In this section two machine learning methodologies were studied; RF and GBR. The influence of sampling the data was studied taken into account the performance of the algorithm in terms of computational time and error of the prediction. Additionally, the use of adaptive weights were shown to have positive influence in the prediction. In any case, it might be difficult to understand the magnitude of the error, due to the lack of comparative approach, which could lead to the weak understanding of the obtained results. For this reason, a Heuristic Model (HM) was designed, taken into account the feature analysis performed in Section 3.1. With this simple and heuristic method, we could understand how difficult is to achieve a good result without using complex models, taken into account only the knowledge of the problem.