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Title:	貸款違約預測：使用Spark平台分析P2P借貸資料 Loan default prediction：analyzing P2P lending data on the spark platform
Authors:	林博仁 Lin, Bo Ren
Contributors:	胡毓忠 Hu, Yuh Jong 林博仁 Lin, Bo Ren
Keywords:	點對點借貸預測 P2P Lending Prediction
Date:	2017
Issue Date:	2017-03-01 17:38:14 (UTC+8)
Abstract:	由於FinTech數位金融的快速崛起，金融相關業務逐漸由線上申辦取代傳統作業。在借貸方面，銀行為了降低呆帳風險，要求融資方必須提供足夠抵押擔保品，而融資方往往因為無擔保品而求救無門，其中包含信用歷史優良的客戶，因此P2P借貸平台為此需求而誕生。本研究探討如何在大數據Spark分析平台上使用Scikit Learning的程式庫來進行自動化機器學習流程，並以優化的角度來進行P2P借貸模型特徵值篩選以及參數和超級參數的最佳化，因而提高預測還款鑑定力。本研究分析資料集是引用美國上市公司Lending Club公開資料，以投資方的角度來分析融資方歷年的借貸資料，從中篩選特徵值，並利用隨機樹演算法結合自動化機器學習流程來完成分析模型的訓練與測試。我們提供預測信用良好的借貸者給投資方參考，並由投資方根據自身的資金狀態從中選擇合適投資的融資方，進而達成精準預測融資方是否還款的目標。 In the rapidly rise FinTech era, traditional financial-related business is gradually replaced by online digital finance. From a new loan, the bank always requires a borrower to provide certain amount of collateral for risk reduction. However, a borrower sometimes cannot meet this requirement, even with a good credit history. A P2P lending platform is created for solving this problem. This study investigates the issue for how to proceed automated machine learning pipeline through P2P lending model’s features selection with parameter and hyper-parameter optimization. By using Scikit Learning libraries on the big data analytics Spark platform, we can predict who are borrowers with good credits. We apply Random Forest machine learning algorithm in the automated machine learning pipeline to analyze the Lending Club open datasets from a lender perspective. A predicted list of high credit borrowers is available for investors to select to achieve high loan return rate.
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Description:	碩士國立政治大學資訊科學系碩士在職專班 103971012
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0103971012
Data Type:	thesis
Appears in Collections:	[Executive Master Program of Computer Science of NCCU] Theses

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