Bagging on heart data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fb0190f7",
   "metadata": {},
   "source": [
    "#                Ensemble Learning: Bagging on heart data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fe31acad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "227ab477",
   "metadata": {},
   "source": [
    "## Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0acbacbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>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",
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       "      <th>0</th>\n",
       "      <td>40</td>\n",
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       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172</td>\n",
       "      <td>N</td>\n",
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       "      <th>1</th>\n",
       "      <td>49</td>\n",
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       "      <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",
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       "      <td>1</td>\n",
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       "      <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",
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       "      <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",
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       "      <th>4</th>\n",
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       "      <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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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",
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       "    <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",
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       "    <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",
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       "    <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",
       "<p>918 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "0     40   M           ATA        140          289          0     Normal   \n",
       "1     49   F           NAP        160          180          0     Normal   \n",
       "2     37   M           ATA        130          283          0         ST   \n",
       "3     48   F           ASY        138          214          0     Normal   \n",
       "4     54   M           NAP        150          195          0     Normal   \n",
       "..   ...  ..           ...        ...          ...        ...        ...   \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",
       "0      172              N      0.0       Up             0  \n",
       "1      156              N      1.0     Flat             1  \n",
       "2       98              N      0.0       Up             0  \n",
       "3      108              Y      1.5     Flat             1  \n",
       "4      122              N      0.0       Up             0  \n",
       "..     ...            ...      ...      ...           ...  \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  \n",
       "\n",
       "[918 rows x 12 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('heart.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "77fa3212",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ac261533",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "  </thead>\n",
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       "      <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",
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       "      <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",
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       "    <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",
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      ],
      "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3f8f0243",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    508\n",
       "0    410\n",
       "Name: HeartDisease, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.HeartDisease.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7b42d425",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8070866141732284"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "410/508"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a2016d",
   "metadata": {},
   "source": [
    "## Treat Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "767120ae",
   "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>76</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>118</td>\n",
       "      <td>529</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>130</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>130</td>\n",
       "      <td>603</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>125</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>67</td>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>115</td>\n",
       "      <td>564</td>\n",
       "      <td>0</td>\n",
       "      <td>LVH</td>\n",
       "      <td>160</td>\n",
       "      <td>N</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Flat</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "76    32   M           ASY        118          529          0     Normal   \n",
       "149   54   M           ASY        130          603          1     Normal   \n",
       "616   67   F           NAP        115          564          0        LVH   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "76     130              N      0.0     Flat             1  \n",
       "149    125              Y      1.0     Flat             1  \n",
       "616    160              N      1.6     Flat             0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Cholesterol>(df.Cholesterol.mean()+3*df.Cholesterol.std())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "54e4ed33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(918, 12)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7232111d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(915, 12)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df[df.Cholesterol<=(df.Cholesterol.mean()+3*df.Cholesterol.std())]\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "48fec727",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
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       "  <tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Age, Sex, ChestPainType, RestingBP, Cholesterol, FastingBS, RestingECG, MaxHR, ExerciseAngina, Oldpeak, ST_Slope, HeartDisease]\n",
       "Index: []"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df[df.MaxHR>(df.MaxHR.mean()+3*df.MaxHR.std())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ff7d4717",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Age, Sex, ChestPainType, RestingBP, Cholesterol, FastingBS, RestingECG, MaxHR, ExerciseAngina, Oldpeak, ST_Slope, HeartDisease]\n",
       "Index: []"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df[df.FastingBS>(df.FastingBS.mean()+3*df.FastingBS.std())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5aa15fb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>ST</td>\n",
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       "      <th>702</th>\n",
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       "      <td>178</td>\n",
       "      <td>270</td>\n",
       "      <td>0</td>\n",
       "      <td>LVH</td>\n",
       "      <td>145</td>\n",
       "      <td>N</td>\n",
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       "      <td>55</td>\n",
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       "      <td>140</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
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       "      <td>122</td>\n",
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       "      <td>62</td>\n",
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       "      <td>160</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>LVH</td>\n",
       "      <td>145</td>\n",
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       "      <td>Down</td>\n",
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       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>58</td>\n",
       "      <td>M</td>\n",
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       "      <td>114</td>\n",
