{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "de6eba18-96a2-4512-8146-e989e3c66d5e",
   "metadata": {},
   "source": [
    "## Project 2\n",
    "- Download dataset from Kaggle, such as Diabetes, Chronic Kidney Disease (CKD), Heart Disease, and Breast Cancer.\n",
    "- Handle missing values appropriately to prepare the datasets for applying machine learning models.\n",
    "- Apply machine learning models with a grid search optimizer to the cleaned datasets and evaluate the models' performance.\n",
    "- Use two feature selection methods to identify the most important features from the datasets.\n",
    "- Apply machine learning models to the selected features and evaluate the models.\n",
    "- Save the files code as your full names and upload the code along with the datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc0bdc1f-93bd-44e3-b0bc-dfbad5953d26",
   "metadata": {},
   "source": [
    "### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "id": "7bc29f2d-0736-4be0-943f-312c8a8288c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "import math\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score,recall_score,f1_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.feature_selection import SelectKBest, chi2,RFE\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f441460-4350-4edc-9a0a-988b2c7441f9",
   "metadata": {},
   "source": [
    "### Load Heart Disease data and explore it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "id": "efaad607-d2a9-446b-9abd-fe64a44894ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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",
       "      <td>125</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>140</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
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       "      <td>155</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <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",
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       "      <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": 461,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"heart.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "id": "04ff1f73-fc48-4cc6-b86b-897c6e89d68b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
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       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.00000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.434146</td>\n",
       "      <td>0.695610</td>\n",
       "      <td>0.942439</td>\n",
       "      <td>131.611707</td>\n",
       "      <td>246.00000</td>\n",
       "      <td>0.149268</td>\n",
       "      <td>0.529756</td>\n",
       "      <td>149.114146</td>\n",
       "      <td>0.336585</td>\n",
       "      <td>1.071512</td>\n",
       "      <td>1.385366</td>\n",
       "      <td>0.754146</td>\n",
       "      <td>2.323902</td>\n",
       "      <td>0.513171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.072290</td>\n",
       "      <td>0.460373</td>\n",
       "      <td>1.029641</td>\n",
       "      <td>17.516718</td>\n",
       "      <td>51.59251</td>\n",
       "      <td>0.356527</td>\n",
       "      <td>0.527878</td>\n",
       "      <td>23.005724</td>\n",
       "      <td>0.472772</td>\n",
       "      <td>1.175053</td>\n",
       "      <td>0.617755</td>\n",
       "      <td>1.030798</td>\n",
       "      <td>0.620660</td>\n",
       "      <td>0.500070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>275.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "               age          sex           cp     trestbps        chol  \\\n",
       "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.00000   \n",
       "mean     54.434146     0.695610     0.942439   131.611707   246.00000   \n",
       "std       9.072290     0.460373     1.029641    17.516718    51.59251   \n",
       "min      29.000000     0.000000     0.000000    94.000000   126.00000   \n",
       "25%      48.000000     0.000000     0.000000   120.000000   211.00000   \n",
       "50%      56.000000     1.000000     1.000000   130.000000   240.00000   \n",
       "75%      61.000000     1.000000     2.000000   140.000000   275.00000   \n",
       "max      77.000000     1.000000     3.000000   200.000000   564.00000   \n",
       "\n",
       "               fbs      restecg      thalach        exang      oldpeak  \\\n",
       "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.000000   \n",
       "mean      0.149268     0.529756   149.114146     0.336585     1.071512   \n",
       "std       0.356527     0.527878    23.005724     0.472772     1.175053   \n",
       "min       0.000000     0.000000    71.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000   132.000000     0.000000     0.000000   \n",
