HRFLM_Heart_disease_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "• Sex: male or female(Nominal)<br>\n",
    "• Age: Age of the patient;(Continuous - Although the recorded ages have been truncated to whole numbers, the concept of age is continuous)<br>\n",
    "Behavioral<br>\n",
    "• Current Smoker: whether or not the patient is a current smoker (Nominal)<br>\n",
    "• Cigs Per Day: the number of cigarettes that the person smoked on average in one day.(can be considered continuous as one can have any number of cigarettes, even half a cigarette.)<br>\n",
    "Medical( history)<br>\n",
    "• BP Meds: whether or not the patient was on blood pressure medication (Nominal)<br>\n",
    "• Prevalent Stroke: whether or not the patient had previously had a stroke (Nominal)<br>\n",
    "• Prevalent Hyp: whether or not the patient was hypertensive (Nominal)<br>\n",
    "• Diabetes: whether or not the patient had diabetes (Nominal)<br>\n",
    "Medical(current)<br>\n",
    "• Tot Chol: total cholesterol level (Continuous)<br>\n",
    "• Sys BP: systolic blood pressure (Continuous)<br>\n",
    "• Dia BP: diastolic blood pressure (Continuous)<br>\n",
    "• BMI: Body Mass Index (Continuous)<br>\n",
    "• Heart Rate: heart rate (Continuous - In medical research, variables such as heart rate though in fact discrete, yet are considered continuous because of large number of possible values.)<br>\n",
    "• Glucose: glucose level (Continuous)<br>\n",
    "Predict variable (desired target)<br>\n",
    "• 10 year risk of coronary heart disease CHD (binary: “1”, means “Yes”, “0” means “No”)\n",
    "Education <br>\n",
    "1 = Some High School; 2 = High School or GED; 3 = Some College or Vocational School; 4 = college<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(r'C:\\Users\\hp\\Dibeties prediction\\dibeties.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <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",
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      ],
      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "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>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",
       "</div>"
      ],
      "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4240, 16)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['male', 'age', 'education', 'currentSmoker', 'cigsPerDay', 'BPMeds',\n",
       "       'prevalentStroke', 'prevalentHyp', 'diabetes', 'totChol', 'sysBP',\n",
       "       'diaBP', 'BMI', 'heartRate', 'glucose', 'TenYearCHD'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "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": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.TenYearCHD.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3596\n",
       "1     644\n",
       "Name: TenYearCHD, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.TenYearCHD.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male                 0\n",
       "age                  0\n",
       "education          105\n",
       "currentSmoker        0\n",
       "cigsPerDay          29\n",
       "BPMeds              53\n",
       "prevalentStroke      0\n",
       "prevalentHyp         0\n",
       "diabetes             0\n",
       "totChol             50\n",
       "sysBP                0\n",
       "diaBP                0\n",
       "BMI                 19\n",
       "heartRate            1\n",
       "glucose            388\n",
       "TenYearCHD           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>582 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      male  age  education  currentSmoker  cigsPerDay  BPMeds  \\\n",
       "14       0   39        2.0              1         9.0     0.0   \n",
       "21       0   43        1.0              0         0.0     0.0   \n",
       "26       0   60        1.0              0         0.0     0.0   \n",
       "33       1   61        NaN              1         5.0     0.0   \n",
       "36       1   56        NaN              0         0.0     0.0   \n",
       "...    ...  ...        ...            ...         ...     ...   \n",
       "4208     0   51        1.0              1         9.0     0.0   \n",
       "4229     0   51        3.0              1        20.0     0.0   \n",
       "4230     0   56        1.0              1         3.0     0.0   \n",
       "4235     0   48        2.0              1        20.0     NaN   \n",
