Hossain, Md Shahadat and Ali, Mohammad and Rahman, Md Whahidur (2025) Data-Driven Strategies for Predicting and Enhancing Rural Business Growth in the United States. American Journal of Technology Advancement, 1 (7). pp. 121-146. ISSN 2997-9382

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Abstract

Rural business development in the United States is a very hot topic in stabilizing the economy in their regions, however; income growth, job provision and infrastructure has remained skewed, thus preventing sustainable growth. The paper follows an analytical method based on data to forecast and improve the growth of rural businesses based on county-level economic statistics provided by the U.S. Bureau of Economic Analysis (BEA). This analysis uses the period between 1969 and 2019. This dataset has 3,199 counties and 32 indicators of the economy, including personal income, net earnings, unemployment compensation, and transfer receipts, which gives a detailed temporal and spatial perspective of the economic activity in rural areas. This study determines the most effective determinants of the performance of rural businesses using statistical modeling, regression analysis, and machine learning methods. Exploratory data analysis and clustering techniques are applied to categorize counties based on economic resilience and growth potential and forecast business performance using predictive models based on historical trends. The results point out that access to broadband, diversification of the economy in terms of employment, and steadfast net revenues are closely linked with the development of rural business. Those counties with adaptive economic diversity and higher levels of education showed more scope of growth than those that were relying on one industry. This study also develops specific policies that could be used by the policymakers that focus on the digital inclusion, the training of the entrepreneurs, and the openness of the financial means to the rural business. The framework suggested shows how data analytics will help to close the information gap between economic prediction and policy implementation and, therefore, promote rational decision-making on rural revitalization. This Study explores a quantitative basis of sustainable rural development and creates a model that can be repeated to predict and improve the business ecosystems at the local level in the United States.

Item Type:	Article
Subjects:	A General Works > AI Indexes (General)
Depositing User:	admin eprints
Date Deposited:	12 Jun 2026 12:51
Last Modified:	12 Jun 2026 12:51
URI:	http://eprints.umsida.ac.id/id/eprint/16572

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