Research on salary level analysis of printing-related jobs based on random forest

Jinwei Li; Jifei Cai; Yongbin Zhang

doi:10.1117/12.2659678

30 November 2022 Research on salary level analysis of printing-related jobs based on random forest

Jinwei Li, Jifei Cai, Yongbin Zhang

Proceedings Volume 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022); 124561E (2022) https://doi.org/10.1117/12.2659678
Event: International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 2022, Qingdao, China

Abstract

Objective: To analyze the salary status of printing-related job postings on the 51job recruitment website, and to analyze the main factors affecting the salary level. Method: The method takes the recruitment information obtained from 51job recruitment website as the original data set, selects the work city, working experience, company nature, educational background as the characteristics, and the average salary as the classification variable of the prediction, and constructs the prediction through the Random Forest (RF) classification algorithm. Model, perform one-hot encoding on the two features of ad(work city) and character(company nature) to get the size of the influencing factors of each city and each company nature, and use grid search and cross-validation methods to select the input parameters of the model, use accuracy, confusion matrix and classification report to verify model accuracy, use feature_importance in sklearn to get the importance of model input feature parameters. Conclusion: The constructed RF model has good accuracy, and the accuracy rate is 0.89; working experience; educational background; private and foreign capital in the nature of the company; Shanghai, Dongguan and Shenzhen in the work city are highly important.

Citation Download Citation

Jinwei Li, Jifei Cai, and Yongbin Zhang "Research on salary level analysis of printing-related jobs based on random forest", Proc. SPIE 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 124561E (30 November 2022); https://doi.org/10.1117/12.2659678

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