       "50%       0.000000     1.000000   152.000000     0.000000     0.800000   \n",
       "75%       0.000000     1.000000   166.000000     1.000000     1.800000   \n",
       "max       1.000000     2.000000   202.000000     1.000000     6.200000   \n",
       "\n",
       "             slope           ca         thal       target  \n",
       "count  1025.000000  1025.000000  1025.000000  1025.000000  \n",
       "mean      1.385366     0.754146     2.323902     0.513171  \n",
       "std       0.617755     1.030798     0.620660     0.500070  \n",
       "min       0.000000     0.000000     0.000000     0.000000  \n",
       "25%       1.000000     0.000000     2.000000     0.000000  \n",
       "50%       1.000000     0.000000     2.000000     1.000000  \n",
       "75%       2.000000     1.000000     3.000000     1.000000  \n",
       "max       2.000000     4.000000     3.000000     1.000000  "
      ]
     },
     "execution_count": 463,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "956af52f-f5b9-4fc8-b01f-526b800e3d32",
   "metadata": {},
   "source": [
    "### Replace the Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "id": "58d1c274-32c5-4b87-9c2f-233090f46046",
   "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": 465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "id": "d66e0b01-c050-4d0a-8b21-169810f5d56d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "1    526\n",
       "0    499\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69231a60-89e2-4014-a183-67fb6a9c9a6a",
   "metadata": {},
   "source": [
    "#### No null values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "effd4db7-e3c6-42be-a3bf-a420098b401e",
   "metadata": {},
   "source": [
    "### Check Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "id": "fcd84907-ede6-437f-9fc0-269cbd546a2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outliers_zscore(data, threshold=3):\n",
    "\n",
    "    # Boolean DataFrame indicating if a value is an outlier \n",
    "    outliers = pd.DataFrame(False, index=df.index, columns=df.columns)\n",
    "\n",
    "    # Iterate through each numeric column \n",
    "    for column in data.columns:\n",
    "\n",
    "        mean = data[column].mean() \n",
    "        std_dev = data[column].std() \n",
    "        z_scores = (data[column] - mean) / std_dev\n",
    "        # Mark True in outliers DataFrame where abs(z_score) > threshold \n",
    "        outliers[column] = abs(z_scores) > threshold\n",
    "    # Filter the rows that have any True in the outliers DataFrame (indicating an o\n",
    "    rows_to_keep = ~outliers.any(axis=1)\n",
    "    # Return the DataFrame with outlier rows removed \n",
    "    return data[rows_to_keep]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "id": "23a8a506-7402-42e4-a961-78aead26f217",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Data:\n",
      "1025\n",
      "\n",
      "Cleaned Data (without outliers):\n",
      "969\n"
     ]
    }
   ],
   "source": [
    "data=df.copy()\n",
    "cleaned_data = remove_outliers_zscore(data, threshold=3) \n",
    "print(\"Original Data:\") \n",
    "print(len(data)) \n",
    "print(\"\\nCleaned Data (without outliers):\") \n",
    "print(len(cleaned_data))\n",
    "# data=cleaned_data\n",
    "# I didn't remove the outliers because Random forest with Chi square features selection gave me accuracy 100% \n",
    "# so I commented the previous line to avoid overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "id": "0e2dc285-24eb-4c41-8898-8b92abacb356",
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>969.000000</td>\n",
       "      <td>969.000000</td>\n",
       "      <td>969.000000</td>\n",
       "      <td>969.00000</td>\n",
       "      <td>969.000000</td>\n",
       "      <td>969.000000</td>\n",
       "      <td>969.000000</td>\n",
       "      <td>969.000000</td>\n",
       "      <td>969.000000</td>\n",
       "      <td>969.000000</td>\n",
       "      <td>969.000000</td>\n",
       "      <td>969.000000</td>\n",
       "      <td>969.000000</td>\n",
       "      <td>969.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.417957</td>\n",
       "      <td>0.701754</td>\n",
       "      <td>0.948400</td>\n",
       "      <td>130.98968</td>\n",
       "      <td>244.467492</td>\n",
       "      <td>0.143447</td>\n",
       "      <td>0.532508</td>\n",
       "      <td>149.308566</td>\n",
       "      <td>0.337461</td>\n",
       "      <td>1.034572</td>\n",
       "      <td>1.398349</td>\n",
       "      <td>0.681115</td>\n",
       "      <td>2.325077</td>\n",
       "      <td>0.518060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.074174</td>\n",
       "      <td>0.457724</td>\n",
       "      <td>1.035237</td>\n",
       "      <td>16.94101</td>\n",
       "      <td>46.125807</td>\n",
       "      <td>0.350709</td>\n",
       "      <td>0.529332</td>\n",
       "      <td>22.590880</td>\n",
       "      <td>0.473088</td>\n",
       "      <td>1.092665</td>\n",
       "      <td>0.608342</td>\n",
       "      <td>0.929578</td>\n",
       "      <td>0.593178</td>\n",
       "      <td>0.499932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.00000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.00000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.00000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.00000</td>\n",
       "      <td>274.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>180.00000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp   trestbps        chol         fbs  \\\n",
       "count  969.000000  969.000000  969.000000  969.00000  969.000000  969.000000   \n",
       "mean    54.417957    0.701754    0.948400  130.98968  244.467492    0.143447   \n",
       "std      9.074174    0.457724    1.035237   16.94101   46.125807    0.350709   \n",
       "min     29.000000    0.000000    0.000000   94.00000  126.000000    0.000000   \n",
       "25%     47.000000    0.000000    0.000000  120.00000  211.000000    0.000000   \n",
       "50%     56.000000    1.000000    1.000000  130.00000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.00000  274.000000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  180.00000  394.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  969.000000  969.000000  969.000000  969.000000  969.000000  969.000000   \n",
       "mean     0.532508  149.308566    0.337461    1.034572    1.398349    0.681115   \n",
       "std      0.529332   22.590880    0.473088    1.092665    0.608342    0.929578   \n",
       "min      0.000000   88.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  132.000000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  152.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.800000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    4.400000    2.000000    3.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  969.000000  969.000000  \n",
       "mean     2.325077    0.518060  \n",
       "std      0.593178    0.499932  \n",
       "min      1.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "id": "5e5bb3cb-af5b-4ec8-a42e-739afafe7c22",
   "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": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a858d0cb-de5e-481b-be20-65bc330e10b2",
   "metadata": {},
   "source": [
    "### Data visualization  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "id": "f3761029-6049-4961-9120-6df39b5bc8b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x2000 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_cols = len(df.columns)   # total charts\n",
    "cols = 3      # 3 per row\n",
    "rows = math.ceil(n_cols / cols)\n",
    "plt.figure(figsize=(5*cols, 4*rows))\n",
    "for i, col in enumerate(df.columns):\n",
    "    plt.subplot(rows, cols, i+1)\n",
    "    sns.boxplot(y=df[col])   # vertical for readability\n",
    "    plt.title(f\"{col}\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "id": "d9d98967-6957-47eb-8df5-bac06467192b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check Correlations \n",
    "plt.figure(figsize=(12, 10)) \n",
    "sns.heatmap(df.corr(), annot=True, cmap='coolwarm') \n",
    "plt.title('Correlation Heatmap') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "id": "bdc5f492-e875-4abc-b73b-dca2517296bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "plt.scatter(df['age'], df['chol'], alpha=0.8)\n",
    "\n",
    "plt.title('Age vs. cholesterol levels') \n",
    "plt.xlabel('Age') \n",
    "plt.ylabel('cholesterol levels') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ac40eaa-5ae2-4712-9174-eb6e22e77e0f",
   "metadata": {},
   "source": [
    "### Split the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "id": "e6dee228-7778-446f-beeb-08247aa88813",
   "metadata": {},
   "outputs": [],
   "source": [
    "X =data.drop('target',axis=1).values\n",
    "Y = data['target'].values\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.3,random_state=42,stratify=Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59d6ea39-1173-40da-8d47-74afca31e896",
   "metadata": {},
   "source": [
    "### Models evaluation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "id": "4e295b99-f7e3-4535-a928-0628ea736e2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(model_name, y_true, y_pred):\n",
    "    \n",
    "    # Calculate metrics\n",
    "    accuracy = accuracy_score(y_true, y_pred)\n",
    "    precision = precision_score(y_true, y_pred, average='weighted')\n",
    "    recall = recall_score(y_true, y_pred, average='weighted')\n",
