Abstract
Introduction: In the present study, data mining is applied to predict the academic performance of university Engineering students at the National Autonomous University of Mexico during the year 2022. The introduction addresses the importance of understanding and anticipating academic performance as a means to implement more effective and personalized educational strategies.Objective: Develop a predictive model capable of identifying determining factors in the academic performance of students and predicting their future performance.Methodology: The methodology used includes the collection of academic and sociodemographic data from students, as well as the use of data mining techniques such as cluster analysis, decision trees and neural networks. The data was preprocessed to ensure quality and divided into training and test sets to validate the predictive model.Results: The results show that the developed model has a high accuracy in predicting academic performance, identifying key variables such as class attendance, participation in extracurricular activities and performance in previous exams. These variables were essential to build a robust and reliable model.Conclusion: the application of data mining has proven to be an effective tool to predict the academic performance of engineering students. This model not only provides a valuable tool for administrators and educators in decision making, but also opens new avenues for future research in the field of personalized education and improving academic performance.