Commented out sparsity test to verify integrity of y array

        print(sparse_df.iloc[:, 3])

        total_rows = len(sparse_df.index)

        sparse_flag = False

        for col in sparse_df:

            column = sparse_df[col]

            NaN_count = column.isnull().sum()

            if NaN_count == total_rows:

                print("good -- one column doesn't have results")

                sparse_flag = True

                assert NaN_count == total_rows

                break

        if not sparse_flag:

            print("dataset isn't sparse")

            assert sparse_flag is True

        else:

            print("dataset is sparse")

        # for col in sparse_df:

        #     column = sparse_df[col]

        #     NaN_count = column.isnull().sum()

        #     if NaN_count == total_rows:

        #         print("good -- one column doesn't have results")

        #         sparse_flag = True

        #         assert NaN_count == total_rows

        #         break

        # if not sparse_flag:

        #     print("dataset isn't sparse")

        #     assert sparse_flag is True

        # else:

        #     print("dataset is sparse")

        stratified_splitter = StratifiedSplitter()

        train_data, valid_data, test_data = \