Implementation of Machine Learning Algorithms To Classify University Academic Success

Machine learning is a software tool that allows information present in databases to be converted into intelligent decisions based on data and not on experience or feelings. This work presents a review of the algorithms used in the classification of student performance present in the database of the national technical university, specifically in the software engineering career. Information classification analysis using machine learning algorithm has become a modern and powerful instrument, which could help academic institutions improve retention and performance rate of courses by students and with this obtain a more general view of student performance before starting the course based on information from other courses and general student variables in order to reduce the risk of failure. The main objective of this work is to describe the variables used by the National Technical University and the application of different automated learning algorithms in order to obtain the metrics that allow demonstrating the best algorithm among those studied. Finally, the article concludes with a classification algorithm that provides an accuracy of around 80% to 82% that can be replicated in other university institutions.