Prediction of Graduate Admission Using Machine Leering

Abstract

The goal of this study is to create a model that may assist students in selecting the best institutions based on their scores and their profiles. We can evaluate candidates across a broad range of disciplines, such as Master of Science (international), Master of Technology (India), and Masters in Business Administration (India and international). We intend to build a machine learning model in order to produce outcomes that may benefit the students in choosing the right University. The dataset includes facts on the university and student profiles, together with a field that indicates whether or not the admission was successful. Key performance indicators have been used to compare the predictions made using a variety of algorithms, including Ensemble Machine Learning (KPI).The dependent variable, or the likelihood of admission to a university, is then evaluated using the model that is performing the best. The chances of admit variable, which has a range of 0 to 1, represents the anticipated likelihood of being accepted to a university. Additionally, we want to build a portal that sorts universities according to their acceptance range before listing them.