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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: May 2026
Volume 11 | Issue 5
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Impact Factor : 8.14
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Issue Published : 120
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| Paper Title: | Automated System for Predicting Optimal Locations for New Retail Stores using Demographic and Economic Factors with Machine Learning |
| Authors Name: | A. Jensilin Mary , K. Kaviya Selvi , M. Madhumitha , E. Mithra , B. Niranjani |
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IJRTI_212081
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| Published Paper Id: | IJRTI2604334 |
| Published In: | Volume 11 Issue 4, April-2026 |
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| Abstract: | The selection of an optimal location is a critical factor influencing the success and sustainability of retail businesses, as it directly impacts customer accessibility, revenue generation, and long-term growth. This paper presents a data-driven framework for predicting suitable retail store locations using machine learning techniques. The proposed system integrates diverse datasets, including demographic, economic, and geographic information, to evaluate location suitability based on key factors such as population density, income distribution, competitor presence, and accessibility. Multiple supervised learning algorithms, namely Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost), are implemented and comparatively analyzed. Feature engineering methods are employed to extract meaningful attributes, including competitor density and accessibility indices, to enhance model performance. The models are evaluated using standard performance metrics such as accuracy, precision, recall, and F1-score. The best-performing model is deployed through a Flask-based web application to provide real-time location predictions. Experimental results demonstrate the effectiveness of the proposed approach in improving prediction accuracy and supporting data-driven decision-making. The system offers a scalable solution for retail planning, enabling businesses to reduce financial risks and optimize strategic investments. |
| Keywords: | Retail Location Prediction, Machine Learning, Business Analytics, XGBoost, Demographic Analysis, Flask Application |
| Cite Article: | "Automated System for Predicting Optimal Locations for New Retail Stores using Demographic and Economic Factors with Machine Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.c521-c525, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604334.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604334
Registration ID:212081
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: c521-c525 Country: Thiruvannamalai, Tamil Nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604334 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604334 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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