GarimaShukla_ECE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c34819aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "from matplotlib.cm import rainbow\n",
    "%matplotlib inline\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "add9acd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "2eb3574e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt=pd.read_csv('heart.csv')\n",
    "dt['HeartDisease'].astype(float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "56ab7e10",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>156</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
       "      <td>98</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>108</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>122</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
       "0   40   M           ATA        140          289          0     Normal    172   \n",
       "1   49   F           NAP        160          180          0     Normal    156   \n",
       "2   37   M           ATA        130          283          0         ST     98   \n",
       "3   48   F           ASY        138          214          0     Normal    108   \n",
       "4   54   M           NAP        150          195          0     Normal    122   \n",
       "\n",
       "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "0              N      0.0       Up             0  \n",
       "1              N      1.0     Flat             1  \n",
       "2              N      0.0       Up             0  \n",
       "3              Y      1.5     Flat             1  \n",
       "4              N      0.0       Up             0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "40429c36",
   "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>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>45</td>\n",
       "      <td>M</td>\n",
       "      <td>TA</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>132</td>\n",
       "      <td>N</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>68</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>141</td>\n",
       "      <td>N</td>\n",
       "      <td>3.4</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>57</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>115</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>57</td>\n",
       "      <td>F</td>\n",
       "      <td>ATA</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>LVH</td>\n",
       "      <td>174</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>38</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>138</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>173</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "913   45   M            TA        110          264          0     Normal   \n",
       "914   68   M           ASY        144          193          1     Normal   \n",
       "915   57   M           ASY        130          131          0     Normal   \n",
       "916   57   F           ATA        130          236          0        LVH   \n",
       "917   38   M           NAP        138          175          0     Normal   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "913    132              N      1.2     Flat             1  \n",
       "914    141              N      3.4     Flat             1  \n",
       "915    115              Y      1.2     Flat             1  \n",
       "916    174              N      0.0     Flat             1  \n",
       "917    173              N      0.0       Up             0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d0f0f9cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 918 entries, 0 to 917\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Age             918 non-null    int64  \n",
      " 1   Sex             918 non-null    object \n",
