🇮🇳 AadhaarPulse: Demographic & Biometric Analytics

AadhaarPulse is a mission-critical dashboard designed for the Unique Identification Authority of India (UIDAI) ecosystem. It provides real-time visibility into Aadhaar enrolments, biometric health scores, and inter-state migration patterns using advanced data visualization and predictive analytics.

---

📊 Core Capabilities

1. Demographic Intelligence

• Enrolment Trending: Visualizes the daily influx of new enrolments vs. demographic updates.
• State-wise Penetration: Interactive heatmap showing saturation levels across Indian states.
• Digital Readiness: Analysis of mobile number seeding vs. email linkage penetration.

2. Biometric Health Monitoring

• MBU (Mandatory Biometric Update) Tracking: Monitors the gap between expected and actual biometric updates for age groups 5-7 and 15-17.
• Failure Analysis: Categorizes authentication failures to improved biometric capture standards.

3. AI-Powered Forecasting

• Predictive Enrolment: Uses Linear Regression to forecast enrolment demand for the next 7 days.
• Resource Planning: Helps enrollment centers allocate staff based on predicted footfall.

4. Security & Governance

• Role-Based Access Control (RBAC): Distinct views for Analysts, Managers, and Admins.
• Audit Logging: Immutable logs of all user activities (Login, Profile Updates).
• Admin Console: Centralized user management and role assignment.

---

📐 Mathematical Models

The dashboard employs statistical models to derive insights from raw government data.

1. Linear Regression (Forecasting)

To predict future enrolments ($y$) based on time ($x$), we use the least squares method:

$$ y = mx + c $$

Where:

• $m$ (Slope): The rate of growth/decline. $$ m = \frac{n(\sum xy) - (\sum x)(\sum y)}{n(\sum x^2) - (\sum x)^2} $$
• $c$ (Intercept): The baseline value. $$ c = \frac{\sum y - m(\sum x)}{n} $$
• $n$: Number of historical data points (days).

This algorithm runs in real-time within ForecastingChart.jsx to predict the next week's trend.

2. Digital Readiness Score (DRS)

A composite index measuring the digital maturity of a state's user base:

$$ DRS = (w_1 \times M_p) + (w_2 \times E_p) + (w_3 \times B_c) $$

Where:

• $M_p$: Mobile Penetration (%)
• $E_p$: Email Linkage (%)
• $B_c$: Biometric Completeness (%)
• Weights: $w_1=0.5, w_2=0.3, w_3=0.2$

---

🛠️ Technology Stack

Component	Technology	Version	Description
Frontend	React	v19	Component-based UI Architecture
Build System	Vite	v6	Lightning-fast HMR and bundling
Styling	Tailwind CSS	v4	Utility-first CSS engine (Zero runtime)
Visualization	Recharts	v2.15	Composable React charting library
Backend	Node.js & Express	v4.18	RESTful API Layer
Database	MongoDB Atlas	v7.0	Cloud-native document store
Authentication	JWT & Bcrypt	v9.0	Stateless secure authentication

---

⚡ Quick Start Guide

Prerequisites

• Node.js v18+
• MongoDB Connection String

1. Installation

Clone the repository and install dependencies.

git clone https://github.com/StartYourRepo/AadhaarPulse.git
cd AadhaarPulse
npm install

2. Environment Setup

Create a .env file in the server/ directory:

MONGO_URI=mongodb+srv://<user>:<password>@cluster.mongodb.net/aadhaar_dashboard
PORT=3000
JWT_SECRET=super_secret_key_123

3. Running Locally

Launch both Frontend and Backend concurrently:

Terminal 1 (Backend):

cd server
npm start

Terminal 2 (Frontend):

npm run dev

The application will be live at http://localhost:5173.

---

🤝 Team Contribution Plan

This project mimics a collaborative Agile workflow.

• Rishab: Frontend Architect (Scaffolding, Routing, Styles)
• Nikhil: Backend Lead (API, Database, Auth Logic)
• Rituraj: Security Engineer (Login/Signup Pages, Admin Panel)
• Swaraj: Data Scientist (Charts, AI Models, Data Scripts)

See TEAM_CONTRIBUTION_PLAN.html for specific commit instructions.

---

📜 License

© 2026 AadhaarPulse Team. Licensed under the MIT License.