ml_pipeline_3.py

# Define the Pipeline
"""
Step1: get the oultet binary columns
Step2: pre processing
Step3: Train a Random Forest Model
"""
model_pipeline = Pipeline(steps=[('get_outlet_binary_columns', OutletTypeEncoder()), 
                                 ('pre_processing',pre_process),
                                 ('random_forest', RandomForestRegressor(max_depth=10,random_state=2))
                                 ])
# fit the pipeline with the training data
model_pipeline.fit(train_x,train_y)

# predict target values on the training data
model_pipeline.predict(train_x)