Zomato Data Analysis - Bengaluru

Columns

• url: The unique URL associated with each restaurant [4].
• address: The full street address of the restaurant [5].
• name: The registered name of the restaurant, with common occurrences like 'Cafe Coffee Day' [6].
• online_order: A boolean indicator specifying whether the restaurant accepts online food orders (True/False) [6].
• book_table: A boolean indicator for whether table booking is available at the restaurant (True/False) [6].
• rate: The average rating given on the Zomato application, with a notable number of missing values and 'NEW' for recently listed places [7].
• votes: The total count of people who have cast a rating for the restaurant [7, 8].
• phone: The contact phone number for the restaurant [8].
• location: The specific area or neighbourhood of Bengaluru where the restaurant is situated, with 'BTM' being a frequently occurring location [8].
• rest_type: The classification of the restaurant, such as 'Quick Bites' or 'Casual Dining' [9].
• dish_liked: A list of dishes that are most frequently liked by customers, though this column has a significant amount of missing data [9].
• cuisines: The types of cuisines served by the restaurant, for example, 'North Indian' or 'Chinese' [9].
• approx_cost(for two people): The estimated cost for a meal for two people at the restaurant [10, 11].
• reviews_list: Customer reviews provided in a JSON format [11].
• menu_item: A list of items available on the restaurant's menu [12].
• listed_in(type): The primary service category of the restaurant, such as 'Delivery' or 'Dine-out' [12].
• listed_in(city): The specific Zomato-listed city section the restaurant falls under [12].

Distribution

Usage

• Performing data cleaning and preprocessing activities, including managing missing values, identifying and removing duplicate entries, and transforming data types [2].
• Exploring both numerical and categorical features to uncover patterns and relationships within the restaurant data [2].
• Conducting visual data analysis to understand trends such as online order availability, table booking rates, popular restaurant locations, and cost distributions [3].
• Developing and testing models for predictive analysis related to restaurant success or customer preferences [1, 3].
• Gaining insights into the restaurant industry landscape and consumer behaviour in Bengaluru [1, 3].

Coverage

License

Who Can Use It

• Aspiring data scientists and analysts seeking practical experience with a large, real-world dataset [1].
• Academics and students engaged in projects related to urban analytics, consumer behaviour, or hospitality industry studies [2, 3].
• Business professionals in the food and beverage sector or market research, looking to understand restaurant trends and customer preferences in Bengaluru [3].
• Anyone interested in applying data analysis techniques to solve problems and draw meaningful conclusions from urban restaurant data [1, 3].

Dataset Name Suggestions

• Bengaluru Restaurants Dataset (Zomato)
• Zomato Bengaluru Restaurant Listings
• Bengaluru Food Scene Data
• Zomato Data Analysis - Bengaluru
• Indian Restaurant Data (Bengaluru)

Attributes