diff --git a/lab-hyper-tuning.ipynb b/lab-hyper-tuning.ipynb
index 847d487..976b5c8 100644
--- a/lab-hyper-tuning.ipynb
+++ b/lab-hyper-tuning.ipynb
@@ -221,11 +221,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code here"
+    "#feature scaling\n",
+    "spaceship = spaceship.dropna().reset_index(drop=True)\n",
+    "spaceship['Cabin'] = spaceship['Cabin'].str.split('/').str[0]\n",
+    "spaceship = spaceship.drop(columns=['PassengerId', 'Name'])\n",
+    "spaceship = pd.get_dummies(spaceship, drop_first=True)\n",
+    "y = spaceship['Transported_True']\n",
+    "X = spaceship.drop(columns=['Transported_True'])\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
+    "print(f\"X_train shape: {X_train.shape}\")\n",
+    "print(f\"X_test shape: {X_test.shape}\")\n",
+    "\n",
+    "#feature selection\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "X_train_scaled = pd.DataFrame(X_train_scaled, columns=X_train.columns, index=X_train.index)\n",
+    "X_test_scaled = pd.DataFrame(X_test_scaled, columns=X_test.columns, index=X_test.index)\n",
+    "X_train_scaled.head()"
    ]
   },
   {
@@ -241,7 +259,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code here"
+    "param_grid = {\n",
+    "    'n_estimators': [100, 200, 300],\n",
+    "    'learning_rate': [0.01, 0.05, 0.1, 0.2],\n",
+    "    'max_depth': [2, 3, 4],\n",
+    "    'min_samples_split': [2, 5, 10],\n",
+    "    'subsample': [0.8, 1.0]\n",
+    "}\n",
+    "\n",
+    "gb = GradientBoostingClassifier(random_state=42)\n",
+    "grid_search = GridSearchCV(\n",
+    "    estimator=gb,\n",
+    "    param_grid=param_grid,\n",
+    "    cv=5,\n",
+    "    scoring='accuracy',\n",
+    "    n_jobs=-1,\n",
+    "    verbose=1\n",
+    ")\n",
+    "grid_search.fit(X_train, y_train)\n",
+    "print(f\"Best parameters: {grid_search.best_params_}\")\n",
+    "print(f\"Best cross-validation accuracy: {grid_search.best_score_:.4f}\")"
    ]
   },
   {
@@ -253,11 +290,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code here"
+    "best_gb = grid_search.best_estimator_\n",
+    "y_pred_best = best_gb.predict(X_test)\n",
+    "accuracy = accuracy_score(y_test, y_pred_best)\n",
+    "print(f\"Tuned Gradient Boosting Test Accuracy: {accuracy:.4f}\")\n",
+    "print(\"\\nClassification Report:\")\n",
+    "print(classification_report(y_test, y_pred_best))\n",
+    "print(\"Confusion Matrix:\")\n",
+    "print(confusion_matrix(y_test, y_pred_best))"
    ]
   },
   {
@@ -287,7 +331,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code here"
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "param_grid = {\n",
+    "    'n_estimators': [100, 200, 300],\n",
+    "    'learning_rate': [0.01, 0.05, 0.1, 0.2],\n",
+    "    'max_depth': [2, 3, 4],\n",
+    "    'min_samples_split': [2, 5, 10],\n",
+    "    'subsample': [0.8, 1.0]\n",
+    "}"
    ]
   },
   {
@@ -302,7 +354,20 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "gb = GradientBoostingClassifier(random_state=42)\n",
+    "grid_search = GridSearchCV(\n",
+    "    estimator=gb,\n",
+    "    param_grid=param_grid,\n",
+    "    cv=5,\n",
+    "    scoring='accuracy',\n",
+    "    n_jobs=-1,\n",
+    "    verbose=2\n",
+    ")\n",
+    "grid_search.fit(X_train, y_train)\n",
+    "print(f\"Best parameters: {grid_search.best_params_}\")\n",
+    "print(f\"Best cross-validation accuracy: {grid_search.best_score_:.4f}\")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -316,7 +381,17 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from sklearn.metrics import accuracy_store, classification_report, confusion_matrix\n",
+    "best_model = grid_search.best_estimator_\n",
+    "y_pred = best_model.predict(X_test)\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "print(f\"Test Accuracy: {accuracy:.4f}\")\n",
+    "print(\"\\nClassification Report:\")\n",
+    "print(classification_report(y_test, y_pred))\n",
+    "print(\"Confusion Matrix:\")\n",
+    "print(confusion_matrix(y_test, y_pred))"
+   ]
   }
  ],
  "metadata": {