PREDICTION OF EMPLOYEE ATTRITION USING ENSEMBLE MACHINE LEARNING ALGORITHMS

The creation of the Employee Attrition Prediction System has been able to effectively solve the problem of talent depletion in organizations through the incorporation of advanced ensemble machine learning methods within an easily navigable web interface. Through the connection between past records of organizational employees and the analysis of such records, the system has become a valuable tool for HR management. Utilizing a rigorous methodological approach, the study employed high-efficiency algorithms such as Random Forest, XGBoost, and Logistic Regression, combined using the ensemble techniques of Voting and Stacking. The strategy was successful in identifying linear and highly non-linear relationships among variables like job satisfaction, work-life balance, and salary. The use of SMOTE has helped to ensure that the system is still fair and accurate despite facing the challenge of imbalanced dataset that usually occurs within a business setting. A combination of two modules allows for smooth communication as well as easy administration on the part of the administrator. As such, users can have real-time attrition predictions as well as educational FAQs available at their disposal while the administrator can manage the whole process efficiently. Testing on both functional and non-functional requirements proves the efficiency of the program since it delivers results instantly. In summary, this project demonstrates the transformative potential of artificial intelligence in workforce planning. By identifying at-risk individuals and the primary drivers of turnover, the technology empowers HR departments to transition from reactive responses to proactive, data-driven retention strategies. Ultimately, this solution provides a scalable and reliable foundation for fostering a more engaged, productive, and stable workforce

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