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

Bogart Yail Marquez; Arturo Realyvásquez-Vargas; Nelson Lopez-Esparza; Carlos E. Ramos

doi:10.47852/bonviewAAES32021326

Authors

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