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Introduction

Peer-to-peer lending has emerged as a popular alternative financing option for individuals and businesses. However, one of the key challenges in peer-to-peer lending is the prediction of credit default. Credit default prediction plays a crucial role in minimizing the risk for lenders and ensuring the sustainability of the peer-to-peer lending platform. By accurately predicting credit default, lenders can make informed decisions on loan approvals and minimize potential losses.

This thesis aims to explore the various factors that influence credit default prediction in peer-to-peer lending and develop a predictive model that can help lenders assess the creditworthiness of borrowers. By leveraging machine learning algorithms and data analytics, this study seeks to improve the accuracy of credit default prediction in peer-to-peer lending platforms.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of Peer-to-Peer Lending
2.2 Credit Default Prediction in Peer-to-Peer Lending
2.3 Factors Influencing Credit Default
2.4 Previous Studies on Credit Default Prediction
2.5 Machine Learning Algorithms for Credit Default Prediction
2.6 Data Analytics in Credit Default Prediction
2.7 The Role of Big Data in Credit Default Prediction
2.8 Regulatory Framework for Peer-to-Peer Lending
2.9 Risk Management in Peer-to-Peer Lending
2.10 Challenges in Credit Default Prediction

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Variable Selection
3.4 Data Preprocessing
3.5 Model Development
3.6 Model Evaluation
3.7 Validation Techniques
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Descriptive Statistics of Dataset
4.2 Variable Importance Analysis
4.3 Model Performance Evaluation
4.4 Comparison of Different Machine Learning Algorithms
4.5 Interpretation of Results
4.6 Implications for Lenders
4.7 Recommendations for Future Research
4.8 Practical Implications

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusions
5.3 Contributions to the Field
5.4 Limitations of the Study
5.5 Future Research Directions
5.6 Final Thoughts

Thesis Overview

The thesis on Credit Default Prediction for Peer-to-Peer Lending aims to explore the factors influencing credit default in peer-to-peer lending platforms and develop a predictive model to aid lenders in assessing borrower creditworthiness. The study will encompass a comprehensive literature review on peer-to-peer lending, credit default prediction, machine learning algorithms, and data analytics. The research methodology will involve data collection, variable selection, model development, and evaluation using validation techniques.

The findings of the study will include descriptive statistics of the dataset, variable importance analysis, model performance evaluation, and comparison of different machine learning algorithms. The discussion will focus on the interpretation of results, implications for lenders, and recommendations for future research and practical implications. The conclusion will summarize the findings, highlight contributions to the field, discuss limitations, propose future research directions, and provide final thoughts on credit default prediction for peer-to-peer lending.

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