Linear regression is a natural starting point. However, it was observed the relationship between the cumulative green and product formation was not linear. To remedy this different power transformations were applied, and they were evaluated by the improvement in accuracy of the transformed features in a linear regression model compared to the nontransformed feature. After evaluating the different power transformations, a Yeo-Johnson transformation was selected as the most promising. The purpose of a Yeo-Johnson transformation is to reduce the skewness and make the distribution of the data more Gaussian. Applying the Yeo-Johnson transformation to cumulative green yielded a more Gaussian-like distribution, as can be observed in Figure . Other models explored included polynomial regression. The polynomial used was a cubic function. This model was considered suitable due to the non-linear relationship between cumulative green and product. Random forest and gradient boosting regression, both based on decision tree models, were also explored.