ml_intro_healthcare.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Aryabb29/Aryabb29.github.io/blob/master/ml_intro_healthcare.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZgqkZPZdi_Pc"
      },
      "source": [
        "#**Introduction to AI & ML**\n",
        "### *Michaela Buchanan - Mark III Systems*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SP0wDI62k7nm"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "If you would like to see some more examples using these algorithms, there is are two other versions of this notebook available. One focuses on predicting car prices and the other looks at a predictive maintenance problem. See the links below to check them out.\n",
        "\n",
        "\n",
        "**Car price prediction:** \n",
        "\n",
        "https://colab.research.google.com/drive/1-7g2CRQinW_ytjZvJbOd74vnqu4OAdoL?usp=sharing\n",
        "\n",
        "**Predictive Maintenance:** \n",
        "\n",
        "https://colab.research.google.com/drive/1IA9JWy_Q60Jcn9N51PNXsu9jdyqavQek?usp=sharing\n",
        "\n",
        "\n",
        "\n",
        "Imports for all the code below. Please run first before anything else!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "byHwkWWKlCmF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84362361-f10b-4323-a24b-909c87b708f9"
      },
      "source": [
        "!git clone https://github.com/michaelabuchanan/ml_workshop.git\n",
        "\n",
        "# data preprocessing\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# logistic regression\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn import datasets\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# random forest\n",
        "from sklearn import tree, metrics\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# xgboost\n",
        "from xgboost import XGBClassifier"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'ml_workshop'...\n",
            "remote: Enumerating objects: 12, done.\u001b[K\n",
            "remote: Counting objects: 100% (12/12), done.\u001b[K\n",
            "remote: Compressing objects: 100% (11/11), done.\u001b[K\n",
            "remote: Total 12 (delta 1), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (12/12), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EQQCGfdiHZhJ"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "###Introduction\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Hs8mq7lyra_"
      },
      "source": [
        "This workshop will be showing both the workflow of a typical ML project as well as demonstrating a few common models. We will be following the ML Project lifecycle mentioned in the previous lecture and covering linear regression, decision trees, random forest, and XGBoost models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4v9HLjpyjef"
      },
      "source": [
        "![ml_lifecycle.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAsoAAAHkCAIAAACoup7MAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAFu/SURBVHhe7d29jhw3+vbh9yj2AHwAPgBHTpwocjSRA0UbKVgn42hhKDGwgBwZq8TRBAYMKNjE0GYLKFIyidMBDDgVIH/vLmzDwP+9p2/OsxySxa6qZn+M5ndBEKaKLBaLZJFPVfdI/+//AAAAhiK8AAAAgxFeAACAwQgvAADAYIQXAABgMMILAAAwGOEFAAAYjPACAAAMRngBAAAGI7wA1nj69OnZ2dm7776rv/Vz2gvc9urVq8ePHz948EBD5eHDh8+fP08Ju7m8vNSoe/TokUs2DcXz8/OLiwudNOXD3ffs2bPo5RcvXqS9dwHhBbCYFgzf7UF7UhpwQ8u8Qoo0RG7sGIwqQHFc26HVSEFGOgB309XVVTzDBMIL4E2mOzzd67fdrTsfB6DlIQ2O27RypBxLKFg5Pz9PRcxAyHvnaGAofNSwqaNSu7/hxdS9NGXdPdZXP1Z21I8RSy8h1EUdS6rQzvQA9OiGrk4PQ5eXl+kc99vUIDmdMXDPpf5YLh0/ju6dVPRtKxaJ4kWI4gzPn7orOzEHIe8dMjVacoQXc+3j9V3+SeRW9Xqw9BLC6SwtqUL7oeZVAHfP44ypQXI6Y+CeS/2xXDp+nFHhRRFbqNiUcENzaUq7jRcYdwjhRc/Stfns7CwdOcizZ89S0fPU68HSSwins7SkCu2Z7oR9vHy6E3SHp1a4jSfFE5H6Y7l0/DhT88nSe+fJkyfpyI3m4c0Xt+fn5ykZJ4/wYi7dAPn3XaeMfQ7e+sGkMszvHl3C1DOBqKgTf4jXE8/Ut8Dqp5+CjlVDiVqg06rq4nTAPVNP5TwmFjR43DLHjbxVjamPsTU7aQBrqKese1C8dbCld40monTkhgpMCZV6WI76RRUcRR1waDyntLtgv1/t1OBOrTJh4KSsOzkVOmHdy5LiucHu0DNBMTfZ1vCioEKaz0Zyb5dVrZoO3fT36by7Oh3xKvHojTM1Mxzm8UBn1z3iBy1FBivW++LW699xavYYlvc2+n9jEF70bF3yddelrDvL3zQ035qsm+biISy3j2+N7E+qdGZpeGGxYBQUgaUcwI2Iy08h9qqn6YEzz74Vsxmx7P1BeLFFapWNurFkVIidv4RsPmoPDC/uVh+nSmfWhRcyFWHwDhaFuB9PM7xYfQscWD3/EF7cH4QXW6RW2WguTkM+aLi8vEzFbTQ/lCG8CLvMrc2vYqz74Alvqvy1JeHFLuqvfxFe3B+EF1ukVtlQ09TtJVc7/w5C/g2Jx48fN2MCwouwy9za/DKH8EEvQv4gQXixC7VeqvENwov7g/Bii9QqG2qa5guM3b/K4G8z2fPnzwkvcqnSmR3n1nrQyx36uiv2Lf+kkvBiF4QX9xnhxRapVTbUNK9evaq/d7njq/X8oxB/Y4vwIpcqndlxbq2nPEvJuN+KjyYJL3ZBeHGfEV5skVplw03T/N7lLr8klhfo32IgvMilSmd2nFubbSK7f8iFu65+fiC82AXhxX1GeLFFapUNN01zcVr9y43FdOYwpXkKwouwp/DibjULhtPNmH8sYoQXuyC8uM8IL7ZIrbIRTZN/VcL8ocYK+Zc54kOW5vpHeBEILzCcIvs6thDCi10QXtxnhBdbpFbZiKap7xlZ928n5L8nGV8Rba5/hBfh8OGFnmvz3+5Je2+72vwr7KpbvI7Sz9qjY1OOGZRZEafOlZdj2qPRogJHfYij61WZLnzHSV+DXyWotCLy1oKtOitp7D8xqdLUDio56m9qMe15/Pix2nB+Kyln8xNP27FlhiguU7QnpR2E2jN6dlGDKLOPCru3p3pfhaj3i3BQNVSzKGndVDxFt6QK9C1ZD2+f8W7NqAWNf/WvbgFdy9Scs+7+1eGpoBs7NtSBu/444YX6I+3K6JqdOl9RTkyIOlHalVHbOXWRZlF362ZIlc5oJKW0VTQEU0G3NeMArz3FXZfSbjhPSqvoTpgTYahTOoUUVKZmhHTkcjq2uPPXjS5dlw4sGmfKjnUWn66Y4jt0jf2hrtRinlpqx6E4U9FZcpjzqsG1tBQN3hkqdT1nSsdvk0c5fcqmes657zo0PDSrpxK30V0wdcaUY4mpcTu/heeMELXn/PGvJl16/9a17d+PHQfuejtOeCHNbl56Sbp105G3R4NOlPZmOnd1R7Oo1X18FKnSmTl3Tkf+HiJodkjJmxjZzytT917Kt5l/m6UV+hVWdDJ/FsupejOfKlRPdbqf+JvRwIrRpUNmBhY5NUWE0YusO510zqgyU6a1dhyKM+ks6Xw39ndeD361TH1S6wyVqUO2SsdPU5Vmri45HbJurlMjrLsWDVHdZamUGyltialqz69Vf4Ssa09Z1KR1bVd0x4G7Pne08ELBVNqbqQdWX7565YGhTpT2Zjp3dUezqN3b/ZBSpTP9O2er5mB9fPM/LaXtLufUHDR/3E+tcLp56lVTxWos5aGDukxxTHN93fpIMWcRXTS6FKzUE4fqrBrmQ0v111XUIZquIr+0rdR0zThPddC1R8OqVmrMZqCmMyrJ2XJzWqZvx6E4U93aezpvfaLa4cOLOoJXh+qGVZ/GE52GgQZDs/e33iAFDdp0ZEZnrIe3mqI5A6gaUTFJe5eYmqLnt/DUCFHFmm9Jm3OOcjbnnJnTRV3bqeuacuCuLxwtvNC11e2uSTAlz6COTIdtmiwfjjpRSsgsWgBCs6ilfXxcqdKZqTtnjmaDSLRJ2u5StmZY0NFsc43+lJzpdLQGSX2/ScRGTSow5Zs2f3Rp3NZTav9wTVtFW2lzZoShbM0brTOGldSc9OfMNXVbzW+Z/amn6V1ugY76RLVFDbJje2rA11UqFu9Cs/fnLzPNpVd1njrj1C2pIVofompMTRrN/H31FKSW6d9WOkUdqauQTvto8W6Oiv6cY/WBndu2oKoeuOtrRwsvpDkQZ06akg/Koqt0opSQWTfNNYua38enIFU6o2GX0pZr3ip5gWlXl27s9NOGDo9oemoGiYfsoJwpLTPnZlh6YD3F12aOLl1jcQPrYueM+TpKmDOfNmOLOfNaXU/bOvLrtlp3341VD9pdboGO5t1RWNQgO7Zn/Ug65wZR7xeL6MxRWk/pMw9svvBo9pFKS8m3rZuT8/7SJae9E+pmkTm3oTTfDWztyno4zb/MA3d90zHDC22mhIyChpS8TT79aXFKezeaJa+b5ppFze/jU5AqnVk9t9aTnWj81Wt/rr5JYs3TD/Wgr4d4PSqas8yc+8eUMx2TmX8XrZ706+lp/lhaelI1Y7RzmBNbWHPk66ZLyRNWt8xe1SNw9S2w1I4Nssvh9VuB+cfWvb+1xeqqLlqZ6j6S5h1dn0jm3/u5fBHZeifWNZwZW0jzZpSlM+fM6eLAXT/lmOGF1E9I6oOU1pU//tZTXt1AMr99c82iZvbxiUiVzqwbLs0lWbbe2M2JQ3RzTs0+uht9h2g81LGFUuuRo2g9Jc9TR/eqT0rbpp7g5oyu+qj60jrqGUqbndmtbnY1Wid/rfl+sQjlC+taZt/qplg9Yy61Y4OsPryeuOYPb6sbrTPvNefJiyXfpdNCmw7LNOtc3wgyP24O+Rm3joe6I2TRY31z/uzPAIu6IBy46zuOHF40+6w/f1k+8dU91Bzri+7q0CxqXVsfS6p0ZuncqvvZi31tzkNDPVhFI37RUpdrDpv+c0CtOZ3NfAaqK7B1dOl0i4KDpnq9nzpvcy5bOm6bTdS/0hUtcwD1CFx6C6y2Y4OsPlz3VzrgxtLer4dQp9HqcH9FCzfD2ZR2W90sK+6mvJD+orPiRmha2kr1uJ3TiQfu+o4jhxfNbtv6GFpEr3UIqROltMyK0SDNopb21nGlSmfmjBVdo6jRmp8a2szFuL5Jdoktit63Fc8uUl9a/SasqZ7dto6uOj5bUef6tp+q8JDpXuq+6z+SrmiZA6ivYl1rrLBjg6w7PH+5azMHdq45OWtnSs4MiWVFh6SDb+hOT2m3NSeBRS9LJO6RrY1Txz0rohmpe7O/2NXjdmurHrjr+44cXkjdgtLvuXw0N1/71MNUFt3VoVnU1j4+KanSQ6nXdvlUdZcGbM5li95ShvpWlDkvz1ZM+vVsuKLOOiQdnKlvlmYTaWdKXkJnzGuun/uzzIqWOYB6BGpPStuzHRtk3eF13Lx06bV60DbvjjqWXbGkWbGQdz47qOP1RSfN7/1+4zTX2nXPM7pVi1cL/dlmxcx54K7vO3540ZwK+y2SN3ozZzMmWDfNNYvaZXU8vFTpQTR8l46zseFF/epPd0JKW0h3eyoiM2fiWDrp53PZcHVj1lOMpLTlFGG4zfX31pBo3XK4b/cqvGiO6lHqszdD3kXXWFDcoDtaOrGFNFf9+VNT3CM6Uf9pVkuMc+ZWz2A6l0+tYGhrxL905jxw1291/PCi2SLNdxJWjKrms5ROlJIz60Z8s6jVY+soUqVX0fgWrbhqPV11/z6csvQm6WiOFt2uKXm5um6ablLaNLVGyn2jP7rql6sDFVPq8CZaZGnLHEbdy9qT0vZsxwZZcXjzgW2Uesmv3yLI1jB0iPq2mtmt+SKy9XFi4PPMUktnzgN3/VbHDy+kOflODdA8lpyaNHWilCOzbpprFrV6dTyKVOnMweZWW3qTdDRfA6zrWWtOjs2YNbd00q9nqIGKUw8c/CssbZnDqEfgwW6BHRtkxeHNIT1K3W5120pK27PmUN9680reRFsjoZQvc7DBU7dtf+Y8cNdvdRLhRXPNmIqV8s/5pl4uDZxhm0X1+/jUpEpnDnZ72NKbpKOebWX+G9Fas8Ct1