In the present work, a random forest regression method was used to learn the complex relationships between the five input and the seven output parameters. A random forest fits a number of decision tree regressors (forest) on several dataset sub-samples averaging them with the main goal of reducing the variance, thus, making it less likely to overfit in comparison to a single decision tree solution. Given the use of out-of-the-bag evaluation technique in the random forest regression method, we do not require a validation dataset. The model was optimized by minimizing the mean square error (MSE).