from sklearn.svm import SVR
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline

# compute minimum and maximum on the training data
scaler = MinMaxScaler().fit(x_train)
# rescale training data
x_train_scaled = scaler.transform(x_train)

svm = SVR(gamma='auto')
# learn an SVM on the scaled training data
svm.fit(x_train_scaled, y_train)
# scale test data and score the scaled data
x_test_scaled = scaler.transform(x_test)
svm.score(x_test_scaled, y_test)


pipe = Pipeline([("scaler", MinMaxScaler()), ("svm", SVR())])

pipe.fit(x_train, y_train)

pipe.score(x_test, y_test)