diff --git "a/stroke-prediction-notebook.ipynb" "b/stroke-prediction-notebook.ipynb"
deleted file mode 100644--- "a/stroke-prediction-notebook.ipynb"
+++ /dev/null
@@ -1,1987 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Stroke Prediction Analysis\n",
-    "\n",
-    "This notebook implements a machine learning pipeline for stroke prediction using tabular data. \n",
-    "\n",
-    "Model creation flow:\n",
-    "1. Data Loading and Initial Exploration\n",
-    "2. Data Cleaning\n",
-    "3. Feature Engineering\n",
-    "4. Model Training and Evaluation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Import Required Libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Suppress warnings\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "import os\n",
-    "import time\n",
-    "import unittest\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.impute import KNNImputer\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.neighbors import KNeighborsClassifier\n",
-    "from sklearn.svm import LinearSVC, SVC\n",
-    "from sklearn.neural_network import MLPClassifier\n",
-    "from sklearn.ensemble import GradientBoostingClassifier\n",
-    "from sklearn.metrics import accuracy_score, f1_score, precision_score\n",
-    "\n",
-    "# Configure logging for the application\n",
-    "logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Load and Explore Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset Shape: (5110, 12)\n",
-      "\n",
-      "Dataset Info:\n",
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 5110 entries, 0 to 5109\n",
-      "Data columns (total 12 columns):\n",
-      " #   Column             Non-Null Count  Dtype  \n",
-      "---  ------             --------------  -----  \n",
-      " 0   id                 5110 non-null   int64  \n",
-      " 1   gender             5110 non-null   object \n",
-      " 2   age                5110 non-null   float64\n",
-      " 3   hypertension       5110 non-null   int64  \n",
-      " 4   heart_disease      5110 non-null   int64  \n",
-      " 5   ever_married       5110 non-null   object \n",
-      " 6   work_type          5110 non-null   object \n",
-      " 7   Residence_type     5110 non-null   object \n",
-      " 8   avg_glucose_level  5110 non-null   float64\n",
-      " 9   bmi                4909 non-null   float64\n",
-      " 10  smoking_status     5110 non-null   object \n",
-      " 11  stroke             5110 non-null   int64  \n",
-      "dtypes: float64(3), int64(4), object(5)\n",
-      "memory usage: 479.2+ KB\n",
-      "\n",
-      "First few rows:\n"
-     ]
-    },
-    {
-     "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>id</th>\n",
-       "      <th>gender</th>\n",
-       "      <th>age</th>\n",
-       "      <th>hypertension</th>\n",
-       "      <th>heart_disease</th>\n",
-       "      <th>ever_married</th>\n",
-       "      <th>work_type</th>\n",
-       "      <th>Residence_type</th>\n",
-       "      <th>avg_glucose_level</th>\n",
-       "      <th>bmi</th>\n",
-       "      <th>smoking_status</th>\n",
-       "      <th>stroke</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>9046</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>Urban</td>\n",
-       "      <td>228.69</td>\n",
-       "      <td>36.6</td>\n",
-       "      <td>formerly smoked</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>51676</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>61.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Self-employed</td>\n",
-       "      <td>Rural</td>\n",
-       "      <td>202.21</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>never smoked</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>31112</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>Rural</td>\n",
-       "      <td>105.92</td>\n",
-       "      <td>32.5</td>\n",
-       "      <td>never smoked</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>60182</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>Urban</td>\n",
-       "      <td>171.23</td>\n",
-       "      <td>34.4</td>\n",
-       "      <td>smokes</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1665</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>79.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Self-employed</td>\n",
-       "      <td>Rural</td>\n",
-       "      <td>174.12</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>never smoked</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      id  gender   age  hypertension  heart_disease ever_married  \\\n",
-       "0   9046    Male  67.0             0              1          Yes   \n",
-       "1  51676  Female  61.0             0              0          Yes   \n",
-       "2  31112    Male  80.0             0              1          Yes   \n",
-       "3  60182  Female  49.0             0              0          Yes   \n",
-       "4   1665  Female  79.0             1              0          Yes   \n",
-       "\n",
-       "       work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
-       "0        Private          Urban             228.69  36.6  formerly smoked   \n",
-       "1  Self-employed          Rural             202.21   NaN     never smoked   \n",
-       "2        Private          Rural             105.92  32.5     never smoked   \n",
-       "3        Private          Urban             171.23  34.4           smokes   \n",
-       "4  Self-employed          Rural             174.12  24.0     never smoked   \n",
-       "\n",
-       "   stroke  \n",
-       "0       1  \n",
-       "1       1  \n",
-       "2       1  \n",
-       "3       1  \n",
-       "4       1  "
-      ]
-     },
-     "execution_count": 110,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Load the dataset\n",
-    "data = pd.read_csv('https://gist.githubusercontent.com/sridhareaswaran/e5b70d93348977849ef4f1f68c1818c7/raw/1a80d4ed327d5c147640061f27e0b7250717e77d/healthcare-dataset-stroke-data.csv')\n",
-    "\n",
-    "# Display basic information\n",
-    "print(\"Dataset Shape:\", data.shape)\n",
-    "print(\"\\nDataset Info:\")\n",
-    "data.info()\n",
-    "\n",
-    "# Display first few rows\n",
-    "print(\"\\nFirst few rows:\")\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Data Analysis and Visualization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-01-20 23:41:34,172 - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
-      "2025-01-20 23:41:34,176 - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Missing Values:\n",
-      "id                     0\n",
-      "gender                 0\n",
-      "age                    0\n",
-      "hypertension           0\n",
-      "heart_disease          0\n",
-      "ever_married           0\n",
-      "work_type              0\n",
-      "Residence_type         0\n",
-      "avg_glucose_level      0\n",
-      "bmi                  201\n",
-      "smoking_status         0\n",
-      "stroke                 0\n",
-      "dtype: int64\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Basic Statistics:\n"
-     ]
-    },
-    {
-     "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>id</th>\n",
-       "      <th>age</th>\n",
-       "      <th>hypertension</th>\n",
-       "      <th>heart_disease</th>\n",
-       "      <th>avg_glucose_level</th>\n",
-       "      <th>bmi</th>\n",
-       "      <th>stroke</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>5110.000000</td>\n",
-       "      <td>5110.000000</td>\n",
-       "      <td>5110.000000</td>\n",
-       "      <td>5110.000000</td>\n",
-       "      <td>5110.000000</td>\n",
-       "      <td>4909.000000</td>\n",
-       "      <td>5110.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>36517.829354</td>\n",
-       "      <td>43.226614</td>\n",
-       "      <td>0.097456</td>\n",
-       "      <td>0.054012</td>\n",
-       "      <td>106.147677</td>\n",
-       "      <td>28.893237</td>\n",
-       "      <td>0.048728</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>21161.721625</td>\n",
-       "      <td>22.612647</td>\n",
-       "      <td>0.296607</td>\n",
-       "      <td>0.226063</td>\n",
-       "      <td>45.283560</td>\n",
-       "      <td>7.854067</td>\n",
-       "      <td>0.215320</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>67.000000</td>\n",
-       "      <td>0.080000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>55.120000</td>\n",
-       "      <td>10.300000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>17741.250000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>77.245000</td>\n",
-       "      <td>23.500000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>36932.000000</td>\n",
-       "      <td>45.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>91.885000</td>\n",
-       "      <td>28.100000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>54682.000000</td>\n",
-       "      <td>61.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>114.090000</td>\n",
