Thailand's national development relies on higher education, posing challenges for the government to enhance graduate competence. High dropout rates impact education quality and student welfare, necessitating a comprehensive study. This research collects a dataset on student dropout and utilizes classification models to predict dropout likelihood at Rajamangala University of Technology Thanyaburi (RMUTT), Thailand. The dataset includes 2,137 undergraduate students from 2013 to 2019 and follows the CRISP-DM model, utilizing internal data sources from ARIT.