Heart Disease Prediction Using Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "da874522",
   "metadata": {},
   "source": [
    "### Importing the Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "67721392",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a20562be",
   "metadata": {},
   "source": [
    "### Data Collection and Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e93898c3",
   "metadata": {},
   "outputs": [
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       "        HeartDiseaseorAttack  HighBP  HighChol  CholCheck   BMI  Smoker  \\\n",
       "0                        0.0     1.0       1.0        1.0  40.0     1.0   \n",
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       "\n",
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       "\n",
       "        NoDocbcCost  GenHlth  MentHlth  PhysHlth  DiffWalk  Sex   Age  \\\n",
       "0               0.0      5.0      18.0      15.0       1.0  0.0   9.0   \n",
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# loading the csv data to a Pandas DataFrame\n",
    "heart_data = pd.read_csv('heart_dataset.csv')\n",
    "heart_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "126d5268",
   "metadata": {},
   "outputs": [
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>4.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
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      "text/plain": [
       "   HeartDiseaseorAttack  HighBP  HighChol  CholCheck   BMI  Smoker  Stroke  \\\n",
       "0                   0.0     1.0       1.0        1.0  40.0     1.0     0.0   \n",
       "1                   0.0     0.0       0.0        0.0  25.0     1.0     0.0   \n",
       "2                   0.0     1.0       1.0        1.0  28.0     0.0     0.0   \n",
       "3                   0.0     1.0       0.0        1.0  27.0     0.0     0.0   \n",
       "4                   0.0     1.0       1.0        1.0  24.0     0.0     0.0   \n",
       "\n",
       "   Diabetes  PhysActivity  Fruits  ...  AnyHealthcare  NoDocbcCost  GenHlth  \\\n",
       "0       0.0           0.0     0.0  ...            1.0          0.0      5.0   \n",
       "1       0.0           1.0     0.0  ...            0.0          1.0      3.0   \n",
       "2       0.0           0.0     1.0  ...            1.0          1.0      5.0   \n",
       "3       0.0           1.0     1.0  ...            1.0          0.0      2.0   \n",
       "4       0.0           1.0     1.0  ...            1.0          0.0      2.0   \n",
       "\n",
       "   MentHlth  PhysHlth  DiffWalk  Sex   Age  Education  Income  \n",
       "0      18.0      15.0       1.0  0.0   9.0        4.0     3.0  \n",
       "1       0.0       0.0       0.0  0.0   7.0        6.0     1.0  \n",
       "2      30.0      30.0       1.0  0.0   9.0        4.0     8.0  \n",
       "3       0.0       0.0       0.0  0.0  11.0        3.0     6.0  \n",
       "4       3.0       0.0       0.0  0.0  11.0        5.0     4.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print first 5 rows of the dataset\n",
    "heart_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cb2f55f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        HeartDiseaseorAttack  HighBP  HighChol  CholCheck   BMI  Smoker  \\\n",
       "253675                   0.0     1.0       1.0        1.0  45.0     0.0   \n",
       "253676                   0.0     1.0       1.0        1.0  18.0     0.0   \n",
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       "253679                   1.0     1.0       1.0        1.0  25.0     0.0   \n",
       "\n",
       "        Stroke  Diabetes  PhysActivity  Fruits  ...  AnyHealthcare  \\\n",
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       "\n",
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       "253679          0.0      2.0       0.0       0.0       0.0  0.0   9.0   \n",
       "\n",
       "        Education  Income  \n",
       "253675        6.0     7.0  \n",
       "253676        2.0     4.0  \n",
       "253677        5.0     2.0  \n",
       "253678        5.0     1.0  \n",
       "253679        6.0     2.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print last 5 rows of the dataset\n",
    "heart_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5d6fa19e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   HeartDiseaseorAttack  HighBP  HighChol  CholCheck  BMI  Smoker  Stroke  \\\n",
       "0                     0       1         1          1   40       1       0   \n",
       "1                     0       0         0          0   25       1       0   \n",
       "2                     0       1         1          1   28       0       0   \n",
       "3                     0       1         0          1   27       0       0   \n",
       "4                     0       1         1          1   24       0       0   \n",
       "\n",
       "   Diabetes  PhysActivity  Fruits  ...  AnyHealthcare  NoDocbcCost  GenHlth  \\\n",
       "0         0             0       0  ...              1            0        5   \n",
       "1         0             1       0  ...              0            1        3   \n",
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       "3         0             1       1  ...              1            0        2   \n",
       "4         0             1       1  ...              1            0        2   \n",
       "\n",
