Unlocking hidden insights from the Zomato restaurant dataset to help businesses make smarter decisions and enhance customer experiences.
This project involves an in-depth analysis of Zomato's restaurant dataset to uncover insights into:
- Customer preferences
- Restaurant trends
- Online service patterns.
The primary goal is to help stakeholders make informed decisions regarding restaurant operations, marketing strategies, and service offerings.
🎯 Analyze the distribution of restaurants across various locations and cuisines
🎯 Examine the relationship between restaurant ratings and features like cost, location, and service type
🎯 Identify trends in online ordering and table booking services
🎯 Provide actionable insights to improve customer satisfaction and overall business performance
| Tool/Library | Description |
|---|---|
| 🐍 Python | Core programming language |
| 🧮 NumPy | Numerical computations |
| 🐼 Pandas | Data preprocessing & analysis |
| 📈 Matplotlib | Static visualizations |
| 🧠 Seaborn | Advanced data visualizations |
| 🧪 Jupyter Notebook | Interactive development environment |
✔️ Handled missing values and inconsistencies
✔️ Converted data types to appropriate formats
✔️ Standardized categorical variables (e.g., Yes/No → Binary)
✔️ Removed duplicate entries to ensure quality
✔️ Cleaned irrelevant or redundant columns for optimal analysis
🗺️ Visualized the distribution of restaurants by location and cuisine
💰 Analyzed the impact of cost for two on restaurant ratings
🛒 Explored the prevalence of online ordering and table booking services
🌟 Identified top-rated restaurants and popular cuisines
📷 Check the
visuals/folder for all saved graphs and charts.
Zomato-Data-Analysis-Using-Python/ ├── data/ │ └── zomato.csv # Raw dataset ├── notebooks/ │ └── zomato_data_analysis.ipynb # Main analysis notebook ├── visuals/ │ └── *.png # Plots and visual outputs └── README.md # Project documentation
The dataset used in this project is publicly available on Kaggle:
🔗 Zomato Restaurants Data on Kaggle
ℹ️ Includes details like restaurant names, locations, cuisines, average cost, rating, votes, and service options.
📍 Certain locations like BTM and Koramangala have a high restaurant density → Possible market saturation
🌐 Restaurants with online ordering enabled tend to have higher average ratings
💸 There's a positive correlation between cost for two and ratings — up to a moderate threshold
🍲 North Indian and Chinese cuisines dominate in popularity across most zones
📊 High-rated restaurants often offer both delivery and dine-in with modern service features
This Zomato dataset analysis offers deep insights into:
- Customer behavior
- Service expectations
- Location-specific trends
📢 Businesses can use these insights to:
- Tailor menus and pricing
- Focus marketing in high-demand areas
- Offer services like online ordering and table booking to increase customer satisfaction and loyalty
🔮 Predict restaurant ratings using machine learning
📍 Geo-mapping of popular food hubs using Folium
📈 Build a live dashboard using Streamlit or Power BI
🗣️ Sentiment analysis on reviews (if available)
🧠 Recommendation system for restaurants/cuisines
Abinesh M
📧 m.abinesh555@email.com
🌐 LinkedIn
💻 GitHub
If you found this project helpful:
🌟 Star this repository
🍴 Fork and contribute improvements
📬 Submit issues and suggestions
This project is licensed under the MIT License.
You’re free to use, modify, and distribute with credit.
“Data is the new oil — and Zomato has a refinery full of it.” 💡
🤖 Possible Add-ons ✨ Real-time dashboard with Streamlit ✨ Predictive modeling using Machine Learning ✨ Integration with Telegram or Discord bot for live updates ✨ Country-wise alert system
👨💻 Author
📧 m.abinesh555@email.com 🌐 LinkedIn 📂 More Projects
🤝 Contributions We welcome contributions!
🍴 Fork the repo
🛠 Make changes
🔁 Submit a pull request
📌 Please follow the code style and include documentation.
📜 License This project is licensed under the MIT License. Feel free to use it for personal or commercial purposes.
🙌 Support If you found this useful, consider leaving a ⭐ on the repo!
📣 Connect & Share If you use this project or build something inspired by it, share it on LinkedIn or GitHub and tag me! Let’s learn and grow together 💪
“In God we trust, all others must bring data.” – W. Edwards Deming