Employee Attrition Prediction System using Machine Learning and Artificial Intelligence

Employee attrition remains one of the most consequential workforce challenges facing contemporary organizations, with replacement costs estimated between 50% and 200% of an affected employee’s annual compensation. This paper presents the design, implementation, and empirical evaluation of an Employee Attrition Prediction System (EAPS) built on supervised machine learning techniques applied to the IBM HR Analytics dataset comprising 1,470 employee records and 35 workforce attributes. Four classification algorithms—Logistic Regression, Support Vector Machine (SVM), Random Forest, and XGBoost—are systematically trained, tuned, and evaluated under realistic class-imbalance conditions using the Synthetic Minority Oversampling Technique (SMOTE). Three domain-informed engineered features are introduced to augment the base feature set: Compensation Ratio, Tenure per Job, and Years Without Change. Experimental results demonstrate that XGBoost achieves superior performance across all five evaluation metrics, attaining 97.2% accuracy, 96.8% precision, 95.4% recall, a macro F1 score of 96.1%, and an AUC-ROC of 0.991 following stratified 10-fold cross-validation and hyperparameter optimization. A modular six-component system architecture is proposed, culminating in an HR decision-support dashboard that leverages SHAP (SHapley Additive exPlanations) values to deliver individualized, interpretable attrition risk assessments to non-technical HR practitioners. The proposed system addresses the critical gap between available HR data and proactive workforce retention strategy, providing organizations with a scalable, evidence-based tool for reducing voluntary turnover and its associated organizational costs.