Análisis y visualización de patrones en datos académicos con Machine Learning

Abstract

Abstract: Patterns identification in academic student data is a relevant topic in the educational field. For this reason, the main objective of this research is to use machine learning algorithms to identify and visualize patterns in students of Information Technology career, which correspond to the academic periods 2020 to 2023. Also, CRISP-DM methodology was used and includes data collection, cleaning and preparation, selection and application of machine learning algorithms, evaluation and validation of results and interpretation of the identified patterns. Among the machine learning algorithms used, the unsupervised clustering method stands out, which allowed the discovery of patterns that facilitated a better understanding. Furthermore, in this research a web system has been developed using Django framework, for visualizing the patterns. This system allows loading academic datasets and visualization of patterns through an interactive dashboard.