# load training and test data with pandas train_df = pd.read_csv('train_data.csv') test_df = pd.read_csv('test_data.csv') # scale values to 0 to 1 for the ANN to work well s = MinMaxScaler(feature_range=(0,1)) # scale training and test data sc_train = s.fit_transform(train_df) sc_test = s.transform(test_df) # print scaling adjustments print('Scalar multipliers') print(s.scale_) print('Scalar minimum') print(s.min_) # convert scaled values back to dataframe sc_train_df = pd.DataFrame(sc_train, columns=train_df.columns.values) sc_test_df = pd.DataFrame(sc_test, columns=test_df.columns.values) # save scaled values to CSV files sc_train_df.to_csv('train_scaled.csv', index=False) sc_test_df.to_csv('test_scaled.csv', index=False)