Capstone project .ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "%matplotlib inline\n",
    "### import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "import seaborn as sns  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('health care diabetes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</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</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 False\n",
       "Glucose                     False\n",
       "BloodPressure               False\n",
       "SkinThickness               False\n",
       "Insulin                     False\n",
       "BMI                         False\n",
       "DiabetesPedigreeFunction    False\n",
       "Age                         False\n",
       "Outcome                     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>88</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>197</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>543</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.158</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "4            0      137             40             35      168  43.1   \n",
       "6            3       78             50             32       88  31.0   \n",
       "8            2      197             70             45      543  30.5   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "2                     0.672   32        1  \n",
       "4                     2.288   33        1  \n",
       "6                     0.248   26        1  \n",
       "8                     0.158   53        1  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Positive = data[data['Outcome']==1]\n",
    "Positive.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100    17\n",
       "99     17\n",
       "129    14\n",
       "125    14\n",
       "111    14\n",
       "106    14\n",
       "95     13\n",
       "Name: Glucose, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Glucose'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  5.,   0.,   4.,  32., 156., 211., 163.,  95.,  56.,  46.]),\n",
       " array([  0. ,  19.9,  39.8,  59.7,  79.6,  99.5, 119.4, 139.3, 159.2,\n",
       "        179.1, 199. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['Glucose'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70    57\n",
       "74    52\n",
       "68    45\n",
       "78    45\n",
       "72    44\n",
       "64    43\n",
       "80    40\n",
       "Name: BloodPressure, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['BloodPressure'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 35.,   1.,   2.,  13., 107., 261., 243.,  87.,  14.,   5.]),\n",
       " array([  0. ,  12.2,  24.4,  36.6,  48.8,  61. ,  73.2,  85.4,  97.6,\n",
       "        109.8, 122. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAD1dJREFUeJzt3X+MZWV9x/H3p6yl9UcDlIHg7tpBs21FUxcyobQ0DZWm8sO4mNRmidGNJVn/wBQbk3bRP7R/kGDqj2qiNKtQ1gZBilg2Sq10S2L8A3SXknVxpUxlC+Nu2bEq0pqoi9/+cc+EK87u3Jk7d+/cp+9XcnPvee5z7vk+99z57Nlnzj2TqkKS1K5fGHcBkqTRMuglqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjVs37gIAzjzzzJqenh53GZI0Ufbt2/edqppaqt+aCPrp6Wn27t077jIkaaIk+c9B+jl1I0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjVsT34yV1rLpHV8Yy3YP3XjlWLar9nhEL0mNM+glqXEGvSQ1zqCXpMYtGfRJNia5P8nBJI8kua5rf1+Sbyd5uLtd0bfO9Ulmkzya5HWjHIAk6cQGOevmGPCuqnooyUuAfUnu6577cFV9oL9zkvOArcCrgJcC/5Lk16vq2dUsXJI0mCWP6KvqSFU91D1+BjgIrD/BKluAO6rqR1X1ODALXLgaxUqSlm9Zc/RJpoHzgQe7pnck2Z/kliSnd23rgSf7VptjkX8YkmxPsjfJ3vn5+WUXLkkazMBBn+TFwGeBd1bVD4CbgFcAm4EjwAcXui6yev1cQ9XOqpqpqpmpqSX/5KEkaYUGCvokL6AX8rdV1d0AVfVUVT1bVT8FPsFz0zNzwMa+1TcAh1evZEnScgxy1k2Am4GDVfWhvvZz+rq9ETjQPd4NbE1yapJzgU3AV1evZEnScgxy1s3FwFuAryd5uGt7N3B1ks30pmUOAW8HqKpHktwJfIPeGTvXesaNJI3PkkFfVV9h8Xn3e0+wzg3ADUPUJUlaJV69UlqjvGqmVouXQJCkxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJatySQZ9kY5L7kxxM8kiS67r2M5Lcl+Sx7v70rj1JPppkNsn+JBeMehCSpOMb5Ij+GPCuqnolcBFwbZLzgB3AnqraBOzplgEuBzZ1t+3ATatetSRpYEsGfVUdqaqHusfPAAeB9cAWYFfXbRdwVfd4C/Cp6nkAOC3JOateuSRpIMuao08yDZwPPAicXVVHoPePAXBW12098GTfanNdmyRpDAYO+iQvBj4LvLOqfnCirou01SKvtz3J3iR75+fnBy1DkrRMAwV9khfQC/nbqururvmphSmZ7v5o1z4HbOxbfQNw+PmvWVU7q2qmqmampqZWWr8kaQmDnHUT4GbgYFV9qO+p3cC27vE24J6+9rd2Z99cBDy9MMUjSTr51g3Q52LgLcDXkzzctb0buBG4M8k1wBPAm7rn7gWuAGaBHwJvW9WKJUnLsmTQV9VXWHzeHeDSRfoXcO2QdUmSVonfjJWkxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXFLBn2SW5IcTXKgr+19Sb6d5OHudkXfc9cnmU3yaJLXjapwSdJgBjmivxW4bJH2D1fV5u52L0CS84CtwKu6dT6e5JTVKlaStHxLBn1VfRn47oCvtwW4o6p+VFWPA7PAhUPUJ0ka0jBz9O9Isr+b2jm9a1sPPNnXZ65rkySNyUqD/ibgFcBm4Ajwwa49i/StxV4gyfYke5PsnZ+fX2EZkqSlrCjoq+qpqnq2qn4KfILnpmfmgI19XTcAh4/zGjuraqaqZqamplZShiRpACsK+iTn9C2+EVg4I2c3sDXJqUnOBTYBXx2uREnSMNYt1SHJ7cAlwJlJ5oD3Apck2UxvWuYQ8HaAqnokyZ3AN4BjwLVV9exoSpckDWLJoK+qqxdpvvkE/W8AbhimKEnS6vGbsZLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjVs37gKkQUzv+MK4S5Amlkf0ktS4JYM+yS1JjiY50Nd2RpL7kjzW3Z/etSfJR5PMJtmf5IJRFi9JWtogR/S3Apc9r20HsKeqNgF7umWAy4FN3W07cNPqlClJWqklg76qvgx893nNW4Bd3eNdwFV97Z+qngeA05Kcs1rFSpKWb6Vz9GdX1RGA7v6srn098GRfv7mu7eck2Z5kb5K98/PzKyxDkrSU1f5lbBZpq8U6VtXOqpqpqpmpqalVLkOStGClQf/UwpRMd3+0a58DNvb12wAcXnl5kqRhrTTodwPbusfbgHv62t/anX1zEfD0whSPJGk8lvzCVJLbgUuAM5PMAe8FbgTuTHIN8ATwpq77vcAVwCzwQ+BtI6hZkrQMSwZ9VV19nKcuXaRvAdcOW5QkafX4zVhJapxBL0mNM+glqXEGvSQ1zqCXpMZ5PXpJP2Oc1/4/dOOVY9t2yzyil6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNW7dMCsnOQQ8AzwLHKuqmSRnAJ8BpoFDwJ9U1feGK1OStFKrcUT/B1W1uapmuuUdwJ6q2gTs6ZYlSWMyiqmbLcCu7vEu4KoRbEOSNKBhg76ALyXZl2R713Z2VR0B6O7PGnIbkqQhDDVHD1xcVYeTnAXcl+Sbg67Y/cOwHeBlL3vZkGVIko5nqCP6qjrc3R8FPgdcCDyV5ByA7v7ocdbdWVUzVTUzNTU1TBmSpBNYcdAneVGSlyw8Bv4IOADsBrZ13bYB9wxbpCRp5YaZujkb+FyShdf5dFV9McnXgDuTXAM8Abxp+DIlSSu14qCvqm8Br1mk/b+BS4cpSpK0evxmrCQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjRv2WjdjN73jC2Pb9qEbrxzbtiVpUB7RS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaN/F/eEQn1zj/0IvaN67PV+t/RMgjeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktS4kZ1emeQy4CPAKcAnq+rGUW1LkoYxztOGT8apnSM5ok9yCvAx4HLgPODqJOeNYluSpBMb1RH9hcBsVX0LIMkdwBbgGyPa3v8rfmlJ0nKMKujXA0/2Lc8Bvz2ibY2NgStpEowq6LNIW/1Mh2Q7sL1b/J8kj65wW2cC31nhumuJ41hbHMfa0uw48v6hXu/XBuk0qqCfAzb2LW8ADvd3qKqdwM5hN5Rkb1XNDPs64+Y41hbHsbY4juGM6vTKrwGbkpyb5BeBrcDuEW1LknQCIzmir6pjSd4B/DO90ytvqapHRrEtSdKJjew8+qq6F7h3VK/fZ+jpnzXCcawtjmNtcRxDSFUt3UuSNLG8BIIkNW6igz7JZUkeTTKbZMe46xlUko1J7k9yMMkjSa7r2s9Icl+Sx7r708dd61KSnJLk35J8vls+N8mD3Rg+0/0yfs1LclqSu5J8s9svvzNp+yPJn3efpwNJbk/yS5OyP5LckuRokgN9bYu+/+n5aPdzvz/JBeOr/DnHGcNfd5+p/Uk+l+S0vueu78bwaJLXjbK2iQ36Cb/MwjHgXVX1SuAi4Nqu9h3AnqraBOzplte664CDfcvvBz7cjeF7wDVjqWr5PgJ8sap+E3gNvTFNzP5Ish74M2Cmql5N7ySIrUzO/rgVuOx5bcd7/y8HNnW37cBNJ6nGpdzKz4/hPuDVVfVbwL8D1wN0P+9bgVd163y8y7SRmNigp+8yC1X1Y2DhMgtrXlUdqaqHusfP0AuV9fTq39V12wVcNZ4KB5NkA3Al8MluOcBrgbu6Lmt+DABJfgX4feBmgKr6cVV9nwnbH/ROrvjlJOuAFwJHmJD9UVVfBr77vObjvf9bgE9VzwPAaUnOOTmVHt9iY6iqL1XVsW7xAXrfKYLeGO6oqh9V1ePALL1MG4lJDvrFLrOwfky1rFiSaeB84EHg7Ko6Ar1/DICzxlfZQP4G+Avgp93yrwLf7/tgT8o+eTkwD/xdNw31ySQvYoL2R1V9G/gA8AS9gH8a2Mdk7o8Fx3v/J/Vn/0+Bf+oen9QxTHLQL3mZhbUuyYuBzwLvrKofjLue5UjyeuBoVe3rb16k6yTsk3XABcBNVXU+8L+s4WmaxXTz11uAc4GXAi+iN8XxfJOwP5YycZ+zJO+hN2V720LTIt1GNoZJDvolL7OwliV5Ab2Qv62q7u6an1r4L2h3f3Rc9Q3gYuANSQ7RmzZ7Lb0j/NO6qQOYnH0yB8xV1YPd8l30gn+S9scfAo9X1XxV/QS4G/hdJnN/LDje+z9RP/tJtgGvB95cz53PflLHMMlBP7GXWejmsm8GDlbVh/qe2g1s6x5vA+452bUNqqqur6oNVTVN773/16p6M3A/8MddtzU9hgVV9V/Ak0l+o2u6lN4ltSdmf9CbsrkoyQu7z9fCGCZuf/Q53vu/G3hrd/bNRcDTC1M8a016f4DpL4E3VNUP+57aDWxNcmqSc+n9YvmrIyukqib2BlxB7zfZ/wG8Z9z1LKPu36P337T9wMPd7Qp6c9x7gMe6+zPGXeuA47kE+Hz3+OXdB3YW+Afg1HHXN+AYNgN7u33yj8Dpk7Y/gL8CvgkcAP4eOHVS9gdwO73fLfyE3tHuNcd7/+lNe3ys+7n/Or0zjdbqGGbpzcUv/Jz/bV//93RjeBS4fJS1+c1YSWrcJE/dSJIGYNBLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktS4/wNmCqInyUGm9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['BloodPressure'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     227\n",
       "32     31\n",
       "30     27\n",
       "27     23\n",
       "23     22\n",
       "33     20\n",
       "18     20\n",
       "Name: SkinThickness, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['SkinThickness'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([231., 107., 165., 175.,  78.,   9.,   2.,   0.,   0.,   1.]),\n",
       " array([ 0. ,  9.9, 19.8, 29.7, 39.6, 49.5, 59.4, 69.3, 79.2, 89.1, 99. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['SkinThickness'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      374\n",
       "105     11\n",
       "140      9\n",
       "130      9\n",
       "120      8\n",
       "100      7\n",
       "94       7\n",
       "Name: Insulin, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Insulin'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([487., 155.,  70.,  30.,   8.,   9.,   5.,   1.,   2.,   1.]),\n",
       " array([  0. ,  84.6, 169.2, 253.8, 338.4, 423. , 507.6, 592.2, 676.8,\n",
       "        761.4, 846. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['Insulin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.0    13\n",
       "31.6    12\n",
       "31.2    12\n",
       "0.0     11\n",
       "33.3    10\n",
       "32.4    10\n",
       "32.8     9\n",
       "Name: BMI, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['BMI'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 11.,   0.,  15., 156., 268., 224.,  78.,  12.,   3.,   1.]),\n",
