diff --git a/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Data/default_data.csv b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Data/default_data.csv new file mode 100644 index 00000000..7a141be5 --- /dev/null +++ b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Data/default_data.csv @@ -0,0 +1,7619 @@ +serial,default,student,credit_balance,income +1,No,No,729.5264952,44361.62507 +2,No,Yes,817.1804066,12106.1347 +3,No,No,1073.549164,31767.13895 +4,No,No,529.2506047,35704.49394 +5,No,No,785.6558829,38463.49588 +6,No,Yes,919.5885305,7491.558572 +7,No,No,825.5133305,24905.22658 +8,No,Yes,808.6675043,17600.45134 +9,No,No,1161.057854,37468.52929 +10,No,No,0,29275.26829 +11,No,Yes,0,21871.07309 +12,No,Yes,1220.583753,13268.56222 +13,No,No,237.045114,28251.69534 +14,No,No,606.7423433,44994.55585 +15,No,No,1112.968401,23810.17405 +16,No,No,286.2325601,45042.41304 +17,No,No,0,50265.31235 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+220,No,No,256.4712276,37657.05592 +221,No,Yes,1153.604998,19224.97523 +222,No,No,771.8958745,31170.08528 +223,No,Yes,1099.630422,17064.04283 +224,No,No,782.8830297,40357.13343 +225,No,No,444.2649062,35924.33879 +226,No,No,504.0283787,24780.96124 +227,No,No,1762.503402,42124.66007 +228,No,Yes,850.0961166,18618.98791 +229,No,No,1412.238721,40785.05977 +230,No,No,715.4968296,47482.56538 +231,No,No,1363.528599,55713.04161 +232,No,No,493.2418898,42495.20308 +233,No,No,1354.752537,33375.13169 +234,No,Yes,571.293911,15416.26935 +235,No,No,956.1513522,44086.70773 +236,No,No,964.820253,34390.74604 +237,No,No,552.634366,50513.16424 +238,No,No,338.2662016,37767.23986 +239,No,No,709.1233514,44041.63009 +240,No,No,978.9782004,57839.05373 +241,No,No,371.0101278,15702.70708 +242,Yes,Yes,1572.856481,14930.17833 +243,No,No,0,40150.21798 +244,Yes,No,1964.476872,39054.58914 +245,No,No,548.0377834,33605.64947 +246,No,No,1257.055222,34045.00882 +247,No,No,1189.918057,38016.8939 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+276,No,Yes,1503.07513,10600.41354 +277,No,No,1362.965595,44217.91604 +278,No,No,908.7715737,45032.84599 +279,No,Yes,1096.820272,16148.06001 +280,No,No,369.2219425,47835.09105 +281,No,No,673.399899,50256.29021 +282,No,No,1038.500008,51173.14138 +283,No,No,0,28762.92378 +284,No,No,527.1940866,32431.11826 +285,No,No,817.4693368,32974.42735 +286,No,Yes,1549.042755,19702.29321 +287,No,Yes,566.7137894,11853.95317 +288,No,No,1496.056465,24935.91874 +289,No,No,862.6566564,42844.834 +290,No,No,1259.149418,31263.64108 +291,No,No,1025.038833,43510.04964 +292,No,Yes,703.2777871,14793.74268 +293,No,Yes,1082.391497,15175.36121 +294,No,No,910.4490235,44054.91642 +295,No,Yes,996.5438622,16995.6604 +296,No,No,353.778226,58514.31808 +297,No,Yes,782.7108275,13300.29747 +298,No,No,1716.595418,51056.86829 +299,No,Yes,502.8986384,15153.96576 +300,No,No,1033.468469,59203.96766 +301,No,No,0,34925.36541 +302,No,Yes,1087.083499,19263.76958 +303,No,No,1047.244249,43899.75925 +304,No,No,1056.927353,38367.86795 +305,No,Yes,979.2244246,26010.8878 +306,No,Yes,847.8187063,15978.63363 +307,No,No,781.7712848,50958.79051 +308,No,Yes,402.4888868,15904.05592 +309,No,No,204.7401817,17534.29054 +310,No,Yes,2022.674643,18336.74317 +311,No,No,748.9370638,44186.61776 +312,No,No,838.2756878,35917.49061 +313,No,No,386.0160493,44694.03096 +314,No,No,812.3992965,49623.10098 +315,No,Yes,1809.926564,6985.135945 +316,No,No,0,30360.54894 +317,No,Yes,1328.183804,24688.47372 +318,No,Yes,1464.936049,26716.68637 +319,No,No,1200.565138,39718.69266 +320,No,No,989.68642,27856.57706 +321,No,No,870.7345799,35539.27022 +322,No,Yes,1200.84739,19849.83141 +323,No,Yes,1428.832047,23204.68146 +324,No,No,0,33951.28873 +325,No,Yes,359.4588665,21783.26949 +326,No,No,265.6839498,40862.75401 +327,No,No,1027.770532,44406.83691 +328,No,No,729.0474335,35042.133 +329,No,No,1042.80492,25867.14772 +330,No,No,230.2305879,37875.61482 +331,No,No,367.4660122,30611.27227 +332,No,Yes,737.7053539,20863.79967 +333,No,No,18.26128536,30349.46042 +334,No,No,1257.760209,30834.77137 +335,No,No,455.5215132,40907.39835 +336,No,No,56.96756035,35458.48156 +337,No,Yes,723.9319853,12213.03714 +338,No,No,612.3831577,41379.76781 +339,No,Yes,1220.177606,15308.12707 +340,No,No,1222.347692,32363.37105 +341,No,No,1165.491138,47684.21945 +342,Yes,No,1642.819997,46856.94704 +343,No,No,944.17356,56088.6838 +344,No,No,749.6080039,41423.94117 +345,No,No,370.3751868,31231.00784 +346,Yes,No,1991.64912,42133.37318 +347,No,No,773.9152266,44562.33812 +348,No,Yes,817.2498113,20480.88706 +349,No,No,1060.71622,50260.46437 +350,Yes,No,1550.449264,56273.51361 +351,No,Yes,454.4669622,15092.43931 +352,No,Yes,1256.239325,17784.55894 +353,No,No,1484.395393,36731.30869 +354,No,Yes,1050.477328,12661.61163 +355,No,No,986.4006189,45430.47221 +356,No,No,717.341927,44266.37654 +357,No,No,692.1432227,47038.09032 +358,Yes,No,1328.892725,34710.06237 +359,No,No,673.4681911,32930.06936 +360,No,No,218.639297,48991.39538 +361,No,No,1218.166966,43954.27749 +362,No,Yes,13.51680255,19322.54503 +363,No,Yes,531.4786999,21726.55897 +364,No,No,165.5381517,33436.45574 +365,No,Yes,1351.849722,25890.50399 +366,No,No,107.282083,54143.96275 +367,No,No,947.1935714,35604.11719 +368,No,Yes,434.5602592,14812.64524 +369,No,No,1020.06724,37458.87925 +370,No,No,662.305318,31597.89762 +371,No,Yes,754.4206129,20767.68302 +372,No,No,289.4895757,44412.8951 +373,No,No,198.0384092,36270.93812 +374,No,Yes,516.0604917,16615.02196 +375,No,No,697.563809,41755.39815 +376,No,Yes,502.3247835,16947.42293 +377,No,Yes,1459.687083,15575.41923 +378,No,No,716.9987227,34418.26022 +379,No,Yes,1037.972705,18324.03006 +380,No,No,673.0161172,29735.9616 +381,No,No,371.9217847,41099.77244 +382,No,No,89.13495796,55604.60753 +383,No,No,1273.280181,40895.28277 +384,No,No,600.723303,18680.51677 +385,No,No,905.0381646,40711.7989 +386,No,No,1061.441562,54437.01032 +387,No,No,1672.639248,46334.78776 +388,No,Yes,883.7026456,19750.094 +389,No,No,373.78033,53936.24178 +390,No,No,1287.795187,52382.6647 +391,No,No,1229.742571,48739.92739 +392,No,Yes,510.613847,22457.05175 +393,No,No,775.6301687,27205.85739 +394,No,No,1152.982887,50206.57693 +395,No,Yes,1303.66259,7986.000336 +396,No,No,1675.278852,28244.50348 +397,No,No,398.277733,30001.79506 +398,No,Yes,739.9017594,25023.87386 +399,No,No,226.4176965,32018.71363 +400,No,No,1217.321712,25173.61961 +401,No,No,639.8205582,29902.00199 +402,No,Yes,883.1669769,12717.49223 +403,No,No,2.287610953,38186.52427 +404,No,No,419.6373477,55967.99317 +405,No,Yes,1544.14025,11102.53474 +406,No,No,1101.015018,32153.01359 +407,Yes,No,1700.599913,30488.98341 +408,No,No,311.8245869,48301.05966 +409,No,No,442.8302223,55074.13083 +410,No,No,728.8149601,17909.58107 +411,No,No,278.8779025,37188.66837 +412,No,No,758.7023275,36625.98955 +413,No,No,620.3396794,22803.74103 +414,No,Yes,1890.167411,18402.46488 +415,No,No,0,28951.4519 +416,No,Yes,1329.198842,23184.01555 +417,No,No,1075.863772,44128.62826 +418,No,No,618.5275092,54956.99054 +419,No,No,1111.086576,27466.89368 +420,No,No,864.0397435,45881.93021 +421,No,No,895.2298096,48512.45564 +422,No,Yes,954.0304337,21908.52716 +423,No,No,947.6349327,46813.51631 +424,No,No,267.3037277,23117.92867 +425,No,Yes,1366.805783,23913.2088 +426,No,No,624.7318835,38325.17376 +427,No,No,13.67938267,25221.63767 +428,No,No,785.8664857,44778.01578 +429,No,Yes,1353.093846,18933.49619 +430,No,No,480.3212639,25553.26285 +431,No,No,0,29507.31325 +432,No,Yes,1221.971243,17916.85609 +433,No,Yes,1184.936523,25108.75671 +434,No,Yes,478.5720556,27512.3449 +435,No,No,221.6904619,34786.10135 +436,No,No,578.3876217,41054.13866 +437,No,Yes,1361.458432,19576.95713 +438,No,Yes,977.8480956,19731.96099 +439,No,No,1184.425245,36887.43997 +440,Yes,Yes,1118.701039,21848.4429 +441,Yes,No,1119.097245,37224.56781 +442,No,No,776.3499793,31329.48485 +443,No,No,1322.297091,51792.40237 +444,No,No,878.0264576,27438.58512 +445,No,No,1268.379515,16921.18088 +446,No,No,0,32611.37307 +447,No,No,0,37262.57409 +448,No,Yes,366.7869573,25833.0631 +449,No,No,747.5610364,39898.76909 +450,No,No,1787.285144,37499.09355 +451,No,Yes,1929.44698,14995.49217 +452,No,No,987.142891,27961.31961 +453,No,Yes,856.3573684,20758.21469 +454,No,No,1101.149707,18153.84432 +455,No,No,704.18452,31515.98168 +456,No,No,555.6234367,40837.73164 +457,No,No,1073.498261,44764.11055 +458,No,No,1126.223023,32842.65237 +459,No,Yes,649.7733186,15708.29638 +460,No,No,977.657621,43743.49673 +461,No,No,1049.889964,42637.51106 +462,No,Yes,1383.566333,15764.34588 +463,No,Yes,1095.46574,14065.16757 +464,No,No,1089.371791,30224.59613 +465,No,No,605.2209683,21792.32152 +466,No,No,1033.885399,49262.04533 +467,No,No,1604.720554,38186.98507 +468,No,No,826.3270259,24679.71504 +469,No,Yes,1176.146823,16670.68079 +470,No,Yes,1275.023199,19937.09803 +471,No,Yes,1290.28314,23538.94414 +472,No,No,1133.474199,51414.49596 +473,No,No,837.4125669,37400.49288 +474,No,Yes,379.0821667,18280.36703 +475,No,Yes,1134.166278,12453.36359 +476,No,No,0,39120.08479 +477,No,No,1491.174803,59394.38308 +478,No,Yes,1695.359586,9582.941897 +479,No,No,705.5733276,54589.23987 +480,No,No,239.1933663,33743.22655 +481,No,Yes,1183.710641,12428.61451 +482,No,No,898.7618389,38267.08158 +483,No,Yes,1524.2904,11719.59348 +484,No,No,777.0237374,35115.01855 +485,No,No,1394.795819,26074.93436 +486,No,Yes,0,20476.15572 +487,No,No,62.57157516,47946.76709 +488,Yes,No,1981.451815,28127.89547 +489,No,No,514.0540348,50053.26193 +490,No,No,722.788039,53714.18607 +491,No,No,152.8663042,33716.86406 +492,No,No,293.1847729,35390.2876 +493,No,No,0,38613.6205 +494,No,Yes,645.3668572,17878.36431 +495,No,Yes,1515.152833,26046.77208 +496,No,Yes,767.4395055,18846.48922 +497,No,Yes,491.8797471,21715.2263 +498,No,No,0,41389.43527 +499,No,No,0,34589.48806 +500,No,No,509.1551641,40132.65921 +501,No,Yes,965.5873701,16440.0997 +502,No,Yes,1868.540072,20489.59811 +503,No,No,403.4972058,41803.77816 +504,No,No,593.1436251,37670.93502 +505,No,No,0,44547.86555 +506,No,No,742.2900159,39367.19331 +507,No,Yes,1110.100457,13013.61057 +508,No,No,771.5512953,49440.40129 +509,No,No,624.3388915,42492.75307 +510,No,Yes,499.9572298,22884.95257 +511,No,No,1088.63752,60650.03613 +512,No,No,1556.491928,49669.66887 +513,No,Yes,797.0843357,23558.68308 +514,No,No,415.9925026,38215.67804 +515,No,Yes,538.2326812,26833.03963 +516,No,Yes,1181.934702,21086.08201 +517,No,Yes,836.0206886,9271.789924 +518,No,No,104.4432151,46928.00549 +519,No,No,564.2094786,53196.62302 +520,No,Yes,804.5394022,25091.46077 +521,No,No,1028.95364,21218.30352 +522,No,No,748.7465837,8983.856902 +523,No,No,66.63208073,28735.97411 +524,No,No,452.8022289,48034.23771 +525,No,Yes,798.4605101,27724.06575 +526,No,Yes,1941.902928,23467.12697 +527,No,No,1446.101012,22996.4323 +528,No,No,761.0640983,61580.03413 +529,No,No,1350.172728,43147.45558 +530,No,No,380.9502006,36943.36199 +531,No,Yes,1602.849607,15906.4668 +532,No,No,147.6101155,37588.56261 +533,No,No,0,36283.08013 +534,No,Yes,733.0725124,18818.16738 +535,No,Yes,1082.747976,26096.33703 +536,No,No,779.6573003,40804.4757 +537,No,No,994.1587122,65254.0758 +538,No,No,769.1813741,50070.78155 +539,No,Yes,684.0985191,4985.169113 +540,No,No,442.5633017,27898.46539 +541,Yes,No,1717.071593,38408.89092 +542,No,No,0,36302.52844 +543,No,No,1092.345865,44717.01258 +544,No,No,497.3349541,35146.79877 +545,No,Yes,1149.680655,16907.70074 +546,Yes,No,1465.210164,58699.9832 +547,No,No,565.3176042,41789.611 +548,No,Yes,922.1378905,12224.18517 +549,No,Yes,175.5993806,11510.05787 +550,No,No,1024.946819,49675.56042 +551,No,No,596.8854323,54091.62476 +552,No,No,750.4104842,53084.90748 +553,No,Yes,790.8700274,14183.97003 +554,No,No,112.3278725,42386.04856 +555,No,Yes,738.3944873,16093.28455 +556,No,No,1157.855561,54419.80857 +557,No,No,1213.243149,45149.51632 +558,No,No,1339.626204,41656.00289 +559,No,No,588.3124302,56520.96251 +560,No,No,721.8081325,56375.72199 +561,No,No,680.820719,43843.47412 +562,No,Yes,815.4065709,12071.7625 +563,No,No,1042.507112,37170.10905 +564,No,Yes,663.2498681,20454.61655 +565,No,Yes,1478.618545,18026.4711 +566,No,No,337.756754,33655.57243 +567,No,Yes,1273.55072,23126.6362 +568,No,No,114.1797259,49562.98927 +569,No,No,0,52295.60093 +570,No,Yes,1516.317689,26377.59349 +571,No,Yes,898.1146731,24164.7511 +572,No,No,790.5905821,48219.59447 +573,No,No,0,29072.72149 +574,No,No,1273.24483,49136.4742 +575,No,No,1279.200448,36063.11411 +576,No,Yes,732.7193809,14568.74741 +577,Yes,No,1763.579088,46227.07454 +578,No,Yes,1280.418448,16310.90553 +579,No,No,1529.937995,36912.8973 +580,No,Yes,345.4676365,18251.49456 +581,No,No,197.9894038,33723.98576 +582,Yes,Yes,1770.969441,15975.5372 +583,No,Yes,639.3719435,17037.0548 +584,No,No,238.8146439,23526.71091 +585,No,No,390.5558431,45338.35721 +586,No,Yes,830.1715111,19476.58758 +587,No,Yes,1378.876076,21613.57847 +588,No,No,242.4034493,37364.42309 +589,No,No,1236.547494,36211.49036 +590,No,No,1063.774937,34914.27525 +591,No,No,126.923179,33819.14332 +592,No,No,758.4655013,38018.14569 +593,No,No,908.9314586,68758.8784 +594,No,No,558.5183424,46297.42541 +595,No,No,1819.242333,36969.55252 +596,No,Yes,1110.681744,19327.57504 +597,No,No,723.2433747,43459.01185 +598,No,No,972.031864,18510.94603 +599,No,No,696.0144306,40741.42024 +600,No,Yes,1178.248909,29362.60461 +601,No,Yes,329.5585109,19371.81209 +602,No,Yes,792.5292385,14546.30177 +603,No,No,1246.037758,33691.72131 +604,No,No,591.7607765,41670.25738 +605,No,No,244.0811388,24854.24982 +606,No,No,1038.54836,49944.86515 +607,No,No,679.0745868,49488.56398 +608,No,No,1061.415836,40585.22164 +609,No,Yes,1268.240691,11914.57827 +610,No,Yes,955.5336858,14736.72088 +611,No,No,1115.82063,27882.80926 +612,No,Yes,601.1632606,22029.19352 +613,No,No,1097.600039,52286.43182 +614,No,No,1042.783073,45982.96632 +615,No,Yes,1321.443053,8624.110689 +616,No,No,1052.39326,37637.65931 +617,No,No,687.9517011,27604.07927 +618,No,Yes,566.4605126,24828.26598 +619,No,No,769.414224,35071.16654 +620,No,No,955.1353421,26372.98582 +621,No,No,643.4095265,28660.14017 +622,No,No,0,42745.30201 +623,No,No,437.6360626,45384.04745 +624,No,No,1278.865642,34673.17099 +625,No,No,1639.390572,30624.77593 +626,No,No,891.6437373,36470.82834 +627,No,Yes,316.4590483,18813.94021 +628,No,No,622.9295074,48874.55577 +629,No,No,107.2775268,42287.30286 +630,No,Yes,990.5432158,12398.4906 +631,No,No,0,45659.9957 +632,No,No,0,33119.95533 +633,No,No,591.2184873,35287.82458 +634,No,Yes,928.0600783,21121.84483 +635,No,No,304.5992239,40785.98918 +636,No,No,235.9394977,31336.51012 +637,No,Yes,1108.053518,10853.23094 +638,No,No,687.306397,24563.75768 +639,No,No,60.18603613,39864.86355 +640,No,No,0,34648.9726 +641,No,No,272.8932571,58730.57286 +642,Yes,No,1531.716459,43930.4001 +643,No,No,949.6086485,47702.57375 +644,No,No,561.9161785,35080.57924 +645,No,No,468.1325222,25231.34623 +646,No,Yes,993.10817,12215.94682 +647,No,No,0,31314.69509 +648,No,Yes,759.4783692,12710.72092 +649,No,No,1340.787209,36440.22832 +650,No,Yes,1521.463396,15210.47407 +651,No,No,133.7433443,30457.01637 +652,Yes,No,780.1725692,51656.87406 +653,No,No,839.9154583,22884.79302 +654,No,No,913.5058729,36670.85422 +655,No,No,924.8814415,35712.63791 +656,No,No,1197.831505,54652.3093 +657,No,No,49.2096659,30451.15286 +658,No,No,970.7160966,48387.64862 +659,No,No,783.0574005,54082.42408 +660,No,Yes,465.0628886,20379.06209 +661,No,No,0,39337.74998 +662,No,Yes,732.2642676,21986.04545 +663,No,No,576.0912613,37679.69859 +664,No,Yes,588.9910563,16309.87545 +665,No,No,863.1407178,43779.47785 +666,No,No,857.575734,49675.37032 +667,No,No,361.5017311,33674.89024 +668,No,No,891.1554271,36023.08501 +669,No,No,918.122206,44738.55603 +670,No,No,1335.028018,21150.85594 +671,No,No,421.4008434,39873.52174 +672,No,Yes,743.6361445,18883.97151 +673,No,No,773.7403725,41872.49476 +674,No,No,731.3934722,45251.88192 +675,No,No,964.1889227,36330.3946 +676,No,Yes,350.0151843,17657.17266 +677,No,No,1206.661362,37125.59135 +678,No,No,1051.947573,52888.1851 +679,No,No,454.7603707,45572.17248 +680,No,No,932.6653075,42758.6007 +681,No,No,535.7458465,36259.46376 +682,No,No,241.8052522,41186.18281 +683,No,No,1026.50671,42042.16794 +684,No,Yes,187.6798856,18278.83397 +685,No,No,197.5383215,58646.9278 +686,No,No,585.7080883,43288.40038 +687,No,Yes,1947.022401,12147.04641 +688,No,No,829.5475975,39717.53761 +689,No,No,196.9384595,35488.44888 +690,No,No,591.863619,31438.77601 +691,No,No,0,39742.10546 +692,No,Yes,1008.291619,11939.29526 +693,No,Yes,1052.227478,17410.75159 +694,No,No,331.6805267,49756.17101 +695,No,No,913.107204,29414.65218 +696,No,Yes,561.3916122,21747.26318 +697,No,No,246.955463,47692.89425 +698,No,No,578.9786101,46304.71748 +699,No,No,557.8087771,62352.84607 +700,No,Yes,788.2517516,10418.18286 +701,No,No,1060.807429,39174.05636 +702,No,Yes,1247.907029,19816.72014 +703,No,No,1184.360723,34259.9902 +704,No,No,1024.578238,33071.54992 +705,No,No,1080.291656,43367.89957 +706,No,No,540.2790448,26267.15635 +707,No,No,1448.835465,33835.73626 +708,No,Yes,561.1940353,27421.11126 +709,No,No,1191.480307,30040.57215 +710,No,No,565.1389875,38202.58394 +711,No,No,482.9821952,35845.19283 +712,No,No,950.3500933,37486.24955 +713,No,Yes,350.7029135,21235.36042 +714,Yes,Yes,1551.023469,19027.50863 +715,No,No,981.5962751,37747.91335 +716,No,No,1442.129805,10921.61503 +717,No,No,1060.33586,56607.25397 +718,No,No,1154.890792,61794.34633 +719,No,Yes,544.1397693,20056.82854 +720,No,No,1137.175454,27588.93331 +721,No,No,414.0840495,47811.41892 +722,No,Yes,436.0083144,17504.44777 +723,No,No,987.8147997,25809.98028 +724,No,No,679.3918418,46603.54641 +725,No,No,1377.772007,51633.32955 +726,No,No,856.8119605,32437.13183 +727,No,No,1036.676833,44923.80275 +728,No,No,0,46826.80447 +729,No,No,961.4720707,51936.75989 +730,No,No,275.9880913,35622.82945 +731,No,No,426.6935421,30769.34477 +732,No,No,290.2519082,46214.15923 +733,No,No,934.9697035,42325.74999 +734,No,Yes,668.9911556,26342.04846 +735,No,Yes,2004.727568,27136.53798 +736,No,No,1312.86573,29938.25946 +737,No,Yes,662.2735565,15092.05102 +738,No,No,524.639806,36120.89226 +739,No,Yes,1212.589568,21058.34866 +740,No,No,961.3157694,30290.8065 +741,Yes,Yes,1504.290178,13965.18604 +742,No,Yes,613.656318,25040.48845 +743,No,Yes,1233.343605,13402.07329 +744,No,No,1221.385959,36961.31512 +745,No,No,1217.056807,46256.78049 +746,No,No,628.7576984,43205.1753 +747,No,Yes,819.0973161,15957.94364 +748,No,Yes,843.4934551,12710.02571 +749,No,No,1173.559815,29141.36573 +750,No,Yes,1195.590283,13329.5964 +751,No,No,1202.883124,12288.12708 +752,No,No,24.87182349,29316.97033 +753,No,No,853.2414216,29484.05238 +754,No,No,398.7757053,40223.90981 +755,No,Yes,1521.172396,18149.88619 +756,No,Yes,1160.221793,15941.05088 +757,No,No,985.6140627,49948.46711 +758,No,No,583.8207193,38215.96476 +759,No,Yes,638.6919826,18148.30171 +760,No,No,384.9977195,25380.70345 +761,No,No,1050.764231,31558.89842 +762,Yes,Yes,1871.938387,18077.48709 +763,No,No,793.7617176,35157.73998 +764,No,Yes,591.6606513,21790.50243 +765,No,No,419.4764413,24001.5122 +766,No,Yes,514.2838902,18185.4797 +767,No,No,1027.895035,21551.61109 +768,No,Yes,1029.681549,15977.32114 +769,No,Yes,953.6263238,18363.0685 +770,No,Yes,1463.337765,11579.15945 +771,No,No,1172.459499,34690.13828 +772,No,Yes,548.2728639,12048.82289 +773,No,No,1752.883789,48250.10462 +774,No,Yes,555.9206802,23909.70649 +775,No,Yes,1560.931752,13621.56916 +776,No,Yes,839.8835566,16883.33886 +777,No,Yes,1112.817978,13634.90763 +778,No,No,1309.253772,43278.05928 +779,No,No,940.5908748,41560.47099 +780,No,No,1019.248977,41195.37012 +781,No,No,433.6690195,32904.69772 +782,No,No,1015.115425,42050.46652 +783,No,No,75.17766307,52765.97828 +784,No,No,1050.316376,39585.62422 +785,No,No,48.91185684,35155.17247 +786,No,No,1721.64778,48236.12633 +787,No,No,1607.35144,48700.84805 +788,No,No,1180.447542,43914.41982 +789,No,No,609.8771346,40875.94654 +790,No,Yes,1054.200208,19440.97318 +791,No,Yes,793.8204657,19074.03173 +792,No,No,879.6756179,30021.38335 +793,No,No,1237.647422,29151.23466 +794,No,Yes,1412.764358,18234.45922 +795,No,Yes,1174.716483,13025.76945 +796,No,No,363.3448038,44325.78885 +797,No,No,919.4857763,44928.18464 +798,No,Yes,1195.05967,21648.65668 +799,No,Yes,794.1377878,15721.79966 +800,No,No,10.18881851,33776.82546 +801,No,No,0,31083.22146 +802,No,Yes,498.5059792,16967.61976 +803,No,Yes,1227.161107,18459.41861 +804,Yes,No,1902.612991,53394.07623 +805,No,Yes,1059.204637,11742.37986 +806,No,Yes,450.3053908,21177.61108 +807,No,No,418.5398704,55002.73341 +808,No,No,1070.480677,32939.36671 +809,No,No,1306.770573,56640.63403 +810,No,Yes,1315.337298,8431.175004 +811,No,No,74.5334252,19146.24563 +812,No,No,66.15233049,38927.12451 +813,No,No,56.42071165,54217.33825 +814,No,No,1056.596875,25123.85962 +815,No,No,259.2375778,51355.37462 +816,No,Yes,1143.692578,23500.90128 +817,No,Yes,0,15682.0685 +818,No,No,877.7939338,30239.72072 +819,No,No,919.077555,45315.55745 +820,No,No,94.93247587,27212.95124 +821,No,No,333.3741889,29413.14899 +822,No,No,197.9107165,30971.19725 +823,No,No,624.910088,41047.55579 +824,No,No,138.2522518,54406.50225 +825,No,No,787.5517717,46185.552 +826,No,No,1030.927564,36271.33456 +827,No,No,1049.747643,30159.20989 +828,No,No,0,35738.98757 +829,No,Yes,1130.056787,18467.10219 +830,No,No,795.5412235,51303.14014 +831,No,Yes,1503.596238,18710.61448 +832,No,No,436.1350124,46071.66052 +833,No,No,800.7055933,36314.43633 +834,Yes,Yes,1881.049952,16580.45056 +835,No,Yes,1445.45803,18632.61606 +836,No,No,1166.100236,23994.40391 +837,No,No,163.7471037,38970.0289 +838,No,No,0,46353.80212 +839,No,No,1322.052937,47814.1742 +840,No,No,1283.523253,61525.69618 +841,No,No,1000.898434,43059.0486 +842,No,No,249.860323,47602.1165 +843,No,No,500.2221784,38991.01248 +844,No,No,568.0944851,36374.43392 +845,No,Yes,1047.085154,13714.27938 +846,No,Yes,186.6683743,15976.63786 +847,No,No,782.778332,39224.40927 +848,No,No,388.5483505,18007.83296 +849,No,Yes,842.5722263,17279.52182 +850,No,No,556.1156313,44197.90846 +851,No,No,1234.578618,48112.48054 +852,No,No,412.8943419,42123.21721 +853,No,No,683.3573938,29269.33352 +854,No,No,872.2261716,48101.61169 +855,No,No,415.7654285,24740.7536 +856,No,Yes,1518.018809,16740.44308 +857,No,No,254.3944552,44612.14757 +858,No,No,1507.249195,24057.51794 +859,No,No,900.5675031,40848.69179 +860,No,No,595.1451336,46011.94663 +861,No,Yes,163.9398231,21083.00451 +862,No,No,389.6132519,43472.6743 +863,No,Yes,1334.97118,14834.86429 +864,No,Yes,1961.728657,17864.09925 +865,No,No,1173.161493,36439.67873 +866,No,No,1102.418242,23218.0838 +867,No,No,1228.907919,46411.82006 +868,Yes,No,1505.831475,29525.7494 +869,No,Yes,1217.623913,21160.4293 +870,No,Yes,843.159091,12042.89422 +871,No,No,1004.532904,59403.93001 +872,No,No,294.0629444,57500.11105 +873,No,No,484.7234795,24569.56729 +874,No,No,775.5561388,34586.25334 +875,No,Yes,1339.312769,21031.85983 +876,No,No,801.8550344,39156.62749 +877,No,No,551.8871003,37573.14139 +878,No,Yes,522.3811806,23440.06422 +879,No,Yes,1237.454723,20660.46938 +880,No,No,320.0059169,57563.48923 +881,No,No,960.6700306,30093.72754 +882,No,No,272.0195177,29773.60932 +883,No,No,927.1659182,33781.81511 +884,No,No,75.79525931,55189.68599 +885,No,No,1285.851381,37635.95408 +886,No,No,1471.775073,32478.04391 +887,No,Yes,808.0147191,14485.46844 +888,No,No,747.3535557,42900.02495 +889,No,No,327.7957317,23865.3281 +890,No,Yes,851.3417919,18786.58034 +891,No,No,110.8543651,27370.30437 +892,No,No,541.7555693,57322.10502 +893,No,No,0,36269.29254 +894,No,No,1201.360264,51740.89005 +895,No,No,57.2198379,18982.55571 +896,No,Yes,785.0934846,32945.82807 +897,No,Yes,383.6396643,17445.18244 +898,No,No,1107.304157,52765.05866 +899,No,No,396.9859854,55454.63104 +900,No,No,0,44807.37827 +901,No,No,924.2415348,45207.38763 +902,No,No,602.4937249,33202.53438 +903,No,Yes,828.739825,17962.31109 +904,No,Yes,1.611175572,20837.33546 +905,No,Yes,894.0057891,18002.32519 +906,No,No,1234.476479,31313.37458 +907,No,No,406.0139804,42642.23737 +908,No,Yes,1051.998449,12376.09598 +909,No,Yes,1085.83808,18890.22808 +910,No,No,1656.173272,41133.12685 +911,No,Yes,242.6526089,20426.79397 +912,No,Yes,602.3064955,12927.7128 +913,No,No,187.5973026,59660.99704 +914,No,No,331.2970819,34497.74268 +915,No,No,911.3877951,57131.32037 +916,No,No,391.4082005,39761.56184 +917,No,No,1450.349184,33957.18196 +918,No,No,819.9169885,28960.11281 +919,No,Yes,1590.176413,18666.41221 +920,No,Yes,1256.61487,22493.50126 +921,Yes,Yes,1889.33211,22652.10963 +922,No,No,743.6853749,10774.97191 +923,No,No,343.1944994,29555.90547 +924,No,No,534.9731547,44079.33287 +925,No,No,1207.697906,34857.54031 +926,No,No,564.8138972,39829.41683 +927,No,Yes,495.3652633,22938.13687 +928,No,No,902.5138231,38647.52189 +929,No,Yes,1741.273354,18544.05165 +930,No,No,1166.642625,52700.10969 +931,No,Yes,631.15977,23245.07076 +932,No,Yes,1076.977966,14668.38071 +933,Yes,No,1243.554025,37634.3464 +934,No,No,2113.019023,34747.75578 +935,No,No,1296.060997,43313.28299 +936,No,No,1088.674077,47147.72595 +937,No,No,824.7317041,42313.51575 +938,No,No,684.2948414,37011.18328 +939,No,No,37.3648865,26221.5179 +940,No,Yes,878.1490055,18414.57007 +941,No,No,904.2415222,40683.46035 +942,No,Yes,942.042215,22181.39455 +943,No,Yes,957.4925363,16323.36476 +944,No,No,285.2908758,17606.63593 +945,No,Yes,1468.382748,19846.06475 +946,No,No,322.7130195,27668.5761 +947,No,No,414.1340631,40877.22645 +948,No,No,1105.002812,36812.70609 +949,No,No,0.445756769,31934.83798 +950,No,No,619.7013996,13902.179 +951,No,No,293.8693202,56676.80175 +952,No,Yes,637.3827283,19704.91942 +953,No,No,1109.528617,69325.07982 +954,No,No,1223.112944,52903.46137 +955,No,Yes,1183.69295,13081.66106 +956,No,No,569.7908726,54335.2241 +957,No,No,625.5319229,46026.48791 +958,No,No,833.6552993,16908.7781 +959,No,No,866.0459226,44827.26588 +960,No,Yes,945.4798492,17279.28681 +961,No,No,1554.845545,43901.80532 +962,No,No,575.7683453,51927.7468 +963,No,No,1141.603687,38722.96119 +964,No,No,1146.64022,45376.55648 +965,No,No,0,34305.91868 +966,No,No,434.7147713,35454.75542 +967,No,No,1026.13443,39135.63696 +968,No,Yes,1302.797206,29252.36143 +969,No,No,690.7058706,59561.9052 +970,No,No,574.4516329,40727.64293 +971,No,No,0,43346.62226 +972,No,Yes,251.3060237,13058.60666 +973,No,Yes,1252.113954,16490.70693 +974,No,No,207.8952069,52249.31234 +975,Yes,No,1753.084389,48965.34697 +976,No,Yes,1279.935227,21107.52024 +977,No,No,862.8765137,36461.90113 +978,No,No,1143.541405,33394.9791 +979,No,No,1253.18164,71238.5506 +980,No,No,624.0839722,25557.63967 +981,No,Yes,1027.86107,14322.08836 +982,Yes,No,1964.014684,50553.53452 +983,No,No,752.4261091,32539.64501 +984,No,No,953.9298177,32640.14405 +985,No,No,773.2042127,66749.43344 +986,No,No,432.7910885,52238.08744 +987,No,No,797.7340162,31616.80024 +988,No,No,240.8415528,56089.15425 +989,No,No,662.3158574,31815.34656 +990,No,Yes,1056.36555,15432.39599 +991,No,No,741.0875961,43015.53915 +992,No,No,0,42284.68217 +993,No,No,981.9544898,41123.21247 +994,No,No,1569.835666,42781.3833 +995,No,No,136.5055213,45596.10783 +996,No,Yes,731.9517153,18117.42791 +997,No,No,717.8107678,32040.93126 +998,No,Yes,1005.176223,18262.17614 +999,No,No,561.9283609,31192.91272 +1000,Yes,No,2033.19179,44998.28744 +1001,No,No,638.8174557,46704.738 +1002,No,Yes,1104.949687,11528.99966 +1003,No,No,0,29514.03361 +1004,No,No,633.7666725,34290.60492 +1005,No,Yes,1526.025134,20894.07666 +1006,No,No,522.7662715,43026.32913 +1007,No,No,853.1031276,47381.72423 +1008,No,No,1225.225152,39338.86212 +1009,No,Yes,1559.627869,22047.78161 +1010,No,No,260.3399357,34932.49376 +1011,No,No,1320.943245,50505.3669 +1012,No,No,1443.64839,45089.38954 +1013,No,No,791.6382334,30303.60678 +1014,No,No,922.6664691,33445.48969 +1015,No,No,80.75286364,35887.54636 +1016,No,No,193.3585386,34728.02143 +1017,No,No,939.0985018,45519.01898 +1018,No,No,1336.803015,30787.15572 +1019,Yes,No,1488.779562,49803.29308 +1020,No,Yes,375.1967469,13709.20533 +1021,No,Yes,773.3811632,18978.18845 +1022,No,No,581.4231695,44600.6628 +1023,No,No,461.0502494,43147.77203 +1024,Yes,Yes,1424.559323,25398.19744 +1025,No,Yes,1139.396214,17139.84809 +1026,No,No,1032.20327,41673.73415 +1027,No,No,96.64183869,44556.21942 +1028,No,No,338.7794082,42678.71319 +1029,No,Yes,1331.425462,13793.18518 +1030,No,No,0,34479.62349 +1031,No,No,616.25397,42436.68644 +1032,No,No,599.8008625,30146.63574 +1033,No,Yes,1466.56289,20904.44788 +1034,No,No,593.3038909,49925.32392 +1035,No,No,786.98126,49432.95276 +1036,No,No,926.9853267,48753.31287 +1037,No,No,1175.420433,37462.64333 +1038,No,Yes,1697.749667,23295.84328 +1039,No,No,575.5143833,54203.19021 +1040,No,No,1386.302444,54404.48169 +1041,No,Yes,1222.245092,23152.7231 +1042,No,No,1556.194194,26905.58075 +1043,No,No,1103.92188,52821.53485 +1044,Yes,No,1496.07211,40214.62083 +1045,No,Yes,975.642874,25920.32191 +1046,No,Yes,781.1742571,23403.06377 +1047,No,Yes,940.4354663,18939.84279 +1048,No,No,375.32962,32531.21576 +1049,No,No,627.8761333,51770.45835 +1050,No,No,1304.383209,49371.95691 +1051,No,No,1496.23237,37534.32924 +1052,No,Yes,1005.593829,15851.48265 +1053,No,No,828.8577543,46060.85293 +1054,No,Yes,1144.35852,18903.37844 +1055,No,No,1386.191949,42537.99079 +1056,No,Yes,1321.805206,11453.61153 +1057,No,No,1121.151251,35765.70649 +1058,No,No,547.8633422,45966.35106 +1059,No,No,813.9973412,42461.56142 +1060,No,No,75.18398526,52166.00597 +1061,No,No,598.2192978,28621.12542 +1062,No,Yes,63.03632327,12308.01832 +1063,No,No,0,48642.25451 +1064,No,No,1553.703309,35929.16355 +1065,No,No,813.7160939,46419.99708 +1066,No,No,1386.176753,42875.00602 +1067,No,No,933.7673154,43693.52116 +1068,No,Yes,1240.0956,20134.64729 +1069,No,No,0,33781.65631 +1070,No,No,613.5818624,30833.80206 +1071,No,No,253.5167956,53125.48345 +1072,No,Yes,947.8858454,17094.47889 +1073,No,Yes,112.8119095,16100.57083 +1074,No,Yes,827.0413054,18892.82129 +1075,No,No,484.679499,43900.09156 +1076,No,Yes,271.2497049,25157.73142 +1077,No,No,1018.248906,52509.74244 +1078,No,No,0,43730.76665 +1079,No,Yes,0,16421.48992 +1080,No,No,1101.803715,36555.4627 +1081,No,Yes,1471.897822,18913.03822 +1082,No,No,1057.71318,40707.61877 +1083,No,No,352.5505372,17626.16713 +1084,No,No,1029.819308,50635.89081 +1085,No,No,1308.663094,35964.79193 +1086,No,Yes,1581.790489,10537.52984 +1087,No,No,918.1189019,53710.2153 +1088,No,No,637.8002911,40323.55907 +1089,No,No,369.041788,38468.24465 +1090,No,Yes,985.0926986,18499.96154 +1091,No,No,306.9569266,32428.81291 +1092,No,No,1205.913239,43715.41376 +1093,No,No,1079.502963,40504.91545 +1094,No,No,555.2884886,40046.42198 +1095,No,No,905.0116944,39293.98251 +1096,No,Yes,1348.956074,20870.26798 +1097,No,Yes,1461.833249,19252.23729 +1098,No,No,443.4469923,29811.36345 +1099,Yes,No,2024.105018,51508.86888 +1100,No,No,1516.551152,39368.14187 +1101,No,Yes,520.5576465,18256.28839 +1102,No,No,850.5480987,44501.91504 +1103,No,No,926.4896733,49919.96729 +1104,No,Yes,1275.824225,9611.963157 +1105,No,No,249.5983832,20684.23741 +1106,No,Yes,1564.471411,19372.8216 +1107,No,No,443.1755645,37639.98243 +1108,No,No,323.5469083,33991.99111 +1109,No,Yes,512.3202025,24949.62161 +1110,No,Yes,524.7606336,14154.91347 +1111,No,No,741.4204609,38660.99965 +1112,No,Yes,1994.049188,14305.11147 +1113,No,No,1175.715193,24000.89494 +1114,No,No,284.0487757,41243.85479 +1115,No,No,565.5053845,39109.30967 +1116,No,No,47.74566544,31150.37469 +1117,No,No,776.9548658,45083.3202 +1118,No,No,353.7390971,65526.80974 +1119,No,No,76.13224922,53594.42001 +1120,No,No,839.6035976,45994.64473 +1121,No,Yes,754.4840226,22425.53548 +1122,No,Yes,833.1207736,23630.27367 +1123,No,No,277.2949179,27548.9503 +1124,No,No,1594.697189,37091.87383 +1125,No,No,0,40393.47543 +1126,No,Yes,1281.617091,14236.09199 +1127,No,No,1469.703786,40174.95035 +1128,No,No,0,38165.68831 +1129,No,No,891.403241,46611.73164 +1130,No,No,493.7649099,51518.05525 +1131,No,Yes,884.6140116,24306.65035 +1132,No,No,0,32582.74556 +1133,No,No,1028.520386,47944.60096 +1134,No,Yes,425.8317403,13275.91441 +1135,No,No,1132.358062,52489.76422 +1136,No,No,965.2738039,46218.46056 +1137,Yes,No,2499.01675,51504.29396 +1138,No,Yes,1435.181264,11346.84187 +1139,No,Yes,1391.19636,8637.963307 +1140,No,Yes,1804.413369,17665.54905 +1141,No,Yes,321.7950672,19229.22587 +1142,No,No,1506.669829,41931.70448 +1143,Yes,Yes,1402.267516,12104.31561 +1144,Yes,No,1379.430717,38881.96935 +1145,No,No,1232.978729,57182.33212 +1146,No,No,134.8580208,35755.86662 +1147,No,Yes,422.9140174,18787.33703 +1148,No,No,1373.564213,21785.25275 +1149,No,No,661.4258876,55591.55703 +1150,No,No,1427.718421,27425.67495 +1151,No,No,938.6378621,39459.76704 +1152,No,No,608.4289941,56483.0569 +1153,No,No,1477.532394,46965.13826 +1154,No,Yes,1470.588741,17876.29269 +1155,No,Yes,1488.598859,20739.62679 +1156,No,No,0,49608.00617 +1157,No,Yes,328.0383342,19566.26724 +1158,No,Yes,1333.806128,21087.32352 +1159,No,No,764.5347169,40593.28542 +1160,No,No,110.326679,52106.2049 +1161,Yes,Yes,2502.684931,14947.51975 +1162,No,No,604.3155154,36592.75684 +1163,No,Yes,1595.288158,22645.04006 +1164,No,No,0,50076.26357 +1165,No,No,440.6376501,50902.13614 +1166,No,Yes,876.9186246,13372.51292 +1167,No,No,1366.890314,35566.95637 +1168,No,No,1129.975937,32795.04634 +1169,No,No,1077.020824,56862.06694 +1170,No,No,1411.074241,26040.00459 +1171,No,No,0,45788.49211 +1172,No,No,861.652948,27355.80502 +1173,No,No,1185.661115,56483.5372 +1174,No,No,901.5539184,57100.41767 +1175,No,Yes,773.7302157,14841.98962 +1176,No,No,1387.973385,30716.64306 +1177,No,No,1207.694726,37355.94434 +1178,No,Yes,455.7164377,12587.40145 +1179,No,Yes,550.8950488,19110.07447 +1180,No,Yes,1484.805423,22965.17668 +1181,No,Yes,441.6261268,15261.70803 +1182,No,No,810.4120752,28056.78391 +1183,No,No,1137.039899,49810.89999 +1184,No,Yes,1402.407716,13870.96968 +1185,No,No,1541.812806,29374.92888 +1186,No,No,898.9807259,63041.22478 +1187,No,Yes,700.3311593,18702.956 +1188,No,Yes,895.4580635,23795.5515 +1189,No,No,770.1321919,48489.37866 +1190,No,No,841.951731,53779.01825 +1191,No,No,44.31361993,30449.25019 +1192,No,No,377.3003603,42257.49319 +1193,No,No,264.8011505,49216.24815 +1194,No,No,522.0216391,37341.89878 +1195,No,No,1681.916005,41866.55806 +1196,No,No,1069.539368,55421.55279 +1197,No,No,375.7009493,37925.97462 +1198,No,Yes,670.991526,19835.96905 +1199,No,No,318.9125682,49305.55317 +1200,No,Yes,955.1202176,25314.85309 +1201,No,No,1123.793439,24901.99634 +1202,No,No,993.5808787,34836.25272 +1203,No,No,278.8052603,54559.11666 +1204,No,No,1553.289604,44150.16209 +1205,No,No,0,34522.88556 +1206,No,No,483.5913011,36497.46846 +1207,No,Yes,311.5305491,18534.43072 +1208,No,No,536.2538616,32994.15136 +1209,No,No,127.7079183,27483.895 +1210,Yes,Yes,1507.333948,23898.87823 +1211,No,No,1351.03593,40946.60552 +1212,No,No,723.1989866,42751.76524 +1213,No,Yes,1041.202799,22618.42415 +1214,No,No,1102.110109,34246.69491 +1215,No,Yes,420.3702643,16419.71297 +1216,Yes,No,1278.4073,36675.60688 +1217,No,No,542.469658,52741.81067 +1218,No,No,401.8290422,47175.61452 +1219,No,No,506.5058716,51114.31972 +1220,No,No,1160.499604,23498.3002 +1221,No,No,1277.795913,56605.59886 +1222,No,Yes,1200.624471,18973.90768 +1223,No,Yes,1418.586072,11556.69531 +1224,No,Yes,480.3397036,14592.8404 +1225,No,No,370.5300188,31139.72184 +1226,No,No,737.6852405,51138.26546 +1227,No,No,501.921446,41366.67789 +1228,No,No,1682.201224,30441.55485 +1229,No,Yes,813.4527916,21073.98883 +1230,No,No,683.8262514,46937.62287 +1231,No,Yes,1036.600444,15197.24851 +1232,No,Yes,471.1433887,24945.03775 +1233,No,No,336.4196404,39707.04233 +1234,No,Yes,443.7230439,13136.83042 +1235,No,No,967.1388884,35269.12297 +1236,No,No,549.2759696,15706.21509 +1237,No,No,1601.160056,17974.74118 +1238,No,No,902.1378198,59695.29046 +1239,No,No,0,37553.55105 +1240,No,No,1433.183548,41744.31508 +1241,No,Yes,502.7977355,13376.05334 +1242,No,No,433.4528187,35858.9302 +1243,No,No,406.4267183,36750.49898 +1244,No,No,935.481005,56610.81557 +1245,No,Yes,1545.830641,12160.84228 +1246,No,No,93.94056279,19811.33054 +1247,No,Yes,192.4111547,24050.37172 +1248,No,No,716.9202275,24015.51912 +1249,No,Yes,1157.139538,21222.41554 +1250,No,No,72.16974028,39560.63498 +1251,No,Yes,1417.268258,20464.02574 +1252,No,No,1195.483956,38452.64134 +1253,No,No,559.1186503,55007.2651 +1254,No,No,0,37499.68927 +1255,No,No,359.2257409,25243.09848 +1256,Yes,Yes,2123.369217,23836.46426 +1257,No,No,1316.542384,20353.49883 +1258,No,No,1172.30089,34513.61085 +1259,No,No,1453.083637,36828.32719 +1260,No,Yes,323.9570867,12327.24399 +1261,No,No,1152.550664,27346.03061 +1262,No,No,1333.998166,57662.12193 +1263,No,No,709.6586991,37992.21295 +1264,No,No,731.3091222,33126.84977 +1265,No,Yes,279.3155744,19742.03706 +1266,No,No,0,17059.36832 +1267,No,Yes,630.704463,16501.809 +1268,No,No,543.0371297,31268.09516 +1269,No,No,618.881502,27906.58962 +1270,No,Yes,1053.161735,25222.97884 +1271,No,Yes,0,19622.57717 +1272,No,Yes,1762.352183,17032.34235 +1273,No,No,0,43943.24491 +1274,No,No,469.1519745,30366.7196 +1275,No,No,699.7625588,32881.30451 +1276,No,No,446.1427252,42611.58355 +1277,No,No,571.1799476,39682.80287 +1278,No,No,952.3348213,44864.15486 +1279,No,No,584.6670228,55682.4667 +1280,No,No,174.3678659,47750.12061 +1281,No,Yes,671.8017009,15299.25989 +1282,No,No,1339.55551,53585.76133 +1283,No,No,0,49232.70655 +1284,No,Yes,1579.070977,21101.20461 +1285,No,No,917.3235409,40193.8381 +1286,No,No,251.4282215,25962.05919 +1287,No,No,998.3620611,38385.29131 +1288,No,No,828.2325106,42246.21736 +1289,No,Yes,1500.283089,16943.02902 +1290,No,No,1142.637976,26731.09036 +1291,No,Yes,1415.198823,16737.51871 +1292,No,No,198.7309179,33512.93037 +1293,No,No,341.7692502,41662.74261 +1294,No,Yes,146.7353634,12716.21283 +1295,No,No,1576.306916,30547.79997 +1296,No,No,1830.471547,24053.47692 +1297,No,No,368.8007286,34526.03537 +1298,No,Yes,1026.576284,23636.55277 +1299,No,No,0,36225.50725 +1300,No,No,241.3360311,40122.40162 +1301,No,No,1272.053891,44895.5933 +1302,No,No,361.2007027,49395.02674 +1303,No,No,1464.009916,32023.59552 +1304,No,Yes,815.0774084,18445.6597 +1305,No,Yes,1026.128247,16430.64112 +1306,No,No,630.7255595,30466.37852 +1307,No,No,1624.126432,16054.30421 +1308,No,No,744.7326369,44965.04641 +1309,No,No,1439.296779,36170.59837 +1310,No,Yes,1491.050998,13033.12274 +1311,No,Yes,0,13334.24013 +1312,No,No,1162.699796,27206.66185 +1313,No,No,1463.593161,34064.87872 +1314,No,Yes,1309.697543,15536.23876 +1315,No,No,920.6474223,37936.08914 +1316,No,Yes,6.318864439,18912.61406 +1317,No,No,128.9911619,56946.4364 +1318,No,Yes,1323.289063,21149.31369 +1319,No,No,383.2157693,15233.31635 +1320,No,No,267.380973,32265.11891 +1321,No,No,429.074698,48109.80825 +1322,No,No,711.003891,51035.74577 +1323,No,Yes,309.5241479,12135.03813 +1324,No,Yes,1317.926258,14070.16706 +1325,No,No,427.3328427,55503.07363 +1326,No,No,872.8569657,31471.54539 +1327,No,No,1105.556173,38886.03581 +1328,No,No,678.0189216,59416.77886 +1329,No,Yes,1164.63073,17929.65298 +1330,No,No,214.9016261,33904.57772 +1331,No,No,721.5867078,50952.48482 +1332,No,Yes,224.338769,18282.2166 +1333,No,Yes,1199.237774,13809.35049 +1334,No,No,580.0545844,37278.27931 +1335,No,No,821.4936019,34739.39481 +1336,No,No,602.8629316,37849.13424 +1337,No,Yes,377.7676037,14021.5889 +1338,No,No,727.632558,38431.15806 +1339,No,Yes,600.4308598,18538.30171 +1340,No,No,993.5916771,26682.32857 +1341,No,No,1096.587037,47235.45313 +1342,No,No,1603.333397,43474.6376 +1343,No,No,1146.971428,30637.2973 +1344,No,No,1342.17534,58049.99404 +1345,No,Yes,1663.687014,19847.69861 +1346,No,No,1005.247966,41308.25485 +1347,No,No,962.8949273,25063.49298 +1348,No,Yes,1092.620656,27128.16725 +1349,No,No,919.3433464,43287.90231 +1350,No,Yes,176.8405782,20021.69339 +1351,No,No,460.6044655,38818.18164 +1352,No,Yes,782.5448463,20593.52669 +1353,No,No,613.7429339,56951.3597 +1354,No,No,362.1034983,39658.95045 +1355,No,No,861.6819237,49672.34718 +1356,No,Yes,936.4734896,19389.48003 +1357,No,No,1222.737042,68564.99068 +1358,No,No,794.4867872,44145.21585 +1359,No,No,0,13573.5624 +1360,Yes,No,2220.966201,40725.09621 +1361,No,No,523.2162452,38063.40167 +1362,Yes,No,1907.377311,42346.82846 +1363,No,Yes,1809.463468,18804.72386 +1364,No,No,1392.261315,32283.38838 +1365,No,No,1349.466456,35870.01951 +1366,No,No,621.7362508,51595.75172 +1367,No,Yes,1273.581981,18475.19238 +1368,No,No,636.2405203,17355.7575 +1369,No,No,680.4518871,33192.4018 +1370,No,No,836.8498663,27936.51892 +1371,No,Yes,1115.10674,19169.07158 +1372,No,Yes,799.7433408,16147.57662 +1373,No,Yes,996.3868788,20070.81392 +1374,No,No,1632.808045,33455.90111 +1375,No,No,439.8374803,24452.6466 +1376,No,No,276.7453134,50523.67789 +1377,No,No,1715.315063,22824.09762 +1378,No,No,848.6936359,29042.5594 +1379,No,Yes,323.5033574,19144.81572 +1380,No,No,793.6122324,24973.20848 +1381,No,No,612.0784475,39443.95741 +1382,No,Yes,1418.429004,14907.07001 +1383,No,Yes,1466.939439,19139.62963 +1384,No,Yes,440.7900554,19896.02967 +1385,No,No,0,53432.61549 +1386,No,No,885.180222,46571.77169 +1387,No,No,635.9697054,42451.61434 +1388,No,No,664.8163017,32894.80407 +1389,No,No,1100.238779,34281.25517 +1390,No,Yes,1606.320366,13422.30712 +1391,No,Yes,1704.427857,20892.31412 +1392,No,No,1295.991633,39799.99754 +1393,No,No,468.412124,22943.12569 +1394,No,No,303.7832118,44745.11955 +1395,No,No,971.709025,33774.89191 +1396,Yes,No,1758.420947,49787.45657 +1397,No,Yes,1334.701119,24621.96305 +1398,No,No,982.6992113,40194.46538 +1399,No,No,52.03888628,14680.10005 +1400,No,No,401.5035397,25010.80009 +1401,No,No,1456.849378,40186.53088 +1402,No,No,772.9245771,42678.38808 +1403,No,Yes,1844.361718,14238.11325 +1404,No,Yes,1015.511397,21362.93892 +1405,No,No,988.6780269,31210.24323 +1406,No,No,1072.744106,45704.83957 +1407,No,No,1576.900672,39835.34872 +1408,No,No,840.1051582,33976.70754 +1409,No,Yes,1210.438929,19022.19227 +1410,No,Yes,714.5496128,13431.84165 +1411,No,Yes,607.094029,16373.93446 +1412,No,No,413.3635225,43071.18631 +1413,No,No,1184.212015,34990.58061 +1414,No,No,273.6022741,27996.5771 +1415,No,Yes,871.8152195,9788.57126 +1416,No,No,1724.556817,46397.45231 +1417,No,Yes,1107.40038,15661.57973 +1418,No,Yes,1161.407345,18420.29889 +1419,No,Yes,107.6025084,17941.73917 +1420,No,No,136.4220356,42719.89406 +1421,No,No,682.035984,30288.9418 +1422,No,Yes,791.5303591,18342.73735 +1423,No,No,457.8690747,43134.54801 +1424,No,No,1514.963236,36403.03614 +1425,No,No,816.0938872,37763.03651 +1426,No,No,1241.954243,41952.11577 +1427,No,No,932.2025461,49214.70512 +1428,No,No,608.1084466,46962.78701 +1429,No,Yes,627.5932466,15423.20763 +1430,No,Yes,593.0552412,22786.91671 +1431,No,No,187.3101926,31140.74672 +1432,No,No,736.4757788,39538.3308 +1433,No,Yes,838.0811589,23698.70725 +1434,No,No,511.5159962,43737.18181 +1435,No,No,355.3596461,65798.92003 +1436,No,No,1138.396686,24098.34886 +1437,No,No,828.0049554,53806.31628 +1438,No,No,1218.553005,51224.91731 +1439,No,No,316.0121309,34250.5216 +1440,No,Yes,480.4697653,14866.61225 +1441,No,No,152.5030074,39502.8368 +1442,No,No,1129.316466,46689.9794 +1443,No,Yes,887.29064,14958.55147 +1444,No,No,938.4369137,34184.94288 +1445,No,No,1076.665439,43972.69386 +1446,Yes,No,1575.487818,35735.45501 +1447,No,No,148.1459807,43104.38609 +1448,Yes,No,1865.635779,49604.88213 +1449,No,Yes,633.7691889,14109.82181 +1450,No,Yes,0,23422.13722 +1451,No,No,922.817168,50800.35901 +1452,No,No,4.712141697,28521.36688 +1453,No,No,1198.175927,42968.63988 +1454,No,No,1726.618698,43814.96127 +1455,No,No,1075.219721,30485.05974 +1456,No,Yes,883.9235437,14740.13868 +1457,No,No,1099.292985,47625.30546 +1458,No,No,948.9651331,45456.9288 +1459,No,Yes,814.6300753,22948.15776 +1460,No,No,687.2929387,35989.22765 +1461,No,No,693.6715894,32578.60631 +1462,No,No,814.8431184,28526.21683 +1463,No,No,1031.974601,59555.86581 +1464,No,No,1319.483579,62054.98639 +1465,No,No,1366.026022,51551.10832 +1466,No,No,1033.455043,55487.21989 +1467,No,No,0,50231.44734 +1468,No,No,857.5823611,54372.89419 +1469,No,No,1156.289558,33959.73853 +1470,No,No,1713.26359,34005.8614 +1471,No,Yes,902.9807197,15076.33854 +1472,No,Yes,212.0421829,16179.30775 +1473,No,No,632.8039803,51149.42525 +1474,No,Yes,378.0211154,19571.3923 +1475,No,No,999.5380168,43934.85626 +1476,No,No,970.8525458,49043.78232 +1477,No,No,1319.187613,35466.24186 +1478,No,No,286.3299636,59576.65032 +1479,No,No,830.5718756,39723.76866 +1480,No,Yes,657.5321356,19395.73893 +1481,No,No,786.6410085,57704.64461 +1482,No,No,111.0619105,40675.32738 +1483,No,No,298.2365837,27838.68412 +1484,No,Yes,819.8586726,19802.46107 +1485,Yes,No,1790.674983,44607.42902 +1486,No,No,883.4719996,44207.33303 +1487,No,Yes,384.8313281,17844.51751 +1488,Yes,No,1567.610704,38785.35768 +1489,No,Yes,927.7727236,18148.06928 +1490,No,No,796.3261454,28616.70904 +1491,No,No,652.5533166,32438.0581 +1492,No,No,315.0122984,44690.49774 +1493,No,No,1609.797453,38756.45469 +1494,No,No,851.7962751,57950.77175 +1495,No,No,460.2078669,44527.27275 +1496,No,No,1203.828964,41484.93943 +1497,Yes,No,2074.807589,38988.85925 +1498,No,No,1243.708322,37926.10524 +1499,No,Yes,1373.037835,19457.6093 +1500,No,No,449.4126863,52892.25015 +1501,No,No,126.5585946,69541.9486 +1502,No,No,387.2361429,24243.94446 +1503,Yes,Yes,2332.878254,11770.23412 +1504,No,No,1359.438493,24411.13474 +1505,No,No,469.3317131,58360.38912 +1506,No,No,111.2949539,40086.69012 +1507,No,No,1443.875165,54355.72614 +1508,No,No,396.820316,51560.0281 +1509,No,Yes,146.1511448,15160.48947 +1510,No,No,607.2000058,36615.82944 +1511,No,No,1708.149967,46416.94558 +1512,No,No,956.533337,53702.54156 +1513,No,Yes,676.6132706,16148.18637 +1514,No,Yes,238.3181069,17194.15414 +1515,No,Yes,0,15967.61421 +1516,No,Yes,740.7379512,13065.56594 +1517,No,Yes,933.7116933,11810.55457 +1518,No,No,860.0343646,33740.69911 +1519,No,No,1032.940825,31152.14069 +1520,No,No,0,36949.94989 +1521,No,No,835.2734997,51471.14295 +1522,No,No,188.4087559,35073.5146 +1523,No,No,506.3249831,43631.29653 +1524,No,No,496.7518122,35955.63928 +1525,No,Yes,739.1794564,15819.73057 +1526,No,Yes,916.5369368,20130.91526 +1527,No,No,1559.752146,37227.26882 +1528,No,No,392.2395399,44983.2018 +1529,No,No,1065.82556,39718.94925 +1530,No,No,523.7088979,48091.74453 +1531,No,No,789.564771,39199.02953 +1532,No,No,805.1953048,38222.25523 +1533,No,No,1255.543375,45142.27359 +1534,No,No,654.808846,44255.7672 +1535,No,No,716.7649108,51528.49866 +1536,No,No,686.0723086,32318.42224 +1537,No,No,1530.370017,29479.02245 +1538,No,No,879.3738243,29697.14649 +1539,No,No,738.3278043,37273.17115 +1540,No,No,1647.426764,49156.15931 +1541,No,Yes,503.2166226,21371.24632 +1542,No,No,215.0253294,40399.40994 +1543,No,Yes,820.656301,24820.04824 +1544,No,No,990.6748042,56141.11376 +1545,No,Yes,914.1064962,15546.78367 +1546,No,Yes,1021.011613,8970.912036 +1547,No,No,1143.431379,35773.40185 +1548,No,Yes,763.3973889,15224.26346 +1549,Yes,No,1532.3263,42152.36171 +1550,No,No,1860.741148,39551.03583 +1551,No,No,1101.296311,27781.45239 +1552,No,No,541.9968025,43873.28941 +1553,No,Yes,696.5835269,2981.279548 +1554,No,No,1536.232276,30635.57179 +1555,No,No,1333.197313,33782.90385 +1556,No,Yes,869.509201,18056.45478 +1557,No,No,1048.55214,40916.12455 +1558,No,No,12.07926653,32670.41964 +1559,No,No,699.3825817,34276.5925 +1560,No,No,855.4263748,48653.58256 +1561,No,Yes,562.3041982,20164.31101 +1562,No,No,62.17004972,28660.74751 +1563,No,No,977.8897501,45005.25642 +1564,No,No,1685.450623,60890.3648 +1565,No,No,1257.79532,21679.4905 +1566,No,No,671.784325,53374.954 +1567,No,No,63.09747039,38493.08725 +1568,No,No,686.3844319,28953.27033 +1569,No,No,582.944764,40368.54382 +1570,No,No,808.6251972,32316.37274 +1571,No,No,1077.980307,17705.21896 +1572,No,Yes,1716.089774,22755.26192 +1573,No,No,417.7275813,31206.29045 +1574,No,No,1300.995166,42493.95555 +1575,No,No,896.678116,46328.5442 +1576,No,No,136.82832,13585.9975 +1577,No,No,29.17509908,38871.47869 +1578,No,No,282.9095623,35445.91861 +1579,No,No,858.5426304,19071.84843 +1580,No,No,864.0471984,27690.11354 +1581,No,No,1225.975008,42203.20816 +1582,No,No,696.4287643,38028.29972 +1583,No,Yes,1840.217987,26480.71927 +1584,No,Yes,1227.485273,16717.62059 +1585,No,No,1716.675134,39543.29108 +1586,No,No,330.6224277,11732.25138 +1587,No,Yes,915.6568136,17710.8547 +1588,No,No,133.0970308,34578.99685 +1589,No,No,420.611587,33293.46686 +1590,No,No,1058.058796,27196.25858 +1591,Yes,No,1972.16682,34362.63501 +1592,No,No,790.2389859,43646.14313 +1593,No,No,131.6247137,40158.70898 +1594,No,No,0,37468.48077 +1595,No,Yes,106.2506074,17638.79973 +1596,No,Yes,712.2537066,21407.04362 +1597,No,No,906.2232258,44626.94267 +1598,No,No,127.4099032,49735.64164 +1599,No,No,1698.071916,48595.70465 +1600,No,Yes,1448.035606,18989.39705 +1601,No,No,711.4394514,40507.82124 +1602,No,No,326.6289568,52696.72518 +1603,No,No,352.9438765,59372.71187 +1604,No,Yes,1247.067238,15522.58415 +1605,No,No,439.068967,47899.16968 +1606,No,No,1030.210058,40711.45317 +1607,No,No,0,33168.05805 +1608,No,No,1113.276529,51457.53906 +1609,No,No,1341.077777,40199.84141 +1610,Yes,Yes,2269.946966,18021.10595 +1611,No,No,1384.264924,27737.60852 +1612,No,No,0,32724.50637 +1613,No,No,745.8132048,42762.47499 +1614,No,No,909.6336239,41915.52066 +1615,No,No,196.3740083,57397.2772 +1616,No,No,866.1746688,41365.45638 +1617,No,Yes,1460.747667,24960.35938 +1618,No,No,760.7767006,44851.56026 +1619,No,Yes,1165.283215,10354.93762 +1620,No,No,1085.425572,39274.83387 +1621,No,Yes,724.385576,18641.49842 +1622,No,No,114.8844751,27365.4222 +1623,No,No,794.5875684,52507.47963 +1624,No,No,1093.256619,28811.27349 +1625,No,No,943.9378335,48198.40061 +1626,Yes,No,1861.060871,55671.72366 +1627,No,No,900.8387591,33130.70934 +1628,No,Yes,1682.796142,23344.07394 +1629,No,No,196.1954156,26612.5603 +1630,No,No,1181.123749,29181.839 +1631,No,Yes,1101.15809,13142.18149 +1632,No,Yes,1506.860473,14914.82336 +1633,No,Yes,1213.840554,17374.06395 +1634,No,Yes,547.2463251,17445.90172 +1635,No,No,1708.574079,34890.71713 +1636,No,No,248.0414824,36257.16145 +1637,No,No,386.2650712,45776.20668 +1638,No,No,527.4632744,35708.16925 +1639,No,No,1546.842501,51161.9795 +1640,No,No,0,47214.4336 +1641,No,No,0,55781.20484 +1642,No,No,955.4664338,47520.17558 +1643,No,Yes,750.8620345,16932.32771 +1644,No,Yes,1255.748135,12209.57707 +1645,No,No,818.0608834,35902.4718 +1646,No,Yes,1469.366668,11337.42299 +1647,No,No,331.7207347,46853.74585 +1648,Yes,No,1456.546056,51508.57434 +1649,No,Yes,1290.854293,19532.73153 +1650,No,Yes,711.0304958,12685.82486 +1651,No,No,734.3172028,33240.03903 +1652,No,No,329.2188974,48133.82442 +1653,No,No,1231.506211,35068.79074 +1654,No,No,847.054148,28539.1079 +1655,No,No,811.4289115,55119.89696 +1656,No,Yes,1656.857598,15359.43651 +1657,No,Yes,1547.153248,14344.28959 +1658,No,Yes,590.718726,13041.19846 +1659,No,Yes,893.9664662,15092.77603 +1660,No,No,0,34066.43811 +1661,No,No,800.15468,51730.70461 +1662,No,Yes,685.5225351,12361.8634 +1663,No,No,1262.757445,68579.10466 +1664,No,No,770.1150264,49856.69012 +1665,No,No,1181.000359,35123.74007 +1666,No,Yes,527.653898,17819.96534 +1667,No,No,441.4899198,39209.90013 +1668,No,No,921.9744134,38340.56889 +1669,No,Yes,875.3623525,14637.17693 +1670,No,No,1033.121155,66304.77382 +1671,No,No,1046.743543,40822.44741 +1672,No,Yes,1319.706449,23373.9312 +1673,No,Yes,698.5122051,26768.91312 +1674,No,No,570.9814914,47021.00672 +1675,No,No,842.5417747,33897.92651 +1676,No,No,0,47256.06211 +1677,No,No,850.3848881,44547.48551 +1678,No,Yes,963.1879064,12799.50938 +1679,No,No,1136.331132,42149.86899 +1680,No,Yes,1945.490483,14941.31796 +1681,No,Yes,1442.543601,13049.31938 +1682,No,No,135.6290155,39925.70017 +1683,No,No,423.8769467,36783.05143 +1684,No,No,299.7795953,28917.53781 +1685,No,Yes,701.5393706,17607.45782 +1686,No,No,283.207207,39263.39202 +1687,No,Yes,1357.849734,11402.54063 +1688,No,Yes,1327.864163,11020.90643 +1689,No,Yes,946.4499823,15179.21205 +1690,No,No,663.5052822,53102.24551 +1691,No,No,115.840084,47519.84972 +1692,No,No,800.4862112,37715.83633 +1693,No,No,640.0638938,52413.85897 +1694,No,No,877.2830136,27405.1355 +1695,No,Yes,1382.087692,15552.25205 +1696,No,No,751.3945389,50910.24562 +1697,No,No,0,39893.27193 +1698,No,No,1120.703239,29613.88758 +1699,No,Yes,1312.051431,28483.13551 +1700,No,No,389.0553278,22581.62365 +1701,No,No,861.6904462,58029.64023 +1702,No,No,1079.396005,38864.1448 +1703,Yes,No,1893.289792,31821.72558 +1704,No,No,337.4959039,55853.26513 +1705,No,No,1142.351706,45580.57481 +1706,No,Yes,847.0564853,13741.32707 +1707,No,Yes,1575.238116,24469.4914 +1708,No,Yes,403.3470535,19690.34551 +1709,No,Yes,566.5231194,25398.39838 +1710,Yes,Yes,2009.685744,25694.49616 +1711,No,No,954.5981678,50139.09299 +1712,No,No,1057.940415,45514.67253 +1713,No,No,381.3874762,28265.08609 +1714,No,No,793.1868556,35555.78028 +1715,No,No,1078.910385,54419.78655 +1716,No,Yes,1311.098483,21189.2384 +1717,No,No,38.29557625,37186.55159 +1718,No,No,756.2166996,36513.92121 +1719,No,No,56.86937772,56245.0654 +1720,No,No,1189.748039,30315.88513 +1721,No,No,1337.390877,51507.50092 +1722,No,Yes,1255.499215,16703.01737 +1723,No,No,1211.867926,42777.58234 +1724,No,No,318.8772708,50069.02433 +1725,No,No,336.0705452,38595.91155 +1726,No,No,934.4904926,31043.40833 +1727,No,No,812.4882979,42721.3007 +1728,No,No,1184.683904,37937.95265 +1729,No,Yes,1737.607593,7153.539666 +1730,No,No,94.12072071,36303.94158 +1731,No,No,994.6744382,33002.33905 +1732,No,No,0,21809.21851 +1733,No,No,1006.46883,44563.7708 +1734,No,No,331.9228185,47779.92656 +1735,No,No,967.1352554,23278.66591 +1736,No,No,132.158429,26878.47213 +1737,No,No,813.7409929,30975.21409 +1738,No,Yes,0,15986.24197 +1739,No,No,529.264959,38814.6067 +1740,No,No,743.0559317,40944.5638 +1741,No,No,27.7011327,45540.9439 +1742,No,No,1299.924669,44214.54091 +1743,No,No,906.5417626,27074.77474 +1744,No,No,1451.179476,53556.5521 +1745,No,No,1027.566531,48558.08728 +1746,No,No,1047.227421,40974.72813 +1747,No,No,1452.603293,28989.04395 +1748,No,Yes,1190.544112,20291.17198 +1749,Yes,No,1858.904515,35525.21393 +1750,No,Yes,1152.520224,12699.40587 +1751,No,No,831.5951866,37372.82827 +1752,No,No,922.6966762,34622.61934 +1753,No,Yes,469.4564151,13944.8934 +1754,No,No,474.8292191,41288.8291 +1755,No,Yes,1485.272118,10300.85544 +1756,No,Yes,943.3475559,19977.89177 +1757,No,No,1789.464512,27427.00457 +1758,No,No,659.5431977,21835.1718 +1759,No,Yes,1319.534005,24032.54412 +1760,No,Yes,514.3682658,18934.68347 +1761,No,No,1244.465198,40036.00808 +1762,No,No,946.8100507,41237.76925 +1763,No,Yes,535.6860201,11019.60693 +1764,No,No,682.8184588,33026.00374 +1765,No,No,1190.48596,38553.57479 +1766,No,No,171.0011892,42749.13071 +1767,No,Yes,1238.016616,26854.36628 +1768,No,No,1037.157699,36034.57837 +1769,No,Yes,464.8353104,19121.22865 +1770,No,No,124.699032,65211.07421 +1771,No,No,667.2030608,44682.83156 +1772,No,No,1111.060998,50301.47568 +1773,No,No,1142.39654,33740.44298 +1774,No,No,778.8301656,44257.33103 +1775,No,No,1412.024755,47727.56463 +1776,No,No,986.081816,52009.39965 +1777,No,No,1251.784019,32721.14404 +1778,No,No,149.0244884,33398.46237 +1779,No,No,465.0855346,43480.56849 +1780,No,No,1031.126796,41022.09564 +1781,Yes,No,1478.124069,31515.34449 +1782,No,No,789.0007057,35798.47264 +1783,No,Yes,790.2959403,14086.44837 +1784,No,Yes,1123.595047,16082.90571 +1785,No,No,1370.109496,40101.37913 +1786,No,No,396.5135872,41969.74677 +1787,No,No,682.0783655,30736.88765 +1788,No,No,338.735981,44923.04553 +1789,No,Yes,1056.329476,16000.84177 +1790,No,No,927.211781,44368.88309 +1791,No,No,634.6961045,32594.69223 +1792,No,No,833.533113,40526.30562 +1793,No,Yes,1345.230627,18518.71569 +1794,No,No,550.8989669,45347.78162 +1795,No,No,495.7089542,24845.43297 +1796,No,No,661.4261522,22610.3103 +1797,No,No,136.1995896,20935.61911 +1798,No,No,480.1925853,26084.40714 +1799,No,No,230.8689248,32798.78259 +1800,No,No,93.5701987,42930.77918 +1801,No,Yes,508.8248517,22663.70983 +1802,No,Yes,970.5159466,16584.82829 +1803,No,No,1324.296671,46875.39427 +1804,No,No,932.9638468,32105.44192 +1805,No,No,0,31418.36914 +1806,No,Yes,984.7631618,17039.97332 +1807,No,No,725.1205004,47549.20075 +1808,No,No,246.8238097,40953.34276 +1809,No,No,1051.321091,23859.4318 +1810,No,Yes,926.8926126,19445.93717 +1811,No,Yes,1024.024885,18545.89964 +1812,No,No,699.8420242,41985.25767 +1813,No,Yes,1036.323514,14108.45452 +1814,No,No,321.1088067,36275.14289 +1815,No,No,0,41152.17395 +1816,No,Yes,1299.187324,19878.74735 +1817,No,No,763.3753093,40978.14118 +1818,No,Yes,808.7427719,9775.581396 +1819,No,No,808.5075036,34543.0188 +1820,No,Yes,677.5528258,20745.01533 +1821,No,Yes,904.0089267,13996.5019 +1822,No,Yes,102.1667716,26266.36282 +1823,No,No,1218.346515,43930.98989 +1824,No,Yes,774.4138371,12289.86437 +1825,No,No,1309.564374,47432.43278 +1826,No,No,921.9989908,31553.73405 +1827,No,No,629.6962085,60230.21901 +1828,No,No,77.50293863,31050.15582 +1829,Yes,No,1772.855484,42080.10684 +1830,No,No,960.2376921,53418.51083 +1831,No,No,664.6529879,56677.62141 +1832,No,No,109.918734,33419.00689 +1833,Yes,Yes,1790.360537,14306.8236 +1834,No,No,1425.801629,34015.84987 +1835,No,Yes,1167.620124,23502.21282 +1836,No,No,891.1921462,35862.1673 +1837,No,Yes,1124.259312,23168.92345 +1838,No,No,1796.114376,31862.57529 +1839,No,No,237.9761856,38229.51786 +1840,No,No,1353.264924,43888.88306 +1841,No,Yes,1176.55006,18662.59883 +1842,No,Yes,1229.441498,12158.04466 +1843,No,No,270.548125,34258.83123 +1844,No,Yes,722.0554201,14661.52456 +1845,Yes,No,1170.198974,46692.10478 +1846,No,No,840.1365052,38738.17715 +1847,No,Yes,873.6412372,21810.61199 +1848,No,No,434.2433404,57146.4261 +1849,No,No,605.2744474,58469.47549 +1850,No,No,428.8421619,36980.13927 +1851,No,No,1662.762192,36691.56219 +1852,No,No,682.7124117,33175.18305 +1853,No,No,598.513777,42584.12081 +1854,No,No,1088.254765,32617.59348 +1855,No,No,911.0522329,38629.51836 +1856,No,No,538.1600228,44654.09318 +1857,No,No,721.7438218,21162.74272 +1858,No,Yes,438.5173671,17400.39085 +1859,No,Yes,516.865449,20179.34606 +1860,No,No,811.1712029,42185.44357 +1861,No,No,775.9818346,68179.7612 +1862,No,No,285.4000014,39398.56535 +1863,Yes,No,1554.816319,26430.4894 +1864,No,No,1057.015212,54652.71033 +1865,No,Yes,673.8124271,17672.28798 +1866,No,No,689.6873624,24085.43483 +1867,No,Yes,1400.344589,14768.69622 +1868,No,No,601.4172905,41490.61584 +1869,No,Yes,754.8595587,18996.8367 +1870,No,No,639.6652615,49977.29496 +1871,No,No,765.7724451,35122.02738 +1872,No,No,220.3689577,24019.31056 +1873,No,No,47.30266754,38293.31114 +1874,No,Yes,780.7223987,23819.78879 +1875,No,No,168.8134955,57262.7545 +1876,No,No,1557.640951,46030.26209 +1877,Yes,No,1891.109614,34448.69385 +1878,No,Yes,688.7475484,26662.3766 +1879,No,No,452.0078771,39990.76569 +1880,No,No,574.4911928,31632.62424 +1881,No,No,572.1661387,43807.21905 +1882,No,No,946.770746,26618.52276 +1883,No,No,523.9086948,43928.63793 +1884,No,No,1146.358385,44840.02496 +1885,No,No,1646.613919,39347.4568 +1886,No,No,1216.202309,43656.12889 +1887,No,No,1184.899625,53896.90225 +1888,No,No,1416.065555,40371.28459 +1889,No,No,431.9427414,46658.01381 +1890,No,Yes,521.0330664,16887.16039 +1891,No,Yes,806.9961112,15964.99698 +1892,No,No,775.4439275,33781.78107 +1893,No,No,942.2799083,24683.38081 +1894,No,Yes,1261.257138,26085.07612 +1895,No,No,509.3708922,39546.47264 +1896,No,No,1389.386452,39707.98955 +1897,Yes,Yes,1836.161765,18944.80788 +1898,No,No,196.4829904,41972.98495 +1899,No,No,1348.888866,40548.75271 +1900,No,No,661.6020253,43157.11928 +1901,No,No,718.9867228,21044.67383 +1902,No,No,451.1293241,39849.71324 +1903,No,Yes,114.4182416,12540.90728 +1904,No,Yes,889.0123127,10382.9368 +1905,No,No,1046.135324,47218.81679 +1906,No,Yes,1263.874831,18520.06367 +1907,No,No,1109.034634,29759.6363 +1908,No,No,698.4053812,33265.43584 +1909,No,No,1328.967126,51721.37161 +1910,No,Yes,1199.809244,19538.02749 +1911,No,No,1261.778453,26767.92328 +1912,No,No,605.3656943,39002.22964 +1913,No,No,550.6398862,35648.39063 +1914,No,No,894.2750955,36513.5392 +1915,No,No,1163.534574,46173.76824 +1916,No,Yes,1061.755086,17502.81964 +1917,No,Yes,1204.54963,19058.98454 +1918,No,No,1666.415624,18916.37486 +1919,No,No,412.9020609,49060.92374 +1920,No,No,595.0659734,29842.88605 +1921,No,Yes,836.0057683,7404.715843 +1922,No,No,178.1545157,28785.9621 +1923,No,Yes,627.6453208,17868.44485 +1924,No,No,1004.255095,36895.86555 +1925,No,No,1195.399568,34333.92991 +1926,No,No,515.6356355,51072.84702 +1927,No,No,1242.110378,37980.75837 +1928,No,Yes,153.2610397,14027.79074 +1929,No,No,1086.740286,42451.99308 +1930,No,No,280.9590497,29998.8573 +1931,No,No,955.8814143,48657.79037 +1932,No,No,1093.282258,18844.25039 +1933,No,No,734.0879454,22342.55035 +1934,No,No,793.8293877,45089.55206 +1935,No,Yes,410.4961872,25838.19736 +1936,No,Yes,677.6531966,18930.0772 +1937,No,No,814.5538745,39148.55837 +1938,No,No,800.1773456,43547.39817 +1939,No,Yes,300.7955043,19673.67406 +1940,Yes,Yes,1748.680849,13715.42438 +1941,No,No,0,54901.82221 +1942,Yes,No,1807.684491,42308.95445 +1943,No,No,833.0705629,36330.26209 +1944,No,No,794.464327,32431.82055 +1945,No,No,631.5980113,46841.11096 +1946,No,No,108.2494526,45264.56479 +1947,No,No,1040.396434,41395.28825 +1948,No,Yes,1207.052483,24747.88997 +1949,No,No,67.23043907,45751.44793 +1950,No,No,438.3989038,40576.74212 +1951,No,No,708.579795,21728.8202 +1952,No,No,775.4022116,41857.57803 +1953,No,No,0,49459.27548 +1954,No,No,610.5067355,52124.23357 +1955,No,No,845.3432645,34216.75436 +1956,No,No,181.9197785,37725.24736 +1957,No,No,272.9878407,63566.36599 +1958,No,No,805.0963327,40236.89963 +1959,No,No,226.8557048,33014.89333 +1960,No,No,0,39576.43465 +1961,No,No,691.9288288,23957.71488 +1962,No,No,1674.19783,54658.07415 +1963,No,Yes,715.7174174,27285.33324 +1964,No,No,339.4334297,44889.87491 +1965,No,Yes,767.3371317,21979.64653 +1966,No,No,1088.488096,38171.37207 +1967,No,No,28.56739084,35414.57828 +1968,No,Yes,792.5775616,21347.27345 +1969,No,Yes,1093.11502,22593.08161 +1970,No,No,408.4892466,35984.56168 +1971,No,No,1214.830968,38224.96205 +1972,No,Yes,850.1598866,26764.29246 +1973,No,Yes,1078.662963,25945.17896 +1974,No,No,403.6464529,55458.89125 +1975,No,No,1378.169887,48152.98785 +1976,No,No,306.9952026,43504.30524 +1977,No,No,1569.380835,42414.62536 +1978,No,Yes,1377.269683,17077.24211 +1979,No,Yes,656.379057,26578.88356 +1980,No,Yes,1654.632735,23687.35721 +1981,No,No,702.0847515,47811.75807 +1982,No,No,1110.060616,40236.50149 +1983,No,Yes,718.211754,18788.74812 +1984,No,No,737.1504318,50544.78419 +1985,No,No,672.3800476,40236.8732 +1986,No,No,1298.002763,42219.91124 +1987,No,No,489.3993741,37338.58292 +1988,No,Yes,636.104269,20763.82786 +1989,No,Yes,1406.799827,16686.62801 +1990,No,No,997.809843,50287.87374 +1991,No,No,0,49792.75726 +1992,No,Yes,691.2247423,16872.15171 +1993,No,No,485.8689097,14213.70558 +1994,No,No,730.1796753,42685.18467 +1995,No,No,152.1195126,41847.23184 +1996,No,Yes,1105.925571,13126.21424 +1997,No,No,0,47592.72814 +1998,No,No,815.737668,48788.10334 +1999,No,No,568.629894,28122.59409 +2000,No,No,589.9444194,24854.60458 +2001,No,No,1139.567166,36363.6248 +2002,No,Yes,344.5228968,21341.65384 +2003,Yes,No,2005.575128,36636.00859 +2004,No,No,1391.033809,51881.81867 +2005,No,No,1338.884597,32500.58487 +2006,No,No,78.78255363,37605.45251 +2007,No,No,764.6451796,49100.94838 +2008,No,No,602.6168777,56017.12754 +2009,No,No,834.6900595,42468.91366 +2010,No,No,1391.516591,30152.80387 +2011,Yes,No,1823.751426,53526.35641 +2012,No,No,1010.070759,39456.90834 +2013,No,Yes,1069.80034,14736.147 +2014,No,No,1116.065771,37074.06137 +2015,No,No,428.4022839,36340.65107 +2016,No,No,147.4885752,44482.26878 +2017,No,Yes,689.519195,15600.04737 +2018,No,No,939.4207168,55360.03312 +2019,No,Yes,591.1402653,19937.02257 +2020,No,No,1139.264364,41869.31867 +2021,No,No,500.6492512,31353.75002 +2022,No,No,1109.408616,64213.19249 +2023,No,Yes,733.928449,28105.6062 +2024,No,No,687.9567922,40354.21604 +2025,No,No,118.5984509,35974.76401 +2026,No,No,0,42009.04182 +2027,No,No,634.8650438,49681.22453 +2028,No,No,615.4712543,38161.6557 +2029,No,No,457.9577436,36211.19499 +2030,No,Yes,854.1493647,12843.43651 +2031,No,No,770.9545178,42673.99315 +2032,No,Yes,0,24283.33598 +2033,No,No,58.4438978,44838.69499 +2034,Yes,Yes,1893.664334,21526.35063 +2035,No,No,768.0436339,35951.12467 +2036,No,No,336.2424173,46279.52586 +2037,No,No,852.9713399,42773.77146 +2038,No,Yes,1300.707924,13675.52018 +2039,No,No,743.5160765,33764.57181 +2040,No,No,293.4523575,38528.44736 +2041,No,Yes,1472.948461,23877.59266 +2042,No,No,756.1601309,52778.05363 +2043,No,No,465.4956002,42358.16154 +2044,No,Yes,1041.438743,24847.8812 +2045,No,No,709.7382874,46363.34367 +2046,No,No,427.11769,34193.16093 +2047,No,No,612.2257762,60323.35727 +2048,No,No,1322.152854,47892.88998 +2049,No,No,673.7476812,25013.06314 +2050,Yes,No,1135.047349,48982.22585 +2051,No,Yes,509.6129401,21928.91099 +2052,No,No,113.669024,38513.33229 +2053,No,Yes,560.4317644,24511.21021 +2054,No,No,1163.73601,32467.74804 +2055,No,No,133.5089142,49516.34083 +2056,No,No,897.4202657,36296.74814 +2057,No,Yes,793.5627844,13446.05629 +2058,No,No,942.7868536,27736.64512 +2059,No,No,953.8070084,40908.40612 +2060,No,No,814.2436789,29631.11007 +2061,No,No,1387.704469,26863.17233 +2062,No,Yes,601.345719,15897.138 +2063,No,No,656.5227013,34229.3741 +2064,No,Yes,1790.544092,21938.20923 +2065,Yes,Yes,1925.982795,17763.3503 +2066,No,No,0,16834.80271 +2067,No,No,310.1186425,31445.77108 +2068,No,Yes,956.7203919,14464.80738 +2069,No,No,205.1731978,47284.25351 +2070,No,Yes,1572.803778,24557.35211 +2071,No,Yes,1455.684182,14822.83306 +2072,No,Yes,1011.458814,28316.75761 +2073,No,No,413.5990058,31466.22059 +2074,No,Yes,0,19281.71466 +2075,No,No,377.1938454,41901.84737 +2076,No,No,0,51480.38735 +2077,No,Yes,0,13407.75859 +2078,No,Yes,527.0560852,22986.28382 +2079,No,Yes,1470.330834,15752.68179 +2080,No,No,776.5683574,54662.516 +2081,No,No,1096.203668,41016.21823 +2082,No,No,130.4529103,46094.07715 +2083,No,No,1331.607309,27665.67547 +2084,No,Yes,598.0818313,12385.82408 +2085,No,No,177.0108651,53545.01511 +2086,No,No,937.0330528,27174.5267 +2087,No,No,0,46581.36178 +2088,No,Yes,970.2107314,19245.19816 +2089,No,No,268.8794915,48006.15004 +2090,No,Yes,708.9778283,15968.91656 +2091,No,No,596.0091827,32983.5435 +2092,Yes,Yes,1944.677459,13026.04673 +2093,No,Yes,1805.293071,10405.42666 +2094,No,No,405.3880438,28493.35072 +2095,No,No,849.1290111,26970.73609 +2096,No,No,988.4077153,32458.76937 +2097,Yes,Yes,2261.848162,20030.16512 +2098,No,No,1221.599764,40865.31289 +2099,No,No,1057.3564,45099.09159 +2100,No,Yes,292.3000209,17321.22712 +2101,No,Yes,940.0797321,18581.57718 +2102,No,No,368.1969332,32107.67872 +2103,No,No,703.3860084,38419.41383 +2104,No,No,1018.22126,39893.30287 +2105,No,No,240.2256121,25042.40173 +2106,No,Yes,446.6348475,18309.79074 +2107,No,No,1277.793381,35620.156 +2108,No,No,519.5840264,30546.86437 +2109,No,No,933.5212001,40378.42312 +2110,No,Yes,1074.577871,18574.35373 +2111,No,Yes,1697.753989,19021.0677 +2112,No,No,1240.664707,46148.19747 +2113,No,No,573.5848026,54427.92115 +2114,No,No,486.057014,54794.3922 +2115,No,No,455.3484167,41378.99712 +2116,No,No,1110.43993,39311.53452 +2117,No,No,1225.065552,49090.36931 +2118,No,No,286.2582428,28838.32131 +2119,No,No,1444.630477,40364.55588 +2120,No,No,1031.044588,38199.04679 +2121,No,Yes,382.0032763,19951.72172 +2122,No,No,1337.313266,41529.55205 +2123,No,No,463.9018129,39626.76943 +2124,No,No,948.7779787,24094.78649 +2125,No,No,626.5161419,49207.0013 +2126,No,No,350.0715602,45354.74619 +2127,Yes,Yes,1492.963421,11054.06844 +2128,No,No,847.6358955,33199.37538 +2129,No,No,241.9407399,47843.07974 +2130,No,No,616.5386778,31925.18324 +2131,No,No,733.4148453,36724.89331 +2132,No,No,1100.972882,50237.25782 +2133,No,No,665.6278337,55093.3824 +2134,No,No,174.935485,55044.37007 +2135,No,No,124.2272969,51265.5462 +2136,No,No,344.5450091,38463.68526 +2137,No,No,954.9482703,42963.66173 +2138,No,No,1629.325936,44911.64353 +2139,No,No,913.044919,31841.63497 +2140,No,No,1306.843023,49036.7417 +2141,No,Yes,2308.893236,19110.26641 +2142,No,No,1042.42039,58234.76647 +2143,No,No,1264.875626,44359.06062 +2144,No,Yes,1348.645456,21789.38222 +2145,No,No,1256.892288,23100.0095 +2146,No,No,1046.631553,45717.62805 +2147,No,No,0,22535.50636 +2148,No,No,432.6554265,22986.93175 +2149,No,No,563.1501915,43028.5972 +2150,No,No,721.0934339,41411.14368 +2151,No,No,741.5927598,24393.31595 +2152,No,No,1071.305347,19529.03519 +2153,No,Yes,1589.638598,22541.89203 +2154,No,No,1194.597579,38222.50611 +2155,No,No,269.5879188,38650.34609 +2156,No,Yes,455.5990263,20549.13483 +2157,No,Yes,795.5407915,10257.67249 +2158,No,No,595.3688043,31418.81567 +2159,No,Yes,539.8347931,13119.68585 +2160,Yes,No,1322.723979,23229.90913 +2161,No,No,1267.295791,36784.83699 +2162,No,No,1091.999738,34704.51229 +2163,No,Yes,951.7855506,14354.63488 +2164,No,Yes,559.4983021,16994.57867 +2165,No,Yes,1222.141327,9080.1457 +2166,No,Yes,1026.104495,20991.39643 +2167,No,No,1140.594209,25116.66829 +2168,No,No,556.4448383,39707.10879 +2169,No,Yes,306.6107704,11511.88422 +2170,No,No,1271.250986,42533.26766 +2171,No,No,333.0727083,61508.75656 +2172,No,No,1174.519506,23942.20577 +2173,No,No,473.9614391,35133.07041 +2174,Yes,Yes,1724.963232,22170.68734 +2175,No,Yes,1056.770651,14194.659 +2176,No,No,841.734663,38023.33074 +2177,No,No,590.8781726,52977.64198 +2178,No,Yes,1070.666361,19312.8406 +2179,No,No,914.9723795,52871.32437 +2180,No,No,1130.512899,35071.67338 +2181,Yes,No,1706.956841,21738.79633 +2182,No,No,839.3255069,43909.00396 +2183,No,Yes,330.9361079,21272.30738 +2184,No,Yes,762.5850193,24738.11845 +2185,No,No,986.8181659,32386.16236 +2186,No,No,916.0730001,42201.8206 +2187,No,No,1263.676788,25695.48137 +2188,No,No,0,42118.19666 +2189,No,No,441.0087864,47707.12518 +2190,No,No,349.0544114,46698.02743 +2191,No,No,423.9854717,37979.78533 +2192,No,Yes,1295.029713,21717.46955 +2193,No,No,681.7244755,31153.63754 +2194,No,No,1153.02131,20586.27811 +2195,No,No,1470.342593,26136.74369 +2196,No,Yes,1040.873311,12177.03896 +2197,No,No,1007.268679,28245.32008 +2198,No,No,667.1445832,44863.06899 +2199,No,Yes,634.6799702,17023.68286 +2200,No,No,298.7045166,39599.66158 +2201,No,No,909.48422,48358.44097 +2202,Yes,Yes,1737.537815,20974.03459 +2203,No,No,203.2638172,46518.13241 +2204,No,Yes,0,19750.39622 +2205,No,Yes,1889.678319,14756.90002 +2206,Yes,No,1558.463471,41648.32617 +2207,No,No,1201.927539,45382.61357 +2208,No,No,1051.604407,47529.21731 +2209,No,No,641.5253832,18350.73342 +2210,No,No,883.907073,41668.1062 +2211,No,Yes,953.809047,18241.74144 +2212,No,Yes,1168.895545,14970.87009 +2213,No,No,431.3307401,58551.89326 +2214,No,Yes,1291.915263,17910.06461 +2215,No,Yes,335.3579479,5118.745551 +2216,No,No,30.8244501,35960.60602 +2217,No,Yes,1119.237574,15214.11563 +2218,Yes,Yes,1812.079998,17962.27016 +2219,No,No,385.6908387,44650.78296 +2220,No,Yes,651.5500049,17743.06562 +2221,No,No,1717.495025,38791.05764 +2222,No,Yes,1181.513221,11784.80226 +2223,No,No,745.2795147,34704.62025 +2224,No,Yes,1453.309747,15183.72922 +2225,No,Yes,1333.097471,29363.97174 +2226,No,Yes,571.8863591,15632.76928 +2227,No,No,494.6532294,40377.66836 +2228,No,No,163.5826085,58220.78047 +2229,No,Yes,1371.468372,24248.55196 +2230,No,No,1129.202659,40688.63559 +2231,No,No,0,53663.16668 +2232,No,No,84.85654884,53760.46526 +2233,No,No,681.4512395,48892.52469 +2234,No,Yes,1366.477534,11268.77433 +2235,No,No,82.0884565,27357.59993 +2236,No,No,602.9962261,33888.40919 +2237,No,No,176.3231998,54798.69875 +2238,No,Yes,713.7991565,20676.90519 +2239,No,No,137.7246167,44747.32768 +2240,No,No,159.9734731,30567.80131 +2241,Yes,Yes,1956.923906,15574.38998 +2242,No,No,183.5763822,51149.28157 +2243,No,No,975.3354849,50751.03859 +2244,No,No,0,44216.92938 +2245,No,No,424.285797,35675.7411 +2246,No,No,1477.669207,42011.80796 +2247,No,Yes,1211.363648,14186.38867 +2248,No,No,903.5493047,46971.56217 +2249,No,Yes,1076.233587,18096.70148 +2250,No,No,988.667818,28486.33975 +2251,No,No,1311.088803,32797.98992 +2252,No,No,0,40056.69266 +2253,No,No,289.3299083,45559.17639 +2254,No,No,22.53717523,59536.27562 +2255,No,Yes,287.6074133,24440.2252 +2256,No,No,25.82837121,22720.09454 +2257,No,Yes,725.7933681,23202.96396 +2258,No,Yes,1137.028318,25117.76297 +2259,No,No,1335.115552,30115.15496 +2260,No,No,1193.676973,34360.9095 +2261,No,Yes,0,18162.77049 +2262,No,No,1047.72339,45181.7486 +2263,No,No,0,48271.87044 +2264,No,No,327.3225081,29816.38032 +2265,No,No,771.6829167,38854.24248 +2266,No,Yes,873.6786524,17655.71003 +2267,No,No,974.9077893,36763.14597 +2268,No,No,309.8239952,28139.62585 +2269,No,No,286.8523269,18402.16475 +2270,No,No,509.8522537,49345.42564 +2271,No,Yes,840.3386901,21097.37031 +2272,No,No,223.6869181,23513.29538 +2273,No,No,1407.224571,22806.84821 +2274,No,No,1178.982625,45545.16842 +2275,No,No,1298.484815,39143.29192 +2276,No,No,627.3533533,37616.46594 +2277,No,No,907.1697759,27631.5678 +2278,No,No,872.1518416,40524.33874 +2279,No,No,620.3203556,52536.77665 +2280,No,No,380.333108,30890.0736 +2281,No,No,1073.168533,51668.96083 +2282,No,No,883.233754,32310.74502 +2283,No,Yes,1558.217831,7364.830078 +2284,Yes,No,2023.733603,32094.62781 +2285,No,Yes,803.9611048,13172.87051 +2286,No,No,1060.204707,27870.15539 +2287,No,No,869.8545396,33589.13955 +2288,No,Yes,1306.483031,13984.78526 +2289,No,No,1597.898741,36524.71624 +2290,No,No,1351.962465,43630.03692 +2291,No,No,0,29475.38138 +2292,No,No,442.5909249,38947.35437 +2293,No,Yes,963.2011987,15938.0661 +2294,No,No,1063.797087,41157.35715 +2295,No,No,214.4115627,42758.81282 +2296,No,No,1258.576399,33571.14452 +2297,No,No,506.6271509,49068.87087 +2298,No,No,38.40778522,38702.10168 +2299,No,No,1237.5474,37387.15415 +2300,No,No,0,47437.51224 +2301,No,No,555.6652909,46584.7305 +2302,No,No,747.9375239,44030.46149 +2303,No,Yes,1000.621768,17223.63797 +2304,No,No,773.6729475,40427.74193 +2305,No,No,744.7661997,35067.64372 +2306,No,No,953.1233642,51257.1439 +2307,No,No,998.3757695,37816.8439 +2308,No,Yes,1098.955685,21025.92746 +2309,No,No,521.6873539,48700.15308 +2310,No,No,82.73038975,27627.22408 +2311,No,No,219.1150032,31490.53342 +2312,No,No,276.3798286,38721.86797 +2313,No,Yes,1180.363817,24063.31274 +2314,No,No,620.9185967,39099.30763 +2315,No,No,414.8575624,38030.82462 +2316,No,No,1389.890556,48689.73039 +2317,No,Yes,703.0119004,21411.93007 +2318,No,No,493.5494228,52241.92216 +2319,No,Yes,1351.411543,14003.47572 +2320,No,Yes,461.7827648,17609.83052 +2321,No,No,143.1022619,44846.90101 +2322,No,No,1280.003234,30274.3356 +2323,No,Yes,1764.68273,17995.72861 +2324,Yes,No,1648.474017,55548.25418 +2325,No,No,0,37409.00299 +2326,No,No,610.5977516,22635.06647 +2327,No,No,355.1485857,55634.33065 +2328,No,Yes,472.5111008,19903.89205 +2329,No,No,786.0139114,52693.94375 +2330,No,No,772.7320741,47161.2767 +2331,No,No,751.2972186,25013.61407 +2332,No,No,1433.71078,42798.56551 +2333,No,No,1037.008599,36622.79223 +2334,No,Yes,1827.13495,19616.00973 +2335,No,No,764.5059389,37869.21429 +2336,No,No,589.5420167,28237.82068 +2337,No,No,754.1274899,41341.8026 +2338,No,No,911.9264415,41249.28174 +2339,No,No,311.918279,39694.48373 +2340,No,Yes,933.2872312,20694.58778 +2341,Yes,No,1374.474712,35805.70886 +2342,No,Yes,322.3119543,10056.11055 +2343,No,Yes,703.8387655,19318.05933 +2344,No,Yes,534.6929068,18729.56624 +2345,No,No,0,26626.48588 +2346,No,No,770.5198985,44509.57539 +2347,No,No,1015.725881,31820.94127 +2348,No,No,421.1464996,61655.34805 +2349,No,No,1125.091402,33564.07555 +2350,No,No,857.380822,28755.18076 +2351,No,No,363.9414625,46155.53922 +2352,No,No,1157.779701,32219.83173 +2353,No,No,993.302551,28501.85601 +2354,No,Yes,403.4594274,15333.87629 +2355,Yes,Yes,2134.934488,20330.86487 +2356,No,Yes,1234.942716,26919.11596 +2357,No,Yes,805.1042517,22174.02889 +2358,No,No,0,41933.09577 +2359,No,Yes,157.5140425,24506.18683 +2360,No,No,257.8976572,39477.03186 +2361,No,No,1056.915063,53080.58634 +2362,No,No,0,48290.85403 +2363,No,Yes,1730.291081,19446.67572 +2364,No,No,694.5007107,14150.91438 +2365,No,No,1546.760526,32928.63613 +2366,No,No,428.6653053,45570.79095 +2367,No,Yes,1764.532309,18148.52408 +2368,No,Yes,1181.979697,17223.36723 +2369,No,No,943.1323898,30178.73406 +2370,No,No,0,41234.89175 +2371,No,No,1087.193767,54961.28848 +2372,No,No,14.82095305,32320.26552 +2373,No,No,0,47207.05072 +2374,No,No,836.0105904,40178.9347 +2375,No,No,1494.521135,42280.40785 +2376,No,Yes,1318.528957,15821.15235 +2377,No,No,431.1279151,37772.80471 +2378,No,No,1335.473573,45847.38211 +2379,No,No,1141.442176,27914.40225 +2380,No,No,795.1192916,38710.38133 +2381,No,No,1005.444088,40615.3422 +2382,No,No,481.2401636,49010.75551 +2383,No,No,427.340143,27260.73546 +2384,No,Yes,1157.593925,17996.90333 +2385,No,No,890.2130229,43643.5824 +2386,No,Yes,1321.329159,20302.21947 +2387,No,No,1167.943622,57256.00741 +2388,No,Yes,1197.170953,10834.11138 +2389,No,Yes,922.8853555,22988.84265 +2390,No,No,834.7118946,28553.47188 +2391,No,Yes,1264.586941,14649.15502 +2392,No,No,1603.665223,25802.30347 +2393,No,No,838.2760677,55082.31753 +2394,No,Yes,850.6772791,21251.63951 +2395,No,Yes,1508.307713,17645.30502 +2396,No,No,1272.480579,34570.57066 +2397,No,No,981.8397963,23636.97634 +2398,No,No,487.6950209,46480.03597 +2399,No,No,1046.416673,47598.30737 +2400,No,No,1533.917257,42888.64105 +2401,No,No,709.9535652,37302.62213 +2402,No,Yes,1610.015858,19649.48443 +2403,No,Yes,645.6885305,14211.14714 +2404,No,No,377.7139958,46729.33299 +2405,No,No,994.99017,39315.95589 +2406,No,No,1240.665744,57303.79193 +2407,No,No,736.5906124,37031.48477 +2408,No,No,1201.230618,46478.15132 +2409,No,Yes,647.3673882,16897.78966 +2410,No,No,1161.239934,52064.47135 +2411,No,Yes,1521.912738,21370.79389 +2412,No,No,805.5206283,43383.70654 +2413,No,No,1155.399548,34539.56438 +2414,No,No,613.1111421,38632.13064 +2415,No,No,623.9576763,46188.29496 +2416,No,No,1000.90104,46469.10252 +2417,No,Yes,807.7488274,18760.3452 +2418,No,No,1156.040208,38295.57124 +2419,No,No,637.283464,32885.70199 +2420,No,Yes,1241.690987,20105.068 +2421,No,No,741.1930622,31104.00445 +2422,No,No,1169.834113,39386.37003 +2423,No,No,0,35821.78653 +2424,No,No,1088.3032,33673.36068 +2425,No,Yes,0,22187.96621 +2426,No,No,608.9485801,26463.37476 +2427,No,Yes,431.3027892,15606.24584 +2428,No,No,512.6534379,39631.69523 +2429,No,Yes,1241.61642,12058.59762 +2430,No,No,0,53058.25521 +2431,No,No,561.4337464,37884.9611 +2432,No,No,245.2442839,35132.22117 +2433,No,No,1081.063663,34138.32374 +2434,No,No,492.3264548,42317.24242 +2435,No,No,1349.57175,50645.29057 +2436,No,Yes,1150.43168,11666.98975 +2437,No,No,357.8658809,42499.03229 +2438,No,No,331.1052765,45104.8152 +2439,No,Yes,1457.673369,20042.86397 +2440,No,Yes,852.3139525,15956.64884 +2441,No,No,1378.940514,43998.1414 +2442,No,Yes,1005.521508,22128.0802 +2443,No,No,1420.993454,34126.42169 +2444,No,No,570.2955412,30615.33055 +2445,No,Yes,1228.990373,17304.6435 +2446,No,Yes,1142.935257,20942.00334 +2447,No,No,759.3458148,32066.70673 +2448,No,No,846.6835117,41651.21558 +2449,Yes,No,2133.464209,28237.88234 +2450,No,No,354.5743684,30954.47668 +2451,No,No,0,46450.96604 +2452,No,No,1051.401733,30072.74931 +2453,No,No,0,41371.66119 +2454,No,No,887.4681371,30563.77026 +2455,No,No,688.1093445,20098.59346 +2456,No,No,1257.34063,40810.17277 +2457,No,No,1175.817635,41447.58481 +2458,No,No,0,34838.43927 +2459,No,No,608.5517388,22555.50859 +2460,No,No,0,32608.48884 +2461,Yes,Yes,2110.556858,19345.10473 +2462,No,No,696.9001673,38713.31706 +2463,No,No,559.4685833,61187.81057 +2464,No,No,715.3929218,49473.37638 +2465,No,Yes,2026.863631,20469.92949 +2466,No,Yes,1187.358891,24267.39648 +2467,No,No,785.7906701,42887.59272 +2468,No,No,845.9203698,42799.43953 +2469,No,Yes,1032.670815,13901.11897 +2470,No,No,861.2727392,38111.03287 +2471,No,No,1601.87795,27885.16355 +2472,No,No,955.9270221,10836.72352 +2473,No,Yes,877.8798035,18217.61365 +2474,No,No,1043.758264,31523.6869 +2475,No,Yes,1080.071527,11239.53116 +2476,No,No,589.2178954,47722.17011 +2477,No,Yes,769.5312437,23048.54061 +2478,No,No,951.0475319,33326.5872 +2479,No,No,1189.158934,36223.87056 +2480,No,No,1709.344285,35290.52311 +2481,No,Yes,1402.553926,16607.56425 +2482,No,No,250.8795835,51662.22782 +2483,No,No,681.4176227,26426.22921 +2484,No,No,1333.248659,39196.66301 +2485,No,No,858.7611254,29150.34401 +2486,No,Yes,992.7762449,14204.67215 +2487,No,No,265.3022839,32276.46294 +2488,No,No,754.4775776,31592.19989 +2489,No,Yes,584.0812286,24440.02035 +2490,No,No,1175.233377,47684.46113 +2491,No,No,1388.664149,31021.55008 +2492,No,No,410.099265,37328.45502 +2493,No,No,59.13249864,34162.23526 +2494,No,Yes,1939.71236,13836.98607 +2495,No,No,886.1490576,24466.12539 +2496,No,No,770.9994019,40990.6215 +2497,No,Yes,306.4109271,13594.18823 +2498,No,No,1022.101638,37095.21594 +2499,No,No,847.9840353,24950.53392 +2500,No,No,1596.227808,42993.57653 +2501,No,Yes,1635.222942,8531.038085 +2502,No,No,0,45217.85066 +2503,No,No,1306.796916,48799.51191 +2504,No,No,932.9523691,35858.18915 +2505,No,Yes,1052.875142,13486.14694 +2506,No,Yes,1304.634346,13496.87987 +2507,No,Yes,2052.133463,13130.6536 +2508,No,No,1216.725616,52377.8489 +2509,No,No,526.470865,44241.56562 +2510,No,No,153.4479361,36813.19871 +2511,No,No,905.6415057,47271.34912 +2512,No,No,1068.873957,41690.04867 +2513,No,No,0,30049.10026 +2514,No,Yes,992.440019,14207.26834 +2515,No,No,964.6025631,36676.57165 +2516,No,No,493.6354264,43524.24978 +2517,No,No,835.6564355,44234.17039 +2518,No,No,480.0930869,57068.13046 +2519,No,No,637.60187,52347.76202 +2520,No,Yes,2134.015627,17897.64661 +2521,No,No,1475.207229,42243.17748 +2522,No,No,887.3432884,55751.7209 +2523,No,No,1376.309911,43234.13992 +2524,No,No,1304.700879,45162.65104 +2525,No,No,225.506104,54534.70972 +2526,No,No,1261.268219,39984.52959 +2527,No,Yes,30.58367172,16246.57305 +2528,No,Yes,437.0352358,16261.20757 +2529,No,No,922.6374027,52205.50687 +2530,No,No,412.7468149,44650.07843 +2531,No,No,693.1013449,54492.74796 +2532,No,Yes,915.4626482,27283.31983 +2533,No,Yes,1322.492812,22930.445 +2534,No,No,1610.086988,40498.99811 +2535,No,No,1113.134257,27110.60301 +2536,No,No,0,47803.25837 +2537,No,No,1163.146385,44424.82596 +2538,No,No,924.2953475,26397.59729 +2539,Yes,Yes,1983.234475,25687.92975 +2540,No,No,725.7005868,49337.40235 +2541,No,Yes,1030.496155,16662.73157 +2542,No,Yes,1038.97702,13739.71928 +2543,No,Yes,292.8574108,15220.93587 +2544,No,Yes,495.1139113,14881.84062 +2545,No,No,659.0404108,47942.24982 +2546,No,No,291.9681397,48511.60308 +2547,No,No,471.3565174,45573.30811 +2548,No,No,855.822381,56219.80884 +2549,No,No,918.6817794,48160.52429 +2550,No,No,1251.32255,37881.82096 +2551,Yes,No,1066.884084,44918.41232 +2552,No,Yes,1427.124037,12553.88673 +2553,No,Yes,1263.880058,12418.80765 +2554,No,Yes,1128.141493,10159.24188 +2555,No,No,694.3985831,36570.42544 +2556,No,Yes,805.3980049,17902.57919 +2557,No,No,519.5172955,39704.69296 +2558,No,Yes,1051.297161,16299.61175 +2559,No,No,421.8969022,37323.75941 +2560,No,Yes,622.4137203,13072.02796 +2561,No,Yes,1005.264248,21394.12401 +2562,No,Yes,537.4925429,9421.714858 +2563,No,Yes,189.9987142,24716.95613 +2564,No,No,309.8628233,56100.05441 +2565,No,No,969.4457592,33411.80109 +2566,No,Yes,775.5892326,17030.57885 +2567,No,No,1296.474737,48682.3735 +2568,No,No,1037.155516,38490.38489 +2569,No,No,978.6521803,25742.11973 +2570,No,No,965.0461321,47987.31975 +2571,No,Yes,816.0427981,17894.79377 +2572,No,Yes,716.1857509,7997.622944 +2573,No,No,1130.608881,43936.64641 +2574,No,No,383.7049011,41311.24395 +2575,No,No,1008.154522,20326.59218 +2576,No,No,652.8174543,41161.85735 +2577,No,Yes,1232.794737,16279.1854 +2578,No,No,803.224401,52869.80781 +2579,No,Yes,1443.431696,22693.65775 +2580,No,Yes,1288.44856,21216.96257 +2581,No,No,0,43193.23215 +2582,No,Yes,1053.332641,10212.50236 +2583,No,Yes,744.7879024,22343.09739 +2584,No,No,412.6876132,33923.45804 +2585,No,No,943.4020371,35281.72849 +2586,No,No,1192.974028,39504.47602 +2587,No,Yes,1071.511583,11082.0629 +2588,No,No,1493.270467,41005.21748 +2589,No,Yes,1132.837721,15703.0252 +2590,No,Yes,597.4840406,14796.74967 +2591,No,Yes,792.9754861,17346.17092 +2592,No,No,1376.075401,22317.56559 +2593,No,No,1060.016921,39757.43817 +2594,No,No,532.3267381,30851.27777 +2595,No,No,0,28711.75713 +2596,No,Yes,894.1258092,24670.1189 +2597,No,No,906.4363882,37309.68181 +2598,No,No,0,28727.12016 +2599,No,No,506.4259139,35333.18222 +2600,No,Yes,943.5017211,14123.70357 +2601,No,Yes,0,14874.80021 +2602,No,No,442.4895649,54996.84275 +2603,No,Yes,380.9633077,20117.83003 +2604,No,No,961.999353,37073.19238 +2605,Yes,No,1789.093391,48331.12686 +2606,No,No,76.47608896,27283.69174 +2607,No,No,1067.139026,53995.7305 +2608,No,No,0,39592.43693 +2609,No,No,253.1319822,40973.64654 +2610,No,No,1056.928348,55409.4596 +2611,No,No,179.7000705,37727.53512 +2612,No,Yes,990.9357627,18197.22873 +2613,No,No,693.7537183,36723.44877 +2614,No,No,965.2768178,37705.24322 +2615,No,No,300.8762062,51423.69464 +2616,No,No,1265.044932,47297.7227 +2617,No,No,558.6705138,40335.61099 +2618,No,No,1183.983311,53599.74553 +2619,No,Yes,1165.840209,20495.1586 +2620,No,Yes,1542.191227,17265.91861 +2621,No,Yes,587.7677591,22669.42433 +2622,No,No,0,31005.03576 +2623,No,No,389.9766946,27329.97931 +2624,No,No,647.075901,55522.62393 +2625,No,No,70.98316299,40012.53291 +2626,No,Yes,115.5453555,12670.02762 +2627,No,No,90.93344719,42451.73558 +2628,No,No,1230.424256,51716.7399 +2629,No,No,201.5583994,27924.84656 +2630,No,Yes,1562.846155,10353.90391 +2631,No,Yes,622.8952091,11019.87756 +2632,No,Yes,1142.126955,9184.732762 +2633,No,Yes,1796.26764,21126.17721 +2634,No,No,947.4239701,38505.78387 +2635,No,No,419.4405795,34332.66131 +2636,No,No,1660.827702,53754.20072 +2637,No,No,391.2404617,24972.24591 +2638,No,No,613.4309365,34745.00747 +2639,No,Yes,1024.108807,18667.92565 +2640,No,Yes,0,25976.06511 +2641,No,No,1252.958338,33876.18903 +2642,No,No,708.8374818,55675.75986 +2643,No,No,266.6470861,16515.30843 +2644,No,Yes,1339.363614,19496.16522 +2645,No,No,781.5953359,49457.1053 +2646,No,Yes,1021.370111,19032.50901 +2647,No,No,948.4942785,40739.85993 +2648,No,Yes,1312.93378,20208.54461 +2649,No,No,0,50861.78885 +2650,No,No,0,24566.48337 +2651,No,No,0,30922.60421 +2652,No,Yes,859.3369637,13993.65454 +2653,No,No,646.157668,45696.83161 +2654,Yes,Yes,1707.914634,10591.71742 +2655,No,No,133.673455,28492.4003 +2656,No,Yes,80.59567582,16145.6377 +2657,No,Yes,631.6008477,17845.07415 +2658,No,No,1236.617914,40668.7254 +2659,No,No,742.5833733,40085.43995 +2660,No,Yes,97.03146278,12731.75755 +2661,No,No,1054.22211,50900.24256 +2662,No,No,959.600287,29850.21207 +2663,No,No,1258.852277,33683.18173 +2664,No,No,443.2464668,36547.16104 +2665,No,No,1605.102415,26126.32944 +2666,No,No,0,25964.84434 +2667,No,No,832.6459751,25106.37478 +2668,No,Yes,443.5370288,19632.17542 +2669,No,No,817.4317693,40088.05051 +2670,No,No,881.6637997,43725.99753 +2671,No,No,1154.094274,48400.24115 +2672,No,Yes,961.5755637,9681.493893 +2673,No,Yes,516.1219595,17538.46162 +2674,No,Yes,1357.411868,22572.79013 +2675,No,No,1036.030594,35261.35578 +2676,No,Yes,900.6093417,17802.92726 +2677,No,No,1516.326025,46393.26454 +2678,No,Yes,1015.204535,22208.32892 +2679,No,No,232.105744,40368.95685 +2680,No,Yes,1082.284133,19451.81628 +2681,No,No,682.4396608,17992.97118 +2682,No,Yes,1218.13888,18144.98483 +2683,No,No,802.4131422,47444.4928 +2684,No,No,143.8548983,38014.87349 +2685,No,No,1087.465794,32908.52712 +2686,No,Yes,895.7539556,14753.29733 +2687,No,No,660.2854965,58956.51318 +2688,No,Yes,630.9734892,19087.17478 +2689,No,Yes,1648.966029,15402.48208 +2690,No,Yes,975.9555974,18246.81572 +2691,No,No,297.0053783,22955.08212 +2692,No,No,0,47328.23291 +2693,No,No,0,32629.94485 +2694,No,Yes,789.2148949,18231.83026 +2695,Yes,No,1804.036475,31318.29603 +2696,No,No,1266.113867,29698.51627 +2697,No,Yes,135.1876434,20414.41217 +2698,No,No,28.81374346,36941.0634 +2699,No,Yes,0,15347.91429 +2700,No,No,750.6989964,49150.34585 +2701,No,No,805.3051169,57386.1288 +2702,No,No,1093.803005,29573.23823 +2703,No,No,483.9652713,44335.95811 +2704,No,Yes,1534.394884,19592.10939 +2705,No,No,1349.187291,48013.6902 +2706,No,No,158.5704427,20575.46501 +2707,No,Yes,172.4620199,25850.38313 +2708,No,No,1280.762861,53675.56755 +2709,No,Yes,213.1732752,22811.45698 +2710,No,No,0,32248.53657 +2711,No,Yes,704.0136486,13903.45573 +2712,No,No,708.8139762,38295.23381 +2713,No,No,257.3963831,49477.78564 +2714,No,Yes,1188.744296,22867.07059 +2715,No,No,0,62886.70923 +2716,No,No,1321.703891,45581.79478 +2717,No,Yes,882.5819675,17749.97205 +2718,No,Yes,1418.134148,11964.42509 +2719,No,No,1245.422205,36897.78011 +2720,No,No,339.5292685,58747.38773 +2721,No,Yes,824.5478304,29088.11135 +2722,No,No,241.4426903,30171.62449 +2723,No,No,1510.79325,47684.23946 +2724,No,No,396.64125,52174.95427 +2725,No,Yes,1633.697803,15786.31732 +2726,No,No,650.4183411,47122.07247 +2727,No,Yes,1382.438231,15680.61049 +2728,No,No,613.4802945,24367.86142 +2729,No,No,1424.351218,37845.99925 +2730,No,Yes,914.1081756,19053.00589 +2731,No,Yes,802.0089304,15469.65262 +2732,No,Yes,539.674903,18852.27385 +2733,No,No,337.5759999,52192.27472 +2734,No,Yes,813.4948376,16681.3503 +2735,No,No,477.882122,39834.03081 +2736,No,No,342.1916021,32154.75355 +2737,No,No,840.365781,45316.83541 +2738,No,Yes,865.5655922,16643.24096 +2739,No,No,645.5916366,44402.10623 +2740,No,No,1755.38891,35031.532 +2741,No,No,498.7494508,51690.06347 +2742,No,No,587.7810817,30859.63634 +2743,No,Yes,824.6165935,10062.57593 +2744,No,No,0,32660.72526 +2745,No,Yes,880.803636,20436.43903 +2746,No,No,917.1221347,48227.81937 +2747,No,Yes,789.7175406,19280.54426 +2748,No,No,763.8556247,52938.03547 +2749,No,Yes,1062.16651,17383.25855 +2750,No,No,944.3325674,44444.14918 +2751,No,No,877.501423,25587.74393 +2752,No,No,1344.103406,41417.34592 +2753,Yes,Yes,1543.099582,18304.65057 +2754,No,No,380.3174501,38864.58415 +2755,No,Yes,1176.893303,23634.81573 +2756,No,No,733.1999149,34524.91886 +2757,No,No,348.9970288,48942.02578 +2758,No,Yes,1380.087526,19245.83269 +2759,No,No,943.1657194,40369.69803 +2760,No,No,1248.885849,51897.39491 +2761,No,No,713.223492,51107.8601 +2762,No,No,834.2356082,36721.86221 +2763,No,No,0,26200.0384 +2764,No,No,1558.883862,37489.72211 +2765,No,No,1362.186619,37736.7496 +2766,No,No,1581.521505,45729.88805 +2767,No,No,324.8549714,39752.35419 +2768,No,No,1112.73574,47527.12558 +2769,No,No,379.5987261,32578.43583 +2770,No,No,1549.098151,26239.78187 +2771,No,No,1489.415915,46732.76781 +2772,No,No,548.2063638,41647.72716 +2773,No,No,561.968473,37637.00503 +2774,No,No,595.4867014,34470.41803 +2775,No,No,267.5139688,27364.86548 +2776,No,Yes,1793.303188,21034.87965 +2777,No,Yes,1773.153542,10177.86674 +2778,No,No,618.8603727,37333.33383 +2779,No,No,205.6438081,37766.49369 +2780,No,No,1011.431543,36364.75385 +2781,No,No,0,38130.64567 +2782,No,Yes,1154.432105,15221.17239 +2783,No,No,292.5159807,43705.37904 +2784,No,Yes,1106.946277,19233.77389 +2785,Yes,No,1665.954621,30070.13526 +2786,No,Yes,968.671101,22212.74103 +2787,No,No,797.2493693,39928.58818 +2788,No,Yes,636.8978227,18620.23895 +2789,No,Yes,1011.334848,14101.2903 +2790,No,No,791.0352223,47303.67664 +2791,No,No,942.4747495,36965.04557 +2792,No,No,706.9742779,46165.15453 +2793,No,No,767.4348125,55990.87461 +2794,No,No,568.8436683,37074.54081 +2795,No,Yes,1271.562957,18999.61681 +2796,No,No,938.9391417,44280.78635 +2797,No,No,1012.194199,34631.70605 +2798,No,No,1355.493231,18935.51151 +2799,No,No,912.0487219,43486.95708 +2800,No,No,289.5988343,25724.77064 +2801,Yes,Yes,2035.861557,14435.84319 +2802,No,Yes,1602.179886,17858.06791 +2803,No,Yes,0,20067.80976 +2804,No,Yes,1202.390705,14036.62088 +2805,No,No,554.7076529,28164.19855 +2806,No,Yes,229.9887235,2541.200814 +2807,No,No,768.979148,35503.90896 +2808,No,Yes,1211.040146,16898.12947 +2809,No,No,0,35648.38116 +2810,No,No,0,27537.73535 +2811,No,Yes,859.9366511,15966.71713 +2812,No,No,0,47556.30125 +2813,No,Yes,1471.538391,14959.66188 +2814,No,Yes,582.8738672,17730.5511 +2815,No,Yes,463.1193392,25858.28175 +2816,No,No,986.1541303,48940.49003 +2817,No,No,534.7460545,29685.12923 +2818,No,No,1402.690527,39656.6322 +2819,No,No,967.748001,46278.07452 +2820,No,No,884.0225178,36889.42825 +2821,No,Yes,455.6999383,19080.80899 +2822,No,No,0,66915.57179 +2823,No,No,1141.23821,37917.66454 +2824,No,No,365.2358798,21358.00005 +2825,No,No,38.37592458,33579.35623 +2826,No,Yes,731.9706113,13593.14051 +2827,No,No,1006.719786,47784.31847 +2828,No,No,1107.801156,34561.94954 +2829,No,No,777.5308087,45673.67391 +2830,No,No,1501.426087,35398.95712 +2831,No,Yes,862.0600385,12085.45308 +2832,No,Yes,12.1864203,19910.96897 +2833,No,No,0,30208.04644 +2834,No,No,1820.32549,31309.99848 +2835,No,No,576.0689929,40179.83005 +2836,No,No,837.7526213,31017.90114 +2837,No,No,399.793885,34875.33233 +2838,No,Yes,578.7550989,20909.83537 +2839,No,No,1106.540875,46565.05995 +2840,No,No,390.7456114,38442.29606 +2841,No,Yes,274.1946278,15421.55166 +2842,No,No,1476.877642,42271.33341 +2843,No,No,578.4607424,20306.93601 +2844,No,No,1064.147713,28192.12588 +2845,No,No,1598.303417,46404.66469 +2846,No,No,863.4518296,33529.88065 +2847,No,No,1157.759857,32574.5029 +2848,No,Yes,406.3860491,24086.95249 +2849,No,No,309.2444877,44202.7075 +2850,No,No,943.9512443,28353.63001 +2851,No,No,1206.791937,42916.70139 +2852,No,Yes,1448.060458,21028.28675 +2853,No,No,1166.593989,26977.62275 +2854,No,No,800.308672,61281.30081 +2855,No,No,714.9260001,31032.8697 +2856,No,No,471.2550831,28417.74199 +2857,No,No,662.1644451,30390.06007 +2858,No,No,770.4319619,53398.31473 +2859,No,No,597.3268696,55496.77711 +2860,No,No,140.8530225,48183.27674 +2861,No,No,1035.552944,29423.23203 +2862,No,Yes,784.1087812,11560.5677 +2863,No,Yes,442.027425,17072.33664 +2864,No,No,888.081432,54072.65702 +2865,No,No,704.2029083,44775.12201 +2866,No,Yes,1016.450616,15099.5302 +2867,No,No,1036.460728,42914.98167 +2868,No,Yes,1060.220034,22795.94299 +2869,No,No,1243.92806,38075.4383 +2870,No,Yes,1291.977346,26571.87456 +2871,No,No,69.25247581,20222.86624 +2872,No,No,196.3892855,29932.66203 +2873,No,Yes,709.0271029,18305.73452 +2874,No,No,1034.149962,52106.8387 +2875,No,No,868.0060301,50549.53346 +2876,No,No,1236.006993,48800.35685 +2877,No,No,12.91234955,46282.25773 +2878,No,Yes,826.4536365,21342.70303 +2879,No,Yes,703.9900094,20752.17544 +2880,No,Yes,817.1214384,13152.03413 +2881,No,No,1210.014631,45066.13324 +2882,No,Yes,766.4062474,22296.9837 +2883,No,No,1643.654052,33980.82583 +2884,No,No,1053.014778,54009.63194 +2885,No,No,240.0774345,41508.9557 +2886,No,No,62.65294851,44440.70548 +2887,No,Yes,924.2332039,11194.66888 +2888,No,Yes,944.0710359,26027.68765 +2889,No,No,295.7854428,51837.75233 +2890,Yes,No,2085.586978,35657.22567 +2891,No,No,474.3552638,58068.62224 +2892,No,No,504.7233526,54297.11366 +2893,No,No,292.3841444,33961.10045 +2894,No,No,593.2804678,40227.14388 +2895,No,No,76.06408834,50054.76235 +2896,No,Yes,1270.09281,16809.00645 +2897,No,No,877.3436869,40689.47859 +2898,No,Yes,932.5609425,20980.8055 +2899,No,No,279.9633883,30262.11714 +2900,No,No,1155.175006,40398.39935 +2901,No,No,0,45431.75759 +2902,No,Yes,1216.767562,23378.57282 +2903,No,Yes,0,15274.46903 +2904,No,Yes,728.5493992,22891.92938 +2905,No,No,0,48365.57759 +2906,No,Yes,643.5652672,21323.04944 +2907,No,No,973.0823643,27289.27132 +2908,No,Yes,1177.957425,14385.16707 +2909,No,No,196.9871114,41314.99362 +2910,No,No,1526.268669,44845.13629 +2911,No,No,441.3960846,56060.20361 +2912,No,No,1004.641773,56533.31307 +2913,No,No,1258.069534,36645.14057 +2914,No,No,1563.993419,37119.60197 +2915,No,No,367.3515763,29905.52786 +2916,No,Yes,1222.768041,18276.43406 +2917,No,Yes,1582.202813,19682.87249 +2918,No,No,647.130283,32725.38665 +2919,No,No,0,33146.49534 +2920,No,Yes,1585.333029,15079.48894 +2921,No,Yes,837.9723362,25773.18265 +2922,No,Yes,686.7821494,10543.55632 +2923,No,No,947.842159,37891.03704 +2924,No,Yes,1205.055008,18101.67631 +2925,No,No,1254.131055,37456.26199 +2926,No,No,949.1238463,50528.53387 +2927,No,No,530.24602,27610.70309 +2928,No,No,833.6899703,54558.14011 +2929,No,No,638.4653109,36173.98245 +2930,Yes,Yes,2387.314867,28296.91472 +2931,No,Yes,839.3908902,21383.23117 +2932,No,No,294.634281,63285.02511 +2933,No,No,1178.179438,34720.93616 +2934,No,No,1149.720136,57890.51596 +2935,No,No,909.7642677,42468.27822 +2936,No,Yes,1391.508027,18539.59036 +2937,No,Yes,1029.249632,18018.35831 +2938,No,No,1504.59694,30773.74284 +2939,No,No,1276.4893,35122.85361 +2940,No,No,874.9150425,25023.32309 +2941,No,No,1104.689463,51516.33629 +2942,No,No,0,47056.19919 +2943,No,No,698.800444,37466.0203 +2944,No,No,1055.410868,33945.14276 +2945,No,Yes,131.2846105,14491.84407 +2946,No,No,795.903786,44181.67186 +2947,No,No,611.9542968,39291.20373 +2948,No,No,1670.423008,41409.77115 +2949,No,No,1423.149545,33591.23154 +2950,No,No,770.6419392,29623.47129 +2951,No,No,1315.469609,29808.35223 +2952,No,No,599.204105,53218.11086 +2953,No,No,584.356741,34128.76699 +2954,No,No,27.27187849,51659.87841 +2955,No,No,65.72759508,32332.18265 +2956,No,No,1338.461116,23909.54695 +2957,No,Yes,1120.422968,16151.25285 +2958,No,No,726.0293438,41996.12441 +2959,No,No,1353.588284,44372.79392 +2960,No,No,531.7442107,38122.1924 +2961,No,No,1824.641385,36762.80884 +2962,No,Yes,894.4654681,14035.49914 +2963,No,Yes,1275.538565,16445.62472 +2964,No,No,550.6243941,19106.26579 +2965,No,Yes,270.3910262,13158.44223 +2966,No,No,651.9986041,35266.12844 +2967,No,No,718.3511021,50765.89263 +2968,No,No,1008.94654,31235.78292 +2969,No,Yes,1510.747812,13390.92 +2970,No,No,1393.368283,38371.97746 +2971,No,No,586.5882744,45519.83771 +2972,No,No,793.4139267,45873.17997 +2973,No,No,285.9101691,43263.63064 +2974,No,Yes,550.3810558,20322.76863 +2975,No,Yes,716.491995,16785.38532 +2976,No,No,383.6864669,39946.48497 +2977,No,No,252.3333091,38431.81869 +2978,No,No,1164.10427,40167.02331 +2979,No,No,1080.185777,38398.8661 +2980,No,No,906.4785442,47101.99063 +2981,No,No,457.8000986,26567.24108 +2982,No,No,1215.867359,42762.12183 +2983,No,No,217.3615206,45083.11137 +2984,No,Yes,1076.727705,12502.64492 +2985,No,Yes,58.06606208,23639.23362 +2986,No,No,1615.225001,55219.52001 +2987,No,Yes,1119.001789,22811.3938 +2988,No,No,671.9580672,17621.45385 +2989,No,No,634.2179264,38672.74965 +2990,No,Yes,831.8185363,16164.48461 +2991,No,Yes,373.0625165,22018.9821 +2992,No,No,776.4548762,58481.54391 +2993,No,Yes,1031.303939,12195.37983 +2994,No,No,910.6724158,43795.5817 +2995,No,No,1010.809065,42163.4536 +2996,No,No,1090.35035,48280.94632 +2997,No,No,1008.550343,53801.61564 +2998,No,No,0,50127.18047 +2999,No,Yes,1824.368004,20074.2811 +3000,No,No,730.1644185,44411.67827 +3001,No,No,991.1317826,30941.54852 +3002,No,No,1356.516665,35030.63502 +3003,No,No,576.6355769,41699.20979 +3004,No,No,1334.554191,43385.82181 +3005,No,No,1487.661808,47624.3796 +3006,No,No,778.6934925,48551.97108 +3007,No,No,444.1963823,37599.42019 +3008,No,No,1023.36216,34632.10269 +3009,No,Yes,153.4000958,17840.38488 +3010,No,No,661.7606164,47169.35685 +3011,No,No,312.6147065,34918.61291 +3012,No,No,645.3725645,30102.03554 +3013,No,No,0,49010.27332 +3014,No,No,779.6147234,22975.49302 +3015,No,Yes,569.4987908,19755.52114 +3016,No,No,1287.765895,38507.11939 +3017,No,No,1177.81507,32294.54565 +3018,No,No,795.3685571,25704.64664 +3019,No,No,877.039197,24902.73549 +3020,No,No,823.9919883,36701.53017 +3021,No,No,1019.215358,52089.64463 +3022,No,No,942.7374821,38908.88112 +3023,No,No,6.787861759,40145.33909 +3024,No,No,0,55158.15124 +3025,No,Yes,1420.204205,22790.79583 +3026,No,Yes,1254.662583,18189.52076 +3027,No,No,0,37796.40346 +3028,No,No,1140.071899,33605.37793 +3029,No,Yes,747.9585112,19869.39018 +3030,No,No,1598.602553,48717.26315 +3031,No,No,1226.370717,21426.30698 +3032,No,No,929.8199349,50611.98595 +3033,No,No,807.405738,43853.88425 +3034,No,No,345.3649412,56965.70211 +3035,No,No,982.4509449,42907.02619 +3036,No,Yes,1067.670767,14485.94849 +3037,No,No,742.5406142,39017.95032 +3038,No,No,1060.178447,30948.29027 +3039,No,No,523.7533678,33767.05071 +3040,No,No,697.982915,29799.3192 +3041,Yes,Yes,1500.894533,15802.23288 +3042,No,Yes,1682.897631,14378.8906 +3043,No,Yes,1264.04207,6466.513408 +3044,No,No,1352.462336,34247.78002 +3045,No,No,376.8789166,50671.32275 +3046,No,No,629.5612815,37255.36509 +3047,No,Yes,709.4142298,11355.11271 +3048,No,No,541.3462788,42332.24628 +3049,No,No,421.1723819,42721.47777 +3050,No,No,500.8042199,47910.96385 +3051,No,No,0,41663.34011 +3052,No,No,1089.365873,43360.16015 +3053,No,No,603.8474825,56468.12224 +3054,Yes,No,1165.539853,50341.5739 +3055,No,No,1457.622602,43155.3627 +3056,No,No,390.0550264,27108.21366 +3057,No,No,584.1412164,35517.22911 +3058,No,No,901.0122829,13498.12653 +3059,No,Yes,1380.096737,14142.07544 +3060,No,No,1146.067689,39902.44189 +3061,No,No,1058.025969,43327.02611 +3062,No,No,474.3796621,35980.75279 +3063,No,Yes,969.1435189,22425.40576 +3064,No,Yes,1616.738878,12557.82288 +3065,No,No,1185.028651,33298.5004 +3066,No,No,472.6332289,41257.38603 +3067,No,No,1257.625123,30964.23458 +3068,No,No,640.9184388,37306.69399 +3069,No,No,105.7442477,42442.84735 +3070,No,No,672.4267927,25947.24541 +3071,No,No,568.666447,45186.97594 +3072,No,Yes,368.1982988,26670.01211 +3073,No,No,1546.548506,57266.82959 +3074,No,No,230.2203941,28930.25452 +3075,No,No,815.0547024,34200.18335 +3076,No,Yes,164.3055117,17995.85826 +3077,No,Yes,1785.797516,15291.6708 +3078,No,No,661.0522978,27873.63791 +3079,No,Yes,979.8771549,13522.42151 +3080,No,Yes,778.2499052,23805.91057 +3081,No,Yes,190.6192783,21744.50932 +3082,No,No,523.813717,45284.18935 +3083,No,No,175.6988688,40044.47032 +3084,No,No,977.9479232,38368.48417 +3085,No,No,837.6953592,44260.76441 +3086,No,Yes,1436.32082,27658.38667 +3087,No,No,638.229501,53812.79788 +3088,No,No,714.0116591,48631.36436 +3089,No,No,738.2532477,36086.09213 +3090,No,No,507.5265349,58027.77092 +3091,No,Yes,1033.833494,15818.0614 +3092,No,Yes,19.73946092,20514.17137 +3093,No,No,434.704864,48898.37012 +3094,No,No,0,45334.83933 +3095,No,No,629.7640719,53069.32515 +3096,No,Yes,522.7677589,26900.28072 +3097,No,Yes,294.6564875,11681.46233 +3098,No,Yes,469.4256329,14647.00227 +3099,No,No,838.4370383,40903.48069 +3100,No,No,1407.639515,52746.13315 +3101,No,Yes,1417.225499,16053.97426 +3102,No,No,363.9940646,38110.44311 +3103,No,Yes,128.2518222,17076.5736 +3104,Yes,Yes,1894.168201,28321.68455 +3105,No,Yes,1624.795642,10623.59745 +3106,No,No,645.9492708,46406.33798 +3107,No,No,684.0849049,38184.56838 +3108,No,No,1168.284538,46904.60382 +3109,No,No,766.1240549,51614.0562 +3110,No,Yes,738.2686012,14063.07546 +3111,No,No,525.8903383,45530.50912 +3112,No,No,631.2535067,38877.57033 +3113,No,No,0,65943.78394 +3114,No,No,913.9139657,53809.33036 +3115,No,No,578.7525663,60498.92546 +3116,No,No,1503.849951,43888.39666 +3117,No,Yes,555.2657969,11581.22066 +3118,Yes,Yes,1289.24621,13624.54526 +3119,No,No,545.5665107,35714.65113 +3120,No,No,1112.348992,33468.82218 +3121,No,No,564.9074481,35820.88568 +3122,No,No,591.8414074,38879.50988 +3123,No,Yes,908.1228119,22084.27165 +3124,Yes,Yes,2169.196187,18195.2669 +3125,No,Yes,1108.105366,15337.30227 +3126,No,No,0,27167.26039 +3127,No,No,957.5919029,35330.85535 +3128,No,No,448.0399683,29537.31025 +3129,No,No,1188.072875,40704.70722 +3130,No,Yes,1223.885814,19520.009 +3131,No,Yes,959.967042,18413.86385 +3132,No,No,734.7218364,35010.02587 +3133,No,No,0,47628.41183 +3134,No,No,1423.284258,48914.19708 +3135,No,No,217.1635013,26975.49181 +3136,No,No,672.0451389,36705.34046 +3137,No,No,830.7653898,45958.98422 +3138,No,No,652.5281789,58750.496 +3139,No,No,533.9891482,64930.2398 +3140,No,Yes,1626.634079,17721.63006 +3141,No,No,1382.120977,30395.68823 +3142,No,No,0,38663.20051 +3143,No,No,1165.689152,38110.36478 +3144,No,No,1190.420507,34198.05133 +3145,No,No,435.2146571,39505.41246 +3146,No,Yes,1382.422544,17166.04257 +3147,No,No,1265.954833,27233.56306 +3148,No,No,1303.626132,40010.46516 +3149,No,Yes,240.1013667,21142.0419 +3150,No,Yes,326.4386248,21109.43181 +3151,No,No,562.8019307,41934.59733 +3152,No,No,531.4863606,28836.19262 +3153,No,Yes,594.5443798,17875.33907 +3154,No,No,745.7338929,60180.56113 +3155,No,Yes,1218.638905,29899.80685 +3156,No,No,266.4604812,37622.05376 +3157,Yes,No,1899.546842,46076.23711 +3158,No,Yes,717.601673,13729.05176 +3159,No,No,507.3345958,56075.10284 +3160,No,Yes,1422.774586,14044.5335 +3161,No,No,1253.699614,37645.79996 +3162,No,No,1301.650765,38918.99417 +3163,Yes,Yes,2415.316994,17429.50337 +3164,No,Yes,1367.013875,11293.46926 +3165,No,No,449.0515984,31624.90568 +3166,No,No,505.6978729,31971.31291 +3167,No,No,1150.458785,54982.75183 +3168,No,No,981.4922134,30385.06908 +3169,No,Yes,612.2660524,16270.83564 +3170,No,Yes,1122.292386,20351.33607 +3171,No,No,59.79707128,36000.07269 +3172,No,Yes,1353.011845,26207.68628 +3173,No,No,1232.233527,50731.86426 +3174,No,No,924.0131534,28803.77469 +3175,No,No,517.8245459,65501.62076 +3176,No,Yes,1795.71687,12547.82909 +3177,No,No,983.56056,26050.13005 +3178,No,Yes,990.3542603,18998.09078 +3179,No,No,1529.517006,52681.70666 +3180,No,No,272.8731293,44249.67868 +3181,No,Yes,636.2066156,19468.43387 +3182,Yes,No,1751.347088,38381.5858 +3183,No,No,721.9203797,46269.83721 +3184,No,No,511.4175111,25768.28549 +3185,No,No,563.6872028,41354.35016 +3186,No,No,1110.720922,47221.44762 +3187,No,No,403.0343765,48874.15116 +3188,No,No,430.0904233,29246.37709 +3189,No,No,278.4739843,33187.76893 +3190,Yes,No,2228.472283,27438.34899 +3191,No,No,1201.782617,30099.16237 +3192,No,No,490.7984044,44686.10315 +3193,No,Yes,792.8559724,8699.598343 +3194,No,No,870.6623709,37180.06765 +3195,No,Yes,1149.209836,11337.05925 +3196,No,No,493.533675,49735.56761 +3197,No,Yes,874.3483758,10845.01883 +3198,No,No,1097.94754,23224.12783 +3199,No,No,976.1491224,29998.36307 +3200,No,No,1373.725379,43124.21315 +3201,No,Yes,1282.762841,22360.19633 +3202,No,No,636.7284738,45649.17081 +3203,No,No,1823.231922,24744.85406 +3204,No,No,605.8638006,34515.19566 +3205,No,No,0,27888.82603 +3206,No,No,491.5203106,37128.7743 +3207,No,No,248.3623967,37868.55976 +3208,No,No,1036.503746,37151.41064 +3209,No,No,1130.893933,35235.36568 +3210,No,Yes,1048.538983,10392.06411 +3211,No,No,1251.888502,50174.32907 +3212,Yes,No,1578.847496,61220.13315 +3213,No,No,0,40341.33883 +3214,No,No,1531.962958,33595.43278 +3215,No,No,935.4652503,30909.93091 +3216,No,Yes,1056.181067,16873.51176 +3217,No,Yes,1060.776827,21430.79122 +3218,No,No,253.8508786,41472.12196 +3219,No,No,323.1232557,36119.42882 +3220,No,No,1051.224565,31749.63903 +3221,No,Yes,925.9470583,14258.26743 +3222,No,Yes,831.2419342,13702.99993 +3223,No,No,280.8368683,56567.00833 +3224,No,Yes,1092.34255,20465.15382 +3225,No,No,702.276627,49708.95525 +3226,No,No,663.0958877,32756.25991 +3227,No,No,224.7422764,25386.99487 +3228,No,No,1108.680514,60725.52684 +3229,No,Yes,0,20071.82169 +3230,No,No,352.7980607,36751.97786 +3231,No,Yes,1390.185426,21735.24694 +3232,No,No,599.647361,32471.21624 +3233,No,No,514.5454923,45763.89983 +3234,No,Yes,484.8943167,15883.66443 +3235,No,No,278.7204414,49231.1908 +3236,No,No,661.0557337,35806.05151 +3237,No,No,1110.29634,27501.12265 +3238,No,No,1306.257568,29376.44137 +3239,Yes,Yes,1966.062966,17735.7788 +3240,No,No,588.5647713,33224.44475 +3241,No,No,0,42869.15883 +3242,No,No,1289.602237,35164.88976 +3243,No,No,1054.278499,50795.20662 +3244,No,No,0,43513.11466 +3245,No,No,897.8072418,37899.66779 +3246,No,Yes,673.7855907,13344.85695 +3247,No,No,636.3350235,49487.60089 +3248,No,No,412.0716146,48347.29698 +3249,Yes,No,1898.323497,61011.21595 +3250,No,No,1606.403675,27382.32539 +3251,No,No,0,41040.19512 +3252,No,Yes,0,20157.25126 +3253,No,No,486.3844235,53051.72368 +3254,No,No,1738.110835,38821.6263 +3255,No,No,0,62746.83679 +3256,No,No,355.9366266,34730.88552 +3257,No,No,0,24818.35863 +3258,No,Yes,1925.987451,20441.67444 +3259,No,No,819.1118002,55716.54873 +3260,No,Yes,588.756707,24015.96067 +3261,No,No,975.5719981,28804.00025 +3262,No,No,127.1619255,40727.555 +3263,No,No,713.4368134,48176.42822 +3264,No,No,737.7230425,48994.81285 +3265,No,Yes,687.0644195,14059.45763 +3266,No,No,444.5145836,41957.74494 +3267,No,No,1061.284237,32839.34486 +3268,No,No,1425.495582,37189.02514 +3269,No,Yes,638.2619325,21282.65109 +3270,No,Yes,1101.444967,20936.92692 +3271,No,No,888.2445875,41489.19125 +3272,No,No,951.2087248,20616.0807 +3273,No,Yes,1152.096644,15852.42345 +3274,No,No,883.1603001,27125.90696 +3275,No,No,1126.988011,47378.01918 +3276,No,No,742.8212302,34353.48791 +3277,No,Yes,1532.116645,19462.15441 +3278,No,Yes,266.7686301,28452.59827 +3279,No,Yes,310.4305997,16479.99239 +3280,No,No,1702.301357,46589.06262 +3281,No,No,649.7824955,39655.39818 +3282,No,No,648.3214545,45392.28403 +3283,No,No,995.2741519,24782.87676 +3284,No,No,330.0748824,45757.37558 +3285,No,No,1358.573433,20700.70608 +3286,Yes,No,1166.798527,30367.61086 +3287,No,No,671.6695725,26142.91161 +3288,No,Yes,484.0854836,12045.31042 +3289,No,No,1404.627553,47712.18407 +3290,No,No,617.001387,54489.74386 +3291,No,Yes,964.6248897,21436.55559 +3292,No,No,466.579143,24719.39552 +3293,No,No,618.801159,25927.57156 +3294,No,No,1310.952365,28978.46412 +3295,No,No,123.9274635,28311.8309 +3296,No,No,786.2642192,45304.79336 +3297,No,No,748.2105894,46981.93176 +3298,Yes,Yes,2124.489239,12651.07432 +3299,No,Yes,326.3912434,16988.9233 +3300,No,Yes,474.8827825,14986.64329 +3301,No,No,59.9658271,24889.25459 +3302,No,No,1611.333882,47894.27665 +3303,No,No,545.6052116,27236.55814 +3304,No,No,0,46590.44064 +3305,No,No,1249.354139,52232.25119 +3306,No,Yes,587.8747541,18448.10027 +3307,Yes,No,1302.734742,43680.06524 +3308,No,Yes,908.3727454,27416.62435 +3309,No,Yes,1252.133019,13860.93503 +3310,No,No,277.1686519,37177.03609 +3311,No,No,623.5748091,57483.50421 +3312,No,No,943.1993902,41765.1425 +3313,No,Yes,1815.445231,18919.41697 +3314,No,Yes,927.4246504,20498.52765 +3315,Yes,Yes,2008.032985,18601.40309 +3316,No,No,1028.235235,31297.96196 +3317,No,No,408.236017,51131.51326 +3318,No,No,1355.436847,42630.66866 +3319,No,Yes,376.7707988,16042.23892 +3320,No,No,856.4791573,69124.2685 +3321,No,No,1110.743705,43991.92013 +3322,No,No,437.814279,27855.75497 +3323,No,No,1013.705407,60574.80587 +3324,Yes,No,1922.829046,56202.5762 +3325,No,No,1066.792794,49629.90301 +3326,No,No,465.5532801,33998.84758 +3327,No,No,899.4833121,36361.97808 +3328,No,No,592.8006667,34355.90955 +3329,No,No,164.0010194,42695.39792 +3330,No,No,0,62975.12874 +3331,No,Yes,1335.131324,17044.3654 +3332,No,Yes,1549.61638,11927.53195 +3333,No,No,1441.070696,33852.6776 +3334,No,No,1157.652068,39080.26353 +3335,No,No,899.4620065,36335.21952 +3336,No,No,331.167032,44752.22929 +3337,No,No,439.2017918,42132.01363 +3338,No,No,489.7176909,27043.23056 +3339,No,No,652.9936792,37143.58123 +3340,No,No,206.8208783,49221.14852 +3341,No,Yes,932.4874049,14950.09473 +3342,No,No,1098.232451,42331.89108 +3343,No,Yes,1959.59552,19677.93573 +3344,No,Yes,1706.953766,20804.65612 +3345,No,No,1255.883711,55971.18547 +3346,No,No,537.9544088,54481.308 +3347,No,No,349.1618062,38844.34984 +3348,No,No,167.6060988,20150.90276 +3349,No,No,1612.465315,42281.23114 +3350,No,No,551.2540524,48007.99181 +3351,No,Yes,1521.496682,16830.56514 +3352,No,No,826.9601242,61365.44432 +3353,No,No,770.0819346,38010.26111 +3354,No,Yes,1109.425497,10973.08825 +3355,No,No,559.3240273,53542.41272 +3356,No,Yes,390.2781979,9050.002781 +3357,No,No,1567.023862,42258.77335 +3358,No,No,170.2602366,62708.16202 +3359,No,No,528.8502036,46222.28896 +3360,No,No,0,49471.7494 +3361,No,Yes,1000.415807,14071.00854 +3362,No,No,538.7486464,34834.44571 +3363,No,No,435.4028043,35168.72709 +3364,No,No,110.6605307,64525.9379 +3365,No,No,153.563343,49405.26098 +3366,No,Yes,856.6247281,19056.86784 +3367,No,Yes,934.2520481,20747.21223 +3368,No,No,783.1229601,36514.69014 +3369,No,Yes,1271.112495,17451.04119 +3370,No,No,910.7598227,33660.71897 +3371,No,No,748.6520804,40612.21576 +3372,No,No,668.9422598,42028.92384 +3373,No,Yes,1061.932041,18589.62145 +3374,No,No,766.0088421,45155.09721 +3375,No,Yes,1396.234603,15837.25338 +3376,No,No,265.4358914,34040.54849 +3377,Yes,No,2080.937247,34494.17121 +3378,No,No,324.6912643,47103.45782 +3379,No,Yes,1135.010767,24005.0051 +3380,Yes,No,1731.680766,56228.92168 +3381,No,Yes,1133.232966,16749.46145 +3382,No,No,782.6486901,37497.087 +3383,No,Yes,745.5654746,12896.6329 +3384,No,No,1444.361237,51154.50812 +3385,No,Yes,1340.5292,19318.84043 +3386,Yes,No,1903.667219,41173.50399 +3387,No,No,317.9035736,42209.48734 +3388,No,No,901.3177479,43242.61209 +3389,No,No,527.9834823,39950.95852 +3390,No,Yes,915.6537558,16888.81242 +3391,No,No,1143.550354,42694.80392 +3392,Yes,No,1488.558775,22256.86369 +3393,No,No,1024.230284,27104.85794 +3394,No,No,619.9038223,29217.35234 +3395,No,No,1007.248656,34893.13769 +3396,No,No,0,42577.81452 +3397,No,No,1342.047594,51442.56558 +3398,No,Yes,1478.169785,22564.58488 +3399,No,No,149.3577795,52310.41153 +3400,No,No,279.7242752,52008.41581 +3401,No,No,869.8087827,49312.50649 +3402,No,No,1493.287852,56364.25489 +3403,No,No,1127.822686,43344.89429 +3404,No,No,924.6927054,38676.97044 +3405,No,No,311.507988,43379.63468 +3406,No,No,696.5100668,28610.19466 +3407,No,Yes,295.6334521,25452.47585 +3408,No,No,464.762682,17862.22687 +3409,No,Yes,750.4126745,17291.52689 +3410,No,No,890.4076532,52943.3834 +3411,No,No,0,37455.19888 +3412,No,Yes,340.697866,12351.82596 +3413,No,No,724.2438058,43399.71828 +3414,No,No,979.8550462,40374.05156 +3415,No,No,33.45702647,30510.03842 +3416,No,No,1203.736179,40969.08235 +3417,No,No,735.7308567,23225.1771 +3418,No,Yes,884.9188466,27208.77127 +3419,No,No,825.8499757,48216.71189 +3420,No,Yes,1734.58584,14165.27886 +3421,No,Yes,670.4934929,16513.04426 +3422,No,No,874.8472856,36709.10831 +3423,No,No,0,40021.35536 +3424,No,No,827.3504736,43833.4237 +3425,No,No,1508.841378,31739.27192 +3426,No,Yes,0,19095.83037 +3427,No,Yes,914.515479,7525.584256 +3428,No,No,302.9612445,51030.43953 +3429,No,Yes,275.4858932,13448.33419 +3430,No,No,589.5580943,42335.44703 +3431,No,No,1120.267458,45153.04007 +3432,No,Yes,1416.966156,11104.56632 +3433,No,No,1270.176678,44185.35771 +3434,No,No,848.2344863,35186.5775 +3435,No,No,560.9680738,43254.71541 +3436,No,No,619.6155048,45463.3009 +3437,No,No,1197.579775,18449.02078 +3438,Yes,No,1585.729401,52274.86012 +3439,No,No,478.3633261,28262.86813 +3440,No,No,1144.912479,41370.51658 +3441,No,No,778.7519229,58860.38076 +3442,No,Yes,1675.422141,13703.88109 +3443,No,No,680.7697735,56759.23459 +3444,No,Yes,1113.577978,17593.52985 +3445,No,No,339.3513975,38097.01668 +3446,No,No,908.8712521,55612.17236 +3447,No,No,480.787555,11193.4157 +3448,No,Yes,874.5830207,17378.28727 +3449,No,No,0,42515.16283 +3450,No,No,1353.522203,52048.50412 +3451,No,No,1073.950033,28845.74203 +3452,No,Yes,1402.287769,13276.24908 +3453,No,No,759.1480034,24473.1066 +3454,No,No,4.109498145,38326.19836 +3455,No,No,194.5543901,38794.14591 +3456,No,Yes,597.5996145,16526.67733 +3457,No,Yes,1067.781115,18641.72431 +3458,No,No,379.2910135,43096.48981 +3459,No,Yes,583.5997492,15179.68613 +3460,No,No,465.5222142,30792.88359 +3461,No,No,1144.122747,33624.94143 +3462,No,Yes,1025.087573,29019.32622 +3463,No,No,203.9580437,63638.18732 +3464,No,Yes,439.5817266,17823.50194 +3465,No,No,0,40574.42079 +3466,No,No,720.8575686,42753.72169 +3467,No,No,1385.79514,44983.29656 +3468,No,No,1127.507577,64618.74214 +3469,No,No,808.0001678,36076.0798 +3470,No,Yes,280.2481537,25307.37708 +3471,No,No,378.7230203,15738.33184 +3472,No,Yes,885.5168464,21211.3259 +3473,No,No,0,37482.48573 +3474,No,Yes,1006.942246,19065.69279 +3475,No,No,490.645063,37998.71435 +3476,No,Yes,141.0265496,11314.39178 +3477,No,No,761.8855412,46421.76407 +3478,No,No,1787.60382,33312.80668 +3479,No,No,964.5413058,33908.05138 +3480,No,No,931.5769842,52703.79122 +3481,No,No,10.0565726,37673.31655 +3482,No,Yes,1971.663236,22040.26274 +3483,No,Yes,1171.519916,17642.26775 +3484,No,No,784.383692,44927.4957 +3485,No,No,503.4985373,20476.07294 +3486,No,Yes,295.4807145,17324.04163 +3487,Yes,Yes,1475.057438,20210.27262 +3488,No,No,950.4713695,38990.17386 +3489,No,Yes,1083.702362,17522.73069 +3490,No,Yes,1334.666264,18064.77834 +3491,Yes,No,1332.386178,53517.35049 +3492,No,No,314.5480494,43440.60406 +3493,No,Yes,683.1898968,12367.69713 +3494,No,Yes,1087.412755,17060.59429 +3495,No,No,443.6347834,46611.02063 +3496,No,No,426.5528803,34191.29522 +3497,No,No,532.9847408,55847.28595 +3498,No,Yes,1378.217339,20119.8292 +3499,No,No,470.0359134,29309.12726 +3500,No,Yes,470.3093927,10062.0836 +3501,No,No,98.39661755,25308.58993 +3502,No,No,400.0591104,30160.28898 +3503,No,No,1265.725868,20816.95333 +3504,No,Yes,700.3351717,15905.2127 +3505,No,No,1266.772636,47991.05013 +3506,No,No,116.3537354,30765.3518 +3507,No,Yes,1023.638535,15738.16001 +3508,No,No,823.6641694,24533.20314 +3509,No,Yes,1328.671531,13389.43379 +3510,No,No,1032.185178,46541.64947 +3511,No,Yes,1093.639058,14790.76919 +3512,No,No,587.764496,39618.81736 +3513,No,No,1379.184314,48254.60001 +3514,No,Yes,444.0253084,23691.98322 +3515,No,Yes,597.6024871,21296.6123 +3516,No,No,738.1944102,35134.26535 +3517,No,No,563.4911731,47142.44985 +3518,No,Yes,1518.715646,23877.81985 +3519,No,No,1610.719681,30098.82399 +3520,No,Yes,542.3896938,14294.59508 +3521,No,No,586.1690704,31466.85134 +3522,No,Yes,648.8853641,24457.36408 +3523,No,Yes,913.0464559,17139.5796 +3524,No,No,1650.896019,43478.12821 +3525,No,No,758.5061124,37485.90813 +3526,No,No,1955.557356,45507.9115 +3527,No,No,446.9644659,27626.60257 +3528,No,No,874.9416209,39807.67955 +3529,No,Yes,1124.567376,28936.90622 +3530,No,No,0,29427.91484 +3531,No,No,184.5691415,44021.30216 +3532,No,No,1133.799783,39860.16322 +3533,No,Yes,1148.923139,19717.42629 +3534,No,No,1040.272472,35639.5543 +3535,No,No,753.9608286,21507.99492 +3536,No,No,818.4962932,34903.67755 +3537,No,Yes,1228.655816,17460.64216 +3538,No,Yes,16.4148836,24291.34942 +3539,No,No,763.900776,64967.64045 +3540,No,Yes,1667.308827,15614.82091 +3541,No,No,637.4918793,48942.61214 +3542,No,No,911.1320579,31265.21771 +3543,No,No,125.0538441,50059.54712 +3544,No,Yes,548.2788232,15308.59999 +3545,No,No,795.0348699,34169.66973 +3546,No,No,649.1590457,37015.27499 +3547,No,No,0,51758.41029 +3548,No,No,292.3343828,41036.12926 +3549,No,No,1285.991803,31068.12253 +3550,No,No,845.8543023,44480.65532 +3551,No,Yes,1660.111798,26838.93305 +3552,No,No,610.5686385,36145.01702 +3553,No,Yes,1137.421085,16177.27291 +3554,No,Yes,1115.768562,11496.54181 +3555,No,No,302.1642898,28805.06267 +3556,No,No,1074.143357,35054.58442 +3557,No,No,510.960176,32151.84898 +3558,No,No,974.4105653,43861.80192 +3559,No,No,46.18328844,42533.71536 +3560,No,No,1731.604528,58075.94369 +3561,No,No,1429.393379,31387.70033 +3562,No,No,503.5138088,62115.10508 +3563,Yes,Yes,2179.221428,15302.7765 +3564,No,No,18.78828058,24699.09684 +3565,No,No,1047.139689,46007.98061 +3566,No,Yes,1049.603574,11137.86104 +3567,No,No,1372.935822,35467.75766 +3568,No,No,958.3266322,28145.84248 +3569,No,No,826.6385929,47358.54902 +3570,No,No,1005.163082,42127.99447 +3571,No,No,1132.862485,25402.471 +3572,No,No,618.610524,45154.74784 +3573,No,No,1073.825421,47935.00532 +3574,No,No,1108.800997,39676.55456 +3575,No,No,583.7120008,42590.12414 +3576,No,No,835.5334433,23160.57862 +3577,No,No,698.6738292,29580.0691 +3578,No,No,929.5761899,40442.06312 +3579,No,No,916.5979585,41432.4345 +3580,No,No,1324.198769,10892.32916 +3581,No,Yes,1219.376677,11361.82386 +3582,No,No,265.2958484,45828.01885 +3583,No,Yes,1255.425835,19885.76115 +3584,No,No,1524.436703,18878.55448 +3585,No,No,326.3972934,30156.35542 +3586,No,No,1821.003152,54051.98234 +3587,No,Yes,1357.423466,25733.3094 +3588,No,No,1187.473801,53318.99336 +3589,No,Yes,925.9048607,20952.58614 +3590,No,No,1451.785006,27853.96778 +3591,No,No,32.06807899,38788.66047 +3592,No,No,685.1975865,36015.41886 +3593,No,No,1071.89915,54791.80557 +3594,No,No,874.4902512,50078.45942 +3595,No,No,926.3096412,38093.97752 +3596,No,No,1235.640809,53171.29688 +3597,No,Yes,1155.504667,24840.76175 +3598,Yes,No,1360.385782,28992.00182 +3599,No,No,234.9957765,40394.42105 +3600,No,No,854.6912433,26672.18171 +3601,No,Yes,1405.658927,19701.75543 +3602,No,Yes,1161.026177,13913.44196 +3603,No,No,329.3476893,20708.98772 +3604,No,No,1541.320802,32508.3839 +3605,No,No,1456.652456,59361.91835 +3606,No,No,0,24565.57165 +3607,No,No,83.08037273,45442.82986 +3608,No,No,95.8981855,31437.42323 +3609,No,No,674.804717,47653.61598 +3610,No,No,917.7839803,44466.88503 +3611,No,Yes,759.8033264,14217.69888 +3612,No,No,379.0630675,14792.51184 +3613,Yes,Yes,2004.394001,18860.84665 +3614,No,No,1469.603836,43436.95207 +3615,No,No,200.2748084,36926.39755 +3616,No,No,843.4296594,39512.06905 +3617,No,No,676.7633506,34400.86089 +3618,No,No,46.09208958,42025.76231 +3619,No,No,542.8420254,48897.75254 +3620,No,No,89.90989542,30004.4708 +3621,No,No,908.1844428,37319.62257 +3622,No,No,0,46306.93629 +3623,No,Yes,1326.848392,20790.25397 +3624,No,Yes,993.8172666,12531.0255 +3625,No,Yes,1084.381324,20881.45652 +3626,No,No,1197.71154,30377.25003 +3627,No,No,1378.297068,43411.66192 +3628,No,No,182.7419949,32359.20921 +3629,No,No,529.6503069,35102.27513 +3630,No,Yes,1632.354361,15763.58457 +3631,No,Yes,1119.202526,20619.50975 +3632,No,No,787.1368176,45772.61403 +3633,No,Yes,969.7841591,21412.60098 +3634,No,No,1256.98265,38083.13036 +3635,No,No,1015.045427,31026.41301 +3636,No,Yes,1197.608957,17452.00004 +3637,No,Yes,847.4580256,16926.35002 +3638,No,No,598.4386356,52896.0597 +3639,No,Yes,983.0922765,17001.64919 +3640,No,No,1296.267423,45226.12102 +3641,No,No,412.397142,30206.95882 +3642,Yes,No,1504.755378,26183.24535 +3643,No,Yes,367.3634193,16295.56247 +3644,No,No,0,27889.3047 +3645,No,Yes,894.7254906,19335.63404 +3646,No,No,75.83412558,42075.04784 +3647,No,No,642.5352176,34893.78594 +3648,No,Yes,1777.284555,14567.81398 +3649,No,No,370.0332879,44507.21131 +3650,No,No,653.373457,52276.5854 +3651,No,No,793.9121634,48907.68384 +3652,No,No,917.0752778,19003.77498 +3653,No,No,509.7784393,38614.41313 +3654,No,No,947.8137895,42094.47015 +3655,No,Yes,860.8353999,19514.76352 +3656,No,No,302.2270486,35232.46044 +3657,No,No,891.8873793,35062.53819 +3658,No,No,1377.558752,38364.67915 +3659,No,Yes,1222.958464,15174.97246 +3660,No,No,378.9704244,39492.01195 +3661,No,No,0,41523.75248 +3662,No,Yes,1103.913001,28552.53733 +3663,No,Yes,704.4725219,21140.27564 +3664,No,Yes,1402.31057,21566.55688 +3665,No,Yes,68.37660782,10996.08786 +3666,No,No,409.5960723,30627.50294 +3667,No,Yes,655.7009447,19915.23285 +3668,No,Yes,654.8146682,14504.39641 +3669,No,No,1255.920125,43929.14322 +3670,No,No,1217.960888,33443.30641 +3671,No,No,691.8931985,35562.60937 +3672,No,Yes,548.1362888,19501.34107 +3673,No,No,437.5460694,31537.12693 +3674,No,No,1067.842439,70700.64784 +3675,No,No,521.4397781,31981.85812 +3676,No,Yes,1348.99532,25805.80996 +3677,No,No,674.6637003,41798.39162 +3678,No,No,516.3590418,25891.75965 +3679,No,Yes,1470.338563,19647.14233 +3680,No,No,859.0079101,43101.87381 +3681,No,No,1114.088015,59200.137 +3682,No,No,773.3268067,51787.78944 +3683,No,Yes,803.968975,16553.77283 +3684,No,No,376.2925648,43940.04806 +3685,No,No,829.7296771,36164.15418 +3686,No,No,299.8906333,34920.18154 +3687,No,Yes,148.6570947,15645.77712 +3688,No,No,705.113586,47752.15923 +3689,No,No,467.8641353,43308.69657 +3690,No,No,382.0942862,38348.28619 +3691,No,No,450.3090116,46172.72509 +3692,No,No,379.9145929,39064.04852 +3693,No,Yes,151.7714011,18659.07094 +3694,No,No,1535.224473,35282.22802 +3695,No,No,1596.87343,34655.88003 +3696,No,No,778.0601546,52545.57805 +3697,No,No,107.7066719,42199.96763 +3698,No,Yes,961.7978206,13389.06941 +3699,No,Yes,235.2497893,13360.92519 +3700,No,No,935.3381557,57139.30486 +3701,No,No,0,34563.04588 +3702,No,No,10.30689072,37590.30958 +3703,No,Yes,2370.463612,24251.95872 +3704,No,No,1447.022281,37322.71022 +3705,No,No,829.0233191,25161.89851 +3706,No,No,1474.061544,47040.80859 +3707,No,No,913.6383267,50351.1552 +3708,No,No,1028.886338,34257.63277 +3709,No,No,563.8917207,54147.70839 +3710,No,Yes,747.8216828,19330.3084 +3711,No,Yes,811.5399409,13828.96866 +3712,No,Yes,797.3736475,23438.1458 +3713,No,No,701.9181566,53151.76073 +3714,No,No,1195.359822,42107.364 +3715,Yes,No,1672.506063,66466.46089 +3716,No,No,889.9012,20805.61404 +3717,No,Yes,579.7213046,18555.75259 +3718,No,No,1143.200459,28900.72779 +3719,Yes,Yes,1237.621866,14862.70546 +3720,No,No,740.9570805,48683.22038 +3721,No,Yes,1470.474586,18621.17043 +3722,No,No,507.6786163,45839.13418 +3723,No,Yes,452.3023556,19922.64012 +3724,No,No,431.9049281,30115.15283 +3725,No,No,388.9354653,36932.81012 +3726,No,Yes,647.2601142,18905.85384 +3727,No,No,1069.130301,42361.04675 +3728,No,No,517.9885181,45495.00543 +3729,No,No,996.2511833,23720.98747 +3730,No,No,995.1223306,47286.83091 +3731,No,No,0,55069.81221 +3732,No,No,480.5711421,36192.09032 +3733,No,No,413.8588246,42453.68188 +3734,No,No,730.0720039,47074.22892 +3735,No,No,11.30778333,43520.4888 +3736,No,Yes,1507.892451,14632.25766 +3737,No,Yes,1129.113909,13580.25696 +3738,No,No,1159.93658,46789.88063 +3739,No,No,817.9824403,40658.58991 +3740,No,Yes,901.1752435,16745.06685 +3741,No,Yes,1778.121512,17060.92745 +3742,No,No,1036.756686,34912.78946 +3743,No,No,199.1361487,49292.54565 +3744,No,Yes,1295.690551,23191.23653 +3745,No,Yes,468.2576618,6365.564576 +3746,No,No,688.1054911,27080.74254 +3747,No,No,202.3212081,37188.56938 +3748,No,No,621.0034435,37249.43644 +3749,No,No,963.3324811,30100.76227 +3750,No,No,1106.697012,51879.71848 +3751,No,No,1391.407689,30427.20258 +3752,No,No,198.5947905,58598.66764 +3753,No,No,90.68269663,37914.40131 +3754,No,No,460.873865,49243.44479 +3755,No,No,1182.444832,34784.78813 +3756,No,No,748.4344222,43215.82973 +3757,No,Yes,1727.554591,11608.12845 +3758,No,No,831.4782222,39079.78845 +3759,No,No,655.6291501,40257.93299 +3760,No,Yes,787.1367086,22434.97645 +3761,No,Yes,760.8953501,20070.148 +3762,No,No,0,44387.5749 +3763,No,No,523.3884426,53115.59936 +3764,No,No,481.5910034,35817.76129 +3765,No,No,0,26275.25654 +3766,No,Yes,1414.657799,17954.85255 +3767,No,Yes,1103.216776,22410.9083 +3768,No,No,948.6964528,38247.91323 +3769,No,Yes,725.5724195,9019.545512 +3770,No,No,1727.349001,40791.14292 +3771,No,No,891.6073042,43925.96442 +3772,No,No,667.290493,66538.34422 +3773,No,Yes,1060.26371,15661.409 +3774,No,Yes,830.8605751,18259.66422 +3775,No,No,319.1745767,44410.53156 +3776,No,No,1147.274484,44871.54944 +3777,No,No,664.5115204,47878.83383 +3778,No,No,1066.899512,35567.76207 +3779,No,Yes,374.9767674,19806.51909 +3780,No,No,549.1913427,43688.68042 +3781,No,Yes,943.5813708,17730.91926 +3782,No,No,111.2850398,42292.29777 +3783,No,No,504.7395253,59560.17966 +3784,No,No,798.4773511,32921.63394 +3785,Yes,No,2008.458596,35145.04762 +3786,No,No,965.9265722,29219.07078 +3787,No,No,1768.139978,30762.10521 +3788,No,No,435.0553769,32198.02488 +3789,No,Yes,685.6319445,15892.09254 +3790,No,Yes,745.2306213,12877.5095 +3791,No,No,161.7594874,43676.05741 +3792,No,No,0,32558.47509 +3793,No,No,1389.286467,38373.01452 +3794,No,No,802.0926754,45052.58045 +3795,No,Yes,335.8226355,16123.11335 +3796,No,No,1031.786257,39260.00622 +3797,No,No,893.2416447,43816.22547 +3798,No,No,1007.277995,45124.04044 +3799,No,No,786.8023084,38023.61481 +3800,No,Yes,1814.558895,24177.27252 +3801,No,No,116.2188756,50109.02341 +3802,No,No,839.9240942,47347.67671 +3803,No,Yes,824.6917018,15158.62479 +3804,No,No,239.3844788,25485.17338 +3805,No,No,933.7511521,52319.15351 +3806,No,No,459.3876653,37630.61332 +3807,No,Yes,870.0239927,15955.88515 +3808,No,No,232.2075504,11151.04762 +3809,No,No,150.7838407,32106.48364 +3810,No,No,1215.170473,55230.64819 +3811,No,No,1435.360095,33728.89232 +3812,No,No,338.0683219,47340.02669 +3813,No,Yes,1056.947399,20304.09812 +3814,No,No,194.1734166,28743.16437 +3815,No,No,298.5096896,50229.77974 +3816,No,Yes,938.339329,21911.72721 +3817,No,No,1297.392431,47095.22157 +3818,No,No,690.2283828,56958.59936 +3819,No,No,1013.906431,34166.82649 +3820,No,No,1213.794105,48796.84928 +3821,No,No,0,39269.83592 +3822,No,Yes,1399.950257,11010.21245 +3823,No,No,590.99973,50246.71554 +3824,No,No,1438.833691,35532.39932 +3825,No,No,438.2346852,48859.26102 +3826,No,No,783.0015156,37428.22004 +3827,No,No,1864.946403,48678.41519 +3828,No,No,208.0504439,46840.51927 +3829,No,No,621.1651351,32168.5529 +3830,No,No,994.1999643,52689.53943 +3831,No,No,674.6401585,42463.52497 +3832,No,No,537.5871865,46953.37681 +3833,No,No,690.3409422,29924.22946 +3834,No,Yes,1369.408276,18144.35026 +3835,No,No,654.277589,37706.59487 +3836,No,No,798.031314,29934.14323 +3837,No,No,763.2248929,48170.35992 +3838,No,No,1663.616348,52975.26361 +3839,No,No,988.3939471,49804.28375 +3840,No,No,1419.631892,40295.2688 +3841,No,Yes,1196.041822,19665.8292 +3842,No,No,618.0513849,16944.34449 +3843,No,No,1028.181249,32249.84567 +3844,No,No,523.5010994,39168.17425 +3845,No,No,517.4596209,39543.5871 +3846,No,Yes,764.5629974,5082.992631 +3847,No,No,206.9488464,53058.56141 +3848,No,No,847.9815066,43023.58501 +3849,No,No,0,40540.69441 +3850,No,No,631.7471322,37936.04343 +3851,No,No,1014.599104,51438.7102 +3852,No,No,157.2800282,41415.39603 +3853,No,No,525.4851998,43737.90426 +3854,No,No,962.9486146,28660.93057 +3855,No,No,594.1061337,24069.21303 +3856,Yes,Yes,2321.882221,21331.31478 +3857,No,Yes,1128.780156,17231.80847 +3858,No,Yes,2118.800574,18791.85208 +3859,No,No,290.5648739,13239.87284 +3860,No,No,1244.392244,33880.29922 +3861,No,No,1177.917268,49061.72291 +3862,No,No,0,44225.61586 +3863,No,Yes,492.6570685,10154.15626 +3864,No,No,0,62689.03303 +3865,No,No,530.2512986,39311.32571 +3866,No,No,0,45104.09403 +3867,No,No,1028.174611,51228.30211 +3868,No,Yes,488.2385515,33003.42295 +3869,No,No,4.458938299,46970.49642 +3870,No,No,1636.316153,35814.79198 +3871,No,No,107.9218494,33764.20207 +3872,No,Yes,129.184357,20570.8542 +3873,No,No,956.9854006,38285.7479 +3874,No,Yes,823.0640631,17254.39842 +3875,No,No,458.9984676,33163.54535 +3876,No,Yes,867.9046789,18949.02734 +3877,No,Yes,117.8094413,18752.8302 +3878,No,No,474.6661055,42058.77731 +3879,No,Yes,1180.529146,19139.30023 +3880,No,No,315.8506084,47328.98275 +3881,No,Yes,552.2040873,22554.57852 +3882,Yes,No,1342.262874,35691.63736 +3883,No,No,407.289756,45376.23591 +3884,No,No,1624.956465,47574.55972 +3885,No,Yes,548.4547299,21672.53989 +3886,No,No,908.8882984,42205.2065 +3887,No,Yes,969.8986849,17940.11913 +3888,No,Yes,966.350838,11776.52915 +3889,No,No,628.5669713,37617.11918 +3890,No,Yes,882.9774249,8833.668596 +3891,No,No,824.9954903,34277.5259 +3892,No,No,762.9128371,41617.81395 +3893,No,No,1246.229525,49479.57682 +3894,No,No,60.96076167,41548.11727 +3895,No,No,1377.978816,34683.61245 +3896,No,No,1215.252546,43793.11584 +3897,No,No,494.9019178,13980.36118 +3898,No,No,839.8737554,39158.23755 +3899,No,No,982.3816071,29157.29227 +3900,No,Yes,927.5165876,4143.118844 +3901,No,No,433.8578193,30581.58287 +3902,No,No,1213.03372,17726.67011 +3903,No,No,316.0043508,26518.46264 +3904,No,Yes,973.9031102,21590.0349 +3905,No,No,0,25603.53326 +3906,No,No,713.7511068,37230.07372 +3907,No,No,997.4654347,22061.79981 +3908,No,No,1326.064864,34945.80095 +3909,No,Yes,1135.361184,24314.03688 +3910,No,No,0,50805.03659 +3911,No,No,43.89647321,48543.59909 +3912,No,No,666.370177,50105.2676 +3913,No,Yes,0,18426.26072 +3914,Yes,Yes,2334.123559,19335.88929 +3915,No,No,303.791175,28525.40173 +3916,No,No,634.4950473,35131.30153 +3917,No,Yes,853.8183574,18750.75063 +3918,No,No,747.4553934,43898.75063 +3919,No,Yes,1120.030273,19170.45835 +3920,No,No,1290.191542,36898.92872 +3921,No,No,1348.785018,33488.41292 +3922,Yes,No,1632.894118,44326.58484 +3923,No,No,693.6306933,32594.66376 +3924,No,No,467.5830885,43435.83661 +3925,No,No,618.1253382,27868.79389 +3926,No,Yes,1217.667355,20977.23519 +3927,No,No,872.0713576,36004.64481 +3928,No,No,0,28659.48838 +3929,No,No,894.7344842,37903.52646 +3930,No,Yes,361.6104669,21685.69223 +3931,No,Yes,937.197434,18411.82443 +3932,No,No,0,55760.50194 +3933,No,No,339.5318647,31092.75765 +3934,No,No,423.7145506,32640.29407 +3935,No,Yes,1529.061705,17358.04298 +3936,No,No,894.1144875,47681.62283 +3937,No,No,760.7208238,40854.46907 +3938,No,No,883.0162511,37961.15592 +3939,No,Yes,1322.585497,15304.26039 +3940,No,No,2036.993189,31384.13 +3941,No,No,751.3999173,54993.40784 +3942,No,No,578.8362774,25522.51998 +3943,No,No,829.2816512,58070.11256 +3944,No,No,1160.061265,51895.30404 +3945,No,No,1197.771881,44047.69118 +3946,No,No,417.5922108,48035.60964 +3947,No,No,777.8244481,43910.98856 +3948,No,No,625.7461988,29644.2083 +3949,No,No,1382.775774,36232.31933 +3950,No,No,986.3058026,25342.30479 +3951,No,No,937.7383255,24932.72113 +3952,No,No,0,44525.84935 +3953,No,No,131.576673,51106.99485 +3954,No,Yes,1096.184606,15535.54866 +3955,No,No,499.1739193,44773.57533 +3956,No,No,1236.21872,37884.5661 +3957,No,No,637.014541,27828.1376 +3958,Yes,No,2147.312578,58271.39083 +3959,No,No,516.2165693,42446.04118 +3960,No,Yes,113.276476,21068.16403 +3961,No,No,352.3277028,40716.17481 +3962,No,No,918.014298,26798.07141 +3963,No,Yes,1378.913117,14329.77123 +3964,No,Yes,482.5452583,21369.27509 +3965,No,No,813.2006512,49477.51151 +3966,No,Yes,1330.485181,13765.44133 +3967,No,No,1373.533033,56878.33506 +3968,No,Yes,1411.607365,16677.30692 +3969,No,No,313.8050859,37720.7621 +3970,Yes,Yes,1803.944567,29100.81923 +3971,No,No,964.5246557,40814.65291 +3972,No,No,1139.370702,42759.38253 +3973,No,Yes,1009.431089,21904.69277 +3974,No,No,615.4653882,25865.18062 +3975,No,No,673.6980514,41891.42597 +3976,No,Yes,1288.551958,24918.41508 +3977,No,Yes,2388.174009,7832.135644 +3978,No,No,0,34545.09712 +3979,No,Yes,1119.38661,20011.46887 +3980,No,No,1352.703443,35743.00396 +3981,No,Yes,886.3485401,17534.46708 +3982,No,Yes,1468.79522,25038.8165 +3983,No,No,727.3734929,46329.02775 +3984,No,No,593.4338377,36995.82154 +3985,No,Yes,1168.340641,13580.94664 +3986,No,No,541.784475,56100.10543 +3987,No,No,1055.414789,29967.17338 +3988,No,No,399.7235905,43243.6661 +3989,No,Yes,552.8777042,14320.85979 +3990,No,No,1189.752134,50838.51722 +3991,No,No,698.0014773,38135.5587 +3992,No,No,431.5360552,46941.05894 +3993,No,No,851.1035675,53433.57417 +3994,No,No,1457.716054,37453.83977 +3995,No,Yes,992.2802505,13958.29148 +3996,No,No,387.7613417,40063.72764 +3997,No,No,794.1762128,43335.98504 +3998,No,No,412.2548906,37088.21233 +3999,No,Yes,1163.452512,27308.69093 +4000,No,Yes,880.281524,20177.40029 +4001,No,No,752.2218482,48764.02273 +4002,No,No,40.09310013,33278.36519 +4003,No,Yes,1242.5214,18331.00352 +4004,No,Yes,1671.813691,20074.50933 +4005,No,No,1097.039083,40021.35302 +4006,No,No,918.7473435,28327.55036 +4007,No,No,1185.288927,39926.60693 +4008,No,No,997.4773427,44748.79194 +4009,No,Yes,741.4423677,15499.91932 +4010,No,Yes,1234.249913,12268.36496 +4011,No,No,0,52585.68587 +4012,No,Yes,985.2622511,30295.63096 +4013,No,No,1023.198467,40866.01884 +4014,No,No,423.4304738,46000.79668 +4015,No,Yes,1007.139484,17217.50506 +4016,No,No,893.3552679,49960.30616 +4017,No,No,1008.357462,39610.62537 +4018,No,No,691.996757,45428.53932 +4019,No,No,410.2368039,36502.37857 +4020,No,Yes,1215.38361,15994.04102 +4021,No,No,599.0349705,49813.1267 +4022,No,No,355.251657,50160.70311 +4023,No,No,869.8984352,33540.04952 +4024,No,No,1786.463952,54207.16779 +4025,No,Yes,1221.813756,12603.30286 +4026,No,No,0,39164.02235 +4027,No,No,1621.245375,38590.89809 +4028,No,No,1121.496586,39395.89936 +4029,No,No,736.6375029,55187.91982 +4030,No,No,1739.535924,51623.28754 +4031,No,No,1478.621444,41986.04773 +4032,No,No,903.5644525,35296.4479 +4033,No,No,739.6528792,48283.18702 +4034,No,No,228.9558548,36783.00525 +4035,No,No,1313.559525,39989.00907 +4036,No,No,903.9258639,27395.79614 +4037,No,No,667.6598304,42078.68486 +4038,No,No,539.8879468,33856.45403 +4039,No,No,1877.114809,43442.32501 +4040,No,No,739.8597477,30853.29309 +4041,No,Yes,408.5629744,18471.77433 +4042,No,Yes,482.3251509,30588.03024 +4043,No,No,1097.817753,43065.61873 +4044,No,No,1580.866727,36513.61672 +4045,No,Yes,920.2311149,16088.2081 +4046,No,No,425.0537454,37351.35552 +4047,No,No,0,53273.34166 +4048,No,Yes,928.1822759,16968.86752 +4049,No,No,617.3487635,38106.78833 +4050,Yes,No,1673.486349,49310.33291 +4051,No,No,1023.051552,19354.39792 +4052,No,No,1459.561402,44334.61697 +4053,No,No,1235.777517,35140.4588 +4054,No,No,621.9781679,43472.23346 +4055,No,No,280.9443867,50404.03297 +4056,No,No,326.652236,45562.57424 +4057,No,No,902.762305,52528.23748 +4058,No,Yes,672.6932215,13795.96275 +4059,No,Yes,710.7799965,17625.96752 +4060,No,No,901.2084412,48855.45084 +4061,Yes,Yes,2216.017669,20911.69564 +4062,No,No,1210.592999,40182.70682 +4063,No,No,828.9707387,33423.93637 +4064,No,Yes,533.4927964,14707.29581 +4065,No,Yes,768.8230469,22138.2159 +4066,No,No,746.6703307,40354.55847 +4067,No,Yes,730.7394952,7299.520099 +4068,No,Yes,1308.024588,17353.86042 +4069,No,No,481.2610496,33500.5653 +4070,No,Yes,1409.989102,21569.89077 +4071,No,No,524.6079138,40663.04341 +4072,No,Yes,1122.1379,13929.47367 +4073,No,No,1105.878816,42110.4244 +4074,Yes,No,1319.816454,36548.81835 +4075,No,Yes,823.7366728,19041.81838 +4076,No,No,172.9757855,47060.63142 +4077,No,Yes,1497.364809,10576.34148 +4078,Yes,Yes,1819.940936,19393.48297 +4079,No,No,389.3958811,20200.37994 +4080,Yes,Yes,2047.417007,13678.50803 +4081,No,No,909.8655536,44879.27758 +4082,No,No,686.1632948,33190.42713 +4083,No,Yes,0,20565.77765 +4084,No,Yes,1676.097684,25687.91439 +4085,No,No,971.960011,33543.33493 +4086,No,No,887.594632,59416.15976 +4087,No,No,1312.847706,64402.60891 +4088,No,No,1808.722658,30020.6429 +4089,No,No,1170.743464,49832.9514 +4090,No,No,439.352384,44620.26515 +4091,No,No,103.0423418,45798.6747 +4092,No,No,1473.897564,32860.10099 +4093,No,No,780.7092515,37023.11715 +4094,No,No,1144.242002,28018.99557 +4095,No,No,1148.509336,37392.16784 +4096,No,No,667.4121637,31150.02654 +4097,No,No,1013.785194,50268.99231 +4098,No,Yes,1418.38865,12788.71903 +4099,No,Yes,1345.288297,23169.40413 +4100,No,Yes,1493.106066,14295.66474 +4101,No,No,828.2653037,36241.66013 +4102,No,Yes,208.8969152,17966.36114 +4103,No,No,567.2940844,39841.22835 +4104,No,No,686.869876,32359.33746 +4105,No,Yes,1926.289008,12283.40851 +4106,No,No,814.286312,36613.70709 +4107,No,No,1569.518216,43024.97068 +4108,No,No,0,42035.33898 +4109,No,Yes,0,13226.15496 +4110,No,Yes,1828.614325,20225.80611 +4111,No,No,777.6460629,50869.72861 +4112,Yes,No,1644.696623,61474.93445 +4113,No,No,295.1286576,36539.08483 +4114,No,No,781.1237141,22327.67598 +4115,No,No,673.9266568,39508.62412 +4116,No,No,343.2495905,58411.67754 +4117,No,Yes,1302.340309,16292.26714 +4118,No,No,225.8196601,54124.20604 +4119,No,Yes,1456.858054,14530.3112 +4120,No,No,955.0907543,52224.49279 +4121,No,No,777.0230001,47369.11683 +4122,No,No,0,23616.53901 +4123,No,No,527.2520163,49280.30094 +4124,No,No,29.84898288,36972.93414 +4125,No,No,125.1039485,16157.21831 +4126,No,Yes,1095.868822,16127.52466 +4127,No,No,0,41194.07139 +4128,No,No,973.9988496,48095.06797 +4129,No,No,1288.252682,42226.46201 +4130,No,No,269.2892196,33846.50864 +4131,No,No,1105.890238,35450.72727 +4132,No,No,360.8923852,37581.7918 +4133,No,No,933.104366,24113.91729 +4134,No,No,1184.210495,38409.53551 +4135,No,Yes,1850.612146,20409.03805 +4136,No,Yes,1517.804161,12958.65092 +4137,No,No,969.5504078,41301.97154 +4138,No,No,683.8090455,41004.65902 +4139,No,No,915.0752059,23682.91332 +4140,No,No,0,47895.80492 +4141,No,No,606.5810048,32497.44886 +4142,No,No,689.9194521,37695.79145 +4143,No,No,561.6228589,38894.55088 +4144,No,No,303.517761,43791.10542 +4145,Yes,No,1823.404105,14664.13812 +4146,No,Yes,1698.291816,11764.86211 +4147,No,Yes,1355.313216,11070.85497 +4148,No,Yes,1009.911576,18001.8645 +4149,No,No,887.9032989,29642.14403 +4150,No,No,308.5335358,39121.36406 +4151,No,No,1512.256314,35652.76786 +4152,No,No,0,39858.12723 +4153,No,No,1274.603005,55206.70233 +4154,No,Yes,1795.219846,7112.408499 +4155,No,No,848.9972626,44379.75335 +4156,No,No,588.7801861,58764.36376 +4157,No,No,551.1743516,24975.02706 +4158,No,Yes,1084.044084,13680.1133 +4159,No,No,723.4157186,35938.13978 +4160,Yes,No,698.5673519,31754.52342 +4161,No,No,910.7396182,40222.7125 +4162,No,Yes,1581.065936,17331.32583 +4163,No,No,948.6897016,39671.53388 +4164,No,No,199.1745658,21753.35696 +4165,No,Yes,1759.412325,11556.18453 +4166,No,No,780.6589645,29691.49427 +4167,No,Yes,1770.796815,18837.42017 +4168,Yes,Yes,2182.604344,20780.69245 +4169,No,No,696.0127587,35920.66202 +4170,No,No,563.9177259,47839.61726 +4171,No,No,1041.071523,41485.62372 +4172,No,No,293.6553752,38893.60459 +4173,No,Yes,1489.04815,17934.49337 +4174,No,No,237.4898898,44614.71632 +4175,No,No,1266.796021,34652.10888 +4176,No,No,1059.86583,33567.84503 +4177,No,Yes,704.2927042,13472.48319 +4178,No,No,0,45701.08516 +4179,No,No,0,47846.95657 +4180,No,No,499.6227806,42052.87012 +4181,No,No,728.8188595,33662.07097 +4182,No,No,1238.105177,37544.94213 +4183,No,No,776.8998746,37685.4493 +4184,No,No,202.0704329,28814.17531 +4185,No,No,625.3990249,52074.96822 +4186,No,No,1089.302178,28232.14128 +4187,No,No,992.9635257,45540.44568 +4188,No,No,352.7465359,41548.88295 +4189,No,No,416.7476666,69386.90159 +4190,No,No,937.9231754,31664.0669 +4191,No,Yes,15.15379273,13430.41584 +4192,No,No,1128.673594,42566.29755 +4193,No,Yes,1924.559775,8097.917975 +4194,No,No,211.0755018,50506.22019 +4195,No,No,23.52912629,42814.81856 +4196,No,No,891.3948219,43535.93994 +4197,No,No,707.1074635,18675.91728 +4198,No,Yes,1018.099338,18239.2817 +4199,No,No,1404.099156,28764.49671 +4200,No,No,817.0858884,55892.7261 +4201,No,Yes,908.8486338,20332.39106 +4202,No,No,1216.195156,47561.71276 +4203,No,No,176.9040458,43150.26853 +4204,No,No,401.708353,51338.21601 +4205,No,No,1214.211192,45778.02671 +4206,No,Yes,1835.564112,17807.44952 +4207,No,No,66.77272206,43189.66453 +4208,No,No,1343.185883,30960.61603 +4209,No,No,1309.405746,34469.98017 +4210,No,Yes,463.0916614,21248.65294 +4211,Yes,No,1672.031483,50327.36776 +4212,No,No,357.7445593,45516.77611 +4213,No,No,705.0950458,38297.85495 +4214,No,Yes,453.6439697,9770.006437 +4215,No,Yes,1799.149349,20215.62382 +4216,No,No,748.1381646,21153.48246 +4217,No,No,650.0893552,46496.51016 +4218,No,Yes,341.4855933,21278.82614 +4219,No,No,1476.478933,50701.84532 +4220,No,No,1134.344472,41748.18345 +4221,No,Yes,852.3586673,20948.97088 +4222,No,Yes,930.663263,22919.99717 +4223,No,No,1027.017571,47046.01236 +4224,No,No,1494.254119,48133.93844 +4225,Yes,No,1396.789181,40371.12474 +4226,No,No,44.05939884,50765.69828 +4227,No,No,938.2801803,33341.96319 +4228,No,No,776.9646792,36704.0519 +4229,No,Yes,1589.649633,15262.42162 +4230,No,Yes,1247.556321,16410.89037 +4231,No,No,0,30143.96371 +4232,Yes,Yes,2291.617688,20837.20945 +4233,No,No,676.9595811,33013.82476 +4234,No,No,135.9982356,59369.64841 +4235,No,No,630.6085404,52491.55867 +4236,No,No,315.9532575,38938.97575 +4237,No,Yes,564.738467,15411.19159 +4238,No,No,663.2441573,25728.86844 +4239,No,No,72.12325678,35374.86457 +4240,No,No,491.036498,37835.61321 +4241,No,No,1331.895676,38903.19798 +4242,No,No,755.3396629,37027.2546 +4243,No,No,684.1860237,45400.01736 +4244,No,Yes,407.3404404,25376.72863 +4245,No,No,0,37727.46358 +4246,No,No,130.3159693,44559.81729 +4247,No,No,1224.454493,46811.61439 +4248,No,No,1525.35393,52675.7637 +4249,No,No,618.9984399,17122.34756 +4250,No,No,353.6383342,43145.17966 +4251,No,No,422.6445292,58345.01452 +4252,No,No,25.73626917,28163.69297 +4253,No,No,697.6213981,48469.8814 +4254,No,No,462.1383867,44546.23719 +4255,No,No,599.6677199,36584.07624 +4256,No,No,1251.235906,43191.31209 +4257,No,No,1432.298465,31250.58298 +4258,No,Yes,975.9154041,12102.81789 +4259,No,Yes,445.9290605,10629.61835 +4260,No,No,694.4124308,47705.87128 +4261,No,Yes,320.0122023,14704.47769 +4262,No,No,1102.302032,38872.12992 +4263,No,No,0,39250.09552 +4264,No,No,165.9730086,28439.58796 +4265,No,No,1258.878806,40858.86844 +4266,No,No,1372.761993,30219.15009 +4267,No,No,1464.028962,43700.14263 +4268,No,Yes,1594.093167,14135.21128 +4269,No,No,0,31741.04562 +4270,No,No,1595.910033,55712.29374 +4271,No,No,901.1760737,55064.63649 +4272,No,No,789.7507972,39022.35325 +4273,No,No,1203.205839,43809.25995 +4274,No,Yes,1399.338053,22736.21907 +4275,No,No,1125.876488,49359.34272 +4276,No,No,236.3384662,39260.06019 +4277,No,No,218.1463145,27166.69754 +4278,No,No,413.5765513,49424.31038 +4279,No,No,469.0539803,25680.17493 +4280,No,No,923.9925164,27818.24687 +4281,No,No,182.6280599,31623.93645 +4282,No,No,478.4017447,30338.35686 +4283,No,No,918.5461513,53331.27843 +4284,No,Yes,873.9305469,29870.74737 +4285,No,No,748.7048356,48036.0791 +4286,No,No,146.8181908,48280.70993 +4287,No,No,1266.640989,41930.58627 +4288,No,Yes,883.8818318,14888.51811 +4289,No,Yes,1353.981432,17431.60535 +4290,No,Yes,1777.744671,24820.85627 +4291,No,No,695.4933217,48899.34318 +4292,No,No,0,33313.31107 +4293,No,No,377.7266313,49730.70458 +4294,No,No,975.737182,39453.22631 +4295,No,Yes,918.590687,14936.70819 +4296,No,No,1313.500378,31145.70835 +4297,No,No,1302.399854,15468.34474 +4298,No,No,1246.626138,41930.155 +4299,No,No,198.385708,37085.60371 +4300,No,No,395.6077739,27750.8215 +4301,No,Yes,638.0655848,17922.40349 +4302,No,No,677.2048392,52080.52336 +4303,No,No,681.57575,29980.33054 +4304,No,No,1114.945775,45832.94832 +4305,No,No,558.1784844,12740.86464 +4306,No,No,531.4779785,35314.07104 +4307,No,Yes,1080.969996,23760.75888 +4308,No,No,150.7705446,31216.78586 +4309,Yes,Yes,1665.70832,21024.27165 +4310,No,No,1086.193489,52761.41304 +4311,No,No,0,41435.43794 +4312,No,No,1253.923498,34304.76691 +4313,No,No,100.4631161,24687.85307 +4314,No,No,961.1524669,51179.46012 +4315,No,Yes,1055.861054,9045.241626 +4316,No,No,89.4101022,24086.60114 +4317,No,No,918.9503478,47761.40271 +4318,No,No,995.3870476,44217.68564 +4319,No,No,1774.233089,42955.82797 +4320,No,No,701.6914324,35335.78263 +4321,No,No,1177.920843,30169.29581 +4322,No,No,1308.267257,43611.46156 +4323,No,No,1114.536871,35044.72982 +4324,No,Yes,815.5486691,16908.13976 +4325,No,Yes,1010.506784,6786.38809 +4326,No,No,752.9859192,45523.49679 +4327,No,Yes,729.1897184,18329.72087 +4328,No,No,1095.471779,53581.15671 +4329,No,No,355.1701761,30440.89519 +4330,No,No,1308.987967,31777.20188 +4331,No,No,0,40257.7955 +4332,No,No,464.0641049,16670.15281 +4333,No,No,570.9163985,46569.16773 +4334,No,Yes,1045.338813,29601.40394 +4335,No,No,0,39134.93192 +4336,No,No,642.2562314,29223.99534 +4337,No,No,486.8159722,25117.64382 +4338,No,No,1082.119828,43256.40056 +4339,No,Yes,1224.636872,25717.07064 +4340,No,No,953.1473563,29328.03916 +4341,No,No,1323.175214,52019.34997 +4342,No,Yes,139.1928688,24734.08293 +4343,No,No,856.881451,32449.31133 +4344,No,No,311.8892887,46116.13153 +4345,No,No,0,49959.14101 +4346,No,No,498.8541547,47736.23679 +4347,No,Yes,465.8710734,14519.47338 +4348,No,No,892.82027,45379.84427 +4349,No,Yes,456.2406945,17657.60886 +4350,No,No,557.261712,45040.94441 +4351,No,No,0,28094.15709 +4352,No,No,138.6207393,50249.41724 +4353,No,No,241.1631501,36581.25912 +4354,No,No,763.1772393,42481.49875 +4355,No,Yes,1195.813811,19906.73149 +4356,No,No,130.9969303,54366.80276 +4357,No,No,1253.041317,32362.65251 +4358,No,No,1496.098042,44309.9029 +4359,No,Yes,1071.955237,19813.99697 +4360,No,Yes,724.9047355,16643.12695 +4361,No,Yes,194.1850121,20889.852 +4362,No,No,1441.743359,45037.16768 +4363,No,Yes,1678.382001,8154.239774 +4364,No,Yes,2128.434148,22664.21984 +4365,No,Yes,1393.464421,15573.79311 +4366,No,Yes,1437.830363,23062.49487 +4367,No,No,1257.958107,38532.19219 +4368,No,Yes,1329.398351,15971.57324 +4369,No,No,605.1244322,29769.97835 +4370,No,No,601.0816652,45607.34686 +4371,No,No,1164.513406,29874.48879 +4372,Yes,No,2054.452527,37366.76505 +4373,No,No,701.6328413,43340.9203 +4374,No,No,673.0924399,27894.09399 +4375,No,Yes,1262.925919,16935.19311 +4376,No,No,357.9963055,30217.02129 +4377,No,Yes,1052.598123,24418.21395 +4378,No,No,1234.512473,50293.43354 +4379,No,No,1153.802194,37552.62162 +4380,No,No,1375.29736,35810.91421 +4381,No,No,249.1036852,51051.33576 +4382,No,No,874.031434,39664.93613 +4383,No,No,768.1587234,23991.04129 +4384,No,Yes,1236.256423,16389.86381 +4385,No,Yes,641.2207571,23265.69236 +4386,No,Yes,825.5228817,14539.16201 +4387,No,No,931.0411701,42225.36714 +4388,No,No,650.0755295,32560.76104 +4389,No,Yes,0,17577.99634 +4390,No,No,682.6045089,57553.97658 +4391,No,No,1207.105002,50010.82983 +4392,No,Yes,840.2209676,18103.73552 +4393,No,No,894.6279058,39027.63028 +4394,No,No,711.5258161,35468.38215 +4395,No,No,1635.217569,47669.74196 +4396,No,No,740.8693707,46541.21117 +4397,No,No,773.4830759,38647.37067 +4398,No,No,1142.326319,45173.3746 +4399,No,No,894.16832,42212.44583 +4400,No,Yes,14.03384572,13846.80104 +4401,No,No,537.0403329,21364.3105 +4402,No,No,1092.524306,19759.12264 +4403,No,No,541.0713808,29807.40555 +4404,No,No,1049.004118,34088.16979 +4405,No,No,463.9325612,37842.04778 +4406,No,No,214.2726904,56032.64758 +4407,No,Yes,997.7624823,13799.54474 +4408,No,Yes,938.684222,18454.39903 +4409,Yes,No,1350.603261,21541.29879 +4410,No,Yes,935.7571992,5386.176483 +4411,No,No,819.0485492,61602.63667 +4412,No,Yes,1669.091038,16830.75295 +4413,No,No,395.8518569,33708.59238 +4414,No,Yes,810.5424069,17975.15713 +4415,No,Yes,62.23712836,20267.49993 +4416,No,No,1133.112585,23110.65365 +4417,No,No,748.4875955,36453.64512 +4418,Yes,Yes,1337.235187,32761.0534 +4419,No,No,1138.430984,36133.61053 +4420,No,Yes,1247.455532,15382.60965 +4421,No,Yes,875.9797105,17286.69449 +4422,No,No,980.7639386,43650.53446 +4423,No,No,238.9815065,30412.70202 +4424,No,No,185.0101145,40874.83285 +4425,No,Yes,478.9618638,19911.51027 +4426,No,No,145.4765901,41196.4202 +4427,No,No,339.9590471,36132.80569 +4428,No,No,303.9037423,43836.70473 +4429,No,No,536.2142781,47956.52456 +4430,No,Yes,1219.581454,12434.04184 +4431,Yes,Yes,1926.371583,17012.43814 +4432,No,Yes,460.202332,14782.45877 +4433,No,No,825.2878657,46147.95575 +4434,No,No,31.25654574,44970.76801 +4435,No,No,535.5848146,51971.70563 +4436,No,No,667.7116286,30803.44466 +4437,No,No,694.3267591,42041.45656 +4438,No,No,81.42293643,51044.40781 +4439,No,No,1324.492949,28691.83661 +4440,No,Yes,1587.132957,25819.48539 +4441,No,No,775.0229447,39137.07673 +4442,No,No,0,46542.98528 +4443,No,Yes,819.7277126,10175.30995 +4444,No,No,1416.224397,31303.16331 +4445,No,No,1005.293851,34010.27821 +4446,No,Yes,1419.360362,20118.5582 +4447,No,No,230.0066559,44232.13848 +4448,No,Yes,1559.45787,20707.31861 +4449,No,No,672.7082326,47532.58768 +4450,No,Yes,457.2357469,5296.710921 +4451,No,No,968.4589678,45299.00083 +4452,No,No,257.5463322,29967.20428 +4453,No,Yes,1130.54464,20862.90449 +4454,No,No,486.5760502,27687.34117 +4455,No,No,207.3038092,49999.49574 +4456,No,No,110.9151208,58185.55984 +4457,No,No,678.4851464,30196.04569 +4458,No,Yes,515.1499965,10214.70526 +4459,No,Yes,837.9118858,20984.17906 +4460,No,No,709.6172205,34624.83575 +4461,No,Yes,745.2824716,17803.75736 +4462,No,Yes,1288.393705,22919.63773 +4463,No,Yes,819.7423451,20792.83096 +4464,Yes,No,1708.65972,38203.50947 +4465,No,No,1061.613602,38250.77775 +4466,No,No,372.7412499,35570.21178 +4467,No,No,883.8678853,42866.38395 +4468,No,No,0,41864.25913 +4469,No,No,657.6876177,26454.58575 +4470,No,No,221.0238822,42087.08223 +4471,No,Yes,1009.896004,9804.666039 +4472,No,No,174.4129448,40282.48755 +4473,No,No,358.8483412,44486.90647 +4474,No,No,421.7596249,35522.66598 +4475,No,Yes,1295.851844,10987.26519 +4476,No,No,1005.100951,36275.64879 +4477,No,No,268.9614828,40733.46798 +4478,Yes,Yes,1807.666218,18166.32265 +4479,No,No,1237.861344,33816.18157 +4480,No,Yes,700.1690395,28160.64227 +4481,No,Yes,1236.678437,16298.56815 +4482,No,No,354.7025871,29893.03973 +4483,No,No,657.0219512,47044.33353 +4484,No,No,1444.9225,54599.15776 +4485,No,No,1456.411254,30332.98599 +4486,No,Yes,501.1402608,19467.53765 +4487,No,Yes,1155.222175,18442.34093 +4488,No,No,804.4697537,46869.32017 +4489,No,Yes,1473.633713,17816.62085 +4490,No,No,1345.380592,30175.94167 +4491,No,Yes,545.6124774,13020.95914 +4492,No,No,1134.279848,37658.23096 +4493,No,No,212.3613685,32283.40531 +4494,No,No,424.7352349,22327.4342 +4495,No,No,658.4636449,39242.75542 +4496,No,No,1023.795885,42335.73719 +4497,No,No,815.5669074,31752.07715 +4498,No,Yes,953.42174,14062.05354 +4499,No,No,905.6456347,35876.79569 +4500,No,No,623.092054,42035.45262 +4501,No,No,287.2001519,45952.72892 +4502,No,Yes,1310.793503,16742.95252 +4503,No,Yes,1509.663402,16777.56234 +4504,No,No,1616.108734,33046.18794 +4505,No,No,710.1593099,32649.61987 +4506,No,No,1213.658672,45662.64661 +4507,No,No,1004.116828,52822.40438 +4508,No,No,36.35843995,44603.42733 +4509,No,No,1349.192332,51189.11227 +4510,Yes,No,1854.604154,38406.35646 +4511,No,No,126.1532047,33964.05061 +4512,No,Yes,987.1540956,13002.48555 +4513,No,Yes,1127.993934,15186.86869 +4514,Yes,No,1436.311549,18810.80414 +4515,No,No,0,41564.76983 +4516,No,Yes,1068.860742,20804.37868 +4517,No,No,0,62128.66663 +4518,No,Yes,568.9755524,17291.81469 +4519,No,No,1608.745469,41572.2737 +4520,No,No,885.0040673,48753.55996 +4521,No,Yes,886.9510173,21792.60979 +4522,No,No,1358.132472,49903.59708 +4523,No,No,215.7527192,22254.58917 +4524,No,Yes,410.4667487,25574.43832 +4525,No,No,1146.614029,40870.90133 +4526,No,Yes,1262.393294,24797.60757 +4527,No,No,778.4746661,39360.16409 +4528,No,No,444.3040741,43424.93499 +4529,No,No,826.9536725,16124.62568 +4530,No,No,204.694596,42076.57612 +4531,No,No,819.5307335,38456.61504 +4532,No,No,1386.296964,31739.51324 +4533,No,No,698.9032763,28682.95742 +4534,No,No,756.4334686,33296.56712 +4535,No,No,739.9018425,47349.53197 +4536,No,No,220.2291177,37451.01761 +4537,No,Yes,458.9677371,18444.29785 +4538,No,No,0,36584.757 +4539,No,No,102.2583724,23476.74536 +4540,No,No,215.068135,47815.5997 +4541,No,No,906.0813599,45720.01304 +4542,No,No,735.927001,18373.4865 +4543,No,No,944.3217169,34546.11681 +4544,No,No,1071.007369,40758.00204 +4545,No,No,1345.451737,43322.19302 +4546,No,Yes,250.17301,17439.62927 +4547,No,No,265.7290045,60182.67719 +4548,No,No,1006.053988,27002.06581 +4549,No,Yes,1670.126895,14746.89261 +4550,No,Yes,291.9623649,7105.292025 +4551,No,No,381.5209693,31750.77732 +4552,No,Yes,851.7297296,14726.37602 +4553,No,No,1375.049224,42557.68909 +4554,No,No,816.7337205,40949.36444 +4555,No,No,819.018615,49111.39157 +4556,No,Yes,1026.15356,25393.7688 +4557,No,No,853.4381453,58757.68136 +4558,No,Yes,1200.376613,10817.56196 +4559,No,No,1037.134803,43368.22337 +4560,No,No,0,28363.7137 +4561,No,No,357.1381462,35623.46527 +4562,No,Yes,970.4260895,20569.64004 +4563,No,No,812.3479812,33654.67742 +4564,No,No,91.94732281,44337.4059 +4565,No,No,1396.037916,46378.73051 +4566,No,No,72.65008166,50786.97919 +4567,No,No,1168.092556,27823.45422 +4568,No,Yes,1098.454046,16837.21032 +4569,Yes,No,1549.894673,47916.29486 +4570,No,No,1039.098374,26217.13205 +4571,No,No,309.838215,54945.58168 +4572,No,No,1477.528611,36709.66844 +4573,No,No,79.53894967,34417.37572 +4574,No,No,1219.347882,34310.29194 +4575,No,No,812.5880324,43980.13997 +4576,No,No,557.3749689,35067.26624 +4577,No,No,1688.314557,32723.45163 +4578,No,No,1137.791157,21103.42962 +4579,No,No,856.2829599,40394.04958 +4580,No,No,1126.410483,35125.67772 +4581,No,No,541.0218143,29519.88015 +4582,No,No,682.8796097,43142.05452 +4583,No,No,538.9498876,35634.2028 +4584,No,Yes,1305.809699,28929.38474 +4585,No,No,735.0188559,25305.25395 +4586,No,Yes,344.3089096,14256.24175 +4587,No,No,1060.695812,36920.68241 +4588,No,No,848.6763909,38742.05979 +4589,No,Yes,1245.267507,16772.61937 +4590,Yes,No,1563.535198,51704.91997 +4591,Yes,Yes,1433.167635,18087.68637 +4592,No,Yes,1238.58951,10514.82864 +4593,No,No,625.6150864,53655.27048 +4594,No,Yes,1681.572465,20059.67479 +4595,Yes,Yes,1681.481506,10155.32399 +4596,No,No,602.6381471,70021.64844 +4597,No,No,681.2130022,37287.22978 +4598,No,Yes,966.0269814,27960.37189 +4599,No,Yes,539.1600365,17824.26071 +4600,No,No,921.3042946,25350.4002 +4601,No,Yes,1325.346923,21487.6237 +4602,No,No,0,46714.55812 +4603,No,Yes,900.2746082,22986.61368 +4604,No,No,695.3708301,31779.39667 +4605,No,Yes,1270.996404,15069.04871 +4606,No,No,1388.561139,54935.9412 +4607,No,No,635.741696,21297.1199 +4608,No,No,484.5157687,39775.24989 +4609,No,No,1185.096271,42969.71809 +4610,No,No,656.9802829,32225.29529 +4611,No,No,1084.925654,36908.68843 +4612,No,No,1309.559449,36851.26912 +4613,No,No,847.5306542,40982.4198 +4614,No,No,0,35600.14937 +4615,No,No,78.16808284,56254.17531 +4616,No,Yes,251.9717446,11757.96327 +4617,No,No,871.2313984,31770.62398 +4618,No,Yes,918.7587912,14304.59736 +4619,No,No,0,31774.64754 +4620,No,No,450.0573591,36121.23739 +4621,No,Yes,1079.37288,14223.26681 +4622,No,Yes,320.2304819,14185.27463 +4623,No,No,799.3700279,34293.72751 +4624,No,No,1206.592155,25646.0822 +4625,No,No,0,39830.09162 +4626,No,No,603.6616705,44663.73612 +4627,No,No,672.0628046,33850.97884 +4628,No,Yes,959.2223533,16082.44763 +4629,No,No,670.4719051,24265.5869 +4630,No,No,587.2594598,33732.21623 +4631,No,Yes,602.3076763,11019.58695 +4632,No,Yes,937.6060593,19962.19804 +4633,No,No,1502.757749,53129.78304 +4634,No,No,1063.155675,34642.06476 +4635,No,No,511.6137514,36796.79478 +4636,No,No,926.9698728,61937.56967 +4637,No,Yes,1004.364637,13422.37946 +4638,No,No,367.6680959,50025.94458 +4639,No,Yes,256.7070707,22166.79439 +4640,No,Yes,1777.685651,21573.90002 +4641,No,No,717.7428338,49347.02181 +4642,No,Yes,532.3821353,17704.81438 +4643,No,Yes,1346.679507,16104.89497 +4644,No,No,334.4664195,40156.24449 +4645,No,No,0,36179.82686 +4646,No,Yes,774.4984602,10333.80055 +4647,No,No,779.2599906,38387.72127 +4648,No,No,881.9391119,62222.9807 +4649,No,No,1563.230205,31126.32678 +4650,No,No,991.0609685,37597.32306 +4651,No,No,430.6510942,38372.02125 +4652,No,No,806.3776849,40454.06788 +4653,No,No,438.7666361,32762.18623 +4654,No,Yes,1457.858845,17365.56672 +4655,No,No,366.2048882,30631.96311 +4656,No,No,707.6469316,35268.01743 +4657,No,Yes,244.3821578,19773.07246 +4658,No,No,903.6157164,20444.42412 +4659,No,Yes,1031.498886,24636.30022 +4660,No,No,914.3782799,40659.12596 +4661,No,No,1047.25165,39222.05889 +4662,No,No,884.4943566,30520.35327 +4663,No,No,434.7788265,39099.09185 +4664,No,Yes,36.84403824,16942.30534 +4665,No,No,189.3622325,36706.81197 +4666,No,No,366.3212813,40904.76363 +4667,No,Yes,1092.464572,14659.71807 +4668,No,No,732.734289,39024.14923 +4669,No,No,594.4405256,28723.34186 +4670,No,Yes,604.5927,14896.59187 +4671,No,No,1732.843605,30322.86931 +4672,No,No,577.4333324,43585.04414 +4673,No,No,0,38626.42292 +4674,No,No,54.67522087,32279.56843 +4675,No,No,75.95176394,60826.99403 +4676,No,No,550.5327125,27223.85787 +4677,No,Yes,1343.874484,17280.43863 +4678,No,No,1350.428847,26450.29174 +4679,No,No,866.3379875,35141.42764 +4680,No,No,6.071892351,46930.57516 +4681,No,No,501.3451781,45368.23118 +4682,Yes,No,1488.469925,36457.22328 +4683,No,No,1462.786737,60296.39834 +4684,Yes,Yes,1567.679282,19172.67945 +4685,No,No,771.7893469,42139.07027 +4686,No,Yes,1534.9684,17084.58112 +4687,No,Yes,801.0497163,14679.83344 +4688,No,No,1724.369132,32610.81033 +4689,No,No,0,50258.53894 +4690,No,Yes,1277.374387,19675.17784 +4691,No,No,786.9874342,33289.85082 +4692,No,Yes,231.7707259,13702.47092 +4693,No,No,173.9594605,53965.05121 +4694,No,No,1184.927422,40958.64399 +4695,No,No,1308.206711,32215.53337 +4696,No,No,203.3000811,53055.69443 +4697,No,No,1092.098834,32122.05118 +4698,Yes,Yes,1902.969565,12464.30304 +4699,No,Yes,1004.438273,16456.27725 +4700,No,No,366.7015515,60456.11777 +4701,No,Yes,1148.73389,23812.62699 +4702,No,No,0,33272.15215 +4703,No,No,476.2352711,37627.35002 +4704,No,Yes,477.7829458,22753.59547 +4705,No,Yes,970.832756,19096.88941 +4706,No,No,1268.593973,42012.89659 +4707,No,No,657.5311129,40807.2813 +4708,No,No,0,47447.79657 +4709,Yes,No,1661.752645,21871.46297 +4710,Yes,No,2075.188754,40882.49422 +4711,No,No,915.7715639,24243.18016 +4712,No,Yes,1048.274763,20659.14486 +4713,No,No,987.5662506,49542.40423 +4714,No,No,535.1353429,44448.60703 +4715,No,No,1057.932601,26628.44074 +4716,No,No,1023.147921,43698.81669 +4717,No,No,749.0385161,53711.46049 +4718,No,Yes,1143.329016,17367.92553 +4719,No,Yes,1269.226506,18552.42414 +4720,No,No,0,35282.56729 +4721,No,Yes,723.3718471,17623.58567 +4722,No,No,1000.376782,40272.30202 +4723,No,Yes,1069.693058,10722.24946 +4724,No,Yes,1391.335002,18309.421 +4725,No,No,1075.824171,22982.22663 +4726,No,No,548.6453079,38094.42617 +4727,Yes,Yes,2109.999395,17774.52529 +4728,No,Yes,649.3601165,10524.3261 +4729,No,No,863.138441,58119.7166 +4730,No,No,861.6448507,39272.00904 +4731,No,No,567.4891943,22304.57395 +4732,No,Yes,642.5364898,14763.37718 +4733,No,Yes,1045.657466,23335.04633 +4734,No,No,738.1441283,43693.06639 +4735,No,Yes,0,11537.89323 +4736,No,Yes,1137.954162,22110.95576 +4737,No,No,462.6746283,50229.58589 +4738,No,No,1006.201561,42689.27369 +4739,No,No,584.1932422,36676.78915 +4740,No,No,1314.765205,30067.41065 +4741,No,No,0,30236.45633 +4742,No,Yes,1005.707006,10887.49987 +4743,No,No,113.5712644,32803.83265 +4744,No,No,967.7398523,26210.79214 +4745,No,No,0,45486.45868 +4746,No,No,651.3251349,51583.2502 +4747,No,No,1035.315645,53066.10488 +4748,No,Yes,58.9719451,20914.93102 +4749,No,No,0,60480.57797 +4750,No,Yes,1190.021728,16240.76757 +4751,No,No,37.99253638,48739.83538 +4752,No,No,652.266674,48639.44148 +4753,No,No,1529.687857,40977.41388 +4754,No,No,626.9285568,42139.18446 +4755,No,Yes,756.9956094,26674.39078 +4756,No,Yes,496.7969074,21058.96159 +4757,No,Yes,396.348074,19541.0461 +4758,No,No,1590.641641,36242.497 +4759,No,Yes,1331.980568,20672.33954 +4760,No,No,36.08799035,37015.55736 +4761,No,No,338.4767539,32666.51253 +4762,No,Yes,992.3483203,22324.9956 +4763,No,Yes,1123.340012,20785.64023 +4764,No,No,743.6231452,37333.57412 +4765,No,No,1111.691517,43659.07059 +4766,No,No,969.6560183,40142.13415 +4767,No,No,974.5078184,25346.77821 +4768,No,Yes,285.6496186,26874.75458 +4769,No,No,596.6200269,30764.66205 +4770,No,No,1417.912113,38015.16636 +4771,No,Yes,1055.017327,24188.04975 +4772,No,No,1001.221971,44801.401 +4773,No,No,771.4410045,44810.29936 +4774,No,Yes,672.5680785,22788.36738 +4775,Yes,No,1511.610952,53506.94493 +4776,No,No,601.6896467,31522.78485 +4777,No,Yes,1316.652076,12165.729 +4778,No,No,122.0943088,46609.39748 +4779,No,No,840.0150181,51350.63767 +4780,No,Yes,1765.893938,18053.49972 +4781,No,Yes,1752.73615,15596.89143 +4782,No,No,1147.978075,40515.69046 +4783,No,No,1839.322208,38104.09111 +4784,No,No,1208.395128,32430.18277 +4785,No,No,952.1275301,49137.39569 +4786,No,No,1251.151035,45205.2458 +4787,No,No,215.5414226,55278.43265 +4788,No,No,1294.457122,40768.45105 +4789,No,No,463.0855991,46760.49589 +4790,No,No,815.2441831,26231.46541 +4791,No,No,854.5112787,34463.76511 +4792,No,No,1062.058711,35781.12549 +4793,No,No,1035.160236,67450.68807 +4794,No,No,880.396518,60206.08655 +4795,No,No,847.432632,37303.5744 +4796,No,No,529.1890785,50195.11301 +4797,No,No,1163.016893,38902.76362 +4798,No,No,570.0246843,40987.06233 +4799,No,No,0,29254.86823 +4800,No,Yes,673.4736889,20926.49855 +4801,No,No,1728.335976,33488.30436 +4802,No,No,1285.828594,43507.46606 +4803,No,Yes,1511.894197,22794.07105 +4804,No,No,676.6846401,24989.83369 +4805,No,No,1010.516286,36298.99498 +4806,No,Yes,613.1568831,17665.4777 +4807,No,Yes,1134.296094,18991.82121 +4808,No,No,295.843231,30602.26689 +4809,No,No,206.5425226,14909.3671 +4810,No,Yes,1194.884549,18943.40411 +4811,No,No,1551.092739,44596.74595 +4812,No,No,1210.176036,29460.1916 +4813,No,No,407.8607445,47826.21377 +4814,No,No,1561.933701,56614.52158 +4815,No,No,602.5456297,42798.07875 +4816,No,Yes,640.364464,14938.76657 +4817,No,No,927.6691817,39934.14448 +4818,No,Yes,1436.676385,14418.16602 +4819,No,No,552.5513185,48267.67339 +4820,No,No,839.2218564,43213.69411 +4821,No,Yes,1248.954189,14024.90498 +4822,No,No,929.9259221,34535.45527 +4823,No,No,1198.832807,40275.0895 +4824,No,No,479.5462007,24805.9234 +4825,No,No,1523.372674,44603.32637 +4826,No,No,990.8069149,44966.20673 +4827,No,No,890.5655077,14327.99078 +4828,No,No,678.4668286,40803.57496 +4829,No,No,1462.223173,29574.23457 +4830,No,No,0,48781.47896 +4831,No,Yes,490.3217974,24403.07667 +4832,No,Yes,2216.329753,24737.08176 +4833,No,No,1336.363246,22245.51252 +4834,No,No,355.0605964,36888.83432 +4835,No,Yes,1094.521679,15617.52141 +4836,No,Yes,1969.407748,23674.3763 +4837,No,No,1274.159685,41943.67752 +4838,No,No,224.1700297,33542.30818 +4839,No,Yes,781.4523056,18301.5838 +4840,No,No,1360.952931,46419.81699 +4841,No,No,210.797283,34468.89916 +4842,No,No,671.7326056,26431.2726 +4843,No,No,275.063482,28266.17198 +4844,No,No,306.1895161,30587.00678 +4845,No,No,293.614967,35945.66026 +4846,No,No,394.4129448,52725.15994 +4847,No,No,1002.030641,49319.33865 +4848,No,No,357.5171949,29711.35968 +4849,No,No,959.0630532,55089.99026 +4850,No,No,734.3358294,49128.53775 +4851,No,No,325.3312147,32961.65703 +4852,No,No,910.4827507,47298.72064 +4853,No,No,496.7739601,38316.13544 +4854,No,No,416.632532,53256.27963 +4855,No,Yes,1525.172306,21427.09362 +4856,No,No,124.7541714,45763.7922 +4857,No,No,0,52069.29467 +4858,No,No,854.9134418,39190.29567 +4859,No,No,796.1739999,27278.39687 +4860,No,No,1044.863161,41726.23363 +4861,No,No,1690.668077,55412.75947 +4862,No,No,935.7129441,34067.05422 +4863,No,No,306.4591959,36812.62605 +4864,No,No,698.4405643,21903.0381 +4865,No,No,973.9908207,45941.02201 +4866,No,No,455.0177823,45611.47645 +4867,No,Yes,1133.699627,26119.30468 +4868,No,No,598.1930735,42734.43969 +4869,No,No,615.477641,45792.89996 +4870,No,No,1270.130906,47817.55662 +4871,No,No,956.9226131,50834.40834 +4872,No,Yes,1095.076812,18087.71987 +4873,No,No,143.5630209,54411.26212 +4874,No,Yes,604.0550916,21187.71056 +4875,No,No,0,38954.1075 +4876,No,No,0,37825.22141 +4877,No,Yes,1179.994525,18954.39379 +4878,No,No,337.6513682,36787.01961 +4879,No,No,855.5967839,40412.72569 +4880,No,No,437.5777389,55720.97296 +4881,No,Yes,596.8065278,17397.34524 +4882,No,Yes,1725.386954,19915.92949 +4883,No,No,619.5406416,39049.50042 +4884,No,No,627.9877394,56266.1534 +4885,No,No,186.5235201,36717.51458 +4886,No,Yes,1093.02753,12179.03819 +4887,No,No,684.2704638,42437.91476 +4888,No,No,963.0926687,33110.30411 +4889,No,No,190.5034301,53690.59123 +4890,No,No,68.30593198,52323.90214 +4891,No,No,1336.96984,33148.76865 +4892,No,No,372.4258549,53695.29638 +4893,No,No,1095.028241,36282.47457 +4894,No,No,813.133978,59309.47668 +4895,No,No,31.70412287,42191.65919 +4896,No,No,655.8320417,37267.58281 +4897,No,No,1340.240429,37464.31876 +4898,No,No,881.2565114,41728.8461 +4899,No,No,684.7150446,27951.19723 +4900,No,No,1158.379804,61054.76393 +4901,No,No,945.2682188,38738.53539 +4902,No,Yes,465.5836287,15625.63353 +4903,No,No,999.6348552,51385.69973 +4904,No,No,1143.680795,48044.89701 +4905,No,Yes,669.3807882,16805.19372 +4906,No,Yes,838.9709429,21407.4524 +4907,No,Yes,1384.737597,23083.66709 +4908,No,Yes,1401.073191,21811.26825 +4909,No,No,1591.193583,37501.48689 +4910,No,No,54.05059372,46662.11281 +4911,No,No,862.9197102,36774.21591 +4912,No,No,712.2077207,29546.48988 +4913,No,No,908.9265484,40757.09412 +4914,No,No,779.0764379,31710.97405 +4915,No,Yes,99.38256245,16725.24507 +4916,No,No,1081.001337,32344.57858 +4917,No,No,1124.249135,27521.15158 +4918,No,No,923.4065669,33122.22004 +4919,No,No,1096.21847,30653.26275 +4920,No,No,999.6678059,51420.97671 +4921,No,No,0,16601.63528 +4922,No,Yes,517.8219594,12673.90607 +4923,No,No,1461.887463,38559.07348 +4924,No,No,699.0538658,41722.80607 +4925,No,Yes,1241.061898,4664.565047 +4926,No,No,1380.749804,52664.87659 +4927,No,No,561.9646064,40555.4076 +4928,No,No,1122.746958,47140.829 +4929,No,Yes,736.2440566,13756.54552 +4930,No,No,643.4811548,45925.88666 +4931,No,No,1141.157938,44333.21063 +4932,No,Yes,1273.891137,20104.43016 +4933,No,Yes,1306.046537,20776.38865 +4934,No,Yes,840.9889092,15406.20741 +4935,No,No,289.2454385,46550.52551 +4936,No,No,758.1342851,33220.57549 +4937,No,No,930.7169386,46501.27571 +4938,Yes,Yes,2177.150869,17659.74782 +4939,No,No,0,24892.91569 +4940,No,Yes,565.8300588,21042.22772 +4941,No,No,1076.126584,23632.5203 +4942,No,Yes,1031.86993,18668.48355 +4943,No,Yes,768.4037418,15417.84154 +4944,No,Yes,690.4210492,19273.73239 +4945,No,No,469.8442413,51308.31851 +4946,No,No,617.8600254,50177.77338 +4947,No,Yes,1779.049699,15689.77663 +4948,No,No,0,47669.70408 +4949,Yes,No,1928.280283,35492.12823 +4950,No,No,1123.71926,56217.6849 +4951,No,No,794.6461084,41033.58771 +4952,No,Yes,1428.066883,19818.29166 +4953,Yes,No,1028.767207,40346.83327 +4954,No,No,1006.202977,50501.76154 +4955,No,No,691.7517135,45420.96912 +4956,No,No,524.8381501,41268.42365 +4957,No,No,493.9141608,37409.18393 +4958,No,No,1520.980478,37510.53936 +4959,No,No,665.0397566,47062.25309 +4960,No,Yes,1520.442101,18462.43066 +4961,No,No,1094.780473,34190.87652 +4962,No,No,735.0910408,44933.32315 +4963,No,No,681.6935764,33327.11303 +4964,No,No,376.034544,50748.27174 +4965,Yes,No,2037.943354,43016.07218 +4966,No,Yes,1630.199589,14232.66153 +4967,No,Yes,0,21881.70591 +4968,No,No,184.4275319,36731.62695 +4969,No,No,349.6606656,39391.32231 +4970,No,No,673.5552703,49169.72854 +4971,No,No,82.72452491,42048.4448 +4972,No,No,1247.120605,50539.90745 +4973,No,Yes,640.639543,29236.63019 +4974,No,No,273.4469724,52492.74982 +4975,No,No,0,35377.14073 +4976,No,No,803.8311868,33417.77241 +4977,No,No,1248.375749,37469.86462 +4978,No,No,0,40348.31418 +4979,No,Yes,1307.204973,19381.54132 +4980,No,No,0,45793.39304 +4981,No,No,1186.098705,50353.92543 +4982,No,Yes,1037.573018,18769.57902 +4983,Yes,Yes,1878.001146,17473.18399 +4984,No,No,736.2348369,36313.63355 +4985,No,No,260.1621754,33551.7153 +4986,No,Yes,1103.681782,14225.72134 +4987,No,Yes,886.0593168,10378.64229 +4988,No,No,221.1637828,43072.89425 +4989,No,No,1098.057751,29402.87314 +4990,No,No,0,38686.67529 +4991,No,No,826.9498104,46946.05245 +4992,No,No,569.9677215,38982.01743 +4993,No,No,1316.057411,45308.56384 +4994,No,No,86.72551869,51257.58346 +4995,No,No,1458.835325,24850.58478 +4996,No,No,1243.451348,41184.89891 +4997,No,No,790.75576,41909.10767 +4998,No,No,1997.17281,50273.60103 +4999,No,No,960.9126678,29304.45772 +5000,No,No,646.8567437,28836.75713 +5001,No,Yes,287.6494893,15441.36675 +5002,No,No,630.6653369,56266.7585 +5003,No,No,610.500867,28664.04266 +5004,No,No,1224.229414,34490.225 +5005,No,No,326.8736761,48756.02758 +5006,No,Yes,325.2828199,15644.24197 +5007,No,Yes,623.7578593,19191.96692 +5008,No,No,1394.476776,44092.54868 +5009,No,No,929.0854677,35571.77665 +5010,No,No,887.743411,43478.88519 +5011,No,No,0,26598.64322 +5012,No,Yes,127.2227631,9801.500167 +5013,No,No,986.358895,41688.06434 +5014,No,Yes,1430.325502,20846.96332 +5015,Yes,No,1026.358855,56182.12993 +5016,No,No,556.0174984,33705.07865 +5017,No,No,1018.436236,53962.27769 +5018,No,Yes,1083.711391,17040.5194 +5019,No,No,1602.806943,34700.26596 +5020,No,No,921.3478541,47610.9552 +5021,No,Yes,769.4497618,21717.94372 +5022,No,Yes,289.8341427,13914.60372 +5023,No,No,955.3391649,46310.10752 +5024,No,No,387.7866998,38745.36115 +5025,No,Yes,1340.966389,18725.95421 +5026,No,No,1252.894039,35587.7236 +5027,No,No,435.3931594,32907.33247 +5028,No,No,491.0485061,37219.52108 +5029,No,No,1112.509304,44298.38564 +5030,No,No,828.9802098,55696.8037 +5031,No,No,568.3520036,38963.95924 +5032,No,Yes,672.410411,16508.42503 +5033,No,No,931.7841072,37280.41015 +5034,No,Yes,982.7730973,24145.52943 +5035,No,Yes,686.5287583,25779.78334 +5036,No,No,1069.526911,34694.00075 +5037,No,No,863.9548546,17159.16338 +5038,No,Yes,976.4514847,10541.57023 +5039,Yes,No,1586.502199,48925.93338 +5040,No,No,544.1262485,49900.58402 +5041,No,No,198.9291418,50463.77549 +5042,No,No,1026.194095,35685.48373 +5043,No,No,1317.564864,40731.76397 +5044,No,No,0,33322.48236 +5045,No,No,358.6137769,47688.80472 +5046,No,Yes,1511.37288,14982.59851 +5047,No,No,359.9166787,52248.11807 +5048,No,Yes,529.3431063,17245.80061 +5049,No,Yes,888.564819,21820.21718 +5050,Yes,No,1954.333299,35067.53492 +5051,No,No,131.747076,36947.77862 +5052,No,No,518.8990204,46192.55437 +5053,No,Yes,1593.327492,11570.72862 +5054,Yes,Yes,1473.034561,18108.96167 +5055,No,No,1527.924799,44640.74474 +5056,No,No,1349.908479,48103.03078 +5057,No,No,371.8818634,46682.54695 +5058,No,No,758.0210778,55278.59194 +5059,No,No,1306.832034,57561.41126 +5060,No,No,605.8472399,58892.90548 +5061,No,No,1085.243811,21805.79231 +5062,No,Yes,1582.23461,20214.68954 +5063,No,No,1078.48346,40944.26978 +5064,No,No,711.6638177,35741.20216 +5065,No,No,1204.459183,39418.70709 +5066,No,Yes,885.2098957,18693.72712 +5067,No,No,1795.883711,25878.51931 +5068,No,No,230.9162266,36139.41058 +5069,No,Yes,1038.555598,17055.79504 +5070,No,No,731.4771282,18843.27132 +5071,No,No,1734.228044,37621.63317 +5072,No,No,640.5203498,30327.60774 +5073,No,No,218.1148541,38766.10763 +5074,No,No,912.3904235,26857.1719 +5075,No,No,726.0421485,42272.43784 +5076,No,No,408.6190178,35622.59839 +5077,No,No,309.7409324,33333.56472 +5078,No,No,1392.619372,54027.19947 +5079,No,Yes,587.0449399,15939.81952 +5080,No,No,898.2726563,48233.8755 +5081,No,No,325.8410056,37392.68151 +5082,No,No,896.0271359,30452.03684 +5083,No,No,803.7645777,48013.61015 +5084,No,No,509.1956334,46368.39407 +5085,No,Yes,1031.017732,16568.633 +5086,No,No,782.4133944,34830.62629 +5087,No,No,1168.377486,42388.12102 +5088,No,No,561.6230018,36266.80588 +5089,No,No,850.5184574,35683.83229 +5090,No,No,752.5102185,18960.36558 +5091,No,No,245.5059615,31307.24364 +5092,Yes,No,1733.824679,26330.51173 +5093,No,No,590.666032,59574.21772 +5094,No,Yes,711.5828226,17780.75983 +5095,No,Yes,831.8453438,19797.35659 +5096,No,No,1344.711281,26215.39184 +5097,No,No,224.6911451,55040.61273 +5098,No,No,361.7426655,54071.34056 +5099,No,No,1322.810964,48016.61734 +5100,No,No,1043.374397,28079.26268 +5101,No,No,1475.518485,48653.68375 +5102,No,No,812.0838869,41821.29978 +5103,No,Yes,287.7923623,13060.00857 +5104,No,No,775.3097895,38721.24589 +5105,No,No,344.6240771,43246.57799 +5106,No,Yes,372.4308104,17243.0493 +5107,No,Yes,2088.529109,18078.32917 +5108,No,No,946.3981669,30770.06673 +5109,No,No,675.3767263,36921.57596 +5110,No,No,1012.768454,36406.24109 +5111,No,Yes,1011.937209,18509.29713 +5112,No,No,452.3726509,59429.12314 +5113,No,No,1092.491259,30654.68526 +5114,No,No,71.57768976,61588.2545 +5115,No,No,1319.041718,57768.89998 +5116,No,No,266.2777587,41204.29499 +5117,No,No,370.963649,25321.35282 +5118,No,No,848.7244264,41585.95255 +5119,No,No,507.3961316,35353.40553 +5120,No,Yes,267.3278291,19698.71388 +5121,No,No,420.8993319,39764.70243 +5122,No,No,129.5964485,52169.23157 +5123,No,No,327.2404373,40061.62451 +5124,No,No,678.0711056,63791.8242 +5125,No,Yes,199.1270927,19913.6025 +5126,No,No,1215.698245,33598.97076 +5127,No,No,460.6815058,37129.27082 +5128,No,Yes,1300.720433,9266.529731 +5129,No,No,534.568708,37281.04021 +5130,No,No,1308.529172,57190.87487 +5131,No,No,507.065032,42753.19192 +5132,No,No,197.3272654,24562.11966 +5133,No,Yes,819.834292,16412.99702 +5134,No,No,0,57003.59377 +5135,No,No,1489.780332,30296.61044 +5136,No,No,581.5208186,53100.57248 +5137,No,Yes,286.2915646,19553.63561 +5138,No,No,371.8652201,45949.91421 +5139,No,Yes,992.7802211,16585.32428 +5140,No,Yes,758.1385236,23758.57763 +5141,No,No,1174.194909,35533.48452 +5142,No,Yes,1196.098264,23851.22139 +5143,No,No,613.2314024,34664.57133 +5144,No,No,968.9723403,33763.19028 +5145,No,Yes,1151.298716,14384.76708 +5146,No,No,848.7027399,35192.83887 +5147,No,Yes,989.0907701,20262.02194 +5148,No,No,92.740663,40722.59065 +5149,No,No,1654.934175,33179.78392 +5150,No,No,212.4979287,53619.47571 +5151,No,No,73.89323483,44308.9054 +5152,No,No,692.155234,37592.26085 +5153,No,No,957.5839622,40315.77284 +5154,No,No,69.02387754,41566.58207 +5155,No,No,1394.125443,42921.01708 +5156,No,No,1317.866468,36610.88611 +5157,No,No,602.0963956,53553.72247 +5158,No,No,430.1182212,41105.28763 +5159,No,No,211.9830282,33448.24478 +5160,No,No,544.1431456,33861.5539 +5161,No,Yes,943.8430643,16701.78309 +5162,Yes,No,1412.192448,37283.0081 +5163,No,No,1416.44477,33099.49688 +5164,No,No,193.2831647,37376.64971 +5165,No,No,857.2364484,67124.05709 +5166,No,No,1132.078482,29715.30494 +5167,No,No,1489.357027,37758.41289 +5168,No,No,1527.359325,33923.52877 +5169,No,Yes,1427.049384,18268.52683 +5170,No,No,964.8630373,43744.98335 +5171,No,No,326.1509299,32919.97105 +5172,No,No,866.2996685,65931.95111 +5173,No,No,626.1601542,34007.90835 +5174,No,No,836.1928646,43275.14523 +5175,No,No,826.5580205,44371.74734 +5176,No,No,102.1225226,33300.18752 +5177,No,No,1609.297031,52752.96843 +5178,No,Yes,140.4556219,11179.49786 +5179,No,No,1147.980695,20766.39507 +5180,No,No,855.2925657,35749.03279 +5181,No,No,1682.201856,36366.99129 +5182,No,No,689.8207736,30304.7803 +5183,No,No,1706.046305,32900.71144 +5184,No,No,479.5219091,43743.62231 +5185,No,Yes,765.3633568,11976.40849 +5186,No,No,894.3291029,45250.73031 +5187,No,No,993.6470928,26768.95668 +5188,No,Yes,378.4925197,18659.77213 +5189,No,Yes,576.0650075,13536.60904 +5190,Yes,Yes,2113.629761,21100.72179 +5191,No,No,542.6962061,40996.21233 +5192,No,Yes,493.6295461,20500.21263 +5193,No,No,0,29322.63139 +5194,No,No,1185.581253,47782.49713 +5195,No,Yes,1719.871119,22431.858 +5196,No,No,495.7500691,44361.39119 +5197,No,No,533.8941544,33785.74614 +5198,No,No,150.1427055,38610.37731 +5199,No,Yes,1298.901493,14975.26493 +5200,No,No,957.2389599,57698.40822 +5201,No,No,836.9332916,51031.93983 +5202,No,Yes,1400.702824,23083.26016 +5203,No,No,857.329142,25742.17377 +5204,No,No,876.7655553,49291.16656 +5205,No,No,979.3167149,27534.91214 +5206,No,Yes,491.7043208,8352.208988 +5207,No,No,1621.711643,37970.47402 +5208,No,No,643.6155094,44251.01588 +5209,No,Yes,343.7986381,19971.77621 +5210,Yes,No,1711.169093,18579.10247 +5211,No,No,1170.027406,33332.62578 +5212,No,Yes,638.2043997,14641.38373 +5213,No,Yes,1457.839411,18496.93206 +5214,No,No,383.860155,31749.67842 +5215,No,No,618.0316829,44419.54767 +5216,No,No,565.2182999,29015.72406 +5217,No,No,290.1459587,32862.55529 +5218,No,Yes,576.9442788,22800.86705 +5219,No,No,633.1271156,54034.35501 +5220,No,Yes,1350.629002,11854.98444 +5221,No,Yes,893.5582716,23033.81221 +5222,No,Yes,895.3189658,20869.35594 +5223,No,No,1470.572491,44490.32107 +5224,No,No,789.2565112,43835.34027 +5225,No,No,218.7690046,52074.66272 +5226,No,Yes,531.6938027,15794.58161 +5227,No,Yes,1268.537855,21336.8901 +5228,No,No,0,34040.31291 +5229,No,No,1046.562865,26555.48894 +5230,No,No,389.9921752,29032.1127 +5231,No,No,642.2346279,35800.08382 +5232,No,No,391.9899522,35012.86961 +5233,No,No,1483.689011,58310.51235 +5234,No,Yes,438.5747709,17439.11495 +5235,No,No,897.8616937,47557.5241 +5236,No,No,0,41693.38584 +5237,No,Yes,776.6390702,15338.8886 +5238,No,No,112.4707089,34320.02615 +5239,No,No,634.477776,56726.12001 +5240,No,No,791.9474961,47886.52327 +5241,No,No,1088.285306,43134.53141 +5242,No,Yes,2018.358536,15472.84958 +5243,No,No,1470.635273,40080.21475 +5244,No,Yes,611.2088966,19733.2518 +5245,No,No,1117.080028,42388.41555 +5246,No,No,1589.990919,42268.27985 +5247,No,No,0,57898.41516 +5248,No,No,636.2386682,39172.36337 +5249,No,Yes,714.3971711,17412.71798 +5250,No,Yes,946.7796039,12881.40211 +5251,No,No,108.0567758,41840.32581 +5252,No,No,816.8883528,52492.34594 +5253,No,No,5.88381628,45840.47246 +5254,No,No,761.7506343,13889.13253 +5255,No,Yes,1173.781343,15823.80548 +5256,No,No,1418.475373,38034.14443 +5257,No,Yes,864.3571268,10484.7705 +5258,No,Yes,1101.160092,14307.00108 +5259,No,No,0,46615.69803 +5260,No,No,0,43782.25586 +5261,No,No,582.3172218,29239.84607 +5262,No,No,369.0504876,38418.86652 +5263,No,No,966.0518775,10174.7298 +5264,No,No,399.4456151,54329.92293 +5265,No,No,51.88848659,41839.17339 +5266,No,No,460.8794155,40046.02933 +5267,No,No,1139.012728,36958.57801 +5268,No,No,707.5731029,48790.40225 +5269,No,Yes,1034.603151,18614.0375 +5270,No,No,118.8698001,42823.57196 +5271,No,Yes,461.0043408,15554.02977 +5272,No,No,1057.275,42731.87641 +5273,No,No,1035.485881,41714.37377 +5274,No,No,1010.279513,34188.98465 +5275,No,No,0,41199.79936 +5276,No,No,656.0237254,36349.43699 +5277,No,No,539.1375008,23561.43052 +5278,No,No,1377.462061,49406.0727 +5279,No,Yes,1322.145996,21272.57594 +5280,No,Yes,1393.589491,23261.86324 +5281,No,No,1324.013984,45114.74031 +5282,No,No,1020.529557,35652.66346 +5283,No,No,2033.540235,41629.23729 +5284,No,No,888.6429126,19942.60287 +5285,No,No,1350.116267,39555.96231 +5286,Yes,Yes,1936.061862,13377.82884 +5287,No,Yes,1406.917364,16427.10078 +5288,No,No,282.8381245,29478.13801 +5289,No,No,601.0825714,25319.3661 +5290,No,Yes,1639.426608,14682.77666 +5291,No,Yes,48.52809588,16647.30616 +5292,No,No,324.5045318,51139.87685 +5293,No,Yes,886.4015079,13995.88959 +5294,No,No,967.4415738,51674.41937 +5295,No,No,711.7376077,45419.88194 +5296,No,No,1547.88079,50638.33959 +5297,No,Yes,1200.594445,13772.98912 +5298,No,No,0,45201.12871 +5299,No,No,1454.981589,43821.22135 +5300,No,No,355.4423386,46147.51787 +5301,No,No,0,46927.78445 +5302,No,No,0,49784.33635 +5303,No,No,282.8653131,39961.03982 +5304,No,No,303.7151177,29048.03524 +5305,No,No,157.7630016,36206.28569 +5306,No,No,300.0004665,41189.4522 +5307,No,No,463.9820725,43330.55244 +5308,No,No,1654.234956,39580.12971 +5309,No,No,762.5744974,45526.89479 +5310,No,Yes,1887.881716,21901.1825 +5311,No,No,1206.263492,34621.77331 +5312,No,Yes,247.6531036,20444.75243 +5313,No,No,576.7961199,39209.83287 +5314,No,Yes,1214.408306,18055.23847 +5315,No,No,987.7455283,40243.62002 +5316,No,Yes,1257.01613,19247.90061 +5317,No,No,524.0856189,40319.11814 +5318,No,Yes,1236.207092,23364.04868 +5319,No,No,1008.675001,68610.41206 +5320,No,Yes,1474.779605,17382.46739 +5321,No,No,772.3753146,36997.33674 +5322,No,No,701.5525937,39526.57719 +5323,No,No,0,28798.03997 +5324,No,Yes,1165.895511,14969.28725 +5325,No,No,0,28226.55795 +5326,No,No,1429.221048,40564.88631 +5327,No,Yes,806.6692357,17359.67476 +5328,No,No,528.0893156,46389.34068 +5329,No,No,963.4281482,60084.12648 +5330,No,Yes,764.5271667,22578.63567 +5331,No,No,573.699137,49465.75485 +5332,No,No,1410.36924,62395.03815 +5333,No,No,816.7410271,47272.33361 +5334,No,Yes,1220.524914,12195.36195 +5335,No,No,245.4387533,54133.03831 +5336,No,Yes,1083.911282,8584.077577 +5337,No,No,485.3488065,14676.29551 +5338,No,No,677.758794,50309.06472 +5339,No,No,286.6608747,34499.53263 +5340,No,Yes,843.9202067,20754.02417 +5341,No,No,831.8366199,41247.6422 +5342,No,No,1024.326377,33832.48963 +5343,No,No,1277.123098,42472.90827 +5344,No,Yes,1221.943911,19127.35023 +5345,No,No,967.2492817,27663.13407 +5346,No,Yes,1556.904418,19272.23648 +5347,No,Yes,419.729555,24679.73147 +5348,No,No,276.1648843,53625.71409 +5349,No,Yes,1407.497272,14688.3889 +5350,No,No,1041.088023,39515.84302 +5351,No,No,182.3254442,39703.26885 +5352,No,No,162.2184867,41381.27071 +5353,No,No,405.2739806,52571.18413 +5354,No,No,887.1583614,51519.12648 +5355,Yes,No,1844.883839,35508.67542 +5356,No,No,792.9815259,37549.95 +5357,No,Yes,942.1401049,23835.38547 +5358,No,No,938.7858968,14524.35762 +5359,No,No,18.90420269,35590.23258 +5360,No,Yes,599.4709832,18575.40842 +5361,No,Yes,679.809547,19263.12332 +5362,No,No,601.2632926,24400.09625 +5363,No,No,132.0810393,51694.73956 +5364,No,No,743.0329689,25186.4259 +5365,No,No,728.1868975,52515.06755 +5366,No,No,467.2475637,33644.25784 +5367,No,Yes,1104.933348,17377.49815 +5368,No,Yes,310.1981818,14672.9257 +5369,No,No,1575.712705,45957.24472 +5370,No,Yes,1370.248937,13358.2742 +5371,No,No,1593.432803,73554.2335 +5372,No,No,463.0022177,42297.4578 +5373,No,Yes,786.1060239,20954.45157 +5374,No,Yes,731.5118011,26505.34452 +5375,No,Yes,468.2732193,27054.89382 +5376,No,No,219.1457095,37993.04368 +5377,No,Yes,846.95907,20659.85327 +5378,No,No,702.1592628,57468.40444 +5379,No,Yes,1381.644434,11059.56807 +5380,No,No,1455.505837,33455.04969 +5381,No,No,1357.909718,43377.11218 +5382,No,No,1032.627079,23349.83013 +5383,No,No,798.8596367,48336.12931 +5384,No,Yes,117.9030818,14649.85806 +5385,No,No,234.743604,27499.95362 +5386,No,No,890.2668722,36631.49842 +5387,No,Yes,518.4788393,18635.50971 +5388,No,No,1124.49596,31739.56375 +5389,No,No,1468.788055,38521.8702 +5390,No,No,938.1061788,13405.21094 +5391,No,No,722.0291625,26984.25629 +5392,No,No,610.641708,42899.84813 +5393,No,No,407.7713007,18555.32354 +5394,No,No,1057.228817,58133.52784 +5395,No,No,1262.404516,57158.75502 +5396,No,Yes,1082.674545,22417.55615 +5397,Yes,No,1969.942924,29415.75329 +5398,No,No,760.0996428,45921.34313 +5399,No,No,929.3962543,23133.65981 +5400,No,No,1177.249598,35419.61031 +5401,No,No,309.2706808,42226.65131 +5402,No,No,981.9439338,38483.53189 +5403,No,No,200.162804,24280.13016 +5404,No,No,1630.48301,54323.42289 +5405,No,No,947.2354163,28310.49378 +5406,No,No,537.3963542,40828.14012 +5407,No,Yes,1292.568784,14859.24008 +5408,No,No,356.8276454,39444.82535 +5409,No,No,500.155117,34437.70689 +5410,No,No,1345.70698,40802.88652 +5411,No,Yes,1091.139756,19990.83533 +5412,No,No,1025.527625,22741.3913 +5413,No,No,403.2400249,42993.43641 +5414,No,No,699.3426727,36957.68057 +5415,No,No,1445.805164,15666.05743 +5416,No,Yes,963.8992578,16561.25564 +5417,No,No,542.7656016,52625.41571 +5418,No,No,766.9290949,25376.10376 +5419,No,No,808.4291276,29212.90473 +5420,No,No,736.5367299,46416.18292 +5421,No,No,1076.139823,31845.95153 +5422,No,Yes,731.3148595,11510.15147 +5423,No,No,236.0037059,38202.51384 +5424,No,No,1382.454952,40394.3086 +5425,No,Yes,915.5715469,22586.33828 +5426,No,No,176.5969458,48957.75638 +5427,No,Yes,639.9996263,19900.0064 +5428,No,Yes,950.6780086,20435.81781 +5429,No,No,1320.711115,29105.44244 +5430,No,Yes,1203.029399,15952.45202 +5431,No,No,1335.935254,19482.20628 +5432,Yes,Yes,1647.282248,16154.46228 +5433,No,Yes,1434.386464,15761.69962 +5434,No,No,307.4762385,36120.96859 +5435,No,Yes,1277.8949,20649.92999 +5436,No,No,998.3930699,46051.92286 +5437,No,No,561.343259,44403.99429 +5438,No,Yes,1577.083581,15230.8206 +5439,No,Yes,412.0714443,12588.97113 +5440,Yes,Yes,1758.406571,14272.27378 +5441,No,Yes,251.4681938,19373.88802 +5442,No,No,1353.131735,40164.04859 +5443,No,No,10.19445669,32641.93256 +5444,No,No,933.5547342,33633.14869 +5445,No,Yes,1692.752538,23743.84195 +5446,No,No,663.2504792,36276.86395 +5447,No,No,51.53040142,36851.91287 +5448,No,No,797.7426471,53398.92859 +5449,No,No,0,50243.93117 +5450,No,No,1217.072762,62764.09766 +5451,No,No,0,44980.29304 +5452,No,No,741.844666,45808.54028 +5453,No,No,455.1844905,43513.35063 +5454,No,Yes,1788.140822,26228.71044 +5455,No,No,1253.773412,28153.01406 +5456,No,No,918.0933737,44112.81017 +5457,No,No,0,30241.94858 +5458,No,Yes,699.1734013,17572.83106 +5459,No,No,171.996377,33505.49343 +5460,No,No,1326.348854,46587.08029 +5461,No,No,1275.495303,40026.28595 +5462,Yes,Yes,2247.421889,17926.72301 +5463,No,No,993.7721191,52645.66309 +5464,No,No,997.3795041,34824.84806 +5465,No,Yes,652.3726691,12139.05712 +5466,No,No,1124.886189,41989.0342 +5467,No,No,1608.818447,36721.10348 +5468,No,Yes,776.606473,4755.25219 +5469,No,No,845.0207249,33895.23785 +5470,No,No,570.0546987,42157.80771 +5471,No,No,186.4381344,13325.12133 +5472,No,No,0,48672.95601 +5473,No,No,1429.488716,31995.488 +5474,No,No,295.0566741,39479.75168 +5475,No,No,896.5812766,54840.05787 +5476,No,Yes,1562.532324,13754.33407 +5477,No,No,889.3087417,33036.24479 +5478,No,Yes,1805.682955,20727.64022 +5479,No,No,1334.105928,43704.43086 +5480,No,No,527.9402237,56820.82074 +5481,No,No,1282.076946,52753.07657 +5482,No,No,932.8287296,48165.18024 +5483,No,Yes,1850.888247,21575.76333 +5484,No,No,455.1907842,36488.15785 +5485,No,No,748.2866448,45626.99455 +5486,No,No,874.4787423,33485.52051 +5487,No,Yes,538.7065362,16350.36018 +5488,No,No,841.8517272,48615.01457 +5489,No,No,1814.168195,28322.83919 +5490,No,Yes,365.7044087,15953.97561 +5491,No,No,401.1807575,39686.67595 +5492,No,No,2096.136391,49992.52981 +5493,No,No,1429.878559,33452.58756 +5494,No,Yes,1737.347715,19202.71019 +5495,No,No,1043.390338,45309.94897 +5496,No,Yes,1155.456548,22194.47788 +5497,No,Yes,931.649113,19179.44679 +5498,No,No,146.7554609,49925.06939 +5499,No,No,1406.947652,27667.83603 +5500,No,Yes,705.8968256,18447.87609 +5501,No,No,641.8039407,32628.64126 +5502,No,No,379.6035635,42862.48889 +5503,No,No,706.2267919,33920.45697 +5504,No,No,577.1319969,43616.09623 +5505,No,No,90.99981606,52387.59199 +5506,No,No,549.3990418,48424.40438 +5507,Yes,Yes,1102.434982,17391.77965 +5508,No,Yes,1228.338796,18129.69406 +5509,No,Yes,974.0143849,20108.10358 +5510,No,No,263.6560618,50782.8322 +5511,No,No,847.0691474,35581.29012 +5512,No,No,835.1918091,39977.13147 +5513,No,No,757.9077112,37088.83957 +5514,No,Yes,1013.665766,13821.09008 +5515,No,Yes,643.5770009,28765.91053 +5516,No,Yes,757.2727635,18876.08916 +5517,No,No,1248.278943,41100.62617 +5518,No,No,699.2858502,41475.9821 +5519,No,No,866.4026312,18586.72717 +5520,No,No,697.6663826,35402.26796 +5521,No,No,899.0968928,39630.63879 +5522,No,No,674.2046296,46481.95268 +5523,No,No,902.9239586,33778.40323 +5524,No,No,1591.755806,46259.65998 +5525,No,No,484.2173586,30178.57524 +5526,No,No,751.6872866,51327.31727 +5527,No,No,604.1949471,38292.7691 +5528,No,No,640.6334699,46075.41506 +5529,No,Yes,782.4451651,23447.21338 +5530,No,No,1026.955197,40307.01102 +5531,No,Yes,862.9022538,16157.86653 +5532,No,Yes,780.5121258,14813.34158 +5533,No,No,878.0093852,15262.93511 +5534,No,No,481.8620283,32928.239 +5535,No,Yes,1579.990032,13274.7315 +5536,No,No,1.674025903,23001.66708 +5537,No,No,514.8490253,37656.84787 +5538,No,No,2087.678741,44997.36435 +5539,No,Yes,973.5147102,20770.48403 +5540,No,No,528.8724969,45235.49719 +5541,No,No,1131.412434,37663.22687 +5542,No,No,955.3433705,40368.59789 +5543,No,No,759.0322605,45774.38354 +5544,No,No,1023.852414,31492.99837 +5545,No,No,1096.077756,30374.83472 +5546,No,No,829.3296907,48734.16617 +5547,No,No,792.3568717,39911.42723 +5548,No,No,882.2645345,18379.51486 +5549,No,Yes,733.8470899,16400.12834 +5550,No,No,553.5194362,45385.31133 +5551,No,Yes,822.9595949,8918.702539 +5552,No,No,493.9599904,34621.75764 +5553,No,No,88.25405671,43927.31589 +5554,No,No,0,45077.57458 +5555,No,No,611.8257349,21716.53436 +5556,No,Yes,1096.692136,20856.58764 +5557,No,No,191.6110706,35119.58138 +5558,No,No,447.9581612,54044.82311 +5559,No,No,1149.689188,39974.56302 +5560,No,Yes,463.3000047,16416.61273 +5561,No,No,1347.635595,58953.09267 +5562,No,Yes,0,16979.89307 +5563,No,No,643.0108474,27735.15824 +5564,No,Yes,365.2229558,15375.15172 +5565,No,No,289.6815694,45991.04432 +5566,No,No,578.2906566,48044.38449 +5567,No,No,1186.932729,51742.57644 +5568,No,No,426.0072838,50874.56518 +5569,No,No,1115.738777,48125.64676 +5570,No,Yes,932.6958142,18743.31374 +5571,No,Yes,1048.487596,22935.59545 +5572,No,No,768.372947,44405.30875 +5573,No,No,785.4947084,50683.15418 +5574,No,Yes,865.2058661,22081.58164 +5575,Yes,No,1801.801183,24152.26483 +5576,No,No,950.3412748,64396.16534 +5577,No,No,787.0429349,46266.94145 +5578,No,No,982.8399888,32419.66505 +5579,No,No,1175.389577,35339.55667 +5580,No,Yes,887.2848722,13132.90135 +5581,No,No,801.9944369,46439.00355 +5582,No,No,1096.246733,42685.10729 +5583,No,No,628.8553316,40490.42605 +5584,No,Yes,567.7225809,14892.32445 +5585,No,Yes,843.0087866,22037.47041 +5586,No,Yes,1217.309875,10520.04307 +5587,No,No,0,10593.92125 +5588,No,No,1186.777951,38581.92786 +5589,No,No,439.0438931,40148.10805 +5590,No,Yes,778.9993533,10489.57353 +5591,No,Yes,742.5493235,24505.01935 +5592,No,Yes,763.3376238,15849.86396 +5593,No,No,408.11603,39388.31756 +5594,No,No,1071.258354,36869.05258 +5595,No,No,1373.575601,22182.41659 +5596,No,No,52.91808918,30506.35885 +5597,No,Yes,1140.454363,18991.88604 +5598,No,No,1162.299227,47513.12477 +5599,No,Yes,1528.49437,22664.34935 +5600,No,No,991.9611672,49910.09688 +5601,No,No,592.4323214,32283.26412 +5602,No,No,848.3276082,30472.71793 +5603,No,No,309.5179821,35293.19357 +5604,No,Yes,425.8446454,15324.758 +5605,No,Yes,1268.486114,19862.04442 +5606,No,Yes,898.6319508,15293.87405 +5607,No,No,274.9569465,24102.39151 +5608,No,No,399.443128,45360.93225 +5609,No,Yes,850.3864062,6389.070569 +5610,No,No,92.90008897,43955.88137 +5611,No,No,854.0233346,32107.30834 +5612,No,No,0,29721.34187 +5613,No,No,1094.812833,32936.05866 +5614,No,No,680.1644594,25391.63308 +5615,No,No,1231.351894,29867.87647 +5616,No,No,0,38228.26017 +5617,No,No,830.0460942,51847.12293 +5618,No,No,1267.001444,32752.19374 +5619,No,No,1623.690272,36747.90398 +5620,No,No,302.7190177,45260.39773 +5621,No,No,439.9619588,28215.47884 +5622,No,Yes,1016.825478,18703.55523 +5623,No,No,1421.204764,47423.79177 +5624,No,No,479.6771518,40995.03621 +5625,No,Yes,925.8969167,12384.20423 +5626,No,Yes,1192.702159,21597.75167 +5627,No,No,1649.91703,36091.38475 +5628,No,No,808.9991026,26379.8867 +5629,No,No,752.0562947,49741.43152 +5630,No,No,727.1907763,27282.19836 +5631,No,No,845.60731,31633.08725 +5632,No,No,445.6618979,46816.10511 +5633,No,No,1459.044966,49753.80492 +5634,No,Yes,988.6416096,22085.45082 +5635,No,No,796.9912085,25159.55486 +5636,No,No,903.4591316,33640.09968 +5637,No,No,1163.083904,45488.75056 +5638,No,No,1217.210527,33865.05058 +5639,No,Yes,740.2286772,23469.3875 +5640,No,No,1259.450524,28881.72525 +5641,No,No,501.391686,42856.19 +5642,No,No,808.6118886,48650.43388 +5643,No,No,413.8882716,53142.0239 +5644,No,Yes,666.4982302,11951.89879 +5645,No,Yes,412.1607883,17679.52177 +5646,No,Yes,372.6524955,22308.04128 +5647,No,No,1235.076654,48687.55716 +5648,No,No,853.2462216,41581.19534 +5649,No,No,1911.668517,52802.08882 +5650,No,No,357.9694607,57012.55088 +5651,No,Yes,698.1579817,19582.81616 +5652,No,No,918.4610912,32468.45074 +5653,Yes,No,1741.914915,35067.42512 +5654,No,No,577.4934767,39015.41684 +5655,No,Yes,1415.681994,21856.32093 +5656,No,Yes,1524.486833,15845.87125 +5657,No,Yes,1171.441401,19213.38573 +5658,No,Yes,396.8014841,26061.76425 +5659,No,No,1236.158124,19682.7109 +5660,No,No,1053.405495,52921.85379 +5661,No,No,1207.040625,42171.98897 +5662,No,No,528.9619452,27065.70528 +5663,No,No,662.703255,48652.16994 +5664,No,No,517.2285076,40248.58607 +5665,No,No,938.8460043,40914.22823 +5666,Yes,No,1823.636559,44260.15637 +5667,No,No,638.7219872,48085.70791 +5668,No,No,929.0226144,23889.68006 +5669,No,Yes,282.2487063,19809.09867 +5670,No,Yes,1429.609328,14827.89454 +5671,No,Yes,672.3581632,24495.03762 +5672,No,No,752.4667639,39896.10937 +5673,No,No,471.346127,30337.14118 +5674,No,No,270.5857589,44958.63127 +5675,Yes,No,2075.327892,43817.49942 +5676,No,No,96.80389905,31371.72608 +5677,No,Yes,568.294951,13286.3505 +5678,No,Yes,990.8805619,21164.66399 +5679,No,No,284.5609721,45542.1738 +5680,No,No,983.8373408,28980.10202 +5681,No,No,924.4373405,40987.48615 +5682,No,No,857.1531969,31252.65135 +5683,No,No,195.0223352,44222.97874 +5684,No,No,1046.008996,45573.38377 +5685,No,Yes,933.3504051,15557.63256 +5686,No,No,0,43646.91172 +5687,No,No,691.8920905,32512.19539 +5688,No,Yes,1097.969621,16275.68364 +5689,No,No,0,45374.99718 +5690,No,Yes,402.909163,22752.32695 +5691,No,No,1230.903161,44303.198 +5692,No,No,959.4820389,31357.44633 +5693,No,No,630.2291255,46713.63044 +5694,No,Yes,1353.49303,16727.66372 +5695,No,Yes,603.57519,15477.35388 +5696,No,No,830.2500906,35377.85706 +5697,No,No,858.1799645,30892.7358 +5698,No,No,380.1735083,37395.71893 +5699,No,Yes,1005.811733,12112.65742 +5700,No,No,1027.825307,33089.46729 +5701,No,No,456.003327,29898.00506 +5702,No,No,1043.09783,30516.2627 +5703,No,No,586.9688854,50317.65289 +5704,No,No,271.9422082,27790.06928 +5705,No,No,419.7290517,37444.53606 +5706,No,Yes,597.1123802,12660.81457 +5707,No,No,342.9573696,43688.53486 +5708,No,No,728.2789548,30028.18011 +5709,No,Yes,513.8367632,9879.115221 +5710,No,No,795.9586091,22570.4834 +5711,No,No,1.976691657,51672.36067 +5712,No,No,1355.64122,36671.65987 +5713,No,Yes,319.2626761,24881.38566 +5714,No,No,1719.169241,57866.05876 +5715,No,Yes,1191.085574,20895.40819 +5716,No,No,984.4391631,25294.86799 +5717,No,No,947.7959041,45284.20595 +5718,No,No,949.0387135,47760.40017 +5719,No,Yes,219.7286195,18401.98242 +5720,No,No,1465.743931,25521.26989 +5721,No,Yes,645.3388269,20122.20229 +5722,No,Yes,856.5644805,23199.97702 +5723,No,Yes,912.8325373,19467.97931 +5724,No,Yes,774.3476913,15147.40509 +5725,No,Yes,1376.340022,17101.42779 +5726,No,No,699.6803861,50267.84987 +5727,No,Yes,903.4586502,17254.9607 +5728,No,Yes,1150.547186,23705.95342 +5729,No,No,751.338737,42736.4281 +5730,No,No,556.077321,25371.21377 +5731,No,No,751.3922405,29875.04689 +5732,No,No,425.9869196,39772.75241 +5733,No,No,297.6808143,26586.53383 +5734,No,No,507.2462937,46106.23331 +5735,No,No,865.6970038,33541.04638 +5736,No,Yes,1265.527377,14672.25618 +5737,No,No,564.4113065,39643.53629 +5738,No,No,621.0412193,50804.67531 +5739,No,No,1205.59069,25920.86062 +5740,No,No,1943.9323,24193.60895 +5741,No,No,415.2887519,35790.13089 +5742,No,Yes,1169.420444,19879.24817 +5743,No,No,685.636884,49260.62537 +5744,No,No,781.9190194,54925.50779 +5745,No,No,651.3743295,44648.69641 +5746,No,No,490.2510328,49222.49138 +5747,No,No,976.2895424,35909.43829 +5748,No,No,1476.838123,41154.88175 +5749,No,No,752.9482339,62329.12133 +5750,No,No,511.605907,29914.17626 +5751,No,Yes,1384.372132,21059.60487 +5752,No,Yes,1031.670446,22440.62198 +5753,No,Yes,1491.507493,23636.16481 +5754,No,No,672.2363541,46336.05827 +5755,No,Yes,644.413564,23319.64913 +5756,No,No,539.5934291,9950.229447 +5757,No,Yes,0,17648.35555 +5758,No,No,919.8126372,44316.35833 +5759,No,No,1249.872794,50418.04452 +5760,No,No,539.8053161,35924.61001 +5761,No,No,1162.11326,49255.14433 +5762,No,No,944.4502979,47463.33358 +5763,No,No,0,29675.04039 +5764,No,Yes,1440.528795,20882.01732 +5765,No,Yes,1558.471919,17470.4767 +5766,No,No,854.1406227,52239.80024 +5767,No,No,468.5494575,53273.91902 +5768,No,No,902.4974099,56947.86374 +5769,No,No,897.2856085,66547.91535 +5770,No,Yes,422.1894101,14366.56905 +5771,No,No,194.5759668,44543.781 +5772,No,Yes,487.5114038,17713.58867 +5773,No,No,1558.860467,42112.3133 +5774,No,No,630.0289508,43547.54217 +5775,No,No,1056.65463,30711.86121 +5776,No,No,1712.470037,40395.34637 +5777,No,No,25.60294273,35464.24798 +5778,No,No,1384.850571,40131.49389 +5779,No,No,961.365709,42619.07773 +5780,No,No,229.214974,68145.13373 +5781,No,No,778.791565,53219.52735 +5782,No,No,653.4362222,41440.92856 +5783,Yes,No,961.4888501,27717.615 +5784,No,No,1084.457564,33080.96538 +5785,No,Yes,869.6677558,20285.25178 +5786,No,No,818.334937,27390.83691 +5787,No,No,416.8611818,39672.5173 +5788,No,No,250.8352449,58139.02159 +5789,No,No,0,48411.0769 +5790,No,Yes,481.5883754,19623.46054 +5791,No,Yes,1506.911513,23375.64593 +5792,No,No,1100.008931,34358.07468 +5793,No,No,220.6667528,64467.7334 +5794,No,Yes,789.2835878,20370.17371 +5795,No,No,41.35917584,47351.83354 +5796,No,No,382.9626972,43645.94779 +5797,No,No,567.9170514,28875.52181 +5798,No,No,244.5090864,33751.24507 +5799,No,Yes,815.341565,26064.36562 +5800,No,No,529.0726813,43914.67143 +5801,No,No,736.4185577,48660.15108 +5802,No,Yes,0,17744.92997 +5803,No,Yes,1260.341165,11696.67776 +5804,No,Yes,708.213553,12092.31447 +5805,No,Yes,99.99761123,4376.810337 +5806,No,No,1143.30818,48630.03384 +5807,No,Yes,892.3838609,17841.5227 +5808,No,No,1250.047245,50883.71442 +5809,No,No,415.6553667,43389.66075 +5810,No,Yes,1058.867384,16059.83275 +5811,No,No,857.6996646,43162.67365 +5812,No,No,1429.767205,37255.9111 +5813,No,Yes,322.266857,19231.93909 +5814,No,No,572.1867296,35926.37624 +5815,No,No,1438.758473,24061.6476 +5816,No,Yes,1742.886938,20344.42801 +5817,No,No,325.2675938,39431.53541 +5818,No,No,1091.418551,43091.15686 +5819,No,No,1252.409529,53237.10354 +5820,No,No,607.4312695,34541.302 +5821,Yes,No,1258.764794,26331.3724 +5822,No,No,1026.702707,33915.04884 +5823,No,Yes,590.5662326,9143.396433 +5824,No,Yes,1218.170346,18918.37715 +5825,No,No,260.6188019,42125.14867 +5826,No,Yes,1498.070591,13274.63056 +5827,No,No,909.9468758,53430.89543 +5828,No,Yes,1167.110583,18676.56977 +5829,No,No,923.6855831,38403.01062 +5830,No,Yes,1275.197078,15793.90917 +5831,No,No,709.2575197,23249.93691 +5832,No,No,682.9880122,41679.75979 +5833,No,No,1355.961899,25208.04886 +5834,No,Yes,1815.889294,15490.02827 +5835,No,No,698.2917247,43876.16188 +5836,No,No,189.1298582,28827.21309 +5837,No,No,1418.70284,35619.96673 +5838,No,No,670.4225823,53666.19234 +5839,No,No,206.0163452,38550.37667 +5840,No,No,1616.962125,47504.6645 +5841,No,No,995.5295997,24126.61036 +5842,No,No,591.0368363,30834.71179 +5843,No,No,1294.903234,52338.22146 +5844,No,Yes,1259.372813,22361.05474 +5845,No,Yes,1313.35267,17437.61583 +5846,No,No,0,24461.85446 +5847,No,Yes,769.4791217,27515.57093 +5848,No,Yes,571.5771218,15325.0791 +5849,No,No,756.0879108,49163.2233 +5850,No,No,183.4286922,39020.22886 +5851,No,No,1054.805589,52290.34067 +5852,No,No,1386.207726,44536.4873 +5853,No,No,596.1879849,45929.44918 +5854,No,Yes,1029.916975,16691.80329 +5855,No,No,417.5384554,31730.8666 +5856,No,No,1218.421391,40449.31171 +5857,No,No,210.7807881,46326.76112 +5858,Yes,No,1178.158858,44437.20214 +5859,No,No,997.6251137,30670.94715 +5860,No,No,637.902181,48425.72265 +5861,No,No,0,54245.1198 +5862,No,No,350.051852,48411.98668 +5863,No,No,1517.918958,49400.17084 +5864,No,No,190.6411053,31463.41998 +5865,No,No,971.5836736,41278.41903 +5866,No,Yes,925.3906782,16427.57101 +5867,No,No,1637.839922,34107.08056 +5868,No,No,224.5924725,27246.0453 +5869,No,No,255.117031,55980.07404 +5870,No,No,1201.016548,41585.83205 +5871,No,No,1075.570111,51370.2097 +5872,No,No,438.4202406,29899.82216 +5873,No,Yes,1246.437178,15756.71825 +5874,No,Yes,1655.11286,17269.79891 +5875,No,No,1063.816802,38457.24929 +5876,No,No,1626.563799,43592.8405 +5877,No,No,409.40402,48746.43435 +5878,No,No,1018.673018,38421.81091 +5879,No,Yes,1090.891882,20421.95772 +5880,No,No,324.885414,36555.39975 +5881,No,Yes,1085.152966,20054.8985 +5882,No,Yes,869.772061,18724.40368 +5883,No,Yes,608.8406483,24523.8541 +5884,No,Yes,1042.972565,9595.587571 +5885,No,Yes,1276.667762,19073.10852 +5886,No,No,928.0940903,55894.66202 +5887,No,No,1592.763904,35084.09317 +5888,No,No,0,39653.92321 +5889,No,No,340.999798,48058.38318 +5890,No,No,81.53186513,40847.81131 +5891,Yes,Yes,2145.607674,23516.19134 +5892,No,No,654.4160098,47244.70418 +5893,No,Yes,1155.780577,15146.89646 +5894,No,No,1745.313528,49090.12856 +5895,No,No,534.7848919,46138.43179 +5896,No,No,584.8374117,39553.1715 +5897,No,No,1365.556851,38511.3198 +5898,No,Yes,946.3671498,12532.65752 +5899,No,No,263.190295,34112.47197 +5900,No,Yes,1090.925059,18935.81822 +5901,No,No,835.189928,44678.83175 +5902,No,No,1265.535986,35133.21729 +5903,No,No,357.5066215,66404.68311 +5904,No,Yes,564.0054409,16502.33737 +5905,No,No,817.9277097,59431.83918 +5906,No,No,1366.278621,41192.46265 +5907,No,No,836.3431371,34559.1584 +5908,No,No,0,27367.78266 +5909,No,Yes,780.2279181,25228.71073 +5910,No,No,995.8773192,52860.58494 +5911,No,No,0,22827.18449 +5912,No,No,554.3445863,31028.9242 +5913,No,No,1204.578841,39583.12053 +5914,No,No,1053.375879,39612.67194 +5915,No,No,821.498847,39318.48939 +5916,No,Yes,470.9123472,24074.36465 +5917,No,Yes,1534.853735,21678.35097 +5918,No,No,1177.976867,50811.75946 +5919,No,Yes,1151.899517,15097.51233 +5920,No,Yes,906.4390691,24662.29072 +5921,No,Yes,1513.542437,13246.37528 +5922,No,No,740.8851866,34196.06746 +5923,No,No,1176.414512,57318.70197 +5924,No,No,208.2950791,37295.80198 +5925,No,Yes,375.3063048,19342.15809 +5926,No,Yes,953.1305453,18057.78649 +5927,No,No,280.9559003,37690.03348 +5928,No,No,399.1294716,38004.10498 +5929,No,No,1229.858122,58399.49667 +5930,No,No,1740.864107,46458.60612 +5931,No,Yes,795.1395535,24606.15429 +5932,No,No,584.8167761,47449.62424 +5933,No,No,1140.748209,39017.07762 +5934,No,No,1202.316722,49914.52204 +5935,No,Yes,476.7204658,10516.86972 +5936,No,No,1059.872271,38698.67273 +5937,No,No,1367.108606,36427.42974 +5938,No,Yes,1604.777659,23999.76034 +5939,No,Yes,1055.682537,25109.50847 +5940,No,No,367.6116872,44046.01751 +5941,No,Yes,0,18032.07378 +5942,No,No,543.8057414,31512.61059 +5943,No,No,192.6274629,38042.01839 +5944,No,No,1184.839953,34527.6868 +5945,No,Yes,1510.777233,19491.69407 +5946,No,No,801.6399094,37441.79212 +5947,No,No,631.0308358,44525.88999 +5948,No,No,279.1168757,30144.4325 +5949,No,No,674.0325619,54832.66573 +5950,No,Yes,957.6302042,15018.91365 +5951,No,No,274.4242206,31292.62745 +5952,No,No,1044.449897,43374.0041 +5953,No,No,319.807607,45202.6354 +5954,No,Yes,429.222463,19442.02909 +5955,No,No,835.5850236,41032.37131 +5956,No,No,2.788920349,54838.80551 +5957,No,Yes,867.4416448,16396.68236 +5958,No,Yes,271.7309257,14215.09288 +5959,No,No,404.1946521,41668.41958 +5960,No,No,1572.811108,18218.47436 +5961,No,No,730.6335593,42300.14687 +5962,No,No,0,56610.2365 +5963,No,No,1046.168559,46078.98231 +5964,No,No,514.8717738,50339.1918 +5965,No,Yes,1181.586009,22142.05215 +5966,No,No,0,45235.29087 +5967,No,No,272.2199278,47757.82441 +5968,No,No,1175.125526,33284.20607 +5969,No,No,812.8896264,48563.79976 +5970,No,No,571.0415013,40389.44633 +5971,No,Yes,1041.147351,12488.73193 +5972,No,No,334.0194949,40916.03325 +5973,No,No,1402.601194,37273.59307 +5974,No,No,1569.870324,55938.76622 +5975,No,Yes,1035.966107,30531.61311 +5976,No,No,1376.113241,63073.44514 +5977,No,Yes,668.642229,20240.95496 +5978,No,No,0,36249.17499 +5979,No,No,1112.655684,35159.21546 +5980,No,No,718.5798774,12571.68936 +5981,No,No,641.5503677,45574.49958 +5982,Yes,Yes,2125.792202,19539.14861 +5983,No,Yes,814.3541028,17973.50916 +5984,No,No,904.0336286,18898.31133 +5985,No,No,795.4973264,25336.32685 +5986,No,Yes,1753.598144,21130.91859 +5987,No,No,284.3956974,60840.80984 +5988,No,Yes,1300.889693,14156.64567 +5989,No,No,1237.843391,31880.17559 +5990,No,No,563.4665954,31151.18709 +5991,No,Yes,6.727869735,21695.70276 +5992,No,No,661.5281862,58519.85979 +5993,No,No,630.7148446,53546.5706 +5994,No,No,721.1416861,40458.95421 +5995,No,Yes,360.0091602,18487.43325 +5996,No,No,751.0309852,45581.71886 +5997,No,No,572.4682971,52797.42938 +5998,No,Yes,242.4629888,23413.35959 +5999,No,No,199.4547896,57455.21016 +6000,No,No,477.5668171,16978.27113 +6001,No,Yes,1184.725034,19797.77015 +6002,No,Yes,604.7587402,19369.34923 +6003,No,No,384.3310694,34232.06923 +6004,No,No,1399.573561,23577.76451 +6005,No,No,328.7781476,48626.35674 +6006,No,No,271.0598661,30465.30757 +6007,No,Yes,145.0021215,19927.0921 +6008,No,Yes,344.1541116,20439.68811 +6009,No,No,985.7527395,22829.58798 +6010,No,Yes,1002.41925,17797.34371 +6011,No,Yes,1004.844323,20249.20743 +6012,No,No,483.4158516,53873.51265 +6013,No,No,1034.601162,41252.72833 +6014,No,Yes,854.6262002,16322.72259 +6015,No,No,796.8367297,29397.88409 +6016,No,No,535.6968565,55828.08177 +6017,No,Yes,1070.639261,14833.02918 +6018,No,No,0,30524.75601 +6019,No,No,1333.901206,23261.84654 +6020,Yes,No,1114.832181,39776.99896 +6021,No,Yes,793.2480916,24314.68239 +6022,No,No,433.93529,26888.08734 +6023,No,Yes,1004.273646,17069.52357 +6024,No,Yes,1112.701189,20901.21374 +6025,No,No,298.7751394,33255.83885 +6026,No,No,796.8463462,20078.78923 +6027,No,Yes,969.3971625,17686.562 +6028,No,No,1075.118346,47566.78403 +6029,No,Yes,0,16451.94239 +6030,No,Yes,44.65556623,26346.81193 +6031,No,Yes,1000.740586,14689.49468 +6032,No,No,1311.999313,26377.97395 +6033,Yes,Yes,2086.536165,17893.72137 +6034,No,No,697.0468594,38743.09318 +6035,No,No,1240.66425,30938.53745 +6036,No,Yes,726.7641457,16079.63675 +6037,No,No,633.5041872,40571.76652 +6038,No,Yes,386.156432,22527.64722 +6039,No,No,0,34701.19596 +6040,No,No,808.5935854,41222.35167 +6041,No,No,1384.392454,35499.52693 +6042,No,No,713.067544,44413.75139 +6043,No,No,590.6765328,26673.8476 +6044,No,No,294.8446533,41591.77996 +6045,No,No,208.0077062,42399.52956 +6046,No,No,220.4493279,33213.32492 +6047,No,No,773.172691,49308.16489 +6048,No,No,1248.477058,37204.07352 +6049,No,No,634.1866103,32255.98924 +6050,No,No,678.1868937,55542.96963 +6051,No,No,1252.621497,27295.51793 +6052,No,Yes,1297.265043,16864.43489 +6053,No,No,0,38870.4968 +6054,No,No,1190.761292,27257.12253 +6055,No,No,196.515562,44226.63177 +6056,No,No,619.5592065,33476.66786 +6057,No,No,438.6250849,41454.72128 +6058,No,No,676.0988601,38571.41375 +6059,No,No,589.914146,49945.38901 +6060,No,No,1068.14635,27859.86445 +6061,No,No,1210.416122,45113.16252 +6062,No,Yes,390.3564738,9114.275289 +6063,No,No,1505.707167,35786.32716 +6064,No,No,490.0559754,35895.35391 +6065,No,No,123.2860681,47467.95895 +6066,No,No,563.2864112,51720.91153 +6067,No,No,409.1603681,45055.89747 +6068,No,Yes,781.6803963,21342.47871 +6069,No,No,870.3027256,24263.12744 +6070,No,No,509.1560111,47444.15145 +6071,No,No,1056.662484,39255.76331 +6072,No,No,394.9902144,41574.83351 +6073,No,Yes,1592.004647,19982.29006 +6074,No,No,0,49250.46411 +6075,No,No,1276.403278,52215.13403 +6076,Yes,No,2413.319449,38540.57271 +6077,No,No,210.1232594,58413.10367 +6078,Yes,No,2187.224846,42205.12305 +6079,No,No,1159.841945,43385.01904 +6080,No,No,1339.603177,46442.35377 +6081,No,Yes,793.3474509,11495.39314 +6082,No,Yes,163.5033913,17545.72664 +6083,No,No,867.6294827,43091.93599 +6084,No,Yes,1231.025804,13363.97805 +6085,No,No,312.4154887,47118.99943 +6086,No,Yes,758.4480112,20936.06864 +6087,No,Yes,1035.823468,13959.42045 +6088,No,No,763.7352796,44125.71873 +6089,No,Yes,728.5435561,12475.46073 +6090,No,No,1066.296558,42247.77855 +6091,No,No,338.7252717,37017.10149 +6092,No,No,264.9424906,39317.57391 +6093,No,No,1167.325681,43337.13006 +6094,No,No,553.7428862,39653.98309 +6095,No,No,485.7928417,38281.31479 +6096,No,No,485.9448148,46447.40528 +6097,No,No,690.4383031,66807.9363 +6098,Yes,No,1588.526206,38014.56885 +6099,No,No,460.4301908,56002.2232 +6100,No,No,275.660215,31259.9782 +6101,No,No,336.9071196,41907.71615 +6102,No,No,307.693119,42220.85392 +6103,No,Yes,1431.077694,20576.77995 +6104,No,Yes,787.6182494,11928.55523 +6105,No,Yes,1660.404013,18861.15466 +6106,No,No,0,41482.70163 +6107,No,No,1744.935803,43978.11235 +6108,No,Yes,758.3096627,12662.126 +6109,No,No,1173.462466,48396.50483 +6110,No,No,1335.541418,53354.84921 +6111,No,Yes,1434.849164,17459.03127 +6112,No,No,591.066666,43869.3159 +6113,No,No,862.7765105,49444.78465 +6114,No,Yes,1233.250011,16076.85426 +6115,No,Yes,911.7867777,20928.39244 +6116,No,No,0,43066.26756 +6117,No,No,286.928184,50618.72261 +6118,No,No,377.5344919,28045.59512 +6119,No,No,811.7924885,36874.61863 +6120,No,Yes,1856.605596,17845.33685 +6121,No,No,607.8379922,33737.17549 +6122,Yes,Yes,2182.348954,22037.85969 +6123,No,Yes,1406.567671,23050.88827 +6124,No,Yes,662.0906031,16310.79451 +6125,No,No,777.0162482,52797.93905 +6126,No,No,0,47236.97377 +6127,No,Yes,769.7114996,11262.63855 +6128,No,No,970.1847021,38784.45915 +6129,No,No,537.9266975,45615.19566 +6130,No,No,768.1548836,39701.93491 +6131,No,No,1150.040923,19536.16037 +6132,No,No,902.5572891,32765.38813 +6133,No,No,442.8275798,42004.19673 +6134,No,No,100.8668444,31389.45857 +6135,No,Yes,904.0563158,23188.71514 +6136,No,No,1396.263723,24379.07154 +6137,No,No,1001.331745,47640.737 +6138,Yes,No,1631.615617,38906.95641 +6139,No,No,790.7405179,45900.29736 +6140,No,No,1097.393094,46807.08168 +6141,No,No,0,37598.86279 +6142,No,Yes,1262.835385,12406.99895 +6143,No,No,738.1929286,54491.14267 +6144,No,No,655.2677484,28642.58257 +6145,No,No,0,32944.99582 +6146,No,No,638.5403332,20607.4593 +6147,No,No,570.9308876,46840.72126 +6148,No,No,1056.443662,31268.737 +6149,No,No,1522.855155,35805.25597 +6150,No,No,977.3215555,35296.28141 +6151,No,No,743.5650136,28893.87601 +6152,No,No,352.0332779,38259.22409 +6153,No,No,318.2094608,48975.36743 +6154,No,Yes,674.8509392,15798.1346 +6155,No,No,268.9543495,55892.72711 +6156,No,Yes,1023.205479,17694.9656 +6157,No,No,718.1569168,42172.00521 +6158,No,No,843.2908052,41692.42221 +6159,No,No,866.729785,36452.72212 +6160,No,No,449.949322,38987.02486 +6161,No,No,459.101423,45043.6664 +6162,No,Yes,760.2195299,17657.98551 +6163,No,No,1160.827632,26022.95259 +6164,No,No,1317.512987,49285.54547 +6165,No,Yes,561.7818671,18470.28261 +6166,No,Yes,176.5338346,15175.58604 +6167,No,Yes,1059.236341,12601.47121 +6168,No,No,526.7984896,28491.4805 +6169,No,No,20.96450385,62862.75183 +6170,No,No,528.9424635,42134.27063 +6171,No,No,1561.53353,54594.84671 +6172,No,No,963.7228112,47605.76859 +6173,No,Yes,1227.365273,12128.14817 +6174,No,No,1209.377359,35071.31662 +6175,No,Yes,671.1758688,17564.95475 +6176,No,No,275.6246467,35761.3165 +6177,No,No,776.188873,34140.98039 +6178,No,Yes,699.303115,19789.53066 +6179,No,Yes,606.1251346,16957.64876 +6180,No,Yes,1231.212923,20000.06255 +6181,Yes,No,1408.438085,48012.91357 +6182,No,No,338.4954707,34332.84324 +6183,No,No,1380.48149,24856.64306 +6184,No,No,1316.42181,45806.90151 +6185,No,No,1046.182045,48752.13176 +6186,No,Yes,904.684255,17281.11917 +6187,No,No,1281.520648,37665.29394 +6188,No,No,0,46597.49508 +6189,No,Yes,501.1116447,22393.27308 +6190,No,No,1085.95015,36532.71876 +6191,No,No,351.9451074,48726.04767 +6192,Yes,No,1803.170259,36192.63022 +6193,No,No,666.622349,44931.93976 +6194,No,No,154.703584,33162.19321 +6195,No,No,360.0891792,44448.9158 +6196,No,No,105.4410038,35291.46335 +6197,No,No,492.4282925,25848.9885 +6198,No,Yes,1232.422469,24093.29644 +6199,No,No,1154.398529,20457.22078 +6200,No,Yes,1113.326964,14579.65698 +6201,No,Yes,699.6980374,22590.66798 +6202,No,Yes,1001.785683,24771.72694 +6203,No,Yes,1217.226947,16449.999 +6204,No,No,1156.933998,45325.34097 +6205,No,Yes,1637.450714,18766.85518 +6206,No,No,824.2414326,53320.28294 +6207,No,No,662.3428972,49885.01912 +6208,No,No,617.6407642,37604.93356 +6209,No,No,271.4519112,34863.1042 +6210,No,Yes,688.1812417,18755.95323 +6211,No,No,797.7091656,39677.69721 +6212,No,No,464.4043311,53215.93158 +6213,No,Yes,976.2022175,23191.7707 +6214,No,No,1430.766147,45672.51997 +6215,No,Yes,1208.144482,17721.29366 +6216,No,No,0,41960.1745 +6217,No,Yes,402.3587995,15256.48418 +6218,No,Yes,824.5143779,15309.27621 +6219,No,Yes,1060.878824,11041.3315 +6220,No,No,310.4959193,47427.32909 +6221,No,No,691.4184986,40232.65666 +6222,No,No,23.15226669,42016.89909 +6223,No,Yes,913.2761576,20446.203 +6224,No,No,970.8164382,35835.35508 +6225,No,No,1148.447538,39662.12241 +6226,No,No,1638.687046,38441.15803 +6227,No,Yes,0,19134.44475 +6228,No,Yes,1448.950207,28273.83539 +6229,No,Yes,1286.839756,17464.99353 +6230,No,No,762.0735996,48268.59139 +6231,No,No,977.5765275,51074.21274 +6232,No,No,368.7680524,45647.29761 +6233,No,No,267.9593388,46134.62266 +6234,No,Yes,503.1563883,18728.29694 +6235,No,No,0,48523.33555 +6236,No,No,282.6028771,56118.3224 +6237,No,No,748.0570433,47775.18082 +6238,No,No,999.6540136,27564.68871 +6239,No,No,494.660496,47035.75468 +6240,No,No,1022.682388,22588.55547 +6241,No,Yes,1135.217871,19062.59687 +6242,No,No,707.8023303,40140.80045 +6243,Yes,Yes,1954.321689,17137.47372 +6244,No,Yes,1282.350409,7230.030471 +6245,No,No,808.6169215,23519.67255 +6246,No,Yes,1219.933751,21765.38212 +6247,No,No,481.8804217,44137.0795 +6248,No,No,409.4117491,53624.45775 +6249,No,No,1157.277347,31369.49194 +6250,Yes,No,1424.93289,27668.95224 +6251,No,No,0,58825.61513 +6252,No,No,687.0691307,34434.21823 +6253,No,No,1435.662933,31507.08928 +6254,No,No,188.544102,49697.23179 +6255,No,No,1084.387499,34558.61578 +6256,No,No,644.6185908,37452.06721 +6257,No,No,1345.563131,38602.11298 +6258,No,Yes,733.7194635,27165.47998 +6259,No,Yes,744.1780148,20773.6274 +6260,No,No,0,29542.0254 +6261,No,Yes,1395.961882,23717.85111 +6262,Yes,No,1920.242332,57242.98341 +6263,No,No,538.4132679,40773.94146 +6264,No,Yes,385.6982623,18352.43625 +6265,No,Yes,1350.757577,19692.91878 +6266,No,No,1567.763856,39201.24186 +6267,No,No,753.6868883,42230.64777 +6268,No,No,72.37195337,28717.94062 +6269,No,Yes,675.0360244,21115.79312 +6270,No,Yes,980.16435,13713.50223 +6271,No,Yes,910.3836047,19163.80699 +6272,No,Yes,1262.884299,14340.50794 +6273,No,No,621.3127192,39372.07719 +6274,No,No,643.4026386,50208.82372 +6275,No,Yes,728.2592917,13438.98556 +6276,No,No,459.2602277,59731.28878 +6277,No,No,289.6166642,49066.83365 +6278,No,Yes,784.7466141,11642.92229 +6279,No,Yes,712.5947732,18441.02612 +6280,No,No,1096.003601,52030.9726 +6281,No,Yes,435.4855311,19899.75457 +6282,No,No,688.0224701,29019.81789 +6283,No,No,599.4283586,36654.64817 +6284,No,No,68.75629448,45811.74294 +6285,No,Yes,765.1369986,14290.26763 +6286,No,No,1273.124571,57688.56805 +6287,No,No,1078.621061,46060.08477 +6288,No,No,1677.348956,44972.70986 +6289,No,No,531.7561839,42365.07367 +6290,No,Yes,631.1726124,11075.89406 +6291,No,No,1081.478534,34072.64381 +6292,No,No,368.5153456,60953.82479 +6293,No,No,343.7269991,38054.5554 +6294,No,No,1715.282689,60137.63368 +6295,No,No,450.3355798,20080.10212 +6296,No,No,740.5807734,20717.54444 +6297,No,No,0,26237.47434 +6298,No,No,475.8690387,35621.69112 +6299,No,Yes,1383.745059,14633.98588 +6300,No,Yes,406.4823849,22708.90108 +6301,No,No,857.2615657,34406.61082 +6302,No,Yes,1637.235201,16751.16975 +6303,No,No,1094.315872,34854.34239 +6304,No,No,1213.127074,47848.0479 +6305,No,Yes,1074.746406,22729.30865 +6306,No,Yes,1010.212633,13469.86665 +6307,No,No,416.1282864,21960.62456 +6308,No,Yes,675.8413465,17801.47436 +6309,No,No,0,36338.8485 +6310,No,No,867.3041732,29541.04315 +6311,No,No,843.9073034,23956.51937 +6312,No,No,766.43043,39078.797 +6313,No,No,1286.537233,46337.12389 +6314,No,No,1142.935154,34401.66711 +6315,No,Yes,925.4112876,15120.33562 +6316,No,Yes,1110.453823,14965.43739 +6317,No,Yes,938.6059755,24392.10404 +6318,No,No,321.0397429,37026.58716 +6319,No,No,943.3876407,39415.59565 +6320,No,No,511.5987702,54002.51166 +6321,No,No,743.8495986,24303.46763 +6322,No,No,1089.577061,46890.2154 +6323,No,No,0,46275.36347 +6324,No,No,0,31511.65746 +6325,No,No,1282.321247,37437.71096 +6326,No,No,1373.796792,36888.92841 +6327,No,No,1228.3343,43577.66949 +6328,No,No,1536.788312,34734.86388 +6329,No,No,735.4704341,53312.53053 +6330,No,Yes,1142.427605,26114.40315 +6331,No,No,1490.715076,36417.40004 +6332,No,No,880.0328325,60203.88156 +6333,No,No,2.84301484,39923.8193 +6334,Yes,Yes,2066.695603,10470.636 +6335,Yes,No,2343.797513,51095.29393 +6336,No,No,929.7454817,36071.52426 +6337,No,Yes,764.6277907,26188.0695 +6338,No,No,504.6044952,31870.01829 +6339,No,No,685.2069414,40555.79988 +6340,No,No,473.027458,43016.86073 +6341,No,No,1302.553499,32235.05245 +6342,No,No,435.0041342,30828.86039 +6343,No,No,601.8385009,37697.37005 +6344,No,No,456.7636577,50160.6337 +6345,No,No,1065.042918,37747.10405 +6346,No,No,1153.331896,37936.08708 +6347,No,No,59.63300148,35709.73846 +6348,No,Yes,1106.430674,16960.69412 +6349,No,Yes,1106.453525,14840.93813 +6350,No,Yes,385.430994,21619.66341 +6351,No,No,0,45025.51165 +6352,No,No,727.957482,53606.97314 +6353,No,No,1028.777609,36276.08514 +6354,No,Yes,1035.916689,10607.90614 +6355,No,No,974.5977187,25316.85875 +6356,No,No,772.8092199,39944.6072 +6357,No,No,1014.973979,41993.64061 +6358,No,Yes,0,10276.25611 +6359,No,Yes,1403.789341,22893.96052 +6360,No,No,580.4233413,25926.23509 +6361,No,No,499.5507444,45954.67124 +6362,No,Yes,748.2362054,11613.22295 +6363,No,No,568.0939695,31392.06695 +6364,No,No,1366.725774,35425.51208 +6365,No,No,511.6788941,32693.85088 +6366,No,Yes,678.2178753,19979.69399 +6367,No,Yes,1692.42056,7761.342904 +6368,No,No,631.8045979,32752.51926 +6369,No,No,585.0500546,34297.56527 +6370,No,No,1173.490792,44134.74487 +6371,No,No,544.2374276,43306.62114 +6372,Yes,Yes,1985.321739,22248.40266 +6373,No,No,1011.060904,38001.7121 +6374,No,No,1231.214517,48156.35756 +6375,No,No,875.842483,45524.5322 +6376,No,No,1516.065384,59194.23921 +6377,No,No,563.1212611,30226.48272 +6378,No,Yes,1244.566927,21717.47587 +6379,No,Yes,1215.643468,16403.68576 +6380,No,No,1077.747127,30133.25763 +6381,No,Yes,1393.916368,16658.48308 +6382,No,No,803.5700674,45353.97068 +6383,No,No,916.7492378,58469.93356 +6384,No,No,147.0493336,52074.5107 +6385,No,No,734.4727806,46805.55302 +6386,No,Yes,996.205915,13647.53574 +6387,No,No,1883.200207,47438.25571 +6388,No,No,1271.115698,40578.31619 +6389,No,No,173.7334181,32480.53398 +6390,No,No,1004.187665,37132.65721 +6391,No,Yes,2033.362496,18549.74539 +6392,No,No,269.3578618,40883.18547 +6393,No,No,441.701436,44227.91027 +6394,No,No,1471.608629,61385.59987 +6395,No,Yes,1133.822004,16602.02802 +6396,No,No,843.3526611,51736.91025 +6397,No,No,250.4683325,42309.08282 +6398,No,No,1569.728273,30604.28645 +6399,No,Yes,995.3471118,11690.74126 +6400,Yes,No,1572.34559,37895.17307 +6401,No,No,1570.416179,49501.81038 +6402,No,No,645.931623,39695.62726 +6403,No,No,1001.853011,32748.63094 +6404,No,Yes,905.2312955,15699.89846 +6405,No,Yes,859.3796898,19287.5037 +6406,No,No,1670.850285,54442.2662 +6407,No,No,391.2566277,33651.40556 +6408,No,No,670.0082484,35919.79677 +6409,No,No,1036.053835,44869.46019 +6410,No,No,481.9894913,19240.31429 +6411,No,Yes,542.7692556,17664.88143 +6412,No,Yes,887.2889243,17409.03112 +6413,No,Yes,1465.849554,17678.73631 +6414,No,Yes,1545.63746,13959.99367 +6415,No,No,262.7913362,28974.75055 +6416,No,Yes,793.7963758,21172.40412 +6417,No,No,1192.766158,45902.50755 +6418,No,Yes,1120.320878,29045.4068 +6419,No,Yes,514.1994978,21482.03481 +6420,No,No,710.7604275,53050.48538 +6421,No,No,1176.243271,27956.7829 +6422,Yes,No,1805.559588,40119.78783 +6423,No,No,124.8284595,39478.30007 +6424,No,No,1056.993239,45489.89668 +6425,No,Yes,904.9946707,19108.74233 +6426,No,No,1402.120138,41275.64547 +6427,No,No,7.069530292,48711.63749 +6428,No,Yes,733.7833904,16124.35425 +6429,No,No,612.960653,43392.88709 +6430,No,No,998.704699,46697.77777 +6431,No,No,1078.457001,33001.12651 +6432,No,No,638.4763864,53034.55233 +6433,No,Yes,890.0481045,15156.91913 +6434,No,No,815.6821325,53817.27975 +6435,No,No,1276.98265,40611.34869 +6436,No,No,671.1857708,44120.64174 +6437,No,No,818.2843466,48490.81219 +6438,No,No,1371.330899,35820.62983 +6439,No,No,897.4957561,47816.41161 +6440,No,Yes,699.6964224,17318.88187 +6441,No,No,848.9407381,35534.98389 +6442,No,No,0,27297.90952 +6443,No,Yes,1660.861064,13694.85645 +6444,No,No,1475.503847,44769.12214 +6445,No,No,0,22431.45354 +6446,No,No,1186.964356,33439.8364 +6447,No,No,844.4038026,36854.63634 +6448,No,Yes,1540.308824,15241.01588 +6449,No,No,679.7075809,29534.82146 +6450,Yes,No,1650.578,31514.25965 +6451,No,Yes,614.7838595,18487.23371 +6452,No,No,1379.678044,31766.2936 +6453,No,Yes,770.1055385,15284.71993 +6454,No,No,1209.540282,40805.77763 +6455,No,Yes,1380.969819,13633.43347 +6456,No,No,1349.106443,28340.43736 +6457,No,Yes,1051.393853,23520.426 +6458,No,Yes,428.4258221,15446.95803 +6459,No,No,1714.18603,52779.08882 +6460,No,Yes,1647.801283,15392.21298 +6461,No,No,482.9758188,47924.67664 +6462,Yes,No,2124.671313,44520.0001 +6463,No,No,297.8382216,34549.46263 +6464,No,No,1201.348042,35858.40429 +6465,No,No,811.2419476,26448.3285 +6466,No,No,0,36201.53887 +6467,No,No,585.8041768,30143.32851 +6468,No,No,511.321855,38510.16091 +6469,No,No,1249.194174,45586.89022 +6470,No,No,48.96555026,27069.73978 +6471,No,No,863.5747099,32889.38396 +6472,No,No,1579.359465,50216.57163 +6473,No,No,800.6093328,50324.55493 +6474,No,Yes,1356.534691,19480.54377 +6475,Yes,Yes,1704.578529,16887.40514 +6476,No,Yes,528.5856887,19621.62922 +6477,No,Yes,1390.507666,15885.51078 +6478,No,Yes,333.5411962,27321.55783 +6479,No,No,1569.556299,36306.01944 +6480,No,No,1509.780887,43650.41851 +6481,No,No,961.2415545,31647.28748 +6482,No,No,1443.507447,47205.34866 +6483,No,No,0,37605.37939 +6484,No,No,895.3959518,48939.73566 +6485,No,No,1355.866843,45537.95492 +6486,No,Yes,960.1139856,24326.06027 +6487,No,No,1164.4764,36997.71898 +6488,No,No,535.1829158,37042.76649 +6489,No,Yes,1424.772927,21852.92988 +6490,No,No,773.1724915,50069.20882 +6491,No,No,685.1925696,33185.01044 +6492,No,No,343.7688061,51626.13569 +6493,No,Yes,1513.541568,23556.6905 +6494,No,No,415.0036341,37859.31446 +6495,No,No,1034.36018,25111.21255 +6496,No,No,1115.896008,21646.30595 +6497,No,Yes,748.7464309,17440.62655 +6498,No,No,291.4087289,54408.78898 +6499,No,Yes,453.9297986,12665.307 +6500,No,No,889.565857,49941.08863 +6501,No,No,345.1906244,48111.44622 +6502,No,No,393.828578,28362.62463 +6503,No,Yes,991.0939963,16842.43614 +6504,No,No,4.466457821,45157.90962 +6505,No,No,660.0589187,39163.32727 +6506,No,Yes,1168.868403,18113.34377 +6507,No,No,528.8673882,38881.47626 +6508,No,No,89.33404767,36515.98159 +6509,No,Yes,196.7767287,15858.3533 +6510,No,No,1637.347759,37676.66312 +6511,No,No,574.9553577,33517.21103 +6512,No,No,969.134944,29705.04132 +6513,No,Yes,1049.241311,23332.48384 +6514,No,No,772.5312697,23222.73977 +6515,No,No,1304.068285,26219.32314 +6516,No,No,875.8752694,37160.9608 +6517,No,No,587.0745299,34975.97647 +6518,No,Yes,495.136133,15519.48512 +6519,No,No,131.6339983,42028.0086 +6520,No,No,1333.182128,19396.03188 +6521,Yes,No,1819.249095,49621.05617 +6522,No,No,953.8160755,45281.36985 +6523,No,No,327.0385825,51348.93727 +6524,No,No,989.63631,27318.94371 +6525,No,No,1109.221483,56680.13753 +6526,No,Yes,0,9321.463734 +6527,No,Yes,635.394658,18344.07546 +6528,No,No,100.021825,48377.64128 +6529,No,No,264.9680541,36842.82266 +6530,No,No,465.8456638,52714.08886 +6531,No,No,1227.2618,49697.04618 +6532,No,No,1378.677643,30862.99636 +6533,No,No,715.6567217,23266.13569 +6534,No,Yes,0,21719.45483 +6535,No,No,1335.230281,43660.39171 +6536,No,No,1576.903647,35117.80535 +6537,No,Yes,1232.590353,22108.58037 +6538,No,No,1613.714114,47151.60976 +6539,No,No,763.7431661,59856.79667 +6540,No,No,741.430159,32951.17936 +6541,No,No,325.5865499,45809.98424 +6542,Yes,No,1797.767019,47068.72461 +6543,No,No,899.9027004,47093.73838 +6544,Yes,No,1605.214586,26149.14822 +6545,No,No,0,42879.39694 +6546,No,No,886.6890235,50507.93081 +6547,No,No,1008.290635,30801.08975 +6548,No,Yes,1034.504453,19105.62822 +6549,No,Yes,1125.734507,23410.09941 +6550,No,Yes,1070.950734,12707.58041 +6551,No,No,920.4084978,40536.64248 +6552,No,Yes,734.7331649,10816.94977 +6553,No,No,1701.688487,38567.20471 +6554,No,No,1453.316173,29830.05674 +6555,No,No,294.0243461,49988.14731 +6556,No,No,1302.479132,43154.39293 +6557,No,No,1135.187984,47075.0323 +6558,No,No,1123.633139,17292.30317 +6559,No,Yes,483.1097993,15067.31371 +6560,No,No,1669.502687,30259.25637 +6561,No,Yes,1051.376889,13271.46469 +6562,No,Yes,778.3409168,23737.26676 +6563,No,No,37.99084851,43787.7819 +6564,No,No,599.8796698,51733.82517 +6565,No,Yes,1037.642867,22889.48898 +6566,No,No,1187.463608,37092.59041 +6567,No,No,1287.205895,37135.36394 +6568,No,No,599.3803801,58909.34426 +6569,No,No,278.0784558,33811.66921 +6570,No,No,536.7008086,35786.51482 +6571,No,No,626.5147862,55155.07786 +6572,No,No,0,54298.85961 +6573,No,No,885.5100067,42683.47973 +6574,No,Yes,1314.841724,15965.41501 +6575,No,No,580.164721,31879.13893 +6576,No,No,1144.530511,40998.92734 +6577,No,No,0,55435.43055 +6578,No,No,321.3085814,33136.33648 +6579,No,No,776.3958736,49705.81618 +6580,No,No,519.3086916,42861.93266 +6581,No,No,892.2810718,40492.10058 +6582,No,Yes,945.0549545,21095.53818 +6583,No,No,840.2368475,24801.36811 +6584,No,No,660.5336718,31480.04082 +6585,No,No,213.010074,38950.60007 +6586,No,No,506.8241417,50493.09526 +6587,No,No,884.6606716,35324.02022 +6588,No,Yes,296.462756,20138.24699 +6589,No,Yes,700.0753377,14828.55059 +6590,No,No,927.468685,44308.28232 +6591,No,Yes,1172.273498,22792.62169 +6592,No,No,841.5262496,40239.6774 +6593,No,No,49.6823973,47026.03855 +6594,No,Yes,903.9166521,20246.39323 +6595,No,No,600.1827009,50578.05423 +6596,No,Yes,1749.122184,21359.13155 +6597,Yes,Yes,1893.023004,16853.32957 +6598,No,Yes,1636.519725,17533.3422 +6599,No,Yes,1533.911936,16453.88308 +6600,No,Yes,1523.722571,15708.95773 +6601,No,Yes,1156.6202,11102.56815 +6602,Yes,Yes,2168.453196,24648.97992 +6603,No,No,456.8307381,39629.58084 +6604,No,No,1616.168586,39654.61886 +6605,No,Yes,1311.860099,15565.1977 +6606,No,Yes,437.0682956,21638.94784 +6607,No,No,683.1146156,31615.36555 +6608,No,Yes,1327.282439,15819.35932 +6609,No,No,797.6309421,54042.36225 +6610,No,No,635.7076076,26084.69605 +6611,No,Yes,1312.88532,16221.20289 +6612,No,Yes,944.8328467,23323.82273 +6613,Yes,No,1838.871369,32129.33982 +6614,No,No,267.3723188,41183.65749 +6615,No,Yes,723.5404147,28289.23591 +6616,No,No,610.0521709,35855.38793 +6617,No,No,1861.317697,46653.58874 +6618,No,No,0,34882.22855 +6619,No,No,1577.972544,50430.23981 +6620,No,Yes,954.1503905,15969.81997 +6621,No,No,204.3961996,38979.62388 +6622,No,Yes,1109.468217,21663.13247 +6623,No,No,771.2248776,47146.43951 +6624,No,No,376.350444,32168.44216 +6625,No,Yes,1523.880095,19342.98352 +6626,No,Yes,1178.211737,12077.01326 +6627,No,Yes,1457.575381,19668.15776 +6628,No,Yes,1292.447316,8231.037423 +6629,No,No,1233.502222,23031.60089 +6630,No,No,542.1405751,22394.1911 +6631,No,No,1381.819269,48394.28962 +6632,No,No,1157.768113,49960.76521 +6633,No,No,744.4938745,24506.6782 +6634,No,No,1387.490549,40993.59534 +6635,Yes,No,1434.127716,47311.53042 +6636,No,No,1483.536563,34156.3742 +6637,No,No,1340.893192,31959.9656 +6638,No,No,972.0944016,26231.14591 +6639,No,No,1511.879387,36381.23765 +6640,No,Yes,1111.997722,20549.62035 +6641,No,Yes,1555.563352,15957.32883 +6642,No,No,1727.365146,41707.4773 +6643,No,No,25.7358489,49089.60408 +6644,Yes,Yes,1895.334933,16394.50811 +6645,No,Yes,417.478257,17787.94782 +6646,No,No,247.1990572,37377.35782 +6647,No,No,91.38769809,44091.876 +6648,No,Yes,617.089796,22801.66397 +6649,No,Yes,793.3337851,10487.93018 +6650,No,Yes,1035.720966,20393.27002 +6651,No,No,826.9817504,35426.1808 +6652,No,No,1734.451564,31578.00495 +6653,No,No,1069.62206,40299.31991 +6654,No,No,626.8384282,41334.18353 +6655,No,No,932.0569663,45505.30722 +6656,No,No,1551.027731,31768.03061 +6657,No,Yes,315.0902684,22551.89125 +6658,No,No,1059.351605,41179.01002 +6659,No,No,976.7040479,59291.72257 +6660,No,Yes,562.5589369,11524.81035 +6661,No,Yes,310.0202771,16382.15293 +6662,No,No,936.944765,40240.49711 +6663,No,No,0,43033.49225 +6664,No,Yes,431.1970473,20301.49367 +6665,No,No,1411.548718,42956.63139 +6666,No,Yes,1183.452815,16189.28839 +6667,No,Yes,1849.603666,16966.88592 +6668,No,No,391.4820793,45821.63323 +6669,No,No,10.88494131,32866.44524 +6670,No,Yes,697.2486326,25730.91758 +6671,No,No,175.054337,22644.99588 +6672,No,Yes,1094.740579,19638.26543 +6673,No,Yes,134.6240411,19464.29871 +6674,No,No,1069.748332,32441.28198 +6675,No,No,1165.812599,47005.57329 +6676,No,No,321.2181021,43257.35349 +6677,No,No,178.2750321,35615.92811 +6678,No,Yes,953.8584818,18121.11336 +6679,No,Yes,1335.812707,12843.44803 +6680,No,Yes,853.5943254,25373.46219 +6681,No,No,639.6256747,38660.92322 +6682,No,No,1051.847749,37160.2733 +6683,No,No,335.1071558,36374.21438 +6684,No,No,1193.762792,34909.30888 +6685,No,No,915.2030835,31422.94555 +6686,No,No,426.0870274,15498.86947 +6687,Yes,Yes,1805.618353,19168.02997 +6688,No,No,717.4789564,37810.47213 +6689,No,No,520.9472873,41039.28245 +6690,No,No,1034.892431,34539.84576 +6691,No,Yes,1022.598336,11739.09598 +6692,No,No,617.5914405,43871.69796 +6693,No,Yes,1322.162293,16984.19826 +6694,No,No,476.5913949,33760.45831 +6695,No,No,1101.091224,43035.56208 +6696,No,Yes,1019.579112,15833.79065 +6697,No,No,329.4482726,43242.87537 +6698,No,Yes,949.2338248,25040.71234 +6699,No,Yes,1230.714628,18581.27461 +6700,No,No,1252.653058,35382.14033 +6701,No,No,0,47019.66323 +6702,No,No,642.2693048,37752.38459 +6703,No,No,512.5135258,44178.8405 +6704,No,No,1114.901552,38155.36839 +6705,No,Yes,1742.346535,10778.15487 +6706,No,No,763.0223125,34931.34829 +6707,No,No,878.4447309,47722.81035 +6708,No,Yes,936.2295509,14217.40152 +6709,No,No,585.2377164,40585.74654 +6710,No,No,347.6140996,40138.0778 +6711,No,No,607.8986423,26117.66517 +6712,No,Yes,1251.085756,21968.26999 +6713,No,No,316.5364349,39995.3188 +6714,No,No,336.7970403,42880.4643 +6715,No,Yes,871.0667958,13144.36558 +6716,No,No,1187.904686,43873.98291 +6717,No,No,86.01278996,43467.50265 +6718,No,No,737.041709,27879.65685 +6719,No,Yes,1419.870721,10401.04666 +6720,No,Yes,402.5033656,12255.87074 +6721,No,No,191.2150007,41921.68285 +6722,No,Yes,1836.780086,15422.23216 +6723,No,Yes,1273.186248,20429.62137 +6724,No,No,1037.600637,40415.82298 +6725,No,No,827.5428855,39655.67701 +6726,No,No,915.9135902,34934.48239 +6727,No,Yes,212.7086441,13155.67206 +6728,No,No,941.1681796,58991.86151 +6729,No,No,487.4168943,34439.90331 +6730,No,No,1889.524775,32509.61626 +6731,No,No,691.2022794,38948.10536 +6732,No,Yes,502.6458212,14425.08417 +6733,No,No,140.0185757,52099.1163 +6734,No,No,0,36484.59415 +6735,No,No,519.4614942,44498.01559 +6736,No,Yes,1031.869798,19322.55235 +6737,No,No,939.6693901,47112.7653 +6738,No,No,0,44902.92935 +6739,No,No,1093.684902,39684.45193 +6740,No,Yes,1623.336605,14338.40878 +6741,No,No,851.2040106,44905.32531 +6742,No,No,1119.702648,33510.35718 +6743,No,No,0,32720.5717 +6744,No,Yes,744.9142665,15711.71642 +6745,No,Yes,303.3458684,15220.59932 +6746,No,No,1522.755927,29165.44319 +6747,No,No,361.3676099,36533.71036 +6748,No,No,1062.727927,48041.6494 +6749,No,Yes,1344.094679,22012.29315 +6750,No,No,387.7319185,55163.79694 +6751,No,No,860.0282429,56687.14354 +6752,No,No,1637.357955,46629.94697 +6753,No,No,975.1985123,30573.25805 +6754,No,No,487.7974797,21418.69468 +6755,No,No,0,49626.41562 +6756,Yes,No,1562.454585,43067.33374 +6757,No,No,1399.126163,26505.02013 +6758,No,Yes,542.064066,9135.886774 +6759,No,No,901.0757265,38613.42665 +6760,No,No,295.8705689,19054.0305 +6761,No,No,90.56141029,50939.52115 +6762,No,No,476.1499315,58837.72311 +6763,No,No,382.8850624,42620.53023 +6764,No,No,1013.963746,49653.79713 +6765,No,Yes,932.6857989,15774.70106 +6766,No,No,360.5749488,42217.38292 +6767,No,No,727.9636084,40258.70172 +6768,No,No,917.9527564,31380.23317 +6769,No,No,1567.165357,38255.05057 +6770,No,No,1006.680488,33348.57536 +6771,No,No,1278.932666,42894.7653 +6772,No,No,0,56493.48609 +6773,No,No,1107.487867,23530.62273 +6774,No,Yes,1719.566851,18696.29875 +6775,No,No,1066.174662,33911.56487 +6776,No,No,0,30172.81059 +6777,No,Yes,1296.103527,15709.45579 +6778,No,No,720.2445985,38493.49193 +6779,No,No,574.6382233,41914.46952 +6780,No,No,241.4300888,34507.77593 +6781,No,No,345.1421431,41943.77284 +6782,No,No,1341.703442,53122.06896 +6783,Yes,No,1847.791373,30202.84624 +6784,No,Yes,1497.067767,9030.402564 +6785,No,No,503.39124,29187.44412 +6786,No,No,1119.575449,42280.80903 +6787,No,No,1172.415172,58067.12982 +6788,No,Yes,1594.384586,21514.6028 +6789,No,No,566.4438526,36039.39407 +6790,No,No,542.4701592,39037.80101 +6791,No,No,1086.420939,38656.66313 +6792,No,Yes,995.0394301,8750.5588 +6793,No,Yes,1200.875976,13833.9822 +6794,No,No,0,56895.96198 +6795,Yes,No,1508.649243,37111.3089 +6796,No,No,1170.547946,52358.80093 +6797,No,No,1356.743836,54489.93878 +6798,No,No,850.0364106,31522.23465 +6799,No,No,1184.140383,25196.76179 +6800,No,No,902.7136645,23847.39229 +6801,No,Yes,1597.890914,24006.18039 +6802,No,No,1079.076123,36636.28005 +6803,No,Yes,1503.578445,18776.53385 +6804,Yes,Yes,1570.65878,16239.15317 +6805,No,No,0,41655.68226 +6806,No,Yes,735.5783648,13976.68591 +6807,No,No,923.3462318,44670.86424 +6808,No,Yes,1269.678223,13998.30673 +6809,No,No,706.4819315,48339.87899 +6810,No,No,1146.875398,64644.90529 +6811,No,Yes,1749.549609,20078.36188 +6812,No,Yes,263.49132,25363.59525 +6813,No,No,468.8699085,44285.32011 +6814,No,No,443.9130878,35241.99297 +6815,No,No,375.720573,35564.41027 +6816,No,No,772.8229977,25556.75717 +6817,No,No,750.1395372,17993.82049 +6818,No,No,414.8042587,37825.37218 +6819,No,No,626.1752277,33930.97505 +6820,No,No,645.2012091,44324.22436 +6821,No,Yes,1287.287235,13215.97025 +6822,No,No,1391.261154,51169.36221 +6823,No,Yes,601.8046898,16266.3026 +6824,No,No,1061.310874,33942.61975 +6825,No,No,272.7504686,42646.70489 +6826,No,Yes,1421.740129,11134.9396 +6827,No,No,1065.626806,36015.68758 +6828,No,No,1167.035059,34467.54286 +6829,No,No,949.1290849,28411.65649 +6830,No,Yes,565.9887284,18645.93665 +6831,Yes,No,1914.106962,55573.70171 +6832,No,No,515.9428051,50678.33694 +6833,No,No,1347.46071,32720.44574 +6834,No,No,124.3059108,44464.63585 +6835,No,No,1007.220296,42183.08973 +6836,No,No,218.3379599,44179.9465 +6837,No,No,1344.55595,34841.26052 +6838,No,No,1063.219526,27887.25053 +6839,No,Yes,624.5431657,14820.46719 +6840,No,No,559.8320113,39144.47684 +6841,No,No,341.3868405,29326.33924 +6842,No,No,574.1644925,44072.0608 +6843,No,Yes,734.8982568,11868.07421 +6844,No,No,875.2953348,46041.75628 +6845,No,No,366.0908026,47096.37807 +6846,No,Yes,1009.993872,19870.53658 +6847,No,No,221.9224906,32988.57136 +6848,Yes,Yes,1957.120295,18805.95213 +6849,No,Yes,1050.431291,19246.33309 +6850,No,No,612.3249889,55326.41284 +6851,Yes,No,1456.96393,49054.19389 +6852,No,Yes,1821.523917,15802.38929 +6853,No,Yes,770.3280295,29059.14469 +6854,No,Yes,1269.906274,771.9677294 +6855,No,Yes,1923.381941,12940.22783 +6856,No,No,890.7594972,20257.37999 +6857,No,No,1341.054788,33778.16204 +6858,No,No,888.2650394,37745.60517 +6859,No,Yes,1189.313959,19545.72712 +6860,No,No,1820.540494,46251.44869 +6861,No,No,481.8409362,36377.64743 +6862,No,No,432.0920648,56732.73728 +6863,No,No,683.9971778,36578.18465 +6864,No,No,354.7019532,44412.19392 +6865,No,Yes,669.7372373,17109.63048 +6866,No,Yes,713.9228744,26049.97564 +6867,No,No,597.0416087,31713.09769 +6868,No,Yes,1306.416879,18996.35032 +6869,No,No,1991.135514,30809.67457 +6870,No,Yes,717.6328948,19590.971 +6871,No,No,390.6066769,38083.5473 +6872,No,Yes,1862.538659,23839.57252 +6873,Yes,No,1143.680503,37751.01734 +6874,No,No,264.372357,45778.30895 +6875,No,No,216.8465333,37160.9411 +6876,No,No,686.8929286,51120.82998 +6877,No,No,1642.192319,24444.31218 +6878,No,Yes,0,15587.9006 +6879,No,Yes,1218.649212,19206.13346 +6880,No,No,1970.859761,37905.25263 +6881,No,Yes,1151.733317,23149.54749 +6882,No,No,539.0777208,36275.45572 +6883,Yes,Yes,2287.173842,18692.14431 +6884,No,No,1218.245802,56816.80894 +6885,No,No,700.4531369,54661.11507 +6886,No,No,1167.231679,30648.4288 +6887,No,No,1492.615028,46174.69799 +6888,No,No,666.3947948,34912.3367 +6889,No,Yes,636.8988095,23005.57595 +6890,No,No,420.727023,37831.57008 +6891,No,No,879.0526491,30633.84194 +6892,No,No,376.2411614,25821.16106 +6893,No,No,961.4024541,25478.81835 +6894,No,No,500.7285411,33406.05411 +6895,No,Yes,369.6799323,24834.41758 +6896,No,No,719.938044,31031.2194 +6897,No,No,1168.347005,39275.19354 +6898,No,No,600.5380969,39260.38212 +6899,No,No,0,18593.91474 +6900,No,No,252.9448968,23443.41646 +6901,No,No,676.4308888,53378.98852 +6902,No,Yes,1066.822188,26348.30364 +6903,No,Yes,1004.650577,13869.7521 +6904,No,No,1010.421141,46217.80956 +6905,No,No,1260.931294,27697.33825 +6906,No,No,402.6324273,58828.79225 +6907,No,Yes,919.4754847,6100.733202 +6908,No,No,1004.653816,42729.18223 +6909,No,No,614.5385663,33811.98762 +6910,No,No,1358.109779,50662.89279 +6911,No,No,160.5248787,29675.83549 +6912,No,Yes,1190.188829,15860.32668 +6913,No,No,262.5992604,48762.28355 +6914,No,No,647.9180241,35494.48529 +6915,No,No,1574.651282,30559.5137 +6916,No,Yes,1658.602257,14821.00456 +6917,No,No,4.814908682,28714.13538 +6918,No,No,1220.312282,36190.99361 +6919,No,No,802.8571022,45806.19917 +6920,No,No,81.87448947,35818.228 +6921,No,Yes,70.73361201,17466.13896 +6922,No,Yes,962.9057964,22795.0325 +6923,No,No,926.694301,44233.64617 +6924,Yes,Yes,1233.445895,12586.47819 +6925,No,Yes,644.1117175,9778.219986 +6926,No,Yes,1338.659659,8078.176047 +6927,No,No,740.3867859,46877.96074 +6928,No,Yes,901.1865026,19204.70162 +6929,No,No,975.3887418,45597.93769 +6930,No,No,298.7196152,49585.87127 +6931,No,No,591.4980139,29507.48706 +6932,No,No,177.0144011,37553.80181 +6933,No,No,292.2719769,60582.22621 +6934,No,No,625.1918331,32826.40965 +6935,No,No,546.9866501,37513.70038 +6936,No,No,727.6753323,23515.61089 +6937,No,Yes,531.9764082,21357.63216 +6938,No,No,311.2127675,51257.87429 +6939,No,Yes,703.0630696,17692.65839 +6940,No,No,1239.683068,52241.45566 +6941,No,No,810.2788394,51283.14786 +6942,No,No,303.4826916,21381.31449 +6943,No,No,1372.035706,36188.23978 +6944,No,No,1146.375563,37053.82846 +6945,No,Yes,730.9627524,20097.40094 +6946,No,No,807.2192163,42138.65307 +6947,No,No,450.4055188,34206.27365 +6948,No,Yes,467.9127389,25424.41007 +6949,No,Yes,684.8910355,17582.98554 +6950,No,No,1013.469261,38280.91701 +6951,No,No,676.6527202,31741.43106 +6952,No,No,287.9668874,37409.97776 +6953,No,No,816.6489096,52163.78155 +6954,No,No,1118.789834,39111.79234 +6955,No,No,532.1536093,43789.33519 +6956,No,Yes,1364.232684,18423.04766 +6957,No,Yes,1464.690871,16542.04507 +6958,No,Yes,997.642404,27054.72741 +6959,No,No,335.3400825,41942.75251 +6960,No,No,1149.012958,35310.12955 +6961,No,Yes,905.5657992,24032.80055 +6962,No,No,649.6732695,32276.42914 +6963,No,No,380.1891882,40689.92044 +6964,No,No,0,48303.87151 +6965,No,Yes,485.4343175,21278.34081 +6966,No,No,649.8419855,54693.15179 +6967,No,No,698.5724534,44949.26908 +6968,No,Yes,1328.953669,20666.70281 +6969,No,No,609.4776899,41798.29235 +6970,Yes,No,1723.187215,43143.16449 +6971,No,No,0,36581.8039 +6972,No,No,594.6012612,36190.75639 +6973,No,Yes,875.7129441,18696.95358 +6974,No,No,949.3149767,50404.27876 +6975,No,No,252.3767271,39723.48373 +6976,No,No,1162.567975,21753.52835 +6977,No,Yes,1244.359563,19232.04744 +6978,No,No,578.4955654,46030.88128 +6979,No,No,443.9009529,50768.44487 +6980,No,No,1316.089253,41314.16952 +6981,No,No,904.4240809,56062.01653 +6982,No,Yes,1037.974595,15580.76888 +6983,No,No,842.9856646,42682.14201 +6984,No,Yes,1258.528735,10920.96459 +6985,No,Yes,297.3907381,19383.79115 +6986,Yes,Yes,1553.349657,18091.94374 +6987,No,Yes,684.9353812,21908.57159 +6988,No,No,1265.501264,32910.39744 +6989,No,No,362.6111511,39296.07799 +6990,No,No,1532.916209,22080.92177 +6991,No,Yes,1341.615739,26319.01559 +6992,No,Yes,1086.161108,23576.78673 +6993,No,No,496.0017626,32818.15707 +6994,No,No,1597.052084,50827.80754 +6995,No,Yes,2092.45853,14514.76996 +6996,No,Yes,1019.984293,23795.42248 +6997,No,No,765.2338783,29096.41726 +6998,No,No,928.9649696,46709.48028 +6999,No,Yes,1787.201938,17553.60725 +7000,No,No,628.1368037,38436.19145 +7001,No,No,351.820396,30513.51793 +7002,No,Yes,265.5039229,23775.43557 +7003,No,Yes,1886.55057,6467.149663 +7004,No,No,57.64704922,38218.18447 +7005,No,No,933.827036,57377.43278 +7006,No,Yes,996.2320726,10707.94266 +7007,No,Yes,1033.056283,15453.0337 +7008,No,Yes,824.5184007,20689.72422 +7009,No,No,358.6476417,56331.77877 +7010,No,No,1393.369463,43439.67407 +7011,No,No,332.4138259,37129.15118 +7012,No,No,1215.08895,39002.49696 +7013,No,Yes,1793.789405,22740.54856 +7014,No,No,874.174255,33821.90601 +7015,Yes,Yes,1893.942955,12399.12474 +7016,No,No,1119.613426,32213.22903 +7017,No,No,234.6993207,28098.89325 +7018,No,Yes,874.0171638,24635.31103 +7019,No,No,1283.228887,47514.99337 +7020,No,Yes,1326.558083,25853.14254 +7021,No,No,1263.798242,47203.82028 +7022,No,Yes,1145.279018,12906.20414 +7023,No,No,1118.851738,30558.38386 +7024,No,No,753.8322965,46957.28188 +7025,No,No,1039.23791,50653.91379 +7026,No,Yes,1947.072679,13157.95656 +7027,No,No,864.7774424,32120.29412 +7028,No,No,1165.851838,48855.50945 +7029,No,No,1101.438796,39045.99391 +7030,No,No,438.5165083,36838.88203 +7031,No,No,750.7200082,47876.89589 +7032,No,Yes,743.4154463,19610.17998 +7033,No,No,1371.351016,34955.4079 +7034,No,No,673.3842033,34891.54278 +7035,No,No,1208.220572,36402.74523 +7036,No,Yes,483.4205666,12596.93401 +7037,No,No,1394.079754,27179.42878 +7038,No,Yes,1405.317174,17933.23701 +7039,No,No,736.3926729,32026.96396 +7040,No,Yes,1167.34909,16356.93057 +7041,No,No,1031.869932,46725.37402 +7042,No,Yes,0,17189.3714 +7043,No,No,0,51226.76161 +7044,No,No,1334.950731,51086.44465 +7045,No,Yes,997.9362831,25788.36315 +7046,No,No,419.4445698,47155.69222 +7047,Yes,Yes,1806.921846,11506.21775 +7048,No,No,474.495396,43522.46238 +7049,No,Yes,531.3584198,11420.5687 +7050,No,No,880.5810244,49547.17602 +7051,No,Yes,1491.166105,18171.62223 +7052,No,No,976.9028073,32837.16555 +7053,No,Yes,642.3822859,13563.46329 +7054,No,Yes,1670.829991,11385.88027 +7055,No,No,1285.371279,39925.2034 +7056,No,No,199.5945257,27579.35339 +7057,No,No,66.51311342,29670.69883 +7058,No,No,577.5319564,22111.86691 +7059,No,No,1142.616307,42053.57047 +7060,No,No,767.1392121,49171.97818 +7061,No,Yes,851.3383722,22829.02478 +7062,No,No,683.9421408,35232.83518 +7063,No,No,485.4404311,38656.45463 +7064,No,No,0,49772.97475 +7065,No,Yes,915.1476615,21545.71736 +7066,No,No,1143.529562,38663.22863 +7067,No,No,365.2423298,27027.40806 +7068,No,No,661.1041871,42945.57029 +7069,No,No,964.7660193,49646.80657 +7070,No,No,1044.786889,41148.54006 +7071,No,No,201.8074002,71878.77264 +7072,No,No,655.6622473,54844.20473 +7073,No,No,10.90579922,48181.68915 +7074,No,No,528.4044347,41066.7319 +7075,No,No,728.3560585,32388.61576 +7076,No,No,938.3867636,18594.12798 +7077,No,Yes,359.9363755,26500.20874 +7078,No,No,1185.149365,32467.41774 +7079,No,Yes,811.0466954,21263.09914 +7080,No,Yes,1089.831454,15056.07787 +7081,No,Yes,288.3188143,18356.56525 +7082,No,No,351.9591074,29281.03071 +7083,No,No,977.1955021,58658.75015 +7084,No,No,919.6724049,40573.17945 +7085,No,No,800.2433379,19051.324 +7086,No,No,771.8254969,40520.8094 +7087,No,No,585.869313,67100.68521 +7088,No,No,1077.812379,26518.08028 +7089,No,No,907.3067584,28461.29374 +7090,No,No,838.017911,42232.94151 +7091,No,No,1006.934931,25733.34781 +7092,No,No,712.4120489,42352.09149 +7093,No,No,508.4532442,31966.40088 +7094,Yes,No,1476.509782,42984.41285 +7095,No,No,1224.471239,37088.0532 +7096,No,No,636.309344,50040.41979 +7097,No,No,1609.698815,60123.6273 +7098,No,Yes,885.212532,11774.41657 +7099,No,Yes,137.0393722,10994.7941 +7100,No,Yes,988.8956298,19449.17918 +7101,No,Yes,563.2901332,22323.83355 +7102,No,No,309.7823539,33671.56261 +7103,No,Yes,821.2572882,17347.43946 +7104,No,No,434.1899042,42205.26489 +7105,Yes,Yes,1321.535379,23735.15989 +7106,No,No,549.8538372,50084.38951 +7107,No,No,1170.019022,48445.21687 +7108,No,No,1238.371887,42321.52715 +7109,No,Yes,0,15698.98123 +7110,No,No,803.946256,42286.28782 +7111,No,Yes,1456.005961,15069.88289 +7112,No,No,954.33059,27180.54596 +7113,No,No,1224.930711,44002.65453 +7114,No,No,796.3908026,47600.49452 +7115,No,No,461.0262193,15755.64723 +7116,No,No,468.2857768,23336.03001 +7117,No,No,321.0127629,30275.54188 +7118,No,No,1653.434709,44965.03236 +7119,No,No,1190.536219,41630.28283 +7120,No,No,178.0473468,35401.70897 +7121,No,Yes,1447.856782,24669.84203 +7122,No,No,0,56043.91022 +7123,No,No,282.3076802,38099.14331 +7124,No,No,28.73880255,41809.20418 +7125,No,Yes,1279.316806,17837.03455 +7126,No,No,0,36061.70076 +7127,No,No,1470.348149,49018.80185 +7128,No,Yes,405.5682901,20523.91231 +7129,No,Yes,509.1722947,19962.81788 +7130,No,No,1207.150842,29875.37904 +7131,No,No,546.3750599,37524.43432 +7132,No,No,86.91865797,34638.9039 +7133,No,No,418.8238889,56320.63386 +7134,No,No,0,56412.00753 +7135,No,No,41.65143751,47522.02367 +7136,No,No,906.7458077,38881.01107 +7137,No,Yes,1499.856349,25156.73644 +7138,No,No,1561.631319,32952.84077 +7139,No,Yes,1615.504802,19566.15796 +7140,No,No,766.0960211,41435.5919 +7141,Yes,Yes,1807.705844,21849.48163 +7142,No,No,594.9167826,43152.53324 +7143,No,No,1458.675647,57786.32312 +7144,No,No,901.288509,44594.34803 +7145,No,No,32.9935989,31644.77062 +7146,No,No,1050.268525,59401.19567 +7147,No,No,1329.096762,60321.61321 +7148,No,No,410.9356245,39076.27282 +7149,No,No,1591.82829,27668.44177 +7150,No,No,561.9897454,40446.00878 +7151,No,Yes,592.1700026,18684.23564 +7152,No,No,624.6963964,30763.46512 +7153,No,No,1433.740497,39445.08409 +7154,No,Yes,635.3621561,13912.21853 +7155,No,No,982.9687181,50700.17386 +7156,No,No,877.4158794,22997.94503 +7157,No,No,433.4299707,49209.2564 +7158,No,No,951.3130442,25540.81596 +7159,No,No,205.0062644,36554.62653 +7160,No,Yes,1214.783347,11789.44984 +7161,No,No,974.8972056,26921.36588 +7162,No,Yes,470.1071813,16014.11331 +7163,No,No,0,30684.73055 +7164,No,No,1666.22423,34918.43253 +7165,No,No,798.6598947,17463.02135 +7166,No,No,286.2674216,47960.43821 +7167,No,No,908.5726294,48426.92744 +7168,No,No,78.74644144,41335.26248 +7169,No,Yes,1051.425965,6583.874438 +7170,No,No,1301.158296,28673.64598 +7171,No,Yes,1506.691675,20680.18265 +7172,No,No,0,29901.39942 +7173,No,Yes,0,15759.91517 +7174,No,No,182.8909632,21286.84999 +7175,No,Yes,1468.969355,15977.66255 +7176,No,No,1291.244338,42098.47895 +7177,No,No,608.9583159,24975.64641 +7178,No,No,713.7206102,17753.76732 +7179,No,No,1446.646704,28951.72738 +7180,No,Yes,889.292843,14273.29372 +7181,No,No,1219.63446,47936.30678 +7182,No,Yes,533.0075803,20556.41715 +7183,No,No,1024.143822,45585.96877 +7184,No,Yes,510.2960401,19682.56083 +7185,No,No,797.3475885,35177.40999 +7186,No,Yes,1686.608874,22175.1316 +7187,No,No,530.5473292,44627.56264 +7188,No,No,1332.531071,36974.6082 +7189,No,No,1401.34681,31403.06548 +7190,No,No,586.8113871,40353.35048 +7191,No,No,628.4195457,34756.24764 +7192,No,Yes,1193.307929,16430.01599 +7193,No,Yes,666.424384,19177.67937 +7194,No,Yes,1017.66712,13553.09143 +7195,No,No,648.3446128,18843.60623 +7196,No,No,952.138823,28642.3638 +7197,No,Yes,1492.065854,11615.98347 +7198,No,Yes,171.5988951,24809.11453 +7199,No,Yes,1231.281094,15716.53226 +7200,Yes,No,1833.646548,43539.78433 +7201,No,Yes,633.1704436,23694.32524 +7202,No,No,655.2077278,30593.14758 +7203,No,No,331.4070861,20588.17187 +7204,No,No,1213.362642,33527.48881 +7205,No,No,881.6498549,50114.70054 +7206,No,Yes,1799.361876,14250.19939 +7207,No,No,1165.936504,42252.26271 +7208,No,Yes,758.6734779,24166.7623 +7209,No,No,269.2901413,43778.73288 +7210,No,Yes,1043.880984,27380.47009 +7211,No,No,893.1145504,43511.65216 +7212,No,Yes,728.4671377,15958.747 +7213,No,No,413.7521931,44803.36028 +7214,No,No,97.26487584,37468.18023 +7215,No,No,765.0464862,44870.68429 +7216,No,Yes,1054.485054,10997.40407 +7217,No,Yes,649.530745,13408.66295 +7218,No,No,742.3840999,34265.36231 +7219,No,No,1016.463702,49635.10485 +7220,No,No,636.3043098,52011.06922 +7221,No,No,976.4285332,31897.56603 +7222,No,No,1197.818051,37487.65054 +7223,No,No,483.4548791,44281.34657 +7224,No,No,1239.492389,40312.23023 +7225,No,No,942.387355,34685.86796 +7226,No,No,712.1312457,48835.99289 +7227,No,No,214.9817904,46693.93774 +7228,No,No,558.3388574,38284.67553 +7229,No,Yes,1134.559731,20964.2127 +7230,No,No,523.6476518,37590.81994 +7231,No,No,593.1607864,46475.03132 +7232,No,Yes,868.7125196,13025.52498 +7233,No,No,607.7211365,41791.88774 +7234,No,No,1059.804101,64315.02621 +7235,No,Yes,0,29106.58374 +7236,No,No,1141.743817,45455.16307 +7237,No,No,1127.259556,47545.8468 +7238,No,No,456.9152179,42506.86315 +7239,No,Yes,385.3975215,13739.16614 +7240,No,No,705.9967717,53423.1498 +7241,No,No,742.5622239,48207.61508 +7242,No,Yes,1310.399734,24267.77776 +7243,No,No,670.9824681,55003.09132 +7244,No,No,441.7254656,39307.50404 +7245,No,No,1075.718431,38020.72359 +7246,No,Yes,675.2406919,18827.78189 +7247,Yes,No,1377.68002,41435.2695 +7248,No,No,302.3999089,32048.74216 +7249,No,No,910.2307775,46274.3436 +7250,No,No,812.5282385,58338.92578 +7251,No,Yes,1298.516266,18755.76764 +7252,No,No,679.8276669,43692.53639 +7253,No,No,1187.283928,56503.57295 +7254,No,Yes,813.1853163,24823.79917 +7255,No,Yes,893.7084711,16399.77852 +7256,No,Yes,894.6573347,23656.84181 +7257,No,Yes,0,18495.77982 +7258,No,Yes,1179.161205,16923.40249 +7259,No,No,1067.379034,56291.60201 +7260,No,Yes,1291.607142,16114.02428 +7261,No,No,119.4719259,47761.14999 +7262,No,No,343.7242848,53705.45963 +7263,No,No,1699.301572,29196.08185 +7264,No,No,1558.596985,38880.44487 +7265,No,Yes,1455.504623,13555.33569 +7266,No,No,1079.372744,66134.71789 +7267,No,No,508.1530596,52772.26145 +7268,No,No,965.0127556,32549.55151 +7269,No,No,553.6490192,47021.49182 +7270,No,Yes,920.7789671,14194.86915 +7271,No,No,637.3381071,49657.39428 +7272,No,No,744.4402059,40280.267 +7273,No,No,1529.152652,43826.29127 +7274,No,No,755.3569917,53091.51529 +7275,No,No,69.56179754,49074.2476 +7276,No,Yes,1567.211982,15877.78648 +7277,No,No,429.352533,17208.71403 +7278,No,No,1348.303554,39165.65917 +7279,No,No,1068.107972,37863.44785 +7280,No,No,1074.791401,25715.17745 +7281,No,No,395.884602,34555.82697 +7282,No,No,1047.13677,46567.92504 +7283,No,Yes,1518.62964,18925.49001 +7284,No,No,964.0462535,46501.9112 +7285,No,Yes,1099.542904,25314.84494 +7286,No,No,737.1769582,54160.85789 +7287,No,No,587.7538164,38885.65921 +7288,No,No,1177.088377,29949.49784 +7289,No,Yes,1182.000082,18102.83375 +7290,No,No,351.2888006,30345.6989 +7291,No,No,658.0236327,30391.63176 +7292,No,Yes,9.514032891,24581.97711 +7293,No,No,515.5802807,43509.04398 +7294,No,No,312.127697,37743.50812 +7295,No,No,820.6918521,28064.7322 +7296,No,No,1498.565036,39948.97799 +7297,No,No,1090.54797,20027.75639 +7298,No,Yes,717.3391896,18382.18719 +7299,No,No,631.5969082,30517.22942 +7300,No,No,1118.929803,49988.09133 +7301,No,Yes,496.5163137,12862.02656 +7302,No,Yes,524.1735101,8272.090321 +7303,No,No,1021.2201,47650.00253 +7304,No,No,749.8830655,28580.92192 +7305,No,No,181.7244389,36858.05867 +7306,No,No,1555.836184,30110.96029 +7307,No,No,895.746675,35531.83709 +7308,No,Yes,400.9045488,20912.28865 +7309,No,No,540.237504,32931.87538 +7310,No,No,403.8100343,40535.16617 +7311,No,Yes,1218.543165,14063.11689 +7312,No,Yes,1252.344171,17621.52959 +7313,No,Yes,384.5909324,21364.23009 +7314,No,No,319.030066,27401.11677 +7315,No,Yes,850.8392962,19413.20394 +7316,No,No,1072.822621,54527.97928 +7317,No,No,1637.319454,43364.95612 +7318,No,Yes,600.9563131,21613.91361 +7319,No,No,1014.725193,40223.99273 +7320,No,No,1311.451279,47805.79785 +7321,No,Yes,1084.54298,17410.84658 +7322,No,Yes,1302.086748,21949.27691 +7323,No,Yes,329.7858539,16476.98691 +7324,No,No,625.8463055,34245.84287 +7325,No,Yes,777.6427309,18190.64527 +7326,No,Yes,1161.881101,13499.83015 +7327,No,No,138.5885867,38437.99189 +7328,No,Yes,740.057882,24710.119 +7329,No,No,824.987977,42408.89554 +7330,No,No,1292.160161,39387.68824 +7331,No,No,528.0608252,34016.88875 +7332,No,No,708.851824,36322.34913 +7333,No,Yes,1066.194241,16736.43384 +7334,No,No,1072.572774,38483.46172 +7335,No,Yes,1315.476272,27959.64347 +7336,Yes,Yes,1988.869747,17762.86591 +7337,No,No,416.2293097,35580.55569 +7338,Yes,No,1721.434918,41999.70334 +7339,No,Yes,1706.760437,21127.48242 +7340,No,No,707.4436697,42656.64914 +7341,No,No,249.1121284,46332.36673 +7342,No,No,446.8616771,49645.34571 +7343,No,No,764.806803,33127.15599 +7344,No,Yes,824.1233648,25108.76629 +7345,No,Yes,786.9150381,12881.68933 +7346,No,No,308.0062759,37361.77514 +7347,No,No,1733.895361,47872.62098 +7348,No,No,996.8525108,49954.70408 +7349,No,No,0,52632.32989 +7350,No,Yes,1070.332099,20053.02853 +7351,No,No,0,39329.80818 +7352,No,No,1250.679653,37012.40758 +7353,No,No,1753.915146,43855.83313 +7354,No,Yes,425.4199728,18109.32069 +7355,No,No,1056.82442,37994.96955 +7356,No,Yes,1546.581832,14981.16867 +7357,No,No,330.3242892,55734.04574 +7358,No,No,1080.155564,35471.98678 +7359,No,No,691.1683584,42342.3507 +7360,No,No,584.2234605,38683.37488 +7361,No,Yes,681.9722899,19925.44439 +7362,No,No,999.4856455,37919.02126 +7363,Yes,No,1757.738499,51991.46349 +7364,No,No,1185.736827,24265.43592 +7365,No,Yes,1770.52557,17447.12692 +7366,No,No,149.82255,33948.86629 +7367,No,No,0,36708.35734 +7368,No,No,771.0160219,47614.73816 +7369,No,No,0,56982.50592 +7370,No,No,607.0946322,27145.63754 +7371,No,No,306.6580976,30135.6288 +7372,No,Yes,364.8266997,22122.52108 +7373,No,No,814.693522,39020.89769 +7374,No,No,1656.038047,29816.05017 +7375,No,No,125.9377001,44260.48182 +7376,No,Yes,1766.649774,15423.43728 +7377,No,No,1593.837919,30570.16108 +7378,No,No,0,30131.97581 +7379,No,Yes,210.8979227,19075.20696 +7380,No,No,449.1416064,40992.30273 +7381,No,No,1125.302093,33408.66034 +7382,No,No,1529.14314,43125.12328 +7383,No,No,483.6375266,47517.655 +7384,No,No,570.9834124,40580.90918 +7385,No,Yes,989.0542835,17276.4439 +7386,No,No,896.7344228,21627.45252 +7387,No,Yes,1533.898986,10488.4126 +7388,No,No,1357.693251,25389.83071 +7389,No,No,40.30111234,33656.71653 +7390,No,Yes,1014.891129,15253.3116 +7391,No,Yes,594.3105933,14159.64178 +7392,No,No,1038.322871,45057.64205 +7393,No,No,1423.384402,48315.00731 +7394,No,No,601.7770832,15298.21319 +7395,No,No,947.5977921,34869.12685 +7396,No,No,296.355437,24625.88522 +7397,No,No,633.8504298,29213.37583 +7398,No,Yes,1154.446367,14301.15712 +7399,No,No,504.6153595,56623.01029 +7400,No,Yes,1176.934525,19829.22646 +7401,No,No,1001.320238,41063.19735 +7402,No,Yes,1352.292826,23660.65483 +7403,No,Yes,1060.060072,18617.21814 +7404,No,No,1371.703275,48298.54454 +7405,No,No,1015.969113,37952.96428 +7406,No,No,1171.41159,52278.2442 +7407,No,No,1021.283608,50389.62074 +7408,No,No,1743.099238,31438.59197 +7409,No,No,84.34924717,59022.17661 +7410,No,No,879.4171517,35228.11056 +7411,No,No,820.945303,34064.67368 +7412,No,No,1085.898368,29771.06456 +7413,No,No,648.3648681,40276.57183 +7414,No,No,0,27614.49927 +7415,No,Yes,313.3960316,18071.68803 +7416,No,No,477.5507621,24273.41864 +7417,No,No,403.2683085,38629.94742 +7418,No,Yes,864.9479861,8624.220719 +7419,No,No,502.0259542,31574.71251 +7420,No,No,971.8148887,35583.19054 +7421,No,No,0,49606.99523 +7422,No,Yes,823.4713732,12331.91623 +7423,No,No,969.8063054,60396.78919 +7424,No,Yes,881.265405,15275.42058 +7425,No,No,1315.191489,42694.97507 +7426,No,No,1149.659956,34635.1377 +7427,No,No,943.5662471,31377.94282 +7428,No,No,270.0725934,36833.64514 +7429,Yes,No,1392.599753,37620.24131 +7430,No,No,0,30836.44051 +7431,No,No,939.6424061,38395.6138 +7432,No,Yes,838.9784353,21176.47917 +7433,No,No,142.6144048,34695.11589 +7434,No,No,1493.033897,30848.61911 +7435,No,No,1193.406554,55017.46259 +7436,No,No,0,19572.30011 +7437,No,Yes,1078.514259,20073.35524 +7438,Yes,Yes,2461.506979,11878.55704 +7439,No,No,614.1406212,17472.91489 +7440,No,No,872.1227091,43154.71996 +7441,No,No,880.0472807,45092.5924 +7442,No,No,433.4001126,25085.07012 +7443,No,No,618.1270378,29836.28565 +7444,No,No,1115.154313,38623.5482 +7445,No,Yes,327.4198831,14938.94435 +7446,No,Yes,888.7186748,15037.6083 +7447,No,Yes,995.9150546,24897.17325 +7448,No,No,1219.390103,42549.91358 +7449,No,Yes,984.3611537,16577.83193 +7450,No,No,980.3971031,41753.07598 +7451,No,No,422.4516417,34797.79654 +7452,No,Yes,311.321864,22648.76345 +7453,No,Yes,1019.647755,2702.982331 +7454,No,Yes,1250.972829,19275.25403 +7455,No,No,1042.699189,10693.6216 +7456,No,No,436.5756274,22389.46339 +7457,No,No,698.074828,37027.81607 +7458,No,No,296.8818307,43136.56168 +7459,No,Yes,610.1853688,19010.12579 +7460,No,No,0,38686.58143 +7461,No,Yes,1230.957508,20573.78631 +7462,No,Yes,712.0267045,17273.11586 +7463,No,No,772.4056333,39023.49164 +7464,No,No,175.1677608,53019.42301 +7465,No,Yes,124.8712711,20886.49619 +7466,No,No,423.3376968,60170.57447 +7467,No,No,364.7738836,31857.18073 +7468,No,Yes,851.9759048,14170.92231 +7469,No,Yes,1619.714151,21781.94056 +7470,No,No,1200.946758,47636.85963 +7471,No,No,765.1320369,39600.48462 +7472,No,No,1343.954922,26365.52625 +7473,No,Yes,1669.763112,23741.5106 +7474,No,No,266.759321,25864.93903 +7475,No,Yes,1039.909419,21212.68268 +7476,No,No,246.7564139,37138.3863 +7477,No,No,1282.567571,36558.83792 +7478,No,Yes,1070.320464,12756.76045 +7479,No,No,766.5125244,43426.8308 +7480,No,No,1102.924309,57828.17878 +7481,No,No,924.9593298,26332.20962 +7482,No,Yes,1034.902391,15945.13985 +7483,No,No,1807.688667,40731.51641 +7484,No,No,520.6546128,39914.91905 +7485,No,No,1018.769969,35813.42074 +7486,No,No,1281.675172,41481.321 +7487,Yes,No,1621.575785,36577.8462 +7488,No,No,0,40772.92686 +7489,No,Yes,1848.826618,18128.56959 +7490,Yes,Yes,2117.120614,12143.4748 +7491,No,No,1002.144124,44445.52727 +7492,No,No,551.1858718,36603.97534 +7493,No,Yes,149.1541,22802.81968 +7494,No,Yes,1360.865633,22310.92951 +7495,No,No,1136.148437,38474.61369 +7496,No,No,717.6673898,27956.27352 +7497,No,Yes,651.5487845,23374.83297 +7498,No,Yes,604.5743501,16153.41429 +7499,No,No,759.0904065,41891.85759 +7500,No,No,1204.957811,24788.76787 +7501,No,No,796.2572608,33760.51826 +7502,No,No,506.4623208,36596.12674 +7503,No,No,160.0124036,48121.18102 +7504,No,No,339.6911806,49109.36138 +7505,No,No,0,35288.33508 +7506,No,Yes,1344.709359,17318.8399 +7507,No,No,368.086167,51601.36084 +7508,No,No,1005.129339,43501.69649 +7509,No,No,712.8636656,47454.74443 +7510,No,No,384.5278841,25950.26756 +7511,No,No,512.6564669,43846.97971 +7512,No,No,0,37112.5978 +7513,No,Yes,721.5173197,18310.94552 +7514,No,Yes,1458.893462,21186.56296 +7515,No,No,388.6092291,23040.44174 +7516,No,No,516.1096447,48303.80104 +7517,No,No,575.7546157,26757.57934 +7518,No,No,0,26100.75191 +7519,No,No,1440.39242,46113.26845 +7520,No,Yes,1136.400756,24962.84907 +7521,No,No,326.519432,20310.45248 +7522,No,No,837.2091498,38032.9563 +7523,No,No,1324.84258,41590.33519 +7524,No,No,244.935779,27369.01052 +7525,Yes,Yes,1875.458895,17820.46333 +7526,No,No,0,30287.49034 +7527,No,No,1793.290612,45117.38191 +7528,No,Yes,1874.494501,19592.06987 +7529,No,No,1036.985599,46388.62453 +7530,No,No,91.41528435,46468.78798 +7531,No,No,438.8123997,39390.72553 +7532,No,Yes,1371.093868,16204.76896 +7533,No,Yes,950.7767872,17711.74176 +7534,No,No,370.0009349,39286.61366 +7535,No,No,688.9057615,43606.92374 +7536,No,No,1452.32898,37691.77019 +7537,No,No,641.9084171,45107.43169 +7538,No,No,618.1391643,50146.42925 +7539,No,No,516.4497663,41922.88849 +7540,No,Yes,720.4352399,24582.88069 +7541,No,Yes,485.0148907,23261.33095 +7542,No,No,929.7554006,30908.8982 +7543,No,No,591.7871599,46381.23725 +7544,No,Yes,753.3835736,12258.60451 +7545,No,No,1255.425942,17917.79479 +7546,No,No,694.5387626,42855.56531 +7547,No,No,431.159894,51727.54032 +7548,No,Yes,23.20605171,22196.65337 +7549,No,Yes,584.5762328,16639.65584 +7550,No,No,877.0294572,35054.9489 +7551,No,No,1300.296924,51338.85741 +7552,No,No,1049.927427,25445.92007 +7553,Yes,No,1099.057701,42590.89445 +7554,No,No,763.7737643,60938.6641 +7555,No,No,506.7765142,40625.48747 +7556,No,No,41.27972799,52066.14735 +7557,No,No,789.6399004,44073.24669 +7558,No,Yes,867.4707644,11905.67895 +7559,No,No,1352.29093,33077.08882 +7560,No,Yes,0,20217.09091 +7561,No,No,337.8749093,30090.53051 +7562,No,No,660.244842,40885.84695 +7563,No,No,1144.481793,45548.38038 +7564,No,Yes,1075.092764,7031.294556 +7565,No,No,535.8972417,43917.5687 +7566,No,Yes,1170.224624,8747.47272 +7567,No,No,922.1241069,48137.37167 +7568,No,No,1064.087172,34677.93179 +7569,No,Yes,1294.285702,17444.60356 +7570,No,No,845.7367046,34676.26423 +7571,No,No,212.6477409,29592.17085 +7572,No,No,501.2866489,36154.65269 +7573,No,No,463.0454558,35621.08035 +7574,No,Yes,722.2273331,18487.04056 +7575,No,No,724.2055922,36489.69055 +7576,No,No,209.0427889,40075.98415 +7577,No,Yes,1108.624099,18761.7671 +7578,No,No,0,60203.59603 +7579,No,No,960.0680599,23482.83561 +7580,No,No,685.3419782,51856.89098 +7581,No,No,1446.604606,45552.0099 +7582,No,No,1187.656684,46731.55487 +7583,No,No,1073.025819,39553.83126 +7584,No,No,1608.68335,40078.22043 +7585,No,No,1160.133222,32978.6596 +7586,No,No,621.1492608,15546.76898 +7587,No,Yes,1367.429631,23617.61236 +7588,No,Yes,909.1019937,7283.763145 +7589,No,No,524.4167931,41535.32466 +7590,No,No,654.4936185,12696.80325 +7591,No,Yes,1010.576124,20982.97031 +7592,No,Yes,130.1477105,15174.67298 +7593,No,No,150.6636179,42800.69692 +7594,No,No,584.8383476,41447.41353 +7595,No,Yes,771.295014,15594.01185 +7596,No,No,1363.481664,35359.75405 +7597,No,Yes,414.8064509,18832.03421 +7598,No,Yes,1647.004573,16614.80417 +7599,No,Yes,562.8425912,19669.76934 +7600,No,No,843.7009112,50891.67332 +7601,No,No,455.376336,36590.55036 +7602,No,No,459.4239213,49958.7568 +7603,No,No,967.7206245,31641.67634 +7604,No,No,84.34446632,42333.53363 +7605,No,No,530.6035028,59740.20997 +7606,No,No,1369.925967,49089.81963 +7607,No,No,692.1562081,39199.4465 +7608,No,Yes,564.638739,24342.03079 +7609,No,Yes,1251.186806,11229.64122 +7610,No,No,378.445644,30562.09552 +7611,No,Yes,0,24000.33023 +7612,No,No,0,37361.5408 +7613,No,No,820.4850172,35951.11998 +7614,No,Yes,246.8670273,16209.53668 +7615,No,No,1490.137485,35882.75867 +7616,No,No,421.038872,17137.16813 +7617,No,No,1147.571897,32012.76535 +7618,No,Yes,1087.917744,18053.0839 diff --git a/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Data/heart.csv b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Data/heart.csv new file mode 100644 index 00000000..c211c934 --- /dev/null +++ b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Data/heart.csv @@ -0,0 +1,304 @@ +age,sex,cp,trtbps,chol,fbs,restecg,thalachh,exng,oldpeak,slp,caa,thall,output +63,1,3,145,233,1,0,150,0,2.3,0,0,1,1 +37,1,2,130,250,0,1,187,0,3.5,0,0,2,1 +41,0,1,130,204,0,0,172,0,1.4,2,0,2,1 +56,1,1,120,236,0,1,178,0,0.8,2,0,2,1 +57,0,0,120,354,0,1,163,1,0.6,2,0,2,1 +57,1,0,140,192,0,1,148,0,0.4,1,0,1,1 +56,0,1,140,294,0,0,153,0,1.3,1,0,2,1 +44,1,1,120,263,0,1,173,0,0,2,0,3,1 +52,1,2,172,199,1,1,162,0,0.5,2,0,3,1 +57,1,2,150,168,0,1,174,0,1.6,2,0,2,1 +54,1,0,140,239,0,1,160,0,1.2,2,0,2,1 +48,0,2,130,275,0,1,139,0,0.2,2,0,2,1 +49,1,1,130,266,0,1,171,0,0.6,2,0,2,1 +64,1,3,110,211,0,0,144,1,1.8,1,0,2,1 +58,0,3,150,283,1,0,162,0,1,2,0,2,1 +50,0,2,120,219,0,1,158,0,1.6,1,0,2,1 +58,0,2,120,340,0,1,172,0,0,2,0,2,1 +66,0,3,150,226,0,1,114,0,2.6,0,0,2,1 +43,1,0,150,247,0,1,171,0,1.5,2,0,2,1 +69,0,3,140,239,0,1,151,0,1.8,2,2,2,1 +59,1,0,135,234,0,1,161,0,0.5,1,0,3,1 +44,1,2,130,233,0,1,179,1,0.4,2,0,2,1 +42,1,0,140,226,0,1,178,0,0,2,0,2,1 +61,1,2,150,243,1,1,137,1,1,1,0,2,1 +40,1,3,140,199,0,1,178,1,1.4,2,0,3,1 +71,0,1,160,302,0,1,162,0,0.4,2,2,2,1 +59,1,2,150,212,1,1,157,0,1.6,2,0,2,1 +51,1,2,110,175,0,1,123,0,0.6,2,0,2,1 +65,0,2,140,417,1,0,157,0,0.8,2,1,2,1 +53,1,2,130,197,1,0,152,0,1.2,0,0,2,1 +41,0,1,105,198,0,1,168,0,0,2,1,2,1 +65,1,0,120,177,0,1,140,0,0.4,2,0,3,1 +44,1,1,130,219,0,0,188,0,0,2,0,2,1 +54,1,2,125,273,0,0,152,0,0.5,0,1,2,1 +51,1,3,125,213,0,0,125,1,1.4,2,1,2,1 +46,0,2,142,177,0,0,160,1,1.4,0,0,2,1 +54,0,2,135,304,1,1,170,0,0,2,0,2,1 +54,1,2,150,232,0,0,165,0,1.6,2,0,3,1 +65,0,2,155,269,0,1,148,0,0.8,2,0,2,1 +65,0,2,160,360,0,0,151,0,0.8,2,0,2,1 +51,0,2,140,308,0,0,142,0,1.5,2,1,2,1 +48,1,1,130,245,0,0,180,0,0.2,1,0,2,1 +45,1,0,104,208,0,0,148,1,3,1,0,2,1 +53,0,0,130,264,0,0,143,0,0.4,1,0,2,1 +39,1,2,140,321,0,0,182,0,0,2,0,2,1 +52,1,1,120,325,0,1,172,0,0.2,2,0,2,1 +44,1,2,140,235,0,0,180,0,0,2,0,2,1 +47,1,2,138,257,0,0,156,0,0,2,0,2,1 +53,0,2,128,216,0,0,115,0,0,2,0,0,1 +53,0,0,138,234,0,0,160,0,0,2,0,2,1 +51,0,2,130,256,0,0,149,0,0.5,2,0,2,1 +66,1,0,120,302,0,0,151,0,0.4,1,0,2,1 +62,1,2,130,231,0,1,146,0,1.8,1,3,3,1 +44,0,2,108,141,0,1,175,0,0.6,1,0,2,1 +63,0,2,135,252,0,0,172,0,0,2,0,2,1 +52,1,1,134,201,0,1,158,0,0.8,2,1,2,1 +48,1,0,122,222,0,0,186,0,0,2,0,2,1 +45,1,0,115,260,0,0,185,0,0,2,0,2,1 +34,1,3,118,182,0,0,174,0,0,2,0,2,1 +57,0,0,128,303,0,0,159,0,0,2,1,2,1 +71,0,2,110,265,1,0,130,0,0,2,1,2,1 +54,1,1,108,309,0,1,156,0,0,2,0,3,1 +52,1,3,118,186,0,0,190,0,0,1,0,1,1 +41,1,1,135,203,0,1,132,0,0,1,0,1,1 +58,1,2,140,211,1,0,165,0,0,2,0,2,1 +35,0,0,138,183,0,1,182,0,1.4,2,0,2,1 +51,1,2,100,222,0,1,143,1,1.2,1,0,2,1 +45,0,1,130,234,0,0,175,0,0.6,1,0,2,1 +44,1,1,120,220,0,1,170,0,0,2,0,2,1 +62,0,0,124,209,0,1,163,0,0,2,0,2,1 +54,1,2,120,258,0,0,147,0,0.4,1,0,3,1 +51,1,2,94,227,0,1,154,1,0,2,1,3,1 +29,1,1,130,204,0,0,202,0,0,2,0,2,1 +51,1,0,140,261,0,0,186,1,0,2,0,2,1 +43,0,2,122,213,0,1,165,0,0.2,1,0,2,1 +55,0,1,135,250,0,0,161,0,1.4,1,0,2,1 +51,1,2,125,245,1,0,166,0,2.4,1,0,2,1 +59,1,1,140,221,0,1,164,1,0,2,0,2,1 +52,1,1,128,205,1,1,184,0,0,2,0,2,1 +58,1,2,105,240,0,0,154,1,0.6,1,0,3,1 +41,1,2,112,250,0,1,179,0,0,2,0,2,1 +45,1,1,128,308,0,0,170,0,0,2,0,2,1 +60,0,2,102,318,0,1,160,0,0,2,1,2,1 +52,1,3,152,298,1,1,178,0,1.2,1,0,3,1 +42,0,0,102,265,0,0,122,0,0.6,1,0,2,1 +67,0,2,115,564,0,0,160,0,1.6,1,0,3,1 +68,1,2,118,277,0,1,151,0,1,2,1,3,1 +46,1,1,101,197,1,1,156,0,0,2,0,3,1 +54,0,2,110,214,0,1,158,0,1.6,1,0,2,1 +58,0,0,100,248,0,0,122,0,1,1,0,2,1 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+57,1,0,130,131,0,1,115,1,1.2,1,1,3,0 +57,0,1,130,236,0,0,174,0,0,1,1,2,0 diff --git a/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Data/iris_data.csv b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Data/iris_data.csv new file mode 100644 index 00000000..d4c3f946 --- /dev/null +++ b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Data/iris_data.csv @@ -0,0 +1,151 @@ +sepal_length,sepal_width,petal_length,petal_width,class +5.1,3.5,1.4,0.2,Iris-setosa +4.9,3,1.4,0.2,Iris-setosa +4.7,3.2,1.3,0.2,Iris-setosa +4.6,3.1,1.5,0.2,Iris-setosa +5,3.6,1.4,0.2,Iris-setosa +5.4,3.9,1.7,0.4,Iris-setosa +4.6,3.4,1.4,0.3,Iris-setosa +5,3.4,1.5,0.2,Iris-setosa +4.4,2.9,1.4,0.2,Iris-setosa +4.9,3.1,1.5,0.1,Iris-setosa +5.4,3.7,1.5,0.2,Iris-setosa +4.8,3.4,1.6,0.2,Iris-setosa +4.8,3,1.4,0.1,Iris-setosa +4.3,3,1.1,0.1,Iris-setosa +5.8,4,1.2,0.2,Iris-setosa +5.7,4.4,1.5,0.4,Iris-setosa +5.4,3.9,1.3,0.4,Iris-setosa +5.1,3.5,1.4,0.3,Iris-setosa +5.7,3.8,1.7,0.3,Iris-setosa +5.1,3.8,1.5,0.3,Iris-setosa +5.4,3.4,1.7,0.2,Iris-setosa +5.1,3.7,1.5,0.4,Iris-setosa +4.6,3.6,1,0.2,Iris-setosa +5.1,3.3,1.7,0.5,Iris-setosa +4.8,3.4,1.9,0.2,Iris-setosa +5,3,1.6,0.2,Iris-setosa +5,3.4,1.6,0.4,Iris-setosa +5.2,3.5,1.5,0.2,Iris-setosa +5.2,3.4,1.4,0.2,Iris-setosa +4.7,3.2,1.6,0.2,Iris-setosa +4.8,3.1,1.6,0.2,Iris-setosa +5.4,3.4,1.5,0.4,Iris-setosa +5.2,4.1,1.5,0.1,Iris-setosa +5.5,4.2,1.4,0.2,Iris-setosa +4.9,3.1,1.5,0.1,Iris-setosa +5,3.2,1.2,0.2,Iris-setosa +5.5,3.5,1.3,0.2,Iris-setosa +4.9,3.1,1.5,0.1,Iris-setosa +4.4,3,1.3,0.2,Iris-setosa +5.1,3.4,1.5,0.2,Iris-setosa +5,3.5,1.3,0.3,Iris-setosa +4.5,2.3,1.3,0.3,Iris-setosa +4.4,3.2,1.3,0.2,Iris-setosa +5,3.5,1.6,0.6,Iris-setosa +5.1,3.8,1.9,0.4,Iris-setosa +4.8,3,1.4,0.3,Iris-setosa +5.1,3.8,1.6,0.2,Iris-setosa +4.6,3.2,1.4,0.2,Iris-setosa +5.3,3.7,1.5,0.2,Iris-setosa +5,3.3,1.4,0.2,Iris-setosa +7,3.2,4.7,1.4,Iris-versicolor +6.4,3.2,4.5,1.5,Iris-versicolor +6.9,3.1,4.9,1.5,Iris-versicolor +5.5,2.3,4,1.3,Iris-versicolor +6.5,2.8,4.6,1.5,Iris-versicolor +5.7,2.8,4.5,1.3,Iris-versicolor +6.3,3.3,4.7,1.6,Iris-versicolor +4.9,2.4,3.3,1,Iris-versicolor +6.6,2.9,4.6,1.3,Iris-versicolor +5.2,2.7,3.9,1.4,Iris-versicolor +5,2,3.5,1,Iris-versicolor +5.9,3,4.2,1.5,Iris-versicolor +6,2.2,4,1,Iris-versicolor +6.1,2.9,4.7,1.4,Iris-versicolor +5.6,2.9,3.6,1.3,Iris-versicolor +6.7,3.1,4.4,1.4,Iris-versicolor +5.6,3,4.5,1.5,Iris-versicolor +5.8,2.7,4.1,1,Iris-versicolor +6.2,2.2,4.5,1.5,Iris-versicolor +5.6,2.5,3.9,1.1,Iris-versicolor +5.9,3.2,4.8,1.8,Iris-versicolor +6.1,2.8,4,1.3,Iris-versicolor +6.3,2.5,4.9,1.5,Iris-versicolor +6.1,2.8,4.7,1.2,Iris-versicolor +6.4,2.9,4.3,1.3,Iris-versicolor +6.6,3,4.4,1.4,Iris-versicolor +6.8,2.8,4.8,1.4,Iris-versicolor +6.7,3,5,1.7,Iris-versicolor +6,2.9,4.5,1.5,Iris-versicolor +5.7,2.6,3.5,1,Iris-versicolor +5.5,2.4,3.8,1.1,Iris-versicolor +5.5,2.4,3.7,1,Iris-versicolor +5.8,2.7,3.9,1.2,Iris-versicolor 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+6.3,2.5,5,1.9,Iris-virginica +6.5,3,5.2,2,Iris-virginica +6.2,3.4,5.4,2.3,Iris-virginica +5.9,3,5.1,1.8,Iris-virginica diff --git a/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Data/train.csv b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Data/train.csv new file mode 100644 index 00000000..1b9b8f6e --- /dev/null +++ b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Data/train.csv @@ -0,0 +1,8069 @@ +ID,Gender,Ever_Married,Age,Graduated,Profession,Work_Experience,Spending_Score,Family_Size,Var_1,Segmentation +462809,Male,No,22,No,Healthcare,1.0,Low,4.0,Cat_4,D +462643,Female,Yes,38,Yes,Engineer,,Average,3.0,Cat_4,A +466315,Female,Yes,67,Yes,Engineer,1.0,Low,1.0,Cat_6,B +461735,Male,Yes,67,Yes,Lawyer,0.0,High,2.0,Cat_6,B +462669,Female,Yes,40,Yes,Entertainment,,High,6.0,Cat_6,A +461319,Male,Yes,56,No,Artist,0.0,Average,2.0,Cat_6,C +460156,Male,No,32,Yes,Healthcare,1.0,Low,3.0,Cat_6,C +464347,Female,No,33,Yes,Healthcare,1.0,Low,3.0,Cat_6,D +465015,Female,Yes,61,Yes,Engineer,0.0,Low,3.0,Cat_7,D +465176,Female,Yes,55,Yes,Artist,1.0,Average,4.0,Cat_6,C +464041,Female,No,26,Yes,Engineer,1.0,Low,3.0,Cat_6,A +464942,Male,No,19,No,Healthcare,4.0,Low,4.0,Cat_4,D +461230,Female,No,19,No,Executive,0.0,Low,,Cat_3,D +459573,Male,Yes,70,No,Lawyer,,Low,1.0,Cat_6,A +460849,Female,Yes,58,No,Doctor,0.0,Low,1.0,Cat_3,B +460563,Female,No,41,No,Healthcare,1.0,Low,2.0,Cat_1,C +466865,Female,No,32,No,Homemaker,9.0,Low,5.0,Cat_3,D +461644,Male,No,31,No,Healthcare,1.0,Low,6.0,Cat_6,B +466772,Male,Yes,58,Yes,Entertainment,1.0,Average,4.0,Cat_6,B +464291,Female,Yes,79,Yes,Artist,0.0,High,1.0,Cat_6,C +466084,Male,Yes,49,Yes,Homemaker,12.0,Low,1.0,Cat_3,A +459675,Female,No,18,No,Healthcare,3.0,Low,4.0,Cat_6,D +465602,Male,Yes,33,Yes,Artist,13.0,Low,2.0,Cat_3,A +459168,Female,No,36,Yes,Artist,5.0,Low,2.0,Cat_6,B +461021,Female,,58,No,Executive,1.0,Average,3.0,Cat_3,B +465083,Male,Yes,56,No,Artist,1.0,Average,3.0,Cat_6,C +467604,Male,No,31,No,Healthcare,9.0,Low,8.0,Cat_4,A +459717,Male,Yes,49,Yes,Artist,1.0,Average,3.0,Cat_6,C +465882,Male,No,35,Yes,Artist,0.0,Low,1.0,Cat_6,B +461996,Female,No,35,No,Artist,5.0,Low,4.0,Cat_6,C +463331,Female,Yes,45,Yes,Artist,0.0,Low,3.0,Cat_6,C +462216,Female,Yes,42,No,Doctor,1.0,Low,2.0,Cat_4,A +462454,Male,No,19,No,Healthcare,1.0,Low,3.0,Cat_2,D +467010,Male,No,26,No,Homemaker,9.0,Low,,Cat_6,D +459861,Female,Yes,83,No,Lawyer,1.0,High,2.0,Cat_6,D +467917,Female,No,27,Yes,Healthcare,3.0,Low,4.0,Cat_1,D +465572,Male,No,32,No,Doctor,5.0,Low,1.0,Cat_4,D +460593,Female,Yes,28,No,Healthcare,1.0,Average,5.0,Cat_3,D +465891,Female,Yes,28,Yes,Artist,8.0,Low,2.0,Cat_6,A +467442,Male,Yes,56,Yes,Artist,,Average,2.0,Cat_6,C +462044,Female,No,47,Yes,Artist,4.0,Low,1.0,Cat_6,B +465686,Female,No,22,No,Healthcare,0.0,Low,2.0,Cat_6,D +464590,Female,No,27,Yes,Artist,14.0,Low,3.0,Cat_6,A +466006,Female,Yes,49,Yes,Artist,1.0,Low,2.0,,C +466152,Male,Yes,40,No,Engineer,8.0,Low,1.0,Cat_6,A +463156,Female,Yes,79,No,Lawyer,,High,2.0,Cat_6,A +465729,Male,Yes,41,Yes,Artist,0.0,Average,4.0,Cat_4,B +462384,Female,No,29,Yes,Healthcare,,Low,1.0,Cat_7,A +462442,Male,No,19,No,Healthcare,0.0,Low,4.0,Cat_6,D +464191,Female,No,45,Yes,Engineer,1.0,Low,1.0,Cat_6,A +462078,Female,Yes,61,No,Artist,1.0,Average,2.0,Cat_2,B +467873,Female,Yes,36,Yes,Artist,1.0,Average,2.0,Cat_6,C +461270,Male,Yes,40,Yes,Artist,1.0,Low,1.0,Cat_3,A +462960,Female,Yes,57,Yes,Entertainment,0.0,Average,2.0,Cat_6,C +464961,Male,Yes,76,No,Marketing,1.0,Low,1.0,Cat_4,A +462110,Male,Yes,32,Yes,Artist,14.0,Average,2.0,Cat_6,B +465769,Male,No,25,Yes,Healthcare,9.0,Low,4.0,Cat_6,D +462267,Male,No,32,,Doctor,8.0,Low,2.0,Cat_6,D +465269,Female,Yes,45,Yes,Artist,1.0,Average,3.0,Cat_3,C +460881,Male,Yes,72,Yes,Lawyer,1.0,Low,,Cat_4,D +461891,Male,Yes,48,Yes,Artist,0.0,Average,2.0,Cat_6,C +466935,Male,Yes,55,Yes,Artist,9.0,Average,2.0,Cat_1,C +464091,Male,Yes,74,Yes,Lawyer,0.0,High,3.0,Cat_6,D +464535,Male,Yes,56,Yes,Lawyer,1.0,High,2.0,Cat_6,A +463108,Male,Yes,59,Yes,Lawyer,7.0,High,2.0,Cat_6,A +461356,Male,No,25,Yes,Entertainment,0.0,Low,2.0,Cat_6,A +464570,Male,Yes,31,Yes,Doctor,0.0,Average,2.0,Cat_6,A +464580,Male,No,27,No,Artist,8.0,Low,4.0,Cat_6,A +465233,Male,Yes,57,Yes,Artist,0.0,Average,4.0,Cat_6,B +467208,Female,Yes,39,Yes,Homemaker,,High,2.0,Cat_6,A +459954,Male,Yes,27,Yes,Healthcare,8.0,Low,2.0,Cat_6,D +460219,Female,Yes,51,Yes,Entertainment,8.0,Average,2.0,Cat_6,A +460877,Female,Yes,47,No,Artist,0.0,Average,5.0,Cat_6,B +460191,Female,No,33,No,Healthcare,8.0,Low,4.0,Cat_6,D +463742,Male,Yes,56,Yes,Artist,0.0,Average,4.0,Cat_6,C +463691,Female,No,33,No,Homemaker,0.0,Low,3.0,Cat_6,B +463250,Female,No,36,Yes,Doctor,0.0,Low,2.0,Cat_6,D +463952,Male,Yes,51,No,Artist,4.0,Low,3.0,Cat_4,A +461558,Male,Yes,45,Yes,Engineer,2.0,Average,2.0,Cat_6,C +466567,Female,No,49,Yes,,1.0,Low,1.0,Cat_6,D +465379,Male,No,30,No,Healthcare,0.0,Low,4.0,Cat_6,C +463814,Female,No,26,No,Healthcare,6.0,Low,3.0,Cat_5,D +466938,Male,Yes,63,Yes,Artist,8.0,Low,1.0,Cat_6,A +460671,Male,Yes,35,No,Entertainment,8.0,Average,4.0,Cat_3,B +462204,Female,No,19,No,Marketing,1.0,Low,3.0,Cat_4,A +461671,Male,No,32,No,Healthcare,7.0,Low,5.0,Cat_6,D +459384,Male,Yes,52,Yes,Artist,0.0,Average,3.0,Cat_6,C +459905,Female,Yes,36,Yes,Artist,5.0,Low,2.0,Cat_6,C +464734,Female,No,42,No,Entertainment,7.0,Low,3.0,Cat_4,D +464125,Male,Yes,40,No,Doctor,0.0,Average,2.0,Cat_3,D +467434,Male,No,39,Yes,Entertainment,0.0,Low,3.0,Cat_1,B +460956,Female,Yes,47,Yes,Artist,1.0,Average,4.0,Cat_3,C +465179,Female,Yes,61,Yes,Artist,1.0,High,2.0,Cat_6,C +463338,Male,Yes,32,No,Entertainment,1.0,Low,2.0,Cat_3,D +467066,Male,Yes,55,No,Artist,0.0,Low,1.0,Cat_6,B +463845,Male,Yes,58,Yes,Artist,,Average,1.0,Cat_6,D +459976,Female,No,38,Yes,Artist,0.0,Low,1.0,Cat_6,D +461334,Male,Yes,63,Yes,Artist,0.0,High,2.0,Cat_6,C +465108,Male,Yes,58,Yes,Doctor,1.0,Average,2.0,Cat_6,C +462600,Male,Yes,30,No,Engineer,3.0,Average,3.0,Cat_4,D +464144,Male,Yes,60,Yes,Artist,0.0,Average,4.0,Cat_6,C +466911,Male,Yes,68,No,Executive,1.0,High,7.0,Cat_6,B +462806,Male,Yes,52,No,Artist,0.0,Low,3.0,Cat_4,A +463426,Male,Yes,38,No,Executive,0.0,High,5.0,Cat_6,A +464065,Female,Yes,35,Yes,Healthcare,10.0,Low,2.0,Cat_6,B +462979,Male,No,18,No,Doctor,1.0,Low,2.0,Cat_6,D +462834,Male,Yes,30,Yes,Homemaker,3.0,Low,2.0,Cat_1,D +460764,Male,No,86,No,Lawyer,1.0,Low,1.0,Cat_6,D +466466,Female,,19,No,Healthcare,6.0,Low,5.0,Cat_3,D +460185,Male,No,25,No,Healthcare,9.0,Low,3.0,Cat_6,D +459749,Male,Yes,59,Yes,Entertainment,,Low,4.0,Cat_4,A +464406,Female,No,39,Yes,Artist,0.0,Low,1.0,Cat_6,A +467758,Female,Yes,50,Yes,Doctor,1.0,Low,,Cat_6,B +462059,Male,No,47,Yes,Artist,1.0,Low,1.0,Cat_6,C +467905,Male,Yes,43,Yes,Executive,0.0,High,3.0,Cat_6,D +461177,Male,Yes,36,No,Executive,8.0,Average,4.0,Cat_1,B +460765,Male,Yes,80,No,Lawyer,1.0,Low,1.0,Cat_6,D +465386,Male,No,36,Yes,Artist,0.0,Low,2.0,Cat_2,C +465827,Male,No,27,No,,2.0,Low,7.0,Cat_4,D +462332,Female,Yes,37,Yes,Artist,1.0,High,2.0,Cat_7,A +464481,Male,Yes,46,Yes,Executive,0.0,High,3.0,Cat_6,C +461011,Male,Yes,55,No,Executive,1.0,High,4.0,Cat_3,B +464749,Female,Yes,47,No,Engineer,9.0,Average,4.0,Cat_4,B +461094,Female,No,28,Yes,Healthcare,1.0,Low,3.0,Cat_3,D +462104,Male,Yes,39,No,Entertainment,9.0,Low,2.0,Cat_3,D +464524,Female,Yes,27,No,Marketing,1.0,Low,2.0,Cat_6,A +466295,Female,Yes,42,No,Engineer,0.0,Low,,Cat_6,A +461787,Male,Yes,72,Yes,Artist,0.0,Average,2.0,Cat_6,C +467857,Female,Yes,28,Yes,Engineer,0.0,Low,2.0,Cat_6,A +460153,Male,No,35,Yes,Entertainment,8.0,Low,5.0,Cat_6,C +465215,Male,Yes,39,Yes,Executive,9.0,High,5.0,Cat_2,A +466746,Female,Yes,69,Yes,Lawyer,0.0,High,2.0,Cat_6,C +464857,Male,No,18,No,Healthcare,6.0,Low,4.0,Cat_4,D +459478,Male,Yes,46,Yes,Artist,,Low,1.0,Cat_6,A +460723,Female,Yes,50,Yes,Engineer,1.0,High,2.0,Cat_4,B +459853,Male,No,30,No,Executive,5.0,Low,5.0,Cat_6,A +459390,Female,No,41,No,Artist,,Low,2.0,Cat_6,B +460410,Female,No,39,Yes,Artist,3.0,Low,4.0,Cat_6,D +463823,Female,No,29,No,Marketing,8.0,Low,4.0,Cat_6,D +462008,Female,Yes,37,Yes,Artist,4.0,Low,4.0,Cat_5,B +459750,Female,Yes,39,Yes,Engineer,0.0,Average,2.0,Cat_6,A +459880,Female,Yes,48,Yes,Artist,0.0,Average,5.0,Cat_7,C +459261,Female,No,32,Yes,Doctor,0.0,Low,3.0,Cat_6,C +465656,Male,Yes,78,No,Lawyer,1.0,Low,,Cat_6,B +465395,Female,Yes,43,No,Engineer,0.0,Average,5.0,Cat_4,B +461631,Male,No,29,No,Marketing,8.0,Low,5.0,Cat_6,C +464904,Female,Yes,28,No,Engineer,9.0,Average,2.0,Cat_4,A +461740,Male,Yes,80,Yes,Lawyer,1.0,Low,1.0,Cat_6,B +467092,Female,No,28,Yes,Healthcare,7.0,Low,4.0,Cat_6,D +464173,Male,Yes,71,Yes,Artist,6.0,Average,3.0,Cat_6,C +464972,Male,Yes,67,No,Lawyer,0.0,High,9.0,Cat_4,A +467959,Female,No,26,No,Engineer,0.0,Low,1.0,Cat_2,A +467688,Female,Yes,47,Yes,Engineer,0.0,Average,2.0,Cat_6,C +460043,Female,Yes,43,Yes,Artist,2.0,Average,2.0,Cat_6,B +466590,Female,No,27,Yes,Healthcare,0.0,Low,4.0,Cat_6,C +461191,Female,Yes,45,Yes,Marketing,8.0,Low,4.0,Cat_6,D +465662,Male,Yes,76,No,Executive,1.0,Low,1.0,Cat_6,C +459231,Female,Yes,43,Yes,Artist,3.0,Average,4.0,Cat_6,C +466495,Female,Yes,78,Yes,Lawyer,1.0,High,2.0,Cat_6,B +459192,Male,Yes,35,Yes,Artist,3.0,Average,3.0,Cat_6,C +465710,Female,No,49,Yes,Artist,1.0,Low,1.0,Cat_6,C +460258,Female,No,25,Yes,Healthcare,1.0,Low,5.0,Cat_6,D +462813,Male,Yes,37,No,Executive,0.0,Average,5.0,Cat_6,A +463595,Female,No,32,No,Healthcare,1.0,Low,5.0,,C +467768,Male,Yes,60,Yes,Doctor,0.0,Average,2.0,Cat_6,C +465291,Female,Yes,45,Yes,Artist,5.0,Average,3.0,Cat_6,C +462996,Female,Yes,49,Yes,Homemaker,9.0,Average,3.0,Cat_6,C +463497,Male,No,32,Yes,Artist,0.0,Low,5.0,Cat_6,D +465301,Female,Yes,40,Yes,Artist,0.0,Low,3.0,Cat_3,B +464702,Male,Yes,59,No,Artist,2.0,Average,2.0,Cat_4,C +459012,Male,Yes,48,Yes,Artist,3.0,Low,5.0,Cat_6,B +465053,Male,Yes,82,Yes,Executive,0.0,High,2.0,Cat_6,C +459420,Female,Yes,70,Yes,Lawyer,0.0,Low,2.0,Cat_6,B +464166,Male,No,31,Yes,Artist,5.0,Low,2.0,Cat_6,B +458987,Female,Yes,72,Yes,Lawyer,0.0,High,2.0,Cat_6,C +467745,Male,Yes,50,Yes,Artist,3.0,Average,4.0,Cat_6,C +464866,Female,No,23,No,Engineer,11.0,Low,1.0,Cat_3,D +461081,Female,No,20,No,Healthcare,2.0,Low,6.0,Cat_6,D +467896,Female,No,22,No,Healthcare,4.0,Low,3.0,Cat_6,D +463991,Female,No,31,Yes,Engineer,8.0,Low,1.0,Cat_6,A +466620,Female,Yes,36,Yes,Artist,7.0,Average,2.0,Cat_6,C +462594,Male,Yes,52,No,Entertainment,,Average,5.0,Cat_4,B +462206,Female,No,29,Yes,Artist,,Low,1.0,Cat_4,B +460697,Male,Yes,40,Yes,Artist,4.0,Average,4.0,Cat_6,C +467934,Female,No,40,Yes,Artist,1.0,Low,1.0,Cat_6,A +460054,Male,Yes,38,Yes,Artist,,Low,2.0,Cat_6,C +459576,Female,Yes,85,No,Lawyer,,Low,,,A +467910,Female,No,21,No,Doctor,1.0,Low,3.0,Cat_7,A +467335,Female,Yes,41,No,Engineer,4.0,Average,2.0,Cat_6,D +460263,Male,Yes,40,Yes,Artist,0.0,Low,2.0,Cat_6,B +459340,Male,Yes,53,Yes,Artist,1.0,Average,3.0,Cat_6,B +465622,Female,Yes,42,Yes,Artist,5.0,Low,2.0,Cat_6,C +459395,Male,Yes,42,No,Executive,8.0,Low,2.0,Cat_6,D +467539,Male,Yes,48,Yes,Artist,,Average,4.0,Cat_7,C +466574,Male,Yes,59,Yes,Artist,7.0,Low,2.0,Cat_6,C +466544,Male,Yes,41,Yes,Executive,1.0,Average,3.0,Cat_6,A +463610,Female,Yes,31,Yes,Artist,0.0,Average,2.0,Cat_6,A +465539,Female,Yes,49,Yes,Artist,1.0,High,5.0,Cat_6,B +459823,Male,Yes,67,Yes,Executive,1.0,Low,2.0,Cat_6,A +467614,Male,Yes,35,No,Artist,4.0,Average,2.0,Cat_6,B +459073,Female,No,26,Yes,Healthcare,0.0,Low,3.0,Cat_6,C +466065,Male,,19,No,Healthcare,9.0,Low,3.0,Cat_3,D +464863,Female,Yes,25,No,Engineer,6.0,Average,8.0,Cat_4,D +460022,Female,Yes,39,Yes,Healthcare,4.0,Low,2.0,Cat_6,A +459929,Male,Yes,63,Yes,Entertainment,0.0,Low,2.0,Cat_6,A +466613,Male,No,23,No,Healthcare,8.0,Low,3.0,Cat_6,D +461149,Female,No,23,No,Healthcare,0.0,Low,3.0,Cat_3,D +466068,Female,Yes,53,Yes,Artist,2.0,Low,1.0,Cat_6,C +461513,Male,No,33,Yes,Doctor,0.0,Low,2.0,Cat_6,B +464761,Female,Yes,50,No,Engineer,,Average,4.0,Cat_2,B +466236,Female,Yes,69,Yes,Engineer,0.0,Low,2.0,Cat_4,B +459664,Male,No,37,Yes,Artist,2.0,Low,1.0,Cat_6,B +461967,Female,No,20,No,Marketing,3.0,Low,4.0,Cat_7,C +460516,Female,,85,No,Lawyer,0.0,High,1.0,Cat_3,C +461710,Female,Yes,49,Yes,Doctor,1.0,Average,4.0,Cat_6,C +462742,Male,No,18,No,Healthcare,0.0,Low,3.0,Cat_3,D +459528,Female,Yes,52,Yes,Doctor,0.0,Average,2.0,Cat_6,C +462691,Male,Yes,25,No,Doctor,,Average,2.0,Cat_4,B +462934,Male,Yes,56,No,Executive,,High,2.0,Cat_6,A +465837,Male,No,62,Yes,,0.0,Low,1.0,Cat_6,A +464613,Female,No,35,,Artist,0.0,Low,3.0,Cat_6,B +467515,Male,Yes,28,Yes,Engineer,0.0,Average,2.0,Cat_6,A +463429,Female,No,28,Yes,Artist,7.0,Low,3.0,Cat_6,A +464024,Male,No,20,No,Healthcare,0.0,Low,5.0,Cat_7,D +460691,Female,No,27,No,Homemaker,8.0,Low,1.0,Cat_3,D +461825,Female,No,56,No,Engineer,,Low,1.0,Cat_6,A +467271,Male,Yes,45,Yes,Artist,4.0,Average,4.0,Cat_6,B +465634,Male,No,25,Yes,Healthcare,1.0,Low,4.0,Cat_4,B +464801,Male,No,40,No,Marketing,1.0,Low,1.0,Cat_4,D +459429,Female,Yes,25,Yes,Healthcare,,Low,2.0,Cat_6,D +459888,Female,Yes,74,No,Lawyer,,High,2.0,Cat_6,D +463467,Female,No,23,No,Healthcare,0.0,Low,4.0,,D +462036,Female,Yes,67,Yes,Lawyer,0.0,High,2.0,Cat_6,B +461282,Male,No,21,No,Healthcare,0.0,Low,4.0,,D +460448,Female,No,38,Yes,Artist,9.0,Low,1.0,Cat_3,A +464464,Male,No,28,No,Healthcare,0.0,Low,4.0,Cat_2,C +462410,Female,No,32,No,Healthcare,1.0,Low,5.0,Cat_7,B +467252,Female,No,33,Yes,,0.0,Low,4.0,,D +459756,Female,Yes,56,No,Lawyer,1.0,High,4.0,Cat_4,B +463789,Female,Yes,53,Yes,Artist,1.0,Low,2.0,Cat_6,C +466947,Male,Yes,57,No,Executive,1.0,High,5.0,Cat_6,C +464150,Female,Yes,59,Yes,Artist,0.0,Average,3.0,Cat_6,C +465693,Female,Yes,25,Yes,Healthcare,1.0,High,4.0,Cat_4,A +461670,Male,Yes,59,Yes,Artist,0.0,Average,6.0,Cat_6,C +466768,Female,Yes,36,Yes,Artist,0.0,Low,4.0,Cat_6,C +461550,Male,Yes,37,Yes,Entertainment,7.0,Low,2.0,Cat_6,A +462438,Male,No,22,No,Doctor,1.0,Low,3.0,Cat_2,D +460704,Male,Yes,43,Yes,Artist,1.0,Low,2.0,Cat_6,B +464884,Male,Yes,41,Yes,Artist,1.0,Low,2.0,Cat_4,A +467050,Female,No,21,No,Engineer,0.0,Low,5.0,Cat_2,B +460738,Female,No,49,No,Engineer,1.0,Low,,Cat_7,A +466176,Male,No,38,Yes,Doctor,8.0,Low,1.0,Cat_2,B +459488,Male,Yes,47,No,Artist,,Average,4.0,Cat_6,A +467773,Male,Yes,70,Yes,Lawyer,1.0,High,3.0,Cat_6,C +465425,Female,No,31,No,Engineer,1.0,Low,4.0,Cat_6,B +462318,Female,Yes,82,No,Lawyer,0.0,Average,2.0,Cat_6,A +466078,Female,Yes,51,No,Executive,,Low,1.0,Cat_6,A +460792,Female,No,42,No,Homemaker,11.0,Low,,Cat_6,A +460197,Male,No,38,Yes,Artist,8.0,Low,3.0,Cat_2,A +459375,Male,Yes,18,No,Executive,0.0,High,4.0,Cat_6,A +463862,Male,Yes,50,Yes,Artist,6.0,Average,3.0,Cat_6,C +460144,Female,No,46,Yes,Doctor,1.0,Low,,Cat_3,A +462721,Female,Yes,72,No,Lawyer,1.0,High,3.0,Cat_6,D +459637,Female,No,33,No,Engineer,,Low,4.0,Cat_6,C +465809,Female,No,25,No,Homemaker,1.0,Low,7.0,Cat_4,B +466915,Female,Yes,75,No,Entertainment,2.0,High,2.0,Cat_2,D +467343,Male,No,72,Yes,Artist,1.0,Low,1.0,Cat_6,B +466268,Female,Yes,72,Yes,Artist,0.0,Average,3.0,Cat_4,C +464868,Male,Yes,36,Yes,Entertainment,6.0,Low,6.0,Cat_4,A +464038,Male,Yes,40,Yes,Artist,1.0,Low,2.0,Cat_6,A +460176,Female,No,28,Yes,Entertainment,0.0,Low,4.0,Cat_6,C +462161,Female,No,28,Yes,Doctor,0.0,Low,3.0,Cat_6,D +464841,Male,,19,No,Entertainment,0.0,High,3.0,Cat_4,D +464893,Male,Yes,43,Yes,Artist,0.0,Low,2.0,Cat_4,B +464443,Female,Yes,35,Yes,Artist,1.0,Average,2.0,Cat_6,B +467619,Male,Yes,65,Yes,Lawyer,1.0,High,2.0,Cat_6,B +465974,Male,Yes,52,Yes,Executive,7.0,High,4.0,Cat_4,C +462075,Female,No,40,Yes,Artist,1.0,Low,1.0,Cat_6,B +459494,Female,Yes,69,No,Engineer,9.0,Low,3.0,Cat_6,A +466921,Female,No,22,No,Healthcare,3.0,Low,3.0,Cat_6,D +463544,Male,Yes,47,Yes,Executive,1.0,Average,4.0,Cat_6,C +461167,Male,Yes,29,Yes,Marketing,1.0,High,2.0,Cat_3,A +467364,Male,Yes,46,Yes,Artist,1.0,Average,3.0,Cat_3,C +462408,Male,No,32,Yes,Artist,1.0,Low,2.0,Cat_6,A +466224,Female,No,38,Yes,Healthcare,0.0,Low,1.0,Cat_2,C +465151,Male,Yes,59,Yes,Artist,0.0,Low,1.0,Cat_6,B +467216,Female,No,26,Yes,Entertainment,1.0,Low,1.0,Cat_6,D +460586,Male,No,26,Yes,Healthcare,1.0,Low,2.0,Cat_3,A +461197,Male,No,28,No,Marketing,0.0,Low,1.0,Cat_3,D +462776,Female,Yes,53,Yes,Engineer,1.0,Average,3.0,Cat_6,C +465058,Female,No,43,,Entertainment,,Low,1.0,Cat_6,B +464088,Male,No,27,No,Doctor,1.0,Low,3.0,Cat_6,D +459471,Female,No,32,No,Artist,,Low,8.0,Cat_6,C +464695,Male,No,26,No,Engineer,0.0,Low,4.0,Cat_4,D +462331,Female,No,31,No,Entertainment,0.0,Low,2.0,Cat_6,D +466523,Male,Yes,39,Yes,Executive,1.0,High,3.0,Cat_3,B +465337,Female,Yes,43,No,Engineer,1.0,Low,3.0,Cat_7,A +460834,Male,Yes,25,No,Doctor,1.0,Low,2.0,Cat_1,A +466824,Male,Yes,40,Yes,Artist,3.0,High,3.0,Cat_6,A +461607,Female,Yes,40,Yes,Entertainment,7.0,Average,2.0,Cat_3,B +465091,Male,Yes,63,Yes,Entertainment,,Average,3.0,Cat_6,C +459532,Female,Yes,27,Yes,Healthcare,,Low,2.0,Cat_6,D +466999,Male,No,27,Yes,Healthcare,1.0,Low,3.0,Cat_6,D +466597,Female,No,19,No,Healthcare,1.0,Low,2.0,Cat_6,D +464075,Female,Yes,86,Yes,Lawyer,0.0,High,2.0,Cat_6,B +463394,Male,Yes,41,Yes,Marketing,,High,2.0,Cat_6,B +462163,Female,Yes,27,Yes,Artist,4.0,Average,2.0,Cat_3,C +463895,Female,Yes,70,No,Doctor,0.0,Average,2.0,Cat_6,B +464767,Female,Yes,53,No,Engineer,8.0,Average,4.0,Cat_4,B +464763,Female,Yes,49,No,Engineer,,Average,4.0,Cat_4,B +460718,Male,Yes,55,No,Executive,3.0,Low,,Cat_4,D +461989,Female,,57,Yes,Engineer,0.0,Average,4.0,Cat_2,C +466374,Male,Yes,83,No,Lawyer,,Low,1.0,Cat_4,B +463738,Male,Yes,43,No,Executive,0.0,High,4.0,Cat_6,A +465967,Male,Yes,36,No,Doctor,6.0,Low,3.0,Cat_6,A +462260,Male,Yes,74,Yes,Lawyer,0.0,Low,2.0,Cat_6,C +467234,Female,No,30,No,Homemaker,0.0,Low,4.0,Cat_6,D +466573,Female,No,37,Yes,Artist,5.0,Low,2.0,Cat_6,B +463229,Female,No,22,No,Marketing,0.0,Low,1.0,Cat_5,B +459768,Male,No,19,No,Healthcare,0.0,Low,4.0,Cat_6,D +462541,Male,Yes,70,Yes,Engineer,4.0,High,2.0,Cat_6,B +467714,Male,Yes,89,Yes,Lawyer,13.0,Low,1.0,Cat_6,A +467730,Male,No,18,No,Healthcare,,Low,4.0,Cat_6,D +459474,Male,No,25,Yes,Doctor,6.0,Low,1.0,Cat_2,D +460093,Female,No,45,Yes,Artist,4.0,Low,1.0,Cat_3,A +467865,Female,No,35,Yes,Artist,3.0,Low,1.0,Cat_4,A +465758,Female,Yes,37,No,Engineer,7.0,Low,1.0,Cat_4,D +464184,Female,Yes,39,No,Engineer,4.0,Average,2.0,Cat_6,B +462618,Female,No,32,Yes,Healthcare,1.0,Low,5.0,Cat_4,D +464226,Female,Yes,62,Yes,Artist,2.0,Average,3.0,Cat_6,C +465432,Female,Yes,58,Yes,Engineer,0.0,Average,4.0,Cat_4,B +464087,Female,No,32,No,Engineer,,Low,4.0,Cat_6,C +466627,Male,No,25,No,Healthcare,0.0,Low,5.0,Cat_6,C +467380,Female,Yes,56,Yes,Artist,0.0,Average,4.0,Cat_6,C +465842,Female,No,36,Yes,Entertainment,10.0,Low,2.0,Cat_6,A +464622,Female,No,30,No,Healthcare,8.0,Low,3.0,Cat_6,A +462083,Male,No,19,No,Healthcare,1.0,Low,5.0,Cat_6,D +460910,Male,No,27,Yes,Healthcare,0.0,Low,2.0,Cat_6,A +460255,Female,No,38,Yes,Doctor,14.0,Low,1.0,Cat_6,A +460109,Male,Yes,46,Yes,Artist,8.0,Average,2.0,Cat_6,C +464306,Male,No,29,Yes,Artist,3.0,Low,1.0,Cat_6,B +466835,Male,Yes,51,No,Entertainment,1.0,Average,4.0,Cat_7,B +465192,Male,Yes,55,No,Artist,0.0,Low,1.0,Cat_6,A +463380,Female,Yes,69,Yes,Engineer,1.0,Low,1.0,Cat_3,A +462391,Female,No,26,No,Healthcare,1.0,Low,8.0,Cat_7,C +467086,Female,Yes,83,Yes,Lawyer,0.0,Low,3.0,Cat_6,A +460306,Female,No,39,Yes,Healthcare,9.0,Low,1.0,Cat_6,D +460752,Male,Yes,35,Yes,Artist,9.0,Low,1.0,Cat_6,A +467264,Male,Yes,19,No,Marketing,2.0,High,5.0,Cat_6,C +461646,Female,Yes,60,Yes,Artist,,High,4.0,Cat_6,C +463570,Male,Yes,35,Yes,Artist,,Average,4.0,Cat_6,C +465277,Female,No,53,Yes,Engineer,,Low,1.0,Cat_6,B +467426,Female,Yes,86,No,Lawyer,,Low,,Cat_6,B +466258,Female,No,29,No,Healthcare,1.0,Low,3.0,Cat_6,B +463719,Female,No,26,No,Doctor,0.0,Low,1.0,Cat_6,D +460032,Male,No,36,Yes,Engineer,8.0,Low,1.0,Cat_6,A +465175,Female,No,22,No,Homemaker,0.0,Low,4.0,Cat_6,D +466610,Male,No,19,No,Marketing,0.0,Low,5.0,Cat_6,A +459036,Female,Yes,20,Yes,Lawyer,1.0,Average,3.0,Cat_3,A +464047,Male,No,43,Yes,Healthcare,3.0,Low,3.0,Cat_6,D +459496,Female,No,42,Yes,Artist,,Low,3.0,Cat_6,A +459997,Male,No,43,Yes,Entertainment,1.0,Low,2.0,Cat_4,A +463382,Female,Yes,50,Yes,Engineer,4.0,High,3.0,Cat_6,B +460967,Male,No,25,Yes,Entertainment,1.0,Low,2.0,Cat_6,A +465094,Male,Yes,61,No,Artist,1.0,High,2.0,Cat_6,B +461229,Female,Yes,28,Yes,Healthcare,0.0,Average,2.0,Cat_6,A +466442,Male,No,38,Yes,Artist,1.0,Low,1.0,Cat_6,D +464527,Male,No,25,No,Executive,12.0,Low,3.0,Cat_6,A +463560,Female,No,18,No,Healthcare,9.0,Low,5.0,Cat_4,D +460011,Male,Yes,43,Yes,Entertainment,1.0,Average,5.0,Cat_5,C +467207,Female,No,27,Yes,Artist,8.0,Low,1.0,Cat_6,B +465177,Female,No,60,Yes,Entertainment,1.0,Low,2.0,Cat_6,D +467531,Male,Yes,75,Yes,Lawyer,2.0,Low,1.0,Cat_6,D +465409,Female,No,31,No,Doctor,,Low,4.0,Cat_6,D +462842,Male,No,23,No,Doctor,6.0,Low,4.0,Cat_2,D +462124,Male,No,36,Yes,Artist,8.0,Low,1.0,Cat_6,A +466265,Male,No,30,No,Doctor,1.0,Low,1.0,Cat_3,C +459789,Male,Yes,56,No,Artist,1.0,Average,2.0,Cat_6,C +464223,Female,Yes,52,Yes,Artist,2.0,Low,2.0,Cat_6,C +460280,Male,Yes,39,Yes,Entertainment,0.0,Low,1.0,Cat_6,D +460331,Female,No,35,No,Artist,5.0,Low,1.0,Cat_6,A +461494,Male,Yes,33,No,Engineer,0.0,Average,2.0,Cat_6,B +460035,Female,No,25,Yes,Artist,0.0,Low,6.0,Cat_6,A +463812,Female,Yes,27,Yes,Artist,0.0,Average,2.0,Cat_4,B +465569,Male,Yes,35,Yes,Artist,1.0,High,3.0,Cat_6,A +466333,Female,Yes,41,Yes,Artist,1.0,Average,2.0,Cat_6,C +467668,Female,Yes,49,Yes,Artist,,Average,3.0,Cat_7,C +462723,Female,Yes,46,No,Artist,8.0,Average,2.0,Cat_6,B +460909,Female,No,31,No,Healthcare,,Low,2.0,Cat_6,D +467777,Male,No,49,Yes,Doctor,,Low,1.0,Cat_6,C +462404,Female,No,26,Yes,Artist,9.0,Low,1.0,Cat_6,A +461799,Male,Yes,43,Yes,Doctor,13.0,High,2.0,Cat_6,B +459933,Female,Yes,30,Yes,Artist,8.0,Low,4.0,Cat_6,A +463717,Female,Yes,70,Yes,Artist,1.0,Average,4.0,Cat_6,C +463652,Female,No,28,Yes,Artist,,Low,1.0,Cat_6,D +462149,Female,Yes,89,No,Lawyer,3.0,High,1.0,Cat_6,D +462769,Female,Yes,42,No,Homemaker,6.0,High,4.0,Cat_6,B +465532,Female,Yes,37,Yes,Entertainment,6.0,Average,3.0,Cat_4,A +462829,Male,Yes,29,No,Executive,1.0,Average,3.0,Cat_6,D +459210,Female,Yes,68,Yes,Artist,1.0,High,2.0,Cat_6,C +464659,Female,No,25,No,Engineer,1.0,Low,1.0,Cat_4,D +461411,Male,No,29,No,Healthcare,1.0,Low,,Cat_6,D +467759,Female,Yes,48,Yes,Doctor,0.0,Low,2.0,Cat_6,C +461475,Female,Yes,51,Yes,Doctor,1.0,Average,3.0,Cat_1,C +462714,Female,Yes,40,Yes,Engineer,1.0,High,5.0,Cat_2,B +466890,Male,Yes,47,Yes,Artist,0.0,Average,6.0,Cat_7,B +462888,Male,Yes,39,Yes,Executive,,High,3.0,Cat_6,B +467479,Male,Yes,35,Yes,Executive,1.0,Average,4.0,Cat_6,C +466799,Female,No,19,No,Healthcare,2.0,Low,3.0,Cat_6,D +466557,Female,No,21,No,Healthcare,7.0,Low,,Cat_6,D +460873,Male,Yes,46,Yes,Entertainment,0.0,Average,3.0,Cat_4,B +466776,Male,Yes,86,Yes,Lawyer,0.0,Low,1.0,Cat_6,B +459462,Male,Yes,66,Yes,Artist,0.0,High,2.0,Cat_6,B +465727,Male,No,42,No,Marketing,1.0,Low,5.0,Cat_4,D +462348,Female,Yes,73,Yes,Lawyer,1.0,High,2.0,Cat_6,B +460941,Male,Yes,72,No,Engineer,1.0,Low,1.0,Cat_6,A +461629,Male,Yes,53,Yes,Artist,0.0,Low,3.0,Cat_6,B +459907,Female,Yes,39,Yes,Artist,8.0,Average,2.0,Cat_6,C +467146,Female,No,27,No,Homemaker,9.0,Low,1.0,Cat_6,D +460916,Male,Yes,63,No,Entertainment,1.0,Average,,Cat_6,B +459196,Female,No,23,No,Healthcare,1.0,Low,3.0,Cat_4,D +465444,Female,No,30,Yes,Entertainment,0.0,Low,4.0,Cat_6,B +464186,Female,Yes,39,Yes,Artist,5.0,Average,2.0,Cat_6,C +466412,Male,Yes,26,Yes,Artist,1.0,Average,3.0,Cat_6,B +459480,Female,Yes,50,Yes,Entertainment,7.0,Low,3.0,Cat_6,A +467880,Female,Yes,45,Yes,Artist,1.0,Average,2.0,Cat_6,C +460216,Female,No,62,No,Entertainment,2.0,Low,1.0,Cat_6,A +467941,Female,No,27,Yes,Doctor,1.0,Low,3.0,Cat_6,A +466090,Male,Yes,57,Yes,Entertainment,1.0,Low,,Cat_6,A +462572,Female,Yes,30,No,Engineer,1.0,Average,7.0,Cat_4,A +467443,Male,No,32,No,Healthcare,7.0,Low,4.0,Cat_3,A +462548,Male,No,18,,Executive,,Low,5.0,Cat_4,A +459701,Male,No,47,No,Artist,3.0,Low,3.0,Cat_5,B +461139,Female,No,32,Yes,Artist,4.0,Low,4.0,Cat_2,A +461115,Female,Yes,32,No,Engineer,5.0,Low,3.0,Cat_1,A +459027,Male,Yes,67,Yes,Doctor,1.0,Average,4.0,Cat_6,B +467340,Female,Yes,77,Yes,Lawyer,0.0,High,2.0,Cat_6,C +461410,Male,Yes,79,No,,0.0,Average,2.0,,C +460857,Male,No,31,No,Entertainment,1.0,Low,1.0,Cat_4,A +464300,Male,Yes,42,Yes,Artist,0.0,Low,3.0,Cat_4,A +460532,Female,No,18,No,,0.0,Low,6.0,Cat_6,D +463287,Male,Yes,50,Yes,Artist,1.0,Average,4.0,Cat_2,C +461392,Male,Yes,71,Yes,Artist,0.0,Average,5.0,Cat_6,C +461546,Female,Yes,72,No,Lawyer,1.0,Low,3.0,Cat_6,A +467780,Male,Yes,47,Yes,Artist,,Low,3.0,Cat_6,A +462130,Male,Yes,45,Yes,Artist,9.0,Average,2.0,Cat_6,C +464684,Female,No,40,No,Engineer,9.0,Low,5.0,Cat_4,A +462276,Female,No,36,Yes,Engineer,4.0,Low,1.0,Cat_6,A +462142,Female,No,42,Yes,Artist,,Low,1.0,Cat_6,B +462417,Male,No,30,No,Healthcare,4.0,Low,4.0,Cat_6,D +466463,Female,No,29,Yes,Healthcare,2.0,Low,4.0,Cat_4,B +464736,Male,No,28,Yes,Engineer,0.0,Low,5.0,Cat_4,D +467520,Male,,22,No,Healthcare,,Low,2.0,Cat_6,D +459835,Male,Yes,85,No,Lawyer,,Low,1.0,Cat_6,D +463855,Female,Yes,40,Yes,Artist,1.0,Average,2.0,Cat_6,C +460344,Female,Yes,52,Yes,Artist,14.0,Average,2.0,Cat_6,C +466211,Male,Yes,47,Yes,Artist,0.0,Low,,Cat_6,C +460139,Male,No,26,No,Artist,6.0,Low,6.0,Cat_5,D +464126,Female,No,55,Yes,Marketing,3.0,Low,1.0,Cat_6,B +461286,Female,No,20,No,Doctor,4.0,Low,4.0,Cat_6,D +461621,Female,Yes,87,Yes,Lawyer,0.0,High,2.0,Cat_6,C +460163,Male,No,28,No,Healthcare,0.0,Low,2.0,Cat_4,A +461958,Male,No,38,No,Artist,2.0,Low,2.0,Cat_3,A +464875,Male,Yes,42,No,Entertainment,1.0,Low,2.0,Cat_4,B +464791,Male,Yes,42,No,Marketing,0.0,High,6.0,Cat_4,A +464579,Female,No,29,No,Engineer,1.0,Low,4.0,Cat_6,D +463531,Male,No,32,Yes,Doctor,0.0,Low,4.0,Cat_6,A +462651,Male,Yes,38,No,Executive,,High,5.0,Cat_4,D +465486,Female,Yes,59,No,Artist,1.0,High,2.0,Cat_6,C +460882,Male,No,22,No,Healthcare,0.0,Low,2.0,Cat_3,D +467162,Female,No,26,No,Homemaker,9.0,Low,,Cat_4,D +461298,Female,Yes,78,Yes,Artist,1.0,High,2.0,Cat_6,C +461595,Female,No,32,Yes,Healthcare,0.0,Low,3.0,Cat_4,A +459260,Male,Yes,37,Yes,Artist,0.0,Average,4.0,Cat_7,C +462772,Male,Yes,37,Yes,Healthcare,1.0,High,4.0,Cat_6,B +462941,Male,No,67,No,Lawyer,,Low,2.0,Cat_6,D +459143,Male,Yes,47,No,Artist,0.0,High,4.0,Cat_6,B +464532,Female,Yes,68,Yes,Lawyer,0.0,High,2.0,Cat_6,A +460056,Male,No,30,Yes,Healthcare,3.0,Low,5.0,Cat_1,C +466761,Female,Yes,48,Yes,Entertainment,2.0,Low,1.0,Cat_1,A +465469,Female,No,43,Yes,Artist,6.0,Low,1.0,Cat_6,C +461886,Male,Yes,33,Yes,Healthcare,9.0,Low,2.0,Cat_6,D +465680,Male,Yes,31,No,Healthcare,0.0,Average,5.0,Cat_4,D +459316,Male,Yes,32,Yes,Healthcare,1.0,Low,2.0,Cat_6,D +465609,Female,No,21,No,Healthcare,1.0,Low,4.0,Cat_2,D +467009,Female,No,29,No,Homemaker,2.0,Low,4.0,Cat_3,D +461766,Female,Yes,35,Yes,Artist,1.0,Low,2.0,Cat_4,A +464938,Male,Yes,46,No,Artist,0.0,Low,3.0,Cat_4,A +462034,Female,Yes,38,Yes,Artist,9.0,High,2.0,Cat_2,B +464049,Female,No,32,Yes,Healthcare,1.0,Low,3.0,Cat_6,C +467925,Female,No,28,Yes,Artist,3.0,Low,7.0,Cat_2,A +463711,Female,Yes,68,Yes,Lawyer,3.0,Low,1.0,Cat_6,A +463846,Female,Yes,45,Yes,Artist,4.0,Low,1.0,Cat_6,C +462064,Male,Yes,58,Yes,Artist,0.0,Average,3.0,Cat_7,C +463249,Male,No,28,No,Entertainment,1.0,Low,2.0,Cat_7,A +464217,Female,No,32,No,Healthcare,12.0,Low,3.0,Cat_6,A +463378,Male,No,30,Yes,Doctor,1.0,Low,1.0,Cat_3,B +463035,Female,Yes,42,No,Homemaker,3.0,Average,4.0,Cat_6,D +459656,Male,No,22,No,,,Low,6.0,Cat_1,D +463114,Male,No,41,Yes,Artist,,Low,3.0,Cat_6,C +465309,Female,No,18,No,Healthcare,6.0,Low,4.0,Cat_7,D +462688,Female,Yes,29,Yes,Healthcare,8.0,Low,3.0,Cat_3,D +459255,Female,No,33,Yes,Healthcare,0.0,Low,3.0,Cat_6,A +467385,Male,No,47,No,Artist,1.0,Low,,Cat_4,A +467492,Female,Yes,57,Yes,Doctor,1.0,Low,2.0,Cat_7,A +460789,Male,Yes,36,Yes,Executive,1.0,Low,3.0,Cat_6,D +467307,Male,Yes,57,Yes,Executive,1.0,High,3.0,Cat_6,C +464026,Male,No,18,No,Healthcare,1.0,Low,2.0,Cat_6,D +464645,Male,,52,Yes,Artist,1.0,Average,5.0,Cat_4,C +462493,Male,Yes,52,Yes,Artist,1.0,Average,6.0,Cat_6,C +460685,Male,No,51,,Artist,6.0,Low,4.0,Cat_4,B +460452,Female,Yes,37,Yes,Entertainment,0.0,Average,2.0,Cat_1,B +459322,Male,No,36,Yes,Marketing,0.0,Low,1.0,Cat_6,A +464066,Female,No,29,Yes,Healthcare,9.0,Low,3.0,Cat_4,A +461114,Male,Yes,45,Yes,Artist,1.0,Average,4.0,Cat_3,C +465165,Male,No,61,Yes,Artist,0.0,Low,1.0,Cat_6,B +464526,Male,Yes,52,Yes,Entertainment,1.0,Average,4.0,Cat_6,A +460666,Female,Yes,49,Yes,Homemaker,14.0,Low,1.0,Cat_6,A +465417,Male,Yes,40,Yes,Entertainment,,Average,2.0,Cat_4,B +464096,Female,No,31,No,Healthcare,1.0,Low,5.0,Cat_4,C +464891,Male,Yes,42,No,Entertainment,1.0,Low,3.0,Cat_4,A +460107,Male,Yes,51,Yes,Engineer,1.0,Average,5.0,Cat_6,C +467708,Female,No,42,Yes,Healthcare,8.0,Low,1.0,Cat_7,D +461873,Male,No,29,Yes,Doctor,1.0,Low,4.0,Cat_6,C +465181,Female,No,33,Yes,Healthcare,8.0,Low,1.0,Cat_4,A +467204,Male,No,27,Yes,Healthcare,,Low,3.0,Cat_6,D +467320,Female,No,21,No,Healthcare,0.0,Low,4.0,Cat_2,D +459468,Male,Yes,35,No,Artist,9.0,Average,3.0,Cat_6,B +467316,Female,No,42,Yes,Doctor,0.0,Low,1.0,Cat_6,A +466182,Female,No,41,Yes,Artist,2.0,Low,1.0,Cat_6,C +463757,Male,No,39,Yes,Artist,0.0,Low,3.0,Cat_6,B +462314,Female,No,28,No,Doctor,14.0,Low,1.0,Cat_6,A +466392,Female,Yes,45,Yes,Engineer,3.0,Average,,Cat_3,B +466652,Female,Yes,61,Yes,Doctor,1.0,Average,2.0,Cat_6,B +461664,Male,Yes,62,Yes,Artist,0.0,Average,4.0,Cat_6,C +464105,Male,Yes,65,Yes,Entertainment,1.0,Average,2.0,Cat_6,C +466790,Male,Yes,45,Yes,Doctor,1.0,Average,2.0,Cat_3,A +463844,Female,Yes,26,Yes,Engineer,9.0,Low,2.0,Cat_6,B +462913,Female,No,25,Yes,Artist,4.0,Low,5.0,Cat_6,C +465541,Female,Yes,45,Yes,Artist,1.0,Average,4.0,Cat_7,C +459149,Male,Yes,43,No,Executive,0.0,High,4.0,Cat_6,B +464508,Male,Yes,49,Yes,Executive,1.0,High,3.0,Cat_6,C +458999,Male,Yes,55,Yes,Artist,1.0,Average,2.0,Cat_6,C +459947,Female,No,37,Yes,Artist,3.0,Low,1.0,Cat_6,B +465500,Male,Yes,65,Yes,Entertainment,1.0,High,2.0,Cat_6,C +466009,Female,No,32,No,,10.0,Low,5.0,Cat_6,C +466338,Female,No,39,No,Engineer,0.0,Low,3.0,Cat_4,A +465122,Female,Yes,40,Yes,Artist,2.0,Average,2.0,Cat_6,C +465230,Male,Yes,72,Yes,Entertainment,1.0,Average,3.0,Cat_6,C +466369,Female,No,38,No,Homemaker,5.0,Low,1.0,Cat_2,B +463939,Male,No,26,No,Marketing,0.0,Low,5.0,Cat_6,D +460825,Male,No,30,Yes,Doctor,1.0,Low,3.0,Cat_4,D +461125,Male,Yes,51,Yes,Homemaker,1.0,Low,,Cat_4,A +463471,Male,No,57,Yes,Marketing,1.0,Low,4.0,Cat_7,A +467415,Female,No,31,Yes,Healthcare,1.0,Low,1.0,Cat_6,D +465879,Male,No,39,Yes,Artist,0.0,Low,1.0,Cat_6,A +463961,Female,No,30,No,Engineer,0.0,Low,4.0,Cat_4,A +462634,Female,Yes,43,No,Artist,0.0,Average,3.0,Cat_4,A +463622,Male,No,27,Yes,Healthcare,8.0,Low,1.0,Cat_3,C +466624,Female,Yes,46,No,Artist,0.0,Average,4.0,Cat_3,C +467785,Male,Yes,43,No,Artist,1.0,Low,2.0,Cat_6,B +461928,Male,Yes,69,Yes,Lawyer,,Low,1.0,,A +460578,Male,Yes,28,No,Entertainment,8.0,Average,4.0,Cat_7,D +460198,Male,No,43,Yes,Entertainment,9.0,Low,1.0,Cat_6,D +467468,Female,No,27,No,Healthcare,,Low,5.0,Cat_7,D +463581,Female,No,28,Yes,Healthcare,1.0,Low,3.0,Cat_6,D +463968,Male,No,33,No,Artist,,Low,4.0,Cat_6,D +465414,Female,No,30,No,Engineer,0.0,Low,1.0,,B +464574,Male,No,58,No,Healthcare,4.0,Low,2.0,Cat_6,D +462312,Female,No,38,Yes,Engineer,8.0,Low,4.0,Cat_6,A +467971,Female,No,31,Yes,Artist,1.0,Low,4.0,Cat_6,D +464746,Male,Yes,32,No,Executive,4.0,Average,5.0,Cat_4,D +459298,Male,Yes,45,Yes,Artist,4.0,Average,4.0,Cat_6,C +466414,Male,Yes,66,Yes,Artist,1.0,Low,3.0,Cat_6,A +460773,Female,Yes,51,Yes,Artist,1.0,Average,3.0,Cat_6,C +461594,Female,Yes,59,Yes,Artist,,Average,2.0,Cat_6,C +462713,Female,No,40,Yes,Engineer,,Low,1.0,Cat_3,B +465163,Male,Yes,46,Yes,Artist,11.0,Average,5.0,Cat_6,C +462222,Male,No,20,No,Healthcare,1.0,Low,6.0,Cat_4,D +459342,Female,Yes,84,No,Lawyer,1.0,High,2.0,Cat_6,C +463006,Female,No,41,Yes,Marketing,,Low,1.0,Cat_6,D +463029,Male,Yes,51,No,Artist,,Average,3.0,Cat_6,C +460767,Male,Yes,57,Yes,Entertainment,8.0,Average,5.0,Cat_2,A +464319,Female,No,33,No,Doctor,0.0,Low,1.0,Cat_6,A +462828,Male,No,25,No,Marketing,3.0,Low,3.0,Cat_5,D +459873,Male,Yes,55,Yes,Artist,,Average,3.0,Cat_4,A +461608,Female,Yes,56,Yes,Executive,,High,4.0,Cat_1,A +465155,Male,Yes,77,Yes,Lawyer,1.0,Low,1.0,Cat_6,C +461704,Male,Yes,30,Yes,Healthcare,1.0,Low,2.0,Cat_6,A +464826,Male,No,23,No,Healthcare,6.0,Low,5.0,Cat_4,D +462677,Female,No,43,Yes,Artist,2.0,Low,4.0,Cat_6,B +465006,Male,No,39,Yes,Entertainment,5.0,Low,1.0,Cat_6,A +463483,Male,Yes,29,No,Executive,1.0,High,2.0,Cat_6,D +459816,Male,Yes,37,Yes,Doctor,,Low,2.0,Cat_6,D +459002,Female,Yes,45,No,Engineer,2.0,Low,2.0,Cat_6,D +465990,Male,Yes,48,No,Executive,2.0,High,2.0,Cat_2,D +466044,Male,Yes,27,No,Entertainment,0.0,Average,4.0,Cat_6,A +461163,Female,Yes,60,No,Entertainment,9.0,Low,4.0,Cat_3,B +467106,Male,Yes,45,Yes,Entertainment,4.0,Average,3.0,Cat_2,B +467373,Male,No,31,Yes,Healthcare,,Low,1.0,Cat_6,D +461256,Male,No,33,No,Doctor,1.0,Low,4.0,Cat_4,C +466522,Male,,18,No,,6.0,High,4.0,Cat_3,D +459010,Female,Yes,48,Yes,Artist,,Average,3.0,Cat_6,B +466537,Female,Yes,83,No,Lawyer,,Low,2.0,Cat_6,A +459046,Male,Yes,57,Yes,Executive,,Low,4.0,Cat_2,A +459335,Male,No,26,Yes,Healthcare,,Low,3.0,Cat_1,D +462695,Male,Yes,51,Yes,Artist,8.0,Low,5.0,Cat_6,A +466208,Male,Yes,67,No,Executive,2.0,High,1.0,Cat_6,C +462128,Male,Yes,38,Yes,Artist,9.0,Average,2.0,Cat_6,C +467329,Female,No,40,Yes,Doctor,0.0,Low,1.0,Cat_6,C +461609,Female,Yes,45,Yes,Artist,5.0,Average,2.0,Cat_6,B +464470,Male,Yes,48,Yes,Artist,0.0,Average,4.0,Cat_6,C +462705,Female,Yes,69,No,Lawyer,0.0,Average,2.0,Cat_6,C +465255,Male,No,21,No,Healthcare,9.0,Low,5.0,Cat_6,D +462536,Female,Yes,48,No,Engineer,1.0,High,,Cat_6,B +461186,Female,No,18,No,Healthcare,,Low,3.0,Cat_3,D +466018,Male,Yes,72,Yes,Artist,0.0,Low,3.0,Cat_4,C +461385,Male,Yes,85,No,Lawyer,,Low,1.0,Cat_6,D +459115,Female,No,21,No,Marketing,1.0,Low,5.0,Cat_4,D +461263,Female,Yes,48,Yes,Artist,1.0,Low,1.0,Cat_4,A +460027,Female,No,37,Yes,Entertainment,6.0,Low,,Cat_6,A +460366,Male,No,28,No,Healthcare,1.0,Low,5.0,Cat_4,D +464106,Male,Yes,55,Yes,Entertainment,1.0,Low,2.0,Cat_6,A +465696,Female,No,56,Yes,Artist,0.0,Low,2.0,Cat_6,C +461627,Male,Yes,50,Yes,Doctor,1.0,Low,2.0,Cat_6,C +467963,Male,No,21,No,Healthcare,1.0,Low,4.0,Cat_6,D +462437,Male,No,18,No,Healthcare,0.0,Low,6.0,Cat_6,D +467543,Male,Yes,68,Yes,Lawyer,0.0,High,2.0,Cat_6,B +465206,Male,Yes,47,Yes,Artist,13.0,Low,1.0,Cat_6,C +463040,Male,Yes,75,No,Lawyer,1.0,High,2.0,Cat_6,B +461668,Male,Yes,50,Yes,Artist,3.0,Average,4.0,Cat_2,C +461955,Male,No,31,Yes,Healthcare,0.0,Low,4.0,Cat_6,D +467139,Female,No,30,Yes,Artist,0.0,Low,3.0,Cat_6,A +459366,Female,Yes,41,Yes,Artist,0.0,Low,4.0,Cat_6,A +460689,Male,Yes,41,,Executive,6.0,High,2.0,Cat_3,A +462167,Female,Yes,35,No,Healthcare,2.0,Low,1.0,Cat_6,A +466752,Male,Yes,37,Yes,Healthcare,0.0,Low,1.0,Cat_6,D +462778,Male,Yes,60,Yes,Entertainment,0.0,Low,,Cat_6,D +466902,Male,Yes,50,Yes,Doctor,0.0,Average,3.0,Cat_6,C +459534,Male,Yes,36,Yes,Artist,0.0,Average,2.0,Cat_6,C +463399,Female,No,26,No,Marketing,3.0,Low,3.0,Cat_3,A +465538,Male,Yes,62,No,,7.0,High,2.0,Cat_6,D +464878,Male,No,22,No,Healthcare,0.0,Low,4.0,Cat_4,D +467599,Female,No,19,No,Healthcare,0.0,Low,3.0,Cat_6,D +466226,Female,Yes,45,Yes,Artist,0.0,Average,5.0,Cat_6,C +463576,Female,No,31,No,Doctor,0.0,Low,4.0,Cat_6,D +464911,Male,Yes,56,Yes,Artist,0.0,Average,3.0,Cat_4,C +466457,Female,Yes,62,,Lawyer,8.0,Low,5.0,Cat_6,A +463909,Female,No,25,Yes,Healthcare,1.0,Low,3.0,Cat_6,B +466099,Female,Yes,73,Yes,Lawyer,1.0,Low,1.0,Cat_6,B +464427,Male,Yes,62,Yes,Doctor,1.0,Average,4.0,Cat_3,C +463951,Male,Yes,46,No,Entertainment,0.0,Average,3.0,Cat_6,A +465638,Female,Yes,51,Yes,Artist,,Low,,Cat_3,C +465289,Male,Yes,25,Yes,Executive,1.0,High,2.0,Cat_6,A +466718,Male,Yes,66,,Executive,0.0,High,2.0,Cat_6,B +464219,Male,No,27,Yes,Marketing,1.0,Low,2.0,Cat_3,A +467098,Male,Yes,87,No,Executive,1.0,Low,2.0,Cat_6,A +467451,Female,Yes,47,Yes,Artist,0.0,Low,1.0,Cat_1,A +465137,Male,Yes,57,Yes,Artist,1.0,Average,4.0,Cat_6,C +462289,Male,Yes,60,Yes,Artist,0.0,Average,4.0,Cat_3,A +467459,Male,Yes,40,Yes,Executive,,High,5.0,Cat_6,B +460308,Female,No,32,Yes,Doctor,7.0,Low,,Cat_2,A +467647,Male,No,28,Yes,Healthcare,2.0,Low,3.0,Cat_6,D +459803,Male,No,39,Yes,Doctor,4.0,Low,3.0,Cat_6,C +465972,Female,No,47,Yes,Marketing,9.0,Low,1.0,Cat_6,D +459414,Female,No,39,Yes,,8.0,Low,,Cat_6,C +466173,Male,No,28,No,Entertainment,1.0,Low,,Cat_3,A +464136,Male,Yes,69,Yes,Artist,0.0,Average,3.0,Cat_6,A +459286,Female,No,42,Yes,Artist,0.0,Low,1.0,Cat_6,D +463580,Female,Yes,46,Yes,Engineer,1.0,Low,1.0,Cat_6,D +466636,Female,No,23,No,Marketing,3.0,Low,3.0,Cat_6,C +461064,Male,No,45,Yes,Artist,1.0,Low,5.0,Cat_6,C +461597,Female,Yes,38,Yes,Doctor,1.0,Average,4.0,Cat_6,B +458990,Female,Yes,71,No,Lawyer,1.0,High,3.0,Cat_6,D +461351,Female,No,26,No,Doctor,0.0,Low,2.0,Cat_6,D +464420,Female,Yes,60,Yes,Artist,8.0,High,2.0,Cat_6,C +462060,Female,Yes,46,Yes,Artist,1.0,Average,2.0,Cat_6,C +465707,Male,Yes,39,Yes,Entertainment,1.0,Average,4.0,Cat_3,C +463733,Female,No,18,No,Healthcare,1.0,Low,3.0,Cat_6,D +463960,Male,Yes,26,Yes,Doctor,1.0,Average,2.0,Cat_7,A +462395,Male,Yes,53,Yes,Artist,1.0,High,2.0,Cat_6,D +461015,Male,Yes,40,Yes,Entertainment,1.0,Average,2.0,Cat_3,C +464965,Male,Yes,76,No,Executive,,Low,7.0,Cat_4,D +466267,Male,Yes,26,Yes,Entertainment,0.0,Average,3.0,Cat_3,A +464056,Female,Yes,65,Yes,Artist,0.0,Average,2.0,Cat_6,C +465898,Female,No,37,Yes,Artist,12.0,Low,2.0,Cat_6,A +465361,Male,No,25,No,Healthcare,0.0,Low,4.0,Cat_3,C +461394,Female,No,21,No,Engineer,0.0,Low,5.0,Cat_2,D +466269,Female,No,25,No,Healthcare,0.0,Low,4.0,Cat_2,D +466213,Male,No,23,No,Healthcare,4.0,Low,5.0,Cat_6,D +465550,Female,No,30,Yes,Healthcare,1.0,Low,6.0,Cat_3,D +463451,Female,No,30,No,Doctor,0.0,Low,4.0,Cat_6,D +463877,Male,Yes,42,No,Entertainment,0.0,Average,4.0,Cat_4,A +459499,Female,Yes,81,No,Lawyer,,High,2.0,Cat_6,D +463876,Male,Yes,49,Yes,Entertainment,1.0,Average,5.0,Cat_6,C +465448,Male,No,32,No,Healthcare,0.0,Low,3.0,Cat_2,A +463041,Female,Yes,43,Yes,Artist,,Average,4.0,Cat_2,B +459325,Male,Yes,26,No,Executive,9.0,High,4.0,Cat_6,D +467233,Female,No,67,No,Artist,1.0,Low,2.0,Cat_6,B +465264,Male,Yes,88,No,Executive,0.0,Low,1.0,Cat_6,A +461811,Female,No,40,Yes,Healthcare,8.0,Low,4.0,Cat_2,D +466370,Female,,48,Yes,Artist,0.0,Low,4.0,Cat_2,C +465368,Male,No,18,No,Healthcare,7.0,Low,3.0,Cat_1,D +461245,Male,Yes,39,Yes,,5.0,Average,3.0,Cat_6,A +467693,Male,Yes,61,Yes,Entertainment,7.0,High,5.0,Cat_6,B +467879,Female,Yes,51,Yes,Artist,,High,4.0,Cat_6,C +461499,Female,Yes,25,No,Engineer,0.0,Average,2.0,Cat_4,A +465936,Male,No,35,Yes,Artist,6.0,Low,3.0,Cat_6,B +459748,Male,Yes,46,No,Executive,0.0,High,2.0,Cat_6,C +466376,Female,Yes,53,No,Homemaker,2.0,High,3.0,Cat_6,B +460215,Male,Yes,48,Yes,Entertainment,9.0,Low,1.0,Cat_6,D +462076,Male,Yes,75,Yes,Lawyer,1.0,Low,2.0,Cat_6,D +465756,Male,No,23,No,Healthcare,1.0,Low,3.0,Cat_4,D +463573,Male,No,29,No,Doctor,1.0,Low,2.0,Cat_6,D +463455,Male,Yes,46,No,Artist,0.0,Low,2.0,Cat_6,B +460841,Female,No,61,Yes,Marketing,1.0,Low,1.0,Cat_6,D +463959,Female,No,37,No,Artist,1.0,Low,1.0,Cat_6,B +461792,Female,No,23,No,Healthcare,6.0,Low,5.0,Cat_2,D +459118,Male,No,21,No,Healthcare,,Low,8.0,Cat_6,D +462791,Female,No,33,No,Healthcare,7.0,Low,4.0,Cat_5,D +460852,Male,Yes,74,No,Executive,0.0,Low,,,D +463102,Male,No,41,No,Entertainment,0.0,Low,2.0,Cat_6,D +463302,Female,Yes,58,Yes,Engineer,,High,4.0,Cat_6,B +462659,Male,Yes,25,Yes,Entertainment,8.0,Average,2.0,Cat_6,A +463408,Male,No,33,No,Marketing,2.0,Low,3.0,Cat_6,D +462071,Male,Yes,43,No,Doctor,0.0,Low,2.0,Cat_6,A +464806,Male,Yes,25,No,Executive,1.0,Low,2.0,Cat_4,D +463664,Female,No,26,No,Engineer,0.0,Low,1.0,Cat_6,A +467795,Male,No,22,No,Healthcare,1.0,Low,5.0,Cat_6,D +459141,Male,Yes,28,No,Artist,0.0,Average,2.0,Cat_6,D +465900,Female,No,36,Yes,Entertainment,7.0,Low,1.0,Cat_6,A +467265,Female,No,28,No,Healthcare,1.0,Low,3.0,Cat_4,C +464490,Male,Yes,85,Yes,Artist,1.0,High,4.0,Cat_6,A +461920,Male,No,30,Yes,Entertainment,0.0,Low,,Cat_6,D +460411,Male,Yes,43,Yes,Healthcare,7.0,High,2.0,Cat_6,A +466491,Male,Yes,82,No,Executive,1.0,High,2.0,Cat_6,B +459062,Male,,48,Yes,Executive,,High,5.0,Cat_6,B +465248,Female,No,18,No,Healthcare,3.0,Low,3.0,Cat_2,D +463308,Male,Yes,53,Yes,Engineer,1.0,Low,2.0,Cat_3,A +463478,Male,Yes,50,Yes,Artist,0.0,Average,4.0,Cat_6,B +465396,Female,Yes,39,No,Engineer,9.0,Average,6.0,Cat_4,D +461954,Female,Yes,35,No,Engineer,2.0,Average,6.0,Cat_2,B +467287,Male,No,28,Yes,Entertainment,,Low,3.0,Cat_4,D +463800,Female,No,33,Yes,Engineer,1.0,Low,3.0,Cat_6,A +459894,Female,Yes,35,Yes,Healthcare,1.0,Low,3.0,Cat_6,C +459778,Male,Yes,48,Yes,Entertainment,0.0,Average,4.0,Cat_6,B +464662,Female,Yes,50,Yes,Artist,1.0,High,3.0,Cat_4,C +460878,Male,Yes,47,No,Entertainment,0.0,Average,4.0,Cat_6,C +462187,Female,No,50,Yes,Artist,4.0,Low,1.0,Cat_6,C +462675,Male,Yes,56,Yes,Artist,,Low,4.0,Cat_3,A +461908,Female,No,26,No,Healthcare,10.0,Low,1.0,Cat_6,D +461951,Male,No,21,No,Healthcare,0.0,Low,4.0,Cat_6,D +465352,Male,Yes,61,Yes,Entertainment,1.0,Average,4.0,Cat_6,C +464258,Female,Yes,88,No,Lawyer,0.0,High,2.0,Cat_6,B +464215,Male,No,72,No,Marketing,1.0,Low,2.0,Cat_6,D +467561,Male,Yes,61,Yes,Artist,1.0,Low,2.0,Cat_6,B +460322,Female,Yes,45,No,Engineer,8.0,Low,,Cat_6,A +464665,Female,Yes,51,No,Engineer,1.0,Average,5.0,Cat_4,B +460670,Male,No,42,Yes,Artist,6.0,Low,2.0,Cat_3,B +461486,Male,Yes,59,No,Entertainment,8.0,Average,3.0,Cat_6,C +459526,Female,No,45,Yes,Healthcare,4.0,Low,1.0,Cat_6,A +465429,Female,Yes,68,Yes,Artist,,Average,3.0,Cat_6,C +466492,Female,Yes,85,Yes,Lawyer,0.0,High,2.0,Cat_6,A +459489,Male,Yes,46,Yes,Doctor,1.0,Average,4.0,Cat_6,B +459681,Female,Yes,32,Yes,Artist,0.0,Average,2.0,Cat_6,B +462971,Female,Yes,70,Yes,Engineer,0.0,Low,1.0,Cat_6,C +461233,Male,Yes,37,Yes,Entertainment,,Low,,Cat_3,D +461221,Female,No,30,No,Marketing,1.0,Low,4.0,Cat_6,B +464090,Female,No,33,Yes,Engineer,1.0,Low,2.0,Cat_6,A +459863,Male,Yes,61,No,Healthcare,,Low,3.0,Cat_6,A +459822,Female,No,37,Yes,Engineer,0.0,Low,1.0,Cat_4,D +462540,Male,Yes,32,Yes,Entertainment,6.0,Low,2.0,Cat_2,B +467411,Male,Yes,52,Yes,Artist,0.0,High,2.0,Cat_6,B +463997,Female,Yes,42,No,Entertainment,0.0,Average,3.0,Cat_6,B +464103,Male,Yes,47,Yes,Artist,3.0,Average,2.0,Cat_6,B +465751,Female,Yes,52,Yes,Artist,9.0,Low,2.0,Cat_6,C +459797,Male,Yes,66,Yes,Entertainment,0.0,Average,3.0,Cat_6,C +460798,Female,No,28,No,Engineer,,Low,6.0,Cat_6,C +464572,Female,Yes,47,Yes,Marketing,1.0,Low,1.0,Cat_6,D +462352,Male,Yes,52,Yes,Entertainment,1.0,Average,3.0,Cat_6,B +460513,Male,No,27,No,Healthcare,2.0,Low,4.0,Cat_3,D +463228,Female,Yes,56,Yes,Artist,0.0,Average,5.0,Cat_6,B +464765,Female,Yes,53,No,Engineer,7.0,Average,6.0,Cat_4,A +463720,Female,Yes,45,Yes,Artist,1.0,Average,4.0,Cat_4,B +460799,Female,No,37,No,Homemaker,1.0,Low,1.0,Cat_1,D +460891,Female,Yes,36,No,Artist,2.0,Average,4.0,Cat_3,C +460311,Male,No,25,Yes,Entertainment,2.0,Low,3.0,Cat_1,D +466727,Female,Yes,45,Yes,Artist,1.0,Average,2.0,Cat_6,B +466157,Male,Yes,72,Yes,Homemaker,9.0,Low,,,A +465138,Male,No,35,Yes,Entertainment,1.0,Low,3.0,Cat_3,A +466361,Female,Yes,72,Yes,Lawyer,1.0,High,2.0,Cat_6,B +463154,Female,Yes,31,No,Artist,1.0,Low,4.0,Cat_6,D +462967,Male,Yes,47,No,Entertainment,4.0,Average,3.0,Cat_6,B +460908,Male,No,20,No,Healthcare,7.0,Low,3.0,Cat_6,D +467560,Female,Yes,73,Yes,Lawyer,1.0,Low,2.0,Cat_6,B +463404,Male,Yes,29,No,Doctor,,High,2.0,Cat_3,B +463281,Male,Yes,25,No,Entertainment,3.0,Average,2.0,Cat_3,D +462466,Male,Yes,52,Yes,Executive,0.0,High,4.0,Cat_6,C +463744,Female,No,27,Yes,Artist,1.0,Low,6.0,Cat_6,C +464209,Male,Yes,52,Yes,Executive,1.0,Average,4.0,Cat_2,C +462528,Female,Yes,49,Yes,Artist,0.0,Low,1.0,Cat_6,C +459518,Male,Yes,60,,,,Average,4.0,Cat_6,B +460061,Male,No,45,Yes,Marketing,0.0,Low,1.0,Cat_3,D +466188,Male,Yes,59,Yes,Artist,4.0,Low,1.0,Cat_4,B +467914,Female,No,18,No,Healthcare,8.0,Low,5.0,Cat_6,D +462758,Male,No,18,No,Healthcare,0.0,Low,3.0,Cat_6,D +465704,Male,Yes,55,No,Artist,1.0,Low,1.0,Cat_6,C +460397,Female,No,59,Yes,Entertainment,1.0,Low,1.0,Cat_6,A +466676,Male,No,22,No,Healthcare,0.0,Low,2.0,Cat_6,D +465024,Male,Yes,52,Yes,Executive,0.0,High,4.0,Cat_6,C +461630,Male,Yes,51,Yes,Artist,1.0,Average,3.0,Cat_6,C +465328,Male,Yes,35,No,Executive,0.0,High,5.0,Cat_6,A +461220,Female,No,26,No,Executive,8.0,Low,3.0,Cat_6,D +463935,Male,Yes,38,Yes,Entertainment,1.0,Average,2.0,Cat_6,C +460514,Male,Yes,43,Yes,Doctor,1.0,Low,4.0,Cat_3,A +466262,Male,No,41,Yes,Entertainment,1.0,Low,2.0,Cat_3,A +467094,Female,No,27,No,Homemaker,8.0,Low,,Cat_4,D +459280,Male,Yes,60,Yes,,0.0,Low,1.0,Cat_6,B +464581,Female,No,25,No,Doctor,,Low,4.0,Cat_6,D +459624,Male,No,18,No,Healthcare,,Low,5.0,Cat_4,D +460890,Male,Yes,47,Yes,Artist,0.0,Average,4.0,Cat_3,C +466415,Male,Yes,71,No,Marketing,1.0,Low,1.0,Cat_6,D +462392,Female,No,32,Yes,Entertainment,1.0,Low,3.0,Cat_6,A +459706,Male,Yes,60,Yes,Entertainment,1.0,Average,3.0,Cat_6,C +464429,Female,No,33,Yes,Healthcare,0.0,Low,,Cat_6,B +467446,Female,No,37,Yes,Artist,9.0,Low,1.0,Cat_2,A +460528,Male,Yes,48,Yes,Homemaker,9.0,Low,1.0,Cat_3,C +464836,Female,,33,No,Engineer,7.0,High,5.0,Cat_4,D +459212,Female,Yes,68,Yes,Artist,1.0,Average,2.0,Cat_6,C +462364,Male,Yes,51,No,Artist,0.0,Low,3.0,Cat_6,A +464549,Male,Yes,52,Yes,Executive,1.0,Low,4.0,Cat_6,C +464555,Male,Yes,31,Yes,Marketing,13.0,High,4.0,Cat_4,D +462027,Male,No,39,No,Engineer,1.0,Low,1.0,Cat_6,A +467110,Male,,60,Yes,Lawyer,1.0,High,1.0,Cat_6,D +467123,Female,Yes,50,Yes,Executive,4.0,High,3.0,Cat_6,B +459599,Female,No,19,No,Healthcare,,Low,4.0,Cat_1,D +467826,Male,No,18,No,Healthcare,0.0,Low,6.0,Cat_6,D +461837,Male,Yes,37,Yes,Artist,0.0,Low,2.0,Cat_6,C +467328,Female,Yes,50,Yes,Doctor,1.0,Average,3.0,Cat_6,B +467962,Female,No,20,No,Healthcare,0.0,Low,,Cat_6,D +464025,Male,No,19,No,Healthcare,1.0,Low,6.0,Cat_6,D +460385,Male,No,31,Yes,Healthcare,0.0,Low,4.0,Cat_6,D +462551,Male,Yes,53,Yes,Doctor,14.0,Average,2.0,Cat_6,B +462047,Female,Yes,26,Yes,Artist,12.0,Average,2.0,Cat_6,B +459958,Male,No,37,Yes,Healthcare,8.0,Low,3.0,Cat_6,D +465690,Female,Yes,49,No,Healthcare,,Average,2.0,Cat_4,B +459221,Male,Yes,70,No,Entertainment,,Low,1.0,Cat_6,B +461059,Female,Yes,48,No,Lawyer,1.0,High,2.0,Cat_6,D +461227,Male,No,21,No,Healthcare,0.0,Low,4.0,Cat_6,D +466246,Male,No,19,No,Healthcare,0.0,Low,4.0,Cat_2,D +462619,Male,,61,No,,0.0,High,,Cat_4,A +459561,Male,No,31,No,Healthcare,,Low,3.0,Cat_4,A +467048,Female,Yes,65,Yes,Lawyer,0.0,High,3.0,Cat_6,B +467362,Male,Yes,73,No,Executive,1.0,Low,1.0,Cat_6,B +459846,Female,Yes,50,Yes,Artist,1.0,Average,3.0,Cat_6,B +460351,Male,Yes,70,Yes,Lawyer,0.0,Low,1.0,Cat_6,A +459515,Male,Yes,58,Yes,Engineer,0.0,Low,1.0,Cat_6,D +460499,Female,,58,No,Marketing,14.0,High,6.0,Cat_3,D +460829,Male,No,18,Yes,Healthcare,1.0,Low,2.0,Cat_6,D +466559,Male,No,32,No,Healthcare,0.0,Low,4.0,Cat_6,D +467236,Female,No,40,Yes,Lawyer,4.0,Low,1.0,Cat_6,B +461816,Female,No,49,Yes,Marketing,0.0,Low,5.0,Cat_6,A +467409,Male,No,35,Yes,Artist,8.0,Low,1.0,Cat_6,A +466299,Female,Yes,40,Yes,Engineer,0.0,Low,2.0,Cat_2,B +464565,Male,No,30,Yes,Marketing,12.0,Low,4.0,Cat_6,D +465906,Female,No,35,Yes,Artist,5.0,Low,1.0,Cat_6,A +460950,Male,Yes,28,No,Doctor,0.0,Low,5.0,Cat_1,D +466654,Male,No,30,,Marketing,8.0,Low,1.0,Cat_6,A +459649,Male,Yes,39,Yes,Healthcare,1.0,Average,2.0,Cat_6,D +461077,Male,No,39,Yes,Entertainment,1.0,Low,8.0,Cat_3,A +464642,Female,Yes,26,Yes,Engineer,6.0,High,7.0,Cat_4,D +463741,Male,Yes,36,Yes,Artist,0.0,High,3.0,Cat_6,C +460594,Female,No,26,Yes,Homemaker,9.0,Low,,Cat_3,D +465253,Male,No,21,No,Healthcare,1.0,Low,3.0,Cat_7,D +459936,Female,No,45,Yes,Artist,2.0,Low,1.0,Cat_6,B +462998,Male,Yes,45,No,Entertainment,10.0,Average,4.0,Cat_6,B +464179,Female,Yes,63,Yes,Artist,0.0,Average,3.0,Cat_6,C +465655,Female,No,45,Yes,Artist,1.0,Low,1.0,Cat_6,A +462988,Female,Yes,27,Yes,Healthcare,9.0,High,2.0,Cat_6,D +463933,Male,Yes,47,No,Executive,0.0,Low,7.0,Cat_4,A +462111,Male,Yes,36,Yes,Artist,6.0,Average,4.0,Cat_6,C +462435,Male,Yes,49,Yes,Artist,,Average,2.0,Cat_6,C +462747,Male,No,25,No,Doctor,7.0,Low,4.0,Cat_6,C +462279,Male,No,32,No,Healthcare,0.0,Low,7.0,Cat_6,D +463383,Male,Yes,41,No,Executive,6.0,High,3.0,Cat_4,A +459743,Female,Yes,50,Yes,Artist,9.0,Average,4.0,Cat_6,A +461762,Female,Yes,63,Yes,Artist,0.0,High,2.0,Cat_6,C +463071,Male,Yes,69,No,Executive,,Low,4.0,Cat_6,D +464121,Female,Yes,62,Yes,Engineer,1.0,Low,1.0,Cat_6,B +465051,Male,Yes,63,Yes,Marketing,1.0,High,3.0,Cat_6,C +460140,Female,Yes,36,Yes,Artist,5.0,Low,2.0,Cat_6,B +466197,Female,Yes,31,No,Engineer,8.0,Low,2.0,Cat_4,C +460433,Female,No,45,No,Entertainment,8.0,Low,2.0,Cat_6,A +462953,Female,Yes,45,Yes,Artist,1.0,Average,4.0,Cat_6,B +462535,Male,No,26,No,Doctor,1.0,Low,3.0,Cat_6,D +462921,Male,Yes,49,No,Artist,,Average,4.0,Cat_6,C +464735,Female,No,38,Yes,Marketing,8.0,Low,3.0,Cat_4,D +464455,Female,Yes,74,Yes,Lawyer,1.0,High,2.0,Cat_6,B +461675,Male,No,61,Yes,Lawyer,1.0,Low,1.0,Cat_6,C +464578,Female,No,26,Yes,Healthcare,0.0,Low,,Cat_6,D +466304,Female,Yes,70,Yes,Lawyer,1.0,High,2.0,Cat_2,B +466850,Female,No,30,No,Doctor,,Low,3.0,Cat_4,D +467041,Female,Yes,57,Yes,Artist,1.0,Average,4.0,Cat_6,C +464775,Female,Yes,35,,,8.0,Average,4.0,Cat_4,A +464858,Male,No,20,No,Healthcare,1.0,Low,3.0,Cat_4,D +462040,Male,No,19,No,Healthcare,5.0,Low,4.0,Cat_2,D +460800,Male,No,52,Yes,Artist,1.0,Low,3.0,Cat_3,B +459663,Male,No,31,Yes,Artist,,Low,6.0,Cat_3,A +462081,Male,No,22,No,Marketing,0.0,Low,3.0,Cat_4,D +463047,Male,Yes,27,Yes,Homemaker,9.0,Low,,Cat_6,D +464500,Female,,39,Yes,Artist,1.0,Low,3.0,Cat_6,B +460756,Female,No,28,No,Homemaker,8.0,Low,,Cat_6,D +462835,Male,Yes,59,No,Engineer,,Average,4.0,Cat_6,B +463766,Female,Yes,57,Yes,Artist,1.0,Average,3.0,,C +463874,Male,No,25,Yes,Healthcare,1.0,Low,4.0,Cat_3,D +463525,Female,Yes,40,No,Engineer,0.0,Low,3.0,Cat_6,A +465653,Female,Yes,53,Yes,Artist,1.0,Low,2.0,Cat_6,A +465905,Female,No,32,Yes,Artist,9.0,Low,1.0,Cat_6,A +465663,Female,No,70,Yes,Artist,0.0,Low,2.0,Cat_6,B +465225,Male,No,48,Yes,Engineer,0.0,Low,1.0,Cat_2,A +465001,Male,Yes,75,Yes,Lawyer,1.0,High,2.0,Cat_6,C +466905,Male,Yes,62,Yes,Artist,0.0,Low,1.0,Cat_6,B +465573,Male,Yes,41,No,Artist,1.0,Average,6.0,Cat_6,B +467008,Male,Yes,55,Yes,Artist,0.0,Average,3.0,Cat_6,C +466995,Female,No,35,Yes,Artist,8.0,Low,,Cat_6,A +463397,Female,No,20,No,Healthcare,4.0,Low,3.0,Cat_4,D +466238,Male,No,20,No,Healthcare,0.0,Low,3.0,Cat_4,D +460531,Female,No,35,No,,0.0,Low,5.0,Cat_6,C +462250,Female,Yes,56,Yes,Entertainment,1.0,Average,3.0,Cat_4,B +462611,Male,Yes,39,No,Executive,1.0,High,7.0,Cat_4,D +459098,Male,Yes,42,Yes,Entertainment,,Low,3.0,Cat_6,D +465701,Male,Yes,35,Yes,Artist,1.0,Average,2.0,Cat_4,B +466242,Female,No,35,No,Engineer,1.0,Low,1.0,Cat_4,A +466308,Male,No,33,Yes,Healthcare,0.0,Low,2.0,Cat_6,C +464039,Male,No,42,Yes,Artist,1.0,Low,2.0,Cat_6,A +459972,Male,No,35,Yes,Artist,5.0,Low,2.0,Cat_6,A +461801,Female,No,27,No,Healthcare,0.0,Low,3.0,Cat_6,A +462716,Male,No,33,No,Entertainment,6.0,Low,2.0,Cat_4,D +467218,Female,No,32,Yes,Artist,9.0,Low,1.0,Cat_6,D +460387,Female,No,35,No,Entertainment,4.0,Low,2.0,Cat_6,D +466441,Male,No,22,No,Healthcare,0.0,Low,9.0,Cat_6,D +462057,Male,Yes,40,Yes,Artist,3.0,Average,4.0,Cat_3,C +466991,Male,Yes,51,Yes,Artist,3.0,Average,2.0,Cat_6,C +459453,Male,Yes,88,Yes,Lawyer,,Low,,Cat_6,B +461714,Male,Yes,82,Yes,Lawyer,1.0,High,2.0,Cat_6,A +463465,Male,Yes,43,Yes,Artist,6.0,Average,3.0,Cat_6,B +467940,Female,No,29,No,Marketing,9.0,Low,4.0,Cat_2,A +461970,Female,No,38,Yes,Artist,9.0,Low,1.0,Cat_6,B +459877,Male,Yes,37,Yes,Artist,0.0,Average,3.0,Cat_6,C +460784,Male,No,32,Yes,Healthcare,0.0,Low,6.0,Cat_7,D +460137,Male,No,26,Yes,Entertainment,5.0,Low,4.0,Cat_6,D +465201,Male,Yes,39,Yes,Entertainment,0.0,Average,4.0,Cat_3,A +465371,Female,No,43,Yes,Artist,0.0,Low,1.0,Cat_6,A +463929,Male,Yes,51,Yes,Doctor,11.0,Average,2.0,Cat_6,C +461998,Male,Yes,52,Yes,Artist,0.0,Average,2.0,Cat_6,C +460828,Male,No,51,No,Entertainment,1.0,Low,1.0,Cat_6,A +467523,Male,No,20,No,Healthcare,,Low,4.0,Cat_4,D +461673,Male,No,33,No,Healthcare,0.0,Low,5.0,Cat_5,C +459788,Female,No,36,Yes,Entertainment,,Low,1.0,Cat_3,A +462645,Female,Yes,39,No,Engineer,,Average,6.0,Cat_4,D +462666,Male,Yes,48,Yes,Artist,0.0,Low,2.0,Cat_6,C +462587,Female,Yes,78,No,Lawyer,1.0,High,2.0,Cat_4,B +459583,Male,Yes,46,Yes,Artist,1.0,Low,1.0,Cat_6,A +467361,Female,Yes,26,No,Homemaker,,Low,4.0,Cat_6,D +459493,Male,No,26,No,Healthcare,0.0,Low,7.0,Cat_6,A +464583,Female,No,37,Yes,Engineer,7.0,Low,,Cat_6,A +462726,Female,Yes,75,Yes,Homemaker,1.0,High,,Cat_6,B +466919,Male,No,19,No,Doctor,,Low,5.0,Cat_3,D +467949,Male,No,22,No,Healthcare,1.0,Low,4.0,Cat_4,D +462406,Male,Yes,27,No,Engineer,6.0,Low,3.0,Cat_6,D +460434,Male,,57,Yes,Artist,3.0,High,3.0,Cat_6,D +463729,Female,No,32,No,Artist,,Low,5.0,Cat_1,B +466827,Male,Yes,37,No,Entertainment,0.0,Average,4.0,Cat_3,D +463641,Female,No,33,Yes,Marketing,1.0,Low,3.0,Cat_5,B +467294,Female,Yes,57,No,Engineer,0.0,Low,1.0,Cat_6,B +460875,Male,No,35,Yes,Entertainment,0.0,Low,3.0,Cat_3,A +459183,Female,No,26,Yes,Artist,1.0,Low,3.0,Cat_2,A +464541,Male,No,31,Yes,Doctor,1.0,Low,2.0,Cat_6,A +461249,Female,No,53,Yes,Artist,1.0,Low,,Cat_7,D +462959,Female,Yes,56,Yes,Engineer,2.0,High,2.0,Cat_6,B +459922,Male,Yes,65,Yes,Lawyer,0.0,Low,1.0,Cat_4,D +467935,Male,No,23,No,Healthcare,7.0,Low,3.0,Cat_6,D +461382,Male,Yes,68,No,Executive,1.0,High,2.0,Cat_6,B +462521,Female,No,32,No,Homemaker,9.0,Low,1.0,Cat_6,D +463296,Female,No,21,No,Healthcare,0.0,Low,5.0,Cat_6,D +467003,Female,Yes,61,Yes,Artist,,Low,2.0,Cat_6,C +459705,Male,Yes,25,Yes,Entertainment,1.0,Average,2.0,Cat_6,A +459984,Female,Yes,36,Yes,Entertainment,0.0,Average,3.0,Cat_6,B +466051,Male,No,25,No,Executive,3.0,Low,1.0,Cat_4,A +463583,Male,Yes,58,Yes,Artist,9.0,Average,4.0,Cat_6,C +466100,Male,Yes,66,Yes,Executive,1.0,Low,2.0,Cat_6,A +461427,Female,No,42,Yes,Artist,1.0,Low,2.0,Cat_6,A +464193,Female,Yes,36,Yes,Doctor,13.0,Low,2.0,Cat_6,B +467835,Male,Yes,58,No,Artist,9.0,Low,2.0,Cat_6,B +463000,Female,No,36,No,Engineer,0.0,Low,1.0,Cat_6,A +462457,Male,No,28,No,Healthcare,2.0,Low,4.0,Cat_6,C +460426,Male,Yes,45,No,Artist,4.0,Average,2.0,Cat_6,C +466024,Female,Yes,35,No,Entertainment,10.0,Low,2.0,Cat_3,A +467031,Male,Yes,59,Yes,Artist,11.0,Average,3.0,Cat_6,C +467440,Male,Yes,49,Yes,Entertainment,0.0,Low,1.0,Cat_6,A +464377,Female,No,51,Yes,Entertainment,0.0,Low,1.0,Cat_6,A +465870,Male,No,40,Yes,Artist,5.0,Low,2.0,Cat_6,A +460677,Female,,49,No,Healthcare,2.0,Average,2.0,Cat_3,D +460095,Female,Yes,74,Yes,Lawyer,1.0,Low,1.0,Cat_6,C +463297,Male,No,27,Yes,Healthcare,4.0,Low,2.0,Cat_6,D +461790,Male,Yes,70,Yes,Artist,0.0,High,2.0,Cat_6,C +467410,Female,Yes,62,Yes,Lawyer,0.0,Low,2.0,Cat_6,C +465904,Male,No,37,Yes,Artist,9.0,Low,1.0,Cat_6,A +459393,Female,No,40,No,Healthcare,8.0,Low,3.0,Cat_3,D +465712,Male,No,47,Yes,,0.0,Low,1.0,Cat_6,B +465771,Male,Yes,52,Yes,Entertainment,1.0,Average,4.0,Cat_6,B +462013,Female,Yes,57,Yes,Executive,0.0,Average,2.0,Cat_6,C +465620,Male,No,37,Yes,Artist,8.0,Low,3.0,Cat_6,C +459840,Male,Yes,60,Yes,Artist,1.0,Low,1.0,Cat_6,B +461460,Male,Yes,43,Yes,Executive,7.0,High,3.0,Cat_6,B +466317,Male,Yes,37,Yes,Entertainment,4.0,Low,1.0,Cat_6,B +461962,Male,Yes,59,Yes,Entertainment,1.0,Average,3.0,Cat_6,B +466071,Female,Yes,60,Yes,Artist,2.0,Low,3.0,Cat_4,B +460796,Male,Yes,69,No,Lawyer,1.0,Low,3.0,Cat_6,C +467012,Male,Yes,49,Yes,Executive,1.0,High,4.0,Cat_6,C +460357,Male,No,23,No,Healthcare,1.0,Low,5.0,Cat_6,D +460182,Female,Yes,43,Yes,Artist,2.0,Average,3.0,Cat_6,B +460491,Female,No,22,No,Healthcare,1.0,Low,7.0,Cat_3,D +466630,Male,No,29,No,Healthcare,0.0,Low,4.0,Cat_6,C +466331,Male,Yes,26,Yes,Marketing,0.0,Low,2.0,Cat_3,D +463912,Female,Yes,36,No,Artist,1.0,Average,4.0,Cat_4,B +461728,Female,No,55,Yes,Entertainment,1.0,Low,1.0,Cat_6,A +463278,Female,Yes,59,Yes,Engineer,0.0,Low,1.0,Cat_3,B +459413,Female,Yes,83,Yes,Artist,,High,2.0,Cat_6,A +465659,Female,No,55,Yes,Doctor,3.0,Low,,Cat_3,C +461746,Male,Yes,53,Yes,Artist,2.0,Average,3.0,Cat_6,C +465574,Male,Yes,62,Yes,Artist,1.0,Average,3.0,Cat_3,B +462170,Male,Yes,73,No,Executive,0.0,High,3.0,Cat_6,A +460626,Male,Yes,45,No,Doctor,9.0,High,4.0,Cat_6,D +466122,Female,,28,Yes,Healthcare,4.0,Low,8.0,Cat_3,C +466678,Male,No,28,Yes,Healthcare,1.0,Low,4.0,Cat_6,D +464930,Male,No,25,No,Marketing,0.0,Low,2.0,Cat_4,A +465447,Male,Yes,52,Yes,Engineer,1.0,Average,3.0,Cat_4,C +467549,Male,Yes,61,Yes,Entertainment,11.0,Average,3.0,Cat_6,A +463718,Female,No,27,Yes,Artist,1.0,Low,4.0,Cat_6,D +467626,Male,Yes,31,Yes,Entertainment,8.0,Average,2.0,Cat_6,B +459058,Male,No,40,Yes,Artist,1.0,Low,4.0,Cat_3,A +461983,Male,No,27,Yes,Artist,1.0,Low,3.0,Cat_6,B +461207,Female,No,60,Yes,Artist,6.0,Low,1.0,Cat_6,B +467585,Male,Yes,40,Yes,Healthcare,6.0,High,2.0,Cat_6,A +464604,Male,Yes,32,No,Entertainment,5.0,Low,2.0,Cat_4,A +462529,Female,Yes,42,Yes,Engineer,1.0,Low,2.0,Cat_6,B +460475,Male,Yes,32,Yes,Doctor,0.0,Average,2.0,Cat_3,D +463553,Male,No,27,Yes,Artist,9.0,Low,2.0,Cat_6,B +460912,Male,No,29,No,Entertainment,1.0,Low,5.0,Cat_6,D +461715,Male,No,43,Yes,Entertainment,0.0,Low,5.0,Cat_2,A +459567,Male,Yes,48,Yes,Doctor,1.0,Average,2.0,Cat_6,C +462340,Male,Yes,62,No,Entertainment,1.0,Average,6.0,Cat_6,B +461834,Female,No,42,Yes,Artist,7.0,Low,2.0,Cat_6,C +459677,Male,Yes,25,Yes,Artist,8.0,Average,2.0,Cat_6,B +465148,Male,No,31,No,Artist,1.0,Low,,,A +467911,Male,Yes,61,Yes,Healthcare,1.0,Low,4.0,Cat_6,D +462981,Female,No,26,No,Homemaker,8.0,Low,1.0,Cat_6,D +463055,Female,No,27,Yes,Doctor,1.0,Low,4.0,Cat_4,B +461884,Female,Yes,40,Yes,Artist,0.0,Low,2.0,Cat_6,B +467834,Female,No,23,No,Healthcare,0.0,Low,4.0,Cat_2,D +463019,Male,Yes,45,Yes,Executive,0.0,High,4.0,Cat_6,B +466996,Female,No,37,Yes,Artist,9.0,Low,1.0,Cat_6,A +463026,Male,Yes,28,Yes,Doctor,,Low,2.0,Cat_6,D +459479,Male,Yes,70,No,Marketing,1.0,Low,2.0,Cat_6,A +465565,Male,Yes,37,Yes,Artist,1.0,Average,2.0,Cat_6,C +459523,Female,No,19,No,Doctor,1.0,Low,4.0,Cat_6,D +464707,Male,Yes,48,No,,1.0,Average,4.0,Cat_4,A +466796,Male,,56,Yes,Artist,0.0,Average,5.0,Cat_6,A +462977,Male,Yes,36,No,Artist,,Average,3.0,Cat_6,B +462369,Male,Yes,63,No,Entertainment,1.0,Average,5.0,Cat_6,C +463053,Female,Yes,68,Yes,Artist,1.0,Average,2.0,Cat_6,C +462139,Female,No,27,Yes,Healthcare,1.0,Low,4.0,Cat_2,C +460922,Male,No,30,Yes,Entertainment,1.0,Low,3.0,Cat_6,D +463706,Female,No,33,Yes,Doctor,0.0,Low,3.0,Cat_6,D +465703,Male,Yes,25,No,Entertainment,2.0,Low,2.0,Cat_4,A +462796,Female,No,37,Yes,Entertainment,1.0,Low,2.0,Cat_7,C +459558,Male,No,30,Yes,Entertainment,0.0,Low,3.0,Cat_6,D +459716,Female,Yes,66,Yes,Engineer,8.0,Average,2.0,Cat_6,C +460599,Female,No,31,No,Engineer,0.0,Low,1.0,Cat_3,D +465775,Female,No,31,Yes,Engineer,9.0,Low,3.0,Cat_6,D +465392,Female,Yes,45,Yes,Artist,8.0,High,5.0,Cat_6,C +464783,Male,Yes,42,No,Entertainment,0.0,Low,3.0,Cat_4,A +459439,Female,Yes,46,Yes,Artist,1.0,Average,2.0,Cat_6,A +464356,Female,Yes,76,Yes,Lawyer,1.0,High,2.0,Cat_6,B +459881,Male,No,25,Yes,Healthcare,8.0,Low,4.0,Cat_6,D +461772,Female,Yes,41,Yes,Engineer,1.0,Average,3.0,Cat_4,B +460551,Male,Yes,49,Yes,Engineer,0.0,Average,2.0,Cat_3,C +466047,Male,No,32,No,,0.0,Low,5.0,Cat_4,A +462248,Male,Yes,55,No,Artist,0.0,Low,2.0,Cat_6,B +465011,Male,Yes,66,Yes,Executive,3.0,High,2.0,Cat_6,C +467898,Female,Yes,68,Yes,Artist,0.0,Low,1.0,Cat_6,C +461035,Male,No,21,No,,7.0,Low,,Cat_3,D +465382,Female,No,39,Yes,Artist,0.0,Low,,Cat_6,B +464460,Female,No,26,Yes,Healthcare,1.0,Low,2.0,Cat_6,D +466291,Male,No,22,No,Healthcare,,Low,,Cat_4,D +461397,Male,No,20,No,Healthcare,0.0,Low,4.0,Cat_6,D +464302,Female,Yes,50,Yes,Doctor,1.0,Low,3.0,Cat_6,B +465458,Female,Yes,84,Yes,Artist,1.0,High,2.0,Cat_6,A +467276,Male,No,43,Yes,Artist,12.0,Low,5.0,Cat_6,C +461129,Female,Yes,63,Yes,Entertainment,0.0,Average,4.0,Cat_6,B +467406,Male,No,37,Yes,Entertainment,9.0,Low,1.0,Cat_6,D +463140,Male,Yes,31,No,Engineer,5.0,Average,3.0,Cat_3,A +460980,Male,No,26,No,Entertainment,1.0,Low,3.0,Cat_3,D +464666,Male,Yes,41,Yes,Entertainment,2.0,Average,3.0,Cat_4,D +464236,Female,Yes,62,Yes,Artist,1.0,High,2.0,Cat_6,B +466898,Male,Yes,45,Yes,Executive,0.0,High,4.0,Cat_6,B +462736,Female,Yes,28,No,Engineer,6.0,Low,3.0,Cat_6,A +467952,Female,Yes,89,Yes,Lawyer,0.0,High,2.0,Cat_6,C +461399,Male,No,20,No,Healthcare,0.0,Low,4.0,Cat_6,D +466320,Female,No,40,Yes,Engineer,9.0,Low,2.0,Cat_4,B +465142,Male,No,23,No,Healthcare,0.0,Low,4.0,Cat_6,D +460334,Male,No,41,Yes,Artist,4.0,Low,1.0,Cat_6,A +460745,Male,Yes,78,No,Lawyer,1.0,High,2.0,Cat_6,A +459812,Female,No,43,Yes,Engineer,0.0,Low,1.0,Cat_3,A +465197,Female,Yes,57,Yes,Artist,0.0,High,4.0,Cat_6,C +462068,Male,Yes,55,Yes,Executive,1.0,High,3.0,Cat_6,C +463592,Female,No,27,Yes,Engineer,11.0,Low,1.0,Cat_6,B +467352,Male,Yes,51,Yes,Executive,1.0,High,4.0,Cat_6,C +465030,Male,Yes,35,Yes,Executive,9.0,High,4.0,Cat_6,C +462095,Male,Yes,49,Yes,Entertainment,1.0,Average,3.0,Cat_1,A +460248,Female,No,33,Yes,Healthcare,7.0,Low,2.0,Cat_6,D +464286,Female,Yes,60,Yes,Artist,1.0,Average,2.0,Cat_6,C +464248,Male,Yes,52,Yes,Artist,8.0,Low,3.0,Cat_6,C +459167,Female,No,42,Yes,Artist,1.0,Low,2.0,Cat_5,C +462710,Female,No,28,Yes,Marketing,2.0,Low,3.0,Cat_6,D +462731,Female,Yes,43,Yes,Artist,5.0,High,3.0,Cat_6,B +464475,Male,No,25,Yes,Healthcare,0.0,Low,4.0,Cat_6,C +466899,Female,Yes,51,Yes,Artist,,Low,2.0,Cat_6,C +460108,Male,Yes,42,Yes,Artist,1.0,Average,3.0,Cat_2,C +460606,Male,No,20,No,Healthcare,,Low,5.0,Cat_7,D +459096,Male,,80,No,Lawyer,0.0,Low,1.0,Cat_6,A +467942,Female,Yes,41,Yes,Artist,1.0,High,4.0,Cat_2,C +463264,Female,Yes,71,Yes,Lawyer,0.0,Low,2.0,Cat_6,C +462089,Female,Yes,42,Yes,Artist,0.0,Average,2.0,Cat_6,C +467756,Male,Yes,56,Yes,Artist,1.0,Average,2.0,Cat_6,C +461323,Female,Yes,41,Yes,Healthcare,6.0,Average,2.0,Cat_6,C +459563,Male,Yes,72,Yes,Entertainment,7.0,Low,2.0,Cat_6,A +467602,Female,No,30,No,Entertainment,1.0,Low,4.0,Cat_6,D +462562,Female,No,25,Yes,Entertainment,0.0,Low,6.0,Cat_2,C +461588,Male,No,33,Yes,Marketing,0.0,Low,4.0,Cat_2,D +465691,Male,No,26,Yes,Artist,,Low,,Cat_4,A +460894,Male,Yes,38,No,Executive,5.0,High,3.0,Cat_6,A +459151,Male,Yes,60,Yes,Lawyer,9.0,Low,2.0,Cat_6,B +466417,Female,Yes,79,No,Lawyer,1.0,High,3.0,Cat_6,A +466837,Female,No,38,Yes,Marketing,9.0,Low,4.0,Cat_5,D +466918,Male,Yes,56,Yes,Artist,1.0,Average,1.0,Cat_6,C +464133,Male,Yes,73,No,Homemaker,2.0,Low,1.0,Cat_6,A +466101,Female,No,22,No,Healthcare,0.0,Low,7.0,Cat_6,D +464289,Male,Yes,42,No,Marketing,1.0,Low,3.0,Cat_6,A +461936,Male,Yes,72,No,Lawyer,0.0,High,2.0,Cat_6,B +459301,Female,No,33,Yes,Healthcare,0.0,Low,3.0,Cat_6,C +462284,Male,Yes,53,Yes,Executive,1.0,Average,4.0,Cat_6,B +463290,Female,No,25,No,Healthcare,1.0,Low,5.0,Cat_6,B +462413,Male,Yes,46,Yes,Artist,0.0,Average,3.0,Cat_6,C +466814,Male,No,18,No,Healthcare,0.0,Low,3.0,Cat_6,D +460996,Female,No,60,Yes,Engineer,1.0,Low,1.0,Cat_6,A +459714,Male,No,22,No,Doctor,2.0,Low,4.0,Cat_6,C +464829,Male,No,22,No,Healthcare,9.0,Low,5.0,Cat_4,D +467474,Male,No,18,No,Healthcare,3.0,Low,5.0,Cat_6,D +466134,Male,Yes,39,Yes,Doctor,0.0,High,3.0,Cat_6,C +460496,Male,No,19,No,Healthcare,7.0,Low,5.0,Cat_3,D +461995,Female,No,30,No,Artist,7.0,Low,1.0,Cat_6,A +466798,Male,No,23,No,Healthcare,4.0,Low,5.0,Cat_6,D +461086,Male,No,23,No,Healthcare,1.0,Low,7.0,Cat_3,D +459306,Female,No,48,Yes,Artist,4.0,Low,1.0,Cat_6,C +464919,Male,Yes,39,Yes,Artist,9.0,Low,1.0,Cat_4,A +461798,Female,No,56,Yes,Artist,0.0,Low,1.0,Cat_6,A +466987,Male,Yes,63,No,Artist,5.0,Average,2.0,Cat_6,C +459329,Female,Yes,71,No,Engineer,1.0,High,4.0,Cat_3,A +465174,Female,Yes,70,Yes,Entertainment,1.0,High,2.0,Cat_6,B +460979,Male,Yes,84,No,Lawyer,1.0,Low,1.0,Cat_6,A +465272,Male,Yes,39,Yes,Executive,0.0,High,6.0,Cat_6,C +460463,Male,Yes,42,Yes,Artist,6.0,Low,4.0,Cat_3,A +460246,Male,Yes,46,Yes,Artist,4.0,Low,2.0,Cat_6,B +463423,Male,Yes,63,No,Executive,0.0,Average,3.0,Cat_6,B +466992,Female,Yes,55,No,Artist,0.0,Low,3.0,Cat_6,C +461530,Male,Yes,42,Yes,Engineer,1.0,Low,2.0,Cat_6,A +463615,Female,Yes,58,Yes,Artist,,Average,2.0,Cat_6,C +465101,Female,Yes,88,Yes,Lawyer,1.0,Low,1.0,Cat_6,D +464997,Male,Yes,43,Yes,Artist,5.0,Average,2.0,Cat_6,B +462255,Male,Yes,73,Yes,Executive,1.0,High,3.0,Cat_6,C +460734,Male,Yes,35,Yes,Entertainment,8.0,Average,,Cat_2,D +462616,Male,No,30,No,Healthcare,0.0,Low,5.0,Cat_4,D +460968,Male,Yes,30,No,Doctor,11.0,Average,2.0,Cat_3,A +465235,Male,Yes,46,Yes,Healthcare,1.0,High,3.0,Cat_6,C +461818,Female,Yes,43,Yes,Artist,2.0,Average,2.0,Cat_6,C +463171,Female,Yes,36,No,Entertainment,1.0,High,3.0,Cat_3,A +460776,Female,Yes,47,Yes,,2.0,Low,1.0,,D +461634,Male,Yes,63,Yes,Entertainment,1.0,Low,1.0,Cat_6,A +462150,Male,Yes,35,Yes,Artist,7.0,Low,2.0,Cat_6,C +461503,Female,No,31,Yes,Artist,9.0,Low,2.0,Cat_6,B +464408,Female,Yes,45,Yes,Artist,,Average,4.0,Cat_4,C +463163,Male,No,26,No,Executive,2.0,Low,2.0,Cat_3,D +462358,Male,Yes,37,No,Doctor,4.0,Average,2.0,Cat_6,A +460647,Female,Yes,26,No,Doctor,1.0,Average,2.0,Cat_4,D +464888,Male,Yes,37,No,Entertainment,0.0,Low,5.0,Cat_4,A +461652,Female,Yes,53,Yes,Artist,1.0,Average,3.0,Cat_6,C +467062,Female,No,55,Yes,Artist,1.0,Low,1.0,Cat_3,A +467157,Male,Yes,42,Yes,Executive,9.0,High,6.0,Cat_7,B +462509,Female,Yes,46,Yes,Artist,0.0,Average,2.0,Cat_6,C +459469,Male,Yes,30,Yes,Healthcare,,Low,6.0,Cat_6,A +465499,Male,No,63,Yes,Artist,0.0,Low,1.0,Cat_6,A +464913,Male,Yes,52,Yes,Entertainment,0.0,Low,4.0,Cat_4,B +465969,Female,No,28,Yes,Doctor,12.0,Low,2.0,Cat_7,D +464922,Female,No,20,No,Healthcare,0.0,Low,5.0,Cat_4,D +463022,Male,Yes,60,Yes,Executive,4.0,High,3.0,Cat_6,C +464704,Male,Yes,42,Yes,Healthcare,0.0,Average,4.0,Cat_4,B +462345,Male,Yes,46,Yes,Doctor,1.0,Average,6.0,Cat_4,B +461439,Male,Yes,37,Yes,Artist,1.0,Average,2.0,Cat_6,C +461599,Female,No,42,Yes,Artist,8.0,Low,1.0,Cat_6,B +465705,Female,Yes,46,Yes,Artist,1.0,Average,4.0,Cat_6,C +461043,Male,Yes,49,No,Healthcare,0.0,Low,7.0,Cat_4,D +467551,Female,Yes,89,No,Lawyer,0.0,High,2.0,Cat_6,B +464053,Female,Yes,38,No,Entertainment,4.0,Low,1.0,Cat_6,B +459282,Male,Yes,48,Yes,Artist,0.0,Average,2.0,Cat_6,C +461649,Female,Yes,51,Yes,Artist,3.0,Low,4.0,Cat_6,B +464389,Male,Yes,73,Yes,Artist,1.0,High,2.0,Cat_2,C +464293,Male,No,30,Yes,Healthcare,1.0,Low,4.0,Cat_6,C +461739,Female,Yes,46,Yes,Artist,1.0,Low,,Cat_6,C +465320,Female,Yes,65,Yes,Lawyer,1.0,Average,2.0,Cat_6,B +463707,Female,No,36,No,Artist,0.0,Low,1.0,Cat_5,B +465436,Female,No,25,Yes,Healthcare,0.0,Low,3.0,Cat_5,B +464562,Female,No,31,No,,8.0,Low,3.0,Cat_6,D +465078,Male,Yes,43,Yes,Executive,0.0,High,4.0,Cat_6,B +460568,Female,Yes,33,Yes,Homemaker,0.0,High,2.0,Cat_3,C +459636,Male,No,30,Yes,Entertainment,14.0,Low,1.0,Cat_6,D +466445,Female,,21,No,Healthcare,,Low,4.0,Cat_6,D +460129,Female,No,25,Yes,Engineer,8.0,Low,2.0,Cat_6,A +465373,Male,Yes,49,Yes,Artist,4.0,Average,4.0,Cat_6,C +463966,Female,No,31,Yes,Artist,9.0,Low,1.0,Cat_6,B +465467,Male,,33,No,Healthcare,1.0,Low,3.0,Cat_6,C +460918,Female,No,27,Yes,Doctor,14.0,Low,8.0,Cat_6,C +465954,Female,No,33,Yes,Entertainment,11.0,Low,3.0,Cat_6,A +465153,Male,Yes,35,Yes,Artist,1.0,Average,2.0,Cat_6,A +466502,Male,No,19,No,Healthcare,1.0,Low,5.0,Cat_3,D +460819,Female,Yes,25,No,Marketing,0.0,High,5.0,Cat_4,D +464993,Male,Yes,57,No,Executive,4.0,High,4.0,Cat_6,C +463103,Male,Yes,45,No,Executive,1.0,Average,4.0,Cat_6,B +466302,Female,Yes,47,No,Homemaker,3.0,High,3.0,Cat_6,B +466521,Male,Yes,20,,Marketing,1.0,Average,3.0,Cat_3,A +464288,Female,No,33,Yes,Doctor,9.0,Low,2.0,Cat_6,A +460240,Male,Yes,45,Yes,Artist,3.0,Low,1.0,Cat_6,C +459188,Male,No,27,No,Healthcare,1.0,Low,3.0,Cat_6,C +465411,Female,Yes,38,Yes,Engineer,0.0,Low,1.0,Cat_5,D +465595,Female,No,38,No,Engineer,0.0,Low,3.0,Cat_4,C +466021,Male,No,87,No,Lawyer,1.0,Low,1.0,Cat_6,A +461831,Male,No,30,Yes,Doctor,0.0,Low,3.0,Cat_6,C +462556,Male,Yes,57,Yes,Entertainment,0.0,Average,7.0,Cat_5,C +467321,Female,Yes,63,Yes,Artist,9.0,Low,2.0,Cat_6,C +459327,Male,No,50,No,Doctor,9.0,Low,3.0,Cat_6,D +461626,Male,Yes,68,Yes,Executive,0.0,High,2.0,Cat_6,C +463337,Female,Yes,33,No,Entertainment,1.0,Low,2.0,Cat_3,A +467615,Female,Yes,37,Yes,Doctor,0.0,Low,2.0,Cat_6,A +463597,Female,No,25,No,Engineer,0.0,Low,6.0,Cat_6,C +463784,Female,No,29,No,Engineer,0.0,Low,5.0,Cat_4,A +459513,Female,No,32,Yes,Healthcare,3.0,Low,2.0,Cat_6,C +465804,Female,Yes,68,No,Engineer,0.0,Low,1.0,Cat_4,A +465103,Male,Yes,86,Yes,Lawyer,1.0,High,2.0,Cat_6,A +464007,Male,Yes,49,Yes,Artist,0.0,Low,3.0,Cat_6,B +463060,Female,Yes,32,Yes,Homemaker,12.0,High,2.0,Cat_6,D +461359,Male,Yes,28,Yes,Healthcare,1.0,Low,4.0,Cat_7,D +467002,Female,No,26,Yes,Homemaker,9.0,Low,,Cat_6,D +465014,Male,Yes,83,No,Lawyer,1.0,Low,1.0,Cat_6,A +459100,Female,No,63,Yes,Artist,14.0,Low,1.0,Cat_6,B +464083,Male,Yes,65,No,Lawyer,0.0,High,2.0,Cat_6,C +464552,Female,Yes,38,Yes,Artist,1.0,Average,3.0,Cat_4,C +460218,Male,Yes,61,Yes,Entertainment,1.0,Low,1.0,Cat_6,D +460297,Female,Yes,46,Yes,Artist,8.0,Low,2.0,Cat_6,B +467171,Female,No,30,Yes,Doctor,1.0,Low,2.0,Cat_6,C +464809,Male,Yes,46,,Entertainment,,Average,5.0,Cat_4,B +463008,Male,No,53,No,Entertainment,1.0,Low,,Cat_6,A +464023,Male,No,22,No,Healthcare,0.0,Low,5.0,Cat_7,D +467929,Female,Yes,42,Yes,Artist,1.0,Average,2.0,Cat_6,C +461049,Female,Yes,57,Yes,Doctor,0.0,Low,7.0,Cat_7,B +465049,Female,Yes,49,No,Engineer,6.0,Low,2.0,Cat_6,A +460751,Female,No,25,No,Artist,1.0,Low,3.0,Cat_6,D +461690,Female,No,31,Yes,Engineer,0.0,Low,1.0,Cat_4,A +464084,Male,No,25,No,Healthcare,1.0,Low,5.0,Cat_4,D +465644,Male,No,33,Yes,Healthcare,0.0,Low,5.0,Cat_6,C +459470,Male,No,29,Yes,Healthcare,9.0,Low,6.0,Cat_6,D +462700,Male,Yes,38,Yes,Artist,0.0,Average,4.0,Cat_6,B +465067,Male,Yes,62,Yes,Executive,8.0,High,2.0,Cat_6,C +464247,Male,Yes,59,No,Engineer,0.0,Low,1.0,Cat_6,A +459465,Female,Yes,27,Yes,Entertainment,3.0,Low,5.0,Cat_6,B +463894,Female,Yes,51,Yes,Artist,0.0,Average,3.0,Cat_2,C +466089,Male,No,59,No,Entertainment,0.0,Low,3.0,Cat_6,B +459355,Female,Yes,78,No,Homemaker,,Low,1.0,Cat_6,A +467227,Female,No,37,Yes,Artist,12.0,Low,1.0,Cat_4,A +459054,Male,Yes,37,Yes,Executive,9.0,High,4.0,Cat_6,C +463609,Male,Yes,30,No,Artist,0.0,Average,2.0,Cat_5,A +466260,Male,Yes,43,Yes,Entertainment,0.0,Average,2.0,Cat_6,B +464283,Male,No,40,Yes,Artist,5.0,Low,2.0,Cat_6,A +465646,Female,No,40,Yes,Artist,6.0,Low,2.0,Cat_6,B +461103,Female,No,30,No,Engineer,1.0,Low,5.0,Cat_3,D +464378,Female,Yes,40,Yes,Artist,9.0,Average,2.0,Cat_6,B +462661,Female,Yes,62,Yes,Artist,2.0,High,3.0,Cat_6,C +460266,Male,No,22,No,Healthcare,0.0,Low,8.0,Cat_6,D +462983,Female,Yes,66,Yes,Lawyer,1.0,High,2.0,Cat_6,A +459022,Male,Yes,67,Yes,Artist,10.0,Low,1.0,Cat_6,D +463231,Male,Yes,50,Yes,Artist,1.0,Average,3.0,Cat_6,A +465863,Male,No,27,No,Healthcare,5.0,Low,3.0,Cat_6,D +463557,Male,No,28,Yes,Doctor,1.0,Low,3.0,Cat_6,A +467636,Male,Yes,45,Yes,Doctor,1.0,Average,2.0,Cat_6,C +463539,Male,Yes,59,Yes,Artist,1.0,Average,2.0,Cat_4,B +459633,Male,No,19,No,Healthcare,1.0,Low,2.0,Cat_6,D +462304,Male,Yes,62,Yes,Artist,1.0,Low,4.0,Cat_4,B +466988,Female,No,72,Yes,Lawyer,0.0,Low,,Cat_6,B +467467,Male,No,19,No,Healthcare,0.0,Low,4.0,Cat_6,D +464871,Female,No,28,Yes,Entertainment,6.0,Low,3.0,Cat_4,B +462258,Male,No,25,No,Doctor,1.0,Low,4.0,Cat_6,D +467813,Male,Yes,36,Yes,Entertainment,0.0,Low,2.0,Cat_4,A +459068,Female,No,33,Yes,Healthcare,2.0,Low,2.0,Cat_6,A +462701,Male,Yes,59,No,Entertainment,0.0,Average,4.0,Cat_6,B +466536,Male,No,20,No,Healthcare,4.0,Low,4.0,Cat_4,D +465668,Male,No,23,No,Healthcare,0.0,Low,5.0,Cat_4,D +462234,Male,Yes,27,No,,1.0,Average,6.0,Cat_4,D +460340,Female,No,46,Yes,Artist,10.0,Low,2.0,Cat_6,A +466820,Female,Yes,74,No,Lawyer,1.0,High,2.0,Cat_6,A +462922,Male,No,26,No,Doctor,8.0,Low,1.0,Cat_3,D +465326,Male,Yes,76,Yes,Artist,,Low,1.0,Cat_6,A +464130,Male,No,41,Yes,Artist,1.0,Low,3.0,Cat_6,A +463832,Female,No,32,No,Marketing,,Low,,Cat_6,D +462735,Female,Yes,30,No,Artist,0.0,Average,2.0,Cat_1,B +464412,Male,Yes,70,No,Lawyer,1.0,Low,2.0,Cat_6,D +464468,Male,Yes,50,Yes,Artist,1.0,Low,3.0,Cat_6,C +463422,Male,Yes,39,No,Executive,,High,4.0,Cat_6,B +459498,Female,Yes,42,Yes,Artist,,Low,2.0,Cat_6,C +460173,Female,Yes,40,Yes,Artist,1.0,Low,3.0,Cat_6,C +463091,Female,Yes,39,Yes,Homemaker,8.0,Low,1.0,Cat_6,D +467213,Female,Yes,88,Yes,Lawyer,1.0,High,2.0,Cat_6,B +461804,Female,No,43,Yes,Artist,0.0,Low,1.0,Cat_6,A +466649,Male,No,19,No,Healthcare,1.0,Low,3.0,Cat_3,D +460533,Female,Yes,45,No,Artist,6.0,Low,1.0,Cat_6,A +465820,Female,,30,Yes,,,Average,4.0,Cat_4,B +466329,Female,Yes,39,,Doctor,2.0,Low,1.0,Cat_3,A +461892,Female,No,35,Yes,Healthcare,0.0,Low,1.0,Cat_2,D +463656,Male,Yes,53,Yes,Doctor,0.0,Low,3.0,Cat_6,A +462850,Male,No,21,No,Healthcare,1.0,Low,4.0,Cat_6,D +460603,Female,No,22,,Entertainment,0.0,Low,3.0,Cat_3,A +460707,Male,Yes,38,No,Artist,1.0,Average,3.0,Cat_6,C +463122,Male,Yes,36,Yes,Executive,9.0,Average,3.0,Cat_6,B +467588,Male,Yes,63,Yes,Artist,0.0,Average,4.0,Cat_6,C +462552,Male,No,31,Yes,Artist,1.0,Low,2.0,Cat_6,A +466048,Male,No,25,No,Entertainment,3.0,Low,1.0,Cat_4,A +467845,Male,Yes,52,Yes,Entertainment,1.0,Low,2.0,Cat_6,C +459810,Male,No,19,No,Healthcare,1.0,Low,4.0,Cat_6,D +461267,Female,No,35,Yes,Healthcare,,Low,1.0,Cat_4,D +466572,Female,No,30,No,Healthcare,0.0,Low,4.0,Cat_6,D +464092,Female,Yes,59,Yes,Artist,0.0,Average,5.0,Cat_6,C +467025,Female,No,25,No,Engineer,1.0,Low,3.0,Cat_6,D +466110,Male,Yes,77,Yes,Lawyer,0.0,Low,1.0,Cat_6,A +462353,Female,Yes,58,Yes,Doctor,1.0,Average,2.0,Cat_6,C +459904,Female,Yes,38,Yes,Artist,6.0,Low,4.0,Cat_6,C +462011,Male,Yes,29,No,Marketing,1.0,Low,4.0,Cat_4,D +462367,Male,Yes,56,Yes,Artist,1.0,Average,3.0,Cat_6,B +460220,Female,No,37,Yes,Entertainment,1.0,Low,2.0,Cat_2,D +461429,Male,Yes,49,Yes,Doctor,1.0,Average,4.0,Cat_6,C +462668,Female,No,41,Yes,Artist,2.0,Low,1.0,Cat_6,B +467339,Male,,55,Yes,Artist,1.0,Average,3.0,Cat_6,B +461699,Male,No,28,Yes,Doctor,0.0,Low,1.0,Cat_6,D +464885,Female,No,35,No,Engineer,1.0,Low,2.0,Cat_4,A +460989,Male,No,23,No,Healthcare,1.0,Low,3.0,Cat_1,D +462156,Female,Yes,43,Yes,Artist,4.0,Average,2.0,Cat_3,C +461182,Male,No,18,No,Healthcare,,Low,,Cat_3,D +460507,Male,No,30,No,Doctor,1.0,Low,4.0,Cat_3,D +462875,Male,No,20,No,Healthcare,1.0,Low,3.0,Cat_6,D +461708,Male,Yes,84,No,Lawyer,0.0,High,2.0,Cat_6,B +459766,Female,No,42,Yes,Engineer,,Low,1.0,Cat_6,A +462137,Male,No,28,Yes,Doctor,1.0,Low,1.0,Cat_6,A +459630,Male,Yes,73,Yes,Lawyer,1.0,Average,2.0,Cat_6,A +462699,Female,Yes,61,Yes,Lawyer,,High,4.0,Cat_2,B +465897,Female,Yes,42,Yes,Artist,9.0,Low,1.0,Cat_6,A +465628,Male,Yes,49,Yes,Artist,2.0,Low,2.0,Cat_2,A +462987,Female,Yes,84,Yes,Lawyer,,High,2.0,Cat_6,C +463492,Male,No,22,No,Healthcare,,Low,4.0,Cat_6,D +462321,Female,No,42,Yes,Engineer,6.0,Low,1.0,Cat_6,A +460511,Female,No,31,No,Doctor,0.0,Low,4.0,Cat_3,D +459225,Female,Yes,49,Yes,Artist,0.0,Average,4.0,Cat_6,C +467894,Male,Yes,58,Yes,Artist,0.0,Average,2.0,Cat_6,C +465076,Male,Yes,72,Yes,Artist,0.0,High,5.0,Cat_6,C +467573,Male,Yes,45,Yes,Artist,6.0,Average,4.0,Cat_6,B +465266,Male,Yes,49,Yes,Entertainment,0.0,Average,5.0,Cat_4,B +462215,Female,No,20,No,Marketing,1.0,Low,3.0,Cat_4,D +466279,Female,No,31,No,,1.0,Low,4.0,Cat_3,A +467131,Male,Yes,50,Yes,Artist,0.0,Low,2.0,Cat_6,B +459456,Female,No,65,No,Lawyer,,Low,1.0,Cat_6,D +460225,Female,Yes,78,Yes,Lawyer,3.0,High,2.0,Cat_6,C +463825,Female,Yes,45,Yes,Artist,1.0,Average,3.0,,B +462597,Male,Yes,29,No,Healthcare,,Low,4.0,Cat_4,A +466360,Male,No,25,No,Healthcare,0.0,Low,3.0,Cat_4,B +465542,Female,Yes,62,No,Doctor,,Average,5.0,Cat_3,A +465848,Female,No,28,Yes,Healthcare,0.0,Low,1.0,Cat_7,D +459043,Female,Yes,49,No,Engineer,1.0,Low,1.0,Cat_7,A +463069,Male,No,29,Yes,Artist,7.0,Low,1.0,Cat_6,B +459795,Male,No,52,Yes,Artist,0.0,Low,1.0,Cat_6,A +464954,Male,No,23,No,Healthcare,1.0,Low,4.0,Cat_4,D +459713,Male,No,18,No,Healthcare,,Low,3.0,Cat_6,D +463068,Male,Yes,26,Yes,Artist,9.0,Average,2.0,Cat_6,B +464834,Male,Yes,51,No,Executive,1.0,Low,4.0,Cat_4,D +462908,Male,Yes,27,No,Executive,10.0,High,3.0,Cat_6,A +464032,Male,No,20,No,Healthcare,1.0,Low,3.0,Cat_6,D +459307,Male,Yes,62,Yes,Entertainment,0.0,Low,1.0,Cat_6,B +462224,Female,Yes,63,No,Engineer,4.0,Average,8.0,Cat_4,D +465611,Male,No,53,Yes,Doctor,0.0,Low,1.0,Cat_6,C +466322,Male,No,28,No,Doctor,0.0,Low,3.0,Cat_3,B +461548,Female,No,33,Yes,Engineer,1.0,Low,3.0,Cat_6,A +461870,Female,Yes,45,Yes,Artist,0.0,Average,2.0,Cat_6,C +466966,Male,Yes,61,Yes,Entertainment,1.0,Average,2.0,Cat_6,C +459207,Female,Yes,62,,Doctor,0.0,Average,4.0,Cat_6,B +465672,Male,No,42,No,Executive,9.0,Low,3.0,Cat_4,D +459317,Male,Yes,52,Yes,Executive,1.0,High,4.0,Cat_6,C +464362,Female,Yes,51,Yes,Artist,1.0,Low,1.0,Cat_6,C +465683,Female,No,49,No,Entertainment,1.0,Low,2.0,Cat_4,A +462253,Male,Yes,51,Yes,Engineer,0.0,Low,2.0,Cat_4,A +459421,Male,Yes,29,No,Healthcare,0.0,Average,4.0,Cat_6,A +459987,Male,Yes,53,Yes,Healthcare,,High,2.0,Cat_6,D +460036,Male,Yes,40,Yes,,3.0,Low,1.0,Cat_6,D +461613,Female,No,25,Yes,Healthcare,8.0,Low,4.0,Cat_5,C +461168,Female,Yes,29,No,Engineer,5.0,Average,2.0,Cat_3,A +459247,Male,Yes,60,Yes,Executive,0.0,Average,5.0,Cat_6,C +460770,Female,No,25,Yes,Healthcare,5.0,Low,3.0,Cat_3,A +465047,Male,Yes,55,Yes,Executive,8.0,High,2.0,Cat_6,C +467013,Male,Yes,66,Yes,Executive,0.0,High,2.0,Cat_6,C +465894,Male,No,27,Yes,Artist,8.0,Low,6.0,Cat_6,D +462702,Male,Yes,51,No,Entertainment,1.0,Average,4.0,Cat_4,A +465431,Male,Yes,35,No,Artist,0.0,Low,2.0,Cat_6,B +466386,Male,Yes,65,Yes,Executive,1.0,Low,2.0,Cat_4,B +467221,Female,No,20,No,Doctor,,Low,4.0,Cat_6,C +464057,Male,No,32,Yes,Marketing,0.0,Low,5.0,Cat_6,B +467217,Female,Yes,79,Yes,Lawyer,,High,2.0,Cat_6,C +462186,Female,No,21,No,Doctor,0.0,Low,3.0,Cat_1,D +463432,Male,No,36,Yes,Artist,0.0,Low,1.0,Cat_6,B +466753,Female,Yes,70,Yes,Artist,1.0,Average,2.0,Cat_3,C +465123,Female,No,28,Yes,Healthcare,1.0,Low,3.0,Cat_6,A +459965,Male,No,25,Yes,Artist,7.0,Low,1.0,Cat_2,A +466030,Female,Yes,36,No,Entertainment,8.0,Low,2.0,Cat_6,B +462582,Male,No,20,No,Healthcare,0.0,Low,4.0,Cat_4,D +461466,Male,Yes,49,No,Artist,0.0,Low,2.0,Cat_7,A +459676,Female,No,28,Yes,Artist,,Low,2.0,Cat_6,A +464380,Female,Yes,49,Yes,Artist,6.0,Average,6.0,Cat_6,C +466846,Male,No,18,No,Healthcare,2.0,Low,4.0,Cat_2,D +465184,Female,Yes,46,No,Engineer,1.0,Average,5.0,Cat_6,C +459481,Male,Yes,47,No,Lawyer,0.0,Low,2.0,Cat_6,A +467263,Female,No,32,No,Marketing,,Low,1.0,Cat_4,D +465136,Male,Yes,47,Yes,Artist,0.0,Average,2.0,Cat_6,C +464253,Female,Yes,49,Yes,Artist,,Average,4.0,Cat_6,C +466534,Male,No,28,No,Healthcare,0.0,Low,9.0,Cat_6,D +466411,Male,Yes,58,Yes,Artist,1.0,Average,3.0,Cat_3,B +462576,Female,No,21,No,Healthcare,9.0,Low,4.0,Cat_3,D +467704,Male,Yes,53,Yes,Artist,1.0,Average,2.0,Cat_6,C +465303,Female,Yes,67,Yes,Artist,1.0,Average,3.0,Cat_2,C +467703,Male,No,26,Yes,Healthcare,1.0,Low,5.0,Cat_6,D +465493,Female,No,26,No,Artist,0.0,Low,3.0,Cat_4,A +461322,Male,Yes,40,Yes,Artist,0.0,Average,2.0,Cat_6,C +461706,Male,No,25,Yes,Healthcare,0.0,Low,3.0,Cat_6,D +463629,Male,No,41,Yes,Artist,1.0,Low,1.0,Cat_6,B +465140,Male,Yes,45,Yes,Executive,5.0,High,3.0,Cat_7,B +459308,Female,Yes,47,Yes,Artist,0.0,Average,2.0,Cat_6,C +459582,Male,Yes,27,No,Entertainment,,Average,3.0,Cat_6,A +460859,Male,Yes,50,Yes,Entertainment,,Low,4.0,Cat_6,D +461915,Male,Yes,51,No,Artist,,Average,2.0,Cat_2,B +466358,Female,,28,No,Engineer,0.0,Low,4.0,Cat_6,A +459708,Female,No,39,Yes,Engineer,1.0,Low,1.0,Cat_6,A +459179,Male,Yes,60,No,Entertainment,0.0,Average,3.0,Cat_6,B +462654,Male,Yes,39,No,Artist,1.0,Average,6.0,Cat_4,A +466256,Female,Yes,65,Yes,Engineer,1.0,Low,1.0,Cat_6,B +465649,Female,Yes,43,Yes,Marketing,6.0,Low,1.0,Cat_6,A +467333,Female,Yes,58,Yes,Artist,,Average,6.0,Cat_2,C +465652,Female,Yes,31,No,Marketing,0.0,Low,2.0,Cat_2,D +460360,Female,Yes,38,Yes,Artist,2.0,Low,1.0,Cat_4,A +464472,Female,No,27,Yes,Artist,0.0,Low,3.0,Cat_6,C +467422,Female,Yes,58,Yes,Artist,0.0,Average,2.0,Cat_6,C +463116,Male,Yes,62,Yes,Artist,1.0,High,2.0,Cat_6,B +460177,Male,No,48,Yes,Artist,1.0,Low,1.0,Cat_6,B +465081,Male,Yes,57,Yes,Executive,7.0,High,2.0,Cat_6,C +467810,Male,Yes,42,Yes,Artist,0.0,Average,5.0,Cat_6,C +465063,Female,Yes,68,Yes,Lawyer,1.0,High,2.0,Cat_6,C +462690,Male,Yes,38,Yes,Engineer,,Average,4.0,Cat_6,B +467752,Male,Yes,31,No,Executive,,Average,3.0,Cat_4,A +466430,Female,Yes,51,No,Engineer,0.0,Low,4.0,Cat_6,A +465713,Male,Yes,59,Yes,Artist,0.0,Average,4.0,Cat_6,C +462555,Male,Yes,36,Yes,Artist,1.0,Average,2.0,Cat_4,C +459668,Male,Yes,32,No,,5.0,Average,3.0,Cat_6,B +461615,Male,No,25,Yes,Doctor,1.0,Low,2.0,Cat_6,D +461532,Male,Yes,48,Yes,Entertainment,1.0,Low,1.0,Cat_6,A +465295,Female,,37,Yes,Doctor,8.0,Average,1.0,Cat_6,A +459175,Female,Yes,70,Yes,Artist,1.0,Average,2.0,Cat_6,C +463739,Female,Yes,42,No,Marketing,0.0,Low,3.0,Cat_6,B +464339,Male,No,26,No,Artist,0.0,Low,2.0,Cat_6,D +465074,Male,No,25,Yes,Artist,1.0,Low,4.0,Cat_6,A +465282,Female,No,36,Yes,Artist,0.0,Low,3.0,Cat_6,A +460488,Male,No,42,No,Doctor,0.0,Low,1.0,Cat_3,D +464554,Female,No,35,Yes,Artist,0.0,Low,7.0,Cat_2,C +462814,Female,Yes,22,No,Engineer,8.0,Average,2.0,Cat_5,C +462073,Female,No,36,Yes,Artist,6.0,Low,1.0,Cat_6,C +459901,Male,Yes,47,Yes,Artist,0.0,Average,3.0,Cat_6,C +460858,Male,No,19,No,Healthcare,1.0,Low,4.0,Cat_3,D +461895,Male,Yes,36,Yes,Entertainment,1.0,Low,2.0,Cat_6,B +467308,Female,No,28,Yes,Doctor,0.0,Low,3.0,Cat_3,C +467584,Male,Yes,52,No,Doctor,1.0,Average,4.0,Cat_6,B +466444,Male,Yes,50,Yes,Doctor,,Average,4.0,Cat_3,C +462622,Male,Yes,47,No,Executive,0.0,Low,4.0,Cat_4,D +460100,Female,No,29,Yes,Doctor,1.0,Low,1.0,Cat_3,A +466591,Female,No,23,No,Healthcare,0.0,Low,4.0,Cat_6,D +462849,Male,Yes,50,Yes,Artist,0.0,Average,4.0,Cat_6,C +463235,Female,No,21,No,,1.0,Low,4.0,Cat_6,D +466410,Female,Yes,56,Yes,Artist,0.0,Low,1.0,Cat_6,B +464141,Male,Yes,32,Yes,Entertainment,9.0,High,2.0,Cat_4,A +464634,Female,No,32,Yes,Engineer,,Low,9.0,Cat_4,D +461564,Male,Yes,63,Yes,Executive,1.0,High,2.0,Cat_6,C +463219,Male,Yes,50,No,Entertainment,0.0,Low,2.0,Cat_6,D +461425,Male,Yes,52,Yes,Artist,,Low,4.0,Cat_6,A +464009,Female,Yes,49,Yes,Artist,7.0,Average,4.0,Cat_6,B +460573,Male,No,62,No,Healthcare,8.0,Low,1.0,Cat_3,C +460951,Female,Yes,55,No,Engineer,1.0,Average,7.0,Cat_6,A +462262,Male,No,27,No,Healthcare,1.0,Low,3.0,,B +467126,Female,Yes,45,Yes,Artist,0.0,Low,1.0,Cat_3,B +462924,Female,Yes,31,No,Homemaker,8.0,Average,2.0,Cat_6,D +462488,Male,No,33,Yes,Healthcare,0.0,Low,5.0,Cat_6,D +461640,Male,Yes,52,Yes,Artist,1.0,Average,2.0,Cat_6,C +465504,Male,No,42,Yes,Entertainment,0.0,Low,2.0,Cat_6,B +460902,Male,No,38,Yes,Executive,1.0,Low,3.0,Cat_6,A +459657,Male,Yes,84,Yes,Lawyer,1.0,High,2.0,Cat_6,B +465054,Male,Yes,67,Yes,Executive,1.0,High,3.0,Cat_6,C +464000,Female,No,32,No,Healthcare,0.0,Low,5.0,Cat_7,C +460324,Female,No,33,Yes,Entertainment,12.0,Low,2.0,Cat_6,A +462468,Female,No,30,Yes,Healthcare,2.0,Low,5.0,Cat_6,D +465245,Male,No,18,No,Healthcare,1.0,Low,4.0,Cat_4,D +467919,Female,Yes,47,Yes,Artist,7.0,Average,2.0,Cat_6,C +463716,Male,No,29,No,Doctor,1.0,Low,4.0,Cat_6,D +463769,Male,Yes,63,No,Executive,0.0,High,4.0,Cat_6,B +464907,Male,,55,Yes,Engineer,0.0,Average,2.0,Cat_4,B +460523,Female,No,48,Yes,Entertainment,1.0,Low,,Cat_4,C +462327,Male,Yes,27,Yes,Entertainment,0.0,Low,4.0,Cat_7,D +463882,Female,Yes,43,Yes,Artist,1.0,Low,1.0,Cat_1,A +464446,Female,Yes,50,Yes,Engineer,0.0,Low,,Cat_6,B +462282,Male,No,29,No,Healthcare,1.0,Low,3.0,Cat_6,D +467167,Male,Yes,73,Yes,Artist,1.0,Average,,Cat_3,C +459625,Female,Yes,57,No,Engineer,8.0,Low,1.0,Cat_6,B +466740,Female,Yes,47,No,Engineer,8.0,Low,1.0,Cat_3,A +461434,Female,Yes,32,Yes,Executive,4.0,High,3.0,Cat_7,C +465404,Female,Yes,25,Yes,Entertainment,0.0,Average,2.0,Cat_1,A +462337,Female,Yes,79,Yes,Lawyer,1.0,High,2.0,Cat_4,B +461921,Female,No,41,Yes,Artist,0.0,Low,1.0,Cat_6,A +463763,Male,Yes,36,Yes,Artist,4.0,Average,2.0,Cat_6,C +467749,Male,No,19,No,Healthcare,1.0,Low,9.0,Cat_7,D +464876,Female,Yes,49,No,Engineer,,Average,5.0,Cat_4,A +463850,Male,Yes,26,No,Entertainment,0.0,High,2.0,Cat_6,D +460381,Male,No,28,Yes,Artist,13.0,Low,2.0,Cat_6,A +460946,Male,No,84,No,Artist,0.0,Low,1.0,Cat_6,D +464329,Male,Yes,81,No,Lawyer,1.0,Low,2.0,Cat_6,D +467209,Female,No,35,Yes,Healthcare,1.0,Low,1.0,Cat_6,D +461897,Male,Yes,55,Yes,Doctor,0.0,Average,2.0,Cat_6,C +462920,Male,Yes,50,No,Executive,0.0,High,5.0,Cat_6,B +460994,Male,Yes,78,Yes,Lawyer,0.0,Low,5.0,Cat_6,A +462868,Male,Yes,36,No,Entertainment,0.0,Average,4.0,Cat_4,A +459964,Male,No,47,Yes,Marketing,1.0,Low,1.0,Cat_6,A +462663,Male,Yes,48,No,Entertainment,6.0,Average,5.0,Cat_4,B +464116,Female,Yes,69,Yes,Marketing,1.0,Low,2.0,Cat_6,C +467119,Male,Yes,38,No,Marketing,0.0,High,4.0,Cat_7,B +465042,Male,Yes,36,Yes,Artist,1.0,Average,4.0,Cat_6,C +464762,Female,Yes,52,No,Engineer,9.0,Average,4.0,Cat_4,B +460009,Male,No,46,Yes,Artist,0.0,Low,1.0,Cat_6,B +460583,Male,No,18,No,Marketing,1.0,Low,6.0,Cat_3,D +467827,Female,No,22,No,Healthcare,0.0,Low,4.0,Cat_6,D +463795,Male,No,18,No,Doctor,1.0,Low,6.0,Cat_6,B +464313,Male,Yes,30,No,Homemaker,1.0,Average,3.0,Cat_6,D +463672,Male,No,32,Yes,Engineer,,Low,3.0,Cat_6,D +459230,Male,Yes,43,No,Artist,7.0,High,4.0,Cat_6,B +462245,Female,Yes,49,Yes,Artist,1.0,Average,3.0,Cat_6,C +462926,Female,No,40,Yes,Homemaker,9.0,Low,2.0,Cat_6,A +460743,Male,Yes,55,Yes,Artist,0.0,Average,3.0,Cat_6,C +464656,Female,No,33,No,Healthcare,1.0,Low,7.0,Cat_4,B +467656,Male,No,31,Yes,Healthcare,1.0,Low,5.0,Cat_6,D +460978,Female,No,35,Yes,Healthcare,3.0,Low,6.0,Cat_2,D +467684,Male,No,40,Yes,Healthcare,8.0,Low,3.0,Cat_2,D +467089,Male,Yes,69,No,Artist,0.0,Average,3.0,Cat_6,B +466091,Female,Yes,46,Yes,Artist,1.0,Low,4.0,Cat_4,B +464582,Female,Yes,63,Yes,Marketing,0.0,Low,2.0,Cat_6,D +467004,Female,No,50,No,Artist,0.0,Low,,Cat_2,A +458997,Male,Yes,61,Yes,Artist,1.0,Average,2.0,Cat_6,B +466025,Female,No,50,No,Engineer,1.0,Low,1.0,Cat_3,A +464689,Female,No,25,Yes,Marketing,2.0,Low,,Cat_4,A +464507,Male,Yes,53,Yes,Artist,0.0,High,3.0,Cat_6,C +459204,Female,Yes,35,Yes,Engineer,0.0,Average,2.0,Cat_6,B +467521,Male,No,19,No,Healthcare,7.0,Low,3.0,Cat_6,D +462743,Male,No,26,No,Healthcare,3.0,Low,4.0,Cat_3,D +459245,Female,Yes,38,No,Engineer,0.0,Average,4.0,Cat_6,B +463884,Female,No,26,No,Artist,1.0,Low,1.0,Cat_6,D +463860,Male,Yes,39,Yes,Healthcare,0.0,High,3.0,Cat_6,C +459184,Male,Yes,39,Yes,Entertainment,1.0,Low,4.0,Cat_4,A +459332,Female,Yes,72,Yes,Homemaker,1.0,Low,,Cat_6,B +460201,Female,No,45,Yes,Artist,2.0,Low,1.0,Cat_6,A +464989,Male,Yes,47,No,Entertainment,0.0,Average,4.0,Cat_6,C +467782,Female,No,30,Yes,Entertainment,0.0,Low,5.0,Cat_2,A +464923,Male,Yes,48,No,Executive,5.0,High,2.0,Cat_4,A +459364,Female,Yes,76,Yes,Marketing,0.0,Low,2.0,Cat_6,B +462119,Male,Yes,71,Yes,Entertainment,0.0,Low,2.0,Cat_6,A +465132,Female,No,41,Yes,Artist,0.0,Low,1.0,Cat_6,A +466039,Female,No,29,No,Entertainment,0.0,Low,,Cat_4,A +463873,Male,Yes,57,Yes,Artist,1.0,Average,5.0,Cat_6,C +467633,Male,Yes,49,No,Entertainment,0.0,Low,1.0,Cat_6,A +462780,Male,Yes,21,No,Healthcare,1.0,High,8.0,Cat_4,B +462445,Male,No,18,No,Healthcare,0.0,Low,3.0,Cat_6,D +461052,Male,No,22,No,Healthcare,,Low,3.0,Cat_7,D +460664,Female,No,33,No,Homemaker,8.0,Low,1.0,Cat_6,D +465992,Male,Yes,53,Yes,Entertainment,1.0,Low,3.0,Cat_6,A +463990,Male,Yes,55,Yes,Artist,1.0,Average,2.0,Cat_4,B +463128,Male,Yes,73,Yes,Lawyer,0.0,High,4.0,Cat_6,A +463536,Male,Yes,89,Yes,Executive,,High,2.0,Cat_6,C +460775,Male,Yes,59,Yes,Artist,0.0,Low,2.0,Cat_6,A +464118,Male,No,38,Yes,Entertainment,0.0,Low,2.0,Cat_6,D +459264,Female,No,42,Yes,Artist,0.0,Low,2.0,Cat_6,C +464069,Female,Yes,83,Yes,Lawyer,1.0,High,2.0,Cat_6,A +461185,Female,No,22,No,Healthcare,,Low,4.0,Cat_3,D +465564,Male,Yes,46,No,Artist,0.0,Average,4.0,Cat_3,B +466387,Male,Yes,37,Yes,Entertainment,8.0,Average,2.0,Cat_6,B +462887,Female,Yes,42,No,Engineer,1.0,Low,3.0,Cat_6,A +465977,Male,Yes,71,Yes,Artist,0.0,Average,2.0,Cat_6,C +462775,Female,No,18,No,Healthcare,7.0,Low,3.0,Cat_6,D +460286,Female,No,41,Yes,Artist,4.0,Low,1.0,Cat_6,D +466294,Male,Yes,27,Yes,Healthcare,0.0,Low,5.0,Cat_4,D +466037,Female,No,53,No,Entertainment,1.0,Low,1.0,Cat_4,A +466335,Female,No,28,No,Healthcare,0.0,Low,3.0,Cat_4,B +465750,Male,Yes,35,Yes,,3.0,Average,2.0,Cat_6,C +461933,Male,No,18,No,Doctor,0.0,Low,2.0,Cat_5,D +466681,Male,No,19,No,Healthcare,2.0,Low,4.0,Cat_6,D +466605,Female,No,32,No,Healthcare,0.0,Low,4.0,Cat_3,B +461332,Male,No,30,No,Healthcare,1.0,Low,4.0,Cat_2,C +461610,Male,No,36,Yes,Doctor,0.0,Low,1.0,Cat_6,B +467707,Male,Yes,82,Yes,Lawyer,5.0,High,2.0,Cat_6,A +460558,Female,Yes,37,Yes,Engineer,,Low,1.0,Cat_3,C +465158,Male,No,45,Yes,Artist,1.0,Low,1.0,Cat_6,A +460353,Male,No,31,Yes,,6.0,Low,1.0,Cat_4,D +459903,Male,Yes,27,Yes,Entertainment,9.0,Low,4.0,Cat_6,C +464021,Female,No,18,No,Healthcare,1.0,Low,5.0,Cat_6,D +463325,Male,Yes,22,No,Doctor,1.0,Low,4.0,Cat_6,A +459660,Female,No,22,No,Healthcare,2.0,Low,6.0,Cat_6,D +466423,Female,No,42,No,Homemaker,8.0,Low,1.0,Cat_3,D +465585,Male,Yes,69,No,Doctor,1.0,Low,1.0,Cat_6,A +462671,Female,Yes,43,Yes,Homemaker,8.0,High,2.0,Cat_6,A +459603,Female,Yes,89,No,Lawyer,3.0,High,2.0,Cat_6,A +463614,Female,No,26,No,Healthcare,1.0,Low,4.0,Cat_3,C +465152,Male,No,43,Yes,Artist,1.0,Low,3.0,Cat_6,B +460525,Male,Yes,30,Yes,Entertainment,1.0,Average,2.0,Cat_3,C +466108,Female,Yes,60,Yes,Executive,0.0,Low,2.0,Cat_6,B +465400,Female,Yes,38,Yes,Doctor,7.0,High,4.0,Cat_3,C +465405,Female,,29,Yes,Homemaker,0.0,High,4.0,Cat_2,B +465681,Male,Yes,45,Yes,Entertainment,0.0,Average,2.0,Cat_4,A +466251,Male,No,20,No,Healthcare,3.0,Low,4.0,Cat_4,D +460417,Female,Yes,51,Yes,Marketing,9.0,Low,1.0,Cat_6,A +460569,Male,Yes,36,Yes,Entertainment,1.0,Average,4.0,Cat_3,C +461162,Female,No,41,No,Marketing,9.0,Low,3.0,Cat_3,B +459241,Male,Yes,46,No,Entertainment,,Low,5.0,Cat_6,D +463628,Female,No,30,Yes,Artist,9.0,Low,1.0,Cat_6,A +467817,Female,No,23,No,Homemaker,0.0,Low,5.0,Cat_5,B +460406,Male,Yes,39,Yes,Healthcare,4.0,Low,2.0,Cat_6,B +464122,Male,Yes,63,Yes,Artist,0.0,Average,4.0,Cat_6,B +459658,Male,Yes,60,Yes,Artist,1.0,Average,3.0,Cat_6,C +462757,Male,No,20,No,Healthcare,1.0,Low,3.0,Cat_6,D +459529,Female,Yes,89,No,Lawyer,1.0,High,2.0,Cat_6,A +464916,Female,,31,,Doctor,9.0,High,2.0,Cat_4,A +465920,Male,No,43,Yes,Executive,1.0,Low,4.0,Cat_6,A +463262,Female,Yes,70,No,Lawyer,3.0,High,2.0,Cat_6,A +465631,Male,No,35,Yes,Artist,9.0,Low,1.0,Cat_6,A +467164,Male,Yes,42,Yes,Artist,0.0,High,2.0,Cat_6,B +467859,Male,Yes,39,Yes,Doctor,6.0,Average,2.0,Cat_3,C +461703,Female,Yes,29,Yes,Healthcare,0.0,Low,2.0,Cat_6,A +460667,Female,No,38,No,Homemaker,8.0,Low,,Cat_6,A +466097,Female,Yes,74,No,Lawyer,1.0,Low,1.0,Cat_6,A +464390,Male,Yes,41,Yes,Artist,1.0,Low,2.0,Cat_6,C +465423,Male,Yes,51,Yes,Entertainment,0.0,Average,4.0,Cat_7,C +461337,Male,Yes,50,Yes,Executive,1.0,High,3.0,Cat_6,C +463888,Male,No,23,No,Marketing,0.0,Low,3.0,Cat_6,D +460390,Male,No,36,Yes,Artist,9.0,Low,3.0,Cat_6,A +465286,Male,No,36,Yes,Artist,8.0,Low,1.0,Cat_6,B +460294,Female,Yes,50,Yes,Doctor,1.0,Low,2.0,Cat_6,D +460509,Male,No,23,No,Healthcare,,Low,4.0,Cat_3,D +461248,Female,No,37,No,Healthcare,1.0,Low,1.0,Cat_6,D +464336,Male,Yes,65,Yes,Executive,0.0,High,4.0,Cat_6,C +467595,Female,No,26,Yes,Healthcare,0.0,Low,1.0,Cat_6,D +466406,Male,Yes,55,Yes,Entertainment,1.0,Low,3.0,Cat_6,A +465092,Male,Yes,65,Yes,Executive,0.0,High,2.0,Cat_6,C +459492,Female,Yes,72,Yes,Lawyer,1.0,High,2.0,Cat_6,A +461426,Male,Yes,51,Yes,Artist,7.0,Average,2.0,Cat_6,C +467022,Male,No,25,Yes,Artist,1.0,Low,5.0,Cat_6,D +467330,Male,Yes,38,No,Executive,0.0,High,3.0,Cat_6,C +459691,Male,Yes,55,No,Artist,,Average,2.0,Cat_6,A +466161,Male,Yes,46,No,Artist,0.0,Average,,Cat_6,C +461320,Female,No,39,Yes,Artist,0.0,Low,3.0,Cat_6,B +466283,Female,Yes,62,Yes,Artist,0.0,High,2.0,Cat_6,B +463705,Female,Yes,37,Yes,Artist,4.0,Average,2.0,Cat_2,C +465821,Female,No,28,No,Engineer,3.0,Low,7.0,Cat_4,A +467457,Female,No,32,Yes,Marketing,1.0,Low,6.0,Cat_6,B +466543,Male,No,31,Yes,Doctor,14.0,Low,2.0,Cat_7,A +463772,Female,No,31,No,Healthcare,1.0,Low,3.0,Cat_6,D +465881,Male,No,43,Yes,Artist,8.0,Low,,Cat_6,A +459757,Male,Yes,67,Yes,Artist,1.0,Average,2.0,Cat_6,B +464452,Female,No,72,Yes,Lawyer,0.0,Low,1.0,Cat_6,C +463839,Female,Yes,57,No,Engineer,0.0,Low,3.0,Cat_6,B +462777,Male,,58,No,Executive,,High,3.0,Cat_6,A +464368,Male,Yes,36,Yes,Entertainment,4.0,Average,2.0,Cat_6,B +466923,Female,No,21,No,Homemaker,9.0,Low,1.0,Cat_6,D +459878,Male,Yes,47,No,Entertainment,1.0,Average,5.0,Cat_6,C +467279,Female,No,25,Yes,Entertainment,8.0,Low,3.0,Cat_6,D +465604,Female,Yes,41,No,Engineer,9.0,Average,2.0,Cat_3,B +460787,Male,No,29,Yes,Healthcare,0.0,Low,4.0,Cat_6,A +466240,Male,No,21,No,Healthcare,0.0,Low,3.0,Cat_4,D +466621,Male,Yes,66,Yes,Lawyer,0.0,Low,1.0,Cat_6,C +464619,Male,Yes,27,Yes,Entertainment,10.0,Low,2.0,Cat_6,B +462722,Female,No,27,No,Homemaker,,Low,4.0,Cat_6,C +467404,Male,Yes,52,Yes,Artist,4.0,Low,1.0,Cat_6,C +461314,Male,Yes,46,Yes,Artist,1.0,Average,3.0,Cat_6,C +463340,Female,No,23,No,Healthcare,0.0,Low,4.0,Cat_3,D +467870,Male,No,27,Yes,Entertainment,0.0,Low,5.0,Cat_6,B +462200,Female,Yes,52,Yes,Doctor,1.0,Low,3.0,Cat_4,B +465591,Male,Yes,65,No,Artist,0.0,Average,2.0,Cat_4,A +461002,Male,No,42,No,Entertainment,,Low,3.0,Cat_3,D +460812,Male,Yes,59,No,Entertainment,1.0,Low,2.0,Cat_6,A +467105,Female,Yes,45,Yes,Lawyer,0.0,Low,1.0,Cat_6,B +460630,Male,Yes,18,No,Executive,,Average,,Cat_3,D +462037,Female,Yes,61,No,Engineer,1.0,High,3.0,Cat_4,B +461726,Female,Yes,53,Yes,Artist,1.0,Average,4.0,Cat_3,C +463577,Female,Yes,37,Yes,Doctor,1.0,Average,2.0,Cat_6,C +463038,Male,Yes,63,Yes,Executive,0.0,High,5.0,Cat_6,D +464753,Female,No,33,No,Marketing,,Low,5.0,Cat_4,D +464928,Female,Yes,50,Yes,Artist,1.0,Low,3.0,Cat_4,C +467312,Female,Yes,62,Yes,Artist,1.0,Low,2.0,Cat_6,C +464887,Male,Yes,49,Yes,Artist,1.0,Average,8.0,Cat_4,C +460555,Male,Yes,46,Yes,Homemaker,6.0,Average,3.0,Cat_3,C +466661,Male,No,27,,Doctor,0.0,Low,,Cat_2,A +461329,Male,Yes,53,Yes,Executive,1.0,High,4.0,Cat_6,C +459648,Female,Yes,42,Yes,Artist,2.0,High,2.0,Cat_6,C +460835,Female,Yes,28,Yes,Artist,0.0,Low,1.0,Cat_5,A +463827,Female,Yes,49,Yes,Engineer,1.0,Average,4.0,Cat_6,B +460868,Male,Yes,35,Yes,Artist,1.0,Average,2.0,Cat_6,B +462566,Female,Yes,27,No,Doctor,0.0,Low,,Cat_3,C +467485,Male,Yes,58,Yes,Lawyer,0.0,High,2.0,Cat_6,B +467351,Male,Yes,41,No,Artist,8.0,Average,2.0,Cat_6,C +465864,Male,Yes,32,Yes,Entertainment,9.0,Average,2.0,Cat_6,D +467232,Male,No,33,No,Entertainment,0.0,Low,2.0,Cat_6,A +466855,Male,Yes,69,No,Entertainment,0.0,Low,5.0,Cat_3,A +467238,Female,No,31,No,Doctor,6.0,Low,4.0,Cat_6,D +467537,Female,Yes,68,No,Lawyer,,High,3.0,Cat_6,D +463654,Female,No,39,No,Engineer,4.0,Low,3.0,Cat_3,D +459047,Female,No,21,No,Healthcare,1.0,Low,5.0,Cat_4,D +462994,Female,Yes,39,No,Artist,,Average,3.0,Cat_6,B +464363,Female,Yes,41,Yes,Artist,9.0,Low,1.0,Cat_6,C +462565,Male,Yes,49,Yes,Artist,1.0,Average,4.0,Cat_6,C +463226,Female,Yes,38,Yes,Executive,0.0,Average,2.0,Cat_6,A +461232,Female,Yes,36,Yes,Executive,,High,,Cat_3,A +467671,Female,Yes,52,Yes,Artist,9.0,Low,4.0,Cat_6,C +460374,Female,No,46,Yes,Artist,6.0,Low,2.0,Cat_6,C +463077,Male,No,28,No,Doctor,4.0,Low,3.0,Cat_6,C +462719,Female,Yes,55,Yes,Artist,0.0,High,5.0,Cat_6,B +459729,Female,No,41,Yes,Healthcare,1.0,Low,1.0,Cat_6,D +459084,Male,Yes,47,No,Executive,0.0,High,2.0,Cat_7,B +467924,Male,Yes,52,Yes,Artist,0.0,Low,3.0,Cat_4,A +465439,Female,Yes,45,Yes,Artist,4.0,High,4.0,Cat_6,C +462899,Male,No,32,No,Healthcare,,Low,3.0,Cat_6,D +466712,Male,Yes,78,Yes,Lawyer,1.0,Low,1.0,Cat_6,B +467433,Male,No,45,,Artist,0.0,Low,1.0,Cat_6,C +462642,Male,Yes,39,No,Executive,,Average,7.0,Cat_4,D +460983,Male,Yes,38,No,Doctor,0.0,High,4.0,Cat_6,D +464008,Female,Yes,50,Yes,Artist,1.0,Average,4.0,Cat_6,C +461702,Female,Yes,53,Yes,Engineer,8.0,High,4.0,Cat_6,A +464312,Male,No,43,Yes,Artist,1.0,Low,1.0,Cat_3,A +462818,Male,Yes,59,Yes,Artist,1.0,Average,3.0,Cat_6,C +466045,Female,No,38,No,Entertainment,8.0,Low,2.0,Cat_6,B +461889,Male,Yes,42,Yes,Artist,2.0,Average,2.0,Cat_6,C +467945,Male,Yes,65,Yes,Artist,0.0,Average,2.0,Cat_6,C +464322,Male,Yes,79,Yes,Lawyer,0.0,Low,2.0,Cat_6,A +465637,Female,No,41,Yes,Artist,1.0,Low,1.0,Cat_4,D +462058,Male,Yes,42,Yes,Entertainment,9.0,Low,2.0,Cat_6,A +459368,Male,Yes,28,No,Executive,,Low,7.0,Cat_4,A +462500,Male,Yes,49,Yes,Doctor,1.0,Average,2.0,Cat_6,C +466948,Male,Yes,58,Yes,Artist,1.0,Low,2.0,Cat_7,B +461606,Male,Yes,69,Yes,Lawyer,0.0,High,3.0,Cat_6,A +466184,Female,No,35,Yes,Artist,3.0,Low,1.0,Cat_6,C +466014,Female,Yes,47,No,Engineer,0.0,High,2.0,Cat_4,A +462890,Female,No,25,Yes,Marketing,12.0,Low,1.0,Cat_6,D +464909,Female,Yes,38,Yes,Homemaker,0.0,Low,2.0,Cat_4,A +459554,Female,No,25,No,Engineer,8.0,Low,3.0,Cat_6,D +462429,Male,Yes,76,No,Lawyer,0.0,Low,1.0,Cat_6,A +463605,Female,No,23,No,Healthcare,0.0,Low,5.0,Cat_5,D +459531,Male,No,38,Yes,Marketing,3.0,Low,,Cat_6,D +464608,Female,Yes,49,Yes,Artist,0.0,Average,3.0,Cat_6,C +459960,Female,Yes,56,Yes,,1.0,Low,1.0,Cat_6,B +459597,Female,Yes,71,Yes,Artist,1.0,High,2.0,Cat_6,C +464497,Female,No,39,No,Engineer,0.0,Low,3.0,Cat_3,A +467150,Female,No,41,Yes,Artist,8.0,Low,3.0,Cat_6,A +464832,Male,Yes,66,No,Lawyer,1.0,Low,,Cat_4,D +466829,Female,Yes,27,No,Homemaker,10.0,Low,1.0,Cat_6,D +459508,Female,No,23,No,Healthcare,,Low,3.0,Cat_6,D +463506,Female,Yes,35,Yes,Artist,2.0,Average,2.0,Cat_6,C +466475,Female,Yes,33,No,Artist,,Low,2.0,Cat_1,B +462546,Female,No,21,No,Executive,8.0,Low,,Cat_7,D +467448,Female,Yes,49,No,Engineer,0.0,Low,1.0,Cat_6,A +462852,Male,Yes,42,No,Engineer,9.0,Low,3.0,Cat_6,D +462072,Male,No,39,Yes,Artist,0.0,Low,1.0,Cat_6,A +463211,Male,Yes,68,Yes,Lawyer,8.0,Low,1.0,Cat_5,A +464059,Male,Yes,57,Yes,Artist,1.0,Low,1.0,Cat_6,A +463746,Male,No,38,Yes,Artist,0.0,Low,1.0,Cat_6,A +465671,Female,Yes,35,No,Engineer,2.0,Average,5.0,Cat_4,D +461729,Female,Yes,41,Yes,Marketing,0.0,Low,3.0,Cat_6,D +464849,Male,Yes,40,Yes,Artist,0.0,Average,4.0,Cat_4,C +461100,Female,No,32,Yes,Healthcare,,Low,3.0,Cat_3,C +466434,Male,Yes,53,Yes,Artist,0.0,Average,4.0,Cat_6,B +467305,Female,Yes,38,Yes,Homemaker,8.0,Average,3.0,Cat_6,B +466270,Female,Yes,68,No,Marketing,0.0,Low,2.0,Cat_6,D +463479,Male,No,30,No,Executive,1.0,Low,2.0,Cat_6,D +467471,Male,Yes,52,No,Entertainment,4.0,Average,5.0,Cat_6,C +459159,Male,Yes,80,Yes,Lawyer,1.0,Low,1.0,Cat_6,A +465980,Female,No,31,Yes,Healthcare,1.0,Low,7.0,Cat_6,A +467713,Female,Yes,38,No,Executive,,Average,4.0,Cat_4,A +467528,Male,Yes,87,No,Executive,0.0,High,2.0,Cat_6,C +461278,Male,No,23,No,Healthcare,1.0,Low,4.0,Cat_6,D +466964,Male,No,45,Yes,Artist,5.0,Low,1.0,Cat_4,C +460607,Male,Yes,36,Yes,Artist,9.0,Average,4.0,Cat_7,A +462825,Male,Yes,41,No,Executive,1.0,High,2.0,Cat_4,B +459899,Female,Yes,48,Yes,Artist,0.0,Low,4.0,Cat_6,C +460936,Male,Yes,70,Yes,Executive,1.0,High,5.0,Cat_3,B +458991,Female,Yes,84,Yes,Lawyer,3.0,High,2.0,Cat_6,A +460028,Male,Yes,39,Yes,Healthcare,11.0,Average,2.0,Cat_2,D +464281,Female,No,39,Yes,Artist,0.0,Low,1.0,Cat_6,A +463443,Male,Yes,50,Yes,Artist,1.0,Average,4.0,Cat_6,C +460949,Male,Yes,40,Yes,Doctor,1.0,Low,5.0,Cat_6,A +466602,Male,Yes,39,Yes,Artist,,Average,2.0,Cat_4,C +459044,Male,Yes,38,Yes,Artist,4.0,Average,3.0,Cat_6,C +460786,Male,Yes,71,No,Lawyer,1.0,High,2.0,Cat_6,B +465968,Female,No,35,Yes,Artist,10.0,Low,1.0,Cat_6,A +460039,Male,Yes,27,Yes,Healthcare,5.0,Low,5.0,Cat_6,D +462892,Male,Yes,33,No,Healthcare,8.0,Average,3.0,Cat_6,C +462652,Male,Yes,37,No,Artist,,Average,3.0,Cat_4,B +465050,Male,Yes,74,Yes,Lawyer,1.0,High,2.0,Cat_6,B +461793,Male,No,20,No,Marketing,0.0,Low,2.0,Cat_6,D +460781,Female,Yes,30,No,Entertainment,1.0,High,4.0,Cat_5,D +463649,Male,No,22,No,Doctor,0.0,Low,4.0,Cat_6,B +466969,Female,No,43,Yes,Homemaker,9.0,Low,1.0,Cat_6,A +462030,Male,No,36,Yes,Artist,11.0,Low,2.0,Cat_6,C +463735,Male,No,29,Yes,Healthcare,4.0,Low,2.0,Cat_6,C +464134,Female,No,43,Yes,Entertainment,0.0,Low,6.0,Cat_6,A +460195,Male,No,38,Yes,Entertainment,7.0,Low,1.0,Cat_6,D +464089,Female,Yes,40,Yes,Artist,5.0,High,5.0,,B +466643,Female,Yes,68,Yes,Lawyer,9.0,High,4.0,Cat_6,C +461313,Male,Yes,57,Yes,Artist,1.0,Average,,Cat_6,C +460418,Female,No,35,Yes,Entertainment,9.0,Low,2.0,Cat_6,A +461303,Female,No,23,No,Doctor,7.0,Low,4.0,Cat_2,C +467049,Male,No,50,Yes,Artist,1.0,Low,1.0,Cat_6,B +459674,Female,No,32,Yes,Doctor,0.0,Low,5.0,Cat_6,D +466910,Male,Yes,59,Yes,Entertainment,9.0,Low,1.0,Cat_6,A +466459,Female,No,30,Yes,Healthcare,3.0,Low,5.0,Cat_6,B +463085,Female,No,35,Yes,Artist,0.0,Low,1.0,Cat_6,A +461533,Male,No,48,Yes,Healthcare,1.0,Low,2.0,Cat_6,D +461187,Male,No,29,No,Healthcare,9.0,Low,4.0,Cat_3,D +461432,Female,Yes,40,Yes,Artist,1.0,Average,2.0,Cat_6,C +463978,Male,Yes,38,Yes,Artist,1.0,Average,3.0,Cat_2,C +459383,Male,Yes,36,No,Engineer,0.0,Low,1.0,Cat_6,D +464020,Male,No,20,No,Healthcare,0.0,Low,4.0,Cat_6,D +467394,Male,No,29,No,Healthcare,6.0,Low,3.0,Cat_6,C +464539,Male,Yes,42,No,Executive,3.0,High,3.0,Cat_4,A +461529,Female,Yes,35,Yes,Entertainment,1.0,Low,1.0,Cat_6,D +464943,Female,Yes,37,No,Engineer,1.0,Average,6.0,Cat_4,B +466147,Female,Yes,52,No,Artist,0.0,Average,4.0,Cat_6,C +466263,Male,No,53,Yes,Artist,0.0,Low,2.0,Cat_4,C +461812,Male,Yes,30,No,Artist,9.0,High,6.0,Cat_2,A +466848,Female,Yes,65,Yes,Artist,1.0,Low,2.0,Cat_6,B +467933,Female,No,23,No,Healthcare,1.0,Low,3.0,Cat_6,D +463957,Female,No,32,No,Entertainment,0.0,Low,3.0,Cat_4,C +462175,Female,No,36,Yes,Homemaker,9.0,Low,1.0,Cat_2,A +465818,Male,No,27,No,Entertainment,1.0,Low,3.0,Cat_4,B +467190,Male,Yes,36,Yes,Executive,0.0,Low,2.0,Cat_3,B +467848,Male,No,37,Yes,Artist,12.0,Low,1.0,Cat_6,B +462249,Male,No,29,No,Entertainment,0.0,Low,3.0,Cat_6,D +459311,Male,No,29,Yes,Executive,0.0,Low,5.0,Cat_6,C +462610,Male,No,27,Yes,Doctor,1.0,Low,6.0,Cat_4,D +467309,Female,No,35,Yes,Engineer,2.0,Low,1.0,Cat_7,B +467365,Male,No,40,Yes,Artist,1.0,Low,1.0,Cat_6,A +461913,Male,No,37,Yes,Entertainment,6.0,Low,4.0,Cat_2,A +462617,Male,No,19,No,Healthcare,0.0,Low,5.0,Cat_4,D +465485,Male,No,29,Yes,Healthcare,0.0,Low,4.0,Cat_6,D +461316,Female,Yes,53,Yes,Artist,0.0,Average,2.0,Cat_3,C +460501,Male,No,25,Yes,Healthcare,0.0,Low,,Cat_3,D +460556,Female,Yes,38,Yes,,1.0,Low,1.0,Cat_3,C +463036,Male,Yes,48,Yes,Artist,,Low,6.0,Cat_6,B +461079,Female,No,26,No,Engineer,4.0,Low,1.0,Cat_7,A +465120,Female,No,30,Yes,Healthcare,0.0,Low,4.0,Cat_6,D +464671,Male,No,30,No,Entertainment,1.0,Low,2.0,Cat_4,D +465367,Male,Yes,69,No,Executive,1.0,High,3.0,Cat_6,B +467088,Male,Yes,57,Yes,Artist,0.0,Low,1.0,Cat_3,A +463887,Female,Yes,56,Yes,Artist,4.0,Average,2.0,Cat_6,C +460250,Male,No,27,Yes,Healthcare,8.0,Low,4.0,Cat_6,D +465316,Male,No,21,No,Healthcare,0.0,Low,6.0,Cat_4,D +464713,Female,Yes,71,Yes,Artist,0.0,High,4.0,Cat_1,B +465764,Female,No,39,Yes,Entertainment,8.0,Low,4.0,Cat_4,D +467080,Female,Yes,87,Yes,Lawyer,1.0,High,2.0,Cat_6,A +460683,Male,Yes,59,No,Entertainment,6.0,Average,3.0,Cat_3,B +460042,Male,No,32,Yes,Marketing,8.0,Low,1.0,Cat_6,D +463025,Male,Yes,40,No,Executive,7.0,Low,4.0,Cat_6,A +459731,Female,Yes,58,Yes,Artist,0.0,High,3.0,,B +460955,Male,No,19,No,Healthcare,,Low,5.0,Cat_4,D +462179,Female,No,31,Yes,Marketing,0.0,Low,9.0,Cat_7,D +463147,Male,No,31,No,Healthcare,,Low,4.0,Cat_6,B +462639,Female,Yes,43,No,Engineer,,Average,2.0,Cat_4,D +465722,Male,Yes,47,No,,1.0,Low,5.0,Cat_3,A +464799,Male,No,35,No,Marketing,0.0,Low,4.0,Cat_4,D +461934,Female,Yes,46,Yes,Artist,8.0,Average,2.0,Cat_6,C +459146,Male,Yes,69,Yes,Artist,1.0,Low,2.0,Cat_6,A +461448,Male,Yes,69,Yes,Doctor,0.0,High,3.0,Cat_6,C +466419,Female,Yes,45,Yes,Entertainment,3.0,Average,5.0,Cat_3,B +465443,Male,Yes,38,Yes,Artist,0.0,Low,4.0,Cat_6,A +459105,Male,Yes,39,Yes,Executive,5.0,High,3.0,Cat_6,C +459941,Male,Yes,65,Yes,Artist,4.0,Low,,Cat_6,B +463232,Female,No,23,No,Healthcare,2.0,Low,5.0,Cat_6,D +464737,Female,Yes,58,No,Engineer,0.0,Average,6.0,Cat_4,A +467922,Male,Yes,84,Yes,Entertainment,0.0,High,2.0,Cat_6,D +465329,Male,Yes,35,Yes,Artist,5.0,Average,2.0,Cat_6,C +466512,Female,No,22,No,Healthcare,1.0,Low,2.0,Cat_6,D +465408,Male,No,30,No,Executive,,Low,4.0,Cat_6,D +466339,Male,No,23,No,Healthcare,0.0,Low,2.0,Cat_4,D +462870,Female,No,41,Yes,Artist,3.0,Low,3.0,Cat_6,D +464335,Male,Yes,50,Yes,Executive,4.0,High,3.0,Cat_6,C +461353,Male,Yes,50,Yes,Executive,0.0,High,4.0,Cat_6,A +463920,Female,No,28,Yes,Homemaker,8.0,Low,1.0,Cat_6,D +463712,Female,Yes,37,Yes,Doctor,1.0,Average,2.0,Cat_6,C +467465,Male,No,19,No,Healthcare,,Low,6.0,Cat_6,D +463637,Female,No,30,Yes,Healthcare,,Low,5.0,Cat_2,A +466917,Male,Yes,43,No,Executive,3.0,Low,,Cat_6,B +464663,Male,Yes,73,No,Executive,1.0,High,2.0,Cat_4,D +460919,Female,No,19,No,Healthcare,0.0,Low,,Cat_6,D +465000,Male,Yes,50,Yes,Executive,1.0,High,3.0,Cat_6,C +461226,Male,Yes,51,Yes,,1.0,Low,2.0,Cat_6,D +467500,Female,Yes,32,Yes,Artist,4.0,High,2.0,Cat_6,C +461536,Female,No,39,Yes,Doctor,2.0,Low,1.0,Cat_6,B +464296,Male,No,43,Yes,Doctor,1.0,Low,4.0,Cat_6,A +463142,Female,No,33,Yes,Artist,5.0,Low,2.0,Cat_4,B +461004,Male,Yes,61,Yes,Artist,1.0,Average,3.0,Cat_3,C +462323,Female,Yes,70,Yes,Doctor,0.0,Average,2.0,Cat_6,C +467899,Male,Yes,66,Yes,Artist,1.0,Average,2.0,Cat_6,B +461941,Female,Yes,72,Yes,Artist,0.0,Low,1.0,Cat_6,C +461383,Male,Yes,71,Yes,Artist,0.0,High,2.0,Cat_6,B +466616,Male,No,18,No,Healthcare,8.0,Low,9.0,Cat_6,D +464658,Male,Yes,33,No,Artist,1.0,Average,2.0,Cat_4,B +460699,Male,No,28,Yes,Artist,9.0,Low,1.0,Cat_6,A +460180,Female,Yes,27,Yes,Marketing,11.0,Average,2.0,Cat_6,A +462683,Female,Yes,38,No,Engineer,1.0,Low,5.0,Cat_1,B +464988,Male,Yes,63,Yes,Artist,0.0,Average,7.0,Cat_6,C +466593,Male,Yes,70,Yes,Lawyer,0.0,High,3.0,Cat_6,A +464542,Female,No,26,Yes,Healthcare,10.0,Low,1.0,Cat_6,D +465678,Male,Yes,36,No,Entertainment,1.0,Low,2.0,Cat_4,A +467862,Female,Yes,47,Yes,Artist,0.0,Low,3.0,Cat_6,C +459394,Male,No,28,No,Entertainment,9.0,Low,2.0,Cat_6,A +460091,Female,No,26,Yes,Entertainment,1.0,Low,5.0,Cat_6,C +461023,Male,Yes,65,No,Lawyer,,High,2.0,Cat_3,B +461260,Male,No,20,No,Marketing,1.0,Low,3.0,Cat_4,D +463632,Male,Yes,30,Yes,Artist,0.0,Average,2.0,Cat_6,C +460047,Male,No,41,Yes,Entertainment,8.0,Low,2.0,Cat_6,A +460378,Female,No,40,Yes,Doctor,1.0,Low,1.0,Cat_6,A +464758,Female,Yes,47,No,Engineer,1.0,Average,4.0,Cat_4,B +460931,Male,Yes,40,Yes,Entertainment,2.0,Average,5.0,Cat_6,A +463240,Female,Yes,53,No,Engineer,0.0,Average,3.0,Cat_6,A +461126,Female,No,28,Yes,Homemaker,,Low,2.0,Cat_4,B +459698,Male,Yes,60,Yes,Artist,1.0,Low,2.0,Cat_6,A +461598,Female,No,27,Yes,Artist,8.0,Low,1.0,Cat_6,A +465979,Male,Yes,43,Yes,Artist,2.0,Low,2.0,Cat_6,B +463974,Female,No,33,Yes,Homemaker,7.0,Low,3.0,Cat_2,C +466231,Male,Yes,56,Yes,Entertainment,0.0,Average,2.0,Cat_6,C +459833,Male,Yes,70,Yes,Executive,1.0,High,2.0,Cat_6,C +462886,Male,Yes,70,No,Doctor,1.0,High,2.0,Cat_6,D +467628,Male,Yes,73,No,Executive,,High,3.0,Cat_6,C +467488,Female,Yes,38,Yes,Entertainment,,Average,2.0,Cat_2,A +465998,Female,No,40,Yes,Engineer,6.0,Low,3.0,Cat_6,D +459338,Male,Yes,88,No,Executive,,Low,,Cat_6,D +466942,Female,No,29,Yes,Healthcare,1.0,Low,4.0,Cat_6,D +467195,Male,Yes,27,Yes,Executive,,High,2.0,Cat_6,A +466310,Female,Yes,40,Yes,Engineer,0.0,Average,2.0,Cat_6,B +467617,Male,Yes,82,Yes,Lawyer,1.0,Low,1.0,Cat_6,A +459975,Female,No,38,Yes,Artist,7.0,Low,1.0,Cat_6,B +464386,Male,Yes,39,Yes,Doctor,0.0,Average,3.0,Cat_6,B +462005,Male,Yes,39,Yes,Artist,0.0,Average,2.0,Cat_2,C +465859,Male,No,47,Yes,Artist,0.0,Low,1.0,Cat_6,A +464394,Male,Yes,75,Yes,Lawyer,0.0,High,2.0,Cat_6,A +466120,Female,Yes,37,No,Marketing,0.0,Low,4.0,Cat_6,D +460964,Male,Yes,84,No,Lawyer,1.0,High,2.0,Cat_6,A +461975,Female,No,42,Yes,Entertainment,6.0,Low,2.0,Cat_6,A +462172,Male,Yes,39,Yes,Doctor,0.0,Average,2.0,Cat_1,D +464563,Male,Yes,42,Yes,Artist,0.0,Average,2.0,Cat_6,B +464945,Female,Yes,40,Yes,Artist,1.0,Average,2.0,Cat_4,A +463688,Female,No,52,Yes,Doctor,1.0,Low,1.0,Cat_4,B +461750,Male,No,52,No,Artist,5.0,Low,1.0,Cat_6,B +464400,Male,No,31,Yes,Entertainment,0.0,Low,4.0,Cat_6,D +463052,Male,Yes,37,Yes,Marketing,1.0,Low,2.0,Cat_6,D +461372,Male,No,27,Yes,Artist,3.0,Low,1.0,Cat_2,A +463367,Female,No,35,Yes,Engineer,4.0,Low,4.0,Cat_2,C +467926,Male,Yes,79,Yes,Artist,0.0,High,2.0,Cat_6,C +461483,Female,No,28,Yes,Doctor,1.0,Low,4.0,Cat_6,C +467355,Male,No,31,No,Healthcare,0.0,Low,4.0,Cat_6,D +466416,Male,Yes,88,No,Executive,0.0,High,3.0,Cat_6,D +460330,Female,No,51,Yes,Entertainment,4.0,Low,1.0,Cat_6,A +459650,Male,Yes,38,Yes,Entertainment,,Low,2.0,Cat_6,D +466789,Female,Yes,53,Yes,Artist,0.0,Average,2.0,Cat_3,B +466679,Female,No,43,Yes,Artist,8.0,Low,2.0,Cat_6,B +459362,Male,Yes,55,No,Executive,0.0,Low,2.0,Cat_6,A +466128,Male,No,35,Yes,Artist,4.0,Low,2.0,Cat_6,A +462474,Female,Yes,25,No,Engineer,1.0,Average,2.0,Cat_4,A +459782,Male,Yes,58,Yes,Artist,6.0,Average,4.0,Cat_6,C +463113,Female,No,32,Yes,Doctor,1.0,Low,6.0,Cat_6,D +466683,Female,Yes,49,No,Artist,1.0,Average,4.0,Cat_3,C +462103,Female,Yes,53,No,Entertainment,9.0,High,2.0,Cat_6,A +459227,Female,Yes,31,No,Engineer,9.0,Low,4.0,Cat_6,A +467761,Male,No,39,Yes,Artist,1.0,Low,2.0,Cat_6,B +459163,Male,No,42,Yes,Entertainment,1.0,Low,1.0,Cat_6,A +462455,Male,No,20,No,Healthcare,1.0,Low,5.0,Cat_2,D +460178,Male,Yes,50,Yes,Entertainment,1.0,Low,2.0,Cat_6,A +462803,Male,No,26,No,Doctor,0.0,Low,6.0,Cat_2,B +460427,Male,Yes,46,Yes,Artist,1.0,Average,3.0,Cat_6,C +466343,Male,No,30,Yes,Doctor,0.0,Low,2.0,Cat_3,A +461583,Female,Yes,60,Yes,Doctor,6.0,Average,2.0,Cat_6,C +466461,Male,Yes,84,No,Lawyer,0.0,Low,1.0,Cat_6,D +462689,Female,No,37,Yes,Homemaker,,Low,1.0,Cat_6,D +462740,Male,Yes,63,Yes,Artist,6.0,Low,1.0,Cat_6,B +461355,Female,No,38,Yes,Healthcare,0.0,Low,2.0,Cat_6,D +465009,Male,Yes,51,Yes,Entertainment,0.0,Average,4.0,Cat_6,C +467658,Male,No,20,No,Healthcare,0.0,Low,4.0,Cat_3,D +462837,Female,No,25,No,Healthcare,14.0,Low,3.0,Cat_6,D +463907,Female,No,33,Yes,Doctor,0.0,Low,5.0,Cat_6,D +462728,Female,No,31,No,Engineer,0.0,Low,1.0,Cat_6,D +461175,Male,Yes,57,Yes,Healthcare,1.0,Low,3.0,Cat_6,B +464872,Male,Yes,42,Yes,Artist,1.0,Low,4.0,Cat_4,B +460830,Male,Yes,40,No,Entertainment,2.0,Average,5.0,Cat_2,B +459741,Male,Yes,66,No,Lawyer,0.0,High,3.0,Cat_6,B +461154,Male,No,21,No,Homemaker,0.0,Low,4.0,Cat_3,A +464990,Male,Yes,73,No,Executive,1.0,High,2.0,Cat_6,B +467886,Female,Yes,46,Yes,Artist,0.0,Low,2.0,Cat_6,C +459201,Female,Yes,66,Yes,Artist,0.0,Average,2.0,Cat_6,C +463357,Male,Yes,27,Yes,Doctor,0.0,Average,2.0,Cat_5,D +460539,Female,Yes,35,Yes,Homemaker,,Low,,Cat_3,D +462380,Female,Yes,53,No,Artist,1.0,High,4.0,Cat_6,A +459884,Male,Yes,80,No,Lawyer,8.0,Low,1.0,Cat_6,D +461651,Female,Yes,63,Yes,Artist,1.0,Average,3.0,Cat_6,C +465336,Female,No,41,Yes,Doctor,9.0,Low,1.0,Cat_4,A +461338,Male,Yes,43,Yes,Artist,1.0,Average,4.0,Cat_6,C +461050,Male,Yes,47,Yes,,1.0,High,3.0,Cat_6,B +460485,Female,No,18,No,Marketing,9.0,Low,5.0,Cat_3,B +467901,Female,No,33,Yes,Entertainment,1.0,Low,4.0,Cat_6,B +463713,Male,No,30,No,Engineer,9.0,Low,3.0,Cat_4,D +464278,Male,Yes,85,Yes,Executive,1.0,High,2.0,Cat_6,D +461192,Female,Yes,46,Yes,Artist,,High,,Cat_6,C +462285,Male,Yes,48,Yes,Doctor,,Low,2.0,,A +463044,Male,Yes,80,Yes,Lawyer,,High,2.0,Cat_6,B +463086,Male,No,33,Yes,Artist,8.0,Low,,Cat_5,A +459595,Female,Yes,36,Yes,Artist,4.0,High,7.0,Cat_6,C +461419,Male,Yes,27,No,Entertainment,3.0,Average,2.0,Cat_6,B +465767,Male,No,28,No,Healthcare,0.0,Low,5.0,Cat_4,B +460598,Female,Yes,35,Yes,Doctor,0.0,Average,3.0,Cat_3,A +460714,Male,No,26,Yes,Entertainment,1.0,Low,3.0,Cat_6,D +466482,Female,Yes,39,Yes,Homemaker,,Average,3.0,Cat_3,A +461584,Female,Yes,56,Yes,Artist,1.0,High,4.0,Cat_2,B +462066,Female,No,26,Yes,Artist,13.0,Low,2.0,Cat_6,B +463369,Male,Yes,31,Yes,Entertainment,4.0,Low,2.0,Cat_6,B +466586,Male,No,21,No,Healthcare,0.0,Low,2.0,Cat_6,D +464649,Male,Yes,39,No,Entertainment,1.0,Average,4.0,Cat_4,A +463209,Female,No,51,Yes,Artist,0.0,Low,1.0,Cat_6,C +466298,Male,Yes,45,No,Doctor,0.0,Low,4.0,Cat_4,A +466929,Female,No,33,Yes,Homemaker,11.0,Low,,Cat_6,D +466702,Male,Yes,62,,Lawyer,0.0,Low,1.0,Cat_6,C +459238,Female,Yes,80,Yes,Lawyer,,High,2.0,Cat_6,A +463904,Male,Yes,77,Yes,Executive,1.0,Low,1.0,Cat_6,D +463996,Female,No,29,No,Healthcare,,Low,5.0,Cat_4,D +462208,Female,Yes,33,No,Engineer,7.0,Average,5.0,Cat_4,B +461755,Male,No,29,No,Artist,1.0,Low,3.0,Cat_2,A +466736,Female,No,20,No,Healthcare,1.0,Low,,Cat_6,D +466872,Male,Yes,43,Yes,Artist,1.0,Low,2.0,Cat_3,B +467104,Female,No,18,No,Healthcare,1.0,Low,3.0,Cat_6,D +463476,Male,Yes,53,Yes,Artist,3.0,Average,3.0,Cat_6,B +463953,Male,Yes,28,Yes,Entertainment,3.0,Average,3.0,Cat_7,B +462202,Female,No,43,Yes,Entertainment,1.0,Low,4.0,Cat_4,A +467830,Male,No,31,Yes,Entertainment,1.0,Low,3.0,Cat_6,D +463752,Female,No,28,Yes,Healthcare,,Low,2.0,Cat_3,D +461770,Male,Yes,65,Yes,Artist,1.0,Low,2.0,Cat_6,A +466525,Male,Yes,33,Yes,Artist,0.0,Average,2.0,Cat_6,B +462349,Male,Yes,49,Yes,Artist,0.0,Average,6.0,Cat_6,C +463964,Male,No,33,No,Healthcare,1.0,Low,4.0,Cat_6,C +459353,Male,Yes,53,Yes,Artist,6.0,Average,2.0,Cat_6,C +459709,Female,Yes,47,Yes,Artist,7.0,Average,4.0,Cat_6,C +461858,Male,No,31,No,Entertainment,1.0,Low,7.0,Cat_4,D +460450,Male,Yes,52,Yes,Executive,1.0,High,4.0,Cat_4,B +466520,Male,,61,,Engineer,2.0,Average,2.0,Cat_3,B +467646,Male,No,31,No,Healthcare,,Low,7.0,Cat_6,D +460780,Female,No,30,Yes,Healthcare,1.0,Low,2.0,Cat_3,A +463514,Male,Yes,63,Yes,Entertainment,,Average,2.0,Cat_6,C +465105,Male,Yes,69,No,Executive,9.0,Average,2.0,Cat_6,B +460445,Male,No,29,Yes,Engineer,0.0,Low,4.0,Cat_3,D +462961,Female,Yes,56,No,Engineer,1.0,Average,5.0,Cat_6,B +463932,Female,Yes,72,Yes,Artist,1.0,Average,3.0,Cat_6,B +459145,Male,Yes,88,No,Lawyer,,Low,1.0,Cat_2,A +465651,Male,No,30,No,Engineer,1.0,Low,4.0,Cat_6,D +463721,Female,No,31,Yes,Healthcare,1.0,Low,4.0,Cat_2,B +467369,Male,No,41,Yes,Artist,2.0,Low,3.0,Cat_6,A +461361,Female,No,31,Yes,Healthcare,9.0,Low,7.0,Cat_5,D +465608,Male,No,20,No,Healthcare,,Low,5.0,Cat_3,D +464793,Female,Yes,26,No,Marketing,7.0,High,3.0,Cat_4,A +463736,Male,Yes,48,No,Entertainment,,Average,5.0,Cat_6,C +462799,Male,No,26,No,Healthcare,,Low,,Cat_5,D +459006,Male,No,20,No,Healthcare,1.0,Low,4.0,Cat_6,D +460204,Male,Yes,41,Yes,Artist,4.0,Low,4.0,Cat_6,A +465257,Male,No,20,,Healthcare,4.0,Low,5.0,Cat_2,D +466762,Male,Yes,63,Yes,Homemaker,1.0,Low,3.0,Cat_6,B +464318,Male,Yes,88,Yes,Executive,1.0,Low,1.0,Cat_6,D +459634,Female,No,39,Yes,Engineer,,Low,3.0,Cat_6,B +465416,Male,Yes,55,No,Engineer,1.0,High,4.0,Cat_6,A +467275,Male,No,32,Yes,Healthcare,,Low,,Cat_6,D +462117,Male,No,32,Yes,Entertainment,1.0,Low,2.0,Cat_6,D +460710,Female,Yes,38,Yes,Artist,0.0,Average,3.0,Cat_6,A +460098,Female,Yes,62,Yes,Engineer,1.0,Low,1.0,Cat_3,B +465787,Male,No,18,No,Healthcare,1.0,Low,5.0,Cat_4,D +463365,Male,Yes,67,No,Executive,1.0,High,2.0,Cat_6,D +464042,Female,Yes,42,Yes,Artist,9.0,Low,2.0,Cat_6,B +467627,Female,No,45,Yes,Engineer,0.0,Low,1.0,Cat_6,A +464720,Male,Yes,46,No,Doctor,0.0,Average,3.0,Cat_4,D +461123,Male,No,20,No,Healthcare,2.0,Low,4.0,Cat_4,D +465413,Female,Yes,41,Yes,Engineer,0.0,High,3.0,Cat_6,A +465126,Female,Yes,35,No,Engineer,7.0,Average,4.0,Cat_4,A +465950,Female,Yes,52,Yes,Artist,6.0,Low,2.0,Cat_6,A +466734,Female,Yes,63,Yes,Artist,1.0,Average,4.0,Cat_6,B +466222,Male,Yes,51,No,Executive,5.0,Low,3.0,Cat_2,C +466142,Female,Yes,29,Yes,Entertainment,0.0,Average,2.0,Cat_6,A +467888,Male,No,27,No,Doctor,0.0,Low,4.0,Cat_6,B +461992,Female,No,37,Yes,Doctor,1.0,Low,1.0,Cat_4,A +460522,Female,Yes,29,No,Homemaker,,Average,2.0,Cat_4,C +459973,Female,No,50,Yes,Artist,0.0,Low,2.0,Cat_6,A +460695,Male,Yes,74,Yes,Lawyer,1.0,Low,1.0,Cat_6,A +464260,Female,Yes,72,Yes,Artist,3.0,Average,3.0,Cat_6,C +464140,Female,Yes,83,Yes,Lawyer,1.0,High,2.0,Cat_6,B +465606,Male,Yes,37,Yes,Engineer,1.0,Average,3.0,Cat_3,A +459050,Female,No,19,No,Healthcare,0.0,Low,3.0,Cat_6,D +462330,Female,No,30,No,Artist,1.0,Low,1.0,Cat_6,B +459940,Female,No,46,Yes,Engineer,1.0,Low,2.0,Cat_1,B +466352,Female,Yes,65,Yes,Doctor,1.0,Low,2.0,Cat_6,B +463558,Female,Yes,88,Yes,Lawyer,1.0,Average,2.0,Cat_6,C +460049,Male,Yes,43,Yes,Entertainment,0.0,Average,2.0,Cat_5,A +464148,Male,Yes,50,Yes,Executive,9.0,High,3.0,Cat_2,C +465503,Male,No,35,Yes,Artist,8.0,Low,2.0,Cat_6,D +465082,Male,Yes,67,Yes,Lawyer,0.0,Low,1.0,Cat_3,A +459832,Male,Yes,66,Yes,Executive,0.0,Low,1.0,Cat_6,B +464609,Female,No,39,Yes,Artist,3.0,Low,2.0,Cat_6,C +466703,Female,Yes,46,Yes,Homemaker,0.0,Average,,Cat_6,C +466864,Female,No,32,No,Homemaker,8.0,Low,1.0,Cat_6,D +461458,Female,No,40,Yes,Artist,6.0,Low,1.0,Cat_6,B +459218,Male,Yes,88,Yes,Lawyer,0.0,High,3.0,Cat_6,C +462860,Male,No,30,Yes,Healthcare,8.0,Low,5.0,Cat_6,D +462033,Male,No,32,Yes,Entertainment,1.0,Low,1.0,Cat_6,A +467695,Male,Yes,43,No,Marketing,1.0,Low,2.0,Cat_7,D +463090,Female,Yes,55,No,Engineer,3.0,Average,2.0,Cat_6,B +459219,Female,Yes,47,Yes,Artist,0.0,Average,3.0,Cat_6,C +460148,Female,Yes,73,No,Engineer,1.0,Low,,,A +461603,Male,No,39,Yes,Artist,,Low,1.0,Cat_6,C +466359,Male,No,22,No,Healthcare,0.0,Low,4.0,Cat_6,D +464655,Male,Yes,39,Yes,,0.0,Average,5.0,Cat_4,A +466088,Female,No,31,No,Healthcare,0.0,Low,7.0,Cat_6,B +461205,Male,No,27,Yes,Healthcare,,Low,3.0,Cat_2,D +462822,Male,No,19,No,Healthcare,0.0,Low,4.0,Cat_6,D +459076,Male,Yes,69,No,Doctor,0.0,Average,2.0,Cat_6,C +463410,Male,Yes,37,No,Doctor,,Average,2.0,Cat_6,B +463879,Male,Yes,45,Yes,Executive,0.0,High,2.0,Cat_6,C +462827,Female,No,22,No,Doctor,1.0,Low,3.0,Cat_3,A +461500,Female,No,26,Yes,Doctor,0.0,Low,3.0,Cat_6,B +461553,Male,No,39,Yes,Artist,1.0,Low,3.0,Cat_7,A +467912,Male,Yes,61,Yes,Entertainment,1.0,Low,2.0,Cat_5,A +465029,Female,Yes,74,No,Lawyer,1.0,High,2.0,Cat_6,C +460062,Male,No,29,Yes,Artist,4.0,Low,1.0,Cat_3,D +462863,Male,No,29,Yes,Homemaker,5.0,Low,1.0,Cat_6,D +460465,Male,No,36,No,Executive,0.0,Low,3.0,Cat_6,D +460335,Female,No,63,Yes,Doctor,0.0,Low,2.0,Cat_6,B +466067,Male,Yes,69,Yes,Artist,0.0,Average,5.0,Cat_3,C +463962,Female,,35,Yes,Artist,12.0,Low,2.0,Cat_6,B +459807,Male,Yes,81,Yes,Lawyer,1.0,Low,1.0,Cat_6,A +463364,Female,Yes,71,No,Lawyer,1.0,High,2.0,Cat_6,B +463599,Female,No,31,No,Healthcare,10.0,Low,2.0,Cat_3,C +462686,Male,Yes,41,Yes,Entertainment,9.0,Average,4.0,,A +462949,Male,Yes,35,No,Homemaker,1.0,Average,3.0,Cat_6,B +460275,Female,No,52,Yes,Artist,6.0,Low,1.0,Cat_6,A +461184,Male,No,19,No,Healthcare,,Low,4.0,Cat_3,D +460728,Female,,39,Yes,Artist,0.0,Low,1.0,Cat_7,B +462416,Male,Yes,45,No,Artist,0.0,Low,2.0,Cat_4,B +460846,Female,No,19,No,Marketing,1.0,Low,4.0,Cat_6,D +459542,Male,No,23,No,Healthcare,2.0,Low,5.0,Cat_6,D +459080,Male,Yes,77,No,Lawyer,0.0,Low,2.0,Cat_6,D +463106,Female,Yes,84,Yes,Lawyer,0.0,Low,1.0,Cat_6,B +464728,Male,Yes,47,No,Entertainment,1.0,High,4.0,Cat_4,D +466474,Male,No,29,Yes,Entertainment,6.0,Low,3.0,Cat_6,A +461711,Female,No,35,Yes,Artist,8.0,Low,3.0,Cat_2,A +460347,Male,Yes,40,Yes,Artist,14.0,Low,2.0,Cat_6,C +467689,Male,No,35,No,Healthcare,1.0,Low,1.0,Cat_6,D +460260,Male,No,20,No,Healthcare,0.0,Low,3.0,Cat_2,D +466344,Male,No,43,Yes,Entertainment,0.0,Low,2.0,Cat_2,A +466437,Male,Yes,56,No,Executive,0.0,Low,1.0,Cat_6,D +460423,Female,Yes,53,Yes,Artist,3.0,Low,1.0,Cat_7,A +459590,Male,Yes,22,No,Executive,1.0,High,3.0,Cat_6,D +464668,Male,Yes,32,No,Healthcare,1.0,High,4.0,Cat_4,D +463049,Male,Yes,51,No,Executive,2.0,High,5.0,Cat_6,A +460458,Female,No,41,No,Marketing,1.0,Low,,Cat_3,D +462407,Male,Yes,37,No,Doctor,2.0,Average,4.0,Cat_6,A +459450,Male,Yes,55,Yes,Artist,4.0,Average,4.0,Cat_6,A +463255,Male,No,42,Yes,Entertainment,3.0,Low,2.0,Cat_6,A +462574,Male,No,20,No,Healthcare,1.0,Low,5.0,Cat_4,D +465761,Female,No,42,Yes,Artist,12.0,Low,2.0,Cat_4,A +467728,Female,Yes,38,No,Engineer,1.0,High,5.0,Cat_6,B +465841,Female,No,52,Yes,Engineer,3.0,Low,1.0,Cat_6,A +460117,Female,Yes,48,Yes,Marketing,9.0,Low,1.0,Cat_6,D +466619,Male,Yes,37,Yes,Entertainment,5.0,Average,2.0,Cat_6,C +461722,Male,No,27,No,Doctor,1.0,Low,3.0,Cat_6,B +460898,Male,Yes,48,No,Artist,3.0,Average,4.0,Cat_6,B +463627,Female,No,25,Yes,Marketing,5.0,Low,2.0,Cat_4,D +460403,Female,No,56,Yes,Doctor,3.0,Low,1.0,Cat_6,A +459818,Female,Yes,68,No,Lawyer,1.0,Low,1.0,Cat_6,D +463732,Male,No,21,No,Healthcare,0.0,Low,4.0,Cat_6,D +462499,Female,,72,No,Lawyer,8.0,Average,2.0,Cat_6,D +467112,Male,No,43,Yes,Artist,1.0,Low,2.0,Cat_2,B +459902,Male,Yes,52,Yes,Lawyer,8.0,High,2.0,Cat_6,C +463233,Male,Yes,50,No,Executive,0.0,Average,5.0,Cat_6,B +467721,Male,No,41,Yes,Entertainment,0.0,Low,1.0,Cat_4,D +465915,Male,Yes,40,Yes,Entertainment,8.0,Average,2.0,Cat_6,B +464506,Male,No,32,Yes,Healthcare,8.0,Low,2.0,Cat_4,D +460624,Male,Yes,57,Yes,Homemaker,5.0,High,2.0,Cat_6,A +462020,Female,No,25,Yes,Artist,13.0,Low,2.0,Cat_6,C +464320,Male,Yes,74,No,Lawyer,0.0,High,2.0,Cat_6,A +467687,Male,No,38,Yes,Entertainment,,Low,4.0,Cat_6,D +461561,Female,Yes,46,Yes,Artist,0.0,Average,2.0,Cat_6,C +465953,Female,No,50,Yes,Artist,1.0,Low,1.0,Cat_6,D +463309,Male,Yes,50,Yes,Artist,0.0,Average,2.0,Cat_3,B +459134,Female,Yes,69,Yes,Lawyer,0.0,High,2.0,Cat_6,C +464195,Male,No,29,Yes,Artist,0.0,Low,1.0,Cat_6,A +467696,Male,Yes,84,Yes,Lawyer,,High,3.0,Cat_6,B +459586,Male,Yes,73,No,Executive,1.0,Low,,Cat_6,C +466504,Male,Yes,38,No,Executive,9.0,High,5.0,Cat_6,B +466792,Female,Yes,82,No,Lawyer,0.0,Low,1.0,Cat_6,A +459875,Male,Yes,71,No,Lawyer,0.0,High,,Cat_6,A +463837,Female,Yes,50,No,Engineer,0.0,Low,3.0,Cat_7,B +465384,Male,Yes,56,Yes,Artist,0.0,High,2.0,Cat_6,B +462050,Male,No,19,No,Healthcare,4.0,Low,3.0,Cat_2,D +465268,Male,Yes,29,No,Executive,12.0,Low,3.0,Cat_6,B +461370,Male,No,26,Yes,Doctor,1.0,Low,3.0,Cat_6,D +464225,Female,No,31,No,Doctor,1.0,Low,5.0,Cat_3,C +466665,Male,Yes,42,Yes,Artist,0.0,Average,2.0,Cat_6,A +462290,Female,No,33,Yes,Doctor,1.0,Low,1.0,Cat_6,B +459326,Male,Yes,35,No,Healthcare,1.0,Low,5.0,Cat_6,D +464926,Female,Yes,46,No,Engineer,0.0,High,3.0,Cat_4,A +460343,Female,No,67,Yes,Artist,0.0,Low,2.0,Cat_6,B +463626,Female,No,35,No,Artist,7.0,Low,2.0,Cat_6,D +464266,Male,No,28,No,Doctor,8.0,Low,3.0,Cat_6,A +464491,Female,No,43,No,Doctor,8.0,Low,1.0,Cat_4,D +467698,Female,Yes,40,No,Artist,3.0,Average,,Cat_6,A +463440,Female,Yes,73,Yes,Artist,0.0,Average,3.0,Cat_1,C +463602,Female,No,21,No,Healthcare,8.0,Low,4.0,Cat_6,D +464706,Female,No,32,No,Doctor,1.0,Low,3.0,Cat_4,D +467220,Female,No,36,No,,,Low,,Cat_4,D +460313,Male,No,32,Yes,Artist,13.0,Low,3.0,Cat_6,A +465497,Male,No,25,No,Homemaker,14.0,Low,3.0,Cat_5,A +465846,Female,No,40,Yes,Artist,5.0,Low,1.0,Cat_6,A +463374,Female,No,19,No,Healthcare,0.0,Low,3.0,Cat_6,D +462704,Male,Yes,48,Yes,Executive,5.0,High,5.0,Cat_6,B +462160,Female,No,36,Yes,Artist,3.0,Low,3.0,Cat_6,C +461365,Male,No,27,No,Healthcare,1.0,Low,3.0,Cat_6,D +464205,Female,Yes,82,No,Entertainment,0.0,High,2.0,Cat_6,B +459294,Male,No,23,No,Healthcare,9.0,Low,4.0,Cat_6,D +460318,Male,No,30,Yes,Entertainment,14.0,Low,3.0,Cat_6,D +467967,Male,No,30,Yes,Entertainment,8.0,Low,4.0,Cat_6,A +461797,Male,No,30,Yes,Healthcare,1.0,Low,3.0,Cat_6,D +462980,Male,No,28,Yes,Entertainment,1.0,Low,5.0,Cat_6,C +462361,Male,Yes,60,Yes,Artist,0.0,Low,2.0,Cat_4,A +461855,Female,Yes,52,Yes,Artist,1.0,Average,2.0,Cat_6,C +461069,Female,No,19,No,Healthcare,,Low,,Cat_3,D +467522,Female,No,19,No,Healthcare,0.0,Low,2.0,Cat_6,D +463327,Male,Yes,40,No,Executive,1.0,Low,5.0,Cat_5,D +459824,Male,No,38,Yes,Entertainment,8.0,Low,1.0,Cat_6,D +461051,Female,Yes,71,Yes,Entertainment,1.0,Average,2.0,Cat_6,B +461142,Female,No,40,Yes,Engineer,1.0,Low,3.0,Cat_3,B +461521,Male,Yes,68,Yes,Artist,0.0,High,2.0,Cat_6,B +459422,Male,Yes,50,Yes,Entertainment,,Low,4.0,Cat_6,A +467792,Male,No,21,No,Healthcare,0.0,Low,4.0,Cat_6,D +467432,Male,No,29,Yes,Healthcare,,Low,3.0,Cat_6,B +464016,Female,,29,Yes,Entertainment,0.0,High,1.0,Cat_1,A +464654,Female,Yes,39,No,Engineer,1.0,Average,4.0,Cat_4,B +459111,Male,Yes,41,Yes,Executive,0.0,Low,4.0,Cat_6,B +459408,Male,No,28,No,Doctor,,Low,1.0,Cat_6,D +466473,Female,No,22,No,Healthcare,0.0,Low,3.0,Cat_2,D +464365,Male,No,35,Yes,Artist,,Low,1.0,Cat_6,A +460046,Female,Yes,49,Yes,Artist,3.0,Low,2.0,Cat_6,B +466698,Female,Yes,26,Yes,Healthcare,8.0,Low,2.0,Cat_3,D +460609,Female,No,33,No,Healthcare,,Low,1.0,Cat_4,C +459927,Female,No,27,No,Engineer,0.0,Low,2.0,Cat_3,D +467057,Female,No,50,No,Entertainment,8.0,Low,2.0,Cat_6,A +466052,Female,Yes,37,Yes,Homemaker,5.0,Low,2.0,Cat_4,D +464210,Male,No,43,Yes,Marketing,0.0,Low,2.0,Cat_6,A +465873,Male,No,35,Yes,Entertainment,13.0,Low,1.0,Cat_6,D +459802,Female,Yes,50,Yes,Artist,1.0,Low,1.0,Cat_6,C +464817,Male,No,31,Yes,Artist,7.0,Low,1.0,Cat_4,A +465540,Female,Yes,29,No,Engineer,,High,2.0,Cat_6,D +460845,Female,No,20,No,Healthcare,0.0,Low,2.0,Cat_6,D +459072,Male,Yes,66,Yes,Entertainment,1.0,Average,2.0,Cat_6,C +465694,Male,Yes,37,Yes,Healthcare,1.0,Average,2.0,Cat_4,A +466381,Male,Yes,52,Yes,Artist,2.0,Average,2.0,Cat_6,C +460947,Male,Yes,45,Yes,Executive,7.0,High,5.0,Cat_6,C +467338,Female,No,27,Yes,Healthcare,2.0,Low,6.0,Cat_6,D +467189,Female,Yes,46,Yes,Artist,1.0,High,4.0,Cat_6,C +466456,Female,Yes,43,No,Doctor,9.0,Low,1.0,Cat_2,A +463805,Female,No,32,No,Doctor,8.0,Low,6.0,Cat_6,C +462427,Male,Yes,87,Yes,Lawyer,9.0,Low,4.0,Cat_6,C +462785,Female,Yes,40,Yes,Engineer,,Average,5.0,Cat_3,B +466906,Male,No,59,Yes,Artist,1.0,Low,2.0,Cat_6,B +461842,Male,Yes,63,Yes,Artist,1.0,Average,3.0,Cat_6,C +462229,Male,No,23,No,,0.0,Low,8.0,Cat_4,D +462372,Female,Yes,65,No,Lawyer,,High,2.0,Cat_6,A +462400,Male,No,47,Yes,Artist,0.0,Low,1.0,Cat_6,B +466598,Female,Yes,45,Yes,Artist,9.0,Low,2.0,Cat_6,C +466940,Male,Yes,79,No,Lawyer,0.0,Low,1.0,Cat_6,D +461754,Male,Yes,65,No,Entertainment,1.0,High,2.0,Cat_6,B +461376,Female,No,38,Yes,Healthcare,0.0,Low,2.0,Cat_6,B +459427,Female,Yes,28,Yes,Healthcare,,Low,2.0,Cat_6,D +462025,Male,No,20,No,Healthcare,2.0,Low,3.0,Cat_4,D +465720,Female,Yes,36,Yes,Artist,1.0,Low,2.0,Cat_6,C +466354,Male,No,22,No,Healthcare,0.0,Low,4.0,Cat_6,D +463857,Male,Yes,56,No,Healthcare,0.0,High,4.0,Cat_7,B +465849,Female,No,32,Yes,Doctor,5.0,Low,3.0,Cat_4,A +464100,Female,No,33,Yes,Healthcare,1.0,Low,1.0,Cat_6,D +461444,Female,Yes,68,Yes,Artist,1.0,High,2.0,Cat_6,C +461828,Male,Yes,53,Yes,Artist,,Average,3.0,Cat_6,C +464711,Female,Yes,38,Yes,Artist,7.0,Average,3.0,Cat_1,B +462759,Male,No,22,No,Healthcare,,Low,5.0,Cat_2,D +465349,Female,Yes,49,Yes,Artist,0.0,High,4.0,Cat_6,C +459449,Male,Yes,43,Yes,Artist,,Low,2.0,Cat_6,B +463407,Female,No,33,No,Doctor,0.0,Low,1.0,Cat_3,D +466632,Female,Yes,57,Yes,Artist,1.0,High,2.0,Cat_6,C +464641,Female,Yes,65,Yes,Lawyer,2.0,Low,6.0,Cat_1,D +460290,Female,,50,Yes,Artist,,High,1.0,,B +462154,Male,Yes,36,Yes,Entertainment,0.0,Average,2.0,Cat_6,C +464754,Female,Yes,43,No,Engineer,4.0,Average,7.0,Cat_4,A +460167,Female,No,18,No,Entertainment,1.0,Low,5.0,Cat_2,C +461987,Female,Yes,43,No,Engineer,1.0,Average,2.0,Cat_6,B +463259,Male,Yes,75,Yes,Lawyer,0.0,Low,1.0,Cat_6,A +464421,Female,Yes,27,No,Marketing,,High,4.0,Cat_6,D +461628,Male,Yes,39,Yes,Healthcare,11.0,Average,2.0,Cat_6,B +459126,Male,Yes,61,Yes,Artist,2.0,Low,2.0,Cat_6,B +460639,Female,Yes,73,Yes,Lawyer,,Low,5.0,Cat_1,D +465143,Male,No,26,Yes,Engineer,0.0,Low,5.0,Cat_4,C +460860,Male,Yes,52,Yes,Artist,7.0,Average,4.0,Cat_6,B +467629,Male,Yes,50,Yes,Artist,1.0,Low,1.0,Cat_6,B +461720,Female,No,51,Yes,Artist,1.0,Low,1.0,Cat_4,B +460086,Male,Yes,50,No,Artist,1.0,Average,3.0,Cat_6,C +465832,Male,Yes,53,Yes,Executive,9.0,High,5.0,Cat_4,B +467070,Male,Yes,40,No,Executive,5.0,Low,4.0,Cat_6,D +459602,Female,Yes,86,No,Lawyer,,Low,1.0,Cat_6,D +463541,Female,No,26,No,Healthcare,0.0,Low,3.0,Cat_5,B +462431,Male,No,32,No,Entertainment,9.0,Low,4.0,Cat_6,A +461387,Female,No,22,No,Healthcare,5.0,Low,3.0,Cat_2,D +461693,Female,Yes,36,Yes,Artist,1.0,Average,4.0,Cat_6,B +466793,Female,,62,Yes,Artist,4.0,Average,3.0,Cat_6,B +466010,Female,Yes,69,No,Doctor,0.0,Low,1.0,Cat_3,A +465674,Female,No,39,Yes,Entertainment,1.0,Low,2.0,Cat_4,A +459405,Female,Yes,81,No,Doctor,0.0,High,2.0,Cat_6,A +465989,Female,No,28,No,Healthcare,9.0,Low,2.0,Cat_6,D +465598,Male,No,28,Yes,Healthcare,6.0,Low,5.0,Cat_2,D +466093,Male,No,52,Yes,Entertainment,1.0,Low,5.0,Cat_4,B +462773,Female,Yes,40,Yes,Artist,4.0,Average,5.0,Cat_6,B +467087,Male,No,68,Yes,Lawyer,0.0,Low,,Cat_6,B +465763,Female,Yes,37,No,Artist,0.0,Average,2.0,Cat_4,D +462958,Male,No,26,No,Marketing,1.0,Low,5.0,Cat_6,D +467056,Female,No,31,No,Homemaker,8.0,Low,1.0,Cat_6,A +461990,Female,Yes,68,Yes,Lawyer,0.0,Low,2.0,Cat_6,C +464480,Male,Yes,42,Yes,Artist,1.0,Average,3.0,Cat_6,C +461318,Female,Yes,56,Yes,Artist,1.0,Average,6.0,Cat_5,C +467398,Male,Yes,37,No,Entertainment,1.0,Low,2.0,Cat_6,A +464222,Male,Yes,58,Yes,Artist,1.0,Low,1.0,Cat_6,D +465427,Male,No,33,Yes,Artist,7.0,Low,1.0,Cat_6,A +460652,Female,No,31,No,Homemaker,8.0,Low,1.0,Cat_4,D +463642,Female,No,27,Yes,Healthcare,0.0,Low,3.0,Cat_2,B +462672,Male,No,25,No,Healthcare,7.0,Low,4.0,Cat_4,D +466112,Male,Yes,51,Yes,Entertainment,1.0,Average,2.0,Cat_6,B +459831,Male,Yes,37,No,Engineer,8.0,Average,3.0,Cat_4,D +463027,Male,Yes,69,Yes,Executive,1.0,Average,4.0,Cat_6,B +462467,Female,Yes,45,No,Doctor,1.0,Low,1.0,Cat_6,A +461469,Male,Yes,38,Yes,Artist,0.0,Low,2.0,Cat_6,C +461562,Male,Yes,51,Yes,Artist,,Average,4.0,Cat_2,C +467051,Female,No,19,No,Healthcare,0.0,Low,4.0,Cat_6,D +465928,Female,No,32,Yes,Healthcare,6.0,Low,3.0,Cat_6,D +462491,Male,No,31,Yes,Artist,1.0,Low,5.0,Cat_6,C +460642,Male,No,19,No,Healthcare,13.0,Low,3.0,Cat_3,D +460988,Male,Yes,41,Yes,Entertainment,0.0,Average,3.0,Cat_4,C +460634,Female,No,39,No,Entertainment,,Low,6.0,Cat_3,D +461108,Male,No,21,No,Healthcare,1.0,Low,4.0,Cat_3,D +464175,Female,No,30,No,Healthcare,0.0,Low,,Cat_6,D +463329,Male,No,23,No,Healthcare,0.0,Low,6.0,Cat_3,D +466153,Male,Yes,65,No,Entertainment,0.0,Average,2.0,Cat_6,C +467027,Male,No,31,No,Executive,4.0,Low,5.0,Cat_1,D +459707,Female,Yes,74,Yes,Lawyer,,Low,,Cat_6,D +466710,Female,Yes,73,No,Artist,1.0,High,2.0,Cat_6,C +465616,Male,Yes,42,Yes,Doctor,9.0,Low,2.0,Cat_6,C +463173,Male,No,47,Yes,Artist,0.0,Low,1.0,Cat_3,B +465779,Female,No,19,No,Healthcare,0.0,Low,5.0,Cat_2,D +465826,Male,Yes,45,No,Executive,3.0,Low,9.0,Cat_4,D +463063,Female,No,25,No,Marketing,,Low,7.0,Cat_2,B +463575,Female,No,28,Yes,Entertainment,0.0,Low,3.0,Cat_6,D +466480,Female,Yes,40,Yes,Artist,,Average,2.0,Cat_4,C +463175,Female,,40,No,Engineer,1.0,Low,4.0,Cat_3,A +462046,Female,No,30,Yes,Doctor,8.0,Low,,Cat_4,D +466075,Female,Yes,50,Yes,Engineer,1.0,Average,4.0,Cat_4,B +461723,Male,No,26,Yes,Healthcare,8.0,Low,2.0,Cat_6,C +460783,Male,Yes,82,Yes,Lawyer,0.0,Low,1.0,Cat_6,B +465962,Male,Yes,33,Yes,Healthcare,0.0,Low,2.0,Cat_7,A +462781,Male,No,50,No,Marketing,1.0,Low,2.0,Cat_7,C +466020,Male,Yes,83,No,Lawyer,0.0,Low,1.0,Cat_6,D +461502,Female,No,37,Yes,Artist,9.0,Low,1.0,Cat_6,B +463280,Male,Yes,37,Yes,Artist,6.0,Average,2.0,Cat_3,A +466936,Male,Yes,55,No,Lawyer,1.0,High,3.0,Cat_6,C +460552,Male,Yes,61,Yes,Artist,0.0,Low,2.0,Cat_3,C +459345,Female,Yes,39,Yes,Artist,3.0,Low,1.0,Cat_6,B +464256,Male,Yes,40,Yes,Artist,0.0,Low,2.0,Cat_6,C +467097,Male,Yes,55,Yes,Artist,2.0,Low,1.0,Cat_6,B +462571,Male,Yes,50,Yes,Healthcare,1.0,Average,1.0,Cat_6,D +463765,Male,No,26,Yes,Doctor,13.0,Low,3.0,Cat_6,D +460152,Female,No,53,Yes,Artist,1.0,Low,1.0,Cat_6,C +463415,Female,No,28,Yes,Healthcare,,Low,5.0,Cat_6,B +459587,Female,No,32,Yes,Artist,7.0,Low,3.0,Cat_6,A +460995,Female,No,31,Yes,Marketing,0.0,Low,1.0,Cat_6,D +465426,Male,No,30,No,Healthcare,0.0,Low,4.0,Cat_6,B +461452,Female,No,31,Yes,Doctor,0.0,Low,4.0,Cat_3,A +460797,Male,Yes,47,Yes,Artist,4.0,Low,1.0,Cat_3,A +466618,Male,Yes,49,Yes,Artist,3.0,Average,2.0,Cat_6,C +462168,Male,Yes,37,Yes,Executive,1.0,High,3.0,Cat_6,B +465338,Male,Yes,47,Yes,Artist,13.0,Average,4.0,Cat_6,C +463501,Male,Yes,52,Yes,Executive,0.0,High,2.0,Cat_6,C +460817,Female,No,20,No,Entertainment,,Low,7.0,Cat_3,C +459726,Male,Yes,79,No,Lawyer,0.0,High,2.0,Cat_6,B +463516,Male,Yes,47,No,Executive,1.0,High,6.0,Cat_6,C +466751,Female,No,20,,Healthcare,1.0,Low,4.0,Cat_6,D +463359,Male,No,23,No,Healthcare,1.0,Low,4.0,Cat_6,D +463488,Female,No,29,No,Engineer,9.0,Low,4.0,Cat_4,A +465872,Female,No,33,Yes,Engineer,10.0,Low,1.0,Cat_6,A +460588,Male,No,21,No,Healthcare,0.0,Low,4.0,Cat_3,D +460236,Male,No,18,No,Healthcare,8.0,Low,4.0,Cat_6,D +460802,Male,Yes,70,Yes,Lawyer,0.0,High,2.0,Cat_6,C +459467,Female,Yes,56,Yes,Artist,0.0,Average,2.0,Cat_6,C +466953,Male,Yes,42,Yes,Artist,2.0,Average,2.0,Cat_6,B +459086,Female,No,28,No,Healthcare,1.0,Low,2.0,Cat_6,B +464915,Male,Yes,41,Yes,Executive,,High,4.0,Cat_4,B +460827,Male,Yes,81,Yes,Lawyer,9.0,Low,1.0,Cat_6,D +464272,Male,No,30,Yes,Artist,9.0,Low,2.0,Cat_6,A +461063,Male,Yes,53,No,Lawyer,0.0,Low,2.0,Cat_6,D +467302,Male,No,26,Yes,Executive,9.0,Low,4.0,Cat_6,D +461935,Male,Yes,51,Yes,Artist,1.0,Low,3.0,Cat_6,C +459089,Male,No,19,No,Artist,9.0,Low,7.0,Cat_7,B +462365,Male,Yes,75,Yes,Lawyer,0.0,High,2.0,Cat_6,B +459628,Female,Yes,51,Yes,Engineer,0.0,Low,1.0,Cat_6,A +467332,Female,Yes,70,Yes,Homemaker,3.0,Low,2.0,Cat_6,B +460443,Female,No,38,Yes,Doctor,0.0,Low,1.0,Cat_6,A +462195,Female,No,21,No,Healthcare,0.0,Low,4.0,Cat_4,D +466228,Male,Yes,80,No,Lawyer,0.0,Low,1.0,Cat_4,B +461952,Female,Yes,19,No,Artist,,High,2.0,Cat_4,D +467589,Male,No,33,No,Entertainment,1.0,Low,1.0,Cat_6,D +466107,Male,Yes,48,Yes,Entertainment,1.0,Average,6.0,Cat_6,C +462199,Female,No,33,No,Healthcare,8.0,Low,3.0,,D +466452,Female,No,33,Yes,Engineer,0.0,Low,3.0,Cat_1,D +467074,Female,No,31,No,Healthcare,1.0,Low,4.0,Cat_6,C +464233,Female,Yes,48,Yes,Artist,1.0,Average,2.0,Cat_6,C +462225,Male,Yes,49,No,Engineer,1.0,Average,8.0,Cat_4,D +465970,Female,Yes,38,No,Artist,1.0,Low,1.0,Cat_6,B +460094,Female,No,39,Yes,Artist,0.0,Low,1.0,Cat_3,A +467428,Female,Yes,46,Yes,Entertainment,2.0,Average,2.0,Cat_6,B +465623,Male,Yes,41,Yes,Artist,8.0,Average,2.0,Cat_6,C +467516,Female,Yes,31,No,Artist,0.0,High,3.0,Cat_4,A +462975,Male,Yes,37,Yes,Entertainment,8.0,Average,2.0,Cat_6,C +462389,Female,Yes,58,No,Engineer,1.0,Low,1.0,Cat_6,B +461556,Female,Yes,49,Yes,Engineer,1.0,Average,2.0,Cat_6,A +465964,Female,Yes,47,No,Marketing,2.0,Low,1.0,Cat_6,D +460724,Male,No,30,Yes,Healthcare,1.0,Low,4.0,Cat_6,D +460660,Male,No,41,,Entertainment,6.0,Low,1.0,Cat_6,A +466787,Female,Yes,33,Yes,Doctor,0.0,Low,2.0,Cat_6,A +467256,Male,No,33,No,Healthcare,0.0,Low,4.0,Cat_2,B +467414,Male,No,27,Yes,Healthcare,,Low,1.0,Cat_6,D +466742,Male,Yes,45,No,Doctor,8.0,Low,2.0,Cat_3,A +466017,Female,Yes,45,Yes,Homemaker,1.0,Low,1.0,Cat_3,B +463079,Female,No,35,No,Marketing,,Low,,Cat_3,D +459679,Female,No,21,No,Entertainment,1.0,Low,4.0,Cat_6,D +465930,Female,No,36,Yes,Artist,9.0,Low,2.0,Cat_6,A +464821,Female,Yes,56,No,,0.0,Low,1.0,Cat_4,D +460175,Female,No,28,No,Artist,0.0,Low,5.0,Cat_2,C +466725,Female,Yes,89,No,Lawyer,0.0,High,2.0,Cat_6,A +465095,Male,Yes,48,Yes,Executive,1.0,High,4.0,Cat_6,C +465452,Male,Yes,47,No,Entertainment,0.0,High,5.0,Cat_6,C +463473,Male,No,23,No,Healthcare,,Low,3.0,Cat_6,D +463213,Female,No,18,No,Healthcare,3.0,Low,4.0,Cat_6,D +467653,Male,Yes,58,Yes,Lawyer,2.0,High,2.0,Cat_6,C +463218,Male,Yes,42,No,Entertainment,9.0,Average,3.0,Cat_6,A +461289,Male,No,22,No,Healthcare,0.0,Low,3.0,Cat_6,D +459983,Female,Yes,50,Yes,Artist,2.0,Low,1.0,Cat_6,B +465845,Female,Yes,40,Yes,Entertainment,,Low,2.0,Cat_6,B +462363,Male,Yes,73,No,Executive,0.0,High,2.0,Cat_6,D +460575,Female,No,27,No,Artist,,Low,,Cat_3,D +461509,Male,Yes,40,Yes,Artist,1.0,Average,3.0,Cat_6,B +460542,Female,Yes,81,No,Lawyer,1.0,Low,,Cat_3,B +461148,Male,,37,Yes,Artist,1.0,Average,3.0,Cat_3,A +464064,Female,Yes,39,No,Doctor,0.0,Low,2.0,Cat_6,B +460545,Male,Yes,83,No,Lawyer,0.0,High,3.0,Cat_3,D +459303,Male,Yes,55,Yes,Artist,0.0,Low,1.0,Cat_6,B +462568,Male,Yes,51,Yes,Doctor,,Low,3.0,Cat_6,A +462635,Male,Yes,42,No,Engineer,5.0,Average,4.0,Cat_4,A +464456,Male,Yes,27,Yes,Artist,1.0,Low,4.0,Cat_6,C +465106,Male,Yes,53,Yes,Executive,1.0,High,2.0,Cat_6,C +467605,Female,Yes,40,Yes,Engineer,1.0,Average,7.0,Cat_6,A +466116,Male,Yes,62,Yes,Artist,0.0,Average,9.0,Cat_3,C +461388,Male,No,21,No,Healthcare,,Low,5.0,Cat_2,D +466155,Male,Yes,40,Yes,Entertainment,1.0,Average,2.0,,A +467176,Male,No,27,No,Healthcare,1.0,Low,3.0,Cat_3,A +459400,Female,Yes,84,No,Lawyer,0.0,High,2.0,Cat_6,B +466784,Male,No,26,No,Doctor,4.0,Low,,Cat_6,D +460320,Male,No,26,No,Artist,11.0,Low,4.0,Cat_6,C +466454,Male,No,33,Yes,Entertainment,1.0,Low,1.0,Cat_6,A +467028,Male,No,27,Yes,Artist,0.0,Low,1.0,Cat_6,D +464827,Female,Yes,46,No,Entertainment,9.0,Average,5.0,Cat_4,D +465463,Female,No,28,No,Doctor,,Low,5.0,Cat_2,A +464501,Female,No,25,No,Healthcare,1.0,Low,3.0,Cat_6,A +460659,Female,Yes,47,Yes,Marketing,,Average,3.0,Cat_6,A +463715,Female,No,32,No,Artist,3.0,Low,4.0,Cat_6,C +461028,Female,No,21,No,Healthcare,6.0,Low,4.0,Cat_4,D +466766,Male,Yes,73,Yes,Lawyer,1.0,High,4.0,Cat_6,C +462475,Male,Yes,35,Yes,Artist,0.0,Average,2.0,Cat_6,B +465770,Male,No,52,Yes,Artist,1.0,Low,1.0,Cat_6,B +466823,Male,Yes,40,Yes,Artist,1.0,Low,2.0,Cat_6,B +463505,Male,No,31,Yes,Healthcare,,Low,1.0,Cat_6,D +467083,Female,Yes,88,Yes,Lawyer,1.0,High,2.0,Cat_6,A +467675,Female,No,39,No,,,Low,1.0,Cat_6,A +464080,Female,No,41,Yes,Artist,1.0,Low,2.0,Cat_6,C +462287,Female,Yes,73,Yes,Artist,,Average,2.0,Cat_6,C +466503,Male,Yes,45,Yes,Artist,7.0,Average,5.0,Cat_6,B +461847,Male,Yes,41,No,Entertainment,0.0,Low,4.0,Cat_6,A +461447,Male,Yes,75,Yes,Lawyer,0.0,High,2.0,Cat_6,C +459982,Male,No,39,Yes,Entertainment,9.0,Low,2.0,Cat_6,A +463316,Female,Yes,89,No,Lawyer,0.0,High,2.0,Cat_6,A +466758,Male,Yes,45,Yes,Doctor,0.0,Low,1.0,Cat_6,A +464307,Female,Yes,50,Yes,Artist,1.0,Average,2.0,Cat_6,C +461344,Male,Yes,43,No,Healthcare,6.0,High,2.0,Cat_7,B +463353,Male,Yes,50,Yes,Executive,4.0,Average,4.0,Cat_2,B +467349,Female,No,53,No,Entertainment,0.0,Low,,Cat_6,D +462607,Female,,37,No,Engineer,2.0,Average,3.0,Cat_4,D +467172,Male,No,40,,Artist,8.0,Low,2.0,Cat_6,C +464324,Male,Yes,76,Yes,Lawyer,1.0,Low,1.0,Cat_3,D +462108,Female,Yes,27,Yes,Homemaker,,Average,2.0,Cat_6,A +459288,Male,Yes,41,Yes,Artist,1.0,Low,2.0,Cat_6,A +460050,Male,Yes,50,Yes,Entertainment,3.0,Low,2.0,Cat_6,A +464994,Male,Yes,56,Yes,Executive,8.0,High,5.0,Cat_6,C +464903,Male,Yes,28,No,Entertainment,0.0,Average,2.0,Cat_4,A +461251,Female,No,46,Yes,Marketing,0.0,Low,,Cat_6,D +463165,Female,Yes,72,Yes,Doctor,1.0,Average,2.0,Cat_6,A +460419,Female,Yes,41,No,Doctor,,Low,1.0,Cat_7,A +465981,Male,No,28,No,Artist,0.0,Low,2.0,Cat_6,A +465281,Female,Yes,27,Yes,Artist,8.0,Average,2.0,Cat_6,C +463781,Female,No,32,No,Healthcare,2.0,Low,4.0,Cat_4,D +466569,Female,No,20,No,Doctor,9.0,Low,4.0,Cat_6,A +466547,Male,Yes,45,Yes,Executive,1.0,Low,5.0,Cat_6,B +464631,Male,No,59,Yes,Artist,1.0,Low,2.0,Cat_6,C +465957,Female,No,46,Yes,Marketing,1.0,Low,1.0,Cat_6,D +459848,Male,Yes,46,Yes,Artist,1.0,Average,3.0,Cat_6,C +461642,Male,No,41,Yes,Artist,9.0,Low,2.0,Cat_6,A +467259,Male,Yes,70,Yes,Lawyer,1.0,High,2.0,Cat_6,B +459388,Female,,25,Yes,Homemaker,,Low,1.0,Cat_6,D +463439,Male,Yes,42,Yes,Artist,9.0,Average,3.0,Cat_6,B +464764,Female,Yes,60,No,Engineer,1.0,High,9.0,Cat_4,A +467073,Male,No,37,Yes,Engineer,9.0,Low,1.0,Cat_6,D +460480,Male,No,18,No,Healthcare,0.0,Low,7.0,Cat_3,D +464419,Male,Yes,52,Yes,Artist,1.0,Average,3.0,Cat_6,C +463686,Female,No,32,No,Artist,2.0,Low,4.0,Cat_2,A +459411,Male,Yes,74,No,Lawyer,8.0,Low,1.0,Cat_6,A +463774,Male,Yes,29,Yes,Healthcare,1.0,Average,2.0,Cat_6,A +463671,Male,Yes,37,Yes,Entertainment,0.0,Average,3.0,Cat_6,C +466986,Male,Yes,45,No,Entertainment,1.0,Average,3.0,Cat_6,B +465508,Male,No,22,No,Healthcare,1.0,Low,4.0,Cat_6,D +463284,Female,Yes,60,Yes,Engineer,0.0,Low,,Cat_3,B +465351,Female,Yes,51,Yes,Artist,8.0,Average,2.0,Cat_6,B +464001,Male,Yes,59,Yes,Artist,0.0,Average,2.0,Cat_6,C +459483,Male,Yes,55,Yes,Artist,1.0,Average,2.0,Cat_7,A +466013,Male,No,22,No,Healthcare,2.0,Low,3.0,Cat_4,D +459763,Male,No,18,No,Artist,,Low,4.0,Cat_4,D +466490,Female,Yes,84,No,Lawyer,0.0,High,2.0,Cat_6,B +463024,Male,No,35,Yes,Artist,,Low,1.0,Cat_6,A +463665,Male,Yes,50,Yes,Entertainment,0.0,Average,3.0,Cat_6,C +463459,Male,Yes,73,Yes,Artist,5.0,High,3.0,Cat_6,C +462756,Female,No,23,No,Engineer,1.0,Low,4.0,Cat_6,B +461414,Male,No,30,Yes,Entertainment,,Low,5.0,Cat_3,D +463915,Female,No,21,Yes,Healthcare,0.0,Low,5.0,Cat_2,D +463543,Male,No,18,No,Healthcare,1.0,Low,4.0,Cat_6,D +462768,Male,Yes,46,Yes,Artist,0.0,Average,3.0,Cat_6,B +461876,Male,No,32,Yes,Entertainment,9.0,Low,2.0,Cat_6,D +462135,Female,No,36,Yes,Healthcare,6.0,Low,3.0,Cat_2,D +458998,Male,Yes,37,No,Doctor,9.0,Low,2.0,Cat_6,D +466432,Male,No,42,Yes,Entertainment,1.0,Low,1.0,Cat_7,A +464722,Male,Yes,35,Yes,Doctor,1.0,Low,,Cat_4,D +460389,Female,Yes,42,Yes,Healthcare,9.0,Average,2.0,Cat_6,D +460663,Female,No,33,No,Homemaker,9.0,Low,1.0,Cat_6,D +463484,Female,Yes,35,No,Engineer,1.0,High,2.0,,D +467771,Female,Yes,59,Yes,Artist,0.0,Average,5.0,Cat_6,C +462084,Male,Yes,70,Yes,Artist,1.0,Low,1.0,Cat_6,B +463621,Male,Yes,53,No,Entertainment,1.0,Low,1.0,Cat_6,A +467021,Female,Yes,80,No,Lawyer,1.0,High,2.0,Cat_6,D +459056,Male,Yes,56,No,Executive,,High,3.0,Cat_6,B +467563,Male,Yes,39,Yes,Artist,0.0,Average,2.0,Cat_2,C +459820,Female,Yes,73,Yes,Artist,,Average,3.0,Cat_3,C +467306,Female,Yes,58,Yes,Artist,0.0,Average,5.0,Cat_6,C +464865,Female,No,30,No,Homemaker,8.0,Low,9.0,Cat_4,A +463585,Female,No,23,No,Healthcare,0.0,Low,3.0,Cat_6,D +466186,Male,Yes,59,Yes,Artist,9.0,Average,4.0,Cat_6,C +459619,Female,Yes,56,Yes,Doctor,0.0,Low,2.0,Cat_6,D +466699,Female,Yes,40,Yes,Artist,8.0,Low,1.0,Cat_6,B +465517,Female,Yes,68,Yes,Artist,1.0,Low,2.0,Cat_6,A +459237,Male,No,29,Yes,Artist,1.0,Low,6.0,Cat_6,A +461757,Female,Yes,55,Yes,Artist,1.0,Average,3.0,Cat_6,C +466309,Male,No,21,No,Healthcare,1.0,Low,4.0,Cat_6,D +466532,Female,No,19,No,Engineer,1.0,Low,4.0,Cat_6,D +460832,Female,Yes,41,No,Healthcare,1.0,Average,4.0,Cat_6,D +461531,Male,No,41,Yes,Artist,4.0,Low,1.0,Cat_3,A +467304,Male,No,33,No,Doctor,1.0,Low,2.0,Cat_3,C +463124,Male,Yes,31,No,Artist,,Average,2.0,Cat_6,B +461463,Male,No,41,Yes,Artist,6.0,Low,1.0,Cat_3,B +464566,Male,Yes,35,Yes,Executive,0.0,High,4.0,Cat_6,A +462070,Male,Yes,41,Yes,Entertainment,9.0,Average,2.0,Cat_6,B +464162,Male,Yes,81,Yes,Lawyer,,High,2.0,Cat_6,C +466554,Male,No,26,No,Healthcare,0.0,Low,3.0,Cat_6,D +462793,Male,Yes,42,No,Executive,3.0,High,5.0,Cat_6,B +465657,Female,Yes,53,Yes,Artist,0.0,Average,2.0,Cat_6,C +460530,Female,Yes,33,Yes,Entertainment,3.0,Average,3.0,Cat_4,D +464045,Male,Yes,39,Yes,Artist,0.0,Low,2.0,Cat_6,C +465633,Male,Yes,83,Yes,Executive,,High,2.0,Cat_6,B +462905,Female,No,25,No,Artist,6.0,Low,3.0,Cat_6,C +466254,Male,No,40,Yes,Entertainment,0.0,Low,2.0,Cat_2,A +460617,Female,No,22,No,Healthcare,0.0,Low,4.0,Cat_3,D +465468,Female,Yes,37,Yes,Artist,1.0,Average,2.0,Cat_6,C +466125,Female,Yes,65,Yes,Artist,0.0,Low,2.0,Cat_6,B +465207,Male,Yes,49,Yes,Artist,0.0,Average,2.0,Cat_6,C +463332,Female,Yes,38,Yes,Engineer,1.0,Low,2.0,Cat_6,C +459924,Female,Yes,65,Yes,Artist,0.0,Average,2.0,Cat_6,C +466143,Female,Yes,48,No,Engineer,0.0,Average,5.0,,C +466375,Male,Yes,52,Yes,Entertainment,1.0,Low,4.0,Cat_2,C +462951,Male,Yes,73,Yes,Lawyer,2.0,Low,1.0,Cat_6,D +465654,Female,No,48,Yes,Engineer,1.0,Low,1.0,Cat_6,B +460987,Male,No,25,Yes,Doctor,8.0,Low,2.0,Cat_6,D +461681,Male,No,31,Yes,Healthcare,9.0,Low,3.0,Cat_7,D +462820,Male,No,25,No,Entertainment,8.0,Low,9.0,Cat_6,C +467518,Male,No,21,No,Doctor,0.0,Low,,Cat_6,D +467525,Male,No,20,No,Healthcare,0.0,Low,4.0,Cat_6,D +465876,Male,No,25,No,Executive,0.0,Low,3.0,Cat_6,A +464199,Male,Yes,25,Yes,Artist,8.0,Low,2.0,Cat_7,A +462746,Male,No,29,No,Entertainment,1.0,Low,3.0,Cat_6,B +463261,Female,Yes,72,No,Entertainment,7.0,Average,3.0,Cat_6,B +467548,Male,Yes,48,Yes,Artist,0.0,High,2.0,Cat_6,B +460924,Male,No,28,No,Doctor,1.0,Low,5.0,Cat_2,D +463368,Female,Yes,42,Yes,Artist,3.0,High,3.0,Cat_2,C +459957,Female,Yes,32,Yes,Artist,8.0,Low,3.0,Cat_6,A +459191,Male,Yes,49,Yes,Artist,0.0,Low,1.0,Cat_6,B +462479,Female,Yes,55,Yes,Artist,1.0,Average,2.0,Cat_6,C +466570,Female,Yes,40,Yes,Artist,0.0,Average,3.0,Cat_6,A +459407,Female,No,37,Yes,Doctor,0.0,Low,3.0,Cat_6,A +467331,Male,No,27,No,Healthcare,3.0,Low,4.0,Cat_6,D +463377,Female,Yes,32,Yes,Homemaker,4.0,High,2.0,Cat_3,B +467456,Male,Yes,36,No,Executive,7.0,High,4.0,Cat_6,A +463058,Female,No,28,Yes,Homemaker,11.0,Low,1.0,,D +467401,Male,Yes,72,No,Entertainment,0.0,Average,,Cat_1,C +464029,Male,No,18,No,Entertainment,1.0,Low,4.0,Cat_6,D +466056,Female,No,36,Yes,Artist,1.0,Low,1.0,Cat_4,B +467130,Female,Yes,29,No,Homemaker,,High,3.0,Cat_3,A +465002,Female,Yes,88,Yes,Lawyer,1.0,High,2.0,Cat_6,C +463155,Female,No,38,Yes,Engineer,,Low,1.0,Cat_6,D +459651,Male,Yes,87,Yes,Lawyer,,Low,1.0,Cat_6,B +459961,Male,Yes,51,Yes,Artist,8.0,Low,3.0,Cat_2,A +466109,Male,Yes,51,Yes,Entertainment,1.0,Average,4.0,Cat_6,B +461543,Male,No,31,No,Artist,3.0,Low,2.0,Cat_6,A +459502,Female,Yes,47,Yes,Artist,1.0,Average,3.0,Cat_6,C +459864,Female,Yes,70,No,Artist,,High,2.0,Cat_6,A +460441,Male,No,31,No,Healthcare,,Low,4.0,Cat_6,B +461942,Male,No,29,Yes,Healthcare,0.0,Low,6.0,Cat_6,D +462755,Female,No,20,No,Healthcare,0.0,Low,,Cat_6,D +465098,Female,No,41,Yes,Artist,1.0,Low,,Cat_4,A +459872,Male,Yes,68,No,Entertainment,4.0,Average,2.0,Cat_6,A +462053,Male,Yes,28,Yes,Artist,,Low,2.0,Cat_6,B +467040,Male,No,32,Yes,Doctor,0.0,Low,4.0,Cat_3,D +465079,Male,Yes,75,Yes,Lawyer,1.0,Low,1.0,Cat_6,A +467697,Female,No,39,Yes,Healthcare,0.0,Low,1.0,Cat_6,A +466555,Male,No,18,No,Healthcare,1.0,Low,3.0,Cat_6,D +463457,Female,Yes,42,Yes,Artist,2.0,Average,2.0,Cat_6,B +462653,Male,Yes,48,Yes,Executive,0.0,Average,4.0,Cat_4,A +461390,Female,Yes,70,Yes,Artist,0.0,High,2.0,Cat_6,C +460217,Male,Yes,45,Yes,Artist,1.0,Average,2.0,Cat_6,B +463684,Female,No,29,Yes,Healthcare,0.0,Low,4.0,Cat_6,A +463764,Male,Yes,50,Yes,Entertainment,1.0,Average,3.0,Cat_6,C +465310,Female,No,22,No,Healthcare,0.0,Low,6.0,Cat_4,D +462489,Male,Yes,46,Yes,Artist,0.0,Low,2.0,Cat_6,C +465677,Male,Yes,39,No,Artist,,Low,2.0,Cat_4,A +459737,Female,Yes,81,Yes,Lawyer,8.0,High,2.0,Cat_6,C +465357,Female,Yes,49,Yes,Artist,1.0,Average,3.0,Cat_6,C +462159,Male,No,43,Yes,Doctor,1.0,Low,,Cat_6,A +461747,Male,Yes,66,Yes,Artist,1.0,High,2.0,Cat_6,B +465940,Female,No,53,Yes,Artist,0.0,Low,2.0,,A +464323,Female,No,29,Yes,Artist,12.0,Low,1.0,Cat_6,A +467248,Female,Yes,86,Yes,Lawyer,0.0,High,2.0,Cat_6,C +463265,Male,Yes,70,Yes,Artist,1.0,Average,3.0,Cat_6,C +459946,Female,Yes,36,Yes,Artist,1.0,Average,2.0,Cat_6,B +463161,Male,No,35,Yes,Artist,1.0,Low,2.0,Cat_3,A +460790,Female,Yes,42,Yes,Artist,0.0,High,2.0,Cat_2,C +463118,Female,Yes,49,Yes,Doctor,,High,3.0,Cat_6,B +460864,Female,No,20,No,Artist,0.0,Low,3.0,Cat_6,D +461039,Female,Yes,27,No,Engineer,1.0,Average,9.0,Cat_3,D +460299,Male,Yes,56,Yes,Artist,4.0,Low,1.0,Cat_6,B +462378,Male,Yes,67,No,Artist,1.0,Low,1.0,Cat_6,B +463081,Female,Yes,51,Yes,Artist,0.0,Low,1.0,Cat_6,B +460923,Male,Yes,49,Yes,Artist,1.0,Average,3.0,Cat_6,C +462120,Male,Yes,51,No,Artist,1.0,Low,3.0,Cat_3,A +463980,Female,No,26,Yes,Artist,1.0,Low,9.0,Cat_6,C +461464,Male,No,59,Yes,Entertainment,,Low,3.0,Cat_6,B +466695,Female,Yes,31,Yes,Entertainment,1.0,Low,1.0,Cat_6,A +465833,Male,Yes,43,No,Executive,1.0,High,3.0,Cat_4,A +467490,Male,Yes,59,No,Executive,7.0,Average,5.0,Cat_6,B +459765,Female,No,35,Yes,Engineer,4.0,Low,1.0,Cat_6,A +467570,Male,No,18,No,Healthcare,14.0,Low,,Cat_6,D +464561,Female,No,48,Yes,Entertainment,9.0,Low,2.0,Cat_6,A +459409,Female,Yes,88,Yes,Lawyer,1.0,High,2.0,Cat_6,B +462864,Female,No,30,Yes,Healthcare,12.0,Low,6.0,Cat_6,D +461009,Female,No,22,No,Healthcare,6.0,Low,3.0,Cat_3,D +459117,Male,Yes,66,Yes,Artist,1.0,High,2.0,Cat_6,C +464131,Female,Yes,81,No,Engineer,0.0,High,2.0,Cat_6,B +464079,Female,Yes,36,No,Doctor,14.0,Average,2.0,Cat_6,A +462478,Male,Yes,30,No,Doctor,,Average,2.0,Cat_4,A +465375,Female,No,50,Yes,Engineer,1.0,Low,1.0,Cat_6,B +461637,Male,Yes,51,Yes,Artist,1.0,Low,2.0,Cat_7,B +459252,Male,Yes,66,Yes,Lawyer,0.0,High,3.0,Cat_6,A +461438,Female,Yes,39,Yes,Engineer,8.0,Average,2.0,Cat_6,C +463956,Female,No,28,No,Entertainment,0.0,Low,5.0,Cat_2,D +459682,Male,Yes,35,Yes,Executive,,Average,3.0,Cat_6,A +459892,Female,Yes,50,Yes,Artist,1.0,Low,1.0,Cat_6,A +467720,Male,No,27,Yes,Entertainment,,Low,2.0,Cat_6,A +463148,Male,Yes,60,Yes,Marketing,1.0,High,5.0,Cat_6,C +460862,Male,Yes,52,No,Entertainment,1.0,Average,4.0,Cat_3,B +460382,Male,Yes,32,No,Marketing,4.0,Low,3.0,Cat_6,D +461650,Female,Yes,42,Yes,Doctor,0.0,Low,1.0,Cat_4,B +467501,Male,No,48,Yes,Entertainment,1.0,Low,2.0,Cat_6,D +459486,Male,Yes,75,Yes,Lawyer,,High,2.0,Cat_6,A +467319,Male,No,26,Yes,Homemaker,8.0,Low,2.0,Cat_6,A +459604,Female,No,21,No,Healthcare,0.0,Low,7.0,Cat_3,D +463554,Female,No,53,No,Artist,7.0,Low,1.0,Cat_6,C +467652,Male,No,32,Yes,Doctor,0.0,Low,3.0,Cat_6,D +465298,Female,Yes,56,Yes,Doctor,1.0,Average,2.0,Cat_3,C +462494,Female,No,36,Yes,Artist,8.0,Low,4.0,Cat_3,D +467344,Male,Yes,83,Yes,Lawyer,0.0,Low,2.0,Cat_6,B +463442,Male,Yes,42,Yes,Executive,1.0,Low,2.0,Cat_6,D +467219,Female,Yes,66,No,Lawyer,,High,2.0,Cat_6,A +464360,Male,Yes,62,Yes,Executive,0.0,High,2.0,Cat_6,B +462505,Male,Yes,40,No,Executive,0.0,Low,4.0,Cat_4,D +459914,Male,No,38,Yes,Artist,2.0,Low,1.0,Cat_4,D +461255,Male,No,18,No,Healthcare,1.0,Low,5.0,Cat_4,D +462360,Female,Yes,62,Yes,Artist,3.0,Low,2.0,Cat_6,C +466571,Male,No,23,No,Healthcare,1.0,Low,4.0,Cat_6,D +465033,Female,Yes,56,Yes,,0.0,Average,2.0,Cat_6,B +464575,Male,No,42,Yes,Artist,8.0,Low,1.0,Cat_6,A +465650,Female,No,32,No,Marketing,0.0,Low,4.0,Cat_6,A +464640,Female,No,28,No,Marketing,0.0,Low,5.0,Cat_4,A +459694,Male,Yes,77,No,Lawyer,0.0,Low,,Cat_6,D +465216,Male,Yes,35,Yes,Executive,8.0,High,4.0,Cat_6,C +460818,Female,No,30,Yes,Healthcare,8.0,Low,,Cat_2,D +463927,Female,No,53,Yes,Engineer,1.0,Low,2.0,Cat_6,A +459506,Female,No,27,Yes,,1.0,Low,1.0,Cat_6,A +467001,Female,,53,Yes,Entertainment,8.0,Average,4.0,Cat_2,B +460265,Male,Yes,52,Yes,Executive,1.0,High,4.0,Cat_6,B +464178,Male,No,29,Yes,Doctor,3.0,Low,3.0,Cat_4,A +461586,Male,No,36,Yes,Artist,6.0,Low,3.0,Cat_2,B +461764,Female,No,41,Yes,Artist,0.0,Low,1.0,Cat_6,A +459297,Male,Yes,72,Yes,Executive,0.0,High,4.0,Cat_6,C +459548,Female,Yes,50,Yes,Engineer,1.0,Low,2.0,Cat_6,A +463452,Female,Yes,79,Yes,Lawyer,0.0,High,2.0,Cat_6,C +467767,Female,No,23,No,Marketing,4.0,Low,4.0,Cat_6,D +465927,Female,No,25,Yes,Healthcare,0.0,Low,1.0,Cat_6,D +465857,Female,No,40,Yes,Doctor,1.0,Low,1.0,Cat_6,B +460581,Male,Yes,27,Yes,Healthcare,2.0,Average,2.0,Cat_3,A +459501,Female,Yes,62,Yes,Artist,4.0,Average,7.0,Cat_7,A +466670,Male,Yes,29,Yes,Healthcare,2.0,Average,2.0,Cat_2,B +460678,Female,No,31,No,Doctor,6.0,Low,2.0,Cat_3,A +459490,Male,Yes,37,No,Engineer,,Average,2.0,Cat_6,A +460230,Male,No,28,Yes,Healthcare,1.0,Low,4.0,Cat_6,C +463653,Female,No,26,Yes,Artist,1.0,Low,5.0,Cat_6,A +463726,Female,No,33,No,Healthcare,1.0,Low,3.0,Cat_6,B +467858,Male,Yes,59,Yes,Artist,,Low,4.0,Cat_6,A +466907,Male,Yes,57,Yes,Artist,,Average,2.0,Cat_6,C +467249,Female,No,43,Yes,Marketing,1.0,Low,1.0,Cat_6,D +465533,Female,Yes,50,Yes,Entertainment,0.0,Average,4.0,Cat_2,B +462297,Female,No,29,Yes,Artist,14.0,Low,2.0,Cat_6,B +460596,Male,No,29,No,Engineer,0.0,Low,5.0,Cat_3,A +462633,Female,,28,Yes,Doctor,,High,8.0,Cat_4,D +467709,Female,Yes,37,Yes,Artist,5.0,Low,2.0,Cat_6,D +467413,Female,Yes,75,Yes,Lawyer,2.0,High,4.0,Cat_6,B +464672,Male,No,19,No,Entertainment,0.0,Low,6.0,Cat_4,A +465807,Female,No,42,Yes,Artist,1.0,Low,1.0,Cat_3,B +462760,Male,No,22,No,Healthcare,1.0,Low,5.0,,D +466529,Female,Yes,28,Yes,Doctor,0.0,Average,2.0,Cat_2,A +464434,Male,Yes,45,Yes,Artist,0.0,Average,4.0,Cat_6,B +462359,Male,Yes,51,Yes,Entertainment,0.0,Low,3.0,,D +464802,Male,No,40,No,Entertainment,1.0,Low,1.0,Cat_4,A +467643,Male,No,42,Yes,Artist,12.0,Low,1.0,Cat_3,A +461977,Female,No,49,Yes,Artist,3.0,Low,1.0,Cat_6,C +461738,Female,Yes,46,Yes,,,Average,3.0,Cat_6,C +464520,Male,Yes,46,No,Entertainment,8.0,Low,2.0,Cat_6,D +464484,Male,Yes,29,Yes,Artist,1.0,Average,3.0,Cat_3,B +465391,Female,Yes,72,Yes,Lawyer,0.0,High,2.0,Cat_6,A +460611,Male,No,26,Yes,Healthcare,0.0,Low,3.0,Cat_3,C +466159,Female,Yes,50,Yes,Engineer,2.0,Average,3.0,Cat_6,C +463623,Male,No,33,Yes,Doctor,0.0,Low,3.0,Cat_6,C +459254,Male,Yes,57,Yes,Doctor,,Low,1.0,Cat_6,A +463594,Female,Yes,35,Yes,Artist,7.0,Average,2.0,Cat_6,C +465118,Female,Yes,51,Yes,Artist,1.0,Average,6.0,Cat_6,C +460761,Male,Yes,52,Yes,Entertainment,0.0,Average,6.0,Cat_4,C +459132,Male,No,20,No,Entertainment,5.0,Low,2.0,Cat_6,D +461265,Male,No,33,No,Marketing,1.0,Low,6.0,Cat_4,D +463446,Male,Yes,58,Yes,Artist,0.0,Average,3.0,Cat_3,C +467902,Female,No,37,No,Artist,,Low,1.0,Cat_6,A +460591,Female,Yes,38,Yes,Doctor,1.0,Low,4.0,Cat_3,A +464139,Female,No,27,No,Healthcare,14.0,Low,1.0,Cat_6,D +466433,Male,Yes,43,Yes,Entertainment,8.0,Low,2.0,Cat_6,B +459023,Female,No,18,No,Healthcare,0.0,Low,5.0,Cat_6,D +466476,Female,No,29,No,Healthcare,0.0,Low,3.0,Cat_6,D +460090,Male,Yes,86,No,Lawyer,2.0,Average,2.0,Cat_6,A +461016,Male,No,32,No,Healthcare,7.0,Low,5.0,Cat_3,D +464630,Female,Yes,52,No,Engineer,0.0,Low,6.0,Cat_4,A +462464,Male,Yes,53,Yes,Artist,1.0,Average,4.0,Cat_6,C +459026,Male,No,29,No,Healthcare,9.0,Low,3.0,Cat_3,D +464376,Male,Yes,58,Yes,Artist,2.0,Low,1.0,Cat_6,A +465256,Male,No,22,No,Healthcare,,Low,5.0,Cat_2,D +461783,Male,,72,,Lawyer,0.0,High,,Cat_6,A +467478,Female,Yes,39,Yes,Artist,0.0,Average,4.0,Cat_6,C +463094,Male,Yes,40,Yes,Engineer,12.0,Average,3.0,Cat_6,A +463257,Female,No,23,No,Healthcare,3.0,Low,5.0,Cat_6,D +465060,Female,Yes,79,Yes,Lawyer,14.0,High,3.0,Cat_6,C +461696,Female,Yes,70,Yes,Lawyer,0.0,High,2.0,Cat_6,B +466130,Female,No,27,No,Artist,0.0,Low,6.0,Cat_6,D +460498,Male,Yes,47,No,Executive,1.0,High,2.0,Cat_3,B +459248,Male,Yes,58,Yes,Executive,3.0,Average,4.0,Cat_3,B +464924,Male,Yes,26,No,Entertainment,,High,6.0,Cat_4,D +462620,Male,Yes,40,No,Engineer,6.0,Average,5.0,Cat_4,D +462657,Male,Yes,35,Yes,Executive,1.0,Average,2.0,Cat_6,A +467822,Male,Yes,31,Yes,Healthcare,0.0,Low,2.0,Cat_6,C +460876,Female,Yes,43,Yes,Artist,1.0,High,4.0,Cat_3,C +461395,Male,No,21,No,Doctor,4.0,Low,3.0,Cat_2,D +460731,Female,No,29,Yes,Artist,1.0,Low,3.0,Cat_6,C +463582,Female,No,18,No,Healthcare,1.0,Low,3.0,Cat_6,D +461423,Male,No,28,Yes,Artist,0.0,Low,3.0,Cat_4,B +460879,Female,No,27,Yes,Healthcare,0.0,Low,4.0,Cat_6,C +467376,Female,Yes,47,Yes,Doctor,8.0,Average,2.0,Cat_6,C +463701,Male,Yes,52,Yes,Marketing,1.0,High,4.0,Cat_6,C +466667,Female,No,42,No,Marketing,5.0,Low,2.0,Cat_6,D +464345,Female,Yes,57,Yes,Artist,2.0,High,3.0,Cat_6,C +467889,Male,Yes,61,Yes,Executive,0.0,High,2.0,Cat_6,C +461709,Male,No,33,Yes,Artist,0.0,Low,3.0,Cat_4,C +463515,Female,Yes,41,Yes,Artist,11.0,Average,4.0,Cat_6,C +460080,Female,No,39,Yes,Doctor,9.0,Low,3.0,Cat_6,A +460375,Male,No,40,Yes,Doctor,4.0,Low,2.0,Cat_6,C +466364,Female,Yes,52,Yes,Artist,1.0,Low,3.0,Cat_2,C +465551,Female,Yes,48,Yes,Artist,,Average,2.0,Cat_3,B +463096,Female,No,33,Yes,Doctor,,Low,3.0,Cat_6,D +460896,Male,No,43,Yes,Artist,0.0,Low,1.0,Cat_3,A +460702,Female,No,51,Yes,Artist,0.0,Low,1.0,Cat_6,B +466001,Female,Yes,36,Yes,Entertainment,6.0,Low,2.0,Cat_6,B +465738,Male,Yes,38,Yes,Entertainment,8.0,Low,2.0,Cat_3,B +460804,Female,No,30,Yes,Artist,0.0,Low,5.0,Cat_2,A +464426,Male,No,39,Yes,Marketing,1.0,Low,4.0,Cat_3,D +465880,Female,No,46,Yes,Entertainment,1.0,Low,2.0,Cat_6,A +461342,Male,Yes,43,No,Executive,3.0,High,4.0,Cat_6,B +459078,Male,Yes,70,Yes,Lawyer,0.0,High,2.0,Cat_6,B +460396,Female,Yes,41,Yes,Entertainment,9.0,Low,1.0,Cat_7,D +461098,Female,Yes,39,Yes,Engineer,1.0,Low,1.0,Cat_3,A +463076,Female,No,51,Yes,Artist,0.0,Low,2.0,Cat_6,C +460976,Male,No,20,No,Healthcare,1.0,Low,4.0,Cat_6,D +465536,Female,Yes,30,Yes,Doctor,,Average,2.0,Cat_3,B +460399,Male,No,26,Yes,Marketing,10.0,Low,1.0,Cat_6,D +460449,Male,No,31,No,Marketing,4.0,Low,1.0,Cat_3,D +463807,Female,No,33,Yes,Healthcare,0.0,Low,3.0,Cat_4,B +459323,Female,No,21,No,Doctor,,Low,,Cat_6,D +459727,Male,Yes,83,No,Lawyer,,High,2.0,Cat_6,B +463263,Female,Yes,70,Yes,Engineer,0.0,High,2.0,Cat_6,B +466998,Female,Yes,60,No,Artist,,Average,3.0,Cat_6,C +466729,Male,No,33,Yes,Healthcare,1.0,Low,,Cat_6,A +462608,Female,No,71,No,Engineer,1.0,Low,1.0,Cat_4,A +465495,Male,Yes,29,Yes,Engineer,5.0,Low,2.0,Cat_4,A +461244,Female,No,41,No,Marketing,1.0,Low,1.0,Cat_6,D +459173,Male,Yes,55,Yes,Artist,1.0,Average,2.0,Cat_6,C +465795,Male,No,29,No,Entertainment,1.0,Low,4.0,Cat_4,C +467345,Male,Yes,76,Yes,Lawyer,1.0,Low,1.0,Cat_6,A +467290,Female,No,23,No,Healthcare,0.0,Low,,Cat_6,D +462220,Female,No,18,No,Healthcare,7.0,Low,3.0,Cat_4,D +465737,Female,Yes,49,Yes,Engineer,6.0,Low,3.0,Cat_3,D +459423,Male,No,19,No,Healthcare,9.0,Low,5.0,Cat_6,D +464445,Male,Yes,81,Yes,Executive,1.0,High,3.0,Cat_6,C +461378,Female,No,35,Yes,Artist,9.0,Low,1.0,Cat_6,B +462286,Male,Yes,59,No,Doctor,0.0,Average,2.0,Cat_6,B +463678,Female,No,32,No,Healthcare,1.0,Low,5.0,Cat_4,B +465601,Male,Yes,35,Yes,Artist,,Average,4.0,Cat_3,D +462647,Female,Yes,56,No,,,Average,3.0,Cat_4,D +465714,Female,Yes,63,Yes,Artist,1.0,Low,1.0,Cat_2,C +466312,Female,No,21,No,Healthcare,0.0,Low,3.0,Cat_4,D +459697,Female,Yes,77,No,Lawyer,0.0,High,3.0,Cat_6,A +460057,Male,No,36,Yes,Artist,2.0,Low,1.0,Cat_6,C +464960,Female,No,22,No,Healthcare,0.0,Low,5.0,Cat_4,D +459064,Male,Yes,26,No,Executive,5.0,High,4.0,Cat_6,D +461277,Male,Yes,82,Yes,Lawyer,7.0,Low,1.0,Cat_6,A +463403,Male,No,18,No,Healthcare,2.0,Low,4.0,Cat_6,D +463863,Male,Yes,35,Yes,Artist,0.0,Average,2.0,Cat_6,C +467064,Male,Yes,36,No,Doctor,0.0,Average,2.0,Cat_7,A +467856,Male,Yes,59,Yes,Artist,1.0,Low,2.0,Cat_6,A +462251,Male,Yes,61,No,Artist,0.0,Average,2.0,Cat_6,C +465372,Female,No,37,Yes,Entertainment,0.0,Low,1.0,Cat_6,A +463970,Female,No,25,No,Healthcare,1.0,Low,5.0,Cat_4,B +463547,Female,No,28,No,Healthcare,0.0,Low,4.0,Cat_2,B +465331,Female,Yes,56,Yes,Artist,,Low,2.0,Cat_6,C +460785,Male,No,29,Yes,Healthcare,0.0,Low,4.0,Cat_7,B +461788,Female,Yes,86,Yes,Lawyer,1.0,High,2.0,Cat_6,C +461617,Male,No,45,Yes,Artist,0.0,Low,2.0,Cat_6,A +460661,Female,No,33,No,Homemaker,9.0,Low,1.0,Cat_6,D +463363,Male,No,20,No,Healthcare,1.0,Low,4.0,,D +462520,Female,No,25,Yes,Artist,,Low,5.0,Cat_4,B +461568,Male,Yes,59,Yes,Artist,1.0,Average,3.0,Cat_6,C +464769,Female,,51,No,Engineer,9.0,Average,4.0,Cat_4,B +466615,Female,No,20,No,Healthcare,3.0,Low,3.0,Cat_1,D +465688,Female,Yes,62,,Artist,1.0,Average,3.0,Cat_3,C +462109,Male,Yes,75,No,Lawyer,0.0,Low,1.0,Cat_6,D +459565,Male,No,47,Yes,Entertainment,2.0,Low,,Cat_1,A +462766,Female,No,32,No,Homemaker,8.0,Low,1.0,Cat_6,D +466682,Male,Yes,62,,Entertainment,2.0,Average,4.0,Cat_3,C +459690,Female,No,43,Yes,Artist,1.0,Low,1.0,Cat_6,A +459347,Male,Yes,69,Yes,Lawyer,1.0,Low,1.0,Cat_6,B +465766,Male,Yes,38,No,Entertainment,1.0,Low,3.0,Cat_4,A +467575,Female,No,29,No,Engineer,1.0,Low,4.0,Cat_6,C +459774,Male,Yes,56,Yes,Artist,0.0,Low,1.0,Cat_6,D +464860,Female,Yes,51,No,Engineer,,Average,3.0,Cat_4,B +464932,Male,Yes,53,Yes,Executive,9.0,Low,1.0,Cat_4,D +461814,Female,No,37,Yes,Artist,1.0,Low,1.0,Cat_3,A +463428,Male,Yes,28,No,Executive,1.0,High,5.0,Cat_6,A +459671,Female,Yes,48,Yes,Artist,,Average,3.0,Cat_4,A +464451,Female,Yes,74,Yes,Artist,0.0,Average,2.0,Cat_6,B +464043,Male,Yes,51,Yes,Entertainment,1.0,Average,4.0,Cat_6,C +461784,Female,Yes,88,Yes,Lawyer,0.0,Low,1.0,Cat_6,D +463389,Female,Yes,50,Yes,Artist,0.0,Average,2.0,Cat_3,C +462233,Male,No,19,No,Healthcare,1.0,Low,7.0,Cat_4,D +463826,Female,No,28,Yes,Healthcare,5.0,Low,,Cat_6,D +465121,Female,No,32,Yes,Artist,6.0,Low,1.0,Cat_3,B +466168,Female,Yes,45,Yes,Executive,1.0,High,3.0,Cat_3,B +459841,Male,Yes,56,Yes,Healthcare,,High,3.0,Cat_6,B +467342,Female,No,33,Yes,Artist,1.0,Low,2.0,Cat_6,C +459495,Male,No,29,Yes,Artist,,Low,3.0,Cat_6,D +464962,Male,Yes,53,Yes,Executive,1.0,High,4.0,Cat_4,B +460874,Female,No,18,No,Healthcare,1.0,Low,3.0,Cat_6,D +463247,Male,No,45,Yes,Entertainment,1.0,Low,1.0,Cat_7,A +460771,Male,Yes,86,Yes,Artist,0.0,Low,,Cat_6,A +465774,Female,No,26,Yes,Doctor,9.0,Low,4.0,Cat_6,D +463551,Male,Yes,46,Yes,Doctor,7.0,Average,2.0,Cat_6,C +464502,Male,Yes,35,Yes,Healthcare,6.0,Average,2.0,Cat_6,A +461407,Female,No,32,Yes,Marketing,8.0,Low,6.0,Cat_1,A +465218,Male,Yes,36,Yes,Artist,,Average,2.0,Cat_6,C +466081,Female,Yes,25,Yes,Artist,8.0,Average,2.0,Cat_4,C +467336,Female,Yes,63,Yes,Artist,0.0,Average,4.0,Cat_6,C +462440,Female,No,23,No,Healthcare,9.0,Low,4.0,Cat_6,D +459389,Female,No,26,Yes,Engineer,9.0,Low,8.0,Cat_6,A +466446,Female,Yes,46,Yes,Artist,1.0,High,3.0,Cat_6,C +464385,Male,Yes,70,No,Engineer,0.0,Average,2.0,Cat_6,B +467872,Female,Yes,40,Yes,Engineer,1.0,Average,4.0,Cat_5,B +459857,Male,No,33,Yes,Entertainment,6.0,Low,1.0,Cat_6,A +466701,Male,Yes,48,Yes,Homemaker,10.0,Low,3.0,Cat_6,C +461169,Male,No,39,Yes,Entertainment,9.0,Low,2.0,Cat_3,D +459519,Female,Yes,45,Yes,Artist,,Average,2.0,Cat_6,A +460795,Male,Yes,61,Yes,Artist,1.0,Average,3.0,Cat_6,C +464511,Male,Yes,52,Yes,Marketing,1.0,Low,3.0,Cat_3,D +466399,Female,Yes,26,No,Entertainment,1.0,Average,2.0,Cat_4,A +465603,Female,Yes,45,Yes,Homemaker,8.0,Low,,Cat_3,A +466079,Female,Yes,40,No,Engineer,1.0,Low,2.0,Cat_6,A +459769,Male,No,42,Yes,Marketing,1.0,Low,,Cat_6,D +462450,Male,No,19,No,Healthcare,0.0,Low,,Cat_6,D +460587,Female,No,38,Yes,Artist,,Low,1.0,Cat_3,B +461490,Female,Yes,73,Yes,Artist,2.0,High,2.0,Cat_6,C +460895,Female,No,29,Yes,Doctor,9.0,Low,5.0,Cat_3,B +465104,Male,Yes,35,Yes,Homemaker,9.0,Average,4.0,Cat_6,A +467503,Female,No,26,Yes,Artist,1.0,Low,4.0,Cat_6,C +460316,Female,No,39,No,Healthcare,4.0,Low,3.0,Cat_6,D +467198,Male,No,28,Yes,Healthcare,1.0,Low,5.0,Cat_6,D +460348,Female,No,37,Yes,Artist,1.0,Low,2.0,Cat_6,C +467460,Female,Yes,36,Yes,Homemaker,8.0,Average,2.0,Cat_7,B +466838,Male,Yes,41,No,Executive,0.0,Low,3.0,Cat_1,D +463561,Female,No,39,Yes,Engineer,1.0,Low,1.0,Cat_6,A +466170,Male,Yes,74,Yes,Lawyer,1.0,Low,1.0,Cat_2,C +463578,Female,No,23,No,Healthcare,0.0,Low,3.0,Cat_4,D +461352,Male,No,25,No,Healthcare,3.0,Low,5.0,Cat_2,D +465814,Female,Yes,50,No,Artist,0.0,Average,5.0,Cat_4,B +467690,Female,Yes,67,No,Lawyer,,High,2.0,Cat_6,D +463809,Female,No,30,Yes,Healthcare,2.0,Low,3.0,Cat_6,B +460701,Male,Yes,66,No,Entertainment,0.0,Average,2.0,Cat_6,B +461592,Male,Yes,29,Yes,Entertainment,0.0,Low,2.0,Cat_6,B +467692,Female,Yes,43,No,Artist,4.0,Low,1.0,Cat_6,B +459033,Female,Yes,56,Yes,Artist,0.0,Low,1.0,Cat_6,B +466424,Female,Yes,42,Yes,Engineer,1.0,High,3.0,Cat_3,A +459937,Female,No,39,Yes,Artist,7.0,Low,1.0,Cat_6,A +466404,Female,No,40,Yes,Engineer,0.0,Low,1.0,Cat_6,A +463504,Male,No,26,Yes,Healthcare,,Low,1.0,Cat_6,D +459944,Female,Yes,39,Yes,Artist,0.0,Low,1.0,Cat_6,A +464073,Female,No,46,Yes,Engineer,1.0,Low,2.0,Cat_7,D +462169,Female,Yes,40,Yes,Artist,1.0,High,3.0,Cat_6,B +463032,Female,Yes,78,No,Lawyer,0.0,High,2.0,Cat_6,A +467374,Female,Yes,45,Yes,Homemaker,8.0,High,4.0,Cat_6,B +459269,Male,Yes,58,Yes,Artist,0.0,Average,2.0,Cat_6,C +466769,Female,No,31,Yes,Entertainment,0.0,Low,4.0,Cat_6,A +465792,Female,Yes,57,Yes,Engineer,1.0,Low,4.0,Cat_3,C +462280,Male,No,32,Yes,Healthcare,0.0,Low,4.0,Cat_6,B +464616,Male,Yes,27,No,Entertainment,6.0,Low,4.0,Cat_6,A +465202,Male,No,27,No,Entertainment,6.0,Low,4.0,Cat_6,D +464384,Male,No,25,Yes,Healthcare,9.0,Low,4.0,Cat_4,C +466655,Male,Yes,57,No,Executive,1.0,Low,2.0,Cat_6,C +466600,Male,No,18,No,Healthcare,11.0,Low,1.0,Cat_4,D +467815,Female,Yes,69,Yes,Artist,0.0,Low,2.0,Cat_6,C +464966,Male,Yes,59,No,Engineer,1.0,Low,2.0,Cat_4,A +467803,Female,Yes,36,Yes,Entertainment,7.0,Average,2.0,Cat_6,B +466810,Male,No,19,No,Healthcare,1.0,Low,3.0,Cat_6,D +459896,Female,Yes,40,Yes,Artist,9.0,Low,2.0,Cat_6,C +464862,Female,No,20,No,Engineer,8.0,Low,5.0,Cat_4,D +461751,Female,Yes,63,Yes,Artist,1.0,Average,3.0,Cat_6,C +466965,Female,No,42,Yes,Homemaker,,Low,1.0,Cat_6,A +465157,Male,Yes,42,Yes,Entertainment,3.0,Average,4.0,Cat_6,B +462544,Male,Yes,30,Yes,Entertainment,,Average,3.0,Cat_4,D +464652,Female,Yes,25,No,Engineer,7.0,Average,2.0,Cat_4,D +466401,Male,Yes,48,No,Executive,1.0,Average,4.0,Cat_6,A +461902,Female,Yes,52,Yes,Artist,0.0,Low,3.0,Cat_6,B +463164,Male,Yes,35,Yes,Artist,8.0,Average,2.0,Cat_6,C +461660,Female,Yes,74,No,Lawyer,0.0,Low,1.0,Cat_6,D +466054,Female,Yes,33,No,Homemaker,8.0,Average,4.0,Cat_4,D +464725,Male,Yes,45,No,Executive,11.0,High,9.0,Cat_4,A +463507,Male,Yes,49,No,Artist,1.0,Low,1.0,Cat_6,C +465888,Male,Yes,37,Yes,Doctor,8.0,Average,2.0,Cat_6,A +466103,Female,Yes,40,No,Engineer,0.0,Low,5.0,Cat_6,C +461261,Female,No,29,No,Artist,0.0,Low,3.0,Cat_4,A +462203,Male,Yes,72,No,Executive,2.0,High,7.0,Cat_4,A +463303,Female,Yes,49,Yes,Homemaker,2.0,Average,4.0,Cat_6,B +465943,Male,No,26,No,Healthcare,0.0,Low,,Cat_6,D +462997,Male,Yes,55,Yes,Entertainment,0.0,Average,4.0,Cat_6,C +460338,Male,No,28,Yes,Entertainment,14.0,Low,2.0,Cat_6,D +460000,Male,No,52,Yes,Entertainment,1.0,Low,1.0,Cat_3,A +462531,Female,Yes,59,No,Homemaker,,High,2.0,Cat_6,B +459417,Male,No,19,No,Healthcare,,Low,,Cat_6,D +466479,Male,Yes,52,Yes,Artist,,Average,3.0,Cat_6,C +460122,Female,No,41,No,Artist,0.0,Low,1.0,Cat_6,A +466087,Male,,22,No,Healthcare,8.0,High,6.0,Cat_4,D +459350,Female,Yes,56,Yes,Engineer,0.0,High,2.0,Cat_6,B +460676,Female,Yes,51,Yes,Marketing,9.0,Average,1.0,Cat_3,A +463179,Male,Yes,57,Yes,Entertainment,0.0,Low,2.0,Cat_6,A +461137,Male,Yes,60,Yes,Entertainment,0.0,Low,4.0,Cat_6,A +467555,Male,No,27,No,Entertainment,14.0,Low,3.0,Cat_6,D +464211,Male,Yes,50,Yes,Doctor,0.0,Average,3.0,Cat_2,B +461413,Female,No,40,Yes,Artist,2.0,Low,1.0,Cat_6,B +467660,Female,Yes,69,Yes,Artist,9.0,High,2.0,Cat_6,C +461193,Female,No,41,Yes,Marketing,1.0,Low,1.0,Cat_6,D +460772,Male,Yes,70,No,Entertainment,1.0,Low,1.0,Cat_6,A +465702,Male,No,32,Yes,Entertainment,1.0,Low,1.0,Cat_4,A +459001,Female,No,20,No,Marketing,,Low,4.0,Cat_6,C +467006,Female,No,73,Yes,Lawyer,0.0,Low,1.0,Cat_6,B +466927,Male,No,19,No,Engineer,7.0,Low,5.0,Cat_2,D +464107,Male,Yes,70,No,Artist,1.0,Average,2.0,Cat_6,C +460605,Male,Yes,33,Yes,Doctor,9.0,Average,2.0,Cat_4,D +459847,Female,Yes,49,Yes,Artist,1.0,Average,5.0,Cat_6,C +460906,Female,Yes,89,Yes,Lawyer,1.0,High,3.0,Cat_6,A +459720,Male,No,25,No,Doctor,6.0,Low,1.0,Cat_6,D +463294,Female,Yes,35,Yes,Engineer,5.0,Low,2.0,Cat_6,B +463751,Female,,39,Yes,Doctor,5.0,Low,,Cat_2,B +465209,Male,Yes,47,Yes,Artist,0.0,Average,2.0,Cat_3,C +461222,Female,No,48,Yes,Artist,1.0,Low,1.0,Cat_7,D +464813,Male,Yes,45,Yes,Engineer,5.0,Average,5.0,Cat_6,B +462855,Male,No,20,No,Healthcare,,Low,,Cat_6,D +463777,Female,Yes,46,Yes,Artist,2.0,Low,2.0,Cat_6,C +463210,Male,Yes,84,,Lawyer,1.0,Low,1.0,Cat_6,A +461672,Female,No,26,No,Engineer,1.0,Low,2.0,Cat_6,A +467711,Female,No,31,Yes,Artist,,Low,1.0,Cat_6,A +459662,Male,Yes,51,Yes,Entertainment,0.0,High,5.0,Cat_3,A +466515,Male,Yes,52,No,Executive,9.0,Average,4.0,Cat_6,A +464946,Male,Yes,53,Yes,Executive,9.0,Low,1.0,Cat_4,D +460880,Male,No,42,Yes,Artist,4.0,Low,2.0,Cat_2,A +459537,Female,No,47,Yes,Homemaker,9.0,Low,3.0,Cat_4,A +463635,Female,Yes,41,Yes,Artist,0.0,Low,3.0,Cat_6,A +467650,Male,Yes,52,Yes,Artist,2.0,Average,4.0,Cat_6,A +467891,Female,No,30,Yes,Doctor,0.0,Low,5.0,Cat_6,D +459052,Male,Yes,47,No,Artist,0.0,Low,2.0,Cat_6,A +466439,Male,Yes,61,No,Engineer,0.0,Low,4.0,Cat_6,D +464571,Male,No,33,No,Healthcare,,Low,2.0,Cat_6,A +460758,Male,Yes,31,No,Executive,9.0,Average,2.0,Cat_6,A +462001,Female,Yes,31,No,Entertainment,0.0,Average,,Cat_4,D +460172,Male,Yes,47,Yes,Artist,1.0,Average,3.0,Cat_6,C +466657,Female,Yes,86,Yes,Lawyer,0.0,High,2.0,Cat_6,C +461381,Male,Yes,31,Yes,Entertainment,0.0,Average,2.0,Cat_6,A +464275,Male,No,30,No,Doctor,0.0,Low,5.0,Cat_6,D +463571,Female,No,32,Yes,Marketing,0.0,Low,3.0,Cat_2,B +460017,Male,Yes,41,Yes,Artist,7.0,High,2.0,Cat_3,C +462171,Female,Yes,37,No,Engineer,3.0,High,2.0,Cat_6,D +460436,Female,No,51,No,Entertainment,1.0,Low,1.0,Cat_6,A +459527,Male,Yes,46,Yes,Artist,1.0,Average,4.0,Cat_6,B +459278,Male,Yes,65,No,Executive,,High,2.0,Cat_6,A +466851,Male,No,38,Yes,Healthcare,8.0,Low,1.0,Cat_6,A +460483,Female,,38,Yes,Executive,0.0,High,1.0,Cat_3,D +466133,Female,No,21,No,Healthcare,7.0,Low,5.0,Cat_2,D +459678,Male,No,21,No,Healthcare,0.0,Low,5.0,Cat_6,D +464012,Female,Yes,75,Yes,Lawyer,3.0,High,2.0,Cat_6,A +464897,Male,Yes,68,No,Executive,0.0,High,2.0,Cat_4,D +463593,Female,No,22,No,Doctor,1.0,Low,5.0,Cat_6,C +464559,Female,No,35,Yes,Healthcare,9.0,Low,1.0,Cat_6,D +463572,Male,Yes,35,Yes,Artist,1.0,Average,4.0,Cat_6,C +466496,Male,Yes,72,Yes,Artist,0.0,Average,2.0,Cat_6,B +461132,Male,Yes,68,Yes,Artist,2.0,Low,1.0,Cat_6,B +459165,Male,No,27,No,Doctor,7.0,Low,3.0,Cat_6,C +461073,Female,No,33,No,Entertainment,1.0,Low,1.0,Cat_3,A +467868,Female,Yes,69,Yes,Entertainment,1.0,High,2.0,Cat_6,B +459825,Male,No,22,No,Healthcare,0.0,Low,5.0,Cat_2,D +463300,Male,Yes,47,No,Entertainment,1.0,Low,2.0,Cat_6,A +467532,Male,Yes,53,Yes,Artist,8.0,Low,1.0,Cat_6,A +462646,Male,Yes,63,,,,Average,2.0,Cat_4,D +465999,Female,Yes,32,No,,8.0,Low,1.0,Cat_6,D +461166,Female,Yes,41,Yes,Entertainment,5.0,High,,Cat_3,D +460143,Male,Yes,38,Yes,Entertainment,0.0,Low,2.0,Cat_4,A +463436,Female,No,25,No,Doctor,0.0,Low,3.0,Cat_6,B +465090,Male,Yes,57,Yes,Artist,0.0,Average,2.0,Cat_6,B +466368,Male,No,37,Yes,Doctor,,Low,1.0,Cat_2,B +466809,Female,Yes,33,Yes,Entertainment,0.0,Low,5.0,Cat_6,B +462676,Female,Yes,48,Yes,Entertainment,,Average,4.0,Cat_6,A +466924,Female,No,22,No,Healthcare,0.0,Low,6.0,Cat_6,D +466719,Male,Yes,68,No,Executive,1.0,High,2.0,Cat_6,A +460141,Male,No,35,Yes,Artist,13.0,Low,1.0,Cat_6,B +467936,Male,No,27,No,Healthcare,1.0,Low,4.0,Cat_6,D +465799,Male,Yes,52,Yes,Artist,1.0,Low,4.0,Cat_6,C +461745,Male,Yes,46,No,Entertainment,8.0,Low,1.0,Cat_3,A +465548,Female,No,40,Yes,Artist,0.0,Low,4.0,Cat_3,A +459359,Male,Yes,46,Yes,Entertainment,,Average,4.0,Cat_6,C +461810,Female,Yes,62,Yes,Homemaker,1.0,Low,1.0,Cat_2,B +460024,Female,Yes,43,Yes,Entertainment,8.0,Low,1.0,Cat_6,B +462952,Male,Yes,46,Yes,Entertainment,,Low,,Cat_6,B +461862,Female,No,42,Yes,Artist,9.0,Low,1.0,Cat_6,C +462136,Female,No,31,Yes,Healthcare,,Low,4.0,Cat_6,D +464676,Male,No,19,No,Entertainment,1.0,Low,6.0,Cat_4,C +461601,Male,Yes,36,Yes,Artist,,Average,2.0,Cat_6,C +466465,Male,Yes,43,Yes,Engineer,5.0,High,2.0,Cat_3,A +462067,Male,No,32,No,Doctor,9.0,Low,4.0,Cat_3,D +460549,Male,No,40,Yes,Artist,8.0,Low,2.0,Cat_3,B +466732,Female,Yes,48,Yes,Engineer,0.0,High,2.0,Cat_4,B +467579,Male,No,38,Yes,Doctor,7.0,Low,1.0,Cat_6,C +465824,Male,Yes,68,No,Lawyer,2.0,Low,3.0,Cat_4,A +464637,Female,No,38,Yes,Marketing,,Low,4.0,Cat_4,D +463343,Male,Yes,35,Yes,Artist,2.0,Low,3.0,Cat_2,B +467206,Female,No,52,Yes,Artist,,Low,1.0,Cat_6,C +466756,Male,Yes,60,Yes,Artist,0.0,Average,2.0,Cat_6,C +462386,Female,No,26,Yes,Healthcare,1.0,Low,5.0,Cat_6,B +467402,Female,No,28,Yes,Artist,5.0,Low,4.0,Cat_6,D +464976,Male,Yes,75,No,Lawyer,1.0,Low,1.0,Cat_4,D +464983,Female,Yes,25,No,Entertainment,2.0,Low,2.0,Cat_6,D +466974,Female,Yes,35,Yes,Artist,8.0,Average,2.0,Cat_6,C +462866,Male,Yes,56,Yes,Doctor,0.0,Low,1.0,Cat_6,A +466453,Male,Yes,41,Yes,Entertainment,2.0,Average,2.0,Cat_6,A +463529,Male,No,37,Yes,Marketing,,Low,1.0,Cat_6,A +463651,Female,No,36,Yes,Healthcare,1.0,Low,2.0,Cat_6,A +463555,Female,No,31,Yes,Artist,0.0,Low,2.0,Cat_6,A +464314,Male,Yes,68,No,Lawyer,0.0,Low,2.0,Cat_6,D +461350,Male,No,30,No,Healthcare,1.0,Low,4.0,Cat_2,D +462354,Male,Yes,62,No,Artist,1.0,Average,2.0,Cat_6,C +462145,Female,No,39,Yes,Homemaker,10.0,Low,1.0,Cat_2,D +461041,Female,Yes,72,No,Lawyer,0.0,High,7.0,Cat_3,D +461946,Male,No,31,Yes,Healthcare,1.0,Low,3.0,Cat_6,C +466105,Female,Yes,65,Yes,Artist,1.0,Average,4.0,Cat_6,C +467598,Male,Yes,68,Yes,Artist,1.0,Low,1.0,Cat_6,A +464639,Male,Yes,39,No,Entertainment,1.0,Average,4.0,Cat_4,B +463143,Female,Yes,52,Yes,Entertainment,,Low,2.0,Cat_3,B +465791,Female,No,28,No,Healthcare,8.0,Low,4.0,Cat_3,C +464418,Male,Yes,43,Yes,Artist,8.0,Average,2.0,Cat_6,B +460782,Male,Yes,77,Yes,Artist,1.0,High,2.0,Cat_6,A +467425,Female,No,42,Yes,Artist,13.0,Low,1.0,Cat_6,C +461986,Male,No,31,Yes,Healthcare,14.0,Low,4.0,Cat_6,B +461306,Male,No,21,No,Doctor,,Low,4.0,Cat_6,C +465554,Female,No,85,Yes,Lawyer,0.0,Low,3.0,Cat_6,A +467030,Male,No,26,Yes,Artist,1.0,Low,2.0,Cat_6,A +461753,Female,No,35,Yes,Artist,0.0,Low,1.0,Cat_6,B +464142,Female,Yes,76,Yes,Artist,1.0,High,2.0,Cat_6,C +467047,Female,No,37,No,Homemaker,9.0,Low,1.0,Cat_6,D +461863,Male,No,33,No,Doctor,2.0,Low,4.0,Cat_6,A +460476,Male,Yes,52,No,Doctor,,Average,4.0,Cat_3,B +464176,Female,Yes,40,Yes,Artist,0.0,Low,1.0,Cat_6,B +463989,Male,No,79,No,Lawyer,0.0,Low,1.0,Cat_4,D +462356,Male,No,47,Yes,Engineer,0.0,Low,3.0,Cat_6,A +459871,Female,Yes,78,No,Entertainment,3.0,Average,2.0,Cat_6,A +466889,Male,Yes,50,Yes,Executive,0.0,High,4.0,Cat_6,C +465948,Female,Yes,46,Yes,Artist,1.0,Average,2.0,Cat_6,B +461496,Male,Yes,47,Yes,Homemaker,,Low,1.0,Cat_6,B +462002,Female,No,37,No,Artist,0.0,Low,2.0,Cat_6,D +462592,Female,Yes,35,No,Artist,11.0,Low,2.0,Cat_4,A +465816,Male,Yes,51,No,Executive,9.0,Average,3.0,Cat_4,A +464343,Female,No,51,Yes,Artist,1.0,Low,1.0,Cat_6,B +459692,Female,Yes,71,Yes,Lawyer,8.0,High,2.0,Cat_6,C +460543,Male,Yes,80,No,Lawyer,7.0,Low,,Cat_3,C +463206,Male,Yes,77,No,Lawyer,0.0,Low,1.0,Cat_6,A +467565,Male,Yes,60,Yes,Artist,0.0,Low,3.0,Cat_6,C +467691,Female,Yes,53,Yes,Artist,,Average,3.0,Cat_6,C +459520,Female,Yes,43,Yes,Doctor,0.0,Low,1.0,Cat_6,B +460816,Female,Yes,49,No,,11.0,Low,2.0,Cat_3,A +464120,Male,No,30,No,Healthcare,8.0,Low,3.0,Cat_6,A +463276,Male,Yes,42,No,Executive,,Average,2.0,Cat_3,A +462398,Female,Yes,62,Yes,Artist,1.0,Low,4.0,Cat_4,C +461006,Male,,22,No,Healthcare,1.0,High,,Cat_3,D +463194,Female,Yes,48,Yes,Artist,0.0,Average,2.0,Cat_3,B +462974,Male,Yes,66,Yes,Lawyer,0.0,Low,1.0,Cat_6,D +463414,Male,Yes,25,No,Executive,0.0,High,3.0,Cat_1,A +460958,Male,No,26,Yes,Healthcare,1.0,Low,5.0,Cat_6,D +463493,Male,Yes,42,Yes,Executive,,High,4.0,Cat_6,B +467540,Male,Yes,52,Yes,Healthcare,0.0,Average,4.0,Cat_6,C +466562,Female,No,18,No,Healthcare,5.0,Low,4.0,Cat_6,D +459195,Female,Yes,58,Yes,Artist,1.0,Average,2.0,Cat_6,C +463352,Male,Yes,37,Yes,,0.0,Average,4.0,Cat_6,B +463948,Male,Yes,39,No,Executive,2.0,High,5.0,Cat_6,B +464949,Male,Yes,19,No,Executive,0.0,High,3.0,Cat_4,A +461969,Female,Yes,46,No,Engineer,9.0,Average,1.0,Cat_4,A +462127,Male,Yes,68,Yes,Lawyer,0.0,High,2.0,Cat_6,C +467512,Male,Yes,41,No,Artist,0.0,Average,2.0,Cat_6,B +461366,Male,No,25,Yes,Healthcare,0.0,Low,1.0,Cat_6,D +464674,Male,Yes,50,No,Artist,1.0,Average,5.0,Cat_4,B +460540,Female,Yes,30,No,Artist,1.0,Average,2.0,Cat_3,D +466813,Male,Yes,59,Yes,Artist,1.0,Low,,Cat_6,A +459228,Female,Yes,52,Yes,Artist,0.0,Average,4.0,Cat_6,C +462615,Male,No,31,No,Doctor,4.0,Low,7.0,Cat_4,D +460712,Male,Yes,80,Yes,Lawyer,0.0,Low,1.0,Cat_6,D +462945,Male,Yes,51,Yes,Artist,1.0,Average,4.0,Cat_6,B +459200,Female,Yes,39,Yes,Artist,2.0,Average,4.0,Cat_6,C +465087,Male,No,29,Yes,Doctor,1.0,Low,4.0,Cat_6,B +459108,Male,No,23,No,Healthcare,,Low,5.0,Cat_6,D +467760,Female,Yes,53,Yes,Artist,4.0,Average,5.0,Cat_6,C +464726,Male,Yes,33,Yes,Executive,11.0,High,9.0,Cat_4,A +462146,Male,Yes,69,Yes,Lawyer,0.0,High,4.0,Cat_6,C +464188,Female,No,33,No,Doctor,1.0,Low,3.0,Cat_6,D +461055,Female,Yes,52,Yes,Engineer,,Average,3.0,Cat_3,B +461718,Female,Yes,48,No,Executive,9.0,Average,3.0,Cat_2,B +467667,Male,Yes,28,No,Doctor,8.0,Low,2.0,Cat_6,A +461997,Female,No,29,Yes,Artist,8.0,Low,1.0,Cat_6,B +460361,Female,Yes,47,Yes,Artist,0.0,Low,1.0,Cat_4,B +460315,Female,No,40,No,Doctor,14.0,Low,6.0,Cat_7,D +463021,Male,Yes,72,No,Lawyer,0.0,High,2.0,,A +467739,Female,Yes,63,No,Artist,1.0,Average,4.0,Cat_6,C +463937,Male,No,27,Yes,Entertainment,0.0,Low,4.0,Cat_2,C +462598,Female,Yes,38,No,Doctor,9.0,Average,5.0,Cat_4,D +467742,Male,Yes,59,Yes,Artist,4.0,High,4.0,Cat_6,B +462918,Male,Yes,38,Yes,Doctor,11.0,Average,3.0,Cat_6,A +465308,Female,No,22,No,Doctor,1.0,Low,4.0,Cat_7,B +463396,Female,No,25,No,Doctor,3.0,Low,4.0,Cat_3,C +466967,Male,Yes,49,Yes,Artist,1.0,Low,1.0,Cat_6,A +465145,Male,Yes,45,Yes,Artist,0.0,Average,2.0,Cat_6,C +466611,Male,No,21,No,Marketing,7.0,Low,3.0,Cat_6,B +463117,Female,No,26,No,Engineer,0.0,Low,3.0,Cat_6,D +462056,Female,Yes,52,Yes,Artist,8.0,Average,4.0,Cat_6,A +465514,Male,No,27,Yes,Entertainment,1.0,Low,5.0,Cat_4,D +460310,Male,No,32,No,Healthcare,12.0,Low,4.0,Cat_6,D +461662,Female,Yes,89,Yes,Lawyer,1.0,High,2.0,Cat_6,A +464448,Male,Yes,39,Yes,Artist,1.0,Average,2.0,Cat_6,C +461473,Female,Yes,41,Yes,Artist,3.0,Average,3.0,Cat_6,C +461587,Male,No,26,Yes,Healthcare,0.0,Low,3.0,Cat_6,B +459021,Male,Yes,45,Yes,Artist,7.0,Low,2.0,Cat_2,A +465861,Female,No,40,Yes,Entertainment,14.0,Low,1.0,Cat_6,A +465874,Female,No,26,Yes,Artist,1.0,Low,2.0,Cat_6,D +460325,Female,No,63,Yes,Healthcare,3.0,Low,1.0,Cat_6,B +460333,Female,No,40,No,Entertainment,12.0,Low,4.0,Cat_6,A +467043,Male,No,53,Yes,Entertainment,4.0,Low,1.0,Cat_6,A +461937,Female,No,41,Yes,Doctor,1.0,Low,,Cat_6,B +467262,Female,Yes,77,No,Lawyer,1.0,High,2.0,Cat_6,D +465794,Female,No,32,No,,0.0,Low,4.0,Cat_4,C +460421,Male,Yes,30,Yes,Artist,,Low,1.0,Cat_7,B +462180,Female,Yes,60,Yes,Engineer,4.0,Average,4.0,Cat_2,B +462681,Female,Yes,59,Yes,Entertainment,7.0,Average,2.0,Cat_6,B +465461,Male,Yes,72,No,Entertainment,1.0,Average,3.0,Cat_6,C +459356,Male,No,63,Yes,Artist,4.0,Low,1.0,Cat_6,A +459216,Female,No,42,Yes,Artist,1.0,Low,2.0,Cat_6,A +465283,Male,Yes,53,No,Executive,0.0,High,2.0,Cat_6,B +467450,Male,Yes,81,No,Executive,9.0,High,2.0,Cat_6,C +465296,Female,Yes,76,Yes,Lawyer,5.0,High,3.0,Cat_6,A +461239,Female,No,40,Yes,Entertainment,,Low,1.0,Cat_6,D +463870,Female,No,28,No,Healthcare,1.0,Low,2.0,Cat_4,A +467550,Male,No,27,Yes,Doctor,0.0,Low,2.0,Cat_3,A +459382,Female,No,41,Yes,Engineer,,Low,1.0,Cat_6,A +463134,Male,No,29,Yes,Entertainment,,Low,1.0,Cat_6,D +459917,Male,No,43,Yes,Artist,11.0,Low,1.0,Cat_3,B +463037,Male,Yes,57,Yes,Entertainment,1.0,Average,4.0,Cat_6,B +461209,Female,No,31,No,Healthcare,9.0,Low,1.0,Cat_6,D +461524,Male,No,68,Yes,Lawyer,1.0,Low,1.0,Cat_6,D +465038,Male,Yes,46,No,Executive,0.0,High,4.0,Cat_6,A +461849,Male,No,32,No,Healthcare,0.0,Low,4.0,Cat_6,A +459654,Male,Yes,51,Yes,Artist,5.0,Average,3.0,Cat_6,B +463620,Male,No,19,No,Healthcare,1.0,Low,4.0,Cat_2,D +466862,Female,No,26,,Healthcare,9.0,Low,4.0,Cat_6,D +463794,Female,No,32,Yes,Healthcare,,Low,5.0,Cat_2,D +461743,Male,Yes,62,Yes,Doctor,1.0,High,2.0,Cat_6,C +466994,Male,Yes,47,Yes,Artist,1.0,Average,2.0,Cat_6,C +467210,Male,Yes,28,Yes,Homemaker,13.0,Average,2.0,Cat_6,B +460059,Female,No,48,No,Entertainment,2.0,Low,,Cat_4,D +466946,Male,Yes,61,Yes,Entertainment,0.0,Average,2.0,Cat_6,C +462189,Female,No,25,No,Marketing,1.0,Low,4.0,Cat_4,B +466011,Male,Yes,74,Yes,Lawyer,1.0,High,2.0,Cat_4,B +462162,Male,Yes,38,No,Artist,1.0,Low,2.0,Cat_6,B +463977,Male,Yes,28,Yes,Healthcare,9.0,High,2.0,Cat_6,B +466692,Female,,27,No,Engineer,1.0,Low,1.0,Cat_5,A +465232,Male,Yes,56,Yes,Executive,0.0,High,4.0,Cat_6,B +461018,Male,No,25,No,Marketing,0.0,Low,3.0,Cat_3,A +465607,Female,No,37,Yes,Artist,7.0,Low,2.0,Cat_3,B +467801,Female,Yes,50,Yes,Doctor,0.0,Average,2.0,Cat_7,C +465858,Female,Yes,48,Yes,Artist,3.0,Low,1.0,Cat_6,A +467117,Male,No,48,Yes,Artist,0.0,Low,1.0,Cat_6,A +462762,Male,Yes,48,Yes,Artist,0.0,High,4.0,Cat_6,B +462973,Male,Yes,39,Yes,Artist,9.0,Average,2.0,Cat_3,B +466551,Male,Yes,36,Yes,Artist,9.0,Average,2.0,Cat_6,B +461569,Male,Yes,63,Yes,Artist,1.0,Low,1.0,Cat_6,C +459253,Male,Yes,36,Yes,Artist,1.0,Average,4.0,Cat_6,C +467755,Male,Yes,37,Yes,Artist,6.0,Low,2.0,Cat_6,C +459943,Male,No,42,Yes,Entertainment,2.0,Low,3.0,Cat_6,B +465480,Male,Yes,39,No,Doctor,1.0,High,2.0,Cat_6,A +466029,Female,No,28,No,Executive,0.0,Low,1.0,Cat_2,A +466806,Male,Yes,40,Yes,Healthcare,0.0,Low,1.0,Cat_6,D +466214,Female,Yes,88,Yes,Lawyer,0.0,Low,2.0,Cat_6,C +462782,Male,Yes,57,Yes,Executive,7.0,High,2.0,Cat_4,C +461479,Female,Yes,51,Yes,Artist,1.0,Average,3.0,Cat_6,C +461027,Female,,43,No,Engineer,8.0,Average,5.0,Cat_4,A +460494,Female,No,28,No,Marketing,1.0,Low,4.0,Cat_3,D +460565,Female,Yes,42,No,Homemaker,8.0,Average,4.0,Cat_3,C +465247,Male,No,18,No,Healthcare,6.0,Low,7.0,Cat_6,D +465993,Female,No,27,No,Engineer,0.0,Low,2.0,Cat_6,D +459463,Female,Yes,62,Yes,Artist,,Average,3.0,Cat_6,C +460170,Male,Yes,68,No,Artist,0.0,Low,1.0,Cat_6,C +463200,Male,Yes,42,Yes,Entertainment,0.0,Average,2.0,Cat_4,A +458986,Male,No,18,No,Healthcare,7.0,Low,4.0,Cat_6,D +467390,Male,Yes,46,Yes,Artist,0.0,Low,2.0,Cat_6,C +459349,Female,Yes,48,Yes,Artist,0.0,High,4.0,Cat_6,C +467144,Male,Yes,35,Yes,Entertainment,1.0,Low,2.0,Cat_6,A +460750,Male,Yes,56,Yes,Entertainment,0.0,Average,2.0,Cat_6,C +464263,Female,Yes,88,Yes,Lawyer,1.0,Low,,Cat_6,D +462644,Male,Yes,25,No,Engineer,,Average,3.0,Cat_4,D +463386,Female,Yes,43,Yes,Doctor,6.0,Low,1.0,Cat_3,B +463770,Male,No,25,No,Healthcare,1.0,Low,4.0,Cat_6,D +459434,Female,Yes,78,No,Lawyer,1.0,Low,1.0,Cat_6,C +467417,Female,Yes,53,Yes,Artist,4.0,Average,6.0,Cat_6,C +461048,Male,Yes,33,Yes,Doctor,10.0,Average,2.0,Cat_2,D +461428,Male,No,49,Yes,Artist,1.0,Low,1.0,Cat_6,C +461213,Female,No,31,Yes,Healthcare,1.0,Low,4.0,Cat_6,A +464975,Male,Yes,65,Yes,Doctor,5.0,Low,,Cat_4,A +461565,Male,Yes,51,Yes,Entertainment,4.0,Average,2.0,Cat_2,C +462213,Male,No,19,No,Doctor,1.0,Low,2.0,Cat_4,D +467716,Male,Yes,41,Yes,Artist,4.0,Average,3.0,Cat_6,B +460285,Female,No,57,Yes,Doctor,0.0,Low,2.0,Cat_6,B +461455,Female,Yes,35,Yes,Artist,0.0,Average,2.0,Cat_6,B +467920,Female,No,40,No,Engineer,8.0,Low,1.0,Cat_6,A +467244,Male,No,26,Yes,Healthcare,0.0,Low,5.0,Cat_6,D +463107,Male,Yes,86,Yes,Lawyer,,Low,2.0,Cat_6,A +463460,Male,Yes,52,Yes,Artist,,Average,2.0,Cat_6,B +462976,Female,Yes,50,Yes,Engineer,0.0,Average,2.0,Cat_6,C +467669,Male,Yes,82,No,Lawyer,0.0,High,2.0,Cat_6,A +461589,Male,Yes,41,Yes,Artist,1.0,Average,2.0,Cat_6,B +463269,Male,Yes,36,Yes,Entertainment,2.0,Average,2.0,Cat_3,A +464815,Male,Yes,22,No,Doctor,2.0,High,9.0,Cat_4,B +465552,Female,,23,No,Healthcare,0.0,Low,7.0,Cat_6,D +463587,Female,No,27,No,Engineer,1.0,Low,4.0,Cat_6,D +467200,Female,No,75,Yes,Lawyer,0.0,Low,2.0,Cat_6,C +462436,Male,No,32,Yes,Healthcare,0.0,Low,3.0,Cat_6,D +463207,Female,Yes,51,Yes,Artist,1.0,Average,2.0,Cat_6,C +463853,Female,Yes,58,Yes,Artist,0.0,Average,4.0,Cat_3,B +459443,Female,Yes,50,Yes,Artist,8.0,Average,2.0,Cat_6,B +466489,Male,Yes,55,Yes,Homemaker,1.0,Average,4.0,Cat_6,C +464388,Female,No,30,No,Engineer,11.0,Low,1.0,Cat_4,D +467662,Male,No,29,Yes,Artist,,Low,1.0,Cat_4,D +466832,Female,Yes,69,Yes,Artist,0.0,Average,2.0,Cat_4,C +464496,Female,Yes,41,No,Marketing,1.0,High,5.0,Cat_3,A +465271,Male,Yes,47,No,Executive,0.0,High,5.0,Cat_6,B +467621,Male,Yes,76,Yes,Lawyer,1.0,Low,1.0,Cat_6,A +467526,Male,No,21,No,Healthcare,,Low,3.0,Cat_6,D +464050,Female,No,30,Yes,Artist,0.0,Low,1.0,Cat_6,A +460510,Male,Yes,35,Yes,Entertainment,,Average,2.0,Cat_3,C +462339,Male,No,33,Yes,Artist,3.0,Low,5.0,Cat_6,A +467670,Male,No,25,No,Engineer,,Low,3.0,Cat_6,A +464844,Female,No,31,Yes,Healthcare,1.0,Low,4.0,Cat_4,D +464681,Female,Yes,25,No,Engineer,4.0,Average,5.0,Cat_4,D +467640,Female,Yes,38,Yes,Artist,,Low,3.0,Cat_6,D +459381,Male,Yes,56,Yes,Artist,,Low,2.0,Cat_6,B +461095,Female,No,21,No,Healthcare,,Low,3.0,Cat_3,D +464963,Female,Yes,47,Yes,Entertainment,1.0,Low,2.0,Cat_4,B +461422,Male,Yes,52,Yes,Executive,1.0,Average,6.0,Cat_6,B +460853,Female,Yes,63,Yes,Artist,1.0,High,3.0,Cat_6,C +461505,Female,Yes,39,No,Artist,0.0,High,3.0,Cat_6,B +467832,Female,No,20,No,Healthcare,4.0,Low,,,D +466398,Male,No,21,No,Healthcare,1.0,Low,4.0,Cat_3,D +465587,Male,Yes,42,Yes,Entertainment,,Low,5.0,Cat_3,B +467109,Male,No,38,No,Doctor,0.0,Low,5.0,Cat_3,D +460350,Female,No,43,Yes,Healthcare,2.0,Low,1.0,Cat_4,D +466863,Female,No,28,No,Homemaker,10.0,Low,1.0,Cat_6,D +459767,Male,No,21,No,Healthcare,4.0,Low,4.0,Cat_6,D +465640,Male,Yes,50,Yes,Artist,7.0,Average,2.0,Cat_6,C +462903,Male,Yes,72,Yes,Entertainment,,Average,2.0,Cat_6,B +463631,Male,No,25,No,Healthcare,0.0,Low,5.0,Cat_3,D +467797,Male,Yes,58,Yes,Artist,1.0,High,2.0,Cat_6,C +467818,Female,Yes,37,Yes,Artist,5.0,High,3.0,Cat_6,C +459120,Male,No,23,No,Healthcare,5.0,Low,5.0,Cat_2,D +464905,Male,,81,No,Executive,,High,2.0,Cat_4,A +463326,Female,No,23,No,Healthcare,0.0,Low,3.0,Cat_2,D +466493,Male,Yes,77,No,Lawyer,0.0,High,2.0,Cat_6,A +466847,Female,No,33,No,Doctor,,Low,2.0,Cat_6,A +464396,Male,Yes,35,Yes,Entertainment,1.0,Average,3.0,Cat_6,B +463946,Male,No,25,No,Healthcare,4.0,Low,3.0,Cat_4,D +466687,Male,No,22,No,Healthcare,0.0,Low,3.0,Cat_6,D +467908,Female,Yes,42,Yes,Engineer,0.0,High,4.0,Cat_6,B +461062,Female,No,25,No,Executive,1.0,Low,3.0,Cat_6,D +466526,Male,No,33,No,Healthcare,1.0,Low,5.0,Cat_2,B +464423,Male,Yes,51,Yes,Entertainment,0.0,Average,5.0,Cat_6,C +465284,Female,No,31,No,Marketing,8.0,Low,4.0,Cat_6,D +463530,Female,No,32,No,Entertainment,12.0,Low,5.0,Cat_6,D +462415,Male,No,32,No,Doctor,0.0,Low,1.0,Cat_6,A +460066,Female,No,45,No,Marketing,0.0,Low,1.0,Cat_6,D +464242,Male,No,37,Yes,Engineer,1.0,Low,3.0,Cat_6,D +465259,Female,No,23,No,Healthcare,,Low,4.0,Cat_1,D +460083,Female,No,52,Yes,Artist,9.0,Low,1.0,Cat_6,B +461669,Female,Yes,42,No,Engineer,0.0,Average,2.0,Cat_6,A +461060,Male,Yes,59,Yes,Executive,1.0,High,5.0,Cat_6,C +459198,Female,Yes,66,Yes,Artist,0.0,Average,3.0,Cat_6,C +466163,Male,Yes,79,,Lawyer,1.0,Low,1.0,Cat_4,D +465362,Female,Yes,52,Yes,Engineer,0.0,Average,4.0,Cat_6,B +464678,Male,Yes,47,Yes,Executive,1.0,High,5.0,Cat_4,C +464514,Male,Yes,42,Yes,Artist,0.0,Average,3.0,Cat_7,A +463848,Male,Yes,48,Yes,Artist,1.0,Average,4.0,Cat_1,C +462495,Male,Yes,84,No,Lawyer,0.0,High,2.0,Cat_6,A +462144,Female,No,39,Yes,Artist,3.0,Low,1.0,Cat_6,A +461000,Male,Yes,61,No,Marketing,1.0,High,2.0,Cat_3,A +459319,Female,Yes,46,No,Artist,1.0,High,2.0,Cat_6,C +467581,Female,Yes,33,Yes,Engineer,2.0,Average,2.0,Cat_6,D +463669,Female,Yes,31,Yes,Marketing,7.0,High,4.0,Cat_2,A +460604,Female,Yes,41,No,Entertainment,9.0,High,2.0,Cat_4,A +464912,Male,,32,Yes,Doctor,,Average,2.0,Cat_4,A +466645,Male,Yes,66,Yes,Lawyer,5.0,Low,2.0,Cat_6,C +459715,Male,No,20,No,Healthcare,2.0,Low,4.0,Cat_6,D +463828,Male,Yes,43,No,Engineer,0.0,Average,2.0,Cat_1,C +467438,Male,No,28,No,Entertainment,6.0,Low,3.0,Cat_6,A +463031,Female,Yes,38,Yes,Artist,1.0,Average,4.0,Cat_6,B +465219,Male,Yes,42,Yes,Healthcare,5.0,Low,2.0,Cat_4,C +467497,Male,Yes,51,Yes,Artist,1.0,Low,3.0,Cat_6,D +462112,Male,Yes,78,Yes,Lawyer,1.0,Low,1.0,Cat_6,D +461773,Male,Yes,69,Yes,Entertainment,1.0,High,3.0,Cat_6,B +465178,Female,Yes,89,Yes,Lawyer,0.0,High,2.0,Cat_6,B +465599,Female,Yes,47,Yes,Healthcare,0.0,Average,6.0,Cat_1,C +459079,Male,Yes,84,Yes,Lawyer,,High,2.0,Cat_6,A +460261,Female,Yes,58,Yes,Lawyer,1.0,High,4.0,Cat_1,C +466749,Male,No,21,,Healthcare,1.0,Low,5.0,Cat_3,D +459037,Female,No,23,No,Engineer,1.0,Low,,Cat_6,D +465139,Male,Yes,40,No,Executive,9.0,Average,2.0,Cat_6,D +465786,Female,No,19,No,Doctor,0.0,Low,3.0,Cat_4,D +465847,Female,No,40,Yes,Lawyer,1.0,Low,1.0,Cat_6,A +459977,Female,No,41,Yes,Artist,4.0,Low,1.0,Cat_6,D +461242,Male,Yes,52,Yes,Healthcare,1.0,Low,2.0,Cat_6,D +464995,Male,Yes,45,No,Executive,3.0,High,4.0,Cat_4,D +461508,Male,No,33,Yes,Artist,8.0,Low,1.0,Cat_6,A +459174,Male,No,43,Yes,Artist,1.0,Low,2.0,Cat_4,B +465852,Male,Yes,39,Yes,Doctor,1.0,Low,2.0,Cat_4,B +466892,Male,Yes,51,Yes,Artist,1.0,Average,3.0,Cat_6,C +463693,Male,Yes,35,Yes,Artist,9.0,High,3.0,Cat_6,C +464190,Male,No,37,Yes,Executive,3.0,Low,1.0,Cat_6,D +459391,Female,No,40,Yes,Engineer,1.0,Low,4.0,Cat_6,D +462390,Female,No,28,No,Healthcare,0.0,Low,4.0,Cat_3,B +467177,Male,No,69,Yes,Marketing,8.0,Low,,Cat_6,A +461343,Male,Yes,27,Yes,Artist,6.0,Average,2.0,Cat_6,B +459661,Male,Yes,62,Yes,Executive,0.0,High,3.0,Cat_6,C +459854,Female,Yes,57,Yes,Artist,0.0,High,2.0,Cat_6,C +458995,Male,Yes,62,No,Executive,0.0,High,4.0,Cat_6,B +459751,Male,No,28,No,Marketing,0.0,Low,6.0,Cat_6,B +466951,Female,Yes,52,Yes,Artist,0.0,High,3.0,Cat_6,C +459971,Female,No,40,Yes,Healthcare,8.0,Low,1.0,Cat_6,A +465370,Female,No,53,Yes,Artist,0.0,Low,1.0,Cat_6,B +462178,Female,Yes,73,Yes,Lawyer,7.0,Low,2.0,Cat_6,A +463277,Female,Yes,26,No,Engineer,6.0,Low,2.0,Cat_3,D +461836,Male,Yes,53,Yes,Artist,6.0,Average,3.0,Cat_6,C +466836,Female,No,42,Yes,Doctor,0.0,Low,1.0,Cat_4,D +464449,Female,No,67,Yes,Engineer,0.0,Low,,Cat_6,D +461443,Female,Yes,70,Yes,Artist,1.0,Average,2.0,Cat_6,C +459222,Female,Yes,68,Yes,Artist,1.0,Average,3.0,Cat_6,C +459549,Male,No,22,No,Healthcare,9.0,Low,5.0,Cat_4,D +462292,Male,Yes,65,Yes,Artist,1.0,Low,1.0,Cat_6,A +467505,Male,Yes,43,Yes,Artist,0.0,Low,4.0,Cat_6,D +463992,Male,Yes,39,Yes,Artist,0.0,Average,2.0,Cat_6,B +459560,Male,Yes,89,No,Lawyer,1.0,High,2.0,Cat_6,B +464944,Female,Yes,37,Yes,Entertainment,0.0,Low,2.0,Cat_4,B +459333,Male,No,29,Yes,Healthcare,1.0,Low,2.0,Cat_6,D +467286,Male,No,27,Yes,Healthcare,1.0,Low,3.0,Cat_4,D +459011,Male,Yes,88,No,Lawyer,1.0,Low,1.0,Cat_6,A +467076,Female,Yes,48,Yes,Doctor,0.0,Average,2.0,Cat_6,B +462577,Female,Yes,70,No,Marketing,0.0,High,2.0,Cat_4,D +460346,Female,Yes,40,Yes,Artist,12.0,Average,,Cat_6,C +459900,Male,No,52,Yes,Artist,6.0,Low,1.0,Cat_6,C +462990,Female,No,25,Yes,Healthcare,3.0,Low,5.0,Cat_4,D +464625,Female,No,48,Yes,Artist,0.0,Low,2.0,Cat_6,B +464790,Female,No,42,Yes,Artist,,Low,1.0,Cat_4,C +460012,Female,Yes,50,Yes,Artist,1.0,High,3.0,Cat_2,C +459968,Male,No,39,Yes,Artist,11.0,Low,1.0,Cat_7,A +464221,Male,Yes,56,Yes,Entertainment,0.0,Low,,Cat_4,A +465031,Female,Yes,67,Yes,,1.0,Average,2.0,Cat_6,A +465522,Female,No,29,Yes,Healthcare,2.0,Low,8.0,Cat_5,B +465212,Male,Yes,46,Yes,Executive,,High,4.0,Cat_3,B +461575,Male,No,31,No,Doctor,1.0,Low,4.0,Cat_6,C +460001,Male,Yes,41,Yes,Artist,4.0,Average,2.0,Cat_2,A +460778,Male,No,27,Yes,Artist,0.0,Low,8.0,Cat_4,D +460700,Male,Yes,56,Yes,Entertainment,1.0,Average,3.0,Cat_6,B +460616,Male,Yes,32,Yes,Homemaker,2.0,Average,2.0,Cat_3,C +462665,Female,Yes,59,Yes,Artist,4.0,Average,4.0,Cat_7,C +461945,Female,Yes,87,Yes,Artist,0.0,Low,1.0,Cat_6,C +465798,Female,No,41,Yes,Healthcare,0.0,Low,3.0,Cat_6,C +461201,Female,No,59,Yes,Artist,,Low,1.0,Cat_6,C +465134,Female,Yes,55,Yes,Entertainment,1.0,Average,5.0,Cat_6,B +462893,Female,Yes,52,No,Artist,1.0,Average,3.0,Cat_4,C +459208,Female,Yes,61,Yes,Artist,1.0,High,2.0,Cat_3,C +461896,Male,Yes,46,Yes,Artist,1.0,Low,1.0,Cat_6,B +459868,Female,Yes,46,Yes,Engineer,1.0,Average,2.0,Cat_6,C +464568,Male,Yes,36,Yes,Artist,8.0,Average,2.0,Cat_6,B +462492,Male,Yes,36,No,Artist,1.0,Average,4.0,Cat_2,B +467559,Male,Yes,58,Yes,Executive,0.0,Average,4.0,Cat_6,C +464277,Male,No,25,Yes,Artist,2.0,Low,1.0,Cat_6,A +462798,Male,Yes,65,Yes,Artist,7.0,High,2.0,Cat_6,C +461924,Female,Yes,89,No,Lawyer,1.0,High,3.0,Cat_6,B +460998,Male,No,27,Yes,,0.0,Low,3.0,Cat_3,B +463630,Male,No,39,Yes,Entertainment,1.0,Low,2.0,Cat_6,A +462173,Male,Yes,89,Yes,Executive,3.0,Low,3.0,Cat_6,A +463569,Female,Yes,59,No,Marketing,1.0,Average,4.0,Cat_1,D +460044,Female,Yes,47,Yes,Artist,0.0,Low,2.0,Cat_1,B +461026,Female,Yes,41,Yes,Artist,7.0,Average,2.0,Cat_6,B +461468,Male,Yes,66,Yes,Entertainment,,Average,3.0,Cat_6,C +467224,Female,No,19,No,Entertainment,0.0,Low,4.0,Cat_6,D +462879,Male,Yes,74,Yes,Lawyer,1.0,High,2.0,Cat_6,A +460349,Male,,35,Yes,Artist,3.0,Average,2.0,Cat_6,C +459591,Male,Yes,50,Yes,Artist,1.0,Average,3.0,Cat_6,C +459133,Male,No,23,No,Entertainment,1.0,Low,3.0,Cat_4,D +465465,Female,Yes,28,Yes,Artist,8.0,Low,2.0,Cat_6,B +460231,Male,Yes,59,Yes,Executive,11.0,High,3.0,Cat_6,C +461111,Female,Yes,42,Yes,Doctor,8.0,Low,1.0,Cat_6,A +459203,Male,Yes,35,Yes,Engineer,0.0,Low,2.0,Cat_6,B +463540,Male,Yes,56,Yes,Artist,8.0,Average,3.0,Cat_6,C +461971,Male,Yes,50,Yes,Entertainment,1.0,Average,4.0,Cat_3,B +467183,Male,Yes,39,Yes,Entertainment,4.0,Average,4.0,Cat_6,B +466548,Female,Yes,45,Yes,Artist,1.0,Average,2.0,Cat_6,B +460747,Male,Yes,52,Yes,Artist,1.0,Average,4.0,Cat_3,C +459721,Male,Yes,76,Yes,Lawyer,0.0,Low,2.0,Cat_6,A +463204,Male,No,36,No,Entertainment,1.0,Low,1.0,Cat_6,A +467618,Male,No,26,No,Doctor,6.0,Low,2.0,Cat_6,D +467903,Male,Yes,39,Yes,Entertainment,9.0,Low,2.0,Cat_6,A +466249,Female,No,18,No,Healthcare,8.0,Low,4.0,Cat_4,D +464467,Female,No,43,Yes,Healthcare,1.0,Low,2.0,Cat_4,A +461138,Male,Yes,63,Yes,Entertainment,1.0,High,2.0,Cat_7,B +463392,Female,Yes,36,Yes,Artist,5.0,Low,2.0,Cat_4,B +466671,Female,,33,No,Healthcare,2.0,Low,3.0,Cat_2,C +463017,Female,Yes,81,No,Lawyer,0.0,High,2.0,Cat_6,A +463908,Female,Yes,37,Yes,Artist,9.0,Low,2.0,Cat_6,C +464154,Female,Yes,47,Yes,Artist,1.0,Average,4.0,Cat_6,C +461272,Male,Yes,67,Yes,Artist,0.0,Low,1.0,Cat_3,B +461765,Female,No,36,Yes,Artist,1.0,Low,1.0,Cat_6,A +467099,Female,No,30,No,Entertainment,0.0,Low,3.0,Cat_6,A +464151,Male,No,40,Yes,Doctor,12.0,Low,1.0,Cat_2,D +461304,Male,No,19,No,Marketing,2.0,Low,4.0,Cat_6,C +459569,Male,No,32,Yes,Healthcare,,Low,1.0,Cat_6,D +461457,Female,Yes,32,No,Doctor,1.0,Low,5.0,Cat_6,A +467851,Male,No,33,No,Marketing,,Low,5.0,Cat_6,D +463510,Female,No,27,Yes,Healthcare,1.0,Low,7.0,Cat_2,A +465519,Female,Yes,73,No,Lawyer,1.0,High,2.0,Cat_6,A +461092,Female,Yes,29,Yes,Homemaker,0.0,High,2.0,Cat_3,A +461180,Male,No,28,No,Entertainment,0.0,Low,3.0,Cat_3,D +462473,Male,No,69,No,Engineer,1.0,Low,,Cat_4,A +463028,Male,No,33,No,Doctor,1.0,Low,4.0,Cat_6,D +461276,Male,Yes,67,Yes,Marketing,1.0,Low,2.0,Cat_7,D +460940,Male,Yes,73,Yes,Artist,0.0,Low,1.0,Cat_6,A +462955,Male,Yes,42,Yes,Artist,1.0,Low,2.0,Cat_3,B +467725,Male,No,32,Yes,Healthcare,9.0,Low,2.0,Cat_6,D +465217,Male,Yes,53,Yes,Executive,1.0,High,5.0,Cat_3,A +463657,Male,Yes,42,Yes,Executive,0.0,High,2.0,Cat_6,B +465851,Female,Yes,50,No,Artist,9.0,High,2.0,Cat_4,A +466561,Female,No,19,No,Healthcare,3.0,Low,3.0,Cat_6,D +459577,Male,Yes,82,Yes,Lawyer,0.0,Low,1.0,Cat_6,D +466818,Male,Yes,82,No,Executive,0.0,High,2.0,Cat_6,B +464543,Female,No,38,Yes,Artist,7.0,Low,1.0,Cat_6,A +462640,Male,Yes,27,No,Executive,,Low,2.0,Cat_4,D +467649,Male,Yes,38,Yes,Doctor,0.0,Average,5.0,Cat_6,B +466016,Female,No,48,Yes,Homemaker,1.0,Low,1.0,Cat_3,B +462547,Male,No,22,No,Healthcare,9.0,Low,4.0,Cat_6,D +462963,Male,Yes,30,Yes,Artist,0.0,Average,2.0,Cat_3,C +466305,Male,No,31,No,Healthcare,1.0,Low,3.0,Cat_2,B +462631,Female,Yes,35,No,Engineer,2.0,High,4.0,Cat_4,D +460706,Female,No,27,No,Engineer,6.0,Low,6.0,Cat_4,D +460282,Male,Yes,40,No,Executive,0.0,Low,3.0,Cat_6,D +459041,Male,Yes,62,Yes,Artist,8.0,Low,1.0,Cat_6,D +460114,Male,Yes,30,Yes,Healthcare,8.0,Low,2.0,Cat_6,C +460147,Male,Yes,67,Yes,Entertainment,1.0,Average,3.0,Cat_6,C +462123,Male,No,38,Yes,Artist,0.0,Low,2.0,Cat_6,C +465768,Female,No,37,Yes,Artist,7.0,Low,4.0,Cat_4,A +463236,Female,No,26,Yes,Healthcare,5.0,Low,2.0,Cat_6,D +462487,Male,Yes,61,Yes,Entertainment,2.0,Average,4.0,Cat_6,C +463954,Male,Yes,22,No,Entertainment,1.0,Average,3.0,Cat_4,D +466609,Female,Yes,47,Yes,Artist,0.0,Average,5.0,Cat_6,C +461741,Male,Yes,67,Yes,Lawyer,1.0,High,2.0,Cat_6,B +464416,Male,Yes,38,Yes,Artist,1.0,Low,6.0,Cat_6,A +467513,Female,Yes,36,No,Engineer,,Average,4.0,Cat_4,D +465428,Female,No,36,Yes,Engineer,0.0,Low,1.0,Cat_4,A +464632,Male,No,29,No,Entertainment,0.0,Low,8.0,Cat_4,D +462999,Female,,29,No,Engineer,0.0,High,4.0,Cat_6,A +462554,Male,No,28,No,Healthcare,1.0,Low,8.0,Cat_4,C +460075,Female,No,25,Yes,Artist,,Low,1.0,Cat_6,B +460838,Male,No,40,Yes,Artist,0.0,Low,,Cat_6,A +464889,Male,Yes,41,No,Executive,1.0,Low,7.0,Cat_4,D +465490,Female,Yes,35,Yes,Artist,0.0,Average,5.0,Cat_4,C +465909,Male,Yes,39,Yes,Entertainment,9.0,Low,1.0,Cat_6,A +465117,Female,Yes,73,Yes,Artist,1.0,High,2.0,Cat_6,C +459085,Male,No,22,No,Marketing,4.0,Low,3.0,Cat_6,A +464709,Male,Yes,40,No,Executive,8.0,High,5.0,Cat_4,D +460572,Female,No,30,Yes,Engineer,0.0,Low,4.0,Cat_3,C +464612,Male,No,32,Yes,Doctor,1.0,Low,2.0,Cat_6,A +465424,Male,No,38,Yes,Entertainment,1.0,Low,3.0,Cat_6,A +463093,Female,Yes,31,No,Healthcare,,Low,4.0,Cat_2,C +466803,Female,,41,Yes,Marketing,0.0,Low,2.0,Cat_6,D +463248,Female,Yes,26,No,Homemaker,8.0,Average,4.0,Cat_2,D +464155,Male,No,41,No,Doctor,0.0,Low,3.0,Cat_4,B +467424,Male,Yes,56,Yes,Entertainment,2.0,Average,2.0,Cat_6,D +465220,Male,Yes,61,No,Executive,0.0,High,2.0,Cat_1,B +463004,Male,Yes,48,Yes,Artist,0.0,Average,2.0,Cat_6,C +463315,Male,Yes,40,Yes,Executive,0.0,High,4.0,Cat_3,B +466498,Male,Yes,83,Yes,Lawyer,6.0,High,3.0,Cat_6,D +463234,Female,No,22,No,Engineer,1.0,Low,4.0,Cat_6,D +462453,Male,No,22,No,Healthcare,9.0,Low,5.0,Cat_6,D +464486,Male,,22,No,Healthcare,0.0,Low,3.0,Cat_4,D +467111,Male,Yes,72,Yes,Artist,0.0,Low,1.0,Cat_6,B +466355,Male,No,27,Yes,Healthcare,0.0,Low,2.0,Cat_6,C +463230,Female,Yes,35,Yes,Engineer,5.0,High,3.0,Cat_6,A +463127,Male,Yes,84,No,Executive,,High,2.0,Cat_6,D +465018,Male,No,42,Yes,Entertainment,0.0,Low,3.0,Cat_6,A +464940,Female,Yes,30,No,Engineer,1.0,Low,2.0,Cat_4,A +465294,Male,Yes,73,No,Lawyer,0.0,Low,2.0,Cat_6,D +461525,Male,Yes,68,Yes,Entertainment,1.0,Low,2.0,Cat_6,D +460058,Male,Yes,46,Yes,Artist,0.0,Low,1.0,Cat_4,B +464692,Female,No,48,No,Marketing,1.0,Low,1.0,Cat_3,D +466342,Female,Yes,49,Yes,Artist,0.0,Average,5.0,Cat_6,C +459214,Female,No,26,Yes,Engineer,0.0,Low,3.0,Cat_6,C +464243,Female,No,49,Yes,Artist,1.0,Low,2.0,Cat_6,C +467273,Female,Yes,81,Yes,Lawyer,7.0,High,2.0,Cat_6,B +459652,Female,No,26,No,Engineer,,Low,4.0,Cat_4,A +466187,Female,Yes,56,Yes,Artist,4.0,Average,2.0,Cat_3,C +466421,Male,Yes,26,No,Doctor,4.0,Average,3.0,Cat_3,D +461541,Male,No,36,Yes,Executive,9.0,Low,3.0,Cat_6,D +462709,Female,No,32,No,Doctor,0.0,Low,3.0,Cat_6,C +466233,Female,Yes,56,Yes,Homemaker,5.0,Low,1.0,Cat_6,C +461881,Male,No,40,Yes,Artist,1.0,Low,1.0,Cat_6,A +466371,Male,No,19,No,Healthcare,1.0,Low,4.0,Cat_2,D +467717,Female,Yes,66,No,Engineer,0.0,High,5.0,Cat_4,B +464917,Female,Yes,76,No,Lawyer,,Low,1.0,Cat_4,D +466031,Female,No,30,No,Entertainment,6.0,Low,1.0,Cat_6,A +467039,Male,Yes,26,No,Homemaker,9.0,Average,2.0,Cat_6,A +464334,Female,No,33,Yes,Healthcare,1.0,Low,3.0,Cat_6,D +459185,Female,No,25,Yes,Entertainment,5.0,Low,1.0,Cat_6,D +460580,Female,Yes,27,No,Engineer,9.0,Low,2.0,Cat_3,D +464808,Male,Yes,42,No,Entertainment,1.0,Low,4.0,Cat_4,A +465583,Male,No,35,Yes,Artist,2.0,Low,2.0,Cat_6,A +462042,Female,No,29,No,Healthcare,0.0,Low,3.0,Cat_6,D +460013,Female,No,68,No,,2.0,Low,3.0,Cat_7,D +461776,Male,Yes,72,No,Engineer,0.0,High,4.0,Cat_4,B +467251,Female,No,21,No,Engineer,1.0,Low,6.0,Cat_6,C +464937,Male,Yes,50,Yes,Artist,0.0,Low,2.0,Cat_4,D +464072,Male,Yes,49,Yes,Executive,1.0,Low,2.0,Cat_6,A +460836,Female,Yes,33,No,Artist,6.0,Average,2.0,Cat_6,C +467323,Female,Yes,52,Yes,Artist,7.0,Average,4.0,Cat_6,C +464245,Female,Yes,89,Yes,Lawyer,2.0,Low,1.0,Cat_6,A +459030,Male,Yes,46,No,Executive,6.0,High,6.0,Cat_6,C +459059,Male,Yes,87,No,Executive,,High,2.0,Cat_6,A +460926,Male,Yes,50,Yes,Artist,1.0,Average,4.0,Cat_4,B +463803,Female,No,30,Yes,Entertainment,0.0,Low,2.0,Cat_6,D +466540,Male,No,22,No,Healthcare,2.0,Low,2.0,Cat_4,D +459083,Male,Yes,58,Yes,Artist,8.0,Average,3.0,,C +467699,Female,Yes,32,Yes,Doctor,0.0,Average,2.0,Cat_4,C +465773,Male,No,29,No,Entertainment,8.0,Low,4.0,Cat_6,D +464984,Male,No,32,Yes,Artist,1.0,Low,1.0,Cat_6,D +466215,Male,Yes,53,Yes,Artist,3.0,Average,4.0,Cat_6,C +460508,Male,Yes,27,Yes,Healthcare,2.0,High,3.0,Cat_3,D +460229,Female,Yes,68,Yes,Lawyer,2.0,High,2.0,Cat_6,C +459735,Female,No,43,No,Artist,,Low,1.0,Cat_6,B +459289,Male,No,39,Yes,Doctor,1.0,Low,1.0,Cat_6,A +461853,Male,No,20,No,Healthcare,6.0,Low,3.0,Cat_6,D +465345,Female,No,40,Yes,Artist,0.0,Low,,Cat_6,B +464513,Male,Yes,38,Yes,Executive,6.0,High,3.0,Cat_6,B +465806,Female,No,26,Yes,Marketing,0.0,Low,2.0,Cat_3,A +463534,Female,No,31,Yes,Engineer,1.0,Low,4.0,Cat_4,A +467053,Male,No,22,No,Healthcare,6.0,Low,4.0,Cat_6,D +459730,Male,Yes,35,No,,1.0,Low,2.0,Cat_6,D +464269,Male,No,37,Yes,Entertainment,0.0,Low,,Cat_1,A +466839,Female,No,26,No,Homemaker,8.0,Low,2.0,Cat_6,D +466510,Male,No,51,Yes,Artist,6.0,Low,1.0,Cat_6,A +464299,Female,No,35,Yes,Artist,12.0,Low,3.0,Cat_6,A +463773,Male,Yes,36,Yes,Artist,9.0,Average,2.0,Cat_6,C +462133,Female,Yes,46,Yes,Doctor,9.0,High,3.0,Cat_2,A +466895,Male,Yes,46,Yes,Marketing,,Low,1.0,Cat_6,D +460848,Male,Yes,53,No,Entertainment,10.0,Average,3.0,Cat_6,A +459324,Female,No,18,No,Healthcare,1.0,Low,3.0,Cat_6,D +467214,Male,No,26,No,Engineer,0.0,Low,4.0,Cat_3,D +462009,Female,Yes,32,Yes,Healthcare,8.0,High,2.0,Cat_2,A +464929,Male,Yes,69,No,Doctor,0.0,Average,3.0,Cat_4,B +463149,Male,Yes,60,Yes,Artist,3.0,Average,2.0,Cat_6,C +463447,Male,Yes,60,No,Engineer,0.0,Average,2.0,Cat_6,B +464833,Male,Yes,51,No,Executive,,Low,9.0,Cat_4,D +462088,Male,Yes,26,Yes,Artist,0.0,Average,2.0,Cat_6,C +459992,Male,No,48,Yes,Artist,1.0,Low,1.0,Cat_6,B +462270,Male,Yes,51,Yes,Doctor,1.0,Average,4.0,Cat_6,B +466711,Male,Yes,72,No,Executive,1.0,High,4.0,Cat_6,B +459614,Female,No,43,Yes,Artist,1.0,Low,2.0,Cat_6,B +459850,Male,No,35,Yes,Artist,0.0,Low,1.0,Cat_6,B +460665,Male,No,29,No,Entertainment,5.0,Low,1.0,Cat_6,D +462841,Male,No,19,No,Healthcare,8.0,Low,6.0,Cat_6,D +461761,Female,No,27,Yes,Healthcare,0.0,Low,8.0,Cat_6,C +461573,Female,No,40,Yes,Engineer,1.0,Low,5.0,Cat_6,C +463199,Female,Yes,52,Yes,Artist,1.0,Low,3.0,Cat_4,A +465317,Male,No,23,No,Healthcare,0.0,Low,4.0,Cat_4,D +461832,Male,No,31,Yes,Healthcare,,Low,3.0,Cat_6,C +464596,Female,Yes,35,Yes,Executive,0.0,Low,2.0,Cat_6,B +466059,Male,Yes,25,No,Entertainment,1.0,Average,2.0,Cat_4,A +463104,Female,Yes,47,No,Lawyer,0.0,Low,1.0,Cat_6,A +459990,Male,Yes,37,Yes,Healthcare,1.0,Average,2.0,Cat_6,A +463330,Male,No,20,No,Healthcare,1.0,Low,4.0,Cat_6,D +459893,Female,Yes,68,No,Lawyer,,Average,2.0,Cat_6,D +467212,Female,No,28,Yes,Doctor,1.0,Low,3.0,Cat_6,B +460913,Male,,22,No,Healthcare,1.0,Low,5.0,Cat_4,D +462266,Male,No,25,Yes,Doctor,8.0,Low,5.0,Cat_6,D +461020,Female,,20,No,,,High,,Cat_3,D +462838,Female,Yes,32,Yes,Homemaker,0.0,Low,3.0,Cat_5,B +460159,Female,No,18,No,Entertainment,1.0,Low,7.0,Cat_2,B +460974,Male,Yes,79,No,Lawyer,1.0,Low,1.0,Cat_6,D +460312,Female,No,36,Yes,Entertainment,7.0,Low,5.0,,D +459919,Female,No,49,Yes,Artist,0.0,Low,1.0,Cat_4,B +467381,Male,Yes,49,Yes,Entertainment,0.0,Low,4.0,Cat_6,B +461707,Female,Yes,45,Yes,Artist,1.0,Average,4.0,Cat_6,C +462461,Male,No,28,Yes,Doctor,1.0,Low,3.0,Cat_6,D +465378,Female,Yes,57,Yes,Artist,4.0,Average,,Cat_6,C +459533,Female,No,41,Yes,Artist,1.0,Low,1.0,Cat_6,A +461091,Female,Yes,37,Yes,Artist,3.0,Average,3.0,Cat_3,B +466467,Female,Yes,36,Yes,Artist,1.0,Average,3.0,Cat_3,B +462476,Male,Yes,45,Yes,Artist,4.0,Average,2.0,Cat_6,C +459197,Female,Yes,39,Yes,,0.0,Average,2.0,Cat_6,A +464357,Male,No,40,Yes,Artist,7.0,Low,1.0,Cat_6,A +459915,Female,No,29,Yes,Healthcare,1.0,Low,3.0,Cat_4,D +459547,Male,Yes,89,No,Lawyer,,Low,2.0,Cat_6,B +463319,Female,Yes,73,No,Lawyer,1.0,High,3.0,Cat_6,B +466323,Male,No,28,No,Doctor,4.0,Low,1.0,Cat_3,B +459363,Male,No,53,Yes,Artist,2.0,Low,1.0,Cat_6,A +464700,Male,No,25,No,Doctor,2.0,Low,,Cat_4,D +461794,Female,No,19,No,Healthcare,5.0,Low,5.0,Cat_2,D +462563,Female,Yes,52,Yes,Artist,0.0,Average,2.0,Cat_6,C +465922,Female,No,31,Yes,Healthcare,9.0,Low,3.0,Cat_6,D +464341,Male,No,35,Yes,Artist,2.0,Low,2.0,Cat_6,A +467529,Male,Yes,47,Yes,Artist,,Average,2.0,Cat_6,B +464294,Male,Yes,68,No,Executive,1.0,High,2.0,Cat_6,C +461923,Female,Yes,42,Yes,Artist,2.0,Average,2.0,Cat_6,C +466843,Female,No,38,No,Homemaker,13.0,Low,,Cat_6,A +462880,Male,Yes,25,No,Executive,1.0,High,3.0,Cat_6,B +464402,Male,No,37,Yes,Entertainment,7.0,Low,1.0,Cat_6,A +462596,Female,No,31,No,Healthcare,0.0,Low,,Cat_4,D +466841,Male,No,21,No,Healthcare,1.0,Low,6.0,Cat_4,D +464536,Male,Yes,28,Yes,Artist,0.0,Average,3.0,Cat_6,A +462765,Female,Yes,50,Yes,Artist,1.0,High,8.0,Cat_6,B +461364,Female,No,46,No,Doctor,0.0,Low,2.0,Cat_6,B +465836,Male,No,19,No,Executive,13.0,Low,3.0,Cat_7,D +465208,Male,Yes,37,Yes,Artist,1.0,Average,2.0,Cat_6,C +460416,Male,No,28,Yes,Healthcare,14.0,Low,1.0,Cat_6,D +459787,Male,Yes,71,No,Lawyer,0.0,Low,1.0,Cat_6,A +464276,Female,Yes,66,No,Marketing,0.0,High,2.0,Cat_1,C +460560,Female,Yes,57,Yes,Homemaker,9.0,Average,2.0,Cat_3,C +467449,Female,Yes,86,Yes,Lawyer,1.0,Low,2.0,Cat_6,C +467625,Female,Yes,40,Yes,Artist,0.0,Average,2.0,Cat_6,A +460559,Female,No,49,Yes,Healthcare,8.0,Low,1.0,Cat_3,D +463847,Male,Yes,41,No,Artist,2.0,Average,3.0,Cat_6,C +464669,Female,Yes,42,No,Engineer,0.0,Low,4.0,Cat_4,D +465472,Female,Yes,87,No,Entertainment,,High,3.0,Cat_6,A +461393,Female,Yes,80,Yes,Artist,6.0,Average,2.0,Cat_6,C +463141,Male,Yes,60,Yes,Artist,,Low,1.0,Cat_3,A +464149,Male,Yes,38,Yes,Artist,1.0,Average,4.0,Cat_6,C +461777,Female,Yes,74,Yes,Lawyer,1.0,High,2.0,Cat_6,A +464505,Female,No,30,No,Doctor,9.0,Low,5.0,Cat_6,D +463345,Female,No,20,No,Healthcare,4.0,Low,4.0,Cat_6,D +459609,Female,No,43,Yes,Artist,1.0,Low,1.0,Cat_4,A +465412,Male,No,32,Yes,Doctor,7.0,Low,4.0,Cat_6,D +465195,Male,Yes,53,Yes,Executive,1.0,High,3.0,Cat_6,B +462550,Female,No,23,No,Healthcare,2.0,Low,6.0,Cat_1,D +466896,Male,Yes,52,No,Artist,7.0,Low,1.0,Cat_6,C +463851,Female,Yes,45,Yes,Artist,4.0,Average,4.0,Cat_2,C +462452,Male,No,22,No,Healthcare,1.0,Low,,Cat_4,D +464399,Male,Yes,63,No,Entertainment,0.0,Average,5.0,Cat_6,C +462257,Female,Yes,81,Yes,Lawyer,,High,2.0,Cat_6,A +464782,Male,Yes,35,No,Marketing,0.0,Low,3.0,Cat_4,D +465624,Male,No,39,Yes,Artist,8.0,Low,3.0,Cat_6,C +462508,Male,Yes,37,Yes,Healthcare,0.0,Low,7.0,Cat_4,D +465085,Male,Yes,83,Yes,Lawyer,,High,2.0,Cat_6,A +462872,Female,Yes,47,No,Engineer,10.0,Low,2.0,Cat_3,D +467535,Male,Yes,65,Yes,Lawyer,1.0,High,2.0,Cat_6,A +465793,Male,No,25,No,Healthcare,1.0,Low,5.0,Cat_4,C +465892,Male,No,40,Yes,Artist,9.0,Low,3.0,Cat_6,A +463666,Male,No,27,Yes,Executive,0.0,Low,6.0,Cat_6,D +459015,Male,No,19,No,Healthcare,0.0,Low,3.0,Cat_6,D +459699,Female,,61,Yes,Engineer,9.0,Average,3.0,Cat_6,B +466189,Female,Yes,56,Yes,Artist,0.0,Average,2.0,Cat_3,C +466348,Male,No,53,Yes,Artist,1.0,Low,2.0,Cat_4,C +459461,Female,Yes,39,Yes,Artist,0.0,Low,1.0,Cat_1,C +460620,Male,Yes,55,Yes,Entertainment,0.0,Average,4.0,Cat_3,C +463318,Male,Yes,65,Yes,Lawyer,0.0,Low,2.0,Cat_1,A +462626,Female,No,26,Yes,Engineer,0.0,Low,6.0,Cat_4,B +467554,Female,No,28,Yes,Healthcare,1.0,Low,3.0,Cat_6,D +463502,Male,Yes,40,Yes,Executive,,High,4.0,Cat_6,A +466651,Male,No,40,No,Entertainment,5.0,Low,1.0,Cat_3,A +466885,Male,Yes,42,No,Executive,1.0,Average,4.0,Cat_6,B +467439,Male,Yes,37,Yes,Entertainment,1.0,Low,4.0,Cat_6,A +461749,Female,No,22,No,Healthcare,0.0,Low,5.0,Cat_6,D +467134,Female,Yes,33,No,Healthcare,0.0,Low,5.0,Cat_5,D +462753,Male,Yes,28,No,Executive,0.0,Average,3.0,Cat_6,D +462259,Male,Yes,51,Yes,Artist,,Low,2.0,Cat_6,B +463537,Female,No,32,Yes,Healthcare,1.0,Low,3.0,Cat_6,D +463217,Female,Yes,33,Yes,Healthcare,2.0,High,1.0,Cat_5,A +463689,Female,Yes,40,Yes,Doctor,1.0,Low,,Cat_6,B +466380,Male,Yes,36,Yes,Entertainment,5.0,Low,2.0,Cat_6,A +463101,Female,No,27,No,Homemaker,8.0,Low,1.0,Cat_6,D +466366,Male,No,18,No,Healthcare,0.0,Low,3.0,Cat_2,D +463864,Male,Yes,81,No,Executive,,High,2.0,Cat_6,B +465716,Female,No,59,Yes,Artist,0.0,Low,2.0,Cat_6,C +463014,Male,Yes,53,Yes,Executive,0.0,High,3.0,Cat_6,B +459808,Male,Yes,38,No,Doctor,,Average,4.0,Cat_6,B +462443,Male,No,20,No,Healthcare,1.0,Low,2.0,Cat_4,D +460453,Female,No,40,Yes,Doctor,7.0,Low,1.0,Cat_6,A +466485,Male,Yes,37,Yes,Executive,9.0,High,2.0,Cat_3,A +467445,Male,Yes,31,No,Doctor,8.0,Low,2.0,Cat_2,A +459448,Male,Yes,40,Yes,Executive,,High,3.0,Cat_6,C +463500,Male,Yes,35,Yes,Executive,0.0,High,4.0,Cat_6,A +461507,Male,Yes,35,Yes,Entertainment,0.0,Low,2.0,Cat_6,D +465918,Female,No,39,Yes,Artist,5.0,Low,3.0,Cat_6,B +467533,Male,Yes,79,Yes,Lawyer,2.0,Low,1.0,Cat_6,D +467029,Male,Yes,42,Yes,Artist,8.0,Average,2.0,Cat_6,C +465749,Male,Yes,42,No,Engineer,1.0,Average,3.0,Cat_6,C +460768,Female,No,32,No,Engineer,9.0,Low,4.0,Cat_5,A +460146,Female,Yes,52,Yes,Engineer,1.0,Average,3.0,Cat_3,A +464998,Male,Yes,59,Yes,Artist,1.0,High,2.0,Cat_6,C +467715,Male,Yes,51,Yes,Executive,4.0,High,3.0,Cat_6,C +462201,Female,No,32,Yes,Healthcare,0.0,Low,1.0,Cat_4,D +467454,Male,Yes,49,Yes,Artist,1.0,Low,1.0,Cat_6,A +460645,Female,No,40,No,Doctor,1.0,Low,2.0,Cat_3,C +465129,Female,No,61,Yes,Entertainment,3.0,Low,1.0,Cat_6,A +464264,Female,Yes,86,No,Lawyer,8.0,High,2.0,Cat_6,B +465913,Female,Yes,29,Yes,Artist,10.0,Low,2.0,Cat_6,B +461068,Male,No,46,No,Marketing,1.0,Low,4.0,Cat_3,D +466689,Male,No,28,Yes,Artist,8.0,Low,3.0,Cat_6,C +460071,Female,No,53,Yes,Engineer,1.0,Low,1.0,Cat_6,A +459817,Female,Yes,65,No,Marketing,,Low,,Cat_6,D +460477,Male,No,32,No,Entertainment,0.0,Low,6.0,Cat_3,D +467766,Female,No,31,No,Healthcare,6.0,Low,3.0,Cat_6,C +460008,Male,No,25,No,Doctor,9.0,Low,4.0,Cat_6,A +463775,Female,No,33,No,Engineer,,Low,3.0,Cat_6,C +461820,Female,Yes,38,Yes,Doctor,0.0,Average,2.0,Cat_6,C +459226,Female,Yes,43,Yes,Artist,0.0,Average,2.0,Cat_1,C +460961,Male,No,20,No,Healthcare,1.0,Low,2.0,Cat_3,D +463503,Male,Yes,33,Yes,Entertainment,1.0,Low,2.0,Cat_7,A +466365,Female,Yes,50,,Artist,0.0,Average,3.0,Cat_2,C +462632,Female,Yes,37,No,Engineer,,Average,4.0,Cat_4,A +460404,Male,Yes,38,Yes,Artist,5.0,Low,7.0,Cat_6,A +460492,Female,No,43,No,Marketing,10.0,Low,,Cat_3,D +461571,Male,No,27,No,Healthcare,1.0,Low,4.0,Cat_6,C +467624,Male,Yes,35,No,Entertainment,0.0,Average,5.0,Cat_6,D +463880,Female,Yes,36,No,Engineer,0.0,Low,3.0,Cat_2,B +462846,Female,No,20,No,Marketing,7.0,Low,2.0,Cat_6,D +459233,Female,Yes,53,Yes,Marketing,6.0,Low,1.0,Cat_6,D +464751,Male,Yes,36,No,Doctor,,Low,1.0,Cat_4,A +465939,Female,No,32,Yes,Engineer,0.0,Low,2.0,Cat_6,D +467118,Male,Yes,55,Yes,Entertainment,1.0,Average,3.0,Cat_7,B +463815,Male,Yes,37,Yes,Entertainment,0.0,Average,2.0,Cat_6,C +460871,Female,No,27,Yes,Healthcare,,Low,,Cat_6,D +459127,Male,No,43,Yes,Healthcare,1.0,Low,7.0,Cat_6,D +460675,Female,No,32,Yes,Engineer,,Low,,Cat_3,D +460165,Female,No,40,Yes,Entertainment,0.0,Low,1.0,Cat_3,A +461678,Female,Yes,87,No,Lawyer,1.0,Low,1.0,Cat_6,A +465096,Male,Yes,86,No,Lawyer,13.0,Low,1.0,Cat_6,A +464522,Female,No,42,Yes,Engineer,11.0,Low,1.0,Cat_6,D +460886,Male,No,37,Yes,Artist,0.0,Low,1.0,Cat_6,B +462302,Female,No,25,No,Doctor,,Low,7.0,Cat_6,D +462184,Male,No,19,No,Doctor,0.0,Low,4.0,Cat_6,D +462956,Male,Yes,42,No,Artist,7.0,Average,5.0,Cat_6,B +466422,Male,Yes,33,No,Doctor,0.0,Low,4.0,Cat_4,D +465324,Male,Yes,41,Yes,Artist,8.0,Low,2.0,Cat_6,B +460668,Female,No,27,Yes,Doctor,,Low,2.0,Cat_3,D +466275,Male,Yes,84,No,Lawyer,,Low,,Cat_6,D +467257,Male,No,22,No,Healthcare,10.0,Low,4.0,Cat_6,D +460254,Female,No,32,Yes,Artist,1.0,Low,2.0,Cat_6,B +463158,Male,Yes,83,Yes,Executive,0.0,High,3.0,Cat_6,B +467866,Male,Yes,50,Yes,Artist,1.0,Average,3.0,Cat_6,C +461685,Female,No,30,Yes,Healthcare,4.0,Low,4.0,Cat_6,D +467875,Female,No,23,No,Healthcare,,Low,4.0,Cat_6,D +460208,Male,No,27,No,Artist,9.0,Low,8.0,Cat_6,D +462151,Female,No,35,Yes,Artist,8.0,Low,1.0,Cat_6,B +462853,Male,No,23,No,Healthcare,1.0,Low,2.0,Cat_6,D +465239,Male,Yes,56,Yes,Artist,0.0,Average,3.0,Cat_6,B +461624,Female,Yes,41,Yes,Artist,1.0,Average,,Cat_2,B +467508,Female,No,25,No,Doctor,1.0,Low,3.0,Cat_6,B +467318,Male,Yes,41,Yes,Doctor,,Low,2.0,Cat_6,C +466867,Female,No,27,No,Homemaker,14.0,Low,1.0,Cat_6,D +467738,Male,Yes,43,Yes,Doctor,3.0,Average,2.0,Cat_6,B +465590,Male,Yes,40,Yes,Artist,1.0,Average,5.0,Cat_6,A +464980,Female,,60,No,Engineer,1.0,High,2.0,Cat_4,B +459805,Female,Yes,45,No,Artist,4.0,Average,4.0,Cat_6,B +465013,Female,Yes,82,No,Lawyer,0.0,High,2.0,Cat_6,C +459240,Female,No,28,Yes,Artist,1.0,Low,1.0,Cat_6,A +462344,Female,Yes,63,Yes,Artist,0.0,High,4.0,Cat_6,C +460388,Female,No,35,Yes,Artist,13.0,Low,2.0,Cat_6,B +464218,Female,Yes,89,No,Lawyer,1.0,High,2.0,Cat_6,A +466589,Male,No,20,No,Healthcare,,Low,4.0,Cat_6,D +467556,Female,No,40,Yes,Healthcare,,Low,1.0,Cat_4,A +459887,Male,Yes,46,No,Artist,3.0,Average,2.0,,C +465955,Male,No,25,Yes,Marketing,8.0,Low,3.0,Cat_6,A +460033,Female,,46,Yes,Entertainment,6.0,Low,3.0,Cat_6,A +462928,Male,Yes,49,Yes,Artist,2.0,Average,4.0,Cat_2,C +460889,Female,Yes,47,Yes,Artist,1.0,Low,2.0,Cat_6,B +459669,Male,Yes,30,Yes,Artist,0.0,Average,2.0,Cat_6,B +467849,Female,Yes,40,Yes,Doctor,0.0,Low,1.0,Cat_2,A +466873,Male,Yes,39,Yes,Healthcare,7.0,Average,5.0,Cat_3,A +464837,Male,Yes,61,Yes,Artist,1.0,Average,5.0,Cat_4,B +462581,Female,No,28,No,Engineer,1.0,Low,4.0,Cat_4,A +466237,Male,No,18,No,Healthcare,0.0,Low,3.0,Cat_4,D +462707,Female,Yes,55,Yes,Artist,0.0,Average,4.0,Cat_6,C +467060,Male,No,39,No,Engineer,5.0,Low,2.0,Cat_6,D +464794,Female,Yes,47,No,Engineer,8.0,Average,,Cat_4,A +460281,Female,No,29,Yes,Doctor,10.0,Low,5.0,Cat_6,D +460164,Male,Yes,66,No,Engineer,1.0,Low,2.0,Cat_6,A +460431,Female,No,46,Yes,Engineer,1.0,Low,1.0,Cat_6,D +466901,Female,Yes,52,Yes,Artist,0.0,Average,3.0,Cat_7,C +463328,Female,No,23,No,Healthcare,0.0,Low,4.0,Cat_6,D +462246,Female,Yes,84,No,Lawyer,1.0,High,2.0,Cat_6,B +462371,Female,No,27,No,Healthcare,5.0,Low,3.0,Cat_2,D +462022,Female,Yes,37,Yes,Artist,1.0,Average,3.0,Cat_3,B +460662,Female,No,46,No,Homemaker,9.0,Low,1.0,Cat_6,D +464022,Female,No,22,No,Healthcare,0.0,Low,3.0,Cat_6,D +461819,Male,Yes,42,Yes,Artist,0.0,Average,2.0,Cat_6,C +465735,Female,Yes,35,Yes,Artist,1.0,Low,2.0,Cat_3,B +462804,Male,Yes,39,No,Executive,5.0,High,3.0,Cat_4,D +464138,Female,No,27,Yes,Healthcare,0.0,Low,4.0,Cat_6,B +464224,Male,Yes,65,No,Engineer,1.0,High,2.0,Cat_6,B +463289,Female,Yes,39,Yes,Artist,0.0,Average,3.0,Cat_3,C +463223,Female,No,22,No,Entertainment,,Low,3.0,Cat_6,D +462038,Male,No,31,Yes,Artist,4.0,Low,4.0,Cat_6,A +463341,Female,Yes,48,No,Engineer,1.0,Average,6.0,Cat_3,A +463185,Female,Yes,58,Yes,Artist,1.0,Average,2.0,Cat_6,C +459258,Female,Yes,74,Yes,Lawyer,1.0,High,,Cat_6,B +464379,Female,Yes,40,Yes,Engineer,12.0,Average,3.0,Cat_6,B +464521,Male,No,26,No,Doctor,2.0,Low,4.0,Cat_6,B +459575,Male,No,35,Yes,Artist,0.0,Low,1.0,Cat_6,A +467732,Female,No,43,Yes,Healthcare,8.0,Low,1.0,,D +460003,Male,No,49,Yes,Doctor,5.0,Low,1.0,Cat_6,D +462226,Male,Yes,26,No,Artist,7.0,Low,4.0,Cat_4,A +461274,Female,Yes,74,Yes,Lawyer,,High,2.0,Cat_6,A +462992,Female,Yes,56,Yes,Engineer,,High,3.0,Cat_3,B +466675,Female,Yes,49,Yes,Artist,2.0,Average,2.0,Cat_6,C +461124,Male,No,19,No,Healthcare,1.0,Low,8.0,Cat_4,D +464638,Male,No,39,Yes,Artist,2.0,Low,3.0,Cat_4,B +462374,Male,Yes,37,Yes,Executive,0.0,High,4.0,Cat_6,B +463526,Male,No,41,Yes,Doctor,0.0,Low,1.0,Cat_6,A +462912,Female,No,26,Yes,Homemaker,8.0,Low,2.0,Cat_6,D +462604,Female,Yes,38,No,Doctor,1.0,Average,3.0,Cat_4,A +460708,Male,Yes,35,Yes,Artist,0.0,Low,3.0,Cat_6,C +467015,Male,Yes,49,Yes,Artist,0.0,Average,2.0,Cat_6,C +464745,Male,,60,No,Engineer,0.0,Average,3.0,Cat_4,A +467245,Female,No,38,No,Doctor,0.0,Low,1.0,Cat_6,C +464798,Male,Yes,52,,Entertainment,,Average,5.0,Cat_4,B +463619,Male,Yes,70,No,Executive,1.0,High,2.0,Cat_6,A +467246,Male,No,21,No,Doctor,9.0,Low,3.0,Cat_7,D +462969,Male,Yes,58,Yes,Artist,8.0,Low,1.0,Cat_3,B +460959,Female,No,28,No,Artist,5.0,Low,5.0,Cat_3,C +461911,Male,Yes,57,No,Executive,0.0,Low,1.0,Cat_6,A +459882,Male,Yes,37,Yes,Artist,1.0,Average,2.0,Cat_6,B +459632,Female,Yes,52,No,Marketing,1.0,High,3.0,Cat_4,D +462613,Female,Yes,35,No,Engineer,0.0,Average,5.0,Cat_4,D +466455,Female,Yes,30,Yes,Doctor,4.0,Low,2.0,Cat_6,D +462394,Male,Yes,35,Yes,Artist,0.0,Average,2.0,Cat_6,C +462010,Female,No,31,Yes,Artist,14.0,Low,3.0,Cat_2,A +461371,Male,Yes,37,Yes,Doctor,0.0,Average,2.0,Cat_6,A +462376,Male,Yes,71,Yes,Executive,0.0,High,2.0,Cat_6,B +459845,Male,Yes,66,Yes,Healthcare,0.0,Low,1.0,Cat_4,D +462658,Female,Yes,25,Yes,Homemaker,9.0,Average,2.0,,A +462583,Male,No,26,Yes,Artist,0.0,Low,4.0,Cat_4,D +459858,Female,No,31,No,Healthcare,8.0,Low,4.0,Cat_6,D +459091,Male,Yes,67,Yes,Artist,0.0,Average,6.0,Cat_1,B +467135,Female,Yes,51,Yes,Artist,1.0,Average,5.0,Cat_6,C +460413,Female,No,39,Yes,Artist,7.0,Low,1.0,Cat_6,B +466206,Female,Yes,60,Yes,Artist,2.0,Average,2.0,Cat_3,C +463192,Female,Yes,46,No,Entertainment,4.0,Average,,Cat_4,A +467382,Male,Yes,36,Yes,Doctor,9.0,Average,2.0,Cat_6,C +466750,Female,No,18,,Healthcare,0.0,Low,3.0,Cat_3,D +463801,Female,No,30,No,Engineer,0.0,Low,1.0,Cat_6,A +467296,Female,Yes,69,Yes,Artist,,Average,2.0,Cat_3,C +460651,Male,No,32,No,Homemaker,8.0,Low,,Cat_4,A +460296,Female,Yes,60,Yes,Artist,1.0,Low,2.0,Cat_6,A +463524,Female,Yes,40,Yes,Artist,6.0,Average,2.0,Cat_6,C +461775,Female,Yes,83,No,Lawyer,0.0,High,2.0,Cat_6,B +465711,Male,Yes,51,Yes,Artist,0.0,Average,,Cat_6,C +462833,Male,Yes,33,No,Doctor,0.0,Average,5.0,Cat_6,D +465506,Male,Yes,43,Yes,Entertainment,1.0,Average,4.0,Cat_4,B +463366,Male,Yes,71,No,Lawyer,0.0,Low,5.0,Cat_3,D +464987,Male,Yes,50,Yes,Executive,1.0,High,5.0,Cat_6,B +459114,Male,Yes,57,Yes,Artist,1.0,Average,2.0,Cat_6,C +467928,Male,Yes,35,Yes,Artist,2.0,Average,2.0,Cat_6,C +462881,Female,Yes,66,Yes,Artist,0.0,High,3.0,Cat_6,B +466524,Female,Yes,46,Yes,Artist,0.0,Low,1.0,Cat_3,B +463191,Male,No,26,No,Healthcare,1.0,Low,1.0,Cat_4,D +460861,Male,No,21,No,Healthcare,1.0,Low,3.0,Cat_3,D +465610,Female,No,42,Yes,Artist,8.0,Low,2.0,Cat_6,C +460206,Female,Yes,40,Yes,Entertainment,1.0,Low,2.0,Cat_6,B +462876,Male,No,27,No,Doctor,0.0,Low,4.0,Cat_1,D +465333,Female,Yes,37,Yes,Artist,2.0,Low,2.0,Cat_7,C +463121,Female,Yes,70,,Lawyer,,High,,Cat_6,A +460379,Male,No,35,Yes,Artist,9.0,Low,3.0,Cat_6,A +462991,Male,No,28,Yes,Healthcare,0.0,Low,1.0,Cat_6,D +460307,Male,No,27,No,Entertainment,6.0,Low,2.0,Cat_6,D +464383,Female,Yes,81,Yes,Lawyer,1.0,Low,1.0,Cat_6,C +467931,Male,No,25,Yes,Doctor,0.0,Low,2.0,Cat_6,D +466594,Male,Yes,72,Yes,Executive,1.0,High,2.0,Cat_6,C +461200,Female,No,33,Yes,Doctor,0.0,Low,5.0,Cat_6,C +465600,Male,Yes,40,Yes,Engineer,7.0,Low,2.0,Cat_3,B +459688,Male,No,52,Yes,Artist,3.0,Low,1.0,Cat_6,B +463549,Male,No,23,No,Healthcare,0.0,Low,4.0,Cat_6,D +466696,Female,Yes,48,Yes,Artist,2.0,Average,4.0,Cat_6,C +462906,Female,No,31,No,Healthcare,0.0,Low,4.0,Cat_6,D +461527,Female,No,35,Yes,Artist,1.0,Low,1.0,Cat_6,B +461354,Female,No,63,No,Entertainment,0.0,Low,1.0,Cat_2,B +462483,Male,Yes,63,Yes,,1.0,Average,2.0,Cat_6,C +463938,Male,,28,Yes,Engineer,1.0,Low,2.0,Cat_4,B +461661,Female,Yes,37,Yes,Artist,7.0,Average,2.0,Cat_2,C +463532,Female,Yes,67,Yes,Artist,9.0,Low,3.0,Cat_6,B +465627,Female,No,37,Yes,Healthcare,1.0,Low,1.0,Cat_6,B +465242,Male,No,22,No,Healthcare,7.0,Low,4.0,Cat_3,D +465170,Female,Yes,46,Yes,Entertainment,4.0,Low,3.0,Cat_6,C +467359,Male,Yes,41,Yes,Artist,6.0,Average,2.0,Cat_6,C +460415,Female,No,59,Yes,Entertainment,1.0,Low,2.0,Cat_6,A +465984,Male,,23,No,Healthcare,8.0,High,5.0,Cat_6,D +459432,Male,Yes,84,Yes,Lawyer,0.0,Low,3.0,Cat_6,A +464241,Female,Yes,89,,Artist,1.0,High,2.0,Cat_6,C +467141,Male,Yes,50,Yes,Artist,1.0,Low,3.0,Cat_6,A +466933,Female,Yes,52,No,Engineer,0.0,Low,,Cat_4,A +465035,Female,Yes,74,Yes,Lawyer,12.0,High,2.0,Cat_6,C +461415,Male,No,48,Yes,Entertainment,0.0,Low,3.0,Cat_6,A +461957,Male,Yes,47,Yes,Entertainment,1.0,Average,4.0,Cat_6,C +465523,Female,Yes,45,Yes,Engineer,0.0,High,5.0,Cat_6,C +459631,Male,,53,Yes,Executive,,High,3.0,Cat_6,D +465279,Male,Yes,53,Yes,Healthcare,0.0,High,4.0,Cat_6,A +463782,Female,No,29,Yes,Artist,8.0,Low,1.0,Cat_6,A +463243,Male,No,19,No,Healthcare,0.0,Low,3.0,Cat_6,D +465489,Female,No,50,Yes,Engineer,1.0,Low,5.0,Cat_6,A +467100,Female,No,42,Yes,Artist,0.0,Low,1.0,Cat_2,B +460154,Male,No,29,Yes,Entertainment,8.0,Low,4.0,Cat_6,C +459753,Male,Yes,40,Yes,Marketing,,Average,2.0,Cat_2,A +464030,Male,No,18,No,Entertainment,1.0,Low,4.0,Cat_6,D +463220,Male,Yes,46,No,Engineer,0.0,Average,3.0,Cat_6,B +465237,Male,Yes,68,Yes,Lawyer,,High,2.0,Cat_6,D +467564,Male,Yes,72,Yes,Artist,0.0,Average,2.0,Cat_6,B +465113,Male,Yes,66,Yes,Lawyer,1.0,High,2.0,Cat_4,C +459516,Female,,63,Yes,Artist,,Average,2.0,Cat_6,C +465965,Female,Yes,41,No,Marketing,8.0,Low,2.0,Cat_6,A +460907,Male,Yes,82,No,Lawyer,1.0,High,3.0,Cat_6,A +467314,Male,Yes,43,No,Artist,0.0,Average,2.0,Cat_6,C +459951,Male,Yes,53,Yes,Artist,0.0,Low,1.0,Cat_6,A +467295,Male,No,29,No,Healthcare,0.0,Low,3.0,Cat_6,C +466212,Female,Yes,53,Yes,Artist,0.0,Average,3.0,Cat_6,C +463533,Female,Yes,87,Yes,Artist,1.0,High,2.0,Cat_2,C +467890,Male,Yes,62,Yes,Entertainment,0.0,Average,4.0,Cat_6,B +467541,Male,Yes,43,Yes,Artist,1.0,High,3.0,Cat_6,A +460183,Male,No,36,No,Entertainment,8.0,Low,2.0,Cat_6,D +463634,Female,No,30,Yes,Doctor,0.0,Low,3.0,Cat_6,A +463100,Female,No,37,No,Homemaker,8.0,Low,1.0,Cat_6,D +460550,Female,Yes,59,Yes,Artist,0.0,Average,4.0,Cat_3,C +465867,Female,Yes,35,Yes,Artist,8.0,High,5.0,Cat_6,B +462964,Male,Yes,39,No,Artist,7.0,Average,3.0,Cat_6,C +466685,Female,No,19,No,Healthcare,0.0,Low,5.0,Cat_6,D +460719,Female,Yes,75,Yes,Lawyer,1.0,Low,,Cat_3,D +463005,Female,Yes,40,Yes,Homemaker,10.0,Average,2.0,Cat_4,B +466377,Male,Yes,62,Yes,,8.0,Average,2.0,Cat_6,C +464796,Male,Yes,43,No,Marketing,3.0,High,4.0,Cat_4,A +460682,Male,Yes,43,No,Artist,6.0,Low,2.0,Cat_3,B +459744,Male,Yes,29,Yes,Artist,9.0,Average,2.0,Cat_6,B +460084,Female,No,27,Yes,Healthcare,1.0,Low,2.0,Cat_6,D +461119,Female,No,23,No,Healthcare,6.0,Low,3.0,Cat_3,D +463441,Male,Yes,42,Yes,Artist,8.0,High,4.0,Cat_6,C +466971,Male,No,65,Yes,Executive,9.0,Low,3.0,Cat_6,D +463208,Male,Yes,57,Yes,Executive,1.0,Average,5.0,Cat_6,C +467877,Female,Yes,46,Yes,Artist,0.0,Average,2.0,Cat_6,C +464471,Male,Yes,30,Yes,Doctor,0.0,Low,3.0,Cat_6,D +467360,Male,Yes,51,Yes,Artist,0.0,Average,4.0,Cat_6,B +461576,Male,Yes,39,Yes,Artist,10.0,Low,2.0,Cat_4,C +461966,Female,No,41,Yes,Artist,1.0,Low,1.0,Cat_6,C +463398,Male,No,29,No,Marketing,9.0,Low,4.0,Cat_3,C +466096,Male,Yes,76,Yes,Lawyer,0.0,Low,2.0,Cat_4,B +464433,Male,No,33,Yes,Artist,,Low,,Cat_2,A +461082,Male,No,23,No,Healthcare,8.0,Low,4.0,Cat_1,D +465942,Female,Yes,33,Yes,Marketing,8.0,High,2.0,Cat_6,D +464354,Female,No,43,Yes,Engineer,3.0,Low,2.0,Cat_4,A +464252,Male,Yes,35,Yes,Artist,1.0,Average,4.0,Cat_6,C +463463,Male,Yes,32,No,Doctor,0.0,Low,2.0,Cat_6,A +465592,Male,Yes,65,Yes,Entertainment,1.0,Low,1.0,Cat_6,B +462214,Male,Yes,49,No,Doctor,1.0,Low,2.0,Cat_4,D +466707,Female,Yes,48,Yes,Homemaker,1.0,Low,4.0,Cat_6,B +459956,Female,Yes,39,Yes,Healthcare,8.0,Average,2.0,Cat_6,D +460847,Male,No,27,Yes,Artist,1.0,Low,3.0,Cat_6,A +463222,Female,No,36,Yes,Healthcare,2.0,Low,,Cat_6,A +462816,Male,Yes,76,No,Entertainment,0.0,Low,1.0,Cat_6,D +461979,Male,Yes,35,Yes,Artist,9.0,Average,2.0,Cat_6,C +467170,Male,Yes,35,Yes,Artist,8.0,Low,4.0,Cat_7,A +462609,Female,Yes,48,No,Doctor,4.0,Low,1.0,Cat_4,D +466332,Male,No,23,No,Healthcare,0.0,Low,3.0,Cat_2,D +459594,Female,Yes,42,Yes,Artist,5.0,Low,1.0,Cat_6,A +464147,Male,Yes,41,Yes,Artist,0.0,High,3.0,Cat_6,C +463007,Male,Yes,29,No,Executive,,High,3.0,Cat_6,D +464119,Male,Yes,36,Yes,Artist,0.0,Average,2.0,Cat_6,C +463310,Male,Yes,60,Yes,Entertainment,0.0,Low,2.0,Cat_3,A +465706,Female,Yes,46,Yes,Artist,1.0,Average,3.0,Cat_6,C +459826,Female,No,31,No,,,Low,1.0,Cat_7,C +466174,Female,No,20,No,Healthcare,8.0,Low,4.0,Cat_2,D +463596,Female,No,31,No,Healthcare,0.0,Low,4.0,Cat_6,C +465455,Male,No,26,No,Doctor,0.0,Low,2.0,Cat_6,D +462867,Male,Yes,28,No,Artist,1.0,Low,2.0,Cat_6,A +459140,Female,No,18,No,Healthcare,7.0,Low,6.0,Cat_6,D +464295,Female,No,30,No,Healthcare,7.0,Low,6.0,Cat_4,C +460067,Female,No,39,Yes,Entertainment,11.0,Low,1.0,Cat_6,A +467222,Male,Yes,74,Yes,Entertainment,1.0,Average,2.0,Cat_6,A +461482,Female,No,52,Yes,Artist,9.0,Low,1.0,Cat_6,C +462514,Male,No,25,Yes,Healthcare,,Low,2.0,Cat_4,D +459796,Male,Yes,66,Yes,Artist,,Average,3.0,Cat_6,C +463722,Male,Yes,55,Yes,Executive,9.0,High,2.0,Cat_6,C +462278,Male,No,31,Yes,Healthcare,0.0,Low,4.0,Cat_6,C +463799,Female,No,41,Yes,Artist,0.0,Low,4.0,Cat_2,B +464259,Male,Yes,59,Yes,Artist,1.0,Average,4.0,Cat_6,B +467159,Female,Yes,39,Yes,Homemaker,8.0,High,2.0,Cat_6,D +460669,Male,,48,Yes,Executive,0.0,Low,2.0,Cat_1,B +462003,Male,Yes,79,No,Lawyer,9.0,Low,3.0,Cat_6,D +461216,Female,No,43,No,,,Low,,Cat_6,A +464298,Male,No,26,No,Doctor,1.0,Low,1.0,Cat_6,A +465039,Female,Yes,88,No,Lawyer,0.0,High,2.0,Cat_6,B +467973,Female,Yes,66,Yes,Engineer,0.0,Average,3.0,Cat_6,A +467731,Male,Yes,56,Yes,Artist,,Average,4.0,Cat_6,C +461721,Male,No,26,Yes,Entertainment,7.0,Low,5.0,Cat_5,D +463166,Female,No,41,Yes,Artist,1.0,Low,1.0,Cat_4,A +465944,Female,Yes,36,No,Engineer,4.0,High,3.0,Cat_7,A +461061,Female,No,38,Yes,Engineer,3.0,Low,1.0,Cat_2,B +463010,Male,Yes,81,No,Executive,0.0,Low,1.0,Cat_6,D +464756,Female,No,25,No,Engineer,5.0,Low,3.0,Cat_4,B +467366,Male,No,42,Yes,Artist,,Low,1.0,Cat_4,A +464006,Male,No,32,No,Healthcare,0.0,Low,5.0,Cat_6,B +461206,Male,No,28,Yes,Healthcare,9.0,Low,1.0,Cat_6,D +466216,Male,No,29,No,Entertainment,1.0,Low,4.0,Cat_4,A +463690,Female,No,28,No,Healthcare,7.0,Low,4.0,Cat_6,D +465929,Male,No,26,Yes,Healthcare,8.0,Low,5.0,Cat_6,D +461736,Male,Yes,47,Yes,Entertainment,1.0,Average,4.0,Cat_6,C +460402,Female,Yes,50,Yes,Doctor,1.0,Low,2.0,Cat_7,A +467367,Male,Yes,70,Yes,Lawyer,1.0,Low,2.0,Cat_6,B +464770,Female,No,38,No,Engineer,,Low,1.0,Cat_4,A +464918,Male,No,39,No,Artist,1.0,Low,1.0,Cat_4,D +461639,Male,Yes,60,Yes,Artist,0.0,Average,2.0,Cat_6,A +464254,Male,Yes,63,Yes,Entertainment,0.0,Average,3.0,Cat_6,C +462579,Female,Yes,68,No,Lawyer,1.0,Low,2.0,Cat_4,B +466976,Male,Yes,47,Yes,Artist,0.0,Low,1.0,Cat_6,B +467079,Male,Yes,40,Yes,Artist,1.0,Low,2.0,Cat_6,B +467724,Male,Yes,46,No,Artist,1.0,High,3.0,Cat_6,B +459792,Male,Yes,42,Yes,Artist,6.0,Low,3.0,Cat_6,A +464789,Female,Yes,48,Yes,Artist,0.0,Average,5.0,Cat_4,B +467143,Female,No,53,No,Lawyer,1.0,Low,3.0,Cat_6,D +464370,Male,Yes,65,Yes,Entertainment,0.0,Average,2.0,Cat_6,B +459415,Male,No,26,No,Doctor,0.0,Low,4.0,Cat_6,A +459387,Male,No,35,Yes,Doctor,0.0,Low,1.0,Cat_6,D +467895,Female,No,27,Yes,Healthcare,0.0,Low,4.0,Cat_6,C +459838,Female,Yes,86,No,Lawyer,0.0,High,2.0,Cat_6,C +463695,Female,No,30,Yes,Doctor,1.0,Low,4.0,Cat_2,A +464512,Male,Yes,43,No,Executive,8.0,Low,3.0,Cat_6,A +467419,Female,Yes,38,Yes,Artist,4.0,Low,1.0,Cat_6,B +462511,Female,Yes,66,Yes,Lawyer,1.0,High,2.0,Cat_6,C +464124,Female,Yes,74,Yes,Doctor,1.0,Low,2.0,Cat_6,B +459638,Male,No,25,Yes,Healthcare,,Low,,Cat_6,D +466194,Male,Yes,89,No,Lawyer,0.0,Low,1.0,Cat_6,A +461807,Male,No,30,Yes,Doctor,10.0,Low,4.0,Cat_6,D +460456,Male,Yes,33,No,Executive,0.0,High,5.0,Cat_3,D +462932,Female,Yes,37,Yes,Homemaker,3.0,High,3.0,Cat_6,A +461297,Male,Yes,69,No,Lawyer,0.0,High,2.0,Cat_6,C +464430,Female,Yes,51,Yes,Engineer,6.0,Average,2.0,Cat_6,C +459416,Male,No,19,No,Artist,10.0,Low,3.0,Cat_6,A +464743,Male,Yes,45,Yes,Entertainment,0.0,Average,2.0,Cat_4,B +466276,Female,No,32,No,Doctor,0.0,Low,5.0,Cat_4,D +467829,Female,No,22,No,Doctor,3.0,Low,4.0,Cat_2,D +463813,Female,No,26,No,Engineer,1.0,Low,6.0,Cat_6,A +461733,Female,Yes,43,Yes,Artist,0.0,High,3.0,Cat_6,B +466925,Male,No,19,No,Healthcare,7.0,Low,2.0,Cat_6,D +467846,Male,Yes,48,Yes,Artist,1.0,Average,3.0,Cat_6,C +459596,Female,Yes,61,Yes,Doctor,1.0,Average,2.0,Cat_6,B +460597,Female,No,20,No,Marketing,1.0,Low,2.0,Cat_3,D +467788,Male,No,22,No,Healthcare,0.0,Low,4.0,Cat_6,D +467966,Female,No,31,Yes,Entertainment,9.0,Low,3.0,Cat_7,D +464503,Male,No,25,Yes,Artist,9.0,Low,3.0,Cat_6,A +460242,Male,Yes,41,Yes,Healthcare,8.0,Low,3.0,Cat_6,C +467014,Male,Yes,36,Yes,Executive,9.0,High,3.0,Cat_6,C +466637,Male,Yes,48,Yes,Artist,1.0,High,5.0,Cat_2,C +462553,Male,No,27,No,Artist,1.0,Low,1.0,Cat_6,B +459287,Male,No,35,Yes,Lawyer,1.0,Low,1.0,Cat_4,D +464074,Female,Yes,39,Yes,Doctor,0.0,Average,3.0,Cat_6,C +463734,Male,Yes,38,No,Executive,0.0,Average,3.0,Cat_6,A +466575,Female,Yes,57,Yes,Artist,7.0,Average,3.0,Cat_6,C +459193,Male,No,38,Yes,Artist,1.0,Low,1.0,Cat_6,A +462143,Male,Yes,80,No,Artist,1.0,Low,2.0,Cat_6,A +467700,Male,Yes,57,Yes,Entertainment,,Average,2.0,Cat_6,C +467140,Male,Yes,61,Yes,Entertainment,9.0,Average,2.0,Cat_6,C +463356,Male,No,29,Yes,Healthcare,5.0,Low,4.0,Cat_2,D +466797,Female,No,23,No,Healthcare,1.0,Low,4.0,Cat_6,D +459234,Female,Yes,83,No,Lawyer,1.0,Low,1.0,Cat_6,D +460442,Male,Yes,37,Yes,Entertainment,0.0,Average,3.0,Cat_6,B +463901,Male,Yes,35,No,Artist,0.0,Low,6.0,Cat_4,C +465494,Male,Yes,41,Yes,Doctor,9.0,Average,2.0,Cat_6,B +461552,Female,Yes,38,Yes,Artist,,Average,2.0,Cat_6,B +459239,Male,Yes,81,No,Entertainment,1.0,Low,1.0,Cat_6,A +466379,Female,Yes,58,Yes,Homemaker,2.0,Low,1.0,Cat_6,C +464759,Female,No,38,No,Engineer,8.0,Low,1.0,Cat_4,A +461994,Male,Yes,18,No,Doctor,10.0,Low,2.0,Cat_6,D +461802,Male,No,37,Yes,Artist,1.0,Low,4.0,Cat_6,B +462527,Male,Yes,43,No,Artist,4.0,Average,2.0,Cat_6,C +461800,Female,No,36,Yes,Doctor,4.0,Low,1.0,Cat_2,B +460933,Female,Yes,76,Yes,Lawyer,0.0,High,2.0,Cat_6,A +460189,Male,No,33,No,Entertainment,14.0,Low,1.0,Cat_6,D +459031,Male,Yes,43,No,Executive,1.0,High,5.0,Cat_4,B +464673,Male,Yes,47,No,Executive,1.0,High,7.0,Cat_2,D +459101,Male,Yes,81,No,Lawyer,8.0,Low,2.0,Cat_6,B +460136,Male,No,27,Yes,Artist,10.0,Low,3.0,Cat_6,D +460973,Male,No,29,No,Entertainment,1.0,Low,5.0,Cat_6,D +460187,Female,No,38,Yes,Doctor,4.0,Low,3.0,Cat_6,A +460679,Female,No,19,,Healthcare,9.0,Low,4.0,Cat_3,D +459700,Male,Yes,37,Yes,Executive,0.0,Average,5.0,Cat_6,B +464787,Male,No,47,No,Entertainment,1.0,Low,1.0,Cat_4,A +467201,Female,No,33,Yes,Engineer,,Low,5.0,Cat_7,D +462614,Male,Yes,51,No,Entertainment,1.0,Average,5.0,Cat_4,D +460813,Female,Yes,86,No,Lawyer,1.0,High,2.0,Cat_6,A +462698,Female,Yes,56,Yes,Entertainment,1.0,Average,2.0,Cat_6,A +463344,Male,Yes,59,Yes,Artist,3.0,Average,2.0,Cat_6,B +463633,Male,Yes,33,Yes,Doctor,0.0,Average,2.0,Cat_6,B +466278,Female,No,31,No,,0.0,Low,2.0,Cat_3,A +464964,Female,Yes,71,No,Artist,,High,2.0,Cat_4,B +462317,Female,No,32,No,Entertainment,3.0,Low,6.0,Cat_4,D +467353,Male,Yes,26,No,Artist,3.0,Low,2.0,Cat_6,A +464169,Male,,89,No,Lawyer,1.0,High,2.0,Cat_6,D +464553,Male,Yes,70,Yes,Artist,1.0,Low,2.0,Cat_6,B +463518,Male,Yes,30,No,Executive,5.0,High,4.0,Cat_6,A +459711,Male,Yes,37,No,Executive,9.0,High,6.0,Cat_6,D +460788,Female,No,30,Yes,Doctor,0.0,Low,9.0,Cat_4,A +459392,Female,No,23,No,Doctor,0.0,Low,3.0,Cat_6,C +467887,Female,No,42,Yes,Artist,0.0,Low,2.0,Cat_1,B +467743,Male,Yes,48,No,Executive,1.0,High,4.0,Cat_1,B +460368,Male,No,28,Yes,Healthcare,0.0,Low,7.0,Cat_6,C +463126,Female,Yes,65,Yes,Artist,3.0,Average,2.0,Cat_6,C +467046,Female,No,28,No,Doctor,0.0,Low,5.0,Cat_3,A +463099,Male,Yes,85,Yes,Lawyer,,Low,1.0,Cat_6,B +462439,Male,No,18,No,Healthcare,1.0,Low,4.0,Cat_6,D +464182,Male,Yes,35,Yes,Artist,4.0,Average,2.0,Cat_2,C +464810,Female,No,27,No,Engineer,1.0,Low,1.0,Cat_4,A +464234,Male,Yes,68,Yes,Entertainment,0.0,Low,3.0,Cat_2,A +464683,Male,Yes,52,No,Engineer,0.0,Average,6.0,Cat_4,B +463349,Male,No,20,No,Healthcare,5.0,Low,5.0,Cat_2,D +465866,Male,Yes,35,Yes,Artist,7.0,Low,3.0,Cat_6,D +465261,Female,No,21,Yes,Healthcare,0.0,Low,4.0,Cat_1,D +459780,Female,No,42,No,Entertainment,1.0,Low,3.0,Cat_6,A +462031,Female,Yes,50,Yes,Artist,0.0,Average,2.0,Cat_6,C +464933,Male,No,25,No,,0.0,Low,1.0,Cat_4,D +463941,Female,No,32,No,Artist,1.0,Low,6.0,Cat_4,D +466842,Male,No,19,No,Healthcare,8.0,Low,5.0,Cat_2,D +459014,Male,No,22,No,Healthcare,0.0,Low,4.0,Cat_6,D +462420,Male,No,27,Yes,Healthcare,1.0,Low,3.0,Cat_6,C +465250,Male,No,22,No,Healthcare,0.0,Low,4.0,Cat_4,D +466513,Female,Yes,55,Yes,Artist,1.0,Average,3.0,Cat_6,B +461791,Female,No,22,No,Lawyer,8.0,Low,1.0,Cat_6,D +467160,Male,Yes,40,No,,0.0,Average,2.0,Cat_3,A +464586,Female,No,46,Yes,Artist,1.0,Low,1.0,Cat_3,B +461391,Male,Yes,65,Yes,Entertainment,3.0,Low,3.0,Cat_6,C +466658,Female,No,18,No,Healthcare,0.0,Low,4.0,Cat_6,D +459728,Female,Yes,53,Yes,Artist,2.0,Average,4.0,Cat_6,C +464618,Male,,35,Yes,Artist,5.0,Low,1.0,Cat_6,B +462219,Male,Yes,59,No,Executive,0.0,Low,5.0,Cat_4,B +467553,Female,No,32,Yes,Healthcare,8.0,Low,3.0,Cat_6,D +459655,Male,Yes,39,Yes,Artist,1.0,Average,3.0,Cat_6,B +462182,Male,Yes,88,No,Artist,7.0,Low,1.0,Cat_6,D +459119,Male,Yes,61,Yes,Executive,9.0,High,3.0,Cat_6,C +460721,Male,No,21,No,Healthcare,1.0,Low,4.0,Cat_6,D +459517,Male,No,31,Yes,Healthcare,1.0,Low,4.0,Cat_6,D +461441,Male,Yes,58,Yes,Artist,1.0,Average,4.0,Cat_6,C +465390,Female,No,27,Yes,Marketing,3.0,Low,3.0,Cat_6,D +466160,Female,Yes,51,Yes,Engineer,1.0,Average,2.0,Cat_6,C +463842,Female,Yes,48,Yes,Engineer,0.0,High,4.0,Cat_6,B +465902,Male,No,25,Yes,Executive,3.0,Low,3.0,Cat_6,D +467034,Male,Yes,89,No,Executive,0.0,High,2.0,Cat_6,A +466247,Female,,51,Yes,Artist,0.0,Low,4.0,Cat_2,C +465747,Female,No,40,Yes,Artist,6.0,Low,1.0,Cat_6,C +464203,Male,No,56,Yes,Artist,2.0,Low,1.0,Cat_6,B +462373,Male,Yes,36,Yes,Entertainment,9.0,High,3.0,Cat_6,B +465364,Female,No,38,Yes,Doctor,5.0,Low,5.0,Cat_2,A +464280,Male,Yes,47,Yes,Entertainment,1.0,Low,4.0,Cat_6,C +460993,Male,Yes,61,Yes,Artist,1.0,Average,4.0,Cat_6,C +464227,Male,Yes,89,Yes,Lawyer,0.0,High,2.0,Cat_6,C +461141,Male,Yes,55,No,Homemaker,2.0,Low,1.0,Cat_3,A +465297,Male,Yes,88,No,,,High,3.0,Cat_6,A +467282,Female,Yes,43,Yes,Artist,4.0,Average,2.0,Cat_6,A +459790,Male,No,28,No,Doctor,10.0,Low,3.0,Cat_6,C +464670,Male,Yes,42,No,Entertainment,0.0,Average,4.0,Cat_4,B +462093,Male,No,33,Yes,Artist,1.0,Low,,Cat_4,D +459398,Female,Yes,60,No,Lawyer,,High,2.0,Cat_6,C +461136,Male,No,59,Yes,Artist,1.0,Low,1.0,Cat_6,B +461379,Male,No,31,Yes,Healthcare,0.0,Low,,Cat_5,D +461956,Female,No,31,Yes,Artist,2.0,Low,,Cat_6,A +460283,Male,Yes,43,Yes,Doctor,14.0,Low,2.0,Cat_6,A +460337,Female,No,42,Yes,Marketing,10.0,Low,3.0,Cat_6,D +463275,Male,Yes,29,No,Entertainment,7.0,Average,2.0,Cat_3,D +461234,Female,No,53,Yes,Artist,3.0,Low,,Cat_4,A +466861,Female,No,21,No,Healthcare,8.0,Low,7.0,Cat_2,D +463177,Male,No,41,Yes,Entertainment,7.0,Low,2.0,Cat_1,D +461874,Male,Yes,48,Yes,Doctor,0.0,Average,3.0,Cat_3,A +462506,Male,No,31,No,Healthcare,1.0,Low,4.0,Cat_6,C +466868,Male,No,26,Yes,Doctor,4.0,Low,3.0,Cat_2,A +461253,Female,No,18,No,Healthcare,0.0,Low,5.0,Cat_4,D +460466,Female,No,19,No,Healthcare,8.0,Low,,Cat_6,D +461830,Male,Yes,42,Yes,Executive,1.0,High,3.0,Cat_6,C +464748,Female,Yes,52,No,Engineer,9.0,Average,5.0,Cat_4,B +461904,Female,Yes,50,Yes,Artist,4.0,Average,2.0,Cat_5,C +466737,Male,No,33,No,Entertainment,1.0,Low,,Cat_6,D +459153,Female,Yes,75,Yes,,0.0,High,2.0,Cat_6,B +467562,Male,Yes,71,Yes,Executive,0.0,High,3.0,Cat_6,A +465168,Female,Yes,59,No,Entertainment,8.0,Average,2.0,Cat_6,B +459162,Female,Yes,85,Yes,Lawyer,0.0,High,2.0,Cat_6,A +462831,Male,Yes,29,No,Executive,1.0,High,3.0,Cat_6,D +465576,Male,Yes,43,No,Executive,4.0,Average,5.0,Cat_4,A +464474,Female,No,52,Yes,Artist,1.0,Low,1.0,Cat_6,A +460566,Male,Yes,36,Yes,Homemaker,,Average,2.0,Cat_3,C +465169,Female,No,59,Yes,Doctor,0.0,Low,4.0,Cat_6,B +460235,Female,No,23,No,Healthcare,1.0,Low,3.0,Cat_6,D +462580,Female,No,39,Yes,Artist,0.0,Low,1.0,Cat_4,B +467313,Male,Yes,39,Yes,Entertainment,1.0,Average,2.0,Cat_6,B +460400,Female,Yes,46,Yes,Entertainment,5.0,Low,2.0,Cat_6,A +463406,Male,No,33,No,Marketing,0.0,Low,4.0,Cat_3,C +459135,Male,Yes,53,Yes,Executive,0.0,High,4.0,Cat_6,C +465862,Female,,50,Yes,Artist,8.0,Average,1.0,Cat_6,A +462972,Female,Yes,62,Yes,Artist,1.0,Low,1.0,Cat_6,B +466284,Male,Yes,46,Yes,Artist,1.0,Average,5.0,Cat_2,C +464410,Male,Yes,40,Yes,Artist,0.0,Average,2.0,Cat_6,C +466005,Male,No,62,Yes,Entertainment,1.0,Low,4.0,Cat_2,C +460821,Female,No,31,Yes,Healthcare,,Low,2.0,Cat_4,D +459206,Female,No,25,No,Artist,1.0,Low,3.0,Cat_6,C +467571,Male,No,23,No,Marketing,0.0,Low,,,C +461156,Male,No,22,No,Marketing,3.0,Low,6.0,Cat_3,D +465515,Female,Yes,47,Yes,Artist,1.0,High,6.0,Cat_6,B +460883,Male,Yes,68,Yes,Artist,0.0,High,2.0,Cat_6,B +465380,Male,No,25,No,Entertainment,5.0,Low,2.0,Cat_6,D +467435,Female,No,30,No,Artist,0.0,Low,,Cat_6,C +461218,Male,Yes,27,No,Executive,0.0,Low,6.0,Cat_6,D +467186,Male,Yes,38,Yes,Entertainment,2.0,Average,3.0,Cat_3,B +459827,Female,No,33,No,,,Low,1.0,Cat_7,C +461859,Female,Yes,46,No,Artist,5.0,Low,5.0,Cat_6,C +467821,Female,Yes,37,Yes,Artist,1.0,Low,2.0,Cat_6,C +462525,Female,Yes,41,Yes,Artist,0.0,Average,2.0,Cat_6,C +462787,Female,No,29,No,Artist,1.0,Low,1.0,Cat_3,A +459055,Female,No,19,No,Entertainment,1.0,Low,5.0,Cat_6,D +462605,Male,Yes,82,No,Executive,1.0,High,2.0,Cat_4,D +461252,Female,Yes,48,Yes,Doctor,1.0,Low,1.0,Cat_3,D +464340,Male,Yes,67,Yes,Artist,1.0,High,2.0,Cat_6,B +461872,Female,Yes,77,Yes,Lawyer,0.0,High,2.0,Cat_6,B +459746,Male,,56,Yes,Artist,1.0,Average,2.0,Cat_6,B +466274,Male,Yes,69,Yes,Executive,0.0,Low,,Cat_6,A +467499,Male,Yes,40,Yes,Artist,,Low,3.0,Cat_6,C +467423,Male,Yes,61,Yes,Artist,0.0,Low,1.0,Cat_6,B +463865,Male,Yes,46,Yes,Artist,0.0,Average,5.0,Cat_6,B +467504,Female,,31,No,Doctor,1.0,Low,4.0,Cat_6,D +458984,Male,Yes,39,Yes,Artist,0.0,Average,3.0,Cat_6,C +459142,Female,Yes,89,Yes,Artist,0.0,High,3.0,Cat_6,C +462993,Male,Yes,30,No,Artist,,Average,3.0,Cat_6,B +467947,Female,,59,Yes,Artist,1.0,Average,4.0,Cat_6,C +466389,Female,Yes,47,Yes,Engineer,0.0,Average,2.0,Cat_6,A +460632,Female,No,22,No,Healthcare,0.0,Low,3.0,Cat_3,D +465626,Female,No,42,Yes,Artist,7.0,Low,3.0,Cat_6,C +461768,Male,Yes,69,Yes,Artist,0.0,High,2.0,Cat_6,C +462242,Female,No,30,Yes,Entertainment,0.0,Low,5.0,Cat_1,D +466949,Male,Yes,39,No,Executive,12.0,High,7.0,Cat_6,B +465479,Male,No,27,No,Artist,1.0,Low,1.0,Cat_4,D +464845,Male,Yes,30,No,Engineer,1.0,Average,3.0,Cat_4,A +465066,Male,Yes,47,Yes,Executive,7.0,High,4.0,Cat_6,C +466817,Female,Yes,81,No,Lawyer,0.0,High,2.0,Cat_6,B +466436,Male,Yes,42,No,Doctor,0.0,Average,3.0,Cat_6,B +467974,Female,No,43,Yes,Artist,1.0,Low,1.0,Cat_4,B +460341,Female,No,31,No,Entertainment,11.0,Low,5.0,Cat_7,D +467463,Female,Yes,39,Yes,Artist,8.0,Average,3.0,Cat_6,B +466235,Female,Yes,57,Yes,Artist,0.0,Average,4.0,Cat_4,C +459371,Male,Yes,35,Yes,,,Low,2.0,Cat_6,A +460233,Male,No,43,Yes,Doctor,0.0,Low,,Cat_6,D +466094,Female,No,31,No,Healthcare,0.0,Low,3.0,Cat_6,B +461451,Female,No,28,Yes,Engineer,1.0,Low,6.0,Cat_2,A +460473,Male,Yes,58,Yes,Artist,0.0,Average,2.0,Cat_4,B +461786,Male,Yes,71,Yes,Lawyer,0.0,High,2.0,Cat_6,C +466745,Female,Yes,36,Yes,Homemaker,0.0,Average,2.0,Cat_6,B +460124,Female,No,43,Yes,Artist,1.0,Low,3.0,Cat_6,A +459910,Male,Yes,69,Yes,Artist,1.0,Low,2.0,Cat_6,B +464082,Male,Yes,77,Yes,Lawyer,0.0,Low,1.0,Cat_6,A +461487,Male,Yes,50,Yes,Artist,1.0,Average,4.0,Cat_6,C +465188,Female,Yes,50,Yes,Artist,1.0,Average,4.0,Cat_6,B +467255,Female,No,40,Yes,Healthcare,6.0,Low,2.0,Cat_6,D +466993,Female,Yes,43,Yes,Engineer,9.0,Average,2.0,Cat_6,C +467151,Male,No,37,No,Entertainment,,Low,1.0,Cat_6,D +467480,Male,Yes,35,Yes,Artist,,Average,,Cat_6,D +464403,Female,Yes,65,Yes,Lawyer,0.0,High,3.0,Cat_6,C +461518,Female,Yes,46,Yes,Artist,0.0,Average,4.0,Cat_3,B +464848,Male,Yes,35,No,Entertainment,1.0,Average,4.0,Cat_4,B +460715,Female,Yes,86,No,Lawyer,0.0,High,2.0,Cat_6,A +466963,Male,No,63,No,Entertainment,7.0,Low,1.0,Cat_6,A +466508,Female,Yes,40,Yes,Artist,9.0,Average,5.0,Cat_6,B +460125,Female,Yes,55,Yes,Artist,10.0,Low,1.0,Cat_6,B +462325,Male,No,37,Yes,Healthcare,1.0,Low,4.0,Cat_4,B +461537,Male,No,31,Yes,Artist,0.0,Low,1.0,Cat_6,B +461679,Male,No,28,Yes,Healthcare,4.0,Low,4.0,Cat_6,D +463559,Female,Yes,48,Yes,Engineer,1.0,High,3.0,Cat_6,B +464330,Male,No,38,Yes,Artist,,Low,2.0,Cat_6,A +467018,Male,Yes,57,Yes,Artist,1.0,Average,5.0,Cat_6,C +463160,Female,No,48,No,Entertainment,,Low,1.0,Cat_6,B +467736,Female,Yes,79,Yes,Lawyer,4.0,High,,Cat_6,B +466104,Male,Yes,81,Yes,Lawyer,1.0,Low,1.0,Cat_6,B +467825,Female,No,21,No,Marketing,1.0,Low,8.0,Cat_6,C +466599,Female,No,33,Yes,Healthcare,1.0,Low,2.0,Cat_6,C +466724,Male,No,38,Yes,Artist,0.0,Low,2.0,Cat_6,B +463062,Female,Yes,37,Yes,Artist,,Average,3.0,Cat_6,B +467881,Female,No,33,Yes,Artist,10.0,Low,1.0,Cat_7,A +462578,Male,Yes,38,Yes,Artist,8.0,Average,2.0,Cat_4,B +459177,Female,Yes,39,Yes,Artist,1.0,Average,4.0,Cat_6,A +459232,Female,Yes,59,Yes,Artist,0.0,High,2.0,Cat_6,C +459302,Female,Yes,57,Yes,Artist,,Average,4.0,Cat_2,C +467019,Male,Yes,80,Yes,Lawyer,1.0,Low,1.0,Cat_6,C +462122,Male,Yes,42,No,Entertainment,6.0,Low,3.0,Cat_6,B +459776,Male,Yes,75,Yes,Lawyer,2.0,Low,1.0,Cat_6,D +466074,Female,No,29,No,Engineer,9.0,Low,2.0,Cat_4,A +461528,Female,No,33,Yes,Engineer,1.0,Low,2.0,Cat_6,A +464375,Female,Yes,63,Yes,Artist,1.0,Low,2.0,Cat_6,B +460138,Male,No,33,Yes,Entertainment,9.0,Low,6.0,Cat_6,D +460232,Male,No,22,No,Healthcare,8.0,Low,4.0,Cat_6,D +464444,Male,Yes,36,Yes,Artist,8.0,Average,2.0,Cat_6,C +467783,Female,Yes,53,Yes,Artist,0.0,Average,3.0,Cat_6,C +460497,Male,Yes,35,Yes,Entertainment,6.0,Low,5.0,Cat_3,D +465124,Female,Yes,29,Yes,Healthcare,6.0,Average,2.0,Cat_6,A +464602,Female,Yes,31,Yes,Healthcare,0.0,Low,1.0,Cat_1,D +465581,Male,Yes,38,Yes,Artist,0.0,Low,4.0,Cat_3,A +462750,Male,No,22,No,Healthcare,9.0,Low,8.0,Cat_6,D +461258,Female,Yes,57,Yes,Artist,1.0,Average,3.0,Cat_4,C +464958,Female,Yes,42,No,Engineer,1.0,Average,6.0,Cat_4,B +462537,Male,No,78,Yes,Entertainment,,Low,1.0,Cat_6,C +462268,Female,Yes,77,Yes,Lawyer,6.0,High,2.0,Cat_6,B +460920,Male,Yes,79,Yes,Lawyer,1.0,High,2.0,Cat_6,C +463816,Male,Yes,53,Yes,Healthcare,3.0,High,4.0,Cat_7,B +461712,Female,Yes,42,Yes,Artist,1.0,Low,2.0,Cat_6,A +461846,Female,No,72,Yes,Artist,1.0,Low,2.0,Cat_2,B +464315,Male,Yes,71,No,Executive,1.0,Average,2.0,Cat_6,C +467607,Female,No,25,No,Engineer,9.0,Low,1.0,Cat_6,D +459259,Male,Yes,74,Yes,Lawyer,,High,2.0,Cat_6,C +465224,Male,Yes,61,Yes,Executive,0.0,Low,3.0,Cat_6,D +463181,Male,Yes,48,Yes,Doctor,5.0,Average,3.0,Cat_5,B +467932,Female,No,28,No,Healthcare,0.0,Low,4.0,Cat_6,D +466876,Female,Yes,43,Yes,Entertainment,0.0,Average,2.0,Cat_2,C +463401,Female,No,18,No,Healthcare,,Low,3.0,Cat_3,D +464071,Male,Yes,82,Yes,Lawyer,0.0,High,2.0,Cat_6,B +464202,Male,Yes,40,Yes,Artist,14.0,Low,1.0,Cat_6,A +467163,Male,No,32,No,Healthcare,9.0,Low,3.0,Cat_6,D +463453,Male,No,27,Yes,Healthcare,,Low,4.0,Cat_6,B +466795,Male,,55,Yes,Entertainment,,Average,5.0,Cat_6,B +461932,Female,No,21,No,Healthcare,3.0,Low,2.0,Cat_4,D +467280,Female,No,28,No,Entertainment,10.0,Low,2.0,Cat_6,D +463658,Male,Yes,43,Yes,Artist,3.0,Average,3.0,Cat_6,C +460579,Female,Yes,38,No,Engineer,0.0,Average,5.0,Cat_3,A +462310,Male,Yes,63,Yes,Artist,5.0,Average,4.0,Cat_6,C +460321,Male,Yes,25,Yes,Artist,3.0,Average,2.0,Cat_6,B +464537,Male,Yes,41,Yes,Artist,8.0,Average,3.0,Cat_6,A +463335,Male,No,27,Yes,Doctor,,Low,1.0,Cat_4,A +467346,Male,Yes,37,Yes,Artist,1.0,Average,3.0,Cat_6,C +466126,Female,Yes,57,No,Engineer,0.0,Low,1.0,Cat_6,B +464058,Male,Yes,86,Yes,Healthcare,0.0,Low,1.0,Cat_6,D +462232,Male,Yes,56,No,Executive,1.0,High,8.0,Cat_4,A +463034,Female,No,27,Yes,Homemaker,9.0,Low,1.0,Cat_6,D +459543,Male,Yes,70,No,Executive,1.0,High,2.0,Cat_6,A +459635,Female,No,22,No,Healthcare,8.0,Low,4.0,Cat_6,D +464097,Male,Yes,35,Yes,Doctor,5.0,High,2.0,Cat_6,D +466930,Male,No,31,No,Healthcare,2.0,Low,2.0,Cat_5,A +463866,Male,Yes,40,Yes,Artist,1.0,Average,4.0,Cat_6,C +465387,Female,Yes,43,Yes,Artist,0.0,Low,2.0,Cat_6,C +463246,Female,Yes,38,No,Engineer,9.0,Low,2.0,Cat_6,A +459719,Male,Yes,84,Yes,Lawyer,,Low,,Cat_4,D +462241,Female,No,28,No,Entertainment,1.0,Low,4.0,Cat_6,D +462198,Female,No,39,No,Engineer,2.0,Low,,Cat_3,D +463283,Female,No,48,Yes,Artist,1.0,Low,1.0,Cat_3,B +466983,Female,Yes,41,Yes,Artist,8.0,Average,2.0,Cat_6,C +462812,Female,No,29,No,Healthcare,0.0,Low,3.0,Cat_6,C +462496,Male,Yes,45,No,Artist,1.0,Average,3.0,Cat_6,A +461782,Female,Yes,45,Yes,Artist,,High,5.0,Cat_6,C +460405,Female,No,41,Yes,Entertainment,5.0,Low,2.0,Cat_6,D +460757,Female,Yes,28,No,Homemaker,9.0,High,3.0,Cat_6,A +467396,Male,No,20,,Healthcare,0.0,Low,3.0,Cat_6,D +466546,Female,Yes,69,Yes,Lawyer,1.0,High,2.0,Cat_6,B +459138,Male,Yes,53,Yes,Artist,1.0,Average,3.0,Cat_6,C +464830,Male,,47,,Executive,1.0,Average,5.0,Cat_4,B +459194,Female,Yes,81,Yes,Lawyer,1.0,High,2.0,Cat_6,C +465043,Male,Yes,48,Yes,Doctor,9.0,Average,5.0,Cat_6,C +461435,Female,Yes,45,Yes,Artist,8.0,Average,2.0,Cat_6,C +462512,Female,Yes,28,No,Entertainment,,Average,2.0,Cat_4,A +467677,Male,Yes,57,No,Entertainment,0.0,High,3.0,Cat_4,D +464216,Male,Yes,51,Yes,Artist,,High,3.0,Cat_6,A +463279,Male,No,27,No,Entertainment,0.0,Low,3.0,Cat_3,A +460395,Female,No,27,Yes,Entertainment,8.0,Low,3.0,Cat_6,D +462308,Male,Yes,30,Yes,Healthcare,1.0,High,2.0,Cat_4,B +461309,Female,No,21,No,Healthcare,1.0,Low,4.0,Cat_6,D +459310,Male,No,38,Yes,Entertainment,1.0,Low,2.0,Cat_2,D +459786,Male,Yes,57,No,Marketing,1.0,High,3.0,Cat_6,A +467215,Female,Yes,78,Yes,Lawyer,1.0,High,2.0,Cat_6,C +460613,Female,Yes,46,Yes,Homemaker,8.0,Average,3.0,Cat_3,C +461682,Male,Yes,75,No,Executive,1.0,High,2.0,Cat_6,C +466979,Female,Yes,39,Yes,Homemaker,,Low,1.0,Cat_3,A +467077,Male,,61,Yes,Artist,1.0,Average,5.0,Cat_6,C +465605,Female,Yes,52,Yes,Engineer,4.0,Low,1.0,Cat_3,B +463896,Female,No,25,No,Homemaker,0.0,Low,3.0,Cat_2,C +463481,Female,Yes,37,Yes,Artist,,Average,3.0,Cat_7,B +463323,Male,Yes,31,Yes,Healthcare,2.0,Low,2.0,Cat_6,D +464033,Female,No,18,No,Healthcare,1.0,Low,4.0,Cat_6,D +467637,Male,No,36,Yes,Engineer,,Low,2.0,Cat_3,D +465456,Female,Yes,59,Yes,Artist,3.0,High,5.0,Cat_1,B +467831,Male,Yes,39,Yes,Artist,0.0,Average,2.0,Cat_6,C +466041,Female,Yes,52,,Engineer,1.0,Low,1.0,Cat_4,A +464285,Female,No,25,Yes,Artist,9.0,Low,1.0,Cat_6,B +465697,Female,Yes,47,No,Artist,7.0,Low,1.0,Cat_6,C +465199,Male,Yes,32,Yes,Entertainment,1.0,Low,2.0,Cat_6,A +466057,Female,Yes,38,No,Doctor,1.0,Average,2.0,Cat_4,D +467327,Male,Yes,52,Yes,Artist,3.0,Average,4.0,Cat_6,C +460495,Male,No,21,No,Healthcare,0.0,Low,6.0,Cat_3,D +466180,Male,Yes,70,No,Lawyer,0.0,Low,1.0,Cat_6,B +467954,Male,No,31,No,Healthcare,8.0,Low,4.0,Cat_6,D +467915,Female,No,23,No,Doctor,0.0,Low,4.0,Cat_6,C +464786,Male,No,33,No,Marketing,1.0,Low,3.0,Cat_4,D +462811,Male,Yes,37,Yes,Marketing,,High,2.0,Cat_6,B +459220,Female,Yes,77,Yes,Lawyer,1.0,High,2.0,Cat_6,A +461196,Female,Yes,42,Yes,Engineer,8.0,Low,2.0,Cat_3,D +465069,Male,Yes,73,Yes,Healthcare,1.0,Average,3.0,Cat_6,C +464959,Female,Yes,40,Yes,Engineer,1.0,Average,6.0,Cat_4,B +460537,Female,No,33,Yes,Artist,0.0,Low,3.0,Cat_3,A +461019,Male,No,18,No,Healthcare,0.0,Low,3.0,Cat_3,D +467240,Female,,22,No,Doctor,6.0,High,3.0,Cat_6,D +460116,Female,Yes,43,Yes,,1.0,Low,2.0,Cat_6,D +466319,Female,,29,No,Healthcare,1.0,Low,4.0,Cat_6,C +465801,Male,,37,Yes,Doctor,8.0,Low,6.0,Cat_6,C +461686,Male,Yes,59,Yes,Artist,1.0,Low,2.0,Cat_6,B +459263,Female,No,32,No,Healthcare,8.0,Low,4.0,Cat_6,D +460487,Female,No,42,No,Homemaker,,Low,1.0,Cat_3,B +462947,Female,Yes,46,Yes,Artist,1.0,Low,1.0,Cat_6,B +463875,Male,Yes,53,Yes,Entertainment,1.0,Low,3.0,Cat_2,A +459704,Male,Yes,41,Yes,Artist,1.0,Average,3.0,Cat_6,B +467741,Male,No,21,No,Healthcare,1.0,Low,3.0,Cat_4,D +464978,Male,Yes,62,No,Executive,0.0,High,2.0,Cat_4,B +465402,Male,No,31,No,Healthcare,,Low,,Cat_6,D +465191,Female,No,58,Yes,Engineer,1.0,Low,1.0,Cat_6,A +459459,Male,Yes,36,Yes,Artist,4.0,Low,1.0,Cat_6,A +467458,Female,No,28,Yes,Entertainment,0.0,Low,2.0,Cat_6,D +459223,Female,Yes,47,Yes,Doctor,1.0,Average,4.0,Cat_6,C +465762,Female,No,36,No,Engineer,9.0,Low,,Cat_4,D +466882,Male,No,36,Yes,Artist,0.0,Low,,Cat_6,A +463170,Female,,33,Yes,Healthcare,0.0,Low,5.0,Cat_3,D +462296,Female,Yes,46,Yes,Artist,0.0,High,4.0,Cat_6,C +466625,Male,Yes,49,Yes,Artist,0.0,Average,2.0,Cat_7,C +462043,Male,No,33,No,Healthcare,0.0,Low,5.0,Cat_6,C +466777,Female,Yes,49,Yes,Homemaker,8.0,Average,2.0,Cat_6,C +459959,Male,Yes,25,Yes,Artist,9.0,Low,4.0,Cat_6,D +462470,Male,Yes,80,Yes,Executive,3.0,Low,1.0,Cat_6,B +460805,Male,No,36,Yes,Healthcare,8.0,Low,4.0,Cat_2,A +460026,Male,No,26,No,Engineer,7.0,Low,1.0,Cat_6,D +463244,Female,Yes,45,No,Doctor,1.0,Low,1.0,Cat_6,B +463902,Female,Yes,41,Yes,Entertainment,,Average,4.0,Cat_6,C +462032,Female,Yes,69,Yes,Artist,0.0,Low,2.0,Cat_6,C +467291,Male,Yes,53,Yes,Entertainment,1.0,Average,4.0,Cat_6,D +467847,Male,Yes,41,Yes,Artist,8.0,High,2.0,Cat_2,A +462243,Male,Yes,43,No,Executive,1.0,Low,4.0,Cat_6,A +466626,Female,Yes,51,Yes,Artist,1.0,Average,2.0,Cat_7,C +464372,Male,Yes,45,Yes,Artist,1.0,Average,6.0,Cat_5,C +465005,Male,Yes,50,Yes,Executive,4.0,High,3.0,Cat_6,C +466185,Male,Yes,57,Yes,Doctor,0.0,Average,4.0,Cat_6,C +462165,Male,Yes,70,Yes,Lawyer,3.0,High,2.0,Cat_6,D +464740,Female,Yes,59,No,Artist,1.0,Low,3.0,Cat_4,C +467391,Male,Yes,84,Yes,Lawyer,,Low,1.0,Cat_6,C +463753,Female,No,30,Yes,Healthcare,0.0,Low,5.0,Cat_2,D +460892,Male,Yes,43,Yes,Engineer,,Average,4.0,Cat_7,A +459852,Male,No,40,Yes,Artist,7.0,Low,1.0,Cat_6,A +460777,Male,Yes,74,No,Lawyer,0.0,Low,1.0,Cat_6,D +463202,Male,No,33,No,Marketing,0.0,Low,3.0,Cat_3,D +466973,Female,Yes,63,Yes,Marketing,2.0,Low,1.0,Cat_6,D +460739,Male,Yes,41,Yes,Executive,,High,5.0,Cat_6,A +459217,Female,Yes,60,No,Lawyer,1.0,Average,3.0,Cat_6,B +462148,Male,Yes,62,Yes,Artist,0.0,Average,,Cat_4,C +462265,Male,No,31,Yes,Doctor,3.0,Low,4.0,Cat_6,C +460455,Female,Yes,28,Yes,Homemaker,9.0,Average,2.0,Cat_3,B +462402,Female,Yes,37,No,Entertainment,1.0,Average,2.0,Cat_4,D +459386,Male,Yes,70,No,Lawyer,0.0,High,2.0,Cat_6,B +461526,Male,Yes,78,No,Entertainment,0.0,Low,2.0,Cat_6,D +466782,Male,No,19,No,,9.0,Low,3.0,Cat_4,D +461538,Female,No,38,Yes,Artist,8.0,Low,8.0,Cat_2,B +460736,Male,No,19,No,Entertainment,0.0,Low,3.0,Cat_6,C +459859,Female,Yes,28,No,Doctor,9.0,Low,1.0,Cat_7,A +466252,Female,Yes,73,No,Executive,0.0,Low,3.0,Cat_4,B +462240,Male,Yes,84,No,Lawyer,1.0,High,2.0,Cat_6,A +466920,Male,No,21,No,Healthcare,7.0,Low,4.0,Cat_3,D +466293,Male,Yes,79,No,Lawyer,0.0,High,2.0,Cat_6,B +466926,Male,No,20,No,Healthcare,0.0,Low,,Cat_6,D +464422,Male,No,59,No,Entertainment,,Low,1.0,Cat_3,A +461517,Male,Yes,42,Yes,Artist,0.0,Average,3.0,Cat_6,B +466763,Male,Yes,55,Yes,Homemaker,0.0,Low,4.0,Cat_6,B +460822,Male,Yes,41,Yes,Artist,0.0,Low,2.0,Cat_6,A +460957,Male,Yes,48,Yes,Entertainment,1.0,Low,,Cat_4,B +466715,Male,Yes,72,Yes,Executive,1.0,High,4.0,Cat_6,C +459843,Male,Yes,52,No,Artist,,Average,6.0,Cat_6,B +459653,Female,No,25,Yes,,8.0,Low,1.0,Cat_6,D +460045,Male,Yes,56,Yes,Artist,0.0,Low,2.0,Cat_6,B +464249,Male,Yes,80,No,Lawyer,,High,2.0,Cat_6,C +467723,Male,Yes,83,No,Executive,0.0,Low,7.0,Cat_6,D +459764,Male,Yes,48,Yes,Artist,1.0,Average,2.0,,B +466664,Female,Yes,47,Yes,Doctor,1.0,Average,2.0,Cat_4,A +464867,Female,Yes,47,Yes,Doctor,1.0,Average,2.0,Cat_4,B +459742,Female,Yes,83,No,Lawyer,1.0,High,2.0,Cat_6,C +463768,Female,Yes,38,No,Engineer,1.0,Low,1.0,Cat_4,A +465636,Male,No,29,Yes,Healthcare,3.0,Low,3.0,Cat_4,D +467639,Male,Yes,50,Yes,Entertainment,0.0,Average,4.0,Cat_6,B +464760,Female,Yes,52,No,Engineer,6.0,Average,4.0,Cat_4,B +464986,Male,Yes,67,No,Lawyer,,High,2.0,Cat_6,A +462024,Male,No,33,Yes,Entertainment,1.0,Low,8.0,Cat_4,C +464730,Male,Yes,36,No,Executive,1.0,High,6.0,Cat_4,D +465823,Male,Yes,65,No,Entertainment,7.0,Average,4.0,Cat_4,D +465546,Female,No,25,No,Marketing,1.0,Low,9.0,Cat_5,D +460460,Male,Yes,27,No,Entertainment,4.0,Low,4.0,Cat_3,B +467202,Male,Yes,22,No,Doctor,1.0,Low,4.0,Cat_6,A +467184,Male,Yes,51,No,Engineer,0.0,Average,3.0,Cat_6,D +465327,Female,No,42,Yes,Entertainment,1.0,Low,1.0,Cat_6,D +459314,Male,Yes,46,Yes,Artist,,Average,4.0,Cat_6,C +466072,Female,No,23,No,Healthcare,1.0,Low,3.0,Cat_3,D +466086,Male,Yes,80,Yes,Lawyer,0.0,High,2.0,Cat_6,C +459343,Female,Yes,40,Yes,Entertainment,12.0,Low,6.0,Cat_6,A +459224,Female,Yes,48,Yes,Artist,0.0,Average,2.0,Cat_6,C +465473,Female,No,55,Yes,Artist,3.0,Low,1.0,Cat_6,A +466887,Male,Yes,36,Yes,Entertainment,1.0,Low,3.0,Cat_3,A +463682,Male,Yes,41,Yes,Artist,12.0,Average,2.0,Cat_6,B +460963,Female,No,32,Yes,Artist,0.0,Low,3.0,Cat_6,B +467033,Male,Yes,37,Yes,Entertainment,8.0,Average,2.0,Cat_2,D +463883,Male,No,30,No,Healthcare,0.0,Low,2.0,Cat_4,B +466716,Female,Yes,79,Yes,Lawyer,1.0,Low,1.0,Cat_6,C +464251,Male,Yes,65,Yes,Artist,0.0,Average,4.0,Cat_2,B +463843,Male,Yes,49,Yes,Executive,0.0,High,4.0,Cat_6,A +461476,Male,Yes,53,Yes,Artist,1.0,Average,4.0,Cat_2,C +467787,Male,No,20,No,Healthcare,8.0,Low,5.0,Cat_6,D +463130,Female,Yes,78,No,Lawyer,1.0,High,2.0,Cat_6,B +467722,Male,No,32,Yes,Doctor,6.0,Low,5.0,Cat_6,D +465241,Male,No,23,No,Healthcare,3.0,Low,2.0,Cat_4,D +460345,Female,Yes,40,Yes,Artist,1.0,Average,2.0,Cat_6,C +463512,Female,No,30,No,Engineer,0.0,Low,3.0,Cat_6,B +466255,Male,Yes,45,Yes,Entertainment,0.0,Low,4.0,Cat_2,C +463449,Male,Yes,25,No,Healthcare,1.0,Average,2.0,Cat_6,D +459276,Female,No,20,No,Healthcare,0.0,Low,4.0,Cat_6,D +460564,Female,Yes,28,,Homemaker,6.0,Average,3.0,Cat_3,C +463644,Female,No,27,Yes,Healthcare,1.0,Low,3.0,Cat_7,D +466253,Male,No,19,No,Healthcare,0.0,Low,3.0,Cat_2,D +463760,Female,No,27,No,Doctor,1.0,Low,,,A +461717,Female,No,26,Yes,Healthcare,0.0,Low,4.0,Cat_2,A +462733,Male,Yes,55,No,Artist,0.0,Average,2.0,Cat_6,C +466146,Female,Yes,41,No,Engineer,8.0,Average,3.0,Cat_6,C +461866,Male,No,25,No,Healthcare,0.0,Low,4.0,Cat_6,D +459876,Female,Yes,67,Yes,Artist,1.0,Average,2.0,Cat_6,B +463434,Female,Yes,25,No,Engineer,,Average,2.0,Cat_6,D +461869,Male,Yes,87,Yes,Lawyer,0.0,Low,1.0,Cat_6,B +461228,Male,Yes,26,No,Doctor,9.0,Low,6.0,Cat_6,D +462962,Female,Yes,37,Yes,Artist,4.0,Low,1.0,Cat_3,A +465869,Male,No,42,Yes,Doctor,1.0,Low,1.0,Cat_6,A +464181,Female,No,31,Yes,Healthcare,6.0,Low,4.0,Cat_2,D +459740,Female,No,20,No,Healthcare,0.0,Low,3.0,Cat_6,D +463852,Male,Yes,51,Yes,Entertainment,,High,4.0,Cat_6,D +462774,Female,Yes,88,Yes,Lawyer,0.0,Low,1.0,Cat_6,D +464567,Male,No,33,Yes,Healthcare,8.0,Low,4.0,Cat_2,D +459271,Female,No,55,Yes,Artist,1.0,Low,1.0,Cat_6,A +460990,Male,No,22,No,Healthcare,1.0,Low,3.0,Cat_6,D +463802,Female,No,39,Yes,Engineer,1.0,Low,3.0,Cat_6,B +466672,Male,Yes,62,Yes,Artist,0.0,Low,3.0,Cat_6,C +462919,Female,Yes,37,No,Homemaker,13.0,Average,3.0,Cat_6,A +463153,Female,No,26,Yes,Homemaker,9.0,Low,1.0,Cat_2,D +460464,Male,No,27,Yes,Artist,9.0,Low,2.0,Cat_3,D +461375,Male,No,29,Yes,Healthcare,1.0,Low,4.0,Cat_2,D +467956,Female,No,31,Yes,Entertainment,1.0,Low,1.0,Cat_6,B +467024,Male,Yes,40,Yes,Entertainment,9.0,Average,5.0,Cat_6,B +461146,Male,Yes,56,Yes,Entertainment,0.0,Low,2.0,Cat_3,C +462388,Female,Yes,45,Yes,Artist,1.0,High,5.0,Cat_6,C +460355,Female,No,40,Yes,Entertainment,5.0,Low,1.0,Cat_3,A +461854,Male,No,30,Yes,Healthcare,9.0,Low,3.0,Cat_6,D +458985,Male,No,23,No,Healthcare,1.0,Low,4.0,Cat_6,D +466356,Male,Yes,46,Yes,Artist,0.0,Low,2.0,Cat_6,C +460078,Female,Yes,39,Yes,Healthcare,8.0,High,2.0,Cat_1,D +464969,Female,Yes,65,No,Lawyer,0.0,High,9.0,Cat_4,A +461769,Male,No,40,Yes,Artist,0.0,Low,1.0,Cat_6,B +465193,Male,Yes,36,No,Engineer,,Average,,Cat_3,D +463548,Female,No,18,No,Healthcare,3.0,Low,4.0,Cat_4,D +467116,Male,Yes,73,Yes,Executive,1.0,High,2.0,Cat_6,B +462140,Female,Yes,67,No,Engineer,,Average,4.0,Cat_4,A +461577,Female,Yes,50,Yes,Artist,1.0,Average,3.0,Cat_6,C +460740,Male,Yes,38,Yes,Artist,4.0,Average,3.0,Cat_6,B +466844,Male,No,18,No,Healthcare,0.0,Low,5.0,Cat_6,D +466085,Male,No,19,No,,9.0,Low,4.0,Cat_4,D +460150,Male,No,19,No,Healthcare,0.0,Low,4.0,Cat_4,D +467955,Female,Yes,39,Yes,Artist,9.0,Low,,Cat_4,D +463242,Female,No,26,No,Engineer,5.0,Low,2.0,Cat_6,A +466833,Male,Yes,58,Yes,Artist,7.0,Low,2.0,Cat_6,B +460371,Female,No,28,Yes,Artist,1.0,Low,2.0,Cat_6,C +465180,Female,Yes,53,Yes,Artist,1.0,Average,2.0,Cat_7,C +461279,Male,No,18,No,Doctor,4.0,Low,2.0,Cat_6,A +459109,Male,Yes,49,Yes,Entertainment,7.0,Average,4.0,Cat_6,C +467519,Male,No,19,No,Healthcare,0.0,Low,3.0,Cat_6,D +461398,Male,No,20,No,Doctor,0.0,Low,4.0,Cat_6,D +467188,Male,Yes,55,Yes,Artist,2.0,Low,2.0,Cat_6,B +459318,Female,No,32,Yes,Engineer,0.0,Low,3.0,Cat_6,D +461823,Male,No,27,No,Healthcare,10.0,Low,4.0,Cat_6,C +459732,Male,Yes,72,Yes,Executive,1.0,High,5.0,Cat_6,A +461317,Female,No,59,Yes,Artist,1.0,Low,1.0,Cat_6,C +461459,Female,Yes,35,Yes,Artist,0.0,Low,3.0,Cat_6,A +460429,Female,No,32,Yes,Engineer,9.0,Low,1.0,Cat_6,A +464577,Female,Yes,28,Yes,Healthcare,10.0,High,2.0,Cat_6,D +466552,Male,Yes,19,No,Entertainment,1.0,Low,2.0,Cat_6,A +461231,Male,Yes,52,Yes,Artist,2.0,High,1.0,Cat_4,D +463650,Male,No,29,No,Doctor,1.0,Low,3.0,Cat_6,D +462674,Female,Yes,51,Yes,Homemaker,12.0,Average,4.0,Cat_3,B +465275,Male,No,27,Yes,Healthcare,1.0,Low,5.0,Cat_6,D +465684,Female,No,47,Yes,Entertainment,0.0,Low,1.0,Cat_3,A +465492,Male,Yes,61,No,Entertainment,1.0,Low,1.0,Cat_2,A +464510,Female,Yes,75,No,Lawyer,0.0,High,2.0,Cat_6,D +463588,Male,No,19,No,Healthcare,1.0,Low,1.0,Cat_6,D +466341,Male,Yes,48,Yes,Artist,0.0,Average,2.0,Cat_5,C +459397,Male,Yes,59,Yes,Artist,3.0,Average,2.0,Cat_6,B +460888,Male,Yes,42,No,Homemaker,8.0,Low,1.0,Cat_6,A +460272,Female,No,86,Yes,Lawyer,1.0,Low,1.0,Cat_6,A +460612,Male,No,33,Yes,Entertainment,9.0,Low,4.0,Cat_3,C +462530,Female,Yes,37,Yes,Artist,,High,2.0,Cat_6,A +462227,Male,Yes,49,No,Executive,3.0,High,5.0,Cat_7,B +459684,Male,Yes,52,Yes,Entertainment,,Average,3.0,Cat_6,B +459568,Male,No,41,Yes,Artist,,Low,3.0,Cat_6,C +464517,Male,No,26,Yes,Healthcare,1.0,Low,,Cat_5,D +467892,Male,No,23,No,Doctor,1.0,Low,5.0,Cat_6,D +461012,Male,No,35,No,Doctor,1.0,Low,3.0,Cat_3,D +463003,Male,Yes,37,Yes,Artist,2.0,Average,2.0,Cat_6,C +467592,Male,Yes,43,Yes,Entertainment,7.0,Low,2.0,Cat_6,A +462366,Male,Yes,50,No,Engineer,1.0,Low,2.0,Cat_6,D +465908,Female,No,50,No,Engineer,1.0,Low,1.0,Cat_6,A +466759,Female,No,26,No,Healthcare,3.0,Low,,Cat_6,D +463400,Female,No,20,No,Healthcare,8.0,Low,3.0,Cat_1,D +467075,Female,Yes,31,No,Homemaker,9.0,Average,2.0,Cat_6,A +462087,Female,No,31,Yes,Healthcare,9.0,Low,3.0,Cat_2,A +462522,Male,Yes,41,No,Executive,1.0,Average,5.0,Cat_4,B +459615,Male,Yes,84,No,Lawyer,1.0,Low,1.0,Cat_6,A +464899,Female,No,22,,Healthcare,8.0,Low,5.0,Cat_4,D +460157,Male,Yes,57,No,,1.0,Low,5.0,Cat_4,D +467794,Female,No,23,No,Healthcare,0.0,Low,4.0,Cat_3,D +461040,Female,Yes,69,No,Lawyer,0.0,Low,1.0,Cat_3,D +466443,Female,Yes,68,Yes,Artist,4.0,Low,2.0,Cat_6,B +463975,Female,No,30,No,Healthcare,8.0,Low,4.0,Cat_4,B +464063,Female,No,26,Yes,Doctor,1.0,Low,1.0,Cat_6,D +461917,Male,Yes,79,,Lawyer,1.0,Low,1.0,Cat_6,D +464055,Male,Yes,32,Yes,Doctor,0.0,Low,,,D +461326,Female,No,36,Yes,Artist,1.0,Low,1.0,Cat_6,C +467196,Male,No,18,No,Entertainment,0.0,Low,5.0,Cat_6,C +461633,Male,No,30,Yes,Healthcare,8.0,Low,4.0,Cat_7,D +462045,Female,No,46,Yes,Artist,9.0,Low,1.0,Cat_6,B +466264,Male,Yes,33,Yes,,0.0,Low,2.0,Cat_3,D +466289,Male,No,19,No,Healthcare,2.0,Low,5.0,Cat_2,D +463562,Male,No,33,Yes,Entertainment,0.0,Low,4.0,Cat_6,C +460089,Female,Yes,65,Yes,Artist,13.0,Low,1.0,Cat_6,B +459336,Male,Yes,26,Yes,Artist,6.0,Average,2.0,Cat_6,A +467350,Female,No,30,Yes,Homemaker,6.0,Low,1.0,Cat_6,D +463565,Female,No,33,Yes,Entertainment,1.0,Low,4.0,Cat_6,D +463905,Female,Yes,27,Yes,Engineer,0.0,Average,4.0,Cat_7,A +467269,Male,Yes,65,Yes,Artist,0.0,Average,2.0,Cat_6,C +464934,Male,Yes,77,No,Lawyer,1.0,Low,2.0,Cat_4,D +463923,Female,No,20,No,Engineer,0.0,Low,4.0,Cat_6,D +465445,Female,No,27,Yes,Homemaker,6.0,Low,7.0,Cat_6,B +460517,Male,Yes,78,,Lawyer,,Low,,Cat_3,C +463661,Female,Yes,51,Yes,Doctor,1.0,Average,4.0,Cat_6,B +459370,Male,No,18,No,Healthcare,13.0,Low,4.0,Cat_6,D +466019,Female,Yes,63,Yes,Artist,5.0,Low,1.0,Cat_3,C +467107,Male,Yes,36,Yes,Engineer,1.0,Average,3.0,Cat_4,B +461683,Male,Yes,43,Yes,Executive,1.0,High,4.0,Cat_6,B +459053,Male,Yes,46,Yes,Executive,0.0,High,4.0,Cat_6,C +466894,Male,No,51,Yes,Artist,0.0,Low,3.0,Cat_6,B +460005,Male,No,43,Yes,Engineer,0.0,Low,1.0,Cat_6,A +467969,Female,Yes,43,Yes,Artist,0.0,Average,2.0,Cat_6,C +463120,Female,No,37,Yes,Entertainment,1.0,Low,1.0,Cat_6,A +466106,Female,Yes,63,No,Artist,0.0,Average,5.0,Cat_6,C +467644,Male,No,30,No,Doctor,,Low,5.0,Cat_6,D +461976,Female,Yes,37,Yes,Entertainment,5.0,Average,2.0,Cat_3,B +460885,Male,Yes,53,No,Artist,8.0,Average,3.0,Cat_6,B +463692,Female,Yes,26,Yes,Healthcare,6.0,High,7.0,Cat_2,D +467674,Male,Yes,40,Yes,Artist,0.0,Average,2.0,Cat_6,C +465982,Male,Yes,41,Yes,Entertainment,0.0,Average,4.0,Cat_6,B +464349,Male,Yes,66,Yes,Artist,2.0,Low,1.0,Cat_6,C +467113,Male,Yes,80,Yes,Lawyer,,Low,1.0,Cat_6,D +463881,Female,Yes,37,No,Engineer,0.0,Average,5.0,Cat_2,B +462247,Female,Yes,42,No,Artist,1.0,High,4.0,Cat_6,B +463886,Female,No,38,Yes,Artist,1.0,Low,2.0,Cat_6,C +460304,Female,No,53,No,Lawyer,1.0,Low,1.0,Cat_6,D +466458,Male,No,33,No,Executive,5.0,Low,2.0,Cat_6,A +465830,Female,Yes,52,No,,7.0,Average,7.0,Cat_4,A +462889,Male,Yes,33,No,Executive,,High,4.0,Cat_6,B +462449,Male,No,19,No,Healthcare,1.0,Low,3.0,Cat_6,D +462101,Male,Yes,53,Yes,Artist,,Average,3.0,Cat_6,A +467558,Male,Yes,50,Yes,Doctor,0.0,Average,8.0,Cat_6,C +467247,Female,No,26,No,Engineer,0.0,Low,2.0,Cat_6,D +461663,Male,No,20,No,Healthcare,1.0,Low,3.0,Cat_6,D +459571,Female,Yes,53,Yes,Executive,0.0,High,4.0,Cat_6,C +459385,Female,Yes,41,Yes,Artist,0.0,Average,2.0,Cat_7,C +466191,Male,Yes,48,Yes,Executive,0.0,High,3.0,Cat_2,C +465062,Male,Yes,56,No,Executive,0.0,High,4.0,Cat_6,B +467108,Female,No,66,Yes,Artist,0.0,Low,1.0,Cat_6,D +466957,Male,Yes,35,No,Entertainment,8.0,Average,4.0,Cat_6,A +463298,Male,Yes,50,No,Entertainment,0.0,Low,2.0,Cat_6,A +461155,Female,Yes,51,Yes,Artist,3.0,Low,2.0,Cat_3,B +466650,Female,No,30,No,Healthcare,8.0,Low,3.0,Cat_4,D +464633,Male,No,30,No,Executive,1.0,Low,4.0,Cat_4,D +464469,Female,No,50,Yes,Artist,9.0,Low,1.0,Cat_6,B +459710,Male,Yes,61,Yes,Artist,,Low,1.0,Cat_6,B +465319,Male,No,19,No,Healthcare,0.0,Low,5.0,Cat_6,D +462041,Female,No,22,No,Doctor,0.0,Low,2.0,Cat_4,D +459539,Female,No,35,Yes,Healthcare,9.0,Low,1.0,Cat_6,D +466158,Female,Yes,47,Yes,Engineer,0.0,Low,4.0,Cat_6,B +460430,Male,No,32,No,Doctor,12.0,Low,4.0,Cat_6,D +461953,Male,Yes,33,No,Healthcare,11.0,High,2.0,Cat_6,D +463982,Female,No,25,Yes,Healthcare,,Low,2.0,Cat_4,A +467546,Female,No,28,No,Healthcare,1.0,Low,4.0,Cat_6,B +464861,Male,No,32,No,Homemaker,,Low,2.0,Cat_4,B +459667,Female,Yes,50,Yes,Healthcare,1.0,High,6.0,Cat_5,C +467447,Female,Yes,37,Yes,Engineer,,Average,3.0,Cat_6,B +461373,Male,No,53,Yes,Artist,1.0,Low,1.0,Cat_6,B +464587,Female,Yes,49,Yes,Artist,1.0,Low,2.0,Cat_3,A +467811,Male,Yes,72,Yes,Lawyer,1.0,Low,1.0,Cat_6,D +464333,Male,Yes,60,Yes,Executive,0.0,High,3.0,Cat_6,B +466978,Male,No,48,Yes,Artist,4.0,Low,1.0,Cat_6,C +461705,Female,No,32,No,Healthcare,1.0,Low,4.0,Cat_6,D +461161,Male,No,48,Yes,Entertainment,1.0,Low,2.0,Cat_3,A +467860,Female,No,33,Yes,Entertainment,0.0,Low,1.0,Cat_7,B +462409,Female,Yes,26,No,Engineer,7.0,Low,2.0,Cat_6,A +461093,Female,Yes,38,Yes,Artist,1.0,Average,4.0,Cat_3,C +466350,Female,Yes,56,Yes,Artist,0.0,Low,1.0,Cat_4,C +462273,Female,No,41,Yes,Entertainment,1.0,Low,,Cat_6,D +460512,Female,Yes,43,Yes,Engineer,0.0,High,4.0,Cat_3,A +462320,Female,Yes,51,Yes,Artist,0.0,Average,2.0,Cat_2,C +465355,Female,Yes,47,Yes,Artist,7.0,Average,2.0,Cat_6,C +462877,Male,No,28,Yes,Artist,1.0,Low,3.0,Cat_4,D +466259,Male,No,20,No,Healthcare,1.0,Low,2.0,Cat_6,D +462424,Female,Yes,38,No,Artist,0.0,Low,3.0,Cat_6,C +459028,Male,Yes,57,Yes,Executive,1.0,High,3.0,Cat_6,C +463708,Female,Yes,28,No,Engineer,1.0,Average,2.0,Cat_4,A +461405,Male,No,39,Yes,Artist,1.0,Low,1.0,Cat_6,B +466656,Male,Yes,89,Yes,Marketing,1.0,High,2.0,Cat_6,C +464409,Female,Yes,81,Yes,Lawyer,0.0,Average,2.0,Cat_6,A +463754,Male,No,33,Yes,Entertainment,1.0,Low,4.0,Cat_2,D +464628,Male,Yes,32,Yes,Artist,5.0,Average,2.0,Cat_3,A +460447,Male,,79,No,Lawyer,0.0,Low,1.0,Cat_3,A +466754,Female,Yes,41,Yes,Artist,1.0,Average,3.0,Cat_6,B +465023,Male,Yes,43,Yes,Artist,9.0,Average,2.0,Cat_2,C +466954,Female,Yes,68,No,Doctor,8.0,Average,2.0,Cat_6,C +460274,Female,Yes,58,Yes,Artist,7.0,Low,2.0,Cat_6,C +466507,Male,Yes,51,Yes,Entertainment,1.0,Average,5.0,Cat_6,B +462281,Male,Yes,47,Yes,Artist,0.0,Low,2.0,Cat_6,C +462930,Female,Yes,55,Yes,Homemaker,13.0,Average,3.0,Cat_5,B +465125,Female,No,39,Yes,Artist,9.0,Low,1.0,Cat_6,A +464127,Female,No,33,Yes,Healthcare,1.0,Low,5.0,Cat_2,B +462164,Female,Yes,62,Yes,Artist,9.0,Low,1.0,Cat_6,B +463949,Male,Yes,51,No,Executive,0.0,High,4.0,Cat_7,B +460380,Male,No,29,Yes,Entertainment,8.0,Low,3.0,Cat_6,D +461324,Female,No,48,Yes,Artist,4.0,Low,1.0,Cat_6,C +463482,Male,No,41,No,Engineer,9.0,Low,7.0,Cat_4,C +465723,Male,No,42,Yes,Healthcare,9.0,Low,1.0,Cat_3,D +467948,Female,No,20,No,Healthcare,0.0,Low,3.0,Cat_6,D +464473,Female,Yes,60,Yes,Artist,0.0,Average,4.0,Cat_6,C +467325,Female,Yes,72,Yes,Lawyer,1.0,High,2.0,Cat_6,B +462017,Male,No,25,Yes,Entertainment,9.0,Low,1.0,Cat_2,A +463152,Female,Yes,57,Yes,Doctor,1.0,Average,4.0,Cat_6,C +462174,Female,Yes,40,Yes,Engineer,8.0,Average,4.0,Cat_4,D +461511,Male,Yes,35,No,Doctor,9.0,Low,2.0,Cat_6,B +464095,Female,Yes,31,Yes,Engineer,1.0,Low,2.0,Cat_6,A +460096,Female,Yes,78,No,Lawyer,1.0,Low,1.0,Cat_5,A +462129,Female,No,41,Yes,Artist,9.0,Low,1.0,Cat_6,B +464111,Male,Yes,57,Yes,Artist,0.0,Average,3.0,Cat_2,D +463699,Female,Yes,41,Yes,Marketing,9.0,Low,3.0,Cat_6,B +466307,Male,Yes,49,Yes,Artist,1.0,Low,2.0,Cat_6,C +459980,Male,No,40,No,Engineer,1.0,Low,2.0,Cat_6,A +467115,Male,Yes,89,Yes,Lawyer,0.0,Low,2.0,Cat_6,B +464261,Male,Yes,61,Yes,Executive,0.0,High,2.0,Cat_6,C +462098,Female,Yes,38,No,Entertainment,3.0,Average,3.0,Cat_6,B +460002,Female,Yes,58,Yes,Doctor,5.0,Average,3.0,Cat_2,D +467322,Female,Yes,46,Yes,Artist,0.0,Average,2.0,Cat_1,C +463584,Female,No,22,No,Engineer,,Low,3.0,Cat_6,D +466633,Male,Yes,39,Yes,Entertainment,6.0,Average,2.0,Cat_6,C +460966,Female,Yes,58,Yes,Engineer,1.0,Average,4.0,Cat_6,B +461084,Female,Yes,38,Yes,Artist,8.0,Average,2.0,Cat_6,B +461165,Male,Yes,28,No,Artist,1.0,Average,2.0,Cat_3,A +461454,Female,Yes,35,Yes,Artist,7.0,High,4.0,Cat_7,B +459284,Female,Yes,70,Yes,Lawyer,1.0,High,2.0,Cat_6,C +461116,Male,Yes,30,No,Executive,4.0,High,4.0,Cat_3,D +459235,Male,Yes,55,No,Entertainment,1.0,Low,,Cat_4,A +461377,Female,Yes,57,Yes,Artist,,Low,1.0,Cat_6,B +467840,Female,,22,No,Healthcare,0.0,Low,6.0,Cat_2,D +462362,Male,Yes,63,Yes,Entertainment,0.0,Average,4.0,Cat_6,B +462217,Female,No,42,Yes,Executive,,Low,1.0,Cat_4,B +464325,Male,Yes,27,Yes,Healthcare,4.0,High,3.0,Cat_6,B +460063,Female,No,43,Yes,Artist,1.0,Low,1.0,Cat_3,A +460727,Male,Yes,69,No,Executive,0.0,High,4.0,Cat_3,B +467612,Male,Yes,67,Yes,Executive,1.0,Low,,Cat_7,B +463317,Male,Yes,72,Yes,Marketing,0.0,Low,1.0,Cat_6,A +466036,Female,Yes,89,Yes,Lawyer,1.0,Low,,Cat_6,A +459320,Male,Yes,26,Yes,Executive,0.0,Low,3.0,Cat_6,D +464457,Female,No,53,Yes,Artist,9.0,Low,1.0,Cat_6,B +463702,Male,Yes,49,Yes,Artist,1.0,Average,4.0,Cat_6,B +463890,Female,No,28,No,Artist,1.0,Low,5.0,Cat_1,A +467420,Male,Yes,52,Yes,Executive,1.0,High,4.0,Cat_6,C +466871,Male,Yes,43,Yes,Executive,,High,4.0,Cat_6,B +466878,Male,Yes,38,No,Artist,8.0,High,5.0,Cat_6,A +462660,Female,Yes,27,No,Homemaker,9.0,Average,2.0,Cat_6,A +466691,Male,No,18,No,Healthcare,5.0,Low,4.0,Cat_2,D +463182,Male,Yes,59,Yes,Entertainment,1.0,Low,3.0,Cat_6,B +463321,Male,No,22,No,Healthcare,1.0,Low,3.0,Cat_1,D +462532,Male,No,32,Yes,Doctor,0.0,Low,4.0,Cat_2,D +464588,Female,Yes,35,Yes,Artist,0.0,Low,2.0,Cat_6,B +467436,Male,Yes,51,Yes,Artist,5.0,Average,4.0,Cat_6,C +465260,Female,No,22,No,Healthcare,8.0,Low,6.0,Cat_6,D +467566,Male,Yes,49,No,Executive,,High,3.0,Cat_6,B +464284,Female,Yes,42,Yes,Artist,,Low,,Cat_4,B +462895,Male,No,32,No,Entertainment,1.0,Low,3.0,Cat_7,D +459460,Male,No,55,No,Lawyer,,Low,1.0,Cat_1,D +461985,Male,No,31,Yes,Entertainment,,Low,,Cat_5,A +463168,Female,Yes,36,Yes,Doctor,7.0,Average,2.0,Cat_6,C +466853,Male,No,32,Yes,Healthcare,1.0,Low,3.0,Cat_6,B +464840,Male,No,18,No,Healthcare,0.0,Low,4.0,Cat_4,D +460135,Female,No,31,Yes,Healthcare,4.0,Low,3.0,Cat_6,B +464814,Male,Yes,53,Yes,Artist,0.0,High,7.0,Cat_4,D +464856,Female,No,42,Yes,Engineer,8.0,Low,3.0,Cat_4,A +462940,Male,No,75,No,Lawyer,,Low,2.0,Cat_6,A +463083,Male,No,30,Yes,Artist,7.0,Low,3.0,Cat_6,D +461283,Male,No,19,No,Healthcare,1.0,Low,4.0,Cat_6,D +459487,Male,Yes,67,No,Lawyer,,Low,,Cat_6,D +466181,Male,Yes,46,Yes,Entertainment,1.0,Low,2.0,Cat_3,C +462465,Male,Yes,51,Yes,Engineer,1.0,Low,2.0,Cat_6,B +464780,Female,No,32,No,Engineer,1.0,Low,9.0,Cat_4,D +466741,Female,,53,No,Artist,9.0,Low,2.0,Cat_6,B +465385,Female,No,46,Yes,Artist,5.0,Low,1.0,Cat_2,B +464270,Female,No,45,Yes,Artist,3.0,Low,1.0,Cat_6,A +467807,Male,Yes,50,Yes,Executive,1.0,Average,3.0,Cat_6,B +466118,Female,Yes,36,No,Engineer,1.0,Low,3.0,Cat_4,A +460068,Female,No,40,Yes,Artist,8.0,Low,1.0,Cat_6,B +467444,Female,Yes,40,Yes,Artist,,Low,2.0,Cat_6,D +460937,Male,Yes,43,No,Artist,1.0,High,9.0,Cat_4,B +459550,Male,No,19,No,Healthcare,8.0,Low,5.0,Cat_4,D +461539,Male,Yes,42,No,Entertainment,6.0,Average,2.0,Cat_2,A +464228,Male,No,29,Yes,Healthcare,1.0,Low,1.0,Cat_2,D +461593,Female,Yes,37,Yes,Doctor,9.0,Average,2.0,Cat_6,C +461949,Female,No,38,No,Artist,14.0,Low,1.0,Cat_6,A +466171,Female,Yes,79,Yes,Lawyer,0.0,Low,1.0,Cat_3,A +462682,Male,No,25,No,Entertainment,2.0,Low,7.0,Cat_6,D +463384,Female,No,29,No,Marketing,1.0,Low,5.0,Cat_3,D +460584,Female,No,33,Yes,Homemaker,1.0,Low,1.0,Cat_3,A +467502,Female,No,26,No,Healthcare,,Low,6.0,Cat_7,D +459433,Male,No,26,No,Doctor,1.0,Low,4.0,Cat_6,D +467452,Female,Yes,38,Yes,Artist,9.0,Average,2.0,Cat_6,C +462501,Male,Yes,63,Yes,Artist,1.0,Average,,Cat_6,C +462121,Female,No,60,Yes,Artist,1.0,Low,3.0,Cat_6,C +464890,Female,No,33,No,Engineer,0.0,Low,7.0,Cat_4,A +461172,Female,Yes,71,Yes,Lawyer,1.0,Low,1.0,Cat_3,A +460855,Male,Yes,45,Yes,Artist,,Low,2.0,Cat_6,A +461268,Female,Yes,60,Yes,Artist,0.0,Average,2.0,Cat_4,B +465731,Female,Yes,31,Yes,Artist,0.0,Average,2.0,Cat_4,A +462861,Female,No,26,Yes,Healthcare,8.0,Low,1.0,Cat_6,D +458989,Female,Yes,42,Yes,Engineer,1.0,Low,1.0,Cat_6,B +463527,Female,No,35,No,Artist,13.0,Low,1.0,Cat_6,A +463293,Female,No,20,No,Healthcare,1.0,Low,3.0,Cat_6,D +465166,Male,No,25,Yes,Healthcare,4.0,Low,5.0,Cat_4,D +460194,Female,No,23,No,Entertainment,0.0,Low,2.0,Cat_2,D +462711,Male,Yes,53,Yes,Artist,0.0,Average,5.0,Cat_6,C +466932,Female,No,49,Yes,Doctor,,Low,2.0,Cat_6,D +467769,Female,No,35,Yes,Doctor,1.0,Low,1.0,Cat_3,B +461898,Male,Yes,60,Yes,Executive,5.0,High,3.0,Cat_6,C +460840,Male,Yes,86,Yes,Lawyer,0.0,Low,1.0,Cat_6,A +466137,Female,,31,No,Healthcare,0.0,Low,4.0,Cat_6,C +466607,Male,No,18,No,Marketing,2.0,Low,2.0,Cat_3,D +462939,Male,Yes,38,Yes,Doctor,0.0,High,4.0,Cat_7,A +465698,Male,No,39,Yes,Artist,8.0,Low,2.0,Cat_3,A +461240,Female,No,45,No,Marketing,1.0,Low,1.0,Cat_6,D +461357,Male,No,28,Yes,Healthcare,0.0,Low,4.0,Cat_6,D +459512,Male,Yes,65,Yes,Lawyer,0.0,High,2.0,Cat_6,B +467775,Female,No,41,Yes,Artist,0.0,Low,1.0,Cat_7,C +462871,Male,Yes,48,No,Entertainment,1.0,Average,3.0,Cat_6,B +464454,Female,Yes,52,Yes,Engineer,7.0,High,4.0,Cat_6,C +459879,Female,No,26,Yes,Healthcare,,Low,1.0,Cat_6,D +466802,Female,,43,Yes,Engineer,1.0,Low,1.0,Cat_6,A +459074,Female,No,30,Yes,Healthcare,0.0,Low,4.0,Cat_4,C +462539,Female,Yes,77,No,Lawyer,1.0,Low,1.0,Cat_6,A +460133,Female,No,37,No,Artist,4.0,Low,1.0,Cat_6,A +461780,Female,No,65,Yes,Lawyer,0.0,Low,5.0,Cat_6,A +466945,Male,Yes,61,Yes,Artist,0.0,Average,2.0,Cat_6,C +463922,Female,No,32,No,Healthcare,2.0,Low,5.0,Cat_1,C +466328,Male,Yes,49,Yes,Artist,0.0,Low,3.0,Cat_3,C +466195,Male,No,19,No,Healthcare,1.0,Low,4.0,Cat_6,D +462300,Male,Yes,50,Yes,Artist,1.0,Average,3.0,Cat_6,C +460412,Female,No,31,No,Healthcare,0.0,Low,5.0,Cat_2,D +467823,Male,Yes,45,Yes,Doctor,6.0,Average,2.0,Cat_6,C +467726,Male,Yes,82,Yes,Lawyer,0.0,High,2.0,Cat_6,C +463061,Male,No,33,No,,5.0,Low,3.0,Cat_5,D +460134,Male,Yes,39,Yes,Entertainment,9.0,High,3.0,Cat_6,D +463237,Male,No,20,No,Healthcare,1.0,Low,5.0,Cat_6,D +466770,Female,Yes,85,Yes,Lawyer,0.0,Low,5.0,Cat_6,C +464198,Male,Yes,49,Yes,Entertainment,1.0,Low,2.0,Cat_6,A +459344,Female,Yes,76,Yes,Artist,1.0,High,2.0,Cat_6,C +464152,Male,Yes,37,Yes,Artist,0.0,High,4.0,Cat_6,B +464104,Male,Yes,62,No,Artist,0.0,Average,3.0,Cat_6,C +461034,Female,No,23,No,Marketing,2.0,Low,6.0,Cat_3,D +460755,Male,No,33,Yes,Artist,0.0,Low,1.0,Cat_6,D +465019,Male,Yes,53,Yes,Artist,0.0,Low,3.0,Cat_6,A +462902,Male,Yes,46,No,Executive,,Average,3.0,Cat_1,C +460060,Male,Yes,28,No,Engineer,6.0,Average,2.0,Cat_4,A +465739,Female,No,87,No,Lawyer,1.0,Low,2.0,Cat_6,A +461695,Female,Yes,45,Yes,Artist,3.0,Average,2.0,Cat_6,A +466297,Female,No,21,No,Healthcare,1.0,Low,4.0,Cat_4,D +465544,Female,No,51,Yes,Artist,0.0,Low,1.0,Cat_3,A +466207,Female,Yes,37,Yes,Doctor,9.0,Average,4.0,Cat_2,C +464431,Male,,67,Yes,Executive,0.0,High,3.0,Cat_6,B +462397,Male,Yes,49,Yes,Artist,1.0,Average,4.0,Cat_6,C +460014,Female,No,66,No,Entertainment,2.0,Low,3.0,Cat_6,D +460202,Male,Yes,42,Yes,Entertainment,1.0,Low,2.0,Cat_6,D +459834,Male,Yes,45,No,Doctor,1.0,Average,3.0,Cat_6,B +464011,Male,No,32,Yes,Healthcare,,Low,4.0,Cat_6,D +460048,Male,No,47,Yes,Doctor,1.0,Low,1.0,Cat_6,B +464546,Female,Yes,38,Yes,Engineer,8.0,Low,3.0,Cat_6,A +459687,Male,Yes,50,No,Executive,,High,4.0,Cat_6,B +467737,Female,Yes,51,Yes,Entertainment,7.0,Average,2.0,Cat_6,A +463936,Male,Yes,32,No,Entertainment,1.0,Average,2.0,Cat_6,B +460422,Male,Yes,53,Yes,Entertainment,1.0,High,,Cat_6,B +467784,Female,Yes,45,Yes,Artist,9.0,Low,1.0,Cat_7,C +467067,Female,No,32,No,Healthcare,0.0,Low,4.0,Cat_6,C +466779,Male,No,22,No,,2.0,Low,2.0,Cat_3,D +465661,Male,No,59,Yes,Entertainment,1.0,Low,1.0,Cat_6,C +459616,Female,No,21,No,Healthcare,,Low,4.0,Cat_1,D +463745,Male,Yes,62,Yes,Artist,1.0,Average,3.0,Cat_3,A +466706,Male,Yes,67,Yes,Lawyer,0.0,Low,2.0,Cat_6,C +467816,Female,No,73,Yes,Artist,0.0,Low,1.0,Cat_6,C +466418,Male,Yes,85,No,Lawyer,0.0,Low,1.0,Cat_4,D +467882,Female,No,40,Yes,Artist,0.0,Low,1.0,Cat_6,B +463914,Female,Yes,27,No,Artist,,Low,2.0,Cat_6,A +465498,Male,Yes,41,Yes,Engineer,9.0,Average,2.0,Cat_2,A +461070,Male,Yes,46,Yes,Doctor,0.0,Low,3.0,Cat_3,D +461171,Male,Yes,41,Yes,Artist,,Average,3.0,Cat_3,A +467403,Female,Yes,38,Yes,Artist,7.0,Average,3.0,Cat_4,B +464382,Female,Yes,55,Yes,Artist,1.0,Average,4.0,Cat_6,C +465449,Female,No,43,Yes,Artist,9.0,Low,2.0,Cat_6,B +465190,Female,Yes,48,Yes,Artist,1.0,High,4.0,Cat_6,C +467310,Female,Yes,88,Yes,Lawyer,0.0,High,3.0,Cat_6,B +463131,Male,Yes,66,No,Entertainment,1.0,High,2.0,Cat_6,A +467301,Female,Yes,73,No,Lawyer,1.0,Low,2.0,Cat_6,B +466202,Male,Yes,49,Yes,Artist,9.0,Average,2.0,Cat_6,C +459945,Male,Yes,48,Yes,Artist,0.0,Low,2.0,Cat_6,C +465187,Female,Yes,39,No,Doctor,0.0,Average,4.0,Cat_4,C +460253,Male,Yes,52,Yes,Artist,4.0,Average,2.0,Cat_6,B +463216,Male,No,30,Yes,Artist,8.0,Low,1.0,Cat_6,A +465615,Male,Yes,61,Yes,Artist,3.0,Average,2.0,Cat_6,C +462055,Male,No,33,Yes,Healthcare,0.0,Low,7.0,Cat_4,A +465234,Male,Yes,68,Yes,Artist,0.0,Low,1.0,Cat_6,C +460720,Female,Yes,48,Yes,Artist,1.0,Average,2.0,Cat_6,B +467582,Male,Yes,39,Yes,Executive,0.0,High,3.0,Cat_2,B +466046,Female,Yes,36,No,Entertainment,1.0,Average,2.0,Cat_4,A +467622,Male,Yes,76,Yes,Lawyer,1.0,High,2.0,Cat_6,C +462664,Male,Yes,69,No,Artist,2.0,Low,2.0,Cat_6,A +461731,Male,Yes,79,No,Entertainment,0.0,Low,1.0,Cat_6,B +459061,Female,Yes,39,Yes,Healthcare,9.0,High,3.0,Cat_6,A +462630,Male,No,32,No,Doctor,,Low,6.0,Cat_4,D +462311,Male,Yes,56,Yes,Executive,,High,9.0,Cat_6,B +461396,Male,No,23,No,Doctor,0.0,Low,5.0,Cat_6,B +464900,Male,Yes,71,No,Lawyer,1.0,Low,2.0,Cat_4,D +463445,Male,Yes,48,Yes,Artist,0.0,Average,3.0,Cat_6,A +463824,Female,Yes,37,No,Artist,1.0,Low,3.0,Cat_6,B +459451,Female,Yes,46,Yes,Artist,0.0,Low,2.0,Cat_6,C +459070,Female,Yes,66,No,Lawyer,,Low,1.0,Cat_6,A +463674,Female,No,25,Yes,Engineer,0.0,Low,7.0,Cat_4,C +460119,Male,No,26,Yes,Healthcare,0.0,Low,4.0,Cat_6,D +465227,Male,No,23,No,Healthcare,6.0,Low,3.0,Cat_6,D +462612,Female,Yes,32,No,Doctor,,High,6.0,Cat_4,B +467610,Female,Yes,84,Yes,Lawyer,2.0,Low,1.0,Cat_6,B +465159,Male,Yes,57,No,Entertainment,0.0,Low,1.0,Cat_6,A +465956,Male,Yes,39,Yes,Entertainment,4.0,Average,4.0,Cat_6,A +462422,Male,Yes,71,Yes,Lawyer,1.0,Low,2.0,Cat_6,A +466266,Male,Yes,48,Yes,Artist,1.0,Low,3.0,Cat_3,C +464573,Female,Yes,39,Yes,Artist,7.0,Average,3.0,Cat_6,B +463306,Female,Yes,60,Yes,Engineer,1.0,High,4.0,Cat_3,B +462065,Female,No,43,Yes,Homemaker,14.0,Low,1.0,Cat_6,D +463456,Male,Yes,46,No,Entertainment,1.0,High,3.0,Cat_6,B +461914,Female,Yes,55,Yes,Artist,6.0,Low,,Cat_6,C +459779,Female,Yes,62,Yes,Entertainment,1.0,Low,1.0,Cat_6,A +466663,Male,No,57,Yes,Doctor,0.0,Low,1.0,Cat_3,A +464504,Male,No,29,Yes,Healthcare,0.0,Low,4.0,Cat_6,D +465883,Male,Yes,33,No,Artist,,Low,4.0,Cat_6,A +465262,Male,Yes,60,Yes,Entertainment,7.0,Average,4.0,Cat_2,B +466205,Female,Yes,58,Yes,Artist,6.0,Low,1.0,Cat_6,C +464128,Male,Yes,52,No,Entertainment,0.0,Average,4.0,Cat_5,B +460482,Female,Yes,30,No,Homemaker,9.0,Low,3.0,Cat_3,B +466055,Male,Yes,52,Yes,Entertainment,1.0,Average,3.0,Cat_4,B +465511,Male,Yes,31,Yes,Artist,0.0,Average,2.0,Cat_6,A +460106,Female,Yes,67,Yes,Entertainment,1.0,Low,,Cat_6,A +467527,Male,Yes,62,Yes,Artist,2.0,Average,4.0,Cat_6,B +460927,Male,Yes,38,Yes,Healthcare,1.0,High,4.0,Cat_3,D +464172,Male,Yes,60,Yes,Artist,3.0,High,3.0,Cat_6,B +461442,Male,Yes,47,Yes,Artist,1.0,Average,6.0,Cat_2,C +459851,Male,No,53,Yes,Artist,3.0,Low,1.0,Cat_6,B +466179,Male,Yes,71,No,Lawyer,0.0,Low,1.0,Cat_6,B +459019,Male,Yes,45,No,Executive,10.0,High,3.0,Cat_6,C +463945,Female,No,37,No,Homemaker,,Low,2.0,Cat_1,B +463274,Male,Yes,58,Yes,Artist,7.0,Low,1.0,Cat_3,B +459606,Male,Yes,38,Yes,Artist,1.0,Average,3.0,Cat_6,C +462807,Female,No,28,Yes,Homemaker,,Low,1.0,Cat_6,C +467654,Female,Yes,56,Yes,Artist,,High,4.0,Cat_6,C +467418,Male,Yes,49,Yes,Artist,1.0,Average,4.0,Cat_6,C +467712,Male,Yes,50,Yes,,1.0,Average,4.0,Cat_7,A +465299,Male,Yes,56,Yes,Executive,,High,2.0,Cat_6,B +461616,Male,No,28,Yes,Artist,,Low,1.0,Cat_6,B +461547,Male,No,70,No,Entertainment,0.0,Low,1.0,Cat_6,B +460811,Female,Yes,52,No,Engineer,1.0,High,2.0,Cat_6,A +463891,Male,Yes,75,Yes,Lawyer,1.0,Low,1.0,Cat_6,A +467534,Male,Yes,48,No,Entertainment,5.0,Average,2.0,Cat_2,C +462748,Male,Yes,51,Yes,Artist,1.0,Low,3.0,Cat_6,C +459380,Male,Yes,62,Yes,Artist,1.0,Low,1.0,Cat_6,A +466783,Female,No,43,Yes,Healthcare,0.0,Low,6.0,Cat_3,A +462832,Female,No,22,No,Doctor,,Low,4.0,Cat_6,D +461134,Male,Yes,32,Yes,Doctor,0.0,Low,3.0,Cat_6,D +465741,Female,Yes,43,Yes,Healthcare,9.0,Low,2.0,Cat_6,C +464785,Male,Yes,35,No,Doctor,5.0,Low,4.0,Cat_4,A +464902,Female,Yes,68,No,Lawyer,0.0,Low,1.0,Cat_4,D +460629,Female,Yes,36,Yes,Entertainment,,Average,3.0,Cat_3,B +464271,Female,Yes,36,Yes,Artist,0.0,Average,3.0,Cat_2,C +467408,Male,Yes,47,Yes,Artist,0.0,Average,4.0,Cat_6,A +461822,Male,Yes,37,Yes,Artist,9.0,High,2.0,Cat_6,C +459378,Male,No,20,No,Healthcare,1.0,Low,4.0,Cat_6,D +463818,Female,Yes,40,Yes,Artist,2.0,Low,1.0,Cat_6,B +460644,Female,Yes,40,Yes,Homemaker,2.0,Average,2.0,Cat_3,C +466743,Male,No,26,No,Entertainment,1.0,Low,2.0,Cat_6,A +464212,Female,Yes,56,Yes,Marketing,0.0,Average,2.0,Cat_6,C +463709,Male,Yes,35,Yes,Artist,1.0,High,2.0,Cat_6,A +466145,Male,Yes,56,No,Entertainment,1.0,Average,3.0,Cat_6,C +465679,Male,Yes,47,Yes,Healthcare,1.0,Low,2.0,Cat_4,D +464436,Male,Yes,88,Yes,Lawyer,0.0,High,2.0,Cat_4,A +459094,Female,No,31,No,Healthcare,1.0,Low,5.0,Cat_6,C +466183,Male,Yes,50,Yes,Entertainment,0.0,Average,2.0,Cat_3,C +461102,Male,Yes,43,No,Entertainment,0.0,Average,3.0,Cat_3,A +463012,Male,Yes,47,No,Engineer,,Low,1.0,Cat_6,D +462211,Male,No,18,No,Healthcare,1.0,Low,3.0,Cat_4,D +459639,Female,Yes,56,Yes,Artist,,Low,1.0,Cat_6,B +465687,Male,Yes,88,Yes,Lawyer,1.0,Low,1.0,Cat_6,A +465742,Female,Yes,45,Yes,Artist,3.0,Low,3.0,Cat_6,C +466324,Female,No,28,No,Engineer,0.0,Low,1.0,Cat_4,A +461827,Male,Yes,57,Yes,Artist,0.0,Low,1.0,Cat_6,C +459403,Male,Yes,46,Yes,Artist,,Average,4.0,Cat_7,C +459442,Male,Yes,85,Yes,Executive,0.0,High,2.0,Cat_6,C +460424,Female,No,35,No,Entertainment,0.0,Low,1.0,Cat_6,A +461170,Female,No,40,No,Engineer,0.0,Low,3.0,Cat_3,A +465931,Male,Yes,38,Yes,Artist,6.0,Low,1.0,Cat_7,D +466800,Female,No,18,No,Healthcare,6.0,Low,3.0,Cat_6,D +462869,Male,No,31,Yes,Doctor,8.0,Low,1.0,Cat_6,D +462678,Female,Yes,48,Yes,Artist,4.0,Average,3.0,Cat_6,C +466391,Female,Yes,58,Yes,Engineer,0.0,Average,,Cat_6,A +464701,Male,No,31,No,Doctor,0.0,Low,2.0,Cat_4,A +467125,Male,Yes,46,No,Entertainment,1.0,Average,4.0,Cat_3,C +461190,Female,No,31,No,Engineer,0.0,Low,2.0,Cat_6,D +461691,Male,No,31,Yes,Healthcare,3.0,Low,4.0,Cat_1,D +461208,Female,No,32,Yes,Homemaker,5.0,Low,1.0,Cat_6,D +460052,Female,No,81,Yes,Lawyer,1.0,Low,1.0,Cat_3,B +464999,Male,Yes,66,Yes,Lawyer,1.0,High,2.0,Cat_6,C +462191,Female,No,22,No,Doctor,3.0,Low,2.0,Cat_4,B +467045,Male,Yes,51,Yes,Entertainment,0.0,Low,2.0,Cat_6,A +459104,Male,No,22,No,Healthcare,0.0,Low,,Cat_6,D +462558,Female,No,33,Yes,Engineer,0.0,Low,6.0,Cat_6,D +460342,Female,No,29,Yes,Engineer,10.0,Low,1.0,Cat_6,D +466647,Female,Yes,41,Yes,Artist,7.0,High,2.0,Cat_6,C +459038,Female,No,18,No,Healthcare,1.0,Low,4.0,Cat_6,D +460074,Female,No,48,Yes,Lawyer,4.0,Low,1.0,Cat_6,A +465016,Male,Yes,78,No,Executive,0.0,High,2.0,Cat_6,B +459842,Male,No,25,Yes,Healthcare,,Low,3.0,Cat_6,D +466230,Male,Yes,46,Yes,Artist,0.0,Average,2.0,Cat_6,B +467096,Male,Yes,53,Yes,Entertainment,4.0,Low,2.0,Cat_2,A +466012,Male,Yes,56,,Artist,2.0,Average,3.0,Cat_4,B +467059,Male,Yes,47,Yes,Artist,2.0,Low,1.0,Cat_6,D +463023,Female,Yes,40,No,Homemaker,6.0,Low,1.0,Cat_6,D +464557,Male,No,27,No,Artist,8.0,Low,4.0,Cat_6,D +467052,Male,No,18,No,Healthcare,2.0,Low,,Cat_2,D +464461,Male,Yes,41,Yes,,1.0,Average,4.0,Cat_6,A +464733,Female,Yes,31,No,Engineer,1.0,High,4.0,Cat_4,A +465433,Female,No,40,Yes,Artist,,Low,1.0,Cat_2,B +459799,Male,Yes,47,Yes,Artist,1.0,Low,2.0,Cat_6,B +466808,Female,No,43,No,Engineer,0.0,Low,1.0,Cat_6,A +459321,Male,Yes,81,Yes,Entertainment,1.0,Low,1.0,Cat_6,B +464035,Male,Yes,62,Yes,Executive,1.0,High,4.0,Cat_6,C +466975,Male,Yes,51,Yes,Artist,0.0,Average,2.0,Cat_6,C +467524,Male,No,20,No,Healthcare,,Low,5.0,Cat_6,D +460270,Male,No,42,Yes,Doctor,9.0,Low,1.0,Cat_6,B +465529,Female,No,33,No,Doctor,0.0,Low,4.0,Cat_6,A +464246,Female,Yes,59,Yes,Artist,0.0,Average,2.0,Cat_6,C +465639,Male,Yes,36,Yes,Healthcare,8.0,High,2.0,Cat_6,D +464346,Male,Yes,55,Yes,Executive,2.0,High,3.0,Cat_6,C +460953,Male,Yes,59,No,Entertainment,1.0,Low,2.0,Cat_6,D +464556,Female,No,39,Yes,Artist,1.0,Low,3.0,Cat_6,A +467762,Female,No,39,Yes,Engineer,0.0,Low,4.0,Cat_6,C +467285,Male,Yes,53,No,Executive,,High,4.0,Cat_6,B +467128,Female,No,32,No,Homemaker,1.0,Low,1.0,Cat_6,A +459503,Male,Yes,35,Yes,Healthcare,5.0,High,4.0,Cat_6,C +466402,Male,Yes,40,Yes,Artist,0.0,Average,4.0,Cat_6,B +466834,Male,Yes,60,Yes,Entertainment,1.0,Low,3.0,Cat_6,A +467710,Female,No,37,Yes,Artist,9.0,Low,4.0,Cat_6,A +461559,Male,,52,Yes,Artist,0.0,Low,4.0,Cat_4,C +463227,Female,Yes,35,Yes,Engineer,1.0,Average,2.0,Cat_6,A +464779,Female,No,27,No,Engineer,1.0,Low,1.0,Cat_4,A +462883,Female,Yes,28,Yes,Artist,8.0,Low,4.0,Cat_6,A +459018,Male,Yes,85,No,Executive,1.0,High,2.0,Cat_6,D +459464,Male,No,29,Yes,Artist,,Low,2.0,Cat_6,B +466774,Male,Yes,78,Yes,Lawyer,0.0,Low,2.0,Cat_6,C +459920,Female,Yes,51,Yes,Artist,6.0,High,2.0,Cat_4,B +467906,Female,No,33,No,Healthcare,0.0,Low,5.0,Cat_1,D +460386,Male,Yes,29,Yes,Healthcare,0.0,Low,2.0,Cat_6,D +467069,Female,No,29,No,Healthcare,0.0,Low,3.0,Cat_6,D +465025,Male,Yes,73,Yes,Executive,0.0,High,2.0,Cat_6,C +466984,Female,No,43,Yes,Homemaker,9.0,Low,,Cat_6,D +462650,Female,No,33,No,Entertainment,8.0,Low,5.0,Cat_4,D +459510,Female,No,23,No,Healthcare,0.0,Low,3.0,Cat_6,D +466229,Male,Yes,56,Yes,Executive,0.0,High,1.0,Cat_6,B +466856,Female,Yes,65,No,Engineer,1.0,Average,3.0,Cat_6,A +461623,Female,Yes,41,Yes,Artist,11.0,Average,2.0,Cat_6,B +461839,Male,Yes,58,Yes,Artist,1.0,Average,2.0,Cat_6,C +467883,Female,Yes,48,Yes,Artist,0.0,Average,2.0,Cat_6,C +460038,Female,No,36,Yes,Engineer,9.0,Low,5.0,Cat_6,D +467744,Male,No,36,Yes,Artist,9.0,Low,1.0,Cat_6,D +462385,Male,No,23,No,Doctor,4.0,Low,3.0,Cat_6,C +462916,Female,Yes,68,No,Artist,6.0,Average,3.0,Cat_6,C +463339,Female,Yes,27,Yes,Engineer,4.0,Low,2.0,Cat_3,B +460981,Female,Yes,42,No,Marketing,0.0,Average,2.0,Cat_6,A +458993,Male,Yes,29,No,Doctor,0.0,Low,2.0,Cat_4,A +459966,Female,No,31,Yes,Doctor,1.0,Low,3.0,Cat_6,B +459538,Female,No,45,Yes,Homemaker,11.0,Low,1.0,Cat_6,B +459424,Male,Yes,50,Yes,Doctor,,Low,3.0,Cat_6,B +465975,Male,No,42,Yes,Artist,8.0,Low,1.0,Cat_6,A +464600,Female,No,55,Yes,Artist,0.0,Low,1.0,Cat_6,B +465760,Female,No,43,Yes,Engineer,8.0,Low,2.0,Cat_4,A +466577,Male,No,30,Yes,Healthcare,1.0,Low,1.0,Cat_6,D +467324,Male,No,32,No,Doctor,5.0,Low,1.0,Cat_7,D +464387,Female,No,27,Yes,Artist,9.0,Low,5.0,Cat_6,B +462481,Male,No,33,Yes,Entertainment,1.0,Low,1.0,Cat_6,A +463042,Male,Yes,51,No,Executive,,High,4.0,Cat_6,C +465365,Male,No,31,No,Entertainment,1.0,Low,4.0,Cat_2,D +463762,Male,Yes,31,No,Executive,1.0,High,3.0,Cat_6,A +460914,Female,No,36,Yes,Artist,9.0,Low,2.0,Cat_6,C +466123,Male,Yes,70,Yes,Entertainment,0.0,Low,1.0,Cat_6,A +465803,Male,Yes,37,,,2.0,High,5.0,Cat_6,C +460506,Female,Yes,52,Yes,Engineer,0.0,Low,3.0,Cat_3,B +463066,Female,No,28,Yes,Healthcare,9.0,Low,3.0,Cat_6,D +465110,Male,Yes,42,Yes,Entertainment,9.0,Low,5.0,Cat_6,D +461445,Male,Yes,52,Yes,Artist,3.0,Average,4.0,Cat_6,C +466330,Male,No,29,No,Doctor,0.0,Low,3.0,Cat_3,C +461578,Female,Yes,61,Yes,Artist,0.0,Average,4.0,Cat_6,C +466363,Female,Yes,38,,Engineer,5.0,Low,2.0,Cat_2,B +461118,Female,No,30,Yes,Healthcare,6.0,Low,,Cat_3,A +465568,Male,Yes,39,Yes,Executive,3.0,High,3.0,Cat_6,B +459130,Female,Yes,74,No,Lawyer,0.0,High,2.0,Cat_6,A +459629,Female,Yes,47,Yes,Entertainment,3.0,Average,2.0,Cat_6,B +463892,Male,No,38,Yes,Artist,9.0,Low,1.0,Cat_6,A +461950,Female,No,40,Yes,Healthcare,1.0,Low,1.0,Cat_2,D +463499,Male,Yes,48,Yes,Executive,0.0,Low,2.0,Cat_6,A +467946,Female,Yes,40,Yes,Artist,0.0,Low,2.0,Cat_6,B +461540,Male,No,38,Yes,,3.0,Low,2.0,Cat_2,B +462679,Male,No,63,Yes,Doctor,1.0,Low,1.0,Cat_6,D +459181,Male,Yes,58,No,Artist,0.0,High,3.0,Cat_6,D +463612,Female,Yes,51,Yes,Engineer,1.0,High,3.0,Cat_6,C +463393,Female,Yes,38,Yes,Artist,6.0,Low,2.0,Cat_3,B +465527,Female,No,26,No,Entertainment,6.0,Low,8.0,Cat_4,D +462316,Male,Yes,60,Yes,Artist,1.0,Average,3.0,Cat_6,C +467510,Male,Yes,51,Yes,Entertainment,3.0,Low,3.0,Cat_6,B +459755,Female,Yes,56,No,Artist,,Average,3.0,Cat_6,C +466049,Female,No,25,Yes,Doctor,0.0,Low,1.0,Cat_4,A +465211,Male,No,43,No,Artist,6.0,Low,2.0,Cat_6,B +460367,Male,Yes,73,Yes,Artist,1.0,Low,2.0,Cat_6,B +463591,Male,No,22,No,Healthcare,,Low,4.0,Cat_6,D +464585,Male,Yes,66,Yes,Lawyer,1.0,High,2.0,Cat_4,C +463698,Female,Yes,53,No,Engineer,0.0,Average,2.0,Cat_6,B +462114,Male,No,21,No,Healthcare,1.0,Low,4.0,Cat_6,D +461856,Female,Yes,45,Yes,Artist,3.0,High,4.0,Cat_2,C +467511,Male,Yes,57,Yes,Entertainment,,Low,1.0,Cat_6,A +464593,Female,No,35,Yes,Doctor,1.0,Low,4.0,Cat_2,B +459643,Male,Yes,40,Yes,Executive,0.0,High,4.0,Cat_6,B +459695,Male,Yes,35,Yes,Artist,,High,2.0,Cat_6,A +460213,Male,Yes,40,Yes,Entertainment,5.0,Low,1.0,Cat_6,D +461046,Male,Yes,50,Yes,Artist,0.0,Low,1.0,Cat_6,B +463409,Female,Yes,43,No,Entertainment,7.0,Average,5.0,Cat_2,B +463299,Female,Yes,49,Yes,Artist,0.0,Low,4.0,Cat_6,B +465719,Female,Yes,37,Yes,Artist,0.0,Low,2.0,Cat_6,C +463448,Male,Yes,40,Yes,Artist,8.0,Average,2.0,Cat_6,B +466248,Male,No,41,Yes,Doctor,1.0,Low,1.0,Cat_2,B +462802,Female,Yes,39,Yes,Artist,,Average,4.0,Cat_4,D +463205,Female,No,25,No,Doctor,0.0,Low,4.0,Cat_6,D +465648,Female,No,32,No,Entertainment,0.0,Low,2.0,Cat_2,A +467774,Female,No,39,Yes,Homemaker,1.0,Low,6.0,Cat_6,A +465525,Female,Yes,38,Yes,Healthcare,3.0,Average,3.0,Cat_2,D +467776,Female,No,25,Yes,Doctor,0.0,Low,4.0,Cat_6,D +460850,Female,Yes,61,No,Engineer,0.0,Low,1.0,Cat_6,B +463919,Female,No,31,Yes,Homemaker,9.0,Low,1.0,Cat_6,D +460809,Male,Yes,72,Yes,Artist,2.0,Low,1.0,Cat_6,B +465708,Female,Yes,60,Yes,Artist,0.0,High,2.0,Cat_3,C +467138,Female,No,26,No,Homemaker,13.0,Low,1.0,Cat_6,D +459003,Male,Yes,51,Yes,Doctor,0.0,High,5.0,Cat_4,C +466167,Male,Yes,56,No,,1.0,Low,2.0,Cat_4,B +464257,Male,Yes,35,,Entertainment,1.0,High,2.0,Cat_6,D +464479,Male,Yes,55,Yes,Entertainment,1.0,Low,1.0,Cat_6,B +464204,Female,Yes,39,Yes,Artist,2.0,High,2.0,Cat_5,C +460073,Female,Yes,42,Yes,Engineer,2.0,Low,1.0,Cat_6,B +460289,Female,Yes,60,Yes,Artist,2.0,Average,6.0,Cat_4,C +464052,Male,No,36,Yes,Artist,0.0,Low,1.0,Cat_6,A +459069,Male,Yes,25,Yes,Doctor,0.0,Average,2.0,Cat_6,A +465800,Male,No,25,No,Healthcare,1.0,Low,3.0,Cat_6,C +461497,Male,No,29,No,Doctor,5.0,Low,5.0,Cat_2,B +464005,Female,No,27,No,Healthcare,0.0,Low,5.0,Cat_6,C +459588,Male,Yes,52,Yes,Artist,1.0,Low,1.0,Cat_6,A +464477,Male,Yes,36,No,Entertainment,,Low,2.0,Cat_6,A +459476,Male,Yes,41,Yes,Artist,0.0,High,4.0,Cat_6,A +464432,Male,No,31,No,Engineer,8.0,Low,2.0,Cat_3,D +462264,Male,Yes,45,Yes,Entertainment,0.0,Average,4.0,Cat_6,C +466257,Female,No,32,No,Healthcare,0.0,Low,4.0,Cat_6,C +460121,Male,Yes,55,Yes,Engineer,1.0,Average,3.0,Cat_6,A +463640,Male,No,28,Yes,Healthcare,1.0,Low,3.0,Cat_6,D +464462,Female,Yes,70,Yes,Artist,5.0,High,2.0,Cat_6,C +461024,Male,Yes,82,Yes,Lawyer,1.0,Low,1.0,Cat_6,A +464229,Female,No,26,No,Engineer,2.0,Low,7.0,Cat_6,D +466680,Male,No,22,No,Engineer,0.0,Low,4.0,Cat_6,D +459557,Female,,29,No,Entertainment,1.0,High,,Cat_1,D +466063,Female,Yes,48,Yes,Entertainment,0.0,Low,1.0,Cat_3,B +465205,Male,Yes,48,Yes,Entertainment,0.0,Low,2.0,Cat_6,D +467476,Male,No,32,Yes,Healthcare,1.0,Low,4.0,Cat_6,D +463724,Male,No,29,No,Healthcare,0.0,Low,5.0,Cat_4,B +465868,Male,No,35,Yes,Artist,6.0,Low,3.0,Cat_6,D +466148,Male,Yes,39,No,Entertainment,0.0,Average,4.0,Cat_6,C +464273,Female,No,26,No,Artist,2.0,Low,2.0,Cat_6,D +466641,Male,No,20,No,Healthcare,1.0,Low,4.0,Cat_6,D +467044,Female,No,35,Yes,Marketing,0.0,Low,1.0,Cat_7,D +461312,Male,No,18,No,Healthcare,3.0,Low,3.0,Cat_2,D +464450,Female,Yes,56,Yes,Artist,0.0,High,2.0,Cat_6,B +462929,Male,No,40,No,Homemaker,,Low,1.0,Cat_4,A +467900,Female,No,37,No,Engineer,3.0,Low,1.0,Cat_6,A +462155,Female,Yes,57,Yes,Artist,,High,3.0,Cat_6,C +467651,Male,No,23,No,Healthcare,0.0,Low,4.0,Cat_6,D +459686,Male,Yes,52,Yes,Artist,1.0,Low,1.0,Cat_6,C +466985,Female,Yes,45,No,Engineer,0.0,Average,7.0,Cat_7,A +465258,Male,No,20,No,Healthcare,5.0,Low,4.0,Cat_3,D +467718,Female,No,20,No,Healthcare,2.0,Low,6.0,Cat_2,D +460288,Male,Yes,61,Yes,Healthcare,1.0,Average,7.0,Cat_3,B +461991,Male,No,23,No,Doctor,0.0,Low,4.0,Cat_6,B +460110,Female,No,52,Yes,Artist,0.0,Low,2.0,,C +460128,Male,Yes,25,Yes,Healthcare,8.0,Low,2.0,Cat_6,D +466614,Male,No,21,No,Healthcare,1.0,Low,6.0,Cat_5,D +462573,Male,Yes,59,Yes,Artist,0.0,Average,6.0,Cat_4,B +465346,Female,No,37,Yes,Marketing,1.0,Low,3.0,Cat_6,D +465022,Female,Yes,72,Yes,Lawyer,0.0,High,2.0,Cat_6,C +459931,Male,Yes,50,Yes,Artist,0.0,Low,1.0,Cat_6,A +464538,Male,No,19,No,Healthcare,0.0,Low,,Cat_3,D +464708,Male,No,20,No,Healthcare,0.0,Low,8.0,Cat_4,D +462303,Female,Yes,32,No,Artist,0.0,Average,4.0,Cat_6,A +464114,Female,No,36,No,Doctor,0.0,Low,2.0,Cat_6,A +463638,Female,Yes,29,No,Doctor,0.0,Average,2.0,Cat_7,B +461074,Male,Yes,25,No,Entertainment,,Average,,Cat_3,D +463351,Female,No,22,,Healthcare,3.0,Low,4.0,Cat_5,D +466223,Male,Yes,66,Yes,Artist,1.0,Low,1.0,Cat_4,B +464550,Male,Yes,50,Yes,Artist,,Low,4.0,Cat_6,C +460641,Male,,53,Yes,Artist,,Average,3.0,Cat_3,C +464560,Male,No,39,Yes,Entertainment,9.0,Low,2.0,Cat_6,A +459106,Male,Yes,60,Yes,Artist,1.0,Low,2.0,Cat_6,C +461492,Male,Yes,70,Yes,Entertainment,8.0,Average,2.0,Cat_6,C +462185,Male,No,18,No,Doctor,0.0,Low,3.0,Cat_6,D +463967,Female,No,33,Yes,Engineer,0.0,Low,2.0,Cat_6,A +462421,Male,No,53,Yes,Artist,0.0,Low,2.0,Cat_6,A +467587,Male,Yes,27,Yes,Artist,9.0,Average,3.0,Cat_6,C +466767,Male,Yes,68,Yes,Lawyer,0.0,Low,2.0,,C +460401,Female,No,27,Yes,Artist,0.0,Low,3.0,Cat_7,A +460372,Male,Yes,37,Yes,Artist,4.0,Average,2.0,Cat_6,C +464086,Male,Yes,42,Yes,Artist,1.0,Average,4.0,Cat_6,B +464705,Male,No,32,No,Artist,3.0,Low,7.0,Cat_4,A +463791,Female,No,28,Yes,Engineer,7.0,Low,1.0,Cat_6,B +461514,Male,No,31,No,Healthcare,1.0,Low,4.0,Cat_6,B +464078,Male,Yes,88,No,Lawyer,0.0,Low,,Cat_6,B +459251,Male,No,35,Yes,Artist,0.0,Low,1.0,Cat_6,B +460470,Male,No,36,No,Healthcare,,Low,4.0,Cat_4,B +462923,Male,Yes,33,No,Homemaker,9.0,Average,2.0,Cat_6,A +461017,Female,No,40,Yes,Homemaker,13.0,Low,1.0,Cat_3,A +465700,Male,Yes,25,Yes,Entertainment,1.0,High,2.0,Cat_3,D +460113,Male,Yes,67,Yes,Artist,1.0,Average,2.0,Cat_6,C +461047,Female,,18,No,Healthcare,1.0,Average,3.0,Cat_7,D +466073,Female,Yes,62,No,Engineer,8.0,High,2.0,Cat_4,A +463201,Female,No,21,No,Healthcare,0.0,Low,2.0,Cat_4,D +464941,Female,No,28,No,Healthcare,,Low,4.0,Cat_4,D +462909,Female,No,36,No,Engineer,,Low,1.0,Cat_6,A +461345,Male,No,27,No,Doctor,0.0,Low,3.0,Cat_6,D +461939,Female,Yes,36,Yes,Entertainment,1.0,Average,4.0,Cat_4,A +467552,Male,Yes,53,No,Executive,0.0,Average,4.0,Cat_6,C +461882,Female,No,30,Yes,Doctor,0.0,Low,5.0,Cat_7,C +467800,Female,Yes,53,Yes,Artist,8.0,Average,3.0,Cat_6,C +465997,Female,No,33,No,Doctor,1.0,Low,3.0,Cat_6,D +462291,Female,No,36,Yes,Marketing,,Low,3.0,Cat_6,D +466981,Female,Yes,53,No,Engineer,0.0,Low,3.0,Cat_5,A +467397,Male,No,29,No,Doctor,8.0,Low,4.0,Cat_6,B +462589,Female,No,28,No,Engineer,8.0,Low,8.0,Cat_4,D +467957,Male,Yes,47,Yes,Artist,1.0,Low,1.0,Cat_6,B +459426,Male,No,23,No,,1.0,Low,4.0,Cat_6,D +460264,Female,Yes,35,Yes,Entertainment,0.0,Low,4.0,Cat_1,A +466916,Male,Yes,87,Yes,Lawyer,3.0,Low,1.0,Cat_6,C +463822,Male,Yes,56,Yes,Artist,0.0,Average,4.0,Cat_6,C +461203,Female,Yes,26,No,Lawyer,0.0,Low,9.0,Cat_7,C +459124,Female,Yes,75,Yes,Artist,1.0,High,3.0,Cat_6,B +467011,Female,Yes,77,Yes,Lawyer,,High,2.0,Cat_6,B +467495,Male,Yes,66,No,Artist,2.0,High,3.0,Cat_6,C +464292,Male,Yes,55,Yes,Artist,0.0,Average,2.0,Cat_6,C +461065,Male,Yes,61,Yes,Executive,,Low,6.0,Cat_3,B +461010,Male,Yes,39,Yes,Entertainment,1.0,Average,3.0,Cat_3,A +463109,Male,Yes,61,Yes,Executive,9.0,High,2.0,Cat_6,C +462480,Male,No,40,No,Engineer,2.0,Low,4.0,Cat_6,D +465229,Male,Yes,43,Yes,Executive,0.0,High,3.0,Cat_6,B +462074,Female,No,57,Yes,Artist,1.0,Low,1.0,Cat_6,B +461645,Female,No,37,Yes,Artist,0.0,Low,2.0,Cat_6,C +463662,Male,Yes,41,Yes,Entertainment,2.0,Average,2.0,Cat_6,C +467798,Female,No,20,No,,,Low,2.0,Cat_6,D +463756,Male,Yes,68,No,Executive,1.0,Average,2.0,Cat_6,B +466581,Female,Yes,43,Yes,Artist,9.0,High,4.0,Cat_6,C +464680,Male,No,21,No,Healthcare,1.0,Low,5.0,Cat_4,D +464727,Male,Yes,38,Yes,Engineer,1.0,Average,4.0,Cat_3,B +466413,Male,Yes,62,Yes,Artist,1.0,Average,5.0,Cat_6,A +462847,Female,No,30,Yes,Doctor,,Low,7.0,Cat_5,C +462625,Male,No,32,Yes,Artist,0.0,Low,3.0,Cat_4,A +459048,Female,No,18,No,Healthcare,0.0,Low,5.0,Cat_6,D +461348,Male,Yes,35,Yes,Healthcare,1.0,Low,1.0,Cat_6,D +464719,Female,No,28,No,Doctor,3.0,Low,4.0,Cat_6,A +465561,Male,Yes,25,No,Artist,1.0,Average,4.0,Cat_4,B +462585,Male,Yes,42,No,,8.0,Low,,Cat_4,D +463976,Female,No,29,No,Healthcare,5.0,Low,4.0,Cat_4,A +465617,Male,No,40,Yes,Doctor,9.0,Low,,Cat_6,C +464044,Female,No,40,Yes,Entertainment,1.0,Low,3.0,Cat_6,B +466880,Male,Yes,39,Yes,Artist,4.0,Average,,Cat_6,C +463333,Female,No,18,No,Healthcare,0.0,Low,3.0,Cat_6,D +462507,Male,No,39,No,Entertainment,0.0,Low,3.0,Cat_6,A +460589,Male,Yes,49,No,Entertainment,1.0,Average,6.0,Cat_3,B +460615,Female,No,25,Yes,Healthcare,,Low,,Cat_3,C +460754,Female,No,33,Yes,Homemaker,8.0,Low,1.0,Cat_6,A +464170,Male,Yes,38,No,Artist,8.0,Low,1.0,Cat_6,B +463660,Male,Yes,85,Yes,Lawyer,1.0,Low,1.0,Cat_6,A +466200,Male,Yes,51,Yes,Doctor,8.0,Average,3.0,Cat_4,B +460999,Male,No,26,Yes,Engineer,1.0,Low,3.0,Cat_3,D +461327,Female,Yes,35,Yes,Artist,1.0,Average,3.0,Cat_6,C +461178,Male,Yes,33,No,Entertainment,1.0,Low,3.0,Cat_3,D +465150,Male,No,41,Yes,Entertainment,0.0,Low,1.0,Cat_6,A +462882,Male,Yes,41,Yes,Homemaker,2.0,Average,3.0,Cat_3,B +460544,Male,No,77,Yes,Lawyer,0.0,Low,1.0,Cat_3,C +459979,Female,No,41,No,Marketing,0.0,Low,2.0,Cat_6,B +463157,Male,Yes,46,Yes,Entertainment,5.0,Low,3.0,Cat_6,A +466169,Male,Yes,63,Yes,Artist,1.0,Low,1.0,Cat_1,C +467427,Female,Yes,37,Yes,Artist,8.0,High,2.0,Cat_6,C +464392,Male,Yes,58,,Artist,1.0,Average,2.0,Cat_6,B +459065,Male,Yes,51,Yes,Engineer,,Low,2.0,Cat_6,D +465810,Female,No,28,No,Engineer,1.0,Low,2.0,Cat_4,D +465156,Male,Yes,46,Yes,Artist,,Average,5.0,Cat_6,C +464112,Male,No,28,Yes,Doctor,1.0,Low,2.0,Cat_6,D +465877,Female,No,48,Yes,Artist,6.0,Low,1.0,Cat_6,D +464321,Female,No,28,,Engineer,0.0,Low,3.0,Cat_3,D +464337,Male,No,36,Yes,Artist,5.0,Low,1.0,Cat_6,D +465566,Male,No,50,Yes,Artist,1.0,Low,3.0,Cat_3,B +460394,Female,No,32,Yes,Healthcare,9.0,Low,3.0,Cat_6,D +460962,Male,Yes,55,Yes,Executive,9.0,High,4.0,Cat_6,C +465323,Female,Yes,43,Yes,Artist,0.0,Low,1.0,Cat_6,A +462152,Female,No,30,Yes,,8.0,Low,2.0,Cat_6,B +460384,Female,Yes,32,Yes,Marketing,8.0,Low,2.0,Cat_6,D +461760,Male,Yes,82,Yes,Artist,0.0,High,2.0,Cat_6,C +461075,Male,Yes,33,Yes,Entertainment,,Average,2.0,Cat_3,B +461512,Male,No,33,Yes,Doctor,,Low,4.0,Cat_6,D +465276,Male,Yes,55,Yes,Artist,1.0,Average,5.0,Cat_6,C +464935,Female,No,40,,Marketing,6.0,Low,1.0,Cat_4,A +462670,Male,Yes,89,Yes,Doctor,,Low,1.0,Cat_6,A +467270,Female,No,33,Yes,Doctor,0.0,Low,4.0,Cat_6,C +463221,Male,No,22,No,Healthcare,,Low,3.0,Cat_6,D +464358,Male,Yes,58,Yes,Artist,0.0,Average,2.0,Cat_6,B +462181,Female,No,32,Yes,Healthcare,8.0,Low,3.0,Cat_6,C +459178,Male,Yes,48,Yes,Executive,0.0,High,5.0,Cat_6,C +466117,Male,Yes,62,Yes,Artist,1.0,Average,2.0,Cat_3,C +463683,Female,Yes,39,Yes,Artist,14.0,Average,2.0,Cat_6,B +462720,Male,Yes,35,Yes,Marketing,,Low,5.0,Cat_6,D +460223,Male,No,47,No,Entertainment,1.0,Low,1.0,Cat_6,A +465829,Female,Yes,35,Yes,Doctor,7.0,Average,3.0,Cat_4,A +461523,Male,Yes,78,Yes,Lawyer,0.0,High,5.0,Cat_6,B +461099,Female,No,42,No,Artist,0.0,Low,4.0,Cat_3,A +467405,Female,No,37,Yes,Artist,1.0,Low,1.0,Cat_4,C +467958,Female,No,43,Yes,Doctor,0.0,Low,1.0,Cat_6,A +467283,Male,Yes,48,Yes,Artist,1.0,Average,3.0,Cat_2,C +463238,Male,No,22,No,Healthcare,0.0,Low,5.0,Cat_6,D +460228,Male,No,31,No,Healthcare,2.0,Low,5.0,Cat_2,A +464123,Female,No,51,Yes,Artist,0.0,Low,1.0,Cat_6,B +467441,Male,No,42,Yes,Artist,,Low,1.0,Cat_6,A +464110,Male,Yes,58,Yes,Doctor,1.0,High,2.0,Cat_6,B +460943,Male,Yes,50,Yes,Entertainment,1.0,Average,4.0,Cat_6,B +467603,Male,Yes,71,Yes,Lawyer,1.0,High,2.0,Cat_6,C +464973,Male,No,82,,Lawyer,1.0,Low,2.0,Cat_4,A +460576,Male,Yes,49,No,Executive,9.0,High,5.0,Cat_3,B +466635,Male,Yes,53,Yes,Artist,4.0,Average,2.0,Cat_6,C +461085,Female,No,31,No,Engineer,1.0,Low,1.0,Cat_3,A +465850,Female,No,21,No,Healthcare,9.0,Low,7.0,Cat_4,D +467694,Male,Yes,61,Yes,Executive,0.0,High,2.0,Cat_6,C +462497,Male,No,35,Yes,Artist,1.0,Low,2.0,Cat_6,B +465732,Male,Yes,47,Yes,Marketing,0.0,Low,2.0,Cat_4,D +466032,Male,,28,No,Entertainment,,Low,2.0,Cat_6,B +459759,Female,Yes,38,Yes,Artist,0.0,Average,3.0,Cat_6,C +465399,Male,No,39,Yes,Entertainment,1.0,Low,1.0,Cat_4,B +459107,Male,Yes,55,Yes,Artist,1.0,Low,2.0,Cat_6,C +461135,Female,No,19,No,Healthcare,0.0,Low,3.0,Cat_6,D +462557,Female,Yes,58,No,Engineer,,Average,2.0,Cat_4,C +460769,Female,Yes,76,No,Lawyer,1.0,High,2.0,Cat_6,B +465839,Male,No,19,No,Healthcare,,Low,4.0,Cat_6,D +463187,Female,Yes,51,Yes,Artist,8.0,Low,2.0,Cat_6,C +464070,Male,No,39,Yes,Artist,5.0,Low,2.0,Cat_6,B +462826,Male,No,22,No,Healthcare,0.0,Low,3.0,Cat_3,D +462099,Female,Yes,47,Yes,Doctor,,Average,5.0,Cat_2,C +466628,Male,No,32,No,Healthcare,8.0,Low,4.0,,C +462844,Female,Yes,30,No,Engineer,0.0,Average,2.0,Cat_6,D +463045,Female,Yes,35,Yes,Homemaker,13.0,High,7.0,Cat_6,C +462236,Male,No,35,Yes,Entertainment,13.0,Low,2.0,Cat_4,C +467429,Female,Yes,42,Yes,Artist,1.0,Low,,Cat_6,B +460029,Female,Yes,35,Yes,Healthcare,12.0,Average,2.0,Cat_2,D +465393,Male,Yes,36,Yes,Artist,7.0,Average,2.0,Cat_6,C +462091,Male,No,18,No,Healthcare,0.0,Low,5.0,Cat_4,D +459860,Male,Yes,75,No,Executive,1.0,High,2.0,Cat_6,D +461636,Male,No,46,Yes,Artist,3.0,Low,1.0,Cat_6,C +459431,Male,Yes,68,No,Artist,0.0,Low,2.0,Cat_6,A +459419,Male,Yes,67,Yes,Doctor,1.0,Average,2.0,Cat_6,B +459806,Female,Yes,50,No,Engineer,1.0,Average,4.0,Cat_6,B +463080,Male,No,32,No,Artist,0.0,Low,4.0,Cat_5,D +461044,Male,No,21,No,Healthcare,1.0,Low,5.0,Cat_4,D +467770,Female,Yes,48,Yes,Artist,9.0,High,4.0,Cat_3,C +463245,Male,Yes,37,Yes,Entertainment,9.0,Average,2.0,Cat_6,A +459281,Male,Yes,41,Yes,Artist,0.0,Low,2.0,Cat_6,B +458983,Female,Yes,63,Yes,Executive,0.0,High,5.0,Cat_6,C +459815,Male,No,42,Yes,Artist,9.0,Low,1.0,Cat_6,A +461038,Female,Yes,49,Yes,Engineer,1.0,High,5.0,Cat_3,B +465287,Male,No,23,No,Healthcare,0.0,Low,4.0,Cat_3,D +465313,Male,No,31,No,Entertainment,0.0,Low,3.0,Cat_6,B +464529,Male,No,45,Yes,Artist,9.0,Low,1.0,Cat_6,D +460391,Male,Yes,21,No,Entertainment,0.0,Low,3.0,Cat_6,D +464744,Male,Yes,55,Yes,Entertainment,0.0,Low,4.0,Cat_4,A +462741,Female,No,26,Yes,Artist,2.0,Low,2.0,Cat_6,A +460454,Male,No,51,Yes,Artist,,Low,1.0,Cat_6,B +461506,Male,No,27,No,Doctor,1.0,Low,3.0,Cat_6,D +460207,Male,No,41,Yes,Entertainment,11.0,Low,4.0,Cat_6,B +463486,Male,Yes,43,No,Executive,,High,8.0,Cat_4,D +459736,Female,Yes,28,Yes,Artist,0.0,Low,2.0,Cat_6,A +467187,Male,Yes,47,No,Engineer,1.0,Average,3.0,Cat_6,C +460247,Female,No,25,No,Doctor,8.0,Low,,Cat_6,D +466384,Female,Yes,72,Yes,Engineer,0.0,High,2.0,Cat_4,B +462763,Male,No,23,No,Healthcare,0.0,Low,4.0,Cat_6,D +465660,Male,Yes,48,Yes,Artist,1.0,Low,3.0,Cat_6,C +461493,Female,Yes,33,Yes,Artist,8.0,Average,3.0,Cat_6,A +466282,Male,Yes,31,Yes,Healthcare,9.0,Low,,Cat_6,A +460111,Male,Yes,72,Yes,Executive,8.0,High,2.0,Cat_6,C +465305,Male,Yes,66,No,Executive,1.0,Low,2.0,Cat_6,D +461947,Female,No,33,Yes,Healthcare,0.0,Low,4.0,Cat_2,B +465008,Female,Yes,70,Yes,Artist,0.0,High,2.0,Cat_6,C +459928,Female,No,35,Yes,Artist,7.0,Low,1.0,Cat_6,B +466639,Male,No,38,Yes,Artist,1.0,Low,1.0,Cat_6,B +459402,Female,Yes,51,Yes,Artist,1.0,Average,4.0,Cat_6,C +466470,Male,Yes,67,Yes,Homemaker,1.0,Average,2.0,Cat_2,B +465914,Female,No,46,Yes,Artist,3.0,Low,2.0,Cat_6,B +465675,Female,No,47,No,Entertainment,0.0,Low,1.0,Cat_4,A +461815,Male,Yes,55,Yes,Artist,1.0,Low,1.0,Cat_3,B +461335,Male,Yes,51,No,Executive,9.0,High,2.0,Cat_6,C +461974,Female,Yes,47,Yes,Artist,0.0,Low,1.0,Cat_6,A +463267,Male,Yes,31,No,Homemaker,9.0,Low,1.0,Cat_4,A +464201,Female,No,30,No,Healthcare,0.0,Low,9.0,Cat_6,C +463942,Male,No,26,No,Healthcare,0.0,Low,4.0,Cat_4,D +459092,Male,Yes,60,Yes,Executive,0.0,High,4.0,Cat_6,B +463472,Male,No,21,No,Healthcare,,Low,4.0,Cat_6,D +461264,Male,No,19,No,Doctor,0.0,Low,3.0,Cat_4,D +462379,Female,Yes,60,Yes,Artist,1.0,Average,3.0,Cat_6,C +461554,Male,No,25,Yes,Healthcare,0.0,Low,3.0,Cat_6,B +465778,Female,No,21,No,Healthcare,0.0,Low,,Cat_2,D +466426,Female,No,25,Yes,Engineer,,Low,2.0,Cat_3,A +466721,Male,Yes,36,Yes,Doctor,7.0,Average,2.0,Cat_6,A +465822,Female,Yes,52,Yes,Engineer,1.0,Average,5.0,Cat_4,B +461779,Female,Yes,83,Yes,Lawyer,1.0,High,2.0,Cat_6,B +465537,Female,Yes,56,Yes,Artist,0.0,High,5.0,Cat_3,A +465044,Female,Yes,47,Yes,Engineer,0.0,Average,4.0,Cat_6,B +459171,Male,Yes,40,Yes,Artist,1.0,Average,2.0,Cat_6,C +466303,Male,Yes,67,Yes,Lawyer,0.0,High,2.0,Cat_2,B +464547,Male,No,41,Yes,Artist,0.0,Low,2.0,Cat_3,B +462584,Female,Yes,40,No,Entertainment,0.0,Low,4.0,Cat_4,D +467399,Male,Yes,29,Yes,Doctor,1.0,Low,3.0,Cat_6,D +462082,Female,No,41,Yes,Artist,10.0,Low,1.0,Cat_6,C +462575,Male,Yes,50,No,Executive,1.0,High,4.0,Cat_6,B +459895,Male,Yes,38,Yes,,9.0,Average,2.0,Cat_6,C +459644,Female,No,33,No,Marketing,9.0,Low,4.0,Cat_2,D +466587,Female,No,48,No,Healthcare,0.0,Low,4.0,Cat_6,C +460082,Male,No,37,Yes,Artist,8.0,Low,1.0,Cat_6,A +461380,Male,Yes,37,Yes,Artist,1.0,Average,2.0,Cat_6,B +465475,Female,Yes,36,Yes,Entertainment,0.0,Average,3.0,Cat_6,B +459883,Male,No,63,Yes,Artist,0.0,Low,1.0,Cat_2,B +466686,Female,No,22,No,Healthcare,1.0,Low,4.0,Cat_6,D +463750,Male,Yes,41,Yes,Artist,3.0,Average,3.0,Cat_6,C +460327,Female,No,42,Yes,Artist,12.0,Low,1.0,Cat_6,B +461349,Male,No,26,Yes,Doctor,0.0,Low,5.0,Cat_6,D +465350,Female,No,35,Yes,Artist,0.0,Low,3.0,Cat_6,A +459473,Male,Yes,78,No,Lawyer,,High,2.0,Cat_6,A +460893,Male,No,57,Yes,Entertainment,4.0,Low,1.0,,A +461665,Male,Yes,61,Yes,Entertainment,4.0,Average,2.0,Cat_5,B +461981,Male,No,18,No,Healthcare,4.0,Low,3.0,Cat_3,D +467685,Male,Yes,50,No,Marketing,1.0,High,6.0,Cat_4,A +465524,Female,No,51,Yes,Artist,8.0,Low,1.0,Cat_6,C +459605,Female,,19,No,Healthcare,8.0,Low,,Cat_3,D +464220,Male,Yes,65,Yes,Executive,1.0,High,2.0,Cat_6,A +463450,Male,No,26,Yes,Healthcare,1.0,Low,3.0,Cat_6,B +467481,Male,Yes,50,Yes,Artist,0.0,Average,2.0,Cat_6,A +465958,Female,No,36,Yes,Artist,5.0,Low,,Cat_6,D +461384,Male,Yes,71,Yes,Lawyer,2.0,High,,Cat_6,A +464478,Female,Yes,75,No,Lawyer,0.0,High,2.0,Cat_6,B +465549,Female,No,52,Yes,Healthcare,0.0,Low,1.0,Cat_3,D +459267,Female,No,31,No,Healthcare,4.0,Low,3.0,Cat_6,A +467293,Male,Yes,80,Yes,Lawyer,,Low,1.0,Cat_6,D +461875,Male,No,33,No,Doctor,0.0,Low,3.0,Cat_6,B +463971,Female,Yes,57,Yes,Artist,0.0,Average,4.0,Cat_6,C +461789,Male,Yes,67,Yes,Executive,1.0,High,2.0,Cat_6,C +461909,Female,No,30,No,Marketing,0.0,Low,4.0,Cat_6,D +459556,Male,Yes,82,Yes,Lawyer,,Low,2.0,Cat_6,D +465244,Male,No,23,No,Healthcare,1.0,Low,4.0,Cat_4,D +466937,Male,Yes,62,No,Entertainment,1.0,Average,2.0,Cat_6,B +459296,Male,Yes,70,Yes,Artist,1.0,Average,2.0,Cat_6,C +459352,Male,Yes,38,Yes,Doctor,4.0,Average,4.0,Cat_3,B +461225,Female,No,32,No,Marketing,11.0,Low,6.0,Cat_6,D +460097,Male,,43,Yes,Artist,0.0,Low,2.0,Cat_3,B +464235,Female,Yes,59,No,Engineer,,Average,4.0,Cat_6,C +463212,Male,No,28,Yes,Healthcare,0.0,Low,5.0,Cat_6,D +460925,Male,Yes,42,Yes,Artist,1.0,Average,3.0,Cat_6,C +461189,Male,Yes,87,No,Lawyer,,Low,1.0,Cat_3,B +460803,Female,No,26,No,Marketing,0.0,Low,4.0,Cat_4,D +464881,Male,Yes,59,Yes,Entertainment,0.0,Low,,Cat_4,C +464694,Female,No,30,No,Engineer,1.0,Low,3.0,Cat_4,A +467161,Female,Yes,30,Yes,Homemaker,9.0,Low,2.0,Cat_6,B +466640,Female,No,19,No,Marketing,1.0,Low,4.0,Cat_6,D +464054,Male,Yes,31,Yes,Artist,8.0,Average,2.0,Cat_6,C +459154,Male,Yes,88,Yes,Lawyer,0.0,High,2.0,Cat_6,A +465734,Male,Yes,40,Yes,Artist,0.0,Low,3.0,Cat_1,A +466612,Female,No,21,No,Healthcare,8.0,Low,2.0,Cat_6,D +463282,Male,No,38,Yes,Artist,1.0,Low,1.0,Cat_3,B +460681,Female,No,36,Yes,Homemaker,6.0,Low,1.0,Cat_3,B +466944,Male,Yes,47,No,Entertainment,1.0,Low,1.0,Cat_6,A +465376,Female,No,30,Yes,Entertainment,1.0,Low,4.0,Cat_4,A +465273,Female,No,35,Yes,Healthcare,9.0,Low,2.0,Cat_2,D +465162,Male,Yes,58,Yes,Artist,1.0,Average,3.0,Cat_6,B +459784,Female,Yes,52,Yes,Engineer,0.0,High,5.0,Cat_6,B +461057,Male,Yes,46,Yes,Artist,0.0,High,4.0,Cat_3,B +459522,Male,Yes,47,Yes,Entertainment,,Average,4.0,Cat_6,B +464910,Female,Yes,45,Yes,Artist,0.0,Average,5.0,Cat_4,C +462238,Male,Yes,60,No,Entertainment,,Low,2.0,Cat_3,A +461826,Male,Yes,35,Yes,Artist,12.0,Average,2.0,Cat_6,A +461301,Male,Yes,71,Yes,Lawyer,0.0,Average,2.0,Cat_6,C +462995,Male,Yes,36,Yes,Artist,0.0,Average,5.0,Cat_6,C +464331,Female,No,53,Yes,Engineer,0.0,Low,1.0,Cat_6,D +464487,Male,No,53,No,Engineer,1.0,Low,3.0,Cat_4,A +465647,Male,No,31,Yes,Artist,9.0,Low,1.0,Cat_6,A +463730,Male,No,20,No,,2.0,Low,2.0,Cat_6,D +467132,Female,Yes,66,Yes,Engineer,0.0,Low,2.0,Cat_6,B +467120,Male,Yes,37,Yes,Artist,8.0,Low,2.0,Cat_6,B +467802,Female,Yes,47,Yes,Artist,0.0,High,5.0,Cat_6,C +464985,Female,Yes,66,Yes,Lawyer,1.0,High,3.0,Cat_6,C +460224,Female,No,25,No,Healthcare,1.0,Low,3.0,Cat_6,B +460303,Female,No,43,Yes,Artist,2.0,Low,1.0,,A +465923,Female,No,30,Yes,Engineer,9.0,Low,5.0,Cat_6,D +461948,Female,Yes,45,Yes,Artist,1.0,Average,4.0,Cat_6,C +461194,Female,No,33,No,Doctor,1.0,Low,8.0,Cat_4,C +460590,Male,Yes,37,Yes,Artist,0.0,Average,3.0,Cat_3,B +463291,Male,Yes,35,Yes,Engineer,5.0,Average,4.0,Cat_6,B +466530,Male,No,21,No,Healthcare,1.0,Low,4.0,,D +461321,Male,Yes,50,Yes,Artist,13.0,Low,1.0,Cat_6,C +466623,Male,Yes,56,,Entertainment,4.0,Average,4.0,Cat_3,C +466062,Male,Yes,56,Yes,Artist,4.0,Low,3.0,Cat_3,B +465252,Female,No,20,No,Healthcare,8.0,Low,3.0,Cat_3,D +461031,Male,Yes,59,No,Executive,1.0,High,2.0,Cat_6,C +467230,Male,No,21,No,Healthcare,0.0,Low,4.0,Cat_6,D +465374,Male,Yes,37,Yes,Entertainment,1.0,Average,3.0,Cat_4,C +464159,Female,Yes,68,Yes,Artist,1.0,Low,1.0,Cat_4,B +463064,Male,Yes,33,No,Executive,9.0,High,2.0,Cat_6,D +462927,Female,No,26,No,Homemaker,9.0,Low,2.0,Cat_6,A +459428,Female,No,43,No,Marketing,0.0,Low,2.0,Cat_4,D +463270,Male,Yes,52,No,Executive,1.0,Low,5.0,,A +459921,Male,No,37,Yes,Artist,8.0,Low,1.0,Cat_3,A +462564,Male,No,55,Yes,Executive,,Low,5.0,Cat_6,C +462434,Male,Yes,57,Yes,Artist,0.0,Average,4.0,Cat_6,C +463625,Male,Yes,35,No,Artist,1.0,Average,3.0,Cat_4,A +465871,Male,No,33,No,Entertainment,8.0,Low,1.0,Cat_6,A +461131,Male,Yes,68,Yes,Artist,0.0,High,,Cat_6,A +463151,Male,Yes,60,Yes,Artist,1.0,Low,1.0,Cat_3,A +459794,Male,Yes,52,Yes,Artist,0.0,Low,2.0,Cat_6,B +462770,Male,No,18,No,Healthcare,,Low,3.0,Cat_6,D +467590,Male,No,25,Yes,Artist,,Low,1.0,Cat_6,A +465004,Male,No,35,Yes,Entertainment,0.0,Low,3.0,Cat_7,D +464851,Male,No,18,No,Doctor,0.0,Low,6.0,Cat_4,D +467383,Female,Yes,77,Yes,Lawyer,0.0,High,2.0,Cat_6,B +466277,Male,No,18,No,Healthcare,0.0,Low,5.0,Cat_3,D +462115,Male,No,18,No,Healthcare,0.0,Low,3.0,Cat_6,D +463958,Female,No,25,No,Healthcare,0.0,Low,5.0,Cat_2,C +460023,Male,Yes,49,Yes,Entertainment,0.0,Low,1.0,Cat_6,A +460085,Female,No,37,Yes,Marketing,0.0,Low,1.0,Cat_4,D +463930,Male,Yes,49,Yes,Artist,1.0,Average,2.0,Cat_6,C +463458,Male,Yes,26,Yes,Entertainment,1.0,Average,2.0,Cat_6,B +467147,Male,No,45,Yes,Entertainment,0.0,Low,1.0,Cat_6,A +461930,Female,No,42,Yes,Artist,0.0,Low,1.0,Cat_4,A +464398,Male,Yes,48,No,Marketing,0.0,Low,5.0,Cat_6,D +461133,Male,No,23,No,Healthcare,1.0,Low,4.0,Cat_6,D +466382,Female,Yes,52,Yes,Engineer,4.0,High,2.0,Cat_4,B +466914,Male,Yes,81,Yes,Entertainment,1.0,Low,1.0,Cat_6,D +459986,Male,Yes,60,Yes,Artist,0.0,Low,1.0,Cat_6,B +465102,Male,Yes,57,Yes,Lawyer,1.0,High,2.0,Cat_6,B +459209,Female,No,21,No,Entertainment,1.0,Low,4.0,Cat_6,C +464174,Female,No,43,Yes,Engineer,0.0,Low,4.0,Cat_6,D +461516,Male,Yes,32,Yes,Entertainment,0.0,Average,3.0,Cat_6,A +463252,Male,Yes,50,Yes,Artist,0.0,Average,2.0,Cat_6,B +462795,Male,No,25,No,Entertainment,13.0,Low,3.0,Cat_6,A +461927,Male,No,25,No,Healthcare,1.0,Low,4.0,Cat_2,C +467337,Male,Yes,56,Yes,Artist,1.0,Average,4.0,Cat_6,C +465645,Female,No,36,Yes,Artist,9.0,Low,2.0,Cat_6,C +465844,Female,No,46,Yes,Engineer,0.0,Low,1.0,Cat_6,D +460425,Female,No,27,No,Doctor,,Low,3.0,Cat_7,A +461181,Male,No,21,No,Healthcare,,Low,4.0,Cat_3,D +460305,Female,,49,Yes,Entertainment,1.0,High,1.0,Cat_6,D +461290,Male,No,22,No,Healthcare,,Low,2.0,Cat_6,D +460256,Male,No,53,Yes,Artist,2.0,Low,2.0,Cat_2,B +466825,Female,Yes,41,Yes,Artist,1.0,Low,2.0,Cat_6,A +464051,Male,Yes,53,Yes,Doctor,0.0,Average,2.0,Cat_6,C +464643,Female,Yes,79,No,Engineer,0.0,High,2.0,Cat_4,A +467239,Female,No,33,No,Engineer,1.0,Low,3.0,Cat_6,D +461113,Female,No,23,No,Healthcare,1.0,Low,4.0,Cat_3,D +466980,Male,Yes,72,Yes,Lawyer,1.0,High,2.0,Cat_6,A +463663,Female,No,37,Yes,,0.0,Low,2.0,Cat_6,A +466077,Male,Yes,35,Yes,Doctor,9.0,High,3.0,Cat_6,A +462641,Female,Yes,42,No,,,Average,,Cat_4,D +460520,Female,No,28,Yes,Doctor,1.0,Low,3.0,Cat_3,C +466429,Male,Yes,36,No,Healthcare,1.0,Low,2.0,Cat_3,A +460161,Male,No,38,Yes,Artist,0.0,Low,1.0,Cat_3,B +461579,Female,Yes,43,Yes,Artist,4.0,High,3.0,Cat_6,C +467155,Male,Yes,47,No,Executive,0.0,High,4.0,Cat_6,B +460746,Male,No,22,No,Marketing,1.0,Low,3.0,Cat_6,A +464901,Male,Yes,82,No,Executive,1.0,High,4.0,Cat_4,A +462685,Female,No,32,No,Healthcare,,Low,3.0,Cat_4,D +464238,Male,Yes,87,Yes,Lawyer,0.0,High,2.0,Cat_6,A +466135,Male,Yes,47,Yes,Artist,1.0,Low,2.0,Cat_3,B +465196,Female,No,33,Yes,Artist,3.0,Low,2.0,Cat_4,D +463110,Male,Yes,68,No,Executive,1.0,High,2.0,Cat_6,A +461549,Female,Yes,68,Yes,Artist,,Average,2.0,Cat_6,B +463820,Male,Yes,45,Yes,Executive,8.0,High,4.0,Cat_6,C +462819,Male,Yes,49,Yes,Artist,0.0,Average,3.0,Cat_6,C +462456,Male,No,18,No,Healthcare,0.0,Low,8.0,Cat_6,D +467909,Female,No,45,Yes,Doctor,0.0,Low,1.0,Cat_6,A +463391,Male,Yes,62,Yes,Doctor,5.0,Low,1.0,Cat_3,B +467211,Female,Yes,75,Yes,Lawyer,0.0,Low,1.0,Cat_6,B +462256,Female,Yes,59,Yes,Homemaker,0.0,Average,4.0,Cat_6,B +459491,Female,Yes,39,Yes,Artist,1.0,Average,4.0,Cat_6,C +464664,Male,Yes,39,No,Entertainment,1.0,Average,5.0,Cat_4,B +459925,Male,Yes,68,No,Executive,1.0,High,2.0,Cat_4,D +459412,Male,Yes,62,Yes,,0.0,Average,3.0,Cat_6,B +463224,Male,Yes,50,Yes,Executive,0.0,High,4.0,Cat_6,B +459066,Male,Yes,47,Yes,Executive,8.0,High,4.0,Cat_6,C +463387,Male,,20,No,Marketing,3.0,Low,2.0,Cat_3,C +463714,Male,Yes,57,No,,1.0,Average,2.0,Cat_4,C +462052,Male,Yes,51,Yes,Artist,1.0,Low,2.0,Cat_6,C +463304,Female,No,41,Yes,Doctor,0.0,Low,1.0,Cat_4,A +463521,Male,Yes,45,Yes,Executive,1.0,High,4.0,Cat_6,A +464703,Male,Yes,40,No,Doctor,1.0,Average,5.0,Cat_4,A +463542,Female,Yes,27,No,Artist,6.0,High,5.0,Cat_2,A +463425,Male,Yes,43,No,Entertainment,3.0,Low,,Cat_6,D +465353,Male,Yes,73,Yes,Artist,1.0,Average,2.0,Cat_6,C +459696,Male,Yes,43,Yes,Engineer,0.0,Average,2.0,Cat_6,B +462147,Female,Yes,82,Yes,Lawyer,,Low,1.0,Cat_6,A +460815,Male,Yes,55,Yes,Artist,0.0,High,2.0,Cat_3,A +463413,Male,Yes,41,No,Doctor,1.0,Average,2.0,Cat_4,A +460481,Male,No,25,Yes,Entertainment,1.0,Low,2.0,Cat_3,B +463336,Female,Yes,62,No,Engineer,4.0,Low,1.0,Cat_4,B +462485,Female,Yes,37,No,Engineer,0.0,Low,2.0,Cat_6,A +465676,Female,Yes,51,Yes,Entertainment,,Average,2.0,Cat_4,B +459357,Male,Yes,69,No,Lawyer,0.0,Low,1.0,Cat_6,D +464428,Female,Yes,89,Yes,Entertainment,6.0,Average,4.0,Cat_2,A +465341,Male,No,29,Yes,Healthcare,,Low,6.0,Cat_2,D +463295,Male,No,28,No,Entertainment,0.0,Low,3.0,Cat_6,A +467750,Male,Yes,40,Yes,Artist,,Average,3.0,Cat_6,C +461877,Female,No,38,Yes,Artist,7.0,Low,2.0,Cat_6,B +467793,Male,No,18,No,Healthcare,3.0,Low,4.0,Cat_6,D +466816,Male,Yes,81,Yes,Lawyer,1.0,High,2.0,Cat_6,B +462271,Male,Yes,45,Yes,Executive,2.0,Average,4.0,Cat_2,B +463677,Male,Yes,42,No,Executive,1.0,High,5.0,Cat_6,C +461400,Male,No,36,No,Marketing,0.0,Low,1.0,Cat_6,D +466140,Male,Yes,30,Yes,Entertainment,0.0,Low,2.0,Cat_6,A +463435,Female,No,33,Yes,Healthcare,0.0,Low,3.0,Cat_6,D +463858,Male,Yes,37,No,Artist,0.0,Average,2.0,Cat_6,C +459777,Male,Yes,35,No,Artist,9.0,Low,3.0,Cat_7,A +463545,Female,No,31,Yes,Doctor,0.0,Low,2.0,Cat_4,D +462188,Female,Yes,53,No,Engineer,0.0,Average,4.0,Cat_4,B +465966,Male,No,36,Yes,Doctor,9.0,Low,1.0,Cat_6,B +464869,Male,Yes,37,Yes,Entertainment,0.0,Low,2.0,Cat_4,A +459564,Male,Yes,80,Yes,Lawyer,2.0,Low,1.0,Cat_6,A +459099,Male,Yes,30,Yes,Doctor,8.0,Average,2.0,Cat_6,A +459559,Female,Yes,39,No,Engineer,,Average,3.0,Cat_6,D +461054,Female,No,18,No,Marketing,,Low,,Cat_3,D +465994,Male,No,32,No,Doctor,3.0,Low,3.0,Cat_7,D +461243,Male,Yes,49,Yes,Marketing,0.0,Low,5.0,Cat_6,D +464163,Male,Yes,67,Yes,Executive,0.0,High,4.0,Cat_6,C +459781,Male,No,47,No,Entertainment,1.0,Low,1.0,Cat_6,A +462021,Male,Yes,35,No,Engineer,1.0,Low,6.0,Cat_6,A +464004,Female,Yes,26,No,Marketing,0.0,Low,5.0,Cat_4,D +459918,Female,No,43,Yes,Artist,1.0,Low,1.0,Cat_3,B +463783,Female,No,30,No,Healthcare,,Low,,,C +465725,Male,No,29,Yes,Doctor,0.0,Low,2.0,Cat_3,A +461150,Female,,20,No,Marketing,0.0,High,2.0,Cat_3,A +466022,Female,Yes,67,No,Engineer,,High,3.0,Cat_7,B +462086,Male,No,41,Yes,Doctor,4.0,Low,2.0,Cat_6,B +459627,Male,No,40,Yes,Artist,14.0,Low,2.0,Cat_2,A +462524,Female,Yes,40,Yes,Doctor,,Low,2.0,Cat_6,C +462764,Male,No,18,No,Healthcare,,Low,4.0,Cat_6,D +462907,Male,No,29,No,Healthcare,2.0,Low,5.0,Cat_6,D +464766,Female,Yes,51,No,Engineer,7.0,Average,5.0,Cat_4,B +467608,Male,No,32,Yes,Engineer,6.0,Low,1.0,Cat_6,D +467763,Female,No,48,Yes,Artist,1.0,Low,1.0,Cat_2,B +467972,Male,No,22,No,Artist,1.0,Low,3.0,Cat_6,D +459484,Female,Yes,35,Yes,Entertainment,1.0,Average,2.0,Cat_6,A +459622,Female,No,51,Yes,Artist,2.0,Low,1.0,Cat_6,B +462023,Female,Yes,46,No,Engineer,8.0,Low,1.0,Cat_6,A +461484,Female,No,27,Yes,Healthcare,1.0,Low,5.0,Cat_6,C +459020,Female,Yes,56,Yes,Artist,0.0,Average,4.0,Cat_6,C +466028,Male,Yes,50,Yes,Artist,4.0,Average,4.0,Cat_3,B +460362,Male,Yes,59,Yes,Healthcare,0.0,Low,3.0,Cat_4,D +464171,Female,Yes,61,Yes,Artist,0.0,Low,1.0,Cat_6,C +465144,Male,No,32,Yes,Doctor,9.0,Low,1.0,Cat_6,A +461861,Female,No,26,Yes,Healthcare,1.0,Low,5.0,Cat_4,C +467843,Female,No,23,No,Healthcare,,Low,4.0,Cat_6,D +459017,Male,Yes,50,Yes,Executive,,High,6.0,Cat_6,C +463606,Male,No,22,No,Marketing,0.0,Low,4.0,Cat_2,C +467871,Female,No,29,No,Marketing,4.0,Low,3.0,Cat_6,B +461730,Male,Yes,46,Yes,Artist,1.0,Low,3.0,Cat_6,B +467514,Male,No,43,Yes,Artist,,Low,4.0,Cat_6,D +460529,Male,No,33,No,Artist,,Low,,Cat_3,C +463169,Female,Yes,85,Yes,Lawyer,0.0,Low,1.0,Cat_3,D +460646,Male,Yes,57,Yes,Entertainment,,Low,,Cat_3,C +460102,Male,Yes,43,Yes,Artist,9.0,High,2.0,Cat_4,B +460490,Female,No,18,No,Healthcare,0.0,Low,4.0,Cat_3,D +459511,Male,Yes,87,No,Artist,1.0,Low,1.0,Cat_6,A +466601,Male,Yes,40,Yes,Artist,1.0,Average,2.0,Cat_4,C +467616,Male,Yes,25,Yes,Entertainment,1.0,Low,2.0,Cat_6,A +459659,Female,,18,No,Healthcare,,Low,3.0,Cat_6,D +459811,Female,Yes,48,Yes,Artist,1.0,High,3.0,Cat_6,C +460087,Female,Yes,56,Yes,Entertainment,0.0,Average,2.0,Cat_6,B +466723,Female,No,32,No,Entertainment,1.0,Low,3.0,Cat_6,A +461580,Female,No,38,Yes,Artist,7.0,Low,7.0,Cat_2,C +465026,Female,No,31,No,Doctor,1.0,Low,2.0,Cat_6,A +465825,Female,Yes,36,No,Executive,9.0,Average,5.0,Cat_4,A +467844,Male,No,37,Yes,Healthcare,0.0,Low,2.0,Cat_6,D +463348,Female,Yes,40,Yes,Doctor,1.0,Low,2.0,Cat_6,A +466314,Male,No,18,No,Healthcare,5.0,Low,4.0,Cat_4,D +462518,Female,No,26,No,Healthcare,0.0,Low,6.0,Cat_5,A +459601,Female,No,23,No,Healthcare,,Low,2.0,Cat_7,D +461806,Male,No,27,Yes,Healthcare,9.0,Low,2.0,Cat_6,D +459584,Female,No,20,No,Healthcare,1.0,Low,3.0,Cat_6,D +463133,Male,Yes,47,No,Artist,1.0,Average,4.0,Cat_6,C +459856,Male,Yes,50,No,Entertainment,1.0,Average,3.0,Cat_6,B +460986,Male,No,37,Yes,Doctor,0.0,Low,3.0,,A +465360,Female,Yes,49,Yes,Artist,1.0,Average,2.0,Cat_6,C +466869,Male,Yes,29,Yes,Healthcare,1.0,Low,3.0,Cat_3,D +465884,Male,Yes,37,Yes,Executive,6.0,High,3.0,Cat_6,D +459300,Female,Yes,58,Yes,Artist,0.0,Low,,Cat_6,C +461485,Male,Yes,59,No,Engineer,1.0,Average,3.0,Cat_6,C +466579,Male,No,21,No,Marketing,0.0,Low,5.0,Cat_1,D +467706,Female,Yes,42,Yes,Artist,,Average,4.0,Cat_6,A +463125,Male,No,29,No,Healthcare,0.0,Low,6.0,Cat_6,B +463673,Male,No,33,Yes,Healthcare,,Low,5.0,Cat_6,B +465899,Female,No,38,Yes,Doctor,9.0,Low,2.0,Cat_6,B +459703,Male,Yes,37,Yes,Artist,0.0,Low,3.0,Cat_6,A +464317,Male,Yes,60,Yes,Entertainment,1.0,Average,3.0,Cat_6,C +465093,Male,Yes,55,Yes,Doctor,0.0,Low,1.0,Cat_6,C +464361,Male,Yes,50,Yes,Artist,1.0,Average,2.0,Cat_6,C +459299,Male,No,36,Yes,Artist,,Low,3.0,Cat_6,B +462334,Male,No,47,Yes,Artist,9.0,Low,2.0,Cat_6,B +459989,Male,Yes,52,Yes,Artist,6.0,Average,2.0,Cat_6,B +464757,Female,Yes,42,No,Executive,0.0,Average,5.0,Cat_4,A +462293,Male,No,32,No,Healthcare,0.0,Low,3.0,Cat_6,D +462638,Female,No,33,Yes,Engineer,,Low,2.0,Cat_4,D +464820,Female,No,25,No,Artist,1.0,Low,1.0,Cat_4,B +465709,Male,Yes,68,Yes,Entertainment,1.0,Average,2.0,Cat_6,C +462277,Male,Yes,36,Yes,Entertainment,4.0,Average,2.0,Cat_2,B +465223,Male,Yes,51,Yes,Artist,,Average,4.0,Cat_6,C +464776,Female,Yes,47,No,Engineer,,Average,5.0,Cat_4,B +465228,Male,Yes,62,Yes,Artist,0.0,High,2.0,Cat_6,C +461368,Male,No,32,Yes,Doctor,4.0,Low,5.0,Cat_6,D +460698,Female,Yes,66,No,Artist,,Average,2.0,Cat_6,B +460435,Female,Yes,48,Yes,Artist,0.0,Low,1.0,Cat_6,B +465080,Male,Yes,70,Yes,Lawyer,8.0,Average,2.0,Cat_6,C +459272,Female,Yes,60,Yes,Artist,0.0,High,2.0,Cat_6,C +466578,Male,No,45,No,Artist,2.0,Low,1.0,Cat_6,B +466646,Female,Yes,48,Yes,Homemaker,1.0,Low,2.0,Cat_3,B +466408,Male,Yes,76,Yes,Lawyer,1.0,Low,2.0,Cat_3,A +460948,Male,Yes,53,Yes,Engineer,4.0,Low,3.0,Cat_3,B +462244,Male,Yes,83,No,Executive,1.0,High,3.0,Cat_6,B +459410,Female,Yes,79,Yes,Lawyer,1.0,High,2.0,Cat_6,B +466668,Female,Yes,49,Yes,Engineer,0.0,Low,1.0,Cat_2,B +467437,Female,Yes,76,Yes,Lawyer,0.0,High,2.0,Cat_6,B +465267,Male,Yes,37,No,Engineer,12.0,Average,5.0,Cat_4,B +464465,Female,Yes,37,Yes,Artist,,Average,6.0,Cat_3,B +463849,Female,Yes,35,No,Artist,0.0,Low,2.0,Cat_6,B +467191,Female,Yes,31,No,Homemaker,12.0,Average,2.0,Cat_6,B +460970,Male,No,21,No,Marketing,1.0,Low,5.0,Cat_6,B +463943,Male,No,71,Yes,Entertainment,1.0,Low,2.0,Cat_6,C +463778,Female,No,27,No,Engineer,1.0,Low,4.0,Cat_5,A +462792,Male,Yes,52,No,Homemaker,,Average,4.0,Cat_6,A +459739,Male,Yes,72,No,Engineer,1.0,High,,Cat_6,D +462894,Male,Yes,43,Yes,Artist,2.0,Average,4.0,Cat_3,C +465963,Male,Yes,46,Yes,Artist,0.0,Low,2.0,Cat_6,B +466791,Male,Yes,88,No,Lawyer,1.0,Low,1.0,Cat_6,A +460807,Male,No,29,No,Healthcare,1.0,Low,6.0,Cat_6,D +462328,Male,No,27,No,Engineer,0.0,Low,5.0,Cat_6,D +460618,Male,Yes,55,Yes,Lawyer,,Low,4.0,Cat_3,C +460041,Female,Yes,48,No,Artist,8.0,Average,3.0,Cat_6,A +466840,Male,No,20,No,Healthcare,4.0,Low,5.0,Cat_2,D +461585,Male,No,38,Yes,Entertainment,1.0,Low,1.0,Cat_7,A +460538,Female,,22,No,Healthcare,0.0,High,3.0,Cat_3,D +462570,Female,No,29,No,Homemaker,1.0,Low,6.0,Cat_6,D +461845,Female,Yes,48,Yes,Doctor,1.0,Average,4.0,Cat_6,C +465182,Female,No,65,Yes,Lawyer,0.0,Low,1.0,Cat_6,A +463868,Male,Yes,51,No,Engineer,1.0,Average,7.0,Cat_4,B +464028,Female,No,21,No,Healthcare,0.0,Low,5.0,Cat_6,D +466438,Female,No,42,Yes,Artist,0.0,Low,1.0,Cat_6,A +462196,Male,Yes,56,No,Marketing,1.0,Average,3.0,Cat_4,B +463043,Female,Yes,86,Yes,Lawyer,9.0,High,2.0,Cat_6,C +464316,Male,Yes,59,Yes,Artist,0.0,Average,4.0,Cat_6,C +466595,Male,Yes,86,Yes,Executive,1.0,High,2.0,Cat_6,C +465240,Male,Yes,59,Yes,Artist,4.0,High,2.0,Cat_7,D +467303,Male,Yes,38,Yes,Doctor,3.0,Average,2.0,Cat_6,C +465388,Female,No,31,Yes,Marketing,0.0,Low,1.0,Cat_6,D +462014,Female,No,45,Yes,Artist,1.0,Low,1.0,Cat_6,B +465401,Female,Yes,48,Yes,Artist,10.0,Low,2.0,Cat_6,B +461096,Female,No,33,No,Engineer,9.0,Low,4.0,Cat_3,B +462801,Male,Yes,38,Yes,Entertainment,9.0,Average,3.0,Cat_6,B +463829,Female,Yes,49,Yes,Artist,1.0,Average,4.0,Cat_6,C +459770,Female,Yes,48,Yes,Artist,8.0,Average,2.0,Cat_6,A +461421,Male,Yes,37,Yes,Artist,,Average,3.0,Cat_6,B +463737,Male,No,28,Yes,Healthcare,7.0,Low,4.0,Cat_2,C +462433,Female,Yes,33,Yes,Doctor,0.0,Average,2.0,Cat_6,B +465311,Male,No,20,No,Healthcare,0.0,Low,4.0,Cat_6,D +464804,Male,Yes,27,No,Entertainment,0.0,Low,1.0,Cat_4,A +464721,Male,Yes,39,Yes,Artist,3.0,Average,4.0,Cat_6,B +465131,Female,Yes,87,No,Lawyer,0.0,Low,1.0,Cat_6,C +467371,Male,No,30,Yes,Healthcare,1.0,Low,4.0,Cat_6,B +465348,Male,,30,No,Executive,,High,,Cat_6,D +459004,Female,Yes,51,Yes,Artist,1.0,High,5.0,Cat_6,C +465290,Male,Yes,46,Yes,Executive,1.0,High,5.0,Cat_6,B +464165,Female,No,55,No,Entertainment,3.0,Low,1.0,Cat_6,A +462502,Male,No,39,No,Entertainment,0.0,Low,4.0,Cat_6,A +467572,Male,Yes,63,Yes,Artist,1.0,Average,4.0,Cat_6,A +464117,Male,Yes,84,Yes,Lawyer,0.0,High,2.0,Cat_6,C +461688,Male,Yes,41,Yes,Executive,7.0,High,2.0,Cat_6,C +467148,Male,No,28,Yes,Artist,,Low,1.0,Cat_6,A +460130,Male,No,27,Yes,Healthcare,2.0,Low,4.0,Cat_1,A +462425,Male,No,35,Yes,Doctor,3.0,Low,1.0,Cat_6,A +460326,Female,Yes,47,Yes,Artist,6.0,Low,2.0,Cat_6,B +465903,Female,No,51,Yes,Artist,0.0,Low,1.0,Cat_6,B +461961,Female,Yes,32,Yes,Artist,8.0,Low,3.0,Cat_6,B +467855,Male,Yes,31,Yes,Engineer,1.0,Low,2.0,Cat_6,D +459213,Female,No,38,Yes,Healthcare,0.0,Low,4.0,Cat_6,A +461120,Male,No,20,No,Healthcare,1.0,Low,3.0,Cat_3,D +467927,Male,No,31,Yes,Doctor,8.0,Low,6.0,Cat_2,A +466908,Male,Yes,55,Yes,Entertainment,1.0,Low,2.0,Cat_6,A +461042,Male,No,19,No,Healthcare,0.0,Low,,Cat_4,D +466497,Male,No,83,Yes,Lawyer,0.0,Low,1.0,Cat_6,D +465027,Male,Yes,41,Yes,Doctor,0.0,Low,2.0,Cat_6,B +463550,Female,Yes,38,Yes,Artist,1.0,Average,2.0,Cat_4,C +462943,Male,Yes,61,Yes,Executive,5.0,High,2.0,Cat_6,B +462954,Female,Yes,48,Yes,Homemaker,12.0,Low,1.0,Cat_6,A +463567,Male,Yes,40,Yes,Doctor,1.0,Average,2.0,Cat_6,B +466124,Female,No,47,Yes,Artist,0.0,Low,1.0,Cat_6,B +463354,Male,Yes,53,Yes,Executive,4.0,High,4.0,Cat_6,D +463461,Male,No,29,No,Doctor,,Low,4.0,Cat_4,A +465586,Male,Yes,72,No,Artist,1.0,Low,1.0,Cat_6,B +466178,Female,Yes,50,Yes,Artist,1.0,Average,4.0,Cat_6,C +461929,Male,Yes,63,Yes,Entertainment,0.0,Low,1.0,Cat_6,A +465752,Male,Yes,42,Yes,Artist,5.0,Average,2.0,Cat_6,C +462931,Male,Yes,40,No,Entertainment,14.0,Average,3.0,Cat_6,D +467853,Female,Yes,63,Yes,Artist,1.0,Average,5.0,Cat_3,C +467277,Male,No,25,Yes,Artist,13.0,Low,3.0,Cat_6,A +459049,Male,Yes,49,Yes,Executive,2.0,High,4.0,Cat_2,B +464098,Female,No,28,No,Healthcare,0.0,Low,6.0,Cat_5,A +459438,Female,Yes,69,Yes,Artist,0.0,High,2.0,Cat_6,C +466815,Male,No,31,No,Healthcare,0.0,Low,5.0,Cat_4,C +459747,Female,Yes,36,Yes,Artist,1.0,Average,5.0,Cat_6,C +461014,Male,No,21,No,Healthcare,9.0,Low,4.0,Cat_3,D +462007,Female,Yes,36,Yes,Artist,1.0,Average,2.0,Cat_6,C +465446,Female,No,41,Yes,Healthcare,12.0,Low,1.0,Cat_1,A +461416,Female,No,31,Yes,Artist,13.0,Low,2.0,Cat_6,B +463089,Male,Yes,51,Yes,Executive,7.0,High,4.0,Cat_6,C +463373,Male,Yes,55,Yes,Artist,1.0,Low,3.0,Cat_3,A +461910,Male,Yes,31,Yes,Artist,,Average,2.0,Cat_2,B +465470,Female,Yes,61,No,Engineer,1.0,Average,2.0,Cat_6,C +465985,Male,No,31,Yes,Artist,1.0,Low,3.0,Cat_6,D +462715,Female,Yes,55,Yes,Artist,1.0,High,4.0,Cat_6,B +461925,Female,No,33,Yes,Artist,0.0,Low,5.0,Cat_3,C +465743,Female,Yes,50,Yes,Artist,1.0,Average,5.0,Cat_2,C +463675,Male,Yes,71,No,Entertainment,1.0,Low,2.0,Cat_6,A +459712,Male,No,18,No,Healthcare,,Low,4.0,Cat_6,D +464305,Female,Yes,73,Yes,Lawyer,1.0,High,2.0,Cat_6,C +467178,Female,Yes,58,No,Lawyer,0.0,High,4.0,Cat_6,C +464185,Male,Yes,65,Yes,Artist,5.0,High,2.0,Cat_6,A +467082,Female,Yes,75,Yes,Artist,1.0,High,2.0,Cat_6,C +459948,Female,No,62,Yes,Engineer,0.0,Low,1.0,Cat_6,B +465991,Female,No,38,Yes,Healthcare,0.0,Low,5.0,Cat_6,D +465407,Female,No,27,Yes,Homemaker,3.0,Low,7.0,Cat_6,D +466225,Male,No,19,No,Healthcare,0.0,Low,3.0,Cat_2,D +465321,Male,Yes,43,Yes,Entertainment,,Low,4.0,Cat_3,A +461843,Female,Yes,50,Yes,Artist,0.0,High,2.0,Cat_3,C +465935,Male,No,27,Yes,Entertainment,9.0,Low,1.0,Cat_6,A +463758,Male,No,33,No,Healthcare,9.0,Low,3.0,Cat_7,C +464651,Female,Yes,26,No,Doctor,0.0,Average,3.0,Cat_4,D +460526,Female,Yes,28,,,8.0,Average,4.0,Cat_3,C +467085,Male,No,45,Yes,Entertainment,1.0,Low,2.0,Cat_6,A +463965,Male,No,25,Yes,Doctor,7.0,Low,1.0,Cat_6,A +467156,Male,Yes,37,Yes,Artist,6.0,Low,1.0,Cat_6,B +461347,Female,Yes,37,Yes,Artist,5.0,Average,3.0,Cat_6,B +462113,Female,Yes,60,Yes,Artist,8.0,High,6.0,Cat_6,C +461299,Female,Yes,76,Yes,Lawyer,1.0,High,2.0,Cat_6,C +466760,Male,Yes,47,Yes,Doctor,0.0,Average,4.0,Cat_6,A +459760,Female,Yes,42,Yes,Artist,8.0,Low,1.0,Cat_6,A +463972,Male,Yes,66,Yes,Artist,0.0,Low,4.0,Cat_2,C +462051,Male,No,20,No,Healthcare,,Low,5.0,Cat_4,D +467431,Male,Yes,40,Yes,Artist,9.0,Average,3.0,Cat_6,C +463466,Male,No,21,No,Healthcare,13.0,Low,6.0,Cat_6,D +460479,Female,No,42,No,Doctor,8.0,Low,1.0,Cat_3,A +461033,Male,Yes,57,Yes,Doctor,5.0,Average,2.0,Cat_6,C +460839,Male,No,23,No,Healthcare,4.0,Low,3.0,Cat_6,D +462426,Female,Yes,52,Yes,Artist,2.0,Average,4.0,Cat_6,C +461431,Male,Yes,42,Yes,Doctor,1.0,Average,2.0,Cat_1,C +461121,Male,No,28,No,Doctor,0.0,Low,4.0,Cat_3,D +467149,Male,Yes,43,Yes,Artist,9.0,Average,3.0,Cat_6,A +461809,Male,No,30,Yes,Doctor,3.0,Low,2.0,Cat_6,D +463793,Male,No,26,No,Healthcare,1.0,Low,6.0,Cat_4,A +460168,Male,Yes,86,Yes,Executive,0.0,High,2.0,Cat_2,C +463931,Male,Yes,52,Yes,Entertainment,0.0,Average,2.0,Cat_6,C +463046,Female,Yes,43,No,Engineer,0.0,Low,1.0,Cat_6,A +465377,Male,Yes,26,Yes,Artist,5.0,Average,,Cat_6,A +460020,Male,Yes,53,Yes,Artist,1.0,Average,3.0,Cat_6,C +461878,Male,No,33,Yes,Healthcare,3.0,Low,1.0,Cat_6,D +465802,Male,Yes,25,Yes,Healthcare,9.0,Low,2.0,Cat_6,C +466956,Female,Yes,55,Yes,Artist,1.0,Low,1.0,Cat_6,B +462559,Female,Yes,47,Yes,Engineer,9.0,Average,4.0,Cat_2,C +464811,Female,Yes,43,No,Engineer,,Average,,Cat_4,B +467260,Male,No,27,No,Doctor,9.0,Low,4.0,Cat_6,D +458994,Male,Yes,38,Yes,Healthcare,8.0,Average,4.0,Cat_6,C +459772,Female,Yes,69,Yes,Artist,2.0,Average,9.0,Cat_5,C +464895,Male,No,19,No,Doctor,1.0,Low,5.0,Cat_4,C +460625,Female,No,28,No,Marketing,9.0,Low,2.0,Cat_3,D +465263,Male,Yes,65,Yes,Artist,1.0,High,2.0,Cat_6,C +461147,Male,Yes,33,Yes,Artist,2.0,Low,,Cat_3,C +462441,Female,No,22,No,Healthcare,0.0,Low,4.0,Cat_6,D +459828,Female,Yes,25,No,Homemaker,11.0,Average,3.0,Cat_6,D +460328,Female,No,52,Yes,Artist,1.0,Low,2.0,Cat_6,A +466448,Female,No,37,Yes,Artist,0.0,Low,2.0,Cat_6,B +463556,Male,Yes,29,Yes,Entertainment,0.0,Low,2.0,Cat_2,D +461109,Male,No,31,Yes,Healthcare,0.0,Low,3.0,Cat_3,D +460557,Female,No,36,Yes,Marketing,9.0,Low,1.0,Cat_3,C +466066,Female,No,73,Yes,Artist,0.0,Low,1.0,Cat_6,C +459457,Male,Yes,51,Yes,Artist,2.0,Low,2.0,Cat_6,A +464404,Male,No,89,Yes,Artist,1.0,Low,,Cat_6,A +463538,Male,Yes,60,No,Executive,0.0,High,2.0,Cat_6,B +459613,Male,No,40,Yes,Healthcare,,Low,2.0,Cat_6,D +466588,Female,No,22,No,Healthcare,9.0,Low,4.0,Cat_6,D +460112,Male,Yes,28,Yes,Engineer,9.0,Average,2.0,Cat_3,C +466744,Female,Yes,52,Yes,Artist,1.0,Average,3.0,Cat_6,C +461821,Female,No,28,No,Doctor,0.0,Low,4.0,Cat_6,C +463993,Female,Yes,37,Yes,Artist,5.0,Average,2.0,Cat_6,B +467779,Male,Yes,53,Yes,Doctor,2.0,Average,3.0,Cat_6,C +461144,Female,No,23,No,Lawyer,0.0,Low,6.0,Cat_3,B +464308,Female,Yes,75,Yes,Lawyer,0.0,High,2.0,Cat_6,B +459819,Male,Yes,71,Yes,Entertainment,,High,2.0,Cat_6,B +464509,Male,Yes,53,Yes,Artist,0.0,High,4.0,Cat_6,C +464061,Male,Yes,30,Yes,Artist,12.0,Average,2.0,Cat_6,B +466064,Female,Yes,31,No,Entertainment,0.0,Average,2.0,Cat_3,A +461999,Male,No,27,Yes,Doctor,0.0,Low,5.0,Cat_6,B +466913,Male,Yes,65,Yes,Lawyer,0.0,Low,1.0,Cat_6,A +462627,Male,Yes,27,No,Entertainment,,Average,3.0,Cat_4,D +465765,Male,Yes,52,No,Entertainment,0.0,Average,2.0,Cat_4,B +460245,Female,Yes,52,Yes,Artist,3.0,Low,2.0,,B +460158,Female,No,51,No,Engineer,8.0,Low,6.0,Cat_4,D +460703,Female,Yes,30,Yes,Engineer,8.0,Average,5.0,Cat_4,D +465625,Male,No,42,Yes,Artist,11.0,Low,2.0,Cat_6,C +466857,Female,No,19,No,Healthcare,9.0,Low,8.0,Cat_5,D +466828,Male,Yes,50,Yes,Executive,0.0,High,4.0,Cat_6,B +464679,Male,Yes,41,Yes,Artist,0.0,Average,4.0,Cat_4,C +464145,Female,No,36,No,Engineer,14.0,Low,4.0,Cat_6,D +465543,Female,Yes,43,Yes,Healthcare,0.0,Average,2.0,Cat_3,A +467630,Male,Yes,85,No,Lawyer,0.0,Low,1.0,Cat_6,D +465726,Male,No,70,No,,,Low,4.0,Cat_4,D +466218,Female,,22,No,Healthcare,5.0,Low,4.0,Cat_2,D +459645,Male,Yes,43,Yes,Artist,,Average,2.0,Cat_6,B +465521,Female,Yes,47,Yes,Artist,1.0,Average,2.0,Cat_7,C +460300,Female,Yes,60,Yes,Lawyer,0.0,Low,2.0,Cat_6,D +466821,Male,No,30,Yes,Healthcare,0.0,Low,,Cat_1,D +466367,Female,No,37,Yes,Doctor,5.0,Low,1.0,Cat_2,B +464898,Male,No,31,Yes,Healthcare,0.0,Low,2.0,Cat_4,C +459477,Female,No,36,No,Engineer,,Low,2.0,Cat_6,D +464698,Male,Yes,82,Yes,Lawyer,0.0,Low,,Cat_4,A +467129,Female,,49,No,Marketing,1.0,Average,3.0,Cat_4,A +462712,Male,No,33,Yes,Homemaker,,Low,1.0,Cat_6,D +465632,Male,No,37,Yes,Artist,4.0,Low,7.0,Cat_7,A +460735,Male,Yes,35,Yes,Engineer,1.0,Average,4.0,Cat_3,A +467812,Male,No,50,Yes,Artist,1.0,Low,1.0,Cat_2,B +464950,Male,No,21,No,Healthcare,1.0,Low,4.0,Cat_4,D +459562,Male,Yes,59,No,Entertainment,1.0,Average,2.0,Cat_7,C +464839,Female,No,20,No,Healthcare,1.0,Low,5.0,Cat_4,D +465959,Female,No,49,Yes,Engineer,9.0,Low,1.0,Cat_6,A +463624,Male,Yes,62,Yes,Executive,0.0,High,4.0,Cat_6,C +466227,Male,Yes,61,Yes,Artist,0.0,Average,5.0,Cat_6,C +463056,Male,Yes,43,Yes,Homemaker,,Average,2.0,Cat_4,A +465334,Female,Yes,41,No,Marketing,5.0,Average,3.0,Cat_6,A +460595,Male,Yes,21,No,Doctor,1.0,Low,3.0,Cat_3,D +461618,Female,Yes,42,Yes,Artist,,High,3.0,Cat_6,C +464955,Female,Yes,38,No,Engineer,1.0,Average,6.0,Cat_4,B +459642,Male,Yes,36,Yes,Executive,,High,4.0,Cat_6,B +460844,Male,Yes,51,Yes,Healthcare,,Low,2.0,Cat_6,A +463188,Female,Yes,48,Yes,Artist,2.0,Low,2.0,Cat_6,C +463889,Female,No,29,Yes,Homemaker,,Low,1.0,Cat_1,D +459509,Male,No,30,Yes,Healthcare,1.0,Low,1.0,Cat_6,D +460211,Male,Yes,25,,Artist,6.0,Low,1.0,Cat_6,A +464838,Male,No,19,No,Healthcare,0.0,Low,4.0,Cat_2,D +462134,Male,Yes,88,No,Lawyer,1.0,Low,1.0,,D +464635,Male,Yes,43,Yes,Healthcare,1.0,High,4.0,Cat_4,B +465213,Male,Yes,38,Yes,Entertainment,2.0,Average,2.0,Cat_6,B +460317,Male,No,32,No,Doctor,10.0,Low,2.0,Cat_6,D +460196,Female,No,38,Yes,Healthcare,8.0,Low,2.0,Cat_6,D +463643,Female,No,43,Yes,Artist,0.0,Low,3.0,Cat_6,A +459285,Male,No,76,Yes,Lawyer,0.0,Low,2.0,Cat_6,A +464558,Male,No,30,Yes,Healthcare,9.0,Low,5.0,Cat_6,D +463950,Male,No,31,No,Healthcare,1.0,Low,4.0,Cat_7,D +464657,Male,No,23,No,Healthcare,0.0,Low,3.0,Cat_3,D +461922,Male,Yes,70,No,Lawyer,0.0,High,3.0,Cat_1,B +465421,Female,No,25,No,Engineer,1.0,Low,1.0,Cat_4,A +463617,Male,No,29,No,Marketing,1.0,Low,2.0,Cat_6,D +461574,Female,Yes,50,Yes,Artist,1.0,Average,3.0,Cat_6,C +467638,Male,Yes,71,Yes,Artist,1.0,Average,2.0,Cat_6,C +460762,Female,No,18,No,Healthcare,0.0,Low,5.0,Cat_4,D +467568,Male,No,42,Yes,Artist,8.0,Low,,Cat_6,A +462623,Male,No,35,No,Healthcare,,Low,1.0,Cat_4,D +459762,Female,Yes,45,Yes,Artist,8.0,Average,2.0,,B +460408,Female,No,41,Yes,Artist,2.0,Low,1.0,Cat_6,A +461089,Male,No,20,No,Marketing,0.0,Low,3.0,Cat_3,B +466866,Male,Yes,27,No,Entertainment,14.0,Average,2.0,Cat_6,A +467781,Male,Yes,27,Yes,Healthcare,1.0,High,2.0,Cat_6,D +467867,Female,No,18,No,Healthcare,0.0,Low,4.0,Cat_4,D +459309,Female,Yes,41,Yes,Artist,9.0,High,2.0,Cat_2,B +465040,Male,Yes,67,No,Lawyer,1.0,Average,2.0,Cat_6,A +462387,Female,Yes,28,Yes,Artist,0.0,Low,2.0,Cat_7,A +459621,Female,No,29,No,Healthcare,8.0,Low,,,D +463646,Male,Yes,45,No,Artist,1.0,Average,4.0,Cat_6,B +462810,Male,Yes,36,No,Executive,8.0,High,4.0,Cat_6,C +460065,Female,Yes,79,Yes,Artist,,Average,2.0,Cat_6,C +463999,Female,No,33,No,Engineer,1.0,Low,3.0,Cat_6,C +461785,Female,Yes,87,Yes,Artist,1.0,Average,2.0,Cat_6,C +461238,Female,Yes,52,Yes,Artist,3.0,Low,1.0,Cat_6,A +464200,Female,No,32,No,Healthcare,1.0,Low,3.0,Cat_6,D +465819,Female,No,42,Yes,Engineer,1.0,Low,7.0,Cat_4,A +462018,Male,No,22,No,Healthcare,8.0,Low,5.0,Cat_2,D +459275,Female,No,18,No,Doctor,2.0,Low,4.0,Cat_6,D +461498,Female,No,26,No,Doctor,1.0,Low,4.0,Cat_2,D +461894,Female,Yes,66,Yes,Artist,0.0,Average,2.0,Cat_6,C +461176,Female,Yes,66,No,Engineer,6.0,Average,4.0,Cat_5,B +460759,Male,Yes,69,No,Lawyer,1.0,Low,2.0,Cat_1,B +460570,Male,No,25,Yes,Marketing,,Low,5.0,Cat_3,C +463928,Male,Yes,55,Yes,Artist,1.0,Low,2.0,Cat_6,A +465753,Female,Yes,49,Yes,Artist,6.0,Low,2.0,Cat_6,C +467354,Male,Yes,61,Yes,Doctor,3.0,Low,2.0,Cat_6,A +467326,Female,No,47,Yes,Artist,0.0,Low,1.0,Cat_1,C +461331,Male,Yes,59,Yes,Executive,0.0,High,5.0,Cat_6,C +462744,Male,Yes,38,Yes,Artist,3.0,Average,4.0,Cat_6,B +461436,Male,Yes,55,Yes,Artist,8.0,Average,2.0,Cat_2,C +463057,Female,No,25,No,Homemaker,1.0,Low,1.0,Cat_2,D +467609,Male,No,36,No,Artist,2.0,Low,4.0,Cat_6,A +466451,Male,Yes,41,Yes,Entertainment,0.0,Low,1.0,Cat_6,A +463680,Female,No,39,No,Engineer,1.0,Low,3.0,Cat_3,A +463589,Female,No,20,No,Healthcare,1.0,Low,3.0,Cat_6,D +460459,Female,No,33,No,Engineer,0.0,Low,5.0,Cat_3,D +459128,Female,Yes,78,No,Lawyer,0.0,High,3.0,Cat_6,B +464957,Female,Yes,87,No,Lawyer,0.0,Low,1.0,Cat_4,A +464835,Male,No,19,No,Healthcare,1.0,Low,6.0,Cat_4,D +466428,Female,No,23,No,,0.0,Low,2.0,Cat_6,D +466210,Female,Yes,60,Yes,Artist,1.0,High,4.0,Cat_6,C +462351,Male,Yes,77,No,Lawyer,1.0,High,2.0,Cat_6,C +460015,Female,Yes,43,No,Entertainment,5.0,Low,1.0,Cat_3,D +463355,Female,Yes,51,Yes,Artist,6.0,Average,3.0,Cat_6,C +465354,Male,No,18,No,Doctor,0.0,Low,5.0,Cat_2,C +464373,Female,No,46,Yes,Artist,0.0,Low,2.0,Cat_6,B +459611,Female,No,20,No,Healthcare,1.0,Low,5.0,Cat_6,D +466487,Female,No,27,Yes,Healthcare,0.0,Low,5.0,Cat_4,A +462166,Male,Yes,26,Yes,Artist,0.0,Low,2.0,Cat_1,C +461056,Female,Yes,57,Yes,Artist,0.0,High,3.0,Cat_3,C +461488,Male,Yes,53,Yes,Artist,6.0,Average,2.0,Cat_6,C +465745,Male,Yes,38,Yes,Artist,2.0,Low,2.0,Cat_6,C +464415,Male,Yes,41,No,Doctor,1.0,Low,3.0,Cat_6,A +462727,Female,Yes,81,No,Lawyer,0.0,High,2.0,Cat_6,A +463268,Female,Yes,38,No,Engineer,0.0,Low,3.0,Cat_4,A +462085,Female,No,30,Yes,Healthcare,0.0,Low,1.0,Cat_6,D +465226,Male,Yes,37,No,Entertainment,2.0,Low,3.0,Cat_6,A +461465,Male,Yes,28,Yes,Artist,9.0,Average,2.0,Cat_6,A +461409,Male,Yes,43,No,Artist,1.0,High,3.0,Cat_6,B +467791,Male,No,21,No,Doctor,0.0,Low,5.0,Cat_7,D +463761,Male,Yes,33,Yes,Artist,1.0,Average,3.0,Cat_6,A +466098,Male,Yes,78,Yes,Lawyer,1.0,High,2.0,Cat_6,A +460471,Female,No,18,No,,0.0,Low,6.0,Cat_4,D +465783,Male,No,23,No,Healthcare,1.0,Low,5.0,Cat_3,D +467796,Female,No,27,No,Healthcare,9.0,Low,2.0,Cat_6,A +464859,Female,Yes,45,Yes,Artist,1.0,Average,3.0,Cat_6,B +467386,Male,No,31,No,Doctor,0.0,Low,1.0,Cat_6,D +461984,Male,Yes,42,Yes,Artist,2.0,Average,2.0,Cat_6,C +467205,Female,No,37,Yes,Healthcare,1.0,Low,3.0,Cat_6,C +464525,Male,No,33,Yes,Entertainment,9.0,Low,2.0,Cat_6,D +467103,Male,No,32,Yes,Healthcare,1.0,Low,3.0,Cat_6,A +461067,Male,No,21,No,Healthcare,1.0,Low,3.0,Cat_3,D +462569,Female,No,32,No,Doctor,7.0,Low,4.0,Cat_3,D +462069,Male,Yes,36,Yes,Artist,0.0,Average,2.0,Cat_6,B +465614,Male,Yes,43,Yes,Artist,9.0,Low,2.0,Cat_6,C +464822,Male,Yes,37,No,Entertainment,,Average,3.0,Cat_4,A +460252,Male,Yes,41,Yes,Artist,0.0,Low,2.0,Cat_6,A +466603,Male,Yes,41,Yes,Engineer,1.0,Average,2.0,Cat_6,C +462884,Female,Yes,55,No,Artist,1.0,Low,2.0,Cat_3,B +462418,Male,Yes,61,No,Engineer,9.0,Average,4.0,Cat_6,C +465699,Female,No,38,No,Artist,13.0,Low,2.0,Cat_4,D +466488,Male,Yes,62,Yes,Entertainment,0.0,Average,6.0,Cat_6,C +462783,Female,No,33,No,Marketing,0.0,Low,4.0,Cat_3,D +467063,Male,Yes,53,Yes,Entertainment,1.0,Average,2.0,Cat_6,B +461919,Male,No,38,Yes,Doctor,0.0,Low,3.0,Cat_6,A +461844,Male,Yes,51,No,Artist,2.0,Average,4.0,Cat_7,C +460600,Female,No,41,Yes,Artist,2.0,Low,,Cat_3,A +462636,Female,Yes,36,No,Marketing,,Low,4.0,Cat_4,D +464981,Male,Yes,45,Yes,Artist,0.0,Average,4.0,Cat_6,C +466034,Female,Yes,71,No,Engineer,2.0,Low,1.0,Cat_6,B +461078,Female,No,26,No,Homemaker,0.0,Low,6.0,Cat_3,A +465664,Female,Yes,84,Yes,Lawyer,1.0,Low,1.0,Cat_6,A +467037,Male,Yes,56,No,Lawyer,0.0,High,2.0,Cat_6,C +465474,Male,No,27,No,Marketing,8.0,Low,4.0,Cat_4,D +459158,Male,No,36,No,Engineer,3.0,Low,3.0,Cat_6,D +461545,Female,Yes,70,Yes,Artist,1.0,Low,3.0,Cat_7,B +463819,Male,Yes,25,,Artist,8.0,Low,,Cat_6,A +464397,Female,Yes,40,Yes,Engineer,0.0,Average,2.0,Cat_6,C +464003,Male,No,22,No,Healthcare,0.0,Low,4.0,Cat_2,D +466221,Male,Yes,48,Yes,Artist,4.0,Low,1.0,Cat_6,B +465172,Female,Yes,41,Yes,Artist,9.0,Average,2.0,Cat_6,C +460621,Male,Yes,77,Yes,Lawyer,0.0,Low,2.0,Cat_3,C +467702,Male,Yes,72,Yes,Artist,6.0,Low,1.0,Cat_6,D +461036,Female,No,21,No,Healthcare,1.0,Low,3.0,Cat_3,D +460376,Female,No,29,Yes,Healthcare,3.0,Low,4.0,Cat_2,C +467348,Female,Yes,33,Yes,Homemaker,,Average,3.0,Cat_6,D +462595,Female,No,29,Yes,Artist,7.0,Low,7.0,Cat_4,D +461195,Male,No,18,No,Healthcare,0.0,Low,2.0,Cat_4,D +466353,Female,Yes,38,Yes,Engineer,0.0,Average,2.0,Cat_6,B +466464,Female,Yes,48,Yes,Homemaker,3.0,Average,2.0,Cat_3,B +466287,Female,Yes,48,Yes,Doctor,1.0,Average,2.0,Cat_2,A +462035,Female,No,37,Yes,Artist,3.0,Low,1.0,Cat_6,C +463082,Male,No,32,Yes,Healthcare,9.0,Low,5.0,Cat_6,D +463600,Male,No,33,No,Healthcare,3.0,Low,5.0,Cat_2,B +463679,Male,No,31,No,Healthcare,,Low,8.0,Cat_4,D +465812,Male,Yes,47,No,Artist,0.0,Average,5.0,Cat_4,A +465048,Male,Yes,63,Yes,Artist,1.0,Average,3.0,Cat_4,C +460025,Female,No,26,Yes,Healthcare,5.0,Low,2.0,Cat_6,D +466764,Male,Yes,65,Yes,Lawyer,11.0,High,2.0,Cat_6,C +462125,Male,Yes,22,No,Healthcare,1.0,Average,4.0,Cat_7,D +459551,Female,Yes,42,No,Engineer,4.0,Low,1.0,Cat_6,B +467152,Male,Yes,43,No,Artist,8.0,Low,1.0,Cat_6,A +461174,Female,No,23,No,Marketing,,Low,4.0,Cat_3,B +466420,Male,No,27,Yes,Artist,5.0,Low,4.0,Cat_3,B +460051,Male,No,42,Yes,Artist,7.0,Low,1.0,Cat_6,A +463144,Male,Yes,45,Yes,Executive,1.0,High,3.0,Cat_6,C +465438,Male,No,33,Yes,Doctor,9.0,Low,6.0,Cat_4,D +460438,Male,Yes,58,No,Executive,0.0,High,4.0,Cat_3,B +459032,Male,No,21,No,Doctor,0.0,Low,3.0,Cat_6,A +461199,Female,No,85,Yes,Homemaker,4.0,Low,1.0,Cat_6,D +462061,Male,No,41,No,Doctor,1.0,Low,3.0,Cat_6,B +467623,Male,Yes,76,Yes,,1.0,High,2.0,Cat_6,A +466119,Female,Yes,57,No,Engineer,0.0,Low,1.0,Cat_3,B +460952,Male,Yes,38,No,Doctor,0.0,Average,5.0,Cat_6,A +459024,Male,Yes,50,Yes,Artist,0.0,Average,5.0,Cat_6,C +461632,Male,No,25,Yes,Artist,,Low,1.0,Cat_6,D +464010,Male,No,33,No,Healthcare,1.0,Low,4.0,Cat_6,C +464405,Male,No,62,Yes,Entertainment,,Low,1.0,Cat_6,D +459189,Male,No,26,No,Healthcare,1.0,Low,6.0,Cat_6,C +466972,Male,Yes,25,Yes,Artist,12.0,Low,2.0,Cat_6,A +467072,Male,Yes,63,No,Executive,1.0,High,3.0,Cat_6,C +462448,Male,No,19,No,Healthcare,,Low,3.0,Cat_6,D +467078,Female,Yes,26,No,Healthcare,,High,2.0,Cat_7,C +460437,Female,No,26,Yes,Artist,9.0,Low,1.0,Cat_6,A +463830,Female,No,30,Yes,Engineer,3.0,Low,3.0,Cat_6,D +463475,Female,Yes,36,Yes,Doctor,0.0,Low,1.0,Cat_6,B +462237,Female,No,22,No,Healthcare,1.0,Low,9.0,Cat_4,D +466804,Female,,40,Yes,Homemaker,4.0,Low,1.0,Cat_3,A +460184,Male,No,27,No,Doctor,9.0,Low,4.0,Cat_6,D +461287,Male,Yes,42,Yes,Executive,1.0,High,5.0,Cat_6,B +462039,Male,No,21,No,Healthcare,1.0,Low,2.0,Cat_2,D +467482,Male,Yes,38,Yes,Executive,0.0,High,3.0,Cat_6,C +459081,Male,Yes,63,Yes,Artist,0.0,Average,4.0,Cat_6,C +463048,Female,Yes,47,Yes,Homemaker,,High,5.0,Cat_6,A +460711,Female,No,25,Yes,Engineer,8.0,Low,3.0,Cat_6,D +462984,Female,Yes,69,Yes,Artist,1.0,High,2.0,Cat_6,B +459447,Male,Yes,53,Yes,Artist,9.0,Low,1.0,Cat_6,B +466008,Male,Yes,36,Yes,Artist,1.0,Low,2.0,Cat_6,C +467567,Male,No,27,Yes,Healthcare,1.0,Low,5.0,Cat_6,D +461001,Female,No,59,Yes,Artist,0.0,Low,,Cat_3,B +462836,Male,No,19,No,Healthcare,1.0,Low,3.0,Cat_6,D +462176,Male,Yes,29,No,Executive,0.0,Average,2.0,,D +459580,Female,No,51,Yes,Lawyer,6.0,Low,1.0,Cat_6,B +460160,Male,No,43,Yes,Entertainment,5.0,Low,1.0,Cat_3,D +460808,Male,No,42,Yes,Artist,1.0,Low,1.0,Cat_6,B +459640,Female,Yes,72,Yes,Doctor,,High,2.0,Cat_6,C +467199,Male,No,37,Yes,Artist,7.0,Low,2.0,Cat_6,A +466939,Male,Yes,66,No,Lawyer,0.0,High,2.0,Cat_6,B +461796,Male,Yes,39,Yes,Artist,1.0,Average,2.0,Cat_1,A +464686,Female,No,21,No,Entertainment,5.0,Low,5.0,Cat_4,D +460843,Male,No,21,No,Healthcare,1.0,Low,4.0,Cat_6,D +460370,Female,Yes,28,Yes,Executive,8.0,High,2.0,Cat_6,C +465059,Male,Yes,56,Yes,Artist,0.0,Average,4.0,Cat_6,C +465531,Female,No,40,Yes,Homemaker,9.0,Low,,Cat_6,D +465925,Male,No,26,Yes,Healthcare,1.0,Low,1.0,Cat_6,D +464828,Male,No,18,No,Healthcare,8.0,Low,6.0,Cat_4,D +462230,Male,No,23,No,Healthcare,1.0,Low,8.0,Cat_4,D +466154,Female,Yes,45,Yes,Artist,1.0,Average,5.0,Cat_3,B +462197,Female,Yes,65,No,Engineer,1.0,Average,3.0,Cat_4,A +461692,Female,Yes,87,No,Artist,0.0,Low,1.0,Cat_6,A +462019,Female,Yes,56,Yes,Artist,5.0,Average,4.0,Cat_6,C +460763,Male,No,47,Yes,Artist,1.0,Low,2.0,Cat_6,B +465740,Female,No,68,Yes,Doctor,1.0,Low,1.0,Cat_6,A +460865,Male,No,18,No,Healthcare,1.0,Low,3.0,Cat_4,D +459991,Male,Yes,40,Yes,Artist,6.0,Low,1.0,Cat_6,A +463564,Female,Yes,41,Yes,Doctor,0.0,Low,1.0,Cat_6,B +463749,Male,No,23,No,Healthcare,1.0,Low,4.0,Cat_6,D +464037,Male,No,35,Yes,Artist,9.0,Low,1.0,Cat_6,B +459180,Male,Yes,66,Yes,Executive,1.0,High,2.0,Cat_6,C +460901,Male,No,22,No,Doctor,8.0,Low,3.0,Cat_6,D +465312,Male,No,23,No,Healthcare,0.0,Low,4.0,Cat_6,D +466585,Male,No,18,No,Marketing,1.0,Low,4.0,Cat_1,D +460444,Male,Yes,60,No,Artist,0.0,Low,1.0,Cat_6,B +462209,Female,Yes,40,No,Artist,0.0,Low,1.0,Cat_4,D +462649,Female,Yes,30,No,Engineer,,Average,8.0,Cat_4,D +464132,Male,No,28,No,Entertainment,1.0,Low,2.0,Cat_6,D +467601,Male,Yes,42,No,Artist,6.0,Low,2.0,Cat_6,B +464458,Male,No,46,Yes,Artist,1.0,Low,2.0,Cat_6,B +466346,Female,No,23,No,Healthcare,9.0,Low,4.0,Cat_4,D +465471,Male,Yes,43,Yes,Artist,0.0,Low,1.0,Cat_6,B +460179,Female,No,20,No,Entertainment,0.0,Low,6.0,Cat_2,B +461833,Female,No,27,Yes,Healthcare,5.0,Low,,Cat_6,C +459801,Female,No,27,Yes,Engineer,4.0,Low,2.0,Cat_6,D +461719,Male,Yes,82,Yes,Lawyer,0.0,Low,1.0,Cat_6,D +460831,Male,No,21,No,Healthcare,0.0,Low,4.0,Cat_6,D +463833,Female,No,25,No,Doctor,,Low,4.0,Cat_4,A +464974,Male,Yes,60,No,Artist,0.0,Average,2.0,Cat_4,B +465344,Female,Yes,71,Yes,Lawyer,0.0,High,2.0,Cat_6,C +460398,Female,No,39,Yes,Entertainment,7.0,Low,1.0,Cat_6,A +463681,Male,Yes,83,Yes,Lawyer,,Low,1.0,Cat_6,D +459725,Female,Yes,35,Yes,Artist,1.0,Low,2.0,Cat_6,B +466580,Male,No,19,No,Healthcare,1.0,Low,4.0,Cat_7,D +464207,Male,No,53,Yes,Artist,0.0,Low,1.0,Cat_6,A +461281,Male,No,23,No,Healthcare,4.0,Low,9.0,Cat_2,D +465781,Male,No,20,No,Healthcare,8.0,Low,,Cat_3,D +461285,Male,No,20,No,Healthcare,,Low,3.0,Cat_6,D +463360,Female,Yes,50,Yes,Artist,4.0,Low,1.0,Cat_6,B +461622,Male,Yes,35,Yes,Executive,4.0,High,4.0,Cat_6,C +462911,Male,Yes,32,Yes,Doctor,11.0,Average,2.0,Cat_6,A +463963,Female,Yes,33,No,Engineer,5.0,High,3.0,Cat_6,D +467297,Female,Yes,53,Yes,Artist,1.0,Average,3.0,Cat_6,C +461744,Male,Yes,47,Yes,Artist,0.0,Average,4.0,Cat_6,B +464696,Female,No,37,Yes,Engineer,0.0,Low,1.0,Cat_4,A +460929,Male,Yes,50,Yes,Artist,0.0,Average,4.0,Cat_6,C +462840,Female,No,23,No,Healthcare,9.0,Low,3.0,Cat_6,D +460251,Female,No,30,Yes,Entertainment,9.0,Low,1.0,Cat_6,D +465476,Female,No,28,No,Healthcare,3.0,Low,2.0,Cat_6,A +461534,Male,Yes,39,Yes,Artist,1.0,Low,1.0,Cat_6,B +465097,Male,Yes,81,No,Lawyer,1.0,High,2.0,Cat_6,A +464391,Female,Yes,65,No,Engineer,0.0,Average,2.0,Cat_6,B +461619,Male,Yes,53,Yes,Executive,,High,3.0,Cat_6,C +466519,Male,Yes,37,,Entertainment,0.0,Low,,Cat_3,B +460833,Male,Yes,30,Yes,Healthcare,0.0,Low,2.0,Cat_6,A +464425,Male,No,40,Yes,Artist,8.0,Low,1.0,Cat_6,B +462231,Male,No,28,Yes,Artist,4.0,Low,6.0,Cat_4,B +464688,Male,Yes,28,Yes,Doctor,0.0,Low,9.0,Cat_4,D +466141,Female,Yes,60,Yes,Homemaker,1.0,Low,4.0,Cat_6,C +464435,Male,Yes,48,Yes,Entertainment,1.0,Low,3.0,Cat_6,A +467824,Male,Yes,70,Yes,Lawyer,3.0,Low,1.0,Cat_6,D +459670,Female,Yes,39,Yes,Entertainment,7.0,Average,2.0,Cat_6,B +462515,Male,Yes,42,No,Doctor,5.0,Average,5.0,Cat_6,B +461604,Male,Yes,38,No,Entertainment,1.0,Average,2.0,Cat_6,B +462472,Male,No,35,Yes,Artist,0.0,Low,1.0,Cat_6,C +465149,Male,Yes,43,Yes,Entertainment,1.0,Average,3.0,Cat_6,B +459673,Female,Yes,83,Yes,Lawyer,1.0,Low,1.0,Cat_6,B +465507,Male,Yes,43,Yes,Artist,8.0,Low,1.0,Cat_6,B +467393,Female,Yes,46,Yes,Artist,0.0,Average,4.0,Cat_6,C +460713,Male,Yes,50,Yes,Doctor,0.0,Average,3.0,Cat_6,B +465366,Female,No,38,Yes,Artist,8.0,Low,1.0,Cat_6,B +465073,Male,Yes,69,No,Executive,1.0,High,2.0,Cat_6,C +464626,Female,Yes,38,Yes,Artist,1.0,Average,2.0,Cat_6,C +465450,Female,No,26,Yes,Healthcare,0.0,Low,3.0,Cat_6,D +461157,Male,No,37,No,Healthcare,0.0,Low,5.0,Cat_3,A +465032,Male,Yes,72,Yes,Executive,0.0,High,2.0,Cat_6,C +460965,Male,Yes,52,Yes,Artist,1.0,Average,4.0,Cat_6,B +466166,Male,Yes,85,Yes,Lawyer,1.0,High,2.0,Cat_4,A +460131,Female,No,29,Yes,Healthcare,6.0,Low,3.0,Cat_6,D +465577,Male,No,26,No,Healthcare,0.0,Low,4.0,Cat_3,C +463334,Female,No,33,Yes,Doctor,0.0,Low,6.0,Cat_4,C +462100,Female,No,40,Yes,Healthcare,0.0,Low,1.0,Cat_2,D +459125,Female,No,32,Yes,Artist,,Low,7.0,Cat_6,A +467734,Male,Yes,79,Yes,Lawyer,2.0,High,2.0,Cat_6,B +462865,Female,No,25,Yes,Healthcare,,Low,1.0,Cat_6,D +465932,Male,No,35,No,Artist,1.0,Low,1.0,Cat_7,A +460377,Female,Yes,68,Yes,Lawyer,0.0,Low,1.0,Cat_6,C +463138,Female,No,28,No,Homemaker,,Low,,Cat_3,D +461202,Male,No,33,Yes,Healthcare,,Low,4.0,Cat_6,C +466288,Male,Yes,36,Yes,Artist,7.0,Average,5.0,Cat_2,B +459161,Male,Yes,51,Yes,Executive,3.0,High,5.0,Cat_6,C +466780,Female,No,36,Yes,Artist,11.0,Low,,Cat_6,A +466959,Male,Yes,25,Yes,Artist,8.0,Low,4.0,Cat_6,D +463786,Female,Yes,37,Yes,Artist,,Average,2.0,Cat_6,C +459093,Female,Yes,39,Yes,Doctor,8.0,Average,2.0,,C +463747,Female,Yes,35,Yes,Artist,0.0,Average,2.0,Cat_6,C +460259,Male,No,21,No,Healthcare,1.0,Low,4.0,Cat_2,D +464494,Female,Yes,55,Yes,Artist,1.0,Average,4.0,Cat_3,C +466563,Female,No,20,No,Marketing,,Low,4.0,Cat_6,D +466722,Male,Yes,35,No,Executive,1.0,High,3.0,Cat_6,C +460561,Female,No,39,No,Engineer,6.0,Low,1.0,Cat_3,C +461522,Male,Yes,83,No,Executive,1.0,High,2.0,Cat_6,D +459039,Male,Yes,45,Yes,Artist,1.0,Average,2.0,Cat_6,C +459452,Male,Yes,40,Yes,Artist,9.0,Low,2.0,Cat_6,C +461713,Female,No,27,Yes,Artist,1.0,Low,5.0,Cat_6,C +461567,Male,Yes,51,No,Entertainment,7.0,Average,3.0,Cat_6,C +461495,Male,Yes,35,Yes,Artist,1.0,Average,3.0,Cat_6,B +461602,Female,No,26,Yes,Artist,0.0,Low,,Cat_6,A +461781,Male,Yes,57,Yes,Artist,0.0,Low,2.0,Cat_6,C +462126,Male,Yes,20,No,Healthcare,1.0,Average,4.0,Cat_7,D +461247,Female,No,49,Yes,Artist,4.0,Low,1.0,Cat_6,A +461620,Male,No,52,Yes,Entertainment,1.0,Low,2.0,Cat_6,B +463286,Male,Yes,35,Yes,Artist,1.0,Average,3.0,Cat_6,C +466553,Male,Yes,39,Yes,Healthcare,1.0,Low,3.0,Cat_6,D +466239,Male,Yes,69,Yes,Executive,0.0,Low,2.0,Cat_4,B +462393,Female,No,26,Yes,Entertainment,6.0,Low,2.0,Cat_6,D +465502,Male,Yes,36,Yes,Artist,8.0,Low,4.0,Cat_4,D +459337,Female,Yes,25,Yes,Engineer,6.0,Average,2.0,Cat_6,A +466934,Female,Yes,60,Yes,Artist,0.0,Low,1.0,Cat_6,B +467965,Female,No,37,Yes,Healthcare,1.0,Low,2.0,Cat_6,A +465840,Female,No,42,No,Artist,0.0,Low,1.0,Cat_5,D +465689,Male,No,22,No,Healthcare,9.0,Low,4.0,Cat_3,D +462982,Male,Yes,41,No,Doctor,2.0,Average,4.0,Cat_6,A +464262,Male,Yes,75,No,Executive,0.0,Low,1.0,Cat_6,D +463095,Female,Yes,37,Yes,Homemaker,,High,2.0,Cat_6,A +461687,Female,Yes,77,Yes,Artist,0.0,High,2.0,Cat_6,B +467124,Male,Yes,57,No,,1.0,Average,2.0,Cat_4,B +464499,Male,Yes,43,Yes,Executive,1.0,High,4.0,Cat_6,B +467778,Female,No,31,Yes,Healthcare,8.0,Low,4.0,Cat_6,C +466280,Male,No,33,No,Doctor,1.0,Low,2.0,Cat_3,B +463523,Male,Yes,60,Yes,Executive,1.0,High,4.0,Cat_6,B +462937,Female,Yes,61,Yes,Artist,1.0,Low,2.0,Cat_6,C +460649,Female,No,29,No,Homemaker,8.0,Low,1.0,Cat_4,D +460911,Male,No,25,Yes,Healthcare,1.0,Low,4.0,Cat_6,D +462269,Male,Yes,83,Yes,Artist,2.0,High,2.0,Cat_6,A +459279,Female,No,38,Yes,Artist,7.0,Low,1.0,Cat_6,C +461566,Male,No,28,Yes,Artist,0.0,Low,4.0,Cat_7,C +462324,Male,Yes,65,Yes,Entertainment,0.0,High,2.0,Cat_6,A +463253,Female,Yes,61,Yes,Doctor,0.0,Average,4.0,Cat_7,B +465528,Female,Yes,46,Yes,,0.0,Low,2.0,Cat_6,C +464667,Female,Yes,51,No,Engineer,4.0,Low,2.0,Cat_4,D +462606,Male,Yes,36,No,Executive,2.0,High,3.0,Cat_4,D +466961,Female,Yes,55,Yes,Artist,2.0,Average,2.0,Cat_6,C +463431,Male,Yes,43,Yes,Artist,,Average,4.0,Cat_4,B +459585,Female,Yes,46,Yes,Artist,0.0,Average,4.0,Cat_6,A +459304,Female,Yes,43,Yes,Artist,8.0,Average,2.0,Cat_6,B +462523,Male,Yes,57,No,Doctor,4.0,Low,3.0,Cat_4,B +464805,Male,Yes,35,No,Doctor,0.0,Low,1.0,Cat_4,A +465036,Male,Yes,65,Yes,Artist,0.0,Average,2.0,Cat_6,C +467953,Female,Yes,37,Yes,Artist,0.0,Low,3.0,Cat_6,A +464381,Male,Yes,35,Yes,Artist,4.0,Average,2.0,Cat_6,A +466115,Male,Yes,37,No,Executive,1.0,Low,2.0,Cat_6,A +467606,Male,Yes,38,Yes,Executive,0.0,Average,3.0,Cat_6,B +466340,Male,No,19,No,Healthcare,1.0,Low,3.0,Cat_4,D +461453,Male,Yes,41,Yes,Artist,8.0,Low,,Cat_6,D +460628,Male,Yes,27,Yes,Entertainment,,Low,,Cat_3,B +459916,Male,No,36,Yes,Artist,0.0,Low,1.0,Cat_3,B +462341,Male,Yes,61,Yes,Artist,2.0,Average,2.0,Cat_6,C +467613,Male,Yes,53,No,Artist,1.0,Average,6.0,Cat_6,A +462706,Male,Yes,45,Yes,Artist,0.0,Low,1.0,Cat_6,C +466095,Female,,27,Yes,Healthcare,1.0,Low,6.0,Cat_6,C +466393,Female,Yes,42,No,Entertainment,1.0,Low,6.0,Cat_3,B +466735,Male,No,18,No,Healthcare,0.0,Low,,Cat_6,D +461296,Female,Yes,83,Yes,Lawyer,0.0,High,2.0,Cat_6,C +463987,Female,No,36,Yes,Artist,0.0,Low,1.0,Cat_6,B +462094,Female,No,37,Yes,Healthcare,,Low,1.0,Cat_6,D +463073,Male,Yes,42,Yes,Artist,1.0,Average,3.0,Cat_6,C +466539,Female,Yes,80,No,Lawyer,1.0,High,2.0,Cat_6,C +463311,Female,No,18,No,Healthcare,0.0,Low,5.0,Cat_3,D +462355,Male,Yes,61,No,Entertainment,1.0,Low,,Cat_6,A +461960,Male,Yes,57,Yes,Entertainment,0.0,Low,2.0,Cat_6,A +461848,Male,No,36,No,Marketing,0.0,Low,2.0,Cat_6,D +461963,Female,No,43,Yes,Artist,8.0,Low,1.0,Cat_6,A +459268,Female,No,33,No,Homemaker,7.0,Low,,Cat_6,D +465477,Male,No,31,No,Healthcare,1.0,Low,3.0,Cat_2,B +460619,Male,No,52,Yes,Entertainment,,Low,,Cat_3,C +466472,Male,No,38,Yes,Doctor,4.0,Low,1.0,Cat_2,B +467727,Male,Yes,45,Yes,Artist,7.0,High,4.0,Cat_3,A +459341,Male,Yes,72,Yes,Lawyer,0.0,High,2.0,Cat_6,C +466486,Female,Yes,43,Yes,Doctor,9.0,Low,2.0,Cat_4,A +460284,Female,No,29,Yes,Entertainment,8.0,Low,2.0,Cat_6,A +463704,Male,No,45,Yes,Artist,1.0,Low,2.0,Cat_6,A +460690,Female,,51,No,Homemaker,8.0,Low,,Cat_3,A +467061,Female,No,29,Yes,Homemaker,13.0,Low,1.0,Cat_6,A +464545,Male,No,43,Yes,Artist,7.0,Low,3.0,Cat_6,A +464239,Female,Yes,42,No,Artist,0.0,Average,3.0,Cat_6,A +464996,Male,Yes,47,No,Entertainment,9.0,Average,6.0,Cat_6,C +461339,Male,No,26,Yes,Healthcare,1.0,Low,3.0,Cat_6,B +461581,Female,Yes,60,Yes,Lawyer,1.0,High,3.0,Cat_6,C +460623,Male,Yes,41,Yes,Executive,5.0,Low,2.0,Cat_1,A +467493,Male,Yes,47,Yes,Executive,1.0,High,4.0,,B +467407,Male,No,27,Yes,Artist,7.0,Low,3.0,Cat_6,D +461110,Male,No,18,No,Healthcare,,Low,3.0,Cat_3,D +460055,Female,No,46,Yes,Artist,8.0,Low,1.0,Cat_6,C +466568,Female,Yes,35,Yes,Artist,,Average,2.0,Cat_6,B +467032,Female,No,25,Yes,Artist,2.0,Low,2.0,Cat_6,A +463590,Male,No,18,No,Healthcare,,Low,3.0,Cat_4,D +467225,Female,No,22,No,Healthcare,1.0,Low,3.0,Cat_6,D +466509,Male,Yes,56,No,Executive,1.0,Average,3.0,Cat_6,B +463087,Female,Yes,35,Yes,Homemaker,9.0,Average,2.0,Cat_4,B +460524,Female,Yes,40,,Homemaker,9.0,Average,,Cat_4,C +459535,Male,Yes,50,Yes,Artist,8.0,Average,2.0,Cat_6,C +467384,Male,No,42,Yes,,,Low,1.0,Cat_6,D +466390,Female,Yes,61,Yes,Homemaker,1.0,Average,,Cat_3,B +463636,Male,No,33,No,Doctor,1.0,Low,6.0,Cat_4,A +466717,Female,Yes,69,No,Entertainment,1.0,Low,2.0,Cat_3,B +466731,Female,Yes,38,Yes,Engineer,4.0,Low,2.0,Cat_4,A +466201,Male,Yes,42,Yes,Doctor,1.0,Average,5.0,Cat_4,C +467729,Male,Yes,61,Yes,Entertainment,3.0,Average,6.0,Cat_6,B +460234,Female,No,20,No,Healthcare,4.0,Low,6.0,Cat_2,D +464342,Female,Yes,46,Yes,Engineer,1.0,Average,2.0,Cat_6,C +460915,Male,Yes,40,No,Entertainment,5.0,Low,4.0,Cat_3,D +466002,Male,Yes,51,Yes,Doctor,1.0,High,2.0,Cat_3,C +459641,Female,Yes,58,Yes,Artist,4.0,Average,5.0,Cat_6,C +463995,Female,No,33,Yes,Healthcare,1.0,Low,5.0,Cat_6,C +463903,Male,No,26,No,Entertainment,0.0,Low,2.0,Cat_6,A +466378,Male,Yes,58,No,Executive,4.0,High,3.0,Cat_6,C +459270,Male,Yes,65,Yes,Homemaker,0.0,High,2.0,Cat_6,A +461257,Male,Yes,41,Yes,Engineer,1.0,Average,2.0,Cat_4,C +461667,Male,Yes,42,Yes,Doctor,0.0,Average,2.0,Cat_6,C +465214,Male,Yes,25,No,Engineer,14.0,Low,1.0,Cat_6,D +461212,Female,No,30,No,Homemaker,10.0,Low,6.0,Cat_6,D +466080,Male,,33,No,Marketing,1.0,High,1.0,Cat_6,D +463608,Male,Yes,25,Yes,,,Low,,Cat_7,A +461697,Male,Yes,79,No,Lawyer,1.0,Low,1.0,Cat_6,D +459913,Female,Yes,51,No,Healthcare,0.0,High,3.0,Cat_4,D +461288,Male,No,26,Yes,Doctor,1.0,Low,4.0,Cat_6,C +465669,Female,Yes,37,No,Entertainment,2.0,Average,4.0,Cat_4,A +460934,Female,No,25,No,Doctor,0.0,Low,2.0,Cat_6,D +466785,Male,No,41,Yes,Marketing,0.0,Low,2.0,Cat_6,D +465034,Female,Yes,83,Yes,Executive,1.0,High,3.0,Cat_6,A +463947,Male,Yes,35,Yes,Entertainment,0.0,Average,3.0,Cat_6,C +465785,Male,No,21,No,Healthcare,3.0,Low,7.0,Cat_4,D +462856,Female,No,28,Yes,Engineer,0.0,Low,3.0,Cat_6,D +459898,Male,Yes,45,Yes,Artist,0.0,Average,2.0,Cat_6,C +463790,Male,Yes,51,No,Executive,0.0,Low,4.0,Cat_4,B +465487,Male,No,25,Yes,Entertainment,1.0,Low,3.0,Cat_6,A +465147,Male,No,29,Yes,Healthcare,2.0,Low,3.0,Cat_6,C +463405,Male,Yes,45,No,Marketing,1.0,Low,2.0,Cat_3,D +460648,Female,,29,No,Homemaker,8.0,Low,1.0,Cat_5,D +464771,Female,Yes,47,No,Engineer,8.0,Average,5.0,Cat_4,B +459358,Female,No,56,Yes,Lawyer,,Low,2.0,Cat_6,D +459152,Female,No,40,Yes,Artist,8.0,Low,2.0,Cat_6,B +464605,Female,Yes,65,Yes,Lawyer,1.0,Low,1.0,Cat_6,A +466481,Male,No,40,Yes,Artist,2.0,Low,1.0,Cat_3,B +462097,Male,Yes,42,Yes,Doctor,8.0,Average,2.0,Cat_2,B +460636,Male,Yes,45,Yes,Artist,2.0,Average,3.0,Cat_3,D +463174,Female,Yes,58,Yes,Engineer,1.0,Low,1.0,Cat_3,A +463239,Female,No,30,No,Doctor,1.0,Low,3.0,Cat_2,D +460081,Male,Yes,35,Yes,Artist,10.0,High,2.0,Cat_6,B +466891,Female,Yes,53,No,Homemaker,9.0,Low,1.0,Cat_6,A +463519,Female,Yes,58,Yes,Artist,0.0,Average,2.0,Cat_6,C +467632,Female,Yes,88,Yes,Artist,1.0,High,2.0,Cat_6,C +465339,Female,No,27,No,Marketing,0.0,Low,3.0,Cat_2,C +467611,Male,No,39,Yes,Artist,9.0,Low,1.0,Cat_6,A +462732,Female,Yes,41,Yes,Artist,,Low,1.0,Cat_6,B +465481,Female,No,28,No,Artist,1.0,Low,4.0,Cat_4,B +459095,Male,Yes,35,No,Entertainment,9.0,Low,3.0,Cat_1,A +464896,Male,Yes,76,No,Executive,1.0,High,2.0,Cat_4,A +460414,Female,No,33,Yes,Healthcare,0.0,Low,1.0,Cat_6,D +461420,Female,Yes,59,Yes,Doctor,5.0,Low,2.0,Cat_6,A +463375,Female,Yes,45,Yes,Doctor,0.0,Low,1.0,Cat_4,B +459546,Female,Yes,81,No,Lawyer,,High,2.0,Cat_6,B +463796,Female,No,33,No,Engineer,1.0,Low,3.0,Cat_6,D +459236,Female,Yes,86,No,Lawyer,0.0,High,2.0,Cat_6,B +459593,Male,No,19,No,Healthcare,,Low,4.0,Cat_6,D +466576,Female,No,20,No,Healthcare,0.0,Low,4.0,Cat_6,D +460633,Male,Yes,37,Yes,,,High,2.0,Cat_3,A +462012,Female,No,25,No,Healthcare,0.0,Low,8.0,Cat_7,B +461198,Female,No,89,Yes,Lawyer,,Low,1.0,Cat_6,D +462533,Female,No,33,Yes,Entertainment,0.0,Low,3.0,Cat_6,A +460171,Female,Yes,62,Yes,Artist,0.0,High,2.0,Cat_6,C +466385,Female,Yes,55,Yes,Artist,4.0,Average,4.0,Cat_7,C +465418,Male,No,39,Yes,Artist,0.0,Low,1.0,Cat_1,A +464411,Male,No,30,Yes,Healthcare,3.0,Low,6.0,Cat_7,D +463254,Female,Yes,37,No,Executive,0.0,High,2.0,Cat_1,A +461456,Female,No,40,No,Marketing,0.0,Low,1.0,Cat_6,A +461557,Male,No,38,Yes,Artist,6.0,Low,4.0,Cat_2,A +462946,Male,Yes,52,No,Artist,1.0,Low,2.0,Cat_6,A +463616,Female,Yes,48,No,Engineer,1.0,Average,5.0,Cat_3,B +462593,Female,Yes,60,No,Engineer,1.0,Average,5.0,Cat_4,B +464693,Female,No,36,Yes,Engineer,9.0,Low,1.0,Cat_4,A +459618,Male,Yes,59,Yes,Doctor,0.0,Average,2.0,Cat_4,C +466494,Male,Yes,71,No,Entertainment,0.0,Average,3.0,Cat_6,A +462223,Male,Yes,78,No,,0.0,Low,1.0,Cat_4,D +462432,Male,Yes,37,Yes,Executive,1.0,Average,2.0,Cat_6,B +464268,Female,No,51,Yes,Artist,0.0,Low,1.0,Cat_6,A +461305,Male,No,22,No,Healthcare,1.0,Low,3.0,Cat_6,D +462006,Male,Yes,28,Yes,Artist,1.0,Average,2.0,Cat_6,B +463917,Female,No,22,No,Healthcare,1.0,Low,8.0,Cat_4,D +463979,Female,Yes,48,Yes,Homemaker,1.0,High,2.0,Cat_2,C +465780,Female,No,23,No,Healthcare,1.0,Low,4.0,Cat_6,D +464795,Female,No,31,No,Engineer,1.0,Low,8.0,Cat_4,A +467596,Male,Yes,37,No,Artist,1.0,Low,5.0,Cat_3,C +459250,Male,Yes,50,Yes,Doctor,1.0,Average,4.0,Cat_6,C +462680,Male,Yes,42,Yes,Doctor,,Average,4.0,Cat_6,C +460468,Female,No,50,Yes,Executive,1.0,Low,1.0,Cat_6,A +466131,Female,No,43,Yes,Artist,1.0,Low,1.0,Cat_6,D +461311,Male,No,20,No,Marketing,0.0,Low,4.0,Cat_6,C +464067,Female,No,28,Yes,Entertainment,0.0,Low,4.0,Cat_2,B +462299,Female,No,33,No,Healthcare,7.0,Low,6.0,Cat_6,C +466165,Female,No,49,Yes,Artist,0.0,Low,1.0,Cat_2,B +462177,Male,No,43,,Artist,6.0,Low,5.0,Cat_2,B +460237,Female,No,40,Yes,Healthcare,9.0,Low,1.0,Cat_4,D +459087,Female,No,21,No,Doctor,0.0,Low,3.0,Cat_6,C +464971,Female,Yes,36,No,Engineer,1.0,Low,5.0,Cat_4,A +460472,Male,Yes,49,No,Doctor,1.0,Average,3.0,Cat_4,A +461145,Female,Yes,36,Yes,Entertainment,0.0,Low,2.0,Cat_3,A +466982,Male,Yes,56,Yes,Artist,,Average,2.0,Cat_6,B +466061,Female,No,41,,,2.0,Low,1.0,Cat_3,A +465960,Female,No,32,Yes,Marketing,8.0,Low,1.0,Cat_6,D +462561,Female,Yes,48,Yes,Artist,4.0,Average,2.0,Cat_6,C +465555,Female,Yes,66,No,Lawyer,,High,2.0,Cat_4,D +462096,Female,No,43,Yes,Homemaker,,Low,1.0,Cat_4,A +466015,Male,Yes,69,No,Entertainment,3.0,Low,8.0,Cat_6,A +459372,Female,No,27,Yes,Healthcare,,Low,4.0,Cat_6,B +463196,Female,Yes,67,Yes,Engineer,1.0,Low,1.0,Cat_6,A +463598,Female,Yes,32,No,Engineer,3.0,Low,2.0,Cat_6,C +464850,Female,,51,No,Artist,,Average,3.0,Cat_4,B +462978,Male,Yes,43,No,Entertainment,,Average,3.0,Cat_6,B +461988,Male,Yes,36,No,Engineer,0.0,Average,2.0,Cat_6,B +461635,Male,Yes,43,Yes,Executive,6.0,High,3.0,Cat_6,C +464687,Male,Yes,36,Yes,Artist,9.0,Average,5.0,Cat_4,D +466336,Male,,23,No,Healthcare,3.0,Low,4.0,Cat_4,D +459685,Male,Yes,36,Yes,Artist,0.0,Average,2.0,Cat_7,C +466617,Female,No,23,No,Healthcare,4.0,Low,3.0,Cat_6,D +465973,Female,Yes,50,Yes,Artist,8.0,Average,2.0,Cat_6,B +459312,Male,No,22,No,Healthcare,1.0,Low,4.0,Cat_4,D +459525,Female,Yes,62,Yes,Artist,,High,3.0,Cat_6,C +467863,Female,Yes,35,Yes,Doctor,0.0,Average,3.0,Cat_6,C +461659,Female,Yes,52,Yes,Artist,0.0,Average,2.0,Cat_6,C +466272,Female,Yes,68,Yes,Lawyer,0.0,Average,3.0,Cat_6,C +459292,Male,No,46,Yes,Artist,1.0,Low,2.0,Cat_6,C +460115,Female,No,28,Yes,Healthcare,,Low,1.0,Cat_3,A +463710,Male,No,49,Yes,Entertainment,0.0,Low,1.0,Cat_2,B +463856,Male,Yes,37,Yes,Artist,0.0,Average,2.0,Cat_4,C +467545,Male,Yes,53,Yes,Artist,9.0,Average,2.0,Cat_6,B +466027,Female,Yes,70,No,Engineer,7.0,Low,1.0,Cat_3,B +460409,Female,No,43,Yes,Artist,8.0,Low,4.0,Cat_6,D +465342,Male,Yes,83,Yes,Executive,0.0,High,2.0,Cat_6,A +460249,Female,No,25,Yes,Entertainment,9.0,Low,2.0,Cat_6,A +464231,Male,Yes,45,Yes,Artist,0.0,Average,4.0,Cat_6,C +461676,Female,No,29,Yes,Engineer,1.0,Low,1.0,Cat_6,A +462338,Male,Yes,43,Yes,Artist,9.0,Average,2.0,,C +467597,Female,Yes,65,Yes,Lawyer,0.0,Low,1.0,Cat_6,A +459256,Female,Yes,46,Yes,Artist,0.0,High,4.0,Cat_6,B +459962,Male,Yes,59,Yes,Artist,8.0,Low,1.0,Cat_6,B +463546,Female,No,30,Yes,Healthcare,0.0,Low,2.0,Cat_6,C +464197,Male,Yes,29,No,Healthcare,5.0,Low,2.0,Cat_6,D +465146,Male,Yes,58,Yes,Artist,9.0,Low,1.0,Cat_6,B +462412,Male,Yes,71,No,Artist,1.0,Low,5.0,Cat_6,C +464279,Male,Yes,53,No,Executive,1.0,Low,5.0,Cat_6,A +465466,Male,,31,No,Healthcare,1.0,Low,3.0,Cat_6,C +463381,Male,Yes,36,Yes,Doctor,8.0,Average,4.0,Cat_3,C +464230,Male,No,33,No,Engineer,0.0,Low,4.0,Cat_1,D +465394,Male,No,32,Yes,Engineer,0.0,Low,3.0,Cat_4,B +467600,Male,Yes,41,Yes,Engineer,,Average,5.0,Cat_6,C +461105,Female,No,49,Yes,Entertainment,0.0,Low,1.0,Cat_3,D +464328,Male,Yes,84,No,Lawyer,1.0,High,2.0,Cat_6,B +462797,Male,Yes,30,Yes,Healthcare,0.0,Low,4.0,Cat_6,A +461008,Male,No,26,No,Healthcare,1.0,Low,4.0,Cat_3,A +461412,Female,Yes,41,Yes,Artist,5.0,High,3.0,Cat_5,B +462283,Male,No,25,No,Healthcare,4.0,Low,5.0,Cat_6,D +467683,Male,Yes,26,Yes,Healthcare,9.0,High,3.0,Cat_2,D +463727,Female,No,27,No,Doctor,0.0,Low,5.0,Cat_3,A +467174,Male,Yes,41,No,Executive,0.0,High,3.0,,A +459497,Female,Yes,80,Yes,Lawyer,0.0,Low,1.0,Cat_6,C +464407,Female,Yes,50,Yes,Healthcare,1.0,Low,3.0,Cat_6,D +462738,Female,No,28,Yes,Healthcare,,Low,3.0,Cat_2,D +465116,Female,Yes,66,Yes,Artist,0.0,Low,2.0,Cat_6,C +462957,Male,Yes,58,No,Executive,1.0,High,5.0,Cat_6,B +465292,Female,No,42,Yes,Doctor,0.0,Low,3.0,Cat_6,B +464359,Female,No,37,Yes,Artist,0.0,Low,3.0,Cat_6,B +461838,Male,Yes,49,Yes,Artist,0.0,Low,1.0,Cat_6,C +464624,Male,No,39,Yes,Entertainment,3.0,Low,2.0,Cat_1,A +459034,Female,No,21,No,Healthcare,,Low,5.0,Cat_6,D +466705,Male,Yes,69,No,Lawyer,0.0,Low,3.0,Cat_6,C +459849,Female,Yes,50,Yes,Artist,1.0,High,5.0,Cat_6,C +463273,Male,Yes,37,Yes,Entertainment,8.0,Average,2.0,Cat_3,B +461551,Male,Yes,61,No,Entertainment,1.0,Low,2.0,Cat_6,A +464129,Male,Yes,52,Yes,Executive,1.0,High,4.0,Cat_1,C +464031,Female,No,23,No,Healthcare,1.0,Low,4.0,Cat_6,D +464287,Male,Yes,35,Yes,Artist,,Average,3.0,Cat_2,C +466069,Female,Yes,49,Yes,Artist,0.0,High,4.0,,B +460791,Female,No,27,Yes,Engineer,9.0,Low,7.0,Cat_3,C +464348,Male,No,43,Yes,Artist,1.0,Low,2.0,Cat_6,A +460904,Female,No,35,No,Entertainment,1.0,Low,1.0,Cat_6,B +466484,Female,Yes,52,No,Executive,1.0,Low,1.0,Cat_3,A +467663,Male,Yes,31,No,Doctor,,Average,2.0,Cat_6,D +467885,Female,No,43,Yes,Doctor,0.0,Low,,Cat_4,A +461674,Male,Yes,48,Yes,Executive,0.0,High,4.0,Cat_6,C +460601,Female,Yes,37,Yes,,0.0,Low,3.0,Cat_3,A +464297,Male,No,26,No,Artist,8.0,Low,5.0,Cat_5,A +462322,Female,No,49,Yes,Artist,1.0,Low,2.0,Cat_2,C +459974,Male,No,37,Yes,Entertainment,0.0,Low,1.0,Cat_6,A +461003,Male,No,30,No,Executive,13.0,Low,4.0,Cat_3,D +464939,Male,No,43,No,Entertainment,0.0,Low,1.0,Cat_4,D +461905,Female,No,42,Yes,Artist,0.0,Low,3.0,Cat_6,C +466952,Male,Yes,60,Yes,Artist,0.0,Average,2.0,Cat_6,C +464741,Male,Yes,71,No,Executive,1.0,High,,Cat_1,A +460484,Female,No,23,No,Healthcare,4.0,Low,7.0,Cat_3,D +462346,Male,Yes,81,Yes,Lawyer,9.0,Low,1.0,Cat_6,B +465441,Female,Yes,45,Yes,Engineer,2.0,Low,1.0,Cat_4,A +461689,Male,No,33,Yes,Artist,1.0,Low,2.0,Cat_6,C +464846,Male,No,25,No,Entertainment,8.0,Low,2.0,Cat_4,D +462254,Male,Yes,38,No,Executive,1.0,Average,5.0,Cat_2,B +462708,Male,Yes,25,Yes,Executive,8.0,High,2.0,Cat_6,A +467806,Male,Yes,62,Yes,Artist,1.0,High,2.0,Cat_6,B +461173,Male,Yes,45,Yes,Artist,1.0,Low,4.0,Cat_3,B +465912,Female,No,63,Yes,Artist,1.0,Low,2.0,Cat_6,B +461341,Female,Yes,39,Yes,Artist,0.0,High,2.0,Cat_6,A +465135,Female,No,25,No,Engineer,1.0,Low,1.0,Cat_6,B +461654,Female,No,67,Yes,Artist,0.0,Low,1.0,Cat_7,B +459147,Male,Yes,81,Yes,Entertainment,1.0,Low,1.0,Cat_6,D +464611,Male,No,31,Yes,Artist,1.0,Low,4.0,Cat_7,C +460356,Male,No,22,No,Healthcare,1.0,Low,4.0,Cat_6,D +461467,Female,Yes,73,,Lawyer,,High,2.0,Cat_1,A +461591,Female,No,33,No,Engineer,1.0,Low,2.0,Cat_4,B +466505,Female,Yes,42,No,Artist,8.0,High,5.0,Cat_6,B +463112,Male,Yes,70,Yes,Lawyer,,Low,1.0,Cat_3,A +464717,Male,Yes,25,No,Executive,4.0,High,8.0,Cat_4,D +459051,Male,Yes,66,Yes,Artist,,Average,2.0,Cat_6,C +464620,Female,Yes,41,Yes,Artist,1.0,High,2.0,Cat_6,B +462333,Female,Yes,40,Yes,Artist,1.0,Low,1.0,Cat_6,D +463910,Female,Yes,50,Yes,Artist,0.0,Average,2.0,Cat_6,C +464196,Female,,36,Yes,Entertainment,8.0,Low,1.0,Cat_6,A +459262,Female,No,31,No,Entertainment,9.0,Low,4.0,Cat_6,C +467808,Female,Yes,63,Yes,Artist,0.0,Average,2.0,Cat_6,C +460992,Male,Yes,57,No,Executive,0.0,High,4.0,Cat_6,A +459950,Female,No,40,Yes,Artist,0.0,Low,1.0,Cat_6,A +461053,Male,Yes,39,No,Executive,,High,4.0,Cat_3,D +467461,Female,Yes,45,Yes,Artist,8.0,High,3.0,Cat_6,B +463779,Female,No,32,No,Healthcare,0.0,Low,,Cat_7,D +464177,Male,No,25,Yes,Healthcare,1.0,Low,3.0,Cat_6,D +463918,Female,No,22,No,Healthcare,0.0,Low,4.0,Cat_4,D +464326,Female,No,38,Yes,Artist,1.0,Low,3.0,Cat_6,A +465612,Male,Yes,37,Yes,Artist,9.0,Average,2.0,Cat_6,C +463872,Male,Yes,78,No,Lawyer,1.0,High,2.0,Cat_2,A +466883,Female,Yes,37,Yes,Artist,2.0,Average,2.0,Cat_6,B +467477,Male,Yes,43,Yes,Healthcare,5.0,Average,2.0,Cat_6,D +461307,Male,No,23,No,Healthcare,1.0,Low,4.0,Cat_6,D +463509,Male,No,43,No,Artist,0.0,Low,1.0,Cat_3,D +461470,Male,Yes,43,Yes,Artist,2.0,Average,2.0,Cat_6,C +466138,Male,Yes,71,No,Lawyer,3.0,High,2.0,Cat_6,D +460034,Female,Yes,38,Yes,Artist,8.0,Low,1.0,Cat_6,D +460650,Female,,20,No,Homemaker,13.0,Low,1.0,Cat_4,D +466738,Female,No,27,Yes,Healthcare,0.0,Low,,Cat_6,D +465088,Male,Yes,48,Yes,Executive,9.0,High,4.0,Cat_6,B +467253,Female,No,35,Yes,Engineer,,Low,2.0,Cat_6,D +462375,Male,Yes,53,Yes,Artist,1.0,Average,3.0,Cat_6,C +466337,Female,Yes,41,No,Engineer,0.0,Low,2.0,Cat_4,A +465924,Female,No,28,Yes,Marketing,0.0,Low,2.0,Cat_6,D +465788,Male,No,22,No,Healthcare,1.0,Low,6.0,Cat_4,D +459570,Male,No,27,Yes,Healthcare,,Low,3.0,Cat_6,D +463934,Female,Yes,43,Yes,Artist,3.0,Average,2.0,Cat_6,C +459437,Male,Yes,68,Yes,Lawyer,,High,2.0,Cat_6,B +466728,Male,Yes,28,No,Doctor,0.0,Average,2.0,Cat_6,A +461737,Female,Yes,48,Yes,Doctor,1.0,Average,6.0,Cat_4,B +460972,Male,Yes,42,Yes,Artist,14.0,Low,2.0,Cat_6,A +461210,Female,No,38,Yes,Healthcare,0.0,Low,4.0,Cat_6,A +463111,Female,No,42,Yes,Engineer,6.0,Low,3.0,Cat_6,D +465513,Male,No,43,Yes,Entertainment,1.0,Low,2.0,Cat_6,B +461732,Female,Yes,46,Yes,Artist,1.0,Average,2.0,Cat_2,C +459647,Female,No,43,Yes,Engineer,1.0,Low,2.0,Cat_6,B +460257,Female,No,28,Yes,Healthcare,1.0,Low,6.0,Cat_6,D +464437,Female,Yes,48,Yes,Engineer,0.0,Average,4.0,Cat_6,B +461544,Male,Yes,82,No,Lawyer,1.0,High,2.0,Cat_6,A +462749,Male,Yes,49,Yes,Artist,1.0,Low,2.0,Cat_6,C +461472,Female,Yes,41,Yes,Artist,0.0,Average,2.0,Cat_6,C +467659,Male,Yes,66,Yes,Entertainment,0.0,Average,2.0,Cat_6,B +466859,Male,No,22,No,Healthcare,0.0,Low,5.0,Cat_2,D +460101,Female,No,31,Yes,Healthcare,9.0,Low,3.0,Cat_3,D +461885,Female,Yes,49,Yes,Artist,3.0,High,5.0,Cat_6,C +460982,Male,Yes,77,No,Lawyer,1.0,High,2.0,Cat_6,A +460503,Male,No,41,Yes,Entertainment,0.0,Low,2.0,Cat_3,B +460814,Male,Yes,78,No,Entertainment,0.0,Low,1.0,Cat_6,D +459981,Male,No,41,Yes,Artist,7.0,Low,1.0,Cat_6,A +463776,Female,Yes,39,Yes,Artist,3.0,Average,2.0,Cat_6,C +467746,Male,Yes,50,Yes,Executive,0.0,High,3.0,Cat_6,B +460151,Male,Yes,35,Yes,Artist,3.0,Average,2.0,Cat_6,C +467145,Female,Yes,52,Yes,Doctor,1.0,Average,3.0,Cat_6,B +459581,Female,No,35,No,,,Low,,Cat_7,A +461101,Female,Yes,50,Yes,Engineer,4.0,Average,4.0,Cat_3,A +464240,Male,Yes,52,No,Engineer,,Low,1.0,Cat_2,D +461424,Male,Yes,63,No,Healthcare,0.0,Average,4.0,Cat_4,B +464237,Female,Yes,47,Yes,Artist,0.0,Low,3.0,Cat_6,B +463566,Female,Yes,41,Yes,Homemaker,,Average,4.0,Cat_2,C +459995,Female,No,38,No,Artist,4.0,Low,2.0,Cat_6,A +465896,Male,No,32,No,Doctor,2.0,Low,2.0,Cat_6,D +463639,Male,No,31,No,Healthcare,0.0,Low,6.0,Cat_6,D +463292,Male,Yes,36,No,Executive,1.0,High,3.0,Cat_6,B +462484,Male,Yes,67,Yes,Artist,0.0,Low,1.0,Cat_6,D +459680,Male,No,41,Yes,Entertainment,,Low,1.0,Cat_6,A +465198,Male,Yes,53,Yes,Entertainment,8.0,Average,4.0,Cat_6,C +467820,Male,Yes,51,Yes,Artist,0.0,Low,1.0,Cat_3,B +465817,Male,Yes,56,No,Executive,0.0,Low,2.0,Cat_4,A +467661,Female,Yes,45,Yes,Artist,,High,,Cat_6,B +464244,Male,Yes,72,Yes,Executive,1.0,High,4.0,Cat_6,B +465717,Female,Yes,42,Yes,Artist,2.0,Average,2.0,Cat_6,C +460461,Female,Yes,26,No,Homemaker,9.0,Average,2.0,Cat_3,A +465535,Female,No,29,No,Entertainment,9.0,Low,,Cat_2,A +466931,Male,No,37,Yes,Entertainment,1.0,Low,2.0,Cat_3,A +461106,Male,No,18,No,Healthcare,1.0,Low,4.0,Cat_3,D +465860,Female,,31,Yes,Doctor,1.0,Low,3.0,Cat_6,B +463379,Male,No,29,No,Marketing,1.0,Low,7.0,Cat_6,D +459331,Female,No,70,Yes,Lawyer,0.0,Low,1.0,Cat_6,D +466396,Female,No,19,No,Healthcare,1.0,Low,5.0,Cat_3,D +463135,Male,Yes,46,Yes,Artist,5.0,Average,2.0,Cat_3,C +463418,Male,Yes,53,No,Executive,1.0,High,4.0,Cat_6,A +467300,Female,Yes,50,Yes,Artist,0.0,Low,1.0,Cat_6,B +465518,Female,Yes,43,Yes,Artist,1.0,Average,3.0,Cat_4,B +461964,Female,No,23,No,Healthcare,0.0,Low,4.0,Cat_7,D +463190,Female,Yes,61,Yes,Artist,0.0,Average,2.0,Cat_4,B +461771,Male,Yes,27,Yes,Doctor,9.0,Average,2.0,Cat_6,A +467496,Female,Yes,63,Yes,,4.0,Average,3.0,Cat_6,A +466082,Female,Yes,83,Yes,Lawyer,1.0,Low,1.0,Cat_6,C +461241,Male,Yes,32,Yes,Healthcare,5.0,Low,2.0,Cat_6,D +460741,Male,Yes,58,Yes,Artist,1.0,Average,5.0,Cat_6,B +461752,Male,Yes,67,Yes,Doctor,1.0,Average,3.0,Cat_6,C +461030,Female,No,19,No,Healthcare,8.0,Low,5.0,Cat_3,D +459097,Male,Yes,88,Yes,Lawyer,0.0,High,2.0,Cat_6,A +464615,Male,Yes,35,Yes,Artist,7.0,Low,2.0,Cat_6,A +460928,Male,Yes,45,Yes,Artist,0.0,Average,4.0,Cat_6,B +467682,Female,Yes,35,Yes,Artist,9.0,Average,4.0,Cat_6,B +463480,Female,Yes,27,No,Healthcare,0.0,Low,2.0,Cat_6,D +462370,Male,Yes,35,Yes,Executive,4.0,High,3.0,Cat_6,B +459148,Female,No,20,No,Healthcare,2.0,Low,3.0,Cat_4,D +462588,Male,Yes,41,No,Entertainment,12.0,Average,3.0,Cat_4,A +466102,Female,No,33,No,Healthcare,1.0,Low,6.0,Cat_3,C +464482,Male,Yes,58,Yes,Artist,0.0,Low,1.0,Cat_6,C +463078,Male,Yes,86,No,Executive,0.0,High,2.0,Cat_6,A +464646,Male,Yes,61,No,Engineer,7.0,Average,5.0,Cat_4,D +461151,Male,No,32,Yes,Artist,0.0,Low,3.0,Cat_3,D +466397,Male,No,20,No,Healthcare,0.0,Low,3.0,Cat_3,D +463743,Male,No,32,No,Artist,0.0,Low,4.0,Cat_6,D +467753,Female,Yes,69,No,Artist,0.0,High,2.0,Cat_6,C +465666,Female,No,35,Yes,Entertainment,9.0,Low,4.0,Cat_6,C +463105,Female,No,72,Yes,Lawyer,1.0,Low,2.0,Cat_6,D +465280,Female,Yes,37,Yes,Artist,0.0,Low,1.0,Cat_6,C +466811,Male,No,22,No,Healthcare,0.0,Low,,Cat_4,D +467158,Female,No,25,No,Healthcare,0.0,Low,,Cat_6,D +465578,Male,No,33,Yes,Healthcare,4.0,Low,1.0,Cat_6,D +466560,Male,Yes,40,No,Executive,0.0,Average,4.0,Cat_4,D +459897,Female,Yes,55,Yes,Artist,1.0,Low,2.0,Cat_6,C +466440,Male,Yes,23,No,Executive,1.0,High,9.0,Cat_6,A +464647,Female,Yes,51,No,Doctor,1.0,High,1.0,Cat_4,D +460851,Male,Yes,69,No,Doctor,0.0,Average,3.0,Cat_6,B +467122,Female,Yes,73,No,Lawyer,0.0,High,2.0,Cat_6,C +460222,Female,,72,Yes,Lawyer,4.0,High,,Cat_6,D +460462,Female,No,20,No,Marketing,1.0,Low,3.0,Cat_3,B +459440,Female,No,53,Yes,Artist,1.0,Low,1.0,Cat_6,B +459521,Female,Yes,46,Yes,Artist,0.0,Average,4.0,Cat_1,A +465772,Female,Yes,53,Yes,Engineer,0.0,Low,4.0,Cat_1,B +462218,Male,Yes,53,No,Executive,0.0,Average,5.0,Cat_4,B +467673,Male,Yes,50,No,Executive,0.0,High,4.0,Cat_6,B +461295,Female,Yes,66,Yes,Artist,1.0,Average,3.0,Cat_6,C +466035,Female,Yes,86,Yes,Lawyer,1.0,Low,2.0,Cat_6,B +459830,Female,No,27,Yes,Doctor,8.0,Low,1.0,Cat_6,A +462080,Female,No,42,Yes,Artist,8.0,Low,1.0,Cat_6,C +459330,Male,No,33,No,Marketing,11.0,Low,9.0,Cat_6,D +467058,Male,Yes,82,No,Executive,1.0,High,2.0,Cat_6,B +461122,Male,Yes,26,No,Entertainment,8.0,Low,2.0,Cat_4,D +464886,Male,Yes,38,Yes,Engineer,0.0,Average,4.0,Cat_4,B +463697,Female,No,32,Yes,Artist,6.0,Low,7.0,Cat_4,A +460592,Male,No,20,No,Healthcare,1.0,Low,5.0,Cat_3,D +465995,Male,No,27,No,Entertainment,0.0,Low,3.0,Cat_2,D +461293,Male,No,22,No,Healthcare,0.0,Low,4.0,Cat_6,D +467020,Male,Yes,80,Yes,Lawyer,0.0,High,3.0,Cat_6,B +461153,Female,Yes,32,Yes,Artist,,High,3.0,Cat_3,A +467175,Male,Yes,77,Yes,Lawyer,0.0,Low,1.0,Cat_6,D +465154,Male,No,22,No,Healthcare,1.0,Low,2.0,Cat_4,D +466666,Female,Yes,32,Yes,Healthcare,0.0,Low,2.0,Cat_6,A +467583,Male,Yes,47,Yes,Executive,9.0,Low,,Cat_6,B +462859,Male,Yes,49,Yes,Entertainment,1.0,Average,2.0,Cat_6,A +467483,Male,Yes,55,Yes,Executive,0.0,High,9.0,Cat_6,A +466950,Male,No,28,Yes,Entertainment,1.0,Low,2.0,Cat_3,D +463417,Male,Yes,59,Yes,Artist,,Low,,Cat_6,B +466884,Female,Yes,42,No,Doctor,1.0,Low,1.0,Cat_6,A +464564,Female,No,37,Yes,Artist,8.0,Low,,Cat_6,B +463203,Female,No,20,No,Healthcare,1.0,Low,3.0,Cat_7,D +464716,Male,Yes,43,Yes,Marketing,0.0,High,6.0,Cat_4,D +466162,Female,Yes,73,Yes,Doctor,0.0,Low,1.0,Cat_4,B +462383,Female,No,46,No,Engineer,1.0,Low,3.0,Cat_3,D +462970,Male,Yes,28,No,Doctor,0.0,Average,2.0,Cat_6,D +463988,Female,Yes,35,Yes,Entertainment,9.0,Average,2.0,Cat_2,A +466500,Female,Yes,48,Yes,Artist,3.0,High,3.0,Cat_4,B +460826,Female,No,32,Yes,Artist,0.0,Low,3.0,Cat_4,D +466714,Male,Yes,66,Yes,Executive,0.0,Average,2.0,Cat_6,C +467876,Female,Yes,47,Yes,Artist,8.0,High,2.0,Cat_6,C +465204,Male,Yes,28,Yes,Healthcare,1.0,Low,2.0,Cat_6,C +461851,Female,Yes,35,Yes,Artist,3.0,Average,2.0,Cat_6,C +460823,Female,Yes,75,Yes,Lawyer,1.0,High,2.0,Cat_6,B +459328,Male,Yes,48,Yes,Executive,1.0,High,3.0,Cat_6,B +466541,Male,No,41,Yes,Entertainment,1.0,Low,4.0,Cat_6,A +461817,Female,No,25,No,Doctor,1.0,Low,3.0,Cat_6,D +459187,Female,Yes,52,No,Doctor,0.0,Average,3.0,Cat_4,B +464352,Male,Yes,58,Yes,Doctor,0.0,Average,2.0,Cat_6,C +462752,Female,No,26,No,Healthcare,,Low,1.0,Cat_6,D +459814,Female,No,49,Yes,Artist,0.0,Low,3.0,Cat_2,B +460309,Male,No,28,Yes,Marketing,9.0,Low,5.0,Cat_7,D +467169,Male,No,53,Yes,Artist,0.0,Low,1.0,Cat_6,B +461362,Female,No,27,Yes,Doctor,0.0,Low,4.0,Cat_6,D +462942,Female,No,33,Yes,Entertainment,0.0,Low,4.0,Cat_4,A +463088,Female,Yes,89,Yes,Lawyer,0.0,High,2.0,Cat_6,C +463924,Female,No,40,Yes,Artist,1.0,Low,1.0,Cat_5,B +462105,Male,Yes,47,No,Executive,,High,4.0,Cat_4,B +462794,Male,No,27,No,Healthcare,10.0,Low,5.0,Cat_6,D +462399,Male,Yes,37,Yes,Executive,1.0,Low,6.0,Cat_6,D +465593,Male,Yes,56,No,Lawyer,0.0,High,3.0,Cat_3,A +465437,Male,Yes,48,Yes,Executive,2.0,High,5.0,Cat_6,C +465457,Female,No,32,Yes,Engineer,0.0,Low,1.0,Cat_6,A +463167,Male,Yes,39,Yes,Doctor,1.0,Average,3.0,Cat_6,A +463176,Male,Yes,39,Yes,Artist,,Average,4.0,Cat_3,B +462141,Female,No,39,Yes,Artist,9.0,Low,1.0,Cat_6,C +460760,Male,Yes,60,No,Artist,1.0,High,4.0,Cat_3,B +461938,Female,No,33,Yes,Engineer,9.0,Low,2.0,Cat_6,A +465041,Female,Yes,50,No,Artist,0.0,Average,4.0,Cat_4,C +462896,Male,Yes,58,Yes,Entertainment,,Low,2.0,Cat_6,A +467226,Female,No,20,No,Entertainment,1.0,Low,4.0,Cat_6,C +467228,Male,No,29,Yes,Healthcare,,Low,4.0,Cat_6,C +459800,Female,No,37,No,Engineer,0.0,Low,1.0,Cat_6,D +466175,Male,Yes,25,Yes,Doctor,1.0,Low,2.0,Cat_6,C +460212,Male,Yes,37,Yes,Entertainment,11.0,Low,6.0,Cat_6,D +466912,Male,Yes,68,Yes,Engineer,0.0,Low,1.0,Cat_6,D +460373,Male,No,20,No,Healthcare,2.0,Low,3.0,Cat_6,D +466383,Male,,22,No,Healthcare,9.0,Low,3.0,Cat_4,D +463728,Female,Yes,30,No,Doctor,1.0,Average,6.0,Cat_6,B +466596,Female,Yes,83,Yes,Artist,0.0,High,2.0,Cat_6,C +465945,Female,No,52,Yes,Artist,0.0,Low,1.0,Cat_6,D +464825,Female,No,19,No,Healthcare,7.0,Low,1.0,Cat_4,D +462235,Female,Yes,36,Yes,Engineer,11.0,Low,2.0,Cat_4,C +463477,Male,No,31,No,Healthcare,1.0,Low,4.0,Cat_4,C +465744,Male,Yes,51,Yes,Artist,0.0,Average,2.0,Cat_2,C +467093,Female,Yes,53,Yes,Artist,8.0,Average,2.0,Cat_6,A +464351,Female,No,31,Yes,Healthcare,1.0,Low,1.0,Cat_6,D +466955,Male,Yes,57,No,Entertainment,0.0,Low,4.0,Cat_3,A +462305,Male,Yes,36,Yes,Artist,0.0,Average,5.0,Cat_6,C +461519,Male,No,35,No,Doctor,,Low,3.0,Cat_4,A +465885,Female,No,41,Yes,Marketing,1.0,Low,2.0,Cat_6,A +461022,Male,Yes,69,Yes,Artist,0.0,Low,1.0,Cat_3,A +463146,Male,Yes,37,No,Entertainment,11.0,Average,2.0,Cat_6,B +466807,Female,,41,Yes,Healthcare,0.0,Low,1.0,Cat_4,A +459150,Female,No,49,Yes,Doctor,0.0,Low,1.0,Cat_6,A +467114,Male,Yes,59,Yes,Marketing,0.0,Average,3.0,Cat_6,B +466395,Female,Yes,37,Yes,Doctor,2.0,High,2.0,Cat_4,A +465028,Female,Yes,68,Yes,Lawyer,3.0,High,4.0,Cat_6,C +467194,Male,No,29,No,Entertainment,,Low,3.0,Cat_6,D +464189,Female,No,42,Yes,Artist,1.0,Low,2.0,Cat_6,A +466897,Male,,52,No,Doctor,0.0,High,3.0,Cat_6,D +466043,Female,Yes,46,Yes,Artist,1.0,Average,4.0,Cat_6,B +460585,Male,No,49,No,Entertainment,1.0,Low,3.0,Cat_3,D +461217,Female,No,33,Yes,Engineer,1.0,Low,5.0,Cat_6,D +465759,Male,Yes,33,No,Artist,1.0,Low,3.0,Cat_4,A +463485,Female,No,25,No,Engineer,,Low,3.0,Cat_6,D +464781,Male,No,32,No,Marketing,0.0,Low,3.0,Cat_4,D +459425,Female,Yes,87,Yes,Lawyer,0.0,High,2.0,Cat_6,A +460716,Male,Yes,52,Yes,Entertainment,0.0,Average,4.0,Cat_6,B +464864,Female,No,27,No,Engineer,14.0,Low,8.0,Cat_4,D +467250,Male,Yes,63,Yes,Entertainment,6.0,Low,2.0,Cat_6,D +460169,Female,Yes,89,Yes,Lawyer,1.0,High,2.0,Cat_2,C +461204,Female,Yes,86,Yes,Artist,,Low,4.0,,D +462401,Male,Yes,31,No,Executive,1.0,Average,2.0,Cat_4,D +464290,Female,No,41,Yes,Artist,0.0,Low,1.0,Cat_6,C +462692,Female,Yes,35,No,Artist,,Average,2.0,Cat_6,B +464599,Female,Yes,50,Yes,Artist,1.0,High,7.0,Cat_2,B +464952,Female,Yes,70,No,Engineer,0.0,Average,2.0,Cat_4,B +460502,Female,Yes,55,Yes,Engineer,1.0,Average,3.0,Cat_3,B +463897,Female,Yes,36,Yes,Homemaker,8.0,Average,5.0,Cat_6,B +464401,Female,Yes,66,Yes,Lawyer,4.0,Low,1.0,Cat_6,B +460631,Male,,47,Yes,Marketing,,High,2.0,Cat_3,D +462621,Female,Yes,49,No,Engineer,5.0,Average,5.0,Cat_4,D +466092,Male,Yes,65,Yes,Artist,1.0,Average,4.0,Cat_6,B +466469,Female,No,23,No,Healthcare,1.0,Low,4.0,Cat_3,D +459804,Male,Yes,45,No,Artist,3.0,High,4.0,Cat_6,C +460548,Male,No,33,No,Entertainment,1.0,Low,5.0,Cat_3,D +459733,Male,Yes,48,Yes,Executive,1.0,High,3.0,Cat_6,A +466830,Male,No,28,Yes,Healthcare,5.0,Low,4.0,Cat_6,D +466849,Female,No,27,Yes,Artist,0.0,Low,8.0,Cat_4,C +461090,Female,Yes,45,Yes,Engineer,1.0,Low,3.0,Cat_3,D +466531,Female,Yes,49,Yes,Entertainment,2.0,Average,3.0,Cat_6,A +464752,Female,Yes,46,,Marketing,,Low,1.0,Cat_4,D +467378,Female,Yes,56,Yes,Artist,1.0,Average,2.0,Cat_6,C +462830,Male,No,21,No,Healthcare,,Low,1.0,Cat_2,D +461087,Female,Yes,29,No,Artist,0.0,Average,2.0,Cat_3,A +461857,Male,No,33,No,Entertainment,,Low,3.0,Cat_6,C +465754,Female,Yes,57,Yes,Artist,0.0,Average,4.0,Cat_6,C +466708,Male,Yes,65,No,Entertainment,1.0,Low,1.0,Cat_6,A +465133,Female,Yes,61,Yes,Engineer,1.0,Average,5.0,Cat_6,B +467266,Female,No,32,No,Healthcare,0.0,Low,4.0,Cat_3,C +464304,Male,Yes,35,Yes,Artist,1.0,Low,1.0,Cat_3,A +464393,Female,Yes,82,Yes,Lawyer,1.0,High,2.0,Cat_6,A +464883,Female,Yes,37,No,Engineer,1.0,Low,4.0,Cat_4,A +464002,Female,No,26,No,Engineer,0.0,Low,5.0,,A +465203,Male,No,32,Yes,Healthcare,2.0,Low,3.0,Cat_6,C +461860,Female,Yes,81,Yes,Lawyer,1.0,High,2.0,Cat_6,C +459934,Male,No,49,Yes,Artist,0.0,Low,1.0,Cat_6,B +461408,Male,Yes,46,Yes,Marketing,1.0,Low,3.0,Cat_6,D +459545,Female,No,20,No,Doctor,,Low,4.0,Cat_6,D +460737,Male,,66,Yes,Artist,0.0,Low,,Cat_3,B +459967,Male,No,27,Yes,Entertainment,9.0,Low,3.0,Cat_6,B +464493,Female,Yes,66,Yes,Lawyer,6.0,Average,4.0,Cat_5,B +463020,Male,Yes,31,Yes,Artist,0.0,Average,3.0,Cat_1,D +466704,Male,Yes,53,Yes,Doctor,4.0,Average,2.0,Cat_6,C +462950,Female,No,29,Yes,Doctor,0.0,Low,5.0,Cat_3,D +459862,Male,Yes,70,No,Executive,,High,2.0,Cat_6,A +465560,Male,Yes,60,Yes,Entertainment,1.0,Average,5.0,Cat_3,B +465114,Female,No,43,No,Doctor,4.0,Low,6.0,Cat_7,B +464908,Male,Yes,73,Yes,Entertainment,5.0,Low,1.0,Cat_4,A +459761,Female,No,47,No,Engineer,,Low,,Cat_6,A +464109,Male,Yes,47,No,Entertainment,0.0,Average,3.0,Cat_6,C +463748,Male,No,20,No,Healthcare,1.0,Low,3.0,Cat_6,D +461808,Male,Yes,38,Yes,Artist,0.0,Low,2.0,Cat_7,B +461716,Female,Yes,56,Yes,Lawyer,5.0,High,9.0,Cat_6,C +466409,Male,Yes,51,Yes,Artist,0.0,Average,5.0,Cat_6,C +464160,Male,Yes,27,Yes,Artist,4.0,Average,2.0,Cat_6,A +462459,Male,No,30,Yes,Artist,2.0,Low,2.0,Cat_6,A +463347,Male,Yes,49,Yes,Artist,1.0,Average,4.0,Cat_2,A +461117,Female,No,30,Yes,Healthcare,0.0,Low,6.0,Cat_3,D +467023,Male,Yes,26,No,Doctor,9.0,Average,2.0,Cat_6,A +462637,Female,Yes,58,No,Marketing,,Average,6.0,Cat_4,D +465068,Male,Yes,42,Yes,Artist,3.0,Average,4.0,Cat_6,B +463731,Male,No,23,No,Healthcare,0.0,Low,,Cat_6,D +462239,Male,Yes,49,Yes,Executive,1.0,High,5.0,Cat_5,C +467472,Male,Yes,59,Yes,Executive,3.0,High,4.0,Cat_6,B +460269,Male,No,31,Yes,Healthcare,8.0,Low,,Cat_7,D +466286,Female,Yes,49,Yes,Artist,0.0,Average,5.0,Cat_2,C +467017,Male,No,31,Yes,Engineer,,Low,4.0,Cat_2,A +463767,Male,Yes,32,Yes,Artist,9.0,Average,2.0,Cat_6,C +466653,Male,Yes,41,Yes,Entertainment,5.0,Low,1.0,Cat_6,A +464982,Male,Yes,71,Yes,Executive,1.0,High,4.0,Cat_6,B +465021,Male,Yes,62,Yes,Entertainment,0.0,Average,3.0,Cat_6,C +462513,Male,No,26,Yes,Artist,,Low,4.0,Cat_4,A +460079,Female,No,41,No,Doctor,0.0,Low,1.0,Cat_6,A +460092,Male,No,41,Yes,Doctor,2.0,Low,2.0,Cat_6,C +462534,Male,No,38,Yes,Artist,,Low,1.0,Cat_6,A +466172,Male,Yes,53,Yes,Artist,3.0,Average,3.0,Cat_6,C +463370,Male,Yes,46,Yes,Doctor,2.0,Low,4.0,Cat_3,A +463030,Male,Yes,50,Yes,Executive,1.0,High,3.0,Cat_6,C +465359,Female,Yes,40,No,Artist,1.0,Low,1.0,Cat_6,C +462504,Female,No,49,No,Artist,9.0,Low,1.0,Cat_1,B +465937,Female,No,30,Yes,Healthcare,9.0,Low,3.0,Cat_6,D +460534,Male,Yes,36,Yes,Artist,0.0,Average,4.0,Cat_6,B +461032,Female,Yes,66,Yes,Lawyer,0.0,High,3.0,Cat_6,C +463285,Male,Yes,31,No,Marketing,,Low,4.0,Cat_3,D +466773,Female,Yes,37,Yes,,0.0,Low,,Cat_6,C +463568,Female,No,26,No,Healthcare,1.0,Low,,Cat_5,D +463785,Female,No,27,No,Healthcare,0.0,Low,4.0,Cat_4,C +467127,Male,Yes,70,No,Lawyer,,Low,1.0,Cat_6,D +467469,Male,Yes,43,No,Entertainment,,Average,3.0,Cat_4,D +461358,Male,No,42,No,Entertainment,1.0,Low,3.0,Cat_6,A +464803,Male,No,28,No,Doctor,1.0,Low,1.0,Cat_4,A +460938,Male,No,38,Yes,Artist,,Low,1.0,Cat_6,A +460960,Male,Yes,48,Yes,Entertainment,6.0,Low,2.0,Cat_7,B +461461,Male,Yes,36,No,Entertainment,9.0,Average,2.0,Cat_6,B +460192,Male,Yes,41,Yes,,0.0,Low,2.0,Cat_7,D +466960,Male,Yes,40,Yes,Artist,1.0,Low,2.0,Cat_6,B +466674,Female,Yes,57,Yes,Artist,0.0,High,2.0,Cat_6,C +467747,Female,Yes,50,Yes,Artist,,High,4.0,Cat_6,C +460037,Female,No,42,Yes,Entertainment,5.0,Low,7.0,Cat_6,A +461310,Male,No,20,No,Marketing,,Low,5.0,Cat_6,C +460166,Female,No,21,No,Healthcare,1.0,Low,7.0,Cat_2,D +459454,Male,Yes,38,No,Artist,,Average,2.0,Cat_6,A +461403,Female,No,27,Yes,Healthcare,0.0,Low,4.0,Cat_6,D +459129,Female,No,26,Yes,Marketing,9.0,Low,,Cat_3,D +464453,Female,Yes,46,Yes,Artist,4.0,High,5.0,Cat_6,C +463926,Female,No,27,No,Engineer,0.0,Low,6.0,Cat_6,D +462718,Female,No,25,No,Marketing,8.0,Low,5.0,Cat_6,D +464440,Male,Yes,87,Yes,Lawyer,0.0,High,2.0,Cat_6,A +467197,Male,No,27,Yes,Healthcare,1.0,Low,3.0,Cat_6,D +465070,Female,Yes,48,Yes,Marketing,6.0,Low,1.0,Cat_6,C +466127,Male,Yes,51,Yes,Doctor,1.0,Average,4.0,Cat_6,A +466941,Male,Yes,81,Yes,Lawyer,,High,2.0,Cat_6,B +467918,Male,Yes,48,Yes,Artist,7.0,Average,2.0,Cat_6,C +462696,Male,Yes,52,No,Artist,0.0,High,2.0,Cat_6,A +464135,Female,Yes,68,Yes,Lawyer,0.0,High,2.0,Cat_6,B +467586,Male,Yes,35,Yes,Artist,3.0,Average,3.0,Cat_6,B +460794,Female,Yes,39,Yes,Homemaker,9.0,Low,1.0,Cat_6,D +460155,Female,Yes,53,Yes,Artist,2.0,High,2.0,Cat_2,C +464606,Male,Yes,41,Yes,Artist,1.0,Average,2.0,Cat_6,C +465856,Male,Yes,41,Yes,Artist,11.0,High,2.0,Cat_6,A +463214,Female,No,25,No,Engineer,1.0,Low,1.0,Cat_6,A +462848,Female,Yes,23,No,Healthcare,7.0,High,2.0,Cat_6,B +465293,Female,Yes,47,Yes,Artist,1.0,High,5.0,,C +461666,Female,Yes,35,Yes,Artist,0.0,High,2.0,Cat_7,C +459773,Male,Yes,51,Yes,Executive,1.0,Low,3.0,Cat_6,C +463647,Female,No,38,Yes,Engineer,1.0,Low,1.0,Cat_6,A +464156,Male,Yes,62,Yes,Artist,10.0,Low,1.0,Cat_6,B +460457,Female,No,43,Yes,Entertainment,8.0,Low,,Cat_3,A +463139,Male,Yes,60,Yes,Executive,1.0,High,6.0,Cat_2,B +464355,Male,Yes,47,Yes,Executive,1.0,High,4.0,Cat_6,C +462183,Male,No,22,No,Marketing,0.0,Low,4.0,Cat_6,D +466697,Female,No,31,No,Marketing,0.0,Low,3.0,Cat_6,D +466730,Female,Yes,38,Yes,Engineer,3.0,Low,2.0,Cat_6,A +466394,Female,Yes,42,No,Entertainment,6.0,Average,5.0,Cat_6,A +459009,Female,Yes,50,Yes,Artist,1.0,Average,3.0,Cat_6,C +460554,Male,Yes,38,No,Artist,5.0,Average,3.0,Cat_3,B +462498,Female,Yes,55,Yes,Doctor,0.0,Average,3.0,Cat_6,B +459926,Female,Yes,71,Yes,Artist,1.0,Average,2.0,Cat_6,C +465910,Male,Yes,38,No,Artist,10.0,Average,4.0,Cat_7,A +459166,Male,Yes,77,Yes,Artist,1.0,Low,1.0,Cat_6,A +462545,Male,No,33,No,Engineer,1.0,Low,3.0,Cat_4,D +459406,Male,Yes,72,No,Executive,0.0,High,2.0,Cat_6,B +464772,Female,Yes,43,No,Engineer,9.0,Average,4.0,Cat_4,A +465491,Female,No,35,Yes,Artist,0.0,Low,3.0,Cat_6,B +463123,Female,No,33,Yes,Doctor,1.0,Low,3.0,Cat_6,A +462015,Male,Yes,27,Yes,Entertainment,0.0,Average,2.0,Cat_6,A +461107,Male,No,21,No,Healthcare,0.0,Low,6.0,Cat_3,D +459265,Female,Yes,41,Yes,Artist,0.0,Average,2.0,Cat_6,C +462789,Female,No,19,No,Healthcare,,Low,6.0,Cat_3,D +465356,Female,,50,No,Artist,1.0,High,4.0,Cat_6,C +460295,Male,No,29,Yes,Artist,9.0,Low,3.0,Cat_6,A +463871,Female,Yes,46,No,Artist,8.0,Low,1.0,Cat_6,B +464594,Male,Yes,46,Yes,Artist,1.0,Low,1.0,Cat_2,D +460694,Female,Yes,53,No,Engineer,3.0,Low,1.0,Cat_6,A +464369,Male,Yes,36,Yes,Executive,1.0,High,4.0,Cat_6,B +463983,Female,Yes,36,No,Engineer,0.0,Average,2.0,Cat_4,A +463973,Male,Yes,56,No,Executive,,High,5.0,Cat_4,B +465721,Male,Yes,25,Yes,Entertainment,1.0,High,2.0,Cat_3,D +467347,Female,No,32,No,Entertainment,,Low,4.0,Cat_6,D +461325,Male,No,61,Yes,Entertainment,1.0,Low,3.0,Cat_2,C +465335,Male,Yes,61,No,Entertainment,1.0,Average,2.0,Cat_7,B +461871,Male,Yes,53,Yes,Artist,0.0,Average,2.0,Cat_6,C +466296,Male,Yes,69,No,Executive,1.0,High,4.0,Cat_6,C +462252,Male,No,47,Yes,Entertainment,4.0,Low,1.0,Cat_6,A +460209,Female,Yes,26,Yes,Entertainment,5.0,Low,7.0,Cat_6,D +465890,Female,No,39,Yes,Artist,9.0,Low,1.0,Cat_6,B +459745,Male,Yes,47,No,Artist,0.0,Low,4.0,Cat_1,A +465715,Female,Yes,69,Yes,Artist,7.0,Low,1.0,Cat_6,A +467168,Male,Yes,46,Yes,Artist,8.0,Average,2.0,Cat_1,B +465805,Male,Yes,50,Yes,Engineer,,Low,1.0,Cat_3,C +461417,Female,No,37,Yes,Doctor,1.0,Low,3.0,Cat_6,D +462938,Female,No,50,Yes,Artist,6.0,Low,1.0,Cat_6,B +463869,Female,Yes,52,Yes,Doctor,0.0,Average,4.0,Cat_6,C +459071,Male,No,28,Yes,Healthcare,5.0,Low,4.0,Cat_6,C +460193,Male,No,42,Yes,Entertainment,3.0,Low,2.0,Cat_2,A +462936,Female,No,43,Yes,Engineer,,Low,1.0,Cat_6,A +464015,Female,No,42,Yes,Artist,,Low,2.0,Cat_6,A +467961,Male,Yes,45,Yes,Executive,1.0,High,5.0,Cat_4,B +464442,Male,Yes,57,Yes,Lawyer,1.0,High,2.0,Cat_6,B +465834,Male,No,48,Yes,Entertainment,0.0,Low,3.0,Cat_2,A +462857,Male,No,18,No,Healthcare,1.0,Low,3.0,Cat_6,D +467268,Male,Yes,88,Yes,Lawyer,,Low,1.0,Cat_6,A +463474,Male,Yes,51,No,Artist,3.0,Average,2.0,Cat_6,C +463607,Female,No,29,Yes,Engineer,1.0,Low,5.0,Cat_1,B +460793,Female,Yes,28,No,Artist,,Low,3.0,Cat_3,B +464874,Female,Yes,47,Yes,Artist,0.0,Average,4.0,Cat_4,C +465907,Female,No,25,Yes,Marketing,8.0,Low,,Cat_1,D +465186,Female,Yes,38,No,Artist,4.0,Average,4.0,Cat_1,C +463861,Male,Yes,37,Yes,Artist,1.0,Low,1.0,Cat_3,A +464027,Male,No,22,No,Healthcare,9.0,Low,4.0,Cat_6,D +460985,Female,Yes,61,Yes,Engineer,0.0,Average,2.0,Cat_6,C +466870,Male,Yes,42,Yes,Artist,0.0,Average,2.0,Cat_6,C +466060,Male,Yes,60,Yes,Artist,1.0,Average,2.0,Cat_3,B +461273,Male,Yes,73,Yes,Entertainment,1.0,High,4.0,Cat_6,B +464925,Male,,60,No,Executive,6.0,Average,2.0,Cat_4,A +465673,Female,No,27,,Marketing,8.0,Low,4.0,Cat_4,D +461648,Female,Yes,49,Yes,Artist,1.0,Low,1.0,Cat_6,C +467631,Male,Yes,72,Yes,Lawyer,3.0,High,2.0,Cat_6,C +467804,Female,Yes,43,Yes,Artist,1.0,Average,2.0,Cat_6,C +465946,Female,Yes,50,Yes,,1.0,Low,2.0,Cat_3,A +460076,Female,No,49,Yes,,0.0,Low,2.0,Cat_6,D +466000,Male,Yes,38,Yes,Doctor,5.0,Low,2.0,Cat_1,A +459445,Female,Yes,35,Yes,Artist,0.0,Average,3.0,Cat_6,C +466292,Female,Yes,82,No,Lawyer,1.0,High,2.0,Cat_6,B +463804,Male,Yes,63,Yes,Artist,9.0,Low,1.0,Cat_6,A +464081,Male,Yes,73,Yes,Executive,11.0,High,2.0,Cat_6,C +462543,Female,Yes,38,Yes,Artist,4.0,Low,1.0,Cat_3,B +463444,Male,Yes,61,Yes,Lawyer,,High,2.0,Cat_6,B +463051,Female,No,42,Yes,Homemaker,10.0,Low,1.0,Cat_6,A +465893,Female,Yes,43,Yes,Engineer,0.0,Average,2.0,Cat_6,B +466812,Male,No,31,No,Homemaker,14.0,Low,1.0,Cat_6,D +463129,Male,Yes,69,No,Doctor,,Low,1.0,Cat_6,A +467091,Female,No,42,Yes,Entertainment,1.0,Low,3.0,Cat_6,D +460268,Male,Yes,32,Yes,Artist,9.0,Average,2.0,Cat_6,B +465757,Male,Yes,39,Yes,Artist,3.0,Average,4.0,Cat_4,A +460021,Female,Yes,48,Yes,Artist,0.0,Average,4.0,Cat_6,B +463859,Male,Yes,39,Yes,Artist,6.0,Average,2.0,Cat_6,C +460053,Male,Yes,55,Yes,Artist,9.0,Average,2.0,Cat_1,C +466659,Male,Yes,60,Yes,Doctor,1.0,High,2.0,Cat_3,B +466533,Female,No,51,Yes,Entertainment,1.0,Low,1.0,Cat_6,A +465307,Female,Yes,61,Yes,Artist,0.0,Average,3.0,Cat_4,B +464592,Female,No,43,Yes,Artist,1.0,Low,1.0,Cat_6,A +460975,Male,Yes,86,,Lawyer,1.0,High,2.0,Cat_6,B +461647,Female,Yes,55,Yes,Artist,2.0,Average,3.0,Cat_6,C +467462,Female,Yes,43,Yes,Artist,1.0,Average,5.0,Cat_6,C +467594,Male,Yes,45,No,Executive,0.0,Average,3.0,Cat_4,B +464255,Female,Yes,50,Yes,Artist,1.0,Average,5.0,Cat_4,C +461701,Male,Yes,40,No,Artist,1.0,Average,2.0,Cat_5,C +466516,Male,No,19,No,Healthcare,0.0,Low,3.0,Cat_6,D +464036,Female,No,25,Yes,Doctor,3.0,Low,1.0,Cat_6,A +461887,Female,No,89,Yes,Lawyer,2.0,Low,1.0,Cat_6,D +464093,Female,No,25,No,Engineer,1.0,Low,,Cat_7,D +466177,Female,Yes,49,Yes,Artist,0.0,Average,3.0,Cat_6,C +467486,Female,Yes,46,Yes,Engineer,1.0,Average,5.0,Cat_6,B +466775,Female,Yes,83,Yes,Lawyer,0.0,Low,2.0,Cat_6,C +467412,Male,Yes,35,Yes,Executive,6.0,Average,4.0,Cat_6,B +460546,Female,Yes,71,Yes,Lawyer,1.0,Low,2.0,Cat_3,C +466204,Male,Yes,50,Yes,Artist,0.0,Average,3.0,Cat_6,C +459000,Male,Yes,58,No,Executive,12.0,High,2.0,Cat_6,C +460635,Male,Yes,29,No,Healthcare,,High,2.0,Cat_3,D +466781,Male,Yes,35,Yes,Artist,1.0,Average,4.0,Cat_3,A +464992,Male,Yes,67,Yes,Executive,0.0,High,2.0,Cat_6,C +464921,Male,Yes,35,No,Doctor,0.0,Average,5.0,Cat_4,C +458982,Male,Yes,61,Yes,Executive,1.0,High,3.0,Cat_6,C +461045,Female,No,29,No,Entertainment,0.0,Low,2.0,Cat_3,B +466644,Female,No,49,Yes,Homemaker,0.0,Low,,Cat_6,C +459623,Male,Yes,46,No,Entertainment,,Average,5.0,Cat_4,B +459346,Male,Yes,47,Yes,Executive,0.0,Average,5.0,Cat_2,B +464183,Female,No,37,No,Artist,4.0,Low,2.0,Cat_6,C +466642,Male,No,18,No,Healthcare,9.0,Low,3.0,Cat_6,D +460519,Female,No,23,No,Entertainment,0.0,Low,2.0,Cat_3,C +462687,Male,No,29,No,Homemaker,11.0,Low,3.0,Cat_3,D +464847,Female,Yes,52,No,Artist,,Average,4.0,Cat_4,A +467921,Female,No,43,Yes,Artist,1.0,Low,4.0,Cat_6,A +460863,Male,Yes,66,Yes,Artist,0.0,Low,5.0,Cat_6,A +464168,Male,Yes,45,Yes,Executive,1.0,High,3.0,Cat_6,C +464819,Female,Yes,67,Yes,Lawyer,1.0,Low,1.0,Cat_4,A +465718,Female,Yes,49,Yes,Marketing,9.0,Low,2.0,Cat_6,C +461025,Male,Yes,38,No,Executive,1.0,High,4.0,Cat_6,D +466243,Female,Yes,38,No,Engineer,1.0,Low,2.0,Cat_4,A +463059,Female,No,32,Yes,Homemaker,10.0,Low,1.0,Cat_6,D +466962,Male,Yes,48,Yes,Artist,8.0,Average,2.0,Cat_6,C +463067,Female,No,42,Yes,Homemaker,7.0,Low,1.0,Cat_6,A +465755,Female,No,50,Yes,Artist,8.0,Low,3.0,Cat_6,C +460088,Female,Yes,33,Yes,Homemaker,,High,2.0,Cat_6,D +466301,Male,Yes,50,Yes,Artist,0.0,Average,3.0,Cat_2,C +467884,Female,Yes,30,Yes,Engineer,8.0,High,6.0,Cat_4,D +459908,Male,Yes,89,No,Lawyer,0.0,Low,,Cat_6,A +459315,Female,Yes,27,Yes,Doctor,1.0,Low,2.0,Cat_4,D +465343,Female,Yes,43,Yes,Engineer,1.0,Average,4.0,Cat_6,B +467772,Female,No,25,No,Marketing,1.0,Low,2.0,Cat_6,D +463601,Female,No,19,No,Healthcare,4.0,Low,5.0,,D +460292,Female,No,27,Yes,Marketing,12.0,Low,3.0,Cat_6,A +466566,Male,No,43,Yes,Artist,1.0,Low,1.0,Cat_6,B +459500,Male,Yes,37,Yes,Entertainment,1.0,Average,4.0,Cat_6,B +460267,Female,No,27,Yes,Healthcare,1.0,Low,9.0,Cat_6,C +467298,Male,Yes,61,Yes,Healthcare,1.0,Low,2.0,Cat_6,A +467095,Male,Yes,41,No,Engineer,0.0,Average,2.0,Cat_7,C +465919,Female,No,50,Yes,Artist,0.0,Low,1.0,Cat_6,B +461835,Female,Yes,60,Yes,Artist,1.0,Average,2.0,Cat_6,C +461302,Male,No,20,No,Healthcare,0.0,Low,4.0,Cat_6,D +461275,Male,Yes,66,Yes,Marketing,1.0,High,2.0,Cat_6,B +463115,Male,Yes,36,Yes,Homemaker,5.0,Low,1.0,Cat_6,D +466362,Male,Yes,65,Yes,Lawyer,1.0,High,2.0,Cat_6,B +462784,Female,No,30,No,Executive,,Low,1.0,Cat_3,C +467498,Male,Yes,56,Yes,Lawyer,,Low,3.0,Cat_1,D +463490,Male,No,20,No,Healthcare,1.0,Low,4.0,Cat_6,D +463836,Female,Yes,38,Yes,Artist,0.0,Low,1.0,Cat_6,B +459923,Male,No,31,Yes,Marketing,0.0,Low,2.0,Cat_3,D +462590,Female,Yes,30,No,Engineer,,Average,3.0,Cat_4,D +464164,Female,Yes,81,Yes,Lawyer,0.0,High,2.0,Cat_6,C +460298,Female,No,52,Yes,Lawyer,4.0,Low,1.0,Cat_6,A +467819,Male,Yes,57,Yes,Artist,0.0,Low,1.0,Cat_3,B +460722,Male,Yes,30,No,Entertainment,1.0,Average,3.0,Cat_3,A +466407,Male,Yes,55,No,Entertainment,1.0,Low,3.0,Cat_4,A +462411,Male,No,38,Yes,Artist,8.0,Low,1.0,Cat_6,A +466198,Male,Yes,70,No,Lawyer,0.0,High,2.0,Cat_3,C +462153,Male,Yes,59,No,Artist,0.0,Low,1.0,Cat_6,B +467180,Male,Yes,39,Yes,Healthcare,1.0,Low,3.0,Cat_3,D +461481,Female,Yes,58,Yes,Artist,9.0,Average,3.0,Cat_6,C +464589,Male,Yes,30,No,Entertainment,11.0,Low,2.0,Cat_5,D +461471,Female,Yes,73,Yes,Artist,1.0,Average,2.0,Cat_6,C +463981,Female,No,25,Yes,Artist,5.0,Low,1.0,,C +459793,Male,Yes,40,Yes,Artist,0.0,High,,Cat_6,A +464146,Male,No,38,Yes,Entertainment,1.0,Low,2.0,Cat_6,A +464498,Male,Yes,45,Yes,Artist,,Average,3.0,Cat_3,B +459589,Male,No,25,No,Healthcare,1.0,Low,5.0,Cat_6,D +459775,Male,No,36,Yes,Artist,,Low,2.0,Cat_2,B +459829,Male,Yes,53,Yes,Artist,3.0,High,3.0,Cat_7,D +463314,Female,No,27,No,Healthcare,0.0,Low,4.0,Cat_3,A +459723,Male,Yes,49,Yes,Artist,6.0,Average,2.0,Cat_6,C +459122,Female,Yes,51,Yes,Artist,6.0,Average,5.0,Cat_6,C +462221,Male,No,33,No,Healthcare,0.0,Low,7.0,Cat_4,D +463528,Male,Yes,67,No,Executive,,High,2.0,Cat_6,A +459112,Female,Yes,72,Yes,Lawyer,1.0,High,,,C +462694,Male,Yes,61,Yes,Entertainment,6.0,Average,2.0,Cat_6,A +466757,Female,Yes,47,No,Engineer,0.0,Low,1.0,Cat_6,A +464413,Female,Yes,36,Yes,Engineer,0.0,Low,1.0,Cat_3,B +464690,Male,Yes,40,No,Engineer,2.0,Average,,Cat_4,D +466425,Male,Yes,32,Yes,Entertainment,1.0,Average,2.0,Cat_3,A +460474,Female,Yes,40,No,Homemaker,9.0,Average,5.0,Cat_4,A +467261,Female,Yes,77,Yes,Lawyer,0.0,High,2.0,Cat_6,C +463411,Male,Yes,42,Yes,Entertainment,,Average,2.0,Cat_6,C +463322,Female,Yes,72,Yes,Lawyer,1.0,High,4.0,Cat_2,B +466193,Male,Yes,84,No,Lawyer,0.0,Low,1.0,Cat_6,A +461555,Male,No,27,No,Entertainment,,Low,1.0,Cat_6,D +459738,Male,Yes,32,Yes,Artist,1.0,Low,2.0,Cat_6,D +459008,Female,Yes,52,Yes,Artist,1.0,Average,3.0,Cat_6,B +462004,Female,Yes,53,Yes,Artist,,High,6.0,Cat_3,C +459836,Male,,32,Yes,Healthcare,8.0,High,5.0,Cat_7,D +461250,Female,Yes,47,Yes,Lawyer,2.0,High,4.0,Cat_6,D +461926,Female,Yes,63,Yes,Artist,4.0,Average,4.0,Cat_6,C +459578,Male,Yes,35,Yes,Executive,2.0,Average,5.0,Cat_6,B +466003,Female,Yes,27,Yes,Marketing,9.0,Low,1.0,Cat_3,C +465460,Male,No,49,Yes,Artist,0.0,Low,1.0,Cat_6,B +462862,Female,No,31,No,Homemaker,8.0,Low,1.0,Cat_6,D +466388,Male,,20,No,Healthcare,0.0,Low,2.0,Cat_6,D +465084,Male,Yes,61,Yes,Executive,5.0,High,2.0,Cat_6,C +465777,Female,No,30,Yes,Artist,9.0,Low,5.0,Cat_6,A +466688,Male,No,23,No,Healthcare,1.0,Low,3.0,Cat_4,D +461029,Male,Yes,42,No,Entertainment,8.0,Low,2.0,Cat_4,A +462132,Male,Yes,47,,Artist,4.0,Low,1.0,Cat_6,B +464812,Female,Yes,48,Yes,Engineer,0.0,High,5.0,Cat_4,A +459351,Female,Yes,57,Yes,Artist,0.0,High,,Cat_1,C +465099,Male,Yes,39,No,Entertainment,0.0,Average,2.0,Cat_6,C +465730,Male,Yes,43,Yes,Executive,6.0,High,3.0,Cat_4,A +462138,Female,Yes,56,Yes,Artist,1.0,Low,2.0,Cat_6,B +466144,Female,Yes,48,No,Artist,0.0,Low,,,C +465630,Male,No,26,Yes,Artist,,Low,1.0,,B +460332,Male,No,28,Yes,Healthcare,0.0,Low,5.0,Cat_6,D +462342,Female,Yes,71,Yes,Lawyer,8.0,High,2.0,Cat_6,C +459007,Female,Yes,42,Yes,Artist,1.0,Average,3.0,Cat_6,C +465130,Female,Yes,50,Yes,Artist,0.0,Average,9.0,Cat_6,C +459702,Female,Yes,62,Yes,Lawyer,1.0,Low,3.0,Cat_6,A +461401,Male,Yes,43,Yes,Artist,1.0,Average,3.0,Cat_6,A +463916,Female,No,32,No,Engineer,9.0,Low,4.0,Cat_5,A +459938,Male,No,61,Yes,Artist,1.0,Low,1.0,Cat_6,B +460199,Female,Yes,39,Yes,Entertainment,5.0,Low,2.0,Cat_6,B +466684,Female,Yes,46,Yes,Artist,0.0,Average,2.0,Cat_1,C +465978,Male,Yes,50,Yes,Artist,1.0,Average,3.0,Cat_6,B +465682,Male,Yes,39,Yes,Artist,0.0,Low,4.0,Cat_4,A +462693,Female,No,35,Yes,Artist,1.0,Low,1.0,Cat_6,A +459734,Female,Yes,85,Yes,Artist,1.0,Low,,Cat_6,A +459952,Female,No,35,Yes,Entertainment,7.0,Low,2.0,Cat_6,A +466450,Female,No,33,Yes,Healthcare,1.0,Low,1.0,Cat_6,D +466556,Male,No,21,No,Healthcare,1.0,Low,3.0,Cat_6,D +459932,Female,No,46,Yes,Artist,9.0,Low,1.0,Cat_6,B +460420,Female,No,37,Yes,Artist,1.0,Low,1.0,Cat_6,A +466928,Female,No,26,,Healthcare,0.0,Low,7.0,Cat_6,D +463513,Male,Yes,79,Yes,Artist,,High,2.0,Cat_6,B +460123,Female,Yes,33,Yes,Engineer,9.0,Low,2.0,Cat_6,D +462357,Male,No,25,Yes,Artist,1.0,Low,3.0,Cat_6,A +459866,Female,No,51,Yes,Artist,,Low,1.0,Cat_6,B +464094,Male,Yes,53,Yes,Executive,1.0,High,4.0,Cat_6,B +467852,Female,No,41,Yes,Artist,10.0,Low,1.0,Cat_6,B +462526,Female,Yes,46,Yes,Artist,3.0,Average,5.0,Cat_6,C +461677,Male,No,32,Yes,Healthcare,8.0,Low,,Cat_6,D +463611,Male,No,62,Yes,Artist,0.0,Low,,Cat_6,A +467389,Male,No,43,No,Engineer,1.0,Low,,Cat_6,D +463180,Male,Yes,36,Yes,Artist,0.0,Low,3.0,Cat_3,C +461572,Female,Yes,57,Yes,Artist,0.0,Average,3.0,Cat_2,C +464607,Female,Yes,49,Yes,Artist,0.0,Average,4.0,Cat_2,C +463921,Female,Yes,49,Yes,,,Low,1.0,Cat_6,B +467679,Female,Yes,51,Yes,Artist,,Average,4.0,Cat_2,C +463694,Female,No,40,No,Marketing,,Low,,Cat_6,D +467751,Male,Yes,48,Yes,Artist,1.0,Average,3.0,Cat_6,C +465563,Male,Yes,59,Yes,Entertainment,1.0,Average,5.0,Cat_3,B +465589,Male,Yes,40,Yes,Artist,0.0,Average,3.0,Cat_6,A +459215,Female,Yes,58,Yes,Artist,0.0,Low,3.0,Cat_3,C +466826,Male,Yes,43,Yes,Entertainment,0.0,Low,2.0,Cat_6,A +461164,Female,No,39,Yes,Doctor,9.0,Low,1.0,Cat_3,B +459305,Male,Yes,29,No,Healthcare,13.0,High,2.0,Cat_6,B +460181,Female,Yes,37,Yes,Marketing,9.0,Low,1.0,Cat_6,A +466447,Female,Yes,49,Yes,Artist,0.0,Average,4.0,Cat_7,B +467735,Male,No,26,Yes,Doctor,1.0,Low,2.0,Cat_6,A +463755,Female,Yes,63,No,Artist,0.0,High,2.0,Cat_6,B +460930,Male,No,26,Yes,Artist,10.0,Low,5.0,Cat_6,D +465246,Male,No,21,No,Healthcare,1.0,Low,5.0,Cat_4,D +459683,Female,Yes,56,Yes,Engineer,1.0,Average,3.0,Cat_6,B +463491,Male,Yes,27,Yes,Doctor,9.0,Average,3.0,Cat_6,D +466058,Male,Yes,35,No,Entertainment,1.0,Low,,Cat_4,A +459985,Female,No,39,Yes,Doctor,8.0,Low,2.0,Cat_6,B +464523,Female,No,30,Yes,Healthcare,13.0,Low,,Cat_6,D +462815,Male,Yes,84,No,Lawyer,1.0,Low,1.0,Cat_6,A +464187,Female,No,41,No,Engineer,9.0,Low,1.0,Cat_6,A +461246,Male,Yes,51,Yes,Artist,,Average,5.0,Cat_6,C +460655,Male,No,19,Yes,Marketing,,Low,4.0,Cat_6,D +467137,Female,Yes,59,Yes,Engineer,0.0,Low,1.0,Cat_6,D +462477,Male,No,25,Yes,Entertainment,7.0,Low,3.0,Cat_6,A +463906,Female,Yes,40,Yes,Engineer,0.0,Average,4.0,Cat_6,B +465855,Male,No,42,Yes,Artist,1.0,Low,1.0,Cat_2,A +467334,Male,Yes,73,No,Executive,0.0,High,2.0,Cat_6,C +461600,Male,No,30,Yes,Marketing,1.0,Low,5.0,Cat_6,C +465828,Female,No,29,No,Entertainment,9.0,Low,4.0,Cat_4,D +463494,Male,Yes,47,,Healthcare,,Average,4.0,Cat_6,D +460279,Female,Yes,27,No,Entertainment,6.0,High,2.0,Cat_6,B +463136,Male,Yes,35,Yes,Artist,1.0,High,3.0,Cat_6,B +459869,Female,No,23,No,Healthcare,1.0,Low,3.0,Cat_6,D +467916,Female,Yes,48,Yes,Artist,6.0,Low,1.0,Cat_6,C +465853,Female,Yes,33,Yes,Healthcare,,Average,2.0,Cat_6,A +461890,Female,Yes,45,Yes,Artist,0.0,Average,2.0,Cat_6,C +466582,Female,No,23,No,Healthcare,1.0,Low,3.0,Cat_6,D +461076,Male,No,42,Yes,Entertainment,1.0,Low,8.0,Cat_3,A +466713,Female,Yes,69,Yes,Artist,1.0,High,4.0,Cat_6,C +460753,Female,No,33,No,Healthcare,1.0,Low,6.0,Cat_6,A +460262,Male,No,28,No,Executive,9.0,Low,4.0,Cat_6,D +465249,Female,No,19,No,Healthcare,1.0,Low,4.0,Cat_6,D +466083,Female,No,21,No,Marketing,2.0,Low,2.0,Cat_3,D +459137,Male,Yes,42,Yes,Artist,7.0,Average,2.0,Cat_6,C +459485,Female,Yes,87,Yes,Lawyer,,High,2.0,Cat_6,A +461734,Female,Yes,81,No,,,High,2.0,Cat_6,B +467416,Female,No,38,Yes,Doctor,8.0,Low,1.0,Cat_4,B +463137,Female,No,31,Yes,Homemaker,8.0,Low,1.0,Cat_6,D +459839,Male,Yes,45,No,Engineer,1.0,Average,5.0,Cat_6,B +465665,Female,Yes,83,Yes,Lawyer,1.0,Low,1.0,Cat_6,A +466922,Female,No,22,No,Healthcare,0.0,Low,,Cat_6,D +464747,Female,Yes,69,No,Engineer,0.0,Average,4.0,Cat_4,A +464920,Male,No,36,Yes,Artist,2.0,Low,1.0,Cat_4,B +461638,Male,Yes,70,Yes,Entertainment,0.0,High,2.0,Cat_6,A +461972,Male,Yes,40,No,,,Average,5.0,Cat_3,B +467790,Male,No,21,No,Healthcare,1.0,Low,6.0,Cat_6,D +463402,Male,No,19,No,Healthcare,7.0,Low,4.0,Cat_4,D +462900,Male,Yes,39,Yes,Artist,0.0,Average,3.0,Cat_6,B +460688,Male,Yes,28,No,Doctor,5.0,Low,2.0,Cat_3,D +466033,Female,Yes,40,Yes,Artist,0.0,Average,4.0,Cat_6,B +459620,Female,Yes,29,No,Marketing,,Low,,,D +467121,Male,Yes,53,Yes,Doctor,1.0,Low,,Cat_6,A +461440,Female,Yes,42,Yes,Artist,1.0,Average,2.0,Cat_6,C +459598,Male,Yes,40,No,Artist,,Average,2.0,Cat_4,A +465670,Female,No,48,No,Engineer,12.0,Low,1.0,Cat_4,D +463985,Female,Yes,29,Yes,Artist,4.0,Low,2.0,Cat_1,D +466347,Male,Yes,71,No,Executive,5.0,Low,3.0,Cat_4,B +459077,Female,Yes,82,No,Lawyer,0.0,High,2.0,Cat_6,A +460339,Female,Yes,50,Yes,Entertainment,4.0,Low,2.0,Cat_6,A +459970,Male,No,40,Yes,Artist,1.0,Low,2.0,Cat_6,A +463464,Male,No,18,No,Healthcare,6.0,Low,4.0,Cat_6,D +459313,Male,Yes,42,No,Entertainment,0.0,Average,4.0,Cat_6,C +466990,Male,Yes,39,Yes,Entertainment,9.0,Average,2.0,Cat_6,B +460077,Female,No,32,Yes,Artist,,Low,1.0,Cat_6,A +460369,Female,Yes,50,Yes,Artist,,Low,2.0,Cat_6,C +464108,Female,Yes,60,Yes,Engineer,1.0,Average,3.0,Cat_7,C +463792,Female,No,38,No,Engineer,,Low,1.0,Cat_6,B +462261,Male,Yes,60,No,Engineer,0.0,Average,3.0,Cat_6,C +463312,Male,Yes,40,No,Engineer,3.0,Low,1.0,Cat_3,A +462667,Female,Yes,40,No,Homemaker,2.0,Average,2.0,Cat_6,A +462917,Male,Yes,65,No,Doctor,1.0,Low,1.0,Cat_1,D +463898,Female,No,38,Yes,Healthcare,0.0,Low,2.0,Cat_6,C +459157,Male,No,37,Yes,Engineer,1.0,Low,1.0,Cat_6,D +460287,Male,Yes,53,Yes,Artist,0.0,Low,1.0,Cat_6,B +460521,Male,No,39,Yes,Artist,1.0,Low,,Cat_4,C +460684,Male,Yes,62,No,Entertainment,8.0,Low,2.0,Cat_3,B +462319,Male,Yes,47,Yes,Artist,1.0,Average,2.0,Cat_2,C +460614,Male,,38,Yes,Entertainment,,Average,4.0,Cat_3,C +462925,Female,Yes,30,No,Homemaker,,Average,2.0,Cat_3,A +465128,Female,Yes,62,No,Artist,1.0,Average,3.0,Cat_6,B +465222,Male,Yes,69,No,Doctor,8.0,Low,2.0,Cat_6,A +467850,Female,No,19,No,Healthcare,0.0,Low,2.0,Cat_4,D +465488,Male,No,18,No,Healthcare,0.0,Low,4.0,Cat_6,D +461266,Female,No,35,Yes,Artist,,Low,1.0,Cat_4,A +467453,Male,No,49,No,Healthcare,,Low,1.0,Cat_6,D +467672,Female,Yes,36,Yes,Artist,7.0,Average,2.0,Cat_6,C +466606,Female,Yes,35,Yes,Engineer,0.0,Low,3.0,Cat_3,B +465596,Male,,36,Yes,Healthcare,8.0,High,2.0,Cat_4,B +462309,Male,No,30,Yes,Healthcare,,Low,1.0,Cat_6,D +461840,Male,Yes,47,Yes,Executive,8.0,High,4.0,Cat_6,C +461284,Male,No,23,No,Artist,1.0,Low,4.0,Cat_6,D +467878,Female,Yes,42,Yes,Doctor,0.0,Average,4.0,Cat_6,A +465553,Female,Yes,47,Yes,Artist,8.0,Average,4.0,Cat_3,C +463346,Male,Yes,40,Yes,Healthcare,1.0,Low,3.0,Cat_6,D +466232,Male,Yes,59,No,Executive,0.0,High,3.0,Cat_6,C +465183,Female,Yes,38,Yes,Artist,3.0,Low,2.0,Cat_7,C +462382,Male,Yes,73,Yes,Lawyer,1.0,High,2.0,Cat_6,B +461363,Male,No,45,Yes,Artist,0.0,Low,1.0,Cat_6,A +465658,Male,Yes,71,Yes,Artist,0.0,Average,4.0,Cat_6,C +460748,Female,Yes,60,No,Engineer,0.0,Low,2.0,Cat_3,A +466217,Male,No,27,Yes,Doctor,0.0,Low,2.0,Cat_6,C +465484,Female,Yes,57,Yes,Artist,0.0,Low,2.0,Cat_6,B +459401,Male,No,26,No,Healthcare,1.0,Low,3.0,,C +459783,Female,Yes,18,No,Artist,,High,,Cat_1,B +462326,Female,Yes,42,Yes,Artist,4.0,Average,2.0,Cat_6,C +459870,Male,Yes,49,No,Executive,11.0,High,4.0,Cat_6,A +467676,Male,Yes,40,Yes,Entertainment,8.0,Low,2.0,Cat_6,D +464488,Female,No,40,Yes,Artist,5.0,Low,9.0,Cat_6,C +466076,Male,No,19,No,Healthcare,8.0,Low,3.0,Cat_6,D +462275,Male,Yes,42,Yes,Artist,1.0,Average,2.0,Cat_6,C +459396,Female,No,50,Yes,Engineer,0.0,Low,1.0,Cat_6,B +464417,Male,Yes,72,No,Entertainment,2.0,Average,2.0,Cat_6,C +467311,Male,Yes,67,Yes,Lawyer,1.0,High,3.0,Cat_6,B +464034,Male,Yes,37,Yes,Entertainment,3.0,Low,1.0,Cat_6,A +467748,Male,Yes,38,Yes,Executive,,High,4.0,Cat_6,C +465010,Female,No,42,Yes,Marketing,,Low,3.0,Cat_6,D +459886,Male,Yes,26,No,Artist,1.0,Low,4.0,Cat_6,A +466435,Male,Yes,32,No,Executive,1.0,High,5.0,Cat_6,D +463885,Male,Yes,51,Yes,Artist,1.0,Low,1.0,Cat_4,A +462915,Male,Yes,61,No,Engineer,5.0,Average,3.0,Cat_6,B +462824,Male,Yes,38,No,Engineer,0.0,Average,3.0,Cat_4,B +460696,Male,Yes,73,No,Lawyer,1.0,Low,1.0,Cat_6,D +466662,Female,No,30,No,Engineer,1.0,Low,,Cat_4,A +461658,Female,Yes,62,Yes,Artist,0.0,Average,2.0,Cat_5,C +467142,Male,Yes,40,Yes,Executive,0.0,Low,4.0,Cat_6,B +460127,Male,No,42,Yes,Healthcare,8.0,Low,3.0,Cat_6,D +467542,Male,No,30,Yes,Healthcare,9.0,Low,1.0,Cat_6,D +462901,Female,Yes,46,No,Homemaker,9.0,Low,1.0,Cat_3,A +465547,Female,Yes,68,,Lawyer,0.0,Low,1.0,Cat_6,A +464797,Female,Yes,38,No,Engineer,9.0,Average,4.0,Cat_4,A +460867,Male,Yes,40,Yes,Doctor,0.0,Average,5.0,Cat_6,B +464438,Male,Yes,50,No,Entertainment,0.0,Average,3.0,Cat_6,C +460779,Female,No,28,No,Entertainment,1.0,Low,2.0,Cat_3,A +458996,Female,Yes,71,No,,1.0,Low,1.0,Cat_6,A +460276,Female,Yes,37,Yes,Artist,3.0,Low,2.0,Cat_6,C +466478,Female,Yes,51,Yes,Artist,0.0,Average,4.0,Cat_3,C +463834,Female,Yes,41,Yes,Engineer,1.0,Average,4.0,Cat_6,C +465796,Female,Yes,65,Yes,Artist,0.0,Average,4.0,Cat_4,C +460654,Female,Yes,48,No,Marketing,0.0,Average,2.0,Cat_4,D +459752,Female,Yes,63,Yes,,,Average,3.0,Cat_6,B +459057,Male,Yes,68,No,Lawyer,,High,,Cat_6,D +465641,Female,Yes,47,Yes,Artist,0.0,Average,2.0,Cat_6,C +467854,Male,No,28,Yes,Artist,9.0,Low,2.0,Cat_6,A +459291,Female,No,38,Yes,Entertainment,0.0,Low,1.0,Cat_6,A +460749,Male,No,36,Yes,Entertainment,1.0,Low,2.0,Cat_6,A +467861,Female,No,27,No,Artist,,Low,4.0,Cat_4,D +459446,Male,Yes,36,No,Entertainment,,Average,3.0,Cat_6,C +459229,Female,Yes,65,Yes,Lawyer,0.0,Average,4.0,Cat_6,B +466038,Female,No,27,No,Homemaker,9.0,Low,1.0,Cat_4,A +459507,Male,Yes,69,No,Lawyer,,High,2.0,Cat_1,D +467494,Female,Yes,80,Yes,Lawyer,0.0,High,,Cat_6,A +466794,Male,,59,Yes,Entertainment,,Average,4.0,Cat_6,A +464682,Male,Yes,50,,Executive,4.0,Average,9.0,Cat_4,D +459472,Female,Yes,77,No,Lawyer,,High,2.0,Cat_6,A +466023,Male,Yes,68,No,Executive,,High,3.0,Cat_7,B +467538,Male,Yes,50,Yes,Artist,0.0,Low,3.0,Cat_6,B +459266,Male,No,26,Yes,Healthcare,5.0,Low,5.0,Cat_6,D +467642,Male,Yes,46,Yes,Artist,2.0,Average,4.0,Cat_2,C +466477,Male,Yes,36,Yes,Artist,,Average,2.0,,C +459579,Female,Yes,40,Yes,Artist,4.0,Average,5.0,Cat_4,B +464143,Male,No,33,Yes,Healthcare,9.0,Low,5.0,Cat_6,D +467678,Male,No,56,No,Marketing,1.0,Low,2.0,Cat_6,D +466053,Female,No,25,No,Doctor,2.0,Low,1.0,Cat_4,D +467038,Male,Yes,61,Yes,Lawyer,1.0,High,2.0,Cat_6,C +466822,Female,Yes,42,Yes,Entertainment,0.0,Average,4.0,Cat_3,B +464371,Male,Yes,49,Yes,Artist,1.0,Low,4.0,Cat_3,C +462788,Male,Yes,48,No,Entertainment,1.0,Average,3.0,Cat_3,C +459334,Male,Yes,36,Yes,Healthcare,3.0,Low,3.0,Cat_6,D +465236,Male,Yes,47,Yes,Artist,0.0,Low,1.0,Cat_6,C +461433,Male,Yes,70,Yes,Doctor,1.0,High,3.0,Cat_6,C +466345,Male,No,21,No,Healthcare,1.0,Low,4.0,Cat_2,D +459942,Male,No,51,Yes,Artist,5.0,Low,1.0,Cat_6,B +462739,Male,Yes,46,Yes,Artist,0.0,Low,2.0,Cat_6,A +463412,Male,No,33,Yes,Artist,,Low,2.0,Cat_6,A +467243,Male,No,26,Yes,Healthcare,1.0,Low,3.0,Cat_6,D +463215,Female,Yes,37,Yes,Engineer,0.0,High,2.0,Cat_6,A +460887,Male,Yes,47,Yes,Entertainment,2.0,Average,3.0,Cat_3,B +467950,Female,No,38,Yes,Entertainment,0.0,Low,2.0,Cat_6,D +460407,Female,No,36,Yes,Artist,8.0,Low,1.0,Cat_6,B +466565,Male,Yes,29,No,Artist,6.0,Average,2.0,Cat_6,B +461590,Male,Yes,41,No,Executive,1.0,High,4.0,Cat_6,B +461128,Male,No,20,No,Healthcare,7.0,Low,3.0,Cat_6,D +460145,Female,No,36,Yes,Artist,0.0,Low,1.0,Cat_2,B +462192,Female,No,23,No,Healthcare,0.0,Low,6.0,Cat_4,D +467470,Male,Yes,53,Yes,Engineer,1.0,Average,3.0,Cat_6,B +462734,Male,Yes,69,Yes,Artist,,Average,2.0,Cat_6,C +467937,Male,No,28,Yes,Doctor,0.0,Low,3.0,Cat_6,C +463395,Male,No,31,No,Marketing,,Low,1.0,Cat_6,B +466648,Male,Yes,36,Yes,Executive,6.0,High,2.0,Cat_6,C +460866,Male,No,28,Yes,Doctor,1.0,Low,9.0,Cat_4,B +463097,Female,Yes,45,Yes,Artist,1.0,Average,3.0,Cat_7,C +461614,Female,No,25,,Healthcare,1.0,Low,1.0,Cat_4,C +459771,Female,,30,Yes,Doctor,0.0,Low,3.0,Cat_3,A +466196,Female,Yes,78,Yes,Lawyer,6.0,Low,1.0,Cat_4,A +466372,Female,Yes,60,Yes,Artist,1.0,Average,5.0,Cat_6,C +467400,Male,Yes,61,No,Artist,,Low,2.0,Cat_6,A +459367,Female,No,22,No,Doctor,11.0,Low,4.0,Cat_1,D +463390,Female,Yes,50,Yes,Artist,0.0,Average,2.0,Cat_3,C +460504,Male,,43,No,Executive,,Average,3.0,Cat_3,A +465478,Male,Yes,51,No,Entertainment,1.0,Low,3.0,Cat_6,A +466739,Male,No,31,No,Healthcare,0.0,Low,,Cat_6,D +461262,Female,No,27,No,Engineer,1.0,Low,7.0,Cat_4,A +463376,Female,Yes,45,Yes,Executive,7.0,High,2.0,Cat_6,C +459536,Male,Yes,32,Yes,Healthcare,6.0,Low,2.0,Cat_6,D +466351,Female,No,22,No,Healthcare,2.0,Low,3.0,Cat_4,D +465916,Male,No,42,Yes,Artist,3.0,Low,1.0,Cat_6,B +461867,Male,No,30,Yes,Artist,0.0,Low,4.0,Cat_6,A +461236,Female,No,30,Yes,Healthcare,9.0,Low,1.0,Cat_6,D +461330,Male,Yes,60,No,Executive,0.0,High,2.0,Cat_6,C +466271,Male,No,33,No,Entertainment,1.0,Low,3.0,Cat_2,B +463511,Female,No,27,Yes,Artist,1.0,Low,1.0,Cat_3,B +462567,Female,No,36,No,Engineer,0.0,Low,1.0,Cat_6,A +466506,Male,Yes,50,,Artist,1.0,Average,4.0,Cat_6,A +460278,Male,Yes,29,Yes,Artist,0.0,Low,5.0,Cat_4,A +467841,Female,No,18,No,Healthcare,2.0,Low,5.0,Cat_6,D +465167,Female,Yes,42,Yes,Artist,9.0,Average,3.0,Cat_3,C +463579,Male,Yes,45,Yes,Artist,,Average,2.0,Cat_6,B +462779,Female,Yes,61,No,Marketing,,Average,,Cat_6,D +464161,Male,Yes,83,No,Lawyer,1.0,Low,1.0,Cat_6,A +463462,Female,Yes,41,No,Engineer,0.0,Average,4.0,Cat_6,A +459963,Female,No,40,Yes,Engineer,9.0,Low,1.0,Cat_7,A +459172,Male,No,21,No,Healthcare,3.0,Low,3.0,Cat_2,D +467475,Female,Yes,66,Yes,Artist,0.0,Average,3.0,Cat_6,C +464303,Male,Yes,43,No,Artist,0.0,Average,2.0,Cat_3,B +461308,Male,No,18,No,Healthcare,0.0,Low,2.0,Cat_6,D +463288,Female,,59,Yes,Artist,9.0,Average,2.0,Cat_6,C +467231,Male,No,22,No,Healthcare,,Low,4.0,Cat_6,D +460149,Male,Yes,50,Yes,Artist,1.0,Low,2.0,Cat_6,C +459955,Male,No,47,Yes,Engineer,1.0,Low,2.0,Cat_6,D +463668,Female,No,29,No,Engineer,0.0,Low,6.0,Cat_4,C +463424,Female,Yes,62,Yes,Artist,5.0,Average,5.0,Cat_6,C +462767,Male,No,21,No,Healthcare,2.0,Low,4.0,Cat_6,D +465736,Female,No,36,Yes,Artist,8.0,Low,2.0,Cat_3,B +461449,Female,Yes,38,Yes,Artist,5.0,High,3.0,Cat_4,B +462591,Male,No,25,No,Doctor,4.0,Low,,Cat_4,D +464531,Male,Yes,38,No,Engineer,0.0,High,6.0,Cat_4,A +467007,Female,Yes,35,No,Engineer,0.0,High,1.0,Cat_6,A +460190,Female,No,30,Yes,Entertainment,9.0,Low,3.0,Cat_6,D +466149,Female,No,30,No,Doctor,3.0,Low,4.0,Cat_6,A +464214,Male,No,28,Yes,Healthcare,1.0,Low,3.0,Cat_6,D +461211,Female,No,53,Yes,Entertainment,0.0,Low,1.0,Cat_6,A +467430,Male,Yes,48,Yes,Artist,4.0,Average,2.0,Cat_6,C +462624,Male,Yes,31,No,Entertainment,1.0,Low,6.0,Cat_4,D +464936,Male,No,28,No,,1.0,Low,1.0,Cat_4,D +461542,Male,No,26,Yes,Artist,5.0,Low,1.0,Cat_1,B +465302,Female,Yes,57,Yes,Artist,1.0,High,3.0,Cat_2,B +460291,Female,Yes,37,Yes,Artist,6.0,Low,2.0,Cat_6,B +460742,Male,No,52,Yes,Artist,0.0,Low,3.0,Cat_6,B +464350,Female,Yes,86,Yes,Lawyer,0.0,High,2.0,Cat_6,C +465746,Female,No,41,Yes,Artist,,Low,1.0,Cat_6,C +466325,Female,No,25,No,Marketing,1.0,Low,2.0,Cat_3,A +464353,Female,No,51,Yes,Artist,0.0,Low,1.0,Cat_6,A +463172,Male,Yes,32,No,Entertainment,2.0,Low,2.0,Cat_3,A +465582,Male,,40,Yes,Artist,1.0,Low,1.0,Cat_7,A +467136,Male,Yes,41,No,Entertainment,0.0,Average,4.0,Cat_4,A +466693,Male,No,23,No,Healthcare,0.0,Low,2.0,Cat_6,D +461727,Female,No,52,Yes,Artist,1.0,Low,3.0,Cat_6,C +461160,Male,Yes,49,Yes,Artist,1.0,Low,2.0,Cat_3,A +461360,Female,Yes,30,Yes,Healthcare,0.0,Average,4.0,Cat_6,D +466900,Female,Yes,46,Yes,Artist,9.0,Average,4.0,Cat_6,C +467166,Female,Yes,25,No,Homemaker,8.0,Average,2.0,Cat_6,D +460541,Female,Yes,36,Yes,Homemaker,1.0,Average,3.0,Cat_3,D +466622,Female,No,47,Yes,Entertainment,1.0,Low,1.0,Cat_6,A +465971,Male,Yes,57,Yes,Artist,0.0,Low,1.0,Cat_6,D +466909,Male,Yes,61,Yes,Artist,0.0,Low,2.0,Cat_6,B +460637,Female,No,19,No,Healthcare,,Low,,Cat_3,D +465983,Male,,19,No,Healthcare,0.0,Low,3.0,Cat_6,D +462288,Male,Yes,39,Yes,Artist,0.0,Average,2.0,Cat_4,C +464627,Male,Yes,37,Yes,Artist,0.0,Low,4.0,Cat_3,A +460205,Male,Yes,55,Yes,Artist,4.0,Low,2.0,Cat_6,B +460854,Female,No,22,No,Marketing,1.0,Low,2.0,Cat_1,D +463725,Male,Yes,46,Yes,Artist,1.0,Low,2.0,Cat_6,C +461694,Male,No,32,Yes,Doctor,0.0,Low,4.0,Cat_4,D +465917,Male,No,36,Yes,Artist,13.0,Low,1.0,Cat_6,B +463604,Male,No,23,No,Healthcare,2.0,Low,3.0,Cat_2,D +467473,Male,No,18,No,Healthcare,2.0,Low,3.0,Cat_6,D +462462,Male,Yes,30,Yes,Entertainment,,Average,3.0,Cat_6,C +465089,Male,Yes,71,No,Executive,0.0,High,4.0,Cat_6,C +467491,Male,Yes,47,Yes,Artist,1.0,Average,4.0,Cat_6,C +461418,Male,No,45,Yes,Artist,0.0,Low,4.0,Cat_6,A +464540,Female,Yes,42,Yes,Engineer,1.0,Average,4.0,Cat_4,A +462985,Male,Yes,32,Yes,Doctor,9.0,Average,3.0,Cat_6,A +465921,Female,No,25,Yes,Artist,8.0,Low,1.0,Cat_6,B +462193,Male,No,28,Yes,Healthcare,,Low,6.0,Cat_4,B +467754,Male,Yes,35,Yes,Artist,,Average,4.0,Cat_6,C +459672,Male,Yes,47,No,Artist,3.0,Average,3.0,Cat_6,C +465815,Male,Yes,41,No,Doctor,0.0,Average,2.0,Cat_4,A +459379,Female,No,19,No,Healthcare,3.0,Low,3.0,Cat_6,D +460824,Male,Yes,66,Yes,,0.0,Low,2.0,Cat_2,B +458988,Female,No,29,No,Marketing,2.0,Low,3.0,Cat_6,D +466583,Female,Yes,38,Yes,Doctor,0.0,Average,2.0,Cat_6,A +467951,Male,No,31,Yes,Entertainment,1.0,Low,3.0,Cat_6,A +461742,Male,Yes,87,Yes,Executive,1.0,High,2.0,Cat_6,C +466321,Male,Yes,41,Yes,Entertainment,2.0,Low,2.0,Cat_4,C +464102,Female,Yes,63,Yes,Artist,7.0,Average,3.0,Cat_6,C +465695,Female,No,33,No,Healthcare,,Low,3.0,Cat_4,B +467657,Male,No,23,No,Healthcare,0.0,Low,4.0,Cat_3,D +467203,Female,Yes,36,Yes,Artist,5.0,Average,2.0,Cat_6,C +460329,Male,No,45,Yes,Entertainment,6.0,Low,2.0,Cat_6,A +465926,Male,Yes,26,Yes,Healthcare,6.0,Low,2.0,Cat_6,D +467635,Female,No,41,Yes,Artist,9.0,Low,1.0,Cat_6,B +461127,Male,No,23,No,Healthcare,1.0,Low,5.0,Cat_4,D +462486,Male,Yes,51,Yes,Executive,1.0,Average,5.0,Cat_6,C +463011,Female,Yes,81,Yes,Homemaker,0.0,High,2.0,Cat_6,B +464715,Male,Yes,61,No,Marketing,1.0,Average,4.0,Cat_4,D +462725,Male,Yes,77,Yes,Lawyer,0.0,High,2.0,Cat_6,B +465325,Female,Yes,50,Yes,Artist,4.0,Low,2.0,Cat_6,B +464699,Male,No,29,No,Doctor,1.0,Low,5.0,Cat_4,D +465270,Female,Yes,43,Yes,Artist,0.0,Average,4.0,Cat_6,C +463189,Female,Yes,69,Yes,Engineer,1.0,Low,1.0,Cat_4,A +460162,Female,No,21,No,Healthcare,1.0,Low,4.0,Cat_2,D +466244,Female,No,30,No,Healthcare,3.0,Low,3.0,Cat_4,B +466234,Female,Yes,49,Yes,Engineer,,Low,1.0,Cat_4,B +466403,Female,Yes,49,No,Engineer,1.0,Average,4.0,Cat_6,A +459156,Female,Yes,81,Yes,Lawyer,1.0,High,2.0,Cat_6,A +463944,Male,No,29,No,Artist,0.0,Low,5.0,Cat_4,A +466888,Male,Yes,50,Yes,Entertainment,1.0,Low,1.0,Cat_6,A +461430,Male,Yes,57,Yes,Artist,1.0,Average,2.0,Cat_3,C +464489,Male,Yes,45,Yes,Artist,0.0,Average,3.0,Cat_6,C +464738,Female,Yes,66,No,Engineer,0.0,Average,2.0,Cat_4,A +459953,Male,Yes,53,Yes,Artist,9.0,Low,2.0,Cat_6,B +465318,Male,No,21,No,Doctor,6.0,Low,6.0,Cat_7,C +461959,Female,Yes,62,Yes,Artist,0.0,Low,2.0,Cat_6,C +459029,Male,Yes,70,No,Executive,,High,2.0,Cat_6,B +467237,Male,Yes,82,Yes,Lawyer,0.0,High,2.0,Cat_6,D +459724,Female,Yes,39,No,Artist,0.0,Average,4.0,,A +459891,Male,Yes,49,Yes,Doctor,,Average,2.0,Cat_1,A +462628,Female,Yes,28,No,,,Average,3.0,Cat_4,D +461756,Female,Yes,35,Yes,Engineer,0.0,Average,2.0,Cat_4,C +463433,Female,Yes,50,Yes,Doctor,0.0,Low,3.0,Cat_6,C +465724,Male,No,35,Yes,Artist,9.0,Low,2.0,Cat_3,A +466904,Male,Yes,55,Yes,Artist,0.0,Average,2.0,Cat_6,C +462463,Male,Yes,61,No,Artist,1.0,Average,2.0,Cat_6,C +461515,Male,Yes,26,No,Artist,0.0,Low,2.0,Cat_6,A +464192,Female,Yes,61,No,Lawyer,0.0,High,3.0,Cat_6,D +467357,Male,Yes,60,Yes,,0.0,Low,2.0,Cat_6,D +467904,Female,No,25,Yes,Artist,13.0,Low,1.0,Cat_6,A +460921,Male,Yes,45,No,Executive,1.0,High,3.0,Cat_6,C +464842,Male,No,35,Yes,Artist,0.0,Low,2.0,Cat_4,A +467733,Male,Yes,62,No,Executive,9.0,High,3.0,Cat_6,B +460142,Female,Yes,53,Yes,Artist,0.0,Average,3.0,Cat_6,B +461504,Female,Yes,49,Yes,Artist,9.0,Low,2.0,Cat_6,B +460174,Female,No,43,Yes,Artist,8.0,Low,4.0,Cat_6,C +461965,Male,No,19,No,Healthcare,0.0,Low,6.0,Cat_6,D +467506,Female,No,38,Yes,Healthcare,,Low,1.0,Cat_4,A +463998,Female,No,27,No,Engineer,0.0,Low,3.0,Cat_6,B +462821,Male,No,18,No,Healthcare,0.0,Low,3.0,Cat_1,D +463787,Female,Yes,43,Yes,Artist,1.0,Low,2.0,Cat_5,C +460203,Female,Yes,36,Yes,Entertainment,8.0,Low,1.0,Cat_6,A +460104,Male,No,53,Yes,Entertainment,1.0,Low,3.0,Cat_4,A +465434,Female,No,26,Yes,Healthcare,1.0,Low,7.0,Cat_2,B +461259,Male,No,59,Yes,Entertainment,0.0,Low,1.0,Cat_4,A +465797,Male,Yes,36,Yes,Artist,1.0,Average,2.0,Cat_2,C +467388,Male,No,35,,,0.0,Low,,Cat_6,D +466373,Male,Yes,63,Yes,Executive,1.0,High,1.0,Cat_6,B +464495,Female,No,33,No,Marketing,1.0,Low,9.0,Cat_6,C +462329,Male,Yes,42,No,Executive,2.0,High,4.0,Cat_6,C +463508,Female,Yes,31,No,Artist,0.0,Low,3.0,Cat_6,A +462062,Male,No,22,No,Healthcare,0.0,Low,3.0,Cat_7,D +466150,Male,Yes,51,Yes,Artist,1.0,Low,2.0,Cat_6,B +466220,Male,No,42,Yes,Doctor,8.0,Low,1.0,Cat_2,B +464648,Male,Yes,28,No,Executive,0.0,High,4.0,Cat_4,A +464167,Male,Yes,53,Yes,Executive,4.0,High,3.0,Cat_6,C +465012,Male,Yes,62,No,Executive,0.0,High,4.0,Cat_6,B +464584,Female,No,27,Yes,Healthcare,9.0,Low,1.0,Cat_6,D +463258,Female,Yes,53,Yes,Artist,1.0,Average,3.0,Cat_6,A +465987,Male,,20,No,Healthcare,1.0,Low,3.0,Cat_2,D +467765,Male,Yes,28,No,Entertainment,9.0,High,4.0,Cat_6,D +463687,Female,No,31,No,Healthcare,0.0,Low,5.0,Cat_2,D +467557,Female,Yes,58,Yes,Artist,,Average,2.0,Cat_6,C +461477,Female,No,31,Yes,Healthcare,0.0,Low,3.0,Cat_2,C +462935,Male,Yes,79,No,Entertainment,1.0,Low,1.0,,D +465017,Male,Yes,52,Yes,Executive,1.0,High,4.0,Cat_6,C +461140,Female,No,30,No,Homemaker,3.0,Low,,Cat_3,D +462430,Male,Yes,42,Yes,Executive,1.0,High,3.0,Cat_6,C +463225,Female,Yes,43,Yes,Artist,,Average,2.0,Cat_6,B +462897,Male,No,33,Yes,Healthcare,3.0,Low,2.0,Cat_2,D +466136,Female,No,19,Yes,Artist,0.0,Low,2.0,Cat_6,A +466989,Female,Yes,40,Yes,Artist,9.0,Average,2.0,Cat_2,B +462428,Male,Yes,67,Yes,Entertainment,2.0,Average,3.0,Cat_6,C +462446,Male,No,22,No,Healthcare,0.0,Low,4.0,Cat_6,D +467254,Female,Yes,45,Yes,Artist,9.0,Low,2.0,Cat_6,B +464077,Female,Yes,60,Yes,Artist,4.0,High,2.0,Cat_6,C +465808,Male,Yes,41,Yes,Artist,0.0,Average,3.0,Cat_3,D +464660,Male,No,29,Yes,Entertainment,0.0,Low,5.0,Cat_4,C +462989,Male,,32,Yes,Healthcare,1.0,Low,1.0,Cat_6,D +464855,Female,Yes,37,No,Engineer,,Average,9.0,Cat_4,A +465430,Male,No,28,No,Healthcare,4.0,Low,5.0,Cat_7,C +463119,Female,Yes,88,Yes,Lawyer,,High,2.0,Cat_6,C +460493,Male,No,42,Yes,Entertainment,2.0,Low,1.0,Cat_3,B +459170,Female,No,36,Yes,Artist,0.0,Low,1.0,Cat_6,B +461219,Female,No,28,Yes,Healthcare,8.0,Low,3.0,Cat_6,D +467054,Male,No,23,No,Healthcare,1.0,Low,3.0,Cat_6,D +465107,Male,Yes,81,No,Executive,0.0,Low,1.0,Cat_6,D +459274,Female,Yes,82,Yes,Lawyer,0.0,High,2.0,Cat_6,B +459540,Female,Yes,70,Yes,Lawyer,0.0,Low,1.0,Cat_6,B +467536,Male,Yes,82,No,Executive,1.0,Low,1.0,Cat_6,D +461611,Female,Yes,38,Yes,Artist,5.0,Average,2.0,Cat_6,C +466164,Female,No,43,Yes,Engineer,9.0,Low,1.0,Cat_6,A +466638,Female,No,42,Yes,Artist,8.0,Low,2.0,Cat_6,B +466733,Female,No,29,No,Homemaker,1.0,Low,,Cat_4,A +460518,Female,Yes,38,Yes,Engineer,9.0,Average,3.0,Cat_3,C +464115,Female,No,38,Yes,Engineer,0.0,Low,3.0,Cat_6,D +460383,Female,No,36,Yes,Engineer,7.0,Low,3.0,Cat_6,D +466943,Male,No,53,Yes,Artist,0.0,Low,1.0,Cat_6,B +462054,Male,No,25,Yes,Healthcare,0.0,Low,,Cat_4,D +465988,Female,Yes,58,Yes,Homemaker,0.0,High,2.0,Cat_5,B +462503,Male,Yes,52,Yes,Executive,0.0,High,4.0,Cat_6,C +461215,Female,No,36,Yes,Artist,13.0,Low,1.0,Cat_6,D +460608,Male,No,19,No,Healthcare,6.0,Low,8.0,Cat_7,D +461698,Female,Yes,67,Yes,Artist,0.0,High,2.0,Cat_6,C +459373,Male,No,31,Yes,Healthcare,,Low,4.0,Cat_6,D +465911,Female,No,28,Yes,Artist,9.0,Low,1.0,Cat_6,A +462315,Male,Yes,65,Yes,Artist,1.0,Average,3.0,Cat_6,B +461563,Female,Yes,60,Yes,Artist,1.0,High,2.0,Cat_6,C +465007,Male,Yes,74,Yes,Lawyer,1.0,Low,1.0,Cat_6,D +464777,Female,Yes,52,No,Engineer,8.0,Average,4.0,Cat_2,B +461480,Female,Yes,63,Yes,Artist,1.0,High,3.0,Cat_6,C +460486,Female,No,18,No,Healthcare,12.0,Low,3.0,Cat_3,D +459994,Male,No,25,No,Entertainment,0.0,Low,3.0,Cat_6,A +459042,Female,Yes,41,Yes,Engineer,1.0,Average,3.0,Cat_6,C +461280,Male,No,20,No,Healthcare,7.0,Low,4.0,Cat_2,D +463070,Male,Yes,68,No,Entertainment,1.0,Low,1.0,Cat_6,D +464447,Male,Yes,69,Yes,Artist,0.0,Low,1.0,Cat_6,D +459617,Male,No,31,Yes,Entertainment,0.0,Low,5.0,Cat_6,D +464714,Female,No,43,Yes,Doctor,1.0,Low,6.0,Cat_7,C +465160,Male,Yes,62,Yes,Executive,1.0,High,3.0,Cat_6,C +460239,Male,Yes,42,Yes,Artist,5.0,Average,2.0,Cat_6,C +465952,Male,No,25,Yes,Healthcare,8.0,Low,3.0,Cat_6,D +461005,Male,No,23,No,Healthcare,0.0,Low,5.0,Cat_3,D +465782,Female,No,21,No,Healthcare,5.0,Low,5.0,Cat_3,D +466886,Female,No,40,No,Marketing,1.0,Low,1.0,Cat_6,D +464013,Male,No,27,No,Healthcare,0.0,Low,1.0,Cat_6,D +466604,Male,No,32,Yes,Healthcare,1.0,Low,,Cat_6,D +466535,Male,No,26,Yes,Artist,8.0,Low,1.0,Cat_6,D +466786,Male,No,32,No,Executive,0.0,Low,2.0,Cat_3,D +460314,Female,No,39,No,Healthcare,4.0,Low,3.0,Cat_6,D +461088,Male,Yes,49,Yes,Artist,9.0,Low,1.0,Cat_3,B +464653,Female,Yes,59,No,Artist,3.0,Average,4.0,Cat_4,A +460567,Male,No,39,,Entertainment,8.0,Low,4.0,Cat_3,C +462771,Female,Yes,46,Yes,Marketing,,High,3.0,Cat_6,B +463358,Female,Yes,62,Yes,Doctor,4.0,Average,3.0,Cat_6,B +466970,Female,Yes,36,Yes,Homemaker,9.0,Low,2.0,Cat_4,A +461491,Female,Yes,70,Yes,Artist,8.0,Average,2.0,Cat_6,C +464344,Female,Yes,58,Yes,Artist,1.0,Low,1.0,Cat_6,B +463655,Male,Yes,39,Yes,Artist,13.0,Low,2.0,Cat_6,B +463427,Female,Yes,29,Yes,Engineer,1.0,Low,2.0,Cat_6,D +463016,Male,Yes,58,Yes,Artist,1.0,Low,2.0,Cat_6,B +464099,Female,Yes,43,Yes,Artist,5.0,Average,2.0,Cat_4,A +461446,Female,Yes,66,Yes,Lawyer,1.0,High,2.0,Cat_6,C +460241,Male,Yes,40,Yes,Artist,8.0,Average,2.0,Cat_6,C +465567,Male,Yes,35,Yes,Entertainment,1.0,Low,2.0,Cat_3,A +460536,Male,No,33,No,Engineer,2.0,Low,4.0,Cat_3,D +464366,Male,Yes,55,Yes,Entertainment,2.0,Average,2.0,Cat_6,C +459339,Male,Yes,28,Yes,Healthcare,8.0,High,2.0,Cat_6,A +467466,Female,No,19,No,Doctor,6.0,Low,2.0,Cat_3,A +465340,Female,No,28,Yes,Artist,3.0,Low,4.0,Cat_2,D +460319,Female,Yes,40,No,Doctor,2.0,Average,2.0,Cat_7,D +466542,Female,Yes,73,Yes,Lawyer,1.0,High,2.0,Cat_6,B +459482,Male,Yes,66,No,Marketing,1.0,Low,4.0,Cat_6,D +459369,Male,Yes,35,No,Engineer,,Average,7.0,Cat_4,A +467705,Male,Yes,33,Yes,Artist,,Low,2.0,Cat_6,A +467838,Male,Yes,60,Yes,Artist,1.0,Average,2.0,Cat_6,C +465784,Female,No,19,No,Healthcare,0.0,Low,4.0,Cat_3,D +464301,Female,Yes,51,Yes,Doctor,0.0,Average,3.0,Cat_6,B +462885,Male,Yes,28,No,Executive,1.0,High,3.0,Cat_6,D +466831,Male,Yes,85,Yes,Artist,1.0,Low,1.0,Cat_6,A +466114,Male,Yes,52,Yes,Entertainment,1.0,Average,5.0,Cat_6,C +463313,Female,Yes,55,Yes,Engineer,1.0,Low,1.0,Cat_3,A +461237,Male,No,30,Yes,Doctor,0.0,Low,5.0,Cat_2,A +463184,Female,Yes,60,Yes,Artist,1.0,Average,2.0,Cat_6,C +467591,Female,Yes,41,Yes,Artist,1.0,High,5.0,Cat_7,B +462350,Male,Yes,35,Yes,Artist,1.0,Average,2.0,Cat_6,C +466564,Female,No,23,No,Marketing,0.0,Low,4.0,Cat_6,D +465221,Male,Yes,88,Yes,Artist,0.0,High,2.0,Cat_6,C +467569,Male,No,19,No,Healthcare,,Low,4.0,Cat_6,D +464157,Male,Yes,88,Yes,Marketing,0.0,High,2.0,Cat_6,C +460725,Male,Yes,41,Yes,Artist,0.0,High,2.0,Cat_2,B +462294,Male,Yes,30,No,Healthcare,1.0,Average,4.0,Cat_6,D +462336,Male,Yes,46,No,Artist,1.0,Low,4.0,Cat_6,B +462301,Female,No,27,No,,12.0,Low,3.0,Cat_6,D +464137,Female,Yes,74,No,Artist,5.0,High,2.0,Cat_7,C +462673,Female,No,28,No,Homemaker,8.0,Low,,Cat_6,D +467229,Female,No,29,Yes,Healthcare,0.0,Low,4.0,Cat_6,C +459524,Female,No,35,Yes,Artist,1.0,Low,1.0,Cat_6,C +461655,Female,Yes,35,Yes,Homemaker,9.0,Average,2.0,Cat_6,A +463145,Female,No,38,Yes,Homemaker,0.0,Low,4.0,Cat_6,B +467577,Female,Yes,48,Yes,Artist,1.0,Average,7.0,Cat_6,C +467836,Male,Yes,63,No,Executive,1.0,High,3.0,Cat_6,C +463018,Female,No,40,Yes,Homemaker,9.0,Low,1.0,Cat_6,D +467272,Male,Yes,76,Yes,Lawyer,8.0,High,2.0,Cat_6,A +467181,Female,Yes,50,Yes,Artist,9.0,Low,2.0,Cat_6,B +460939,Female,Yes,87,Yes,Lawyer,0.0,Low,2.0,Cat_6,B +465440,Male,Yes,62,No,Engineer,1.0,Low,5.0,Cat_6,D +465512,Male,Yes,38,Yes,Entertainment,,Average,4.0,Cat_3,D +466190,Male,Yes,53,Yes,Executive,0.0,Low,2.0,Cat_2,C +465570,Male,Yes,46,Yes,Artist,1.0,Average,4.0,Cat_3,C +463075,Male,Yes,52,Yes,Artist,0.0,Low,4.0,Cat_6,C +460932,Female,No,32,Yes,Doctor,8.0,Low,1.0,Cat_6,D +459139,Female,Yes,53,Yes,Artist,1.0,Average,2.0,Cat_4,C +460277,Female,No,20,No,Entertainment,1.0,Low,5.0,Cat_2,B +460244,Female,No,51,Yes,Doctor,0.0,Low,3.0,Cat_6,B +465265,Female,Yes,56,Yes,Artist,2.0,Average,2.0,Cat_6,B +462207,Male,No,26,No,Doctor,0.0,Low,6.0,Cat_4,D +466327,Male,Yes,31,Yes,Entertainment,5.0,Average,3.0,Cat_3,A +465330,Male,No,28,Yes,Engineer,0.0,Low,3.0,Cat_4,C +461235,Female,No,50,Yes,Healthcare,,Low,1.0,Cat_6,D +464675,Female,Yes,41,No,Doctor,0.0,Average,9.0,Cat_4,D +461386,Female,Yes,82,No,Lawyer,0.0,High,2.0,Cat_6,B +464548,Female,Yes,47,No,,1.0,Low,1.0,Cat_4,A +462751,Male,No,31,No,Healthcare,3.0,Low,9.0,Cat_6,D +459016,Female,No,18,No,Healthcare,0.0,Low,6.0,Cat_6,D +461803,Female,No,33,No,Doctor,0.0,Low,4.0,Cat_6,D +459277,Male,Yes,38,No,Entertainment,0.0,Low,2.0,Cat_6,B +461748,Male,No,27,Yes,Healthcare,1.0,Low,5.0,Cat_6,D +464768,Female,Yes,43,No,Engineer,9.0,Average,4.0,Cat_4,A +463468,Male,Yes,43,Yes,Artist,,Average,4.0,Cat_6,B +460238,Female,No,36,Yes,Artist,9.0,Low,3.0,Cat_4,A +459455,Female,Yes,79,Yes,Lawyer,1.0,Low,1.0,Cat_6,D +464327,Female,Yes,50,Yes,Artist,9.0,Low,2.0,Cat_6,C +463696,Female,No,48,Yes,Marketing,0.0,Low,1.0,Cat_7,A +460856,Male,Yes,42,Yes,Artist,0.0,Average,3.0,Cat_3,C +462601,Female,Yes,67,No,Lawyer,1.0,High,4.0,Cat_4,D +467544,Male,Yes,56,No,Artist,0.0,Low,2.0,,C +464515,Male,No,27,No,Healthcare,0.0,Low,4.0,Cat_6,D +461901,Male,No,30,Yes,Artist,5.0,Low,4.0,Cat_6,C +466677,Female,Yes,47,Yes,Entertainment,3.0,Average,3.0,Cat_6,A +467809,Male,Yes,46,Yes,Doctor,1.0,Average,2.0,Cat_6,C +462313,Male,Yes,55,Yes,Artist,1.0,Average,2.0,Cat_4,C +461402,Female,No,32,Yes,Entertainment,1.0,Low,5.0,Cat_6,D +460810,Female,Yes,89,No,Lawyer,1.0,High,2.0,Cat_6,D +459242,Male,Yes,65,Yes,Executive,0.0,Low,4.0,Cat_6,B +460801,Female,Yes,30,No,Entertainment,3.0,High,2.0,Cat_6,A +462306,Male,Yes,79,No,Lawyer,8.0,High,2.0,Cat_6,A +463913,Male,No,36,Yes,Doctor,1.0,Low,1.0,Cat_6,A +464729,Male,No,36,No,Marketing,,Low,4.0,Cat_4,D +467133,Female,Yes,72,Yes,Lawyer,1.0,High,,Cat_6,C +465403,Female,No,27,No,Healthcare,9.0,Low,5.0,Cat_7,A +463759,Male,Yes,53,Yes,Executive,9.0,High,4.0,Cat_6,C +463685,Female,No,27,Yes,Healthcare,1.0,Low,5.0,Cat_4,B +461224,Female,Yes,33,Yes,Doctor,8.0,High,2.0,Cat_6,D +459504,Male,Yes,77,Yes,Lawyer,1.0,High,2.0,Cat_6,A +461805,Female,No,31,Yes,Healthcare,1.0,Low,3.0,Cat_6,D +463495,Male,No,30,No,Healthcare,,Low,4.0,Cat_6,B +462212,Male,No,23,No,Engineer,0.0,Low,4.0,Cat_4,D +464977,Female,Yes,66,No,,1.0,Average,2.0,Cat_4,B +465381,Female,No,25,No,Marketing,0.0,Low,4.0,Cat_6,C +460019,Female,No,46,Yes,Doctor,6.0,Low,,Cat_6,C +465389,Male,Yes,46,Yes,Artist,1.0,Low,2.0,Cat_6,B +463162,Male,Yes,38,Yes,Executive,0.0,High,6.0,Cat_6,A +464718,Male,No,25,No,Healthcare,0.0,Low,2.0,Cat_3,B +462519,Male,No,22,No,Healthcare,1.0,Low,5.0,Cat_4,D +459530,Female,Yes,37,Yes,Lawyer,1.0,High,2.0,Cat_6,C +461066,Female,Yes,63,No,Marketing,,Average,3.0,Cat_3,A +459444,Male,Yes,68,Yes,Artist,1.0,High,2.0,Cat_6,C +465843,Female,No,37,Yes,Artist,0.0,Low,1.0,Cat_2,B +460869,Male,Yes,35,Yes,Artist,1.0,Average,2.0,Cat_6,B +462029,Male,No,60,No,Entertainment,0.0,Low,1.0,Cat_6,A +459865,Male,Yes,47,Yes,Artist,0.0,Average,3.0,Cat_6,C +464968,Male,No,43,No,Executive,0.0,Low,5.0,Cat_6,A +462272,Male,Yes,56,No,Artist,0.0,Average,2.0,Cat_6,A +464879,Female,Yes,28,Yes,Doctor,1.0,Average,2.0,Cat_4,A +462684,Female,No,32,No,Healthcare,7.0,Low,4.0,Cat_6,D +462194,Male,Yes,61,No,Engineer,0.0,Low,3.0,Cat_4,A +465415,Male,No,31,No,Artist,0.0,Low,7.0,Cat_2,C +465251,Male,No,19,No,Healthcare,3.0,Low,5.0,Cat_6,D +460733,Male,No,50,Yes,Marketing,3.0,Low,4.0,Cat_6,D +459418,Male,Yes,66,No,Executive,1.0,High,2.0,Cat_6,D +464265,Male,Yes,56,Yes,Entertainment,1.0,Low,2.0,Cat_6,C +463421,Male,Yes,53,Yes,Entertainment,0.0,Average,2.0,Cat_4,B +459013,Male,Yes,47,Yes,Artist,3.0,Average,4.0,Cat_6,C +466875,Female,No,36,Yes,Marketing,0.0,Low,2.0,Cat_3,D +463817,Female,No,33,Yes,Healthcare,1.0,Low,5.0,Cat_6,D +466139,Female,Yes,51,No,Doctor,0.0,Average,3.0,Cat_6,C +460227,Male,No,33,No,Healthcare,1.0,Low,3.0,Cat_6,C +464250,Male,Yes,53,Yes,Executive,6.0,High,3.0,Cat_6,C +462118,Male,No,30,Yes,Artist,1.0,Low,3.0,Cat_6,C +467235,Female,No,31,Yes,Artist,0.0,Low,2.0,Cat_5,D +467648,Male,Yes,41,No,Entertainment,2.0,Average,5.0,Cat_6,A +466306,Male,No,18,No,Healthcare,1.0,Low,4.0,Cat_6,D +464739,Male,Yes,67,Yes,Artist,1.0,Low,1.0,Cat_4,B +466121,Female,Yes,57,Yes,Artist,1.0,Average,6.0,Cat_3,C +464441,Male,Yes,35,Yes,Artist,0.0,Low,2.0,Cat_2,A +466660,Male,No,22,No,Healthcare,1.0,Low,4.0,Cat_6,D +458992,Female,No,27,Yes,Marketing,0.0,Low,1.0,Cat_6,D +464623,Male,Yes,35,Yes,Entertainment,9.0,High,2.0,Cat_2,A +465530,Female,Yes,56,Yes,Artist,1.0,Average,4.0,Cat_6,C +462891,Male,No,27,No,Healthcare,1.0,Low,3.0,Cat_6,C +459360,Female,No,32,Yes,Healthcare,,Low,3.0,Cat_6,C +459088,Female,No,20,No,Healthcare,1.0,Low,5.0,Cat_6,D +459911,Male,No,19,No,Healthcare,4.0,Low,6.0,Cat_2,D +466199,Female,Yes,38,Yes,Artist,1.0,Low,2.0,Cat_3,C +460018,Female,Yes,33,Yes,Artist,1.0,Average,2.0,Cat_6,C +465580,Male,Yes,39,No,Executive,6.0,Average,2.0,Cat_4,D +466070,Female,Yes,61,Yes,Artist,1.0,Average,5.0,Cat_6,C +462471,Male,No,30,Yes,Artist,1.0,Low,1.0,Cat_6,A +461759,Male,Yes,49,Yes,Artist,1.0,Low,2.0,Cat_6,C +462933,Male,Yes,55,,Entertainment,,High,5.0,Cat_6,B +462048,Female,No,19,No,Healthcare,0.0,Low,4.0,Cat_6,D +463050,Male,Yes,49,Yes,Artist,0.0,Average,4.0,Cat_6,B +465597,Female,No,43,Yes,Entertainment,14.0,Low,1.0,Cat_6,A +462092,Female,Yes,35,Yes,Homemaker,7.0,Average,4.0,,B +467574,Male,No,32,Yes,Healthcare,4.0,Low,,Cat_2,D +463940,Female,No,26,No,Healthcare,1.0,Low,8.0,Cat_4,C +459718,Male,Yes,65,Yes,Executive,1.0,Low,1.0,Cat_6,B +460210,Male,No,29,,Entertainment,8.0,Low,9.0,Cat_6,A +461899,Female,No,20,No,Doctor,1.0,Low,6.0,Cat_6,D +459544,Female,No,23,No,Healthcare,,Low,4.0,Cat_6,D +466273,Female,No,82,No,Lawyer,0.0,Low,3.0,Cat_6,D +461813,Male,No,29,Yes,Healthcare,1.0,Low,2.0,Cat_3,D +462851,Male,No,19,No,Healthcare,0.0,Low,3.0,Cat_6,D +466860,Male,No,21,No,Marketing,0.0,Low,5.0,Cat_1,C +464617,Female,No,39,Yes,Artist,1.0,Low,1.0,Cat_6,A +462228,Female,No,29,No,Homemaker,0.0,Low,4.0,Cat_4,A +460903,Male,No,30,No,Doctor,7.0,Low,3.0,Cat_6,D +464518,Male,Yes,30,No,Executive,0.0,High,4.0,Cat_6,D +462800,Female,Yes,62,Yes,Lawyer,9.0,Average,2.0,Cat_4,B +467489,Male,No,30,Yes,Healthcare,0.0,Low,1.0,Cat_6,D +464894,Male,No,22,No,Healthcare,,Low,5.0,Cat_4,D +464533,Male,Yes,37,No,Executive,,High,6.0,Cat_6,B +467278,Female,Yes,84,Yes,Artist,0.0,High,2.0,Cat_6,B +466026,Female,,49,No,Entertainment,0.0,Low,1.0,Cat_3,A +464601,Female,No,25,Yes,Healthcare,9.0,Low,2.0,Cat_6,D +461980,Male,Yes,52,Yes,Artist,2.0,Low,3.0,Cat_3,A +461489,Female,Yes,70,Yes,Lawyer,1.0,High,2.0,Cat_6,C +466778,Male,Yes,37,Yes,Entertainment,1.0,Low,2.0,Cat_3,A +465621,Male,No,42,Yes,Doctor,8.0,Low,1.0,Cat_6,C +461758,Male,No,33,Yes,Healthcare,8.0,Low,,Cat_3,D +460935,Male,Yes,87,Yes,Lawyer,0.0,Low,1.0,Cat_6,D +465895,Female,Yes,31,No,Artist,7.0,Average,2.0,Cat_6,B +465278,Male,Yes,38,No,Marketing,,High,4.0,Cat_7,C +462655,Male,No,38,Yes,Entertainment,0.0,Low,2.0,Cat_4,D +465100,Male,Yes,56,Yes,Artist,1.0,High,2.0,Cat_6,C +464274,Female,Yes,56,Yes,Artist,1.0,Average,5.0,Cat_6,C +467153,Female,Yes,51,Yes,Artist,0.0,Low,,Cat_6,A +459399,Female,No,20,No,Doctor,1.0,Low,2.0,Cat_6,D +466852,Female,No,23,No,Marketing,4.0,Low,4.0,Cat_6,C +464101,Male,Yes,62,Yes,Entertainment,9.0,Average,2.0,Cat_6,C +465501,Male,Yes,40,Yes,Artist,13.0,High,1.0,Cat_4,D +462516,Female,No,28,No,Engineer,,Low,3.0,Cat_6,D +459082,Male,,45,Yes,Artist,1.0,Low,,Cat_6,A +466405,Female,Yes,43,Yes,Entertainment,0.0,Average,2.0,Cat_3,A +465594,Male,Yes,29,No,Executive,4.0,Low,6.0,Cat_3,D +465685,Male,No,38,Yes,Healthcare,6.0,Low,2.0,Cat_3,D +461774,Female,Yes,81,Yes,Lawyer,1.0,High,3.0,Cat_6,B +467341,Male,No,35,Yes,Artist,3.0,Low,1.0,Cat_6,B +461612,Female,No,31,Yes,Healthcare,8.0,Low,5.0,Cat_6,C +466629,Male,No,28,No,Healthcare,1.0,Low,5.0,Cat_6,C +467036,Male,Yes,66,No,Lawyer,0.0,Low,1.0,Cat_6,B +465306,Male,Yes,69,No,Executive,0.0,Average,4.0,Cat_2,D +467165,Female,No,25,No,Homemaker,,Low,1.0,Cat_6,D +465789,Male,No,18,No,Healthcare,4.0,Low,4.0,Cat_4,D +465422,Female,Yes,40,Yes,Artist,1.0,Average,2.0,Cat_6,C +462049,Male,Yes,39,Yes,Artist,14.0,Average,2.0,Cat_6,B +466997,Female,,50,No,Engineer,1.0,Low,2.0,Cat_6,D +465838,Female,Yes,52,No,Doctor,3.0,Average,2.0,Cat_6,A +467379,Male,Yes,59,Yes,Entertainment,0.0,Average,2.0,Cat_6,B +462396,Male,Yes,50,Yes,Doctor,,Average,2.0,Cat_6,C +465285,Female,Yes,32,No,Engineer,9.0,Low,4.0,Cat_6,D +461292,Female,Yes,81,No,Lawyer,,High,2.0,Cat_3,D +466004,Male,Yes,52,Yes,Artist,0.0,Average,6.0,Cat_4,C +464853,Male,Yes,39,No,Executive,8.0,Average,4.0,Cat_4,A +467455,Female,No,37,Yes,Artist,8.0,Low,2.0,Cat_6,C +465667,Male,No,23,No,Healthcare,1.0,Low,3.0,Cat_2,D +463437,Male,Yes,49,Yes,Artist,1.0,Average,3.0,Cat_6,B +461291,Male,No,18,No,Healthcare,0.0,Low,2.0,Cat_6,D +459889,Male,Yes,63,Yes,Homemaker,8.0,Average,3.0,Cat_6,B +466460,Male,Yes,85,No,Lawyer,1.0,Low,1.0,Cat_6,D +460674,Female,No,31,Yes,Entertainment,0.0,Low,3.0,Cat_3,A +460132,Male,No,39,Yes,Healthcare,3.0,Low,2.0,Cat_6,D +463613,Female,Yes,48,Yes,Artist,0.0,Average,6.0,Cat_6,A +465231,Male,Yes,65,No,Artist,0.0,Average,2.0,Cat_6,C +463002,Male,Yes,41,Yes,Artist,0.0,High,5.0,Cat_6,B +464018,Male,No,22,No,,0.0,Low,7.0,Cat_1,D +464685,Male,No,35,No,Executive,3.0,Low,4.0,Cat_4,D +465406,Female,No,33,Yes,Healthcare,1.0,Low,1.0,Cat_6,D +467299,Female,No,27,Yes,Healthcare,1.0,Low,4.0,Cat_6,B +461879,Male,Yes,37,Yes,Executive,0.0,Average,3.0,Cat_4,B diff --git a/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Essentials_Classification.ipynb b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Essentials_Classification.ipynb new file mode 100644 index 00000000..aeaf0a19 --- /dev/null +++ b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Essentials_Classification.ipynb @@ -0,0 +1,687 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "id": "ba179c16", + "metadata": {}, + "outputs": [], + "source": [ + "import ipywidgets as widgets\n", + "from ipywidgets import Button, Layout\n", + "from IPython.display import display, clear_output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b202e74f", + "metadata": {}, + "outputs": [], + "source": [ + "%%html\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\"Vertica\n", + "
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\n", + "\"Vertica\n", + "
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\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66310b5b-1491-4cd8-adf9-e2b64ce0e04a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a68b326", + "metadata": {}, + "outputs": [], + "source": [ + "%%html\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8426607f", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining a function for multiple choice widgets\n", + "\n", + "def create_multipleChoice_widget(options, correct_answer):\n", + " if correct_answer not in options:\n", + " options.append(correct_answer)\n", + " \n", + " correct_answer_index = options.index(correct_answer)\n", + " \n", + " radio_options = [(words, i) for i, words in enumerate(options)]\n", + " alternativ = widgets.RadioButtons(\n", + " options = radio_options,\n", + " description = '',\n", + " disabled = False\n", + " )\n", + " \n", + " description_out = widgets.Output()\n", + "# with description_out:\n", + "# print(description)\n", + " \n", + " feedback_out = widgets.Output()\n", + "\n", + " def check_selection(b):\n", + " a = int(alternativ.value)\n", + " if a==correct_answer_index:\n", + " s = widgets.HTML('
Correct!
