Heart Disease Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction of Heart Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing all required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\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": "markdown",
   "metadata": {},
   "source": [
    "Here we will use 3 algorithms:\n",
    "\n",
    "1.KNeighborsClassifier\n",
    "\n",
    "2.DecisionTreeClassifier\n",
    "\n",
    "3.RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "#finding correlation of all features in dataset\n",
    "cor_mat=df.corr()\n",
    "top_cor_features=cor_mat.index\n",
    "plt.figure(figsize=(20,20))\n",
    "hm=sns.heatmap(df[top_cor_features].corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000025409B472C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000025402BA1088>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002540891EC08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000025408F0D4C8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000254089B8848>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000254092C2F88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002540899DA48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000254089330C8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000254092D2F08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002540897E388>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002540A594908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000254092FF9C8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000025408ED5B08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002540895FC48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000025408EC1D48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000025408F70F48>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J+7Jh2dquwrmqabwE+K2ZHVjFhisI5f0gM3tF0rls7Nw/CNwiaXkW5aZB5bg0aqRHP2FmjwDTYjPvPwO/kLRV4vgkS4Dvmdns8h2SSrX3SjaW/EZpRFC//EYtFOUN1T8CqyR9VdLmkgZI2l3SeyRNAo4ltC0eDVwgqVQ7GkroIHkphn2lD2leDRwraRdJbwa+lV52CsXBkvaXtCmhXfgeM1sSr83ese15LaET9vWmWtpY7iHk+1RJgyRNBj4KXFkW72qgU9KusZx9u8K5KmpMeGLdSdJRMY1BUfdd4nFDgReiY98L+JcK536Y0Of0I0kfqzPPzaaqnwCQdKSkrePN9qV4zOuEztd/ENrJS/wY+Fqp/0LSWyQdGvfdCLxV0smSNpM0VNLecd/V8bgto8/ZYGRQmhTCuZvZ64Q/xh6EHvAVwE+AUcBlhA6+p2OTzCXAzySJ0L62J2G0xk3AtX1I81fA+cBcYDHwh7gry+FprcgVBIf0AmEI3xExfAvgYsKogScInan/2QwDm4GZ/R34GHAQobz+F3C0mf2lLN6vCG3hdxDK2R0VTldRYzNbTah5H06oyS8ndIxuFo87Efh3SasJlZOrqYCZPQh8BLhY0kH9y3Hz6cFPvCVGmQI8LGkNoXP1cDN7xcxeJjz9/y42w+