HeartStroke_LogisticRegression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heart Stroke Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Insight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('framingham.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>male</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>currentSmoker</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>prevalentStroke</th>\n",
       "      <th>prevalentHyp</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "      <th>TenYearCHD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>26.97</td>\n",
       "      <td>80.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>28.73</td>\n",
       "      <td>95.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>127.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>25.34</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>28.58</td>\n",
       "      <td>65.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>23.10</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   male  age  education  currentSmoker  cigsPerDay  BPMeds  prevalentStroke  \\\n",
       "0     1   39        4.0              0         0.0     0.0                0   \n",
       "1     0   46        2.0              0         0.0     0.0                0   \n",
       "2     1   48        1.0              1        20.0     0.0                0   \n",
       "3     0   61        3.0              1        30.0     0.0                0   \n",
       "4     0   46        3.0              1        23.0     0.0                0   \n",
       "\n",
       "   prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  \\\n",
       "0             0         0    195.0  106.0   70.0  26.97       80.0     77.0   \n",
       "1             0         0    250.0  121.0   81.0  28.73       95.0     76.0   \n",
       "2             0         0    245.0  127.5   80.0  25.34       75.0     70.0   \n",
       "3             1         0    225.0  150.0   95.0  28.58       65.0    103.0   \n",
       "4             0         0    285.0  130.0   84.0  23.10       85.0     85.0   \n",
       "\n",
       "   TenYearCHD  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           1  \n",
       "4           0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>male</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>currentSmoker</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>prevalentStroke</th>\n",
       "      <th>prevalentHyp</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "      <th>TenYearCHD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4240.000000</td>\n",
       "      <td>4240.000000</td>\n",
       "      <td>4135.000000</td>\n",
       "      <td>4240.000000</td>\n",
       "      <td>4211.000000</td>\n",
       "      <td>4187.000000</td>\n",
       "      <td>4240.000000</td>\n",
       "      <td>4240.000000</td>\n",
       "      <td>4240.000000</td>\n",
       "      <td>4190.000000</td>\n",
       "      <td>4240.000000</td>\n",
       "      <td>4240.000000</td>\n",
       "      <td>4221.000000</td>\n",
       "      <td>4239.000000</td>\n",
       "      <td>3852.000000</td>\n",
       "      <td>4240.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.429245</td>\n",
