multicollinearity ridgecv

# Import Libraries
from sklearn.linear_model import RidgeCV

# Define the range of alpha values to consider
alphas = np.logspace(-5, 5, 100)

# Fit the RidgeCV model
ridge_cv = RidgeCV(alphas=alphas, cv=3)  # 3-fold cross-validation
ridge_cv.fit(X_train, y_train)

# Get the best alpha value
best_alpha = ridge_cv.alpha_
print("Best alpha:", best_alpha)

# Evaluate the model with the best alpha value
y_pred = ridge_cv.predict(X_test)