Initial project setup for fraud detection system

- Define project structure with data, experiments, models, and src directories
- Outline key tasks: EDA, feature engineering, model training, API and UI development
- Document dataset features and project requirements
- Create comprehensive README with implementation roadmap

src/model_training.py

@@ -133,6 +133,41 @@ def plot_feature_importance(model, feature_names):
     else:
         importances = model.named_steps['classifier'].feature_importances_
 
+    # Get the transformed feature names from the pipeline
+    if hasattr(model, 'named_steps'):
+        # For pipeline models, get the feature names from the preprocessor
+        preprocessor = model.named_steps['preprocessor']
+        # Get the transformed feature names
+        transformed_features = []
+
+        # Handle numerical features (they keep their names)
+        numerical_features = preprocessor.transformers_[0][2]  # Numerical features list
+        transformed_features.extend(numerical_features)
+
+        # Handle categorical features (they get expanded with one-hot encoding)
+        categorical_features = preprocessor.transformers_[1][2]  # Categorical features list
+        categorical_transformer = preprocessor.transformers_[1][1]  # OneHotEncoder
+        if hasattr(categorical_transformer, 'get_feature_names_out'):
+            # For newer scikit-learn versions
+            cat_feature_names = categorical_transformer.get_feature_names_out(categorical_features)
+        else:
+            # For older scikit-learn versions
+            cat_feature_names = categorical_transformer.named_steps['onehot'].get_feature_names(categorical_features)
+        transformed_features.extend(cat_feature_names)
+
+        # Handle binary features (they pass through)
+        binary_features = preprocessor.transformers_[2][2]  # Binary features list
+        transformed_features.extend(binary_features)
+
+        # Use the transformed feature names
+        feature_names = transformed_features
+
+    # Make sure the lengths match
+    if len(feature_names) != len(importances):
+        print(f"Warning: Feature names length ({len(feature_names)}) doesn't match importances length ({len(importances)})")
+        # Use generic feature names if lengths don't match
+        feature_names = [f'Feature {i}' for i in range(len(importances))]
+
     # Create a DataFrame for visualization
     feature_importance = pd.DataFrame({
         'Feature': feature_names,