-       "      <td>33.100000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>72940.000000</td>\n",
-       "      <td>82.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>271.740000</td>\n",
-       "      <td>97.600000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 id          age  hypertension  heart_disease  \\\n",
-       "count   5110.000000  5110.000000   5110.000000    5110.000000   \n",
-       "mean   36517.829354    43.226614      0.097456       0.054012   \n",
-       "std    21161.721625    22.612647      0.296607       0.226063   \n",
-       "min       67.000000     0.080000      0.000000       0.000000   \n",
-       "25%    17741.250000    25.000000      0.000000       0.000000   \n",
-       "50%    36932.000000    45.000000      0.000000       0.000000   \n",
-       "75%    54682.000000    61.000000      0.000000       0.000000   \n",
-       "max    72940.000000    82.000000      1.000000       1.000000   \n",
-       "\n",
-       "       avg_glucose_level          bmi       stroke  \n",
-       "count        5110.000000  4909.000000  5110.000000  \n",
-       "mean          106.147677    28.893237     0.048728  \n",
-       "std            45.283560     7.854067     0.215320  \n",
-       "min            55.120000    10.300000     0.000000  \n",
-       "25%            77.245000    23.500000     0.000000  \n",
-       "50%            91.885000    28.100000     0.000000  \n",
-       "75%           114.090000    33.100000     0.000000  \n",
-       "max           271.740000    97.600000     1.000000  "
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Check missing values\n",
-    "print(\"Missing Values:\")\n",
-    "print(data.isnull().sum())\n",
-    "\n",
-    "# Visualize distribution of target variable\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "sns.countplot(data=data, x='stroke')\n",
-    "plt.title('Distribution of Stroke Cases')\n",
-    "plt.show()\n",
-    "\n",
-    "# Basic statistics\n",
-    "print(\"\\nBasic Statistics:\")\n",
-    "data.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exploratory Data Analysis (EDA)\n",
-    "\n",
-    "We will analyze the dataset to examine the distribution of various features and explore their relationships with the target variable, stroke."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.histplot(data['age'], kde=True, bins=30)\n",
-    "plt.title('Age Distribution')\n",
-    "plt.xlabel('Age')\n",
-    "plt.ylabel('Frequency')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Distribution of average glucose level\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.histplot(df['avg_glucose_level'], kde=True, bins=30)\n",
-    "plt.title('Average Glucose Level Distribution')\n",
-    "plt.xlabel('Average Glucose Level')\n",
-    "plt.ylabel('Frequency')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Distribution of BMI\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.histplot(data['bmi'], kde=True, bins=30)\n",
-    "plt.title('BMI Distribution')\n",
-    "plt.xlabel('BMI')\n",
-    "plt.ylabel('Frequency')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-01-20 23:41:34,480 - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
-      "2025-01-20 23:41:34,484 - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-01-20 23:41:34,568 - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
-      "2025-01-20 23:41:34,572 - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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Z2/XOnyTmUMCdhDkUcPe6neZPd0Uodb1iY2M1ePBg47yoqEgnT55U+fLl5eDgYMfKYC85OTkKDAzUkSNHZDKZ7F0OABvi7z8sFotOnTqlgIAAe5dy22MOhb/i30/g7sa/AXe3a50/3RWhVIUKFeTk5KQTJ05YXT9x4oT8/Pwu6u/q6ipXV1era15eXreyRNwhTCYT/6ACdyn+/t/d7qYVUsWud/4kMYfCpfHvJ3B349+Au9e1zJ/uio3OXVxc1LBhQ61bt864VlRUpHXr1slsNtuxMgAAgNsT8ycAAHCr3RUrpSRp8ODB6tGjhxo1aqT77rtPU6ZM0ZkzZ/T888/buzQAAIDbEvMnAABwK901odRTTz2l3377TaNGjVJGRobq1aunVatWXbR5J3Aprq6uGj169EUfSQBQ8vH3H3cz5k/4J/j3E7i78W8AroWD5W77fmMAAAAAAADY3V2xpxQAAAAAAABuL4RSAAAAAAAAsDlCKQAAAAAAANgcoRQAAAAAAABsjlAKAIAbUFBQYO8SAAAA7gh//vmnvUvAbYpQCgCA62CxWLR9+3Z9+umn+v333+1dDgDc1rZs2aKMjAx7lwHATrKyshQXF6f77rtPv/zyi73LwW2IUAq4Tnv37tXChQuVmJioo0eP2rscADbm4OCgzMxMffnllxoyZIi9ywGA29q0adP0zjvv2LsMADaWn5+vhQsXqmfPnlq2bJkOHTqk1atX27ss3IYIpYBrtHv3bj3yyCNq3LixJkyYoA4dOuiJJ57QunXr7F0aABvJy8uTJLVv3149e/bU3LlztWnTJjtXBQC3F4vFIklKSUnRypUrZTab7VwRAFuxWCzasWOHunbtqokTJ6p06dI6deqUKleurIceesje5eE2RCgFXIMRI0aoUaNGOnPmjBYsWKD58+drz549atWqlfr06WPv8gDcYsX7R7m4uEiS3njjDT3//POqU6eOzp07Z8/SAOC2UxxKzZw5U3Xq1NFjjz1m54oA2MKRI0c0aNAgDRw4UGfPntWYMWM0bNgwVaxYUR988IFCQ0PtXSJuQ872LgC4nZ0/f16vvvqqli9frg8++EDPPvusypYta7T36NFDc+fO1bZt29S0aVM7VgrgZjt8+LAKCwtVrVo1OTtf+J/LJUuW6JVXXlF+fr5GjhypLl26qFKlSnauFABuL46Ojjp27Ji+/fZbvfPOOypVqpQKCwvl5ORk79IA3AIWi0Xvvfeeli9frlKlSumRRx7R888/r3vuuUcRERGyWCx64IEHjP5FRUVydGR9DC4glAKu4Mcff9SyZcs0fvx4devWzVglUWzXrl0qLCxUlSpV7FMggFsmPj5eS5cu1Q8//KB9+/apT58++v7779WzZ0/1799fYWFhF/2bAAB3K4vFIgcHB0nSqVOnNGHCBGVlZalBgwY6e/asSpcurby8PP7dBEogBwcHlSpVSsHBwRoyZIjCw8MlSZ9//rmOHTumFStWyNvbW4WFhfrll1+0bNkyvfjii3J3d7dz5bgdOFiK19cCuMisWbP06aefav369SpdurRxvbCwUMuXL9ewYcPUsmVLffLJJ3asEsCtcO7cOVWoUEFhYWHatWuXWrRooaFDh6pp06by8PCwd3kAcNspKipSUlKSEhMT9dFHH6mgoEChoaE6efKkypUrJw8PDzVv3lyvvfaavUsFcJP9NZiWLuzH265dO/Xp00ejRo3Sb7/9psTERE2cOFG7d+/WqlWr1Lp1aztWjNsFK6WAK3B1dVVGRoYKCwuNa8Wbdq5evVr169fX+PHj7VghgFvF3d1d77//vvr06aMpU6aoS5cu8vHxsXdZAHBb2rt3r9auXatly5YpMzNTI0aMUFRUlLZs2aLq1atrw4YNcnJy0u7du3X+/Hm5ubnZu2QAN1FxIFUcTs2ePVuBgYF6/vnntWnTJr311ltas2aNnnrqKX377bfy8vKyb8G4bbBSCriKWrVqKTAwUI0aNVJ6eroyMzN15MgR9erVSwMGDGAZOlDCValSRR06dNDUqVPtXQoA3LaGDBmizz//XB06dNCYMWPk4uKimTNn6sCBAzpw4IAee+wxRUZGqmHDhvYuFcAttnLlSvXu3VuDBg1SVlaW3nnnHZnNZr3//vuqX7++JCk/P1/Ozs5Wq6twdyKUAq7ixx9/1KJFizRv3jyFhYWpYcOG6tevnypWrGjv0gDYwOrVqzVy5Eh9/fXX8vX1tXc5AHBb2rt3rw4fPqx27dopJSVFAwYMUFpamry9vTV69GitW7dOX331lQ4cOCAPDw82OgZKqLy8PLVq1UqbN2+Wl5eX3NzcFB8fr8jISKWkpOjUqVMKCAhQSEiIHBwc+LcAhFLA9fj7Z6UB3B127dpl/GYPAHB5BQUFevTRR3XmzBl17NhRo0aN0tGjR+Xp6ak2bdqoatWq+uijj5hTASXY/fffr23btumdd97R0KFDtWTJEr355pvKy8vTgQMHVK9ePbVr105jx44llAKhFAAAAICb49dff1WDBg20ZMkSPfTQQ3ruueeUnp6u9evXa8qUKVq1apWWLl1q9QUyAEqWP//8UwUFBapYsaKmTp2qGTNm6NFHH1VERIRq1KihHTt26Pnnn9f27dsVHh5OMHWXY6NzAAAAADdFWlqafH195efnJ0kaO3asGjVqpO3bt+vQoUO69957CaSAEs7Ly0sODg7at2+fxo0bp/79+2vQoEEqX768JKlq1ar6+uuvNWPGDH300UcEUnc5/usDAAAAuCkefPBB/fHHH9q6daskKSgoSAMHDpTZbNbs2bPVtm1bO1cI4FYr/mhuWlqaAgICNHjwYCOQkqQzZ84oNTVV3t7eki5skYK7FyulAAAAANw0o0eP1ptvvqlatWqpYcOGGjx4sLKysvTII4+oefPmOnv2rPLz8+Xp6SmJPTuBkqqoqEi///67Veh06tQpLV26VKdPn1abNm0kib//dzn2lAIAAABwUz3//PM6fPiwxo8frwcffFC5ublydXXV66+/rnfffVdt27bVE088oWeffVaFhYVycnKyd8kAboHatWurUaNGioiIULly5fT1119r3rx56t69u6ZOnSo3Nzd7lwg7I5QCAAAAcFP98ccfys7OVtWqVY2VUOvXr9djjz2myZMn6/fff9ebb76pQ4cOqUKFCmx0DJRQe/fu1fjx47VhwwZ5e3vL0dFR77zzjtq1a2fv0nCbIJQCAAAAcEusW7dOBQUFioqK0v79+/XAAw9o//798vf31+OPP67SpUtr7ty5fIQPKMHOnz+vrKws/f7776pdu7Zxnb/3kNjoHAAAAMAtkJubq2nTphmbnru6uioiIkJr1qyRJHXu3Fk//fSTfv/9d/4fU6AEc3Nzk5+fnxFIFRUVSWIvKVxAKAXAZiIiIjRw4EB7l3HDnnvuOXXs2NE4v9PHAwDAreTq6qr77rtPCQkJkqRq1arp7Nmz+vPPPyVJ9evX19y5c1WhQgV7lgnAxvioLv6K/2sAUOIdPnxYDg4OSklJuanPXbp0qcaPH39TnwkAQEny2muv6dixY4qLi9NPP/2kX375RampqZKkmjVr6t5777VzhQAAeyKUAlCi5eXl3bJne3t7y8PD45Y9HwCAkmDWrFlav369atWqJW9vb7300kv2LgkAcJsglAJgU0VFRRo2bJi8vb3l5+enMWPGGG1ZWVnq3bu3KlasKJPJpBYtWmj37t1G+6FDh9ShQwf5+vqqbNmyaty4sdauXWv1/CpVqmj8+PHq3r27TCaT+vbtq+DgYEkXPibg4OCgiIiIq9ZZWFiowYMHy8vLS+XLl9ewYcP09++F+PvH9z766CPde++9cnNzk6+vrx5//HGrccfFxSk4OFju7u6qW7eulixZYvW+Xr16Ge01atTQ1KlTrd63YcMG3XfffSpTpoy8vLz0wAMP6JdffjHav/zySzVo0EBubm6qWrWqxo4dq4KCgquOFQCAW6lt27b6/PPPtWPHDs2cOVM1a9a0d0kAgNsEoRQAm5ozZ47KlCmj7du3a8KECRo3bpwSExMlSU888YQyMzP1zTffKDk5WQ0aNFDLli118uRJSdLp06f1yCOPaN26ddq1a5fatGmj9u3bKz093eod7777rurWratdu3Zp5MiR2rFjhyRp7dq1On78uJYuXXrVOidNmqT4+HjNnj1b3333nU6ePKlly5Zdtv/OnTv18ssva9y4cUpNTdWqVavUrFkzoz0uLk5z587VjBkztG/fPg0aNEjPPvusNm7cKOlCaFWpUiUtXrxY+/fv16hRo/T6669r0aJFkqSCggJ17NhRDz/8sH744QclJSWpb9++xgaRmzdvVvfu3fXKK69o//79+vjjjxUfH68333zzWv/TAABwy/j5+alevXoKCwtjPxkAgMHB8vdf/QPALRIREaHCwkJt3rzZuHbfffepRYsWateunaKjo5WZmSlXV1ejPSQkRMOGDVPfvn0v+czatWurX79+GjBggKQLK6Xq169vFSAdPnxYwcHB2rVrl+rVq3dNtQYEBGjQoEEaOnSopAuhUHBwsBo2bKjly5cb46lXr56mTJmipUuX6vnnn9fRo0cv+khfbm6uvL29tXbtWpnNZuN67969dfbsWc2fP/+SNQwYMEAZGRlasmSJTp48qfLly2vDhg16+OGHL+obGRmpli1bKjY21rj2xRdfaNiwYTp27Ng1jRkAAAAAbMnZ3gUAuLuEh4dbnfv7+yszM1O7d+/W6dOnVb58eav2c+fO6dChQ5IurJQaM2aMEhISdPz4cRUUFOjcuXMXrZRq1KjRP6oxOztbx48fV5MmTYxrzs7OatSo0UUf4SvWqlUrBQUFqWrVqmrTpo3atGmjxx57TKVLl9bBgwd19uxZtWrVyuqevLw81a9f3zifNm2aZs+erfT0dJ07d055eXlGiObt7a3nnntOUVFRatWqlSIjI/Xkk0/K399fkrR7925t2bLFamVUYWGhzp8/r7Nnz6p06dL/6GcCAAAAADcboRQAmypVqpTVuYODg4qKinT69Gn5+/trw4YNF93j5eUlSRoyZIgSExP17rvvKiQkRO7u7nr88ccv2sy8TJkyt6r8y/Lw8NB//vMfbdiwQWvWrNGoUaM0ZswYff/99zp9+rQkKSEhQffcc4/VfcWrwhYsWKAhQ4Zo0qRJMpvN8vDw0MSJE7V9+3aj72effaaXX35Zq1at0sKFCzVixAglJiaqadOmOn36tMaOHatOnTpdVJubm9stHDkAAAAA3BhCKQC3hQYNGigjI0POzs6qUqXKJfts2bJFzz33nB577DFJF1ZOHT58+KrPdnFxkXRh5dC18PT0lL+/v7Zv327sC1VQUGDsc3U5zs7OioyMVGRkpEaPHi0vLy+tX79erVq1kqurq9LT0y/50bvisd1///168cUXjWvFK8T+qn79+qpfv75iY2NlNps1f/58NW3aVA0aNFBqaqpCQkKuaYwAAAAAYG+EUgBuC5GRkTKbzerYsaMmTJig6tWr69ixY0pISNBjjz2mRo0a6d5779XSpUvVvn17OTg4aOTIkSoqKrrqs318fOTu7q5Vq1apUqVKcnNzk6en5xXveeWVV/T222/r3nvvVc2aNfXee+8pKyvrsv1XrFihn3/+Wc2aNVO5cuW0cuVKFRUVqUaNGvLw8NCQIUM0aNAgFRUV6cEHH1R2dra2bNkik8mkHj166N5779XcuXO1evVqBQcH6/PPP9f3339vfHNgWlqaPvnkEz366KMKCAhQamqqfvrpJ3Xv3l2SNGrUKLVr106VK1fW448/LkdHR+3evVt79+7VG2+8ce3/IQAAAADARvjqCwC3BQcHB61cuVLNmjXT888/r+rVq6tLly765Zdf5OvrK0l67733VK5cOd1///1q3769oqKirrhyqZizs7Pef/99ffzxxwoICFCHDh2ues+rr76qbt26qUePHsbH6YpXaF2Kl5eXli5dqhYtWig0NFQzZszQv/71L9WqVUuSNH78eI0cOVJxcXEKDQ1VmzZtlJCQYIROL7zwgjp16qSnnnpKTZo00R9//GG1aqp06dI6cOCAOnfurOrVq6tv376KiYnRCy+8IEmKiorSihUrtGbNGjVu3FhNmzbV5MmTFRQUdNWxAgAAAIA98O17AAAAAIA7Vnx8vAYOHHjFVe3XwsHBQcuWLVPHjh1vSl0Aro6VUgAAAACAO9ZTTz2l//73v/YuA8ANIJQCcFcqW7bsZY/NmzfbuzwAAAC7+/s3HNvT5WrJz8+Xu7u7fHx8bFwRgJuBUArAXSklJeWyR6NGjexdHgAAwEWKiooUFxen4OBgubu7q27dulqyZImKiopUqVIlTZ8+3ar/rl275OjoqF9++UWSlJWVpd69e6tixYoymUxq0aKFdu/ebfQfM2aM6tWrp5kzZyo4OFhubm5XrSkiIkIvvfSSBg4cqHLlysnX11effvqpzpw5o+eff14eHh4KCQnRN998Y9xTWFioXr16GeOoUaOGpk6davXc5557Th07dtSbb76pgIAA1ahRQ4cPH5aDg4MWLlyohx9+WG5ubpo3b57i4+Pl5eVldf+XX36pBg0ayM3NTVWrVtXYsWNVUFBgtP/0009q1qyZ3NzcFBYWpsTExGv+7wDg5uHb9wDclUJCQuxdAgAAwHWJi4vTF198oRkzZujee+/Vpk2b9Oyzz2r16tV6+umnNX/+fPXv39/oP2/ePD3wwAPGF5888cQTcnd31zfffCNPT099/PHHatmypf773//K29tbknTw4EH9+9//1tKlS+Xk5HRNdc2ZM0fDhg3Tjh07tHDhQvXv31/Lli3TY489ptdff12TJ09Wt27dlJ6ertKlSxsh2uLFi1W+fHlt3bpVffv2lb+/v5588knjuevWrZPJZLooMHrttdc0adIk1a9fX25ublq9erVV++bNm9W9e3e9//77euihh3To0CH17dtXkjR69GgVFRWpU6dO8vX11fbt25Wdna2BAwde938PADeBBQAAAABwWzt//ryldOnSlq1bt1pd79Wrl+Xpp5+27Nq1y+Lg4GD55ZdfLBaLxVJYWGi55557LNOnT7dYLBbL5s2bLSaTyXL+/Hmr+6tVq2b5+OOPLRaLxTJ69GhLqVKlLJmZmddc18MPP2x58MEHjfOCggJLmTJlLN26dTOuHT9+3CLJkpSUdNnnxMTEWDp37myc9+jRw+Lr62vJzc01rqWlpVkkWaZMmWJ172effWbx9PQ0zlu2bGl56623rPp8/vnnFn9/f4vFYrGsXr3a4uzsbPn111+N9m+++cYiybJs2bJrGziAm4KVUgAAAABwmzt48KDOnj2rVq1aWV3Py8tT/fr1Va9ePYWGhmr+/Pl67bXXtHHjRmVmZuqJJ56QJO3evVunT59W+fLlre4/d+6cDh06ZJwHBQWpYsWK11VbeHi48WcnJyeVL19ederUMa75+vpKkjIzM41r06ZN0+zZs5Wenq5z584pLy9P9erVs3punTp15OLictH7rrbVwu7du7Vlyxa9+eabxrXCwkKdP39eZ8+e1Y8//qjAwEAFBAQY7Waz+doGC+CmIpQCAAAAgNvc6dOnJUkJCQm65557rNpcXV0lSV27djVCqfnz56tNmzZGCHX69Gn5+/trw4YNFz37r/sxlSlT5rprK1WqlNW5g4OD1TUHBwdJF/bEkqQFCxZoyJAhmjRpksxmszw8PDRx4kRt377d6jmXq+VqNZ4+fVpjx45Vp06dLmq7ln2yANgOoRQAAAAA3ObCwsLk6uqq9PR0Pfzww5fs88wzz2jEiBFKTk7WkiVLNGPGDKOtQYMGysjIkLOzs6pUqWKjqi9ty5Ytuv/++/Xiiy8a1/66WuufatCggVJTUy+7h2hoaKiOHDmi48ePy9/fX5K0bdu2m/Z+ANeOUAoAAAAAbnMeHh4aMmSIBg0apKKiIj344IPKzs7Wli1bZDKZ1KNHD1WpUkX333+/evXqpcLCQj366KPG/ZGRkTKbzerYsaMmTJig6tWr69ixY0pISNBjjz1m028fvvfeezV37lytXr1awcHB+vzzz/X9998rODj4pjx/1KhRateunSpXrqzHH39cjo6O2r17t/bu3as33nhDkZGRql69unr06KGJEycqJydH//d//3dT3g3g+jjauwAAAAAAwNWNHz9eI0eOVFxcnEJDQ9WmTRslJCRYhTldu3bV7t279dhjj8nd3d247uDgoJUrV6pZs2Z6/vnnVb16dXXp0kW//PKLseeTrbzwwgvq1KmTnnrqKTVp0kR//PGH1aqpfyoqKkorVqzQmjVr1LhxYzVt2lSTJ082voXQ0dFRy5Yt07lz53Tfffepd+/eVvtPAbAdB4vFYrF3EQAAAAAAALi7sFIKAAAAAAAANkcoBQAAAAC4SHp6usqWLXvZIz093d4lArjD8fE9AAAAAMBFCgoKdPjw4cu2V6lSRc7OfHcWgBtHKAUAAAAAAACb4+N7AAAAAAAAsDlCKQAAAAAAANgcoRQAAAAAAABsjlAKAAAAAAAANkcoBQAAAAAAAJsjlAIAAAAAAIDNEUoBAAAAAADA5gilAAAAAAAAYHP/D6Yw2IhN4qRrAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-01-20 23:41:34,844 - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
-      "2025-01-20 23:41:34,847 - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "categorical_columns = ['gender', 'hypertension', 'heart_disease', 'ever_married', 'work_type', 'Residence_type', 'smoking_status', 'stroke']\n",
-    "\n",
-    "# Create subplots for the categorical columns\n",
-    "for i in range(0, len(categorical_columns), 2):\n",
-    "    fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
-    "\n",
-    "    sns.countplot(x=categorical_columns[i], data=data, ax=axes[0])\n",
-    "    axes[0].set_title(categorical_columns[i], fontsize=10, fontweight='bold', color='navy')\n",
-    "    axes[0].tick_params(axis='x', rotation=300)\n",
-    "    for container in axes[0].containers:\n",
-    "        axes[0].bar_label(container)\n",
-    "\n",
-    "    if i + 1 < len(categorical_columns):\n",
-    "        sns.countplot(x=categorical_columns[i + 1], data=data, ax=axes[1])\n",
-    "        axes[1].set_title(categorical_columns[i + 1], fontsize=10, fontweight='bold', color='navy')\n",
-    "        axes[1].tick_params(axis='x', rotation=300)\n",
-    "        for container in axes[1].containers:\n",
-    "            axes[1].bar_label(container)\n",
-    "    else:\n",
-    "        axes[1].axis(\"off\") \n",
-    "\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Correlation Heatmap\n",
-    "\n",
-    "A correlation heatmap provides a visual representation of the relationships between numeric features in the dataset. By analyzing the heatmap, we can identify how strongly one feature is linearly related to another. This helps in understanding feature interactions and determining which features might have a significant influence on the target variable.\n",
-    "\n",
-    "Let’s plot a heatmap to examine the correlations between all numeric features in the dataset and identify any strong positive or negative relationships. Features with high correlation (either positive or negative) may provide insights for feature selection or highlight potential multicollinearity issues."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Correlation heatmap\n",
-    "numeric_df = data.select_dtypes(include=[np.number])\n",
-    "plt.figure(figsize=(12, 8))\n",
-    "sns.heatmap(numeric_df.corr(), annot=True, cmap='coolwarm', fmt='.2f')\n",
-    "plt.title('Correlation Heatmap')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. Data Preprocessing & Feature Engineering\n",
-    "\n",
-    "#### Drop id column\n",
-    "The id column is dropped as it serves as a unique identifier for each row but does not contribute to the predictive power of the model.\n",
-    "\n",
-    "#### Remove missing values\n",
-    "Remove data entries with missing 'bmi' as it corresponds no impact to model accuracy being less in number\n",
-    "\n",
-    "#### Binary Encoding\n",
-    "Convert categorical features with only two unique values into binary numeric format for easier processing by machine learning models:\n",
-    "- ever_married: Encoded as 0 for “No” and 1 for “Yes”.\n",
-    "- Residence_type: Encoded as 0 for “Rural” and 1 for “Urban”.\n",
-    "\n",
-    "#### One-Hot Encoding for Multi-Class Categorical Features\n",
-    "- For features with more than two categories, such as gender, work_type, and smoking_status, apply one-hot encoding to create separate binary columns for each category.\n",
-    "- The onehot_encode function is assumed to handle the transformation, creating additional columns for each category while dropping the original column.\n",
-    "\n",
-    "#### Split Dataset into Features and Target\n",
-    "- Separate the target variable (stroke) from the features:\n",
-    "- X: Contains all feature columns used as input for the model.\n",
-    "- y: Contains the target column, which indicates whether a stroke occurred.\n",
-    "\n",
-    "#### Train-Test Split\n",
-    "- Split the dataset into training and testing sets to evaluate model performance effectively. This ensures the model is tested on unseen data and helps prevent overfitting.\n",
-    "- The specific split ratio (e.g., 70% train, 30% test) can be customized as needed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# def onehot_encode(df, column):\n",
-    "#     df = df.copy()\n",
-    "#     dummies = pd.get_dummies(df[column], prefix=column)\n",
-    "#     df = pd.concat([df, dummies], axis=1)\n",
-    "#     df = df.drop(column, axis=1)\n",
-    "#     return df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def onehot_encode(df, column):\n",
-    "    \"\"\"\n",
-    "    Perform one-hot encoding on a specified categorical column in the dataframe.\n",
-    "\n",
-    "    Parameters:\n",
-    "    df (pandas.DataFrame): The input dataframe.\n",
-    "    column (str): The name of the column to apply one-hot encoding.\n",
-    "\n",
-    "    Returns:\n",
-    "    pandas.DataFrame: A new dataframe with the original column replaced by one-hot encoded columns.\n",
-    "    \"\"\"\n",
-    "    # Log the start of the process\n",
-    "    logging.info(f\"Starting One-Hot Encoding for column: {column}\")\n",
-    "    \n",
-    "    # Check if the column exists in the dataframe\n",
-    "    if column not in df.columns:\n",
-    "        logging.error(f\"Column '{column}' not found in dataframe!\")\n",
-    "        raise ValueError(f\"Column '{column}' not found in dataframe!\")\n",
-    "\n",
-    "    # Create a copy of the dataframe\n",
-    "    df = df.copy()\n",
-    "\n",
-    "    # Create dummy variables\n",
-    "    logging.info(f\"Creating dummy variables for column: {column}\")\n",
-    "    dummies = pd.get_dummies(df[column], prefix=column)\n",
-    "\n",
-    "    # Concatenate the dummies with the original dataframe\n",
