Heart_disease_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('./Heart.csv')"
   ]
  },
  {
   "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>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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking for balanced dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f169e64dd50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('target',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performing Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation=df.corr()\n",
    "correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target      1.000000\n",
      "cp          0.433798\n",
      "thalach     0.421741\n",
      "slope       0.345877\n",
      "restecg     0.137230\n",
      "fbs        -0.028046\n",
      "chol       -0.085239\n",
      "trestbps   -0.144931\n",
      "age        -0.225439\n",
      "sex        -0.280937\n",
      "thal       -0.344029\n",
      "ca         -0.391724\n",
      "oldpeak    -0.430696\n",
      "exang      -0.436757\n",
      "Name: target, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(correlation['target'].sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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",
       "    </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",
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       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>298</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>299</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>301</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>302</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     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  \n",
       "0        0   0     1  \n",
       "1        0   0     2  \n",
       "2        2   0     2  \n",
       "3        2   0     2  \n",
       "4        2   0     2  \n",
       "..     ...  ..   ...  \n",
       "298      1   0     3  \n",
       "299      1   0     3  \n",
       "300      1   2     3  \n",
       "301      1   1     3  \n",
       "302      1   1     2  \n",
       "\n",
       "[303 rows x 13 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.drop('target',axis=1)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "298    0\n",
       "299    0\n",
       "300    0\n",
       "301    0\n",
       "302    0\n",
       "Name: target, Length: 303, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df['target']\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Selection For categorical Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "bestfeatures = SelectKBest(score_func=chi2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit = bestfeatures.fit(x,y)\n",
    "scores = pd.DataFrame(fit.scores_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfcolumns = pd.DataFrame(x.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "featureScores = pd.concat([dfcolumns,scores],axis=1)\n",
    "featureScores.columns = ['Label','Score'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Label</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>thalach</td>\n",
       "      <td>188.320472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>oldpeak</td>\n",
       "      <td>72.644253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>ca</td>\n",
       "      <td>66.440765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>cp</td>\n",
       "      <td>62.598098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>exang</td>\n",
       "      <td>38.914377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>chol</td>\n",
       "      <td>23.936394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>age</td>\n",
       "      <td>23.286624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>trestbps</td>\n",
       "      <td>14.823925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>slope</td>\n",
       "      <td>9.804095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>sex</td>\n",
       "      <td>7.576835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>thal</td>\n",
       "      <td>5.791853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>restecg</td>\n",
       "      <td>2.978271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>fbs</td>\n",
       "      <td>0.202934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Label       Score\n",
       "7    thalach  188.320472\n",
       "9    oldpeak   72.644253\n",
       "11        ca   66.440765\n",
       "2         cp   62.598098\n",
       "8      exang   38.914377\n",
       "4       chol   23.936394\n",
       "0        age   23.286624\n",
       "3   trestbps   14.823925\n",
       "10     slope    9.804095\n",
       "1        sex    7.576835\n",
       "12      thal    5.791853\n",
       "6    restecg    2.978271\n",
       "5        fbs    0.202934"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featureScores.sort_values(by='Score',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr=ExtraTreesClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n",
       "                     criterion='gini', max_depth=None, max_features='auto',\n",
       "                     max_leaf_nodes=None, max_samples=None,\n",
       "                     min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                     min_samples_leaf=1, min_samples_split=2,\n",
