09. Support Vector Machine (Iris ,diabetes).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use SVM where its easier to segregate data and plot Hyper plane\n",
    "Ref: https://towardsdatascience.com/https-medium-com-pupalerushikesh-svm-f4b42800e989"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**you can tune your model by using the parameters--> C(regularization),gamma and kernel.  --> use shift+tab to know the different values of Kernel**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WORKING\n",
    "At first approximation what SVMs do is to find a separating line(or hyperplane) between data of two classes. SVM is an algorithm that takes the data as an input and outputs a line that separates those classes if possible.\n",
    "\n",
    "\n",
    "According to the SVM algorithm we find the points closest to the line from both the classes.These points are called support vectors. Now, we compute the distance between the line and the support vectors. This distance is called the margin. **Our goal is to maximize the margin.** The hyperplane for which the margin is maximum is the optimal hyperplane.\n",
    "\n",
    "Thus for a space of n dimensions we have a hyperplane of **n-1 dimensions** separating it into two parts\n"
   ]
  },
  {
   "attachments": {
    "SVM.png": {
     "image/png": 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NY6u1gwWJgLKyMnz2s5/E+aHfY3Mtp7bcimVZ2LJli5kCJKU0cypra2tvmB94bV3vZj7iw3g9/XheopgIgOwDSPRwsPGzPwGwZM0lhGcVZGAjR/9uEwB1oLrbUKXrGqkzkxfC8xI9DGIhfQAQ0cMz1/h5Ixs/R3CHNgwn3AAgADtQyYIQEQMgEUXOoDqHfnUGuWIp8sVyFiRClHsanhqGkAUQMocFISIGQCKKnAanHh48VMlNHG2PICdUb77awU0sBhEtKJwDSBQDckU+MqxMrJSrWYwIUe4AlHsGlsiDkCtYECJiALwZjkoQPTxVNuf9RZoTPgbAhR3YCMuSLAgRLSg8BExENM+UGoPrdMKy0iHtNSwIETEA3gkPARPNn0lvAiNqGLPeLIsRQW64GfCmIQNlbP1CRAyAt8NDwETzK+SFcCi8F8ecQyxGRHdiZ+A6zYCVABnYwIIQEQMgEUVOn+pGi3scIcwizuLltCLFdbrgqVFIWQQhMlgQP14Dz4XjOTyiRMQASBR75ho/V7Hxc8R4noITOmI+Wu1gNQvig1nvEg45e3DcqWcxiBZTAOQeG9GDu9z4+SwbP0eYcs/AUwMQchmEzGdBfHDa7Ua9sx/D3iCnFhEthgDIP1Si+cPGz/5ww1caPwfY+Nkvje4R87VS1rAYRIshABLR/LigRnDaPYVMK4uNnyNIucNwndOwRDaEXciC+GBI9WNAnUWOlYc8sYwFIboNmyUgirIgAhfl9kZkWdmIs+JZkAhxw40AQrADFbCsIAvig0bnmBn5LrerOfJNtNgCIOcAEj2YTCsLdfZ2CA7wR+5zS03AdU7CslIg7bUsiB87Pp4y7/0aewtWSTbfJlo0AZB7a0TzQ/CyYxGnw5/nTcIO1MISKSyIT8rtaghYkBYPbhEtmgBIRA9GeQoT3hgsCKSJdBYkQjwvBMcc/g1CBipYEN92fARHvYnu5W+GJSCKDn2qGwedPehXZ1iMSAZvpxueOg9pF0LIJSyID2a9WXPZQ17ykGgRB0DOASS6P/XOfrS7LUi1OPoXSc6V1i+SrV98c8Q5gEPhPXDhsBhEd4lzAImiwLsbP7P9RaQotx/KPQsh8k3zZ4q882oIx53DSLcyEQQveUh0t3gImCgKsPGzPy5f9s2DDNbAsvhx6odm5zgcOCizK2Hz5A8iBkCiWMHGz/5Q6gJc9xQskQlps+5+mPImcUq1I9lKQZEsYUGI7gH7ANJNua6Lnp4eHDt2DGfOnMHMzAwCgQDy8vJQU1OD4uJi2Pbi2duemJjA7t27zejYjh07kJqaGjXL0Oo2YhaXsEluZePnSP6NhE8A3oyZ+2dZCSyID0657SYEVsgaEwKJaBEGQB62Wliamprwr//6r9i/f7/5d1xcHMbHx00wfPTRR/HCCy9g48aNi2dDceoUfvjDHyI7OxtVVVW+BMCHsQzKU0iyUlBtb0axXMc3bqR2VNVFuOFWwEqEDJSxID5wPMcc/pWwsd6uZEGIFmsApIVDh7y3334bL774ogl5f/Inf4Lc3FycPXsWr732Gjo6OtDd3Y3169fjnXfeMSFxx44d5qbt3bvX/H5NTQ127tyJgwcPmn9v2LABSUlJaG5uhlIKFRUVqK2tRVpamvn5ne6j/62ft76+Hn19febfOTk5qK6uxtq1a01I1Y+hl0kvWyAQwMjICILBoFmmhoYGLFmyBD/+8Y/x/ve/H+Xl5VfXWf9M/65eT70e+fn5cBzHPNa+fftQV1eHRx55BKdPn8bRo0cxODgIKaV5Hr2emZmZpm6dnZ04fPgw+vv7zfOXlJSYn585c8aEv+uXQf/+/azT1q1bzSisVibLYcFCgJcfi9zfiNMJzxuDDFRCsOeiL8IImZOeCq0iLBE5LAgRAyDNh8nJSUxNTSE9Pd2EsrKyMjNKW1VVZYKK/rcOSL/73e/wzW9+E1/96levBkAdmL7+9a/js5/9LLZs2WLC11//9V+bIKdvAwMDZoRRB6EvfelLePbZZ+/qPm1tbfjRj36EPXv2mCBk27ZZxtLSUnzmM5/B9u3brz6ODlCFhYVISUkxgUr/7vDwMIQQ6Orqwujo6LvWVwe2//zP/zRhMSsrywTAoaEhvPTSS/jpT3+KL3/5y0hMTMS//du/mQCckZFhDunqQPyJT3wCH/rQh8zj/su//AsOHTpk7qvro5fxox/9qKnjzZahpaXlvtZpzZo1JgAKS/DMxwjzPBdO+BgACTuwkQXxiYSNzYFtCIA7PkQMgDQ/H6xSmpE3fauvr8ff/M3fmMC3YsUKrF69Gps3bzahKhQK3dXj6eCoQ49SyoQYHVx0kNKBSweompqaO95HB6Lf/va35t/6/s8//7wJWT/5yU/M93Nzc82ImX4M/Vizs7PmcXQA1WFNr9OJEydMgNW/e+3on6bXTwfdX//612hvb8fjjz+OkydPmtBWUlJigtdbb72FX/7ylyaMfvKTnzRzJL///e/jlVdewcqVK03wffnll01o089x/vx5/OpXv0JjY6MJgTogX7sMRUVFZn3uZ510AJz1LiHkhRBnxSFoMQRGinL74KlBCFkIIXNZEJ8ErSCCDH9E0RMAeRLIwrB161Yz6qVDT2dnJ37xi18gHA6bETkdVnSgWb9+/V0HQE2HrsceewxLly41j/n666+bQ6pnzpy5430aGhrQ2tqKS5cumSD1wQ9+0IyWjYyMmOClw1pvb+/V59TLpu+jQ5amw1QgEDCje/p7OhReSz/ftQFweHjYhL+uri5zqFavt/7++Pi4Wd433ngDY2NjZrROf+/AgQPm/o7jYNu2bXjve99rfqaDpf75unXrTGC8dhn0fR9knfaE3zRXPtgeeIJv2AhyQocvf3gG2fjZL2EvDA8KNgJmFJyIFnEA5EkgC4sOPB/4wAfMaNNcSNNh6LXXXjMnhwghTKi6WXBXSpnb9ZKTk81NS0hIMDcdfmZnZ+94Hx3IJicnzQhZamqqCVJaSkqKuemf6dscHfAyMzPven314+kAWFxcbIKeDnQ6gOn1rKioMMs0PT1t/j0zM2PmAGq1tbUm0KWlpZmf6+VNSkoy99PLWVdXd8vnnFvm+1mnQXXO9P7LEfk8BBxByh2CcnthiRwIuZIF8cG0dxHHnEOIRwI22nUsCNFiD4C0cJw/f94EoL6+PhNg9E0HwbkA1NzcbM5ovXDhgjm0qsOfDmn65jjO1bOFrzc2NmZ+psPOxMSECTcrVqwwoelO99EhSwclvQz6+/p59HPP3ScvL88Erzn6Z/p2Pb2stxplLi0tNSHwt7/9rTnU29vba0buysvLTSDTz69D2uOPP45Pf/rTZkR0dHTU7Lzoxzx48KAJh1NTU2b99bI2NDSY+YxVVVU3LIN+vPtdJzZ+9ocTbtD/DztQBYtNh33R43biqHMQZbKSo39EDIA0n3Sw2bt3r5mLVltbi/e85z1m9EmHkqNHj5rwsmTJEhPKMjMzTThqbGw0o4Ozs7M4fvz4TUNJU1OTmRO3atUq0w9PB8jCwkIsX778jvepqakxI5Gvv/66OWGiqKgI8fHx5iQUHbjWr19v7ncrcydY6FBXX19vTva4dgRTy8nJuXoYWD+uXoePfexjZh6e/v3i4mKzjq2trSYEX7x40TzWsmXL8NRTT5mwqMPjvn37zBw9vewvv/yyea78/PwblqGystIcIr7XdWLjZ38oNQ7ldMCy0iDtYhbEj9fAU2hyjpmz3sttnoBDFFUBkHMA/afD3a5du8zIVUNDgwk7CQkJJhjq1+fxxx838+Ly8vJMQHziiSdMAPzOd75jQpAOOklJSTccCpZSmsbS//3f/21GEDdt2mTCZXZ29h3vo4PQM888Yw4F64D1d3/3d+a+Oig9++yz5izcax/nejpQ6mVra2vDf/zHf5hDttcHQP14OgDq8KZDmA5kFRUVJvzqMPj000+bEKrX9Zvf/KYZ5dO/s2LFChOE9c/Pnj1rRk/1z3WthBD48Ic/bMKj4zg3LMP9rNPVxs82Gz9HkhtuhedNwQ5ugSWSWRAfDKhzGPIGkC+WI0fksSBE0RAAeRhr4dABbtu2bSYgHT9+HOfOnTNn/AaDQTNKpkPR3KhYVVUV/vIv/9KMDOpANHdiyI4dO8x/x8X9YX5aeXm5CTZdXV3m3xs3bjQB79pDtbe7j368P//zPzf/7u3tNeFy7sokJSUlJmzp5f7KV75i7nPtc+tl/sIXvmDCbEpKyg3hb44OaH/2Z39mev7pYLdly5ar7029LF/84hdNQB0aGjLPp+ugQ7CuS1ZW1tXl6+/vN/XSwU8v39xJKNcvQ2lp6T2tUzgYQofbapo/F8tSvlkjtmN6Ca5zArDiIQMbWBCfNLlHLv89y2oWg+hBc1coFPq953mP+b0g3/72t83cs89//vNmBIqixze+8Q187WtfM6/tV7/61ZteAeNu7kPAqBpBo3MEGSILFXYNCxIhTrgV4UuvQtqlCCZ8kAXxwZgaxU9mf4R4KwEfj/tTxHP0m+j+w59lvcU5gEQL2MzMjGkLoyUmJiKMMIq99eaqHxdw4V33jYuLM4fqheDE+PnmhuvNV7Z+8c+kN4EVohDL5UqGP6J5wDmA9NDd6rDsvd4n1iilzKH1X776KkIXLyIt+dbzzpTnYXlBAR7btQtFJSUs3ny+Du5ZKPcchFwGIZeyID5Jt9JRF9iGFCuNxSCKpgDIOYDRa/v27eb2oPeJRefPn0fnnj3Ibm7GkhQBCCB+CrCu208aVgqH16xBbl4eA+A8c0JXRv8CHP3zU4pg8COKygBIRO8212x7Q3Y2CnKBwnUKWTNAYS8gpwAxDVhXTrJuVwo/vXjR9BSk+aPUCFynG5bIgrBXsSBEFD3bGJaAaOFr3AicKAdkCPAk4GQA4VzASb38b3o43HATgFnIQDksXm/ZF0NqAIfCe9HjdrEYRNEcADkHkOjdZjOA84VA8jiwvAUIDF0eAfQE4KZdDoIi3YIdYK3m9bNITZnef5aVDGmvY0F80uQcxSFnD2Y8jm4TRWUA5BxAopubWA9kxQGlzUBcCBBhwB4FgoOAnLh8n8RUoCx5GivGe6DGhlm0eeA67fC8CQi7BILzz/x576sxdLudSLFSUSiLWBCiaAyARHSjkD2L8ZWzWDYFFLVft9PkAPY4EBgE1LgOhgqyrxWXXv83zO79OdyRfhbwPnle+Mp1fwOwA5UsiE863DbMYNo0PU+wElkQonnENjBEC9ho+jBmLg1jSSeQMnXz+1guMD3uoSU5EU7qSpQnpsLpaYbbdxIivxCBsi2Q2ctZzHug3F54ahhCFkHIHBbEj50fbxZtbhOCCGKd5NVXiKI2APIQMNF1IcRT0PtD6U0W0k7cxQYTAm5BGeJ3PQf3bBfCJw/DPdNpbjJnBQLrt0DkFsKSPGvkTq62fmHjZ9+cUadxwRvBalGCTMGrQxFFbQAkohtljmcj71Q24kY67n5nKhgPe9V6yIK1cPt74LQcgDvUZ24iKx+B0lrIpUWwAjyr9abB2x2AcvtgiVwIuYIF8cmIOm+u/FFlM4QTMQASxRBhCSRZyQhZSdgvBMZuM0o+YFkYDAZRbv/hT9qSNuzlayCXFUENn0G4eT/cgdOY3fMqREYO7OJq2CtKYMVzbtW1nPAxAC7sQDUsi6OlflkhV2KZKECe4NVXiGIiAHIOIBHgeA4chJG3LBflTz+NtrQ0dN7m/mHPw7rCQqxdv/6Gn1mWZQ4B65s70g+n9RDcc6cQOvQbOG2HYBdVQhaWQiSmxnzdlRqD63TCstIh7TV8I/ooTyxjEYhiIQByDiDRHxx363FRTaEmZwue/chHULdz5x13jtLS0rBkye3nSsmsfMjtH4IaHUK4/SjcM+0IHfs9rPaj5rCxvbocIiUjZuvuhlsAbxoyWAVLcGSUiBgAiShCLqjzOBLebw7/bra2IysrGVlZWfP6HCIjB3Gbn4EqrYXT1QinpwXhE/vMf9sFa2Gv2QiRHlsT7z1vBq5zArASIAPr+Ub0Sa/bjTHvAgrEKqSLTBaEiAGQKDa0uI2YxSw22Y8gzop/qM8lUrMQ3Pi4mQ/o9jTDOdWE8MkjcLqbIZcXI7C2BiIzLybq7jpd8NQopL0BgsHDF47nmKt+THqTKAjy2stEMRUAOQeQYtmUN4lOtw1JVoppfhspIjkNYsMjsIsq4PS2weloMGHQ7W2DzF8Fu3QzZHb0zsnyPAUndMT0xreD1Xwj+uSs6kW/OsvRP6JYCoCcA0gEdLknMelNoNLeZC5/FfG/w4RkBNZugr1qA9wzHQifrIfT1w7nTAdkTsGVXoIFsER0nR2r3LPw1IBp+yJkPt+IPmlyjpqvFXYNi0EUKwGQKNaFvBCaneMIIIj10t/Lj5legqvLIQvWwe3vRrjlINzB0+YmlixFoLQOMn81rEAwKmrvhq80fg6w55xfLqjzpvlzprUEy0VB1K+vUgqnTp3CxMQEVq9ejfT0dL4JiAGQKBbNejPIFrlYbRUjS2QviGWy7IDpFSiXr4Ea6kO4+YAJgbPv/AwiIxeBtdWQy0tgxSUs3g2xOwzX6YElsiHsQr4RfdLqNiKMMEplOQJWMLrXtbUL77y9FwcO7sXE+Dgqymvw+BM7UV2zHklJSXwzUGwGQM4BpFilN3p19nbEP+QTP+4rCFoCMnelubnnz13tJTh74NcQqYdgr6mEXFkKkZiy6OruhhvNhfTsQAWsKA8eC1XIm8WEN47lYiXWyHVRva4Nx1rwg+++ibdeO4+xkTx4Xi5O7HPQcOgNfPIz5/H0ex5HcnIC3xQUOwGQcwAp1sVbCea20MklSyEf/TDU6KA5Y9g904HQ0d9BtB+FXLUB9uoNEMmL43CWpybhOidhWSmQ9lq+CX1kRv4Qh1SRFrXreKKpDd/71ut465cC1qUPI9O+fJUTFbqE5v3H8N2LhxAMCjzz7FOwbR6goxgJgESxLOyFdRyBjcCi2RkSGbmI2/JeqPEROF0NcE63Idy0B05nA+yVpWZUUKQt7F6CrtMGz5s0c/8skcI3ok+CVhwKZXRfeWVychJvvnEQ+38fgnXpaQTEH9orCSseiXIrOo+P46c/OYqiolUoLeMOCTEAUpQ6cOAAWpqakJ6SAiFvf1ZpUlIS1paVYcXKlVE3WnxBnUer24QMK8uMgiw2Ii0LweonYZfUwDl1Am73CYTbDsPpPnG5l2BJDURm7oJbbs8LwTGHf4OQgUr+QdJDNTAwgPa2fsyM5yNe3Ly3ZpxYjxNHelBff5wBkGIvAHIOYOw4uG8ffvOtb6EyMREJdwiAg8nJ2Pq//hc+/j//J+Li4qKqDi1uI445h7A98MSiDrciOR3Biu1QaypN/0Cns8FcWcQ93Qa5dBXs0jrIJQunl6ByeuCp85B2CYRcwj9In5xy280I+GpZHNUnf4yNjeHCsAMnnHzLLa+00jE5LjE2Nsk3BsVOAOQcwNijI9/q8XHsGhxE+h1e//+wLNMyIRwOR1UAvLbxc7RMfheJKRDraq/pJXgETu/Jd/cSzCmAJYSvy+lcaf0i2frFN6PqAg6G9yDZSjEBMJoFAgEE4yxYwrnNAEgYtm0hwPl/FEsBkGIwAEqJbClRKgSy7hAAcwFcisIa+N34+aHu1MUlmCuLXO0l2HoQ7kCPuYklyxAou9JL0A5EfNmU2w/lnoEQ+RByGf8YfXLSbcZ5bwhr5Nqob/2ydOlSLF8ZBxkYAdyb38e1BpG/1EVefjbfHBR7AZCHgClWhBdQ4+eHGgQDQdgFa818QDXYi3DLfriDfZh9+xUzNzCwdhPksjUR7SV4+bJvHmSw2rS4oci75M2g3W1BPBJQIsuifn2zsrJQvakUB3Y3YKDjJIJ49xw/xxuDCjShZmsGamo4J5ViKADyEDDFmj7VgxFvGKtlyYJp/PxQ/8aFgMwvNDd3+Cyc1oNmZHB2/y/N2cL2mirYK9eZy9E9TEpdgOuegiUyIe0ivhF90uOewrg3irVyPdJERtSvr5QSWx+pwdm+cfz03w9jsOcCpCqEhQCUNQpHHsOmR8fwsT9+H1YUcFSaYigAEsWaKW8KJaIM5bI65tZdZi+DzP4I1IWBP/QSPPIGnPYjl3sJrlr/0HoJuuFmwJsxc/8siw13/dLoHjFfK2Pour9Ll+bhIx97ComJe/Cbnzeip/0EQiEPOblA9eZUPPcnz6C2biPfHMQASBTNVohCrBArkW5lxmwNRGYe4ra+D2r8PJyOBji9bQg3vvPuXoKpWfP2fJ66CDfcAliJkIEyvgl9MqouYNa7ZHr/5YqlMbXuy1fk4KMffxxrS/PQ1dmH2VAI2dnp5rDvysICvjkodgMg5wBSrMgQmSzCXBBMW4Lgpqdgr73SS7DnBMKth8x/yxVXeglm5Dzw87hOJzxvzPT9E4IX3/dTnb0N6TH6N5CRkYFHdzyCR3fwfUAMgJwDSDFD7+To/5ngwxMQ3h0EUzIQrHwUqrgK7ukWOJ2NcDqPwz3dCrm0CPa6WnMpuvuruwsnfMw0ILIDPMzm984Pd4CIGACJYkqr24QJbwylsgJpFkehbhoEE1MgSjfDXlUO50w7nPajcE63wulrh8xbiUDZFoicFfe046jcPnhqEEIWQshcFpmIGACJ/KA8D8NK4YRSd2wE3Q8gLQqmB1z0pnDY2QsXLsokWz3ciRWfiIA5O7gU7rlTCLceNl/1TWYvg122BTJ/FSx5548yJ3S58bMdZONnvziegzOqBxI2VshCFoSIAfAPOAcwdhStWYNTmzdj97lzCN4hAIaSk1FUXGy66S9mXe5JTHjjqJDVSBVpfBPcbRAMxJkQKFeUQA2cRrjlANyhPri7XzYnkgTWXeklGIy/+c6GOwTlnoYlciAkJ9r75Yw6jYPhvSiURQyARAyAVz7gOQcw5tRt2YL0tDQMDw/jTq9+UnIyitetQzC4eK8WEPZCOOE0IIAA1ttVfAPcz+eEkJBLV5ubO3wG4ZaDUP3dmN33C4j07Mu9BAvWwUpIetfvOeEG/f+wA1WwrAAL6ZMG5xCGvH48InayGEQMgBSrMpcswZZHH42Z9f1D4+diLBE5fAM8IJm9HHLn8su9BNvq4Z7tRKj+dTgn6y9fgq6wDCIpDUqNQzkdsKw0SLuYhfPJsBrEOXUGS6wcLBUrWBAiBkCi2NDgHDZfq2QdizGPTC/BR94PNTYMp+MYnL6TCDXshtV+DHZhGbBiBl7cFOzgZlgimQXzSbNz3Mx93WBXQVqSBSFiAHw3zgGkaDTjzSDBSjLX/M0XvMzTQwmC6dkI1j5tWsU4XY1wepoRbtkHt+0kZEkRRBlH//wy5U3glGpHspViLn1IRAyAV3EOIEW7WvsRxFsJ7P33sINgSgaCVTthF29EqOvncE82whpPhjUjAV75zRe9bjdcz0WZXYEki6OwRAyARDEiwUowN4pgEExKhbVGwc5fj6D3JBBk/f2SbKWgLrANBWI1i0HEAEhE9