solutions/python_lecture_ai_in_finance_solutions.ipynb

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      "name": "Python Lecture - AI in Finance.ipynb",
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      "name": "python"
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        "# Python Lecture: AI in Finance\n",
        "**Instructor:** Alyona Galyeva\n",
        "\n",
        "In this lecture you will explore how AI can be used in Finance, how to make a simple AI model in Python, what are the consequences of using this model in real life.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "***Learning objectives***\n",
        "\n",
        "After completing this lecture, you will be able to:\n",
        "\n",
        "* explain how AI can be used in Finance\n",
        "* build, train and evaluate a simple AI model in Python\n",
        "* understand and combat consequences of using this AI model in real life\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Prerequisites: \n",
        "* Laptop\n",
        "* Access to the internet\n",
        "* Basic Python knowledge\n"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "# Introduction (5 min)"
      ],
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      "cell_type": "markdown",
      "source": [
        "## About Alyona Galyeva\n",
        "\n",
        "[Alyona Galyeva](https://nl.linkedin.com/in/alyonagalyeva) is a Principal Data & AI Engineer at LINKIT, PyLadies Amsterdam organiser and Microsoft AI MVP, who found joy in building, optimizing and deploying end-to-end AI and Data Engineering Solutions."
      ],
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NNAxkP+rFPqKD/AFQqWgBKfH98UynJ94UAWrnoKkX97HUdyQQKZbybTtNICSc7UCilh/1RqvO+56likURkGgAtvvNT42xKy+tQwyBWJPeo2f8AeblpgOnQh89jUO1sZxV1bhGHz0ySdCu1RQBI3/Hv+FUFbawNTGYmPZVekBpuomTIqqts+7mokldOh4qU3TkYFAE07hE8sVGtsGQEHmqrMWOTTlkdOAaAL8cYhyxNVfOxLvHSoWkdupplAGoRFMMmk/dQjNZmSOlGSetAEkshkbJqwJlEOzvVKigAq1DceWNrciqtFMCzPMJBhRiq1BpKACiiigAooooAKKKBQAUUUUAJRRRQAUUUUAFFFFABRRRQAUUUUAJRRRQAUUUUAFFFFABRRRQAtFJS0AJRS0lABRRRQAtFFFMBaKSloAUUyPqaeKZH95vrQA49abTz1pKBCVIopAKlAoGAFPApKWkAopabmnZoADTKfTSKQCUUUUwCiikoAWkopM0AIaSikoAQ0006m0wGmmmnUUgGUmKdQBTAkjZ16Urkk00ClxQIZzS4p2KKAGbRS0tJQAlNNOppoAjNMNPNMNADaWkpaADFFLRQAUhpaQ0DGGkpxpKAEpTQKD1oAKUUU9FLHA70CNCwiyxmbovT60l7L/AKvELbwhB2HP1rBlcu5JpDIqKXFGaoRes+kn+7UNS2h4f/AHTUeeKBIUCkc8GikbpSGUG606H/AFg+tNbqaWL/AFi/WkM0JfumqKVek6GqKdTQxIlFSr1FRipF60ii7ULj5qsVFIO9AENSR9aZUkfrSAfJ0pIf9YKV+lJF98UAa1LSUUhhRRRQAUUUUAFFFFABRRRQAUUUUAFJS0lABRRS0AJRRS0AJRRRQAUUUUAFFFFACUUUUAFFFFAH/9SxRRRQAUUUUAFFFFABRRRQAUUUUALRSUUALRRRQAUlFFABS0UlAC0lFJkCgBeaCcdaiabbwvNVWeRzzxQBZaZV4FV2ld/YUh2qM1UlmGMCgCQlByx5qk7b2yOlMJyeaOlIBKKWlpgIBS0lLQAUlGaSgBaWkowTQAc0tOHFITQAZpKKWgBtLRSFqAFpKQZNOoAAO5pcimk5pKAHZpRTcU7NABnFMJzSE0lABingU3NLmgB+2gZFMzT92etADqeKYPan0AIyA1DkocGrFIVDjBpAIoDc1IIz2qtgxn2q1FKOhoGOCU8LU2MjNGKkoi20mKlIphoAjIqM1KeKqSt2FCBkbHLU2kpwqiSSMbnxWkq4qjbLlia0gKTGhQKdQBS0hhS0UUDEopaKACiiigApKdSUANpMU6koEFJS0UAJSUtFADaQ040w0AFNNLSUDENUpJMnatTzvsT3NUlHc00JscBSMc8ClALnAqysQXrTETxH5RUk5/cmol4psj7hgmkUUQCBzTwSOlTcVGyZ6UySSIgtkcGr6NuFY2Shz3FaMUmcMKARbpaSlpFBRiiigBpHFItPpi9aAHD71PI9KYetKCelAgNLRQOaYC0ox3o4FIOaAHZzSj5RzSZA6UAFvYUCLMUrJUshSZc9CKq8KPangg9KYrETIcbhUVWd2wE1WyDUlBQBmikkfy19zQAplRG55q0l2h4xisgcnJ71MioOSaZLNTz1NVpLnjAqsSnY1ATk0xClsnNJnnJpyJvOBU4tu5NICruOcipFDt2q0I1XtTqLjKot/wC8alEaL2qSkNICM/KcrU6sGFRU05XlaAEvTiA461SMhSMZ5zVi5lV4tveqocYCvTAtQOGXParP0qmijZtSnK7RYRu9AFjFGwU7cKb856CkAoUDvUgYVDibsKeol6MtMB5cCkLr60FM9RTfKXqTQAu5ScZpcr61EyRL1akCxuCyngUAT/L60fL61AoiPRqlEanoaQDuKVcA80nlmneWTQA9Vg3eppZJsfKlM8sL7UbE9aYEZeQjGaqOCh3Fjmrx2jpVdxvOcUARxiaY4KcetaWPs8fy4DU0ynaFUYqAkMfmJNAiB1aU5kfNAhHUNU2I+wqFnIOyNeaAIkh2Mc8k1bjiIBYjpUiW3llXc8mtBgojP0oAyN8r8A4FGxR1JJpfJlUEgZpsSzlsuuBQBIASOBimlW+tSRwzbsseKt4VR1oGVVFxjC/KKBbueXerJLsMRikWymc5dsUAIsMa/eNWUSEcAZqUWqcFjnFOJiioEQO0adsVTdg/J6VJPKCflGaoubhhhFxSGTJJFu2oKsInmuB6VQt4Jg3z961LL/WMPSgDRAwMUtFFUIY7bRUfmNSO2TTKhsY4knqaTdjpTaKVwJ42LdalqOIYWpKtCAjPWmeWvpT6KYEDhV6VHTpDlqZWbYwooopXAcmd1WcgdaiiHepGUMMVcQFyKMiq4gP96jyD/eqhE7bSOarEAGneQf71NMDjoaQCZFJQUZetJzQAtJRRQAUlLSUAJSUtNoADUchxGafUM5xEaAMGTqagqaSoaYD+1MFSH7tRigBGNNpzjHWmigBaKKKAEJNA5pCMmn9BigBaBim0tAElM4pRSGgA4ozSGkoAXNG6kpKAHZpd3FR0o5IFAHQ2MvkQe5qqzM8pd6mgHCrTZD++IHakBTuzwKpCrV2elVM0wHClpuaM0ALyORU8dyyHBqDNN6mgDaiu0bg1aHlyDsa5sipUkkT7poA2mgSMF14rD6sx96sm7kZNhqsOBQAUlKaSgApKWkoAKKKKACiiigApaKSgAooooAKKKKACiiigApaKKACtjR7fzJjO3ROn1NY4BJwK7WztxbW6xd+p+poAs0UUUAFFFFABRRQTgZNAFedv4RVanMSxJPem1Ixayr//AFZrVrKvuYzVIlkFv/qhUtQ23+pFTUDEpc4opKAHFi3WmjiikoAXrSUUUAFFFFACUtJRQAUUlFABRRRQAUUUYoAKSlpKACinbGxuxxTaACiipIkDuFNICOip54xG2BUFMApKWkoAKKKKACnKjNyozTa0bQDy2pAZ2MUuaV/vn600UwFqZ4WRQ5qEda0rjmAUgMyikopgGKKKKAClpM0UAFFLSUAFFLikoASiil5oAKSiigAopcUlABRS0UAJSmilNACUUUUALiikzRQAtJRRTAWikooEOHWo0+8akpgGHNADj1pQKMc1IooGAFSAUYopAFFJRQAuaUU0U9cUAPC5pStPWnYqLgViKbUzYqI4qkA2ilpKYCU00pppoAM03NLSYNMBKaacQabg0AJmkzSGk5oAXNKDTKcKAJFoqVI+9NZNtAhlFJSUALRRSUwEpKWkpAMIqM1KajIoAbRS0UAJS0hoFAC0lFJQMSilpKAAdaKUCigAFadhDlzK3Rf51nqpJwK3tot4AnoMn+tIRRvpf4RWVmpJpC7k1DTQMWm0tJTEXrTo/wBKiqW1+630qPtQACkbpS0h6UhlFutEf3x9aG60iffH1pDNGTpVJepq61Ul+8abEiYVIKYKeKkov54qN+RUoXIFIyHFICtUsfSmYqdF4FADH6UkX3xUjrhaZH98UAatFHaikMKWiigBKKWkoAKKKKACiiigAoopKACloooASlpKKACiiigAooooAKKKKAEopaSgAooooAKKKKAP/9WxRRRQAUUUUAFFFFABRRRQAUUUUAFFFLQAUlFFACUtFIWC0ALikOB1NMLk9KTZu+9QA0yjovNMKyP14qb5EHyimE45NADNu360wlVGWNNZ+9Z8spY4oAfPNu4WqvPeijrQAhoxS4ozQAtJRiloACfSm0tJQACjGTSilzigBQMClzTKU0ALRTc0uaAFzSdqTNITQApNJSCjNAC5ozTaKAFooxS7aAEyaOTTwBS0AR7TTtlLg0tADdgpdgpdpoKtQAmwetGz3oKmja1AChWHSn8iovmFG9u9AE3NO96jEnHNPVgaQDiAw5qAqUOR0qejgjFAE1vNu+U1bxWSylTuWr1vMJBtPWkykyU1CancYqsxpDGOeKpMcmp5W4qvTQmGKDRUscZY0xF63TCVbAqNBgCpRUlIUUUUtAxKKKKACilooASilooAKSlpKACkpaSgQUlLRQAlJS000ANJplONNoGFKBRSigDOuW3SbfSozwKSTmU08j5lFUiGWoU2Jk9TTycc0/HAFV7ltqADvQURPMTwtRgMafCm8/SrAAHSgRX5Uc1IpzUzKHXBqohK5X0NAD5kym4dqSB/lxVlRlSPUVRjGGYUAzYjOVzT6htzmOp6RSEpaKKACmjhqdTT1oAU0mTSmnbcAE96AD3ozS9ODSdKBAMd6M54FGM07IUUAKq92pzOBUJcnpQCMcdaYh/L9eBU4G1eKqjJNTbjjAoAJM5xUY4kA7U9TlsNSrgMc0AOSHLN7VSMiySFGHK1pwE4JqjfxeUvnoOT1oEVmXbTMimQ3Q3gyjitaS2SRfNXpQIzKcCB1FWRCd3A4qz9nyKBmbuwflqaOVy201K1viq5jdTkUAXKSoY2fOGqUlR1NKwDqTFRGZBULXJ6KKdmK5ZIpCyryapmWVvaojG7H5jT5GLmC4MZO5TzUH3hT3jCDNQjdu46U+WwJkkLur4PStINHKN1Z2MGrqxbkAU1JRbCKRk0xsL0akMUhXaDSiFRy9IZIWKgDNIRK3Q0phR+TTwgXoaBEBimPeozbSnq1WXkCDrVZrtlGcUDG/Yc/eNWFtVVNmeDVdbi5k+4tWTHdeXkjmgQ0WkQqQJEneq6m4PBFWFQ9WFACNcRp71A945/1aGrmwelMZ4k64oAzw11K4LDAqWSGd2ypwKlW6V32JTDeKGKmgBohlHVqkVHXqaBOrdqkUK/zGgCJplQ8nNV5LvjCA1f2xntSSGONckUAQQl3QAfeNakFsEHzcvUMQEMIkxy1AZwd+eaYE8sTj527VBHIGf94eO1RyPLJwTxUHknOaANCSb+FOaom6cMRtqVPl61IApPSkBBHNPKcKprQjtWIzJ1p8ZUdKtbgBTAqyK0K5BqrFcTySbcYFXHmh6t2qubyIAkYwKAIZbm43EKpxUG+eQ8rSvfRE9aj+2R9jQBZCkDnrUMkwjHJqIypJ1akMEPVuaQFiKYupftV21ZQd/rVKMRhMDpUscgP7uPmgDZBB6Uh6VlCeSM7TVlZt4o5gHHrSUUVABRRRSAtJ92nVFG2Ripa1QgooopgU2+8aSpJFwc1HWTGFFFFICzH92pKgibtU9aIQ05HIoVg3SnVXdWX50qgLFFQxyq/HepqAEZQwxVVlKmrdNZQwwaAKlIaeyFTTKQCUlKRTaACkpabQAlQXJxEanqrdn91QBiPUQqR6aOtMB56UsSEnNLip0wooApz/fxTMUsh3SE0zNAC0lFFACilNJTuooAbS0Gm0APpKKMGgApdtGDS0AJgUcUZpKADFNVTvFOp8fMgoA24OGHsKhf/WE1IhwajPU0gM66PzAVWNT3By9QUwAGlptOoAM0UlLQAZpQaSlxQA4UUuMUUAJSUppKACkpaSgAooooAKKKKACiiigAooooAKKKKACiiigApaSigDU0q38658xh8sfP49q6uqOn2/2a2VT95vmP41eoAKKKKACiiigAqGZsDb61NVF23MTSYDaKKKQwrLvv9W1alZd79w1SEyrbf6qp6r2v+qqxQAlFFKOtABtbGcU2tUoGirLYYOKAEqVImcZFRd61Yl2x4pAZRGOtJT5PvmmgE9KYDaWlKsOoptABRUqRO3IFI0TqeRSAFiZxkVH0rTgjYRkGqckLoCxoAgrRVFeDis2r9o2coaAKJGDilUZIFSTrtkNLbrukHtTAtz4SELWbV66O5wgp6wRIMvSAzqnt/wDWirhhikX5KqRKVmANAEl598VTxV66HzrS3KKsQIFAGfS4OM44orRdR9nB9qAM4Ak4FTC3lIzipLd0TJbrTzeNngcUAVHRkOGFXrP7jU+YLLDvFMsujCgCAW7yOT0Gae1mwGQc06ecqdiUyC4YPhjkGgCqQQcHrWjP/wAe4qK7QAhx3qWb/j2FAGeiNI21avi3hQfOaLVQqGSqchkdiSDQBaktUK7o6zyMcVoWm8Eq3Sqtwu2UigCCiiimAUUUUALiijNJQAUUlFAC0tNooAWikzRmgAoopaAClxxQBk1YCDFMRVop7DBpvFABRQKWkMSilzRTATFLiiigQuKnkUbEPrmq9WW5iT8aQxgUU+kHSlNABmkJpKaTQAuaKSigB1OBpgpaALKtTi1Qj7palx8u6psAMajp+PlyajIIqgDNNJoHPSjafSgBtJTipHWl2NjNAEdJmpNjEZFM2knb3pgMJppNTGIgE8cU0REgMTgUAQ5pCSDUvllZVFKV8x2JIHOKAIMmnq1PMBDBQfrStGAhZTnFAi2jAimSMKhiBbPOAOtPcKULA9KdxWIcijipiiLjcTz2pBH85HYUhjFQscCmkGrSlAGK9h1qCMBmwaAI6SrCKNpZvoKdhVIXGSaAK6J5jbahZdpI9K0EwsjEdhUDqvlA45NAFTFGKveUp2LjGRk08hQGDABe3rQBnEcU3FW7fG/B5yKlRPLQ/wB5jtFAFHaT0pu0mr7SFHEUYwB+tSbQjuw7CgDNCk9KcImPQE1biGyF2/CnSSNHEiL1IpDKAFJin44oCknimBesId0nmEcL/OnX03G0HrVwAW1uF74yfrWBNIXck0gIs0lFFUIKSiigC7a9G+lR0+17j2plAgoPSimmgCm3Wmr94U5utNHWpKNFulU1+8att0qqPvmmxImWpFqMVKtSUaaDgVJjjFMT7oqUUgKBXnFXVUBRUDr+8q0OlAEEq/LmoE+8KtS/cqsvUUDNIdKKQdKdSASiiigAooooAKKKKACilpKACkpaKACiiigBKKKWgBKKKKACiiigAooooAKKKKAEpaKSgAooooA//9axRRRSAKKKKACiiimAUUUUAFLSUtABRRSUALTc0jEngUoUDrQA3k0m3mnk02kAcDik5oprN2FADWbbxUTA4y1SYHU1BK+1SxoApzTH7i1UzQSWOfWlFACYzSgYpaQ0wEJoAoAooAKWgCloASijNIOtADqSlNJQAUlGaSgBaKM0maAEoopwU0ANp22nAU6gBm0Uop+KBigBuKXbT6KAGgUuKXFOpANoxS0tABRRRQAUUYoxQAcUYFLgUYoAYUU1GYiOlTbe9LzQBEHZeDUgYZyKMA9absHagB9R8xtvWngGgjPFAFxXEiZqF6gjYxNz0NXGXcMikUigwJpoQ1dMVSLCB1oCxUS3J5NXY4woqUDAxS4ouOwAU+kpaQBS0UUDCiiigAooooAKKKSgBaSiigApKWkoAKKKKAENRk080w0ANNJS0lAC0optLQBlScSn61Kw+6aS5Xa+71oX54/pVEGgOQDVO76rU8TbkHrUdwhZM+lIoLfjNOc7eaihcdat7VYc0xIiMqBc5qmuWckd6na2GeDUiRheBSGSLwpPtWeh+djV2dwke0dTVFOFJpiZqWv+qqxUUAxEKmpFIKKSloAKaadTTQAHkVOfntgR1FQCnoxClaABTux604gZqMcdKfy3SgQjMAOOtV2cDlz+FPzhsd6qyxkvmgBTKzcDgVLGGpY4C3WrQULxTEIF29aaz7etSE5qN130AQmTJ+XrVyNMDfKahQJGeBzT5CZBQI0ONodelDoJoyjd6r2pKptfpTmc52oelAjm5ITHMYz2NadtcD7hPAqG4ZHul2/jVK4QpNxxmmlcDpRLDjqKY13EvGaxoo8jk1YCKO1X7MjmLTXydFGartcSN0XFPUKO1P4p8qFzMqkyt14qMqe5q0xFQMydzT0DUiwBRu9KC8f1pPMHYU7hYXcxo570wyGmb6QWHSkYqFSByKGbPWowcVMikS53VNFMYz83SqsZ5q4IPNwfSsyi+0xCAr3pCJWwT0pvk7gOelS7/mCZpFDGE+fl6U9BN/HSmYA4qQMx+7zQA0rnqKURo3Wp1jPVzipPKhI+9QIaLiCEYqSS7Cp061CbS3Y5LZqeSKCRQD2oAqCbigMzdKjMEQPBNPAVBxQArBmGCcVXNrG33uae8yjtmq7Tv/CpNAE6W8UZyvFKI4QegqG2SZt0kgwKpm4fcRtNAGoXiHAFKCSMhar2iSzvkrjFbEitGmQRQBkmS4JwiVC8NwWUy9CavfaGHUgVWknMsigHgGgC9dxswVEOMCoUQx/eOabdXSrKATVRroH7oJpgaDEdah8xs4QZqGFppjtUYrRW1cDLMBQBDHBJIcyHAqcQ7XxnIpslrOfuPVmKJkT94cmgCwsSIOBTHlReDTllQnbms6/jOd6nFAitPAszE5xUf2ZNmzPFQZI6uTUqsB6mgYCygHanfZIR2p2WbpUbJJ6mkBKscEQzxUE8wbCoM0nlSN2zTNkqnAAFACtlht6CrNmfJbavOaoSAkbc81o2sYRd/U0AWpE3nd3quMqasbqz7h3V969KTQGlHKD8rdanK4rIt7lHcbuCK1PPhP8AEKkY6im+dF/eFMaaP+8KQiYNjmnicDrVHzU/vUhlj9ad2Bo/aEpfPj9azPMi9RRvi9afMwNJpI2HWoCV7GqgeL1p4eP1pNgWNwo3CoQ0frRuj9RSGS7gOc1Zjk38VURY2OSwq2rRKMAiriImoqEzxL1YVVmv4kGEOTViH3OyIeZnBpLa8jm+XPNY80jzHc5p1rAFlVkOT3pAdJRR2opgIygiqrKVNW6RgCKAKVIRUjKVNMpAR0lSEVGaAEqleH5QKu1nXh6CgDJfrQvWhutKnWmBKTiml+MCkc1HQBEetFL3ooAQUtJS0AJThxRR3oAeMGkOO1JnFGaAE5o3UUhFAC7jS5ptGaAHUlJmjNAC1Lb8y1DVi15kNAGuoOM1C3U1a3DZiqTHhjSAy5TlzTKc3LE0lMBBS7c0tAoATbilxTjSUAJSdelLTsYoAKWikoASiikoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACtDTbf7RcjI+VPmNZ9dbpdv5FsHYfNJyfp2oA0qKKKACiiigAooooAimbC49aqU+RtzZplIYUUUUgErOvPuGtGs68+41UhMo2n+rqzVWz/ANWfrVo0AJQOtFA60Aaqn5RVG4Xa+fWrTHEQNMmG+PcKQFSJdzgVpg87faqdsvJapom3SNQBUZC0pUetWz5cC80yIfviar3LEyfSgC2kkc3y1XMIEwXsagjOHBq9MQpV6ACWXycKBVd7ncBxzVp0SdciqEsLR9elAGhDKXjJ9Koyzs+VNWbX/VmqD/eP1oAbU0DbJAagpRwc0wL12vRxRZrgFzUx/ewZ9qRf3cGaQFGVsyFqP3kvvSRr5kmDV+SRIBtA5oAbbRuh+boaibi5qaCdpHx2qKT/AI+RQA67+8tPuf8AUim3XUU+fmAUAZlabf8AHt+FZlahGLbB9KAM+OJpTgVcW1jXlzToPkg3DrWezux5NAGqQohIXpVez/iqZFIt8H0qCy6tQBUl/wBYaan3hU1whWQnsaSCMvIPQUwLl3/qxRJ/x7D6VHeOOEFSvzaj6UgHW5AgzUf2qIdqS0cFTGarTQOrHAyKALH2xR0WqcsnmPup8ds79eBULLtYj0pgNooooAKKKKACikpKAFooooAKKKKAEpaKKAClzSUUAKDg1YD8VWpQaBCscmm0UUwFpKKWgYlLRRQIKKKWgAqxn90n1NV6lA+QN70AOBpM0maM0hgTSUUlAC0UlLTAWnU3NGaAJ0GUYUvUlR2FRLJsB460ivtOaQEwxux2UU0sMHnOahDkHIpxbPGMUAPj4yfQUq/dLGogxAI9aUMRQA8EEgUwkluaQsSc+lBYnigBz5L4FOJ5YjrjFR729aRccnOKAFwViYnvTJc4VPalkcFQoOfU1XLEnJNMRa48/HZRTEUgGUjJPQVVLHrnrSFm9aALcZIL7vvGo5C+0BgAPQVWyetOye9AFqP5oiq9c09sCMID35qqKd2oAnchph6CnbgzOM9aq0UAWMqsZUHJNRIdrg02igCZ5BuAXoKfujD785qrS0AT712v6t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      ],
      "metadata": {
        "id": "V4qmVIY2oQFl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## About PyLadies Amsterdam\n",
        "\n",
        "[PyLadies Amsterdam](https://amsterdam.pyladies.com/) is a local chapter of a global non-profit organization PyLadies.\n",
        "\n",
        "We are acting on diversity in technology by organizing coding events tailored by level and topic so Python programmers can do what they love: program and help peers to write cleaner and better code.\n",
        "\n",
        "We make sure the audience and stage contains at least 50% women."
      ],
      "metadata": {
        "id": "LFJ4b607orYM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# AI in Finance: Theory (20 min)"
      ],
      "metadata": {
        "id": "phD4L3TPao-R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## What differentiates financial sector in using AI\n",
        "\n",
        "The challenges provided by an increasing adoption of AI are not exclusive to the financial sector. Still, from an AI-perspective finance differs from other industries in a number of important ways that warrant an adequate regulatory and supervisory response.\n",
        "\n",
        "One such difference is that ***the financial sector is commonly held to a higher societal standard*** than many other industries, as trust in financial\n",
        "institutions is considered essential for an effective financial system. Furthermore, many consumers regard financial institutions like banks, insurance\n",
        "companies, and pension funds ***to be providers of a utility function***. The data that these companies extract from their customers are therefore regarded as a by-product of this function, instead of a commercial asset for the financial sector to monetise. Even data usage that is acceptable from a strictly legalistic perspective can result in material reputational damage if it does not correspond to the expectations of society at large.\n",
        "\n",
        "Another aspect that makes finance special is ***the potential impact of AI on financial stability***. The financial system is not impervious to shocks. As\n",
        "experienced in the global financial crisis, problems can propagate through the system rapidly. When AI increasingly manages financial transactions, relatively small incidents could have major consequences. Given the increasing importance\n",
        "of tech giants in providing AI-related services and infrastructure, the concept of systemic importance may also need to be extended to include these companies at some point.\n",
        "\n",
        "Given the inherent interconnectivity of the financial system, ***the rise of AI has a strong international dimension***. An adequate policy response will require close international cooperation and clear minimum standards and guidelines for the sector to adhere to. Regulatory arbitrage in the area of AI could have dangerous consequences and should be prevented where possible.\n",
        "\n",
        "Finance can also be different from other industries as many financial services have ***a very specific data environment***. Because\n",
        "of cultural and legal differences data are, in many cases, only representative for domestic markets. Good examples of this are data pertaining to credit, pensions and mortgages. This may provide a challenge for the development of data-hungry AI systems, especially for relatively small markets as that of the Netherlands. Furthermore, because of ***continuous changes to the financial regulatory framework***, historic data quickly becomes less representative and unusable for training AI-enabled systems.\n",
        "\n",
        "The progress of AI in beating humans in increasingly difficult games should also give us pause. In its design, the stock market is set up as a perfect\n",
        "information game, in which all participants have access to the same information.Banking and insurance can arguably be regarded as imperfect information games: customers have information regarding their financial position and risk profile\n",
        "that is unavailable to banks and insurance companies. AIs like AlphaGo and Liberatus are making huge strides in ***beating humans in both perfect and\n",
        "imperfect information games***. Now that computer performance is progressing to the point where it equals the raw performance capabilities of the human brain, we should consider the possibility that at some point – perhaps sooner than we think – AIs could surpass human agents in trading, banking and insurance as well.\n",
        "\n",
        "Finally, the increasing importance of AI for the financial sector also invites us ***to rethink our traditional supervisory paradigms***. When it comes to AI, strong operational controls could be more important than capital and liquidity buffers. We will increasingly come across business models that are driven primarily by the monetization of data, rather than by product fees and interest margins."
      ],
      "metadata": {
        "id": "4lJCqUkCcFs5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Current adoption of AI in Finance\n",
        "\n",
        "AI adoption in the financial sector already is quite widespread, even though we might not always be aware of its presence. \n",
        "\n",
        "**Customer interaction**\n",
        "\n",
        "AI-enabled chatbots that use natural language processing techniques to provide a\n",
        "24/7 first line of customer interaction. Intelligent chatbots can save costs dramatically as they reduce the need for customer service call centres. The Dutch insurance company ***ASR***’s chatbot-based website is a typical example of such an intelligent chatbot. Increasingly, companies opt to integrate\n",
        "their chatbots into existing platforms such as Facebook Messenger or WhatsApp, as well as speech-enabled platforms such as Google Assistant, Apple Siri and Amazon Alexa.\n",
        "\n",
        "**Client oboarding**\n",
        "\n",
        "Nowadays, when we want to open a new bank account, we are likely to encounter AI during the onboarding process. ***Rabobank***, for instance, uses UK-based iProov’s deep-learning driven ‘Face Verifier’ to match a selfie with the face image from the customer’s identity document, including liveness check. Rabobank uses this service in conjunction with ‘ReadID’ to verify the ID document’s authenticity, which also uses AI techniques such as optical character recognition.\n",
        "\n",
        "**Account information services**\n",
        "\n",
        "AI is also commonly used in account information services, such as ***ABN AMRO***’s ‘Grip’ app. Grip uses machine learning to automatically classify incoming and outgoing transactions and assigns them to categories that are relevant to the app’s user. The app also provides insight into possible saving\n",
        "opportunities as well as more general budgeting services. With the recent implementation of PSD2 more such apps by third-party service providers are\n",
        "expected to be introduced. \n",
        "\n",
        "**Fraud detection**\n",
        "\n",
        "Dutch fintech company ***FRISS*** is an AI-enabled service provider that is used by various Dutch insurance companies for fraud detection in claims\n",
        "management. Their system uses expert knowledge, proprietary data, and external data sources to provide real time risk scores. FRISS also offers AI-driven risk assessment at underwriting and CDD solutions for onboarding new clients.\n",
        "\n",
        "**Investor services**\n",
        "\n",
        "***ING***’s ‘Katana’ tool helps bond traders with their pricing decisions. Katana employs predictive analytics, based on historic and real-time data,\n",
        "to help traders decide what price to quote when buying and selling bonds for their clients. This tool is a typical example of how AI can be used for\n",
        "augmentation (rather than for automation) as it analyses and visualises relevant information for the user to decide on.\n",
        "\n",
        "**Transaction monitoring**\n",
        "\n",
        "***BusinessForensics***’ HQ Craft is an example of an AI-driven solution for real-time AML transaction monitoring. This big data processing platform uses\n",
        "data analysis of network transactions, forensic analysis, fraud management and machine learning. This Netherlands-based company provides its services to various Dutch banks and insurance companies.\n",
        "\n",
        "**Car insurance**\n",
        "\n",
        "***Fairzekering*** is a Dutch AI-enabled car insurance product. The insurance company analyses their customers’ driving behaviour based on the data collected by a smart module ‘Chipin’ that is installed in their customers’ cars. Depending on your risk score, you get a 0-35% discount on your monthly rate. Customers also have access to a dashboard that provides them with additional insights in their driving behaviour.\n",
        "\n",
        "**Document analysis**\n",
        "\n",
        "***JPMorgan Chase*** developed an AI-driven Contract Intelligence platform called ‘COiN’ to analyse legal documents and extract important data points and\n",
        "clauses. According to JPMorgan, the system was used to review 12,000 annual commercial credit agreements, which would normally take 360,000 hours to review manually. A number of Dutch banks and insurance companies have indicated that\n",
        "they are also implementing automated solutions to analyse documents.\n",
        "\n",
        "**Customer relationship management**\n",
        "\n",
        "***Salesforce*** is an AI-enabled CRM platform that offers companies a wide variety of tools to support their client interaction. One of their products, ‘Einstein’, that now is a part of Tableau CRM, uses AI to provide lead scoring, activity capture, opportunity insights, and account insights services. Various Dutch financial firms are among Salesforce’s customers.\n",
        "\n",
        "**Portfolio management and risk analytics**\n",
        "\n",
        "***BlackRock***’s ‘Aladdin’ is an example of a financial platform that increasingly applies AI. Aladdin is a widely used operating system that combines AI with sophisticated risk analytics, comprehensive portfolio\n",
        "management, trading execution and investment operations tools on a single platform. Today, Aladdin is used to manage an estimated 18 trillion US dollars\n",
        "in assets globally. BlackRock’s platform is used by numerous Dutch financial institutions, including major banks and pension funds.\n"
      ],
      "metadata": {
        "id": "TBwjvmVQQL65"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## What's next for AI in Finance\n",
        "\n",
        "The latest report of the World Economic Forum (WEF) titled “The Future of AI and Automation in Financial Services” provides an insightful forward-looking perspective. Although it is hard to predict how AI will exactly shape the\n",
        "financial sector landscape in years to come, the examples provided by the WEF report illustrate the wide variety of potential innovations nicely. \n",
        "\n",
        "**Deposits and lending**\n",
        "\n",
        "The following use cases aim to solve the core customer need of frictionless access to funding and secure movement of data, while also creating a compelling business case for banks of the future:\n",
        "\n",
        "* ***Just-in-time lending***: Tailored small to mid-sized business lending products and advice provided just when the customer's funding needs arise; offered by institutions that factor multiple data sources into a continuous risk analysis and pricing engine.\n",
        "\n",
        "* ***Trusted data steward***: A financial institution (or syndicate of institutions) serves as a central consent management platform that links sources of customer identity attributes (i.e. data) to users of that data (e.g. service providers) and uses Al to scan and \"police\" the third parties misusing client data.\n",
        "\n",
        "**Investment management**\n",
        "\n",
        "The following use cases will help investment managers provide more tailored financial planning and outcomes to customers (both consumers and businesses), while also generating new sources of alpha:\n",
        "\n",
        "* ***Outcomes-based investment products***: Customers are offered the opportunity to purchase investment products that guarantee specific life experiences or outcomes; these products are structured as tokenized contracts that could subsequently be traded on a secondary market.\n",
        "\n",
        "* ***Verifiable impact investing***: A service that provides SMEs the ability to issue investment tokens, directly linked to verified financial and operational data, in order to establish credibility and raise funding from investors who gain access to new and trusted sources of alpha.\n",
        "\n",
        "**Payments**\n",
        "\n",
        "As payments globally move to more real-time, frictionless rails, the next evolution of payments will see increased automation (which benefits the institutions involved) and an enhanced, data-rich purchasing experience (desired by customers)\n",
        "\n",
        "* ***Machine-to-machine (M2M) payment protocol***: A payment network, or consortium of payment networks, banks, wallet providers and other financial institutions, develops a protocol through which asynchronous M2M payments are decisioned, authenticated and transmitted/received on behalf of consumers.\n",
        "\n",
        "* ***Augmented purchases***: A frictionless shopping experience (delivered through AR) providing contextual access to product data and seamless transaction execution, whether the customer is browsing the product virtually or physically\n",
        "\n",
        "**Insurance**\n",
        "\n",
        "Customers are demanding more transparent, digital, and personalized insurance experiences; the following two use cases address these customer needs and are feasible given technology development\n",
        "\n",
        "* ***Dynamic life + health insurance***: An integrated life and health product that proactively serves the customer by collecting real-time data as they engage in everyday activities; this improves the insurance company's risk analysis, allowing for dynamically priced products and personalized customer rewards.\n",
        "\n",
        "* ***Connected post-claims experience***: A post-claims experience created through a connected ecosystem of product/service providers that creates a differentiated claim-handling process to drive customer loyalty and increase back-office efficiency.\n",
        "\n",
        "**Capital markets**\n",
        "\n",
        "The following use cases support capital markets players in their efforts to adopt more data-driven approaches to investing and client service, as well as to improve optimization across different fundamental calculations:\n",
        "\n",
        "* ***Cross bank collateral optimization***: The ability to rapidly allocate collateral in the most efficient way across the entire institution to minimize cost of delivery and transformation costs, using quantum computing and Al.\n",
        "\n",
        "* ***Quantum trade optimization***: Applying quantum computing to supercharge the optimization of trading strategies and asset allocation, minimizing the cost and market impact of trading.\n",
        "\n",
        "* ***Data-linked green bonds***: Green bonus that fund sustainable projects linked to trusted sources of data that help monitor sustainability outcomes and can trigger covenant events.\n",
        "\n",
        "**Market infrastructure**\n",
        "\n",
        "The following use cases were prioritized as significant infrastructural changes expected over the next decade, the use cases would help solve common points of inefficiency for a large number of industry participants:\n",
        "\n",
        "* ***Global fund transfer network***: A DLT fund transfer network that optimizes and improves the speed and transparency of the flow of high-value fund transfers across jurisdictions.\n",
        "\n",
        "* ***Global corporate actions ledger***: A single, permissioned ledger where data on mandatory and voluntary corporate actions are published in machine-readable formats to drive consistency and reduce intermediation.\n",
        "\n",
        "* ***Distributed KYC/AML utility***: A platform that simplifies customer onboarding and continuously monitors transactions, using the combined data and analytical capabilities of all institutions to reduce duplication of effort\n",
        "\n",
        "It is hard to predict whether and how these global trends described by the WEF and other reports will materialise in Europe and in the Netherlands. To a large extent, this will also depend on future regulatory responses. \n",
        "\n",
        "The Payment Service Directive (PSD2), for instance, is expected to enable a number of AI-driven innovations, most notably in deposits, lending and payments. \n",
        "\n",
        "The General Data Protection Regulation (GDPR), on the other hand, sets explicit\n",
        "and implicit constraints on potential innovations by regulating the manner in which companies may use personal data in their product and service offerings. \n",
        "\n",
        "Within Europe, the Dutch financial market seems to be well-suited to become a front-runner in the adoption of AI. Amsterdam and Eindhoven are positioning themselves as upcoming AI hubs and ING has launched a new AI Fintech Lab in\n",
        "collaboration with Delft University of Technology."
      ],
      "metadata": {
        "id": "FDHhFfXsRmVt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# AI in Finance: Use Case (50 min)\n",
        "\n",
        "**Credit Card Application Validation**\n",
        "\n",
        "Your collegues are responsible for the the credit card application validations. Last months the amount of credit card applications grew significantly, so they are not capable of handling all credit card requests anymore. \n",
        "\n",
        "You are tasked to find a solution using an AI model, that should predict the status of a credit card application decision based on available historical data.   \n",
        "\n",
        "To do this you will retrieve data, prepare data for the modelling, train the AI model, and evaluate it. "
      ],
      "metadata": {
        "id": "HD1T7aFGapTG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Historical data\n",
        "\n",
        "From another department you have received a dataset, which consists of observations covering the demographic data and credit history of applicants.\n",
        "\n",
        "> This dataset is taken from the book \"Econometric Analysis\" by William H. Greene."
      ],
      "metadata": {
        "id": "R674N2hsqCZX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's start with the import of Python libraries."
      ],
      "metadata": {
        "id": "FCopzp2S0rZS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Python libraries for data transformations\n",
        "import pandas as pd\n",
        "\n",
        "# Python library for serializing (in your case used to save models)\n",
        "import joblib\n",
        "\n",
        "# Python library for data preprocessing, modelling, evaluations\n",
        "from sklearn.model_selection import train_test_split \n",
        "from sklearn.ensemble import RandomForestClassifier \n",
        "from sklearn.metrics import confusion_matrix, classification_report, ConfusionMatrixDisplay"
      ],
      "metadata": {
        "id": "v5-DEPiUsl68"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Shap library does not included in our environment, that's why we need to install it manually via pip."
      ],
      "metadata": {
        "id": "UAR3LtpW1XUJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install shap"
      ],
      "metadata": {
        "id": "ETE6-vH8HXKp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39fa50cb-45fe-4308-faac-a7006113d106"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: shap in /usr/local/lib/python3.7/dist-packages (0.40.0)\n",
            "Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.7/dist-packages (from shap) (21.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from shap) (1.4.1)\n",
            "Requirement already satisfied: slicer==0.0.7 in /usr/local/lib/python3.7/dist-packages (from shap) (0.0.7)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from shap) (1.3.5)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from shap) (1.21.6)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from shap) (1.0.2)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.7/dist-packages (from shap) (0.51.2)\n",
            "Requirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.7/dist-packages (from shap) (4.64.0)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from shap) (1.3.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>20.9->shap) (3.0.9)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba->shap) (0.34.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba->shap) (57.4.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->shap) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->shap) (2022.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->shap) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->shap) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->shap) (1.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Python library for models interpretability\n",
        "import shap"
      ],
      "metadata": {
        "id": "wJDHm45K5koQ"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "As all necessary libraries are installed, let's move to the next step: loading of the dataset with Pandas library. Our dataset is represented by a CSV file. Pandas library helps us to read the CSV file, to replace all \"yes\" values with Boolean True and all \"no\" values with Boolean False."
      ],
      "metadata": {
        "id": "wuGRF308tS4U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# read csv file using Pandas, replace all \"yes\" values with Boolean True, all \"no\" values with Boolean False\n",
        "credit_card = pd.read_csv(\"https://raw.githubusercontent.com/pyladiesams/ai-in-finance-python-lecture-beginner-may2022/master/dataset/credit_card_data.csv\", true_values=[\"yes\"], false_values=[\"no\"])"
      ],
      "metadata": {
        "id": "R1V38wV6cvuo"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# show the shape of the underlying dataframe\n",
        "credit_card.shape"
      ],
      "metadata": {
        "id": "nOET2x0gcxGz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b29dfac-e380-4e51-f47e-4d73b2d02c5b"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1319, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# show a couple of first rows of the dataframe with column names\n",
        "credit_card.head()"
      ],
      "metadata": {
        "id": "IqBuk1_ccw3S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "2bd28ca0-b5c1-4199-bf54-da02efb66671"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   card  reports       age  income     share  expenditure  owner  selfemp  \\\n",
              "0  True        0  37.66667  4.5200  0.033270   124.983300   True    False   \n",
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              "\n",
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          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we see that our dataset has 1319 records (rows) and 12 columns."
      ],
      "metadata": {
        "id": "0WIDprdK2Yfc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Variables Description\n",
        "\n",
        "* **card**: Dummy variable, 1 if application for credit card accepted, 0 if not\n",
        "* **reports**: Number of major derogatory reports\n",
        "* **age**: Age n years plus twelfths of a year\n",
        "* **income**: Yearly income (divided by 10,000)\n",
        "* **share**: Ratio of monthly credit card expenditure to yearly income\n",
        "* **expenditure**: Average monthly credit card expenditure\n",
        "* **owner**: 1 if owns their home, 0 if rents\n",
        "* **selfempl**: 1 if self employed, 0 if not.\n",
        "* **dependents**: 1 + number of dependents\n",
        "* **months**: Months living at current address\n",
        "* **majorcards**: Number of major credit cards held\n",
        "* **active**: Number of active credit accounts"
      ],
      "metadata": {
        "id": "lbPIZjKRHe1J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The next logical step should be **EDA** (exploratory data analysis), it is needed to: \n",
        "\n",
        "* validate assumptions whether the dataset is giving answer to your business problem\n",
        "* find which variables require more attention\n",
        "* understant outliers better\n",
        "\n",
        "It's a tremendous chunk of work usually performed by data scientists and/or data analysts.\n",
        "\n",
        "In your case, we assume that the department, that shared data with you, have already performed EDA and sent only important data."
      ],
      "metadata": {
        "id": "kqZ_oLvW2lLO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset cleaning\n",
        "\n",
        "Our model is not capable to take data as it is. Before showing any data to any model we need to \"clean\" the dataset. \n",
        "In the majority of the cases data cleaning consists of the next steps:\n",
        "* type casting\n",
        "* working with missing values\n",
        "* working with outliers."
      ],
      "metadata": {
        "id": "GIDlzTS5OEFv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Type casting\n",
        "\n",
        "It is the method to convert the variable data type into a certain data type. \n",
        "\n",
        "In your case look for categorical values, which you can't use directly in your model. In case of Pandas DataFrame look for \"object\" type variables."
