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simple_imputer = SimpleImputer(strategy='median')
std_scaler = StandardScaler()

pipe_num = Pipeline([('imputer', simple_imputer), ('scaler', std_scaler)])

s_imputer = SimpleImputer(strategy='constant', fill_value='unknown')
ohe_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)
pipe_cat = Pipeline([('imputer', s_imputer), ('encoder', ohe_encoder)])

col_transformer = ColumnTransformer([('num_preproc', pipe_num, [x for x in features.columns if features[x].dtype!='object']),
                                     ('cat_preproc', pipe_cat, [x for x in features.columns if features[x].dtype=='object'])])

model = VotingClassifier(estimators=[
        ('gbc', GradientBoostingClassifier(n_estimators=10, max_depth=50000, random_state=42)),
        ('nb', GaussianNB()),
        ('rf', KNeighborsClassifier(n_neighbors=3))], voting='soft')

final_pipe = Pipeline([('preproc', col_transformer),
                       ('model', model)])

final_pipe.fit(X_train_1, y_train_1)
preds = final_pipe.predict(X_test_1)