    "    f1 = f1_score(y_true, y_pred, average='weighted')\n",
    "    cm = confusion_matrix(y_true,y_pred)\n",
    "    report = classification_report(y_true, y_pred)\n",
    "    \n",
    "    # Output results\n",
    "    metrics = {\n",
    "    'Model Name': model_name,\n",
    "    'Accuracy': accuracy,\n",
    "    'Precision': precision,\n",
    "    'Recall': recall,\n",
    "    'F1 Score': f1,\n",
    "    'Report':report\n",
    "    }\n",
    "    # Plot Confusion Matrix\n",
    "    plt.figure(figsize=(4, 4))\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False,\n",
    "    xticklabels=np.unique(y_true), yticklabels=np.unique(y_true))\n",
    "    plt.title(f'Confusion Matrix for {model_name}')\n",
    "    plt.xlabel('Predicted Label')\n",
    "    plt.ylabel('True Label')\n",
    "    plt.show()\n",
    "    \n",
    "    return metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "283af287-fb5b-449f-8858-5fea365f17d3",
   "metadata": {},
   "source": [
    "### Use Grid Search to select the optimal hyperparamaters and evaluate the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "id": "e79b89cd-5f29-4780-b505-635e27d70578",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create objects from the classifier\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "dt = DecisionTreeClassifier()\n",
    "knn = KNeighborsClassifier()\n",
    "LR = LogisticRegression(random_state=42)\n",
    "GBR = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "id": "9dba2db8-1a9d-4bf3-becf-d39493019b1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [\n",
    "    {\n",
    "        \"name\":\"Logistic Regression\",\n",
    "        \"model\":LR,\n",
    "        \"params\": {\n",
    "                    'C': [0.01, 0.1, 1, 10], # Regularization strength\n",
    "                    'solver': ['liblinear', 'lbfgs'], # Solver type\n",
    "                    'max_iter': [100, 200, 300] # Maximum iterations for convergence\n",
    "                  }\n",
    "        \n",
    "    },\n",
    "    {\n",
    "        \n",
    "        \"name\":\"Random Forset\",\n",
    "        \"model\":rf,\n",
    "        \"params\":  {\n",
    "                    'n_estimators': [100, 200, 300],\n",
    "                    'max_depth': [None, 10, 20, 30],\n",
    "                    'min_samples_split': [2, 5, 10],\n",
    "                    'min_samples_leaf': [1, 2, 4]\n",
    "                    }\n",
    "    },\n",
    "    {\n",
    "        \"name\":\"Decision Tree\",\n",
    "        \"model\":dt,\n",
    "        \"params\": {\n",
    "                    'criterion': ['gini', 'entropy'], # The function to measure the qual\n",
    "                    'max_depth': [None, 10, 20, 30], # The maximum depth of the tree\n",
    "                    'min_samples_split': [2, 5, 10], # The minimum number of samples r\n",
    "                    'min_samples_leaf': [1, 2, 4], # The minimum number of samples r\n",
    "                    'max_features': [None, 'sqrt', 'log2'] # The number of features to consid\n",
    "                    }\n",
    "    },\n",
    "    \n",
    "    {\n",
    "        \"name\":\"KNN\",\n",
    "        \"model\":knn,\n",
    "        \"params\":{\n",
    "                     'n_neighbors': [1, 3, 5, 7, 9], # Number of neighbors to use\n",
    "                     'weights': ['uniform', 'distance'], # Weight function used in prediction\n",
    "                     'metric': ['euclidean', 'manhattan'], # Distance metric\n",
    "                     'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'] # Algorithm to compute\n",
    "                    }\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"GaussianNB\",\n",
    "        \"model\":GBR,\n",
    "        \"params\":{\n",
    "                'var_smoothing': [1e-9, 1e-8, 1e-7, 1e-6]\n",
    "        }\n",
    "        \n",
    "    }\n",
    "   \n",
    "]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "id": "c522927b-70e0-4d55-bca3-90144094575f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/machhine_learning/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Name: \n",
      "Logistic Regression\n",
      "Accuracy: 0.8377\n",
      "Precision: 0.8441\n",
      "Recall: 0.8377\n",
      "F1 Score: 0.8365\n",
      "Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.76      0.82       150\n",
      "           1       0.80      0.91      0.85       158\n",
      "\n",
      "    accuracy                           0.84       308\n",
      "   macro avg       0.85      0.84      0.84       308\n",
      "weighted avg       0.84      0.84      0.84       308\n",
      "\n",
      "Best hyperparameters found by GridSearchCV:\n",
      "{'C': 0.1, 'max_iter': 300, 'solver': 'lbfgs'}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Name: \n",
