Project3_MLRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'C:/Users/smail/Downloads/50-Startups.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.08</td>\n",
       "      <td>110594.11</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>Florida</td>\n",
       "      <td>146121.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.96</td>\n",
       "      <td>91790.61</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>California</td>\n",
       "      <td>144259.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.75</td>\n",
       "      <td>127320.38</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>Florida</td>\n",
       "      <td>141585.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.39</td>\n",
       "      <td>135495.07</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>California</td>\n",
       "      <td>134307.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.24</td>\n",
       "      <td>156547.42</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>Florida</td>\n",
       "      <td>132602.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.11</td>\n",
       "      <td>121597.55</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>California</td>\n",
       "      <td>126992.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.16</td>\n",
       "      <td>114175.79</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>Florida</td>\n",
       "      <td>124266.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.86</td>\n",
       "      <td>113867.30</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>California</td>\n",
       "      <td>118474.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>California</td>\n",
       "      <td>107404.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>105733.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.60</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>California</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>Florida</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>California</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.00</td>\n",
       "      <td>116983.80</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>California</td>\n",
       "      <td>14681.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0   165349.20       136897.80        471784.10    New York  192261.83\n",
       "1   162597.70       151377.59        443898.53  California  191792.06\n",
       "2   153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3   144372.41       118671.85        383199.62    New York  182901.99\n",
       "4   142107.34        91391.77        366168.42     Florida  166187.94\n",
       "5   131876.90        99814.71        362861.36    New York  156991.12\n",
       "6   134615.46       147198.87        127716.82  California  156122.51\n",
       "7   130298.13       145530.06        323876.68     Florida  155752.60\n",
       "8   120542.52       148718.95        311613.29    New York  152211.77\n",
       "9   123334.88       108679.17        304981.62  California  149759.96\n",
       "10  101913.08       110594.11        229160.95     Florida  146121.95\n",
       "11  100671.96        91790.61        249744.55  California  144259.40\n",
       "12   93863.75       127320.38        249839.44     Florida  141585.52\n",
       "13   91992.39       135495.07        252664.93  California  134307.35\n",
       "14  119943.24       156547.42        256512.92     Florida  132602.65\n",
       "15  114523.61       122616.84        261776.23    New York  129917.04\n",
       "16   78013.11       121597.55        264346.06  California  126992.93\n",
       "17   94657.16       145077.58        282574.31    New York  125370.37\n",
       "18   91749.16       114175.79        294919.57     Florida  124266.90\n",
       "19   86419.70       153514.11             0.00    New York  122776.86\n",
       "20   76253.86       113867.30        298664.47  California  118474.03\n",
       "21   78389.47       153773.43        299737.29    New York  111313.02\n",
       "22   73994.56       122782.75        303319.26     Florida  110352.25\n",
       "23   67532.53       105751.03        304768.73     Florida  108733.99\n",
       "24   77044.01        99281.34        140574.81    New York  108552.04\n",
       "25   64664.71       139553.16        137962.62  California  107404.34\n",
       "26   75328.87       144135.98        134050.07     Florida  105733.54\n",
       "27   72107.60       127864.55        353183.81    New York  105008.31\n",
       "28   66051.52       182645.56        118148.20     Florida  103282.38\n",
       "29   65605.48       153032.06        107138.38    New York  101004.64\n",
       "30   61994.48       115641.28         91131.24     Florida   99937.59\n",
       "31   61136.38       152701.92         88218.23    New York   97483.56\n",
       "32   63408.86       129219.61         46085.25  California   97427.84\n",
       "33   55493.95       103057.49        214634.81     Florida   96778.92\n",
       "34   46426.07       157693.92        210797.67  California   96712.80\n",
       "35   46014.02        85047.44        205517.64    New York   96479.51\n",
       "36   28663.76       127056.21        201126.82     Florida   90708.19\n",
       "37   44069.95        51283.14        197029.42  California   89949.14\n",
       "38   20229.59        65947.93        185265.10    New York   81229.06\n",