      ],
      "metadata": {
        "id": "j0fFXM51V51E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# show the dataframe general info\n",
        "credit_card.info()"
      ],
      "metadata": {
        "id": "1eRp1iHhc2yc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a68b591-2b6a-483d-9144-c5b380b22bdc"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1319 entries, 0 to 1318\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   card         1319 non-null   bool   \n",
            " 1   reports      1319 non-null   int64  \n",
            " 2   age          1319 non-null   float64\n",
            " 3   income       1319 non-null   float64\n",
            " 4   share        1319 non-null   float64\n",
            " 5   expenditure  1319 non-null   float64\n",
            " 6   owner        1319 non-null   bool   \n",
            " 7   selfemp      1319 non-null   bool   \n",
            " 8   dependents   1319 non-null   int64  \n",
            " 9   months       1319 non-null   int64  \n",
            " 10  majorcards   1319 non-null   int64  \n",
            " 11  active       1319 non-null   int64  \n",
            "dtypes: bool(3), float64(4), int64(5)\n",
            "memory usage: 96.7 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you see you do not have any \"object\" types in a dataset as you have already did type casting, while reading CSV file and loading it in a Pandas data frame."
      ],
      "metadata": {
        "id": "zPAM0Qhl6MX4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Missing values\n",
        "\n",
        "\n",
        "Missing values are often common in datasets. \n",
        "\n",
        "In case of missing values you can:\n",
        "* drop columns/rows with missing values\n",
        "* impute: fill missing values with some number. eg: mean, mode etc.\n",
        "* extend imputation: imputate, in addition with a new column showing location of imputed values."
      ],
      "metadata": {
        "id": "7RdPEqR6V7bD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# show calculated missing values per variable\n",
        "credit_card.isna().sum(axis=0)"
      ],
      "metadata": {
        "id": "D9U7z8Pnc5lL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0ac5681-793b-4a0b-e117-ea9cfaaf2a9f"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "card           0\n",
              "reports        0\n",
              "age            0\n",
              "income         0\n",
              "share          0\n",
              "expenditure    0\n",
              "owner          0\n",
              "selfemp        0\n",
              "dependents     0\n",
              "months         0\n",
              "majorcards     0\n",
              "active         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In your case you are lucky, as there are no missing values in this dataset."
      ],
      "metadata": {
        "id": "1xJimRYgiixe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Outliers\n",
        "\n",
        "They are values that are significantly distant from most observations. There are different methods of handling them, however, this is outside of scope of this lecture."
      ],
      "metadata": {
        "id": "eX-Fm-tfV89o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset preprocessing\n",
        "\n",
        "Now you have the data that could be further transformed to be used by your AI model. How the data further will be processed heavily depends on the model type.\n",
        "\n",
        "In your case you need to perform the following steps:\n",
        "\n",
        "* Features and Target split \n",
        "* Train Test Split"
      ],
      "metadata": {
        "id": "6GLWfdb3UaW9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Features and Target split\n",
        "\n",
        "Remember, you was tasked to find a solution, that should predict the status of a credit card application decision based on available historical data. It means that you have a binary classification task. "
      ],
      "metadata": {
        "id": "0EZDpm4T6WdB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# show a couple of first rows of the dataframe with column names\n",
        "credit_card.head()"
      ],
      "metadata": {
        "id": "eeHB2H1yc8Jd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "cd9d1beb-1c8d-4430-c87d-3d019ff88d3c"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   card  reports       age  income     share  expenditure  owner  selfemp  \\\n",
              "0  True        0  37.66667  4.5200  0.033270   124.983300   True    False   \n",
              "1  True        0  33.25000  2.4200  0.005217     9.854167  False    False   \n",
              "2  True        0  33.66667  4.5000  0.004156    15.000000   True    False   \n",
              "3  True        0  30.50000  2.5400  0.065214   137.869200  False    False   \n",
              "4  True        0  32.16667  9.7867  0.067051   546.503300   True    False   \n",
              "\n",
              "   dependents  months  majorcards  active  \n",
              "0           3      54           1      12  \n",
              "1           3      34           1      13  \n",
              "2           4      58           1       5  \n",
              "3           0      25           1       7  \n",
              "4           2      64           1       5  "
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      ]
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    {
      "cell_type": "markdown",
      "source": [
        "Prediction target (usually referred as \"y\").\n",
        "\n",
        "In your case, True if credit card application is granted and False if not."
      ],
      "metadata": {
        "id": "yjaDKzN0qG6-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# store prediction target in y variable\n",
        "y=credit_card.card\n",
        "\n",
        "# show y\n",
        "y"
      ],
      "metadata": {
        "id": "PqHtTRxvc97k",
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          "base_uri": "https://localhost:8080/"
        },
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      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        True\n",
              "1        True\n",
              "2        True\n",
              "3        True\n",
              "4        True\n",
              "        ...  \n",
              "1314     True\n",
              "1315    False\n",
              "1316     True\n",
              "1317     True\n",
              "1318     True\n",
              "Name: card, Length: 1319, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Features (usually referred as \"X\")\n",
        "\n",
        "In your case, all variables used to make predictions except the target."
      ],
      "metadata": {
        "id": "nVPn6xUkqmN1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# store features in X variable by dropping targer variable, show X\n",
        "X=credit_card.drop(['card'], axis=1)\n",
        "\n",
        "#show X\n",
        "X"
      ],
      "metadata": {
        "id": "D2CuyiRfc_d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "24b9be5d-d618-4669-ae4c-67b7e1946b64"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      reports       age  income     share  expenditure  owner  selfemp  \\\n",
              "0           0  37.66667  4.5200  0.033270   124.983300   True    False   \n",
              "1           0  33.25000  2.4200  0.005217     9.854167  False    False   \n",
              "2           0  33.66667  4.5000  0.004156    15.000000   True    False   \n",
              "3           0  30.50000  2.5400  0.065214   137.869200  False    False   \n",
              "4           0  32.16667  9.7867  0.067051   546.503300   True    False   \n",
              "...       ...       ...     ...       ...          ...    ...      ...   \n",
              "1314        0  33.58333  4.5660  0.002146     7.333333   True    False   \n",
              "1315        5  23.91667  3.1920  0.000376     0.000000  False    False   \n",
              "1316        0  40.58333  4.6000  0.026513   101.298300   True    False   \n",
              "1317        0  32.83333  3.7000  0.008999    26.996670  False     True   \n",
              "1318        0  48.25000  3.7000  0.111619   344.157500   True    False   \n",
              "\n",
              "      dependents  months  majorcards  active  \n",
              "0              3      54           1      12  \n",
              "1              3      34           1      13  \n",
              "2              4      58           1       5  \n",
              "3              0      25           1       7  \n",
              "4              2      64           1       5  \n",
              "...          ...     ...         ...     ...  \n",
              "1314           0      94           1      19  \n",
              "1315           3      12           1       5  \n",
              "1316           2       1           1       2  \n",
              "1317           0      60           1       7  \n",
              "1318           2       2           1       0  \n",
              "\n",
              "[1319 rows x 11 columns]"
            ],
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now you have nicely splitted features X and target y."
      ],
      "metadata": {
        "id": "AlWteeKTAOhp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train Test Split\n",
        "\n",
        "Your dataset should be splitted at least into train and test sets.\n",
        "\n",
        "**Train dataset** is used to train the AI model. **Test dataset** to test its performance. \n",
        "\n",
        "If you prepare to train a couple of models (to choose later which one performs the best) or finetune your model you also need **validation dataset**. It means that your will split the original dataset into train, validation, and test datasets. In such a case you will train models using train dataset, evaluate models using validation dataset and finally test the best model with test dataset."
      ],
      "metadata": {
        "id": "ZajkAAfoAcIf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you are going to train only one model without any finetuning you need to split your dataset into train/test. Let's assume that 70% of the dataset will be used for training and 30% for model testing. \n",
        "\n",
        "Scikit-learn has a wonderful function implemented for you to split train/test sets."
      ],
      "metadata": {
        "id": "AlUI7WuwrKCB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# split X and y into train test set, keep 30% of the data for testing, with random state equal to zero\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
      ],
      "metadata": {
        "id": "FZds9PlIdBga"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modelling\n",
        "\n",
        "A process of creating a mathematical representation of a real-world process. \n"
      ],
      "metadata": {
        "id": "2BSZimueoVj_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model training\n",
        "\n",
        "For your binary classification task you are going to use **Random forest** - a classification algorithm consisting of many decisions trees."
      ],
      "metadata": {
        "id": "_goqafpOn7T7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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VAsHH2qN7umULmD5jKaGQCQt4OtYYX5VdAeg8kSyZQwixcAkOddli4vinKmnVk0NooFAV4UUy9WVdAzhwytxRbRC8pitDUAIJ/of8poUTK2YL8K2nWpIUFRihm8AID87ZOXSEC0Orw4QgDGZFocxYIAL4qHkvKCDvBqpbzyZZwqGhsAkF99cGmJN7u1GK42tA7R4ACAvLVTTNFqeJGUtoQGR7EgwIviYYoq+DI6ImKZfEoJAMgvlhMsisCnVoY4RpAGBwDkrbSYInpYUZRVK/pkFA0CvCiis10XQQfm/mMPo7EBAHnFcQeT3q3wO2JVBB8oAwD8KyBKDBUokgsQYl4oGpzsKB62KpJ3tNFMYwMA8mswqVKiJFUEJdViOhkaHACQt1q87322FkdhjTQNjmJBgBfFw5bC/+Ou3He1vZrGBgDkFctMilbJIiiplkxRBLIBAH5liu1l4i0G2qZPRtEgwIviodTWgv/SpOl2YsZyGhsAkFfaW1u9OUOFzxZHtdHgAIC8tVjSovWmIgkC7KTBUSwI8KJ4aPuVgj/jbRE1p/1dGhsAkFfDq9OlRaQIBpNakqIzq2hxAEDe9snfF0dEFUdaP6Veo8VRLAjwophO978X/PIu7dJOOwMA8rMbVtsKfyApbbIpzWASAJDfHL2tKMqp2xfT2CiaS22qAEUj2PZowZ/xSidoaABAXrKltuA/aBW9TV3o5TYEACB/GXZ94RfS6VDzhAXIUTxva6oARcORNunQOwq2fModVzoP0tAAgLw0ru0WCRRw+Szvq6BP0NAAgLwfOs5N/VVE64ItoLc2jVpIS6OYEOBF8aiUFlF6SeEWUNsiag0NDQDIy8Hk0bJTOnThphIKiYjT9nNaGgDgE6sKdhHyoJca6mc0MYoJAV4Uz8ByhqRE5MWCLaAj28Vqf4aWBgDk8ZXnD71BV8FdZIhIq7NJzZO1NDIAwB99cuYbUlKgZevQKTWn9SkaGcV1mQ0Uk3Tm/oL8Koo7sDR0gzpZttLIAID81XanOAVYLO+roM4NtC8AwDdDyNmpv0lbgY6Ntb6HFkaxIcCL4urETk8vF6VXFtyZX6JF7MxVtDAAIK9tl+2Sdp4vuHLZuk0C+nkaGADgL85vCy5Ng9a2aPv/aFsUGwK8KELtH5TSAnsXt+uX1anpZbQtACCfqbMlKUoeKriCaftVdXLqNVoYAOCvjtl8UEQnC6xUG9W81N9oXBQbArwovj5sjqyRNv1CwRTI0SIq/TVaFgDgi354bvttIk7hpBSytIi2bqRlAQD+65Nb/ymiGwpmFm/ILZT+AS2LYkSAF8XqJ6J0qkDKskjezSRoUgCAf65A9TkFsbCLKSK285Ca1/o0jQoA8KVA+0e8lH9+p0Qk6dSpOe3/Q6OiKC+vqQIUIzWn7TERifv+k0p3YCmpa9SFYtOqAADf9MOzO16WDucx3xfE1klJd3yeFgUA+LZPPlHWSVLf5P/okLbFynyOFkWxIsCLItZ2lmjd6tvDD3gDy/lqbmYxbQkA8B3T/IaIs9PnpbhRnS4tNCYAwNd2tv+XaGeVb49/18StB9TJrEuD4qWoAhQz/WLJXNHGC6L9+FbQiyXTdpo6XTpoSQCAL/vhF8q/JUH5uaR9eAWt9Do1u+1QWhEAUBB98rOhD0qJ9ZhkfHjwhk6p2W0hWhHFjBm8KGpqdscCsZ3bffdOULpVVPoSgrsAAF/3w/Nab5KU/MF3Uw4cvUpGtB1BCwIACqZPPj35uKT1Df6LEjlbxbCn04IodgR4QUd2asdXxdYP78pn6wNBEUm11ag56RW0HgDA90a3Xi5iv+KfA9ZtYqc+rWZIisYDABSUeW0/FMf5vb8G9MbX1MnJ1TQeih0BXkBELrzi8IuWv1WyJf87L52UlPNxdYa8RasBAAqBmiGp8246/OzX1paK5P0i3rpNlPNFdXpmES0HACi4PllEq7ntX37l9dKU4/XJ+dwx64w4+no1p/VeWg4gwAt4GkOhB75025TR//vPUSKmk4+dl2zdacoVv5rcqOa2/5UWAwAUilmzZpWvXl12z5dvO1T+vqQyXweRsuG9gMz/46R71JyO+2k1AECBMiOR6K1f+Z/DAr97fIzYjsrLPrk1aciNf5r4gjq17ac0GdD55qUKUORUJBJ5wDTN87TWemFDhSoNiMw4rF1MQ+fJOoRa3tkSkMt+OUWvXFs2bvz48bJ+/foXaDoAgN/FYrGAiNxuWeYF6YzSjy+pVFUTkzJ1QkoMlR/9sNYiG7cH5HO/mKKXra44bsKEcVvWr1//Cq0HACg0NTU1Xw8EAjdoLeql18qlosTJq7Gx2ye3tJty+e2H6Rcahk+bNHH88Obm5idoOYAAL4pcJBL5k2EYF2mttVJKmYbIotfLpfHdkISPaJdhJfZB7si0PPTSCLnhnkPk3a0BZRgipmmeNn78+PHr16//By0IAPCrqqqqymAw+IBlWRc5jqOVEmWZIk8mKt0+T447sk1CAeegDygfWDhCrv39IbK1xRJDOaKUcfbEiRPTzc3NC2hFAECBCESj0VsCgcANtm17Y2PL1LJgRYWsXFcis6a2S2XZQR4bKy3PL62Qa+6YLG+vDynLcLRhGCdPnDhx8siRI5/YtGmTTTOimBHgRdGKRCKPZmfuuh1YV7+htKzeGJJ/LKmUk49uk9GVmc6PKw9UZ6ZFayW2I3LzX8fLbx4dJ+0pJUbnIbrHaxjG8ePHjx+xfv16Pq0EAPiRMWXKlLsMw/jEv/phJe6/hiHy+jsl8o9Xh8upM1v08GHegPKAjii1FsnYSub/caLc+cRYcbS4/bDqPA63H37fxIkTNzQ3Ny+hKQEAfhcOh79pmub8nmNjJZYpsmZzUP768nA5/qg2PX5k+oD3yS53bPyrR8bLjX+eIMm0IaYhXcehlIqaplne3Nz8JC2JYqaoAhSjSCTy/wzD+MLuwd3u3MFce1ItrP9VwxNBS64SURMP0LDSXrCiou2Wh8dWvLauREqDeq9dqHvYmUzmx3V1dd+hRQEAflFVVVVZUVFxh2VZF2ZnCe2lLxRHKzGUfvWpH7/5z5FlttsPDz8gB2g66X8uqWy5+eEJI9/dYknA6vm1VPfawf1pGIbSWv9HPB7/uTv2pGUBAD4UiEajN1qWdV0mk+m1T9ZaSUdK1vz5urfunDk1dYWImnRA1l8ztZ14o2zrzx4cP3ZFU6kE9/LNHrdfNk1T2bb9240bN/7bunXr2mlWFCMWWUPRiUQiTxmG8QXZFSTt/UMO7bxeXqLPC53e9kNpbjtUtL5h/x6ZFlF6jRj29O/9v7FHrtoQTJaFdK+fj7rjS9M0vx0Oh/9IqwIA/KC6ujpYWVl5r2EYXnC39y2VGEpWx+O1J4w6a+e3paNtihjyk11za/cn53VJ2zN+/sDYmo3bre0BS/YYSGavHTrjvP8VjUavpmUBAD4dG/9AKZUN7vbaJ4vo1obl8WmzPrvtRmlrO0oc52siTmq/Hpxy1ovjnPb00+bUN94pWRUM7D0PsNsvd35gfPnYsWNvolVRrJjBi2JiRSKRuwzD+FxfM3c7B22vtbS0zHvjjTc297j/SSmXkrIfiGEfL9oMi5aKffuGirZFpEm01Iul71antD+cfWTmzJkjA4FAnVLqsD6OUxuG4XZo/5lIJH5IEwMA8tXkyZNLx40bd4thGJfpfuK0WutEbW1tNDuq7Lr/ESmTESU/FTGOE5FZoqRU9CA7YvcYlGRE6VUiEhed+q2aaz/XbdA7Tyn1kFJqdF/9sGmaynGcb8Xj8VuYyQsA8AkzHA5fFwwGf9T7zN2uvm7Ntm3bjn/77bc37vHYwvIfi9aniJaIKKnYt/QN2hEt74jhvCqO3K/mdfx/2UdmzZo1NRAIPCkiVX31yZZlqUwmc/uOHTu+2djYmKSZUUwI8KJoRKPRR5RS5/S3neM4r2itz6mrq9vUa+fxWwlIdXCqKGOciHxdtHGG1jKynznB2cFkSkS/KUruEJV5WszgJnVS64a9bT5z5szJgUAgrpQal8Nx35dIJD5NSwMA8k0sFitzHOcvhmF8qDNg21dv+dr27dsjfQ3M9MtSKu3BwySkpohtXS2GnpNOqxG7p1PopcdMijIaxMn8XpR+RtpTm9UHZGMv/fC0QCDwqlJqVG+Dyew1tVLqP+Lx+E9obQBAnjOj0egtSqmvdk58kt46T631exs3bqxat27d1l775GfFEiM4VULGeMmoK0SrD6RtNSZg6v7XsVHaHRuvEkfdK5n0Q6Ktbep97e/sbdPq6upRpaWlL4vIUb0Pt7smct0Zj8cvpalRTAjwoijO80gk8oBpmp/YtUp37yFYx3HWtba2RnafuZuLEZNO+vxxR3b8Yer4DqksdaSizJZgQEtLhylK621f/8imb4w9dvt9aqp0DGS/06dPrygvL1+rVO+5B7MdWSaT+UF9ff33us92AgDgYIrFYgHbtn9pWdblffXDnX3Z6/F4/JjBvM606Se8cOSk1NxDxyZleJnt3dx++L0WS9IZJ3PzDas+po6w/zHQ/YbD4eMNw3hCKTWyr364cybv1+Lx+C9pdQBAvopEIt+wLOtmx3H63E5rvaG2tnaKiAx4Juwh00769ZGTkle6Y+ORw2ypLHOkNOTI9lZT2joM+cX1jRfK2/Y/1LnSNpD9zpo1a5xlWYuUUlP765Nt276ltrb2G4yNUSwI8KIYOrD7DMP4ZH9pGRzHWdja2nreYIK7na9zhVLGb2wn27F0e5dpWbW0Xo4VibcNZt+zZs2aalnW0311ZPKvhdd+V1dXdzktDwA42Kqrq4eFQqF7TdP8SOds176+AprYsWPHaY2NjTsG81qxWOwlrdXJjqO9kVy2H3Z7fq11cml9bclgy1FTUxO2LOspERnT22Cysx9WjuPMTyQSP6L1AQB5JhCNRn9hmuZXel/ktGtsvD6ZTJ7Y0NCwZjAvFI1G7xRlXOL1ybpnn2wYhiRqXx10LCoWix0mIk+IyNF9XFN4qQwdx7mro6PjqoaGhhTNj0LHImsoaOFw+DHTND+ZQ87d5n0J7ma5r2CZu24Bq/Pm/a7VCSeY1mD3u3Tp0lVKqRN0P0kLHcfRpmleVlNTcwutDwA42NeZoVDoV4ZhfMTtn/qZWLCmtrY2Otjg7r/6YS3mbv1wZ7+8T5Ma6uvr6zKZzGyt9Y69v+4u7iWFaZo/jMViV9L8AIB8EolE/s0wjK/0961WrXVLIpGYONjgbtdFgBIxjT37ZNPYtwm18Xh8TTqdPk5rvbb36wGl3KGzYRiXlJSUkD4JxXHhTRWggDuwP5im+cEc0jIsbmlpmbWvwd39LR6Pb06n04dprRv76sjcn4FA4OuRSOS/OAsAAAdDdXX1sEgkco9lWZ93x4r9DCRfEZFj8r1M9fX1bxiGcbaIbOqjLNJ53fHraDT6HyJicjYAAA6yQDQa/VkgEPhpfx+4aq3XtLa2zsz3Ai1durRVa32G1vr1vrbrnAD1b9Fo9LeTJ08u5VRAISPAi4IUjUb/aRiGO6iUfoK7r7e2tp6T78HdrGXLlq1TSs0TkXRf23V+Wnl9OBy+h7MBAHAgxWKxQCgU+qNhGJ9yB1b9fPlkdW1t7Ynx+OBSGB1oS5YseTGVSkVFZPveHs9ec7hlVkr9OBqNXs0ZAQA4mMLh8I1KqX/PZDK6rxXBtdattbW1U19//fXVfihXIpFo3LJlS1hEVvW2jdsvd6ajuGz8+PE3czagkBHgRaGxotHo3Uqp9/U3otRaN7S2ts7xS3A3Kx6PN9u2fUhfX0npLJ+2LOvicDh8A6cFAOBAmDx5cqnjOL82TfNj3cZWqvcuLT7Vb2tCLFu2bJ3W+lwR2dzbYFL+tcjLL6LR6LeYyQsAOAjMSCQyPxgMXtctZWFvC502bdu2bZqIOH4qYFNTU0cmkzlDRN7sbZvOdA1un3xlJBK5vaqqKsSpgUJEgBcFJRKJ/E0p9Vnpf+bu4paWllP9FtzNqqur26S1ntvb4LJbRyamaX4/HA7/mbMDALA/xWKxsjFjxjxkGMaX+/uQVURea29vPyU7rvRbWWtraxdkMpnjtdZb+trOtm23bD+PRqPXcoYAAA4gMxqN3moYxo39zdx1HGfrli1bwm+//fZGPxa0vr5+dSqVOlFE3uhrbOzWg2EYV1VWVt7O6YFCRIAXBXMuh8PhB03TPDuHmbtr2tvbP+zX4G5WIpFoCgQC03v7mmi38mrTNC+KRCI/8NssKQCAP8RisYDW+heWZX2wsx/ubYaQ+9hr8Xi82u8rWrsDSq21W96tvQ0muy289uNYLPY1zhQAwAGgwuHwNaZpfkX+NfFpr/2y4zjrE4nEpKampm1+LvCyZcveS6fTc7XWfaZr6BwbXxKJRG5hbIxCQ4AXBSESidxnmubHc1gRdEFLS0ts5cqVWwqh3IsWLdqayWTC/XVk7k/TNP8zEon8jrMFADCUqqurh9m2/ZBhGJd1+wpob31SoqWl5cRCKXsikVgiIqf3tfCa/Gsm762xWGw+ZwwAYD8KRCKRXwUCgZs6+x7pY2zcnE6nTxCRZCEUfOnSpRsNwzhVRFb2NTZ268U0za9Ho9E7q6urg5wyKBQEeOF70Wj0cdM0L+xvUKm1fretre08v8/c3V19ff1q0zSP11r3mS/JcRxtGMaXI5HIrZw1AIChupYMhUK3m6Z5Tn8rc4tIUzwej73++us7C6kCamtrl9q2PVtrvaO3wWR2Jq9S6sZoNHoVpw0AYH+IRCLfME3zqv4mPjmOs7O2tnbS8uXL1xZS+ZcsWbI2nU4fJyJretsmm8rQMIwvlZSU/JSzBgVzUU4VwM/C4fDdSqmzcpi5u6itrW1Woczc3d2rr766JZ1OT9Fa95lcXnbN5P1aOBymIwMA7JPq6uphkUjkXsuyPtcZvOwruLs4EAgcU6h1UVdX96bjOB/qayavO5js/LD19kgk8h0WXgMADKFAJBK5KRAI/KS/D1y11k0dHR0zC7Uili5d2ioiZ/Q1k1c6J0CZpnlNLBa7Y/LkyaWcQvA7ArzwrWg0+rRpmv0uqKa1XtnW1nZOoQZ3s5YtW7bONM15IpLua7vOhdeuDYfD93IWAQAGIxaLBUKh0D1KqU+6A6R+0t+vjsfjJy1atKi9kOukrq7upUwmE+ktN372WqVz1tAPjzvuOHLyAgCGamx8o2EY3+xvQTWtdWttbe3U1157ramQ6yMej7+1ffv2sIj0mcqwM43Fl8eNG/ffnEXwOwK88CMrEom4g8oz+htROo6zoqWlZW6hB3ezXn311fW2bR/S11dSOjt2bVnWp8Ph8Hc5nQAAA3HSSSeVOo7zG9M0P9o5iFR9fNC6JB6PTy2WhUzq6+vfEZFzRGRzb4PJbD+slPrvSCRyrXtdw1kFABgks6amZr5lWdd1S1nY20KnTTt27HD7ZF0MFdPY2JjMZDJnaK3f6G2bbguvXRGLxX49ZcqUEk4p+BUBXvhOJBL5P8MwLpZ+Zu46jrO4tbX1tELLudufurq6TSIyt6+viWbzDpmm+b1wOHw/ZxUAIBexWKyso6Pjr4ZhXNLfh6wi8pqInJIdVxZLHcXj8YWO4xynte7zw2Xbtt2B+E+j0ei1nFkAgEEwI5HIbZZl3djfzF3HcbZu27atprGxcVMxVVB9ff1q27ZP1Fq/3tfYuHMm75WjR4++ndMKfkWAF746X6PR6EOmaX6ov0Gl1rqptbX1nGIL7nYbXK5JpVLTtdbb+qknbZrmBeFw+MZimV0FABic6urqoOM4t1iWdVZnP9zbDCH3sYZ4PF4dj8fTxVhXiUSiSUTO6i3I233hNdM0fxSNRq/hDAMADICaNWvWNYFA4Cr518Sn3vrl5kQiMfHtt9/eXowVVV9fv81xnLla67d7rcx/zeT9UiwWu5VYGfyIkxa+EYlE/qyUOi+HBdUWtLS0HFeswd2sZcuWvae1DvfXkbk/LcuaHw6H7+QsAwDszfTp0ytCodBDpmle2u0roL31LYn29vYTi73Oamtr40qp0/v6Ro38aybvL6LR6A2caQCAHAQikcjtoVDopkwm09/Ep+adO3ceLyKpYq6wurq6Tel0+tTObxf1Ojbu7JO/FolE7qqurg5yqsFPCPDCFyKRyBOmaV7Q36BSa/1OS0vLx4s9uJuVSCSagsHg8Vpru6/tOlcQ/VIkErmNWgMA7MYsLS293TCMD/e3MnfngmqxhoaGFqrN+0bNMtu2Z/e18Fq3mbzfj0ajX6HWAAB9qamp+TfTNK/sb+KT4zg7amtrJ7355pvvUGu7FiUXkeP6Wq+mWyrDL4RCoZ9Ra/ATArzIe9Fo9I+GYXwgh5m7L7e0tIQJ7va0aNGirbZtT+kvubw3gjfNqyORyM+pNQCAdM7cjUQifwoEAp91u9q++mERWbxhw4Zqaq2nurq6N7XWH9Rab+zjGsabyWsYxq8ikch8t0um5gAAuwlEIpGbQqHQT/v7wFVr3ZRMJmdSZT3F4/G2zm/XrOxrO7d+Lcv6ejQavTMWi5VRc/ADArzIa5FI5Bml1Gek/wXVXm9razuX4O7e1dfXv5NKpeaJSJ+5EN0BpmEY36qpqfkTtQYAxS0WiwXKysruUUpd6A50+kl/vzoej5+0bt26dmpuT7W1tYu01pG+ZvJ264dvjMViX6fWAADdhcPhGw3D+GZ/C6pprVtqa2sPb2hoWEOt7WnJkiVvt7e312itV/e2Tbd0DZdorW+m1uAHBHiRrwKRSORewzBO729E6TjOstbW1jkrV67cQrX1bvny5RtEZJLWuqmv7dz6DgQCn4pEIt+n1gCgOMVisTLbtn9rmuZHOgeRqrcPWrXWr8bj8anUWt8SicS7SqkP95aTt1uQVxuGcXMsFrtORCxqDgCKnhkOh28IBoPXdUtZ2FufvHrHjh3TqLK+NTQ0pLTWp/X3LdfOhdeuiEajv5ny/7N3JvBVVUcDnznn3rckBGQHQXFBwCgk9z6kLgjiLoq7gLi0tgpata3VutZS6vLVurR1t1qtVVlCXRB364IrCi8vIZCy71vYE7K9d+898/1e8iIReAt73r3z/6lR8oTcmXtmzsyZM9OjR4Alx7RkOMHLtEhM03xbCDEK0lTuEtH0urq6IVy5mxnhcHiDEOJkIlqfxpGBEOIPhmFMZqkxDMN4i1AolKOUelNKeU26Q9Z4jFRcXHwSSy0zZs6c+TUihogo5aG04zhx0f/ZMIw7WGoMwzCeRhqG8YSUcly6yl2l1KaqqqqChQsXrmexpScSiSxDxAHpkryJSt7r27dv/zRLjWnJcIKXaXHvpGEYbwohzk4XVBLR0urq6mFcubvLweUKy7J6E9HmNPIlIcSlhYWF96cZqMMwDMO4hPz8fJ9S6u9SyjMTfjhZhVCc2eFw+Jh07X+YHf0wIp6ZLMnbfPCapmn3G4ZxC0uNYRjGk2BhYeEtuq7fANsKn5L55XWtW7futnDhwioWW+aEw+FKpdRAAFiUVAnbKnmvMU3zcc6jMS0VfjGZFoVhGEVCiAszmAj6RW1tbX+u3N09ysrKNhORQUSLUzmy+FdN0+4pLCx8kaXGMAzjbnr37p3n9/vfklJe2+wKaDIfUVxTU3MiS223A8pix3EGJ2vX0ERi8NpjpmmOZakxDMN4Ct00zad1XX/Ytu10hU/rhRD9p02bVs9i23VKSkrW27Yd98n/SxUbJ3zyzaFQ6MX8/HwfS45paXCCl2kxGIbxkZTyknRBJRGttixrOFfu7hmRSGRZfX39cURkp/qcUoqklD8rLCx8gqXGMAzjWmQwGHxaCHFOusncALAkHA73nzdv3lYW2+5TWlo6Ryl1YqrBa02VvFLKP4ZCoZtZagzDMN6gX79+twohrk9X+ERENcXFxZ1mzpy5gqW2Rz55la7rIQBIOq+mqZUhIv40GAw+zFJjWhqc4GVaBIZhvCqEOCODyt3vYrGYmRgYxuwh5eXlmxzHOYyI5qVyZNBYyXuTYRg8QZRhGMZl9O7dO88wjAm6rl8ZjxVT+WEAmL569epjWWp7h0gkshAAziKidSmCd0j0/3vcNM0/AIBkyTEMw7gWvbCw8LFAIPB/6Q5clVIriegYFtneYfr06XWWZQ1JVcmbkDsJIX5lmuY/Q6FQDkuOaSlwgpc54BiG8bkQ4gpIP1BtAREN4+Tu3qW0tHSVbduD0vVQTAxe+61hGBNYagzDMO4gFArpwWDwNUS8LB6wpGl/vyQcDp+wZs2aWpbc3iMcDn+HiIWpKnmb/DAijguFQr9mqTEMw7iTwsLC+6SUt2QwUC0aiUQOjUQiy1hqe49Zs2YtqaurKySiJck+02zw2s+J6DGWGtNS4AQvc0CDSsMwxgshBqeLKJVSc+rr6weVlJTwRNB948jWAcDBRLQ01ecSzeVHxjceLDWGYZis98M5Sql/aJo2LBFEYrKDViL6PhwOH8FS2zeEw+E1juMMJaL1yYJJ2DYA9dFQKHQnAGgsOYZhGNegFRYWjvX5fHc0a1mYzCcv9/l8PeL/ymLb+5SXl8cQ8ZR0t1wTsfEY0zSf6969e5AlxxxoOMHLHDCUUu8IIS6HNJW7SqmZRDSkvLx8LUttnwaXGyzLOjlZcNnMkYGU8veFhYWvs9QYhmGyk0Ryd4oQ4mfpDlnjsU5xcfFJLLV9S0lJyTeWZZlKqZQzBhzHiavs/wzDuIOlxjAM4wqkYRhPSCn/mEHl7lZN0/p+9913fKt138bGy6uqqgakS/ImKnlHd+zY8WmWGnOg4QQvc6Ac2JtSyjPTBZVEtMyyrPO4cnf/UFZWttJxnF5EtCmNXkhKebFhGA+mGcTDMAzDtDB69uzpV0o9LqU8PeGHk1UIxb9XFg6HjwEAmyW33/xwfH+0IVkw2TR4TdO0+/v3738L+2GGYZisBgsKCm7Vdf162Fb4tFO7rpSqaN26dafvv/++isW271m4cGEVIg6M/2tS5SUqeTVN+5lpmk9yn3zmQMIJXma/Y5pmkRDiwgwGqn2tlDqOe+7uX0pLS7cgogEAi1I5svhXKeVdhmH8i6XGMAyTHfTu3TuvVatWb0kpf9HsCmgyW19cV1d3Iktt/zJr1qxipdTgVDdqYFsl72OJwWsMwzBM9qGbpvmsz+d7yLbtdIVP65VSoWnTptWz2PYf4XB4AwAMSjV4ramSVwhxo2maL+bn5/tYcsyBgBO8zH7FMIyPhRAXpwsqlVKrHMe5mCt3D5gjWx6LxY5LN3gtMUH06sLCwidZagzDMC0eLScn5xkp5dnpJnMj4uJwONy/vLy8msW2/ykpKSmPRqMnENGWZMFkUyWvlPKPoVDoZpYawzBMdlFYWHibEGJ0usInIqouLi7uVFpauoqldkBi4zUVFRUhAFiaYt+EiaHkVwcCgUdYasyBgBO8zH4jMVDt9Awc2HTLskKJwV/MAaKsrGyzUuowAJibypE1ZAw07cbCwsK/stQYhmFaJr17984zDGOilPKKuKtN5YcB4NtVq1b1ZakdWObMmbPIcZyziGhdij0TJPr/PZ6o5OWroQzDMC0f3TCMv/l8vgfTHbgqpVYQ0bEssgPLypUr62zbHpKqkjehL5JS3mwYxkuhUCiHJcfsTzjBy+wXDMOYluFAtQWIOIzbMrQMIpHIasdxBqWr5E0MXvtNYWHhRJYawzBMyyIUCunBYHA8Il5CjaT6+JJwOHzimjVrallyB57S0tLv6+vrC4iocmffb9pTxXWKiONCodCvWWoMwzAtm8LCwvuFEL/OYKBaNBKJHBqJRJax1FqET14KAHGfvCTZZ5q1a/gZEXEBFLNf4QQvs8+DStM0JwghBqWLKJVSZZZlnZzoc8O0EBJtMg4mosWpPpdoLj8ivmFhqTEMw7QM+vXrl6uUekHTtPMSQSSmOGj9LhwOH8FSa1mUl5evJaJzklXyNkvyxgPKR03TvAsANJYcwzBMi0MzDGOcz+e7vVnLwmSDTpc5jnMoi6xlEQ6HLUQ8Jd0t18RQ8tGGYfyje/fuQZYcsz/gBC+zT1FKvYuIIyF95e4MIcSpXLnbYh3ZBsdxBqUa+NLUd0hKeU9hYeEbLDWGYZgDSygUypFSThFCXJ3ukBUAysPh8ECWWsskEol86ziOqZTamOpzjuPE9fxgKBS6k6XGMAzTopCmaT4phPhDBpW7VcFgsC+3LGyxsfHy6urqAUQ0L1VsnKjkva5z587PsNSY/QEneJl95sAMw3hLSnlGBpW7Sy3LGsaVuy2b0tLSVY7j9AKAlHpKnFZeZBjG/6XqJ8UwDMPsO3r27Ol3HOdJKeVpCT+crEIo/r1Z4XD4GACwWXIt2w8rpeL7qg3JgsmmwWtCiPtCodCt7IcZhmFaBFhQUHCbpmljYFvhEyaJjdd26dKl09dff72VxdZymTdv3lZEHAgAC5IqPVHJK4T4qWEYT3GffGZfwwleZp9gGMZ/hBAXpBuoppT6iogGcOVu1gSXWxDRBIBFqRxZ/KuU8k7TNF9mqTEMw+xfevfunZeXl/e2pmnXNLsCmsxmhy3LOpGlljV+OBKLxU5ONXgNtlXyPmIYxh9ZagzDMAcU3TCM53w+359t205Z+JSw7aH3338/ymJr+YTD4Q1CiEEAUJ4qNo77ZCnlL03TfDE/P9/HkmP2FZzgZfY6hmH8V0p5YbqgUim1koguTvR4ZbKEmTNnrojFYscRUSzV5xLJ/asSp5VcQcQwDLN/0HJycp4RQpyZbjI3ACwKh8PHzZo1q4bFlj3Mnj17bjQaPZGItiQLJpsqeTVN+0MoFPoVS41hGObAUFhY+Dsp5XUZFD5tLS4u7hyJRFaz1LKHGTNmrN2wYUOIiJYm+0xTK0MhxNXBYPBhlhqzr+AEL7NXSQxUOy2dAyOibx3HCXFyNzspKyvbTESHA8D/UjkyaKzk/aVhGH9jqTEMw+xbevfunVdYWDhJSnlF3NWm8cPfVFRU9GWpZSdz5sxZpJQ6k4gqUui4oZIXEf+eqOTlq6EMwzD7D90wjL/7fL4H0h24KqVWCCGOZZFlJ8uWLasnolNSVfIm9ExCiF+FQqGXQqFQDkuO2dtwgpfZa5imOS3DgWoLqqurz+em8dlNJBJZ7TjOYACwUn0ucVr5K9M0J7LUGIZh9g2hUEgPBoPjhRAXUyOpPr6kuLj4pJUrV9ax5LKXkpKSGbFYrICIKnf2/aa9WMIPjw2FQr9mqTEMw+wfCgsL74/HQBkMVItGIpFDw+HwcpZaVsfGy8LhcNwnL0n2maZ2DQDwMyJ6jKXG7G04wcvslaDSNM2JiDgoXURJRLMsyzp5/vz5PFDNHcHleiFE11Q9eRN6JyHECMMwHmSpMQzD7F369euXq5R6QdO08xJBJKY4aJ0eDoePYKm5g9mzZ1dYlnV2sp68zZK8cT/8qGEY9wwePFhjyTEMw+wztMLCwvt8Pt/tzVoWJht0utRxnENZZK7BFkIMTnfLNTGUfEwoFPpH9+7dgyw2Zm/BCV5mj1FKvY+IIyB95e6M6urq03igmruYMWPGRtu2BxPR+jSODIQQdxmG8SZLjWEYZu8QCoVyhBBvCyGuTnfICgDl4XB4IEvNXZSVlU1HxEKl1MZUn0u0a7i/urr6LpYawzDMPkGapvmUlPL3GVTuVuXl5fXlW63uYubMmSvq6uoGENG8VLFxopL3us6dOz/DUmP2FpzgZfYEzTCMKVLK09IFlUqpJY7jnMeVu+6ktLR0leM4vVIleWFbBdGFpmn+mQevMQzD7Bk9e/b0E9GTmqadmvDDySqE4t+bFQ6HjwEAhyXnPsLh8Bql1OnJ/HDzwWtCiD+Zpnkb+2GGYZi9iigsLLxd07TRsK3wKZlfXtOlS5dO06ZNq2axuY/y8vJqKeVJRDQ/2WeaKnmFED8NhUJPAgDfrmH23AixCJjdxTCM/wghzs9goNqXQogBfDrpbkpLS7cIIUIAsDCVI2swPELcYRjGKyw1hmGY3aN37955eXl5U4UQ1zS7AprMD5fW1dWdxFJzvR8usSzr5GTtGppIVPI+HAqFxrHUGIZh9gq6YRj/0HX9Qdu207UsXIeIoffffz/KYnMvM2bM2BiLxQYR0ZxUsXHCJ99omuaL+fn5PpYcsydwgpfZLUzT/ERKeUG6oFIptUIpdUk4HObKXQ8wc+bMFbZtH0dEKTcsiQmiVxiG8TRXEDEMw+wagwcP1nJycp4VQpyRbjK34zjLI5GIUV5ezlVCHqCsrGyeZVknENHmZMFks0ree03T5MFrDMMwe0hBQcHtUspfpCt8UkpVFRcXdw6Hw2tYau5n9uzZFRs3buyfbvBaopXhVYFA4BGWGrMncIKX2WUMw5iEiKdm4MC+IaJQSUnJepaadygtLd2CiIcTUcrm8vGvUsobTNN8nKXGMAyTGT179mxdVVU1WUo5CgDS+eHvNmzY0Iel5i3KysoWA8AZRJR05kE8mHQch4QQf0tU8vLVUIZhmF1HN03zCb/ff3+6A1ciWi6lPJZF5i2WLVtWj4inpKrkTezZSEp5cygU+lcoFMphyTG7Ayd4mV3CNM0vhBDDIf1AtQV1dXXnc3LXm4TD4TWIOAgArFSfiweYiHiTYRiTWGoMwzCpCYVCel5e3nhEvJAaSRUorIpEIsevXLmyjiXnPYqLi8O2bfcjosqdfb9pD5d4h/4QCoW4kpdhGGbXY+MH4rFMBgPVosXFxT1mzpy5gqXmydh4eXFxcUG6St7E4LWfEtFjLDVmd+AEL5NxUJmo3D05g4FqpY7jDJw7d+5GlpynHdkGIURXIlqY6nPx90lKOdwwjP9jqTEMw+ycfv365RLRi1LKcxNBJCY7aFVKzYhEIt1Zat5m1qxZ62zbPitZJW+zJC8JIR4JhUK/Hzx4MFfyMgzDpEcrLCx8QNO03zVrWZhsoNpSIjqEReZ5HMuyBgFAyluuidh4TCgUep4reZldhRO8TEYQ0QcZVu5+X1dXdxoPVGMg0VyeiAYnm+oNP+47dKdpmlNYagzDMD8mvsEXQkxFxCszOGRdEIlEjmepMdCY5P2uvr6+UCmV8tDdcZz4q3Xf1q1b72apMQzDpEQahvG0lPLuDCp3q2zbPpZvtTLQ2EJppWVZxwHA3FSxcaKS91oieoalxuwKnOBl0qGZpvm2EOLUdEElES1yHGcYV+4yzYlEIqsR8ah0U70Tp9/nm6b5ENsmhmGYRnr06BFQSj2ladqQhB9OViFESqnZkUikVzymZMkxTZSXl68FgNOSHbY2H7wmpRxnmubv2A8zDMPsFFFYWHiHruvXwbbCp2R+eXXPnj07zpo1q4bFxjQRfx9qamoGEtH8ZJ9pVsl7dSgUepr75DMZGygWAZMK0zTfQMRh6QaqEdEX1dXVx3PlLrMzwuFwpWVZoVTtGpreLyHE7aZpvspSYxjG6/Tu3TuvXbt270gpf9bsCmgyP1y6evVqrtxldkokEilVSp2U7rDVcZz4e/YX0zTHsdQYhmF+hG6a5vOapj1g23a62zTrpJShyZMnx1hszPbMnTt3o23bJ6cavNaskveGUCj0r/z8fB9LjkkHJ3iZpJim+YkQYlgGQeVyRLxk/vz5G1hqTDISV1IGEFF9mg1R/H273DTNZ1JNomUYhnEzoVBIz8nJeU4IcVq6ydxKqWWRSMSoqKjgKiEmKSUlJQts2z6eiDYlCyabVfL+vn///r9hqTEMwzRSUFBwhxDi5xnExpWRSKTzjBkz1rLUmGTMmjVrXVVVVYiIFif7DG4bunBFIBB4lKXGpIMTvMxOMQyjCBFPTVe5q5T6WinVPxwOc3KXSUtZWdlmIjqSiMrTOTIhxPWGYTzJUmMYxmsMGDCgtVJqspTy8nismMoPO44zff369Uez1JgMA8oltm2foZRamyI5AYmqob+GQqE/8dVQhmE8js8wjKf8fv996Q5ciWi5pmnHssiYTFi4cGFUCHFKqkpeSBRASSlvMk3z5X79+uWy5JhkcIKX2QHDML4UQlwG6QeqLairq7uAm8Yzu0IkElmtlDoFAKxUn0sMXvulYRiTWGoMw3iFUCikW5Y1HhEvoEZS+eFVJSUlJ6xcubKOJcdkyqxZs4qJqB8RVe7s+017v8TohXtDodCvWWoMw3g4Nn4gHpPYtg1pBqpFi4uLe3z//fcrWWpMpsycOXNFcXFx3CcvSfaZpnYNiHi1ruuPsdSYZHCCl/mB/Px8n2EYk4UQAzOY0h1xHGcgD1RjdofEocDBRLQg1ecSzeWHJwavMQzDuN0Pt1JKvSSlPHfbrTxM5oe/j0Qi3VlqzO76YcdxziSiimTBZJMfFkI8YprmH0KhkM6SYxjGQ2iGYTyo6/ptibYMkGKg2hIiOoRFxuwmyrKsQeluuSZ88mjTNP8ZCoVyWGzM9nCCl/kBv9//oRDiUkhfuft9NBo9nQeqMXtCOBzeQESnJJvq3cyRxb/ebprm2yw1hmHcSnyj7vf7pwohrsjgkHVBJBLhgWrMHlFaWvp9LBYrIKKUbbYS7RrGEdHdLDWGYTyCNAzjGSHEXbZtU5rK3Sqfz3cM32pl9oSysrKVPp+vPwDMTRUbJyp5f05Ez7LUmO3hBC8TRzNNc6qU8pR0QSURLSSi88rLyzex2Jg9JRKJrHYcp1eyCqJm713ckQ0zDONhtlsMw7iNHj16BJRSzzTzw8kqhMhxnLJIJNIr/p8sOWZPmT17dtz/nkZE65IFk80Gr/0xFArdwX6YYRiXIwoKCu7Sdf1a2Fb4lMwvr4pGox2nT5/OrZKYPSb+HtXV1Z1ERPOSfaapkldKeZVpms/w7RrmR8aLRcAYhvEWIp6XwUC1abW1tcfz6SSzNyktLd1iWVZ/IlqYypHFv0opbzNNczxLjWEYt9CzZ8/Wbdu2fVdKeXUGk7lLKioquHKX2asUFxfPIqKT0h22Jip5/2ya5p9YagzDuBTdMIx/6rp+n23b6W7TrNN1PVReXh5jsTF7i/Ly8k1KqZOJaHaq2DhRyXs9APwrPz/fx5JjgBO8jGEYn0opz80gqFxWU1NzKffcZfYFZWVlKx3HOY6I6tJspOLv6QjTNJ9NNcGWYRgmGwiFQnpeXt5zUspT003mVkotiUQi5po1a2pZcszeJhKJLHQc53gi2pQsmGxWyXtP//79b2GpMQzjwtj4TinlzzKIjbdEIpHO3333XQVLjdnblJSUrK+qquoPAIuSfQa3DWsYFQwGefAa0wAneL3twCYLIYakq9wloi8Rsf/8+fM3sNSYfUVpaekWRDySiOakc2RCiDGGYTzNUmMYJlsZMGBAa8dxXpdSjoy72lR+2HGc6evXrz+GpcbsYz+81HGc05RSa1PsCZsqeR8zTfN+ANBYcgzDuACfYRjP6Lr+p3QHrkS0zHGcY1lkzL5k4cKF0Wg0OiRVJS8kCqCEEDeapvlKv379clly3oYTvB4lFAp9meFAtfn19fUXhsNhTu4y+5xwOLwGEU8BACvV5+IBphDiesMwilhqDMNkoQ/WLcuaIIQYRo2k8sOrSkpKTli5ciX392P2OaWlpSVCiL5EVLmz7zftGRN++B7TNH/DUmMYJtspKCh4IB5b2LYNaQaqRYuLiw8rLS1dxVJj9jWzZ89eUVxcXEBEi5N9plm7hit1Xf8rS83bcILXY+Tn5/tM05wMAAMzmNJdTEQDeaAasz8Jh8MbampquhLR/FSfSzSXv8w0zb+w1BiGySI/3Eop9bKUcui223U7DyYdx5keiUS6s9SY/e2HlVJnJKvkbXphlVJxP/ywaZpjecgLwzBZil5YWPhnv99/W6ItAySr3lVKLRZCsE9m9jfKcZxB6W65xt9fIcR1pmm+GAqFclhs3oQTvB4jEAh8iIiZVO5+F41Gz+CBasyBYO7cuRsR8ZRkU72bObL419+ZpjmVpcYwTEsnvuH2+/3vCCEuz+CQdX5JSckJLDXmQFBSUjKDiPoRUcobXIl2DX8kortZagzDZBnSMIxnpJR3ZDBQrap169bH8K1W5kBQWlq6auPGjf0B4H+pYuNEJe81RPQcS82bcILXO0GlHgqFpgohTskgqFxARMO4cpc5kITD4TWO4/QGgDWpPpc4bT/PMIyHefAawzAtle7duwcdx3lWSjk44YcxmU1zHGdWJBLpzVJjDiQlJSXrY7HYaURUkSyYbDZ47Y+hUOhOji0YhskSZEFBwd2apv1iO3u2s9h4ZTQa7Tht2rR6FhtzoFi2bFl9XV3dQACYm+wzTZW8UsorTdN8lm/XeA/ehHmHNwHgvHQD1ZRSn0ej0eO5cpdpCZSWlm6JxWIDiGhBKkfWsEuT8jbDMCay1BiGaWkMGDCgdceOHd/TNO2qDCZzl1RUVHDlLtMimD179izLsk5MluRtIlHJ+3+mad7HUmMYpoWjG4bxT13X/5SwXZAiNq5wHCdUXl4eY7ExB5pEAd7JqQavNavkHUNEL/fs2dPPkvMOPPm2BTD2s8Fap/UQaNOqTuQGg0KvcdDKlWTZ1bS1NqBq9NaxXw19P7q7v38oFPoUEYdkkNxdWldXd+ncuXO5cnd3EAIo/ndcxIiNXxMlWgQgFvfrlwu33VYDw4c7LKzMKSsrW1lQUDBA07RVAJCT4v0lIcRw0zQ3FxcX39Ao9t3jsqLLZNtgW3+N9GvVUSFsPYhQCZCjb1K+mJ+0blWxl095OQq4+38GwzAth9HPjda1tpp/PdgypgcxvuY1q458Vh3l+W21Nbo1Onn45N0K7vLz8322bf9DCHFKBn54cSQSMVkju0eDQZay8ev2flipxg8VFUn2w7vshxcXFBQcL6WciYjtdxZMNqlASnl3KBTaFA6HH92TPzPuhwcctCXQ1a6UVjQoOuoOVgKA7g+orbV1anlrzRo3hKvpGMadwfFYDY480g9CSLAsAT4fQk4OgWURbPUrqNFj8Kuhux0bFxYW3i2l/Gk6n0xEmyORSBdWyO4jBYFABaIxPG74W1HcLzfWGdJnoOEQsFlSmRMOhzfk5+eHgsFgOQAcubPPNL3XQojL27RpsxkAbtzDNSng4IMDkJur/bAm4+i6Ar9fQTRqw2WX1QMix8YHGL7OfIB4+r/nnt4HKk7M11e286HqXqkCxxBgayDIEUg+ALAVQR0AVgfRWpyLdfPmqoO3zrEOnrV0y0HvjBs+uTqTP8c0zf8g4iUZVAx9UV1dfcn8+fO5r9Cu8MIL7URu6/NRYJ+cVasG+LdWDbFzc0FJCUr3NXgxdOI+C53aDu3nopBlBLAWidaDdD6zh4/6loWYGaFQqCsRfYSIx6Y0aoiglHquuLj4+l35/X/95nWFS6DjGeV2t66boFXHg6DmGA1UJ4UYIBABAFACVB0Q1FsgVmymVnN6yA1VfeWKhd1wy9S/XPDiatYSw2QHj793jn+Bddjp5U7nAbPsHq0l0uGtoK4PArQmgQFSEESAKALVgYDqGse/oAb88wu05dX52opI5zUr3x835p3aDOxWG6XUa1LKc1O1ZYDG5O63mzZtOnXZsmWctNoVXn29u5D2OaT7e7QrLf4F6noXO5gDStcbfDFIAWjZAKSoplu3EnScOQC4jpAqlM/3OVx00fcsxMwwDKMAAD4QQnRJspekRECJSqkHi4uLx8b3s5n+/k/+98ITDqc1g/vpKzsdJOs6bbRb9VOAbREhB4HiftghwPj62KqDvbK9qJ093+5SXRo9eP5a1e7d28+dvJa1xDBZSFGR1IhOApInE6h2CHAIAR4NBO0AKYAAQUCIEWE9AtUC4GIC+h8AVBNBuVLWO3DFFZsz+JN8hYWFj+u6PiZR4ZgqNl5mWdbAsrKylaygzKEv27QFig4F3enz+cy2F1sk89vlWRD0EeiaAp8GELUQopaAvkfWzAYHy0HSKlC4CRznG/g0+jmOA8WSTE/fvn2767r+fqrYONGuIe6TX7Msa8ysWbNqMt9fFfUVkk4nid0kYVsCKkCAjkSQi4QBaJiGQ/UIoppArQWCMiVEJSq1VCF9ACNHLmIt7X84wbsfue2jM3IHQGzSqf45Z7YXtVpc/I0zotIfdBBgXFkU/6uWdPgm2vt/xdGDr7njvLeSBiamaX6FiCel+72VUvOj0egJ3HN3F5g48TQJ8gUB1IOar6e4Mokw1WIjBAJq/G8FZAGIiU5d9Ri45hoO6tPQp0+f9rm5uWsAQM/gvZ4ciUSGp/rM6OdG69Ql8OcvYz1vWOB0DYgmxRCkXZUNxbsN+o7rFOk43+J1x4mFtzx+wfPcJoJhWihj3x9x2NxYj5c/i/Y5abNqJZqsMmWwHWos2G80DTkiqgb75s46Xi4edfewCTvthRYKhXSl1BREPCfh6FMld1dFIhGezL0LiPHjrxRCPgyAnSGdH0782g9+uPkND6IoSfy3c8QRN0H//hZLNjW9evXq0KpVq4WI2CZJMNlw0Jo4bP1dcXHxI6kX5Vjx8sBvnz9F/9+oQ7VNPvhhMRJmujeOK9MCCd/Hjlg9o/6Q3/723KlFrCmGyQJee60tSvm8BHk+AmmUWPuYgU+m5l+ISCEsUY72c7jiki+S/T8FBQWPaJp2K2yb4ZGs5240EokEWEGZQ1/I0wB9zwLiEdvseFyVRLCzTVZDPNwkf2oy6fHPOoDOu7AxejVeAFtZsum3p6ZpLkLEw5PqZtu7/nw4HB6d7jeUEyc+CCBuRsCcJl+MGeYNf7y/ws0g1P32iBF/ZTXtxxeCRbCPg8k3rup0+kGzzmuD9b/u22ZdP4jWAdiiKUjcHfk3pp5QIQR1mL2layUBPPpt/aFvjRn63zJIXAcNBALjM6zcnamUGso9d9OjFxWFFOAlQPBbEQj4ob6+0WDu5jqiRouJoGlAPh+o2prpAOIZVUlTYMzwSpb4zunTp0/7nJycrxGxdypHJoRAIno0HA7f1vx7o58L6as6nH1eBR50/Xqt05nLrLYgrCjIhhhxd9TZmPSxQSIEAtDbWQKS7CcLAiveGX/eox+yxhjmwPLrt6/t/Hns6HM1pHsX6ocfVlkvQDoWiETGdnesd9zy20KHQFBAH3tRxVblu++sYOTTp8/9Z8N04969e+fl5OT8Q0o5Ms1c04bK3UgkciJrKp3YCWHi6wN0oIuUgNuFz4cQjUEiv7fHfrjBF9dH/0tKPevUVX8Ev/gFB5ZJME0zBABTEbFrKj8spUTbtscJIR4Ih8M/JM/HvndF69N8ZWe0E3W/7JW35VTd2Qpg4R6OBiFq2BsHJMyv6uwQ4KOzrU5TLj3r229YYwzTgnj11dag+YdqCLdDIGBgLAbgOLCbm/BmWQ2E+D6cauvqlMD7lLKnweWXN61/vbCw8AGfz/c727bTxcaLlFIncGycgdX9Vu8Lln4eCLgHgpgLdY1bpD3OMWkA4AeAaqcEBDwClvgEh9TyDY0kFBQUdJNSfpBhJe+/LMu66UeVvEVFPunAqYB0Dfr8wzG+rbKsH4Lc3X09Gv5fnw8ovr6V/TwQTXKE+JzbZO1bOMG7D5nw4am3nROYdXsQYx192HRDDWkvyb3pgAQJBSiCqs+j+Z+cfvr3F5um+TkiDk73GyilvrNt+5yysrLNrK0U/PvfuUIPvC0QjgPEvMaDyD3chOwswGxSqqI1Soi71MjL/s3C3zn5+fldAoFAKSJ2yuA9fy8SiZwb//cxU8cMnVp73FNbILe7BZrWUGkEsJfWZGOilxJtj/xgRXvra8pODMwe+sTQl3iTyDAHgKum/vqZKbUDhsdItLNBQmPh/V4LbaDpiE8iQQCjW8/yz36n6IK/XG0YxidCiEHpNsdKqfmRSKQ3ayo1uePHd65H7SNEOAoRg4053b1lu3e6uVpvK/EbGHXpBJb+zgmFQh2I6H+I2CFZMAnb+gCOC4fDf4z/9+MfXXj5xf4Zf+sotnbwoS2atrJ7T5eJhD8i2Arrp1u95kwQx5/39JCnOTnAMAcYMWny7Uh0KwJ0bB774F625Q2FoUR1CuAb9fBD5xQ6zrNSyp9vZ5d25pOr/H5/l+nTp9extlLIdyrkQNucd0DBcSCwVVMd9b7JLcU3brQJlPMwDor9maW/c3r27Olv06ZNCQD0Se4dfyj8ey0cDl8Z/zVt0qRBROJVBOiCCDoQ7QMFNvpkIooRwHJHExfCpZfOYa3tGzjBuw94/5Of9GtF0ckDD5rbC+pFs+sH+9bWgubggs2d4L5/tIJ5qwLkqJSDXOYJIQaGw2HuuZuM557L0du0O5982gS0nbjQ9ocWG1YlBYOgamuLlbIvhiuuWL4nA8NcHFy2IaJyRDw4nSOrO6jdg91/dWKHmVrf0ZVbCUSy60J7GQs06NG6FvpG590qRPT5ty/4C1eEMcw+ZuxYEJ/3HWtWY94LJXRkATZU6xDs6zVPgGQLHx5ZswT8//kYAmvXEhLhzjLK1EhpJBIxWGMpePPNg0TUukQI8QKSiscdez0RsBM33Hgf0edDZccmOzG4F64cPo+VsSN9+/btq+v6x4g/apOxw7suhIZH96gde8NNWv4JufNHQCyuSbFP45Cm7RSggxugPSyN5l3+TlXvD8ZdNGULa45h9jMTJx6tgfgb+vxnQizadO0C93lUJQQiEBw+cSLkLl8GGI/lkoTHSqkVW7duPWrhwoVRVlgSgc6EHKjzDwKURYCYB4oow44ae/KnNvbP8wGCTWFwrNE4yCpmbexIfn5+u2Aw+BUAHJ3KJ0spsb5Vq3+U/eY3FbpD90Istk8OWpLsrxqqeh3LvlM5sX/BqFEVrLm9Cyd49zLvfHTC86cE5l6VK6J+oEQ13360u4CEjoM09bs2+H+Tu4Dt7BhbKqU+j0ajl3DP3eQEJ0w4OCa0dwSiAUrtF6O3Q3CJiERUrcj5i7r88vtYKzuSuJLyGSIelWS3BjXdusGaYedBdduO8Y3dfkn0NFenAgRdAnaFjfOuDXw26p5hk3hTwjD7iJvfO8f/Xe0pf1vsdL5+E+SBIEWYQS/PvbrmEVHGYtCurAy6fv4pCGfHm2iO4xRv2LBh4MqVK7lKKBmvFvXVNPwX6JqJltXQ6xxpv+oSSEoAR21WqMaqESOe5MPWHenXr9/hUspvhRA7TfIqAhp0zFa4a8R67NwuCuA0dtvA/atJAF2HFfWtiz+J9Rl5zZkfL2DNMcz+QZs0+S4gGotC+EEp2p/5h8SNDESloPXCBdD9g/dBq6/fITpXSlU4jtNv1qxZ61hjSWT5WU4X0OFV0PA0OFDd6kWDU6kHoKfArrsTh2Q+xNMrJFoZfp60XQMR1HY9mFYOHYp1nbsA2gdGhKTpALa1kBSMcUYN/5Q1t/fgBO9e4s8fn97mDLn6LdO/5BRQYn8ndrdft4CagkWrAjD68UOhslb+oGql1JK6urrj5s6du5G1loTXJg2SUnwqSMmGgPLArhMCTUOyrHdtiRfD8OExVtCPSVTyrkLE3OaLgBBhU78CWHHeMEDLOrDGDqEhzdRa1OOFOTMufum8x99kzTHMXrYFz43W23fuNudjq+AozTnAprKhQFFA60WL4NCpU0BGEwVBiOA4zqKSkpKerLEUTPhPf80nZ8RtN9CB362S3w9OXewvNG/2XTBuHE/33g7DMHog4szm7Rrie1EhCC45cTPcObwCSOGB2xg37Y1RwRq9K7xX3du89vT3I6w5htm3iPGTPpEB/6kQix1YS56ICwIb1sMRk4tA37r1B5+slNoUiUTas7ZSiO9DOARycpc1zLU84MecRKAjguW8AVvrR+FQ4Irr7cjPz/cFg8E5ANBzOycIWw87HJZccmncQf/wawdUm8EgiNq6C2KXD3+bNbe30g7MHvP7d686enSrz149JLDZhBaSfmtYr4JgzUYd7vhXN5i9NAgAalpNTc2l8+fP57YMyTYiE4tuEESPIWJgf1btptRl/OfQNHQc+yvlBC+CUcNYfzs6si7BYPAjAOjbtIlbc8qpsP644xq74iY2cQdakwoEtNXrwRTz7//oovvuZc0xzN7hojd+c3S56vnfeU63g3WyqEXsb4hAaRoE169vSPIG162LB5LfbNq06bRly5bVs9Z2wtixQvQ+9kbh1x5vqNptIX64wYD7fEix2Ee2Xx8FF13Eh+Tbceyxx/bz+XwfNA1eC/ocuPWidXDhoM2JAWotIuQgAIU1kAMzYofePOS0yJOsOYbZBxQVtZMKPhFCFJJSLcWONxy8atXVcMh770DrxYvjBmGp4zgDS0tLV7HSksjsC98FILTXgVC2iIzgDz4ZEGJqNqBzKQ6McRul7SgoKOimadr7DbExACghYP0JJ8KakwcBOk4L2Vg1XrGjhv2V/bBTufEeGDPGYu3tGZzg3UPuf/fiHtflfju9k6zs0jJF2miHr3qsx/y3/wsnVFVxW4ZkaBOL7gSAB7FhUhYBYstbHg5ghRIH9YbhZ1Syxn5Mnz592ufm5q4BpfQll1wKVUf1ahyq3bIsbuOrBUCnBuf8/cMLxt3CmmOYPePNzwYfdOeWyzcvjLYD0SLqPZu74MYDJ722Fnr9+8W1pZ9/0ZU1lsIPj598M/yQ3MX93V4jfSCCiA7B/9TIy/JZWzv3wzk5OYsQsc0DV6+Cs8yqFhduNBoIAvBLeGr9kFtvOvftx1hzDLO3Y6rJMQTSW9wPlvDJwrLg8EkTY/PemepnbaUQ1xf+syAoP4B6bEG53WbKbPiB1CY8uY4rsJNgmuZiJDp87cBBUDFwYGNs3IJ0+cOmXWoAjvOcNfKy61lre5puYHabe965rNs1uTPeP9K/ri/ZCC0wH5gwfUSOz1/5ZmXfEZed+cVHrLkdEROKRgiBE5Fabnu9bcElfakEDIXhw6tZcz9m4MDCjuuGnL5u8TE/ASQFLXJRJmjlV3AczfvtRxeP/StrjmF2j8um3NIzEus5a6HdJaijQy11X+OggMOCm+2fOHMGTbjob9+y5nbcj2qTJt0AwdynsLa2xeqx6UaNsqwZjsQzYPhwPmzdjpPOH3L+LSfNn3LJkE1A9aKluuHG1pw+iR9W59909qnfPd0SLh4zTNZTVNROOjBdIBzVgm5g7MwAgAoEAOtrL3FGjnyDFbcTGX3lPwd88j2I4g+tjFtkpkMigqJVoNQJOKh+BWvux/S84IL82sOP+H7toMG5De3CWrBTRr8f7frYX6lLh9thyBDur7ybCBbB7hEq+nObG1t/9fmR+rq+4DQcBrbMiKnhwA1RxqIHDfHP/fCG1685grW33SKYUDRCIkxoycldaJo2S0RSiJMlwUzW3I60u23opMV9j/+hcr0lszUq4Aurz2O/ePume1hzDLN7zLN6fLnU6diik7txJClaXttG+8I+dup1b17XizW3HZPfPAMIG5K71ML9MNk2CcTjNIKnWXE/5rKiy3wv/nbpzy45cQtQfYv2wRjfG0PMgYG+hU9O+OjUE1h7DLM3fB18JgS26ORuApL19YAgxstJk85izW0nnGLoASTfhihCy03uJjIdTkNZdjdAUUQzIYe192MWjbzyinUnnJjzwyyIlpzniEZJCrhFX7PhZ6y53YcTvLvJCx1eeqCr2NoTCFt8Jqnxp0NqL6vot22nFf98ys/zWIMJJkw4GAVmUwUlklIkCHrLCZP+zlX427jird/d/Ha0/xBs7PXV8hUJAA4hTa4//ndXv31DP9Ygw+waQ9+49/1ZdEQXbGltGVK44jXUrn2Eer1AxPuvHxg/vrMG6kPcJids8UFI/J3LyR0FrxVxENKM0R2WjezVastFjYrEFr07TuyNIRdqYchBS7++7Y2LOrEGGWb3kRMmPiF0Xz8ioiyx4/F/+IHgeXiuqA1rsBH6DAJQEywHRK25tWzp1hwCeDzU+u9kDTbjP/85WbbKufuHlgzY4iNkxLgByct5Hl59lVth7SYcYOwGL3x47g2FOWtvBFKQRQk2BJLQU69oc33ed1x10rQZQfG6QOxKmD150h82JYg3a+PHH89aBLj49Vt7fBzLf0gnG7Ir6U1YS4E2xVbvKaxFhsmcE9946J7/Wn3P1px6yqY1L5RDperIk0+bcv9E1iIAvPpqa03IL1CpxJ35LLHc8ZeupoY0DV+CiZPPY0UCPPLuRRcP9C98GeqybaC5oM7WGrixXfH7V7x3c2vWJMPsRjw1cfLFiOJGiMVafHJ3h4241A6RbYBbGMaFMRN00IJvgBA5DVNDsol6IPBp99JXwd+wJgH0Vyaamq3+izU1WdV/KLG/Ak3zfQYTJhzGmtyNXQ2LYNfoWPRUq0tbff80WBZlYfFkQ8OG4w5ae+VTH190tOdf/kmTfyn8geMbprsSZaMyEVD/glclQLVo/dk6ahPMvp8cAUnRbHXEYVe//ZuXWJMMk56RU246eLVz0O+Jth14Zc+KJwTbpm/qe15w/TvXe/5aqNADvwAUvUCprNJl4mdFJAId4E546aWAl/V4WdEtwZ/lfXVfQNVn5d6YSMBh/i3mb/yfDmULyzC7yNixGhA91WQXs24j7jgkAAaIoqLfel6X9f5TQIpzQBFlQ7nnDrq0Gn7uW+kL6OrxNSlsXd6DAL5me5Zs2l/FX79OiPIGNrC7sbdmEezKBvYy+UmHx//ZRtVk8c14BKithaF68fQB7z3u3UqF8eOPEkQPQzSabSfNP9DQ30qihhMm/8PL6/KiKXfc+YlTcLhGdrYOSEHNqac3644bdfWbNw9gS8swqVnjdP3XcuiUzQk1dED4vozm/8nTihw/voMg5yHMyjiyGT79JBHMvdbLqhxz0Bc3tJXR/GzdGzf81LYFXWXl+KveuJ5bNTDMLiCPPuYp9Pu6AGTnoMKmOFAouMXzrRpITIDGC8pZ6pQRQWJ3wJy7Pb0me+WfoWny4mx+hvjeUGra7forr5hsZXcNTvDuAre2/1+HQ/2Vw7e17cnW9SLgMLm+9UP+l+/y7IuPchQg5mRzD1tM9OOVoEbA+P/08aIeH5py/sFf1h/9S2HHsjZR36TLOvL5ip0jbmJLyzDJGTd15Igv1TFnSMfK6on3BEhz5WEDbp967aVe1aUutQ9QSJ0Qs1qXEIsB+nxPeHldFuStf1QoO5v3xkCE0E3fgFe2iXDLJIbJlDeLCgXAaIjFKNvngqCud5dtlGereOnLnNdAivZZ15phe+KuqBXcRFO9O3ANJb4Ltp39z2HbQLrvQza0uwYneHeBGgi82EZsyf7FkvhnH7l65KvvDfBeFW9RUVAgjsUs91/QdOqs6a0lOqO8uCbfVcedUyeDh7hj2BzBJr3DVXe8+VPuN8QwO2Hmc6C/Gjv5drRikPWBJCik+hhMjfV7xZMD14qKQoQiBFnaIunHlhtIWBbIiUXPe3FdvvXRwH93gIqG+oGsXpPxt1BJOs03p+Dx98432OIyTDrjR6hF4TaA7GuXtFMsC1D3/wHefPMgz6nyE+gGSMPAgSy/UtP4YkINArQNfuLFZSkmTLoNpZSUpRX12++vELADTJx8GRvcXXgHWASZMXrqNeapreYNBds9IusS3HqYX+YM85ouEcQLqGkILjB8Dc/j2AB+371eXJeVqtWT9S5ZkwhAq+tzoRR6vMkWl2F25I4Ofyrc6OQZAt1guxEkKFpgdQmMmHqH5yqGpIIbGwarIbrBdiMQxR/lXJgw4RAv6fGGd2444sxA2flgS3DDQSs1FD5R8OycspFscRkmDRMmtAcC1yReGpJJtg0YjXnvRobuvxgI87L8nK7JK8d3iQSEBfRl4CRP6fHVV1sLFD8Hx3HFoUvTM2hA18Bzz+lsdDODE7wZcmVOyR/Ast1RKNiE5cCh+oanPBdYEo0C2ya3KLOhekgRyImTH/GSHn//zhXnl4peAQFELnkk1MimT2J9C1//bEB3troMs53LEvrVldAKXeSIG9Kb0+p7nTr2s8GaZxRZVNQOES+Cxp5RrtElCNlVonasl9bkVYFvztCA2rhlSTY8BREQiV+xxWWYdFkE/wXo9/nANUY87pKIJOFP4CWPVfGi/FVjqQm6xycjBAFgoLfWpO8YAHLjIP1zoG3b9mx0M3wNWATpufvtyzv301YYQEiuejBCGpC7pM1d71012Cu61CYW3YTxzbubfHL8L9sGBPJUxcm70dCfRbSu4UV2jy4pvrmEZzYNHcOWl2F+zGpsfxMo231+CdTZn6w/q61X9CgRf4K67rrguaEiGdQ9XlqTebLuch1dtiYRqFdeReCpDy66gK0uw6Sw5WQ/CbGYy5Y/IAg8CnJinpltQt/CYZCDPd33YPHwUNzgqTWJeA0K96X3UNdBU+oXbHUzgxO8GXBebtmRuqRDs78nzQ7LBcFScKZvzq8948QAjgO39IrakQ5y4sTTvKDHRz48r8//7IO7S1TkLlViw9PMVocMeG5miK+iMEyCu9676rqFTldwR3uGH7NG64xdfVvO9owyFfwdLMuFGwwCkZvrmeugY8eOFYfk1Ax2YVIAIWpDH7H8r2x5GWbnaEVFJwifP0DgPp+MQoAkDxXNOLnPQtSlYX8e9qCPoY1ndCnxOlDKfc8V3zOSuJctb2ZwgjcTIQk8OUfWu/PhCCnkW3oEFBX5XK/IoqI2CHCKi5/QTw4c6YU1+UlNQUiQagXkykQ9BNEa+P6KM1uz9WWYRmZEe45uGK7mwjVPlgVVFPyDV3SJuu8o93TWaR5JAqFlg5gw6Xov6LHziSWXtZGbsn642k5RAk4ILD4MRhMftDLMzuydQ2PAttxZMOM4cbvmoWpBOgUcVz4XQH08Og486Ak1vvFGgfD7AVx46NLwTAG/H15/lVsYZgAneDMgpvBacJRbQy20SDv8ieC/j3C9Im07AJp2qGuD5oZTZ3miF9ZksXN4Nws1dOvzbdA75My0Dg2w9WWYRqbHjuouQZErN16f8iEAAIAASURBVGLKgVLRq6cnFDlx8mWoHLf6YGyYwo7wUy+oshuuvw1iBODKG1EIQYzhwxdfehFbX4bZEQI8wo0HddBYxE8ip1UrT+jxK70ACP3gSmUiQsP4JHGZF3SJ0djvEi1T3BgfI8b3VzF9NFvfDOIKFkF6jm21uaeL2nzuQI60Ww/MWdDO9YrUtGNQSre76qGeCCzFlktcuq9soMbS4OTAohvY+jIMwD1vX3G8AmyNoFzpiIUAqrBawV/eu/RMt+tSIpzfWB3lVhcc90ziMC+sy/6+ZYXgoKuf8QRtUYgtMMNsx4QJBwuEw8mdlYKASiFYMdAmTb7V/cqUZySe2p3GnIAAqSONdX/OS4A4wdX7K6UABQxgA5zJu8Ck5p//zGvr2+TqRwxoUegiK13fn0ZXaoSrDV/D1VetoxeWpU3iaDc/HykFNeQ7kw0wwwAsUl26I5DfnUUJjaVQoBxY5HR2/cBTIuoKLj6do4YwmQJQ9O9D3azHNq893bar2KLFH9bNz3msb2UeW2CG2Q4p2wNAe3SrU0ZsbNNAygNDyLGrux+PAEgAnKYNdL0mkXKBXP6MhNy+MAM4wZuGhw7+YDhYClwbWMZRBPOtg12fTHIAT3Z7ghf8foDHH/e7XZdVvrauDrpQKVhntz6GLTDDAMxzurVRIKTb1/wSp2Mv1wcgAHlurfqCpjYNBEFJflf3w/9t22nHN9R7Ebq6hNcmefjo50ZzH16GaQ5RLiAGXf2MSgGhcH/rJAXuTpg1+Sjy93X1cxYV9QbAHECX7yEROsP48R3YCKeGE7xpaC8qjwPH5cchBKAQTNe/7ISHubxyiFAp0Lp0cb0u14t24OZTSgSCNbJTDltghgFYYHfSbZdvV5AIltnt27r6IadO7YAIHRDcHoKABja5OgDpJisP94LtqSVfj669NvvZCjNMM5TSwfUt7xoKoFyd/KSZoIOAQzzy0nZx9eMRHQwA7h+YT3AQaNpBbIRTwwneNATBaef2cncgAkRyfw9ehKC7Hw+woT8NgKsDr9unnJ8XJXffDEUiqMJWbIAZBgB0dPLI5TlBBILN1MrdEXNlZQAIvJAsEyTdHWjlYtQTDkoRtgramyVbYYbZhiYEuv2UjhprP92dJ1kMGgB448o7un3vIYNA5IW8ns8Tiew93YSyCNJsYiHqiVMCHZT7n1PzwC07InCUOtTdG8vWR1HDMH0Xby8FUjVx0RDDxGkrarq4P/hAqnSCmrt9sOYHQL8nQkkhXL3haIX1nrhhQgA5raP1HCsxzI9CDdHW/Ua8IciQUFTk3gOejiCB0Bt9xhFdnRSUZAcAPZHX0xtuEDCp0wgsgjTxiADN9RW8jfkkzQPG3Qu7LhDk7gCaQPg9UFWPBMgGmGEayiFJ94DtRnJ76wIhEEB5Y9+p3L3h8HlEj4ggfFouO2OGae6uBAU88qgIrda6Oz4m8MgNBeX2/ZUAb8SN2LiXZFK+DiyC1NQobYsX1otFYovrH9K2PLCiBaCAFa4OLP20CN1u2wkgiDE2wAwDAFUUrPDCc7YSUdvVD+j4ogAY9YAqCVC5esNRCYFaTxgfgtqtdfWKrTDDbAMdrPDGg4INQ3/lXp8VAAcQqj2hSyVc7ZMdpeoB0AO+iiywhcVWODWc4E1DLfg2uz7BiwgOic3utwnk/sASEQTiUjc/4rgzX1mnoXJ1EW/82VpTDRtghgGArcq/yf39/hDayhrH1Q/pV15J8CpwpKtP6GptnycSvIhUExNtHLbCDLMNB2zHA5dbgQjcnTBrDQ4gVXnipUWX5wAcrAcgDyR40QJhc4I3DZzgTUOlypnreik1RM5ioesdNUCF69s0CAFR257ldl0e5PL9CKGADs5GrhpiGADoLjY70uVxFiFCV6x096lOZWUVkSeCSQdRufo5N0KbNV6wPRo568s3teVgkmG2s3GgvLBFxXpXP94xYAGBN6qxhcsL2YTaCES2+5ckVENUcAVUuteBRZCab+t7TAbd5WJChO64bqoHHPV0EO7WJTk2wKhRG9yuyQ6xDa4+iSUU0F1snscWmGEAjvWvrpLkKLev+Z56xSJXK/Kaa+oRcaMXkgIOqsVufsJPant87wXbkyfq5758zcv1bIUZphlS1oJS7u4jJkQ8PF7iak/VcHmI1nnjpbXnuHxNzgFE19+sIcI1sHjOGjbCacwXiyA1ky76+1Ib8hKXpt2JQzoIhNWuNwqIU0G6vJd8fdQTvZT8GHN18IwCobWIfsUWmGEADoe1FYAQc/eaF3CE3DjD7bpUSB5I8EIdjBw5380P+N4ljy7bSgEFLp94utDpXMkWmGG2DxydLQDg7tktQgAhuN4ng4JNnnhnfVF3H0oOHx4joDq3q5EQKmHcOL7hms58sQjSU7Klw3pA925it9pa3VfRnltd78Pqaz4jcnvXKPREUrBS5byHLm634dcINin9Bba+DAMwuv2UmQRU41bzHX+uPD0G957zSpH7tUlfuP2glcAbFVFhq8diEO6Os0qsHnyThmG2Z+TI5QSw3NXPqOvgBAPPul6XSN96YN+xFQeA6w+XCcQcV99URgQimssGOD2c4M2AaifwH3Dt1H4CHdTyN9b2Xe56RQaDdRCLubYHDyGCIprqhTXZRVbO0VzcaqijvZ4URZey9WUYgD4DN249Vl+1wUF3blkUSiik+ZsRwfX909SIEU+Aprn3AePPRs6HXliXc+2ur4GOLr3h1vBM9Lea095iC8ww24GoSNAKNz8i1dUDDBvm+tgYT47+F9zcUFnGH1J94ZFl+U/QdVfvrwjla2yA08MJ3gzoKte/ZpPPrS4MltttF0655u9bXK/Ijh2rQAj3VrgSWChhhRfW5ODAvP+hi6+iCILiE/W51Wx9GaaR/vqSN0hz6cZVk9BVbH7GK7pU0eh6tw48JV0HR1bd7wU9ftq9/4MKc10aKRPMsruvn3Xx79ax9WWYndhxB//j2sM6IUABTfGONkWpazNCfgBwxL1e0KIzYsTrbr6prCwLYMQl09n6ZrCiWQTpebO2/5qttu7OwVU6wNv1/Ys8ocghQ2xQKuzeB6QtTm3tx15Q5QNDX57eSa/coMidSYKOovKrccPeqWXryzCNPD3smbsP9te4slawk6iCpdG2T3tFlwRwlyurTBCBauuWwfAxnujbOvmYcbE5W9svcOXD+QC+jfZ+iC0vwyTh8uHjXds2SSAg0t89o0s9dh0EXPpsdVSDg2sjXlGlsmIfgxtvu0kJSOJfbHgzgxO8GXDXeW8sroRA2H3X0AiWRzvQnUMn/dsrurQlvkSA7ruK0nDaDN/ANdd4Ztrzuf5Zryjd56pVSQ1TbRVcGJw+hS0vw/yYo62F08iFG9egUxM5Kzh9s2cCEL/+OkWjrvTDJGisl9ZkLtQ9Sa4LJQjWx9rC9We//RhbXYZJjuPYL7iyiteyKxzbXuwVPeLxVhhqyH2zeBo6CClPzTNRjv0nN9Y+ESLYfnE3W90Mt6Msgswoqj7xH6C7LMHrAyiOHT7OU4ocPny5AvrObU3Iye8HVV31cy+p8qnznrnnSLnWVQleByQYvmWr7zn3jc/Y6jLMj4mhvC9XxFz3XD3Ehg88VbFfUlKtULiuXzw5TqUiLwys2cab1k++tkHUuCspgLAxlvMqW1yGSWPzbDmO3Ni+VUAYrrhimaeUic7fwW2JQSILUHzoKT1GtYWglOt6R6Ntfw+2vYWtbqYmjMmIO86d9MbM2iM3uKn4c3V92+ghUPM3r+lS2bGzyU3XQ4UAVR/9AK69dpPXdHkErX5YuCRZTwDg9ws4MzB7FFtchtmRnwa/iASpfoFbDnXizxEQlv3EQS/+3VOKHDfOBoAp4LL7vQ7i1zBy5HwvqfK2syaHS2M95gI6rlmVUSXtSH23Ira4DJOGmg0V4Kh3XfVMmgY20b2e0yXh+0Aum21CtAIH1r7vKT1eM3wtAXzutjkHDtFbMHx4HRvdzOAE7y6wjDpdYMmga55ng93qlf5nhKs8p8grr6yiWGyCa5IESsWUre7y4prsrNU80xqqXXOid5Ra+u19Q1+ZxtaWYXbk2rM/3HS6b/bLSmT/ldC4/1G6H84Pznys72lLKrymSzXyspcIcJlbghDSdSCkX3txXf51y6BLonprcEUbMyRYbHdeMGrotKlscRkmDWPGWCThn655HimBlD0FRo4s9poqcVD9N4A03TWZoSABkLjOi8vSyQ3e4Kr2hQjVam75w2xwM4cTvLvAtOgx4Yr6nM9dsFTAAkkPVp3/ZwAgTxo/h14CIFfc9VWAX8MVw0u8qMdXzn9kiaEv+8QBkdXFYPGf3Q+W6iXW/o0tLcMk57