We validated our models on a holdout/validation dataset representing 15% of the total data and used the F1 score (a metric that considers both the precision and recall of a classifier) to measure each model's quality. The F1 scores on the validation dataset for the three ML models generated were all very high, with values between 0.98-0.99 (see Table ).