Different-ML-Models-on-Heart-Attack-Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#             Different-ML-Models-on-Heart-Attack-Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Random Forest\n",
    "- Logistic Regression\n",
    "- KNearest Neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier for Heart Attack Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# data splitting\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# accuracy checkers\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "\n",
    "# random-forest-classifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.read_csv('./kaggle/heart.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "d.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
       "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = d['target']\n",
    "X = d.drop('target',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
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       "      <th>0</th>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "0        0   0     1  \n",
       "1        0   0     2  \n",
       "2        2   0     2  \n",
       "3        2   0     2  \n",
       "4        2   0     2  \n",
       "..     ...  ..   ...  \n",
       "298      1   0     3  \n",
       "299      1   0     3  \n",
       "300      1   2     3  \n",
       "301      1   1     3  \n",
       "302      1   1     2  \n",
       "\n",
       "[303 rows x 13 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "298    0\n",
       "299    0\n",
       "300    0\n",
       "301    0\n",
       "302    0\n",
       "Name: target, Length: 303, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(212, 13) (91, 13)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3 )\n",
    "print(X_train.shape,X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=50)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating instance\n",
    "clf = RandomForestClassifier(n_estimators=50)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accuracy Checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8131868131868132\n"
     ]
    }
   ],
   "source": [
    "asr = accuracy_score(y_test,pred)\n",
    "print(asr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[28,  7],\n",
       "       [10, 46]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.80      0.77        35\n",
      "           1       0.87      0.82      0.84        56\n",
      "\n",
      "    accuracy                           0.81        91\n",
      "   macro avg       0.80      0.81      0.81        91\n",
