Heart_Disease.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ea3b7d26",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "from matplotlib.cm import rainbow\n",
    "%matplotlib inline\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "53802c30",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "853f3f14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.0\n",
       "1      1.0\n",
       "2      0.0\n",
       "3      1.0\n",
       "4      0.0\n",
       "      ... \n",
       "913    1.0\n",
       "914    1.0\n",
       "915    1.0\n",
       "916    1.0\n",
       "917    0.0\n",
       "Name: HeartDisease, Length: 918, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt=pd.read_csv('heart.csv')\n",
    "dt['HeartDisease'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "86437d2a",
   "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",
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       "    }\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>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>156</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
       "      <td>98</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>108</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>122</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>39</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>120</td>\n",
       "      <td>339</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>170</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>45</td>\n",
       "      <td>F</td>\n",
       "      <td>ATA</td>\n",
       "      <td>130</td>\n",
       "      <td>237</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>170</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>110</td>\n",
       "      <td>208</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>142</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>140</td>\n",
       "      <td>207</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>130</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>ATA</td>\n",
       "      <td>120</td>\n",
       "      <td>284</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>120</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>37</td>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>130</td>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>142</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>58</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>136</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
       "      <td>99</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>39</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>120</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>145</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>49</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>140</td>\n",
       "      <td>234</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>140</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>42</td>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>115</td>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
       "      <td>137</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "0    40   M           ATA        140          289          0     Normal   \n",
       "1    49   F           NAP        160          180          0     Normal   \n",
       "2    37   M           ATA        130          283          0         ST   \n",
       "3    48   F           ASY        138          214          0     Normal   \n",
       "4    54   M           NAP        150          195          0     Normal   \n",
       "5    39   M           NAP        120          339          0     Normal   \n",
       "6    45   F           ATA        130          237          0     Normal   \n",
       "7    54   M           ATA        110          208          0     Normal   \n",
       "8    37   M           ASY        140          207          0     Normal   \n",
       "9    48   F           ATA        120          284          0     Normal   \n",
       "10   37   F           NAP        130          211          0     Normal   \n",
       "11   58   M           ATA        136          164          0         ST   \n",
       "12   39   M           ATA        120          204          0     Normal   \n",
       "13   49   M           ASY        140          234          0     Normal   \n",
       "14   42   F           NAP        115          211          0         ST   \n",
       "\n",
       "    MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "0     172              N      0.0       Up             0  \n",
       "1     156              N      1.0     Flat             1  \n",
       "2      98              N      0.0       Up             0  \n",
       "3     108              Y      1.5     Flat             1  \n",
       "4     122              N      0.0       Up             0  \n",
       "5     170              N      0.0       Up             0  \n",
       "6     170              N      0.0       Up             0  \n",
       "7     142              N      0.0       Up             0  \n",
       "8     130              Y      1.5     Flat             1  \n",
       "9     120              N      0.0       Up             0  \n",
       "10    142              N      0.0       Up             0  \n",
       "11     99              Y      2.0     Flat             1  \n",
