Predicting a Rise in Employee Attrition Rates Through the Utilization of People Analytics

Title

Predicting a Rise in Employee Attrition Rates Through the Utilization of People Analytics

Creator

Arora S.; Kr Jha A.; Upadhyay S.

Description

Modern organizations have a multitude of technological tools at their disposal to augment decision-making processes, with artificial intelligence (AI) standing out as a pivotal and extensively embraced technology. Its application spans various domains, including business strategies, organizational management, and human resources. There's a growing emphasis on the significance of talent capital within companies, and the rapid evolution of AI has significantly reshaped the business landscape. The integration of AI into HR functions has notably streamlined the analysis, prediction, and diagnosis of organizational issues, enabling more informed decision-making concerning employees. This study primarily aims to explore the factors influencing employee attrition. It seeks to pinpoint the key contributors to an employee's decision to quit an organization and develop a futuristic data driven model to forecast the possibility of an employee leaving the organization. The study involves training a model using an employee turnover dataset from IBM analytics, including a total of thirty-five features and approximately one thousand and five hundred samples. Post-training, the model's performance is assessed using classical metrics. The Gaussian Nae Bayes classifier emerged as the algorithm delivering the most accurate results for the specified dataset. It notably achieved the best recall (0.54) indicating its ability to correctly identify positive observations and maintained false negative of merely 4.5%. 2023 IEEE.

Source

Proceedings of the 2023 12th International Conference on System Modeling and Advancement in Research Trends, SMART 2023, pp. 349-355.

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Artificial Intelligence; HR Analytics; Machine learning; Supervised learning

Coverage

Arora S., School of Business & Management, Christ University, Ghaziabad, India; Kr Jha A., Institute of Technology and Science, Department of Management, Ghaziabad, India; Upadhyay S., Banasthali Vidyapeeth, Department of Computer Science, Rajasthan, India

Rights

Restricted Access

Relation

ISBN: 979-835036986-1

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/SMART59791.2023.10428268

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186744709&doi=10.1109%2fSMART59791.2023.10428268&partnerID=40&md5=79cdd8da3f55e43738fa0b9c28bb7501