print('Standard Deviations = ', scaler.scale_)
## Modeling and Predictions
# L1_cv
l1_cv = LassoCV(cv=5, max_iter=10000)
#100 Regularization coefficients evenly spaced between 0.1 and 1000
l1_cv.alphas = tuple(np.linspace(0.1, 1000, 100))
l1_cv.fit(train, y_train)
res_l1 = l1_cv.predict(test)
print(res_l1)
# L2_cv
l2_cv = RidgeCV(cv=None, store_cv_values=True)
#100 Regularization coefficients evenly spaced between 0.1 and 1000
l2_cv.alphas = tuple(np.linspace(0.1, 10000, 100))
l2_cv.fit(train, y_train)
res_l2 = l2_cv.predict(test)
print(res_l2)
# res_xgb
model_xgb = xgb.XGBRegressor(colsample_bytree=0.2,
gamma=0.0,
learning_rate=0.05,
max_depth=6,
min_child_weight=1.5,
n_estimators=7200,
reg_alpha=0.9,
reg_lambda=0.6,
subsample=0.2,