ULH3vA1BbFHBdsX/JpSd1rw/56h1d1aStrMPkD7kgKWNZrMHz4Qi/qcfwlTy37auuh/wSZ/XtjCPjgqeozL2NLyzAZxlPDh7/pELjisI4cB2zbvtazyjyp7gzwu+GgDgDq8DscXPOpJ/U4bFgtgPqVK4bZ+v1AQD9L3PxiMoQTvLvAE0OfiL5da/6pDjTK3rwoAfgkTKwdcPekix5e5Flljhr+MSFOigdm2R2OoFIx+oWX1+XHh4y9/Eitoiqbhy8pIaG3vvar1y96iK+FMkwaDP+Sn7TF6qx+Bg0cpxNWj/G0Iq+4YhnV171E2Z8UWOW0buVpP7wimvv7TVartVldMyAEfLbliKefOv+pOWxlGSZz8jZbhgKIZvtzEMCjMGrUBq/qEREcqMffZX12iGgr2PaVXl6T9pYtz6qYNSvL1yNQffQjZ8SI19nK7uJ2hkWwa9x47tufvVUXegH07O3FO7u2y9dXn/Xfh7yuS2fE8KvJcbJ2aij5fIA2jYKrhy/xsh6xP1h9fCtOP8ifnQXZChCO8q2HsHPrqWxhGSY9z5/7dEk3bfO/s3bjDQKO1Vcu/vSS37/ieT9MzlhAyOo2OwrUYzB0aNTLerzm3Glr34gaT4PM3uKHLXZwxbf2MfezhWWYXaPyl1dsJqK7s/mwjgDqnJHDb/O8Mq2aZ4FU9h5yIQAI+g8OiS70tB7HjLEAVbYP0q8FQb9nC7vrcIJ3Nxh15qejZ9X2KKFs3MCqVlu/sI66yautGXYItG34f/bOAzyu4njgM/ve3am4YmwDNpg4AhtZ0jXZJjQH00JCM7hgevsDgSSA6aGaHnoJBEIJBNNM7wFC70j33p1kyzYIg8HYGHfJqvd25/89+UwM6K1s1bvT/L6P7xy9i7Q7s7Mzu293ZmfXqWdk45uTryT98BRrEeCFA68r/5Xz3T0ZGFBCDjTLUeKbA3AqSNYkw2waidFnnT5MrPpOZdjJfQKE4caqmrIR54ZYiwAwffq3CsSfKQOv95IhXH2+pg4//GZWJMD/7fvalW81FL0KAjPOEzdCAP5Vt/sFF/1u1lLWJMNsPurwqTcTwMeZmKqBgJJCiQhrEQD3hHUgzf8DoMy7Ei8IgFQV7tpwAmsSQB5++DOK6F7KwNvKbpuJ8AZn2rQy1mQ7TIFF0D7erB01ZY3KXZs5+6QEFAjAi+tKjj5931firMEUR02tUoTXgMi0TQJY5wiaBlOn8qbg+mtFVEbnnj7O9+UiBzPHkSk0YM/AvFkvHHzNy6xFhtkMmx8D6w7K/XCXEcZyJ5PeVvYTDU27+6oOw1KoZy2m5sFpk2cpA2dQpvlhqT511q46kDX4P96rD09e7AzIrAJFPj+811hw2ozfP/Moa5Bh2o9sbtyHSK3OqHmcSCkUZzYfMXk+azAVX02o/RjAmQSZVs9WwSIg3Js1uJFNTp92snKcZzMpTnZjQXLkbXL6lMtZg+2DN3jbyYyDX6g+d83kP6yjnAzIx0vgkAEvrdnxnGP+8N/nWXs/8wfTp1zlkJydCW+daX01yWZn6XfbwtSp61h7GwUkU0F+eNiF2xeaizMjfxYiTPTNTbw06arjWHsMs/ncfuDD3xThN4flY1MzZcDcbYCEvQJzb3v0kFv/y9r7mR+ePPkWRfLFjHlljviDdOCElmuQzI/MPPCf9ectP/rIGtpwAILS2ioVAcyrH/Tofh/8/h7WHsN0kGOOqXOAxhJCbabE4UrgS2ralLtYeT8Tze7NL4FU10OmlLAmagSC03GPer6F8fP4qrnx/wBhYaa0l6TzqUw2XsSaaz+8wdsB7j/47g+fb4ocso5y1qVvEEsthSM+aNrx1oP2/YSvEXpJadq0aUqpJyD9NwlqHYTfwllnrWGttU6pqN55uLlqhYL03rAv8n3z5huTLg2zxhim/bxw2LUv7JE778RBvsa03krqazTB7rnzz37y4L+dz1rzWIRMnXqwIngq3a8TEuJKRyb3gaOmVrHWfsljk6+d//faPX+7WA78Ia2jKVNALPmrpwvn3HECzJypWHMM0wkcfviXDpoHpvvpJ0IERXCHmjb1YFZa6+BuDecDwq3gT/tX6E0g8CDco55vQ7bGsceudJLNExTB/LQ+zCYEKKBPpJL7wzHH1LHiOmC7LIKOc+mrR0y5pO/Ls01Kpp9ITQGzasbecPR+b53HmtqEueWxJ143DGMfUOkX67uLXscUBTBp0pesqTZkVQ6+kkW31Fclh5sijWJMarlBJGB3//xF7x568fasKYbpHA558YJ932vY6T81KjetwldqeZNOtG9u5XUvHXzVX1lTbTB7tiEUPiuADsA0jFGJaLXj4Dg4emo1K0vPZf85KnJ27qvv9BUNfdNryUEtubDLkiNnj59YOY01xTBdwIsvbmfWNSxEgLR8YycFvqSmTuEUO5syY74XuB7QODctt42QmgBgEu5W/yprqg3+NXsrM0CfgMAR6aZJajkqjjHZsG53OP74RlZWx+ATvJ3AFfs/+uQNq/fd5Xs1YFE6mcoqym/+sH77s3hzd9NR06ftq4j+RkRpde1SAX7uIO3Im7ub6O9LIXlAf3urMebiMnchR+nhvMAPjtzVv2AWb+4yTOfy3IHXvb63b86U4caqFSqNtngHiXVrDs0pO5M3dzeRqVOlOnzKQQRwdzqdGWo57QX4iUNyd97c3TRm/m6W9cc1J45drAbGVNrs8RCspTyY62xz6/h/VBzBWmKYLuLAA78BAbsowC/T6dQgETUqgOt4c3cz1lR7NJ0HBJemVeE1bCmo9jlQ0wG8ubuJHD/1ewdkqQJ6LZ0K266vv0CPS6dpL97c7STzYBF0Hre/sn9glLmyfN/8iiJQRo8GsJ8nhzbMwe12PGzPdxezZtrB00+PNRV9hrKHT/IKAVKpD9ThU3dnpbRvjpv8/Lk3vJosPbvZwR4MKAEG5jTBNN+7R//9gPtmsVoYpms49fVTh3xUO+qVeclh0Z5shwKErWjlmmMHfbj7Nfs+PIc104749ImnDjZJPY4AgZ5ujAJ6TK5dcxKccgoXx9tMLnv72Jzd5Lwn986pPABI9GhsvIbym99p3OHASft+9DprhmG6gX//O18Ech4UBJOxR60fAJPNpMzARDl9yjusmHbI8IO8ICh4ExAHpUFzysGs2w9/A6tYM5vJPff4jL4DrxACLujxMQWkFOAMJeDvXDi+MwNoppMZkXPlOfkNR/9mCYwY2pzKQITdZiZr6wx4I94PbnxqyIr6JmfHysrK1ayTdvLoo0NNw3wYCCZ29xWj1Mml7wHpFmfatOtZGe1nxyE7lhi7FyW+230CNAwZ2vKmELtRj8JxoM/XX8OIN16pst/9YAxrhGG6loPGDN41vue0D1aUhCDZty905+kh1+bN+nrYorICtnnj9eU/fFsz6pu137Afbi9PPVViOHQPAkQRyNfNfpiAaJEC+KeaPu1aVkb76dM/OOnEg5uf+dMfvodfb93c7Qf6GpoQPp6fD1c/PfzTt96O7dKyZ88wTLfwm622GrHkDwdUrIhG+zUP3KLbfbLR1AT9P19A277yMvrWrRv/yRdffMZaaac8P8kfCo56GAh3A8Dc7m+AWg6Ij+Hu9WewNtrPHoMGjV0+fufPluy1N9RvvXW3ro1bkBL6fPcdbPOf/6wa+tZ/C98CWMZa6Tx4g7eTiUQic4lE4cC+SZq86xo8YZ+VEPB1w0VDJHgz3hf+8fJg+HJpgAwDkUiVWZY1jrXSAWbPNkwpdiGhZgmC7brFdxEpAnGbBOd2mD79a1ZCh21yNRINUIEArN1hB1gycW9wcnO7ZfLL/f572Oa/r0Pe99+TIEKp1EO2bR/HWmGYriEajeYRwXtIKprs0weWj98ZVkRLW67YY9dP3jBwTiUM/fAD8NfUECCiUuoO27b/wprpAFNmG8bBal8w8HGB2K9b/LBAIIdukJS8Fo48kjfoO0BhYeF2OTk5nxHhkPxchfuGa+CMg36Afnmy65chgiBRnQc3PjsUqr4JEEBLbHyMbdsPs2YYpnsIhUJPGIhTZW4urAyGYNmuu4EyzW7xyX0XLoSt334TAqtWtfw9AihfunTphKVLl/JtjI6I9u3cncEUswFh227JgyfcP0KPQnPD2bgnfM8aaD8lJSX5pml+hAAlyjChduSvYMle+0Bzv35dbpNuLJ6zYgVs8+Yb0OfbbwmlRAXwmG1ZnDKpE+EN3k4kEomciYi3EFHLWrI5KWBwfwdmTFoGu49ZB31yFIiWCaozxL4+Tl1XL/C7lT64/umh8OG8PpAfUC0vRte3oWVxeaxt2/9m7XQc87GnLiGUf0QQW3b2SaL11WaxlpC+NP2+Q5smTeKN3c6xyX8j4tEb7KGleJ6S8P3uE2BVKAxusKkMo7MssuX3YDIJvrp1MPjTT2HLWBmQ6WtxaK5RAkATEe1u23Y5a4dhumQhebDP53tOKfXjCfrm/v1h8X77Q92wbUAFcjrt9FCLzROB0dwM/pUrYNib/4X8xYtbFq4bAiwhBNTU1AxbsGDBEtZOJ/jhJ568GIhOBYCh7v/ETvfDsJaAKqSSh8ERR6xgiXeKTV5mmubl610ggSMRfCbBmQf/AAeMrYH8XAlmp8XG61MiNTYj/LDWhDteHAKvlPeDXD+5IfOPfyMWi/kBIMnaYZguj8NLTNNMOI7T8tJTSAnS54Ml++wDawt2BJmT0+mnB9FxKLB6NW799lvQr/oLIJ/vfzE6IjQ3N0+uqKh4mrXTCfPte4HjAIxLAWE4APg6d2upxWnUAahqIHks7pGsYIl3nNLS0mmI+PiGOBlTheV/2HlnWFE6br1NGkanqhGlbLndtmUsBkM++ajl929YG5umiURUVFZWNpe100lzIIugc4hGo/2JqAoRt9l4uaAIW4LZ7Yc0w86j1y069HD/iu3kskgeNrWzQDSBBAFfOFv90L+PvPWCq3POqvwmb7Brm6bxywBZKbWwvr4+tGDBglrWUifw73/nG/7cPQFoTwQ4AQEGdGxBSYoAywnwbgUyAYcfbrGQO21RWSiE+AwA8lo2dzfakHEdTbJfP1hX8OuPBk4Krl3c1H+f5bKvKdr5Gtp1jdsbK9b16Yv3N9z1n9Pyly7xGclkS9D685ODSqm3bdueyBpimE4PWkcS0Zc/nWP/Z/MNg4eAE9rhgz6/+fXWXzhDfl1PgXZ6YQA/StrBWLqksRHuNx5/c3Let4sLWwpFuFNNagN5w4slIvrOsqztAcBhLXUCs2b1A9Pc0yDjIESaCgB9sEN+GCQBfkJK/lMJvwWHH8o5kzuJYDD4W7/f/7ZS6hfL9qYkwrBBDhy93+rbxozPG7e9uWLcQKxv96rS/QtfycE1S9f4X/33o2Ive2Hels1JBJ+58d/90SbftyxrD9YQw3QdxcXFA30+3zeI2OdH29vIJzdtsQWs2WGH+Ut+u1czCizGDuxLpKrwrxJAD2z7/LMjBi5YMLXlUMcGv/wzpJQ7xuPxL1hLHYfuAR8U5e0DCty1zXEAMKhDW0xICgjngEF3gmxO4O7OpyzlziEajY5GxHnrX7j+1Cm7Nunk5cHKMUVPLd17375AaiIi+jpgky1FDVHRKyNefH7bfl8tHGs0Nq7fPP5lnFyzZs2a7RYuXLiWtdRxeIO38wzmLkT84y8MJoUQAhYsWDqopmZxSzLwu/5z8KVFvm+OLfF9s01/bDAUoNigD8T/HSVBBAIC1QyGqkoOq0k0bftyPLndBbcddF9LrpJx48b9iogW/jx4/lHB7i8guiIWi13GWuoCZs8uMB26jgRMRBR5QGS06JEI16t0wyynIHV8RLr/KYJFQtBdzrRpd7AQu4ZwOPyJEGK85+SHCOXl5T/Ogee/eNwR8+Twc+LOtqOWOgNMRBBArk3SRjEnpUq3uj8k9StzeUPE//UnO9B3F888+PGWU7klJSWH+f3+p1qbC1xHZhiG+8sOKisre5G1xDCd6offAYAJnsEmUb3jOBMrKio+nf12YZ/363a/uCK53fRE87ZD6lTAWG/z7tyNP92JcmNPAOUHRxX7vl0V9i1+PoSVl5xy4LstJzxDodDZhmHc0FpMtVHweo1lWRexljofc/bscUrCJShgNyTIhfU58738sBssKWrZbMcvJMi74PDD/8lS7BoikchSANgKvU/Nf1NXV7fz/Pnz3e/Bra8detpOxuI/hsyvfz3EXGcoQuPH2HhjL+xqV7keGuXnzlb1ljPi3bnrhs68ZtKj8ZT/nyWEONJjHqD171rV9Hg8/iRriWG6hlAodIdhGH/S+GSSUh6TSCRmwezZhiA6CwlPQmhJiWe0/Oe5nnLnclIEVAsEb5p+869Nhx22MDXv7IyIbwJAnuZvv2dZ1gTWUudD7wdGAhpXANB+QNgXAARsWFNtPJH/71+uLh0gWAwGPQivN16HMzlPehetjd8XQuymsYs6N462LCvWsn/1xBNTgXAGAoxBAH+LLn8eX/1vj4PWx1fURCDKUSSvlVOPeCNlk8cBwAOoCQY4pVnnwRu8nbOo3FcI8ZqUklobuG4sSUQ32rZ9bmv//92f+evW++cuCG+JDYPyqCkv30jmNJHRvA4C9TUqr2Zuw1bz7z/0rnkaY/2nEOL/vJynYRgopfyNZVmfsLa6mMefCRrUtAMh9hWEeYBoKqR6RVQPYCwHlbT42mfXU1paejoi/t3LJl0cxzm6JahszW4+gL7XrJ1a+j0M3KpW5fRplL5cgaDyVGNDX39T7TCx8qtz9n+mzGu/IRwOv61xoC17Ps3NzdvNmTPnW9YWw3SckpKSA30+3ws/pmP5ebCDCFJKTz/84hu7bvdR869L1lD+gFoZ6Nss/D6fTDp9jKZ1/aGppthXPefo371brYkDPgaAnTVBc00ymRxTWVm5mLXVxTz2VCmQHAmC8gWIfEEklKJGZUCdIcRS2dxcDkcdVcOC6vKF5MVCiCs3ymDUml0cb1nWg6096/+vfw04Z/AbpVv61g7NF015fag5NwlC1qhAfZ0TWPelHFx9x8H325q/v1gIMUxjk59blrUTF1xjmM4nFAoVImJCCGG2ugGx/gDSc7FYbFKrv+DRR4caZISkwEHCxL7CUQElDAkg1yHAOpnEz+GoqZVefz8SiVwnhDjf67BFKkyYFIvFnmdtdS30dn4R+NSOANgXhMgHSSagagJ3bezQcoCmOOfV7Za18WFE9JQuTlZKXW1Z1sWt/oLbbw+YQ4eOdyQME0D5YBi5IBWAYdQpVHVAxiKYeuinqc3e1uLkZxBxktdhSCJqTiaTJZWVlQtYWx2DN3g7SEFBQb9+/fp9hIhjvAxGKbVCCFEQi8W65Nj52LFjt5JSViHiQA8nhkRkr1y5cpdFixY1staYbGbMmDG/DgQC5Yg4QGOTZbZt/yZ1ororAttdDMP40MOBbbDJf1uWddxP3mMzDNMuwuFwvRBCV9F5USwW274LY4FA//792/Kv5bFYbCxri+kFGNFoVJuSREr5Vjwe36urGlBYWFiQl5f3hddiMrXBNCsWix3N6mKYziUSiSxBxK29nhNRLSIOisViXZYLOxqNfg0AIzRtaLIsK4e1xfSSODnp9cIlZQ9fWJa1Yxfao4+I1iCi7mT9t5Zlbcfa6hiCRdAx+vbte4wQYkwqWGz1bYhpmud21eauS1lZ2fdKqWtaO6i4oU1CiPCgQYMOZ40x2Y7P5zsPEQd42aRLMpk8o6s2d13i8fhHSqkHdTaJiMdEo9FxrDGG6RilpaWPGYaRSx47OUSUNAzj4K5sQ3V1dZOU8kxNG0gIURoMBo9jjTG9YCEZh/+lQ2jNHmqWLVt2YFe2oaqqaiERPaRZSLocFQwG2Q8zTCcSDAYvFkJsrbF/BQCndeXmrsu6desOJKJmjU8OhMPhx1hjTLYTiUSeMwzD1NikG8NO6so2pOz95JT9e9nktqFQ6ELWWMfgDd4OMHbs2K2EEHd4nQ6A9ScUHiorK3uwq9sSj8dvlFI+rwlk3Y9/DR06NJ81x2QrpaWlu5imebLX89Te6jlz5sz5uKvbYtv2iUqpz3XfIaLXWGsM035CodA+RHSYUoo0ub1e+uyzzxLd4IfvBIDFHnMPSinJMIxriouLh7PmmCxeSJ4hhCjyukED62/R3LR06dL6Lm6KchznTABIesXFbvMMw7ht+PDhuaw5hukUn7yDYRhnt+GTP7csa1ZXt2XBggWVAPCyl09OtfEwN45gzTHZSjgc3h8A/qCzSaXU84lEYm5Xt8WyrEcQcYHOJoUQ54bD4QLWXPvhDd4O4DjOh6A/obDGcZwruqs9SqnzAKDeoy0t+YaGDRv2PmuOyVaUUu/oXrgopaqXLFnyj+5qDhGdS0RSY5P9I5HI/aw5hmkXaBjG+QDg8wpapZTSsqzDuissQMQpXmtat41CiK0NwziRVcdkI8XFxQMB4AzQ3KAhomXxeLxbYuNEIrGmqanpFN1tGsMwdh4yZMherD2G6QSnjHiuEGKAl/27P66vr5/YXe2xLOtQpZTj5ZMR0SeEuIjTVjJZikDEixDR1GzuJuPx+LTualAymfytZv5w42Q3jpjBquuA0lkE7SMcDh+NiNvrTigg4s2VlZULu6tNiUTicyK6x8tgUhvRwVAoNJk1yGQbkUjkFjdQI80Or1LqnG44NfQjtm2/QETv6WwSEQ8PBoNjWIMMs9l++CIhxF5eNu+6ZkQ8oDvzXMdisU+J6Fqv525TTdO8PBKJTGQNMtmG3++/QwjxqzYWd91auX7OnDn/klI+p4kL3I8Xg8Hg9qxBhmk/wWDwKNM0/08ppYvDz5o/f/7Sbm7aQRqfTIZhTHDjCdYgk22EQqGZhmHsqlsbE9Hvu7NNFRUVPyilZmjmCDJN84+RSGQaa7B98AZvO+WGiHchotCcGloZi8Wu7O6GWZY1QynV4LHYbXkxYhjGrfymkskmotHo1oh4JuhPDZXH4/Fur5Zr2/ZE3Yk+AMgzTfMu1iLDbDojRozYSghxpZSSNDb/shDize5uW3Nz811E9J0meHU/HmEtMlm2kJyAiEfqNneklFf3RIVsKeVNRNTg9TyVquFfrEWGaR8lJSX5QohbddfAiWhBMpl8qLvb1q9fvzcA4EWvONyNI9x4oqioaFvWJJMtjB8/fnvDMC7WxckA8Fw8Hn+nu9umlPqXOx942aQ7jyil7uT0Se2DN3jbQTgcflgI0UeXqBoRd+7BJv6mjQTWw6LR6N9Zk0y2oJR6TveciFZYlvWbnmqf4zjHadrmOt49IpHIaaxJhmmbaDTqGzRo0AOgf6EjhRAXd3URl9aorKxcTESXeT1HRNfmt4pEIn9lbTLZQDAYHCCEeKCNzZ0fHMe5oyfaV1FR8QEA3O21xk3F83uEQqHfszYZph0bCkJcKYQYpEuTZhjGuZWVlau7u23vvvuu4zjOpV4p0zbMWX6//59ufMHaZDKdwsJCfzKZvFcXJyulXLu42F2mdnf7EonEGiI6T+OT3fli0ODBg69kbbZjPmYRbPbC8ghEnJ4qzuAVxD5g23Z1T7XRtu2EUupBLyemlHLb+MeSkpL9WKNMphOJRK4TQozTXcNGxAt7woFtIB6PP0JE5V7PUwHxNaFQaAfWKMO0SbEQYn+Nvbs2dVlZWVm8B/3w/UT0iVcTU+282l3zsjqZjF9MCDENEUdqnrs2edKcOXOW9VQbLcuaQURLvWLj9an/xGzWJsNsHoWFhVuYpnkWeGwmuT9y16VlZWUv9lQbE4mEGw9c6H2QsWWe+l1DQ0OYNcpkOvn5+VEhxN5t+ORzuqOwmiZOfkFK+VgbOfLPTuX2ZzYnJmMRbDrDhw/PJaKzNMfcXWNZrZTq8VM5iHgJANRpnruR7CUFBQUB1iyTqQSDwWEAcGzq9I3XG8o5sVjsgR5uqkNEZ3vZYuqzPwCczVplGG9cn6WU+kx3SkhKOce27at7uq2NjY37EFGtR6zQ0oFwOPwxa5XJZMLh8Damad4NmpNCyWTyRdu2X+zptiaTyQOIKOllk0KI/HA4zKkaGGYzyMnJmeO6NK+brUqpH2pra0/t6XZalnWDuybQrOEhEAh8yNfCmUwmGo3mKaU+0sXJyWTSjsfjt/V0W5uamo5TSi3z8sluH3w+XwVrdfPgDd7NYKuttpohhCj1FKYQ7oLud4lEYk1Pt9W27SUAcKjbJi9M09y1f//+J7BmmYydwIS4DRG30lXrTSaTe7vxZRrY5HtSyvN0pwdM0zwlGo3uxpplmNYZMGDAg0IIQ5ciiYjSovpuVVXVOqXUbR5zE6Y2lMaGw+E/s2aZTAURX0vdDPOyyeVKqTPSoa0VFRUWALzmZZOpFBNHchFEhtk0wuHwJYi4ta7ouFLq6urq6qZ0aK9SaoYbJ3jMVa5PNgcPHvwga5bJVJRS/4aNDhK0Ms4b0yhObpZSXqeLkxFxuDvPsGY3Hd7g3URKSkqGIOJVbRSPeKqqquqzdGlzLBZ7XUr5lmYCcB3ZXYWFhX1Yw0wGBpWlPp/vMF1lUMdxLurJK6E/xzCMO5VSX3k9d7uilHqHtcswvyQSieyslDpUt5AEgA/j8fgb6dLmeDx+CREt9wpeU58z+Aoak4lEo9GTAGCMziaFELMqKiq+Spc2W5Z1qEpVOmzNJhHRBwAXsHYZRs/o0aO3dv0XaE7vA8BX8Xj89jTyyW8Q0Xte9p+ayw5x4w3WMJNplJaW7oqIB7cRJ7+TSCTSZq2ZSCRuJaKvdHEyAJxRWFi4FWt40+AN3k2goKAgYBjGCx7xYAtKqZpkMnl+urWdiP5EROs07Ybc3NwXAMBkTTOZQigUGoyIr0gpdTa5oKGh4Y50ancsFqsnonM09kpCCCMSidzMWmaYX3APIvo1+e9XNTY27p+GfniCNhATYnufz3cTq5fJJKZMmWIQ0bWpTVHPFEnl5eUz0qzpSSnlIbqCa4Zh7BOJRM5lLTOMN7m5uQ8KIQZovtKslEq7W2n9+vX7vRsvtPYsNZ35AeA+1jCTaRDRfYho6oqdWpZ1QLq1GxF3JSLPWjmGYQzKycnhk/WbCG/wbpoD210IMR40yeMNw7ivsrJyYbq13bbteUT0vJcTS/1zz3A4vAtrmskgjkPEwaAv6HDVggULatPQJp8hooo2bPKMaDS6NauZYdYTjUavEUKU6E7sG4Yxtaqqqjkd/bBS6jqv50opMgzj+KKiIvbDTMawcOHClxBxS51Nrl27dq90bLvP5/uPUuo/Xn7YtUkiur6kpGQIa5phfklxcfGRhmHsq7vZSkQXp1IGphXvvvuu09TUNFnTbhJCjHHjDtY0k0Fx8q2IOFrnk4loCgDIdGt7LBZbiogX6OJkIcR+4XD4aNZ02/AGb9vGkuf3+5/XfUdK+Vl5eXnaFkeybfsoImqrSuJsPsXLZIhN/towjOu9nqc2d59IJBKz0rUPjY2NY4lotaYPgoheZG0zTMuJ/UIiOiuVH9PrVMK7Sqn30jbYEuJ2APBM1SClJL/f/2BBQUE/1jiT7oTD4QOJ6Heu6Wls8s6FCxcuT8f2x2KxJABcTETSo+0tdTVM07yfY2OG+SklJSVDfD7fdTqfrJT6tqam5p507UNOTs4HRPSOl092+6aUmrHTTjuNYY0z6U4kEikholPbsMk3bdv+KF37sGLFivuJ6BudTSLiNaFQaDBrvI01B4tAj1LqdQDIc+M9jyCwCRFPS/d+ENH/EZFXjgkSQgwNh8PPscaZDBjL74MmebxSakV9ff3p6dyH1ClDXUoX14lFQ6HQlaxxprdjGMZFiJijK1ColJqS2rRJS2Kx2NLGxsZTdYVPEXGHvLy8KaxxJp0pKSnJR8SrXHv0OihERI5lWX/xip3TAcuyYlLKa1qbVzYskIUQB4wbN443eBhm480DIU5FxOGa5+A4zqTq6uqaNPbJScMwJuviCiFEICcn51LWOJMBXIaIAd0Xvv7668MAwEnXDixatGgNER3kFSe7turOO0R0Oqu7jTmaReBNJBLZTwgxLrWR5HVC4TE3SEz3vti2/TERPe21rkwl496HK/gz6UwoFLpwE6r1XjV//vyV6d4Xy7IeIKIqnU0KIU4uLi4ezppneivRaHRPwzCO8PLDqRP7F8bj8eXp3pe5c+c+k0wmPa+Fu59+v/8+Ts/CpDOBQOBYwzBKQJ8iabLrjtO9L4lE4lKl1Hyv5+60k0wmPykoKAiw5hkGoKioaLTf75+pi8OllA9UVlam/dq4rKxspZTyQq+XPG4fTdOcGolEJrLmmXSlpKRkP9M0PQsQp17GnrV69eq16d4X27YTjuM8pFsbBwKBy0aPHr0ja94b3uDV83cA8Gk2kupt2z4+Uzpj2/aRXqd4NySVJ6LbWe1MOlJYWLiFEOJ80FTrJaLP4/H4bRnSJVlfX7+31+mBlE0O8fl817P2md7IyJEj+xPRy20UU/y4qakpY4oSIuKfiWilx/y14WLCa6x9Jh0pLi4uJqI7dUWHHcd5IB6PP58pfVJKnU1EzV42KYTI6d+//wOsfYYB0+fzaX0yEX1rGMZFmdIhIcRNRPSBZn5wP15y1yCsfibdiEajW5qm+VwbcfJ7iHhnpvTJ7/ef784jnotnKSEvL++VaDTq4xHgMa+xCFonFAqdbRhGgS5RdXNz828zrFtJAPh9G5WDw9Fo9FgeAUy6EQgEbhZC9NcElfD111+PzaQ+zZ8/f6lS6jLNV1ybnB4Oh0t5BDC9jQEDBtyEiLng8VIndVrh5nQsrOaFbdvViPhIa89SfXS7NSYSiRzHI4BJM9Dv9z/QRlGlWiHEbZnUqXg8/goRfehlk6kXL4eGQqGxPASY3kwkEvmTEGKk7vQuIv6zrKzs+0zpUyq1022tHYDa0Ec3DsnJybmRRwCTbiilbkTEHE2crJRSN6dzCrOf8+mnny5TSt2n88mI+GsiOo1HQOvwBm8rBIPBXQ3DuFFKqSse8dTcuXPLM61vlmW9IaV82cto3D4DwIPFxcXFPBKYdCEcDh9jGMaxuoWlUuqyVatW1WRa32pqam4los895pmW0wOI+DJX82Z6E9FodDQAeN6QSV05eygWiz2VaX2LxWJnENEX3l1DNza7nmM0Jp2IRCJTAaBUZ5MAcKVlWRWZ1rfa2tr9lVI1XgaZygF+D48CprcyatSovgBwNWhu0Sml3o7FYldloE9+ioju1+XjBYBjueAak2Y+uQQRj20jTr4nk27UbCAej19BRO95+eTUP6/lwsStw4uHX2IYhnEe6K+BS0S8IJ2LR2hQUspLdakaUkK4hMcHkw6MGzeuHyL+tY0K+suam5tvzsT+VVdX1xDR33SFXhBxCCLyiT6m16CU+iC10en1fF0mpUhqJdDYQ3ebRggxOBwOP8EjgUkXEPFx0G/uVFuWdUOG+uEmIYRngcMNN9xCodBfeSQwvZE+ffq8j4h5Xjdb3R83NTVNztT+2bZ9slKqTjP/iZycnLd5JDBpxPu6lxJSyjW2bWfsKddkMnmI135Vqhh5bv/+/d/iYfBLeAPvZ0Sj0UlCiIM0E3xLoupYLPZlpvaxoqLCAoCZuknBNM0p4XB4Px4RTBpM8OcIIUZproOB4zjHVVVVrcvgwPIBKeVsnU0ahvG3cDhcwCOC6QV++ArDMAZpFpIOAJyUyX0sKytbJqW822NOQ6UUCSEmh8Ph/XlEMD1NOBx+KRX/etlknZTykEzuY0NDwztKqQ/bsMlLUrcLGKbXEIlEjgKAYBsFjq+uqqpaleFdPSEVX7Q2x5FhGIODweA1PCKYNLDJvwkh+uniZCnliZncx8rKytVKqWs9HmOq79FwOHwMj4ifwhu8v+RJTdpd12A+sG37jkzvpGVZVyql5mr62XItnIcD05OEQqHBhmFcorNJKeU/Kioq/pPxk7EQpyulVnsFlqmY+gMeFUw2E41GRxPRGanUJF5vPL60LCvTT7dSQUHBn4hI6r6EiFdHo9E8HhlMD9rkwYi4Xxu3aF5JJBJzM7mfVVVVzUKIKboXrYiYQ0R8ipfpNYwcObJ/W6kZELEhHo9fnAVr49mI+IVHHzG1yXs6v+RhenhtXAgAp+leuADAgoqKimcyva/uvEJEDV42mfq8nFM1/GxPgUXwPyKRyKupQNXrbUijUuq8LOkuSSn/pKsc7BpONBp9nEcG00MYiBhzzdHLJpVSywDgqmzobCwWWyGlvEIXWAohhkYikXN5aDDZipTyIiFEP036AtXY2PjbbOjrk08+KZVS03XpWQzDCDuOczSPDKaHcH3PJYhoajZ3V9u2PTVL/PDSZDI508smXT9smubRkUhkdx4aTG+gf//+M4QQ22kXlER7Zkt/a2tr99CkYMTUqcnLeGQwPbY4NozLEbGPOx497FEmk8ms8VGmae6hey6E+FXfvn3P4ZGxkUxYBOsJBoPjAGBf3dsQIcSTtm1/nC19TiQS7xDRf1v1YKlAFgAODQaDIR4hTHdTUlJyPCIO97LJ1I/+Ztv2kiyyyVuJ6Csvm0z988LCwsIteIQw2UY0Gj3VNM2jdCf2DcM4saqq6vts6XM8Hn9SSvmQ13OllBvc3l1UVMQnhpiesMk7hBBRr5esrltKJpP7ZFOf6+rqrpVSfur1XErZcmK5sLCwD48QJpspLi7eyzCMS9u42XpzLBb7NFv6/Pnnn69QSh2r6S8IIQ4vLS09hUcI091EIpEzEXGKrg6UUuqYysrK1dnS588++6wcAG7S2aRhGJdEo9F9eYSshzd4U5im+SQiCk1uodry8vKsy/Fh2/YfiKjJI3B3ZeEzDOM+HiFMd+IunEzTvCNVvdqzoItt27dkW9+llBHtpC3EwEAgcDuPEiabiEaj/QHgOqUUaYK4OUqpZ7MuEBPidiLS5RAnn8/3b6/TGgzTRTY5mohObyPv5tOVlZWJbOp3dXW1GxPf2tqm9ka3afoEAoGbeZQwWRyH+/1+/y1t+OSVjuP8Pdv6Xltb+7xSqlLTb3Ic5/oRI0YM4JHCdBdjx44dREQzUzbpdXo30dDQ8GK29V0pdTcRrdI8d83yJnfe4pHCG7wthMPhfyDidrpE1UKIydnaf6XUNJ0TE0JEw+HwtTxSmO4iJyfnOSFEjsYm1xHR3tnY90QisYaILtc5McMwjgwGg0fxSGGyBSK6EgD6t1ER+NRYLLY22/oei8UsIrpak5YChBBjw+HwoTxSmO6goKAgQER3pwqreY1L2djYOAMAnGzrv23bjyulHtGlT0HE48ePH78DjxYmG/H7/ScCQLHXc9cMlFJ/rqio+Crb+l5dXV1DRKfqfLJhGP222GKLq3mkMN2FlPJqIYQ212xjY+PJCxYsqM1Cn1wNAH/xskn354hY5Pf7T+KRwhu8UFJSEhFCHKMrHgEA78VisdezVQbxePx5pdQHHgaDKdn8qbS0dBSbDNPVBIPB4xBxrzZs8l7bthdlqwzq6upuJqJlOptMXUfpzyOGyQKbH2Ca5p9TG0mtpmMholmJROLDbJWBbdvXOY7zrZfNpz5n82hhuoMBAwZEDcOYAB6FldwfSSn/WlVV9U0Wx8ZHK6UavZ4jouk4zjs8WphsxOfz3QWawmpSyo/i8fhjWWz/HymlZule8pimedqOO+64JY8WpqsZOnToEMMwTtH5ZKXUvVVVVZ9lqwwsy3qEiLzSwWBq3rqTRwtv8LqT89UAkKcpHuEIIY7LdjkQ0SlepzRSt+T7KKWuYpNhupLCwsI+hmGcrwsqlVLfL1269OJslkPq7etJmjeVKITYkYjO5FHDZDiGYRjzpZTglVNMKfVVY2PjiVkfkAlxAADUe/hoEkKISCTyHA8ZpisJhUKDiegDpZTnd6SU7yUSieuzXRZSypOISHrZJCJuE4lEruRRw2QT4XD4U9f+NbfoVgPAEdkuB8uyTiCiL73s35VRnz59KqPRqI9HDdOFa2P/NttsU6WzSaXUF0KI07NdFkqpaan5x9Mmw+HwR719zPTqDd5gMHiIYRi/83qOiNDc3DylvLz822yXRTwerxJCnKC7HmsYxuRoNLobT7VMV5GTk3O2EGK0ziYbGxv3WLp0aX0vCCxfklLe6/Xc9fGIeHlRUdFQHjlMphIKhS5HxKG6PJ+IeGtVVVVzL7D5CqXU6x4y2HCb5vfRaPQPPHKYLlsYCPGgO+Q0mzuNiDizN8gikUg8AgALNTbp/vNPwWBwRx45TDYQDocPR8RS19Q1h5+eyuZbdBuRJKI7vOw/FbdspUurxjAdJRAIzBRCDGojH/6tsVgsme2ycOcdInrGyyZT89b4SCQytVfHcb214yNHjhximuazUkpdFcKPA4HAy71FJvX19Y8opRIaebhO/b2CgoJ+PN0ync3o0aOLDMO4XFfQQSn1wLx5877oLTJRSs0kouUeAXaLnPx+/8d8eoDJRILB4DDDMP4MmhP7RFQei8V6TVFB27YnK6VqNMGrTyl1KRdcY7qCSCQyEQD21S0kAeA5y7Le6i0ySSaTEzU33EAIMUAIcRmPHibTKSws9CPiVYgovHyMUmqxbdsn9xaZWJZ1m1LqU41Pdj9PLyoq2pZHENPZhMPhEYjYVpz8cTwev6sXxcknEdESj8eYmr/+VlBQEOit46a3bvCKAQMG3NDWlxDxrN7wNmQDVVVVzUqpc7yebwj48/Pzr+cpl+lMhg8fnpufn/+g7kooEa2pq6u7pDfJJZFIfEdEN3t5sNQm76+I6HgeRUzGOWIhngUAzzzSSikHEffuZWKRhmHsoynuQoZhjItGo+yHma7gEUQ0NQvJHyzLmt6bBFJZWbmYiE7SxCZgGMYR0Wj0WB4+TCaTm5v7mBDi116n92H9C499eptcamtr93XjEc1X+vt8vmd4BDGdDSK+gIj5Gv/TWFdXt19vk4uu0Hoqpdn2/fv3f7jXrq96Y6ej0egYIcQxoElUTUSPxWKxT3ubbOLx+H8BwPOKKKxP1XBKUVHRaJ52mc5i0KBBByJiFPQFXW5csGDBkt4mG9u2r/PKAZbKj+3+8+ZRo0b15ZHEZJAfPsIwjLGaa+Dux4xYLLa2t8mmvLzcklI+52XzqVsO5xQWFhbwSGI6i3A4fLsQYiudTTY1NR3aG2WzatWqp4lontdz1yaJ6A4ufMpkKqFQaBcAOFRX4FhKee+cOXMW9DbZVFdX1yDimZq5kQzDKI1EInzYgulMmzxJCFGiG3eO45yVqtvS29bG85VS97cRJ08pKSnplalFe+UGLxG9oXk56T6fb1nWEb11QunTp88fiOg7r+eu3/f5fK/w1Mt0BoWFhX2EEI/obNJxnP8kEomre6uMHMfZh4gavBy8ECI/Ly/veR5NTCZQXFw8nIhu0i0kAWBRbW3tP3uryUspZ3gVd9pATk7OPb35ChrTeYTD4d8g4ik6mySi1+fOndsri5csWrRojVLqj7rvIGJfALiCRxOTaUSj0TwhxN9Bfw28SUp5vlcx1GynsbHxXgD42sP2N2woXevGNzyimE5YG28nhLha55MRcWFFRcX9vVREFAgEznPnJS+bdD99Pt+t7vzW24QjemEQ+yAiDvVyUOsL8KmLe/Ok8u677zpKqfN0b4yEEL+KRCK38BTMdJRAIPC+EMLUjLcGIjqnN8uooqLiK6XU3brAUgixZzQaPYJHFJPuGIZxtBBiK6+gVQgBUspDqqurm3q5zV/Zmog2uk0z0efzhXhEMR1eKRGdi4h+zQsXWLt27dTeurkD62+4vUtEz3r54VSqhr8UFxcP5BHFZBKO4+wrhAh7bhas98nTKysrV/dWGVVVVTU3NDQc6MrCaw4QQgz1+Xyn8IhiOkpubu6JQoghbcTJ+wNAsrfK6JNPPlnV1NR0hJdNpuwymkwmf9fbZNOrNniLiopGI+KhqY0kryD2bdu2n+7tE0s8Hn8UAMo1gawrw+Oi0eiveRpm2ksoFDoYEYvbqAx6TyKRmNvbZaWUusSr4NoG2SmlLi0sLOzDI4tJV0pLS3f1+XzX6E7sSylvTiQScfbD8ZlSyg81cwLk5eW9P3r06EE8spj2Eo1Gj/X5fJO8XrKm0padsHDhwrW9XVY1NTXTiehrj8ckpQTTNBMAYPLIYjKBMWPG/Nrn8z3bhk9+PpFIPNvbZTVv3ry5juPcqPHJZBjGxdFodDceWUx7iUQiE4UQl7ZRdPzaeDz+RW+X1dy5c5+RUr7g9dyd13w+39PhcLhXpTTrVRu8Pp/vIUTsqznqDpZl7c1Ty3osy9rV6zBHKvfnACLiU7xMuzEM4x+IaOiuhMXj8bNYUi0n+uoQcQ/dm0rDMEbl5OScz9Ji0hWl1GNKKdAUcfkGEW9kSaWCNCGuIKJmj/nRlaEvNzf3IZYU0x6Ki4sHKqXu0xU4VUq9W1NT8yhLqyUXZ5NS6k6vuDh1w23bSCRyOUuLyQQCgcB9rivR3KKrcxyHU4+kSCaTNxLRIs186crscTckZ2kx7VkaA8DDbRQd/0pKyfsv/7O5q4ioXhMnu9zXq9YOvaWj0Wj0GNM0x+kqgxLRcWwmP/VjRHSGRl5kGMaBxcXFB7OomHbY5DVCiK11qUCklL9hSf2PWCw2X0r5sMbJbTg9UMzSYtKNcDh8ASJu65q35qXO9bFYbClL60ebfx0AdNfCSQixTzgc3oOlxWzuGsA0zXs2pEhqzSaJSCWTySt7c7qUnxOPx29UStleNpn656nBYHAYS4tJZ1K36H6ru0VHRPdVVFRYLK31zJkzZ5lS6m86n4yIw8Lh8IUsLaYda+O/IuI2bdxsvTYejy9naf3ok8sA4P424uQJ7nzXa4K7XrKoLCCi29t4G1Iei8UeYTP5KStWrPgnEVV4PXdl6vP57uNAltlMm9yfiC5so8jSw4lEwmZp/ZTa2toLiWiNziaJ6F8777xzLkuLSRcKCwv9QogNhRK9FpJvWJZ1J0vrp1iWdbiXzafmTz8AcPzCbBZjx47dSQgxBTwKK7k/UkrdVVlZ+SZL66esWrVqou5KOyIOMk1zNkuKSWcMw3gE9IXV5tu2fSZL6qfE4/F/uPGKxie7n1cCQD5Li9kM8onoijZ88gvxePxeFtUv4uS/ENHnOpsUQvSam0i9YoNXCHE6IvbXObHm5uZzAMBhE/kpixYtaiSimW04sS1dGbO0mE0hGo36EHHGhuHjEVQ2NDY2XsLS+iVffPHFd4h4u674khAi2tDQcCBLi0kXcnJyYu7Q1BVT/O677yaxpDw5yGtDKXWbZng0Gr2BxcRsKlLKj3XPlVJL4/H4n1lSrcbGa4hohs4mEXGXSCRyJEuLSUcikcgziJiv8ckSEaeypFrnhx9+OBgA6jX278r4E5YUs6mEw2ErlfOePHxynRBiOkuqdRBxsjtvedmkECIvGo32ihevWb/BGwqFdkHEMzWDARzHuXjOnDnvsmm0jm3bzwDAjZriyiCEuJCvhTObyLFCiL3bsMmjqqqqvmFRtU4sFrtMSvm61/NUNe9ZBQUF/VhaTBosJE9GxKI2roFev2zZsjqWVutYlvUhALzlMWeiUsoV75+DweA4lhazCQvJuxCxryZtWVJKyS/uNSxfvvxuIvpWZ5MAcH04HN6GpcWkmf3vRUQH6m7REdEzsViskqXVOosXL25w4xYv+0/FO0XhcPhPLC1mE+Lk04UQO+riZAC4JhaL1bO0PNfGlUT0nM4nK6UmlZSUTMx2WWT9Bq8Q4m3dNSql1FfJZPI2Ngs9a9euvVIp9YPuO0T0IUuKacskEfHeNmzyzYqKimdYVHqklGcTUZOHLboBgq9fv37/ZUkxPUkwGByAiBeB/hroWtu2uSiRHlVfX+95mipV+DQghDiHRcW0sZAsEUIc08YLl3lcNV/P4sWLG5RSk70Kn7qyFUJsw/U9mDTDHZeXCyFMzUYS2LbNp3fbwLKsmW2kTwIhxLnFxcUDWVqMF4WFhVsAwF/biJNXW5Z1DUtLj23bk3VxsjvvmaZ5pdcN4mxBZHkQewsi+nWF1RDx/KqqqnVsEnqqq6trlFLneT1PLRT6BoPBmSwtxgMjGo2+0Ua13noAmMGiapuKioo5Sql7vZxYSsaRSCTC196ZnjN6w7gKEbdr42tcIGwTmD9//kpEPNVrTe7avGmaU8Lh8OEsLUbDzQCQryng0uD3+/dkMbVNIpH4zHGc23Sxsc/nu5pP1jNptDY+Rwixm25trJQ6iCW1aUgpd9M9d+Mfn893HUuK8SI3N/dvQohtNH7EHUe7sKQ22SYP0flkwzB2CYVCZ2ezDLJ2gzcUCg0GgDNA8zYEAMpisdiTbAqbRjwefwgAvvRwYD++qdxxxx23ZGkxrdjkeACY6HVqKJV3aLZlWRUsrU22yT8TUdLLJhHRAAC+ocD0CNFodDchxOm6E/tSymvY5jed5cuXPySl/ECzMHc/Hh41alRflhbzc8Lh8AwhxF6azQjXJo/65JNPVrG0NpnriWix18LctUnDMJ5mMTE9TXFx8UgAuE5XdFwp9bxt26+wtDaNRCIxVyl1pddzdw4QQpwcDod/x9JiWomT90XEk9qIk2fGYrH5LK1NXhu/rJR6QRcnCyH+FgwGd8xWGWTtBq8Q4lnd1RMiqonFYuPZDDabEq9r4bD+tFZufn7+gywmZmOmTJliIOIzoHnhopT62rbt41lam0dzc/MumnmODMPYNhKJ/I0lxXSroygpySei+1J5KL3G53Ip5T9YWpvOokWLGhHxKiL6xQp9w8l9IYSZl5d3T28ppMtsGsXFxcMR8UqdTSqlYgDwGktr07FtewkA3Njas41ycQ4vLS09j6XF9CCmz+e7AxGFVyxORA4AXAUAksW16Sil7iGiHzTP3Tn3dn7xymyMOx6I6M424uRlyWTybpbWZuGYpnlVaj77hU9OfQohxC3uvJiNAsjK4D8cDl8hhNhVcw3c5Xz3n2wDm0cqufcFOidmGMYfwuHwWSwtZgPV1dVPCSGGamzScRznGJbU5jNnzpxyKeXDXovLVOBwXigU2pulxXQXiHgkIo7yDD6EcP3F/1VWVi5maW0elmW9RkT3t/aubKPbNNPHjBkziqXFbMA0zcsQMU9js9DU1HR0RUUFFzvcfJu8zetk/QabJKJrCwsL/SwtpieIRCK/EUL8XueTieg827bLWVqbRyKR+E4pdbxXPu6UfHfIz88/gaXFbKBPnz7HI2KBziYR8eiqqqrvWVqbR1lZWZmU8vw2bPL3wWBwt2zsf9Zt8I4ePXprRDyhjcqgn9u2zW9D2snQoUP/QURfewWyruyFEGdGo1FO1cC0XNNGxP11BV2klC9XVFS8z9JqH0R0KRE1eq/bW8R+IS8ume6goKCgn8/nuwc0J/allG/G4/HnWVrtw7btk5VSy3XfycnJeY8lxbiUlpaO8vl8J+lSJAHAHXPnzp3H0mof33333f5efjj1clsEAoEPWFJMT6z3EfG9Nq6Bz7Nt+xYWVfuIx+OvSCn/qwvCDcO4dcSIEQNYWkxxcfFAIcRtoL/Z+nIsFnuDpdU+EonEzVLKBbrvGIbxtvuRdRN+tnUoNzf3RkQc5mUs7o8Nw/g9D/v28+qrrzY1NTUd4pUBIyX77YjoMpYWQ0Q3I2JA48BkMpk8iiXVISf2NQAcoclKA6ZpTszJyeFq3kyX07dv33faKKa4Qkp5EkuqYyDi6a1dQYP/bShtGQ6H72RJ9W6GDx+eq5T6SErvW9dSyso1a9acy9JqP8uXL18HADd5xcWp9Cljw+HwqSwtpjuJRCKPbOQXWvMXjVLK01lSHaOxsfFEr1QNruyVUjBo0KC3WVKMaZrvuuNBY5PLmpqa/siS6jCneaUWTb3w/nF+zCayaoM3Go0Wm6Z5hC71AhFdUl5evpDHe8eYO3duQil1s+YrZBjGn0aOHLkdS6tXB5VHGYYx1suBpTYkD6yqqlrH0uoYlmU9q5R6XzP3gRDingkTJpgsLaYL/fCxiBjSndgnoidTLyWYDhCLxZ5ExHkec+uG3J9HBYPBMSyt3svgwYOvRcQtvGwy9fO/V1dXN7G0OuyHLyaiGi+bTH2eHY1G+7O0mO6gtLR0HCIe1oZP/jSRSPDGYwepqqr6hoie0vlkAAhGIhE+bNGLCYfDJyBiURs2+cScOXO+ZWl12Ce/BQCftWGThwaDwXHZ1O+s2eAtLCx0g9e3Um9DvDY4qh3H+TsP987BMIxriOgbD1m3VCns37//h4WFhX1YWr2PoqKi0Yj4sJSSNKd337Bt+3WWVueQTCZPJ6I6D5ts8WO1tbWvZmtSeaZnmTBhgklEV2IKD5tfYNv2aSytzqG2tnYiESW9gldE7GcYxk0sqd7J+PHjt0fEk0FzDRQRX7cs658src5BSjlBd5tGCFEAAFewpJjugIj+AQA+zUbSCtu2J7KkOgfbtt