xjZtcRtLwyNukuIFzLku4HxrSWA48AB8Q0/p3Qkfw4odP1F2TlM7JoyM/rQuhcmU8YUVAaZjacMKLgkfi7ZT/PvQvhLj+wyv6dY7qlZRVwMqED7elE+MEp5ncKYfjbYmBGhf2bEYZ3LibUJDsadB3GEm6KCwk1wy9UiDOZcNMt6fKtZpefRunQU5kgdAAujtf1Q8TO15Tsqfn/QWhfPj/a8hCwZwN167Fc52GppYzHeCcQms7St6HZIjRY8G427gk/u1RAgBnAWX043ycIvfxbAjcA/1PjcQMId/Tt4x/5yxnkdQChU+dtvDESYdeyOCfyxuifw4GrGnQdRpWcAaFp4K8VbJsM3NjsMtMMHaqVibjvQcJNeYd4fS9N2bnX9P8ADiaMGBFhxNM9DdKs13Kdh6WHazsK2I/QarJzvEGdnIUNhWiWqZOphD8I8Xejcb498K+E9rhHCbX2E2o87v3Ao2ZWaeRDWqx/tdtCE0Dp1e4kybz/Anh/bK7KFDNbZmZ/iuurCbWb0T0fVTz6ocNUwrDHV83scYJjKO8ATZtq/4+pwGUWuBsYJmlUxrZAbeW66fRwbTclDEddTWhiu57QJJc67ebcDbhV0rz4SjPASDNbBuGCEF72qO1kZlPM7C1mNtzMPlE6Tw0cTnzhIXKSpIck/VTSlrWm3wuj2XCY1VNs7DjWx7EwLG0lsFVK6deEpA7g3YRmoXL2lfSgpF8lXrwpJBV0qFQmKl3Tn5nZN1Myoy//j1rKVxY0K91+k7y2ZvaEme1uZoPNbLSZnRJvUqnTbs59PzPbk9Dx8bk4kqahxBENHwP+OwZdSHgbbg/CMKlz0kqqQlj5kK5a4mRGHLZ3DeGxdFXZ7j8RXud+F3AB8D+NsqvRVNChWpnI+nr15f/RrLLT1DLbV3op49mmHduEqkeQxhJGnLyVMBxoppmdJ2k4oTOug9BWd5iZvRgf688jtMm9DHSWHk+qMWLECOvo6ABg7dq1DB48uI4stSbJfM+bN2+F9TDhUi3E8dGnmdmHwDWG9DUup4ga15MP1zgbai7HdXQMpNbRMmHCBCsxd+5ca0eS+SaF17wpe7XbNU5f4/KliBrXkw/XOBtqLce9vqFqoZ2t1Oa2WlKpY2AqYUQDhI6WLsLsa+s7WoC7JQ2TNMpqbI+e//RKOivMFZ3n72XmETNbJ6n0aveA5D7XOHtc42KT0nz2mdKn6QfKOn026GiJr5BD9Q6PDZy7EnM0jxw5kq6uLgBGbh6+TlROaX9RWbNmTep5tMSr3RMnTsxtu6TjOOlTs3Mv7xjoYcRcTR0eFuZcmAnB8UyePBmAC2ZfzznzNzar+4jJtZraknR1dVHSwHGc3vGno56pabRMnP/jGmC2mZVe0X+mNK41/j4bw3M517jjOE470atzj6NfLgEWmtn3E7tuAI6J68cQBuOXwo9WYB9gZa3t7Y7jOE461NIssx/h81fzJT0Qw74OnAlcLel4wmeoSjOi3UwYMbOYMBTy2FQtdhzHcXqlltEyd1G5HR3Ca/Tl8Q34XJ12OY7jOHWQycc6nL6xZMkSvvjFL/LKK6+wySabQHzFO80XxRzHaS/abfqBXDJw4EBOOOEEFi5cyN133w2wjaRdCS+H3W7hyzG3x20Ir4ePi8t0wuvqjuM463HnngNGjRrFTjvtBMDQoUMB/sYbL4rlaUY+x3FaBG+WyRnd3d0Ab8ZfFEudLF4Uc5y84s49R6xZs4ZDDjkEYIm/KJYuxx13HNdddx2jR49mwYIFgPdppM1xxx3HjTfeyDbbbOMa5wBvlskJ69at45BDDuGII46ANz7O6y+KpURnZydnnXVWebD3aaRIZ2cnt9xyS3mwa9wk3LnnADPj7LPPZpddduFLX/pScpe/KJYSkyZNYosttigP9j6NFJk0aRLDhw8vD3aNm4Q3y+SA3/3ud9x2220sX76cPfbYA2BXSQfjL4plTV19GlD8fo2+9lMsX76ctWvXJo+pW2Onf7hzzwH7778/c+fOXT9xmKQ/xxkdwV8UawY1f+2n6P0afZ3Qrru7m8GDB9dyTM0a5/EGWindRqVd6w3XnbvTzjxT+taA92lkRt0a5/EGWmk2ykalXesN19vcnXbG+zSyxzVuEl5zd9qCadOmceutt7Jq1SrGjBkDMALv00iVadOm0dXVxYoVK1zjHODO3WkL5syZs8HjrKQVZvY83qeRGnPmzNlg2zVuLt4s4ziOU0DcuTuO4xQQb5ZpMNW+mj5ryuAGW+I4TpHxmrvjOE4BcefuOI5TQNy5O47jFBB37o7jOAXEnbvjOE4B8dEyjtNAqo2W6j7zww22xCk6XnN3HMcpIO7cHcdxCog3yzhODvDmGidtvObuOI5TQNy5O47jFBB37o7jOAXE29ydwuGTszmOO3fHyTXVblTgna1Oz7hzd5wWxUfYOD3hbe6O4zgFJJOau6QpwHnAAOAnZnZmFum0M65x9rSqxq1Uo29VjVuB1GvukgYAPwIOAnYFpknaNe102hnXOHtc4+xxjbMli5r7XsBiM3sMQNKVwFTgzxmk1a7kUuPTTjuNxYsXc/nllzfTjLTIpcb1kMMafW417u7uZocdduC1115j4MC+u0lJPPLII+y44479tqHeUV9ZOPfRwJLE9lPA3uWRJE0HpsfNNZIWxfURwIqN4p+VspU544CzNsj39r1Ez6vG2wKbzZ49+/G6z5QBedC4BsYD3cDqfhzbLxLXvQP4O7A0sbu/+YDWK8dJ7TcFxg8aNGheX06QSHvCuHHjFgCv9tuaKtRajrNw7qoQZhsFmM0EZm50sHSfmU3MwK5c08d851JjSacBO5rZkWmfOw1aQWNJ3cDnzOw3/Th2gJm93tfjEsfPAp4ys28mwrL8P+aqHCe1l9QBPA7sY2br+nEuAz5hZovTsi9x7pryncVomaeAsYntMWxYE2gakr4q6WlJqyUtkvR+SZtImiHpUUnPS7pa0vAY/1OSHpO0Rdw+SNJySVs3NyfN17iSlhXifEzSw5JektQlaZfEvm5JX5P0Z0kvSvqZpDcl9n9E0gPx2N9Lemej8hZpuMaSfg5sB/xS0hpJp0r671jmVkq6U9JuifizJF0o6WZJa4EDJG0l6ZeSVkm6V9Lpku5KHPMOSbdJeiFet8Ni+HTgCODUmPYvs8xrpOnluES59sBhcdcRkp6UtELSNxLx95L0h1g+l0n6oaRNm2F7Vcws1YXwNPAYsAPh0eZBYLc+HH9f2jbF8+5MeATcNm53AG8HTgbuJhSszYCLgDmJ42YDs4CtCAXvIxnZV3O+m61xD1qeBlwew3YC1gIHAoOAU4HFwKZxfzewgPDnHg78Djg97tsTeJbwiD4AOCbG36zoGsd8fiCxfRwwNJbNc4EHEvtmASuB/QgVtTcBV8blzYROyiXAXTH+4Lh9bMzfnoTH+90S5zs9zbKSR41r0T6WaQMuBjYH3kVoYtkl7p8A7BPz0AEsBE5OnMsIT7FZ6FZTvrO6aAcDfwUeBb7Rx2OnZ2TTjtFhfAAYlAhfCLw/sT0KeA0YGLeHAU8C84GLsrCtP/lupsY9aHkabzj3/wtcndi3CfA0MDludwOfLcvPo3H9QuC7ZWkuAt5bdI0pc+5l+4ZFp/GWuD0LuCyxf0Asuzsnwk7nDef+KeB/y855EfDtxPnKnXsm/8dmalyL9rzh3Mck9v8ROLzKsScD1yW2s3TuNeU7k3HuZnYzcHM/j92obS0NzGyxpJMJDmg3Sb8GvkTokLhO0j8S0V8HRgJPm9lLkv47xj0kC9uifX3KdzM17kHLJNsCTySO+YekJYROtBLJzrQn4jEQrskxkv4tsX/TxP7+2t0yGsP6oYLfAw4FtgZKZXQEocYOG2q4NaEmmQxLrm8P7C3ppUTYQODn1WzI6v+YOH/ufEUZyxPrLwNDACTtBHwfmEh4ShoI9Knztb/Umu+2ekPVzK4ws/0JhdyAswiF/yAzG5ZY3mRmTwNI2oPwaDwHOL9ZtueNKlomWUqiJ1+SCE0wTyfiJNtbt+ON9tYlwPfKrsmbzWxO2vnIIckOxX8hDA38APAWQm0SNuyITMZ/DlhHaGIskdR4CfDbMl2HmNkJFc7VjvQl/xcCfwHGmdkWwNep3EHcNNrGuUvaWdL7JG0GvAL8jVBD/zHwPUnbx3hbS5oa198EXE64cMcCoyWd2JQM5IgetExyNfDh2Gk9CDiF0Gb5+0Scz0kaEzuwvw5cFcMvBj4raW8FBkv6sKShmWYsHzwDvC2uDyVo9jyhdvgfPR1oYaTMtcBpkt4s6R3A0YkoNwI7STpK0qC4vCfR0Z1Mux3pS/6HAqsIQzPfAZzQS/yGkyvnLmlK7MFfLGlGyqffDDiT0IG0HNiG4FDOA24AbpW0mtC5WhprewZhaNiFZvYqcCRwuqRxaRkl6aeSnpW0IK1z9pJeGhpX03I9ZraIoNcFMd5HgY+a2d8T0a4AbiV0qj1GaB/GzO4DPgP8EHiR0BHb2U9bG6pxCvqeAXwzNp0MJzRXPU14sefuGo4/iVDLX05obplDHGttZquBDwKHE56SlhOeuDaLx14C7BpHgPxPHNE0P45auq8fecmEDP1EUvtP9hL3y4Qnq9WEyshVPUevD0ljJc2VtFBhBNoXej0oiwb/fnYSDCB0qryNN3rOd222XQ3I9yTCqIUF7aQxPXQctqrGedI3YdNZwKV1XKMRzbS/FTRuUL5HAXvG9aGETuge852nmvv6V5Et1O5KryIXGjO7E3ihQcm5xtnSdH3jOPZ3xuasvYDjgesaaUPGNF3jZmBmy8zsT3F9NWGU3+iejsmTc6/0KnKPxjt9xjXOljzoO5TQ7r6W0O9xDnB9P89lhObKefElpzyQB42bisLbs+8G7ukpXp4+1lHTq8hOXeRGYzPraEa6GdN0fc3sXsJ7CGmwn5ktlbQNcJukv8SnoGbSdI2biaQhwDWEF6ZW9RQ3TzX33LyKXGBc42wplL5