Fair and Transparent Student Admission Prediction Using Machine Learning Models

Abstract

Student admission prediction is a crucial aspect of academic planning, offering insights into enrollment trends, resource allocation, and institutional growth. However, traditional methods often lack the ability to address fairness and transparency, leading to potential biases and inequities in the decision-making process. This paper explores the development and evaluation of machine learning models designed to predict student admissions while prioritizing fairness and interpretability. We employ a diverse set of algorithms, including Logistic Regression, Decision Trees, and ensemble methods, to forecast admission outcomes based on academic, demographic, and extracurricular features. Experimental results on real-world datasets highlight the effectiveness of the proposed models in achieving competitive predictive performance while adhering to fairness metrics such as demographic parity and equalized odds. Our findings demonstrate that machine learning can not only enhance the accuracy of admission predictions but also support equitable access to education by promoting transparency and accountability in automated systems.

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