PAo95y5yaRC2InsueinArkaBWD4I1pQO8kLbYE4B5CiyUVvCofCe80EeIosJ3T5pBs7wMbPREQLNgByDiBFoy73JA45ezCihlmMCFJqBK7TDUtkQdgcefLLjDeDfvcsRtUFFoNogeEhYKKHZK7xsw3bTH6nyHHDTQBmIe2tsKw4FsQnrU6j6X9ZbW9GBjKvfn94eBj1h4/gXH8/Vq8uREVFBbKyslgwolgOgDwETNGiT51m42c/PkPUFNxwGywrGTKwjgXxybR3EY3uEdP7L11cDn/T09PYv+8QfvfaUdQfaMXZs2dRUlyC6ro2PPlMLWrrqmDbHJcgiqkAyEPAFG3+0Pi5lsWIINdph+eNQwaqIXi9Zd90ux2Y9CawXlYhxUo14e93b+zHj/7pILqOpkCqxxGnFM7WB3C26Tw6TvwO4S862PZoLaRko2iimAmARNFkUPWjX51BrshHvljOgkSI54XhhI8DCMAO8MxfvyhPock5BgFhrvzheR727tmPH/1/h9HfUItsqwLSjr96fyc8gfY9+/FDuRdx8UFs3lLNIhI9ZOxIS/QQnFdDKBCrUGNvYePnSAYPtxeeGoKQBRCSh939MqDOYtgbRJ5YhhyRh8HBQbz+60PoOpaCFKsG0op/90iElYoUPILGfQpvvr4Hk5OTLCJRrAVAzgGkaJAr8lEX2I5CUcRiRJATqr8cKIJs/eKnJvcYLFiokJdH8tpPduL0yRDiUAjrFtcBtq0U4FIO+k6N4dy5cywi0UPGOYBEDwFP+og85Q5AuX2wRC6EXMGC+ChPLEWGlYUCucr8e2L8IibHPAgv6fYjEl4Spic9XLx4kUUkipUASET0IJzwMQAu7ED1LUeZKDIq7XePwKalJyM108KYmLrt73liCsnpAikpKSwi0UPGyUlE86jb7cCR8AFcUOdZjAhSagyu0wnLSoe017AgC0zJ2jUoXBuPWXRDeaGb3iesxmAlDKFgdQby8vJYNKKHjH0AfTY1NYWOjg7Mzs7esRFqcnIyMjMzERfHxrYLUdgLmcu+TXrjWCUZQiLJDbcA3jRksAqWSGRBfDLtXcQlbwaJVhLirYSr38/NzcVTz9Siu/kAzh07iiRvI8Q1DbodbwKT1n6UPyLw5K5tHAEkiqUAGItzAHXY7e7uxos//jEu9PSgMDv71iMcnof4zEw89oEPYPO2bXznLkB9qgdD3gBWyxJkiiUsSMT+jmbgOicAKwEysJ4F8dGh8B4TAB8LvueGn23bvhVOyMKPvnUI7fWtsN1lsFQiPDkFxJ3D+q0u/tdf7MTGal41hyimAmCsmp2dxcTZs0jcvRtrbjP6edHzsD8pCRnLljEALlBs/OwP1+mCp0Yh7Q0QIpMF8cmg6jdn/+aKfMQh/oafJyQkYOcTWxCXIPH7Nw7hzKk2zFz0kJIuUFicjqfeswM1m6rYBJqIATD6WZaFjIwMLM/MxGoAf+w4t7zvqOfh3OwsQuEwC7cgN37ncO5q4+dlLEiEeJ6CEzpipjPbQTYP9lOjc9R8LZfVtzyio0PgY48/iorK9ejr6zNn+6ampmL58uXms5CIYjgAsg8gLUbHnSPw4KFSbmLj5whS7ll4asC0fREynwXxybgaw2m3C2lWOgpl0R13fLOysu4455mIHq4Fs6ViH0BarC55M+aC98WyFCslGz9Hkhu+0vg5wMbPfupwWzCDafM3kGDxJByixYCHgInmQaXchHgr3twoMpQ7DNfpgSWWQNiFLIhPZr1ZtLnNCCIO6+QGFoSIAZAoNsRbCVgql7MQEeaGGwGEYAcqYVlBFsSv1wEOVsrVSEQSMgQP6xIxABIRPSSemoTrnIRlpUDaa1kQHwURh832dkhuTogYAB/og50ngdAiEfbCaHaPw4aNDXYVCxJBOvx53qSZ+2cJNg32dSNi2RxLIFqEeBII0X3qUz2m8W2/OsNiRHQnMQQnfNyMPckAmwb7acabxkVvEspzWQyixbbzxhL4KxgMAnFx2CMERm/TAHXa83AqMRErEhJYtAWiwTmEWVxCmaxkMSJIOT3w1HlIuwRCZrMgPpn0Jsx1r5OsJNTYW1gQIgZAuheZmZnYvGsXjiQnY/Q2jaA9z0N1ZiZq6upYtAWAjZ/941xp/SLZ+sVXXe5JNLlHUWXXQli8egcRA+ADirU5gElJSdj51FOo27YNSqnb3jcYDJpO+uS/BqeejZ99oNx+0/xZiHwIyeDtl7AXRovTCBsBlEkehidiAHwAsTwHMC4uztxocRhVIzjtnkKGlXXHqx7Q/HJCR3UMhAxWw2Lw9s1Z1YsRbxgrxWpkCR6GJ2IAJIoBQ2rQbPTWyjLEsfFzxCh1Aa7bBUtkQNoM3n5qunLd3wq7hsUgYgAkig0ZIhObre3IFrksRgS54WbAmzFz/yyLUyH8MqKGcUb1IstagmWigAUhYgCcH+wDSAtdjshjESL9uaCm4YZbACsRMlDGgvho2ruIYrkOK0QhAlaABSFiAHww7ANIRLfiOh3wvDHT90+IdBbER5liCeqs7Ui0klgMIgZAoug3oM5iWA0iTyzj4d8I8jwXTvgYAAk7UM2C+CzJSga4v0606PE0OqK7dCi819w8cJpCJCm3D54ahJArICSDt5/CXtjciIgB8CHs7XPjSgvPoDqHXtWNFJGGbIshJJKc0OXGz3aQjZ/91K/O4JCzx1wCkYgYAOcN5wDSQjbX+LlKbuJ7NYKUOwTlnoYlciAkzzj10zHnMI46B6GgWAwiBkCi6HfhSuPnTCsLK+VqFiSCnHCD/n/YgUpYPOPUN6NqBH1uj2l+XiAKWRAiBkCi6NfqNmIWl1AqK9j4OYKUGodyOmBZaZB2CQviozb3BEKYxTq5AUGLVy0iYgB8CDgHkBaSWW8Wk94ECsQq0/uMIscNt8HzpiAD62CJZBbEJzPeNNrdViQgEcWylAUhihLsA0h0B6Wy3Ix6pIg0FiNiO4KX4DpNgBUHGShnQXzU43ZhwhvDOlmONPZgJGIAJIoFcVYc5/35wHW64akRSLsUQmSxID666E1itShBhc0ejEQMgA91z5+HgIliPgCG2fploVgpi8wtlpufO46D3t4zOHjwEMbHJ1Faug7l5aXIzMzkG4QYAB8UDwHTQtPiNJrWL2Wygu/PCFLuOXMTcqm5kb9i/ao3g4PDeGv3Xrzx5gns338S4+NTKN/Qg23bOvD+921GZSXnRRIDIFH0fOirc6bpbY6VbwIgRc5c42cZ4Ogf+Wt4eAQ/feVt/MsPW9DRmQflFZtzJ/cfttHc2ovu7tfwF3/uYePGMhaLGACJokGDU2/O/n3EfoyjfxGk1Ahc9xQskQVpr2JBfNTptmFcjWGNXIs0kRFz6x8KhfDmm2/jBz9qReeppyAC697VNmN8qh+/+s2bSE7+PVJTk1BUtJJvGlpU2AaG6DqjVxo/Z7Dxc8S54SbAm4W0y2Gx56JvZr1L5rrXx93DMVuD9vZT+PkvTuBkRwGsm7SAEjIf07Pb8Nob57F330Fuu4gB8H5xlIUWiparjZ/L2fg5kjt/asr0/rOsZNP7j/zT6/ZgxBvGUrEiJkf/tBMnWnGi2QWsWzcht0QBBgbjcKqrBzMzM3zjEAMg0WJ10ZtEh9uKJCsZJWx6G1Gu0w7PG4ewiyHYc9FXje4R87Vc1sRsDSYnpzA5ZcOybt370LKkea9OTSlMTEzwjUOLCucAEl1jSA0gHgkolGvY+DmCPC8MJ3zcfCTZgSoWxEfDagAD6iyyrVzki2Wxu3EMBGGbLWT4Du/dEOwAEAwG+eahRYVzAImukWSloC6wDRvsShYjgpTbC08NQciVEDKHBfHRCacBCgob7I0QVuweJFq2NAcFKyxIMXyb7dVFxMfPIC83HWlp3GEkBsD7wjmAtBDkiDysliVItlJZjAiaa/3Cxs/+mvVmEcIsVotirJJrYroWFRWlqKvNQFyg2ZydftMA6B5FzUYPdXWbIKXkG4gYAImI7pZyB6HcPlgiF0KuYEF8tkFuRF1gu5kHG8vy8vLwRx/eike3T8K23oRy+81UBVwZ+fPcPajc0IRPfbICVVXsA0iLD+cAEpnWLxcwpPqRLjKRK/JZkAhywkcBuLADG82kevJPnBWHZbKAhbhi06Yq/D9/qZCethdv7X4JIxeSoBSQEH8Jm6ol/vT5jdi1aycSExNZLGIAfFCcA0h+OO7Uo091Y2fgaRYjgpQag+t0mjMtpV3MgtCCYlkWtmypQVZWBrZs3ofe3vNwVRhZWVmord2Eqsoyhj9iAJyPPzQiP4yqEbS7LUi0kjj6F2FuuAXwpiGDVbAEN6R+anWa4MIx/S+lxYND1youXm1u09PTZpBChz5us2ix4xxAinls/OwPz5uB6zQDVgJkYD0L4qMRNWyufd3lnmQxbkMHv6SkJIY/igrczaOoMjs7i7179+JkSwvSkpJue9+EhAQUVqzGycIWJAk2fo401+mCpy5A2hsgRCYL4utO0HFMeOOotGs5+kfEAOjXqADnANKDeeftt7H3n/8ZW1NSbrunfi4xEcmfqcGy51diU+IWNn6O6N+5ghs6CsCCHaxmQXx00ZtCp3vSnPVbJEtYECIGwMjikDrNh7i4ONi2jXWTk3huZgbiNu+r/xuwcHA4gApvEzZIXn0ikpR7Fkr1m7YvQnLepZ9Oue2Y8iZRLjcihf0viRgAiRazTMtCkRC3DYBZloUCdxWq1CZkiWwWLYLc8JXGzwE2fvb1dfBcNDvHISGx3uZOEFEs4aXgKGZZLrBstgBLHfY9iyTlDsN1emCJJRB2IQviozHvAjx4WCPXIVvksiBEMYSHgIkootxwI4AQ7EAlLIsX0Pd3BECaq36kWzwJh4gBkCgGnC70cDpgIZl/ARHlqUm4zklYVgqkvZYF8VmGyEQGGP6IYnMHkCjGjGZ4OFwHdJZwukGk6fDneZMm/FkihQUhImIAvDJCwDmA9JC1lQHDuUDWgAXLYT0i97cdghM+DiAIGahgQXwU8mbR4baix+1iMYgYAP3FOYAUCReTPHQWA0kXgaIO1iOSlNMDT52HtAshJM+69lOP6sLB8B4MqX4WgyhGcQYURaUxz0OPUje0gekqAs4kA0UNQHhcQXHEOWKcK61fJFu/+MrzPDQ4hzHqjWClLGJBiBgAiaJDwbJl6Couxn9OTuLa+OfZHk7XzmAqaGFqIAHjyxNRs3Qp4uN5/d+HTbn9pvmzJfIg5DIWxEeDqt/ccsVS5Ao24SZiAFxAe6dED+LRHTuQlpyMiYmJd31/SPQje+0ZrFd5yM9bgYT4eKyvrDQXd6eHyzGXfVOwgzWwLJ575qdG94j5WiFrWAwiBkD/cQ4gzZfCoiJzu96wGsAFNWIa3mZuX8JCRYhSF+C6XbBEBqTNQ45+GlejOO2eQpqVgUK5mgUhYgAkin7ZIs/cKLLccDPgzZi5f5aVwIL4qFf1IGAFUCYrEM/XgogBkIjoYfDUNNxwC2AlQgbKWBCfpVnpqLO3Y4XkJfiIGAAX2gaDcwBpnl30pjCoziHJSjYT3ylyXKcTnjdm+v4Jkc6C+KxArmIRiMhgH0CKei1OIw6G92LSm2AxIroz58IJHwUgYQc2siBERAsIDwFTVJvyJtHsNkDB4+hfhCm3D54ahJCFEJJzL/3+O7igziPZSkGm4AlQRMRrAVOU63JPmpG/NXItUqxUFiSCnNDlxs92kI2f/dbkHMMhZw+mvYssBhFd/mxeaAvEOYA0X0JeCM3OcQQQxHpZyYJEkHKHoNxeWCIHQhawID6aVONocY9DQnL0j4iu4hxAilp9qhsj3jAKZCGyBK89G0lO+DiAMOxAJSwrwIL4qNM9aUb+1sh1SLTY9JyIFlgAJJpvHW4bkpGCKlnHYkSQUuNQTjssKxXSLmFBfBT2wmhxGxFAAKWyggUhoqt4CJiiVoFYhRWiEPmC156NJNdpg+dNwQ5uhiWSWRAfnVWnccE7j0JRhCwe/iWihRgAeQiY5luZzRGPyO/AzcINNwGIgwyUsyA+a3VPIIg4VNi87i8RLdAASESLn3JOwVMjkPY6CJHFgvhsuShAnrUUywRPxCEiBkCKcoPqHGa9S6bvX5wVz4JEkBOea/1Sy2IsAOV2NYtARIsjAHIOID2IKW8SB8N7zKT33CAbP0eScs+Zm5BLzY2IiBYutoGhqNLlnsRpdQpJIoWjfxE21/hZBtj42W/jasyMhM940ywGES3sAEj0oNj42T9KjcB1T8ESWZD2KhbER67nmqt+1If3sxhExABI0Y+Nn30MHeETgDcLaW+AxZFXX/WrM2hzTyCEEBKsRBaEiBZHAOQcQLpfDc5h85WNnyP8N6um4IZbYVnJkIFSFsRnje4R87WCJ4AQ0W3wLOAo0tzcjMbjxxEMBJCUePs9fyklVhcVYVVREYRY/APB59UQxrxRNn6OVOibmTJfrYRkuE4HPG8cMrARQqSxOD66oEbQ555GhpVl/haIiBgAY0Bbayte+ud/xpKJCSxNvv0VGEalRMkzz+DDn/oUli6NjjM26+xtyBI5PKEoAtTEBYRbDwG2hFrZDivdhh2oYmH8/gxwmxDCLDbJLQhacSwIES38AMiN9oOLj4vD0nAY5d3dWC/lbe+7D0DfsmUYHh6OigC4ROSYG0Xo7zUhGVYgiHD3ATin6hFYXg2vUgHs/eybGW/aXP86AYkoljwUT0SLJADSg4uLi8OSuDiskxKb7Nu/tIN6g8HQTfdJpGYiWPcM1JIeqJZ04FwSZodfhFy2BnZpHWRWHosUYR48rJVlSLSSkCrSWRAiYgCk6BXyZjGkBhC0gsgR+SxIJAOHGAWWhhDM2wV7dAvCrUfgnG6Be7aTQdAHCUhEnb0dFqJzx25ychJHjx4185c3btyIpKQkvuhE0RQAeRYw3YtO96SZ91QqKxgAI8wJHwPgwk6og51WDrlsrQl/4dZDDII+sCwrKsNfKBTC4cONeON3e7Bv7zuQUmD79h146sntqKmpMIGQiBZxAOQcQLrnDYMXMq1fJr1x7AwwYESSUmPm7F/LSoe011z+Gw4EYReWmdDHIBhZE2oMCgqpVhqEJaMq/L21ez++9d2DOHTUw6XZGvP9xvYpNDS9gc//2TS2b6tDIBDgm4BosQZAonvVe6Xxc5Fcy8bPEeaGWwBvGjJYCUu8+1Acg2BkjalR1Dv7kGKlotZ+JKrWbd++g/j7f3ob9U3rYAVqEReXYL7vqCnsPngA4fBbCAZsbNu2mW8EonvERtC0aM01ft4oa1mMiP6NzsB1mgErATKw4Zb3mwuC8U99AnHbPggrNdMEwdk3XsTsnlfhjgywmPOg3W1Gq9ukKx5Vo3/nzp3Dq7+oR/3xTFiBOlgi4Q/vLZEMK7AFh4+n4le/OYyhoSG+EYjuEQ8B06I0oM5hQJ1FnliKPDZ+jijX6YKnLkDa6yFE5p3/tjki+NDMepdw0m1GHOLNGcDRpKW1HSdap+CJTRDixssL6hAYdlagua0ZHZ3dyMlhGyiiRRkAaR54HhwAU56H8TuMpE4DUIt54+A0Iog4VNl13HmI6FtMwQ0dNaNNdrDm3nbyGATnXa/bba6AUyxLkX4XYXwxGRmZwPkLArBu3dJGeWkYGPIwPDTKNwMRA2DsysrOxqWcHPxMShy6w+Xd+oTAysxMpKamLsp1zRNLkSmysFKs4gsfQco9C6X6IeRyCHl/Z10zCM6fq9f9lTVRuX7WXd6DE4eIoiAAcg7g/VtdVISnPv1ptNXW3nFULEdKlFdVIS9vcW5oy+wKvuA+cMP1lz84ApsefOPOIPhAhtSAmQqRY+WZHaJosyQrDUuyPPQNjd3yPsIaR162hZxsNr4mWrQBkIfxHlxaWhre8+yz5kY035R7Hq7TA0ssgbDnb+SVQfD+zHqz2CA3YrlcCWGJqFu/srISrF93BA3Np+B5G2BZwXcPFqhp2FYvStemorh4Nd8QRIs1ABLdjVE1gkvejJnvlGAlsiAR5IaPAwjBDlTcsDFmEIy8HJGLJSLbnAASjfLz8/H+99agrWM3jrYch2fXwLoSdD0vDDjHsKlyHO995gmeAELEAEjRLOTN4pCzF67n4PHgMyxIBHlqEq5zEpaVAmmve6jPxSB4d+Ks+Khfx23btuDSrIPvfL8BRxpPIeRkmBl/8cELqNvk4XMv7MDmzZv4B0oUDQGQcwDpVnpVD9rdFtP4maN/kaXDn+dNmrl/lkiJyHMyCN76M/ISZsxl3+KthOgOuXFxePyxR5CUmIS3dh9ET+8ZWBawujAJTzzxCDbVVCAYDPIPlGgxB0DOAaQ7YeNnvwJHCE64EUAQMhD5k28YBN/tjDqNU24HVsiVWC1Lon59ExISsHPnFlRVlaK/v99sK/Lz8xdtBwMiBkCiezDX+DmXjZ8jTjk98NQwpF0CIf275B6D4GVHnYPmMoir5JqYeh+mpaWZGxExAFIMOe4chgcPVXITR4sjzLnS+kUGFkavuVgOgufVEM6qPiyxcrBUrOCbk4iiJwByDiBdb1RdwBnVazZ6KyXbPUSScvtN82dL5JnmzwtJLAbBZuc4XDgosythW9x/J6IoCIAc1aHbqZK1pvVLLJz5uJA45rJvylz2zVqgveZiJQhOeZM4pdqRbKWgKAbm/hFRjARAolvJEJmoEVtYiAhT6gJctwuWyIC0ixb88kZ7EOxy200IrJA1JgQSETEAEtG8c8PNgDcDGdgEaxG1G4nGIOh5nul/uU5uwHq7km9OIoq+AMg5gDRHeQrnvUFYEMgWuSxIJP8O1TTccAtgJUIGyhblOkRbEFwtS1CEtUgTvO4tEUVRAOQcQLpen+pBo3MUK+UqBsAIc51OeN6Y6fsnFnngiIYgqD8f060MvjGJKPoCINH1jjgHcFb1otquYzEiyPNcOOGjACTswMaoWa/FGgQ9zzMtkDSxQE/EISIGwHn5sCMaVP3oV2eQK/KRL5azIBGk3D54ahBCroSQ0ddPb7EFwTb3BMa9UayT5RwFJKLoC4A8BEzXOu7UQ0GhUm7iqEeEOaHLjZ/tYHRfZH8xBMEZbxqHnL0IeyGUyQq+OYko+gIg0ZxRNYLTbhcyrEw2fo4w5Q5Bub2wRLYZAYwFCzkI9ridmPDGUCrLkcqTP4iIAZCiWZt7ArOYRbXcgvhF1H4kGjjh4wDCsANVsKxATK37QguCnueh0TkGCxbK7Wq+OYkougMg5wBSupWJOns7imUpixFBSo1DOe2wrFRIO3avNLFQguCAOochrx95YpmZC0tEFJUBkHMAaU6pXc4i+MB12uB5U7ADm2GJ5Jivh99BsMk9Yr5WSI7+EVEUB0Ai8o/nzcINNwGIgwwwgPsdBGe9Wf3MKJFlnAdLd6SUguM4EELAtrlZJwZAWmQuelPmrMdEKxmJViILEskNiNMNT41A2usgZBYLsgCCYKW9CUHEcR4s3VZv7zns23cIHR2tiI9PxMaNm1BdvQGZmWksDi2uAMg5gLGrPrwPF72L2BncxWJEmBO+3PpFBjaxGAsgCMZZcci1OO+Pbu/EiVP4z/94G7/+dSdO91xCMDiF9RvewHPPDeEDH3wM+fnsG0mLIAByDmBsG1T9OOE2mEu+JYCjf5Gk3HNQ7lkIsRRCLmVBIhwEHcfByZMnzX+vXbuWh/DorrS3d+EH//IafvbKLCYndkFYy+GEPBw70oKxsWYALj723BPIzMxksWhhB0CKbWz87J+5xs8yuIk7YhEMgkopHD/eird3v4ODh/aa79XWbkP69gRUVZWiyq7l60E3NT09jd276/H6axOYGH8CUqy68mYEBB5Bz6l4vPR/j2B1UQ6eemoHC0YMgLTwnHHDOOuOocXpxhKRhZWyiEWJIKVG4LrdsEQWpL2KBYlQEHQcB3v2HML3/3kfDrwzjYnJy7U/1NaOZLcRn8l8FhsKNiIQCLCwdIOhoSGcaOrD+fN5fwh/1xDWBrS2NOLYsSbs2LEFwWCQRaOFHwA5BzC2XPQU3gydxzEnFzuChZj0bMRz0CNi3PAJwLsEGdgMy4pnQSIUBPc3n8LfffP3OPL/s/enwXFc1/03/u1tBoN9JQAuAAgSJAGQAAiQ4CKKErVZlmVRtmX7J0d2YstKbNk/J3GVXvxT5bdJVVx25Z88FaeSSLYVPbIsW7ZlLdZi7aJIgju4giRAEgRI7MQOzPT21DlkI8MRlgGIZQCcD6sLw5menu7b99z+3nvPOfeTfGjuViQZ11f5cIv+iN7+Vrz3H+0o//wB7Ny5XQpU+BSdnZ24ejWEUDAZujZKHVR8MK14dLQPoLu7G0uWLJFCE2JXAMpUx+IkVQlCUeoQr8Sh116J5wZ7Uagb2GwEsFSTAeoZ7Ww5/bDN01CUBGiGJN2eLSEYunwWZ949i8ZDRdBxB1T1RpRvUi/Ula1w+0pw8MUA/mjvR1HRSuTmSjCI8OmBkutjJco4z1QVjuuyq4EgxLQAFBYn8Qqwy5eOXb4E6G4SjlpBnLZCqLNCWK7p2GIEkK8Z0KWDMO3Y1jm4bg80YyNUVVJGzJYQPPH75xE834CHMjQ0BT/A8b5KdJhLoBSdBQJDwKkqYMCPE7UHcOLEWRGAwqdIS0vDkiU6fL4+OPZoAtGGpg4iIyOA1FRZQ1oQASjEIAlKIi/7ptA/RcEGw48Gy8QBcxiNtoVGuw85qoZKIw5Fug8BCRCZphEEC5Z5lJsA3aiQAplFIdiwdDM+CfYiy0zGqsBZZBptODy8EQ3FZ4CQD+7pYsDtQGc70NnZKwUnfIrs7GwUrclBYlIruruuQo1YKtBx61BQMIyS4vWIixPXDmGeCEDxAVxcREb8+hUVxYYfa3UfLtsWaswhXLJNvB4cQLo5jDLdjxLdh