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

---

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

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())