Project Title: Predictive Analytics for Loan Default Prediction Using Machine Learning

Project Title: Predictive Analytics for Loan Default Prediction Using Machine Learning

Project Summary:

The project will focus on predicting loan defaults in the field of peer-to-peer lending using machine learning techniques. It will utilize the Lending Club loan dataset available on Kaggle, which includes detailed borrower information and loan characteristics. The dataset contains features such as loan amount, interest rate, borrower credit history, annual income, and loan status, which are essential for building predictive models. The project will involve data preprocessing, feature engineering, and training models such as Logistic Regression, Random Forest, and Gradient Boosting. The results will be presented through performance metrics and visualizations to provide insights into factors influencing loan defaults.

Research Focus:

The research will examine the key factors influencing loan defaults and the effectiveness of various machine learning models in predicting these defaults. It will also explore how different features impact the predictive accuracy and what patterns can be identified in borrower behavior that leads to defaults. Additionally, the study will investigate the potential for improving risk management practices in peer-to-peer lending platforms through the application of these predictive models.

References:

1. Sayah, F. (2021). Lending Club Loan Defaulters Prediction. Kaggle.

- This notebook provides a comprehensive approach to loan defaulter prediction using machine learning models, guiding the methodology and model selection for my project.

- [Lending Club Loan Defaulters Prediction on Kaggle](https://www.kaggle.com/code/faressayah/lending-club-loan-defaulters-prediction)

2. Chen, J., & Zhang, Q. (2019). Machine learning methods for default risk prediction in peer-to-peer lending. *Information Sciences*, 482, 287-299.

- This paper compares various machine learning methods for predicting default risk in P2P lending, highlighting the importance of feature selection and model evaluation metrics.

- [Link to the paper](https://www.sciencedirect.com/science/article/abs/pii/S0020025518309813)

Using these references and the Lending Club dataset, the project will explore the prediction of loan defaults to improve risk management in peer-to-peer lending. The dataset, sourced from Kaggle, will be thoroughly analyzed, and the insights will be used to develop robust machine learning models. The performance of these models will be evaluated using metrics like accuracy, precision, recall, and ROC-AUC, and the findings will be visualized to provide clear and actionable insights for lenders.