  This article addresses the challenge of validating the admission committee's
decisions for undergraduate admissions. In recent years, the traditional review
process has struggled to handle the overwhelmingly large amount of applicants'
data. Moreover, this traditional assessment often leads to human bias, which
might result in discrimination among applicants. Although classical machine
learning-based approaches exist that aim to verify the quantitative assessment
made by the application reviewers, these methods lack scalability and suffer
from performance issues when a large volume of data is in place. In this
context, we propose deep learning-based classifiers, namely Feed-Forward and
Input Convex neural networks, which overcome the challenges faced by the
existing methods. Furthermore, we give additional insights into our model by
incorporating an interpretability module, namely LIME. Our training and test
datasets comprise applicants' data with a wide range of variables and
information. Our models achieve higher accuracy compared to the best-performing
traditional machine learning-based approach by a considerable margin of 3.03\%.
Additionally, we show the sensitivity of different features and their relative
impacts on the overall admission decision using the LIME technique.