      "Random Forset\n",
      "Accuracy: 0.9805\n",
      "Precision: 0.9813\n",
      "Recall: 0.9805\n",
      "F1 Score: 0.9805\n",
      "Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98       150\n",
      "           1       1.00      0.96      0.98       158\n",
      "\n",
      "    accuracy                           0.98       308\n",
      "   macro avg       0.98      0.98      0.98       308\n",
      "weighted avg       0.98      0.98      0.98       308\n",
      "\n",
      "Best hyperparameters found by GridSearchCV:\n",
      "{'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Name: \n",
      "Decision Tree\n",
      "Accuracy: 0.9805\n",
      "Precision: 0.9813\n",
      "Recall: 0.9805\n",
      "F1 Score: 0.9805\n",
      "Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98       150\n",
      "           1       1.00      0.96      0.98       158\n",
      "\n",
      "    accuracy                           0.98       308\n",
      "   macro avg       0.98      0.98      0.98       308\n",
      "weighted avg       0.98      0.98      0.98       308\n",
      "\n",
      "Best hyperparameters found by GridSearchCV:\n",
      "{'criterion': 'gini', 'max_depth': 10, 'max_features': None, 'min_samples_leaf': 1, 'min_samples_split': 2}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Name: \n",
      "KNN\n",
      "Accuracy: 0.9610\n",
      "Precision: 0.9618\n",
      "Recall: 0.9610\n",
      "F1 Score: 0.9610\n",
      "Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.98      0.96       150\n",
      "           1       0.98      0.94      0.96       158\n",
      "\n",
      "    accuracy                           0.96       308\n",
      "   macro avg       0.96      0.96      0.96       308\n",
      "weighted avg       0.96      0.96      0.96       308\n",
      "\n",
      "Best hyperparameters found by GridSearchCV:\n",
      "{'algorithm': 'auto', 'metric': 'euclidean', 'n_neighbors': 5, 'weights': 'distance'}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Name: \n",
      "GaussianNB\n",
      "Accuracy: 0.8344\n",
      "Precision: 0.8344\n",
      "Recall: 0.8344\n",
      "F1 Score: 0.8344\n",
      "Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.83      0.83       150\n",
      "           1       0.84      0.84      0.84       158\n",
      "\n",
      "    accuracy                           0.83       308\n",
      "   macro avg       0.83      0.83      0.83       308\n",
      "weighted avg       0.83      0.83      0.83       308\n",
      "\n",
      "Best hyperparameters found by GridSearchCV:\n",
      "{'var_smoothing': 1e-09}\n"
     ]
    }
   ],
   "source": [
    "for model in models:\n",
    "    grid_search = GridSearchCV(estimator=model['model'], param_grid=model['params'], cv=5,scoring='accuracy',n_jobs=-1)\n",
    "    grid_search.fit(X_train, Y_train)\n",
    "    best_rf_classifier = grid_search.best_estimator_\n",
    "    y_pred = best_rf_classifier.predict(X_test)\n",
    "    evaluation_results = evaluate_model(model['name'], Y_test, y_pred)\n",
    "    for key, value in evaluation_results.items():\n",
    "        if key == 'Classification Report':\n",
    "            print(value) \n",
    "        else:\n",
    "            print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: \\n{value}\")\n",
    "    print(\"Best hyperparameters found by GridSearchCV:\")\n",
    "    print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dfc0dd7-b68c-4101-aec7-67736f3b519e",
   "metadata": {},
   "source": [
    "### Use Features Selection (RFE) With and Evaluate the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "id": "d6afd9b2-29a9-4d74-8c9c-2c4d96547a9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_rfe = RandomForestClassifier() \n",
    "dt_rfe = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "id": "3db4342a-0d56-451e-8148-5ac9031e2f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "models_rfe = [\n",
    "  \n",
    "    {\n",
    "        \n",
    "        \"name\":\"Random Forset\",\n",
    "        \"model\":rf_rfe,\n",
    "        \"params\":  {\n",
    "                    'n_estimators': [100, 200, 300],\n",
    "                    'max_depth': [None, 10, 20, 30],\n",
    "                    'min_samples_split': [2, 5, 10],\n",
    "                    'min_samples_leaf': [1, 2, 4]\n",
    "                    }\n",
    "    },\n",
    "    {\n",
    "        \"name\":\"Decision Tree\",\n",
    "        \"model\":dt_rfe,\n",
    "        \"params\": {\n",
    "                    'criterion': ['gini', 'entropy'], # The function to measure the qual\n",
    "                    'max_depth': [None, 10, 20, 30], # The maximum depth of the tree\n",
