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) license.