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shera21/e-commerce-sales-and-customer-analytics

E-Commerce Sales and Customer Analytics Dataset (150k Transactions) Dataset Summary This dataset contains 150,000 simulated e-commerce transactions spanning from 2021 to 2025. It is tailored for business intelligence, sales analytics, customer behavior modeling, and end-to-end data analytics/machine learning portfolio projects. The dataset captures detailed transactional records covering order details, customer profiles, product metrics, pricing strategy… See the full description on the dataset page: https://huggingface.co/datasets/shera21/e-commerce-sales-and-customer-analytics.

sourceHugging Facecc-by-sa-4.0updated 27d agoView on Hugging Face
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Dataset Card

language:

  • —en license: cc-by-4.0 size_categories:
  • —100K<n<1M task_categories:
  • —tabular-classification
  • —tabular-regression tags:
  • —ecommerce
  • —sales-analytics
  • —customer-analytics
  • —business-intelligence
  • —EDA pretty_name: E-Commerce Sales and Customer Analytics (2021-2025) ---

E-Commerce Sales and Customer Analytics Dataset (150k Transactions)

Dataset Summary

This dataset contains 150,000 simulated e-commerce transactions spanning from 2021 to 2025. It is tailored for business intelligence, sales analytics, customer behavior modeling, and end-to-end data analytics/machine learning portfolio projects.

The dataset captures detailed transactional records covering order details, customer profiles, product metrics, pricing strategy, discounts, delivery performance, returns, loyalty metrics, and marketing channels.

Key Features & Columns Included

  • —Orders & Sales: Order IDs, timestamps, quantities, unit prices, discounts, gross revenue, net revenue, and profit margins.
  • —Customer Profile & Behavior: Customer IDs, loyalty tier, purchase frequency, lifetime value, customer ratings, and return behavior.
  • —Product Analytics: Product category, sub-category, stock status, and product performance metrics.
  • —Operations & Logistics: Payment methods, shipping speed, delivery statuses, return reasons, and delivery delays.
  • —Marketing Attribution: Acquisition marketing channels, campaign performance, and promotion usage.

Primary Use Cases

  • —Exploratory Data Analysis (EDA): Analyze seasonality, profit margins, and regional purchasing patterns.
  • —SQL & Dashboarding: Perform complex window functions, cohort retention modeling, and build executive dashboards in Power BI, Tableau, or Excel.
  • —Machine Learning:
  • —Customer Churn Prediction (Classification)
  • —Customer Lifetime Value (LTV) Forecasting (Regression)
  • —Customer Segmentation (RFM / Clustering)

How to Load in Python

python
from datasets import load_dataset

# Load dataset directly from Hugging Face Hub
dataset = load_dataset("shera21/e-commerce-sales-and-customer-analytics")

# Convert to Pandas DataFrame
df = dataset["train"].to_pandas()
print(df.head())