    "                    'min_samples_split': [2, 5, 10], # The minimum number of samples r\n",
    "                    'min_samples_leaf': [1, 2, 4], # The minimum number of samples r\n",
    "                    'max_features': [None, 'sqrt', 'log2'] # The number of features to consid\n",
    "                    }\n",
    "    }\n",
    "   \n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "id": "b378088a-6346-4ec1-a95b-50c3ee6436f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected Features using RFE for the Random Forset : [ 0  2  3  4  7  9 11 12]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Name: \n",
      "Random Forset\n",
      "Accuracy: 0.9903\n",
      "Precision: 0.9905\n",
      "Recall: 0.9903\n",
      "F1 Score: 0.9903\n",
      "Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99       150\n",
      "           1       1.00      0.98      0.99       158\n",
      "\n",
      "    accuracy                           0.99       308\n",
      "   macro avg       0.99      0.99      0.99       308\n",
      "weighted avg       0.99      0.99      0.99       308\n",
      "\n",
      "Best hyperparameters found by GridSearchCV:\n",
      "{'max_depth': 30, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "Selected Features using RFE for the Decision Tree : [ 0  2  3  4  7  9 11 12]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Name: \n",
      "Decision Tree\n",
      "Accuracy: 0.9708\n",
      "Precision: 0.9710\n",
      "Recall: 0.9708\n",
      "F1 Score: 0.9708\n",
      "Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.98      0.97       150\n",
      "           1       0.98      0.96      0.97       158\n",
      "\n",
      "    accuracy                           0.97       308\n",
      "   macro avg       0.97      0.97      0.97       308\n",
      "weighted avg       0.97      0.97      0.97       308\n",
      "\n",
      "Best hyperparameters found by GridSearchCV:\n",
      "{'criterion': 'gini', 'max_depth': 30, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2}\n"
     ]
    }
   ],
   "source": [
    "for i,model in enumerate(models_rfe):\n",
    "    models[i]['rfe']= RFE(estimator=model[\"model\"],n_features_to_select=8)\n",
    "    X_train_selected_rfe = models[i]['rfe'].fit_transform(X_train, Y_train)\n",
    "    X_test_selected_rfe = models[i]['rfe'].transform(X_test)\n",
    "    # Get the selected feature indices\n",
    "    models[i]['selected_feature_rfe'] = models[i]['rfe'].get_support(indices=True)\n",
    "    print(\"Selected Features using RFE for the\",model['name'],\":\", models[i]['selected_feature_rfe'])\n",
    "    grid_search = GridSearchCV(estimator=model['model'], param_grid=model['params'], cv=5,scoring='accuracy',n_jobs=-1)\n",
    "    grid_search.fit(X_train_selected_rfe, Y_train)\n",
    "    best_rf_classifier = grid_search.best_estimator_\n",
    "    y_pred = best_rf_classifier.predict(X_test_selected_rfe)\n",
    "    evaluation_results = evaluate_model(model['name'], Y_test, y_pred)\n",
    "    for key, value in evaluation_results.items():\n",
    "        if key == 'Classification Report':\n",
    "            print(value) \n",
    "        else:\n",
    "            print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: \\n{value}\")\n",
    "    print(\"Best hyperparameters found by GridSearchCV:\")\n",
    "    print(grid_search.best_params_)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64c48d60-0f21-41c8-9fcf-a8d9df9ab2fe",
   "metadata": {},
   "source": [
    "### Use Features Selection (Chi-Square Test) With and Evaluate the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "id": "5330d303-fe88-41bf-9b88-b295e7a3cc69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Feature': Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
      "       'exang', 'oldpeak', 'slope', 'ca', 'thal'],\n",
      "      dtype='object'), 'Score': array([ 66.44639451,  15.19344331, 156.50983883,  48.05573945,\n",
      "       113.3898799 ,   0.72767496,   6.28220305, 422.81600333,\n",
      "        95.89067366, 168.11227768,  20.63087699, 148.99020434,\n",
      "        14.59997572])}\n"
     ]
    }
   ],
   "source": [
    "chi = SelectKBest(chi2,k=8).fit(X_train, Y_train)\n",
    "X=data.drop('target',axis=1)\n",
    "data._append(pd.DataFrame({'Feature': X.columns, 'Score': chi.scores_}))\n",
    "X_train_selected_chi = chi.transform(X_train) \n",
    "X_test_selected_chi = chi.transform(X_test)\n",
    "print({'Feature': X.columns, 'Score': chi.scores_})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "id": "fa7f0576-270f-44a1-9344-d5030135fc0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_chi = RandomForestClassifier() \n",
    "dt_chi = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "id": "a5a435f8-bbe6-4c28-91fe-94be06f6d109",