       "   MentHlth  PhysHlth  DiffWalk  Sex  Age  Education  Income  \n",
       "0        18        15         1    0    9          4       3  \n",
       "1         0         0         0    0    7          6       1  \n",
       "2        30        30         1    0    9          4       8  \n",
       "3         0         0         0    0   11          3       6  \n",
       "4         3         0         0    0   11          5       4  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_data['HeartDiseaseorAttack'] = heart_data['HeartDiseaseorAttack'].astype('int64')\n",
    "heart_data['HighBP'] = heart_data['HighBP'].astype('int64')\n",
    "heart_data['HighChol'] = heart_data['HighChol'].astype('int64')\n",
    "heart_data['CholCheck'] = heart_data['CholCheck'].astype('int64')\n",
    "heart_data['BMI'] = heart_data['BMI'].astype('int64')\n",
    "heart_data['Smoker'] = heart_data['Smoker'].astype('int64')\n",
    "heart_data['Stroke'] = heart_data['Stroke'].astype('int64')\n",
    "heart_data['Diabetes'] = heart_data['Diabetes'].astype('int64')\n",
    "heart_data['PhysActivity'] = heart_data['PhysActivity'].astype('int64')\n",
    "heart_data['Fruits'] = heart_data['Fruits'].astype('int64')\n",
    "heart_data['Veggies'] = heart_data['Veggies'].astype('int64')\n",
    "heart_data['HvyAlcoholConsump'] = heart_data['HvyAlcoholConsump'].astype('int64')\n",
    "heart_data['AnyHealthcare'] = heart_data['AnyHealthcare'].astype('int64')\n",
    "heart_data['NoDocbcCost'] = heart_data['NoDocbcCost'].astype('int64')\n",
    "heart_data['GenHlth'] = heart_data['GenHlth'].astype('int64')\n",
    "heart_data['MentHlth'] = heart_data['MentHlth'].astype('int64')\n",
    "heart_data['PhysHlth'] = heart_data['PhysHlth'].astype('int64')\n",
    "heart_data['DiffWalk'] = heart_data['DiffWalk'].astype('int64')\n",
    "heart_data['Sex'] = heart_data['Sex'].astype('int64')\n",
    "heart_data['Age'] = heart_data['Age'].astype('int64')\n",
    "heart_data['Education'] = heart_data['Education'].astype('int64')\n",
    "heart_data['Income'] = heart_data['Income'].astype('int64')\n",
    "\n",
    "\n",
    "heart_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e639b8f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(253680, 22)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of rows and columns in the dataset\n",
    "heart_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "48200d18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['HeartDiseaseorAttack', 'HighBP', 'HighChol', 'CholCheck', 'BMI',\n",
       "       'Smoker', 'Stroke', 'Diabetes', 'PhysActivity', 'Fruits', 'Veggies',\n",
       "       'HvyAlcoholConsump', 'AnyHealthcare', 'NoDocbcCost', 'GenHlth',\n",
       "       'MentHlth', 'PhysHlth', 'DiffWalk', 'Sex', 'Age', 'Education',\n",
       "       'Income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# showing all the column headers\n",
    "heart_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8dd61bb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 253680 entries, 0 to 253679\n",
      "Data columns (total 22 columns):\n",
      " #   Column                Non-Null Count   Dtype\n",
      "---  ------                --------------   -----\n",
      " 0   HeartDiseaseorAttack  253680 non-null  int64\n",
      " 1   HighBP                253680 non-null  int64\n",
      " 2   HighChol              253680 non-null  int64\n",
      " 3   CholCheck             253680 non-null  int64\n",
      " 4   BMI                   253680 non-null  int64\n",
      " 5   Smoker                253680 non-null  int64\n",
      " 6   Stroke                253680 non-null  int64\n",
      " 7   Diabetes              253680 non-null  int64\n",
      " 8   PhysActivity          253680 non-null  int64\n",
      " 9   Fruits                253680 non-null  int64\n",
      " 10  Veggies               253680 non-null  int64\n",
      " 11  HvyAlcoholConsump     253680 non-null  int64\n",
      " 12  AnyHealthcare         253680 non-null  int64\n",
      " 13  NoDocbcCost           253680 non-null  int64\n",
      " 14  GenHlth               253680 non-null  int64\n",
      " 15  MentHlth              253680 non-null  int64\n",
      " 16  PhysHlth              253680 non-null  int64\n",
      " 17  DiffWalk              253680 non-null  int64\n",
      " 18  Sex                   253680 non-null  int64\n",
      " 19  Age                   253680 non-null  int64\n",
      " 20  Education             253680 non-null  int64\n",
      " 21  Income                253680 non-null  int64\n",
      "dtypes: int64(22)\n",
      "memory usage: 42.6 MB\n"
     ]
    }
   ],
   "source": [
    "# getting some info about the data\n",
    "heart_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5b5a9946",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>HeartDiseaseorAttack</th>\n",
       "      <th>HighBP</th>\n",
       "      <th>HighChol</th>\n",
       "      <th>CholCheck</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoker</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>PhysActivity</th>\n",
       "      <th>Fruits</th>\n",
       "      <th>...</th>\n",
       "      <th>AnyHealthcare</th>\n",
       "      <th>NoDocbcCost</th>\n",
       "      <th>GenHlth</th>\n",
       "      <th>MentHlth</th>\n",
       "      <th>PhysHlth</th>\n",
       "      <th>DiffWalk</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.094186</td>\n",
