Machine learning to increase applicants in the admission process of a public University in Lima-Peru

In recent years, admission to public universities by applicants has been a process of increasing competition, because the academic offer has been changing considerably, due to the increase in professional careers in private universities, weakening in In some cases, the study programs of public universities, which is why the present research was proposed, which aims to Implement a predictive model of machine learning to increase the number of applicants to the admission process in a public university, the method used was to use the information from the admissions processes of the years 2018 and 2019 of the public university, before the pandemic, to evaluate the data between seven machine learning classifiers under the conditions of only categorical data, categorical and numerical data and finally data standardized. The results show that the Logistic Regression, Decision tree classification and Random Forest Classification models, in that order, allow the evaluation of the corresponding information, supported by the confusion matrix and the indicator f1-score that allows to properly validate the results for groups that are not homogeneous in the data.