Assignment(ENSEMBLE4).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "47e4c0ad",
   "metadata": {},
   "source": [
    "Build a random forest classifier to predict the risk of heart disease based on a dataset of patient\n",
    "information. The dataset contains 303 instances with 14 features, including age, sex, chest pain type,\n",
    "resting blood pressure, serum cholesterol, and maximum heart rate achieved.\n",
    "\n",
    "Dataset link: https://drive.google.com/file/d/1bGoIE4Z2kG5nyh-fGZAJ7LH0ki3UfmSJ/view?usp=share_link\n",
    "\n",
    "**Q1.** Preprocess the dataset by handling missing values, encoding categorical variables, and scaling the\n",
    "numerical features if necessary.<br>\n",
    "**Q2.** Split the dataset into a training set (70%) and a test set (30%).<br>\n",
    "**Q3.** Train a random forest classifier on the training set using 100 trees and a maximum depth of 10 for each\n",
    "tree. Use the default values for other hyperparameters.<br>\n",
    "**Q4.** Evaluate the performance of the model on the test set using accuracy, precision, recall, and F1 score.<br>\n",
    "**Q5.** Use the feature importance scores to identify the top 5 most important features in predicting heart\n",
    "disease risk. Visualise the feature importances using a bar chart.<br>\n",
    "**Q6.** Tune the hyperparameters of the random forest classifier using grid search or random search. Try\n",
    "different values of the number of trees, maximum depth, minimum samples split, and minimum samples\n",
    "leaf. Use 5-fold cross-validation to evaluate the performance of each set of hyperparameters.<br>\n",
    "**Q7.** Report the best set of hyperparameters found by the search and the corresponding performance\n",
    "metrics. Compare the performance of the tuned model with the default model.<br>\n",
    "**Q8.** Interpret the model by analysing the decision boundaries of the random forest classifier. Plot the\n",
    "decision boundaries on a scatter plot of two of the most important features. Discuss the insights and\n",
    "limitations of the model for predicting heart disease risk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bfdb015b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, classification_report\n",
    "from sklearn.preprocessing import LabelEncoder, OrdinalEncoder, OneHotEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b7a5da09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "0        0   0     1       1  \n",
       "1        0   0     2       1  \n",
       "2        2   0     2       1  \n",
       "3        2   0     2       1  \n",
       "4        2   0     2       1  \n",
       "..     ...  ..   ...     ...  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('dataset.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "90d698e8",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>303.0</td>\n",
       "      <td>54.366337</td>\n",
       "      <td>9.082101</td>\n",
       "      <td>29.0</td>\n",
       "      <td>47.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>303.0</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>94.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>303.0</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>126.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>274.5</td>\n",
       "      <td>564.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>303.0</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>71.0</td>\n",
       "      <td>133.5</td>\n",
       "      <td>153.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>303.0</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>303.0</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>303.0</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.544554</td>\n",
       "      <td>0.498835</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count        mean        std    min    25%    50%    75%    max\n",
       "age       303.0   54.366337   9.082101   29.0   47.5   55.0   61.0   77.0\n",
       "sex       303.0    0.683168   0.466011    0.0    0.0    1.0    1.0    1.0\n",
       "cp        303.0    0.966997   1.032052    0.0    0.0    1.0    2.0    3.0\n",
       "trestbps  303.0  131.623762  17.538143   94.0  120.0  130.0  140.0  200.0\n",
       "chol      303.0  246.264026  51.830751  126.0  211.0  240.0  274.5  564.0\n",
       "fbs       303.0    0.148515   0.356198    0.0    0.0    0.0    0.0    1.0\n",
       "restecg   303.0    0.528053   0.525860    0.0    0.0    1.0    1.0    2.0\n",
       "thalach   303.0  149.646865  22.905161   71.0  133.5  153.0  166.0  202.0\n",
       "exang     303.0    0.326733   0.469794    0.0    0.0    0.0    1.0    1.0\n",
       "oldpeak   303.0    1.039604   1.161075    0.0    0.0    0.8    1.6    6.2\n",
       "slope     303.0    1.399340   0.616226    0.0    1.0    1.0    2.0    2.0\n",
       "ca        303.0    0.729373   1.022606    0.0    0.0    0.0    1.0    4.0\n",
       "thal      303.0    2.313531   0.612277    0.0    2.0    2.0    3.0    3.0\n",
       "target    303.0    0.544554   0.498835    0.0    0.0    1.0    1.0    1.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1ce0905a",
   "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",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c67a291d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_features=['sex', 'cp', 'fbs', 'restecg', 'exang','slope', 'ca', 'thal', 'oldpeak']\n",
    "num_features = ['age', 'trestbps', 'chol', 'thalach']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "247136cf",
   "metadata": {},
   "source": [
    "### Check for class imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5165881e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.target.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb9494da",
