🍽️ Zomato Restaurant Data Analysis
Understanding customer preferences and restaurant trends is crucial for making informed business decisions in the food industry. This project explores Zomato’s restaurant dataset using Python to uncover meaningful insights into customer choices and dining patterns.
📌 Objectives
This analysis aims to answer key business questions: ✅ Do more restaurants provide online delivery compared to offline services? ✅ Which types of restaurants are most favored by the general public? ✅ What price range do couples prefer for dining out?
🛠️ Tech Stack
Python (Data Analysis & Visualization) Pandas – Data manipulation NumPy – Numerical computing Matplotlib / Seaborn – Data visualization Jupyter Notebook / Google Colab – Interactive analysis
📂 Project Structure
Zomato-Data-Analysis/ │── data/ # Dataset files │── notebooks/ # Jupyter/Colab notebooks │── images/ # Charts & visualizations │── zomato_analysis.ipynb # Main analysis notebook │── README.md # Project documentation
🔍 Key Insights
Online vs Offline Delivery – Majority of restaurants prefer offering online delivery due to changing customer preferences. Most Favored Restaurant Types – Casual dining and quick bites are among the most popular choices. Price Range for Couples – Most couples prefer moderately priced restaurants over luxury dining.
📊 Visualizations
Bar charts showing restaurant type distribution Pie charts comparing online vs offline delivery Heatmaps analyzing price preferences by couples
📈 Future Improvements
Add sentiment analysis on restaurant reviews. Predict restaurant ratings using machine learning models. Explore geographical distribution of restaurants.
📜 License This project is licensed under the MIT License.
🏁 Conclusion
This analysis of Zomato’s restaurant dataset highlights how customer preferences are shaping the food industry. The findings reveal that:
Online delivery is more popular than offline services, indicating the growing influence of convenience-driven dining.
Casual dining and quick bites dominate as the most preferred restaurant types, showing that affordability and accessibility matter to customers.
Couples generally prefer mid-range price categories, suggesting that restaurants offering good value for money can attract more dining pairs.
Overall, the study emphasizes the importance of data-driven decision making for restaurants and food delivery platforms. By understanding customer choices, businesses can tailor their services, optimize pricing strategies, and strengthen their market presence.