diff --git a/Module_3/Project/Heart_disease_prediction/xgboost_heart_disease.ipynb b/Module_3/Project/Heart_disease_prediction/xgboost_heart_disease.ipynb
index e370802e..f47f4b46 100644
--- a/Module_3/Project/Heart_disease_prediction/xgboost_heart_disease.ipynb
+++ b/Module_3/Project/Heart_disease_prediction/xgboost_heart_disease.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -31,39 +31,30 @@
     }
    ],
    "source": [
-    "# Loading the dataset you provided earlier\n",
     "df = pd.read_csv('Cleveland data.csv', header=None)\n",
-    "\n",
-    "# Assigning column names based on the screenshot\n",
     "df.columns = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang',\n",
     "              'oldpeak', 'slope', 'ca', 'thal', 'target']\n",
     "\n",
-    "# Remap the target variable: 0 -> 0, 1 -> 1, 2 -> 1, 3 -> 1, 4 -> 1 (0 for no disease, 1 for disease)\n",
     "df['target'] = df.target.map({0: 0, 1: 1, 2: 1, 3: 1, 4: 1})\n",
-    "\n",
-    "# Handle missing values in 'thal' and 'ca' columns by replacing NaN with the mean\n",
     "df['thal'] = df.thal.fillna(df.thal.mean())\n",
     "df['ca'] = df.ca.fillna(df.ca.mean())\n",
     "\n",
-    "# Distribution of target vs age\n",
     "plt.figure(figsize=(10, 6))\n",
     "sns.histplot(data=df, x='age', hue='target', multiple='stack', kde=False)\n",
     "plt.title('Distribution of Target vs Age')\n",
     "plt.xlabel('Age')\n",
     "plt.ylabel('Count')\n",
-    "\n",
-    "# Display the plot\n",
     "plt.show()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -73,30 +64,27 @@
     }
    ],
    "source": [
-    "# Creating a barplot of age vs sex with hue set to target\n",
     "plt.figure(figsize=(10, 6))\n",
     "sns.barplot(data=df, x='sex', y='age', hue='target')\n",
     "\n",
-    "# Adding labels and title\n",
     "plt.title('Barplot of Age vs Sex with Target Hue')\n",
     "plt.xlabel('Sex')\n",
     "plt.ylabel('Age')\n",
     "\n",
-    "# Show the plot\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy for training set for Naive Bayes = 0.76\n",
-      "Accuracy for test set for Naive Bayes = 0.69\n"
+      "Accuracy for training set for KNN = 0.76\n",
+      "Accuracy for test set for KNN = 0.69\n"
      ]
     }
    ],
@@ -135,15 +123,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy for training set for Naive Bayes = 0.66\n",
-      "Accuracy for test set for Naive Bayes = 0.67\n"
+      "Accuracy for training set for SVM = 0.66\n",
+      "Accuracy for test set for SVM = 0.67\n"
      ]
     }
    ],
@@ -170,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -204,15 +192,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy for training set for Naive Bayes = 1.0\n",
-      "Accuracy for test set for Naive Bayes = 0.75\n"
+      "Accuracy for training set for DecisionTreeClassifier = 1.0\n",
+      "Accuracy for test set for DecisionTreeClassifier = 0.75\n"
      ]
     }
    ],
@@ -242,15 +230,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy for training set for Naive Bayes = 0.98\n",
-      "Accuracy for test set for Naive Bayes = 0.8\n"
+      "Accuracy for training set for RandomForestClassifier = 0.98\n",
+      "Accuracy for test set for RandomForestClassifier = 0.8\n"
      ]
     }
    ],
@@ -282,23 +270,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/Users/microwave/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
-      "  warnings.warn(\n"
+      "Accuracy for training set for AdaBoost = 0.91\n",
+      "Accuracy for test set for AdaBoost = 0.84\n"
      ]
     },
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Accuracy for training set for Naive Bayes = 0.91\n",
-      "Accuracy for test set for Naive Bayes = 0.84\n"
+      "/Users/microwave/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
+      "  warnings.warn(\n"
      ]
     }
    ],
@@ -328,15 +316,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy for training set for Naive Bayes = 1.0\n",
-      "Accuracy for test set for Naive Bayes = 0.85\n"
+      "Accuracy for training set for GradientBoosting = 1.0\n",
+      "Accuracy for test set for GradientBoosting = 0.85\n"
      ]
     }
    ],
@@ -369,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -408,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {