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A comprehensive analysis of pizza sales data using SQL, uncovering key insights into sales trends, popular pizzas, and customer preferences. This project utilises four datasets to provide a detailed look at the restaurant's performance and operational efficiency.

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Comprehensive-SQL-Analysis-of-Pizza-store-sales

A comprehensive analysis of pizza sales data using SQL, uncovering key insights into sales trends, popular pizzas, and customer preferences. This project utilises four datasets to provide a detailed look at the restaurant's performance and operational efficiency.

Project Overview

This project involves a comprehensive analysis of pizza sales data using SQL. The objective is to uncover key trends and insights by solving a range of queries from basic to complex. The project is divided into four main datasets: Pizzas, Pizza Types, Orders, and Order Details.

Datasets

  1. Pizzas Dataset
  • pizza_id: Unique identifier for each pizza.
  • pizza_type_id: Identifier linking the pizza to its type.
  • size: Size of the pizza (e.g., Small, Medium, Large).
  • price: Price of the pizza.
  1. Pizza Types Dataset
  • pizza_type_id: Unique identifier for each pizza type.
  • name: Name of the pizza type.
  • category: Category of the pizza (e.g., Vegetarian, Non-Vegetarian).
  • ingredients: Ingredients used in the pizza.
  1. Orders Dataset
  • order_id: Unique identifier for each order.
  • date: Date when the order was placed.
  • time: Time when the order was placed.
  1. Order Details Dataset
  • order_details_id: Unique identifier for each order detail.
  • order_id: Identifier linking the order to the order details.
  • pizza_id: Identifier linking the order detail to the pizza.
  • quantity: Number of pizzas ordered.

SQL Queries

The analysis involves solving a range of SQL queries, from easy to hard, to extract meaningful insights from the data. The queries cover various aspects such as:

  • Total sales by pizza type and size.
  • Most popular pizzas.
  • Sales trends over time.
  • Order frequency and customer preferences.
  • Detailed analysis of ingredients and categories.

Findings

Some of the key findings from the analysis include:

  • The most popular pizza types and sizes.
  • Peak sales periods and trends over time.
  • Customer preferences and ordering patterns.
  • Insights into ingredient usage and pizza categories.

Contributing

If you would like to contribute to this project, please fork the repository and submit a pull request. For any issues or suggestions, feel free to open an issue.

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A comprehensive analysis of pizza sales data using SQL, uncovering key insights into sales trends, popular pizzas, and customer preferences. This project utilises four datasets to provide a detailed look at the restaurant's performance and operational efficiency.

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