Diabetes_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To predict diabetes using PIMA diabetes data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"./data/pima-data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>num_preg</th>\n",
       "      <th>glucose_conc</th>\n",
       "      <th>diastolic_bp</th>\n",
       "      <th>thickness</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>diab_pred</th>\n",
       "      <th>age</th>\n",
       "      <th>skin</th>\n",
       "      <th>diabetes</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",
       "      <td>1.3790</td>\n",
       "      <td>True</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",
       "      <td>1.1426</td>\n",
       "      <td>False</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>0.0000</td>\n",
       "      <td>True</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>0.9062</td>\n",
       "      <td>False</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>1.3790</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_preg  glucose_conc  diastolic_bp  thickness  insulin   bmi  diab_pred  \\\n",
       "0         6           148            72         35        0  33.6      0.627   \n",
       "1         1            85            66         29        0  26.6      0.351   \n",
       "2         8           183            64          0        0  23.3      0.672   \n",
       "3         1            89            66         23       94  28.1      0.167   \n",
       "4         0           137            40         35      168  43.1      2.288   \n",
       "\n",
       "   age    skin  diabetes  \n",
       "0   50  1.3790      True  \n",
       "1   31  1.1426     False  \n",
       "2   32  0.0000      True  \n",
       "3   21  0.9062     False  \n",
       "4   33  1.3790      True  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if any null value is present\n",
    "data.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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5Fk1fMk95W/KjHUajtWDBz+rSua2ystooISFOxw87QJ9Mmb9Tu3/8Y4JGXHqMEhPjq0z/+ON56pTVRt33au9VyI3Sgm+z1SWrlbI6tVJCfJyOP6avPvnshyptUlKSyl9vLS7V9pvxS5av0yED9pQktW6VohYpSVr0/WrPYm8sFuYUqXN6orLSExXv9+m4fVpqytLNVdp0TEtUj7bJ8u0w0sFkKi0LalvAqTTgtC3o1LpZ1e86QjqefJSWvxQ6n9vw5XwlpKcqqV3bndpt+HK+inPW7TR9y4pV2rTwRzmSab8ax71fJ65bTwXWZiu4brUUKFPJF58o4YBBVdokHDBIJdMmSpJKv5qq+N4H7LSexEOPUcnMjz2JGYi0JpGISOqQqa3ZOeXvi1flKKlD5s5tVlXf5ru/jFGvu/+so76bql6j/6IfRv19p8/IOv905X40LUJb0LD5W2UosH5t+fvghlz5W1c9MfC1bqvA+vAduGBAwaJCWYu00PIZHdTmoZfUavSTiu/Zf6f1Jw8aqq0z2OlWtragVO1SE8rft0tNUG7Btl22/9/cdRq0V7oXoQG1tjZ3k9q1q7g7nJnZUmvXVr14++67lcrJ2ajBg/tWmV5UVKJnn/tIV105zJNYG7O16wrULjOt/H1mZqrWrtv5QuLV/36po095WA88Olm3/t/xkqR9urfTJ9N+UFlZQCtXbdS3P6zRmh3+jSDlFpaqXYuKfXJmSs375Mr6d2iuAVktNPiZRRr89EL9rkuqurVO2v2CTVCzjpkqWllxrlaUnaNmHTNrWAJoGHyt2iq4oWIktoHgbQAAIABJREFUWjAvV76WO5wrt2yrYF7FubIr2iJLSavSJvGQo1Qy66Mq01L+eLPSx7yg5FMuikjsqOCCLib/REudakSY2WGSulZezjn30i4XaCiqe87SuVq36TLiHH37t3uVM36y2p96nPo9fo++PPni8matBx2srAtO18zfn7vzOpqC6h5j3bF/q2+kwMb1yh15klxBvuL23Eet/na/1l1zjtzWLeWtkgYeo02PjKrHgBu/nbq3BuMXrNei1YV66SKGr6Nhqe57XHlXHAwGde99b+reey/Yqd1jj7+nCy8coubNuWDbHVdNR1dXf+C8Mw/WeWcerAkfLNCTz3+msaNO0/CT9tPSn9dp+AVPq0P7dO3XL0t+f5O4h1En1e2Sa1vi4ZeNJVqWV6xPLustSbrsrSWak12oAzul1F+AsaKaTq3u+w00PL/yWqTS3iWuWy+50mIFspeXTysYd6eCG9fLkpqpxXX3KDjwWJXM+KCeYgYiq9aJCDN7WVI3SfMkBcKTnaRqExFmNlLSSEn6U2KGjk3w9m5sl8vOVecLz5Qkbf5moZI7tdP2getJHdupeE3V52OLV+UouWPFoxWV23Q659TywpVr3p6kfo+NLm/XoncP9Xt8tL4afpm25TXN57ICG3Llb1NxR8LXOkOBvPVV2gQ35MrfJiOUDfb55WuWIlcQuiPnwneNypb9oLKcbMV1yNK2paFhw3Fdu8v8fpUtqzqMuKlrl5qgnEqPWuTklyqjxc5DeWcu26xnpq/Sixf1UkIcFw9oWNplpisnp+KRorVrNyojo+Luz5YtJfpp8WpdcEGoSO269fm64sqn9OS4yzV/wc/68MO5evDBt5VfsFU+nykxMV5/OO9IrzejwWuXkaqcSqMY1q7NV0abFrtsf/zQPhp13wRJUlycXzffcFz5vLMveVZds1pHLthGKjMlQTkFFfvktYWlaptSu8crPl6ySfu2b65mCaHH5QZ2TdWCNVtIRIR1v/Jc7XVZ6Hxuw+yFapZVca7WrFM7bV2du6tFgQYjmJcrX+uM8ve+VhkKblq/c5tWGQrmrZN8flmz5nKFFaPXEg89eqfHMoIbQ+twxUUqmfmR4rr1IhGBRqMuVyYHSvqdc+5K59zV4T/X7Kqxc+4Z59yBzrkDvU5CSNKKZ1/T9IGnaPrAU5Tz/sfqdM4pkqT0AfuqLL9AJWurPjtYsnadygq3KH3AvpKkTuecorUTP5EkFefkqvXAgyRJrY84RFuW/ixJSurUXge++pjmXfZnbVnyszcb1gBtW/y9/O2z5M9oL8XFKXngMSqZXfUxleLZ05U8ODTUN+mwISpZOEeS5EtNl3yhr6E/s4Pi2mepbG3F88fJg47R1umTPdqSxqNPxxSt2FCs7I3FKg0ENenbPA3uUbUA2ndrtujO95br8bN7qHVznjdGw9O3bxf9vCJXK7PXq7S0TO9P/FpDBvcrn9+iRbK+nPWApnwyWlM+Ga3+++6hJ8ddrr59uui1V24sn37hBYP1x5G/JwmxC317ddTPv+Rp5aqNKt1Wpvc/Wqghh+9Tpc3Pv2wofz11xk/q0jmUbNhaXKqiraEL7M+/XCJ/nE977ZkhVNWnXTP9sqlE2ZtLtC0Q1KQfNmrwnmm7X1BS+9QEzckuUFnQaVvAaU52ofZsxUif7RaPe02T9jtFk/Y7RdnvfKw9Lgidz7U+eF9t21xQbS0IoKEpW/aD/O06yde2veSPU+IhR6n06xlV2pR+M0OJh4ceN0w46Eht+/briplmSjh4sEpmVUpE+PwVj274/UrY7zAFspdFelOAelOXRzMWSWonaeeS5g1c7oefKWPoERo8/yMFirZq/pU3l88bNOMdTR8YOqgtvH5U+c93rvtomnInhy6mF1x9m3qPvVm+uDgFSkq08NrbJUl7/+VPim+Zrj5/v0OS5MoCmnHkcI+3rgEIBpT/7INqdcejks+nrZ9MUNnK5Uo5Z6S2LfleJbOnq+jj8Uq/bpTajntTwcJ8bXroVklSQq/9lHLOSCkQkIIBbX5qbJXsb9JhRytvNNWZdxTnM90yrKsue+VHBZ3Tqf3bqntGMz32abZ6d2iuIT1a6sGPflFRaUDXvxGqrNwhLUFPnBP6qc4//Os7LV+/VUWlAQ3++ze6+6Q9NZAaEnX22iV36ci991eblHStHDNed7z3rJ6fOSHaYTUacXF+3X7rWRox4nEFgkENP+1Qde/eQf94dIL69Omio4b02/1KsFtxcX7d/ufjNeKalxQIBDX8pP3VvVuG/vHUJ+rTs6OOOmIfvfLfLzXrq6WKi/MrNTVJY+84TZK0IW+LLr36Jfl8psy2qbr/ziZ4jKuFOJ/p5sGd9Me3lirgnE7t01p7tUnW45+vUe92zTS4W5oW5mzRdeOXK784oKnLNuuJWTl698KeGto9XV/9UqBTX/peJtPAri10ZLfaJTGamtUTP1OHYUfoxCWh87kvLq44nztu7juatF/ofK7/2JvU9dwTFNcsWaes/ExLn3tDC+98XK0O7KvD335cCS1T1fHEwep759Wa2OeEaG1Oo8Rx71cKBlT4wsNK+8vfJZ9fxZ+9p8Cq5Wo2fITKlv+g0m9mqHjqe2pxxW1q+dB/FNySr4LH7ihfPH6f/grmrQsVuyyfGK+0v/5d8sdJPr+2LZqt4injo7BxTUc06ynEIqvts3Vm9qmk/pK+klSyfbpz7qTdLfteag/+1SLsgKO4iIy0jDN6RDuEmBc3/adoh9AkuCfuiXYIsa9w/e7b4DfZ9vrb0Q6hSXjj8rnRDiHmnXd5y903wm+yLp9fCvNCm1c/r2V1nMZp1YkDYvKatuOE2VH5d6vLiIhRkQoCAAAAAAA0DbVORDjnPotkIAAAAAAAIPbtNhFhZjOccwPNrEBVf6HKJDnnXGrEogMAAAAAIMpcICafzIia3SYinHMDw3/v+re+AAAAAAAAaqHWP99pZpdWM+2++g0HAAAAAADEsroUqzzdzIqdc69KkpmNk8QPXQMAAAAAgFqrSyLiNEnjzSwo6ThJec65KyMTFgAAAAAAiEW1KVbZqtLbEZLekfS5pLvMrJVzLi9SwQEAAAAAEG0uGIx2CDGlNiMivtbOv5ZxvKRh4fd71ndQAAAAAAAgNu22WKVzbg/n3J6S/iqpv3NuD0nPS5ov6fQIxwcAAAAAAGJIrX81Q9Ktzrl8Mxso6RhJL0h6MiJRAQAAAACAmFSXYpWB8N/HS3rKOfeumY2q/5AAAAAAAGg4XMDtvhFqrS4jIlaZ2dOSzpQ00cwS67g8AAAAAABo4uqSSDhT0oeSjnXObZLUStJNEYkKAAAAAADEpFo/muGcK5L0v0rv10haE4mgAAAAAABAbKpLjQgAAAAAAJocF6RGRH2ixgMAAAAAAPAMiQgAAAAAAOAZEhEAAAAAAMAz1IgAAAAAAKAGQWpE1CtGRAAAAAAAAM+QiAAAAAAAAJ4hEQEAAAAAADxDjQgAAAAAAGrgAtSIqE+MiAAAAAAAAJ4hEQEAAAAAADxDIgIAAAAAAHiGRAQAAAAAAPAMxSoBAAAAAKiBC1Kssj4xIgIAAAAAAHiGRAQAAAAAAPAMiQgAAAAAAOAZakQAAAAAAFADF6BGRH1iRAQAAAAAAPAMiQgAAAAAAOAZEhEAAAAAAMAz1IgAAAAAAKAGLkiNiPrEiAgAAAAAAOAZEhEAAAAAAMAzJCIAAAAAAIBnqBEBAAAAAEANqBFRvxgRAQAAAAAAPEMiAgAAAAAAeIZEBAAAAAAA8AyJCAAAAAAAUC0zO9bMfjSzJWb212rmJ5rZf8LzvzSzrrtbJ8UqAQAAAACogQs0zWKVZuaX9ISkYyRlS5ptZuOdc99VanappI3Oub3M7GxJYyWdVdN6GREBAAAAAACqc5CkJc65Zc65Ukn/lnTyDm1OlvRi+PWbko4yM6tppSQiAAAAAABAdTpKWlnpfXZ4WrVtnHNlkjZLal3TSklEAAAAAADQBJnZSDObU+nPyB2bVLPYjs+p1KZNFdSIAAAAAACgBsFgbNaIcM49I+mZGppkS8qq9L6TpNW7aJNtZnGS0iTl1fS5niQihr000IuPadJKTzwt2iHEvA3njI52CDFvXbw/2iE0CVtveyjaIcS8pNMPiHYIMc9tC0Y7hCbh7PcHRzuEmDf01YXRDiHmtU0NRDuEJiE2L9Mhabak7ma2h6RVks6WdO4ObcZLulDSLEmnS5rinGNEBAAAAAAAqBvnXJmZXSXpQ0l+Sc875741s7skzXHOjZf0T0kvm9kShUZCnL279ZKIAAAAAAAA1XLOTZQ0cYdpt1d6XSzpjLqsk0QEAAAAAAA1CPJEYL3iVzMAAAAAAIBnSEQAAAAAAADPkIgAAAAAAACeoUYEAAAAAAA1oEZE/WJEBAAAAAAA8AyJCAAAAAAA4BkSEQAAAAAAwDMkIgAAAAAAgGcoVgkAAAAAQA0oVlm/GBEBAAAAAAA8QyICAAAAAAB4hkQEAAAAAADwDDUiAAAAAACoQdBFO4LYwogIAAAAAADgGRIRAAAAAADAMyQiAAAAAACAZ6gRAQAAAABADYLBaEcQWxgRAQAAAAAAPEMiAgAAAAAAeIZEBAAAAAAA8Aw1IgAAAAAAqAE1IuoXIyIAAAAAAIBnSEQAAAAAAADPkIgAAAAAAACeIREBAAAAAAA8Q7FKAAAAAABqQLHK+sWICAAAAAAA4BkSEQAAAAAAwDMkIgAAAAAAgGeoEQEAAAAAQA2oEVG/GBEBAAAAAAA8QyICAAAAAAB4hkQEAAAAAADwDDUiAAAAAACoATUi6hcjIgAAAAAAgGdIRAAAAAAAAM+QiAAAAAAAAJ6hRgQAAAAAADWgRkT9YkQEAAAAAADwDIkIAAAAAADgGRIRAAAAAADAMyQiAAAAAACAZyhWCQAAAABADShWWb8YEQEAAAAAADxDIgIAAAAAAHiGRAQAAAAAAPBMk6sRMf3HjRozfpmCzun0AZm6bHBWlfkvTFulN2fnyO8ztWoer9FndFfHlkn6fnWh7nx7qQqLA/L7pD8OydKwfdtGaSsavs+nf6+x976jYCCoU08/RJdedlS17T76cL7+7/oX9dp/r1fvPll6f8LXevH5T8vn//TTGv37zRu0T8+OXoXeoCXse4hSLr5O8vlV/Ml4Fb37ctUGcfFKvep2xe25j1zBZm1+5FYF1+VIkvyduyl15F9kyc0l55T3t0ukbaVKv+MJ+Vq2listkSRtGn2dXP5GrzetwYjvd7Can3+dzOdT8dQJ2jrhlaoN4uLV4orbFNe1h4KFm1Xw2O0Krs9R4mFDlXzCueXN/FndtOnWSxRYsVhptzwmS28jbQv18eb7rpPL3+TlZjVYvu4HKP74P0o+nwJzPlTZtDeqzu/aR/HHj5Rl7qHS/9yn4LefS5IsPUMJ594i+XySL05lX0xQ4KuJ0diERmH6vDW658V5CgadTh+yh0ae3LPK/H+9/6PenLJcfr+pVYtE3XP5AHVs21yS9OCr8/XZ3DWSpCtO66Vhh3X2PP7GYMaKfI2dtloB53Rar1YacWBmlflzVhXq/umr9dP6rbr/2C4aule6JOmr7ELdP31VebvlG0t0/++76KhuaZ7G3xhM/36Dxry9WEEnnX5we112dJcq81+Y+ove/GJN6BwuJV6jz+6pjq2SyucXFpfp+Pu+1NF92+q24Xt7HX6DxXGvYfnn+bfohL6/U27BRvW9+7xoh4NKqBFRv5pUIiIQdLr7naX654g+ykxL0JmPz9PgXq21V2az8jY9OzbXG4f0V3KCX6/PWqMHJ/6sh8/bR0nxft131t7q2iZZufklGv7oPA3cu6VSk5tUF9ZKIBDUmNH/09PPXa7MzDSde9bDOnJwb3Xbq12Vdlu2FOu1V6arb7+Kk9rjTzxAx594gCRp8U+rde1Vz5OE2M58anHpjdo4+loFN+Sq5b3Pq2TOdAVW/VzeJHnIiXJbCpR3zRlKPOxopZz3J+U/cpvk8yvt6lHKf/xOla1YIktJlcrKypfLf3SUypb9EIWNamDMp5SLbtTme69TMC9X6Xc/p9JvZlTp46QjT1BwS4E23niWEg45Ss3PuVIFj92ukpmTVTJzsiTJn7WnUm+4T4EVi8uXKxx3p8qW08dVmE/xJ16p0n/dIpe/XolXPKLA91/IrVtZ3sRtylXpm39X3KDhVRZ1BXkqefpGKVAmJSQp8ZonFfj+C6kgz+utaPACwaDuev4bPX/LEcpsnawzbv5YQw7ooL06VVzo9uzaUm+O6abkxDi9PnmJHnx1gR6+7lBN/Wa1vvt5k94eO1Sl24I6/65PdXj/9kppFh/FLWp4AkGne6au0jOn7Kl2KfE6+z+LNXjPNHWrdBHcvkWC7j46Sy9+s67Ksgd1StGb5/SQJG0uLtOwl37QYZ1beBp/YxAIOt391k/65+X9lZmeqDMfnqPBfdpor3bNy9v07NhCb9zQMXQO9/kqPThhqR6+sHf5/EcnLteAbunRCL/h4rjX4Lww6309PvVNvXTR7dEOBYioJvVoxoKVBercOklZrZOUEOfTsH3basp3G6q0ObhbupIT/JKkfTu30NrNoUzuHm2T1bVNsiQpIzVRrVPilbdlm7cb0EgsWviLsjq3Uaes1opPiNOxx+2nqVMW7dTuiUcn6aJLBysxsfoT2knvz9Vxw/aPdLiNRtxevVSWk61g7mopUKaSmR8rccDhVdokHDhIW6eG7gqXfPGpEvocGJq+70Eq+2WJylYskSS5wnzJkdbdUVy3ngqszVZwXbiPv/hECQcMqtIm4YBBKpkW6uPSr6YqvvcBO60n8dBjVDLzY09ibsx8nfaWy1sttzFHCpQpsGCa/D0PrdLGbcqVW/vzzt/XQFnojyT54yUzb4JuhBYsyVPndinKykxRQpxfww7rrE/mrK7S5pDeGUpODCXW9+3eWjl5RZKkpavyNaBnW8X5fWqWFKd9Oqdr+vwcz7ehoVu4tkid0xOUlZaoeL9Px+2drk+Xba7SpmNqgnq0Sa7xqzp5yWYN7NJCyfFN6vSsVhb8kq/ObZKV1SY5dA63X6amLFpfpc3B3VtWnMN1SdXaTcXl875dWaD1haX6XY9Wnsbd0HHca3imL5mnvC350Q4DiLhaH+nMrJuZJYZfH2lm15hZo0or524uVbv0xPL3mWmJWru5dJft35q9VoN6tNxp+oKVBdpW5tS50p0OVMhdu1nt2lV8NTLapWttbtUTsu+/y1ZOziYdcWTvHRcv9+EH83Ts8ftFLM7Gxt+qrYIbcsvfBzfkyteqbTVt1oYbBOSKCmUt0uRv31lyTmk3P6yW972gZidVHeqXeuWtann/i2o2/OKIb0dD5tuxj/Ny5WtZtY99LdsqmBduEwzIFW2RpVQdQp14yFEqmfVRlWkpf7xZ6WNeUPIpF0Uk9kYptbXc5ooLCZe/XpbWutaLW1obJV79hJL+/KLKpr3JaIhdWJu3Ve1bV4z8a9cqWWvztu6y/ZufLtfh/dtLknp0Tte0eWu0taRMG/NL9OV3uVqzoSjiMTc2uVu2qV1KQvn7zJR4rS2s+82KDxZv0rC9dz7vgJS7qUTt0ivOu0LncCW7bP/Wl2s0qGdofxIMOo19d4luOrFbxONsbDjuAYiWujxX8JakA81sL0n/lDRe0muShkUisEhw1Uzb1Z2J8d/kalF2oV6+vG+V6bn5pfrLv3/SvWd2l8/HHbjqOLdzT5sq+ioYDOrBse/qrjHn7HIdC+avUFJSvLp3bx+RGBul6r6sO/b1rtr4/YrfZ1/l/e0SuZJitbz9MW1b9qO2LZqj/EdHKbhxnSypmdJuHKPg4cepeNqkyGxDg/cr+7jS3iWuWy+50mIFspeXTysYd6eCG9fLkpqpxXX3KDjwWJXM+KCeYm7EavOdroHbvF4lj/1JatFKiX+4TYFFM6QtPINcG7s89k1foW+X5enlOwZLkgbu206LluXpnNunqFVqovp3b604jn07qe5rW9dBOuu2bNPi9Vt5LGMXqj2H20Xb8XNytGhlgV6+KnQz4/XPV+nwnq3UviU3kHbGcQ+orequcfDr1WXsX9A5VybpVEmPOOeul7TLq0QzG2lmc8xszjOTG8bzYZlpCcrZVJE9X7u5RBmpCTu1m7l4k56eslLjLuqphLiKLiosLtPl//pW1/6+i/p3SfUk5sYos126cnIqLgZyczYpI6Oiv7ZsKdGSxTkaceETOu7ou7Vg/gpd+6d/6ttFFc+FfziJxzJ2FNiQK1/rjPL3vtYZCm5cX02bcIE0n1/WLEWuMF/BDbkq/W6uXMFmqbREJXNnKX6P0DPJwY2h55VdcZGKZ0xW3F69vNmgBiiYt0Mft8pQcNP6ndu0Crfx+WXNmocedQlLPPTonYanbv93csVFKpn5keK6Nd0+rmLzellam/K3ltpGLv9XjGooyFNw7S/ydd31CKumLLNVcpVRDDl5W5XRMnmndjMXrtVTb3+ncTcNVEK8v3z65af20jtjh+r5W46Qc1KX9lwo7ygzJV45hRUjLNcWblNG87rV0fhw8SYN6ZameD+Jnupkpicqp9KjFms3lygjLXGndjN/zNPTH63QuEv7lp/Dzfs5X6/NWKWj7pql+8cv1buzc/TQhKWexd6QcdwDEC11SURsM7NzJF0o6b3wtF0eZZ1zzzjnDnTOHThy6D6/JcZ607dTC63YsFXZecUqLQtq4vx1Gtyz6rOC360q1Kj/LdETF/VS60rDLEvLgrr6pe918v4ZOrZfmx1XjUp698nSLyvWKTt7g7aVlumDSXN1xOA+5fNbtEjWZzPv1qSPb9Okj29Tv3276B9PXKrefUK/YBIMBjX5w/k6dhiPZVRWtvR7xbXPkq9te8kfp8TDjlbJnOlV2pR+PUPJR4YGKSUeMlil334dmj7/S8V13ktKSJR8fiX03E9l2ctDJxQtwsMr/X4lHPA7la1c5ul2NSRly36Qv12nij4+5CiVfj2jSpvSb2Yo8fBQHyccdKS2hftYkmSmhIMHq2RWpRMyn79iCKvfr4T9DlMgu+n2cWXBVT/JWneQtcyU/HHy9ztcgR++qN3Cqa2luPA+OilFvi695NavqnmZJqpvt1ZakVOo7NxClZYFNHHmLxpyQIcqbb5bvlF3PDtH424aqNZpFXeNA8GgNhaEEvg/rtikn37ZpN/1q/prEJD6ZDbTik2lyt5com2BoCb9tElH7lG3X72Y9BOPZdSkb1YLrVi3VdkbtobO4eau1eDeVc/Hvssu0Kg3ftQTI/qqdYuKc7gHzu+lKXccpk9uP1R/PqmbTh7QTjfymIYkjnsAoqcuj2ZcLOlySfc455ab2R6SXtnNMg1KnN9068ndNOKfixQMSqcNyFT3ds316OQV6tMpRUN6tdYDE5erqDSg618JjeJon56ocRf10gcL1mvO8nxtKirTO1+HnpMbc2Z39eyQEs1NapDi4vz62y2n6YrLnlEwGNQppx6kvbq30xOPTVLv3lk6ckifGpf/es4yZWamqVNW7Z8VbxKCARU8/5DSb3lE5vNp66fvKZC9XM3PvEzbln6v0q9naOuUCUq96g61evQNucJ8bX7kNkmS21KgovdfV6t7n5ecU8ncWSqdO1NKTAqtzx8n+XwqXThbxR+/G+UNjaJgQIUvPKy0v/w99BOpn72nwKrlajZ8hMqW/6DSb2aoeOp7anHFbWr50H8U3JKvgsfuKF88fp/+CuatCxX9Kp8Yr7S//l3yx0k+v7Ytmq3iKeOjsHENUDCobROeVMJFoyXzKfDNZLncXxR31B8UXLVYwR++lHXsrsTzbpOSU+Tf52C5o/6gkkevkK9tZ8UPGxEaQmymshlvhYpaYidxfp9uu3h/XTpmmoJBp+GD91D3rDQ9+t9F6rNnSw05sKMeeHW+ikrKdN0jsyRJ7ds005M3DVRZmdMfRoV+UjklOU73X3Ww4vwUUtxRnM908xEddfn4ZQoEpVN7tdJerZP0+Bc56p2RrMF7pmnR2iJd+/7PKigJ6LOf8zXuyxy9c17oRs2q/FLlFJbqwI7Nd/NJTVec36dbh++tEU/PVzDodNrB7dW9fXM9OmmZ+mSlakifNnpg/FIVlQR0/QvfSpLat0zUuBH9ohx5A8dxr8F57ZK7dOTe+6tNSrpWjhmvO957Vs/PnBDtsIB6Z3V51sXMkiV1ds79WJcPCb5zKQ/URFjpiadFO4SYl3/O6GiHEPN8VIr3RPOudbtTi7pLOn3nqvKoX9s+r9OpCH6luD35lYlIy3t1YbRDiHltUwPRDqFJcE9+EdPPln2c0SMmr2mPzv0xKv9udfnVjBMlzZP0Qfh9fzMjvQkAAAAAAGqtLrcfR0k6SNImSXLOzZO0RwRiAgAAAAAAMaouiYgy59zmHabF5PAUAAAAAAAQGXUpVrnIzM6V5Dez7pKukTQzMmEBAAAAAIBYVJdExNWSbpFUIuk1SR9KujsSQQEAAAAA0FAEg9GOILbUJRFxvHPuFoWSEZIkMztD0hv1HhUAAAAAAIhJdakR8bdaTgMAAAAAAKjWbkdEmNlxkoZJ6mhmj1aalSqpLFKBAQAAAACA2FObRzNWS5oj6SRJX1eaXiDp+kgEBQAAAABAQ0GNiPq120SEc26+pPlm9lq4fWfn3I8RjwwAAAAAAMScutSIOFbSPEkfSJKZ9Tez8RGJCgAAAAAAxKS6JCJGSTpI0iZJcs7Nk9S1/kMCAAAAAACxqi4/31nmnNtsZhELBgAAAACAhoYaEfWrLomIRWZ2riS/mXWXdI2kmZEJCwAAAAAAxKK6PJpxtaTekkokvS4pX9J1kQgKAAAAAADEplqPiHDOFUm6xczGht66gsiFBQAAAAAAYlGtExFmNkDS85JahN9vlnSJc+7rCMUGAAAAAEDUUSOiftWlRsQ/JV3pnJsuSWY2UNK/JPWLRGBQ0C4RAAASc0lEQVQAAAAAACD21KVGRMH2JIQkOedmSOLxDAAAAAAAUGu7HRFhZvuHX35lZk8rVKjSSTpL0tTIhQYAAAAAAGJNbR7NeGiH93dUeu3qMRYAAAAAABDjdpuIcM4N9iIQAAAAAAAaIopV1q+6FKuUmR0vqbekpO3TnHN31XdQAAAAAAAgNtW6WKWZPaVQXYirJZmkMyR1iVBcAAAAAAAgBtXlVzMOc85dIGmjc+5OSYdKyopMWAAAAAAAIBbV5dGMreG/i8ysg6QNkvao/5AAAAAAAGg4qBFRv+qSiHjPzNIlPSDpG4V+MeO5iEQFAAAAAABiUq0TEc65u8Mv3zKz9yQlOec2RyYsAAAAAAAQi3abiDCzIc65KWZ2WjXz5Jz7X2RCAwAAAAAAsaY2IyIOlzRF0okKPY6xnYXfk4gAAAAAAMSsoNt9G9RebRIRBWZ2g6RFCiUeLDydfwoAAAAAAFAntUlEpIT/7iFpgKR3FUpGnChpWoTiAgAAAAAAMWi3iQjn3J2SZGaTJe3vnCsIvx8l6Y2IRgcAAAAAAGJKXX6+s7Ok0krvSyV1rddoAAAAAABoYILBaEcQW+qSiHhZ0ldm9rZC9SFOlfRiRKICAAAAAAAxqdaJCOfcPWY2SdKg8KSLnXNzIxMWAAAAAACIRXUZESHn3DeSvolQLAAAAAAAIMb5oh0AAAAAAABoOuo0IgIAAAAAgKaGYpX1ixERAAAAAADAMyQiAAAAAACAZ0hEAAAAAAAAz1AjAgAAAACAGlAjon4xIgIAAAAAAHiGRAQAAAAAAPAMiQgAAAAAAOAZakQAAAAAAFADakTUL0ZEAAAAAAAAz5CIAAAAAAAAniERAQAAAAAAPGPOuWjH0CCZ2Ujn3DPRjiOW0ceRRx97g36OPPo48ujjyKOPvUE/Rx59HHn0MWIdIyJ2bWS0A2gC6OPIo4+9QT9HHn0cefRx5NHH3qCfI48+jjz6GDGNRAQAAAAAAPAMiQgAAAAAAOAZEhG7xjNZkUcfRx597A36OfLo48ijjyOPPvYG/Rx59HHk0ceIaRSrBAAAAAAAnmFEBAAAAAAA8AyJiHpgZnHRjgHArplZupldGX59pJm9t4t2z5lZrxrWM8rM/i9SccYyM5tZz+vramaLwq8PNLNH63P9sahyn/2KZTuY2Zv1HROAhs/MfjazNtVMr9f9eizZfr5gZneZ2dG7aTvVzA6sw7r7m9mw3x4lEF2NMhERPpn63syeNbNvzWyymSVX/o9sZm3M7Ofw64vM7B0zm2Bmy83sKjO7wczmmtkXZtaqhs+aamaPmNlMM1tkZgeFp48ys2fMbLKkl8zMb2YPmNlsM1tgZn8Mt/OZ2bhwnO+Z2UQzOz3yvfTrmNkLDTm+WFKXg9Qulj/SzA6r7eeEX9f5s8L/fx6va3wNTLqkK3fXyDk3wjn3nQfxNDnOud1+V3/Duuc4566J1PohOedWO+ea3LEhkhcT9SlG9tNoZCK5X48VzrnbnXMf1/Nq+0siEYFGr1EmIsK6S3rCOddb0iZJw3fTvo+kcyUdJOkeSUXOuf0kzZJ0wW6WbR7e2V4p6flK0w+QdLJz7lxJl0ra7JwbIGmApMvMbA9Jp0nqKqmvpBGSDq31FqJJ+A0HqSMl1ekkIEIHxMbgPkndzGyepAckpZjZm2b2g5m9amYmVb2QMLNjzewbM5tvZp/suEIzu8zMJlVKgo41s6/M7CczGxRus6sEZXszm2Zm88IJzkHhti+E3y80s+s96x0PmFlh+O8jw/1VXf/fZ2bfhfvqwfC0KsnR7evZYd3lo1zCF47Phz9jmZmRoKgqzsxeDPfxm2bWzEJ3O8eY2Swzm2Nm+5vZh2a21Mwul37baIpYEI19p5n5vfy8xiR8c+nr8E2ekeFpl4b3v1MtdKPq8fD0tmb2Vng/PNvMfhfd6Bs2M2tuZu+Hj32LzOysSvOSzewDM7ss/H63+/WmxMxuMbMfzexjST3C08qPYWZ2e/g7uMhCNzMr99EfbOebns3Dx7PZFrp5erKZJUi6S9JZ4XOIs6prF16+d/i8ZF54n9/d2x4BataYExHLnXPzwq+/VuhivyafOucKnHPrJG2WNCE8fWEtln1dkpxz0ySlmll6ePp459zW8Ouhki6w0IXOl5JaK5QsGSjpDedc0DmXI+nT2mycF8zstvAB4yMze912GHJulYbiWWjo89Tw6xQz+1f4YmmBmQ0PTz8nPG2RmY0NT6v24srMuoUPZl+b2XQz26eGODPN7O3wQXG+hUcBWGhUy6Lwn+vC06odLROet5eZfRxexzdm1q3eO3U3fu1BysyusYoLtH+bWVdJl0u6PnyAGWRmXczsk3CbT8ysczWfX/mzBoQPevPDB6oWNYSeFf73+tHM7ggv3zX8/XnRKl3U1Gd/1aO/SlrqnOsv6SZJ+0m6TlIvSXtKqnJiamZtJT0rabhzbl9JZ+ww/ypJJ0o6pdI+IM45d1B4vXeEp+0qQXmupA/D8ewraZ5Cdzg6Ouf6OOf6SvpXfXZAA7NT/1toZNqpkno75/pJGv0b1r+PpN8rlHi+w8zif2O8saSHpGfCfZyvipFCK51zh0qaLukFSadLOkShE94mpb4vJnbxGaPM7GUzm2Jmi63iwu5IM/vUzF5T6PxEZvaHShcTT1s4QWFmF1vowvsz7bAPawIucc4dIOlASdeYWUdJtyn0nT1GoX3Adv+Q9HB4Pzxc0nNeB9vIHCtptXNuX+dcH0kfhKenKHTu/Jpz7tlqlqvxuBrrzOwASWcr1A+nKXTM39HjzrkB4X5NlnRCpXnV3fS8RdKU8Hd3sEI3UuIl3S7pP865/s65/1TXzsyaK3Se+I/wucaBkrLrdaOB36gx1zYoqfQ6oNB/6DJVJFeSamgfrPQ+qN33w44/LbL9/ZZK00zS1c65Dys3NLPjd7PuqLDQXd/hCu0w4yR9o1BCpzZuU+jiqm94XS3NrIOksQqNEtkoabKZnSJppcIXV+G225M4z0i63Dm32MwOljRO0pBdfN6jkj5zzp0aPgFLCe/wL5Z0sEJ9/2X4ZGyjQgmgc5xzl5nZf8Pb+YqkVyXd55x728yS5HEiboeD1K76/HHn3F3h9i8rdJCaoNCF9B7OuRIzS3fObTKzpyQVOue23zmeIOkl59yLZnaJQv12yi5iSZD0H0lnOedmm1mqpK3VtQ07SKFRRUWSZpvZ+5LWK3SSfqlz7nMze16hA+iDdeuZqPjKOZctSRZKHnaVNKPS/EMkTXPOLZck51xepXnnK3QwP8U5t63S9P+F/66cGB0qqZ9V3NFPU+j7OVvS8+EL5Hecc/PMbJmkPc3sMUnvS5pcHxvaQFXX/19IKpb0XPj7VW0dj1p63zlXIqnEzHIlZYoTsO1WOuc+D79+RdL2ESPjw38vlJTinCuQVGBmxZX22zHvN+6npfDFhJkdrtDFRJ8aPq6fQvua5pLmhr/3Unh/65xbbmY9JZ0l6XfOuW1mNk7SeWb2kaQ7FTrmblboJsfc37Dpjc01ZnZq+HWWQvvlz7bvq83sDUl7h+cfLalXpXxRqpm1CH/HsbOFkh600A2l95xz08N9966k+51zr+5iud0dV2PdIElvO+eKJMnMxlfTZrCZ/VlSM0mtJH2rin1H+U1PM9t+03OopJOs4kZhkqSdbjLV0G6WpFvMrJOk/znnFv/WjQTqU2MeEVGdnxU6KEuhuzn15SxJMrOBCl2Ab66mzYeSrth+583M9g5nI2dIGm6hWhGZCg2nbwgGSnrXObc1fDCesLsFKjla0hPb3zjnNiqU+Z3qnFvnnCtT6KL/cEnlF1dmdqykfDNLUeiRgjfCB6unJbWv4fOGSHoy/FmBcP8PVGiHv8U5V6jQReCgcPudRsuE7/Z3dM69HV5P8faDhYfKD1LOuXxVnPhXNtjMvjSzhQptd+/w9AWSXjWzPyiUcKvOoZJeC79+WaE+2pUekta4/2/v3mPsKMs4jn+fLSoRKloSTUAR0aASgYrgBRNbA8YYL0BCQ9R4wT8wpCDGbEgAQ5bUW2r/EI0XLGBRYkyk1RisUiUtl4JsldZCCmoCrZEYLQ2oiIlGfv7xPNM9PTuzPe2ue/19kv1jz3nPzJw558w78zzv8460DUDS3+tz6/ILSfsq+7+hZ9n9FzUTrXM26Q9k9gcjg/EByMbD5AnWyzuW2bu8JkC5tP5eJWlTja56B/AE8L2I+Gj9jk4HtgArmd9Zu3H7v75/bwbWkwG0Jgu3P8BcmefnH87yJ7vB80hXYL03ON8fuF9I+28yx2noHkHZpumDnyQDCc0IitEmCAqcQ57XbKv+8hwy2/wWxvrcf5OB5QUhIpaT5yFvqxFr24HfTfCSoWrbHIePdxCim6Tfk9+5h4AvRsS19dRW4D19I4B6+bjbfd5AJcC+AVxYiby1HJg0bTs2Bzkys/nuniDpkbbFt7WT9H3gA2Si6Y6I6Er4mc2I+RaIWEMGA+4Dxs3uOwlP1TK/RQ61bnMjsAt4MLKO9gbyILyezMQ1jz1AZi9m2iC1e10jTNou0lqX13FxNQQ83XPAXCrp9Yew7Z3rK22d4WypVTzcTuq9ZPDnTcBvYrA7tXSui4kvtAdZlg7y+GzzD2Ci0pN+9wPLqoyCOHBC2+3AJ4Gf1EigibQGKCPilcBfa3jrTcAZkWVQQ5LWk6OOzjiE7Z3zKkB5jKSN5PDepfXUbsYCzOeRw1Lt8J0QEc1cRR9kYWUsBzXVFxODrqdrtOUtPX3layWNDLDs+ewY4ClJz0aWdb6VzDAvqxGaR3DgvGGbgMuafyJiKdap+rVnJd1Knlc3fdG1wD7y+2/j3Q1cEDmPxmKyfLNXc5x4svq7/oRpW9LzDuDyJvgTEW+stv3nNK3tIuIk4DFJXyWDqqdN/m2aTZ05GYiQtLsZ6l//r5E0IulRSadJOlvSZyWdWM+vk3RZT/sTKwMx7rkO62uZb5A0Wq8baYbE1//PSbpa0qnV7p2S/ibpOWBY0ilkEONkqu5zht0LvD8ijqwDYlsJyW7GLgAm6tRfQgZYlkXerWQReYJ7V9vFVWWZHo+IFfX6iIjTJ9jWO4FLq+2iKiO4Gzg/cqK1o8ja8nu6FlDr/FNkuQgR8YKY/vkMDquTiogh4BWSNgNXkneAOJrxHdF95JBigA8z8QXGo8BxEXFWrWPxQYIb74qIJZHzbZxPZkZgjlzUSNoHbK0g4ZcHaL8XuATYEBG/pS/bKOleYBj4abTc0qxHV4ByObAjIraTv63rgeOBLZX1XAdcdSjvcR5YDNweETuBu4Bmss615LFllMwC/7Pj9TaYR4CP1X5eQo02s/3+HxcTXc6rPvhY8piwraXNncCFEfHSWu6SCmQ+ACyPiGMr0Lmi5bXz1c/JSVd3AqvIsq4ngC+Q++WX5HG32fefAs6MnMtoF1k3b91OBUarL7qGA+fr+TRwZESsnpEtm8UkPUieK+wgk5D39D3/NNmfPQT8mPG/97ak5yoy+L6zziFW1eObyXKjHZGTiXa1uwh4uD7L1wHfnaK3azYlQlqoAfXBRE7QOCzp15NcxovJIcWrJa2bko2bpIgYIS8e9wB7yVELbydrAm+LnPn/JuAvZOd+pqTldfLVZOf/C1wnaUNEfIi8eApgo6QrK8DwHcaCXldJ+lllmr9JlmQ8D/iBqua2ZTtfRs4pcVKt71JJ90fEZ4BPVLMbJX0lchLH2zU2J8UwWe88Ejlb8A3kaJn/ACskPTa5vXhoIuIa8i4te8iRMrvIGuJmn3+ODCbsJufX2EPe5WUzmQUK4FZJX4qIk4HbyKHTl1f7m8n3txe4WNIf63N+RtKaiFjXs66zgK+R86v8Czi3ylz6t/nj5G2ijgJeQ05UdV3t643kifvZwB+Aj8xAyYuZ2ZQ5nON09TFbqNFUwIvICRVHO9YxAhwHvJqs5V4taW2VHQxLel9P24vIvnWI7LtWSvpVRFxcj/+ZvPhZNEBiZd6KiKMlPVNB9R8BNzflmGZmNvs4EFEi4uuMn+H3eknzdub6nk77heTF5CUV0TU7qP6gj5mZDaY3QDzT2zJfRN7y91xy1Mom4Ar5JNfMbNZaiBPJtJK0cqa3YQZ8OyJOITvtWxyEMDMzs7lI0vDBW5mZ2WzhERE2a9Rw2P461x9K+vxMbM9CExHvJm/B2utxSRe0tTczs4lV+cQVfQ9vXaDJDzMzs/0ciDAzMzMzMzOzaTMn75phZmZmZmZmZnOTAxFmZmZmZmZmNm0ciDAzMzMzMzOzaeNAhJmZmZmZmZlNGwcizMzMzMzMzGza/A+8Dpn1R7cGcwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Correlation\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "#get correlations of each features in dataset\n",
    "corrmat = data.corr()\n",
    "top_corr_features = corrmat.index\n",
    "plt.figure(figsize=(20,20))\n",
    "#plot heat map\n",
    "g=sns.heatmap(data[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>num_preg</th>\n",
