  The on-time graduation rate among universities in Puerto Rico is
significantly lower than in the mainland United States. This problem is
noteworthy because it leads to substantial negative consequences for the
student, both socially and economically, the educational institution and the
local economy. This project aims to develop a predictive model that accurately
detects students early in their academic pursuit at risk of not graduating on
time. Various predictive models are developed to do this, and the best model,
the one with the highest performance, is selected. Using a dataset containing
information from 24432 undergraduate students at the University of Puerto Rico
at Mayaguez, the predictive performance of the models is evaluated in two
scenarios: Group I includes both the first year of college and pre-college
factors, and Group II only considers pre-college factors. Overall, for both
scenarios, the boosting model, trained on the oversampled dataset, is the most
successful at predicting who will not graduate on time.
