After identifying the different models' performance and their respective evaluation metrics (discussed in previous sections), we combined multiple models to improve the overall performance and yield a consensus prediction by generating an ensemble learning. We selected the five models summarized in we decided to prioritize not only models with high precision but also those that were more susceptible to making a correct prediction of success. Therefore, we selected models with good recall, prioritizing these models over others with high-balanced accuracy and high F1 values. ). This result suggested that even when metrics were equal, Ensemble1 was better at identifying TP than individual LRG models. Furthermore, Ensemble 2 was better than LRG models predicting TP.