       "                     min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                     n_jobs=None, oob_score=False, random_state=None, verbose=0,\n",
       "                     warm_start=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.07089985 0.05318978 0.1198887  0.06274476 0.05770704 0.01955415\n",
      " 0.03749901 0.08317518 0.10383701 0.08459674 0.06886495 0.13317157\n",
      " 0.10487125]\n"
     ]
    }
   ],
   "source": [
    "print(corr.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ca          0.133172\n",
       "cp          0.119889\n",
       "thal        0.104871\n",
       "exang       0.103837\n",
       "oldpeak     0.084597\n",
       "thalach     0.083175\n",
       "age         0.070900\n",
       "slope       0.068865\n",
       "trestbps    0.062745\n",
       "chol        0.057707\n",
       "sex         0.053190\n",
       "restecg     0.037499\n",
       "fbs         0.019554\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance=pd.Series(corr.feature_importances_,index=x.columns)\n",
    "feature_importance.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f169e210ed0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance.sort_values(ascending=True).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Droping the features which are not correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['fbs','restecg'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>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>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>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>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>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>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  thalach  exang  oldpeak  slope  ca  thal  \\\n",
       "0   63    1   3       145   233      150      0      2.3      0   0     1   \n",
       "1   37    1   2       130   250      187      0      3.5      0   0     2   \n",
       "2   41    0   1       130   204      172      0      1.4      2   0     2   \n",
       "3   56    1   1       120   236      178      0      0.8      2   0     2   \n",
       "4   57    0   0       120   354      163      1      0.6      2   0     2   \n",
       "\n",
       "   target  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 12)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age [63 37 41 56 57 44 52 54 48 49 64 58 50 66 43 69 59 42 61 40 71 51 65 53\n",
      " 46 45 39 47 62 34 35 29 55 60 67 68 74 76 70 38 77]\n",
      "\n",
      "\n",
      "sex [1 0]\n",
      "\n",
      "\n",
      "cp [3 2 1 0]\n",
      "\n",
      "\n",
      "trestbps [145 130 120 140 172 150 110 135 160 105 125 142 155 104 138 128 108 134\n",
      " 122 115 118 100 124  94 112 102 152 101 132 148 178 129 180 136 126 106\n",
      " 156 170 146 117 200 165 174 192 144 123 154 114 164]\n",
      "\n",
      "\n",
      "chol [233 250 204 236 354 192 294 263 199 168 239 275 266 211 283 219 340 226\n",
      " 247 234 243 302 212 175 417 197 198 177 273 213 304 232 269 360 308 245\n",
      " 208 264 321 325 235 257 216 256 231 141 252 201 222 260 182 303 265 309\n",
      " 186 203 183 220 209 258 227 261 221 205 240 318 298 564 277 214 248 255\n",
      " 207 223 288 160 394 315 246 244 270 195 196 254 126 313 262 215 193 271\n",
      " 268 267 210 295 306 178 242 180 228 149 278 253 342 157 286 229 284 224\n",
      " 206 167 230 335 276 353 225 330 290 172 305 188 282 185 326 274 164 307\n",
      " 249 341 407 217 174 281 289 322 299 300 293 184 409 259 200 327 237 218\n",
      " 319 166 311 169 187 176 241 131]\n",
      "\n",
      "\n",
      "thalach [150 187 172 178 163 148 153 173 162 174 160 139 171 144 158 114 151 161\n",
      " 179 137 157 123 152 168 140 188 125 170 165 142 180 143 182 156 115 149\n",
      " 146 175 186 185 159 130 190 132 147 154 202 166 164 184 122 169 138 111\n",
      " 145 194 131 133 155 167 192 121  96 126 105 181 116 108 129 120 112 128\n",
      " 109 113  99 177 141 136  97 127 103 124  88 195 106  95 117  71 118 134\n",
      "  90]\n",
      "\n",
      "\n",
      "exang [0 1]\n",
      "\n",
      "\n",
      "oldpeak [2.3 3.5 1.4 0.8 0.6 0.4 1.3 0.  0.5 1.6 1.2 0.2 1.8 1.  2.6 1.5 3.  2.4\n",
      " 0.1 1.9 4.2 1.1 2.  0.7 0.3 0.9 3.6 3.1 3.2 2.5 2.2 2.8 3.4 6.2 4.  5.6\n",
      " 2.9 2.1 3.8 4.4]\n",
      "\n",
      "\n",
      "slope [0 2 1]\n",
      "\n",
      "\n",
      "ca [0 2 1 3 4]\n",
      "\n",
      "\n",
      "thal [1 2 3 0]\n",
      "\n",
      "\n",
      "target [1 0]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in df.columns:\n",
    "    print(i,df[i].unique())\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### sex,cp,restecg,exang,slope,ca,thal,target are categorical column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex_0</th>\n",
       "      <th>sex_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>298</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>299</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>301</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>302</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sex_0  sex_1\n",
       "0        0      1\n",
       "1        0      1\n",
