🍽️ Zomato Bangalore Data Analysis
Exploring Bangalore’s food scene with Data Science & Machine Learning
📖 Project Overview
This project dives into Zomato’s Bangalore dataset with 50,000+ records of restaurants. The goal was to clean, preprocess, and analyze patterns in cuisines, locations, restaurant types, and customer behavior. From online ordering preferences to hotspot locations like Koramangala, I uncovered insights that reflect how Bangalore eats.
- 🔍 Cleaned raw data with missing values & inconsistencies
- 📊 Visualized key trends like cuisines, locations & ratings
- 🤖 Explored machine learning methods for prediction
- 🚀 Delivered insights for businesses & customers
📊 Mini Visuals
A glimpse of the analysis (see full visuals on GitHub).
👉 Tap on any image to view it in full screen
🎯 Key Takeaways
- 🍛 North Indian cuisine dominates customer preferences
- 📍 Koramangala 5th Block leads as Bangalore’s food hub
- 📲 Online ordering boosts ratings significantly
🛠️ Tech Stack Used
Python
Pandas / NumPy
Matplotlib / Seaborn
Machine Learning
Jupyter Notebook
🚀 Explore the Project
Curious to explore the full analysis?
Check the complete project on GitHub.
🔮 Future Improvements
- Use machine learning to predict ratings
- Interactive dashboards
- Expand datasets
Shuba S
Developer focused on Python, JavaScript, full-stack apps, and creative projects.
