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@article{192804,
        author = {K.TAMILSELVI and N.SUVAATHI and G.KANISHA and M.ANANDHA HARINI},
        title = {A Survey on Assessing Global Economic Vitality: A Machine Learning Approach to GDP Classification},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {2054-2056},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192804},
        abstract = {In an era of volatile global markets, the accurate assessment of economic health is paramount for policymakers and investors. Traditional econometric models, while foundational, often struggle to capture the non-linear complexities inherent in global economic interactions. This paper presents a machine learning framework utilizing the Random Forest classifier to categorize national economic health based on Gross Domestic Product (GDP) growth. Utilizing a comprehensive dataset of over 200 countries (2010–2025), we engineer features from key fiscal and monetary indicators—including inflation, public debt, and unemployment—to predict discrete growth categories (High, Moderate, Low). Our tuned Random Forest model achieves an accuracy of 0.6561, distinguishing itself from baseline models and validating the potential of ensemble learning methods to provide robust, scalable risk analysis tools for the global economy.},
        keywords = {Random Forest, Economic Health, GDP Growth, Ensemble Learning, Macroeonomic Indicators, Risk Analysis, Machine Learning},
        month = {February},
        }