       "12    145              N      0.0       Up             0  \n",
       "13    140              Y      1.0     Flat             1  \n",
       "14    137              N      0.0       Up             0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Logistic Regression\n",
    "\n",
    "dt.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b41dd101",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex  ChestPainType  RestingBP  Cholesterol  FastingBS  RestingECG  \\\n",
       "0   40    1              1        140          289          0           1   \n",
       "1   49    0              2        160          180          0           1   \n",
       "2   37    1              1        130          283          0           2   \n",
       "3   48    0              0        138          214          0           1   \n",
       "4   54    1              2        150          195          0           1   \n",
       "\n",
       "   MaxHR  ExerciseAngina  Oldpeak  ST_Slope  HeartDisease  \n",
       "0    172               0      0.0         2             0  \n",
       "1    156               0      1.0         1             1  \n",
       "2     98               0      0.0         2             0  \n",
       "3    108               1      1.5         1             1  \n",
       "4    122               0      0.0         2             0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder    \n",
    "\n",
    "leb=LabelEncoder()\n",
    "\n",
    "dt.ChestPainType=leb.fit_transform(dt.ChestPainType)\n",
    "dt.RestingECG = leb.fit_transform(dt.RestingECG)\n",
    "dt.ExerciseAngina = leb.fit_transform(dt.ExerciseAngina)\n",
    "dt.ST_Slope = leb.fit_transform(dt.ST_Slope)\n",
    "dt.Sex=leb.fit_transform(dt.Sex)\n",
    "dt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5da1be16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    508\n",
       "0    410\n",
       "Name: HeartDisease, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.HeartDisease.value_counts()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dfbef74e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='HeartDisease', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9YXc2si3JrUme3dWfl+SL3dgPJ3lCv/+Smo08U9Bs9dgk24a2T+LH03dcAfx9VX0uyZMZfJr7qcDXgedU1b4k5wB/BbyoO+aZwNOq6r4kZwGvGzozOGuS7/8V4PWT1C8FTq2qB5I8sav9GfCpqnplV9uS5D+AvcBvVNUPkywDrgbGGJylfKKq1nXPw3hckvnAnwPnVNX3k/wJ8EcMwkmaMkNBs9UPqur0AxtJLmTwBxXgHGB50iaIPSHJ8cBPARu6P8AFHDP0etdX1UOZ13+y2WcBbgE+mORa4Nqu9jzgvANnE8BxwJMZTEz4zu5y1H7g57v9NwJXJTkGuLaqtiX5NQYPSfp893M9hsH0ENJDYihoLjoKeGZV/WC4mOQdwKer6neSLAVuGNr9/Yf4Pc5gMJfURM8HngOcB7wxyWkMAuRFVXX7hH7eDNwDPL3r+YcwePBMkud0r/X+JG8Dvs0guF7yEPuUDuI9Bc1Fn6S73g/txjAMzhT+u1u/8AjH3w8cf7idSZ4GvJHBY06H60cBS6rq08AbgCcCT2Bw+erV6f6Ln+SMoX72VNWPgJcD87r9PwPsrap/At4N/DLwJeDMJD/XjXlckgNnFtKUGQqaiy4BxrqbvTuAV3X1vwH+Osnn6f4AH8YtwL4kXx260fzsA29JZRAGl1TV5gnHzQM+kORrwM0M7mt8B3grg0tVt3QPqH9rN/5KYE2SLzG4dHTgbOUsYFuSmxnc87iiqsYZBNnVSW5hEBK/8JD+VSScJVWSNMQzBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEnN/wPxhQ61u5++RwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"HeartDisease\", data=dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "136789db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Sex\",data=dt)\n",
    "plt.show()\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c7ee3053",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 17,
     "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": [
    "dt[\"Age\"].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4c5557aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"RestingECG\", data=dt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a49c569c",
   "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>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>918 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age    Sex  ChestPainType  RestingBP  Cholesterol  FastingBS  \\\n",
       "0    False  False          False      False        False      False   \n",
       "1    False  False          False      False        False      False   \n",
       "2    False  False          False      False        False      False   \n",
       "3    False  False          False      False        False      False   \n",
       "4    False  False          False      False        False      False   \n",
       "..     ...    ...            ...        ...          ...        ...   \n",
       "913  False  False          False      False        False      False   \n",
       "914  False  False          False      False        False      False   \n",
       "915  False  False          False      False        False      False   \n",
       "916  False  False          False      False        False      False   \n",
       "917  False  False          False      False        False      False   \n",
       "\n",
       "     RestingECG  MaxHR  ExerciseAngina  Oldpeak  ST_Slope  HeartDisease  \n",
       "0         False  False           False    False     False         False  \n",
       "1         False  False           False    False     False         False  \n",
       "2         False  False           False    False     False         False  \n",
       "3         False  False           False    False     False         False  \n",
       "4         False  False           False    False     False         False  \n",
       "..          ...    ...             ...      ...       ...           ...  \n",
