Application of Ordinary Least Squares Regression and Neural Networks in Predicting Employee Turnover in the Industry

Authors

  • Bogart Yail Marquez Department of Systems and Computing, National Technological Institute of Mexico (Campus Tijuana), Mexico https://orcid.org/0000-0001-7334-374X
  • Arturo Realyvásquez-Vargas Department of Industrial Engineering, National Technological Institute of Mexico (Campus Tijuana), Mexico
  • Nelson Lopez-Esparza Department of Systems and Computing, National Technological Institute of Mexico (Campus Tijuana), Mexico
  • Carlos E. Ramos Department of Systems and Computing, National Technological Institute of Mexico (Campus Tijuana), Mexico

DOI:

https://doi.org/10.47852/bonviewAAES32021326

Keywords:

artificial intelligence techniques, staff turnover, data mining, tensor flow, ordinary least squares regression

Abstract

Employee turnover, also known as labor turnover or employee attrition, refers to the flow of employees entering and leaving an organization within a specific time. It is an indicator used to measure the number of employees who leave a company and are replaced by new hires. This project aims to create and implement an artificial intelligence model using the Python programming language and the TensorFlow library. The focus is developing a dashboard to facilitate the model training process and enable predictions related to employee turnover in the business context. The goal is to enhance predictive capabilities and provide valuable strategic and human resources talent management and decision-making insights. By harnessing the power of artificial intelligence, the project aims to identify patterns and factors that influence employee turnover. This, in turn, will enable the implementation of preventive measures and corrective actions to reduce turnover rates and maintain workforce stability in the company.

 

Received: 10 July 2023 | Revised: 8 August 2023 | Accepted: 11 September 2023

 

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

 

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

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Published

2023-09-12

How to Cite

Marquez, B. Y., Realyvásquez-Vargas, A., Lopez-Esparza, N., & Ramos, C. E. (2023). Application of Ordinary Least Squares Regression and Neural Networks in Predicting Employee Turnover in the Industry. Archives of Advanced Engineering Science, 2(1), 30–36. https://doi.org/10.47852/bonviewAAES32021326

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Section

Articles