diff --git a/app/run.py b/app/run.py
index 5206582..4e6ab8b 100644
--- a/app/run.py
+++ b/app/run.py
@@ -177,6 +177,8 @@
 
     else:
         st.error(f"Failed to load model info: {info_response.status_code}")
+        st.error(f"Error: {info_response.text}")
+        st.error(f"Base URL: {API_BASE_URL}")
 
 except (requests.exceptions.ConnectionError, requests.exceptions.Timeout) as e:
     st.error(
diff --git a/models/v1/metadata.pkl b/models/v1/metadata.pkl
index 8788127..cc0d8c5 100644
Binary files a/models/v1/metadata.pkl and b/models/v1/metadata.pkl differ
diff --git a/models/v1/model.pkl b/models/v1/model.pkl
index 37eaa6b..2145af5 100644
Binary files a/models/v1/model.pkl and b/models/v1/model.pkl differ
diff --git a/models/v1/preprocessor.pkl b/models/v1/preprocessor.pkl
index 2c5bf43..5a30aab 100644
Binary files a/models/v1/preprocessor.pkl and b/models/v1/preprocessor.pkl differ
diff --git a/models/v2/metadata.pkl b/models/v2/metadata.pkl
index 5c8bc43..b02260c 100644
Binary files a/models/v2/metadata.pkl and b/models/v2/metadata.pkl differ
diff --git a/models/v2/model.pkl b/models/v2/model.pkl
index f04c60a..d5f6bf0 100644
Binary files a/models/v2/model.pkl and b/models/v2/model.pkl differ
diff --git a/src/model.py b/src/model.py
index 7eb19ee..989f56b 100644
--- a/src/model.py
+++ b/src/model.py
@@ -114,21 +114,50 @@ def save_model(model, preprocessor, metadata, version: str) -> str:
 
 
 def get_feature_importance(model, preprocessor):
-    """Get feature importance from the trained model."""
+    """Get feature importance from the trained model, aggregated by original features."""
     if not hasattr(model, 'feature_importances_'):
         return None
 
     # Get feature names after preprocessing
     if hasattr(preprocessor, 'get_feature_names_out'):
-        feature_names = preprocessor.get_feature_names_out()
+        transformed_feature_names = preprocessor.get_feature_names_out()
     else:
-        feature_names = [f"feature_{i}" for i in range(
+        transformed_feature_names = [f"feature_{i}" for i in range(
             len(model.feature_importances_))]
 
-    importance_df = pd.DataFrame({
-        'feature': feature_names,
-        'importance': model.feature_importances_
-    }).sort_values('importance', ascending=False)
+    # Create mapping from transformed features to original features
+    original_importance = {}
+    
+    for i, transformed_name in enumerate(transformed_feature_names):
+        # Get raw importance and format as percentage
+        importance = model.feature_importances_[i] * 100
+        
+        # Extract original feature name from transformed name
+        # OneHotEncoder creates names like 'cat_transformer__feature_name_category'
+        # RobustScaler creates names like 'num_transformer__feature_name'
+        if '__' in transformed_name:
+            parts = transformed_name.split('__')
+            if len(parts) >= 2:
+                original_feature = parts[1]
+                # For one-hot encoded features, remove the category suffix
+                if '_' in original_feature and parts[0] == 'cat_transformer':
+                    original_feature = '_'.join(original_feature.split('_')[:-1])
+            else:
+                original_feature = transformed_name
+        else:
+            original_feature = transformed_name
+            
+        # Aggregate importance by original feature
+        if original_feature in original_importance:
+            original_importance[original_feature] += importance
+        else:
+            original_importance[original_feature] = importance
+
+    # Create DataFrame and sort by importance
+    importance_df = pd.DataFrame([
+        {'feature': feature, 'importance': importance}
+        for feature, importance in original_importance.items()
+    ]).sort_values('importance', ascending=False)
 
     return importance_df.to_dict()