Vs66adMmXWjcY7hTbTI0pY5jHoEHuwW2LFBVhx+4ItN58h03j0PV1/s1rcR8uDmiwVbW6Y5/0+tTcPVV9efmg7c9VudRHgh9ZeDmi+g8s/5Op+ZNcfEummuWVS/j09NqnTmwGNu6U3S0XzRtUt3rHsdsmjSLz7Os3WjcY7hTbTIopY5mCNOuzs2yIrD67l0fxfbHNuHnF7qjwM664Llh0w9oIbmxw0HG9JLZ85Ddv0cpxJeNN/q1I9cebZOlKoTpUyZdWOiWdTBJushUqUzBx5zS2+Sjroo2aU71g2VRZP+ulOsNryJFlnUMgdTt8nBboEdG2TF4SlfZn8X2xzbB2tbq9fUmb8ZoAPTrml1+8uiHtxFPW77N3LKlDlwXxROJbxoRsH1VyvipZZ03vcOnNCbRfX7+NSkSmcOPOaW3iQddVGyS3esGyr1pNM5ZN0pVhveRIssapmDqdvkYLfAjg2y4vCUL7O/i22O7YO1rdW/2dGJG/KFZk5H1O0vi3pwF/W47d/IKVPmwH1ROJXwQppff8tfc+XvsfuB58AJvVlU/0IWGVjUlFTpzIHH3NKbpKMuSnZpw2b/bh0q9aTTOWTdKVYb3kSLLGqZg6nbZOAtoBVL0kZlxwZZcXjKlxl4sYXm2N7f6Zq0RuSPnTb18Jm//+70WqjbXxb14C7qcdu/kVOmzIH7onBC4UXzU678s7H8Q+X+l2sGTujNovoXMp8L172hS5sz1tdxnXMHHnNLb5KOuijZpTua/bt1qNSTTueQdadYbXgTLbKoZQ6mbpOBt4AL19NR84P8HRtkxeEpX2bgxRaaY3t/p5tSf7Y+9UuFEYh0PlvP1e0vi3pwF/W47d/IKVPm8H2RO6Hwovn7+vEl5CK1/9tEAyf0ZlH9C5kvAqatX0fahU+RO/CYW3qTdOQhZtilO9Z9dauedDqH5N9HDvvrgubXmEaN2K0WtczB1CNwVPvrqSCVOHGlOzbIisPrR3lJaaM1J+0DTy+S90KoH9jym33mHbHuq9+jLJ05D9n1c5xQeCHNxcOjJB8ZW3/xSSdKWTPrxkSzqJlDsy+/JWaG0uukc2QOfP8vvUk66tlWdumOdQXWR/VHV8qU2d8v7zWvqPlgvQ9LW+Yw6hE46hbIp6x6PZMdG2TF4fXFyiGfXuZ8ZXK4eu2o33DH5+/za9ic//c6XeeWzpwH7vqtTiu8aIaKHiV5w/W/GCzNMbFummsWtfVC5sivqPM11d2lc2R06pR2EEtvko7mCNk6Hjo6EW3H0kk/5rVcShutOWL7HyYOtGI5PIB6BA65BfKmnooXd2yQFYcf+PVV3bZy+CWtHvbFK+H8JeKiaDsdk9nfs0Fh6cx53DeXtdMKL0RxZcp9Q3vyB33ZOvvXQ03WTXPNonbvsLxY3QZp736k02SGzK3zLb1JOpovY3dZO5vjLaVNWzrpN4OY/cWU6QSZg02IK5bDA6hH4JBbIC92KsbdsUFWHN78vG9/8WVdQ9nr89KUupfzMCLuwaWfRNfFymHip/rU/ZnzwF2/1cmFF834K2/lOf97Qr54h0V3dWgWtXp1NA3NfFXbd/en02TUnintIJbeJH31m4DVa2cRttqcN59LJ/3mS5f9vWLVPZLOkdkalA+xtGUOox6Bu98Cxe9DTq03OzbIisObIficoHmd5leL9je2O+rFNa5abRLfS1g639a/+CqL3n+stnTmPHDXb3Vy4UVzxs/N6ddmTLBummsWtcvqKMWzbP9rqrtLp8nc6fCiebevWzubRc2p24pJv/7W1Y5vrTSVKK7Seetrb0Yz68Z/Trfe1mVjRcscwPDwQvds3qGdZ54dG2Td4c34csd5RmWq95uF1BH/0jcENR2uPlpa5/yxzXw755HH0rmiuWbvPoXq0lRIv5Xqcbt1djpw1/edXHgh9WANM0etTpQOyKyb5ppFzbyQpiLEPkBomc6U2f3eWGTFTdKhAVAv1VOvpvvqkTazO1ZM+vUhssszULRq80V0Pc9qz5x7Z0rcCP05cUXLHEA9Ane5BXT5xcjp9OOODbLu8OastcsbhahG89G/mNNs3S0Z3GW60xfdI3VNHPlFf63r9+KB0HZZs2MS013ZKacet1tnzgN3fd8phhfNwWozm6nZxIvu6tAsauaF1OpLW1erRdKZMrvMrSusuEn6YsSHFVFas2dnfmZcV2BrPzajohXVtpjvpu6IsS8wNANG5TVTp70tK1rmAOoRuPoWqGMLtcz+4q3Vh9eXLOte8sWspQufutKiTaTfLH35cj7zlrTmXZbfC4tKC2q3utghQ0jFdjpl3cx54K7vOMXwQpeRDqjMLKG5cqyb5ppFzaxGIb9nwrpeXySdKbP6xlinfpJe14ChOYksfVqqJ8T5zbJu0m9+FrNiWMb3k/r3/KjXpDpF9ODWNWNdy+xbPeGuuwXyMCv0n3l2bJDVh6uq6YDMiquOtVkX3hk8zdP5zcFS+W2yYvDULRZWR/PSvHnXvaHJb8z+TFjPUXNmzgN3fcd+w4siUJgfOTZX4vmDoxkTrBipkke+YenqqPz1QBHtTDn2KZ0ss8tttkI6a2bdM0Su7hfdA/NjtXoO2vHwmaOrueQvGk5x6q33fB4WBO3pxwcFtUkM3TmzTH3r9VdfUZnrpun56rtv6WyrRqs73fqDuY5sZg4VqwfM/HfUu4ezujR1ug/Ug2zaO2HIApwXsnXkNGnEpuMrOw6z3eN1jaJ8advapClfZuYlNPtif10/Zb/hRXGR84PZevGQ+fdVcyJY97zSDHTmD1NdSD2/hB2H+xzNSEsWrTG7aA70+SOho+4arSJzrive+OX6i0ShHmAzb11Vr17qdBvPjDDikues9KI8MUcEVWBmIJVPMfPP6PxBB3Y6xadYNPEt1VxvdNKUvI0OV/WiHQr9cnThzQgvJc9QH67uS2kz7DJ95bfJzAVml9OprfL1W0WlhOWa1ZA5k0PHjjevbo388K1N2pw55z8eHLjrm/YVXqjF4xVuTgNISXMaqL6v5syJ6sJmbGE+e8q6TacojZKpqVaV1CnUi+rdqSkp7Djc+1QTjYypOugSFi2oK6gCzTvE1BdTbTjf0rtdDd4clktvoXpgzF8gm5OUqITOeNBFxVG6xvlNp5z1GNCe/iWr7/K5ftEZ6zu3uVTkfTG/9RbRKTTIm60tGpxTDa7W1oGq1dSxofPMo0KmHi3mDH5lyLsgp/ac3x3NZUYld6bTovcX3R0rTifFTNUcMPOpcVJBmR3LtHU3r5KUIWXd2Hr3FflzqoAGZ+d04cBdXxsZXnRaZErnjUKxDKhNU0JFt3HKtJAqnIq4seISVlMvprMOlUpfLh2/s87bmo5OTNChe6x5C6kOujHyKVjla0Q1F9oVYVY9Tuqx1DFVbVVGlVRVY+7Q3a4LyZt00UpvKqQ5JyoO0BJbtJL2FD2ozTlzWVB7piMzKiTaWWdUc+V9oZM6aYhU6EEUfbFuAonBs+PhU6aifA1CdUosNuplbRYjc8UC0z9dPra1WdyV+nnHJc2KMSxL75opW2/elG8zznUtRWbddFM1qes8R7/3D9z1hdMNL3TlKdNGZwJSj6ZMC9Uds+72XmfIXVRLpS+Xjt/Zupskvy2X0tjIZ6j5tj5UTanHSf8mb9L9vLTaihLWVVhWjG1Vr3PfdRTPBh2abdUO6bBBUtH7p8qnU95YN4HE4Nnx8A4tac0Qs0O9v/quXHE60dSxengXNKhSoRudp9N1Vty8ovuiE6nvI7yQA3d97nTDC8kbpdMrdzG8UP91rmgX6QTLpeN3dvjwQtSY6rv5N7wCi13OWI+TeizN4WrXnybUdGnrVvqc5m49oMxpJVVJFdtliGr+7V+XUne/oqZ0gv2rO70eGHNEOTsevpUeaWauNP2FcKb5p9vxfmzKh98+nuUW3by677ZGTnsKL+zAXW/7/WoncGCapHS/6UYt7iXd4dqpO0e32e43T70MzJ/im6aqrU1NTGMnR12+1n41hU6XhxqaKLVH1Zh6ebuCaq6VI5+C3QvD31hgPvWvAjv1i/oi9cqGukk7lTRqdbE4XTG2dXaP7bGnC/GqX4M87dqPuHnzcR4TzkkN9QN3PeEFsNjw8ALAQFom053JvXk8hBfAYoQXwCnL79BR3+fAUoQXwGKEF8Api88pzvfzC3qYg/ACWIzwAjhZz7NfGxn+jVHMR3gBLFaHFxf7+SUIAEvF9xbPDvu/H6BAeAEsVocXPCQBp+Aq+weTxv7KFZYivAAWK8KLff/mG4CZ4vdRH636T6YwEOEFsFgRXmhGSwkAjid/dcELxaMjvACS+IcmH2z7PzyLfyCIiQzYnyc3/8a84vj+v/t0fvPfcRHxnwLCC+CaQgRPTOHRxH/llT8hCRMZsD/1B5FT/6rss5v/Rlx59vQvgWIRwgvgWv1tTXnY+i/E8n9PVxmYyID9Kf75alH0UH9n83n2f4zxT86fCMIL4NrFxP9crDkr/il+PTblk93Z2Rn/ICCwV/FVzcKTJ0989+muzPPw2yKng/ACSIpvVPRNfXQCYCDFEPFaok/ZeG9xUggvgP9pfkRSODs74wkJOJjLy8utof/jGf/jOQ6M8AK45dWrV/4/i+O/LRA9GD3ifxIHjke3nmKI/NNJ35W6WwksThPhBQAAGIzwAgAADEZ4AQAABiO8AAAAgxFeAACAwQgvAADAYIQXAABgMMILAAAwGOEFAAAYjPACAAAMRngBAAAGI7wAAACDEV4AAIDBCC8AAMBghBcAAGAwwgsAADAY4QUAABiM8AIAAAxGeAEAAAYjvAAAAIMRXgAAgMEILwAAwGCEFwAAYDDCCwAAMBjhBQAAGIzwAgAADEZ4AQAABiO8AAAAg40JL94FAAC4QXgBAAAG48MRAAAwGOEFAAAYjPACAAAMRngBAAAGI7wAAACDEV4AAIDBCC8AAMBghBcAAGAwwgsAADAY4QUAABiM8AIAAAxGeAEAAAYjvAAAAIMRXgAAgMEILwAAwGCEFwAAYDDCCwAAMBjhBQAAGIzwAgAADEZ4AQAABiO8AAAAgxFeAACAwQgvAADAYIQXAABgMMILAAAwGOEFAAAYjPACAAAMRngBAAAGI7wAAACDEV4AAIDBCC8AAMBghBcAAGAwwgsAADAY4QUAABiM8AIAAAxGeAEAAAYjvAAAAIMRXgAAgMEILwAAwGBvVHjx9OnTd1sePHhwfn7+7NmzlG80n/fRo0dpexvlVH4dlbbviBcvXmya8139kHatcnl5qe5Qp7g0t8OQwl3CzIYddTkAgNr68OLXl1+8/suf9Cdtn4Cp8CI8fvw4ZR2K8GK+q6srBxYPHz5UO8i+wwudqFnskDMCAJpWhhe/ff3Vdx+9dbLhRdq+oSXtyZMnTtrHou7zaqVM29so555qsldD1mNFeCpB3ZG292/3OgMAlloTXvz68ouILe5EeGHn5+dOffXqVdo1COHFfL72Qy72u9cZALDU4vDi3//4q0KKHz5555eLP9+t8OLq6sqpw7+EQXgx3+4lLHX4MwIAFoQXv3391Y+fvufY4o/X3/7y5Yd3K7wQp8a67vzeVMxxdnaWp4o/VfF+efjw4cXFRUrLuByHF/7eYuRvhjKd8EKrYHzt0d9Iba6Lm+KvL1M1fPz4sfOrnvnpVFV/7UCpyqOcKWGGV69eqXpx4a6GeLOukvP7dKIftJm/JXIT1SImi8K9aW4on+758+dRfr9ZnDR1Ru135uYZTUnRqqLT6ewpLeNTuMA5/Q4A98eC8MKxxS8Xf/bmmxRexDczJFK1xsQCk9PiUXy84nK0FsaKlau/TzoVXvh7CbX6mwreryWtrqEz10Up58wIQ1cXC3kuyvT6HZrVEEUnSnIeN1FtZnjRbJl6Cfd+V2/qjHn/eo83g8Iy7y/U/ehT6G/VxHlydX4AuD8WhBcKLH59+UXauIPhRXw4Em8gnN9rmBbU/Ak1lkzt9xrpB/Tr47NF0bxfOXWIkpxfp4vHWWVwTvMZi50R4mi/4wD9HWcsnp69U+t3fjoXKz5KBToM0uLna1F9Nkdv4XJ0iBrKJeRvDiQPL3TeaKioZOTX394TNgWUAYo0F3vXxC2TN0vUsIjzro+vCm/ulOYZI1BQcBCnm+pHt7NyqibKE+MkurI+KQDcE2u+2ml3LryIJ2AvGxL561XQC1i9P9ak/NE5ytFRadeN5kLonfla1SzW/DCtSCJtbzhzUT2dIj7OiBDK4ok8bU+LmhQBjbjakq+a0VDFSh+VKa5oU8Cy8EKKy1HhjmmaUVdReHOn1GeMYv0GKBddHINHYmcdt7nmdTkAcE/ci/AifwDNZ/zmmiF6DPX+eokVF5WvKFPlSKxheVF1eOHQp45mrF5Kr0tsVc/lKH/avqGK+ZC0Pc1X16yJTudCYqmOYpsN5ZimKMr55yz24oYqQitzPfM2lE0B68OLCMKKUMncC3mg0+l3F6X6p20AuGfezPBiilbflHXD+evVK5aZtH1bnepypoIDZ84Xwjq88NKV78nV+a9LbC2Zrkke+gQfkjamdWoS63Gct99Q9fot3lPXvJnZF958B+ArLeq5KWB9eOGQZSomqFNdh2a/u/CpogDgjXdfwgutDfUTtvPXa0Bn2ZBYlvxZu0yVY9qv1HzJ9x4dlbZvlsC+vATvqZdM1yQvOUwdUnC25tuIuPAoxKfbKhpKvKeuRhSetjfqhgrNK90UUBbe3Cn1GX26qU80fMb8zZD3NPvdhTeTAOA+uEffvahNLQ+dZUNiWYoVq59f+4tU79FRaftmCezLS/Ceesl0TfKSw9QhuVevXnWyTV34Vnlp9R6LwtP2Rt1QoXmlmwLKwps7pT5j53QSF5u2u/3uwptJAHAfEF401gDvP/zbi3oJnDKV3zXJSw5ThxScbdHbi0WLaFFCiMLT9kbdUKF5pZsCysKbO6U+o0/H2wsA2B3hRWMNWPfdi6m1pP42g5exfI/zqOS0vY0yS71kuiZ5yWHqkIKzNUuI9TgKcVPkK+5WRQkhCk/bG3VDheaVbgooC2/ulPqM9bcrcnWq69DM78KnigKANx7hRWMN6P/miPIrKX8b4XKav+AQReXLm0vIl0YvXXmZOeWXvIRNkY0l0zXJSw5ThxRck+abG7VGUUhcXf7tiqBsapPi67TOX1dDe5yUtjfqhgrNK90UUBbe3Cn1GSNwrH9zJH5nNQ8BXQdVMm1nXHgzCQDuA8KL9hrgha1eZWNNyperOG/9r1Z4tS4ij3rVjGLrdTr+oaf8tx+9J6+DuSZ5yWHqkEKcrsip9VWtUSf5WurAKPLfofAiYoj68xGfTql55OGdzfHjwptJAHAfEF70lgenesnXuqInVy8/zV9wNf3sOEB/O7aQ4i1Ic9WMzHkJUXKR2TvrJdP5i8w2dUjNYYGuNKIl1T9iC8kLiRcYyhCXGfmL9Vicua5GNHja3mg2lDWvdFNAWXi8j8njM2me0cWKIozoBf3snUX46Myd8dNMAoD7YG548evLLxxMbP2jsMOHxJ78nxLfq1gb0vY2neVB4jm+oOWqWDJdjtZUBx+FYk2S5qqpMiPCKNQP095fL9KuSVGyTR1SU03yYCL43+ySopCpCz/L/s+R4KS6Gl6PJW1vDAkvouTgDM0zSlxmoa6G60B4AQA1woveGqDVUYtNrJ1a/utYQVSOC9GTrvJrWY38xVJnnVVT5TtVdN6pEpyhTvIVNUueOqRJEYYKiQtRlXzh3qwLKS5c0YkOL4Iwc4a6BO1xUtre6DRU80o3BTQKVwAUrSrO0DyjKX+EeuoFXVpdprgOKjltZ1x4MwkA7oP1H44AAAA0EV4AAIDBCC8AAMBghBcAAGAwwgsAADAY4QUAABiM8AIAAAxGeAEAAAYjvAAAAIMRXgAAgMEILwAAwGCEFwAAYDDCCwAAMBjhBQAAGIzwAgAADEZ4AQAABiO8AAAAgxFeAACAwQgvAADAYIQXAABgMMILAAAwGOEFAAAYjPACAAAMRngBAAAGI7wAAACDEV4AAIDBCC8AAMBghBcAAGAwwgsAADAY4QUAABiM8AIAAAxGeAEAAAYjvAAAAIMRXgAAgMEILwAAwGCEFwAAYDDCCwAAMBjhBQAAGIzwAgAADPZGhRdPnz59t+Xs7Oz8/PzFixcp3x3x6tUr1//Bgwdp12E9evRIZ1erpu3Ror/S9ghXV1cqUDVP2//3fz6FPHz4MO2acHl5mbLeLmHf3M6HPKOvcU93hAvf37ABcCcsCy/+eP3tL19++MMn77z+y5/858dP3/v3P/6q/SnHUU2FF0EzuNbslHutZ8+eab0/wGKgE6V6v/vu8+fP094D8rJ3t8KLx48fq8B84fQpTMFH2tvy5MmTlI/wYhu3VXNsuHDCC+CeWxBe/Pdff//uo7cisMj/aP9vX3+V8h3P1HKlp9JYOfQIu2OE4bMcYDE4Pz93nUWrZtp7QPsOL4arX13Ipv2u3wDp7/61nJ2dRc5DLvaHd90iu4UXd25sADiwueGFogdHEt9//PZ//vk3v674/ZuXv3z5oWOOU4gwpsILU5AxZOU4THjhT0ZUYQcZ+iElHNCdW0LqVxeiPeIkBRBpb0VHKYOiT1/1vvv3uK5bhPACwD7NDS/8gYj+rj8HUVThCOPHT99Lu46kH17I8+fPnWGXzxoOE15cXFzoLFoU4yOSw38+creWkOarC9k03nXr+QdFmSnhNscfanZf9b7797g2LUF4AWCPZoUX8eri929epl23/fLlh85w3C9hbA0vRI+nylB/y09T7fn5uV+PmyZQLTYpeSMlVFLyhhY5LVQ+i+nnJ0+eLP1ExiVoUYwveKqQlJbxLO91QpnjvJ2vss65UsuXEEc5Uy9RVKZSdeGxqVP4XZG4BervPTQDNTdgVE8/aHPqWnKOD+qcLkc/uMyoZMG11dl91UWtZH7P+rrcbopm/P7J+dWMzpPL2zn4EP2g8nVeV09/6+eZLWnNpOuiW22l6ilndJx+UOXzuNYdXcvL9566cF2Ihlk0oLqj2Xo+hQvMr135dS11fgCnaW548fPnH3ReTvz68guHF/oh7ToGz6SStlviZUA+R2sK885aPW/WUvLtL2MWNEXOnxlVNx/iTa9Pml69mVP1lKQZuXkJ9WI280rNhXvZU+WdrfkSxcU6KXqhoMsp3hzUK59K8FpSay7MwS1WX4L4cP2gxUw/RKvmdF4l+XBfdVHUop71denv5lFqq5Tvhs/odg7OrJLzgMbmtGRoJm2KuRUBNE8Uom6rwwt1ULP8+loivND+ejCokPn3EYAjWvDVzo7//uvvDi+mXm8chmdSSdstXocklsk4Sj9EzKEfYn+xsHl/PZXHzKtoIGZMTYU63LNkva5MiU9GvOnFT4qJWFQN7ffCGfXX395frHxLr9SFKNWbjnKaV6ETiX5oNoLq76WlWZ9oSSUpg/Yoc/SOCvF5pVixco5vmhl8rH5QUf45Cg8+hS/fVx21kqU96+vSTqXGIcrvbpKinkU7m3MqSYVEv8QZi1izaMlcM+m66NvVcB18ruijvPFjwFizzub8eeEqMAZANH5ci/6OM4pbW+XrkHwk6KZw/uZJAZyaMeHFz59/oNji+4/fTttH4plU0vYE54lJSpO+JuvmpwNetJqLRz2Va7rUbKjpOG1nlKRDmq8fmlSO8ueroCdWVTVt3/AsL0X9NV/7kLyQpVdaLCG+ChXrzaBTaL/r5sapr1T10U4VmK86RUt6XZFiJRO1qtqkWXNR/rycgsv0z27Y4jJVN+fxIuerzktb2rO+LqkPceFFP3pntLNtCrhu7QhoLFopb42iJXPNpE0BZXhRn8t0dcqsK03bG80626bsW4W7Diq/6NkYpXmPxAXqvO6R4HLUF2kbwAkbEF7ENzP+88+/pV1H4tlH0vYE52nOjAUvHsXU7LM0p/IpXv8kbXc5c7GKe/mvl23P8vV+8XPnnMuU5pUWS0gsw3nIIq6bVyY3TrM+taIlO+FFn8sp1r/gMv2zlmRv5uuWrz1CAV910RRTmj3r+kh9Ia5Av51tU0D7Czfu2XyVLVoy10y6Lrp6iTLF/VtUr1ln25R9q3DHEM1rcYPkoz2GQd2hGmNOStsATtiu4cUfr7/1L5Uc/ddGxDPp1tnHeZozY8EzXTE1N+frrTbnnDUtesItnrD9hkCK50vP8s2J2/Wcc5nSvNJ6CfFKk9fND6ART3ipFi2BW1evoiVjqdbCqestnlynFBWouUz/HKfIly6v1rHHV100RcemvEZ40XzIntnOcl3oRAQQQVLa7o7JZtLm6LnhhUsoqtess23K/l/hERM0T1en6gfvaQ4AJ6UNACdsp/Di929edn5h9fA8D26dfZynmBk1qWmR9qRZS5k2fJbmVC5awJRBqX5iKzRn2IKWJeUs3hCIVlDtn/Ne3VzPOmn+ldaFO8rJnzUdT+R5XP+gTZ1O2erVwjXMW1I5fZTpkhXKaDWtXwMEF5KHCwUXlTZugok4qQOO/Ip81XmtbH7P1tcVvHYWST5j3oZyXejEgHEhErFm54zNpM3RjcLVjGrwogdtTp3N+aPwqO1UvOhU9bI3I783C50kACdlfXgR/9zFicQW4pm0P/vE82u+fvuhvCPl2+hM5V5rO+oJvRDVm1I8pndmedezSFp0pc3CvbhG63m1zpd/rSI6xMFQTgcWRbmGRUuqDZvLm2per0/ao2I7ry7Eh6eNrI9c5/pdka+6rtXmoEl5zzavy7x2Fkk+Y9E414VuCy8itXPGZtLm6FuFqyWbzR7m1NmcPwqP2nqz5tQoqp+/kwTgpKwML/79j7/6+xY/f/7BicQW4pm0P/sUq4vEE7NmzOKdfHOmm5rK/WQvWu10lnzFFSflE3pTvPfuyEvuzPKuZ5609EqbhTtAUVShn1WCftay5KSC6ql2KN6U5KW5hnVLikpWDZVBqfG2oM7pEnSWtN3iY9PGTZ3Fj8teUyNaEtc2P9fSnu1cl9u5SPIZ85aR60InBkx01sC3FxFbqH+LkzZLaNbZNsU0wot8vOWcytsL4A2zOLxQMOHfE9EfBRlp72nwPNiffTwtxooYi43Xy0KsK2l7Y2oq9wStFaieRuMsxcRdq1e7nBfamIilM8u7npG04kqbhUdOFehYLa/PFC3JvjRdQto13ZIFnSheuuRfPdH+ra8uxAemjQ2XpgNVK/+QEjZ81XmtlvZs57q8dhZJPmPRzteFToyEiEHTdveMjimLpM3R/6tz9GmzK5slNOtsm5L+V/jq7154s9BJAnBSloUXv339lb9s8f3Hbx/3X9Bq8iTbmX1iGo3n3ZjLmnNfrGppe2NqKnfO5oTrZViaZwle7fIFuOBPIiI2ks4s73pG0oornSrcUY4uyutuLLr6QTVUavF8b66PpO2qJbWMaQmPrsk1K+/Dm/lzPjBtbMQw8AXqvClhwzujVrLJu6Bni+vK+UKKJJ+xKP+60OobvubOyoeBz5jvCWpSJRVnvC46q7MPF28WmiU062ybkm41iAdM0c7mU+dj3k0kafu2ThKAk7IgvIgPRH767P3T+UAk158l9ZzkaS6fKOPhqX5MjJVD0q4Nn6WYbWVqDo3zSj7n1vxU2lxRLKoU63dnlnc9I2nFlU4VHsub/i7ehXgpal6CH4IlbVctGcXWLwkiIIgGVB61qk7nzQ4fmDZuRI+IWibt3fBV5/27tGeL68p57SySfMaina8L3Sjip+jH/E1DLMlFYBfvOYozemfUObLVLe9OkTl1NuevG0RtVVTPnaikvJy4lrR9WycJwEmZFV4omFBIocDiu4/e+u+//p72nh7PYvXsoxk51jZNZ8Uc6hVR+2Pd1SToopwk3m/N+V1iItZ+n0J/a21QyVFOPufWvGDXy39QgS4nTt2Z5X0JedLSK50qPBYAKRa/iAMUdsTFxlkkX6S9M9YtZfNio3ZQOUUber9zio8tzt6kbJI2bsR40IWnXTd81VErWdqzxXXl3HRFks9YtPN1oZucTvJ5oyl0Xu8JromayDVRY/oaXUJxRu2RvIO8R4dHsKUf/LbMJRclOCJRZfILt+uCbjeIqhrVjrGnH6LO+bW4iSRt39ZJAnBSZoUX8T+Wbf2Tf2LS3LlXntY78tkzaI/nvoIyx2KZst7wlB0882qK1CFpV0aFR0RSz8XBU7wyp+0JxecjrkmxMplbI09aeqWdwr0wqLRikRMtgZuSGlRgnt81dOvZVA1FZ4y+83KlPd7s8+Fp40b0SH11vuq8Vkt7tr6u4LWzSPIZi5pcF7op1qk5nzfluxE9mFNg1DyjU/M6T/WaSmhejrvAeSzq7828cJnqWTVscS2usKTt2zpJAE7KfQkvNDl2nnQ1wWkajelPU55fD8RTnbMFza3K7yVWYubVfj0yxn79oE3tVJJ3FnNuzo+DKjZtT4hlQHXTpteeYmUyt0aRtOhKO4X7yXiqtj5LtINOp6iobn/XsF63VHi+nCtDvDYwH9jp0JwLSRsZV8/NmPNVN2s1s2eb12VeO4skn7FoZ+0RF6skn0UtqYbNmyKnzI4+RQ3oAptndJ68zqIAJQ4X/aw92u8hV1+Oell5YixF/b1ZFC5FG3rs1dfiCkvavq2TBOCkLPtqJ2AOL7z8HJgWJC1pWqXS9hvK62i9SAPAnUB4gcW8wEvaPiy/G5j56uLuIrwAcKcRXmCZq6urx5uvOhYv8w/jnry6EMILAHca4QVmiU/E7WHr10cPwN8D8PdF3mxuZ8ILAHcU4QVmifDiQffbhft2dnamChzr7Ifk1ia8AHBHEV4AAIDBCC8AAMBghBcAAGAwwgsAADAY4QUAABiM8AIAAAxGeAEAAAYjvAAAAIMRXgAAgMEILwAAwGCEFwAAYDDCCwAAMBjhBQAAGIzwAgAADEZ4AQAABiO8AAAAgxFeAACAwQgvAADAYIQXAABgMMILAAAwGOEFAAAYjPACAAAMRngBAAAGI7wAAACDEV4AAIDBCC8AAMBghBcAAGAwwgsAADAY4QUAABiM8AIAAAxGeAEAAAYjvAAAAIMRXgAAgMEILwAAwGCEFwAAYLA3JLx49OjRuzMoWzpgBJ90lzKfPn3qiqVtAADeCMvCiz9ef/vLlx/++Ol7r//yJ//Rz9qj/SnHkRBeAABwOhaEF//559++++itCCzyP9r/29dfpXwn4MWLF1629UPaBQAADmVuePHff/3dkcQPn7yjn73z929eRsyhv7Xp/UdHeCGXl5cPHz5UI4x9ZwMAwFazwos/Xn/rGOKnz95PuzK/ff2VU3/+/IO069gIL+Ts7MyNQHgBADiwWeGFAgiFDt9//PbUdyz8bQz9nbaPrRNe+OsO+ls/P3v2zGuwN02HnJ+fx9osWp4vLi5ScsbfvciPFR/in5Xkch48ePD48eOrqyvvD65Msfy7WNf8+fPnfgMhqtVUtPTq1asnT55EnXWITqcfVL6SUiYAAA5l2Vc7p9zF8ELrsfOINp3qVbmpfgfQCS+0qEdYEBRkXF5epnwbrsxUeNGsjEKilO+GylTJKfm2lAMAgMMaEF7EhyP//sdf065j2xpeeAlXBPD8+fOUcJMk+iHeNOiH2F8s7Z3wQrGLlnzl98sDVcOvFopIIiqTtjdcrKOfqIn+9n4Vm7+Q0M+OLXw679QP3qlCvAcAgEPaKbzIv9r5wyfvpL0nYGt4IYot0q4b/nyh+TmIXyTo77S90QkvpHhREVXKg4NOeCFFTSKSyEMi5XHm4nSKMLw/Px0AAIexOLz45csPFUzkfxRenMI/fZGbE17U34To8GrdjAOa4UURiJiDg7xKnfBCgU7azpyfnyspP6NLqEMl0X6pWwAAgH0bE178/PkHp/NbqbI1vGgu3h0ucH54kb9gCM4/M7xofq7h/PkZvYfwAgBwUgZ8OPLDJ+84yNDPKeHYtoYXxYqe0yFa2r3G11KmDefJF3txtuai7vx5UrMyzWLN+fOk+BBk6rOYRS9pAAAYYsBXO+Xnzz9whHEi7zBica2X+eaKHjq/OWIp38YphBfiX1E5OzuLVybx1c7z83PvAQDgkMaEF3+8/tYflJzIL4+sCy/8yxpO1VKdfykyCkzbG804wNkOGV5cXV05mCgo7OB7nQCAoxgTXojDi1++/DBtH9WK8EIrsQ9pPvEr2nBq2t44hfBCsYV/5VXBhF9j+Ofm778AAHAYs8KLf//jrz988k7nqxXx9uLuhhedQyQ+NEnbG6cQXrhi/PsWAICTMiu88D/K2flqhb97oT9397sXl5eXPqT+pY/4+qSkXRunEF44c+ffCwcA4PBmhRfx73Lq71++/DD+7/U/Xn/733/9/afP3nds8cvFn73/6FaEF+JPGR48eBARxtXNP9npJPF+a8YBznaw8CI+tampnMePHxN2AAAOb+53LxRS+BdQp/7UH4tE0q8vv0i7DmVdeDH1n3c83Pzb4f45Zd1oxgHOdrDw4tWrV87vv5ueVf9NCQAAe7Xsq51+V/H9x29H6PDjp+/9cvHn5mcikeeuhBeiCENP/BFkxHckr66uvMfZrBkHONthwgvFFv46Z/N02ulvZuhy+BUSAMAhLQsvcFKa/1R5QRmkGX8AALAnhBd3mF9mnJ2dTb2ciN+2Lf5NTwAA9orw4g57cfMZkCKMZ8+e5f/+t5IuLi78KY9S014AAA6C8OJui39pdIpiC15dAAAOjPDizvNXOOOf7DRtnp+f8zsjAICjILwAAACDEV4AAIDBCC8AAMBghBcAAGAwwgsAADAY4QUAABiM8AIAAAxGeAEAAAYjvAAAAIMRXgAAgMEILwAAwGCEFwAAYDDCCwAAMBjhBQAAGIzwAgAADEZ4AQAABiO8AAAAgxFeAACAwQgvAADAYIQXAABgMMILAAAwGOEFAAAYjPACAAAMRngBAAAGI7wAAACDEV4AAIDBCC8AAMBghBcAAGAwwgsAADAY4QUAABiM8AIAAAxGeAEAAAYjvAAAAIMRXgAAgMEILwAAwGBvSHhxfn7+7rvvPnz4MG1PcLZnz56l7dkePXqkA58+fZq2N7Sn3jnFJejvtD2U6uDKpG0AAI5qp/Di929efvfRW6//8qcfP30v7ToSRQxeX6+urtKuyqtXr5xHP6Rds51CeHF5eXl2dqZC0naG8AIAcFJ2Ci8UVSi2OIXwQlGF19eLi4u0q+IQ5Pz8PG0v0QwvFtk9vHjx4sX1FRJDAABO3vrw4t//+KsCi58//+AUwgt5+PChlt7O5yOrPxkRwgsAAOZbGV789vVXiip++fJD/TmR8OLi4sKr79TnI05d8cmIEF4AADDfyvDih0/e+f7jt/94/a0/HzmF8CI+H2m+n3j+/LmSitVdC/b5+bm/0GDK0Px4pRle+BAVkrZvuFinqnAdpZhmKrxQtR8/fuxXL6afnzx5kodBPrYWp9YptFkXLsqj8h88eOBDVDE1RUrL+BQuUBmiPspfXyAAAH1rwotfLv6skOLXl1/o59MJL8SLolbEtJ3REqukPHTwnqZ6nfbqOye8iC+Z5lQx160ouZnZFBBEhOGz17aGF/FGp6BrTzlu+BSORZwnt+4TJQDAvbU4vFBUcf2xyMWfvXlS4YUe+r0c1p+A+PE9Pjfxeiz6IXbqh9hfLKgzwwv97J3Kf3l5qT15mZJHAJFZ8ZAzi2quU7u2RRAQ+dN2xqcowouIXVSOr1F/x2uV4lp8gW5AJUV+789jHQAAtloWXvzx+tvvP37bH4t4z0mFF1qkr1fOKjjwJyMPs299ah09Oztrfg7ix/diafcquzW8cLb8RJaHHWnXZvlXzua7FkcGqmHa3lgUXigacIyiK027bjizOIYw11yKNolymh+pAADQtCy88O+J/Pdff0/bJxZeiL9IUazZjhiawUTNS3u+VItX3354EcFNcyVufjgyRQu/i0rbG4vCi/hYpPnWwRFD3iC+wCKgMb/wKK4dAICOBeGFogpFEj999n7a3ji18MKv97V8pu0NxxzxAUSfV/EV4UWs6N4suISi2I66qEXhhWOCqdPVqa5e/apDXDjhBQBgvrnhxR+vv/3uo7f0Jz4WsVMLL2INjlcIfqnQfC5XZi2oXllrKdOG8/TDCy/D9Scj5hLyFd2uNl/O0H6/UShE4bIovPDpmuGCOH8ehDl/cYHmzM0kAACa5oYXDiPyj0Xs1MIL8TodX57w+4x6ofUnJh0p30Zz9XW2iAB8onyNz7mEIjW+fTllx/BiKiZwfknbhBcAgKFmhRf+BzqbMcQJhheOG+LRvPnJiEMB0bL6/Pnz/AsKzVW8ufo6W0QAXobnv73wF05FNVSckX/RUpy0Y3jB2wsAwFFsDy/i/y2b+ScddjyxbCukaH4yomDCGZq/tRGHp+2N5urrbEV4Id4suIQ8AvCXPVW3+tuXUcPV4cW67140YwgX3kwCAKBpe3jhf+hi/p902FH58xE9u/vrlsVDfKzT+eId4kOTtL3RXH2dLQpxKCPxtY9c/ZsjztxctuNDk9XhRec3R7TH7VP/5kizMi68mQQAQNPc715MOcEPR8RP52dnZ17Ui/W+EwfkX4ZIuzbmhBfiD2LyZd6i2DwpYqC0fUPVc5KsDi8ihqjLd2al5pEH4QUAYKA3M7zIowSto2lvxnGAkiLC8G9waKeTxPttZngRH6wovvF3KaJYyyOAeE1ycXHhlV5/q+aqVdQhL1wcMejAPDIQnyIvXOK8ijCiMvrZO3UiZzPCCwDAQHsML/xZif74fyc5JK2+mzX0mhbjtDeTvyHIPXz4MEKElHVjZngheWQTdK766w6qpF+uFJQ53q8UhUfEEPL9RXghEcEU6liB8AIAMNCbGV6Il3OpPwExLeFafSPI0Erv7yLoEd97nM3mhxeiPXH2s7MznSXeYRQRgCKMJ5t/njwya9NvJryzLlzl5EFJ7NTPdXghuvyojC5WlanLFMILAMBAu4YXAAAABcILAAAwGOEFAAAYjPACAAAMRngBAAAGI7wAAACDEV4AAIDBCC8AAMBghBcAAGAwwgsAADAY4QUAABiM8AIAAAxGeAEAAAYjvAAAAIMRXgAAgMEILwAAwGCEFwAAYDDCCwAAMBjhBQAAGIzwAgAADEZ4AQAABiO8AAAAgxFeAACAwQgvAADAYIQXAABgMMILAAAwGOEFAAAYjPACAAAMRngBAAAGI7wAAACDEV4AAIDBCC8AAMBghBcAAGAwwgsAADAY4QUAABiM8AIAAAxGeAEAAAYjvAAAAIMRXgAAgMEILwAAwGBvVHjx9OnTdzfS9p316NEjXYUuJ20fw4sXL96MxjxZbt68l+e3uYe6xknaxrRoVf2QdnWdwt031tIWqM0fmUCYG178+vKL13/5U//P79+8TLmPhPBioN0nlMvLy7Ozs11KmOPZs2dqrgcPHri2Dx8+fPz48dXVVUo+tidPnqhWza50hfOk+W3uoU54MUdzcdWw0ZhpNiDhRW3+yATCyPAiZT0ez7lvwD3wZoQX+56SInxp0vqR8h3Voq6c32KEFzvqNCDhRW3f9zLeSHPDi1++/FABhP5O2yfJU8YbcA8QXmx1dXXlNxb6W5HEq1evtFN/P3/+/OHDhz6vfnbmIyK8OE2dBiS8qO31Xsaball48e9//DVtnyRPGW/APUB4sZWb6OzszIFFTnscYSg17TqeRV05v8U6qyPm6DTgKdx9Y8W40g9p10J7vZfxploWXvz68ou0fZI8Zcy/B/QE/OTJk3jBrjXp4uIipVVmZnYdPDflT9L6Yf7r+nyCyws5Pz+fmiC0X6n5hwUqpHM5/sqCc+qo+vsKnQlFJ9J+HZK2K1Fyoai86uCi5MGDByrw8vIypXUpgHBfTLXG/JHQucxmki/N5+10TRxb0OEpx813L5pHpe1pvkCXpkaLZuwMM/WvWjgqLPpZzVjHZ0vHsFP1g4rSKeKtUj6o9IOzNbtYByq/OjRt36ZUH1sMUe+vj/K5VFtvFq3qn2tOFXfxoruvFuNElVRRvjF1jfpZe5xHqc4m/cLn3yn56cTFijebp9g6FUiUkLaBGWaHFxd/fsPCC90wngcLmk3i/g/zM7sO+ls3rfPkdOumfF0xwbm0Qj3Fq9iUVlFRKVOmmV8XmM9ZUxOKv6sYc3dTzFaFmN3UaFN1bi5gS0W7pe1pnXmzmeRLU1Kz/lH5OLaQd4f3RJtIpzIFX6BKa55IfZTy3WiORlO/7ziGvV+FxEoc8kHl1Lpu4hM1k8zNXoTLWvuvz1GFLEVpRav655pTxefS5bsdCjppytflQvLoJPjeabZtXfiiO6XZBRIl5INNpvKr14rC549MIMwNL3789D2HF/rz8+cffP/x29rUnx8+eeeXLz/84/W3Kd9RxXSQtqdpPnK4oLvLc5PutDg8XwNkUWbv9+SiRwc/B+jvePioJ4WaD/ffmiVdiO7wmAvyaSJqoh/isUM/xP7ijHl+XYj26KJcPT27eI80JxTPiXm2js6UFPOdFgwXNXV1K6hAF6WLSrumdSrZTIpO0d/R4Prb++ul2vuVM21ntF/yi+1UpuBO1GXqjDqFh6Wq0VxIolg1iHOK6qne9MAuIgYX7prPGcPeqfwqLZKicI0W71Ff55s5lxx1q/nYokPdC1KEHS4tWqDZqnGNaTvjC/ffcfepbh5UdRc3+XBdrDho0FFR4Ti7C1cNo/DN0f+z6E7xSVVIZC7imyJ/nFSd5fwxFWin62bNNgT6loUXEVUUf7776K3fvv4qZT0e37Rz7gHfh7q70vaNuIvyCXRR5qhDMWVL3Pxpe5pzikpLuzY0Behw7VeGtGszyWoKK2ZY89yU1yRK0FFp1w0Vov1xLfWEoqlHx0pnGchNTUmxv35W84WrJml7IV1dzKeqZz4/TpmqpDSTomuKBo+GLS7K+Yt+tE0xO4UXUg9L92Pev+pTZSvWZlOSMhcNvnQMO3M9MOJy3FbqDm8W2dR02llfSE6H+Ni0veGOlvx2EHdE2phoVV9jcaBFFxf3SNS/Hre1aKhiEHrxlqI74gKb42HOnbI1s+SFuwXqXhNXMu/9ZhsCfcvCC/3RD//559/8ukJ/62fFFicSYcS0mLYnxJ3cnCZ8a8XNvyizRB00aaZdN/pF5eq5I3TKr3nxUGlp++YpsHm4kjRfx6pZTCjK73Urj6X6pqYkN1pzOYkmqqe8KXGWnGbGObGFdObNZlKna3xd6qC0veH8xU67Lnrn8KJuKD8i553eEUtm2t5YOoa9pw5YpehrBwRFTo/SIlyrefjFeV1zly9RVbdhfks2W9XX2Gwld1kRQpnr3+zNggup28QXK3XH+QLz8bDoTulkVqM5c164T9fstSi8aFXxJjDHgu9efP/x2z9//kHazijI8FuNHz55J+06kpgW0/aEWGLT9m1F6qLM0pm2xM9VW6enqblJ4j7PZ4opzpxXxnPQVPVyxYTSqdKUqSmp3wgzmyjEWXK6zDntI1OVlGZSpx3c9UXNnb95OddF7xZeNKMcJ83pYtuc8NYZ+yXUHbQpoD0gixuk+bLETbQ1HFSbK1u0fAQlHtIR9bry2u9NabZq5xpdn/rNjTgpv/YpUzldmWbs4kPyZlx0p3QyRwtE4RFA5KfLFalRgjeBOeaGF33//dff/W7juC8wdGvNuQecrRnmS9xIfixYlFmcvzltiWcQTYhpe0J/Frs+X/W0p5po5vWBtZTppuQ5UUI+oXhml6nrapqakrxz6hXOzCZq0sOWivV6I0svs9BM6nSNu75I6uS/Lvr2/N6pTMHnanbHVJLWbyVpv9ehQl6NTuHiK8o7aFNAe6GKK/INog7KN8UvIabOlfMjeIQmWv5djiOYiAb8giHKl2ardq7RF6gMaTvTSSpM5XRlZp5XmzLzTulkjhaIPoo9fTHJNNsQ6BsTXojDi+P+u1ueMrbeA87WvMOluBUXZZZ+fs8IU6nB2fKJJnd9vtupnmo7Ur5tJefi0vyYqFnby/bUZFeLEtL2De+MFivMbKK+eAW9tbZTlZRmUqcB3fVFUif/ddG326FTmYLP1WylZlI0yJS8Gp3CxVeUp24KaHdoXFGkehRF5OfgIN499G1KSu85FGc41HCA4vcBDl8iBLFmq3au0RfY7LJOUmEqpysz87zalGbDivO7qIjbmpmjBSI19vRFZSK/N4E57m948ca8vchfLWg1jY9LpZ4UXPKix3rR3K1J3PN4MXd3TE1J3jnzmWw1P8VuLWeqktJM6nSNu75I6uS/Lvr2etCpTMHnUuFpO1Mnqak3pV73nRZy9WNK2HBSXo1O4eIryht2U8CWhS1uEAc6jgbE3ZQP2g6HJhr5Kk0/1G8sfKXF5xrNVu1coy+w2WWdpMJUTldm5nm1KTPvlE7maIHoo2abdCzND8iY8OKP1986vDjuP+vpKWPrPeAHpqlsReqizOI6TIUjnc9Hc51ZzBOreKaIp5bmOhrrStq+Kbk5uxViQpGYlfyaZGv9bWpK6jfCzCbaauaVdubNuvWk0zXaWSd18l8XfXtV7lSm4HM1r65O8uqr2KJexZtPvS5h/hjeFNBe2OobJM6o/A5Yt4aAwaGJ8rvYeOfhCFt/e3wW70KardppwE6XdZIKUzldmZnnXXSn6OepzNEC0csxjUTY19dsQ6BvVnjx33/9/afP3u+8mfjPP//m8OJOfPcibq3mhOibPKa8RZkl6lA8I4onU2kWlXOxxUOYFfN1PXHkPNtK2s4Or1caJWkFiukpSs4nax2lSU3qw2tTU5KaSzubq1cc0p/11IAqpNk+wWtqcx7PxRnrK6pbT+o1ILjri6RO/uuiDxJebIps18ELtuTVcAkycwx7T7M73IZFX3sAKMlhwdbbIcTZXUJUz+2ms2gA5/ut2aqdBux0WSepMJXTlZl53kV3SiezWtiZ8152dKK7Pm1ndC+oJVWfuCmabQj0zQovInpQnJF2ZRRS+HdTf/z0vbTrSDxlzLkHfCfXt2LcRfl9uChz1EH5ixXL97/u6rQ9zWdUzmKV9W2vpJjKO9FPrBySdmUTdDHxRckRTExNKL7A5lpSmCoh9td19oXr77Q9IRp5amWKa89joynNnNGwknZtuIZF65lrVSR18l8XfZDwwgtJ/YmYrtFJklfDJcjMMezMMtWGxRrm3lEhGnJFUVupSi6zuB/jQjr3adre6DRgp8s6SYWpnK7MzPNGzefcKTHm864U9WA0Wt3L6oKii8VBoZLS9pKRCYS5H4788Mk7jjB+ufhzvKL4/ZuXv3z5Yfy7F9r0/mPxDTPnHoi7Rfenl3DdY5oEPUkVa+eizK6D7mel6m/fz1rUPS9L83Gh4LlDVIhmDd//KsrThHbmz2cOC7Qz5iClxtxxXcrtBvEjoyiPS9ZFueQ5E4oO8YUXz4hNzqkm8omCL1Cpao2oQ+x0I3fokJgxVbgb2fRzXKDypL1dnkx1Xq+OKtydG73gbOadarq0nXGbF0kqSjtVWl5Juy76IOGFL1CitfW3Lla1ihGSV8MlqPWUQX87qTOGvTNaxqdw+dqjU3hP0KaTpLh9torOLaKluMY6imq2qsaYdxbXIp0u7iQVpnK6MnnvhOYh3qnmir7r3Cm+KbTfI1k0J8SdInkvq7SYOmKSUYHRy3nmZhsCfXPDiz9ef/vz5x84wqj/KPioY4tIPdj/VKI70/fAlPyu1h2V9t6mu6uYDWV+ZtdBJ4obMjdzMtXhuvObJ63nFG1qZ0rOaFrR5OKfU9Ybnp4KRcmdCcUVyxtzSt0j3q9Gy2e9oDpMvZAoqISYB5ua/dikhbNuQFVPh/vnlG/DTVcvG+KLLZJUSFF4ZPDmuknc52p2QZ3UaW31uH/OqxElzBzD3q/MbpxcMahCRAP5eeeIKhXjJG6WevzEIWn7RlHbaDHvL/rROkmFqZyuTJwr1zyk03f1lU5lnmptdc1U4WrPlGljqg2BjmVf7Sz+w5HvPnrrp8/e/88//5aSbzvx8EJ0d+nGi9lfC1JxU+VmZnYdfCIdEkugbuOZC6focM8yuqtVgk+qv1WB5juDom46l5/JlNl7nC2nysfMolBGz3yam1LaRn9C8XNPMVs16ULyKSzt3VAlizrMeSOS84Xn5etn7ZlTsZzOGw2omuhnt4bLdB5rrgHmrq+TPAyidyKDN/Oq9ts853MV49maSbocNa97TfIer7syL2HOGHaqS9CxLtDDtRhUQeUoj3Km7SXckkXJ2ryuRKvpplpVh6iG0SbRYp0u7iQVpnK6MnGuXKfw+XeKLiq6QFSmpylv5r1szp8XrjapC59qQ6BjWXiBrXSv6iZsTh/AnbB0DG/WnVmxZnB4oWUybQN44xBeDEZ4gbvuAOGFCtchl9u+ZAPg7iK8GIzwAnfdXsOLV5tvzi4qH8BdRHgxGOEF7ro9hRfOZg8mvvIJ4I1BeDEY4QXuun2HF+fn58QWwBuP8AIAAAxGeAEAAAYjvAAAAIMRXgAAgMEILwAAwGCEFwAAYDDCCwAAMBjhBQAAGIzwAgAADEZ4AQAABiO8AAAAgxFeAACAwQgvAADAYIQXAABgMMILAAAwGOEFAAAYjPACAAAMRngBAAAGI7wAAACDEV4AAIDBCC8AAMBghBcAAGAwwgsAADAY4QUAABiM8AIAAAxGeAEAAAYjvAAAAIMRXgAAgMEILwAAwGCEFwAAYDDCCwAAMBjhBQAAGIzwAgAADEZ4AQAABnujwot3N54+fZq2K0rqZ7hXHj16pNbQ32l7OZcwpz13P9ddocuc2SZ7dX8aHMBpWhxe/P7Ny18u/vzDJ++8/suf9Oe7j9766bP3//PPv6Xko9o6sxNe5HZfgVwC4UVOlzmzTXb37NmzBw8eNFv1/jQ4gNO0LLxQGKF4woFF8efnzz9ImY5n68xOeJHbfQVyCbRn7noIHqpNPJ6JIQCcoAXhxX//9XdHEj988o5+9k6/zPB+/eCdx7J1Zvd0zHJohBf7cD0ECS8A3Htzw4s/Xn/7/cdvK4b48dP30q7Mv//xV0cYijbSrmPYOrN7OmY5NMKLfbgegoQXAO69ueGFA4jvPnpLcUbaddsPn7zz02fv//ryi7R9DFtndk/HRYYXL16cn58/ePDAhz98+PDJkydXV1cp+bZXr17pcOWJzNrUzpTcpRPpEC8GPqkL0R5tOo+K0tnPzs60X393ClcNI6eoJhcXFymtkp8uinVw0FycVPjjx4+jcOV59uxZSsu4hKI9m+qceWuoMjqdu6B/1YW8kMvLy/oanS24GjpKScrgM0bji36OmogKfP78eUqrqE3ykeAm8mbRJt6ZnyhMJfW7wDtrKbnbNSonGkpXqrOo6VJaxhn8s8pxTZx/6u4AgDA3vPB3OU/hCxYdnhCbU6opqcjgPTVNo/Wcqz2x8OQ08zYn6IKWEGX2OuEDc1rGtObFchW0Jx2fUVHNmihzvaY2T6ecoh+8Nuea+UU5i8I7a1ihzhmt0WzV5oXUlhbiaugoX7tp06mKz9Ku27SgOkNOO1NyJnYWbeKdcaJcM2lqpEVN0nbFqdLsGrVGs9qSxy7m/TokbytT3eYMeAD32azw4vdvXvqzj+O+nNjKc19ntVNSnsGLk+hhLqZLLfOeTzWH5ouTntg84ys1nmgjs/72ng6fzk+B8YJEO71Hf2vq1yn8EkKndm2leC0Ra49O6mrnmbWuOJvFNXoN1h6dNzJLkV9X5P3RJipca4/PqGo7mzXXsKY6pyum/boKiSbVxfpcc4p1ITpch+hvbWpn3hqxHpur4b/zTpcIqnSIu0Z/x1N+UZkoP/pRRUVmKfJ7p6tXqJNU/+jfuKLognwwuBq6nLSdqRtcIrZQISpTe1S+zuKdRfW8Uxeo8+rskd/DtXlSAAizwgtFFQ4vYvPnzz+IXyG5u7+Y6k1Nl94Mmkm1UxNoPuF6vtZc7Hk2OLOS6ue/gkpTNikWaa1M3i/5gidesYqpPGqStm9E+XlNtmbOC9e1eA0rVmWJSno1NRfeafBQ54wKqPWKJnW/1HWuRSF1v7gQyZvU1ZCiC+LCi/0S5cSFz8mcX6l4p2qbtjN1kgvRKfKmFgdA+XB1zrwHQ6fBI5ILzlzcCM4sxZiMcooGB4DcrPDily8/VBiheOL655vfEyn+/Pjpe1NfyzgYz3rFzJ7zdBwZvFnMqk2a6DdlN6Zm0bOgkrYuh5152QGKgom0fcMrSr5+xDLfrInDkSinn1kVVlJeuE8nzZXDhedrar2GTemsdnVYFtVO29M6S6bUQUBzHRX3oDQv3OXEa4NOZu1xUtEm3qnapu1MnTQVu4hqrkuI83oA5z0Y6gZ39zVHaTR4Hkl4Tx1oimvYvBwAsAXhhQIIf8FTP8Qvpv729Vc/f/5BRBjeeSyeEDurnafjyBCrqWbe/lwZK0ravi0WubQ9oZPNi0EsYMGH5OtHvyZFaj+zT5oX7hUo35OrFzOX0GnwUOeM1miu6E5KG9P6LV9fjqtRL5n9Cy9S+5mVJEWbeKdqm7YzRVKs9M3MhbpHgq80r4ZjgqnOqlO1Kc24zYXPqSGAe2tBePHDJ+9899FbijDS3sx//vk3RxjH/ZTEE+LUBCqejvMMfoIP2tRTo8KOYs3zgVvlD3+1zlo4NV/Xh7gmU29KIr9r0s/sk+aLk/f0aR1KuVtr2JQ6Z6c1pJOU6xfiy59TYe9vvjCQopx+ZiVJcQrvrPtXiqS4ombUVXDFVJ+0namvdFNqO1wQ51fklLa7dXbmZhIA2ILwov9+wu8wfvrs/bR9DP3nM/F0nGfQJK5NfzaRU1F5Nh+4VX/CjZUjbWem5uv6ENekuaJI5HdRWgI7mX3SPNV7tkq5b/LnDTWlztlpDekk5fqFRK+l7ekK9y+kKMch6VRm5yxSvdOdUiiS+ldUcMVU+bSdqa9oU+rkEHX+vKhOfmeeKgoAZNlXOzsvJ+IDlLR9DPWUWvB03MxwdXX17NkzrccuxCKnD8wn3xU6K8fUfF0f4prs9e1Fs32a5uevc3ZaQzpJuX4hvnzeXtimVN5eADiQWeHFH6+/dXgRX7monU54kU+RBWeYmmGDQg0/ocaK4i8x5AvVCrFypO3M1HxdH7LouxdefmKz4JPq77R9862CTgMWXEKxlDbVOTutIZ2kXBTSXIx9OfkFTlW4zpkrUl3IVGYlSXEK76z7V9UukuKK6sw192+zJq5kXg2N3mJPrk7VpjSr4cLn1BDAvTUrvJCfPntf0UPnn9VSYNHPcAD+tf6ph3XxHOrf99PMrmVDe4pf/zNP3OLN+MJd89sVmmfPNv9qRdqeECtH2s5Mzdf1IVGTznfuIj7oZ3YIlS9O/ShKbaJDlCdtt9awKXXOTmtIJykXhdS/fiL+zCt/zTBV4QjL6jBFezxs4sL7mZ1UnMI7616ILxfnXe/TNd+OKEmXECNWZ1HOvAdDfaUOkpp3RzRjPry9J69YcOHNJACwueFF//OR+Grncf/dLU3fnhObi43n4vyXEr38NMMCTe6bkv63wnlKjZU7aEXxOn2Y8EKcuV4nInNejq9Rh6TtG7Gw5Un1Uhq08Dgpb1vXpF6ta3XOTmtIJykXhahuReQXQUC+f6rCceH1ou5ho9QIJrS6X5fbKscBbp3k8xYjRAW6dyTvMheSn9F8RfkAdt3yHgydBq+jHGcuynHmvGLB+ZtJAGBzwwuJL3j+/PkHv339lXfmv5h69P8xVbzSiybWWFf0Q8z7+dwa4YiChpgrtXh41pZ8sVEh3qlTRCH6wWesF4NazO9pOzM1XzcPiZ06yteoU2vt8QJZrGH5NfqpN79AKRaVWJWVPwpXSOHCi8yudr6GTalzdlpDOkm5vClUQ4c+qnBcYBEOdioch6jTo6EiyiwC1tivo9zvaiudyzulOEUEc5Ff/aJAQXX2+Mm7Xud1ayvJ+92/mwJu1STGpFLTrhvNK/VOFa78Ue3Y6e4O1+USXgBYa0F4IRFh1H/q2CKSDvlKQ1NkPBEWYvnJxbxf0xzqKThoSfC8X9AZi6m5KdbCtJ2Zmq+nDpmqtla4os7SzKwL8XKoU6d8N2KhLdQN4moXa1hTnbPTGtJJykUhqljdNVqeF1U4YtBCM38zs84o+qE+xPtzXtFdpaLrp0ZaETuKDw/azPcX1VBr1NUQnUtnTJluOKkek9KsMwDkloUX8vs3L3/+/AP/5+z6ox+02QwgjhJeiOZQPZzl066m1HgkrWmK16wdQYnX3ToQMRWSZ1bJmsGLNWxKZ0Gdmq87h7jasQh16iwqJ56tVXkdqAtRzbUZC1LO+aNw5WkW7mo3V99CnbNzadJJyuWFqBeiQXSNzVptrbBW2WgoFaUCdYqUVlGbxGodI2HqFErSTo8cjzGHpM5fn8UjLe/fOgIQX3UMSJXm/Z0r1d0R1daBU7eGMzQvf6rOABAWhxfA6cjDCwDA6SC8wB1GeAEAp4nwAncY4QUAnCbCC9xhhBcAcJoIL3CHEV4AwGkivAAAAIMRXgAAgMEILwAAwGCEFwAAYDDCCwAAMBjhBQAAGIzwAgAADEZ4AQAABiO8AAAAgxFeAACAwQgvAADAYIQXAABgMMILAAAwGOEFAAAYjPACAAAM9X//9/8BxnXhvlu6gfYAAAAASUVORK5CYII=)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oaoQmknJFdLL"
      },
      "source": [
        "\n",
        "---\n",
        "\n",
        "###Problem Definition\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rQVWopn6yxMU"
      },
      "source": [
        "Classify breast tumors as malignant or benign based on features such as radius, texture, smoothness, etc. Our goal is to experiment with implementing different machine learning algorithms to make these predictions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ELQD4DppH7GF"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "### Data Analysis/Preprocessing\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kfpLQ2S1y1KY"
      },
      "source": [
        "The first step we need to take is to look at our data. In the first cell you ran, you cloned a GitHub repo which contained the `breast-cancer.csv` file. A few rows of this file are shown below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AwgHuD2TIPkr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "outputId": "30dc3060-44bf-45cd-8176-889f61d4445e"
      },
      "source": [
        "data = pd.read_csv('./ml_workshop/breast-cancer.csv')\n",
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0      842302         M        17.99         10.38          122.80     1001.0   \n",
              "1      842517         M        20.57         17.77          132.90     1326.0   \n",
              "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
              "3    84348301         M        11.42         20.38           77.58      386.1   \n",
              "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
              "..        ...       ...          ...           ...             ...        ...   \n",
              "564    926424         M        21.56         22.39          142.00     1479.0   \n",
              "565    926682         M        20.13         28.25          131.20     1261.0   \n",
              "566    926954         M        16.60         28.08          108.30      858.1   \n",
              "567    927241         M        20.60         29.33          140.10     1265.0   \n",
              "568     92751         B         7.76         24.54           47.92      181.0   \n",
              "\n",
              "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0            0.11840           0.27760         0.30010              0.14710   \n",
              "1            0.08474           0.07864         0.08690              0.07017   \n",
              "2            0.10960           0.15990         0.19740              0.12790   \n",
              "3            0.14250           0.28390         0.24140              0.10520   \n",
              "4            0.10030           0.13280         0.19800              0.10430   \n",
              "..               ...               ...             ...                  ...   \n",
              "564          0.11100           0.11590         0.24390              0.13890   \n",
              "565          0.09780           0.10340         0.14400              0.09791   \n",
              "566          0.08455           0.10230         0.09251              0.05302   \n",
              "567          0.11780           0.27700         0.35140              0.15200   \n",
              "568          0.05263           0.04362         0.00000              0.00000   \n",
              "\n",
              "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
              "0    ...        25.380          17.33           184.60      2019.0   \n",
              "1    ...        24.990          23.41           158.80      1956.0   \n",
              "2    ...        23.570          25.53           152.50      1709.0   \n",
              "3    ...        14.910          26.50            98.87       567.7   \n",
              "4    ...        22.540          16.67           152.20      1575.0   \n",
              "..   ...           ...            ...              ...         ...   \n",
              "564  ...        25.450          26.40           166.10      2027.0   \n",
              "565  ...        23.690          38.25           155.00      1731.0   \n",
              "566  ...        18.980          34.12           126.70      1124.0   \n",
              "567  ...        25.740          39.42           184.60      1821.0   \n",
              "568  ...         9.456          30.37            59.16       268.6   \n",
              "\n",