      "weighted avg       0.82      0.81      0.81        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.10349015, 0.03759543, 0.13645364, 0.08542226, 0.07253931,\n",
       "       0.01055848, 0.02503171, 0.14273507, 0.05173256, 0.12790835,\n",
       "       0.04929249, 0.09092779, 0.06631276])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6578f70c88>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAD4CAYAAAAQP7oXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAbnUlEQVR4nO3deZxcVZn/8c83JAFJyEYCwyZNEhiWQCJ0IpvIpoOAAhJ+EUSIOGaQQQYZQFRG8/MnDosjghlHA6OJgsgkDOgAsu8IJB2ykKAQljjsEpKwhC2E5/fHPTFFp5Lc7q6qe7v7+3696tW3zj333qfq1cnT556q8ygiMDMzy6NH0QGYmVnn4aRhZma5OWmYmVluThpmZpabk4aZmeXWs+gA6mnw4MHR1NRUdBhmZp3KrFmzFkfEkGr7unTSaGpqoqWlpegwzMw6FUl/Xtu+Lp003nt5CS//xxVFh2FmrQz5yvFFh2Dt5DkNMzPLzUnDzMxyc9IwM7PcnDTMzCw3Jw0zM8ut0KQh6TpJsyQtkDQhtX1J0uOSZki6TNKk1D5E0jWSZqbHPkXGbmbWHRX9kduTImKJpA8BMyXdAPwLsDvwOnAHMDf1vQS4OCLuk/Rh4GZgp9YnTMlnAsDWgzZtwEswM+s+ik4ap0k6Km1vA3wBuDsilgBImgbskPYfDOwsadWx/ST1jYg3Kk8YEZOByQCjth3qYiFmZjVUWNKQtD9ZItgrIt6UdBfwJ6qMHpIewJ4R8XZjIjQzs9aKnNPoDyxNCWNHYE+gD/BxSQMl9QSOruh/C/DVVU8kjWpotGZmVmjSuAnoKemPwPnAg8BzwPeBGcD9wCLg1dT/NKBZ0jxJjwInNzxiM7NurrDbUxHxDvCp1u2SWiJichppXAtcl/ovBsY1NkozM6tU9ER4NRMlHQxsRHZL6rr2nqjnkEFeGM3MrIZKlzQi4syiYzAzs+r8jXAzM8vNScPMzHIr3e2pWnr3L4t4dtJJRYfRZWx96s+LDsHMCuaRhpmZ5eakYWZmuTlpmJlZbk4aZmaWW+FJQ1IfSTdImitpvqRxkvaQdHeqtXGzpC0k9Ux1NPZPx/2rpPMKDt/MrFspw6enDgGej4jDACT1B34PHBERL0saB5wXESdJGg9Ml/TVdNxHW5+ssp7GVgP7NOglmJl1D2VIGo8A/ybpAuB6YCkwArg11c7YAHgBICIWSPpV6rdXRLzb+mSV9TR2+/Bg19MwM6uhwpNGRDwuaXfgUOB7ZNX6FkTEXms5ZFdgGbBZg0I0M7OkDHMaWwJvRsQVwEVkt5yGSNor7e8laZe0/VlgELAf8GNJAwoK28ysWyp8pEE2crhI0vvACuArwHvApWl+oyfwI0kvkdXdOCginpE0iaxu+IkFxW1m1u0UnjQi4mbg5iq79qvStqpeOBFxad2CMjOzqgq/PWVmZp1H4SONeuq9WZMX2TMzqyGPNMzMLDcnDTMzy61L3556ffFC7rz8sKLDMDNrlwP+/oaiQ1iDRxpmZpabk4aZmeXmpGFmZrk5aZiZWW5OGmZmlltpPz0l6QTgTCCAecBK4G2gGegHnBER1xcXoZlZ91PKpJFWtT0X2DsiFksaBPwQaALGAMOAOyUNj4i3Wx371yJMmw/aqKFxm5l1dWW9PXUgMC0iFgNExJLU/l8R8X5ELASeAnZsfWBETI6I5oho7r9J78ZFbGbWDZQ1aaxN60p8rsxnZtZAZU0adwDHSNoUIN2eIrX1kDQMGAo8VlSAZmbdUSnnNFIt8POAuyWtBGanXf8LzCCbCD+59XyGmZnVVymTBkBETAWmrnouaQpwW0ScXFhQZmbdXFlvT5mZWQmVdqTRWkSMb+sxmwzevpSrRJqZdVYeaZiZWW5OGmZmllunuT3VHq+88jhTpn6y6DDMGm78ibcUHYJ1UR5pmJlZbk4aZmaWm5OGmZnlts6kIWmApFNqcSFJ4yVtWfF8kaTBtTi3mZk1xvpGGgOANZKGpPZMoI8HtlxfJzMzK6/1/ed/PjBM0hxgBVkRpKVkS5LvIOl44DSgN/AQqxPMf5IVSwrg58Az6fmVkt4C9kr9zpb0KeAt4LiIeCItF7JGsaVUY+MX6Vo9gKPTEulmZtYg60sa5wAjImKUpP2BG9LzpyXtBIwD9omIFZJ+AnweWABsFREjILvFFRHLJJ0KnBkRLakd4NWI2DVV6fsRcHi6bhOtii0BJwOXRMSVknoDG1QLuLII06abugiTmVkttXUifEZEPJ22DwL2AGamkchBZMuVPwUMlfRjSYcAr63jfFdV/Nyror1asaUHgG9K+