Predicting Undergraduate Applicants for Enrollment Using Binary Classification Machine Learning Techniques

Abstract

This study aimed to develop a binary classification machine learning model to predict undergraduate applications for enrollment. It used the 2024 student admissions data, such as the applicant’s general weighted average, College Admission Test results, interview score, and personal information, and employed Logistic Regression, Naïve Bayes, K-Nearest Neighbors, and Support Vector Machine models. The dataset was preprocessed using imputation, one-hot label encoding, standardization, and SMOTE to handle the class imbalance. The model performance was evaluated using accuracy, precision, recall, and F1 score, with Support Vector Machine emerging as the best-performing model, with an accuracy of 82%. To enhance model transparency and stakeholder trust, explainability methods under Explainable AI (XAI) were employed to interpret how and why predictions were made. These findings support the ethical use of artificial intelligence in admissions and provide a policy framework for a data-driven selection process. The model’s predictive and interpretative capabilities can help the university streamline the admission process, optimize resources, and maintain fairness. Future researchers can include real-time data and broader factors to improve the adaptation and support inclusive education goals that are associated with SDGs and the Times Higher Education Impact Rankings.