The primary model type used with feature configs 'B' to 'F' was the feedforward neural network. FNNs have a structure of fully connected layers, also known as 'dense layers'. Every node in a dense layer has individual connections to all nodes in the previous layer. The number of tuneable parameters in a FNN are defined upon model instantiation, i.e. it is independent of the training process. The more parameters a model has, the greater its 'capacity', meaning that it is capable of fitting a larger set of functions . Nevertheless, high-capacity models are prone to overfitting and require a regularisation strategy. Overfitting is the learning of specific data sequences or dependencies rather than general patterns. Two regularisation strategies were used in this work, the main one being 'early stopping'. An additional set of results was produced by combining early stopping and 'dropout'.