main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Heart Disease Binary Classification</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import SimpleImputer,IterativeImputer\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score,StratifiedKFold\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, RandomForestRegressor\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score, confusion_matrix,make_scorer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Inspecting the Data for Missing Values</h2>\n",
    "\n",
    "For this project we are required to train a model on one dataset and test the dataset on 2 or more datasets to ensure that the model is a good fit and was not over/underfitted in training.\n",
    "\n",
    "Therefore since multiple datasets were used we will focus only on the features common to all three datasets and remove the rest.\n",
    "\n",
    "The common features found in all three datasets were:\n",
    "1. age\n",
    "2. sex\n",
    "3. chestpain type\n",
    "4. blood pressure\n",
    "5. cholesterol\n",
    "6. fbs\n",
    "7. restecg\n",
    "8. max heart rate\n",
    "9. exang\n",
    "10. ST depression(old peak)\n",
    "11. slope\n",
    "12. Num Vessels\n",
    "13. thal\n",
    "14. heart_disease\n",
    "\n",
    "The initial model will be trained on the UCI Heart Disease Dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>dataset</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalch</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>typical angina</td>\n",
       "      <td>145.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>True</td>\n",
       "      <td>lv hypertrophy</td>\n",
       "      <td>150.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.3</td>\n",
       "      <td>downsloping</td>\n",
       "      <td>0.0</td>\n",
       "      <td>fixed defect</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>Male</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>160.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>False</td>\n",
       "      <td>lv hypertrophy</td>\n",
       "      <td>108.0</td>\n",
       "      <td>True</td>\n",
       "      <td>1.5</td>\n",
       "      <td>flat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>67</td>\n",
       "      <td>Male</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>120.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>False</td>\n",
       "      <td>lv hypertrophy</td>\n",
       "      <td>129.0</td>\n",
       "      <td>True</td>\n",
       "      <td>2.6</td>\n",
       "      <td>flat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>reversable defect</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>Male</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>non-anginal</td>\n",
       "      <td>130.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>False</td>\n",
       "      <td>normal</td>\n",
       "      <td>187.0</td>\n",
       "      <td>False</td>\n",
       "      <td>3.5</td>\n",
       "      <td>downsloping</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>41</td>\n",
       "      <td>Female</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>atypical angina</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>False</td>\n",
       "      <td>lv hypertrophy</td>\n",
       "      <td>172.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1.4</td>\n",
       "      <td>upsloping</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  age     sex    dataset               cp  trestbps   chol    fbs  \\\n",
       "0   1   63    Male  Cleveland   typical angina     145.0  233.0   True   \n",
       "1   2   67    Male  Cleveland     asymptomatic     160.0  286.0  False   \n",
       "2   3   67    Male  Cleveland     asymptomatic     120.0  229.0  False   \n",
       "3   4   37    Male  Cleveland      non-anginal     130.0  250.0  False   \n",
       "4   5   41  Female  Cleveland  atypical angina     130.0  204.0  False   \n",
       "\n",
       "          restecg  thalch  exang  oldpeak        slope   ca  \\\n",
       "0  lv hypertrophy   150.0  False      2.3  downsloping  0.0   \n",
       "1  lv hypertrophy   108.0   True      1.5         flat  3.0   \n",
       "2  lv hypertrophy   129.0   True      2.6         flat  2.0   \n",
       "3          normal   187.0  False      3.5  downsloping  0.0   \n",
       "4  lv hypertrophy   172.0  False      1.4    upsloping  0.0   \n",
       "\n",
       "                thal  num  \n",
       "0       fixed defect    0  \n",
       "1             normal    2  \n",
       "2  reversable defect    1  \n",
       "3             normal    0  \n",
       "4             normal    0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Datasets\\heart_disease_uci.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalch</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
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       "      <th>num</th>\n",
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       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>typical angina</td>\n",
       "      <td>145.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>True</td>\n",
       "      <td>lv hypertrophy</td>\n",
       "      <td>150.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.3</td>\n",
       "      <td>downsloping</td>\n",
       "      <td>0.0</td>\n",
       "      <td>fixed defect</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>Male</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>160.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>False</td>\n",
       "      <td>lv hypertrophy</td>\n",
       "      <td>108.0</td>\n",
       "      <td>True</td>\n",
       "      <td>1.5</td>\n",
       "      <td>flat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>Male</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>120.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>False</td>\n",
       "      <td>lv hypertrophy</td>\n",
       "      <td>129.0</td>\n",
       "      <td>True</td>\n",
       "      <td>2.6</td>\n",