-    "    df = pd.concat([df, dummies], axis=1)\n",
-    "    \n",
-    "    # Drop the original column\n",
-    "    logging.info(f\"Dropping original column: {column}\")\n",
-    "    df = df.drop(column, axis=1)\n",
-    "    \n",
-    "    logging.info(f\"One-Hot Encoding completed for column: {column}\")\n",
-    "    return df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def preprocess_inputs(df):\n",
-    "    \"\"\"\n",
-    "    Preprocess the input dataframe by handling missing data, encoding categorical features,\n",
-    "    and performing data scaling and imputation.\n",
-    "\n",
-    "    Steps:\n",
-    "    1. Drop the 'id' column from the dataframe.\n",
-    "    2. Handle missing values by removing rows with missing values.\n",
-    "    3. Apply binary encoding for 'ever_married' and 'Residence_type'.\n",
-    "    4. Apply one-hot encoding for categorical columns like 'gender', 'work_type', and 'smoking_status'.\n",
-    "    5. Split the dataframe into features (X) and target variable (y).\n",
-    "    6. Perform train-test split with 70% of data for training.\n",
-    "    7. Impute missing values using KNNImputer for both the training and test datasets.\n",
-    "    8. Scale features using StandardScaler.\n",
-    "\n",
-    "    Parameters:\n",
-    "    df (pandas.DataFrame): The input dataframe.\n",
-    "\n",
-    "    Returns:\n",
-    "    tuple: A tuple containing the training and test sets (X_train, X_test, y_train, y_test).\n",
-    "    \"\"\"\n",
-    "    logging.info(\"Starting data preprocessing.\")\n",
-    "    \n",
-    "    # Make a copy of the dataframe to avoid modifying the original\n",
-    "    df = df.copy()\n",
-    "\n",
-    "    # Drop id column\n",
-    "    logging.info(\"Dropping 'id' column.\")\n",
-    "    df = df.drop('id', axis=1)\n",
-    "    \n",
-    "    # Drop rows with missing target value (BMI column)\n",
-    "    logging.info(\"Dropping rows with missing target value (BMI).\")\n",
-    "    df.dropna(how='any', inplace=True)\n",
-    "    \n",
-    "    # Binary encoding\n",
-    "    logging.info(\"Performing binary encoding on 'ever_married' and 'Residence_type' columns.\")\n",
-    "    df['ever_married'] = df['ever_married'].replace({'No': 0, 'Yes': 1})\n",
-    "    df['Residence_type'] = df['Residence_type'].replace({'Rural': 0, 'Urban': 1})\n",
-    "    \n",
-    "    # One-hot encoding\n",
-    "    logging.info(\"Performing one-hot encoding on categorical columns: 'gender', 'work_type', 'smoking_status'.\")\n",
-    "    for column in ['gender', 'work_type', 'smoking_status']:\n",
-    "        df = onehot_encode(df, column=column)\n",
-    "    \n",
-    "    # Split df into X and y\n",
-    "    logging.info(\"Splitting data into features (X) and target (y).\")\n",
-    "    y = df['stroke']\n",
-    "    X = df.drop('stroke', axis=1)\n",
-    "    \n",
-    "    # Train-test split\n",
-    "    logging.info(\"Splitting data into training and testing sets (70-30).\")\n",
-    "    X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, shuffle=True, random_state=1)\n",
-    "    \n",
-    "    # KNN imputation of missing values\n",
-    "    logging.info(\"Performing KNN imputation to fill missing values.\")\n",
-    "    imputer = KNNImputer()\n",
-    "    imputer.fit(X_train)\n",
-    "    X_train = pd.DataFrame(imputer.transform(X_train), index=X_train.index, columns=X_train.columns)\n",
-    "    X_test = pd.DataFrame(imputer.transform(X_test), index=X_test.index, columns=X_test.columns)\n",
-    "    \n",
-    "    # Scale X\n",
-    "    logging.info(\"Scaling features using StandardScaler.\")\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train)\n",
-    "    X_train = pd.DataFrame(scaler.transform(X_train), index=X_train.index, columns=X_train.columns)\n",
-    "    X_test = pd.DataFrame(scaler.transform(X_test), index=X_test.index, columns=X_test.columns)\n",
-    "    \n",
-    "    logging.info(\"Data preprocessing completed successfully.\")\n",
-    "    \n",
-    "    return X_train, X_test, y_train, y_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-01-20 23:41:35,073 - INFO - Starting data preprocessing.\n",
-      "2025-01-20 23:41:35,076 - INFO - Dropping 'id' column.\n",
-      "2025-01-20 23:41:35,077 - INFO - Dropping rows with missing target value (BMI).\n",
-      "2025-01-20 23:41:35,078 - INFO - Performing binary encoding on 'ever_married' and 'Residence_type' columns.\n",
-      "2025-01-20 23:41:35,080 - INFO - Performing one-hot encoding on categorical columns: 'gender', 'work_type', 'smoking_status'.\n",
-      "2025-01-20 23:41:35,080 - INFO - Starting One-Hot Encoding for column: gender\n",
-      "2025-01-20 23:41:35,081 - INFO - Creating dummy variables for column: gender\n",
-      "2025-01-20 23:41:35,082 - INFO - Dropping original column: gender\n",
-      "2025-01-20 23:41:35,082 - INFO - One-Hot Encoding completed for column: gender\n",
-      "2025-01-20 23:41:35,083 - INFO - Starting One-Hot Encoding for column: work_type\n",
-      "2025-01-20 23:41:35,083 - INFO - Creating dummy variables for column: work_type\n",
-      "2025-01-20 23:41:35,084 - INFO - Dropping original column: work_type\n",
-      "2025-01-20 23:41:35,084 - INFO - One-Hot Encoding completed for column: work_type\n",
-      "2025-01-20 23:41:35,084 - INFO - Starting One-Hot Encoding for column: smoking_status\n",
-      "2025-01-20 23:41:35,085 - INFO - Creating dummy variables for column: smoking_status\n",
-      "2025-01-20 23:41:35,086 - INFO - Dropping original column: smoking_status\n",
-      "2025-01-20 23:41:35,086 - INFO - One-Hot Encoding completed for column: smoking_status\n",
-      "2025-01-20 23:41:35,086 - INFO - Splitting data into features (X) and target (y).\n",
-      "2025-01-20 23:41:35,087 - INFO - Splitting data into training and testing sets (70-30).\n",
-      "2025-01-20 23:41:35,089 - INFO - Performing KNN imputation to fill missing values.\n",
-      "2025-01-20 23:41:35,092 - INFO - Scaling features using StandardScaler.\n",
-      "2025-01-20 23:41:35,094 - INFO - Data preprocessing completed successfully.\n"
-     ]
-    }
-   ],
-   "source": [
-    "X_train, X_test, y_train, y_test = preprocess_inputs(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>age</th>\n",
-       "      <th>hypertension</th>\n",
-       "      <th>heart_disease</th>\n",
-       "      <th>ever_married</th>\n",
-       "      <th>Residence_type</th>\n",
-       "      <th>avg_glucose_level</th>\n",
-       "      <th>bmi</th>\n",
-       "      <th>gender_Female</th>\n",
-       "      <th>gender_Male</th>\n",
-       "      <th>gender_Other</th>\n",
-       "      <th>work_type_Govt_job</th>\n",
-       "      <th>work_type_Never_worked</th>\n",
-       "      <th>work_type_Private</th>\n",
-       "      <th>work_type_Self-employed</th>\n",
-       "      <th>work_type_children</th>\n",
-       "      <th>smoking_status_Unknown</th>\n",
-       "      <th>smoking_status_formerly smoked</th>\n",
-       "      <th>smoking_status_never smoked</th>\n",
-       "      <th>smoking_status_smokes</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>478</th>\n",
-       "      <td>1.023001</td>\n",
-       "      <td>-0.322666</td>\n",
-       "      <td>-0.227441</td>\n",
-       "      <td>0.729299</td>\n",
-       "      <td>0.958933</td>\n",
-       "      <td>-0.405669</td>\n",
-       "      <td>-0.833400</td>\n",
-       "      <td>0.833235</td>\n",
-       "      <td>-0.832733</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>2.582565</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>-1.171720</td>\n",
-       "      <td>-0.42133</td>\n",
-       "      <td>-0.395617</td>\n",
-       "      <td>-0.657467</td>\n",
-       "      <td>2.235287</td>\n",
-       "      <td>-0.779172</td>\n",
-       "      <td>-0.426108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3798</th>\n",
-       "      <td>-1.688827</td>\n",
-       "      <td>-0.322666</td>\n",
-       "      <td>-0.227441</td>\n",
-       "      <td>-1.371180</td>\n",
-       "      <td>0.958933</td>\n",
-       "      <td>0.151093</td>\n",
-       "      <td>-1.144398</td>\n",
-       "      <td>-1.200142</td>\n",
-       "      <td>1.200864</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>-0.387212</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>-1.171720</td>\n",
-       "      <td>-0.42133</td>\n",
-       "      <td>2.527696</td>\n",
-       "      <td>1.520988</td>\n",
-       "      <td>-0.447370</td>\n",
-       "      <td>-0.779172</td>\n",
-       "      <td>-0.426108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2615</th>\n",
-       "      <td>-0.666335</td>\n",
-       "      <td>-0.322666</td>\n",
-       "      <td>-0.227441</td>\n",
-       "      <td>0.729299</td>\n",
-       "      <td>-1.042825</td>\n",
-       "      <td>-1.051083</td>\n",
-       "      <td>-1.027774</td>\n",
-       "      <td>0.833235</td>\n",
-       "      <td>-0.832733</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>-0.387212</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>0.853446</td>\n",
-       "      <td>-0.42133</td>\n",
-       "      <td>-0.395617</td>\n",
-       "      <td>1.520988</td>\n",
-       "      <td>-0.447370</td>\n",
-       "      <td>-0.779172</td>\n",
-       "      <td>-0.426108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>425</th>\n",
-       "      <td>-1.688827</td>\n",
-       "      <td>-0.322666</td>\n",
-       "      <td>-0.227441</td>\n",
-       "      <td>-1.371180</td>\n",
-       "      <td>0.958933</td>\n",
-       "      <td>-0.615210</td>\n",
-       "      <td>-1.960767</td>\n",
-       "      <td>0.833235</td>\n",
-       "      <td>-0.832733</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>-0.387212</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>-1.171720</td>\n",
-       "      <td>-0.42133</td>\n",
-       "      <td>2.527696</td>\n",
-       "      <td>1.520988</td>\n",
-       "      <td>-0.447370</td>\n",
-       "      <td>-0.779172</td>\n",
-       "      <td>-0.426108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3123</th>\n",
-       "      <td>0.400614</td>\n",
-       "      <td>-0.322666</td>\n",
-       "      <td>-0.227441</td>\n",
-       "      <td>0.729299</td>\n",
-       "      <td>-1.042825</td>\n",
-       "      <td>-0.930865</td>\n",
-       "      <td>0.125509</td>\n",
-       "      <td>-1.200142</td>\n",
-       "      <td>1.200864</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>-0.387212</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>0.853446</td>\n",
-       "      <td>-0.42133</td>\n",
-       "      <td>-0.395617</td>\n",
-       "      <td>-0.657467</td>\n",
-       "      <td>-0.447370</td>\n",
-       "      <td>1.283413</td>\n",
-       "      <td>-0.426108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3036</th>\n",
-       "      <td>-0.132860</td>\n",
-       "      <td>-0.322666</td>\n",
-       "      <td>-0.227441</td>\n",
-       "      <td>0.729299</td>\n",
-       "      <td>0.958933</td>\n",
-       "      <td>-0.016137</td>\n",
-       "      <td>-0.522403</td>\n",
-       "      <td>-1.200142</td>\n",
-       "      <td>1.200864</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>-0.387212</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>0.853446</td>\n",
-       "      <td>-0.42133</td>\n",
-       "      <td>-0.395617</td>\n",
-       "      <td>1.520988</td>\n",
-       "      <td>-0.447370</td>\n",
-       "      <td>-0.779172</td>\n",
-       "      <td>-0.426108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2899</th>\n",
-       "      <td>0.533983</td>\n",
-       "      <td>-0.322666</td>\n",
-       "      <td>-0.227441</td>\n",
-       "      <td>0.729299</td>\n",
-       "      <td>0.958933</td>\n",
-       "      <td>-0.818707</td>\n",
-       "      <td>-0.250280</td>\n",
-       "      <td>-1.200142</td>\n",
-       "      <td>1.200864</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>-0.387212</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>0.853446</td>\n",
-       "      <td>-0.42133</td>\n",
-       "      <td>-0.395617</td>\n",
-       "      <td>-0.657467</td>\n",
-       "      <td>-0.447370</td>\n",
-       "      <td>1.283413</td>\n",
-       "      <td>-0.426108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>962</th>\n",
-       "      <td>-0.444054</td>\n",
-       "      <td>-0.322666</td>\n",
-       "      <td>-0.227441</td>\n",
-       "      <td>-1.371180</td>\n",
-       "      <td>-1.042825</td>\n",
-       "      <td>-0.625508</td>\n",
-       "      <td>-0.366904</td>\n",
-       "      <td>-1.200142</td>\n",
-       "      <td>1.200864</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>-0.387212</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>0.853446</td>\n",
-       "      <td>-0.42133</td>\n",
-       "      <td>-0.395617</td>\n",
-       "      <td>1.520988</td>\n",
-       "      <td>-0.447370</td>\n",
-       "      <td>-0.779172</td>\n",
-       "      <td>-0.426108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4157</th>\n",
-       "      <td>-1.288722</td>\n",
-       "      <td>-0.322666</td>\n",
-       "      <td>-0.227441</td>\n",
-       "      <td>-1.371180</td>\n",
-       "      <td>-1.042825</td>\n",
-       "      <td>0.143257</td>\n",
-       "      <td>-0.535361</td>\n",
-       "      <td>0.833235</td>\n",
-       "      <td>-0.832733</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>-0.387212</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>-1.171720</td>\n",
-       "      <td>-0.42133</td>\n",
-       "      <td>2.527696</td>\n",
-       "      <td>1.520988</td>\n",
-       "      <td>-0.447370</td>\n",
-       "      <td>-0.779172</td>\n",
-       "      <td>-0.426108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>275</th>\n",
-       "      <td>1.067457</td>\n",
-       "      <td>-0.322666</td>\n",
-       "      <td>-0.227441</td>\n",
-       "      <td>0.729299</td>\n",
-       "      <td>-1.042825</td>\n",
-       "      <td>-0.240678</td>\n",
-       "      <td>-0.068864</td>\n",
-       "      <td>0.833235</td>\n",
-       "      <td>-0.832733</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>2.582565</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>-1.171720</td>\n",
-       "      <td>-0.42133</td>\n",
-       "      <td>-0.395617</td>\n",
-       "      <td>-0.657467</td>\n",
-       "      <td>-0.447370</td>\n",
-       "      <td>-0.779172</td>\n",
-       "      <td>2.346822</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3436 rows × 19 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           age  hypertension  heart_disease  ever_married  Residence_type  \\\n",
-       "478   1.023001     -0.322666      -0.227441      0.729299        0.958933   \n",
-       "3798 -1.688827     -0.322666      -0.227441     -1.371180        0.958933   \n",
-       "2615 -0.666335     -0.322666      -0.227441      0.729299       -1.042825   \n",
-       "425  -1.688827     -0.322666      -0.227441     -1.371180        0.958933   \n",
-       "3123  0.400614     -0.322666      -0.227441      0.729299       -1.042825   \n",
-       "...        ...           ...            ...           ...             ...   \n",
-       "3036 -0.132860     -0.322666      -0.227441      0.729299        0.958933   \n",
-       "2899  0.533983     -0.322666      -0.227441      0.729299        0.958933   \n",
-       "962  -0.444054     -0.322666      -0.227441     -1.371180       -1.042825   \n",
-       "4157 -1.288722     -0.322666      -0.227441     -1.371180       -1.042825   \n",
-       "275   1.067457     -0.322666      -0.227441      0.729299       -1.042825   \n",
-       "\n",
-       "      avg_glucose_level       bmi  gender_Female  gender_Male  gender_Other  \\\n",
-       "478           -0.405669 -0.833400       0.833235    -0.832733     -0.017062   \n",
-       "3798           0.151093 -1.144398      -1.200142     1.200864     -0.017062   \n",
-       "2615          -1.051083 -1.027774       0.833235    -0.832733     -0.017062   \n",
-       "425           -0.615210 -1.960767       0.833235    -0.832733     -0.017062   \n",
-       "3123          -0.930865  0.125509      -1.200142     1.200864     -0.017062   \n",
-       "...                 ...       ...            ...          ...           ...   \n",
-       "3036          -0.016137 -0.522403      -1.200142     1.200864     -0.017062   \n",
-       "2899          -0.818707 -0.250280      -1.200142     1.200864     -0.017062   \n",
-       "962           -0.625508 -0.366904      -1.200142     1.200864     -0.017062   \n",
-       "4157           0.143257 -0.535361       0.833235    -0.832733     -0.017062   \n",
-       "275           -0.240678 -0.068864       0.833235    -0.832733     -0.017062   \n",
-       "\n",
-       "      work_type_Govt_job  work_type_Never_worked  work_type_Private  \\\n",
-       "478             2.582565               -0.070514          -1.171720   \n",
-       "3798           -0.387212               -0.070514          -1.171720   \n",
-       "2615           -0.387212               -0.070514           0.853446   \n",
-       "425            -0.387212               -0.070514          -1.171720   \n",
-       "3123           -0.387212               -0.070514           0.853446   \n",
-       "...                  ...                     ...                ...   \n",
-       "3036           -0.387212               -0.070514           0.853446   \n",
-       "2899           -0.387212               -0.070514           0.853446   \n",
-       "962            -0.387212               -0.070514           0.853446   \n",
-       "4157           -0.387212               -0.070514          -1.171720   \n",
-       "275             2.582565               -0.070514          -1.171720   \n",
-       "\n",
-       "      work_type_Self-employed  work_type_children  smoking_status_Unknown  \\\n",
