Customer Segmentation Analysis Project

Overview

This project performs customer segmentation analysis using the Online Retail Dataset from the UCI Machine Learning Repository. The analysis combines SQL operations, K-means clustering, and statistical analysis to derive meaningful customer segments and insights.

Dataset

We use the Online Retail Dataset from UCI Machine Learning Repository. This dataset contains:

• 541,909 transactions
• 8 attributes
• Transactions from 01/12/2010 to 09/12/2011
• Multi-country e-commerce sales data

Data Dictionary

Column	Description	Type
InvoiceNo	Invoice number (6-digit unique)	Nominal
StockCode	Product code (5-digit unique)	Nominal
Description	Product name	Nominal
Quantity	Quantity per transaction	Numeric
InvoiceDate	Invoice date and time	Numeric
UnitPrice	Unit price in sterling	Numeric
CustomerID	Customer number (5-digit unique)	Nominal
Country	Country of customer	Nominal

Project Structure

customer-segmentation/
│
├── data/                      # Data files
│   ├── raw/                  # Raw data
│   └── processed/            # Processed data
│
├── notebooks/                # Jupyter notebooks
│   ├── 01_data_preparation.ipynb
│   ├── 02_exploratory_analysis.ipynb
│   └── 03_segmentation_analysis.ipynb
│
├── src/                      # Source code
│   ├── __init__.py
│   ├── data_processing.py
│   ├── feature_engineering.py
│   └── visualization.py
│
├── tests/                    # Unit tests
│   └── test_data_processing.py
│
├── requirements.txt          # Project dependencies
├── setup.py                  # Package setup file
└── README.md                # Project documentation

Installation

1. Clone the repository:

git clone https://github.com/yourusername/customer-segmentation.git
cd customer-segmentation

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

Usage

1. Data Preparation:

python src/data_processing.py

2. Run Analysis:

python src/feature_engineering.py

3. Or use Jupyter notebooks:

jupyter notebook notebooks/01_data_preparation.ipynb

Features

• RFM (Recency, Frequency, Monetary) Analysis
• K-means Clustering
• SQL-based Customer Analytics
• Interactive Visualizations
• Statistical Analysis

Results

The analysis identifies distinct customer segments based on:

• Purchase behavior
• Transaction frequency
• Monetary value
• Product preferences
• Geographic distribution

Detailed results and visualizations are available in the notebooks.

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• UCI Machine Learning Repository for the dataset
• scikit-learn documentation and community
• Python data science community