2RVk8K7BRz7ElynDapWCFUT/6BZrfM+PxqtJdjXthWrEuJZBOqpHUDQD7dpOXAtHS5a4PcriPNLEIjwaRITE3HXXZtx4vjreOP1vQgFd0JR0gFYcNGI3GVHsPvh5di0uUwKS4h9ASg+gIuPa04n/01V0j91/1VFQb5u8NZiWzhkDuO8HcL7oUF+Xaz7UGbEIVOE4JTwUr/ovs1SGLPM8hU5yC9U0XShGw1DFbg0VAg43XD3DwI916fiNb0d+YUa7ysIo7Fhwzp890kLfv8nePut36CnJwBVVZCb240vf6UYX/7KHVi6VOqPMA8EoLC4aHWu4Kh1kFc7SNXHT1Sao+n4nJaITsfGcXMYp6wQasxh1JpBrNINbDICyJWAkahx7FY49mUoajZUbYUUyCyzenUBtmzLx4mjF9DdthJQ84HuTKD7hjh3LiF1yQVs2ZbH+wrCaFCnuapqAxITA9hY+TGam69B0zSsWlWG227bhry8ZVJIwvwSgDIFvDg4Zh3CWfsUCoxVUY/+Zqga7vQnsD/gGSuEWiuIk1aIX+dpBqqNOP6ryWjyuFjmEV4lQDc2QlFkBHW2SU1NxX2f2YrLjW/gzVc+Rm9XN1zn+kiNorYiKf0E7vlsgPcRB35hItauXc3b4OAg/z8+XlZSEuaZAJQp4MXDNacTF+16+IcDaD/ehS57L0pLS5GcnBzV95NVDdW+AMoMP+otEwfNYVywTd5yVR2bjDgeGYyTgJFP4TjdHP2rKKnQ9NVSIHPEunWr8K1vfwYp2R/g7dNv4Gp9HIwr8Vie7+COu1bg4S9s430EIVpE+AnzVgAKi4fT9nEEMYz4+lT8z9PP4fLly9i+fTvuvPNOlJeXIykpKarjkMArNfxYp/vQaJs8LUx/Xwn2I8PUUK5f/0wCRv4X2zwNuIPQfOVQ1AQpkDmkuHg1dqZfwmDrScQ3JiDnagFWrMhCeUUpcnLEd0sQBBGAwgJiwO3HWfs09JCBjv3dePPNN3H16lUcPHgQV65cQUZGBoqLJ5eUWFMUrNR9vF21LRw0h3hk8N3QII8Olug+bDDieAp5MeO6Q7CtE4ASgGasl8o412LctdCccgnLk5fiK+u/gUxVorEFQVjEAlB8ABc29XYd+tweGJficXzvEbS0tLDjcn9/P69vGQwGb+n4uZqOz2tJ6HBs1JrDnFR6nzmMY1YQqzUfqow4DipZlILDaoDrdEHTS6Gq6VIZ55grzmV0uG1YoRaI+BMEYfEKQPEBXPiQuPfBjzK3CjU1h1FTU8N+f4ZhoKenhx3e09LSbvqOZVlobm7m7y5btizqxfEzVQ13+RPYH/DUjYCR47zKSJBXFqk2Alih6ZxuZnGUvQM7dIiXjtJ9VVIZY4Ba6zD/LdPlfgiCsIgFoLA4WKWtgXJVR92+X6OpqQkbNmyAqqo4evQoC8DwqEfHcfj9F154Ab29vdi6dSu2bduGwsJC+P3+qH4vWdWw1RdAueHHeSvEOQTrbZO3paqOzUYcrz3sX+ABI47dDMe5ClVbDlWTpcXmmi6nA5edS0hXMrFczR91H9u2cfHiRRw5coRtZWhoiDtAOTk5qKqqQlFREXR9/jThZMMffPABd/Z37twZddDXQjsHQRABKCw6qNFVbQ2Ha45g37598Pl8WLNmDT/YamtrefQvIeF6YILrujh58iT+67/+C7///e95ivj999/HI488gr/6q7/i702GgKKyH2Cx7ueVRWrMYVy2Tbwc7EemqaGCA0b8SFQXphC0zRuJn41NUhFjgCanEUlKMjbolfApvlH3OX78OJ599lns3buX/0+dnp6eHhaGt99+O7797W9j48aN8+aaGxoa8Itf/AJZWVmoqKiYE/EVC+cgCCIAx0B8ABcmITeIYXcYrc2t2LNnD49sFBcXY+3atSz+kpKSePTPG9E4e/Ysnn76abz++ussEEk8NjU1obW1lX0Gp1zhFQWrdB9vV2wLB8whXnv4z6FBXn+4VPdjveFH+gIKGHHsDtjWRShqJlR9pVTGGCBdyUC1sQPL1bzRBbtt48MPP8Tzzz/PIu/RRx/l9V+bm5vx1ltv4dy5c7hw4QKnT/roo49YJO7cuZM3gmyMvl9VVYU77rgD+/fv5/+vX7+eO1nUuXIcB2VlZdi8eTNSUlL484n2of/T7x48eJCj9+n/S5YsQWVlJdsyiVQ6Bp0TnZthGOjs7OTOHp3T0aNHkZmZif/5n//Bgw8+yDMAHvQZfZeuk64jNzeXXUDoWJ988gmqq6s5W8ClS5dw+PDhkbaAfoeuMz09ncvt/PnzOHDgAAeX0e9TZ5E+p/aDxF/kOdD3p3JN27Zt41FYQRABOA2jQ8LC5Zh1CN1OF5oOtPHDiu43NearV6/mxjUtLW1k+pca42effZZH/qjxXbZsGTfM9Je+k5eXNy3ntFTTsVtLQrtj4ZgZ5ITSn5hDOGoNo0jzccLp7AUQMGKbtSTBoRtlUMYYbRJml+Va/oT79PX18cg32QWJspKSErabiooKtgf6Pwmkd999Fz/5yU/wox/9aEQAkmD6p3/6J/zN3/wNu06Q+PrHf/xHFnK0tbS08AgjCaG/+7u/w2c/+9mo9jlz5gx++ctf4uOPP2YhRB02OkfqzD3++OPYsWPHyHFIQBUUFHDnjgQVfbe9vZ1dPurr63Ht2rWbrpcE2wsvvMBiMSMjgwVgW1sbfvvb3+J3v/sdnnrqKc5199xzz7EApjajt7eXBfHXvvY17N69m4/7zDPPsH8x7UvlQ+f4pS99ictxtHM4derUlK6J2i4RgIIIQEEYh2tOJ45YNehr68fFva3cQ1+5ciWLubi4OHR3d4/4/zU3N+NXv/oVfv3rXyMQCPBDrqGhgXv21dXV3OuONhAkWrJUHff4r/sDnrSCOG4GOWr4lBVEgWZw0unlqj4vOymu0wfbOgNFSYKmr5XKOE/QNI1H3mg7ePAg/vmf/5ltYcWKFewDu2XLFhZVoVAo6g42iR7HcVjEkHAhIUWCiwRUVVXVhPuQIHrzzTf5/7T/17/+dRZZL774Ir+fnZ3NI2Z0DDpWMBjk45AAJbFG13TixAkWsPTd8NE/gq6PhO6f/vQnngHYtWsX6urqWLStWbOGhdf777+P1157jcXoY489xjMJ//3f/82dxfz8fBa+L730Eos2+o2Ojg6eRaitrWURSAI5/BxWrVrF1zOVayIBKAgiAAVhHLzEz0PHTBz65BBM02QhR4KOet09PT3cmFJvnXr79NChh9CDDz7IPXHahxpiEozUYN8kcG64DEyHOEtRNWz3xaPCiMNZK4Qj5jDO2SbODZlYpurY7ItDoeaDbx4JQduqg+v2QTeqoKji7zTXdDvX0Om2I1VJQ4aaNe6+ZCNPPfUUix7qNL366qtsO5mZmSxWSNCUlpZGLQAJEl133nknli5dysd8++23eUq1qalpwn2OHj2K06dPY3h4mIXUQw89xKNlnZ2dLLxIrDU2No78Jp0b7ePZLNkwdd4yMjL4vciIf/q9cAHY3t7O4q++vp7bArpuep/aCzrfP//5z9x5pDaC3tu3bx/vT+3IbbfdhgceeIA/I2FJn69bt44FY/g50L63ck2CIAJwOkcsxAdwQeElfg5eC6F9/zVuoPPy8ljMrVixghtmasSpUd6/fz83/tRoP/roo/wgev7553mf22+/nb/j9/t5NJAa5r1797IfUEJCAjfI1Kunh4h6i4Ec8YrKIrBU9+OibWK/OYRm28IfhvuRpWrYaMRhreZDQowHjLhuCBZP//qgGeVSGWOAY/YBXLGbcIdx74T7kuD5/Oc/z6NNnkgjMfTWW2+xiwTVcxJVo7Wd1IGiLZLExETeiEAgwBuJn/D8m2PtQ4Ksr6+PR8i89E1EUlISb/QZbR4k8NLTo883SccjAVhUVMRCjwQd2TldZ1lZGZ/T4OAg/39oaIhtn9i8eTMLupSUFP6czpfaBNqPzpM6mmPhnfNMXZMgiACcRA9VWFicv5H4Wb/sx+XT11NZUINMDzUeEbnRgz9//jxP9XZ1dfHIxle/+lX2bTp69Cg37iT+1q69PoVJPfLnnnuOxWFbW9uIAKR9Hn74YX4g0EPgVjEUBUW6j7fmG5HDFywTbwUHUKMMcbAIicS0GA0YcayLcJ12aHoRVC1LKuOcj/51oc46hYASP+HoX0dHBwugy5cvs714NuMJoJMnT47Yi6ZpLP5IpNFmWdZItPCnzqG7mz8jsdPb28vihjpi4fYy1j5khySU6Bzoffod+m1vn5ycnJEoftyYxh4tYIvOdayOfnFxMYvAN998k6d6ydbXrVvHnUESZPT7JNJ27drF2QBM02Q/Pnp+0DGpE0ltDLUpdP10rtSGtLS0cAcx8hzoeNNxTYIgAlAQwjDdEE5aR2HAh+1ZtyPuvhTuPd91113sAzgwMMAPGmp06+vruXf9+c9/nh2v6b29e/fyVMw999zD4s5b7Jwa4GXLlvHDory8nKePz549y74/Fy9exA9/+EOeAprOTsUyzcAXNANttsVBInVWCB+HhniaeI3uw0Y9DktiLGDEupH6RZPULzFBnX2SXSEqtM3wK3Hj245pcuDBiy++yB2az3zmM2wfJEoOHz7M4iUzM5NFGdkUiaPa2loeHQwGgzh27Nio9f/48ePsE0f298EHH7CALCgowPLlyyfcp6qqikci3377bXbLoE5XXFwcd9RIcJWWlvJ+443w6brOou7gwYMc7BE+gkksWbJkZBqYjkvX8Mgjj3Dnj75fVFTE13j69GkWwdSG0LGoPaB2gsQiicdPPvmE2wU6d2oX6Ldyc3M/dQ7UfpSUlEz5mgRBBOA0IlPAC0gAwuIktwElHlWFm7Hu6yW47777uEdNDTL1yru7u3lfeu/ee+/FX//1X3Mj/8wzz3ADnZKSwuLP83WinjrVkS1btvADKisri/enB9WPf/xj9peqrKzk1Bn0G/QwpAafGvXpgETefVoiNhs2B4ycNIM4YtLfEFbqBqqNOBaLcz76Z7dw8mdFzeHkz8LcMuwOoc4+hTjEYY1WMuH+JO7IHshGjh49ymInEAiwMKT6v2vXLvaLy8nJYYFInSoSgP/xH//BIoiEDtlU5FQwdZ6OHDmCl19+mUcQN23axOKS7GiifUgI3X///TwVTALrX/7lX0aWcfzsZz/LUbjhx4mE7JXO7cyZMxzoRbYdKQDpeCQASbyRCCNBVlZWxuKXxCC1HyRC6Vp/8pOf8CgffWfFihUshOnz5uZmHj2lz6msVFXlmQFqJ6hjGXkOt3JNgiACcBqQKeCFhwESRLdBhzEi8goLC0c+9/v9HN1HDTw1yt/5znd4mqauro4b49bWVh7JIwGYnJzM02HUu9+/fz9HP1KP3stZRgKPtoGBAf4e/aWG/7333uPe/o4dO3gUYSIh2NHRMeJoP160cZqqYYcvnkf+ztrXA0bqrBBvyzUdW4wARxAbc1SvLV72zYHuq4SywFc5mQ802hfQ415j8Zeqpk1sO4bBdZ8E0rFjx3DlyhWu89SpWbJkCduMNypGNvO3f/u3PDJIgsjrLO3cuZNfh6+as2HDBhY29fX1/H/qKJHAC5/WHG8fOt6TTz7J/ye7InHprUxCtkw2R+f9D//wD7xP+G/TOX//+99nMZuUlPQp8edBbQF1BD0/4a1bt448H+hcfvCDH7BAbWtr49+jciARTOWSkZExcn5Xr17l8iLhR+fnBaFEnkNxcfGUr0kQRAAK829EYniYe7nUuFEjOZE4p30mm37FUAwWgWNBPfYvfOEL3MguW7aMp2Oo8d27d+9IHi96CND7JOjeeOMN/PSnP+U8gdTQU8/8gw8+4FECEm3nzp3jc6RefSAQ4NenTp3Cz3/+c/YxfOKJJ3i6Z7wyefvtt3n0gBr/O++8c8Kcgwmqio3q9YCRC3YINaFhNNkWmuw+LFE1ziW4RvdxYMmsjf4512Db9VDUNGi6pKqIBeqdOiQgEWVa9Ov+ktAgwTVRpC91gEig0BbOvfd+OtAkMTGR6/VDDz005vHG24fai4KCgnGnRamzRdto9v7AAw/wNh5eu0DbaMKYBGpkCpnwEURqT8ZaKWisc5jqNQmCCEBhXhEKhfDRhx/i3XfegeI4CEzQozV0HVXV1di2c+dIdOBEDLoDcOEigHioY4gfasxXr159Uz6t7u5ujna8du0a98xJAFKjTULuvffe4ympFStW4Itf/CJP9TY3N/NUEQnGvr4+FoP0wKTzJOG6ZMkSFockKu++++4RATg0NMR/w53fBwcHWUT+5je/YcFIx4o26bRPUbBW92ON5kOzY2F/aIgjiN8IDvDrDYYfJbofqbMQMGKbJwF3CJpRBUUJSIWPAfLUQqxQVyJHXSqFIQiCCMDREB/A2eHShQs4+dJLWN3WhrQJ0pmc1XUEH30Ua24Iq4m45nTimHUQKWo6yicx4sEVUtd59IFEGwk9EnlEa2sr+0PhRqTgI488wpGRXV1dLPxIAJLg27JlC08HeVNGOTk5PM3T0dHBopLE75kzZ1g0DgwM8JQS/Qbt4/f72ZFc0zQeRVy2bNmky5V+d7lmYHnAQKtt8dTwWTuED0NDOGwGsVb3ocLwc/LpGbEfZ/C6AFTioRklUtFjhFJ9btPwRDOFKdOcgiACcE4QH8DZg/2IMjNRlZKCB9raUDbB/i8BOH8jxUQ0nLJrcdw+gu3KnWOO/o0FCcydO3eybx9uTNngRnQgiTlVVVnMHTlyZETMffDBBzyd7S0Vl5//v8tskbCj7507d479fl566SW88sorvK4niczdu3fz94LBIEcSZ2RkcBQyPQCTkpJuqZyzNR33a4nY4ti8ushJK4hD5jBOmEEU6gY2GwFejm46sa3zcN1uaEYZ1Ch8zYTFQTRTmDLNKQgiAAVhygy4/Thnn0aCkog1WvGUOwMkxMJZuXIlT/u2tbXxdPDPfvYz9tnp7u5mEeg4Do/8RS4Vl5mZyRsJxD/96U/sR9jQ0MBpL+644w6OGszKyuLo4WeeeYaXo/vWt77F7+v69JhHmqphpz+e/QHrrCCOWkGctkK89vCKGwEj+ZoB/RY7Qa5rwzKPANCgGxulMsYAnU47htxBzvsXUOKlQARBEAEoLEyuJ37uRZlehSQ1ZdqOGx8fz6KMhOHevXt5FJDEWmdnJ08NDwwMcPSgF2Xsuu6If2BdXR0LRBKKubm5nGvw3nvvZQGYl5fH++3Zs4eDP5YsWcIpL2aCRFVFlS/ACaQbLBMHzGE02hYa7T5kqxqqjDhOOh2YYsCIY1+G67RA1fKhajlSGeeYkBvEfutj2K6Fe3yfkwIRBEEE4PijGOIDOF8JT/y8XquY9uMnJydz/rOtW7dyEEdcXBynvjh//jynfLhw4QIngs7OzmafwDfffJMFoJfOIiEhgdNbfO973+MoQZ/Px/XtwIEDLCpN0+To3+rqah79o+OdPXuWReHatWtHElHfKn5FRbHhZ3/AphsBI5dsE68HB5AeGkaZ4Uex7uO1iSfD9dQvgO6TxM+xQJNzCfV2HQrVNTL6JwiCCEBh4dLoXOSF7gu1NRMudXUreOuTEiUlJfjmN7/Jok/TNLzzzjsc7PHWW2/hxIkTHMyxc+dOFoiNjY2cE4y+460X3NrayjkHSURmZWWxDyGJQ9u2eWWAZ555htcXfuKJJzg4ZTpRFQV5moG8gIEW28Jhcxjn7BDeDw2yr+A63YdyIw6ZUQhBx26DY1+CombxCKAw99Ra1wV5mV4phSEIgghAYeFiuiGs1zZinV46a7+Znp7OU7oVFRW8QgCJthdffJEF38aNG/mzsrIyvPDCC7x8FAm+/v5+Hk0kDh8+zAIwGAzy/rfddhuLy8uXL/OUcE1NDQtA2k6ePMmpYlatWsXRxlOJEh6LHE3HA1oiuhwbteYwTlkhniI+bgaxSjewyQggd5yAEcs8xmuv6EYFFMWQyjjHtDutuOI0IVNZgqXqCikQQRBEAI6FRAHPf/K0lbzFKwmz+rt+v59zCWZlZfG0cGJiIk8Rb9u2jVNa6LrOI4Ou67IAbG9vZwHY2dnJ4q+uro79CUn8FRcX834k/Pbu3ctLRxH19fX8/3PnzrHw+9rXvoZHH310WkUgC1pVw53+BFQZAZy2gqi1gjg5EjBiYIsRx6OGWpi9OE4PHOssFCUZmr5GKmIMcNI6Bhs21usV0BRNCkQQBBGAwsJltoVfJCkpKSziKisrOegjEAjwVC+9TktL4/+TAKRt1apVvMQWCcCBgQGeJt6+fTunfmlpaRmZFo6Pj+dVB3bv3s1pZ55//nn+7Le//S1PFZO4pP1JdC5duvSmxNIe0S4vF06SqqLaF2B/wHrLxEFzmBNL05aj6thkxGG1biBOUWFbZ+C6/dCNLVDURKmIc8yA24dGpwEpSiq7QwiCIIgAFBYktmsjhCA0aPApc59ANlKEkQhcvXo1Rwl3dXWxAOzt7WUhd+LECRZvJP685aXCp4VJUH7nO9/h4JPu7m4cP36cP2tqauLF8vfs2cNpZegYW7duHVk032Mqy8uFQwKv1PCzP2CjbaKGI4dNvBrsR4apoUxXURiqRSL80IwNUhljhFKtglMh0SYIgiACMAokCnh2iI+PR1tCAl5SVXwywUogZxQFOX7/mKsDnLZrOd/Zen0jMpSsmLze6upq/P3f/z3nECSx1tjYyGKup6eHPyMBmJaWdtO0MP3/3nvvZWFnGAYHl9CGG0vJkQj0+Xycm5BEHolB0zTxjW98Y8TH8FaWlwtHUxSs1H28XbUtHDKHcN428e5wN/aZSSjxFaBSSUWGVO05J0FJQpWxVQpCEAQRgNEgPoCzy5p161D92GO4UF+P3gnKfoWmoXLr1k8lZ8aN6a6D1l44cFCJLTF7vcuWLePl40isqarKo3t33XUXdzg2bdrEASBE+LQwvX/bbbexmKP9amtrWTQS69atw2OPPcZ+hu+//z4LQxKNe/fu5eXkPAE4HcvLRZKr6XhQS0KHY+NQ/x6choNDKMWpoR6s1nycTzBHk8F9QRAEYR4IQGF2WVFQgC8/9lhUy7uROA8EAhxgEcl5++yMJH6eCUiE5ebmjvzfE3AJCQkcQDLetHBrayuLu4aGBt539+7deOihh1jU1dfXjwg+0zR5IxobG1kUTufycuGku63YqZxCpT8b9b40HLeCvJ22ghwossUI8EojqnSuZo1Opx0uXKQrmZNeBlEQBEEEoDDjkKiLj4+/peTGM534eaYh4VdW9r8rIY81Ley67k1+gVVVVewTSOLPsixOP9Pc3MwCc/ny5bz2sG3bPCX89NNPz8jycuDEzwf5b5q/GluNAMoNP85bIc4h2GCbvOWqOjYbcZxKxi+CZIbFXwdqrD1IU9JRrcuauoIgiACcFOIDOH+YrcTPs0VycvKo08JdXV03+QWSMCwuvr7OcVNTEw4dOsQjhCT+SFBmZGTw+x9//PGYy8uRQKTfUVV1JCn1ZHCcTtj2BShqOjT9+vJ3AUXFBiMOxbqfVxapMYdx2Tbxx2A/Mk0NFbofa3U/RxgL089puxbn7TPYod8lo3+CIIgAjBbxAZx/HLUO8N8KbfOCuJ68vLybpoUzMzP5/XC/wM2bN7MATExM5PQyNTU1LPLoNYnG6upqrstjLS8XCoXYl5COR8KSRGdRURGLzaVLl0YtBm3zBOAOQzO2QFFunprXFQWrdB9vV2wLB80hTiXz59AgJ5cu0f3YYPg556AwPQy6Ayz+4pUErNbWSoEIgiACUFiYhNwgUpU0ZGlLkKsuWzDXFTkt7Lou+vr62FcyPT39Jr9AL19gQ0MDL0VHnxUWFo65vBxx4sQJ/Pu//ztef/11XpGEhGR+fj4Hm+zevZsF5kTT8q7TD9s8DUVJgGYUj7vvUk3HQxwwYuGoGeSE0nvNIRyzhlGk+VBpxCFbAkZumQv2efaFLdXKY94XVhAEATIFLNwKm/Xb4FN8C366q7y8HE8++SSuXLmCHTt2sE8f1dODBw+y0AuFQjzKR0IvLi5uzOXlPL/AN954g0cDv/SlL2HlypU8IvjKK69wupgf/OAHnCtwvITRtnUOrtsDzdgINUqxkanquMd/3R/wlBXkJeaO8SojQRRoBrb4Aliu6jISPwUc18EJ6whUqJwKSRAEQQTgJJAHz/zCp/hjIunzbNTLgoICrFixgnP/adr1adOuri6e+j179iz7/JH4W7t27ZjLy7FQcBxOJk3bsmXLePWRr3zlKyz8/u3f/g2vvvoq/87q1atZGI7eQbJgmUfZdHVj8oE3KaqGbb54lBtxOGeFcNgc5nyC54dMLFN1bPbFoVDzwSf2GDUtTjPa3BZe83eJmiMFIgiCCEBhYWK5Jhy4JEEWjbM7Cb/ExJtXdSgsLERFRcXI9G9CQgL2798/6vJy3jHWrFnDyaAvX77M08B5eXm8zxe/+EUeYSQB2dTUNKYAdOxGuE4bVK0QqrZkytcTr6gsAktuBIzsN4fQZFv4w3A/slQNG404rNF8SJSAkQkhW6jSt2KZmieFIQiCCEBhYTLg9uOodRDxiEe5vmnRlkN6ejoefvhhFmq6rqO0tHTMPIKeW4OqqjyF/Nhjj+GZZ57BO++8w/6F3/zmN1lIPvXUU+wXuGzZMv4OCcKjR4+yICTxuGLFChQVnkb2EsAXmJ6yNxQFq3Ufb803IocvWCbeCg6gRhniZejW636kScDImOSqS5GjLuWlEAVBEEQAThHxAYxtztt1OGzt48TPiznVhaIoHCV89913j7xHwm+s5eVqamp4tZBNmzbhq1/9KvsKPv/88zyN3N3dje9+97v4whe+wLkFyQZOnjyJF154gUXi5cuXRwTgxvIgdu9+ELfdnoXANFvvMs3AFzQD7baFI9Yw6qwQ9oSGcNQcxhrdh416HJZIwMin0BQpE0EQ5mHb9aMf/eivABTM9Ym0tLSwP1Vubu5IxKQQW5huCO+bb8GChV3GZzjlhfC/DA8Pc6CHz+fjRNGf/exneVq4vb0dv/zlL/GLX/xixC+wtLSUE0ZfvXoVR44cYZ/CoqIinlZubGzkEULa/8qVKygpKcEdd9wBx+nCnk9O4NIlDfkFq3n6OBrf2aamppF1i0dbzSWSBFXlFDJrdR/iFAVdjo0LtoWTVggtjoVkRUWyjAjCcW30uX0wYS4Kf1hBEBbUIMZFEYBC1Fx06lFrH0KBtmpRT/+ORUpKCgd8bNiwgSOHly9fzgJN13WcOXOG1wzu6enhxNArV67kfXt7e1kAtrW1sSisqKjgYJCnn36aVxeh43z/+9/HN77+MEpL+nHxwim890EDjz5u3LhxRNC5rstBJl5yaY+hoSG8/PLLLCbpeOnp6ZyaJhriFJWXlCu+MQXc7dpotC3UWkFctE0Wh8mKBm2RBoyQPZywj8BQDKSrmWIAgiDMKwEocxdC1HiJnzcukMTPM0FkHkEiMTGRVwKpr6/Hu+++i5/+9Ke8ekh+fj4LQ8uyOFAkPj6eR/z27NmDixcv8neDwSC/vnSpHyvzFaxbV4xXX/+Icw+SaExOTsaFCxfY9/DSpUs8yldSUsL5BLOzszkX4VtvvYXf//73LAArKytHopKjviZVxUY1DqW6HxfsEPsJNtkWmux+LFE1ziVYpPuQsMhcAg5ae9HiXMF6TVK/CIIw/xAfQCEqWp0ruOo0IVtdilx1uRTIJNmwYQO+973vcUoZEoHPPvssjw4ODAywANy5cyfnEqyrq8Pp06d5KpkEIu3z3HPPYe8nGqoqV+HESZNH+uhz+t6JEyd4evnVV19lX0ESgGvXrsWjjz7KQSr79u3jVUm8QJVbGV33KQovJUdbk22iJjSMC7aJN4ID2B8awnrDzyIxdRFMD7c6V1n8Zau5vAmCIIgAnCKSBzC26XV6UKQV8yb3agqGpus8veulfTl8+DD7+gWDQY76veeee1i4HTt2jEf2DMPgAJP77rsPRw7/CXv2vIf/+X8Pobt7kAVgeno6T+/+7ne/42CSnp4e9iEsKSnhoJOXX36ZPydBeeXKFZ5y3rZtG//WdLBcM7A8YKDVtnDEHMZZO4SPQkM4YgbZd7DC8CNLXbgTDLXWIf5bplWKPQiCIAJQWLhkq7nIUrORrMgyV7cCCbddu3ZxIAiJNhKAKSkpPE1MQsLbfD4fC7qHH34Y27e04/bbFBw8shR79x7iqd7CwkIeKXz11Vc5gKSsrAyPP/44jyS2trayD+Gvf/1rFoFEdXU1tm7dOpLIetrqhabjfi0RWxwbJ8zrK4scMof59UrdQLURh6WasaDuYY/TjUt2A1KUVORrq6RSC4IgAlBYuCSrqVII0wgJvNGCMfLz83lramriKd0rzYeQmtSFioqNWF++G3fffZl9ApOTk/Haa6+xj19aWhoefPBBfPnLX+ZVSbyRxHfeeYdTzCxdupRHHem4M0WaquF2fzwnkK6zgjhqXV93uM4KYYWmo9oI8JJz+gIYLTtrn8IQBlGqVSCgxEtlFgRBBOB0ID6AsXc/HDigx7aqSOqPmaakpITTxzQ0NODPf/4z4F7EsqU2uq4lIzNrEPfeey92796Njz76iHMOWpbFvn07d+5k8Ycb7hR03+gzx3E4IGTbtm0sOmeaRFVFlS+A9UYcGqwQDpjDHDncaPchW9VQZcRx0un4eRow4rgOR/1u0CqxTiuVCisIggjAW0X8aGKTersO7W4rVmvrkKVkS4HMMN4KI2QPL7/8En770gcIDlsImQpKShrYh3D16tUc0dvU1MTfIQFIm0draytHHPf393PKGRJ/9J3ZxK8oKDb87A/Y5FioCQ1x6pjXgwNIDw1hAy9D5+O1iecbRVoxVE2V0T9BEEQACguTkBtCjbUHvW4P1mglUiCzRH5+Pr7+9a+jZJ2JQ4dS0d6RC91IZRG3fv16HskbHh5m/0FVVTknoDf6Z9s2r0dcU1PDo3+VlZXsbxgIBMb8vQsXLnAOThKLa9eu5XQ004WqKJxLMC9goMW2cNgcxjk7hA9Cg/x6ne5DueFH5jwJGFEVFQlIlEoqCIIIQGHhctm5gE63Hau0NchQs6RAZpHUFB07tgewpfpu2PgSNC2ZhZkXxJGVlcXCr729naOJL126xKODx48f58jguro69g3cvn071q1bN+bvkGD8+OOPeeWRpUuX4oknnsCdd945I9eUo+l4QEvklUWOm0GcsoI8RUyvC3UDm40AcmN4qTnbtWHBhAqNp4EFQRBEAE4j4gMYO3iJnyu0aimM2RYb1lm4bh/i4qpgxH06dUt5eTlHE1+9ehUffPABC8P8/HzU1tbiww8/5BHCrVu3sgBMTBx7xOrKlSucK7CmpoYFIG0nT57E4OAgVq1ahS1btkxb6hiPdFXDHf54TiB9xgrimEViMMRBI3na9cjhfM2IuRVGTtpH0eNcwwa9CqlKmlRSQRBEAE4H4gMYW1xP/Nx8I/HzMimQWe0EhWCZxwD4oBnlo+5TUFCAb3zjGzzKd/DgQZw7d46XU7x06RLnAUxKSmLxt2HDhnE7WyT89u7dywEjRH19Pf+fjkfC72tf+xonlZ5uEUgkqSo2+wLYYPhRb5k4aA6znyBtOaqGTUYcr0kciIGAkX63l1f+UKCgUtkilVQQBBGAwsLkqHUQLlxUaJtEnM8yjnUJrtMOTS+Cqo0+9a5p2khi6YaGBly7do1H/V588UUWb8XFxRz8kZo6dvqe1tZWXkLu/PnzPL1MYnH37t0IhUKcXJo+++1vf8urh+i6zgIzMTGRRwnH8ymcLHGKilLDz/6Al20T+zly2MSrwQFkmMMo169/ljyHASP19lkMuP0o1zYhQUmSSioIggjA6R/9kCnguSbkBmHAQIlWhnytUApklrHMg9dFnrFpwn3T09N5I7q6uth+UlJSWLSRQByPw4cPs8gLBoMcKPKd73yHp5W7u7vZl5A+a2pq4lVF9uzZw0KTBODWrVt5beOCgoJpvW5NUVCg+3hrsS0eETxvh/BuaJBfF+s+lBlxyJhlIWi5Fk5ax6BDR4leJhVUEAQRgNOJjDLFFuX6JvgUP/xKnBTGLOLYLXDsJihqDlRtcmsukxD83Oc+h40bN3J08GiJpj06OztZ4HnBIvfeey8LO8MweDSRNmJoaIhFoM/n4yXq3n77bRaDpmnyFHRycvKMlEOOpuNBLRGdjo1j5jBOWyEeGay1glit+TifYM4sBYw0O40cDJWnrkSmukQqqSAIIgCFhQkJvwxFon7nAit0iGQgdF8llCn4vmmahhUrVky437Fjx1gADgwMYNOmTTwCSGLOdV0OJDl+/Djvt27dOjz22GM8nfz++++zMCTRuHfvXl6/eKYEoEeGquEufwL7A5IIJAF43LoeQZx/I2AkTzM43cxMUWsd5r9lepVUUEEQRAAKgjC9OM412HY9FDUNmj5ziZt7e3tZ/J04cYKndMODRVpbW1ncNTQ08Aji7t278dBDD/G0cn19/YjgM02Tt3A8942ZGM1PVjVs8QVQZvhx3grxesMNtslbrqpjsxHHqWTipjlgJOQGEVACKNXKsVzNl0oqCIIIwJlCfADnjpAbwhGrBj7Fh426pH6ZbWzzJOAOQTOqoCiBGfudxsZGHuHr6elBdXU1C8C0tDS2vXC/wKqqKvYJJPFnWRZHGDc3N/Mo4/Lly5GTk8N5BL0RQRKPCQkJnD6moqKCg0VUdXoFWUBReRWRYt3PgSL7zWEOHPljsB+ZpnYjYMTPEcbTRaW+hUfFJfefIAgiAGcA8QGcey47F1BjfcyJnzdCBOCsdnycwesCUImHZszsqivJycm46667WPBt2rSJfQZxI4gk3C+QhGFxcTF/1tTUhEOHDrHII/FXVlbGq4+QKHzuuec4aritrW1EANJ3H374YV6HeDojhkcaLkVBoe7j7QoHjAxxKpl3bgSMlOh+Ti+TfosBIyT80hW/VFBBEEQACgsXSfw8d9jWebhuNzRjA1R1ZpMM5+Xljfj1kWDLzMzk98P9Akm4eUmkHcfhfIH79u3j1yQaq6ureXRP0zTOEUhCsby8nJerO3v2LF566SVcvHgRP/zhD9m/cCY7eEs1HQ9pSehwLBw1g6izQthrDuGYNTwSMJKtSVMnCIIgAlD4FJL4ee5wXRuWeQSABt2onJXfJOFXVlYWdg4u+vr6OJdgenr6TX6BLS0tLAwbGhqQnZ3NnxUWFvJqIfS9LVu2YOXKlewzWFRUxCuT/PjHP+agkcrKSh5hpN+byTWHiUxVxz3+6/6Ap6wgLzFXeyNgpEAzUO0LYIWqRyVGHdfGAWsvVKjYbGyXSioIggjAmX8Yig/gXCCJn+cOx26C67RA1fKhajlzdh7l5eV48skneXm4HTt2cBJpsseDBw+yAAyFQuwXSAKwvb0db775Jvbv38/v5+bmYv369Sz04uLieBsYGOApY/pL/w9fc/jb3/42C0ciEAhMa51LUTVs88Wj3IjDOSuEw5xP0MT5IRNLVR3Vvjis1Hzwj/Ob1BmqsT7GMjUPmyECUBAEEYAzhoiOueOa04lLdgPSlAxJ/DwHWKHriZ9136Y5tb+CggJOITM0NMRTu7jhF7hv3z4eucvIyGDxR/v84Q9/wE9/+lMe1aP3s7KyeORv1apVHB187tw5zimYkpLCAi98zeHly5fztPOePXtYPO7atYuP6/dPr69dvKKyCCzR/bjEASNDaLIt/GG4H1mqhgrdj7W6H4mjBIzU2l7ql0qpoIIgiAAUFqgAdLuwVF2OlVqRJH6eZRy7HY59CYqaxSOAcw0Jv8TExJveKyws5Kheb/q3vb0d7733Hk8Jkxj84he/yFO9zc3NePfdd1nk9fX1sRgsLS3lUcG33357ZM3hYDDI4q+lpYX/n5yczP6IM4WhKFit+3hrtk0cMIfRYJl4OzTIr0sNP9brfqTdCBi55nThsn0BqUo6VqgrpZIKgiACUFiYpCnpqDZ28ANPmF0s8ygAE7pRDiUG04ykp6dzNO/KlSt5PWASdIcPH2bxRhQXF+ORRx7hoJCuri4WfiQAFUXhKd7NmzdzdPAnn3zCeQT9fj/7Cd555538fdp/48aNI6N/ruuyKKTv0+9NN8s0g7f2sICRPaEhHDWHUaT7UKnHocE5gSCC2KhVwy8RwIIgiACcHcQHcA4EoJohhTAXdd3phWOdhaIkQ9PXxuQ5khDLzMzE3XffPfLekiVLOAegqqro6OjAkSNHeCr3zJkzPA3c39/PkcHbt29Hfn4+j/6RABweHmZB+OSTT/JawvT9np4eFn+O47BA3LNnD+cpjIuLY7FJ+2dnZ495fvT7pmnyORpG9AI6S9Vx742AkZNWECfMIAvCoBvEFfcU4hDAGq1EKqkgCCIAZ+NBIwiLCds6A9fth25UQ1ET5815r1y5kqd929racOrUKfzsZz9j/8Hu7m4WgSTmSLht27aNR/hI/NH7aWlpuP/++3n0z4sAJqFHnb5jx47h5z//OV577TWeSiZRuG7dOvyf//N/8OUvf5kDRyLbCRKUJC737dvHwSl03Ly8vEldS6qq4TZfPCr0OJy1Q2i1W5GMNBTqOUiZ4XQ8giAIIgCFOaHJbuT0L8u0POSoS6VAZhHXDcIyawH4OffffILE23333cfBH3v37uVRuNTUVHR2dvLU8MDAAIu3wsJC/jx8zeGdO3eyELypHjY14YUXXsCvf/1rXqauqKiIp5bpuH/4wx/YL5FEJ4k/EnrZ2dn8enBwkINNfvOb3+D8+fPsczhZAeiRoKrYqMahR0vBgLsDKWqKVFJBEEQAzu6DUaaAZ4sD1h60OM14WH1UCmOWcawLcJ1OaPo6qFrmvDv/5ORkjt7dunUrRw3HxcWxbyAJsatXr3J0cG1t7ZhrDnvYts3pYV577TVcu3aNU9F861vf4uTRbW1tnDbmV7/6FU8XU9vw3e9+l30O6ff8fj/uueceFogpKSk87XyrpKipSEGqVFBBEEQAzhYyBTy7tDpX0Ow0YomaK6N/c4BlXk/9ohmb5vV1BAKBkaXeSkpK8M1vfpNH6BISEni94dHWHA6HxOKePXs4opg++/znP89TvhkZGSwOjx07xpHFvb29fNy+vj5uKxobG3nJOtrv8ccfZzGYlJQkFUsQBGG+CUBhdpHEz3OHY1+BYzdDVZdC1RaO+E5PT2cBV1FRwf55ruuyiItcczgcEnEnTpzgyN/S0lJOQE2iLrxTGAqF+PPNmzfz57qus2h8+umneer5W9/6Fk9J30rUcJ/biwb7HBKVJF4LWxAEQQSgsODoupH4OV0SP88JVugQ/9V8VQtOfPv9fl4P2KOwsPBTaw6Hc/XqVd6IdevW8ebR0tLCU8mDg4Mcebx9+3Y+dnNzM08b79u3j9//zGc+w/vfylJz5+zTOGTuw0a9GtCkjgqCIAJw1hEfwJnntF2LIIaxSd8miZ9nGcfphG03QFHToekLX3xHrjkciW3bHDVMJCYmjiShtiyLBR5t9HllZSX7BcbFxeHAgQMcXGKaJgeFVFdXs5AOX2ruiSeeGMk1OBEhN4hTVi0cODL6JwiCCMDZRqYhZ4eQG0LQHcYqdS2KtGIpkFnGNk8A7jA0YwsUEd+8ksjy5cvZp+/UqVM4dOgQrylMr1988UWeIk5LS+PRv3Xr1vHawp988gkHm2RlZfH7a9as+dRSc/n5+exXODg4yMEhJCDpt9RRln1rdC7imtuJVdpayYkpCIIIQGHhUqyVwVAMJCnJUhiziOsMwDZPQ1ESoBmLW3xblsV/Sew99NBDPN178OBB/PjHP+bpWxJ4+/fv52Xjtm3bxkIvMTERH374IQtAen/jxo0jo4Ik/Lyl5mg7fvw473fp0iUWkPfeey/+4i/+YtTRyFrr+pR8uVYllVQQBBGAwsLEp/iQqy2TgpgDbOssXLcHmlEBdZHnmSNhRgKP2LBhA5566ike/evo6OBRvqtXr3L0cFJS0kj6mM7OThZ1dXV1HPxB4q+4uPimUcFAIMDv3X///Tz9/Oabb/L27LPPshAsKCjgFDYe7U4rrjpNyFKykauKXQiCIAJw7kZJxAdQWIC4rnVj3V8durFx0ZcHCb3f/e53PNX7+OOP47HHHmPR1tXVxaN3//mf/8ltQWlpKQtAEnzvvvvuSFLpnTt38vskED/66KORUUF673vf+x7uuusuXhrO5/NxPkIShyQc29vbbxKALP7UHJRplVAVif4QBGHxoMbKiYgP4Mxz1DrAm4js2cexG+E6bVC1PKjakkVfHsuXL+fRuKtXr+KVV15hEadpGqdyOXDgAAvDhIQEHuXbuHEj5wEcLal0+KigN9VL3/H7/ezvR5vXtpAgjFwvOFPNxhb9NhRqRVJJBUFYVMgU8CKhxbmCGvNjZKtLUa5tkgKZZazQ9cTPuk/KnsjJycHDDz/MwRsk4P71X/8V77//Pq8GskUtl6oAACdUSURBVG/fPp7+3bFjBwu6jIwMnDx5ctSk0uGjgps2beLveCN8fX19nBaGRGZSUhKnpMnKyrrpPJaqy+VmCIIgAlBYuByzDiCIIIq1DTLaOss4diuPACpqNlRthRQIwKN9JOTi4uI4infPnj344x//iO7ubh7527VrF/7yL/+S07xQfSVRd9ddd92UVHqsUUGPU6dOcXBIf38//xZ9x1u1RBAEQQRgjCHTk9NPl9MhiZ/nEMs8AsCGblRAUaTP5eHz+VjMrVy5ksVdXV0djwB6uQPLy8tHRvPy8vLYT9BLKp2VlTXmqCAxODjI4vDIkSMsMul74SuRdDhtvGWqS3gTBEEQAThHyKjUzHGKEz8HsUnfLomfZxnH6YZtnYeipEDTxc9sNDIyMjiog7bxiEwqPdqooMe5c+dYAHZ0dPCoIAnA8OnfQ9Y+Xg/7PuMhuQGCIIgAFBYe/W4fzttneI1TSfw8+9jmacAdgObbBkVNkAKZRiJHBb2l5oLBIIu/gwcPctAHfb558+aRTman044G+yxSlDRkqllSkIIgiAAUFh5dTgeSlVSsUAsk8fMs47pDsK0TgBIHzVgvBTIDjLbU3LVr13gEsKOjgyONt2/fzsvDeZyya2HBQoleBl0xpBAFQRABGBsPTfEBnE4SlSRU6zuQLktczTq21QDX6YKml0JV06VAZgm/389BJMFgELm5uewf6C0BN+D2o96uQ7ySwEu/CYIgiACcY8QHcGZIVzMh0mNuOjJ26BDVbOg+WWJsNklLS8MDDzzAQSR0H8JH/y7Y59Hn9mK9ViEj4lHQ19fHy/QlJiZy6h5ppwVBBKAgCOPg2M1wnKtQteVQtVwpkFlG0zT2EbzpnrgOLjsXkaakY71eIYU0DgMDAzhy5BTef/9DnDt3Aunp2di6dQe2batEXt5SKSBBEAE4/cgU8PTQ4bThitOETCULSyX33KxjmzcSPxuS+DmWKFSLsEpbw8u/CaPT2zuAN998H8/+4iSOHAL6+/NhGH589H4t7n+gHY9+bRdKSwukoARBBOD0IFML08tR6wAu2vW42/eAFMYs49gdsK0LUNRMqLo8KGMFVVGxVi+VghiHYDCIP//5A/w///8jOHV8E1RUIk6Ph+tYaGw4jxee/xiW9Wf89d/ch8LCPCkwQZjPbaIUwcKjy+ngNBcBJSBLXc0BtlkLIATdKIOi+KVAhHnDqZNn8etfHcfJ2vXQlB1QlPgbHXQdqrIOvdd24a03uvHRR4cQCoWkwARBBKAQS5y2j19f9k0vk8TPs4zr9MG26qAoSdB0iTKNFVqcZlywz2HIHZTCGIeDBw/j8EEHqlI+xgNjFVqupOP0qQucbkcQBBGAQozQ7/bhnH0aCZL4eU6wrbNw3V5o+hooqkSZxgIk+vabH6PWOiyFMV7dtW309A5gYMAHBSlj7meaSWhpCaGrq0sKTRBEAAqxQr1dx2kuVmtrJc3FLOO6IVjmMQAGNKNcCiRGuGTXo9G5wLn/AjemNIUxHgiqhuspE+1x9nKgaQpHWguCIAJwGh+iEgU85R68a/EyV8vUPJRqIkBmG8e6BNdph6YXQNVkibFYaU9q7esjf2W65GMcDxJ0mZmpSE0NwnHbxyxPn68bK/LikJ2dLYUmCCIAbx2JAp4eVmlrUa3fhgxZ43TWsW6kftEk9UvM0Opc5S1bXYpsVfIxTsS2bZtRvc2A7R6G6356FNDFCaxc1Y2NFcVITpYZBkGYz0gi6IXUg1d05GuFUhBzgGO3wLGboKg5nPxZiA1q7UP8t0yT0b9oKCoqxDf+shpXmt9FzSevQFM3QVVS4GIYUM5g1ZrTePQvVmLL1grptAuCCEBBEKzQYfaN0n2VUBRxrY0F+txetDlXkaVko0A6RlFz++074P7/XPzyF/tRs68R3dcAvx9YW2ziK18tw+c+dweyc2SBSUEQATjNiA/g1Dhnn0bIDXHkr0/xSYHMIo5zDbZ9HoqaBk1fLQUSQ5TpVUhUkhGnBKQwokTTNOzadSeWLl2OfXsPoLOrB3H+OKwrXoXKyg1ITU2VQhIEEYDTh0wnTJ0epxvHrEMIICCpX+YA2zwJuEPQjCooIjRihiQlWQI/boG1a1fz5nXKpY0WBBGAQgxSpK1DspIqo3+zjOsMXheASjw0o0QKRFhwiPATBBGAQoySoqaiXJXI07nAturhut3QjA1Q1TQpkBjhqtPMI1c56lJeA1gQBEG4GckDKAhTrqs2LPMwAA26USkFEiO0O62oMT9Gg3NOCkMQBCHWBaBMMwjzDcduguu0cNoXVcuRAokRTlhHeeWPFCVVRv8EQRBiXQAKwnzDS/ysS+LnmKHP7cUF5xxH/hZqRVIggiAIIgAFYfpw7HZe+k1Rs6Dq+VIgMUK9XYcBt5/Xwk5QEqVABEEQ5osAFB9AYT5gmUcBmNCNciiKIQUSA5iuiVNWLXQYKNHKpEAEQRDmgwAUH0BhvuA6vXCss1CUZGj6WimQGOGKcxnX3E6sUPNlLWxBEIQJkDQwgjBJbOsMXLcfulENRZVpxthpzAxs0XdgqbpCCkMQBGG+CUCZAhZiGdcNwjJrAfg5958QOyzTVvAmCIIgTIxMAQvCJHCsC3CdTmh6AVQtUwpEEARBmJfIFLAgTAIv9YtmbJbCiJV74lroca9Bh44UWY1FEAQhKiQNjCBEiWNf4U1Vc6FqS6VAYoQG+yyv/NHutklhCIIgzFcBKD6AQqxihQ5RDYXm2yQuCzGC7do4bO3HBec8r/whCIIgzDMBKA9UIZZxnC7YdgMUNR2aXigFEiNcdZrQ7rYiV12OLDVbCkQQBGG+CUBBiGVs8zjgDkPTN0BR4qRAYoRa+xD/LdMrpTAEQRBEAArC9OE6A7DN01CUBGhGsRRIjNDldOKyfQlpSgZWqAVSIIIgCPNZAIoPoBBr2NY5uG4PVL0IqpoiBRIjnLGPI4QgirX18Cl+KRBBEIT5KADFB1CIRVzXgmUe4YxJulEhBRJDHcUEJQkb9WoUaTIqKwiCMFkkD6AgjINjN8J12qBqhVA1CTKIJYq19VCgwFB8UhiCIAgiAAVh+rBC1xM/675NUhgxhKIo8EGmfQVBEBaMABQfQCFWcOxWHgFU1GyossZszGC6IQTdIAzFgF8isgVBEKaE+AAKwhhY5lEANvv+KYoMlscKtdYh1FgfY9gdksIQBEGY7wJQEGIJx+mGY52DoqRA04ukQGKEHucaDls1aHIa4ZPRP0EQhIUjAGUKWIgFbPM0XHcAmlECRU2QAokRztqnMIwhrNGKEVACUiCCIAjzXQDKFLAQO52QYdjWCUCJg2aslwKJEYLuMM7YJ+GHH2s1uS+CIAgLQgAKQqxgW/VwnS5oWiFUNV0KJEZotC+i2+1CnrYSaXJfBEEQRAAKwnThui7s0CEAiqR+iTFG1v3VqqQwBEEQbhFJAyMIYTh2MxznKlRtOVQtVwokRjDdEK/5m6UtQY66TApEEARhoQhA8QEUYgHbvJH42ZDRv1hjs76dc/+pikxcCIIgLBgBKAhzjWN3wLYuQFEzoeoFUiAxhKH4ZMk3QRAEEYCCMP3YZi2AEHRjAxRFlhmLmfviWnDgQoMmo3+CIAjThOQBFASqd04/bOssFCUJmr5OCiRGMN0Qaqw9OGLtl8IQBEGYRsQHUBBuoBllUNREKGqyFEaM0OQ04qC1FyvV1TL6JwiCsBAFoCDMaQdETYTh3y4FEWPUWtdTv2zQK6UwBEEQphHpUguCEJN0Ou244lxGhpKFZWqeFIggCMJCFoDiAygIAnHKPgYLFkr0cuiKTFYIgiAsSAEoPoCCIHgMuP04b9chQUnEKm2NFIggCMI0I91qQRBijj63F9nKUmSruUhSJChHEARBBKAgCAueBCUR1cZtSBTxJwiCsDgEoPgACoKQpCTLyJ8gCMIMIj6AgiAIgiAIIgAFQRAEQRAEEYCziEwBC4IgCIIgLBIBKFPAgiAIgiAIi0wACoIgCIIgCCIABUEQBEEQhMUgAMUHUBAEQRAEYZEIQPEBFARBEARBWGQCUBAEQRAEQRABKAiCIAiCICwGASg+gIIgCIIgCItEAIoPoCAIgiAIwiITgIIgCIIgCIIIQEEQBEEQBGExCEDxARQEQRAEQVgkAlB8AAVBEARBEBaZABQEQRAEQRBEAAqCIAiCIAiLQQCKD6AgCIIgCMIiEYDiAygIgiAIgrDIBKAgCIIgCIIgAlAQBEEQBEGYAfTe3l5NVSenA30+HwzDGHef4eFh2LYtJTwBlmVhcHAQoVAImqYhPj4efr9/xsuW7qHHRPdSmB4GBgamfK+m294SEhLE3sTexN7E3sTeFrMAVFXVNxn/O7qhdDOjMRDaJ5pjUwXxROhiCgKha+3p6UFiYiKSk5O5bOn/VBbjle9kynYshoaGRsraNE02TjombRMZqDD1MqcGcLIN6EzY22J8IIm9ib2JvYm9ib2FCcDk5GR7MmqbKnO0Io0KWtf1qI65GINA6Lrj4uJGKiSVVVJSEvdcU1NTp6VsJ9vw0dbf38/nRY2nBOdML5N9IM2UvS1GxN7E3sTexN7E3sIEYFdX10gJBAKBm4ZOCSqscIHoOM6n9hkL+m54AdNNpYInhR8MBm/qKSxG6LojKzn1VKIpj8iyJVJSUkZe0z2jfcaDekP0+95f71zoHpPxdnV18T2jHpwwfT1isTexN7E3sTexN7G3OReAgUAgRAVNBdrX14f09PSbCp6Mobe3lwufVPTQ0BAreKrkHqOpaNqXbrR3bNo/LS2Nv9/d3c3qW1VV/pw2+g0yGtqPXocTefzRfm869pmt73jv0fXT9VIl9PahMiAjGQ+qsJFGFN7QecZHFX28yk29ITIEuld0DtRAkpHQedH3EhIS2MiuXbvGQ/gTnZcwPuG93mjszSMaH93wOuH53dA9ozpA9kqfe8eZ7KjIQoHqL9kXPaQ9xN7E3sTexN4Wq73pfr/fpcIklexV5HB/Bc8hlj4jQ/nwww9x5syZSf2IaZpcwFT4JABJ9ET2DOi9mpoariy0z2KBypWumcqD7oM3HT5aZZxIWFIZh/dk6X5FlvNYYtQT7HQutIU3gtR4eg7V3nnNpDC+1e/MlaCf7HeogQp3ivb2oTpANjPaSES0x6UGjuyW7hvdv3Cfm4VUhpP9DrUzXptGNkJtDZV/pL1NdBx6kIULDLIfb2opmvOm+0vfoXOhB1P473sPrsjzms/lHu0+M3kO3r0OFyS0jzeSFN4Rn8xx6fv0zOzs7GSRSZ+H/8Z8KkN6XlC9TkpKuuVnG9Vrquck3jx7o7odLrZHFSVRTv3SuY/nSxj+mTewRPZE1+YNPtFr+qy7u5vPazqnneeFAAz/D1Xirq4urrzhDQ8VEqlkr8AyMjKiGt6mz6iRoxtBDzp6TQ8jT7WHf5caQs84w4VM5PFH+73p2Ge2vhP5HpUNVUBvtJTKnf5Gc+zRxGS4APRE3WQgI4nswXrnSe/TcScbNS6MbR9UnlTfw8uU3qMHSbSjEZF1gOyIvk+2NlonYLETPjvh2dtUCH9Ieh2uyY4i0LnQdyLtzRtJEnubH/ZGz7eFYG+kAXbu3IlNmzZNy/GorL2oXqrnaWlpc+J35w1sUCeAtAyJXM9W6X16vtG9I5teTO2lHqmoqQKQMYT3bskoSM1TZb///vsnZXAkKOlYdAxS2d7IRCT0GfUOaN//+3//76JvqCYrLDs6OpCZmXnTCEPkfYz2uN53qUcU/kALBoMjUx3hRhxLIn2uBf1kv0Nl6o2Qh+OlTgif4ojmd6i37d03eh2Z0mIhluFcnAuJM3qYhdtbf3//p6b7ojk+fZfqgffwCY9epN+htncmriPWy30mzoFsypsCDN/Ha9siR+7GOy69JnHjCUpqM+lvpIiczTK81ePR9U/H6F+k+IoVvGngyBE/L0UNicC5EqlzLgC9m0WVmrbwG0eFQ8qZGqVo8+pQ4+WNJtJrzyFTGJ/JTNF5UBmHizVvWn8q5e31lsgYwn1mvIguqhsTRXEJ0UONkVfm4ZC9jdVhGg0SIEuWLOFOnCdQprsxF0bvcHlTwFO1C2pXI+0NN6auxN5i1968QRNvtFbsLfah+0uinTrI4fZGzzavE71Y7G3U8W4yAqrQkT0EqtzeUPlEUINIvSpvaJwasWiibRZrxFQsGoknAiPFoTdKKEwPc2lvgtib2JvYm9jb4rS3UQWgrutcEJGZ1L0RvGgyrJMhkUF4TrYT5dyR/FexB/VsqXEL9y303p8oBF+InrmwN0HsTexN7E3sbXHb25jejt6ULxVOuFMkFU5PT8+nAkXCGRoaYj8Iv9/PvSRS1KP5sUyE5xtD5+A4Dm+44ZNIm2ew0Wb29npqEzHeUkDRLFNzq99HDC0dRPUgsndL10f3mO5NZNlP9fq8qS5EBLN4Tvp0r+m3xipXqmNeXfGCjcJ7+N5xvPoyGUffqSxNNN79884zvDzHs7e2trYRJ+qx7I2+R8cQexN7E3sTexN7E3u7JQHo+TZE+rV4wi4ywCD8RAcHB9n/iF7T9ycK+x6tEtExvOVbvAg5z7nWMxYqCNrX64FNZIReGPp4BjXRUkATLVNzq99HjC0dRGVFPWIvb2P4+6OV5WSvj/an43uNstfwhTdqVI/IICPL1TMK+sxL4OoN7UdGd9JxvN6el6qBziEaf5/JLk000f2j6/VSTnjXlpycPKa9eaMMY9mb5yNGf+n6xN7E3sTexN7E3sTeJrI3faLCoR+nCwqPbqPXdLKjKWSqrFRR6UI9w4tWjXoXTJuXNzC8Yof3kKgwPEdeL4zfi8gaLxUDfXe8xJzRLAU03jXd6vcRY0sHeb0KL2dZZC/pVq6P6godlzoLY6Vf8O7laBnkOzs7+f5TRQ8/NzIEqi9eT80LkKHr8AyRPu/t7R35fjQNRbT1eLz7R9frGadX5+k66O9Y9kb25C2mHmlvXnJber+9vZ0fJpOxt+7ubi57sTexN7E3sText8VlbxPW3KSkpJEDhV8EVXx6n37QU6JeclP6zFs1JDxn4ETD17TR73k9KioQ+g06rreUC/2W1yuiC/aSOtJ3SDh6OX7GukHegtThTLYHF7lMza1+P3y9xFiEKldkYmKvLow2VRLpQzPa9dE+4b1nr2fp5Wej34qmYQ0fuqfvU53zEh975+gZi5eDks6Fjk8NOdUFOpeJHkrh94yO4V1zeA6x8IZ+vN5/eDnSMT1XCS8Fw2j2Rvu1tLRwedFr+kvH8uo7XTdt2dnZUd1TKm8vLZMXDSn2JvYm9ib2Jva2eOxNj6Zw6GB0kPAQdzp5ej98uNPLiO4NodP+0Qzje1PF4UkYydCpstNvhKdbiOyNhSd19G5CZHh3+DlH5luLNJZobkh4g3Or35+Kv8ts493nSKgSkuGEG0i010fv0z3yFuf2ejBUqb2kut6C3dHg9XjI4MYa9vaWg6LfDm/YqWEez68l/Jo8Xw6vznvnG76aw3jnPFomfPpt74E0mr3Rb9Hv02f0fW8qiHqIdG70e3QNS5YsidrevKSnns2IvYm9ib2JvYm9LS57iyrtORmEN7oX+b5XAGRA3tC15+wYreqnk/aWQ/PwRhfHy6tEn3nJG8OHZ8lgR6u43nBv+DZZPMfc6fr+fMjy7w2RjzbdEFn5o7m+8NxZZHjUwNFG79G9o79paWkjPjbRNLCeT+p494SMkPbx/FS9c6E6NF7kX/g1edNDkQ1IeN7Fsc7Bm46K7FHS98PTDkTaG+1P5+CNQnh5Nakh8dbops+j9auhsiI7CbdPsTexN7E3sText8Vlb1HfHS9HUrj69S6UKigZKe3jZcmPNiEmGR4VcrgxeUs1RXMMrydIPToPLyQ/mogoITqmMz+j58BK9zl8SZ5II6J7G35fx+sdRTuk7k1phPf4vASg0Rijt1JDeMPgJU+Ppq570wzh1+VNCYS/N5a9eR2soaGhEadisTexN7E3sTdB7G2y9qZORiXThUQ6RnqLn9OPkHF4C6JHuyamF4ETOXw6mbD68FHH8F5dLDmbLkToHnuOy5OB7q3nOjDeFApVeLqv1NsdzVCoUnd0dIz0pqPFGxUIf6hEPhAmU9+8cphoqsNbXYf2jbQjOmb4SMdo9uZ1srxeJx1L7E3sTexN7E0Qe5uKvU1qfJaMwQtJjnyfKjkpTOrxRDuv7fWEwofGvUjgyTiNkkHS74afFxUuGW40vUxh6gYyVf8Ob7piot5/cnIy16dw52mPrKws9p+hv5N1Mo48d8/fI9r6Fjkt5aUNGIvwiC3az8vlNNYDaTR785ZhpPe9B5bYm9ib2JvYmyD2NhV7Uyd7UXSykVFYXm+I3p9I9YYz2rrCXoTLZLOqj9ZITKaXKUye8ZKlTvS9aPxLIn1apvpbVM8iN2+IfsQQJuGrMtq01Gi99HC8YXxvBMDr/YVfK70XOVUVbm9enqjwfGZib2JvYm9ib4LY21TsTZ/K8CYZUriBeUY2Wt6kiQwkcn/q5UylIOiCI0cm6fxkCaWZNZCpOPnS9yYzpB0NXoJWr6fsPTCoLo11jpHpISbjB+I1vt5oAP2fHhxjNRpeDi9v6io1NZWH9sN9g0ZLPurZmxdJRucZngrgVu3Ny3cm9ib2JvYm9iYsLnvTp3IydODIxIlTEW6jfWcqc+4ISw8QOTIpQ+TTU/mmc51Luk/TFR3mRTyRcVBj6+XzisyUPhGTHe73/IHCp4O8B0qkf0/4dJSXl8yr9+EPMC8dxWj2RtfopXjwnNsnW4ZT7VyJvYm9ib2JvYm9LTx70+ey4CPXYbwVo6HjRDrFyuLcM3efvHs1mTU+JzreVI7T09PDhhGZbd1bYsdbZ3OsjsxUz4O+5y3Z5P2u90CJfCCFj5aHRyYahvGpHGdexF/kyIE3neT1/kbLHD9Ve5tKGYi9ib2JvYm9ib3Nb3uLuSQ9U513F2YOalBHq0ij+bjMVo/LM46kpKSRtTS9969du8bTPf39/SOGErnRNY3nRB4NkY7k9P/RohO99SO93/UeNqP59Yz2Hj2gvLVCvWOJvYm9ib2JvQlib7dib3qs3ATpzcQuVMkiI5TCp1nmAjIA6vmHuyFQD5kabzKYiQx3vES00RKeFsIjclqKyslbyJvOLfx8w53bx5uWCvclotd0feL8LfYm9ib2Joi93Yq9xdwI4GjZt4W5gxrM0XpCU+0dTQdewFG4PxDVGS/r+mydV/i0VPioRHi04f/H3rn1RFasb7z6/5+4ncw4zIygo+PMcGoMeEAFISZEtyRceeGFXpv9RfwaemPiB1Gzd4zKQQEPCBo8jAqtIKKChEQyuvN7s99OUa5uVh+gG+d5kk7D6rXeqnrrfWo9dVi14hGI9MGprPPjaanYRjod1Sw0stWBIL6Jb+Kb+HZ6+dZSAQihfRErfxeLxdDb21sXQbLm3Zu9uPN2hC8+Tf24v7/fshd8ewOd5oee0UnX91HTUr4IPZ2Oikc1qk1LcWPyJ70anY6K+dZoh0t8E9/EN/FNfDvdfGupAIzJQHCPj4+HJ554oi5bNAKpc9IXOR9H7+Hvvp6DwEsXWdNI5tno8riQPoEeWri2hkYi3Y/Mp6Xi6ais0YhQZY8zvyHFNzHiOZ2qqJdvjUJ8E9/EN/FNfDvdfGupAIx3p4YsnZ2d4d577829Q3warOmj18c9jEtFnYaXXdeL3377LXODSn/NTauQ9aR4rXWdFS/1xrDfeOIbiu+VWW06Kj0/vnH4Fg9+nS/2baQRyNoNPn0xvvgmvolv4pv4dnvwrW0EoMPXSNT6yL0P/cfImgKo5PCYoHl7bRDk79pDol58wWlKjlb2jkKFB4Zq3Vi2WY/qhyrTUvQuq01HOSpNS+3u7pano6iLRn0uvolv4pv4Jr6Jb20hAMkgBUtfx0NQ1/LUla/ViBWvv4fxyH1wkg0201eoVAMNTiNTBO0KfE/vqKOj41AwcpwPx1uJrIXUWettTvKGlDUtRfxVm46K8541LRU/nUZ+G4018U18E9/EN/FNfGsLAQjOnTv3lw0uCTqCrxaCpIG6t7eX66Xd/mRZ7Pw8JPHFwo3ubdVuwO+Q48KFC4cCiIDk+F133dXyhcc0hmn9cCxvo0oZm9nDo0HPmpbKMx1V6aZGw00efXSjWetxxDfxTXwT38Q38a0tBKDPwcf71njPyV/KXQ1+XTwUDuEgcJ7ei+8OnzYwbqMSOUgX+9VeSH6aQDm2t7eN8JcuXTrU+OGLX375xXpGzerFN9pDSslw9uzZ8ouwq4E6pe7Onz9f8zTMUTeUdFqK+DhqOiq+KaSNLWXixlRpo1LxTXwT38Q38U18q5dvbbERNKr7559/Nud7oFMZVBgk4fcs8BsVevHixUPBiwK+fPlyrrQJchwWD5nSs6I3QFDwux+nl8B5pEGaXFfvu4tbhTiAvCx84/e0V0QA7ezs2Df+bJctB7xhinvG5I04ISa4GWQt4iUuqDOPJ/7P04vOe0NKp0zwZd61RJU2qfWYTteqiG/im/gmvp0WviGy5ubmwurqqsURQpsyjI2Nhb6+vtx28NnNmzeNO52dneXjP/30kx3HX+T57rvvDjdu3KiZb1tbW+GLL74w2/39/RVF+TvvvBOWl5ftGmJ/YGAgPProoyYum8G3UqkUPvvss3DlyhUrx3HxLZcA/PHHH//SGGUZJsF0aJtAP4qQ2KZyvDJwAMeoFApEpUBSt0OF8uH/+B15VDYOInjzNp6ch7om3zERaQAIJhpwH8L3POB80qq0mDb1A0HnJIZ06fBu6ksPxEp+zLKRVV7yl9qJ64K/SZvv+HoI5LuOUxe1PBFFfaUxkKd8eWMlblSJl/gpLvyMjThmvFzeADpB8WH6+H+aL2Kxq6srV35oYGhU0xeB55mOim9qNI6+ENnrAn/GNwHxTXwT38S308Q30vjggw/C2tqaiSXS/eSTT8wn4Pr167n4hpBeWFgI3d3d5huPH65xUfvNN9+YeMNmrXxDSM7OzoYHH3wwUwBSX5Rheno6rK+vm+jDj/xPnicnJ5vCN/I3MzMThoeHTQAeB98ODg7+cSZvEMVqOws4Om14fv3119wNmWeejFMxVArHCFTsEjAekFQa5HViUKEQg+90eDcPsE+6BGP83j3SSBsTH2IlPwQ9AVPND1QMgRmD66oNNdN7OcqPR9ngt9RONfgWCJTL99KqdxQiKxaqla/WWPHGyuuNQOdDXqk7Gjo+8dN27iuIVWmNRJqvLBLnmZbyUQnSrmU0wZ9EjPnmXEj9Kb6Jb+Kb+HZa+EYcECMTExPhxRdftLK8+eabNsq1uroafvjhh3DfffeFnp6eMD8/b4IZcbW8vBy+/vpryxe/4RvyxnWIQQQRIgm/4CN+w7eUk7wj1q5du2YxiC18QNxhm++PP/7Y0sd+sVi0uCamFhcXw8bGhtkdHR0N999//yEfkNeHH37YykIn4vXXX7eRw8HBQbsO26SLHeILO/xN+pTdy3fPPfdYvpaWlixdxN7jjz9efhCE81dWVmx0kzJcuXIlDA0NWbw2yrfNzc3CmVqGcGtFrcPH7igcSsEJVA9SCuTD5/46IBzmQ7xUer1PLJEuQUsgE1Q+LE4lxFMFpOdbDcS9sGq+yZqHJ8+1LK5tho2sG5DbhTSUkw9lb3QHdGzXGi/1TDWQT+rCe378TxmIG68/7JIfGjX8xTHqjhirJ59H3ZCw52XxpxLz3tQ91ogxb3i9QcuTT/FNfBPfxLd25Bt28S3Hvc745hh5QuQgiLq7u22ED4GEiJubmzN7iC7ySyeJ8/EJvyME+Z3rZmdny+kh/BCOpEuaX331lYlMBBeiD1u9vb1m30dHS6WSjUAj7MkXImt6etrK+Pzzzx8a6Y/LwsfF2Pr6uglYxCSCizxRzyMjIyZ4qWeEI3a5bmpqKnz77beIMcsTx70zyTd1hg3iE/H3/vvvW34nJyebwrdcAhBVXM8C3rgXU+scuCt9f2Q7fgckNr3RaUYDGqI1LVSkD3F7pfIbjnXnxj2wPL6Jicvfte4DlW4BUI+NrDrNmqZqRqNMINaat3pjxevN16741IVPIfgwOTHCed5TypPPdAg9zw0yvtHXY4OGHhs+GuCxnsef4pv4Jr6Jb+3IN459//33Ng28vb1twmRjYyM89NBDJsr420HZ8Ql55DjlHxwcNEHGca7t7+83Ufbaa6/ZyNvVq1fLI5Fch1Dq6+sLzz33nPl4aWnJ7CCivvzyy/IoHtdwHFGG+Nvd3TV+kq8XXnghvPLKKyYm4zoHcVmwT4dofHzcfsMmAoz65G+vT3zjdl999dXymkUX/5yDGESs+mgdIpY0OAfB++mnn5ovseMivgG+HZzJS5JWgEKf9IaMOLSWBcC1+qYZ+yq1895Mragz783X8gTXceWzUa5AZBpE8U18E9/Et78L3/b29srrRxEnCwsLJi5dNCFsQvT6OT7FYjE89dRT9tvi4qJNjyLg/I1hly5dKqeHLRdniMbPP//cpowRdSsrKyaafBSRNIeGhmyKdnR01OxPT0+X7Xd0dJgQxp6PssV15usKvSyk/eSTT4bHHnssfPfdd+Un0uPzOAe7iDS/DuFYKpVM5CE8L1++bOWP1z8iIMk7ZcXG2NiY5R2BWC128sRWoVC4dSYIgiAIgiAcIxA/zzzzTHjppZdMxLz11ls2knb16tWy8EEI+YMu+/v7NrXb29trI3oIOQRRoVCoOs2PaJ2YmDCbMzMzJur4jIyMhGeffdZG2Oi8HBwc1GU/LUsqsLPKAnZ2dsrTy5SNNCj/1tZWePrpp60jgnCNR+0QjXzSvDer4yABKAiCIAjCiWF4eNgE1/z8vI1uIWhmZ2dNJJVKJRsNQzy999575fVtnHfhwoW/PHSSgmv7+/tNaC0tLdk1XV1d4d1337UnhLE1OjpqQqse+9XgI3NpWcL/1kZ+9NFHNj28ublpo4YIU4Tg22+/bUIYgbi2tmbCECBQyV+ad0RrM/D/L7/88r9IRyEpCIIgCMJxAGF07do1Ez2+Zu38+fMmfHzN28WLF8MDDzxgI4Q3btwwsYQAOnfuXHjkkUdsLWBHR4ed48KK6/lwDsf5XL9+3dYAnj171qaL+fuPP/4wUdjT02P2sZPXPv9XKkuMO++8szw6F5fljjvuMAHH7wjNgYGBsgDk/Fu3blmei8Wi5Zc0+eZ//JDmvRn7aRYKhZuF33///d9//vnnPxWegiAIgiAIzcXKykp44403TGBOTU21RZ4KhcJ/NAUsCIIgCIJwTOjs7LSHT47aT/mkIQEoCIIgCIJwTOjq6sr9lpuTxP+pagRBEARBEG4vSAAKgiAIgiBIAAqCIAiCIAgSgIIgCIIgCIIEoCAIgiAIgiABKAiCIAiCIEgACoIgCIIgCBKAgiAIgiAIQktxJoTwYaFQkCcEQRAEQRBuD3z43wAAAP//ALAQxf5XUSoAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![SVM.png](attachment:SVM.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Kernel Functions\n",
    "\n",
    "**SVM algorithms use a group of mathematical functions that are known as kernels. The function of a kernel is to require data as input and transform it into the desired form.**\n",
    "\n",
    "Different SVM algorithms use differing kinds of kernel functions. These functions are of different kinds—for instance, linear, nonlinear, polynomial, radial basis function (RBF), and sigmoid.\n",
    "\n",
    "The most preferred kind of kernel function is RBF. Because it's localized and has a finite response along the complete x-axis."
