Investing for hippies #GoGreen

For our project, we analyzed two datasets, one containing stocks, their prices, and other associated data, and the other containing information about the environment, mostly carbon dioxide emissions. The stock data was reported daily while the environmental data was reported yearly, so we analyzed the data on a quarterly basis to avoid losing the volatility of each stock. We wanted to identify whether a stock would rise or fall, so we classified each quarter into a drop, a rise of 0 to 0.5%, or a rise of greater than 0.5%. Based on this classifier, we developed a model that will predict whether a stock will rise, fall, or stay the same given a change in carbon dioxide emissions, enabling investors to make optimal investment decisions while protecting the environment.