# 3 Most Interesting Analytical Questions This Dataset Could Answer Based on the evidence gathered, here are three analytically substantive questions this Zomato restaurant dataset is well-positioned to answer: ## 1. **Which cuisine types are underserved in high-performing markets?** **Why this is interesting:** With **1,851 distinct cuisine types** across **147 cities** and **17 countries**, combined with rating data (Aggregate rating 0.0-4.9) and engagement metrics (Votes 0-10,934), this dataset enables identification of cuisine gaps in markets with high restaurant performance. This directly supports market expansion and niche identification decisions. **Data support:** - Cuisines dimension with 1,851 variants - Geographic coverage: 147 cities, 17 country codes - Performance metrics: Aggregate rating, Votes, Rating text/color - 45 pre-aggregated gold tables include breakdowns by Cuisines ## 2. **How do service capabilities (online delivery, table booking) correlate with pricing power and customer engagement across different price segments?** **Why this is interesting:** The dataset uniquely captures both service features (Has Table booking, Has Online delivery, Is delivering now) and multiple pricing/engagement dimensions. This enables analysis of whether digital capabilities justify premium pricing or drive higher customer engagement, segmented by the 4-tier price range. **Data support:** - Service features: Has Table booking, Has Online delivery, Is delivering now (all boolean) - Pricing: Average Cost for two (range 0-800,000), Price range (1-4), Currency (11 types) - Engagement: Votes (0-10,934), Aggregate rating (0.0-4.9) - Gold tables include aggregations by delivery/booking/price dimensions ## 3. **What is the competitive pricing landscape for similar cuisines across different currencies/markets, and where are the arbitrage opportunities?** **Why this is interesting:** With **11 distinct currencies** and cost data ranging from 0 to 800,000 in local currency units, combined with cuisine and location data, this enables sophisticated cross-market price benchmarking and identification of markets where specific cuisines may be over/underpriced relative to quality. **Data support:** - Currency dimension: 11 types (Botswana Pula to Turkish Lira) - Average Cost for two with extreme range (0-800,000) - Cuisines: 1,851 distinct types - Quality proxy: Aggregate rating, Votes - Gold tables include aggregations by Currency --- ## What Remains Uncertain Due to repeated tool failures, I could not execute specific queries to validate: - Actual distribution of restaurants across countries/cities - Correlation strengths between variables - Specific cuisine-market combinations with data gaps - Statistical significance of pricing variations However, all three questions are **firmly grounded in the confirmed schema** and the 45 pre-aggregated gold tables that exist in the dataset.