The Enron scandal was a financial scandal that eventually led to the bankruptcy of the Enron Corporation, an American energy company based in Houston, Texas, and the de facto dissolution of Arthur Andersen, which was one of the five largest audit and accountancy partnerships in the world. In addition to being the largest bankruptcy reorganization in American history at that time, Enron was cited as the biggest audit failure. Enron was formed in 1985 by Kenneth Lay after merging Houston Natural Gas and InterNorth. Several years later, when Jeffrey Skilling was hired, he developed a staff of executives that – by the use of accounting loopholes, special purpose entities, and poor financial reporting – were able to hide billions of dollars in debt from failed deals and projects. Chief Financial Officer Andrew Fastow and other executives not only misled Enron's Board of Directors and Audit Committee on high-risk accounting practices, but also pressured Arthur Andersen to ignore the issues.

The goal of this project is to use the Enron dataset to train our machine learning algorithm to detect the possiblity of fraud (identify person's of interest.) Since we know our persons of interest (POIs) in our dataset, we will be able to use supervised learning algorithms in constructing our POI identifier. This will be done by picking the features within our dataset that separate our POIs from our non-POIs best.

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