Zomato Exploratory Data Analysis 🍽️📊

📌 Project Overview

This project performs an Exploratory Data Analysis (EDA) on Zomato restaurant data to help users discover high-quality cuisines, popular food hubs, and value-for-money restaurants in their locality.
The analysis focuses on understanding restaurant distribution, ratings, pricing, and service availability across different regions.

🎯 Motivation

Good quality food plays an important role in everyday life, yet finding the best cuisine within a budget can be challenging.
This project aims to support food lovers and the community by identifying top-rated restaurants, dominant cuisines, and areas with the best dining options using data-driven insights.

📊 Dataset Description

The dataset contains detailed information about restaurants listed on Zomato, including:

• Restaurant details (ID, name, address, locality)
• Geographic information (city, country, latitude, longitude)
• Cuisine types
• Pricing details (average cost for two, price range, currency)
• Ratings and votes
• Online delivery and table booking availability

🛠️ Tools & Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Colab Notebook

🔍 Exploratory Data Analysis Performed

• Country-wise distribution of Zomato-listed restaurants
• City-level analysis with a focus on NCR regions
• Aggregate rating distribution and rating categories
• Identification of unrated vs rated restaurants
• Relationship between ratings and restaurant count
• Analysis of restaurant presence across major localities
• Insights into food hubs and restaurant concentration

📌 Key Insights

• Zomato’s restaurant data is heavily concentrated in India, making the analysis India-centric.
• A large number of restaurants are unrated, indicating newly listed or low-engagement restaurants.
• Most restaurants fall within the average to good rating range, while excellent-rated restaurants are relatively rare.
• New Delhi dominates restaurant listings in the NCR region, followed by Gurgaon and Noida.
• Highly rated restaurants across Indian cities show consistently high average ratings, indicating uniform food quality across regions.

🧠 Conclusion

This EDA provides meaningful insights into restaurant quality, pricing, and regional food trends on Zomato.
The findings can help users make better dining decisions and enable further analysis for recommendation systems or business insights.