   ]
  },
  {
   "attachments": {
    "svm%20HyperParams.png": {
     "image/png": 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F+eXnzJ2pdn7V6ysbK1oTc00P4OYwBAwQAG9abExYEuYXjQm9nDX0BXah6IqNpVh/tnVI4380rl8sndD4G04mNYoXeG1+yOvPH6/oM6WKHOdRAP0WAAH0fgBs2eASlUxNb2VN3R+Xw6QUdN7dIyX9D/dJW7ZN6OCxXM1moeWLnO4fLemB0bLmx7wPAAiAAKYJgPkMBMBha7UjKun3aU2vZw09klQ5wF2yq1rSxnKs44syNeS1yDgtsI6ePwC38b3QY6gBBGaem8EewJbdcUlVY7Q/bWjCFxzgLqpaq/VRom1RSYtdRPgD0N8BkBpAoIMN3cxsAFxonTa7RKd8oXezZs/8nk3vNe6L8BMAcG0MAQMDcaU3swGwZW9c1sG8qdfTRhgSns2Foc8VuV4bb4blaT6NMy3KovD6dlcTahQBgAAIDHYAzGcwAC53UZgQciBr6v081ZZZWhfwRJrrZycn9P0/1PXaa4XO5YVGXaFdu3J9+6FMX15S1RImSABAbwdAagCBzgXAmewBbLknLoWJIH9o1rTZxV0v5ThT5PrF8Qn9+79r6K3vRfJvRSrVrRplr+e3pRo72pD7lvSVFdUwWQIAMPW90COoAQT6LwCudrHWuFhHikwfzcLC0EfyTL96t6kDP3Qyz5dkx5xMZmTGrOwLJb37I6dfHWjoSJ7yIQCASzAEjDnhhbSuN5s1zfOtD/XVFxOJMdqUlLQ2LskO4ObKrkMBsOXhuBz2Bv59WtPaKO7q7+Xbt2vJcykrrv8cACAAAn3q+fq4fvLO69r5/ocaal7d2/PpyhXasXO3vrVoeZjBOmhsBwPgWhdrrY10NM/CbXkXdwdZ5SLdty7W/icaOvyxlXk3kvFG3nj5zZlWPJnpvvUlrXIxjQQAejkAUgOI2/og57nWvbpfn/3//qsWnz5z1eM/+8KTOrxmnc4uWDrYAbDVvma4A9QZoweTiv62fl7PpDV9y4107fdaYJ2+uLSi898o9MN5TX3wVkNN45V4o3Xbjb72mVhfWVEZyPccAPoiAFIDiDv9IC89dVp3vfOuVhw/edXjb+7aoQ/yfGCPz8VZwJ05BmtdrAU2CmsCfpJnWtHFXsD11Vh/unFY25Y29Hba1JjNNVw4bY0T7R0uaWXMQAcA9GwABNA5nawBbImN0QNxWX/bGNNzaU3/qIu9gC0r4khfXxDpi74aFoFOjAk3AMC1OgYADEBDnwqAnSux2BQlWmad3s1SHZuFGcFqT/YZtpbwBwAEQAC2wz2ALSVjdF9cUVNez6d1DjoAEABvHpNAgP4MgC1bolirbBSWhTlZZBx4ACAAXh+TQIDOB8C8wwGwbKz2xWXVvQ9rMwIAehOTQDAnOC99vHaNfvbFz2n++fNXPf76zrtUjWO5Ab3OcF3qAWzZEMUaTa3eypraG2Va4jjNAAABEOiAHeWqjj/4iA5v36GjxdUhp1ytaNfCRZpvBnM9uKndT7oRACvtXsAfN8ZDL+BX3DAfUAAgAF4fNYC4HbvKQ1q2ar3Orpx+nbtYRkus06gdzHlP1tiwH1o3AmDLtigJ4e/tvKl9RaYllmtNzO53CmVGQI8GQBon7kRiTNgWbBWd2tMHQE32fOZdCoAVY7U3LusnU72AJXoB0T1N7/V2valnm3WNmVzbTUn7ymUtjtkRBui5AAigc1wXh4CnXOgFzJraF9MLiO4YTwv9ujah771U13P7M42r0F3rU31rZ6ZvLB3S6iqfQ4AACAwI28VJIFOq7VrAnzTG9WJa15fpBUSHZd7rN/UJ/Z8/n9D+/+Bk3q6G+99Ym+v8N5pKvyp9Z+2QlrM9IEANIDAYAbC7Q8BTLu0F3EsvIDrsYJrqb16vaf9/top+Ubn4wOFIH50z+sG8pjYuivT1+XwOAdYBBAaAm4UeQLV7AffGZU14H3oBgU56td7Qay97mTeTq79j3oz1/n6vd7KmUjoaALaCAwajobcDoC+6/t/eHiVabF3oBWR3EHTS2aLQ2Qkv07i6Q8HkJkwOGbO5UhEAAQIgMEgBUN0PgFO9gLmXXk+bvBnomFFjNVo28qWrA553PqwWMFTYsCwUwPdCj6EGEJhbAVDtXsBtURJ6AVs3oBN2lhLt3GOk7enVD27LtG6HtDlKFFNyBLAOIDBIATCfpQBYNVb3J2U1GjX9oVkL/S+bXCxHu8cM2lxK9K1dFX38nQm9Ua9Lr8RSbmTX5Vrxj1J9/dFE+4bKHCiAZWCAweBmuQewZYmN9GhSCZNB/lt9TGtdrMeSCnsFY8aUjNET1aqyL3r95wU1vf37TKmXVm6WvnFvSd9eWdUqloABCIDAoLCzsBD0dJa6SA+ZSijBP5A19V/rmXZEJe2Jyxq2lCTjzo3GVl+aP6Tlj0Z6+96mUiMtT5z2VcqEP6CXAyA1gEAnAqDtiQDYMt86fbU8rLuypp5u1kKP4EdFprujkrZGiSKGhXGHWhcTjwxV9HB1criXEiOghwMgDRTofADMeyAATlkfJVpkI72TN/VKWtePGmN6LYv1UFLROhfzpoHvFWAQAiCAznHtnUCKHgqALSPWap8ta42N9HxaD4v0fq92TluiRI/ElTBkDAAYgADIEDAw86wxPRkAp7SC3tfcsHblqX7bnNChLNXRPNOmKAn7CS+0jjcRAOZiAKSrHuhgAOyhGsDrWetirSqP6sM81e/TWthH+Eie6t64HNZvGzJMFAGAORUAAXQ+AOY9HgBbnDHaECVa7iK9kTb0RtbUr5oTYbLII3ElDA9bLhgBgAAI4OYC4Gz3ALb+6zVfKPc+7MZgrtiSq/UqIzO5bE3FWO1LKqHn76W0oVezun7aGNe7eTP0CK5koggAzJ0ASA0gMHcD4MGsqZ83xnU0a6rsddWOrPMkPVYa0u7K8MX7rNMTpWrYTq4VAg9kqQ7m57UnKoc9hkdYPxAA+jcAUgMIdI7rkQA45gu9eO606r/+lfa+/Nplj2XO6Zn79ip+8guXBcApy12k5W5Ym93k+oF/SGuhVvCRpBImiwAA+jAAAuicXukBXGYjbWpmWvHU7/Xf/Ye/vOyxepLo/4gijX3ui9f9Oza26wNfTRuhLvBnjXEdKzLtikoaZbYwABAAAVwRAH0xJ36fqrFhwei1LtbLaT0EwfeyVI8lVa2PqA0EgBt/L/QYagCBzgXAfpgFfCtWukhfKg3pkaSqc77QTxpjej1t8IYDwA1QAwgMANcn6wDe1knMmDAZZIl1eqZZCzOFTxSZHk4qKrFuIAD0dgAE0Dl2DgfAKWtcrPklGyaIvJo2VArBsBJ+AgAIgAABcI4asS4MCa90kX7frCnz0mNJpa9GGKbKYBgVATBQAZAaQGDuBsDEGEVJot889rA+mT/vsscy5/TO3nv0xB2eApwx2hOX1fBeTzcnwmKDn0mqPR/63qmn+l2jppMm0wrFeqRc0cYSE1oAzPEAyNUu0PkAmGt2L7BGjNWj8xYp+do3Vfvmty57rBV1Hiu8HoorM/Lfuj8u69Mi13PNuhYbp7viUk++N7W00NONmr73Sk3PvJDpZFpoRTXTH+5t6s/2VPVwucL5EcDcDYAAOufiJJB8Vl9HyRg9XB3RwxrpykXlY0lFJ4o81AUudk5LbG+d8nLv9Zt6Tf/7L8f02ned7GtVxU2jE0OFfrCvqWN/MSbzqNHDlQofYgAEQAC3GIbau+4W8mG4cVB6lEas0+64pKeaE3ora2pJ0lunvLfrTf3lizW99u8ixb+4JOSddPInnJ6La/qrhRNatTvS2pjhYAAzh3UAgUEIgKYVAKd6AQerjW2LEjkpBMC8x84vz9XrevGZQu6Vq7eyMxNWeiXRy6/leqXG2oYA5mgApMYF6KxeGQbutqqxGjZOtaLQ+R7aCSXzXmfSQuOfSubs9Kdif9Lq5CfSqWbBBxjAjGIIGBiYq71rzwRu+ELni0Jp6B2cHC627Zq9VoCK+/wCbcRYNU3Rcz2AJlz8XudS3PjJ5/DxBTDXA+AgDgG/njX0h8aE6lmm5Irff5Gx2lse0voSReCYmQCYq9DJPNOBPNV7eTNMkqh7r0o7ZtTaQ8RTc2abUgiAS4zTiLUqyWiBdVpgrRYYp3mt+3p4x41zRa6TRaayMeH198zJ1xgtSpxGl6c6vjCX+2Sa0/HSQsvXSsvKjg8wgLkZAAd5CPiTPNMvjx5W+bnntfnQhxfubySxXty9S8UDjxAAMSMB8EyR65fZmN7P3IWgV7R7+hYZF9bpq3kfHmmdHBry4c80vdcZn4d/Hr+khrDUDjKJsVpsXbi1guJS2wqGrid6Dl9NGzpbFNpdqoTX2UserJR1/wOp/v53qfxRJ+MvHq9iXq54b6r7dkW6u1LiAwxgbgbAQbbIOm07e15bf/pLff4Xv77YczE8pH//L/9cJ+5/iIOEmbjMChMhlvq6VthRrXWxNkZx+PyVrxOMvPdqyodgeK4odNrnOl3kOu0LTfgihKvw70WuA62g2c6HrWC4wFottVEIhEtdFPbrrXYxhH2QpdqfNbQuirUz6r0QtbmU6M/ureiTfzGm5+Oa7Oux1DCyw17uvqYe+Y70jzdXtcJxqgZAAJxzWl+HSV5oZGxci06fufjmZJkqdWb/4c4dyzN9nOcaMkaPxxXdF43K3mTvnDEmDPuWjDTfOq3V5cuRZK1g6Iuw6PKJItenRabjxWRv4bH27WIElYaNDVu1tcLgfDPZa7hghoeRW8H05bShd7Om1rs4LAUzantzGPXhckX/6+el7y2f0GsvNnRuwmvhfKN9u52+c1dV+8r0/gEYgADIMjDAzMq91wtpXW/kqe5LStocRTcd/m7qJGKMFhqnhdZpyyX3N7wPvYIniiyEwBPtfz7vC72fNfV+u75QYYayQiBcGHojTagtXGCd5hmroXbdobvBa677IpRTvJ+nOpinOpFnIfg9nFTCcHTPnoSN0WfLVa3dHemVLQ2dy7wWRFZ7KiXW/gMw9wMgy8AAnXG8yPVRnsprcjZvt/YDLhmj5S4Kt92XXOCN+UInizy8rhNhgsbk8PG5ogi3iXaN4dTKeM12QKwYG8Jh3F7RsBx+F4Xex9bfOfnHfHh+1ZgQ/PbF5Z7t+bvShiQJNwAYqAAIoDMO52mYxLHcxZLSMAt4Ni/0RsJsYqcNl9yft4PhqXZtYSsQjk/VF/rJWcpjIehd/DOXBsSWUWO1wsVabSNtipLQmwgAIAACA+mcL1SXV0WTgahQ7y0q7IzRPDM5c3jDNI9n3ocQWPOFsrCUjVfhJWsmewaH2nWKjCQAQJ8GwEGsASwZo8bIsH776IM6uXjRhfvr5USHtm/Vg9RF4k7ClYyi1s30bgC84YnKGA23brK8oQAwlwLgIF+5L7OR7l2xRq//8Z/ok/zijElvjHYkJd1THuKTits23G5bE+3c148BEAAwRwPgIFtgnT43ukD3j8y7apv+skzYxgq4XfOtC4O/U/Vzt1oD6L1X2v7nhCFWACAAYuYMhw3rCXqYecusC0Oop4pCXv6WegDPFrleHm/ohbQRBl8fiMvaM1QKS7MAAAiAM4Z1AIGZNWIn19QzhQ2zgQtfSDfRkXcuLfTTsxP67m/r2v9CEQLg0/sy/fmjub4wr6qR+PIQOLXu35kiD1vIpX5ysoZtT/KwYes4E5ZvqYaJG1YVc+P1/QAAczgAMnsP6JxtLtEan2is8CrcjXsAG77Qb8Ym9G9/Utfr/y6SeSVWbqTn7k5Vm6ip+jWjL4xWQ6g7XGTanzb0QZ6GmboKdYaTP5vt0Kf2vsJTC7NMVbouaAdBZxR2BVk0tZ+wdaFHnPMCAMzxAAigczZEsZYVsQ77XI2bGAL+OM/0syN1vfFTI/vMxa3IzDMlvbEh0z/cX9PKahR29ngrayr1PtQHLneR5hsbhpzjEAQVFp/O/eT2bHl7x47zYU2/yZB4zk8uAP2BssvW9htq/X020mrXusUhFJYpkwAAAiCAm7PAOi21ifZnuY4XTd1oNZWjRaaPD3kVH0a6cjnl4kOn/e+l+v6yc1rSDmjbXKL1UXzL+/nm3ocFn08Xhc74PATDE0WuI3kW7n8/T8OtUC30CC63TmtdrM0s9AwAcysAUgMIdMYSG+usL3Qwb+jzN2j5JiyqrGlrBfMhrzOVTIlKerJU1V1R6bZfkzNGo8aF7drW6fJ9b8eLQseLLAwxf5inISR+kKd6N0/1y+aEVthI26Mk7PqxmDAIAP0ZAKn1ATpr2EzW1R3NUx3NszBcey1rXKTVWyW/MZOevxjw8jWZssfr2rHF6TuV0dAT1ylD1mqDTbShPTDc8IWO5rkO5s0QAs8UuX7frOmp5oRWulh7opI2RnHYGQQA0CcBEEBnWVltcYmOpk39tjmhP66MXvO5K2ykL66o6M0vT+jNj+vyr8QqlhTK/vGE9j5h9M/mj3Q0/E2nZKzWRa1brM9JYcbxW1lDr2fN0EN4LM+0OHXaFZe0LUrCTGMAQJ8EQIaAgc4FwIXWhaHdd/NUh7I01O1N+1xj9FipqtrnpP84OqE3/pCqtizT3Y8a/Y8bR/VoqTLrv88C6/RwUtX9cSVMWnkza4R6wZ80xsOs5EeSija4OPwuAIAeDYAMAQMdDoDGhpq+R5KyDjeNnk1rWuWiMEt3OiOx1RcWVFR+VPrBnjElLtIfD4/o/nL5lid7dPQkZozWRnG4nSgyvZjW9UGW6u/qY2Eo+7GkqmWOwQ4A6MkACKDDAbA99XeFc5oXlfRKWtfrWUP3xOVr/pkhY1WKjfaNlPREaUhbuzzse6uW2EhfLg3r0zjX75oTejNr6nxjLITAzT3+2gGAAAhgxrl2ACyU6964rI/yTM80a1pmI624Rg/Zs2ktBMWHk+qMhr/DeRrCp5EJexVfaqGxoWfyTiZzLLJO3yyPaHvW1PPNmn7ZGA/rDe69TtgFAALgLKIGEOgMeyEAFqF+7rGkop83xkMI/EKpquErgthLaV0vpY0QmvbNYHBqtfF3s6b++tQJ2U8+1drzjQuPpZGVW7pQT65epUcrw3f839rSXiLmt80JPdushYVmdkUlSk4AEAB75YVwQga6EwDz9k4gm6IkbN32cloPQXB3XNZ6F2vMF3o1bei1rKGdUaJHk2qos5vZ12JUOXpay7//lPa8cujC/edGyvr1V+/Tm99YOCMBUO3JIk8mQyEEPtWcCL/9HnoCARAAAQwCd0kP4JSdcUklY/Tr5oT+pn4+DJ2asOae164o0b64MuPhr3WxV7VWy8Yb2r3/Qz3+6/0XHju5cFgH7t6omb4cHLJWDyYVnWrkerpZ06ix2kBNIICB7hQAMCCN/eoA2LI5SvT5ZEgbXSJ5acRYfSap6qGkEoLTXDHPuvB7eUm/a9Y0URR8KAAMLGoAccty78PkgD80a8oKf83emsQXuiep6L7yEIvy9lIA9FcHn/VRrHUuCtHQtNcBnItWuVibXaxXs4bezZu62zIUDIAACNy0A826frP/Fa179nktO3Fy+uds3qjzjz+hdWs3aZ0jAPZKAMw1fc+XMUaDsKPuPXE5LBh9IGtqZ1QK+xEDAAEQuIHWF+YSb3T/ex/oS3/5fd39xtvTPu/vv/EVvX3PXo17htp64n27xhDwoFnuorC+4dEiC5/NUeP4cAAgAM4WZgH3H+u9XFEoyvPpHy8KGYb0e+f96rEAWCsn+mDNYu3fsebCfWfmVfXpwmEt7/DpIDJG5wuv00WhUUsABEAABEAA7Lhl1mnpssU6+PWHdPrBHRfubyaRyhtXamNc6uh/f7F1Op5n9E4DIAACIABeS80XOl3kOlsUOu+LsH5gJh9m1JaMUUkm/Bw2NiyxMmqdkuv06q+0kb60dKnemz9PV/YRL3aRtiSdnZxRtHumqUwFQADsEcwCBjobAHPdXBtrBb63s6beyZo663Ol3ocFnFvS9t+RSyq376vLh5021F7oedhazTdWy22kpS7SUutCOHTGhNu2UiXcZsMnRRZe7wKGfwEQAGcXNYBAZ126F/D1nCpyvZjW9UbaUM17eaMQ3Ja4SAuMDcHOySgyUuEnl41pyOtcUVz4ec7nOlfk+lS5DudZKzGGxxZbF/YeXmCtVtko/HO1y2sN5qH3Uppn7FX7EAMAARDAnHIzQ8AHs2bYKeOTItNCY3V3XAr76S61keJbuEgrvA9byp0o8jDR4liR6XCequm9DuVNvZ55DYUQaVQxRutcrDUuDr2Fwx0OhB8XWdjpZI2LwrA1ABAAewBDwP0jjSKNVas6OzL9nq21SkmFpcqqZwKgmQqA07exV9K6nk3r4aTwhaSqHXHpthfwtsaE5VWunGFb84WO5ZkOF9mFYHi0yEOv40tpPfRNLrZOq10ceghXu2jGZ+nuTxvhGOyISnwoABAAZxtDwP1loYs0tnGDvv8n39LTn56a9jkfbVin9aOjYWsx9EAAvM5C0O9lzTDsu8BYPVGqarHtzKmhYqzWR4nWa3If3tR7fVrkocfxozzVh3mmk3kebq+GU8JkD+H6ViAMdYRR2K84uY3zRea9XssaOlJkui8ua4OL+VAAIAACt2JLUtaTO3br4JZt15xJudh77S5VtYQ6q55wrYWg389SPdWsab5x+mypqoVdfL9iY8LCzK3bvXE51OedLnJ9lGchFB7OM53yeZiI0rrV5UPtXus1LjAufLZGrQ2TOVoXGlcOU08FzA/yVEfyNPzztqikvXE5DD8DAAEQuAULo1hfieZzIPrIdDWA58OEj1pY0mVfUu5q+Js2pBoTloFp3e5tl4Sc80XYteNInoUewnFf6JM805EwlUNqymtyMNeECSVJexZyKh9q/Xx76LkVcO+Jy9oZl1SmVxoAAbC3UAMIdC8AvpE1Q7B6MKlo7XWGRI/lmV7O6mELtXuicscnakwxxmiecZpnXei5a2n4ItQMnmzfThR5CHjnvQ89fq3gZ/xk715kFIaMl9iS7oqSsBwNAIAaQGCAAqC5LACOF4UOZE0NWRtm+l7Lq+MN/eDEuJ5u1MJyMN+q5npicUWry7Nz+igZqxWudbs6sLYCYLPd6xe1F6nm3AIAPRwAAXQ6AF4+CeTDPA09aXfFpWsO/b453tT/8+q4fvKzTGNHEkUyOr6jrrNfL/SddUNaGvfWKSQ25paWqwEAAiCAOc1pMuQV7f1vD+RNTciH5Vam0wqHP/54Qr/6Ya6J75UVHZ183qEtqb5faWr1Hzt9feEQkykAoC87BXoMNYBApxr7xRrAzPtQOzdi7DVr/8LSMB+mOvuikz16MSTaA7E+eNHoxXONMGMXAEAAvG3U6QCdbuwXawDP+0JjRREC4LUmdByr5/r4Q6/8+NXDw9lxo0+O+jAJAwBAAATQs439Yg3gp0WumnxYZPmaF2UX/s9P+yCXbADQv6gBBK7hTFHozawR1plbNM0kiVFjtdS6vllT7kINoIqwtl5Juu4i3csrkVZvMnpvVSG9c8XftaLQitVWy1hWBQAIgDOBGkD0gsL7MEv2++dP6+zJ49o4Xrvs8dwaRfPn67PLVumhykhf/E6XDgGPtyeCDF8nvG5wse5bk+j1vU19+lYufTIZFv3WVJv2SfePljSfBZUBgAB4J6gBRM+FQHnl585o8X/7sXY+8/xlj41XKnrq85/V/q99s48C4MVJIDXv1Wjdd512t8A6fXlJRYe/muu/VSc08VokV0irHi/0nc+V9FC1ct0/DwAgAAJ9pRVsRqzV0kaqja++rq//8KeXPX563qje37xReR8FoEsDYOG9YkmlG1TybR1K9M/vHtLKdRM6dCJXUhjdu6Ksx+dXtChmj2cAIAAC6PFQa2VkQs9m1p7Y4W4iv+4sl7QuiXV8URYi5FIbhT13AQAEwBlDDSDQwRAoo1xeuS+USspusrkNW6thm3AAAWDOfB/0CGoAgW40eNtub5PJLxcXXABAAAQwp7mpANjeDzijxx0ABhJDwMB1ZM7q+OJFOrBx/WX3nxseChNBFvTdFd9kALQXegDRN59F7/VensrLa6NLFDNqAmAuBECGgNFrRo3V4uFRvf3EZzS2aeNljzWSWPnWrVpzjX10ez0ATvYA2guTQdDb3q2l+t14Tb8rJpQbr4dsRZ+pVrW1Ql0mgD4PgECvWWCcnliwRCsf/azSaXqmF7lId5WH+jIA2nbwy8h/Pe/t801998C4fvzbVMc+NPLG6IVVDR14tNA/3z6sHcOEQAAEQGDmGocx2l6uhttccXESSBEqApkE0ts+TjP9zYcT+vu/zXT6r8tyH0yesk+tzvSjow1V/tm4/tUOozVxzMECcIvfBz2GGkCgcy5OAmn3AHJIetqL9bp+/lpTp38ey3xw8XrdHI509meJfvZSU8/W6xwoAP0bAKkBBLrQ4Nt790byYuCwt7Uuho/UMx15U/LvXT1YYz50On7A6JN6rpwLZwD9GgABdKPBtwNgexZwk+DQ03Ij5blkpuuqzYzyTEzkAUAABHCTAVBeTUljvuCg9ChjjObHVvOWSMWCq98nP6/QyGKvBYmVYwQFQL8HQGoAgc4HwKTdA1invfW0+0tlPbDPydydytuL75WXl9md6oH7ovAcALhVrAMIDGAAjNvNrc7wYU/bXIr1p3srOvYvxvXswgnpnUjhLduY6f5vSn92/5C2lqjmBNDHARBA503NAo7bwa/BEHBPa10YP1Sp6H/5vPS3Wyf03luZvJc2bjf65pqqHq5UwnJFAEAABHBNF3sAvUwIgD6UXdAD38MnaWP0WLmizZtivbc2DdF9QxxrpY143wDMnQBIDSDQ+QBo5BXLqCmvVGJJmB7XCnorXBxuADAz3wc9dIID0J0AmKtQud3m6gwDA8DAYQgYGCBTNYCFCs23VlnuwzAwAGDQOgQADFCDvxgA5xmnxBhmAgPAAKIGEBjQAFgyRoWkGm0OfabhC50pJj/D863jgAD9HACpAQS6EQDNhQA4NQlknBpA9JGDtVS/qo3rTdPU/Mzpi6Uh3TOSqGQY0AL6MgAC6EYAvDgJpNS66PKeSSDoGwfGU/3HA2P6u2caOvqhVDHSG/vG9C+fqOjx+RVCINDPAZAhYKBznCaHywoVqrZ73Sdoc+gDx/NMf/fhhP7+B5k+/ZuK4kORssTrd/uaKlTT8BetHh6ucKCAm+4Q6BEMAQNdaPDm4hBwWSbMAD5X0AOI3vdao6F/eKuhk/8Qy7wXyxRGpm6lZ0t68fdevx2ra4LebKD/AiCAbjT4i5NAqu3hMr400Q+ONXIdPST5Ty7/2jKpUXHK6OS5QqeKnAMFEAABXCsA5q0AaK28MRoXARC9LzFGsZP8NN9aJpQ38IUG9HUApAYQuHmp92FJjNbPTBdvxTXW9rt0IeghY8IwcEaTQx/YWI618R7JbE8vu79YlCvZmGvFAqeFLAkD3DSWgQH61Nki16+aE3o1rSvykmkv8RJ5r3vish4qD2nY2iuu+C5OAklkFBmp6X0IkDFtED1sa5LoW7sqOvHNut6eaKg45EIXRvyZhh593OnxkYrKzAIG+i8AArh1z6R1vfrLZ/TIH9650Of3zuYVGn9inzasW69hm1wRAC8OAbcuuqyM6ipU84ViQ+8JelfrYubzI1WlX5b+anlNH76daig2um+f05/eXdXuUsJBAgiAwNw3zzrNM1YPPntA//O//uGF+3/ytX16e882Tay9emzXXbIQdMuQsTrnC417r1EOKXrcwtjpqyuqWr3A6aOHMw07q7srJW2IYw4O0O8BkBpAoHMuzAL2UwHQhHuYCYx+scA6PV6tSlWOBXBn3wc9ghpAoBsN/mINoNo9gCIAAgABEMBcbvCXDwFXjAn/NE7POwAMFGoAgT5Wyb3e2rlWf/nnn71w3zvbVqs6XFZipguAFyeBtIwYG5aCSUUABAAC4CyiBhC4efcnZTWeeEDHH77nwn3znNWu0VEttVc370vXAWyZ2g1knCFgACAAzgZqAIFbt7c8pM3LK2FP3wttSdKosRqxV1d42CsCYMUY1eV1ZsD3Ay68V9quiWE9RAAEQAA9rWSMltzC+n32Gj2AgzwJ5FAj1e/qNR0yqYZzqz1xSXdXS2G2KQAQALuEIWCgc66sAbw4BDyY7e618w39lwPj+vkrTR05LFUKo517Mn37wUxfXTCkpWVCIAACYEcxBAx0LwBeOgTcijh1Xyj3Xm6A2uE7jaa+u39cf//XucZ+XVH0oVPTSC/tTHXuaEPma0bfXF2lJxAAARBAnwdAc3kAbAW+srFhEkjd+7Aw9KD4bbOmnzyXauJvq7JHJ0Ne+O2fK+m9WPrB+po2LHb6TJUVhwHMxQ4BAAPjylnALYutU1VmoOoAx4tCR8dynTtopaNX9/D595w+ekM63Mj40AAgAHYDNYBAJxv81QFwqtdvIJeCMV7eXH3O8Wby7GhEaQoAAmBnz8PUAAKzEgCtjCbkNTZAF19D1mrlkNPotkJanV99PtqWacPd0voSVTIACIAA5kgAzOkB1KOlir72QKLRrzfkN6fyQ4WKkULFww1t++Ncf7S7op3lhA8NgDmJy1tggLhph4AndwgetLUAN5cS/cW2YSX/dFy/2F7X8UNGsZE2Pyj9k/sq+uL8qkaYAQyAANgd1AACnXNxCNhfFgA1oGsB7hhO9K/uNrp7S13HGrkSGW2uxtpTKWk+4Q8AARDAnAqA/mLdW9WYEAcHdT/gtXEcblMXn9QjAyAAApibAfCKIeCqjIoBPzYEPwCD9X3AyRcYuACYTzMEPMj7AQPAoKEHEBgg000CKRujhrwaRT5w28EBAAEQfePZZk0vNyZUyQtdWabe+ureWqpoV3lIJb7IcYXphoBbgW+hdWF3jJr3GuZzAwAEwG5jFvCNtcLfj/a/pLveeFuj4xOXPXZ0xTId3veAFq3dpPVRzMHCDQNgy7CxGlcRJoIMszwoABAAu4UawJtXyYsQ/r7x3f+iVUePX/bYbx95UAfXrtena9ZrvQiAmD4A5lcEwNaJoO69