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@article{197131,
        author = {G. Satish and Mamidisetti Pooja Sree and Kudupudi Teja Sree and Varada Gayathri and Nethala Suma Sree and Dr. Y. Venkat},
        title = {HR Attrition Analysis and Prediction System Using Ensemble Methods},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {5831-5835},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197131},
        abstract = {Employee attrition continues to be a major concern for organizations, affecting both productivity and long-term stability. This study presents a machine learning-based approach to predict employee attrition using ensemble techniques. The dataset used includes various employee attributes such as job role, salary, performance ratings, and work-life balance indicators. Initially, data preprocessing methods such as handling missing values, encoding categorical variables, and feature scaling are applied to ensure data quality.
Multiple classification models including Decision Tree, Random Forest, and Gradient Boosting are developed and evaluated. To enhance predictive performance, ensemble methods such as Voting and Stacking are implemented by combining individual model outputs. The experimental results indicate that ensemble techniques outperform standalone models in terms of accuracy and consistency. The proposed system provides meaningful insights that can assist HR departments in identifying employees at risk of leaving and taking proactive retention measures.},
        keywords = {HR Analytics, Employee Attrition, Machine Learning, Ensemble Methods, Predictive Modeling},
        month = {April},
        }