This project focuses on analyzing an e-commerce dataset to uncover key business insights such as top-performing products, regional sales patterns, and monthly sales trends.
It demonstrates end-to-end data analysis — from cleaning and preprocessing to visualization — using Python’s data analytics libraries.
Objective:
To explore, clean, and analyze e-commerce sales data to identify:
- Best-selling products and profit trends
- Regional and category-wise performance
- Monthly sales and profit fluctuations
| Category | Tools Used |
|---|---|
| Programming Language | Python |
| Libraries | Pandas, NumPy, Matplotlib, Seaborn |
| Environment | VS Code |
| Version Control | Git, GitHub |
- The Sales and Profit trend shows significant monthly variations influenced by category and region.
- Top products contribute a large share of overall revenue, revealing a skewed product performance.
- Regional sales comparison highlights where the business performs best.
- Monthly analysis helps identify seasonal sales patterns and growth opportunities.