       " array([ 0.  ,  6.71, 13.42, 20.13, 26.84, 33.55, 40.26, 46.97, 53.68,\n",
       "        60.39, 67.1 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IEuAu4FBVva/vRxObO8lMkvO75V8Efo/eB3APA3/YbTZRmavqtqraVFWz9N7D/1xVf8wEZwZI8sIkLz69TG8++QAT/P6oqv8Enkrysm7oGuAJJjhzn5v46RQOrCTzuD90GPEDi+uBf6M3L/un485zjpz3AseB/6V3dnEzvXnZvcCT3fOF4855RubfoTd18Diwv3tcP8m5gd8EvtxlPgD8WTf+EuBR4Ai9fwY/f9xZz5L/auDT05C5y/eV7nHw9J+/SX5/dPm2AvPde+QfgQumIPMLgO8Av9w3tuzMfoNWkhowzdM4kqQhWfaS1ADLXpIaYNlLUgMse0lqgGUvSQ2w7CWpAZa9JDXg/wDg/nHqetrsBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['BMI'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</th>\n",
       "      <td>768.0</td>\n",
       "      <td>3.845052</td>\n",
       "      <td>3.369578</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>768.0</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>0.000</td>\n",
       "      <td>99.00000</td>\n",
       "      <td>117.0000</td>\n",
       "      <td>140.25000</td>\n",
       "      <td>199.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>768.0</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>0.000</td>\n",
       "      <td>62.00000</td>\n",
       "      <td>72.0000</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>122.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>768.0</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>32.00000</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>768.0</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>127.25000</td>\n",
       "      <td>846.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>768.0</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.000</td>\n",
       "      <td>27.30000</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>36.60000</td>\n",
       "      <td>67.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>768.0</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.24375</td>\n",
       "      <td>0.3725</td>\n",
       "      <td>0.62625</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>768.0</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>21.000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>41.00000</td>\n",
       "      <td>81.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <td>768.0</td>\n",
       "      <td>0.348958</td>\n",
       "      <td>0.476951</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count        mean         std     min       25%  \\\n",
       "Pregnancies               768.0    3.845052    3.369578   0.000   1.00000   \n",
       "Glucose                   768.0  120.894531   31.972618   0.000  99.00000   \n",
       "BloodPressure             768.0   69.105469   19.355807   0.000  62.00000   \n",
       "SkinThickness             768.0   20.536458   15.952218   0.000   0.00000   \n",
       "Insulin                   768.0   79.799479  115.244002   0.000   0.00000   \n",
       "BMI                       768.0   31.992578    7.884160   0.000  27.30000   \n",
       "DiabetesPedigreeFunction  768.0    0.471876    0.331329   0.078   0.24375   \n",
       "Age                       768.0   33.240885   11.760232  21.000  24.00000   \n",
       "Outcome                   768.0    0.348958    0.476951   0.000   0.00000   \n",
       "\n",
       "                               50%        75%     max  \n",
       "Pregnancies                 3.0000    6.00000   17.00  \n",
       "Glucose                   117.0000  140.25000  199.00  \n",
       "BloodPressure              72.0000   80.00000  122.00  \n",
       "SkinThickness              23.0000   32.00000   99.00  \n",
       "Insulin                    30.5000  127.25000  846.00  \n",
       "BMI                        32.0000   36.60000   67.10  \n",
       "DiabetesPedigreeFunction    0.3725    0.62625    2.42  \n",
       "Age                        29.0000   41.00000   81.00  \n",
       "Outcome                     0.0000    1.00000    1.00  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Week 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2.,  0.,  0.,  0.,  0.,  0.,  3., 13., 38., 61., 61., 36., 27.,\n",
       "        14.,  7.,  3.,  1.,  1.,  0.,  1.]),\n",
       " array([ 0.   ,  3.355,  6.71 , 10.065, 13.42 , 16.775, 20.13 , 23.485,\n",
       "        26.84 , 30.195, 33.55 , 36.905, 40.26 , 43.615, 46.97 , 50.325,\n",
       "        53.68 , 57.035, 60.39 , 63.745, 67.1  ]),\n",
       " <a list of 1 Patch objects>)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Positive['BMI'],histtype='stepfilled',bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.9    8\n",
       "31.6    7\n",
       "33.3    6\n",
       "30.5    5\n",
       "32.0    5\n",
       "31.2    5\n",
       "32.4    4\n",
       "Name: BMI, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Positive['BMI'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  4.,  9., 28., 26., 36.,\n",
       "        27., 29., 22., 24., 21., 25., 14.]),\n",
       " array([  0.  ,   9.95,  19.9 ,  29.85,  39.8 ,  49.75,  59.7 ,  69.65,\n",
       "         79.6 ,  89.55,  99.5 , 109.45, 119.4 , 129.35, 139.3 , 149.25,\n",
       "        159.2 , 169.15, 179.1 , 189.05, 199.  ]),\n",
       " <a list of 1 Patch objects>)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Positive['Glucose'],histtype='stepfilled',bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "125    7\n",
       "158    6\n",
       "128    6\n",
       "115    6\n",
       "129    6\n",
       "146    5\n",
       "162    5\n",
       "Name: Glucose, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Positive['Glucose'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([16.,  0.,  0.,  0.,  0.,  1.,  0.,  1.,  6.,  6., 19., 37., 56.,\n",
       "        36., 41., 31.,  7.,  4.,  4.,  3.]),\n",
       " array([  0. ,   5.7,  11.4,  17.1,  22.8,  28.5,  34.2,  39.9,  45.6,\n",
       "         51.3,  57. ,  62.7,  68.4,  74.1,  79.8,  85.5,  91.2,  96.9,\n",
       "        102.6, 108.3, 114. ]),\n",
       " <a list of 1 Patch objects>)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Positive['BloodPressure'],histtype='stepfilled',bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70    23\n",
       "76    18\n",
       "78    17\n",
       "74    17\n",
       "72    16\n",
       "0     16\n",
       "82    13\n",
       "Name: BloodPressure, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Positive['BloodPressure'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([88.,  1.,  4., 10., 18., 30., 41., 34., 23., 15.,  1.,  1.,  1.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  1.]),\n",
       " array([ 0.  ,  4.95,  9.9 , 14.85, 19.8 , 24.75, 29.7 , 34.65, 39.6 ,\n",
       "        44.55, 49.5 , 54.45, 59.4 , 64.35, 69.3 , 74.25, 79.2 , 84.15,\n",
       "        89.1 , 94.05, 99.  ]),\n",
       " <a list of 1 Patch objects>)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Positive['SkinThickness'],histtype='stepfilled',bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     88\n",
       "32    14\n",
       "33     9\n",
       "30     9\n",
       "39     8\n",
       "35     8\n",
       "36     8\n",
       "Name: SkinThickness, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Positive['SkinThickness'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([141.,   6.,  23.,  33.,  24.,  12.,   7.,   7.,   2.,   1.,   1.,\n",
       "          5.,   3.,   1.,   1.,   0.,   0.,   0.,   0.,   1.]),\n",
       " array([  0. ,  42.3,  84.6, 126.9, 169.2, 211.5, 253.8, 296.1, 338.4,\n",
       "        380.7, 423. , 465.3, 507.6, 549.9, 592.2, 634.5, 676.8, 719.1,\n",
       "        761.4, 803.7, 846. ]),\n",
       " <a list of 1 Patch objects>)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Positive['Insulin'],histtype='stepfilled',bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      138\n",
       "130      6\n",
       "180      4\n",
       "156      3\n",
       "175      3\n",
       "194      2\n",
       "125      2\n",
       "Name: Insulin, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Positive['Insulin'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "BloodPressure = Positive['BloodPressure']\n",
    "Glucose = Positive['Glucose']\n",
    "SkinThickness = Positive['SkinThickness']\n",
    "Insulin = Positive['Insulin']\n",
    "BMI = Positive['BMI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(BloodPressure, Glucose, color=['b'])\n",
    "plt.xlabel('BloodPressure')\n",
    "plt.ylabel('Glucose')\n",
    "plt.title('BloodPressure & Glucose')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g =sns.scatterplot(x= \"Glucose\" ,y= \"BloodPressure\",\n",
    "              hue=\"Outcome\",\n",
    "              data=data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "B =sns.scatterplot(x= \"BMI\" ,y= \"Insulin\",\n",
    "              hue=\"Outcome\",\n",
    "              data=data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "S =sns.scatterplot(x= \"SkinThickness\" ,y= \"Insulin\",\n",
    "              hue=\"Outcome\",\n",