              "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "0             0.16220            0.66560           0.7119   \n",
              "1             0.12380            0.18660           0.2416   \n",
              "2             0.14440            0.42450           0.4504   \n",
              "3             0.20980            0.86630           0.6869   \n",
              "4             0.13740            0.20500           0.4000   \n",
              "..                ...                ...              ...   \n",
              "564           0.14100            0.21130           0.4107   \n",
              "565           0.11660            0.19220           0.3215   \n",
              "566           0.11390            0.30940           0.3403   \n",
              "567           0.16500            0.86810           0.9387   \n",
              "568           0.08996            0.06444           0.0000   \n",
              "\n",
              "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
              "0                  0.2654          0.4601                  0.11890  \n",
              "1                  0.1860          0.2750                  0.08902  \n",
              "2                  0.2430          0.3613                  0.08758  \n",
              "3                  0.2575          0.6638                  0.17300  \n",
              "4                  0.1625          0.2364                  0.07678  \n",
              "..                    ...             ...                      ...  \n",
              "564                0.2216          0.2060                  0.07115  \n",
              "565                0.1628          0.2572                  0.06637  \n",
              "566                0.1418          0.2218                  0.07820  \n",
              "567                0.2650          0.4087                  0.12400  \n",
              "568                0.0000          0.2871                  0.07039  \n",
              "\n",
              "[569 rows x 32 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-92994991-8132-41a7-8152-77e8cdc1aee7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842302</td>\n",
              "      <td>M</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.30010</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>...</td>\n",
              "      <td>25.380</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.16220</td>\n",
              "      <td>0.66560</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>842517</td>\n",
              "      <td>M</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>...</td>\n",
              "      <td>24.990</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>M</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>...</td>\n",
              "      <td>23.570</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>M</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>...</td>\n",
              "      <td>14.910</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>M</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>...</td>\n",
              "      <td>22.540</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</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",
              "      <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>926424</td>\n",
              "      <td>M</td>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>...</td>\n",
              "      <td>25.450</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>926682</td>\n",
              "      <td>M</td>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>...</td>\n",
              "      <td>23.690</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>926954</td>\n",
              "      <td>M</td>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>...</td>\n",
              "      <td>18.980</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>927241</td>\n",
              "      <td>M</td>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>...</td>\n",
              "      <td>25.740</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>92751</td>\n",
              "      <td>B</td>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 32 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "brxgByKTQOVz"
      },
      "source": [
        "First let's check out what datatypes we have in our dataset. If the data types are not what we expect, we could run into problems with our preprocessing and later on with our training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "djIagM3nQup0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0217ee2-a717-4c5a-8735-b71cf46cadbf"
      },
      "source": [
        "data.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                           int64\n",
              "diagnosis                   object\n",
              "radius_mean                float64\n",
              "texture_mean               float64\n",
              "perimeter_mean             float64\n",
              "area_mean                  float64\n",
              "smoothness_mean            float64\n",
              "compactness_mean           float64\n",
              "concavity_mean             float64\n",
              "concave points_mean        float64\n",
              "symmetry_mean              float64\n",
              "fractal_dimension_mean     float64\n",
              "radius_se                  float64\n",
              "texture_se                 float64\n",
              "perimeter_se               float64\n",
              "area_se                    float64\n",
              "smoothness_se              float64\n",
              "compactness_se             float64\n",
              "concavity_se               float64\n",
              "concave points_se          float64\n",
              "symmetry_se                float64\n",
              "fractal_dimension_se       float64\n",
              "radius_worst               float64\n",
              "texture_worst              float64\n",
              "perimeter_worst            float64\n",
              "area_worst                 float64\n",
              "smoothness_worst           float64\n",
              "compactness_worst          float64\n",
              "concavity_worst            float64\n",
              "concave points_worst       float64\n",
              "symmetry_worst             float64\n",
              "fractal_dimension_worst    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vBcVGv6zOCgX"
      },
      "source": [
        "In general, you can't provide text input to a ML model. As you can see above almost all of our columns are of type float64, which means they are decimal numbers and should be fine to feed into our model. However, the diagnosis column is represented by a M for malignant or a B for benign. Normally we would have to encode this information numerically so that our model can use it. However, luckily for us, scikit-learn is smart enough to know what to do with this kind of Y data. If this had not been the case then we would have done something like assign 0 to represent M and 1 to represent B."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nh4XMDo-gETV"
      },
      "source": [
        "One last thing to check before we train. If any of the values are missing in our dataset, they will be listed as NaN (not a number) in our dataframe. When we go to train, our algorithm won't know what to do with these values so we are going to go ahead and remove any rows with missing values now. We should also check for and remove any duplicated columns as well."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aDkrtiSegTBg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "outputId": "c51089e5-596a-4524-dd06-c75e81605aae"
