For the gradient boosting trees model, there are several hyperparameters to optimize -includ-ing the number of trees, maximum tree depth, minimum sample split, learning rate, and the loss function. Optimization started using the common starting parameters of 1000 trees, unlimited maximum tree depth, minimum sample split of 2, learning rate of 0.01, and the least squares loss function. Parameters were manually sampled, comparing the mean square error (MSE) score. This initial sampling did not noticeably affect the performance relative to the random forest and the neural network model. Therefore a more exhaustive grid search over these hyperparameters was not performed.