RetenSure: Ensemble Learning for Managing Employee Attrition

This paper proposes a perspective on predicting employee attrition to safeguard the company’s core values such as teamwork, innovation, and organizational integrity. Employee attrition, signifying the departure of individuals from a company, introduces a spectrum of challenges, including disruptions in team unity, diminished morale, and heightened costs associated with recruiting and onboarding new staff. Addressing employee attrition is crucial for workplace stability, productivity, and financial efficiency. Recent research on employee attrition lacks generalizability in terms of accuracy improvement when employing a single machine learning (ML) algorithm. This paper proposes the use of an ensemble learning model to predict and mitigate turnover rates effectively and is named Retensure. The proposed approach involves combining multiple techniques, providing greater accuracy compared to the conventional reliance on a single method. These insights contribute to fostering a positive work environment and ensuring the sustained success of organizations in the dynamic and ever-evolving business landscape. This paper utilizes the IBM analytics dataset to identify and predict employee attrition. The results of the proposed pipeline are showcased using the IBM analytics dataset, and a comparative analysis is conducted against the outcomes of the RetenSure approach and other state-of-the-art methods.