Health Facility Resilience to Power Outages in the United States

This project aims to address the critical challenge of natural disasters disrupting both energy supply and health services. It proposes developing an emergency power allocation model, especially drawing from renewable energy sources, for mobile health units and backup systems in the U.S.

Problem Statement

Can we accurately predict which U.S. health entities are most vulnerable during a hazardous natural disaster and the amount of renewable emergency power allocated for mobile backup units?

Key Results

(UPDATE IN README.md) Enumerate the main results of this project in a list and describe them.

EXAMPLE:

1. Recorded over 1,000 unique prompts and their responses generated by ChatGPT
2. Identified three biases in ChatGPT's responses
   • When prompted about this world event
   • When prompted about this field of science
   • When prompted about this political party

Methodologies

Algorithms to be used:

• Multi-Output Gradient Boosting Regressor
• Prophet
• Neural Networks

Data Sources

• U.S. Health Facility Database from Kaggle: Link to Kaggle Dataset
• Electric Power Consumption Forecasting from Kaggle: Link to Kaggle Dataset
• Weather and Renewable Energy Analysis from Kaggle: Link to Kaggle Dataset

Technologies Used

• Python
• pandas
• Google Collab
• Numpy

Authors

This project was completed in collaboration with:

• Chelsea Ross (chelsea@example.com)
• Tram Tran (tram@example.com)
• Gulzira Abdullah (gulzira@example.com)
• Sumodha (sumodha@example.com)