       "2        1      0\n",
       "3        0      1\n",
       "4        1      0\n",
       "..     ...    ...\n",
       "298      1      0\n",
       "299      0      1\n",
       "300      0      1\n",
       "301      0      1\n",
       "302      1      0\n",
       "\n",
       "[303 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_sex=pd.get_dummies(data=df['sex'],prefix='sex')\n",
    "new_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chestPain_0</th>\n",
       "      <th>chestPain_1</th>\n",
       "      <th>chestPain_2</th>\n",
       "      <th>chestPain_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>298</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>299</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>301</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>302</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     chestPain_0  chestPain_1  chestPain_2  chestPain_3\n",
       "0              0            0            0            1\n",
       "1              0            0            1            0\n",
       "2              0            1            0            0\n",
       "3              0            1            0            0\n",
       "4              1            0            0            0\n",
       "..           ...          ...          ...          ...\n",
       "298            1            0            0            0\n",
       "299            0            0            0            1\n",
       "300            1            0            0            0\n",
       "301            1            0            0            0\n",
       "302            0            1            0            0\n",
       "\n",
       "[303 rows x 4 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_cp=pd.get_dummies(df['cp'],prefix='chestPain')\n",
    "new_cp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>exang_0</th>\n",
       "      <th>exang_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>3</td>\n",
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       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>299</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>301</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>302</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     exang_0  exang_1\n",
       "0          1        0\n",
       "1          1        0\n",
       "2          1        0\n",
       "3          1        0\n",
       "4          0        1\n",
       "..       ...      ...\n",
       "298        0        1\n",
       "299        1        0\n",
       "300        1        0\n",
       "301        0        1\n",
       "302        1        0\n",
       "\n",
       "[303 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_exang=pd.get_dummies(df['exang'],prefix='exang')\n",
    "new_exang"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>slope_0</th>\n",
       "      <th>slope_1</th>\n",
       "      <th>slope_2</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>299</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>301</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>302</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     slope_0  slope_1  slope_2\n",
       "0          1        0        0\n",
       "1          1        0        0\n",
       "2          0        0        1\n",
       "3          0        0        1\n",
       "4          0        0        1\n",
       "..       ...      ...      ...\n",
       "298        0        1        0\n",
       "299        0        1        0\n",
       "300        0        1        0\n",
       "301        0        1        0\n",
       "302        0        1        0\n",
       "\n",
       "[303 rows x 3 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_slope=pd.get_dummies(df['slope'],prefix='slope')\n",
    "new_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>299</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>301</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>302</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>303 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     thal_0  thal_1  thal_2  thal_3\n",
       "0         0       1       0       0\n",
       "1         0       0       1       0\n",
       "2         0       0       1       0\n",
       "3         0       0       1       0\n",
       "4         0       0       1       0\n",
       "..      ...     ...     ...     ...\n",
       "298       0       0       0       1\n",
       "299       0       0       0       1\n",
       "300       0       0       0       1\n",
       "301       0       0       0       1\n",
       "302       0       0       1       0\n",
       "\n",
       "[303 rows x 4 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_thal=pd.get_dummies(df['thal'],prefix='thal')\n",
    "new_thal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>ca_3</th>\n",
       "      <th>ca_4</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>301</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ca_0  ca_1  ca_2  ca_3  ca_4\n",
       "0       1     0     0     0     0\n",
       "1       1     0     0     0     0\n",
       "2       1     0     0     0     0\n",
       "3       1     0     0     0     0\n",
       "4       1     0     0     0     0\n",
