🍕 Pizza Sales Analysis Using SQL

📄 Overview

This project analyzes pizza sales data using SQL queries to generate insights into customer preferences, popular pizzas, and sales trends. The analysis leverages order details, pizza categories, and pricing data.

🔑 Key Insights

• Total Orders & Revenue
  Analyze overall sales volume and the total revenue generated.

• Highest-Priced Pizza & Common Pizza Size
  Identify the most expensive pizza and the most commonly ordered size.

• Top 5 Most Ordered Pizzas
  Rank pizzas by order volume to find the most popular options.

• Orders by Pizza Category
  Breakdown orders by pizza categories (e.g., Veg, Non-Veg).

• Orders Distribution by Hour
  Understand customer order patterns throughout the day.

• Average Pizzas Ordered Per Day
  Calculate the daily average number of pizzas sold.

• Top 3 Pizzas by Revenue
  Identify which pizzas contribute the most to overall revenue.

• Revenue Contribution by Pizza Type
  Analyze how much revenue each pizza category (type) contributes.

• Cumulative Revenue Over Time
  Visualize how revenue accumulates over a specific time period.

• Top 3 Pizzas by Category
  Identify the top 3 pizzas in each category based on sales.

🛠️ Tools, Concepts & Technologies

• SQL Clauses:
  ORDER BY, GROUP BY, JOINS (INNER, LEFT, RIGHT, OUTER)

• Functions:

  • Aggregate: COUNT, SUM, AVG
  • DateTime: DATE, HOUR
  • Subqueries

• Window Functions:
  ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE()

📊 Datasets Used

• Order_Details
• Pizza_Types
• Orders
• Pizzas