      },
      "source": [
        "data.dropna(inplace=True)\n",
        "data = data.loc[:,~data.columns.duplicated()]\n",
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0      842302         M        17.99         10.38          122.80     1001.0   \n",
              "1      842517         M        20.57         17.77          132.90     1326.0   \n",
              "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
              "3    84348301         M        11.42         20.38           77.58      386.1   \n",
              "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
              "..        ...       ...          ...           ...             ...        ...   \n",
              "564    926424         M        21.56         22.39          142.00     1479.0   \n",
              "565    926682         M        20.13         28.25          131.20     1261.0   \n",
              "566    926954         M        16.60         28.08          108.30      858.1   \n",
              "567    927241         M        20.60         29.33          140.10     1265.0   \n",
              "568     92751         B         7.76         24.54           47.92      181.0   \n",
              "\n",
              "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0            0.11840           0.27760         0.30010              0.14710   \n",
              "1            0.08474           0.07864         0.08690              0.07017   \n",
              "2            0.10960           0.15990         0.19740              0.12790   \n",
              "3            0.14250           0.28390         0.24140              0.10520   \n",
              "4            0.10030           0.13280         0.19800              0.10430   \n",
              "..               ...               ...             ...                  ...   \n",
              "564          0.11100           0.11590         0.24390              0.13890   \n",
              "565          0.09780           0.10340         0.14400              0.09791   \n",
              "566          0.08455           0.10230         0.09251              0.05302   \n",
              "567          0.11780           0.27700         0.35140              0.15200   \n",
              "568          0.05263           0.04362         0.00000              0.00000   \n",
              "\n",
              "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
              "0    ...        25.380          17.33           184.60      2019.0   \n",
              "1    ...        24.990          23.41           158.80      1956.0   \n",
              "2    ...        23.570          25.53           152.50      1709.0   \n",
              "3    ...        14.910          26.50            98.87       567.7   \n",
              "4    ...        22.540          16.67           152.20      1575.0   \n",
              "..   ...           ...            ...              ...         ...   \n",
              "564  ...        25.450          26.40           166.10      2027.0   \n",
              "565  ...        23.690          38.25           155.00      1731.0   \n",
              "566  ...        18.980          34.12           126.70      1124.0   \n",
              "567  ...        25.740          39.42           184.60      1821.0   \n",
              "568  ...         9.456          30.37            59.16       268.6   \n",
              "\n",
              "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "0             0.16220            0.66560           0.7119   \n",
              "1             0.12380            0.18660           0.2416   \n",
              "2             0.14440            0.42450           0.4504   \n",
              "3             0.20980            0.86630           0.6869   \n",
              "4             0.13740            0.20500           0.4000   \n",
              "..                ...                ...              ...   \n",
              "564           0.14100            0.21130           0.4107   \n",
              "565           0.11660            0.19220           0.3215   \n",
              "566           0.11390            0.30940           0.3403   \n",
              "567           0.16500            0.86810           0.9387   \n",
              "568           0.08996            0.06444           0.0000   \n",
              "\n",
              "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
              "0                  0.2654          0.4601                  0.11890  \n",
              "1                  0.1860          0.2750                  0.08902  \n",
              "2                  0.2430          0.3613                  0.08758  \n",
              "3                  0.2575          0.6638                  0.17300  \n",
              "4                  0.1625          0.2364                  0.07678  \n",
              "..                    ...             ...                      ...  \n",
              "564                0.2216          0.2060                  0.07115  \n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
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              "      <td>842517</td>\n",
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              "      <td>1326.0</td>\n",
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              "      <td>M</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>...</td>\n",
              "      <td>23.570</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>M</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
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              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>...</td>\n",
              "      <td>14.910</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
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              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>M</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>...</td>\n",
              "      <td>22.540</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
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              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
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              "      <td>0.24390</td>\n",
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              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
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              "      <td>926682</td>\n",
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              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>...</td>\n",
              "      <td>23.690</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
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              "      <td>0.1628</td>\n",
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              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>...</td>\n",
              "      <td>18.980</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
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              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
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              "      <td>25.740</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
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              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
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              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this case it appears there weren't any duplicates or missing data rows based on the fact that we still have 569 rows after performing our cleaning. However it never hurts to check!"