       "      <td>flat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>reversable defect</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>Male</td>\n",
       "      <td>non-anginal</td>\n",
       "      <td>130.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>False</td>\n",
       "      <td>normal</td>\n",
       "      <td>187.0</td>\n",
       "      <td>False</td>\n",
       "      <td>3.5</td>\n",
       "      <td>downsloping</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>Female</td>\n",
       "      <td>atypical angina</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>False</td>\n",
       "      <td>lv hypertrophy</td>\n",
       "      <td>172.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1.4</td>\n",
       "      <td>upsloping</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   age     sex               cp  trestbps   chol    fbs         restecg  \\\n",
       "0   63    Male   typical angina     145.0  233.0   True  lv hypertrophy   \n",
       "1   67    Male     asymptomatic     160.0  286.0  False  lv hypertrophy   \n",
       "2   67    Male     asymptomatic     120.0  229.0  False  lv hypertrophy   \n",
       "3   37    Male      non-anginal     130.0  250.0  False          normal   \n",
       "4   41  Female  atypical angina     130.0  204.0  False  lv hypertrophy   \n",
       "\n",
       "   thalch  exang  oldpeak        slope   ca               thal  num  \n",
       "0   150.0  False      2.3  downsloping  0.0       fixed defect    0  \n",
       "1   108.0   True      1.5         flat  3.0             normal    2  \n",
       "2   129.0   True      2.6         flat  2.0  reversable defect    1  \n",
       "3   187.0  False      3.5  downsloping  0.0             normal    0  \n",
       "4   172.0  False      1.4    upsloping  0.0             normal    0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df[['id','dataset']],axis=1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 920 entries, 0 to 919\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       920 non-null    int64  \n",
      " 1   sex       920 non-null    object \n",
      " 2   cp        920 non-null    object \n",
      " 3   trestbps  861 non-null    float64\n",
      " 4   chol      890 non-null    float64\n",
      " 5   fbs       830 non-null    object \n",
      " 6   restecg   918 non-null    object \n",
      " 7   thalch    865 non-null    float64\n",
      " 8   exang     865 non-null    object \n",
      " 9   oldpeak   858 non-null    float64\n",
      " 10  slope     611 non-null    object \n",
      " 11  ca        309 non-null    float64\n",
      " 12  thal      434 non-null    object \n",
      " 13  num       920 non-null    int64  \n",
      "dtypes: float64(5), int64(2), object(7)\n",
      "memory usage: 100.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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    {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
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       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>typical angina</td>\n",
       "      <td>145.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>True</td>\n",
       "      <td>lv hypertrophy</td>\n",
       "      <td>150.0</td>\n",
       "      <td>False</td>\n",
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       "      <td>160.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>False</td>\n",
       "      <td>lv hypertrophy</td>\n",
       "      <td>108.0</td>\n",
       "      <td>True</td>\n",
       "      <td>1.5</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>Male</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>120.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>False</td>\n",
       "      <td>lv hypertrophy</td>\n",
       "      <td>129.0</td>\n",
       "      <td>True</td>\n",
       "      <td>2.6</td>\n",
       "      <td>flat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>reversable defect</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>Male</td>\n",
       "      <td>non-anginal</td>\n",
       "      <td>130.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>False</td>\n",
       "      <td>normal</td>\n",
       "      <td>187.0</td>\n",
       "      <td>False</td>\n",
       "      <td>3.5</td>\n",
       "      <td>downsloping</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>Female</td>\n",
       "      <td>atypical angina</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>False</td>\n",
       "      <td>lv hypertrophy</td>\n",
       "      <td>172.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1.4</td>\n",
       "      <td>upsloping</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex               cp  trestbps   chol    fbs         restecg  \\\n",
       "0   63    Male   typical angina     145.0  233.0   True  lv hypertrophy   \n",
       "1   67    Male     asymptomatic     160.0  286.0  False  lv hypertrophy   \n",
       "2   67    Male     asymptomatic     120.0  229.0  False  lv hypertrophy   \n",
       "3   37    Male      non-anginal     130.0  250.0  False          normal   \n",
       "4   41  Female  atypical angina     130.0  204.0  False  lv hypertrophy   \n",
       "\n",
       "   thalch  exang  oldpeak        slope   ca               thal  num  \n",
       "0   150.0  False      2.3  downsloping  0.0       fixed defect    0  \n",
       "1   108.0   True      1.5         flat  3.0             normal    2  \n",
       "2   129.0   True      2.6         flat  2.0  reversable defect    1  \n",
       "3   187.0  False      3.5  downsloping  0.0             normal    0  \n",
       "4   172.0  False      1.4    upsloping  0.0             normal    0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>thalch</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>ca</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>920.000000</td>\n",
       "      <td>861.000000</td>\n",
       "      <td>890.000000</td>\n",
       "      <td>865.000000</td>\n",
       "      <td>858.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>920.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.510870</td>\n",
