# Interface
class DataProcessor:
  def __init__(self, train_data_loader, test_data_loader, data_transformer, model):
    self.train_data_loader = train_data_loader
    self.test_data_loader = test_data_loader
    self.data_transformer = data_transformer
    self.model = model

  def run(self):
    # Load train and test data using data loaders
    train_df = self.train_data_loader.get_data()
    test_df = self.test_data_loader.get_data()

    # Transform the data using the data transformer
    X_train, y_train, X_test = self.data_transformer.transform_data(train_df, test_df)

    # Fit the model and make prediction
    self.model.fit(X_train, y_train)
    test_df['Vehicles'] = self.model.predict(X_test)

    # Save the transformed training data and test data
    self.train_data_loader.save_data(pd.concat([X_train, y_train], axis=1))
    self.test_data_loader.save_data(test_df)

# Create a data processor instance
process = DataProcessor(
    train_data_loader=CSVDataLoader(file_path='train.csv'),
    test_data_loader=CSVDataLoader(file_path='test.csv'),
    data_transformer=DataTransformer(),
    model=LGBMModel(num_leaves=16, n_estimators=80)
)

# Run the data processing pipeline
process.run()