Dark Mode
Food Item Classification Data
Food & Beverage Consumption
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This collection details food items available within a centralised repository supporting seamless food delivery operations. The data focuses on critical attributes of menu items, allowing for efficient classification and retrieval within a larger application structure. The utility of this dataset lies in its ability to support core functions, such as identifying items, classifying them by dietary requirements, and managing menu configurations for restaurants.
Columns
- f_id: The unique identifier assigned to a specific food item.
- item: The established name of the food item on the menu.
- veg_or_non_veg: A binary classification that designates whether the item is vegetarian or non-vegetarian.
- Note: A fourth column exists but its description is not presently available.
Distribution
The information is delivered in a tabular format, typically a CSV file, labelled 'food.csv' and sized at 17.21 MB. The dataset features approximately 372,000 unique records. The data quality is assessed as highly usable, scoring 10.00. The expected update frequency is 'Never'.
Usage
This data is ideal for building advanced food item recommendation engines or developing sophisticated filtering mechanisms based on dietary needs. It can be employed for operational planning, helping to analyse the ratio of vegetarian (73%) to non-vegetarian (27%) offerings across the menus. Furthermore, it serves as foundational input for data models simulating food delivery trends and inventory management.
Coverage
The dataset specifically details attributes of menu items, including classification and naming. Geographic scope, temporal range, or demographic details related to user interaction are not provided in this specific file.
License
CC0: Public Domain
Who Can Use It
- Software Engineers: To integrate item data into application back-ends and build user interfaces that rely on accurate dietary labelling.
- Business Analysts: To conduct market analysis regarding food type availability and menu diversity within the delivery platform.
- Academic Researchers: To study large-scale food naming conventions and item categorisation strategies in the digital marketplace.
Dataset Name Suggestions
- Food Item Classification Data
- Delivery App Menu Items
- Zomato Food Item Listing
- Dietary Item Categorisation
Attributes
Original Data Source: Food Item Classification Data
QUALITYLoading...