近幾年來許多遊戲開發商在線上遊戲和手機遊戲上, 主要都是以免費遊玩遊戲(Free-to-Play, F2P) 為其商業模式。遊戲營收為玩家在遊戲中儲值及購買遊戲幣(In-App-Purchase, IAP)，因此越多玩家持續留在遊戲中
對於儲值及購買量有很大的影響。遊戲開發商普遍希望能及時挽回流失玩家，尤其是新進玩家流失率較高，若能準確的預測出即將流失的玩家及分析流失原因，開發商就能對其進行挽留並提出對應的策略於遊戲當中，玩家若能因此續玩遊戲就可能有效的增加營收。本論文的目標為如何運用玩家的行為資料，有效進行資料探勘並且運用機器學習模型(Machine Learning) 進行訓練及預測出不錯的結果。

本論文提出一巨量資料探勘及機器學習框架於新進玩家流失預測，以下四個階段將說明此框架的核心內容：(1) 資料預處理階段、(2) 資料分析階段、(3) 機器學習模型訓練階段以及(4) 模型預測結果分析階段。論文中訓練資料來自於一款麻將遊戲真實玩家遊玩紀錄，原始資料需經由本框架四大階段進行處理及訓練，並分析其實驗結果進而將模型運用於新進玩家流失預測中。機器學習模型訓練階段中結合K-means 分群及若干分類演算法，先將資料特徵相近玩家分在同一群體中，再針對不同群體進行分類演算法的訓練，希望能因此提高模型的表現。最終實驗顯示先分群再分類演算法的模型表現優於只進行分類演算法的模型，其中又以分群結合輕量極限梯度模(LightGBM) 分類演算法的表現較好。最終預測出哪些玩家傾向會流失，再分析可能影響流失的玩家行為。

In recent years, numerous game companies allow players access to online and mobile games for free (Free-to-Play, F2P). The revenue of a F2P game comes from in-game purchases (In-APP-Purchase, IAP). However, F2P is different from telecommunication services in which player churn can be easily identified by the user unsubscribing. Compared with retaining current players, the game company operator is more challenging to recruit new players. Therefore, new player churn prediction is worth studying for the game industry.

In this thesis, we propose a framework of big data mining and churn prediction model which includes 4 steps: (1) Data Pre-Processing (2) Data Analysis (3) Machine Learning (4) Result and Prediction Analysis. Our research data containing players' behaviors are from the mahjong mobile game. The raw data need to be processed and analyzed in Data Pre-Processing and Data Analysis steps respectively. The analyzed data with key features is the training data for Machine Learning step. We will evaluate the Machine Learning prediction results to find the best model and analyze the behaviors which cause churning in Result and Prediction Analysis step. According to our experiment results, the classification models with K-means clustering perform better than classification models without K-means. The Machine Learning model LightGBM with K-means has better performance and the highest recall values. Finally, our framework is able to predict and find the new players who tend to churn.

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