Zomato Restaurant Analysis

The basic idea of analyzing the Zomato dataset is to get a fair idea about the factors affecting the aggregate rating of each restaurant, establishment of different types of restaurant at different places, Bengaluru being one such city has more than 12,000 restaurants with restaurants serving dishes from all over the world. With each day new restaurants opening the industry has’nt been saturated yet and the demand is increasing day by day. Inspite of increasing demand it however has become difficult for new restaurants to compete with established restaurants. Most of them serving the same food. Bengaluru being an IT capital of India. Most of the people here are dependent mainly on the restaurant food as they don’t have time to cook for themselves. With such an overwhelming demand of restaurants it has therefore become important to study the demography of a location. What kind of a food is more popular in a locality. Do the entire locality loves vegetarian food. If yes then is that locality populated by a particular sect of people for eg. Jain, Marwaris, Gujaratis who are mostly vegetarian. These kind of analysis can be done using the data, by studying different factors.

Problem Statement : In this challenge, we are analysing the Zomato Restaurant dataset to find the more insights about the Restaurant business.

Source : https://www.kaggle.com/himanshupoddar/zomato-bangalore-restaurants

Data Description :

url : contains the url of the restaurant in the zomato website

address : contains the address of the restaurant in Bengaluru

name : contains the name of the restaurant

online_order : whether online ordering is available in the restaurant or not

book_table : table book option available or not

rate : contains the overall rating of the restaurant out of 5

votes : contains total number of rating for the restaurant as of the above mentioned date

phone : contains the phone number of the restaurant

location : contains the neighborhood in which the restaurant is located

rest_type : restaurant type

dish_liked : dishes people liked in the restaurant

cuisines : food styles, separated by comma

approx_cost(for two people) : contains the approximate cost for meal for two people

reviews_list : list of tuples containing reviews for the restaurant, each tuple consists of two values, rating and review by the customer

menu_item : contains list of menus available in the restaurant

listed_in(type) : type of meal

listed_in(city) : contains the neighborhood in which the restaurant is listed

Real-world/Business Objectives and Constraints :

  1. The cost of a mis-classification can be high.
  2. No strict latency concerns.
  3. It will help everyone to unterstand the insights of a restaurant business.

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