xnyhZRwmACAAovc4KfT8tcLf2iOfXPbYO6dO66OMDexxrc/OtXsAW8aYCAIAA9IhAGBgGGPU+p9v/e+Scosha1UY6TwBEAAIgLOBGkCg043+6mHgEWNDz/I4ARAACIDdRA0g0B3TDQOPGquKjDKuvwCAAAhgLjb6qwPgsLFhLcCTRc4BAoABwDIwfSiW9MnaNfrJlz+vRWfOXvbYm9u2KBsZYf4vbikAjlgb9gY5XmSq+UIVw7UhABAAu4Ah4Ju3vVzV0fsf1Intd+l0fnmPjU8S7Vy0RCscE7xx/QCYX7YbiNUqF+uDPNXxPNe6iAAIAARA9FYALFW1ZMUanVtWTPPlLi2yTgus40BhWk6TF1tXLgWzykYhAB4pMq2jDxkACIDoLbExWm4iLaeTBrfhWruBrHSRlEpH8lRShQMFAHP6u6DHUAMIdLjRGzdtAFxho3BCOFpkymmHAEAA7AZqAIFuNfrph4CHrNV84zTuvc6yHiAAEAABzKVGP/1+wC1rXRQWhD4choEBAARAAHPCtfYDbtkYTU7+eI8ACAAEwG6iBhDodKO/dgBc7eIwyejDPA3rAQIA5ibWAQRmyAvNml5q1FTKi8sWUWld0qwrlbW7XA177vZyAKwaG5aDOZin+jjPtClKeGMBgAAI4Fpeb9b1w7cPaMMbH2jxuYkL95+aN6S39mxVeesW7S1XeyYATlcD2LIlSkIAPJA1CYAAQAAEcD3DhbTt3Y/12b/8B206dOzC/W9vXqlnqmUd3bBO6qEAWPhCmqbjfYOLw4nh/TxV5r0ieucBgADYadQAop8b02itoRVHT2vdRycu3H9upKJqrdkzr/N6Q8At86zTUhvp4yILewOvdOwKAgBzDesAAgPG3SAAtmyLEpVl9EGeccAAgAAIoP8b/Y0D4JYoCUO/B7JmGAYGABAAAczxALjQOi2wNgwDf1LQCwgABMAOowYQ6E4AzHX9df52RaXw87W0wUEDgDmGdQCBAXMzNYAtm1yiqpkIu4KM+0JDhgGDfnEwa+rZZl2LrdP9SVnzreOgAOjNAAj0u5IxOr52mX70jQf14omzF+7/ePkCnV69ROUeuci5OAR8/d72YWu1wSV6PWvoUJZqZ1ziTe5x3ns9N9HQd98b09On61pWtfqnCwt9aXlFK6uc7gH0cABkCBj9aktS1pE9O/Tx1vU6XlzyObZGd1eHtCkp91gAzG/43N3RZAB8LWsQAPvAi/WG/vXz5/Xrv/ZKD1f0acXr3z5QV/Mfe/3RmqHQIwgAYggYmDnrk5L+eOESjU8ztDpkrEZ6pQfQ2LA/3Y2GgFtWuVgLjNWRPNPpItcCAkTPGity/fLchH73q0L+v1aVnJl8rw4db+gHG+vauNjpC0NDHCgA7c4AADMUrIzmWauVNrrqNs9YWfXWEHCuG/e2x8borqgUnvtW1uRN7mGfFrmOf1pIhyLZM5cE9XciHX5VOlxjNjcAAiAwsG52EsiUnXGiWCYMBTcp0ehZrcsLVxjZ/OoHIt+6McoCoIcDIDWAQKcb/c3XALYsspHWuEgni1wf5ikHsEeF92mhU2l7rmLZJe/tXak27ZM2VdnSD8BF1AACAxoA85vsAWy5Ny7r3TzVi2ldm6OEg9iDhqzVEwsreuPzuX7erKn5cqQoku76eqHv3FfRzgrvG4AeDIAAuuNWh4Bb1ro4zCD9KE91ssi02HLq6EU7yyX997ulVUsn9P4Xcw05o8+tr+jx+WWNMoEHAAEQGFz2NgJgbIx2RyX9Q3Mi7AzyZIlTR6+6p1rSmnWRPlmdqSSjFS4KazoCwOXfBT2GGkCgSwHwFtvatihR1ZgwG3jCFxzIHrbIOu2KS9oSJ4Q/AL0dAKkBBLobAPObnAQyZb512uRijfki7AwCACAAAugTt1MDOGVvPLmbyYtZnQMJAARAAP3T6G9uL+DprHCxlrtIh/NMR1gSBgAIgDOFGkCgWwHw9ur47m/3Aj6X0gsIAATAO0QNINAfAXBDe0mY97M0LA4NACAAAuiTAJjfZgAsGau7o5Ia8nqVXkAAIADOBIaAgc5yd9gD2LI9SjRibFgS5nzBkjAAQAC8TQwBA11q9ObOA+CodWFdwHO+0NtZg4MKAARAAL3d6O88ALbsiUthp4nXsoaa9NwDAAEQQO8HwPwOA+ASG4UJISeKXIfyJgcWAAiAt48aQKCznCbLLe60B7DlvqQc/pZnmQwCAATA20ENINCtRm9nLACudrFWsTA0ABAAAfR6o3czFgBbHmwvDP1Mk15AACAAAujRRm9mNACud4mWWqf381TH84wDDAAEwFtHDSDQ6UbfHgL2MxMAE2O0Ny4rldeL1AICAAHwVlADCHQ3AOaauQWct0SJFhird/KmTrM9HAAQAAH0FjeDk0CmDBmrXXFJE95rf8rC0ABAAATQY41+5gNgy86opEXG6d28qTG2hwMAAuCtoAYQ6M8AOL+9PdzZgu3hAIAAeJOoAQT6OwC23BUnal3CvZw11OBiDgAIgAB6KwDmHQiAYXu4aHJ7uPcytocDAAIggJ7gOtgD2PJAXA4rDT6X1inpAAAC4M3hCwPodKOfCoCdaWsrXazVLtLHRabDBQtDAwAB8DqoAQS619asjLz8jC0GfaUH40r4+UyzxgEHAAIggN5o+J0dBl7nYi2zTofyVMfYHg4ACIA3whAw0P8BMG5vD5fI6E2WhAEAAuC1MAQMzEYA7NwF15YoCWsDHshStocDAAIggF4JgLk6F8yqxmpjFOtTn2s/vYAAQAAEMLs6vRTMlJ1RSVVj9Hra0LhnezgAIAACmMWG3/kh4JYF1mmLS3TGFzrAwtAAQAAE0AsBsPO9cnvjsiJJL6V1ZUzyAgAC4HSYBQx0oeGb7gXAZS7SWhfrWJHrgzzl4AMAAfAiZgED3Wz4ndsPeDoPxOXw87m0zsEHAAIggNngujgE3LLGxVpunT7KUx1lYWgAIAACmI2G390A6IzRvvbC0G+wJAwAEACvRA0gMPcCYMvm9sLQ72apzrAwNAAQAEUNIDArATDvYgCsGKv1LtYpn+tNloQBAAIggO5ys9AD2LIrLoVh4P1ZQw16+wGAAAigmw1/sse96PLuHIus0/oo1qcsCQMABMBLUQMIdKPhz04PYMveqBR+vsiSMABAAKQGEOhmw3fhZz4LAXC1i7XUOh3OUx1nSRgAGOwACKB73NQQ8CwEwMgY7YnLakW/V1gSBgAIgAC61fBnbwi4ZatLNGys3smaOl8UvCEAMOgBkBpAYO4HwBFrtcXFGisKvZuzJAwADGwApAYQGJwA2LKnvT/wKynDwAAwsAEQQPcDYD6LAXCZdVrqnI4WmY4xGQQABjsAMgQMdJ7rgR5AY4x2t5eEeZXJIAAwmAGQIWCgiw3fzH4AbNkaJaoYowNZUzXPZBAAGLgACKCbDb83AuCodWF/4HO+0KGMnUEAgAAIoOMBMNfs97rdzTAwABAAqQEEOs/1SA+g2juDLDSTO4OcKnLeHAAYpABIDSDQzYbfOwEwNkY7okStS793MtYEBICBCoAAutnwZ28v4OnsiEthe7o3soZyRgEAgAAIYObN5l7A01lonRZZp+NFrmMFawICwMAFQGoAgW40/PYQcA8tvbI9SsLPtxgGBoDBCYDUAAKzEADVOwFwS5QoksKagBkXggAwGAEQwGAHwAXWaZmLdNoXDAMDAAEQwCAEwJa73OQw8JsMAwPAYAVAagCB7gXAvMcC4Ob2MPC7WVMp5wIAmPsBkBpAoHtcj/YAzrdOK1ykM77QUYaBAWDuB0AA3Wz4vRkAW+6KSkokHWQYGAAGJwAyBAwMdgDc5OKwTuHBPGVRaACY6wGQIWCgiw3f9G4AnGedRqzViSLXSfYGBoC5HQABdLPh9+YkkCkbXRx+vp+nvFkAQAAEMBNcDw8Bt2xoLwfzHgEQAAYjAFIDCHSj4fd2AFzunKrG6FieacIXvGEAMFcDIDWAwGwEwN684CoZq1U2VkNeR3KWgwGAORsAAXQ/AObq3UkWGyLqAAGAAAhgxvR6DWAIgFMTQbImpSEAMNcDICd6oBsNvx0Ae7i9LbBOy6zTuC/CziAAgDkYAKkBBGYhAKq3g9Xadi/gBwwDA8DcDIAACIBXWmKdmpI+ZiIIABAAAcxMAMx7PACusFH4+UlBAASAOR0AqQEEOs/1SQ/gAutUMUani1w16gABYO4FQGoAgW42fNMXATAyRktspEzSiZx9gQFgzgVAAN1s+P3RA6gwDOzCT4aBAYAACOCOGv5kqMr7IgBSBwgAcz4AUgMIdKPhTw4BexU93+ZWuHYAZCYwAMy9AEgNINDd9tYKgT6EwN4OgKPGaqGxaniv8YKJIAAwpwIggG43/v4YBjbtiSCtoHqCYWAAmJsBkCFgoDtcn8wEbqkYo4ak0ywFAwBzKwAyBAx0u/H3z0zg+e2ZwGcLloIBgDkVAAEQAK9lnpl8rWfoAQQAAiCAAQmAdvK1nmUSCADMzQBIDSDQ3QDYD2sBzjftIWDPEDAAzKkASA0g0F2uj3oAK8aoJKOa92owDAwAcycAAuhy4zf9EwBbF4ij1oYVC88xDAwABEAAt9v4+ycAtiyyTnErANIDCABzLwBSAwh0NwDmfRIAh9o9luMEQAC4Y1GvvBBqAIHumqcR1dW4EAT74Wo1lcKWcACAORIAAXTXPXanJnxNIxrqk5PV5EViJgIgABAAAdyWTWad1Ecd71H7tabkPwC4Y9QAAuiTq1V6AAFgzgVAagABXD8ATmIpaACYQwEQAK4bANsXiRmjBABAAAQwIAGQIWAAmLsBkBpAANOJjcJC0JwhAGAOBUBqAAEAAAYsAALA9dS9DwtBR+JiEQAIgAAGQq1dHlJhtAAA7lh07tw5Z+2t5cAkSRTH8fWv1ut15TkLNtxIlmWamJhQs9mUc07ValWlUqnjx7b1Hk650XuJmTE+Pn7b79VMt7ehoaE+DIBFOwBa2htob3y/0d7uNABaa5Nbqb9rvaGtN/NmGkjrOTfzd7c+IFMhdJAmgbR+17Nnz2p4eFijo6Ph2Lb+vXUsrnd8b+XYXvPLtFa7cKzTNA2Ns/V3tm43aqC4/WPeOgHe6gm0E+2tPwPgnfUA0t5ob7Q3vt9ob5cEwNHR0fxW0nbrw3yzIa11oKMouqm/cxAngbR+73K5fOED2TpWIyMj4cp1/vz5M3Jsb/XE17qNjY2F19U6eTI5Z2bd6hdSp9pbX36h684CIO2N9kZ74/uN9nZJADx16tSFI1CpVC7rOm1pHaxLA2JRFFc951paf/bSA9x6U1sHvpXwG43GZVcKg6j1e1/5If//2Tuf3jiOJIsXCXoJgiYlSpSIvRrwF/Aa8M3Yo7+MP+EeduC5DHzy0cBcBxhD/E/RJiwB0uBX8GuEQllVWVVNdpP9HtBgs7oqK//Ey4zIjIzEUqmpj1y34NmzZ4vvtBn39AFriPfrr/JCG0Pei4uLts2w4IzlWcTm20QFcOYSsPlmvplvHt/Mt6AA7u3tvaOiqdC3b982L168+KTiIcPNzU1b+WjRd3d3rQaPkAslLZp7aWilzf1HR0ft81dXV632vb293f7Oh3dAGu7je0ROv/S+ZdzzUM/oGuWnvAih7qEOIEkfENhMotjRiXwIep9wYw1BBNqKPNBBQhLyxXP7+/styS4vL9sp/KF8Gf2IVm8N34QaH90oE/K7oc2QAfjK70pn7KzI+iiA82YAkV/4xSAtmG/mm/l2PzDfHoECuLu7+5HKREuWIEd/BTnE8htE+emnn5pff/111Evev3/fVjCVjwKI0pMtA679/PPPrbBwz6aAeqXM1AftoOXwkjAOKZbUcbRkaa9cz13KqBR28sIndoJ0nnKoVr7uUzGe+8yqFPqxz9BBRado3YMMwJnSTERtunRw8JZ2o/2iz81jrcNfTo4aeob/O71q9j5Oywv9jPo0OEJfQ/1nvg2lw0AWFQz4o6WlmrLSvjxDXhiY4vs1cOV8rTPnlpWX+8yD2joqJNyjmaRoiI9Jl+cZM8/Pz1slk9/jOx5THTJeINcHBwezxzbkGjlHeRPfkO2obBeVksqlX/Le50sYf9PEEnyibJp84ju/XV1dtfla5rLzo1AA4z8I8cXFRSu8seOhktCSVWEvX76smt7mNzo5GoKBju8MRtLa47N0hCJnVGRy+qX3LeOeh3omX6NuEEDNllLv/K1Ju6RMRgVQSt0YQJJswSqfXCfdsbvGjW5+UJ/Ie6xTrjGQ1M5GZBmARzwP10pGwGPF7uV/N/+1vdX881//brZmLKvF1QnxbQriICmDa+wsAnnhmcw3zSSZb4+Db4xvT4Fv6ADff/998+233y4lPepau3qR86Ojo5X43WliAyMAXQYlV1zlOuMbbQenN0kJ3MkaNQIAGaJ1CynQ5hH2H374YRThUChJizTQsjUzkcFvWAfc++OPP258RzVWsTw7O2uOj48/mWHI7Vibrp7FIooD2p9//rlY6ogkXiclfdUK/dhnqFPNkEcodEJc4qh5D9a22o3vOaTFk6jD/1l9XlDOGMwi325vbz9b7qtJn2eRAw0+cfci76HvvY9yrLNhfF95gFNaAoz3qG/LM3d96fId5UYKJX0mf7MS+ZB1ODc9yr+M2b+sfK0LtAycZ/wUogYlcFVK6soVQDUWQs0nNhyVg+ZMp1QbV4fOS7OJfJdDptGPMUt0AnUclTUt60+pb1lLkCH6zGhHF7IxtIvLqAedkeo8Ar51GUwloIC8fv26NeKkoCy7MzfKBpeWgKfygn418635a+nKfFtfvmnSRLO15tv6g/ZFacdAjnxjbJMRvSl8K853QwIEOlsICLemyodAh4hVpalxOrGa3TabumNqHUkiJTArh5olNJaDVfLNMN/MN/PNfNtMvhUVwJ2dnbYiciR1zeDVRFiHSBBCTrZDMXcc/2r9gGVL5xZ9C3V9aAu+UY9V8M0w38w3881822y+dXo7asmXyolOkVTO9fX1ZxtFIu7u7lo/iN3d3dZKQqMu+bEMQb4x5OHDhw/tp/nLJ5GPCFsb2VuW2hD6jgKqOaZm7vPNGh0dhBxk65by0ca0Ta77qeXTUleTNrPISZ+25l1d9YqMSVa02Sha+EpH8jLG0XfK0UR97ad8xvrs49ubN28WTtRdfOM50jDfzDfzzXwz38y3WQqgfBuyX4sUu7zBIGb0jz/+aP2P+M7zQ9u+S0JEGjq+RTvk5FwrslAR3CsLbIiE2obeR6iho4CGjqmZ+3yzZkcHUVdYxIrbGK+X6nJs+bif9NUpq+OLnRpyBCFzvYoU/KYArpraz7s7SUfWnkI1kIcaf5+xRxMNtR/lVcgJle3w8LCTb5pl6OKbfMT4S/nMN/PNfDPfzDfzbYhvO0OVw8spUNzdxncyW9KQEVYElYKKeLXaqArMR3EDo2BHC4nKkCOvtvFrR1ZfKAae7QvMWXMUUF+Z5j7frNnRQbIqFLMsW0lzyoeskC7GQlf4BbVlKYL8+fl52/4IeswbREBeZKlpgwzlEBH5/ebmZvF8TUdRK8d97Ud5RU7JPOXgbxff4JMOU898U3Bbrp+enraDyRi+XV1dtXVvvplv5pv5Zr5tFt8GJffg4GCRUCwEgs91XihNVMFN+U2nhsSYgUPT13x4nywqKoR3kK6OcuFdsooosII68gyKo2L8dDWQDqSOGGvB5WNq5j4fz0tcRyBcOTCxZKG0VJJ9aErl455oPcuyVHw23lXTscape55H5hT4WHkUWRSDkryQPh05skBehgal2GakoTLHGGKxo++z/mM9kqZcJRSCocQ37vvtt9/a+uI7f0lL8k65+ZycnFS1KfWtsEzaDWm+mW/mm/lmvm0O33ZqKofESCRucSfzXI/TnYqIril07q+ZxtdScQzCCNERdt4Rwy1kaywGdVQj5O3dMc853lomS02DxA5n7vNT/F0eGmrnDIQQ4kSC1JaP67SRDueWBYNQK6iuDuyugSweCNc17a3joHh37NjpmPv8WmKZ5MshmVd+42kOfXkuRcLn3RqQSnzjXbyf33heS0FYiOSN91GG169fV/NNQU/FGfPNfDPfzDfzbbP4VhX2HEJodi9fVwVAIE1dy9mxVusn0zoOTdDsYl9cJX5T8MY4PQthS4Kr6d74GQs55i7r+ccQ5V9T5KXlhiz8NeWLsbMgHh0cH67Rdvw9Ojpa+NjUdLDySe1rE0jIPfJTVV6Qob6df7FMWh7KHUiMu9iVBy1HZYuS52PYgcw37icPmoVQXE06Ep3Rze+1fjXUFTyJ/DTfzDfzzXwz3zaLb9WtoxhJUftVQRFQSMo9ipJfGxAT4lHJkUw6qqkmDVmCWHSCtuTX7Igy6rDM+IxyYKWd45E8mUS0bWzXPuuodkpdSxrR4lMA0Boy6qSG2DEoeHqNrGuZIZZLSwLxWhffZGDd3d0tnIrNN/PNfDPfDPNtLN+2x2jJFCQ7Rurwc14COXQgeu2ZmNqBk6dPx2yrj7OO0apbJ2fTpwjaWI7LY0DbynWgbwkFgaddsXZLREGoz87OFtZ0LTQrEAeVPCCMkTfVw9BSh07X4d7MI9KMMx0lvsnIktVJWuab+Wa+mW+G+TaFb6PmZyGDtiTn6wg5GiYWT+26tiyhODWuncBjnEYhJO+N+aJyIW6NlWlMJ8hU/w4tVwxZ/4eHh608Redp4dWrV63/DH/HOhnnvMvfo1be8rKUwgZ0Ie7Y4j7FcuoakEp80zGMXNeAZb6Zb+ab+WaYb1P4tj22UGQ278KSNcT1Ia03onSusHa4jI2qXuokxliZxnj0BUsdeq7GvyT7tEx9F3KWP5qiXxBhhK9KaVmqZKVHaBpfMwCy/mJZuZaXqiLfFCcqxjMz38w38818M8y3KXzbmTK9CZEiwUSyUtykIYLk+7FyplQEBc4zk+TPRyjdL0GmOPny3Jgp7RooQKssZQ0YyFJXHnN4iDF+IOp8NRvA/wwcXZ2GYnhp6er58+ft1H70DSoFHxXftJOMfMZQAHP5pnhn5pv5Zr6Zb8Zm8W1nSmZIOAdOnKK4lZ6ZsubehPAAeWbSU+TLEb5lnnNJOy1rd5h2PEEOOlvF88qR0ocwdrpf/kBxOUgDSvbvictRiksmuY8DmMJRlPhGGRXiQc7tY+twqnFlvplv5pv5Zr49Pb7trLLi8zmMc0hDOtkp1odz3187qa3GnPE5lN6UdK6vr1ti5GjrOmJH52x2GTJT88FzOrJJ79WAkgekOFsedyZ+8cUXn8U4046/PHOg5SRZf6XI8VP5NqUOzDfzzXwz38y3x823tQvSM3Xd3bg/0KGWBKnk4/JQFpfIcXBwsDhLU9cvLy/b5Z7b29sFUfKHMvU5kdcgO5Lzf2l3os6P1Hs12JT8ekrXGKB0VqjSMt/MN/PNfDPMtzl821mXRrA1s75AyPIOpbjMsgpAACz/6IaAhUznDWGGiNsXiLYWMSyEkJelqCcd5E3eYn6jc3vfslT0JeI75bPzt/lmvplvhvk2h29rNwNYir5trA50mCVLaKp1tAxow1H0B0JmFHX9ofIVl6XirETcbRhnIPLGqdL9cVkqppGXo5aFOaEODPPNfDPfzLfHy7eVKoAQWk6sfP/666+br776ahJBSuvuy3bu3ETI+TTX493d3coO+FYHnfODZfTQ7T20LCUn9LwcFWc1+palGJi002vuclTk21yDy3wz38w38818e9x8W6kCGMmAcH/33XfNN998MyktOoFcOfkg5/uwHp66PweCl52s6SRrAl3eF/IO9GaFvjV0EjkemZal4nJUaTai6YlxpgEpDmLIc16qmMq3uTDfzDfzzXwz3x4331aqAMbo1JDl+Pi4OTk5qY4Qn4U1b72+72lcGuoxHHY9Fbe3t8UAlTrmZlUo7RQf29YleZkqwxp44oCiWJl9y1H5/jhwKMSDnpOz75xOoBQNPh+Mb76Zb+ab+Wa+bQbf1kYBFOQjMXbLvab+I0pLAF0VHglaa7VBkKdqIdEucjjN5FilddR0bBgaG1h2WVv1m55lKazLvuUooWtZ6u3bt4vlKNpibp2bb+ab+Wa+mW/m21oogGSQguXjeBDqMbuu5KsRNV6dwzgYBycF2MxHqPSBDmfOEsG6grrHOnr27Nknwsh1PlxfJUqO1CV/m4cckErLUshf33JUzHtpWSruTiO/c2XNfDPfzDfzzXwz39ZCAQT7+/ufBbhE6BC+MQTJgvr7779XHdqtnWWx8mtIImfhubGt1g3UO+Q4PDz8RIAQSK4fHBys3PGYzjC3D9dqO1XKuEwLjw69tCxVsxzVNajRcZNHzW4syx/HfDPfzDfzzXwz39ZCAdQafIxbI8tJh3L3Qc/FqXAIB4FrrBdFh88djNLoIgfvJf2+A8kfEyjHxcVFS/ijo6NPOj/q4urqqrWMlmXFz7WQMhn29vYWB2H3gTal7b788svRyzBDA0pelkI+hpaj4qCQO1vKxMDUFajUfDPfzDfzzXwz36bybS08PNG6Nf0q0Bg0Vh9J+I2G5fkovGjAtdO46giixo1lRUeAUJCWKl6HhGv6mHdNPbt4VVBZ+FA2ynh2dtb+j1VEubTUgABdX1+39fPixYu1IIfaTIdjC3SoyAFt0xUEk/Ken58v5GWMFV4zIOXOkvoqbenvstRLy1KS6WWGJDDfzDfzzXwz38y3qhKfnp5+Ni1amn7mhfmlVPgQIUmbxrm5uVlYNlyjUSgsDYhWrHQQZj78H8/IQ2AhzfPnz6uncbkP7Zp8R6KhQSMYCI6m8JWH/f399l1dzrS5HhAs8gQQyDy9m+uSPFHmrnospVEqL/nL6cS24Dvv5m98HtIr6jhtMWZHFO2VZaCmfLWyEjtV5CXu4qKeSSPKjMolnwjkRXWYt//nfCGLr169qsoPAwbkzAeB1yxH5UFNjshqC+pT8mO+mW/mm/lmvplvc/n2/v373a137979/8ePH/+3LzM8dHx8PFqTRbtWA9QAcpBxBJ5GkUZOgyGoEkh+h7wiBr9DDP5CtLG+G3ovz8Vz9/qmWMkPQo/G3Vc3NAwdVSQI7xhjbeR6nJLGEBQCgXIplhbvHOsPQd3wTI1/yhxZaYIzL4LOJ+c17rZTXUF8+UhwrS+fY+UeGSRdLc3wbuTj5cuXVfVIG5AG+dZ7KSPXazop8818M9/MN/PNfKvh25s3b/5RXcPZkbUGY6ePKSAaLFYZ2q+IwIcCaVpexwHRoJGwU3cs8V4EmMq+vLxs06HyIExcKuB9CjUQrbC+uimtw5PnMc61y0ijRAilCykoJx/KPnf5g7THysuUpQbySVvQbnQa/E8ZkBu1H+mSH4hLfXGNtkPGpuRzaEaB9FQW7Uqs7WQka5rKV9uUHMnNN/PNfDPfzDfzbQ7fqmYA5xzkTYVP3U2lWD0UJJ4BSQWoEiJxlgGIAAl4pzRsiEAZdD5fLE9N3XC/yEvDTIkOH+txahq58yxNm88FbTaVuHNkhfaSzwp1oyUETZMjI7KKxuRzbEDQLA/q3MdYfViLU/Ngvplv5pv5Zr6Zb0P5/PDhw9+rFEDDMAzDMAzjaWBra+tv264GwzAMwzCMzYIVQMMwDMMwDCuAhmEYhmEYhhVAwzAMwzAMwwqgYRiGYRiGYQXQMAzDMAzDsAJoGIZhGIZhWAE0DMMwDMMwrAAahmEYhmEYVgANwzAMwzAMK4CGYRiGYRiGFUDDMAzDMAzDCqBhGIZhGIZhBdAwDMMwDMMI2Gma5petrS3XhGEYhmEYxmbgl/8EAAD//wrTP72/VBbNAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning Hyper Params:\n",
    "### C\n",
    "It controls the trade off between smooth decision boundary and classifying training points correctly. A large value of c means you will get more training points correctly. **Higher the value of C -> Causes more accurate fit -> might Overfit**\n",
    "![svm%20HyperParams.png](attachment:svm%20HyperParams.png)\n",
    "### gamma\n",
    "If gamma has a very high value, then the decision boundary is just going to be dependent upon the points that are very close to the line which effectively results in ignoring some of the points that are very far from the decision boundary. This is because the closer points get more weight and it results in a wiggly curve as shown in previous graph.On the other hand, if the gamma value is low even the far away points get considerable weight and we get a more linear curve.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "..                        ...  ...      ...  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv(\"/Users/sudhanshukumar/Documents/Machine Learning/0 csv files/diabetes.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.298000</td>\n",
       "      <td>109.980000</td>\n",
       "      <td>68.184000</td>\n",
       "      <td>19.664000</td>\n",
