Anticipating Graduate Program Admission Through Implementation of Deep Learning Models

Abstract

Acceptance into a graduate program must be part of a student’s academic journey. Every year, a huge number of people apply to schools and universities, and the admissions process may be tough and time-consuming. Many factors are considered while evaluating a student's application, including academic achievement, test scores, LOR, and extracurricular activities. However, selecting the best choices can still be arbitrary and prone to mistakes. As a result, it is required to develop a more efficient and objective technique of evaluating an applicant's chances of admission to a graduate course based on their application materials. The purpose of this study is to develop a ML model that can predict a student’s prospects of acceptance into a graduate school. The model will be trained using a dataset containing different characteristics, such as GRE scores, GPA, and letters of recommendation. The dataset will be preprocessed to cope with missing values, outliers, and categorical data. A variety of ML methods, including LR, DT, and SVM, will be used to build the model. The algorithm’s efficacy will be measured using a variety of measures, including accuracy, precision, recall, and F1 score. The best-performing model will then be picked and used to evaluate the admissions outcomes of fresh applicants.