       "      <td>132.132404</td>\n",
       "      <td>199.130337</td>\n",
       "      <td>137.545665</td>\n",
       "      <td>0.878788</td>\n",
       "      <td>0.676375</td>\n",
       "      <td>0.995652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.424685</td>\n",
       "      <td>19.066070</td>\n",
       "      <td>110.780810</td>\n",
       "      <td>25.926276</td>\n",
       "      <td>1.091226</td>\n",
       "      <td>0.935653</td>\n",
       "      <td>1.142693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-2.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>268.000000</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>603.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age    trestbps        chol      thalch     oldpeak          ca  \\\n",
       "count  920.000000  861.000000  890.000000  865.000000  858.000000  309.000000   \n",
       "mean    53.510870  132.132404  199.130337  137.545665    0.878788    0.676375   \n",
       "std      9.424685   19.066070  110.780810   25.926276    1.091226    0.935653   \n",
       "min     28.000000    0.000000    0.000000   60.000000   -2.600000    0.000000   \n",
       "25%     47.000000  120.000000  175.000000  120.000000    0.000000    0.000000   \n",
       "50%     54.000000  130.000000  223.000000  140.000000    0.500000    0.000000   \n",
       "75%     60.000000  140.000000  268.000000  157.000000    1.500000    1.000000   \n",
       "max     77.000000  200.000000  603.000000  202.000000    6.200000    3.000000   \n",
       "\n",
       "              num  \n",
       "count  920.000000  \n",
       "mean     0.995652  \n",
       "std      1.142693  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      1.000000  \n",
       "75%      2.000000  \n",
       "max      4.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that most of the columns contain missing values so we will have to do data imputation and we may have to drop some columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of missing values in each column is:\n",
      " ca          66.413043\n",
      "thal        52.826087\n",
      "slope       33.586957\n",
      "fbs          9.782609\n",
      "oldpeak      6.739130\n",
      "trestbps     6.413043\n",
      "thalch       5.978261\n",
      "exang        5.978261\n",
      "chol         3.260870\n",
      "restecg      0.217391\n",
      "age          0.000000\n",
      "sex          0.000000\n",
      "cp           0.000000\n",
      "num          0.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(f\"Percentage of missing values in each column is:\\n {(df.isna().sum()/len(df) *100).sort_values(ascending=False)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num\n",
       "0    411\n",
       "1    265\n",
       "2    109\n",
       "3    107\n",
       "4     28\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['num'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['trestbps',\n",
       " 'chol',\n",
       " 'fbs',\n",
       " 'restecg',\n",
       " 'thalch',\n",
       " 'exang',\n",
       " 'oldpeak',\n",
       " 'slope',\n",
       " 'ca',\n",
       " 'thal']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()[df.isnull().sum() > 0].sort_values(ascending=False)\n",
    "missing_data_cols = df.isnull().sum()[df.isnull().sum() > 0].index.tolist()\n",
    "missing_data_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_cols = ['thal', 'ca', 'slope', 'exang', 'restecg','fbs', 'cp', 'sex', 'num']\n",
    "bool_cols = ['fbs', 'exang']\n",
    "numeric_cols = ['oldpeak', 'thalch', 'chol', 'trestbps', 'age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "\n",
    "for col in categorical_cols:\n",
    "    df[col] = le.fit_transform(df[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_imputer = SimpleImputer(strategy=\"mean\")\n",
    "cat_imputer = SimpleImputer(strategy=\"most_frequent\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputed_df = df.copy()\n",
    "imputed_df['trestbps'] = num_imputer.fit_transform(imputed_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputed_df['trestbps'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in missing_data_cols:\n",
    "    if col in categorical_cols:\n",
    "        imputed_df[col] = cat_imputer.fit_transform(imputed_df[[col]])\n",
    "    elif col in numeric_cols:\n",
    "        imputed_df[col] = num_imputer.fit_transform(imputed_df[[col]])\n",
    "    else:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalch</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>63.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>37.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</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>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps   chol  fbs  restecg  thalch  exang  oldpeak  slope  \\\n",
       "0   63    1   3      63.0  233.0    1        0   150.0      0      2.3      0   \n",
       "1   67    1   0      67.0  286.0    0        0   108.0      1      1.5      1   \n",
       "2   67    1   0      67.0  229.0    0        0   129.0      1      2.6      1   \n",
       "3   37    1   2      37.0  250.0    0        1   187.0      0      3.5      0   \n",
       "4   41    0   1      41.0  204.0    0        0   172.0      0      1.4      2   \n",
       "\n",
       "   ca  thal  num  \n",
       "0   0     0    0  \n",
       "1   3     1    2  \n",
       "2   2     2    1  \n",
       "3   0     1    0  \n",
       "4   0     1    0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputed_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 920 entries, 0 to 919\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       920 non-null    int64  \n",
      " 1   sex       920 non-null    int32  \n",
      " 2   cp        920 non-null    int32  \n",
      " 3   trestbps  920 non-null    float64\n",
      " 4   chol      920 non-null    float64\n",
      " 5   fbs       920 non-null    int32  \n",
      " 6   restecg   920 non-null    int32  \n",
      " 7   thalch    920 non-null    float64\n",
      " 8   exang     920 non-null    int32  \n",
      " 9   oldpeak   920 non-null    float64\n",
      " 10  slope     920 non-null    int32  \n",
      " 11  ca        920 non-null    int64  \n",
      " 12  thal      920 non-null    int32  \n",
      " 13  num       920 non-null    int64  \n",
      "dtypes: float64(4), int32(7), int64(3)\n",
      "memory usage: 75.6 KB\n"
     ]
    }
   ],
   "source": [
    "imputed_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Handling Outliers</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since outliers are usually wrt continuous data we will only examine our numeric columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>oldpeak</th>\n",
       "      <th>thalch</th>\n",
       "      <th>chol</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.878788</td>\n",
       "      <td>137.545665</td>\n",
       "      <td>199.130337</td>\n",
       "      <td>53.510870</td>\n",
       "      <td>53.510870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.053774</td>\n",
       "      <td>25.138494</td>\n",
       "      <td>108.957634</td>\n",
       "      <td>9.424685</td>\n",
       "      <td>9.424685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.600000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>177.750000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>221.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>267.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.200000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>603.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          oldpeak      thalch        chol    trestbps         age\n",
       "count  920.000000  920.000000  920.000000  920.000000  920.000000\n",
       "mean     0.878788  137.545665  199.130337   53.510870   53.510870\n",
       "std      1.053774   25.138494  108.957634    9.424685    9.424685\n",
       "min     -2.600000   60.000000    0.000000   28.000000   28.000000\n",
       "25%      0.000000  120.000000  177.750000   47.000000   47.000000\n",
       "50%      0.800000  138.000000  221.000000   54.000000   54.000000\n",
       "75%      1.500000  156.000000  267.000000   60.000000   60.000000\n",
       "max      6.200000  202.000000  603.000000   77.000000   77.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputed_df[numeric_cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 20))\n",
    "\n",
    "colors = ['red', 'green', 'blue', 'orange', 'purple']\n",
    "\n",
    "for i, col in enumerate(numeric_cols):\n",
    "    plt.subplot(3, 2, i+1)\n",
    "    sns.boxplot(x=imputed_df[col], color=colors[i])\n",
    "    plt.title(col)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Machine Learning Algorithms</h1>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scaling Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "imputed_df[numeric_cols] = scaler.fit_transform(imputed_df[numeric_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "imputed_df[numeric_cols] = scaler.fit_transform(imputed_df[numeric_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputed_df.drop('num', axis=1)\n",
    "y = imputed_df['num']\n",
    "\n",
    "# split the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2 , random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputed_df.drop('num', axis=1)\n",
    "y = imputed_df['num']\n",
    "\n",
    "# split the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2 , random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Support Vector Machine</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.5380434782608695\n",
      "Precision score:  0.5380434782608695\n",
      "Recall score:  0.5380434782608695\n",
      "F1 score:  0.5380434782608695\n"
     ]
    }
   ],
   "source": [
    "svm_model = SVC()\n",
    "svm_model.fit(X_train, y_train)\n",
    "\n",
    "# predict the test data\n",
    "y_pred = svm_model.predict(X_test)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y_test, y_pred))\n",
    "print('Precision score: ', precision_score(y_test, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y_test, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y_test, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Logistic Regression</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.5380434782608695\n",
      "Precision score:  0.5380434782608695\n",
      "Recall score:  0.5380434782608695\n",
      "F1 score:  0.5380434782608695\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(max_iter=1000)\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# predict the test data\n",
    "y_pred = lr.predict(X_test)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y_test, y_pred))\n",
    "print('Precision score: ', precision_score(y_test, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y_test, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y_test, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation Scores: [0.4673913  0.52173913 0.5326087  0.57608696 0.67391304 0.67391304\n",
      " 0.54347826 0.51086957 0.39130435 0.34782609]\n",
      "Mean CV Score: 0.5239130434782608\n"
     ]
    }
   ],
   "source": [
    "log_reg = LogisticRegression(max_iter=1000)  # Set max_iter for convergence\n",
    "\n",
    "# Perform cross-validation\n",
    "scores = cross_val_score(log_reg, X, y, cv=10)  # k-fold cross-validation\n",
    "\n",
    "# Print the cross-validation scores\n",
    "print(\"Cross-Validation Scores:\", scores)\n",
    "print(\"Mean CV Score:\", scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>K Nearest Neighbours</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score at k = 1:  0.5\n",
      "Accuracy score at k = 2:  0.5054347826086957\n",
      "Accuracy score at k = 3:  0.5108695652173914\n",
      "Accuracy score at k = 4:  0.5597826086956522\n",
      "Accuracy score at k = 5:  0.532608695652174\n",
      "Accuracy score at k = 6:  0.5163043478260869\n",
      "Accuracy score at k = 7:  0.5271739130434783\n",
      "Accuracy score at k = 8:  0.5597826086956522\n",
      "Accuracy score at k = 9:  0.5217391304347826\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,10):\n",
