This dataset offers a detailed overview of restaurant information, including their location, cuisines, average cost, and user ratings. It is designed to facilitate the analysis of various factors influencing restaurant popularity, such as cuisine type, pricing, and the availability of booking and delivery services. The dataset can be instrumental in developing personalised restaurant recommendation systems and gaining insights into the broader food service industry.

Columns

• Restaurant ID: A unique identifier assigned to each restaurant.
• Restaurant Name: The official name of the dining establishment.
• City: The city where the restaurant is located.
• Address: The specific street address of the restaurant.
• Locality: The neighbourhood or area within the city where the restaurant is situated.
• Longitude: The geographical longitude coordinate of the restaurant's location.
• Latitude: The geographical latitude coordinate of the restaurant's location.
• Cuisines: Describes the types of food offered, such as Japanese, Thai, Chinese, or Mughlai.
• Average Cost for two: The estimated cost for a meal for two people at the restaurant.
• Currency: The currency used for the average cost (e.g., Indian Rupees (Rs.), Dollar ($)).
• Has Table booking: Indicates whether the restaurant accepts table reservations (Yes/No).
• Has Online delivery: Indicates if the restaurant provides online food delivery services (Yes/No).
• Is delivering now: Shows if the restaurant is currently offering delivery (Yes/No).
• Price range: A categorisation of the restaurant's price level, ranging from 1 (least expensive) to 4 (most expensive).
• Aggregate rating: The overall rating of the restaurant based on customer reviews.
• Rating colour: A colour code representing the rating (e.g., Dark green, Green, Yellow, Orange, Red, White).
• Rating text: A text description of the rating (e.g., Excellent, Very good, Good, Average, Poor, Not rated).
• Votes: The total number of user votes or reviews received by the restaurant.

Distribution

The dataset is typically provided in CSV format and comprises approximately 9,531 records. * Average Cost for two: Costs predominantly fall within the 0.00 - 80,000.00 range. * Currency: Indian Rupees (Rs.) accounts for 91% of the entries, while Dollar ($) accounts for 5%. * City: New Delhi represents 57% of the restaurants, Gurgaon 12%, and other cities account for 31%. There are 8,918 unique city values. * Locality: Connaught Place and Rajouri Garden each represent 1% of localities, with 98% falling into other categories. There are 9,330 unique locality values. * Longitude: Values range from -158 to 175, with a significant concentration between 75.00 and 108.28 (8,064 entries). * Latitude: Values range from -41.3 to 56, with a large number of entries between 26.78 and 36.52 (7,911 entries). * Cuisines: North Indian cuisine accounts for 10%, North Indian, Chinese for 5%, and other cuisine combinations for 85%.

Usage

This dataset is ideal for: * Developing restaurant recommendation systems to suggest personalised dining options based on user preferences, location, and restaurant attributes. * Analysing factors affecting restaurant popularity, such as cuisine type, pricing, table booking availability, and online delivery services. * Gaining insights into the food delivery industry dynamics. * Solving problem statements related to the influence of location on cost, the relationship between cuisine type and ratings, the correlation between cost and ratings, and the impact of booking/delivery options on ratings.

Coverage

The dataset's geographic scope is global, with a strong focus on cities like New Delhi (57%) and Gurgaon (12%) in India, and other cities making up the remaining 31%. The time range and specific demographic scope of the data are not specified in the available information.

License

CC0

Who Can Use It

• Data Scientists and Analysts: For conducting deep dives into restaurant trends and consumer behaviour.
• Developers: To build and improve food delivery applications and recommendation engines.
• Researchers: For academic studies on the hospitality and food service industries.
• Businesses: Seeking to understand market dynamics and competitor landscapes.

Dataset Name Suggestions

• Global Restaurant Insights Dataset
• Zomato Restaurant Data
• Foody Restaurant Popularity Data
• Restaurant Dining Trends Dataset
• Worldwide Restaurant Attributes

Attributes

Original Data Source: Global Zomato Dataset