jqwbUS8Ve2ElUWYNtmkVxtfnpmZrUtbk8byim0BUyNiVHr8bURMjIilwEjgLrLRweXrOF/k2AaIiPg18BmyW1k3SjqwjbGbmVkHrS9pvA5sspZ9twNjJW0GWaEkSdumT0T1iIhryOp8776Oc42r+PlARfsaxZYkDQWeiohLgd8Cu63/5ZmZWS2tc04jIl6RdL+k+WR/4b9Use9RSecCt0jqQTZR/o+p3y9SG8A30s8pwE9bTYQPlDQPeAc4tuLSaxRbkvR/gC9IWgG8CHy/vS/azMzaRxHlKrOdPj11fURM7+i5ttuuX3xn4p4dD8qsk/HaU9YRkmZFRHO1ff5GuJmZ5Va6kUYtNTc3R0tLS9FhmJl1Kh5pmJlZTThpmJlZbl26CNNzSxfyrWmHFB2GdXPnHXNT0SGY1YxHGmZmlpuThpmZ5VaKpCFpiqSxbejflL5waGZmDVSKpGFmZp1DIUlD0gmS5kmaK+lXqXk/SX+Q9NSqUYcyF0maL+kRSePWcVozM6uzhn96KhVTOhfYOyIWSxoE/BDYAtiXbBn03wHTgc8Co8hWzR1Mtgz7PY2O2czMMkWMNA4EpkXEYoCIWJLar0s1NB4FNk9t+wJXRcTKiHgJuBsYva6TS5ogqUVSy5uvvVunl2Bm1j2VaU7jnYpttfcklUWYNu7XuwZhmZnZKkUkjTvI6mVsClkdjnX0vRcYJ2kDSUOA/ciWTDczswI0fE4jIhZIOg+4W9JKYPY6ul9LVntjLlk1v7Mj4kVJTXUP1MzM1lDIMiIRMRWYuo79fdPPAM5Kj8r9i4ARdQzRzMyqKNOchpmZlVyXXrBwq4Hbe7E4M7Ma8kjDzMxyc9IwM7PcnDTMzCy3Lj2nsXDZcxx63Tfrdv4bj/x+3c5tZlZGHmmYmVluThpmZpZbaZKGpNMk/VHSlZLOLDoeMzNbU2mSBnAK8AlgYdGBmJlZdaVIGpJ+CgwFfg98DRgp6QFJCyV9OfXZQtI9kuakokwfKzJmM7PuqBSfnoqIkyUdAhwAnAocBewJ9AFmS7oBOBa4OSLOk7QBsHG1c0maAEwA2GhIv0aEb2bWbZQiaVTx24h4C3hL0p3AGGAm8HNJvcgKNs2pdmBETAYmA/QfvkU0KmAzs+6gFLenqmj9n31ExD1k9TSeA6ZIOqHxYZmZdW9lTRpHSNooFWran6w2+LbASxFxGXA5sHuRAZqZdUdlvT01D7gTGAz8v4h4XtKJwFmSVgBvAB5pmJk1WGmSRkQ0pc2Ja9m/zsJNZmZWf2W9PWVmZiVUmpFGPWw/YCsvKmhmVkMeaZiZWW5OGmZmlluXvj21cOliDrvmsqLDqJsbjv5y0SGYWTfjkYaZmeXmpGFmZrk5aZiZWW4NTRqSjpS0cyOvaWZmtdOhpKFMW85xJOCkYWbWSbU5aUhqkvSYpF8C84EvpIJJD0uaJqlv6ne+pEclzZP0A0l7A58BLkqFlIalx02SZkm6V9KO6djNJV0raW567J3a/yVd+z5JV7ksrJlZY7X3I7fbAycCTwD/DRwcEcslfR04Q9K/kxVS2jEiQtKAiFgm6XfA9RExHUDS7cDJEbFQ0keBnwAHApcCd0fEUangUl9Jo4GjgZFAL+BhYFbrwD5QhGnwoHa+PDMzq6a9SePPEfGgpMPJbjfdLwmgN/AA8CrwNvCfkq4Hrm99gjQi2RuYlo4F2DD9PJC0im1ErARelbQPWXGmt4G3Jf1PtcA+UIRpWJOLMJmZ1VB7k8by9FPArRFxbOsOksYABwFjyUq4HtiqSw9gWUSMamcMZmbWYB399NSDwD6ShgNI6iNphzSK6B8RNwJfI7ulBPA6sAlARLwGPC3pmHSsJK3qdzvwldS+gaT+wP3Ap1Nxpr7A4R2M3czM2qhDSSMiXgbGA1dJmkd2a2pHssRwfWq7DzgjHfIbskJKsyUNAz4PfEnSXGABcETq90/AAZIeIZu32DkiZgK/IyvQ9HvgEbLbYGZm1iBtvj0VEYuAERXP7wBGV+k6psqx97PmR24PqdLvJVYnkEo/iIiJkjYG7qHKRLiZmdVPZ1uwcHL6cuBGwNSIeLjogMzMuhNFdN0PGDU3N0dLS0vRYZiZdSqSZkVEc7V9XnvKzMxyc9IwM7PcOtucRps8sXQZn55+bdFhmJk11P+MPapu5/ZIw8zMcnPSMDOz3Jw0zMwstzYlDUkDJJ2StvdPixG25fgpksa25Zj2XsvMzGqvrSONAcAp9QjEzMzKr61J43xgmKQ5wEVkdS6mS/qTpCuV1jiX9G1JMyXNlzR5VXultfWRNFzSban40sNpjSrWdi0zM2uctiaNc4An03LmZwEfAU4nW09qKLBP6jcpIkZHxAjgQ1RfkXZtfa4E/j0iRpLV23ghta/tWh8gaYKkFkkt7772WhtfnpmZrUtHJ8JnRMSzEfE+MAdoSu0HSHoorVJ7ILBLlWPX6CNpE2CriLgWICLejog313OtD4iIyRHRHBHNvfv16+DLMzOzSh39ct87FdsrgZ6SNiIr29ocEc9Imki2wOBf5emT51odjN3MzNqorSONvxZRWodV//kvTsWSqn1aqmqfiHgdeFbSkQCSNkzLoJuZWQm06a/1iHhF0v2S5gNvAS9V6bNM0mXAfOBFYGYb+3wB+Jmk7wIrgGPaEqOZmdVPl14afcCw4fGxCy4qOgwzs4bq6NpT61oavUvPCwwfOKCuC3eZmXU3XkbEzMxyc9IwM7PcnDTMzCy3Lj2n8eTS5Rx9zYyiw7AaueboMUWHYNbteaRhZma5OWmYmVluThpmZpabk4aZmeVW06Qh6XhJMyTNkfQzSdtKWihpsKQeku6V9MnU9zpJsyQtkDSh4hxvSDov1dN4UNLmqX1Yev6IpO9JeqOWsZuZ2frVLGlI2gkYB+yT6m2sBD4OXAD8B/DPwKMRcUs65KSI2ANoBk6TtGlq7wM8mOpp3AN8ObVfAlwSEbsCz9YqbjMzy6+WI42DgD2Amamy30HA0Ii4HOgHnAycWdH/NElzgQeBbYDtU/u7wKp64LNYXTdjL2Ba2v712oKoLML0zmvLOvyizMxstVp+T0PA1Ij4xgcas6XNt05P+wKvS9ofOBjYKyLelHQXq5dLXxGrV1Fsc92MiJgMTAYYOGynrrsao5lZAWo50rgdGCtpMwBJgyRtS3Z76krg28BlqW9/YGlKGDsCe+Y4/4PA0Wn7czWM28zMcqpZ0oiIR4FzgVskzQNuJbu1NBq4ICKuBN6V9EXgJrIqf38EzidLCOtzOnBGOvdw4NVaxW5mZvnUdBmRiLgauLpV854V+z9b0f6ptZyjb8X2dGB6evocsGdEhKTPAX9bk6DNzCy3zrT21B7AJEkClgEnFRyPmVm302mSRkTcC4xsyzHDBvbxIndmZjXkb4SbmVluThpmZpabk4aZmeXWaeY02uOlZSv44bUvFh2GmXUjZxz1N0WHUFceaZiZWW5OGmZmlluHksbalieXNEXS2I6cu8o5x0uaVMtzmplZ23ikYWZmueVOGpLOkDQ/PU5vtU+SJkl6TNJtwGYV+xZJujAVT5ohaXhqHyLpGkkz02Of1D5G0gOSZkv6g6Q1lguRdFjqM7jdr9zMzNos16enJO0BfBH4KNkS6A9Juruiy1Fka0HtDGwOPAr8vGL/qxGxq6QTgB8Bh5MVVbo4Iu6T9GHgZmAn4E/AxyLiPUkHA99n9eq2SDoKOAM4NCKWVol1AjABYOCQrfK8PDMzyynvR273Ba6NiOUAkv4b+FjF/v2AqyJiJfC8pDtaHX9Vxc+L0/bBwM7ZUlIA9JPUl2zZ9KmStgcC6FVxngPJKv19MiJeqxZoZT2NbYaPdD0NM7MaatT3NKLKdg+yVWvfruyYJrvvjIijJDUBd1XsfhIYCuwAtNQrWDMzqy7vnMa9wJGSNpbUh+x21L0V++8BxknaQNIWwAGtjh9X8fOBtH0L8NVVHSSNSpv9yZZBBxjf6jx/JrtV9UtJu+SM3czMaiRX0oiIh4EpwAzgIeDyiJhd0eVaYCHZXMYvWZ0YVhmYiif9E/C11HYa0CxpnqRHyWqIA1wI/Kuk2VQZCUXEn4DPA9MkDcsTv5mZ1YZWl+Ou0wWkRUBzRCyu64Wq2Gb4yPjaRTc3+rJm1o11hWVEJM2KiOZq+/w9DTMzy63uE+ER0VTva6zN5gN6dYmsb2ZWFh5pmJlZbk4aZmaWW5eup/H6kve464qXiw7D2mj/44cUHYKZrYVHGmZmlpuThpmZ5eakYWZmuTU0aUi6S1LVL4yYmVn5eaRhZma51S1pSOoj6QZJc1PhpnGt9h+bCjPNl3RBRfsbki6WtEDS7ZKGpPZhkm6SNEvSvZJ2rFfsZmZWXT1HGocAz0fEyIgYAdy0aoekLYELyOpjjAJGSzoy7e4DtETELsDdwHdS+2TgqxGxB3Am8JNqF5U0QVKLpJZXX3ulHq/LzKzbqmfSeAT4hKQLJH0sIl6t2DcauCsiXo6I94AryQo5AbwPXJ22rwD2TcWZ9iZb2XYO8DNgi2oXjYjJEdEcEc39+21ah5dlZtZ91e3LfRHxuKTdgUOB70m6vb2nIktuyyJi1Po6m5lZ/dRzTmNL4M2IuAK4CNi9YvcM4OOSBkvaADiW7FbUqpjGpu3jgPtSadenJR2Tzi1JI+sVu5mZVVfP21O7AjPS7aTvAN9btSMiXgDOAe4E5gKzIuK3afdyYIyk+WRzHt9N7Z8HviRpLrAAOKKOsZuZWRX1vD11M9C6AtL+FfuvAq5ay7FnVGl7mmxy3czMCuLvaZiZWW6lW+U2IvrW6lybDOrpFVPNzGrIIw0zM8vNScPMzHIr3e2pWlrx4gpeuPCFosMwK70tzq76XVmzNXikYWZmuTlpmJlZbk4aZmaWm5OGmZnl5qRhZma5lTZpSDpB0rxUxOlXkj4t6SFJsyXdJmnzomM0M+tuSvmRW0m7AOcCe0fEYkmDyJZI3zMiQtLfA2cD/1zl2AnABICtBmzVwKjNzLq+UiYNstVtp0XEYoCIWCJpV+BqSVsAvYGnqx0YEZPJqvwxcuuR0aB4zcy6hdLenqrix8CkiNgV+Adgo4LjMTPrdsqaNO4AjpG0KUC6PdUfeC7tP7GowMzMurNS3p6KiAWSzgPulrQSmA1MJKsRvpQsqWxXYIhmZt1SKZMGQERMBaa2av5ttb5mZtYYpU0atdDrb3p5ITYzsxoq65yGmZmVkJOGmZnl5qRhZma5dek5jRV/eYOXLr2v6DCsi9j8tH2LDsGscB5pmJlZbk4aZmaWWyFJQ9IASaek7f0lXd/G46dIGluf6MzMbG2KGmkMAE4p6NpmZtZORU2Enw8MkzQHWAEslzQdGAHMAo5PS6B/G/g08CHgD8A/RIRXrjUzK0hRI41zgCcjYhRwFvAR4HRgZ2AosE/qNykiRkfECLLEcfj6TixpgqQWSS1L3lhWn+jNzLqpskyEz4iIZyPifWAO0JTaD0jV+h4hq7Gxy/pOFBGTI6I5IpoH9R1Qv4jNzLqhsnxP452K7ZVAT0kbAT8BmiPiGUkTcQ0NM7NCFTXSeB3YZD19ViWIxZL6Av60lJlZwQoZaUTEK5LulzQfeAt4qUqfZZIuA+YDLwIzGxymmZm1UtjtqYg4bi3tp1ZsnwucW6XP+PpFZmZma1OWiXAzM+sEyjIRXhe9NuvrRebMzGrIIw0zM8tNXfkL1pJeBx4rOo42GgwsLjqINupsMXe2eMExN0JnixfqF/O2ETGk2o4ufXsKeCwimosOoi0ktTjm+ups8YJjboTOFi8UE7NvT5mZWW5OGmZmlltXTxqTiw6gHRxz/XW2eMExN0JnixcKiLlLT4SbmVltdfWRhpmZ1ZCThpmZ5dZpk4akQyQ9JukJSedU2b+hpKvT/ockNVXs+0Zqf0zS35U5XkmfkDRL0iPp54GNiLcjMVfs/7CkNySd2RlilrSbpAckLUjvd0OW4u/A70YvSVNTrH+U9I2SxLufpIclvSdpbKt9J0pamB4nNiLejsQsaVTF78Q8SePKHnPF/n6SnpU0qaaBRUSnewAbAE+SVfnrDcwFdm7V5xTgp2n7c8DVaXvn1H9DYLt0ng1KHO9HgC3T9gjgubK/xxX7pwPTgDPLHjPZd5bmASPT803r/XtRg5iPA36TtjcGFgFNJYi3CdgN+CUwtqJ9EPBU+jkwbQ8syXu8tph3ALZP21sCLwADyhxzxf5LgF+TVUCtWWyddaQxBngiIp6KiHeB3wBHtOpzBDA1bU8HDpKk1P6biHgnIp4GnkjnK2W8ETE7Ip5P7QuAD0nasM7xdihmAElHAk+nmBulIzF/EpgXEXMhW74/IlaWPOYA+kjqSVYO+V3gtaLjjYhFETEPeL/VsX8H3BoRSyJiKXArcEid4+1QzBHxeEQsTNvPA38Bqn5TuiwxA0jaA9gcuKXWgXXWpLEV8EzF82dTW9U+EfEe8CrZX495jq21jsRb6Wjg4Yh4h/prd8zKimZ9Hfi/DYizajxJW97nHYCQdHMa8p/dgHg/EE/SlpinA8vJ/vr9X+AHEbGkBPHW49iOqMl1JY0h+6v/yRrFtS7tjllSD+DfgLrcFu7qy4h0GZJ2AS4g+4u47CYCF0fEG2ng0Rn0BPYFRgNvArdLmhURtxcb1jqNISuPvCXZ7Z57Jd0WEU8VG1bXI2kL4FfAiRGxxl/2JXMKcGNEPFuPf3+ddaTxHLBNxfOtU1vVPmn43h94JeextdaReJG0NXAtcEJENOKvnA/Ek7Ql5o8CF0paBJwOfFPSqdRfR2J+FrgnIhZHxJvAjcDudY+4YzEfB9wUESsi4i/A/UC91yHqyL+fIv7tdfi6kvoBNwDfiogHaxzb2nQk5r2AU9O/vx8AJ0g6v2aR1XtCp06TRD3JJtG2Y/Uk0S6t+vwjH5w8/K+0vQsfnAh/ivpPhHck3gGp/2c7y3vcqs9EGjcR3pH3eSDwMNmEck/gNuCwksf8deAXabsP8CiwW9HxVvSdwpoT4U+n93pg2h5Uhvd4HTH3Bm4HTm/E73AtYm61bzw1nghv2JtQhzf1UOBxsvuL30pt3wU+k7Y3IvvkzhPADGBoxbHfSsc9BnyqzPGSlbtdDsypeGxW5phbnWMiDUoaNfi9OJ5s4n4+cGHZYwb6pvYFZAnjrJLEO5ps5LacbES0oOLYk9LreAL4Yone46oxp9+JFa3+/Y0qc8ytzjGeGicNLyNiZma5ddY5DTMzK4CThpmZ5eakYWZmuTlpmJlZbk4aZmaWm5OGmZnl5qRhZma5/X92fqKn2pBitwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=clf.feature_importances_, y=X.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genrating Model on Selected Relevant Features from feature importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = d[['thalach','cp','oldpeak','ca','age','chol','trestbps','exang','slope','thal','fbs']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(212, 11) (91, 11)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape,X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_ = RandomForestClassifier(n_estimators=50)\n",
