P2P Investment Data Analytics: A Case Study of Lending Club

Abstract

In this study, a forecasting model is constructed for default risk by applying several machine learning models, including logistic regression, random forest, gaussian naive bayes, artificial neural networks, support vector machine, and XGBoost. Cross validation and voting ensemble learning are also utilized for best suitable hyperparameters as well. Dataset comes from Lending Club and then cleaned, normalized and standardized it into an ideal input dataset which are splitting into training and testing ones later. Among the methods, XGBoost achieved the best performance.