Features of Quality Health Plan-Eligible Uninsured Adults

What it does

My project is on identifying the most impactful demographic features in quality health plan-eligible uninsured adults using random forest and light gradient boosting machine regression models.

How we built it

I cleaned and modeled the data using random forest regressor from the sklearn library in Python and light gradient boosting machine regression models.

Challenges we ran into

It was difficult to determine the exact objective of what I wished to accomplish through this dataset, so I had to conduct extensive research regarding health insurance and its related policies.

Accomplishments that we're proud of

I was able to determine the important demographic features of QHP-eligible uninsured adults and thereby prove that it is not necessarily a "public health crisis," but more so that they are covered by other forms of health insurances like employer coverage.

What we learned

I learned how to employ the light gradient boosting machine regression model effectively to achieve results with high accuracy and efficiency.

What's next for the project

I wish to investigate further into other forms of health insurance (i.e., public vs private health insurance) as well as between different countries that have more effectively implemented universal health care unlike the U.S.

Built With

• python