🍕 Pizza Sales Analysis Using SQL

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📌 Project Overview

This project focuses on analyzing pizza sales data using SQL to uncover valuable business insights.
The analysis includes:

• Revenue trends
• Popular pizza types and sizes
• Order distribution by time
• Category-wise contributions

These insights can help optimize menu offerings and improve decision-making.

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🎯 Objective

To analyze pizza sales data and answer key business questions such as:

• ✅ What is the total revenue generated?
• ✅ Which is the most popular pizza size?
• ✅ What are the top-selling pizza types?
• ✅ When is the peak ordering time?
• ✅ Which pizzas contribute the most to revenue?

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📊 Dataset Details

The dataset consists of 4 tables:

1. orders

• order_id – Unique identifier for each order
• date – Date of the order
• time – Time of the order

2. order_details

• order_id – Foreign key referencing orders
• pizza_id – Foreign key referencing pizzas
• quantity – Number of pizzas ordered

3. pizzas

• pizza_id – Unique identifier for pizza
• pizza_type_id – Links to pizza type
• size – Pizza size (S, M, L, XL)
• price – Price of the pizza

4. pizza_types

• pizza_type_id – Unique pizza type identifier
• name – Pizza name
• category – Category of pizza (Veg, Non-Veg, etc.)
• ingredients – Ingredients used

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🛠 Tools Used

• Database: MySQL
• Queries: SELECT, JOIN, GROUP BY, ORDER BY, Aggregate Functions, Window Functions

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✅ Key Analysis Performed

✔ Total Orders & Revenue
✔ Highest Priced Pizza
✔ Most Common Pizza Size
✔ Top 5 Most Ordered Pizza Types
✔ Category-wise Quantity Sold
✔ Distribution of Orders by Hour
✔ Average Pizzas per Day
✔ Top 3 Pizzas by Revenue
✔ Percentage Contribution of Each Pizza Type
✔ Cumulative Revenue Over Time
✔ Top 3 Pizzas by Revenue per Category

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🔍 Key Insights

✔ Total Orders: 21,350
✔ Total Revenue: $817,860
✔ Most Popular Size: Large
✔ Peak Ordering Time: 12 PM – 1 PM
✔ Top Pizza by Revenue: Thai Chicken Pizza

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📌 Questions Covered

✅ Basic Queries

1. Retrieve the total number of orders placed.
2. Calculate the total revenue generated from pizza sales.
3. Identify the highest-priced pizza.
4. Identify the most common pizza size ordered.
5. List the top 5 most ordered pizza types along with their quantities.

✅ Intermediate Queries

6. Total quantity of each pizza category ordered.
7. Distribution of orders by hour of the day.
8. Category-wise distribution of pizzas.
9. Average pizzas ordered per day.
10. Top 3 pizza types by revenue.

✅ Advanced Queries

11. Percentage contribution of each pizza type to revenue.
12. Cumulative revenue over time.
13. Top 3 pizza types by revenue in each category.

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🧾 Sample SQL Queries

✅ 1. Total Orders

SELECT COUNT(order_id) AS total_orders FROM orders;

✅ 2. Total Revenue

SELECT ROUND(SUM(orders_details.quantity * pizzas.price), 2) AS total_sales FROM orders_details JOIN pizzas ON pizzas.pizza_id = orders_details.pizza_id;

✅ 3. Highest Priced Pizza

SELECT pizza_types.name, pizzas.price FROM pizza_types JOIN pizzas ON pizza_types.pizza_type_id = pizzas.pizza_type_id ORDER BY pizzas.price DESC LIMIT 1;

(📌 Full queries available in pizza_sales_analysis.sql file)

📄 Project Report

For detailed SQL queries, outputs, and analysis screenshots, check the full project report:
SQL Queries & Outputs (PDF)

📂 Project Structure Pizza-Sales-SQL-Project/ │ ├── README.md
├── LICENSE ├── pizza_sales_analysis.sql # Combined SQL file (all queries) ├── Questions List.txt # List of all questions ├── SQL_Queries_and_Outputs.pdf # PDF report with query & output screenshots ├── queries/ # Individual query files │ ├── Question 1.sql │ ├── Question 2.sql │ └── ... Question 13.sql ├── dataset/ # CSV files │ ├── orders.csv │ ├── orders_details.csv │ ├── pizzas.csv │ └── pizza_types.csv

▶ How to Run Install MySQL and create a database. Import the pizza sales dataset (tables: orders, order_details, pizzas, pizza_types). Run queries from pizza_sales_analysis.sql file in MySQL Workbench or any SQL client.

🏷 Tags #SQL #DataAnalysis #MySQL #PizzaSales #PortfolioProject #BusinessInsights

👤 Author Faizan Shaikh 📌 LinkedIn Profile | 📌 GitHub Profile ✅ Live GitHub Repository