   "metadata": {},
   "source": [
    "**Observation**:\n",
    "- The dataset is imbalanced, it is expected that the model will not perform good for class 0 data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28745e4e",
   "metadata": {},
   "source": [
    "### Splitting into train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f6ecbd5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dependent and independent features\n",
    "y = df.pop('target')\n",
    "x = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e34ee159",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into train and test sets\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=24)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daaddc3c",
   "metadata": {},
   "source": [
    "### Scaling numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f41a3b15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale the numerical features\n",
    "scaler = MinMaxScaler()\n",
    "x_train[num_features] = scaler.fit_transform(x_train[num_features])\n",
    "x_test[num_features] = scaler.transform(x_test[num_features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cdd79a90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.581395</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.433962</td>\n",
       "      <td>0.216895</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>0.372093</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.150943</td>\n",
       "      <td>0.292237</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.709677</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>0.302326</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.132075</td>\n",
       "      <td>0.267123</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.653226</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0.651163</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.245283</td>\n",
       "      <td>0.353881</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.232558</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.433962</td>\n",
       "      <td>0.248858</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.879032</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age  sex  cp  trestbps      chol  fbs  restecg   thalach  exang  \\\n",
       "77   0.581395    1   1  0.433962  0.216895    0        1  0.750000      1   \n",
       "109  0.372093    0   0  0.150943  0.292237    0        0  0.709677      0   \n",
       "230  0.302326    1   2  0.132075  0.267123    0        1  0.653226      0   \n",
       "226  0.651163    1   1  0.245283  0.353881    0        0  0.258065      0   \n",
       "46   0.232558    1   2  0.433962  0.248858    0        0  0.879032      0   \n",
       "\n",
       "     oldpeak  slope  ca  thal  \n",
       "77       0.0      2   0     2  \n",
       "109      0.0      2   0     2  \n",
       "230      0.0      2   0     2  \n",
       "226      1.4      1   1     3  \n",
       "46       0.0      2   0     2  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8f36945",
   "metadata": {},
   "source": [
    "### Training Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "50dd63fa",
   "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>RandomForestClassifier(max_depth=10)</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\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=10)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=100, max_depth=10)\n",
    "clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d503f66",
   "metadata": {},
   "source": [
    "### Evaluating performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5d77c674",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d933ef92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.74      0.81        42\n",
      "           1       0.80      0.92      0.86        49\n",
      "\n",
      "    accuracy                           0.84        91\n",
      "   macro avg       0.84      0.83      0.83        91\n",
      "weighted avg       0.84      0.84      0.83        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# classification report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "38a4c4c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix\n",
    "ConfusionMatrixDisplay.from_estimator(clf, x_test, y_test)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac409883",
   "metadata": {},
   "source": [
    "**Observation**:\n",
    "- As expected, the model is not performing well on the minority class (class 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7230ecf",
   "metadata": {},
   "source": [
    "### Feature importance to find top 5 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "37ca1653",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 features:\n",
      "1. thalach (0.141220)\n",
      "2. oldpeak (0.130540)\n",
      "3. thal (0.108328)\n",
      "4. cp (0.096030)\n",
      "5. age (0.095752)\n"
     ]
    }
   ],
   "source": [
    "# get feature importances and sort them in descending order\n",
    "importances = clf.feature_importances_\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# print the top 5 features\n",
    "print(\"Top 5 features:\")\n",
    "for i in range(5):\n",
    "    print(\"%d. %s (%f)\" % (i + 1, x_train.columns[indices[i]], importances[indices[i]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "759e85d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot feature importances in a bar chart\n",
    "plt.bar(range(x_train.shape[1]), importances[indices], align='center')\n",
    "plt.xticks(range(x_train.shape[1]), x_train.columns[indices], rotation=90)\n",
