Market Minder is a comprehensive analytics project that leverages machine learning and data analysis to derive actionable insights from retail data. The project implements RFM analysis, customer segmentation, and predictive modeling to help businesses understand their customers better.
- 📈 RFM (Recency, Frequency, Monetary) Analysis
- 👥 Customer Segmentation using K-means Clustering
- 🔮 Churn Prediction Models
- 🛒 Market Basket Analysis
- 📊 Interactive Data Visualizations
- Python 3.11.5
- Pandas & NumPy for data manipulation
- Scikit-learn for machine learning models
- XGBoost & LightGBM for advanced modeling
- Matplotlib & Seaborn for visualizations
- Jupyter Notebook for development
bash jupyter notebook notebooks/MarketMinder\ Submission.ipynb
- Run the cells in sequence to:
- Load and preprocess the data
- Perform RFM Analysis
- Generate customer segments
- Build prediction models
- Visualize results
- Successfully segmented customers into 4 distinct groups
- Achieved high accuracy in churn prediction
- Generated valuable insights through market basket analysis
- Created comprehensive visualizations for business insights
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
Mrunmay More
- GitHub: @mrunmaymore
- LinkedIn: Mrunmay More
- Prof. Krystyn Gutu for project guidance
- Online Retail dataset providers
- Open source community for amazing tools and libraries
⭐️ If you find this project useful, please consider giving it a star!
This project is licensed under the MIT License - see the LICENSE file for details.