Finally, to make a more accurate prediction, we used a random forest regression model, which is an ensemble learning method for regression that combines predictions from multiple decision tree models. As expected, random forest predictions are better than all the other algorithms that we have tried with R 2 value of 0.941 and 0.135 RMSE on the test set. Figure summarizes the different ML regression models that we have tried. We note that Yang et al. compared random forest regression models with deep neural networks (DNN) and showed DNN model performs better than the random forest regression model . However, DNNs typically require much more data than what is available for this study.