A Systematic Analysis of Machine and Deep Learning Frameworks for Human Resource Attrition Dataset

Employee attrition poses significant challenges for organizations, affecting productivity, workforce stability, and operational costs. The ability to predict turnover with high accuracy can assist businesses to craft meaningful retention strategies and improve human resource management strategies. This paper, systematically review the literature on using deep learning methods for employee attrition prediction and the capabilities of uncovering complex patterns and relationships from HR data. The study opens with a consideration of common attrition predictors, including job satisfaction and workload, as well as the effect of data preprocessing and feature engineering on model performance. Different deep learning architectures are discussed with respect to applicability and effectiveness in this field. The traditional models are evaluated based on the datasets used and metrics compared, and some discuss common challenges, such as imbalanced data, model interpretability, and ethics. It also looks at practical applications like proactive retention efforts and workforce planning, showcasing the transformative impact of deep learning on human resource analytics. Lastly, the review suggests ways forward for future research in areas such as explainable AI, different architectures, and the need for a more ethical approach to the context in which AI operates to ensure fairness and transparency. The present study aims to provide guidance on using deep learning techniques to solve employee attrition problems for practitioners.