mtjT+Pkto2tmruRYBBdO4L8RRZ9cAs83s2l7jxwb6nk44FrgMeCvhJYqZZnZeHL52FWHsbTdwmJm9GMczn0d48+xloLPUVlSNESNGWEdHBwBr165l8OD2+5BxMt/z5s1bYWapzl/jGrvGjcA13pCsbexR4/720gJnAzNi+AzgrLh+MPArwuPTPsA9vaUxYcIEKzF37lxrR5L5JoP5PFxj17gRuMYbkrWNPWnca5u7mS0DlsX11ZJKvbRTgckx2qVAF/DVGH5ZTPhuScMkjYrnaXvqnYC/1WnEByPaXeNG4BpXplyXU8avo3PGTU35IEqfOlTLemlHlhy2mS2LnS5QvTd7A+euxAT8I0eOpKurC4A1a9asXy8ip4yvPDV00fPtOE5jqdm5l/fShqb1ylErhPU4Af/EiRNt8uTJAHR1dVFaLyKdPdR4ipxvx3EaS02jZar00j4jaVTcP4owBQnUh9wAAA/CSURBVCy0cW+24zhOXujVucfRL5cAC83s+4ldNxA+okD8vT4RfnR8Q24fYKW3tzuO4zSWWppl9gOOAuZLeiCGfZ0wcdTVko4nfMzi0LjvZsKImcWEoZDHpmqx4ziO0yu1jJa5i+rzFG/03cw4SuZzddrlOI7j1EGeph9wnMIw/+mVFTvPmzEkzmlP8jT9gOM4jpMS7txzwIqbz+UTn/gEu++++/owScMl3Sbpkfi7ZQyXpPPjhwoekrRn0wx3HCe3uHPPAUPGf4Czzir/BCkzgNvNbBxwe9wGOAgYF5fphG85Oo7jbIA79xzwprG7s8UWW5QHTyVM60D8/Xgi/LI4tcTdwLDS+waO4zglvEM1v9Q1vQPkc4qHatMvpGmPT/HgFIF652Fy59561PyxgjxO8VBt+oXuIyZnmsaKm89l0JL7GD16NAsWhO9kpzlttRP7ji5yjfOCN8vkF5/eIUW8XyN7XON84c49v/j0Dini/RrZ4xrnC2+WyQHP3XA2n7t4AatWrWLMmDEAI/DpHRpBZv0aIzev3PZflDb/av0aa9euZe3atcl81q2x0z/cueeArT926gZT/kpaYWbP49M7NIu6+zUumH0958zf+O+VZt9CM6nWd3LmHoMZPLim6atr1jiPAwOqUX7TK93k+2NnvYMP3Lk77cwzpa+Eeb9GZtStcR4HBlSj/KZ3yvh1nDN/YL9u6vUOPvA2d6ed8X6N7HGNm4TX3J22wPs1ssc1zhfu3J22wPs1ssc1zhfeLOM4jlNAcldz93mwHcdx6sdr7o7jOAXEnbvjOE4BcefuOI5TQNy5O47jFBB37o7jOAXEnbvjOE4BcefuOI5TQNy5O47jFBB37o7jOAXEnbvjOE4BcefuOI5TQNy5O47jFJDcTRzmZI9PzuY4xcdr7o7jOAXEa+6O47Q1HVW+VQqt/TSbSc1d0hRJiyQtljQjizTaHdc4e1zj7HGNsyN15y5pAPAj4CBgV2CapF3TTidJd3c3kli3bl2/jpfE4sWL67Khs7OTb37zm3Wdo1aaoXEWrLjpB7x458+bbUZFiqJxnnGNsyWLmvtewGIze8zM/g5cCUxNO5GOjg5+85vfpH3aViEzjbPSddasWey///6pnzdDGlKO2xzXOEMUvlOb4gmlTwJTzOzTcfsoYG8zO6ks3nRgetzcGVgU10cAK2pIajzQDawGNo3b8/pp9gRgAfBqP48H6AD+Dizt5/HJfG9vZltXi5ixxkld02SrmO6iRFgH9WnWV/KicZFxjTckaxura2xmqS7AocBPEttHARf04fj7aojzc+AfwN+ANcCpgAHHAE9GMb+RiL8X8AfgJWAZ8ENg08R+A3aM6x8G7gdWAUuA08rS3h/4fTzXEqAzhs8iPGLeRHCM9wBvTzPfWWvcg67HR13vjPH2SWjwIDA5cY5O4LGowePAEcAuwCvA6/G8LyU0+zFwW4z/21hYk9fl8/F8K4D/B2wS9+0Y46+M+65qBY2LvrjG+bExi2aZp4Cxie0xpFwzM7OjCM7mo2Y2BLg67tqfcGd/P/AtSbvE8NeBLxLuovvG/SdWOf1a4GhgGMHRnyDp4wCStgN+BVwAbA3sATyQOHYa8B1gS2Ax8L1681qFTDTuQdf3Ehz0hySNJtzATgeGA18GrpG0taTBwPnAQWY2FPg/wANmthD4LPAHMxtiZsMSyR4BfJdwbR4AZpeZ9QlgIrAn4ZH9uBj+XeBWgtZjCNckTTIvx45rnCVZOPd7gXGSdpC0KXA4cEMG6VTiO2b2NzN7kFCjfBeAmc0zs7vNbJ2ZdQMXERzWRphZl5nNN7N/mNlDwJxE3COA35jZHDN7zcyeN7Okc7/WzP5oZusITmqPbLLZcI1PM7O1ZvY34EjgZjO7OWp0G3AfcHCM+w9gd0mbm9kyM3u4l3PfZGZ3mtmrwDeAfSUl//BnmdkLZvYkcC7hBgrwGrA9sK2ZvWJmd6WU1xLNLMftgmucIak79+jYTgJ+DSwErq7hD55kZh3JL