There is a marked imbalance in the prediction classes: 86.8% of the data have the label 'Pass' and the remaining 13.2% of the data have the label 'Fail'. We use a class-balanced weighting scheme in the process of training the RF classifier, that supports a class-balanced weight for the loss function. The design of the AdaBoost algorithm that results in higher weights for misclassified samples does not warrant an explicit weight. Further, to tune the parameters of all models, we use a grid search of parameter values on the training data, for the combination that yields the highest prediction accuracy.