      " 2   ChestPainType   918 non-null    object \n",
      " 3   RestingBP       918 non-null    int64  \n",
      " 4   Cholesterol     918 non-null    int64  \n",
      " 5   FastingBS       918 non-null    int64  \n",
      " 6   RestingECG      918 non-null    object \n",
      " 7   MaxHR           918 non-null    int64  \n",
      " 8   ExerciseAngina  918 non-null    object \n",
      " 9   Oldpeak         918 non-null    float64\n",
      " 10  ST_Slope        918 non-null    object \n",
      " 11  HeartDisease    918 non-null    int64  \n",
      "dtypes: float64(1), int64(6), object(5)\n",
      "memory usage: 86.2+ KB\n"
     ]
    }
   ],
   "source": [
    "dt.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c0f2d5e7",
   "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>Age</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.510893</td>\n",
       "      <td>132.396514</td>\n",
       "      <td>198.799564</td>\n",
       "      <td>0.233115</td>\n",
       "      <td>136.809368</td>\n",
       "      <td>0.887364</td>\n",
       "      <td>0.553377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.432617</td>\n",
       "      <td>18.514154</td>\n",
       "      <td>109.384145</td>\n",
       "      <td>0.423046</td>\n",
       "      <td>25.460334</td>\n",
       "      <td>1.066570</td>\n",
       "      <td>0.497414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-2.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>173.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>267.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>603.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age   RestingBP  Cholesterol   FastingBS       MaxHR  \\\n",
       "count  918.000000  918.000000   918.000000  918.000000  918.000000   \n",
       "mean    53.510893  132.396514   198.799564    0.233115  136.809368   \n",
       "std      9.432617   18.514154   109.384145    0.423046   25.460334   \n",
       "min     28.000000    0.000000     0.000000    0.000000   60.000000   \n",
       "25%     47.000000  120.000000   173.250000    0.000000  120.000000   \n",
       "50%     54.000000  130.000000   223.000000    0.000000  138.000000   \n",
       "75%     60.000000  140.000000   267.000000    0.000000  156.000000   \n",
       "max     77.000000  200.000000   603.000000    1.000000  202.000000   \n",
       "\n",
       "          Oldpeak  HeartDisease  \n",
       "count  918.000000    918.000000  \n",
       "mean     0.887364      0.553377  \n",
       "std      1.066570      0.497414  \n",
       "min     -2.600000      0.000000  \n",
       "25%      0.000000      0.000000  \n",
       "50%      0.600000      1.000000  \n",
       "75%      1.500000      1.000000  \n",
       "max      6.200000      1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "21b61d59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "#get correlations of each features in dataset\n",
    "corrmat =dt.corr()\n",
    "top_corr_features = corrmat.index\n",
    "plt.figure(figsize=(20,20))\n",
    "#plot heat map\n",
    "g=sns.heatmap(dt[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "358c0e3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Age'}>,\n",
       "        <AxesSubplot:title={'center':'RestingBP'}>,\n",
       "        <AxesSubplot:title={'center':'Cholesterol'}>],\n",
       "       [<AxesSubplot:title={'center':'FastingBS'}>,\n",
       "        <AxesSubplot:title={'center':'MaxHR'}>,\n",
       "        <AxesSubplot:title={'center':'Oldpeak'}>],\n",
       "       [<AxesSubplot:title={'center':'HeartDisease'}>, <AxesSubplot:>,\n",