    "    knn = KNeighborsClassifier(n_neighbors= i)\n",
    "    knn.fit(X_train, y_train)\n",
    "\n",
    "    # predict the test data\n",
    "    y_pred = knn.predict(X_test)\n",
    "\n",
    "    print(f'Accuracy score at k = {i}: ', accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.5597826086956522\n",
      "Precision score:  0.5597826086956522\n",
      "Recall score:  0.5597826086956522\n",
      "F1 score:  0.5597826086956522\n"
     ]
    }
   ],
   "source": [
    "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors= 8)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "# predict the test data\n",
    "y_pred = knn.predict(X_test)\n",
    "\n",
    "#scores = cross_val_score(kf, X, y, cv=kf)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y_test, y_pred))\n",
    "print('Precision score: ', precision_score(y_test, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y_test, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y_test, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Decision Tree</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.5597826086956522\n",
      "Precision score:  0.5597826086956522\n",
      "Recall score:  0.5597826086956522\n",
      "F1 score:  0.5597826086956522\n"
     ]
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train,y_train)\n",
    "\n",
    "y_pred = dt.predict(X_test)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y_test, y_pred))\n",
    "print('Precision score: ', precision_score(y_test, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y_test, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y_test, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Testing Models on Alternative Datasets</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = pd.read_csv(\"Datasets\\heart.csv\")\n",
    "df2 = pd.read_csv(\"Datasets\\Heart_Disease_Prediction.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalch',\n",
       "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'num'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.columns\n",
    "df2 = df2.rename(columns={'Age':'age', 'Sex':'sex', 'Chest pain type':'cp', 'BP':'trestbps', 'Cholesterol':'chol', 'FBS over 120':'fbs',\n",
    "       'EKG results':'restecg', 'Max HR':'thalch', 'Exercise angina':'exang', 'ST depression':'oldpeak',\n",
    "       'Slope of ST':'slope', 'Number of vessels fluro':'ca', 'Thallium':'thal', 'Heart Disease':'num'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalch</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>322</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Presence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>115</td>\n",
       "      <td>564</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Presence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>128</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>269</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalch  exang  oldpeak  slope  \\\n",
       "0   70    1   4       130   322    0        2     109      0      2.4      2   \n",
       "1   67    0   3       115   564    0        2     160      0      1.6      2   \n",
       "2   57    1   2       124   261    0        0     141      0      0.3      1   \n",
       "3   64    1   4       128   263    0        0     105      1      0.2      2   \n",
       "4   74    0   2       120   269    0        2     121      1      0.2      1   \n",
       "\n",
       "   ca  thal       num  \n",
       "0   3     3  Presence  \n",
       "1   0     7   Absence  \n",
       "2   0     7  Presence  \n",
       "3   1     7   Absence  \n",
       "4   1     3   Absence  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num\n",
       "Absence     150\n",
       "Presence    120\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['num'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num\n",
       "Absence     150\n",
       "Presence    120\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['num'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 270 entries, 0 to 269\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       270 non-null    int64  \n",
      " 1   sex       270 non-null    int64  \n",
      " 2   cp        270 non-null    int64  \n",
      " 3   trestbps  270 non-null    int64  \n",
      " 4   chol      270 non-null    int64  \n",
      " 5   fbs       270 non-null    int64  \n",
      " 6   restecg   270 non-null    int64  \n",
      " 7   thalch    270 non-null    int64  \n",
      " 8   exang     270 non-null    int64  \n",
      " 9   oldpeak   270 non-null    float64\n",
      " 10  slope     270 non-null    int64  \n",
      " 11  ca        270 non-null    int64  \n",
      " 12  thal      270 non-null    int64  \n",
      " 13  num       270 non-null    object \n",
      "dtypes: float64(1), int64(12), object(1)\n",
      "memory usage: 29.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['num'] = le.fit_transform(df2['num'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</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>0</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>294</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
       "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
       "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
       "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
       "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   2     3       0  \n",
       "1   0     3       0  \n",
       "2   0     3       0  \n",
       "3   1     3       0  \n",
       "4   3     2       0  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1025 entries, 0 to 1024\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       1025 non-null   int64  \n",
      " 1   sex       1025 non-null   int64  \n",
      " 2   cp        1025 non-null   int64  \n",
      " 3   trestbps  1025 non-null   int64  \n",
      " 4   chol      1025 non-null   int64  \n",
      " 5   fbs       1025 non-null   int64  \n",
      " 6   restecg   1025 non-null   int64  \n",
      " 7   thalch    1025 non-null   int64  \n",