       "      <td>49.580189</td>\n",
       "      <td>1.979444</td>\n",
       "      <td>0.494104</td>\n",
       "      <td>9.005937</td>\n",
       "      <td>0.029615</td>\n",
       "      <td>0.005896</td>\n",
       "      <td>0.310613</td>\n",
       "      <td>0.025708</td>\n",
       "      <td>236.699523</td>\n",
       "      <td>132.354599</td>\n",
       "      <td>82.897759</td>\n",
       "      <td>25.800801</td>\n",
       "      <td>75.878981</td>\n",
       "      <td>81.963655</td>\n",
       "      <td>0.151887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.495027</td>\n",
       "      <td>8.572942</td>\n",
       "      <td>1.019791</td>\n",
       "      <td>0.500024</td>\n",
       "      <td>11.922462</td>\n",
       "      <td>0.169544</td>\n",
       "      <td>0.076569</td>\n",
       "      <td>0.462799</td>\n",
       "      <td>0.158280</td>\n",
       "      <td>44.591284</td>\n",
       "      <td>22.033300</td>\n",
       "      <td>11.910394</td>\n",
       "      <td>4.079840</td>\n",
       "      <td>12.025348</td>\n",
       "      <td>23.954335</td>\n",
       "      <td>0.358953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>83.500000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>15.540000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>23.070000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>25.400000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>28.040000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>696.000000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>142.500000</td>\n",
       "      <td>56.800000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "              male          age    education  currentSmoker   cigsPerDay  \\\n",
       "count  4240.000000  4240.000000  4135.000000    4240.000000  4211.000000   \n",
       "mean      0.429245    49.580189     1.979444       0.494104     9.005937   \n",
       "std       0.495027     8.572942     1.019791       0.500024    11.922462   \n",
       "min       0.000000    32.000000     1.000000       0.000000     0.000000   \n",
       "25%       0.000000    42.000000     1.000000       0.000000     0.000000   \n",
       "50%       0.000000    49.000000     2.000000       0.000000     0.000000   \n",
       "75%       1.000000    56.000000     3.000000       1.000000    20.000000   \n",
       "max       1.000000    70.000000     4.000000       1.000000    70.000000   \n",
       "\n",
       "            BPMeds  prevalentStroke  prevalentHyp     diabetes      totChol  \\\n",
       "count  4187.000000      4240.000000   4240.000000  4240.000000  4190.000000   \n",
       "mean      0.029615         0.005896      0.310613     0.025708   236.699523   \n",
       "std       0.169544         0.076569      0.462799     0.158280    44.591284   \n",
       "min       0.000000         0.000000      0.000000     0.000000   107.000000   \n",
       "25%       0.000000         0.000000      0.000000     0.000000   206.000000   \n",
       "50%       0.000000         0.000000      0.000000     0.000000   234.000000   \n",
       "75%       0.000000         0.000000      1.000000     0.000000   263.000000   \n",
       "max       1.000000         1.000000      1.000000     1.000000   696.000000   \n",
