台灣各知名大學之間,彼此常存在著多年的競爭,且隨著近年來教育發展越趨國際 化,使得競爭早已不僅止於這些國內名校間。而想要提昇大學競爭力最根本的作法,不外 乎是積極延攬好的人才,並長期培育提昇學校的研究實力,因此若能將校內優秀的大學畢 業生保留下來繼續修讀研究所,將有助於提升學校之研究水準與聲譽。
本研究將機器學習方法應用於預測學生大學畢業後是否願意繼續修讀同校之研究所, 並以國立清華大學資訊工程學系之畢業生為資料來源,透過機器學習分類技術如J48決策 樹、隨機森林、支持向量機等方法,建立畢業學生流失預測模型並瞭解影響學生流失之重 要特徵,提供給校方作為擬定未來發展策略之參考。

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.

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