       "      <td>318</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
       "      <td>140</td>\n",
       "      <td>N</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Down</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "166   50   M           ASY        140          231          0         ST   \n",
       "702   59   M            TA        178          270          0        LVH   \n",
       "771   55   M           ASY        140          217          0     Normal   \n",
       "791   51   M           ASY        140          298          0     Normal   \n",
       "850   62   F           ASY        160          164          0        LVH   \n",
       "900   58   M           ASY        114          318          0         ST   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "166    140              Y      5.0     Flat             1  \n",
       "702    145              N      4.2     Down             0  \n",
       "771    111              Y      5.6     Down             1  \n",
       "791    122              Y      4.2     Flat             1  \n",
       "850    145              N      6.2     Down             1  \n",
       "900    140              N      4.4     Down             1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Oldpeak>(df.Oldpeak.mean()+3*df.Oldpeak.std())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "895aa80a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(909, 12)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df1[df1.Oldpeak<=(df1.Oldpeak.mean()+3*df1.Oldpeak.std())]\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9473962d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>109</th>\n",
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       "      <td>241</td>\n",
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       "      <td>N</td>\n",
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       "      <th>241</th>\n",
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       "      <td>0</td>\n",
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       "      <td>142</td>\n",
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       "      <td>ASY</td>\n",
       "      <td>200</td>\n",
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       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>140</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
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       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>61</td>\n",
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       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>ST</td>\n",
       "      <td>70</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
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       "      <th>592</th>\n",
       "      <td>61</td>\n",
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       "      <td>190</td>\n",
       "      <td>287</td>\n",
       "      <td>1</td>\n",
       "      <td>LVH</td>\n",
       "      <td>150</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Down</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>56</td>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>200</td>\n",
       "      <td>288</td>\n",
       "      <td>1</td>\n",
       "      <td>LVH</td>\n",
       "      <td>133</td>\n",
       "      <td>Y</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Down</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>192</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>LVH</td>\n",
       "      <td>195</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "109   39   M           ATA        190          241          0     Normal   \n",
       "241   54   M           ASY        200          198          0     Normal   \n",
       "365   64   F           ASY        200            0          0     Normal   \n",
       "399   61   M           NAP        200            0          1         ST   \n",
       "592   61   M           ASY        190          287          1        LVH   \n",
       "732   56   F           ASY        200          288          1        LVH   \n",
       "759   54   M           ATA        192          283          0        LVH   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "109    106              N      0.0       Up             0  \n",
       "241    142              Y      2.0     Flat             1  \n",
       "365    140              Y      1.0     Flat             1  \n",
       "399     70              N      0.0     Flat             1  \n",
       "592    150              Y      2.0     Down             1  \n",
       "732    133              Y      4.0     Down             1  \n",
       "759    195              N      0.0       Up             1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.RestingBP>(df.RestingBP.mean()+3*df.RestingBP.std())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e4b8feaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(902, 12)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2[df2.RestingBP<=(df2.RestingBP.mean()+3*df2.RestingBP.std())]\n",
    "df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1ce33200",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ATA', 'NAP', 'ASY', 'TA'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ChestPainType.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c211bf87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Normal', 'ST', 'LVH'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.RestingECG.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "28d980c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['N', 'Y'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ExerciseAngina.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0c13207",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Up', 'Flat', 'Down'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ST_Slope.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "474c4a9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Normal</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</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               0      0.0         0             0  \n",
       "1               0      1.0         1             1  \n",
       "2               0      0.0         0             0  \n",
       "3               1      1.5         1             1  \n",
       "4               0      0.0         0             0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df3.copy()\n",
    "df4.ExerciseAngina.replace(\n",
    "    {\n",
    "        'N' :0,\n",
    "        'Y' :1\n",
    "    },\n",
    "    inplace =True\n",
    ")\n",
    "df4.ST_Slope.replace(\n",
    "    {'Up' : 0,\n",
    "     'Flat':1,\n",
    "     'Down':2\n",
    "    },\n",
    "    inplace = True\n",
    ")\n",
    "df.RestingECG.replace(\n",
    "    {\n",
    "        'Normal':0,\n",
    "        'ST':1,\n",
    "        'LVH':2\n",
    "    },\n",
    "    inplace = True\n",
    ")\n",
    "df.ChestPainType.replace(\n",
    "    {'ATA':0,\n",
    "     'NAP':1,\n",
    "     'ASY':2,\n",
    "     'TA':3},\n",
    "    inplace=True\n",
    ")\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "02bf7eb7",
   "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>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "      <th>Sex_M</th>\n",
       "      <th>ChestPainType_ATA</th>\n",
       "      <th>ChestPainType_NAP</th>\n",
       "      <th>ChestPainType_TA</th>\n",
       "      <th>RestingECG_Normal</th>\n",
       "      <th>RestingECG_ST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</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",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  RestingBP  Cholesterol  FastingBS  MaxHR  ExerciseAngina  Oldpeak  \\\n",
       "0   40        140          289          0    172               0      0.0   \n",
       "1   49        160          180          0    156               0      1.0   \n",
       "2   37        130          283          0     98               0      0.0   \n",
       "3   48        138          214          0    108               1      1.5   \n",
       "4   54        150          195          0    122               0      0.0   \n",
       "\n",
       "   ST_Slope  HeartDisease  Sex_M  ChestPainType_ATA  ChestPainType_NAP  \\\n",
       "0         0             0      1                  1                  0   \n",