       "\n",
       "             sysBP        diaBP          BMI    heartRate      glucose  \\\n",
       "count  4240.000000  4240.000000  4221.000000  4239.000000  3852.000000   \n",
       "mean    132.354599    82.897759    25.800801    75.878981    81.963655   \n",
       "std      22.033300    11.910394     4.079840    12.025348    23.954335   \n",
       "min      83.500000    48.000000    15.540000    44.000000    40.000000   \n",
       "25%     117.000000    75.000000    23.070000    68.000000    71.000000   \n",
       "50%     128.000000    82.000000    25.400000    75.000000    78.000000   \n",
       "75%     144.000000    90.000000    28.040000    83.000000    87.000000   \n",
       "max     295.000000   142.500000    56.800000   143.000000   394.000000   \n",
       "\n",
       "        TenYearCHD  \n",
       "count  4240.000000  \n",
       "mean      0.151887  \n",
       "std       0.358953  \n",
       "min       0.000000  \n",
       "25%       0.000000  \n",
       "50%       0.000000  \n",
       "75%       0.000000  \n",
       "max       1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28251cddc48>"
      ]
     },
     "execution_count": 6,
     "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": [
    "#analyzing missing data \n",
    "sns.heatmap(data.isnull(), yticklabels= False , cbar = False)\n",
    "#we have some missing data in the education , cigs per day . total cholestrol , bmi and glucose so we have to fill them "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4240 entries, 0 to 4239\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   male             4240 non-null   int64  \n",
      " 1   age              4240 non-null   int64  \n",
      " 2   education        4135 non-null   float64\n",
      " 3   currentSmoker    4240 non-null   int64  \n",
      " 4   cigsPerDay       4211 non-null   float64\n",
      " 5   BPMeds           4187 non-null   float64\n",
      " 6   prevalentStroke  4240 non-null   int64  \n",
      " 7   prevalentHyp     4240 non-null   int64  \n",
      " 8   diabetes         4240 non-null   int64  \n",
      " 9   totChol          4190 non-null   float64\n",
      " 10  sysBP            4240 non-null   float64\n",
      " 11  diaBP            4240 non-null   float64\n",
      " 12  BMI              4221 non-null   float64\n",
      " 13  heartRate        4239 non-null   float64\n",
      " 14  glucose          3852 non-null   float64\n",
      " 15  TenYearCHD       4240 non-null   int64  \n",
      "dtypes: float64(9), int64(7)\n",
      "memory usage: 530.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2825286e848>"
      ]
     },
     "execution_count": 8,
     "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": [
    "sns.countplot( x = 'prevalentStroke' , data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x282528e6948>"
      ]
     },
     "execution_count": 9,
     "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": [
    "sns.countplot(x = 'education' , data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2825294bd08>"
      ]
     },
     "execution_count": 10,
     "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": [
