This dataset contain detailed information about pizza orders, including specifics about the pizza variants, quantities, pricing, dates, times, and categorization details.

pizza_id: A unique identifier assigned to each distinct pizza variant available for ordering. order_id: A unique identifier for each order made, which links to multiple pizzas. pizza_name_id: An identifier linking to a specific name of the pizza. quantity: The number of units of a specific pizza variant ordered within an order. order_date: The date when the order was placed. order_time: The time when the order was placed. unit_price: The cost of a single unit of the specific pizza variant. total_price: The aggregated cost of all units of a specific pizza variant in an order. pizza_size: Represents the size of the pizza (e.g., small, medium, large). pizza_category: Indicates the category of the pizza, such as vegetarian, non-vegetarian, etc. pizza_ingredients: Provides a list or description of the ingredients used in the pizza. pizza_name: Specifies the name of the specific pizza variant ordered.

This is a realistic and structured pizza sales dataset covering the time span from **2024 to 2025. ** Whether you're a beginner in data science, a student working on a machine learning project, or an experienced analyst looking to test out time series forecasting and dashboard building, this dataset is for you.

📁 What’s Inside? The dataset contains rich details from a pizza business including:

✅ Order Dates & Times ✅ Pizza Names & Categories (Veg, Non-Veg, Classic, Gourmet, etc.) ✅ Sizes (Small, Medium, Large, XL) ✅ Prices ✅ Order Quantities ✅ Customer Preferences & Trends

It is neatly organized in Excel format and easy to use with tools like Python (Pandas), Power BI, Excel, or Tableau.

💡** Why Use This Dataset?** This dataset is ideal for:

📈 Sales Analysis & Reporting 🧠 Machine Learning Models (demand forecasting, recommendations) 📅 Time Series Forecasting 📊 Data Visualization Projects 🍽️ Customer Behavior Analysis 🛒 Market Basket Analysis 📦 Inventory Management Simulations

🧠 Perfect For: Data Science Beginners & Learners BI Developers & Dashboard Designers MBA Students (Marketing, Retail, Operations) Hackathons & Case Study Competitions

pizza, sales data, excel dataset, retail analysis, data visualization, business intelligence, forecasting, time series, customer insights, machine learning, pandas, beginner friendly

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

Intermediate: Join the necessary tables to find the total quantity of each pizza category ordered. Determine the distribution of orders by hour of the day. Join relevant tables to find the category-wise distribution of pizzas. Group the orders by date and calculate the average number of pizzas ordered per day. Determine the top 3 most ordered pizza types based on revenue.

Advanced: Calculate the percentage contribution of each pizza type to total revenue. Analyze the cumulative revenue generated over time. Determine the top 3 most ordered pizza types based on revenue for each pizza category.

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About Dataset Who doesn't like pizza? This dataset contains about 1000 images of pizza and 1000 images of dishes other than pizza. It can be used for a simple binary image classification task.

All images were rescaled to have a maximum side length of 512 pixels.

This is a subset of the Food-101 dataset. Information about the original dataset can be found in the following paper: Bossard, Lukas, Matthieu Guillaumin, and Luc Van Gool. "Food-101 – Mining Discriminative Components with Random Forests." In European conference on computer vision, pp. 446-461. Springer, Cham, 2014.

The original dataset can be found in the following locations: https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/ https://www.kaggle.com/datasets/dansbecker/food-101 https://paperswithcode.com/dataset/food-101 https://www.tensorflow.org/datasets/catalog/food101

Number of instances in each class: Pizza: 983 Not Pizza: 983

Acknowledgements The Food-101 data set consists of images from Foodspotting [1] which are not property of the Federal Institute of Technology Zurich (ETHZ). Any use beyond scientific fair use must be negociated with the respective picture owners according to the Foodspotting terms of use [2].

[1] http://www.foodspotting.com/ [2] http://www.foodspotting.com/terms/

🍕🍽️ Pizza Restaurant Sales

Problem: - Pizza restaurant has recently seen a decline in sales and plans to increase them by looking at customer and order data. To do this, the management plans to perform a thorough analysis of order data and consumer behaviour in order to spot important trends and areas for improvement.

Background: - An overview of pizza sales data from January 2015 to December 2015 is given in this report. To find trends and patterns in pizza sales, data was gathered from pizza joints across the United States and analysed.

About the dataset📅¶

This dataset contains 4 tables in CSV format

The Orders table contains the date & time that all table orders were placed

The Order_Details table contains the different pizzas served with each order in the Orders table, and their quantities

The Pizzas table contains the size and price for each distinct pizza in the Order Details table, as well as its broader pizza type

The Pizza_Types table contains details on the pizza types in the Pizzas table, including their name as it appears on the menu, the category it falls under, and its list of ingredients.

Pizza

## Overview

Pizza is a dataset for classification tasks - it contains Food annotations for 1,966 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

jairNeto/pizza dataset hosted on Hugging Face and contributed by the HF Datasets community

1) Data Introduction • The Pizza or Not Pizza? dataset is a computer vision image dataset designed for binary classification to distinguish between images of pizza and non-pizza food items.

