🍕 Pizza Hut Sales Analysis using SQL

📌 Project Overview

This project focuses on analyzing Pizza Hut sales data using SQL to answer real-world business questions.
The goal is to derive meaningful insights related to revenue, customer ordering patterns, pizza popularity, and category-wise performance using structured query language.

A total of 13 business-driven SQL queries were written and optimized to demonstrate strong command over SQL fundamentals and advanced concepts.

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📊 Dataset Content

The analysis is performed on a relational dataset consisting of four CSV files.
Each table represents a different aspect of Pizza Hut’s sales operations.

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🍽️ orders.csv

Contains order-level information.

Column Name	Description
order_id	Unique identifier for each order
date	Date on which the order was placed
time	Time at which the order was placed

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🧾 order_details.csv

Contains item-level details for each order.

Column Name	Description
order_details_id	Unique identifier for each order line item
order_id	References the corresponding order
pizza_id	References the pizza ordered
quantity	Number of pizzas ordered

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🍕 pizzas.csv

Contains pricing and size information for pizzas.

Column Name	Description
pizza_id	Unique identifier for each pizza
pizza_type_id	References the pizza type
size	Pizza size (S, M, L, XL, XXL)
price	Price of the pizza

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📋 pizza_types.csv

Contains descriptive information about pizza types.

Column Name	Description
pizza_type_id	Unique identifier for each pizza type
name	Name of the pizza
category	Pizza category (Classic, Veggie, Chicken, Supreme)
ingredients	Ingredients used in the pizza

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🔗 Dataset Relationships

• orders.order_id → order_details.order_id
• order_details.pizza_id → pizzas.pizza_id
• pizzas.pizza_type_id → pizza_types.pizza_type_id

These relationships enable comprehensive sales, revenue, and category-level analysis using SQL joins.

❓ Business Questions Solved

1. Total number of orders placed
2. Total revenue generated from pizza sales
3. Highest priced pizza
4. Most common pizza size ordered
5. Top 5 most ordered pizza types by quantity
6. Total quantity ordered per pizza category
7. Hourly distribution of orders
8. Category-wise pizza distribution
9. Average number of pizzas ordered per day
10. Top 3 pizza types based on revenue
11. Percentage contribution of each pizza type to total revenue
12. Cumulative revenue analysis over time
13. Top 3 pizzas by revenue within each pizza category

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🛠 SQL Concepts & Techniques Used

• INNER JOINs
• GROUP BY & Aggregate Functions (SUM, COUNT, AVG)
• Subqueries
• Window Functions (RANK)
• Date & Time Analysis
• Revenue & Performance Metrics

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🎯 Key Learnings

• Translating business questions into SQL logic
• Writing optimized and readable SQL queries
• Applying window functions for ranking and cumulative analysis
• Understanding relational database structures and joins

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✅ Conclusion

This project demonstrates practical SQL skills required for data analyst roles.
It highlights the ability to analyze transactional data, extract business insights, and present results in a structured and professional manner.

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