Authors - Reynaldo F. Agunod, Janelli M. Mendez Abstract - Higher education institutions collect large volumes of student data but these are underutilized for institutional planning. This study applies the CRISP-DM framework to enrolment records of a freshman cohort of 1,916 students across four academic years (2021-2025) across 28 academic programs from a private higher education institution in Central Visayas, Philippines, to forecast institutional progression metrics using predictive analytics. Descriptive analytics and three predictive models were applied based on their suitability for the dataset with 3-4 data points, namely: Linear Regression, Holt-Winters Exponential Smoothing, and ARIMA. Six institutional performance metrics were analyzed: enrolment, retention, persistence, attrition, program shifts, and graduation. Key findings reveal a continuous 29.6% enrolment decline within the cohort, an im-proving retention and persistence profile, a program-shift surge largely due to migrations from Accountancy to Finance, and a rapidly increasing graduation rate. Linear Regression (OLS) was identified as the most effective forecasting model for the study’s single-cohort dataset.
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