Work

Employee Turnover Analysis Using Machine Learning Algorithms

Year: 2024

Type: preprint

Abstract: Employee's knowledge is an organization asset. Turnover may impose apparent and hidden costs and irreparable damages. To overcome and mitigate this risk, employee's condition should be monitored. Due to high complexity of analyzing well-being features, employee's turnover predicting can be delegated to machine learning techniques. In this paper, we discuss employee's attrition rate. Three different supervised learning algorithms comprising AdaBoost, SVM and RandomForest are used to benchmark employee attrition accuracy. Attained models can help out at establishing predictive analytics.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2402.03905

Authors: Karimi, Mahyar, Viliyani, Kamyar Seyedkazem

Institution: Amirkabir University of Technology

Language: English

Topic: AI and HR Technologies

Subfield: Organizational Behavior and Human Resource Management

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Decent work and economic growth

Open Access status: green

OpenAlex ID: W4391675613

Cites:

Cited by: 3

Related to: 10