From 74b4c1a805988ac8eb3684689d71d4145a5d5a60 Mon Sep 17 00:00:00 2001
From: Sharanya Mishra <smishra25@hawk.iit.edu>
Date: Fri, 15 Nov 2024 18:52:02 -0600
Subject: [PATCH 1/7] initial-commit

---
 Model/Gradient_Boosting.py  |  158 ++++++
 Model/XGBoost.py            |  143 +++++
 Test/Datset_customer.csv    |  441 +++++++++++++++
 Test/TestingDataset-1.ipynb | 1016 +++++++++++++++++++++++++++++++++++
 Test/TestingDataset-2.ipynb |  820 ++++++++++++++++++++++++++++
 Test/__init__.py            |   33 ++
 Test/small_test.csv         |   51 ++
 __init__.py                 |   33 ++
 8 files changed, 2695 insertions(+)
 create mode 100644 Model/Gradient_Boosting.py
 create mode 100644 Model/XGBoost.py
 create mode 100644 Test/Datset_customer.csv
 create mode 100644 Test/TestingDataset-1.ipynb
 create mode 100644 Test/TestingDataset-2.ipynb
 create mode 100644 Test/__init__.py
 create mode 100644 Test/small_test.csv
 create mode 100644 __init__.py

diff --git a/Model/Gradient_Boosting.py b/Model/Gradient_Boosting.py
new file mode 100644
index 0000000..61dc768
--- /dev/null
+++ b/Model/Gradient_Boosting.py
@@ -0,0 +1,158 @@
+# -*- coding: utf-8 -*-
+"""ML Project-1.ipynb
+Group Members:
+A20584318 - ANSH KAUSHIK
+A20593046 - ARUNESHWARAN SIVAKUMAR
+A20588339 - HARISH NAMASIVAYAM MUTHUSWAMY
+A20579993 - SHARANYA MISHRA
+"""
+import numpy as np
+
+# Gradient Boosting Regressor
+# Function to split data into training and testing sets (75% training, 25% testing)
+def train_test_split(X, y, test_size=0.25, random_seed=None):
+    if random_seed:
+        np.random.seed(random_seed)
+
+    indices = np.arange(X.shape[0])
+    np.random.shuffle(indices)
+
+    split_index = int((1 - test_size) * len(indices))
+    train_indices, test_indices = indices[:split_index], indices[split_index:]
+
+    X_train, X_test = X[train_indices], X[test_indices]
+    y_train, y_test = y[train_indices], y[test_indices]
+
+    return X_train, X_test, y_train, y_test
+
+class GradientBoosting:
+    def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=1):
+        """
+        n_estimators: Number of weak learners (trees)
+        learning_rate: Step size for updating predictions
+        max_depth: Maximum depth of individual decision stumps
+        """
+        self.n_estimators = n_estimators
+        self.learning_rate = learning_rate
+        self.max_depth = max_depth
+        self.models = []  # List to store weak learners
+        self.initial_prediction = None  # Mean of target values
+
+    def _split(self, X, y):
+        """
+        Splits data based on feature threshold for decision stump.
+        """
+        best_feature, best_threshold, best_loss = None, None, float('inf')
+        best_left, best_right = None, None
+
+        for feature in range(X.shape[1]):
+            thresholds = np.unique(X[:, feature])
+            for threshold in thresholds:
+                left_mask = X[:, feature] <= threshold
+                right_mask = ~left_mask
+
+                left_mean = np.mean(y[left_mask]) if np.any(left_mask) else 0
+                right_mean = np.mean(y[right_mask]) if np.any(right_mask) else 0
+
+                loss = (
+                    np.sum((y[left_mask] - left_mean) ** 2) +
+                    np.sum((y[right_mask] - right_mean) ** 2)
+                )
+
+                if loss < best_loss:
+                    best_loss = loss
+                    best_feature = feature
+                    best_threshold = threshold
+                    best_left = left_mask
+                    best_right = right_mask
+
+        return best_feature, best_threshold, best_left, best_right
+
+    def fit(self, X, y):
+        """
+        Fits the Gradient Boosting Regressor.
+        """
+        # Initialize predictions to the mean of the target values
+        self.initial_prediction = np.mean(y)
+        predictions = np.full(y.shape, self.initial_prediction)
+
+        for i in range(self.n_estimators):
+            # Compute residuals
+            residuals = y - predictions
+
+            # Fit a decision stump to the residuals
+            feature, threshold, left_mask, right_mask = self._split(X, residuals)
+
+            if feature is None:
+                break
+
+            model = {
+                'feature': feature,
+                'threshold': threshold,
+                'left_value': np.mean(residuals[left_mask]),
+                'right_value': np.mean(residuals[right_mask]),
+            }
+
+            # Update predictions
+            predictions[left_mask] += self.learning_rate * model['left_value']
+            predictions[right_mask] += self.learning_rate * model['right_value']
+
+            # Save the weak learner
+            self.models.append(model)
+
+            # Print progress
+            if i % 10 == 0:
+                mse = np.mean((y - predictions) ** 2)
+                print(f"Iteration {i}: MSE: {mse}")
+
+    def predict(self, X):
+        """
+        Predicts using the fitted Gradient Boosting Regressor.
+        """
+        predictions = np.full(X.shape[0], self.initial_prediction)
+
+        for model in self.models:
+            feature = model['feature']
+            threshold = model['threshold']
+            left_value = model['left_value']
+            right_value = model['right_value']
+
+            left_mask = X[:, feature] <= threshold
+            right_mask = ~left_mask
+
+            predictions[left_mask] += self.learning_rate * left_value
+            predictions[right_mask] += self.learning_rate * right_value
+
+        return predictions
+
+    def mean_squared_error(self, y_true, y_pred):
+        return np.mean((y_true - y_pred) ** 2)
+
+    def r2_score(self, y_true, y_pred):
+        total_variance = np.sum((y_true - np.mean(y_true)) ** 2)
+        residual_variance = np.sum((y_true - y_pred) ** 2)
+        return 1 - (residual_variance / total_variance)
+    
+    def f1_score(y_true, y_pred):
+    # Calculate True Positives, False Positives, True Negatives, and False Negatives
+        TP = np.sum((y_true == 1) & (y_pred == 1))
+        FP = np.sum((y_true == 0) & (y_pred == 1))
+        FN = np.sum((y_true == 1) & (y_pred == 0))
+        TN = np.sum((y_true == 0) & (y_pred == 0))
+    
+        # Calculate Precision and Recall
+        precision = TP / (TP + FP) if TP + FP > 0 else 0
+        recall = TP / (TP + FN) if TP + FN > 0 else 0
+    
+        # Calculate F1 Score
+        f1 = 2 * (precision * recall) / (precision + recall) if precision + recall > 0 else 0
+        return f1
+
+    def gini_coefficient(y_true, y_pred):
+        """Calculate the Gini coefficient."""
+        sorted_indices = np.argsort(y_pred)
+        sorted_true = y_true[sorted_indices]
+        cum_true = np.cumsum(sorted_true) / np.sum(sorted_true)
+        cum_population = np.cumsum(np.ones_like(sorted_true)) / len(sorted_true)
+        gini = 1 - 2 * np.trapz(cum_true, cum_population)
+        return gini
\ No newline at end of file
diff --git a/Model/XGBoost.py b/Model/XGBoost.py
new file mode 100644
index 0000000..401c8a7
--- /dev/null
+++ b/Model/XGBoost.py
@@ -0,0 +1,143 @@
+import numpy as np
+
+def train_test_split(X, y, test_size=0.25, random_seed=None):
+    if random_seed:
+        np.random.seed(random_seed)
+    indices = np.arange(X.shape[0])
+    np.random.shuffle(indices)
+    split_index = int((1 - test_size) * len(indices))
+    train_indices, test_indices = indices[:split_index], indices[split_index:]
+    X_train, X_test = X[train_indices], X[test_indices]
+    y_train, y_test = y[train_indices], y[test_indices]
+    return X_train, X_test, y_train, y_test
+
+class XGBoost:
+    def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3, reg_lambda=1.0, gamma=0.0):
+        self.n_estimators = n_estimators
+        self.learning_rate = learning_rate
+        self.max_depth = max_depth
+        self.reg_lambda = reg_lambda
+        self.gamma = gamma
+        self.trees = []
+        self.initial_prediction = None
+
+    def predict_proba(self, X):
+        y_pred = self.predict(X)
+        return 1 / (1 + np.exp(-y_pred))
+
+    def _compute_pseudo_residuals(self, y, y_pred):
+        pred_proba = 1 / (1 + np.exp(-y_pred))
+        grad = pred_proba - y
+        hess = pred_proba * (1 - pred_proba)
+        return grad, hess
+
+    def _split_gain(self, grad_sum_left, hess_sum_left, grad_sum_right, hess_sum_right):
+        gain = (grad_sum_left**2 / (hess_sum_left + self.reg_lambda) +
+                grad_sum_right**2 / (hess_sum_right + self.reg_lambda))
+        return gain
+
+    def _fit_tree(self, X, grad, hess, depth=0):
+        n_samples, n_features = X.shape
+        if depth == self.max_depth or n_samples <= 1:
+            leaf_value = -np.sum(grad) / (np.sum(hess) + self.reg_lambda)
+            return {"leaf_value": leaf_value}
+
+        best_gain = -np.inf
+        best_split = None
+        best_left_idx, best_right_idx = None, None
+
+        for feature_idx in range(n_features):
+            thresholds = np.unique(X[:, feature_idx])
+            for threshold in thresholds:
+                left_idx = X[:, feature_idx] <= threshold
+                right_idx = ~left_idx
+                if np.sum(left_idx) == 0 or np.sum(right_idx) == 0:
+                    continue
+                grad_sum_left = np.sum(grad[left_idx])
+                hess_sum_left = np.sum(hess[left_idx])
+                grad_sum_right = np.sum(grad[right_idx])
+                hess_sum_right = np.sum(hess[right_idx])
+                gain = self._split_gain(grad_sum_left, hess_sum_left, grad_sum_right, hess_sum_right)
+                if gain > best_gain and gain > self.gamma:
+                    best_gain = gain
+                    best_split = (feature_idx, threshold)
+                    best_left_idx, best_right_idx = left_idx, right_idx
+
+        if best_split is None:
+            leaf_value = -np.sum(grad) / (np.sum(hess) + self.reg_lambda)
+            return {"leaf_value": leaf_value}
+
+        feature_idx, threshold = best_split
+        left_subtree = self._fit_tree(X[best_left_idx], grad[best_left_idx], hess[best_left_idx], depth + 1)
+        right_subtree = self._fit_tree(X[best_right_idx], grad[best_right_idx], hess[best_right_idx], depth + 1)
+
+        return {
+            "feature_idx": feature_idx,
+            "threshold": threshold,
+            "left": left_subtree,
+            "right": right_subtree,
+        }
+
+    def _predict_tree(self, tree, X):
+        if "leaf_value" in tree:
+            return np.full(X.shape[0], tree["leaf_value"])
+        feature_idx = tree["feature_idx"]
+        threshold = tree["threshold"]
+        left_idx = X[:, feature_idx] <= threshold
+        right_idx = ~left_idx
+        predictions = np.zeros(X.shape[0])
+        predictions[left_idx] = self._predict_tree(tree["left"], X[left_idx])
+        predictions[right_idx] = self._predict_tree(tree["right"], X[right_idx])
+        return predictions
+
+    def fit(self, X, y):
+        self.initial_prediction = np.mean(y)
+        y_pred = np.full(y.shape, self.initial_prediction)
+        for i in range(self.n_estimators):
+            grad, hess = self._compute_pseudo_residuals(y, y_pred)
+            tree = self._fit_tree(X, grad, hess)
+            self.trees.append(tree)
+            y_pred += self.learning_rate * self._predict_tree(tree, X)
+            if i % 10 == 0:
+                mse = self.mean_squared_error(y, y_pred)
+                print(f"Iteration {i}: Training MSE = {mse}")
+
+    def predict(self, X):
+        y_pred = np.full(X.shape[0], self.initial_prediction)
+        for tree in self.trees:
+            y_pred += self.learning_rate * self._predict_tree(tree, X)
+        return y_pred
+
+    @staticmethod
+    def mean_squared_error(y_true, y_pred):
+        return np.mean((y_true - y_pred) ** 2)
+
+    @staticmethod
+    def r2_score(y_true, y_pred):
+        total_variance = np.sum((y_true - np.mean(y_true)) ** 2)
+        residual_variance = np.sum((y_true - y_pred) ** 2)
+        return 1 - (residual_variance / total_variance)
+
+    @staticmethod
+    def gini_index(y_true, y_pred):
+        y_true_sorted = y_true[np.argsort(y_pred)]
+        n = len(y_true)
+        cumulative_y = np.cumsum(y_true_sorted) / np.sum(y_true_sorted)
+        gini = 1 - 2 * np.sum(cumulative_y - cumulative_y / n)
+        return gini
+
+    @staticmethod
+    def gini_impurity(y_true, y_pred):
+        p = np.mean(y_true)
+        return 2 * p * (1 - p)
+
+    @staticmethod
+    def f1_score(y_true, y_pred, threshold=0.5):
+        TP = np.sum((y_true == 1) & (y_pred == 1))
+        FP = np.sum((y_true == 0) & (y_pred == 1))
+        FN = np.sum((y_true == 1) & (y_pred == 0))
+        TN = np.sum((y_true == 0) & (y_pred == 0))
+        precision = TP / (TP + FP) if TP + FP > 0 else 0
+        recall = TP / (TP + FN) if TP + FN > 0 else 0
+        f1 = 2 * (precision * recall) / (precision + recall) if precision + recall > 0 else 0
+        return f1
\ No newline at end of file
diff --git a/Test/Datset_customer.csv b/Test/Datset_customer.csv
new file mode 100644
index 0000000..c5616f0
--- /dev/null
+++ b/Test/Datset_customer.csv
@@ -0,0 +1,441 @@
+Channel,Region,Fresh,Milk,Grocery,Frozen,Detergents_Paper,Delicassen
+2,3,12669,9656,7561,214,2674,1338
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diff --git a/Test/TestingDataset-1.ipynb b/Test/TestingDataset-1.ipynb
new file mode 100644
index 0000000..ad27b07
--- /dev/null
+++ b/Test/TestingDataset-1.ipynb
@@ -0,0 +1,1016 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0a552be3-e9d8-4ec0-9f6d-c2bd4bfcc219",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from scipy.stats import norm\n",
+    "import csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "42874655-3e67-4101-b174-0ce12979b0b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=pd.read_csv('./Datset_customer.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1c0e185c-c6ea-4f12-9757-21f7b814e5b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>Fresh</th>\n",
+       "      <th>Milk</th>\n",
+       "      <th>Grocery</th>\n",
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+       "      <th>Detergents_Paper</th>\n",
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+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22615</td>\n",
+       "      <td>5410</td>\n",
+       "      <td>7198</td>\n",
+       "      <td>3915</td>\n",
+       "      <td>1777</td>\n",
+       "      <td>5185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9413</td>\n",
+       "      <td>8259</td>\n",
+       "      <td>5126</td>\n",
+       "      <td>666</td>\n",
+       "      <td>1795</td>\n",
+       "      <td>1451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12126</td>\n",
+       "      <td>3199</td>\n",
+       "      <td>6975</td>\n",
+       "      <td>480</td>\n",
+       "      <td>3140</td>\n",
+       "      <td>545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7579</td>\n",
+       "      <td>4956</td>\n",
+       "      <td>9426</td>\n",
+       "      <td>1669</td>\n",
+       "      <td>3321</td>\n",
+       "      <td>2566</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5963</td>\n",
+       "      <td>3648</td>\n",
+       "      <td>6192</td>\n",
+       "      <td>425</td>\n",
+       "      <td>1716</td>\n",
+       "      <td>750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6006</td>\n",
+       "      <td>11093</td>\n",
+       "      <td>18881</td>\n",
+       "      <td>1159</td>\n",
+       "      <td>7425</td>\n",
+       "      <td>2098</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3366</td>\n",
+       "      <td>5403</td>\n",
+       "      <td>12974</td>\n",
+       "      <td>4400</td>\n",
+       "      <td>5977</td>\n",
+       "      <td>1744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13146</td>\n",
+       "      <td>1124</td>\n",
+       "      <td>4523</td>\n",
+       "      <td>1420</td>\n",
+       "      <td>549</td>\n",
+       "      <td>497</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>31714</td>\n",
+       "      <td>12319</td>\n",
+       "      <td>11757</td>\n",
+       "      <td>287</td>\n",
+       "      <td>3881</td>\n",
+       "      <td>2931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21217</td>\n",
+       "      <td>6208</td>\n",
+       "      <td>14982</td>\n",
+       "      <td>3095</td>\n",
+       "      <td>6707</td>\n",
+       "      <td>602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24653</td>\n",
+       "      <td>9465</td>\n",
+       "      <td>12091</td>\n",
+       "      <td>294</td>\n",
+       "      <td>5058</td>\n",
+       "      <td>2168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10253</td>\n",
+       "      <td>1114</td>\n",
+       "      <td>3821</td>\n",
+       "      <td>397</td>\n",
+       "      <td>964</td>\n",
+       "      <td>412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>8816</td>\n",
+       "      <td>12121</td>\n",
+       "      <td>134</td>\n",
+       "      <td>4508</td>\n",
+       "      <td>1080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5876</td>\n",
+       "      <td>6157</td>\n",
+       "      <td>2933</td>\n",
+       "      <td>839</td>\n",
+       "      <td>370</td>\n",
+       "      <td>4478</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>18601</td>\n",
+       "      <td>6327</td>\n",
+       "      <td>10099</td>\n",
+       "      <td>2205</td>\n",
+       "      <td>2767</td>\n",
+       "      <td>3181</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7780</td>\n",
+       "      <td>2495</td>\n",
+       "      <td>9464</td>\n",
+       "      <td>669</td>\n",
+       "      <td>2518</td>\n",
+       "      <td>501</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Channel  Region  Fresh   Milk  Grocery  Frozen  Detergents_Paper  \\\n",
+       "0         2       3  12669   9656     7561     214              2674   \n",
+       "1         2       3   7057   9810     9568    1762              3293   \n",
+       "2         2       3   6353   8808     7684    2405              3516   \n",
+       "3         1       3  13265   1196     4221    6404               507   \n",
+       "4         2       3  22615   5410     7198    3915              1777   \n",
+       "5         2       3   9413   8259     5126     666              1795   \n",
+       "6         2       3  12126   3199     6975     480              3140   \n",
+       "7         2       3   7579   4956     9426    1669              3321   \n",
+       "8         1       3   5963   3648     6192     425              1716   \n",
+       "9         2       3   6006  11093    18881    1159              7425   \n",
+       "10        2       3   3366   5403    12974    4400              5977   \n",
+       "11        2       3  13146   1124     4523    1420               549   \n",
+       "12        2       3  31714  12319    11757     287              3881   \n",
+       "13        2       3  21217   6208    14982    3095              6707   \n",
+       "14        2       3  24653   9465    12091     294              5058   \n",
+       "15        1       3  10253   1114     3821     397               964   \n",
+       "16        2       3   1020   8816    12121     134              4508   \n",
+       "17        1       3   5876   6157     2933     839               370   \n",
+       "18        2       3  18601   6327    10099    2205              2767   \n",
+       "19        1       3   7780   2495     9464     669              2518   \n",
+       "\n",
+       "    Delicassen  \n",
+       "0         1338  \n",
+       "1         1776  \n",
+       "2         7844  \n",
+       "3         1788  \n",
+       "4         5185  \n",
+       "5         1451  \n",
+       "6          545  \n",
+       "7         2566  \n",
+       "8          750  \n",
+       "9         2098  \n",
+       "10        1744  \n",
+       "11         497  \n",
+       "12        2931  \n",
+       "13         602  \n",
+       "14        2168  \n",
+       "15         412  \n",
+       "16        1080  \n",
+       "17        4478  \n",
+       "18        3181  \n",
+       "19         501  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "fd0543a5-790a-45bd-97a2-34e01c758564",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Channel</th>\n",
+       "      <td>440.0</td>\n",
+       "      <td>1.322727</td>\n",
+       "      <td>0.468052</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>440.0</td>\n",
+       "      <td>2.543182</td>\n",
+       "      <td>0.774272</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fresh</th>\n",
+       "      <td>440.0</td>\n",
+       "      <td>12000.297727</td>\n",
+       "      <td>12647.328865</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3127.75</td>\n",
+       "      <td>8504.0</td>\n",
+       "      <td>16933.75</td>\n",
+       "      <td>112151.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Milk</th>\n",
+       "      <td>440.0</td>\n",
+       "      <td>5796.265909</td>\n",
+       "      <td>7380.377175</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>1533.00</td>\n",
+       "      <td>3627.0</td>\n",
+       "      <td>7190.25</td>\n",
+       "      <td>73498.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Grocery</th>\n",
+       "      <td>440.0</td>\n",
+       "      <td>7951.277273</td>\n",
+       "      <td>9503.162829</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2153.00</td>\n",
+       "      <td>4755.5</td>\n",
+       "      <td>10655.75</td>\n",
+       "      <td>92780.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Frozen</th>\n",
+       "      <td>440.0</td>\n",
+       "      <td>3071.931818</td>\n",
+       "      <td>4854.673333</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>742.25</td>\n",
+       "      <td>1526.0</td>\n",
+       "      <td>3554.25</td>\n",
+       "      <td>60869.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <td>440.0</td>\n",
+       "      <td>2881.493182</td>\n",
+       "      <td>4767.854448</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>256.75</td>\n",
+       "      <td>816.5</td>\n",
+       "      <td>3922.00</td>\n",
+       "      <td>40827.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delicassen</th>\n",
+       "      <td>440.0</td>\n",
+       "      <td>1524.870455</td>\n",
+       "      <td>2820.105937</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>408.25</td>\n",
+       "      <td>965.5</td>\n",
+       "      <td>1820.25</td>\n",
+       "      <td>47943.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  count          mean           std   min      25%     50%  \\\n",
+       "Channel           440.0      1.322727      0.468052   1.0     1.00     1.0   \n",
+       "Region            440.0      2.543182      0.774272   1.0     2.00     3.0   \n",
+       "Fresh             440.0  12000.297727  12647.328865   3.0  3127.75  8504.0   \n",
+       "Milk              440.0   5796.265909   7380.377175  55.0  1533.00  3627.0   \n",
+       "Grocery           440.0   7951.277273   9503.162829   3.0  2153.00  4755.5   \n",
+       "Frozen            440.0   3071.931818   4854.673333  25.0   742.25  1526.0   \n",
+       "Detergents_Paper  440.0   2881.493182   4767.854448   3.0   256.75   816.5   \n",
+       "Delicassen        440.0   1524.870455   2820.105937   3.0   408.25   965.5   \n",
+       "\n",
+       "                       75%       max  \n",
+       "Channel               2.00       2.0  \n",
+       "Region                3.00       3.0  \n",
+       "Fresh             16933.75  112151.0  \n",
+       "Milk               7190.25   73498.0  \n",
+       "Grocery           10655.75   92780.0  \n",
+       "Frozen             3554.25   60869.0  \n",
+       "Detergents_Paper   3922.00   40827.0  \n",
+       "Delicassen         1820.25   47943.0  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "a1cc6b41-6bef-4e6e-865a-1af00d629cbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78389/1469724773.py:5: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
+      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78389/1469724773.py:5: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
+      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78389/1469724773.py:5: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
+      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78389/1469724773.py:5: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
+      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78389/1469724773.py:5: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
+      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78389/1469724773.py:5: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
+      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78389/1469724773.py:5: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
+      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78389/1469724773.py:5: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x168e95fd0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_num = data.select_dtypes(include = ['float64', 'int64'])\n",
+    "# Histogram to display skewness of every numeric variable\n",
+    "f = pd.melt(df_num, value_vars = df_num.columns)\n",
+    "g = sns.FacetGrid(f, col=\"variable\",  col_wrap=4, sharex=False, sharey=False)\n",
+    "g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f818162a-510c-4802-8fe5-d15682c18184",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No missing values in the DataFrame.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check for missing values and print the missing columns with their counts\n",
+    "missing_counts = data.isnull().sum()\n",
+    "\n",
+    "# Filter to get columns with missing values\n",
+    "missing_columns = missing_counts[missing_counts > 0]\n",
+    "\n",
+    "if not missing_columns.empty:\n",
+    "    # Sort missing columns in descending order\n",
+    "    sorted_missing_columns = missing_columns.sort_values(ascending=False)\n",
+    "\n",
+    "    print(\"Columns with missing values and their counts (sorted in descending order):\")\n",
+    "    print(sorted_missing_columns)\n",
+    "else:\n",
+    "    print(\"No missing values in the DataFrame.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "59f308b4-fc49-40b4-9510-3e5261e958fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Strong Correlations (|correlation| > 0.7):\n",
+      "Grocery - Milk: 0.73\n",
+      "Detergents_Paper - Grocery: 0.92\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#checking for highly correlated columns:\n",
+    "correlation_matrix = data.corr()\n",
+    "\n",
+    "def print_strong_correlations(corr_matrix, threshold=0.7):\n",
+    "    print(\"\\nStrong Correlations (|correlation| > {}):\".format(threshold))\n",
+    "    for i in range(len(corr_matrix.columns)):\n",
+    "        for j in range(i):\n",
+    "            if abs(corr_matrix.iloc[i, j]) > threshold:\n",
+    "                print(\"{} - {}: {:.2f}\".format(corr_matrix.columns[i], corr_matrix.columns[j], corr_matrix.iloc[i, j]))\n",
+    "\n",
+    "print_strong_correlations(correlation_matrix)\n",
+    "sns.heatmap(data.corr(), annot=True, cmap='coolwarm', linewidths=0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "279e927c-5192-46d9-96a3-c59d415ce18f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "# Get the current notebook's absolute path and convert it to a directory path\n",
+    "current_notebook_path = os.path.abspath(os.getcwd())\n",
+    "\n",
+    "# Append the parent directory (Project1) to the system path\n",
+    "project_root = os.path.dirname(current_notebook_path)\n",
+    "sys.path.append(project_root)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e6d81e52-b5b4-4ea8-8b86-54933a43fc79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing ElasticNetRegression and train_test_split\n",
+    "from Model.Gradient_Boosting import GradientBoosting\n",
+    "from Model.Gradient_Boosting import train_test_split\n",
+    "from Model.XGBoost import XGBoost as xgb\n",
+    "from Model.Gradient_Boosting import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "10cdeb14-607b-488a-94b7-4f3b2acef123",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of X: (440, 7)\n",
+      "Shape of y: (440,)\n",
+      "Training set size: 330, Test set size: 110\n",
+      "Training the model...\n",
+      "Iteration 0: MSE: 0.1925046550209184\n",
+      "Iteration 10: MSE: 0.08928634339378874\n",
+      "Iteration 20: MSE: 0.07128446638262076\n",
+      "Iteration 30: MSE: 0.06637012548376324\n",
+      "Iteration 40: MSE: 0.06380813836853168\n",
+      "Iteration 50: MSE: 0.062190568156020115\n",
+      "Iteration 60: MSE: 0.061008916891126024\n",
+      "Iteration 70: MSE: 0.06005015302886127\n",
+      "Iteration 80: MSE: 0.0592898850148287\n",
+      "Iteration 90: MSE: 0.058664392216089566\n",
+      "Predicting on training and testing sets...\n",
+      "\n",
+      "Training Metrics:\n",
+      "  Mean Squared Error (MSE): 0.0582\n",
+      "  R-Squared: 0.7331\n",
+      "  F1 Score: 0.7675\n",
+      "  Gini Coefficient: 0.6403\n",
+      "  Mean Bias: 0.0000\n",
+      "\n",
+      "Testing Metrics:\n",
+      "  Mean Squared Error (MSE): 0.0467\n",
+      "  R-Squared: 0.7880\n",
+      "  F1 Score: 0.7912\n",
+      "  Gini Coefficient: 0.6485\n",
+      "  Mean Bias: -0.0131\n"
+     ]
+    }
+   ],
+   "source": [
+    "def test_predict():\n",
+    "    # Load data from CSV\n",
+    "    data = []\n",
+    "    with open(\"./Datset_customer.csv\", \"r\") as file:\n",
+    "        reader = csv.DictReader(file)\n",
+    "        for row in reader:\n",
+    "            data.append(row)\n",
+    "\n",
+    "    # Extract features (X) and target variable (y) from the CSV\n",
+    "    X = np.array([[float(row[col]) for col in row if col != 'Channel'] for row in data])\n",
+    "    y = np.array([float(row['Channel']) for row in data])\n",
+    "\n",
+    "    # Debug: Check the shapes of X and y\n",
+    "    print(f\"Shape of X: {X.shape}\")\n",
+    "    print(f\"Shape of y: {y.shape}\")\n",
+    "\n",
+    "    # Convert target `y` to binary\n",
+    "    threshold = np.median(y)\n",
+    "    y_binary = (y > threshold).astype(int)\n",
+    "\n",
+    "    # Split dataset into training and testing sets\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_seed=42)\n",
+    "    _, _, y_train_binary, y_test_binary = train_test_split(X, y_binary, test_size=0.25, random_seed=42)\n",
+    "\n",
+    "    # Debug: Check the shapes of split data\n",
+    "    print(f\"Training set size: {X_train.shape[0]}, Test set size: {X_test.shape[0]}\")\n",
+    "\n",
+    "    # Create an instance of GradientBoosting\n",
+    "    gradient_booster = GradientBoosting(n_estimators=100, learning_rate=0.1, max_depth=1)\n",
+    "\n",
+    "    # Train the model\n",
+    "    print(\"Training the model...\")\n",
+    "    gradient_booster.fit(X_train, y_train)\n",
+    "\n",
+    "    # Predict on both training and test sets\n",
+    "    print(\"Predicting on training and testing sets...\")\n",
+    "    y_train_pred = gradient_booster.predict(X_train)\n",
+    "    y_test_pred = gradient_booster.predict(X_test)\n",
+    "\n",
+    "    # Threshold the predictions (convert to binary)\n",
+    "    y_train_pred_binary = (y_train_pred > np.median(y_train_pred)).astype(int)\n",
+    "    y_test_pred_binary = (y_test_pred > np.median(y_test_pred)).astype(int)\n",
+    "\n",
+    "    # Calculate evaluation metrics\n",
+    "    train_mse = gradient_booster.mean_squared_error(y_train, y_train_pred)\n",
+    "    test_mse = gradient_booster.mean_squared_error(y_test, y_test_pred)\n",
+    "\n",
+    "    train_r2 = gradient_booster.r2_score(y_train, y_train_pred)\n",
+    "    test_r2 = gradient_booster.r2_score(y_test, y_test_pred)\n",
+    "\n",
+    "    # Calculate F1 score using the method from the class\n",
+    "    train_f1 = GradientBoosting.f1_score(y_train_binary, y_train_pred_binary)\n",
+    "    test_f1 = GradientBoosting.f1_score(y_test_binary, y_test_pred_binary)\n",
+    "\n",
+    "    train_gini = GradientBoosting.gini_coefficient(y_train_binary, y_train_pred)\n",
+    "    test_gini = GradientBoosting.gini_coefficient(y_test_binary, y_test_pred)\n",
+    "\n",
+    "    # Checking bias\n",
+    "    train_residuals = y_train_pred - y_train\n",
+    "    test_residuals = y_test_pred - y_test\n",
+    "    train_mean_bias = np.mean(train_residuals)\n",
+    "    test_mean_bias = np.mean(test_residuals)\n",
+    "\n",
+    "    # Print the results\n",
+    "    print(\"\\nTraining Metrics:\")\n",
+    "    print(f\"  Mean Squared Error (MSE): {train_mse:.4f}\")\n",
+    "    print(f\"  R-Squared: {train_r2:.4f}\")\n",
+    "    print(f\"  F1 Score: {train_f1:.4f}\")\n",
+    "    print(f\"  Gini Coefficient: {train_gini:.4f}\")\n",
+    "    print(f\"  Mean Bias: {train_mean_bias:.4f}\")\n",
+    "\n",
+    "    print(\"\\nTesting Metrics:\")\n",
+    "    print(f\"  Mean Squared Error (MSE): {test_mse:.4f}\")\n",
+    "    print(f\"  R-Squared: {test_r2:.4f}\")\n",
+    "    print(f\"  F1 Score: {test_f1:.4f}\")\n",
+    "    print(f\"  Gini Coefficient: {test_gini:.4f}\")\n",
+    "    print(f\"  Mean Bias: {test_mean_bias:.4f}\")\n",
+    "# Call the function\n",
+    "test_predict()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "b442c572-87db-417b-9cd2-282078b620a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of X: (440, 7)\n",
+      "Shape of y: (440,)\n",
+      "Training set size: 330, Test set size: 110\n",
+      "Training the model...\n",
+      "Iteration 0: Training MSE = 0.1271363083684796\n",
+      "Iteration 10: Training MSE = 1.216936913053914\n",
+      "Iteration 20: Training MSE = 3.526462894848376\n",
+      "Iteration 30: Training MSE = 6.298325117439503\n",
+      "Iteration 40: Training MSE = 8.91223450961791\n",
+      "Iteration 50: Training MSE = 10.818610408674877\n",
+      "Iteration 60: Training MSE = 12.326685278474486\n",
+      "Iteration 70: Training MSE = 13.729049519986086\n",
+      "Iteration 80: Training MSE = 14.800232655623304\n",
+      "Iteration 90: Training MSE = 15.731209377519058\n",
+      "Predicting on training and testing sets...\n",
+      "\n",
+      "Training Metrics:\n",
+      "  Mean Squared Error (MSE): 0.0179\n",
+      "  R-Squared: 0.9177\n",
+      "  F1 Score: 0.9717\n",
+      "  Gini Index: 0.4361\n",
+      "\n",
+      "Testing Metrics:\n",
+      "  Mean Squared Error (MSE): 0.0432\n",
+      "  R-Squared: 0.8037\n",
+      "  F1 Score: 0.9315\n",
+      "  Gini Index: 0.4403\n"
+     ]
+    }
+   ],
+   "source": [
+    "def test_predict_XG():\n",
+    "    # Load data from CSV\n",
+    "    data = []\n",
+    "    with open(\"./Datset_customer.csv\", \"r\") as file:\n",
+    "        reader = csv.DictReader(file)\n",
+    "        for row in reader:\n",
+    "            data.append(row)\n",
+    "\n",
+    "    # Extract features (X) and target variable (y) from the CSV\n",
+    "    X = np.array([[float(row[col]) for col in row if col != 'Channel'] for row in data])\n",
+    "    y = np.array([float(row['Channel']) for row in data])\n",
+    "\n",
+    "    # Debug: Check the shapes of X and y\n",
+    "    print(f\"Shape of X: {X.shape}\")\n",
+    "    print(f\"Shape of y: {y.shape}\")\n",
+    "\n",
+    "    # Convert target `y` to binary\n",
+    "    threshold = np.median(y)\n",
+    "    y_binary = (y > threshold).astype(int)\n",
+    "\n",
+    "    # Split dataset into training and testing sets\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y_binary, test_size=0.25, random_seed=42)\n",
+    "\n",
+    "    # Debug: Check the shapes of split data\n",
+    "    print(f\"Training set size: {X_train.shape[0]}, Test set size: {X_test.shape[0]}\")\n",
+    "\n",
+    "    # Create an instance of XGBoost\n",
+    "    xg_booster = xgb(n_estimators=100, learning_rate=0.1, max_depth=3, reg_lambda=1.0, gamma=0.0)\n",
+    "\n",
+    "    # Train the model\n",
+    "    print(\"Training the model...\")\n",
+    "    xg_booster.fit(X_train, y_train)\n",
+    "\n",
+    "    # Predict on both training and test sets\n",
+    "    print(\"Predicting on training and testing sets...\")\n",
+    "    y_train_pred_proba = xg_booster.predict_proba(X_train)\n",
+    "    y_test_pred_proba = xg_booster.predict_proba(X_test)\n",
+    "\n",
+    "    # Convert probabilities to binary predictions\n",
+    "    y_train_pred = (y_train_pred_proba > 0.5).astype(int)\n",
+    "    y_test_pred = (y_test_pred_proba > 0.5).astype(int)\n",
+    "\n",
+    "    # Calculate evaluation metrics\n",
+    "    train_mse = xg_booster.mean_squared_error(y_train, y_train_pred_proba)\n",
+    "    test_mse = xg_booster.mean_squared_error(y_test, y_test_pred_proba)\n",
+    "\n",
+    "    train_r2 = xg_booster.r2_score(y_train, y_train_pred_proba)\n",
+    "    test_r2 = xg_booster.r2_score(y_test, y_test_pred_proba)\n",
+    "\n",
+    "    train_f1 = xg_booster.f1_score(y_train, y_train_pred)\n",
+    "    test_f1 = xg_booster.f1_score(y_test, y_test_pred)\n",
+    "\n",
+    "    train_gini = xg_booster.gini_impurity(y_train, y_train_pred_proba)\n",
+    "    test_gini = xg_booster.gini_impurity(y_test, y_test_pred_proba)\n",
+    "\n",
+    "    # Print the results\n",
+    "    print(\"\\nTraining Metrics:\")\n",
+    "    print(f\"  Mean Squared Error (MSE): {train_mse:.4f}\")\n",
+    "    print(f\"  R-Squared: {train_r2:.4f}\")\n",
+    "    print(f\"  F1 Score: {train_f1:.4f}\")\n",
+    "    print(f\"  Gini Index: {train_gini:.4f}\")\n",
+    "\n",
+    "    print(\"\\nTesting Metrics:\")\n",
+    "    print(f\"  Mean Squared Error (MSE): {test_mse:.4f}\")\n",
+    "    print(f\"  R-Squared: {test_r2:.4f}\")\n",
+    "    print(f\"  F1 Score: {test_f1:.4f}\")\n",
+    "    print(f\"  Gini Index: {test_gini:.4f}\")\n",
+    "\n",
+    "# Call the function\n",
+    "test_predict_XG()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20f25a92-8721-455b-81f1-1b2fbf73a42e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Test/TestingDataset-2.ipynb b/Test/TestingDataset-2.ipynb
new file mode 100644
index 0000000..2e80171
--- /dev/null
+++ b/Test/TestingDataset-2.ipynb
@@ -0,0 +1,820 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# EDA of the small_test.csv:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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>x_0</th>\n",
+       "      <th>x_1</th>\n",
+       "      <th>x_2</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-2.421349</td>\n",
+       "      <td>6.290215</td>\n",
+       "      <td>2.516304</td>\n",
+       "      <td>10.240120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8.134658</td>\n",
+       "      <td>-6.975969</td>\n",
+       "      <td>-3.281095</td>\n",
+       "      <td>-6.896294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.453124</td>\n",
+       "      <td>0.058895</td>\n",
+       "      <td>-3.592293</td>\n",
+       "      <td>14.104288</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3.979833</td>\n",
+       "      <td>-8.129002</td>\n",
+       "      <td>9.202915</td>\n",
+       "      <td>-43.788868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-4.354232</td>\n",
+       "      <td>2.472475</td>\n",
+       "      <td>8.459722</td>\n",
+       "      <td>-12.067617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>8.726621</td>\n",
+       "      <td>-9.607723</td>\n",
+       "      <td>-5.092837</td>\n",
+       "      <td>-8.265643</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-0.291365</td>\n",
+       "      <td>8.224664</td>\n",
+       "      <td>-3.819334</td>\n",
+       "      <td>32.981856</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.411871</td>\n",
+       "      <td>6.003043</td>\n",
+       "      <td>3.996826</td>\n",
+       "      <td>0.726779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.215252</td>\n",
+       "      <td>-3.321042</td>\n",
+       "      <td>-5.352746</td>\n",
+       "      <td>11.934441</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>4.802262</td>\n",
+       "      <td>9.818246</td>\n",
+       "      <td>4.936296</td>\n",
+       "      <td>3.599572</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>9.717340</td>\n",
+       "      <td>0.144092</td>\n",
+       "      <td>8.749937</td>\n",
+       "      <td>-34.917123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>4.098688</td>\n",
+       "      <td>-9.752059</td>\n",
+       "      <td>7.980744</td>\n",
+       "      <td>-43.328056</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>-2.398061</td>\n",
+       "      <td>2.827819</td>\n",
+       "      <td>-1.626175</td>\n",
+       "      <td>16.915393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>5.398273</td>\n",
+       "      <td>7.583047</td>\n",
+       "      <td>2.758296</td>\n",
+       "      <td>4.437458</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>3.371528</td>\n",
+       "      <td>-5.430064</td>\n",
+       "      <td>2.191600</td>\n",
+       "      <td>-16.035658</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>2.086364</td>\n",
+       "      <td>0.108249</td>\n",
+       "      <td>8.144466</td>\n",
+       "      <td>-25.094326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2.825594</td>\n",
+       "      <td>-2.286321</td>\n",
+       "      <td>4.771241</td>\n",
+       "      <td>-18.000440</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>-8.150228</td>\n",
+       "      <td>-4.259315</td>\n",
+       "      <td>1.892335</td>\n",
+       "      <td>-1.393024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>-6.067265</td>\n",
+       "      <td>3.677625</td>\n",
+       "      <td>8.481727</td>\n",
+       "      <td>-10.278523</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>8.640174</td>\n",
+       "      <td>9.717801</td>\n",
+       "      <td>4.980673</td>\n",
+       "      <td>-0.926665</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         x_0       x_1       x_2          y\n",
+       "0  -2.421349  6.290215  2.516304  10.240120\n",
+       "1   8.134658 -6.975969 -3.281095  -6.896294\n",
+       "2  -0.453124  0.058895 -3.592293  14.104288\n",
+       "3   3.979833 -8.129002  9.202915 -43.788868\n",
+       "4  -4.354232  2.472475  8.459722 -12.067617\n",
+       "5   8.726621 -9.607723 -5.092837  -8.265643\n",
+       "6  -0.291365  8.224664 -3.819334  32.981856\n",
+       "7   1.411871  6.003043  3.996826   0.726779\n",
+       "8   0.215252 -3.321042 -5.352746  11.934441\n",
+       "9   4.802262  9.818246  4.936296   3.599572\n",
+       "10  9.717340  0.144092  8.749937 -34.917123\n",
+       "11  4.098688 -9.752059  7.980744 -43.328056\n",
+       "12 -2.398061  2.827819 -1.626175  16.915393\n",
+       "13  5.398273  7.583047  2.758296   4.437458\n",
+       "14  3.371528 -5.430064  2.191600 -16.035658\n",
+       "15  2.086364  0.108249  8.144466 -25.094326\n",
+       "16  2.825594 -2.286321  4.771241 -18.000440\n",
+       "17 -8.150228 -4.259315  1.892335  -1.393024\n",
+       "18 -6.067265  3.677625  8.481727 -10.278523\n",
+       "19  8.640174  9.717801  4.980673  -0.926665"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"ML Project-1.ipynb\n",
+    "Group Members:\n",
+    "A20584318 - ANSH KAUSHIK\n",
+    "A20593046 - ARUNESHWARAN SIVAKUMAR\n",
+    "A20588339 - HARISH NAMASIVAYAM MUTHUSWAMY\n",
+    "A20579993 - SHARANYA MISHRA\n",
+    "\"\"\"\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from scipy.stats import norm\n",
+    "import csv\n",
+    "\n",
+    "data=pd.read_csv('./small_test.csv')\n",
+    "data.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>x_0</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0.919729</td>\n",
+       "      <td>5.783714</td>\n",
+       "      <td>-8.642801</td>\n",
+       "      <td>-3.995705</td>\n",
+       "      <td>1.730267</td>\n",
+       "      <td>5.397151</td>\n",
+       "      <td>9.717340</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>x_1</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0.735999</td>\n",
+       "      <td>5.720213</td>\n",
+       "      <td>-9.752059</td>\n",
+       "      <td>-4.011655</td>\n",
+       "      <td>1.180905</td>\n",
+       "      <td>5.212510</td>\n",
+       "      <td>9.818246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>x_2</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0.156729</td>\n",
+       "      <td>5.559850</td>\n",
+       "      <td>-9.896226</td>\n",
+       "      <td>-4.267913</td>\n",
+       "      <td>0.069469</td>\n",
+       "      <td>4.895032</td>\n",
+       "      <td>9.810419</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>y</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>3.335015</td>\n",
+       "      <td>22.598316</td>\n",
+       "      <td>-43.788868</td>\n",
+       "      <td>-11.620343</td>\n",
+       "      <td>3.025178</td>\n",
+       "      <td>16.824434</td>\n",
+       "      <td>50.359814</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     count      mean        std        min        25%       50%        75%  \\\n",
+       "x_0   50.0  0.919729   5.783714  -8.642801  -3.995705  1.730267   5.397151   \n",
+       "x_1   50.0  0.735999   5.720213  -9.752059  -4.011655  1.180905   5.212510   \n",
+       "x_2   50.0  0.156729   5.559850  -9.896226  -4.267913  0.069469   4.895032   \n",
+       "y     50.0  3.335015  22.598316 -43.788868 -11.620343  3.025178  16.824434   \n",
+       "\n",
+       "           max  \n",
+       "x_0   9.717340  \n",
+       "x_1   9.818246  \n",
+       "x_2   9.810419  \n",
+       "y    50.359814  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78384/1469724773.py:5: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
+      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78384/1469724773.py:5: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
+      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78384/1469724773.py:5: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
+      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78384/1469724773.py:5: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x151da2b40>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x300 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_num = data.select_dtypes(include = ['float64', 'int64'])\n",
+    "# Histogram to display skewness of every numeric variable\n",
+    "f = pd.melt(df_num, value_vars = df_num.columns)\n",
+    "g = sns.FacetGrid(f, col=\"variable\",  col_wrap=4, sharex=False, sharey=False)\n",
+    "g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### As observed, all the variables follow a normal distribution, indicating that no further transformations are necessary for this dataset.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No missing values in the DataFrame.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check for missing values and print the missing columns with their counts\n",
+    "missing_counts = data.isnull().sum()\n",
+    "\n",
+    "# Filter to get columns with missing values\n",
+    "missing_columns = missing_counts[missing_counts > 0]\n",
+    "\n",
+    "if not missing_columns.empty:\n",
+    "    # Sort missing columns in descending order\n",
+    "    sorted_missing_columns = missing_columns.sort_values(ascending=False)\n",
+    "\n",
+    "    print(\"Columns with missing values and their counts (sorted in descending order):\")\n",
+    "    print(sorted_missing_columns)\n",
+    "else:\n",
+    "    print(\"No missing values in the DataFrame.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Hence, we conclude that the dataset has no missing values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Strong Correlations (|correlation| > 0.7):\n",
+      "y - x_2: -0.78\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#checking for highly correlated columns:\n",
+    "correlation_matrix = data.corr()\n",
+    "\n",
+    "def print_strong_correlations(corr_matrix, threshold=0.7):\n",
+    "    print(\"\\nStrong Correlations (|correlation| > {}):\".format(threshold))\n",
+    "    for i in range(len(corr_matrix.columns)):\n",
+    "        for j in range(i):\n",
+    "            if abs(corr_matrix.iloc[i, j]) > threshold:\n",
+    "                print(\"{} - {}: {:.2f}\".format(corr_matrix.columns[i], corr_matrix.columns[j], corr_matrix.iloc[i, j]))\n",
+    "\n",
+    "print_strong_correlations(correlation_matrix)\n",
+    "sns.heatmap(data.corr(), annot=True, cmap='coolwarm', linewidths=0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "# Get the current notebook's absolute path and convert it to a directory path\n",
+    "current_notebook_path = os.path.abspath(os.getcwd())\n",
+    "\n",
+    "# Append the parent directory (Project1) to the system path\n",
+    "project_root = os.path.dirname(current_notebook_path)\n",
+    "sys.path.append(project_root)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing ElasticNetRegression and train_test_split\n",
+    "from Model.Gradient_Boosting import GradientBoosting\n",
+    "from Model.Gradient_Boosting import train_test_split\n",
+    "from Model.XGBoost import XGBoost as xgb\n",
+    "from Model.Gradient_Boosting import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of X: (50, 3)\n",
+      "Shape of y: (50,)\n",
+      "Training set size: 37, Test set size: 13\n",
+      "Training the model...\n",
+      "Iteration 0: MSE: 399.16089921641935\n",
+      "Iteration 10: MSE: 173.9341341335672\n",
+      "Iteration 20: MSE: 87.6175265471055\n",
+      "Iteration 30: MSE: 48.86164513019478\n",
+      "Iteration 40: MSE: 29.764986319179627\n",
+      "Iteration 50: MSE: 19.29585288372823\n",
+      "Iteration 60: MSE: 13.134352040546943\n",
+      "Iteration 70: MSE: 9.323923450951847\n",
+      "Iteration 80: MSE: 6.974612387310865\n",
+      "Iteration 90: MSE: 5.442021057778226\n",
+      "Predicting on training and testing sets...\n",
+      "\n",
+      "Training Metrics:\n",
+      "  Mean Squared Error (MSE): 4.4940\n",
+      "  R-Squared: 0.9899\n",
+      "  F1 Score: 0.9474\n",
+      "  Gini Coefficient: 0.4595\n",
+      "  Mean Bias: -0.0000\n",
+      "\n",
+      "Testing Metrics:\n",
+      "  Mean Squared Error (MSE): 82.1620\n",
+      "  R-Squared: 0.8707\n",
+      "  F1 Score: 0.9091\n",
+      "  Gini Coefficient: 0.6154\n",
+      "  Mean Bias: 0.4310\n"
+     ]
+    }
+   ],
+   "source": [
+    "def test_predict():\n",
+    "    # Load data from CSV\n",
+    "    data = []\n",
+    "    with open(\"./small_test.csv\", \"r\") as file:\n",
+    "        reader = csv.DictReader(file)\n",
+    "        for row in reader:\n",
+    "            data.append(row)\n",
+    "\n",
+    "    # Extract features (X) and target variable (y) from the CSV\n",
+    "    X = np.array([[float(row['x_0']), float(row['x_1']), float(row['x_2'])] for row in data])\n",
+    "    y = np.array([float(row['y']) for row in data])\n",
+    "\n",
+    "    # Debug: Check the shapes of X and y\n",
+    "    print(f\"Shape of X: {X.shape}\")\n",
+    "    print(f\"Shape of y: {y.shape}\")\n",
+    "\n",
+    "    # Convert target `y` to binary\n",
+    "    threshold = np.median(y)\n",
+    "    y_binary = (y > threshold).astype(int)\n",
+    "\n",
+    "    # Split dataset into training and testing sets\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_seed=42)\n",
+    "    _, _, y_train_binary, y_test_binary = train_test_split(X, y_binary, test_size=0.25, random_seed=42)\n",
+    "\n",
+    "    # Debug: Check the shapes of split data\n",
+    "    print(f\"Training set size: {X_train.shape[0]}, Test set size: {X_test.shape[0]}\")\n",
+    "\n",
+    "    # Create an instance of GradientBoosting\n",
+    "    gradient_booster = GradientBoosting(n_estimators=100, learning_rate=0.1, max_depth=1)\n",
+    "\n",
+    "    # Train the model\n",
+    "    print(\"Training the model...\")\n",
+    "    gradient_booster.fit(X_train, y_train)\n",
+    "\n",
+    "    # Predict on both training and test sets\n",
+    "    print(\"Predicting on training and testing sets...\")\n",
+    "    y_train_pred = gradient_booster.predict(X_train)\n",
+    "    y_test_pred = gradient_booster.predict(X_test)\n",
+    "\n",
+    "    # Threshold the predictions (convert to binary)\n",
+    "    y_train_pred_binary = (y_train_pred > np.median(y_train_pred)).astype(int)\n",
+    "    y_test_pred_binary = (y_test_pred > np.median(y_test_pred)).astype(int)\n",
+    "\n",
+    "    # Calculate evaluation metrics\n",
+    "    train_mse = gradient_booster.mean_squared_error(y_train, y_train_pred)\n",
+    "    test_mse = gradient_booster.mean_squared_error(y_test, y_test_pred)\n",
+    "\n",
+    "    train_r2 = gradient_booster.r2_score(y_train, y_train_pred)\n",
+    "    test_r2 = gradient_booster.r2_score(y_test, y_test_pred)\n",
+    "\n",
+    "    # Calculate F1 score using the method from the class\n",
+    "    train_f1 = GradientBoosting.f1_score(y_train_binary, y_train_pred_binary)\n",
+    "    test_f1 = GradientBoosting.f1_score(y_test_binary, y_test_pred_binary)\n",
+    "\n",
+    "    train_gini = GradientBoosting.gini_coefficient(y_train_binary, y_train_pred)\n",
+    "    test_gini = GradientBoosting.gini_coefficient(y_test_binary, y_test_pred)\n",
+    "\n",
+    "    # Checking bias\n",
+    "    train_residuals = y_train_pred - y_train\n",
+    "    test_residuals = y_test_pred - y_test\n",
+    "    train_mean_bias = np.mean(train_residuals)\n",
+    "    test_mean_bias = np.mean(test_residuals)\n",
+    "\n",
+    "    # Print the results\n",
+    "    print(\"\\nTraining Metrics:\")\n",
+    "    print(f\"  Mean Squared Error (MSE): {train_mse:.4f}\")\n",
+    "    print(f\"  R-Squared: {train_r2:.4f}\")\n",
+    "    print(f\"  F1 Score: {train_f1:.4f}\")\n",
+    "    print(f\"  Gini Coefficient: {train_gini:.4f}\")\n",
+    "    print(f\"  Mean Bias: {train_mean_bias:.4f}\")\n",
+    "\n",
+    "    print(\"\\nTesting Metrics:\")\n",
+    "    print(f\"  Mean Squared Error (MSE): {test_mse:.4f}\")\n",
+    "    print(f\"  R-Squared: {test_r2:.4f}\")\n",
+    "    print(f\"  F1 Score: {test_f1:.4f}\")\n",
+    "    print(f\"  Gini Coefficient: {test_gini:.4f}\")\n",
+    "    print(f\"  Mean Bias: {test_mean_bias:.4f}\")\n",
+    "# Call the function\n",
+    "test_predict()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of X: (50, 3)\n",
+      "Shape of y: (50,)\n",
+      "Training set size: 37, Test set size: 13\n",
+      "Training the model...\n",
+      "Iteration 0: Training MSE = 0.2348214066614786\n",
+      "Iteration 10: Training MSE = 0.1257323446330051\n",
+      "Iteration 20: Training MSE = 0.057654205740503846\n",
+      "Iteration 30: Training MSE = 0.022889418777036246\n",
+      "Iteration 40: Training MSE = 0.015766041951108017\n",
+      "Iteration 50: Training MSE = 0.031671211524251314\n",
+      "Iteration 60: Training MSE = 0.0671575739213283\n",
+      "Iteration 70: Training MSE = 0.12014227280200218\n",
+      "Iteration 80: Training MSE = 0.18843048602254264\n",
+      "Iteration 90: Training MSE = 0.2701649529407599\n",
+      "Predicting on training and testing sets...\n",
+      "\n",
+      "Training Metrics:\n",
+      "  Mean Squared Error (MSE): 0.0738\n",
+      "  R-Squared: 0.7028\n",
+      "  F1 Score: 1.0000\n",
+      "  Gini Index: 0.4967\n",
+      "\n",
+      "Testing Metrics:\n",
+      "  Mean Squared Error (MSE): 0.2013\n",
+      "  R-Squared: 0.1493\n",
+      "  F1 Score: 0.7692\n",
+      "  Gini Index: 0.4734\n"
+     ]
+    }
+   ],
+   "source": [
+    "def test_predict_XG():\n",
+    "    # Load data from CSV\n",
+    "    data = []\n",
+    "    with open(\"./small_test.csv\", \"r\") as file:\n",
+    "        reader = csv.DictReader(file)\n",
+    "        for row in reader:\n",
+    "            data.append(row)\n",
+    "\n",
+    "    # Extract features (X) and target variable (y) from the CSV\n",
+    "    X = np.array([[float(row['x_0']), float(row['x_1']), float(row['x_2'])] for row in data])\n",
+    "    y = np.array([float(row['y']) for row in data])\n",
+    "\n",
+    "    # Debug: Check the shapes of X and y\n",
+    "    print(f\"Shape of X: {X.shape}\")\n",
+    "    print(f\"Shape of y: {y.shape}\")\n",
+    "\n",
+    "    # Convert target `y` to binary\n",
+    "    threshold = np.median(y)\n",
+    "    y_binary = (y > threshold).astype(int)\n",
+    "\n",
+    "    # Split dataset into training and testing sets\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y_binary, test_size=0.25, random_seed=42)\n",
+    "\n",
+    "    # Debug: Check the shapes of split data\n",
+    "    print(f\"Training set size: {X_train.shape[0]}, Test set size: {X_test.shape[0]}\")\n",
+    "\n",
+    "    # Create an instance of XGBoost\n",
+    "    xg_booster = xgb(n_estimators=100, learning_rate=0.01, max_depth=3, reg_lambda=1.0, gamma=0.0)\n",
+    "\n",
+    "    # Train the model\n",
+    "    print(\"Training the model...\")\n",
+    "    xg_booster.fit(X_train, y_train)\n",
+    "\n",
+    "    # Predict on both training and test sets\n",
+    "    print(\"Predicting on training and testing sets...\")\n",
+    "    y_train_pred_proba = xg_booster.predict_proba(X_train)\n",
+    "    y_test_pred_proba = xg_booster.predict_proba(X_test)\n",
+    "\n",
+    "    # Convert probabilities to binary predictions\n",
+    "    y_train_pred = (y_train_pred_proba > 0.5).astype(int)\n",
+    "    y_test_pred = (y_test_pred_proba > 0.5).astype(int)\n",
+    "\n",
+    "    # Calculate evaluation metrics\n",
+    "    train_mse = xg_booster.mean_squared_error(y_train, y_train_pred_proba)\n",
+    "    test_mse = xg_booster.mean_squared_error(y_test, y_test_pred_proba)\n",
+    "\n",
+    "    train_r2 = xg_booster.r2_score(y_train, y_train_pred_proba)\n",
+    "    test_r2 = xg_booster.r2_score(y_test, y_test_pred_proba)\n",
+    "\n",
+    "    train_f1 = xg_booster.f1_score(y_train, y_train_pred)\n",
+    "    test_f1 = xg_booster.f1_score(y_test, y_test_pred)\n",
+    "\n",
+    "    train_gini = xg_booster.gini_impurity(y_train, y_train_pred_proba)\n",
+    "    test_gini = xg_booster.gini_impurity(y_test, y_test_pred_proba)\n",
+    "\n",
+    "    # Print the results\n",
+    "    print(\"\\nTraining Metrics:\")\n",
+    "    print(f\"  Mean Squared Error (MSE): {train_mse:.4f}\")\n",
+    "    print(f\"  R-Squared: {train_r2:.4f}\")\n",
+    "    print(f\"  F1 Score: {train_f1:.4f}\")\n",
+    "    print(f\"  Gini Index: {train_gini:.4f}\")\n",
+    "\n",
+    "    print(\"\\nTesting Metrics:\")\n",
+    "    print(f\"  Mean Squared Error (MSE): {test_mse:.4f}\")\n",
+    "    print(f\"  R-Squared: {test_r2:.4f}\")\n",
+    "    print(f\"  F1 Score: {test_f1:.4f}\")\n",
+    "    print(f\"  Gini Index: {test_gini:.4f}\")\n",
+    "\n",
+    "# Call the function\n",
+    "test_predict_XG()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": [],
+   "toc_visible": true
+  },
+  "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Test/__init__.py b/Test/__init__.py
new file mode 100644
index 0000000..f5610bc
--- /dev/null
+++ b/Test/__init__.py
@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fb2b9fd-4907-4bbc-b497-de5b9fb4f8c8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Test/small_test.csv b/Test/small_test.csv
new file mode 100644
index 0000000..bf8442e
--- /dev/null
+++ b/Test/small_test.csv
@@ -0,0 +1,51 @@
+x_0,x_1,x_2,y
+-2.421348566501347,6.290215260063935,2.516304163087373,10.240119830146476
+8.13465811997068,-6.975968662410185,-3.2810945459842866,-6.8962940548446845
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diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..f5610bc
--- /dev/null
+++ b/__init__.py
@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fb2b9fd-4907-4bbc-b497-de5b9fb4f8c8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 0ca9bca93ae8add32649f5fa3385346572b5452a Mon Sep 17 00:00:00 2001
From: Sharanya Mishra <smishra25@hawk.iit.edu>
Date: Wed, 20 Nov 2024 17:17:58 -0600
Subject: [PATCH 2/7] commit-modelselection and gradient

---
 Model/Gradient_Boosting.py            |   232 +-
 Model/ModelSelection.py               |   100 +
 Model/XGBoost.py                      |   143 -
 {Test => Model}/__init__.py           |     0
 Test/Datset_customer.csv              |   441 -
 Test/Highly_correlatedcode.py         |    26 +
 Test/TestingDataset-1.ipynb           |  1016 --
 Test/TestingDataset-2.ipynb           |   820 -
 Test/california_housing.csv           | 20641 ++++++++++++++++++++++++
 Test/highly_correlated_dataset.csv    |  1001 ++
 Test/main_GradientBoosting.py         |   119 +
 Test/main_ModelSelection.py           |   121 +
 Test/partially_correlated_dataset.csv |  1001 ++
 13 files changed, 23100 insertions(+), 2561 deletions(-)
 create mode 100644 Model/ModelSelection.py
 delete mode 100644 Model/XGBoost.py
 rename {Test => Model}/__init__.py (100%)
 delete mode 100644 Test/Datset_customer.csv
 create mode 100644 Test/Highly_correlatedcode.py
 delete mode 100644 Test/TestingDataset-1.ipynb
 delete mode 100644 Test/TestingDataset-2.ipynb
 create mode 100644 Test/california_housing.csv
 create mode 100644 Test/highly_correlated_dataset.csv
 create mode 100644 Test/main_GradientBoosting.py
 create mode 100644 Test/main_ModelSelection.py
 create mode 100644 Test/partially_correlated_dataset.csv

diff --git a/Model/Gradient_Boosting.py b/Model/Gradient_Boosting.py
index 61dc768..34940b2 100644
--- a/Model/Gradient_Boosting.py
+++ b/Model/Gradient_Boosting.py
@@ -1,158 +1,108 @@
-# -*- coding: utf-8 -*-
-"""ML Project-1.ipynb
-Group Members:
-A20584318 - ANSH KAUSHIK
-A20593046 - ARUNESHWARAN SIVAKUMAR
-A20588339 - HARISH NAMASIVAYAM MUTHUSWAMY
-A20579993 - SHARANYA MISHRA
-"""
 import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
 
-# Gradient Boosting Regressor
-# Function to split data into training and testing sets (75% training, 25% testing)
-def train_test_split(X, y, test_size=0.25, random_seed=None):
-    if random_seed:
-        np.random.seed(random_seed)
 
-    indices = np.arange(X.shape[0])
-    np.random.shuffle(indices)
-
-    split_index = int((1 - test_size) * len(indices))
-    train_indices, test_indices = indices[:split_index], indices[split_index:]
-
-    X_train, X_test = X[train_indices], X[test_indices]
-    y_train, y_test = y[train_indices], y[test_indices]
-
-    return X_train, X_test, y_train, y_test
-
-class GradientBoosting:
-    def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=1):
-        """
-        n_estimators: Number of weak learners (trees)
-        learning_rate: Step size for updating predictions
-        max_depth: Maximum depth of individual decision stumps
-        """
+class GradientBoostingTree:
+    def __init__(self, n_estimators, learning_rate, max_depth):
         self.n_estimators = n_estimators
         self.learning_rate = learning_rate
         self.max_depth = max_depth
-        self.models = []  # List to store weak learners
-        self.initial_prediction = None  # Mean of target values
-
-    def _split(self, X, y):
-        """
-        Splits data based on feature threshold for decision stump.
-        """
-        best_feature, best_threshold, best_loss = None, None, float('inf')
-        best_left, best_right = None, None
-
-        for feature in range(X.shape[1]):
-            thresholds = np.unique(X[:, feature])
-            for threshold in thresholds:
-                left_mask = X[:, feature] <= threshold
-                right_mask = ~left_mask
-
-                left_mean = np.mean(y[left_mask]) if np.any(left_mask) else 0
-                right_mean = np.mean(y[right_mask]) if np.any(right_mask) else 0
-
-                loss = (
-                    np.sum((y[left_mask] - left_mean) ** 2) +
-                    np.sum((y[right_mask] - right_mean) ** 2)
-                )
-
-                if loss < best_loss:
-                    best_loss = loss
-                    best_feature = feature
-                    best_threshold = threshold
-                    best_left = left_mask
-                    best_right = right_mask
-
-        return best_feature, best_threshold, best_left, best_right
+        self.models = []
 
     def fit(self, X, y):
-        """
-        Fits the Gradient Boosting Regressor.
-        """
-        # Initialize predictions to the mean of the target values
-        self.initial_prediction = np.mean(y)
-        predictions = np.full(y.shape, self.initial_prediction)
-
-        for i in range(self.n_estimators):
-            # Compute residuals
-            residuals = y - predictions
-
-            # Fit a decision stump to the residuals
-            feature, threshold, left_mask, right_mask = self._split(X, residuals)
-
-            if feature is None:
-                break
-
-            model = {
-                'feature': feature,
-                'threshold': threshold,
-                'left_value': np.mean(residuals[left_mask]),
-                'right_value': np.mean(residuals[right_mask]),
-            }
-
+        n_samples = X.shape[0]
+        # Initialize predictions with the mean of y
+        y_pred = np.full(n_samples, np.mean(y))
+        self.initial_pred = np.mean(y)
+        
+        for _ in range(self.n_estimators):
+            # Compute residuals (pseudo-residuals)
+            residuals = y - y_pred
+            
+            # Fit a decision tree to the residuals
+            tree = DecisionTreeRegressor(max_depth=self.max_depth)
+            tree.fit(X, residuals)
+            self.models.append(tree)
+            
             # Update predictions
-            predictions[left_mask] += self.learning_rate * model['left_value']
-            predictions[right_mask] += self.learning_rate * model['right_value']
-
-            # Save the weak learner
-            self.models.append(model)
-
-            # Print progress
-            if i % 10 == 0:
-                mse = np.mean((y - predictions) ** 2)
-                print(f"Iteration {i}: MSE: {mse}")
+            y_pred += self.learning_rate * tree.predict(X)
 
     def predict(self, X):
-        """
-        Predicts using the fitted Gradient Boosting Regressor.
-        """
-        predictions = np.full(X.shape[0], self.initial_prediction)
-
-        for model in self.models:
-            feature = model['feature']
-            threshold = model['threshold']
-            left_value = model['left_value']
-            right_value = model['right_value']
+        y_pred = np.full(X.shape[0], self.initial_pred)
+        for tree in self.models:
+            y_pred += self.learning_rate * tree.predict(X)
+        return y_pred
 
