from sklearn.pipeline import Pipeline
                    from sklearn.preprocessing import StandardScaler, OneHotEncoder
                    from sklearn.compose import ColumnTransformer
                    from sklearn.linear_model import LogisticRegression
                    
                    numeric_features = ['income', 'age', 'debt_ratio']
                    numeric_transformer = Pipeline(steps=[('scaler', StandardScaler())])
                    
                    categorical_features = ['employment_type', 'residence_state']
                    categorical_transformer = Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))])
                    
                    preprocessor = ColumnTransformer(
                        transformers=[
                            ('num', numeric_transformer, numeric_features),
                            ('cat', categorical_transformer, categorical_features)
                        ]
                    )
                    
                    model = Pipeline(steps=[
                        ('preprocessor', preprocessor),
                        ('classifier', LogisticRegression())
                    ])