Abstract
In today’s society, frequent transfers of personnel and work have led to a lack of stability in the company’s business. In order to avoid employee resignation, it is urgent to propose a credible framework to analyze the behavior of employees and provide a valuable reference for whether they are willing to resign. Based on the data mining technology and machine learning algorithm, this paper analyzes the resignation information of Kaggle employees. By comparing the original features of the data set, new features can be calculated using Price-Muller’s employee resignation theory, and the new features will be input into a BP neural network and classified, so as to predict whether employees have a tendency to leave.
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Wang, B. (2021). Performance Evaluation of Price-Muller Model Based on Classification Algorithm. In: Dai, HN., Liu, X., Luo, D.X., Xiao, J., Chen, X. (eds) Blockchain and Trustworthy Systems. BlockSys 2021. Communications in Computer and Information Science, vol 1490. Springer, Singapore. https://doi.org/10.1007/978-981-16-7993-3_50
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DOI: https://doi.org/10.1007/978-981-16-7993-3_50
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