    "              data=data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</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>0.221898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</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>0.466581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</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>0.065068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</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>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>0.130548</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>0.292695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</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>0.173844</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>0.238356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</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>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Pregnancies   Glucose  BloodPressure  SkinThickness  \\\n",
       "Pregnancies                  1.000000  0.129459       0.141282      -0.081672   \n",
       "Glucose                      0.129459  1.000000       0.152590       0.057328   \n",
       "BloodPressure                0.141282  0.152590       1.000000       0.207371   \n",
       "SkinThickness               -0.081672  0.057328       0.207371       1.000000   \n",
       "Insulin                     -0.073535  0.331357       0.088933       0.436783   \n",
       "BMI                          0.017683  0.221071       0.281805       0.392573   \n",
       "DiabetesPedigreeFunction    -0.033523  0.137337       0.041265       0.183928   \n",
       "Age                          0.544341  0.263514       0.239528      -0.113970   \n",
       "Outcome                      0.221898  0.466581       0.065068       0.074752   \n",
       "\n",
       "                           Insulin       BMI  DiabetesPedigreeFunction  \\\n",
       "Pregnancies              -0.073535  0.017683                 -0.033523   \n",
       "Glucose                   0.331357  0.221071                  0.137337   \n",
       "BloodPressure             0.088933  0.281805                  0.041265   \n",
       "SkinThickness             0.436783  0.392573                  0.183928   \n",
       "Insulin                   1.000000  0.197859                  0.185071   \n",
       "BMI                       0.197859  1.000000                  0.140647   \n",
       "DiabetesPedigreeFunction  0.185071  0.140647                  1.000000   \n",
       "Age                      -0.042163  0.036242                  0.033561   \n",
       "Outcome                   0.130548  0.292695                  0.173844   \n",
       "\n",
       "                               Age   Outcome  \n",
       "Pregnancies               0.544341  0.221898  \n",
       "Glucose                   0.263514  0.466581  \n",
       "BloodPressure             0.239528  0.065068  \n",
       "SkinThickness            -0.113970  0.074752  \n",
       "Insulin                  -0.042163  0.130548  \n",
       "BMI                       0.036242  0.292695  \n",
       "DiabetesPedigreeFunction  0.033561  0.173844  \n",
       "Age                       1.000000  0.238356  \n",
       "Outcome                   0.238356  1.000000  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### correlation matrix\n",
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2278a586278>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### create correlation heat map\n",
    "sns.heatmap(data.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2278a71d710>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(8,8))\n",
    "sns.heatmap(data.corr(),annot=True,cmap='viridis')  ### gives correlation value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2278bde9f28>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(8,8))\n",
    "sns.heatmap(data.corr(),annot=True)  ### gives correlation value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regreation and model building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</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</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = data.iloc[:,[0,1,2,3,4,5,6,7]].values\n",
    "label = data.iloc[:,8].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(features,\n",
    "                                                label,\n",
    "                                                test_size=0.2,\n",
    "                                                random_state =10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train,y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7833876221498371\n",
