Published on: April 2026
SMART BUSINESS LOCATION ANALYZER SYSTEM
M.Sanjay Kumar B.Shiva Shankar T.Shyam Prasad A.Anil Kumar
Mr. Shaik Nagur Vali
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Abstract
The Smart Business Location System is a framework that uses data to help business owners choose the best places to open new businesses. Choosing the right location is very important for the success of a business, but traditional methods often use gut feelings, manual surveys, and limited market research, which can lead to bad choices. This study tackles these issues by combining geospatial analytics with machine learning to make location evaluation more accurate and scalable.
The system uses hyperlocal data from sources like Google Places API and OpenStreetMap. This data includes business listings, ratings, reviews, road networks, and geographic coordinates. Geospatial analysis is used to find important indicators like the number of competitors, how easy it is to get to them, how many people they affect, and how available the infrastructure is. These features are improved even more. using feature engineering methods to train machine learning models like Logistic Regression, Random Forest, and XGBoost.
How to Cite this Paper
Kumar, M., Shankar, B., Prasad, T. & Kumar, A. (2026). Smart Business Location Analyzer System. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.123
Kumar, M.Sanjay, et al.. "Smart Business Location Analyzer System." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.123.
Kumar, M.Sanjay,B.Shiva Shankar,T.Shyam Prasad, and A.Anil Kumar. "Smart Business Location Analyzer System." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.123.
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References
[1] M. Li and Z. Zhou, “Geospatial Data Analysis for Urban Business Location Optimization,” Journal of Geographic Information Systems, vol. 12, no. 3, pp. 145–158, 2021.[2] T. Mitchell, Machine Learning, 1st ed. New York, USA: McGraw-Hill, 2017.
[3] J. Dean et al., “Large-Scale Machine Learning for Predictive Analytics,” in Proc. IEEE Int. Conf. Data Mining, 2020, pp. 45–52.
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- •Published on: Apr 07 2026
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