Predictive model for the early detection of students with high risk of academic dropout

Abstract

The results of 4 predictive models, logistic regression, decision trees, KNN and a neural network are compared to predict the academic dropout of students at the National Intercultural University of the Amazon, applied to a dataset extracted from the system's database. of academic management of the university, which contains socioeconomic and academic performance data which were processed and formatted using onehotencoding techniques in order to apply the predictive models already mentioned. For data processing and formatting, Transac Sql queries were used and the application of predictive models was done through Knime Software and using Python through Google Colab. The results obtained by applying 4 predictive models are very good since they all exceeded 80% of Accuracy, which guarantees that they can be put into production for the benefit of the university and thus can make better decisions when addressing academic dropout. . It is concluded that applying a predictive model in universities for the early detection of students with high risk of academic dropout is viable and very beneficial so that universities, through their academic managers, can apply more focused strategies to reduce their academic dropout rates.