Zomato Restaurant Data Analysis 🍽️

Data analysis of Zomato restaurant data using Python

A comprehensive data science project analyzing restaurant trends, digital transformation impact, and customer preferences using real-world Zomato data

📊 Project Overview

This project analyzes 148 restaurants to uncover insights about:

• Restaurant type distribution and pricing strategies
• Impact of digital presence (online ordering) on ratings and costs
• Customer behavior patterns and rating correlations
• Best value restaurants based on price-quality ratio

🔍 Key Findings

• Digital Advantage: Restaurants with online ordering have 10.6% higher ratings and command 42.2% premium pricing
• Market Distribution: Dining establishments dominate (74.3%), followed by Cafes (15.5%)
• Price-Quality Correlation: Premium restaurants (>₹600) achieve higher average ratings (3.79) compared to budget options (3.53)
• Service Premium: Only 5.4% offer table booking, but they show significantly higher ratings (4.19 vs 3.60)

🏆 Performance Metrics

Metric	Value	Insight
Average Rating	3.63/5.0	Room for industry improvement
Average Cost	₹418	Mid-range market positioning
Online Adoption	39.2%	Significant growth opportunity
High-Rated Restaurants	23%	Quality differentiation exists

---

🛠️ Technical Implementation

Core Technologies

• Python 3.8+ - Primary programming language
• Pandas - Data manipulation and analysis
• NumPy - Numerical computations and statistical operations
• Matplotlib & Seaborn - Advanced data visualization
• Jupyter Notebook - Interactive development environment

Data Science Techniques Applied

• ✅ Data Cleaning: Rating format standardization, missing value handling
• ✅ Exploratory Data Analysis (EDA): Statistical summaries and distribution analysis
• ✅ Correlation Analysis: Identifying key performance relationships
• ✅ Segmentation Analysis: Restaurant categorization and comparison
• ✅ Business Intelligence: Actionable insight generation

---

📈 Visualizations

The project includes comprehensive visualizations:

• Restaurant type distribution analysis
• Rating vs cost correlation plots
• Online vs offline performance comparisons
• Price category breakdowns
• Top performer identification

🚀 Getting Started

Installation

1. Clone the repository

git clone https://github.com/Jrsandy26/zomato-data-analysis.git
cd zomato-data-analysis

2.Ensure you have Python 3.8+ installed

python --version

3. Install required packages

pip install pandas numpy matplotlib seaborn jupyter

4.Launch Jupyter Notebook

jupyter notebook notebooks/zomato_analysis.ipynb

Load and preprocess data df = pd.read_csv('data/Zomato-data.csv')

Analyze specific restaurant types cafes_analysis = analyze_specific_type('Cafes')

Find best value restaurants best_value = find_best_value_restaurants(max_cost=400, min_rating=3.5)

---

📊 Key Visualizations

Restaurant Type Distribution

• Dining: 110 restaurants (74.3%)
• Cafes: 23 restaurants (15.5%)
• Buffet: 7 restaurants (4.7%)
• Other: 8 restaurants (5.4%)

Digital vs Traditional Performance

Metric	Online Restaurants	Offline Restaurants	Difference
Avg Rating	3.86	3.49	+10.6% ⬆️
Avg Cost	₹510	₹359	+42.2% ⬆️
Avg Votes	559	75	+645% ⬆️

---

💼 Business Applications

For Restaurant Owners

• Digital Strategy: ROI analysis for online ordering implementation
• Pricing Optimization: Data-driven pricing strategies by restaurant type
• Service Enhancement: Table booking as differentiation opportunity

For Food Delivery Platforms

• Market Segmentation: Targeted acquisition strategies
• Partner Development: Supporting offline restaurants' digital transition
• Quality Metrics: Rating improvement programs

For Investors & Analysts

• Market Trends: Digital transformation impact quantification
• Investment Decisions: High-potential restaurant category identification
• Risk Assessment: Performance correlation analysis

---

🧠 Technical Skills Demonstrated

Data Science Core Data cleaning and preprocessing Statistical analysis and correlation Hypothesis testing and validation Business intelligence and insights

Programming & Tools Python programming and libraries Jupyter Notebook development Git version control Documentation and presentation

---

📈 Detailed Insights

Price Segmentation Analysis

• Budget (≤₹300): 67 restaurants, 3.53 avg rating
• Mid-range (₹301-600): 54 restaurants, 3.69 avg rating
• Premium (>₹600): 27 restaurants, 3.79 avg rating

Correlation Findings

• Rating ↔ Votes: 0.490 (Strong positive correlation)
• Rating ↔ Cost: 0.275 (Moderate positive correlation)
• Digital Presence ↔ Performance: Significant positive impact

Top Performers

1. Onesta - 4.6/5.0 rating (2,556 votes)
2. Empire Restaurant - 4.4/5.0 rating (4,884 votes)
3. Meghana Foods - 4.4/5.0 rating (4,401 votes)

---

🔮 Future Enhancements

• Predictive Modeling: Rating prediction based on features
• Sentiment Analysis: Customer review text analysis
• Location Analysis: Geographic performance patterns
• Temporal Trends: Time-based performance evolution
• Competitive Analysis: Market positioning strategies

---

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

---

👤 About the Author

Sandeep Sai Kumar

🎓 Aspiring Data Scientist | 🐍 Python Developer

• 🌐 Portfolio: sandeep26.vercel.app
• 💼 LinkedIn: linkedin.com/in/sandeepsai26
• 📧 Email: sandeepsai.work@gmail.com
• 🐱 GitHub: @Jrsandy26

---

🙏 Acknowledgments

• Dataset Source: Zomato restaurant data platform
• Inspiration: Restaurant industry digital transformation trends
• Tools: Python Data Science ecosystem and open-source community
• Methodology: Industry best practices for data analysis

---

⭐ If this project helped you, please consider giving it a star! ⭐

Made with ❤️ and Python