def get_transformed_feature_names(column_transformer, columns):
    features = []
    for _, transformer, cols in column_transformer.transformers_:
        if isinstance(transformer, OrdinalEncoder):
            features.extend(cols)
        elif isinstance(transformer, OneHotEncoder):
            features.extend(transformer.get_feature_names(cols))
        elif transformer == "passthrough":
            features.extend(columns[cols])
        else:
            raise NotImplementedError(f"Transformer {transformer} is not implemented.")
    return np.array(features)

select_pipeline.fit(titanic_train_X, titanic_train_y)
feature_names = get_transformed_feature_names(select_pipeline['columntransformer'], titanic_train_X.columns)

print(feature_names[select_pipeline['selectkbest'].get_support()])