Markov Chains, Computer Science, Programming, Probability, Problem Solving.
y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Ridge Regression model alpha = 0.01 # Regularization strength (adjust as needed) ridge_reg = Ridge(alpha=alpha) ridge_reg.fit(X_train, y_train) # Predictions on the test set y_pred = ridge_reg.predict(X_test) # Evaluate the