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Discover the fascinating world of Zomato through simple data analysis! This project, done using Python, helps us understand where restaurants are, what people like, and how prices relate to reviews. Explore colorful charts and graphs to uncover interesting patterns and trends in the world of food and dining.

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Zomato Exploratory Data Analysis

Zomato

Overview

This project involves the exploratory data analysis (EDA) of Zomato data to gain insights into restaurant trends, customer preferences, and other relevant information.

Table of Contents

Dataset

The dataset used for this project is sourced from Zomato's API, providing information about restaurants, cuisines, user ratings, and more.

Installation

Clone this repository: https://github.com/abhijithkj369/Zomato-EDA.git

Exploratory Data Analysis

Geographic Distribution:

Restaurants on Zomato are geographically diverse, with concentrations in urban areas. Hotspots of culinary activity were identified, showcasing the platform's global reach. Customer Ratings Analysis:

User ratings exhibit a predominantly positive trend, with a notable concentration around the 4.0 mark. The majority of restaurants maintain high ratings, reflecting overall customer satisfaction. Cuisine Trends:

Popular cuisines on Zomato include a mix of global and local flavors, with a preference for diverse culinary experiences. Insights into specific cuisine preferences provide valuable information for both users and restaurants. Price Range Impact:

A correlation between price range and customer ratings was explored, revealing nuanced relationships. Restaurants with higher price ranges generally maintain positive reviews, but exceptions exist. Top-Rated Restaurants:

Identification of top-rated restaurants highlighted exceptional establishments across various cuisines and price ranges. These insights can guide users seeking premium dining experiences or specific culinary delights. The project's data-driven approach, supported by Python, Jupyter Notebooks, and data analysis libraries, succeeded in unraveling the intricacies of Zomato's dataset. The findings not only provide actionable insights for Zomato users and restaurants but also lay the groundwork for potential future analyses and enhancements.

Contributing

Feel free to contribute to this project. Fork the repository, make changes, and submit a pull request. Your contributions are welcome!

License

This project is licensed under the MIT License.

Happy coding!

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Discover the fascinating world of Zomato through simple data analysis! This project, done using Python, helps us understand where restaurants are, what people like, and how prices relate to reviews. Explore colorful charts and graphs to uncover interesting patterns and trends in the world of food and dining.

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