Abstract
The competition between top universities in Taiwan has been existing for years, but since the development of education is becoming more international, the competition has become more intense and has expended to more than universities in Taiwan. The basic and most significant way to improve competitiveness of a university is to attract more talented and qualified students to come and study, good students with good training would definitely help to improve research capability. Thus, for the university, keeping talented undergraduate students to continue their graduate study would be also helpful to raise the reputation and research level for the university. This research applies machine learning techniques on churn prediction in undergraduate students continuing their graduate study at the same university, while using data of National Tsing Hua University computer science students as the data resource. Through machine learning classification methods like J48 Decision Tree, Random Forest and Support Vector Machine, we develop prediction models to detect possible churners and analyze the most important factors that affect students to churn.