       "      <td>0.429001</td>\n",
       "      <td>0.424121</td>\n",
       "      <td>0.962670</td>\n",
       "      <td>28.382364</td>\n",
       "      <td>0.443169</td>\n",
       "      <td>0.040571</td>\n",
       "      <td>0.296921</td>\n",
       "      <td>0.756544</td>\n",
       "      <td>0.634256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.951053</td>\n",
       "      <td>0.084177</td>\n",
       "      <td>2.511392</td>\n",
       "      <td>3.184772</td>\n",
       "      <td>4.242081</td>\n",
       "      <td>0.168224</td>\n",
       "      <td>0.440342</td>\n",
       "      <td>8.032119</td>\n",
       "      <td>5.050434</td>\n",
       "      <td>6.053875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.292087</td>\n",
       "      <td>0.494934</td>\n",
       "      <td>0.494210</td>\n",
       "      <td>0.189571</td>\n",
       "      <td>6.608694</td>\n",
       "      <td>0.496761</td>\n",
       "      <td>0.197294</td>\n",
       "      <td>0.698160</td>\n",
       "      <td>0.429169</td>\n",
       "      <td>0.481639</td>\n",
       "      <td>...</td>\n",
       "      <td>0.215759</td>\n",
       "      <td>0.277654</td>\n",
       "      <td>1.068477</td>\n",
       "      <td>7.412847</td>\n",
       "      <td>8.717951</td>\n",
       "      <td>0.374066</td>\n",
       "      <td>0.496429</td>\n",
       "      <td>3.054220</td>\n",
       "      <td>0.985774</td>\n",
       "      <td>2.071148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</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>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       HeartDiseaseorAttack         HighBP       HighChol      CholCheck  \\\n",
       "count         253680.000000  253680.000000  253680.000000  253680.000000   \n",
       "mean               0.094186       0.429001       0.424121       0.962670   \n",
       "std                0.292087       0.494934       0.494210       0.189571   \n",
       "min                0.000000       0.000000       0.000000       0.000000   \n",
       "25%                0.000000       0.000000       0.000000       1.000000   \n",
       "50%                0.000000       0.000000       0.000000       1.000000   \n",
       "75%                0.000000       1.000000       1.000000       1.000000   \n",
       "max                1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 BMI         Smoker         Stroke       Diabetes  \\\n",
       "count  253680.000000  253680.000000  253680.000000  253680.000000   \n",
       "mean       28.382364       0.443169       0.040571       0.296921   \n",
       "std         6.608694       0.496761       0.197294       0.698160   \n",
       "min        12.000000       0.000000       0.000000       0.000000   \n",
       "25%        24.000000       0.000000       0.000000       0.000000   \n",
       "50%        27.000000       0.000000       0.000000       0.000000   \n",
       "75%        31.000000       1.000000       0.000000       0.000000   \n",
       "max        98.000000       1.000000       1.000000       2.000000   \n",
       "\n",
       "        PhysActivity         Fruits  ...  AnyHealthcare    NoDocbcCost  \\\n",
       "count  253680.000000  253680.000000  ...  253680.000000  253680.000000   \n",
       "mean        0.756544       0.634256  ...       0.951053       0.084177   \n",
       "std         0.429169       0.481639  ...       0.215759       0.277654   \n",
       "min         0.000000       0.000000  ...       0.000000       0.000000   \n",
       "25%         1.000000       0.000000  ...       1.000000       0.000000   \n",
       "50%         1.000000       1.000000  ...       1.000000       0.000000   \n",
       "75%         1.000000       1.000000  ...       1.000000       0.000000   \n",
       "max         1.000000       1.000000  ...       1.000000       1.000000   \n",
       "\n",
       "             GenHlth       MentHlth       PhysHlth       DiffWalk  \\\n",
       "count  253680.000000  253680.000000  253680.000000  253680.000000   \n",
       "mean        2.511392       3.184772       4.242081       0.168224   \n",
       "std         1.068477       7.412847       8.717951       0.374066   \n",
       "min         1.000000       0.000000       0.000000       0.000000   \n",
       "25%         2.000000       0.000000       0.000000       0.000000   \n",
       "50%         2.000000       0.000000       0.000000       0.000000   \n",
       "75%         3.000000       2.000000       3.000000       0.000000   \n",
       "max         5.000000      30.000000      30.000000       1.000000   \n",
       "\n",
       "                 Sex            Age      Education         Income  \n",
       "count  253680.000000  253680.000000  253680.000000  253680.000000  \n",
       "mean        0.440342       8.032119       5.050434       6.053875  \n",
       "std         0.496429       3.054220       0.985774       2.071148  \n",
       "min         0.000000       1.000000       1.000000       1.000000  \n",
       "25%         0.000000       6.000000       4.000000       5.000000  \n",
       "50%         0.000000       8.000000       5.000000       7.000000  \n",
       "75%         1.000000      10.000000       6.000000       8.000000  \n",
       "max         1.000000      13.000000       6.000000       8.000000  \n",
       "\n",
       "[8 rows x 22 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical measures about the data\n",
