Global Restaurant Insights Dataset

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

• 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

• 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

License

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