0+svwwMAZC0k6QbJS2XtAr4D0JNcSMk7S1prqTnJK0k1DhLcccCj/Y1/RqpOd9N0HhJYn174FBJL5UWwhPTKDNbC3yKoNkySTdJeket5zazNcALwLZV0n4ise9UQMAfJT0s6Th6J88aFwXXeEOaZmMmLzGZ2c3Azf08tlYx+tITfCGhHX2ama2WdDLwySpxryC0yR9kZq9IOpc3nPsSQvt96vQh36X4WWlcSddk2BLg52b2mSrn/jXwa0mbE5puLgb+qcp5IfFYLmkIoalnadn+0h9+u9I+M1sOfCYetz/wG0l3mlnVMa050riwuMYb0kwbW3n6gWeAt9UYdyihg3RNrEme0EvcF6Jj3wv4l8S+2cAHJB0maaCkrSRl1fTSLHrT9XLgo5I+JGmApDdJmixpjKSRkj4W295fJXSevp4475j4+J3kYEn7x/DvAveYWbK2/hVJW8ammi8AVwFIOlTSmBjnRcLN43UcxwFa27mfAXwzNgtUq4WX+DLBSa8m1CSv6iHuicC/S1oNfIs3OhWJ7b4HA6cQmg8eILbrF4gedY2OdyrwdeA5Qk3+K4SytAlBm6UEfd7LGx3XdxBq4MslJYeGXQF8O8afQOjXSHI9YYjrA4SO3Eti+HuAeyStIbTTfsHMHu93rh2nYKQ+zr0eJE0BzgMGEIZIndlkkzJH0k+BjwDPmtnuDUivZTSWZMC4nppaajzPWOAy4K2EDt+ZZnZeCiZWSqtl9E2TRpbjVtC4kWWuKs0ag1m+EC7Uo4QmgU0Jo112bbZdDcj3JMIwvwWu8Ub2rn//oM7zjAL2jOtDgb9mke9W0zflvDekHLeKxo0qcz0teWqWactXkc3sTkKTRCNoV42Xmdmf4vpqwsiM0Rkk1Zb6QkPLcUto3MAyV5U8OffRbDjs7SkaLEYb0FIam5msziaZciR1AO8mvEGcNi2lb4vSchpnXOaqkifnrgph+ekQKAZtrXEcankNcLKZrcoiiQphbaNvg2gpjRtQ5qqnHduEmsqIESOso6OjrnOsXbuWwYMHp2NQgym3fd68eSushwmX+kNPGreydn0hmc8sNJa0L+Ft3g/F7a8BmNkZaaaTV2IN9UbLsEO1lTSWNAi4Efi1mX2/4QY0u+PBzJgwYYLVy9y5c+s+R7Mot50MJhvqSeNW1q4vJPOZhcaElwIfA3bgjc6+3dJOJ68LYZbPrDtUW0JjwhPGZcC5zbIhT80yjtNI3pL2Ca3+1+lbFklzCDOv7izpKUnHZ5FOC2m8H2GWy/dJeiAuB/d2UJr4N1RroKjfWEyDatq0gC4rszip1fE6fStjZtN6j5VaWrnX2MJEdpX6BxqG19wdx3EKiDt3x3GcAuLO3XEcp4B4m7vTKz31OTiOk0+85u44jlNA3Lk7juMUEG+WqZMWHgroOE6B8Zq74zhOAfGau5MJ/kTjOM3FnXvOkdRN+Dzg68A6M5soaTjhU4EdQDdwmJm92CwbHcfJH3U1y0jqljQ/zptwXwwbLuk2SY/E3y3TMbWtOcDM9jCziXF7BnC7mY0Dbo/bjuM460mjzd0dT+OZClwa1y8FPt5EWxzHySFZNMtMBSbH9UuBLuCrGaSTOjl9WceAW+PHoi8ys5nASDNbBuFzXpK2qXSgpOnAdICRI0fS1dVVMYE1a9ZU3Qdwyvh19di/AT2lkzW95dNxikRdH+uQ9DjwIsEBXWRmMyW9ZGbDEnFeNLONmmbKHM+EK6+8st92QPjjDhkypK5zzH86vYkCx4+ufUbZctsPOOCAeaUnIUnbmtnS6MBvA/4NuKEWjZNMnDjR7rvvvor7urq6mDx5ctVj07zpNbNDNZlPSfMST5uOUzjqrbnvl3Q8kv5S64GxBjoTguPpybnUQm8OqhY603RiR0yuOW5PtpvZ0vj7rKTrCB8IfkbSqFhrHwU8W7fBjuMUirra3JOOB9jA8QC446kPSYMlDS2tAx8EFgA3AMfEaMcA1zfHQsdx8kq/nbs7noYwErhL0oPAH4GbzOwW4EzgQEmPAAfGbcdxnPXU0ywzErhOUuk8V5jZLZLuBa6On9l6Eji0fjPbEzN7DHhXhfDngfennV5OO5Qdx+kH/XbujXY8juM4Tu343DKO4zgFxJ274zhOAXHn7jiOU0DacuIw7zh0HKfoeM3dcRyngLhzdxzHKSBt2SzjNA//iIfjNAavuTuO4xSQQtfcm9lx6jVUx3GaidfcHcdxCog7d8dxnALizt1xHKeAuHN3HMcpIO7cHcdxCkihR8s4rYOPLnKcdPGau+M4TgEpRM29Y8ZNnDJ+XaofuC4y5bVk185xikchnLtTXHp6Ec2bbBynOt4s4ziOU0AyqblLmgKcBwwAfmJmZ9Z7zqLMwV4pH6eMX8fkPp4nC40dxykOqTt3SQOAHwEHAk8B90q6wcz+nHZa7YprHPARNo5TnSxq7nsBi83sMQBJVwJTgZocT1Fq6BlTl8ZFp1oZmjVlcIMtcZzmkYVzHw0sSWw/BexdHknSdGB63FwjaVE9iX4eRgAr6jlHs/g8jPj8kRvYvn0vh6SqcStr1xcOOGuDfPamseO0NFk4d1UIs40CzGYCM1NLVLrPzCamdb5G0g/bU9W4lbXrC+2ST8eBbEbLPAWMTWyPAZZmkE474xo7jtMjWTj3e4FxknaQtClwOHBDBum0M66x4zg9knqzjJmtk3QS8GvCML2fmtnDaadTgdSaeJpAn2zPQONW1q4vtEs+HQeZbdRU6ziO47Q4/oaq4zhOAXHn7jiOU0BaxrlL+qmkZyUtSIQNl3SbpEfi75YxXJLOl7RY0kOS9mye5SBprKS5khZKeljSFxplfyvr1heaqbHj5JGWce7ALGBKWdgM4HYzGwfcHrcBDgLGxWU6cGGDbKzGOuAUM9sF2Af4nKRdaYz9s2hd3fpCMzV2nPxhZi2zAB3AgsT2ImBUXB8FLIrrFwHTKsXLwwJcT5gXpiH2F0W3PGvsiy95W1qp5l6JkWa2DCD+bhPDK72eP7rBtlVEUgfwbuAemmd/y+nWF3KiseM0lVZ37tWo6fX8RiNpCHANcLKZreopaoWwRtifS936Qgto7DgNodWd+zOSRgHE32djeO5ez5c0iOB0ZpvZtTG4Wfa3jG59IWcaO05TaXXnfgNwTFw/htDOWgo/Oo6I2AdYWXo0bwaSBFwCLDSz7yd2Ncv+ltCtL+RQY8dpLs1u9K91AeYAy4DXCLWu44GtCCMgHom/w2NcET5m8SgwH5jYZNv3JzzyPwQ8EJeDG2F/K+vWKhr74kseF59+wHEcp4C0erOM4ziOUwF37o7jOAXEnbvjOE4BcefuOI5TQNy5O47jFBB37o7jOAXEnbvjOE4B+f/ISpcGijawVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25409c717c8>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='target', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Counter plot is used to count the number of people having heart disease and not having heart disease.\n",
    "\n",
    "0 shows count of person having no heart disease which is less than 140 and 1 shows count of person having heary disease which is more than 160 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After visualizing the dataset, we found that we have some catogerical variables and we need to convert them into dummy variables before training the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.get_dummies(df,columns=['sex','fbs','cp','restecg','exang','slope','ca','thal'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After this, we will be doing standard scaling. The reason to do standard scaling is that there are some features that are having lots of variation and are measured in different units. Whenever we find features that are measured in different units, we should prefer standard scaling because that will help you to rescale/scale down value within same distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss=StandardScaler()\n",
    "columns_to_scale=['age','trestbps', 'chol', 'thalach', 'oldpeak'] #on the remaining features\n",
    "dataset[columns_to_scale]=ss.fit_transform(dataset[columns_to_scale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>thalach</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>target</th>\n",
       "      <th>sex_0</th>\n",
       "      <th>sex_1</th>\n",
       "      <th>fbs_0</th>\n",
       "      <th>fbs_1</th>\n",
       "      <th>...</th>\n",
       "      <th>slope_2</th>\n",
       "      <th>ca_0</th>\n",
       "      <th>ca_1</th>\n",
       "      <th>ca_2</th>\n",
       "      <th>ca_3</th>\n",
       "      <th>ca_4</th>\n",
       "      <th>thal_0</th>\n",
       "      <th>thal_1</th>\n",
       "      <th>thal_2</th>\n",
       "      <th>thal_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.952197</td>\n",
       "      <td>0.763956</td>\n",
       "      <td>-0.256334</td>\n",
       "      <td>0.015443</td>\n",
       "      <td>1.087338</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.915313</td>\n",
       "      <td>-0.092738</td>\n",
       "      <td>0.072199</td>\n",
       "      <td>1.633471</td>\n",
       "      <td>2.122573</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.474158</td>\n",
       "      <td>-0.092738</td>\n",
       "      <td>-0.816773</td>\n",
       "      <td>0.977514</td>\n",
       "      <td>0.310912</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.180175</td>\n",
       "      <td>-0.663867</td>\n",
       "      <td>-0.198357</td>\n",
       "      <td>1.239897</td>\n",
       "      <td>-0.206705</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.290464</td>\n",
       "      <td>-0.663867</td>\n",
       "      <td>2.082050</td>\n",
       "      <td>0.583939</td>\n",
       "      <td>-0.379244</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  trestbps      chol   thalach   oldpeak  target  sex_0  sex_1  \\\n",
       "0  0.952197  0.763956 -0.256334  0.015443  1.087338       1      0      1   \n",
       "1 -1.915313 -0.092738  0.072199  1.633471  2.122573       1      0      1   \n",
       "2 -1.474158 -0.092738 -0.816773  0.977514  0.310912       1      1      0   \n",
       "3  0.180175 -0.663867 -0.198357  1.239897 -0.206705       1      0      1   \n",
       "4  0.290464 -0.663867  2.082050  0.583939 -0.379244       1      1      0   \n",
       "\n",
       "   fbs_0  fbs_1  ...  slope_2  ca_0  ca_1  ca_2  ca_3  ca_4  thal_0  thal_1  \\\n",
       "0      0      1  ...        0     1     0     0     0     0       0       1   \n",
       "1      1      0  ...        0     1     0     0     0     0       0       0   \n",
       "2      1      0  ...        1     1     0     0     0     0       0       0   \n",
       "3      1      0  ...        1     1     0     0     0     0       0       0   \n",