      " 8   exang     1025 non-null   int64  \n",
      " 9   oldpeak   1025 non-null   float64\n",
      " 10  slope     1025 non-null   int64  \n",
      " 11  ca        1025 non-null   int64  \n",
      " 12  thal      1025 non-null   int64  \n",
      " 13  num       1025 non-null   int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 112.2 KB\n"
     ]
    }
   ],
   "source": [
    "df3 = df3.rename(columns={'thalach':'thalch','target':'num'})\n",
    "df3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalch      0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "num         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_df2 = df2.copy()\n",
    "\n",
    "scaled_df2[numeric_cols] = scaler.fit_transform(scaled_df2[numeric_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_df3 = df3.copy()\n",
    "\n",
    "scaled_df3[numeric_cols] = scaler.fit_transform(scaled_df3[numeric_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2 = scaled_df2.drop('num', axis=1)\n",
    "y2 = scaled_df2['num']\n",
    "\n",
    "X3 = scaled_df3.drop('num', axis=1)\n",
    "y3 = scaled_df3['num']\n",
    "\n",
    "# split the data into train and test sets\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2 , random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Logistic Regression</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.5518518518518518\n",
      "Precision score:  0.5518518518518518\n",
      "Recall score:  0.5518518518518518\n",
      "F1 score:  0.5518518518518518\n"
     ]
    }
   ],
   "source": [
    "y_pred = lr.predict(X2)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y2, y_pred))\n",
    "print('Precision score: ', precision_score(y2, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y2, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y2, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.24097560975609755\n",
      "Precision score:  0.24097560975609755\n",
      "Recall score:  0.24097560975609755\n",
      "F1 score:  0.24097560975609755\n"
     ]
    }
   ],
   "source": [
    "y_pred = lr.predict(X3)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y3, y_pred))\n",
    "print('Precision score: ', precision_score(y3, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y3, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y3, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>SVM</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.5703703703703704\n",
      "Precision score:  0.5703703703703704\n",
      "Recall score:  0.5703703703703704\n",
      "F1 score:  0.5703703703703704\n"
     ]
    }
   ],
   "source": [
    "y_pred = svm_model.predict(X2)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y2, y_pred))\n",
    "print('Precision score: ', precision_score(y2, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y2, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y2, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.19219512195121952\n",
      "Precision score:  0.19219512195121952\n",
      "Recall score:  0.19219512195121952\n",
      "F1 score:  0.19219512195121952\n"
     ]
    }
   ],
   "source": [
    "y_pred = svm_model.predict(X3)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y3, y_pred))\n",
    "print('Precision score: ', precision_score(y3, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y3, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y3, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>KNN</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.5592592592592592\n",
      "Precision score:  0.5592592592592592\n",
      "Recall score:  0.5592592592592592\n",
      "F1 score:  0.5592592592592592\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn.predict(X2)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y2, y_pred))\n",
    "print('Precision score: ', precision_score(y2, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y2, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y2, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.16682926829268294\n",
      "Precision score:  0.16682926829268294\n",
      "Recall score:  0.16682926829268294\n",
      "F1 score:  0.16682926829268294\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn.predict(X3)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y3, y_pred))\n",
    "print('Precision score: ', precision_score(y3, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y3, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y3, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Decision Tree</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.5148148148148148\n",
      "Precision score:  0.5148148148148148\n",
      "Recall score:  0.5148148148148148\n",
      "F1 score:  0.5148148148148148\n"
     ]
    }
   ],
   "source": [
    "y_pred = dt.predict(X2)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y2, y_pred))\n",
    "print('Precision score: ', precision_score(y2, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y2, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y2, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.19902439024390245\n",
      "Precision score:  0.19902439024390245\n",
      "Recall score:  0.19902439024390245\n",
      "F1 score:  0.19902439024390245\n"
     ]
    }
   ],
   "source": [
    "y_pred = dt.predict(X3)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y3, y_pred))\n",
    "print('Precision score: ', precision_score(y3, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y3, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y3, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Models Trained on Second Dataset</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Logistic Regression</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X2, y2, test_size=0.2 , random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.9074074074074074\n",
      "Precision score:  0.9074074074074074\n",
      "Recall score:  0.9074074074074074\n",
      "F1 score:  0.9074074074074074\n"
     ]
    }
   ],
   "source": [
    "lr2 = LogisticRegression(max_iter=1000)\n",
    "lr2.fit(X_train, y_train)\n",
