SANKHYA (संख्या) - Predictive Governance Dashboard

"From Numbers to Decisions"

A next-generation predictive analytics dashboard for UIDAI's Aadhaar ecosystem, analyzing 5M+ records to forecast demand, identify stressed districts, and optimize resource allocation.

---

Screenshots SCREENSHOTS FOR THE WEBSITE

🚀 Quick Start

# Navigate to backend
cd sankhya/backend

# Install dependencies
pip install -r requirements.txt

# Generate data & AI forecast
python generate_data.py
python ai_forecaster.py

# Run server
python app.py

Access: http://localhost:5000/login.html

---

📊 Features

Core Analytics

Feature	Description
DSI Scoring	Demand Stress Index (0-10 scale) for each district
Blue Zone Detection	High senior population areas requiring attention
DEZ Identification	Digital Exclusion Zones with low activity
Migration detection	shows major migration activity

Deviation Thresholds:

Range	Status	Action
±0 to ±20%	✅ Normal	No action required
±20% to ±40%	⚠️ Warning	Monitor closely
> ±40%	🚨 Critical	Immediate investigation

Use Case: Detects unusual cross-border migration patterns that may indicate:

• Mass migration events
• Data anomalies
• Potential fraud patterns
• System issues

Interactive Map

• 1,000+ district markers with DSI coloring
• 300+ Aadhaar center locations
• Zoom-responsive dot sizing
• Filters: Pincode, State, District, Zone
• View modes: Normal, State Avg, District Avg

Dashboard Pages

1. Command Center - KPIs, Map, Forecasts
2. Demographic Hub - Population analytics
3. Migration Radar - Inter-state flow analysis
4. Resource Lab - Capacity optimization
5. System Health - Anomaly detection

---

📈 Data Sources

Dataset	Records	Description
Demographic	2.07M	Age, population, pincode data
Biometric	1.86M	Authentication records
Enrollment	1.00M	New enrollments
Total	4.93M	Combined dataset

---

🛠 Tech Stack

Layer	Technologies
Frontend	Tabler UI, Leaflet.js, Chart.js, ApexCharts
Backend	Flask, Python 3.x
Data Processing	Pandas, NumPy
Maps	OpenStreetMap + Leaflet.js
AI/ML	Custom forecasting algorithms

---

📁 Project Structure

sankhya/
├── backend/
│   ├── app.py              # Flask server
│   ├── generate_data.py    # Data pre-processor
│   ├── ai_forecaster.py    # AI prediction model
│   ├── data_processor.py   # Analytics engine
│   └── requirements.txt    # Dependencies
├── data/
│   ├── sankhya_data.json   # Generated analytics
│   └── ai_forecast.json    # AI predictions
├── css/
│   └── custom.css          # Premium styling
├── images/
│   └── sankhya_logo.png    # Branding assets
├── index.html              # Main dashboard
├── login.html              # Authentication
└── [other pages].html      # Feature pages

---

🎨 Design Features

• 🇮🇳 Indian flag tricolor decorative strips
• Premium fonts (Poppins, Inter)
• Glassmorphism effects
• Dark/Light mode support
• Responsive layout

---

📐 DSI Formula

DSI = (V × Wa + S × Ws) / C + R

Where:
  V  = Transaction volume
  S  = Senior population ratio
  C  = Center capacity
  R  = Error rate
  Wa = Volume weight (0.4)
  Ws = Senior weight (0.3)

Thresholds:

• 🟢 Low: 0 - 3.3
• 🟡 Medium: 3.3 - 6.6
• 🔴 Critical: 6.6 - 10

---

👥 Team

Developed for UIDAI Hackathon

---

📄 License

MIT License

---