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@article{178844,
        author = {Arpana Sinha},
        title = {AttriSense: Data-Driven Analysis for Employee Attrition},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4253-4259},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178844},
        abstract = {Organizations have a major problem from employee attrition, which affects productivity and raises recruitment expenses. The objective of this project is to forecast employee attrition through the use of multiple machine learning models, such as Artificial Neural Networks, Random Forest and Support Vector Machines. The models were trained by examining employee-related characteristics from the IBM HR Analytics dataset, including job role, satisfaction level and performance rating in order to detect trends that result in attrition. HR departments may lower turnover and retain important personnel by using this predictive technique to drive their decisions.},
        keywords = {Employee Attrition, IBM HR Analytics dataset, Machine Learning Models- Linear Regression, Random Forest, K-Nearest Neighbor, ANN, RNN, Data Science, Predictive Modeling.},
        month = {May},
        }