       "1         1             1      0                  0                  1   \n",
       "2         0             0      1                  1                  0   \n",
       "3         1             1      0                  0                  0   \n",
       "4         0             0      1                  0                  1   \n",
       "\n",
       "   ChestPainType_TA  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  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = pd.get_dummies(df4, drop_first=True)\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "75498f80",
   "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>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>Sex_M</th>\n",
       "      <th>ChestPainType_ATA</th>\n",
       "      <th>ChestPainType_NAP</th>\n",
       "      <th>ChestPainType_TA</th>\n",
       "      <th>RestingECG_Normal</th>\n",
       "      <th>RestingECG_ST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</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",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</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",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  RestingBP  Cholesterol  FastingBS  MaxHR  ExerciseAngina  Oldpeak  \\\n",
       "0   40        140          289          0    172               0      0.0   \n",
       "1   49        160          180          0    156               0      1.0   \n",
       "2   37        130          283          0     98               0      0.0   \n",
       "3   48        138          214          0    108               1      1.5   \n",
       "4   54        150          195          0    122               0      0.0   \n",
       "\n",
       "   ST_Slope  Sex_M  ChestPainType_ATA  ChestPainType_NAP  ChestPainType_TA  \\\n",
       "0         0      1                  1                  0                 0   \n",
       "1         1      0                  0                  1                 0   \n",
       "2         0      1                  1                  0                 0   \n",
       "3         1      0                  0                  0                 0   \n",
       "4         0      1                  0                  1                 0   \n",
       "\n",
       "   RestingECG_Normal  RestingECG_ST  \n",
       "0                  1              0  \n",
       "1                  1              0  \n",
       "2                  0              1  \n",
       "3                  1              0  \n",
       "4                  1              0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df5.drop(\"HeartDisease\",axis='columns') \n",
    "y = df5.HeartDisease\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4eb99110",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.42896269,  0.46089071,  0.85238015, ..., -0.22914788,\n",
       "         0.80934904, -0.49062742],\n",
       "       [-0.47545956,  1.5925728 , -0.16132855, ..., -0.22914788,\n",
       "         0.80934904, -0.49062742],\n",
       "       [-1.74679706, -0.10495034,  0.79657967, ..., -0.22914788,\n",
       "        -1.23556086,  2.03820649],\n",
       "       ...,\n",
       "       [ 0.37209878, -0.10495034, -0.61703246, ..., -0.22914788,\n",
       "         0.80934904, -0.49062742],\n",
       "       [ 0.37209878, -0.10495034,  0.35947592, ..., -0.22914788,\n",
       "        -1.23556086, -0.49062742],\n",
       "       [-1.64085227,  0.3477225 , -0.20782894, ..., -0.22914788,\n",
       "         0.80934904, -0.49062742]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_scaler = scaler.fit_transform(X)\n",
    "X_scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ac843e25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.24489796, 0.75675676, 0.55791506, ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.42857143, 0.86486486, 0.34749035, ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.18367347, 0.7027027 , 0.54633205, ..., 0.        , 0.        ,\n",
       "        1.        ],\n",
       "       ...,\n",
       "       [0.59183673, 0.7027027 , 0.25289575, ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.59183673, 0.7027027 , 0.45559846, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.20408163, 0.74594595, 0.33783784, ..., 0.        , 1.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler  = MinMaxScaler()\n",
    "X_scaler = scaler.fit_transform(X)\n",
    "X_scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7d9a514f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test =train_test_split(X_scaler,y,test_size=0.2,random_state = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3ffc0bca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(721, 14)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "758faa86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181, 14)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da9bce79",
   "metadata": {},
   "source": [
    "#### Train a model using standalone support vector machine and then using bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b371ba56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6895334561080417"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores = cross_val_score(SVC(),X ,y, cv=5)\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50d810f4",
   "metadata": {},
   "source": [
    "### use bagging now with svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4a9cde46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6839656230816453"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "beg_model = BaggingClassifier(base_estimator=SVC(),n_estimators=100, max_samples =0.8,random_state=0)\n",
    "scores = cross_val_score(beg_model,X ,y, cv=5)\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "609bcdf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    405\n",
       "0    316\n",
       "Name: HeartDisease, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b00fdc60",
   "metadata": {},
   "source": [
    "As you can see above, using bagging in case of SVM doesn't make much difference in terms of model accuracy. Bagging is effective when we have high variance and instable model such as decision tree. Let's explore how bagging changes the performance for a decision tree classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ed1c037",
   "metadata": {},
   "source": [
    "### Train a model using decision tree and then using bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b4ab0dee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.75690608, 0.8121547 , 0.77222222, 0.68333333, 0.63333333])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores = cross_val_score(DecisionTreeClassifier(), X, y, cv=5)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bb2428fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7315899324739104"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d0301440",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8103499079189687"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_model = BaggingClassifier(\n",
    "    base_estimator = DecisionTreeClassifier(),\n",
    "    n_estimators = 80,\n",
    "    max_samples = 0.8,\n",
    "    oob_score=True,\n",
    "    random_state = 0\n",
    ")\n",
    "scores = cross_val_score(bag_model, X, y, cv=5)\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a453b588",
   "metadata": {},
   "source": [
    "#### You can see that with bagging the score improved from 71.93% to 81.03%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23e21dd3",
   "metadata": {},
   "source": [
    "## Train a model using Random Forest which itself uses bagging underneath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f7596027",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8347452424800492"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "scores = cross_val_score(RandomForestClassifier(), X, y, cv=5)\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af6d7682",
   "metadata": {},
   "source": [
    "#### Random forest gave even a better performance with 82.5% as score. Underneath it used bagging where it sampled not only data rows but also the columns (or features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b1cd2a3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "407386c1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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