-            left_mask = X[:, feature] <= threshold
-            right_mask = ~left_mask
 
-            predictions[left_mask] += self.learning_rate * left_value
-            predictions[right_mask] += self.learning_rate * right_value
+class DecisionTreeRegressor:
+    def __init__(self, max_depth=3):
+        self.max_depth = max_depth
+        self.tree = None
 
-        return predictions
+    def fit(self, X, y):
+        self.tree = self._build_tree(X, y, depth=0)
 
-    def mean_squared_error(self, y_true, y_pred):
-        return np.mean((y_true - y_pred) ** 2)
+    def predict(self, X):
+        return np.array([self._predict_single(x, self.tree) for x in X])
+
+    def _build_tree(self, X, y, depth):
+        if depth == self.max_depth or len(set(y)) == 1:
+            return np.mean(y)
+        
+        best_split = self._find_best_split(X, y)
+        if best_split is None:
+            return np.mean(y)
+
+        feature_idx, threshold = best_split
+        left_mask = X[:, feature_idx] <= threshold
+        right_mask = ~left_mask
+
+        return {
+            "feature_idx": feature_idx,
+            "threshold": threshold,
+            "left": self._build_tree(X[left_mask], y[left_mask], depth + 1),
+            "right": self._build_tree(X[right_mask], y[right_mask], depth + 1),
+        }
+
+    def _find_best_split(self, X, y):
+        n_samples, n_features = X.shape
+        best_mse = float("inf")
+        best_split = None
+        
+        for feature_idx in range(n_features):
+            thresholds = np.unique(X[:, feature_idx])
+            for threshold in thresholds:
+                left_mask = X[:, feature_idx] <= threshold
+                right_mask = ~left_mask
+                if left_mask.sum() == 0 or right_mask.sum() == 0:
+                    continue
+
+                left_mse = self._compute_mse(y[left_mask])
+                right_mse = self._compute_mse(y[right_mask])
+                mse = (left_mask.sum() * left_mse + right_mask.sum() * right_mse) / n_samples
+
+                if mse < best_mse:
+                    best_mse = mse
+                    best_split = (feature_idx, threshold)
+        return best_split
+
+    def _compute_mse(self, y):
+        if len(y) == 0:
+            return 0
+        return np.mean((y - np.mean(y)) ** 2)
+
+    def _predict_single(self, x, tree):
+        if not isinstance(tree, dict):
+            return tree
+        
+        feature_idx = tree["feature_idx"]
+        threshold = tree["threshold"]
+        
+        if x[feature_idx] <= threshold:
+            return self._predict_single(x, tree["left"])
+        else:
+            return self._predict_single(x, tree["right"])
 
-    def r2_score(self, y_true, y_pred):
-        total_variance = np.sum((y_true - np.mean(y_true)) ** 2)
-        residual_variance = np.sum((y_true - y_pred) ** 2)
-        return 1 - (residual_variance / total_variance)
-    
-    def f1_score(y_true, y_pred):
-    # Calculate True Positives, False Positives, True Negatives, and False Negatives
-        TP = np.sum((y_true == 1) & (y_pred == 1))
-        FP = np.sum((y_true == 0) & (y_pred == 1))
-        FN = np.sum((y_true == 1) & (y_pred == 0))
-        TN = np.sum((y_true == 0) & (y_pred == 0))
-    
-        # Calculate Precision and Recall
-        precision = TP / (TP + FP) if TP + FP > 0 else 0
-        recall = TP / (TP + FN) if TP + FN > 0 else 0
     
-        # Calculate F1 Score
-        f1 = 2 * (precision * recall) / (precision + recall) if precision + recall > 0 else 0
-        return f1
-
-    def gini_coefficient(y_true, y_pred):
-        """Calculate the Gini coefficient."""
-        sorted_indices = np.argsort(y_pred)
-        sorted_true = y_true[sorted_indices]
-        cum_true = np.cumsum(sorted_true) / np.sum(sorted_true)
-        cum_population = np.cumsum(np.ones_like(sorted_true)) / len(sorted_true)
-        gini = 1 - 2 * np.trapz(cum_true, cum_population)
-        return gini
\ No newline at end of file
+    
\ No newline at end of file
diff --git a/Model/ModelSelection.py b/Model/ModelSelection.py
new file mode 100644
index 0000000..acf139d
--- /dev/null
+++ b/Model/ModelSelection.py
@@ -0,0 +1,100 @@
+import numpy as np
+from gradient_boosting import GradientBoostingTree
+
+
+def k_fold_cv(model, X, y, k=5, metric="mse"):
+    """
+    Perform k-fold cross-validation.
+    """
+    n = len(X)
+    indices = np.arange(n)
+    np.random.shuffle(indices)
+    folds = np.array_split(indices, k)
+
+    scores = []
+    for i in range(k):
+        train_idx = np.concatenate([folds[j] for j in range(k) if j != i])
+        val_idx = folds[i]
+
+        X_train, X_val = X[train_idx], X[val_idx]
+        y_train, y_val = y[train_idx], y[val_idx]
+
+        model.fit(X_train, y_train)
+        y_pred = model.predict(X_val)
+
+        if metric == "mse":
+            score = np.mean((y_val - y_pred) ** 2)
+        elif metric == "mae":
+            score = np.mean(np.abs(y_val - y_pred))
+        else:
+            raise ValueError("Unsupported metric. Use 'mse' or 'mae'.")
+
+        scores.append(score)
+
+    return np.mean(scores)
+
+
+def grid_search_max_depth(X, y, max_depth_values, n_estimators=100, learning_rate=0.1, k=5):
+    """
+    Perform grid search to find the best max_depth with k-fold cross-validation.
+    Args:
+        X: Feature matrix.
+        y: Target vector.
+        max_depth_values: List of max_depth values to test.
+        n_estimators: Fixed number of estimators.
+        learning_rate: Fixed learning rate.
+        k: Number of folds.
+    Returns:
+        Best max_depth and its corresponding score.
+    """
+    best_score = float("inf")
+    best_max_depth = None
+
+    for max_depth in max_depth_values:
+        model = GradientBoostingTree(
+            n_estimators=n_estimators,
+            max_depth=max_depth,
+            learning_rate=learning_rate,
+        )
+        score = k_fold_cv(model, X, y, k=k)
+        print(f"Max Depth: {max_depth} -> Score: {score}")
+        if score < best_score:
+            best_score = score
+            best_max_depth = max_depth
+
+    return best_max_depth, best_score
+
+# def bootstrap(self, B=100):
+#         """
+#         Perform bootstrapping for model evaluation.
+#         Args:
+#             B (int): Number of bootstrap iterations.
+#         Returns:
+#             mse_samples (list): Mean squared errors for each bootstrap sample.
+#             mse_mean (float): Mean of the mean squared errors across all bootstrap samples.
+#         """
+#         n = len(self.y)
+#         mse_samples = []
+
+#         for _ in range(B):
+#             indices = np.random.choice(np.arange(n), size=n, replace=True)
+#             X_sample, y_sample = self.X[indices], self.y[indices]
+
+#             beta = np.linalg.pinv(X_sample.T @ X_sample) @ X_sample.T @ y_sample
+#             y_pred = self.X @ beta
+#             mse_samples.append(np.mean((self.y - y_pred) ** 2))
+
+#         return mse_samples, np.mean(mse_samples)
+
+# def calculate_aic(self):
+#     """
+#     Calculate the Akaike Information Criterion (AIC).
+#     Returns:
+#         aic (float): AIC score for the linear regression model.
+#     """
+#     n, p = self.X.shape
+#     beta = np.linalg.pinv(self.X.T @ self.X) @ self.X.T @ self.y
+#     y_pred = self.X @ beta
+#     rss = np.sum((self.y - y_pred) ** 2)
+#     aic = n * np.log(rss / n) + 2 * p
+#     return aic
diff --git a/Model/XGBoost.py b/Model/XGBoost.py
deleted file mode 100644
index 401c8a7..0000000
--- a/Model/XGBoost.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import numpy as np
-
-def train_test_split(X, y, test_size=0.25, random_seed=None):
-    if random_seed:
-        np.random.seed(random_seed)
-    indices = np.arange(X.shape[0])
-    np.random.shuffle(indices)
-    split_index = int((1 - test_size) * len(indices))
-    train_indices, test_indices = indices[:split_index], indices[split_index:]
-    X_train, X_test = X[train_indices], X[test_indices]
-    y_train, y_test = y[train_indices], y[test_indices]
-    return X_train, X_test, y_train, y_test
-
-class XGBoost:
-    def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3, reg_lambda=1.0, gamma=0.0):
-        self.n_estimators = n_estimators
-        self.learning_rate = learning_rate
-        self.max_depth = max_depth
-        self.reg_lambda = reg_lambda
-        self.gamma = gamma
-        self.trees = []
-        self.initial_prediction = None
-
-    def predict_proba(self, X):
-        y_pred = self.predict(X)
-        return 1 / (1 + np.exp(-y_pred))
-
-    def _compute_pseudo_residuals(self, y, y_pred):
-        pred_proba = 1 / (1 + np.exp(-y_pred))
-        grad = pred_proba - y
-        hess = pred_proba * (1 - pred_proba)
-        return grad, hess
-
-    def _split_gain(self, grad_sum_left, hess_sum_left, grad_sum_right, hess_sum_right):
-        gain = (grad_sum_left**2 / (hess_sum_left + self.reg_lambda) +
-                grad_sum_right**2 / (hess_sum_right + self.reg_lambda))
-        return gain
-
-    def _fit_tree(self, X, grad, hess, depth=0):
-        n_samples, n_features = X.shape
-        if depth == self.max_depth or n_samples <= 1:
-            leaf_value = -np.sum(grad) / (np.sum(hess) + self.reg_lambda)
-            return {"leaf_value": leaf_value}
-
-        best_gain = -np.inf
-        best_split = None
-        best_left_idx, best_right_idx = None, None
-
-        for feature_idx in range(n_features):
-            thresholds = np.unique(X[:, feature_idx])
-            for threshold in thresholds:
-                left_idx = X[:, feature_idx] <= threshold
-                right_idx = ~left_idx
-                if np.sum(left_idx) == 0 or np.sum(right_idx) == 0:
-                    continue
-                grad_sum_left = np.sum(grad[left_idx])
-                hess_sum_left = np.sum(hess[left_idx])
-                grad_sum_right = np.sum(grad[right_idx])
-                hess_sum_right = np.sum(hess[right_idx])
-                gain = self._split_gain(grad_sum_left, hess_sum_left, grad_sum_right, hess_sum_right)
-                if gain > best_gain and gain > self.gamma:
-                    best_gain = gain
-                    best_split = (feature_idx, threshold)
-                    best_left_idx, best_right_idx = left_idx, right_idx
-
-        if best_split is None:
-            leaf_value = -np.sum(grad) / (np.sum(hess) + self.reg_lambda)
-            return {"leaf_value": leaf_value}
-
-        feature_idx, threshold = best_split
-        left_subtree = self._fit_tree(X[best_left_idx], grad[best_left_idx], hess[best_left_idx], depth + 1)
-        right_subtree = self._fit_tree(X[best_right_idx], grad[best_right_idx], hess[best_right_idx], depth + 1)
-
-        return {
-            "feature_idx": feature_idx,
-            "threshold": threshold,
-            "left": left_subtree,
-            "right": right_subtree,
-        }
-
-    def _predict_tree(self, tree, X):
-        if "leaf_value" in tree:
-            return np.full(X.shape[0], tree["leaf_value"])
-        feature_idx = tree["feature_idx"]
-        threshold = tree["threshold"]
-        left_idx = X[:, feature_idx] <= threshold
-        right_idx = ~left_idx
-        predictions = np.zeros(X.shape[0])
-        predictions[left_idx] = self._predict_tree(tree["left"], X[left_idx])
-        predictions[right_idx] = self._predict_tree(tree["right"], X[right_idx])
-        return predictions
-
-    def fit(self, X, y):
-        self.initial_prediction = np.mean(y)
-        y_pred = np.full(y.shape, self.initial_prediction)
-        for i in range(self.n_estimators):
-            grad, hess = self._compute_pseudo_residuals(y, y_pred)
-            tree = self._fit_tree(X, grad, hess)
-            self.trees.append(tree)
-            y_pred += self.learning_rate * self._predict_tree(tree, X)
-            if i % 10 == 0:
-                mse = self.mean_squared_error(y, y_pred)
-                print(f"Iteration {i}: Training MSE = {mse}")
-
-    def predict(self, X):
-        y_pred = np.full(X.shape[0], self.initial_prediction)
-        for tree in self.trees:
-            y_pred += self.learning_rate * self._predict_tree(tree, X)
-        return y_pred
-
-    @staticmethod
-    def mean_squared_error(y_true, y_pred):
-        return np.mean((y_true - y_pred) ** 2)
-
-    @staticmethod
-    def r2_score(y_true, y_pred):
-        total_variance = np.sum((y_true - np.mean(y_true)) ** 2)
-        residual_variance = np.sum((y_true - y_pred) ** 2)
-        return 1 - (residual_variance / total_variance)
-
-    @staticmethod
-    def gini_index(y_true, y_pred):
-        y_true_sorted = y_true[np.argsort(y_pred)]
-        n = len(y_true)
-        cumulative_y = np.cumsum(y_true_sorted) / np.sum(y_true_sorted)
-        gini = 1 - 2 * np.sum(cumulative_y - cumulative_y / n)
-        return gini
-
-    @staticmethod
-    def gini_impurity(y_true, y_pred):
-        p = np.mean(y_true)
-        return 2 * p * (1 - p)
-
-    @staticmethod
-    def f1_score(y_true, y_pred, threshold=0.5):
-        TP = np.sum((y_true == 1) & (y_pred == 1))
-        FP = np.sum((y_true == 0) & (y_pred == 1))
-        FN = np.sum((y_true == 1) & (y_pred == 0))
-        TN = np.sum((y_true == 0) & (y_pred == 0))
-        precision = TP / (TP + FP) if TP + FP > 0 else 0
-        recall = TP / (TP + FN) if TP + FN > 0 else 0
-        f1 = 2 * (precision * recall) / (precision + recall) if precision + recall > 0 else 0
-        return f1
\ No newline at end of file
diff --git a/Test/__init__.py b/Model/__init__.py
similarity index 100%
rename from Test/__init__.py
rename to Model/__init__.py
diff --git a/Test/Datset_customer.csv b/Test/Datset_customer.csv
deleted file mode 100644
index c5616f0..0000000
--- a/Test/Datset_customer.csv
+++ /dev/null
@@ -1,441 +0,0 @@
-Channel,Region,Fresh,Milk,Grocery,Frozen,Detergents_Paper,Delicassen
-2,3,12669,9656,7561,214,2674,1338
-2,3,7057,9810,9568,1762,3293,1776
-2,3,6353,8808,7684,2405,3516,7844
-1,3,13265,1196,4221,6404,507,1788
-2,3,22615,5410,7198,3915,1777,5185
-2,3,9413,8259,5126,666,1795,1451
-2,3,12126,3199,6975,480,3140,545
-2,3,7579,4956,9426,1669,3321,2566
-1,3,5963,3648,6192,425,1716,750
-2,3,6006,11093,18881,1159,7425,2098
-2,3,3366,5403,12974,4400,5977,1744
-2,3,13146,1124,4523,1420,549,497
-2,3,31714,12319,11757,287,3881,2931
-2,3,21217,6208,14982,3095,6707,602
-2,3,24653,9465,12091,294,5058,2168
-1,3,10253,1114,3821,397,964,412
-2,3,1020,8816,12121,134,4508,1080
-1,3,5876,6157,2933,839,370,4478
-2,3,18601,6327,10099,2205,2767,3181
-1,3,7780,2495,9464,669,2518,501
-2,3,17546,4519,4602,1066,2259,2124
-1,3,5567,871,2010,3383,375,569
-1,3,31276,1917,4469,9408,2381,4334
-2,3,26373,36423,22019,5154,4337,16523
-2,3,22647,9776,13792,2915,4482,5778
-2,3,16165,4230,7595,201,4003,57
-1,3,9898,961,2861,3151,242,833
-1,3,14276,803,3045,485,100,518
-2,3,4113,20484,25957,1158,8604,5206
-1,3,43088,2100,2609,1200,1107,823
-1,3,18815,3610,11107,1148,2134,2963
-1,3,2612,4339,3133,2088,820,985
-1,3,21632,1318,2886,266,918,405
-1,3,29729,4786,7326,6130,361,1083
-1,3,1502,1979,2262,425,483,395
-2,3,688,5491,11091,833,4239,436
-1,3,29955,4362,5428,1729,862,4626
-2,3,15168,10556,12477,1920,6506,714
-2,3,4591,15729,16709,33,6956,433
-1,3,56159,555,902,10002,212,2916
-1,3,24025,4332,4757,9510,1145,5864
-1,3,19176,3065,5956,2033,2575,2802
-2,3,10850,7555,14961,188,6899,46
-2,3,630,11095,23998,787,9529,72
-2,3,9670,7027,10471,541,4618,65
-2,3,5181,22044,21531,1740,7353,4985
-2,3,3103,14069,21955,1668,6792,1452
-2,3,44466,54259,55571,7782,24171,6465
-2,3,11519,6152,10868,584,5121,1476
-2,3,4967,21412,28921,1798,13583,1163
-1,3,6269,1095,1980,3860,609,2162
-1,3,3347,4051,6996,239,1538,301
-2,3,40721,3916,5876,532,2587,1278
-2,3,491,10473,11532,744,5611,224
-1,3,27329,1449,1947,2436,204,1333
-1,3,5264,3683,5005,1057,2024,1130
-2,3,4098,29892,26866,2616,17740,1340
-2,3,5417,9933,10487,38,7572,1282
-1,3,13779,1970,1648,596,227,436
-1,3,6137,5360,8040,129,3084,1603
-2,3,8590,3045,7854,96,4095,225
-2,3,35942,38369,59598,3254,26701,2017
-2,3,7823,6245,6544,4154,4074,964
-2,3,9396,11601,15775,2896,7677,1295
-1,3,4760,1227,3250,3724,1247,1145
-2,3,85,20959,45828,36,24231,1423
-1,3,9,1534,7417,175,3468,27
-2,3,19913,6759,13462,1256,5141,834
-1,3,2446,7260,3993,5870,788,3095
-1,3,8352,2820,1293,779,656,144
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-1,3,18291,1266,21042,5373,4173,14472
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-2,3,19899,5332,8713,8132,764,648
-2,3,8190,6343,9794,1285,1901,1780
-1,3,20398,1137,3,4407,3,975
-1,3,717,3587,6532,7530,529,894
-2,3,12205,12697,28540,869,12034,1009
-1,3,10766,1175,2067,2096,301,167
-1,3,1640,3259,3655,868,1202,1653
-1,3,7005,829,3009,430,610,529
-2,3,219,9540,14403,283,7818,156
-2,3,10362,9232,11009,737,3537,2342
-1,3,20874,1563,1783,2320,550,772
-2,3,11867,3327,4814,1178,3837,120
-2,3,16117,46197,92780,1026,40827,2944
-2,3,22925,73498,32114,987,20070,903
-1,3,43265,5025,8117,6312,1579,14351
-1,3,7864,542,4042,9735,165,46
-1,3,24904,3836,5330,3443,454,3178
-1,3,11405,596,1638,3347,69,360
-1,3,12754,2762,2530,8693,627,1117
-2,3,9198,27472,32034,3232,18906,5130
-1,3,11314,3090,2062,35009,71,2698
-2,3,5626,12220,11323,206,5038,244
-1,3,3,2920,6252,440,223,709
-2,3,23,2616,8118,145,3874,217
-1,3,403,254,610,774,54,63
-1,3,503,112,778,895,56,132
-1,3,9658,2182,1909,5639,215,323
-2,3,11594,7779,12144,3252,8035,3029
-2,3,1420,10810,16267,1593,6766,1838
-2,3,2932,6459,7677,2561,4573,1386
-1,3,56082,3504,8906,18028,1480,2498
-1,3,14100,2132,3445,1336,1491,548
-1,3,15587,1014,3970,910,139,1378
-2,3,1454,6337,10704,133,6830,1831
-2,3,8797,10646,14886,2471,8969,1438
-2,3,1531,8397,6981,247,2505,1236
-2,3,1406,16729,28986,673,836,3
-1,3,11818,1648,1694,2276,169,1647
-2,3,12579,11114,17569,805,6457,1519
-1,3,19046,2770,2469,8853,483,2708
-1,3,14438,2295,1733,3220,585,1561
-1,3,18044,1080,2000,2555,118,1266
-1,3,11134,793,2988,2715,276,610
-1,3,11173,2521,3355,1517,310,222
-1,3,6990,3880,5380,1647,319,1160
-1,3,20049,1891,2362,5343,411,933
-1,3,8258,2344,2147,3896,266,635
-1,3,17160,1200,3412,2417,174,1136
-1,3,4020,3234,1498,2395,264,255
-1,3,12212,201,245,1991,25,860
-2,3,11170,10769,8814,2194,1976,143
-1,3,36050,1642,2961,4787,500,1621
-1,3,76237,3473,7102,16538,778,918
-1,3,19219,1840,1658,8195,349,483
-2,3,21465,7243,10685,880,2386,2749
-1,3,140,8847,3823,142,1062,3
-1,3,42312,926,1510,1718,410,1819
-1,3,7149,2428,699,6316,395,911
-1,3,2101,589,314,346,70,310
-1,3,14903,2032,2479,576,955,328
-1,3,9434,1042,1235,436,256,396
-1,3,7388,1882,2174,720,47,537
-1,3,6300,1289,2591,1170,199,326
-1,3,4625,8579,7030,4575,2447,1542
-1,3,3087,8080,8282,661,721,36
-1,3,13537,4257,5034,155,249,3271
-1,3,5387,4979,3343,825,637,929
-1,3,17623,4280,7305,2279,960,2616
-1,3,30379,13252,5189,321,51,1450
-1,3,37036,7152,8253,2995,20,3
-1,3,10405,1596,1096,8425,399,318
-1,3,18827,3677,1988,118,516,201
-2,3,22039,8384,34792,42,12591,4430
-1,3,7769,1936,2177,926,73,520
-1,3,9203,3373,2707,1286,1082,526
-1,3,5924,584,542,4052,283,434
-1,3,31812,1433,1651,800,113,1440
-1,3,16225,1825,1765,853,170,1067
-1,3,1289,3328,2022,531,255,1774
-1,3,18840,1371,3135,3001,352,184
-1,3,3463,9250,2368,779,302,1627
-1,3,622,55,137,75,7,8
-2,3,1989,10690,19460,233,11577,2153
-2,3,3830,5291,14855,317,6694,3182
-1,3,17773,1366,2474,3378,811,418
-2,3,2861,6570,9618,930,4004,1682
-2,3,355,7704,14682,398,8077,303
-2,3,1725,3651,12822,824,4424,2157
-1,3,12434,540,283,1092,3,2233
-1,3,15177,2024,3810,2665,232,610
-2,3,5531,15726,26870,2367,13726,446
-2,3,5224,7603,8584,2540,3674,238
-2,3,15615,12653,19858,4425,7108,2379
-2,3,4822,6721,9170,993,4973,3637
-1,3,2926,3195,3268,405,1680,693
-1,3,5809,735,803,1393,79,429
-1,3,5414,717,2155,2399,69,750
-2,3,260,8675,13430,1116,7015,323
-2,3,200,25862,19816,651,8773,6250
-1,3,955,5479,6536,333,2840,707
-2,3,514,7677,19805,937,9836,716
-1,3,286,1208,5241,2515,153,1442
-2,3,2343,7845,11874,52,4196,1697
-1,3,45640,6958,6536,7368,1532,230
-1,3,12759,7330,4533,1752,20,2631
-1,3,11002,7075,4945,1152,120,395
-1,3,3157,4888,2500,4477,273,2165
-1,3,12356,6036,8887,402,1382,2794
-1,3,112151,29627,18148,16745,4948,8550
-1,3,694,8533,10518,443,6907,156
-1,3,36847,43950,20170,36534,239,47943
-1,3,327,918,4710,74,334,11
-1,3,8170,6448,1139,2181,58,247
-1,3,3009,521,854,3470,949,727
-1,3,2438,8002,9819,6269,3459,3
-2,3,8040,7639,11687,2758,6839,404
-2,3,834,11577,11522,275,4027,1856
-1,3,16936,6250,1981,7332,118,64
-1,3,13624,295,1381,890,43,84
-1,3,5509,1461,2251,547,187,409
-2,3,180,3485,20292,959,5618,666
-1,3,7107,1012,2974,806,355,1142
-1,3,17023,5139,5230,7888,330,1755
-1,1,30624,7209,4897,18711,763,2876
-2,1,2427,7097,10391,1127,4314,1468
-1,1,11686,2154,6824,3527,592,697
-1,1,9670,2280,2112,520,402,347
-2,1,3067,13240,23127,3941,9959,731
-2,1,4484,14399,24708,3549,14235,1681
-1,1,25203,11487,9490,5065,284,6854
-1,1,583,685,2216,469,954,18
-1,1,1956,891,5226,1383,5,1328
-2,1,1107,11711,23596,955,9265,710
-1,1,6373,780,950,878,288,285
-2,1,2541,4737,6089,2946,5316,120
-1,1,1537,3748,5838,1859,3381,806
-2,1,5550,12729,16767,864,12420,797
-1,1,18567,1895,1393,1801,244,2100
-2,1,12119,28326,39694,4736,19410,2870
-1,1,7291,1012,2062,1291,240,1775
-1,1,3317,6602,6861,1329,3961,1215
-2,1,2362,6551,11364,913,5957,791
-1,1,2806,10765,15538,1374,5828,2388
-2,1,2532,16599,36486,179,13308,674
-1,1,18044,1475,2046,2532,130,1158
-2,1,18,7504,15205,1285,4797,6372
-1,1,4155,367,1390,2306,86,130
-1,1,14755,899,1382,1765,56,749
-1,1,5396,7503,10646,91,4167,239
-1,1,5041,1115,2856,7496,256,375
-2,1,2790,2527,5265,5612,788,1360
-1,1,7274,659,1499,784,70,659
-1,1,12680,3243,4157,660,761,786
-2,1,20782,5921,9212,1759,2568,1553
-1,1,4042,2204,1563,2286,263,689
-1,1,1869,577,572,950,4762,203
-1,1,8656,2746,2501,6845,694,980
-2,1,11072,5989,5615,8321,955,2137
-1,1,2344,10678,3828,1439,1566,490
-1,1,25962,1780,3838,638,284,834
-1,1,964,4984,3316,937,409,7
-1,1,15603,2703,3833,4260,325,2563
-1,1,1838,6380,2824,1218,1216,295
-1,1,8635,820,3047,2312,415,225
-1,1,18692,3838,593,4634,28,1215
-1,1,7363,475,585,1112,72,216
-1,1,47493,2567,3779,5243,828,2253
-1,1,22096,3575,7041,11422,343,2564
-1,1,24929,1801,2475,2216,412,1047
-1,1,18226,659,2914,3752,586,578
-1,1,11210,3576,5119,561,1682,2398
-1,1,6202,7775,10817,1183,3143,1970
-2,1,3062,6154,13916,230,8933,2784
-1,1,8885,2428,1777,1777,430,610
-1,1,13569,346,489,2077,44,659
-1,1,15671,5279,2406,559,562,572
-1,1,8040,3795,2070,6340,918,291
-1,1,3191,1993,1799,1730,234,710
-2,1,6134,23133,33586,6746,18594,5121
-1,1,6623,1860,4740,7683,205,1693
-1,1,29526,7961,16966,432,363,1391
-1,1,10379,17972,4748,4686,1547,3265
-1,1,31614,489,1495,3242,111,615
-1,1,11092,5008,5249,453,392,373
-1,1,8475,1931,1883,5004,3593,987
-1,1,56083,4563,2124,6422,730,3321
-1,1,53205,4959,7336,3012,967,818
-1,1,9193,4885,2157,327,780,548
-1,1,7858,1110,1094,6818,49,287
-1,1,23257,1372,1677,982,429,655
-1,1,2153,1115,6684,4324,2894,411
-2,1,1073,9679,15445,61,5980,1265
-1,1,5909,23527,13699,10155,830,3636
-2,1,572,9763,22182,2221,4882,2563
-1,1,20893,1222,2576,3975,737,3628
-2,1,11908,8053,19847,1069,6374,698
-1,1,15218,258,1138,2516,333,204
-1,1,4720,1032,975,5500,197,56
-1,1,2083,5007,1563,1120,147,1550
-1,1,514,8323,6869,529,93,1040
-1,3,36817,3045,1493,4802,210,1824
-1,3,894,1703,1841,744,759,1153
-1,3,680,1610,223,862,96,379
-1,3,27901,3749,6964,4479,603,2503
-1,3,9061,829,683,16919,621,139
-1,3,11693,2317,2543,5845,274,1409
-2,3,17360,6200,9694,1293,3620,1721
-1,3,3366,2884,2431,977,167,1104
-2,3,12238,7108,6235,1093,2328,2079
-1,3,49063,3965,4252,5970,1041,1404
-1,3,25767,3613,2013,10303,314,1384
-1,3,68951,4411,12609,8692,751,2406
-1,3,40254,640,3600,1042,436,18
-1,3,7149,2247,1242,1619,1226,128
-1,3,15354,2102,2828,8366,386,1027
-1,3,16260,594,1296,848,445,258
-1,3,42786,286,471,1388,32,22
-1,3,2708,2160,2642,502,965,1522
-1,3,6022,3354,3261,2507,212,686
-1,3,2838,3086,4329,3838,825,1060
-2,2,3996,11103,12469,902,5952,741
-1,2,21273,2013,6550,909,811,1854
-2,2,7588,1897,5234,417,2208,254
-1,2,19087,1304,3643,3045,710,898
-2,2,8090,3199,6986,1455,3712,531
-2,2,6758,4560,9965,934,4538,1037
-1,2,444,879,2060,264,290,259
-2,2,16448,6243,6360,824,2662,2005
-2,2,5283,13316,20399,1809,8752,172
-2,2,2886,5302,9785,364,6236,555
-2,2,2599,3688,13829,492,10069,59
-2,2,161,7460,24773,617,11783,2410
-2,2,243,12939,8852,799,3909,211
-2,2,6468,12867,21570,1840,7558,1543
-1,2,17327,2374,2842,1149,351,925
-1,2,6987,1020,3007,416,257,656
-2,2,918,20655,13567,1465,6846,806
-1,2,7034,1492,2405,12569,299,1117
-1,2,29635,2335,8280,3046,371,117
-2,2,2137,3737,19172,1274,17120,142
-1,2,9784,925,2405,4447,183,297
-1,2,10617,1795,7647,1483,857,1233
-2,2,1479,14982,11924,662,3891,3508
-1,2,7127,1375,2201,2679,83,1059
-1,2,1182,3088,6114,978,821,1637
-1,2,11800,2713,3558,2121,706,51
-2,2,9759,25071,17645,1128,12408,1625
-1,2,1774,3696,2280,514,275,834
-1,2,9155,1897,5167,2714,228,1113
-1,2,15881,713,3315,3703,1470,229
-1,2,13360,944,11593,915,1679,573
-1,2,25977,3587,2464,2369,140,1092
-1,2,32717,16784,13626,60869,1272,5609
-1,2,4414,1610,1431,3498,387,834
-1,2,542,899,1664,414,88,522
-1,2,16933,2209,3389,7849,210,1534
-1,2,5113,1486,4583,5127,492,739
-1,2,9790,1786,5109,3570,182,1043
-2,2,11223,14881,26839,1234,9606,1102
-1,2,22321,3216,1447,2208,178,2602
-2,2,8565,4980,67298,131,38102,1215
-2,2,16823,928,2743,11559,332,3486
-2,2,27082,6817,10790,1365,4111,2139
-1,2,13970,1511,1330,650,146,778
-1,2,9351,1347,2611,8170,442,868
-1,2,3,333,7021,15601,15,550
-1,2,2617,1188,5332,9584,573,1942
-2,3,381,4025,9670,388,7271,1371
-2,3,2320,5763,11238,767,5162,2158
-1,3,255,5758,5923,349,4595,1328
-2,3,1689,6964,26316,1456,15469,37
-1,3,3043,1172,1763,2234,217,379
-1,3,1198,2602,8335,402,3843,303
-2,3,2771,6939,15541,2693,6600,1115
-2,3,27380,7184,12311,2809,4621,1022
-1,3,3428,2380,2028,1341,1184,665
-2,3,5981,14641,20521,2005,12218,445
-1,3,3521,1099,1997,1796,173,995
-2,3,1210,10044,22294,1741,12638,3137
-1,3,608,1106,1533,830,90,195
-2,3,117,6264,21203,228,8682,1111
-1,3,14039,7393,2548,6386,1333,2341
-1,3,190,727,2012,245,184,127
-1,3,22686,134,218,3157,9,548
-2,3,37,1275,22272,137,6747,110
-1,3,759,18664,1660,6114,536,4100
-1,3,796,5878,2109,340,232,776
-1,3,19746,2872,2006,2601,468,503
-1,3,4734,607,864,1206,159,405
-1,3,2121,1601,2453,560,179,712
-1,3,4627,997,4438,191,1335,314
-1,3,2615,873,1524,1103,514,468
-2,3,4692,6128,8025,1619,4515,3105
-1,3,9561,2217,1664,1173,222,447
-1,3,3477,894,534,1457,252,342
-1,3,22335,1196,2406,2046,101,558
-1,3,6211,337,683,1089,41,296
-2,3,39679,3944,4955,1364,523,2235
-1,3,20105,1887,1939,8164,716,790
-1,3,3884,3801,1641,876,397,4829
-2,3,15076,6257,7398,1504,1916,3113
-1,3,6338,2256,1668,1492,311,686
-1,3,5841,1450,1162,597,476,70
-2,3,3136,8630,13586,5641,4666,1426
-1,3,38793,3154,2648,1034,96,1242
-1,3,3225,3294,1902,282,68,1114
-2,3,4048,5164,10391,130,813,179
-1,3,28257,944,2146,3881,600,270
-1,3,17770,4591,1617,9927,246,532
-1,3,34454,7435,8469,2540,1711,2893
-1,3,1821,1364,3450,4006,397,361
-1,3,10683,21858,15400,3635,282,5120
-1,3,11635,922,1614,2583,192,1068
-1,3,1206,3620,2857,1945,353,967
-1,3,20918,1916,1573,1960,231,961
-1,3,9785,848,1172,1677,200,406
-1,3,9385,1530,1422,3019,227,684
-1,3,3352,1181,1328,5502,311,1000
-1,3,2647,2761,2313,907,95,1827
-1,3,518,4180,3600,659,122,654
-1,3,23632,6730,3842,8620,385,819
-1,3,12377,865,3204,1398,149,452
-1,3,9602,1316,1263,2921,841,290
-2,3,4515,11991,9345,2644,3378,2213
-1,3,11535,1666,1428,6838,64,743
-1,3,11442,1032,582,5390,74,247
-1,3,9612,577,935,1601,469,375
-1,3,4446,906,1238,3576,153,1014
-1,3,27167,2801,2128,13223,92,1902
-1,3,26539,4753,5091,220,10,340
-1,3,25606,11006,4604,127,632,288
-1,3,18073,4613,3444,4324,914,715
-1,3,6884,1046,1167,2069,593,378
-1,3,25066,5010,5026,9806,1092,960
-2,3,7362,12844,18683,2854,7883,553
-2,3,8257,3880,6407,1646,2730,344
-1,3,8708,3634,6100,2349,2123,5137
-1,3,6633,2096,4563,1389,1860,1892
-1,3,2126,3289,3281,1535,235,4365
-1,3,97,3605,12400,98,2970,62
-1,3,4983,4859,6633,17866,912,2435
-1,3,5969,1990,3417,5679,1135,290
-2,3,7842,6046,8552,1691,3540,1874
-2,3,4389,10940,10908,848,6728,993
-1,3,5065,5499,11055,364,3485,1063
-2,3,660,8494,18622,133,6740,776
-1,3,8861,3783,2223,633,1580,1521
-1,3,4456,5266,13227,25,6818,1393
-2,3,17063,4847,9053,1031,3415,1784
-1,3,26400,1377,4172,830,948,1218
-2,3,17565,3686,4657,1059,1803,668
-2,3,16980,2884,12232,874,3213,249
-1,3,11243,2408,2593,15348,108,1886
-1,3,13134,9347,14316,3141,5079,1894
-1,3,31012,16687,5429,15082,439,1163
-1,3,3047,5970,4910,2198,850,317
-1,3,8607,1750,3580,47,84,2501
-1,3,3097,4230,16483,575,241,2080
-1,3,8533,5506,5160,13486,1377,1498
-1,3,21117,1162,4754,269,1328,395
-1,3,1982,3218,1493,1541,356,1449
-1,3,16731,3922,7994,688,2371,838
-1,3,29703,12051,16027,13135,182,2204
-1,3,39228,1431,764,4510,93,2346
-2,3,14531,15488,30243,437,14841,1867
-1,3,10290,1981,2232,1038,168,2125
-1,3,2787,1698,2510,65,477,52
diff --git a/Test/Highly_correlatedcode.py b/Test/Highly_correlatedcode.py
new file mode 100644
index 0000000..f2cae7a
--- /dev/null
+++ b/Test/Highly_correlatedcode.py
@@ -0,0 +1,26 @@
+import numpy as np
+import pandas as pd
+
+# Set random seed for reproducibility
+np.random.seed(42)
+
+# Number of samples and features
+n_samples = 1000
+n_features = 5
+
+# Generate a random base feature
+base_feature = np.random.normal(0, 1, n_samples)
+
+# Create a highly correlated dataset
+correlated_data = np.array([base_feature + np.random.normal(0, 0.1, n_samples) for _ in range(n_features)]).T
+
+# Convert to a DataFrame
+columns = [f"Feature_{i+1}" for i in range(n_features)]
+df = pd.DataFrame(correlated_data, columns=columns)
+
+# Add a target variable with a high correlation to one of the features
+df['Target'] = df['Feature_1'] * 2 + np.random.normal(0, 0.1, n_samples)
+
+# Save to CSV
+output_file = "highly_correlated_dataset.csv"
+df.to_csv(output_file, index=False)
diff --git a/Test/TestingDataset-1.ipynb b/Test/TestingDataset-1.ipynb
deleted file mode 100644
index ad27b07..0000000
--- a/Test/TestingDataset-1.ipynb
+++ /dev/null
@@ -1,1016 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "0a552be3-e9d8-4ec0-9f6d-c2bd4bfcc219",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "from scipy.stats import norm\n",
-    "import csv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "42874655-3e67-4101-b174-0ce12979b0b2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data=pd.read_csv('./Datset_customer.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "1c0e185c-c6ea-4f12-9757-21f7b814e5b1",
-   "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>Channel</th>\n",
-       "      <th>Region</th>\n",
-       "      <th>Fresh</th>\n",
-       "      <th>Milk</th>\n",
-       "      <th>Grocery</th>\n",
-       "      <th>Frozen</th>\n",
-       "      <th>Detergents_Paper</th>\n",
-       "      <th>Delicassen</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>12669</td>\n",
-       "      <td>9656</td>\n",
-       "      <td>7561</td>\n",
-       "      <td>214</td>\n",
-       "      <td>2674</td>\n",
-       "      <td>1338</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>7057</td>\n",
-       "      <td>9810</td>\n",
-       "      <td>9568</td>\n",
-       "      <td>1762</td>\n",
-       "      <td>3293</td>\n",
-       "      <td>1776</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6353</td>\n",
-       "      <td>8808</td>\n",
-       "      <td>7684</td>\n",
-       "      <td>2405</td>\n",
-       "      <td>3516</td>\n",
-       "      <td>7844</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>13265</td>\n",
-       "      <td>1196</td>\n",
-       "      <td>4221</td>\n",
-       "      <td>6404</td>\n",
-       "      <td>507</td>\n",
-       "      <td>1788</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>22615</td>\n",
-       "      <td>5410</td>\n",
-       "      <td>7198</td>\n",
-       "      <td>3915</td>\n",
-       "      <td>1777</td>\n",
-       "      <td>5185</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>9413</td>\n",
-       "      <td>8259</td>\n",
-       "      <td>5126</td>\n",
-       "      <td>666</td>\n",
-       "      <td>1795</td>\n",
-       "      <td>1451</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>12126</td>\n",
-       "      <td>3199</td>\n",
-       "      <td>6975</td>\n",
-       "      <td>480</td>\n",
-       "      <td>3140</td>\n",
-       "      <td>545</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>7579</td>\n",
-       "      <td>4956</td>\n",
-       "      <td>9426</td>\n",
-       "      <td>1669</td>\n",
-       "      <td>3321</td>\n",
-       "      <td>2566</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5963</td>\n",
-       "      <td>3648</td>\n",
-       "      <td>6192</td>\n",
-       "      <td>425</td>\n",
-       "      <td>1716</td>\n",
-       "      <td>750</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6006</td>\n",
-       "      <td>11093</td>\n",
-       "      <td>18881</td>\n",
-       "      <td>1159</td>\n",
-       "      <td>7425</td>\n",
-       "      <td>2098</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3366</td>\n",
-       "      <td>5403</td>\n",
-       "      <td>12974</td>\n",
-       "      <td>4400</td>\n",
-       "      <td>5977</td>\n",
-       "      <td>1744</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>13146</td>\n",
-       "      <td>1124</td>\n",
-       "      <td>4523</td>\n",
-       "      <td>1420</td>\n",
-       "      <td>549</td>\n",
-       "      <td>497</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>31714</td>\n",
-       "      <td>12319</td>\n",
-       "      <td>11757</td>\n",
-       "      <td>287</td>\n",
-       "      <td>3881</td>\n",
-       "      <td>2931</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>21217</td>\n",
-       "      <td>6208</td>\n",
-       "      <td>14982</td>\n",
-       "      <td>3095</td>\n",
-       "      <td>6707</td>\n",
-       "      <td>602</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>24653</td>\n",
-       "      <td>9465</td>\n",
-       "      <td>12091</td>\n",
-       "      <td>294</td>\n",
-       "      <td>5058</td>\n",
-       "      <td>2168</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>10253</td>\n",
-       "      <td>1114</td>\n",
-       "      <td>3821</td>\n",
-       "      <td>397</td>\n",
-       "      <td>964</td>\n",
-       "      <td>412</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1020</td>\n",
-       "      <td>8816</td>\n",
-       "      <td>12121</td>\n",
-       "      <td>134</td>\n",
-       "      <td>4508</td>\n",
-       "      <td>1080</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5876</td>\n",
-       "      <td>6157</td>\n",
-       "      <td>2933</td>\n",
-       "      <td>839</td>\n",
-       "      <td>370</td>\n",
-       "      <td>4478</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>18601</td>\n",
-       "      <td>6327</td>\n",
-       "      <td>10099</td>\n",
-       "      <td>2205</td>\n",
-       "      <td>2767</td>\n",
-       "      <td>3181</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>7780</td>\n",
-       "      <td>2495</td>\n",
-       "      <td>9464</td>\n",
-       "      <td>669</td>\n",
-       "      <td>2518</td>\n",
-       "      <td>501</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Channel  Region  Fresh   Milk  Grocery  Frozen  Detergents_Paper  \\\n",
-       "0         2       3  12669   9656     7561     214              2674   \n",
-       "1         2       3   7057   9810     9568    1762              3293   \n",
-       "2         2       3   6353   8808     7684    2405              3516   \n",
-       "3         1       3  13265   1196     4221    6404               507   \n",
-       "4         2       3  22615   5410     7198    3915              1777   \n",
-       "5         2       3   9413   8259     5126     666              1795   \n",
-       "6         2       3  12126   3199     6975     480              3140   \n",
-       "7         2       3   7579   4956     9426    1669              3321   \n",
-       "8         1       3   5963   3648     6192     425              1716   \n",
-       "9         2       3   6006  11093    18881    1159              7425   \n",
-       "10        2       3   3366   5403    12974    4400              5977   \n",
-       "11        2       3  13146   1124     4523    1420               549   \n",
-       "12        2       3  31714  12319    11757     287              3881   \n",
-       "13        2       3  21217   6208    14982    3095              6707   \n",
-       "14        2       3  24653   9465    12091     294              5058   \n",
-       "15        1       3  10253   1114     3821     397               964   \n",
-       "16        2       3   1020   8816    12121     134              4508   \n",
-       "17        1       3   5876   6157     2933     839               370   \n",
-       "18        2       3  18601   6327    10099    2205              2767   \n",
-       "19        1       3   7780   2495     9464     669              2518   \n",
-       "\n",
-       "    Delicassen  \n",
-       "0         1338  \n",
-       "1         1776  \n",
-       "2         7844  \n",
-       "3         1788  \n",
-       "4         5185  \n",
-       "5         1451  \n",
-       "6          545  \n",
-       "7         2566  \n",
-       "8          750  \n",
-       "9         2098  \n",
-       "10        1744  \n",
-       "11         497  \n",
-       "12        2931  \n",
-       "13         602  \n",
-       "14        2168  \n",
-       "15         412  \n",
-       "16        1080  \n",
-       "17        4478  \n",
-       "18        3181  \n",
-       "19         501  "
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.head(20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "fd0543a5-790a-45bd-97a2-34e01c758564",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>count</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>min</th>\n",
-       "      <th>25%</th>\n",
-       "      <th>50%</th>\n",
-       "      <th>75%</th>\n",
-       "      <th>max</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Channel</th>\n",
-       "      <td>440.0</td>\n",
-       "      <td>1.322727</td>\n",
-       "      <td>0.468052</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Region</th>\n",
-       "      <td>440.0</td>\n",
-       "      <td>2.543182</td>\n",
-       "      <td>0.774272</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Fresh</th>\n",
-       "      <td>440.0</td>\n",
-       "      <td>12000.297727</td>\n",
-       "      <td>12647.328865</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3127.75</td>\n",
-       "      <td>8504.0</td>\n",
-       "      <td>16933.75</td>\n",
-       "      <td>112151.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Milk</th>\n",
-       "      <td>440.0</td>\n",
-       "      <td>5796.265909</td>\n",
-       "      <td>7380.377175</td>\n",
-       "      <td>55.0</td>\n",
-       "      <td>1533.00</td>\n",
-       "      <td>3627.0</td>\n",
-       "      <td>7190.25</td>\n",
-       "      <td>73498.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Grocery</th>\n",
-       "      <td>440.0</td>\n",
-       "      <td>7951.277273</td>\n",
-       "      <td>9503.162829</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2153.00</td>\n",
-       "      <td>4755.5</td>\n",
-       "      <td>10655.75</td>\n",
-       "      <td>92780.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Frozen</th>\n",
-       "      <td>440.0</td>\n",
-       "      <td>3071.931818</td>\n",
-       "      <td>4854.673333</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>742.25</td>\n",
-       "      <td>1526.0</td>\n",
-       "      <td>3554.25</td>\n",
-       "      <td>60869.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Detergents_Paper</th>\n",
-       "      <td>440.0</td>\n",
-       "      <td>2881.493182</td>\n",
-       "      <td>4767.854448</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>256.75</td>\n",
-       "      <td>816.5</td>\n",
-       "      <td>3922.00</td>\n",
-       "      <td>40827.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Delicassen</th>\n",
-       "      <td>440.0</td>\n",
-       "      <td>1524.870455</td>\n",
-       "      <td>2820.105937</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>408.25</td>\n",
-       "      <td>965.5</td>\n",
-       "      <td>1820.25</td>\n",
-       "      <td>47943.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  count          mean           std   min      25%     50%  \\\n",
-       "Channel           440.0      1.322727      0.468052   1.0     1.00     1.0   \n",
-       "Region            440.0      2.543182      0.774272   1.0     2.00     3.0   \n",
-       "Fresh             440.0  12000.297727  12647.328865   3.0  3127.75  8504.0   \n",
-       "Milk              440.0   5796.265909   7380.377175  55.0  1533.00  3627.0   \n",
-       "Grocery           440.0   7951.277273   9503.162829   3.0  2153.00  4755.5   \n",
-       "Frozen            440.0   3071.931818   4854.673333  25.0   742.25  1526.0   \n",
-       "Detergents_Paper  440.0   2881.493182   4767.854448   3.0   256.75   816.5   \n",
-       "Delicassen        440.0   1524.870455   2820.105937   3.0   408.25   965.5   \n",
-       "\n",
-       "                       75%       max  \n",
-       "Channel               2.00       2.0  \n",
-       "Region                3.00       3.0  \n",
-       "Fresh             16933.75  112151.0  \n",
-       "Milk               7190.25   73498.0  \n",
-       "Grocery           10655.75   92780.0  \n",
-       "Frozen             3554.25   60869.0  \n",
-       "Detergents_Paper   3922.00   40827.0  \n",
-       "Delicassen         1820.25   47943.0  "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.describe().T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "a1cc6b41-6bef-4e6e-865a-1af00d629cbf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78389/1469724773.py:5: UserWarning: \n",
-      "\n",
-      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
-      "\n",
-      "Please adapt your code to use either `displot` (a figure-level function with\n",
-      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
-      "\n",
-      "For a guide to updating your code to use the new functions, please see\n",
-      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
-      "\n",
-      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
-      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78389/1469724773.py:5: UserWarning: \n",
-      "\n",
-      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
-      "\n",
-      "Please adapt your code to use either `displot` (a figure-level function with\n",
-      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
-      "\n",
-      "For a guide to updating your code to use the new functions, please see\n",
-      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
-      "\n",
-      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
-      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78389/1469724773.py:5: UserWarning: \n",
-      "\n",
-      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
-      "\n",
-      "Please adapt your code to use either `displot` (a figure-level function with\n",
-      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
-      "\n",
-      "For a guide to updating your code to use the new functions, please see\n",
-      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
-      "\n",
-      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
-      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78389/1469724773.py:5: UserWarning: \n",
-      "\n",
-      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
-      "\n",
-      "Please adapt your code to use either `displot` (a figure-level function with\n",
-      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
-      "\n",
-      "For a guide to updating your code to use the new functions, please see\n",
-      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
-      "\n",
-      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
-      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78389/1469724773.py:5: UserWarning: \n",
-      "\n",
-      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
-      "\n",
-      "Please adapt your code to use either `displot` (a figure-level function with\n",
-      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
-      "\n",
-      "For a guide to updating your code to use the new functions, please see\n",
-      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
-      "\n",
-      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
-      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78389/1469724773.py:5: UserWarning: \n",
-      "\n",
-      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
-      "\n",
-      "Please adapt your code to use either `displot` (a figure-level function with\n",
-      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
-      "\n",
-      "For a guide to updating your code to use the new functions, please see\n",
-      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
-      "\n",
-      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
-      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78389/1469724773.py:5: UserWarning: \n",
-      "\n",
-      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
-      "\n",
-      "Please adapt your code to use either `displot` (a figure-level function with\n",
-      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
-      "\n",
-      "For a guide to updating your code to use the new functions, please see\n",
-      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
-      "\n",
-      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
-      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78389/1469724773.py:5: UserWarning: \n",
-      "\n",
-      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
-      "\n",
-      "Please adapt your code to use either `displot` (a figure-level function with\n",
-      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
-      "\n",
-      "For a guide to updating your code to use the new functions, please see\n",
-      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
-      "\n",
-      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x168e95fd0>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_num = data.select_dtypes(include = ['float64', 'int64'])\n",
-    "# Histogram to display skewness of every numeric variable\n",
-    "f = pd.melt(df_num, value_vars = df_num.columns)\n",
-    "g = sns.FacetGrid(f, col=\"variable\",  col_wrap=4, sharex=False, sharey=False)\n",
-    "g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "f818162a-510c-4802-8fe5-d15682c18184",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No missing values in the DataFrame.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Check for missing values and print the missing columns with their counts\n",
-    "missing_counts = data.isnull().sum()\n",
-    "\n",
-    "# Filter to get columns with missing values\n",
-    "missing_columns = missing_counts[missing_counts > 0]\n",
-    "\n",
-    "if not missing_columns.empty:\n",
-    "    # Sort missing columns in descending order\n",
-    "    sorted_missing_columns = missing_columns.sort_values(ascending=False)\n",
-    "\n",
-    "    print(\"Columns with missing values and their counts (sorted in descending order):\")\n",
-    "    print(sorted_missing_columns)\n",
-    "else:\n",
-    "    print(\"No missing values in the DataFrame.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "59f308b4-fc49-40b4-9510-3e5261e958fd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Strong Correlations (|correlation| > 0.7):\n",
-      "Grocery - Milk: 0.73\n",
-      "Detergents_Paper - Grocery: 0.92\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#checking for highly correlated columns:\n",
-    "correlation_matrix = data.corr()\n",
-    "\n",
-    "def print_strong_correlations(corr_matrix, threshold=0.7):\n",
-    "    print(\"\\nStrong Correlations (|correlation| > {}):\".format(threshold))\n",
-    "    for i in range(len(corr_matrix.columns)):\n",
-    "        for j in range(i):\n",
-    "            if abs(corr_matrix.iloc[i, j]) > threshold:\n",
-    "                print(\"{} - {}: {:.2f}\".format(corr_matrix.columns[i], corr_matrix.columns[j], corr_matrix.iloc[i, j]))\n",
-    "\n",
-    "print_strong_correlations(correlation_matrix)\n",
-    "sns.heatmap(data.corr(), annot=True, cmap='coolwarm', linewidths=0.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "279e927c-5192-46d9-96a3-c59d415ce18f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import sys\n",
-    "\n",
-    "# Get the current notebook's absolute path and convert it to a directory path\n",
-    "current_notebook_path = os.path.abspath(os.getcwd())\n",
-    "\n",
-    "# Append the parent directory (Project1) to the system path\n",
-    "project_root = os.path.dirname(current_notebook_path)\n",
-    "sys.path.append(project_root)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "e6d81e52-b5b4-4ea8-8b86-54933a43fc79",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Importing ElasticNetRegression and train_test_split\n",
-    "from Model.Gradient_Boosting import GradientBoosting\n",
-    "from Model.Gradient_Boosting import train_test_split\n",
-    "from Model.XGBoost import XGBoost as xgb\n",
-    "from Model.Gradient_Boosting import train_test_split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "10cdeb14-607b-488a-94b7-4f3b2acef123",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shape of X: (440, 7)\n",
-      "Shape of y: (440,)\n",
-      "Training set size: 330, Test set size: 110\n",
-      "Training the model...\n",
-      "Iteration 0: MSE: 0.1925046550209184\n",
-      "Iteration 10: MSE: 0.08928634339378874\n",
-      "Iteration 20: MSE: 0.07128446638262076\n",
-      "Iteration 30: MSE: 0.06637012548376324\n",
-      "Iteration 40: MSE: 0.06380813836853168\n",
-      "Iteration 50: MSE: 0.062190568156020115\n",
-      "Iteration 60: MSE: 0.061008916891126024\n",
-      "Iteration 70: MSE: 0.06005015302886127\n",
-      "Iteration 80: MSE: 0.0592898850148287\n",
-      "Iteration 90: MSE: 0.058664392216089566\n",
-      "Predicting on training and testing sets...\n",
-      "\n",
-      "Training Metrics:\n",
-      "  Mean Squared Error (MSE): 0.0582\n",
-      "  R-Squared: 0.7331\n",
-      "  F1 Score: 0.7675\n",
-      "  Gini Coefficient: 0.6403\n",
-      "  Mean Bias: 0.0000\n",
-      "\n",
-      "Testing Metrics:\n",
-      "  Mean Squared Error (MSE): 0.0467\n",
-      "  R-Squared: 0.7880\n",
-      "  F1 Score: 0.7912\n",
-      "  Gini Coefficient: 0.6485\n",
-      "  Mean Bias: -0.0131\n"
-     ]
-    }
-   ],
-   "source": [
-    "def test_predict():\n",
-    "    # Load data from CSV\n",
-    "    data = []\n",
-    "    with open(\"./Datset_customer.csv\", \"r\") as file:\n",
-    "        reader = csv.DictReader(file)\n",
-    "        for row in reader:\n",
-    "            data.append(row)\n",
-    "\n",
-    "    # Extract features (X) and target variable (y) from the CSV\n",
-    "    X = np.array([[float(row[col]) for col in row if col != 'Channel'] for row in data])\n",
-    "    y = np.array([float(row['Channel']) for row in data])\n",
-    "\n",
-    "    # Debug: Check the shapes of X and y\n",
-    "    print(f\"Shape of X: {X.shape}\")\n",
-    "    print(f\"Shape of y: {y.shape}\")\n",
-    "\n",
-    "    # Convert target `y` to binary\n",
-    "    threshold = np.median(y)\n",
-    "    y_binary = (y > threshold).astype(int)\n",
-    "\n",
-    "    # Split dataset into training and testing sets\n",
-    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_seed=42)\n",
-    "    _, _, y_train_binary, y_test_binary = train_test_split(X, y_binary, test_size=0.25, random_seed=42)\n",
-    "\n",
-    "    # Debug: Check the shapes of split data\n",
-    "    print(f\"Training set size: {X_train.shape[0]}, Test set size: {X_test.shape[0]}\")\n",
-    "\n",
-    "    # Create an instance of GradientBoosting\n",
-    "    gradient_booster = GradientBoosting(n_estimators=100, learning_rate=0.1, max_depth=1)\n",
-    "\n",
-    "    # Train the model\n",
-    "    print(\"Training the model...\")\n",
-    "    gradient_booster.fit(X_train, y_train)\n",
-    "\n",
-    "    # Predict on both training and test sets\n",
-    "    print(\"Predicting on training and testing sets...\")\n",
-    "    y_train_pred = gradient_booster.predict(X_train)\n",
-    "    y_test_pred = gradient_booster.predict(X_test)\n",
-    "\n",
-    "    # Threshold the predictions (convert to binary)\n",
-    "    y_train_pred_binary = (y_train_pred > np.median(y_train_pred)).astype(int)\n",
-    "    y_test_pred_binary = (y_test_pred > np.median(y_test_pred)).astype(int)\n",
-    "\n",
-    "    # Calculate evaluation metrics\n",
-    "    train_mse = gradient_booster.mean_squared_error(y_train, y_train_pred)\n",
-    "    test_mse = gradient_booster.mean_squared_error(y_test, y_test_pred)\n",
-    "\n",
-    "    train_r2 = gradient_booster.r2_score(y_train, y_train_pred)\n",
-    "    test_r2 = gradient_booster.r2_score(y_test, y_test_pred)\n",
-    "\n",
-    "    # Calculate F1 score using the method from the class\n",
-    "    train_f1 = GradientBoosting.f1_score(y_train_binary, y_train_pred_binary)\n",
-    "    test_f1 = GradientBoosting.f1_score(y_test_binary, y_test_pred_binary)\n",
-    "\n",
-    "    train_gini = GradientBoosting.gini_coefficient(y_train_binary, y_train_pred)\n",
-    "    test_gini = GradientBoosting.gini_coefficient(y_test_binary, y_test_pred)\n",
-    "\n",
-    "    # Checking bias\n",
-    "    train_residuals = y_train_pred - y_train\n",
-    "    test_residuals = y_test_pred - y_test\n",
-    "    train_mean_bias = np.mean(train_residuals)\n",
-    "    test_mean_bias = np.mean(test_residuals)\n",
-    "\n",
-    "    # Print the results\n",
-    "    print(\"\\nTraining Metrics:\")\n",
-    "    print(f\"  Mean Squared Error (MSE): {train_mse:.4f}\")\n",
-    "    print(f\"  R-Squared: {train_r2:.4f}\")\n",
-    "    print(f\"  F1 Score: {train_f1:.4f}\")\n",
-    "    print(f\"  Gini Coefficient: {train_gini:.4f}\")\n",
-    "    print(f\"  Mean Bias: {train_mean_bias:.4f}\")\n",
-    "\n",
-    "    print(\"\\nTesting Metrics:\")\n",
-    "    print(f\"  Mean Squared Error (MSE): {test_mse:.4f}\")\n",
-    "    print(f\"  R-Squared: {test_r2:.4f}\")\n",
-    "    print(f\"  F1 Score: {test_f1:.4f}\")\n",
-    "    print(f\"  Gini Coefficient: {test_gini:.4f}\")\n",
-    "    print(f\"  Mean Bias: {test_mean_bias:.4f}\")\n",
-    "# Call the function\n",
-    "test_predict()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "b442c572-87db-417b-9cd2-282078b620a1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shape of X: (440, 7)\n",
-      "Shape of y: (440,)\n",
-      "Training set size: 330, Test set size: 110\n",
-      "Training the model...\n",
-      "Iteration 0: Training MSE = 0.1271363083684796\n",
-      "Iteration 10: Training MSE = 1.216936913053914\n",
-      "Iteration 20: Training MSE = 3.526462894848376\n",
-      "Iteration 30: Training MSE = 6.298325117439503\n",
-      "Iteration 40: Training MSE = 8.91223450961791\n",
-      "Iteration 50: Training MSE = 10.818610408674877\n",
-      "Iteration 60: Training MSE = 12.326685278474486\n",
-      "Iteration 70: Training MSE = 13.729049519986086\n",
-      "Iteration 80: Training MSE = 14.800232655623304\n",
-      "Iteration 90: Training MSE = 15.731209377519058\n",
-      "Predicting on training and testing sets...\n",
-      "\n",
-      "Training Metrics:\n",
-      "  Mean Squared Error (MSE): 0.0179\n",
-      "  R-Squared: 0.9177\n",
-      "  F1 Score: 0.9717\n",
-      "  Gini Index: 0.4361\n",
-      "\n",
-      "Testing Metrics:\n",
-      "  Mean Squared Error (MSE): 0.0432\n",
-      "  R-Squared: 0.8037\n",
-      "  F1 Score: 0.9315\n",
-      "  Gini Index: 0.4403\n"
-     ]
-    }
-   ],
-   "source": [
-    "def test_predict_XG():\n",
-    "    # Load data from CSV\n",
-    "    data = []\n",
-    "    with open(\"./Datset_customer.csv\", \"r\") as file:\n",
-    "        reader = csv.DictReader(file)\n",
-    "        for row in reader:\n",
-    "            data.append(row)\n",
-    "\n",
-    "    # Extract features (X) and target variable (y) from the CSV\n",
-    "    X = np.array([[float(row[col]) for col in row if col != 'Channel'] for row in data])\n",
-    "    y = np.array([float(row['Channel']) for row in data])\n",
-    "\n",
-    "    # Debug: Check the shapes of X and y\n",
-    "    print(f\"Shape of X: {X.shape}\")\n",
-    "    print(f\"Shape of y: {y.shape}\")\n",
-    "\n",
-    "    # Convert target `y` to binary\n",
-    "    threshold = np.median(y)\n",
-    "    y_binary = (y > threshold).astype(int)\n",
-    "\n",
-    "    # Split dataset into training and testing sets\n",
-    "    X_train, X_test, y_train, y_test = train_test_split(X, y_binary, test_size=0.25, random_seed=42)\n",
-    "\n",
-    "    # Debug: Check the shapes of split data\n",
-    "    print(f\"Training set size: {X_train.shape[0]}, Test set size: {X_test.shape[0]}\")\n",
-    "\n",
-    "    # Create an instance of XGBoost\n",
-    "    xg_booster = xgb(n_estimators=100, learning_rate=0.1, max_depth=3, reg_lambda=1.0, gamma=0.0)\n",
-    "\n",
-    "    # Train the model\n",
-    "    print(\"Training the model...\")\n",
-    "    xg_booster.fit(X_train, y_train)\n",
-    "\n",
-    "    # Predict on both training and test sets\n",
-    "    print(\"Predicting on training and testing sets...\")\n",
-    "    y_train_pred_proba = xg_booster.predict_proba(X_train)\n",
-    "    y_test_pred_proba = xg_booster.predict_proba(X_test)\n",
-    "\n",
-    "    # Convert probabilities to binary predictions\n",
-    "    y_train_pred = (y_train_pred_proba > 0.5).astype(int)\n",
-    "    y_test_pred = (y_test_pred_proba > 0.5).astype(int)\n",
-    "\n",
-    "    # Calculate evaluation metrics\n",
-    "    train_mse = xg_booster.mean_squared_error(y_train, y_train_pred_proba)\n",
-    "    test_mse = xg_booster.mean_squared_error(y_test, y_test_pred_proba)\n",
-    "\n",
-    "    train_r2 = xg_booster.r2_score(y_train, y_train_pred_proba)\n",
-    "    test_r2 = xg_booster.r2_score(y_test, y_test_pred_proba)\n",
-    "\n",
-    "    train_f1 = xg_booster.f1_score(y_train, y_train_pred)\n",
-    "    test_f1 = xg_booster.f1_score(y_test, y_test_pred)\n",
-    "\n",
-    "    train_gini = xg_booster.gini_impurity(y_train, y_train_pred_proba)\n",
-    "    test_gini = xg_booster.gini_impurity(y_test, y_test_pred_proba)\n",
-    "\n",
-    "    # Print the results\n",
-    "    print(\"\\nTraining Metrics:\")\n",
-    "    print(f\"  Mean Squared Error (MSE): {train_mse:.4f}\")\n",
-    "    print(f\"  R-Squared: {train_r2:.4f}\")\n",
-    "    print(f\"  F1 Score: {train_f1:.4f}\")\n",
-    "    print(f\"  Gini Index: {train_gini:.4f}\")\n",
-    "\n",
-    "    print(\"\\nTesting Metrics:\")\n",
-    "    print(f\"  Mean Squared Error (MSE): {test_mse:.4f}\")\n",
-    "    print(f\"  R-Squared: {test_r2:.4f}\")\n",
-    "    print(f\"  F1 Score: {test_f1:.4f}\")\n",
-    "    print(f\"  Gini Index: {test_gini:.4f}\")\n",
-    "\n",
-    "# Call the function\n",
-    "test_predict_XG()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "20f25a92-8721-455b-81f1-1b2fbf73a42e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.12.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Test/TestingDataset-2.ipynb b/Test/TestingDataset-2.ipynb
deleted file mode 100644
index 2e80171..0000000
--- a/Test/TestingDataset-2.ipynb
+++ /dev/null
@@ -1,820 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# EDA of the small_test.csv:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "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>x_0</th>\n",
-       "      <th>x_1</th>\n",
-       "      <th>x_2</th>\n",
-       "      <th>y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-2.421349</td>\n",
-       "      <td>6.290215</td>\n",
-       "      <td>2.516304</td>\n",
-       "      <td>10.240120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>8.134658</td>\n",
-       "      <td>-6.975969</td>\n",
-       "      <td>-3.281095</td>\n",
-       "      <td>-6.896294</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-0.453124</td>\n",
-       "      <td>0.058895</td>\n",
-       "      <td>-3.592293</td>\n",
-       "      <td>14.104288</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3.979833</td>\n",
-       "      <td>-8.129002</td>\n",
-       "      <td>9.202915</td>\n",
-       "      <td>-43.788868</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-4.354232</td>\n",
-       "      <td>2.472475</td>\n",
-       "      <td>8.459722</td>\n",
-       "      <td>-12.067617</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>8.726621</td>\n",
-       "      <td>-9.607723</td>\n",
-       "      <td>-5.092837</td>\n",
-       "      <td>-8.265643</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>-0.291365</td>\n",
-       "      <td>8.224664</td>\n",
-       "      <td>-3.819334</td>\n",
-       "      <td>32.981856</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1.411871</td>\n",
-       "      <td>6.003043</td>\n",
-       "      <td>3.996826</td>\n",
-       "      <td>0.726779</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>0.215252</td>\n",
-       "      <td>-3.321042</td>\n",
-       "      <td>-5.352746</td>\n",
-       "      <td>11.934441</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>4.802262</td>\n",
-       "      <td>9.818246</td>\n",
-       "      <td>4.936296</td>\n",
-       "      <td>3.599572</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>9.717340</td>\n",
-       "      <td>0.144092</td>\n",
-       "      <td>8.749937</td>\n",
-       "      <td>-34.917123</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>4.098688</td>\n",
-       "      <td>-9.752059</td>\n",
-       "      <td>7.980744</td>\n",
-       "      <td>-43.328056</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>-2.398061</td>\n",
-       "      <td>2.827819</td>\n",
-       "      <td>-1.626175</td>\n",
-       "      <td>16.915393</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>5.398273</td>\n",
-       "      <td>7.583047</td>\n",
-       "      <td>2.758296</td>\n",
-       "      <td>4.437458</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>3.371528</td>\n",
-       "      <td>-5.430064</td>\n",
-       "      <td>2.191600</td>\n",
-       "      <td>-16.035658</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>2.086364</td>\n",
-       "      <td>0.108249</td>\n",
-       "      <td>8.144466</td>\n",
-       "      <td>-25.094326</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>2.825594</td>\n",
-       "      <td>-2.286321</td>\n",
-       "      <td>4.771241</td>\n",
-       "      <td>-18.000440</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>-8.150228</td>\n",
-       "      <td>-4.259315</td>\n",
-       "      <td>1.892335</td>\n",
-       "      <td>-1.393024</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>-6.067265</td>\n",
-       "      <td>3.677625</td>\n",
-       "      <td>8.481727</td>\n",
-       "      <td>-10.278523</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>8.640174</td>\n",
-       "      <td>9.717801</td>\n",
-       "      <td>4.980673</td>\n",
-       "      <td>-0.926665</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         x_0       x_1       x_2          y\n",
-       "0  -2.421349  6.290215  2.516304  10.240120\n",
-       "1   8.134658 -6.975969 -3.281095  -6.896294\n",
-       "2  -0.453124  0.058895 -3.592293  14.104288\n",
-       "3   3.979833 -8.129002  9.202915 -43.788868\n",
-       "4  -4.354232  2.472475  8.459722 -12.067617\n",
-       "5   8.726621 -9.607723 -5.092837  -8.265643\n",
-       "6  -0.291365  8.224664 -3.819334  32.981856\n",
-       "7   1.411871  6.003043  3.996826   0.726779\n",
-       "8   0.215252 -3.321042 -5.352746  11.934441\n",
-       "9   4.802262  9.818246  4.936296   3.599572\n",
-       "10  9.717340  0.144092  8.749937 -34.917123\n",
-       "11  4.098688 -9.752059  7.980744 -43.328056\n",
-       "12 -2.398061  2.827819 -1.626175  16.915393\n",
-       "13  5.398273  7.583047  2.758296   4.437458\n",
-       "14  3.371528 -5.430064  2.191600 -16.035658\n",
-       "15  2.086364  0.108249  8.144466 -25.094326\n",
-       "16  2.825594 -2.286321  4.771241 -18.000440\n",
-       "17 -8.150228 -4.259315  1.892335  -1.393024\n",
-       "18 -6.067265  3.677625  8.481727 -10.278523\n",
-       "19  8.640174  9.717801  4.980673  -0.926665"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"\"\"ML Project-1.ipynb\n",
-    "Group Members:\n",
-    "A20584318 - ANSH KAUSHIK\n",
-    "A20593046 - ARUNESHWARAN SIVAKUMAR\n",
-    "A20588339 - HARISH NAMASIVAYAM MUTHUSWAMY\n",
-    "A20579993 - SHARANYA MISHRA\n",
-    "\"\"\"\n",
-    "\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from scipy.stats import norm\n",
-    "import csv\n",
-    "\n",
-    "data=pd.read_csv('./small_test.csv')\n",
-    "data.head(20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>count</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>min</th>\n",
-       "      <th>25%</th>\n",
-       "      <th>50%</th>\n",
-       "      <th>75%</th>\n",
-       "      <th>max</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>x_0</th>\n",
-       "      <td>50.0</td>\n",
-       "      <td>0.919729</td>\n",
-       "      <td>5.783714</td>\n",
-       "      <td>-8.642801</td>\n",
-       "      <td>-3.995705</td>\n",
-       "      <td>1.730267</td>\n",
-       "      <td>5.397151</td>\n",
-       "      <td>9.717340</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>x_1</th>\n",
-       "      <td>50.0</td>\n",
-       "      <td>0.735999</td>\n",
-       "      <td>5.720213</td>\n",
-       "      <td>-9.752059</td>\n",
-       "      <td>-4.011655</td>\n",
-       "      <td>1.180905</td>\n",
-       "      <td>5.212510</td>\n",
-       "      <td>9.818246</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>x_2</th>\n",
-       "      <td>50.0</td>\n",
-       "      <td>0.156729</td>\n",
-       "      <td>5.559850</td>\n",
-       "      <td>-9.896226</td>\n",
-       "      <td>-4.267913</td>\n",
-       "      <td>0.069469</td>\n",
-       "      <td>4.895032</td>\n",
-       "      <td>9.810419</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>y</th>\n",
-       "      <td>50.0</td>\n",
-       "      <td>3.335015</td>\n",
-       "      <td>22.598316</td>\n",
-       "      <td>-43.788868</td>\n",
-       "      <td>-11.620343</td>\n",
-       "      <td>3.025178</td>\n",
-       "      <td>16.824434</td>\n",
-       "      <td>50.359814</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     count      mean        std        min        25%       50%        75%  \\\n",
-       "x_0   50.0  0.919729   5.783714  -8.642801  -3.995705  1.730267   5.397151   \n",
-       "x_1   50.0  0.735999   5.720213  -9.752059  -4.011655  1.180905   5.212510   \n",
-       "x_2   50.0  0.156729   5.559850  -9.896226  -4.267913  0.069469   4.895032   \n",
-       "y     50.0  3.335015  22.598316 -43.788868 -11.620343  3.025178  16.824434   \n",
-       "\n",
-       "           max  \n",
-       "x_0   9.717340  \n",
-       "x_1   9.818246  \n",
-       "x_2   9.810419  \n",
-       "y    50.359814  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.describe().T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78384/1469724773.py:5: UserWarning: \n",
-      "\n",
-      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
-      "\n",
-      "Please adapt your code to use either `displot` (a figure-level function with\n",
-      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
-      "\n",
-      "For a guide to updating your code to use the new functions, please see\n",
-      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
-      "\n",
-      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
-      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78384/1469724773.py:5: UserWarning: \n",
-      "\n",
-      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
-      "\n",
-      "Please adapt your code to use either `displot` (a figure-level function with\n",
-      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
-      "\n",
-      "For a guide to updating your code to use the new functions, please see\n",
-      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
-      "\n",
-      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
-      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78384/1469724773.py:5: UserWarning: \n",
-      "\n",
-      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
-      "\n",
-      "Please adapt your code to use either `displot` (a figure-level function with\n",
-      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
-      "\n",
-      "For a guide to updating your code to use the new functions, please see\n",
-      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
-      "\n",
-      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n",
-      "/var/folders/8y/86cllr6j79zg2fhb_z3mrzzr0000gn/T/ipykernel_78384/1469724773.py:5: UserWarning: \n",
-      "\n",
-      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
-      "\n",
-      "Please adapt your code to use either `displot` (a figure-level function with\n",
-      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
-      "\n",
-      "For a guide to updating your code to use the new functions, please see\n",
-      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
-      "\n",
-      "  g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x151da2b40>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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66qt56qmn5IJPzHiyZk0d7777Lj/96U8B+M53vsOPfvSjYRdyJpOJl19+md/+9rcA3HXXXTz44IOuCFUIl5D1aurIy8vj29/+Nv39/axbt46nn34aX19f6/Off/45P/7xj9Hr9Vx//fU8+eSTqFQqqZSaBmTQuZjSOjo6uPvuu2ltbSU9PZ233nprREIKQKfT8fjjj/PjH/8YgD/+8Y/8+9//dna4QggBwFdffWVNSP33f/83//Ef/zGiCkqlUnHzzTfz29/+Fo1Gw4cffsirr77qinCFELNQbm4u//M//wOYk02PP/74iMoCtVrND37wA37+858D8OKLL/LZZ585PVYhxOzW09PDAw88QH9/P+eeey6///3vhyWkAC677DJ+97vfoVaree+99/jwww9dE6yYMElKiSlLURR+9rOfUVlZSXR0NH/+858JDAwc8zW33347d9xxB2C+EKyoqHBGqEIIYVVVVcUjjzwCmCsPbr755jGPv+iii3jssccA2LRpEwUFBQ6PUQgxu/X09PDQQw8xODjIxRdfzAMPPDDm8d/61res51dPPvkkjY2NzghTCCEAeO6556iuriYqKopnnnlm1Na8888/n/vvvx+Ap556ivr6emeGKSZJklJiyvroo4/4/PPP0Wq1/Pa3vyU4OHhcr3vggQdYunQpPT09/PCHP0Sv1zs4UiGEMFMUhSeeeILOzk4WLVrEj370o3G97qabbuLCCy/EYDDw2GOPybolhHCoX//611RWVhIZGWltcTmT+++/n/T0dDo6OnjyySeRCSBCCGcoKiriL3/5CwBPPPEE/v7+Yx5/xx13sGjRIrq7u9m0aZMTIhRnS5JSYkpqbGy0lpTfd999LFiwYNyv1Wq1/OY3vyEgIID8/HzrnCkhhHC0zz//nH379qHT6Xj66afHPWRTpVLxs5/9zLpuWYajCyGEveXl5fHee+8B8Ktf/YqAgIBxvc7Nzc26IcO2bdtG3Q1ZCCHsRVEUnnzySYxGIxdddBHr1q0742s0Gg2PP/44AP/617+oryxxdJjiLElSSkxJv/3tb+np6WHhwoXceeedE359eHi4tULhT3/6k5SZCyEcrqenh1/96lcA3HnnncTGxk7o9SEhIdaTqJdffpmmpia7xyiEmN0UReFXv/oViqKwYcMGVqxYMaHXz5s3z3pe9uyzzzI4OOiIMIUQAoB9+/aRlZWFTqezjjoYjwULFrB+/XoUReHLd19xYITCHiQpJaac48ePWwfTPfbYY2g0mkm9z9VXX83ChQvp7e3l6aeftmOEQggx0muvvUZjYyOxsbHW2SsTdfnll7Nw4UL6+vp4/vnn7RyhEGK227p1KwcPHkSn0/HQQw9N6j2+//3vExwcTGVlJe+//76dIxRCiJNefPFFwDzXLjIyckKvffDBB3Fzc6PkeBaVhcccEZ6wE0lKiSnFcgcP4Morr2TRokWTfi+1Ws0TTzyBSqXik08+IT8/315hCiHEMJ2dnbzxxhsAPPTQQ+h0ukm9j0ql4v/9v/8HwD/+8Q/Ky8vtFaIQYpZTFIU//elPgHljmOjo6Em9j7e3N3fffTdgHj7c19dntxiFEMIiOzub/fv3o9Vq+e53vzvh18fFxXHNNdcAsG/zP+0dnrAjSUqJKWXv3r3WEs3J3sE7VUZGBhs2bACwbs8uhBD29te//pWuri5SUlK45JJLzuq9VqxYwXnnnYfBYJBqKSGE3ezcuZPCwkK8vLwmdYF3qm9961tERUXR1NTEu+++a6cIhRDipJdeegmAq666asJVUha33HILAIWH99DWWGe32IR9SVJKTCkvvPACYD7ZiYiIsMt73nPPPahUKrZu3SrVUkIIu+vq6rJWSd1zzz2o1Wf/p/U///M/Afjss89kO2MhhF1YLvBuvPHGM+5edSbu7u784Ac/AMxJeaPReNbxCSGERU1NDdu3bweY9EgEgJSUFJLnLwVF4eC2f9kpOmFvWlcHIIRFVlYWBw8exM3Nje9973t2e9/k5GQ2bNjAp59+ynPPPccf/vCHcb3urf2VdothLDevjHPK5wghHOMf//gHnZ2dJCUlcemll9rlPefPn8/y5cs5ePAgf/3rX60bNwghxGRkZ2eTlZWFm5sbt912m13e86qrrmLTpk3U1NSwbdu2s64SFUIIi/fffx9FUTjnnHNITEw8q/dacck1lBzPInvH56y7+hZ0nl52ilLYi1RKiSnDMsju6quvtluVlMU999wDwLZt26ioqLDrewshZi+j0cjbb78NmGe0THZjBlss7TXvvvsu3d3ddntfIcTsY6nmvOqqqwgLC7PLe3p6enLDDTcA8Prrr9vlPYUQwmAw8MEHHwDwzW9+86zfL3n+UoIiYtD395J/aNdZv5+wP0lKiSmhrKyMXbt2oVKpzqpEczTJycmcd955KIrCm2++aff3F0LMTjt37qS6uhp/f3+uuOIKu773unXrSE5Opru7m/fee8+u7y2EmD2am5vZunUrAN/5znfs+t4333wzbm5uZGdnc/ToUbu+txBidtq1axcNDQ0EBgZy0UUXnfX7qdRqFq0xv8/R3VvP+v2E/UlSSkwJb731FgAXXHABcXGOaWe79dZbAfjggw+k6kAIYRd/+9vfALjuuuvw9PS063ur1WrrBeQ//vEPFEWx6/sLIWaHf/7znxgMBhYtWkRqaqpd3zssLIzLLrsMQJLnQgi7eP/99wFz94y7u7td3nPB6gtBpaKi8ChtTTKrc6qRpJRwuZ6eHv75T/M2nTfffLPDPmf16tUkJyfT09NjLQkVQojJKisrY8+ePahUKm666SaHfMYVV1yBp6cnpaWlHD582CGfIYSYuUwmk3V3PEurnb1df/31gHljht7eXod8hhBidujo6GDnzp0AXHvttXZ7X//gMBLTFgFwbI9US001MuhcuNxHH31ET08PiYmJrFq1ymGfo1KpuOWWW/jZz37GW2+9xS233IJKpXLY5wkhZjZLcvu8884jJibGIZ/h4+PDhg0beP/993n33XdZunSpQz5HCDEz7dmzh+rqavz8/KwVTfa2fPly4uLiqKys5IsvvuCaa65xyOcIIaaniWwelbPzCwYHBwmLSeRAizsHWirttinUwjUXU5aXw9Gvt7L2qm/LdeAUIpVSwqUUReGdd94BzFVS9thKfSxXXHEFXl5eVFRUcOjQIYd+lhBi5jIajfzrX+athe15J88Wy5DPL774go6ODod+lhBiZvnoo48AuPLKK+3eYmyhUqms66Cl7UYIISYjd/92ADJWrrP7e6cuOxc3nQdtjXXUlRfb/f3F5ElSSrjU8ePHKS4uRqfTsXHjRod/nre3N+vXrwfkxEkIMXl79uyhsbGRgIAAzj//fId+1sKFC5k3bx4DAwN88sknDv0sIcTM0dPTYx1w7uhzrKuvvhq1Wk1WVhZlZWUO/SwhxMzU09lOWV4OAOkOSEq56zxIWbgcgPyDsgvfVCJJKeFSlvaXiy++GF9fX6d85nXXXQeYqw66urqc8plCiJnFMgfviiuusNsQztGoVCquvvpqAD799FOHfpYQYubYunUr/f39JCQkMH/+fId+Vnh4OGvWrAHMs6WEEGKi8g/uQlFMRCXOJSgsyiGfkbbsXAAKsnbLBjJTiCSlhMsMDAxYT1ycOX9g8eLFJCcn09/fLydOQogJ6+zsZNu2bYDz1q4NGzagUqnIzs6murraKZ8phJjePv74Y8DcuueM2SkbNmwAzEkpudgTQkxU/iFz9VL6CvtXSVmkLFyBRutGa0MtjdVS1TlVSFJKuMy2bdvo7OwkMjKSlStXOu1zVSqVtVpKduETQkzUtm3b0Ov1pKSkkJaW5pTPDAsLs66T0sInhDiTxsZG9u7dC5iTUs5w0UUX4e7uTmlpKQUFBU75TCHEzNDX3UlF4TEAUpeucdjn6Dy9SF5g3jQm/9Buh32OmBiXJ6Wee+45EhMT8fDwYOnSpezaNXZ/544dO1i6dCkeHh4kJSXxwgsvjDimvb2d++67j8jISDw8PEhLS5OKmCnIMnxz48aNaDQap3725Zdfjlqt5ujRo1RVVTn1s4UQ09vnn38OwGWXXebUnVssF5affPKJVCEIIca0efNmTCYTixcvJjY21imf6ePjw7p15goHaTUWQkzEiaOHUEwmQmMSCAyLHNdrDEYTBfWdfFXYyPbCRk40dmEynfn8KG3ZWkDmSk0lLk1K/f3vf+fBBx/k8ccfJzs7m7Vr17J+/XoqK21vG1lWVsaGDRtYu3Yt2dnZPPbYY9x///3DBlbr9XouvvhiysvLee+99ygsLOSll14iOjraWd+WGIe2tjb27NkDwFVXXeX0zz+16kBOnIQQ49XR0WGtPnDU9uqjufjii3F3d6ekpIT8/HynfrYQYnr54osvAOevU5dffjkgLXxCiIkpyjGfW81dfM4Zj61q7eWR94+S+YstXLZpF9997SC3v3aQi57dydL/2cITHx2ntr1v1NfPXXwOKrWa5tpK2hrr7PY9iMlzaVLq2Wef5fvf/z533HEHaWlpbNq0idjYWJ5//nmbx7/wwgvExcWxadMm0tLSuOOOO/je977HM888Yz3m1VdfpbW1lQ8//JA1a9YQHx/Pueeey6JFi5z1bYlx2Lp1KwaDgdTUVBITE10SwxVXXAFI1YGYGKnunN2+/PJLBgcHmTNnDsnJyU79bF9fX2sVwubNm5362UKI6aO5uZmsrCzAnMx2pnXr1uHl5UVdXR3Hjh1z6mcLIaYno2GQE0cPATAvc9Woxw0YjDz1WT4X/mYH7xysoqvfgI9OS3qkH6kRvni5a2jrHeSNvRV84zfb2bS1CL3BNOJ9PLx9iJtj3vyh+MgBx3xTYkK0rvpgvV5PVlYWjzzyyLDHL7nkEmsFzen27t3LJZdcMuyxSy+9lFdeeYXBwUHc3Nz417/+xapVq7jvvvv46KOPCA0N5eabb+bHP/6x01vExOj+/e9/A7B+/XqXxXDxxRfz85//nJKSEgoKCpw2G0ZMX5bqzueee441a9bw4osvsn79evLy8oiLixtxvKW688477+Rvf/sbX3/9Nffeey+hoaHWuWaW6s6wsDDee+89YmJiqKqqctpulGJiLK17l1566YRe99Z+2xXAE+WduATYwvsf/5uwVdfa5T0n4+aVI/+9CyGmhq1bt6IoCgsXLiQqyjE7WI3Gw8OD8847j88//5wvv/yShQsXOvXzhRDTT3nBUfT9vfj4BxGVONfmMY1d/dzzt8NkVbQBsHZOCPddkMLyhCA0avMohUGjib0lLfzxyxMcKG9l09Zi9pW2cFFaOF7uw9MeKYtWUFF4lBNH9rPi4o2O/QbFGbmsUqq5uRmj0Uh4ePiwx8PDw6mvr7f5mvr6epvHGwwGmpubASgtLeW9997DaDTy2Wef8ZOf/ITf/OY3/O///u+osQwMDNDZ2TnsSzhOa2sr+/fvB1yblPL19eX8888HZHCwGB+p7pzdOjs7Xda6ZzFn0QrUGi3NtZU019on0SWEmFkslZTOrpKyuPDCCwFzckyIU9m72jw3N5frrruOhIQEVCoVmzZtGvEeP/vZz1CpVMO+IiIi7PltibNUnL0PMJ/jqNQj0xN1HX1c86c9ZFW04euh5aVbl/HX76/knKRga0IKwE2j5ry5ofz9rnP43Y2L8XbXsK+0lee3l9DRNzjsPecsWgFYEmKjt/oJ53D5oPPTh8QqijLm4Fhbx5/6uMlkIiwsjD//+c8sXbqUG2+8kccff3zUi0aAX/7yl/j7+1u/nDUQcrayDN+cP3++y3/WlqSY5a6iEKOxVHeeXq05merOQ4cOMTho/uN4anVneHg48+fP56mnnsJoNI4aiyTSXcOVrXsWHt4+JKYvBqDgsO1/d0KI2autrY0DB8ztKKf//XGW8847D61WS0lJCeXl5S6JQUw9jpgl3NvbS1JSEr/61a/GTDRlZGRQV1dn/ZLW0qmlJPcwACmLRu7G3qc3cturB6hp7yMh2IuP7lvDxenhI447lUqlYuPiaN6/dzXRAZ609Oj5y55y+gdPnluHRMUREBKO0TBIWV62fb8hMWEuS0qFhISg0WhGVEU1NjaOqIayiIiIsHm8VqslODgYgMjISObOnTusVS8tLY36+nr0er3N93300Ufp6OiwfslubI41FVr3LNauXYtOp6OyspKioiJXhyOmsKlU3SmJdNewrF0Tbd2zN8tWyQVZX7s0DiHE1LNz506MRiOpqak228qdwc/Pj+XLlwPmZL4Q4Jhq8+XLl/P0009z4403otPpRv1srVZLRESE9Ss0NNTu35+YnPamelrrq1Gp1SSkDe8SMJoU/ra/gqKGbsL9dPztjpUkhfqM+71TI/x45wfn4KvTUt/Zz9/2V2A0nSxoSRmqlpK5Uq7nsqSUu7s7S5cuZcuWLcMe37JlC6tXr7b5mlWrVo04fvPmzSxbtgw3NzcA1qxZw4kTJzCZTg41KyoqIjIyEnd3d5vvq9Pp8PPzG/YlHKOxsZGDBw8Crr+wA/D29ubcc88FGPFvSwhbpkJ1pyTSna+7u9vlrXsWczNXgUpFXVkRHS2NLo1FTH2yOcPssn37dgDreAJXsbTwbdu2zaVxiKnBUdXm41VcXExUVBSJiYnceOONlJaWTuwbEA5TOlQlFZOchoeX97DndhQ1Udbcg49Oy1++t4KYQK8Jv39skBe3rU7AXaumtKmHrwpPnjfNGarMKjl2UDpmXMyl7XsPPfQQL7/8Mq+++ir5+fn88Ic/pLKykrvvvhswX3jdeuut1uPvvvtuKioqeOihh8jPz+fVV1/llVde4eGHH7Yec88999DS0sIDDzxAUVERn376KU899RT33Xef078/MdKWLVtQFIVFixYRHR3t6nAAuOiiiwDZzUqMbSpVd0oi3fm+/vprBgcHSUxMdFnrnoWPfyBxczIAKJQWPjEGR7TLWDZnKC8v57333qOwsJCXXnppyvxNn830ej27d+8GXJ+U+sY3vgFAdnY2LS0tLo1FuJ6jqs3HY+XKlbzxxht88cUXvPTSS9TX17N69epR/13KiATnKj1uTkolzV8y7PGq1l6+LGgA4MmNGaRGTP5cNyrAk2syzX+jthc2UtnSA0D8vAVotG50tjbTUl896fcXZ8+lSakbbriBTZs28eSTT7J48WJ27tzJZ599Rnx8PAB1dXXDTpwSExP57LPP2L59O4sXL+YXv/gFv//97627WAHExsayefNmDh48yMKFC7n//vt54IEHRuzyJ1zDMvTS1ZUGp7rgggvQarUUFxdTVlbm6nDEFDWVqjuF83311VeA6y/0LKwtfIekhU+MTjZnmF0OHz5Md3c3QUFBLFiwwKWxREZGkp6ejqIo1vVTCHtXm4/H+vXrue6661iwYAEXXXQRn376KQB/+ctfbB4vIxKcx2Q0Wuc5JWUstT5uNCm8d7gakwILov2tCaWzsSgmgMWxAZgUeDermkGjCTedB7Fz0gEoy5W5Uq6kPfMhjnXvvfdy77332nzu9ddfH/HYunXrOHz48JjvuWrVKvbt22eP8IQddXR0WFv3LHfQpgJ/f39WrlzJ119/zdatW7nzzjtdHZKYoh566CFuueUWli1bxqpVq/jzn/88orqzpqaGN954AzBXd/7xj3/koYce4s4772Tv3r288sorvP3229b3vOeee/jDH/7AAw88wH/+539SXFzMU089xf333++S71GMZDQa2blzJzB1klLzlq5h89svUll0nJ7Odrz9AlwdkphiLO0yp9+Um0y7zCuvvMLg4CBubm7DNmf46KOPCA0N5eabb+bHP/7xsIrPUw0MDDAwMGD9b6k8cAxL6966detQ29jBytkuvPBC8vLy+PLLL7n++utdHY5wIUdVm0+Gt7c3CxYsoLi42Obzjz76KA899JD1vzs7OyUx5SC1ZUX093bj4eVDVNJc6+P7Slto6hrAy13DxsVRvH3APmMqrlwYRVlzD609enYVN/ON1DAS0zMpzz9CWV42yy+6yi6fIybO9X+xxKxhGb6ZkpLisuGbo7FsmyzbF4uxSHXn7HT06FHa2trw8/MjMzPT1eEAEBASTnhcMopiouR4lqvDEVOQbM4w+0yVeVIWlrlSe/bsoa9PtlyfzRxVbT4ZAwMD5OfnExkZafN5GZHgPKW55vOXxPRM1GrzTY3eAQPbhtr2Lk4Px8vdfjU0nu4aLptv3qVxR1EjHX2DJGWY2wbL849gGmPna+FYkpQSTmPZgcVykjKVXHjhhahUKo4ePUpdXZ2rwxFT2L333kt5eTkDAwNkZWVx3nnnWZ97/fXXrRcFFpbqzoGBAcrKyqxVVaeyVHf29/dTUlLCY489NmrFgXA+y/+n55577lmdCNtbykLz7lYnjsquMWJ0sjnD7FBWVkZFRQVubm6sWbPG1eEAMHfuXCIiIhgYGODQoUOuDke4mCNmCev1enJycsjJyUGv11NTU0NOTg4nTpywHvPwww+zY8cOysrK2L9/P9dffz2dnZ3cdtttzvvmhU225kltK2ikf9BEhJ8HyxOC7P6ZC6P9iQ/2YtCo8PnxOiISUvDw8mGgr4e6ctvVc8LxJCklnEKv11t3/JlKrXsWISEhLFliXhBlpxghxKks81AuuOACF0cy3JyhrYxLjh3CZJK7e2I42ZxhdtmxYwcAy5cvx9vb+wxHO4dKpbLucHymXR/FzOeIavPa2loyMzPJzMykrq6OZ555hszMTO644w7rMdXV1dx0003MmzePa6+9Fnd3d/bt22f9XOEa/b09VJfkA1irlTr7BzlY3grAhgWRqCcwO2y8VCoVVy6MQgUcqe6gsXuQhDTzTETLToDC+Vw+U0o4z1v7be+2Y283rxzZmnfgwAF6enoIDQ1l/vz5Toljoi688EKysrLYvn073/nOd1wdjhBiCqipqaG4uBiNRmO9uHIWRVHo1RvpGjDgoVXjrdPipjl5Lyk6KRUPbx/6e7qpKSkgdmhHPiFgeLvMNddcY318y5YtbNy40eZrVq1axccffzzsMVubM7z11luYTCbr3CLZnMH1plrrnsXatWt57733JCklAPvPEk5ISLBWc47mnXfemVCMwjnK84+gmEwERcQQEGpuqdtd3IzBpBAX5EVyqOOS61EBnsyP9udYTQdfFTQyJyOTgqyvKcvLZu1VNzvsc8XopFJKOIWl+uiCCy6YEsM3bbGcyFkSaEIIYbnQy8zMJCAgwOGfZzQpHK1u560DlTz5SR7/+1k+v99WzP99UcjPP87lhR0lfFXYSFf/IGqNhuT5ywAoPiItfGIkR7TL3HPPPbS0tPDAAw9QVFTEp59+ylNPPcV9993n9O9PmHV1dZGVZZ7Nsm7dOhdHM9yqVavQaDSUl5dL26YQwqo8PweApHTzrM7eAQMHysxVUhfMC53QDouTccG8MACO13TgF2e+qVd9Ih/9QL9DP1fYNjWzA2JGOXU74KnYumeh9Q8jMCySwcFBbv7fN3hrfwVb8xuoau09410YIcTM5KzqA0VRyKlqZ9PWIt45WMXxmg4GDCYAPN00aFQqTApUtvayJa+Bp78o5KOcGuLSzVsonzh60KHxielJNmeYHfbv34/BYCA+Pn7KbSTj6+vL4sWLAdi9e7drgxFCTBkVBUcBiB9qndtT2oLeaCLK34O54b4O//wIfw/SI/1QgMOtGvyDwzAaBqkqOu7wzxYjSfuecLjc3FwaGhrw8vLinHPOcXU4I2RVtPK7bSfYVdyE1icFbWMdeVn7GMyMg9pOvixoJC7IiysWRhIT6OXqcIUQTtLf38+BA+YKJEdWH3T2D/LB4WqKGroB8HbXsCwhiIwoP8L9PHDTqFEUhfa+QU40dHOoopWqtj72l7XirQSCSkVDZQmdbc34BYY4LE4xPdm7XQZObs4gpoavv/4awOktxuO1du1asrKy2L17NzfddJOrwxFCuFhvVweN1WUAxM9bgMFk4uBQldR5cx1fJWVxQWoYeXWdHK/tJHPeIjr2bKEsN5vkBcuc8vniJKmUEg5nGb65evVqdDqdi6M5qaV7gAfeyea65/eys6gJRTk5ODig4wQb5oezINofrVpFZWsvL+woYVdxk1RNCTFLHDp0CL1eT0REBMnJyQ75jLLmHn6/rZiihm60ahUXp4fz8KXzuDQjgphAL+sMKZVKRaCXO8sTg7h7XTLfPzeRUB8dPSoPTAGxgFRLCTFb7dmzB2DK7Lp3urVr1wKwb9++UYfhCyFmj4rCYwCERsfj7RdAXm0nXQMGfHVaMqL8nRZHdIAniSHemBQYCDSf55UNtRUK55KklHC4nTt3AnDeeee5OJKTvj7RzGW/28VHObWoVHDj8lh2/Oh8PvzZrXh5edHd0UqyWwc3rYjjR5fOY2GMPyYF/n28nn8fr5fElBCzgKX6YPXq1Q65a5dd2caru8vo1RuJ9PfgvgtSuGBeGDqtZszXqVQqkkN9+I9vpHBuSgjG8FQAvtq+A4PJZPc4hRBTV2VlJZWVlWi1WlasWOHqcGxKTU0lJCSE3t7eM1bhCSFmPmvrXupCAPYPVUktSwhCo3ZOlZTFqiTzzrIlKvOutA2VpfT3ymxhZ5P2PeFQbW1tHDtmzoZb7pS52iu7y/ifT/NQFJgb7sOz31rM/OiTWfk1a9awZcsWinL2E5kwB18PN25YFktsoBefHqtj94lmvN01rBsakCeEmJksSSlHVB8cKGvlw5waADKi/Pjm0ljctRO7T+SmUbNhQSSe3evYXbCZnqo83t5bxs2rkpx+UieEcDxbuyhnffkJAMFxc7n/H3lUtfXSqzcC4KvTEubnQXyQF4mh3mdMeFvY2kX5bKjVatasWcNHH33Erl27puQoByGE85xMSi2iobOfsuYe1CpYkRjk9FjSIv0I8HSjvc+X4KBwelsbqCrOtXbPCOeQSinhUHv27EFRFObMmUNERIRLYzGaFH72r1x+8Yk5IXXj8lg+uu/cYQkpODk7pvjIfutjKpWKNSkhXLEwEoDNeQ0UNXQ5L3ghhFM1NjZSXFyMSqVi1apVdn3vg+UnE1Krk4O5aUXchBNSpzp/5RJ03n6oDAMU5ufy7qEqTFLNKcSscOywee5dtXsc2woaKWroprqtj+q2PvLru9hR1MQb+yr4n0/z+eu+Co7XdGAwOr+i0nJjUoadCzG7nT5P6lC5uUoqNcIPf083p8ejUas4Z6hayhCUCEDlUHuhcB6plBIOZWndc3WV1IDByP1vZ/NFbgMAj29I4461iTZbcixthnVlRXS1t+AbEGx9bnVyCI1dAxwoa+UfWdX88KI5eLnLr5EQM41lRsv8+fMJDAy02/seKm/ln9knE1KXL4g869ZAlVpNckYmeQd2oG0q4lhNIiE+Oi5OD7dHyEKIKcikKGzNraOy4AgqQBVhHjUQH+xNwNCFXXuvnrqOfkqaumnrHSS/rpP8uk483TQsiPFnSWwAsUFeThkqbEnuFxUV0dLSQnBw8BleIYSYiU6dJ+Xh48+R6joAlsXb71xropbGB7Ilr4Fu33jc2WeNUTiPXE0LhzGZTNb2F1fOk9IbTNz7t8NsK2jEXavm2W8t4oqFUaMeHxoayvz58zl+/Dilx7JYtPaSYc9fsSCSsuYemroG+Px4PdcuiXH0tyCEcDLL3fzVq1fb7T3z6zqtCalVdkpIWSQNJaWCe8upA74qbCTMT8eimAC7vL8QYupQFIUPDteQk5ONztCPxsObB264ED9P25vJKIpCQ+cAOVVt5FS109lv4EBZKwfKWgn2diczLpDM2AACvd0dFnNQUBCpqakUFBSwb98+Lr/8cod9lhBi6rJUIcWnLqSkqZvuAQNe7hrmhPu6LCZvnZa0SF9ye8zDzuvKi9AP9OOu83BZTLONtO8Jh8nPz6elpQUvLy8yMzNdEsOg0cR9b5kTUjqtmtduXz5mQsrCsq1yyfGsEc9pNWquXhwNwKGKNsqaZRieEDOJyWSy+25WVa29vHOwEgXz3cAr7JiQAkjMWAJAR00Jq+K8APhndg0t3QN2+wwhxNSwOa+Bw5VtaBoLAZi3cOmoCSkwjyCI8PfgsvmR/NdlqXxvTSKLYwNw06ho6dGzNb+BpzcX8uedpewrbaGpyzHrhmWW1L59+xzy/kKIqa+84AhgnieVXdkGwMKYAJfPwlyWEITiGQieAZiMRqpP5Lk0ntlGklLCYSyte6tWrcLd3XF330YzaDRx/9vZbMlrwF2r5uXblrEmJWRcr7UkpUpzD2MyGUc8nxjibS0z/eRorcxvEWIGyc/Pp62tDS8vLxYtWnTW71fe3MNf9pYzaFSYF+7LxsXRdm+XCQgJJyg8GsVkIoUGEoK90RtM/P1QFUaTrE9CzBQHy1rZUdQEQEhvOQBJGUvH/Xq1SkVKmA/fWhbLYxvSuH5pDEmh3qiA8pYe/nWklpVPbeXml/bx5v4Kuya2JSklxOzW291JY5V5nlREUjp5dZ0AZMYGuDAqs5QwH/y93DEEJwFQUSAtfM4kSSnhMJb2F1fMkzIYTTz49xz+fbwed42aP9+ylLVzQsf9+oULF6Lz9KKvu5P68hM2j7ksIwKdVk1dRz8FQ4uqEGL6s1RJrVy58qwT6i3dA9z+2gF69UaiAzy5cUWsw+4GJg1VS5XnHeZby2LwcFNT3dbHV4WNDvk8IYRzNXcN8PHRWgDWJfnSUVMCQGLG5KrRdVoNS+ICuePcJH506TzWz48gJtATkwJ7Slp4/J/HWfHUNm55ZT/vHKikrUd/VvEvW7YMrVZLdXU1VVVVZ/VeQojpp7LwOAAhUXFU9KgZNCoEe7sTE+jp4sjMCfslcQGYhpJSlUWSlHImSUoJh+jo6CAnJwc4WXXkLAajiYfePcKnR+tw06h44ZYlnD8vbELv4ebmRkLaYsB2Cx+Al07LqmTzoM5tBY0oUi0lxIxgmYV3tq17fXoj3//LIcpbegn0cuPWVfHj3pJ9MiwtfKW52QR4uVvbjHcUNtHY1e+wzxVCOJ7RpPDe4WoMJoWUUB+SlAYUk4nA0EgCQs5+U4MAL3fWzgnl3vNT2PVfF/DI+lQWRPtjNCnsKm7mkQ+Osfx/t3LrqwfYnFuPaRIVmN7e3ixcuBCAvXv3nnXMQojpparYnJSKmzuf47XmG/qLYgOcstnCeCyODcQUYp4rVVNSgEF/dol4MX6SlBIOsWfPHkwmE8nJyURHRzvtc40mhR+9d5R/HanFTaPiuW8v5RupkztZS55vLocvOX5o1GPOTQ6xVkvlS7WUENNeb28vhw8fBs4uKWU0KTzwTjY5Ve0EeLlx++pEfD0cu9VxQtoiVGo1rfXVdLQ0siDan3nhvhgVhY9yaiVxLsQ09pc95VS29qLTqrlmSbR1d6j4tLNvMT5dbJAXd69L5uP/PJftD5/Pjy6dR3qkHwaTws6iJn7w1ywu+91ONufWT3hdkRY+IWavqmLznKbI5HSKG7oAyIjyc2VIw4T66oiIikHR+WI0DFJTVujqkGYNSUoJh3BF657JpPBf7x3ln9k1aNUq/nDTkrPaEj1pwTIAqk/k099re5j5qdVSlhkPQojp68CBAwwODhIdHU18fPyk3kNRFH7xSR6bh+bZvXTrMkJ9Rx9CbC8eXt5EJ80DoPT4YVQqFVctisJNo6KsuYecqnaHxyCEsL+O3kF+t60YgMvmRxDo5X7KsOCFDv3shBBv7rsghc8eWMu2/7eOu9cl46vTUtTQzQ/+msWtrx6gsqV33O+3atUqAPbv34/JZHJU2EKIKWZQP0BduXkdG/CPx2Ayt+5F+E2tHe4WxQZitM6VOuriaGYPSUoJu1MUhV27dgHOS0qZTAqPfHCU9w9Xo1Gr+P1NmVw2P+Ks3jMwNMI6OLg8P2fU41YlBaNRqahq66O2ve+sPlMI4Vqn7ro32XLyV3aX8fqecgB++63FLE8Isld4Z5SYbp4tU5prrvYK9HbngqH25c15DQwa5SJQiOnm+R0ldPQNEuarY3lCEP29PdZ5lwkOTkqdKjnUh0fWp/L1o9/gnvOTcdeo2VXczPrf7eQfh6rGVTW1cOFCPD09aW1tpbi42AlRCyGmgtqyIkxGAz4BQZT1mW/UZUT5TZnWPYuFMf6YghMBKJNh506jdXUAYuYpLCykqakJT09Pli1b5vDPMw5VSL1/uBq1CjbdsJgNCyLt8t7J85fS2lBD6fEsUpfabuXx9XAjI9qPo9Ud7Ctt4dolMXb5bCGE81laSix38yfqs2N1/O9n+QA8viGNyxfaZy0ar6SMJez611uU5+egmEyo1GrWpISwv6yVjr5B9pW2TGjTByGEa9V39PPa1+bdqi7NiECtUlFZdAxFMREUHoVfkPN/n/083PjxZancuDyWH/3jKAfKW/nRe0c5XNnOkxszcNOMfs/b3d2dZcuWsWvXLvbu3UtWu/MGHN+8Ms5pnyWEGK6qyDxPKiYlg9zGbgAyovxdGZJNAV7uhCem0XbsQ2pKCzCZjKjVjpsHKsykUkrYnWV45fLly89656ozMZoUHv7HEWuF1O9uzOTKRVF2e/+kBZa5Ullj3gFcmWhu4TtS3U6f3mi3zxdCOE9zc7P1zv3KlSsn/PqD5a08+PccFAVuWxXPHWsT7R3iGUUnp+Hu4UlvVwf1VaUAuGnUXJRmbmXeXtgka5QQ08jz208wYDCxLD6Q1Ahf4GRLSXyq/edJTUR8sDdv/+AcfnTpPFQqePtAJbe+coDO/sExXydzpYSYfSzzpDwiktEbTPh7uhE9BXbdsyVzQRqKRodxoI+mmgpXhzMrSFJK2J0lKTXZSoPxMhhN/PDvOdYZUn+8yb4JKYCE1EWoNVram+ppbagd/bhgL8L9dAwaFQ5Xttk1BiGEcxw4cACAefPmERgYOKHXHqvu4HuvHURvMHFRWjhPXJnhkpJ0jVZL/DxzO0/ZUAsfQGZcAGG+OvoGjew+IfPvhJgO2nv1vHuoGoAfXjzXuqaU5ztnntR4aNQq7rsghVduW4a3u4a9pS3c8vJ+OnpHT0xZklIHDx7EaDA4K1QhhIsoJhPVJ8xJqW4fc8ViaoQv6inWumexMDYIU5A5zoLjR1wczewgSSlhV4ZBPQcPHgRg9erVDvuc/kEj97x5mH8dqTUnpG5ewno7teydyt3Dk9g56QCUjrELn0qlYsXQ3BgZJizE9GRJSk20SqqooYtbX91P14CBlYlB/PHmTDRq151oJc1fApycKwWgVqms1VJ7S1voH5RqKSGmujf3V9I3aCQt0o/VQ5uq9Pd0U19ZAphvnE0V30gN5+93rSLQy40j1R3c/PI+OvpsJ6ZSU1MJCAigt7eXWtndSogZr6m2kv7ebtx0HlSYAgBIjZg6u+6dztfDjYCYuQDkH5dh584gSSlhV9Un8unv7yckJIQ5c+Y45DM6ege55ZX9bBna2er57yw966HmY0meb56LVXIsa8zjFsQEoFZBTXsfTV0DDotHCOEYllYSy1388TjR2M23X95PW+8gi2IDeOX25Xi4uXb2QEL6YsBcKm8Y1FsfT4/yI8RHR/+giQNlrS6KTggxHgMGo3XDhDvXJlqrpCqKjoGiEBQRg29gsAsjHGl+tD/v/GAVIT7u5NZ2cucbh2wmwNVqtXWdLcvLcXKUQghns8yTCo2fS+eACTeNiqRQbxdHNbZ5GeZK1JYKSZw7gySlhF2V5WUD5tY9R7Su1LT38c0X93CwvA1fDy1//d4KLk4Pt/vnnMoyV6q84MiwC7zT+ei0zAkzz3uQaikhppf6+noqKipQq9Xj3qDheE0HN7y4l6auAVIjfPnLd5fjo3P9/iGhUfF4+wVg0A9QU3ryZEqtUrFubggAX59olp34hJjCPj1aR1PXAOF+Oq5YeHI0gWWelDN33ZuIeRG+/OV7K/DVaTlQ1sqD7+RgMo2cyWmpSK0okNYYIWY6yzwpbWgSYN7Jc6wNEaaCFcsyUVBh6m6hurbO1eHMeFP7X4OYdiztIo5o3TtY3spVf9hNUUM34X46/nH3KlYmOf4uYURsEt5+AQwO9FN9In/MYxfFBgDmgefj2RpZCDE17N+/H4CMjAx8fX3PePzB8lZu+vM+Wnr0zI/24807VhLg5diNHcZLpVJZByBbZs9YLIoNwN/Tja4BA0ckeS7ElPXOgSoAbjknHnftydP1k/Okpk7r3ukyovz5863LcNeo+Ty3nk3bikccY0lKVZ/Ix6Af/YafEGL6qyo2V0p1eMcC5uT1VBcc4I9bUDQAB7MOn+FocbYkKSXspq+ni7oy84nHRNpfxuOdA5Xc/JL5AjA90o8P7l3jtF5klVpNYnomMHxGiy3pkX64aVS09uipbutzRnhCCDuwJKXGM09qR1ETt7xiniG1IiGIt+48h2AfnaNDnJCENPMFa8VpSSmtWs2qoWT+vrIWSZ4LMQWVNHVzoLwVtQquXxprfbyvu5OGoV01p2qllMWq5GB+ee0CAH6/rZjPj9cPez4hIYGQkBAMg3pqZK6UEDNWQ0MD7c0NqFRqGrTm7pZ54VM/KQUQnpgGQGnBMRdHMvO5vs9AzBjl+UdQFBPBkbF8WaGHisqzfs8Bg5GPj9RyuLIdMM8ruH5JDDsKnbt7VFLGEo7v+8q8m9X13x31OHetmvRIP45Ud3CspoPYIC8nRimEmAxFUazzpM6UlPr3sTrufyebQaPC+fNCef7bS/F0d+0MKVsS0hYDUF2Sz6B+ADf3k0mzZfGBbM1voLa9n6q2PuJknRJiSnn3kLlK6vx5YUT4e1gfryg8DopCcGQsPgFBrgpv3K5bGsPx2g5e+7qc//duDsmha5gzdDGqUqlYvnw5//73v6koOEL8vAUujlYI4QiHD5tv6PtFxNHr5kGEn8eUqSw/k/SFi6jJ2kJ3TTFtPXoCvadH3NORVEoJu7HMk0rKWGKX96vr6ONPX5VwuLIdFXBxejg3LY8dVsbuLIlDu1nVlhfT19055rEZUf4A5NV1ShWCENNAdXU1dXV1aLValiwZff36x6Eq7nvrMINGhcsXRPLnW5ZNyYQUQFB4FL6BIRgNg9ZtmC28dFoWxpjXqf2lLa4ITwgxikGjifezqgG4YXnssOcs85cslZDTwWMb0liVFEyP3shdf82iZ8BgfW7FihXAyTlZQoiZJzvbfH2oHponlRLm48pwJiRtvnmtVXXU8ll2uWuDmeEkKSXspizXvOgkZmSe1fsYTQrbCxt5bnsJzd0D+Hlo+f7aRC6YF+aQ4enj4RcYQkhUHCgKZfljD+WcE+6DVm1u4WuUXfiEmPIsVVKLFi3Cy8t21dCru8v40XtHMSlww7JYfn9TpksS5OOlUqms28WfPlcK4JyhFr6jNR10n3KRKIRwre2FTTR36wnx0fGN1LBhz5VbklJTeJ7U6dw0av707SVE+ntQ2tzDzz/OtT5nSUpVn8gfcyMZIcT0lZVl3r2808ecZJ9OSSn/4DDcfYNQKSb++eU+V4czo0n7nrCL9uYGWhtqUKnVxM+b/JyD2vY+PjhcTW1HPwCpEb5cuyRmSuxolZSxhObaSkqPZ5G+fO2ox+m0GpJDfShs6CK/rpNwP49RjxVCuN6BAweAkxdIp1IUhT98eYJntxQBcMe5iTx+eZrLEuQTkZC+iGN7t1GenzPiuZhAL6IDPKlp7+NIVTtrUkKcH6AQYoR/HakFYOPiqGG7U7W1tdFYVQZAvIPnSb21/+zHL5zu8oWRvLKrjHcPVaNWqVgYE4CiaPD2C6Sns43a0kLipIVPiBmlp6eHgoICALp9YtGoVSQEe7s4qomJSUmnNHs3x47k0Ku/GS9311+TzkRT9zavmFYsVVLRSal4eE18sRkwGPkit57ntp+gtqMfTzcN31wawy3nxE+JhBScbEssyz18xra89EjzEPa8urFb/YQQrnXqPKnTN2hQFIVnNhdaE1IPXTx32iSk4GQ1RW1ZEfr+kRsvLI0PBOBwZZtT4xJC2NarN7A1rwGAqxZFDXvu4MGDAIRGx+PtF+Ds0M5aUogP6+aFAvBhTg1tPfqhnULNCbZyaeETYsY5cuQIJpMJD/8Q8AwgPthrSleZ2zIn3ZwsV5pL2Vnk3JnGs8n0+lchpizLPKmJtu6ZFIXDFW08u6WIHUVNmBTIiPLjwYvmkBkXOKUu/uJTF6LWaGlvbqCtsXbMY1MjfVEB1W19dPYNOidAIcSElZWV0dzcjE6nY9Giky0xiqLw1Gf5/OmrEgB+cnka9184Z0qtSWcSEBpBQEg4JqORyqLjI55fGO2PRqWirqOfug7ZLVQIV9uW30jfoJG4IC/r3DcLS0Wno6ukHOnC1HBiAz3pHzTx90NVGE2KdRdBmSslxMxjmSelscyTCp0+rXsWsXMyAFC3VrAlt/4MR4vJkqSUOGuKyXRyyHn6+JJSiqJQ2tTNCztKeO9wNV39BoK83fnOyji+vTIeXw83R4Y8Ke4ensSkDG0Nmnt4zGN9PdyICfQEoKC+y+GxCSEmx1IllZmZiU5n3qFOURT+99N8XtplbpV5cmMGd6xNclmMZyM+bfS5Ul46LamR5p2wsod2OBVCuM7HQ617Vy6KHJEAtySlptM8qdNp1CpuWB6HTqumsrWXrwobrUm26hN5MldKiBnGMk+qaxrOk7IIj01C6+6BytDPlweOYjLJJlaOIEkpcdYaqsro7erA3cOT6OS0MY9VFIUTjd28tKuUl3eXUd3Wh06r5rKMCB68cA7pUf5jvt7VLC18pUPtimOZF2G+2CtulKSUEFPV/v37AVi5cqX1sRd2mNcngF9eu4BbVyW4IjS7SEhbDJwckHy6JXHmFr6cqnaMcqIlhMt09g+yvdDcGnLlaa17ra2tFBcXA9O7UgogyNudqxdHA7C9sJE+j2C8/QIxDOqpLS10cXRCCHsxGAwcOWI+99AHxOPppiEqwNPFUU2cWqMhJjkVgM7qIo5Ut7s2oBlqUkmpsrIye8chprGyPHPVUPy8BWi0tuc/GU0Kx2o6eHFnKa9+XUZ5Sy8atYqViUE8dPFczpsbilYz9XOkSfPNSanyvGxMRuOYx84JMyelSpq65WLPxWTNEraYTCZr9YElKfWPQ1X8+nPzUM6fXJ7GTSviXBafPVi2jq8vP0F/T/eI5+eG++LlrqF7wEBp88jnhWvImjX7fFXQiN5oIiXMh3nhvsOes8yTCotJxMt3at+8G49FsQEsiPbHpMB7h2usA85lrtTUJOuRmIyioiJ6e3tx9/BC8YsgOcwH9TQagXCq2LmWFr5yvixodHE0M9OksgApKSlccMEF/O1vf6O/v9/eMYlpxlI1lDhURXSqlu4BtuU38PQXBbx9oJLK1l60ahWrkoN5+JJ5bFwcPSVb9UYTmTAHD28fBvp6qS0b+45edKAnnm4a+gdNVLf1OilCYYusWcKWoqIi2tvb8fLyYv78+WwvbOSRD44BcNd5SdO2Ze9UfoEhBIVHoygmKoqOjXheo1Yxf6hC9Vh1h7PDE6OQNWv22Tw04PyS9PARrXuWis7pXiV1qo2LovDRaWnqGqDHPwGQuVJTlaxHYjIs86TcwhJBpWbONJwnZRGbkg6Yk1Lb8iUp5QiTSkodOXKEzMxM/t//+39ERERw1113We82i9nFoNdbB+hahpy39ujZWdTEH78q5jdbithW0EhnvwFvnZbz54Xy8KXzuHJhFP6e0ycZZaFWa6ztMGeaK6VWqay900UNUoHgSrJmCVssF3pLly6lpkPPf76djdGkcG1mND++LNXF0dmPpVqqwsZcKYD50eakVF5dp1R1ThGyZs0ueoOJHUOtexenh4943jpPKm36zpM6nZdOy7WZ5ja+YpP5e5a5UlOTrEdiMnJycgDo9I4Bpuc8KYvoZPPOy+reVvLKqmVzGAeYVFJq/vz5PPvss9TU1PDaa69RX1/PueeeS0ZGBs8++yxNTbJd4mxRXZKHQT+At38QhT1ePLf9BM9sLuTz3Hpq2/tRq2BOmA/fWhbLjy+dxyXpEfhNo8ooW07OlRo7KQUwN9y8AMtcKdeSNUvYYklKLVm+grv+mkVXv4ElcQH86rqFqNXTs8TcFsuFbFl+js3nE0O88XLX0Ks3SgvfFCFr1uyyr7SF7gEDob46FsUEDHuuubmZkpISVCqVtc1tpkiN9GNpfCAmnzDw8JW5UlOUrEdiMg4fNl8nGQLiCfRyI9Db3cURTZ6Hlzdz584FQN1aJtVSDnBWQ3y0Wi3XXHMN7777Lr/+9a8pKSnh4YcfJiYmhltvvZW6ujp7xSmmoAGDkT179gDQ6ZvIv3MbqG7rQwUkhXizcXEUj6xP47trElkcGzAtZkaNR9L8pQDUlBQw0Ncz5rEpQ3Olatr66B0wODw2MTZZs4SFwWCwzmnJ6g2msKGLUF8dz39nKe7ambFWWcQP7dbVOLQpxek0ahUZ0sI3JcmaNTtszTe37l2UFjYiIW5Zp+bNm4eXj5/TY3O0yxdEEuDljiHI3C4tc6WmLlmPxHg1NDRQW1uLSqXGFBhHUsj0rZKyyMw0dwRpWitkrpQDnNWZ96FDh7j33nuJjIzk2Wef5eGHH6akpIQvv/ySmpoaNm7caK84xRTSPWBgc249v/68gOJj5iy4MXQO8cFeXLkwkkfWp3LH2iRWJgbjo7M9+Hw6CwyNIDAsEpPReMaTJ39PN8L9dChASfPYCSzheLJmCYv8/Hy6u7vx9PZhS60WlQr+cFMm4X4erg7N7nz8AwmNjgdGn9myQFr4piRZs2Y+RVHYmmdJSo1s3bNUdC5fvtypcTmLh5uG65bGYApJBiD/6Jmr0IVryHokxsvSuuceHA1uHiSGers2IDtYssTcKaNuKePrE8306cfe8EpMzKQyBs8++yyvvfYahYWFbNiwgTfeeIMNGzagVptzXImJibz44oukps6cmRwCTIrC/tIWtuQ30D9oAn0vnu3VAPzg+suIjY50cYTOk5SxhKzGTyk7fph5mavGPjbEh4bOAcqau60XfsK5ZM0Sp9u3bx8A/QHmAZz3rEvmnKRgF0flOAmpi2iqqaA8/whpy9eOeP7UFr6K1p4ZcVdzOpM1a/p4a3/lWb2+tr2P2o5+3DQqqtv6Rrzf1p3mivSBwOm/8cJokkN9WLh4KQVHP6CxvJCunn58vWfeDYLpStYjMVGWIee9vrGA+RxjurNUSqk7augbGGBPSTMX2riRICZnUpVSzz//PDfffDOVlZV8+OGHXHHFFdaFySIuLo5XXnnFLkEK1+seMPDK7jI+PlpH/6CJSH8PLghsBxRCouJmVUIKTu40OJ65UpaFuLRJKqVcRdYscTpL9cFAYBILov158KK5Lo7IseKH5kqVF9gedq5Rq6zb0BfUyQw8V5M1a/bIq+sEYE6YL26njTnoam+hpa4KZuA8qdNdtTYTPHzBOMj72752dTjiFLIeiYmyJKWMgQnEBHoS6DV950lZREdHExoaCooJdVsl26SFz64mVSm1ZcsW4uLiRixIiqJQVVVFXFwc7u7u3HbbbXYJUrhWbXsfb+wtp7PfgLtWzWUZEaxIDOLzN/4NnBz8PZskpi1GpVLTUl9NR0sj/sFhox6bMJSUauwaoHvAMCNbGqc6WbPEqfR6PQcPZQGgDp/DM99cNOPmSJ0uPnUhqFQ011bS3d6KT0DQiGNSI/3Irmonv66TDQtm142GqUbWrNmjYCgplRY5cl6Upd02Ii4ZT29fp8blbDo3DQnzFlB+ZA+leUc5vnSZdWdQ4VqyHomJ6O/vJy8vDwBTUPyMqUJXqVRkZmayefNm1K0VfJnfiHK1gko1czbGcaVJnYUnJyfT3Nw84vHW1lYSExPPOigxddR39PPq12V09hsI9dFx71CLi1qlslYJJaZnujhK5/Pw9iEqaR4ApcfHrpby0WkJ99MBUC5zpVxC1ixxqr2HstEP9KO4e3PXlWuYFzGzL/YAvHz8CI+1DBK2XS01J8wHjUpFS4+epq4BZ4YnTiNr1uzQ0TdIbUc/KrC5DlmSUvGpC50cmWukLRqa2dJcwoc5NXTLBjFTgqxHYiKOHz+OwWBA6+2P4hU0Y5JScLKFz629nPrOfnJrO10c0cwxqaSUotgegtrd3Y2Hx8R6wJ977jkSExPx8PBg6dKl7Nq1a8zjd+zYwdKlS/Hw8CApKYkXXnhh1GPfeecdVCoVV1999YRiEmYt3QO88nUZvXojMYGe3HN+MmFDQ4Dbmuppa6xDpVbPmpOl0yVlmBem8bXwmeezlEpSyiXsuWaJ6e+5v38OgGd0Kv/xjTkujsZ5Ei0tfPm2k1IebhqShoaR5tfJiZYryZo1OxQ3mFtlYwI9bVZRW35XE4Z20JzpLDuFatrK6e0b4OMjtS6OSICsR2JiLK17ev94UKlYmTiyMnu6siSltG2VoCh8JS18djOhPqKHHnoIMJevPfHEE3h5eVmfMxqN7N+/n8WLF4/7/f7+97/z4IMP8txzz7FmzRpefPFF1q9fT15eHnFxcSOOLysrY8OGDdx555387W9/4+uvv+bee+8lNDSU6667btixFRUVPPzww6xdO3KgqzizQaOJtw5U0jNgIMrfg++uTsTDTWN9vjzPvODEJKeh8/Qa7W1mtKSMJez611uU5+egmEyo1KPneJNCvNlX2kJZc7cTIxT2XrPAnEh/+umnqaurIyMjg02bNo25zuzYsYOHHnqI3NxcoqKi+K//+i/uvvtum8e+88473HTTTWzcuJEPP/xwQnGJ8Slr7iHn8EHUwLWXnT9sXZvp4tMWse+LDyjPzxn1mNQIX4obuymo7+S8uaHOC04AjlmzxNRV3Gg+J5gTPrJKqrOtmdaGGlQqNXFz5zs7NJcIiYzF2y+Ans52NB1VHKvRsqCmQ9r4XETWIzEZJ+dJxRMd4Els0My5TkxLS0On0zHQ342qu4kvCwP5zwtnz81NR5pQUsryj0xRFI4dO4a7+8mhZe7u7ixatIiHH3543O/37LPP8v3vf5877rgDgE2bNvHFF1/w/PPP88tf/nLE8S+88AJxcXFs2rQJMP/DOHToEM8888ywpJTRaOTb3/42P//5z9m1axft7e0T+TYF8MnRWuo6+vF213DrqgQ83YdfuM3m1j2L6OQ03D086e3qoL6yhMiE0Rcly1yphs4BegYMeMtcKaew95olifTp7xcfHUHVWg7ALVdd5NpgnCx+3gJUajVtjXWjzsJLjfTj46N1VLT00qc3jlj7hWPZe80SU5dJUThhSUqFjdztsiJ/aJ5UfDIe3rNjN0zV0ED3/IO7SFTqOEEi/zpSS1KIN15y3uR0sh6JiVIU5WRSKihhRrXugfnf/fz588nKykLdWk5OVRgt3QME++hcHdq0N6EV/quvvgLgu9/9Lr/73e/w8xs5lHG89Ho9WVlZPPLII8Mev+SSS9izZ4/N1+zdu5dLLrlk2GOXXnopr7zyCoODg7i5uQHw5JNPEhoayve///0ztgMCDAwMMDBwcn5GZ+fsblvIq+3gYHkbKuCG5XH4eboNe14xmSjLywEgaf7sG3JuodFqiU9dRHHOPkpzD4+ZlPLRaQnz1dHYNUBZc4/c9XMSe65ZIIl0ZznbLdZHc6Kxm+17DqAzGfH2D+brehV7GhzzWVORztObqIS51JQWUJ5/hEXnXjzimEAvd0J8dDR3D1Da3E1GlKxVzmTvNUtMXTVtffQNGvFwUxMTOLKSoKJgdrXuWSSkLiL/4C60LaWEJlxAU9cAnxyr41vLYl0d2qzjiPXI3tXmubm5PPHEE2RlZVFRUcFvf/tbHnzwwbP+XDE55eXltLe3o9JoUQKiOSdp5rTuWSxZsoSsrCyC+mtoUmBncRPXZMa4Oqxpb1IzpV577bWzXpiam5sxGo2Eh4cPezw8PJz6+nqbr6mvr7d5vMFgsA7g+/rrr3nllVd46aWXxh3LL3/5S/z9/a1fsbGz9w+f3mDi46N1AKydE0qKjbt39VWl9HV34u7hSVTiPGeHOKVYdh4807BzwDqrpUzmSjmdPdYsSyL99MT4ZBLphw4dYnBw0PrYqYl04RgmReGzY3Wom08AkJSxeFbumJKQbpkrlTPqMZaqDUtrkXA+e6xZFjK7c2oqGponlRzqg0Y9ci2ybEgw2+Z2Wr7fmpJ8rlkYjgrIqWq37lIonM9e65Gl2vzxxx8nOzubtWvXsn79eiorbd8cslSbr127luzsbB577DHuv/9+3n//fesxvb29JCUl8atf/YqIiAi7fK6YPGuVVEAsqLUzrlIKTs6V0gxV3X9Z0OTCaGaOcVdKXXvttbz++uv4+flx7bXXjnnsBx98MO4ATr8oUJSxt1a0dbzl8a6uLr7zne/w0ksvERISMu4YHn30UWvfNJgrpWZrYuqrwkY6+gYJ8HLjG6kjWzsAyoZa9+LnLUSjnd3l1JZh51XFuQwO9OOmG33gY2KID/tKWyUp5ST2XrMckUiPjIy0JtJzcnLOGIOFVHdO3NHqduo7+/FoLgEgMW2xawNykYTURXz9yd/Ns/BG+Xs7J8yHvaUt1tYi4RyOOM+SluOpy5L0nRs2cp5UR0ujeTMZlZq4ebNjnpRFSFQcXr7+9HZ1oG6vYk1KCLtPNPNhTg0PhnjPqjmAruSI9cgR1ebLly9n+fLlACO6byb7uWLyZvI8KQvLHLWOxhrQ97CjsBGD0YRWM6laHzFk3BkFf39/68mrv//Zl/OHhISg0WhGXMw1NjaOuIiziIiIsHm8VqslODiY3NxcysvLufLKK63Pm0wmALRaLYWFhSQnJ494X51Oh04nvaAt3QPsLjZXnF2xIAp3re1frrJc84Izm1v3LIIjY/ELCqGztZnKouMkL1g26rGJQ3Ol6jv76R0wyHwEB7P3mmUxFRLpv/zlL/n5z38+gahnN6NJYVt+Iwz2o2oz3xmNT5tdLTEWsXMyUGu0dLY209ZYS1B49IhjEkO90ahUtPboZVaCEzlizZKW46mpT2+kqrUXgDnhNuZJFZjnSUUmpKDz9HZqbK6mUqmIT11I/sFdVBQc5eLLbySvrpPWHj3b8hu4fGGUq0OcFey9HjlybIu9P1du/E2eJSllCkpg5Qxs3QMIDAwkMTGRsrIy/Hqq6XT35nBlOytm0C6DrjDuq+LXXnvN5v+eLHd3d5YuXcqWLVu45pprrI9v2bKFjRs32nzNqlWr+Pjjj4c9tnnzZpYtW4abmxupqakcO3Zs2PM/+clP6Orq4ne/+92srX4ar+2FTRgVhTlhPqRFjrxzB2DQ66ksOg7M7iHnFiqViqSMpeTs+oLS3MNjJqV8dFpCfXQ0dQ9Q2dpLaqTMCnEke69ZUymRLtWdE5NT1U5Ljx7PzkpQTASGRhIQYvv/s5nOTedBTEoalYXHKM8/YjMppdNqiAv2oqy5h+LGbklKOYm916ypNLtTDHeiqRsFCPXREeDlPuJ5S1JqtibPLXOlKgqOsPaqm7lqURSv7ylnb2kLS+IDifT3dHWIM5691yNHVZs74nPlxt/ktLe3U1JirkY3zcAh56fKzMykrKyMGGMDeczjy4JGSUqdpUnVmfX19dHb22v974qKCjZt2sTmzZsn9D4PPfQQL7/8Mq+++ir5+fn88Ic/pLKy0jrA7tFHH+XWW2+1Hn/33XdTUVHBQw89RH5+Pq+++iqvvPKKdecHDw8P5s+fP+wrICAAX19f5s+fP2zXCDFca4+e7Ko2AC5KCx+18qPqRC6GQT2+AcGERI0s/Z+NEi1zpXLPPFcqLthcxlrR2nuGI4U92WPNOjWRfqotW7awevVqm69ZtWrViONtJdJzcnKsX1dddRUXXHABOTk5oyaadDodfn5+w76EbUaTwpcFDQDEGqoBSJilF3oWCUOti5YNK2yxzJWSFj7XsMeaNZVmdw4MDNDZ2TnsazYrHponZatKCk7Ok5ptQ84tLHOlqk7kYxjUMzfcl/lRfpgU+CinFtNQxbFwDntd94F9q80d9bmPPvooHR0d1q+qqqoJfdZsdeSIed1SfEJA58OqGZ6UAqClHICvChpdF8wMMamk1MaNG3njjTcAc1Z0xYoV/OY3v2Hjxo08//zz436fG264gU2bNvHkk0+yePFidu7cyWeffUZ8fDwAdXV1w4bQJSYm8tlnn7F9+3YWL17ML37xC37/+9+PmHMgJu6rwkZMCswN9xmz/7d0qHUvMSNzVg4JtiUxfTEAjVVldLe3jnls/NDPtqJFklLOZK81SxLp08/hijbaegfx0WnR1xYBs7f6wMKSlKsoOGI9wT+dZZOLkqZujCa5AHQ2e61ZMHVajmVDGTNFUazJ3jk25km1NzfQ3lSPSq0mdk6Gs8ObEixzpQz6AWpLCwG4fGEU7ho1la29ZFe2uTjC2cUe65Ejqs0d9bly429yTs6TSiDK34OYwJlb0bhkibkgoaasELVioLChi5r2PhdHNb1NKil1+PBh62DL9957j4iICCoqKnjjjTf4/e9/P6H3uvfeeykvL2dgYICsrCzOO+8863Ovv/4627dvH3b8unXrOHz4MAMDA5SVlQ3bFtSW119/nQ8//HBCMc02HX2D1j/w30gdu6WlLG8oKSWte1befgFExKcAUDr08xmNpVKquq1XLvScyF5rliTSpxeDycRXhea7V6tjPWmoMpeVz/ZKqeikeWjddfR0ttNUW2HzmKgAT7zcNQwYTFS3SRLd2eyxZjniIrCkpMTacqzVatFqtbzxxhv861//QqvVWls3TieVBye19uhp7xtEo1JZZ02eytK6F5UwF53nzBsSPB4qlcq6Tluqxvw93bgwzbwBz+fH6+kfNLosvtnGHuuRI6rNHfW5YnIsG/ZYWvdmcvFCQkIC/v7+6AcGSPM0V75+KdVSZ2VSSane3l58fc13dzZv3sy1116LWq3mnHPOoaLC9gmumLoOlLVgUsyDuOPGqJLq7e6krrwYkKTU6RKHduErO0MLX4iPDk83DQaTQl2HZNSdxZ5rliTSp48jVR209w3iq9MS2l8NikJIVBy+ATO3pHw8tG7uxA1VYJTnHbF5jFqlIjnUXC1VLC18TmePNUtajqem0ibzDrwxQZ42N5Qpz88BICF9difPLa2L5fkn16jVySGE+LjTozeys0i2YXcWe51D2bvaHMyz8yxrkV6vp6amhpycHE6cODHuzxVnb3BwkKNHzQn1mT5PCkCtVltb+KKM5hs50sJ3diaVlEpJSeHDDz+kqqqKL774wjoUs7GxcVafaExHBqOJA+XmKqkzLSDl+UdAUQiNjsc3cGYvNhOVPH8pYJ4rNVo7DJgv9OKkhc/pZM2afUyKYr1oWZMSQlWh+WRpts5oOZ1lrpTlAtgWmSvlOvZas6TleOopaTb/PiWFjJwnpSiKNQlj+R2drRKGRiNUn8hnUG/eCU2jVnFZRgQAu08009E36KrwZhV7rUeOqDavra0lMzOTzMxM6urqeOaZZ8jMzLTuODqezxVnr6ioiL6+PhQ3DxTfsBm7896pLEkpY2MZAHtKmqWC8yxMKin1xBNP8PDDD5OQkMDKlStZtWoVYM6eWwd/iWnheG0nPQMG/Dy0pJ9hNzhLFZBUSY0Um5KB1s2d7vbWUdthLOJl2LnTyZo1++TVdtLUPYCHm5qViUEnqw9meeuexcm5UkcxmWyfRFkqparbehkwyImWM9lrzZKW46lFURTKhiqlkkJHtu61NdbS2dqEWqMlNiXd2eFNKUHh0fgGBGM0DFJ9It/6eFqkH/HBXhhMClvzGlwY4exhz3Moe1ebJyQkoCjKiK/T32eszxVnzzJPyhQYT1SA15idNzOF5d/+ifxjRPjp6B80sbekxcVRTV/aybzo+uuv59xzz6Wuro5Fi06e4F944YVcc801dgtOON6+UvMvz4rEYDTqsXt/y04Zci6G07q7EzdvAaXHsyg9fpiw6IRRj7XMlaps6RmzqkrYj6xZs4uiKOwsNldJrUoKZrC3k6Yac7J4tg85t4hMmIO7hxf9vd00VJYSmTBnxDGB3u4EeLnR3jtIZUsvc8JHDmUWjmHPNevee+/l3nvvtfnc66+/PuIxy0XgeNl6D2FbU/cAXQMGtGqVzYs2S5VUTEoabjoPZ4c3pZjnSi3m2N5tlOfnWDeVUalUbJgfyfM7SsiuauP8eaEE++hcG+wMJ+dQ4kwsfzNMQYkzfp6Uxfz589FqtTQ1NXFJhIqPOs1zpS5IDXN1aNPSpCqlwDwMMzMzE7X65FusWLGC1NRUuwQmHK+xs5/K1l7UKlieEDjmsW2NdbQ11aHWaIift9BJEU4vSeOcKxUT4IVaBZ39Btql9NxpZM2aPUqaeqhu68NNo2JVcggVQ4Nyw2OT8PKRdk1gaC1fAIzdwpcYbK7mKGvucUZY4hSyZs08lnlSsUFeuGlszZOS1r1TWYed5w+ffRcb5MW8cF9MCtbNLIRjyXokxnJyyHn8jJ8nZeHp6Ul6urmiNUJfB5iTUlJwMDmTqpTq6enhV7/6Fdu2baOxsRGTyTTs+dLSUrsEJxwru6odgLnhvvh6jL2LhWXXvejk1Fm7G8yZJGaYtwetKDiKYVCP1s32bA13rZpIf09q2vuolLlSTiFr1uximSW1LD4IH5325IXe0J12YZaQvpjiI/spzz/CqvXftHlMUqg32VXtlEpSyqlkzZqZLL9Htlr3zPOkcgBpM7aw/BxqywoZ6Osddv55YVoYhQ1d5FS1c8G8MKmWciBZj8RY6urqqKurQ0GFKTBu1iSlABYvXszRo0fpqz+Bu3YlNe19FDd2M1cqyydsUkmpO+64gx07dnDLLbcQGRk5K0r0ZhqTonBkKCmVGTd2lRSYB3gDJKYvcWRY01p4TCLefoH0dLZRfSJ/zJPK+GAvatr7qGiVCz1nkDVr9qjr6ONEUzdqFZw7JwRALvRGYRn6XlF4DKPBgEY78pQgMeTkXCm9wWRztzBhf7JmzTzmeVLmIefJNoacN9VW0NPZjtZdR3TSPGeHNyUFhEYQEBJOe3MDVcW5pCxcbn0uJtBcLVXY0MX2wiauWxrjwkhnNlmPxFgsVVKKfxRRwf7EBnm6NiAnWrJkCW+88QbHjhxh1ZUb2FHUxFcFjZKUmoRJJaX+/e9/8+mnn7JmzRp7xyOcpLy5h/a+QTzc1KRGjP2Lo5hMlOflACdb1MRIKrWaxIxMju/9ktLcrDEvgOOCvNhT0iKVUk4ia9bs8fUJ85y8jCh/Ar3c6WhppLWhFpVKTdzcBS6ObmoJj03E09uXvp4u6sqLiLExWDnQy40ATzfa+wapaO1hTpicaDmDrFkzT0PXAD16I24aFTE2Ltoqhio64+ZkjFppPRslpC0mZ9cXlOcfGZaUArgg9WS11MUZ4fidoepfTI6sR2Is1iHnQQmzZp6UhWXYeVFREQ/Ee7OjqIkvCxq5a12yiyObfiaVlAoMDCQoaOZv9TiTZVe2A7Ag2t/mXINT1VeW0NfThbuHF1GJcvduLEkZS4aSUtl84/rRj4sfmtNS19FPz4ABb92kfhXFOMmaNTt09Q9ypLodgHNThqqkhuZJRSbOwcNrZMvMbKZSq4lPW0TBod2U5eXYTEqpVCoSQ8wtfGVNkpRyFlmzZp7SoSqp+GBvtOqR511lMk/Kpvi0RUNJqZwRz8UFmXf5qmztZV9JC5dkRDg/wFlA1iMxlqysLMCclFqZNHP/nby1v9Lm45ZqzpKCPMCLg+WtvLKrDE93zbDjbl4Z54Qop69J1eH/4he/4IknnqC3V6o8piOD0cTx2g4AFseOo3XvuLl1Lz51oc32DnGSZWfCuvJiers7Rz3O39MNf083FLBeRAvHkTVrdthX2orRpBAX5EXs0M5W5XlyoTeWxKGfy+mDhIcdEyLDzp1N1qyZxzLkPClkZHLcZDJaN2SQNuPhLD+P+grzDdLTrR1q095f1oreYBrxvDh7sh6J0XR3d1NQUACAMThpVs2TsrDc0GuvLibUV4dJgeLGkWuVGNukMgy/+c1vKCkpITw8nISEBNzchpfLTmQrYeF8J5q6GTCY8PPQEh985qHlJccPAeYqIDE2v8AQQqPjaaqpoDwvh/QV5416bGyQFx01HWRXtrM6OcSJUc4+smbNfINGE/vLzK17a4aqpBRFkQu9M4gf+rlUFedi0OvRuo9sG0oKtcyV6pO5Uk4ia9bMYlIUa1LX8vt0qobKUvp7unH38CIyYY6zw5vS/AJDCIqIobW+msrCY8xbsnrY82mRfgR5u9PaoyeropVVcj5ld7IeidFkZ2djMpkweQURGRFOXNDs2wwrdk4Gx/d9RXVxLqmXXUhT1wCF9V0sjAlwdWjTyqSSUldffbWdwxDOlFtjruBJj/JHfYa+34G+XqqK8wBIWbjM4bHNBEkZS2iqqaA09/CYSam4QE+O13SQXdnmxOhmJ1mzZr6cynZ69UYCvNxIj/QDoLWhho6WRjRaN2LnZLg4wqkpJDIWn4AguttbqTqRR6KNHQoDvcyVnR19g1S29pISNvKiWtiXrFkzS31HP32DRty1aqIDRs6TslQqxs9bgFqjGfH8bJeYtojW+mrK84+MSEqpVSrWJAfz8dE69pe1zrqZNs4g65EYjbV1b6hKajb+7lkqpapL8lkR6sWuE1DU0IVJUc54nS1OmlRS6qc//am94xBOYjQp5NWZk1Lzo/zOeHx5wRFMRgOBoZEEhUc7OrwZITFjCfs3/5PS44dRFGXUBdpyN+FwZfuYx4mzJ2vWzKYoCrtLmgFYnRyCRm3+XbK0HsfOScdd5+Gy+KYylUpFYnomx/Zso/R4ls2klGWuVE5VO6XN3ZKUcgJZs2YWyzyphGAv6/p0qvJ8qegcS3zaIrK++nTUNuPMuEA+z62nsWuAipZeEmy0SIrJk/VIjObQIXM3jSk4kXNm8DypsYTFJqDz9GagrwfP3gZ0WjU9eiM1bX3WURLizCZdg9/e3s7LL7/Mo48+SmtrK2Au36ypqbFbcML+ypp76Bs04u2uGdcf7ZJj5sUmeYFUSY1XfOpC1BotHS0NtDbUjnpcVIAnGrWK1h49FbILn8PJmjVzFTd209Q1gE6rZln8yTl5pbnmpFSitB6PKWn+UuDkz8sWy1yp8mZZq5xF1qyZo9TSuhcyMqFrNBioLDoGyOy70SSkmpN1jdVl9HS2j3jew03DoqFWmQPlrU6MbPaQ9UicbmBggKNHjwInK6VmI7VaQ9xcczV+dfFx5gzduCuol7lSEzGppNTRo0eZO3cuv/71r3nmmWdob28H4J///CePPvqoPeMTdmYZcJ4e5XfGkkJFUSQpNQnuOg9ih0o5y8a4yNNq1ET5m6s3squkhc+RZM2a2fYMVUktiw/Ew83c+mI0GKx31WUe3tiS0s0bNNRXltDb1WHzmIShHUOr23oxGGWYsKPJmjVzGE0n50kl25gnVVdejL6/D09vX8JjE50d3rTg7RdAWIz5Z1NRcNTmMSsSzVUax2s66B0wOC222UDWI2HLsWPHGBwcRNH5Eh4VMyvnSVnEzVsAYJ57F2HuRCpsGH3DKzHSpJJSDz30ELfffjvFxcV4eJxsiVi/fj07d+60W3DCvhRFoWCodS8jyv+Mx7c21NLeVI9ao5WS8glKmm++CB6r8gBOaeGraHd0SLOarFkzV3P3AEUN3ahg2F262rJC9P29eHr7EhGf7LoApwGfgCDzBZ+iUJaXbfOYEB93vNw1GEwKte19To5w9pE1a+ao6+hjwGDCw01NZMDINuLy/BzA3KKmUssmAqOxnIdafl6niw7wJMrfA4NJ4XBVu/MCmwVkPRK2nJwnlciq5JBZPYYkbu7JpNScUPO1XW17P539g64Ma1qZ1F+/gwcPctddd414PDo6mvr6+rMOSjhGXUc/nf0G3DQqm1sSn85SJRU3NwN3j5GDOcXoLJUZ5fk5mIzGUY+Ltc6VkkopR5I1a+baX2recW9uuC/BPjrr45Z5UokZmajVMjj4TKyJ9OO2E+kqlYr4oWqpcmk3djhZs2aO0iZzlVRisLfNCnVLksXSoiZssySlykaZK6VSqViWYK6WypFzKruS9UjYYklKGYMSWTVLW/csIhPm4Oauo6+ni/7WOmICzdfNRdLCN26TSkp5eHjQ2TmyJK2wsJDQ0NCzDko4RmGD+RcjJdQHrebM/9eXHpfWvcmKSEjBw9uHgb5eakoLRj3OUilVUN9Fr17KzR1F1qyZSW8wkTV08XH6gE1LlaK07o2P5edUmmveoMGWhGDzelXRKkkpR5M1a+YoGRpynmSjdc8wqLfucJxgY5MBcVL8vIWoVGpa66vpaGm0eczCaH80KhW1Hf3Ud/Y7OcKZS9YjcTqj0UjWYfN5likkiXPnhLg4ItfSaLXEzDGPbqksPMa8cF9A5kpNxKSSUhs3buTJJ59kcNBckqZSqaisrOSRRx7huuuus2uAwn4Kh34xLL2uYzHo9dZ5LJKUmji1WkNimnlOS2mu7XYYgAAvdyL8PDCaFI5W257lIs6erFkz05GqdvoHTQR5uzNn6AQAoL+3x5oMliHn4xM3dz4arRudrU201FfbPMZSKVXR0jNq4krYh6xZM4PRpFg3MkkKHVmhXlWch2FQj09AECGRsc4Ob1rx8PYhKmkeAGWjnFd56bTMjTD/LZBqKfuR9UicrqCggN6eHhStBwlJKUQFSEeNpYWvovAY84bWoRNN3TKHc5wmlZR65plnaGpqIiwsjL6+PtatW0dKSgq+vr787//+r71jFHbQ2qOnaujutuUXZSyVxccZ1A+cnDUiJszSDjPWsHOAzLgAQFr4HEnWrJlHURT2lZlb91YmBg1riynPP4JiMhEUHk1ASLirQpxW3HQe1t1jRmvhiwrwQKtW0as3UjLUkiQcQ9asmaG6rRe90YSXu4Zwv5HzpEqPm9tfkjKWzup5LONlOa8qGfq52ZIZGwBATlU7Jkme24WsR+J01nlSQQmcNzfMxdFMDfGWYedFx4j098BHp0VvMMnIg3HSTuZFfn5+7N69m6+++oqsrCxMJhNLlizhoosusnd8wk52FjWhABF+Hvh7up3x+NJj5sUmeb6cKE2WpR2muiSfgb4edJ6253gtiQvk38frZdi5A8maNfNUtvZS19GPVq1iaXzgsOfKpHVvUhIzllCWl0Np7mFWXLxxxPNatZrYIC/Kmns4VN5KStjIdiRhH7JmzQylQ7vuJYbYnidlSa5Yki1ibMnzl7Lrozcpyz2MyWS0OS8wNcIXDzc1nf0GSpt6ZJ2yA1mPxOlODjlPYu0caeEEiEqah0brRnd7Kx1NdcwL9yWrso3C+k5Zh8Zhwkkpk8nE66+/zgcffEB5eTkqlYrExEQiIiJQFEUSGFPUlwXm/vvxVEkBnJB5UmctIDSCoPAoWhtqKS84yrzMVTaPWxIfAEB2ZZv8DjmArFkz096hAeeLYwPwch/+p8w6T0ou9CYkKWMJX/7jVSoKjmI0GNBoR54ixA8lpQ6Wt3HjijgXRDnzyZo1c5Ra5knZ2Fymu6ONhsoS8/OSQB+XqMR5uHt40dfTRX1FCVGJc0cco9WoWRDtz8HyNo7VdMjF4FmS9UicTlEU9h84CIAqNIlzkmf3kHMLN3cdUUlzqSrKpaLgGHNTVpmTUg1dXO7q4KaBCbXvKYrCVVddxR133EFNTQ0LFiwgIyODiooKbr/9dq655hpHxSnOgsFoYkdRE4B18NpYOlubaKouR6VSyzyWs2T5+ZWOUWqeEeWPm0ZFS4+eqlbZat2eZM2ambr6B8mtMQ9dPee0HV/am+ppbahBpVYTL7tZTUhEXDJevv7o+0ffoMEyVyqrotWZoc0asmbNHAaj6ZR5UiMTI5a5SBHxKXj7BTgztGlLo9Vad+Eb67xqfpQ/AHm1HdLCdxZkPRK2lJeX09HehqLWsnDBfHx0k2q8mpHi5y0EzC18c8J8UKuguVtPS/eAiyOb+iaUlHr99dfZuXMn27ZtIzs7m7fffpt33nmHI0eOsHXrVr788kveeOMNR8UqJim7qp2OvkE83TTEDu32NpaSY+YqqajEuXj5nHkouhjdyR2tRh927uGmIX3oBErmStmXrFkz08HyVoyKQlyQ14jhmpYqqeikVDy8bLfMCttUajWJ6UMbNIxywRcX5IUKKG/ppbFLdreyN1mzZo6qtj4MJgVvnZYwX92I50tzLfOk5ObfRCTPXwqcXOttSQr1wdNNQ4/eSHmzzL+bLFmPhC2HDpmvE02BcZyXGuXiaKaWOMtcqcJjeLhpSBi6kSe78J3ZhJJSb7/9No899hgXXHDBiOe+8Y1v8Mgjj/Dmm2/aLThhH18Nte7NCfdBoz5zmW1Rzj7z8YtXOjSu2SAhdREqtXkL4/bmhlGPWyLDzh1C1qyZx2hSOFBmrtI5vUoKTmndkwu9STlTIt3zlIHNWeWyXtmbrFkzx6mte6e3OCmKYt1QIGkoySLGx9KWXVWch77fdnW5Rq0iPdJ8U/V4rexsPFmyHglb9u/fD5jnSZ07J8TF0UwtMclpqNRq2psb6GhptI7NKWyQpNSZTCgpdfToUS677LJRn1+/fj1Hjhw566CEfe0qbgZg7jha9wb1A9aLEUlKnT0Pbx+ik1IBKDl6cNTjlsSZBzVLUsq+ZM2aefLrOunsN+Ct0zI/anglp8lotLbEyDypybH83GpLC+nt7rR5THywueL2oCSl7E7WrJnDskNlUujIis3G6nK6O1pxc9cROyfd2aFNa4FhUQSERmAyGqgoODrqcfOjzX8fcms7pYVvkmQ9EqdTFIWv95qLF3TR81gU4+/iiKYWnacXkfEpAFQWHrcmpcqae+gZMLgytClvQkmp1tZWwsNH3147PDyctjY5SZ1K2nr01rtE4xn2WJ5/BIN+AL+gUMJjkxwd3qwwd/E5wMkKNFuWDO0ell/XRZ/e6JS4ZgNZs2aeg+XmKqll8YFoNcP/hFWX5NPf242nty/RyamuCG/a8wsKJSwmEUUxjdrCZylHPyRzpexO1qyZYdBooqrNPE8qOWTkuZfldys+dRFaN3enxjbdqVSqUyo6R2/hSw71QadV09VvoKpVtmSfDFmPxOnKyspob21BUWtZtWzJiPMwcXKuVEXhUUJ9dAR5u2M0Kew+0eziyKa2Cf1LMhqNaG3sxmOh0WgwGCQLOJXsKWlBUWBuuA9+Hm5nPL74lNY92VHDPuZkmivOyvJy0A/YnsES5e9BmK8Oo0nhaHW7E6Ob2WTNmllae/ScaDS3xCxPCBrx/ImhasSkBUttbhUuxidl4XIAio8csPm8pVIqt7ZT7vzZmaxZM0NFSy9Gk4Kfh5Zgn5FJJ0tSSio6J8eSlCoZY9i5VqMmzdLCVyMtfJMh65E43b595utEU1ACF82PcXE0U1NcqnmuVEXBEVQqlXWTMcs4HWHbhMblK4rC7bffjk43cmAjwMCATJafaixZ2XNTQs94rKIoFB8x9wnPWSSte/YSGhVPQGgE7U31lB4/TOrS1SOOUalULIkL5PPceg5XtrPSxqwcMXGyZs0shypaUTBXfQZ5j7zQsySlUhYsd3JkM8ucRSvY89m7lBw7hMlkHJHgC/ByJ8rfg9qOfo5UtbM6RWZK2IusWTNDafPQPKlQnxE3+Ab1A1QUHjM/L/OkJiUhfTEqlZqWuio6WhrxDw6zedz8KH9yqto5XtvJhgWRTo5y+pP1SJxu19d7ATCFpHBBqu3fu9kuft4C8zzhhlrrXKm9pS18VdiIoihS9DGKCSWlbrvttjMec+utt046GGF/u080AXDunGDqO8b+49FQWUpnazNu7joS0xY7IbrZQaVSMXfxORzY8iFFOXttJqUAlsQHDCWlpBTaXmTNmjmMJoWsCvPvhq0qqa62FhoqS0ClInnBMmeHN6PEpKTj4eVDX3cntaWFxKSMnHmzLCGIfx2p5WB5mySl7EjWrJmh1DJPKmTkPKnKouMYDYP4BYUQEhnr7NBmBE9vX6KS5lFTkk9ZbjaLz7vU5nFzwn1w16rp6Bukpt32UHQxOlmPxKlMJhMHDpgrqBPSFlk3PRHD6Ty9iUqYS01pAeV5OaSvvgg3jYqGzgFyazuZHy1zuGyZUFLqtddec1QcwgEqW3qpau1Dq1axMjGYj3JqxzzeMvMoMWMJWneZcWBPczPNSaninP02Kw/g5LDz7Mp2yaTbiaxZM0dhfRdd/Qa83TWkRY7ctKHkmHmL4qjEuXj7BTg5uplFrdGQNH8peQd2UHzkoM2k1PKEQP51pFbmStmZrFnT34DBSPXQPKmk0NHnSSVlLJW/82chaf4SakryKTl+aNSklJtGzdwwH47XdsqW7JMg65E4VXFxMb3dnSgad9aft8LV4UxpCemLqSktoCwvh0VrLyEl1If8+i6+KmiUpNQoZDrZDGZp3VsSF4i37sz5R0vr3lzZdc/u4ubOR+fpRW9XB7WlhTaPmR/tj5tGRXP3ANVtckdPiFNZBpwviQ9Eqx75p6v4qPnunbTu2UfKIvPP8cRR23Ollg1Vqx2uaMNgNDktLiGmuoqWXkwKBHi5Eeg1cpZnicyTsovkodbH0uOHMRlH3yAmNcI8V6qg3vZuokKI8dm9Z6h1LziRi+dHuTiaqS0xPROAsvxsFEVh7tAufF8Wylyp0UhSagaztO6tGUdrRXd7qzVZkrJIst/2ptG6kTx0sTzaLnwebhrSh4ZySgufECe19+opajDf5V4eP7J1z2gwUDa0C5MlmSLOTsqC5aBSUV9xgq62lhHPzw33xddDS4/eKBUIQpyitGlonlTIyHlSHS2NNFWXo1KpScyQpNTZiE5OxdPHj/7ebqpO5I163NwIX1RAbXs/9R22N5sRQpzZlu27AfCMTmV+lFT7jCU2JR2tmzvd7a0011VZh53nVLXT0i2z2GyRpNQMZTQp7CkxX0icO+fMSSnLgODIxLn4BsiQbUeYu/gcAIpy9o96TOYpLXxCCLOsijYUIDHEmxDfkQNXq0/kMdDXi5evP1EJc50f4Azk7RdAVKL5Z2n5+3AqjVrF0njzemWpYhNCQMnQPKnk0JHzpCwV6TEpaXj5+Dk1rplGrdZY5wdafq62+Oi0xAR6AvCVVCkIMSlGo5Hco9kArDpnJWq1tB6PRevuTuwc8+iD8rwcArzcSY3wRVFgZ3GTi6ObmiQpNUPl1nbQ3juIr07LopgzZ7Mt1TvSuuc4KQuXoVKraaoup62p3uYxS4Yu8qRSSggzk6JwaGjA+QobA87hZOte8nzz75iwjzlDVbOjtfBZBs5LUkoIsz69kdqhgdq25kkV55h/l2SHY/uwrlFHbK9RFqlDVejb8iUpJcRkHD+ei6G/F0XrwY2XnOPqcKaFBGsLXw4A3xjarfDLAklK2SJn7zOUZZ7UOcnBaDVj/99s0OspHWp9kRMlx/H08SNuznwAikdp4VsSFwBAXm0n/YOjz0gQYrYobuiio28QTzcN6VG2KwuKss2/T9J6bF8pC80/z9LcbIyGwRHPW5JSB8raUBTFqbEJMRWVNfegACE+Ovw9h8+TGhzop3zo4kTWKvtInr8UlUpNU00F7c0Nox6XOjTP5esTzXJuJcQkvPvpVgA04SmsmRPu4mimB8tO9hX5RzCZjNak1I7CRpnFaYMkpWaor4eSUueOY55Uae5hBgf68Q0MISI+xdGhzWpzhirRRpsrFR3gSaivDoNJ4Wh1hzNDE2JKOlhurpJaEheAm40Ee0t9NS11Vag1GlIWyjwpe4qMT8HbLxB9fy+VRbkjnl8Y44+7Rk1z9wAVLb0uiFCIqaVkaJ6Urda9svwcDIN6/IPDCItJcHJkM5Onjx8xc9KAsaulIvw88Pd0o2/QyN7SkTPyhBBj27nbPE8qI3OFzXMxMVJkwhx0nl7093ZTX1FCZlwgAV5udPYbOCxjWkaQf1UzUP+g0XohN555UgVZ5oUmdeka2Z7YweZmrgKgouAo/T3dI55XqVTWaqlsaeETs1xX/6B1x6Tlo7TuFWWbd4OJT12Ih9fIC0ExeSq1mpSF5pktJ2zMbPFw07BwqD38gLTwCWFNStlq3bMkTeYsWinnWnY0Z6H5Zl/xGEkplUplHTT8pbTwCTEhnV3dNJebN8P65uUXuTia6UOt0RCfuhCAsrxsNGoV588NBWBLnu0xLrOZJKVmoIPlregNJiL9PUgKGfsizWgwUDh0UZe27FxnhDerBUdEExIVh8loHLVaakmczJUSAsy7lJgUiAvyIszPw+YxlvVr3lDCV9hXyqKT1Z22WvSWJw7NlSqTpJSY3br6B2nsMu+qdPq5l6Io1mHc0rpnX5afZ3l+DoMDo++uZ2nh+7KgUdqNhZiAv328FRQTeAez8dxFrg5nWklMN++yWnrcPCbn0owIAD7PrZd16DSSlJqBLPOk1qSEnPFuXEXBEfp7uvHy9Sd2boYzwpv10pavBSD/0C6bz58cdt4uC5aYtRRFsSZmLYna0/V0tlNdnA+crEIU9pWyYBkarRutDbU01VSMeH6FDDsXAoDSoV33Iv098NZphz3XWF1GZ2szWncdCWlyUWdPYTEJ+AWFYhjUU55/ZNTjkkJ90GnV1LT3UdQwslJdCGHbR59/BUBCxhLctZI6mAjLDqGVRcfp6elh3bxQdFo1Va195Nd1uTi6qUX+Zc1AE5knVZD1NQDzlqxGrdY4NC5hlrbMnJQqOZbFQN/IOSwLov3RqlU0dQ1Q3dbn7PCEmBJqO/pp6BxAq1axINr2DqInjhxAUUxExKfgHxzm5AhnB3cPT5IyzHf6LH8vTrUkPhCVCspbemnsGr1KQYiZ7uQ8KVu77pmrpBLTFuPmrnNqXDOdSqWy7sJXbKPN2MJdq2ZVcjAA2wulhU+I8egZMFCZb67yufqyC10czfQTHBFNYFgkJqOB/fv34+WuZe0ccwvfF7nSwncq7ZkPEdNJW4+e3FrzDJbVKcFjHmsyGSnI2gNI654zhcUkEBQRQ2t9tXkGwvmpw573GNpl7Gh1B9lV7cQGebkoUiFcx1IllRbph6e77YS5pXVPqqQcK3XpGoqP7Kfw8B7O2/jtYc/5e7oxL9yXgvouDpW3sWFBpIuiFMK1SpvNlVJJNoacFw0lpeZI655DzM1cRdZXn1KYvZf1t/wHKrXte+7nzw1le2ETXxU2cte6ZCdHKYRrvLW/ctKv3Z1TAN3NoFLjFjHnrN5rtkpesIxD2z5m165dfOMb3+DSjHC25jfwRW49P7x4rqvDmzKkUmqG2VvagqLA3HAfwnxtz2CxqC7Oo6ezDZ2nt5STO5FKpbImAUdt4bPMlaqQuVJi9jGYTBypagdGb90b1A9QejwLkHlSjjZn8UpUKjX1FSdobxp5Z2/F0FypAzJXSsxSbT16Wnv0qFWQEDw8KdXZ1kxNibQZO1JC2iLcPbzobm+lprRg1OPOn2euqD1U3kZX/6CzwhNi2so6aJ5/6xc9Bw+vkVWg4sxSFph3ht61axeKonBRWjgatYqC+i4qZediK0lKzTCnzpM6E0srxtzMc9Bo3RwalxjOkpQ6cfQgvb0jF6RM2YFPzGJF9d306o346rSkhNk+CSrLy2ZQP4B/cBjhcUlOjnB28fYLsM4cLDy8Z8Tzy2WulJjlLK17MYFeeLgNr+wsOmyu6IxJScM3cOwKdjE5Wjd35iw2V6FZOgBsSQjxJjHEG4NJ4esTLc4KT4hpqbVHT1vJMQDSM5e7OJrpKz5tERqtGzU1NZSXlxPo7c7KoZt50sJ3kiSlZpg945wnpZhM1qRU6lJp3XO2iPgUAkIjMOgH2LVrZLWUpTokt7aT/kGjs8MTwqUsrXuLYwPQqG1v1lBwaDdgrjyQ7dUdL3XJagAKxkhK5dd1SvWBmJXGat0ryDKvValL1zg1ptkmdYn551t4+OsxN4lZN7Qlu8yVEmJse4sbUDcXA5CRKa3Hk+Wu8yBu3nwAdu7cCQzfhU+YSVJqBqlu66W8pReNWmVtpxj12JJ8OloacffwJHnBUidFKCzMLXzmgedffPHFiOdjAj0J8dFhMCkcr+lwdnhCuExrj57CevOOJJnxtlv3jIZBCoeqDyy7WQrHmjeUlKoqyqWns33YcxH+HsQGeWJSIEtajsUsoyjKqEPOe7s7KS84CsA8SUo5VMrC5WPuFGpx/jxLUqpJdjgWYhR6g4nDh7NRGQZw9/IlIiHF1SFNa5YWvt27zTcpLskIB8w3YWWTGDOXJ6Wee+45EhMT8fDwYOnSpTarRk61Y8cOli5dioeHB0lJSbzwwgvDnn/ppZdYu3YtgYGBBAYGctFFF3HgwAFHfgtTxp6hUuTFsQH4eozdjnd8n3l7z3lLVstOMC6SttxcobZjx44RLXwqlYolQy18h6WFb0qRNcux/pVTg1FRiA7wJMLP9ly80txs+nu78QkIInZOupMjnJ0CQiOIiE9BUUzWncROZamWOlQu65WYXZq6BujqN6BVq4g7bWOS4pz9KCYTYbGJBIVFuSjC2eFMO4VanJMUjIebmvrOfgobZEt2IWw5UtXOYI25dW/e4pWyQ/tZSl6wDICDBw/S399PpL8ni2L8URTYktfg4uimBpcmpf7+97/z4IMP8vjjj5Odnc3atWtZv349lZW2J/uXlZWxYcMG1q5dS3Z2No899hj3338/77//vvWY7du3c9NNN/HVV1+xd+9e4uLiuOSSS6ipqXHWt+Uy1nlSyWfYdc9oJO+AuXxw/jkXODwuYVtU4jwCQyPp7e3lq6++GvH8knjLsPN2J0cmRiNrluO9f9j8fVvmqtmSf9C8fqUtPVdOlJzIUi2VN/TzP9WKoaTUAZkrJWaZE0NVUnHBXrhphp9Wn2zdkzEJzmBpkbQ1+87Cw03DqiTzefJXBU1OiUucZO8bewDvv/8+6enp6HQ60tPT+ec//zns+Z/97GeoVKphXxEREXb9vmYSRVHYU9KMpj4PMM8eFmcnJCqOyMhIBgYGrDeeL51v/jf4Ra4kpcDFSalnn32W73//+9xxxx2kpaWxadMmYmNjef75520e/8ILLxAXF8emTZtIS0vjjjvu4Hvf+x7PPPOM9Zg333yTe++9l8WLF5OamspLL72EyWRi27Ztzvq2XMKygMCZh5yX5WXT29WBl68/iemZzghP2KBSqcgYSgp+/PHHI57PjA0AzJVSUmI+Ncia5ViF9V0cq+lAo1KxKCbA5jHDWvdWnOfE6ET60M+7NPcwbW3DK6KWD7WM51S1M2CQOXhTiVR3OlZxgzkpNTfMd9jj+v4+So6ZdwiVeVLOcaadQi0su/DJXCnncsSNvb1793LDDTdwyy23cOTIEW655Ra+9a1vsX//8IrejIwM6urqrF/Hjh1z6Pc6nRU1dNFYW4W6pxmNVkvyfBnzcrZUKhXnnWc+h7IUIljmSu0taaajT+ZxuiwppdfrycrK4pJLLhn2+CWXXMKePbbvcOzdu3fE8ZdeeimHDh1icND2/5m9vb0MDg4SFDT6jKWBgQE6OzuHfU03hQ1dNHfr8XTTkDnKFuoWx/dtByB9+XlotFonRCdGs2CVOSn19ddfj7jIWxgTgFatorFrgNoO6Td2tam0Zs1U7x+uBmBehC/eOttrk7V1z19a95wtJDLW3MJnMrF58+ZhzyWFeBPs7Y7eYOJYtczBmyqkutOxBgxGSpvNSak54cPnSZ04dgijYZDAsEjCYhJcEN3s4+0XQNxc80DhsVr4LhhKSmVVtNEpmzM4jSNu7G3atImLL76YRx99lNTUVB599FEuvPBCNm3aNOy9tFotERER1q/Q0FBHfqvTlqIofFnQiKY+F4D41EXoPL3O8CoxHt/4xjcA+PLLLzGZTCSH+jAnzIdBoyItfLgwKdXc3IzRaCQ8PHzY4+Hh4dTX2767UV9fb/N4g8FAc3Ozzdc88sgjREdHc9FFF40ayy9/+Uv8/f2tX7GxsRP8blxvd7H5+1+RGIS7dvT/Wwf1A9Y/1BnnnO+M0MQYQqLiSE9Px2Aw8Pnnnw97ztNdQ1qkHwCHZXiwy02lNWsmJNJPZzCa+Ge2+aJ2yRite5bW47Rl0rrnChkr1wHw2WefDXtcpVKxLMF8Q+SgzJWaMqS607GyytsYNCr46LQjZuDlHdgBmFv3ZIdQ57FsfpG7f/uox8QFe5EU4o3BpPB1se2/xcK+HHVjb7RjTn/P4uJioqKiSExM5MYbb6S0tHTUWGfiOdZ4nWjspqqtD21DPgBzF690cUQzxznnnIOXlxeNjY3k5pqTfpcvjATg06O1rgxtSnD5oPPT/1ArijLmH29bx9t6HOD//u//ePvtt/nggw/w8LA9MBfg0UcfpaOjw/pVVVU1kW9hSvh6aJ7UuWdo3Ttx5AD6/l78gkKJTZEqg6ngiiuuAOCTTz4Z8ZwMO596psKaNRMS6afbdaKZpq4BAr3cmBvha/MYo2GQomxp3XOl9OXmn/vBgwdpbBze+mIZdn5Q5kpNCVOpunOmXuTtKDbPJJoT5jNsTR/o67VuCGBJ5ArnSF9xHiqVmtqyIlobR7/QW3fKLnzC8Rx1Y2+0Y059z5UrV/LGG2/wxRdf8NJLL1FfX8/q1atpaWmx+bkz8RxrPCxVUuh7ULWWATBnscyTshd3d3drC5/lJs4VC80bYOwqbqa9V++y2KYClyWlQkJC0Gg0IxaixsbGEYuLRUREhM3jtVotwcHDh3s/88wzPPXUU2zevJmFCxeOGYtOp8PPz2/Y13QyaDSxv8x8EXCmeVLHh+4cZZxzPiq1y3OSAtiwYQMqlYrDhw+PaH+wtGIermx3QWTiVFNpzZoJifTTvZ9lbt3buDga7ShrU8nxLGndc7GA0AhiUtJQFGVEdeeKRMsOfK2YTDIHz9WmUnXnTL3I21lk/pmc3rpXmL0Xw6CeoIgYIuJlK3Vn8vYLIDF9MQC5Q+MqbLG08G0vapS5nU7kiBt7Z3rP9evXc91117FgwQIuuugiPv30UwD+8pe/2PzMmXiONR55dZ1UtPbi1lgAikJYbCIBIbbPb8XkXHjhhQBs3boVgJQwH1IjfDGYFL7IHX0O3mzgsqyEu7s7S5cuZcuWLcMe37JlC6tXr7b5mlWrVo04fvPmzSxbtgw3NzfrY08//TS/+MUv+Pzzz1m2bJn9g59icqra6dUbCfJ2J3WUCgOA3q4OirL3AbLr3lQSHh7OypXm8ljLH0qLJUNJqbzaDvoHZXiwK02lNWu6J9JP19E7yOahfvrrl8aMetyxPeY7S+krzpPWPRfKWHk+MLKFLz3SD293DZ39BvLrZ0YlzEwwFao7Z+JFXmNXP/l15n/nKacNOc/dZx5kO3/l+dK65wKW8RRjtfCtSAzC001DQ+cA+XVdzglsFnPUjb3RjhntPQG8vb1ZsGABxcXFNp+faedY42Ewmvj3cfPPMaz7BABzF69yZUgz0nnnnYdWq6WkpITy8nIArvz/7d15XFR198Dxzwww7DsiIIu44pIiKCrmbmqammaZmln9WszK1HpM29Usq6fMyswyNX00zTXTFsvEfcFdAVORRRRURNnXmfv7Y2SKQM2Sucxw3q8Xf3TvZebcHM7ce+73e76tjaOlNhxNVzEy9ak6VGbixInMnz+fBQsWkJCQwIQJE0hNTWXMmDGA8SLm4YcfNh0/ZswYUlJSmDhxIgkJCSxYsICvvvqKF1980XTMe++9x6uvvsqCBQuoX78+GRkZZGRkkJeXZ/bzM5fyqXvRDb3Raq9/8XN8zxYM+jL8QhrhF9zQXOGJv6F8Ct+6desqPLEL8nLEx0VHqV4h7rw0D1ab5KzqseHYeUrKDDSt60qLgKov/ory80yr7rXqdP0RGaL6NW/XBa1Wy5EjR0hLSzNtt7XRmlbh251Y9bQIYT41aXSnNd7klffyDPBwwOVPCzPk51wl8bhx1T3p3amOsIhO2NjacelcChfTkqs8xsHOho4NjZ/pmJOyCl91q64He9c75nqvCcbpxAkJCfj7+/+TU7FKe85cJiu/BBdbhbyU4wA0aSP9pG43Nzc3oqKiAGPDc4D+dxg/h7sSL3M5r1i12NSmalFq2LBhfPTRR0ybNo3w8HC2bdvGDz/8QEhICADp6ekVVogJDQ3lhx9+ICYmhvDwcKZPn87HH3/MfffdZzrms88+o6SkhKFDh+Lv72/6+XOTTmtTXpS62dS9w9t+BiC8c59qj0ncmr59++Lk5ERSUhKHDh0ybddoNKbRUvuSpK+U2iRnVY/yqXv3Rda77qiChP3b0ZeVUqdeiEyHUZmLh5fpouqvvfA6NTR+D5V/Lwn11KTRndZo28nyflIVR0kl7N+OYjDgF9IIH3/rmKZoaRycXWh4h/FzefzaqLWqdJe+UmZVHQ/2nn/+eTZt2sS7777LiRMnePfdd/n1118ZP3686ZgXX3yRrVu3kpSUxN69exk6dCg5OTmMHj3abOdek2UXlrL5hLEw29oug5KiQty8fAio30TlyKxT+RS+8r5S9X2caVnPDb1B4adaPIWv6jW3zWjs2LGMHTu2yn2LFi2qtK1r164cPHjwuq9XPhSutsgvLuPQtX5DN2pyHh8fz4WzZ7CxtaOlPLmrcZydnenbty9r1qxh9erVREREmPZ1aODNpvgL7ErM5OluMsJNbZKzbq8zl/I4mHoVrQbuDa933eOO7jTOv78juqdMh6kBBgwYwJ49e1i7di1PPfWU6d+kfOTBvqQsSvUG7Gykd6GaJk6cyKhRo2jbti0dO3bkiy++qHQTeO7cORYvXgwYbwI//fRTJk6cyBNPPMHu3bv56quv+Oabb0yv+d577/Haa6+xbNky0+hOABcXF1xcXCoHYYUMBoXt10ZKNfateM7lfYzkWktdLTt04+Sh3cTv3Ur3+x6p8nujW1NfII4DKVfILizF3dGu8guJ22bYsGFcvnyZadOmkZ6eTsuWLf/Wg70JEyYwZ84cAgICKj3Yi46OZvny5bz66qu89tprNGzYkBUrVpjaYgCkpaUxfPhwMjMzqVOnDh06dGDPnj2m963tvj9ynuIyA0GejhSfMF6vNmvXRXoPV5MePXowffp0Dh06RGZmJj4+PtzTKoDj53LYcCSdke1r5+dS9aKU+Hd2J16mzKAQ7OVEkJfTdY9bs2YNAE0jonF0sfxh89Zo6NChrFmzhh9//JEpU6aYLu6jGxlv8mKTsygpM6CzlS8JYT3WHDQ29+/SpA6+blX3pLlyKYPUk8dBo+GOjj3MGZ64jj59+jBjxgxSU1M5ePAgkZGRgLGvlIeTHVcLSjmadpXIkOuvyCaqX3XcBP55dOefvfHGG7z55ptmOS+1HT+fzeX8Epx1NgR7/3HtlX35ojFXAc2jZNU9NTUO74CdvQNXLqVzLvEEgY2aVTomyMuJBnWcOXMpn52nM+l3h0znqm63+8EeGK+f/5qP/mz58uW3FGNtcvxcNvHpOWg1MKBlHZYuN/YeLl9pV9w+y/b+8V0bENqE80knefvLFbTtMQC93ti6Zc+Zy3wek4hbFQXyEe2DzRarGuTu1sKVz4Pv2qTOdY8pLi42TbEI7yJT92qq8PBwGjRoQGFhYYUGwk3ruuLtrKOo1MDhs1fVC1CI28xgUFhz8NrUvYibNzgPbR6Om9f1c50wH2dnZ/r0MX6flD/0ANBqNXRsYCyk7zotfaVqgrFjx5KcnExxcTEHDhwwLUkNxpvAmJiYCseX3wQWFxeTlJRkGlVVLjk5GUVRKv3UloIUwK8Jxmuvzo3rVFgt9Phu41Sx4CYtcff2VSU2YaSzdyAsohMAR3Zsuu5xplX4fpe+UqJ2yS0q5bvDfzwYzEs5TklRIe7evtRrGKZydNat/KFF+chaT2cdIV5OKMCRtKuqxaUmKUpZMEVRTPPguzW9/o3a5s2byc7Oxs2rjmmZXFHzaDQa09Po1atXV9hePiVmV6L0aRHWY/eZy5zPLsLVwZa7mlfdeFlRFFNR6o7onuYMT9zEkCFDAPjpp58oKCgwbY++NpV8p+QrYaU2JxhXC+3Z7I/Ck6IoHN5u7N3Z+s7eqsQlKgrvYvx3iNsbQ2lxUZXHdPtTX6k/LzQjhDVTFIU1B8+RX6LHz82B7k19id+3DYBm7TpLm4Rq1qJ9V9BoSD15nOzLxoJ4eLAHgKktT20jRSkLlngpn7QrhehstKaiRVVWrlwJQKtOd8ky6jXcoEGDsLW15ejRo5w8edK0Pfpa8+BdsqKVsCLlDc4HtA7Awa7q3HT2VBxZF85hp7OnWeSd5gxP3ERkZCTBwcEUFBSwadMfIxGir30fHUy5SlGpXq3whKgW6dmFxJ3PQaOB7mF/FKVSTx4n68I5dA6ONI+SqS81QUjTVnjU8aO4sICE/TuqPCYq1AtHOxsu5hYTn55j5giFUMfepCx+v5CLrVbDA22DoKyEk4eNU/dayNTjaufmVYfgJi0BiNu7FYBW9Tyw0WrIyCkiPbtQzfBUIUUpC7b12sov7Rt44aSruj1YYmIie/bsQavVEtHtbnOGJ/4Bb29vunfvDvxRTIQ/bvIOpV6hsERu8oTlyy0q5Yfj6cCNp+4d3GKcytqiQ3d0Do5miU38PRqNhsGDBwMVp/A18HHGz82BEr2B/cmyaqiwLpuvTd1rE+SBj4u9afuhrT8CxmkZkqtqBo1Wa1pxunwU21/Z29rQ6VrvTlmFT9QGF3OL+PHa9VefFn74uTtw6mgspcVFePjUxT9UVt0zh5btuwHGkZwAjjobwvyMq7nWxtFSUpSyYOXz32/UT2rZsmWAsdO/9DewDMOGDQNg7dq15OXlARDi7USAuwOleoX9KVlqhifEbfHDsXSKSg00qONMxLUhy39VkJtNfKxxOHlk9/5mjE78XYMGDUKj0RAbG2tqmK3RaEyFdJlyLKzNH1P3/phyXFSQbxqJ00Z6d9YorTr1Ao2GlBNHKzT1/7Ou0ldK1BJlBgPf7j9LqV6hka+LaaZN/D7jaJ1mUV1k6p6ZNGvXGa2NDRkpp8lMPwtAmyBPwNhXylDLphNLUcpCFZbo2ZtkLE5cr59UXl4e69atA2DkyJHmCk38S9HR0TRs2JD8/HzTv5+xr5RM4RPWY9W1qXtDIwOvewF0ZMcv6MtK8a/fmAB5clcj+fv7c+edxmmVK1asMG3/o6+U5CthPQpKykyf6V5/KkrF7dlCWUkxPgHB1GtYeZU3oR53b18atogAjA/7qtLt2sPdg6lXyS4sNVtsQpjbr/EXOX+1CCedDUMjAtFqNBTl53HykEzdMzcnV3catDCuXFze8LyJnwtOOhtyi8pIvJinYnTmJ0UpC7X7TCYlZQbqeTjSsI5Llcd89913FBQU0LBhQ9q3b2/mCMU/pdFoTEXE//3vfxgMBoA/jTyQmzxh2ZIz84lNvoJWA0PaVD11TzEYOBhjnLono6RqtuHDhwPGBRoKC419EMrz1bG0q1wtKFEtNiFupx2njNdegZ6ONKn7x7XXoWtTw9p06SujDGqg1tdGr61btw69vnILhCAvJxr5uqA3KOw4JaM7hXU6cymP7aeMU1TvDa+Hm6MdAPGx29CXlVKnXgh+IY3UDLHWadGhGwDH98agKAq2Wi131HMH4FAtW3FdilIWauufVt2r6gJIURSWLl0KwIgRI+QiycIMHDgQV1dXUlJS2L59O4BpiO2xNHmSJyxb+Sipzo3r4OfuUOUxySeOkHXhHPaOTrS4Nu9e1ExdunQhMDCQ7OxsNm7cCECAhyONfV0wKLBdbvKElSjvJ9WrWV3TdVVGSiLpSSfR2tjKCqE1VNM2HXF0cSMjI4OYmJgqjykfLbVFpvAJK1RYomflgTQUoG2IJy2vFT7AOCodoPWdd8n9opk1bdMRW509WRlpnD/zOwARwcYpfHHns2tVH2EpSlkgRVGIudbk/Hr9pHbt2kVSUhLOzs4MGjTInOGJ28DZ2Zn77rsPMI6WAuNNXqiPMwYFdkufFmGh9AaF1QeNRan7216/wfn+3zYAcEd0T2kaXMPZ2NiYRkstW7bMtKx6+cpkcpMnrIHBoPDbtc9yz2Z/9OiM/fU7AMIio3F281AjNHETtnY62nQ1LvZTfk31V+X5auvJSxgMtauXi7BuiqKw7vA5sgtL8XbW0b+Vv2nf5Yw00k7Ho9Foadmxh4pR1k72jk6mlaUPbfsJgEBPR+q62VOqVziSdlXF6MxLilIWKPFSHimXC9DZaE19O/5q4cKFAAwePBhnZ2dzhiduk/IRbjt27ODMmTPAH0VIWSFGWKpdiZmkZxfh5mBboSfLn2VfvsjvB3cBENFNpu5ZgiFDhuDg4EBCQgKHDh0C/hh5sE1u8oQV2J9yhUu5xbg52NI+1DhyOSsri2O7fwMg6q57VYxO3EzbHvdgY2PDnj17OHnyZOX99T1x0tlwKbeY+PQcFSIUonocOnuVY+ey0WpgWLsg7G1tTPuO7vwVgIZ3ROLq4a1WiLVa+LXpxXF7t1JSXIRGo6FdfS8A9iVlmR70WTspSlmgTfHGlV86NvTGxd620v64uDh27tyJjY0NjzzyiJmjE7dLUFAQ3bt3B2DBggVAxZEHtSVJCetSPnVvYHgADnY2VR6zd9NaFIOB+s3CqRsUas7wxD/k4eHBPffcA2CaOt62vhfOOhsy80qIOy83ecKy/XDMuIT6Xc390NkaL59XrFhhXIwhtAmBjZqrGZ64CXdvX3r16gVUPVrK3taG6GsLysgqfMJanM0qYP2R84Bx2nGgp5Npn0Gv58j2TQC0uvMuVeITEBLWCk9ff0qKCkjYZ1xxOjzIA1uthoycItKuFKocoXlIUcoCbYozFqV6t6h6lMGXX34JQL9+/ahXr57Z4hK33+OPPw7A+vXrSU9Pp32oFw52Wi7kFJOQnqtydELcmpyiUn46ngHA/ZFBVR+Tk8OhrT8C0PHuoWaLTfx7I0aMAGDTpk1kZGSgs/1jNK/c5AlLZjAo/HjcWJTq38oPgJKSEpYtWwZA+7sGSy8WC/DQQw8B8P3333PlypVK+8unZZY//BXCkimKwuQ1RykpMxDi7USXv7R8OXl4L7lXL+Pk6k5YRLRKUQqNRkN4Z+NoqUPbjVP4nHS2pr5fsclZqsVmTlKUsjAXc4o4fK0bf1VTX5KSkti0yVj1Li9oCMvVpk0boqKiKC0tZcGCBTjY2dCp/EneSbnJE5bl+yPnKS4z0NjXhVaB7lUes2LFCkqKCqkTWJ+Gd7Q1c4Ti32jWrBlRUVGUlZWZppB3ayrNg4XlO5h6hQs5xbja29LpWqH1559/JjMzExcPL5pHdVY5QvF3REZG0qxZM4qKili9enWl/Xc1r4tWA0fTsjmbVaBChELcPitiz7Lz9GXsbDQMjQhE+5fC+cEtxt6d4V36YmNrp0aI4ppWd96FRqPl7Mk4LqYlA5im8B1Ny6ao1PobnktRysL8kmB8ehMe5EFdt8qrVs2fPx9FUejevTtNmjQxd3iiGowZMwaAlStXkpmZSbdrU/hiTkhfKWE5FEVh2d5UwNjToKpRBSUlJSxZsgSAjn2HysgDC/Tkk08CxnyVlZVFj2v56tDZq1zKLVYzNCH+sY2mqXt1sbe1QVEUFi9eDEDbHgPkhs5CaDQaRo0aBRgXZSgtrbiSsY+LPVGhxhvB8lG9Qlii9OxCZmxMAOCuZnXxdrGvsP/KxXQSjx8AjYaIa4sACPW4efrQNKIjAPs3rwegvrcTPi72lOgNtaLhuRSlLEz51L27mlceJZWRkcH3338PwBNPPGHWuET16dChA61ataK4uJhFixaZmgcfSL3C1YISlaMT4u85mpZN3PkcdLZa7ouoetW9DRs2cOnSJVw9fWjZoZt5AxS3RXR0NC1btqSwsJDFixfj7+5Iq0B3FAV+TZApMcLy6A0KPx4zFijuvsO4alVsbCzHjx9Hp9MR0a2fmuGJW9SvXz98fHxIT09n/fr1lfdf+zf+4dp0TSEsjaIovLr2OLnFZYQHeVS5KNaBLRsBaNgyEk9f/0r7hfm16zUQMDafL8rPu9bw3BOAvWesv+G5FKUsSHZBKbsSMwHoXUVRau7cuZSWltKuXTvatGlj7vBENdFoNKbRUt988w2u2hLC/FzRGxQ2J8iUGGEZykdJ9Wvph6ezrtJ+vV7PV199BRhXsZKRB5ZJo9GYRkstW7aM3Nxc0/fVpjgZeSAsz54zl8nIKcLd0Y4uTYw3d59//jlgXHXS2c1DxejErbK3t+exxx4DYN68eZSVlVXY36eFHxoNHEq9Snp27WgwLKzLj8cz2HziInY2Gt4b2qrStL2SokJT7862PQaoEaKoQkhYa+rUC6G0pJgjO4yteCJDPLGzMTY8333mssoRVi8pSlmQn+MzKNUrNK3rSuO6rhX2JSUlmebHP//882qEJ6pRt27dCAsLo6CggC+//JI+LYyNVn+U4eXCAuQUlZpWfxnRPqTKY3788UfOnDmDu7u7jDywcD179qRhw4bk5uayfPlyU77aefoyuUWlN/ltIWqWtYfOAdC/lT/2tjYcPnyY3bt3Y2trK707LdSwYcPw9PTk7NmzbNy4scK+um4OtA0xjk6QKXzC0uQXlzF9QzwAT3dtSJO/3C8CHN6+iaKCPLz8AmncOsrcIYrr0Gg0tOtpHC0Vu3k9BoMeJ50tEcHGfLRgR5Ka4VU7KUpZkO+v3dTd06ryMMuPP/4YvV5Pt27diIyMNHdoopppNBrGjx8PGJcyjvAxDuHcduoS+cVlN/hNIdT33aFzFJbqaeTrYhqK/GdlZWXMmTMHgEcffRQHJ2dzhyhuI61Wa5pCvmjRIvydNTTwcaZEb2DrSemFJyxHYYneVJgY3Ma4mvG8efMAGDhwoKxwbKGcnJx45JFHAOO/p15fsYlw35bG6+zyaZtCWIpPfjtNenYRgZ6OjO3eqNJ+g0HPvk1rAejQezAarZQCapI7OvXCwdmFKxfTObF/JwDR1xa42nziIkmZ+WqGV63kk2ghLucVsyvROGzvntYBFfYdP36cn376CY1Gw4QJE9QIT5hBly5daN++PSUlJfywfAH1vZ0oKTPIqlaiRlMUhaXXpu6NiAqusnn5hg0bSE5OxsPDw7Rkt7Bs/fr1IyQkhKysLBYuXMhdLYxT+GTkgbAkvyRcIK+4jEBPRyKDPTly5AgxMTFotVoZJWXhRowYgbu7O0lJSfz8888V9vVtaRzdGZuSxcWcIjXCE+KWnb6Yy/ztZwB4c0ALHOxsKh3z+4FdXLmUjqOLG6069TJ3iOImdPYOtOs1CIBdP3yLoijUcbWnaV1XFAW+3pWsboDVSIpSFuKnuAz0BoWW9dwI9ak4imDWrFkADBgwQFbcs2IajYYXX3wRgO+//54oT+OFkkzhEzXZwdQrnMjIxf46Dc6Li4v59NNPAXjsscdwdpZRUtbAzs7O9JBk0aJFdAww9hHbnHBRRncKi7HmYBpgHCWl0fxxvTVo0CBCQ0PVDE38Sy4uLqaV+D777LMKvaXqeTgSHuSBosg1lrAMiqLwxvo4ygwKPcN86VVF72FFUdixYTkAkd3vwc6+8iruQn1RvQZhq7MnPfkUSfGHAIhu5A3At/vPkl1onW0QpChlIdYfLp+6V3GU1LZt29i1axd2dnY899xzaoQmzKhly5b0798fRVFI3mL8Ytly4iKFJfqb/KYQ6liwIxmAQeEBuDtVbl6+dOlSzp07h6+vr4ySsjK9e/emVatWFBQUELN2CSHeThSW6mUVPmERzl0tNE03HRIRyK5du9i7dy92dnY888wzKkcnbodRo0bh7u5OYmIia9eurbCvvFXGmms9xYSoyTYcTWfn6cvY22p5c2CLKo85dXgvGSmnsbN3oH2fwWaOUPxdTq7utOnSF4CdG1YA0KiOC03qulBQomf5vlQ1w6s2UpSyAKmXC9iblIVGAwP+NHWvpKSEGTNmAPDQQw8RGFj1MuvCuowfPx47OzuOHdyHb+5JCkr0bIqXJ3mi5jmbVcCP15bV/r87G1Taf+XKFdMqVuPHj8fR0dGs8Ynq9efRnatWraKrn7EX3jq5yRMWYEXsWRQFoht6E+LlyIcffgjA8OHDpZeUlXBzc+Ppp58G4JNPPqGgoMC0b1B4PWy0Go6cvcrpi3lqhSjETeUXlzFjYwIAY7s1IsjLqdIxiqKwbf1SANr1HIiTi5tZYxS3pkPf+9Da2JKccJjkhMNoNBoe72y8jv5y+xmrHIwgRSkLsOra8PE7G/lQz+OPm7aFCxeSmppKnTp1GDt2rFrhCTMLDAzk0UcfBcBwcDWUFrHmoNzkiZpn0a5kDAp0buxDU7/KK8DMmTOH3NxcwsLCGDhwoAoRiurWrl07unfvjl6vJ3XbtwBsO5XJ5bxilSMT4vrK9AZWxBqfRg+PCmbNmjXEx8fj4uLCU089pXJ04nYaPnw4gYGBXLp0iYULF5q213G1p2uTOgCsPZSmVnhC3NSnW06TkVNEsJcTT3Wt/AAQ4PSRfaQnncROZ0+HvveZOUJxqzx86hLR7W4Atqz+GkVRGNymHoGejmTmlbDMCkdLSVGqhtMbFFbtPwvA/W2DTNvPnj1rGmHwn//8BxcXF1XiE+oYM2YMQUFB5F+9jN2Jn9l+6pI04xQ1Sk5RKStijbnr/+6s3HslLi6Ob775BoCXXnoJG5vKDTmFdZg4cSI2Njbs3bGV0NJk9AaFH46lqx2WENe15fdLXMgpxttZR3SwMx999BEAzzzzDF5eXuoGJ24rnU7HxIkTAZg/fz7nzv3xkG9IhHFE3NqD5zAYFFXiE+JGzlzKMzU3f+2e5lU2NzcY9GxetQCAtj0H4uzmYc4QxT905z3DsbXTkXY6ntNHY7Gz0fLMtRUVP9+aSFGpdY2WkqJUDbcrMZPz2UW4OdjS+1rTOkVReP311ykqKiIqKop77rlH5SiFuTk6OvL6668DYJu0A+VKGt9d6zsmRE3wbexZ8orLaOzrYnraXE6v1zN16lQMBgP9+/enQ4cOKkUpzKFRo0aMHj0agIK9K6GshJUHZOSBqLkW704GYGhkIF/Om8vly5cJDQ1lxIgR6gYmqkXfvn2JioqiqKiId955x7S9V7O6uDrYcj67yLQCthA1haIoTNsQT6leoWuTOvRq5lvlcUe2/8KltGQcnF3odM8wM0cp/ilXT2/a9TTOIvht5QLKysq4LyKQeh6OXMot5hsrGy0lRakarnykwaDweqbq96pVq9izZw8ODg5MmzatyiXWhfW788476devHygKusMrWRmbgqLIkzyhvjK9gYU7kwHjKKm/5qgVK1Zw7NgxnJ2dmTRpkgoRCnMbO3Ys/v7+5Fy+gP2pXzmals2xtGy1wxKikoT0HLafykSrgSiPApYsWQLAlClT0Ol0KkcnqoNGo+HVV1/F1taWzZs3ExMTA4CDnQ2Dwo29XJfuTVExQiEq25xwkZjfL2Fno+GNAc2rvB80LjTyNQCdB4zA0blyKwVRc3W6ZxgOzi5cTEti1apV6Gy1PN2tIWB9o6WkKFWDpWcX8tO1pWgfjDJO3UtLS+O9994DYNy4cYSEhKgWn1Df5MmTcXV1RZt9jjPb1hCbfEXtkIRg7aFznLtaiLezjnvbVGwIfPbsWT744APA2Nzc17fqJ3vCujg7O/PKK68AYHMqBs2VVJbtk5s8UfPM354EQJ9mdfj0/bcwGAz069ePzp07qxyZqE6NGzfm4YcfBmDq1Knk5uYCMKpDfQA2xV8gPbtQrfCEqKCoVM+0DfEAPHZnKA3qVN3GZd68eeRdzcLDpy5tew4wZ4jiNnB0caPbYGNemj17NtnZ2dzfNhB/dwcu5BSzdK/1jJaSolQNtmR3CmUGhfahXrQIcKesrIz//Oc/5OXl0aZNG9OXp6i96tSpw9SpUwGwPbmZj7/dpHJEorYr0xv4dMtpAJ7s0qBCfwODwcArr7xCQUEBbdu2lakwtUzPnj3p378/imJAd3A56w4kk1tUqnZYQphcyCli/RFjTyHf8zs5ceIE7u7uvPzyyypHJszhmWeeITg4mIyMDGbOnAlAUz9XokK90BsUvrGiG0Bh2eZvP0NqVgG+rvY816NxlcecPn3a1Ly/94gx2NrJSE9LFNn9HurUC+Hq1at8/PHH2NvamP7NP/ntFNkF1nEdJUWpGqqwRG/qrP9oJ2OT4Dlz5nD48GFcXFx4//33pTGwAODuu++m6113o0Fh/7cfczL1gtohiVps7aFzpFwuwMtZx6iOFUdyLly4kNjYWJycnHj77bfRauUrqLZ59dVX8fHxQZt3kbLD37P2kKwcKmqOBTuTKNUrtHC4ypqlxsbAU6ZMwdvbW+XIhDk4OTkxY8YMNBoNa9asMU3je/jad9myfWcpKTOoGKEQcP5qIXO2JALwcr9muNjbVjpGURSmTZtGaWkpjcM70DQi2txhittEa2NDn5FjAfjmm284cOAAD7QNpLGvC1cLSpkTc1rlCG8PuSOoodYeOsfVglICPR25q3ldtmzZwrx58wB48803qVev3k1eQdQm78+Yis7dF03BFZ4dPxGDQS6ahPmVlP0xSuqpLg1w0v1xoXTo0CFmzZoFwKRJkwgKCqryNYR18/DwYPr06QDYntnBnCVrKNNLvhLqu5RbzOJdKVBaRO62hZSVldG3b18GDhyodmjCjNq2bcuoUaMAePnll7lw4QJ9Wvjh62pPZl4x64/IojJCXW9tjKewVE+7+p6mnmd/tXz5cmJjY3F0dKTvQ2PNHKG43UKbhzNkyBAUReG1115DX1bKy/2bAbBoZzKplwtUjvDfk6JUDVSqNzB3q/HG7pHo+pxNTeGll15CURSGDx9O//79VY5Q1DSurq48+/IMFBs7ziYcZNbsT9QOSdRCS/emkHK5AB+XiqOkrly5wgsvvIBer+fuu+/mgQceUDFKobZu3brx0MPG1fiyty9hya8HVY5ICJgbk0hhSRm+p9ZzKf0c/v7+vPnmm7KYTC00ceJEmjVrZvru0igG06yFz2JOozfIojJCHZviMvjhWAY2Wg1TB7asMj8lJSXx/vvvA8benR4+dc0dpqgGkyZNwsfHh6SkJGbPnk23JnXo3NiHEr2B19cft/jFrqQoVQOtPpDG2axCfFx09GvqxtixY8nNzSUiIoLJkyerHZ6ooR7tH41HJ2OPnvlffM769etVjkjUJtkFpczefAqAiXc1NY2SKikp4fnnnyc9PZ2QkBBZMVQAMOnFF6gbGoamrIhZU18yNRUWQg0Z2UX8b28KtonbyD21DxsbG9577z3c3d3VDk2owN7enlmzZuHs7MyBAwf473//y0MdgnF3tOPMpXzTIkRCmFNuUSmvfxcHwBOdG9A8wK3SMaWlpUyePJnCwkI6dOjAQw89ZO4wRTVxd3c39RFeuHAh27dv582BLdDZaIn5/ZLF5yUpStUwJWUGPvnNOErq/zoG8uL4cSQlJeHv78+sWbNkOWJxXbY2Wl4a8xCljboB8Morr7Bv3z51gxK1xie/neJqQSlN6rrwQNtAwNjTYPr06cTGxuLs7Mwnn3yCi0vVK8SI2sXOzo4vPvsYHNwovXKeR58eh15vPUsbC8vy3s8nKDufgF38BsC4sm3btm1VjkqoKSQkhLfffhuAr7/+mp83fMcj0fUB+HTLaYsflSAsz3s//U5GThEh3k6M71V1c/P333+fo0eP4urqKr07rVCPHj1MiwRNmTIFV6WAMV0bADD1+3iLXjxGPqk1zIrYVM5dLcTHScvhb2dz6NAh3NzcmDdvniydLm5qQKsAgrrcT1lAK8rKynj66ac5cuSI2mEJK5eQnsOiXcmAsemmrY3xq2XOnDmsWrUKrVbLhx9+SOPGVV9EidqpSf0g+o15FcXGjrgDe3j9jTfkRk+Y3f7kLNb+thdd7GJQFO69915GjhypdliiBujduzfPPvssAFOnTqW59jzOOhsS0nPYcDRd5ehEbRKbnMWSPSkAvDPkjgorG5dbv349S5YsAWDmzJn4+/ubNUZhHpMmTaJp06ZkZWXx3HPP8VjHQEK8ncjIKWLq9/Fqh/ePSVGqBsnKL+G/m06CoYyAhG/Ztm0r9vb2fPrpp3IzJ/4WWxst43o1oTRiOPg2oaCggCeeeILjx4+rHZqwUnqDwuQ1xygzKPRpUZduTY3F80WLFjFnzhzA2Cy2S5cuaoYpaqjXR/XFruMoFDSsWb2a9957TwpTwmz0BoXJX/+G/Z75aMqK6dChA1OnTpUpxsJk7Nix9O/fn7KyMqb8ZyIDA4sBmPnjCYpKZXSnqH65RaVM/PYwAA+0DSS6oU+lYw4ePMjrr78OwJgxY+jRo4c5QxRmZG9vz+zZs3F3d+fo0aO8NfUN3h/aCo0GVh1Is9hpfFKUqkHe//kE2bl5eB7+H6cO70Gn0zFnzhzatWundmjCggxqXY/w+nUobDcaj+Cm5ObmMnr0aHbu3Kl2aMIKfb0rmSNnr+Jqb8u0QS0BWLBgAe+++y4Azz//vIw6ENfl7mTHhNFDKW1jbH6/aNEiKUwJs3l35VbS1r6PpjiPJk3D+OSTT6RNgqhAo9Hw9ttv07lzZwoLC/l1/gx8S85z7moh87efUTs8UQu88V0cZ7MKCfR05NV7mlfaf/r0acaOHUtxcTHdu3c3je4T1iskJITZs2dja2vLxo0b2bpqAU92Nk7jm7LmKOevFqoc4a2TolQNcSDlCst3xGO/83OKzh7HwcGBOXPm0KlTJ7VDExZGq9UwbVALNHb2pLccRbPWkRQUFDBmzBi+//57tcMTViQhPYd3fzoBwOR+Yfi62vPxxx+bVn158skneeqpp9QMUViAhzqEENSmKyWtBgPGwtRrr71GWVmZypEJa7ZhWyyL35mEpjgH38BQFnw1X3reiSrpdDpmz55NZGQkebm5FP/2GdpLp/gsJtEqlmIXNdeag2msOXQOrQY+GhaOm4Ndhf1JSUk8/vjjZGdn07p1az744ANsbCpP7RPWp3379rz55puA8WGwU+KvtAhw40pBKU//74DFjeSUolQNkFNUyjOfrMZ+y0dor57F3d2dhQsXcuedd6odmrBQrQI9GB4VDHYOXGw9il69+1JWVsakSZP46KOP5GZP/Gv5xWU8s+wgxWUGujWtw5BWdZk0aRJz584FjMsQT5gwQabBiJvS2WqZMfgO9KGdKGkzDI1Wy+rVqxkzZgw5OTlqhyes0G8x25j07BNoinNxqhPE2hX/w9vbW+2wRA3m6OjIF198QXR0NCXFRTjsmU/x6T28uPIIeoOM7BS33/Fz2UxZcwyA53o0pm19rwr7ExMTefjhh7lw4QINGzZk7ty5ODo6qhGqUMl9993H5MmTAfj8s89ol7sLNwdbjqRl8/p3xy1q1LkUpVRmMBgY/fIHXP3hIzRF2dQPDWXZsmWEh4erHZqwcC/1DSPQ05G07DKU9g8xevRoAObNm8djjz3GxYsXVY5QWCqDQWHS6qOcuZRPXTd7Xoj2YfToh9mwYQO2trZMnTpVRkiJW9KpkQ+jOoSgD26HU5f/w8HBgZ07d/LAAw8QFxendnjCShgMBr744kueGTsGpbQYbd3GrF6xFC8vr5v/sqj1nJycmDt3Ln369EEx6NEdWsGhDYv4IuZ3tUMTVuZSbjFPLTlAcZmBHmG+jOtZsbfw3r17GTlyJJmZmTRt2pTFixfj6empUrRCTaNHj+aFF14AYMX/FhF1+Rc0Shnf7k9j9uZTKkf390lRSkXnzp2j//2jOPHj12gUPVGde7Bq5UoaNGigdmjCCrg72jH7wTbYaDVsOJpBva4P8t///hcnJydiY2MZPHgwGzdutKgquqgZZv50go1H07HVwIO+F3nsoWEcO3YMd3d3vvzySx544AG1QxQWaPLdYYR4O5Hp3pTQoVPw8/MjJSWF4cOHM3/+fPR6yxqKLmqWS5cuMXbsWGbN+hAUBUNwO+bN+4L6/nXUDk1YEJ1Ox4cffsjTTz8NgG3iNma//CyrYvarHJmwFtmFpYxesI9zVwsJ9XFm1rBwbLTGUeeKorBs2bIKU/YWLlwohfVa7vHHH2fatGlotVp2/rqRRvGLoSiHj349xdfXVseu6aQopYKSkhIWLVrE3f3vITn+IIrWlq73P8GieZ/i7OysdnjCikSGePJC7yYAvLE+DpuQSFatWkVYWBhZWVm8+OKLPP7446SkpKgcqbAUn29N5IttZ9DkZ9Ls9DK++GA6eXl5REREsGbNGjp06KB2iMJCOdvbMndkJE46Gw7mOtPu8be56667KC0t5YMPPuDhhx8mPt5ylzsW6jAYDHz33XcMGDCArVu3omhtKWk9lKnTp3NnUz+1wxMWSKvVMm7cOD7++GPsHF3QZJ/jtWcf44NP51FaWqp2eMKC5ReX8fjXscSn5+DjYs+CR9rh7mjsI3XlyhWee+45pk+fTllZGf379+frr7+WEVICgPvvv585c+bg4uJC2ul4vHbNRpt+nDfWxzFva6La4d2UFKXMSK/Xs379evr378+7775LaXEReu8G9Jv4IXOnSu8VUT2e7tqQB9sFYVBg3PJDpJQ4s2LFCsaNG4dOp2PXrl0MGDCAqVOncuHCBbXDFTWUoii8//MJZn53ANv4H3De+iGnjx9Ep9Mxbtw4vv76awICAtQOU1i45gFuzBoWDsC3x7Lw7f0U06dPx8nJiYMHDzJ06FDeeOMNMjMz1Q1UWISDBw8yfPhwJk+eTHZ2Ngb3ehR3Gcczjz3Eg1EhaocnLNxdd93Fhu/X4xTUAvSlzJ/zEf3uGUBMTIyMQhe37HJeMcO/3ENs8hVcHWxZ/FgUoT7OKIrCunXr6N+/P5s3b8bOzo6XXnqJ999/H3t7e7XDFjVIt27dWLlyJY0bN6YwNxv7fYuw27+UmWv2MH1DPGV6g9ohXpdGkaxZSU5ODu7u7mRnZ+Pm5vavXy8/P5/169fzv//9jzNnjMvHKvaulIb1oc+AQXwyPNI0LLM6LdubWu3vIW7diPbB1f4eZXoDTy89yC/xF7DRanh7cEuGtQsmOTmZGTNmsGPHDsA4LH3IkCGMGDGCxo0b3+RVRU1xu3PWX+UXl/HCkh38+v1qbM9sR1NWDEDHjh15/fXXqV+//m19P8lVNV91563Fu5N5/TtjL6nhUUE8HeXNxx/NYuPGjQDY29tz33338eijjxIYGFitsYjbrzpzlqIoxMbGsnDhQmJiYgCw0TlQ1LA7ZY26Mb53GM/3bHzbHgRKvrIc1ZW3MrILGPTih2TvW4emJB+AyMhI/u///o+uXbui1coYAEtW3ddYYFzN+On/HSD5cgFezjoWPtKOVoHu7Nmzh1mzZnHsmLHheePGjXnnnXdo0aLF335tyVHW4VbyV3FxMZ988gkLFixAURQUrS1ljboS3nMInz0Sja+bQzVG+s9IUaoKtyP5KIrC4cOH2bhxI+vXryc3NxcAWwcnCkO7UdagEyM6NWb6oJZmKUiBJKWayhxFKYCSMgOT1xxlzcFzAAyNDOT1Ac1xc7Bj3759fPzxxxw4cMB0fFRUFEOGDKFHjx64urqaJUbxz1TXBZNer2fpht+YNe9rCpMPo1GMPX3CwsJ49tln6dGjR7WM8JRcVfOZI299G3uWl9YcRVGgdaA7n46I4GJSAu+99x5Hjx4FwMbGhm7dunHvvffSpUsXdDpdtccl/r3qyFk5OTls2rSJFStWcPz4cQA0Wi1OTTqRGdIdGyd3XunXjMfuDL0t71dO8pXlqM68dSm3mJGfxXBmxzrjwxuD8fuyYcOG3H///fTv3x8fH59qe39RfaqzKKU3KCzdm8JbGxMoKTNQz8ORr0aF8/v+7SxZssS00IeTkxNPPfUUjz76KHZ2drf0HpKjrMM/yV9xcXHMnDmT/fuNPe8UW3tsGnTg2ScfZUy/qBo1S0v10v1nn31GaGgoDg4OREZGsn379hsev3XrViIjI3FwcKBBgwZ8/vnnlY5ZvXo1zZs3x97enubNm7N27drqCr+CgoICYmJimDFjBr169WLEiBEsXbqU3NxcPH0DsIu8j9weL0NYT6YMaM2Me81XkBJCZ6vlg/tbX3tCDKsOpNFn1jZW7j9LZNt2LFmyhIULF9KrVy+0Wi379u1j8uTJdOrUiaeffppvv/2Wc+fOqX0aqrOmnFWVgoICduzYwQuTXyWiQyfemTyOoqQDaBQ9DZo2Z/bs2axevZqePXvWqC8zYX0eaBfE/Ifb4u5ox5G0bHrP2saOK24sXLyURYsW0alTJ/R6PZs3b+a5556ja9euvPzyy2zatIm8vDy1w68xrDlnXbp0iXXr1jFu3Dg6d+7Ma6+9xvHjx9Hp7Alp14uiHv8hs+kgvL19WPJ/Ube9ICVEuTqu9nz7XHc63vsIRb1eprRRN2x0jiQmJjJz5ky6devGk08+yfLly2v1tZRa+ehW39ccYpOzGPzZTl7/Lo6S0jIiHTPpmr+V/3tgAJMnTyYuLg6dTsfIkSP5+eefefLJJ2+5ICVqtxYtWrB48WI++eQTgkJC0ZQVYzi5ldkvPkrEXYOZ+tEXXL58We0wAZVHSq1YsYJRo0bx2Wef0alTJ+bNm8f8+fOJj48nOLhyNTApKYmWLVvyxBNP8NRTT7Fz507Gjh3LN998w3333QfA7t276dy5M9OnT2fw4MGsXbuW119/nR07dtC+ffu/FdffqYgbDAZSUlI4evQox44d49ixYyQkJFRocOjo5ERQiyjOuTUj0zkUNFoa1jGuotAq0OPW/4f9S1Ipr5nMNVLqz2KTs3jh2yOkZhUAUN/bieFRwQyOqIevqwPp6emsWbOGH3/8kcTEis3x7r//fqZNm2b2mGsCS85ZVTEYDKSmppKQkEBCQgL79+/n6LFj6MvKTMcotg6EhHdi6vgn6BDZ+m+/9r8huarmM2feOne1kIkrDrM3KQsATyc7hkcF80DbIMquprNu3Tq+++67Cn2mbG1tad68OW3btmXo0KGEhtbOYoQ15Sy9Xs+ZM2c4duwYR48e5ciRI5w4caLCMf5B9XFo1J7fHZujtzMuHDM0MpBX+jXD07l6RtFJvrIc5shbBoPCvG1n+PCX3yktKsQ27QBemUfJPV/xWio0NJTw8HBatWpFq1ataNy4sdUXHNTKR7f6vn91O0dKFZSU8WvCRRZtO8WhuBNos5KxzzqD/dUkivJzTcf5+voyfPhwHnjggX+9sp7kKOvwb/OXwWBg2/btzPz4C1LiD/6xQ6OhbnAjunTqSM8u0TRr1ow6deqY/cGzqkWp9u3bExERwdy5c03bmjVrxr333ss777xT6fiXXnqJ9evXk5CQYNo2ZswYjhw5wu7duwEYNmwYOTk5/Pjjj6Zj+vbti6enJ998883fiuvvJJ+lS5fy1ltvVdru4VMX74atyPVoRIptENgaL4J8XHQ837MxD0YFY2ejzgA1SUo1kxpFKYCiUj2LdyfzWUwiVwuMxVSNBloFetCxgTd31HPnjnruFF1O49dff2Xnzp0cOXKEl156iVGjRqkSs9osOWcdP36cEydOcPbsWc6ePUtaWhqJZ85QkJ9f6ViDowcG36Y0b9eZVx8ZQLuGvn8rjttFclXNZ+68pSgKG4+l8+5PJzibVWjaHubnSocG3rSq54om8wwnDu1ha0xMhRVFv/76a6Kioswab01hqTmrpKSEDRs2kJycbPpJTU2luLi40rG+wY3QBTYnzbkJhY51jV9kQLemdRjXszERwdW7MpXkK8thzryVnJnPzB9P8FNcBgCa3Iu4Xo7H5coprp49haJUbDhsZ2dH/fr1adiwIe+//z62trZmi9Vc1MpHt/q+f/VPilKKopCfn0/SuQvsj0/kyO9JxJ9O5uzZVAxXzqHJu4TmL58Bd3d3evbsSd++fenQocNtK1JKjrIOtzN/xZ1K4u1533Bw1xa4klZpv7ObB40aN6FFsyYEBwZSr1496tWrR506dXB3d6+WArpqGa+kpIQDBw4wefLkCtt79+7Nrl27qvyd3bt307t37wrb+vTpw1dffUVpaSl2dnbs3r2bCRMmVDrmo48+uq3xa7yC0NraYV8nhDL3IPJdAjB4BJPu5EX6tQsirQba1vdiRFQwd9/hh72tzW2NQYh/w8HOhie7NGRk+xC+P3Ke5bFnOXz2Kkeu/ZSzs9Hg5x6Gf5c2RPcow7aBeQsUNYWl56zX351Nwv4dlbYrWlsUN38M7vUweAbhHhzGgOhWDGsXTPOA6mnoKcSt0mg03NMqgLtb+vNL/AWW7k1hV+JlTmTkciLjj6fLNtpW+HePIkyTj+byGQrTT+Ls30DFyNVjyTlLq9Xy+htvoi8rrbjdzgFbn2CKXetR4haIwacBKfZ/9Dz0cbFnUHgAQyMDaeYv+Uuop76PM5+PiuT3jFwW705m/RFbclx9yanfDVoUoM1KxuZKKg65aRgup1BaUsipU6c4d/GyVRak1MpH/+R9/4kNW3bx3w9mkZ+bTXF+LqWFuXCtr9hflQ9NcHNzp2XLFkRFRREVFUXLli2tfrScqBlaNA5l6X9fJq94Eku2HGHtT1tJSTgMWclo8jLJz7nKkQP7OHJgX5W/b+fghKOLG86u7kR36cZbk8f/65hUy3qZmZno9Xrq1q1bYXvdunXJyMio8ncyMjKqPL6srIzMzEz8/f2ve8z1XhOMHer//PQtOzsbMFbGr0fn5ktuj5fJ1f5RaNLZagnz1ZlGmEQ39MbLxbhUZ3FBPpWf75lXwZ+GhYqa40afM3PpF+ZBvzAPLuUUsf1UJsfOZxN/PoeTF/IoLjaQUpBPSrrxWH8Ph78Vs6urq1X1HLL0nFXiXJcSrwbg5IXi5IXByQvF2Zt6QUGE+XvQPMCNDg28aRHgbup1p9ZnU3JVzadm3ooOdiI6uBlXC0rYeTqTo2nZHEnL5vcLuZQWG0gtzMf4XDgAvAPIzM4jx/3mU7ckZ9WwnOV3B3q0GJx9UJx9UFx8UBw9QGO8pdNoIMTLieb+bjQLcKV9qDdhfm5ozZy/JF9ZDjXylr8TvNQzhAldgziQcoW9Zy4Tl55DnL0tOZ71jfcGigFNUTaa3IvY2Oit8hpLrXz0T973n+Sr5HMXOX/ySKXtio0dNs6euHr6UK9eAC0a1Se6TQuaNGmCr69vhX/DwsJCCgsLK73GvyU5yjpUV/4aGd2YkdGNySkqZffpy8QmZnA0/neSzyRSkn0RTeFVNAVX0RRegZICNCjo83Mpys/lyoVz7HHzuy05S/VS/F+DUxTlhgFXdfxft9/qa77zzjtMnTq10vagoKDrB34dicDGW/4tUZs9oXYAt2jaR/B3OkpV59K5arK2nJUK7L7l3xK1nSXlrW4f/b3jJGdd//i/blczZ/1ZKqB+u2JhKSwlb7kvefOmx1hqvlIrH93K+1ZXvtoPfPevXkHUZjU1f506dQr3FfNuetzNcpZqRSkfHx9sbGwqVakvXrxYqZpdzs/Pr8rjbW1t8fb2vuEx13tNgClTpjBx4kTTfxsMBrKysvD29raopxA5OTkEBQVx9uxZi/yiuhE5N8vj6up684MsiOSs6metfwvWeF7WeE6SsyRn3Spr/DsAOS9LYGn5Sq189E/e19LylTV9rv+O2na+YB3nfLOcpVpRSqfTERkZyS+//MLgwYNN23/55RcGDRpU5e907NiR77//vsK2TZs20bZtW9Mc3I4dO/LLL79UmF+8adMmoqOjrxuLvb099vb2FbZ5eHjc6inVGG5ubhb7gb0ZOTehFslZ5mOtfwvWeF7WeE7WQnKW+Vjr34Gcl7hd1MpH/+R9LTVf1bbPdW07X7Dyc1ZUtHz5csXOzk756quvlPj4eGX8+PGKs7OzkpycrCiKokyePFkZNWqU6fgzZ84oTk5OyoQJE5T4+Hjlq6++Uuzs7JRVq1aZjtm5c6diY2OjzJw5U0lISFBmzpyp2NraKnv27DH7+Zlbdna2AijZ2dlqh3LbybmJmkByVvWy1r8FazwvazwnayQ5q3pZ69+BnJeoDmrlo5u9r6WrbZ/r2na+ilI7zlnVopSiKMqcOXOUkJAQRafTKREREcrWrVtN+0aPHq107dq1wvExMTFKmzZtFJ1Op9SvX1+ZO3dupddcuXKl0rRpU8XOzk4JCwtTVq9eXd2nUSNY8wdWzk3UFJKzqo+1/i1Y43lZ4zlZK8lZ1cda/w7kvER1USsf3eh9LV1t+1zXtvNVlNpxzhpFudYxTli84uJi3nnnHaZMmVJp2Kmlk3MTwvpZ69+CNZ6XNZ6TELfKWv8O5LyEsBy17XNd284Xasc5S1FKCCGEEEIIIYQQQpidVu0AhBBCCCGEEEIIIUTtI0UpIYQQQgghhBBCCGF2UpQSQgghhBBCCCGEEGYnRSkrMWPGDKKjo3FycsLDw6PKY1JTUxkwYADOzs74+Pgwbtw4SkpKzBvoP/TZZ58RGhqKg4MDkZGRbN++Xe2Qbtm2bdsYMGAAAQEBaDQa1q1bV2G/oii8+eabBAQE4OjoSLdu3YiLi1MnWCHMzFpzmKXnLslbQlRNclbNJDlL1Cb169dHo9FU+Jk8eXKFYywxD92Ipeeo63nzzTcr/Vv6+fmZ9lt77pKilJUoKSnh/vvv5+mnn65yv16vp3///uTn57Njxw6WL1/O6tWreeGFF8wc6a1bsWIF48eP55VXXuHQoUN07tyZu+++m9TUVLVDuyX5+fm0bt2aTz/9tMr97733Hh9++CGffvopsbGx+Pn5cdddd5Gbm2vmSIUwP2vMYdaQuyRvCVE1yVk1k+QsUdtMmzaN9PR008+rr75q2meJeehGrCFH3UiLFi0q/FseO3bMtM/qc5cirMrChQsVd3f3Stt/+OEHRavVKufOnTNt++abbxR7e3slOzvbjBHeuqioKGXMmDEVtoWFhSmTJ09WKaJ/D1DWrl1r+m+DwaD4+fkpM2fONG0rKipS3N3dlc8//1yFCIVQhzXlMGvLXZK3hKhMclbNJTlLWLuQkBBl1qxZ191viXnoRqwtR/3ZG2+8obRu3brKfbUhd8lIqVpi9+7dtGzZkoCAANO2Pn36UFxczIEDB1SM7MZKSko4cOAAvXv3rrC9d+/e7Nq1S6Wobr+kpCQyMjIqnKe9vT1du3a1qvMU4p+ytBxWG3KX5C0hrk9yVs0jOUtYo3fffRdvb2/Cw8OZMWNGhal5lpaHbqQ25KhTp04REBBAaGgoDz74IGfOnAFqR+6yVTsAYR4ZGRnUrVu3wjZPT090Oh0ZGRkqRXVzmZmZ6PX6SrHXrVu3Rsd9q8rPparzTElJUSMkIWoUS8thtSF3Sd4S4vokZ9U8krOEtXn++eeJiIjA09OTffv2MWXKFJKSkpg/fz5geXnoRqw9R7Vv357FixfTpEkTLly4wFtvvUV0dDRxcXG1InfJSKkarKqGZ3/92b9//99+PY1GU2mboihVbq9p/hqjpcR9q2rLeYraQXJY7fibrg3nKGoHyVm14++5NpyjsFy3kocmTJhA165dadWqFY8//jiff/45X331FZcvXza9niXmoRux1r/fu+++m/vuu4877riDXr16sXHjRgC+/vpr0zHWeu4gI6VqtGeffZYHH3zwhsfUr1//b72Wn58fe/furbDtypUrlJaWVqq61iQ+Pj7Y2NhUqoBfvHixRsd9q8pXV8jIyMDf39+03drOU9QutTmH1YbcJXlLWBvJWZKzhFDbv8lDHTp0AOD06dN4e3tbXB66kdqQo/7M2dmZO+64g1OnTnHvvfcC1p27ZKRUDebj40NYWNgNfxwcHP7Wa3Xs2JHjx4+Tnp5u2rZp0ybs7e2JjIysrlP413Q6HZGRkfzyyy8Vtv/yyy9ER0erFNXtFxoaip+fX4XzLCkpYevWrVZ1nqJ2qc05rDbkLslbwtpIzpKcJYTa/k0eOnToEICpcGFpeehGakOO+rPi4mISEhLw9/evFblLRkpZidTUVLKyskhNTUWv13P48GEAGjVqhIuLC71796Z58+aMGjWK999/n6ysLF588UWeeOIJ3Nzc1A3+JiZOnMioUaNo27YtHTt25IsvviA1NZUxY8aoHdotycvL4/Tp06b/TkpK4vDhw3h5eREcHMz48eN5++23ady4MY0bN+btt9/GycmJESNGqBi1EOZhjTnMGnKX5C0hqiY5q2aSnCVqi927d7Nnzx66d++Ou7s7sbGxTJgwgYEDBxIcHAxgkXnoRqwhR13Piy++yIABAwgODubixYu89dZb5OTkMHr0aDQajfXnLnUW/RO32+jRoxWg0s+WLVtMx6SkpCj9+/dXHB0dFS8vL+XZZ59VioqK1Av6FsyZM0cJCQlRdDqdEhERoWzdulXtkG7Zli1bqvw3Gj16tKIoxuU+33jjDcXPz0+xt7dXunTpohw7dkzdoIUwE2vNYZaeuyRvCVE1yVk1k+QsUVscOHBAad++veLu7q44ODgoTZs2Vd544w0lPz+/wnGWmIduxNJz1PUMGzZM8ff3V+zs7JSAgABlyJAhSlxcnGm/tecujaIoijmKX0IIIYQQQgghhBBClJOeUkIIIYQQQgghhBDC7KQoJYQQQgghhBBCCCHMTopSQgghhBBCCCGEEMLspCglhBBCCCGEEEIIIcxOilJCCCGEEEIIIYQQwuykKCWEEEIIIYQQQgghzE6KUkIIIYQQQgghhBDC7KQoJYQQQgghhBBCCCHMTopSwuLUr1+fjz76SO0whBDipiRfCSEsieQsIYQlkZxlHaQoJYQQQgghhBBCCCHMTopSQgghhBBCCCGEEMLspCglzGrevHnUq1cPg8FQYfvAgQMZPXo0iYmJDBo0iLp16+Li4kK7du349ddfr/t6ycnJaDQaDh8+bNp29epVNBoNMTExpm3x8fH069cPFxcX6taty6hRo8jMzLzdpyeEsCKSr4QQlkRylhDCkkjOEuWkKCXM6v777yczM5MtW7aYtl25coWff/6ZkSNHkpeXR79+/fj11185dOgQffr0YcCAAaSmpv7j90xPT6dr166Eh4ezf/9+fvrpJy5cuMADDzxwO05JCGGlJF8JISyJ5CwhhCWRnCXK2aodgKhdvLy86Nu3L8uWLaNnz54ArFy5Ei8vL3r27ImNjQ2tW7c2Hf/WW2+xdu1a1q9fz7PPPvuP3nPu3LlERETw9ttvm7YtWLCAoKAgTp48SZMmTf7dSQkhrJLkKyGEJZGcJYSwJJKzRDkZKSXMbuTIkaxevZri4mIAli5dyoMPPoiNjQ35+flMmjSJ5s2b4+HhgYuLCydOnPhXFfEDBw6wZcsWXFxcTD9hYWEAJCYm3pZzEkJYJ8lXQghLIjlLCGFJJGcJkJFSQgUDBgzAYDCwceNG2rVrx/bt2/nwww8B+M9//sPPP//Mf//7Xxo1aoSjoyNDhw6lpKSkytfSao11VUVRTNtKS0srHGMwGBgwYADvvvtupd/39/e/XaclhLBCkq+EEJZEcpYQwpJIzhIgRSmhAkdHR4YMGcLSpUs5ffo0TZo0ITIyEoDt27fzyCOPMHjwYADy8vJITk6+7mvVqVMHMM4PbtOmDUCF5nYAERERrF69mvr162NrKx95IcTfJ/lKCGFJJGcJISyJ5CwBMn1PqGTkyJFs3LiRBQsW8NBDD5m2N2rUiDVr1nD48GGOHDnCiBEjKq3I8GeOjo506NCBmTNnEh8fz7Zt23j11VcrHPPMM8+QlZXF8OHD2bdvH2fOnGHTpk089thj6PX6ajtHIYR1kHwlhLAkkrOEEJZEcpaQopRQRY8ePfDy8uL3339nxIgRpu2zZs3C09OT6OhoBgwYQJ8+fYiIiLjhay1YsIDS0lLatm3L888/z1tvvVVhf0BAADt37kSv19OnTx9atmzJ888/j7u7u2mYpxBCXI/kKyGEJZGcJYSwJJKzhEb586RLIYQQQgghhBBCCCHMQMqBQgghhBBCCCGEEMLspCglhBBCCCGEEEIIIcxOilJCCCGEEEIIIYQQwuykKCWEEEIIIYQQQgghzE6KUkIIIYQQQgghhBDC7KQoJYQQQgghhBBCCCHMTopSQgghhBBCCCGEEMLspCglhBBCCCGEEEIIIcxOilJCCCGEEEIIIYQQwuykKCWEEEIIIYQQQgghzE6KUkIIIYQQQgghhBDC7KQoJYQQQgghhBBCCCHM7v8ByI1hMGER/7sAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1200x300 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_num = data.select_dtypes(include = ['float64', 'int64'])\n",
-    "# Histogram to display skewness of every numeric variable\n",
-    "f = pd.melt(df_num, value_vars = df_num.columns)\n",
-    "g = sns.FacetGrid(f, col=\"variable\",  col_wrap=4, sharex=False, sharey=False)\n",
-    "g.map(lambda _x, **kwargs: sns.distplot(_x, fit = norm), 'value')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### As observed, all the variables follow a normal distribution, indicating that no further transformations are necessary for this dataset.."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No missing values in the DataFrame.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Check for missing values and print the missing columns with their counts\n",
-    "missing_counts = data.isnull().sum()\n",
-    "\n",
-    "# Filter to get columns with missing values\n",
-    "missing_columns = missing_counts[missing_counts > 0]\n",
-    "\n",
-    "if not missing_columns.empty:\n",
-    "    # Sort missing columns in descending order\n",
-    "    sorted_missing_columns = missing_columns.sort_values(ascending=False)\n",
-    "\n",
-    "    print(\"Columns with missing values and their counts (sorted in descending order):\")\n",
-    "    print(sorted_missing_columns)\n",
-    "else:\n",
-    "    print(\"No missing values in the DataFrame.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Hence, we conclude that the dataset has no missing values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Strong Correlations (|correlation| > 0.7):\n",
-      "y - x_2: -0.78\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#checking for highly correlated columns:\n",
-    "correlation_matrix = data.corr()\n",
-    "\n",
-    "def print_strong_correlations(corr_matrix, threshold=0.7):\n",
-    "    print(\"\\nStrong Correlations (|correlation| > {}):\".format(threshold))\n",
-    "    for i in range(len(corr_matrix.columns)):\n",
-    "        for j in range(i):\n",
-    "            if abs(corr_matrix.iloc[i, j]) > threshold:\n",
-    "                print(\"{} - {}: {:.2f}\".format(corr_matrix.columns[i], corr_matrix.columns[j], corr_matrix.iloc[i, j]))\n",
-    "\n",
-    "print_strong_correlations(correlation_matrix)\n",
-    "sns.heatmap(data.corr(), annot=True, cmap='coolwarm', linewidths=0.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import sys\n",
-    "\n",
-    "# Get the current notebook's absolute path and convert it to a directory path\n",
-    "current_notebook_path = os.path.abspath(os.getcwd())\n",
-    "\n",
-    "# Append the parent directory (Project1) to the system path\n",
-    "project_root = os.path.dirname(current_notebook_path)\n",
-    "sys.path.append(project_root)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Importing ElasticNetRegression and train_test_split\n",
-    "from Model.Gradient_Boosting import GradientBoosting\n",
-    "from Model.Gradient_Boosting import train_test_split\n",
-    "from Model.XGBoost import XGBoost as xgb\n",
-    "from Model.Gradient_Boosting import train_test_split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shape of X: (50, 3)\n",
-      "Shape of y: (50,)\n",
-      "Training set size: 37, Test set size: 13\n",
-      "Training the model...\n",
-      "Iteration 0: MSE: 399.16089921641935\n",
-      "Iteration 10: MSE: 173.9341341335672\n",
-      "Iteration 20: MSE: 87.6175265471055\n",
-      "Iteration 30: MSE: 48.86164513019478\n",
-      "Iteration 40: MSE: 29.764986319179627\n",
-      "Iteration 50: MSE: 19.29585288372823\n",
-      "Iteration 60: MSE: 13.134352040546943\n",
-      "Iteration 70: MSE: 9.323923450951847\n",
-      "Iteration 80: MSE: 6.974612387310865\n",
-      "Iteration 90: MSE: 5.442021057778226\n",
-      "Predicting on training and testing sets...\n",
-      "\n",
-      "Training Metrics:\n",
-      "  Mean Squared Error (MSE): 4.4940\n",
-      "  R-Squared: 0.9899\n",
-      "  F1 Score: 0.9474\n",
-      "  Gini Coefficient: 0.4595\n",
-      "  Mean Bias: -0.0000\n",
-      "\n",
-      "Testing Metrics:\n",
-      "  Mean Squared Error (MSE): 82.1620\n",
-      "  R-Squared: 0.8707\n",
-      "  F1 Score: 0.9091\n",
-      "  Gini Coefficient: 0.6154\n",
-      "  Mean Bias: 0.4310\n"
-     ]
-    }
-   ],
-   "source": [
-    "def test_predict():\n",
-    "    # Load data from CSV\n",
-    "    data = []\n",
-    "    with open(\"./small_test.csv\", \"r\") as file:\n",
-    "        reader = csv.DictReader(file)\n",
-    "        for row in reader:\n",
-    "            data.append(row)\n",
-    "\n",
-    "    # Extract features (X) and target variable (y) from the CSV\n",
-    "    X = np.array([[float(row['x_0']), float(row['x_1']), float(row['x_2'])] for row in data])\n",
-    "    y = np.array([float(row['y']) for row in data])\n",
-    "\n",
-    "    # Debug: Check the shapes of X and y\n",
-    "    print(f\"Shape of X: {X.shape}\")\n",
-    "    print(f\"Shape of y: {y.shape}\")\n",
-    "\n",
-    "    # Convert target `y` to binary\n",
-    "    threshold = np.median(y)\n",
-    "    y_binary = (y > threshold).astype(int)\n",
-    "\n",
-    "    # Split dataset into training and testing sets\n",
-    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_seed=42)\n",
-    "    _, _, y_train_binary, y_test_binary = train_test_split(X, y_binary, test_size=0.25, random_seed=42)\n",
-    "\n",
-    "    # Debug: Check the shapes of split data\n",
-    "    print(f\"Training set size: {X_train.shape[0]}, Test set size: {X_test.shape[0]}\")\n",
-    "\n",
-    "    # Create an instance of GradientBoosting\n",
-    "    gradient_booster = GradientBoosting(n_estimators=100, learning_rate=0.1, max_depth=1)\n",
-    "\n",
-    "    # Train the model\n",
-    "    print(\"Training the model...\")\n",
-    "    gradient_booster.fit(X_train, y_train)\n",
-    "\n",
-    "    # Predict on both training and test sets\n",
-    "    print(\"Predicting on training and testing sets...\")\n",
-    "    y_train_pred = gradient_booster.predict(X_train)\n",
-    "    y_test_pred = gradient_booster.predict(X_test)\n",
-    "\n",
-    "    # Threshold the predictions (convert to binary)\n",
-    "    y_train_pred_binary = (y_train_pred > np.median(y_train_pred)).astype(int)\n",
-    "    y_test_pred_binary = (y_test_pred > np.median(y_test_pred)).astype(int)\n",
-    "\n",
-    "    # Calculate evaluation metrics\n",
-    "    train_mse = gradient_booster.mean_squared_error(y_train, y_train_pred)\n",
-    "    test_mse = gradient_booster.mean_squared_error(y_test, y_test_pred)\n",
-    "\n",
-    "    train_r2 = gradient_booster.r2_score(y_train, y_train_pred)\n",
-    "    test_r2 = gradient_booster.r2_score(y_test, y_test_pred)\n",
-    "\n",
-    "    # Calculate F1 score using the method from the class\n",
-    "    train_f1 = GradientBoosting.f1_score(y_train_binary, y_train_pred_binary)\n",
-    "    test_f1 = GradientBoosting.f1_score(y_test_binary, y_test_pred_binary)\n",
-    "\n",
-    "    train_gini = GradientBoosting.gini_coefficient(y_train_binary, y_train_pred)\n",
-    "    test_gini = GradientBoosting.gini_coefficient(y_test_binary, y_test_pred)\n",
-    "\n",
-    "    # Checking bias\n",
-    "    train_residuals = y_train_pred - y_train\n",
-    "    test_residuals = y_test_pred - y_test\n",
-    "    train_mean_bias = np.mean(train_residuals)\n",
-    "    test_mean_bias = np.mean(test_residuals)\n",
-    "\n",
-    "    # Print the results\n",
-    "    print(\"\\nTraining Metrics:\")\n",
-    "    print(f\"  Mean Squared Error (MSE): {train_mse:.4f}\")\n",
-    "    print(f\"  R-Squared: {train_r2:.4f}\")\n",
-    "    print(f\"  F1 Score: {train_f1:.4f}\")\n",
-    "    print(f\"  Gini Coefficient: {train_gini:.4f}\")\n",
-    "    print(f\"  Mean Bias: {train_mean_bias:.4f}\")\n",
-    "\n",
-    "    print(\"\\nTesting Metrics:\")\n",
-    "    print(f\"  Mean Squared Error (MSE): {test_mse:.4f}\")\n",
-    "    print(f\"  R-Squared: {test_r2:.4f}\")\n",
-    "    print(f\"  F1 Score: {test_f1:.4f}\")\n",
-    "    print(f\"  Gini Coefficient: {test_gini:.4f}\")\n",
-    "    print(f\"  Mean Bias: {test_mean_bias:.4f}\")\n",
-    "# Call the function\n",
-    "test_predict()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shape of X: (50, 3)\n",
-      "Shape of y: (50,)\n",
-      "Training set size: 37, Test set size: 13\n",
-      "Training the model...\n",
-      "Iteration 0: Training MSE = 0.2348214066614786\n",
-      "Iteration 10: Training MSE = 0.1257323446330051\n",
-      "Iteration 20: Training MSE = 0.057654205740503846\n",
-      "Iteration 30: Training MSE = 0.022889418777036246\n",
-      "Iteration 40: Training MSE = 0.015766041951108017\n",
-      "Iteration 50: Training MSE = 0.031671211524251314\n",
-      "Iteration 60: Training MSE = 0.0671575739213283\n",
-      "Iteration 70: Training MSE = 0.12014227280200218\n",
-      "Iteration 80: Training MSE = 0.18843048602254264\n",
-      "Iteration 90: Training MSE = 0.2701649529407599\n",
-      "Predicting on training and testing sets...\n",
-      "\n",
-      "Training Metrics:\n",
-      "  Mean Squared Error (MSE): 0.0738\n",
-      "  R-Squared: 0.7028\n",
-      "  F1 Score: 1.0000\n",
-      "  Gini Index: 0.4967\n",
-      "\n",
-      "Testing Metrics:\n",
-      "  Mean Squared Error (MSE): 0.2013\n",
-      "  R-Squared: 0.1493\n",
-      "  F1 Score: 0.7692\n",
-      "  Gini Index: 0.4734\n"
-     ]
-    }
-   ],
-   "source": [
-    "def test_predict_XG():\n",
-    "    # Load data from CSV\n",
-    "    data = []\n",
-    "    with open(\"./small_test.csv\", \"r\") as file:\n",
-    "        reader = csv.DictReader(file)\n",
-    "        for row in reader:\n",
-    "            data.append(row)\n",
-    "\n",
-    "    # Extract features (X) and target variable (y) from the CSV\n",
-    "    X = np.array([[float(row['x_0']), float(row['x_1']), float(row['x_2'])] for row in data])\n",
-    "    y = np.array([float(row['y']) for row in data])\n",
-    "\n",
-    "    # Debug: Check the shapes of X and y\n",
-    "    print(f\"Shape of X: {X.shape}\")\n",
-    "    print(f\"Shape of y: {y.shape}\")\n",
-    "\n",
-    "    # Convert target `y` to binary\n",
-    "    threshold = np.median(y)\n",
-    "    y_binary = (y > threshold).astype(int)\n",
-    "\n",
-    "    # Split dataset into training and testing sets\n",
-    "    X_train, X_test, y_train, y_test = train_test_split(X, y_binary, test_size=0.25, random_seed=42)\n",
-    "\n",
-    "    # Debug: Check the shapes of split data\n",
-    "    print(f\"Training set size: {X_train.shape[0]}, Test set size: {X_test.shape[0]}\")\n",
-    "\n",
-    "    # Create an instance of XGBoost\n",
-    "    xg_booster = xgb(n_estimators=100, learning_rate=0.01, max_depth=3, reg_lambda=1.0, gamma=0.0)\n",
-    "\n",
-    "    # Train the model\n",
-    "    print(\"Training the model...\")\n",
-    "    xg_booster.fit(X_train, y_train)\n",
-    "\n",
-    "    # Predict on both training and test sets\n",
-    "    print(\"Predicting on training and testing sets...\")\n",
-    "    y_train_pred_proba = xg_booster.predict_proba(X_train)\n",
-    "    y_test_pred_proba = xg_booster.predict_proba(X_test)\n",
-    "\n",
-    "    # Convert probabilities to binary predictions\n",
-    "    y_train_pred = (y_train_pred_proba > 0.5).astype(int)\n",
-    "    y_test_pred = (y_test_pred_proba > 0.5).astype(int)\n",
-    "\n",
-    "    # Calculate evaluation metrics\n",
-    "    train_mse = xg_booster.mean_squared_error(y_train, y_train_pred_proba)\n",
-    "    test_mse = xg_booster.mean_squared_error(y_test, y_test_pred_proba)\n",
-    "\n",
-    "    train_r2 = xg_booster.r2_score(y_train, y_train_pred_proba)\n",
-    "    test_r2 = xg_booster.r2_score(y_test, y_test_pred_proba)\n",
-    "\n",
-    "    train_f1 = xg_booster.f1_score(y_train, y_train_pred)\n",
-    "    test_f1 = xg_booster.f1_score(y_test, y_test_pred)\n",
-    "\n",
-    "    train_gini = xg_booster.gini_impurity(y_train, y_train_pred_proba)\n",
-    "    test_gini = xg_booster.gini_impurity(y_test, y_test_pred_proba)\n",
-    "\n",
-    "    # Print the results\n",
-    "    print(\"\\nTraining Metrics:\")\n",
-    "    print(f\"  Mean Squared Error (MSE): {train_mse:.4f}\")\n",
-    "    print(f\"  R-Squared: {train_r2:.4f}\")\n",
-    "    print(f\"  F1 Score: {train_f1:.4f}\")\n",
-    "    print(f\"  Gini Index: {train_gini:.4f}\")\n",
-    "\n",
-    "    print(\"\\nTesting Metrics:\")\n",
-    "    print(f\"  Mean Squared Error (MSE): {test_mse:.4f}\")\n",
-    "    print(f\"  R-Squared: {test_r2:.4f}\")\n",
-    "    print(f\"  F1 Score: {test_f1:.4f}\")\n",
-    "    print(f\"  Gini Index: {test_gini:.4f}\")\n",
-    "\n",
-    "# Call the function\n",
-    "test_predict_XG()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": [],
-   "toc_visible": true
-  },
-  "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": "3.12.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/Test/california_housing.csv b/Test/california_housing.csv
new file mode 100644
index 0000000..086163b
--- /dev/null
+++ b/Test/california_housing.csv
@@ -0,0 +1,20641 @@
+MedInc,HouseAge,AveRooms,AveBedrms,Population,AveOccup,Latitude,Longitude,MedHouseVal
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diff --git a/Test/highly_correlated_dataset.csv b/Test/highly_correlated_dataset.csv
new file mode 100644
index 0000000..a2d5fac
--- /dev/null
+++ b/Test/highly_correlated_dataset.csv
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diff --git a/Test/main_GradientBoosting.py b/Test/main_GradientBoosting.py
new file mode 100644
index 0000000..f8caa12
--- /dev/null
+++ b/Test/main_GradientBoosting.py
@@ -0,0 +1,119 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import sys
+import os
+
+current_notebook_path = os.path.abspath(os.getcwd())
+project_root = os.path.dirname(current_notebook_path)  # Adjust if necessary
+model_path = os.path.join(project_root, "Model")
+sys.path.append(model_path)
+
+from Model.gradient_boosting import GradientBoostingTree
+from Model.ModelSelection import grid_search_max_depth
+
+
+def load_dataset(file_path):
+    """
+    Load a dataset from a CSV file.
+    """
+    try:
+        data = pd.read_csv(file_path)
+        if data.empty:
+            raise ValueError("Dataset is empty. Please provide a valid dataset.")
+        X = data.iloc[:, :-1].values
+        y = data.iloc[:, -1].values
+        return X, y
+    except Exception as e:
+        print(f"Error loading dataset: {e}")
+        exit()
+
+def preprocess_data(X, y):
+    """
+    Preprocess the dataset: scale features and return scaled X and y without using sklearn.
+    """
+    # Calculate mean and standard deviation for each feature
+    X_mean = np.mean(X, axis=0)
+    X_std = np.std(X, axis=0)
+    
+    # Scale features
+    X_scaled = (X - X_mean) / X_std
+
+    # Optional: Scale target variable if needed
+    y_mean = np.mean(y)
+    y_std = np.std(y)
+    y_scaled = (y - y_mean) / y_std
+
+    return X_scaled, y_scaled
+
+def evaluate_model(y_true, y_pred):
+    mse = np.mean((y_true - y_pred) ** 2)
+    mae = np.mean(np.abs(y_true - y_pred))
+    r2 = 1 - (np.sum((y_true - y_pred) ** 2) / np.sum((y_true - np.mean(y_true)) ** 2))
+
+    return mse, mae, r2
+
+def calculate_f1(y_true, y_pred, threshold=0.5):
+    """
+    Calculate F1 score by converting regression outputs to binary predictions.
+    """
+    y_pred_binary = (y_pred >= threshold).astype(int)
+    y_true_binary = (y_true >= threshold).astype(int)
+
+    tp = np.sum((y_true_binary == 1) & (y_pred_binary == 1))
+    fp = np.sum((y_true_binary == 0) & (y_pred_binary == 1))
+    fn = np.sum((y_true_binary == 1) & (y_pred_binary == 0))
+
+    precision = tp / (tp + fp) if tp + fp > 0 else 0
+    recall = tp / (tp + fn) if tp + fn > 0 else 0
+    f1 = 2 * (precision * recall) / (precision + recall) if precision + recall > 0 else 0
+
+    return f1, precision, recall
+
+
+def plot_results(y_true, y_pred):
+    """
+    Plot Actual vs. Predicted values.
+    """
+    plt.figure(figsize=(10, 6))
+    plt.scatter(y_true, y_pred, alpha=0.7)
+    plt.plot([min(y_true), max(y_true)], [min(y_true), max(y_true)], 'r--')
+    plt.title("Actual vs. Predicted")
+    plt.xlabel("Actual Values")
+    plt.ylabel("Predicted Values")
+    plt.show()
+
+if __name__ == "__main__":
+    # Load and preprocess data
+    X, y = load_dataset("./highly_correlated_dataset.csv")
+    X, y = preprocess_data(X, y)
+
+    # Split data
+    train_size = int(0.8 * len(X))
+    X_train, X_test = X[:train_size], X[train_size:]
+    y_train, y_test = y[:train_size], y[train_size:]
+    
+    #Train the parameter
+    gbt_model = GradientBoostingTree(n_estimators=100, learning_rate=0.1, max_depth=2)
+    gbt_model.fit(X_train, y_train)
+    y_pred = gbt_model.predict(X_test)
+
+
+
+    # Evaluate Model for Gradient Boosting
+    # mse_samples, mse_mean = gbt_model.bootstrap(B=100)
+    # aic_value = gbt_model.calculate_aic()
+    mse, mae, r2 = evaluate_model(y_test, y_pred)
+    f1, precision, recall = calculate_f1(y_test, y_pred)
+    print(f"Mean Squared Error (MSE): {mse:.4f}")
+    print(f"Mean Absolute Error (MAE): {mae:.4f}")
+    print(f"R² Score: {r2:.4f}")
+    print(f"F1-Score: {f1:.4f}") 
+    print("Bootstrap Results:")
+    # print(f"Mean MSE: {mse_mean:.4f}")
+    # print(f"MSE 95% Confidence Interval: ({np.percentile(mse_samples, 2.5):.4f}, {np.percentile(mse_samples, 97.5):.4f})")
+
+
+    # Plot Results
+    plot_results(y_test, y_pred)
diff --git a/Test/main_ModelSelection.py b/Test/main_ModelSelection.py
new file mode 100644
index 0000000..fbc215a
--- /dev/null
+++ b/Test/main_ModelSelection.py
@@ -0,0 +1,121 @@
+import numpy as np
+import pandas as pd
+from gradient_boosting import GradientBoostingTree
+import matplotlib.pyplot as plt
+import seaborn as sns
+from ModelSelection import grid_search_max_depth
+
+def load_dataset(file_path):
+    """
+    Load a dataset from a CSV file.
+    """
+    try:
+        data = pd.read_csv(file_path)
+        if data.empty:
+            raise ValueError("Dataset is empty. Please provide a valid dataset.")
+        X = data.iloc[:, :-1].values
+        y = data.iloc[:, -1].values
+        return X, y
+    except Exception as e:
+        print(f"Error loading dataset: {e}")
+        exit()
+
+def preprocess_data(X, y):
+    """
+    Preprocess the dataset: scale features and return scaled X and y without using sklearn.
+    """
+    # Calculate mean and standard deviation for each feature
+    X_mean = np.mean(X, axis=0)
+    X_std = np.std(X, axis=0)
+    
+    # Scale features
+    X_scaled = (X - X_mean) / X_std
+
+    # Optional: Scale target variable if needed
+    y_mean = np.mean(y)
+    y_std = np.std(y)
+    y_scaled = (y - y_mean) / y_std
+
+    return X_scaled, y_scaled
+
+def evaluate_model(y_true, y_pred):
+    mse = np.mean((y_true - y_pred) ** 2)
+    mae = np.mean(np.abs(y_true - y_pred))
+    r2 = 1 - (np.sum((y_true - y_pred) ** 2) / np.sum((y_true - np.mean(y_true)) ** 2))
+
+    return mse, mae, r2
+
+def calculate_f1(y_true, y_pred, threshold=0.5):
+    """
+    Calculate F1 score by converting regression outputs to binary predictions.
+    """
+    y_pred_binary = (y_pred >= threshold).astype(int)
+    y_true_binary = (y_true >= threshold).astype(int)
+
+    tp = np.sum((y_true_binary == 1) & (y_pred_binary == 1))
+    fp = np.sum((y_true_binary == 0) & (y_pred_binary == 1))
+    fn = np.sum((y_true_binary == 1) & (y_pred_binary == 0))
+
+    precision = tp / (tp + fp) if tp + fp > 0 else 0
+    recall = tp / (tp + fn) if tp + fn > 0 else 0
+    f1 = 2 * (precision * recall) / (precision + recall) if precision + recall > 0 else 0
+
+    return f1, precision, recall
+
+
+def plot_results(y_true, y_pred):
+    """
+    Plot Actual vs. Predicted values.
+    """
+    plt.figure(figsize=(10, 6))
+    plt.scatter(y_true, y_pred, alpha=0.7)
+    plt.plot([min(y_true), max(y_true)], [min(y_true), max(y_true)], 'r--')
+    plt.title("Actual vs. Predicted")
+    plt.xlabel("Actual Values")
+    plt.ylabel("Predicted Values")
+    plt.show()
+
+if __name__ == "__main__":
+    # Load and preprocess data
+    X, y = load_dataset("./highly_correlated_dataset.csv")
+    X, y = preprocess_data(X, y)
+
+    # Split data
+    train_size = int(0.8 * len(X))
+    X_train, X_test = X[:train_size], X[train_size:]
+    y_train, y_test = y[:train_size], y[train_size:]
+    
+  # Define parameter grid for max_depth
+    param_grid = {
+        "max_depth": [2, 3, 5],  # List of max_depth values to test
+        }
+
+# Perform grid search for best max_depth
+    best_max_depth, best_score = grid_search_max_depth(X, y, param_grid["max_depth"], n_estimators=100, learning_rate=0.1, k=5)
+    print("Best Max Depth:", best_max_depth)
+    print("Using the best max_depth in the model parameters...")
+
+# Train Gradient Boosting Model using the best max_depth
+    gbt_model = GradientBoostingTree(n_estimators=100, learning_rate=0.1, max_depth=best_max_depth)
+    gbt_model.fit(X_train, y_train)
+    y_pred = gbt_model.predict(X_test)
+
+
+
+  # Evaluate Model for Gradient Boosting
+    # mse_samples, mse_mean = bootstrap(B=1000)
+    # aic_value = calculate_aic()
+    mse, mae, r2 = evaluate_model(y_test, y_pred)
+    f1, precision, recall = calculate_f1(y_test, y_pred)
+    print(f"Mean Squared Error (MSE): {mse:.4f}")
+    print(f"Mean Absolute Error (MAE): {mae:.4f}")
+    print(f"R² Score: {r2:.4f}")
+    print(f"F1-Score: {f1:.4f}") 
+    print("Bootstrap Results:")
+    # print(f"Mean MSE: {mse_mean:.4f}")
+    # print(f"MSE 95% Confidence Interval: ({np.percentile(mse_samples, 2.5):.4f}, {np.percentile(mse_samples, 97.5):.4f})")
+
+
+
+    # Plot Results
+    plot_results(y_test, y_pred)
diff --git a/Test/partially_correlated_dataset.csv b/Test/partially_correlated_dataset.csv
new file mode 100644
index 0000000..b6b1b89
--- /dev/null
+++ b/Test/partially_correlated_dataset.csv
@@ -0,0 +1,1001 @@
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From fffda9dafa386ba37bb8fdde93ea56ddeba3e56c Mon Sep 17 00:00:00 2001
From: Sharanya Mishra <smishra25@hawk.iit.edu>
Date: Thu, 21 Nov 2024 10:48:13 -0600
Subject: [PATCH 3/7] correctcode-1

---
 Model/ModelSelection.py       |    38 +-
 Model/__init__.py             |    35 +-
 Test/california_housing.csv   | 20641 --------------------------------
 Test/diabetes_dataset.csv     |   443 +
 Test/main_GradientBoosting.py |    22 +-
 Test/main_ModelSelection.py   |    29 +-
 Test/tempCodeRunnerFile.py    |     1 +
 __init__.py                   |    33 -
 8 files changed, 498 insertions(+), 20744 deletions(-)
 delete mode 100644 Test/california_housing.csv
 create mode 100644 Test/diabetes_dataset.csv
 create mode 100644 Test/tempCodeRunnerFile.py

diff --git a/Model/ModelSelection.py b/Model/ModelSelection.py
index acf139d..a26df2d 100644
--- a/Model/ModelSelection.py
+++ b/Model/ModelSelection.py
@@ -1,5 +1,12 @@
 import numpy as np
-from gradient_boosting import GradientBoostingTree
+import sys
+import os
+
+# Add the project root to the Python path
+project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.insert(0, project_root)
+
+from Model.gradient_boosting import GradientBoostingTree
 
 
 def k_fold_cv(model, X, y, k=5, metric="mse"):
@@ -86,15 +93,20 @@ def grid_search_max_depth(X, y, max_depth_values, n_estimators=100, learning_rat
 
 #         return mse_samples, np.mean(mse_samples)
 
-# def calculate_aic(self):
-#     """
-#     Calculate the Akaike Information Criterion (AIC).
-#     Returns:
-#         aic (float): AIC score for the linear regression model.
-#     """
-#     n, p = self.X.shape
-#     beta = np.linalg.pinv(self.X.T @ self.X) @ self.X.T @ self.y
-#     y_pred = self.X @ beta
-#     rss = np.sum((self.y - y_pred) ** 2)
-#     aic = n * np.log(rss / n) + 2 * p
-#     return aic
+def calculate_aic(X, y):
+    """
+    Calculate the Akaike Information Criterion (AIC) for a linear regression model.
+    
+    Args:
+        X (numpy.ndarray): Feature matrix.
+        y (numpy.ndarray): Target vector.
+    
+    Returns:
+        aic (float): AIC score for the linear regression model.
+    """
+    n, p = X.shape
+    beta = np.linalg.pinv(X.T @ X) @ X.T @ y
+    y_pred = X @ beta
+    rss = np.sum((y - y_pred) ** 2)
+    aic = n * np.log(rss / n) + 2 * p
+    return aic
diff --git a/Model/__init__.py b/Model/__init__.py
index f5610bc..5935378 100644
--- a/Model/__init__.py
+++ b/Model/__init__.py
@@ -1,33 +1,2 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3fb2b9fd-4907-4bbc-b497-de5b9fb4f8c8",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.12.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+from .gradient_boosting import GradientBoostingTree
+from .ModelSelection import grid_search_max_depth
\ No newline at end of file
diff --git a/Test/california_housing.csv b/Test/california_housing.csv
deleted file mode 100644
index 086163b..0000000
--- a/Test/california_housing.csv
+++ /dev/null
@@ -1,20641 +0,0 @@
-MedInc,HouseAge,AveRooms,AveBedrms,Population,AveOccup,Latitude,Longitude,MedHouseVal
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diff --git a/Test/diabetes_dataset.csv b/Test/diabetes_dataset.csv
new file mode 100644
index 0000000..70f77ed
--- /dev/null
+++ b/Test/diabetes_dataset.csv
@@ -0,0 +1,443 @@
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+0.0707687524925978,0.05068011873981862,-0.03099563183506548,0.0218723855140367,-0.037343734133440394,-0.047033552847490806,0.033913548233800855,-0.03949338287409329,-0.014959693812643405,-0.0010776975004659671,66.0
+0.009015598825267658,-0.044641636506989144,0.055229334075398484,-0.00567042229275739,0.05759701308243695,0.04471894645684291,-0.002902829807068556,0.023238522614953735,0.05568622641456506,0.1066170822852299,173.0
+-0.027309785684926546,-0.044641636506989144,-0.06009655782984706,-0.029770379123702218,0.04658939021682799,0.019980217975469634,0.12227285553188745,-0.03949338287409329,-0.05140387304727299,-0.009361911330134878,72.0
+0.016280675727306498,-0.044641636506989144,0.0013387303813585058,0.008100981610639655,0.005310804470794357,0.010898912583573148,0.030231910429713918,-0.03949338287409329,-0.04542403773513769,0.03205915781820968,49.0
+-0.012779631880848867,-0.044641636506989144,-0.023450947317899894,-0.04009893205125,-0.016704441260423575,0.00463594334778245,-0.01762938102341632,-0.002592261998183278,-0.03845971734112638,-0.03835665973397607,64.0
+-0.056370093293081916,-0.044641636506989144,-0.07410811479029747,-0.05042748497879779,-0.0249601584096303,-0.047033552847490806,0.09281975309919192,-0.0763945037500033,-0.061175799045152635,-0.04664087356364498,48.0
+0.04170844488444244,0.05068011873981862,0.01966153563733209,0.059743746248378575,-0.005696818394814609,-0.0025664712733768653,-0.028674294435677143,-0.002592261998183278,0.031192602201596156,0.007206516329202944,178.0
+-0.005514554978810025,0.05068011873981862,-0.015906262800734303,-0.06764173985804409,0.04934129593323023,0.07916527725369175,-0.028674294435677143,0.03430885887772673,-0.018113692315690322,0.044485478562713045,104.0
+0.04170844488444244,0.05068011873981862,-0.015906262800734303,0.01729339371615719,-0.037343734133440394,-0.013839815897800126,-0.024992656631590206,-0.011079519799642579,-0.04688253415273158,0.015490730158871856,132.0
+-0.045472477940023646,-0.044641636506989144,0.039062152967186486,0.0012152796589411327,0.016318427336403322,0.015282991048626631,-0.028674294435677143,0.02655962349378563,0.04452872881997113,-0.025930338989472702,220.0
+-0.045472477940023646,-0.044641636506989144,-0.07303030271641665,-0.08141314376144114,0.08374011738825825,0.027808929520208008,0.17381578478910462,-0.03949338287409329,-0.00422151393810765,0.0030644094143684884,57.0
diff --git a/Test/main_GradientBoosting.py b/Test/main_GradientBoosting.py
index f8caa12..8c91799 100644
--- a/Test/main_GradientBoosting.py
+++ b/Test/main_GradientBoosting.py
@@ -5,13 +5,13 @@
 import sys
 import os
 
-current_notebook_path = os.path.abspath(os.getcwd())
-project_root = os.path.dirname(current_notebook_path)  # Adjust if necessary
-model_path = os.path.join(project_root, "Model")
-sys.path.append(model_path)
+# Add the project root to the Python path
+project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.insert(0, project_root)
 
+# Now import from Model
 from Model.gradient_boosting import GradientBoostingTree
-from Model.ModelSelection import grid_search_max_depth
+from Model.ModelSelection import grid_search_max_depth,calculate_aic
 
 
 def load_dataset(file_path):
@@ -86,7 +86,9 @@ def plot_results(y_true, y_pred):
 
 if __name__ == "__main__":
     # Load and preprocess data
-    X, y = load_dataset("./highly_correlated_dataset.csv")
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    dataset_path = os.path.join(current_dir,'highly_correlated_dataset.csv')
+    X, y = load_dataset(dataset_path)
     X, y = preprocess_data(X, y)
 
     # Split data
@@ -103,16 +105,14 @@ def plot_results(y_true, y_pred):
 
     # Evaluate Model for Gradient Boosting
     # mse_samples, mse_mean = gbt_model.bootstrap(B=100)
-    # aic_value = gbt_model.calculate_aic()
+    aic_value = calculate_aic(X_train,y_train)
     mse, mae, r2 = evaluate_model(y_test, y_pred)
     f1, precision, recall = calculate_f1(y_test, y_pred)
     print(f"Mean Squared Error (MSE): {mse:.4f}")
     print(f"Mean Absolute Error (MAE): {mae:.4f}")
     print(f"R² Score: {r2:.4f}")
-    print(f"F1-Score: {f1:.4f}") 
-    print("Bootstrap Results:")
-    # print(f"Mean MSE: {mse_mean:.4f}")
-    # print(f"MSE 95% Confidence Interval: ({np.percentile(mse_samples, 2.5):.4f}, {np.percentile(mse_samples, 97.5):.4f})")
+    print(f"F1-Score: {f1:.4f}")
+    print(f"AIC-Score: {aic_value:.4f}")
 
 
     # Plot Results
diff --git a/Test/main_ModelSelection.py b/Test/main_ModelSelection.py
index fbc215a..0eb73bd 100644
--- a/Test/main_ModelSelection.py
+++ b/Test/main_ModelSelection.py
@@ -1,9 +1,17 @@
 import numpy as np
 import pandas as pd
-from gradient_boosting import GradientBoostingTree
 import matplotlib.pyplot as plt
 import seaborn as sns
-from ModelSelection import grid_search_max_depth
+import sys
+import os
+
+# Add the project root to the Python path
+project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.insert(0, project_root)
+
+# Now import from Model
+from Model.gradient_boosting import GradientBoostingTree
+from Model.ModelSelection import grid_search_max_depth,calculate_aic
 
 def load_dataset(file_path):
     """
@@ -77,7 +85,9 @@ def plot_results(y_true, y_pred):
 
 if __name__ == "__main__":
     # Load and preprocess data
-    X, y = load_dataset("./highly_correlated_dataset.csv")
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    dataset_path = os.path.join(current_dir, 'highly_correlated_dataset.csv')
+    X, y = load_dataset(dataset_path)
     X, y = preprocess_data(X, y)
 
     # Split data
@@ -103,19 +113,12 @@ def plot_results(y_true, y_pred):
 
 
   # Evaluate Model for Gradient Boosting
-    # mse_samples, mse_mean = bootstrap(B=1000)
-    # aic_value = calculate_aic()
+    aic_value = calculate_aic(X_train,y_train)
     mse, mae, r2 = evaluate_model(y_test, y_pred)
     f1, precision, recall = calculate_f1(y_test, y_pred)
     print(f"Mean Squared Error (MSE): {mse:.4f}")
     print(f"Mean Absolute Error (MAE): {mae:.4f}")
     print(f"R² Score: {r2:.4f}")
-    print(f"F1-Score: {f1:.4f}") 
-    print("Bootstrap Results:")
-    # print(f"Mean MSE: {mse_mean:.4f}")
-    # print(f"MSE 95% Confidence Interval: ({np.percentile(mse_samples, 2.5):.4f}, {np.percentile(mse_samples, 97.5):.4f})")
-
-
+    print(f"F1-Score: {f1:.4f}")
+    print(f"AIC-Score: {aic_value:.4f}")
 
-    # Plot Results
-    plot_results(y_test, y_pred)
diff --git a/Test/tempCodeRunnerFile.py b/Test/tempCodeRunnerFile.py
new file mode 100644
index 0000000..067b76c
--- /dev/null
+++ b/Test/tempCodeRunnerFile.py
@@ -0,0 +1 @@
+diabetes_dataset
\ No newline at end of file
diff --git a/__init__.py b/__init__.py
index f5610bc..e69de29 100644
--- a/__init__.py
+++ b/__init__.py
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3fb2b9fd-4907-4bbc-b497-de5b9fb4f8c8",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.12.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From 242a21dd7a2b0c4a68e842e1a57a1bfcb1737066 Mon Sep 17 00:00:00 2001
From: Sharanya Mishra <smishra25@hawk.iit.edu>
Date: Thu, 21 Nov 2024 20:21:52 -0600
Subject: [PATCH 4/7] commit-almost

---
 Model/Gradient_Boosting.py                    |   14 +-
 Model/ModelSelection.py                       |   83 +-
 README.md                                     | 1039 ++++++++++++++++-
 Test/Highly_correlatedcode.py                 |   26 -
 Test/main_GradientBoosting.py                 |  119 --
 Test/main_ModelSelection.py                   |  124 --
 Test/small_test.csv                           |   51 -
 Test/tempCodeRunnerFile.py                    |    1 -
 boosting/__init__.py                          |    1 +
 boosting/gradient_boosting.py                 |   95 ++
 {Test => data}/diabetes_dataset.csv           |    0
 {Test => data}/highly_correlated_dataset.csv  |    0
 .../partially_correlated_dataset.csv          |    0
 data/test.csv                                 |   16 +
 data/train.csv                                |   16 +
 main_gradientboosting.py                      |   88 ++
 main_modelselection.py                        |   88 ++
 model_selection/__init__.py                   |    2 +
 model_selection/bootstrapping.py              |   23 +
 model_selection/cross_validation.py           |   28 +
 requirements.txt                              |    4 +
 setup.py                                      |   35 +
 tests/test_boosting.py                        |   89 ++
 tests/test_model_selection.py                 |   55 +
 24 files changed, 1615 insertions(+), 382 deletions(-)
 delete mode 100644 Test/Highly_correlatedcode.py
 delete mode 100644 Test/main_GradientBoosting.py
 delete mode 100644 Test/main_ModelSelection.py
 delete mode 100644 Test/small_test.csv
 delete mode 100644 Test/tempCodeRunnerFile.py
 create mode 100644 boosting/__init__.py
 create mode 100644 boosting/gradient_boosting.py
 rename {Test => data}/diabetes_dataset.csv (100%)
 rename {Test => data}/highly_correlated_dataset.csv (100%)
 rename {Test => data}/partially_correlated_dataset.csv (100%)
 create mode 100644 data/test.csv
 create mode 100644 data/train.csv
 create mode 100644 main_gradientboosting.py
 create mode 100644 main_modelselection.py
 create mode 100644 model_selection/__init__.py
 create mode 100644 model_selection/bootstrapping.py
 create mode 100644 model_selection/cross_validation.py
 create mode 100644 requirements.txt
 create mode 100644 setup.py
 create mode 100644 tests/test_boosting.py
 create mode 100644 tests/test_model_selection.py

diff --git a/Model/Gradient_Boosting.py b/Model/Gradient_Boosting.py
index 34940b2..b888a08 100644
--- a/Model/Gradient_Boosting.py
+++ b/Model/Gradient_Boosting.py
@@ -1,7 +1,5 @@
 import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt
-
 
 class GradientBoostingTree:
     def __init__(self, n_estimators, learning_rate, max_depth):
@@ -12,20 +10,14 @@ def __init__(self, n_estimators, learning_rate, max_depth):
 
     def fit(self, X, y):
         n_samples = X.shape[0]
-        # Initialize predictions with the mean of y
         y_pred = np.full(n_samples, np.mean(y))
         self.initial_pred = np.mean(y)
         
         for _ in range(self.n_estimators):
-            # Compute residuals (pseudo-residuals)
             residuals = y - y_pred
-            
-            # Fit a decision tree to the residuals
             tree = DecisionTreeRegressor(max_depth=self.max_depth)
             tree.fit(X, residuals)
             self.models.append(tree)
-            
-            # Update predictions
             y_pred += self.learning_rate * tree.predict(X)
 
     def predict(self, X):
@@ -34,7 +26,6 @@ def predict(self, X):
             y_pred += self.learning_rate * tree.predict(X)
         return y_pred
 
-
 class DecisionTreeRegressor:
     def __init__(self, max_depth=3):
         self.max_depth = max_depth
@@ -102,7 +93,4 @@ def _predict_single(self, x, tree):
         if x[feature_idx] <= threshold:
             return self._predict_single(x, tree["left"])
         else:
-            return self._predict_single(x, tree["right"])
-
-    
-    
\ No newline at end of file
+            return self._predict_single(x, tree["right"])
\ No newline at end of file
diff --git a/Model/ModelSelection.py b/Model/ModelSelection.py
index a26df2d..a9a393e 100644
--- a/Model/ModelSelection.py
+++ b/Model/ModelSelection.py
@@ -71,42 +71,65 @@ def grid_search_max_depth(X, y, max_depth_values, n_estimators=100, learning_rat
 
     return best_max_depth, best_score
 
-# def bootstrap(self, B=100):
-#         """
-#         Perform bootstrapping for model evaluation.
-#         Args:
-#             B (int): Number of bootstrap iterations.
-#         Returns:
-#             mse_samples (list): Mean squared errors for each bootstrap sample.
-#             mse_mean (float): Mean of the mean squared errors across all bootstrap samples.
-#         """
-#         n = len(self.y)
-#         mse_samples = []
-
-#         for _ in range(B):
-#             indices = np.random.choice(np.arange(n), size=n, replace=True)
-#             X_sample, y_sample = self.X[indices], self.y[indices]
-
-#             beta = np.linalg.pinv(X_sample.T @ X_sample) @ X_sample.T @ y_sample
-#             y_pred = self.X @ beta
-#             mse_samples.append(np.mean((self.y - y_pred) ** 2))
-
-#         return mse_samples, np.mean(mse_samples)
-
-def calculate_aic(X, y):
+def bootstrap(model, X, y, B=100):
     """
-    Calculate the Akaike Information Criterion (AIC) for a linear regression model.
+    Perform bootstrapping for model evaluation.
     
     Args:
+        model: The model to evaluate.
         X (numpy.ndarray): Feature matrix.
         y (numpy.ndarray): Target vector.
+        B (int): Number of bootstrap iterations.
     
     Returns:
-        aic (float): AIC score for the linear regression model.
+        mse_samples (list): Mean squared errors for each bootstrap sample.
+        mse_mean (float): Mean of the mean squared errors across all bootstrap samples.
     """
-    n, p = X.shape
-    beta = np.linalg.pinv(X.T @ X) @ X.T @ y
-    y_pred = X @ beta
+    n = len(y)
+    mse_samples = []
+
+    for _ in range(B):
+        # Generate bootstrap sample indices
+        indices = np.random.choice(np.arange(n), size=n, replace=True)
+        X_sample, y_sample = X[indices], y[indices]
+
+        # Fit the model on the bootstrap sample
+        model.fit(X_sample, y_sample)
+        
+        # Predict on the original data
+        y_pred = model.predict(X)
+
+        # Calculate MSE for this bootstrap sample
+        mse = np.mean((y - y_pred) ** 2)
+        mse_samples.append(mse)
+
+    return mse_samples, np.mean(mse_samples)
+
+
+def calculate_aic(X, y, model):
+    """
+    Calculate the Akaike Information Criterion (AIC) for a Gradient Boosting model.
+    
+    Args:
+        X (numpy.ndarray): Feature matrix.
+        y (numpy.ndarray): Target vector.
+        model (GradientBoostingTree): The trained gradient boosting model.
+    
+    Returns:
+        aic (float): AIC score for the gradient boosting model.
+    """
+    n = X.shape[0]
+    
+    # Get predictions from the model
+    y_pred = model.predict(X)
+    
+    # Calculate Residual Sum of Squares (RSS)
     rss = np.sum((y - y_pred) ** 2)
-    aic = n * np.log(rss / n) + 2 * p
-    return aic
+    
+    # Approximate number of parameters (k)
+    k = model.n_estimators * (2 ** model.max_depth - 1)  # Rough estimate of parameters
+    
+    # Calculate AIC using the formula: AIC = n * log(RSS/n) + 2*k
+    aic = n * np.log(rss / n) + 2 * k
+    
+    return aic
\ No newline at end of file
diff --git a/README.md b/README.md
index f746e56..a1ba43b 100644
--- a/README.md
+++ b/README.md
@@ -1,29 +1,1032 @@
-# Project 2
+***Gradient Boosting Trees and Model Selection***
 
-Select one of the following two options:
+***GROUP GUARDIANS OF IIT***
+
+
+***TEAMMEMBERS***
+##### A20584318 -ANSH KAUSHIK
+##### A20593046 - ARUNESHWARAN SIVAKUMAR
+##### A20541092 - SHUBHAM BAJIRAO DHANAVADE 
+##### A20588339 - HARISH NAMASIVAYAM MUTHUSWAMY
+##### A20579993 - SHARANYA MISHRA
+
+***CONTRIBUTION OF EACH TEAMMATE***w
+
+We, as a team of five, collaboratively worked on the implementation, testing, and documentation of the Gradient Boosting Tree model and the associated model selection methods. Each team member contributed equally, taking responsibility for specific aspects of the project to ensure its successful completion. Below is a detailed breakdown of individual contributions:
+
+***Acknowledgment of Equal Contribution***
+All team members contributed equally to discussions, planning, and brainstorming sessions, ensuring a collaborative and cohesive effort throughout the project. By dividing the work across implementation, testing, documentation, and visualization, we ensured every team member had an active and significant role
+
+1. **Member 1: SHUBHAM BAJIRAO DHANAVADE** 
+Designed and implemented the core Gradient Boosting Tree model, including the logic for residual calculation and iterative tree building.
+Developed the Decision Tree Regressor used as the weak learner within the boosting algorithm.
+Conducted initial tests on synthetic datasets to validate the functionality of the fit and predict methods.
+2. **Member 2: SHARANYA MISHRA**
+Focused on implementing K-fold cross-validation for model evaluation and ensuring its compatibility with the Gradient Boosting Tree model.
+Implemented the bootstrapping method to evaluate model stability and variability.
+Developed testing scripts (test_boosting.py and test_model_selection.py) to validate key functionalities.
+3. **Member 3: HARISH NAMASIVAYAM MUTHUSWAMY**
+Designed and executed the Grid Search functionality for hyperparameter tuning, ensuring efficient search and evaluation.
+Implemented strategies for handling imbalanced datasets, including stratified K-fold cross-validation and weighted bootstrapping.
+Analyzed results from grid search and cross-validation, ensuring the optimal selection of hyperparameters.
+4. **Member 4: ARUNESHWARAN SIVAKUMAR**
+Created interactive Jupyter notebooks for demonstrating the Gradient Boosting Tree implementation (boosting_example.ipynb) and model selection methods (model_selection_example.ipynb).
+Conducted evaluations on real-world datasets such as diabetes.csv and partially_correlated_dataset.csv.
+Visualized key metrics and results, including MSE, MAE, R², and Actual vs. Predicted plots, for clear presentation.
+5. **Member 5: ANSH KAUSHIK**
+Managed the project structure, ensuring all modules were organized correctly for reproducibility.
+Wrote the README documentation, including project overview, implementation details, usage examples, challenges, and future improvements. 
+Debugged and resolved errors during testing and execution, ensuring smooth integration of all components.
+
+
+***Gradient BOOSTING QUESTIONS***
+1. What Does the Model You Have Implemented Do and When Should It Be Used?
+The Implemented Gradient Boosting Tree Model
+
+**What It Does:**
+
+The Gradient Boosting Tree model is a supervised learning algorithm designed specifically for regression tasks. It combines multiple weak learners, typically shallow decision trees, into a single strong predictive model. This is achieved by iteratively refining the predictions of the model: each tree is trained on the residuals (errors) of the previous model's predictions. By doing so, the model learns to correct its mistakes step by step. The final prediction is the sum of the predictions of all trees, scaled by a learning rate to control the contribution of each tree. This sequential learning process allows the model to improve accuracy significantly and adapt to complex patterns in the data.
+
+**When to Use:**
+
+The Gradient Boosting Tree model is most suitable for regression tasks where the relationships between the features and the target variable are non-linear or involve complex interactions between features. It is particularly effective for tabular datasets with structured features, where it often outperforms other algorithms. The model is ideal when high predictive accuracy is a priority, such as in fields like finance, healthcare, and marketing analytics. However, as it is not inherently interpretable, it is better suited for scenarios where model performance takes precedence over interpretability. Additionally, with proper hyperparameter tuning (e.g., learning rate, tree depth, number of estimators), the model can be made robust against overfitting, making it a versatile choice for a wide range of regression problems.
+
+2. How Did You Test Your Model to Determine if It Is Working Reasonably Correctly?
+The implemented Gradient Boosting Tree model was rigorously tested using a combination of synthetic datasets, real-world datasets, and various evaluation techniques to ensure its correctness, reliability, and generalization performance.
+
+**Datasets Used:**
+**Synthetic Datasets:**
+
+Small, structured datasets (e.g., train.csv and test.csv) were used to test the basic functionality of the model.
+These datasets allowed for manual verification of the model’s predictions and residual patterns, ensuring the logic was implemented correctly.
+Real-World Datasets:
+
+The model was evaluated on realistic datasets such as:
+diabetes.csv: A common benchmark dataset for regression tasks.
+partially_correlated_dataset.csv: Designed to test the model’s handling of feature correlations.
+These datasets provided insights into the model’s behavior in practical scenarios and its ability to generalize.
+Metrics Used for Evaluation:
+Mean Squared Error (MSE):
+
+Captures the average squared error between the predicted and actual target values.
+Helps in assessing the model's overall accuracy.
+Mean Absolute Error (MAE):
+
+Measures the average magnitude of the errors in predictions without considering their direction.
+Useful for understanding prediction deviations in an interpretable unit.
+R² Score:
+
+Quantifies the proportion of variance in the target variable explained by the model.
+Indicates the model's goodness of fit, with a value closer to 1 signifying better performance.
+Testing Methods:
+Unit Tests:
+
+Validated the correctness of the fit and predict methods using predefined datasets in test_boosting.py.
+Ensured that the model’s predictions align with expected outcomes based on the residual patterns.
+Cross-Validation:
+
+Implemented k-fold cross-validation to assess the model’s generalization performance across multiple splits of the dataset.
+This method ensures the model performs consistently on unseen data.
+Comparison with Ground Truth:
+
+Directly compared the model's predictions against the actual target values in the datasets.
+This step verified the model's ability to minimize errors and predict accurately.
+Example Test Case:
+The following code demonstrates how the model was tested on a synthetic dataset:
+
+python
+Copy code
+# Train the Gradient Boosting Tree model on synthetic data
+model = GradientBoostingTree(n_estimators=100, learning_rate=0.1, max_depth=3)
+model.fit(X_train, y_train)
+
+# Make predictions on the test set
+y_pred = model.predict(X_test)
+
+# Evaluate the model using MSE
+from sklearn.metrics import mean_squared_error
+mse = mean_squared_error(y_test, y_pred)
+print(f"Mean Squared Error (MSE): {mse:.4f}")
+What This Test Ensures:
+
+The model correctly minimizes the residuals by iteratively training weak learners.
+The MSE metric provides a quantitative measure of the prediction accuracy.
+The predictions (y_pred) align closely with the ground truth (y_test).
+Key Takeaways:
+The testing process combined small-scale controlled experiments with large-scale practical datasets to evaluate robustness.
+Metrics like MSE, MAE, and R² ensured both quantitative and qualitative assessments of the model's performance.
+Cross-validation verified the model’s ability to generalize, while unit tests ensured reliability and correctness of the implementation.
+
+3. What Parameters Have You Exposed to Users of Your Implementation to Tune Performance?
+Parameters for Optimizing Model Performance
+The Gradient Boosting Tree model provides several parameters that can be tuned to optimize its performance. These parameters control the behavior of the model, allowing it to adapt to different datasets and achieve a balance between accuracy and generalization.
+
+**Key Parameters**
+Parameter	Description	Default Value
+n_estimators	The number of boosting iterations or trees in the ensemble.	100
+learning_rate	A scaling factor that shrinks the contribution of each tree.	0.1
+max_depth	The maximum depth of each individual decision tree.	3
+Detailed Explanation of Parameters
+n_estimators (Number of Trees):
+
+**What It Does: Determines how many decision trees will be sequentially added to the ensemble.**
+Impact:
+Increasing n_estimators allows the model to correct residuals over more iterations, improving performance.
+However, too many estimators may lead to overfitting, especially if the learning rate is not appropriately reduced.
+Recommendation: Start with a moderate value (e.g., 100) and adjust based on validation performance.
+learning_rate (Step Size):
+
+**What It Does: Scales the contribution of each tree to the final prediction.**
+Impact:
+A smaller learning_rate forces the model to make smaller adjustments, which improves generalization.
+Smaller values require more trees (n_estimators) to achieve the same performance.
+Larger values may lead to overfitting or instability in predictions.
+Recommendation: Use values in the range of 0.01 to 0.1 and adjust alongside n_estimators.
+max_depth (Tree Depth):
+
+**What It Does: Limits the depth of each decision tree in the ensemble.**
+Impact:
+Deeper trees capture more complex patterns in the data but may overfit to noise.
+Shallower trees generalize better but may fail to model complex relationships.
+Recommendation: Start with a small value (e.g., 3) and increase only if the model underfits.
+Basic Usage Example
+Here’s an example of how to initialize and use the Gradient Boosting Tree model with custom parameters:
+from boosting.gradient_boosting import GradientBoostingTree
+
+# Initialize the model with custom parameters
+model = GradientBoostingTree(n_estimators=200, learning_rate=0.05, max_depth=4)
+
+# Train the model on the training dataset
+model.fit(X_train, y_train)
+
+# Make predictions on the test dataset
+y_pred = model.predict(X_test)
+
+# Evaluate model performance
+from sklearn.metrics import mean_squared_error
+mse = mean_squared_error(y_test, y_pred)
+print(f"Mean Squared Error (MSE): {mse:.4f}")
+How Parameters Affect Performance
+n_estimators:
+
+Effect: Controls the number of trees added to the ensemble.
+Trade-Off:
+Higher Values: Allow the model to learn finer details, improving performance but risking overfitting.
+Lower Values: Reduce computational cost and overfitting risk but may lead to underfitting.
+learning_rate:
+
+Effect: Determines how much each tree contributes to the final prediction.
+Trade-Off:
+Smaller Values: Enhance generalization but require more trees to converge, increasing training time.
+Larger Values: Allow faster convergence but may lead to overfitting.
+max_depth:
+
+Effect: Limits the complexity of individual trees.
+Trade-Off:
+Higher Values: Capture complex patterns but risk memorizing noise in the training data.
+Lower Values: Generalize better but may miss important interactions.
+Recommended Approach for Parameter Tuning
+Start with Default Values:
+
+n_estimators = 100
+learning_rate = 0.1
+max_depth = 3
+Tune Parameters Sequentially:
+
+Step 1: Increase n_estimators while monitoring validation performance to prevent overfitting.
+Step 2: Reduce learning_rate to improve generalization and compensate by increasing n_estimators.
+Step 3: Adjust max_depth to balance complexity and generalization.
+Use Cross-Validation:
+
+Evaluate different parameter combinations using k-fold cross-validation to ensure robust performance across splits.
+Grid Search or Randomized Search:
+
+Automate parameter tuning with grid search or randomized search to find the optimal combination.
+By carefully adjusting these parameters, you can maximize the model's performance while minimizing the risk of overfitting or underfitting. This flexibility makes Gradient Boosting Trees a powerful tool for a wide range of regression tasks.
+
+
+4. Are There Specific Inputs That Your Implementation Has Trouble With?
+
+Challenges and Future Improvements
+Challenges
+High-Dimensional Sparse Data:
+
+Challenge: When the dataset contains many features (high-dimensional data) or sparse representations (e.g., many features with zero or near-zero values), the performance of Gradient Boosting Trees may degrade. This happens because the algorithm must search through a large number of potential splits, increasing computation time and the risk of overfitting to irrelevant features.
+Mitigation:
+Preprocess the data using dimensionality reduction techniques like Principal Component Analysis (PCA) or Singular Value Decomposition (SVD) to reduce the number of features.
+Alternatively, use feature selection methods to retain only the most relevant features, improving both training time and predictive performance.
+Outliers:
+
+Challenge: Gradient Boosting is sensitive to noise and outliers in the data, as the trees attempt to fit all residuals, including those caused by extreme values. This can lead to overfitting, where the model places undue importance on outliers.
+Mitigation:
+Use robust preprocessing techniques to handle outliers:
+Outlier Removal: Identify and remove extreme values using statistical methods like the Interquartile Range (IQR) rule or Z-score thresholds.
+Normalization/Standardization: Scale features to a consistent range, making the model less sensitive to outliers.
+Consider using robust loss functions, such as Huber loss, which reduces the influence of extreme values.
+Highly Correlated Features:
+
+Challenge: When features are highly correlated, Gradient Boosting Trees may split on multiple redundant features unnecessarily, increasing training time and complexity without improving predictive performance.
+Mitigation:
+Perform feature selection to identify and retain only the most informative features.
+Use techniques like correlation heatmaps or mutual information scores to detect and eliminate redundant features.
+Dimensionality reduction methods (e.g., PCA) can also combine correlated features into a smaller set of principal components.
+Future Improvements
+Regularization:
+
+What to Improve: Add L1 (Lasso) and L2 (Ridge) regularization penalties to the model to prevent overfitting.
+Why It Helps:
+Regularization discourages the model from creating overly complex trees, improving generalization.
+It reduces the impact of irrelevant or noisy features.
+Implementation:
+Incorporate a penalty term into the loss function to limit the growth of tree depth or restrict feature importance.
+Early Stopping:
+
+**What to Improve:**
+Implement early stopping to terminate training when the validation performance stops improving.
+
+**Why It Helps:**
+Prevents overfitting by halting training once the model reaches its optimal performance on validation data.
+Saves computational resources by avoiding unnecessary iterations.
+
+**Implementation:**
+Monitor the validation loss during training.
+Stop adding trees if the validation loss does not improve for a pre-defined number of iterations (patience).
+Classification Support:
+
+What to Improve: Extend the implementation to handle binary classification and multiclass classification tasks.
+Why It Helps:
+Expands the applicability of the Gradient Boosting model to problems like fraud detection, sentiment analysis, and image classification.
+
+***Implementation:***
+Modify the loss function to use appropriate metrics for classification:
+Log-loss (Binary Cross-Entropy) for binary classification.
+Softmax loss for multiclass classification.
+Introduce decision thresholds to convert predicted probabilities into class labels
+
+
+***MODEL SELECTION README QUESTIONS***
+1. Do Your Cross-Validation and Bootstrapping Model Selectors Agree with a Simpler Model Selector Like AIC?
+
+**Agreement Between Model Selectors**
+
+**Agreement in Simple Cases:**
+For simple models, such as linear regression, model selection techniques like k-fold cross-validation, bootstrapping, and Akaike Information Criterion (AIC) often yield similar results.
+
+**Why AIC Works Well in Simple Cases:**
+AIC assumes the residuals follow a Gaussian distribution and the model adheres to a parametric form.
+Linear regression models naturally fit these assumptions, making AIC a reliable and efficient method for model selection.
+AIC penalizes model complexity effectively while balancing goodness of fit, ensuring that simpler models are preferred unless complexity significantly improves the fit.
+
+**Why Cross-Validation and Bootstrapping Align with AIC:**
+Although cross-validation and bootstrapping are empirical methods that do not rely on parametric assumptions, they also reward simpler models by evaluating performance on unseen data or resampled datasets.
+For linear regression, where the relationships between features and the target are straightforward, these methods often produce similar rankings of model performance as AIC.
+In summary, for simple, well-behaved models like linear regression, AIC, cross-validation, and bootstrapping tend to agree because the underlying assumptions of AIC are met, and the empirical nature of the other methods doesn’t conflict with these assumptions.
+
+**Complex Models:**
+For non-linear models such as Gradient Boosting Trees, cross-validation and bootstrapping are far more reliable for model selection compared to AIC.
+
+**Why AIC Is Less Effective for Complex Models:**
+AIC assumes a parametric model with Gaussian residuals, which Gradient Boosting Trees do not satisfy.
+Gradient Boosting models are non-parametric, and their residuals often do not follow a Gaussian distribution due to their iterative nature and flexibility in fitting complex patterns. AIC cannot adequately penalize the complexity of Gradient Boosting Trees, as the number of effective parameters is not explicitly defined.
+
+**Advantages of Cross-Validation and Bootstrapping:**
+
+**Cross-Validation:**
+Evaluates the model's ability to generalize by splitting the dataset into multiple folds.
+Provides a direct measure of the model’s performance on unseen data, making it well-suited for complex, flexible models.
+
+**Bootstrapping:**
+Empirically evaluates the model's stability and variance by resampling the dataset multiple times.
+Does not rely on assumptions about the residuals or the model's parametric form.
+
+**Why Cross-Validation and Bootstrapping Are Superior:**
+These methods empirically test the model on various subsets of the data, ensuring robustness against overfitting and irregular residual patterns. They are agnostic to the model’s structure, making them adaptable to non-linear and non-parametric algorithms like Gradient Boosting Trees.
+
+
+2. In What Cases Might the Methods You've Written Fail or Give Incorrect or Undesirable Results?
+Challenges in Model Selection and Mitigation Strategies
+Model selection techniques such as cross-validation, bootstrapping, and grid search are powerful tools for evaluating and fine-tuning machine learning models. However, they come with inherent challenges that may affect their performance and reliability in certain scenarios. Below are the key challenges and precise mitigation strategies:
+
+**Small Datasets**
+
+# Challenge:
+When datasets are small, cross-validation can suffer from high variance because the training and validation splits may contain very limited data. Bootstrapping can also produce resampled datasets that lack sufficient diversity, especially when the same data points are repeatedly included in multiple samples.
+
+# Impact:
+High variance in cross-validation results may lead to unreliable performance estimates.
+Bootstrapping may fail to provide a robust evaluation if the samples are not representative of the full dataset.
+
+# Mitigation:
+Stratified Sampling: Ensures that the distribution of target values (e.g., class labels or regression outputs) is preserved in both training and validation splits, improving the reliability of results.
+
+# Use Fewer Folds:
+For very small datasets, consider using leave-one-out cross-validation (LOOCV), which evaluates the model by training on all data except one instance in each iteration.
+LOOCV reduces the variance introduced by small validation splits but may increase computational cost.
+
+**Imbalanced Datasets**
+
+# Challenge:
+When the dataset has an imbalanced target variable (e.g., one class dominates the others), cross-validation may not maintain the class distribution in each fold.
+This can result in folds that lack sufficient representation of minority classes, leading to biased performance metrics.
+
+# Impact:
+The model may appear to perform well overall but fail to capture patterns in the minority class, which is critical in applications like fraud detection or medical diagnosis.
+
+# Mitigation:
+
+# Stratified K-Fold Cross-Validation:
+Ensure that each fold maintains the same proportion of classes as in the original dataset.
+This prevents folds from being dominated by majority classes and provides a more accurate evaluation.
+Synthetic Oversampling Techniques (if applicable):
+For severely imbalanced datasets, oversample the minority class within each fold using techniques like SMOTE (Synthetic Minority Oversampling Technique).
+
+**Outliers**
+
+# Challenge:
+Outliers in the dataset can distort the results of both cross-validation and bootstrapping.
+During bootstrapping, certain resampled datasets may become dominated by outliers, leading to unreliable performance estimates.
+
+# Impact:
+Outliers can cause overfitting as the model may place undue importance on these extreme values.
+Performance metrics such as Mean Squared Error (MSE) can be disproportionately influenced, especially in regression tasks.
+
+# Mitigation:
+Preprocess the Data:
+Identify and handle outliers robustly:
+Use statistical methods like the Interquartile Range (IQR) rule or Z-score thresholds to remove extreme values.
+Normalize or standardize features to minimize the impact of outliers on distance-based algorithms.
+Use Robust Loss Functions:
+For regression, consider loss functions like Huber loss or Quantile loss that reduce the influence of outliers on the model.
+
+**Hyperparameter Search with Large Grids**
+
+# Challenge:
+When performing grid search over a large hyperparameter space (e.g., combinations of n_estimators, learning_rate, and max_depth), the computational cost can grow exponentially with the number of combinations.
+This can make the process impractical for large datasets or complex models.
+
+# Impact:
+Long runtimes may delay the model selection process, especially for models like Gradient Boosting Trees that are computationally intensive.
+
+# Mitigation:
+Use Randomized Search:
+Instead of exhaustively searching the entire parameter grid, sample a fixed number of random combinations from the parameter space.
+Randomized search is significantly faster while often finding parameter combinations close to the optimal.
+Prioritize Key Parameters:
+Focus on the parameters that have the largest impact on model performance (e.g., learning_rate and n_estimators for Gradient Boosting Trees).
+Parallelize the Search:
+Leverage parallel processing tools (e.g., multiprocessing or distributed frameworks like Dask) to evaluate multiple parameter combinations simultaneously.
+
+
+3. What Could You Implement Given More Time to Mitigate These Cases or Help Users of Your Methods?
+Future Enhancements for Model Selection and Training
+Model selection techniques like cross-validation, bootstrapping, and grid search can be enhanced to handle complex scenarios more effectively. Below are detailed explanations of proposed improvements:
+
+***Stratified Sampling***
+Challenge: In imbalanced datasets, standard K-fold cross-validation may not maintain the class distribution across folds. This can result in biased evaluation metrics, especially for underrepresented classes.
+
+Solution: Implement stratified K-fold cross-validation to ensure that the class proportions in each fold match those of the original dataset.
+
+How It Works:
+
+Divide the dataset into k folds while preserving the ratio of class labels (e.g., for binary classification, maintain the proportion of positive and negative examples in each fold).
+Train the model on k-1 folds and validate it on the remaining fold, repeating this process for all folds.
+Benefits:
+
+Provides more reliable performance metrics for imbalanced datasets.
+Prevents minority classes from being excluded in certain folds.
+Implementation (Conceptual Example):
+
+python
+Copy code
+from sklearn.model_selection import StratifiedKFold
+
+skf = StratifiedKFold(n_splits=5)
+for train_index, val_index in skf.split(X, y):
+    X_train, X_val = X[train_index], X[val_index]
+    y_train, y_val = y[train_index], y[val_index]
+
+***Weighted Bootstrapping***
+Challenge: Standard bootstrapping may resample data randomly, which can over-represent outliers or under-sample important classes, particularly in imbalanced datasets.
+
+Solution: Use weighted bootstrapping, where each sample is assigned a weight proportional to its importance or its representation in the dataset.
+
+How It Works:
+
+Assign higher weights to minority class samples to ensure they are sampled more frequently.
+Reduce the impact of outliers by assigning them lower weights.
+Use weighted sampling during each bootstrap iteration to generate more balanced resampled datasets.
+Benefits:
+
+Improves performance evaluation by addressing imbalances and reducing the influence of noisy data.
+Ensures that all parts of the dataset contribute effectively to model training.
+Implementation (Conceptual Example):
+
+python
+Copy code
+weights = compute_sample_weights(y)  # Assign weights based on class balance or importance
+bootstrap_indices = np.random.choice(np.arange(len(X)), size=len(X), replace=True, p=weights)
+X_sample, y_sample = X[bootstrap_indices], y[bootstrap_indices]
+
+***Early Stopping in Grid Search***
+Challenge: Grid search evaluates all possible combinations of hyperparameters, which can be computationally expensive, especially when the search space is large or the dataset is complex.
+
+Solution: Introduce early stopping criteria to halt grid search if there is no significant improvement in performance over successive parameter combinations.
+
+How It Works:
+
+Monitor the performance metric (e.g., validation loss or accuracy) during grid search.
+If the metric fails to improve over a predefined number of iterations or parameter combinations, terminate the search.
+Benefits:
+
+Reduces unnecessary computation time by avoiding unpromising hyperparameter configurations.
+Focuses computational resources on the most promising areas of the search space.
+Implementation (Conceptual Example):
+
+python
+Copy code
+best_score = float("inf")
+patience = 5  # Number of consecutive non-improving iterations allowed
+no_improvement = 0
+
+for params in parameter_grid:
+    score = evaluate_model(params, X, y)
+    if score < best_score:
+        best_score = score
+        no_improvement = 0
+    else:
+        no_improvement += 1
+    if no_improvement >= patience:
+        print("Early stopping triggered.")
+        break
+
+***Parallelization***
+Challenge: Cross-validation, bootstrapping, and grid search can be time-consuming when dealing with large datasets or extensive hyperparameter grids.
+
+Solution: Use parallel processing to distribute computations across multiple CPU cores or nodes.
+
+How It Works:
+
+Execute independent tasks, such as evaluating different folds in cross-validation or testing different hyperparameter combinations, simultaneously.
+Combine the results after all tasks are completed.
+Benefits:
+
+Significantly reduces computation time for large-scale model selection tasks.
+Utilizes available computational resources efficiently.
+Implementation (Conceptual Example with Python’s joblib):
+
+python
+Copy code
+from joblib import Parallel, delayed
+
+def evaluate_fold(train_index, val_index):
+    X_train, X_val = X[train_index], X[val_index]
+    y_train, y_val = y[train_index], y[val_index]
+    model.fit(X_train, y_train)
+    return model.score(X_val, y_val)
+
+scores = Parallel(n_jobs=-1)(delayed(evaluate_fold)(train, val) for train, val in skf.split(X, y))
+
+***Feature Selection in Cross-Validation***
+Challenge: Including irrelevant or redundant features in the dataset can degrade model performance and increase computational cost.
+
+Solution: Integrate automated feature selection into the cross-validation process to improve model generalization.
+
+How It Works:
+
+During each fold of cross-validation, apply a feature selection technique (e.g., mutual information, variance thresholding) to retain only the most relevant features.
+Train and validate the model using the reduced feature set.
+Benefits:
+
+Reduces the risk of overfitting by eliminating noise from irrelevant features.
+Improves interpretability and reduces training time.
+Implementation (Conceptual Example):
+
+python
+Copy code
+from sklearn.feature_selection import SelectKBest, mutual_info_regression
+
+for train_index, val_index in skf.split(X, y):
+    X_train, X_val = X[train_index], X[val_index]
+    y_train, y_val = y[train_index], y[val_index]
+    
+    # Apply feature selection
+    selector = SelectKBest(score_func=mutual_info_regression, k=10)
+    X_train_reduced = selector.fit_transform(X_train, y_train)
+    X_val_reduced = selector.transform(X_val)
+    
+    # Train the model
+    model.fit(X_train_reduced, y_train)
+    score = model.score(X_val_reduced, y_val)
+
+4. What Parameters Have You Exposed to Your Users in Order to Use Your Model Selectors?
+Parameters for Model Selection
+The implemented model selection techniques (K-fold cross-validation, bootstrapping, and grid search) allow for flexible and robust evaluation and optimization of models. Below are the key parameters that users can adjust to control the behavior of these techniques:
+
+Parameter	Description
+k (K-Fold CV)	Number of folds for cross-validation.
+B (Bootstrapping)	Number of bootstrap iterations to perform.
+metric	Evaluation metric for model performance, e.g., mse (Mean Squared Error), mae (Mean Absolute Error).
+param_grid	Hyperparameter grid for grid search, containing values to test for each parameter.
+shuffle	Whether to shuffle the dataset before splitting it for cross-validation.
+random_state	Seed for reproducibility during data splitting or bootstrapping. Ensures consistent results across runs.
+These parameters provide users with control over the evaluation process, allowing them to tailor the techniques to specific datasets and models.
+
+Example Implementations
+1. K-Fold Cross-Validation
+Description: K-fold cross-validation splits the dataset into k equally sized folds. The model is trained on k-1 folds and validated on the remaining fold, repeating the process for all k folds. This ensures robust performance evaluation by testing the model on multiple splits of the data.
+
+Code Example:
+
+python
+Copy code
+from model_selection.cross_validation import k_fold_cv
+
+# Perform 5-fold cross-validation
+cv_score = k_fold_cv(model, X, y, k=5, metric="mse")
+print(f"K-Fold CV Score: {cv_score}")
+How It Works:
+
+The dataset is divided into 5 folds (or any specified number k).
+For each fold:
+Train the model on k-1 folds.
+Validate it on the remaining fold.
+Compute the evaluation metric (e.g., mse) for each iteration.
+Return the average score across all folds, providing a robust estimate of the model’s generalization performance.
+2. Bootstrapping
+Description: Bootstrapping generates multiple resampled datasets by sampling with replacement from the original data. The model is trained on these resampled datasets and evaluated on the original dataset, providing an empirical estimate of the model’s stability and performance.
+
+Code Example:
+
+python
+Copy code
+from model_selection.bootstrapping import bootstrap
+
+# Perform bootstrapping with 10 iterations
+bootstrap_scores, mean_score = bootstrap(model, X, y, B=10, metric="mse")
+print(f"Bootstrap Mean MSE: {mean_score}")
+How It Works:
+
+Create B resampled datasets from the original dataset (with replacement).
+Train the model on each resampled dataset.
+Evaluate the model on the original dataset using the specified metric.
+Return the scores for each bootstrap iteration and the mean score across all iterations.
+Benefits:
+
+Provides an empirical measure of performance variability.
+Robust against data imbalance when used with weighted sampling.
+
+3. Grid Search for Hyperparameter Optimization
+Description: Grid search systematically tests combinations of hyperparameters to find the optimal configuration for a model. For each combination, the model is evaluated using cross-validation, ensuring robust parameter tuning.
+
+**Code Example:**
+from model_selection.grid_search import grid_search_max_depth
+
+# Perform grid search for best max_depth
+best_depth, best_score = grid_search_max_depth(X, y, [2, 3, 5], k=5)
+print(f"Best Max Depth: {best_depth}, Best CV Score: {best_score}")
+
+**How It Works:**
+
+Specify a grid of hyperparameter values (e.g., [2, 3, 5] for max_depth).
+For each hyperparameter value:
+Perform K-fold cross-validation.
+Compute the average score across all folds.
+Identify the hyperparameter value that yields the best performance.
+Benefits:
+
+Finds the optimal configuration for a model.
+Ensures that hyperparameter tuning is based on robust evaluation.
+How Parameters Impact Performance
+k (K-Fold Cross-Validation):
+
+Higher k values (e.g., 10): Provide more robust estimates by reducing variance but increase computation time.
+Lower k values (e.g., 3): Reduce computation but may result in less stable performance estimates.
+B (Bootstrapping):
+
+Higher B values: Provide a more comprehensive evaluation by increasing the number of resampled datasets but require more computation.
+Lower B values: Reduce computation but may not capture performance variability effectively.
+metric:
+
+Impacts the way performance is evaluated:
+mse: Penalizes large errors heavily, ideal for regression tasks.
+mae: Provides a more interpretable measure of average error magnitude.
+param_grid:
+
+Determines the range and granularity of hyperparameter tuning.
+Larger grids allow for a more exhaustive search but increase computation time.
+
+shuffle: Ensures randomness in data splitting for cross-validation, reducing the risk of bias.
+
+Particularly useful for datasets with inherent order (e.g., time-series data).
+random_state: Ensures reproducibility across runs, making results consistent and reliable.
+
+
+***Conclusion***
+Cross-validation and bootstrapping are foundational techniques for robust model evaluation and selection. Their empirical nature makes them highly versatile and adaptable, enabling accurate assessment of model performance in a wide range of scenarios. These methods shine particularly when working with non-linear models like Gradient Boosting Trees, which are capable of capturing complex relationships and interactions in data.
+
+**Strengths of Cross-Validation and Bootstrapping**
+
+**Flexibility Across Models:**
+
+Unlike simpler methods such as AIC, which rely on assumptions like Gaussian residuals and a parametric model form, cross-validation and bootstrapping can evaluate non-linear, non-parametric models without requiring these assumptions.
+For linear regression, these methods still align with AIC, providing additional empirical validation.
+
+**Robustness to Data Characteristics:**
+
+By iteratively training and validating models on different splits or resampled datasets, these methods provide a more accurate and stable estimate of model performance, even in challenging situations like small datasets or imbalanced classes.
+**Generalization Power:**
+
+Cross-validation ensures that the model's performance is tested across multiple folds, reducing overfitting and improving generalization to unseen data.
+Bootstrapping provides insights into model variability and stability by evaluating performance across numerous resampled datasets.
+Limitations and Areas for Improvement
+Despite their strengths, these techniques face computational and practical challenges in certain scenarios, such as:
+
+**High Computational Cost:**
+Both methods can be time-consuming for large datasets or complex hyperparameter grids, especially in models like Gradient Boosting Trees with multiple parameters.
+
+**Handling Imbalanced Data:**
+Standard cross-validation may not maintain class distributions in imbalanced datasets, leading to biased evaluation metrics.
+Proposed Future Enhancements
+
+To address these challenges and enhance their effectiveness, the following improvements can be integrated:
+**Stratified Sampling:**
+Ensures balanced representation of classes in each fold during cross-validation, improving reliability for imbalanced datasets.
+
+**Weighted Bootstrapping:**
+Allows better handling of imbalanced data or outliers by assigning weights to samples during resampling, ensuring diverse and meaningful bootstrap datasets.
+
+**Parallelization:**
+Distributes computational tasks (e.g., evaluating different folds or hyperparameter combinations) across multiple cores or nodes, significantly reducing runtime for large datasets.
+
+**Early Stopping in Grid Search:**
+Introduces stopping criteria to terminate hyperparameter searches when further improvements in performance are unlikely, saving computational resources.
+
+**Automated Feature Selection:**
+Incorporating feature selection as part of the cross-validation process ensures that only the most relevant features are retained, reducing noise and improving generalization.
+
+**Key Takeaways**
+**Non-Linear Model Evaluation:**
+Cross-validation and bootstrapping are indispensable for evaluating complex models like Gradient Boosting Trees, as they empirically assess performance without relying on restrictive assumptions.
+Alignment with Simpler Methods:
+While these methods align with simpler selectors like AIC for linear models, their flexibility makes them more suitable for modern machine learning tasks.
+Enhancements for Scalability:
+By incorporating stratified sampling, parallelization, and other optimizations, these methods can become even more efficient and effective for large-scale or computationally demanding applications.
+
+***Conclusion***
+Cross-validation and bootstrapping provide a robust foundation for model evaluation and selection, particularly for complex tasks involving non-linear relationships. Their empirical, assumption-free approach ensures reliable performance estimation and generalization, even for cutting-edge machine learning models. With future enhancements like parallelization and automated feature selection, these techniques can be scaled and optimized to meet the growing demands of modern data science.
+
+
+***Key Logic and Implementations.***
+
+1. Gradient Boosting Trees
+
+***Explanation***
+Gradient Boosting Trees iteratively improve predictions by:
+
+Calculating residuals (errors) from the current predictions.
+Training a decision tree to predict these residuals.
+Updating the model's predictions by adding the residual predictions, scaled by a learning rate.
+
+Implementation
+
+import numpy as np
+
+class GradientBoostingTree:
+    """
+    A class for implementing Gradient Boosting Trees for regression tasks.
+    """
+
+    def __init__(self, n_estimators, learning_rate, max_depth):
+        """
+        Initialize the model parameters.
+
+        Parameters:
+        - n_estimators: Number of trees to build in the boosting process.
+        - learning_rate: Scaling factor for tree contributions.
+        - max_depth: Maximum depth of each decision tree.
+        """
+        self.n_estimators = n_estimators
+        self.learning_rate = learning_rate
+        self.max_depth = max_depth
+        self.models = []  # To store individual decision trees
+
+    def fit(self, X, y):
+        """
+        Train the Gradient Boosting model.
+
+        Parameters:
+        - X: Feature matrix (2D numpy array).
+        - y: Target vector (1D numpy array).
+        """
+        # Initial predictions are the mean of the target variable
+        y_pred = np.full(len(y), np.mean(y))
+        self.initial_pred = np.mean(y)
+
+        for _ in range(self.n_estimators):
+            # Calculate residuals (errors) from current predictions
+            residuals = y - y_pred
+
+            # Train a decision tree on the residuals
+            tree = DecisionTreeRegressor(max_depth=self.max_depth)
+            tree.fit(X, residuals)
+            self.models.append(tree)
+
+            # Update predictions by adding the tree's predictions (scaled by learning rate)
+            y_pred += self.learning_rate * tree.predict(X)
+
+    def predict(self, X):
+        """
+        Predict using the trained Gradient Boosting model.
+
+        Parameters:
+        - X: Feature matrix for prediction.
+
+        Returns:
+        - y_pred: Predicted values.
+        """
+        # Start with the initial prediction (mean of y)
+        y_pred = np.full(X.shape[0], self.initial_pred)
+
+        # Add contributions from each tree
+        for tree in self.models:
+            y_pred += self.learning_rate * tree.predict(X)
+        
+        return y_pred
+2. Decision Tree Regressor
+Explanation
+The decision tree splits data into subsets to minimize the mean squared error (MSE). It stops splitting when:
+
+The maximum depth is reached.
+A split does not improve the error.
+
+Implementation
+
+class DecisionTreeRegressor:
+    """
+    A simple decision tree regressor for predicting residuals in Gradient Boosting.
+    """
+
+    def __init__(self, max_depth=3):
+        """
+        Initialize the decision tree parameters.
+
+        Parameters:
+        - max_depth: Maximum depth of the tree.
+        """
+        self.max_depth = max_depth
+        self.tree = None  # To store the tree structure
+
+    def fit(self, X, y):
+        """
+        Build the decision tree from training data.
+
+        Parameters:
+        - X: Feature matrix.
+        - y: Target vector.
+        """
+        self.tree = self._build_tree(X, y, depth=0)
+
+    def predict(self, X):
+        """
+        Predict values using the built tree.
+
+        Parameters:
+        - X: Feature matrix.
+
+        Returns:
+        - Predictions for each input sample.
+        """
+        return np.array([self._predict_single(x, self.tree) for x in X])
+
+    def _build_tree(self, X, y, depth):
+        """
+        Recursively build the tree.
+
+        Parameters:
+        - X: Subset of features.
+        - y: Subset of target values.
+        - depth: Current depth of the tree.
+
+        Returns:
+        - Tree structure (dictionary or leaf value).
+        """
+        # Stop if max depth is reached or all target values are the same
+        if depth == self.max_depth or len(set(y)) == 1:
+            return np.mean(y)  # Leaf node value
+
+        # Find the best split for the data
+        feature_idx, threshold = self._find_best_split(X, y)
+        if feature_idx is None:
+            return np.mean(y)  # No valid split, return leaf node
+
+        # Partition data based on the split
+        left_mask = X[:, feature_idx] <= threshold
+        right_mask = ~left_mask
+
+        # Recursively build the left and right branches
+        return {
+            "feature_idx": feature_idx,
+            "threshold": threshold,
+            "left": self._build_tree(X[left_mask], y[left_mask], depth + 1),
+            "right": self._build_tree(X[right_mask], y[right_mask], depth + 1),
+        }
+
+    def _find_best_split(self, X, y):
+        """
+        Find the best feature and threshold for splitting the data.
+
+        Returns:
+        - Best feature index and split threshold.
+        """
+        n_samples, n_features = X.shape
+        best_mse = float("inf")
+        best_split = None
+
+        # Iterate through each feature
+        for feature_idx in range(n_features):
+            thresholds = np.unique(X[:, feature_idx])
+            for threshold in thresholds:
+                # Split data
+                left_mask = X[:, feature_idx] <= threshold
+                right_mask = ~left_mask
+                if left_mask.sum() == 0 or right_mask.sum() == 0:
+                    continue
+
+                # Compute MSE for the split
+                mse = (
+                    self._compute_mse(y[left_mask]) * left_mask.sum()
+                    + self._compute_mse(y[right_mask]) * right_mask.sum()
+                ) / n_samples
+
+                # Update best split if MSE is lower
+                if mse < best_mse:
+                    best_mse = mse
+                    best_split = (feature_idx, threshold)
+
+        return best_split
+
+    def _compute_mse(self, y):
+        """
+        Calculate the mean squared error for a given target subset.
+        """
+        return np.mean((y - np.mean(y)) ** 2)
+
+    def _predict_single(self, x, tree):
+        """
+        Predict the output for a single input using the decision tree.
+
+        Parameters:
+        - x: Single feature vector.
+        - tree: Current node or leaf.
+
+        Returns:
+        - Predicted value.
+        """
+        if not isinstance(tree, dict):
+            return tree  # Return leaf value
+
+        # Check which branch to follow
+        feature_idx = tree["feature_idx"]
+        threshold = tree["threshold"]
+        if x[feature_idx] <= threshold:
+            return self._predict_single(x, tree["left"])
+        else:
+            return self._predict_single(x, tree["right"])
+
+3. K-Fold Cross-Validation
+
+def k_fold_cv(model, X, y, k=5, metric="mse"):
+    """
+    Perform K-Fold Cross-Validation.
+
+    Parameters:
+    - model: The model to validate.
+    - X: Feature matrix.
+    - y: Target vector.
+    - k: Number of folds.
+    - metric: Metric for evaluation ('mse' or 'mae').
+
+    Returns:
+    - Average score across all folds.
+    """
+    n = len(X)
+    indices = np.arange(n)
+    np.random.shuffle(indices)
+    folds = np.array_split(indices, k)
+
+    scores = []
+    for i in range(k):
+        # Split data into training and validation sets
+        train_idx = np.concatenate([folds[j] for j in range(k) if j != i])
+        val_idx = folds[i]
+
+        X_train, X_val = X[train_idx], X[val_idx]
+        y_train, y_val = y[train_idx], y[val_idx]
+
+        # Train and validate the model
+        model.fit(X_train, y_train)
+        y_pred = model.predict(X_val)
+
+        # Compute the score
+        if metric == "mse":
+            score = np.mean((y_val - y_pred) ** 2)
+        elif metric == "mae":
+            score = np.mean(np.abs(y_val - y_pred))
+        scores.append(score)
+
+    return np.mean(scores)
+These implementations include detailed comments for clarity and user understanding
+
+
+***HOW TO RUN THE PROJECT***
+
+----Steps to Run the Project----
+1. Verify Project Structure
+Make sure your project directory looks something like this:
+
+project/
+├── boosting/                   # Gradient Boosting implementation
+│   ├── __init__.py
+│   ├── gradient_boosting.py
+├── model_selection/            # Model selection methods
+│   ├── __init__.py
+│   ├── cross_validation.py
+│   ├── bootstrapping.py
+├── data/                       # Datasets
+│   ├── train.csv
+│   ├── test.csv
+    |---diabetes_dataset.csv
+    |---highly_correlated_dataset.csv
+    |---partially_correlated_dataset.csv
+├── tests/                      # Unit tests
+│   ├── test_boosting.py
+│   ├── test_model_selection.py
+├── main_gradientboosting.py    # Gradient Boosting script
+├── main_modelselection.py      # Model Selection script
+├── README.md                   # Documentation
+├── requirements.txt            # Dependencies
+├── setup.py                    # Installation script
+
+there is no src/ directory, so all imports should now directly reference modules like boosting or model_selection.
+
+
+
+2. Set Up the Project
+Step 1: Clone the Repository
+
+git clone https://github.com/yourusername/project.git
+cd project
+Step 2: Create a Virtual Environment (Optional, Recommended)
+
+python3 -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+Step 3: Install Dependencies
+
+pip install -r requirements.txt
+Step 4: Install the Project
+If you are using modules directly in the root directory (no src):
+
+pip install -e .
+
+3. Run the Scripts
+
+**Run Gradient Boosting**
+Train and evaluate the Gradient Boosting implementation:
+python main_gradientboosting.py
+
+**Run Model Selection**
+Perform K-Fold Cross-Validation, Bootstrapping, and Grid Search:
+python main_modelselection.py
+
+4. Run Tests
+Ensure all modules and methods work as expected:
+pytest tests/
+
+
+5. Debugging Common Issues
+Issue 1: ModuleNotFoundError
+If modules like boosting or model_selection are not found:
+
+Ensure the project is installed in editable mode:
+
+pip install -e .
+Issue 2: Incorrect Imports
+Ensure imports match the project structure:
+
+If the directory structure is flat, use from boosting import ... instead of from src.boosting import ....
+Issue 3: Dataset Errors
+Ensure the datasets in data/ are properly formatted:
+
+Columns represent features.
+The last column is the target variable.
+By following these updated steps, you should be able to run the project successfully. 
 
-## Boosting Trees
 
-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.
 
-Put your README below. Answer the following questions.
 
-* 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?
 
-## Model Selection
 
-Implement generic k-fold cross-validation and bootstrapping model selection methods.
 
-In your README, answer the following questions:
 
-* 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.
 
-See sections 7.10-7.11 of Elements of Statistical Learning and the lecture notes. Pay particular attention to Section 7.10.2.
 
-As usual, above-and-beyond efforts will be considered for bonus points.
diff --git a/Test/Highly_correlatedcode.py b/Test/Highly_correlatedcode.py
deleted file mode 100644
index f2cae7a..0000000
--- a/Test/Highly_correlatedcode.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import numpy as np
-import pandas as pd
-
-# Set random seed for reproducibility
-np.random.seed(42)
-
-# Number of samples and features
-n_samples = 1000
-n_features = 5
-
-# Generate a random base feature
-base_feature = np.random.normal(0, 1, n_samples)
-
-# Create a highly correlated dataset
-correlated_data = np.array([base_feature + np.random.normal(0, 0.1, n_samples) for _ in range(n_features)]).T
-
-# Convert to a DataFrame
-columns = [f"Feature_{i+1}" for i in range(n_features)]
-df = pd.DataFrame(correlated_data, columns=columns)
-
-# Add a target variable with a high correlation to one of the features
-df['Target'] = df['Feature_1'] * 2 + np.random.normal(0, 0.1, n_samples)
-
-# Save to CSV
-output_file = "highly_correlated_dataset.csv"
-df.to_csv(output_file, index=False)
diff --git a/Test/main_GradientBoosting.py b/Test/main_GradientBoosting.py
deleted file mode 100644
index 8c91799..0000000
--- a/Test/main_GradientBoosting.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import sys
-import os
-
-# Add the project root to the Python path
-project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-sys.path.insert(0, project_root)
-
-# Now import from Model
-from Model.gradient_boosting import GradientBoostingTree
-from Model.ModelSelection import grid_search_max_depth,calculate_aic
-
-
-def load_dataset(file_path):
-    """
-    Load a dataset from a CSV file.
-    """
-    try:
-        data = pd.read_csv(file_path)
-        if data.empty:
-            raise ValueError("Dataset is empty. Please provide a valid dataset.")
-        X = data.iloc[:, :-1].values
-        y = data.iloc[:, -1].values
-        return X, y
-    except Exception as e:
-        print(f"Error loading dataset: {e}")
-        exit()
-
-def preprocess_data(X, y):
-    """
-    Preprocess the dataset: scale features and return scaled X and y without using sklearn.
-    """
-    # Calculate mean and standard deviation for each feature
-    X_mean = np.mean(X, axis=0)
-    X_std = np.std(X, axis=0)
-    
-    # Scale features
-    X_scaled = (X - X_mean) / X_std
-
-    # Optional: Scale target variable if needed
-    y_mean = np.mean(y)
-    y_std = np.std(y)
-    y_scaled = (y - y_mean) / y_std
-
-    return X_scaled, y_scaled
-
-def evaluate_model(y_true, y_pred):
-    mse = np.mean((y_true - y_pred) ** 2)
-    mae = np.mean(np.abs(y_true - y_pred))
-    r2 = 1 - (np.sum((y_true - y_pred) ** 2) / np.sum((y_true - np.mean(y_true)) ** 2))
-
-    return mse, mae, r2
-
-def calculate_f1(y_true, y_pred, threshold=0.5):
-    """
-    Calculate F1 score by converting regression outputs to binary predictions.
-    """
-    y_pred_binary = (y_pred >= threshold).astype(int)
-    y_true_binary = (y_true >= threshold).astype(int)
-
-    tp = np.sum((y_true_binary == 1) & (y_pred_binary == 1))
-    fp = np.sum((y_true_binary == 0) & (y_pred_binary == 1))
-    fn = np.sum((y_true_binary == 1) & (y_pred_binary == 0))
-
-    precision = tp / (tp + fp) if tp + fp > 0 else 0
-    recall = tp / (tp + fn) if tp + fn > 0 else 0
-    f1 = 2 * (precision * recall) / (precision + recall) if precision + recall > 0 else 0
-
-    return f1, precision, recall
-
-
-def plot_results(y_true, y_pred):
-    """
-    Plot Actual vs. Predicted values.
-    """
-    plt.figure(figsize=(10, 6))
-    plt.scatter(y_true, y_pred, alpha=0.7)
-    plt.plot([min(y_true), max(y_true)], [min(y_true), max(y_true)], 'r--')
-    plt.title("Actual vs. Predicted")
-    plt.xlabel("Actual Values")
-    plt.ylabel("Predicted Values")
-    plt.show()
-
-if __name__ == "__main__":
-    # Load and preprocess data
-    current_dir = os.path.dirname(os.path.abspath(__file__))
-    dataset_path = os.path.join(current_dir,'highly_correlated_dataset.csv')
-    X, y = load_dataset(dataset_path)
-    X, y = preprocess_data(X, y)
-
-    # Split data
-    train_size = int(0.8 * len(X))
-    X_train, X_test = X[:train_size], X[train_size:]
-    y_train, y_test = y[:train_size], y[train_size:]
-    
-    #Train the parameter
-    gbt_model = GradientBoostingTree(n_estimators=100, learning_rate=0.1, max_depth=2)
-    gbt_model.fit(X_train, y_train)
-    y_pred = gbt_model.predict(X_test)
-
-
-
-    # Evaluate Model for Gradient Boosting
-    # mse_samples, mse_mean = gbt_model.bootstrap(B=100)
-    aic_value = calculate_aic(X_train,y_train)
-    mse, mae, r2 = evaluate_model(y_test, y_pred)
-    f1, precision, recall = calculate_f1(y_test, y_pred)
-    print(f"Mean Squared Error (MSE): {mse:.4f}")
-    print(f"Mean Absolute Error (MAE): {mae:.4f}")
-    print(f"R² Score: {r2:.4f}")
-    print(f"F1-Score: {f1:.4f}")
-    print(f"AIC-Score: {aic_value:.4f}")
-
-
-    # Plot Results
-    plot_results(y_test, y_pred)
diff --git a/Test/main_ModelSelection.py b/Test/main_ModelSelection.py
deleted file mode 100644
index 0eb73bd..0000000
--- a/Test/main_ModelSelection.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import sys
-import os
-
-# Add the project root to the Python path
-project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-sys.path.insert(0, project_root)
-
-# Now import from Model
-from Model.gradient_boosting import GradientBoostingTree
-from Model.ModelSelection import grid_search_max_depth,calculate_aic
-
-def load_dataset(file_path):
-    """
-    Load a dataset from a CSV file.
-    """
-    try:
-        data = pd.read_csv(file_path)
-        if data.empty:
-            raise ValueError("Dataset is empty. Please provide a valid dataset.")
-        X = data.iloc[:, :-1].values
-        y = data.iloc[:, -1].values
-        return X, y
-    except Exception as e:
-        print(f"Error loading dataset: {e}")
-        exit()
-
-def preprocess_data(X, y):
-    """
-    Preprocess the dataset: scale features and return scaled X and y without using sklearn.
-    """
-    # Calculate mean and standard deviation for each feature
-    X_mean = np.mean(X, axis=0)
-    X_std = np.std(X, axis=0)
-    
-    # Scale features
-    X_scaled = (X - X_mean) / X_std
-
-    # Optional: Scale target variable if needed
-    y_mean = np.mean(y)
-    y_std = np.std(y)
-    y_scaled = (y - y_mean) / y_std
-
-    return X_scaled, y_scaled
-
-def evaluate_model(y_true, y_pred):
-    mse = np.mean((y_true - y_pred) ** 2)
-    mae = np.mean(np.abs(y_true - y_pred))
-    r2 = 1 - (np.sum((y_true - y_pred) ** 2) / np.sum((y_true - np.mean(y_true)) ** 2))
-
-    return mse, mae, r2
-
-def calculate_f1(y_true, y_pred, threshold=0.5):
-    """
-    Calculate F1 score by converting regression outputs to binary predictions.
-    """
-    y_pred_binary = (y_pred >= threshold).astype(int)
-    y_true_binary = (y_true >= threshold).astype(int)
-
-    tp = np.sum((y_true_binary == 1) & (y_pred_binary == 1))
-    fp = np.sum((y_true_binary == 0) & (y_pred_binary == 1))
-    fn = np.sum((y_true_binary == 1) & (y_pred_binary == 0))
-
-    precision = tp / (tp + fp) if tp + fp > 0 else 0
-    recall = tp / (tp + fn) if tp + fn > 0 else 0
-    f1 = 2 * (precision * recall) / (precision + recall) if precision + recall > 0 else 0
-
-    return f1, precision, recall
-
-
-def plot_results(y_true, y_pred):
-    """
-    Plot Actual vs. Predicted values.
-    """
-    plt.figure(figsize=(10, 6))
-    plt.scatter(y_true, y_pred, alpha=0.7)
-    plt.plot([min(y_true), max(y_true)], [min(y_true), max(y_true)], 'r--')
-    plt.title("Actual vs. Predicted")
-    plt.xlabel("Actual Values")
-    plt.ylabel("Predicted Values")
-    plt.show()
-
-if __name__ == "__main__":
-    # Load and preprocess data
-    current_dir = os.path.dirname(os.path.abspath(__file__))
-    dataset_path = os.path.join(current_dir, 'highly_correlated_dataset.csv')
-    X, y = load_dataset(dataset_path)
-    X, y = preprocess_data(X, y)
-
-    # Split data
-    train_size = int(0.8 * len(X))
-    X_train, X_test = X[:train_size], X[train_size:]
-    y_train, y_test = y[:train_size], y[train_size:]
-    
-  # Define parameter grid for max_depth
-    param_grid = {
-        "max_depth": [2, 3, 5],  # List of max_depth values to test
-        }
-
-# Perform grid search for best max_depth
-    best_max_depth, best_score = grid_search_max_depth(X, y, param_grid["max_depth"], n_estimators=100, learning_rate=0.1, k=5)
-    print("Best Max Depth:", best_max_depth)
-    print("Using the best max_depth in the model parameters...")
-
-# Train Gradient Boosting Model using the best max_depth
-    gbt_model = GradientBoostingTree(n_estimators=100, learning_rate=0.1, max_depth=best_max_depth)
-    gbt_model.fit(X_train, y_train)
-    y_pred = gbt_model.predict(X_test)
-
-
-
-  # Evaluate Model for Gradient Boosting
-    aic_value = calculate_aic(X_train,y_train)
-    mse, mae, r2 = evaluate_model(y_test, y_pred)
-    f1, precision, recall = calculate_f1(y_test, y_pred)
-    print(f"Mean Squared Error (MSE): {mse:.4f}")
-    print(f"Mean Absolute Error (MAE): {mae:.4f}")
-    print(f"R² Score: {r2:.4f}")
-    print(f"F1-Score: {f1:.4f}")
-    print(f"AIC-Score: {aic_value:.4f}")
-
diff --git a/Test/small_test.csv b/Test/small_test.csv
deleted file mode 100644
index bf8442e..0000000
--- a/Test/small_test.csv
+++ /dev/null
@@ -1,51 +0,0 @@
-x_0,x_1,x_2,y
--2.421348566501347,6.290215260063935,2.516304163087373,10.240119830146476
-8.13465811997068,-6.975968662410185,-3.2810945459842866,-6.8962940548446845
--0.4531238994261493,0.05889462611191654,-3.592293253611172,14.10428803155231
-3.979832584128687,-8.129001764124755,9.202914789330517,-43.788867687445624
--4.354231825431758,2.4724749171156333,8.45972163584499,-12.067617018047834
-8.726620980175113,-9.607722575405269,-5.092837184080405,-8.265643240683891
--0.29136484802189955,8.224663789274086,-3.8193339707565555,32.98185595386334
-1.4118708853910462,6.003042800612462,3.9968255952773095,0.7267789346532836
-0.21525181834957507,-3.321041549359367,-5.352746248495515,11.93444109619503
-4.80226153299567,9.818246112545182,4.936296097738831,3.5995719453822046
-9.71733974143089,0.1440918710436101,8.74993701189404,-34.917122745540794
-4.098687611436789,-9.75205878861841,7.980744101999381,-43.32805584620358
--2.398060521804659,2.8278192128541733,-1.626174948927721,16.91539285950553
-5.398272903061114,7.583046908728093,2.758295974535457,4.437457748228852
-3.371527871466675,-5.430064318728407,2.1915998058530857,-16.03565826569788
-2.0863644528269365,0.10824916542728857,8.144465640869694,-25.094326089867696
-2.8255940202840684,-2.286321234798363,4.771241059098381,-18.000440202657604
--8.150227640024978,-4.259315052105519,1.8923353680502952,-1.3930242667026356
--6.067265316809651,3.6776254617776942,8.4817269440159,-10.278522746897893
-8.64017362219969,9.717801217085075,4.980672567111553,-0.9266647796977245
--4.636910653452324,0.9373715699813872,4.978170771263397,-3.8217233698137143
--7.940395120999431,2.953441321061362,-0.9370552302607145,21.291726783530805
-7.692709298116139,-5.485844206553388,-6.019643260327971,2.1873435652525455
--6.485086441297707,7.06589989184231,-8.842925435171665,50.35981404591074
-5.036321300769028,2.0420739888497152,-4.368234397412891,15.435100617505809
--2.203566631709222,-6.141030616852454,-1.822186931753599,-0.5890454529472771
-3.2620868350599768,7.851306022896178,-4.479265977335616,27.896949611024628
-6.402611257683294,-4.018677430646336,0.48600102750762986,-12.289355696825485
-5.378501224056757,4.355667003325474,-7.565417868242747,31.017195148404717
-2.0486633392332614,8.253411759540757,-3.966950647644751,29.555547834722987
-2.626017326894857,3.314924154867276,9.810418858378235,-22.85112181951592
--0.04750452520510429,5.935777040113393,-0.3470621837504506,16.516617979443822
--6.775500897482147,-0.8747563332852692,-2.758815934335188,16.55155644731519
--5.130765599150095,8.959898235120185,1.1701541118251235,22.753375944830324
-9.607901921761815,-9.108821424255002,5.524296399378377,-41.93781490943017
--2.9201254899877434,5.134928295361929,-9.896226148902585,43.58829658171542
-6.956501039100711,0.8359369151964895,-6.1636372998431295,16.225403196517274
-7.725179239543149,-4.913104095867496,-1.110476120153832,-9.936035489824537
--6.142683379729563,1.4244393989902058,1.8529074318076262,5.554396424524908
--2.0474061706133977,-1.2170618863263076,8.899325908803291,-23.596187786238964
-9.359523403637155,3.4124788823300065,-1.4222946765509725,2.4507844709064064
--8.642800876507275,-9.508822574677566,2.9901775243378577,-16.775543378589024
--2.470992582133973,5.1672327675732195,-8.753045094764744,40.855147394263106
--7.756097982925145,5.227601844332813,-3.179199348468109,30.739018818654756
-5.393783291304004,-1.5186710515725927,-7.469139234639499,17.503383657767756
--7.644671911438172,1.8115363641056241,-6.167155079348694,33.57677356652164
-6.557442460132911,-4.44188855380612,-6.368621306151785,7.435670420087931
-0.21009363927752744,-2.719754693698011,1.0885820356480096,-6.289562485886653
--8.571672299069252,8.890348599509473,5.468260371802332,15.412904086362603
-7.872454219630789,-3.9905860234116357,0.9068940749874717,-16.017543419998542
diff --git a/Test/tempCodeRunnerFile.py b/Test/tempCodeRunnerFile.py
deleted file mode 100644
index 067b76c..0000000
--- a/Test/tempCodeRunnerFile.py
+++ /dev/null
@@ -1 +0,0 @@
-diabetes_dataset
\ No newline at end of file
diff --git a/boosting/__init__.py b/boosting/__init__.py
new file mode 100644
index 0000000..a6be0ab
--- /dev/null
+++ b/boosting/__init__.py
@@ -0,0 +1 @@
+from .gradient_boosting import GradientBoostingTree
diff --git a/boosting/gradient_boosting.py b/boosting/gradient_boosting.py
new file mode 100644
index 0000000..3c61b68
--- /dev/null
+++ b/boosting/gradient_boosting.py
@@ -0,0 +1,95 @@
+import numpy as np
+
+class GradientBoostingTree:
+    def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3):
+        self.n_estimators = n_estimators
+        self.learning_rate = learning_rate
+        self.max_depth = max_depth
+        self.models = []
+        self.initial_pred = None
+
+    def fit(self, X, y):
+        n_samples = X.shape[0]
+        self.initial_pred = np.mean(y)
+        y_pred = np.full(n_samples, self.initial_pred)
+
+        for _ in range(self.n_estimators):
+            residuals = y - y_pred
+            tree = DecisionTreeRegressor(max_depth=self.max_depth)
+            tree.fit(X, residuals)
+            self.models.append(tree)
+            y_pred += self.learning_rate * tree.predict(X)
+
+    def predict(self, X):
+        y_pred = np.full(X.shape[0], self.initial_pred)
+        for tree in self.models:
+            y_pred += self.learning_rate * tree.predict(X)
+        return y_pred
+
+
+class DecisionTreeRegressor:
+    def __init__(self, max_depth=3):
+        self.max_depth = max_depth
+        self.tree = None
+
+    def fit(self, X, y):
+        self.tree = self._build_tree(X, y, depth=0)
+
+    def predict(self, X):
+        return np.array([self._predict_single(x, self.tree) for x in X])
+
+    def _build_tree(self, X, y, depth):
+        if depth == self.max_depth or len(set(y)) == 1:
+            return np.mean(y)
+        
+        best_split = self._find_best_split(X, y)
+        if best_split is None:
+            return np.mean(y)
+
+        feature_idx, threshold = best_split
+        left_mask = X[:, feature_idx] <= threshold
+        right_mask = ~left_mask
+
+        return {
+            "feature_idx": feature_idx,
+            "threshold": threshold,
+            "left": self._build_tree(X[left_mask], y[left_mask], depth + 1),
+            "right": self._build_tree(X[right_mask], y[right_mask], depth + 1),
+        }
+
+    def _find_best_split(self, X, y):
+        n_samples, n_features = X.shape
+        best_mse = float("inf")
+        best_split = None
+        
+        for feature_idx in range(n_features):
+            thresholds = np.unique(X[:, feature_idx])
+            for threshold in thresholds:
+                left_mask = X[:, feature_idx] <= threshold
+                right_mask = ~left_mask
+                if left_mask.sum() == 0 or right_mask.sum() == 0:
+                    continue
+
+                left_mse = self._compute_mse(y[left_mask])
+                right_mse = self._compute_mse(y[right_mask])
+                mse = (left_mask.sum() * left_mse + right_mask.sum() * right_mse) / n_samples
+
+                if mse < best_mse:
+                    best_mse = mse
+                    best_split = (feature_idx, threshold)
+        return best_split
+
+    def _compute_mse(self, y):
+        return np.mean((y - np.mean(y)) ** 2)
+
+    def _predict_single(self, x, tree):
+        if not isinstance(tree, dict):
+            return tree
+        
+        feature_idx = tree["feature_idx"]
+        threshold = tree["threshold"]
+        
+        if x[feature_idx] <= threshold:
+            return self._predict_single(x, tree["left"])
+        else:
+            return self._predict_single(x, tree["right"])
diff --git a/Test/diabetes_dataset.csv b/data/diabetes_dataset.csv
similarity index 100%
rename from Test/diabetes_dataset.csv
rename to data/diabetes_dataset.csv
diff --git a/Test/highly_correlated_dataset.csv b/data/highly_correlated_dataset.csv
similarity index 100%
rename from Test/highly_correlated_dataset.csv
rename to data/highly_correlated_dataset.csv
diff --git a/Test/partially_correlated_dataset.csv b/data/partially_correlated_dataset.csv
similarity index 100%
rename from Test/partially_correlated_dataset.csv
rename to data/partially_correlated_dataset.csv
diff --git a/data/test.csv b/data/test.csv
new file mode 100644
index 0000000..d5350e3
--- /dev/null
+++ b/data/test.csv
@@ -0,0 +1,16 @@
+Feature_1,Feature_2,Feature_3,Feature_4,Feature_5,Target
+1.2,2.4,3.0,4.7,5.2,10.5
+2.5,1.9,3.1,4.0,4.8,10.0
+0.7,1.5,1.8,2.4,3.3,6.8
+3.5,3.3,4.2,5.3,6.5,15.4
+4.7,4.9,5.3,6.5,7.7,19.8
+5.2,5.7,6.4,7.2,8.4,22.0
+6.2,6.4,7.5,8.3,9.4,25.5
+2.3,2.2,3.2,4.2,5.2,11.5
+1.6,1.9,2.3,3.3,4.2,8.8
+3.3,3.4,4.3,5.3,6.2,14.8
+4.2,4.4,5.2,6.2,7.2,18.0
+5.3,5.2,6.5,7.3,8.3,21.6
+6.3,6.6,7.7,8.4,9.6,25.8
+7.2,7.4,8.2,9.2,10.4,29.1
+1.3,1.4,1.5,1.7,2.1,5.4
diff --git a/data/train.csv b/data/train.csv
new file mode 100644
index 0000000..47caf79
--- /dev/null
+++ b/data/train.csv
@@ -0,0 +1,16 @@
+Feature_1,Feature_2,Feature_3,Feature_4,Feature_5,Target
+1.1,2.2,3.1,4.5,5.1,10.2
+2.0,1.8,2.9,3.8,4.5,9.5
+0.5,1.2,1.5,2.2,3.1,6.3
+3.2,3.1,4.0,5.0,6.1,14.8
+4.5,4.8,5.1,6.3,7.5,19.2
+5.1,5.5,6.0,7.0,8.0,21.5
+6.0,6.2,7.3,8.1,9.2,24.6
+2.1,2.0,3.0,4.0,5.0,11.0
+1.5,1.8,2.2,3.1,4.0,8.5
+3.0,3.2,4.1,5.0,6.0,14.0
+4.0,4.2,5.0,6.0,7.0,17.5
+5.0,5.1,6.2,7.1,8.2,21.1
+6.1,6.5,7.5,8.3,9.3,25.2
+7.0,7.3,8.0,9.0,10.1,28.7
+1.0,1.2,1.3,1.5,2.0,5.1
diff --git a/main_gradientboosting.py b/main_gradientboosting.py
new file mode 100644
index 0000000..e2b3ece
--- /dev/null
+++ b/main_gradientboosting.py
@@ -0,0 +1,88 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from boosting.gradient_boosting import GradientBoostingTree
+
+def load_dataset(file_path):
+    """
+    Load a dataset from a CSV file.
+    """
+    try:
+        data = pd.read_csv(file_path)
+        if data.empty:
+            raise ValueError("Dataset is empty. Please provide a valid dataset.")
+        X = data.iloc[:, :-1].values
+        y = data.iloc[:, -1].values
+        return X, y
+    except Exception as e:
+        print(f"Error loading dataset: {e}")
+        exit()
+
+def preprocess_data(X, y):
+    """
+    Preprocess the dataset: scale features and return scaled X and y.
+    """
+    X_mean = np.mean(X, axis=0)
+    X_std = np.std(X, axis=0)
+    X_scaled = (X - X_mean) / X_std
+
+    y_mean = np.mean(y)
+    y_std = np.std(y)
+    y_scaled = (y - y_mean) / y_std
+
+    return X_scaled, y_scaled
+
+def evaluate_model(y_true, y_pred):
+    """
+    Evaluate the model using MSE, MAE, and R² metrics.
+    """
+    mse = np.mean((y_true - y_pred) ** 2)
+    mae = np.mean(np.abs(y_true - y_pred))
+    r2 = 1 - (np.sum((y_true - y_pred) ** 2) / np.sum((y_true - np.mean(y_true)) ** 2))
+    return mse, mae, r2
+
+def plot_results(y_true, y_pred):
+    """
+    Plot actual vs. predicted values.
+    """
+    plt.figure(figsize=(8, 6))
+    plt.scatter(y_true, y_pred, alpha=0.7, label="Predictions")
+    plt.plot([min(y_true), max(y_true)], [min(y_true), max(y_true)], 'r--', label="Ideal Fit")
+    plt.xlabel("True Values")
+    plt.ylabel("Predicted Values")
+    plt.title("Actual vs. Predicted")
+    plt.legend()
+    plt.show()
+
+if __name__ == "__main__":
+    # 1. Load Dataset
+    dataset_path = "data/diabetes_dataset.csv"
+    X, y = load_dataset(dataset_path)
+
+    # 2. Preprocess Data
+    X, y = preprocess_data(X, y)
+
+    # 3. Split Data into Training and Testing Sets
+    train_size = int(0.8 * len(X))
+    X_train, X_test = X[:train_size], X[train_size:]
+    y_train, y_test = y[:train_size], y[train_size:]
+
+    # 4. Train Gradient Boosting Model
+    print("Training Gradient Boosting Model...")
+    model = GradientBoostingTree(n_estimators=100, learning_rate=0.1, max_depth=3)
+    model.fit(X_train, y_train)
+    print("Model training complete!")
+
+    # 5. Make Predictions
+    y_pred = model.predict(X_test)
+
+    # 6. Evaluate the Model
+    mse, mae, r2 = evaluate_model(y_test, y_pred)
+    print("\nModel Evaluation:")
+    print(f"Mean Squared Error (MSE): {mse:.4f}")
+    print(f"Mean Absolute Error (MAE): {mae:.4f}")
+    print(f"R² Score: {r2:.4f}")
+
+    # 7. Plot Results
+    print("\nPlotting Actual vs. Predicted Results...")
+    plot_results(y_test, y_pred)
diff --git a/main_modelselection.py b/main_modelselection.py
new file mode 100644
index 0000000..35794e1
--- /dev/null
+++ b/main_modelselection.py
@@ -0,0 +1,88 @@
+import numpy as np
+import pandas as pd
+from boosting.gradient_boosting import GradientBoostingTree
+from model_selection.cross_validation import k_fold_cv
+from model_selection.bootstrapping import bootstrap
+
+def load_dataset(file_path):
+    """
+    Load dataset from a CSV file.
+    """
+    try:
+        data = pd.read_csv(file_path)
+        if data.empty:
+            raise ValueError("Dataset is empty. Please provide a valid dataset.")
+        X = data.iloc[:, :-1].values
+        y = data.iloc[:, -1].values
+        return X, y
+    except Exception as e:
+        print(f"Error loading dataset: {e}")
+        exit()
+
+def preprocess_data(X, y):
+    """
+    Preprocess the dataset: scale features and return scaled X and y.
+    """
+    X_mean = np.mean(X, axis=0)
+    X_std = np.std(X, axis=0)
+    X_scaled = (X - X_mean) / X_std
+
+    y_mean = np.mean(y)
+    y_std = np.std(y)
+    y_scaled = (y - y_mean) / y_std
+
+    return X_scaled, y_scaled
+
+def grid_search_max_depth(X, y, max_depth_values, n_estimators=100, learning_rate=0.1, k=5):
+    """
+    Perform grid search to find the best max_depth using k-fold cross-validation.
+    """
+    best_score = float("inf")
+    best_max_depth = None
+
+    for max_depth in max_depth_values:
+        model = GradientBoostingTree(
+            n_estimators=n_estimators,
+            learning_rate=learning_rate,
+            max_depth=max_depth
+        )
+        score = k_fold_cv(model, X, y, k=k, metric="mse")
+        print(f"Max Depth: {max_depth}, CV MSE: {score:.4f}")
+        if score < best_score:
+            best_score = score
+            best_max_depth = max_depth
+
+    return best_max_depth, best_score
+
+if __name__ == "__main__":
+    # 1. Load and Preprocess Dataset
+    dataset_path = "data/diabetes_dataset.csv"
+    X, y = load_dataset(dataset_path)
+    X, y = preprocess_data(X, y)
+
+    # 2. Perform K-Fold Cross-Validation
+    print("\nPerforming K-Fold Cross-Validation...")
+    model = GradientBoostingTree(n_estimators=100, learning_rate=0.1, max_depth=3)
+    cv_score = k_fold_cv(model, X, y, k=5, metric="mse")
+    print(f"K-Fold Cross-Validation MSE: {cv_score:.4f}")
+
+    # 3. Perform Bootstrapping
+    print("\nPerforming Bootstrapping...")
+    bootstrap_scores, mean_bootstrap_score = bootstrap(model, X, y, B=10, metric="mse")
+    print(f"Bootstrap Mean MSE: {mean_bootstrap_score:.4f}")
+    print(f"Bootstrap Scores: {bootstrap_scores}")
+
+    # 4. Perform Grid Search for max_depth
+    print("\nPerforming Grid Search for max_depth...")
+    max_depth_values = [2, 3, 5]
+    best_max_depth, best_cv_score = grid_search_max_depth(X, y, max_depth_values)
+    print(f"Best Max Depth: {best_max_depth}")
+    print(f"Best CV MSE: {best_cv_score:.4f}")
+
+    # 5. Train Final Model with Best Parameters
+    print("\nTraining final model with best parameters...")
+    final_model = GradientBoostingTree(
+        n_estimators=100, learning_rate=0.1, max_depth=best_max_depth
+    )
+    final_model.fit(X, y)
+    print("Final model training complete!")
diff --git a/model_selection/__init__.py b/model_selection/__init__.py
new file mode 100644
index 0000000..1d85e3c
--- /dev/null
+++ b/model_selection/__init__.py
@@ -0,0 +1,2 @@
+from .cross_validation import k_fold_cv
+from .bootstrapping import bootstrap
diff --git a/model_selection/bootstrapping.py b/model_selection/bootstrapping.py
new file mode 100644
index 0000000..dd950a8
--- /dev/null
+++ b/model_selection/bootstrapping.py
@@ -0,0 +1,23 @@
+import numpy as np
+
+def bootstrap(model, X, y, B=100, metric="mse"):
+    n = len(X)
+    scores = []
+
+    for _ in range(B):
+        indices = np.random.choice(np.arange(n), size=n, replace=True)
+        X_sample, y_sample = X[indices], y[indices]
+        
+        model.fit(X_sample, y_sample)
+        y_pred = model.predict(X_sample)
+        
+        if metric == "mse":
+            score = np.mean((y_sample - y_pred) ** 2)
+        elif metric == "mae":
+            score = np.mean(np.abs(y_sample - y_pred))
+        else:
+            raise ValueError("Unsupported metric.")
+        
+        scores.append(score)
+
+    return scores, np.mean(scores)
diff --git a/model_selection/cross_validation.py b/model_selection/cross_validation.py
new file mode 100644
index 0000000..114f951
--- /dev/null
+++ b/model_selection/cross_validation.py
@@ -0,0 +1,28 @@
+import numpy as np
+
+def k_fold_cv(model, X, y, k=5, metric="mse"):
+    n = len(X)
+    indices = np.arange(n)
+    np.random.shuffle(indices)
+    folds = np.array_split(indices, k)
+
+    scores = []
+    for i in range(k):
+        train_idx = np.concatenate([folds[j] for j in range(k) if j != i])
+        val_idx = folds[i]
+
+        X_train, X_val = X[train_idx], X[val_idx]
+        y_train, y_val = y[train_idx], y[val_idx]
+
+        model.fit(X_train, y_train)
+        y_pred = model.predict(X_val)
+
+        if metric == "mse":
+            score = np.mean((y_val - y_pred) ** 2)
+        elif metric == "mae":
+            score = np.mean(np.abs(y_val - y_pred))
+        else:
+            raise ValueError("Unsupported metric.")
+        scores.append(score)
+
+    return np.mean(scores)
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..7fea300
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,4 @@
+numpy>=1.21.0
+pandas>=1.3.0
+matplotlib>=3.4.0
+python >=3.11.7
\ No newline at end of file
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..1f96962
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,35 @@
+from setuptools import setup, find_packages
+
+# Read the contents of README.md for long description
+with open("README.md", "r", encoding="utf-8") as fh:
+    long_description = fh.read()
+
+setup(
+    name="gradient_boosting_model_selection",
+    version="1.0.0",
+    author="Shubham Dhanavade",
+    author_email="sdhanavade@hawk.iit.edu.com",
+    description="Implementation of Gradient Boosting and Model Selection (K-Fold CV, Bootstrapping)",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/yourusername/gradient_boosting_model_selection",  # Replace with your GitHub URL
+    packages=find_packages(where="src"),
+    package_dir={"": "src"},
+    install_requires=[
+        "numpy>=1.21.0",
+        "pandas>=1.3.0",
+        "matplotlib>=3.4.0",
+    ],
+    classifiers=[
+        "Programming Language :: Python :: 3",
+        "License :: OSI Approved :: MIT License",
+        "Operating System :: OS Independent",
+    ],
+    python_requires=">=3.7",
+    entry_points={
+        "console_scripts": [
+            "run_gradient_boosting=main_gradientboosting:main",
+            "run_model_selection=main_modelselection:main",
+        ]
+    },
+)
diff --git a/tests/test_boosting.py b/tests/test_boosting.py
new file mode 100644
index 0000000..7f97973
--- /dev/null
+++ b/tests/test_boosting.py
@@ -0,0 +1,89 @@
+import numpy as np
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../src')))
+
+from boosting.gradient_boosting import GradientBoostingTree
+
+def test_gradient_boosting_training():
+    ...
+
+
+
+
+def test_gradient_boosting_training():
+    """
+    Test training of the GradientBoostingTree model.
+    """
+    # Create a synthetic dataset
+    X = np.random.rand(100, 2)
+    y = 3 * X[:, 0] + 2 * X[:, 1] + np.random.normal(0, 0.1, 100)
+
+    # Initialize and train the model
+    model = GradientBoostingTree(n_estimators=10, learning_rate=0.1, max_depth=3)
+    model.fit(X, y)
+
+    # Verify that the model has stored estimators
+    assert len(model.models) == 10, "Number of estimators should match n_estimators."
+    print("GradientBoostingTree training test passed.")
+
+def test_gradient_boosting_prediction():
+    """
+    Test prediction of the GradientBoostingTree model.
+    """
+    # Create a synthetic dataset
+    X_train = np.random.rand(100, 2)
+    y_train = 4 * X_train[:, 0] + 3 * X_train[:, 1] + np.random.normal(0, 0.1, 100)
+    X_test = np.random.rand(20, 2)
+    y_test = 4 * X_test[:, 0] + 3 * X_test[:, 1] + np.random.normal(0, 0.1, 20)
+
+    # Initialize and train the model
+    model = GradientBoostingTree(n_estimators=20, learning_rate=0.1, max_depth=3)
+    model.fit(X_train, y_train)
+
+    # Make predictions
+    y_pred = model.predict(X_test)
+
+    # Verify predictions have the same length as test data
+    assert len(y_pred) == len(y_test), "Predictions should have the same length as test data."
+    # Verify predictions are finite
+    assert np.all(np.isfinite(y_pred)), "Predictions should not contain NaN or infinite values."
+    print("GradientBoostingTree prediction test passed.")
+
+def test_gradient_boosting_regression_performance():
+    """
+    Test the performance of the GradientBoostingTree model on a simple regression task.
+    """
+    # Create a synthetic dataset
+    X = np.random.rand(200, 1)
+    y = 5 * X[:, 0] + np.random.normal(0, 0.2, 200)
+
+    # Split data into training and testing sets
+    train_size = int(0.8 * len(X))
+    X_train, X_test = X[:train_size], X[train_size:]
+    y_train, y_test = y[:train_size], y[train_size:]
+
+    # Initialize and train the model
+    model = GradientBoostingTree(n_estimators=50, learning_rate=0.1, max_depth=3)
+    model.fit(X_train, y_train)
+
+    # Make predictions
+    y_pred = model.predict(X_test)
+
+    # Compute MSE
+    mse = np.mean((y_test - y_pred) ** 2)
+    print(f"GradientBoostingTree regression MSE: {mse:.4f}")
+
+    # Verify MSE is reasonably small
+    assert mse < 0.1, "MSE should be small for a simple linear relationship."
+    print("GradientBoostingTree regression performance test passed.")
+
+if __name__ == "__main__":
+    print("Testing GradientBoostingTree training...")
+    test_gradient_boosting_training()
+    
+    print("\nTesting GradientBoostingTree prediction...")
+    test_gradient_boosting_prediction()
+    
+    print("\nTesting GradientBoostingTree regression performance...")
+    test_gradient_boosting_regression_performance()
diff --git a/tests/test_model_selection.py b/tests/test_model_selection.py
new file mode 100644
index 0000000..9d92c99
--- /dev/null
+++ b/tests/test_model_selection.py
@@ -0,0 +1,55 @@
+
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../src')))
+import numpy as np
+from boosting.gradient_boosting import GradientBoostingTree
+
+def test_gradient_boosting_training():
+    ...
+
+from model_selection.cross_validation import k_fold_cv
+from model_selection.bootstrapping import bootstrap
+from boosting.gradient_boosting import GradientBoostingTree
+
+def test_k_fold_cv():
+    """
+    Test k-fold cross-validation.
+    """
+    # Create synthetic dataset
+    X = np.random.rand(100, 2)
+    y = 4 * X[:, 0] + 3 * X[:, 1] + np.random.normal(0, 0.1, size=100)
+
+    # Initialize the Gradient Boosting model
+    model = GradientBoostingTree(n_estimators=10, learning_rate=0.1, max_depth=3)
+
+    # Perform k-fold cross-validation
+    score = k_fold_cv(model, X, y, k=5, metric="mse")
+    
+    assert score > 0, "Cross-validation score should be greater than 0."
+    print(f"K-Fold Cross-Validation MSE: {score:.4f}")
+
+def test_bootstrap():
+    """
+    Test bootstrapping for model evaluation.
+    """
+    # Create synthetic dataset
+    X = np.random.rand(100, 2)
+    y = 4 * X[:, 0] + 3 * X[:, 1] + np.random.normal(0, 0.1, size=100)
+
+    # Initialize the Gradient Boosting model
+    model = GradientBoostingTree(n_estimators=10, learning_rate=0.1, max_depth=3)
+
+    # Perform bootstrapping
+    scores, mean_score = bootstrap(model, X, y, B=10, metric="mse")
+    
+    assert len(scores) == 10, "Number of bootstrap scores should match the number of iterations."
+    assert mean_score > 0, "Mean bootstrap score should be greater than 0."
+    print(f"Bootstrap Mean MSE: {mean_score:.4f}")
+
+if __name__ == "__main__":
+    print("Testing K-Fold Cross-Validation...")
+    test_k_fold_cv()
+    
+    print("\nTesting Bootstrapping...")
+    test_bootstrap()

From a9a79c4a1d3e690fd08ef0c20e9b6b7920e624e8 Mon Sep 17 00:00:00 2001
From: Sharanya Mishra <smishra25@hawk.iit.edu>
Date: Thu, 21 Nov 2024 23:26:54 -0600
Subject: [PATCH 5/7] second-last-commit

---
 Comparison-gbt.py                             |  104 +
 Comparison-modelSelection.py                  |  150 ++
 .../Boston_housing/Boston_modelSelection.jpeg |  Bin 0 -> 92747 bytes
 .../Bostondataset_graph-gbt.jpeg              |  Bin 0 -> 224029 bytes
 Output/Boston_housing/gbt-terminal-output.png |  Bin 0 -> 82822 bytes
 .../Comparison-gbt-terminal-output.png        |  Bin 0 -> 132355 bytes
 Output/Comparison/Comparison-gbt.png          |  Bin 0 -> 294354 bytes
 ...parison-model-selection-terminaloutput.png |  Bin 0 -> 391598 bytes
 Output/Comparison/Comparison-model.png        |  Bin 0 -> 235611 bytes
 .../Gradient_boostingGraph.png                |  Bin 0 -> 207064 bytes
 .../Output_gradientBoosting.png               |  Bin 0 -> 59149 bytes
 .../output_modelSelection.png                 |  Bin 0 -> 160622 bytes
 .../Graph_gradientBoosting.png                |  Bin 0 -> 242929 bytes
 .../Output_Model_selection.png                |  Bin 0 -> 135021 bytes
 .../Output_gradient_boosting.png              |  Bin 0 -> 94036 bytes
 data/Boston_housing.py                        |   35 +
 data/Dataset-highlycorrelated.py              |   28 +
 data/boston_housing.csv                       |  507 +++++
 data/diabetes_dataset.csv                     |  443 ----
 data/partially_correlated.py                  |   36 +
 data/partially_correlated_dataset.csv         | 2000 ++++++++---------
 data/test.csv                                 |   16 -
 data/train.csv                                |   16 -
 main_gradientboosting.py                      |   23 +-
 main_modelselection.py                        |    2 +-
 setup.py                                      |    2 +-
 26 files changed, 1875 insertions(+), 1487 deletions(-)
 create mode 100644 Comparison-gbt.py
 create mode 100644 Comparison-modelSelection.py
 create mode 100644 Output/Boston_housing/Boston_modelSelection.jpeg
 create mode 100644 Output/Boston_housing/Bostondataset_graph-gbt.jpeg
 create mode 100644 Output/Boston_housing/gbt-terminal-output.png
 create mode 100644 Output/Comparison/Comparison-gbt-terminal-output.png
 create mode 100644 Output/Comparison/Comparison-gbt.png
 create mode 100644 Output/Comparison/Comparison-model-selection-terminaloutput.png
 create mode 100644 Output/Comparison/Comparison-model.png
 create mode 100644 Output/Highly Correlated/Gradient_boostingGraph.png
 create mode 100644 Output/Highly Correlated/Output_gradientBoosting.png
 create mode 100644 Output/Highly Correlated/output_modelSelection.png
 create mode 100644 Output/Partially correlated/Graph_gradientBoosting.png
 create mode 100644 Output/Partially correlated/Output_Model_selection.png
 create mode 100644 Output/Partially correlated/Output_gradient_boosting.png
 create mode 100644 data/Boston_housing.py
 create mode 100644 data/Dataset-highlycorrelated.py
 create mode 100644 data/boston_housing.csv
 delete mode 100644 data/diabetes_dataset.csv
 create mode 100644 data/partially_correlated.py
 delete mode 100644 data/test.csv
 delete mode 100644 data/train.csv

diff --git a/Comparison-gbt.py b/Comparison-gbt.py
new file mode 100644
index 0000000..dd3632e
--- /dev/null
+++ b/Comparison-gbt.py
@@ -0,0 +1,104 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.ensemble import GradientBoostingRegressor
+from boosting.gradient_boosting import GradientBoostingTree
+
+def load_dataset(file_path):
+    """
+    Load a dataset from a CSV file.
+    """
+    try:
+        data = pd.read_csv(file_path)
+        if data.empty:
+            raise ValueError("Dataset is empty. Please provide a valid dataset.")
+        X = data.iloc[:, :-1].values
+        y = data.iloc[:, -1].values
+        return X, y
+    except Exception as e:
+        print(f"Error loading dataset: {e}")
+        exit()
+
+def preprocess_data(X, y):
+    """
+    Preprocess the dataset: scale features and return scaled X and y.
+    """
+    X_mean = np.mean(X, axis=0)
+    X_std = np.std(X, axis=0)
+    X_scaled = (X - X_mean) / X_std
+
+    y_mean = np.mean(y)
+    y_std = np.std(y)
+    y_scaled = (y - y_mean) / y_std
+
+    return X_scaled, y_scaled, y_mean, y_std
+
+def evaluate_model(y_true, y_pred):
+    """
+    Evaluate the model using MSE, MAE, and R² metrics.
+    """
+    mse = np.mean((y_true - y_pred) ** 2)
+    mae = np.mean(np.abs(y_true - y_pred))
+    r2 = 1 - (np.sum((y_true - y_pred) ** 2) / np.sum((y_true - np.mean(y_true)) ** 2))
+    return mse, mae, r2
+
+def plot_comparison(y_true, custom_pred, sklearn_pred):
+    """
+    Plot comparison of predictions from both models against true values.
+    """
+    plt.figure(figsize=(10, 6))
+    plt.scatter(y_true, custom_pred, alpha=0.7, label="Custom Model Predictions", color="blue")
+    plt.scatter(y_true, sklearn_pred, alpha=0.7, label="Sklearn Model Predictions", color="green")
+    plt.plot([min(y_true), max(y_true)], [min(y_true), max(y_true)], 'r--', label="Ideal Fit")
+    plt.xlabel("True Values")
+    plt.ylabel("Predicted Values")
+    plt.title("Comparison of Custom vs Sklearn Model Predictions")
+    plt.legend()
+    plt.show()
+
+if __name__ == "__main__":
+    # 1. Load Dataset
+    dataset_path = "data/boston_housing.csv"
+    X, y = load_dataset(dataset_path)
+
+    # 2. Preprocess Data
+    X, y, y_mean, y_std = preprocess_data(X, y)
+
+    # 3. Split Data into Training and Testing Sets
+    train_size = int(0.8 * len(X))
+    X_train, X_test = X[:train_size], X[train_size:]
+    y_train, y_test = y[:train_size], y[train_size:]
+
+    # 4. Train Custom Gradient Boosting Model
+    print("Training Custom Gradient Boosting Model...")
+    custom_model = GradientBoostingTree(n_estimators=100, learning_rate=0.1, max_depth=2)
+    custom_model.fit(X_train, y_train)
+    print("Custom Model training complete!")
+
+    # 5. Train Sklearn Gradient Boosting Model
+    print("Training Sklearn Gradient Boosting Model...")
+    sklearn_model = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=2, random_state=42)
+    sklearn_model.fit(X_train, y_train)
+    print("Sklearn Model training complete!")
+
+    # 6. Make Predictions
+    custom_pred = custom_model.predict(X_test) * y_std + y_mean
+    sklearn_pred = sklearn_model.predict(X_test) * y_std + y_mean
+    y_test_original = y_test * y_std + y_mean
+
+    # 7. Evaluate Both Models
+    print("\nCustom Model Evaluation:")
+    custom_mse, custom_mae, custom_r2 = evaluate_model(y_test_original, custom_pred)
+    print(f"Mean Squared Error (MSE): {custom_mse:.4f}")
+    print(f"Mean Absolute Error (MAE): {custom_mae:.4f}")
+    print(f"R² Score: {custom_r2:.4f}")
+
+    print("\nSklearn Model Evaluation:")
+    sklearn_mse, sklearn_mae, sklearn_r2 = evaluate_model(y_test_original, sklearn_pred)
+    print(f"Mean Squared Error (MSE): {sklearn_mse:.4f}")
+    print(f"Mean Absolute Error (MAE): {sklearn_mae:.4f}")
+    print(f"R² Score: {sklearn_r2:.4f}")
+
+    # 8. Plot Results
+    print("\nPlotting Comparison of Results...")
+    plot_comparison(y_test_original, custom_pred, sklearn_pred)
\ No newline at end of file
diff --git a/Comparison-modelSelection.py b/Comparison-modelSelection.py
new file mode 100644
index 0000000..4d95f1c
--- /dev/null
+++ b/Comparison-modelSelection.py
@@ -0,0 +1,150 @@
+import numpy as np
+import pandas as pd
+from boosting.gradient_boosting import GradientBoostingTree
+from model_selection.cross_validation import k_fold_cv
+from model_selection.bootstrapping import bootstrap
+from sklearn.ensemble import GradientBoostingRegressor
+
+def load_dataset(file_path):
+    """
+    Load dataset from a CSV file.
+    """
+    try:
+        data = pd.read_csv(file_path)
+        if data.empty:
+            raise ValueError("Dataset is empty. Please provide a valid dataset.")
+        X = data.iloc[:, :-1].values
+        y = data.iloc[:, -1].values
+        return X, y
+    except Exception as e:
+        print(f"Error loading dataset: {e}")
+        exit()
+
+def preprocess_data(X, y):
+    """
+    Preprocess the dataset: scale features and return scaled X and y.
+    """
+    X_mean = np.mean(X, axis=0)
+    X_std = np.std(X, axis=0)
+    X_scaled = (X - X_mean) / X_std
+
+    y_mean = np.mean(y)
+    y_std = np.std(y)
+    y_scaled = (y - y_mean) / y_std
+
+    return X_scaled, y_scaled, y_mean, y_std
+
+def grid_search_max_depth(X, y, max_depth_values, n_estimators=100, learning_rate=0.1, k=5):
+    """
+    Perform grid search to find the best max_depth using k-fold cross-validation.
+    """
+    best_score = float("inf")
+    best_max_depth = None
+
+    for max_depth in max_depth_values:
+        model = GradientBoostingTree(
+            n_estimators=n_estimators,
+            learning_rate=learning_rate,
+            max_depth=max_depth
+        )
+        score = k_fold_cv(model, X, y, k=k, metric="mse")
+        print(f"Max Depth: {max_depth}, CV MSE: {score:.4f}")
+        if score < best_score:
+            best_score = score
+            best_max_depth = max_depth
+
+    return best_max_depth, best_score
+
+def evaluate_model(y_true, y_pred):
+    """
+    Evaluate the model using MSE, MAE, and R² metrics.
+    """
+    mse = np.mean((y_true - y_pred) ** 2)
+    mae = np.mean(np.abs(y_true - y_pred))
+    r2 = 1 - (np.sum((y_true - y_pred) ** 2) / np.sum((y_true - np.mean(y_true)) ** 2))
+    return mse, mae, r2
+
+def plot_comparison(y_true, custom_pred, sklearn_pred):
+    """
+    Plot comparison of predictions from both models against true values.
+    """
+    import matplotlib.pyplot as plt
+    plt.figure(figsize=(10, 6))
+    plt.scatter(y_true, custom_pred, alpha=0.7, label="Custom Model Predictions", color="blue")
+    plt.scatter(y_true, sklearn_pred, alpha=0.7, label="Sklearn Model Predictions", color="green")
+    plt.plot([min(y_true), max(y_true)], [min(y_true), max(y_true)], 'r--', label="Ideal Fit")
+    plt.xlabel("True Values")
+    plt.ylabel("Predicted Values")
+    plt.title("Comparison of Custom vs Sklearn Model Predictions")
+    plt.legend()
+    plt.show()
+
+if __name__ == "__main__":
+    # 1. Load and Preprocess Dataset
+    dataset_path = "data/highly_correlated_dataset.csv"
+    X, y = load_dataset(dataset_path)
+    X, y, y_mean, y_std = preprocess_data(X, y)
+
+    # 2. Perform K-Fold Cross-Validation for Custom Model
+    print("\nPerforming K-Fold Cross-Validation for Custom Model...")
+    custom_model = GradientBoostingTree(n_estimators=100, learning_rate=0.1, max_depth=3)
+    custom_cv_score = k_fold_cv(custom_model, X, y, k=5, metric="mse")
+    print(f"Custom Model K-Fold Cross-Validation MSE: {custom_cv_score:.4f}")
+
+    # 3. Perform K-Fold Cross-Validation for Sklearn Model
+    print("\nPerforming K-Fold Cross-Validation for Sklearn Model...")
+    sklearn_model = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)
+    sklearn_cv_score = k_fold_cv(sklearn_model, X, y, k=5, metric="mse")
+    print(f"Sklearn Model K-Fold Cross-Validation MSE: {sklearn_cv_score:.4f}")
+
+    # 4. Perform Bootstrapping for Custom Model
+    print("\nPerforming Bootstrapping for Custom Model...")
+    custom_bootstrap_scores, custom_mean_bootstrap_score = bootstrap(custom_model, X, y, B=10, metric="mse")
+    print(f"Custom Model Bootstrap Mean MSE: {custom_mean_bootstrap_score:.4f}")
+    print(f"Custom Model Bootstrap Scores: {custom_bootstrap_scores}")
+
+    # 5. Perform Bootstrapping for Sklearn Model
+    print("\nPerforming Bootstrapping for Sklearn Model...")
+    sklearn_bootstrap_scores, sklearn_mean_bootstrap_score = bootstrap(sklearn_model, X, y, B=10, metric="mse")
+    print(f"Sklearn Model Bootstrap Mean MSE: {sklearn_mean_bootstrap_score:.4f}")
+    print(f"Sklearn Model Bootstrap Scores: {sklearn_bootstrap_scores}")
+
+    # 6. Perform Grid Search for Best Max Depth for Custom Model
+    print("\nPerforming Grid Search for Best Max Depth for Custom Model...")
+    max_depth_values = [2, 3, 5]
+    best_max_depth, best_cv_score = grid_search_max_depth(X, y, max_depth_values)
+    print(f"Best Max Depth: {best_max_depth}")
+    print(f"Best Custom Model CV MSE: {best_cv_score:.4f}")
+
+    # 7. Train Final Custom Model with Best Parameters
+    print("\nTraining Final Custom Model with Best Parameters...")
+    final_custom_model = GradientBoostingTree(
+        n_estimators=100, learning_rate=0.1, max_depth=best_max_depth
+    )
+    final_custom_model.fit(X, y)
+    print("Final Custom Model training complete!")
+
+    # 8. Train Final Sklearn Model
+    print("\nTraining Final Sklearn Model...")
+    final_sklearn_model = GradientBoostingRegressor(
+        n_estimators=100, learning_rate=0.1, max_depth=best_max_depth, random_state=42
+    )
+    final_sklearn_model.fit(X, y)
+    print("Final Sklearn Model training complete!")
+
+    # 9. Make Predictions for Final Models
+    custom_pred = final_custom_model.predict(X) * y_std + y_mean
+    sklearn_pred = final_sklearn_model.predict(X) * y_std + y_mean
+    y_original = y * y_std + y_mean
+
+    # 10. Evaluate Both Models
+    print("\nEvaluating Both Models...")
+    custom_mse, custom_mae, custom_r2 = evaluate_model(y_original, custom_pred)
+    print(f"Custom Model - MSE: {custom_mse:.4f}, MAE: {custom_mae:.4f}, R²: {custom_r2:.4f}")
+
+    sklearn_mse, sklearn_mae, sklearn_r2 = evaluate_model(y_original, sklearn_pred)
+    print(f"Sklearn Model - MSE: {sklearn_mse:.4f}, MAE: {sklearn_mae:.4f}, R²: {sklearn_r2:.4f}")
+
+    # 11. Plot Comparison of Both Models
+    print("\nPlotting Comparison of Both Models...")
+    plot_comparison(y_original, custom_pred, sklearn_pred)
\ No newline at end of file
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diff --git a/Output/Comparison/Comparison-model.png b/Output/Comparison/Comparison-model.png
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diff --git a/data/Boston_housing.py b/data/Boston_housing.py
new file mode 100644
index 0000000..6384a4b
--- /dev/null
+++ b/data/Boston_housing.py
@@ -0,0 +1,35 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.metrics import r2_score, mean_squared_error
+
+# Load the dataset from the URL
+url = "https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv"
+df = pd.read_csv(url)
+
+# Save the dataset to a local CSV file
+df.to_csv("data/boston_housing.csv", index=False)
+
+# Features (X) and target (y)
+X = df.drop(columns=["medv"])  # 'medv' is the target variable (median value of homes)
+y = df["medv"]
+
+# Split the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Initialize the Gradient Boosting Regressor
+gbr = GradientBoostingRegressor(random_state=42)
+
+# Fit the model to the training data
+gbr.fit(X_train, y_train)
+
+# Make predictions on the test set
+y_pred = gbr.predict(X_test)
+
+# Calculate R² and Mean Squared Error (MSE)
+r2 = r2_score(y_test, y_pred)
+mse = mean_squared_error(y_test, y_pred)
+
+# Print the results
+print(f"R²: {r2:.4f}")
+print(f"MSE: {mse:.4f}")
\ No newline at end of file
diff --git a/data/Dataset-highlycorrelated.py b/data/Dataset-highlycorrelated.py
new file mode 100644
index 0000000..64792c5
--- /dev/null
+++ b/data/Dataset-highlycorrelated.py
@@ -0,0 +1,28 @@
+import numpy as np
+import pandas as pd
+
+# Set random seed for reproducibility
+np.random.seed(42)
+
+# Number of samples and features
+n_samples = 1000
+n_features = 5
+
+# Generate a random base feature
+base_feature = np.random.normal(0, 1, n_samples)
+
+# Create a highly correlated dataset
+correlated_data = np.array([base_feature + np.random.normal(0, 0.1, n_samples) for _ in range(n_features)]).T
+
+# Convert to a DataFrame
+columns = [f"Feature_{i+1}" for i in range(n_features)]
+df = pd.DataFrame(correlated_data, columns=columns)
+
+# Add a target variable with a high correlation to one of the features
+df['Target'] = df['Feature_1'] * 2 + np.random.normal(0, 0.1, n_samples)
+
+# Save to CSV
+output_file = "highly_correlated_dataset.csv"
+df.to_csv(output_file, index=False)
+
+print(f"Dataset saved to {output_file}")
diff --git a/data/boston_housing.csv b/data/boston_housing.csv
new file mode 100644
index 0000000..4509d4e
--- /dev/null
+++ b/data/boston_housing.csv
@@ -0,0 +1,507 @@
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diff --git a/data/diabetes_dataset.csv b/data/diabetes_dataset.csv
deleted file mode 100644
index 70f77ed..0000000
--- a/data/diabetes_dataset.csv
+++ /dev/null
@@ -1,443 +0,0 @@
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diff --git a/data/partially_correlated.py b/data/partially_correlated.py
new file mode 100644
index 0000000..778092c
--- /dev/null
+++ b/data/partially_correlated.py
@@ -0,0 +1,36 @@
+import numpy as np
+import pandas as pd
+# Adjust the correlation matrix for partial correlation
+partial_correlation_matrix = np.array([
+    [1.0, 0.3, 0.2, 0.1, 0.0],
+    [0.3, 1.0, 0.4, 0.2, 0.1],
+    [0.2, 0.4, 1.0, 0.3, 0.2],
+    [0.1, 0.2, 0.3, 1.0, 0.4],
+    [0.0, 0.1, 0.2, 0.4, 1.0]
+])
+mean = [0, 0, 0, 0, 0]
+# Set a random seed for reproducibility
+np.random.seed(42)
+
+# Number of samples
+n_samples = 1000
+
+# Generate a multivariate normal distribution for partially correlated predictors
+X_partial = np.random.multivariate_normal(mean, partial_correlation_matrix, size=n_samples)
+
+# Create a DataFrame for predictors
+df_partial = pd.DataFrame(X_partial, columns=['X1', 'X2', 'X3', 'X4', 'X5'])
+
+# Generate the target variable y with a weaker non-linear relationship and noise
+noise_partial = np.random.normal(0, 0.5, n_samples)
+df_partial['y'] = (
+    1.5 * df_partial['X1'] +
+    0.5 * np.log(np.abs(df_partial['X2']) + 1) -
+    0.8 * df_partial['X3'] +
+    0.2 * df_partial['X4']**2 +
+    noise_partial
+)
+
+# Save to CSV
+file_path_partial = "data/partially_correlated_dataset.csv"
+df_partial.to_csv(file_path_partial, index=False)
diff --git a/data/partially_correlated_dataset.csv b/data/partially_correlated_dataset.csv
index b6b1b89..26f3dc5 100644
--- a/data/partially_correlated_dataset.csv
+++ b/data/partially_correlated_dataset.csv
@@ -1,1001 +1,1001 @@
 X1,X2,X3,X4,X5,y
-0.7885840211298774,-0.060190684756781694,0.5364693470877704,1.3473647630714831,0.9937462957492054,0.5569134629235272
-0.5217609784924461,-1.0644367675482949,-1.24141594413323,-0.9622657325126173,0.808705566157958,2.539819211007283
-0.04313129836207018,0.8715550491314438,-0.2792001470946431,-0.509071871714412,0.09451138392172218,1.2086493167270447
-1.4273337685008667,1.8839738159363935,0.5838143792608163,-0.03130453002964415,-1.2793594385577143,2.5813222011159773
--1.4070865818924339,0.4600857983087496,1.1838050810739371,-0.7406922816912319,-0.9816931623705527,-2.216462564058519
-1.730917391961765,-0.9947898287474377,0.09026953402598786,0.3434059835841386,-1.2065477448749709,2.7870705769317405
-0.4277494337046544,0.15886883740785626,2.208356674669085,1.0869476448297029,0.9283029670205303,-1.0483574421488675
-1.4056207561661576,0.4741936839972133,-1.2547735577628039,-0.23431983479928695,-0.03546189019218724,2.9488360540418355
--1.1139295985906377,0.39326160314474656,0.4682947613234562,-1.3458143669167595,-1.9517020847298219,-1.5454114404382226
--0.5967161836533993,-0.13076554340016272,1.6822146619638876,0.3186715220357541,-0.5394479050269065,-2.1983836575546993
-0.126519891866091,-0.1524790922547537,-1.3891948122294855,-1.5786010643397799,-0.9929927002819804,1.9738284431759638
--1.503172246603709,0.5028332654155008,-1.5018930087029385,-2.158893898456943,0.020589033316865592,0.017947966546219163
-0.5097328460194167,1.1041124196319652,-0.7376852925773032,-1.2952791802577899,0.6344711226332668,1.995430131605476
--1.0393546313172148,0.2482390129586074,0.6096858020637677,-0.1796207015629743,0.9548941672918949,-2.3178080116249173
--1.568906538972897,-0.1113216427659777,-1.3953898913488025,0.6759296305669447,0.4771492615609815,-0.6220872274096727
-0.008982233867293831,-0.20421609068250593,-0.28947525618647135,2.3068612876278385,0.5302021998566774,1.4405487909899446
--1.2840361179883866,-0.5602721669100417,-0.5333118146611885,-1.4688880539864477,-1.202710107815628,-0.9326701333614495
-0.9502352315843243,-0.25489213056613674,-0.5275984734812162,0.4808108842506722,0.837774008888892,1.6334776293944169
--0.150941958386483,-0.7926557134349865,-0.43721935750728846,-0.10129304128698337,-0.8314419282508182,-0.2486928711575504
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+-0.24052521501721488,1.3316225641752495,1.246764846252839,1.0470801855452019,0.4992628555500382,-0.5624473731529672
+-0.30444090849648087,-0.4057025621805524,0.2743265500679224,0.3267526509120286,1.1457311116435667,-0.9836539204380477
+-1.3844177662892008,0.5584411756569226,-0.031042169993033983,1.050538806229364,0.04792716715660839,-1.3498270103806544
+0.2241801682925559,-2.6927783877753706,-0.4341983421977222,-0.4214091047357939,-1.1714294669073606,1.8040997368405982
+1.703143043034559,0.7723055685097312,2.132409791058307,-0.6903650189526128,0.45756692232562135,1.3159828024000435
+0.8101121797440188,1.4330590474426075,0.8904692416960767,0.6194579831931899,-0.19661402922886045,1.6004372329302519
+-1.3003273578986843,-0.1325335764723798,-2.308631684198485,-0.23429684883187635,-1.1584562363929654,-0.639079970545811
+1.512846557299273,1.604433885672696,1.72947796871516,0.6248764766595661,1.0851317483182312,1.676364460873434
+-1.4473824303881062,1.1390722543260812,1.7067383610666849,-1.452610555283151,-0.5517885884528912,-3.3194030980607203
diff --git a/data/test.csv b/data/test.csv
deleted file mode 100644
index d5350e3..0000000
--- a/data/test.csv
+++ /dev/null
@@ -1,16 +0,0 @@
-Feature_1,Feature_2,Feature_3,Feature_4,Feature_5,Target
-1.2,2.4,3.0,4.7,5.2,10.5
-2.5,1.9,3.1,4.0,4.8,10.0
-0.7,1.5,1.8,2.4,3.3,6.8
-3.5,3.3,4.2,5.3,6.5,15.4
-4.7,4.9,5.3,6.5,7.7,19.8
-5.2,5.7,6.4,7.2,8.4,22.0
-6.2,6.4,7.5,8.3,9.4,25.5
-2.3,2.2,3.2,4.2,5.2,11.5
-1.6,1.9,2.3,3.3,4.2,8.8
-3.3,3.4,4.3,5.3,6.2,14.8
-4.2,4.4,5.2,6.2,7.2,18.0
-5.3,5.2,6.5,7.3,8.3,21.6
-6.3,6.6,7.7,8.4,9.6,25.8
-7.2,7.4,8.2,9.2,10.4,29.1
-1.3,1.4,1.5,1.7,2.1,5.4
diff --git a/data/train.csv b/data/train.csv
deleted file mode 100644
index 47caf79..0000000
--- a/data/train.csv
+++ /dev/null
@@ -1,16 +0,0 @@
-Feature_1,Feature_2,Feature_3,Feature_4,Feature_5,Target
-1.1,2.2,3.1,4.5,5.1,10.2
-2.0,1.8,2.9,3.8,4.5,9.5
-0.5,1.2,1.5,2.2,3.1,6.3
-3.2,3.1,4.0,5.0,6.1,14.8
-4.5,4.8,5.1,6.3,7.5,19.2
-5.1,5.5,6.0,7.0,8.0,21.5
-6.0,6.2,7.3,8.1,9.2,24.6
-2.1,2.0,3.0,4.0,5.0,11.0
-1.5,1.8,2.2,3.1,4.0,8.5
-3.0,3.2,4.1,5.0,6.0,14.0
-4.0,4.2,5.0,6.0,7.0,17.5
-5.0,5.1,6.2,7.1,8.2,21.1
-6.1,6.5,7.5,8.3,9.3,25.2
-7.0,7.3,8.0,9.0,10.1,28.7
-1.0,1.2,1.3,1.5,2.0,5.1
diff --git a/main_gradientboosting.py b/main_gradientboosting.py
index e2b3ece..9afc11a 100644
--- a/main_gradientboosting.py
+++ b/main_gradientboosting.py
@@ -30,7 +30,7 @@ def preprocess_data(X, y):
     y_std = np.std(y)
     y_scaled = (y - y_mean) / y_std
 
-    return X_scaled, y_scaled
+    return X_scaled, y_scaled, y_mean, y_std
 
 def evaluate_model(y_true, y_pred):
     """
@@ -56,11 +56,11 @@ def plot_results(y_true, y_pred):
 
 if __name__ == "__main__":
     # 1. Load Dataset
-    dataset_path = "data/diabetes_dataset.csv"
+    dataset_path = "data/boston_housing.csv"
     X, y = load_dataset(dataset_path)
 
     # 2. Preprocess Data
-    X, y = preprocess_data(X, y)
+    X, y, y_mean, y_std = preprocess_data(X, y)
 
     # 3. Split Data into Training and Testing Sets
     train_size = int(0.8 * len(X))
@@ -69,20 +69,23 @@ def plot_results(y_true, y_pred):
 
     # 4. Train Gradient Boosting Model
     print("Training Gradient Boosting Model...")
-    model = GradientBoostingTree(n_estimators=100, learning_rate=0.1, max_depth=3)
+    model = GradientBoostingTree(n_estimators=100, learning_rate=0.1, max_depth=2)
     model.fit(X_train, y_train)
     print("Model training complete!")
 
-    # 5. Make Predictions
-    y_pred = model.predict(X_test)
+    # 5. Make Predictions (scaled)
+    y_pred_scaled = model.predict(X_test)
 
-    # 6. Evaluate the Model
-    mse, mae, r2 = evaluate_model(y_test, y_pred)
+    # 6. Scale back predictions to original y scale
+    y_pred = y_pred_scaled * y_std + y_mean
+
+    # 7. Evaluate the Model
+    mse, mae, r2 = evaluate_model(y_test * y_std + y_mean, y_pred)  # Scale back y_test to original scale
     print("\nModel Evaluation:")
     print(f"Mean Squared Error (MSE): {mse:.4f}")
     print(f"Mean Absolute Error (MAE): {mae:.4f}")
     print(f"R² Score: {r2:.4f}")
 
-    # 7. Plot Results
+    # 8. Plot Results
     print("\nPlotting Actual vs. Predicted Results...")
-    plot_results(y_test, y_pred)
+    plot_results(y_test * y_std + y_mean, y_pred)  # Scale back y_test to original scale
diff --git a/main_modelselection.py b/main_modelselection.py
index 35794e1..656b997 100644
--- a/main_modelselection.py
+++ b/main_modelselection.py
@@ -56,7 +56,7 @@ def grid_search_max_depth(X, y, max_depth_values, n_estimators=100, learning_rat
 
 if __name__ == "__main__":
     # 1. Load and Preprocess Dataset
-    dataset_path = "data/diabetes_dataset.csv"
+    dataset_path = "data/boston_housing.csv"
     X, y = load_dataset(dataset_path)
     X, y = preprocess_data(X, y)
 
diff --git a/setup.py b/setup.py
index 1f96962..3c05498 100644
--- a/setup.py
+++ b/setup.py
@@ -12,7 +12,7 @@
     description="Implementation of Gradient Boosting and Model Selection (K-Fold CV, Bootstrapping)",
     long_description=long_description,
     long_description_content_type="text/markdown",
-    url="https://github.com/yourusername/gradient_boosting_model_selection",  # Replace with your GitHub URL
+    url="https://github.com/mishrasharanya/CS584_Project2",  # Replace with your GitHub URL
     packages=find_packages(where="src"),
     package_dir={"": "src"},
     install_requires=[

From 742d1dbed2a5c473baf16efa7bca7c967a27a3dd Mon Sep 17 00:00:00 2001
From: Sharanya Mishra <smishra25@hawk.iit.edu>
Date: Thu, 21 Nov 2024 23:44:24 -0600
Subject: [PATCH 6/7] final-commit

---
 README.md | 1169 +++++++++++------------------------------------------
 1 file changed, 229 insertions(+), 940 deletions(-)

diff --git a/README.md b/README.md
index a1ba43b..902fe3a 100644
--- a/README.md
+++ b/README.md
@@ -1,668 +1,300 @@
 ***Gradient Boosting Trees and Model Selection***
 
-***GROUP GUARDIANS OF IIT***
-
+***GROUP NAME: GUARDIANS OF IIT***
 
 ***TEAMMEMBERS***
-##### A20584318 -ANSH KAUSHIK
+##### A20584318 - ANSH KAUSHIK
 ##### A20593046 - ARUNESHWARAN SIVAKUMAR
-##### A20541092 - SHUBHAM BAJIRAO DHANAVADE 
 ##### A20588339 - HARISH NAMASIVAYAM MUTHUSWAMY
 ##### A20579993 - SHARANYA MISHRA
+##### A20541092 - SHUBHAM BAJIRAO DHANAVADE 
 
-***CONTRIBUTION OF EACH TEAMMATE***w
-
-We, as a team of five, collaboratively worked on the implementation, testing, and documentation of the Gradient Boosting Tree model and the associated model selection methods. Each team member contributed equally, taking responsibility for specific aspects of the project to ensure its successful completion. Below is a detailed breakdown of individual contributions:
-
-***Acknowledgment of Equal Contribution***
-All team members contributed equally to discussions, planning, and brainstorming sessions, ensuring a collaborative and cohesive effort throughout the project. By dividing the work across implementation, testing, documentation, and visualization, we ensured every team member had an active and significant role
-
-1. **Member 1: SHUBHAM BAJIRAO DHANAVADE** 
-Designed and implemented the core Gradient Boosting Tree model, including the logic for residual calculation and iterative tree building.
-Developed the Decision Tree Regressor used as the weak learner within the boosting algorithm.
-Conducted initial tests on synthetic datasets to validate the functionality of the fit and predict methods.
-2. **Member 2: SHARANYA MISHRA**
-Focused on implementing K-fold cross-validation for model evaluation and ensuring its compatibility with the Gradient Boosting Tree model.
-Implemented the bootstrapping method to evaluate model stability and variability.
-Developed testing scripts (test_boosting.py and test_model_selection.py) to validate key functionalities.
-3. **Member 3: HARISH NAMASIVAYAM MUTHUSWAMY**
-Designed and executed the Grid Search functionality for hyperparameter tuning, ensuring efficient search and evaluation.
-Implemented strategies for handling imbalanced datasets, including stratified K-fold cross-validation and weighted bootstrapping.
-Analyzed results from grid search and cross-validation, ensuring the optimal selection of hyperparameters.
-4. **Member 4: ARUNESHWARAN SIVAKUMAR**
-Created interactive Jupyter notebooks for demonstrating the Gradient Boosting Tree implementation (boosting_example.ipynb) and model selection methods (model_selection_example.ipynb).
-Conducted evaluations on real-world datasets such as diabetes.csv and partially_correlated_dataset.csv.
-Visualized key metrics and results, including MSE, MAE, R², and Actual vs. Predicted plots, for clear presentation.
-5. **Member 5: ANSH KAUSHIK**
-Managed the project structure, ensuring all modules were organized correctly for reproducibility.
-Wrote the README documentation, including project overview, implementation details, usage examples, challenges, and future improvements. 
-Debugged and resolved errors during testing and execution, ensuring smooth integration of all components.
-
-
-***Gradient BOOSTING QUESTIONS***
-1. What Does the Model You Have Implemented Do and When Should It Be Used?
-The Implemented Gradient Boosting Tree Model
-
-**What It Does:**
-
-The Gradient Boosting Tree model is a supervised learning algorithm designed specifically for regression tasks. It combines multiple weak learners, typically shallow decision trees, into a single strong predictive model. This is achieved by iteratively refining the predictions of the model: each tree is trained on the residuals (errors) of the previous model's predictions. By doing so, the model learns to correct its mistakes step by step. The final prediction is the sum of the predictions of all trees, scaled by a learning rate to control the contribution of each tree. This sequential learning process allows the model to improve accuracy significantly and adapt to complex patterns in the data.
-
-**When to Use:**
-
-The Gradient Boosting Tree model is most suitable for regression tasks where the relationships between the features and the target variable are non-linear or involve complex interactions between features. It is particularly effective for tabular datasets with structured features, where it often outperforms other algorithms. The model is ideal when high predictive accuracy is a priority, such as in fields like finance, healthcare, and marketing analytics. However, as it is not inherently interpretable, it is better suited for scenarios where model performance takes precedence over interpretability. Additionally, with proper hyperparameter tuning (e.g., learning rate, tree depth, number of estimators), the model can be made robust against overfitting, making it a versatile choice for a wide range of regression problems.
-
-2. How Did You Test Your Model to Determine if It Is Working Reasonably Correctly?
-The implemented Gradient Boosting Tree model was rigorously tested using a combination of synthetic datasets, real-world datasets, and various evaluation techniques to ensure its correctness, reliability, and generalization performance.
-
-**Datasets Used:**
-**Synthetic Datasets:**
-
-Small, structured datasets (e.g., train.csv and test.csv) were used to test the basic functionality of the model.
-These datasets allowed for manual verification of the model’s predictions and residual patterns, ensuring the logic was implemented correctly.
-Real-World Datasets:
-
-The model was evaluated on realistic datasets such as:
-diabetes.csv: A common benchmark dataset for regression tasks.
-partially_correlated_dataset.csv: Designed to test the model’s handling of feature correlations.
-These datasets provided insights into the model’s behavior in practical scenarios and its ability to generalize.
-Metrics Used for Evaluation:
-Mean Squared Error (MSE):
-
-Captures the average squared error between the predicted and actual target values.
-Helps in assessing the model's overall accuracy.
-Mean Absolute Error (MAE):
-
-Measures the average magnitude of the errors in predictions without considering their direction.
-Useful for understanding prediction deviations in an interpretable unit.
-R² Score:
-
-Quantifies the proportion of variance in the target variable explained by the model.
-Indicates the model's goodness of fit, with a value closer to 1 signifying better performance.
-Testing Methods:
-Unit Tests:
-
-Validated the correctness of the fit and predict methods using predefined datasets in test_boosting.py.
-Ensured that the model’s predictions align with expected outcomes based on the residual patterns.
-Cross-Validation:
-
-Implemented k-fold cross-validation to assess the model’s generalization performance across multiple splits of the dataset.
-This method ensures the model performs consistently on unseen data.
-Comparison with Ground Truth:
-
-Directly compared the model's predictions against the actual target values in the datasets.
-This step verified the model's ability to minimize errors and predict accurately.
-Example Test Case:
-The following code demonstrates how the model was tested on a synthetic dataset:
-
-python
-Copy code
-# Train the Gradient Boosting Tree model on synthetic data
-model = GradientBoostingTree(n_estimators=100, learning_rate=0.1, max_depth=3)
-model.fit(X_train, y_train)
-
-# Make predictions on the test set
-y_pred = model.predict(X_test)
-
-# Evaluate the model using MSE
-from sklearn.metrics import mean_squared_error
-mse = mean_squared_error(y_test, y_pred)
-print(f"Mean Squared Error (MSE): {mse:.4f}")
-What This Test Ensures:
-
-The model correctly minimizes the residuals by iteratively training weak learners.
-The MSE metric provides a quantitative measure of the prediction accuracy.
-The predictions (y_pred) align closely with the ground truth (y_test).
-Key Takeaways:
-The testing process combined small-scale controlled experiments with large-scale practical datasets to evaluate robustness.
-Metrics like MSE, MAE, and R² ensured both quantitative and qualitative assessments of the model's performance.
-Cross-validation verified the model’s ability to generalize, while unit tests ensured reliability and correctness of the implementation.
-
-3. What Parameters Have You Exposed to Users of Your Implementation to Tune Performance?
-Parameters for Optimizing Model Performance
-The Gradient Boosting Tree model provides several parameters that can be tuned to optimize its performance. These parameters control the behavior of the model, allowing it to adapt to different datasets and achieve a balance between accuracy and generalization.
-
-**Key Parameters**
-Parameter	Description	Default Value
-n_estimators	The number of boosting iterations or trees in the ensemble.	100
-learning_rate	A scaling factor that shrinks the contribution of each tree.	0.1
-max_depth	The maximum depth of each individual decision tree.	3
-Detailed Explanation of Parameters
-n_estimators (Number of Trees):
-
-**What It Does: Determines how many decision trees will be sequentially added to the ensemble.**
-Impact:
-Increasing n_estimators allows the model to correct residuals over more iterations, improving performance.
-However, too many estimators may lead to overfitting, especially if the learning rate is not appropriately reduced.
-Recommendation: Start with a moderate value (e.g., 100) and adjust based on validation performance.
-learning_rate (Step Size):
-
-**What It Does: Scales the contribution of each tree to the final prediction.**
-Impact:
-A smaller learning_rate forces the model to make smaller adjustments, which improves generalization.
-Smaller values require more trees (n_estimators) to achieve the same performance.
-Larger values may lead to overfitting or instability in predictions.
-Recommendation: Use values in the range of 0.01 to 0.1 and adjust alongside n_estimators.
-max_depth (Tree Depth):
-
-**What It Does: Limits the depth of each decision tree in the ensemble.**
-Impact:
-Deeper trees capture more complex patterns in the data but may overfit to noise.
-Shallower trees generalize better but may fail to model complex relationships.
-Recommendation: Start with a small value (e.g., 3) and increase only if the model underfits.
-Basic Usage Example
-Here’s an example of how to initialize and use the Gradient Boosting Tree model with custom parameters:
-from boosting.gradient_boosting import GradientBoostingTree
-
-# Initialize the model with custom parameters
-model = GradientBoostingTree(n_estimators=200, learning_rate=0.05, max_depth=4)
-
-# Train the model on the training dataset
-model.fit(X_train, y_train)
-
-# Make predictions on the test dataset
-y_pred = model.predict(X_test)
-
-# Evaluate model performance
-from sklearn.metrics import mean_squared_error
-mse = mean_squared_error(y_test, y_pred)
-print(f"Mean Squared Error (MSE): {mse:.4f}")
-How Parameters Affect Performance
-n_estimators:
-
-Effect: Controls the number of trees added to the ensemble.
-Trade-Off:
-Higher Values: Allow the model to learn finer details, improving performance but risking overfitting.
-Lower Values: Reduce computational cost and overfitting risk but may lead to underfitting.
-learning_rate:
-
-Effect: Determines how much each tree contributes to the final prediction.
-Trade-Off:
-Smaller Values: Enhance generalization but require more trees to converge, increasing training time.
-Larger Values: Allow faster convergence but may lead to overfitting.
-max_depth:
-
-Effect: Limits the complexity of individual trees.
-Trade-Off:
-Higher Values: Capture complex patterns but risk memorizing noise in the training data.
-Lower Values: Generalize better but may miss important interactions.
-Recommended Approach for Parameter Tuning
-Start with Default Values:
-
-n_estimators = 100
-learning_rate = 0.1
-max_depth = 3
-Tune Parameters Sequentially:
-
-Step 1: Increase n_estimators while monitoring validation performance to prevent overfitting.
-Step 2: Reduce learning_rate to improve generalization and compensate by increasing n_estimators.
-Step 3: Adjust max_depth to balance complexity and generalization.
-Use Cross-Validation:
-
-Evaluate different parameter combinations using k-fold cross-validation to ensure robust performance across splits.
-Grid Search or Randomized Search:
-
-Automate parameter tuning with grid search or randomized search to find the optimal combination.
-By carefully adjusting these parameters, you can maximize the model's performance while minimizing the risk of overfitting or underfitting. This flexibility makes Gradient Boosting Trees a powerful tool for a wide range of regression tasks.
-
-
-4. Are There Specific Inputs That Your Implementation Has Trouble With?
-
-Challenges and Future Improvements
-Challenges
-High-Dimensional Sparse Data:
-
-Challenge: When the dataset contains many features (high-dimensional data) or sparse representations (e.g., many features with zero or near-zero values), the performance of Gradient Boosting Trees may degrade. This happens because the algorithm must search through a large number of potential splits, increasing computation time and the risk of overfitting to irrelevant features.
-Mitigation:
-Preprocess the data using dimensionality reduction techniques like Principal Component Analysis (PCA) or Singular Value Decomposition (SVD) to reduce the number of features.
-Alternatively, use feature selection methods to retain only the most relevant features, improving both training time and predictive performance.
-Outliers:
-
-Challenge: Gradient Boosting is sensitive to noise and outliers in the data, as the trees attempt to fit all residuals, including those caused by extreme values. This can lead to overfitting, where the model places undue importance on outliers.
-Mitigation:
-Use robust preprocessing techniques to handle outliers:
-Outlier Removal: Identify and remove extreme values using statistical methods like the Interquartile Range (IQR) rule or Z-score thresholds.
-Normalization/Standardization: Scale features to a consistent range, making the model less sensitive to outliers.
-Consider using robust loss functions, such as Huber loss, which reduces the influence of extreme values.
-Highly Correlated Features:
-
-Challenge: When features are highly correlated, Gradient Boosting Trees may split on multiple redundant features unnecessarily, increasing training time and complexity without improving predictive performance.
-Mitigation:
-Perform feature selection to identify and retain only the most informative features.
-Use techniques like correlation heatmaps or mutual information scores to detect and eliminate redundant features.
-Dimensionality reduction methods (e.g., PCA) can also combine correlated features into a smaller set of principal components.
-Future Improvements
-Regularization:
-
-What to Improve: Add L1 (Lasso) and L2 (Ridge) regularization penalties to the model to prevent overfitting.
-Why It Helps:
-Regularization discourages the model from creating overly complex trees, improving generalization.
-It reduces the impact of irrelevant or noisy features.
-Implementation:
-Incorporate a penalty term into the loss function to limit the growth of tree depth or restrict feature importance.
-Early Stopping:
-
-**What to Improve:**
-Implement early stopping to terminate training when the validation performance stops improving.
-
-**Why It Helps:**
-Prevents overfitting by halting training once the model reaches its optimal performance on validation data.
-Saves computational resources by avoiding unnecessary iterations.
-
-**Implementation:**
-Monitor the validation loss during training.
-Stop adding trees if the validation loss does not improve for a pre-defined number of iterations (patience).
-Classification Support:
-
-What to Improve: Extend the implementation to handle binary classification and multiclass classification tasks.
-Why It Helps:
-Expands the applicability of the Gradient Boosting model to problems like fraud detection, sentiment analysis, and image classification.
-
-***Implementation:***
-Modify the loss function to use appropriate metrics for classification:
-Log-loss (Binary Cross-Entropy) for binary classification.
-Softmax loss for multiclass classification.
-Introduce decision thresholds to convert predicted probabilities into class labels
-
-
-***MODEL SELECTION README QUESTIONS***
-1. Do Your Cross-Validation and Bootstrapping Model Selectors Agree with a Simpler Model Selector Like AIC?
-
-**Agreement Between Model Selectors**
-
-**Agreement in Simple Cases:**
-For simple models, such as linear regression, model selection techniques like k-fold cross-validation, bootstrapping, and Akaike Information Criterion (AIC) often yield similar results.
-
-**Why AIC Works Well in Simple Cases:**
-AIC assumes the residuals follow a Gaussian distribution and the model adheres to a parametric form.
-Linear regression models naturally fit these assumptions, making AIC a reliable and efficient method for model selection.
-AIC penalizes model complexity effectively while balancing goodness of fit, ensuring that simpler models are preferred unless complexity significantly improves the fit.
-
-**Why Cross-Validation and Bootstrapping Align with AIC:**
-Although cross-validation and bootstrapping are empirical methods that do not rely on parametric assumptions, they also reward simpler models by evaluating performance on unseen data or resampled datasets.
-For linear regression, where the relationships between features and the target are straightforward, these methods often produce similar rankings of model performance as AIC.
-In summary, for simple, well-behaved models like linear regression, AIC, cross-validation, and bootstrapping tend to agree because the underlying assumptions of AIC are met, and the empirical nature of the other methods doesn’t conflict with these assumptions.
-
-**Complex Models:**
-For non-linear models such as Gradient Boosting Trees, cross-validation and bootstrapping are far more reliable for model selection compared to AIC.
-
-**Why AIC Is Less Effective for Complex Models:**
-AIC assumes a parametric model with Gaussian residuals, which Gradient Boosting Trees do not satisfy.
-Gradient Boosting models are non-parametric, and their residuals often do not follow a Gaussian distribution due to their iterative nature and flexibility in fitting complex patterns. AIC cannot adequately penalize the complexity of Gradient Boosting Trees, as the number of effective parameters is not explicitly defined.
-
-**Advantages of Cross-Validation and Bootstrapping:**
-
-**Cross-Validation:**
-Evaluates the model's ability to generalize by splitting the dataset into multiple folds.
-Provides a direct measure of the model’s performance on unseen data, making it well-suited for complex, flexible models.
-
-**Bootstrapping:**
-Empirically evaluates the model's stability and variance by resampling the dataset multiple times.
-Does not rely on assumptions about the residuals or the model's parametric form.
-
-**Why Cross-Validation and Bootstrapping Are Superior:**
-These methods empirically test the model on various subsets of the data, ensuring robustness against overfitting and irregular residual patterns. They are agnostic to the model’s structure, making them adaptable to non-linear and non-parametric algorithms like Gradient Boosting Trees.
-
-
-2. In What Cases Might the Methods You've Written Fail or Give Incorrect or Undesirable Results?
-Challenges in Model Selection and Mitigation Strategies
-Model selection techniques such as cross-validation, bootstrapping, and grid search are powerful tools for evaluating and fine-tuning machine learning models. However, they come with inherent challenges that may affect their performance and reliability in certain scenarios. Below are the key challenges and precise mitigation strategies:
-
-**Small Datasets**
-
-# Challenge:
-When datasets are small, cross-validation can suffer from high variance because the training and validation splits may contain very limited data. Bootstrapping can also produce resampled datasets that lack sufficient diversity, especially when the same data points are repeatedly included in multiple samples.
-
-# Impact:
-High variance in cross-validation results may lead to unreliable performance estimates.
-Bootstrapping may fail to provide a robust evaluation if the samples are not representative of the full dataset.
+***CONTRIBUTION***
+We, as a team of five, collaboratively worked on the implementation, testing, and documentation of the Gradient Boosting Tree model and the associated model selection methods. Each team member contributed equally, taking responsibility for specific aspects of the project to ensure its successful completion.
 
-# Mitigation:
-Stratified Sampling: Ensures that the distribution of target values (e.g., class labels or regression outputs) is preserved in both training and validation splits, improving the reliability of results.
+**CS584 Project 2:**
+**Gradient Boosting and Model Selection**
 
-# Use Fewer Folds:
-For very small datasets, consider using leave-one-out cross-validation (LOOCV), which evaluates the model by training on all data except one instance in each iteration.
-LOOCV reduces the variance introduced by small validation splits but may increase computational cost.
+This repository contains two independent projects:
 
-**Imbalanced Datasets**
+Gradient Boosting: Implements and evaluates the Gradient Boosting algorithm for classification and regression tasks.
+Model Selection: Focuses on evaluating and comparing machine learning models using robust methods like bootstrapping and cross-validation.
 
-# Challenge:
-When the dataset has an imbalanced target variable (e.g., one class dominates the others), cross-validation may not maintain the class distribution in each fold.
-This can result in folds that lack sufficient representation of minority classes, leading to biased performance metrics.
+*********************************************************1. Gradient Boosting******************************************************
+**Purpose:**
+The Gradient Boosting project focuses on implementing and evaluating the Gradient Boosting algorithm, a popular ensemble method for improving model accuracy by sequentially reducing prediction errors.
 
-# Impact:
-The model may appear to perform well overall but fail to capture patterns in the minority class, which is critical in applications like fraud detection or medical diagnosis.
+**Project Structure (Gradient Boosting):**
+    1. main_gradientboosting.py: The primary script to execute Gradient Boosting.
+    2. boosting/: folder containing Gradient boosting model
+            gradient_boosting.py: Initialization of Model for Gradient Boosting.
+    3. comparison-gbt.py: This shows the comparison between sk learn model and our model.
+    4. Datasets: Located in the data/ directory:
+            highly_correlated_dataset.csv and partially_correlated_dataset.csv:
+                These are synthetic datasets generated from code, designed to test the impact of feature correlation on model performance.
+            boston_housing.csv, highly_correlated_dataset.csv, partially_correlated_dataset 
+    5. comparison-gbt.py - the comaprison file for our model and sk learn
+**Setup for Gradient Boosting:**
+1. Clone the Repository:
+        git clone <"https://github.com/mishrasharanya/CS584_Project2.git">  
+        cd CS584_Project2-main  
 
-# Mitigation:
+2. Install Dependencies:
+    Ensure Python 3.11.7 is installed, then run:
+        pip install -r requirements.txt  
 
-# Stratified K-Fold Cross-Validation:
-Ensure that each fold maintains the same proportion of classes as in the original dataset.
-This prevents folds from being dominated by majority classes and provides a more accurate evaluation.
-Synthetic Oversampling Techniques (if applicable):
-For severely imbalanced datasets, oversample the minority class within each fold using techniques like SMOTE (Synthetic Minority Oversampling Technique).
+3. Verify Data:
+Ensure datasets are present in the data/ directory. If missing, use a compatible dataset in CSV format.
 
-**Outliers**
+**How to Run Gradient Boosting**
+1. Execute the Script:
+    Run the Gradient Boosting pipeline:
+        test_boosting.py - this is a test file to test if its working.
+        main_gradientboosting.py - This is the main file to be run to get the output.
+        comparison-gbt.py - this is the file to comapare with sk learn with our model.
 
-# Challenge:
-Outliers in the dataset can distort the results of both cross-validation and bootstrapping.
-During bootstrapping, certain resampled datasets may become dominated by outliers, leading to unreliable performance estimates.
 
-# Impact:
-Outliers can cause overfitting as the model may place undue importance on these extreme values.
-Performance metrics such as Mean Squared Error (MSE) can be disproportionately influenced, especially in regression tasks.
+Output:
+    Model training progress and evaluation metrics (MSE, MAE, R2).
+    Visualizations (Actual Vs predicted Graph) saved to the output directory.
 
-# Mitigation:
-Preprocess the Data:
-Identify and handle outliers robustly:
-Use statistical methods like the Interquartile Range (IQR) rule or Z-score thresholds to remove extreme values.
-Normalize or standardize features to minimize the impact of outliers on distance-based algorithms.
-Use Robust Loss Functions:
-For regression, consider loss functions like Huber loss or Quantile loss that reduce the influence of outliers on the model.
+**Customization for Gradient Boosting**
+    Hyperparameters: Modify parameters like the learning rate, number of iterations, or max depth directly in Gradient_Boosting.py.
+    Dataset: Replace the default dataset (data/train.csv) with a custom dataset by updating main_gradientboosting.py.
 
-**Hyperparameter Search with Large Grids**
 
-# Challenge:
-When performing grid search over a large hyperparameter space (e.g., combinations of n_estimators, learning_rate, and max_depth), the computational cost can grow exponentially with the number of combinations.
-This can make the process impractical for large datasets or complex models.
+*************************************************Gradient BOOSTING QUESTIONS******************************************************
 
-# Impact:
-Long runtimes may delay the model selection process, especially for models like Gradient Boosting Trees that are computationally intensive.
+1. What Does the Model You Have Implemented Do and When Should It Be Used?
+The Gradient Boosting Tree model is a powerful supervised learning algorithm for regression tasks. It builds an ensemble of weak learners (shallow decision trees) by iteratively training each tree to correct the residual errors of the previous ones. This sequential approach allows the model to capture complex, non-linear relationships in data. The final prediction combines the outputs of all trees, scaled by a learning rate to balance learning and generalization.
 
-# Mitigation:
-Use Randomized Search:
-Instead of exhaustively searching the entire parameter grid, sample a fixed number of random combinations from the parameter space.
-Randomized search is significantly faster while often finding parameter combinations close to the optimal.
-Prioritize Key Parameters:
-Focus on the parameters that have the largest impact on model performance (e.g., learning_rate and n_estimators for Gradient Boosting Trees).
-Parallelize the Search:
-Leverage parallel processing tools (e.g., multiprocessing or distributed frameworks like Dask) to evaluate multiple parameter combinations simultaneously.
+Ideal for structured, tabular data, Gradient Boosting excels in tasks requiring high predictive accuracy, such as finance, healthcare, and marketing. Despite being less interpretable than simpler models, it is highly effective when model performance is prioritized. Careful tuning of hyperparameters like learning rate, tree depth, and the number of trees is crucial to prevent overfitting and optimize results, making this model a versatile choice for complex regression problems.
 
 
-3. What Could You Implement Given More Time to Mitigate These Cases or Help Users of Your Methods?
-Future Enhancements for Model Selection and Training
-Model selection techniques like cross-validation, bootstrapping, and grid search can be enhanced to handle complex scenarios more effectively. Below are detailed explanations of proposed improvements:
+2. How Did You Test Your Model to Determine if It Is Working Reasonably Correctly?
+Gradient Boosting Tree Model Testing Overview
 
-***Stratified Sampling***
-Challenge: In imbalanced datasets, standard K-fold cross-validation may not maintain the class distribution across folds. This can result in biased evaluation metrics, especially for underrepresented classes.
+The model was rigorously tested using synthetic and real-world datasets to ensure reliability and accuracy.
 
-Solution: Implement stratified K-fold cross-validation to ensure that the class proportions in each fold match those of the original dataset.
+**Datasets Used:**
+Synthetic: Small datasets (Synthetic generated Datasets namely partially correlated and highly correlated) tested basic functionality and logic correctness.
+Real-World: Benchmarks like boston_housing.csv assessed generalization and practical performance.
 
-How It Works:
+**Evaluation Metrics:**
+Mean Squared Error (MSE): Measures overall accuracy by averaging squared errors.
+Mean Absolute Error (MAE): Captures average prediction deviation in interpretable units.
+R² Score: Quantifies explained variance; closer to 1 indicates a better fit.
 
-Divide the dataset into k folds while preserving the ratio of class labels (e.g., for binary classification, maintain the proportion of positive and negative examples in each fold).
-Train the model on k-1 folds and validate it on the remaining fold, repeating this process for all folds.
-Benefits:
+**Testing Methods:**
+Unit Tests: Validated model functions (fit, predict) with predefined data.
+Cross-Validation: Ensured consistency across multiple data splits.
+Ground Truth Comparison: Verified prediction alignment with actual target values.
+Key tests confirmed minimized residuals, strong predictive accuracy, and robust generalization, combining controlled experiments and practical scenarios for reliability.
 
-Provides more reliable performance metrics for imbalanced datasets.
-Prevents minority classes from being excluded in certain folds.
-Implementation (Conceptual Example):
+3. What Parameters Have You Exposed to Users of Your Implementation to Tune Performance?
+**Optimizing Gradient Boosting Tree Performance**
 
-python
-Copy code
-from sklearn.model_selection import StratifiedKFold
+The Gradient Boosting Tree model offers key parameters for tuning to balance accuracy and generalization:
 
-skf = StratifiedKFold(n_splits=5)
-for train_index, val_index in skf.split(X, y):
-    X_train, X_val = X[train_index], X[val_index]
-    y_train, y_val = y[train_index], y[val_index]
+n_estimators (Number of Trees)
 
-***Weighted Bootstrapping***
-Challenge: Standard bootstrapping may resample data randomly, which can over-represent outliers or under-sample important classes, particularly in imbalanced datasets.
+Determines the number of boosting iterations.
+Higher values improve performance but can lead to overfitting if unchecked.
+Start with 100 and adjust based on validation performance.
+learning_rate (Step Size)
 
-Solution: Use weighted bootstrapping, where each sample is assigned a weight proportional to its importance or its representation in the dataset.
+Scales each tree's contribution to the final prediction.
+Smaller values (e.g., 0.01–0.1) improve generalization but require more trees.
+Larger values risk overfitting.
+max_depth (Tree Depth)
 
-How It Works:
+Limits tree complexity to control overfitting.
+Deeper trees capture more patterns but may memorize noise.
+Start with 2 and increase only if underfitting occurs.
+Tuning Tips
 
-Assign higher weights to minority class samples to ensure they are sampled more frequently.
-Reduce the impact of outliers by assigning them lower weights.
-Use weighted sampling during each bootstrap iteration to generate more balanced resampled datasets.
-Benefits:
+Begin with default values: n_estimators=100, learning_rate=0.1, max_depth=2.
+Tune sequentially: Adjust n_estimators, lower learning_rate, and tweak max_depth.
+Use cross-validation and grid or random search for robust tuning.
+By carefully optimizing these parameters, the model can adapt to various datasets and tasks, maximizing accuracy while avoiding overfitting.
 
-Improves performance evaluation by addressing imbalances and reducing the influence of noisy data.
-Ensures that all parts of the dataset contribute effectively to model training.
-Implementation (Conceptual Example):
+4. Are There Specific Inputs That Your Implementation Has Trouble With?
+**Challenges and Future Improvements for Gradient Boosting Trees**
+**Challenges**
+**High-Dimensional Sparse Data**
+Issue: Slower computation and risk of overfitting due to irrelevant features.
+Outliers
+Issue: Sensitivity to extreme values can lead to overfitting.
+Highly Correlated Features
+
+Issue: Redundant splits increase complexity without adding value.
+
+
+**Regularization**
+Add L1/L2 penalties to reduce overfitting and improve generalization by limiting tree complexity.
+Early Stopping
+Halt training when validation performance plateaus, preventing overfitting and saving resources.
+Classification Support
+Extend the model for binary and multiclass tasks using log-loss or softmax loss, enabling applications in fraud detection and sentiment analysis.
+These enhancements ensure robust, efficient, and versatile performance across diverse datasets and tasks.
+
+*****************************************************2. Model Selection***********************************************************
+**Purpose**
+The Model Selection project demonstrates robust techniques for evaluating and selecting the best machine learning models. It uses bootstrapping and cross-validation to ensure reliable model performance comparisons.
+
+**Project Structure (Model Selection):**
+    1. main_modelselection.py: The main script for performing model selection.
+    2. model_selection/: folder containing boot strapping and cross validation.
+        bootstrapping.py: Implements bootstrapping for model evaluation.
+        cross_validation.py: Cross-validation utilities for splitting datasets.
+    3. Datasets: Located in the data/ directory:
+        highly_correlated_dataset.csv and partially_correlated_dataset.csv:
+            These are synthetic datasets generated from code, designed to test the impact of feature correlation on model performance.
+        Boston_housing.csv: A real-world dataset.
+
+**Setup for Model Selection:**
+ 
+1. Install Dependencies:
+Install required Python libraries:
+    pip install -r requirements.txt  
+2. Verify Data:
+    Ensure datasets are present in the data/ directory. Synthetic datasets (highly_correlated_dataset.csv and partially_correlated_dataset.csv) are pre-generated and included. If needed, refer to the provided code to regenerate these datasets.
+
+**How to Run Model Selection**
+1. Execute the Script:
+        Run the model selection pipeline:
+            test_modelselection.py - this is a test file to test if its working.
+            main_modelselection.py - This is the main file to be run to get the output.
+            comparison-modelselection.py - this is a comparison file betweern our model and SK learn
+Output:
+    Performance metrics (K- Fold MSE, K Fold Crossvalidation, Bootstrap Mean MSE, Bootstrap Scores, Max Depth- 3 nos CV MSE-3 nos and Best max Depth And Best CV MSE) 
+    Summary of the best-performing model.
+
+**Customization for Model Selection**
+    1. Evaluation Method:
+        Choose between bootstrapping or cross-validation in main_modelselection.py.
+    2. Hyperparameters:
+        Update cross-validation folds or bootstrapping parameters in cross_validation.py or bootstrapping.py.
+    3. Models:
+        Add or modify models in ModelSelection.py for comparison.
+
+*****************************************************MODEL SELECTION QUESTIONS**********************************************
+1. Do Your Cross-Validation and Bootstrapping Model Selectors Agree with a Simpler Model Selector Like AIC?
 
-python
-Copy code
-weights = compute_sample_weights(y)  # Assign weights based on class balance or importance
-bootstrap_indices = np.random.choice(np.arange(len(X)), size=len(X), replace=True, p=weights)
-X_sample, y_sample = X[bootstrap_indices], y[bootstrap_indices]
+**Agreement Between Model Selectors**
 
-***Early Stopping in Grid Search***
-Challenge: Grid search evaluates all possible combinations of hyperparameters, which can be computationally expensive, especially when the search space is large or the dataset is complex.
+**Simple Models (e.g., Linear Regression):**
+Techniques like AIC, cross-validation, and bootstrapping often agree.
+Why AIC Works: Assumes Gaussian residuals and penalizes complexity, aligning well with linear regression's assumptions.
+Cross-Validation/Bootstrapping: Evaluate on unseen or resampled data, reinforcing simpler models, similar to AIC.
 
-Solution: Introduce early stopping criteria to halt grid search if there is no significant improvement in performance over successive parameter combinations.
+**Complex Models (e.g., Gradient Boosting Trees):**
+Cross-validation and bootstrapping are more reliable than AIC.
+Why AIC Falls Short: Assumes parametric structure and Gaussian residuals, which do not apply to non-linear, non-parametric models.
 
-How It Works:
+**Advantages of Cross-Validation/Bootstrapping:**
+Cross-Validation: Directly measures generalization on unseen data.
+Bootstrapping: Tests stability and variance through resampling.
 
-Monitor the performance metric (e.g., validation loss or accuracy) during grid search.
-If the metric fails to improve over a predefined number of iterations or parameter combinations, terminate the search.
-Benefits:
+These methods adapt to flexible models like Gradient Boosting Trees, ensuring robust and reliable model selection without reliance on strict parametric assumptions.
 
-Reduces unnecessary computation time by avoiding unpromising hyperparameter configurations.
-Focuses computational resources on the most promising areas of the search space.
-Implementation (Conceptual Example):
+2. In What Cases Might the Methods You've Written Fail or Give Incorrect or Undesirable Results?
 
-python
-Copy code
-best_score = float("inf")
-patience = 5  # Number of consecutive non-improving iterations allowed
-no_improvement = 0
+**Challenges in Model Selection and Mitigation Strategies**
 
-for params in parameter_grid:
-    score = evaluate_model(params, X, y)
-    if score < best_score:
-        best_score = score
-        no_improvement = 0
-    else:
-        no_improvement += 1
-    if no_improvement >= patience:
-        print("Early stopping triggered.")
-        break
+**Small Datasets**
 
-***Parallelization***
-Challenge: Cross-validation, bootstrapping, and grid search can be time-consuming when dealing with large datasets or extensive hyperparameter grids.
+Challenge: High variance in cross-validation; bootstrapping lacks diversity.
+Mitigation: Use stratified sampling to preserve target distribution and consider leave-one-out cross-validation (LOOCV) for reliable estimates.
 
-Solution: Use parallel processing to distribute computations across multiple CPU cores or nodes.
+**Imbalanced Datasets**
 
-How It Works:
+Challenge: Cross-validation may not maintain class proportions, leading to biased metrics.
+Mitigation: Apply stratified k-fold cross-validation to maintain class balance and use oversampling techniques like SMOTE for minority classes.
 
-Execute independent tasks, such as evaluating different folds in cross-validation or testing different hyperparameter combinations, simultaneously.
-Combine the results after all tasks are completed.
-Benefits:
+**Outliers**
 
-Significantly reduces computation time for large-scale model selection tasks.
-Utilizes available computational resources efficiently.
-Implementation (Conceptual Example with Python’s joblib):
+Challenge: Outliers distort evaluation and cause overfitting.
+Mitigation:  normalize features
 
-python
-Copy code
-from joblib import Parallel, delayed
+**Hyperparameter Search with Large Grids**
 
-def evaluate_fold(train_index, val_index):
-    X_train, X_val = X[train_index], X[val_index]
-    y_train, y_val = y[train_index], y[val_index]
-    model.fit(X_train, y_train)
-    return model.score(X_val, y_val)
+Challenge: Large grids lead to high computational costs.
+Mitigation: Use randomized search for faster exploration, focus on impactful parameters (e.g., learning_rate), and parallelize the search with multiprocessing or distributed frameworks.
+By addressing these challenges with targeted strategies, model selection can be made more efficient and reliable.
 
-scores = Parallel(n_jobs=-1)(delayed(evaluate_fold)(train, val) for train, val in skf.split(X, y))
 
-***Feature Selection in Cross-Validation***
-Challenge: Including irrelevant or redundant features in the dataset can degrade model performance and increase computational cost.
+3. What Could You Implement Given More Time to Mitigate These Cases or Help Users of Your Methods?
+**Future Enhancements for Model Selection and Training**
 
-Solution: Integrate automated feature selection into the cross-validation process to improve model generalization.
+**Stratified Sampling**
 
-How It Works:
+Challenge: Imbalanced datasets may lead to biased evaluation in cross-validation.
+Solution: Use stratified K-fold cross-validation to maintain class proportions in all folds.
+Benefit: Provides reliable metrics, especially for minority classes.
+Weighted Bootstrapping
 
-During each fold of cross-validation, apply a feature selection technique (e.g., mutual information, variance thresholding) to retain only the most relevant features.
-Train and validate the model using the reduced feature set.
-Benefits:
+Challenge: Standard bootstrapping over-samples outliers or misses minority classes.
+Solution: Assign higher weights to minority samples and lower weights to outliers during resampling.
+Benefit: Improves balance and reduces noise impact.
+Early Stopping in Grid Search
 
-Reduces the risk of overfitting by eliminating noise from irrelevant features.
-Improves interpretability and reduces training time.
-Implementation (Conceptual Example):
+Challenge: Large search spaces make grid search computationally expensive.
+Solution: Stop the search when performance stops improving over a set number of iterations.
+Benefit: Saves time and focuses resources on promising parameters.
+Parallelization
 
-python
-Copy code
-from sklearn.feature_selection import SelectKBest, mutual_info_regression
+Challenge: Cross-validation and grid search are time-intensive.
+Solution: Use parallel processing to distribute tasks across multiple cores or nodes.
+Benefit: Significantly reduces computation time.
+Feature Selection in Cross-Validation
 
-for train_index, val_index in skf.split(X, y):
-    X_train, X_val = X[train_index], X[val_index]
-    y_train, y_val = y[train_index], y[val_index]
-    
-    # Apply feature selection
-    selector = SelectKBest(score_func=mutual_info_regression, k=10)
-    X_train_reduced = selector.fit_transform(X_train, y_train)
-    X_val_reduced = selector.transform(X_val)
-    
-    # Train the model
-    model.fit(X_train_reduced, y_train)
-    score = model.score(X_val_reduced, y_val)
+Challenge: Irrelevant features increase overfitting and computational cost.
+Solution: Apply automated feature selection during cross-validation to retain only relevant features.
+Benefit: Improves generalization, interpretability, and efficiency.
+These strategies enhance the efficiency, accuracy, and robustness of model selection and training processes.
 
 4. What Parameters Have You Exposed to Your Users in Order to Use Your Model Selectors?
-Parameters for Model Selection
-The implemented model selection techniques (K-fold cross-validation, bootstrapping, and grid search) allow for flexible and robust evaluation and optimization of models. Below are the key parameters that users can adjust to control the behavior of these techniques:
-
-Parameter	Description
-k (K-Fold CV)	Number of folds for cross-validation.
-B (Bootstrapping)	Number of bootstrap iterations to perform.
-metric	Evaluation metric for model performance, e.g., mse (Mean Squared Error), mae (Mean Absolute Error).
-param_grid	Hyperparameter grid for grid search, containing values to test for each parameter.
-shuffle	Whether to shuffle the dataset before splitting it for cross-validation.
-random_state	Seed for reproducibility during data splitting or bootstrapping. Ensures consistent results across runs.
-These parameters provide users with control over the evaluation process, allowing them to tailor the techniques to specific datasets and models.
-
-Example Implementations
-1. K-Fold Cross-Validation
-Description: K-fold cross-validation splits the dataset into k equally sized folds. The model is trained on k-1 folds and validated on the remaining fold, repeating the process for all k folds. This ensures robust performance evaluation by testing the model on multiple splits of the data.
-
-Code Example:
-
-python
-Copy code
-from model_selection.cross_validation import k_fold_cv
-
-# Perform 5-fold cross-validation
-cv_score = k_fold_cv(model, X, y, k=5, metric="mse")
-print(f"K-Fold CV Score: {cv_score}")
-How It Works:
-
-The dataset is divided into 5 folds (or any specified number k).
-For each fold:
-Train the model on k-1 folds.
-Validate it on the remaining fold.
-Compute the evaluation metric (e.g., mse) for each iteration.
-Return the average score across all folds, providing a robust estimate of the model’s generalization performance.
-2. Bootstrapping
-Description: Bootstrapping generates multiple resampled datasets by sampling with replacement from the original data. The model is trained on these resampled datasets and evaluated on the original dataset, providing an empirical estimate of the model’s stability and performance.
-
-Code Example:
-
-python
-Copy code
-from model_selection.bootstrapping import bootstrap
-
-# Perform bootstrapping with 10 iterations
-bootstrap_scores, mean_score = bootstrap(model, X, y, B=10, metric="mse")
-print(f"Bootstrap Mean MSE: {mean_score}")
-How It Works:
-
-Create B resampled datasets from the original dataset (with replacement).
-Train the model on each resampled dataset.
-Evaluate the model on the original dataset using the specified metric.
-Return the scores for each bootstrap iteration and the mean score across all iterations.
-Benefits:
-
-Provides an empirical measure of performance variability.
-Robust against data imbalance when used with weighted sampling.
-
-3. Grid Search for Hyperparameter Optimization
-Description: Grid search systematically tests combinations of hyperparameters to find the optimal configuration for a model. For each combination, the model is evaluated using cross-validation, ensuring robust parameter tuning.
-
-**Code Example:**
-from model_selection.grid_search import grid_search_max_depth
-
-# Perform grid search for best max_depth
-best_depth, best_score = grid_search_max_depth(X, y, [2, 3, 5], k=5)
-print(f"Best Max Depth: {best_depth}, Best CV Score: {best_score}")
-
-**How It Works:**
+Key Parameters for Model Selection
 
-Specify a grid of hyperparameter values (e.g., [2, 3, 5] for max_depth).
-For each hyperparameter value:
-Perform K-fold cross-validation.
-Compute the average score across all folds.
-Identify the hyperparameter value that yields the best performance.
-Benefits:
+K-Fold Cross-Validation
 
-Finds the optimal configuration for a model.
-Ensures that hyperparameter tuning is based on robust evaluation.
-How Parameters Impact Performance
-k (K-Fold Cross-Validation):
+k: Number of folds. Higher k (e.g., 10) gives robust estimates but is computationally expensive.
+shuffle & random_state: Ensures randomness and reproducibility during data splits.
+metric: Determines performance evaluation (e.g., MSE for regression).
+Bootstrapping
 
-Higher k values (e.g., 10): Provide more robust estimates by reducing variance but increase computation time.
-Lower k values (e.g., 3): Reduce computation but may result in less stable performance estimates.
-B (Bootstrapping):
+B: Number of resampling iterations. Higher B improves variability estimates but increases computation.
+metric: Measures performance (e.g., MSE, MAE).
+Weighted Sampling: Enhances robustness for imbalanced data.
+Grid Search
 
-Higher B values: Provide a more comprehensive evaluation by increasing the number of resampled datasets but require more computation.
-Lower B values: Reduce computation but may not capture performance variability effectively.
-metric:
+param_grid: Defines hyperparameter values to explore. Larger grids improve results but increase runtime.
+Early Stopping: Halts search when performance stabilizes, saving time.
+Parallelization: Speeds up grid search by processing multiple combinations simultaneously.
+Strengths
 
-Impacts the way performance is evaluated:
-mse: Penalizes large errors heavily, ideal for regression tasks.
-mae: Provides a more interpretable measure of average error magnitude.
-param_grid:
-
-Determines the range and granularity of hyperparameter tuning.
-Larger grids allow for a more exhaustive search but increase computation time.
-
-shuffle: Ensures randomness in data splitting for cross-validation, reducing the risk of bias.
-
-Particularly useful for datasets with inherent order (e.g., time-series data).
-random_state: Ensures reproducibility across runs, making results consistent and reliable.
-
-
-***Conclusion***
-Cross-validation and bootstrapping are foundational techniques for robust model evaluation and selection. Their empirical nature makes them highly versatile and adaptable, enabling accurate assessment of model performance in a wide range of scenarios. These methods shine particularly when working with non-linear models like Gradient Boosting Trees, which are capable of capturing complex relationships and interactions in data.
-
-**Strengths of Cross-Validation and Bootstrapping**
-
-**Flexibility Across Models:**
-
-Unlike simpler methods such as AIC, which rely on assumptions like Gaussian residuals and a parametric model form, cross-validation and bootstrapping can evaluate non-linear, non-parametric models without requiring these assumptions.
-For linear regression, these methods still align with AIC, providing additional empirical validation.
-
-**Robustness to Data Characteristics:**
-
-By iteratively training and validating models on different splits or resampled datasets, these methods provide a more accurate and stable estimate of model performance, even in challenging situations like small datasets or imbalanced classes.
-**Generalization Power:**
-
-Cross-validation ensures that the model's performance is tested across multiple folds, reducing overfitting and improving generalization to unseen data.
-Bootstrapping provides insights into model variability and stability by evaluating performance across numerous resampled datasets.
-Limitations and Areas for Improvement
-Despite their strengths, these techniques face computational and practical challenges in certain scenarios, such as:
-
-**High Computational Cost:**
-Both methods can be time-consuming for large datasets or complex hyperparameter grids, especially in models like Gradient Boosting Trees with multiple parameters.
-
-**Handling Imbalanced Data:**
-Standard cross-validation may not maintain class distributions in imbalanced datasets, leading to biased evaluation metrics.
-Proposed Future Enhancements
-
-To address these challenges and enhance their effectiveness, the following improvements can be integrated:
-**Stratified Sampling:**
-Ensures balanced representation of classes in each fold during cross-validation, improving reliability for imbalanced datasets.
+Suitable for all model types, including non-parametric models.
+Cross-validation ensures generalization, while bootstrapping assesses variability.
+Challenges
 
-**Weighted Bootstrapping:**
-Allows better handling of imbalanced data or outliers by assigning weights to samples during resampling, ensuring diverse and meaningful bootstrap datasets.
 
-**Parallelization:**
-Distributes computational tasks (e.g., evaluating different folds or hyperparameter combinations) across multiple cores or nodes, significantly reducing runtime for large datasets.
+Future Enhancements:
 
-**Early Stopping in Grid Search:**
-Introduces stopping criteria to terminate hyperparameter searches when further improvements in performance are unlikely, saving computational resources.
+Stratified Sampling: Maintains class balance in folds.
+Weighted Bootstrapping: Handles imbalances and outliers effectively.
+Automated Feature Selection: Improves generalization by reducing noise.
+Parallelization: Cuts runtime for large-scale tasks.
+These adjustments improve the efficiency, accuracy, and scalability of model selection processes.
 
-**Automated Feature Selection:**
-Incorporating feature selection as part of the cross-validation process ensures that only the most relevant features are retained, reducing noise and improving generalization.
+**Notes:**
+Synthetic Datasets:
+    1. highly_correlated_dataset.csv: A synthetic dataset where features have high inter-correlation.
+    2. partially_correlated_dataset.csv: A synthetic dataset with moderate feature correlation.
+    3. These datasets were generated using custom Python code, which can be found in the project.
+    4. Independent Projects: Each project can be run independently. Follow the respective setup and execution instructions for Gradient Boosting or Model Selection.
+    5. Extensibility: New models or datasets can be added easily. Update the relevant scripts and configurations as needed.
+    6. Troubleshooting: Run unit tests or verify dataset formats if errors occur.
 
 **Key Takeaways**
 **Non-Linear Model Evaluation:**
@@ -672,354 +304,11 @@ While these methods align with simpler selectors like AIC for linear models, the
 Enhancements for Scalability:
 By incorporating stratified sampling, parallelization, and other optimizations, these methods can become even more efficient and effective for large-scale or computationally demanding applications.
 
-***Conclusion***
-Cross-validation and bootstrapping provide a robust foundation for model evaluation and selection, particularly for complex tasks involving non-linear relationships. Their empirical, assumption-free approach ensures reliable performance estimation and generalization, even for cutting-edge machine learning models. With future enhancements like parallelization and automated feature selection, these techniques can be scaled and optimized to meet the growing demands of modern data science.
 
+*******
 
-***Key Logic and Implementations.***
-
-1. Gradient Boosting Trees
-
-***Explanation***
-Gradient Boosting Trees iteratively improve predictions by:
-
-Calculating residuals (errors) from the current predictions.
-Training a decision tree to predict these residuals.
-Updating the model's predictions by adding the residual predictions, scaled by a learning rate.
-
-Implementation
-
-import numpy as np
-
-class GradientBoostingTree:
-    """
-    A class for implementing Gradient Boosting Trees for regression tasks.
-    """
-
-    def __init__(self, n_estimators, learning_rate, max_depth):
-        """
-        Initialize the model parameters.
-
-        Parameters:
-        - n_estimators: Number of trees to build in the boosting process.
-        - learning_rate: Scaling factor for tree contributions.
-        - max_depth: Maximum depth of each decision tree.
-        """
-        self.n_estimators = n_estimators
-        self.learning_rate = learning_rate
-        self.max_depth = max_depth
-        self.models = []  # To store individual decision trees
-
-    def fit(self, X, y):
-        """
-        Train the Gradient Boosting model.
-
-        Parameters:
-        - X: Feature matrix (2D numpy array).
-        - y: Target vector (1D numpy array).
-        """
-        # Initial predictions are the mean of the target variable
-        y_pred = np.full(len(y), np.mean(y))
-        self.initial_pred = np.mean(y)
-
-        for _ in range(self.n_estimators):
-            # Calculate residuals (errors) from current predictions
-            residuals = y - y_pred
-
-            # Train a decision tree on the residuals
-            tree = DecisionTreeRegressor(max_depth=self.max_depth)
-            tree.fit(X, residuals)
-            self.models.append(tree)
-
-            # Update predictions by adding the tree's predictions (scaled by learning rate)
-            y_pred += self.learning_rate * tree.predict(X)
-
-    def predict(self, X):
-        """
-        Predict using the trained Gradient Boosting model.
-
-        Parameters:
-        - X: Feature matrix for prediction.
-
-        Returns:
-        - y_pred: Predicted values.
-        """
-        # Start with the initial prediction (mean of y)
-        y_pred = np.full(X.shape[0], self.initial_pred)
-
-        # Add contributions from each tree
-        for tree in self.models:
-            y_pred += self.learning_rate * tree.predict(X)
-        
-        return y_pred
-2. Decision Tree Regressor
-Explanation
-The decision tree splits data into subsets to minimize the mean squared error (MSE). It stops splitting when:
-
-The maximum depth is reached.
-A split does not improve the error.
-
-Implementation
-
-class DecisionTreeRegressor:
-    """
-    A simple decision tree regressor for predicting residuals in Gradient Boosting.
-    """
-
-    def __init__(self, max_depth=3):
-        """
-        Initialize the decision tree parameters.
-
-        Parameters:
-        - max_depth: Maximum depth of the tree.
-        """
-        self.max_depth = max_depth
-        self.tree = None  # To store the tree structure
-
-    def fit(self, X, y):
-        """
-        Build the decision tree from training data.
-
-        Parameters:
-        - X: Feature matrix.
-        - y: Target vector.
-        """
-        self.tree = self._build_tree(X, y, depth=0)
-
-    def predict(self, X):
-        """
-        Predict values using the built tree.
-
-        Parameters:
-        - X: Feature matrix.
-
-        Returns:
-        - Predictions for each input sample.
-        """
-        return np.array([self._predict_single(x, self.tree) for x in X])
-
-    def _build_tree(self, X, y, depth):
-        """
-        Recursively build the tree.
-
-        Parameters:
-        - X: Subset of features.
-        - y: Subset of target values.
-        - depth: Current depth of the tree.
-
-        Returns:
-        - Tree structure (dictionary or leaf value).
-        """
-        # Stop if max depth is reached or all target values are the same
-        if depth == self.max_depth or len(set(y)) == 1:
-            return np.mean(y)  # Leaf node value
-
-        # Find the best split for the data
-        feature_idx, threshold = self._find_best_split(X, y)
-        if feature_idx is None:
-            return np.mean(y)  # No valid split, return leaf node
-
-        # Partition data based on the split
-        left_mask = X[:, feature_idx] <= threshold
-        right_mask = ~left_mask
-
-        # Recursively build the left and right branches
-        return {
-            "feature_idx": feature_idx,
-            "threshold": threshold,
-            "left": self._build_tree(X[left_mask], y[left_mask], depth + 1),
-            "right": self._build_tree(X[right_mask], y[right_mask], depth + 1),
-        }
-
-    def _find_best_split(self, X, y):
-        """
-        Find the best feature and threshold for splitting the data.
-
-        Returns:
-        - Best feature index and split threshold.
-        """
-        n_samples, n_features = X.shape
-        best_mse = float("inf")
-        best_split = None
-
-        # Iterate through each feature
-        for feature_idx in range(n_features):
-            thresholds = np.unique(X[:, feature_idx])
-            for threshold in thresholds:
-                # Split data
-                left_mask = X[:, feature_idx] <= threshold
-                right_mask = ~left_mask
-                if left_mask.sum() == 0 or right_mask.sum() == 0:
-                    continue
-
-                # Compute MSE for the split
-                mse = (
-                    self._compute_mse(y[left_mask]) * left_mask.sum()
-                    + self._compute_mse(y[right_mask]) * right_mask.sum()
-                ) / n_samples
-
-                # Update best split if MSE is lower
-                if mse < best_mse:
-                    best_mse = mse
-                    best_split = (feature_idx, threshold)
-
-        return best_split
-
-    def _compute_mse(self, y):
-        """
-        Calculate the mean squared error for a given target subset.
-        """
-        return np.mean((y - np.mean(y)) ** 2)
-
-    def _predict_single(self, x, tree):
-        """
-        Predict the output for a single input using the decision tree.
-
-        Parameters:
-        - x: Single feature vector.
-        - tree: Current node or leaf.
-
-        Returns:
-        - Predicted value.
-        """
-        if not isinstance(tree, dict):
-            return tree  # Return leaf value
-
-        # Check which branch to follow
-        feature_idx = tree["feature_idx"]
-        threshold = tree["threshold"]
-        if x[feature_idx] <= threshold:
-            return self._predict_single(x, tree["left"])
-        else:
-            return self._predict_single(x, tree["right"])
-
-3. K-Fold Cross-Validation
-
-def k_fold_cv(model, X, y, k=5, metric="mse"):
-    """
-    Perform K-Fold Cross-Validation.
-
-    Parameters:
-    - model: The model to validate.
-    - X: Feature matrix.
-    - y: Target vector.
-    - k: Number of folds.
-    - metric: Metric for evaluation ('mse' or 'mae').
-
-    Returns:
-    - Average score across all folds.
-    """
-    n = len(X)
-    indices = np.arange(n)
-    np.random.shuffle(indices)
-    folds = np.array_split(indices, k)
-
-    scores = []
-    for i in range(k):
-        # Split data into training and validation sets
-        train_idx = np.concatenate([folds[j] for j in range(k) if j != i])
-        val_idx = folds[i]
-
-        X_train, X_val = X[train_idx], X[val_idx]
-        y_train, y_val = y[train_idx], y[val_idx]
-
-        # Train and validate the model
-        model.fit(X_train, y_train)
-        y_pred = model.predict(X_val)
-
-        # Compute the score
-        if metric == "mse":
-            score = np.mean((y_val - y_pred) ** 2)
-        elif metric == "mae":
-            score = np.mean(np.abs(y_val - y_pred))
-        scores.append(score)
-
-    return np.mean(scores)
-These implementations include detailed comments for clarity and user understanding
-
-
-***HOW TO RUN THE PROJECT***
-
-----Steps to Run the Project----
-1. Verify Project Structure
-Make sure your project directory looks something like this:
-
-project/
-├── boosting/                   # Gradient Boosting implementation
-│   ├── __init__.py
-│   ├── gradient_boosting.py
-├── model_selection/            # Model selection methods
-│   ├── __init__.py
-│   ├── cross_validation.py
-│   ├── bootstrapping.py
-├── data/                       # Datasets
-│   ├── train.csv
-│   ├── test.csv
-    |---diabetes_dataset.csv
-    |---highly_correlated_dataset.csv
-    |---partially_correlated_dataset.csv
-├── tests/                      # Unit tests
-│   ├── test_boosting.py
-│   ├── test_model_selection.py
-├── main_gradientboosting.py    # Gradient Boosting script
-├── main_modelselection.py      # Model Selection script
-├── README.md                   # Documentation
-├── requirements.txt            # Dependencies
-├── setup.py                    # Installation script
-
-there is no src/ directory, so all imports should now directly reference modules like boosting or model_selection.
-
-
-
-2. Set Up the Project
-Step 1: Clone the Repository
-
-git clone https://github.com/yourusername/project.git
-cd project
-Step 2: Create a Virtual Environment (Optional, Recommended)
-
-python3 -m venv venv
-source venv/bin/activate  # On Windows: venv\Scripts\activate
-Step 3: Install Dependencies
-
-pip install -r requirements.txt
-Step 4: Install the Project
-If you are using modules directly in the root directory (no src):
-
-pip install -e .
-
-3. Run the Scripts
-
-**Run Gradient Boosting**
-Train and evaluate the Gradient Boosting implementation:
-python main_gradientboosting.py
-
-**Run Model Selection**
-Perform K-Fold Cross-Validation, Bootstrapping, and Grid Search:
-python main_modelselection.py
-
-4. Run Tests
-Ensure all modules and methods work as expected:
-pytest tests/
-
-
-5. Debugging Common Issues
-Issue 1: ModuleNotFoundError
-If modules like boosting or model_selection are not found:
-
-Ensure the project is installed in editable mode:
-
-pip install -e .
-Issue 2: Incorrect Imports
-Ensure imports match the project structure:
-
-If the directory structure is flat, use from boosting import ... instead of from src.boosting import ....
-Issue 3: Dataset Errors
-Ensure the datasets in data/ are properly formatted:
-
-Columns represent features.
-The last column is the target variable.
-By following these updated steps, you should be able to run the project successfully. 
+***Conclusion***
+Cross-validation and bootstrapping provide a robust foundation for model evaluation and selection, particularly for complex tasks involving non-linear relationships. Their empirical, assumption-free approach ensures reliable performance estimation and generalization, even for cutting-edge machine learning models. With future enhancements like parallelization and automated feature selection, these techniques can be scaled and optimized to meet the growing demands of modern data science.
 
 
 

From 1222842fb4f9996c02fc29553eedb26e705a571d Mon Sep 17 00:00:00 2001
From: s-aruneshwaran <100713948+s-aruneshwaran@users.noreply.github.com>
Date: Fri, 7 Nov 2025 11:53:29 -0500
Subject: [PATCH 7/7] Created using Colab

---
 GapFillAI_Close_Your_Skill_Gaps.ipynb | 733 ++++++++++++++++++++++++++
 1 file changed, 733 insertions(+)
 create mode 100644 GapFillAI_Close_Your_Skill_Gaps.ipynb

diff --git a/GapFillAI_Close_Your_Skill_Gaps.ipynb b/GapFillAI_Close_Your_Skill_Gaps.ipynb
new file mode 100644
index 0000000..73754ae
--- /dev/null
+++ b/GapFillAI_Close_Your_Skill_Gaps.ipynb
@@ -0,0 +1,733 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/mishrasharanya/CS584_Project2/blob/main/GapFillAI_Close_Your_Skill_Gaps.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ==========================================================\n",
+        "# Cell 1: Install All Libraries\n",
+        "# ==========================================================\n",
+        "print(\"Installing libraries... (pypdf, openai)\")\n",
+        "!pip install -q pypdf openai\n",
+        "print(\"Libraries installed!\")\n",
+        "print(\"-\" * 40)\n",
+        "\n",
+        "\n",
+        "# ==========================================================\n",
+        "# Cell 2: Import & Configure API Key (OpenRouter Version)\n",
+        "# ==========================================================\n",
+        "import openai\n",
+        "from google.colab import userdata, files\n",
+        "import pypdf\n",
+        "import io\n",
+        "import json\n",
+        "import os\n",
+        "import sys\n",
+        "import urllib.parse\n",
+        "from IPython.display import display, Markdown\n",
+        "\n",
+        "# --- Use the free, powerful Llama 4 model ---\n",
+        "OPENROUTER_MODEL = \"meta-llama/llama-4-maverick:free\"\n",
+        "\n",
+        "try:\n",
+        "    API_KEY = userdata.get('OPENROUTER_API_KEY')\n",
+        "\n",
+        "    client = openai.OpenAI(\n",
+        "        base_url=\"https://openrouter.ai/api/v1\",\n",
+        "        api_key=API_KEY\n",
+        "    )\n",
+        "\n",
+        "    print(f\"✅ OpenRouter Client Configured Successfully!\")\n",
+        "    print(f\"✅ Using Model: {OPENROUTER_MODEL}\")\n",
+        "\n",
+        "except Exception as e:\n",
+        "    print(f\"🚨 Error: Could not get API key. Did you add 'OPENROUTER_API_KEY' to Colab Secrets?\\n{e}\")\n",
+        "    raise SystemExit(\"API Key not found\")\n",
+        "\n",
+        "print(\"-\" * 40)\n",
+        "\n",
+        "\n",
+        "# ==========================================================\n",
+        "# Cell 3: Helper Functions (PDF & AI)\n",
+        "# ==========================================================\n",
+        "\n",
+        "def get_pdf_text_from_bytes(pdf_bytes):\n",
+        "    \"\"\"\n",
+        "    Extracts text from PDF bytes.\n",
+        "    On failure, returns an error string.\n",
+        "    \"\"\"\n",
+        "    try:\n",
+        "        pdf_reader = pypdf.PdfReader(io.BytesIO(pdf_bytes))\n",
+        "        text = \"\"\n",
+        "        for page in pdf_reader.pages:\n",
+        "            text += page.extract_text()\n",
+        "        return text\n",
+        "    except Exception as e:\n",
+        "        return f\"PDF_ERROR: {e}\"\n",
+        "\n",
+        "def call_ai(prompt):\n",
+        "    \"\"\"\n",
+        "    Sends a prompt to the OpenRouter model and returns the response.\n",
+        "    On failure, it now returns the error message as a string.\n",
+        "    \"\"\"\n",
+        "    try:\n",
+        "        # We put all instructions into the 'user' role\n",
+        "        messages = [\n",
+        "            {\"role\": \"user\", \"content\": prompt}\n",
+        "        ]\n",
+        "\n",
+        "        kwargs = {\n",
+        "            \"model\": OPENROUTER_MODEL,\n",
+        "            \"messages\": messages,\n",
+        "            \"max_tokens\": 1000 # Fix for 402 credit limit errors\n",
+        "        }\n",
+        "\n",
+        "        response = client.chat.completions.create(**kwargs)\n",
+        "\n",
+        "        return response.choices[0].message.content\n",
+        "\n",
+        "    except Exception as e:\n",
+        "        # Instead of returning None, return the error string.\n",
+        "        error_message = f\"AI_ERROR: {e}\"\n",
+        "        print(f\"🚨 call_ai failed: {error_message}\", file=sys.stderr)\n",
+        "        return error_message\n",
+        "\n",
+        "print(\"Helper functions defined (with 'no-system-prompt' fix).\")\n",
+        "print(\"-\" * 40)\n",
+        "\n",
+        "\n",
+        "# ==========================================================\n",
+        "# Cell 4: AI Prompts (The \"Brain\") -- ❗️ NEW SIMPLE FORMAT ❗️\n",
+        "# ==========================================================\n",
+        "\n",
+        "# Prompt 1: The Gap Analyzer\n",
+        "PROMPT_1_GAP_ANALYZER = \"\"\"\n",
+        "You are an expert tech recruiter.\n",
+        "Analyze the user's [RESUME] and the [JOB_DESCRIPTION].\n",
+        "Identify the single most important technical skill (e.g., \"Kubernetes\", \"PyTorch\")\n",
+        "that is in the [JOB_DESCRIPTION] but missing from the [RESUME].\n",
+        "\n",
+        "Return ONLY the name of that single skill.\n",
+        "Do NOT add any explanation. Just the skill name.\n",
+        "\n",
+        "[RESUME]:\n",
+        "{resume_text}\n",
+        "\n",
+        "[JOB_DESCRIPTION]:\n",
+        "{job_description}\n",
+        "\"\"\"\n",
+        "\n",
+        "# Prompt 2: The OSS Finder\n",
+        "# We are no longer asking for JSON. We are asking for a simple\n",
+        "# string separated by a pipe character (|). This is MUCH more reliable.\n",
+        "PROMPT_2_OSS_FINDER = \"\"\"\n",
+        "You are an expert open-source mentor.\n",
+        "A junior developer needs to learn the following skill: *{skill_gap}*\n",
+        "\n",
+        "Your goal is to find them a real, impressive open-source project to contribute to.\n",
+        "\n",
+        "1.  Find the single most famous GitHub repo (in 'owner/repo' format) for this skill.\n",
+        "2.  Write a 1-sentence summary of why to contribute.\n",
+        "3.  Generate the exact GitHub search query for \"good first issues\".\n",
+        "\n",
+        "---\n",
+        "VERY IMPORTANT: You MUST return your answer as a single line,\n",
+        "with each item separated by a pipe character (|).\n",
+        "Do NOT add any text, markdown, or explanation.\n",
+        "\n",
+        "FORMAT: github_repo|why_contribute|search_query\n",
+        "\n",
+        "EXAMPLE (if skill_gap was 'PyTorch'):\n",
+        "pytorch/pytorch|Contributing to PyTorch is the best way to prove your ML skills.|is:open is:issue repo:pytorch/pytorch label:\"good first issue\"\n",
+        "\"\"\"\n",
+        "\n",
+        "print(\"AI Prompts are ready.\")\n",
+        "print(\"-\" * 40)\n",
+        "\n",
+        "\n",
+        "# ==========================================================\n",
+        "# Cell 5: The \"App\" - Step 1: Upload Resume\n",
+        "# ==========================================================\n",
+        "#\n",
+        "# Run this cell first to upload your resume.\n",
+        "#\n",
+        "# ==========================================================\n",
+        "print(\"Please upload your PDF resume:\")\n",
+        "sys.stdout.flush()\n",
+        "\n",
+        "uploaded_files = files.upload()\n",
+        "\n",
+        "if not uploaded_files:\n",
+        "    raise SystemExit(\"No file uploaded. Please re-run this cell.\")\n",
+        "\n",
+        "filename = list(uploaded_files.keys())[0]\n",
+        "pdf_bytes = uploaded_files[filename]\n",
+        "\n",
+        "print(f\"\\nSuccessfully uploaded: {filename}\")\n",
+        "print(\"-\" * 40)\n",
+        "\n",
+        "\n",
+        "# ==========================================================\n",
+        "# Cell 6: The \"App\" - Step 2: Paste Job Description -- ❗️ JOB DESC ADDED ❗️\n",
+        "# ==========================================================\n",
+        "#\n",
+        "# ==========================================================\n",
+        "\n",
+        "#@param {type:\"string\"}\n",
+        "job_description = \"\"\"\n",
+        "**Job Title: Software Engineer, Backend Systems**\n",
+        "\n",
+        "**Location:** San Francisco, CA (Hybrid)\n",
+        "\n",
+        "**About Us:**\n",
+        "At TechCorp, we are building the next generation of cloud infrastructure to help businesses scale. We are a fast-paced, product-driven team looking for engineers who are passionate about building reliable, high-performance distributed systems.\n",
+        "\n",
+        "**The Role:**\n",
+        "We are seeking a Backend Software Engineer to join our core services team. You will be responsible for designing, building, and maintaining the APIs and services that power our main platform. You will work closely with frontend engineers, product managers, and SREs to deliver robust, scalable solutions.\n",
+        "\n",
+        "**Responsibilities:**\n",
+        "* Design, develop, and deploy backend microservices in Python and Go.\n",
+        "* Write high-quality, maintainable, and well-tested code.\n",
+        "* Manage and operate cloud infrastructure using modern DevOps principles.\n",
+        "* Collaborate on system architecture, code reviews, and setting best practices.\n",
+        "* Monitor system performance and reliability, and participate in an on-call rotation.\n",
+        "\n",
+        "**Required Qualifications:**\n",
+        "* Bachelor's degree in Computer Science or equivalent practical experience.\n",
+        "* 3+ years of professional software development experience.\n",
+        "* Strong proficiency in at least one of the following: Python, Go, or Java.\n",
+        "* Experience with building and consuming RESTful APIs.\n",
+        "* Experience with cloud platforms (AWS, GCP, or Azure).\n",
+        "* Strong understanding of relational databases (e.g., PostgreSQL, MySQL).\n",
+        "\n",
+        "**Preferred Qualifications (Nice-to-Have):**\n",
+        "* Experience with container orchestration using **Kubernetes** and Docker.\n",
+        "* Experience with Infrastructure as Code (IaC) tools like **Terraform**.\n",
+        "* Familiarity with CI/CD pipelines (e.g., Jenkins, GitHub Actions).\n",
+        "* Knowledge of **PyTorch** or TensorFlow for internal ML tooling.\n",
+        "\"\"\" #@param {type:\"string\"}\n",
+        "\n",
+        "# --- We check if the user pasted the description ---\n",
+        "if len(job_description.strip()) < 50:\n",
+        "    print(\"🚨 Please paste a real job description into the text box above before running the next one!\")\n",
+        "    print(\"----------------------------------------\")\n",
+        "    # This line stops the notebook\n",
+        "    raise SystemExit(\"Job description is missing. Please complete Cell 6.\")\n",
+        "else:\n",
+        "    print(\"✅ Job description pasted successfully.\")\n",
+        "    print(\"You can now run Cell 7 (the final step) below.\")\n",
+        "\n",
+        "print(\"-\" * 40)\n",
+        "\n",
+        "\n",
+        "# ==========================================================\n",
+        "# Cell 7: Run the Analysis! (Formerly Cell 6) -- ❗️ NEW SIMPLE PARSER ❗️\n",
+        "# ==========================================================\n",
+        "\n",
+        "print(\"Starting gapfillai analysis...\")\n",
+        "raw_text_output = None\n",
+        "\n",
+        "try:\n",
+        "    # 1. Process Resume\n",
+        "    print(\"Processing PDF resume...\")\n",
+        "    # pdf_bytes comes from Cell 5\n",
+        "    resume_text = get_pdf_text_from_bytes(pdf_bytes)\n",
+        "    if \"PDF_ERROR:\" in resume_text:\n",
+        "        print(f\"🚨 FAILED TO READ PDF. Error: {resume_text}\")\n",
+        "        raise SystemExit(\"PDF processing failed.\")\n",
+        "    print(\"✅ Resume processed.\")\n",
+        "\n",
+        "    # --- Truncate text ---\n",
+        "    MAX_RESUME_CHARS = 20000\n",
+        "    MAX_JOB_CHARS = 5000\n",
+        "    resume_text_truncated = resume_text[:MAX_RESUME_CHARS]\n",
+        "    # job_description comes from Cell 6\n",
+        "    job_description_truncated = job_description[:MAX_JOB_CHARS]\n",
+        "\n",
+        "    if len(resume_text) > MAX_RESUME_CHARS:\n",
+        "        print(f\"⚠️ Resume is very long, truncating to {MAX_RESUME_CHARS} characters.\")\n",
+        "    if len(job_description) > MAX_JOB_CHARS:\n",
+        "        print(f\"⚠️ Job description is very long, truncating to {MAX_JOB_CHARS} characters.\")\n",
+        "    # --- END OF TRUNCATION ---\n",
+        "\n",
+        "\n",
+        "    # 2. Find Skill Gap\n",
+        "    print(\"Calling AI to find skill gap...\")\n",
+        "    prompt_1 = PROMPT_1_GAP_ANALYZER.format(\n",
+        "        resume_text=resume_text_truncated,\n",
+        "        job_description=job_description_truncated\n",
+        "    )\n",
+        "    skill_gap_raw = call_ai(prompt_1)\n",
+        "\n",
+        "    if \"AI_ERROR:\" in skill_gap_raw:\n",
+        "        print(f\"🚨 AI FAILED TO FIND SKILL GAP.\")\n",
+        "        print(f\"The exact error from the API is:\\n{skill_gap_raw}\")\n",
+        "        raise SystemExit(\"AI failed at step 2.\")\n",
+        "\n",
+        "    # --- Clean the output to get the last line ---\n",
+        "    skill_gap = \"\"\n",
+        "    if skill_gap_raw:\n",
+        "        lines = skill_gap_raw.strip().split('\\n')\n",
+        "        if lines:\n",
+        "            skill_gap = lines[-1].strip().replace(\"`\", \"\")\n",
+        "\n",
+        "    if not skill_gap:\n",
+        "        print(\"🚨 AI FAILED TO FIND SKILL GAP (returned an empty string).\")\n",
+        "        print(\"This means the model couldn't find a clear gap. Did you paste a real job description in Cell 6?\")\n",
+        "        raise SystemExit(\"AI failed at step 2 (empty skill gap).\")\n",
+        "\n",
+        "    print(f\"✅ AI found skill gap: {skill_gap}\\n\")\n",
+        "\n",
+        "    # 3. Find OSS Project\n",
+        "    print(f\"Calling AI to find open-source project for '{skill_gap}'...\")\n",
+        "    prompt_2 = PROMPT_2_OSS_FINDER.format(skill_gap=skill_gap)\n",
+        "    # ❗️ We save the raw output to a new variable for logging\n",
+        "    raw_text_output = call_ai(prompt_2)\n",
+        "\n",
+        "    if \"AI_ERROR:\" in raw_text_output:\n",
+        "        print(f\"🚨 AI FAILED TO FIND OSS PROJECT.\")\n",
+        "        print(f\"The exact error from the API is:\\n{raw_text_output}\")\n",
+        "        raise SystemExit(\"AI failed at step 3.\")\n",
+        "\n",
+        "    print(\"✅ AI found project!\\n\\n\")\n",
+        "\n",
+        "    # 4. Display Results\n",
+        "    print(\"--- 🎯 YOUR PERSONALIZED PLAN ---\")\n",
+        "\n",
+        "    # --- ❗️ NEW PARSING LOGIC ❗️ ---\n",
+        "    # We are no longer parsing JSON. We are splitting a pipe-separated string.\n",
+        "\n",
+        "    # 1. Clean the raw string\n",
+        "    cleaned_output = raw_text_output.strip().replace(\"```\", \"\") # Remove any markdown\n",
+        "\n",
+        "    # 2. Split the string by the pipe character\n",
+        "    parts = cleaned_output.split('|')\n",
+        "\n",
+        "    # 3. Check if we got the 3 parts we expected\n",
+        "    if len(parts) != 3:\n",
+        "        print(f\"🚨 AI returned an invalid format. Expected 3 parts, got {len(parts)}.\")\n",
+        "        print(\"--- RAW AI OUTPUT (The string that failed to parse) ---\")\n",
+        "        print(raw_text_output) # <-- This will now work\n",
+        "        print(\"---------------------------------------------------------\")\n",
+        "        raise SystemExit(\"AI failed at step 3 (bad format).\")\n",
+        "\n",
+        "    # 4. Assign parts to variables\n",
+        "    repo_name = parts[0].strip()\n",
+        "    why_contrib = parts[1].strip()\n",
+        "    search_query = parts[2].strip()\n",
+        "\n",
+        "    # 5. Get the project name from the repo\n",
+        "    project_name = repo_name.split('/')[-1]\n",
+        "\n",
+        "    display(Markdown(f\"## Your #1 Skill Gap: *{skill_gap}*\"))\n",
+        "    display(Markdown(\"---\"))\n",
+        "    display(Markdown(f\"### Project Recommendation: *{project_name}*\"))\n",
+        "    display(Markdown(f\"*GitHub Repo:* `github.com/{repo_name}`\"))\n",
+        "    display(Markdown(f\"*Why Contribute?* {why_contrib}\"))\n",
+        "\n",
+        "    display(Markdown(\"### Recommended 'Good First Issues'\"))\n",
+        "    display(Markdown(f\"*The AI's search query will find real, up-to-date 'good first issues' for you to tackle.*\"))\n",
+        "\n",
+        "    display(Markdown(\"### Find Live Issues on GitHub\"))\n",
+        "    display(Markdown(\"Here is the exact query to find issues you can start on today:\"))\n",
+        "    display(Markdown(f\"```bash\\n{search_query}\\n```\"))\n",
+        "\n",
+        "    query_safe = urllib.parse.quote(search_query)\n",
+        "    github_url = f\"[https://github.com/](https://github.com/){repo_name}/issues?q={query_safe}\"\n",
+        "\n",
+        "    display(Markdown(f\"**[Click here to see live issues on GitHub]({github_url})**\"))\n",
+        "\n",
+        "\n",
+        "except SystemExit as e:\n",
+        "    # This just makes the exit cleaner\n",
+        "    print(f\"\\nScript stopped: {e}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"🚨 An unexpected error occurred ({type(e)}): {e}\")\n",
+        "    if raw_text_output:\n",
+        "         print(\"--- RAW AI OUTPUT ---\")\n",
+        "         print(raw_text_output)\n",
+        "    print(\"-----------------------\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 867
+        },
+        "id": "2IyC48Hw4ksT",
+        "outputId": "6ba06f10-b3a6-4195-b772-fcc6d2f03241"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Installing libraries... (pypdf, openai)\n",
+            "Libraries installed!\n",
+            "----------------------------------------\n",
+            "✅ OpenRouter Client Configured Successfully!\n",
+            "✅ Using Model: meta-llama/llama-4-maverick:free\n",
+            "----------------------------------------\n",
+            "Helper functions defined (with 'no-system-prompt' fix).\n",
+            "----------------------------------------\n",
+            "AI Prompts are ready.\n",
+            "----------------------------------------\n",
+            "Please upload your PDF resume:\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-999014f1-4ce4-4936-bf89-e0a8a1278891\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-999014f1-4ce4-4936-bf89-e0a8a1278891\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving Resume (1).pdf to Resume (1) (6).pdf\n",
+            "\n",
+            "Successfully uploaded: Resume (1) (6).pdf\n",
+            "----------------------------------------\n",
+            "✅ Job description pasted successfully.\n",
+            "You can now run Cell 7 (the final step) below.\n",
+            "----------------------------------------\n",
+            "Starting gapfillai analysis...\n",
+            "Processing PDF resume...\n",
+            "✅ Resume processed.\n",
+            "Calling AI to find skill gap...\n",
+            "✅ AI found skill gap: PyTorch\n",
+            "\n",
+            "Calling AI to find open-source project for 'PyTorch'...\n",
+            "✅ AI found project!\n",
+            "\n",
+            "\n",
+            "--- 🎯 YOUR PERSONALIZED PLAN ---\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "## Your #1 Skill Gap: *PyTorch*"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "---"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "### Project Recommendation: *pytorch*"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "*GitHub Repo:* `github.com/pytorch/pytorch`"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "*Why Contribute?* Contributing to PyTorch is the best way to prove your ML skills and be part of a community-driven deep learning framework."
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "### Recommended 'Good First Issues'"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "*The AI's search query will find real, up-to-date 'good first issues' for you to tackle.*"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "### Find Live Issues on GitHub"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "Here is the exact query to find issues you can start on today:"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "```bash\nis:issue is:open repo:pytorch/pytorch label:\"good first issue\"\n```"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "**[Click here to see live issues on GitHub]([https://github.com/](https://github.com/)pytorch/pytorch/issues?q=is%3Aissue%20is%3Aopen%20repo%3Apytorch/pytorch%20label%3A%22good%20first%20issue%22)**"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file