       "      <th>glucose_conc</th>\n",
       "      <th>diastolic_bp</th>\n",
       "      <th>thickness</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>diab_pred</th>\n",
       "      <th>age</th>\n",
       "      <th>skin</th>\n",
       "      <th>diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>num_preg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129459</td>\n",
       "      <td>0.141282</td>\n",
       "      <td>-0.081672</td>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.017683</td>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.544341</td>\n",
       "      <td>-0.081672</td>\n",
       "      <td>0.221898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glucose_conc</th>\n",
       "      <td>0.129459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.466581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diastolic_bp</th>\n",
       "      <td>0.141282</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>0.065068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thickness</th>\n",
       "      <td>-0.081672</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.074752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insulin</th>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>0.130548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>0.017683</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.292695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diab_pred</th>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>0.173844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>0.238356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skin</th>\n",
       "      <td>-0.081672</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.074752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetes</th>\n",
       "      <td>0.221898</td>\n",
       "      <td>0.466581</td>\n",
       "      <td>0.065068</td>\n",
       "      <td>0.074752</td>\n",
       "      <td>0.130548</td>\n",
       "      <td>0.292695</td>\n",
       "      <td>0.173844</td>\n",
       "      <td>0.238356</td>\n",
       "      <td>0.074752</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              num_preg  glucose_conc  diastolic_bp  thickness   insulin  \\\n",
       "num_preg      1.000000      0.129459      0.141282  -0.081672 -0.073535   \n",
       "glucose_conc  0.129459      1.000000      0.152590   0.057328  0.331357   \n",
       "diastolic_bp  0.141282      0.152590      1.000000   0.207371  0.088933   \n",
       "thickness    -0.081672      0.057328      0.207371   1.000000  0.436783   \n",
       "insulin      -0.073535      0.331357      0.088933   0.436783  1.000000   \n",
       "bmi           0.017683      0.221071      0.281805   0.392573  0.197859   \n",
       "diab_pred    -0.033523      0.137337      0.041265   0.183928  0.185071   \n",
       "age           0.544341      0.263514      0.239528  -0.113970 -0.042163   \n",
       "skin         -0.081672      0.057328      0.207371   1.000000  0.436783   \n",
       "diabetes      0.221898      0.466581      0.065068   0.074752  0.130548   \n",
       "\n",
       "                   bmi  diab_pred       age      skin  diabetes  \n",
       "num_preg      0.017683  -0.033523  0.544341 -0.081672  0.221898  \n",
       "glucose_conc  0.221071   0.137337  0.263514  0.057328  0.466581  \n",
       "diastolic_bp  0.281805   0.041265  0.239528  0.207371  0.065068  \n",
       "thickness     0.392573   0.183928 -0.113970  1.000000  0.074752  \n",
       "insulin       0.197859   0.185071 -0.042163  0.436783  0.130548  \n",
       "bmi           1.000000   0.140647  0.036242  0.392573  0.292695  \n",
       "diab_pred     0.140647   1.000000  0.033561  0.183928  0.173844  \n",
       "age           0.036242   0.033561  1.000000 -0.113970  0.238356  \n",
       "skin          0.392573   0.183928 -0.113970  1.000000  0.074752  \n",
