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@article{179934,
        author = {Kunal Yadav and Areesh Jabbar and Naveen and Mohammad Rizwan and Mrs. Chandana K R},
        title = {Predicting Employee Performance Using Machine Learning: A Data-Driven Approach for HR Optimization},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8562-8565},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179934},
        abstract = {Employee performance prediction is an 
emerging domain within human resource analytics that 
leverages data science to forecast workforce productivity. 
This project applies machine learning algorithms—Decision 
Trees, Random Forests, XGBoost, and Neural Networks—
 to HR datasets to predict performance based on key 
indicators such as experience, education, age, job role, and 
engagement metrics. Implemented using Python with 
libraries like Scikit-learn, TensorFlow, and PyTorch, and 
visualized through Streamlit, this system provides real-time 
predictions and actionable insights to enhance HR decision
making. The project aims to automate performance 
evaluation, reduce bias, and support data-driven strategies 
in workforce planning.},
        keywords = {Employee Performance, Machine Learning,  HR Analytics, Random Forest, XGBoost, Neural  Networks, Streamlit, Predictive Modeling, Data-Driven  HR},
        month = {May},
        }