Update app.py

app.py

@@ -476,9 +476,8 @@ elif option == "SML Classification":
         'Income Bracket': [income_bracket]  # Directly use Income Bracket as numerical
     })
 
-    # OneHotEncode only the categorical variables
-    cat_encoded = pd.DataFrame(ohe.transform(cat_features).todense(),
-                               columns=ohe.get_feature_names_out(['Place of living', 'Education Level', 'Age Group']))
+    cat_encoded = pd.DataFrame(ohe.transform(cat_features), 
+                           columns=ohe.get_feature_names_out(['Place of living', 'Education Level', 'Age Group']))
 
     # Combine categorical and numerical features
     features = pd.concat([num_features, cat_encoded], axis=1)