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Fictitious Pizza Sales Analytics
Food & Beverage Consumption
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About
This dataset captures a full year's sales data from a fictitious pizza establishment. It details each customer order, including the date and time of purchase, and provides in-depth information on the pizzas served. This encompasses details such as the pizza type, size, quantity ordered, price, and a list of their ingredients. It offers a granular view of sales operations and menu specifics for a typical pizza place.
Columns
The dataset is organised across four interconnected tables:
- orders:
order_id: A unique identifier for each customer order.date: The calendar date when the order was placed.time: The specific time of day the order was placed.
- order_details:
order_details_id: A unique identifier for each individual pizza item within an order.order_id: Links back to the main order in the 'orders' table.pizza_id: Connects to specific pizza types and their details in the 'pizzas' table.quantity: The number of identical pizzas (same type and size) ordered.
- pizzas:
pizza_id: A unique identifier for each distinct pizza, defined by its type and size.pizza_type_id: Links to the broader pizza type details in the 'pizza_types' table.size: Specifies the size of the pizza (e.g., Small, Medium, Large, X Large, XX Large).price: The price of the pizza in United States Dollars (USD).
- pizza_types:
pizza_type_id: A unique identifier for each general pizza type.name: The name of the pizza as it appears on the menu.category: The menu category the pizza belongs to (e.g., Classic, Chicken, Supreme, Veggie).ingredients: A comma-separated list of ingredients for the pizza. Note that Mozzarella Cheese is always included, even if not listed, and Tomato Sauce is included unless another sauce is specified.
Distribution
The dataset contains a year's worth of sales records and is typically found in a CSV file format, with the
order_details.csv file being approximately 1.31 MB in size. Key columns like order_details_id, order_id, pizza_id, and quantity each contain 48.6 thousand valid records. There are 91 unique pizza IDs represented in the data. The majority of pizza quantities ordered are 1, with a mean quantity of 1.02.Usage
This dataset is ideal for:
- Sales Trend Analysis: Identifying popular times for orders, best-selling pizzas, and seasonal demand fluctuations.
- Menu Optimisation: Analysing ingredient usage, category performance, and pricing strategies.
- Inventory Management: Simulating stock levels and predicting ingredient requirements.
- Business Performance Monitoring: Gaining insights into a fictitious pizza place's operational efficiency.
- Educational Projects: Practising data cleaning, analysis, and visualisation techniques.
Coverage
The dataset covers one full year of sales data. It pertains to a fictitious pizza establishment, with no specific geographic location or demographic scope provided.
License
CC0: Public Domain
Who Can Use It
This dataset is valuable for a variety of users, including:
- Data Analysts and Scientists: For practicing data manipulation, statistical analysis, and machine learning models (e.g., forecasting).
- Business Owners/Managers: To understand sales patterns, customer preferences, and operational insights for restaurant businesses.
- Students and Researchers: For academic projects related to retail analytics, operations research, or data science education.
- Software Developers: For building simulated point-of-sale (POS) systems or inventory management tools.
Dataset Name Suggestions
- Pizza Place Sales Transactions
- Fictitious Pizza Sales Analytics
- Annual Pizza Order Data
- Restaurant Sales Dataset (Pizza)
- Pizza Business Data
Attributes
Original Data Source: Fictitious Pizza Sales Analytics
QUALITY