Business Segmenter
Year
2025
Tech & Technique
Python, Streamlit, scikit-learn, K-Means, Apriori, Pandas, NumPy
Description
Built Business Segmenter using Python, Streamlit, and scikit-learn, implementing automated K-Means clustering and Apriori market basket analysis for data-driven customer insights and optimized product bundling strategies.
Key Features:
Technical Highlights:
Key Features:
- Automated K-Means clustering for customer segmentation
- Apriori market basket analysis for product bundling
- Interactive visualizations for data insights
- Real-time customer segmentation
- Campaign generation with smart parameter automation
- Responsive dashboard for actionable business intelligence
Technical Highlights:
- Developed analytics platform for real-time segmentation
- Implemented robust data processing pipelines
- Created interactive visualizations for business insights
- Built smart parameter automation for campaign generation
My Role
Full-Stack ML Developer
- Backend: Implemented K-Means clustering and Apriori algorithms
- Frontend: Built interactive dashboard using Streamlit
- Data Processing: Created robust pipelines for customer data
- ML Models: Developed automated segmentation system
- Visualization: Implemented interactive charts and graphs
