Analysis of the academic performance of students of the Economics and Tourism majors with Power BI in the periods (2021)

Abstract

In the university world, there is great relevance in obtaining precise data where academic performance of students can be evaluated since it is a topic of great interest. Many times, the process becomes a bit tedious and complicated to reach a study object. The advantage of using tools for the analysis of academic performance of students is that it provides objective and precise data, which allows identifying areas of improvement, taking measures to address them, quick and efficiently dealing with processes required to handle large flows of data. The selected methodology, CRISP-DM, includes the execution of six stages: understanding the business, understanding the data, data preparation, modeling stages, evaluation, and deployment. Once the database is analyzed, variables and data filters are defined, and finally, the execution of automatic learning algorithms (Decision Tree, Random Forests, Neural Networks, Support Vector Machine) is carried out to obtain academic performance of students using Python language. The objective of this article is to perform an analysis of academic performance in Economics and Tourism students at the Technical University of Manabi for which different algorithms were evaluated, obtaining that the most efficient algorithm is the Random Forest giving precise values, allowing to obtain a dashboard with student statistics.