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

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. Therefore, the contributions are threefold: The first is to apply several Supervised Machine Learning algorithms namely Linear Regression, Support Vector Regression, Decision Tree Regression, and Random Forest Regression. The second purpose is to compare and evaluate algorithms used to create a predictive model based on various evaluation metrics. The last purpose is to determine the most important parameters that influence the chance of admission. 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 is the most important parameter that influences the chance of admission.