04fJ7GJ7t6uA4AAiytXkkAEa/WxclSykrbts9gUXUOlmXtSUSrNDbpMwzj3mzqc9Zs8Obm5h6HiFvq8otJKWcmEok1PNQ7h7KyspWIeJuXwaSuow3Pycnh4lm9D+H3+y9v60uO41zA1Xo7j8rKykoAeEFnk4i4dygUGsfSYjqburq6FxBxW12eP8dxuNhfJ/L555+vSBW0856MhdgvFArxpnovJJlMvoGIuV7PiUjGYjFOW9aJJBKJuOM412tk7vrjv0Qikd1ZWkxXEo1Gb0PEiO47hmEcDgCKpdV5EJE2zkHEodFolGsQ9EIikcgriLhVG+PjdyypTkUahjFVu2khREkkEsmawxBZscFbWFi43Ya8V5pTQy8lEolZPMY7l1gsdjMRvecxQW3QxS1FRUVDWVq9yoH9Vggxzcsm3R85jnNTRUWFxdLqXCzLOoKIFupsUggxOxuTyjM9RzAY3JOINhRT9Fr03F1ZWfkdS6tzaWhoeFUpFfd6nip8esMOO+wwjKXVewiFQn8RQhRocvwppdQfeXOn82lqarpLKbVSZ5MAcG9JSUk+S4vpCoqLi0cppU5OjTWvcfi2UoqLcXYytm0vVUrd5fU8NSfvFw6HeSOvd62N90PEiaQ7BQFwu23bS1hanUtZWdl7RPSOzicT0WlFRUWjs6G/WbHBGwgE3gF98vgflFIn8PDuGohoOhGt83JiiOj3+/1vsaR6FS+3Ua3XTiQSXNCl6ziOiLxORrs2OSwcDj/BYmI6g5KSkvy2qgETUXNNTc2Zuhz5TPuoqqpah4jH6E7xImJe3759L2Fp9Q5KS0tHGoZxibto0djkong8fi9Lq/OZN2/eIgA4RQihs8lRiHgkS4vpCvx+/3VCiBzN+INly5ZNicViSZZW5y+Nbds+06u400b5uG/glzy9g1S6ylvaiJObYrHYDJZWl5DMy8ub2sZttxy/3/+3bOhsxm/whsPh84UQv2ojUfVN8Xh8OY/trsG27SVKqRu8nFhKN4XBYPBkllb2E41Gn0DEHM0LFwcRL+SNnq7Dsqz3AeAlr7g+ZZMHhEKhXVhaTEcxTfMQ0zSLdQtJIprORZy6jlgsVklE97UWBqViIzIM45TCwsICllb2o5Q6HQC21OTdhebm5j+wpLo0Nn5aSqm94eb3++8ZNWpUX5YW05m4sZ1hGIe0kbbw/CVLlqxkaXUZSSnlYboNJcMwivx+/zQWVfaTm5s7zTCMndqIkw/htIVdxwcffLBcSnmhV5zszpemaR40ZsyYXTO9rxm9wVtcXDwQEc8B/duQBbZtX8/DumuJx+NXENESXSArhLhk5MiRXDk4iwmHw0EiOlj3wgUAXrUs6zWWVtdSU1NzjFLK8bJJRAwIIbj4EtMhotHoaCHELKW8b3k7jjPLtu1nWFpdy4oVK/6slKryiIVaCp/m5OR8VFhY6GdpZS/BYHCSYRjaU0BSygvmzp07j6XVtSSTyeOJaKWHTZJrk/n5+XzDjek0QqHQYCHEf6WUOvv/yLbtm1laXUsikXhZSvmQ1/NUYfj7S0tLwyytrPbJIUS8r404+QHbtv/D0upabNu+UUr5sWZuhEAg8Pr48eMzOrVoRm/wmqZ5sxBiS6/niAiLFi0ay8O5eyCisW28qRzer1+/i1hS2QsiPoSIAd2pIcuyDmJJdT3V1dU1Sqk/aPKhkmEYO4dCoeksLaYD3K8LWoloneurWUxdz6JFixoB4K7Wbk9sdJtmcCAQuIallZ1MmDDBNAzjn1JK0tjkV1LKf7G0up7KysqFiPisRzyEKVuNRCKRo1haTCfF4VdvKKzYWiyeKoB9KwA4LK2uxzCM27zSGG5QiZTyHyyp7EUIcbcu7y4R1Uopb2VJdQtOyiZbjZNTn3nNzc1XZfSYy9SGh8Phw0zTPE6XPN5xnKtXrlxZy2O5e7Bte4mU8jav566ufD7fuaFQiKuoZyHRaPRiwzCCmtQM5DjOVJZU95FIJN5USr3htbiUUpJhGI8Gg8EdWVpMe/wwAOzilW4lFSvdUF5ebrO0ugfLsu4EgLe9bD71eepOO+00gqWVfaxdu3YmIm6p+45S6ryKioofWFrdQywW+z+llGfhU0R012LXTZgwwWRpMR0hEomUIOKJXs9T18AfisViT7K0ugc3/lFKXaM5bAFCiPGlpaWHs7Sycm083dWv1/6uOy6EEFdVVlZWsrS6zSafIKJ/t1G34vjCwsJQpvYxIzd4R44c2R8Rr1JKgWbCXL506dJreRh384AS4ioiWqVZWLjfuWn48OG5LK3sIRwO70RE56VO8nmlS/ksHo8/xdLqVqSU8rxNsNvLs6XoJtN9IOJs0KdI+tKyrCtYUt2LZVn7tKG3/JycnJdYUlm3kPSZpvlXnU0CwH/YD3c/TU1Ne7ZR3GVYXV3d4ywppoO8k3ph4LUGW2PbNhcd72bi8fi1SqlqD3+MKd08xpLKPojo0Tbi5Kry8nJOJdrN2LZ9vFKqRhMnG3l5ef/N1P5l5IK+b9++pwohRm/QQStKcQ3mj8uWLavjIdy9xGKxFQBwgabQixvIlgwZMoQDjCxCCHERIvaF/53aa41TuLBa95NIJOJEdIvOJg3DmB4MBvdgaTGbSjgcfl4IITQn9hsR8RCWVI+gpJSnEZHy0A0ZhlEUiUROZ1Fl1UJyrmuOGptcsWLFikksqe6nqqrqWynlvZ4Gu/424mElJSUTWVpMe4hGo1cLIQZq7F86jnMSx+E9Q1NT00FE1ODlk4UQEIlEXmBJZVWc/FpqT8rL5uqbm5sPZUn1TMiEiCe586KXTQLAoFAodF0mdi7jNnh32GGHYaZpXqdJZeIGSnfbtv00j92ewbKse5VSj2sWIYCIf99iiy36sbQyn1AotDciHun13HVuSqk/2radYGn1mE2erZSaq7NJIcSrLClmExeSf0DE3+uKKRLRC+Xl5XNYWj1DPB7/BwB86zEnb8j9eWkwGNyepZUVC8kLEHGHNgqc3pzK08z0wGJy1apVfwGApMYmwTTN66PRaB6Li9lM+9+JiP6slNLZ/9yKigpeG/cQqaKWL3jZf+olz/6RSOQAllbmU1paehAi7qXzyUqpZysrKxewtHqGVKqaeTqbFEKcVlRUNDrT+tal+Z4umz2lzzYDV2+xtVzbdxtfbWCAvx4HOM0IIon1ykdrKJ9qoJ/zZUN+w2o5cO2ZB774A6D+zWK/fv0+Si0eySN5/IrGxkZOzdDDJJPJi3w+3+8RsV8rOmrR3YgRIz5atWpV0c9tCh55ZAAADASfLx+U8oEQCEQiNRsSICoQIglJow6gcTUceeRqlng7uf/+vpCTswUYRl8A8LfIupkE+FKyVgaBoRxQqh5Mcw1MmbICEH+00YKCgoAQ4o3UZoGXA5u7atWqB1nYPbu4lFLOME3zJYAW7f7CJoUQOeFw+BHbto9s65edNfvELVb58gYuk1vkLxcDzNXKFGtlH3TXrlsaTdRHNKghojY5TKxely9rV9826aE1rIKe4bS3p/SpW7PFlitoUJ/vYYC/DgO4RvbBpBTY31hH+aqJtjTrna3Uivot82rXbPHpv1bOnAlK8yuFUuoSIYRn/CClbIjH49NY+j1Lc3PzQTk5OQmPF+IohBiilPojAJz/i6cPPTsIAk0DQKl8cHXt+mAp8Eff4PphRyTBD+ugzrcajp/ENt5eZs3qB4axBSD2afHDLfGOD0BIXC/rgAJoliBEHTjOGliwYBXMnPmjjYZCocEAcAbor4EusiyLY+MeZNGiRY2DBg06WgjxuNchFcMwolLKSQDwSBu/Dm988YBBQ321A4ZhTf6g3BpjoGwWeaIek+CD+mYfrRb5apXs0/ydHLRuWa1cfcHU/65lLaQBs2fngpRbtsTdUgRAKdESe7fYu0GwfqNPwv+zdx5wUlVn//8959w7M1tYepMqRRAQKSqiWLCDYAF2dhdQBAPEEl811sRISP5GoymWJIolKCJsAVRQUFFRY5eqFBEVadLbsmVm7j3n+X9mdsEGKDq7e+fu+X5e3qi0e855fucppzHK4WAv2jbegf79D/sgGhH9bv8pukPoP/5rzCmtGmbx4sW5vXr1GkxE6QcrKFXWZSYCmBv3tN/5BZMn10NGRn3EkAlL2wd8cmLiqPTJ2nIh3FIEg3tw6aU7TY//TCbNSUf98kZw3ToQIpjQp9qv0crcOK5Rqcug9V5Y1g6Ew+r7cTIRyUP9FVrrkiVLlpjHNWu+XnW6bdu7DnPKtU4gEJgA4McfJH/mmfrfqV8dVKPaBQdLYKs9CId3VVW7KNl/4F0vjWzeSmy9ondg7YltxM5W5Wy3IEKDkNDBgNCwhUqcDtEsENMS5a6AIrHXgt5ukbv23UjH9Wv0Uc9de/5zP7gfrmfPnlcS0WOHCmLj/8l13VuWLl16nzHZmuf444//i2VZtx8i2IhPjqy1Hrl06dLpmDajuyR3FAnRBYwWADUHuD6kkCARH9z9v7Hih1IKoN0Ab9aETcRYoQSeQjhsLin/MZ56qoUIhUYLTScSoQUj3t9oACECEAftawajGERbAb2eGWvBVKSGh+f36tXrYSL69Y+sUA5csmSJ2R3qAXr16jWPiC44jCZjzNznYLutb54zutdXuunlH8SO7lysQy1DpJoxo74jA0KRBScRlzJsuJCsYGvHFdC7I2xtbiRKNp1of/FxE3v7U/df+PQqMxJVy80vjmq2zWkwbpFq03ud26hFBqItmai+K+yASxbiPzTTt8YqxgJ6rwux1SK9oY+99ov2cnPRPwY98fr3F1179+59HxHddLidQlrrM5YsWfKWGQlPaD6e+N91mMJAPMAdvGzZshcwrbAvCeQR0EEAbQA0A3M9WJZI+IXv+AYNKO0C2M2MzUxYz1ovtwLWVGfYsBWm53+EmTPbSEf9ikHHE3NLIjoK0A0gpH1IPwzsZcYWEtjAjDWKVRHy8t7o2bPnc1LKiw9VNKws7rRZtGjRetPxntDkNCI6bLKolOq6dOnSld//7w+8PKT/0WLLsFMCX7S3SLVxWDYV0PXSLE3xHEuSSqR2ro7nWAIRJR0G7Q5R7Ottus6GD50Oy9aqhk/dfkHR52YkqpG5c7PEvn1joemUuNYJ3AqghhAUSmhd7D9Qy4BO6D3+L/sYtA2sN0LQWgXxAvbsnI3x453v+eSrhBD/UUodbvfu2EWLFj1uBqLm6d27dz8A/ztUHC6EIKXUP5YsWfJbzJjWVTryMibqKhitQWieKCBJYR08N3Y1QHsA3grGOkW0momLkJPzjun5H2HatEZC2mNA6EOaWxKoJcANKwu8B/PJ8X8pRkKjWM+Cv1JKzsHwYc/17NnzASnldT8SJ5+yZMmS90zH1zwnnnjiWK31o4fSpJSSYrHYr5ctWzbpB79g+vSeBDFcEroAaA0WzUBcH1LKH8bN++tX2APGFk20jlivVsRTkZu7OJltSkqBd9Kc3un1ZUb7NFs9fn6dT0+ydRmQ8E3E37TsR4m3nBK/xQLWRppiYbTtPd0zPvnLdKdbedE120VaWloxgOAh/wDmjYsXL25lTNVTgexuIqr3vYECE4EtC6XNmu3+Mm9kCYKBVhSLVfxcwhB+mm0e+LVxM7NtcDRarIX8tSY9F9nZxd/ebVqrmTIlwwqFurLihykt1ItiTty7HHlfMwiCAMsCYlG0mjMbWWvXQsQcUDzh/57cXdd9c9myZWeaAfBUcHlYTTDzR4sXLz6JGXTmcxPqtqTi/hu54aNv6+MaseuCtIrHOnGh/uS5XceFSAJWQKKPXr6nfWDbNVslvzBv4EPFZkSSw0XPj6mjVeN2LAJPv4Eex5XFCCI+VhVRxU+WeXzctZCAtHAcreXu1rq7tkWz/nnyiol7ioq6tEtLS1vzI8fAn1u0aFF2XP5mVGqeLl26NAuFQu8TUZuDzOlxP8xlzZqrNZddvkUStYTrHrEfPkB8YrAssONsg6OuUHUz3sTgwWVmFCopLMxENNpe2sFpIhDokujrI/TD30TvFTEURSOxtrNmBjI3bGByXSL+Ydittb5zyZIlfzYD4A26d+/ex7Ks1w+2iw/fFOTfW7Ro0Snxf/x4ftP05ap7dpfQxgd6pK3PgqsTFsNMPz3D2h8ui3iOJfFOSfstX8fqZae5JYsHD15kNFoVzJmTjh0lTax0+SiswLmkfqbe4xN1PO6WsmLOdtRTip2b0Lz5ng5jx6ZnZWWtI6K6h8npPykpKTl19erV+8ygeALZs2fPfCHEsIP6ZCnZqVePPr181JeUlt4uUUj8hX6CHaecJN3g7tr1NMaPLzf3MO8vYE1KR5MmTayoM5ls+8xf2tcJjSqX28x9keqsXs3CcQ7qk+PRwKJFi4ajslpmqHGfnGFZ1rtE1P3QuRHv3bFjR7v169fvxpQpGQEROltZ/G8KBFuS41QUb4+A79SvLAvaie1VDq5AkF5FdnbpL61f/aIC74Q5eY0uyFgxroXYPapVRvExcKKAEsmoGzOgCQHGzlh9ONKe/vcp6fUWfJQ5oCKxxMGuZigrLS3tunr16q+MqXqHHj16dJFSLj1wLJwZTmYmdnU7Dnu6duPypk1JxCfU/QMbj1h/vtEkxMLBIBCNlTH4SWWJxzFs2JJaOwDTpjW1pD2WNf9KpIXaIBZLHK+tvE/hF/V1YgXHthHYsxt1P12NBis+QdqWLd8EFcxfL168uLVxYJ7T5DAhRNHB6nOJ3QNEZHdoel/x8AtDEUeMWy+PCrpRFxbUz4p5vm01DMEKggJBgVbu5r0a9Pi5GUv/O2ngpJVmZH4ev5ud1/RN1e2aHarulRvtFkeVRRiSFVPch/4yX5wYK7IttNTb4qP/ZKPC2cfJtZt7H+o3aK1dKWW3hQsXmjvFPETPnj1zhRDTvyV0xOrXx65u3bCnSzcub9SYpHvkAeohhR6fXAIBcDS2g0k/qYieQTi8tNYOQH5+e0n0a9K4DOnpTSkaTSSRyehrrvDDHNy5k+qu/hT1l3+C0I4dB5TPzJsdx+n6ySefmKusvKXJiVLKOw+263r/Cbeep6VP/MOl61sE4I5pnr7HQqxymf1IyrqHiuBIA0ELG0rqbSlGaPr86HFTb7jg2cVmZJLA1KlZQtrXEomRlBY6NrF5RSn8wgDqm99vWRXVOUfNavH6K4GGixcPwiEWXeP/KRKJnLl8+fI3zcB4Kg7vSEQrD1x1xQw3PR27u3bD7m7dUHZUCwhXVZyUSYZPjttN3CfHnAgTnlSan0de+KVanBs3sqT9a4BHIBjqTBW5cdL6WlsWrJIS1Pv0U9RfuQLpmzZ+2yc7QojOCxcu/NIowTscf/zxZ9q2veBQPjk+v5a0bFXwed7wTQRcScFgXVRsTOTkbJglIBiPmyOlTOJpgp7u5uS89Qv+tJ/HP1+6dEg47YMZR4m9qPA5iag+2Xc+HNg0GP/n2e9n0d1FzeGqgxYmHlq8ePF1xkQ96cgmSSnHxSfPHb164+uzz6koAFbGIsm0GSZiqhAn7Q+GdNyZhbPH1LbdvCJ/5rWC9EP0zeTDSb+WpXI3NlX+b6MlS9D89VdBSrFSasyyZcvM3bveg3r37v0ugJMPFplsP/EkbO5/FhM4PryJNZOEdDh5tsOV8iSAbaFpROjtRx676F9XmaE5MsbPufr/pped8vdyDghGVY8Vg4Xkxh9+QM1ff63iL/hWPln5mOLNixcv/psZGe/Ru3fveKB4Wvyft51wIraefgZYiAPzN743nsnzyZUFX8If3ezsibWt30V+4Y0CfA9AVmVxhznJPb0/sNk/jk3feRtN3/4fhG2jvLz8vOXLl883CvAePXv23CCEaPn9/+4qYPyAHRh7wQ6WictRqiR0+1ZITLyPQ/rFWI8b88557UEzMr+AwlkXSOXMEETpFV3MFXlJklOd/QbBQnC9lSuo1YsvILFZ5ns+WSn12JIlS8aZgfGk/q+1LOsh7brY26kTNgy4EDoQ+CY3Trqn+K6/UKApnJs9qtZ1fMGMgRbrGQBC+3X5SxdfDtnPFUlUYuG11by5EEJAM1+1aNGiR4wCvEevXr0eEkJc+/0iLymFnd26YdMFA1lbFqgKrrj9th1W2o5mKR5R4exrflaif6S/4ZlXzu3RWW74S6+sjQMQVfGMD6iihv4AW2PHThv/eK4JXl9WB64SlYEKb49EIu1WrlxZYszTe3Tu3bu5bt1m47Y+J4t97TtAuE6VJZMHEwtsm+C4G5noarXykxe//TiJH7EKC/tB4w8UsM9DzPnFu3WPJGPQtp3YPdT4vXd2ftWjRwfccIN5eMebTqw3gIX7N3zEbaS0VStsPaUf9rVrB+FUk0apYu2TrQB15A2fdgl8feusi++ebUbo8Fz83O9P+Uo1/PPnVtuzomVuZaJOVa7vOPHgJrRzJ5q/sQBZaz47kIRorT9csmRJHzM63qRLdnZmjHnX9r6n2vuOPhoiFqumwK1yigkESEdj60A0TuVmv+Lz7iZMKzxZCnqA0kInUnl59XU0M9gOIG3rFjRcvHDDuieeaG2s35v07t37OGb+kIhC+2s5J3cuxQ0Xb0P7tmVAVFRbepWYyENMS4vbfrGLMy4/++yF75oROgIKC7tYwB8RysimstIqKRgdMu62LFhlZTjqjddRf/nyRDGi8hTdOiLquGjRIscMkDfpOGTI2l09erbd3a07RCxavT7Ztoldp0wzXalzs/N939n5s461oP6GjPSBVFpajT0d16gNq2Qfmr77Tun6xx7NNJbv0Ti5S5dAKBT67MCVZswoa34Utvbrhz2djq1ejcZTrmAAHIvtdSWNBPO87z3k9yNB6BHw8LxB143O/N9fguRmJHbuHdn9T0nJL+OJbPzvfeOTTNzy3xYgIVFSUtJt1apV5lEPLzJpki3r1X9YumqMtizav5pVzVRcJwDSivTTnJNzhV+7W0wr+K0Q9KfE3W5cAxuWK3fyAqQZ/LGaWXQCiorMFQ0e5Pjjjx9n2/YkZsbWk/tiW99TEokCqi+l/JZAKyqUaRRze1lfPXFmg1eundj/TXOH60EY9txtVy+IHvvPvZweqNwgSTUyoWqNeqtXoeW8eSDXjRDRgEWLFr1hRsibWPkFLxHjvMQ5syrcsXsYm9m/MyEGwotuTniIX/taFsz8E7G6nSpeRK++RdaD+WHiD5QTuwAjR5r7zj1I7969ZwG4VGvgtuwtGHbqHghRU/lVhUwdtpyCspMmX3bBq+PNCP04wWmF5yqB5yrj7hrTe3xez9i0EW2enQUrEon/jNkp6GFoeuEkizCGAatmfXIiX3tH5YZP921uPHPmFeTohwVxqOJwYg355Iro60tFfBbCYfPoqQfp0aPHVVLK/0BrbOvTF1tPOw1ayirbUf+TNMqsNWG+zs254CfPLz8pAJk0zn6ww/u/O6XO+j8iGjv88yrVFYhYGhu3hnDjU61fKHpu2WBjkh7k8cIGMlPPEYHQKYl7SmqexC4i5cQ+0YTTEA7v9U1fT54ckumZf6RQ6Nbq3C102ElJCGKttCtCnRC+2LzY7DFatmyZ1rh1y6UbzhtwzK7ux1f3jr7DZEtB9NMfz+0cXDXy4UHTzL2Rlfxm7oDghljP/86lvsN1eaRGCrvfd8TaslBn7Vp0evW5ZR+88nYPM0oeZPrzR0kRe0sQtWetmVCzMk/4BilJafdZvY9+hV+Fd/nJD4v0zJnStgciFmN4oa+JSGt8qkgNQm7uF0YQnoPO6t9T3zV8I07uWgp2CTWeX8UzWUvT+2XHPL9Y1B95Tf83zenIgzFhgiU7df0D1cm4k0pLa1zviSKSEIkTNq1emL131dy59cwgeZDCwrqWRiFJeR4r5Q2fHPcTjGVK8EW+KjwWFkpius+y7RviPrnaC7sH6+vEDei0W6nYBRgx4kMjCO/RvV+/L7b1O63d9t4nQLguatQpf6umorVeqQJWfwwZsu1HA4sf+wW950xIfzpjasGx6TsGIXFs1xMlgIogRDAiFNz5ZPFp1141YHa+MUkvJZXT20oSLwshj/FCUvkDRwZepyLlp2LUqE2pn1Q+W0+kudOEJQeQ6zK81NeJ1WEuYfAlKjf3NSMM73DZnOtav1Hc+d3NVuMWtP9xASJP2A1Li9rj609PE6sueGLov9fV+sFiUJ/n/vrG4li70xOX/cMjUypzYmW7MfbuOclakzd7yD0vGWV5iCmzusmAO08I0dJLfjhhOZZF7KrlCuoSXxQeJ0wIyGO7viuE6O21vkZisZV3MNRolZv7ghGGd/jLK3ln5MVee7lt/ZJg4l1ML2EJfBFttPSabZec/XL4n7vMaH0XWVA0TTDn1cgu/cP4ZBaJ6wujGvr/dE7OJDNSHmLatKZSyFeFtLpBeetwI0sJKLXJdTkHI3Pe8UN3W/mFc0mIAUl6QC15EIG1jkCIW9yc7IeMMDxEYWEnOxqbx8HQ0XG78U4gd2Dj3HpXy3MwfNiaw5rY4ePVM6whZ2x+qntg0/DEA+jeCVi/3V7Asuifu/tfdOPA5+cYy/SEOKSlqYQIoRq5JuCnGo8QcHfvDGD8+JS+n0rk58+V0h6QSCqZyYtdzelpRPuKT3BGjFhkBFLzTHmle8Z95eNLlpc3hQUvXknNUGShb2DN9nMuua3ZRIKuzeN1/qyJ6192jm9lc8yLfjgRHLRNK0YXfNZi9sX3fm0U5gk/3EEq+kQQBz1TfPiOzVBioYLBy13lno4RI1J3t/6CBZbcun2DAJpV292bP0+n5a5AV4TDa41Aap5/vTS468j0996tS2V1avpk5KFNRtNat/HqR4p7nHjvxbP3mVGrjLun578q7cDZ7Lre1XsgCNeJ9EOOP4p1PvDJDaTmhQLU1os++ZuiCna5WvT9sQKS15HT898SQp5WsSnCs30dj4LOVjk5C4xAPMC0aY0kyfcEUYfKR/vJazZD+2M5W3bC0KEbD+mjDvcHXXTG9snd62wZjm9e4fca8b4nuC7GZLzzzJNzzz3VWGeNO7DWlubPSVCI2bvV3YRItIaVVX8Nnn66eUr29aRJ6SK/cKYMpA1IHPPxZnG3YmWorJxZ2m8gf1YvI5KaJfuFG1rctXfsp5XFXY9qlCBZ8TtO58YzZz6wYsLMES1r41iNmjwq1GfmvfNecbp5trhbMVrgL8rr4+NYu/Vj5lzf16ishpk1q4nU/JIQCDHIk4nkgStGLLubkIF5mDo1KyX7mpmsrdtfEkSeLu6i4lX9NEthCfLzjR+uYe6YN6LTpWmL59aVZVkMTxZ3K6d2wUcHd3W6ImvlbBQWSjNygMwvfFAK6enibmLwYlFYVuBtUVSUa0atxnPjgNR4XkjraK8WdysFTyREQynUy3jmmTap2t1WQdETQlqeLe4e6OvEjTjiFeQXXWxEUsM8XthACvky2XYHVBYYvWgzXBHLpcuYfgWPP97gUL/2kAXet17rcW8ve90IRFxPHNs9dMBa0eS6oqzOoLor38aDa4LGSmsOoTCJQG09dkTxUCJhEtRG2oFZKdnXWfV+K4AhiEVToa/j/5tpwV1oVFKzbI81mP+5bt6ysrhLHv5UsnSMV7vNO8/hnn+rjWMVa9Dw7oXc8QKLFTw/VlC8UTWQb0U7Th9XmF3XKK0GCxBRZ40Ata941JS9bDcg14EE9xGBwPOp2NeUX/AKSXl25QOynu7rRLJLqGuxnI7CQycGhiqmsDDtN+mvvXGU2N0a2uMCjdu00jg2sPHMV+rf+3atH7uiol+JQOA3KaH3xIt5TiIvQ37+sUZ4NeiTFT4SRP2glOftBlqzAB0tpP0OJkwQKdfX+fkPkpRjKo/Xe1+jYMsCpmDGtM5GKTXn50Qm5ghQL3IVe9shx8N6hiA+VmZmrTrUrzuocAtePuv0btammw4sMrHHJ6OKJnNDvRPLuw1cdNOUyzKMrVY/oqDgdyI97QJ4fBfLt0UST3hEMO1kmV94b4oFmeeJtNCf6EBTUqCvK++PEfmFH2HuXLMQUwOc++yER97gHscK9nxx91t2Q7w62CHnwtl3/LY2jVXunJtzZtNp11feq50SAVLcoNZSizYfy95PGbXVAIWFUuYXPiWIsvYf/0sBs0kkORLiTKug6DepFfMUjbcs+xxWilOjpys6G7Y8hlTW341gaoZFjf74ryaypFlFWESUEpajJZ8TWtXnxVf6XlNrB276jD5SWI9VPqCYGnpPVC4oS0JOwaRJtlFfDfiJ6QW3EqG7l0+1ft90Ej6ZqIXs0u3fKRYDnUEQV3OKxM0HaiWELOnIR1KxoO4LjRbMuEVIeQp/cwuC54l/qACayOkFDx/Mbn5oSGewdUZo1fz6spRSbHwIkNw1uLnrgOaf/saYazWTnz9CMN1F5eWpklXu92KEaISJcaOcXnhRSnz0MzPbWYpnUySK1EniK4t1iZVhnCDKysYb0VQvA2beMey96DFjLSeSMg4sYTfEiJbE8EZ5l/tuenF0rTjGNPbZa455s7zTI9FSJ9H+lHLEjsOLVIeLhz572/1GddVOX5Lycnj4COghkxytQVI+GJg6tUtKfPS0aY0E84MpsSPr+53turBs+worP//3RjLVy4PzLr6iV/qGMWANpJLdVDwLRCeF1v5rQmF2Zq0buKlTsyzSU4XjgFPt27VmEnSCyGpgHlisfj/RlKR1D6WiT2aGsKxfy+nTL0yV77aYZhIgKbXmVpBmCNs+gzofO8WIppptZvr0U4QU91CKxXIHFvCk9Wt06tTt+z//gwLv63ec8FxTuTuQePsi9SCw5pPqb7779rmjGxuzrSYKC9NsiOvpe0aXKgqpuM4EEoIneH71bMIEIaW+m4BgyvX1N3cOQbr6fjz1VEMjnmoymwVnWOvR9O4IAoJSY4P9NyQuKARibFF+ad+bfjN3gO93f6/g1jdtp/r1qOI2/RTTOFM8oXw11mXkuGfHtTfqqz7fYCn8jzz2MvcR4bpQVnB+KhS+hLBnkpSB1O1rh5nl7Xh6VnMjnurh1OdvqZOdsWgyXJeZKRVTLG4kd+OMBp/Xuqu2hLBGIxjskIpxNyoW0FgQnynz8882Sqw+LGEtFaxTajPOd3AcJmm/gAkTQl7/VFlY9G+yrIYp2dcEsOuyZdkj8MwzvY1yqi1utliI51PopOQPTUcrWCTf/YHP+va/vPnmCT1PCn55HlgyUs+BVRYDCJmRnRidtuB1Y7nV5MAUXQ/bPiHFm8HCDvaiLl3+4umv7HBsDyIKp3RHx/9PSpKBtJeMeqqHlSV9/v0ZWnZAqgaZleyS9fvtdDuN8vNY3fbCFdmfULuxcFUqq5zKKb3hZ7rlfUZ91ZTcdO7yJEmRuonkN/fRNRcFBb/y8neKghnXCVuejhS5muHgfU0QxBnSdv9o1FMtiaR4LGvmg81oV+JgJaVmhkXQkvuHPus0d/7J19aasSss7C0DgfspGk3ZJlQWpQNgYXxy9fmJmwho5uWHvn6CTwZpHY8vvP0ORsGMgQRcnShIp2hfJ75bKUhp34/Jk0NGQdWg0U5driWmRqmcGCfiZikzZGHhI99p23eMy1H3ZsiInbLF3f0aYcFtrV2dp7185lBjvtVAKPAXOE6qt4IQi7GEuBULFnh2YpU2PU+pXaOruKpBKRaEE5CfP8gIqGr539wOjV8s7zmOHZcJnMpzOyIxYCl3muTn8ZpZfuIDpRGkagHgmzqAZl4SPO7SyQsGNjMqrGKmFnYi0EVef9z0p/iGilelaSK8usWxsDCNtB4Hx2WkdF9X9C+FguPibTIiqlpm9X29aRYio/afqkzhuT0RgvYMbrhywpxB6bVh7CxN98F1/dAUFulpPTHt2b5GkVXME8/XEazHIzV3fH/HJ1eqfgxmzWriyY+cMEEIrUdT4mmRlK5fVfR5KNQPCLY1IqoGuxH0J/KBRhOxP+NSTJ9+wG4OFHgnzL3shNPqrT8nRa9m+EF7bXKtHvaGccaCqxaZX/QIxWIpvWvoQOSTOIDGoG07/uzJD5w2bSgFgi2ROhf1/2jQYIGuwoIFllFS1XF9+a//GFMSBJ3ygY8k5pXRZhjy/K1/9eNYDXz2zqEbVMMm8XamvMaJUVyi8Z9d/ecaFVaxLix9GVmyLvkguUkUIUKh5qKw0Jv3tLvcnwR19UNfV7yy78LSeM6oqGpJl7G7WqTtJr+0p1mwrEe3wL6zfD9w+fndAPTxQ45DzITyckjhvmgUWbWI9LLBFAh08Et7yA6kUdS5zZMf17VrGyFomG/6OhqFlWbNMSqqYo127nIzBYN14Ie5PZ5yCdlECHHghPeBAu+A0NInESmFP/KDiuE6tu7u82565dwMY8ZVRGFhM4IeAGb4IdnZ3wbJnIvCwrqem4ykdX1lMZ38M8PKgdi82dzFW0Vc9cJV9ffo9BxK9U1D35KppR0siHS9ZPKCM+r5bby26KyxLqT0xWAxSLLCSueoY29+4YrTjBqrUBSB0O+Rynfvfjd0I0QiIMaD3nRZdL9fHPD+I6EAzsW0aR2NkqqIcWyfX++z0XB81CYngpbW7qf9PnQS1uUkRbovFs/igWA8XyPUQ36h2QBVlX5CWJMRi/lI7w5kKHSDFz/NUmISkX/S4oRGLasDnikyJ1yrdDqkuxCNgv1iO0pBQB7Y/JQo8N4xb0SnE6wv2yTu3mW/FI+IEStDX5QXGjOuIlx0gZCtfdeuYLClFeNjPPVN06Z1FZp7VtxR6KMJVkqQsK42Yqoa1jsNjt9iN/VVAb3yvNgxU3ad3dVP7ZowZ1D6dqvR+T5YTP5OPumSFXo51sMUeKsqkZxWeCPtv9vcJ/rmiltKbVFQkOepj5s5s6VIy+joh1M03+tzktK+0qipang2+7T7ESsH+yl6Y+KTsz6vd8vsUSf7uwjAN/tl8Wy/UyZmkkRne/5R6RRFFhScT5YVYB8FcxWnPRzI/EJvvVPz4INBhOxzobW/jMh1YUl9rVFTVcXNBeMFIBN27RPHnGhLwIaYXpB4pyYxueeGPugHEpnM8FXtCK7AeaGVF2ACmyPgVYAlcSP5MaBzHLCkhzw1GQnRgywrgwB/dXk8YCDcadRURd3LgUfLov5r1z6kQ5Ia66c2bdRt/rrRqV+xOOkjFBOkVma3UJU5B/4NYjH4yTd8c++f8NQRcMvRkyhSDvajHTGfjUmT0o2gkl+AODu46hRoAX9tMiNClHFJ+uJbfZvjTCsaT7YNwH+SJ+YBaNeuvhFoFWgDuD6e2/jOJyeKqHy2l1aqZNPmExM5u880WtEeOh5TClsbRSXfnEmgH1L87t2DajQaBQnxO+wv8ArgUknKV8FHZQCCTIqIR04ZONrYcxVgBy703apZwnA0UyjUx1vClXf6sa8TK06WDVlQcI4RVLLnP9D7sltHyZr9J1GNbdxglJ/a9IHbcQy5Dshfm/QTIl8fat1mQmF2plFlknmmsLeAaMzwZ82RNJ/npeuSGHwiUvwhu0P2tRAnoE6dOkZUySW/07OdXYhj2Wcmk8gXWXDP4IbOFz1/iy/thoW+rvKYvf/2soRCdZCWdpRRaJKZMquJYNHZtz4Z1AVFRcd6KM85zY8+OdEeIZrBck2BN+lx8zP1CDTAlz6rYkdyc8yY0TFR4LWgzvfjVLT/3smzgiu7GYtOLrKgoD8JAT86MWaAlAsxrWCkZ2w5FDwGzD4MFkBwXWjQ5UZVyeX6OWOv3xcTiceufGg3vNo+Gk/Mu6iTH9rz1KtnH/OZ0zxd+FDkROBd0SC2BZrcbFSZXITUXQFO92PBMYFttYbrhjzxLdNnnEbMddinXQ3bhoTsbVSVXE4KfNG4vh0Nkj+TSYqwfXRO2gftfde4qVOziKgp/FqocxwIZU7PJX8e1a3BurVvfbKUmQAaeOJbphS2FoSj2aemREQQQpxlRJVk0tJCCAR8+fZPxbzDGcLl3uKe+dl1O9bbaYHgV42gLmLH/ebBAUFj1clDa/juWOh3BOIqCIHBnvigoqJBxAy/rghXbJRAe6Oq5LLYbXsWKQWwHzXKFI1qvOe08UXRcEFp10HxYfLd7t39w+U4WKObjDKqTC6CZGsiIvg2wREIWNZp3uhrtwOIQj7VaKLgQ0KYRZgks041GQl24dNkEvXsaPDUwOf+O+pvBU8Gow78WqhTCgR9sVFocpHkNoZt+/Zu47gYpOuR3DiEZmBu7ttiutbxrMCcQE/21B51sonZvw1M7L7UzUVdFbkOTgw+jVkTlMM+/vhjdIYx6yQmO0Ic78vrGQ6QeG/QE8eXLMWXw3HhVyfGFa/6tkRhYTOjrOSxJNa2sR93hB7wYUphpXPUKX5oy0K3XRtfJz2ssNhpa46aJX3u1BfBz4Gq68JVuMobyZY4Cv6tpaMintP9jKqSS5qIDoT2sUbZxUanse+OuwqgBYCAn90HpaXbRqHJDkxpOPk5N9YaJGioJ+JKreuRZfk5wAMFQ22NqJLcraArfPVw5venIGYIgTNFI7H3LFQsLvs2cg0Jt0Frsdk8tJZcA/L9YxwEaognnq/jgTm+E1j7uJ8Tc08Dzxz78QH3zTq3SUC4TcjHK3eSFVaoFll+aMtXsYZ1fT6ZYh+HaP78U02RN6l+mI7xdyTOEMTdvfApQuBkX+/6iBMMmTg5ybQIljX28wYaMOCQONN37RIi5OsFHYBIKchpheYIeHJd1sm+XnRN3GGovXLi8nT43Cf7uoBdYxMfd/b3BsWKupFoLXd1hb/biaZp5egZ+loas04S06Z1BLgWvLbMTZBR3qTmA0009LsTA5AJpcwDL0libbB1Qwuqka+dtABHtJ3+yjvdm6RyOybMGZTeUJZ29fVYVf7/+ZFjTjLqTCLBQF2/R6mwAh6Zx/h4X3d1xeMcwLQZnY2wkkfL9O3w8xV40IwsUe47/yVYneT7BR2lAOLTjUqTma5RB9/na2mZHvFZor/fC3VwXQTy87ONspIZN6el+b2JBDpKNLL2Zfl6dTnR0hi+pCb1jVUnB8uyGgFUG472pMF1a7yQTUyWj+/frUBKQAmzVJkkdrl1Q8wUgr8rEiSJ7WXFHVP6ao9dsq5lkWpYG+xyD9KbGnUmuGJpGwAAgABJREFU0xn73w2T5ZW1eWro74QAVJksdzXCSg5j5l3VKXH/rq9zLEIDO+q7DR+scbTvDTSxG5ObG6UmM2v0e9gNTmxsX7AgVPO5MbfyvT1pDQfoZoSVRLshwPc1FXBIZKHc8vHtDAec2ObyeuYek6QFPpRecUWV76cBG7KGjyxOnZoFsEV+F2l8xg0QGXUlh1I3YLEg359aIIYspoyUvl/d3Z0liUR6bbDLEvbI1g8TqBqOvLdD/g/uGEKqxmask0Mzva915f27vo5t6gcd/zVKcN3aYaXSnJxL5hTq87D7wELg5s1ta/5bRKbvd0trhgCMT04WDxe2iPep32sq8UhO1BXl/i/UMRAl29z9l7wkIARwLSjwsgWharbAGwjEgy//F+oSj8kJU+BNEsUISs3C/xoliL1OWkoXR2MNQ8SJOdX/lGo73agzSRQWNojHNr5f/Isnk9On1/xOHSlrQazMgKKGRlzJIUM7zWvD8ktdK+JDLYg6tUHvzGx8chIRtSGN0QwpRI3XVJi4FjyeHw/yLPM+TZKw6+umteDKSxCYREzXjlPRklFiTDs5KInExU21oKkauoafP45EnHgEVgtcWOVbj4ZkEJCaqTZklgwOylhqm05xceK5k9qR/LCRebJYsaIMtWFJjAgolaU1P9foWtHXwqaIEVdycK1AeW1oZ1T7bw8CMZxaYaTGJyc9l/G/nwCUS6Ue+IxaYbtaq5hRVtJsphy1ZCuZ2MtpyvdTEgFZVulaY9rJilpRDpCuFS0lqtkgr9GevSCqHQEYa3PUOElksOMSs++LhkzQ6SgvTeU2yPpSgblWFALqcqTUqDNJTJwYqR3JJAG/Cu/yQJYVqw19rV29w4grOezl4AbUgh19e5yQD6XAe2uD3gXM5qfk+olasFYvBJAe+9IDBryvVmiUxDYjrOQQc91NqB23QWqxj9NitWEyOsH6Yr0x7SQREKW1YzsLHGhds8XVgddFAfg/YtAMkNBGXMkh04q5RLVhZwapelyc0kXDemVu3O7LaoNdhsgUeJNKLXDDniliM9eKna3E2G6ElRw+dlqsr3itwt9LMXtj/jsJqkB7aoULMQXeJBtOLSjwEgFDLttc42kj9D7fF+uIQKRNgTdZjBxZXEsKvEps0vU3Qfg7+Ig6adisshxj2UmiVK4FEKkFTmwPHKvGgzwm+H93n1YuyDVHQ5NES9qyL3EVr58TE44HeIhc33buF6ncjrTo+9FyBGrFCZN21s5PjTqTSJn/XQNHop6oYjPgf41KCWXRW0ZYyeHViyd+rbXP35Ukxk6V/rXvmgVe4vtCgBBQWi43Sk2in4jGfL+rlF1v7B0RhGW+16hlwZVyvlFWMu3X/+VABu8Wm5wGH/r9Caft0WDsSzQy9wwli1GX7mT4v+jIzFuRhi01/iFab6wFK057K38YksBZgeVbo2xt9XMbNRFail1l1BUpfQplYnhlzNXyy9pglzcPKHrbqDOZPgrrfN3AhN/Tn3vkWz7wux9mrYBw2OzoSyIri+u7ftdouQp85D8t8Fe+vwJHSgQ1vWJUmlTL+dj3+Vo05onNIxqYA5+/JZ14K3vYsCVGV0nEVZv9X1Ohr8QeZMyCJXx9hEix2Li2vIm5pDq5bPd9skPYhXC4xs/baBbvxwMxX/c1sBWlpeYYSpI477yPS0OIbfdzgqJJ4hhr8wY/tOVYa6OvFzcUCG2t7REiKKPOJIZwxEv8XoAA0xveiAfEEp8bE9hxthpVJZddKm2lrx91EYS6svxl/809KAbD33fgEBAdMfRLo9JkTqNivq+LjkTQ7I24Q2u9xvdvkJeUmKsLk203zO/7XaMAloqZwV2zS1VdXw9mFpW/d93AufuMWScz2+E5sCwfB60CrPCxJ7o6UnIfAgG/Fyo24MorjUaTyJn2qs8V+XdhgCwLXe1N0/zQlr722o98HVAJCz3sde8YVSa5X0GvsY8XWllKKIkHPDHfQG1nPz9cKSU0iaeNqpJLHYo8BulfjZa5QSjSvlv80EquALjMz0UAHXW+MApNLq6m131dcpQSkHjUE9+i5D4oVe7nOgRIvGxUlex8BPf6uX7FzGCBxeLN/m+6X5RmbfLzYC50jv4qcVGUIXnzat06d8G2/dtAy4Il9JOe+JYxY7ZzWRn72Ykp5o+MqpJLn7TPC4SUvjybEW9TvUAM9w2a/LAf2nPPRf+dGSDXl5sR4k2yAhaOltvvM6pMco6ueSPYx48pRmMOSrzx6JdS6mMw+/aRQI77Cif6gFFVkvtV08KICvpXolrueifacYfvGjZ8WFzve/2c4xDpR41Ck4zUO6GUb6+5YdeFBrxxbRJi63x9TZUQ8fj5P0ZUyXZa0S855utD/TFyeF1ij3Iaxab5+bj9gvLOLxqLTjIDB0Z1Weku3zqxqBNxcnOXeydJwNN+PVLAlgXW2iSWSea3A557rR02gX14PpRJoJuz2lePHvULrFmphD9XlVu5X+PeQZPNToQkU/kglm8fUyTgPTTL9Eb7hg9fw4L8e4VBNLIbRKVGVUnOP2LHbo4o4VO7YbgsP7vxgtmf+7F1WtCHvo27NcO1bbNjP/l8zow1Pm7f+sofNc/IkcVMWO3bOoTrxhTUBiOpJFNeXsLMi3xrN8BeZdNbCc+1NNb6wxhLHx4901jutCi5e/CU94xFV0XyRRN8eTesZUER/umpRJ7EJF8exY23KRZdi+HDdxhFJZ9e1ldTWPqvaCgloRjBP/hqrIJfXSODtg93XBPqUclso8YqIBzexcAavy7Qa8HvY+DAqGcCZ4Wpfj25xCRexIgRe4yokstNF85at4czV8CP17kKxrxo93f9OnY6PW0cQiF/Nk65K+G65lq05PvkGAQW+9Yng1chHN7ime/R7tO+PW7P+BTl5auNqJLM+PFlAP7ny7YJASJeinC4PFHgDW+9cl6ZDuz010NrDAQJ/3M6/c5Yc9XgQr/BSkV9N6cqBRb8N099VGTfl3CV/46i2DZcTeONmqqGr8rq3dKQ/HdaLEsVl3bHxsV+atPdAye/eYxa67vdmDa5bHMs36ixivJ04t9w0J9HwFUMT3kquR0e/lM8PvBlLsn8P1+/tlyDzIz0ehI24Lccq4wyMWrFE3f4duAGD96hy8s+92OxjkEvIBwuMeqsAr+V5v6e/Vh0tCxopoc89U3Dh8/Uruu/u7KJ4hr9H0aPjhhFVYFGXS7wZRwXCMC1rF/H/7ni7Mnll5e+EO05G5a/Vpi/KGsS2/runH8bU64icnOXM3z4ijfzPOze7a2V7dGjt4D4A78FmhxzvoaFD42YqoaT6r29J8jlC/zUJs2JV7sXPz3k/lW+Czog7hPkp6kUkNC7/5T5tLmeoarIyfmII5G1vvINQoAJH2NkeKXnbNrV9/rt5BKDHJ0bNvdxVhE3XTDr6WVlraO+egrEYrxa1qkIN7Yq93clgP+PfXhNg3KjTxllVhEXDd/KMedNX13vQQR23F3IC3vuyksWGOu3h8jZDkB13HWDEVMVMTLnfWb+yldxc1yjkchSDB36JQ4UeAFcdt78sZtijXyzwswQKFf23RMn+vFclIeCBMIjfjsCrpkLMH6847XvcqORq/12TYOCmoJweK9RUtXw0MB50b6BL4riM6IfZnZmwLYJo4Nv+PLBrqGB9+cGOVbqi7FK3BEbwNDQRwUXXLByl1FjVfphvtJXWy+lhK3VAG/6LDGZffRyN8cTY8ItRkVVy3KnzUhY/rneY1esDreWG8b5fm61aDErtd5Pc6tivI6RI1caVVah3dTNzPbVo7lSwlWU58mcfd++ORxz/HPPuWVBxaKTcIL36hB+QoT4fD9ducXMUBpXH2jft39yg2pwnT92JjBikMUzSvpMNSZcxeTkPKVd5Y87ACuORGzUeTneXNkeNWonK/VXv9w3pEGKg8G/GhFVLUUX//XhrvbGXRqpv5tASwv95PIP7xhcOMePY/WXwVMXnxZc/ZYiywdemNCF1kWfuvj+q40Kq5ho/Q9Z8wrfBKpObHp5Xt7Xnvy44cM+BRKP2/kDJ7bOzcm+34ioatmWVffFTZE6vimq7dDp+T3P/Mr/i/OJ+0Yp3y+7MZkZOmQPNYqsYkpKdmlWz/mlOdpVH8Ep8abfu/LKfQye4pv4x3X3cUbaHUZEVUvs0tzPOBZ7wTd2w1gCCwcej/uOx3p2X5eZ2530Dam9i5cTR4dmlJ8wa+LFUz83JlwNE39QnuGHnaVs23Bj5ad7+RtVWcld7DqpvxvOtsGk/4BLLzWPulQD2aH3z84Ipv4u3ia0ByVO1gA/j9XcS/88sLXcniiQpjIBodEQJZcb9VUDl59XyuB/QQo/FCAiLsuHvfyNwXRnlB+ObTPAGny7EVDVc2PfovL3Y+0f5YpuT2mribF07y895+8gqhV3Nqvc7FtZ6xIf6B2s+f+ZuLsaCIeVJjzIzNoPfoKJHvPyfbBqZtHtGvDFXbyKeRIGDzYPj1eHbWv3QQZiKd8QKQFNdyceeazkOxHqvRfnf/1apNsfYFHqXtVAwIpIy89GnvfaaGO61cSll25hrSenfODjOA/i8svXevpDr7xynwZNSHWT0Y7zts7JuduIp3r4/YVFSzvpr54QKV7i7SC3/Pu98I27/T5e3eS6y9JE6p7O0iC0FtsXT2z46Gyjvmrq87ycR5Srnk3pU1hSQkE8ibxhnn7huPSi4Vu1UldxKh/vI4pn7R/rcNg8gFhNDDvvrQc+jLVbBMmpGykHBeZEj/vDw4MeX1SrBk/g7FSvZjNou8oIPWCUWE3k5Cxgor+n9DFwIaEJC3TOsMc8/Z1FRUoJXMIpfhevBnZxZvoEI57qQQ0fPl+BZqZ23GyBlZqsRmQXfddlfY+88197ak5Jj7dTMwBh7NXpelOswXBjttWaKLAqb3y1Ykrl+2J2KeKUKDjq3PC/NNOXqXotBgNRpWEcWDVzXeDlP6VTdA+nnr1AgdBa7oi+PeR311IteOn9Becv0xvTng91Cu71YgYyAhoTGsy8vH//deYF4Or0Ddq9kpWbssemWamdnJd9VUr0dd06k9lxU/aqhsQjLpmhU2rLLkyvcPJbn/RZ7zRJ2U00S/e1Wjzs3HfuqW3j5obDHzIwOWWjj0AQyqJxZmdg9aJyw7fomPtFysYUrJXOCZ+dEh8bDs/nWOTxlPXJwSCUdk/H4MFlRjnV2O+52cNZ6dKU1ahyt7gCP3iQ76BnzBad884Z66ONPk25MoAQ+CTa8i/nn//BImOy1czo/hFh0ynMnHJF3sTuXeJhFXdtpUjQ0KxRJ834KuX62rKggT9jePh1I5rq5fLBr64fl/HGqPqyLOVylCZyX+lvM2cfX1vGisJQM5o/cGlra9eeVLuqIV3GkG2/M3b4WQtWGNVVMyNG7GaIUal4BpyBPa6Dk1PmgwcOjCqXr2Vwaer1NTkccYaZRLIGmAi92m0yxGHppJZMGbs4s3hJ9KjLauvQqVjkNxr8ecptriCCG4neh2HDnjMCrAG7kXypBlLuig9mxKDdQSnV11rfwcD61Otr1joS+SOGDzdxcw3gCu7NzMUpGMtpgMYc7LH6gxZ4JxL0f8tPvayU7dTJEyzG3PLuz5127tI/GFOtIYEMG7ZQE/9fKiWXLAQ0qxEqJ2dBSnV2//4uBF/JzJFU6muOqYd0bvguo5aa4d5B/519nFw/Nk2qlPnmAFzdx/r82qsHvLS6No1Vr36rv+5urzm1mUytDZldrE2Tnxj84ONGbTWU4ORmPw9Jv0cqHVWUEqTpZlwWTq13E0aGP3HBp3Mwhfo6UZziQjV82EyjlprhvLM/enZG2YlTYemUSSMRCmJKWb+hYwa+trLWDtzll5cqSScwczSVPlsz3ufmjX9nlFdDhMOfMPGVKXV9gGWBiR5SeXkvpVRfDx++1U0PncSWnVI+mUHLdF7ORCOWGtPoai3wp1S6qiExn5C+RuVmzzvYzx/ylYiJA/MX3rvnogtLOBRNhXrdJ+Wt3rvwnHcvNVZaw4FEbu7DLPDfVHiAhOOTqtIP67y8aSmZyIfDr7sCNzGgUiKpVPp1FW10i1FJzfL6kDsfP4Y2PiaJE8fpPZxSJh7q6h9cOen5S//yZG0cq+cv+fvKnvKLMekUVezt9B8kCN3sDe+8P+TWMUZlNYubnX03os4jFav7HvfDIK1dd6IzPDs1FwVycxfrWPQKRgqcXiKCYn7NzQ2PNCqp4TrEBa+PebXsuOeV509oMGJsubN2db/6hgtmvWoKAeG9LquzUmLnfkLvWKNWLT8jsSnEUHO5cU5OIaLRGxlIiXHQMWeqys2+KSU7+6KLtrqRWF9OgUfXmBILMMtVXriXUUmNa/TvWuu/psIORWbW7Mbud3NyHjnUrzlsFe5PF02ZNyd6/OXlbHu3yCsU3nE6rpji9jnXmKc3UOHwrxTzRPbwMSa2LCitJ6q88NUp3dk5Of/WAiMYHr7/WAgozfNdiUEY3d/cyekBFg27edwp9uq/BT28yJ0uHPS01tz24qX/7+raPFYvDLl78hmhT/MaBSKeXWqVYBxrbZh5V9rDZxt1eQNH8rVgLvL6jgTF+h6Vl/PHFE8MnoIW13v90TUFflYLnG/U4Q0e3dl26LvR9vO8uw7DiQdcXizvfsfQ89982IxYJXl577qCzvX6RhbNepGWOBUTJ8bMoNU8bl7OPxX0X+FluyECE81Uq1eOSunOHpnzvssIa+ltjbLmZaq85DSjDo/ESDnZtzFwv6c/UlpggSdUdvaNh5XyT/mzbppz5eA/1p05M4Mc20uBB4N4Yezoj046e3kfY5YetMH8/McIcgzFTdFLEyqzThw9yQ1f75vOnl4w1iL6FwEBj6UHrBnzdV7YJJUepN+se/61KNbmKgdSkHdsBjaUOiu0fNKLl9x1jRmlCoY8d1vO69GuT5VwMOilsZLQ6Gx//f7SITeekjhpZvAU9vSih5l4PMFjWwWZtZby7yo8zDenOkRBQZ4ATSGG5bG+hhLifZ2T3dcowns8+0q/Fy9OWzLQa1siytnSU8pOu+bXF7zwiBmlg1BYeJyl8Q4Bdbz2aRr0lUoLdMfFF+8zA+UxphVdbwn+h+d8MsAKPE/n5lzom77+b34vK12857XcOKFRwlqVE25nBOE9aHrhQxbhGnhMowzSCvwg54Zv+NF49Kf8gX8b/MScotjJp37lNvrcGzt5GVERwsLY0Q9c9fnQ/sYUvYnKzR2rWY9lxi7viAPlWmCMr4q7cfJyHmPogRrwzD2liR3czBO0xGCjBm/y9pDbru0Z+Gp8c7m33CuVucaieNeA0JLRprj7XWZdck/BWcFlg9rb2z73wsNrcXvJoJjbJ/TFXcuG3tjXFHe9iZOXfZVmMd5Ld+MzuFgx8vxU3EXFTt7pLusLmXmHVx5iYqIoA3ea4q53ufS8ty9cEOtyW7EOaW/IVGOdaljyP6fjIFPcPQzh8CcuqzM0aKFXdmVWbKrgp1XTRh1NcdejDM++n4mHMHnnCgENKEV0o6+Ku3HG5C52BfpoxqeeeRyRE/39uCnuehfOC/9GCYxion3eiZuhNNGYn1Lcxc+pTL/wSt+FF6Yv7V0zmzITu3ZRzGnqhdLjhowcsGC2McMU4Nn8Y2RELBWEtJrUhgbtUW60H0aO9PUjEWJ6wVpJ1JZraOmpMj2JMfRIlZtbZATgfe6aPbT7v8oHvLWds+qihuxGM6G9vaV0YsaMbnkD3vzKjMrBmTp3QNZ95ee+vcJp1a1yGaX6Nc5AXSuCGzLnDrljQP6zZlRSgMLC1pam5QBn1tTOoQrfwHvdVSub+PrYcGFhmmS8T4zjamyXFgNsSbja7YecnHeMALzPbS+N7DA+9L/VbeV28TNTtKTkWMuc1ht7nrWqlRmRn6x3abk0A4Ivrjm9c+LqOa3U7To3fI8ZlBRgwgQhj+26joCWNVl61ICrykraYcyYDb7OjQsKnxeMi6gm6xCWRSqmRmF49hQjgBRgzpx0qySyjYgzasxoEht3qcQtKe6MK6/8+qf+vp9l56++esK49tb2m9paOzpWvNJTPXIp4aDexelFf9tz/l8fuviJJcbyUguZX/hPIlxJXL3HmRjYy8AUtarxjZhYOx4aEAUFNwim64modXW+pMXMTuL+Ji3uxPBha4zVpw68AJmn7rnn3jVO05G7dUYdqrbgktBU7N3b2dr06OtDJphH+H4ilzx/+x0fxtpfuV1lta3GuRRZoly1sXYU/iFz5vVDznt3mxmJFGLatKZSWLcT86+IKKO6/bAm+qdeufzPmDhR14IZlayCGXeCcSXArah6+1qB+UlX0i0Ih3cZw08dsguvbvbXJnN+V4ecXzUSxWnVlV/F5/Ytqu62Darhg30e+fgeFJEyo3HEcffVBPoVMXpUZ6G3IsKnlwnqJic3d7kZiRRi7tyg2Fd6GzFfTUCTavYTMUX8CBNNrC1+Qkwr+I2QNBYVi6+oXo1ilgvrDuQOWWUMP4WYtNCmul9OkMC1BNSt1iiSuZQlTVJ26Z9x6eg9R/J7f7Z9/7vwjMxm9Z1bzwt+ensmRWTV+TIGiPFBrOPmT1SLIWPfPunD2pEc+JR//ztTNGr8vGCcRVWeXhE08KYOBQbXyqNK/yhMk0fp/0eg6wlUtVvuK85nryPlXOgMH74SMMe1U5VLXr2moVNaf+6CaLeTHF21ZhMiB2cGVs5Z4jTM2Ri+sdz0/pHRe9I4u1OzOo/Ojx13xW6VXqUZZXw2PSmw9rN0UXJO34vv2jQxMb0aUpJnnmknpVUkSPSqjgVABs90Y9GrcfnltW9BYOrULJL2E1KKYaSrvq814WOl1SB8+ukmEyunLpPnnNuxQ9q2Gf1Cn3Wv2hOTjBhZmFd6XMFeGbpm1Dmv7TS9/wsoLJRS6zsJ4s7qKCBp5q2KeCD27v0E48c7ZgBSlCmzmli28xgJcVF1+GQF/lALykU4vLYWajSNFP1JEN9UHWfRNfHXiu3BaFr/Y/SvHZvMfMm0aU2FEJMlxIAq/7tIQLN6T5XpHIzJ2/DzcrZfyji254X7/qeP/cUZNqmjMyliJeOP1SAu1mmlBL2sKHLilLHnv/SosS7/YM0sOFHH4hMsdQe4ebJWuyvvGdyuCYu1wj0YHn7TBJyFjS3mB8B0IoCjCZBJ7OsSBpYKgceccPhpY9n+4V8vDer7t5IL7yzVaceX6GAzhyX90vsA4nGrLRRnILKttbVz8dUZ8//4qwte/tD09i9jzdxmjUeVX3//Kveok1yIo8t1QCZjrAQx6lJZcVA4n4xI/+Dpv174xCTT237ywzNPhqPuBFEPMDdLmh+mxFnvLQAWiXJxc2z0sE9rfWc/+2xDiroPSeY+ILRJoh+Os5cFPpZaPxQz1yL5iltfvPz0cWlv/L6ejPSsQ+WN7cTGWvrFVhNlm0s4uHmryvroD7sv/r9ZQ/++zvR2cpH5hf8B0I+ATsl85InB5Rq0AswzdF7OX01P+4inXmghAqX3CxJ9ALRI1iPlTMRg3g3wMib8ReXkvFrr+5qZZMGMh4n4NGjuCCKbkueT47nxCmIxw80b9jdj2D4iP7+bxXRPIm4Gjkpy3LwLhI8AvtPNyfnol/x5SVtg/O2cvEZD0z4+Oorg+CZi39BO9uZ6MrHB58j+it06Xa1wm69mJf65g0LvDPly9OdmVdLHFBa2hlJtLFgTQehL4NDP+WM0KELQ77mCJgJYXytXJX+8rxtD61YSdAOBBv/cowYMuAysYoG7tOsuw+rVn5mdQv7lujnjWr8VO/aYxqL4j586zU/crOoFjrR4qEBoL7eVt5Xb3/zMbfanwaGFW/510eNGo0lmwiuXNplVemarFrTz1vWq4flrnOZZRzyXMtBY7lPHyq8/3sVpfzrG3raqcPG9a2ii2bHrW2bObCNjseOZrNsF616JYsTPWCFgQpnWvIA0/q5C1pcYOtQUjX7oh5tBqXYWidsA6g8gk36WHyaHwUsU4y6w+5nxw/5m0twB7Xva648vp8Ctx9hbezYTe+wjza/ixrHWbVKyzm30WmOx5x+vRDquu+nCuUajVav3eJzdmrS+VkIOAbjhzykIVCT/vEaD79PA+9iWtQbXDYyaDvYp06e3kswdmeTNguh0AOk/z08gxsBCEN+jIrwao3I/M537A402AOyjJTvXEuMiAA1+Vl8zaQj+VLv4hyb3fVjWGoTDMdPBPmVKYWsZ0B2JxK3gxEJe2s/QJwCKaMKHpPluJfE5wuHPk/F5VXaC5KLnJxw1KPjRLW3ltl5dApubHiX2hBQLm4kkgySBXQHtOixinzot961RjdeucRq/8PsLZzxurKb2YhUUnMwav2ZCF8HUEEQ2wPEfFiDicnCRSGwQA9RugljpajyG4eG3Te8dcVLfkhx1iyD0BFOTyuK6HR8GVHS2A0Al+pqwl1l/CSVn6RHZz5jOq738ff6wsz+OtBz9pdO480r3qPr7dFpAgC1B2gYTa5CrQU59Kot1tTfuaGftWNmDvvzXtYNfWGx6r5ol/kK/dvPR47dr3QbHr3JaNt2i68bHyhasbRATM7mapBNAzOka2LS3vdi+poO9pXDiwGdmmN6rvcgZMwbCVSNBdAw06hMldp9ZAFkVe7uhEn6YdQzgHRq0gqEfRl7eQtN7R8hTM7rLNH0NGMcx0FgwBxP9zGwnblZirUDkgBHTAntJ82eKeDpyc583nVd7mTA375JO1ta89ta2jl2tr+umUSygQZZmsqiiHuhKaHcbZ8ZWOS23feU2Wv4+tf33pPMnLTO9V4M8/ngDysi4nUieQOCjCFQZd8dznLjgya3Ic9jRECUCWAvSc92cnEdM59Xi3PiZwpMU4ddC8rFg0bhiEZYtcMIvU4XNwGWK58bYrSFWM7tTkZs7z/TeEVJY2EIq+i1I9wZEs4rcOBH72BW58X6NIqaBEoC/APhFnZtr6le1menTTyFhjxXQXQBqQJyoqcRjOaui2koVGmXEIHgnNH8GTU+qEVWzm75aHxPsXXhP3Y7BjRnNZGlodVmzffPQswThsLl30XC4iVYCqAvp1EFMELTeC3t4McLmAYgqYfLkehAZGQjqUKKv69Xbh4Fml4Dh0PACWC9Hu2StibWoG0hj1UZ9XXL++Sv3kLmj1ZMsnN+u7se6Q1YpBa327oaSAW2W7qWuMLsMDIdmwoQA2rWrAyszEyKmoXUJbLsY4bDxw0mfUJnw6KNZqF+/LpQSYC7Bvn17zUk2w+E1uiIw6IT/ZrVwSzJjJNTHsTbFi8K3FiceMTF4m6lTs6BUfH4NgqKliEb3YvToiOkYw2F8skCPHlmIROpABwRcUYKurYpxwgnGT1RNLSITUtaDUjYsqxR79xYbjRoOy4IFFr7YnoUAMpHhEIIoRsZn+9B/ormH2WAwGAwGg8FgMBgMBoPBYDAYDIdGmC4wGAwGg8FgMBgMBoPBYDAYDIbUxBR4DQaDwWAwGAwGg8FgMBgMBoMhRTEFXoPBYDAYDAaDwWAwGAwGg8FgSFFMgddgMBgMBoPBYDAYDAaDwWAwGFIUU+A1GAwGg8FgMBgMBoPBYDAYDIYUxRR4DQaDwWAwGAwGg8FgMBgMBoMhRTEFXoPBYDAYDAaDwWAwGAwGg8FgSFFMgddgMBgM/5+9OwGToyr3Bv6+51T3bJnJRshCSARGEobMUt1BcWFRwA1BRS/gdQNUrnJRUXHFBXHD7V75PnHfcGPRT0QUWVwABTGkt5k4JBiWaMi+kGQyS3fVeb/nwGko2+6e7slMSOP/9zwDmenu6qrT1ec971tVpwAAAAAAAACgQaHACwAAAAAAAAAAANCgUOAFAAAAAAAAAAAAaFAo8MKUWbx4cXNXV9e0qdp3lyxZ0r6f9mF27wUAANBQkslkzMUwnorlu2V7+2Nb3JgCY1cAAGjIeDxnzpypyo1pKmN9hdxY41MFOLAwmqBhAkI3EX2BiOJlHs6LyMMi8g+l1F0jIyMPtrS0bEulUsPVlun7/vuVUi+p5f1FhMMwvCOXy11a4Sl62bJlC+Lx+GnMfCERHVXy+keMMd8LguDHq1atup+ITPGx7u7u7lgs9nVmHiOi1UT0rlQqVSjTBouMMW9XSp1PRLMe24GZKQxD+9w/ENGVRLQyk8lsIaIgGkyJ6AoiWioiTUT09nQ63V+lrVuNMc9k5ncS0VlKqTYReey9jDF7iehau7zt27ffv27dutFKy+nt7X2G1vpHzGw/nx2jo6PnDw4O7qiSuM5qbm6+kpnnikhLOp1+rm26Ss9funTp/NbW1m8wc10DBftZisiWTCZzlv29p6enLR6Pf1tE5pZ/ujxKRH8nojtFJK2U2lDu87ESicR3iegwZpYa1+WX6XT6y8Xfly9f/m4ReVX086smDMP78/n8OwcHB/PoJQBgqvX19c3RWn+TiKaX6c8CY8x6rfX6MAxTzJzds2fPzrVr1+6utsxEInGmjUu19pvGmA2ZTOb1lcZ1XV1dc+Px+HHM/F6l1LNt7LIxzK2j7c+vGx0d/drevXtXl8awRCJxl43FNkaMjo5eUC5muTZ4LRG9l5kXFeOj/b+IrBCRbxQKhdtGR0e3rF27dqxk+Z9m5ueISNwY861sNntVlZgYb2lpOdQY82ZmfotSao4xpvheATPfYoz5/PDwcGbNmjV7Ki2ns7Ozo6Oj41pmtvFfmPmtK1eufLDS80844QRvaGjo/xTHDCJyQSaTyVX5SLxkMvljIppT7/4kIjqdTr/QhjM7VhGRy5nZr/DcIRuLmfleY8xdmzZt2rBx48bhCuO79yilXmGXW+N6rEin0x8pxt7e3t4TPc/7JBEVatyUrXv37r1g9erV29FLAMD+sHDhwpa5c+f+lojGyjxcMMbY3GUDEd1DRINhGG7r7+/fO06Mv1xrfWw0Tx2n7xxKp9OnV4rHvu/PN8a8RCl1kVKq+4kHHs9fNxHRj4jou5lM5m/R3Mf3/eVKqU/bWGlz6Aoxn5/97GcfHATBOSJi4/2cSL5q1/+PIvL1oaGhP3Z0dGwpzd0SicSXmHn54+lU+LVsNvvTStu5ePHi5oMOOuiZYRj+t1LqLGaeEcnFRpRSvzTGfCmfz983ODg4VKX2YLfr867tvDAM35jL5R4e5zO5RWsde6zYoPX7V6xYsXKcMdXNzByfwC7196GhoXcUxxPJZPI3RNRULo82xuxg5vXMfJeI/GVsbGxjhVxUJ5PJK+yQpsZ18Ijo1lQq9anI9vw3M7+61hyfiDan0+mz0UP8+/LQBI1BRBYQ0SnMXPbMFaXUEwlcU1OTHdDf2dfX99NsNvu1KstcRkQvqOX9bbDQWpftsGfPnt2+ePHizxDRW5i5ucLrD9Faf0Qp9ZHu7u4jBgYGnkiuYrHYHKXUc11y2LJlyxavNKlIJpMfNMZcrJSaXbINdtttp/8iZn6RMWaP7/vnZzKZa4rP2bZtm549e3afTSrtduTz+YOrBR5jzJeVUs+Lvkfx/8zcRkTnich5s2bNun327NkXp9PpVLlljY6Otre3tz/PrSM1Nzfbz+6MSu8di8VaiOhEIprneY99NblagbepqandPb+us4vtuhhj8pHts4m2bb9ZlT575yI3iLq6r6/v/dlsdmuZp5+mlDqo2GY1WFPyeR7LzMfV+npmbm1pabGNhQIvAOyPWGxjwPOJ6KByfaXtu23/pfXjJ7W0t7ev8H3/5q1bt16+fv36kXLLNMYcqrU+sY4+fGe5vyeTyZgx5gPMfIFSan40dkXW0SZl57e1tZ3f3Nz8+nXr1v34nzIRrZ/7eF5Im7XWrUS0oyRGnq6U+h8iOqI0PrrlP8v+xOPxIB6Pf5KILitZzWfbcYdtnzAMbZJ2VYVYvICZvyoir7AxKxKDi+9j+/1TlVKntrW1rUwkEl9Mp9PXlltWa2trMzO/JJKM3kRESyu178MPP+zNmjWr17aFe6+51T6PxYsXeyJik9bD64h9xSScurq69ODgYOjGKkkiOqHS84uFdNsm8+bN+81BBx307oGBgTXlclxmPr6O9Rnr7OzUa9euLRYYFjLz82t9vYhsVUrZ7wYKvACwXyxYsCAWhuFzKz1u40w0Rnme9zvf92/IZDJfq3Qiidba5tqJOvKQsvlHT09Pm1LqS0qpM5RSc8r0mbYfn0dEF4vIe3t7e5O5XC4TWe6hNrd1OVvZA5h9fX2nB0FgY+j0kgO55OoFNpac0N7ePkxEXySij5es+0uYucs9/09EVLbA29vb62utLyWi04tjm5I2s/nrWZ7nnaWU+n1vb+8nc7nc7RWabFGx9uBqC99avHjxadVOmPI870XFbQuCwLZL1QKv1vrFbhxT7/huTUdHR7EwzCLyfKXUtNJ9wa138dd32tVqamq6ZdmyZW9etWrV5uhzbXw3xvQqpZ5f67jA5dqfivx5aT31GmPMKHqHf28o8DYIZtvPSODO4JVoYHJnZLIrCLLr1F/AzMctX778OStXrjyvQiALXadl/2OY2VTp9FSlMzkOO+ywjDHmcH68VxJ31HCbO3omrvgWdwXEMWNMU5ltK/5aKBNYXiAiH1RKTXfJ8AgzD9k+3LaJUio0xsx0Z+dMM8YsKLOa0eVKhYTyeGa+mZlbIu3yqHttzP3fvkeHS7JPFJFbent7X5bL5VZU+Myiye/L+/r6XpHNZm+okLQ/8bnWMrCwyzfG5JVS5T5bzU+ugIlus9tXSo92h5HHg+iZZO75yu1f9jM9x32WryiznwSuUD/uPuWeLyW/h8UEXkTs68Mqr+Vaz3jr7u5Oep73spKiio3O2Vwu9wv0MABQRzwuRPq5sNgPuVhMkf7S9uvPEpFnzZ0797hZs2adVu7sIdtPRs6CraXfKzt4D8Pwu1rr17v+rdg3bnfLVcaYgovTs5nZ9n9tVTYzLNOPLlRKfZWIDiluu4jY5cdcnCm4+NjqLtvsLLPcIJKAlo0PXV1d85j5frt+bt3tew2JyF4b51372+XPdGOe5SLynUQi4aXT6R9XijUuJtrP5MhEIvE/6XT6PTV8xjRenInFYo+tojEmLDO+qBqLSw/aM3MQiY1haSyOxGO7HS+NxWLLiejgMusf1hmLTTwel3Lh2T7mllGtHcbcGKaqnp6etlgs9powDN+ilErY5nPb1G+M+VAul/tdrWcdAwBEYue/9JnGmGj+Yp93kv3xff/FmUzm5RWWF82Hxo3HIvIveWtnZ2eT1vpapdSpkc7UxvntLjbavt/mS9oddGV30LJc3u+Vy+EXLVo0U2v9i0h/PUxEdnwRF5Ex9/oZNmYqpVpFJFkuHkcOzpbdzmQyOd8Y82eb/xbjqCs4D7s8v+By/Bl23MHML/Q874V9fX1HZ7PZwTK5rimJyyfPmjXrxHXr1t1c7TOOjpfG2yeMMcXPMRqTHtsX3BiIXNv+U5Pbv+3Z80+19LJjvdLc2H52zHxqLBa7m4iWlH5eSqlCdD8Yp95iY2J+H3Jju40j6BlQ4IUGjGdBEPjM3Oo6Dq2UmikihxhjjlBK/YeIHK6Usp/vG3zf35vJZN4+ToD8cD6f/73nedWm7Xg0+sv8+fNbFyxYcKOIHOGCq+2Q7mbmbxcKhZsGBgZ2RoqnnSJyEhGdFo/HC/VsrNb6bGYuXg6bY+b3pNPp30eSwVmxWKxHKXUcEb1SKVX3ZRlLlix5BjP/xBV3bRAeVkp9PgiCn+dyub8Wn9fb29untX4NEb1LRGzyOVtrfc2yZcueU3rUrkxyGVNKfSmZTN6VSqW27etOsG3btvWzZ89+jTHGiyT44nlem1KqWETeGobhO5h5R8n6VOz8gyB4uVJqRyQwNbtE3Q5U7IDIDlxO7+7ufsXAwMANVTb5I8aY340ThLdVabNrC4XCl7TWqsrrR/r7+8c9Uul53ila68tK/ma39bdEhAIvAEwoFodheIpSaqhYfLOxipnn2RjMzC8jouXMbAfcL/A87xddXV2njjOlzLVBEHyhNNn7p8prGJb2eSqZTP4vEb0+kqCuEpHvGGNsDHskWqSNxWInBEHwYiLaWdeA0fPOdMVd+x47mPldmUzmx9HiXTweP9oYcwIRneQODNels7Ozo6mp6TZmbnPvE4rIVwqFwrWrVq26p5iwdXd3H27Xh5nPF5FnuOd/p7u7+4GBgYF7Ki3fJWh2nPO6ZDL5zVQqtXpfd4K1a9fmu7u7z9Vax8skszcRUcwm88aYd4jIA6XJWIX9wa7jufl8/r5IDLTLWcTMJxLRfxJROzPPSSQSX0mn0xdW2F67X34uDMOqcS4Mw92Dg4OVxma/LhQKn9Fah1XaNd/U1LSp2nu4qT22FONvpLBg/5fQWt/i+/7vgiD47wpnJQMAVOqDbL5zvpuSofi32cx8qIgcppR6pctlbD96qu/71+/YseO11c4cJaLb8vn8+7TWzePElH/S0dHxLaXUqZGDZL9TSl0TBMGN/f39WyIx87BYLHaSMeY4V5yt2UEHHfSTyK9bmfm8VCr162gs7ejo6DXGvMBdCVOot01tnDXGZJRSTS7nsrnhVcaYH+ZyuWzkeUtisdgbiejNRDTXXcU0kEwmj0mlUukqbVcsvH+vs7NzyXjTWdUqCIIXuoPYUc90Vx81u5h3os35S54zvHr16l1l4ngYhuGFhUJhpY3H9jMPw7BFa23HHs8novPdU5/h+/4bM5nMdyuNGYno4iAI7qg0xrPjyCAItldoL/v4dWNjY5/1PK+p0vYrpcbQI/x7Q4G3ETNKEYkWHUs961nP+mw+n/+e1voMd2Tz/N7e3rtyudyPKnWwSqkHMpnMynrWY86cOScR0QuLtTYR+WmleQEzmcxamwcR0XcmcHbG6ZGC3nszmczvow+6OQJvtz/JZPJLmzdvbq63Tdva2n7CzIe4X/cqpZ5VLvFzAS3b09Nzs+d5NxLRDGY+LB6P/9JdejpeEDvCGHMeEX1+X/eD9evXj6xfv/5fLoFZsmRJe3t7ezF5GisUCncODg5uqmPRmWw2u6Xkb38kou/5vr+amZfYZcdisUuIqGKBVym1NpVK3bsPg7XNAwMDqUn6zgSlZ0W73zG1AwBMtI9SGzZsWLl169ay0xctXLjw83PmzPksEb2NiJqY+eSmpqYriKjiAVdjzOboZZq16Onp6RORt0XOYvpbOp1eXu6sn4GBgfVE9GMiurre+zAw8/PoyUsAPxYt7lru7OQV9qerq+sKz/Nm1NumHR0d7yaiZcX3KRQKp/b3999SZjseJKLLfd//GTPfZJM3Zm6KxWK/LHdGa3Qz3AHXg0XkS+7A5T6HmIGBgTvLPZBIJIpnd9v/35PNZnM1trX9+evAwEDpvnA3EV2TSCRmE9Fr3LJPGSf+/T2bzd67D9v36MDAwL37emZtPp/Xra2txbmah40xY+4MZ8PMM9x49YWxWOx/Ojs7zyidvxkAoEqfGTJzNpPJrKvwlI/6vn81M5/BzLbfeeXMmTNfuG7dupuqLHP7wMDAQD3rkUgkziGi10WKu9du3br13HJTNPX39z9ERN+2+VWtc/6Sm45JRA6P/OnT0eIuPX7gcbfL3f7Y1dV1uYjMr7NJled5H2bmdjc2CZj5zOgJVpF4vIaILunr6/ux1rrftq876P3hZDL52kr3bYm087z29vYbap2GYDy5XO4PZcZJO5RSj8UwpZR9zl115JCKiO4rk5Pa9v2h7/vK3SdAhWF4IhFVKvAqY8z99Y7xStpqy6pVq/rxjYeqX140wdPPihUrdmez2VeLSLaYhHqed2G1gr671KAuWusvR15/V5WbvkQF1eaVreBg9x7DYRhurvbEVCo1vH79+h11BuNjmfk57teCDUjjndXT39//JxG5IjLFQHcymXx2laR9T/FyEK3155YtW7Z0qj7/6dOn/9Nn2dLSMmk3UzTG/DgyJ+/MRvlOxOPx24Ig+FgQBB8UEZwZBACT1SdW7F9tQpfJZC4iolsig/MT+vr65kzmOmit3xG5ocj6dDp9VA03qjT1FuyY+Rnu/zZBSld77uDgYD56plItOjs7m5j50mKhWkT+t1xxNyqTyaw1xrzPTY9gXzfT9/23VRnrBMVpEJRSL7PJ54Gwr9Q7LmPmz0YOWsaXLl06/0D/rnieZ4wx/SLylTAMTxkbG1uUyWSmh2G4zI4tRKTgTjh4WUtLyzPQuwDAJPaxYSaTOVNEbnXxxf7ts5P5/l1dXTYOX1CcekdE7k+n02+oNP9+dN3qyY3DMJwhIrHiZu/Zs+fW8eLxfffdt66ebfF9fx4zv644tZGIvLdccTfKTclwuptGwPblrw6C4LAqn1cYmTIw4fv+S6Zq35hInaOOsdEPirmxUsofZ7zGB8J3ZcmSJe09PT1n9/b2nun7/hvcZ748kUicn0gkvu37/vd9378kkUi8uLOzswO9S2NBgfdpLAzDsyJztnT19vYumaxl9/b2+kqp4tHDYSK6dAo7zuLlM81KqZYpeIuLI//elclkvlLLizKZzKXFG4owc4sx5swqTx8goo3us6B4PH6tTWYbbZ/SWm+OBEvTKOu9YsWKXC6X+2Qul/scM/8NvQMA7C+7d+8+szinGjMfJSJHTnKMPIeenArosilMnvcUY5iIHDzZy29ra7s4El/2jI6O1nSlSzabvYGZf+WSK8+dBVTpgPZWEVlR3A5jzBd6enoOa8BCxu7IGV/xtra22IG+zv39/dt27959XDqdfkc2m727eKf1XC73SCaTOZeIflf8XGKx2EvQcwDAZBORT9n44m6U1dPd3T1puXFLS8sSZj6anrzS5fWV7l+zj7lYvng2qoio1tbWw6egqc4o3jidmbNa62/X+Lrboge1Pc+7uMrYxbbRD1zs7nBTKFADxuPowfK2RljneDy+QGv9Xc/zrlVKXZVIJH7IzH9g5m8w85uVUm9SSn2KmW9ub2//2WSfmABTCwXep7FcLne/iPzddaLtWuu5k7Vsz/POiXRs/0in07dPYcf5oNsGpbV+15FHHnlQvZeWVrJw4cIWEXmiXfL5/JV1LuLTkX8fValoy8yj+Xz+rOJk/Mzc097efn4DDoxOfhp8NRi9AwDsL2vXrh0Tkf8XSc6Omaxl9/T0vDgSKze7ecWnpuNkXhO58dj/dHV1zXM3jJmcAalSx0fe68F6phYKw/ATkV+fl0wm2yrEsPjQ0NArRWSve89DtNYXNuBudUJkDD8aBMHuRsiDq8yxWHDTeBUtQs8BAJMtm83+uXjCDTPbfPY/JmvZYRge6m4y+lhuvI9T41SUSqV2RfJJ+/N/e3p6Dp7k/CZ6JcydqVRquMZ1KzDzvZGY+6YqYwotIpe4A5aP5dHJZPKDDZgbRw/ab2iYZJi5ePO3gIheZ/ddd9+DURHJu3sSiVLqFK31B9B7NA7Mwfv09wciepNLyrqI6PdlOib7c2hvb+/RsVjsX4KDMUbl8/nN0ZuIGWOOV0oVk7IN9cwdNIGO8yoR+Zy75OV106ZNe4Hv+7caY37ped7vbaCb6LJnzpw5m5kXR4LzdfW8PgiCG+Lx+P+6hLdn+vTpzZHLTaLb4K1aterPvu9fzswfdR3rx4888sir77///m2NsCMlk8nXiMhLIpeFjncDtUXLly9fVmEQpLdu3Xr/OJctza62TwZBsKneS4ABAJ4KSqmVRFScDuA4IvpypX4vmUwuLXcDDncTt70rV6588IlBnOe92MZi2y8rpXan0+mHp3Azbiai1zOzTQIOa2lpeSCRSNwoIrcqpX6TSqU2TnTBixcvbhaR6BU636/n9YVCYZ3WeszdMfyQsbExG4t3VYhNeWY+V0SucXfVvmjZsmX/b9WqVXc3wr6USCSOJaJPRKZL2p7L5R6t8pIFlWKxjaXGmI3ZbHZrldd3+L6/rNxN1uw+WSgUhtxckvtCG2NmaK2Ly70fvQYATEVaycy7ivmvUuoUIvpUhfxzhu/7neVusub6vs3RPEQptTySV984xdvxveL9XJj5MM/zUolE4vYwDG8xxvw6eqPziSieiexy3br6d2PMncwcuHGM6urqmla8YqOUjT3JZPIrRPRBN0ftJ3t7e/9Yzxy5T3E8fjERXR7Z9tuqlzRkcaUxnjU0NLRx9erV26vlxn19fc+sdJM1G49zudzDdX7WMREZM8b8IhaLXTc2Nvao1jrOzK9yN86zgfm98+fP/9jGjRuH0YUc+FDgfZoLw3CT1lrcHDqVLqk0zHyZ1vqyMPzX8TsRtcViMdv5visSxNrpyTs6Tulk39ls9su+77+JmZe5S1AXMPM5zPxGEcn7vv8nZv5oOp2u+yYgTU1NbSLyxNwye/fufbDO1dsbKXjOIqJYtQS2UChcGYvFXq2U6mLm2e3t7b8kouceUJ2C5/0omUzmI8GqTSnVa/8fmefR7lvvqxYviOjSkrOqntin7P4zb968569fv75aAD9DKfXKMAy5TDBq9TzvMpvk4lsOAAc6Y8yu4kFRIlpYaeRvB9Qi8ipjTLmkVImITbS6I33hkcUYxMxTelOqdDp9fSKRuEtEineobmXms4joLGPMqO/7a5n5Q7t3776t3htkdXR0tBDR9GJynM/n65pKZ3R0NGxubt5MRIvczdkqnlnc0tKiU6nUL3zfv1MpdaId0sTj8d8U3/+AqECIhMz85WQyuat4VpaI2ITOJoZzRcRzfzNKqQ9UyyaZ+T1hGL6nQqGj3RU3PlplGacQ0QvLxWI7ZNBaDxJRcl+2d+nSpTOUUi8pjiuJ6E70GgAwRf3rhsgUhrOrPPU4IsqUyY2L97f5HBFFp0WKXnmwborj8Rf6+vqOV0q9zN3L244rXq+1fq3neaHv+3a9L+7v77+nhjn5y+VwYfEKHc/zHqnnxUqph0VkiIhm2OU0NTUdSkT3VXp+KpW6xPf9C900DR4zv9fdsLVwoJRTmPlzyWRym2sbMca0u/HXTKVUs9uXdhljfldlvzNu3mdVYYynW1paPlNydXD09XYseZpS6hXl9kmXX6+M3AC/1u/DSBAEH+rv778i+vcTTjjh9t27d7+AmZ9p33vevHknbdy48Ub0IAc+TNHwNMfMo8Xkz3acFZ6jmHma/VFKlf60a61VuaQh8u/RKd6MIJPJ+CLyBSLa5C4dKK53s9b6ZGb+czKZ/EsikTirrgUHgRYR7dqH1q1bN1pnEJPIXLSxCgnQE1atWrVZKfX5SPst933/jQfQoMcGmFOY+dTij0uAbQCLE5ERka1E9Ep3x/RK+52y+06Z/Wla8eDAeDcUYOa2KstQjTQHMABAxIwKfZ7tOCvGYtsnll4tIyLNkX+PTfWKp9PpF4nI+0Tkfvt+LmbYWNislFqmlLqxo6PjfpusuRvO1CQej2ulVMy1AzU3N9d1ZUssFrPt8sSBSa116zgvKRhj3l28zFVE2nzf/8yBMoWPK6Afx8wvj8Tjk10SH3MH7XcT0WWpVOoPVZbDFfanaS6+2raSccY5rVVicTNHTiWeqJaWls8y80H0+MGQFVu3bn0Y3QQATFH/uifya1OVfKhSvzfNXclSGnOnRVPMqd4OrfUZRPQRIloXmbLBxo64UurZ8Xj8j4lE4j7f98+uZ7nHHntsczTHEpHt9bx+bGwsX8xz7bAmDMNx759jjDnB3dPHxpxXdXd3/8cBtMvYmHtsJB6/XCl1AjPPtzHQtpUxZiQMw0/09/f/qUosteOcjipjvFZmNtVy9CqvLy4jP4HvQ0tpcde64447RovjKneCwnPQezQGnMH7NCciHZGjlDsqPCcUkXXMvLvC4zZJGyztDyKPH7QfNiVIp9Pv7+zs/GhHR8fLiOhSEVnkgmnxTJYkM38/kUjYJPTaGtsnKAZhG4s6Ozs7qswR968rFQSqqampWADPj46Ojlt0TKVSVyUSCRuUT3dn/H64s7PzFwfOLvNY0v5EPCqetesK+b8Nw/BtuVzukfEW4o5eV7pstCUMw0fHWcbm4jxZ5fbJ4vzSAAANkFBGzyjdWqXftI89Uq7Q6M7gXVHymqFIfW2/3Nwjk8n8b1dX15WxWOwYrfVlInKM7ZOVUk0udixi5ivi8XgzEX2xxlgaaK1H3XZSoVBYUM86NTc3axGZVmwLY8ze8V6Ty+WyiUTi3UT0FZt4EdHFvu9/Zc+ePdsPhEBs8+RILLYbFneF3YIx5q8icl4ul8uMtxxm/ruIVLpct7lQKKwdZxmbiGhzhYOynoj8ZV+21ff9S4joXPdeY8z81RruOg8AMNF4HL1h1HCF59i+diczPyQi5U6Is31y6YHIYl4j+yMep1KpAhF9NplMXmGMOZ6IbDw+yhUKlTsZqpOIfpxMJjmVSl1dy3LvueeekUQi8cSYxRgzr571Msa0R3Jzk8/nh2qIx7lkMvkTInqLuxn595YsWXLjmjVr9hwg8XjUhtPiWMzGY7etI8y8Qin15kwm88A4y8kT0Xo3fVSlA6MPVNlv7T65lZn/UWmfZOb7JnnzNxBRcboO3GitQaDA+zSnlDrCdQi2E9pQKfFk5nekUqmb6li0TYCOKM49u7+2x132eb39mT9/fuv8+fPPdZcPvtQVS5uZ+fNE9NNa5gWOx+O7gyDYwcwz7LbMmDFjqbsspNakckExARORzUEQ1HQ5SaFQOCcWi61m5oO11ks6Ojo+oLX+nwqXXOzPQY8dEPxHGIY73Xx7x4rIl1ww2zU6OvrWGm98Y5j5v+vcp0pdlU6nMak7ADQ8Y8x8pVTxrtEPVRq8h2H4vWw2W89NRgaZ+RVuzr/Y/tqewcFBm6jcRUQn0eNFurOJ6JUuHs+0ww+t9ReSyeT3U6nUuGfjep63V0Si8yIuI6KaD3yGYdhUvJGsSw5rCqajo6Pfam5ufgsz99n2U0r9aO3atS9KJBJPeQ0iDMMLlFKrbVKplLLb9gMimsbM9nM+f7zibnGfMsZ8LJPJ/GAf1uWmdDr95qnYyJ6enjOI6ANKKc+NpW5MpVJXoccAgKkShuEhxfm+K51I4g4W/jKVSp1T63JF5CH3Ovufpftre9wN0G62P8lkMiYi5xLR6SJyHDN3uNzuJ8lk8vaJzJUvIgvreX48Hu9y0//Y1+q2trZa5vCVoaGhj7e1tb2cmeeJSHzatGlXE9HLD4BaSmiMeZ+IpN29EF6olPqYO7O3JQzDi3K53AO1NE0Yhm/O5XK3TzBHt/+zY6r378e6QPRMdNQNGwSmaHiaE5GTi52CMWZNlae21vmFj96sbdaSJUva9/e2bdy4cTidTl+ZTqdf7eYBLB6FXZRIJPwak+7tRPSPYhsFQfDmOpP28yO/5uLxeE1TPAwMDOwUkS+5s6ftct6fz+dPs/n9U55VMt+Ty+XuSqfT97S3t3/FDRrs3+c2NTXdVseiWvdxVeL4BgPA02KwpdRJxbNLmfkPVZ7aVM9ygyC4OTKX2+xly5b1PBXbl8lkrkmlUmcz88uIqJjk2kT6rTUmqAUiip6pc24976+1fk5xqgARWTs8PFxTLB4cHMyLyIdFZK97+QuSyeRlT/Xcf25dMplM5s/ZbPbudDp9PTNfHdnemxcvXtxc4+Ja9nF1YsW5GCdTIpG4KBaLfbc4bZOI/CSdTr8WvQUATJVkMjldKdUayeNuqJA/U7X7qlTot7PF/puZT30qts/G0nQ6/c1UKnWam583fHKT5HV1bMufIvHmiHqmL1JKPTcy3hly8X1ca9as2SAi59GT9/h5ke/7vQdALUUFQdBfjMeZTOZTInJPcT09z/um3a9qWZbneft6ZnfT/h6OoNdowJwDTfD0lUgkXlUMYiKyXSm1abKWHYbhDyNnri5ubW095Snc1DCVSv2Kmf8SCUxH1xoImfkfxWDued4b6khkFDO/LfKe97uzmmpNiD9PRA+74Ol5nvfpkruIPyUKhcIT/cIdd9wRhGF4hTGmmCwf5fv+hfh2AQDUxvf9BUqp44oZlogMTNayBwYG7owkVTObmppOfiq3NZVK/YWZB+nJImXNc7YZY34T+fWgvr6+rlpf63neZyLJ2N1r167dW0cstu/758jrX1dys5ynhOd5/zRGHxkZ+ZiIbHHrOGvWrFnfadCvBCcSibeKyGciN9a7c8+ePeftj3krAeDfl4i8mIieUYzH+Xz++knMjde7m4vZeDzd9/2XPpWbunLlyruMMSvoycv759WxLd+I/HpKd3f3jFpe19XVNY2Inh+J69+rZ6VtPBaRG9z0QjGl1Hfd1AZPKa01l8Tnl0fmYD5GRM7AtwsOFCjwPk0tW7ZsKTN/PfKnFdls9m+TtfxsNmsTuL+6IOYR0aXJZDL2FG6yid74zRgTqyPYf6L4Wvu6vr6+T9RyxCqRSHyz+DoR2RuG4dV1ZznMLzbGBPTkHEcHHWj7Ui6Xu5WZv+U+a83MH0omk4vwLQMAqK6np6eNmX8VCU/pkZGR1ZMa/Iz5hotD9ueDS5YsWfAUJ9AhPTnVQryOccXXRKR4k5UOZv54LeOKRCLxPhE5ip6c4+53kbOWarJly5bTRWSti+mLRGTBgbYvDQ4ObjLGXOTODrM/p/X19R3TaN+JRCLxRSK6UinV4j6zbzDzyW4KLgCAKdHZ2dlBRJcxs3bx8uYap52rNV9aQ0SZSPz7Tm9v7yFPcTzeFck5az4bM5/P3+xuqm1fd1gsFvtELa+Lx+PvZOZj6Ml7/PyfCYxpPszM29wy+oo3Qz+QrFixYreIfDxSe/i/z3zmMw/BtwwOBCjwPv3orq6uzng8/msiOpievGnFZ6YgaFwcCRpdRHT3OC+xgSWWSCSO6+zsrOkSA9/3T+/u7j52vHlfenp6FhtjjnyiEbRO17odmUzGJnU/d9vhKaUu6e3tPbHadvT09LyWiN4cCZb35HK5bL1tmEqlHnCT4otSSrm5bg846XT6nSKy0825vEBE/gtfNQCAyrH4yCOPPMjzvFuY2Xcx04jI9ZN90xBm/pqL8/bXOW1tbX/t7e2terZNMpmM9fb29tV6WaHv+52+77+6q6srXu0A6JIlS9qNMXPpyTN476hnW8IwfEckjp8pIudX2/Tly5c/j4g+FLmZ7M6JzDe7fv36EWPMp+wi7LKUUtMOxJ0qm81eHYbhbS6pbFdKXdFIOYfv+3Z938HMMXc2+03pdPqCWi/hBQCYYG68aPr06etE5JnFNNYY8+lJfp8gCIKvFQ9yEtF8rfX3x5nGkG1O3NPT85Ja36Snp+f5y5cv92u54lRr/RJ68oabG2p9j8HBwR1EdG2xgMnM/+X7/nuqvWb58uVna60vi7zftblcbv0E4tygMean7n1teqyjJ3EdKIaHh7/KzA+49Wxrb2//Fb5qcCDAZMmNiX3ff5uIPHZmjO35RKRZRNqUUs9RSp0UmT4hT0SXpVKpP1VamOs0X+L7/rxxzlzdmslkrin+MjY29vumpqbrmPnV7g7hyUQisdoY820iuntkZGTDtGnTTBAEs5m5RynVS0TnENH0lpaWPiKq5TLVo1paWm7wff8PRPR7pdSD+Xx+jed5u0ZGRvbGYrEOz/N8Zn4rMx/qtvtvqVQqU0+D7tmz57yOjo6jXKGaYrHY7xOJxDeDILDJ+SpjzLCITNNa99hgqZQ6tzjvoYjktm/fPuFJ4EXk68x8XvGSoQPY24joKiJqVkp9uLe397e5XK7SXJIsIi/1fX9+1WxPqQdSqdRvyt2dm5l73XQQ1fbJXTt27Lhu3bp11eZb9BKJxBuIaJ6bV/GIyGNHJBKJi91E+Vvt9iHRBIAa+26zaNGitx966KGjkVjcxMxNNgezsTHyXNun/SCdTldNKG2/l0gkLqh2xgozm3Q6fWXx90wmk/N9/3Ii+qi7mcoMz/MGfd//JhH9MZ/PP6yUGmNm+/cuY0y3iJwdi8U6wzB8aXGe9XH66g6t9c+Yub+vr+9G23eLyGoR2SYiw0EQNHuet1Qp9Ual1LH0+Fk4e7dv3/61eto0l8v9IJFI/CcRvdBuqlLqK8lk8qXGmOvCMEyLyKNhGDbHYrGlWuvjReR9bq4+G0M2icgJE/08s9msfW87RjnxQN7vlFIfJaJjiGgGMz8nmUx+MJVKXV5lfznR9/3xzqS247ufVZgi4ZmJROJCu79XeX0+CILrBgYGdlZ6gu/7X1ZKvSMy7hxTSt2RTCbfX7w7eWSdPWPMzzKZzH3oaQBgPMYYm5uc29fXtz0aj939QJJKqVPd38n1ZZfncrnxTkxa4vv+f1W7Jwgzh+l0+uvFG3v39/dfnUwmbQw53z1+cltbWyaRSHxdRFYYY/7ueZ6xcZqIbCxeppR6rdZ6cW9vb6KWm2faOE5E30gkEitF5CZm/lsYhvcrpXYEQTCqtW4RkaO11h+22+pOHtpTKBR+WE+bjo6Ofry5uflEuyxmjjPz5xOJhM25fzM6OpqJx+O7tNbtYRguIyIbp9/g6gG2i99cKBQum+i0O5lM5r8TicSzmHk51Xn28f6yZs2aPT09Pe+JxWK/dHFtSSKROCudTl9baTcVkdMSicTiSmM8pZTNn7en0+kflxlz2h+bG7+t2vzQ9jNIp9NfRq/w7wsF3sZk+7mvRvs6fpy4OXaKHcFuZr4slUp9qYZlnldD37mWiJ4o8Lr5Zs/q6+u7Uyn1PBdAjlRKfZ6IRqZNm7bLrobneW0iMs11+o+JxWIn1VLgffwG0OaxBMUmXTZQxWKxYSLa29raGrjJxmcW92URGVFKfbzeBl27du1u3/dfQUQpIupwbfhWrbVtl+1aa/teMRGZbftfl6zb56y3Sfw4BcbxksqtyWTSDgRuPZB3unQ6fV0ymbyUiI6y26+1/vrixYt7K2y7bZxzx9unROQ6t91BmcdOdJ97NQ9Onz79ZjsOqfSEZDJpB2YXElEiUmgpHpFewsxfcH//6+jo6DVP9c11AKChYvHlJbG4ONedRPqyvUT0o1QqdeE4/aEUY121vtMlOleWJEOX9vX1xWxC5wqe85jZxsLReDz+qLuBZzMRTVdKaXcQjtyBu5tr2dgwDMkdrO12qzvGzDbBswmkVkrNtElgZNs/s27dukfrbNNgy5Ytpx188MFr3XyBdlzxMmZ+qed5dkwx7HmeZuZZLiYX23x7GIZv2sepqGR0dPTVLS0t2w/knS6VSv3F9/1blVJnuoTvHd3d3dcPDAysKbdP2XGauxFtNYNdXV2/GhwcHCrz2LPtzzj75Eg8Hr+biHZWeY8LSn5vEpHP0ZNne5d+jx4kIhR4AaAWM5j5o9G+xBXLpKRPNCJyUSaT+b/jBgSRhM23K51owo8nqTsXLlz4vfXr149EHrpQRNqZ+Ux3lu3hzPx5l6M+6q5eaGHmGS6es42vWuv/reUAY/EG3US03BVAQ631sB1rxONx+5i2+Soze5F1v6C/v39LPQ06ODi4Y8mSJc9ta2vrF5HFLo9/vYic3dTUZMcVo2EYNimlZhQLjm4cM2SMWT4wMPDIvnygYRj+t9b6j3ZccaDudP39/Tcmk8lvE9FblFItIvJFIqpU4OVI4b9iOBWRTUT04wqPH8/Mx1U7+cnu90SEAu+/MUzR0CDCMLQ9eeDOyC2U+bF/t536kIj0G2O+bzvXG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      ],
      "metadata": {
