This project analyzes a pizza sales dataset using SQL to uncover key business insights.
The analysis covers sales performance, revenue generation, ordering patterns, and product popularity.
The dataset consists of 4 CSV files:
- 🧾 orders.csv → order_id, order_date, order_time
- 📊 order_details.csv → order_id, pizza_id, quantity
- 🍕 pizzas.csv → pizza_id, pizza_type_id, size, price
- 🧂 pizza_types.csv → pizza_type_id, name, category, ingredients
- SQL Concepts: Joins, Aggregations, Grouping, Ordering, Window Functions, Subqueries, CTEs
- Tools: MySQL Workbench (for querying), Canva (for presentation)
Basic: total orders, revenue, highest-priced pizza, most common size, top 5 pizzas
Intermediate: category quantities, hourly order distribution, avg pizzas/day, top 3 pizzas by revenue
Advanced: % revenue by pizza, cumulative revenue, top 3 by revenue per category
- Certain pizza types & sizes dominate sales and revenue
- Orders peak during specific hours, reflecting customer behavior
- Some categories consistently outperform others in both quantity and revenue
- SQL-driven insights help optimize menu, pricing, and inventory planning