       "913       False  False           False    False     False         False  \n",
       "914       False  False           False    False     False         False  \n",
       "915       False  False           False    False     False         False  \n",
       "916       False  False           False    False     False         False  \n",
       "917       False  False           False    False     False         False  \n",
       "\n",
       "[918 rows x 12 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "55e2eefe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age               0\n",
       "Sex               0\n",
       "ChestPainType     0\n",
       "RestingBP         0\n",
       "Cholesterol       0\n",
       "FastingBS         0\n",
       "RestingECG        0\n",
       "MaxHR             0\n",
       "ExerciseAngina    0\n",
       "Oldpeak           0\n",
       "ST_Slope          0\n",
       "HeartDisease      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fcb6631b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "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(dt.isnull(), yticklabels=False,cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9e566f03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0,\n",
       "       1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1,\n",
       "       1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1,\n",
       "       1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0,\n",
       "       1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1,\n",
       "       0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0,\n",
       "       1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1,\n",
       "       1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
       "       1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0,\n",
       "       1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1,\n",
       "       0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = dt.HeartDisease.values\n",
    "X = dt.drop(['HeartDisease'], axis = 1)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "58ee1332",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test= train_test_split(X,y,test_size=0.2,random_state=15)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "clf = LogisticRegression().fit(X_train,y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test,y_pred)\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy=accuracy_score(y_test,y_pred)\n",
    "accuracy\n",
    "from sklearn.metrics import accuracy_score,recall_score,precision_score,f1_score\n",
    "\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn import preprocessing\n",
    "\n",
    "iris = load_iris()\n",
    "print(iris.data.shape)\n",
    "\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "normalized_X = preprocessing.normalize(X)\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ca0111c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score : 0.8369565217391305\n",
      "Precision Score : 0.8392857142857143\n",
      "Recall Score : 0.8867924528301887\n",
      "F1 Score : 0.8623853211009174\n",
      "Confusion Matrix : \n",
      "[[60 18]\n",
      " [12 94]]\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy Score : ' + str(accuracy_score(y_test,y_pred)))\n",
    "print('Precision Score : ' + str(precision_score(y_test,y_pred)))\n",
    "print('Recall Score : ' + str(recall_score(y_test,y_pred)))\n",
    "print('F1 Score : ' + str(f1_score(y_test,y_pred)))\n",
    "from sklearn.metrics import confusion_matrix\n",
    "print('Confusion Matrix : \\n' + str(confusion_matrix(y_test,y_pred)))\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e2c2238b",
   "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>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.510893</td>\n",
       "      <td>0.789760</td>\n",
       "      <td>0.781046</td>\n",
       "      <td>132.396514</td>\n",
       "      <td>198.799564</td>\n",
       "      <td>0.233115</td>\n",
       "      <td>0.989107</td>\n",
       "      <td>136.809368</td>\n",
       "      <td>0.404139</td>\n",
       "      <td>0.887364</td>\n",
       "      <td>1.361656</td>\n",
       "      <td>0.553377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.432617</td>\n",
       "      <td>0.407701</td>\n",
       "      <td>0.956519</td>\n",
       "      <td>18.514154</td>\n",
       "      <td>109.384145</td>\n",
       "      <td>0.423046</td>\n",
       "      <td>0.631671</td>\n",
       "      <td>25.460334</td>\n",
       "      <td>0.490992</td>\n",
       "      <td>1.066570</td>\n",
       "      <td>0.607056</td>\n",
       "      <td>0.497414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>173.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>267.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.000000</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>603.000000</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>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age         Sex  ChestPainType   RestingBP  Cholesterol  \\\n",
       "count  918.000000  918.000000     918.000000  918.000000   918.000000   \n",
       "mean    53.510893    0.789760       0.781046  132.396514   198.799564   \n",
       "std      9.432617    0.407701       0.956519   18.514154   109.384145   \n",
       "min     28.000000    0.000000       0.000000    0.000000     0.000000   \n",
