heartDiseaseDetection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heart Disease Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the libraries"
   ]
  },
  {
   "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",
    "from matplotlib import rcParams\n",
    "from matplotlib.cm import rainbow \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('heart.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of the data\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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",
       "      <th>0</th>\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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       "    <tr>\n",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# five elements from the top\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\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",
       "      <th>count</th>\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",
       "      <th>mean</th>\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",
       "      <th>std</th>\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",
       "      <th>min</th>\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",
       "      <th>25%</th>\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",
       "      <th>50%</th>\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",
       "      <th>75%</th>\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",
       "      <th>max</th>\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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# description of the dataset\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "0        0   0     1       1  \n",
       "1        0   0     2       1  \n",
       "2        2   0     2       1  \n",
       "3        2   0     2       1  \n",
       "4        2   0     2       1  \n",
       "..     ...  ..   ...     ...  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  \n",
       "\n",
       "[303 rows x 14 columns]>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# info about the data\n",
    "data.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data['target'])\n",
    "plt.title(\"COUNT vs RESULT\")\n",
    "plt.xlabel(\"RESULT\")\n",
    "plt.ylabel(\"COUNT\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The dataset is balanced as the distribution of the target in respective class is somewhat equal.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor=data.corr()\n",
    "top_corr_feature=cor.index\n",
    "plt.figure(figsize=(20,20))\n",
    "sns.heatmap(data[top_corr_feature].corr(),annot=True,cmap=\"RdYlGn\")\n",
    "plt.title(\"HEATMAP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.get_dummies(data,columns=['sex','cp','fbs','restecg','exang','slope','ca','thal'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>thalach</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>target</th>\n",
       "      <th>sex_0</th>\n",
       "      <th>sex_1</th>\n",
       "      <th>cp_0</th>\n",
       "      <th>cp_1</th>\n",
       "      <th>...</th>\n",
       "      <th>slope_2</th>\n",
       "      <th>ca_0</th>\n",
       "      <th>ca_1</th>\n",
       "      <th>ca_2</th>\n",
       "      <th>ca_3</th>\n",
       "      <th>ca_4</th>\n",
       "      <th>thal_0</th>\n",
       "      <th>thal_1</th>\n",
       "      <th>thal_2</th>\n",
       "      <th>thal_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>150</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>1</th>\n",
       "      <td>37</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>187</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>172</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>178</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>163</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  trestbps  chol  thalach  oldpeak  target  sex_0  sex_1  cp_0  cp_1  \\\n",
       "0   63       145   233      150      2.3       1      0      1     0     0   \n",
       "1   37       130   250      187      3.5       1      0      1     0     0   \n",
       "2   41       130   204      172      1.4       1      1      0     0     1   \n",
       "3   56       120   236      178      0.8       1      0      1     0     1   \n",
       "4   57       120   354      163      0.6       1      1      0     1     0   \n",
       "\n",
       "   ...  slope_2  ca_0  ca_1  ca_2  ca_3  ca_4  thal_0  thal_1  thal_2  thal_3  \n",
       "0  ...        0     1     0     0     0     0       0       1       0       0  \n",
       "1  ...        0     1     0     0     0     0       0       0       1       0  \n",
       "2  ...        1     1     0     0     0     0       0       0       1       0  \n",
       "3  ...        1     1     0     0     0     0       0       0       1       0  \n",
       "4  ...        1     1     0     0     0     0       0       0       1       0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "to_scale=['age','trestbps','chol','thalach','oldpeak']\n",
    "data[to_scale]=sc.fit_transform(data[to_scale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>thalach</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>target</th>\n",
       "      <th>sex_0</th>\n",
       "      <th>sex_1</th>\n",
       "      <th>cp_0</th>\n",
       "      <th>cp_1</th>\n",
       "      <th>...</th>\n",
       "      <th>slope_2</th>\n",
       "      <th>ca_0</th>\n",
       "      <th>ca_1</th>\n",
       "      <th>ca_2</th>\n",
       "      <th>ca_3</th>\n",
       "      <th>ca_4</th>\n",
       "      <th>thal_0</th>\n",
       "      <th>thal_1</th>\n",
       "      <th>thal_2</th>\n",
       "      <th>thal_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.952197</td>\n",
       "      <td>0.763956</td>\n",
       "      <td>-0.256334</td>\n",
       "      <td>0.015443</td>\n",
       "      <td>1.087338</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>1</th>\n",
       "      <td>-1.915313</td>\n",
       "      <td>-0.092738</td>\n",
       "      <td>0.072199</td>\n",
       "      <td>1.633471</td>\n",
       "      <td>2.122573</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.474158</td>\n",
       "      <td>-0.092738</td>\n",
       "      <td>-0.816773</td>\n",
       "      <td>0.977514</td>\n",
       "      <td>0.310912</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.180175</td>\n",
       "      <td>-0.663867</td>\n",
       "      <td>-0.198357</td>\n",
       "      <td>1.239897</td>\n",
       "      <td>-0.206705</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.290464</td>\n",
       "      <td>-0.663867</td>\n",
       "      <td>2.082050</td>\n",
       "      <td>0.583939</td>\n",
       "      <td>-0.379244</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  trestbps      chol   thalach   oldpeak  target  sex_0  sex_1  \\\n",
       "0  0.952197  0.763956 -0.256334  0.015443  1.087338       1      0      1   \n",
       "1 -1.915313 -0.092738  0.072199  1.633471  2.122573       1      0      1   \n",
       "2 -1.474158 -0.092738 -0.816773  0.977514  0.310912       1      1      0   \n",
       "3  0.180175 -0.663867 -0.198357  1.239897 -0.206705       1      0      1   \n",
       "4  0.290464 -0.663867  2.082050  0.583939 -0.379244       1      1      0   \n",
       "\n",
       "   cp_0  cp_1  ...  slope_2  ca_0  ca_1  ca_2  ca_3  ca_4  thal_0  thal_1  \\\n",
       "0     0     0  ...        0     1     0     0     0     0       0       1   \n",
       "1     0     0  ...        0     1     0     0     0     0       0       0   \n",
       "2     0     1  ...        1     1     0     0     0     0       0       0   \n",
       "3     0     1  ...        1     1     0     0     0     0       0       0   \n",
       "4     1     0  ...        1     1     0     0     0     0       0       0   \n",
       "\n",
       "   thal_2  thal_3  \n",
       "0       0       0  \n",
       "1       1       0  \n",
       "2       1       0  \n",
       "3       1       0  \n",
       "4       1       0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=data['target']\n",
    "X=data.drop(['target'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split as tts\n",
    "xtrain,xtest,ytrain,ytest=tts(X,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "              beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "              hidden_layer_sizes=(128, 128), learning_rate='constant',\n",
       "              learning_rate_init=0.001, max_fun=15000, max_iter=300,\n",
       "              momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n",
       "              power_t=0.5, random_state=None, shuffle=True, solver='adam',\n",
       "              tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "              warm_start=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "model=MLPClassifier(hidden_layer_sizes=(128,128),max_iter=300)\n",
    "model.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred=model.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85.24590163934425\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "acc=accuracy_score(ytest,ypred)\n",
    "print(acc*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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