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@article{199307,
        author = {Sahil Balaram kale and Abhijeet Gajbhiye},
        title = {A Study on Machine Learning-Based HR Analytics for Employee Attrition Prediction at HCLTech , Nagpur},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {14687-14695},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199307},
        abstract = {The problem of employee attrition has become vital in the modern information technology industry where human capital is one of the major competitive advantages. The organizations like HCLTech are struggling to retain talented employees because of various factors like lack of job satisfaction, career development and work life balance. The application of machine learning methods in Human Resource (HR) analytics over the last few years has been offering an innovative method in the comprehension and forecasting of employee turnover. This paper examines how HR analytics on machine learning can be applied to predict employee attrition at the context of HCLTech, Nagpur. Through utilization of classification algorithms like Logistic Regression, Decision Tree and Random Forest, the study also seeks to discover trends and correlation among different employee related variables and their effect on attrition behavior. The adopted approach in the study is data-driven and thus, it introduces the use of preprocessing, feature selection, and model evaluation methods to improve the prediction accuracy. The results indicate that machine learning inductions especially ensemble techniques are highly effective in forecasting attritions as well as discovering hidden factors that prompt employees to leave their jobs. The study reveals the strategic value of predictive analytics in facilitating proactive HR interventions, enhancing employee retention, and maximizing organizational performance. In addition, the research adds to the existing knowledge base in HR analytics through highlighting the importance of data science in workforce management today.},
        keywords = {HR analytics, workforce retention, machine learning, predictive modeling, employee attrition, and random forest, logistic regression, organizational behavior.},
        month = {April},
        }