diff --git a/a2/code/hyper_param.py b/a2/code/hyper_param.py
index 0c96de9b..d82d127a 100644
--- a/a2/code/hyper_param.py
+++ b/a2/code/hyper_param.py
@@ -34,12 +34,17 @@ def preprocess_data(df_train, df_test):
 
 def predict():
     hyper_params = {
-            'C': np.logspace(-3, 3, 7, 10, 20),
-            'penalty': ['l1', 'l2', 'elasticnet'],
-            'solver': ['liblinear', 'saga', 'lbfgs', 'newton-cg'],
-            'max_iter': [50, 100, 1000, 2500, 5000],
-            'class_weight': ['balanced', None],
-            'tol': [1e-4, 1e-3, 1e-2, 1e-1, 1],
+            "n_estimators": [50, 100, 200, 300, 400, 500, 600, 700],
+            "learning_rate": [0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5],
+            "max_depth": [1, 3, 4, 5, 6, 7, 8, 9, 10],
+            "min_samples_split": [2, 5, 10, 15, 100],
+            "ccp_alpha": [0.0, 0.001, 0.005, 0.01, 0.05],
+            "loss": ["deviance", "exponential"],
+            "tol": [1e-4, 1e-3, 1e-2, 1e-1, 1e-0],
+            "validation_fraction": [0.1, 0.2, 0.3, 0.4, 0.5],
+            "min_samples_leaf": [1, 2, 5, 10],
+            "subsample": [0.6, 0.7, 0.8, 0.9, 1.0],
+            "max_features": ["auto", "sqrt", "log2"],
     }
 
     df_train, df_test = load_data()
@@ -47,11 +52,11 @@ def predict():
     X_train, y_train, X_test, y_test = preprocess_data(df_train, df_test)
 
     # Model 
-    from sklearn.linear_model import LogisticRegression
+    from sklearn.ensemble import GradientBoostingClassifier
     from sklearn.model_selection import GridSearchCV
 
     model = GridSearchCV(
-        estimator=LogisticRegression(), 
+        estimator=GradientBoostingClassifier(), 
         param_grid=hyper_params,
         cv=5, 
         n_jobs=-1,
@@ -67,3 +72,17 @@ def predict():
 
 if __name__ == "__main__":
     predict()
+
+#### Logistic Regression
+# Best parameters: {'C': 20.0, 'class_weight': None, 'max_iter': 50, 'penalty': 'l2', 'solver': 'lbfgs', 'tol': 0.0001}
+# Accuracy: 0.8660287081339713
+# Hyperparams tuned:
+#    hyper_params = {
+#            'C': np.logspace(-3, 3, 7, 10, 20),
+#            'penalty': ['l1', 'l2', 'elasticnet'],
+#            'solver': ['liblinear', 'saga', 'lbfgs', 'newton-cg'],
+#            'max_iter': [50, 100, 1000, 2500, 5000],
+#            'class_weight': ['balanced', None],
+#            'tol': [1e-4, 1e-3, 1e-2, 1e-1, 1],
+#    }
+######################