📊 E-commerce Business Insights

Turning raw e-commerce transaction data into meaningful business insights using Python and Pandas.

📈 Revenue Analysis   •   👥 Customer Insights   •   🌍 Business Analysis

This project analyzes e-commerce transaction data to understand customer behavior, revenue patterns, and business performance.

The analysis focuses on customer spending, revenue trends, country-wise performance, and customer contribution to overall revenue.

• Analyzed revenue changes over time
• Identified peak and low revenue periods
• Examined overall revenue patterns

• Compared revenue across countries
• Identified high-performing regions
• Analyzed geographic revenue contribution

• Identified high-value customers
• Analyzed customer spending behavior
• Compared new and repeat customers
• Examined customer contribution to total revenue

• A relatively small group of customers contributes a significant share of total revenue.
• Revenue varies across different time periods.
• Customer purchasing behavior differs across countries.
• Repeat customers contribute significantly to overall revenue.
• Revenue concentration can be analyzed using the Pareto principle.

• Python
• Pandas
• NumPy
• Matplotlib
• Jupyter Notebook

ecommerce-business-insights/
│
├── 📁 data/
│   └── dataset files
│
├── 📁 images/
│   ├── revenue.png
│   └── top_countries.png
│
├── 📁 notebook/
│   └── analysis notebook
│
├── 📄 README.md
└── 📄 requirements.txt

git clone https://github.com/AnkanXcoder/ecommerce-business-insights.git

cd ecommerce-business-insights

pip install -r requirements.txt

Open the notebook inside the notebook/ folder using Jupyter Notebook or VS Code.

The project uses e-commerce transaction data for customer, revenue, and business analysis.

A sample dataset is included in the repository where applicable.

The goal of this project is to transform raw transaction data into useful business insights related to:

• Customer value
• Revenue distribution
• Geographic performance
• Purchasing behavior
• Revenue trends

Computer Science & Engineering Student

Interested in:

• 📊 Data Science
• 🤖 Machine Learning
• 💻 Software Development
• 📈 Data Analytics

Built with 🐍 Python and 📊 Data Analysis