ahmedburale / e-commerce

E-commerce Data Analysis Project

Overview

This project generates realistic synthetic e-commerce data, analyzes customer behavior, and visualizes key insights. It covers sales trends, product performance, loyalty program impact, and refund rates.

Key Features

• Data Generation: Synthetic data creation for customers, orders, and geographical locations.
• Data Analysis: Insights on sales trends, product trends, loyalty programs, and refund rates.
• Data Visualization: Clear, color-coded charts to visualize findings.

Steps to Run

1. Clone Repository: git clone <your-repo-url>
2. Install Dependencies: pip install -r requirements.txt
3. Run Notebook: Open and run ecommerce_analysis.ipynb in Jupyter or DataSpell.

Visualizations

• Sales Trends: Monthly sales overview.
• Top Products: Best-selling products.
• Loyalty Programs: Comparison of sales by loyalty program.
• Refund Analysis: Sales impact of refunds.

Results

• Customer Insights: Detailed analysis of purchase frequency, average order value, and geographical distribution.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT