Coronary Heart Disease Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1c50c9eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5bbc24be",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"heart.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f2b72654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e275287c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2b97d387",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>3</th>\n",
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       "      <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",
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       "      <th>5</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
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       "      <td>140</td>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>7</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52</td>\n",
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       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>199</td>\n",
       "      <td>1</td>\n",
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       "      <td>162</td>\n",
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       "      <td>150</td>\n",
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      "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",
       "5   57    1   0       140   192    0        1      148      0      0.4      1   \n",
       "6   56    0   1       140   294    0        0      153      0      1.3      1   \n",
       "7   44    1   1       120   263    0        1      173      0      0.0      2   \n",
       "8   52    1   2       172   199    1        1      162      0      0.5      2   \n",
       "9   57    1   2       150   168    0        1      174      0      1.6      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  \n",
       "5   0     1       1  \n",
       "6   0     2       1  \n",
       "7   0     3       1  \n",
       "8   0     3       1  \n",
       "9   0     2       1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "28ae99b6",
   "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": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "581c8e7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <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",
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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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5b87901a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import missingno\n",
    "missingno.matrix(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ac50ee0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    303.000000\n",
       "mean       0.544554\n",
       "std        0.498835\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        1.000000\n",
       "75%        1.000000\n",
       "max        1.000000\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"target\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b49b1050",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"target\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e49ab51d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fbs         0.028046\n",
      "chol        0.085239\n",
      "restecg     0.137230\n",
      "trestbps    0.144931\n",
      "age         0.225439\n",
      "sex         0.280937\n",
      "thal        0.344029\n",
      "slope       0.345877\n",
      "ca          0.391724\n",
      "thalach     0.421741\n",
      "oldpeak     0.430696\n",
      "cp          0.433798\n",
      "exang       0.436757\n",
      "target      1.000000\n",
      "Name: target, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(dataset.corr()[\"target\"].abs().sort_values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d0be1776",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098447</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>0.279351</td>\n",
       "      <td>0.213678</td>\n",
       "      <td>0.121308</td>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.398522</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>-0.168814</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.068001</td>\n",
       "      <td>-0.225439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.098447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.280937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>-0.068653</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.433798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>0.279351</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>-0.144931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.213678</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.085239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>0.121308</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.028046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>0.137230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>-0.398522</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.421741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>-0.436757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.210013</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-0.168814</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.345877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>-0.391724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>0.068001</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.225439</td>\n",
       "      <td>-0.280937</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.144931</td>\n",
       "      <td>-0.085239</td>\n",
       "      <td>-0.028046</td>\n",
       "      <td>0.137230</td>\n",
       "      <td>0.421741</td>\n",
       "      <td>-0.436757</td>\n",
       "      <td>-0.430696</td>\n",
       "      <td>0.345877</td>\n",
       "      <td>-0.391724</td>\n",
       "      <td>-0.344029</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex        cp  trestbps      chol       fbs  \\\n",
       "age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
       "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
       "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
       "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
       "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
       "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
       "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
       "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
       "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
       "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "\n",
       "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
       "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
       "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
       "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
       "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
       "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
       "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
       "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
       "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
       "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "\n",
       "              thal    target  \n",
       "age       0.068001 -0.225439  \n",
       "sex       0.210041 -0.280937  \n",
       "cp       -0.161736  0.433798  \n",
       "trestbps  0.062210 -0.144931  \n",
       "chol      0.098803 -0.085239  \n",
       "fbs      -0.032019 -0.028046  \n",
       "restecg  -0.011981  0.137230  \n",
       "thalach  -0.096439  0.421741  \n",
       "exang     0.206754 -0.436757  \n",
       "oldpeak   0.210244 -0.430696  \n",
       "slope    -0.104764  0.345877  \n",
       "ca        0.151832 -0.391724  \n",
       "thal      1.000000 -0.344029  \n",
       "target   -0.344029  1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5bbbbea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(dataset.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "57933b76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    165\n",
      "0    138\n",
      "Name: target, dtype: int64\n"
     ]
    },
    {
     "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": [
    "y = dataset[\"target\"]\n",
    "sns.countplot(y)\n",
    "new = dataset.target.value_counts()\n",
    "print(new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "05289a5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of patience without heart problems: 45.54\n",
      "Percentage of patience with heart problems: 54.46\n"
     ]
    }
   ],
   "source": [
    "print(\"Percentage of patience without heart problems: \"+str(round(new[0]*100/303,2)))\n",
    "print(\"Percentage of patience with heart problems: \"+str(round(new[1]*100/303,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "33b3f1cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"sex\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9678f7b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sex', ylabel='target'>"
      ]
     },
     "execution_count": 18,
     "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(dataset[\"sex\"],y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b8c6706c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"cp\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cd05114a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cp', ylabel='target'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y04CtSe5i8EvgZ6vqUz1nmhE/PipJjfOMQJIaZxFIUuMsAklqnEUgSY2zCCSpcRaBJDXOIpCkxnV2z2JpoUvyOuAtDK44eRfwKPBDYAI4G7hmvn7BSG3xC2XSDCR5LvBvwK9U1eEkTwbeAywHXgWcz+DaM8+sqh/2l1Q6MaeGpJl5GfDxqjoMUFWP3TNhU1UdrapvAHuB5/QVUBqXRSDNrqmn2J5y65RnEUgz83ngd5I8BWA4NcRw36Ik5wM/B+zuK6A0LheLpRmoqh1J/hq4NcmjwFeGh+4BvsRgsfgNrg9oPnCxWJolST4AfKqqbu47i3QynBqSpMZ5RiBJjfOMQJIaZxFIUuMsAklqnEUgSY2zCCSpcf8HjOrUQryR7ggAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(dataset[\"cp\"],y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fe156a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cp', ylabel='Density'>"
      ]
     },
     "execution_count": 21,
     "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.distplot(dataset[\"cp\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bbdef617",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"fbs\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5e94f7fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='fbs', ylabel='target'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QpMYZAklqnCGQpMYZAukMJXlPkkNJPpXk/eOeR3q6DIF05n4TuBy4a9yDSGeDIZDOQJI/BX4Y+BzwPuDHk3wxyV1J3tlb84IkX0hyR5KvJfnpcc4sLcQ3lElnKMndwCRz787+ZeDVwCXAV4BXAVuBi6vqd3uf/fCMqnp4TONKC1o67gGk89wtVfVd4LtJbmXusx72Azck+T7gM1V1xzgHlBbiU0PS0zN4Sl29D1Z5LXN3dP1Ekrcu/ljS6AyB9PRsTnJxkucCrwP2J3kh8F9VdR1wPfDycQ4oLcSnhqSn507gVmA58OGq+o8kbwM+kOQx4BHAMwKd07xYLEmN86khSWqcIZCkxhkCSWqcIZCkxhkCSWqcIZCkxhkCSWrc/wJzd7FSAM7wbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(dataset[\"fbs\"],y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3d2135b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"restecg\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cfe5adcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='restecg', ylabel='target'>"
      ]
     },
     "execution_count": 25,
     "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(dataset[\"restecg\"],y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7634fb03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"exang\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3e0520ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='exang', ylabel='target'>"
      ]
     },
     "execution_count": 27,
     "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(dataset[\"exang\"],y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5b8bc603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1], dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"slope\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "32bf713b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='slope', ylabel='target'>"
      ]
     },
     "execution_count": 29,
     "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(dataset[\"slope\"],y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6ed09281",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, 3, 4], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"ca\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2f9b9def",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ca', ylabel='target'>"
      ]
     },
     "execution_count": 31,
     "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(dataset[\"ca\"],y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bebb8529",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 0], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"thal\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1a7dc421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='thal', ylabel='target'>"
      ]
     },
     "execution_count": 33,
