SAMPLE Synthetic Retail Transactions (Worldwide) — Zero-PII, ML-Ready CSV/Parquet for AOV, ...

## What this is
A privacy-safe **synthetic retail transactions** dataset with realistic baskets and prices, **zero PII**, and production-style schema so you can unblock **AOV/CLV/demand/promo POCs** immediately.

## What you get
- Line-item transactions with quantity, unit_price, discount, location, timestamp
- Derived metric: total_value per line/basket
- Formats: CSV and Parquet
- Docs: data dictionary + daily manifest; optional locked schema
- Delivery: secure S3 link or private share (Snowflake/Databricks/BigQuery) on request

## Why synthetic?
- **Zero PII** and GDPR-friendly experimentation
- Start modeling **today** while prod access is pending
- Stable distributions for benchmarking algorithms and pipelines

## Known limits
- Not a drop-in replacement for your production POS/e-com telemetry
- Aggregate patterns approximate real-world behaviour; individual rows are synthetic

## Sample (2,000 rows)
We include a **2,000-row CSV** that mirrors the production schema (nulls 0% across sample columns; unique `transaction_id`; ISO8601 timestamps). Add your own summary stats (AOV, discount rate, category mix) if desired.

## Support & customizations
Need region/category mixes, holiday uplift, or promo elasticity scenarios? We can generate tailored cohorts and refresh cadences.