Prediction of Admission in Master’s Program in Foreign Countries Using Machine Learning Algorithms

In the present competitive era, admissions into master’s programmed in prestigious universities has become an arduous task because of the ever-increasing competition leading to higher standards while shortlisting aspirants and challenging for grabbing an admit. Considering the cumbersome nature of the admission process, students often get muddled because of its various factors under consideration. Keeping this view in context, a sound model is proposed that could predict the chances of getting an admit thereby also reducing workload for the admissions committee by automating this tedious task, along with students leveraging the fact of predicting their chances of an admit beforehand applying. The proposed predictive model makes the review process quite efficient by reducing the time spent on a particular application by reviewers. The focus can be fixated to the parts essential for determining the applicant’s candidature. There are several parameters which are to be taken into consideration while applying for Master Program. For the development of model, we used a public open-sourced dataset (www.kaggle.com) [1] which consists the data of Indian students applying abroad for Master of Science program. Our approach is to develop several regression and classification models to predict the chances of a student getting an admit based on various parameters. We computed the performance of the models and compared them to determine the best model.