A credible predictive model for employment of college graduates based on LightGBM

Authors

DOI:

https://doi.org/10.4108/eai.17-2-2022.173456

Keywords:

employment rate of college students, predict model classification, characteristics prediction Accuracy

Abstract

INTRODUCTION: "Improving the employment rate of college students" directly affects the stability of the country and society and the healthy development of the industry market. The traditional graduate employment rate model only predicts the future employment rate based on changes in historical employment data in previous years.

OBJECTIVES: Quantify the employment factors and solve the employment problems in colleges and universities in a targeted manner.

METHODS: We construct a credible employment prediction model for college graduates based on LightGBM.

RESULTS: We use the model to predict the employment status of students and obtain the special importance which is important to employment of college students.

CONCLUSION: The final result shows that our Model performs well in the two indicators of accuracy and model quality.

References

Patuelli R, Reggiani A, Nijkamp P, et al.Neural networks for regional employment forecasts: are the parameters relevant [J]. Journal of Geographical Systems, 2011, 13(1): 67-85.

Wang Yaru. Research on the employment prediction model and application of college students based on decision tree algorithm [D]. Wuhan: Central China Normal University, 2018.

Qi Hongqiang, Zhang Fukun, Gao Dakun, Wang Huiqiang. The employment rate prediction of college students based on the gray system[J]. Modern Electronic Technology, 2019, 42(11): 174-177.

Hou Jie. University student performance prediction based on education data [D]. Dalian University of Technology, 2020.

Shi Jing. Research on the influence of student behavior on academic performance based on data mining [D]. Central China Normal University, 2017.

Xia Pengbin. Employment prediction of college students based on campus big data [D]. Central China Normal University, 2020.

Zhu Wenqi. Research on the calculation method of user similarity in recommendation system[D]. Chongqing: Chongqing University, 2014.

Ge J, Qiu Y. Concept similarity matching based on semantic distance[C]//2008 fourth international conference on semantics, knowledge and grid. IEEE, 2008: 380-383.

Lu Tongshuang, Wang Hongguo, Liu Yinggang, et al. A method for predicting employment destinations of college students based on stereo data[J]. Computer Integrated Manufacturing System, 2019, 25(No 4): 1032-1036.

Huang Z, Liu G. Prediction model of college students entrepreneurship ability based on artificial intelligence and fuzzy logic model[J]. Journal of Intelligent & Fuzzy Systems, 2021 (Preprint): 1-12.

Ke G, Meng Q, Finley T, et al. Lightgbm: A highly efficient gradient boosting decision tree[J]. Advances in neural information processing systems, 2017, 30: 3146-3154.

Li Zhanshan, Yao Xin, Liu Zhaogeng, et al. Feature selection algorithm based on LightGBM [J]. Journal of Northeastern University (Natural Science Edition), 42(12): 1688.

Fan Yayun, Feng Jingjing, Zhang Xiaowei. The application of Markov model in predicting the employment quality of college graduates[J]. Enterprise Technology and Development, 2018 (2): 230-231.

Wu Gongcai, Zheng Hemin. Research on the Application of Data Mining in the Precision Employment of College Graduates[J]. Electronics World, 2018 (9): 84-84.

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Published

17-02-2022

How to Cite

1.
He Y, Zhu J, Fu W. A credible predictive model for employment of college graduates based on LightGBM. EAI Endorsed Scal Inf Syst [Internet]. 2022 Feb. 17 [cited 2024 Nov. 14];9(6):e4. Available from: https://publications.eai.eu/index.php/sis/article/view/348