Python Notebooks/Heart_Disease_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('heart.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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
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       "      <td>4</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      "age         303 non-null int64\n",
      "sex         303 non-null int64\n",
      "cp          303 non-null int64\n",
      "trestbps    303 non-null int64\n",
      "chol        303 non-null int64\n",
      "fbs         303 non-null int64\n",
      "restecg     303 non-null int64\n",
      "thalach     303 non-null int64\n",
      "exang       303 non-null int64\n",
      "oldpeak     303 non-null float64\n",
      "slope       303 non-null int64\n",
      "ca          303 non-null int64\n",
      "thal        303 non-null int64\n",
      "target      303 non-null int64\n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x231ef2875c8>"
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     "execution_count": 7,
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     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "corr = data.corr()\n",
    "\n",
    "plt.figure(figsize = (15,15))\n",
    "sns.heatmap(corr, annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>age</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098447</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>0.279351</td>\n",
       "      <td>0.213678</td>\n",
       "      <td>0.121308</td>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.398522</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>-0.168814</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.068001</td>\n",
       "      <td>-0.225439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>sex</td>\n",
       "      <td>-0.098447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.280937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cp</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.433798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trestbps</td>\n",
       "      <td>0.279351</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>-0.144931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>chol</td>\n",
       "      <td>0.213678</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.085239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fbs</td>\n",
       "      <td>0.121308</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.028046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>restecg</td>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>0.137230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>thalach</td>\n",
       "      <td>-0.398522</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.421741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>exang</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>-0.436757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>oldpeak</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>slope</td>\n",
       "      <td>-0.168814</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.345877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ca</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>-0.391724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>thal</td>\n",
       "      <td>0.068001</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>target</td>\n",
       "      <td>-0.225439</td>\n",
       "      <td>-0.280937</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.144931</td>\n",
       "      <td>-0.085239</td>\n",
       "      <td>-0.028046</td>\n",
       "      <td>0.137230</td>\n",
       "      <td>0.421741</td>\n",
       "      <td>-0.436757</td>\n",
       "      <td>-0.430696</td>\n",
       "      <td>0.345877</td>\n",
       "      <td>-0.391724</td>\n",
       "      <td>-0.344029</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex        cp  trestbps      chol       fbs  \\\n",
       "age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
       "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
       "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
       "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
       "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
       "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
       "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
       "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
       "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
       "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "\n",
       "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
       "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
       "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
       "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
       "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
       "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
       "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
       "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
       "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
       "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "\n",
       "              thal    target  \n",
       "age       0.068001 -0.225439  \n",
       "sex       0.210041 -0.280937  \n",
       "cp       -0.161736  0.433798  \n",
       "trestbps  0.062210 -0.144931  \n",
       "chol      0.098803 -0.085239  \n",
       "fbs      -0.032019 -0.028046  \n",
       "restecg  -0.011981  0.137230  \n",
       "thalach  -0.096439  0.421741  \n",
       "exang     0.206754 -0.436757  \n",
       "oldpeak   0.210244 -0.430696  \n",
       "slope    -0.104764  0.345877  \n",
       "ca        0.151832 -0.391724  \n",
       "thal      1.000000 -0.344029  \n",
       "target   -0.344029  1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x231f0c99d08>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x = 'target', data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset = pd.get_dummies(data, columns = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = data.copy()\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.drop(['target'], axis = 1)\n",
    "y = dataset['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
       "       'exang', 'oldpeak', 'slope', 'ca', 'thal'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=20)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model = RandomForestClassifier(n_estimators=20)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 1, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = model.predict(X_test)\n",
    "pred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[24,  5],\n",
       "       [ 6, 26]], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of model is 81.97%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Accuracy of model is {round(accuracy_score(y_test, pred)*100, 2)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = RandomForestClassifier(n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import randint\n",
    "param_dist={'max_depth':[3,5,10,None],\n",
    "              'n_estimators':[10,100,200,300,400,500],\n",
    "              'max_features':randint(1,31),\n",
    "               'criterion':['gini','entropy'],\n",
    "               'bootstrap':[True,False],\n",
    "               'min_samples_leaf':randint(1,31),\n",
    "              }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "search_clfr = RandomizedSearchCV(classifier, param_distributions = param_dist, n_jobs=-1, n_iter = 40, cv = 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=9, estimator=RandomForestClassifier(n_jobs=-1), n_iter=40,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'bootstrap': [True, False],\n",
       "                                        'criterion': ['gini', 'entropy'],\n",
       "                                        'max_depth': [3, 5, 10, None],\n",
       "                                        'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000231F11C7448>,\n",
       "                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000231F11C75C8>,\n",
       "                                        'n_estimators': [10, 100, 200, 300, 400,\n",
       "                                                         500]})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_clfr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bootstrap': True, 'criterion': 'gini', 'max_depth': 10, 'max_features': 3, 'min_samples_leaf': 1, 'n_estimators': 100}\n",
      "0.8308008863564419\n"
     ]
    }
   ],
   "source": [
    "params = search_clfr.best_params_\n",
    "score = search_clfr.best_score_\n",
    "print(params)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "claasifier=RandomForestClassifier(n_jobs=-1, n_estimators=400,bootstrap= False,criterion='gini',max_depth=5,max_features=3,min_samples_leaf= 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_jobs=-1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[25,  4],\n",
       "       [ 5, 27]], dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, classifier.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 85.25%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Accuracy is {round(accuracy_score(y_test, classifier.predict(X_test))*100,2)}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(classifier, open('heart.pkl', 'wb'))"
   ]
  }
 ],
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