Salary Prediction Based on the Resumes of the Candidates

Abstract

This paper aims to build a salary prediction model based on the resumes of candidates in a recruitment environment. Point-biserial correlation analysis and random forest feature importance ranking methods are employed for the paper to conduct feature selection after the dataset is cleaned and preprocessed. Then, OLS linear regression is adopted to analyze the features selected, and three different models, including random forest regression, decision tree, and ridge regression, are applied in prediction experiments, helping obtain results to be compared and analyzed based on RMSE and MAE. Finally, a stacking ensemble method can be used to integrate and fuse different models to build the final salary prediction model. This model definitely has practical reference significance for both candidates and recruiters.