https://github.com/vanshuchaudhary/zomato

This Jupyter Notebook contains an exploratory data analysis (EDA) of Zomato restaurant data. It includes data cleaning, visualization, and insights into restaurant ratings, pricing, cuisine distribution, and location-based trends.
https://github.com/vanshuchaudhary/zomato

business-analytics data-analysis data-mining data-science data-visualization datascience matplotlib pandas-dataframe pandas-python python python-3 python-library

Last synced: about 1 month ago
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This Jupyter Notebook contains an exploratory data analysis (EDA) of Zomato restaurant data. It includes data cleaning, visualization, and insights into restaurant ratings, pricing, cuisine distribution, and location-based trends.

• Host: GitHub
• URL: https://github.com/vanshuchaudhary/zomato
• Owner: vanshuchaudhary
• Created: 2025-03-12T19:01:44.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-03-12T19:11:24.000Z (about 1 year ago)
• Last Synced: 2026-01-02T15:19:05.888Z (5 months ago)
• Topics: business-analytics, data-analysis, data-mining, data-science, data-visualization, datascience, matplotlib, pandas-dataframe, pandas-python, python, python-3, python-library
• Language: Jupyter Notebook
• Homepage:
• Size: 479 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
📊 Zomato Data Analysis 🍽️

🔍 Uncover Hidden Insights from Zomato Restaurant Data!

🚀 About This Project

This project dives deep into Zomato's restaurant dataset to explore trends in ratings, pricing, cuisines, and locations. Through data cleaning, visualization, and analysis, we extract valuable insights that help businesses and food lovers understand the restaurant industry better.

📌 Key Features

✅ Data Cleaning & Preprocessing – Handling missing values, duplicates, and formatting issues.

✅ Exploratory Data Analysis (EDA) – Understanding customer behavior and restaurant trends.

✅ Data Visualization – Beautiful charts using Matplotlib & Seaborn.

✅ Price & Ratings Analysis – Identifying affordable and high-rated restaurants.

✅ Cuisines & Locations Trends – Which cuisines are most popular? Where are the best restaurants located?

🛠️ Tech Stack

🔹 Python 🐍

🔹 Jupyter Notebook 📒

🔹 Pandas, NumPy – Data Processing 📊

🔹 Matplotlib, Seaborn – Data Visualization 🎨

📥 Installation & Usage

1️⃣ Clone the repository : git clone https://github.com/yourusername/zomato-analysis.git

cd zomato-analysis

2️⃣ Install dependencies : pip install -r requirements.txt

3️⃣ Run the Jupyter Notebook: jupyter notebook zomato.ipynb

🎯 Insights & Business Value

✅ Find out which cuisines dominate the market.

✅ Discover how restaurant pricing affects customer ratings.

✅ Understand location-based preferences to optimize restaurant placement.

💡 Future Enhancements

🚀 Add Machine Learning Models for restaurant rating prediction.

📊 Implement Interactive Dashboards using Power BI/Tableau.

🌎 Expand the analysis with geo-mapping.

🤝 Contributing

Have ideas for improvement? Feel free to fork, improve, and submit a PR!

📜 License

This project is open-source and available under the MIT License.

💬 Let’s Connect!

🔗 [www.linkedin.com/in/vanshuchaudhary2004]