This project focuses on analyzing pizza sales data using MySQL to extract meaningful business insights. By writing and executing SQL queries, I explored order patterns, revenue trends, and product performance.
The project uses the following tables:
| Table Name | Description |
|---|---|
| pizzas | Contains details about each pizza (ID, type, size, price) |
| order_details | Contains information about orders and quantities of each pizza |
| orders | Contains order date and time details |
| pizza_types | Contains type, category, and ingredients of pizzas |
- Total number of orders placed
- Total revenue generated from pizza sales
- Identification of the highest-priced pizza
- Most common pizza size ordered
- Top 5 most ordered pizza types by quantity
- Total quantity of each pizza category ordered (using joins)
- Distribution of orders by hour of the day
- Category-wise distribution of pizzas
- Average number of pizzas ordered per day (grouped by date)
- Top 3 most ordered pizza types based on revenue
- Percentage contribution of each pizza type to total revenue
- Cumulative revenue generated over time
- Top 3 most ordered pizza types based on revenue within each pizza category
- MySQL
- SQL
- (Optional) ER Diagram Tools: dbdiagram.io, Draw.io
- (Optional) Presentation: PowerPoint or Google Slides
✔ Writing efficient SQL queries
✔ Performing data joins, aggregations, and groupings
✔ Using SQL to derive business insights
✔ Understanding the importance of data-driven decision-making
- Import the SQL scripts into your MySQL database.
- Run the queries in the following order:
table_creation.sqldata_insertion.sql(if applicable)- Analysis queries from
basic_queries.sql,intermediate_queries.sql,advanced_queries.sql
- Review results and insights.
This project demonstrates how SQL can be effectively used to analyze real-world sales data and provide actionable business insights. It can be extended further with dashboards or visualizations in tools like Tableau or Power BI.