    "heart_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ca74fece",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HeartDiseaseorAttack    False\n",
       "HighBP                  False\n",
       "HighChol                False\n",
       "CholCheck               False\n",
       "BMI                     False\n",
       "Smoker                  False\n",
       "Stroke                  False\n",
       "Diabetes                False\n",
       "PhysActivity            False\n",
       "Fruits                  False\n",
       "Veggies                 False\n",
       "HvyAlcoholConsump       False\n",
       "AnyHealthcare           False\n",
       "NoDocbcCost             False\n",
       "GenHlth                 False\n",
       "MentHlth                False\n",
       "PhysHlth                False\n",
       "DiffWalk                False\n",
       "Sex                     False\n",
       "Age                     False\n",
       "Education               False\n",
       "Income                  False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values\n",
    "heart_data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6f50afd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>253675</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253676</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253677</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253678</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253679</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>253680 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        HeartDiseaseorAttack  HighBP  HighChol  CholCheck  BMI  Smoker  \\\n",
       "0                          0       1         1          1   40       1   \n",
       "1                          0       0         0          0   25       1   \n",
       "2                          0       1         1          1   28       0   \n",
       "3                          0       1         0          1   27       0   \n",
       "4                          0       1         1          1   24       0   \n",
       "...                      ...     ...       ...        ...  ...     ...   \n",
       "253675                     0       1         1          1   45       0   \n",
       "253676                     0       1         1          1   18       0   \n",
       "253677                     0       0         0          1   28       0   \n",
       "253678                     0       1         0          1   23       0   \n",
       "253679                     1       1         1          1   25       0   \n",
       "\n",
       "        Stroke  Diabetes  PhysActivity  Fruits  ...  AnyHealthcare  \\\n",
       "0            0         0             0       0  ...              1   \n",
       "1            0         0             1       0  ...              0   \n",
       "2            0         0             0       1  ...              1   \n",
       "3            0         0             1       1  ...              1   \n",
       "4            0         0             1       1  ...              1   \n",
       "...        ...       ...           ...     ...  ...            ...   \n",
       "253675       0         0             0       1  ...              1   \n",
       "253676       0         2             0       0  ...              1   \n",
       "253677       0         0             1       1  ...              1   \n",
       "253678       0         0             0       1  ...              1   \n",
       "253679       0         2             1       1  ...              1   \n",
       "\n",
       "        NoDocbcCost  GenHlth  MentHlth  PhysHlth  DiffWalk  Sex  Age  \\\n",
       "0                 0        5        18        15         1    0   36   \n",
       "1                 1        3         0         0         0    0   28   \n",
       "2                 1        5        30        30         1    0   36   \n",
       "3                 0        2         0         0         0    0   44   \n",
       "4                 0        2         3         0         0    0   44   \n",
       "...             ...      ...       ...       ...       ...  ...  ...   \n",
       "253675            0        3         0         5         0    1   20   \n",
       "253676            0        4         0         0         1    0   44   \n",
       "253677            0        1         0         0         0    0    8   \n",
       "253678            0        3         0         0         0    1   28   \n",
       "253679            0        2         0         0         0    0   36   \n",
       "\n",
       "        Education  Income  \n",
       "0               4       3  \n",
       "1               6       1  \n",
       "2               4       8  \n",
       "3               3       6  \n",
       "4               5       4  \n",
       "...           ...     ...  \n",
       "253675          6       7  \n",
       "253676          2       4  \n",
       "253677          5       2  \n",
       "253678          5       1  \n",
       "253679          6       2  \n",
       "\n",
       "[253680 rows x 22 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_data['Age'] = heart_data['Age']*4\n",
    "heart_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "743784b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    229787\n",