')\n", + " else:\n", + " s = widgets.HTML('
Try Again!
')\n", + " with feedback_out:\n", + " clear_output()\n", + " display(s)\n", + " return\n", + " \n", + " check = widgets.Button(description=\"submit\")\n", + " check.on_click(check_selection)\n", + " \n", + " \n", + " return widgets.VBox([description_out, alternativ, check, feedback_out])\n", + "\n", + "def create_numeric_widget(correct_answer):\n", + " \n", + " #correct_answer_index = options.index(correct_answer)\n", + " \n", + " #radio_options = [(words, i) for i, words in enumerate(options)]\n", + " alternativ = widgets.Text(\n", + " #options = radio_options,\n", + " #description = '',\n", + " disabled = False\n", + " )\n", + " \n", + " description_out = widgets.Output()\n", + "# with description_out:\n", + "# print(description)\n", + " \n", + " feedback_out = widgets.Output()\n", + "\n", + " def check_selection(b):\n", + " try:\n", + " a = int(alternativ.value)\n", + " except ValueError:\n", + " a = float(alternativ.value)\n", + " if a==correct_answer:\n", + " s = widgets.HTML('
Correct!
')\n", + " else:\n", + " s = widgets.HTML('
Try Again!
')\n", + " with feedback_out:\n", + " clear_output()\n", + " display(s)\n", + " return\n", + " \n", + " check = widgets.Button(description=\"submit\")\n", + " check.on_click(check_selection)\n", + " \n", + " \n", + " return widgets.VBox([description_out, alternativ, check, feedback_out])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86dd258d", + "metadata": {}, + "outputs": [], + "source": [ + "%%html\n", + "\n", + "\n", + " \n", + "
\n", + " Classification\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "1eb61592", + "metadata": {}, + "source": [ + "## Exercise" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e0deb276", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " 20 mins\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%html\n", + "\n", + " 20 mins" + ] + }, + { + "cell_type": "markdown", + "id": "7a49c17e", + "metadata": {}, + "source": [ + "These exercises ask you to apply to data science problems what you have learned about classification and VerticaPy." + ] + }, + { + "cell_type": "markdown", + "id": "e4eae0c4", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "id": "e91b7444", + "metadata": { + "tags": [] + }, + "source": [ + "\n", + "### Multi-class Classification\n", + "\n", + "The following exercises use [Customer Segementation](https://www.kaggle.com/datasets/abisheksudarshan/customer-segmentation) dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe401e2b-8c17-49f3-bce6-24a24bf76c59", + "metadata": {}, + "outputs": [], + "source": [ + "text_1 = widgets.HTML(value=\" Question 1: How many classes does the customer category have?\")\n", + "Q1 = create_multipleChoice_widget(['3','6','5','4'],'4')\n", + "display(widgets.VBox([text_1,Q1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4e6614e6", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'widgets' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m text_1 \u001b[38;5;241m=\u001b[39m \u001b[43mwidgets\u001b[49m\u001b[38;5;241m.\u001b[39mHTML(value\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m Question 1: How many empty/NULL values are there in total from each of the following columns: Ever_Married, Graduated, and Family_Size?\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 2\u001b[0m Q1 \u001b[38;5;241m=\u001b[39m create_multipleChoice_widget([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m140 + 74 + 316\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m40 + 74 + 216\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m200 + 33 + 75\u001b[39m\u001b[38;5;124m'\u001b[39m],\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m140 + 74 + 316\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 3\u001b[0m display(widgets\u001b[38;5;241m.\u001b[39mVBox([text_1,Q1]))\n", + "\u001b[0;31mNameError\u001b[0m: name 'widgets' is not defined" + ] + } + ], + "source": [ + "text_1 = widgets.HTML(value=\" Question 2: How many missing (empty/NULL) values are in the in each of the following columns: Ever_Married, Graduated, and Family_Size?\")\n", + "Q2 = create_multipleChoice_widget(['140 + 74 + 316','40 + 74 + 216','200 + 33 + 75'],'140 + 74 + 316')\n", + "display(widgets.VBox([text_1,Q2]))" + ] + }, + { + "cell_type": "markdown", + "id": "c933c4a9-f197-47fd-a21f-f8580f008825", + "metadata": {}, + "source": [ + "Use the `dropna()` method to drop the missing values in the above three columns:\n", + "\n", + "```Python\n", + "data[\"Column\"].dropna()\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "733023c3", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c1baa124a27f45c591caff7ec63b570a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value=\" Question 2: What was the numeric Answer? (Hint 100)\"…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "text_1 = widgets.HTML(value=\" Question 3: What other column has missing values? \")\n", + "Q3 = create_multipleChoice_widget(['ID','Gender','Work_Experience'],'Work_Experience')\n", + "display(widgets.VBox([text_1,Q3]))" + ] + }, + { + "cell_type": "markdown", + "id": "4e2f6f76-34de-404b-99bd-ea7807f099c0", + "metadata": {}, + "source": [ + "Another way of handling missing values is by imputing them. This method replaces missing data with an estimated value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57cfb97e-4129-4dfc-aa48-25d9581937a0", + "metadata": {}, + "outputs": [], + "source": [ + "text_1 = widgets.HTML(value=\" Question 4: Which other columns can we use to impute the missing values in the above mentioned column? \")\n", + "Q4 = create_multipleChoice_widget(['ID + Gender','Age + Gender + Spending Score','Age + Graduated + Spending Score','Work_Experience + Gender + Spending Score'],'Age + Graduated + Spending Score')\n", + "display(widgets.VBox([text_1,Q4]))" + ] + }, + { + "cell_type": "markdown", + "id": "43645861-e384-4633-a701-8f46794c1e49", + "metadata": {}, + "source": [ + "Use the `fillna()` method to impute the missing values:\n", + "```Python\n", + "data[\"Column To Impute\"].fillna(method = \"avg\", by = List of Columns)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1ddeaeb-acfa-446a-b795-eaeb870a49b6", + "metadata": {}, + "outputs": [], + "source": [ + "text_1 = widgets.HTML(value=\" Question 5: After removing the missing values and imputing, does the data look balanced in terms of the classes for Segmentation?\")\n", + "Q5 = create_multipleChoice_widget(['Perfectly','Almost','Not at all'],'Almost')\n", + "display(widgets.VBox([text_1,Q5]))" + ] + }, + { + "cell_type": "markdown", + "id": "f14ec56e-150d-4689-8fc1-48b2ef001c82", + "metadata": {}, + "source": [ + "Build a random forest classifier using the default settings:\n", + "```Python\n", + "from verticapy.learn.ensemble import RandomForestClassifier\n", + "model = RandomForestClassifier(\"RF_customer\")\n", + "model.fit(data, data.get_columns([\"Segmentation\"]), \"Segmentation\")\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb6c50e5-1375-43bc-ae3b-7f8770d8efb4", + "metadata": {}, + "outputs": [], + "source": [ + "text_2 = widgets.HTML(value=\" Question 6: What is the precision for class A (to one decimal place)?\")\n", + "Q2 = create_numeric_widget(0.4)\n", + "display(widgets.VBox([text_2,Q2]))" + ] + }, + { + "cell_type": "markdown", + "id": "6ec4a376-e1db-4626-ac9f-d191aab895d9", + "metadata": {}, + "source": [ + "Increasing the maximum allowed depth sometimes improves results:\n", + "\n", + "```Python\n", + "model=RandomForestClassifier(\"RF_new\",\n", + " max_depth= 18,\n", + " )\n", + "model.fit(data, data.get_columns([\"Segmentation\"]), \"Segmentation\")\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e648ab2c-a8dc-4e45-b785-d654d7800d0a", + "metadata": {}, + "outputs": [], + "source": [ + "text_1 = widgets.HTML(value=\" Question 7: What is the new precision for class A?\")\n", + "Q5 = create_multipleChoice_widget(['0.4-0.5','0.6-0.7','0.8-0.9'],'0.8-0.9')\n", + "display(widgets.VBox([text_1,Q5]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a76fd854-f341-4915-aa58-a62c2ffe6106", + "metadata": {}, + "outputs": [], + "source": [ + "text_1 = widgets.HTML(value=\" Question 7: Which is the most important feature?\")\n", + "Q5 = create_multipleChoice_widget(['Age','Famile_Size','Spending_Score'],'Age')\n", + "display(widgets.VBox([text_1,Q5]))" + ] + }, + { + "cell_type": "markdown", + "id": "3b946cb8", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "id": "cbebc825-303a-4005-b05e-99dcfda01b87", + "metadata": {}, + "source": [ + "\n", + "### Binary-class Classification\n", + "\n", + "The following exercises use the [Heart Disease](https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset) dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e29a24a2-ab10-459b-92e5-5b77c71d0c08", + "metadata": {}, + "outputs": [], + "source": [ + "text_2 = widgets.HTML(value=\" Question 1: What is the precision (to one decimal place)?\")\n", + "Q2 = create_numeric_widget(0.9)\n", + "display(widgets.VBox([text_2,Q2]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea9c5809-ceb5-450a-a002-2511f478eafc", + "metadata": {}, + "outputs": [], + "source": [ + "text_2 = widgets.HTML(value=\" Question 2: What is the cutoff (to one decimal)?\")\n", + "Q2 = create_numeric_widget(0.5)\n", + "display(widgets.VBox([text_2,Q2]))" + ] + }, + { + "cell_type": "markdown", + "id": "e900089a-fdb5-498a-843a-dee593e34ad5", + "metadata": {}, + "source": [ + "Does increasing the cutoff improve accuracy?\n", + "```python\n", + "model.classification_report(cutoff=0.7)\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c0d0914-aa73-4865-9131-d053bcce109f", + "metadata": {}, + "outputs": [], + "source": [ + "text_1 = widgets.HTML(value=\" Question 3: Does the accuracy (not precision) increase?\")\n", + "Q5 = create_multipleChoice_widget(['Yes','No','Stays the same'],'No')\n", + "display(widgets.VBox([text_1,Q5]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80ca567a-c9ca-4d50-ba05-af1960c73bcc", + "metadata": {}, + "outputs": [], + "source": [ + "text_1 = widgets.HTML(value=\" Question 4: Which is the least important feature?\")\n", + "Q5 = create_multipleChoice_widget(['caa','fbs','restecg'],'fbs')\n", + "display(widgets.VBox([text_1,Q5]))" + ] + }, + { + "cell_type": "markdown", + "id": "c02f25ee", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "id": "ca2aa185", + "metadata": {}, + "source": [ + " Author Name: Umar Farooq Ghumman\n", + "
\n", + "Author Contact: umarfarooq.ghumman@vertica.com" + ] + }, + { + "cell_type": "markdown", + "id": "185edfa5", + "metadata": {}, + "source": [ + "### Resources\n", + "\n", + "- [Customer Dataset on Kaggle](https://www.kaggle.com/datasets/abisheksudarshan/customer-segmentation)\n", + "\n", + "- [Heart Dataset on Kaggle](https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset)\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Essentials_Classification_LogisticRegression.ipynb b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Essentials_Classification_LogisticRegression.ipynb new file mode 100644 index 00000000..bac7953e --- /dev/null +++ b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Essentials_Classification_LogisticRegression.ipynb @@ -0,0 +1,1053 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "d8813b06", + "metadata": {}, + "outputs": [], + "source": [ + "import ipywidgets as widgets\n", + "from IPython.display import display, clear_output\n", + "from ipywidgets import Layout" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "73c74fd4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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Correct!
')\n", + " else:\n", + " s = widgets.HTML('
Try Again!
')\n", + " with feedback_out:\n", + " clear_output()\n", + " display(s)\n", + " return\n", + " \n", + " check = widgets.Button(description=\"submit\")\n", + " check.on_click(check_selection)\n", + " \n", + " \n", + " return widgets.VBox([description_out, alternativ, check, feedback_out])\n", + "\n", + "def create_numeric_widget(correct_answer):\n", + " \n", + " #correct_answer_index = options.index(correct_answer)\n", + " \n", + " #radio_options = [(words, i) for i, words in enumerate(options)]\n", + " alternativ = widgets.Text(\n", + " #options = radio_options,\n", + " #description = '',\n", + " disabled = False\n", + " )\n", + " \n", + " description_out = widgets.Output()\n", + "# with description_out:\n", + "# print(description)\n", + " \n", + " feedback_out = widgets.Output()\n", + "\n", + " def check_selection(b):\n", + " try:\n", + " a = int(alternativ.value)\n", + " except ValueError:\n", + " a = float(alternativ.value)\n", + " if a==correct_answer:\n", + " s = widgets.HTML('
Correct!
')\n", + " else:\n", + " s = widgets.HTML('
Try Again!
')\n", + " with feedback_out:\n", + " clear_output()\n", + " display(s)\n", + " return\n", + " \n", + " check = widgets.Button(description=\"submit\")\n", + " check.on_click(check_selection)\n", + " \n", + " \n", + " return widgets.VBox([description_out, alternativ, check, feedback_out])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "dab1e034", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "
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\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%html\n", + "\n", + "\n", + "\n", + " \n", + "
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\n" + ] + }, + { + "cell_type": "markdown", + "id": "1eb61592", + "metadata": {}, + "source": [ + "## Logistic Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8e5bc7f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " 10 mins\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%html\n", + "\n", + " 10 mins" + ] + }, + { + "cell_type": "markdown", + "id": "989fd8da", + "metadata": {}, + "source": [ + "### Table of Contents\n", + "\n", + "- [Introduction to classficiation](#h1_cell)\n", + "- [Limitations of Linear Regression](#h2_cell)\n", + "- [Binary classification: Logistic Regression](#h3_cell)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e359fcc6", + "metadata": {}, + "source": [ + "### Covered in This Module\n", + "- Motivation for classification\n", + "- When to use logistic regression\n", + "- Random forest" + ] + }, + { + "cell_type": "markdown", + "id": "e4eae0c4", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "id": "e91b7444", + "metadata": {}, + "source": [ + "\n", + "### Introduction to classification\n" + ] + }, + { + "cell_type": "markdown", + "id": "7050ec65", + "metadata": {}, + "source": [ + "When the feature of interest is qualitative or categorical (as opposed to qualitative), you should use classification algorithms. In such cases, you cannot use linear regression.\n", + "\n", + "Some examples of use cases where classification is applicable include: \n", + "\n", + "- A. Determining whether a borrower will default on their debt.\n", + "- B. Identifying the illness of a patient.\n", + "- C. Determining whether an email is spam.\n", + "\n", + "There are two types of classification:\n", + "- Binary: Problems where the answer is one of \"Yes\" or a \"No.\" Examples A and C are examples of this type.\n", + "- Multinomial: Problems where there are more than two options for the feature of interest. Example B is an example of this type." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a2c55467", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Binary')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(1, 2)\n", + "\n", + "\n", + "data = np.array([\n", + " [1, 2],\n", + " [2, 3],\n", + " [2.5, 6],\n", + " [1.5,4],\n", + "])\n", + "x, y = data.T\n", + "ax1.scatter(x,y)\n", + "\n", + "data = np.array([\n", + " [3, 2],\n", + " [3.5, 3],\n", + " [3.2, 3],\n", + " [4,2],\n", + "])\n", + "x, y = data.T\n", + "ax1.scatter(x,y,marker='p')\n", + "\n", + "data = np.array([\n", + " [5, 1],\n", + " [6, 4],\n", + " [5.6, 5],\n", + " [7,4],\n", + "])\n", + "x, y = data.T\n", + "ax1.scatter(x,y,marker='s')\n", + "ax1.axvline(x = 2.7, color = 'b',linestyle=\"--\")\n", + "ax1.axvline(x = 4.5, color = 'b',linestyle=\"--\") \n", + "ax1.set_ylabel('X2')\n", + "ax1.set_xlabel('X1')\n", + "ax1.set_title('Multi-class')\n", + "data = np.array([\n", + " [1, 2.5],\n", + " [2, 3.1],\n", + " [2.5, 5.3],\n", + " [1.5,4],\n", + "])\n", + "x, y = data.T\n", + "ax2.scatter(x,y)\n", + "\n", + "data = np.array([\n", + " [3, 2.4],\n", + " [3.5, 2.7],\n", + " [3.2, 5],\n", + " [4,2],\n", + "])\n", + "x, y = data.T\n", + "fig.subplots_adjust(top=1)\n", + "fig.subplots_adjust(right=1.5)\n", + "ax2.scatter(x,y,marker='p')\n", + "ax2.axvline(x = 2.7, color = 'b',linestyle=\"--\")\n", + "ax2.set_xlabel('X1')\n", + "ax2.set_ylabel('X2')\n", + "ax2.set_title('Binary')\n", + "#B=plt.show()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "d0ea4db2", + "metadata": {}, + "source": [ + "In general, the underlying goal of classification problems is determining the probability that a data point falls into a paricular category, and then assigning that datapoint a label based on whether the probability exceeds a given threshold.\n", + "\n", + "There are several methods for calculating these probabilities. Some methods ones include:\n", + "- Logistic regrsesion\n", + "- Linear discriminant analysis\n", + "- k-nearest neighbors\n", + "- Trees\n", + "- Random forest" + ] + }, + { + "cell_type": "markdown", + "id": "f691e3b2", + "metadata": {}, + "source": [ + "
\n", + "Knowledge Check: Predicting the winner of a football match is an example of what type of classification?\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4e6614e6", + "metadata": {}, + "outputs": [], + "source": [ + "Q1 = create_multipleChoice_widget(['Binary','Multi-class'],'Binary')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3daeb0c6", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3d7b601902b3436f871e41b821511ae2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Output(), RadioButtons(options=(('Binary', 0), ('Multi-class', 1)), value=0), Button(descriptio…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Q1)" + ] + }, + { + "cell_type": "markdown", + "id": "3b946cb8", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "id": "3fd84ef3", + "metadata": {}, + "source": [ + "\n", + "### Limitations of Linear Regression" + ] + }, + { + "cell_type": "markdown", + "id": "0ef0675d", + "metadata": {}, + "source": [ + "At first glance, it seems like you could use linear regression and encode the category to find its probability. For example, for binary classification, you could encode a \"Yes\" as 1 and a \"No\", as 0, train the model like any other linear regresson model, and then apply the threshold to classify the result:\n", + "\n", + " $p(X)= \\beta_{0}+\\beta_{1}X$\n", + "\n", + "One issue with this approach is that some of the output probabilities could be greater than 1 or smaller than 0, making them difficult to interpret. Aside from that, this approach could work, and the result for using linear regression for a binary classification use case is identical to linear discriminant analysis. However, this method is still less efficient than logistic regression, which will be covered later in the module.\n", + "\n", + "However, if the problem involves more than two classes, then using linear regression in this way will produce errors. This is because by encoding multiple categories into 0, 1, 2, 3, and so on, we also imply a relationship: that a category is semantically \"between\" another. That is, using linear regression in this way implies that category '1' is between categories '0' and '2,' which could be inaccurate or completely irrelevant information depending on the original problem." + ] + }, + { + "cell_type": "markdown", + "id": "03ec5ae2", + "metadata": {}, + "source": [ + "\n", + "### Logistic Regression" + ] + }, + { + "cell_type": "markdown", + "id": "61ee6fff", + "metadata": {}, + "source": [ + "The main goal behind classification is to calculate the probability that a data point falls in a particular category. Logistic regression actually has this same goal.\n", + "\n", + "To avoid the aforementioned issues of applying linear regression to a classification problem, the probability equation is modified as follows:\n", + "\n", + "\\begin{equation}\n", + "p(X)= \\Large\\frac{ \\mathrm{e}^{\\beta_{0}+\\beta_{1}X}} {1+\\mathrm{e}^{\\beta_{0}+\\beta_{1}X}}\n", + "\\end{equation}" + ] + }, + { + "cell_type": "markdown", + "id": "3041693f", + "metadata": {}, + "source": [ + "This equation always produces an S-shaped curve and ensures that all the values of probability will be between 0 and 1." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "77c2b8df", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Probability')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import math\n", + "x=np.array(list(range(0,100)))\n", + "B0=-5\n", + "B1=0.1\n", + "plt.plot(x,np.exp(B0+B1*x)/(1+np.exp(B0+B1*x)))\n", + "plt.title('S-shaped curve')\n", + "plt.xlabel('X')\n", + "plt.ylabel('Probability')" + ] + }, + { + "cell_type": "markdown", + "id": "29c7a395", + "metadata": {}, + "source": [ + "Rearranging the equation:\n", + "\n", + "\\begin{equation}\n", + "\\frac{p(X)}{1+p(X)}\\ = \\mathrm{e}^{\\beta_{0}+\\beta_{1}X}\n", + "\\end{equation}\n", + "\n", + "Taking the logarithm of both sides, you can make the right-hand side linear:\n", + "\n", + "\\begin{equation}\n", + "\\mathrm{log}{(\\frac{p(X)}{1+p(X)}})\\ = \\beta_{0}+\\beta_{1}X\n", + "\\end{equation}\n", + "\n", + "These coefficients are not linearly related with the probabilities, so making inferences from the coefficients from the changes in $X$ and their results on $Y$ is not as straightforward as it would be with linear regression. Still, the signs of the beta terms indicate a positive or negative correlation. For example, a positive value for $\\beta_{1}$ indicates that an increase in $X$ will be associated with increasing $p(X)$. " + ] + }, + { + "cell_type": "markdown", + "id": "188c5010", + "metadata": {}, + "source": [ + "The next step is to find the coefficients from the training data, typically with maximum likelihood estimation (MLE). MLE is used to fit many non-linear models.\n", + "\n", + "\\begin{equation}\n", + " l(\\beta_{0},\\beta_{1})= \\normalsize \\prod_{i:y_{i}=1} p(x_{i}) \\prod_{i':y_{i'}=0} (1-p(x_{i'}))\n", + "\\end{equation}\n", + "\n", + "MLE finds the coefficients $\\beta_{0}$ and $\\beta_{1}$ such that the $p(X)$ is close to 1 for the values with \"Yes\" label and close to 0 for the values with \"No\" label.\n", + "\n", + "The discussion so far has only concerned a single predictor (that is, $X$ has only one dimension), but this method can be applied and extended to multiple dimensions." + ] + }, + { + "cell_type": "markdown", + "id": "c02f25ee", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "id": "ca2aa185", + "metadata": {}, + "source": [ + " Author Name: Umar Farooq Ghumman\n", + "
\n", + "Author Contact: umarfarooq.ghumman@vertica.com" + ] + }, + { + "cell_type": "markdown", + "id": "185edfa5", + "metadata": {}, + "source": [ + "### Resources\n", + "\n", + "- [Introduction to statistical learning](https://www.statlearning.com/)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c176a62b-72a0-4992-98c9-12ea0fc43197", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8967ba5-5d90-48b3-8bf2-fc2de6e99790", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Essentials_Classification_LogisticRegression_Example.ipynb b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Essentials_Classification_LogisticRegression_Example.ipynb new file mode 100644 index 00000000..d4cee695 --- /dev/null +++ b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Essentials_Classification_LogisticRegression_Example.ipynb @@ -0,0 +1,1666 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "d8813b06", + "metadata": {}, + "outputs": [], + "source": [ + "import ipywidgets as widgets\n", + "from IPython.display import display, clear_output\n", + "from ipywidgets import Layout" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "73c74fd4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b1fec60f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%html\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8b0862f1", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining a function for multiple choice widgets\n", + "\n", + "def create_multipleChoice_widget(options, correct_answer):\n", + " if correct_answer not in options:\n", + " options.append(correct_answer)\n", + " \n", + " correct_answer_index = options.index(correct_answer)\n", + " \n", + " radio_options = [(words, i) for i, words in enumerate(options)]\n", + " alternativ = widgets.RadioButtons(\n", + " options = radio_options,\n", + " description = '',\n", + " disabled = False\n", + " )\n", + " \n", + " description_out = widgets.Output()\n", + "# with description_out:\n", + "# print(description)\n", + " \n", + " feedback_out = widgets.Output()\n", + "\n", + " def check_selection(b):\n", + " a = int(alternativ.value)\n", + " if a==correct_answer_index:\n", + " s = widgets.HTML('
Correct!
')\n", + " else:\n", + " s = widgets.HTML('
Try Again!
')\n", + " with feedback_out:\n", + " clear_output()\n", + " display(s)\n", + " return\n", + " \n", + " check = widgets.Button(description=\"submit\")\n", + " check.on_click(check_selection)\n", + " \n", + " \n", + " return widgets.VBox([description_out, alternativ, check, feedback_out])\n", + "\n", + "def create_numeric_widget(correct_answer):\n", + " \n", + " #correct_answer_index = options.index(correct_answer)\n", + " \n", + " #radio_options = [(words, i) for i, words in enumerate(options)]\n", + " alternativ = widgets.Text(\n", + " #options = radio_options,\n", + " #description = '',\n", + " disabled = False\n", + " )\n", + " \n", + " description_out = widgets.Output()\n", + "# with description_out:\n", + "# print(description)\n", + " \n", + " feedback_out = widgets.Output()\n", + "\n", + " def check_selection(b):\n", + " try:\n", + " a = int(alternativ.value)\n", + " except ValueError:\n", + " a = float(alternativ.value)\n", + " if a==correct_answer:\n", + " s = widgets.HTML('
Correct!
')\n", + " else:\n", + " s = widgets.HTML('
Try Again!
')\n", + " with feedback_out:\n", + " clear_output()\n", + " display(s)\n", + " return\n", + " \n", + " check = widgets.Button(description=\"submit\")\n", + " check.on_click(check_selection)\n", + " \n", + " \n", + " return widgets.VBox([description_out, alternativ, check, feedback_out])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "dab1e034", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "
\n", + " Classification\n", + "
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%html\n", + "\n", + "\n", + "\n", + " \n", + "
\n", + " Classification\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "1eb61592", + "metadata": {}, + "source": [ + "## Logistic Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8e5bc7f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " 15 mins\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%html\n", + "\n", + " 15 mins" + ] + }, + { + "cell_type": "markdown", + "id": "989fd8da", + "metadata": {}, + "source": [ + "### Table of Contents\n", + "\n", + "- [Ingesting the data](#h1_cell)\n", + "- [Data exploration](#h2_cell)\n", + "- [Applying logistic regression](#h2_cell)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e359fcc6", + "metadata": {}, + "source": [ + "### Covered in This Module\n", + "\n", + "- Application of ligstic regression using verticapy\n", + "- Understanding the nuances of logistic regression\n", + "- Interpreting the results of classficiation metrics" + ] + }, + { + "cell_type": "markdown", + "id": "e4eae0c4", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "id": "e91b7444", + "metadata": {}, + "source": [ + "\n", + "### Ingesting data\n" + ] + }, + { + "cell_type": "markdown", + "id": "2bb6a4cd-feee-4132-9d47-cb7c608d151e", + "metadata": {}, + "source": [ + "This example uses the [Default](https://r-data.pmagunia.com/dataset/r-dataset-package-islr-default) dataset, which contains information about people and whether they defaulted on their credit card debt. More specifically, the dataset contains four features for each person:\n", + "- Did the person default? \n", + "- Is the person a student? \n", + "- What is their balance? \n", + "- What is their annual income? \n", + "\n", + "You can build a classification model based on the last three parameters. This model can then be used to predict if a person is likely to default on their credit card debt.\n", + "\n", + "To begin, load the data into VerticaPy:\n", + "\n", + "```python\n", + "import verticapy as vp\n", + "data=vp.read_csv('Data/default_data.csv')\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d89cb1d9-7dd6-41f5-b824-3d1ff9236a3f", + "metadata": {}, + "outputs": [], + "source": [ + "import verticapy as vp\n", + "data=vp.read_csv('Data/default_data.csv')" + ] + }, + { + "cell_type": "markdown", + "id": "ddcf6eb5-823a-4490-afd9-1e1ec7a919d2", + "metadata": {}, + "source": [ + "Examine the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c6235cdd-0372-4078-8b29-d39e5331d7bb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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handle missing values. You can find missing data with `describe()`, which produces a summary of the dataset:\n", + "\n", + "```python\n", + "data.describe()\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "bcc7c637-4dcd-4fe3-8194-daddd881dad2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
count
mean
std
min
approx_25%
approx_50%
approx_75%
max
"default"76180.03360462063533730.1802210133220290.00.00.00.01.0
"student"76180.2936466264111310.4554618712655860.00.00.01.01.0
"credit_balance"7618834.003369570169482.061064442580.0481.06857144819.7684573833331164.039119752502.684931
"income"761833495.34792056613302.1082548578771.967729421269.781300909134558.172441538543731.638382222273554.2335
Rows: 1-4 | Columns: 9
" + ], + "text/plain": [ + " count mean std min \\\\\n", + "\"default\" 7618 0.0336046206353373 0.180221013322029 0.0 \\\\\n", + "\"student\" 7618 0.293646626411131 0.455461871265586 0.0 \\\\\n", + "\"credit_balance\" 7618 834.003369570169 482.06106444258 0.0 \\\\\n", + "\"income\" 7618 33495.347920566 13302.1082548578 771.9677294 \\\\\n", + " approx_25% approx_50% approx_75% \\\\\n", + "\"default\" 0.0 0.0 0.0 \\\\\n", + "\"student\" 0.0 0.0 1.0 \\\\\n", + "\"credit_balance\" 481.06857144 819.768457383333 1164.03911975 \\\\\n", + "\"income\" 21269.7813009091 34558.1724415385 43731.6383822222 \\\\\n", + " max \n", + "\"default\" 1.0 \n", + "\"student\" 1.0 \n", + "\"credit_balance\" 2502.684931 \n", + "\"income\" 73554.2335 \n", + "Rows: 1-4 | Columns: 9" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "7949f175", + "metadata": {}, + "source": [ + "The `count` shows that all columns (`default`, `student`, `credit_balance`, `income`) have the same number of values, so there are no missing values in the dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Data Exploration" + ] + }, + { + "cell_type": "markdown", + "id": "b770860b-e1a7-4d89-b8fd-b4480ca9d83f", + "metadata": {}, + "source": [ + "A graph is often useful for identifying trends. Create a scatter plot for income, balance, and the `default` category:\n", + "\n", + "```python\n", + "data.scatter(['credit_balance','income'],catcol='default')\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "db67ec96-bc39-4bd5-a99b-a5849ba4c188", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data.scatter(['credit_balance','income'],catcol='default')" + ] + }, + { + "cell_type": "markdown", + "id": "10d74835-98d9-4db0-b9da-4e72032a1b8d", + "metadata": {}, + "source": [ + "An obvious correlation is higher balances and defaults, but there is also a less obvious correlation with income. This correlation is easier to see with some other graphs:\n", + "\n", + "```python\n", + "data[\"credit_balance\"].boxplot(by = \"default\")\n", + "\n", + "data[\"income\"].boxplot(by = \"default\")\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e465973e-2190-410f-97b6-1316bf544a8a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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AAFAAZQsAAKAAyhYAAEABlC0AAIACdCh1AACAplq5cmWSZM2qlSVOAqWz4ft/w88D2y5lCwBoNRYsWJAkqfnjgpLmgG3BggULcsABB5Q6BpuhbAEArcbAgQOTJL0HDEx55y6lDQMlsmbVytT8cUHDzwPbLmULAGg1unT5W8Eq79wlnSsqSpwGSmvDzwPbLjfIAAAAKICyBQAAUABlCwAAoADKFgAAQAGULQAAgAIoWwAAAAVQtgAAAAqgbAEAABRA2QIAACiAsgUAAFAAZQsAAKAAyhYAAEABlC0AAIACKFsAAAAFULYAAAAKoGwBAAAUQNkCAAAogLIFAABQAGULAACgAMoWAABAAZQtAACAAihbAAAABVC2AAAACqBsAQAAFEDZAgAAKICyBQAAUABlCwAAoADKFgAAQAGULQAAgAIoWwAAAAVQtgAAAAqgbAEAABSgw5Y+4Pvf/36j5ZNPPrnFwgAAAGwvtrhs3X777Q1fl5WVKVsAAACbsMVl69e//nUROQAAALYrW1y2Dj300JSVlTUsP/LIIy0aCAAAYHuwxWVr3LhxBcQAAADYvmxx2TrllFOKyAEAALBd2apbv7/11lu59dZbc9FFF2XJkiVJkueeey5//vOfWyQcAABAa7XFR7Y2ePHFFzN69OhUVVVlwYIFOf3009OjR4/cd999qa6u3ugW8QAAAG1Js49sTZw4MePGjcurr76azp07N4wfeeSRmTZtWpO2MW3atBx99NHp27dvysrKcv/99zdaP27cuJSVlTX6OPzwwxvNWbJkSU444YRUVlame/fuOe2007Js2bJGc1588cUceOCB6dy5c/r165err766eTsNAADQRM0uW7/5zW/yhS98YaPx//W//ldqamqatI3ly5fngx/8YG666aZ3nHP44Ydn4cKFDR8//OEPG60/4YQT8sorr2TKlCl54IEHMm3atHz+859vWF9bW5sxY8ZkwIABmTlzZr71rW/lkksuyX/8x380cU8BAAC2XLNPI+zUqVNqa2s3Gv/d736XnXbaqUnbOOKII3LEEUe86/P07t17k+tmz56dhx56KL/5zW+y7777Jkm+853v5Mgjj8w111yTvn375q677sqaNWty2223pby8PHvssUdmzZqV6667rlEpAwBajzWrVpY6Qpu1fv36rF2zOh3KO6Vdu626/J9m8v3fejS7bH384x/PZZddlnvvvTdJUlZWlurq6lx44YU57rjjWizgo48+mp49e2bHHXfMRz/60Xzzm9/M+973viTJjBkz0r1794ailSSjR49Ou3bt8vTTT+cTn/hEZsyYkYMOOijl5eUNc8aOHZurrroqf/3rX7Pjjjtu8nlXr16d1atXNyxvKJZ1dXWpq6trsf2D1mLt2rUNn/0MAKVSVVWVzp07p+aPC0odBUqqc+fOqaqq8ppcIk39d2922br22mvzr//6r+nZs2dWrlyZgw8+ODU1NRk5cmT+7d/+rbmbbeTwww/Psccem1122SXz5s3LV77ylRxxxBGZMWNG2rdvn5qamvTs2bPRYzp06JAePXo0nMpYU1OTXXbZpdGcXr16Nax7p7I1adKkXHrppRuNP/zww6moqGiJ3YNWZd68eUmS6dOnZ+HChSVOA7Rl3/nOdzZ5dg3vjddeey3XX399zjnnnOy8886ljtNmVVZW5uWXX87LL79c6iht0ooVK5o0r9llq6qqKlOmTMkTTzyRF154IcuWLcvw4cMzevTo5m5yI8cff3zD13vttVf23nvv7Lbbbnn00Udz2GGHtdjzbMpFF12UiRMnNizX1tamX79+GTNmTCorKwt9btgWPf/880mSUaNGZdiwYSVOA0CpPP/887n++utz/PHHez2gzWrqf/g0u2xtcMABB+SAAw7Y2s00ya677pr3v//9+f3vf5/DDjssvXv3zhtvvNFoztq1a7NkyZKG67x69+6dRYsWNZqzYfmdrgVL/natWKdOnTYa79ixYzp27Li1uwKtTocOHRo++xkAaLu8HkCa/L3f7Ksav/jFL+bGG2/caPy73/1uzj777OZudrNee+21/OUvf0mfPn2SJCNHjsxbb72VmTNnNsx55JFHsn79+uy3334Nc6ZNm9bovMopU6Zk0KBB73gKIQAAwNZqdtn68Y9/vMkjWh/5yEfyX//1X03axrJlyzJr1qzMmjUrSTJ//vzMmjUr1dXVWbZsWc4///w89dRTWbBgQaZOnZp/+Zd/ye67756xY8cmSYYMGZLDDz88p59+ep555pk88cQTmTBhQo4//vj07ds3SfKZz3wm5eXlOe200/LKK6/knnvuybe//e1GpwgCAAC0tGaXrb/85S+pqqraaLyysjKLFy9u0jaeffbZDBs2rOF834kTJ2bYsGG5+OKL0759+7z44ov5+Mc/ng984AM57bTTMmLEiDz++OONTu+76667Mnjw4Bx22GE58sgjM2rUqEbvoVVVVZWHH3448+fPz4gRI3Luuefm4osvdtt3AACgUM2+Zmv33XfPQw89lAkTJjQa/8UvfpFdd921Sds45JBDUl9f/47rf/nLX77rNnr06JG77757s3P23nvvPP74403KBAAA0BKaXbYmTpyYCRMm5M0338xHP/rRJMnUqVNz7bXX5oYbbmipfAAAAK1Ss8vWZz/72axevTr/9m//lssvvzxJMnDgwNx88805+eSTWywgAABAa7RVt34/88wzc+aZZ+bNN99Mly5dssMOO7RULgAAgFZtq99nK0l22mmnltgMAADAdqPZdyNctGhRTjrppPTt2zcdOnRI+/btG30AAAC0Zc0+sjVu3LhUV1fn61//evr06ZOysrKWzAUAANCqNbtsTZ8+PY8//nj22WefFowDAACwfWj2aYT9+vXb7HtkAQAAtGXNLls33HBDvvzlL2fBggUtGAcAAGD70OzTCD/1qU9lxYoV2W233VJRUZGOHTs2Wr9kyZKtDgcAANBaNbts3XDDDS0YAwAAYPvS7LJ1yimntGQOAACA7UqLvKnxqlWrsmbNmkZjlZWVLbFpAACAVqnZN8hYvnx5JkyYkJ49e6Zr167ZcccdG30AAAC0Zc0uWxdccEEeeeSR3HzzzenUqVNuvfXWXHrppenbt2++//3vt2RGAACAVqfZpxH+7Gc/y/e///0ccsghOfXUU3PggQdm9913z4ABA3LXXXflhBNOaMmcAAAArUqzj2wtWbIku+66a5K/XZ+14Vbvo0aNyrRp01omHQAAQCvV7LK16667Zv78+UmSwYMH5957703ytyNe3bt3b5FwAAAArVWzy9app56aF154IUny5S9/OTfddFM6d+6cc845J+eff36LBQQAAGiNmn3N1jnnnNPw9ejRozNnzpzMnDkzu+++e/bee+8WCQcAANBatcj7bCXJgAEDMmDAgJbaHAAAQKu2RWXrxhtvbPLcL37xi1scBgAAYHuxRWXr+uuvb9K8srIyZQsAAGjTtqhsbbj7IAAAAJvX7LsRAgAA8M626gYZr732Wn7605+muro6a9asabTuuuuu26pgAAAArVmzy9bUqVPz8Y9/PLvuumvmzJmTPffcMwsWLEh9fX2GDx/ekhkBAABanWafRnjRRRflvPPOy0svvZTOnTvnxz/+cf70pz/l4IMPzv/+3/+7JTMCAAC0Os0uW7Nnz87JJ5+cJOnQoUNWrlyZHXbYIZdddlmuuuqqFgsIAADQGjW7bHXt2rXhOq0+ffpk3rx5DesWL1689ckAAABasWZfs7X//vtn+vTpGTJkSI488sice+65eemll3Lfffdl//33b8mMAAAArU6zy9Z1112XZcuWJUkuvfTSLFu2LPfcc0/+6Z/+yZ0IAQCANq/ZZWvXXXdt+Lpr16655ZZbWiQQAADA9mCr3mcrSZ599tnMnj07STJ06NCMGDFiq0MBAAC0ds0uW6+99lo+/elP54knnkj37t2TJG+99VY+8pGP5P/9v/+XnXfeuaUyAgAAtDrNvhvh5z73udTV1WX27NlZsmRJlixZktmzZ2f9+vX53Oc+15IZAQAAWp1mH9l67LHH8uSTT2bQoEENY4MGDcp3vvOdHHjggS0SDgAAoLVq9pGtfv36pa6ubqPxdevWpW/fvlsVCgAAoLVrdtn61re+lbPOOivPPvtsw9izzz6bL33pS7nmmmtaJBwAAEBrtUWnEe64444pKytrWF6+fHn222+/dOjwt82sXbs2HTp0yGc/+9kcc8wxLRoUAACgNdmisnXDDTcUFAMAAGD7skVl65RTTtniJ7jyyitzxhlnNNweHgAAoC1o9jVbTXXFFVdkyZIlRT8NAADANqXwslVfX1/0UwAAAGxzCi9bAAAAbZGyBQAAUABlCwAAoADKFgAAQAEKL1sHHnhgunTpUvTTAAAAbFOaXbbat2+fN954Y6Pxv/zlL2nfvn3D8s9//vP06dOnuU8DAADQKjW7bL3TLd1Xr16d8vLyZgcCAADYHnTY0gfceOONSZKysrLceuut2WGHHRrWrVu3LtOmTcvgwYNbLiEAAEArtMVl6/rrr0/ytyNbt9xyS6NTBsvLyzNw4MDccsstLZcQAACgFdrisjV//vwkyaGHHpr77rsvO+64Y4uHAgAAaO22uGxt8Otf/7olcwAAAGxXtqhsTZw4MZdffnm6du2aiRMnbnbuddddt1XB4B9VV1dn8eLFpY7RZs2ePbvRZ0rj/e9/f/r371/qGABAE2xR2Xr++edTV1fX8PU7KSsr27pU8A+qq6szZPCgrFi5qtRR2rwTTzyx1BHatIounTN7zlyFCwBagS0qW39/6qDTCHkvLV68OCtWrsoPRlRmSLf27/4AWtyqdfVZsGJdBla0T+f2/kOlFGa/vS4nzazN4sWLlS0AaAWafc0WlMKQbu0zvHvHUsdosz7yvlInAABoPbaobB177LFNnnvfffdtcRgAAIDtRbstmVxVVdXwUVlZmalTp+bZZ59tWD9z5sxMnTo1VVVVLR4UAACgNdmiI1u33357w9cXXnhhPvnJTzZ6Y+N169bl//yf/5PKysqWTQkAANDKbNGRrb9322235bzzzmsoWknSvn37TJw4MbfddluLhAMAAGitml221q5dmzlz5mw0PmfOnKxfv36rQgEAALR2zb4b4amnnprTTjst8+bNy4c//OEkydNPP50rr7wyp556aosFBAAAaI2aXbauueaa9O7dO9dee20WLlyYJOnTp0/OP//8nHvuuS0WEAAAoDVqdtlq165dLrjgglxwwQWpra1NEjfGAAAA+P81+5qt5G/Xbf3qV7/KD3/4w5SVlSVJXn/99SxbtqxFwgEAALRWzT6y9cc//jGHH354qqurs3r16vzzP/9zunXrlquuuiqrV6/OLbfc0pI5AQAAWpVmH9n60pe+lH333Td//etf06VLl4bxT3ziE5k6dWqLhAMAAGitmn1k6/HHH8+TTz6Z8vLyRuMDBw7Mn//8560OBgAA0Jo1+8jW+vXrs27duo3GX3vttXTr1m2rQgEAALR2zS5bY8aMyQ033NCwXFZWlmXLluUb3/hGjjzyyJbIBgAA0Go1u2xdc801eeKJJzJ06NCsWrUqn/nMZxpOIbzqqquatI1p06bl6KOPTt++fVNWVpb777+/0fr6+vpcfPHF6dOnT7p06ZLRo0fn1VdfbTRnyZIlOeGEE1JZWZnu3bvntNNO2+huiC+++GIOPPDAdO7cOf369cvVV1/d3N0GAABokmaXrX79+uWFF17IV7/61ZxzzjkZNmxYrrzyyjz//PPp2bNnk7axfPnyfPCDH8xNN920yfVXX311brzxxtxyyy15+umn07Vr14wdOzarVq1qmHPCCSfklVdeyZQpU/LAAw9k2rRp+fznP9+wvra2NmPGjMmAAQMyc+bMfOtb38oll1yS//iP/2jurgMAALyrZt0go66uLoMHD84DDzyQE044ISeccEKznvyII47IEUccscl19fX1ueGGG/K1r30t//Iv/5Ik+f73v59evXrl/vvvz/HHH5/Zs2fnoYceym9+85vsu+++SZLvfOc7OfLII3PNNdekb9++ueuuu7JmzZrcdtttKS8vzx577JFZs2bluuuua1TKAAAAWlKzylbHjh0bHV0qwvz581NTU5PRo0c3jFVVVWW//fbLjBkzcvzxx2fGjBnp3r17Q9FKktGjR6ddu3Z5+umn84lPfCIzZszIQQcd1OiuiWPHjs1VV12Vv/71r9lxxx03+fyrV6/O6tWrG5Zra2uT/K1o1tXVtfTu8i7Wrl1b6giwzVi7dq3fQ0DJbHhN9ruItqyp3/vNvvX7+PHjc9VVV+XWW29Nhw7N3sw7qqmpSZL06tWr0XivXr0a1tXU1Gx0ymKHDh3So0ePRnN22WWXjbaxYd07la1Jkybl0ksv3Wj84YcfTkVFRTP2iK0xb968UkeAbcb06dOzcOHCUscA2qgNr8l+F9GWrVixoknzmt2SfvOb32Tq1Kl5+OGHs9dee6Vr166N1t93333N3fQ24aKLLsrEiRMblmtra9OvX7+MGTMmlZWVJUzWNj3//POljgDbjFGjRmXYsGGljgG0URtek/0uoi3bcNbbu2l22erevXuOO+645j78XfXu3TtJsmjRovTp06dhfNGiRdlnn30a5rzxxhuNHrd27dosWbKk4fG9e/fOokWLGs3ZsLxhzqZ06tQpnTp12mi8Y8eO6dix45bvEFuliKOn0Fp16NDB7yGgZDa8JvtdRFvW1O/9Zv8Fe/vttzf3oU2yyy67pHfv3pk6dWpDuaqtrc3TTz+dM888M0kycuTIvPXWW5k5c2ZGjBiRJHnkkUeyfv367Lfffg1zvvrVr6aurq7hH2XKlCkZNGjQO55CCAAAsLWafev3+fPnb/SeV0ny6quvZsGCBU3axrJlyzJr1qzMmjWrYZuzZs1KdXV1ysrKcvbZZ+eb3/xmfvrTn+all17KySefnL59++aYY45JkgwZMiSHH354Tj/99DzzzDN54oknMmHChBx//PHp27dvkuQzn/lMysvLc9ppp+WVV17JPffck29/+9uNThEEAABoac0uW+PGjcuTTz650fjTTz+dcePGNWkbzz77bIYNG9Zwvu/EiRMzbNiwXHzxxUmSCy64IGeddVY+//nP50Mf+lCWLVuWhx56KJ07d27Yxl133ZXBgwfnsMMOy5FHHplRo0Y1eg+tqqqqPPzww5k/f35GjBiRc889NxdffLHbvgMAAIVq9mmEzz//fA444ICNxvfff/9MmDChSds45JBDUl9f/47ry8rKctlll+Wyyy57xzk9evTI3Xffvdnn2XvvvfP44483KRMAAEBLaPaRrbKysrz99tsbjS9dujTr1q3bqlAAAACtXbPL1kEHHZRJkyY1Klbr1q3LpEmTMmrUqBYJBwAA0Fo1+zTCK6+8MgcffHAGDRqUAw88MEny+OOPp7a2No888kiLBQQAAGiNmn1ka4899siLL76YT37yk3njjTfy9ttv5+STT86cOXOy5557tmRGAACAVmeLj2xVV1c3Wr7iiitaLAwAAMD2YovL1sCBA1NWVpb6+vqUlZW5GQYAAMAmbHHZWr9+fRE5AAAAtitbXLb+8T2vNrwBMQAAAP9ji8vW/Pnzi8gBAACwXdnisnX77bcXkQMAAGC70uxbvwMAAPDOlC0AAIACKFsAAAAFULYAAAAKoGwBAAAUQNkCAAAogLIFAABQAGULAACgAMoWAABAAZQtAACAAihbAAAABVC2AAAACqBsAQAAFEDZAgAAKICyBQAAUABlCwAAoAAdSh0AmmLlypVJkjlvry1xEiidDd//G34eAIBtm7JFq7BgwYIkyYkz3y5tENgGLFiwIAcccECpYwAA70LZolUYOHBgkuTOEd0yuJtvW9qmOW+vzYkz3274eQAAtm3+aqVV6NKlS5JkcLcOGd69Y4nTQGlt+HkAALZtbpABAABQAGULAACgAMoWAABAAZQtAACAAihbAAAABVC2AAAACqBsAQAAFEDZAgAAKICyBQAAUABlCwAAoADKFgAAQAGULQAAgAIoWwAAAAXoUOoAAABtxYoVKzJnzpxSx9gqs2fPbvS5tRo8eHAqKipKHYPtnLIFAPAemTNnTkaMGFHqGC3ixBNPLHWErTJz5swMHz681DHYzilbAADvkcGDB2fmzJmljrFVVq5cmQULFmTgwIHp0qVLqeM02+DBg0sdgTZA2QIAeI9UVFRsF0dTDjjggFJHgFbBDTIAAAAKoGwBAAAUQNkCAAAogLIFAABQAGULAACgAMoWAABAAZQtAACAAihbAAAABVC2AAAACqBsAQAAFEDZAgAAKICyBQAAUABlCwAAoADKFgAAQAGULQAAgAIoWwAAAAVQtgAAAAqgbAEAABRA2QIAACiAsgUAAFAAZQsAAKAAyhYAAEABlC0AAIACKFsAAAAFULYAAAAKoGwBAAAUYJsvW5dccknKysoafQwePLhh/apVqzJ+/Pi8733vyw477JDjjjsuixYtarSN6urqHHXUUamoqEjPnj1z/vnnZ+3ate/1rgAAAG1Ih1IHaIo99tgjv/rVrxqWO3T4n9jnnHNOHnzwwfzoRz9KVVVVJkyYkGOPPTZPPPFEkmTdunU56qij0rt37zz55JNZuHBhTj755HTs2DFXXHHFe74vAABA29AqylaHDh3Su3fvjcaXLl2a//t//2/uvvvufPSjH02S3H777RkyZEieeuqp7L///nn44Yfz29/+Nr/61a/Sq1ev7LPPPrn88stz4YUX5pJLLkl5efl7vTsAAEAb0CrK1quvvpq+ffumc+fOGTlyZCZNmpT+/ftn5syZqaury+jRoxvmDh48OP3798+MGTOy//77Z8aMGdlrr73Sq1evhjljx47NmWeemVdeeSXDhg3b5HOuXr06q1evbliura1NktTV1aWurq6gPeWdOO0T/sfatWv9HgKAEmrq6/A2X7b222+/TJ48OYMGDcrChQtz6aWX5sADD8zLL7+cmpqalJeXp3v37o0e06tXr9TU1CRJampqGhWtDes3rHsnkyZNyqWXXrrR+MMPP5yKioqt3Cu21Lx580odAbYZ06dPz8KFC0sdAwDarBUrVjRp3jZfto444oiGr/fee+/st99+GTBgQO6999506dKlsOe96KKLMnHixIbl2tra9OvXL2PGjEllZWVhz8umPf/886WOANuMUaNGveNReQCgeBvOens323zZ+kfdu3fPBz7wgfz+97/PP//zP2fNmjV56623Gh3dWrRoUcM1Xr17984zzzzTaBsb7la4qevANujUqVM6deq00XjHjh3TsWPHFtgTtsTf3xQF2roOHTr4PQQAJdTU1+Ft/tbv/2jZsmWZN29e+vTpkxEjRqRjx46ZOnVqw/q5c+emuro6I0eOTJKMHDkyL730Ut54442GOVOmTEllZWWGDh36nucHAADahm3+cMF5552Xo48+OgMGDMjrr7+eb3zjG2nfvn0+/elPp6qqKqeddlomTpyYHj16pLKyMmeddVZGjhyZ/fffP0kyZsyYDB06NCeddFKuvvrq1NTU5Gtf+1rGjx+/ySNXAAAALWGbL1uvvfZaPv3pT+cvf/lLdtppp4waNSpPPfVUdtpppyTJ9ddfn3bt2uW4447L6tWrM3bs2Hzve99reHz79u3zwAMP5Mwzz8zIkSPTtWvXnHLKKbnssstKtUtshdlvryt1hDZr1br6LFixLgMr2qdz+7JSx2mTfP8DQOtSVl9fX1/qEK1BbW1tqqqqsnTpUjfIKIHq6uoMGTwoK1auKnUUKKmKLp0ze87c9O/fv9RRAKDNamo32OaPbEGS9O/fP7PnzM3ixYtLHaXNmj17dk488cTceeedGTJkSKnjtFnvf//7FS0AaCWULVqN/v37+yNzGzBkyJAMHz681DEAALZ5re5uhAAAAK2BsgUAAFAAZQsAAKAAyhYAAEABlC0AAIACKFsAAAAFULYAAAAKoGwBAAAUQNkCAAAogLIFAABQAGULAACgAMoWAABAAZQtAACAAihbAAAABVC2AAAACqBsAQAAFEDZAgAAKICyBQAAUABlCwAAoADKFgAAQAGULQAAgAIoWwAAAAVQtgAAAAqgbAEAABRA2QIAACiAsgUAAFAAZQsAAKAAyhYAAEABlC0AAIACKFsAAAAFULYAAAAKoGwBAAAUQNkCAAAogLIFAABQAGULAACgAMoWAABAAZQtAACAAihbAAAABVC2AAAACqBsAQAAFEDZAgAAKICyBQAAUABlCwAAoADKFgAAQAGULQAAgAIoWwAAAAVQtgAAAAqgbAEAABRA2QIAACiAsgUAAFAAZQsAAKAAyhYAAEABlC0AAIACKFsAAAAFULYAAAAKoGwBAAAUQNkCAAAogLIFAABQAGULAACgAMoWAABAAZQtAACAAihbAAAABVC2AAAACqBsAQAAFEDZAgAAKICyBQAAUABlCwAAoADKFgAAQAGULQAAgAIoWwAAAAVoU2XrpptuysCBA9O5c+fst99+eeaZZ0odCQAA2E61mbJ1zz33ZOLEifnGN76R5557Lh/84AczduzYvPHGG6WOBgAAbIfaTNm67rrrcvrpp+fUU0/N0KFDc8stt6SioiK33XZbqaMBAADboTZRttasWZOZM2dm9OjRDWPt2rXL6NGjM2PGjBImAwAAtlcdSh3gvbB48eKsW7cuvXr1ajTeq1evzJkzZ5OPWb16dVavXt2wXFtbmySpq6tLXV1dcWHZbq1YsSJz584tdYxm2/Cz8vLLL2ft2rUlTrN1Bg0alIqKilLHAABaqab2gTZRtppj0qRJufTSSzcaf/jhh/2RRrPMmzcv5557bqljbLVTTjml1BG22rXXXpvddtut1DEAgFZqxYoVTZrXJsrW+9///rRv3z6LFi1qNL5o0aL07t17k4+56KKLMnHixIbl2tra9OvXL2PGjEllZWWhedk+rVixIqNGjSp1jGZbuXJl/vjHP2bAgAHp0qVLqeNsFUe2AICtseGst3fTJspWeXl5RowYkalTp+aYY45Jkqxfvz5Tp07NhAkTNvmYTp06pVOnThuNd+zYMR07diwyLtupqqqqfPjDHy51DAAAtlJT+0CbKFtJMnHixJxyyinZd9998+EPfzg33HBDli9fnlNPPbXU0QAAgO1Qmylbn/rUp/Lmm2/m4osvTk1NTfbZZ5889NBDG900AwAAoCWU1dfX15c6RGtQW1ubqqqqLF261DVbAADQhjW1G7SJ99kCAAB4rylbAAAABVC2AAAACqBsAQAAFEDZAgAAKICyBQAAUABlCwAAoADKFgAAQAGULQAAgAIoWwAAAAVQtgAAAAqgbAEAABRA2QIAACiAsgUAAFCADqUO0FrU19cnSWpra0ucBAAAKKUNnWBDR3gnylYTvf3220mSfv36lTgJAACwLXj77bdTVVX1juvL6t+tjpEkWb9+fV5//fV069YtZWVlpY4D77na2tr069cvf/rTn1JZWVnqOACUiNcD+NsRrbfffjt9+/ZNu3bvfGWWI1tN1K5du+y8886ljgElV1lZ6cUVAK8HtHmbO6K1gRtkAAAAFEDZAgAAKICyBTRJp06d8o1vfCOdOnUqdRQASsjrATSdG2QAAAAUwJEtAACAAihbAAAABVC2AAAACqBsAQAAFEDZAgBgsx5//PGceOKJGTlyZP785z8nSX7wgx9k+vTpJU4G2zZlCwCAd/TjH/84Y8eOTZcuXfL8889n9erVSZKlS5fmiiuuKHE62LYpW0CTrFmzJnPnzs3atWtLHQWA99A3v/nN3HLLLfnP//zPdOzYsWH8gAMOyHPPPVfCZLDtU7aAzVqxYkVOO+20VFRUZI899kh1dXWS5KyzzsqVV15Z4nQAFG3u3Lk56KCDNhqvqqrKW2+99d4HglZE2QI266KLLsoLL7yQRx99NJ07d24YHz16dO65554SJgPgvdC7d+/8/ve/32h8+vTp2XXXXUuQCFoPZQvYrPvvvz/f/e53M2rUqJSVlTWM77HHHpk3b14JkwHwXjj99NPzpS99KU8//XTKysry+uuv56677sp5552XM888s9TxYJvWodQBgG3bm2++mZ49e240vnz58kblC4Dt05e//OWsX78+hx12WFasWJGDDjoonTp1ynnnnZezzjqr1PFgm+bIFrBZ++67bx588MGG5Q0F69Zbb83IkSNLFQuA90hZWVm++tWvZsmSJXn55Zfz1FNP5c0338zll19e6miwzXNkC9isK664IkcccUR++9vfZu3atfn2t7+d3/72t3nyySfz2GOPlToeAO+R8vLyDB06tNQxoFUpq6+vry91CGDbNm/evFx55ZV54YUXsmzZsgwfPjwXXnhh9tprr1JHA6Bghx566GZPG3/kkUfewzTQujiyBbyr3XbbLf/5n/9Z6hgAlMA+++zTaLmuri6zZs3Kyy+/nFNOOaU0oaCVULaAzXruuefSsWPHhqNYP/nJT3L77bdn6NChueSSS1JeXl7ihAAU6frrr9/k+CWXXJJly5a9x2mgdXGDDGCzvvCFL+R3v/tdkuQPf/hDPvWpT6WioiI/+tGPcsEFF5Q4HQClcuKJJ+a2224rdQzYpilbwGb97ne/aziF5Ec/+lEOPvjg3H333Zk8eXJ+/OMflzYcACUzY8aMRm92D2zMaYTAZtXX12f9+vVJkl/96lf52Mc+liTp169fFi9eXMpoALwHjj322EbL9fX1WbhwYZ599tl8/etfL1EqaB2ULWCz9t1333zzm9/M6NGj89hjj+Xmm29OksyfPz+9evUqcToAilZVVdVouV27dhk0aFAuu+yyjBkzpkSpoHVQtoDNuuGGG3LCCSfk/vvvz1e/+tXsvvvuSZL/+q//ykc+8pESpwOgSOvWrcupp56avfbaKzvuuGOp40Cr4322gGZZtWpV2rdvn44dO5Y6CgAF6ty5c2bPnp1ddtml1FGg1XGDDKBZOnfurGgBtAF77rln/vCHP5Q6BrRKjmwBG9lxxx1TVlbWpLlLliwpOA0ApfTQQw/loosuyuWXX54RI0aka9eujdZXVlaWKBls+5QtYCN33HFHk+eecsopBSYBoFQuu+yynHvuuenWrVvD2N//R1x9fX3Kysqybt26UsSDVkHZAgBgI+3bt8/ChQsze/bszc47+OCD36NE0PooW0CTrVq1KmvWrGk05vQRgO1Tu3btUlNTk549e5Y6CrRabpABbNby5cszYcKE9OzZM127ds2OO+7Y6AOA7VdTr98FNs37bAGbdcEFF+TXv/51br755px00km56aab8uc//zn//u//niuvvLLU8QAo0Ac+8IF3LVxulATvzGmEwGb1798/3//+93PIIYeksrIyzz33XHbffff84Ac/yA9/+MP8/Oc/L3VEAArQrl273HDDDamqqtrsPDdKgnfmyBawWUuWLMmuu+6a5G/XZ234H8xRo0blzDPPLGU0AAp2/PHHu2YLtoJrtoDN2nXXXTN//vwkyeDBg3PvvfcmSX72s5+le/fuJUwGQJFcrwVbT9kCNukPf/hD1q9fn1NPPTUvvPBCkuTLX/5ybrrppnTu3DnnnHNOzj///BKnBKAorjSBreeaLWCTNry/yobTRz71qU/lxhtvzKpVqzJz5szsvvvu2XvvvUucEgBg26VsAZv0j++v0q1bt7zwwgsN128BALB5TiMEAAAogLIFbFJZWdlGF0e7WBoAoOnc+h3YpPr6+owbNy6dOnVKkqxatSpnnHFGunbt2mjefffdV4p4AADbPGUL2KR/fJPKE088sURJAABaJzfIAAAAKIBrtgAAAAqgbAEAABRA2QIAACiAsgXAdu3RRx9NWVlZ3nrrrSY/ZsWKFTnuuONSWVm5xY/dnEsuuST77LNPi2wLgG2fsgVAqzRu3LhccsklSf72HnALFixosW3fcccdefzxx/Pkk09m4cKFqaqqarFt/71x48blmGOO2Wh8w/5Mnjw5hxxySCHPDUDx3PodAP7BvHnzMmTIkOy5556ljgJAK+bIFgDblZ///Of5wAc+kC5duuTQQw/d5BGv6dOn58ADD0yXLl3Sr1+/fPGLX8zy5cuTJIccckiuvfbaTJs2LWVlZQ1Hln7wgx9k3333Tbdu3dK7d+985jOfyRtvvNGwzcmTJ6d79+6Nnuf+++9PWVnZJnNecsklueOOO/KTn/wkZWVlKSsry6OPPtoS/wQAbCOULQC2G3/6059y7LHH5uijj86sWbPyuc99Ll/+8pcbzZk3b14OP/zwHHfccXnxxRdzzz33ZPr06ZkwYUKS5L777svpp5+ekSNHZuHChbnvvvuSJHV1dbn88svzwgsv5P7778+CBQsybty4Zmc977zz8slPfjKHH354Fi5cmIULF+YjH/lIs7cHwLbHaYQAtEqTJ09u+Lq+vj5J8pWvfCW77bZbrr322iTJoEGD8tJLL+Wqq65qmDtp0qSccMIJOfvss5Mk//RP/5Qbb7wxBx98cG6++eb06NEjFRUVKS8vT+/evRse99nPfrbh61133TU33nhjPvShD2XZsmXZYYcdtjj/DjvskC5dumT16tWNnufv92fcuHFbVegAKC1HtgDYbsyePTv77bdfo7GRI0c2Wn7hhRcyefLk7LDDDg0fY8eOzfr16zN//vx33PbMmTNz9NFHp3///unWrVsOPvjgJEl1dXXL7wgA2wVHtgBoU5YtW5YvfOEL+eIXv7jRuv79+2/yMcuXL8/YsWMzduzY3HXXXdlpp51SXV2dsWPHZs2aNUmSdu3aNRyR2qCurq7ldwCAVkPZAmC7MWTIkPz0pz9tNPbUU081Wh4+fHh++9vfZvfdd2/ydufMmZO//OUvufLKK9OvX78kybPPPttozk477ZS33347y5cvT9euXZMks2bN2ux2y8vLs27duibnAKB1cRohANuNM844I6+++mrOP//8zJ07N3fffXeja7uS5MILL8yTTz6ZCRMmZNasWXn11Vfzk5/8pOEGGZvSv3//lJeX5zvf+U7+8Ic/5Kc//Wkuv/zyRnP222+/VFRU5Ctf+UrmzZu3yef+RwMHDsyLL76YuXPnZvHixY6EAWxnlC0Athv9+/fPj3/849x///354Ac/mFtuuSVXXHFFozl77713Hnvssfzud7/LgQcemGHDhuXiiy9O375933G7O+20UyZPnpwf/ehHGTp0aK688spcc801jeb06NEjd955Z37+859nr732yg9/+MOGN11+J6effnoGDRqUfffdNzvttFOeeOKJZu87ANuesvp/PMEcAACArebIFgAAQAGULQAAgAIoWwAAAAVQtgAAAAqgbAEAABRA2QIAACiAsgUAAFAAZQsAAKAAyhYAAEABlC0AAIACKFsAAAAFULYAAAAK8P8ByCwWlVBVHGgAAAAASUVORK5CYII=", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data.bar(['student','default'])" + ] + }, + { + "cell_type": "markdown", + "id": "f691e3b2", + "metadata": {}, + "source": [ + "
\n", + "Knowledge Check: What kind of classification problem is predicting the winner of a football match?\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "4e6614e6", + "metadata": {}, + "outputs": [], + "source": [ + "Q1 = create_multipleChoice_widget(['Binary','Multi-class'],'Binary')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "3daeb0c6", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1914422a7400451eaa3ca5c461800267", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Output(), RadioButtons(options=(('Binary', 0), ('Multi-class', 1)), value=0), Button(descriptio…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Q1)" + ] + }, + { + "cell_type": "markdown", + "id": "3b946cb8", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "id": "3fd84ef3", + "metadata": { + "tags": [] + }, + "source": [ + "\n", + "### Applying logistic regression" + ] + }, + { + "cell_type": "markdown", + "id": "7b2424cd-2121-46cd-b749-0e531a8a30e6", + "metadata": {}, + "source": [ + "Import the `LogisticRegression` library and create a logistic regression model object:\n", + "\n", + "```python\n", + "from verticapy.learn.linear_model import LogisticRegression\n", + "model = LogisticRegression(\"public.LR_default\")\n", + "```\n", + "\n", + "Fit the model with the data:\n", + "\n", + "```python\n", + "model.fit(data, [\"credit_balance\", \"income\",\"student\"], \"default\")\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "3db5775e-8623-4394-8c65-753d488d6f1c", + "metadata": {}, + "outputs": [], + "source": [ + "from verticapy.learn.linear_model import LogisticRegression\n", + "model = LogisticRegression(\"public.LR_default\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "59bd58f8-976d-4c57-abba-b384f0dd3f13", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\n", + "=======\n", + "details\n", + "=======\n", + " predictor |coefficient|std_err | z_value |p_value \n", + "--------------+-----------+--------+---------+--------\n", + " Intercept | -11.15139 | 0.57746|-19.31113| 0.00000\n", + "credit_balance| 0.00587 | 0.00027|21.60061 | 0.00000\n", + " income | 0.00001 | 0.00001| 0.63721 | 0.52399\n", + " student | -0.60562 | 0.27726|-2.18431 | 0.02894\n", + "\n", + "\n", + "==============\n", + "regularization\n", + "==============\n", + "type| lambda \n", + "----+--------\n", + "none| 1.00000\n", + "\n", + "\n", + "===========\n", + "call_string\n", + "===========\n", + "logistic_reg('public.LR_default', '\"public\".\"_verticapy_tmp_view_dbadmin_6064_718428356_\"', '\"default\"', '\"credit_balance\", \"income\", \"student\"'\n", + "USING PARAMETERS optimizer='newton', epsilon=1e-06, max_iterations=100, regularization='none', lambda=1, alpha=0.5, fit_intercept=true)\n", + "\n", + "===============\n", + "Additional Info\n", + "===============\n", + " Name |Value\n", + "------------------+-----\n", + " iteration_count | 8 \n", + "rejected_row_count| 0 \n", + "accepted_row_count|7618 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(data, [\"credit_balance\", \"income\",\"student\"], \"default\")" + ] + }, + { + "cell_type": "markdown", + "id": "e83aa746-46ef-44b3-8632-64d1577c99cf", + "metadata": {}, + "source": [ + "As shown in fitted model, the p-value for `credit_balance` and `student` are both low, but the p-value for `income` is quite high (that is, greater than 0.05). A high p-value indicates that the predictor, `income`, has little or no effect on the outcome, the likelihood that a person will default.\n", + "\n", + "The `credit_balance` coefficient is positive, suggesting that the greater a person's balance, the more likely it is that they default. On the other hand, the coefficient for `student` is negative, which suggests that being is a student reduces the probability that a person will default. \n", + "\n", + "You can check the accuracy of the model by scoring it against some accuracy metrics. Vertica supports [many metrics](https://www.vertica.com/python/documentation_last/learn/Model/binary_classification_score/), but for simplicity, this example will only concern the following:\n", + "\n", + "- Accuracy\n", + "- Area Under the Curve (AUC)\n", + "- Precision\n", + "- Specificity\n", + "\n", + "```python\n", + "model.score(method=\"accuracy\")\n", + "model.score(method=\"auc\")\n", + "model.score(method=\"precision\")\n", + "model.score(method=\"specificity\")\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "11b227ee-5bef-407f-b329-00deadbb79a1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.9726962457337884\n", + "AUC: 0.9522890455209175\n", + "Precision: 0.7105263157894737\n", + "Specificity: 0.9955175224123879\n" + ] + } + ], + "source": [ + "print('Accuracy:',model.score(method=\"accuracy\"))\n", + "print('AUC:',model.score(method=\"auc\"))\n", + "print('Precision:',model.score(method=\"precision\"))\n", + "print('Specificity:',model.score(method=\"specificity\"))" + ] + }, + { + "cell_type": "markdown", + "id": "14bc7a73-4d76-4504-ba22-cd2a255df0a5", + "metadata": {}, + "source": [ + "The model's accuracy, AUC, and specificity are quite good, but its precision is somewhat lacking because the data is a bit skewed. That is, the dataset contains significantly more non-defaulters than defaulters. This imbalance is shown in the graph below:\n", + "\n", + "```python\n", + "data.bar(['default'])\n", + "data[\"default\"].topk()\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "fa2dcdcc-742d-4bc4-9b7b-ff15a4afa7ca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data.bar(['default'])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "911c5543-90cf-4424-8c38-f4eadfcd65b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
count
percent
❌
736296.64
✅
2563.36
Rows: 1-2 | Columns: 3
" + ], + "text/plain": [ + " count percent \n", + "False 7362 96.64 \n", + "True 256 3.36 \n", + "Rows: 1-2 | Columns: 3" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"default\"].topk()" + ] + }, + { + "cell_type": "markdown", + "id": "7687b6a1-311b-4af7-ba84-063c1e25effd", + "metadata": {}, + "source": [ + "One solution to this problem is to balance the data with `balance()` with the `under` method to allow more from the category with more data points (cases where the individual does not default), otherwise the dataset will only contain 256 values to train from.\n", + "\n", + "You can tweak the ratio (x) to ensure that the defaulters are properly represented in the balanced dataset:\n", + "\n", + "```python\n", + "balanced_data=data.balance(column=\"default\",method=\"under\",x=0.25)\n", + "balanced_data[\"default\"].topk()\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "b647db7f-8692-4c4f-9252-7efc6472ecf1", + "metadata": {}, + "outputs": [], + "source": [ + "balanced_data=data.balance(column=\"default\",method=\"under\",x=0.25)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "2e77c401-60b5-47b3-99d2-0c01946325ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
count
percent
❌
102580.016
✅
25619.984
Rows: 1-2 | Columns: 3
" + ], + "text/plain": [ + " count percent \n", + "False 1025 80.016 \n", + "True 256 19.984 \n", + "Rows: 1-2 | Columns: 3" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "balanced_data[\"default\"].topk()" + ] + }, + { + "cell_type": "markdown", + "id": "70031e55-63ee-46ed-941f-dc1cee246444", + "metadata": {}, + "source": [ + "Let us train again to see if we improved." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "64efeb90-8470-445d-ba57-b7788ef47957", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\n", + "=======\n", + "details\n", + "=======\n", + " predictor |coefficient|std_err | z_value |p_value \n", + "--------------+-----------+--------+---------+--------\n", + " Intercept | -8.87974 | 0.73503|-12.08072| 0.00000\n", + "credit_balance| 0.00589 | 0.00038|15.51593 | 0.00000\n", + " income | -0.00000 | 0.00001|-0.10420 | 0.91701\n", + " student | -0.90451 | 0.37083|-2.43915 | 0.01472\n", + "\n", + "\n", + "==============\n", + "regularization\n", + "==============\n", + "type| lambda \n", + "----+--------\n", + "none| 1.00000\n", + "\n", + "\n", + "===========\n", + "call_string\n", + "===========\n", + "logistic_reg('public.LR_default', '\"public\".\"_verticapy_tmp_view_dbadmin_6064_7892778534_\"', '\"default\"', '\"credit_balance\", \"income\", \"student\"'\n", + "USING PARAMETERS optimizer='newton', epsilon=1e-06, max_iterations=100, regularization='none', lambda=1, alpha=0.5, fit_intercept=true)\n", + "\n", + "===============\n", + "Additional Info\n", + "===============\n", + " Name |Value\n", + "------------------+-----\n", + " iteration_count | 7 \n", + "rejected_row_count| 0 \n", + "accepted_row_count|1281 " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(balanced_data, [\"credit_balance\", \"income\",\"student\"], \"default\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "82512aca-6a0e-4a0a-80f9-fda5d33f205b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.9039812646370023\n", + "AUC: 0.951080411585369\n", + "Precision: 0.7854077253218884\n", + "Specificity: 0.9512195121951219\n" + ] + } + ], + "source": [ + "print('Accuracy:',model.score(method=\"accuracy\"))\n", + "print('AUC:',model.score(method=\"auc\"))\n", + "print('Precision:',model.score(method=\"precision\"))\n", + "print('Specificity:',model.score(method=\"specificity\"))" + ] + }, + { + "cell_type": "markdown", + "id": "56740005-8156-4b37-a675-1a970f9b284d", + "metadata": {}, + "source": [ + "The balanced dataset improved the model's precision at the cost of accuracy. There is always going to be trade-off when working with imbalanced data." + ] + }, + { + "cell_type": "markdown", + "id": "c02f25ee", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "id": "ca2aa185", + "metadata": {}, + "source": [ + " Author Name: Umar Farooq Ghumman\n", + "
\n", + "Author Contact: umarfarooq.ghumman@vertica.com" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Essentials_Classification_RandomForest.ipynb b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Essentials_Classification_RandomForest.ipynb new file mode 100644 index 00000000..742d70b3 --- /dev/null +++ b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Essentials_Classification_RandomForest.ipynb @@ -0,0 +1,958 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "d8813b06", + "metadata": {}, + "outputs": [], + "source": [ + "import ipywidgets as widgets\n", + "from IPython.display import display, clear_output\n", + "from ipywidgets import Layout" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "73c74fd4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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Correct!
')\n", + " else:\n", + " s = widgets.HTML('
Try Again!
')\n", + " with feedback_out:\n", + " clear_output()\n", + " display(s)\n", + " return\n", + " \n", + " check = widgets.Button(description=\"submit\")\n", + " check.on_click(check_selection)\n", + " \n", + " \n", + " return widgets.VBox([description_out, alternativ, check, feedback_out])\n", + "\n", + "def create_numeric_widget(correct_answer):\n", + " \n", + " #correct_answer_index = options.index(correct_answer)\n", + " \n", + " #radio_options = [(words, i) for i, words in enumerate(options)]\n", + " alternativ = widgets.Text(\n", + " #options = radio_options,\n", + " #description = '',\n", + " disabled = False\n", + " )\n", + " \n", + " description_out = widgets.Output()\n", + "# with description_out:\n", + "# print(description)\n", + " \n", + " feedback_out = widgets.Output()\n", + "\n", + " def check_selection(b):\n", + " try:\n", + " a = int(alternativ.value)\n", + " except ValueError:\n", + " a = float(alternativ.value)\n", + " if a==correct_answer:\n", + " s = widgets.HTML('
Correct!
')\n", + " else:\n", + " s = widgets.HTML('
Try Again!
')\n", + " with feedback_out:\n", + " clear_output()\n", + " display(s)\n", + " return\n", + " \n", + " check = widgets.Button(description=\"submit\")\n", + " check.on_click(check_selection)\n", + " \n", + " \n", + " return widgets.VBox([description_out, alternativ, check, feedback_out])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "dab1e034", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "
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\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%html\n", + "\n", + "\n", + "\n", + " \n", + "
\n", + " Classification\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "1eb61592", + "metadata": {}, + "source": [ + "## Random Forest" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8e5bc7f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " 10 mins\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%html\n", + "\n", + " 10 mins" + ] + }, + { + "cell_type": "markdown", + "id": "989fd8da", + "metadata": {}, + "source": [ + "### Table of Contents\n", + "\n", + "- [Introduction to Decision Trees](#h1_cell)\n", + "- [Random Forest](#h2_cell)\n" + ] + }, + { + "cell_type": "markdown", + "id": "e359fcc6", + "metadata": {}, + "source": [ + "### Covered in This Module\n", + "\n", + "- Decision trees\n", + "- Limitations of decision trees\n", + "- Bagging\n", + "- Random forest classifiers" + ] + }, + { + "cell_type": "markdown", + "id": "e4eae0c4", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "id": "e91b7444", + "metadata": {}, + "source": [ + "\n", + "### Introduction to Decision Trees\n" + ] + }, + { + "cell_type": "markdown", + "id": "aca0d9dc", + "metadata": {}, + "source": [ + "In classification problems where the number of classes is greater than two is called multiclass or multinomial classification. Logistic regression is not applicable to these cases.\n", + "\n", + "One method for handling this type of classification is using decision trees. Decision trees are a type of non-linear model that is quite similar to how humans really think.\n", + "\n", + "An example of this classification problem is predicting when a person will get out of bed in the morning, which depends on two main predictors: if it is a workday or exercise day, and three classes: early, average, and late:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "76798435", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7161b2c1f1424bd49d320676b286223c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Image(value=b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\x05\\xb5\\x00\\x00\\x04\\xd7\\x08\\x03\\x00\\x00\\x00\\xee\\xa9h…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "file = open(\"Figures/basic_tree_1.png\", \"rb\")\n", + "image = file.read()\n", + "\n", + "image_0= widgets.Image(\n", + " value=image,\n", + " format='png',\n", + " width='400px'\n", + " )\n", + "\n", + "display(image_0)" + ] + }, + { + "cell_type": "markdown", + "id": "b4e7a234", + "metadata": {}, + "source": [ + "There are three regions completely enclosed by clear boundaries. This creates a tree-like representation:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "cecda99a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "401a0a54f238449aaae5275417c9d038", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Image(value=b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\x06\\x99\\x00\\x00\\x03\\xb7\\x08\\x03\\x00\\x00\\x00\\xefy\\xdc…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "file = open(\"Figures/basic_tree_2.png\", \"rb\")\n", + "image = file.read()\n", + "\n", + "image_0= widgets.Image(\n", + " value=image,\n", + " format='png',\n", + " width='600px'\n", + " )\n", + "\n", + "display(image_0)" + ] + }, + { + "cell_type": "markdown", + "id": "5184a7d5", + "metadata": {}, + "source": [ + "A decision tree has three elements:\n", + "\n", + "1. Root node: The top of the tree\n", + "2. Decision node: Where the branch is split\n", + "3. Leaf node (or terminal node): Where the final output is predicted" + ] + }, + { + "cell_type": "markdown", + "id": "62007597", + "metadata": {}, + "source": [ + "\n", + "The biggest benefits of decision trees include the following:\n", + "\n", + "- They are easy to interpret\n", + "- They are easy to graph\n", + "- You do not need to include a dummy variable to encode the predictors" + ] + }, + { + "cell_type": "markdown", + "id": "0fd80fd7", + "metadata": {}, + "source": [ + "Predicting with decision trees has two steps: \n", + "1. Divide the entire predictor space into $j$ distinct regions.\n", + "2. For every observation that falls into a region $R_{j}$, make the same prediction.\n", + "\n", + "The question then becomes: how do you divide the predictor space into sub-regions?\n", + "\n", + "Before answering that, show the let's hone in on the goal of a classification tree: to classify something based on the training data. You can reduce the error in a classification tree with the following:\n", + "\n", + "\\begin{equation}\n", + " E = 1 - \\operatorname*{max}_k \\hat{p}_{mk},\n", + "\\end{equation}\n", + "\n", + "Where $\\hat{p}_{mk}$ is the proportion of training observations in the $m$th region from $k$th class. However, the above metric is not sensitive to the growth of the tree, so other metrics like the Gini index are preferred:\n", + "\n", + "\\begin{equation}\n", + " G =\\sum_{k=1}^{K} \\hat{p}_{mk} (1-\\hat{p}_{mk})\n", + "\\end{equation}\n", + "\n", + "The Gini index is a measure of variance across all K classes. GI gives a small value when all the \\hat{p}_{mk} are either close to 0 or 1, i.e., the nodes consist of one pure class. Fittingly, the Gini index is also called the purity index.\n", + "\n", + "Going back to dividing the predictor into sub-regions, you can do this using recursive binary splitting.\n", + "\n", + "For example, suppose you have $J$ predictors. Starting from the root node, divide the space using J predictors, and for each of the $j$ predictor, try all possible cutoff values (s). For each of these combinations of $j$ and $s$, calculate the goal (that is, the Gini index or some other metric). The combination of $s$ and $j$ gives us the best goal, is used to split the tree. In the next step, try the remaining predictors and their cutoff values to find the best one. Keep dividing until you reach a predefined number of leaf nodes or you hit the predefined minimum limit of samples in each leaf node.\n", + "\n", + "One big limitation of decision trees is that they are very prone to over-fitting. To address this, decision trees can be pruned.\n", + "\n", + "Another limitation of decision trees is that they are high-variance models, which means that they are very sensitive to the initial dataset. That is, for two sub-samples of the same data, the resulting decision trees could be very different. To address this, random forests are used." + ] + }, + { + "cell_type": "markdown", + "id": "f691e3b2", + "metadata": {}, + "source": [ + "
\n", + "Knowledge Check: Which type of models are decision trees?\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "4e6614e6", + "metadata": {}, + "outputs": [], + "source": [ + "Q1 = create_multipleChoice_widget(['High-variance and low-bias','Low-variance and low-bias','Low-variance and high-bias'],'High-variance and low-bias')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "3daeb0c6", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7aac8b4648e64b2db904d6a45d3fa180", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Output(), RadioButtons(options=(('High-variance and low-bias', 0), ('Low-variance and low-bias'…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Q1)" + ] + }, + { + "cell_type": "markdown", + "id": "3b946cb8", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "id": "3fd84ef3", + "metadata": {}, + "source": [ + "\n", + "### Bagging and random forests" + ] + }, + { + "cell_type": "markdown", + "id": "0ef0675d", + "metadata": {}, + "source": [ + "To address the high-variance of decision trees, you can apply bootstrap aggregation, also known as \"bagging.\" The idea is to create multiple sub-datasets from a larger dataset, and create several models from these sub-datasets, and then average out the results from all these models to give one low-variance value. In formal terms:\n", + "\n", + "\\begin{equation}\n", + " \\hat{f}_{bag}(x)=\\frac{1}{B} \\sum_{b=1}^{B} \\hat{f}^{*b}(x),\n", + "\\end{equation}\n", + "\n", + "Where you have $B$ different bootstrapped training data sets to train the models/function $hat{f}^{*b}(x)$. This equation is more accurate for regression problems. For classification, you should use the most frequently occurring class from all models.\n", + "\n", + "One problem with the above approach is that even with multiple samples, there is a large probability that the most dominant feature will be used in the top split, and if this happens, variance will not be reduced. Instead, it will create bagged trees that look very similar to each other. To address this, use random forest.\n", + "\n", + "Random forest restricts the set of predictors to choose from at each split. That is, at each split, only a subset of $m$ predictors is allowed from the total of $p$ predictors. Typically, you should use $m=\\sqrt{p}$ for the number of variables to randomly sample at each split, but you can experiment with the value to control how much randomness we want. For $m=p$ simple bagging is used." + ] + }, + { + "cell_type": "markdown", + "id": "c02f25ee", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "id": "ca2aa185", + "metadata": {}, + "source": [ + " Author Name: Umar Farooq Ghumman\n", + "
\n", + "Author Contact: umarfarooq.ghumman@vertica.com" + ] + }, + { + "cell_type": "markdown", + "id": "185edfa5", + "metadata": {}, + "source": [ + "### Resources\n", + "\n", + "- [Introduction to statistical learning](https://www.statlearning.com/)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Essentials_Classification_RandomForest_Example.ipynb b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Essentials_Classification_RandomForest_Example.ipynb new file mode 100644 index 00000000..9da5aa15 --- /dev/null +++ b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Essentials_Classification_RandomForest_Example.ipynb @@ -0,0 +1,1482 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "d8813b06", + "metadata": {}, + "outputs": [], + "source": [ + "import ipywidgets as widgets\n", + "from IPython.display import display, clear_output\n", + "from ipywidgets import Layout" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "73c74fd4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b1fec60f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%html\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8b0862f1", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining a function for multiple choice widgets\n", + "\n", + "def create_multipleChoice_widget(options, correct_answer):\n", + " if correct_answer not in options:\n", + " options.append(correct_answer)\n", + " \n", + " correct_answer_index = options.index(correct_answer)\n", + " \n", + " radio_options = [(words, i) for i, words in enumerate(options)]\n", + " alternativ = widgets.RadioButtons(\n", + " options = radio_options,\n", + " description = '',\n", + " disabled = False\n", + " )\n", + " \n", + " description_out = widgets.Output()\n", + "# with description_out:\n", + "# print(description)\n", + " \n", + " feedback_out = widgets.Output()\n", + "\n", + " def check_selection(b):\n", + " a = int(alternativ.value)\n", + " if a==correct_answer_index:\n", + " s = widgets.HTML('
Correct!
')\n", + " else:\n", + " s = widgets.HTML('
Try Again!
')\n", + " with feedback_out:\n", + " clear_output()\n", + " display(s)\n", + " return\n", + " \n", + " check = widgets.Button(description=\"submit\")\n", + " check.on_click(check_selection)\n", + " \n", + " \n", + " return widgets.VBox([description_out, alternativ, check, feedback_out])\n", + "\n", + "def create_numeric_widget(correct_answer):\n", + " \n", + " #correct_answer_index = options.index(correct_answer)\n", + " \n", + " #radio_options = [(words, i) for i, words in enumerate(options)]\n", + " alternativ = widgets.Text(\n", + " #options = radio_options,\n", + " #description = '',\n", + " disabled = False\n", + " )\n", + " \n", + " description_out = widgets.Output()\n", + "# with description_out:\n", + "# print(description)\n", + " \n", + " feedback_out = widgets.Output()\n", + "\n", + " def check_selection(b):\n", + " try:\n", + " a = int(alternativ.value)\n", + " except ValueError:\n", + " a = float(alternativ.value)\n", + " if a==correct_answer:\n", + " s = widgets.HTML('
Correct!
')\n", + " else:\n", + " s = widgets.HTML('
Try Again!
')\n", + " with feedback_out:\n", + " clear_output()\n", + " display(s)\n", + " return\n", + " \n", + " check = widgets.Button(description=\"submit\")\n", + " check.on_click(check_selection)\n", + " \n", + " \n", + " return widgets.VBox([description_out, alternativ, check, feedback_out])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "dab1e034", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "
\n", + " Classification\n", + "
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%html\n", + "\n", + "\n", + "\n", + " \n", + "
\n", + " Classification\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "1eb61592", + "metadata": {}, + "source": [ + "## Random Forest" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8e5bc7f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " 10 mins\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%html\n", + "\n", + " 10 mins" + ] + }, + { + "cell_type": "markdown", + "id": "989fd8da", + "metadata": {}, + "source": [ + "### Table of Contents\n", + "\n", + "- [Ingesting the data](#h1_cell)\n", + "- [Data exploration](#h2_cell)\n", + "- [Applying Random Forest](#h3_cell)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e359fcc6", + "metadata": {}, + "source": [ + "### Covered in This Module\n", + "\n", + "- Using random forest with VerticaPy\n", + "- The nuances of random forest" + ] + }, + { + "cell_type": "markdown", + "id": "e4eae0c4", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "id": "e91b7444", + "metadata": {}, + "source": [ + "\n", + "### Ingesting the data\n" + ] + }, + { + "cell_type": "markdown", + "id": "2bb6a4cd-feee-4132-9d47-cb7c608d151e", + "metadata": {}, + "source": [ + "This example uses the [Iris](https://r-data.pmagunia.com/dataset/iris) dataset, which contains four parameters for each plant:\n", + "\n", + "- Sepal length\n", + "- Sepal width\n", + "- Petal length\n", + "- Petal width\n", + "\n", + "Each plant also has a class (species):\n", + "\n", + "- Setosa\n", + "- Versicolor\n", + "- Virginica\n", + "\n", + "The sepal and petal lengths/widths can be used to train a random forest object to accurately classify plants.\n", + "\n", + "Load the data:\n", + "\n", + "```python\n", + "import verticapy as vp\n", + "data=vp.read_csv('Data/iris_data.csv')\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d89cb1d9-7dd6-41f5-b824-3d1ff9236a3f", + "metadata": {}, + "outputs": [], + "source": [ + "import verticapy as vp\n", + "data=vp.read_csv('Data/iris_data.csv')" + ] + }, + { + "cell_type": "markdown", + "id": "ddcf6eb5-823a-4490-afd9-1e1ec7a919d2", + "metadata": {}, + "source": [ + "Examine the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c6235cdd-0372-4078-8b29-d39e5331d7bb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
123
sepal_length
Numeric(5,2)
123
sepal_width
Numeric(5,2)
123
petal_length
Numeric(5,2)
123
petal_width
Numeric(5,2)
Abc
class
Varchar(30)
14.33.01.10.1Iris-setosa
24.42.91.40.2Iris-setosa
34.43.01.30.2Iris-setosa
44.43.21.30.2Iris-setosa
54.52.31.30.3Iris-setosa
64.63.11.50.2Iris-setosa
74.63.21.40.2Iris-setosa
84.63.41.40.3Iris-setosa
94.63.61.00.2Iris-setosa
104.73.21.30.2Iris-setosa
114.73.21.60.2Iris-setosa
124.83.01.40.1Iris-setosa
134.83.01.40.3Iris-setosa
144.83.11.60.2Iris-setosa
154.83.41.60.2Iris-setosa
164.83.41.90.2Iris-setosa
174.92.43.31.0Iris-versicolor
184.92.54.51.7Iris-virginica
194.93.01.40.2Iris-setosa
204.93.11.50.1Iris-setosa
214.93.11.50.1Iris-setosa
224.93.11.50.1Iris-setosa
235.02.03.51.0Iris-versicolor
245.02.33.31.0Iris-versicolor
255.03.01.60.2Iris-setosa
265.03.21.20.2Iris-setosa
275.03.31.40.2Iris-setosa
285.03.41.50.2Iris-setosa
295.03.41.60.4Iris-setosa
305.03.51.30.3Iris-setosa
315.03.51.60.6Iris-setosa
325.03.61.40.2Iris-setosa
335.12.53.01.1Iris-versicolor
345.13.31.70.5Iris-setosa
355.13.41.50.2Iris-setosa
365.13.51.40.2Iris-setosa
375.13.51.40.3Iris-setosa
385.13.71.50.4Iris-setosa
395.13.81.50.3Iris-setosa
405.13.81.60.2Iris-setosa
415.13.81.90.4Iris-setosa
425.22.73.91.4Iris-versicolor
435.23.41.40.2Iris-setosa
445.23.51.50.2Iris-setosa
455.24.11.50.1Iris-setosa
465.33.71.50.2Iris-setosa
475.43.04.51.5Iris-versicolor
485.43.41.50.4Iris-setosa
495.43.41.70.2Iris-setosa
505.43.71.50.2Iris-setosa
515.43.91.30.4Iris-setosa
525.43.91.70.4Iris-setosa
535.52.34.01.3Iris-versicolor
545.52.43.71.0Iris-versicolor
555.52.43.81.1Iris-versicolor
565.52.54.01.3Iris-versicolor
575.52.64.41.2Iris-versicolor
585.53.51.30.2Iris-setosa
595.54.21.40.2Iris-setosa
605.62.53.91.1Iris-versicolor
615.62.74.21.3Iris-versicolor
625.62.84.92.0Iris-virginica
635.62.93.61.3Iris-versicolor
645.63.04.11.3Iris-versicolor
655.63.04.51.5Iris-versicolor
665.72.55.02.0Iris-virginica
675.72.63.51.0Iris-versicolor
685.72.84.11.3Iris-versicolor
695.72.84.51.3Iris-versicolor
705.72.94.21.3Iris-versicolor
715.73.04.21.2Iris-versicolor
725.73.81.70.3Iris-setosa
735.74.41.50.4Iris-setosa
745.82.64.01.2Iris-versicolor
755.82.73.91.2Iris-versicolor
765.82.74.11.0Iris-versicolor
775.82.75.11.9Iris-virginica
785.82.75.11.9Iris-virginica
795.82.85.12.4Iris-virginica
805.84.01.20.2Iris-setosa
815.93.04.21.5Iris-versicolor
825.93.05.11.8Iris-virginica
835.93.24.81.8Iris-versicolor
846.02.24.01.0Iris-versicolor
856.02.25.01.5Iris-virginica
866.02.75.11.6Iris-versicolor
876.02.94.51.5Iris-versicolor
886.03.04.81.8Iris-virginica
896.03.44.51.6Iris-versicolor
906.12.65.61.4Iris-virginica
916.12.84.01.3Iris-versicolor
926.12.84.71.2Iris-versicolor
936.12.94.71.4Iris-versicolor
946.13.04.61.4Iris-versicolor
956.13.04.91.8Iris-virginica
966.22.24.51.5Iris-versicolor
976.22.84.81.8Iris-virginica
986.22.94.31.3Iris-versicolor
996.23.45.42.3Iris-virginica
1006.32.34.41.3Iris-versicolor
Rows: 1-100 | Columns: 5
" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width \\\\\n", + "1 4.3 3.0 1.1 0.1 \\\\\n", + "2 4.4 2.9 1.4 0.2 \\\\\n", + "3 4.4 3.0 1.3 0.2 \\\\\n", + "4 4.4 3.2 1.3 0.2 \\\\\n", + "5 4.5 2.3 1.3 0.3 \\\\\n", + "6 4.6 3.1 1.5 0.2 \\\\\n", + "7 4.6 3.2 1.4 0.2 \\\\\n", + "8 4.6 3.4 1.4 0.3 \\\\\n", + "9 4.6 3.6 1.0 0.2 \\\\\n", + "10 4.7 3.2 1.3 0.2 \\\\\n", + "11 4.7 3.2 1.6 0.2 \\\\\n", + "12 4.8 3.0 1.4 0.1 \\\\\n", + "13 4.8 3.0 1.4 0.3 \\\\\n", + "14 4.8 3.1 1.6 0.2 \\\\\n", + "15 4.8 3.4 1.6 0.2 \\\\\n", + "16 4.8 3.4 1.9 0.2 \\\\\n", + "17 4.9 2.4 3.3 1.0 \\\\\n", + "18 4.9 2.5 4.5 1.7 \\\\\n", + "19 4.9 3.0 1.4 0.2 \\\\\n", + "20 4.9 3.1 1.5 0.1 \\\\\n", + "21 4.9 3.1 1.5 0.1 \\\\\n", + "22 4.9 3.1 1.5 0.1 \\\\\n", + "23 5.0 2.0 3.5 1.0 \\\\\n", + "24 5.0 2.3 3.3 1.0 \\\\\n", + "25 5.0 3.0 1.6 0.2 \\\\\n", + "26 5.0 3.2 1.2 0.2 \\\\\n", + "27 5.0 3.3 1.4 0.2 \\\\\n", + "28 5.0 3.4 1.5 0.2 \\\\\n", + "29 5.0 3.4 1.6 0.4 \\\\\n", + "30 5.0 3.5 1.3 0.3 \\\\\n", + "31 5.0 3.5 1.6 0.6 \\\\\n", + "32 5.0 3.6 1.4 0.2 \\\\\n", + "33 5.1 2.5 3.0 1.1 \\\\\n", + "34 5.1 3.3 1.7 0.5 \\\\\n", + "35 5.1 3.4 1.5 0.2 \\\\\n", + "36 5.1 3.5 1.4 0.2 \\\\\n", + "37 5.1 3.5 1.4 0.3 \\\\\n", + "38 5.1 3.7 1.5 0.4 \\\\\n", + "39 5.1 3.8 1.5 0.3 \\\\\n", + "40 5.1 3.8 1.6 0.2 \\\\\n", + "41 5.1 3.8 1.9 0.4 \\\\\n", + "42 5.2 2.7 3.9 1.4 \\\\\n", + "43 5.2 3.4 1.4 0.2 \\\\\n", + "44 5.2 3.5 1.5 0.2 \\\\\n", + "45 5.2 4.1 1.5 0.1 \\\\\n", + "46 5.3 3.7 1.5 0.2 \\\\\n", + "47 5.4 3.0 4.5 1.5 \\\\\n", + "48 5.4 3.4 1.5 0.4 \\\\\n", + "49 5.4 3.4 1.7 0.2 \\\\\n", + "50 5.4 3.7 1.5 0.2 \\\\\n", + "51 5.4 3.9 1.3 0.4 \\\\\n", + "52 5.4 3.9 1.7 0.4 \\\\\n", + "53 5.5 2.3 4.0 1.3 \\\\\n", + "54 5.5 2.4 3.7 1.0 \\\\\n", + "55 5.5 2.4 3.8 1.1 \\\\\n", + "56 5.5 2.5 4.0 1.3 \\\\\n", + "57 5.5 2.6 4.4 1.2 \\\\\n", + "58 5.5 3.5 1.3 0.2 \\\\\n", + "59 5.5 4.2 1.4 0.2 \\\\\n", + "60 5.6 2.5 3.9 1.1 \\\\\n", + "61 5.6 2.7 4.2 1.3 \\\\\n", + "62 5.6 2.8 4.9 2.0 \\\\\n", + "63 5.6 2.9 3.6 1.3 \\\\\n", + "64 5.6 3.0 4.1 1.3 \\\\\n", + "65 5.6 3.0 4.5 1.5 \\\\\n", + "66 5.7 2.5 5.0 2.0 \\\\\n", + "67 5.7 2.6 3.5 1.0 \\\\\n", + "68 5.7 2.8 4.1 1.3 \\\\\n", + "69 5.7 2.8 4.5 1.3 \\\\\n", + "70 5.7 2.9 4.2 1.3 \\\\\n", + "71 5.7 3.0 4.2 1.2 \\\\\n", + "72 5.7 3.8 1.7 0.3 \\\\\n", + "73 5.7 4.4 1.5 0.4 \\\\\n", + "74 5.8 2.6 4.0 1.2 \\\\\n", + "75 5.8 2.7 3.9 1.2 \\\\\n", + "76 5.8 2.7 4.1 1.0 \\\\\n", + "77 5.8 2.7 5.1 1.9 \\\\\n", + "78 5.8 2.7 5.1 1.9 \\\\\n", + "79 5.8 2.8 5.1 2.4 \\\\\n", + "80 5.8 4.0 1.2 0.2 \\\\\n", + "81 5.9 3.0 4.2 1.5 \\\\\n", + "82 5.9 3.0 5.1 1.8 \\\\\n", + "83 5.9 3.2 4.8 1.8 \\\\\n", + "84 6.0 2.2 4.0 1.0 \\\\\n", + "85 6.0 2.2 5.0 1.5 \\\\\n", + "86 6.0 2.7 5.1 1.6 \\\\\n", + "87 6.0 2.9 4.5 1.5 \\\\\n", + "88 6.0 3.0 4.8 1.8 \\\\\n", + "89 6.0 3.4 4.5 1.6 \\\\\n", + "90 6.1 2.6 5.6 1.4 \\\\\n", + "91 6.1 2.8 4.0 1.3 \\\\\n", + "92 6.1 2.8 4.7 1.2 \\\\\n", + "93 6.1 2.9 4.7 1.4 \\\\\n", + "94 6.1 3.0 4.6 1.4 \\\\\n", + "95 6.1 3.0 4.9 1.8 \\\\\n", + "96 6.2 2.2 4.5 1.5 \\\\\n", + "97 6.2 2.8 4.8 1.8 \\\\\n", + "98 6.2 2.9 4.3 1.3 \\\\\n", + "99 6.2 3.4 5.4 2.3 \\\\\n", + "100 6.3 2.3 4.4 1.3 \\\\\n", + " class \n", + "1 Iris-setosa \n", + "2 Iris-setosa \n", + "3 Iris-setosa \n", + "4 Iris-setosa \n", + "5 Iris-setosa \n", + "6 Iris-setosa \n", + "7 Iris-setosa \n", + "8 Iris-setosa \n", + "9 Iris-setosa \n", + "10 Iris-setosa \n", + "11 Iris-setosa \n", + "12 Iris-setosa \n", + "13 Iris-setosa \n", + "14 Iris-setosa \n", + "15 Iris-setosa \n", + "16 Iris-setosa \n", + "17 Iris-versicolor \n", + "18 Iris-virginica \n", + "19 Iris-setosa \n", + "20 Iris-setosa \n", + "21 Iris-setosa \n", + "22 Iris-setosa \n", + "23 Iris-versicolor \n", + "24 Iris-versicolor \n", + "25 Iris-setosa \n", + "26 Iris-setosa \n", + "27 Iris-setosa \n", + "28 Iris-setosa \n", + "29 Iris-setosa \n", + "30 Iris-setosa \n", + "31 Iris-setosa \n", + "32 Iris-setosa \n", + "33 Iris-versicolor \n", + "34 Iris-setosa \n", + "35 Iris-setosa \n", + "36 Iris-setosa \n", + "37 Iris-setosa \n", + "38 Iris-setosa \n", + "39 Iris-setosa \n", + "40 Iris-setosa \n", + "41 Iris-setosa \n", + "42 Iris-versicolor \n", + "43 Iris-setosa \n", + "44 Iris-setosa \n", + "45 Iris-setosa \n", + "46 Iris-setosa \n", + "47 Iris-versicolor \n", + "48 Iris-setosa \n", + "49 Iris-setosa \n", + "50 Iris-setosa \n", + "51 Iris-setosa \n", + "52 Iris-setosa \n", + "53 Iris-versicolor \n", + "54 Iris-versicolor \n", + "55 Iris-versicolor \n", + "56 Iris-versicolor \n", + "57 Iris-versicolor \n", + "58 Iris-setosa \n", + "59 Iris-setosa \n", + "60 Iris-versicolor \n", + "61 Iris-versicolor \n", + "62 Iris-virginica \n", + "63 Iris-versicolor \n", + "64 Iris-versicolor \n", + "65 Iris-versicolor \n", + "66 Iris-virginica \n", + "67 Iris-versicolor \n", + "68 Iris-versicolor \n", + "69 Iris-versicolor \n", + "70 Iris-versicolor \n", + "71 Iris-versicolor \n", + "72 Iris-setosa \n", + "73 Iris-setosa \n", + "74 Iris-versicolor \n", + "75 Iris-versicolor \n", + "76 Iris-versicolor \n", + "77 Iris-virginica \n", + "78 Iris-virginica \n", + "79 Iris-virginica \n", + "80 Iris-setosa \n", + "81 Iris-versicolor \n", + "82 Iris-virginica \n", + "83 Iris-versicolor \n", + "84 Iris-versicolor \n", + "85 Iris-virginica \n", + "86 Iris-versicolor \n", + "87 Iris-versicolor \n", + "88 Iris-virginica \n", + "89 Iris-versicolor \n", + "90 Iris-virginica \n", + "91 Iris-versicolor \n", + "92 Iris-versicolor \n", + "93 Iris-versicolor \n", + "94 Iris-versicolor \n", + "95 Iris-virginica \n", + "96 Iris-versicolor \n", + "97 Iris-virginica \n", + "98 Iris-versicolor \n", + "99 Iris-virginica \n", + "100 Iris-versicolor \n", + "Rows: 1-100 | Columns: 5" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "markdown", + "id": "99342433-a8ee-4a26-98d5-2dd26217939f", + "metadata": {}, + "source": [ + "\n", + "### Data Exploration" + ] + }, + { + "cell_type": "markdown", + "id": "e2e28b3e-83e3-4db7-8a35-9674e733371e", + "metadata": {}, + "source": [ + "The next step is to find and handle missing values. You can find missing data with `describe()`, which produces a summary of the dataset:\n", + "\n", + "```python\n", + "data.describe()\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bcc7c637-4dcd-4fe3-8194-daddd881dad2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
count
mean
std
min
approx_25%
approx_50%
approx_75%
max
"sepal_length"1505.843333333333330.8280661279778634.35.15.86.47.9
"sepal_width"1503.0540.4335943113621742.02.83.03.34.4
"petal_length"1503.758666666666671.764420419952261.01.64.355.16.9
"petal_width"1501.198666666666670.7631607417008410.10.31.31.82.5
Rows: 1-4 | Columns: 9
" + ], + "text/plain": [ + " count mean std min \\\\\n", + "\"sepal_length\" 150 5.84333333333333 0.828066127977863 4.3 \\\\\n", + "\"sepal_width\" 150 3.054 0.433594311362174 2.0 \\\\\n", + "\"petal_length\" 150 3.75866666666667 1.76442041995226 1.0 \\\\\n", + "\"petal_width\" 150 1.19866666666667 0.763160741700841 0.1 \\\\\n", + " approx_25% approx_50% approx_75% max \n", + "\"sepal_length\" 5.1 5.8 6.4 7.9 \n", + "\"sepal_width\" 2.8 3.0 3.3 4.4 \n", + "\"petal_length\" 1.6 4.35 5.1 6.9 \n", + "\"petal_width\" 0.3 1.3 1.8 2.5 \n", + "Rows: 1-4 | Columns: 9" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "b770860b-e1a7-4d89-b8fd-b4480ca9d83f", + "metadata": {}, + "source": [ + "The `count` shows that all columns (`sepal_length`, `sepal_width`, `petal_length`, `petal_width`) have the same number of values, so there are no missing values in the dataset.\n", + "\n", + "Next, verify if the data is balanced:\n", + "\n", + "```python\n", + "data[\"class\"].topk()\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "db67ec96-bc39-4bd5-a99b-a5849ba4c188", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
count
percent
Iris-setosa5033.333
Iris-versicolor5033.333
Iris-virginica5033.333
Rows: 1-3 | Columns: 3
" + ], + "text/plain": [ + " count percent \n", + "Iris-setosa 50 33.333 \n", + "Iris-versicolor 50 33.333 \n", + "Iris-virginica 50 33.333 \n", + "Rows: 1-3 | Columns: 3" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"class\"].topk()" + ] + }, + { + "cell_type": "markdown", + "id": "10d74835-98d9-4db0-b9da-4e72032a1b8d", + "metadata": {}, + "source": [ + "The data is perfectly balanced, so there is no need to `balance()` it manually.\n", + "\n", + "The next step is to train the data on the three classes:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "48eb9fd9-3108-4f0e-8193-5ab3884752e0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[, ,\n", + " , ],\n", + " [, ,\n", + " , ],\n", + " [, ,\n", + " , ],\n", + " [,\n", + " ,\n", + " ,\n", + " ]], dtype=object)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data.scatter_matrix()" + ] + }, + { + "cell_type": "markdown", + "id": "f691e3b2", + "metadata": {}, + "source": [ + "
\n", + "Knowledge Check: From graphs above, is there a correlation between `sepal_length` and `sepal_width`?\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4e6614e6", + "metadata": {}, + "outputs": [], + "source": [ + "Q1 = create_multipleChoice_widget(['Yes','No'],'No')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "3daeb0c6", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a75f33395efc45408c1fd4471b9708bc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Output(), RadioButtons(options=(('Yes', 0), ('No', 1)), value=0), Button(description='submit', …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Q1)" + ] + }, + { + "cell_type": "markdown", + "id": "3b946cb8", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "id": "3fd84ef3", + "metadata": { + "tags": [] + }, + "source": [ + "\n", + "### Applying random forest" + ] + }, + { + "cell_type": "markdown", + "id": "7b2424cd-2121-46cd-b749-0e531a8a30e6", + "metadata": {}, + "source": [ + "Import the library and create a random forest model object." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "3db5775e-8623-4394-8c65-753d488d6f1c", + "metadata": {}, + "outputs": [], + "source": [ + "from verticapy.learn.ensemble import RandomForestClassifier\n", + "model = RandomForestClassifier(\"public.RF_iris\")" + ] + }, + { + "cell_type": "markdown", + "id": "f6570975", + "metadata": {}, + "source": [ + "Fit the model with the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "59bd58f8-976d-4c57-abba-b384f0dd3f13", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\n", + "===========\n", + "call_string\n", + "===========\n", + "SELECT rf_classifier('public.RF_iris', '\"public\".\"_verticapy_tmp_view_dbadmin_6157_4587772902_\"', 'class', '\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"' USING PARAMETERS exclude_columns='', ntree=10, mtry=2, sampling_size=0.632, max_depth=5, max_breadth=1000000000, min_leaf_size=1, min_info_gain=0, nbins=32);\n", + "\n", + "=======\n", + "details\n", + "=======\n", + " predictor | type \n", + "------------+----------------\n", + "sepal_length|float or numeric\n", + "sepal_width |float or numeric\n", + "petal_length|float or numeric\n", + "petal_width |float or numeric\n", + "\n", + "\n", + "===============\n", + "Additional Info\n", + "===============\n", + " Name |Value\n", + "------------------+-----\n", + " tree_count | 10 \n", + "rejected_row_count| 0 \n", + "accepted_row_count| 150 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(data, \n", + " [\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"], \n", + " \"class\")" + ] + }, + { + "cell_type": "markdown", + "id": "d6cb4451-cf66-42e1-9604-bec0858dd873", + "metadata": {}, + "source": [ + "Examine which predictors are the most important in building the trees with `features_importance()`:\n", + "\n", + "```python\n", + "model.features_importance()\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "2a0c64d9-b396-4a7e-aba3-ada52b456766", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
importance
sign
petal_width47.231
petal_length32.451
sepal_length15.581
sepal_width4.741
Rows: 1-4 | Columns: 3
" + ], + "text/plain": [ + " importance sign \n", + "petal_width 47.23 1 \n", + "petal_length 32.45 1 \n", + "sepal_length 15.58 1 \n", + "sepal_width 4.74 1 \n", + "Rows: 1-4 | Columns: 3" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model.features_importance()" + ] + }, + { + "cell_type": "markdown", + "id": "b35fb8b4-5f62-4fb5-882e-c918e3984dec", + "metadata": {}, + "source": [ + "`petal_length` seems to be the most important, followed closely by `petal_width`. In contrast, `sepal_width` does not seem to have much impact.\n", + "\n", + "You can also look at the tree with `plot_tree()`:\n", + "\n", + "```python\n", + "model.plot_tree()\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4f68efce-7c23-4166-a221-5ef8b72ccf3c", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3cfcfcc293f04bcebc8d19142e35e26d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Image(value=b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\x02\\x1a\\x00\\x00\\x01v\\x08\\x06\\x00\\x00\\x00(g1\\x9a\\x00\\…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "file = open(\"Figures/iris_tree.PNG\", \"rb\")\n", + "image = file.read()\n", + "\n", + "image_0= widgets.Image(\n", + " value=image,\n", + " format='png',\n", + " width='600px'\n", + " )\n", + "\n", + "display(image_0)" + ] + }, + { + "cell_type": "markdown", + "id": "bcba871c-bf5d-48bb-a0fa-757578bd9b67", + "metadata": {}, + "source": [ + "You can see the different features of the tree, including its depth, number of nodes, etc.\n", + "\n", + "Next, evaluate the quality of the model by scoring it:\n", + "\n", + "```python\n", + "model.report()\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "ac875575-8285-4f4e-badd-0c601b8d810e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Iris-setosa
Iris-versicolor
Iris-virginica
auc1.00.99940000000000020.9994
prc_auc1.00.99885703918722790.998787424969988
accuracy1.00.99333333333333330.9933333333333333
log_loss0.002627919346688290.01956172663113760.0177000815322077
precision1.01.00.9803921568627451
recall1.00.981.0
f1_score1.00.989898989898990.99009900990099
mcc1.00.98503656262240870.985184366143778
informedness1.00.980.99
markedness1.00.99009900990099010.9803921568627452
csi1.00.980.9803921568627451
cutoff0.90.56660.4666
Rows: 1-12 | Columns: 4
" + ], + "text/plain": [ + " Iris-setosa Iris-versicolor Iris-virginica \n", + "auc 1.0 0.9994000000000002 0.9994 \n", + "prc_auc 1.0 0.9988570391872279 0.998787424969988 \n", + "accuracy 1.0 0.9933333333333333 0.9933333333333333 \n", + "log_loss 0.00262791934668829 0.0195617266311376 0.0177000815322077 \n", + "precision 1.0 1.0 0.9803921568627451 \n", + "recall 1.0 0.98 1.0 \n", + "f1_score 1.0 0.98989898989899 0.99009900990099 \n", + "mcc 1.0 0.9850365626224087 0.985184366143778 \n", + "informedness 1.0 0.98 0.99 \n", + "markedness 1.0 0.9900990099009901 0.9803921568627452 \n", + "csi 1.0 0.98 0.9803921568627451 \n", + "cutoff 0.9 0.5666 0.4666 \n", + "Rows: 1-12 | Columns: 4" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.report()" + ] + }, + { + "cell_type": "markdown", + "id": "74ff7f39-749f-4810-9050-74f8ee2c4c66", + "metadata": {}, + "source": [ + "The results show almost perfect results for predicting the type `Iris-setosa`. The other two classes are also predicted very accurately. However, keep in mind that we used this data to train the tree, accurate predictions are expected. A better test is to split the data and to use a test set to check the accuracy.\n", + "\n", + "You can also get a single accuracy metric for all the classes combined with `score()`:\n", + "\n", + "```python\n", + "model.score()\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "fa2dcdcc-742d-4bc4-9b7b-ff15a4afa7ca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.986666666666667" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.score()" + ] + }, + { + "cell_type": "markdown", + "id": "6ef331d7-dbd7-4e9c-9001-b9d5137ea74a", + "metadata": {}, + "source": [ + "This shows the efficacy of random forests towards classifying multi-class problems. " + ] + }, + { + "cell_type": "markdown", + "id": "c02f25ee", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "id": "ca2aa185", + "metadata": {}, + "source": [ + " Author Name: Umar Farooq Ghumman\n", + "
\n", + "Author Contact: umarfarooq.ghumman@vertica.com" + ] + }, + { + "cell_type": "markdown", + "id": "185edfa5", + "metadata": {}, + "source": [ + "### Resources\n", + "\n", + "- [Iris Dataset](https://r-data.pmagunia.com/dataset/r-dataset-package-islr-default)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/Video_1.mp4 b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/Video_1.mp4 new file mode 100644 index 00000000..e69de29b diff --git a/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/basic_tree_1.png b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/basic_tree_1.png new file mode 100644 index 00000000..c5d12006 Binary files /dev/null and b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/basic_tree_1.png differ diff --git a/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/basic_tree_2.png b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/basic_tree_2.png new file mode 100644 index 00000000..72ba7150 Binary files /dev/null and b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/basic_tree_2.png differ diff --git a/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/iris_tree.PNG b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/iris_tree.PNG new file mode 100644 index 00000000..46a94730 Binary files /dev/null and b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/iris_tree.PNG differ diff --git a/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/puzzle.svg.png b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/puzzle.svg.png new file mode 100644 index 00000000..ad8debd8 Binary files /dev/null and b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/puzzle.svg.png differ diff --git a/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/timer.svg.png b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/timer.svg.png new file mode 100644 index 00000000..c0959f9d Binary files /dev/null and b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/timer.svg.png differ diff --git a/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/v2.svg.png b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/v2.svg.png new file mode 100644 index 00000000..a679970a Binary files /dev/null and b/docker-verticapy/enablement/Data Science Essentials/Module - Classification/Figures/v2.svg.png differ diff --git a/docker-verticapy/enablement/VerticaPy-Lesson-Series.md b/docker-verticapy/enablement/VerticaPy-Lesson-Series.md index 777aac90..c8eb369d 100644 --- a/docker-verticapy/enablement/VerticaPy-Lesson-Series.md +++ b/docker-verticapy/enablement/VerticaPy-Lesson-Series.md @@ -138,7 +138,6 @@ For example, the curriculum for the **Data Science Essentials** course is as fol **Data Science Essentials** - _Overview of Data Science_ - - Basic terminology - Datasets - vDataFrame @@ -146,28 +145,26 @@ For example, the curriculum for the **Data Science Essentials** course is as fol - Data types (tabular, unstructured etc.) - Data formats (csv, image, text, etc.) - _Basic data exploration_ + - Descriptive Statistics - Visualizations - - Types of plots + - Types of plots (pie charts, histograms, etc.) - Dimension reduction (TSNE, PCA) - _Basic data preparation_ - Basic operations - Impute - Null or missing values - - Normali ze + - Normalize - Concatenate and Transform - Advanced operations - Outlier detection - Test/Train split -- _Advanced statistics_ - - Hypothesis Testing - - Bootstrapping - - Bayes Rule - _Linear Regression_ - Theory - - Example + - Example Application - Exercise - _Classification_ - - Theory - - Example - - Exercise + - Binary - Logistic Regression + - Binary - Example Application + - Multiclass - Random Forest + - Multiclass- Example Application - _Project_