Accurately predicting which students are best suited for graduate programs is beneficial to both students and colleges. In this paper, we propose a quantitative machine learning approach to predict an applicant's potential performance in the graduate program. Our work is based on a real world dataset consisting of MS in CS [Master of Science in…

Course selection can be a daunting task, especially for first year students. Sub-optimal selection can lead to bad performance of students and increase the dropout rate. Given the availability of historic data about student performances, it is possible to aid students in the selection of appropriate courses. Here, we propose a method to compose a…