       "        <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8c0841f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='HeartDisease', ylabel='count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEECAYAAAAlEzNMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUn0lEQVR4nO3dcUzcd/3H8df3uN6tvYN1HSvGX0cz1rIx5wUahBosEdOKmsyuS6X0tltxnaaoMaBTcG1h062Aa1FX03WbW6ZsDWLpZt10UVg7Yqu4MNtGdqwbrnWzzdZOZrnTfWnL9/eHGRYtlApfjvF5Pv7i7rjvvUs+3LPfL3ffsxzHcQQAMJYn0QMAABKLEACA4QgBABiOEACA4QgBABjOm+gBLtaBAwfk9/sTPQYAvK/Ytq3s7Ozz3va+C4Hf71dWVlaixwCA95VoNDribRwaAgDDEQIAMBwhAADDEQIAMBwhAADDEQIAMBwhAADDEQIAMBwhAADDufbO4htvvFHJycmSpHnz5mndunWqrq6WZVlauHChamtr5fF41NLSoubmZnm9XpWXl6uoqMitkYApzzl7VlZSUqLHwBTj9rpwJQS2bUuSmpqahq5bt26dKioqlJ+fr5qaGrW3tys7O1tNTU1qbW2VbdsKh8MqKCiQz+dzYyxgyrOSknSybUeix8AUk7o07Or2XQlBT0+P/vnPf+q2227TmTNn9LWvfU3d3d3Ky8uTJBUWFmrfvn3yeDzKycmRz+eTz+dTenq6enp6FAqF3BgLAHAeroTgkksu0dq1a/W5z31OR44c0Re+8AU5jiPLsiRJgUBA/f39isViQ4eP3rs+FouNum3btkc9eRLwfsYJFTESN5/3XAnBVVddpfnz58uyLF111VWaPXu2uru7h26Px+NKSUlRMBhUPB4fdv25YTgfzj4KwETjfd6b9LOP7ty5U/X19ZKkN998U7FYTAUFBers7JQkdXR0KDc3V6FQSF1dXbJtW/39/ert7VVmZqYbIwEARuDKHsHKlSv1rW99S6tXr5ZlWdq0aZMuu+wybdy4UY2NjcrIyFBxcbGSkpIUiUQUDoflOI4qKyv50BkAmGSW4zhOooe4GNFolENDmNZ41RD+00S8ami0507eUAYAhiMEAGA4QgAAhiMEAGA4QgAAhiMEAGA4QgAAhiMEAGA4QgAAhiMEAGA4QgAAhiMEAGA4QgAAhiMEAGA4QgAAhiMEAGA4QgAAhiMEAGA4QgAAhiMEAGA4QgAAhiMEAGA4QgAAhiMEAGA4QgAAhiMEAGA4QgAAhiMEAGA4QgAAhiMEAGA4QgAAhiMEAGA4I0NwdnAw0SNgCmJdwFRetzb89ttv66abbtKjjz4qr9er6upqWZalhQsXqra2Vh6PRy0tLWpubpbX61V5ebmKiorcGmeYJI9HT73YOymPhfePGxddnegRgIRwZY/g9OnTqqmp0SWXXCJJqqurU0VFhXbs2CHHcdTe3q4TJ06oqalJzc3NeuSRR9TY2KiBgQE3xgEAjMKVEDQ0NKi0tFRz586VJHV3dysvL0+SVFhYqP379+vQoUPKycmRz+dTcnKy0tPT1dPT48Y4AIBRTPihoV27dmnOnDlasmSJHnroIUmS4ziyLEuSFAgE1N/fr1gspuTk5KH7BQIBxWKxC27ftm1Fo9FxzZiVlTWu+2P6Gu/aGi/WJkbi5tqc8BC0trbKsiz97ne/UzQaVVVVlf72t78N3R6Px5WSkqJgMKh4PD7s+nPDMBK/388vC1zD2sJUNd61OVpIJvzQ0BNPPKHHH39cTU1NysrKUkNDgwoLC9XZ2SlJ6ujoUG5urkKhkLq6umTbtvr7+9Xb26vMzMyJHgcAcAGuvWroXFVVVdq4caMaGxuVkZGh4uJiJSUlKRKJKBwOy3EcVVZWyu/3T8Y4AIBzuBqCpqamoa8ff/zx/7q9pKREJSUlbo4AALgAI99QBgD4N0IAAIYjBABgOEIAAIYjBABgOEIAAIYjBABgOEIAAIYjBABgOEIAAIYjBABgOEIAAIYjBABgOEIAAIYjBABgOEIAAIYjBABgOEIAAIYjBABgOEIAAIYjBABgOEIAAIYjBABgOEIAAIYjBABgOEIAAIYjBABgOEIAAIYjBABgOEIAAIYjBABgOEIAAIYjBABgOK8bGz179qw2bNig1157TUlJSaqrq5PjOKqurpZlWVq4cKFqa2vl8XjU0tKi5uZmeb1elZeXq6ioyI2RAAAjcCUEe/bskSQ1Nzers7NzKAQVFRXKz89XTU2N2tvblZ2draamJrW2tsq2bYXDYRUUFMjn87kxFgDgPFwJwdKlS/Xxj39cknTs2DGlpqZq7969ysvLkyQVFhZq37598ng8ysnJkc/nk8/nU3p6unp6ehQKhdwYCwBwHq6EQJK8Xq+qqqr0m9/8Rvfff7/27Nkjy7IkSYFAQP39/YrFYkpOTh66TyAQUCwWG3W7tm0rGo2Oa7asrKxx3R/T13jX1nixNjESN9emayGQpIaGBt1xxx0qKSmRbdtD18fjcaWkpCgYDCoejw+7/twwnI/f7+eXBa5hbWGqGu/aHC0krrxq6KmnntKDDz4oSZo5c6Ysy9L111+vzs5OSVJHR4dyc3MVCoXU1dUl27bV39+v3t5eZWZmujESAGAEYwrBz372s2GXf/KTn4z6/Z/85Cf10ksv6eabb9batWt15513qqamRlu3btWqVat0+vRpFRcX64orrlAkElE4HNaaNWtUWVkpv9//v/9rAAAXzXIcxxnpxqefflrPPfecOjs7tXjxYkn/emnoK6+8omeeeWbShjxXNBqdkN33p17snYBpMJ3cuOjqRI8gSTrZtiPRI2CKSV0aHvc2RnvuHPVvBEuWLNEVV1yhd955R6tWrZIkeTweXXnlleMeCgAwNYwagksvvVT5+fnKz8/X22+/PfQH37Nnz07KcAAA943pVUN33323nn/+ec2dO1eO48iyLDU3N7s9GwBgEowpBAcPHlRbW5s8Hk5NBADTzZie2efPnz/sfQAAgOljTHsEx48fV1FRkebPny9JHBoCgGlkTCHYsmWL23MAABJkTCF48skn/+u6r3zlKxM+DABg8o0pBKmpqZIkx3H00ksvaXBw0NWhAACTZ0whKC0tHXb59ttvd2UYAMDkG1MIXnvttaGvT5w4oePHj7s2EABgco0pBDU1NUNf+/1+ffOb33RtIADA5BpTCJqamtTX16fXX39d8+bN05w5c9yeCwAwScb0hrJf/epXKi0t1fbt27Vq1Sr9/Oc/d3suAMAkGdMewWOPPaZdu3YNfZTkmjVrtHz5crdnAwBMgjHtEViWpUAgIEkKBoN8eAwATCNj2iNIT09XfX29cnNz1dXVpfT0dLfnAgBMkjHtEZSUlOjSSy/V/v37tWvXLt18881uzwUAmCRjCkF9fb2WLVummpoa7dy5U/X19W7PBQCYJGMKgdfr1YIFCyRJV155JZ9LAADTyJj+RvDBD35QjY2Nys7O1qFDhzR37ly35wIATJIx/de+rq5Oc+bM0fPPP685c+aorq7O7bkAAJNkTHsEfr9fZWVlLo8CAEgEDvYDgOEIAQAYjhAAgOEIAQAYjhAAgOEIAQAYjhAAgOEIAQAYjhAAgOEIAQAYbkynmLgYp0+f1p133qm//vWvGhgYUHl5uRYsWKDq6mpZlqWFCxeqtrZWHo9HLS0tam5ultfrVXl5uYqKiiZ6HADABUx4CHbv3q3Zs2frvvvuU19fn1asWKFrr71WFRUVys/PV01Njdrb25Wdna2mpia1trbKtm2Fw2EVFBTI5/NN9EgAgFFMeAg+9alPqbi4eOhyUlKSuru7lZeXJ0kqLCzUvn375PF4lJOTI5/PJ5/Pp/T0dPX09CgUCo26fdu2FY1GxzVjVlbWuO6P6Wu8a2u8WJsYiZtrc8JD8N6H3MdiMX31q19VRUWFGhoaZFnW0O39/f2KxWJKTk4edr9YLHbB7fv9fn5Z4BrWFqaq8a7N0ULiyh+Ljx8/rltvvVXLly/XDTfcMOwTzeLxuFJSUhQMBhWPx4ddf24YAACTY8JDcPLkSd122236xje+oZUrV0qSrrvuOnV2dkqSOjo6lJubq1AopK6uLtm2rf7+fvX29iozM3OixwEAXMCEHxravn27Tp06pW3btmnbtm2SpPXr1+uee+5RY2OjMjIyVFxcrKSkJEUiEYXDYTmOo8rKSvn9/okeBwBwAZbjOE6ih7gY0Wh0Qo7jPvVi7wRMg+nkxkVXJ3oESdLJth2JHgFTTOrS8Li3MdpzJ28oAwDDEQIAMBwhAADDEQIAMBwhAADDEQIAMBwhAADDEQIAMBwhAADDEQIAMBwhAADDEQIAMBwhAADDEQIAMBwhAADDEQIAMBwhAADDEQIAMBwhAADDEQIAMBwhAADDEQIAMBwhAADDEQIAMBwhAADDEQIAMBwhAADDEQIAMBwhAADDEQIAMBwhAADDEQIAMJxrITh48KAikYgk6ejRo1q9erXC4bBqa2s1ODgoSWppadFNN92kkpIS7dmzx61RAACjcCUEDz/8sDZs2CDbtiVJdXV1qqio0I4dO+Q4jtrb23XixAk1NTWpublZjzzyiBobGzUwMODGOACAUbgSgvT0dG3dunXocnd3t/Ly8iRJhYWF2r9/vw4dOqScnBz5fD4lJycrPT1dPT09bowDABiF142NFhcX64033hi67DiOLMuSJAUCAfX39ysWiyk5OXnoewKBgGKx2AW3bdu2otHouObLysoa1/0xfY13bY0XaxMjcXNtuhKC/+Tx/HvHIx6PKyUlRcFgUPF4fNj154ZhJH6/n18WuIa1halqvGtztJBMyquGrrvuOnV2dkqSOjo6lJubq1AopK6uLtm2rf7+fvX29iozM3MyxgEAnGNS9giqqqq0ceNGNTY2KiMjQ8XFxUpKSlIkElE4HJbjOKqsrJTf75+McQAA57Acx3ESPcTFiEajE7L7/tSLvRMwDaaTGxddnegRJEkn23YkegRMMalLw+PexmjPnbyhDAAMRwgAwHCEAAAMRwgAwHCEAAAMRwgAwHCEAAAMRwgAwHCEAAAMRwgAwHCEAAAMRwgAwHCEAAAMRwgAwHCEAAAMRwgAwHCEAAAMRwgAwHCEAAAMRwgAwHCEAAAMRwgAwHCEAAAMRwgAwHCEAAAMRwgAwHCEAAAMRwgAwHCEAAAMRwgAwHCEAAAMRwgAwHCEAAAM5030AIODg7rrrrv08ssvy+fz6Z577tH8+fMTPRYAGCPhewRtbW0aGBjQT3/6U339619XfX19okcCAKMkPARdXV1asmSJJCk7O1t/+tOfEjwRAJgl4YeGYrGYgsHg0OWkpCSdOXNGXu/5R7NtW9FodNyPe83McW8C08xErKsJ8X85iZ4AU8yJCVibtm2PeFvCQxAMBhWPx4cuDw4OjhgB6V97DQCAiZPwQ0OLFi1SR0eHJOnAgQPKzMxM8EQAYBbLcRwnkQO896qhw4cPy3Ecbdq0SVdffXUiRwIAoyQ8BACAxEr4oSEAQGIRAgAwHCEAAMMRAgMNDg6qpqZGq1atUiQS0dGjRxM9EjDMwYMHFYlEEj2GMRL+PgJMvnNP63HgwAHV19frgQceSPRYgCTp4Ycf1u7duzVzJu/6nCzsERiI03pgKktPT9fWrVsTPYZRCIGBRjqtBzAVFBcXj3p2AUw8QmCgiz2tB4DpjRAYiNN6ADgX/w000LJly7Rv3z6VlpYOndYDgLk4xQQAGI5DQwBgOEIAAIYjBABgOEIAAIYjBABgOEKAaamzs1OVlZXDrtu8ebN27dr1P2/z5Zdf1gsvvCBJikQiWrlypSKRiEpLS3XHHXeor69PknTvvffq2LFj//vwwCTjfQTAGP36179WamqqPvKRj0iSGhoahj5Wdffu3aqpqdHWrVu1fv36RI4JXDRCAONs2bJFL7zwghzHUVlZmT796U/rD3/4g374wx9Kkt599101NDRoxowZKi8v1+zZs5Wfn68nn3xSM2bM0Ic+9KH/2uZnP/tZff/735dt27r99tt111136Z133lFDQ4O8Xq9SUlK0efNm+f1+1dbW6ujRoxocHFRFRYXy8/P17LPP6oknnhja3g9+8ANJUkVFhRzH0enTp3X33XfrmmuuUVNTk55++mlZlqXPfOYzuvXWWyfnB4dpixBg2vr9738/7Jz2r7/+ur74xS/qjTfeUHNzs2zbVklJiQoKCvTKK6/ovvvuU1pamrZv365nn31WN9xwg06cOKHW1lb5fD45jqPU1FSFQqHzPl5KSopOnTo1dLmtrU3Lli3T2rVr9dxzz+nUqVPau3evLrvsMm3atEl9fX265ZZb9Mwzz+jIkSN66KGHNHPmTNXU1Oi3v/2tUlJSlJycrC1btujVV19VLBbTq6++ql/+8pfasWOHLMtSWVmZPvaxjykjI8P1nyemL0KAaWvx4sX63ve+N3R58+bNisfj6u7uHgrEmTNndOzYMaWlpenee+/VrFmz9Oabb2rRokWSpHnz5snn813wsRzH0cmTJ3X55ZcPXbdu3Tpt375da9asUVpamkKhkA4fPqyuri4dOnRo6PH7+vp0+eWXq6qqSoFAQH/+85+VnZ2twsJCHTlyRF/60pfk9XpVXl6uw4cP69ixYyorK5Mk/f3vf9df/vIXQoBxIQQwit/vV35+vr7zne9ocHBQ27Zt07x581RWVqa2tjYFg0FVVVXpvTOveDz/fj2FZVkaHBw873Z37typxYsXD/v+X/ziF1qxYoWqqqr04IMPqqWlRRkZGfrABz6gdevW6d1339UDDzwgr9er+++/X3v37pUkff7zn5fjOOrs7NTcuXP16KOP6o9//KMaGxu1fv16LViwQD/60Y9kWZYee+wxThqIcSMEMEogENCsWbMUDof1j3/8Q0uXLlUwGNTy5ctVUlKilJQUpaam6q233vqv+15//fX67ne/O/QH4qqqqqFP0UpLS1Ntbe2w7//whz+s6upqzZo1SzNmzNC3v/1tpaWlacOGDbrlllsUi8UUDocVDAa1aNEirVixQrNmzVJKSoreeustfeITn1BlZaV+/OMfy+Px6Mtf/rKuvfZaffSjH9Xq1as1MDCgUCiktLQ0939wmNY46RwAGI73EQCA4QgBABiOEACA4QgBABiOEACA4QgBABiOEACA4f4fk+65tlow+FwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='HeartDisease',data=dt,palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fa160099",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Processing\n",
