Project Description

This project focuses on analyzing a Pizza Sales Dataset using SQL to uncover insights about sales performance, customer preferences, and revenue patterns. The database contains multiple tables, including order details, pizza information, categories, and pricing.

By writing SQL queries, the project explores sales trends across different dimensions such as time, category, and pizza type. It also applies advanced analytical techniques like cumulative revenue calculation and percentage contribution analysis.

🎯 Objectives

Understand database structure and relationships between tables.

Perform data exploration to answer business questions.

Use SQL to calculate:

Total orders and revenue

Best-selling pizzas and categories

Order distribution by time of day

Average daily pizza sales

Revenue trends and contribution analysis

📂 Dataset Structure

The dataset consists of the following key tables:

orders → order ID, date, and time of each order

order_details → order line items with pizza ID and quantity

pizzas → pizza ID, size, price, and pizza type ID

pizza_types → pizza type ID, pizza name, and category

📝 SQL Tasks 🔹 Basic Analysis

Count total number of orders placed

Calculate total revenue generated

Find the highest-priced pizza

Identify the most common pizza size

List top 5 most ordered pizza types

🔹 Intermediate Analysis

Find total quantity of pizzas sold per category

Analyze order distribution by hour of the day

Find category-wise distribution of pizzas

Calculate average pizzas ordered per day

Identify top 3 pizzas by revenue

🔹 Advanced Analysis

Calculate percentage contribution of each pizza type to total revenue

Analyze cumulative revenue growth over time

Find top 3 pizzas by revenue within each category

✅ Expected Outcomes

By completing this project, you will:

Strengthen your skills in SQL joins, aggregations, and grouping

Learn time-based and revenue-based analysis

Apply advanced SQL concepts like window functions and percentage contribution

Gain the ability to transform raw sales data into meaningful business insights