Authors - Edimar J. Rato, Janelli M. Mendez Abstract - Student dropout has remained a major problem in all higher education institutions globally, including in the Philippines, where the total college dropout rate in the country was recorded at about 35.15% in the Academic Year 2023–2024. This study aimed to develop a predictive analytics model that identifies dropout and retention patterns among students of Tagbilaran City College to support evidence-based intervention strategies. offered by the school from Academic Year 2021-2024. The algorithms implemented for the supervised learning process include Random Forest and Gradient Boosting, while the algorithm for the unsupervised learning process is K-Means Clustering implemented using the RapidMiner Studio tool. Results revealed that both supervised models had a poor performance due to class imbalance issues as well as a small feature set; the Random Forest model had an accuracy of 59.59%, while it had an AUC of 0.575. The Gradient Boosting model had an accuracy of 60.51%, while it had an AUC of 0.508. The K-Means Clustering model had a good performance since it resulted in three interpretable student risk clusters: a moderate-risk group with a dropout rate of 27.3%, a highest-risk group with a dropout rate of 44.7%, and a lower-risk but larger group with a dropout rate of 41.9%. The Davies-Bouldin Index of 0.967 confirmed adequate cluster separation. The K-Means model demonstrated the most practical utility as an early-warning risk stratification tool applicable at the start of each academic year, forming the foundation of an evidence-based intervention plan to improve student retention at Tagbilaran City College.
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