    "heart=pd.get_dummies(dt,columns=['Sex','ChestPainType','MaxHR','FastingBS','ExerciseAngina','RestingECG'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a9e04c5e",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dataset' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [22]\u001b[0m, in \u001b[0;36m<cell line: 5>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m standardScaler \u001b[38;5;241m=\u001b[39mStandardScaler()\n\u001b[0;32m      4\u001b[0m columns_to_scale\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAge\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCholesterol\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOldpeak\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRestingBP\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m----> 5\u001b[0m heart[columns_to_scale]\u001b[38;5;241m=\u001b[39m standardScaler\u001b[38;5;241m.\u001b[39mfit_transform(\u001b[43mdataset\u001b[49m[columns_to_scale])\n",
      "\u001b[1;31mNameError\u001b[0m: name 'dataset' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "standardScaler =StandardScaler()\n",
    "columns_to_scale=['Age','Cholesterol','Oldpeak','RestingBP']\n",
    "heart[columns_to_scale]= standardScaler.fit_transform(dataset[columns_to_scale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7ade3402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "      <th>Sex_F</th>\n",
       "      <th>Sex_M</th>\n",
       "      <th>ChestPainType_ASY</th>\n",
       "      <th>ChestPainType_ATA</th>\n",
       "      <th>...</th>\n",
       "      <th>MaxHR_194</th>\n",
       "      <th>MaxHR_195</th>\n",
       "      <th>MaxHR_202</th>\n",
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       "      <th>FastingBS_1</th>\n",
       "      <th>ExerciseAngina_N</th>\n",
       "      <th>ExerciseAngina_Y</th>\n",
       "      <th>RestingECG_LVH</th>\n",
       "      <th>RestingECG_Normal</th>\n",
       "      <th>RestingECG_ST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>40</td>\n",
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       "      <td>1.5</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 138 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  RestingBP  Cholesterol  Oldpeak ST_Slope  HeartDisease  Sex_F  Sex_M  \\\n",
       "0   40        140          289      0.0       Up             0      0      1   \n",
       "1   49        160          180      1.0     Flat             1      1      0   \n",
       "2   37        130          283      0.0       Up             0      0      1   \n",
       "3   48        138          214      1.5     Flat             1      1      0   \n",
       "4   54        150          195      0.0       Up             0      0      1   \n",
       "\n",
       "   ChestPainType_ASY  ChestPainType_ATA  ...  MaxHR_194  MaxHR_195  MaxHR_202  \\\n",
       "0                  0                  1  ...          0          0          0   \n",
       "1                  0                  0  ...          0          0          0   \n",
       "2                  0                  1  ...          0          0          0   \n",
       "3                  1                  0  ...          0          0          0   \n",
       "4                  0                  0  ...          0          0          0   \n",
       "\n",
       "   FastingBS_0  FastingBS_1  ExerciseAngina_N  ExerciseAngina_Y  \\\n",
       "0            1            0                 1                 0   \n",
       "1            1            0                 1                 0   \n",
       "2            1            0                 1                 0   \n",
       "3            1            0                 0                 1   \n",
       "4            1            0                 1                 0   \n",
       "\n",
       "   RestingECG_LVH  RestingECG_Normal  RestingECG_ST  \n",
       "0               0                  1              0  \n",
       "1               0                  1              0  \n",
