Project
Predicting Global Healthcare Costs
Aggregated, cleaned, and formatted World Health Organization data on health care expense and non-communicable diseases.
Overview
Methods used: clustering, decision trees, support vector machines, niave Bayes, knn, and neural networks. Achieved accuracy up to 70% in predicting level of healthcare costs globally.
Client
Graduate school project, University of Colorado, Boulder.
My Role
Project Lead
Tools
Python
Dates
2023
"Digging through large datasets looking for value is the modern-day equivalent to prospecting for gold."
Copyright © 2025 Divi. All Rights Reserved.