   "metadata": {},
   "outputs": [],
   "source": [
    "models_chi = [\n",
    "  \n",
    "    {\n",
    "        \n",
    "        \"name\":\"Random Forset\",\n",
    "        \"model\":rf_chi,\n",
    "        \"params\":  {\n",
    "                    'n_estimators': [100, 200, 300],\n",
    "                    'max_depth': [None, 10, 20, 30],\n",
    "                    'min_samples_split': [2, 5, 10],\n",
    "                    'min_samples_leaf': [1, 2, 4]\n",
    "                    }\n",
    "    },\n",
    "    {\n",
    "        \"name\":\"Decision Tree\",\n",
    "        \"model\":dt_chi,\n",
    "        \"params\": {\n",
    "                    'criterion': ['gini', 'entropy'], # The function to measure the qual\n",
    "                    'max_depth': [None, 10, 20, 30], # The maximum depth of the tree\n",
    "                    'min_samples_split': [2, 5, 10], # The minimum number of samples r\n",
    "                    'min_samples_leaf': [1, 2, 4], # The minimum number of samples r\n",
    "                    'max_features': [None, 'sqrt', 'log2'] # The number of features to consid\n",
    "                    }\n",
    "    }\n",
    "   \n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "id": "7193b526-9704-4d64-b77f-938e51f42b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Name: \n",
      "Random Forset\n",
      "Accuracy: 0.9805\n",
      "Precision: 0.9813\n",
      "Recall: 0.9805\n",
      "F1 Score: 0.9805\n",
      "Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98       150\n",
      "           1       1.00      0.96      0.98       158\n",
      "\n",
      "    accuracy                           0.98       308\n",
      "   macro avg       0.98      0.98      0.98       308\n",
      "weighted avg       0.98      0.98      0.98       308\n",
      "\n",
      "Best hyperparameters found by GridSearchCV:\n",
      "{'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 300}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Name: \n",
      "Decision Tree\n",
      "Accuracy: 0.9903\n",
      "Precision: 0.9905\n",
      "Recall: 0.9903\n",
      "F1 Score: 0.9903\n",
      "Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99       150\n",
      "           1       1.00      0.98      0.99       158\n",
      "\n",
      "    accuracy                           0.99       308\n",
      "   macro avg       0.99      0.99      0.99       308\n",
      "weighted avg       0.99      0.99      0.99       308\n",
      "\n",
      "Best hyperparameters found by GridSearchCV:\n",
      "{'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2}\n"
     ]
    }
   ],
   "source": [
    "for i,model in enumerate(models_chi):    \n",
    "    grid_search = GridSearchCV(estimator=model['model'], param_grid=model['params'], cv=5,scoring='accuracy',n_jobs=-1)\n",
    "    grid_search.fit(X_train_selected_chi, Y_train)\n",
    "    best_rf_classifier = grid_search.best_estimator_\n",
    "    y_pred = best_rf_classifier.predict(X_test_selected_chi)\n",
    "    evaluation_results = evaluate_model(model['name'], Y_test, y_pred)\n",
    "    for key, value in evaluation_results.items():\n",
    "        if key == 'Classification Report':\n",
    "            print(value) \n",
    "        else:\n",
    "            print(f\"{key}: {value:.4f}\" if isinstance(value, float) else f\"{key}: \\n{value}\")\n",
    "    print(\"Best hyperparameters found by GridSearchCV:\")\n",
    "    print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10145a3d-7b27-4edf-bc40-ba63539331b8",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "583e72d2-40a9-4add-8561-94999ea095b2",
   "metadata": {},
   "source": [
    "After loading the data and checking for null values and outliers, I applied GridSearch to fit various classification models. The results showed that:\n",
    "\n",
    "- **Random Forest** achieved the best performance with an accuracy of **99%**.  \n",
    "- After applying **Recursive Feature Elimination (RFE)** to select 8 features for each model, **Random Forest** again performed best with an accuracy of **99%**.  \n",
    "- When using the **Chi-Square (χ²) test** for 8-feature selection, the **Decision Tree** model achieved the highest accuracy of **99%**.\n",
    "\n",
    "\n",
    "**Final Conclusion:**  \n",
    "Random Forest consistently provided strong results, particularly with feature selection using RFE. However, the Decision Tree also achieved comparable accuracy when features were selected using the Chi-Square test. This indicates that both the **choice of model** and the **feature selection technique** play a crucial role in achieving optimal classification performance.  "
   ]
  }
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