This project analyzes an e-commerce dataset using Python for data cleaning and Power BI for data visualization. The goal is to uncover insights into sales performance, customer behaviour, product profitability, and shipping performance.

Business Problem
An e-commerce company wants to better understand its sales and customer data to answer questions such as:

Which products generate the highest sales and profit?
Which payment methods are most popular?
Which locations generate the most revenue?
How long do deliveries take?
What are the monthly sales trends?
The analysis aims to support better business decisions through data-driven insights.

Tools Used
Python
Pandas
NumPy
Google Colab
Microsoft Excel
Power BI
Data Cleaning
The dataset was cleaned using Python by performing the following steps:

Removed duplicate records
Renamed columns for readability
Converted date columns to datetime format
Created Delivery Days
Calculated Profit
Calculated Profit Margin
Calculated Shipping Percentage
Extracted Year, Month, Quarter and Weekday from Order Date
Exported the cleaned dataset to Excel
Dashboard Features
The Power BI dashboard includes:

Total Sales
Total Profit
Total Orders
Average Delivery Days
Profit Margin
Monthly Sales Trend
Sales by Product
Sales by Location
Payment Method Distribution
Device Type Distribution
Interactive Slicers
Key Insights
Identified the highest-performing products.
Compared sales across different locations.
Analysed customer purchasing behaviour.
Measured delivery performance.
Evaluated product profitability.
Recommendations
Focus marketing efforts on high-performing products.
Improve delivery performance in slower regions.
Increase inventory for popular products.
Monitor low-profit products for pricing opportunities.
Project Structure
Ecommerce-Data-Analysis
│
├── Ecommerce_Data_Cleaning.ipynb
├── Ecommerce_Cleaned.xlsx
├── PowerBI_Dashboard.pdf (optional)
├── Dashboard_Screenshot.png
└── README.md
Author
Omar Hazem

Computer Engineering Student | Aspiring Data Analyst