International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Mitigating Attrition: Data-Driven Approach Using Machine Learning and Data Engineering

Author(s) Naveen Edapurath Vijayan
Country USA
Abstract This paper presents a novel data-driven approach to mitigating employee attrition using machine learning and data engineering techniques. The proposed framework integrates data from various human resources systems and leverages advanced feature engineering to capture a comprehensive set of factors influencing attrition. The study outlines a robust modeling approach that addresses challenges such as imbalanced datasets, categorical data handling, and model interpretation. The methodology includes careful consideration of training and testing strategies, baseline model establishment, and the development of calibrated predictive models. The research emphasizes the importance of model interpretation using techniques like SHAP values to provide actionable insights for organizations. Key design choices in algorithm selection, hyperparameter tuning, and probability calibration are discussed. This approach enables organizations to proactively identify attrition risks and develop targeted retention strategies, ultimately reducing costs associated with employee turnover and maintaining a competitive edge in talent management.
Keywords Employee attrition, Machine learning, Data engineering, Predictive modeling, Feature engineering, Human resources analytics, Talent retention, Workforce analytics, Attrition prediction, Data-driven HR, SHAP values, Model interpretation, Imbalanced datasets, Probability calibration, Hyperparameter tuning, LightGBM, Proactive talent management, HR information systems, Employee sentiment analysis, Organizational performance metrics.
Published In Volume 1, Issue 1, July-August 2019
Published On 2019-08-21
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E-ISSN 2582-2160
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