We ensure that each of the k test sets contains data from each cluster, drawn randomly and we ensure the samples in the kth test set do not overlap with the samples in the kth training set. While the clustering provides a mechanism to arrive at representative train and test sets for validation, the skew in the distribution between 'Pass' and 'Fail' samples remains. To address the class imbalance, in addition to cluster-based random sampling with the data for Phase3 trials, we also study the effect the addition of Phase2, 'Fail' records has on the prediction accuracy.