Each ML model was trained on 80% of the dataset and validated on the remaining 20% using kfold cross-validation (k = 5) to ensure robustness and minimize overfitting. The performance of the models was evaluated using key metrics, including the coefficient of determination (R 2 ), root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE).