E-Commerce Dataset: Products, Customers, and Trends

Description

This dataset provides a comprehensive view of an e-commerce platform, featuring detailed information about products, customers, pricing, and sales trends. It is designed for data analysis, machine learning, and insights into online retail operations. The dataset is structured to help researchers and analysts explore various aspects of e-commerce, such as product popularity, customer preferences, and shipping performance.

Columns and Their Descriptions

• Product ID: Unique identifier for each product.
• Product Name: The name or title of the product listed in the catalog.
• Category: The category or type of the product (e.g., Electronics, Clothing, Home Decor).
• Price: The price of the product in USD.
• Discount: The discount applied to the product as a percentage of the original price.
• Tax Rate: The applicable tax rate for the product as a percentage.
• Stock Level: The number of units currently available in inventory.
• Supplier ID: A unique identifier for the supplier of the product.
• Customer Age Group: The age group of customers who frequently purchase this product (e.g., Teens, Adults, Seniors).
• Customer Location: The geographical location of customers (e.g., Country, State, or City).
• Customer Gender: The gender(s) of customers most likely to purchase this product (e.g., Male, Female, Both).
• Shipping Cost: The cost of shipping the product in USD.
• Shipping Method: The method of shipping used (e.g., Standard, Express, Overnight).
• Return Rate: The percentage of orders for this product that are returned by customers.
• Seasonality: The season(s) during which the product is most popular (e.g., Winter, Summer, All-Year).
• Popularity Index: A score indicating the product's popularity on a scale of 0 to 100.

Use Cases

This dataset is ideal for:

• Exploratory Data Analysis (EDA): Analyze sales trends, product popularity, and customer preferences.
• Visualization: Create insightful charts to visualize product performance, regional sales, and shipping trends.
• Customer Insights: Understand customer segmentation based on demographics, preferences, and location.
• Machine Learning Applications:
  • Regression: Predict product popularity based on price, discount, and stock level.
  • Clustering: Identify similar product categories for targeted marketing.
  • Classification: Predict whether a product will be returned based on its features.

Sample Data