This project aims to analyze a pizza sales dataset using MySQL Workbench to derive valuable business insights. By writing optimized SQL queries, I explored key sales metrics, customer ordering patterns, and revenue trends. This project demonstrates my proficiency in SQL querying, data aggregation, joins, and analytical functions.
The dataset consists of multiple tables related to pizza sales, including orders, pizzas, and categories. These tables were joined and analyzed to extract meaningful insights. All the datasets requied for this project have been uploaded in the repository.
- Retrieve the total number of orders placed.
- Calculate the total revenue generated from pizza sales.
- Identify the highest-priced pizza.
- Identify the most common pizza size ordered.
- List the top 5 most ordered pizza types along with their quantities.
-
Join the necessary tables to find the total quantity of each pizza category ordered.
-
Determine the distribution of orders by hour of the day.
-
Join relevant tables to find the category-wise distribution of pizzas.
-
Group the orders by date and calculate the average number of pizzas ordered per day.
-
Determine the top 3 most ordered pizza types based on revenue.
-
Calculate the percentage contribution of each pizza type to total revenue.
-
Analyze the cumulative revenue generated over time.
-
Determine the top 3 most ordered pizza types based on revenue for each pizza category.
- MySQL Workbench for writing and executing SQL queries.
- Data Aggregation (SUM, COUNT, AVG, GROUP BY)
- Joins (INNER JOIN, LEFT JOIN) to combine multiple tables.
- Window Functions for advanced analytics.
- CTEs & Subqueries for efficient query structuring.
- SQL Queries: All SQL scripts are available in the repository.
- Dataset: All the datasets required for this project are provided in the reopsitory.