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@@ -22,6 +22,15 @@ short_description: Data-Driven Innovation for Aadhaar
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  ## 🎯 Overview
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  Project Sentinel is an innovative fraud detection system designed specifically for UIDAI Aadhaar enrolment centers. Unlike traditional global threshold-based systems, Sentinel uses **context-aware machine learning** with district-level normalization to identify fraudulent patterns while accounting for India's demographic diversity.
@@ -69,6 +78,15 @@ India's demographic diversity creates a unique challenge:
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  ## πŸš€ Quick Start
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  ### Prerequisites
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  ```bash
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  Python 3.8+
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  1. **Clone the repository**
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  ```bash
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- git clone https://huggingface.co/spaces/your-username/UIDAI
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  cd UIDAI
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  ```
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@@ -114,8 +132,8 @@ UIDAI/
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  β”œβ”€β”€ requirements.txt # Python dependencies
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  β”œβ”€β”€ Dockerfile # Docker configuration
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  β”œβ”€β”€ project_sentinel_notebook.ipynb # ML model & data processing
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- β”œβ”€β”€ sentinel_dashboard_enhanced.py # Streamlit dashboard
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- β”œβ”€β”€ analyzed_aadhaar_data.csv # Processed data (generated)
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  β”œβ”€β”€ docs/
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  β”‚ β”œβ”€β”€ Project_Sentinel_Analysis.docx
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  β”‚ β”œβ”€β”€ Sentinel_Dashboard_Documentation.docx
@@ -374,8 +392,8 @@ This project is licensed under the MIT License - see the [LICENSE](LICENSE) file
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  ## πŸ“§ Contact
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  For questions or support, please contact:
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- - **Email**: princelv84@gmail.com
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- - **Issues**: [GitHub Issues](https://github.com/lovnishverma/UIDAI/issues)
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  - **Discussions**: [GitHub Discussions](https://github.com/lovnishverma/UIDAI/discussions)
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  ---
 
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  ---
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+ ## 🎯 Quick Links
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+
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+ - **πŸ“Š Live Notebook**: [Open in Google Colab](https://colab.research.google.com/drive/1YAQ4nfxltvG_cts3fmGc_zi2JQc4oPOT?usp=sharing)
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+ - **πŸš€ Dashboard Demo**: [Hugging Face Spaces](https://huggingface.co/spaces/lovnishverma/UIDAI)
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+ - **πŸ“– Documentation**: See `/docs` folder
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+ - **πŸ’» Source Code**: Available in this repository
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+
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+ ---
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+
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  ## 🎯 Overview
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  Project Sentinel is an innovative fraud detection system designed specifically for UIDAI Aadhaar enrolment centers. Unlike traditional global threshold-based systems, Sentinel uses **context-aware machine learning** with district-level normalization to identify fraudulent patterns while accounting for India's demographic diversity.
 
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  ## πŸš€ Quick Start
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+ ### **Option 1: Google Colab (Fastest)**
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+ Run the complete analysis in your browser without any setup:
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+ [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1YAQ4nfxltvG_cts3fmGc_zi2JQc4oPOT?usp=sharing)
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+ Click the badge above to open the notebook and run all cells to generate the analyzed data.
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+ ### **Option 2: Local Setup**
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+
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  ### Prerequisites
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  ```bash
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  Python 3.8+
 
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  1. **Clone the repository**
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  ```bash
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+ git clone https://huggingface.co/spaces/lovnishverma/UIDAI
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  cd UIDAI
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  ```
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  β”œβ”€β”€ requirements.txt # Python dependencies
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  β”œβ”€β”€ Dockerfile # Docker configuration
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  β”œβ”€β”€ project_sentinel_notebook.ipynb # ML model & data processing
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+ β”œβ”€β”€ app.py # Streamlit dashboard
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+ β”œβ”€β”€ analyzed_aadhaar_data.csv # Processed data (generated from colab)
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  β”œβ”€β”€ docs/
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  β”‚ β”œβ”€β”€ Project_Sentinel_Analysis.docx
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  β”‚ β”œβ”€β”€ Sentinel_Dashboard_Documentation.docx
 
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  ## πŸ“§ Contact
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  For questions or support, please contact:
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+ - **Email**: sentinel-support@example.com
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+ - **Issues**: [GitHub Issues](https://github.com/lovnnishverma/UIDAI/issues)
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  - **Discussions**: [GitHub Discussions](https://github.com/lovnishverma/UIDAI/discussions)
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  ---