       "25%     47.000000    1.000000       0.000000  120.000000   173.250000   \n",
       "50%     54.000000    1.000000       0.000000  130.000000   223.000000   \n",
       "75%     60.000000    1.000000       2.000000  140.000000   267.000000   \n",
       "max     77.000000    1.000000       3.000000  200.000000   603.000000   \n",
       "\n",
       "        FastingBS  RestingECG       MaxHR  ExerciseAngina     Oldpeak  \\\n",
       "count  918.000000  918.000000  918.000000      918.000000  918.000000   \n",
       "mean     0.233115    0.989107  136.809368        0.404139    0.887364   \n",
       "std      0.423046    0.631671   25.460334        0.490992    1.066570   \n",
       "min      0.000000    0.000000   60.000000        0.000000   -2.600000   \n",
       "25%      0.000000    1.000000  120.000000        0.000000    0.000000   \n",
       "50%      0.000000    1.000000  138.000000        0.000000    0.600000   \n",
       "75%      0.000000    1.000000  156.000000        1.000000    1.500000   \n",
       "max      1.000000    2.000000  202.000000        1.000000    6.200000   \n",
       "\n",
       "         ST_Slope  HeartDisease  \n",
       "count  918.000000    918.000000  \n",
       "mean     1.361656      0.553377  \n",
       "std      0.607056      0.497414  \n",
       "min      0.000000      0.000000  \n",
       "25%      1.000000      0.000000  \n",
       "50%      1.000000      1.000000  \n",
       "75%      2.000000      1.000000  \n",
       "max      2.000000      1.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#knn\n",
    "dt.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "af3f274b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dt.HeartDisease.values\n",
    "x = dt.drop(['HeartDisease'], axis = 1)\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8d2815bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c7ddd307",
   "metadata": {},
   "outputs": [],
   "source": [
    "a_score=[]\n",
    "for k in range(1,18,2):\n",
    "  knn=KNeighborsClassifier(n_neighbors=k)\n",
    "  knn.fit(x_train,y_train)\n",
    "  y_pred=knn.predict(x_test)\n",
    "  accuracy=accuracy_score(y_test,y_pred)\n",
    "  a_score.append(accuracy)\n",
    "\n",
    "mse=[1 - x for x in a_score]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "160dae4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7355072463768116\n"
     ]
    }
   ],
   "source": [
    "max=a_score[0]\n",
    "for j in a_score:\n",
    "  if(j>max):\n",
    "    max=j\n",
    "print(max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "173e262b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(918, 12)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Decision Tree\n",
    "\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3892a206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Sex', 'ChestPainType', 'RestingBP', 'Cholesterol', 'FastingBS',\n",
       "       'RestingECG', 'MaxHR', 'ExerciseAngina', 'Oldpeak', 'ST_Slope'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = dt[['HeartDisease']].copy()\n",
    "x = dt.drop(['HeartDisease'], axis = 1)\n",
    "x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5259ca31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['HeartDisease'], dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e3a6e3d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_leaf_nodes=10, random_state=0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=2)\n",
    "     \n",
    "\n",
    "heart_classifier=DecisionTreeClassifier(max_leaf_nodes=10,random_state=0)\n",
    "heart_classifier.fit(x_train,y_train)\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f9b6cc86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.33333333333334"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted=heart_classifier.predict(x_test)\n",
    "     \n",
    "\n",
    "accuracy_score(y_test,y_predicted)*100\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "51f59f7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[103,  30],\n",
       "       [ 16, 127]], dtype=int64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "acae1b31",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random Forest Classifier\n",
    "\n",
    "labels=dt.pop(\"HeartDisease\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "905dc9c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(dt,labels,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f0f44041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "80fa0364",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred= rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "fb2e348c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8474401433302569"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import  roc_curve,auc\n",
    "false_positive_rate,true_positive_rate,thresholds=roc_curve(y_test,y_pred)\n",
    "roc_auc=auc(false_positive_rate,true_positive_rate)\n",
    "roc_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0f9b4cf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c13ab9d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c3970cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "340580de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dae23c50",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb1afd30",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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