       "..    ...   ...   ...   ...   ...\n",
       "298     1     0     0     0     0\n",
       "299     1     0     0     0     0\n",
       "300     0     0     1     0     0\n",
       "301     0     1     0     0     0\n",
       "302     0     1     0     0     0\n",
       "\n",
       "[303 rows x 5 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_ca=pd.get_dummies(df['ca'],prefix='ca')\n",
    "new_ca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "app=[df,new_sex,new_cp,new_ca,new_thal,new_exang,new_slope]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.concat(app,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <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",
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       "      <td>4</td>\n",
       "      <td>57</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  thalach  exang  oldpeak  slope  ca  ...  \\\n",
       "0   63    1   3       145   233      150      0      2.3      0   0  ...   \n",
       "1   37    1   2       130   250      187      0      3.5      0   0  ...   \n",
       "2   41    0   1       130   204      172      0      1.4      2   0  ...   \n",
       "3   56    1   1       120   236      178      0      0.8      2   0  ...   \n",
       "4   57    0   0       120   354      163      1      0.6      2   0  ...   \n",
       "\n",
       "   ca_4  thal_0  thal_1  thal_2  thal_3  exang_0  exang_1  slope_0  slope_1  \\\n",
       "0     0       0       1       0       0        1        0        1        0   \n",
       "1     0       0       0       1       0        1        0        1        0   \n",
       "2     0       0       0       1       0        1        0        0        0   \n",
       "3     0       0       0       1       0        1        0        0        0   \n",
       "4     0       0       0       1       0        0        1        0        0   \n",
       "\n",
       "   slope_2  \n",
       "0        0  \n",
       "1        0  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'thalach', 'exang', 'oldpeak',\n",
       "       'slope', 'ca', 'thal', 'target', 'sex_0', 'sex_1', 'chestPain_0',\n",
       "       'chestPain_1', 'chestPain_2', 'chestPain_3', 'ca_0', 'ca_1', 'ca_2',\n",
       "       'ca_3', 'ca_4', 'thal_0', 'thal_1', 'thal_2', 'thal_3', 'exang_0',\n",
       "       'exang_1', 'slope_0', 'slope_1', 'slope_2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.drop(['sex','cp','thal','exang','ca','slope'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  trestbps  chol  thalach  oldpeak  target  sex_0  sex_1  chestPain_0  \\\n",
       "0   63       145   233      150      2.3       1      0      1            0   \n",
       "1   37       130   250      187      3.5       1      0      1            0   \n",
       "2   41       130   204      172      1.4       1      1      0            0   \n",
       "3   56       120   236      178      0.8       1      0      1            0   \n",
       "4   57       120   354      163      0.6       1      1      0            1   \n",
       "\n",
       "   chestPain_1  ...  ca_4  thal_0  thal_1  thal_2  thal_3  exang_0  exang_1  \\\n",
       "0            0  ...     0       0       1       0       0        1        0   \n",
       "1            0  ...     0       0       0       1       0        1        0   \n",
       "2            1  ...     0       0       0       1       0        1        0   \n",
       "3            1  ...     0       0       0       1       0        1        0   \n",
       "4            0  ...     0       0       0       1       0        0        1   \n",
       "\n",
       "   slope_0  slope_1  slope_2  \n",
       "0        1        0        0  \n",
       "1        1        0        0  \n",
       "2        0        0        1  \n",
       "3        0        0        1  \n",
       "4        0        0        1  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 26)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1[['age','trestbps','chol','oldpeak','thalach']]=sc.fit_transform(df1[['age','trestbps','chol','oldpeak','thalach']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .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>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>chestPain_0</th>\n",
       "      <th>chestPain_1</th>\n",
       "      <th>...</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",
       "      <th>exang_0</th>\n",
       "      <th>exang_1</th>\n",
       "      <th>slope_0</th>\n",
       "      <th>slope_1</th>\n",
       "      <th>slope_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.952197</td>\n",
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       "      <td>-0.256334</td>\n",
       "      <td>0.015443</td>\n",
       "      <td>1.087338</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\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",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.290464</td>\n",
       "      <td>0.478391</td>\n",
       "      <td>-1.048678</td>\n",
       "      <td>-0.072018</td>\n",
       "      <td>-0.551783</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>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",
       "      <td>6</td>\n",
       "      <td>0.180175</td>\n",
       "      <td>0.478391</td>\n",
