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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19346843 |
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Table of Contents:
- <p>This paper explores the application of data science methodologies to retail transactional data. By leveraging a dataset of over 200,000 transactions, we developed a modular Business Intelligence (BI) dashboard to identify revenue drivers and optimize menu offerings through Pareto analysis and quadrant-based categorization. The analysis successfully identified a 42.9% revenue concentration among core products, providing a data-driven roadmap for menu simplification.</p>