Prediction for Insurance Premiums Based on Random Forest and Multiple Linear Regression

Abstract

Insurance benefit forecasting is very important for insurance, and research on insurance benefit forecasting has been going on all the time. This paper aims to find an efficient and simple model for predicting insurance benefits based on multiple linear regression and machine learning scenarios. To be specific, in the process of prediction, random forest and linear regression are used as prediction models. Through the comparison and analysis of the results, it is found that the random forest is more accurate in predicting the results but lacks interpretability. Although linear regression is not as accurate as random forest, it can clearly explain the model and facilitate analysis and discussion. Although the two models have their own advantages. Based on the comparison and analysis, they can provide some help in referring to whether to use these two models. These results shed light on guiding further exploration of predicting insurance benefits in a simple way.