     "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(dataset[\"thal\"],y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "72931a11",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "predict = dataset.drop(\"target\",axis=1)\n",
    "target = dataset[\"target\"]\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(predict,target,test_size=0.20,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ac238fdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(242, 13)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5aa677e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61, 13)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6a04ccb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(242,)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5c4fe1b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "49344083",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "352d00ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train,Y_train)\n",
    "Y_pred_lr = lr.predict(X_test)\n",
    "Y_pred_lr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1316144e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression : 85.25 %\n"
     ]
    }
   ],
   "source": [
    "lr_score = round(accuracy_score(Y_pred_lr,Y_test)*100,2)\n",
    "print(\"Logistic Regression : \"+ str(lr_score) + \" %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "302eeb92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61,)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "nb = GaussianNB()\n",
    "nb.fit(X_train,Y_train)\n",
    "Y_pred_nb = nb.predict(X_test)\n",
    "Y_pred_nb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a465f9b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes: 85.25 %\n"
     ]
    }
   ],
   "source": [
    "nb_score = round(accuracy_score(Y_pred_nb,Y_test)*100,2)\n",
    "print(\"Naive Bayes: \"+str(nb_score)+\" %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2c506191",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61,)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "sv = svm.SVC(kernel='linear')\n",
    "sv.fit(X_train, Y_train)\n",
    "Y_pred_svm = sv.predict(X_test)\n",
    "Y_pred_svm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1c0b2d3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Linear SVM : 81.97 %\n"
     ]
    }
   ],
   "source": [
    "svm_score = round(accuracy_score(Y_pred_svm,Y_test)*100,2)\n",
    "print(\" Linear SVM : \"+str(svm_score)+\" %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "76d6204e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61,)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=7)\n",
    "knn.fit(X_train,Y_train)\n",
    "Y_pred_knn=knn.predict(X_test)\n",
    "Y_pred_knn.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5cbc66bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " KNN : 67.21 %\n"
     ]
    }
   ],
   "source": [
    "knn_score = round(accuracy_score(Y_pred_knn,Y_test)*100,2)\n",
    "print(\" KNN : \"+str(knn_score)+\" %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d00c71ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(61,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "max_accuracy = 0\n",
    "\n",
    "for x in range(200):\n",
    "    dt = DecisionTreeClassifier(random_state=x)\n",
    "    dt.fit(X_train,Y_train)\n",
    "    Y_pred_dt = dt.predict(X_test)\n",
    "    current_accuracy = round(accuracy_score(Y_pred_dt,Y_test)*100,2)\n",
    "    if(current_accuracy>max_accuracy):\n",
    "        max_accuracy = current_accuracy\n",
    "        best_x = x\n",
    "\n",
    "dt = DecisionTreeClassifier(random_state=best_x)\n",
    "dt.fit(X_train,Y_train)\n",
    "Y_pred_dt = dt.predict(X_test)\n",
    "print(Y_pred_dt.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "78cbf5b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Decision Tree : 81.97 %\n"
     ]
    }
   ],
   "source": [
    "dt_score = round(accuracy_score(Y_pred_dt,Y_test)*100,2)\n",
    "print(\" Decision Tree : \"+str(dt_score)+\" %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2a59a48e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "max_accuracy = 0\n",
    "\n",
    "for x in range(2000):\n",
    "    rf = RandomForestClassifier(random_state=x)\n",
    "    rf.fit(X_train,Y_train)\n",
    "    Y_pred_rf = rf.predict(X_test)\n",
    "    current_accuracy = round(accuracy_score(Y_pred_rf,Y_test)*100,2)\n",
    "    if(current_accuracy>max_accuracy):\n",
    "        max_accuracy = current_accuracy\n",
    "        best_x = x\n",
    "\n",
    "rf = RandomForestClassifier(random_state=best_x)\n",
    "rf.fit(X_train,Y_train)\n",
    "Y_pred_rf = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4080d646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61,)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred_rf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "af10a61b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest : 90.16 %\n"
     ]
    }
   ],
   "source": [
    "rf_score = round(accuracy_score(Y_pred_rf,Y_test)*100,2)\n",
    "print(\"Random Forest : \"+str(rf_score)+\" %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "535e31d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:19:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(61,)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "xgb_model = xgb.XGBClassifier(objective=\"binary:logistic\", random_state=42)\n",
    "xgb_model.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred_xgb = xgb_model.predict(X_test)\n",
    "Y_pred_xgb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8cc494c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " XGBoost : 78.69 %\n"
     ]
    }
   ],
   "source": [
    "xgb_score = round(accuracy_score(Y_pred_xgb,Y_test)*100,2)\n",
    "\n",
    "print(\" XGBoost : \"+str(xgb_score)+\" %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1df32127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Algorithms', ylabel='Accuracy score'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "overall = [lr_score,nb_score,svm_score,knn_score,dt_score,rf_score,xgb_score]\n",
    "algorithms = [\"Logistic Regression\",\"Naive Bayes\",\"Support Vector Machine\",\"K-Nearest Neighbors\",\"Decision Tree\",\"Random Forest\",\"XGBoost\"]    \n",
    "\n",
    "sns.set(rc={'figure.figsize':(15,8)})\n",
    "plt.xlabel(\"Algorithms\")\n",
    "plt.ylabel(\"Accuracy score\")\n",
    "\n",
    "sns.barplot(algorithms,overall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66fde194",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9a5cb30",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}