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

Inssaf El Guabassi, Zakaria Bousalem, Rim Marah, Aimad Qazdar


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.


Machine Learning, Educational Data Mining, Linear Regression, Decision Tree, Support Vector Regression, Random Forest Regression

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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