      ],
      "metadata": {
        "id": "5haDXmjH2M1g"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ujlEfWORktMc"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "###Single Variable Logistic Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7AxWxRRzceX6"
      },
      "source": [
        "While we could spend much more time going through our data to make it easier to train off of, I'm going to move on to training using logistic regression due to time constraints for this session. Logistical regression is very similar to linear regression with the main difference being that we get a categorical output rather than a continuous one. This is what we need to be able to classify into our `M` (malignant) and `B` (benign) categories. To make things simpler we will be starting off with just one variable to make our predictions which means we need to be able to choose the best category from our dataset to use. The code below creates a graph of the distribution of our two categories among data entries in different columns in our dataset. The columns we are testing are listed in the `test_columns` list. Feel free to change which columns are being used to see if you can find one that fits our task even better than the ones used below."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_columns = [\"radius_mean\", \"area_mean\", \"symmetry_mean\", \"perimeter_mean\"]\n",
        "\n",
        "for feature_name in test_columns:\n",
        "  plt.figure()\n",
        "  data.groupby(\"diagnosis\")[feature_name].plot.hist(alpha=0.5, density=True, legend=True)\n",
        "  plt.xlabel(feature_name)"
      ],
      "metadata": {
        "id": "OOg07iLRabRN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9b81b5d2-f90f-4c23-9122-414971f641db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can tell from the graphs above, `symmetry_mean` would be a poor choice to use for our model as there is a lot of overlap between our two categories. A better choice might be `area_mean` which seems to have a smaller overlap. Let's run an example of our logistic regression model using both of these categories to see if our theory is correct. We will start with `symmetry_mean`."
      ],
      "metadata": {
        "id": "HSu4yd9EfJl0"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gE59Cz9aitdQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0eb4e5d6-9ee5-40bf-d39c-7988d523a16c"
      },
      "source": [
        "# feel free to play with different features to use for X, ie perimeter_mean, compactness_mean, etc\n",
        "x_single = data['symmetry_mean']\n",
        "y_single = data['diagnosis']\n",
        "\n",
        "# some of this code comes from https://pythonspot.com/linear-regression/\n",
        "\n",
        "x_single = x_single.values.reshape(len(x_single),1)\n",
        "y_single = y_single.values.reshape(len(y_single),1)\n",
        "\n",
        "# Split the data into training/testing sets\n",
        "x_single_train = x_single[:-250]\n",
        "x_single_test = x_single[-250:]\n",
        "\n",
        "# Split the targets into training/testing sets\n",
        "y_single_train = y_single[:-250]\n",
        "y_single_test = y_single[-250:]\n",
        "\n",
        "# Create logistic regression object\n",
        "sym_model = LogisticRegression()\n",
        "\n",
        "# Train the model using the training sets\n",
        "sym_model.fit(x_single_train, y_single_train)\n",
        "accuracy = sym_model.score(x_single_test, y_single_test)\n",
        "print(f\"Accuracy on test set: {accuracy:.3f}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test set: 0.748\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZRrVLON_dwhv"
      },
      "source": [
        "The code cell above fetches our `symmetry_mean` values as our X and our `diagnosis` labels as our Y. Then we split our data into training and testing sets and train a Logistic Regression model off this data using `scikit-learn` and the `fit()` function. You should see a test set accuracy value printed out at the end which gives us an indication of this model's performance. Keep this accuracy in mind as we will use it for comparison next."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's do the same thing again just using `area_mean` instead of `symmetry_mean` to see if we get an increase in accuracy."
      ],
      "metadata": {
        "id": "jCjUDHlZx0ez"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VpSIeL2Gd7Mg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "828a99b6-3b26-4a6e-a197-1b21c5a8aa39"
      },
      "source": [
        "x_single = data['area_mean']\n",
        "\n",
        "x_single = x_single.values.reshape(len(x_single),1)\n",
        "\n",
        "# Split the data into training/testing sets\n",
        "x_single_train = x_single[:-250]\n",
        "x_single_test = x_single[-250:]\n",
        "\n",
        "# Create logistic regression object\n",
        "area_model = LogisticRegression()\n",
        "\n",
        "# Train the model using the training sets\n",
        "area_model.fit(x_single_train, y_single_train)\n",
        "accuracy = area_model.score(x_single_test, y_single_test)\n",
        "print(f\"Accuracy on test set: {accuracy:.3f}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test set: 0.884\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JyxUvLnn-SmE"
      },
      "source": [
        "You should see an accuracy increase of ~10% when using the mean area of the tumors rather than the mean symmetry. While this level of performance isn't bad given the single-variable nature of this model let's see if we can increase our accuracy with some different model types."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C8d9hXPyyVRH"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "blft5H_gjWhz"
      },
      "source": [
        "###Decision Trees\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sJuuUNczy61c"
      },
      "source": [
        "There's only so far we can get in terms of accuracy with our model when all we have to work with is a straight line. Let's try a decision tree instead to see how that performs for our problem."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CNHmc0RIPUXc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e27614c-38fe-438b-ce7a-22a67932425c"
      },
      "source": [
        "x_tree = data.drop('diagnosis', axis=1)\n",
        "y_tree = data['diagnosis']\n",
        "\n",
        "x_train_tree, x_test_tree, y_train_tree, y_test_tree = train_test_split(x_tree, y_tree, test_size=0.2)\n",
        "\n",
        "# create a classifier object\n",
        "tree_model = DecisionTreeClassifier(random_state = 0) \n",
        "  \n",
        "# fit the classifier with X and Y data\n",
        "tree_model.fit(x_train_tree, y_train_tree)\n",
        "\n",
        "accuracy = tree_model.score(x_test_tree, y_test_tree)\n",
        "\n",
        "print(f\"Accuracy on test set: {accuracy:.3f}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test set: 0.956\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-XCnMFc9r41Z"
      },
      "source": [
        "Here we can see what happens when we are able to use all our data. Take a look at our accuracy this time compared to our logistic regression examples. It's way better than when we were just using a single feature to try to classify our tumors. Now let's see if we can squeeze even more accuracy out of our model by increasing the number of trees we use.\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hLuas59uyC5Q"
      },
      "source": [
        "###Random Forest\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s4fYxsh3zRXL"
      },
      "source": [
        "Now that we are starting to get some good results, let's see if we can do even better with some more complex techniques. What's better than one decision tree? What about a whole forest of decision trees? Run the code below to see what happens."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PkXM5E5Rbz_6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "731aac63-a198-474d-a476-719f8eaf5df2"
      },
      "source": [
        "x_forest = data.drop('diagnosis', axis=1)\n",
        "y_forest = data['diagnosis']\n",
        "\n",
        "x_train_forest, x_test_forest, y_train_forest, y_test_forest = train_test_split(x_forest, y_forest, test_size=0.2)\n",
        "\n",
        "forest_model = RandomForestClassifier()\n",
        "forest_model.fit(x_train_forest, y_train_forest)\n",
        "\n",
        "accuracy = forest_model.score(x_test_forest, y_test_forest)\n",
        "\n",
        "print(f\"Accuracy on test set: {accuracy:.3f}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test set: 0.982\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UOr2rH4q1dk7"
      },
      "source": [
        "Take a look at the accuracy this time. While there is an element randomness in using Random Forest, the values you see should be a slight improvement over the Decision Tree we implemented before.\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wq8No4Y31fxd"
      },
      "source": [
        "###XGBoost"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CHvA8OTTGECS"
      },
      "source": [
        "Now let's move away from bagging and try a boost ensemble model, in this case XGBoost. This is one of the most popular ML algorithms you'll come across in Kaggle competitions and other such sites. Below is the code needed to create your XGBoost model and fit it to your training data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBMdYaWH1kj7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf5ecbe5-72b5-428a-daf7-555cdedfc14f"
      },
      "source": [
        "x_boost = data.drop('diagnosis', axis=1)\n",
        "y_boost = data['diagnosis']\n",
        "\n",
        "x_train_boost, x_test_boost, y_train_boost, y_test_boost = train_test_split(x_boost, y_boost, test_size=0.2)\n",
        "\n",
        "boost_model = XGBClassifier()\n",
        "boost_model.fit(x_train_boost, y_train_boost)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lMp8GauOLHeG"
      },
      "source": [
        "Now that our model is trained, let's see how it is doing on our data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s_j2fYTm8z1I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c33baf6d-aba3-4f1f-e159-8410084a4565"
      },
      "source": [
        "accuracy = boost_model.score(x_test_boost, y_test_boost)\n",
        "\n",
        "print(f\"Accuracy on test set: {accuracy:.3f}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test set: 0.956\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GU0PFdekEnBe"
      },
      "source": [
        "Once again the accuracy result will be different each time you train this model due to the random aspect of XGBoost but from what I saw it is on average an even better model for classifying tumors than random forest was. I was occasionally seeing accuracies of >99% which shows how far one can go using well-known ML algorithms on problem similar to this. \n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "irV2gzF_ElKp"
      },
      "source": [
        "###Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fsH_MscJEp_-"
      },
      "source": [
        "This wraps up our whirlwind tour of machine learning. Hopefully this gives you a good starting point to try using some of these algorithms for your own projects. Some last pieces of advice: take some time to get familiar with all they hyperparameters of the algorithm you are using. I didn't have the time to get into that for this session but scikit learn and XGBoost both have a ton of helpful options you can play with for their algorithms. Finally, don't get discouraged if you start a project and end up with a bunch of errors. Google search is your friend and a large part of getting better at ML projects, just like normal programming, is making mistakes and learning from them. Thanks for your time and happy coding!"
      ]
    }
  ]
}