        "id": "HpTpXLO2oJBU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scikit-learn will help you with your task again. With only 2 lines of code you can define and train your first AI model."
      ],
      "metadata": {
        "id": "RewOmvLmQyZw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create your model with RandomForestClassifier with 11 trees and entropy for the quality of split\n",
        "model = RandomForestClassifier(n_estimators=11, criterion=\"entropy\")\n",
        "\n",
        "# train your model using train set\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "iaXP9L81dF59",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9dcf10c6-7a64-4cea-9f18-ff2af85ba5a0"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(criterion='entropy', n_estimators=11)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Congrats! You have just trained you first ever AI model. \n",
        "\n",
        "Now it is time to see what your model will predict if we show test set to it."
      ],
      "metadata": {
        "id": "5giiCsBoU8zF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# generate predictions for test set and store them in y_pred variable\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# show y_pred\n",
        "y_pred"
      ],
      "metadata": {
        "id": "PV__qOCidHmy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c72329ba-54e8-477b-b9e2-439f607b5f0e"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True, False,  True,  True,  True, False,  True,  True,\n",
              "       False,  True,  True,  True, False,  True,  True, False,  True,\n",
              "       False, False, False,  True,  True,  True,  True,  True,  True,\n",
              "       False,  True,  True,  True, False,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True, False,  True,  True,  True,\n",
              "        True, False,  True, False,  True, False,  True,  True, False,\n",
              "        True,  True, False, False,  True,  True,  True,  True, False,\n",
              "        True,  True,  True,  True,  True, False,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True, False,  True,  True,  True, False,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True, False, False,  True,  True,\n",
              "       False, False,  True,  True,  True,  True, False,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True, False,  True, False,  True,  True,\n",
              "        True,  True, False,  True,  True, False,  True,  True,  True,\n",
              "        True,  True, False,  True, False,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True, False,  True,  True,  True,\n",
              "        True, False,  True,  True,  True,  True, False,  True,  True,\n",
              "       False,  True,  True,  True,  True, False,  True, False,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True, False,  True,\n",
              "       False,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True, False,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True, False, False,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True, False,  True, False,  True,  True,  True,\n",
              "       False,  True,  True,  True,  True, False,  True,  True, False,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "       False,  True,  True,  True,  True,  True,  True, False, False,\n",
              "        True,  True,  True,  True,  True,  True, False,  True,  True,\n",
              "        True,  True,  True, False, False,  True,  True, False, False,\n",
              "        True,  True,  True, False,  True,  True,  True, False,  True,\n",
              "        True,  True,  True, False,  True, False,  True,  True,  True,\n",
              "       False,  True, False,  True, False,  True, False,  True,  True,\n",
              "       False,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True, False,  True,  True, False,  True,  True,  True,\n",
              "        True, False, False,  True,  True,  True,  True, False,  True,\n",
              "        True,  True,  True,  True,  True,  True, False,  True, False,\n",
              "        True,  True,  True, False,  True,  True, False,  True,  True,\n",
              "        True,  True, False,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True, False, False,  True,  True,  True,  True,\n",
              "       False,  True,  True,  True, False, False,  True, False,  True,\n",
              "       False,  True,  True,  True,  True,  True, False,  True,  True,\n",
              "       False,  True,  True, False,  True,  True, False,  True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model evaluation\n",
        "\n",
        "Now you need to evaluate the performance of your model. The best way to access and visualize its perfromance for your classification task is **confusion matrix**. And again Scikit-learn library gives you a beautiful one-liner to create this matrix and one-liner to visualize."
      ],
      "metadata": {
        "id": "GAE4oOT9xWxv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create confusion matrix with model classes as labels\n",
        "confusion_matrix_model = confusion_matrix(y_test, y_pred, labels=model.classes_)"
      ],
      "metadata": {
        "id": "lZLIXQYTdJYC"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# prepate confusion matrix to be plotted, with model classes as display_labels\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix_model,display_labels=model.classes_)\n",
        "\n",
        "# plot your confusion matrix\n",
        "disp.plot()"
      ],
      "metadata": {
        "id": "dZWjrWPVdKts",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "a0dc93d2-4bc4-4bea-e6be-368a3ede19ac"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fd006a52790>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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9Yli7mfUGXfWcZUSc2kbxlWX2/yHww7znz9MM/xlwM7CTpB+STc/2o7wXMDMrq0be7phnbPg1kh4nm6ZNwIkR8WzVIzOz3q/zjwV1mzy94bsB64HbSssi4qVqBmZmfURvSZbA7bz54rLtgPHAXLJhQmZmnaIa6QHJ0wzfv/Rzmo3oy+3sbmbWK+V6YVmpiHhC0iHVCMbM+qDe0gyX9M2Sj3XAJODlqkVkZn1Hb+rgAYaWrDeR3cO8sTrhmFmf0xuSZXoYfWhEfKub4jGzvqbWk6WkfhHRJOmI7gzIzPoO0Tt6wx8luz85U9KtwA3AutaNEXFTlWMzs96ul92z3A5YSfbOndbnLQNwsjSzzusFyXKn1BM+izeTZKsa+fbMrMerkWxSLlnWA0PYMkm2qpFvz8x6ut7QDF8SET/otkjMrG/qBcmyy95PaWbWpugdveHHdFsUZtZ31XrNMiLyvn/XzGyb9YZ7lmZm1edkaWZWQQ95ZUQeTpZmVhjhZriZWS5OlmZmeThZmpnl4GRpZlZBL5t1yMysepwszcwqq5XhjnVFB2BmfZsi31LxPNJVkpZJmlVSNkLSPZLmpa/DU7kk/UzSfElPp1d8l+VkaWbFiQ4slV0NHLdV2bnA9IjYC5iePgN8CNgrLWcBl1U6uZOlmRWri5JlRNwPbD2nxQnA1LQ+FTixpPy3kfkbsIOk0eXO73uWZlaYDo7gGSlpRsnnyyPi8grH7BwRS9L6UmDntL4rsLBkv0WpbAntcLI0s0KpJXe2XBERk7f1OhER0rY/qORmuJkVp2vvWbblldbmdfq6LJUvBsaW7DcmlbXLydLMCtVVveHtuBU4I62fAUwrKT899YofCqwpaa63yc1wMytWFz2ULulaYArZvc1FwPeBHwPXSzoTeBH4ZNr9DuDDwHxgPfC5Sud3sjSzQnXVcMeIOLWdTW95RU5EBHB2R87vZGlmxfJwRzOzCnrJ2x3NzKrKM6WbmeUVtZEtnSzNrFCuWVqXGrx9E9/46Uvsvs8GIuCSc8bx7BNDig6rz9u8UZzzsT1p3FxHcxO89yNrOP3bS1n6Un9+9KVxvLaqH3vtv55//flLNPQPfv39t/HUQ0MB2LRRrF7RwE3PPVPwd1Egv91xS5J2JJvxA2AXoBlYnj4fHBGbuyOOWvalCxYx477tufALe9CvoYUBA2vkrngv1zAg+MkNf2fg4BaaGuGbJ+7Fu49+jRsvH8XH/nk5U05czX/+2xjuvHYEHz1jJV+84OU3jp125UjmzxpYYPQ9Q6108HTLCJ6IWBkREyNiIvBr4NLWzxGxWZJruGUMGtrM/oes5c5rdwSgqbGOda/5R9YTSDBwcPbX3tQomhuFBE89OJT3Hr8agA984lUevnPYW479yy3DmXLiqm6NtydSS76laIX9xUm6GtgIHAg8JOk1YG1EXJS2zwKOj4gFkj4NfA3oDzwCfDkimouJvPvtMnYTa17txzmXvMgeEzYw75lBXPa9MWzaUF90aAY0N8NXjt2Hlxf056OfXcHocZsYPKyZ+vTXNXJ0IyuWNmxxzCuLGnhlYX8mvmdtARH3IEHNdPAUPTZ8DHB4RHyzvR0kvQM4GTgi1UybgdPa2O8sSTMkzWhkU9UCLkJ9v2DP/dbzp9+N4uzj3sHG9XWcfPYrRYdlSX09XHbvXK55fA5zZw5i4fztKh5z3y3Dec9HVlPv/++qPTa8yxSdLG/IUUM8BjgIeEzSzPR5j613iojLI2JyRExuYEAVQi3OiiX9Wb6kP3OfHAzAg7cPZ8/91xcclW1tyLBmDjh8Lc8+Poh1a+ppbsrKVyxpYOQujVvs+9dpO7gJ3qq6sw51maKT5bqS9Sa2jKf1v2cBU0vuce4TEed3V4A9warlDax4uYExe2wEYOJ7XuOleZVrL1Z9q1fWs3ZNVj3ctEE8cf9Qxu61iQOOWMsDf9oBgHtuGMFhx65545iX5g1g7Zp+TJjs//BaH0qvhZplT+olWAAcD5BeHjQ+lU8Hpkm6NCKWSRoBDI2IF4sJsxi//N9j+befL6Bf/xaWvjiAi88ZV3RIBrz6SgMXfX03WlpESwsc+dHVHPqB1xi390Z+9KVxXP2T0ey53waOPfXNtx38ddpw3nfCKqQCA+8pIjoy+W+helKyvJFsfrnZZJ04zwNExBxJ5wF3S6oDGslmC+lTyfIfcwbx1Y/sW3QYtpU9JmzkV/c8/5by0eM28/M75rV5zGe+tbTaYdWW2siV3Z8s22tCR8QG4IPtbLsOuK6KYZlZQXpCEzuPnlSzNLO+JgA3w83McqiNXOlkaWbFcjPczCwH94abmVXSQx44z8PJ0swKkz2UXhvZ0snSzIrVA2YUysPJ0swK5ZqlmVklvmdpZpaHx4abmeXjZriZWQXRM14ZkYeTpZkVq4tqlpIWAK+TvU2hKSImpykdrwN2J5sG8pMRsU2zLhc9+a+Z9XVdO1P6UWmS8Mnp87nA9IjYi2xu3HO3NUwnSzMrlFpaci3b6ARgalqfCpy4rSdysjSz4gTZQ+l5FhjZ+lLCtJzVxtnulvR4ybadI2JJWl8K7LytofqepZkVRkRHHkpfUdK8bst7ImKxpJ2AeyQ9V7oxIkLa9jmOXLM0s2JF5FsqniYWp6/LgJuBg4FXJI0GSF+XbWuYTpZmVqwuSJaSBksa2rpO9oqaWcCtwBlptzOAadsappvhZlac1nuWnbczcLOyV2b2A34fEXdKegy4XtKZZC85/OS2XsDJ0swK1Yme7jdExD+AA9ooXwkc0+kL4GRpZoXKdz+yJ3CyNLPiBE6WZma5eGy4mVllnvzXzCwPJ0szswoioLk22uFOlmZWLNcszcxycLI0M6sgAL+Dx8yskoDwPUszs/ICd/CYmeXie5ZmZjk4WZqZVeKJNMzMKgugC6Zo6w5OlmZWLNcszcwq8XBHM7PKAsLPWZqZ5eARPGZmOfiepZlZBRHuDTczy8U1SzOzSoJobi46iFycLM2sOJ6izcwsJz86ZGZWXgDhmqWZWQXhyX/NzHKplQ4eRY1023eEpOXAi0XHUSUjgRVFB2Ed0lt/Z+MiYlRnTiDpTrKfTx4rIuK4zlyvM3plsuzNJM2IiMlFx2H5+XfWO9QVHYCZWS1wsjQzy8HJsvZcXnQA1mH+nfUCvmdpZpaDa5ZmZjk4WZqZ5eCH0gsmqRl4pqToxIhY0M6+ayNiSLcEZmVJ2hGYnj7uAjQDy9PngyNicyGBWdX4nmXBOpIAnSx7JknnA2sj4qKSsn4R0VRcVNbV3AzvYSQNkTRd0hOSnpF0Qhv7jJZ0v6SZkmZJem8q/6Ckh9OxN0hyYu1Gkq6W9GtJjwA/kXS+pG+VbJ8lafe0/mlJj6bf4X9Jqi8obMvJybJ4A9MfzExJNwMbgZMiYhJwFHCxJG11zKeAuyJiInAAMFPSSOA84P3p2BnAN7vv27BkDHB4RLT7s5f0DuBk4Ij0O2wGTuum+Gwb+Z5l8TakPxgAJDUAP5J0JNAC7ArsDCwtOeYx4Kq07y0RMVPS+4AJwEMpt/YHHu6m78HedENEVJoZ4hjgIOCx9LsaCCyrdmDWOU6WPc9pwCjgoIholLQA2K50h4i4PyXTjwBXS7oEWAXcExGndnfAtoV1JetNbNl6a/09CpgaEd/ptqis09wM73mGActSojwKGLf1DpLGAa9ExBXAb4BJwN+AIyTtmfYZLGnvbozb3moB2e8GSZOA8al8OvBxSTulbSPS79R6MNcse55rgNskPUN23/G5NvaZAnxbUiOwFjg9IpZL+ixwraQBab/zgOerH7K140bgdEmzgUdIv4uImCPpPOBuSXVAI3A2vXdawV7Bjw6ZmeXgZriZWQ5OlmZmOThZmpnl4GRpZpaDk6WZWQ5Oln2UpOaSseU3SBrUiXNdLenjaf03kiaU2XeKpMO34RoL0pDOXOVb7bO2g9faYky3GThZ9mUbImJiROwHbAa+WLpR0jY9gxsR/xQRc8rsMgXocLI0K5qTpQE8AOyZan0PSLoVmCOpXtJPJT0m6WlJXwBQ5heS5kq6F9ip9USS7pM0Oa0fl2ZAeirNpLQ7WVL+RqrVvlfSKEk3pms8JumIdOyOku6WNFvSb8iGCJYl6RZJj6djztpq26WpfLqkUans7ZLuTMc8IGnfrvhhWu/kETx9XKpBfgi4MxVNAvaLiBdSwlkTEe9Oo4IeknQ3cCCwD9nEHTsDc4CrtjrvKOAK4Mh0rhER8aqkX1My96Ok3wOXRsSDknYD7gLeAXwfeDAifiDpI8CZOb6dz6drDCSbpOLGiFgJDAZmRMQ3JH0vnfsrZC8S+2JEzJN0CPAr4Oht+DFaH+Bk2XcNlDQzrT8AXEnWPH40Il5I5R8E3tV6P5Js3PpewJHAtWl2nZcl/bmN8x8K3N96roh4tZ043g9MKJmFbvs0D+eRwMfSsbdLWpXje/qapJPS+tgU60qy2ZuuS+X/DdyUrnE4cEPJtQdg1g4ny75ri6nhAFLSKJ01R8BXI+Kurfb7cBfGUQccGhEb24glN0lTyBLvYRGxXtJ9bDVbU4lI11299c/ArD2+Z2nl3AV8Kc2biaS9JQ0G7gdOTvc0R5NNUry1vwFHShqfjh2Ryl8Hhpbsdzfw1dYPklqT1/1kkxwj6UPA8AqxDgNWpUS5L1nNtlUd0Fo7/hRZ8/414AVJn0jXkKQDKlzD+jAnSyvnN2T3I5+QNAv4L7LWyM3AvLTtt7QxyXBELAfOImvyPsWbzeDbgJNaO3iArwGTUwfSHN7slb+ALNnOJmuOv1Qh1juBfpKeBX5MlqxbrQMOTt/D0cAPUvlpwJkpvtnAW17hYdbKsw6ZmeXgmqWZWQ5OlmZmOThZmpnl4GRpZpaDk6WZWQ5OlmZmOThZmpnl8P8B0sV41XFJgekAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "HFFzT_GBnplB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Besides the classification matrix you also need to see other important evaluation metrics."
      ],
      "metadata": {
        "id": "nN2dfyJaV3TL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# generate classification report and show it as a dictionary\n",
        "classification_report(y_test,y_pred, output_dict=True)"
      ],
      "metadata": {
        "id": "3orYt7TvdMez",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a97745b6-1859-4b3b-bf11-a94216f09dd5"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'False': {'f1-score': 0.9467455621301776,\n",
              "  'precision': 0.9302325581395349,\n",
              "  'recall': 0.963855421686747,\n",
              "  'support': 83},\n",
              " 'True': {'f1-score': 0.9855537720706261,\n",
              "  'precision': 0.9903225806451613,\n",
              "  'recall': 0.9808306709265175,\n",
              "  'support': 313},\n",
              " 'accuracy': 0.9772727272727273,\n",
              " 'macro avg': {'f1-score': 0.9661496671004018,\n",
              "  'precision': 0.9602775693923481,\n",
              "  'recall': 0.9723430463066323,\n",
              "  'support': 396},\n",
              " 'weighted avg': {'f1-score': 0.9774197280679563,\n",
              "  'precision': 0.9777279547159518,\n",
              "  'recall': 0.9772727272727273,\n",
              "  'support': 396}}"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Accuracy** answers the following question: How many credit card approvals did we correctly predict out of all the credit card applications?\n",
        "\n",
        "**Precision** answers the following: How many of those applications we predicted as approved are actually approved?\n",
        "\n",
        "**Recall (Sensitivity)** answers the following question: Of all the applications that are approved, how many of those we correctly predict?\n",
        "\n",
        "**Recall (Specificity)** answers the following question: Of all the applications that are rejected, how many of those did we correctly predict?\n",
        "\n",
        "**F1-score** considers both precision and recall. It is the harmonic mean(average) of the precision and recall. F1 Score is best if there is some sort of balance between precision and recall. Oppositely F1 Score isn’t so high if one measure is improved at the expense of the other.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "M-65Aczexuec"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model export\n"
      ],
      "metadata": {
        "id": "jWu7VYMaAF14"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Export your model to a file"
      ],
      "metadata": {
        "id": "4TcFB82-_3YL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# export model to the file \n",
        "joblib.dump(model, \"model_rf.joblib\")"
      ],
      "metadata": {
        "id": "QRHkDwYadPge",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb1676ee-1f42-40ab-f351-80c162db3d37"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['model_rf.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load your model from the file"
      ],
      "metadata": {
        "id": "oS40Htap_-EI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load model from the file and store it to loaded_model variable\n",
        "loaded_model = joblib.load(\"model_rf.joblib\")\n",
        "\n",
        "# generate predictions for test set and store them in loaded_y_pred variable\n",
        "loaded_y_pred = loaded_model.predict(X_test)\n",
        "\n",
        "# show loaded_y_pred\n",
        "loaded_y_pred"
      ],
      "metadata": {
        "id": "LDjPUjpldQwr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f3094e7-02e6-4467-b3b5-6f0958c9086b"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True, False,  True,  True,  True, False,  True,  True,\n",
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              "       False,  True,  True, False,  True,  True, False,  True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# AI in Finance: Real-Life Consequences (25 min)"
      ],
      "metadata": {
        "id": "OPQ_9aI6bUP4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SAFEST \n",
        "\n",
        "General principles for the use of AI by financial institutions (‘SAFEST’)"
      ],
      "metadata": {
        "id": "ZyXPy5mNCQAe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Soundness\n",
        "\n",
        "AI applications in the financial sector should first and foremost be reliable and accurate, behave predictably, and operate within in the boundaries of applicable rules and regulations, including nonfinancial regulations, such as the GDPR. This aspect becomes particularly important when financial firms start to apply identical (or relatively similar) AI driven solutions and systemic risks might arise. Financial\n",
        "firms applying AI in their business processes should demonstrate that they have taken all necessary measures to ensure their business continuity.\n",
        "\n",
        "1. Ensure general compliance with regulatory\n",
        "obligations regarding AI applications.\n",
        "2. Mitigate financial (and other relevant\n",
        "prudential) risks in the development and use\n",
        "of AI applications.\n",
        "3. Pay special attention to the mitigation of\n",
        "model risk for material AI applications.\n",
        "4. Safeguard and improve the quality of data\n",
        "used by AI applications.\n",
        "5. Be in control of (the correct functioning of)\n",
        "procured and/or outsourced AI applications."
      ],
      "metadata": {
        "id": "vkQbtQ_TCioN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Accountability\n",
        "\n",
        "The workings of AI applications can be complex and\n",
        "difficult to understand. Furthermore, AI applications\n",
        "may not always function as intended and can result\n",
        "in damages for the firm itself, its customers and/\n",
        "or other relevant stakeholders. Especially when\n",
        "AI applications become more material, financial\n",
        "firms should demonstrate unequivocally that they\n",
        "understand their responsibility for AI applications\n",
        "and that they have operationalised accountability\n",
        "for these applications throughout their organisation.\n",
        "Model complexity or third party reliance should\n",
        "never be used as arguments for limiting the\n",
        "organisation’s accountability.\n",
        "\n",
        "6. Assign final accountability for AI applications\n",
        "and the management of associated risks\n",
        "clearly at the board of directors level.\n",
        "7. Integrate accountability in the organisation’s\n",
        "risk management framework.\n",
        "8. Operationalise accountability with regard to\n",
        "external stakeholders."
      ],
      "metadata": {
        "id": "JvwXCI3YCplu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Fairness\n",
        "\n",
        "Although fairness is primarily a conduct risk issue,\n",
        "it is vital for society’s trust in the financial sector\n",
        "that financial firms’ AI applications – individually\n",
        "or collectively – do not inadvertently disadvantage\n",
        "certain groups of customers. Whether we are\n",
        "talking about an AI that monitors transactions to\n",
        "detect money laundering, or one that facilitates loan\n",
        "decisions, financial firms should be able to define\n",
        "their concept of fairness and demonstrate how they\n",
        "ensure that their AI applications behave accordingly.\n",
        "\n",
        "9. Define and operationalise the concept of\n",
        "fairness in relation to your AI applications.\n",
        "10. Review (the outcomes of) AI applications for\n",
        "unintentional bias."
      ],
      "metadata": {
        "id": "w4XlErKIFv9j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Ethics\n",
        "\n",
        "As AI applications take on tasks that previously\n",
        "required human intelligence, financial firms should\n",
        "ensure that the outcomes of these systems do\n",
        "not violate the firm’s ethical standards. This moral\n",
        "obligation goes above and beyond compliance\n",
        "with applicable legal requirements. Financial firms\n",
        "should ensure that their customers, as well as other\n",
        "stakeholders, can trust that they are not mistreated\n",
        "or harmed – directly or indirectly – because of the\n",
        "firm’s deployment of AI, even if such applications\n",
        "operate within the boundaries of applicable laws\n",
        "and regulations.\n",
        "\n",
        "11. Specify objectives, standards, and\n",
        "requirements in an ethical code, to guide the\n",
        "adoption and application of AI.\n",
        "12. Align the (outcome of) AI applications with\n",
        "your organisation’s legal obligations, values\n",
        "and principles."
      ],
      "metadata": {
        "id": "cxEUpc6ZC4ZD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Skills\n",
        "\n",
        "As most AI applications augment human tasks,\n",
        "financial firms’ employees and management will\n",
        "increasingly come to rely on AI applications to\n",
        "support them in their work. Like with many other\n",
        "tools, wrong use can result in accidents and it is\n",
        "the organisation’s responsibility to ensure that\n",
        "their (senior) management, risk management and\n",
        "compliance functions have an adequate level of\n",
        "expertise. From the work floor to the board room,\n",
        "a sufficient understanding of the strengths and\n",
        "limitations of the organisation’s AI-enabled systems\n",
        "is vital.\n",
        "\n",
        "13. Ensure that senior management has a\n",
        "suitable understanding of AI (in relation to\n",
        "their roles and responsibilities).\n",
        "14. Train risk management and compliance\n",
        "personnel in AI.\n",
        "15. Develop awareness and understanding of\n",
        "AI within your organisation."
      ],
      "metadata": {
        "id": "wOQQOgh_C9sx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Transparency\n",
        "\n",
        "Transparency means that financial firms should be\n",
        "able to explain how they use AI in their business\n",
        "processes, and (where reasonably appropriate)\n",
        "how these applications function. Adherence to\n",
        "this principle enables adequate risk management\n",
        "and internal audits. This also means effective\n",
        "supervision of the correct workings of the firm’s AI\n",
        "applications to ensure stable operations. When the\n",
        "materiality of AI applications increases, so should\n",
        "the organisation’s efforts in understanding and\n",
        "controlling the applications' functioning. \n",
        "\n",
        "16. Be transparent about your policy and\n",
        "decisions regarding the adoption and use of\n",
        "AI internally.\n",
        "17. Advance traceability and explainability of\n",
        "AI driven decisions and model outcomes."
