Prediction of Factors Influencing the Starting Salary of College Graduates Based on Machine Learning

It is an important deployment of the Party Central Committee and the State Council to fully promote the employment of college graduates with higher quality, and salary is an important indicator of quality measurement. This paper takes the cross-sectional data of the employment of graduates from a financial and economic university in 2020 as the sample; whether the actual starting salary is a high salary as the dependent variable; and human capital, social capital, labor market as the explanatory variables and uses R to establish a logistic regression model to analyze the determinants of the high salary of graduates. Five machine learning methods, SVM, naive Bayes, CART, random forest, and XGBoost, are used to predict whether graduates can get a high starting salary, compare the advantages and disadvantages of various methods horizontally, optimize the parameters at the same time, and further enhance the performance of the model. Based on the employment data of graduate students in a university of finance and economics in 2020, this paper makes an empirical study. The study shows that academic qualifications, professional disciplines, employment regions, employment industries, the nature of employment units, gender, and whether they have served as student cadres have a significant impact on whether graduates can get “high salaries.” The main factors affecting the starting salary of graduates are the accumulation of human capital and social capital, but the segmentation of labor market is also the main reason affecting the starting salary of graduates. The prediction results of several models show that the integrated models have better performance than single models, and the XGBoost model is the best, which can help predict whether graduates get high salary.