As a graduate student, I know the process of looking into which top university abroad and the best courses overseas one should apply to is a long and arduous one. For example, you probably don’t know the ideal GRE score needed to be accepted into your dream program, or you don’t know which schools offer the program you want to because the information available is scattered. To better deal with this complicated and disorganized process, a couple of friends and I built GradSeer, a graduate application assessment website that helps prospective applicants decide which graduate program and/or school they should apply to.
We knew that graduate admission data is not publicly accessible, so we instead looked at Gradcafe, a public forum with almost 500k data points of graduate admission results across the U.S. (for most graduate programs except M.B.A), submitted by prospective students after they were accepted or rejected into a program. Since we wanted to look at the average credentials of people who got accepted and rejected, we think Gradcafe was quite representative.
To collate information, we built a scraper to get data from the Gradcafe website, cleaned the data, and did various data visualization that you can see on the homepage. We also built a predictive analytic tool based on three Models (Logistic Regression, Support Vectors Machine, and K-Nearest Neighbors) that gives prospective students the likelihood of getting accepted to the program based on credentials. And finally, we set up a Google Cloud instance to build and store the web application.
