A structured e-commerce order dataset built from real Amazon purchase histories voluntarily shared by users on the platform. Each record represents a single order line item with full pricing breakdown (unit price, tax, discounts, shipping), fulfilment status, and shipping location.

The sample includes:

10 unique users
1k order items
Marketplace: Amazon.com

Use Cases

E-commerce analytics: basket analysis, spending patterns, seasonal trends
Price research: cross-marketplace price comparison, tax structure analysis
Demand forecasting: order frequency and product category modelling

Data Collection

Order data was exported directly from personal Amazon accounts by participating users who consented to share their purchase history.

Anonymization

User & order IDs — original identifiers replaced with deterministic SHA-256 pseudonyms (truncated to 8 hex characters), preserving cross-record linkability
Addresses — reduced to city and country only; street names, house numbers, and postal codes are removed
Dates — normalized to YYYY-MM-DD; time components stripped

Dataset Structure

Data Fields

Column	Type	Description
`user_id`	string	User identifier
`order_id`	string	Order identifier
`asin`	string	Amazon Standard Identification Number
`product_name`	string	Full product title
`product_condition`	string	Item condition (e.g. New)
`order_date`	date	Date the order was placed
`quantity`	int	Number of units ordered
`unit_price`	float	Price per unit excluding tax (USD)
`unit_price_tax`	float	Tax per unit (USD)
`currency`	string	Currency code (USD)
`total_amount`	float	Total charged including tax (USD)
`website`	string	Amazon marketplace (Amazon.com)
`shipping_city_country`	string	Shipping destination city and country