Authors:
M. Arjun Raj, Arjyalopa Mishra, George Anna Forest
Addresses:
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India, am0306@srmist.edu.in. Department of Management, National Law University Odisha, Cuttack, Odisha, arjyalopa@nluo.ac.in. Department of Data and Analytics, UCB Pharma, Georgia, United States of America, vijay.boopathy@ucb.com.
Delving into the complex landscape of predicting employee attrition, this research embarks on a journey to uncover employing advanced machine learning techniques to glean profound insights into workforce dynamics and guide strategic decision-making within human resources management. Through meticulous analysis and predictive modeling, our study identifies pivotal factors driving attrition and predicts turnover risks within organizational contexts. The robust performance of our models, evidenced by high area Under the Curve (AUC) scores in Receiver Operating Characteristic (ROC) analysis and informative Precision-Recall curves, underscores their potential to fortify retention initiatives and foster a more engaged and resilient workforce. Moreover, our investigation unveils subtle correlations among variables such as job satisfaction, workload, and career prospects, furnishing actionable insights for HR practitioners and leaders striving to implement targeted retention strategies. Despite inherent limitations like data constraints and model assumptions, our research underscores the transformative capacity of machine learning in HR management, offering pragmatic guidance for organizations devoted to cultivating a culture of engagement, resilience, and sustained prosperity. By embracing data-driven methodologies and prioritizing employee well-being, organizations can position themselves for enduring growth and triumph amidst today’s competitive business milieu. This abstract encapsulates the essence of our study, encapsulating key discoveries, methodologies, and implications, furnishing a comprehensive outlook on our venture into employee attrition prediction and its ramifications for organizational triumph.
Keywords: Advanced Machine Learning Techniques; Workforce Dynamics; Strategic Decision-Making; Human Resources Management; Predictive Modelling; Turnover Risks; Organizational Contexts; Job Satisfaction and Workload.
Received on: 15/07/2023, Revised on: 03/10/2023, Accepted on: 29/11/2023, Published on: 03/03/2024
AVE Trends in Intelligent Technoprise Letters, 2024 Vol. 1 No. 1, Pages: 13-29