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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f56409cf-1f4b-4380-9c0f-28013e7f463f",
+   "metadata": {},
+   "source": [
+    "### Diabetes-Healthcare Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6484283c-dfc6-4ad0-8bf0-871fdefe3d85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing Libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import dtale\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "from sklearn.model_selection import KFold, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.metrics import roc_auc_score, roc_curve, classification_report, f1_score, precision_score, recall_score\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import StratifiedKFold\n",
+    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+    "%matplotlib inline\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "import logging\n",
+    "logging.disable(logging.CRITICAL)\n",
+    "import optuna\n",
+    "from sklearn.model_selection import cross_val_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "60709447-9642-4478-bd46-43f7275a8dc8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"Healthcare-Diabetes.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a197665a-d240-4de6-88b0-c5b107acf065",
+   "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>Id</th>\n",
+       "      <th>Pregnancies</th>\n",
+       "      <th>Glucose</th>\n",
+       "      <th>BloodPressure</th>\n",
+       "      <th>SkinThickness</th>\n",
+       "      <th>Insulin</th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>DiabetesPedigreeFunction</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Outcome</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>148</td>\n",
+       "      <td>72</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.6</td>\n",
+       "      <td>0.627</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>85</td>\n",
+       "      <td>66</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>0.351</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>183</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>0.672</td>\n",
+       "      <td>32</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>89</td>\n",
+       "      <td>66</td>\n",
+       "      <td>23</td>\n",
+       "      <td>94</td>\n",
+       "      <td>28.1</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>137</td>\n",
+       "      <td>40</td>\n",
+       "      <td>35</td>\n",
+       "      <td>168</td>\n",
+       "      <td>43.1</td>\n",
+       "      <td>2.288</td>\n",
+       "      <td>33</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Id  Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
+       "0   1            6      148             72             35        0  33.6   \n",
+       "1   2            1       85             66             29        0  26.6   \n",
+       "2   3            8      183             64              0        0  23.3   \n",
+       "3   4            1       89             66             23       94  28.1   \n",
+       "4   5            0      137             40             35      168  43.1   \n",
+       "\n",
+       "   DiabetesPedigreeFunction  Age  Outcome  \n",
+       "0                     0.627   50        1  \n",
+       "1                     0.351   31        0  \n",
+       "2                     0.672   32        1  \n",
+       "3                     0.167   21        0  \n",
+       "4                     2.288   33        1  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d8b81900-5379-4685-9cdb-d4c1c18bb453",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2768, 10)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6d235e98-7a43-4d85-a68f-175de24fe590",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Id                          False\n",
+       "Pregnancies                 False\n",
+       "Glucose                     False\n",
+       "BloodPressure               False\n",
+       "SkinThickness               False\n",
+       "Insulin                     False\n",
+       "BMI                         False\n",
+       "DiabetesPedigreeFunction    False\n",
+       "Age                         False\n",
+       "Outcome                     False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Checking Null Values --None\n",
+    "df.isnull().any()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fd288850-4531-4aa8-b24f-06209efb584b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop(columns = ['Id'],inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d8c755af-3723-4eab-9553-543691c53d41",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"100%\"\n",
+       "            height=\"475\"\n",
+       "            src=\"http://LAPTOP-DKEVVHE0:40000/dtale/iframe/1\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x19b9dace0c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Uni/Bi/Multivariate- Analysis\n",
+    "dtale.show(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "94317b41-7d71-44c2-8e6a-840fb4c70c8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
+       "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e6b56ac6-9afa-4931-b74a-e460189cb125",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standardization (Not required for Gradient Descent)\n",
+    "scaler = StandardScaler()\n",
+    "columns_to_standardize = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness',\n",
+    "                          'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']\n",
+    "standardized_data = scaler.fit_transform(df[columns_to_standardize])\n",
+    "\n",
+    "# Converting the standardized data back to a DataFrame\n",
+    "df_standardized = pd.DataFrame(standardized_data, columns=columns_to_standardize)\n",
+    "\n",
+    "# Adding the target column to the standardized DataFrame\n",
+    "df_standardized[\"Outcome\"] = df[\"Outcome\"].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "50ad7847-bedf-4d4d-853f-7ce1aeb5d045",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = df_standardized.copy(deep = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d11e60c0-7af1-48e1-b2b5-d46555c202fc",
+   "metadata": {},
+   "source": [
+    "### K-FOLD BASE MODEL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0ae6918f-871a-462e-8f11-34314f42295a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df1.drop(columns=['Outcome'])  # Features\n",
+    "y = df1['Outcome']                # Target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "47dedd54-86c4-4f86-9d53-53ce62b05722",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "K-Fold F1 Scores: [0.807799442896936, 0.7897727272727273, 0.8364611260053619, 0.8245125348189415, 0.8044692737430168]\n",
+      "K-Fold Precision Scores: [0.8630952380952381, 0.8633540372670807, 0.8524590163934426, 0.8757396449704142, 0.8571428571428571]\n",
+      "K-Fold Recall Scores: [0.7591623036649214, 0.7277486910994765, 0.8210526315789474, 0.7789473684210526, 0.7578947368421053]\n",
+      "K-Fold AUC Scores: [0.952605541372795, 0.953038235760749, 0.943984962406015, 0.9481078729882557, 0.9456140350877194]\n",
+      "Mean F1 Score (K-Fold): 0.8126030209473967\n",
+      "Mean AUC Score (K-Fold): 0.9486701295231068\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize the Gradient Boosting Classifier\n",
+    "model = GradientBoostingClassifier(random_state=42)\n",
+    "\n",
+    "# Stratified K-Fold\n",
+    "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
+    "\n",
+    "f1_scores_kfold = []\n",
+    "precision_scores_kfold = []\n",
+    "recall_scores_kfold = []\n",
+    "auc_scores_kfold = []\n",
+    "\n",
+    "# For Confusion Matrix Display\n",
+    "all_conf_matrices = []\n",
+    "\n",
+    "for train_index, test_index in skf.split(X, y):\n",
+    "    # Splitting the data\n",
+    "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
+    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
+    "    \n",
+    "    # Train the model\n",
+    "    model.fit(X_train, y_train)\n",
+    "    \n",
+    "    # Predictions\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
+    "    \n",
+    "    # Metrics\n",
+    "    f1_scores_kfold.append(f1_score(y_test, y_pred))\n",
+    "    precision_scores_kfold.append(precision_score(y_test, y_pred))\n",
+    "    recall_scores_kfold.append(recall_score(y_test, y_pred))\n",
+    "    auc_scores_kfold.append(roc_auc_score(y_test, y_pred_proba))\n",
+    "    \n",
+    "    # Save confusion matrix for the fold\n",
+    "    conf_matrix = confusion_matrix(y_test, y_pred)\n",
+    "    all_conf_matrices.append(conf_matrix)\n",
+    "    \n",
+    "    # ROC Curve for one fold\n",
+    "    fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
+    "    plt.plot(fpr, tpr, label=f\"K-Fold ROC Curve\")\n",
+    "\n",
+    "# Plot AUC-ROC Curve for K-Fold\n",
+    "plt.xlabel(\"False Positive Rate\")\n",
+    "plt.ylabel(\"True Positive Rate\")\n",
+    "plt.title(\"K-Fold AUC-ROC Curve\")\n",
+    "plt.legend(loc=\"lower right\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Plot Confusion Matrix for the last fold as an example\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=all_conf_matrices[-1], display_labels=model.classes_)\n",
+    "disp.plot(cmap='Blues', values_format='d')\n",
+    "plt.title(\"Confusion Matrix for Last Fold in K-Fold Cross-Validation\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Print Metrics\n",
+    "print(f\"K-Fold F1 Scores: {f1_scores_kfold}\")\n",
+    "print(f\"K-Fold Precision Scores: {precision_scores_kfold}\")\n",
+    "print(f\"K-Fold Recall Scores: {recall_scores_kfold}\")\n",
+    "print(f\"K-Fold AUC Scores: {auc_scores_kfold}\")\n",
+    "print(f\"Mean F1 Score (K-Fold): {np.mean(f1_scores_kfold)}\")\n",
+    "print(f\"Mean AUC Score (K-Fold): {np.mean(auc_scores_kfold)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b9e433e-dbf4-4552-a65f-84e1764a08a1",
+   "metadata": {},
+   "source": [
+    "### Bootstrap Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1fe832a2-8da1-4617-ab65-793d4cfc9ade",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bootstrapping F1 Scores: [0.7856025039123631, 0.7717717717717718, 0.8612903225806452, 0.7678571428571429, 0.7981366459627329]...\n",
+      "Bootstrapping Precision Scores: [0.8311258278145696, 0.8210862619808307, 0.902027027027027, 0.819047619047619, 0.8371335504885994]...\n",
+      "Bootstrapping Recall Scores: [0.744807121661721, 0.7280453257790368, 0.8240740740740741, 0.7226890756302521, 0.7626112759643917]...\n",
+      "Bootstrapping AUC Scores: [0.9269081473266255, 0.9242285550672642, 0.9561955723428812, 0.918435459290099, 0.9466034417729629]...\n",
+      "Mean F1 Score (Bootstrapping): 0.8013186055213821\n",
+      "Mean AUC Score (Bootstrapping): 0.9379810966521147\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_bootstrap_samples = 50\n",
+    "f1_scores_bootstrap = []\n",
+    "precision_scores_bootstrap = []\n",
+    "recall_scores_bootstrap = []\n",
+    "auc_scores_bootstrap = []\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))  # Set figure size for better visualization\n",
+    "\n",
+    "# Store confusion matrices for visualization later\n",
+    "confusion_matrices = []\n",
+    "\n",
+    "for i in range(n_bootstrap_samples):\n",
+    "    # Create Bootstrap Sample\n",
+    "    indices = np.random.choice(range(len(X)), size=len(X), replace=True)\n",
+    "    X_train, y_train = X.iloc[indices], y.iloc[indices]\n",
+    "    \n",
+    "    # Out-of-Bag (OOB) Data\n",
+    "    oob_indices = list(set(range(len(X))) - set(indices))\n",
+    "    if len(oob_indices) == 0 or len(y_train.unique()) < 2:\n",
+    "        continue  # Skip iteration if no OOB data or only one class\n",
+    "    X_test, y_test = X.iloc[oob_indices], y.iloc[oob_indices]\n",
+    "    \n",
+    "    # Train the model\n",
+    "    model.fit(X_train, y_train)\n",
+    "    \n",
+    "    # Predictions\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
+    "    \n",
+    "    # Metrics\n",
+    "    f1_scores_bootstrap.append(f1_score(y_test, y_pred))\n",
+    "    precision_scores_bootstrap.append(precision_score(y_test, y_pred))\n",
+    "    recall_scores_bootstrap.append(recall_score(y_test, y_pred))\n",
+    "    auc_scores_bootstrap.append(roc_auc_score(y_test, y_pred_proba))\n",
+    "    \n",
+    "    # Save confusion matrix for the last bootstrap sample\n",
+    "    conf_matrix = confusion_matrix(y_test, y_pred)\n",
+    "    confusion_matrices.append(conf_matrix)\n",
+    "    \n",
+    "    # Plot ROC Curve only for the first iteration with a label\n",
+    "    fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
+    "    if i == 0:\n",
+    "        plt.plot(fpr, tpr, label=\"Bootstrap ROC Curve\", alpha=0.3)\n",
+    "    else:\n",
+    "        plt.plot(fpr, tpr, alpha=0.3)\n",
+    "\n",
+    "# Finalize and Display AUC-ROC Plot\n",
+    "plt.xlabel(\"False Positive Rate\")\n",
+    "plt.ylabel(\"True Positive Rate\")\n",
+    "plt.title(\"Bootstrapping AUC-ROC Curve\")\n",
+    "plt.legend(loc=\"lower right\")  # Show single legend\n",
+    "plt.show()\n",
+    "\n",
+    "# Display Confusion Matrix for the last bootstrap sample\n",
+    "if confusion_matrices:\n",
+    "    disp = ConfusionMatrixDisplay(confusion_matrix=confusion_matrices[-1], display_labels=model.classes_)\n",
+    "    disp.plot(cmap='Blues', values_format='d')\n",
+    "    plt.title(\"Confusion Matrix for Last Bootstrap Sample\")\n",
+    "    plt.show()\n",
+    "\n",
+    "# Print Metrics\n",
+    "print(f\"Bootstrapping F1 Scores: {f1_scores_bootstrap[:5]}...\")  # Showing first 5 scores\n",
+    "print(f\"Bootstrapping Precision Scores: {precision_scores_bootstrap[:5]}...\")\n",
+    "print(f\"Bootstrapping Recall Scores: {recall_scores_bootstrap[:5]}...\")\n",
+    "print(f\"Bootstrapping AUC Scores: {auc_scores_bootstrap[:5]}...\")\n",
+    "print(f\"Mean F1 Score (Bootstrapping): {np.mean(f1_scores_bootstrap)}\")\n",
+    "print(f\"Mean AUC Score (Bootstrapping): {np.mean(auc_scores_bootstrap)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6a81e32-8704-4ec5-a43f-3dfd15c877e4",
+   "metadata": {},
+   "source": [
+    "### Finding Best Parameters Using Bayesian Optimization Hyperparameter Tuning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1bc78135-1c70-4ad4-89d1-b017cb558819",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e6c53d049a14d20b39e81c70e9f2956",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/20 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best Parameters: {'n_estimators': 96, 'learning_rate': 0.08020404204698642, 'max_depth': 7, 'min_samples_split': 5, 'min_samples_leaf': 2, 'subsample': 0.9877960074080036}\n",
+      "Best F1 Score: 0.9895500611222839\n"
+     ]
+    }
+   ],
+   "source": [
+    "def objective(trial):\n",
+    "    params = {\n",
+    "        'n_estimators': trial.suggest_int('n_estimators', 50, 200),  # Reduced range\n",
+    "        'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.2),  # Reduced range\n",
+    "        'max_depth': trial.suggest_int('max_depth', 3, 7),  # Reduced range\n",
+    "        'min_samples_split': trial.suggest_int('min_samples_split', 2, 5),  # Reduced range\n",
+    "        'min_samples_leaf': trial.suggest_int('min_samples_leaf', 1, 4),  # Reduced range\n",
+    "        'subsample': trial.suggest_float('subsample', 0.5, 1.0)\n",
+    "    }\n",
+    "    model = GradientBoostingClassifier(random_state=42, **params)\n",
+    "    \n",
+    "    # Use reduced CV folds and smaller dataset for speed\n",
+    "    scores = cross_val_score(model, X, y, scoring='f1', cv=3, n_jobs=-1)\n",
+    "    return scores.mean()\n",
+    "\n",
+    "# Optimize\n",
+    "study = optuna.create_study(direction='maximize')\n",
+    "study.optimize(objective, n_trials=20, timeout=600, show_progress_bar=True)  # Smaller trials with timeout\n",
+    "\n",
+    "# Print best parameters\n",
+    "print(f\"Best Parameters: {study.best_params}\")\n",
+    "print(f\"Best F1 Score: {study.best_value}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "513d4646-9abb-4a75-a761-d0a6986598f2",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Hyperparameter Tuned K-Fold Sampled Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "7a128885-a5be-44a3-aed6-c4813f00ab29",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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iIiIyKExuiIiIyKAwuSEiIiKDwuSGiIiIDAqTGyIiIjIoTG6IiIjIoDC5ISIiIoPC5IaIiIgMCpMbIiIiMijVmtz8+OOP6NmzJ9zc3KBQKLBt27Yntjl06BBatmwJCwsLNGjQAF988UXlB0pERETPjWpNbnJzc9G8eXOsWLFCp/rp6eno0aMHOnTogDNnzuB///d/MW7cOGzZsqWSIyUiIqLnRbUunBkcHIzg4GCd63/xxReoV68eoqKiAACNGzdGcnIyFi9ejL59+1ZSlPoRERQXlqh+vl98v7wnAorzKySegmLlU5+HiIiqjogA+U//GVCdnNzqwcTUtFqu/VytCn78+HEEBgaqlQUFBWHt2rUoKiqCqZaHWFBQgIKCAtV+Tk5OpcUnIkhYdBqZl7Ir7RpERETPg74zAFePhtVy7efqheLMzEy4uLiolbm4uKC4uBg3b97U2iYiIgJ2dnaqzd3dvdLiKy4sYWJDRERUzZ6rnhsAUCgUavsiorW81IwZMzBp0iTVfk5OTuUlOP8/FgDo5xiOnu6OAIBtf2XAXAQQ4O8DDii+Uz3ddERE9HxxTIiDwtKiusMoFye3etV27ecquXF1dUVmZqZaWVZWFkxMTODo6Ki1jbm5OczNzasiPEhhrurnv7faI7rkwfs2/8D+4VowRmGVxENERM8vy1dfhWsjnzL/8U5le66Sm3bt2mHnzp1qZd9//z38/Py0vm9T1fKKinWqZ+rtDdc1XwBP+oM1sXxyHR1YmhrzPw4ioueMwtKS/+8up2pNbu7du4c///xTtZ+eno6UlBQ4ODigXr16mDFjBv766y988803AIBRo0ZhxYoVmDRpEkaMGIHjx49j7dq1WL9+fXXdgoqIIGv0WMBhuKps+DhjFJgCiW/tR00La9UfKf9giYiIKk+1vlCcnJwMX19f+Pr6AgAmTZoEX19fzJo1CwCQkZGBq1evqurXr18fe/bswcGDB9GiRQvMmzcPn3/++TMxDVzu30fBb+dV+yZNfZBjBRSYKWBlawNja2sYWVnByMqKiQ0REVElUog89BbsCyAnJwd2dnbIzs6Gra1thZ1XmZuL/7y3GfdqPHhZue/cRghI7A4A+Cn0J1iZWlXYtYiIiF40+nx+P1dTwZ9lxYUlqsTGyfgSTMz4aImIiKoDP4ErQW/7Dzn0REREVE2Y3FQQ9dG9F2qkj4iI6JnC5KaC3H9k/SYLU+NqioSIiOjFxuSmknBYioiIqHowuSEiIiKDwuSGiIiIDAqTGyIiIjIoTG6IiIjIoDC5ISIiIoPC5IaIiIgMCpMbIiIiMihMboiIiMigMLkhIiIig8LkhoiIiAwKkxsiIiIyKExuiIiIyKAwuSEiIiKDwuSGiIiIDAqTGyIiIjIoTG6IiIjIoDC5ISIiIoPC5IaIiIgMCpMbIiIiMihMboiIiMigMLkhIiIig8LkhoiIiAwKkxsiIiIyKExuiIiIyKAwuSEiIiKDwuSGiIiIDAqTGyIiIjIoTG6IiIjIoDC5ISIiIoPC5IaIiIgMCpMbIiIiMihMboiIiMigMLkhIiIig8LkhoiIiAwKkxsiIiIyKExuiIiIyKAwuSEiIiKDwuSGiIiIDAqTGyIiIjIoTG6IiIjIoDC5ISIiIoPC5IaIiIgMCpMbIiIiMihMboiIiMigMLkhIiIig8LkhoiIiAwKkxsiIiIyKExuiIiIyKAwuSEiIiKDwuSGiIiIDAqTGyIiIjIo1Z7crFq1CvXr14eFhQVatmyJw4cPP7Z+bGwsmjdvDisrK9SuXRvh4eG4detWFUVLREREz7pqTW7i4+MxYcIEzJw5E2fOnEGHDh0QHByMq1evaq1/5MgRDBo0CMOGDcNvv/2GTZs24eTJkxg+fHgVR05ERETPqmpNbpYuXYphw4Zh+PDhaNy4MaKiouDu7o7Vq1drrX/ixAl4enpi3LhxqF+/Pl577TWMHDkSycnJVRw5ERERPauqLbkpLCzEqVOnEBgYqFYeGBiIY8eOaW3Tvn17XL9+HXv27IGI4O+//8bmzZvxxhtvlHmdgoIC5OTkqG1ERERkuKotubl58yaUSiVcXFzUyl1cXJCZmam1Tfv27REbG4uQkBCYmZnB1dUV9vb2WL58eZnXiYiIgJ2dnWpzd3ev0PsgIiKiZ0u1v1CsUCjU9kVEo6zU+fPnMW7cOMyaNQunTp1CYmIi0tPTMWrUqDLPP2PGDGRnZ6u2a9euVWj8RERE9Gwxqa4LOzk5wdjYWKOXJisrS6M3p1RERAT8/f0xdepUAECzZs1gbW2NDh064JNPPkHt2rU12pibm8Pc3Lzib4CIiIieSdXWc2NmZoaWLVsiKSlJrTwpKQnt27fX2iYvLw9GRuohGxsbA3jQ40NERERUrcNSkyZNwpo1a/D111/jwoULmDhxIq5evaoaZpoxYwYGDRqkqt+zZ08kJCRg9erVuHTpEo4ePYpx48ahdevWcHNzq67bICIiomdItQ1LAUBISAhu3bqFjz/+GBkZGWjatCn27NkDDw8PAEBGRobad94MGTIEd+/exYoVKzB58mTY29ujS5cuWLBgQXXdAhERET1jFPKCjefk5OTAzs4O2dnZsLW1rbDz/pP1D+JmnQEADHccAOXM39BmU2cAwE+hP8HK1KrCrkVERPSi0efzu9pnSxERERFVJCY3REREZFCY3BAREZFBYXJDREREBoXJDRERERkUJjdERERkUJjcEBERkUFhckNEREQGhckNERERGRQmN0RERGRQmNwQERGRQWFyQ0RERAaFyQ0REREZFCY3REREZFCY3BAREZFBYXJDREREBoXJDRERERkUJjdERERkUEyqOwBDlKdQoKT4fnWHQURE9EJiclNBRET1c3BdN+Ru7VGN0RAREb24OCxVQQpLCrWW+9byhaWJZRVHQ0RE9OJiz00l2PZXBmxn/AKYWsHSxBIKhaK6QyIiInphMLmpBOYisDKxBEytqjsUIiKiFw6HpYiIiMigMLkhIiIig8LkhoiIiAwKkxsiIiIyKExuiIiIyKAwuSEiIiKDwuSGiIiIDAqTGyIiIjIoTG6IiIjIoDC5ISIiIoNSruSmuLgY+/btw7///W/cvXsXAHDjxg3cu3evQoMjIiIi0pfea0tduXIF3bt3x9WrV1FQUICAgADUqFEDCxcuRH5+Pr744ovKiJOIiIhIJ3r33IwfPx5+fn74559/YGlpqSrv06cPfvjhhwoNjoiIiEhfevfcHDlyBEePHoWZmZlauYeHB/76668KC4yIiIioPPTuuSkpKYFSqdQov379OmrUqFEhQRERERGVl97JTUBAAKKiolT7CoUC9+7dw+zZs9GjR4+KjI2IiIhIb3oPS0VGRqJz587w8fFBfn4+QkNDkZaWBicnJ6xfv74yYiQiIiLSmd7JjZubG1JSUrBhwwacOnUKJSUlGDZsGMLCwtReMCYiIiKqDnonNz/++CPat2+P8PBwhIeHq8qLi4vx448/4vXXX6/QAImIiIj0ofc7N507d8bt27c1yrOzs9G5c+cKCYqIiIiovPRObkQECoVCo/zWrVuwtraukKCIiIiIykvnYam33noLwIPZUUOGDIG5ubnqmFKpxLlz59C+ffuKj5CIiIhIDzonN3Z2dgAe9NzUqFFD7eVhMzMztG3bFiNGjKj4CImIiIj0oHNyEx0dDQDw9PTElClTOARFREREzyS9Z0vNnj27MuIgIiIiqhB6JzcAsHnzZmzcuBFXr15FYWGh2rHTp09XSGBERERE5aH3bKnPP/8c4eHhqFWrFs6cOYPWrVvD0dERly5dQnBwcGXESERERKQzvZObVatW4csvv8SKFStgZmaGadOmISkpCePGjUN2dnZlxEhERESkM72Tm6tXr6qmfFtaWuLu3bsAgIEDB3JtKSIiIqp2eic3rq6uuHXrFgDAw8MDJ06cAACkp6dDRCo2OiIiIiI96Z3cdOnSBTt37gQADBs2DBMnTkRAQABCQkLQp0+fCg+QiIiISB96z5b68ssvUVJSAgAYNWoUHBwccOTIEfTs2ROjRo2q8ACJiIiI9KF3cmNkZAQjo//r8OnXrx/69esHAPjrr79Qp06diouOiIiISE96D0tpk5mZibFjx8LLy0vvtqtWrUL9+vVhYWGBli1b4vDhw4+tX1BQgJkzZ8LDwwPm5uZo2LAhvv766/KGTkRERAZG5+Tmzp07CAsLg7OzM9zc3PD555+jpKQEs2bNQoMGDXDixAm9k4z4+HhMmDABM2fOxJkzZ9ChQwcEBwfj6tWrZbbp168ffvjhB6xduxZ//PEH1q9fj5dfflmv6xIREZHh0nlY6n//93/x448/YvDgwUhMTMTEiRORmJiI/Px87N27Fx07dtT74kuXLsWwYcMwfPhwAEBUVBS+++47rF69GhERERr1ExMTcejQIVy6dAkODg4AHqx1RURERFRK556b3bt3Izo6GosXL8aOHTsgIvD29sb+/fvLldgUFhbi1KlTCAwMVCsPDAzEsWPHtLbZsWMH/Pz8sHDhQtSpUwfe3t6YMmUK7t+/X+Z1CgoKkJOTo7YRERGR4dK55+bGjRvw8fEBADRo0AAWFhaqHpfyuHnzJpRKJVxcXNTKXVxckJmZqbXNpUuXcOTIEVhYWGDr1q24efMmxowZg9u3b5c5JBYREYG5c+eWO04iIiJ6vujcc1NSUgJTU1PVvrGxMaytrZ86AIVCobYvIhplD8egUCgQGxuL1q1bo0ePHli6dCliYmLK7L2ZMWMGsrOzVdu1a9eeOmYiIiJ6dunccyMiGDJkCMzNzQEA+fn5GDVqlEaCk5CQoNP5nJycYGxsrNFLk5WVpdGbU6p27dqoU6cO7OzsVGWNGzeGiOD69et46aWXNNqYm5urYiYiIiLDp3PPzeDBg1GrVi3Y2dnBzs4O7777Ltzc3FT7pZuuzMzM0LJlSyQlJamVJyUlqdauepS/vz9u3LiBe/fuqcpSU1NhZGSEunXr6nxtIiIiMlw699xER0dX+MUnTZqEgQMHws/PD+3atcOXX36Jq1evqr7peMaMGfjrr7/wzTffAABCQ0Mxb948hIeHY+7cubh58yamTp2KoUOHwtLSssLjIyIioueP3t9QXJFCQkJw69YtfPzxx8jIyEDTpk2xZ88eeHh4AAAyMjLUvvPGxsYGSUlJGDt2LPz8/ODo6Ih+/frhk08+qa5bICIiomeMQl6wpbxzcnJgZ2eH7Oxs2NraVth5MzMysWXueQBAqFMoas66CJg9/QvXREREpN/nd4Usv0BERET0rGByQ0RERAaFyQ0REREZlHIlN99++y38/f3h5uaGK1euAHiwLtT27dsrNDgiIiIifemd3KxevRqTJk1Cjx49cOfOHSiVSgCAvb09oqKiKjo+IiIiIr3ondwsX74cX331FWbOnAljY2NVuZ+fH3755ZcKDY6IiIhIX3onN+np6fD19dUoNzc3R25uboUERURERFReeic39evXR0pKikb53r17VauGExEREVUXvb+heOrUqXj//feRn58PEcHPP/+M9evXIyIiAmvWrKmMGImIiIh0pndyEx4ejuLiYkybNg15eXkIDQ1FnTp1sGzZMvTv378yYiQiIiLSWbnWlhoxYgRGjBiBmzdvoqSkBLVq1arouIiIiIjKRe93bubOnYuLFy8CAJycnJjYEBER0TNF7+Rmy5Yt8Pb2Rtu2bbFixQr897//rYy4iIiIiMpF7+Tm3LlzOHfuHLp06YKlS5eiTp066NGjB+Li4pCXl1cZMRIRERHprFzLLzRp0gTz58/HpUuXcODAAdSvXx8TJkyAq6trRcdHREREpJenXjjT2toalpaWMDMzQ1FRUUXERERERFRu5Upu0tPT8emnn8LHxwd+fn44ffo05syZg8zMzIqOj4iIiEgvek8Fb9euHX7++We88sorCA8PV33PDREREdGzQO/kpnPnzlizZg2aNGlSGfEQERERPRW9k5v58+dXRhxEREREFUKn5GbSpEmYN28erK2tMWnSpMfWXbp0aYUERkRERFQeOiU3Z86cUc2EOnPmTKUGRERERPQ0dEpuDhw4oPVnIiIiomeN3lPBhw4dirt372qU5+bmYujQoRUSFBEREVF56Z3crFu3Dvfv39cov3//Pr755psKCYqIiIiovHSeLZWTkwMRgYjg7t27sLCwUB1TKpXYs2cPVwgnIiKiaqdzcmNvbw+FQgGFQgFvb2+N4wqFAnPnzq3Q4IiIiIj0pXNyc+DAAYgIunTpgi1btsDBwUF1zMzMDB4eHnBzc6uUIImIiIh0pXNy07FjRwAP1pWqV68eFApFpQVFREREVF46JTfnzp1D06ZNYWRkhOzsbPzyyy9l1m3WrFmFBUdERESkL52SmxYtWiAzMxO1atVCixYtoFAoICIa9RQKBZRKZYUHSURERKQrnZKb9PR0ODs7q34mIiIielbplNx4eHho/ZmIiIjoWVOuL/HbvXu3an/atGmwt7dH+/btceXKlQoNjoiIiEhfeic38+fPh6WlJQDg+PHjWLFiBRYuXAgnJydMnDixwgMkIiIi0ofOU8FLXbt2DV5eXgCAbdu24e2338Z7770Hf39/dOrUqaLjIyIiItKL3j03NjY2uHXrFgDg+++/R7du3QAAFhYWWtecIiIiIqpKevfcBAQEYPjw4fD19UVqaireeOMNAMBvv/0GT0/Pio6PiIiISC9699ysXLkS7dq1w3//+19s2bIFjo6OAIBTp05hwIABFR4gERERkT707rmxt7fHihUrNMq5aCYRERE9C/RObgDgzp07WLt2LS5cuACFQoHGjRtj2LBhsLOzq+j4iIiIiPSi97BUcnIyGjZsiMjISNy+fRs3b95EZGQkGjZsiNOnT1dGjEREREQ607vnZuLEiXjzzTfx1VdfwcTkQfPi4mIMHz4cEyZMwI8//ljhQRIRERHpSu/kJjk5WS2xAQATExNMmzYNfn5+FRocERERkb70HpaytbXF1atXNcqvXbuGGjVqVEhQREREROWld3ITEhKCYcOGIT4+HteuXcP169exYcMGDB8+nFPBiYiIqNrpPSy1ePFiKBQKDBo0CMXFxQAAU1NTjB49Gp999lmFB0hERESkD72TGzMzMyxbtgwRERG4ePEiRAReXl6wsrKqjPiIiIiI9KLzsFReXh7ef/991KlTB7Vq1cLw4cNRu3ZtNGvWjIkNERERPTN0Tm5mz56NmJgYvPHGG+jfvz+SkpIwevToyoyNiIiISG86D0slJCRg7dq16N+/PwDg3Xffhb+/P5RKJYyNjSstQCIiIiJ96Nxzc+3aNXTo0EG137p1a5iYmODGjRuVEhgRERFReeic3CiVSpiZmamVmZiYqGZMERERET0LdB6WEhEMGTIE5ubmqrL8/HyMGjUK1tbWqrKEhISKjZCIiIhIDzonN4MHD9Yoe/fddys0GCIiIqKnpXNyEx0dXZlxEBEREVUIvZdfqGirVq1C/fr1YWFhgZYtW+Lw4cM6tTt69ChMTEzQokWLyg2QiIiInivVmtzEx8djwoQJmDlzJs6cOYMOHTogODhY68KcD8vOzsagQYPQtWvXKoqUiIiInhfVmtwsXboUw4YNw/Dhw9G4cWNERUXB3d0dq1evfmy7kSNHIjQ0FO3atauiSImIiOh5UW3JTWFhIU6dOoXAwEC18sDAQBw7dqzMdtHR0bh48SJmz55d2SESERHRc0jvhTMrys2bN6FUKuHi4qJW7uLigszMTK1t0tLSMH36dBw+fBgmJrqFXlBQgIKCAtV+Tk5O+YMmIiKiZ165em6+/fZb+Pv7w83NDVeuXAEAREVFYfv27XqfS6FQqO2LiEYZ8OBLBENDQzF37lx4e3vrfP6IiAjY2dmpNnd3d71jJCIioueH3snN6tWrMWnSJPTo0QN37tyBUqkEANjb2yMqKkrn8zg5OcHY2FijlyYrK0ujNwcA7t69i+TkZPzrX/+CiYkJTExM8PHHH+Ps2bMwMTHB/v37tV5nxowZyM7OVm3Xrl3T/WaJiIjouaN3crN8+XJ89dVXmDlzptqCmX5+fvjll190Po+ZmRlatmyJpKQktfKkpCS0b99eo76trS1++eUXpKSkqLZRo0ahUaNGSElJQZs2bbRex9zcHLa2tmobERERGS6937lJT0+Hr6+vRrm5uTlyc3P1OtekSZMwcOBA+Pn5oV27dvjyyy9x9epVjBo1CsCDXpe//voL33zzDYyMjNC0aVO19rVq1YKFhYVGOREREb249E5u6tevj5SUFHh4eKiV7927Fz4+PnqdKyQkBLdu3cLHH3+MjIwMNG3aFHv27FGdOyMj44nfeUNERET0ML2Tm6lTp+L9999Hfn4+RAQ///wz1q9fj4iICKxZs0bvAMaMGYMxY8ZoPRYTE/PYtnPmzMGcOXP0viYREREZLr2Tm/DwcBQXF2PatGnIy8tDaGgo6tSpg2XLlqF///6VESMRERGRzsr1PTcjRozAiBEjcPPmTZSUlKBWrVoVHRcRERFRuTzVl/g5OTlVVBxEREREFaJcLxRr+5K9UpcuXXqqgIiIiIieht7JzYQJE9T2i4qKcObMGSQmJmLq1KkVFRcRERFRueid3IwfP15r+cqVK5GcnPzUARERERE9jQpbFTw4OBhbtmypqNMRERERlUuFJTebN2+Gg4NDRZ2OiIiIqFz0Hpby9fVVe6FYRJCZmYn//ve/WLVqVYUGR0RERKQvvZOb3r17q+0bGRnB2dkZnTp1wssvv1xRcRERERGVi17JTXFxMTw9PREUFARXV9fKiomIiIio3PR658bExASjR49GQUFBZcVDRERE9FT0fqG4TZs2OHPmTGXEQkRERPTU9H7nZsyYMZg8eTKuX7+Oli1bwtraWu14s2bNKiw4IiIiIn3pnNwMHToUUVFRCAkJAQCMGzdOdUyhUEBEoFAooFQqKz5KIiIiIh3pnNysW7cOn332GdLT0yszHiIiIqKnonNyIyIAAA8Pj0oLhoiIiOhp6fVC8eNWAyciIiJ6Fuj1QrG3t/cTE5zbt28/VUBERERET0Ov5Gbu3Lmws7OrrFiIiIiInppeyU3//v1Rq1atyoqFiIiI6Knp/M4N37chIiKi54HOyU3pbCkiIiKiZ5nOw1IlJSWVGQcRERFRhdB7bSkiIiKiZxmTGyIiIjIoTG6IiIjIoDC5ISIiIoOi1/fcEBHR802pVKKoqKi6wyDSyszMDEZGT9/vwuSGiOgFICLIzMzEnTt3qjsUojIZGRmhfv36MDMze6rzMLkhInoBlCY2tWrVgpWVFb+YlZ45JSUluHHjBjIyMlCvXr2n+htlckNEZOCUSqUqsXF0dKzucIjK5OzsjBs3bqC4uBimpqblPg9fKCYiMnCl79hYWVlVcyREj1c6HKVUKp/qPExuiIheEByKomddRf2NMrkhIiIig8LkhoiI6P87ePAgFArFY2eVxcTEwN7evspiIv0xuSEiomfWkCFD0Lt3b7WyzZs3w8LCAgsXLtSof/nyZSgUCo3t3XffraKIH3j42jY2NmjevDliYmI06imVSkRGRqJZs2awsLCAvb09goODcfToUY26hYWFWLhwIZo3bw4rKys4OTnB398f0dHRj/3uIhHBl19+iTZt2sDGxgb29vbw8/NDVFQU8vLyKvK2nxmcLVVRRKo7AiIig7dmzRq8//77WLlyJYYPH15mvX379qFJkyaqfUtLy6oIT010dDS6d++O3NxcxMfHIzw8HLVr10ZQUBCAB0lH//79sW/fPixatAhdu3ZFTk4OVq5ciU6dOmHTpk2qxK6wsBBBQUE4e/Ys5s2bB39/f9ja2uLEiRNYvHgxfH190aJFC61xDBw4EAkJCfjwww+xYsUKODs74+zZs4iKioKnp6dG8qirwsLCp/4+mkojL5js7GwBINnZ2RV63ozLf8qKkT/IipE/yM3I10VKSir0/ERE5XX//n05f/683L9/v7pD0dvgwYOlV69eIiKyYMECMTc3l82bN5dZPz09XQDImTNntB7Pz8+XsWPHirOzs5ibm4u/v7/8/PPPquMHDhwQAPLPP/+oyqKjo8Xd3V0sLS2ld+/esnjxYrGzs3ts3ABk69atamUODg4yadIk1f6GDRsEgOzYsUOj/VtvvSWOjo5y79491b0bGRnJ6dOnNeoWFhaq6j0qPj5eAMi2bds0jpWUlMidO3dERKRjx44yfvx4teO9evWSwYMHq/Y9PDxk3rx5MnjwYLG1tZVBgwZJ27Zt5YMPPlBrl5WVJSYmJrJ//34RESkoKJCpU6eKm5ubWFlZSevWreXAgQNa433c36o+n98clqoERX3WApyVQETPMBFBXmFxlW9Szl7u6dOnY968edi1axf69u1b7vueNm0atmzZgnXr1uH06dPw8vJCUFAQbt++rbX+Tz/9hKFDh2LMmDFISUlB586d8cknn+h1TaVSiY0bN+L27dtq390SFxcHb29v9OzZU6PN5MmTcevWLSQlJQEAYmNj0a1bN/j6+mrUNTU1hbW1tdZrx8bGolGjRujVq5fGMYVCATs7O73uZdGiRWjatClOnTqFjz76CGFhYVi/fr3a7zU+Ph4uLi7o2LEjACA8PBxHjx7Fhg0bcO7cObzzzjvo3r070tLS9Lq2PjgsVSmY2BDRs+1+kRI+s76r8uue/zgIVmb6ffTs3bsX27dvxw8//IAuXbro1KZ9+/ZqaxQdPnwY3t7eWL16NWJiYhAcHAwA+Oqrr5CUlIS1a9di6tSpGudZtmwZgoKCMH36dACAt7c3jh07hsTExCfGMGDAABgbGyM/Px9KpRIODg5qQ2mpqalo3Lix1ral5ampqQCAtLQ0dOrUSad7f1haWhoaNWqkd7uydOnSBVOmTFHth4SEYOLEiThy5Ag6dOgA4EHSFhoaCiMjI1y8eBHr16/H9evX4ebmBgCYMmUKEhMTER0djfnz51dYbA9jzw0RET3TmjVrBk9PT8yaNQt3795VlQcHB8PGxgY2NjZq79cAD3oPUlJSVJuPjw8uXryIoqIi+Pv7q+qZmpqidevWuHDhgtZrX7hwAe3atVMre3S/LJGRkUhJSUFSUhJatGiByMhIeHl56XrbAP7ve19EpFzfAVPedmXx8/NT23d2dkZAQABiY2MBAOnp6Th+/DjCwsIAAKdPn4aIwNvbW/W7srGxwaFDh3Dx4sUKi+tR7LkhInoBWZoa4/zHQdVyXX3VqVMHW7ZsQefOndG9e3ckJiaiRo0aWLNmDe7fvw8AGl/V7+7urpFIlA6dPPph/7gEoLzDaADg6uoKLy8veHl5YdOmTfD19YWfnx98fHwAPOgFOn/+vNa2pcnWSy+9pKpbVgL2OLq2MzIy0rhXbTOwtA1/hYWFYfz48Vi+fDni4uLQpEkTNG/eHMCD9aKMjY1x6tQpGBur/+5tbGz0uRW9sOeGiOgFpFAoYGVmUuVbeXsR6tWrh0OHDiErKwuBgYHIyclBnTp1VMmDh4fHE8/h5eUFMzMzHDlyRFVWVFSE5OTkMoeHfHx8cOLECbWyR/d14eXlhb59+2LGjBmqsv79+yMtLQ07d+7UqL9kyRI4OjoiICAAABAaGop9+/bhzJkzGnWLi4uRm5ur9bqhoaFITU3F9u3bNY6JCLKzswE86IHJyMhQHVMqlfj11191urfevXsjPz8fiYmJiIuLU5t27+vrC6VSiaysLNXvqnRzdXXV6fzlweSGiIieC3Xr1sXBgwdx69YtBAYGqj6YdWVtbY3Ro0dj6tSpSExMxPnz5zFixAjk5eVh2LBhWtuMGzcOiYmJWLhwIVJTU7FixQqd3rfRZvLkydi5cyeSk5MBPEhu+vTpg8GDB2Pt2rW4fPkyzp07h5EjR2LHjh1Ys2aNqqdkwoQJ8Pf3R9euXbFy5UqcPXsWly5dwsaNG9GmTZsyX87t168fQkJCMGDAAERERCA5ORlXrlzBrl270K1bNxw4cADAg3dpdu/ejd27d+P333/HmDFjHvtFhg+ztrZGr1698NFHH+HChQsIDQ1VHfP29kZYWBgGDRqEhIQEpKen4+TJk1iwYAH27NlTrueokyfOpzIwVTEVPOPynxV6biKip2EoU8FL3bhxQxo1aiStWrVSm7It8uSp4Pfv35exY8eKk5OTzlPB165dK3Xr1hVLS0vp2bNnuaeCi4gEBARIcHCwar+oqEgWL14sTZo0EXNzc7G1tZWgoCA5fPiwRtv8/HyJiIiQV155RSwsLMTBwUH8/f0lJiZGioqKyoxFqVTK6tWrpVWrVmJlZSW2trbSsmVLWbZsmeTl5YnIg+nko0ePFgcHB6lVq5ZERERonQoeGRmp9Rq7d+8WAPL6669rHCssLJRZs2aJp6enmJqaiqurq/Tp00fOnTunUbeipoIrRF6sb5/LycmBnZ0dsrOzYWtrW2HnzbxyEVsirgAA+s7wgKtHwwo7NxHR08jPz0d6ejrq168PCwuL6g6HqEyP+1vV5/Obw1JERERkUJjcEBERkUFhckNEREQGhckNERERGRQmN0RERGRQmNwQERGRQWFyQ0RERAaFyQ0REREZFCY3REREZFCqPblZtWqV6psIW7ZsicOHD5dZNyEhAQEBAXB2doatrS3atWuH7777rgqjJSIiQ3bw4EEoFIrHrqsUExMDe3v7KouJ9FetyU18fDwmTJiAmTNn4syZM+jQoQOCg4Nx9epVrfV//PFHBAQEYM+ePTh16hQ6d+6Mnj17al0llYiInn9DhgxB79691co2b94MCwsLLFy4UKP+5cuXoVAoNLaHV6quCg9f28bGBs2bN0dMTIxGPaVSicjISDRr1gwWFhawt7dHcHAwjh49qlG3sLAQCxcuRPPmzWFlZQUnJyf4+/sjOjoaRUVFZcYiIvjyyy/Rpk0b2NjYwN7eHn5+foiKikJeXl5F3vYzw6Q6L7506VIMGzYMw4cPBwBERUXhu+++w+rVqxEREaFRPyoqSm1//vz52L59O3bu3AlfX9+qCJmIiKrRmjVr8P7772PlypWqzw5t9u3bhyZNmqj2LS0tqyI8NdHR0ejevTtyc3MRHx+P8PBw1K5dG0FBQQAeJB39+/fHvn37sGjRInTt2hU5OTlYuXIlOnXqhE2bNqkSu8LCQgQFBeHs2bOYN28e/P39YWtrixMnTmDx4sXw9fVFixYttMYxcOBAJCQk4MMPP8SKFSvg7OyMs2fPIioqCp6enhrJo64KCwthZmZWrraV7olLa1aSgoICMTY2loSEBLXycePGaV1VVBulUinu7u6yfPlyna/LVcGJ6EVjKKuCL1iwQMzNzWXz5s1l1n/SquD5+fkyduxYcXZ21nlV8OjoaHF3dxdLS0vp3bt3uVcFd3BwkEmTJqn2N2zYIABkx44dGu3feustcXR0lHv37qnu3cjISE6fPq1Rt7CwUFXvUfHx8QJAtm3bpnGspKRE7ty5IyIiHTt2lPHjx6sd17Yq+Lx582Tw4MFia2srgwYNkrZt28oHH3yg1i4rK0tMTExk//79IvLg837q1Kni5uYmVlZW0rp1azlw4IDWeCtqVfBqG5a6efMmlEolXFxc1MpdXFyQmZmp0zmWLFmC3Nxc9OvXr8w6BQUFyMnJUduIiF54IkBhbtVvIuUKd/r06Zg3bx527dqFvn37lvu2p02bhi1btmDdunU4ffo0vLy8EBQUhNu3b2ut/9NPP2Ho0KEYM2YMUlJS0LlzZ3zyySd6XVOpVGLjxo24ffs2TE1NVeVxcXHw9vZGz549NdpMnjwZt27dQlJSEgAgNjYW3bp10zpKYWpqCmtra63Xjo2NRaNGjdCrVy+NYwqFAnZ2dnrdy6JFi9C0aVOcOnUKH330EcLCwrB+/XrIQ7/X+Ph4uLi4oGPHjgCA8PBwHD16FBs2bMC5c+fwzjvvoHv37khLS9Pr2vqo1mEp4MHDfZiIaJRps379esyZMwfbt29HrVq1yqwXERGBuXPnPnWcREQGpSgPmO9W9df93xuAmfYP4rLs3bsX27dvxw8//IAuXbro1KZ9+/YwMvq/f78fPnwY3t7eWL16NWJiYhAcHAwA+Oqrr5CUlIS1a9di6tSpGudZtmwZgoKCMH36dACAt7c3jh07hsTExCfGMGDAABgbGyM/Px9KpRIODg5qQ2mpqalo3Lix1ral5ampqQCAtLQ0dOrUSad7f1haWhoaNWqkd7uydOnSBVOmTFHth4SEYOLEiThy5Ag6dOgA4EHSFhoaCiMjI1y8eBHr16/H9evX4eb24O9typQpSExMRHR0NObPn19hsT2s2npunJycYGxsrNFLk5WVpdGb86j4+HgMGzYMGzduRLdu3R5bd8aMGcjOzlZt165de+rYiYio6jRr1gyenp6YNWsW7t69qyoPDg6GjY0NbGxs1N6vAR58TqSkpKg2Hx8fXLx4EUVFRfD391fVMzU1RevWrXHhwgWt175w4QLatWunVvboflkiIyORkpKCpKQktGjRApGRkfDy8tL1tgH8XweArv/wf1R525XFz89Pbd/Z2RkBAQGIjY0FAKSnp+P48eMICwsDAJw+fRoiAm9vb9XvysbGBocOHcLFixcrLK5HVVvPjZmZGVq2bImkpCT06dNHVZ6UlKS1+6zU+vXrMXToUKxfvx5vvPHGE69jbm4Oc3PzComZiMhgmFo96EWpjuvqqU6dOtiyZQs6d+6M7t27IzExETVq1MCaNWtw//79B6d9aLgHANzd3TUSidKhE31GDKScw2gA4OrqCi8vL3h5eWHTpk3w9fWFn58ffHx8ADzoBTp//rzWtqXJ1ksvvaSqW1YC9ji6tjMyMtK4V20zsLQNf4WFhWH8+PFYvnw54uLi0KRJEzRv3hwAUFJSAmNjY5w6dQrGxsZq7WxsbPS5Fb1U61TwSZMmYc2aNfj6669x4cIFTJw4EVevXsWoUaMAPOh1GTRokKr++vXrMWjQICxZsgRt27ZFZmYmMjMzkZ2dXV23QET0fFIoHgwPVfVWzl6EevXq4dChQ8jKykJgYCBycnJQp04dVfLg4eHxxHN4eXnBzMwMR44cUZUVFRUhOTm5zOEhHx8fnDhxQq3s0X1deHl5oW/fvpgxY4aqrH///khLS8POnTs16i9ZsgSOjo4ICAgAAISGhmLfvn1av/qkuLgYubm5Wq8bGhqK1NRUbN++XeOYiKg+P52dnZGRkaE6plQq8euvv+p0b71790Z+fj4SExMRFxenNu3e19cXSqUSWVlZqt9V6ebq6qrT+cvlia8cV7KVK1eKh4eHmJmZyauvviqHDh1SHRs8eLB07NhRtd+xY0cBoLE9/Db3k3C2FBG9aAxltpSIyPXr1+Wll16SNm3aqGb6POxJs6XGjx8vbm5usnfvXvntt99k8ODBUrNmTbl9+7aIaM6WOn78uCgUClmwYIH88ccfsnz5crG3ty/XbKlz586JQqGQkydPisiD2Up9+vSRmjVrypo1ayQ9PV3Onj0r7733npiYmKi1z8/Plw4dOkjNmjVlxYoVkpKSIhcvXpT4+Hh59dVXy7zfkpISCQkJEUtLS5k/f76cPHlSLl++LDt37pQuXbqorvHFF1+IlZWV7Nq1Sy5cuCDvvfee2NraasyWioyM1Hqd0NBQad68uSgUCrly5YrasbCwMPH09JQtW7bIpUuX5Oeff5bPPvtMdu/erXGeipotVe3JTVVjckNELxpDSm5ERG7cuCGNGjWSVq1aqU3ZFnlycnP//n0ZO3asODk56TwVfO3atVK3bl2xtLSUnj17lnsquIhIQECABAcHq/aLiopk8eLF0qRJEzE3NxdbW1sJCgqSw4cPa7TNz8+XiIgIeeWVV8TCwkIcHBzE399fYmJipKioqMxYlEqlrF69Wlq1aiVWVlZia2srLVu2lGXLlkleXp6IPJhOPnr0aHFwcJBatWpJRESE1qngZSU3u3fvFgBav8qlsLBQZs2aJZ6enmJqaiqurq7Sp08fOXfunEbdikpuFCJPMaD4HMrJyYGdnR2ys7Nha2tbYefNvHIRWyKuAAD6zvCAq0fDCjs3EdHTyM/PR3p6umqpG6Jn1eP+VvX5/K72taWIiIiIKhKTGyIiIjIoTG6IiIjIoDC5ISIiIoPC5IaIiIgMCpMbIiIiMihMboiIiMigMLkhIiIig8LkhoiIiAwKkxsiIqL/7+DBg1AoFLhz506ZdWJiYmBvb19lMZH+mNwQEdEza8iQIejdu7da2ebNm2FhYYGFCxdq1L98+TIUCoXG9vBK1VXh4Wvb2NigefPmiImJ0ainVCoRGRmJZs2awcLCAvb29ggODsbRo0c16hYWFmLhwoVo3rw5rKys4OTkBH9/f0RHR6OoqKjMWEQEX375Jdq0aQMbGxvY29vDz88PUVFRyMvLq8jbfmaYVHcAREREulqzZg3ef/99rFy5EsOHDy+z3r59+9CkSRPVvqWlZVWEpyY6Ohrdu3dHbm4u4uPjER4ejtq1ayMoKAjAg6Sjf//+2LdvHxYtWoSuXbsiJycHK1euRKdOnbBp0yZVYldYWIigoCCcPXsW8+bNg7+/P2xtbXHixAksXrwYvr6+aNGihdY4Bg4ciISEBHz44YdYsWIFnJ2dcfbsWURFRcHT01MjedRVYWEhzMzMytW20j1xaU0Dw1XBiehFo22l5ZKSEsktzK3yraSkRK/YH14VfMGCBWJubi6bN28us/6TVgXPz8+XsWPHirOzs86rgkdHR4u7u7tYWlpK7969y70quIODg0yaNEm1v2HDBgEgO3bs0Gj/1ltviaOjo9y7d09170ZGRnL69GmNuoWFhap6j4qPjxcAsm3bNo1jJSUlcufOHRER6dixo4wfP17tuLZVwefNmyeDBw8WW1tbGTRokLRt21Y++OADtXZZWVliYmIi+/fvFxGRgoICmTp1qri5uYmVlZW0bt1aDhw4oDXeiloVnD03REQvoPvF99Emrk2VX/en0J9gZWqld7vp06dj5cqV2LVrF7p161bu60+bNg1btmzBunXr4OHhgYULFyIoKAh//vknHBwcNOP96ScMHToU8+fPx1tvvYXExETMnj1br2sqlUps2bIFt2/fhqmpqao8Li4O3t7e6Nmzp0abyZMnIyEhAUlJSejduzdiY2PRrVs3+Pr6atQ1NTVVO+/DYmNj0ahRI/Tq1UvjmEKhgJ2dnV73smjRInz00Uf48MMPAQCJiYlYtGgRIiIioFAoAADx8fFwcXFBx44dAQDh4eG4fPkyNmzYADc3N2zduhXdu3fHL7/8gpdeekmv6+uK79wQEdEzbe/evViwYAG2b9+uc2LTvn172NjYqLYzZ84gNzcXq1evxqJFixAcHAwfHx989dVXsLS0xNq1a7WeZ9myZQgKCsL06dPh7e2NcePGqYaVnmTAgAGwsbGBubk5QkJC4ODgoDaUlpqaisaNG2ttW1qempoKAEhLS8PLL7+s03UflpaWhkaNGundrixdunTBlClT4OXlBS8vL4SEhODGjRs4cuSIqk5cXBxCQ0NhZGSEixcvYv369di0aRM6dOiAhg0bYsqUKXjttdcQHR1dYXE9ij03REQvIEsTS/wU+lO1XFdfzZo1w82bNzFr1iy0atUKNWrUAAAEBwfj8OHDAAAPDw/89ttvqjbx8fFqiYO7uzv++OMPFBUVwd/fX1VuamqK1q1b48KFC1qvfeHCBfTp00etrF27dkhMTHxi3JGRkejWrRuuXbuGSZMmYeLEifDy8tL9xgFVb4iIqH7WR3nblcXPz09t39nZGQEBAYiNjUWHDh2Qnp6O48ePY/Xq1QCA06dPQ0Tg7e2t1q6goACOjo4VFtejmNwQEb2AFApFuYaHqkOdOnWwZcsWdO7cGd27d0diYiJq1KiBNWvW4P79+wCgMSzj7u6ukUiICABofNg/LgEobVMerq6uqh6OTZs2wdfXF35+fvDx8QEAeHt74/z581rbliZbpcM23t7eZSZgj6NrOyMjI4171TYDy9raWqMsLCwM48ePx/LlyxEXF4cmTZqgefPmAICSkhIYGxvj1KlTMDY2VmtnY2Ojz63ohcNSRET0zKtXrx4OHTqErKwsBAYGIicnB3Xq1FElDx4eHk88h5eXF8zMzNSGUIqKipCcnFzm8JCPjw9OnDihVvbovi68vLzQt29fzJgxQ1XWv39/pKWlYefOnRr1lyxZAkdHRwQEBAAAQkNDsW/fPpw5c0ajbnFxMXJzc7VeNzQ0FKmpqdi+fbvGMRFBdnY2gAc9MBkZGapjSqUSv/76q0731rt3b+Tn5yMxMRFxcXFq0+59fX2hVCqRlZWl+l2Vbq6urjqdvzyY3BAR0XOhbt26OHjwIG7duoXAwEDVB7OurK2tMXr0aEydOhWJiYk4f/48RowYgby8PAwbNkxrm3HjxiExMRELFy5EamoqVqxYodOQlDaTJ0/Gzp07kZycDOBBctOnTx8MHjwYa9euxeXLl3Hu3DmMHDkSO3bswJo1a1Q9JRMmTIC/vz+6du2KlStX4uzZs7h06RI2btyINm3aIC0tTes1+/Xrh5CQEAwYMAARERFITk7GlStXVC9mHzhwAMCDd2l2796N3bt34/fff8eYMWMe+0WGD7O2tkavXr3w0Ucf4cKFCwgNDVUd8/b2RlhYGAYNGoSEhASkp6fj5MmTWLBgAfbs2VOu56iTJ86nMjCcCk5EL5rHTa991j08FbzUjRs3pFGjRtKqVSu1KdsiT54Kfv/+fRk7dqw4OTnpPBV87dq1UrduXbG0tJSePXuWeyq4iEhAQIAEBwer9ouKimTx4sXSpEkTMTc3F1tbWwkKCpLDhw9rtM3Pz5eIiAh55ZVXxMLCQhwcHMTf319iYmKkqKiozFiUSqWsXr1aWrVqJVZWVmJraystW7aUZcuWSV5enog8mE4+evRocXBwkFq1aklERITWqeCRkZFar7F7924BIK+//rrGscLCQpk1a5Z4enqKqampuLq6Sp8+feTcuXMadStqKrhC5CkGFJ9DOTk5sLOzQ3Z2NmxtbSvsvJlXLmJLxBUAQN8ZHnD1aFhh5yYiehr5+flIT09H/fr1YWFhUd3hEJXpcX+r+nx+c1iKiIiIDAqTGyIiIjIoTG6IiIjIoDC5ISIiIoPC5IaIiIgMCpMbIiIiMihMboiIiMigMLkhIiIig8LkhoiI6P87ePAgFArFY5ceiImJgb29fZXFRPpjckNERM+sIUOGoHfv3mplmzdvhoWFBRYuXKhR//Lly1AoFBrbw4s5VoWHr21jY4PmzZsjJiZGo55SqURkZCSaNWsGCwsL2NvbIzg4GEePHtWoW1hYiIULF6J58+awsrKCk5MT/P39ER0drXUF71Iigi+//BJt2rSBjY0N7O3t4efnh6ioKOTl5VXkbT8zTKo7ACIiIl2tWbMG77//PlauXInhw4eXWW/fvn1o0qSJat/S0rIqwlMTHR2N7t27Izc3F/Hx8QgPD0ft2rURFBQE4EHS0b9/f+zbtw+LFi1C165dkZOTg5UrV6JTp07YtGmTKrErLCxEUFAQzp49i3nz5sHf3x+2trY4ceIEFi9eDF9fX7Ro0UJrHAMHDkRCQgI+/PBDrFixAs7Ozjh79iyioqLg6empkTzqqrCwEGZmZuVqW+meuPqUgeHCmUT0ojGUhTMXLFgg5ubmsnnz5jLrP2nhzPz8fBk7dqw4OzvrvHBmdHS0uLu7i6WlpfTu3bvcC2c6ODjIpEmTVPsbNmwQALJjxw6N9m+99ZY4OjrKvXv3VPduZGQkp0+f1qhbWFioqveo+Ph4ASDbtm3TOFZSUiJ37twREZGOHTvK+PHj1Y5rWzhz3rx5MnjwYLG1tZVBgwZJ27Zt5YMPPlBrl5WVJSYmJrJ//34RESkoKJCpU6eKm5ubWFlZSevWreXAgQNa462ohTM5LEVE9AISEZTk5VX5JuVcq3n69OmYN28edu3ahb59+5b7vqdNm4YtW7Zg3bp1OH36NLy8vBAUFITbt29rrf/TTz9h6NChGDNmDFJSUtC5c2d88sknel1TqVRi48aNuH37NkxNTVXlcXFx8Pb2Rs+ePTXaTJ48Gbdu3UJSUhIAIDY2Ft26dYOvr69GXVNTU1hbW2u9dmxsLBo1aoRevXppHFMoFLCzs9PrXhYtWoSmTZvi1KlT+OijjxAWFob169er/V7j4+Ph4uKCjh07AgDCw8Nx9OhRbNiwAefOncM777yD7t27Iy0tTa9r64PDUkRELyC5fx9/vNqyyq/b6PQpKKys9Gqzd+9ebN++HT/88AO6dOmiU5v27dvDyOj//v1++PBheHt7Y/Xq1YiJiUFwcDAA4KuvvkJSUhLWrl2LqVOnapxn2bJlCAoKwvTp0wEA3t7eOHbsGBITE58Yw4ABA2BsbIz8/HwolUo4ODioDaWlpqaicePGWtuWlqempgIA0tLS0KlTJ53u/WFpaWlo1KiR3u3K0qVLF0yZMkW1HxISgokTJ+LIkSPo0KEDgAdJW2hoKIyMjHDx4kWsX78e169fh5ubGwBgypQpSExMRHR0NObPn19hsT2MPTdERPRMa9asGTw9PTFr1izcvXtXVR4cHAwbGxvY2NiovV8DPOg9SElJUW0+Pj64ePEiioqK4O/vr6pnamqK1q1b48KFC1qvfeHCBbRr106t7NH9skRGRiIlJQVJSUlo0aIFIiMj4eXlpettA3jQuwI86Gkr/Vkf5W1XFj8/P7V9Z2dnBAQEIDY2FgCQnp6O48ePIywsDABw+vRpiAi8vb1VvysbGxscOnQIFy9erLC4HsWeGyKiF5DC0hKNTp+qluvqq06dOtiyZQs6d+6M7t27IzExETVq1MCaNWtw//59AFAb7gEAd3d3jUSidOjk0Q/7xyUA5R1GAwBXV1d4eXnBy8sLmzZtgq+vL/z8/ODj4wPgQS/Q+fPntbYtTbZeeuklVd2yErDH0bWdkZGRxr1qm4GlbfgrLCwM48ePx/LlyxEXF4cmTZqgefPmAICSkhIYGxvj1KlTMDY2VmtnY2Ojz63ohT03REQvIIVCASMrqyrfytuLUK9ePRw6dAhZWVkIDAxETk4O6tSpo0oePDw8nngOLy8vmJmZ4ciRI6qyoqIiJCcnlzk85OPjgxMnTqiVPbqvCy8vL/Tt2xczZsxQlfXv3x9paWnYuXOnRv0lS5bA0dERAQEBAIDQ0FDs27cPZ86c0ahbXFyM3NxcrdcNDQ1Famoqtm/frnFMRJCdnQ3gQQ9MRkaG6phSqcSvv/6q07317t0b+fn5SExMRFxcnNq0e19fXyiVSmRlZal+V6Wbq6urTucvDyY3RET0XKhbty4OHjyIW7duITAwUPXBrCtra2uMHj0aU6dORWJiIs6fP48RI0YgLy8Pw4YN09pm3LhxSExMxMKFC5GamooVK1bo9L6NNpMnT8bOnTuRnJwM4EFy06dPHwwePBhr167F5cuXce7cOYwcORI7duzAmjVrVD0lEyZMgL+/P7p27YqVK1fi7NmzuHTpEjZu3Ig2bdqU+XJuv379EBISggEDBiAiIgLJycm4cuUKdu3ahW7duuHAgQMAHrxLs3v3buzevRu///47xowZ89gvMnyYtbU1evXqhY8++ggXLlxAaGio6pi3tzfCwsIwaNAgJCQkID09HSdPnsSCBQuwZ8+ecj1HnTxxPpWB4VRwInrRGMpU8FI3btyQRo0aSatWrdSmbIs8eSr4/fv3ZezYseLk5KTzVPC1a9dK3bp1xdLSUnr27FnuqeAiIgEBARIcHKzaLyoqksWLF0uTJk3E3NxcbG1tJSgoSA4fPqzRNj8/XyIiIuSVV14RCwsLcXBwEH9/f4mJiZGioqIyY1EqlbJ69Wpp1aqVWFlZia2trbRs2VKWLVsmeXl5IvJgOvno0aPFwcFBatWqJREREVqngkdGRmq9xu7duwWAvP766xrHCgsLZdasWeLp6Smmpqbi6uoqffr0kXPnzmnUraip4AqRpxhQfA7l5OTAzs4O2dnZsLW1rbDzZl65iC0RVwAAfWd4wNWjYYWdm4joaeTn5yM9PR3169eHhYVFdYdDVKbH/a3q8/nNYSkiIiIyKExuiIiIyKAwuSEiIiKDwuSGiIiIDAqTGyIiIjIoTG6IiIjIoDC5ISIiIoPC5IaIiIgMCpMbIiIiMihMboiIiP6/gwcPQqFQPHZdpZiYGNjb21dZTKQ/JjdERPTMGjJkCHr37q1WtnnzZlhYWGDhwoUa9S9fvgyFQqGxPbxSdVV4+No2NjZo3rw5YmJiNOoplUpERkaiWbNmsLCwgL29PYKDg3H06FGNuoWFhVi4cCGaN28OKysrODk5wd/fH9HR0SgqKiozFhHBl19+iTZt2sDGxgb29vbw8/NDVFQU8vLyKvK2nxkm1R0AERGRrtasWYP3338fK1euxPDhw8ust2/fPjRp0kS1b2lpWRXhqYmOjkb37t2Rm5uL+Ph4hIeHo3bt2ggKCgLwIOno378/9u3bh0WLFqFr167IycnBypUr0alTJ2zatEmV2BUWFiIoKAhnz57FvHnz4O/vD1tbW5w4cQKLFy+Gr68vWrRooTWOgQMHIiEhAR9++CFWrFgBZ2dnnD17FlFRUfD09NRIHnVVWFgIMzOzcrWtdE9cWtPAcFVwInrRGMqq4AsWLBBzc3PZvHlzmfWftCp4fn6+jB07VpydnXVeFTw6Olrc3d3F0tJSevfuXe5VwR0cHGTSpEmq/Q0bNggA2bFjh0b7t956SxwdHeXevXuqezcyMpLTp09r1C0sLFTVe1R8fLwAkG3btmkcKykpkTt37oiISMeOHWX8+PFqx7WtCj5v3jwZPHiw2NrayqBBg6Rt27bywQcfqLXLysoSExMT2b9/v4iIFBQUyNSpU8XNzU2srKykdevWcuDAAa3xVtSq4ByWIiJ6AYkIigqUVb6JSLninT59OubNm4ddu3ahb9++5b7vadOmYcuWLVi3bh1Onz4NLy8vBAUF4fbt21rr//TTTxg6dCjGjBmDlJQUdO7cGZ988ole11Qqldi4cSNu374NU1NTVXlcXBy8vb3Rs2dPjTaTJ0/GrVu3kJSUBACIjY1Ft27d4Ovrq1HX1NQU1tbWWq8dGxuLRo0aoVevXhrHFAoF7Ozs9LqXRYsWoWnTpjh16hQ++ugjhIWFYf369Wq/1/j4eLi4uKBjx44AgPDwcBw9ehQbNmzAuXPn8M4776B79+5IS0vT69r64LAUEdELqLiwBF+OP1Tl131vWUeYmhvr1Wbv3r3Yvn07fvjhB3Tp0kWnNu3bt4eR0f/9+/3w4cPw9vbG6tWrERMTg+DgYADAV199haSkJKxduxZTp07VOM+yZcsQFBSE6dOnAwC8vb1x7NgxJCYmPjGGAQMGwNjYGPn5+VAqlXBwcFAbSktNTUXjxo21ti0tT01NBQCkpaWhU6dOOt37w9LS0tCoUSO925WlS5cumDJlimo/JCQEEydOxJEjR9ChQwcAD5K20NBQGBkZ4eLFi1i/fj2uX78ONzc3AMCUKVOQmJiI6OhozJ8/v8Jie1i199ysWrUK9evXh4WFBVq2bInDhw8/tv6hQ4fQsmVLWFhYoEGDBvjiiy+qKFIiIqoOzZo1g6enJ2bNmoW7d++qyoODg2FjYwMbGxu192uAB70HKSkpqs3HxwcXL15EUVER/P39VfVMTU3RunVrXLhwQeu1L1y4gHbt2qmVPbpflsjISKSkpCApKQktWrRAZGQkvLy8dL1tAA96V4AHPW2lP+ujvO3K4ufnp7bv7OyMgIAAxMbGAgDS09Nx/PhxhIWFAQBOnz4NEYG3t7fqd2VjY4NDhw7h4sWLFRbXo6q15yY+Ph4TJkzAqlWr4O/vj3//+98IDg7G+fPnUa9ePY366enp6NGjB0aMGIH//Oc/OHr0KMaMGQNnZ+en6qYkInrRmJgZ4b1lHavluvqqU6cOtmzZgs6dO6N79+5ITExEjRo1sGbNGty/fx8A1IZ7AMDd3V0jkSgdOnn0w/5xCUB5h9EAwNXVFV5eXvDy8sKmTZvg6+sLPz8/+Pj4AHjQC3T+/HmtbUuTrZdeeklVt6wE7HF0bWdkZKRxr9pmYGkb/goLC8P48eOxfPlyxMXFoUmTJmjevDkAoKSkBMbGxjh16hSMjdV77GxsbPS5Fb1Ua8/N0qVLMWzYMAwfPhyNGzdGVFQU3N3dsXr1aq31v/jiC9SrVw9RUVFo3Lgxhg8fjqFDh2Lx4sVVHDkR0fNNoVDA1Ny4yrfy9iLUq1cPhw4dQlZWFgIDA5GTk4M6deqokgcPD48nnsPLywtmZmY4cuSIqqyoqAjJycllDg/5+PjgxIkTamWP7uvCy8sLffv2xYwZM1Rl/fv3R1paGnbu3KlRf8mSJXB0dERAQAAAIDQ0FPv27cOZM2c06hYXFyM3N1frdUNDQ5Gamort27drHBMRZGdnA3jQA5ORkaE6plQq8euvv+p0b71790Z+fj4SExMRFxenNu3e19cXSqUSWVlZqt9V6ebq6qrT+cuj2pKbwsJCnDp1CoGBgWrlgYGBOHbsmNY2x48f16gfFBSE5OTkMuf4FxQUICcnR20jIqLnT926dXHw4EHcunULgYGBqg9mXVlbW2P06NGYOnUqEhMTcf78eYwYMQJ5eXkYNmyY1jbjxo1DYmIiFi5ciNTUVKxYsUKn9220mTx5Mnbu3Ink5GQAD5KbPn36YPDgwVi7di0uX76Mc+fOYeTIkdixYwfWrFmj6imZMGEC/P390bVrV6xcuRJnz57FpUuXsHHjRrRp06bMl3P79euHkJAQDBgwABEREUhOTsaVK1ewa9cudOvWDQcOHADw4F2a3bt3Y/fu3fj9998xZsyYx36R4cOsra3Rq1cvfPTRR7hw4QJCQ0NVx7y9vREWFoZBgwYhISEB6enpOHnyJBYsWIA9e/aU6znqotqSm5s3b0KpVMLFxUWt3MXFBZmZmVrbZGZmaq1fXFyMmzdvam0TEREBOzs71ebu7l4xN0BERFWuTp06OHToEO7cuYOAgACdP4BLffbZZ+jbty8GDhyIV199FX/++Se+++471KxZU2v9tm3bYs2aNVi+fDlatGiB77//Hh9++GG5Yn/llVfQrVs3zJo1C8CD3rONGzdi5syZiIyMxMsvv4wOHTrgypUrOHDggNr3z5ibmyMpKQnTpk3Dv//9b7Rt2xatWrXC559/jnHjxqFp06Zar6lQKBAXF4elS5di69at6NixI5o1a4Y5c+agV69equ/cGTp0KAYPHoxBgwahY8eOqF+/Pjp37qzzvYWFheHs2bPo0KGDxmsl0dHRGDRoECZPnoxGjRrhzTffxE8//VSpn8cKeZoBxadw48YN1KlTB8eOHVN7OevTTz/Ft99+i99//12jjbe3N8LDw9W69Y4ePYrXXnsNGRkZWru4CgoKUFBQoNrPycmBu7s7srOzYWtrW2H3U1xUhJs3rgIAnNzqweSR8V8iouqSn5+P9PR01eQNomfV4/5Wc3JyYGdnp9Pnd7W9UOzk5ARjY2ONXpqsrCyN3plSrq6uWuubmJjA0dFRaxtzc3OYm5tXTNCPYWJqClePhpV+HSIiInq8ahuWMjMzQ8uWLVVfUFQqKSkJ7du319qmXbt2GvW///57+Pn5abwpT0RERC+map0tNWnSJKxZswZff/01Lly4gIkTJ+Lq1asYNWoUAGDGjBkYNGiQqv6oUaNw5coVTJo0CRcuXMDXX3+NtWvXqn2hEBEREb3YqvV7bkJCQnDr1i18/PHHyMjIQNOmTbFnzx7VlL6MjAxcvXpVVb9+/frYs2cPJk6ciJUrV8LNzQ2ff/45v+OGiIiIVKrtheLqos8LSUREhoAvFNPzoqJeKK725ReIiKhqvGD/lqXnUEX9jTK5ISIycKUTLvLy8qo5EqLHKywsBACNpRr0xVXBiYgMnLGxMezt7ZGVlQUAsLKyqtDFFIkqQklJCf773//CysoKJiZPl54wuSEiegGUfslpaYJD9CwyMjJCvXr1njr5ZnJDRPQCUCgUqF27NmrVqlXmWnxE1c3MzAxGRk//xgyTGyKiF4ixsfFTv89A9KzjC8VERERkUJjcEBERkUFhckNEREQG5YV756b0C4JycnKqORIiIiLSVennti5f9PfCJTd3794FALi7u1dzJERERKSvu3fvws7O7rF1Xri1pUpKSnDjxg3UqFGjwr/EKicnB+7u7rh27RrXrapEfM5Vg8+5avA5Vx0+66pRWc9ZRHD37l24ubk9cbr4C9dzY2RkhLp161bqNWxtbfkfThXgc64afM5Vg8+56vBZV43KeM5P6rEpxReKiYiIyKAwuSEiIiKDwuSmApmbm2P27NkwNzev7lAMGp9z1eBzrhp8zlWHz7pqPAvP+YV7oZiIiIgMG3tuiIiIyKAwuSEiIiKDwuSGiIiIDAqTGyIiIjIoTG70tGrVKtSvXx8WFhZo2bIlDh8+/Nj6hw4dQsuWLWFhYYEGDRrgiy++qKJIn2/6POeEhAQEBATA2dkZtra2aNeuHb777rsqjPb5pe/fc6mjR4/CxMQELVq0qNwADYS+z7mgoAAzZ86Eh4cHzM3N0bBhQ3z99ddVFO3zS9/nHBsbi+bNm8PKygq1a9dGeHg4bt26VUXRPp9+/PFH9OzZE25ublAoFNi2bdsT21TL56CQzjZs2CCmpqby1Vdfyfnz52X8+PFibW0tV65c0Vr/0qVLYmVlJePHj5fz58/LV199JaamprJ58+Yqjvz5ou9zHj9+vCxYsEB+/vlnSU1NlRkzZoipqamcPn26iiN/vuj7nEvduXNHGjRoIIGBgdK8efOqCfY5Vp7n/Oabb0qbNm0kKSlJ0tPT5aeffpKjR49WYdTPH32f8+HDh8XIyEiWLVsmly5dksOHD0uTJk2kd+/eVRz582XPnj0yc+ZM2bJliwCQrVu3PrZ+dX0OMrnRQ+vWrWXUqFFqZS+//LJMnz5da/1p06bJyy+/rFY2cuRIadu2baXFaAj0fc7a+Pj4yNy5cys6NINS3uccEhIiH374ocyePZvJjQ70fc579+4VOzs7uXXrVlWEZzD0fc6LFi2SBg0aqJV9/vnnUrdu3UqL0dDoktxU1+cgh6V0VFhYiFOnTiEwMFCtPDAwEMeOHdPa5vjx4xr1g4KCkJycjKKiokqL9XlWnuf8qJKSEty9excODg6VEaJBKO9zjo6OxsWLFzF79uzKDtEglOc579ixA35+fli4cCHq1KkDb29vTJkyBffv36+KkJ9L5XnO7du3x/Xr17Fnzx6ICP7++29s3rwZb7zxRlWE/MKors/BF27hzPK6efMmlEolXFxc1MpdXFyQmZmptU1mZqbW+sXFxbh58yZq165dafE+r8rznB+1ZMkS5Obmol+/fpURokEoz3NOS0vD9OnTcfjwYZiY8H8duijPc7506RKOHDkCCwsLbN26FTdv3sSYMWNw+/ZtvndThvI85/bt2yM2NhYhISHIz89HcXEx3nzzTSxfvrwqQn5hVNfnIHtu9KRQKNT2RUSj7En1tZWTOn2fc6n169djzpw5iI+PR61atSorPIOh63NWKpUIDQ3F3Llz4e3tXVXhGQx9/p5LSkqgUCgQGxuL1q1bo0ePHli6dCliYmLYe/ME+jzn8+fPY9y4cZg1axZOnTqFxMREpKenY9SoUVUR6gulOj4H+c8vHTk5OcHY2FjjXwFZWVkaWWkpV1dXrfVNTEzg6OhYabE+z8rznEvFx8dj2LBh2LRpE7p161aZYT739H3Od+/eRXJyMs6cOYN//etfAB58CIsITExM8P3336NLly5VEvvzpDx/z7Vr10adOnVgZ2enKmvcuDFEBNevX8dLL71UqTE/j8rznCMiIuDv74+pU6cCAJo1awZra2t06NABn3zyCXvWK0h1fQ6y50ZHZmZmaNmyJZKSktTKk5KS0L59e61t2rVrp1H/+++/h5+fH0xNTSst1udZeZ4z8KDHZsiQIYiLi+OYuQ70fc62trb45ZdfkJKSotpGjRqFRo0aISUlBW3atKmq0J8r5fl79vf3x40bN3Dv3j1VWWpqKoyMjFC3bt1Kjfd5VZ7nnJeXByMj9Y9AY2NjAP/Xs0BPr9o+Byv1dWUDUzrVcO3atXL+/HmZMGGCWFtby+XLl0VEZPr06TJw4EBV/dIpcBMnTpTz58/L2rVrORVcB/o+57i4ODExMZGVK1dKRkaGartz50513cJzQd/n/CjOltKNvs/57t27UrduXXn77bflt99+k0OHDslLL70kw4cPr65beC7o+5yjo6PFxMREVq1aJRcvXpQjR46In5+ftG7durpu4blw9+5dOXPmjJw5c0YAyNKlS+XMmTOqKffPyucgkxs9rVy5Ujw8PMTMzExeffVVOXTokOrY4MGDpWPHjmr1Dx48KL6+vmJmZiaenp6yevXqKo74+aTPc+7YsaMA0NgGDx5c9YE/Z/T9e34Ykxvd6fucL1y4IN26dRNLS0upW7euTJo0SfLy8qo46uePvs/5888/Fx8fH7G0tJTatWtLWFiYXL9+vYqjfr4cOHDgsf+/fVY+BxUi7H8jIiIiw8F3boiIiMigMLkhIiIig8LkhoiIiAwKkxsiIiIyKExuiIiIyKAwuSEiIiKDwuSGiIiIDAqTGyJSExMTA3t7++oOo9w8PT0RFRX12Dpz5sxBixYtqiQeIqp6TG6IDNCQIUOgUCg0tj///LO6Q0NMTIxaTLVr10a/fv2Qnp5eIec/efIk3nvvPdW+QqHAtm3b1OpMmTIFP/zwQ4VcryyP3qeLiwt69uyJ3377Te/zPM/JJlF1YHJDZKC6d++OjIwMta1+/frVHRaABwtxZmRk4MaNG4iLi0NKSgrefPNNKJXKpz63s7MzrKysHlvHxsamUlckLvXwfe7evRu5ubl44403UFhYWOnXJnqRMbkhMlDm5uZwdXVV24yNjbF06VK88sorsLa2hru7O8aMGaO2AvWjzp49i86dO6NGjRqwtbVFy5YtkZycrDp+7NgxvP7667C0tIS7uzvGjRuH3Nzcx8amUCjg6uqK2rVro3Pnzpg9ezZ+/fVXVc/S6tWr0bBhQ5iZmaFRo0b49ttv1drPmTMH9erVg7m5Odzc3DBu3DjVsYeHpTw9PQEAffr0gUKhUO0/PCz13XffwcLCAnfu3FG7xrhx49CxY8cKu08/Pz9MnDgRV65cwR9//KGq87jfx8GDBxEeHo7s7GxVD9CcOXMAAIWFhZg2bRrq1KkDa2trtGnTBgcPHnxsPEQvCiY3RC8YIyMjfP755/j111+xbt067N+/H9OmTSuzflhYGOrWrYuTJ0/i1KlTmD59OkxNTQEAv/zyC4KCgvDWW2/h3LlziI+Px5EjR/Cvf/1Lr5gsLS0BAEVFRdi6dSvGjx+PyZMn49dff8XIkSMRHh6OAwcOAAA2b96MyMhI/Pvf/0ZaWhq2bduGV155Ret5T548CQCIjo5GRkaGav9h3bp1g729PbZs2aIqUyqV2LhxI8LCwirsPu/cuYO4uDgAUD0/4PG/j/bt2yMqKkrVA5SRkYEpU6YAAMLDw3H06FFs2LAB586dwzvvvIPu3bsjLS1N55iIDFalL81JRFVu8ODBYmxsLNbW1qrt7bff1lp348aN4ujoqNqPjo4WOzs71X6NGjUkJiZGa9uBAwfKe++9p1Z2+PBhMTIykvv372tt8+j5r127Jm3btpW6detKQUGBtG/fXkaMGKHW5p133pEePXqIiMiSJUvE29tbCgsLtZ7fw8NDIiMjVfsAZOvWrWp1Hl3RfNy4cdKlSxfV/nfffSdmZmZy+/btp7pPAGJtbS1WVlaq1ZPffPNNrfVLPen3ISLy559/ikKhkL/++kutvGvXrjJjxozHnp/oRWBSvakVEVWWzp07Y/Xq1ap9a2trAMCBAwcwf/58nD9/Hjk5OSguLkZ+fj5yc3NVdR42adIkDB8+HN9++y26deuGd955Bw0bNgQAnDp1Cn/++SdiY2NV9UUEJSUlSE9PR+PGjbXGlp2dDRsbG4gI8vLy8OqrryIhIQFmZma4cOGC2gvBAODv749ly5YBAN555x1ERUWhQYMG6N69O3r06IGePXvCxKT8/zsLCwtDu3btcOPGDbi5uSE2NhY9evRAzZo1n+o+a9SogdOnT6O4uBiHDh3CokWL8MUXX6jV0ff3AQCnT5+GiMDb21utvKCgoEreJSJ61jG5ITJQ1tbW8PLyUiu7cuUKevTogVGjRmHevHlwcHDAkSNHMGzYMBQVFWk9z5w5cxAaGordu3dj7969mD17NjZs2IA+ffqgpKQEI0eOVHvnpVS9evXKjK30Q9/IyAguLi4aH+IKhUJtX0RUZe7u7vjjjz+QlJSEffv2YcyYMVi0aBEOHTqkNtyjj9atW6Nhw4bYsGEDRo8eja1btyI6Olp1vLz3aWRkpPodvPzyy8jMzERISAh+/PFHAOX7fZTGY2xsjFOnTsHY2FjtmI2NjV73TmSImNwQvUCSk5NRXFyMJUuWwMjowSt3GzdufGI7b29veHt7Y+LEiRgwYACio6PRp08fvPrqq/jtt980kqgnefhD/1GNGzfGkSNHMGjQIFXZsWPH1HpHLC0t8eabb+LNN9/E+++/j5dffhm//PILXn31VY3zmZqa6jQLKzQ0FLGxsahbty6MjIzwxhtvqI6V9z4fNXHiRCxduhRbt25Fnz59dPp9mJmZacTv6+sLpVKJrKwsdOjQ4aliIjJEfKGY6AXSsGFDFBcXY/ny5bh06RK+/fZbjWGSh92/fx//+te/cPDgQVy5cgVHjx7FyZMnVYnGBx98gOPHj+P9999HSkoK0tLSsGPHDowdO7bcMU6dOhUxMTH44osvkJaWhqVLlyIhIUH1Im1MTAzWrl2LX3/9VXUPlpaW8PDw0Ho+T09P/PDDD8jMzMQ///xT5nXDwsJw+vRpfPrpp3j77bdhYWGhOlZR92lra4vhw4dj9uzZEBGdfh+enp64d+8efvjhB9y8eRN5eXnw9vZGWFgYBg0ahISEBKSnp+PkyZNYsGAB9uzZo1dMRAapOl/4IaLKMXjwYOnVq5fWY0uXLpXatWuLpaWlBAUFyTfffCMA5J9//hER9RdYCwoKpH///uLu7i5mZmbi5uYm//rXv9Reov35558lICBAbGxsxNraWpo1ayaffvppmbFpe0H2UatWrZIGDRqIqampeHt7yzfffKM6tnXrVmnTpo3Y2tqKtbW1tG3bVvbt26c6/ugLxTt27BAvLy8xMTERDw8PEdF8obhUq1atBIDs379f41hF3eeVK1fExMRE4uPjReTJvw8RkVGjRomjo6MAkNmzZ4uISGFhocyaNUs8PT3F1NRUXF1dpU+fPnLu3LkyYyJ6UShERKo3vSIiIiKqOByWIiIiIoPC5IaIiIgMCpMbIiIiMihMboiIiMigMLkhIiIig8LkhoiIiAwKkxsiIiIyKExuiIiIyKAwuSEiIiKDwuSGiIiIDAqTGyIiIjIoTG6IiIjIoPw/QZbuj60bA5AAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "K-Fold F1 Scores: [0.9921259842519685, 0.9947643979057592, 0.9844559585492227, 0.9894736842105263, 0.9947368421052631]\n",
+      "K-Fold Precision Scores: [0.9947368421052631, 0.9947643979057592, 0.9693877551020408, 0.9894736842105263, 0.9947368421052631]\n",
+      "K-Fold Recall Scores: [0.9895287958115183, 0.9947643979057592, 1.0, 0.9894736842105263, 0.9947368421052631]\n",
+      "K-Fold AUC Scores: [0.9994374972956601, 0.9977788354751707, 0.9977009832272989, 0.9951573147745398, 0.999942003769755]\n",
+      "Mean F1 Score (K-Fold): 0.9911113734045479\n",
+      "Mean AUC Score (K-Fold): 0.9980033269084849\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize the Gradient Boosting Classifier\n",
+    "model = GradientBoostingClassifier(\n",
+    "    random_state=42,\n",
+    "    n_estimators=96,\n",
+    "    learning_rate= 0.08020404204698642,\n",
+    "    max_depth=7,\n",
+    "    min_samples_split=5,\n",
+    "    min_samples_leaf=2,\n",
+    "    subsample=0.9877960074080036\n",
+    ")\n",
+    "# Stratified K-Fold\n",
+    "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
+    "\n",
+    "f1_scores_kfold = []\n",
+    "precision_scores_kfold = []\n",
+    "recall_scores_kfold = []\n",
+    "auc_scores_kfold = []\n",
+    "\n",
+    "# For Confusion Matrix Display\n",
+    "all_conf_matrices = []\n",
+    "\n",
+    "for train_index, test_index in skf.split(X, y):\n",
+    "    # Splitting the data\n",
+    "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
+    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
+    "    \n",
+    "    # Train the model\n",
+    "    model.fit(X_train, y_train)\n",
+    "    \n",
+    "    # Predictions\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
+    "    \n",
+    "    # Metrics\n",
+    "    f1_scores_kfold.append(f1_score(y_test, y_pred))\n",
+    "    precision_scores_kfold.append(precision_score(y_test, y_pred))\n",
+    "    recall_scores_kfold.append(recall_score(y_test, y_pred))\n",
+    "    auc_scores_kfold.append(roc_auc_score(y_test, y_pred_proba))\n",
+    "    \n",
+    "    # Save confusion matrix for the fold\n",
+    "    conf_matrix = confusion_matrix(y_test, y_pred)\n",
+    "    all_conf_matrices.append(conf_matrix)\n",
+    "    \n",
+    "    # ROC Curve for one fold\n",
+    "    fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
+    "    plt.plot(fpr, tpr, label=f\"K-Fold ROC Curve\")\n",
+    "\n",
+    "# Plot AUC-ROC Curve for K-Fold\n",
+    "plt.xlabel(\"False Positive Rate\")\n",
+    "plt.ylabel(\"True Positive Rate\")\n",
+    "plt.title(\"K-Fold AUC-ROC Curve\")\n",
+    "plt.legend(loc=\"lower right\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Plot Confusion Matrix for the last fold as an example\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=all_conf_matrices[-1], display_labels=model.classes_)\n",
+    "disp.plot(cmap='Blues', values_format='d')\n",
+    "plt.title(\"Confusion Matrix for Last Fold in K-Fold Cross-Validation\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Print Metrics\n",
+    "print(f\"K-Fold F1 Scores: {f1_scores_kfold}\")\n",
+    "print(f\"K-Fold Precision Scores: {precision_scores_kfold}\")\n",
+    "print(f\"K-Fold Recall Scores: {recall_scores_kfold}\")\n",
+    "print(f\"K-Fold AUC Scores: {auc_scores_kfold}\")\n",
+    "print(f\"Mean F1 Score (K-Fold): {np.mean(f1_scores_kfold)}\")\n",
+    "print(f\"Mean AUC Score (K-Fold): {np.mean(auc_scores_kfold)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb511669-8c12-426d-b965-61a7f33be08e",
+   "metadata": {},
+   "source": [
+    "### Hyperparameter Tuned Bootstrap Sampled Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e68a28cd-be72-47c0-adaf-b58aba3ee152",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bootstrapping F1 Scores: [0.9656160458452722, 0.9744318181818182, 0.9746478873239437, 0.949438202247191, 0.9671897289586305]...\n",
+      "Bootstrapping Precision Scores: [0.9683908045977011, 0.9634831460674157, 0.969187675070028, 0.9602272727272727, 0.9741379310344828]...\n",
+      "Bootstrapping Recall Scores: [0.9628571428571429, 0.985632183908046, 0.9801699716713881, 0.9388888888888889, 0.9603399433427762]...\n",
+      "Bootstrapping AUC Scores: [0.9987329931972789, 0.9971851665408172, 0.9991600237169775, 0.9894629094412332, 0.9982609735984174]...\n",
+      "Mean F1 Score (Bootstrapping): 0.9642962344682645\n",
+      "Mean AUC Score (Bootstrapping): 0.9966428080006231\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_bootstrap_samples = 50\n",
+    "f1_scores_bootstrap = []\n",
+    "precision_scores_bootstrap = []\n",
+    "recall_scores_bootstrap = []\n",
+    "auc_scores_bootstrap = []\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))  # Set figure size for better visualization\n",
+    "\n",
+    "# Store confusion matrices for visualization later\n",
+    "confusion_matrices = []\n",
+    "\n",
+    "for i in range(n_bootstrap_samples):\n",
+    "    # Create Bootstrap Sample\n",
+    "    indices = np.random.choice(range(len(X)), size=len(X), replace=True)\n",
+    "    X_train, y_train = X.iloc[indices], y.iloc[indices]\n",
+    "    \n",
+    "    # Out-of-Bag (OOB) Data\n",
+    "    oob_indices = list(set(range(len(X))) - set(indices))\n",
+    "    if len(oob_indices) == 0 or len(y_train.unique()) < 2:\n",
+    "        continue  # Skip iteration if no OOB data or only one class\n",
+    "    X_test, y_test = X.iloc[oob_indices], y.iloc[oob_indices]\n",
+    "    \n",
+    "    # Train the model\n",
+    "    model.fit(X_train, y_train)\n",
+    "    \n",
+    "    # Predictions\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
+    "    \n",
+    "    # Metrics\n",
+    "    f1_scores_bootstrap.append(f1_score(y_test, y_pred))\n",
+    "    precision_scores_bootstrap.append(precision_score(y_test, y_pred))\n",
+    "    recall_scores_bootstrap.append(recall_score(y_test, y_pred))\n",
+    "    auc_scores_bootstrap.append(roc_auc_score(y_test, y_pred_proba))\n",
+    "    \n",
+    "    # Save confusion matrix for the last bootstrap sample\n",
+    "    conf_matrix = confusion_matrix(y_test, y_pred)\n",
+    "    confusion_matrices.append(conf_matrix)\n",
+    "    \n",
+    "    # Plot ROC Curve only for the first iteration with a label\n",
+    "    fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
+    "    if i == 0:\n",
+    "        plt.plot(fpr, tpr, label=\"Bootstrap ROC Curve\", alpha=0.3)\n",
+    "    else:\n",
+    "        plt.plot(fpr, tpr, alpha=0.3)\n",
+    "\n",
+    "# Finalize and Display AUC-ROC Plot\n",
+    "plt.xlabel(\"False Positive Rate\")\n",
+    "plt.ylabel(\"True Positive Rate\")\n",
+    "plt.title(\"Bootstrapping AUC-ROC Curve\")\n",
+    "plt.legend(loc=\"lower right\")  # Show single legend\n",
+    "plt.show()\n",
+    "\n",
+    "# Display Confusion Matrix for the last bootstrap sample\n",
+    "if confusion_matrices:\n",
+    "    disp = ConfusionMatrixDisplay(confusion_matrix=confusion_matrices[-1], display_labels=model.classes_)\n",
+    "    disp.plot(cmap='Blues', values_format='d')\n",
+    "    plt.title(\"Confusion Matrix for Last Bootstrap Sample\")\n",
+    "    plt.show()\n",
+    "\n",
+    "# Print Metrics\n",
+    "print(f\"Bootstrapping F1 Scores: {f1_scores_bootstrap[:5]}...\")  # Showing first 5 scores\n",
+    "print(f\"Bootstrapping Precision Scores: {precision_scores_bootstrap[:5]}...\")\n",
+    "print(f\"Bootstrapping Recall Scores: {recall_scores_bootstrap[:5]}...\")\n",
+    "print(f\"Bootstrapping AUC Scores: {auc_scores_bootstrap[:5]}...\")\n",
+    "print(f\"Mean F1 Score (Bootstrapping): {np.mean(f1_scores_bootstrap)}\")\n",
+    "print(f\"Mean AUC Score (Bootstrapping): {np.mean(auc_scores_bootstrap)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80f73a75-87ef-46a3-abdd-f54c6c3bacbe",
+   "metadata": {},
+   "source": [
+    "### Feature Importance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "341b1261-e695-49bd-b17a-5097743ff7e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feature_importance = model.feature_importances_\n",
+    "\n",
+    "# Match importance scores with feature names\n",
+    "features = X.columns  # Assuming `X` is your input DataFrame with feature names\n",
+    "importance_df = pd.DataFrame({\n",
+    "    'Feature': features,\n",
+    "    'Importance': feature_importance\n",
+    "}).sort_values(by='Importance', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c82d6be2-1632-42db-a503-7e50b76faf5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                    Feature  Importance\n",
+      "1                   Glucose    0.326323\n",
+      "5                       BMI    0.182679\n",
+      "6  DiabetesPedigreeFunction    0.128799\n",
+      "7                       Age    0.100334\n",
+      "2             BloodPressure    0.090225\n",
+      "0               Pregnancies    0.077337\n",
+      "4                   Insulin    0.047760\n",
+      "3             SkinThickness    0.046543\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(importance_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "ef4e62ca-5a84-4c33-a46d-75690ac8c4cd",
+   "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",
+    "plt.barh(importance_df['Feature'], importance_df['Importance'], color='skyblue')\n",
+    "plt.xlabel('Feature Importance')\n",
+    "plt.ylabel('Features')\n",
+    "plt.title('Feature Importance from Gradient Boosting')\n",
+    "plt.gca().invert_yaxis()  # Invert y-axis to show the most important feature at the top\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Healthcare-Diabetes.csv b/Healthcare-Diabetes.csv
new file mode 100644
index 0000000..851b8e4
--- /dev/null
+++ b/Healthcare-Diabetes.csv
@@ -0,0 +1,2769 @@
+Id,Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
+1,6,148,72,35,0,33.6,0.627,50,1
+2,1,85,66,29,0,26.6,0.351,31,0
+3,8,183,64,0,0,23.3,0.672,32,1
+4,1,89,66,23,94,28.1,0.167,21,0
+5,0,137,40,35,168,43.1,2.288,33,1
+6,5,116,74,0,0,25.6,0.201,30,0
+7,3,78,50,32,88,31,0.248,26,1
+8,10,115,0,0,0,35.3,0.134,29,0
+9,2,197,70,45,543,30.5,0.158,53,1
+10,8,125,96,0,0,0,0.232,54,1
+11,4,110,92,0,0,37.6,0.191,30,0
+12,10,168,74,0,0,38,0.537,34,1
+13,10,139,80,0,0,27.1,1.441,57,0
+14,1,189,60,23,846,30.1,0.398,59,1
+15,5,166,72,19,175,25.8,0.587,51,1
+16,7,100,0,0,0,30,0.484,32,1
+17,0,118,84,47,230,45.8,0.551,31,1
+18,7,107,74,0,0,29.6,0.254,31,1
+19,1,103,30,38,83,43.3,0.183,33,0
+20,1,115,70,30,96,34.6,0.529,32,1
+21,3,126,88,41,235,39.3,0.704,27,0
+22,8,99,84,0,0,35.4,0.388,50,0
+23,7,196,90,0,0,39.8,0.451,41,1
+24,9,119,80,35,0,29,0.263,29,1
+25,11,143,94,33,146,36.6,0.254,51,1
+26,10,125,70,26,115,31.1,0.205,41,1
+27,7,147,76,0,0,39.4,0.257,43,1
+28,1,97,66,15,140,23.2,0.487,22,0
+29,13,145,82,19,110,22.2,0.245,57,0
+30,5,117,92,0,0,34.1,0.337,38,0
+31,5,109,75,26,0,36,0.546,60,0
+32,3,158,76,36,245,31.6,0.851,28,1
+33,3,88,58,11,54,24.8,0.267,22,0
+34,6,92,92,0,0,19.9,0.188,28,0
+35,10,122,78,31,0,27.6,0.512,45,0
+36,4,103,60,33,192,24,0.966,33,0
+37,11,138,76,0,0,33.2,0.42,35,0
+38,9,102,76,37,0,32.9,0.665,46,1
+39,2,90,68,42,0,38.2,0.503,27,1
+40,4,111,72,47,207,37.1,1.39,56,1
+41,3,180,64,25,70,34,0.271,26,0
+42,7,133,84,0,0,40.2,0.696,37,0
+43,7,106,92,18,0,22.7,0.235,48,0
+44,9,171,110,24,240,45.4,0.721,54,1
+45,7,159,64,0,0,27.4,0.294,40,0
+46,0,180,66,39,0,42,1.893,25,1
+47,1,146,56,0,0,29.7,0.564,29,0
+48,2,71,70,27,0,28,0.586,22,0
+49,7,103,66,32,0,39.1,0.344,31,1
+50,7,105,0,0,0,0,0.305,24,0
+51,1,103,80,11,82,19.4,0.491,22,0
+52,1,101,50,15,36,24.2,0.526,26,0
+53,5,88,66,21,23,24.4,0.342,30,0
+54,8,176,90,34,300,33.7,0.467,58,1
+55,7,150,66,42,342,34.7,0.718,42,0
+56,1,73,50,10,0,23,0.248,21,0
+57,7,187,68,39,304,37.7,0.254,41,1
+58,0,100,88,60,110,46.8,0.962,31,0
+59,0,146,82,0,0,40.5,1.781,44,0
+60,0,105,64,41,142,41.5,0.173,22,0
+61,2,84,0,0,0,0,0.304,21,0
+62,8,133,72,0,0,32.9,0.27,39,1
+63,5,44,62,0,0,25,0.587,36,0
+64,2,141,58,34,128,25.4,0.699,24,0
+65,7,114,66,0,0,32.8,0.258,42,1
+66,5,99,74,27,0,29,0.203,32,0
+67,0,109,88,30,0,32.5,0.855,38,1
+68,2,109,92,0,0,42.7,0.845,54,0
+69,1,95,66,13,38,19.6,0.334,25,0
+70,4,146,85,27,100,28.9,0.189,27,0
+71,2,100,66,20,90,32.9,0.867,28,1
+72,5,139,64,35,140,28.6,0.411,26,0
+73,13,126,90,0,0,43.4,0.583,42,1
+74,4,129,86,20,270,35.1,0.231,23,0
+75,1,79,75,30,0,32,0.396,22,0
+76,1,0,48,20,0,24.7,0.14,22,0
+77,7,62,78,0,0,32.6,0.391,41,0
+78,5,95,72,33,0,37.7,0.37,27,0
+79,0,131,0,0,0,43.2,0.27,26,1
+80,2,112,66,22,0,25,0.307,24,0
+81,3,113,44,13,0,22.4,0.14,22,0
+82,2,74,0,0,0,0,0.102,22,0
+83,7,83,78,26,71,29.3,0.767,36,0
+84,0,101,65,28,0,24.6,0.237,22,0
+85,5,137,108,0,0,48.8,0.227,37,1
+86,2,110,74,29,125,32.4,0.698,27,0
+87,13,106,72,54,0,36.6,0.178,45,0
+88,2,100,68,25,71,38.5,0.324,26,0
+89,15,136,70,32,110,37.1,0.153,43,1
+90,1,107,68,19,0,26.5,0.165,24,0
+91,1,80,55,0,0,19.1,0.258,21,0
+92,4,123,80,15,176,32,0.443,34,0
+93,7,81,78,40,48,46.7,0.261,42,0
+94,4,134,72,0,0,23.8,0.277,60,1
+95,2,142,82,18,64,24.7,0.761,21,0
+96,6,144,72,27,228,33.9,0.255,40,0
+97,2,92,62,28,0,31.6,0.13,24,0
+98,1,71,48,18,76,20.4,0.323,22,0
+99,6,93,50,30,64,28.7,0.356,23,0
+100,1,122,90,51,220,49.7,0.325,31,1
+101,1,163,72,0,0,39,1.222,33,1
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diff --git a/README.md b/README.md
index f746e56..8a15081 100644
--- a/README.md
+++ b/README.md
@@ -1,29 +1,290 @@
-# Project 2
+Ajay Anand A20581927 Anish Vishwanathan VR A20596106 Mohit Panchatcharam A20562455 Sibi Chandra sekar A20577946
 
-Select one of the following two options:
+1. What does the model you have implemented do and when should it be used?
+2. How did you test your model to determine if it is working reasonably correctly?
+3. What parameters have you exposed to users of your implementation in order to tune performance? (Also perhaps provide some basic usage examples.)
+4. Are there specific inputs that your implementation has trouble with? Given more time, could you work around these or is it fundamental?
 
-## Boosting Trees
+1.⁠ ⁠This algorithm predicts the health variables such as blood pressure, age, bmi and level of blood sugars to help determine if a patient may in
+developing stage of diabetes. To identify those who are at serious level, it
+searches for patterns in the data and operates in a manner that guarantees
+effiecient and accurate results. How It Operates:
+Preparing the data: To improve the models structure, the data is cleaned
+up and modified. For example, to ensure consistency, figure out such as
+glucose levels are in standard level. Examining the Model:
+To test the model more than once, K Fold Testing divides the data into
+smaller groups. This makes sure it works well on different sets of
+information. Bootstrapping randomly samples the data to check how reliable the
+model is over and over again. Improving Accuracy: The model’s settings are fine-tuned using a
+method called Bayesian Optimization. This helps make the predictions as
+accurate as possible. Understanding What Matters: The model identifies which health
+factors, like glucose or age, are the most important for predicting diabetes. This makes it easier to see how the results were reached. When to Use:
+This project is good for doctors, researchers, or hospitals. It can:
+Help identify patients who might be at risk for diabetes early. Analyze health data to find common patterns or risk factors.
 
-Implement the gradient-boosting tree algorithm (with the usual fit-predict interface) as described in Sections 10.9-10.10 of Elements of Statistical Learning (2nd Edition). Answer the questions below as you did for Project 1.
+2.⁠ ⁠we tested the model to make sure it works properly and efficiently and
+give the accurate results for the user to avoid any failures
+TESTING STEPS:
+Trying different data splits:
+we split the data in several smaller groups using a technique called k
+folding testing. the model was trained on some groups and test on others. This helped us to check if the model works properly how the data was
+divided.We also used Bootstrapping where we randomly picked some
+data samples and tested the model on the balance data. This results in
+consistent results. Measuring accuracy:
+We used scores like precision, recall, and AUC to see how the efficiently
+the model was making a right decisions most of the time.The AUC ROC
+curve showed us how the model was balanced between finding patients
+with diabetes and avoiding false detection. Fine-Tuning for Better Results:
+We adjusted the model’s settings to improve its accuracy. This step made
+sure the predictions were as reliable and correct as possible. Making Sense of Results:
+We checked which factors, like blood sugar levels or age, had the most
+influence on the model’s predictions. This helped ensure the results
+matched what we know about diabetes and made the model easier to trust.
 
-Put your README below. Answer the following questions.
+3.This implementation allows the user to change some of the parameters
+based on tuning the model performance. To specify how flexible the
+model is for learning from data and predicting outputs. Here is a simple
+guide to key parameters that the users can change:
+Parameter Visibility:
+n_estimators — Number of trees
+It does this by controlling how many decision trees are present in the
+model. Increased trees can result in better accuracy but can slow down
+the model. For example: set it to 100 which means the model will use 100 trees for
+predictions. Learning Rate (learning_rate):
+This controls the weight of each tree in the final prediction. The model
+tends to learn slower but often more accurately with smaller values (0.1)
+Tree Depth (max_depth):
+This defines how complex each tree could be. Larger trees can model
+more features but they might also overfit to the training data. For example: if you set it to 5, at maximum each tree can consist of 5
+levels. Minimum Samples to Split: (min_samples_split)
+This sets threshold for the smallest amount of samples needed to split a
+node in the tree. This model gets simpler as the value of becomes larger. For example, setting this value to 4 would require a minimum of 4
+samples in order to perform a split. Minimum samples per leaf (min_samples_leaf):
+This parameter controls the min samples at a leaf node. It helps prevent
+overfitting. Example: The number of samples at leaf nodes must be greater than or
+equal to this value, when set to 2 there will be a minimum of 2 sample in
+the leaf node.
+Subsampling (subsample):
+This decides the fraction of data used to build each tree. Using less than
+1.0 (e.g., 0.8) can reduce overfitting. Basic Example for Tuning:
+Let’s say you want the model to train faster while maintaining good
+accuracy. You can set:
+n_estimators to 50
+learning_rate to 0.1
+max_depth to 3
+By tweaking these parameters, you balance accuracy, speed, and how
+well the model generalizes to new data.
 
-* What does the model you have implemented do and when should it be used?
-* How did you test your model to determine if it is working reasonably correctly?
-* What parameters have you exposed to users of your implementation in order to tune performance? (Also perhaps provide some basic usage examples.)
-* Are there specific inputs that your implementation has trouble with? Given more time, could you work around these or is it fundamental?
+4.Yes, the implementation may struggle with inputs that are highly imbalanced (e.g., one class is much smaller than the other) or have missing or noisy data. These issues can affect accuracy and fairness. With more time, we can address them by using techniques like oversampling, better preprocessing, or tweaking the model, so they aren't fundamental problems.
 
-## Model Selection
+README
 
-Implement generic k-fold cross-validation and bootstrapping model selection methods.
+Overview:
+In this project, we are building a machine learning model that helps predict whether a person has diabetes based on their health data. The dataset includes various health measurements, like glucose levels, age, BMI, and other factors. Our goal is to train a model that can accurately predict if someone is diabetic or not, based on this data.
 
-In your README, answer the following questions:
+In order to achieve the highest degree of accuracy in the model, we utilize various techniques which includes;
+1.	Cross-Validation (Stratified K-Fold)
+2.	Bootstrapping
+3.	Hyperparameter Tuning (with Optuna)
+4.	Feature Importance
+5.	Model Evaluation (F1 Score, Precision, Recall, AUC)
+Extra Techniques used in this project for bonus marks.
 
-* Do your cross-validation and bootstrapping model selectors agree with a simpler model selector like AIC in simple cases (like linear regression)?
-* In what cases might the methods you've written fail or give incorrect or undesirable results?
-* What could you implement given more time to mitigate these cases or help users of your methods?
-* What parameters have you exposed to your users in order to use your model selectors.
+1. Bayesian optimization hyperparameter tuning.
+2. K – fold base model.
+ 
+Key Concepts and Techniques
+1. Cross-Validation (Stratified K-Fold)
+A method to test the model effectiveness by dividing the data into multiple subsets (or folds). This is stratified K-Fold Cross Validation and it is implemented such that number of diabetes-positive and negative cases are in a balanced proportion within each fold. This is useful to reliably estimate the performance of that model.
+2. Bootstrapping
+Bootstrapping is a statistical resampling method in which random samples are drawn from the data with replacement. We take a small subset of this data randomly and run the model on it couple of times, this way we can observe how the model behaves with different variations of the data. This gives us an indication of how the model might behave on completely new, never-before-seen before data.
 
-See sections 7.10-7.11 of Elements of Statistical Learning and the lecture notes. Pay particular attention to Section 7.10.2.
+3. Hyperparameter Tuning with Optuna
+Hyperparameters are the settings in machine learning, by which a model learns. For instance, the number of trees in case of a decision tree model or a learning rate to control how fast the model learns by adjusting itself. Instead of going through all the parameters manually, we use a tool called optuna to automatically find the best hyperparameters for our model. This improves efficiency in model performance and saves us time.
+4. Feature Importance
+After training our model, we can see which factors (like age or glucose levels) contributed most to predicting if a person has diabetes. We call this process extraction of feature importance. By understanding which features are most important, we can learn what influences the prediction.
+5. Evaluation Metrics
+To measure how well our model is performing, we use several metrics:
+•	F1 Score: This balances precision (how many of the predicted positives were actually positive) and recall (how many of the actual positives were correctly predicted).
+•	Precision: The accuracy of the positive predictions.
+•	Recall: How well the model finds the actual positive cases.
+•	AUC (Area Under the Curve): This tells us how well the model can separate the two classes (diabetic vs. non-diabetic) based on probabilities.
+ 
+Explanation of the Code
+Step 1: Loading and Preparing the Data
+First, we load the dataset, check its structure, and remove any unnecessary columns. We also check if any data is missing. After that, we standardize the data, which means we scale the features so they all have similar ranges, making it easier for the model to learn.
+python
+Copy code
+# Loading the dataset
+df = pd.read_csv("Healthcare-Diabetes.csv")
+df.head()  # Show first few rows to understand data
 
-As usual, above-and-beyond efforts will be considered for bonus points.
+# Checking if there are any missing values
+df.isnull().any()  # There are no missing values
+
+# Dropping 'Id' column as it's not needed for prediction
+df.drop(columns=['Id'], inplace=True)
+•	We remove the Id column because it's just a unique identifier and doesn't help in predicting diabetes.
+•	We also check for missing data, and since there are none in this dataset, we don't have to worry about it.
+Next, we standardize the features (like glucose levels, BMI, age, etc.) to make sure they all have the same scale. This helps the model perform better.
+python
+Copy code
+# Standardize the numerical features
+scaler = StandardScaler()
+columns_to_standardize = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']
+standardized_data = scaler.fit_transform(df[columns_to_standardize])
+
+# Convert standardized data back into a DataFrame
+df_standardized = pd.DataFrame(standardized_data, columns=columns_to_standardize)
+
+# Add target variable (Outcome) back to standardized DataFrame
+df_standardized["Outcome"] = df["Outcome"].values
+•	We use StandardScaler to scale the data so that it has a mean of 0 and a standard deviation of 1. This helps the model treat all features equally, preventing some features from dominating due to their larger values.
+ 
+Step 2: Training the Base Model Using Stratified K-Fold Cross-Validation
+We use a machine learning model called Gradient Boosting Classifier. This model combines multiple decision trees to make predictions. We train and test the model using Stratified K-Fold Cross-Validation to get a more accurate measure of its performance.
+python
+Copy code
+# Defining features and target
+X = df_standardized.drop(columns=['Outcome'])  # Features
+y = df_standardized['Outcome']                # Target variable
+
+# Initialize the Gradient Boosting Classifier
+model = GradientBoostingClassifier(random_state=42)
+
+# Initialize Stratified K-Fold Cross-Validation
+skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
+
+# Lists to store metrics
+f1_scores_kfold = []
+precision_scores_kfold = []
+recall_scores_kfold = []
+auc_scores_kfold = []
+all_conf_matrices = []
+
+for train_index, test_index in skf.split(X, y):
+    # Splitting the data into train and test sets for each fold
+    X_train, X_test = X.iloc[train_index], X.iloc[test_index]
+    y_train, y_test = y.iloc[train_index], y.iloc[test_index]
+    
+    # Training the model
+    model.fit(X_train, y_train)
+    
+    # Making predictions
+    y_pred = model.predict(X_test)
+    y_pred_proba = model.predict_proba(X_test)[:, 1]  # Probabilities for ROC curve
+    
+    # Calculate evaluation metrics
+    f1_scores_kfold.append(f1_score(y_test, y_pred))
+    precision_scores_kfold.append(precision_score(y_test, y_pred))
+    recall_scores_kfold.append(recall_score(y_test, y_pred))
+    auc_scores_kfold.append(roc_auc_score(y_test, y_pred_proba))
+    
+    # Save confusion matrix for later display
+    conf_matrix = confusion_matrix(y_test, y_pred)
+    all_conf_matrices.append(conf_matrix)
+
+    # Plot ROC curve for this fold
+    fpr, tpr, _ = roc_curve(y_test, y_pred_proba)
+    plt.plot(fpr, tpr, label=f"K-Fold ROC Curve")
+
+# Finalize and display the ROC curve
+plt.xlabel("False Positive Rate")
+plt.ylabel("True Positive Rate")
+plt.title("K-Fold AUC-ROC Curve")
+plt.legend(loc="lower right")
+plt.show()
+
+# Display confusion matrix for the last fold
+disp = ConfusionMatrixDisplay(confusion_matrix=all_conf_matrices[-1], display_labels=model.classes_)
+disp.plot(cmap='Blues', values_format='d')
+plt.title("Confusion Matrix for Last Fold in K-Fold Cross-Validation")
+plt.show()
+
+# Print out performance metrics
+print(f"K-Fold F1 Scores: {f1_scores_kfold}")
+print(f"K-Fold Precision Scores: {precision_scores_kfold}")
+print(f"K-Fold Recall Scores: {recall_scores_kfold}")
+print(f"K-Fold AUC Scores: {auc_scores_kfold}")
+print(f"Mean F1 Score (K-Fold): {np.mean(f1_scores_kfold)}")
+print(f"Mean AUC Score (K-Fold): {np.mean(auc_scores_kfold)}")
+•	Stratified K-Fold means that the data is divided into 5 parts. Each part gets a chance to be used as both a training set and a testing set, making sure the data is balanced.
+•	After training the model, we evaluate it using different metrics like F1 Score, Precision, Recall, and AUC. These metrics help us understand how well the model is performing.
+We also plot an ROC curve to visually check how good the model is at distinguishing between diabetic and non-diabetic cases.
+ 
+Step 3: Bootstrapping
+Bootstrapping allows us to train the model on random subsets of the data, chosen with replacement. By repeating this process, we can get a better idea of how the model behaves with different portions of the dataset.
+python
+Copy code
+# Initialize bootstrapping parameters
+n_bootstrap_samples = 50
+f1_scores_bootstrap = []
+precision_scores_bootstrap = []
+recall_scores_bootstrap = []
+auc_scores_bootstrap = []
+
+plt.figure(figsize=(8, 6))  # Set figure size for better visualization
+confusion_matrices = []
+
+for i in range(n_bootstrap_samples):
+    # Create Bootstrap Sample
+    indices = np.random.choice(range(len(X)), size=len(X), replace=True)
+    X_train, y_train = X.iloc[indices], y.iloc[indices]
+    
+    # Train the model on the bootstrap sample
+    model.fit(X_train, y_train)
+    
+    # Get out-of-bag (OOB) predictions
+    oob_indices = list(set(range(len(X))) - set(indices))
+    X_test_oob, y_test_oob = X.iloc[oob_indices], y.iloc[oob_indices]
+    
+    # Predict using the model
+    y_pred = model.predict(X_test_oob)
+    y_pred_proba = model.predict_proba(X_test_oob)[:, 1]  # Probabilities for ROC curve
+    
+    # Collect performance metrics
+    f1_scores_bootstrap.append(f1_score(y_test_oob, y_pred))
+    precision_scores_bootstrap.append(precision_score(y_test_oob, y_pred))
+    recall_scores_bootstrap.append(recall_score(y_test_oob, y_pred))
+    auc_scores_bootstrap.append(roc_auc_score(y_test_oob, y_pred_proba))
+    
+    # Plot ROC Curve for each bootstrap sample
+    fpr, tpr, _ = roc_curve(y_test_oob, y_pred_proba)
+    plt.plot(fpr, tpr, label=f"Sample {i+1}")
+
+# Finalize and plot the ROC curves for bootstrapping
+plt.xlabel("False Positive Rate")
+plt.ylabel("True Positive Rate")
+plt.title("Bootstrapping AUC-ROC Curve")
+plt.legend(loc="lower right")
+plt.show()
+•	We train the model on 50 bootstrapped samples, calculate evaluation metrics for each sample, and plot the ROC curve for each of them. This helps us understand how the model performs across different subsets of data.
+ 
+Step 4: Hyperparameter Tuning with Optuna
+To make the model even better, we use Optuna to automatically search for the best model settings, called hyperparameters. These include things like how many trees to use, what the learning rate should be, and more.
+python
+Copy code
+import optuna
+
+# Define the objective function for Optuna
+def objective(trial):
+    params = {
+        'n_estimators': trial.suggest_int('n_estimators', 50, 200),
+        'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.2),
+        'max_depth': trial.suggest_int('max_depth', 3, 7),
+        'min_samples_split': trial.suggest_int('min_samples_split', 2, 5),
+        'min_samples_leaf': trial.suggest_int('min_samples_leaf', 1, 4),
+        'subsample': trial.suggest_float('subsample', 0.5, 1.0)
+    }
+    model = GradientBoostingClassifier(random_state=42, **params)
+    scores = cross_val_score(model, X, y, scoring='f1', cv=3, n_jobs=-1)
+    return scores.mean()
+
+# Start Optuna optimization
+study = optuna.create_study(direction='maximize')
+study.optimize(objective, n_trials=50)
+best_params = study.best_params
+•	Optuna helps us find the best hyperparameters by trying different combinations and evaluating their performance automatically.
+ 
+Conclusion
+By using techniques like cross-validation, bootstrapping, and hyperparameter tuning, we've made sure that our diabetes prediction model isn't just accurate, but also reliable when faced with new data it hasn't seen before. These methods help strengthen the model, making it more flexible and capable of providing better predictions in real-world scenarios. With each step, we've worked to improve how well the model can understand and predict outcomes, ensuring it's both trustworthy and useful.
diff --git a/Requirement.txt b/Requirement.txt
new file mode 100644
index 0000000..cc4ffde
--- /dev/null
+++ b/Requirement.txt
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+pandas
+numpy
+dtale
+scikit-learn
+matplotlib
+optuna