Salary Prediction Analysis for the ‘Slow Employment’ Phenomenon - Based on Random Forest Algorithm

Liang Ye; SiYi Fu; GuangYuan Chen; Jin Lu

doi:10.2991/978-94-6463-238-5_40

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Salary Prediction Analysis for the ‘Slow Employment’ Phenomenon - Based on Random Forest Algorithm

Authors

Liang Ye¹, SiYi Fu², GuangYuan Chen², Jin Lu¹^{, *}

¹Office of Admissions and Career Services, Shanghai University, Shanghai, China

²School of Computer Engineering and Science, Shanghai University, Shanghai, China

^*Corresponding author. Email: lujin221@t.shu.edu.cn

Corresponding Author

Jin Lu

Available Online 26 September 2023.

DOI: 10.2991/978-94-6463-238-5_40 How to use a DOI?
Keywords: Slow employment; Random Forest; Grid search method; Salary prediction
Abstract: The “slow employment” issue among college students is a contemporary employment problem that requires attention. To address this issue and enhance students’ employability, this study uses a university in a developed region as a case study and analyzes data, conducts a questionnaire survey, and performs literature analysis to categorize “slow employment” into active and passive types based on the psychological conditions of economically developed college students. The results show that the highest percentage of slow employment (74.08%) among students in general undergraduate colleges and universities is due to the training methods of schools and the lack of students’ career awareness. The study also examines job skills and salary forecasts to identify the root causes of slow employment among students and proposes corresponding countermeasures.
Copyright: © 2024 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 2023 4th International Conference on Big Data and Informatization Education (ICBDIE 2023)
Series: Advances in Intelligent Systems Research
Publication Date: 26 September 2023
ISBN: 978-94-6463-238-5
ISSN: 1951-6851
DOI: 10.2991/978-94-6463-238-5_40 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Liang Ye
AU  - SiYi Fu
AU  - GuangYuan Chen
AU  - Jin Lu
PY  - 2023
DA  - 2023/09/26
TI  - Salary Prediction Analysis for the ‘Slow Employment’ Phenomenon - Based on Random Forest Algorithm
BT  - Proceedings of the 2023 4th International Conference on Big Data and Informatization Education (ICBDIE 2023)
PB  - Atlantis Press
SP  - 298
EP  - 303
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-238-5_40
DO  - 10.2991/978-94-6463-238-5_40
ID  - Ye2023
ER  -

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