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A Recommender System for Predicting Students' Admission to a Graduate Program using Machine Learning Algorithms

, Faculty of Sciences, Tetouan, Morocco ; , Faculty of Sciences and Technology, Settat Morocco ; , Faculty of Sciences, Tetouan, Morocco ; , ISI Laboratory, FS Semlalia UCA, Marrakech, Morocco

Abstract

In the 21st century, University educations are becoming a key pillar of social and economic life. It plays a major role not only in the educational process but also in the ensuring of two important things which are a prosperous career and financial security. However, predicting university admission can be especially difficult because the students are not aware of admission requirements. For that reason, the main purpose of this research work is to provide a recommender system for early predicting university admission based on four Machine Learning algorithms namely Linear Regression, Decision Tree, Support Vector Regression, and Random Forest Regression. The experimental results showed that the Random Forest Regression is the most suitable Machine Learning algorithm for predicting university admission. Also, the Cumulative Grade Point Average (CGPA) is the most important parameter that influences the chance of admission.

Citation

El Guabassi, I., Bousalem, Z., Marah, R. & Qazdar, A. (2021). A Recommender System for Predicting Students' Admission to a Graduate Program using Machine Learning Algorithms. International Association of Online Engineering. Retrieved March 1, 2021 from .