      "0.7337662337662337\n"
     ]
    }
   ],
   "source": [
    "print(model.score(X_train,y_train))\n",
    "print(model.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[452,  48],\n",
       "       [126, 142]], dtype=int64)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(label,model.predict(features))\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.90      0.84       500\n",
      "           1       0.75      0.53      0.62       268\n",
      "\n",
      "   micro avg       0.77      0.77      0.77       768\n",
      "   macro avg       0.76      0.72      0.73       768\n",
      "weighted avg       0.77      0.77      0.76       768\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(label,model.predict(features)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.834\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2278c4fba90>]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Preparing ROC Curve (Receiver Operating Characteristics Curve)\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# predict probabilities\n",
    "probs = model.predict_proba(features)\n",
    "# keep probabilities for the positive outcome only\n",
    "probs = probs[:, 1]\n",
    "# calculate AUC\n",
    "auc = roc_auc_score(label, probs)\n",
    "print('AUC: %.3f' % auc)\n",
    "# calculate roc curve\n",
    "fpr, tpr, thresholds = roc_curve(label, probs)\n",
    "# plot no skill\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "# plot the roc curve for the model\n",
    "plt.plot(fpr, tpr, marker='.')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=5,\n",
       "            max_features=None, 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, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying Decission Tree Classifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model3 = DecisionTreeClassifier(max_depth=5)\n",
    "model3.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.990228013029316"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7532467532467533"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "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=11, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying Random Forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model4 = RandomForestClassifier(n_estimators=11)\n",
    "model4.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.990228013029316"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model4.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7532467532467533"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model4.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Support Vector Classifier\n",
    "\n",
    "from sklearn.svm import SVC \n",
    "model5 = SVC(kernel='rbf',\n",
    "           gamma='auto')\n",
    "model5.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model5model.score(X_test,y_test).score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6168831168831169"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model5.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=7, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying K-NN\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model2 = KNeighborsClassifier(n_neighbors=7,\n",
    "                             metric='minkowski',\n",
    "                             p = 2)\n",
    "model2.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.836\n",
      "True Positive Rate - [0.         0.06716418 0.23880597 0.44776119 0.60074627 0.75373134\n",
      " 0.88059701 0.98507463 1.        ], False Positive Rate - [0.    0.    0.02  0.056 0.12  0.248 0.428 0.668 1.   ] Thresholds - [2.         1.         0.85714286 0.71428571 0.57142857 0.42857143\n",
      " 0.28571429 0.14285714 0.        ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'True Positive Rate')"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Preparing ROC Curve (Receiver Operating Characteristics Curve)\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# predict probabilities\n",
    "probs = model2.predict_proba(features)\n",
    "# keep probabilities for the positive outcome only\n",
    "probs = probs[:, 1]\n",
    "# calculate AUC\n",
    "auc = roc_auc_score(label, probs)\n",
    "print('AUC: %.3f' % auc)\n",
    "# calculate roc curve\n",
    "fpr, tpr, thresholds = roc_curve(label, probs)\n",
    "print(\"True Positive Rate - {}, False Positive Rate - {} Thresholds - {}\".format(tpr,fpr,thresholds))\n",
    "# plot no skill\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "# plot the roc curve for the model\n",
