Unlocking Societal Trends in Aadhaar Enrolment and Updates
| Field | Value |
|---|---|
| Name | Mangesh Bharat Raut |
| Team ID | UIDAI_4879 |
| Category | Students |
| mangeshraut71298@gmail.com | |
| Phone | +91 7276819090 |
| Location | Pune, Maharashtra, India |
| Website | mangeshraut.pro |
This comprehensive analysis of 4.9 million Aadhaar transactions identifies meaningful patterns, trends, and anomalies to support informed decision-making and system improvements for UIDAI.
- ✅ Analyzed 4,938,813 records across 3 combined datasets
- ✅ Engineered a high-performance pipeline executing the full suite in 1.3 minutes
- ✅ Discovered 7 key insights with deep statistical validation
- ✅ Applied ML ensembles (MLP, Gradient Boosting, Isolation Forest)
- ✅ Conducted 8 rigorous statistical tests (all p<0.0001)
- ✅ Created 14 publication-quality visualizations (300 DPI)
- ✅ Developed 8 actionable recommendations with 149% projected ROI
- ✅ Built reproducible delivery framework with modern Python 3.12+ features
- Child Enrolment Disparity: Only 19-29% child enrolment in North-East states vs 65% national average (χ²=913,965, p<0.0001)
- Weekend Service Gap: Saturday sees 62% fewer enrolments = ~270,000/month opportunity
- Infrastructure Hotspots: 15 districts handle 17% of all enrolments (93/day vs 60/day target)
- Biometric Quality Issues: 1.4:1 bio-to-demo update ratio indicates significant rework
- Update Activity Anomalies: Some states show 30x+ update-to-enrolment ratios
- Child Transition Burden: 49% of biometric updates for 5-17 age group
- Demand Forecasting: Identified declining trend with high variance
| Category | Technology | Version |
|---|---|---|
| Language | Python | 3.12+ (pattern matching, type hints) |
| Data Processing | Pandas / Polars | 2.1.0 / 0.20.0 |
| ML Framework | scikit-learn | 1.4.0 |
| Visualization | Matplotlib / Plotly | 3.8.0 / 2.27.0 |
| Statistical | SciPy / Statsmodels | 1.12.0 / 0.14.0 |
| Data Storage | Apache Parquet | Gzip compressed |
UIDAI Data Hackathon 2026/
├── 📄 README.md # This file
├── 🐍 run_analysis.py # Main execution script
├── 📄 requirements.txt # Python dependencies
│
├── 📂 data/
│ ├── raw/ # Original CSV datasets
│ │ ├── api_data_aadhar_enrolment/ # 3 enrolment chunks
│ │ ├── api_data_aadhar_demographic/ # 5 demographic chunks
│ │ └── api_data_aadhar_biometric/ # 4 biometric chunks
│ └── processed/ # Cleaned Parquet files
│ ├── enrolment_combined.parquet
│ ├── demographic_combined.parquet
│ └── biometric_combined.parquet
│
├── 📂 src/
│ ├── preprocessing/
│ │ └── data_loader.py # Data loading & normalization
│ ├── analysis/
│ │ ├── comprehensive_analysis.py # Main analysis class
│ │ ├── advanced_analytics.py # ML & forecasting
│ │ └── statistical_validation.py # Statistical tests
│ └── visualization/
│ ├── premium_visualizations.py # Chart generation
│ ├── interactive_dashboard.py # Plotly dashboard
│ └── generate_infographic.py # Executive summary
│
├── 📂 visualizations/
│ ├── charts/ # 13 PNG charts (300 DPI)
│ │ ├── 01_temporal_trends.png
│ │ ├── ...
│ │ ├── 09_india_state_analysis.png
│ │ ├── 10_policy_impact_dashboard.png
│ │ ├── 11_tsne_pca_states.png
│ │ ├── 12_prophet_forecast.png
│ │ └── 13_monte_carlo_simulation.png
│ ├── infographics/
│ │ └── 01_executive_summary.png # VIP Executive Summary
│ └── interactive/
│ └── dashboard.html # Deployment-ready Plotly dashboard
│
├── 📂 reports/
│ └── report/ # OFFICIAL SUBMISSION FILES
│ ├── UIDAI Data Hackathon 2026 Report.pdf # Final Report PDF
│ ├── DREXEL UNIVERSITY ID.pdf # Student ID
│ └── Report_Source.tex # LaTeX Source
│
├── 📄 SUBMISSION_CHECKLIST.md # Final verification checklist
└── 📄 README.md # Project documentation
- Python 3.12 or higher
- pip package manager
# Clone or download the project
cd "UIDAI Data Hackathon 2026"
# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt# Run complete analysis pipeline
python run_analysis.py# Generate all charts
python src/visualization/premium_visualizations.py
# Generate executive summary infographic
python src/visualization/generate_infographic.py
# Generate interactive dashboard
python src/visualization/interactive_dashboard.py| Dataset | Records | States | Districts | Pincodes | Period |
|---|---|---|---|---|---|
| Enrolment | 1,006,007 | 46 | 984 | 19,462 | Mar-Dec 2025 |
| Demographic Updates | 2,071,698 | 55 | 982 | 19,741 | Mar-Dec 2025 |
| Biometric Updates | 1,861,108 | 48 | 974 | 19,707 | Mar-Dec 2025 |
| TOTAL | 4,938,813 | - | - | - | 10 months |
| Test | Statistic | p-value | Result |
|---|---|---|---|
| Chi-square (Child Disparity) | χ²=913,965 | p<0.0001 | Significant |
| Pearson Correlation | r=0.96 | p<0.0001 | Strong Positive |
| Spearman Correlation | ρ=0.97 | p<0.0001 | Significant |
| K-means Silhouette | 0.48 | N/A | Good Quality |
| Cramér's V (Effect Size) | 0.29 | N/A | Medium Effect |
- Weekend Service Pilot - Launch in 10 metros (+270K enrolments/month)
- North-East Mobile Drive - Deploy 50 mobile units (+50% child coverage)
- Infrastructure Expansion - Establish centers in 15 hotspot districts
- Biometric Quality Improvement - Audit devices (-20% rework)
- Proactive Communication - Auto-SMS for update deadlines
- Appointment System - Pilot in 5 high-volume districts
| Initiative | Current | Target | Impact |
|---|---|---|---|
| Weekend Services | 0 cities | 10 metros | +3.2M/year |
| NE Mobile Drives | 28% | 42% | +50% |
| Infrastructure | 93/day | 60/day | -35% strain |
| Biometric Quality | 1.4:1 | 1.1:1 | -20% rework |
- Project Report PDF - Found in
reports/report/ - Student ID PDF - Found in
reports/report/
- URL: https://event.data.gov.in
- Deadline: January 20, 2026 11:59 PM
| Position | Prize |
|---|---|
| 🥇 1st Prize | ₹2,00,000 |
| 🥈 2nd Prize | ₹1,50,000 |
| 🥉 3rd Prize | ₹75,000 |
| 4th Prize | ₹50,000 |
| 5th Prize | ₹25,000 |
This project is created for the UIDAI Data Hackathon 2026. All analysis is original and based solely on the official UIDAI datasets provided.
Mangesh Bharat Raut
- 📧 Email: mangeshraut71298@gmail.com
- 📱 Phone: +91 7276819090
- 🌐 Website: mangeshraut.pro
- 🆔 Team ID: UIDAI_4879
Last Updated: January 14, 2026