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Zomato Bengaluru Restaurant Metrics
Food & Beverage Consumption
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Free
About
This dataset provides a detailed collection of information on restaurants located in Bengaluru, India, sourced from Zomato. It captures a wide array of attributes pertaining to dining establishments, including their geographical coordinates, menu offerings, pricing structures, user ratings, and various service types available. The dataset serves as a valuable resource for conducting in-depth market analysis, understanding local culinary trends, and developing predictive analytical models within the restaurant industry. Key features include indicators for online order acceptance, table booking availability, approximate meal costs for two people, and customer review summaries.
Columns
- url: The specific web link to the restaurant's profile on the Zomato platform.
- address: The full postal address of the restaurant premises.
- name: The official and registered name of the restaurant.
- online_order: A binary field indicating whether the restaurant facilitates online food orders (True/False). Approximately 59% of restaurants offer this service.
- book_table: A binary field signifying the availability of table booking services at the restaurant (True/False). Around 12% of establishments provide this option.
- rate: The aggregate rating assigned to the restaurant on the Zomato application. This column includes entries such as 'NEW' for recent listings and has about 15% missing values.
- votes: The total number of customer votes or ratings received by the restaurant. Values range from 0 to 16,832.
- phone: The primary contact telephone number for the restaurant. Approximately 2% of these entries are absent.
- location: The designated area or neighbourhood within Bengaluru where the restaurant is situated (e.g., BTM, HSR). There are 93 distinct locations recorded.
- rest_type: Describes the specific operational format of the restaurant (e.g., Quick Bites, Casual Dining, Cafe). 'Quick Bites' accounts for roughly 37% of the entries.
- dish_liked: A list detailing the most popular or frequently liked dishes by patrons. This column contains a considerable number of missing values, at approximately 54%.
- cuisines: The specific types of food or culinary styles offered by the restaurant (e.g., North Indian, Chinese). There are 2,723 unique combinations of cuisines.
- approx_cost(for two people): The estimated monetary cost for a meal catering to two individuals. Costs range from £40 to £6,000, with about 1% of entries missing.
- reviews_list: A JSON-formatted array containing customer reviews and feedback for the restaurant. This column features 22,500 distinct review lists.
- menu_item: A list of items available on the restaurant's menu. About 77% of these entries are empty.
- listed_in(type): Categorisation based on the primary service offered by the restaurant (e.g., Delivery, Dine-out, Buffet). 'Delivery' is the most frequent category, representing 50%.
- listed_in(city): The particular city block or sub-area in Bengaluru under which the restaurant is listed (e.g., BTM, Koramangala 7th Block). There are 30 unique listing cities.
Distribution
The dataset is structured as a
zomato.csv file, with a file size of 574.07 MB. It consists of 51,717 individual records spread across 17 distinct columns. While the majority of the data is present, certain columns such as rate, phone, dish_liked, and approx_cost(for two people) do contain missing values.Usage
This dataset offers opportunities for various analytical and predictive tasks. It is well-suited for data cleaning and preprocessing exercises, including the removal of redundant columns, renaming fields, managing duplicate entries, and addressing missing values. Furthermore, it facilitates data visualisation to discern patterns related to online ordering adoption, table booking prevalence, optimal restaurant locations, and the correlation between location, restaurant type, and customer ratings. The dataset is also appropriate for various regression analyses, such as linear, decision tree, and random forest regression, to forecast restaurant ratings or typical costs.
Coverage
The dataset's focus is exclusively on restaurants situated within Bengaluru (Bangalore), India. It provides a detailed view of the city's diverse food and dining landscape, encompassing a variety of localities and restaurant formats. Information regarding a specific time range for data collection or demographic segmentation beyond the restaurant's operational details is not explicitly provided.
License
CC0: Public Domain
Who Can Use It
- Data Scientists and Analysts: Professionals seeking to apply data cleaning, visualisation techniques, and regression analysis to gain insights into the restaurant sector.
- Market Researchers: Individuals and teams interested in identifying restaurant trends, popular cuisines, pricing dynamics, and customer preferences within Bengaluru.
- Entrepreneurs: Prospective business owners looking to pinpoint promising areas for new restaurant ventures or popular restaurant styles.
- Academic Researchers and Students: An excellent resource for educational purposes, supporting practical learning in data analysis, machine learning, and data science disciplines using real-world data.
Dataset Name Suggestions
- Bengaluru Food Establishments
- Zomato Bengaluru Restaurant Metrics
- Indian Restaurant Data (Bengaluru Focus)
- Bengaluru Culinary Insights
- Zomato Dining Trends
Attributes
Original Data Source: Zomato Bengaluru Restaurant Metrics
QUALITY