       "2               0                  0              1  \n",
       "3               0                  1              0  \n",
       "4               0                  1              0  \n",
       "\n",
       "[5 rows x 138 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "24eee65d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=heart['HeartDisease']\n",
    "x=heart.drop(['HeartDisease'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "68bf1932",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "knn_score=[]\n",
    "for k in range(1,21):\n",
    "    knn_classifier =KNeighborsClassifier(n_neighbors=k)\n",
    "    score= cross_val_score(knn_classifier,x,y,cv=10)\n",
    "    knn_score.append(score.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "80e60788",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Accuracy_Rate vs Accuracy')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([k for k in range(1,21)], knn_score, color='red')\n",
    "for i in range(1,21):\n",
    "    plt.text(i, knn_score[i-1],(i, knn_score[i-1]))\n",
    "plt.xticks([i for i in range(1,21)])\n",
    "plt.xlabel('Accuracy')\n",
    "plt.ylabel('Accuracy_Rate')\n",
    "plt.title('Accuracy_Rate vs Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fe79b2f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_classifier = KNeighborsClassifier(n_neighbors = 12)\n",
    "score=cross_val_score(knn_classifier,x,y,cv=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "14cbfb34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c86403dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Random Forest Classifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "randomforest_classifier= RandomForestClassifier(n_estimators=10)\n",
    "\n",
    "score=cross_val_score(randomforest_classifier,x,y,cv=10)\n",
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9008a185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='RestingBP',y='Cholesterol',data=dt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "765bec0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age               0\n",
       "Sex               0\n",
       "ChestPainType     0\n",
       "RestingBP         0\n",
       "Cholesterol       0\n",
       "FastingBS         0\n",
       "RestingECG        0\n",
       "MaxHR             0\n",
       "ExerciseAngina    0\n",
       "Oldpeak           0\n",
       "ST_Slope          0\n",
       "HeartDisease      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "60bc5855",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'F'], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.Sex.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "32130b38",
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   ]
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   "execution_count": 45,
   "id": "9f9e13d3",
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   "outputs": [
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    "Sex=pd.get_dummies(dt['Sex'],drop_first=True)\n",
    "Sex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e7c3c3a5",
   "metadata": {},
   "outputs": [
    {
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       "   ASY  ATA  NAP  TA\n",
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      ]
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     "execution_count": 46,
     "metadata": {},
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   ],
   "source": [
    "dt.ChestPainType.unique()\n",
    "pd.get_dummies(dt['ChestPainType']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a1810586",
   "metadata": {},
   "outputs": [
    {
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       "   ATA  NAP  TA\n",
