UIDAI Velocity: Ecosystem Intelligence Platform

A comprehensive data analytics and visualization platform for analyzing Aadhaar (UIDAI) metrics including biometric authentication, demographic data, and enrolment statistics across India.

📋 Table of Contents

• Overview
• Features
• Project Structure
• Tech Stack
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Data Pipeline
• Contributing
• Team
• License

🎯 Overview

This project was developed for the UIDAI Hackathon to provide insightful visualizations and analytics on Aadhaar data. It enables users to explore trends in:

• Biometric Authentication: Track successful biometric authentications across age groups (5-17, 17+)
• Demographic Authentication: Monitor demographic authentication patterns
• Enrolment Statistics: Analyze new Aadhaar enrolments by age groups (0-5, 5-17, 18+)
• Geographic Analysis: Filter and visualize data by state, district, and pincode

✨ Features

• 📊 Interactive TradingView-style Charts - Professional-grade candlestick and line charts for data visualization
• 🗺️ Geographic Filtering - Filter data by state and district
• 📈 Custom Indicators - Add and configure custom analytical indicators
• 🔄 Data Fusion Mode - Combine multiple datasets for comprehensive analysis
• 📰 Event Markers - Track policy changes, technology updates, and news events
• 🤖 AI Analyst Notes - Save and manage analytical insights
• 🌙 Dark Theme - Eye-friendly dark mode interface
• 📱 Responsive Design - Works across desktop and mobile devices

📁 Project Structure

United-India---UIDAI---Hackathon/
├── UIDAI/
│   └── UI/                     # Frontend React Application
│       ├── src/
│       │   ├── components/     # React components
│       │   │   ├── Chart/      # Chart visualization components
│       │   │   ├── Docs/       # Help and documentation
│       │   │   ├── Indicators/ # Indicator management
│       │   │   └── Layout/     # Layout components
│       │   ├── store/          # Zustand state management
│       │   ├── types/          # TypeScript type definitions
│       │   ├── utils/          # Utility functions
│       │   └── data/           # Static data files
│       ├── package.json
│       ├── vite.config.ts
│       └── tailwind.config.js
├── Datascience/                # Data processing pipeline
│   ├── aadhaar_metrics_pipeline.ipynb  # Main data pipeline
│   └── processed/              # Processed output data
└── README.md

🛠️ Tech Stack

Frontend

• React 19 - UI library
• TypeScript - Type-safe JavaScript
• Vite 7 - Build tool and dev server
• Tailwind CSS 4 - Utility-first CSS framework
• Zustand - State management
• Lightweight Charts - TradingView charting library
• Lucide React - Icon library
• PapaParse - CSV parsing

Data Science

• Python - Data processing
• Pandas - Data manipulation
• NumPy - Numerical computing
• Jupyter Notebook - Interactive development

🚀 Getting Started

Prerequisites

• Node.js (v18 or higher)
• npm or yarn
• Python 3.8+ (for data pipeline)

Installation

1. Clone the repository

   git clone https://github.com/VarunKumar-05/United-India---UIDAI---Hackathon.git
   cd United-India---UIDAI---Hackathon

2. Install Frontend Dependencies

   cd UIDAI/UI
   npm install

3. Start Development Server

   npm run dev

4. Build for Production

   npm run build

Data Pipeline Setup (Optional)

1. Install Python dependencies

   pip install pandas numpy jupyter

2. Run the data pipeline

   cd Datascience
   jupyter notebook aadhaar_metrics_pipeline.ipynb

📊 Usage

1. Launch the Application: Start the dev server and open http://localhost:5173

2. Select Data View:

   • Choose a state and district from the filters
   • Select the primary dataset (Biometric, Demographic, or Enrolment)

3. Customize Charts:

   • Toggle between different chart types (candles, line)
   • Add custom indicators
   • Enable fusion mode to overlay multiple datasets

4. Explore Events: Click on event markers to view policy changes, technology updates, and news affecting Aadhaar metrics

5. Access Help: Navigate to #help for detailed documentation

🔄 Data Pipeline

The data pipeline processes raw Aadhaar data from multiple sources:

Data Type	Description	Age Groups
Biometric	Successful biometric authentications	5-17, 17+
Demographic	Successful demographic authentications	5-17, 17+
Enrolment	New Aadhaar enrolments	0-5, 5-17, 18+

Output Metrics:

• Daily enrolment trends
• Biometric vs Demographic usage comparison
• Share ratios and child-share analysis
• State and district-level aggregations

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

👥 Team

• Varun Kumar - @VarunKumar-05
• Thomas Albert - @iwinalbert
• Rishi Lakshan

📄 License

This project is open source and available under the MIT License.

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Made with ❤️ for the UIDAI Hackathon