Equity crowdfunding is an increasingly popular means of raising capital for early stage startups. It enables entrepreneurs to finance their companies with smaller contributions from a variety of people. This paper studies the relationship between the characteristics of a given company and its ability to raise funds on an equity crowdfunding platform. A series of statistical and machine learning models are fit to data from a U.S.-based equity crowdfunding website, including a logistic regression, a CART decision tree, a naïve Bayes classifier, and a support vector machine. This study demonstrates that a connection exists between the probability of a company’s crowdfunding success and its previous funding history, Twitter presence, media buzz, size, location, and its founders’ educational backgrounds. As a whole, however, the classification quality of the various models leaves something to be desired. This suggests the need for additional data inputs and more longitudinal research in the field of equity crowdfunding.