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Capstone task: verified analysis of a real dataset

Points: 200 · Badge requirement: Data Analysis Agent

End to end, with your agent as the tool: take dataset/orders.csv (303 messy rows) through clean → explore → report.

Required output

Your pipeline (any structure you choose) must write:

  • out/results.json with keys:
    • rows_total, duplicates_removed, rows_clean
    • total_revenue (2 decimals), avg_order_value (2 decimals), total_quantity
    • revenue_by_region — {region: total} at 2 decimals
    • top_category — category with the highest total revenue
    • first_date, last_date — ISO dates
  • out/report.md containing the sections ## Data quality, ## Findings, ## Appendix and stating the verified total revenue.

Data quality rules

Drop exact duplicates; drop rows with unparseable amount, unparseable date, or blank region. Everything else stays.

Verification

python verify.py checks out/ against ground truth (tolerance 0.01) and prints the capstone claim code on success. Reference implementation: solution/capstone.py — look at it only after your own attempt.

Self-reported evidence, never a certificate.