The uncertainty in the predictions of the best-performing machine learning model was estimated by using the bootstrap technique. In the bootstrap technique, multiple training datasets are generated by random sampling from the original training dataset with replacement while keeping the size of each sampled dataset the same as our original training dataset. After that, multiple models are developed using these sampled training datasets while keeping the model's hyperparameters constant. We have created 1000 such training datasets and trained 1000 machine learning models. Thus, for each test candidate, 1000 predictions were obtained from which the uncertainties such as the mean and standard deviation of the prediction are estimated.