Swiggy_Data_Analysis_Project

Overview This project demonstrates an advanced SQL analysis of Swiggy's restaurant data, providing insights into various aspects such as restaurant ratings, locations, menu items, pricing, and more.

Key Insights:

1. High-Quality Dining: Query: Count of restaurants with ratings above 4.5. Insight: Highlights top-performing restaurants.
2. Restaurant Hotspot: Query: City with the highest number of restaurants. Insight: Offers strategic expansion insights.
3. Pizza Craze: Query: Count of restaurants with "Pizza" in their name. Insight: Reflects popular cuisine trends.
4. Cuisine Popularity: Query: Most common cuisine type. Insight: Identifies the dominant cuisine.
5. City Ratings: Query: Average ratings of restaurants in each city. Insight: Aids in regional quality assessment.
6. Menu Analysis: Query: Highest priced recommended items per restaurant. Insight: Useful for pricing strategy.
7. Expensive Eateries: Query: Top 5 non-Indian cuisine restaurants based on cost per person. Insight: Highlights premium dining options.
8. Above Average Costs: Query: Restaurants with higher than average cost per person. Insight: Identifies premium dining options.
9. Unique Locations: Query: Restaurants with the same name in different cities. Insight: Useful for brand consistency checks.
10. Main Course Leaders: Query: Restaurant offering the most 'Main Course' items. Insight: Identifies menu leaders.
11. Vegetarian Focus: Query: 100% vegetarian restaurants, ordered alphabetically. Insight: Highlights fully vegetarian options.
12. Affordable Dining: Query: Restaurant with the lowest average item price. Insight: Identifies the most affordable dining option.
13. Menu Variety: Query: Restaurants with the highest number of distinct menu categories. Insight: Highlights diverse menus.
14. Non-Veg Dominance: Query: Restaurant with the highest percentage of non-vegetarian food items. Insight: Identifies non-veg menu leaders.
15. Data Cleanup Query: Price data cleanup and formatting. Insight: Ensures consistent and accurate price data for analysis.

Conclusion: This project showcases my SQL proficiency and ability to derive actionable insights from complex datasets. The findings can be leveraged for strategic decision-making in the food delivery industry.