Pizza Sales SQL Analysis Project

📌 Project Overview

This project involves analyzing pizza sales data to gain insights into business performance. The analysis is performed using SQL (MySQL) to answer critical business questions regarding orders, revenue, and pizza popularity.

📂 Database Schema

The analysis is based on four relational tables:

• orders: Contains order ID, date, and time.
• order_details: Contains order detail id, order id quantity, and pizza id.
• pizzas: Contains pizza id, pizza type id, size, and price.
• pizza_types: Contains pizza type id, name, category, and ingredients.

🛠️ Tech Stack

• Database: MySQL
• Language: SQL
• Tools: MySQL Workbench

🔍 Key Business Questions Solved

I structured the analysis into three levels of complexity to demonstrate a range of SQL skills.

🟢 Basic Analysis

• 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.

🟡 Intermediate Analysis

• 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.

🔴 Advanced Analysis

• Calculate percentage contribution: Calculated the percentage contribution of each pizza type to total revenue.
• Analyze cumulative revenue: Generated a running total of revenue over time to track business growth.
• Determine top 3 best sellers: Identified the top 3 most ordered pizza types based on revenue for each pizza category.

📈 Key Insights & Findings

• Peak Hours: The store sees the highest volume of orders at 12:00 PM and 6:00 PM.
• Category Performance: The "Classic" category contributes to the highest total quantity of orders.
• Best Sellers: The "Thai Chicken Pizza" is the highest revenue-generating pizza.
• Sales Trends: Fridays exhibit a strong cumulative revenue growth pattern.

🚀 How to Use

1. Clone this repository.
2. Import the raw CSV files (or execute the schema creation script) into your SQL database.
3. Open the project_query.sql file to view the queries and execute them to reproduce the analysis. This file contains all the SQL queries corresponding to the business questions listed above.

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Author: SUJEET CHOURASIA

Connect with me on LinkedIn: https://www.linkedin.com/in/sujeet-chourasia/