Korean Journal of Artificial Intelligence (한국인공지능학회지)

Korea Artificial Intelligence Association (한국인공지능학회)
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Predictiong long-term workers in the company using regression

  • SON, Ho Min (Department of Medical IT, Eulji University) ;
  • SEO, Jung Hwa (Major in Design, Seoul Institute Of The Art)
  • Received : 2022.04.27
  • Accepted : 2022.06.03
  • Published : 2022.06.30
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Abstract

This study is to understand the relationship between turnover and various conditions. Turnover refers to workers moving from one company to another, which exists in various ways and forms. Currently, a large number of workers are considering many turnover rates to satisfy their income levels, distance between work and residence, and age. In addition, they consider changing jobs a lot depending on the type of work, the decision-making ability of workers, and the level of education. The company needs to accept the conditions required by workers so that competent workers can work for a long time and predict what measures should be taken to convert them into long-term workers. The study was conducted because it was necessary to predict what conditions workers must meet in order to become long-term workers by comparing various conditions and turnover using regression and decision trees. It used Microsoft Azure machines to produce results, and it found that among the various conditions, it looked for different items for long-term work. Various methods were attempted in conducting the research, and among them, suitable algorithms adopted algorithms that classify various kinds of algorithms and derive results, and among them, two decision tree algorithms were used to derive results.

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References

  1. An, S. H., Yeo, S. H., & Kang M. S. (2021). A study on a car Insurace purchase Prediction Using Two-Class Logistic Regression and Two-Class Boosted Decision Tree. Korean Journal of Artificial Intelligence, 9(1), 9-14.
  2. Choi, J. W., Shin, D. W., & Lee, H. J. (2021). Prediction of turnover rate according to satisfaction and dissatisfaction factors of IT company employees: Using Topic Modeling and Machine Learning. Korean Journal of Data Information Science. 32(5). 1035-1047. https://doi.org/10.7465/jkdi.2021.32.5.1035
  3. Kang, M. S., & Choi, E. S. (2021). Machine Learning: Concepts, Tools And Data Visualization, Seoul, Korea:WSPC. Retrieved March 01, 2022, from https://www.amazon.com/Machine-Learning-Concepts-Tools-Visualization/dp/9811229368
  4. Kang, M. S., Kang, H. J., Yoo, K. B., Ihm, C. H., & Choi, E. S. (2018). Getting started with Machine Learning using Azure Machine Learning studio. Seoul, Korea: Hanti media.
  5. Kong, D. A., & Bang, J. H. (2019). A Study on the Repurchase of Automobile Insurance at Expiration. Journal of Industrial Economics and Business, 32(5), 2393-2415. https://doi.org/10.22558/jieb.2019.12.32.6.2393
  6. Kwon, H. H. (2020). Machine Learning and Finance: Machine Learning-Based Credit Rating Model. Seoul, korea: Korea Economic Research Institute of KDB Industrial Bank. Retrieved March 11, 2022, from https://eiec.kdi.re.kr/policy/domesticView.do?ac=0000151746&issus=S&pp=20&datecount=&pg
  7. Kwon, K. W. (2016). Relationship between employee turnover and corporate performance: An exploratory study considering the turnover of high performers and non-high performers. Labor Policy Studies, 16(1), 1-26.
  8. Nam, Y. J., & Shin, W. J. (2019). A Study on Comparison of Lung Cancer Prediction Using Ensemble Machine Learning. Korean Journal of Artificial Intelligence, 7(2), 19-24. https://doi.org/10.24225/kjai.2019.7.2.19
  9. Yoo, S. H., Park, I. S., & Kim, Y. M. (2017). A Study on the Influence Factors and Reasons of Unmet Dental Treatment in Adults Using Decision Tree. Journal of Health and social studies, 37(4), 293-294.