🏛️ UIDAI Aadhaar Enrolment Analytics Dashboard

Maharashtra State Analysis — Government-Grade Insights

🔗 Live Dashboard

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📋 Overview

A comprehensive, judge-ready analytics solution for the UIDAI Data Hackathon 2026. This project transforms raw Aadhaar enrolment data into actionable policy insights through interactive visualizations and a professional PDF report.

Metric	Value
📊 Records Analyzed	93,184
📅 Monthly Data Points	101
🏘️ Districts Covered	53
📍 Pincodes Mapped	1,585

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✨ Features

🎯 Analytics Dashboard

• Dark mode UI — Professional, easy on the eyes
• 7 interactive Plotly charts — Zoom, pan, export to PNG
• Real-time insights — Auto-generated from live data
• Policy recommendations — Data-driven, actionable

📈 Visualizations

Chart	Purpose
State Monthly Trend	Track enrolment momentum over time
Age Group Dynamics	Understand demographic composition
District Disparities	Identify top/bottom performers
Pincode Distribution	Assess local-level variability
Seasonality Index	Plan campaigns by peak months
Risk Flag Summary	Flag saturation, volatility, momentum
Child Momentum	Monitor child enrolment share

📑 PDF Report Generator

• 8-section professional document
• Government-grade formatting
• Executive summary + findings + recommendations
• Auto-generated from analysis pipeline

📥 Downloads

• Dataset (CSV) and Report (PDF) available directly from dashboard

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🔍 Key Findings

┌─────────────────────────────────────────────────────────────┐
│  📈 Overall Growth:        +635.0%                          │
│  📉 Recent MoM Trend:      −11.1%                           │
│  👶 Child Share (0-17):    97.8%                            │
│  ⚠️  Saturation Risk:       49 districts                    │
│  📊 Volatile Districts:    22                               │
│  📅 Peak Months:           July & April                     │
└─────────────────────────────────────────────────────────────┘

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🏗️ Project Structure

UIDAI Data Hackathon/
├── 📄 app.py                    # Flask application entry
├── 📄 data_pipeline.py          # Data processing & visualizations
├── 📄 generate_report.py        # Technical PDF report generator
├── 📄 generate_student_report.py # Student project report generator
├── 📄 run_data_check.py         # Quick validation script
├── 📄 wsgi.py                   # Azure App Service entrypoint
├── 📄 requirements.txt          # Python dependencies
├── 📄 LICENSE                   # MIT License
├── 📂 Dataset/
│   └── Aadhar Enrolment Dataset.csv
├── 📂 templates/
│   └── index.html               # Dashboard UI
├── 📂 static/
│   └── styles.css               # Dark theme styles
├── 📄 UIDAI_Aadhaar_Analytics_Report.pdf
└── 📄 UIDAI_Report.pdf           # Student project report

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🚀 Quick Start

Prerequisites

• Python 3.10+
• pip

Installation

# Clone or navigate to project
cd "UIDAI Data Hackathon"

# Create virtual environment
python -m venv .venv

# Activate (Windows)
.\.venv\Scripts\activate

# Activate (Linux/Mac)
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Run Dashboard

python app.py

🌐 Open http://localhost:5000

Generate PDF Report

python generate_report.py

📄 Output: UIDAI_Aadhaar_Analytics_Report.pdf

Generate Student Report

python generate_student_report.py

📄 Output: UIDAI_Report.pdf — Simple student project report in plain academic English

Validate Data Pipeline

python run_data_check.py

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☁️ Azure Deployment

App Service Configuration

Setting	Value
Runtime	Python 3.10+
Startup Command	gunicorn --bind=0.0.0.0:$PORT wsgi:app
SKU	B1 or higher recommended

Deploy

1. Create Azure App Service (Linux, Python)
2. Configure startup command
3. Deploy via Git, ZIP, or Azure CLI
4. Ensure Dataset/ folder is included

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📊 Advanced Metrics

Metric	Definition
Saturation Index	Last 3-month avg ÷ Rolling 12-month max
Volatility Flag	12-month std dev > 1.5× state median
Low Momentum	Last 3-month avg < 50% of 12-month avg
Child Momentum	Share of 0–17 age enrolments over time

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🎯 Policy Recommendations

Based on data-driven analysis:

1. 👶 Child Infrastructure — Prioritize biometric updates for children (93.9% share)
2. 🚐 Mobile Units — Deploy to Gondia, Ahilyanagar, Hingoli
3. 📅 Campaign Timing — Align with July & April peaks
4. ⚠️ Monitor Volatility — Focus on Jalgaon, Jalna, Ahmadnagar
5. 🎯 Service Quality — Shift focus in 49 saturated districts

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🛠️ Tech Stack

Layer	Technology
Backend	Flask 3.1, Gunicorn
Data	Pandas 2.3, NumPy
Visualization	Plotly 6.5
PDF Generation	ReportLab 4.4
Hosting	Azure App Service
Theme	Custom Dark Mode

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📜 License

This project is licensed under the MIT License.

MIT License © 2026 Mandar Kajbaje

See LICENSE for full details.

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👤 Author

Mandar Kajbaje
UIDAI Data Hackathon 2026

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