I need to systematically check for data quality issues across the key columns. Let me start by examining potential outliers and impossible values in the numeric measures. ```json { "action": "tool_call", "reasoning": "Check for outliers and impossible values in the three main numeric measures: Average Cost for two, Aggregate rating, and Votes. I'll look at their distributions, extremes, and check if ratings fall within expected bounds (0-5 scale).", "tool": "query_sql", "args": { "sql": "SELECT MIN(\"Average Cost for two\") as min_cost, MAX(\"Average Cost for two\") as max_cost, PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY \"Average Cost for two\") as p25_cost, PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY \"Average Cost for two\") as median_cost, PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY \"Average Cost for two\") as p75_cost, PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY \"Average Cost for two\") as p95_cost, MIN(\"Aggregate rating\") as min_rating, MAX(\"Aggregate rating\") as max_rating, COUNT(CASE WHEN \"Aggregate rating\" > 5.0 THEN 1 END) as ratings_above_5, COUNT(CASE WHEN \"Aggregate rating\" < 0 THEN 1 END) as ratings_below_0, MIN(\"Votes\") as min_votes, MAX(\"Votes\") as max_votes, PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY \"Votes\") as p95_votes FROM raw.\"Zomato Restaurant Dataset\"", "purpose": "Identify outliers and impossible values in numeric measures", "source": "raw" } } ```