y = data.price.values
X = data.drop('price',axis = 1)
#X = np.transpose(X)

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)




from sklearn.linear_model import Ridge
clf = Ridge(alpha=1.0)
clf.fit(X_train, y_train)
clf.coef_



RMSE_train = rmse(y_train, clf.predict(X_train))
RMSE_test = rmse(y_test, clf.predict(X_test))
    print(rmse(y_train, clf.predict(X_train)), rmse(y_test, clf.predict(X_test)))

plt.plot(model_degrees, RMSEs, color="b", label="training error")
plt.plot(model_degrees, RMSEs_ho, color="orange", label="cross validation error")
plt.xlabel("m")
plt.ylabel("RMSE")
plt.legend()