{
    "action": "final",
    "content": "## Data Quality Assessment\n\nThe dataset is **structurally sound** with no duplicates or impossible values, but has **three notable quality characteristics**:\n\n### 1. Mixed Currency Units (Critical Issue)\nThe dataset contains **12 different currencies** without normalized conversion, making cross-restaurant cost comparisons impossible:\n\n- **Indian Rupees** (8,652 restaurants): costs 0–8,000, median 450\n- **Indonesian Rupiah** (21 restaurants): costs 70,000–800,000, median 200,000\n- **Dollar** (482 restaurants): costs 0–500, median 25\n- **Pounds, Dirams, Rand, Real, Lira, Pula, Rupee, Riyal** (9 other currencies)\n\nThe apparent \"outlier\" of 800,000 in `Average Cost for two` is actually **valid** — it's in Indonesian Rupiah (≈$50 USD), not an error.\n\n### 2. Zero-Value Records (Data Gaps)\nSignificant portions of the dataset have missing or placeholder zeros:\n\n- **18 restaurants** (0.2%) have zero cost\n- **2,148 restaurants** (22.5%) have zero ratings\n- **1,094 restaurants** (11.5%) have zero votes\n\nThese likely represent new/unrated establishments rather than errors, but limit analytical utility for those records.\n\n### 3. No Structural Issues Found\n✓ **No duplicate Restaurant IDs** (9,551 distinct IDs for 9,551 rows)  \n✓ **Ratings within valid range** (0.0–4.9, no values >5 or <0)  \n✓ **Price ranges valid** (1–4 as expected)  \n✓ **Coordinates valid** (no impossible lat/lon values)  \n✓ **No format inconsistencies** detected\n\n### Recommendation\nFor meaningful cost analysis, **currency normalization is essential**. The zero-rating records should be filtered out for rating-based analyses but retained for coverage studies.",
    "charts": [
        {
            "kind": "bar",
            "x": "Currency",
            "series": [
                "restaurant_count"
            ],
            "title": "Restaurant Distribution by Currency"
        }
    ]
}