       "4236     0   44        1.0              1        15.0     0.0   \n",
       "\n",
       "      prevalentStroke  prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  \\\n",
       "14                  0             0         0    226.0  114.0   64.0  22.35   \n",
       "21                  0             0         0    185.0  123.5   77.5  29.89   \n",
       "26                  0             0         0    260.0  110.0   72.5  26.59   \n",
       "33                  0             0         0    175.0  134.0   82.5  18.59   \n",
       "36                  0             0         0    257.0  153.5  102.0  28.09   \n",
       "...               ...           ...       ...      ...    ...    ...    ...   \n",
       "4208                0             0         0    340.0  152.0   76.0  25.74   \n",
       "4229                0             1         0    251.0  140.0   80.0  25.60   \n",
       "4230                0             1         0    268.0  170.0  102.0  22.89   \n",
       "4235                0             0         0    248.0  131.0   72.0  22.00   \n",
       "4236                0             0         0    210.0  126.5   87.0  19.16   \n",
       "\n",
       "      heartRate  glucose  TenYearCHD  \n",
       "14         85.0      NaN           0  \n",
       "21         70.0      NaN           0  \n",
       "26         65.0      NaN           0  \n",
       "33         72.0     75.0           1  \n",
       "36         72.0     75.0           0  \n",
       "...         ...      ...         ...  \n",
       "4208       70.0      NaN           0  \n",
       "4229       75.0      NaN           0  \n",
       "4230       57.0      NaN           0  \n",
       "4235       84.0     86.0           0  \n",
       "4236       86.0      NaN           0  \n",
       "\n",
       "[582 rows x 16 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null = data[data.isna().any(axis=1)]\n",
    "null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4240, 16)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we have remove the row having Nan value in education\n",
    "data = data.dropna(axis=0, subset=['education'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4135, 16)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [male, age, education, currentSmoker, cigsPerDay, BPMeds, prevalentStroke, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose, TenYearCHD]\n",
       "Index: []"
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     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for dupicate values in dataset\n",
    "duplicate_data = data[data.duplicated()]\n",
    "duplicate_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>24.33</td>\n",
       "      <td>80.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4208</th>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>25.74</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4229</th>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>25.60</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4230</th>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>22.89</td>\n",
       "      <td>57.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4235</th>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>84.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4236</th>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>126.5</td>\n",
       "      <td>87.0</td>\n",
       "      <td>19.16</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      male  age  education  currentSmoker  cigsPerDay  BPMeds  \\\n",
       "14       0   39        2.0              1         9.0     0.0   \n",
       "21       0   43        1.0              0         0.0     0.0   \n",
       "26       0   60        1.0              0         0.0     0.0   \n",
       "42       0   52        1.0              0         0.0     1.0   \n",
       "49       1   36        3.0              1        20.0     NaN   \n",
       "...    ...  ...        ...            ...         ...     ...   \n",
       "4208     0   51        1.0              1         9.0     0.0   \n",
       "4229     0   51        3.0              1        20.0     0.0   \n",
       "4230     0   56        1.0              1         3.0     0.0   \n",
       "4235     0   48        2.0              1        20.0     NaN   \n",
       "4236     0   44        1.0              1        15.0     0.0   \n",
