Abstract
Machine Learning allows us to reduce the human error probability by providing very strong recommendations, predictions, and decisions based on only the input data. For that reason, it has become one of the most important and common aspects of the digital world. Different application areas adapt and adopt Machine Learning techniques in their systems such as medicine, finance, marketing, business intelligence, healthcare, etc. In our case, we aim to design a recommender system based on Machine Learning techniques in the field of Education. Therefore, the 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.
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El Guabassi, I., Bousalem, Z., Marah, R., Qazdar, A. (2021). RSEPUA: A Recommender System for Early Predicting University Admission. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2021. Lecture Notes in Networks and Systems, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-030-73882-2_20
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DOI: https://doi.org/10.1007/978-3-030-73882-2_20
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