def get_feature_importance_data(data_income, column='Close_x', include_targets=False):
    """

    :param data_income:
    :param column:
    :param include_targets: targets are 'Open_x', 'High_x', 'Low_x' and 'Close_x'
    :return: train and test of X and y
    """
    data = data_income.copy()

    y = data[column]
    if include_targets:
        X = data.drop(column, axis=1).drop('Date', axis=1)
    else:
        X = data.iloc[:, 5:]

    train_samples = int(X.shape[0] * 0.7)

    X_train = X.iloc[:train_samples]
    X_test = X.iloc[train_samples:]

    y_train = y.iloc[:train_samples]
    y_test = y.iloc[train_samples:]

    return (X_train, y_train), (X_test, y_test)