       "1     23893\n",
       "Name: HeartDiseaseorAttack, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the distribution of HeartDiseaseorAttack Variable\n",
    "heart_data['HeartDiseaseorAttack'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "62cd25a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Maximum age present in the dataset\n",
    "heart_data['Age'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "558154af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Minimum age present in the dataset\n",
    "heart_data['Age'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cb72565",
   "metadata": {},
   "source": [
    "### Splitting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b6f301c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = heart_data.drop(['HeartDiseaseorAttack', 'Education', 'Income'], axis=1)\n",
    "y = heart_data['HeartDiseaseorAttack']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6f359745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    160948\n",
       "1     16628\n",
       "Name: HeartDiseaseorAttack, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3)\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "07a6b254",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    160948\n",
       "1    160948\n",
       "Name: HeartDiseaseorAttack, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampler=SMOTE()\n",
    "x_train,y_train=sampler.fit_resample(x_train,y_train)\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a6f2d2b",
   "metadata": {},
   "source": [
    "### Scaling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "17c0340d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.94063356,  1.00560134,  0.17062293, ..., -0.52673161,\n",
       "         1.13385406,  0.01103742],\n",
       "       [-1.06311325,  1.00560134,  0.17062293, ..., -0.52673161,\n",
       "        -0.88194772,  0.01103742],\n",
       "       [-1.06311325,  1.00560134,  0.17062293, ..., -0.52673161,\n",
       "         1.13385406, -1.02133282],\n",
       "       ...,\n",
       "       [ 0.94063356,  1.00560134,  0.17062293, ...,  1.89850008,\n",
       "        -0.88194772,  1.38753108],\n",
       "       [-1.06311325,  1.00560134,  0.17062293, ...,  1.89850008,\n",
       "        -0.88194772,  1.38753108],\n",
       "       [ 0.94063356,  1.00560134, -5.860877  , ..., -0.52673161,\n",
       "        -0.88194772, -0.33308599]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()\n",
    "x_train=scaler.fit_transform(x_train)\n",
    "x_test=scaler.transform(x_test)\n",
    "x_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "627f311b",
   "metadata": {},
   "source": [
    "### Model Training and Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca537eb1",
   "metadata": {},
   "source": [
    "### Logisitic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "450deea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4931490d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training the LogisticRegression model with Training data\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bfff63c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Training data :  0.7496116758207619\n"
     ]
    }
   ],
   "source": [
    "# accuracy on training data\n",
    "x_train_prediction = model.predict(x_train)\n",
    "training_data_accuracy = accuracy_score(y_train,x_train_prediction)\n",
    "print('Accuracy on Training data : ', training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5c60159e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Testing data :  0.7190423630820982\n"
     ]
    }
   ],
   "source": [
    "# accuracy on testing data\n",
    "x_test_prediction = model.predict(x_test)\n",
    "test_data_accuracy = accuracy_score(y_test,x_test_prediction,)\n",
    "print('Accuracy on Testing data : ', test_data_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b41db548",
   "metadata": {},
   "source": [
    "### XGB Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b2ca061a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "              interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,\n",
       "              max_bin=256, max_cat_threshold=64, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "              interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,\n",
       "              max_bin=256, max_cat_threshold=64, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy='depthwise', importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_bin=256, max_cat_threshold=64, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints='()', n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0, ...)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1 = XGBClassifier()\n",
    "model1.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4dc59a10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Training data :  0.7878352014315183\n"
     ]
    }
   ],
   "source": [
    "# accuracy on training data\n",
    "x_train_prediction = model1.predict(x_train)\n",
    "training_data_accuracy = accuracy_score(y_train,x_train_prediction)\n",
    "print('Accuracy on Training data : ', training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9b4f4a0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Testing data :  0.7299222117102911\n"
     ]
    }
   ],
   "source": [
    "# accuracy on testing data\n",
    "x_test_prediction = model1.predict(x_test)\n",
    "test_data_accuracy = accuracy_score(y_test,x_test_prediction,)\n",
    "print('Accuracy on Testing data : ', test_data_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e72d50db",
   "metadata": {},
   "source": [
    "### KNeighbors Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8f0d5ec6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2 = KNeighborsClassifier()\n",
    "model2.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1bef3f5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Training data :  0.8568543877525661\n"
     ]
    }
   ],
   "source": [
    "# accuracy on training data\n",
    "x_train_prediction = model2.predict(x_train)\n",
    "training_data_accuracy = accuracy_score(y_train,x_train_prediction)\n",
    "print('Accuracy on Training data : ', training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "90de423d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Testing data :  0.752772521812257\n"
     ]
    }
   ],
   "source": [
    "# accuracy on testing data\n",
    "x_test_prediction = model2.predict(x_test)\n",
    "test_data_accuracy = accuracy_score(y_test,x_test_prediction,)\n",
    "print('Accuracy on Testing data : ', test_data_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ff61fb",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f361ce3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3 = RandomForestClassifier()\n",
    "model3.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ec7a1bdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Training data :  0.9696672217113602\n"
     ]
    }
   ],
   "source": [
    "# accuracy on training data\n",
    "x_train_prediction = model3.predict(x_train)\n",
    "training_data_accuracy = accuracy_score(y_train,x_train_prediction)\n",
    "print('Accuracy on Training data : ', training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "04a340f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Testing data :  0.8069483864185851\n"
     ]
    }
   ],
   "source": [
    "# accuracy on testing data\n",
    "x_test_prediction = model3.predict(x_test)\n",
    "test_data_accuracy = accuracy_score(y_test,x_test_prediction,)\n",
    "print('Accuracy on Testing data : ', test_data_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0829bf2b",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "68e41c1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model4 = DecisionTreeClassifier()\n",
    "model4.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5e9ed8a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Training data :  0.969673434898228\n"
     ]
    }
   ],
   "source": [
    "# accuracy on training data\n",
    "x_train_prediction = model4.predict(x_train)\n",
    "training_data_accuracy = accuracy_score(y_train,x_train_prediction)\n",
    "print('Accuracy on Training data : ', training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7ec48c30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Testing data :  0.7809576369179018\n"
     ]
    }
   ],
   "source": [
    "# accuracy on testing data\n",
    "x_test_prediction = model4.predict(x_test)\n",
    "test_data_accuracy = accuracy_score(y_test,x_test_prediction,)\n",
    "print('Accuracy on Testing data : ', test_data_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97d4f995",
   "metadata": {},
   "source": [
    "### GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8d6ec43b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model5 = GaussianNB()\n",
    "model5.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "42481190",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Training data :  0.723659194273927\n"
     ]
    }
   ],
   "source": [
    "# accuracy on training data\n",
    "x_train_prediction = model5.predict(x_train)\n",
    "training_data_accuracy = accuracy_score(y_train,x_train_prediction)\n",
    "print('Accuracy on Training data : ', training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6f8afe3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Testingp data :  0.6719094922737306\n"
     ]
    }
   ],
   "source": [
    "# accuracy on testing data\n",
    "x_test_prediction = model5.predict(x_test)\n",
    "test_data_accuracy = accuracy_score(y_test,x_test_prediction,)\n",
    "print('Accuracy on Testingp data : ', test_data_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b85c9e9a",
   "metadata": {},
   "source": [
    "### Building a Predictive System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7ee16b3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n",
      "The Person does not have a Heart Disease\n"
     ]
    }
   ],
   "source": [
    "input_data = (1,1,1,40,1,0,0,0,0,1,0,1,0,5,18,15,1,0,9) # => 0\n",
    "\n",
    "# change the input data to a numpy array\n",
    "input_data_as_numpy_array= np.asarray(input_data)\n",
    "\n",
    "# reshape the numpy array as we are predicting for only on instance\n",
    "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
    "\n",
    "prediction = model3.predict(input_data_reshaped)\n",
    "print(prediction)\n",
    "\n",
    "if (prediction[0]== 0):\n",
    "  print('The Person does not have a Heart Disease')\n",
    "else:\n",
    "  print('The Person has Heart Disease')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aead4797",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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