       "39   38558.51        82982.09        174999.30  California   81005.76\n",
       "40   28754.33       118546.05        172795.67  California   78239.91\n",
       "41   27892.92        84710.77        164470.71     Florida   77798.83\n",
       "42   23640.93        96189.63        148001.11  California   71498.49\n",
       "43   15505.73       127382.30         35534.17    New York   69758.98\n",
       "44   22177.74       154806.14         28334.72  California   65200.33\n",
       "45    1000.23       124153.04          1903.93    New York   64926.08\n",
       "46    1315.46       115816.21        297114.46     Florida   49490.75\n",
       "47       0.00       135426.92             0.00  California   42559.73\n",
       "48     542.05        51743.15             0.00    New York   35673.41\n",
       "49       0.00       116983.80         45173.06  California   14681.40"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:,:-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = data.iloc[:,-1:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sol 1 oneHotEncoding but many columns (of num of # values) so expensive -- reduce as much as possible\n",
    "#Sol 2 responseEncoding when dealing with big data\n",
    "#replacing string data with meaningful data \n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencod = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[:,3] = labelencod.fit_transform(X[:,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "onehotencod = OneHotEncoder(categorical_features =[3]) #numb of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\smail\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:368: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\smail\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:390: DeprecationWarning: The 'categorical_features' keyword is deprecated in version 0.20 and will be removed in 0.22. You can use the ColumnTransformer instead.\n",
      "  \"use the ColumnTransformer instead.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "X = onehotencod.fit_transform(X).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 6.1994480e+04,\n",
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       "       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 6.1136380e+04,\n",
       "        1.5270192e+05, 8.8218230e+04],\n",
       "       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 6.3408860e+04,\n",
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       "       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 5.5493950e+04,\n",
       "        1.0305749e+05, 2.1463481e+05],\n",
       "       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 4.6426070e+04,\n",
       "        1.5769392e+05, 2.1079767e+05],\n",
       "       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 4.6014020e+04,\n",
       "        8.5047440e+04, 2.0551764e+05],\n",
       "       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 2.8663760e+04,\n",
       "        1.2705621e+05, 2.0112682e+05],\n",
       "       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 4.4069950e+04,\n",
       "        5.1283140e+04, 1.9702942e+05],\n",
       "       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 2.0229590e+04,\n",
       "        6.5947930e+04, 1.8526510e+05],\n",
       "       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 3.8558510e+04,\n",
       "        8.2982090e+04, 1.7499930e+05],\n",
       "       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 2.8754330e+04,\n",
       "        1.1854605e+05, 1.7279567e+05],\n",
       "       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 2.7892920e+04,\n",
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       "        9.6189630e+04, 1.4800111e+05],\n",
       "       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 1.5505730e+04,\n",
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       "       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 2.2177740e+04,\n",
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       "       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 1.0002300e+03,\n",
       "        1.2415304e+05, 1.9039300e+03],\n",
       "       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 1.3154600e+03,\n",
       "        1.1581621e+05, 2.9711446e+05],\n",
       "       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        1.3542692e+05, 0.0000000e+00],\n",
       "       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 5.4205000e+02,\n",
       "        5.1743150e+04, 0.0000000e+00],\n",
       "       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        1.1698380e+05, 4.5173060e+04]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0000000e+00, 1.0000000e+00, 1.6534920e+05, 1.3689780e+05,\n",
       "        4.7178410e+05],\n",
       "       [0.0000000e+00, 0.0000000e+00, 1.6259770e+05, 1.5137759e+05,\n",
       "        4.4389853e+05],\n",
       "       [1.0000000e+00, 0.0000000e+00, 1.5344151e+05, 1.0114555e+05,\n",
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       "       [0.0000000e+00, 1.0000000e+00, 1.4437241e+05, 1.1867185e+05,\n",
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       "       [1.0000000e+00, 0.0000000e+00, 1.4210734e+05, 9.1391770e+04,\n",
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       "       [0.0000000e+00, 1.0000000e+00, 1.3187690e+05, 9.9814710e+04,\n",
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       "       [0.0000000e+00, 0.0000000e+00, 1.3461546e+05, 1.4719887e+05,\n",