    "sns.countplot(x = 'prevalentStroke', hue = 'currentSmoker' , data = data )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x282529cbb08>"
      ]
     },
     "execution_count": 11,
     "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": [
    "sns.countplot(x = 'prevalentStroke' , hue ='male' , data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['prevalentStroke'][data['prevalentStroke'] == 1].count() #we have just 25 cases of prevalent stroke "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x229b1f00f48>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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beteSo8U1diXfn6+TSffU8tjQSp/mYEtxQ/JS8AxJ1AwoSfoa2a30ZWDniGim+Rwp6ayWYV9Pzqz5Bznf+SQqJrZLej35t/wjedIW+fdo+3tvIOlZJc565TblfpuFMV3Ha4xkemRxo6T/Bu7H5EZbq5kW5ADv1uSc+RvLibzqu079dMhpaqeMnR0RD5Z0YTOgIOm0iHjkil47S8y7kq2dS8mW7u/aDCZNibk6uehAwKURUdv/+gXg5xHxvhL7KHIy+r41cQfiP5r8opxYWi+18daJiL9VxthktudrWiySHhsRrZdmLyfmcyLiqBU9NkS8XwEPi4rpi1PiHTLb8xExVLLoOt4K/q2NgL9E5dSn0io9EngTOb3vJcCfI+ItFTGfxsSg+Sk1A7wlXuuBveXGrEy6PyInix9NrlO+Enh/tJhTWuLNOJjU5szXfJEHzviTRMTXZnp8JWOLvPy9kGzhnxARH2kbr8R8ANnaA7gkIi6qiVdibg98FlgnIjZWTu5/dUS8rjLu4CKB0yKXb7aJM+N706h8j7oeGf8+8PgO+jPnlDIL4v3ANeSVwpeBjchFNrtFxIkVsZtG2wXNlZKkUyLi0S3jvZ8cFzmsPLQrOfPprUPGOYqJFu7jge8MPh8RVQWvarsX9iQ77t9AviE7kgMrbXU5mPRo8kSw8wzPBROT3FeaJheLOZCsP/Ajsn9qm2ix6qcMHh1LTu06n2yNP0DSFcDTI6JmfX+ztv04yLXtkqpqWWj6IoFDJbVdJLDzlNuDrZK279GTyYGUu2nyFMT1yHmmw8ZrRtcvA34g6VtMXtn44WFjTonf+TQnSf/K9Ev2d7UM93Hgv8grr5OBJ0fE6eW7eQSl2FVLzQDp78sxX0U2uNp6CrB1M+1Q0hfJ8YGhki7ZUGm0nqGyPLVJN8gz3yZMLLE8mPb9XJ0NJkXEPuXmuyJi0qo0tZ869KEp968lBxE/RP4t2vRF7Q+cRc7aaD4si8jWxXvIvsnWIuK3UwZwqwYRyX6zh8XEIoEPkFPbhk66g5e8ZcCii0vgq8i/59OYPK1pCfAfLeI1o+tXlJ/F5acrnUxzaiinCa5FNoA+S84GOHPWF81uteYEUMYyTgco382KsAC8uzQ63kh+ftaj3Xs0aAOyVQ7t+7J/Ctw+In45+KCke5HFmKrUJt3DyIGqC2kxSjiDUQwmHUO2HgYdDTx42EARsWPlscxkJ+CBMbAoICL+Kem/aD9NrjGKWhZicuJuBpVqdTJgUbo6zpd0OPn5rl2RdgCwbkT8afBBSXdiYpZNja6mOTW2j4gHlkv2/SR9iBZXDAMGv9dTZ6lUvWcxsWz+evIk0awma+t9wLmlK6gZiB+2lQvwUfLE98spj9+LnM3x4mmvGEJt0v1zRLSunztVRDyz3Ny3/OHWp+XlS7n8uR+w/pS+w/UYuOxqGXt9ciR7WYc92aJu8yVcOlM/YZnqUzXgRw5OHEhOxfsd2XravTJmp4sERuhJlBVpwGZqvyLtQPIzODVx7UROaXtt5XF2Ms1pQJMYbyyD0n+hbgbCVsoSliKL6DTdXaLie6Rc3XYXsirhUkl3JLsrXwrctU3MyBobPyC7KEWumvtDi1DbRMSLZoh/fOk3rlKbdPeR9FmySMtgP1fNmbWJUbsAY0tyUvcGTO47XEL2Sdb4PFkEpOlQfzGZjGYdGFqOqdWhGgJWb32EQJk7XDONbaaYHy4f7B2oXCSgieXkAjbXlAL4LRLkoH2ZviJt0xZxdoiIV019MCIOK1cjtbqe5nR8uVr8b7KubjC5j3IoEVGzQmxGkvYkp/P9Clhd0oHAh4Ev0eIKdCDuM4GTm4agpA0kPSOGXwY92+9cVakO6mcvHEqOuP+MibNzRMTLaw+sK5IeHhE/6TjmeTGluM1Mj61krNmqQ7Xq0tCIyvGVvuYLIuL+bV4/Q7xZR6lrTrySzoiIh2lyfYxlo+RDxLkkIu4z7HNDxD+aPGGfEB3VnRiIvTqwRssrsJFRVvHaISKukbQxmXwf1fQXV8Sd6Xu57P0fIs6JwAcjV0oOPv44svVctQKztqW7VVQU/OjJuZJ2Z/pobs2J4SZJO0TEDwEkPYLp/V0rpczSWERWsKqqrjTgNcxSjq+t0td8vupWYg3GOwVyBD8iDhx8rozq11ztdLUi7U+SHhoDpTbL8W1LB4MqTNSd+Kgq6k5olimSysUr1VefHfp7lEp3EXGFpF/UJtxiplrRbXLcXmQd7u8wMRj7EOBx5ABtldqW7sHARyLi4toDGZXyQb4UeAHwLvJy+5KI2KMi5tbk1jfrkwntGuClbeerlpg/icqScQOxRlmO72Syz+xMJmreVnUFaOY5tUO3UKa8vpNaz5IeSp68vsDkL+BuwPMj4oy2xzjl32nqTryNXF49VN0JSftGxL7KRRKDX+pmue44XX3+iazj0Hj+4P2KK7HPk7WeP0H+DV4P3C4iXtoi1lrkYo3mqu5nwBebWTs1apPuJcAWZLHgf0D79dij0nx5m0tLZanHk6L9UsPB2OsBVM6lbWLtB1wAfC1q3pTpcbsuxzdjl0CbrgBlhbIXkP3Dpw08tS5wa0TUVkRr3qOIlrVVS4w7kgOQg1/Aj0+d0VARf7DuxFVM1J14QEQ8ZiVjvJGJ/vHmv5Tb1fOJuyTpJbM9Hy0Lo5eZD+9gclH0d7dJlMpdXL4ZEbNWHGujtnvhSZ0cxWg1LYXrJN0f+AN1m2dSBip2K3FWa2b4tD1DF3tRdk+QdBMTJ7Chd00YOM7OyvENeEpMWaapnKvbpivgx2QBmY2YPAd6CXkCaq1c/n+eMs9W0vXknNih/wYR8SdJ10TEs6f8G9O6RVocZ1d1J5pdarckr0SOJT9DO5M1N8ZGk1S1nKXaFXFvAP6z8vCaWLdK+qekdWtO2DOZ9ztHKJcWHwM8gLxEXAd4Z0QcVBHzx8DpTJmf3PYM3TVNL8d34kzT0lrGnqkrYOgBqlFT7vSwe0ScVu7vAHyy7XGOogukxHgu+f78VVkRbhuyddZ2T7NvA89uEkWZgnZU5I62Y2U5f9PapdrTElrbq1pl2dbtyCmDg11pb24Tr1Hb0h17EdFMlzmV3Ea5C2tExF4r/t9WnrK5/EJgs4jYX9LdgbtMHcBZSZ2X45P0WnI/tM1LQmusSy6Fbq0M/nyAXJElOmjlA0uahEsG+6FyL7Zhj63pAtlsypS2daksk1m8PSK+Wk4KTyTnFn8KeFjLeBsDg0WSllJ5Zdc1dbxUe8CbBm6vQW6CUBPvVEZwlbAQWrrvBQ6IiOvK/dsBb4yIt1fE/A/gb8DxdLTDsLJg+z/J5cD3Kcf57YjYtkWsziuClYGe25GrfgYv4ZbU/N4l9q/IS+va1XKD9TFeTC6HPYJs/TwPuDaG3ECz/C03Y4bfm5w+V3UFMTDm8D5yA9HDa1rQkt5Gzh9vynk+EzgyIt5Xc5xdUhZM2poc2H7nwFNLgO93MeA78G+1LqAzKgsh6U77ANdcwpTX707WRbiOicuZiLodhs+JiG3U0b5rkp5B7sRxYUSc1Pa4BuKtBdzcjKYra2I8BfhN7XQkST+KiEfUHmOJNZJdcUvsO5H9pQBndjGQJul4sjrfTuTCgJtK7Nb77ZUTz+BW8TU7XIxEGaj6UtTVoJ4ac3BD1kXk3/Oj0b7q4WZkbZSpmzRU7QY877sXyHmaq0epoavc26xqpRc56HWPqN8tYtDN5YPY7Lt2B1ouB5X0SXJe8o+B/cs809ptek4kV0/9UtI9yCI3hwFPLfFrBjDOUu6w/A0qVzbGaOpjNAM8HyRXuIncz23viDi6MvRzKUuWI+I6SXch65m0VvqDW/UJ96UMVN1e0uLooGZ0cTYTMzduIWdVvaIi3hfJbq8PkFcML2NiYL61hdDSfTM5obmZv/hycrPGAypiHkfO0Zy2XXpFzBeSl8DbkG/2LmR/39BFtyVdRC5cubW0UE+LiNbLK0vMwUL1+wMbRsTuykI6Z0fdrrgzFeCunluqDkscKvfVe3zTui0nxe/WtEgXOkmfJj/vxzF5oGosprdpBJs0wAJo6UbEAcqtZh5HngH37+By+1ZyW6HvM7ll1nrKWORa/rMHjvMZFX2cSyPi1hL3Rqm+Bh+TR4UfS67tJ7JYSdXy1ehwV4OGui9xuGhKd8JfmHkFlK28q8rPIiZvUDkUja4Y/j/Kd+eyMgvqSuBOLWMtM+9buqOwvMndbaaMSVqDXLZ7D3IK2uc6GJy5kVzPDpnAtyj3a2YvHErOcb4KeAs5y+LGMmf5lMo+yHuRI/Z3ioj7S3og8LSIeHdFzGYxTPPfdciFJ63WzUs6gJwJckR56HnkQFrrrWWsG8u7UqJyNZ6yLOqFwO3JgdT1yJ1xTpv1hSuKO9+Tbpkm1PySi8kqQTdUTkdqYt+WXKV0ZdtBldKXeTO5IuvJwK8jYs/K4xrF7IU1yZ097kyp+1oe3x7YIipWuim3Nt8b+PTAIOJFUVFYRxMFb04nq7/9BbgoIu7ZMt4HgDOYqK52KrCdk257pYvmzXS0MaUmr8qj3L6e7P6q2YZ+tdqG0CQRsaB+yPqv72352oOA+5Xb6wMXk2fCK4FdW8a8cOD2auQGl6P4vTeinGQr4+yxMo8NGfOn5b/nDjx2XmXMd5BlPZ9NttB/T3YttY037X0hW7qdv1cL5YdcpvsKchHPo8kVhB+oiHc48AtywPNDZM2VL5M7Qby5RbyHkFtoXVHubwX8T/Xvvar/8KvozT695et+NnB7T+Ab5fadBxPGkDHPme1+y5jbkaPsXwMeRFYc+wPwJ+BJlbFnSj6tfveB159AdoGcU+7vQpY67Or9Xh1Yv+VrX1tOrDeQS5Obn8vJgjS9fW7n2w/ZAp108iK7qtrGO4nchLW5vw4562ZN4OIW8X5CztEebAxcVPt7z/uBtCmd7IvIs1fbPpXBqS2PJ7deJyL+UDFW1VTmByZV569ZldX5ZoIjXpm1O/AZ4N6SriQT2rTK/St5nF2XODycPCl0vijEOt+YcupqvJuBTSLiJrXbhWVRRFyubvcYnP9Jl8m7RtwC/Bp4estY10l6KvnheARlDqCk1ciz6dBiBJX5Gc1mgiMrThMRlwE7KatELYq6AiOPYmIX6GklDhlyv7DIAuDXk4WDrFtdb0x5OHC6pGPL/Z2BI8rnqk352askPZjcY1Bk4+BXK3jNCs37gbQulVH2j5LdCf8TEV8ojz8ReEJEvLEi9hbA7yLiH5IeQ+6o/KUoy5eHjLVsxd3U1Xe1q/G6pIntzWcULeZrag6VOLTulSTZDHb+MCKGqdQ2NdadyNq8jyM/PycDr4vKlYjztqUr6WPM0o0QLebURsQvgCdpYNeI8vhJkv7W7kiXOQZ4SFnt9Tlywvjh5FLbYY1kM0HovDhN67mZs5gzJQ4XulFMFYws3VlVwlTSthHx04j4Izm+0Kl529IdmEv7CHLt9JHl/nPIDvzWlzEztRZrW5CaqL2wN7mdycfUQenArqnD4jSjpDlU4nChGsVUwY6O63zgh8Bbo4MNCqaaty3dmCiU/FJgx5go1HIQOVVlaJIeDmwP3GHKpfF6zL6D6Mq4uQxWvYSJfujqnUdH4I9dJ9yyQOQVdLuP3diXODTWiogzp4wzdDcftr1tyL7lsyTtExFHrOgFw5i3SXfAXcnL2GakeZ3yWBuLy+tXY/Kl8V+pvwx5Gbky7T1lxHQz4NDKmKPQWXGaAV8m51Q+kYF97GoOssQ8U9JgicOxKDJvy1xdxjKaIk+7kIO1q1TkEvoPSvpf4CeaKLvadKVtOGuAFZi33QsNSS8D9iHnrUJOwt43KnZ5kLRJtFjVNR+MojiNRrSPneZAicOFTNLm5FTB7YFryamCLxyH71bpnnw7OZD2CSbvEFM1bWwhJF2RBa33BPYFzgPuHO12ZGhi3ousUr8pA1cLNUmiFOWZ+mZcD5xFbt/SxS4FY0nSmRHxUEmnkrtT/IGsKdvVTh82hiStTl4hbgpsSF4xRrSsBNfhcZ1Kfgb3jIiruo6/ELoXPkmepdaMiOOUOzIcw0Qx6jaOIpcEf5YOJksXJ5RYh5f7zycvZ64n93bbeeaX9WsUI87AZ8r78g5y1sY65bbNb8eSG2P4lMEAAAWKSURBVAGcQ859HxfvjYihFxCtrIXQ0u10R4by+rOjsj7tDDGn7Z7QPKaBep6r2oiK09ym9pLN5p5xmKkwG0kbkfW3N2XyFe2rauIuhHqgne3IMOCbkl4n6S6SNmx+KmOuI2nZZoSSHsrEnNNxGNFtrDVD10zt8V0u6TOSHqeKJXM25/xY0lg0JpbjWLJ+7g+B7w38VFkI3QsfJTfpu6Ok91B2ZKiM2cwBHtxWJajbbfiVwOeVdV9F9m+9sixhHJtNBRnNiPOWZPfJ7uTf4JvAVwYXoNj8MTB+sRrwMkmXkTNhWtd7HpG1a1aZLs+8714AKIVemh0ZvjfOE/vLWnS1Wf7bh+WMOL8oIn7dUfzbAQeSo9ijqEthq5hGUO95FJQ7NH+/qWPSWdyFkHS7ptx3bC9g44h4laR7AltGxPEtYr0oIg4dqBkwybjWCuioOM1gvEeTuzE8max/emREHNNFbLM2JF1LVuq7kVxc08k83YXQvTAKh5Dru7cv939HzmgYOukCa5f/rjPDc2NzRlxecZqmC7bm5CDpcnIq31eBvSPihhW8xKwPG40iqJNuO1tExPPKsl1Kvc5WA0AR8elyc3NyB4brYNll9oeW+8L+jaI4TWOrUaxxN6sRuZv284HNI+K9kv6FHFirKqjjpNvOUuWeYc1g0hYMLIlt6YGD/bgRca2ksSl2ExH7jTD8ncty3S7n/ppVkfRxsv7Jo4D3kt0MB1E3x99Jt6V9yN0X7i7pMLKS2UsrYy6SdLuIuBagTEEbu/dnRMVpDqbM/S2xLpB0OOCka6vS9s0cf4CIuEbS4tqgY/elHnelG+FScofZ7cjO9T0i4urK0B8i5y0eTbagnwu8pzLmKIyiOM24Vpuyhe1mSYuYuKK9PfVz/J10hxURIekbZUXatzqM+yVJZwGPJRP5syKizRYjo3aPiHiOpKdHxBdLi/SkyphjWW3KFiZNbLn+CbJkwB0k7Uc2hKq72Zx02zldpbp8l0FLkh3HRDuo2UzwOkn3JwuDbFoZc6aNKV9YGdOsrTOBbUpD6GxgJ7Ih9JyIuKg2uJNuOzsCr5b0G3Jr7nFbSTNKnRanKZdvD4mIrjamNKu1rJ8rIn4G/KzT4F4cMbzlragZl5U0ozSK4jSSTo2IR3UZ06wtSb8DljvvvHbBklu6Qyots2+Nc3WkEbtc0onknnMnRzdn7e9IelOJuWxhRERcs/yXmI3MbcgruJEUX3JLt4UyTeytEXHFqj6WvpX5yTuT9X4fDFQXpykr0mZaAu0i5tY7VW4yu8L4TrrDk3QyOUH6TCa3zJ62yg5qFeiqOE1J5K8DdiCT72nAQRFxUycHajYEjXgXbifdFkpxlmki4pS+j2VV6Lo4jaSvkqUsDysP7QpsEBHPrT1Ws2FJ2nCUXVtOujaUKcVpjuuiOM1MO3nU7u5hNq48kNaCpCVM9EEuJtdn3xAR6626o+rNKIrTnCtpu4g4HaDsoPGjjv8Ns7HgpNtCREyquCXpGcBDV9Hh9G0UxWkeBuwmqRmY3Bi4pNlhYIHMf7YFwt0LHZF0ekRst6qPY9RGtDHlnNhJwKwLbum2IOlZA3cXAQ9hjAqOj1jnxWmcVG0hcdJtZ+eB27cAvwYWynQxF6cxq+Ck284iZt7loaam7Fzh4jRmFZx02xnrXR5GxcVpzOotWtUHMEctKq1bYHx3eehaRPwT+Pdy+wYnXLPhzftEMSJzZZeHUXBxGrMKnjLWkqT7MrHLw/fGdJeHzrk4jVkdJ10biovTmNVx0rWhuDiNWR0nXRuKi9OY1fHsBRvWuZKWLXd2cRqz4bila0ORdAmwJTCpOA3wT1ycxmyFnHRtKC5OY1bHSdfMrEfu0zUz65GTrplZj5x0zcx65KRrZtaj/wfkeDftRWgy3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#analyzing missing data \n",
    "sns.heatmap(data.isnull(), yticklabels= False , cbar = False)\n",
    "#we have some missing data in the education , cigs per day . total cholestrol , bmi and glucose so we have to fill them "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education          1.000000\n",
       "currentSmoker      0.018528\n",
       "male               0.017415\n",
       "cigsPerDay         0.008323\n",
       "BPMeds            -0.010898\n",
       "totChol           -0.023613\n",
       "prevalentStroke   -0.035142\n",
       "glucose           -0.035843\n",
       "diabetes          -0.038749\n",
       "heartRate         -0.054182\n",
       "TenYearCHD        -0.054248\n",
       "diaBP             -0.061919\n",
       "prevalentHyp      -0.081726\n",
       "sysBP             -0.129496\n",
       "BMI               -0.137747\n",
       "age               -0.166356\n",
       "Name: education, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = data.corr()\n",
    "corr_matrix['education'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x229b22b9688>"
