mlprod:pipeline2.py

from sklearn.compose import ColumnTransformer

# Define you transformers.
preprocessing = ColumnTransformer(transformers=[
    ('encode_categorical_features', OneHotEncoder(), ['categorical_column'])
], remainder='passthrough')

estimator = GradientBoostingClassifier()

# Define steps in the pipeline.
pipeline = Pipeline(steps=[
    ('preprocessing', preprocessing),
    ('model', estimator)
])

pipeline.fit(x_train, y_train)