We used three ensemble-based models (Dietterich 2002) named (i) Random Forest regression (RFR) (Riga1 2017), (ii) Gradient boosted regression (GBR) , and (iii) Extreme gradient boosted regression (XGBR) ). These are tree-based models using several decision trees as a weak learner. The aggregate decision of all weak learners forms a strong and effective learner. All the weak learners are learned in parallel in the RFR model; the aggregate (mean) of the decisions is the model decision. In the GBR model, each sample is weighted equally, then the first weak learner trains the first sample, and so on. After learning, we compute the residual (difference between actual and predicted values), and the next tree is built based on the residual values.