By using national language processing to analyze a huge body of data from news articles, periodicals, Wikipedia, and search engine traffic, Radinsky's team was able to pinpoint two key factors that matter when it comes to predicting Cholera outbreaks in areas that were affected by droughts and storms: Gross Domestic Product (whether the country is rich or poor) and its percentage of water.

Predicting where Cholera outbreaks are likely to occur have huge impacts in our ability to deliver life-saving supplies (like clean water) to reduce casualties in the affected areas.

Machine Learning truly has the power to change the world!

See more examples of Machine Learning in our Everyday Encounters blog series >>