README.md

metadata

title: 'SegmentX: Behavioral Intelligence'
emoji: 🛍️
colorFrom: blue
colorTo: indigo
sdk: docker
pinned: false
license: mit

🛍️ SegmentX: E-Commerce Behavioral Intelligence Portal

SegmentX is a professional behavioral intelligence platform. It transforms e-commerce transactions into actionable segments using RFM and K-Means++. Features include revenue forecasting, Pareto analysis, churn probability, and product affinity mapping to maximize customer retention and mission-critical marketing ROI.

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🚀 Key Features

• Behavioral Segmentation: Automated RFM analysis and K-Means++ clustering for precision customer categorization.
• Advanced Forecasting: Monthly revenue trend analysis to identify seasonality and long-term growth patterns.
• Predictive Analytics: Individual-level Churn Probability (%) and Customer Lifetime Value (CLV) projections.
• Market Basket Insights: Segment-specific product affinity analysis highlighting top-selling items per cluster.
• Strategic Visualization: Heatmaps, 2D PCA projections, and Pareto-based revenue contribution charts.
• Campaign Ready: Interactive direct-to-CSV segment export for immediate use in email marketing tools.

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📁 Project Structure

customer_segmentation/
├── data/
│   ├── raw/                # Cleaned transactions (UK-only)
│   └── processed/          # Aggregated RFM features and scaled data
├── docs/                   # Project documentation and instructions
├── models/                 # Saved K-Means cluster model
├── outputs/                # Visualization plots, segment mapping, and product affinity
├── pipeline/               # Modular Python scripts (Cleaning, RFM, Training)
├── app.py                  # Streamlit dashboard
├── README.md               # User guide and setup instructions
├── Procfile                # Deployment configuration
└── requirements.txt        # Required Python libraries

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🛠️ Installation & Usage

1. Install Dependencies:

   pip install -r requirements.txt
   

2. Launch the Dashboard:

   streamlit run app.py
   

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🚀 Deployment

This project is configured for one-click deployment to Heroku, Streamlit Cloud, or Docker.

• Procfile: Pre-configured for web dyno execution.
• runtime.txt: Specifies Python 3.10 usage.
• .gitignore: Optimized to handle large dataset storage.

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📄 License

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