       "      <td>0.922521</td>\n",
       "      <td>0.146634</td>\n",
       "      <td>0.224643</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",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>-1.143291</td>\n",
       "      <td>-0.663867</td>\n",
       "      <td>0.323431</td>\n",
       "      <td>1.021244</td>\n",
       "      <td>-0.896862</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>-0.260980</td>\n",
       "      <td>2.306004</td>\n",
       "      <td>-0.913400</td>\n",
       "      <td>0.540209</td>\n",
       "      <td>-0.465514</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.290464</td>\n",
       "      <td>1.049520</td>\n",
       "      <td>-1.512490</td>\n",
       "      <td>1.064975</td>\n",
       "      <td>0.483451</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 26 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",
       "5  0.290464  0.478391 -1.048678 -0.072018 -0.551783       1      0      1   \n",
       "6  0.180175  0.478391  0.922521  0.146634  0.224643       1      1      0   \n",
       "7 -1.143291 -0.663867  0.323431  1.021244 -0.896862       1      0      1   \n",
       "8 -0.260980  2.306004 -0.913400  0.540209 -0.465514       1      0      1   \n",
       "9  0.290464  1.049520 -1.512490  1.064975  0.483451       1      0      1   \n",
       "\n",
       "   chestPain_0  chestPain_1  ...  ca_4  thal_0  thal_1  thal_2  thal_3  \\\n",
       "0            0            0  ...     0       0       1       0       0   \n",
       "1            0            0  ...     0       0       0       1       0   \n",
       "2            0            1  ...     0       0       0       1       0   \n",
       "3            0            1  ...     0       0       0       1       0   \n",
       "4            1            0  ...     0       0       0       1       0   \n",
       "5            1            0  ...     0       0       1       0       0   \n",
       "6            0            1  ...     0       0       0       1       0   \n",
       "7            0            1  ...     0       0       0       0       1   \n",
       "8            0            0  ...     0       0       0       0       1   \n",
       "9            0            0  ...     0       0       0       1       0   \n",
       "\n",
       "   exang_0  exang_1  slope_0  slope_1  slope_2  \n",
       "0        1        0        1        0        0  \n",
       "1        1        0        1        0        0  \n",
       "2        1        0        0        0        1  \n",
       "3        1        0        0        0        1  \n",
       "4        0        1        0        0        1  \n",
       "5        1        0        0        1        0  \n",
       "6        1        0        0        1        0  \n",
       "7        1        0        0        0        1  \n",
       "8        1        0        0        0        1  \n",
       "9        1        0        0        0        1  \n",
       "\n",
       "[10 rows x 26 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'trestbps', 'chol', 'thalach', 'oldpeak', 'target', 'sex_0',\n",
       "       'sex_1', 'chestPain_0', 'chestPain_1', 'chestPain_2', 'chestPain_3',\n",
       "       'ca_0', 'ca_1', 'ca_2', 'ca_3', 'ca_4', 'thal_0', 'thal_1', 'thal_2',\n",
       "       'thal_3', 'exang_0', 'exang_1', 'slope_0', 'slope_1', 'slope_2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>thalach</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>sex_0</th>\n",
       "      <th>sex_1</th>\n",
       "      <th>chestPain_0</th>\n",
       "      <th>chestPain_1</th>\n",
       "      <th>chestPain_2</th>\n",
       "      <th>...</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",
       "      <th>exang_0</th>\n",
       "      <th>exang_1</th>\n",
       "      <th>slope_0</th>\n",
       "      <th>slope_1</th>\n",
       "      <th>slope_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>298</td>\n",
       "      <td>0.290464</td>\n",
       "      <td>0.478391</td>\n",
       "      <td>-0.101730</td>\n",
       "      <td>-1.165281</td>\n",
       "      <td>-0.724323</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>299</td>\n",
       "      <td>-1.033002</td>\n",
       "      <td>-1.234996</td>\n",
       "      <td>0.342756</td>\n",
       "      <td>-0.771706</td>\n",
       "      <td>0.138373</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>300</td>\n",
       "      <td>1.503641</td>\n",
       "      <td>0.706843</td>\n",
       "      <td>-1.029353</td>\n",
       "      <td>-0.378132</td>\n",
       "      <td>2.036303</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>301</td>\n",
       "      <td>0.290464</td>\n",
       "      <td>-0.092738</td>\n",
       "      <td>-2.227533</td>\n",
       "      <td>-1.515125</td>\n",
       "      <td>0.138373</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>302</td>\n",
       "      <td>0.290464</td>\n",
       "      <td>-0.092738</td>\n",
       "      <td>-0.198357</td>\n",
       "      <td>1.064975</td>\n",
       "      <td>-0.896862</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          age  trestbps      chol   thalach   oldpeak  sex_0  sex_1  \\\n",
       "0    0.952197  0.763956 -0.256334  0.015443  1.087338      0      1   \n",
