Credit Default of P2P Online Loans Based on Logistic Regression Model Under Factor Space Theory Risk Prediction Research

Abstract

P2P, as the most representative online lending platform with a long history of personal credit development, can provide powerful data support for exploring the problem of personal credit default risk, and Logistic Regression plays an important role in machine learning, and the current research on Logistic Regression mainly stays at the application level. Therefore, based on the Factor Space theory to further deepen the interpretation of Logistic Regression, explore the obvious and hidden relationship of the factors behind it, and give a reasonable expression of Logistic Regression from the perspective of the obvious and hidden factors, take the U.S. lending club as an example, choose the lender information data of the whole year of 2019, and establish the P2P online credit default Logistic Regression prediction model. Considering that the conditional factors contain multiple value states, the One-Hot idea is introduced to improve the precision of the algorithm. The accuracy, recall and other evaluation indexes are chosen to compare and analyse the prediction effect of the model. The results of the model show that Logistic Regression can effectively predict the credit default risk of personal credit, and also provide a more in-depth explanation for the generation of personal credit default risk in the context of new personal loans.