      ]
     },
     "execution_count": 30,
     "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": [
    "sns.heatmap(corr_matrix, yticklabels= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(strategy=\"median\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(add_indicator=False, copy=True, fill_value=None,\n",
       "              missing_values=nan, strategy='median', verbose=0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_num = data.drop('male',axis = 1)\n",
    "imputer.fit(data_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 49. ,   2. ,   0. ,   0. ,   0. ,   0. ,   0. ,   0. , 234. ,\n",
       "       128. ,  82. ,  25.4,  75. ,  78. ,   0. ])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(data_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>age</th>\n",
       "      <th>education</th>\n",
       "      <th>currentSmoker</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>prevalentStroke</th>\n",
       "      <th>prevalentHyp</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "      <th>TenYearCHD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>26.97</td>\n",
       "      <td>80.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>28.73</td>\n",
       "      <td>95.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>127.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>25.34</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>61.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>28.58</td>\n",
       "      <td>65.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>23.10</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  education  currentSmoker  cigsPerDay  BPMeds  prevalentStroke  \\\n",
       "0  39.0        4.0            0.0         0.0     0.0              0.0   \n",
       "1  46.0        2.0            0.0         0.0     0.0              0.0   \n",
       "2  48.0        1.0            1.0        20.0     0.0              0.0   \n",
       "3  61.0        3.0            1.0        30.0     0.0              0.0   \n",
       "4  46.0        3.0            1.0        23.0     0.0              0.0   \n",
       "\n",
       "   prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  \\\n",
       "0           0.0       0.0    195.0  106.0   70.0  26.97       80.0     77.0   \n",
       "1           0.0       0.0    250.0  121.0   81.0  28.73       95.0     76.0   \n",
       "2           0.0       0.0    245.0  127.5   80.0  25.34       75.0     70.0   \n",
       "3           1.0       0.0    225.0  150.0   95.0  28.58       65.0    103.0   \n",
       "4           0.0       0.0    285.0  130.0   84.0  23.10       85.0     85.0   \n",
       "\n",
       "   TenYearCHD  \n",
       "0         0.0  \n",
       "1         0.0  \n",
       "2         0.0  \n",
       "3         1.0  \n",
       "4         0.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_num_prep = pd.DataFrame(X , columns = data_num.columns , index = data_num.index)\n",
    "data_num_prep.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_prep = pd.concat([data['male'],data_num_prep],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>male</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>currentSmoker</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>prevalentStroke</th>\n",