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       "        3.0498162e+05],\n",
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       "        2.2916095e+05],\n",
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       "        2.5266493e+05],\n",
       "       [1.0000000e+00, 0.0000000e+00, 1.1994324e+05, 1.5654742e+05,\n",
       "        2.5651292e+05],\n",
       "       [0.0000000e+00, 1.0000000e+00, 1.1452361e+05, 1.2261684e+05,\n",
       "        2.6177623e+05],\n",
       "       [0.0000000e+00, 0.0000000e+00, 7.8013110e+04, 1.2159755e+05,\n",
       "        2.6434606e+05],\n",
       "       [0.0000000e+00, 1.0000000e+00, 9.4657160e+04, 1.4507758e+05,\n",
       "        2.8257431e+05],\n",
       "       [1.0000000e+00, 0.0000000e+00, 9.1749160e+04, 1.1417579e+05,\n",
       "        2.9491957e+05],\n",
       "       [0.0000000e+00, 1.0000000e+00, 8.6419700e+04, 1.5351411e+05,\n",
       "        0.0000000e+00],\n",
       "       [0.0000000e+00, 0.0000000e+00, 7.6253860e+04, 1.1386730e+05,\n",
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       "       [0.0000000e+00, 1.0000000e+00, 7.8389470e+04, 1.5377343e+05,\n",
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       "       [1.0000000e+00, 0.0000000e+00, 7.3994560e+04, 1.2278275e+05,\n",
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       "       [1.0000000e+00, 0.0000000e+00, 6.7532530e+04, 1.0575103e+05,\n",
       "        3.0476873e+05],\n",
       "       [0.0000000e+00, 1.0000000e+00, 7.7044010e+04, 9.9281340e+04,\n",
       "        1.4057481e+05],\n",
       "       [0.0000000e+00, 0.0000000e+00, 6.4664710e+04, 1.3955316e+05,\n",
       "        1.3796262e+05],\n",
       "       [1.0000000e+00, 0.0000000e+00, 7.5328870e+04, 1.4413598e+05,\n",
       "        1.3405007e+05],\n",
       "       [0.0000000e+00, 1.0000000e+00, 7.2107600e+04, 1.2786455e+05,\n",
       "        3.5318381e+05],\n",
       "       [1.0000000e+00, 0.0000000e+00, 6.6051520e+04, 1.8264556e+05,\n",
       "        1.1814820e+05],\n",
       "       [0.0000000e+00, 1.0000000e+00, 6.5605480e+04, 1.5303206e+05,\n",
       "        1.0713838e+05],\n",
       "       [1.0000000e+00, 0.0000000e+00, 6.1994480e+04, 1.1564128e+05,\n",
       "        9.1131240e+04],\n",
       "       [0.0000000e+00, 1.0000000e+00, 6.1136380e+04, 1.5270192e+05,\n",
       "        8.8218230e+04],\n",
       "       [0.0000000e+00, 0.0000000e+00, 6.3408860e+04, 1.2921961e+05,\n",
       "        4.6085250e+04],\n",
       "       [1.0000000e+00, 0.0000000e+00, 5.5493950e+04, 1.0305749e+05,\n",
       "        2.1463481e+05],\n",
       "       [0.0000000e+00, 0.0000000e+00, 4.6426070e+04, 1.5769392e+05,\n",
       "        2.1079767e+05],\n",
       "       [0.0000000e+00, 1.0000000e+00, 4.6014020e+04, 8.5047440e+04,\n",
       "        2.0551764e+05],\n",
       "       [1.0000000e+00, 0.0000000e+00, 2.8663760e+04, 1.2705621e+05,\n",
       "        2.0112682e+05],\n",
       "       [0.0000000e+00, 0.0000000e+00, 4.4069950e+04, 5.1283140e+04,\n",
       "        1.9702942e+05],\n",
       "       [0.0000000e+00, 1.0000000e+00, 2.0229590e+04, 6.5947930e+04,\n",
       "        1.8526510e+05],\n",
       "       [0.0000000e+00, 0.0000000e+00, 3.8558510e+04, 8.2982090e+04,\n",
       "        1.7499930e+05],\n",
       "       [0.0000000e+00, 0.0000000e+00, 2.8754330e+04, 1.1854605e+05,\n",
       "        1.7279567e+05],\n",
       "       [1.0000000e+00, 0.0000000e+00, 2.7892920e+04, 8.4710770e+04,\n",
       "        1.6447071e+05],\n",
       "       [0.0000000e+00, 0.0000000e+00, 2.3640930e+04, 9.6189630e+04,\n",
       "        1.4800111e+05],\n",
       "       [0.0000000e+00, 1.0000000e+00, 1.5505730e+04, 1.2738230e+05,\n",
       "        3.5534170e+04],\n",
       "       [0.0000000e+00, 0.0000000e+00, 2.2177740e+04, 1.5480614e+05,\n",
       "        2.8334720e+04],\n",
       "       [0.0000000e+00, 1.0000000e+00, 1.0002300e+03, 1.2415304e+05,\n",
       "        1.9039300e+03],\n",
       "       [1.0000000e+00, 0.0000000e+00, 1.3154600e+03, 1.1581621e+05,\n",
       "        2.9711446e+05],\n",
       "       [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 1.3542692e+05,\n",
       "        0.0000000e+00],\n",
       "       [0.0000000e+00, 1.0000000e+00, 5.4205000e+02, 5.1743150e+04,\n",
       "        0.0000000e+00],\n",
       "       [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 1.1698380e+05,\n",
       "        4.5173060e+04]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X[:,1:]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[103015.20159796],\n",
       "       [132582.27760815],\n",
       "       [132447.73845175],\n",
       "       [ 71976.09851258],\n",
       "       [178537.48221056],\n",
       "       [116161.24230166],\n",
       "       [ 67851.69209676],\n",
       "       [ 98791.73374687],\n",
       "       [113969.43533013],\n",
       "       [167921.06569551]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[103282.38],\n",
       "       [144259.4 ],\n",
       "       [146121.95],\n",
       "       [ 77798.83],\n",
       "       [191050.39],\n",
       "       [105008.31],\n",
       "       [ 81229.06],\n",
       "       [ 97483.56],\n",
       "       [110352.25],\n",
       "       [166187.94]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test"
   ]
  }
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