try add smote

model.py

@@ -94,8 +94,8 @@ def main():
 
     # use SMOTE to handle imbalanced data
     
-    # smote = SMOTE(sampling_strategy='auto', random_state=21)
-    # x_resampled, y_resampled = smote.fit_resample(training_x, training_y)
+    smote = SMOTE(sampling_strategy='auto', random_state=21)
+    x_resampled, y_resampled = smote.fit_resample(training_x, training_y)
 
     # Train a MLP model
     model = MLPClassifier(
@@ -103,7 +103,7 @@ def main():
         early_stopping=True,
         hidden_layer_sizes=(64, 128, 64),
         random_state=62)
-    model.fit(training_x, training_y)
+    model.fit(x_resampled, y_resampled)
 
     predictions = model.predict(testing_x)
     # Make predictions