📊 Migra-View: Predictive Aadhaar Intelligence

Unlocking Societal Trends for Data-Driven Policy Intervention

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🌟 Project Vision

Migra-View is an advanced analytics framework designed for the UIDAI Data Hackathon 2026.
By leveraging anonymized Aadhaar enrolment and update datasets, Migra-View identifies high-velocity population shifts and infrastructure demand.

It transforms raw administrative data into actionable Micro-Trends, enabling the government to deploy resources where they are needed most — before the surge happens.

Checkout the streamlit dashboard.

🚀 Key Value Propositions

✅ Infrastructure Stress Forecasting

Predicts demand for Aadhaar Seva Kendras (ASK) with high precision.

✅ Migration Hotspot Detection

Uses biometric update spikes as a proxy for labor and student migration.

✅ Policy-Ready Dashboards

An interactive Streamlit interface for non-technical decision-makers.

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🛠 Tech Stack

Category	Tools / Technologies
Language	Python 3.11+
Modeling	Facebook Prophet (Time-Series Forecasting)
Dashboard	Streamlit
Visualization	Plotly, Seaborn, Matplotlib
Data Handling	Pandas, NumPy

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📁 Project Structure

migra-view-uidai/
├── data/
│   ├── raw/                # Original datasets from =
    └── processed/          # Merged, cleaned, and engineered CSVs
├── notebooks/              # Step-by-step development & EDA
│   ├── 01_data_cleaning.ipynb
│   ├── 02_eda_and_uii.ipynb
│   └── 03_predictive_modeling.ipynb
├── src/                    # Production-grade helper functions
│   ├── __init__.py
│   ├── data_utils.py       # Data pipeline & cleaning logic
│   └── plot_utils.py       # Reusable visualization & modeling functions
├── app/
│   └── dashboard.py        # Interactive Streamlit application
├── docs/                   # Presentation PDF and detailed reports
├── requirements.txt        # Project dependencies
└── README.md               # Project overview

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📈 Model Performance & Insights

The Pitch Statement

“Our Migra-View engine achieved a 93.1% median accuracy rate (MDAPE) for week-ahead infrastructure demand forecasting.

By monitoring the RMSE ($2.8 × 10^7$) to capture high-variance biometric shifts, our framework ensures that sudden migration spikes—often invisible in monthly aggregates—are flagged as high-priority zones for immediate policy intervention.”

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🧠 Technical Rigor

Why MDAPE?

We utilize Median Absolute Percentage Error instead of standard MAPE to remain robust against outliers, providing a truer reflection of performance across diverse Indian districts.

Why RMSE?

We track Root Mean Squared Error to penalize large forecasting misses.
In public service, failing to predict a massive population surge is more critical than minor daily fluctuations.

Micro-Trend Detection

While the model is architected for yearly cycles (academic sessions, harvest migration), it has been optimized for short-term Micro-Trends to provide immediate value even from limited historical snapshots.

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🚀 Getting Started

1. Clone the Repository

git clone https://github.com/your-username/migra-view-uidai.git
cd migra-view-uidai

2. Set Up Virtual Environment

python3 -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -r requirements.txt

3. Run the Dashboard

streamlit run app/dashboard.py

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💡 Policy Recommendations

• Dynamic Staffing:
  Scale ASK personnel in July–August based on forecasted school-enrolment spikes.

• Mobile Units:
  Deploy Aadhaar-on-Wheels to districts identified as High Intensity in the Migra-View Hotspot Map.

• Proactive Planning:
  Use the 90-day forecast to pre-allocate biometric kits to emerging industrial hubs.

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🏛 Developed for the UIDAI Data Hackathon 2026

Unlocking the potential of data for a better-governed India.