    "# predict the test data\n",
    "y_pred = lr2.predict(X_test)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y_test, y_pred))\n",
    "print('Precision score: ', precision_score(y_test, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y_test, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y_test, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.40543478260869564\n",
      "Precision score:  0.40543478260869564\n",
      "Recall score:  0.40543478260869564\n",
      "F1 score:  0.40543478260869564\n"
     ]
    }
   ],
   "source": [
    "y_pred = lr2.predict(X)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y, y_pred))\n",
    "print('Precision score: ', precision_score(y, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.4634146341463415\n",
      "Precision score:  0.4634146341463415\n",
      "Recall score:  0.4634146341463415\n",
      "F1 score:  0.4634146341463415\n"
     ]
    }
   ],
   "source": [
    "y_pred = lr2.predict(X3)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y3, y_pred))\n",
    "print('Precision score: ', precision_score(y3, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y3, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y3, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>SVM</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.5925925925925926\n",
      "Precision score:  0.5925925925925926\n",
      "Recall score:  0.5925925925925926\n",
      "F1 score:  0.5925925925925926\n"
     ]
    }
   ],
   "source": [
    "svm_model2 = SVC()\n",
    "svm_model2.fit(X_train, y_train)\n",
    "\n",
    "# predict the test data\n",
    "y_pred = svm_model.predict(X_test)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y_test, y_pred))\n",
    "print('Precision score: ', precision_score(y_test, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y_test, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y_test, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.45217391304347826\n",
      "Precision score:  0.45217391304347826\n",
      "Recall score:  0.45217391304347826\n",
      "F1 score:  0.45217391304347826\n"
     ]
    }
   ],
   "source": [
    "y_pred = svm_model2.predict(X)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y, y_pred))\n",
    "print('Precision score: ', precision_score(y, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.48097560975609754\n",
      "Precision score:  0.48097560975609754\n",
      "Recall score:  0.48097560975609754\n",
      "F1 score:  0.48097560975609754\n"
     ]
    }
   ],
   "source": [
    "y_pred = svm_model2.predict(X3)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y3, y_pred))\n",
    "print('Precision score: ', precision_score(y3, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y3, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y3, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>KNN</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score at k = 1:  0.7037037037037037\n",
      "Accuracy score at k = 2:  0.7222222222222222\n",
      "Accuracy score at k = 3:  0.7777777777777778\n",
      "Accuracy score at k = 4:  0.7962962962962963\n",
      "Accuracy score at k = 5:  0.8148148148148148\n",
      "Accuracy score at k = 6:  0.8148148148148148\n",
      "Accuracy score at k = 7:  0.8333333333333334\n",
      "Accuracy score at k = 8:  0.8518518518518519\n",
      "Accuracy score at k = 9:  0.8148148148148148\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,10):\n",
    "    knn2 = KNeighborsClassifier(n_neighbors= i)\n",
    "    knn2.fit(X_train, y_train)\n",
    "\n",
    "    # predict the test data\n",
    "    y_pred = knn2.predict(X_test)\n",
    "\n",
    "    print(f'Accuracy score at k = {i}: ', accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.8518518518518519\n",
      "Precision score:  0.8518518518518519\n",
      "Recall score:  0.8518518518518519\n",
      "F1 score:  0.8518518518518519\n"
     ]
    }
   ],
   "source": [
    "knn2 = KNeighborsClassifier(n_neighbors= 8)\n",
    "knn2.fit(X_train, y_train)\n",
    "\n",
    "# predict the test data\n",
    "y_pred = knn2.predict(X_test)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y_test, y_pred))\n",
    "print('Precision score: ', precision_score(y_test, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y_test, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y_test, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.44565217391304346\n",
      "Precision score:  0.44565217391304346\n",
      "Recall score:  0.44565217391304346\n",
      "F1 score:  0.44565217391304346\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn2.predict(X)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y, y_pred))\n",
    "print('Precision score: ', precision_score(y, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.48292682926829267\n",
      "Precision score:  0.48292682926829267\n",
      "Recall score:  0.48292682926829267\n",
      "F1 score:  0.48292682926829267\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn2.predict(X3)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y3, y_pred))\n",
    "print('Precision score: ', precision_score(y3, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y3, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y3, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Decision Tree</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.6851851851851852\n",
      "Precision score:  0.6851851851851852\n",
      "Recall score:  0.6851851851851852\n",
      "F1 score:  0.6851851851851852\n"
     ]
    }
   ],
   "source": [
    "dt2 = DecisionTreeClassifier()\n",
    "dt2.fit(X_train,y_train)\n",
    "\n",
    "# predict the test data\n",
    "y_pred = dt2.predict(X_test)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y_test, y_pred))\n",
    "print('Precision score: ', precision_score(y_test, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y_test, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y_test, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.44782608695652176\n",
      "Precision score:  0.44782608695652176\n",
