This project presents an end to end data analysis and market intelligence framework built on a global food restaurant dataset.
The objective is to uncover strategic insights across markets, cuisines, pricing, engagement, and growth opportunities using data driven reasoning.
The analysis was developed as part of a hackathon submission and focuses on business impact, not just exploratory analysis.
The dataset represents a large scale view of restaurant ecosystems across multiple countries and cities.
- Total restaurants: 9,551
- Countries covered: 15
- Cities covered: 141
- Restaurants in India: 8,652
- India share: 90.6 percent
- Rated restaurants: 7,403
- Rating coverage: 77.5 percent
- Final feature dimensions: 22
This immediately establishes India as the dominant market in both scale and engagement.
India is the core driver of platform activity.
More than 90 percent of restaurants belong to India, which means:
- Any improvement in ranking, pricing, discovery, or quality directly impacts the majority of users
- India is the most effective market for experimentation and optimization
- Non India markets are better suited for niche and premium strategies
Conclusion
India should be treated as the primary experimentation and monetization market.
Strong insights depend on clean data. The following steps were applied to ensure reliability.
- Numeric country codes were mapped to country names
- Zero missing values after mapping
- Incorrect Philippines currency was fixed
- All costs were standardized to INR for fair comparison
Removed non informative columns including:
- Locality Verbose
- Rating color
- Rating text
- Switch to order menu
- Country code
Business Impact
Standardized pricing and clean features enable reliable pricing intelligence and cross market analysis.
Average cost for two was analyzed across countries after INR normalization.
-
India
- Average cost around 623 INR
- Large volume market
- Best suited for scale driven growth
-
United States and emerging markets
- Average cost between 2,222 and 2,353 INR
- Medium scale
- Suitable for feature testing
-
UAE and United Kingdom
- Average cost between 4,113 and 5,785 INR
- Small sample size
- Premium positioning markets
Insight
Non India markets have limited samples and should focus on premium and niche strategies rather than scale.
Restaurants serve multiple cuisines stored as comma separated values.
A hybrid feature engineering approach was used to preserve restaurant identity while enabling cuisine level analysis.
- Original dataset with 9,551 restaurants
- Controlled cuisine explosion to 19,710 cuisine rows
- Validation of 145 unique cuisines
- North Indian: 3,960 restaurants
- Chinese: 2,735 restaurants
- Fast Food: 1,986 restaurants
- Italian: 3.56 average rating
- Continental: 3.52 average rating
- Cafe: 3.32 average rating
- North Indian: 2.51 rating
- Chinese: 2.62 rating
- Fast Food: 2.56 rating
Key Insight
Popularity does not imply satisfaction.
Highly popular cuisines require quality standardization, while high rated cuisines should be boosted in discovery systems.
Menu diversity plays a critical role in engagement and ratings.
- High engagement cuisines consistently exceed 3.5 rating
- Low engagement cuisines cluster below 3.0 rating
Recommendation
Recommendation systems should balance popularity with engagement metrics, not popularity alone.
Insight
This is a discovery problem, not a demand problem.
User engagement exists but ratings are not being captured.
City level strategies are too coarse.
-
Connaught Place
- 3.69 average rating
-
Rajouri Garden
- 3.59 average rating
-
Shahdara
- 1.41 average rating
Business Impact
Locality specific onboarding, audits, and promotions deliver higher ROI than city wide actions.
- RMSE: 0.41 on a 5 point scale
- Strong predictive accuracy
- Price range vs rating: 0.44
- Votes vs rating: 0.31
- Votes: 71 percent
- Average cost for two: 20 percent
- Online delivery: 4 percent
- Price range: 3 percent
- Table booking: 2 percent
Strategic Conclusion
Engagement is the single strongest driver of ratings.
Investment in engagement tools outperforms cosmetic feature additions.
This section analyzes how online delivery and table booking features influence restaurant ratings and customer engagement. The visuals compare rating distributions for restaurants with and without these services, highlighting their relative impact.
| Online Delivery Impact | Table Booking Impact |
|---|---|
 |
 |
Hidden High Quality Growth Opportunities
Certain cuisines show strong ratings with limited competition.
| Cuisine | Restaurant Count | Average Rating |
|---|---|---|
| Sandwich | 53 | 4.086038 |
| Steak | 62 | 3.985484 |
| Sushi | 75 | 3.973333 |
| Breakfast | 41 | 3.965854 |
| Mediterranean | 112 | 3.948214 |
| Bar Food | 39 | 3.933333 |
| Indian | 70 | 3.918571 |
| European | 148 | 3.910811 |
| BBQ | 33 | 3.903030 |
| Seafood | 174 | 3.862069 |
Strategy
These cuisines are ideal for premium discovery, editorial promotion, and supply expansion.
- India is the core growth and experimentation market
- Engagement drives ratings more than pricing or features
- Popular cuisines need quality improvement
- High quality niche cuisines need visibility
- Locality level actions outperform city level strategies
All notebooks used for data cleaning, feature engineering, modeling, and analysis are available here:
🔗 Drive Link : https://drive.google.com/drive/folders/1KS_0ilJyk-KP8BpQ8iAdJ--b2_5zZUYC
Team Name: ASHSUM
Team Members:
 Ashiwad Sinha |
 Sumit Pandey |
This project was built as a hackathon submission with a strong focus on real world business decision making using data.