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@article{178573,
        author = {Rahul Kumar and Shreya Singh and Harsh Ranjan and Dr. Pallavi Goel},
        title = {Body Fitness Prediction Using Random Forest Classifier},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4316-4322},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178573},
        abstract = {Accurate prediction of body fitness levels has become increasingly important in fields such as healthcare, sports science, and personal wellness management. This research presents a machine learning-based approach to classify fitness levels using key physiological and lifestyle attributes including age, body mass index (BMI), physical activity, and dietary habits. Among the models evaluated, the Random Forest classifier demonstrated superior performance due to its ensemble learning capabilities, which effectively reduce overfitting and enhance predictive accuracy. The model was trained and validated on diverse datasets to ensure generalizability across populations. The findings highlight the potential of machine learning in delivering data-driven insights for fitness assessment and personalized health interventions.},
        keywords = {Body Fitness Prediction, Machine Learning, Random Forest, Health Analytics, Lifestyle Data},
        month = {May},
        }