Carbon nanotubes are cylindrical structures made from carbon atoms that are only billionths of a meter in diameter. Though they have only been widely studied since 1991, carbon nanotubes have already been incorporated into a variety of industries for the improved strength, conductivity, and resilience they offer over other materials. At a submicroscopic level, these nanotubes can be classified into three distinct types which are differentiated by their amounts of curvature and bundling. However, it is not known what combination of factors of production results in a given type, as the mechanism behind their manufacturing is not yet fully understood. Our goal is to establish relationships between factors and their resulting varieties of nanotubes. Using scanning electron microscope and transmission electron microscope images provided by our sponsor, Nanocomp Technologies, Inc., we will use supervised learning to automate the classification of the images into the three categories. Then, we will use classification models such as decision trees, clustering, and logistic regression to predict the variety of nanotubes that will result from a given set of inputs.