A data-driven approach in predicting scholarship grants of a local government unit in the Philippines using machine learning

Abstract

Inefficient, tedious, and outdated processes in resource allocation are some of the common hurdles educational institutions and agencies face in managing scholarship grants and selecting potential grantees. In response to the challenge, this study developed a predictive model utilizing a range of machine learning algorithms; by leveraging algorithms like Naïve Bayes, Random Forest, Logistic Regression, Support Vector Machine, and Multilayer Perceptron, the study aimed to enhance the selection process for scholarship programs to match applicants with the most suitable scholarship based on their individual backgrounds and qualifications. A number of measures, including accuracy, precision, recall, and F1-score, were used to assess the performance of the models. Results revealed Logistic Regression as the best-performing model in terms of overall accuracy and balance between precision and recall. Moreover, the Support Vector Machine, Naive Bayes, and Random Forest models demonstrated competitive performance, while the Multilayer Perceptron exhibited the lowest performance among others.