       "      <th>prevalentHyp</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "      <th>TenYearCHD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>26.97</td>\n",
       "      <td>80.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>28.73</td>\n",
       "      <td>95.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>127.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>25.34</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>28.58</td>\n",
       "      <td>65.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>23.10</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   male   age  education  currentSmoker  cigsPerDay  BPMeds  prevalentStroke  \\\n",
       "0     1  39.0        4.0            0.0         0.0     0.0              0.0   \n",
       "1     0  46.0        2.0            0.0         0.0     0.0              0.0   \n",
       "2     1  48.0        1.0            1.0        20.0     0.0              0.0   \n",
       "3     0  61.0        3.0            1.0        30.0     0.0              0.0   \n",
       "4     0  46.0        3.0            1.0        23.0     0.0              0.0   \n",
       "\n",
       "   prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  \\\n",
       "0           0.0       0.0    195.0  106.0   70.0  26.97       80.0     77.0   \n",
       "1           0.0       0.0    250.0  121.0   81.0  28.73       95.0     76.0   \n",
       "2           0.0       0.0    245.0  127.5   80.0  25.34       75.0     70.0   \n",
       "3           1.0       0.0    225.0  150.0   95.0  28.58       65.0    103.0   \n",
       "4           0.0       0.0    285.0  130.0   84.0  23.10       85.0     85.0   \n",
       "\n",
       "   TenYearCHD  \n",
       "0         0.0  \n",
       "1         0.0  \n",
       "2         0.0  \n",
       "3         1.0  \n",
       "4         0.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_prep.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Training model</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "attributes = data_prep.drop('prevalentStroke',axis = 1)\n",
    "labels = data_prep['prevalentStroke']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(attributes,labels, test_size=0.30, \n",
    "                                                    random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: 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, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log_model = LogisticRegression()\n",
    "log_model.fit(X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = log_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      1.00      1.00      1264\n",
      "         1.0       0.00      0.00      0.00         8\n",
      "\n",
      "    accuracy                           0.99      1272\n",
      "   macro avg       0.50      0.50      0.50      1272\n",
      "weighted avg       0.99      0.99      0.99      1272\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.37106918238993"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(y_test,predictions)\n",
    "accuracy * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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