       "1   -1.915313 -0.092738  0.072199  1.633471  2.122573      0      1   \n",
       "2   -1.474158 -0.092738 -0.816773  0.977514  0.310912      1      0   \n",
       "3    0.180175 -0.663867 -0.198357  1.239897 -0.206705      0      1   \n",
       "4    0.290464 -0.663867  2.082050  0.583939 -0.379244      1      0   \n",
       "..        ...       ...       ...       ...       ...    ...    ...   \n",
       "298  0.290464  0.478391 -0.101730 -1.165281 -0.724323      1      0   \n",
       "299 -1.033002 -1.234996  0.342756 -0.771706  0.138373      0      1   \n",
       "300  1.503641  0.706843 -1.029353 -0.378132  2.036303      0      1   \n",
       "301  0.290464 -0.092738 -2.227533 -1.515125  0.138373      0      1   \n",
       "302  0.290464 -0.092738 -0.198357  1.064975 -0.896862      1      0   \n",
       "\n",
       "     chestPain_0  chestPain_1  chestPain_2  ...  ca_4  thal_0  thal_1  thal_2  \\\n",
       "0              0            0            0  ...     0       0       1       0   \n",
       "1              0            0            1  ...     0       0       0       1   \n",
       "2              0            1            0  ...     0       0       0       1   \n",
       "3              0            1            0  ...     0       0       0       1   \n",
       "4              1            0            0  ...     0       0       0       1   \n",
       "..           ...          ...          ...  ...   ...     ...     ...     ...   \n",
       "298            1            0            0  ...     0       0       0       0   \n",
       "299            0            0            0  ...     0       0       0       0   \n",
       "300            1            0            0  ...     0       0       0       0   \n",
       "301            1            0            0  ...     0       0       0       0   \n",
       "302            0            1            0  ...     0       0       0       1   \n",
       "\n",
       "     thal_3  exang_0  exang_1  slope_0  slope_1  slope_2  \n",
       "0         0        1        0        1        0        0  \n",
       "1         0        1        0        1        0        0  \n",
       "2         0        1        0        0        0        1  \n",
       "3         0        1        0        0        0        1  \n",
       "4         0        0        1        0        0        1  \n",
       "..      ...      ...      ...      ...      ...      ...  \n",
       "298       1        0        1        0        1        0  \n",
       "299       1        1        0        0        1        0  \n",
       "300       1        1        0        0        1        0  \n",
       "301       1        0        1        0        1        0  \n",
       "302       0        1        0        0        1        0  \n",
       "\n",
       "[303 rows x 25 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df1.drop('target',axis=1)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "298    0\n",
       "299    0\n",
       "300    0\n",
       "301    0\n",
       "302    0\n",
       "Name: target, Length: 303, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df1['target']\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying ML Alogorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv=ShuffleSplit(n_splits=10,test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LogisticRegression(max_iter=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.87912088 0.83516484 0.87912088 0.87912088 0.85714286 0.86813187\n",
      " 0.89010989 0.83516484 0.86813187 0.78021978]\n"
     ]
    }
   ],
   "source": [
    "print(cross_val_score(lr,x,y,cv=cv,scoring='accuracy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8571428571428573\n"
     ]
    }
   ],
   "source": [
    "print(cross_val_score(lr,x,y,cv=cv,scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc=SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.87912088 0.83516484 0.84615385 0.84615385 0.83516484 0.83516484\n",
      " 0.84615385 0.82417582 0.87912088 0.78021978]\n"
     ]
    }
   ],
   "source": [
    "print(cross_val_score(svc,x,y,cv=cv,scoring='accuracy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8406593406593407\n"
     ]
    }
   ],
   "source": [
    "print(cross_val_score(svc,x,y,cv=cv,scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.73626374 0.78021978 0.78021978 0.76923077 0.72527473 0.79120879\n",
      " 0.79120879 0.69230769 0.76923077 0.69230769]\n"
     ]
    }
   ],
   "source": [
    "print(cross_val_score(dt,x,y,cv=cv,scoring='accuracy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7604395604395604\n"
     ]
    }
   ],
   "source": [
    "print(cross_val_score(dt,x,y,cv=cv,scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf=RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.82417582 0.82417582 0.85714286 0.82417582 0.82417582 0.82417582\n",
      " 0.85714286 0.8021978  0.81318681 0.82417582]\n"
     ]
    }
   ],
   "source": [
    "print(cross_val_score(rf,x,y,cv=cv,scoring='accuracy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8285714285714285\n"
     ]
    }
   ],
   "source": [
    "print(cross_val_score(rf,x,y,cv=cv,scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb=XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.81318681 0.81318681 0.84615385 0.81318681 0.78021978 0.84615385\n",
