**Machine Learning for Access to Healthcare**

#### Report

We made a **linear regression model** using Machine Learning to predict if good healthcare has a positive effect on reducing the amount of ebola cases there are. We scored a country’s healthcare system with three level: 0,1,2. These numbers are determined by how many doctors there are per 100,000 people in that population. Countries with a single digit number on the lower spectrum received a 0, while the ones on the higher spectrum received a 1. Countries with 2 digits in their doctors receive a 2. To get the average rate of infection, we subtracted the two rows of the value column from each other, then created a new column and find a mean of those rates for each country. Once we made our new column, we made a scatter plot to visualize our result. Then we added in a line in our graph to show the direction of our graph. We found negative correlation between the quality of healthcare and the number of infection cases. According to your test, we found **-.708 correlation** between quality of health care and number of infections.
This does not mean that this is the cause of the decrease/preventative of new ebola cases, but experiments can be explored to test out this correlation. Nevertheless, it helps **support the CDC and WHO suggestion about borders**, since this testing shows what an effective way to combat the disease really is. Our last test implies that border has little to no effect on the transmission of disease, but strengthening healthcare, whether its our own or neighbors, could help fight against the disease.

View Python Code on Github