    "clf_.fit(X_train, y_train)\n",
    "pred_ = clf_.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8351648351648352\n"
     ]
    }
   ],
   "source": [
    "asr = accuracy_score(y_test, pred_)\n",
    "print(asr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[38,  6],\n",
       "       [ 9, 38]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, pred_)\n",
    "# Out of 47 actually positives, 38 seem to have predicted correctly\n",
    "# Out of 44 actually negatives, 38 seem to have predicted correctly\n",
    "# 6 were negative but predicted as positive\n",
    "# 9 were positive but predicted as negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.86      0.84        44\n",
      "           1       0.86      0.81      0.84        47\n",
      "\n",
      "    accuracy                           0.84        91\n",
      "   macro avg       0.84      0.84      0.84        91\n",
      "weighted avg       0.84      0.84      0.84        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression Classifier on Heart csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_, X_test_, y_train_, y_test_ = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling\n",
    "X_train_ = scaler.fit_transform(X_train_)\n",
    "X_test_ = scaler.transform(X_test_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgr.fit(X_train_ ,y_train_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_lgr = lgr.predict(X_test_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accuracy Checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8901098901098901\n"
     ]
    }
   ],
   "source": [
    "asl = accuracy_score(y_test_, pred_lgr)\n",
    "print(asl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20, 24],\n",
       "       [16, 31]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, pred_lgr)\n",
    "# Out of 36 actually positives, 20 seem to have predicted correctly\n",
    "# Out of 55 actually negatives, 31 seem to have predicted correctly\n",
    "# 24 were negative but predicted as positive\n",
    "# 16 were positive but predicted as negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.56      0.45      0.50        44\n",
      "           1       0.56      0.66      0.61        47\n",
      "\n",
      "    accuracy                           0.56        91\n",
      "   macro avg       0.56      0.56      0.55        91\n",
