Dark Mode
NYC Food Delivery Order Analysis
Product Reviews & Feedback
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset provides sample transactional data from an online food ordering and delivery application for restaurants in New York City [1]. It captures key details of customer orders placed through a food aggregator company's app, which facilitates direct online orders from customers to restaurants [1, 2]. The primary purpose of this dataset is to enable the analysis of demand patterns for different restaurants, with the ultimate goal of enhancing customer experience and improving business operations [3]. Given the increasing number of restaurants in NYC and the reliance of students and busy professionals on convenient food delivery services, understanding these patterns is crucial for the food aggregator company [1].
Columns
- order_id: A unique identifier for each order [4].
- customer_id: The identifier of the customer who placed the order [5].
- restaurant_name: The name of the restaurant from which the food was ordered, with "Shake Shack" being the most common example [6].
- cuisine_type: The type of cuisine ordered by the customer, such as "American" or "Japanese" [6].
- cost_of_the_order: The financial cost associated with the order [7].
- day_of_the_week: Indicates whether the order was placed on a "Weekend" or a "Weekday" (Monday to Friday), with weekends being more frequent [8].
- rating: The customer's rating for the order, given out of 5. It includes instances where no rating was provided [8].
- food_preparation_time: The time in minutes taken by the restaurant to prepare the food, calculated from order confirmation to pick-up confirmation [9].
- delivery_time: The time in minutes taken by the delivery person to deliver the food package, calculated from pick-up confirmation to drop-off [10].
Distribution
The dataset is provided as a CSV data file, named
food_order.csv, with a size of 123.93 kB [4]. It contains 9 columns and consists of 1898 records or rows [4].Usage
This dataset is ideal for analysing demand for various restaurants and for identifying ways to enhance customer experience within an online food delivery service [3]. It can be used for:
- Understanding customer ordering behaviour and preferences [1].
- Optimising food preparation and delivery times to improve service efficiency [9, 10].
- Evaluating restaurant performance based on order volume, cost, and customer ratings [6-8].
- Informing business strategies for food aggregator companies to improve their offerings and operational effectiveness [3].
Coverage
The dataset primarily covers restaurants and customer orders within New York City [1]. Demographically, it reflects orders made by registered customers, including students and busy professionals who rely on online food delivery due to their lifestyles [1]. A specific time range for the data collection is not explicitly provided in the source material.
License
CC0: Public Domain
Who Can Use It
This dataset is particularly useful for:
- Data Scientists working for food aggregator companies (like FoodHub) to perform data analysis and address key business questions [3].
- Business Analysts interested in market trends, customer behaviour, and operational efficiencies in the food delivery sector.
- Researchers studying urban food consumption, logistics, and digital marketplace dynamics.
- Students undertaking projects in data visualisation, exploratory data analysis, or business intelligence [4].
Dataset Name Suggestions
- NYC Food Delivery Order Analysis
- New York City Restaurant Demand Data
- FoodHub Customer Order Patterns
- NYC Online Food Orders
Attributes
Original Data Source: NYC Food Delivery Order Analysis
QUALITY