aadhar-hackathon

🆔 Aadhaar Hackathon – Data Intelligence & Early Warning System

📌 Project Overview

This project is developed as part of an Aadhaar Hackathon to analyze enrolment, demographic, and biometric datasets and generate actionable insights, early warnings, and decision-support outputs for operational planning and policy support.

The system focuses on:

• Demand analysis
• Capacity vs load evaluation
• Trend detection (MoM growth)
• Risk identification
• Actionable recommendations

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🎯 Objectives

• Analyze Aadhaar enrolment demand at district and monthly levels
• Detect load stress, demand spikes, and capacity risks
• Generate early warning indicators
• Support data-driven operational decisions

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🗂️ Project Structure

aadhaar-hackathon/ │ ├── api_data_aadhar_biometric/ # Raw biometric API data ├── api_data_aadhar_demographic/ # Raw demographic API data ├── api_data_aadhar_enrolment/ # Raw enrolment API data │ ├── notebooks/ # Jupyter notebooks (analysis & experiments) │ ├── outputs/ # Processed datasets & final outputs │ ├── imp_outputs/ # Key output files used for decision logic

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🔍 Key Features & Analysis

1️⃣ Demand Analysis

• Monthly demand aggregation
• District-wise enrolment trends
• Demand categorization: Low / Medium / High

2️⃣ Capacity & Load Assessment

• Monthly capacity vs actual demand
• Load category classification
• Backlog and stress risk identification

3️⃣ Trend Detection

• Month-over-Month (MoM) growth calculation
• Trend labels: Increasing / Stable / Decreasing

4️⃣ Early Warning Indicators

• Demand spikes
• Capacity shortfall alerts
• High-load & increasing-demand risk zones

5️⃣ Decision Logic

Actionable recommendations based on Load Category + Demand Trend, such as:

• Add staff
• Deploy mobile enrolment units
• Buffer capacity planning
• Monitoring-only actions

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🛠️ Tech Stack

• Python
• Pandas
• NumPy
• Matplotlib
• Jupyter Notebook
• Git & GitHub

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📊 Outputs

• Cleaned and merged monthly datasets
• Demand trend and MoM growth files
• Load categorization outputs
• Decision recommendation datasets

Generated files are available in: outputs/ imp_outputs/

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🚀 How to Use

1. Clone the repository

git clone https://github.com/RaihanBasha7/aadhar-hackathon.git
cd aadhar-hackathon
jupyter notebook

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📄 License

This project is created for hackathon and educational purposes only.

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Shaik Raihan Basha
Team Lead