Again to solve the preceding optimization problem, we need to resort to quadratic programming techniques, which are beyond the scope of our learning journey. Therefore, we won't cover the computation methods in detail and will implement the regression algorithm using the SVR package from scikit-learn.

Important techniques of SVC, such as penalty as a trade off between bias and variance, kernel (RBF, for example) handling linear non-separation, are transferable to SVR. The SVR package from scikit-learn also supports these techniques.

Let's solve the previous house price prediction problem with SVR this time:

>>> from sklearn.svm import SVR
>>> regressor = SVR(C=0.1, epsilon=0.02, kernel='linear')
>>> regressor.fit(X_train, y_train)
>>> predictions = regressor.predict(X_test)
>>> print(predictions)
[ 14.59908201 19.32323741 21.16739294 18.53822876 20.1960847
  23.74076575 22.65713954 26.98366295 25.75795682 22.69805145]