Authors : N. Bhavana; P. Venkatesh

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/yzx5h7nt

DOI : https://doi.org/10.38124/ijisrt/25may1110

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Predictive modeling of economic phenomena with machine learning algorithms has gained interest recently. The present research proposes an empirical consideration of building a machine-learning model to predict the Gross Domestic Product (GDP) of India. A dataset was generated that combines aspects of time series analysis and inflation rates. The comparative analysis, utilizing linear regression, investigated to find the best model. Our analysis shows that the model has applications because of the importance of relationships captured by the linear regression model, being recognized as a successful one concerning non-linear characteristics introduced by independent variables concerning GDP. Thus, an accomplished prediction accuracy rate needs to be surpassed by the linear regression model. So this forms an important contribution of advanced machine learning techniques to predictive economics. Having discussed the utility of a good dataset and a better application of linear regression, we add arguments about how much these factors can contribute to the efficiency of prediction at the cost of data and computational resources. The findings from this study will support the development of economic policy while being of use to decision-makers in business and government. Therefore, this study will be of a considerable reference point in future research for the application of advanced algorithms of machine learning and quality data trusted sources for economic forecasting.

Keywords : RF Regressor, Gradient Boosting Regressor and Linear Regression (LR).

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