       "diabetes      0.292695   0.173844  0.238356  0.074752  1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing the diabetes column data from boolean to number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes_map = {True: 1, False: 0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['diabetes'] = data['diabetes'].map(diabetes_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>num_preg</th>\n",
       "      <th>glucose_conc</th>\n",
       "      <th>diastolic_bp</th>\n",
       "      <th>thickness</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>diab_pred</th>\n",
       "      <th>age</th>\n",
       "      <th>skin</th>\n",
       "      <th>diabetes</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",
       "      <td>1.3790</td>\n",
       "      <td>1</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",
       "      <td>1.1426</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>0.0000</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>0.9062</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>1.3790</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_preg  glucose_conc  diastolic_bp  thickness  insulin   bmi  diab_pred  \\\n",
       "0         6           148            72         35        0  33.6      0.627   \n",
       "1         1            85            66         29        0  26.6      0.351   \n",
       "2         8           183            64          0        0  23.3      0.672   \n",
       "3         1            89            66         23       94  28.1      0.167   \n",
       "4         0           137            40         35      168  43.1      2.288   \n",
       "\n",
       "   age    skin  diabetes  \n",
       "0   50  1.3790         1  \n",
       "1   31  1.1426         0  \n",
       "2   32  0.0000         1  \n",
       "3   21  0.9062         0  \n",
       "4   33  1.3790         1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes_true_count = len(data.loc[data['diabetes'] == True])\n",
    "diabetes_false_count = len(data.loc[data['diabetes'] == False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(268, 500)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(diabetes_true_count,diabetes_false_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Train Test Split\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "feature_columns = ['num_preg', 'glucose_conc', 'diastolic_bp', 'insulin', 'bmi', 'diab_pred', 'age', 'skin']\n",
    "predicted_class = ['diabetes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data[feature_columns].values\n",
    "y = data[predicted_class].values\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30, random_state=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check how many other missing(zero) values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of rows : 768\n",
      "number of rows missing glucose_conc: 5\n",
      "number of rows missing glucose_conc: 5\n",
      "number of rows missing diastolic_bp: 35\n",
      "number of rows missing insulin: 374\n",
      "number of rows missing bmi: 11\n",
      "number of rows missing diab_pred: 0\n",
      "number of rows missing age: 0\n",
      "number of rows missing skin: 227\n"
     ]
    }
   ],
   "source": [
    "print(\"total number of rows : {0}\".format(len(data)))\n",
    "print(\"number of rows missing glucose_conc: {0}\".format(len(data.loc[data['glucose_conc'] == 0])))\n",
    "print(\"number of rows missing glucose_conc: {0}\".format(len(data.loc[data['glucose_conc'] == 0])))\n",
    "print(\"number of rows missing diastolic_bp: {0}\".format(len(data.loc[data['diastolic_bp'] == 0])))\n",
    "print(\"number of rows missing insulin: {0}\".format(len(data.loc[data['insulin'] == 0])))\n",
    "print(\"number of rows missing bmi: {0}\".format(len(data.loc[data['bmi'] == 0])))\n",
    "print(\"number of rows missing diab_pred: {0}\".format(len(data.loc[data['diab_pred'] == 0])))\n",
    "print(\"number of rows missing age: {0}\".format(len(data.loc[data['age'] == 0])))\n",
    "print(\"number of rows missing skin: {0}\".format(len(data.loc[data['skin'] == 0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\krish.naik\\AppData\\Local\\Continuum\\anaconda3\\envs\\myenv\\lib\\site-packages\\sklearn\\utils\\deprecation.py:58: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "\n",
    "fill_values = Imputer(missing_values=0, strategy=\"mean\", axis=0)\n",
    "\n",
    "X_train = fill_values.fit_transform(X_train)\n",
    "X_test = fill_values.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\krish.naik\\AppData\\Local\\Continuum\\anaconda3\\envs\\myenv\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "            oob_score=False, random_state=10, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Apply Algorithm\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "random_forest_model = RandomForestClassifier(random_state=10)\n",
    "\n",
    "random_forest_model.fit(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.719\n"
     ]
    }
   ],
   "source": [
    "predict_train_data = random_forest_model.predict(X_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "print(\"Accuracy = {0:.3f}\".format(metrics.accuracy_score(y_test, predict_train_data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Hyper Parameter Optimization\n",
    "\n",
    "params={\n",
    " \"learning_rate\"    : [0.05, 0.10, 0.15, 0.20, 0.25, 0.30 ] ,\n",
    " \"max_depth\"        : [ 3, 4, 5, 6, 8, 10, 12, 15],\n",
    " \"min_child_weight\" : [ 1, 3, 5, 7 ],\n",
    " \"gamma\"            : [ 0.0, 0.1, 0.2 , 0.3, 0.4 ],\n",
    " \"colsample_bytree\" : [ 0.3, 0.4, 0.5 , 0.7 ]\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Hyperparameter optimization using RandomizedSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier=xgboost.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_search=RandomizedSearchCV(classifier,param_distributions=params,n_iter=5,scoring='roc_auc',n_jobs=-1,cv=5,verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def timer(start_time=None):\n",
    "    if not start_time:\n",
    "        start_time = datetime.now()\n",
    "        return start_time\n",
    "    elif start_time:\n",
    "        thour, temp_sec = divmod((datetime.now() - start_time).total_seconds(), 3600)\n",
    "        tmin, tsec = divmod(temp_sec, 60)\n",
    "        print('\\n Time taken: %i hours %i minutes and %s seconds.' % (thour, tmin, round(tsec, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Time taken: 0 hours 0 minutes and 1.02 seconds.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  19 out of  25 | elapsed:    0.7s remaining:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:    0.8s finished\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "# Here we go\n",
    "start_time = timer(None) # timing starts from this point for \"start_time\" variable\n",
    "random_search.fit(X,y.ravel())\n",
    "timer(start_time) # timing ends here for \"start_time\" variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=0.3, gamma=0.0, learning_rate=0.25,\n",
       "       max_delta_step=0, max_depth=3, min_child_weight=7, missing=None,\n",
       "       n_estimators=100, n_jobs=1, nthread=None,\n",
       "       objective='binary:logistic', random_state=0, reg_alpha=0,\n",
       "       reg_lambda=1, scale_pos_weight=1, seed=None, silent=True,\n",
       "       subsample=1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier=xgboost.XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "       colsample_bytree=0.3, gamma=0.0, learning_rate=0.25,\n",
    "       max_delta_step=0, max_depth=3, min_child_weight=7, missing=None,\n",
    "       n_estimators=100, n_jobs=1, nthread=None,\n",
    "       objective='binary:logistic', random_state=0, reg_alpha=0,\n",
    "       reg_lambda=1, scale_pos_weight=1, seed=None, silent=True,\n",
    "       subsample=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "score=cross_val_score(classifier,X,y.ravel(),cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.72727273, 0.77922078, 0.67532468, 0.67532468, 0.7012987 ,\n",
       "       0.74025974, 0.76623377, 0.76623377, 0.77631579, 0.80263158])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7410116199589883"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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