       "\n",
       "      prevalentStroke  prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  \\\n",
       "14                  0             0         0    226.0  114.0   64.0  22.35   \n",
       "21                  0             0         0    185.0  123.5   77.5  29.89   \n",
       "26                  0             0         0    260.0  110.0   72.5  26.59   \n",
       "42                  0             1         0      NaN  148.0   92.0  25.09   \n",
       "49                  0             1         0    194.0  139.0   93.0  24.33   \n",
       "...               ...           ...       ...      ...    ...    ...    ...   \n",
       "4208                0             0         0    340.0  152.0   76.0  25.74   \n",
       "4229                0             1         0    251.0  140.0   80.0  25.60   \n",
       "4230                0             1         0    268.0  170.0  102.0  22.89   \n",
       "4235                0             0         0    248.0  131.0   72.0  22.00   \n",
       "4236                0             0         0    210.0  126.5   87.0  19.16   \n",
       "\n",
       "      heartRate  glucose  TenYearCHD  \n",
       "14         85.0      NaN           0  \n",
       "21         70.0      NaN           0  \n",
       "26         65.0      NaN           0  \n",
       "42         70.0      NaN           1  \n",
       "49         80.0     62.0           0  \n",
       "...         ...      ...         ...  \n",
       "4208       70.0      NaN           0  \n",
       "4229       75.0      NaN           0  \n",
       "4230       57.0      NaN           0  \n",
       "4235       84.0     86.0           0  \n",
       "4236       86.0      NaN           0  \n",
       "\n",
       "[477 rows x 16 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for Missing values after deletion of education\n",
    "data.isna().sum()\n",
    "null = data[data.isna().any(axis=1)]\n",
    "null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male                 0\n",
       "age                  0\n",
       "education            0\n",
       "currentSmoker        0\n",
       "cigsPerDay          27\n",
       "BPMeds              53\n",
       "prevalentStroke      0\n",
       "prevalentHyp         0\n",
       "diabetes             0\n",
       "totChol             49\n",
       "sysBP                0\n",
       "diaBP                0\n",
       "BMI                 18\n",
       "heartRate            1\n",
       "glucose            380\n",
       "TenYearCHD           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From the results it is clear that \n",
    "cigsPerDay has 27 null values,<br>\n",
    "BPMeds has 53 null values,<br>\n",
    "totChol has 49 null values,<br>\n",
    "BMI has 18 null values,<br>\n",
    "glucose has 380 null values<br>\n",
    "heartRate has 1 null value.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of each variable:\n",
      "  male                 0.427328\n",
      "age                 49.552358\n",
      "education            1.979444\n",
      "currentSmoker        0.494800\n",
      "cigsPerDay           9.032863\n",
      "BPMeds               0.029642\n",
      "prevalentStroke      0.006046\n",
      "prevalentHyp         0.311245\n",
      "diabetes             0.025635\n",
      "totChol            236.637788\n",
      "sysBP              132.369287\n",
      "diaBP               82.876663\n",
      "BMI                 25.777345\n",
      "heartRate           75.922835\n",
      "glucose             81.937949\n",
      "TenYearCHD           0.151874\n",
      "dtype: float64\n",
      "\n",
      " Median of each variable:\n",
      "  male                 0.00\n",
      "age                 49.00\n",
      "education            2.00\n",
      "currentSmoker        0.00\n",
      "cigsPerDay           0.00\n",
      "BPMeds               0.00\n",
      "prevalentStroke      0.00\n",
      "prevalentHyp         0.00\n",
      "diabetes             0.00\n",
      "totChol            234.00\n",
      "sysBP              128.00\n",
      "diaBP               82.00\n",
      "BMI                 25.38\n",
      "heartRate           75.00\n",
      "glucose             78.00\n",
      "TenYearCHD           0.00\n",
      "dtype: float64\n",
      "\n",
      " Mode of each variable:\n",