2) Data Utilization (1) Characteristics of the Pizza or Not Pizza? dataset: • The dataset is balanced, consisting of an equal number of pizza and non-pizza food images. All images are collected from real user-generated content, enhancing its practical applicability in real-world scenarios. • The images cover a wide range of food types and preparation environments, providing high visual diversity and realism.

(2) Applications of the Pizza or Not Pizza? dataset: • Binary classification model development for food images: The dataset can be used to train deep learning models that automatically classify whether an image contains pizza or not.

Contents

This pizza sales dataset make up 12 relevant features: - order_id: Unique identifier for each order placed by a table - order_details_id: Unique identifier for each pizza placed within each order (pizzas of the same type and size are kept in the same row, and the quantity increases) - pizza_id: Unique key identifier that ties the pizza ordered to its details, like size and price - quantity: Quantity ordered for each pizza of the same type and size - order_date: Date the order was placed (entered into the system prior to cooking & serving) - order_time: Time the order was placed (entered into the system prior to cooking & serving) - unit_price: Price of the pizza in USD - total_price: unit_price * quantity - pizza_size: Size of the pizza (Small, Medium, Large, X Large, or XX Large) - pizza_type: Unique key identifier that ties the pizza ordered to its details, like size and price - pizza_ingredients: ingredients used in the pizza as shown in the menu (they all include Mozzarella Cheese, even if not specified; and they all include Tomato Sauce, unless another sauce is specified) - pizza_name: Name of the pizza as shown in the menu

🍕The Pizza Challenge

For the Maven Pizza Challenge, you’ll be playing the role of a BI Consultant hired by Plato's Pizza, a Greek-inspired pizza place in New Jersey. You've been hired to help the restaurant use data to improve operations, and just received the following note:

Welcome aboard, we're glad you're here to help!

Things are going OK here at Plato's, but there's room for improvement. We've been collecting transactional data for the past year, but really haven't been able to put it to good use. Hoping you can analyze the data and put together a report to help us find opportunities to drive more sales and work more efficiently.

Here are some questions that we'd like to be able to answer:

1. What days and times do we tend to be busiest?
2. How many pizzas are we making during peak periods?
3. What are our best and worst-selling pizzas?
4. What's our average order value?
5. How well are we utilizing our seating capacity? (we have 15 tables and 60 seats)

That's all I can think of for now, but if you have any other ideas I'd love to hear them – you're the expert!

Thanks in advance,

Mario Maven (Manager, Plato's Pizza)

Colllection Methodology

The public dataset is completely available on the Maven Analytics website platform where it stores and consolidates all available datasets for analysis in the Data Playground. The specific individual datasets at hand can be obtained at this link below: https://www.mavenanalytics.io/blog/maven-pizza-challenge

📌I set up the data model to include all the related instances in one single table so obtaining data for analysis is made easier.

My Inspiration

Complete details were also provided about the challenge in the link if you are interested. The purpose of uploading here is to conduct exploratory data analysis about the dataset beforehand with the use of Pandas and data visualization libraries in order to have a comprehensive review of the data and translate my findings and insights in the form of a single page visualization.

This statistic shows the distribution of pizza restaurants in the United States from 2012 to 2014, by chain. In 2014, **** percent of pizza restaurants in the U.S. were Pizza Hut.

Statistics and facts on the pizza delivery market

Pizza

## Overview

Pizza is a dataset for object detection tasks - it contains Pizza annotations for 893 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

Count Pizza

## Overview

Count Pizza is a dataset for object detection tasks - it contains Pizza annotations for 1,102 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

In the United States between 2022 and 2024, frozen pizza was a very popular meal and its consumer share remained stable throughout the years. In 2024, as much as 87 percent of respondents indicated that they had frozen pizza.

Pizza Production Line Market size was valued at USD 559 Million in 2025 and is expected to reach USD 1.15 Billion by 2035, expanding at a CAGR of 7.5% during the forecast period.

sherryy/random-acts-of-pizza dataset hosted on Hugging Face and contributed by the HF Datasets community

The Pizza Foodservice Market is Segmented by Structure (Chained Outlets and Independent Outlets), Service Model (Delivery-Only (Ghost Kitchens), Dine-In, and Carry-Out/Take-Away), Restaurant Format (Quick-Service (QSR), Fast-Casual, and Full-Service/Casual Dining), Location (Leisure, Lodging, Retail, Standalone, and Travel), and Geography (North America, Europe, and More). The Market Forecasts are Provided in Terms of Value (USD).

NexaAIalex/Pizza dataset hosted on Hugging Face and contributed by the HF Datasets community

Marco's Pizza location dataset — 145 locations in Ohio. Part of CREHQ's multi-unit intelligence platform covering retail, restaurant, financial services, and healthcare brands. Licensed access via enterprise API or dataset purchase. Training on CREHQ data is not permitted.

Pizza delivery audience profile for United States.