Back to the Machine Learning fundamentals: How to write code for Model deployment (Part 3/3)

Target_design_flaw.py

def train():
    # Read Data
    data = pd.read_csv(DATA_INGESTION['data_path'])
    target = DATA_INGESTION['data_map']['target']
    variables = DATA_INGESTION['data_map']['variables']

    #Preprocessing
    flt = data['umbrella_limit']>=0
    data = data[flt]
    data[target] = data[target].map(FEATURES_ENGINEERING['target_encoding'])

    #Split data
    X_train, X_test, y_train, y_test = train_test_split(data[variables], data[target],
                                        test_size=PREPROCESSING['train_test_split_params']['test_size'],
                                        random_state=PREPROCESSING['train_test_split_params']['random_state'])    
    #Train Pipeline
    Pipeline_Fit = pipeline.fit(X_train, y_train)

    #Save Model
    PostProcessing.save(Pipeline_Fit, PIPE_TRAINING['pipe_path'])