Leveraging AI-Driven Strategies to Mitigate Employee Turnover in Commercial Industries

Employee attrition remains a significant obstacle in the corporate sector, adversely influencing organizational coherence and productivity. This article introduces a well-rounded system, meticulously crafted to curtail employee turnover. An extensive analysis employing a diverse set of machine learning models, namely, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Random Forest, CatBoost, and XGBoost, has been performed to construct an efficacious predictive architecture. Despite the widespread adoption of machine learning in forecasting attrition, the interpretational challenges persist, largely attributable to the dependency on feature sets. This research underscores that the Min-Max Scaler empowers the Random Forest classifier to attain an exceptional 88.24% accuracy utilizing a condensed three-feature subset, underscoring the efficacy of feature reduction amidst expansive datasets. The study employs LIME and SHAP frameworks to delve into the interpretability of models, examining the influence of risk variables meticulously. SHAP illuminates both overarching and specific insights, while LIME focuses on dissecting local black-box outputs. This scholarly contribution extends beyond offering a potent AI resolution to the pressing challenge of employee attrition. It accentuates the paramountcy of model interpretability in deriving insightful HR decisions. Furthermore, the investigation accentuates the capability of AIinfused systems to bolster workforce consistency and efficiency in the corporate sphere. It unfolds actionable intelligence regarding the determinants of attrition, marking a significant stride towards a more informed and strategic approach to employee retention and organizational productivity.