def METHOD_NAME(self):
    target = 'E_regression.1'
    extra_columns = ['Material compositions 1', 'Material compositions 2', 'Hop activation barrier', 'E_regression']
    d = LocalDatasets(file_path=os.path.join(mastml_path, 'data/figshare_7418492/All_Model_Data.xlsx'),
                      target=target,
                      extra_columns=extra_columns,
                      group_column='Material compositions 1',
                      as_frame=True)
    data_dict = d.load_data()
    X = data_dict['X']
    y = data_dict['y']
    preprocessor = SklearnPreprocessor(preprocessor='StandardScaler', as_frame=True)
    model = SklearnModel(model='LinearRegression')
    X = preprocessor.evaluate(X,y)
    X_train, X_test, y_train, y_test = train_test_split(X, y)
    model.fit(X=X_train, y=y_train)
    baseline = Baseline_tests()
    baseline.test_mean(X_train, X_test, y_train, y_test, model)
    os.remove("data_preprocessed_.csv")
    os.remove("StandardScaler.pkl")
    return