網路貸款已經在西方國家ex: 美國、英國盛行很久了。全世界第一個網貸平台為英國Zopa, 致力於提供小額貸款給借款人。本研究中，我們使用機器學習的方式去預測Lending Club借款人的違約風險並且找出影響違約因素的因子。我們利用羅吉斯回歸以及隨機森林的方式進行分析。接著比較網路貸款在美國以及中國之間不一樣的地方東西方國家。

Online Peer-to-Peer (P2P) lending has been prevailing in the West countries such as USA, UK. The first company to offer peer-to-peer loans in the world was Zopa in UK which provide platform for borrowers can obtain small loan from lenders. In this study, we use machine learning algorithms to predict borrowers’ default risk and discover factors that impact on the rate of loan default with example and data from LendingClub.com. We use logistic regression and random forest to do analysis and identify what is the most influential factor will affect P2P lending. Finally, we compare the difference of P2P lending market between USA and China.

Acknowledgement i
摘要 ii
Abstract iii
Content iv
List of Table vi
List of Figure vii
1. Introduction 1
1.1. Background 1
1.2. The study organization 2
2. Literature Review 3
3. Methodology 5
3.1. Dataset Description 5
3.2. Logistic Regression 6
3.3. Forest Plot 13
3.4. Random Forest 16
3.5. PPDai’s Funding 18
4. Discussion, Comparison, and Implication 22
4.1. Comparative Evaluation 22
4.2. The Risk of China P2P Platforms 23
4.3. China P2P Platforms 27
4.4. The new policy to solve China P2P platforms problem 30
4.5. Compare China and USA’s P2P lending industry situation 32
5. Conclusion 37
6. Reference 38

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