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@article{192500,
        author = {Samriddhi Kathane and Samruddhi D. Tonghale and Shreya Bommarapu and Titiksha Tijare and Palak Bhimke and Prof. Rahul Suryawanshi},
        title = {Employee Attrition Prediction: A Data-Driven HR Decision Support System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {3173-3177},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192500},
        abstract = {Employee Attrition has become a major challenge for organizations these days, because it causes the loss of trained and experienced employees that leads to increased recruitment costs, reduced productivity and an overall decline in the team performance. Understanding different factors that contribute to this attrition factor and an early prediction to this can help the organizations in retention of the employees. The study examines colorful factors similar as job satisfaction, payment, workload, performance conditions, work-life balance and career growth openings to understand their influence on waste. Different types of Machine learning models such as Logistic Regression, Decision Trees, Random Forest and XGBoost are trained and compared to find the best prediction model. The results show that ensemble models like Random Forest perform particularly well, offering strong and harmonious predictions. Along with prediction, the design also highlights the most important factors contributing to waste, giving HR brigades precious perceptivity to improve hand engagement and retention. Overall, this exploration demonstrates how data- driven approaches can support smarter HR opinion and helps associations maintain a stable and motivated pool.},
        keywords = {Employee Attrition Prediction, Machine Learning, XGBoost, Explainable Artificial Intelligence (XAI), SHAP Values, Workforce Overview, Employee Retention, Decision Tree, Random Forest, Support Vector Machine(SVM), HR Predictive Analytics},
        month = {February},
        }