      "     male   age  education  currentSmoker  cigsPerDay  BPMeds  prevalentStroke  \\\n",
      "0   0.0  40.0        1.0            0.0         0.0     0.0              0.0   \n",
      "1   NaN   NaN        NaN            NaN         NaN     NaN              NaN   \n",
      "2   NaN   NaN        NaN            NaN         NaN     NaN              NaN   \n",
      "\n",
      "   prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  \\\n",
      "0           0.0       0.0    240.0  120.0   80.0  22.54       75.0     75.0   \n",
      "1           NaN       NaN      NaN    NaN    NaN  22.91        NaN      NaN   \n",
      "2           NaN       NaN      NaN    NaN    NaN  23.48        NaN      NaN   \n",
      "\n",
      "   TenYearCHD  \n",
      "0         0.0  \n",
      "1         NaN  \n",
      "2         NaN  \n"
     ]
    }
   ],
   "source": [
    "print(\"Mean of each variable:\\n \",data.mean())\n",
    "print(\"\\n Median of each variable:\\n \",data.median())\n",
    "print(\"\\n Mode of each variable:\\n \",data.mode())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing the Nan Value\n",
    "\n",
    "data['BPMeds'].fillna(data.BPMeds.median(), inplace=True)\n",
    "data['cigsPerDay'].fillna(data.cigsPerDay.median(), inplace=True)\n",
    "data['totChol'].fillna(data.totChol.mean(), inplace=True)\n",
    "data['heartRate'].fillna(data.heartRate.median(), inplace=True)\n",
    "data['BMI'].fillna(data.BMI.mean(), inplace=True)\n",
    "data['glucose'].fillna(data.glucose.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male               0\n",
       "age                0\n",
       "education          0\n",
       "currentSmoker      0\n",
       "cigsPerDay         0\n",
       "BPMeds             0\n",
       "prevalentStroke    0\n",
       "prevalentHyp       0\n",
       "diabetes           0\n",
       "totChol            0\n",
       "sysBP              0\n",
       "diaBP              0\n",
       "BMI                0\n",
       "heartRate          0\n",
       "glucose            0\n",
       "TenYearCHD         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [male, age, education, currentSmoker, cigsPerDay, BPMeds, prevalentStroke, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose, TenYearCHD]\n",
       "Index: []"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null = data[data.isna().any(axis=1)]\n",
    "null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4135, 16)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.get_dummies(data,columns=['education']) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>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",
       "      <th>education_1.0</th>\n",
       "      <th>education_2.0</th>\n",
       "      <th>education_3.0</th>\n",
       "      <th>education_4.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>39</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>46</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>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</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>61</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>46</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   male  age  currentSmoker  cigsPerDay  BPMeds  prevalentStroke  \\\n",
       "0     1   39              0         0.0     0.0                0   \n",
       "1     0   46              0         0.0     0.0                0   \n",
       "2     1   48              1        20.0     0.0                0   \n",
       "3     0   61              1        30.0     0.0                0   \n",
       "4     0   46              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  education_1.0  education_2.0  education_3.0  education_4.0  \n",
       "0           0              0              0              0              1  \n",
       "1           0              0              1              0              0  \n",
       "2           0              1              0              0              0  \n",
       "3           1              0              0              1              0  \n",
       "4           0              0              0              1              0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "male\n",
      "age\n",
      "currentSmoker\n",
      "cigsPerDay\n",
      "BPMeds\n",
      "prevalentStroke\n",
      "prevalentHyp\n",
      "diabetes\n",
      "totChol\n",
      "sysBP\n",