      "weighted avg       0.56      0.56      0.56        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_lgr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNeighborsClassifier on Heart csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_knn = KNeighborsClassifier(n_neighbors=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_k, X_test_k, y_train_k, y_test_k = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling\n",
    "X_train_k = scaler.fit_transform(X_train_k)\n",
    "X_test_k = scaler.transform(X_test_k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=10)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_knn.fit(X_train_k, y_train_k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_knn = clf_knn.predict(X_test_k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accuracy Checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8351648351648352\n"
     ]
    }
   ],
   "source": [
    "ask = accuracy_score(y_test_k, pred_knn)\n",
    "print(ask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[30,  9],\n",
       "       [ 6, 46]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test_k, pred_knn)\n",
    "# Out of 36 actually positives, 30 seem to have predicted correctly\n",
    "# Out of 55 actually negatives, 46 seem to have predicted correctly\n",
    "# 9 were negative but predicted as positive\n",
    "# 6 were positive but predicted as negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.77      0.80        39\n",
      "           1       0.84      0.88      0.86        52\n",
      "\n",
      "    accuracy                           0.84        91\n",
      "   macro avg       0.83      0.83      0.83        91\n",
      "weighted avg       0.84      0.84      0.83        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test_k, pred_knn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison of accuracy of different classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8351648351648352, 0.8901098901098901, 0.8351648351648352]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_score = [asr, asl, ask] # accuracy scores collected earlier\n",
    "# asr - accuracy score random forest\n",
    "# asl - accuracy score logistic\n",
    "# ask - accuracy score knn\n",
    "acc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifiers = ['Random Forest', 'Logistic Regression', 'KNeighborsClassifier']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f65767c83c8>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=classifiers, y=acc_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:** Depends on how many times the model is getting trained, may differ on maximum iterations,features selected and sizes of testing and training sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}