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@@ -33,7 +33,7 @@ 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.
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  ### The Problem We Solve
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@@ -108,13 +108,13 @@ pip install -r requirements.txt
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  3. **Run the Jupyter Notebook** (Data Processing)
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  ```bash
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- jupyter notebook project_sentinel_notebook.ipynb
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  ```
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  This generates `analyzed_aadhaar_data.csv`
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  4. **Launch the Dashboard**
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  ```bash
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- streamlit run sentinel_dashboard_enhanced.py
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  ```
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  5. **Access the application**
@@ -131,12 +131,12 @@ UIDAI/
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  β”œβ”€β”€ README.md # This file
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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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  β”‚ └── Dashboard_Enhancements_Guide.docx
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  └── assets/
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  └── screenshots/ # Dashboard screenshots
@@ -276,10 +276,10 @@ RISK_CATEGORIES = {
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  ### Docker Deployment
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  ```bash
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  # Build image
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- docker build -t sentinel-dashboard .
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  # Run container
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- docker run -p 8501:8501 sentinel-dashboard
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  ```
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  ### Hugging Face Spaces
 
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  ## 🎯 Overview
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+ Project S.T.A.R.K 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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  ### The Problem We Solve
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  3. **Run the Jupyter Notebook** (Data Processing)
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  ```bash
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+ jupyter notebook project_stark_notebook.ipynb
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  ```
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  This generates `analyzed_aadhaar_data.csv`
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  4. **Launch the Dashboard**
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  ```bash
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+ streamlit run app.py
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  ```
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  5. **Access the application**
 
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  β”œβ”€β”€ README.md # This file
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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_stark_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_Stark_Analysis.docx
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+ β”‚ β”œβ”€β”€ Stark_Dashboard_Documentation.docx
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  β”‚ └── Dashboard_Enhancements_Guide.docx
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  └── assets/
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  └── screenshots/ # Dashboard screenshots
 
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  ### Docker Deployment
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  ```bash
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  # Build image
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+ docker build -t app .
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  # Run container
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+ docker run -p 8501:8501 app
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  ```
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  ### Hugging Face Spaces