DATA QUALITY AUDIT — profile first, judge second. Numbers below are the
shape of a real audit; run against your file for actuals.

COMPLETENESS
  column           null %    verdict
  email            0.2%      fine
  signup_date      0%        fine
  last_purchase    34%       ⚠ — is null "never purchased" or missing data?
                             This distinction changes your model. Confirm
                             before imputing.
  region           11%       investigate — clustered in one signup source?

CONSISTENCY
  • 47 rows: last_purchase BEFORE signup_date. Impossible. Data error,
    not an outlier — exclude or fix, don't feature-engineer around it.
  • account_status has "active", "Active", "ACTIVE" — 3 spellings, one
    state. Silent cardinality inflation; models will treat them as
    distinct.

VALIDITY
  • 12 emails don't parse. • 3 signup_dates in the future.

OUTLIERS — and the trap here
  lifetime_value has 8 values above $2M against a $340 median. Before
  you clip them: are they real whales or a currency/units bug? An
  outlier that's a data error and an outlier that's your best customer
  look identical in a boxplot. Check the source, don't just winsorize.

DISTRIBUTION RISK FOR TRAINING
  region is 78% one value. A model trained on this won't generalize
  off that region — that's a sampling problem no cleaning fixes, and
  it's the finding most likely to bite you post-deployment.

REMEDIATION PLAN, ORDERED
  1. Exclude the 47 impossible-date rows (errors, non-negotiable)
  2. Normalize account_status casing
  3. Decide last_purchase null semantics WITH a domain owner — don't
     impute blind
  4. Verify the 8 LTV outliers at source before any clipping
  5. Flag the region skew to whoever owns the model's scope

Point me at the file and I'll run the real profile.