Prediction Probability Of Getting An Admission Into A University Using Machine Learning

Abstract

In today's environment, students often struggle to
find the right higher education institution that
matches their profile. While there are advisory
services and online applications available for
university recommendations, they often charge high
consultancy fees and their accuracy is questionable.
Therefore, the objective of this research is to
develop a model that accurately predicts the
likelihood of admission into universities based on
student profiles. This model will not only predict
admission chances but also analyze how scores
correlate with the probability of acceptance using
historical data. The proposed model utilizes linear
regression and random forest algorithms, with the
CatBoost algorithm demonstrating the highest
accuracy.