Construcción de un modelo para diagnosticar y pronosticar el rendimiento académico de los estudiantes del proyecto curricular de ingeniería catastral de la Universidad Distrital Francisco José de Caldas, utilizando series de tiempo y machine learning.

Abstract

This document presents the research for the construction of a diagnostic and prognostic model of the academic performance of the Cadastral Engineering curricular project using the tools of time series and Machine Learning. An analysis of the results obtained in the preliminary investigations is documented. The purpose is to know the general characteristics of the student and the motivations for abandoning the Engineering career, in this way to be able to predict which processes or activities can be improved over time to improve the student's academic performance. Consequently, the contribution of time series and machine learning will be evaluated and determined to arrive at statistical data results with reliable and effective results for decision making.