    "plt.plot(fpr, tpr, marker='.')\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1=0.620 auc=0.728 ap=0.728\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2278d0052e8>]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Precision Recall Curve for Logistic Regression\n",
    "\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import average_precision_score\n",
    "# predict probabilities\n",
    "probs = model.predict_proba(features)\n",
    "# keep probabilities for the positive outcome only\n",
    "probs = probs[:, 1]\n",
    "# predict class values\n",
    "yhat = model.predict(features)\n",
    "# calculate precision-recall curve\n",
    "precision, recall, thresholds = precision_recall_curve(label, probs)\n",
    "# calculate F1 score\n",
    "f1 = f1_score(label, yhat)\n",
    "# calculate precision-recall AUC\n",
    "auc = auc(recall, precision)\n",
    "# calculate average precision score\n",
    "ap = average_precision_score(label, probs)\n",
    "print('f1=%.3f auc=%.3f ap=%.3f' % (f1, auc, ap))\n",
    "# plot no skill\n",
    "plt.plot([0, 1], [0.5, 0.5], linestyle='--')\n",
    "# plot the precision-recall curve for the model\n",
    "plt.plot(recall, precision, marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1=0.658 auc=0.752 ap=0.709\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2278d025908>]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Precision Recall Curve for KNN\n",
    "\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import average_precision_score\n",
    "# predict probabilities\n",
    "probs = model2.predict_proba(features)\n",
    "# keep probabilities for the positive outcome only\n",
    "probs = probs[:, 1]\n",
    "# predict class values\n",
    "yhat = model2.predict(features)\n",
    "# calculate precision-recall curve\n",
    "precision, recall, thresholds = precision_recall_curve(label, probs)\n",
    "# calculate F1 score\n",
    "f1 = f1_score(label, yhat)\n",
    "# calculate precision-recall AUC\n",
    "auc = auc(recall, precision)\n",
    "# calculate average precision score\n",
    "ap = average_precision_score(label, probs)\n",
    "print('f1=%.3f auc=%.3f ap=%.3f' % (f1, auc, ap))\n",
    "# plot no skill\n",
    "plt.plot([0, 1], [0.5, 0.5], linestyle='--')\n",
    "# plot the precision-recall curve for the model\n",
    "plt.plot(recall, precision, marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1=0.916 auc=0.966 ap=0.958\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2278bde2a20>]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U/KdUU3ADMYW7iEhU1HMXEcmgug8Cpp67iEim5MbBpEb13EVEMiclD+1QuIuIRCkln1JVuIuIRCkln1JVuIuIRKluJhzaA0cPJ1qGwl1EJErHHreX7NCMwl1EJEp1CncRkeyZMit4TXjcXeEuIhKl/mGZ59ZCx6bEyggV7ma2xMy2mVm7md00RJtPm9lLZrbVzH4UbZkiIhXirV8Gr+2PwJqliQV8dakGZpYDVgEfBzqBzWa2zt1fKmizALgZuMDd95rZ+8tVsIhIqr3+s/yEQ2837NwATa2xlxGm594KtLv7DnfvBtYCy4rafA5Y5e57Adz9rWjLFBGpEM2LwPLRmqsJ5hMQJtxnAR0F8535ZYVOBU41s6fMbKOZLYmqQBGRitLUCqdfCbla+OwDifTaIVy42yDLip8hVQ0sAC4GVgA/MLNpA97I7AYzazOztq6uruHWKiJSGZpagycyNSxIrIQw4d4JNBXMzwaKr/HpBB5w96Pu/hqwjSDsT+Duq929xd1bGhsbR1qziEi61TcHr/teT6yEMOG+GVhgZnPNrAZYDqwravNPwCUAZtZAMEyzI8pCRUQqRv2c4HXvzsRKKBnu7t4D3Ag8DLwM3OfuW81spZktzTd7GHjbzF4CHge+7u5vl6toEZFUm9Yf7sn13EteCgng7uuB9UXLbimYduDP8l8iImPb+CkwYXrqh2VERGS46ueke1hGRERGYNqcRIdlFO4iIuVQ3wz7O6CvN5HNK9xFRMqhfk5w+4GEbv2rcBcRKYeEr5hRuIuIlEPCH2RSuIuIlMPUJsASu2JG4S4iUg7VNcFTmTQsIyKSMfXNGpYREcmcBD/IpHAXESmXaXOCSyGPHo590wp3EZFyOXbFzBuxb1rhLiJSLv23/k1g3F3hLiJSLtOSu6+7wl1EpFwmfwCqxyvcRUQypaoKpp2iYRkRkcxJ6Na/CncRkXKqb1a4i4hkTv0cOLIfDu2NdbMKdxGRckro1r8KdxGRcur/IFPMV8yECnczW2Jm28ys3cxuGmT9dWbWZWbP5r/+OPpSRUQqUEIfZKou1cDMcsAq4ONAJ7DZzNa5+0tFTe919xvLUKOISOUaPxXGT0vlsEwr0O7uO9y9G1gLLCtvWSIiGVLfHPuwTMmeOzAL6CiY7wQ+Nki73zWzC4FXgK+6e8cgbSLxme8/PWDZ75wxgz84v5lD3b1c94+bBqy/+tzZ/F5LE3sOdPOFH24ZsP6a8+Zw5Zkz2bXvEF+999kB6z+3aB6XLfwA27ve4z/95IUB67986QL+zYIGtu7az8oHi/+ogf+w5DTOnTOdLa/v4ds/3TZg/S1XLuQjM6fys1d3893HXh2w/ptX/RbzGyfzyEu/4e837Biw/rbPnMXMaRN48Lld/HDjwB7C9645l+mTavhxWwf3b+kcsP7OP2xlQk2Ou5/eyUPPD3yg771/cj4Aq5/czqMvv3XCuvHjcqz5o1YAvvPoqzzVvvuE9fUTa7j9D84F4K9/+kt+8fqJVw3MmDqe/7H8bAD+64NbeWnXOyesn9c4iW9ddQYAN//keXZ0HThh/cKZU/jzKz8CwJ+ufYY39594B75z5tTzH5ecDsDn797C3oPdJ6y/4EMNfGXxAgCuvWMTh4+e+LT6xR9+PzdcOB/Q755+90b4u1c/h1+/uoV/l//96d+ncgrTc7dBlnnR/INAs7ufATwCrBn0jcxuMLM2M2vr6uoaXqUiIpUqV0Pj0V2cemRrbJs09+KcLmpgdj5wq7t/Ij9/M4C7f2uI9jlgj7tPPdn7trS0eFtb24iKFhGpGB2b4B+vgL6jwX1mrn0QmlpH/HZmtsXdW0q1C9Nz3wwsMLO5ZlYDLAfWFW1sRsHsUuDl4RQrIpJZOzdAX08w3dsdzMeg5Ji7u/eY2Y3Aw0AOuMPdt5rZSqDN3dcBXzGzpUAPsAe4row1i4hUjuZFkBsXBHvVuGA+BiWHZcpFwzIiMma03QEPfRU+8U04