    "plt.xlabel('Feature')\n",
    "plt.ylabel('Importance')\n",
    "plt.title('Feature Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f4f9127",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0241fdcc",
   "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>GridSearchCV(cv=5, estimator=RandomForestClassifier(max_depth=10),\n",
       "             param_grid={&#x27;max_depth&#x27;: [10, 20, 30, None],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;n_estimators&#x27;: [50, 100, 150]})</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(max_depth=10),\n",
       "             param_grid={&#x27;max_depth&#x27;: [10, 20, 30, None],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;n_estimators&#x27;: [50, 100, 150]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=10)</pre></div></div></div><div class=\"sk-serial\"><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\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=10)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(max_depth=10),\n",
       "             param_grid={'max_depth': [10, 20, 30, None],\n",
       "                         'min_samples_leaf': [1, 2, 4],\n",
       "                         'min_samples_split': [2, 5, 10],\n",
       "                         'n_estimators': [50, 100, 150]})"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define the hyperparameter grid to search over\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100, 150],\n",
    "    'max_depth': [10, 20, 30, None],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4]\n",
    "}\n",
    "\n",
    "# create a grid search object with 5-fold cross-validation\n",
    "grid_search = GridSearchCV(clf, param_grid, cv=5)\n",
    "\n",
    "# fit the grid search object on the training data\n",
    "grid_search.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a649f301",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters:  {'max_depth': 20, 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "Best mean cross-validation score:  0.8539313399778514\n"
     ]
    }
   ],
   "source": [
    "# print the best hyperparameters and corresponding mean cross-validation score\n",
    "print(\"Best hyperparameters: \", grid_search.best_params_)\n",
    "print(\"Best mean cross-validation score: \", grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c48acad",
   "metadata": {},
   "source": [
    "### Decision boundry of two of the most important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6660d422",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 7\n"
     ]
    }
   ],
   "source": [
    "# choose the two most important features\n",
    "feature1, feature2 = importances.argsort()[-2:]\n",
    "print(feature1, feature2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2f226b48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 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-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 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-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 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-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 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-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" 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-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" 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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier()\n",
    "rfc.fit(x_train[['thalach','oldpeak']], y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c3e94f30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a grid of points covering the range of the two features\n",
    "x_min, x_max = x_train.iloc[:, feature1].min() - 1, x_train.iloc[:, feature1].max() + 1\n",
    "y_min, y_max = x_train.iloc[:, feature2].min() - 1, x_train.iloc[:, feature2].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
    "                     np.arange(y_min, y_max, 0.1))\n",
    "\n",
    "# predict the class of each point in the grid\n",
    "Z = rfc.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "# plot the decision boundaries on a scatter plot of the two features\n",
    "plt.contourf(xx, yy, Z, alpha=0.4)\n",
    "plt.scatter(x_train.iloc[:, feature1], x_train.iloc[:, feature2], c=y_train, s=20, edgecolor='k')\n",
    "plt.xlabel(x_train.columns[feature1])\n",
    "plt.ylabel(x_train.columns[feature2])\n",
    "plt.title('Random Forest Classifier Decision Boundaries')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d946b48e",
   "metadata": {},
   "source": [
    "**Observation**\n",
    "- Its hard to interpret the above decision boundry plot because the model trained for this plot was only trained on two features (the best features), and the model is having a hard time finding a concrete boundry between the two classed based on only two features. Because two features are not enough to separate one class from another properly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbe5d151",
   "metadata": {},
   "source": [
    "**Limitations and Insights**\n",
    "- Well first of all there are only 303 samples in the dataset so its not enough for training a more accurate and robust model.\n",
    "- The dataset is imbalanced so the model will always perform bad on classfying the minority class (unless we handle it by using a technique like upsampling, downsampling or SMOTE etc.)\n",
    "- We're achieving 80 percent accuracy but like mentioned already the model is performing poorly on class 0 data and so i will never suggest such a model to be deployed without addressing the above mentioned issues."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}