      "diaBP\n",
      "BMI\n",
      "heartRate\n",
      "glucose\n",
      "TenYearCHD\n",
      "education_1.0\n",
      "education_2.0\n",
      "education_3.0\n",
      "education_4.0\n"
     ]
    }
   ],
   "source": [
    "## Checking the newly added columns of the dataset after performing dummy encoding\n",
    "for col in data.columns: \n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4135, 19)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>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",
       "      <th>education_1.0</th>\n",
       "      <th>education_2.0</th>\n",
       "      <th>education_3.0</th>\n",
       "      <th>education_4.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [male, age, currentSmoker, cigsPerDay, BPMeds, prevalentStroke, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose, TenYearCHD, education_1.0, education_2.0, education_3.0, education_4.0]\n",
       "Index: []"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate_data = data[data.duplicated()]\n",
    "duplicate_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation between different fearures')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#heat map\n",
    "correlation = data.corr()\n",
    "plt.figure(figsize=(14,14))\n",
    "g = sns.heatmap(correlation, vmax=1, square=True,cmap='coolwarm',annot= True,xticklabels=True,yticklabels=True, fmt='.1g')\n",
    "g.set_yticklabels(g.get_yticklabels(), rotation =0)\n",
    "g.set_xticklabels(g.get_yticklabels(), rotation =90)\n",
    "plt.title('Correlation between different fearures')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Defining the Independant and Dependant variable\n",
    "\n",
    "y = data['TenYearCHD']\n",
    "x = data.drop(['TenYearCHD'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install imbalanced-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3101, 18) (3101,)\n",
      "(1034, 18) (1034,)\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25,random_state=27)\n",
    "print (x_train.shape, y_train.shape)\n",
    "print (x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5238 5238\n"
     ]
    }
   ],
   "source": [
    "## Upsampling the Training set\n",
    "\n",
    "sm = SMOTE(random_state=23, sampling_strategy='minority')\n",
    "x_train_sm, y_train_sm = sm.fit_resample(x_train, y_train)\n",
    "print(len(x_train_sm), len(y_train_sm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5238, 18)\n",
      "(5238,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train_sm.shape)\n",
    "print(y_train_sm.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2619\n",
       "0    2619\n",
       "Name: TenYearCHD, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print(Counter(y_train_sm))\n",
    "y_train_sm.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1776 1776\n"
     ]
    }
   ],
   "source": [
    "## Upsampling the Testing set\n",
    "sm_test = SMOTE(random_state=23, sampling_strategy='minority')\n",
    "x_test_sm, y_test_sm = sm_test.fit_resample(x_test, y_test)\n",
    "print(len(x_test_sm), len(y_test_sm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1776, 18)\n",
      "(1776,)\n"
     ]
    }
   ],
   "source": [
    "## Printing the shapes of testing set after Upsampling\n",
    "print(x_test_sm.shape)\n",
    "print(y_test_sm.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    888\n",
       "0    888\n",
       "Name: TenYearCHD, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_sm.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import VotingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODELLING TIMES(ms) OF HRFLM ARE:\n",
      "********************************************\n",
      "Training time: 6.89\n",
      "Testing time: 0.29\n",
      "********************************************\n",
      "\n",
      "The accuracy score for HRFLM in percentage is: 81.31\n",
      "The precision score for HRFLM is: 0.93\n",
      "The recall score for HRFLM is as follows: 0.68\n",
      "The F1 Score for HRFLM is: 0.78\n",
      "Confusion_Matrix: \n",
      "\n",
      " [[839  49]\n",
      " [283 605]] \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     class 0       0.75      0.94      0.83       888\n",
      "     class 1       0.93      0.68      0.78       888\n",
      "\n",
      "    accuracy                           0.81      1776\n",