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   "source": [
    "ChestPainType=pd.get_dummies(dt['ChestPainType'],drop_first=True)\n",
    "ChestPainType.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "258715d2",
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   "outputs": [
    {
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       "      <td>Y</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
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       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
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       "      <td>122</td>\n",
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      ],
      "text/plain": [
       "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
       "0   40   M           ATA        140          289          0     Normal    172   \n",
       "1   49   F           NAP        160          180          0     Normal    156   \n",
       "2   37   M           ATA        130          283          0         ST     98   \n",
       "3   48   F           ASY        138          214          0     Normal    108   \n",
       "4   54   M           NAP        150          195          0     Normal    122   \n",
       "\n",
       "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  M  ATA  NAP  TA  \n",
       "0              N      0.0       Up             0  1    1    0   0  \n",
       "1              N      1.0     Flat             1  0    0    1   0  \n",
       "2              N      0.0       Up             0  1    1    0   0  \n",
       "3              Y      1.5     Flat             1  0    0    0   0  \n",
       "4              N      0.0       Up             0  1    0    1   0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data=pd.concat([dt,Sex,ChestPainType],axis=1) \n",
    "new_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5d9de2c3",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "      <td>108</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
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       "      <td>54</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>122</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
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      ],
      "text/plain": [
       "   Age  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR ExerciseAngina  \\\n",
       "0   40        140          289          0     Normal    172              N   \n",
       "1   49        160          180          0     Normal    156              N   \n",
       "2   37        130          283          0         ST     98              N   \n",
       "3   48        138          214          0     Normal    108              Y   \n",
       "4   54        150          195          0     Normal    122              N   \n",
       "\n",
       "   Oldpeak ST_Slope  HeartDisease  M  ATA  NAP  TA  \n",
       "0      0.0       Up             0  1    1    0   0  \n",
       "1      1.0     Flat             1  0    0    1   0  \n",
       "2      0.0       Up             0  1    1    0   0  \n",
       "3      1.5     Flat             1  0    0    0   0  \n",
       "4      0.0       Up             0  1    0    1   0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.drop(['Sex','ChestPainType'],axis=1,inplace=True);\n",
    "     \n",
    "\n",
    "new_data.head()\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "dafea0cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Normal\n",
       "1    Normal\n",
       "2        ST\n",
       "3    Normal\n",
       "4    Normal\n",
       "Name: RestingECG, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=new_data.RestingECG\n",
    "     \n",
    "\n",
    "y.head()"
   ]
  }
 ],
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