Sử dụng các mô hình Machine Learning dự đoán tình trạng sinh viên tốt nghiệp đúng hạn

Abstract

The study aims to perform optimal Machine Learning model selection to predict the on-time graduation status of students. By using the dataset of students majoring in Banking faculty from the Banking Academy during the period of 2010-2020 through Machine Learning models such as Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, XGBoost, and CatBoost, the study has chosen Random Forest as the optimal model. The research has identified 2 attributes: Academic processing information and Grade Point Average (GPA) of semesters 1 through 4 have a strong impact on the ability of students to graduate on time or late, and proposed some recommendations to help the school provide solutions to improve the graduation rate of students.