      ],
      "metadata": {
        "id": "fTdfMeXmDD1b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Interpretability\n",
        "\n",
        "You need to understand why your model makes a certain prediction. For this you need a tool to **explain a model** (have a qualitative understanding of the relationship between the prediction of the model and the components of the data that the model used to generate that prediction). \n",
        "\n",
        "Your Random Forest Classifier is interpretable. **Interpretability** refers to how easy it is for humans to understand the processes the model uses to make a prediction. \n",
        "\n",
        "[SHAP (SHapley Additive exPlanations)](https://shap-lrjball.readthedocs.io/en/docs_update/index.html) is a game theoretic approach to explain the output of any machine learning model. "
      ],
      "metadata": {
        "id": "BiTB5T-kHUyV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create model explainer with shap\n",
        "explainer = shap.TreeExplainer(model)\n",
        "\n",
        "# estimate SHAP values for train set\n",
        "shap_values = explainer.shap_values(X_train)\n",
        "\n",
        "# plot SHAP values colored by feature values for train set\n",
        "shap.summary_plot(shap_values,X_train)"
      ],
      "metadata": {
        "id": "aP6QUwT6dT6U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "aab1e1e7-2fac-49fd-cfd4-a39c6f880085"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x424.8 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Leakage\n",
        "\n",
        "A few variables look suspicious. For example, does expenditure mean expenditure on this card or on cards used before appying?\n",
        "\n"
      ],
      "metadata": {
        "id": "XfX5sYY8Cl6R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# store expenditures of all credit card holders in expenditures_cardholders variable\n",
        "expenditures_cardholders = credit_card.expenditure[y]\n",
        "\n",
        "# store expenditures of non credit card holders in expenditures_noncardholders variable\n",
        "expenditures_noncardholders = credit_card.expenditure[~y]\n",
        "\n",
        "print('Fraction of those who did not receive a card and had no expenditures: %.2f' \\\n",
        "      %((expenditures_noncardholders == 0).mean()))\n",
        "print('Fraction of those who received a card and had no expenditures: %.2f' \\\n",
        "      %(( expenditures_cardholders == 0).mean()))"
      ],
      "metadata": {
        "id": "MKok_E7bC9ep",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8604ded-12c9-4544-ad7a-201601c818e5"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fraction of those who did not receive a card and had no expenditures: 1.00\n",
            "Fraction of those who received a card and had no expenditures: 0.02\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As shown above, everyone who did not receive a card had no expenditures, while only 2% of those who received a card had no expenditures. It's not surprising that our model appeared to have a high accuracy. But this also seems to be a case of target leakage, where expenditures probably means expenditures on the card they applied for.\n",
        "\n",
        "Since share is partially determined by expenditure, it should be excluded too. The variables active and majorcards are a little less clear, but from the description, they sound concerning. In most situations, it's better to be safe than sorry if you can't track down the people who created the data to find out more.\n",
        "\n",
        "We would run a model without target leakage as follows:"
      ],
      "metadata": {
        "id": "mcIrqdfcDMeB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# store all \"leaking\" column names as a list in potential_leaks variable\n",
        "potential_leaks = [\"expenditure\", \"share\", \"active\", \"majorcards\"]\n",
        "\n",
        "# create clean set by droping \"leaking\" columns, store in clean_X variable\n",
        "clean_X = X.drop(potential_leaks, axis=1)\n",
        "\n",
        "# split clean_X and y into train test set, keep 30% of the data for testing, with random state equal to zero\n",
        "clean_X_train, clean_X_test, y_train, y_test = train_test_split(clean_X, y, test_size=0.3, random_state=0)\n",
        "\n",
        "# create your clean model with RandomForestClassifier with 11 trees and entropy for the quality of split\n",
        "clean_model = RandomForestClassifier(n_estimators=11, criterion=\"entropy\")\n",
        "# train your clean model using clean train set\n",
        "clean_model.fit(clean_X_train, y_train)\n",
        "# generate predictions for clean test set and store them in clean_y_pred variable\n",
        "clean_y_pred = clean_model.predict(clean_X_test)"
      ],
      "metadata": {
        "id": "Lm6_aYXPddyb"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create clean confusion matrix with model classes as labels\n",
        "clean_confusion_matrix_model = confusion_matrix(y_test, clean_y_pred, labels=clean_model.classes_)\n",
        "\n",
        "# prepate confusion matrix to be plotted, with model classes as display_labels\n",
        "disp = ConfusionMatrixDisplay(clean_confusion_matrix_model,display_labels=clean_model.classes_)\n",
        "# plot your confusion matrix\n",
        "disp.plot()"
      ],
      "metadata": {
        "id": "suFtyVJjdf7u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "5d0d514e-46cf-4a9a-b7b6-1cd4bc0c0e3e"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fd006a98e50>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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/ZQBOlmbWft0gWW6XesLn8UGSbFIlH8/MurwqySalkmUtMIjNk2STKvl4ZtbVdYdm+NKIuKTTIjGznqkbJMvqeD+lmVWv6B694Yd1WhRm1nNVe80yIrK+Jc3MbIt1h2uWZmaV52RpZlZG+19z22mcLM0sN8LNcDOzTJwszcyycLI0M8vAydLMrIxuNuqQmVnlOFmamZXXHR53NDOrODfDzczK8U3pZmYZOVmamZXmJ3jMzDJSY3VkSydLM8uPr1mamWXjZriZWRZVkixr8g7AzHo2Rbap7HGkmyQtkzSvqGyopIckvZj+3SaVS9LVkhZKmiNpYrnjO1maWb4i41TezcARzcrOAaZHxDhgeloG+DwwLk1TgF+VO7iTpZnlJ73dMctU9lARM4Dm7w47Cpia5qcCRxeV/yYK/gZsLWlkqeM7WZpZbprus+yIZngrRkTE0jT/JjAizY8GFhdt93oqa5U7eMwsX5E5Ew6TNKto+bqIuC77aSKkLU+7TpZmlqs2pK8VETGpjYd/S9LIiFiamtnLUvkSYPui7cakslY5WXZRw0dt4uyfv8bWw+sh4P7fbss9Nw7nvGsXMWbn9wAYOKSBdatrOf0fPp5ztD3XsiW9+dl3xvLO8t6g4MgT3+aYb6zgpXn9ufqcMWzaWENtr+DMf3udXfdcD8Az/28Q114wmvp62GpoA5fdtTDnT5Gjyt+Ufh9wMvDT9O+9ReVnSvodsA/wblFzvUWdkiwlbUuhJwrgI0ADsDwt7x0RmzojjmrSUC+uu2QUC+cOoP/ABq554AWenjGYn5y24/vbTLngDdat8WXnPNX2CqZc8AbjPrWB9WtrOPOIXZh40Bpu+PFITjzrTT596BpmTh/MjT8exc/uXMjad2u55twxXHrLS2w3po53Vri+0lHjWUq6DZhMobn+OnAhhSR5u6RTgFeBL6XN7weOBBYC64Gvlzt+p3xTEfE2MAFA0kXA2oi4rGm9pF4RUd8ZsVSLlct6s3JZbwA2rKtl8cJ+DBtZx2sv9ktbBAf98zv87+N2zi9IY9sR9Ww7ovCrO2BQI9t/7D1WLO2NBOvW1AKwbnUtQ0fUAfCXu7fmgCPfYbsxheWth/nXvqOSZUQc38qqw1rYNoAz2nL83P5bk3QzsBHYE3hM0mqKkmi6sfQfI2KRpBOBbwN9gCeA0yOiIZ/IO9+IMZvYebcNPPf0gPfLdttnHauW9+KNV/rmGJkVe3NxH16a159dJ67ntEuWcN7xO3P9JaOIgCvvexGA11/uR0MdnP0vH2P92hqO/sZy/uG4VTlHnqOgLR08ucq7DTcG2D8izmptA0mfAL4MHBAREyg04U9oYbspkmZJmlXHexULuLP1G9DAD29YxLUXjGL92tr3yw85+h3+es/WOUZmxTasq+FH39iR0y5ZwsDBjfxx6jBOvXgJtzy1gFMveoMrzhoLQEM9vDh3AD/6r5f5ya0vcetVH+H1l3r2f3gVvnWow+SdLO/IUEM8DNgLeFLS7LT80eYbRcR1ETEpIib1pnv88tX2Cn54wyL+fNc2PPanDxJjTW1wwJHv8n/vc7LsCurr4Eff2JFDj13FZ458F4CH7hj6/vxB//QOL8wutAqGj6xjr4PX0G9AI1tt28Du+6zl5QX9Wj12j9BxT/BUVN7Jcl3RfD2bx9P0GyRgakRMSNPHI+KizgowP8FZly9m8Yv9uOu64ZutmXjgGhYv7MuKpX1yis2aRMAV3x/L9uPe419OXf5++bYj6pjz+CAAZj86iFE7FVo7+x3xLvOfHEhDPWxcL577+wDGjus+LaG26oSb0jtMV+qKWwT8I0B6qH2nVD4duFfSlRGxTNJQYHBEvJpPmJ3jk3uv47PHreLlBf345UPPA/DrfxvJk38ewsFHuQneVcyfOZDp/z2UnT6xgW9+tnAL19fPfYPv/mwxv7pgNA0Nok/fRr77s8LDImPHvcekyas57bBdUU1wxFdXsuOuG/P8CPmK8OC/W+BO4CRJ8yl04rwAEBELJJ0PPCipBqij0IvVrZPl/JmD+NyoPVpcd/n3xnZyNNaa3fZZx7Q3Zre47hfTXmix/LjTl3Pc6ctbXNcjVUeu7Pxk2VoTOiI2AIe3su73wO8rGJaZ5aQrNLGz6Eo1SzPraQJwM9zMLIPqyJVOlmaWLzfDzcwycG+4mVk5XeSG8yycLM0sN4Wb0qsjWzpZmlm+OmjUoUpzsjSzXLlmaWZWjq9Zmpll4WfDzcyycTPczKyM6LjXSlSak6WZ5cs1SzOzDKojVzpZmlm+1Fgd7XAnSzPLT+Cb0s3MyhHhm9LNzDJxsjQzy8DJ0sysDF+zNDPLxr3hZmZlhZvhZmZlBU6WZmaZVEcr3MnSzPLl+yzNzLJwsjQzKyMCGjqmHS5pEbAGaADqI2KSpKHA74EdgUXAlyJi1ZYcv6ZDojQz21IR2aZsDomICRExKS2fA0yPiHHA9LS8RZwszSxfHZssmzsKmJrmpwJHb+mBnCzNLD8BNEa2CYZJmlU0TWnhaA9Keqpo3YiIWJrm3wRGbGmovmZpZjkKiMzXLFcUNa9b8pmIWCJpO+AhSc9tdqaIkLTFVVQnSzPLT9BhHTwRsST9u0zS3cDewFuSRkbEUkkjgWVbenw3w80sXx1wzVLSQEmDm+aBw4F5wH3AyWmzk4F7tzRM1yzNLF8dc5/lCOBuSVDIa7dGxAOSngRul3QK8CrwpS09gZOlmeWoYwbSiIiXgT1aKH8bOKzdJ8DJ0szyFICHaDMzy8CPO5qZldNxjztWmpOlmeUnILLfZ5krJ0szy1ejm+FmZuX5mqWZWRkR7g03M8vENUszs3KCaGjIO4hMnCzNLD9NQ7RVASdLM8uXbx0yMystgHDN0sysjGjT4L+5crI0s1xVSwePokq67dtC0nIKY9d1R8OAFXkHYW3SXb+zHSJieHsOIOkBCj+fLFZExBHtOV97dMtk2Z1JmlXmPSTWxfg76x78WgkzswycLM3MMnCyrD7X5R2AtZm/s27A1yzNzDJwzdLMLAMnSzOzDHxTes4kNQBzi4qOjohFrWy7NiIGdUpgVpKkbYHpafEjQAOwPC3vHRGbcgnMKsbXLHPWlgToZNk1SboIWBsRlxWV9YqI+vyiso7mZngXI2mQpOmSnpY0V9JRLWwzUtIMSbMlzZN0YCo/XNLjad87JDmxdiJJN0u6VtITwL9LukjSD4rWz5O0Y5o/UdLM9B3+p6TanMK2jJws89c//cHMlnQ3sBE4JiImAocAl0tSs32+CkyLiAnAHsBsScOA84HPpn1nAWd13sewZAywf0S0+rOX9Angy8AB6TtsAE7opPhsC/maZf42pD8YACT1Bn4i6SCgERgNjADeLNrnSeCmtO09ETFb0sHAeOCxlFv7AI930mewD9wREeVGhjgM2At4Mn1X/YFllQ7M2sfJsus5ARgO7BURdZIWAf2KN4iIGSmZfgG4WdIVwCrgoYg4vrMDts2sK5qvZ/PWW9P3KGBqRJzbaVFZu7kZ3vVsBSxLifIQYIfmG0jaAXgrIq4HbgAmAn8DDpD0sbTNQEm7dGLc9mGLKHw3SJoI7JTKpwNflLRdWjc0fafWhblm2fXcAvxB0lwK1x2fa2GbycDZkuqAtcBJEbFc0teA2yT1TdudD7xQ+ZCtFXcCJ0maDzxB+i4iYoGk84EHJdUAdcAZdN9hBbsF3zpkZpaBm+FmZhk4WZqZZeBkaWaWgZOlmVkGTpZmZhk4WfZQkhqKni2/Q9KAdhzrZklfTPM3SBpfYtvJkvbfgnMsSo90Zipvts3aNp5rs2e6zcDJsifbEBETImI3YBNwWvFKSVt0D25EfCMiFpTYZDLQ5mRpljcnSwN4BPhYqvU9Iuk+YIGkWkk/k/SkpDmSTgVQwTWSnpf0MLBd04Ek/VXSpDR/RBoB6Zk0ktKOFJLy91Kt9kBJwyXdmc7xpKQD0r7bSnpQ0nxJN1B4RLAkSfdIeirtM6XZuitT+XRJw1PZzpIeSPs8ImnXjvhhWvfkJ3h6uFSD/DzwQCqaCOwWEa+khPNuRHw6PRX0mKQHgT2Bj1MYuGMEsAC4qdlxhwPXAwelYw2NiJWSrqVo7EdJtwJXRsSjksYC04BPABcCj0bEJZK+AJyS4eP8r3SO/hQGqbgzIt4GBgKzIuJ7ki5Ixz6TwovETouIFyXtA/wSOHQLfozWAzhZ9lz9Jc1O848AN1JoHs+MiFdS+eHAp5quR1J4bn0ccBBwWxpd5w1Jf27h+PsCM5qOFRErW4njs8D4olHohqRxOA8Cjk37/o+kVRk+07clHZPmt0+xvk1h9Kbfp/LfAnelc+wP3FF07r6YtcLJsufabGg4gJQ0ikfNEfCtiJjWbLsjOzCOGmDfiNjYQiyZSZpMIfHuFxHrJf2VZqM1FYl03nea/wzMWuNrllbKNOCbadxMJO0iaSAwA/hyuqY5ksIgxc39DThI0k5p36GpfA0wuGi7B4FvNS1IakpeMygMcoykzwPblIl1K2BVSpS7UqjZNqkBmmrHX6XQvF8NvCLpuHQOSdqjzDmsB3OytFJuoHA98mlJ84D/pNAauRt4Ma37DS0MMhwRy4EpFJq8z/BBM/gPwDFNHTzAt4FJqQNpAR/0yl9MIdnOp9Acf61MrA8AvSQ9C/yUQrJusg7YO32GQ4FLUvkJwCkpvvnAh17hYdbEow6ZmWXgmqWZWQZOlmZmGThZmpll4GRpZpaBk6WZWQZOlmZmGThZmpll8P8Br5Sp3XKNIlAAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# generate classification report and show it as a dictionary\n",
        "classification_report(y_test,clean_y_pred, output_dict=True)"
      ],
      "metadata": {
        "id": "LdVZoZCNdfxP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2d6db97-3db5-47a6-f767-e6998fc685df"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'False': {'f1-score': 0.5135135135135134,\n",
              "  'precision': 0.5846153846153846,\n",
              "  'recall': 0.4578313253012048,\n",
              "  'support': 83},\n",
              " 'True': {'f1-score': 0.888198757763975,\n",
              "  'precision': 0.8640483383685801,\n",
              "  'recall': 0.9137380191693291,\n",
              "  'support': 313},\n",
              " 'accuracy': 0.8181818181818182,\n",
              " 'macro avg': {'f1-score': 0.7008561356387442,\n",
              "  'precision': 0.7243318614919824,\n",
              "  'recall': 0.685784672235267,\n",
              "  'support': 396},\n",
              " 'weighted avg': {'f1-score': 0.809666244448853,\n",
              "  'precision': 0.8054803202839457,\n",
              "  'recall': 0.8181818181818182,\n",
              "  'support': 396}}"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create clean model explainer with shap\n",
        "explainer = shap.TreeExplainer(clean_model)\n",
        "\n",
        "# estimate SHAP values for clean train set\n",
        "shap_values = explainer.shap_values(clean_X_train)\n",
        "\n",
        "# plot SHAP values colored by feature values for train set\n",
        "shap.summary_plot(shap_values,clean_X_train)"
      ],
      "metadata": {
        "id": "tnuMiZMtdfcC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "05ffa039-a6e9-4dbe-8a61-877049855e94"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x309.6 with 1 Axes>"
            ],
            "image/png": 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UELd9CbDazPYFTkppTwOvAleb2eZmNgT4Rs2C7p4BPwQmmdlOAGZWYWaHmtmOrbkRIiIi7VmnDELc/W/Ad4BZwDLgYuDOlLYGOBzYixikzASmAatyWdQsO8vM3gPmAWfRSetTRESkKUKWdaonyzeJmZ0JXODuOzc3rzBpTaeq8GzcUUUXofVkM4sugYhIWxTqSuiUfUIaYmb7AYuABcDuxHuC6KoXERGRFqQgpHYDgRnEK2GWAHcDVxdaIhERkQ5GQUgt3P1OUh8RERER2TTUkVJEREQKoY6prWz27NnZ6NGjiy6GiIhIa6mzY6paQkRERKQQCkJERESkEApCREREpBAKQkRERKQQCkJERESkEApCREREpBAKQkRERKQQCkJERESkELpZWSvriE/R7ZBPytUTcUVEWopuViYiIiJti4IQERERKYSCEBERESmEghAREREphIIQERERKUTXpixkZgOA14Ah7l7VoiVqAW29fCIiItLEIKSzMbNK4BVgoLu/XnBxREREOgSdjhEREZFClNUSYmbbAzcBBwBvAdeUpJ8BnAcMBBYA33L3R1LaBGAE8CxwMvAhcL27T8wtvxswGdgrpU8HLnP31blWiJOB/0zreAo4xd0XlVO+RpTx98DpaZGfuPt30vAz6f3vZpYB3weuTK9/A3oCbwOT3f2/Gq5RERERKbclZDqwFhgE7A+cWpOQfty/BZwA9AG+DfzSzIbllt+fGBzsAHwZON/MvpqW3w74LfBLoD8wHPgXYsCRd2zKpz+wFXBFOeVrZBlfBXYEDgfGm9nnU9oe6f0T7l7h7t9NZTwF+Jy79wQ+CzxZS92JiIhILRpsCTGz/sDBwDB3rwaqzexy4JE0y3nAFe5e01owx8weA44jthQALAK+7+4Z8Eczu4kYKMwgtnA84+43pnnfMLOria0N+UDjcndfmso0g9RiUUb5yi3jS+4+NQ0/bWZ/AQz4XR1VswrYHNjVzJa4+2JgcR3zioiISIlyTscMSO8Lc9NeyQ0PAW4wsx+V5JvvwLkwBSA1qoAjc8t/3syW5dID0KWkHItywx8QT4GUU75yy7how0U2WMdG3P1xMxsPXAL8wsyeBsa7u9e1jIiIiHyknCDkjfQ+GHg5DVfm0hcC33H3u+vJY7CZhVwgUslHAcBCYK67H1ZWiRtfvnLLWJ91tU1095uAm8xsS2AC8ZTSoCauQ0REpFNpMAhx99fN7HHgGjP7N2AL4LLcLNcBE8xsHrED5+bA3sBSd38xzbMDMM7MrgN2A84Azk9pPwcuMLOxxNMzq4hBxM7u/lALlK/cMtZnCTEQ2YkUPJnZZ4EewB+AfwLLif1SREREpAzldkz9KvEH9zXgCWLgAIC730y8GuU24F1i585LgW655Z8gBiJvAg8APyQGHLj7m8BBwBjiaZp3gfuAoY3YjjrL14gy1sndP0zz32lmy8zs20BF2o6lxCtjDiF2nhUREZEyhCzLGp6rGdLlr/u5+8hNuqJ2Ikxas2krvADZuKOKLkLLy2YWXQIRkY4i1JWgm5WJiIhIIRSEiIiISCE2+bNj3H3Cpl6HiIiItD9qCREREZFCbPKOqbKh2bNnZ6NHjy66GCIiIq1FHVNFRESkbVEQIiIiIoVQECIiIiKFUBAiIiIihVAQIiIi0k5MmDCBE088sehitBgFISIiIm3IjBkzMDMqKirYYYcdGDVqFE8++WQhZamqquKggw5iyy235JOf/CRz585t0fw3+c3KRERE2rIwac0mzT+7sPyf2ilTpjBx4kSmTp3KoYceSvfu3XnooYeYNWsW++233yYsZe2OP/54hg8fzpw5c5gzZw5HHXUU8+bNo1+/fi2Sv+4T0sqa+gC7Jj0kTg9hExFpUFsJQqqrq+nfvz+33XYbRx99dK3zTJgwgfnz53PHHXcAcPTRR/PEE0/w4Ycfsscee/CTn/yEXXfdFYA5c+Zw4YUX8tprr9GrVy+++c1vcuGFF7J06VJOPfVUnnzySTbbbDN23XVXfvvb37LZZhueHHnppZfYfffdWbp0KT179gRgxIgRnHDCCZx11lmNqQLdJ0RERKQte+qpp1i5ciVHHHFE2cuMGjWKefPmsXjxYvbaay9OOOGE9WmnnXYaN954I8uXL+e5557j4IMPBmDy5MkMGDCAJUuW8NZbb3HVVVcRwsZxwvPPP8/QoUPXByAAe+yxB88//3wztnJDCkJERETagLfffpttt92Wrl3LP30zduxYevbsSY8ePZgwYQLPPPMM1dXVAHTr1o0XXniB9957jz59+rDXXnutn75o0SIWLlxIt27dGDFiRK1ByPvvv0/v3r03mNa7d2+WL1/ejK3ckIIQERGRNqBv374sXbqUNWvKOz20du1aLr74Yj7+8Y/Tq1cvKisrAVi6dCkA9957L3PmzGHw4MEccMABPPXUUwCMGzeOYcOGccghhzB06FAmTpxYa/4VFRW89957G0x77733NmgZaS4FISIiIm3A8OHD6dGjBzNnltefb8aMGcyaNYu5c+dSXV1NVVUVADV9PffZZx9mzZrF4sWLGTNmDMcccwwAPXv2ZPLkySxYsID777+fKVOm8Oijj26U/6677sqCBQs2aPl45pln1vc5aQkKQkRERNqA3r17c8UVV3DOOecwc+ZMVqxYwerVq3nwwQe56KKLNpp/+fLl9OjRg759+7JixQrGjx+/Pm3VqlVMnz6d6upqunXrRq9evdZ3PH3ggQeYP38+WZbRu3dvunTpslGnVICdd96ZPffck8svv5yVK1dy33338eyzz/KVr3ylxba53QchZlZlZifmxr9oZvPNbLmZnV9k2URERBrjggsuYMqUKVx55ZX069ePgQMHcv311zNmzJiN5j355JMZPHgw/fv3Z5dddmHffffdIH3atGlUVlbSq1cvpk6dyvTp0wGYN28eI0eOpKKiguHDh3P22Wdz0EEH1Vqeu+66C3enT58+XHzxxdxzzz0tdnkudIBLdM2sCrjE3e9I4y8BP3D3HxdasDroEl0REelkOtUlukOBZ4suhIiIiNSvzdwx1czOBb4JbAu8B/zM3ceb2SBgCrAfkAGzgQvcfXnJ8jsCLwFdgEfMbB2wl7u/ZGZnAOcBA4EFwLfc/ZG03ARgBODAWGJg9j3gXuA2YJ+U74nu/re0zOPAX4BhwIHAQuBCd3+wxStGRESkg2oTLSFmtjMwEfiSu/cEdgXuN7PNgd8ALwBDgF2AAcAPS/Nw93+4e0UaPcTdK3IByLeAE4A+wLeBX5rZsNzi+wPzgO2BE4FrgVuBc4BtgL8BPypZ5WmpHFsDVwH3mVllc+pBRESkM2krLSFriOeMdjWzhe6+DHjazI4Cgrtflub70MwuBf7XzM5w97Vl5H0ecIW7P5PG55jZY8BxwJVp2kvufksaftDM3gYezrV8zACml+Q7091/nYanm9nXga8SAxIRERFpQJsIQtx9gZmdAHwduMXMngWuILZ+DDKzZSWLZMRWizfKyH4IcIOZ5VsyugKv58YXlSyzomTaCqD07ixVtYwPKKM8IiIiQhsJQgDc/ZfE0yTdgbOAWcCZxFaK5twZZSHwHXe/uwWKmVdZy/icFl6HiIhIh9UmghAz+wSxxeJ/gA+BamJrxz3ApWY2Hvgv4H1gR+Cz7n5fmdlfB0wws3nAM8DmwN7AUnd/sRnFHmNmXwAeB44BDDipGfmJiIh0Km2iYyrQHbiMeApkGXAu8BV3XwEcTOyQ+iIxOHkU2LPcjN39ZuAa4pUu7wKvApcC3ZpZ5luB81OZLkvlfaWZeYqIiHQa7f5mZUVIl+jOdfcrG5q3lG5WJiIiTTVhwgTmz5/PHXfcUXRRGqNT3axMRESk3ZoxYwZmRkVFBTvssAOjRo3iySefLKQsl156Kbvvvjtdu3ZlwoQJLZ5/m+gTIiIiUpiw8XNZWlQjWqWnTJnCxIkTmTp1Koceeijdu3fnoYceYtasWey3336bsJC1GzZsGNdccw1Tp07dJPmrJaQJ3P3AppyKERERqUt1dTWXXXYZN9xwA0ceeSRbbbUV3bp1Y/To0Vx77bW1LnP00Uez/fbb07t3b/bff3+ef/759Wlz5sxhl112oWfPnvTv359JkyYBsHTpUr70pS+x9dZbs8022zBixAjWrVtXa/6nnHIKo0aNomfP0rtUtAwFISIiIm3AU089xcqVKzniiCPKXmbUqFHMmzePxYsXs9dee3HCCSesTzvttNO48cYbWb58Oc899xwHH3wwAJMnT2bAgAEsWbKEt956i6uuuooQ6uy2sUnpdEwru/8TDzJ69OjGL3ihOpmKiHRkb7/9Nttuuy1du5b/0zx27Nj1wxMmTKBPnz5UV1fTu3dvunXrxgsvvMAee+xBnz596NOnDwDdunVj0aJFLFy4kGHDhjFixIgW35ZyqSVERESkDejbty9Lly5lzZo1Zc2/du1aLr74Yj7+8Y/Tq1cvKisrgXi6BeDee+9lzpw5DB48mAMOOICnnnoKgHHjxjFs2DAOOeQQhg4dysSJEzfJ9pRDQYiIiEgbMHz4cHr06MHMmeW1fM+YMYNZs2Yxd+5cqqurqaqqAqDm1hv77LMPs2bNYvHixYwZM4ZjjjkGgJ49ezJ58mQWLFjA/fffz5QpU3j00Uc3yTY1REGIiIhIG9C7d2+uuOIKzjnnHGbOnMmKFStYvXo1Dz74IBdddNFG8y9fvpwePXrQt29fVqxYwfjx49enrVq1iunTp1NdXU23bt3o1asXm20Wf/IfeOAB5s+fT5Zl9O7dmy5duqxPK7V69WpWrlzJunXrWLNmDStXrmTt2nKeHVseBSEiIiJtxAUXXMCUKVO48sor6devHwMHDuT6669nzJiNLyM++eSTGTx4MP3792eXXXZh33333SB92rRpVFZW0qtXL6ZOncr06fFh8PPmzWPkyJFUVFQwfPhwzj77bA466KBay3PGGWewxRZbcOedd/K9732PLbbYgmnTprXY9uqOqa1s9uzZWZM6poqIiLRPumOqiIiItC0KQkRERKQQCkJERESkEApCREREpBAKQkRERKQQCkJERESkEApCREREpBAKQkRERKQQCkJERESkEApCREREpBAKQkRERKQQCkJERESkEApCREREpBB6im4r69Gjx3OrVq1aWXQ52pOuXbtuu2bNmqVFl6O9UH01nuqs8VRnjdeJ62xplmVfrC2ha2uXpLPbfffdV7q7FV2O9sTMXHVWPtVX46nOGk911niqs43pdIyIiIgUQkGIiIiIFEJBSOu7qegCtEOqs8ZRfTWe6qzxVGeNpzoroY6pIiIiUgi1hIiIiEghdHVMCzCznYGfAX2Bt4GT3X1eyTxdgB8BXwQyYKK739JQWkfVAnU2ATgb+Eea/Xfufk7rlL4YZdbZIcBVwO7Af7n7hbk07WeNr7MJaD+rrc4uBY4D1gKrgfHu/nBK2xK4DdgbWANc6O4PtN4WtK4WqK/bgZFAzaW7d7v791qn9MVTS0jLmArc4O47AzcAN9YyzwnAMGAnYDgwwcwqy0jrqJpbZwA/d/c906tD/zAk5dTZAuB04Npa0rSfNb7OQPtZbXX2B2Afd/80MBb4bzPbIqVdCLzn7sOA0cAtZlbRCuUuSnPrC+Ifgpp9rNMEIKAgpNnMbDtgL+DONOlOYC8z61cy67HAze6+zt2XADOBo8tI63BaqM46lXLrzN3nu/tfiP9AS3Wq+myhOutUGlFnD7v7ijT6LBCILQEQ97Mb03zzAAdGbeKiF6KF6qtTUxDSfAOBN9x9LUB6/0eanjcIWJgbfzU3T31pHVFL1BnAcWb2rJk9YmbDN2WB24By66w+2s8aX2eg/ayhOjsZeNndX0/jnWk/a4n6AjjfzP5qZjPN7FObrrhtj4IQaa+mAkNS8+a1wCwz0z8LaWnaz+phZgcA3wWOL7os7UEd9fVtYJi77w78Engo9d/qFBSENN9rQP+anSa975im570KDM6ND8rNU19aR9TsOnP3N919dRr+dZq+2yYud5HKrbP6aD9rZJ1pP6u7zlKr0B3AGHf/ey6pM+1nza4vd3/D3del4Z8DFcCAVih7m6AgpJncfTHwFz6KbI8H/pzOuXQg4/cAAA9bSURBVOfdDZxhZpul84VjgHvKSOtwWqLOzKx/zUxmtidQCfydDqoRdVYf7WeNrDPtZ7XXmZntA/w3cJS7/6kkm7uBM9N8OwH7AA9tynIXpSXqq2QfO5R4Bc0bm7LcbYku0W0ZZwE/M7PLgHeJ5/wwsznAZe7uwDTgc0DNpVtXuPsrabi+tI6quXV2lZntTTxgVwEnufubrbkBBWiwzsxsP+AuoBcQzOw44LR0OaD2s8bXmfaz2o/NHwNbADearX8e20nu/lfiaavbzWw+sd6+5u7LW3kbWlNz6+tnZvYxYB3wHnC4u3eaTtK6Y6qIiIgUQqdjREREpBAKQkRERKQQCkJERESkEApCREREpBAKQkRERKQQCkKkXiGEQ0MIT+TGDwwhVBVYpFYTQrg9hNBiT5kNIVSGELLceL8QwsIQwrZlLHtWCGFaS5WlPQghjAghLCu6HJ1RCOHExhznLX2sSP021bHRhM99Ygjhu81Zp4IQqVMIIQDXAd9pYL6vhxCeCyG8F0J4N4TgIYRjc+lVIYQTa1luo+kheinlVVGSdmAIIQshvJ9e/wgh3BZC2KZ5W1qMLMuWADNouH63Aq4AJrRCsdqMLMueyLJs66LLUZcQwoQQwtyiy9EZbKq6DiE8HkK4pKXz3dRKj40C98XvA+eEEPo3OGcdFIRIfQ4BugOP1TVDCOF44o/oaUBv4i2Lv0m8aU9THAQMJd64p7bnUazNsqwiy7IKYD/iI+l/0MR1tQU/Bf4thNCrnnlOBP6aZdnLrVSmDYQQuoQQ9F0hIhvIsuxd4EHSHXKbQl8sbURqFbgkhPBY+pf/1xDCp0MIx4cQ5ocQqkMIt4QQuuaWGRRCuCeE8GYIYVEI4aYQQs9c+lUhhAUpv5dDCN/IpVWmVoWTQggvhBCWhxAeCSHskCvWGGBuVv8d7f4f8D9Zlv0+iz5MUfojTayKM4m3eJ5GAzt2lmULgAeAz5SmhRC6pjoZUzL99hDCbWn4CyGE36fWmyUhhLtCCNvVtb5UX/vlxg8MIazJjXcNIYxPLTnLQgi/CyFY7bmt34Z5wFJgZD2zjQF+XVKW80IIL6bP7dUQwtUhhC4p7doQwsyS+Q9M826VxncLITyctrtm+W4prWbfOC2E8AKwAtguhHBcCOGZ1Eq1KIRwY01+abntQwiz0776Ulo+CyFU5uY5I7WaVYcQ/hxCOKSuja6lfm8PIUwLIfw01e8b6fjYM4Twf2n7Hgsh7JhbpiqEcFkI4cl0HHgIYZ9cer37QAihW/pM/57yfzmEcFSILX3jgQPDRy1zQ+vYjgPSOqrTZ3ZmLu3AEMKaEMKxKe/qEMIv8sdxLfk15bvi0yGE36TtXJCW75JL/2yqm/dDCE8S/wjk17llCGFSCOGVEMI7IYSHQgjD6ipjLWXuG0L4eYjfVW+GEH4Wci2YoaRVNLcPDqirrkMIp6bt/VbaHxeHECbXsh8PyOV7aghhfhq+HhgBXJryrPV2/CG2MjwaQvh+2kfeDiGcH0IYnOp0eQjhjyGET+WWadaxktvXb87t6xvtN2m43vop2ZYNTpu10Of+a+J3VNNkWaZXG3gBVcTbaX8K6EZ80NHLwE3AVsSHQC0GTkjzbw7MJzbTbwH0AeYAP83leSKxZSIABwMfAoemtEogI/6Ib0u8ZfXvgJtzy/8eOLeknAcCVbnxo4GVwJXAF4Ct69i2ExuaDvQD/gkcSQwsMmDvknWvyY0PIz7H46d11Ok1wMzceAXwPjAije9HfK5FV2B74H+AO3Pz3w7ckhvPgP3qKc/3Up0NBboQW4eWAn3ydV5LOWcDV9azb7wFHF4y7SvAkPTZfibNc2ZK24V4i/F+ufl/BtyahrcD3iYGed2B/oADl5XsG4+meumetmcUsCvxz8sw4AXg6tw6HgXuTfvSdsDjKZ/KlH4GcZ/dI+Xxr+nzGFbHdpfW7+3EffiwtPxZafn7iQ/82hL4DRvuw1XER6vvnbbjYmAJ0KvMfeD7aTs/nep6APDplDaBGKTXd1wPSWU+Na1jX+Ad4OjcNmbArcT982PE74Fvt+B3Re+0f1wK9EjLLQDG5dLfTnXTPdXHm2x4nE8nfld8LM1zOfAi0K22Y6WWMj9E3M/7pNevgF/V811QmeplQF11nep0NXAD8Tvw48BLwPja8sgtMz83/jhwSQOf4YS0ntP56DhYC8wt+Qx+nVumucfK7cT95vCUx5GpDIPrODbqqp/5JdPWf04t8bmnefYmtlx3r68e66zfpiykV8u/0kE4Ljf+r2mnzP+Q/AK4Lg0fBbxcksfexB/xLnWs4x7gmjRcc4Duk0s/B/hzbvwl4NSSPA7M76Rp2peIj6B+Kx2cjwG7lWzbB8Cyktc6NvziuYj45VnzxfYn4MaSdWdp2XeBV4iPWt8o8Enzf4r4Y7xdGh8LvFTPZ/AlYHFufP0Bm8brDEKIP1DLgf1L8vxrzTZSdxAyHfhxPeVaBRzYwP4zCfhFbvz3wDfTcM9U/59P4xcCvylZ/iukL6zcvrF/A+v8d+APaXhAWmZoLv0LbPjF+hxwckkes6njR4Dag5D8D9eWKf+jc9POZsN9uAr4bm48EJ/y+tWG9oE07/vAYXXMO4GGg5DxwO9Kpl0NPFyyT+eP82uB++rJs4rGfVd8lfhU15BLPxP4exo+IdVJPv17pOOc+CclAwbl0jcDqknHA/UEIcQ/QhmwU27aJ9K0HXLb1JQg5J/Alrlpp5OO8dI8css0JQh5vmTa4lo+g3db8Fi5ndy+nqYtAb5cx7FRV/3UF4Q0+3NP03ZK821XXz3W9dID7NqWRbnhFcT+D0tKptU00w4BBoWNe0hnxH90b4QQziX++xxA/ELdgtgRsq51fpDLH+IPfX19FeIKs+wBYrRMCOGTxIc1PRBCGJKlvZT4L/2O/HIh1ws7hBBSWe/Ismx1mnwrMDGEcGGWZTUPwFqbldlZMcuyv4UQ/kRsEZoC/BtwW26dewNXEf+Zb0mso4pasirHtmnZ2SF3BQzxX1JDj+XuRQyo6rLR5xBiX5zzia0uXYn/Up7OzXIb8HVix+JjgNezLPtdShsCfL5k3wnEf3l5VSXr/BfgMuCTxH/UXYhfxhBbUyB+qdVYWJLfEOCGEMKPctO6Aq9TvvX7a5ZlK+Jus9FxU3oqoyq3TBZCeJX0mTSwD/Qjtiy81IjylRrIxp/ty8CXc+Olx3npcVibxnxXDAQW5o7FmjIMTMMDaknPl3lIen821XeNbrk86lMzTz7Pl3Npi2i6xVmWrciNV9Hw8dYUpWVcQT37XQscK7Wts5z9ojFa6nPvxUd/DhtNfULar4XEiH/rktfmWZa9EUL4PLEp+Uxg2/TDPZv4JVuuPxOb9suWZdmLxB++wcRm13IdTGy2HFtz3pjY9FdB/CfXVLcBp6bzmPsCP8+l3UVsbdk5y7Je1N4RNu994o9SjR1zw0uJXxIjSz6PrbIsm9hAvrsR67ouG3wOIYSBxObfK4n/JHsTm6Tzn+1dwM4hhL2I/4huy6UtJP5rypezdxY7++aty62zOzAz5Tso1de3cuusefT4oNzy+eGa9Y4tWW9FlmVfr2fbW0JlzUAKdgfxUeBT3z6whPjjslMd+a6rY3rea/n1J0PT9NbyGjA4bPhLki/DG7WkV+aGa34gdyr57LbMsuzOMtdfmufQkrTl1H1sQd11vV0IYcuSctd8tjV/XJqSb5O10LHSWLVtR2mdwobb31Kf+27ElqJVTSm4gpD26wGge4id5nqGqH8I4YiU3ot4amQJkIUQDiOep2yMmcRmwjqFEMaGEI4O6V4XqRPYWcALWZa904h1nUk8H/9JYM/02o344/m1RpY77y5icPMj4jnbN3JpvYhNi8tDCIOI50br80fglBBC99SB7PyahPRv4ofApBDCTgAhhIoQ77NS+sW3XgqO+hHPL9dlJht2XK0gHrtLgNUhhH2Bk/ILZFm2DLiPGKjsS+wTUuPngKXPbvMQwmapI9sX6ylDd+I/unezLPswhLALsYm5Zn2vE5u2J6b9sR9QeunjdcCEEDuShhDCFiGE/VLr2aY0NoSwV4gdFscRWzx+ldLq3AfSZ/pj4JoQO/KGEDtKfjrN8iaxNbJ7Peu+E9g7hHByiB2XP0vc129t0S2s36+In934tO9+gvijWFOGB4j71LgQO+LuRezPBECWZYuJLag/DulSzBDC1iGEI0LJZfS1ybLsH8AjwOS0XB9gMvBglmU1//b/CByfjpl+xP4reXXV9WbA99O+NJR4qvFnab1vkwLfEK/w2p3Y2lqab9kdbMvUEsdKY9VWP38hBmlfSsf4EcD+ufSW+tz/hfgd1SQKQtqp1AR5MPEf8ovEL9JHiT/eAA8Tf2z+QPyXfhTxR6kxHgbWhBAOrGeed4nN/n8LIXxA7IuwjHhuvSwhXo0wBpiUZdmb+RexNeczoYGrTOqSZVk1cbtHES+Hzfsa8RzycmKflrsbyO7fiV9Y7xDPud9ekv4dYBYwK4TwHrHz4FnUf5yNBW5P5azLNGCP9CVLlmV/y61rGfGHs7Z/pLcRt/vh3Jc9qV4PItZ5FfEzvI+SnvF5WZa9T/ycrwkhvE9seSk9tfdV4g/868ROzjX1+c+Ux83EzsK3pXW+Svyx6VbPtreEm4hB6LvAscQ+HjX13dA+8G3iZz0zzfM4H/1o3U38J/9miFcwDClZlizLXiH2F/h3YifAacClWZb9oqU2riFpWw8hBrJv8dF3w5SUvozY2fdYYh39CPhJSTZnEDuBPx5CWE7s63Q0sRm+HCcS6+/vxO+rZcDJufRLiH+aFhHr+K6S5euq64XE/e0V4nfPQ8R9rMYpxO+i6rS9pcHfdcSAfFkI4fkyt6VeLXGsNMFG9ZPFS/rPI+7/7wBfJHaGrSlnsz/3EMLWxP17ahPLHTukiNQl/Tsen2XZ/mn8QOKPZmWR5WqPUuvJK1mWhTTej3hVipWcz69t2bOIHUtPqm++tiSEcCgxUNoiK+iLJsR+R5eU9keS9i+EcCrxs23ploxW1xaOlaYIIVxN7I/U5JYcdUyVemVZ9hDx34W0sBR4DC5z3qk0499Gawgh7Ek8N/1XYqe2K4H/bk9fqiKtoaMcK1mW/Wdz89DpGGmsKtr3HUqLtIzY2baj6kM8pfE+8CTwLLE5WEQ2pGMl0ekYERERKYRaQkRERKQQCkJERESkEApCREREpBAKQkRERKQQCkJERESkEApCREREpBD/Hycy5OZI/Y1+AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Concluding Remarks (5 min)"
      ],
      "metadata": {
        "id": "uJqN8-5sd5K9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The main take aways** of this lecture are:\n",
        "\n",
        "* A lot of AI models is already used in Finance and even more to come\n",
        "* Python is a great tool to build AI models\n",
        "* Each and every AI model comes with consequences of using it in real life"
      ],
      "metadata": {
        "id": "kqU72auDeKBq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## Further Resources on AI in Finance\n",
        "\n",
        "* Artificial Intelligence in Finance, ISBN 978-1-492-05543-3\n",
        "* Machine Learning and Data Science Blueprints for Finance, ISBN 978-1-492-07305-5\n",
        "* Machine Learning for Finance, ISBN 978-1-78913-636-4 \n",
        "* Hands-On Artificial Intelligence for Banking, ISBN 978-1-78883-078-2\n",
        "* Python for Algorithmic Trading, ISBN 978-1-492-05335-4\n"
      ],
      "metadata": {
        "id": "v9NCsatYozQ1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Used Materials\n",
        "\n",
        "* [General principles for the\n",
        "use of Artificial Intelligence\n",
        "in the financial sector](https://www.dnb.nl/media/voffsric/general-principles-for-the-use-of-artificial-intelligence-in-the-financial-sector.pdf)\n",
        "* [The Future of AI and Automation in Financial Services](https://www3.weforum.org/docs/WEF_Forging_New_Pathways_2020.pdf)\n",
        "* [Credit Card Dataset](https://www.kaggle.com/datasets/dansbecker/aer-credit-card-data)\n",
        "* [Data Leakage](https://www.kaggle.com/code/alexisbcook/data-leakage) "
      ],
      "metadata": {
        "id": "AUqYArNMdRRZ"
      }
    }
  ]
}