This project is designed to analyze customer data using clustering techniques. It utilizes K-means clustering to segment customers based on their purchasing behavior, specifically focusing on recency, frequency, and monetary value.
- Synthetic dataset generation for customer analysis
- Data preprocessing with feature scaling
- Optimal cluster determination using the Elbow Method
- K-means clustering implementation
- Python 3.8 or higher
- pip (Python package installer)
- Git
- Clone the repository:
git clone [repository-url] cd [project-name] - Create and activate virtual environment:
On Windows:
On macOs/Linux:
python -m venv venv .\venv\Scripts\activatepython3 -m venv venv source venv/bin/activate - Install the required libraries:
pip install -r requirements.txt - Run setup script:
On Windows:
On MacOS/Linux:
.\setup_env.shchmod +x setup_env.sh ./setup_env.sh
To run the main script, execute the following command:
python src/main.py
This will generate the clustering results and save the Elbow Method plot as elbow_method.png.
For detailed documentation, please refer to the docs/README.md file.