      "   macro avg       0.84      0.81      0.81      1776\n",
      "weighted avg       0.84      0.81      0.81      1776\n",
      "\n",
      "Cross Validation Accuracy: 0.89 \n",
      "Cross Validation Standard Deviation: 9.44 %\n",
      "Cross_Validation time: 81.00\n"
     ]
    }
   ],
   "source": [
    "# Create the sub-model\n",
    "HRFLM_estimators = []\n",
    "\n",
    "# Defining 1 Logistic Regression Model\n",
    "model11 = LogisticRegression(random_state = 0,C=1, max_iter=10000)\n",
    "HRFLM_estimators.append(('logistic1', model11))\n",
    "\n",
    "\n",
    "# Defining 3 Random Forest Models\n",
    "model21 = RandomForestClassifier(random_state = 0)\n",
    "HRFLM_estimators.append(('RF1', model21))\n",
    "\n",
    "model22 = RandomForestClassifier(random_state = 0) \n",
    "HRFLM_estimators.append(('RF2', model22))\n",
    "\n",
    "model23 = RandomForestClassifier(random_state = 0)\n",
    "HRFLM_estimators.append(('RF3', model23))\n",
    "\n",
    "\n",
    "# Defining the HRFLM ensemble model\n",
    "HRFLM_ensemble = VotingClassifier(HRFLM_estimators,voting='soft')\n",
    "results = {}\n",
    "#Training the model \n",
    "start = time()\n",
    "HRFLM_ensemble.fit(x_train_sm, y_train_sm)\n",
    "end = time()\n",
    "results['training_time'] = end - start\n",
    "\n",
    "#Testing the model \n",
    "start = time()\n",
    "HRFLM_Prediction = HRFLM_ensemble.predict(x_test_sm)\n",
    "end = time()\n",
    "results['testing_time'] = end - start\n",
    "\n",
    "print(\"MODELLING TIMES(ms) OF HRFLM ARE:\")\n",
    "print(\"********************************************\")\n",
    "print(\"Training time: \"+\"{:.2f}\".format(results['training_time']))\n",
    "print(\"Testing time: \"+\"{:.2f}\".format(results['testing_time']))\n",
    "print(\"********************************************\\n\")\n",
    "\n",
    "\n",
    "## Accuracy Score\n",
    "HRFLM_Accuracy = accuracy_score(y_test_sm, HRFLM_Prediction)\n",
    "print(\"The accuracy score for HRFLM in percentage is: \"+\"{:.2f}\".format(HRFLM_Accuracy*100))\n",
    "\n",
    "## Precision\n",
    "HRFLM_Precision = precision_score(y_test_sm, HRFLM_Prediction)\n",
    "print(\"The precision score for HRFLM is: \"+\"{:.2f}\".format(HRFLM_Precision))\n",
    "\n",
    "## Recall \n",
    "HRFLM_Recall = recall_score(y_test_sm, HRFLM_Prediction)\n",
    "print(\"The recall score for HRFLM is as follows: \"+\"{:.2f}\".format(HRFLM_Recall))\n",
    "\n",
    "## F1 Score\n",
    "HRFLM_F1Score = f1_score(y_test_sm, HRFLM_Prediction)\n",
    "print(\"The F1 Score for HRFLM is: \"+\"{:.2f}\".format(HRFLM_F1Score))\n",
    "\n",
    "## Confusion Matrix \n",
    "HRFLM_Confusion_Matrix=confusion_matrix(y_test_sm,HRFLM_Prediction)\n",
    "print(\"Confusion_Matrix: \\n\\n\",HRFLM_Confusion_Matrix, \"\\n\" )\n",
    "\n",
    "## Classification Report\n",
    "target_names =['class 0', 'class 1']\n",
    "print(classification_report(y_test_sm,HRFLM_Prediction,zero_division=1,target_names=target_names))\n",
    "\n",
    "\n",
    "## Cross Validation\n",
    "start = time()\n",
    "HRFLM_accuracies = cross_val_score(HRFLM_ensemble, X = x_train_sm, y = y_train_sm, cv = 10)\n",
    "print(\"Cross Validation Accuracy: {:.2f} \".format(HRFLM_accuracies.mean()))\n",
    "print(\"Cross Validation Standard Deviation: {:.2f} %\".format(HRFLM_accuracies.std()*100))\n",
    "end = time()\n",
    "results['Cross_Validation time'] = end - start\n",
    "print(\"Cross_Validation time: \"+\"{:.2f}\".format(results['Cross_Validation time']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "HRFLM_pred_proba = HRFLM_ensemble.predict_proba(x_test_sm)[:,1]\n",
    "fpr_HRFLM, tpr_HRFLM, thresholds_HRFLM = roc_curve(y_test_sm, HRFLM_pred_proba)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(fpr_HRFLM, tpr_HRFLM, marker='.', label='HRFLM')\n",
    "# axis labels\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('HRFLM ROC Curve')\n",
    "# show the legend\n",
    "plt.legend()\n",
    "# show the plot\n",
    "plt.savefig('plot.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8935506452398343"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Calculating AUC score of HRFLM from the roc curve\n",
    "HRFLM_AUC_Score = roc_auc_score(y_test_sm, HRFLM_pred_proba)\n",
    "HRFLM_AUC_Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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