Data-Driven Analysis of Pizza Hut Orders and Revenue Trends Using SQL

Abstract This project focuses on analyzing sales data of a pizza business using structured query techniques in a relational database environment. The dataset is organized into multiple related tables such as orders, order details, pizzas, and pizza categories, enabling efficient data management and analysis. SQL operations like joins, aggregate functions, subqueries, and common table expressions (CTEs) are used to extract meaningful insights from the data. The study aims to evaluate important business metrics including total revenue, number of orders, popular products, and category-wise performance. Time-based analysis is performed to identify peak sales hours and customer ordering patterns. Additionally, ranking methods are applied to determine best-selling items and their contribution to overall revenue. The analysis reveals variations in customer preferences and highlights key trends in sales distribution. The results demonstrate that SQL-based data analysis is a powerful and efficient approach for handling large structured datasets. It provides valuable insights for decision-making without requiring complex machine learning models, making it suitable for business intelligence applications.