      " 0.8021978  0.8021978  0.8021978  0.76923077]\n"
     ]
    }
   ],
   "source": [
    "print(cross_val_score(xgb,x,y,cv=cv,scoring='accuracy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8285714285714285\n"
     ]
    }
   ],
   "source": [
    "print(cross_val_score(rf,x,y,cv=cv,scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression model gives the max accuracy i.e. 85%\n",
    "### Min acc=83%\n",
    "### Max acc=87%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparametr tunning for better accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For Logistic Regression\n",
    "params={'C':[0.1,0.01,0.001,1.0,10.0,100.0,1000.0]}\n",
    "grid_search=GridSearchCV(lr,param_grid=params,cv=cv,scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=ShuffleSplit(n_splits=10, random_state=42, test_size=0.3, train_size=None),\n",
       "             error_score=nan,\n",
       "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
       "                                          fit_intercept=True,\n",
       "                                          intercept_scaling=1, l1_ratio=None,\n",
       "                                          max_iter=5000, multi_class='auto',\n",
       "                                          n_jobs=None, penalty='l2',\n",
       "                                          random_state=None, solver='lbfgs',\n",
       "                                          tol=0.0001, verbose=0,\n",
       "                                          warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'C': [0.1, 0.01, 0.001, 1.0, 10.0, 100.0, 1000.0]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8571428571428573\n",
      "{'C': 1.0}\n"
     ]
    }
   ],
   "source": [
    "print(grid_search.best_score_)\n",
    "print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For SVC Classifier\n",
    "params={'kernel':['linear','rbf','poly'],'C':[1,10,100,1000],'gamma':[0.1,0.01,0.001,0.0001,1]}\n",
    "grid_search=GridSearchCV(svc,param_grid=params,cv=cv,scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=ShuffleSplit(n_splits=10, random_state=42, test_size=0.3, train_size=None),\n",
       "             error_score=nan,\n",
       "             estimator=SVC(C=1.0, break_ties=False, cache_size=200,\n",
       "                           class_weight=None, coef0=0.0,\n",
       "                           decision_function_shape='ovr', degree=3,\n",
       "                           gamma='scale', kernel='rbf', max_iter=-1,\n",
       "                           probability=False, random_state=None, shrinking=True,\n",
       "                           tol=0.001, verbose=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'C': [1, 10, 100, 1000],\n",
       "                         'gamma': [0.1, 0.01, 0.001, 0.0001, 1],\n",
       "                         'kernel': ['linear', 'rbf', 'poly']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8494505494505494\n",
      "{'C': 1, 'gamma': 0.1, 'kernel': 'poly'}\n"
     ]
    }
   ],
   "source": [
    "print(grid_search.best_score_)\n",
    "print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For Random Forest Classifier\n",
    "params={'n_estimators':[10,50,30,100,80,40],'criterion':['gini','entropy']}\n",
    "grid_search=GridSearchCV(rf,param_grid=params,cv=cv,scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=ShuffleSplit(n_splits=10, random_state=42, test_size=0.3, train_size=None),\n",
       "             error_score=nan,\n",
       "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
       "                                              class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              max_samples=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators=100, n_jobs=None,\n",
       "                                              oob_score=False,\n",
       "                                              random_state=None, verbose=0,\n",
       "                                              warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'n_estimators': [10, 50, 30, 100, 80, 40]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8263736263736264\n",
      "{'criterion': 'entropy', 'n_estimators': 30}\n"
     ]
    }
   ],
   "source": [
    "print(grid_search.best_score_)\n",
    "print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# From cross val score Logistic Regression gives the max accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=5000,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8726415094339622"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34,  4],\n",
       "       [ 7, 46]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(prediction,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.89      0.86        38\n",
      "           1       0.92      0.87      0.89        53\n",
      "\n",
      "    accuracy                           0.88        91\n",
      "   macro avg       0.87      0.88      0.88        91\n",
      "weighted avg       0.88      0.88      0.88        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%print(classification_report(prediction,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model is has an accuracy of 88%"
   ]
  }
 ],
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