First suggestion: Decrease your polynomial degree to e.g. <= 5. Anything above will enter the realm of overfitting given your number of samples
Second suggestion: Upgrade Scikit learn to the bleeding edge github version, this seems to be a bug related to an exception raised because your matrix is singular.
If you cannot upgrade scikit learn, try using a stronger regularization:
import numpy as np
_, S, _ = np.linalg.svd(X, full_matrices=False)
s = S[0]
alpha = 1.2 * s # you may vary this fraction between 0.1 and larger
regularized_regr=linear_model.Ridge(alpha=alpha)
regularized_regr.fit(X,Y)