Developing a Transparent Anaemia Prediction Model Empowered With Explainable Artificial Intelligence

Employee attrition remains a critical concern for organizations, impacting productivity, morale, and operational continuity. This research introduces a machine learning-based framework for predicting employee attrition using the IBM HR Analytics dataset. The study employs three robust classification algorithms—Support Vector Machine (SVM), Random Forest, and a Stacking Classifier—to model attrition likelihood based on employee demographic, behavioral, and performance-related features. The proposed system is developed using Python for machine learning and backend processing, while the user interface is implemented with HTML, CSS, and JavaScript to enable seamless interaction. By analyzing patterns within historical HR data, the system provides predictive insights to support human resource departments in devising proactive retention strategies. This work represents a novel contribution in the domain of predictive HR analytics, integrating ensemble learning techniques to enhance accuracy and decision support in workforce management. Keywords: Employee Attrition, HR Analytics, Machine Learning, SVM, Random Forest, Stacking Classifier, Predictive Modeling, Python, Web Application.