datset_classification = r'datasets\classification\autos.csv' data = pd.read_csv(datset_classification) X, y = data.drop(columns=['class']), data['class'] X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42) preprocessor = Preprocessor() preprocessor.add_branch("categorical") preprocessor.add_branch('numerical') preprocessor.add_transformer_to_branch("numerical", DtypeSelector(np.number)) preprocessor.add_transformer_to_branch("numerical", GeneralImputer('Simple')) preprocessor.add_transformer_to_branch("categorical", DtypeSelector(np.object)) preprocessor.add_transformer_to_branch( "categorical", GeneralImputer('Simple', strategy='most_frequent')) preprocessor.add_transformer_to_branch("categorical", GeneralEncoder(kind='LE')) final = preprocessor.merge() model = GridSelector('classification') clf_pipe = make_pipeline(final, model) clf_pipe.fit(X_train, y_train)