Due to virtually no difference in scoring among all models, the process was conducted to cover models 1 and 2 of GBR and RFR algorithms. Aforementioned scoring values were obtained by utilising default parameters of machine learning algorithms. Thus it is relevant to determine whether and how varying those parameters impact prediction results. Three parameters were chosen into the parameters tuning. The process was initially assessed by mean absolute error to determine one of the parameters (max_depth in case of GBR and n_estimators for RFR). Mean squared error, maximum error and R 2 were further calculated. Fig. and fig. show selected part of optimisation results of GBR and RFR algorithms accordingly. It is worth pointing that prediction capability of both algorithms benefit from altering parameters but the significant parameters are different. Although the performance improved in both cases, the GBR performed better than RFR.