      "Recall score:  0.44782608695652176\n",
      "F1 score:  0.44782608695652176\n"
     ]
    }
   ],
   "source": [
    "y_pred = dt2.predict(X)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y, y_pred))\n",
    "print('Precision score: ', precision_score(y, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.3229268292682927\n",
      "Precision score:  0.3229268292682927\n",
      "Recall score:  0.3229268292682927\n",
      "F1 score:  0.3229268292682927\n"
     ]
    }
   ],
   "source": [
    "y_pred = dt2.predict(X3)\n",
    "\n",
    "print('Accuracy score: ', accuracy_score(y3, y_pred))\n",
    "print('Precision score: ', precision_score(y3, y_pred, average='micro'))\n",
    "print('Recall score: ', recall_score(y3, y_pred, average='micro'))\n",
    "print('F1 score: ', f1_score(y3, y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Cross Validation</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores:  [0.43478261 0.39673913 0.45652174 0.16304348 0.27173913]\n",
      "Average CV Score:  0.3445652173913044\n",
      "Number of CV Scores used in Average:  5\n"
     ]
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier()\n",
    "\n",
    "sk_folds = StratifiedKFold(n_splits = 5)\n",
    "\n",
    "scores = cross_val_score(clf, X, y, cv = sk_folds)\n",
    "\n",
    "print(\"Cross Validation Scores: \", scores)\n",
    "print(\"Average CV Score: \", scores.mean())\n",
    "print(\"Number of CV Scores used in Average: \", len(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores:  [0.43478261 0.39673913 0.45652174 0.16304348 0.27173913]\n",
      "Average CV Score:  0.3445652173913044\n",
      "Number of CV Scores used in Average:  5\n",
      "Cross Validation Scores:  [0.43478261 0.39673913 0.45652174 0.16304348 0.27173913]\n",
      "Average CV Score:  0.3445652173913044\n",
      "Number of CV Scores used in Average:  5\n"
     ]
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier()\n",
    "\n",
    "scoring = {'accuracy', 'precision','recall', 'f1_score'}\n",
    "\n",
    "sk_folds = StratifiedKFold(n_splits = 10)\n",
    "\n",
    "results = cross_val_score(clf,X2,y2,cv=sk_folds,scoring='precision')\n",
    "\n",
    "print(\"Cross Validation Scores: \", scores)\n",
    "print(\"Average CV Score: \", scores.mean())\n",
    "print(\"Number of CV Scores used in Average: \", len(scores))\n",
    "\n",
    "results = cross_val_score(clf,X2,y2,cv=sk_folds,scoring='f1')\n",
    "\n",
    "print(\"Cross Validation Scores: \", scores)\n",
    "print(\"Average CV Score: \", scores.mean())\n",
    "print(\"Number of CV Scores used in Average: \", len(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores:  [0.43478261 0.39673913 0.45652174 0.16304348 0.27173913]\n",
      "Average CV Score:  0.3445652173913044\n",
      "Number of CV Scores used in Average:  5\n",
      "Cross Validation Scores:  [0.43478261 0.39673913 0.45652174 0.16304348 0.27173913]\n",
      "Average CV Score:  0.3445652173913044\n",
      "Number of CV Scores used in Average:  5\n"
     ]
    }
   ],
   "source": [
    "clf = KNeighborsClassifier(n_neighbors= 8)\n",
    "\n",
    "scoring = {'accuracy', 'precision','recall', 'f1_score'}\n",
    "\n",
    "sk_folds = StratifiedKFold(n_splits = 10)\n",
    "\n",
    "results = cross_val_score(clf,X2,y2,cv=sk_folds,scoring='precision')\n",
    "\n",
    "print(\"Cross Validation Scores: \", scores)\n",
    "print(\"Average CV Score: \", scores.mean())\n",
    "print(\"Number of CV Scores used in Average: \", len(scores))\n",
    "\n",
    "results = cross_val_score(clf,X2,y2,cv=sk_folds,scoring='f1')\n",
    "\n",
    "print(\"Cross Validation Scores: \", scores)\n",
    "print(\"Average CV Score: \", scores.mean())\n",
    "print(\"Number of CV Scores used in Average: \", len(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores:  [0.43478261 0.39673913 0.45652174 0.16304348 0.27173913]\n",
      "Average CV Score:  0.3445652173913044\n",
      "Number of CV Scores used in Average:  5\n",
      "Cross Validation Scores:  [0.43478261 0.39673913 0.45652174 0.16304348 0.27173913]\n",
      "Average CV Score:  0.3445652173913044\n",
      "Number of CV Scores used in Average:  5\n"
     ]
    }
   ],
   "source": [
    "clf = SVC()\n",
    "\n",
    "scoring = {'accuracy', 'precision','recall', 'f1_score'}\n",
    "\n",
    "sk_folds = StratifiedKFold(n_splits = 10) \n",
    "\n",
    "results = cross_val_score(clf,X2,y2,cv=sk_folds,scoring='precision')\n",
    "\n",
    "print(\"Cross Validation Scores: \", scores)\n",
    "print(\"Average CV Score: \", scores.mean())\n",
    "print(\"Number of CV Scores used in Average: \", len(scores))\n",
    "\n",
    "results = cross_val_score(clf,X2,y2,cv=sk_folds,scoring='f1')\n",
    "\n",
    "print(\"Cross Validation Scores: \", scores)\n",
    "print(\"Average CV Score: \", scores.mean())\n",
    "print(\"Number of CV Scores used in Average: \", len(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores:  [0.43478261 0.39673913 0.45652174 0.16304348 0.27173913]\n",
      "Average CV Score:  0.3445652173913044\n",
      "Number of CV Scores used in Average:  5\n",
      "Cross Validation Scores:  [0.43478261 0.39673913 0.45652174 0.16304348 0.27173913]\n",
      "Average CV Score:  0.3445652173913044\n",
      "Number of CV Scores used in Average:  5\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression()\n",
    "scoring = {'accuracy', 'precision','recall', 'f1_score'}\n",
    "\n",
    "sk_folds = StratifiedKFold(n_splits = 5)\n",
    "\n",
    "results = cross_val_score(clf,X2,y2,cv=sk_folds,scoring='precision')\n",
    "\n",
    "print(\"Cross Validation Scores: \", scores)\n",
    "print(\"Average CV Score: \", scores.mean())\n",
    "print(\"Number of CV Scores used in Average: \", len(scores))\n",
    "\n",
    "results = cross_val_score(clf,X2,y2,cv=sk_folds,scoring='f1')\n",
    "\n",
    "print(\"Cross Validation Scores: \", scores)\n",
    "print(\"Average CV Score: \", scores.mean())\n",
    "print(\"Number of CV Scores used in Average: \", len(scores))"
   ]
  }
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