近年來，手機遊戲商大多以免費遊戲為主。但是，免費遊戲在定義玩家是否流失上極為困難，遊戲商容易失去挽回玩家的機會。此外，新進玩家的流失率較高。因此，如果能準確地預測出可能流失的新進玩家，就能即時的採取挽留策略，希望能因此提高新進玩家的留存率，進而有效地提高營收。
本論文對此議題提出一巨量資料探勘框架，此框架將由五大階段組成： (1) 資料前處理階段、(2) 資料分析階段、(3) 機器學習階段、(4) 預測結果分析階段及 (5) 產業應用分析階段。將需先將資料進行前處理以及預測前之資料分析，隨後進行機器學習的訓練，再將預測之結果導入資料特徵重要性分析之中，最後用代理人模型找出玩家流失的規則，完成整體預測與分析之工作。

In recent years, most mobile game developers focus on free-to-play (F2P) games. However, it is extremely difficult for F2P games to define whether players are churning, which makes game operators hard to retain players. In addition, new players have a higher churn rate. Therefore, if operators can accurately predict the new players that may churn, they can immediately adopt a retention strategy to lost players, looking forward that this will increase the retention rate of new players and thus effectively increase revenue.
This thesis proposes a machine learning framework for this topic, which is composed of five major stages: (1) pre-processing stage, (2) analysis stage, (3) machine learning stage, (4) feature extraction analysis stage and (5) businese application analysis stage. To be specific, the framework will need to process the data and data analysis before prediction, and train the model via machine learning algorithm, then analyze important features with the prediction results. Finally, by using surrogate model, the framework will be able to find out the patterns of player churn.

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