/0ujeqsoh2VERGQ0mvIXGE6ZFdsmFe4iIuVWMzl47X4vtk0q3EVEyq22Lng98m5sm1S4i4iUW3/P/Yh67iIi2VFdA7ka6FbPXUQkW2omq+cuIpI5tXU6oSoikjm1deq5i4hkTs1kOPJO6XYRUbiLiMShdrKGZUREMkcnVEVEMkg9dxGRDKqdop67iEjm1OR77n19sWxO4S4iEofayYDD0QMlm0ZB4S4iEoeY7y+jcBcRiUP/nSFjOqmqcBcRicOxnns8Nw9TuIuIxEE9dxGRDKrVmLuISPbUxPs0JoW7iEgc+nvuMT2wI1S4m9kSM9tmZu1mdtNJ2l1tZm5mLdGVKCKSAWm7FNLMcsAq4HJgIbDCzBYO0q4O+Arw86iLFBGpeDWTAEvVCdVWoN3dd7h7N7AWWDZIu78Avg0cjrA+EZFsMIv1gR1hwn0W0FEw35lfdoyZnQ00uftDJ3sjM7vBzNrMrK2rq2vYxYqIVLSayak6oWqDLPNjK82qgNuAr5V6I3df7e4t7t7S2NgYvkoRkSyonZyqE6qdQFPB/GxgV8F8HfBR4Akz2wmcB6zTSVURkSIxPrAjTLhvBhaY2VwzqwGWA+v6V7r7fndvcPdmd28GNgJL3b2tLBWLiFSqGB/YUTLc3b0HuBF4GHgZuM/dt5rZSjNbWu4CRUQyoya+E6rVYRq5+3pgfdGyW4Zoe/HoyxIRyaDaulSNuYuISBRq03W1jIiIRCFlJ1RFRCQKtZOh7yj0HCn7phTuIiJxOXZnyPL33hXuIiJxifHOkAp3EZG41KrnLiKSPTE+R1XhLiISlxifo6pwFxGJS3/P/bl7oGNTWTelcBcRicvb7cHriz+BNUvLGvAKdxGRuPzmxfyEQ2837NxQtk0p3EVE4vKhy6B6AlgOcjXQvKhsmwp14zAREYlAUytcuy7osTcvCubLROEuIhKnptayhno/DcuIiGSQwl1EJIMU7iIiGaRwFxHJIIW7iEgGKdxFRDLI3D2ZDZt1Aa+P8NsbgN0RllMJtM9jg/Z5bBjNPs9x98ZSjRIL99EwszZ3b0m6jjhpn8cG7fPYEMc+a1hGRCSDFO4iIhlUqeG+OukCEqB9Hhu0z2ND2fe5IsfcRUTk5Cq15y4iIieR6nA3syVmts3M2s3spkHW15rZvfn1Pzez5virjFaIff4zM3vJzJ43s0fNbE4SdUap1D4XtLvazNzMKv7KijD7bGafzv+st5rZj+KuMWohfrdPMbPHzeyZ/O/3FUnUGRUzu8PM3jKzF4dYb2b2nfy/x/Nmdk6kBbh7Kr+AHLAdmAfUAM8BC4vafBG4PT+9HLg36bpj2OdLgIn56S+MhX3Ot6sDngQ2Ai1J1x3Dz3kB8AxQn59/f9J1x7DPq4Ev5KcXAjuTrnuU+3whcA7w4hDrrwD+L2DAecDPo9x+mnvurUC7u+9w925gLbCsqM0yYE1++n5gsZlZjDVGreQ+u/vj7n4wP7sRmB1zjVEL83MG+Avg28DhOIsrkzD7/DlglbvvBXD3t2KuMWph9tmBKfnpqcCuGOuLnLs/Cew5SZNlwF0e2AhMM7MZUW0/zeE+C+gomO/MLxu0jbv3APuB98VSXXmE2edC1xMc+StZyX02s7OBJnd/KM7CyijMz/lU4FQze8rMNprZktiqK48w+3wrcI2ZdQLrgS/HU1pihvv/fVjS/CSmwXrgxZf2hGlTSULvj5ldA7QAF5W1ovI76T6bWRVwG3BdXAXFIMzPuZpgaOZigr/ONpjZR919X5lrK5cw+7wCuNPd/9bMzgfuzu9zX/nLS0RZ8yvNPfdOoKlgfjYD/0w71sbMqgn+lDvZn0FpF2afMbPLgG8AS939SEy1lUupfa4DPgo8YWY7CcYm11X4SdWwv9sPuPtRd38N2EYQ9pUqzD5fD9wH4O5PA+MJ7sGSVaH+v49UmsN9M7DAzOaaWQ3BCdN1RW3WAdfmp68GHvP8mYoKVXKf80MU3ycI9kofh4US++zu+929wd2b3b2Z4DzDUndvS6bcSIT53f4ngpPnmFkDwTDNjlirjFaYfX4DWAxgZh8mCPeuWKuM1zrgs/mrZs4D9rv7m5G9e9JnlEucbb4CeIXgLPs38stWEvznhuCH/2OgHdgEzEu65hj2+RHgN8Cz+a91Sddc7n0uavsEFX61TMifswH/HXgJeAFYnnTNMezzQuApgitpngX+bdI1j3J/7wHeBI4S9NKvBz4PfL7gZ7wq/+/xQtS/1/qEqohIBqV5WEZEREZI4S4ikkEKdxGRDFK4i4hkkMJdRCSDFO4iIhmkcBcRySCFu4hIBv1/12+MElkqtHAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Precision Recall Curve for Decission Tree Classifier\n",
    "\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import average_precision_score\n",
    "# predict probabilities\n",
    "probs = model3.predict_proba(features)\n",
    "# keep probabilities for the positive outcome only\n",
    "probs = probs[:, 1]\n",
    "# predict class values\n",
    "yhat = model3.predict(features)\n",
    "# calculate precision-recall curve\n",
    "precision, recall, thresholds = precision_recall_curve(label, probs)\n",
    "# calculate F1 score\n",
    "f1 = f1_score(label, yhat)\n",
    "# calculate precision-recall AUC\n",
    "auc = auc(recall, precision)\n",
    "# calculate average precision score\n",
    "ap = average_precision_score(label, probs)\n",
    "print('f1=%.3f auc=%.3f ap=%.3f' % (f1, auc, ap))\n",
    "# plot no skill\n",
    "plt.plot([0, 1], [0.5, 0.5], linestyle='--')\n",
    "# plot the precision-recall curve for the model\n",
    "plt.plot(recall, precision, marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1=0.914 auc=0.968 ap=0.960\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2278a747cf8>]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Precision Recall Curve for Random Forest\n",
    "\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import average_precision_score\n",
    "# predict probabilities\n",
    "probs = model4.predict_proba(features)\n",
    "# keep probabilities for the positive outcome only\n",
    "probs = probs[:, 1]\n",
    "# predict class values\n",
    "yhat = model4.predict(features)\n",
    "# calculate precision-recall curve\n",
    "precision, recall, thresholds = precision_recall_curve(label, probs)\n",
    "# calculate F1 score\n",
    "f1 = f1_score(label, yhat)\n",
    "# calculate precision-recall AUC\n",
    "auc = auc(recall, precision)\n",
    "# calculate average precision score\n",
    "ap = average_precision_score(label, probs)\n",
    "print('f1=%.3f auc=%.3f ap=%.3f' % (f1, auc, ap))\n",
    "# plot no skill\n",
    "plt.plot([0, 1], [0.5, 0.5], linestyle='--')\n",
    "# plot the precision-recall curve for the model\n",
    "plt.plot(recall, precision, marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}