       "4      1      0  ...        1     1     0     0     0     0       0       0   \n",
       "\n",
       "   thal_2  thal_3  \n",
       "0       0       0  \n",
       "1       1       0  \n",
       "2       1       0  \n",
       "3       1       0  \n",
       "4       1       0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see all the dummy variables(sex_0,sex_1,etc) are created with thier catogerical data.ANd remaining all (age,tresbps,etc) we have dwon dow scaling based on the normal standard distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=dataset['target'] #dependent feature\n",
    "X=dataset.drop(['target'], axis=1) #independent feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "knn_scores=[]\n",
    "for k in range(1,21):\n",
    "    knn_classifier=KNeighborsClassifier(n_neighbors=k)\n",
    "    score=cross_val_score(knn_classifier,X,y,cv=10)\n",
    "    knn_scores.append(score.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Scores')"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([k for k in range(1,21)],knn_scores,color='red')\n",
    "for i in range(1,21):\n",
    "    plt.text(i,knn_scores[i-1],(i,knn_scores[i-1]))\n",
    "plt.xticks([i for i in range(1,21)])\n",
    "plt.xlabel('No. of Neighbors(k)')\n",
    "plt.ylabel('Scores')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_classifier = KNeighborsClassifier(n_neighbors = 12)\n",
    "score=cross_val_score(knn_classifier,X,y,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7455913978494624,\n",
       " 0.7591397849462365,\n",
       " 0.8120430107526883,\n",
       " 0.8053763440860215,\n",
       " 0.8348387096774192,\n",
       " 0.831505376344086,\n",
       " 0.8416129032258064,\n",
       " 0.8417204301075267,\n",
       " 0.8449462365591398,\n",
       " 0.8448387096774193,\n",
       " 0.8381720430107527,\n",
       " 0.8448387096774195,\n",
       " 0.841505376344086,\n",
       " 0.8415053763440861,\n",
       " 0.8280645161290323,\n",
       " 0.8381720430107527,\n",
       " 0.8280645161290323,\n",
       " 0.8249462365591398,\n",
       " 0.821505376344086,\n",
       " 0.8250537634408601]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#list of accuracy\n",
    "knn_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8448387096774195"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The end "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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