Additional feature scaling techniques in scikit-learn

sklearn==1.2.2

Suppose you have the following starter code:

from sklearn.model_selection import train_test_split
from sklearn.datasets import fetch_california_housing
from sklearn.svm import SVR
from sklearn.metrics import mean_absolute_error

data = fetch_california_housing()
X = data.data
Y = data.target

X_train, X_test, y_train, y_test = train_test_split(X, Y, train_size=0.8, random_state=42)

Apply the quantile transformer with the default parameters. Fit Epsilon-Support Vector Regression (svm.SVR()) with the default parameters on the rescaled train, and make the predictions on the test (apply .transform() on the test before making a prediction). Calculate the mean absolute error. Round the answer up to the third decimal.