This data analysis project investigates annual pizza sales to uncover customer behavior and product performance, revealing that while the Large (L) size dominates revenue (contributing over $375k), the XXL size and bottom 5 pizza types represent less than 2% of total sales. By analyzing order distributions, the project identifies a group-dining preference with an average of 2.3 pizzas per order, leading to strategic recommendations such as replacing low-volume items with high-margin bundles and optimizing staffing for Friday and Saturday revenue peaks. Using Python, Pandas, and Seaborn, the analysis provides a 360-degree operational view to help a business owner streamline inventory and maximize profitability through data-driven menu engineering.
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