The Application of Random Forest Classification Model in Credit Risk Assessment of Online Lending

In order to understand the application of the Random Forest classification model in online lending credit risk assessment, a research on the application of the Random Forest classification model in online lending credit risk assessment is proposed. This paper first focuses on the risk control issues faced by the current online lending industry, introduces data mining into personal credit evaluation, and uses the Random Forest algorithm to construct a personal credit evaluation model to predict whether online loan applicants have a tendency to default, providing loan decision support for online lending platforms. Secondly, preprocess the raw data, including standardizing continuous numerical variables, quantifying attribute variables, and interpolating samples with missing values. Finally, fit the data from the training set, establish a credit score interval using the average and standard deviation of the fitted credit scores, and provide the proportion of good and bad customers within this interval as a reference for personal credit classification of users.