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This project serves as a valuable resource for understanding the culinary landscape and making data-driven decisions related to the restaurant business.

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Zomato-EDA-Python

About Dataset

The dataset provides a comprehensive view of the restaurant scene in the 13 metropolitan areas of India( 900 restaurants). Researchers, analysts, and food enthusiasts can use this dataset to gain insights into various aspects such as dining and delivery ratings, customer reviews and preferences, popular cuisines, best-selling items, and pricing information across different cities. It enables the exploration of dining patterns, the comparison of restaurants and cuisines between cities, and the identification of trends in the food industry. This dataset serves as a valuable resource for understanding the culinary landscape and making data-driven decisions related to the restaurant business, customer satisfaction, and food choices in these metropolitan areas of India. In this dataset, we have more than 123000 rows and 12 columns, a fairly large dataset. We will be able to get hands-on experience while performing the following tasks and will be able to understand how real-world problem statement analysis is done. Kaggle

In Data Analysis what all things we do

  • Handling Missing Values
  • Explore numerical features.
  • Explore categorical features.
  • Finding relations between features.

The dataset contains all types of data types along with missing values. Perform data cleaning first before plotting data for EDA. The Zomato Restaurants Dataset for Metropolitan Areas of 13 cities in India provides comprehensive information about restaurants in these urban centers. This dataset consists of 12 columns, each representing a specific attribute of the restaurants.

The columns in the dataset are as follows:

  1. Restaurant Name: The name of the restaurant.
  2. Dining Rating: The rating given by customers for the dining experience at the restaurant.
  3. Delivery Rating: The rating given by customers for the delivery service provided by the restaurant.
  4. Dining Votes: The number of votes or reviews received for the dining experience.
  5. Delivery Votes: The number of votes or reviews received for the delivery service.
  6. Cuisine: The type of cuisine or culinary style offered by the restaurant.
  7. Place Name: The name of place
  8. City Name: The name of the metropolitan area or city where the restaurant is located. The dataset includes the following cities: Hyderabad, Kolkata, Lucknow, Pune, Chennai, Bengaluru, Mumbai, Raipur, Jaipur, Ahmedabad, Kochi, Goa, and New Delhi.
  9. Item Name: The name of a specific dish or item offered by the restaurant.
  10. Best Seller: Indicates whether the item is a best-selling dish or not.
  11. Votes: The number of votes or reviews received for the specific item.
  12. Prices: The prices associated with each item offered by the restaurant.

About

This project serves as a valuable resource for understanding the culinary landscape and making data-driven decisions related to the restaurant business.

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