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Inspiration
As we were looking through the Healthcare dataset, we started to wonder what trends contributed to un-insurance rates.
What it does
We analyzed how different factors affected uninsurance rates amongst the US population. Different factors we looked into include: COVID-19 infection rates, unemployment, education rates, political affiliation, race & ethnicity.
How we built it
We mapped out the data in graphs, charts, and maps to better visualize the data. Specific graphs that we found useful in drawing conclusions were scatterplots, box and whisker plots, and frequency maps.
Challenges we ran into
There were many factors that turned out to be insignificant. COVID-19 infection rates and unemployment had low pearson correlation coefficients, therefore, were uncorrelated.
Accomplishments that we're proud of
We recognized geographic trends and tied them to the demographics and history of the US. Especially, when we mapped out uninsured rates on a frequency map, we noticed the areas that had higher uninsurance rates were near Native American reservations and around borders.
What we learned
We learned that those without a high school diploma, non-Democratic counties, and the Native American and Hispanic populations of the US are generally less insured.
What's next for Insurance Demographics
We are curious why these demographics are less insured and want to explore how to better support these communities. Hopefully by better understanding these reasons in the future, we could extend outreach and provide affordable insurance to all.
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