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pretty_name: Amazon US Orders
license: cc-by-4.0
language:
- en
task_categories:
- other
size_categories:
- 1K<n<10K
tags:
- tabular
- csv
- ecommerce
- orders
- amazon
- transactions
dataset_info:
features:
- name: user_id
dtype: string
- name: order_id
dtype: string
- name: asin
dtype: string
- name: product_name
dtype: string
- name: product_condition
dtype: string
- name: order_date
dtype: date32
- name: quantity
dtype: int64
- name: unit_price
dtype: float64
- name: unit_price_tax
dtype: float64
- name: currency
dtype: string
- name: total_amount
dtype: float64
- name: website
dtype: string
- name: shipping_city_country
dtype: string
configs:
- config_name: default
data_files:
- split: train
path: data/orders.csv
---
This is a sample dataset. To access the full version or request any custom dataset tailored to your needs, contact DataHive at contact@datahive.ai.
# Amazon US Orders
## Dataset Summary
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
### Dataset Description
- **Access:** Free sample dataset
- **Curated by:** https://datahive.ai
- **Language(s):** English
- **License:** Creative Commons Attribution 4.0 (CC BY 4.0)
## 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 |
### Data Splits
Single split containing all records. No train/test separation — this is a raw data export, not a benchmark.
## Licensing Information
This dataset is released under the [Creative Commons Attribution 4.0 International (CC-BY-4.0)](https://creativecommons.org/licenses/by/4.0/) license.
## Dataset Card Contact
contact@datahive.ai
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