       "      <td>68.792000</td>\n",
       "      <td>30.304200</td>\n",
       "      <td>0.429734</td>\n",
       "      <td>31.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.865672</td>\n",
       "      <td>141.257463</td>\n",
       "      <td>70.824627</td>\n",
       "      <td>22.164179</td>\n",
       "      <td>100.335821</td>\n",
       "      <td>35.142537</td>\n",
       "      <td>0.550500</td>\n",
       "      <td>37.067164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "Outcome                                                                      \n",
       "0           3.298000  109.980000      68.184000      19.664000   68.792000   \n",
       "1           4.865672  141.257463      70.824627      22.164179  100.335821   \n",
       "\n",
       "               BMI  DiabetesPedigreeFunction        Age  \n",
       "Outcome                                                  \n",
       "0        30.304200                  0.429734  31.190000  \n",
       "1        35.142537                  0.550500  37.067164  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Outcome\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0=df[df[\"Outcome\"]==0]   #stores all the values for outcome 0\n",
    "df1=df[df[\"Outcome\"]==1]   #stores all the values for outcome 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7ff3e0a05b90>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# from graph we can say that a Hyperplane cannot be drawn properly due to which accuracy is very low\n",
    "\n",
    "plt.scatter(df0[\"Insulin\"],df0[\"Glucose\"], color=\"red\",marker=\"*\")\n",
    "plt.scatter(df1[\"Insulin\"],df1[\"Glucose\"], color=\"green\",marker=\"+\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()    #No null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  \n",
       "0                     0.627   50  \n",
       "1                     0.351   31  \n",
       "2                     0.672   32  \n",
       "3                     0.167   21  \n",
       "4                     2.288   33  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=df.drop('Outcome',axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df.Outcome\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "614"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\n",
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "model=SVC()\n",
    "model.fit(X_train,y_train)   #training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7532467532467533"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6948051948051948"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=SVC(gamma=10.0)     #gamma\n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7792207792207793"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=SVC(C=2)    #regularization\n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7662337662337663"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=SVC(C=5.0)\n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7922077922077922"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " #Kernel -->{'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}\n",
    "\n",
    "model=SVC(kernel='linear')  \n",
    "model.fit(X_train,y_train) \n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iris Data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=sklearn.datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR',\n",
       " 'data',\n",
       " 'feature_names',\n",
       " 'filename',\n",
       " 'frame',\n",
       " 'target',\n",
       " 'target_names']"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(data.data,columns=data.feature_names)\n",
    "df[\"outcome\"]=data.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.target_names   #returns an array of flower(Target) names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'setosa'"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.target_names[0]   #grabbing a value from array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### adding a column named flower and then putting the flower names accordingly from th outcome values\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"flower\"]=data.target_names[df.outcome]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>outcome</th>\n",
       "      <th>flower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>6.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>6.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>6.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "50                7.0               3.2                4.7               1.4   \n",
       "51                6.4               3.2                4.5               1.5   \n",
       "52                6.9               3.1                4.9               1.5   \n",
       "53                5.5               2.3                4.0               1.3   \n",
       "54                6.5               2.8                4.6               1.5   \n",
       "55                5.7               2.8                4.5               1.3   \n",
       "56                6.3               3.3                4.7               1.6   \n",
       "57                4.9               2.4                3.3               1.0   \n",
       "58                6.6               2.9                4.6               1.3   \n",
       "59                5.2               2.7                3.9               1.4   \n",
       "60                5.0               2.0                3.5               1.0   \n",
       "61                5.9               3.0                4.2               1.5   \n",
       "62                6.0               2.2                4.0               1.0   \n",
       "63                6.1               2.9                4.7               1.4   \n",
       "64                5.6               2.9                3.6               1.3   \n",
       "65                6.7               3.1                4.4               1.4   \n",
       "66                5.6               3.0                4.5               1.5   \n",
       "67                5.8               2.7                4.1               1.0   \n",
       "68                6.2               2.2                4.5               1.5   \n",
       "69                5.6               2.5                3.9               1.1   \n",
       "70                5.9               3.2                4.8               1.8   \n",
       "71                6.1               2.8                4.0               1.3   \n",
       "72                6.3               2.5                4.9               1.5   \n",
       "73                6.1               2.8                4.7               1.2   \n",
       "74                6.4               2.9                4.3               1.3   \n",
       "75                6.6               3.0                4.4               1.4   \n",
       "76                6.8               2.8                4.8               1.4   \n",
       "77                6.7               3.0                5.0               1.7   \n",
       "78                6.0               2.9                4.5               1.5   \n",
       "79                5.7               2.6                3.5               1.0   \n",
       "80                5.5               2.4                3.8               1.1   \n",
       "81                5.5               2.4                3.7               1.0   \n",
       "82                5.8               2.7                3.9               1.2   \n",
       "83                6.0               2.7                5.1               1.6   \n",
       "84                5.4               3.0                4.5               1.5   \n",
       "85                6.0               3.4                4.5               1.6   \n",
       "86                6.7               3.1                4.7               1.5   \n",
       "87                6.3               2.3                4.4               1.3   \n",
       "88                5.6               3.0                4.1               1.3   \n",
       "89                5.5               2.5                4.0               1.3   \n",
       "90                5.5               2.6                4.4               1.2   \n",
       "91                6.1               3.0                4.6               1.4   \n",
       "92                5.8               2.6                4.0               1.2   \n",
       "93                5.0               2.3                3.3               1.0   \n",
       "94                5.6               2.7                4.2               1.3   \n",
       "95                5.7               3.0                4.2               1.2   \n",
       "96                5.7               2.9                4.2               1.3   \n",
       "97                6.2               2.9                4.3               1.3   \n",
       "98                5.1               2.5                3.0               1.1   \n",
       "99                5.7               2.8                4.1               1.3   \n",
       "\n",
       "    outcome      flower  \n",
       "50        1  versicolor  \n",
       "51        1  versicolor  \n",
       "52        1  versicolor  \n",
       "53        1  versicolor  \n",
       "54        1  versicolor  \n",
       "55        1  versicolor  \n",
       "56        1  versicolor  \n",
       "57        1  versicolor  \n",
       "58        1  versicolor  \n",
       "59        1  versicolor  \n",
       "60        1  versicolor  \n",
       "61        1  versicolor  \n",
       "62        1  versicolor  \n",
       "63        1  versicolor  \n",
       "64        1  versicolor  \n",
       "65        1  versicolor  \n",
       "66        1  versicolor  \n",
       "67        1  versicolor  \n",
       "68        1  versicolor  \n",
       "69        1  versicolor  \n",
       "70        1  versicolor  \n",
       "71        1  versicolor  \n",
       "72        1  versicolor  \n",
       "73        1  versicolor  \n",
       "74        1  versicolor  \n",
       "75        1  versicolor  \n",
       "76        1  versicolor  \n",
       "77        1  versicolor  \n",
       "78        1  versicolor  \n",
       "79        1  versicolor  \n",
       "80        1  versicolor  \n",
       "81        1  versicolor  \n",
       "82        1  versicolor  \n",
       "83        1  versicolor  \n",
       "84        1  versicolor  \n",
       "85        1  versicolor  \n",
       "86        1  versicolor  \n",
       "87        1  versicolor  \n",
       "88        1  versicolor  \n",
       "89        1  versicolor  \n",
       "90        1  versicolor  \n",
       "91        1  versicolor  \n",
       "92        1  versicolor  \n",
       "93        1  versicolor  \n",
       "94        1  versicolor  \n",
       "95        1  versicolor  \n",
       "96        1  versicolor  \n",
       "97        1  versicolor  \n",
       "98        1  versicolor  \n",
       "99        1  versicolor  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"outcome\"]==1]     #prints the value for all outcome=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#removing the outcome column as we now have flower(Target) column\n",
    "df.drop(\"outcome\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>flower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   flower  \n",
       "0  setosa  \n",
       "1  setosa  \n",
       "2  setosa  \n",
       "3  setosa  \n",
       "4  setosa  "
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()   #final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(\"flower\",axis=1)\n",
    "y=df.flower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0=df[df[\"flower\"]==\"setosa\"]\n",
    "df1=df[df[\"flower\"]==\"virginica\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sepal width')"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df0[\"sepal length (cm)\"],df0[\"sepal width (cm)\"],color=\"red\",marker=\"+\")\n",
    "plt.scatter(df1[\"sepal length (cm)\"],df1[\"sepal width (cm)\"],color=\"green\",marker=\"*\")\n",
    "\n",
    "plt.xlabel(\"Sepal length\")\n",
    "plt.ylabel(\"Sepal width\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=SVC()   #different parameters can be passed here\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuning and tweaking the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#linear kernel is used as there is \n",
    "model=SVC(kernel=\"linear\")  #tweaking the model \n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_test,y_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=SVC(C=10)   #tweaking the model \n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_test,y_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#in most cases on increasing the gamma value --> score decreases\n",
    "model=SVC(gamma=1)   #tweaking the model \n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_test,y_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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