Project Description: A full-stack web application designed to manage the enrollment and optimized slot allocation for a COVID-19 vaccination program. The core feature is a proprietary Machine Learning algorithm that analyzes user-provided health data to automatically prioritize high-risk individuals, ensuring equitable and efficient distribution of limited resources.
- AI-Driven Core Logic: Integrated a custom Machine Learning model built with Scikit-Learn and Random Forest directly into the backend to execute rapid, high-accuracy risk assessment upon enrollment.
- Monolithic Reliability (Django): Built on the stable, secure Django framework, providing a robust, full-featured backend for managing user accounts, authentication, data persistence, and all business logic.
- Optimized Resource Allocation: The platform goes beyond simple FIFO (First-In, First-Out) queuing by applying a Pandas-based prioritization algorithm, dynamically adjusting slot allocation to optimize for public health impact.
- Modern Data Handling: Utilized the Pandas library for complex data manipulation, feature engineering, and pipeline execution before feeding data to the ML model.
| Layer | Technology | Details |
|---|---|---|
| Backend Framework | Django (Python) | Handles user authentication, data models, and RESTful API routing. |
| Machine Learning | Scikit-Learn, Random Forest | Core ML libraries used to develop the high-risk individual prioritization model. |
| Data Processing | Pandas | Used for data cleaning, transformation, and processing large data inputs efficiently. |
| Frontend | HTML/CSS, JavaScript (ES6) | Develops a responsive, client-side interface for user enrollment and dashboard views. |
| APIs | RESTful | Interface for the frontend to securely interact with Django models and the ML prediction endpoint. |
The system achieved 85% accuracy in identifying high-risk individuals based on a predictive model, demonstrating a direct, measurable improvement in public health-focused resource distribution.