In this project, I used several supervised algorithms to assist a fictitious charity organization find donors. The goal with this implementation is to construct a model that
accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations.
Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with. While it
can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available
features.
The dataset for this project originates from the UCI Machine Learning Repository. The datset was donated by Ron Kohavi and Barry Becker, after being published in the article "Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid".