diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 406a5e2..ac3d3eb 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -80,18 +80,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Create and Train a Decision Tree Classifier and print the train and test accuracy\n",
+    "# Create and Train a Decision Tree Classifier and print the train and test accuracy\n",
     "\n",
     "from sklearn.tree import DecisionTreeClassifier\n",
     "from sklearn.metrics import accuracy_score, mean_squared_error\n",
     "\n",
     "# Train Decision Tree\n",
-    "\n",
+    "dt_clf = DecisionTreeClassifier(random_state=0)\n",
+    "dt_clf.fit(X_train_scaled, y_train)\n",
     "\n",
     "# Predictions and evaluation\n",
+    "y_train_pred = dt_clf.predict(X_train_scaled)\n",
+    "y_test_pred = dt_clf.predict(X_test_scaled)\n",
     "\n",
+    "# Evaluate performance\n",
+    "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
+    "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
     "\n",
-    "# Evaluate performance\n"
+    "print(f\"Train Accuracy: {train_accuracy}\")\n",
+    "print(f\"Test Accuracy: {test_accuracy}\")"
    ]
   },
   {
@@ -147,12 +154,19 @@
     "from sklearn.ensemble import BaggingClassifier\n",
     "\n",
     "# Train BaggingClassifier\n",
-    "\n",
+    "weak_bagging_clf = BaggingClassifier(base_estimator=DecisionTreeClassifier(max_depth=1), n_estimators=100, random_state=0)\n",
+    "weak_bagging_clf.fit(X_train_scaled, y_train)\n",
     "\n",
     "# Predictions and evaluation\n",
+    "y_train_weak_bagging_pred = weak_bagging_clf.predict(X_train_scaled)\n",
+    "y_test_weak_bagging_pred = weak_bagging_clf.predict(X_test_scaled)\n",
     "\n",
+    "# Evaluate performance\n",
+    "train_weak_bagging_accuracy = accuracy_score(y_train, y_train_weak_bagging_pred)\n",
+    "test_weak_bagging_accuracy = accuracy_score(y_test, y_test_weak_bagging_pred)\n",
     "\n",
-    "# Evaluate performance\n"
+    "print(f\"Weak Bagging Train Accuracy: {train_weak_bagging_accuracy}\")\n",
+    "print(f\"Weak Bagging Test Accuracy: {test_weak_bagging_accuracy}\")"
    ]
   },
   {
@@ -204,12 +218,19 @@
     "from sklearn.ensemble import AdaBoostClassifier\n",
     "\n",
     "# Train AdaBoost\n",
-    "\n",
+    "adaboost_clf = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1), n_estimators=100, random_state=0)\n",
+    "adaboost_clf.fit(X_train_scaled, y_train)\n",
     "\n",
     "# Predictions and evaluation\n",
+    "y_train_adaboost_pred = adaboost_clf.predict(X_train_scaled)\n",
+    "y_test_adaboost_pred = adaboost_clf.predict(X_test_scaled)\n",
     "\n",
+    "# Evaluate performance\n",
+    "train_adaboost_accuracy = accuracy_score(y_train, y_train_adaboost_pred)\n",
+    "test_adaboost_accuracy = accuracy_score(y_test, y_test_adaboost_pred)\n",
     "\n",
-    "# Evaluate performance\n"
+    "print(f\"AdaBoost Train Accuracy: {train_adaboost_accuracy}\")\n",
+    "print(f\"AdaBoost Test Accuracy: {test_adaboost_accuracy}\")"
    ]
   },
   {