We examined three regression models, random forest (RF), neural network (NN), and gradient-boost decision tree (GBDT). The dataset was split into 8:2 for training and test, respectively. When performing hyperparameter optimization, 25% of the training dataset was used for validation. The same test dataset was used in all regressions, and the prediction performance was evaluated by the typical coefficient of determination (R 2 ), the mean absolute error (MAE), and the root mean squared error (RMSE). Machine learning and hyperparameter optimization were implemented using scikit-learn, pandas, numpy, and optuna libraries. The machine learning model was interpreted using feature importance and Shapley additive explanations (SHAP) .