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Produce a deterministic transformation from repeated measure columns to tidy rows with explicit keys, types and missing-value rules.
## 1. Technical Audit & Business Context | Column | Smell | Business impact | |---|---|---| | `churn_date` | Populated only for churned accounts | This is the label in disguise. Any model that sees it will score ~perfectly in training and be useless in production. | | `mrr` | Right-skewed, a long tail of enterprise accounts | Mean imputation would drag small accounts upward and understate churn risk in the SMB segment, which is where outreach capacity actually sits. | | `signup_date` | Mixed `Y
Audits a raw dataset for data smells, ties each issue to its business impact, then proposes an imputation, encoding and scaling strategy with a leakage-safe pandas and scikit-learn pipeline.