CatBoost and Genetic Algorithm Implementations for University Recommendation Systems

Abstract

Graduating from a prestigious foreign university has recently become a common trend among students. Year after year, the number of students pursuing graduate and post-graduate degrees keep growing. Students choose international colleges for various reasons, including improved educational quality, increased research opportunities, higher degree value, and so forth. Additionally, getting accepted into a foreign university can be highly competitive. The students have a tough time learning about the courses and institutions available to them based on their qualifications. Private agencies are now the sole means to obtain information. This approach might be costly and time- consuming. This article intends to assist students in identifying institutions where they have a better probability of getting accepted in their graduate and/or postgraduate degrees, particularly those who wish to study at international universities. This article proposes a recommendation system leveraging the CatBoost classifier and Genetic algorithm. The experimental results of the proposed method compared to the conventional techniques for recommendation are also demonstrated in this article. Our proposed approach achieved 90.43% accuracy in predicting apt Universities.