E-Commerce Data Analysis

An end-to-end e-commerce analytics project built with Power BI and a cleaned transactional dataset. The dashboard turns raw order data into interactive insights about revenue, products, customers, purchasing patterns, and returns.

Dashboard Preview

Sales Overview

Sales Analysis

Returns Analysis

Project Highlights

• Tracks total sales, invoice volume, customer activity, and quantity sold.
• Identifies top-selling products and highest-spending customers.
• Compares revenue across countries and days of the week.
• Explores monthly revenue trends and sales by hour of day.
• Separates completed sales from returned transactions.
• Analyzes returns by product, country, and month.
• Includes an interactive invoice-date filter across dashboard pages.

Dataset

The cleaned dataset contains transaction-level e-commerce records with the following fields:

Column	Description
InvoiceNo	Unique invoice identifier
StockCode	Product stock code
Description	Product description
Quantity	Number of units in the transaction
InvoiceDate	Transaction date and time
UnitPrice	Price per unit
CustomerID	Customer identifier
Country	Customer country
Year, Month, Day, Hour	Date and time features derived from the invoice date
DayName	Day of the week
Is_Return	Indicates whether the transaction is a return
Sales	Calculated transaction sales value

The dashboard covers transactions from December 2010 to December 2011.

Repository Contents

.
├── Ecommerce Analysis.pbix
├── cleaned_ecommerce_data.csv
└── New folder/
    ├── Screenshot 2026-08-08 175937.png
    ├── Screenshot 2026-08-08 180033.png
    └── Screenshot 2026-08-08 180046.png

How to Use

1. Download or clone this repository.
2. Open Ecommerce Analysis.pbix using Power BI Desktop.
3. If Power BI asks for the data source, point it to cleaned_ecommerce_data.csv.
4. Use the date slicer and dashboard navigation to explore sales and returns.

Tools Used

• Power BI
• Power Query
• DAX
• CSV data processing

Author

Created by abdelrhmanmouse.