Detecting early warning signs of employee attrition using machine learning algorithms

Article type: Research Article

Authors: Borse, Rushikesh^{; *} | Das, Rochishnu | Dash, Devasish | Yadav, Akshay

Affiliations: Great Lakes Institute of Management, Chennai, Tamilnadu, India

Correspondence: [*] Corresponding author. Rushikesh Borse, Great Lakes Institute of Management, Chennai, Tamilnadu, 603102, India. E-mail: [email protected].

Abstract: In the wake of the contemporary competitive business landscape, the retention of employees has become one of the most important yet difficult tasks for any corporate. Retaining top-performing employees not only improves organizational performance but also reduces recruitment costs. In this study, the authors investigate the major drivers leading to employee attrition and using machine learning algorithms implemented on a well proven and validated IBM HR data set. Although the data set tags the samples for a target variable (attrited and non-attrited), the work presented in this paper comes up with another labelling (1. likely to leave, 2. On the verge of leaving, 3. will stay). The data set is evaluated over top 10 Machine learning algorithms and a competitive analysis is made between them based on various factors. The best model has shown a prediction accuracy of over 85% +. Managers are provided with insights and recommendations at the end that will help companies to proactively identify at-risk employees and implement effective retention strategies.

Keywords: Employee attrition, machine learning, early detection of attrition, artificial neural network

DOI: 10.3233/JIFS-219410

Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024