def save_x_y_scalar(X_train, Y_train):
    'Use a normalization method on your current dataset and save the coefficients.\n\n    Args:\n        X_train:    Input X_train\n        Y_train:    Lables Y_train\n    Returns:\n        Normalized X_train,Y_train ( currently using StandardScaler from scikit-learn)\n    '
    scalar_x = StandardScaler()
    X_train = scalar_x.fit_transform(X_train)
    scalar_y = StandardScaler()
    Y_train = scalar_y.fit_transform(Y_train)
    print('dumping StandardScaler objects ..')
    pickle.dump(scalar_y, open('pickles/scalar_y.pickle', 'wb'), protocol=pickle.HIGHEST_PROTOCOL)
    pickle.dump(scalar_x, open('pickles/scalar_x.pickle', 'wb'), protocol=pickle.HIGHEST_PROTOCOL)
    return (X_train, Y_train)
Use a normalization method on your current dataset and save the coefficients.

Args:
    X_train:    Input X_train
    Y_train:    Lables Y_train
Returns:
    Normalized X_train,Y_train ( currently using StandardScaler from scikit-learn)
def save_x_y_scalar(X_train, Y_train):
    'Use a normalization method on your current dataset and save the coefficients.\n\n    Args:\n        X_train:    Input X_train\n        Y_train:    Lables Y_train\n    Returns:\n        Normalized X_train,Y_train ( currently using StandardScaler from scikit-learn)\n    '
    scalar_x = StandardScaler()
    X_train = scalar_x.fit_transform(X_train)
    scalar_y = StandardScaler()
    Y_train = scalar_y.fit_transform(Y_train)
    print('dumping StandardScaler objects ..')
    pickle.dump(scalar_y, open('pickles/scalar_y.pickle', 'wb'), protocol=pickle.HIGHEST_PROTOCOL)
    pickle.dump(scalar_x, open('pickles/scalar_x.pickle', 'wb'), protocol=pickle.HIGHEST_PROTOCOL)
    return (X_train, Y_train)