-       "478                  -0.42133           -0.395617               -0.657467   \n",
-       "3798                 -0.42133            2.527696                1.520988   \n",
-       "2615                 -0.42133           -0.395617                1.520988   \n",
-       "425                  -0.42133            2.527696                1.520988   \n",
-       "3123                 -0.42133           -0.395617               -0.657467   \n",
-       "...                       ...                 ...                     ...   \n",
-       "3036                 -0.42133           -0.395617                1.520988   \n",
-       "2899                 -0.42133           -0.395617               -0.657467   \n",
-       "962                  -0.42133           -0.395617                1.520988   \n",
-       "4157                 -0.42133            2.527696                1.520988   \n",
-       "275                  -0.42133           -0.395617               -0.657467   \n",
-       "\n",
-       "      smoking_status_formerly smoked  smoking_status_never smoked  \\\n",
-       "478                         2.235287                    -0.779172   \n",
-       "3798                       -0.447370                    -0.779172   \n",
-       "2615                       -0.447370                    -0.779172   \n",
-       "425                        -0.447370                    -0.779172   \n",
-       "3123                       -0.447370                     1.283413   \n",
-       "...                              ...                          ...   \n",
-       "3036                       -0.447370                    -0.779172   \n",
-       "2899                       -0.447370                     1.283413   \n",
-       "962                        -0.447370                    -0.779172   \n",
-       "4157                       -0.447370                    -0.779172   \n",
-       "275                        -0.447370                    -0.779172   \n",
-       "\n",
-       "      smoking_status_smokes  \n",
-       "478               -0.426108  \n",
-       "3798              -0.426108  \n",
-       "2615              -0.426108  \n",
-       "425               -0.426108  \n",
-       "3123              -0.426108  \n",
-       "...                     ...  \n",
-       "3036              -0.426108  \n",
-       "2899              -0.426108  \n",
-       "962               -0.426108  \n",
-       "4157              -0.426108  \n",
-       "275                2.346822  \n",
-       "\n",
-       "[3436 rows x 19 columns]"
-      ]
-     },
-     "execution_count": 121,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "478     0\n",
-       "3798    0\n",
-       "2615    0\n",
-       "425     0\n",
-       "3123    0\n",
-       "       ..\n",
-       "3036    0\n",
-       "2899    0\n",
-       "962     0\n",
-       "4157    0\n",
-       "275     0\n",
-       "Name: stroke, Length: 3436, dtype: int64"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y_train"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Oversampling to balance dataset\n",
-    "\n",
-    "Oversampling is done on the dataset by increasing the number of instances of the minority class. Here in the dataset oversampling is done on the minority class (stroke == 1) to balance the dataset by duplicating some of its samples until both classes have an equal number of instances."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "oversampled_data = pd.concat([X_train, y_train], axis=1).copy()\n",
-    "\n",
-    "num_samples = y_train.value_counts()[0] - y_train.value_counts()[1]\n",
-    "new_samples = oversampled_data.query(\"stroke == 1\").sample(num_samples, replace=True, random_state=1)\n",
-    "\n",
-    "oversampled_data = pd.concat([oversampled_data, new_samples], axis=0).sample(frac=1.0, random_state=1).reset_index(drop=True)\n",
-    "\n",
-    "y_train_oversampled = oversampled_data['stroke']\n",
-    "X_train_oversampled = oversampled_data.drop('stroke', axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def oversample_data(X_train, y_train):\n",
-    "    \"\"\"\n",
-    "    Perform oversampling to balance the class distribution in the training dataset.\n",
-    "\n",
-    "    This method oversamples the minority class (stroke=1) by duplicating random samples \n",
-    "    and appending them to the training set.\n",
-    "\n",
-    "    Parameters:\n",
-    "    X_train (pandas.DataFrame): The training feature set.\n",
-    "    y_train (pandas.Series): The training target variable.\n",
-    "\n",
-    "    Returns:\n",
-    "    tuple: A tuple containing the oversampled feature set and target variable (X_train_oversampled, y_train_oversampled).\n",
-    "    \"\"\"\n",
-    "    oversampled_data = pd.concat([X_train, y_train], axis=1).copy()\n",
-    "\n",
-    "    num_samples = y_train.value_counts()[0] - y_train.value_counts()[1]\n",
-    "    new_samples = oversampled_data.query(\"stroke == 1\").sample(num_samples, replace=True, random_state=1)\n",
-    "\n",
-    "    oversampled_data = pd.concat([oversampled_data, new_samples], axis=0).sample(frac=1.0, random_state=1).reset_index(drop=True)\n",
-    "\n",
-    "    y_train_oversampled = oversampled_data['stroke']\n",
-    "    X_train_oversampled = oversampled_data.drop('stroke', axis=1)\n",
-    "\n",
-    "    return X_train_oversampled, y_train_oversampled"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train_oversampled ,y_train_oversampled = oversample_data(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "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>hypertension</th>\n",
-       "      <th>heart_disease</th>\n",
-       "      <th>ever_married</th>\n",
-       "      <th>Residence_type</th>\n",
-       "      <th>avg_glucose_level</th>\n",
-       "      <th>bmi</th>\n",
-       "      <th>gender_Female</th>\n",
-       "      <th>gender_Male</th>\n",
-       "      <th>gender_Other</th>\n",
-       "      <th>work_type_Govt_job</th>\n",
-       "      <th>work_type_Never_worked</th>\n",
-       "      <th>work_type_Private</th>\n",
-       "      <th>work_type_Self-employed</th>\n",
-       "      <th>work_type_children</th>\n",
-       "      <th>smoking_status_Unknown</th>\n",
-       "      <th>smoking_status_formerly smoked</th>\n",
-       "      <th>smoking_status_never smoked</th>\n",
-       "      <th>smoking_status_smokes</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.311702</td>\n",
-       "      <td>-0.322666</td>\n",
-       "      <td>-0.227441</td>\n",
-       "      <td>0.729299</td>\n",
-       "      <td>0.958933</td>\n",
-       "      <td>1.103878</td>\n",
-       "      <td>0.630881</td>\n",
-       "      <td>0.833235</td>\n",
-       "      <td>-0.832733</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>-0.387212</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>0.853446</td>\n",
-       "      <td>-0.421330</td>\n",
-       "      <td>-0.395617</td>\n",
-       "      <td>-0.657467</td>\n",
-       "      <td>-0.447370</td>\n",
-       "      <td>1.283413</td>\n",
-       "      <td>-0.426108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.311702</td>\n",
-       "      <td>3.099183</td>\n",
-       "      <td>-0.227441</td>\n",
-       "      <td>0.729299</td>\n",
-       "      <td>-1.042825</td>\n",
-       "      <td>-0.720428</td>\n",
-       "      <td>0.177342</td>\n",
-       "      <td>0.833235</td>\n",
-       "      <td>-0.832733</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>-0.387212</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>0.853446</td>\n",
-       "      <td>-0.421330</td>\n",
-       "      <td>-0.395617</td>\n",
-       "      <td>-0.657467</td>\n",
-       "      <td>2.235287</td>\n",
-       "      <td>-0.779172</td>\n",
-       "      <td>-0.426108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-1.599915</td>\n",
-       "      <td>-0.322666</td>\n",
-       "      <td>-0.227441</td>\n",
-       "      <td>-1.371180</td>\n",
-       "      <td>0.958933</td>\n",
-       "      <td>-0.416639</td>\n",
-       "      <td>-0.859317</td>\n",
-       "      <td>0.833235</td>\n",
-       "      <td>-0.832733</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>-0.387212</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>-1.171720</td>\n",
-       "      <td>-0.421330</td>\n",
-       "      <td>2.527696</td>\n",
-       "      <td>1.520988</td>\n",
-       "      <td>-0.447370</td>\n",
-       "      <td>-0.779172</td>\n",
-       "      <td>-0.426108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-0.577422</td>\n",
-       "      <td>-0.322666</td>\n",
-       "      <td>-0.227441</td>\n",
-       "      <td>-1.371180</td>\n",
-       "      <td>0.958933</td>\n",
-       "      <td>-0.884748</td>\n",
-       "      <td>-0.535361</td>\n",
-       "      <td>0.833235</td>\n",
-       "      <td>-0.832733</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>-0.387212</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>-1.171720</td>\n",
-       "      <td>2.373437</td>\n",
-       "      <td>-0.395617</td>\n",
-       "      <td>-0.657467</td>\n",
-       "      <td>-0.447370</td>\n",
-       "      <td>-0.779172</td>\n",
-       "      <td>2.346822</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-0.043948</td>\n",
-       "      <td>-0.322666</td>\n",
-       "      <td>-0.227441</td>\n",
-       "      <td>0.729299</td>\n",
-       "      <td>-1.042825</td>\n",
-       "      <td>-0.381715</td>\n",
-       "      <td>-0.639027</td>\n",
-       "      <td>0.833235</td>\n",
-       "      <td>-0.832733</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>-0.387212</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>0.853446</td>\n",
-       "      <td>-0.421330</td>\n",
-       "      <td>-0.395617</td>\n",
-       "      <td>-0.657467</td>\n",
-       "      <td>-0.447370</td>\n",
-       "      <td>-0.779172</td>\n",
-       "      <td>2.346822</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6577</th>\n",
-       "      <td>0.356158</td>\n",
-       "      <td>-0.322666</td>\n",
-       "      <td>-0.227441</td>\n",
-       "      <td>0.729299</td>\n",
-       "      <td>0.958933</td>\n",
-       "      <td>0.394886</td>\n",
-       "      <td>0.358758</td>\n",
-       "      <td>0.833235</td>\n",
-       "      <td>-0.832733</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>-0.387212</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>0.853446</td>\n",
-       "      <td>-0.421330</td>\n",
-       "      <td>-0.395617</td>\n",
-       "      <td>-0.657467</td>\n",
-       "      <td>-0.447370</td>\n",
-       "      <td>1.283413</td>\n",
-       "      <td>-0.426108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6578</th>\n",
-       "      <td>1.289738</td>\n",
-       "      <td>-0.322666</td>\n",
-       "      <td>-0.227441</td>\n",
-       "      <td>0.729299</td>\n",
-       "      <td>-1.042825</td>\n",
-       "      <td>-0.166577</td>\n",
-       "      <td>-0.263238</td>\n",
-       "      <td>0.833235</td>\n",
-       "      <td>-0.832733</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>-0.387212</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>0.853446</td>\n",
-       "      <td>-0.421330</td>\n",
-       "      <td>-0.395617</td>\n",
-       "      <td>-0.657467</td>\n",
-       "      <td>-0.447370</td>\n",
-       "      <td>-0.779172</td>\n",
-       "      <td>2.346822</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6579</th>\n",
-       "      <td>1.156369</td>\n",
-       "      <td>-0.322666</td>\n",
-       "      <td>4.396744</td>\n",
-       "      <td>0.729299</td>\n",
-       "      <td>0.958933</td>\n",
-       "      <td>2.011889</td>\n",
-       "      <td>-0.081822</td>\n",
-       "      <td>-1.200142</td>\n",
-       "      <td>1.200864</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>-0.387212</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>-1.171720</td>\n",
-       "      <td>2.373437</td>\n",
-       "      <td>-0.395617</td>\n",
-       "      <td>-0.657467</td>\n",
-       "      <td>-0.447370</td>\n",
-       "      <td>-0.779172</td>\n",
-       "      <td>2.346822</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6580</th>\n",
-       "      <td>0.578439</td>\n",
-       "      <td>-0.322666</td>\n",
-       "      <td>-0.227441</td>\n",
-       "      <td>0.729299</td>\n",
-       "      <td>0.958933</td>\n",
-       "      <td>-0.517380</td>\n",
-       "      <td>0.203259</td>\n",
-       "      <td>-1.200142</td>\n",
-       "      <td>1.200864</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>-0.387212</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>0.853446</td>\n",
-       "      <td>-0.421330</td>\n",
-       "      <td>-0.395617</td>\n",
-       "      <td>-0.657467</td>\n",
-       "      <td>2.235287</td>\n",
-       "      <td>-0.779172</td>\n",
-       "      <td>-0.426108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6581</th>\n",
-       "      <td>1.645388</td>\n",
-       "      <td>-0.322666</td>\n",
-       "      <td>4.396744</td>\n",
-       "      <td>0.729299</td>\n",
-       "      <td>0.958933</td>\n",
-       "      <td>3.298911</td>\n",
-       "      <td>0.203259</td>\n",
-       "      <td>-1.200142</td>\n",
-       "      <td>1.200864</td>\n",
-       "      <td>-0.017062</td>\n",
-       "      <td>-0.387212</td>\n",
-       "      <td>-0.070514</td>\n",
-       "      <td>-1.171720</td>\n",
-       "      <td>2.373437</td>\n",
-       "      <td>-0.395617</td>\n",
-       "      <td>-0.657467</td>\n",
-       "      <td>2.235287</td>\n",
-       "      <td>-0.779172</td>\n",
-       "      <td>-0.426108</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>6582 rows × 19 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           age  hypertension  heart_disease  ever_married  Residence_type  \\\n",
-       "0     0.311702     -0.322666      -0.227441      0.729299        0.958933   \n",
-       "1     0.311702      3.099183      -0.227441      0.729299       -1.042825   \n",
-       "2    -1.599915     -0.322666      -0.227441     -1.371180        0.958933   \n",
-       "3    -0.577422     -0.322666      -0.227441     -1.371180        0.958933   \n",
-       "4    -0.043948     -0.322666      -0.227441      0.729299       -1.042825   \n",
-       "...        ...           ...            ...           ...             ...   \n",
-       "6577  0.356158     -0.322666      -0.227441      0.729299        0.958933   \n",
-       "6578  1.289738     -0.322666      -0.227441      0.729299       -1.042825   \n",
-       "6579  1.156369     -0.322666       4.396744      0.729299        0.958933   \n",
-       "6580  0.578439     -0.322666      -0.227441      0.729299        0.958933   \n",
-       "6581  1.645388     -0.322666       4.396744      0.729299        0.958933   \n",
-       "\n",
-       "      avg_glucose_level       bmi  gender_Female  gender_Male  gender_Other  \\\n",
-       "0              1.103878  0.630881       0.833235    -0.832733     -0.017062   \n",
-       "1             -0.720428  0.177342       0.833235    -0.832733     -0.017062   \n",
-       "2             -0.416639 -0.859317       0.833235    -0.832733     -0.017062   \n",
-       "3             -0.884748 -0.535361       0.833235    -0.832733     -0.017062   \n",
-       "4             -0.381715 -0.639027       0.833235    -0.832733     -0.017062   \n",
-       "...                 ...       ...            ...          ...           ...   \n",
-       "6577           0.394886  0.358758       0.833235    -0.832733     -0.017062   \n",
-       "6578          -0.166577 -0.263238       0.833235    -0.832733     -0.017062   \n",
-       "6579           2.011889 -0.081822      -1.200142     1.200864     -0.017062   \n",
-       "6580          -0.517380  0.203259      -1.200142     1.200864     -0.017062   \n",
-       "6581           3.298911  0.203259      -1.200142     1.200864     -0.017062   \n",
-       "\n",
-       "      work_type_Govt_job  work_type_Never_worked  work_type_Private  \\\n",
-       "0              -0.387212               -0.070514           0.853446   \n",
-       "1              -0.387212               -0.070514           0.853446   \n",
-       "2              -0.387212               -0.070514          -1.171720   \n",
-       "3              -0.387212               -0.070514          -1.171720   \n",
-       "4              -0.387212               -0.070514           0.853446   \n",
-       "...                  ...                     ...                ...   \n",
-       "6577           -0.387212               -0.070514           0.853446   \n",
-       "6578           -0.387212               -0.070514           0.853446   \n",
-       "6579           -0.387212               -0.070514          -1.171720   \n",
-       "6580           -0.387212               -0.070514           0.853446   \n",
-       "6581           -0.387212               -0.070514          -1.171720   \n",
-       "\n",
-       "      work_type_Self-employed  work_type_children  smoking_status_Unknown  \\\n",
-       "0                   -0.421330           -0.395617               -0.657467   \n",
-       "1                   -0.421330           -0.395617               -0.657467   \n",
-       "2                   -0.421330            2.527696                1.520988   \n",
-       "3                    2.373437           -0.395617               -0.657467   \n",
-       "4                   -0.421330           -0.395617               -0.657467   \n",
-       "...                       ...                 ...                     ...   \n",
-       "6577                -0.421330           -0.395617               -0.657467   \n",
-       "6578                -0.421330           -0.395617               -0.657467   \n",
-       "6579                 2.373437           -0.395617               -0.657467   \n",
-       "6580                -0.421330           -0.395617               -0.657467   \n",
-       "6581                 2.373437           -0.395617               -0.657467   \n",
-       "\n",
-       "      smoking_status_formerly smoked  smoking_status_never smoked  \\\n",
-       "0                          -0.447370                     1.283413   \n",
-       "1                           2.235287                    -0.779172   \n",
-       "2                          -0.447370                    -0.779172   \n",
-       "3                          -0.447370                    -0.779172   \n",
-       "4                          -0.447370                    -0.779172   \n",
-       "...                              ...                          ...   \n",
-       "6577                       -0.447370                     1.283413   \n",
-       "6578                       -0.447370                    -0.779172   \n",
-       "6579                       -0.447370                    -0.779172   \n",
-       "6580                        2.235287                    -0.779172   \n",
-       "6581                        2.235287                    -0.779172   \n",
-       "\n",
-       "      smoking_status_smokes  \n",
-       "0                 -0.426108  \n",
-       "1                 -0.426108  \n",
-       "2                 -0.426108  \n",
-       "3                  2.346822  \n",
-       "4                  2.346822  \n",
-       "...                     ...  \n",
-       "6577              -0.426108  \n",
-       "6578               2.346822  \n",
-       "6579               2.346822  \n",
-       "6580              -0.426108  \n",
-       "6581              -0.426108  \n",
-       "\n",
-       "[6582 rows x 19 columns]"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train_oversampled"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0       0\n",
-       "1       1\n",
-       "2       0\n",
-       "3       0\n",
-       "4       0\n",
-       "       ..\n",
-       "6577    0\n",
-       "6578    1\n",
-       "6579    1\n",
-       "6580    0\n",
-       "6581    1\n",
-       "Name: stroke, Length: 6582, dtype: int64"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y_train_oversampled"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 6. Model Training and Evaluation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-01-20 23:41:35,146 - INFO - Logistic Regression trained successfully.\n",
-      "2025-01-20 23:41:35,147 - INFO - K-Nearest Neighbors trained successfully.\n",
-      "2025-01-20 23:41:35,156 - INFO - Support Vector Machine (Linear Kernel) trained successfully.\n",
-      "2025-01-20 23:41:35,427 - INFO - Support Vector Machine (RBF Kernel) trained successfully.\n",
-      "2025-01-20 23:41:36,692 - INFO - Neural Network trained successfully.\n",
-      "2025-01-20 23:41:37,041 - INFO - Gradient Boosting trained successfully.\n"
-     ]
-    }
-   ],
-   "source": [
-    "models = {\n",
-    "    \"Logistic Regression\": LogisticRegression(),\n",
-    "    \"K-Nearest Neighbors\": KNeighborsClassifier(),\n",
-    "    \"Support Vector Machine (Linear Kernel)\": LinearSVC(),\n",
-    "    \"Support Vector Machine (RBF Kernel)\": SVC(),\n",
-    "    \"Neural Network\": MLPClassifier(),\n",
-    "    \"Gradient Boosting\": GradientBoostingClassifier(),\n",
-    "}\n",
-    "\n",
-    "# Verify that all models can be trained\n",
-    "for name, model in models.items():\n",
-    "    try:\n",
-    "        # Train the model\n",
-    "        model.fit(X_train_oversampled, y_train_oversampled)\n",
-    "        logging.info(f\"{name} trained successfully.\")\n",
-    "    except Exception as e:\n",
-    "        logging.error(f\"Error occurred while training {name}: {str(e)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def train_and_evaluate(models, X_train, y_train):\n",
-    "    \"\"\"\n",
-    "    Train and evaluate multiple models on the given training data.\n",
-    "\n",
-    "    For each model, this method trains the model on the training data, \n",
-    "    generates predictions, and calculates evaluation metrics such as \n",
-    "    accuracy, precision, and F1-score.\n",
-    "\n",
-    "    Parameters:\n",
-    "    models (dict): A dictionary where the keys are model names and the values are the machine learning models.\n",
-    "    X_train (pandas.DataFrame): The training feature set.\n",
-    "    y_train (pandas.Series): The training target variable.\n",
-    "\n",
-    "    Returns:\n",
-    "    pandas.DataFrame: A dataframe containing the evaluation metrics (Accuracy, Precision, F1-Score, and Runtime) \n",
-    "    for each model.\n",
-    "    \"\"\"\n",
-    "    # List to hold the metrics for each model\n",
-    "    metrics = []\n",
-    "\n",
-    "    # Loop through each model in the models dictionary\n",
-    "    for name, model in models.items():\n",
-    "        try:\n",
-    "            # Record start time for model training\n",
-    "            start_time = time.time()\n",
-    "            logging.info(f\"Training {name}...\")\n",
-    "            \n",
-    "            # Train the model on the training data\n",
-    "            model.fit(X_train, y_train)\n",
-    "            \n",
-    "            # Record end time and calculate the training time\n",
-    "            end_time = time.time()\n",
-    "            training_time = end_time - start_time\n",
-    "\n",
-    "            # Generate predictions for training data\n",
-    "            y_train_pred = model.predict(X_train)\n",
-    "\n",
-    "            # Calculate evaluation metrics\n",
-    "            accuracy = accuracy_score(y_train, y_train_pred)\n",
-    "            precision = precision_score(y_train, y_train_pred, average='weighted')\n",
-    "            f1 = f1_score(y_train, y_train_pred, average='weighted')\n",
-    "\n",
-    "            # Append the results to the metrics list\n",
-    "            metrics.append({\n",
-    "                \"Model\": name,\n",
-    "                \"Accuracy\": accuracy,\n",
-    "                \"Precision\": precision,\n",
-    "                \"F1-Score\": f1,\n",
-    "                \"Runtime (s)\": training_time\n",
-    "            })\n",
-    "\n",
-    "            # Log successful training of the model\n",
-    "            logging.info(f\"{name} trained successfully in {training_time:.2f} seconds.\")\n",
-    "\n",
-    "        except Exception as e:\n",
-    "            # Log the error if something goes wrong\n",
-    "            logging.error(f\"Error occurred while training {name}: {str(e)}\")\n",
-    "\n",
-    "    # Return a DataFrame containing the metrics for each model\n",
-    "    return pd.DataFrame(metrics)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-01-20 23:41:37,050 - INFO - Training Logistic Regression...\n",
-      "2025-01-20 23:41:37,077 - INFO - Logistic Regression trained successfully in 0.02 seconds.\n",
-      "2025-01-20 23:41:37,077 - INFO - Training K-Nearest Neighbors...\n",
-      "2025-01-20 23:41:37,137 - INFO - K-Nearest Neighbors trained successfully in 0.00 seconds.\n",
-      "2025-01-20 23:41:37,137 - INFO - Training Support Vector Machine (Linear Kernel)...\n",
-      "2025-01-20 23:41:37,147 - INFO - Support Vector Machine (Linear Kernel) trained successfully in 0.01 seconds.\n",
-      "2025-01-20 23:41:37,148 - INFO - Training Support Vector Machine (RBF Kernel)...\n",
-      "2025-01-20 23:41:37,237 - INFO - Support Vector Machine (RBF Kernel) trained successfully in 0.04 seconds.\n",
-      "2025-01-20 23:41:37,237 - INFO - Training Neural Network...\n",
-      "2025-01-20 23:41:37,928 - INFO - Neural Network trained successfully in 0.69 seconds.\n",
-      "2025-01-20 23:41:37,929 - INFO - Training Gradient Boosting...\n",
-      "2025-01-20 23:41:38,156 - INFO - Gradient Boosting trained successfully in 0.22 seconds.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model Training Metrics:\n",
-      "                                 Model  Accuracy  Precision  F1-Score  Runtime (s)\n",
-      "                   Logistic Regression  0.958091   0.959848  0.937872     0.017967\n",
-      "                   K-Nearest Neighbors  0.958964   0.948760  0.941506     0.001619\n",
-      "Support Vector Machine (Linear Kernel)  0.957800   0.917380  0.937154     0.007353\n",
-      "   Support Vector Machine (RBF Kernel)  0.958382   0.960115  0.938581     0.035789\n",
-      "                        Neural Network  0.964785   0.960093  0.954771     0.687653\n",
-      "                     Gradient Boosting  0.966822   0.966917  0.956661     0.222741\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_metrics_data = train_and_evaluate(models, X_train, y_train)\n",
-    "\n",
-    "print(\"Model Training Metrics:\")\n",
-    "print(train_metrics_data.to_string(index=False))"
-   ]
-  }
- ],
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