There are many essential properties to consider in the search for a novel superconductor such as pressure information, the material's critical magnetic field, its critical current density, phase diagram information, and additional structural data. When considering the entire materials discovery process, synthesizing and screening candidate materials for superconductivity is the final objective. Critical temperature is the most reasonable superconductor property to predict since pressure, critical magnetic field, and critical current density are more difficult, intensive, and expensive to measure and less evident in current data. In regard to extrapolation performance for superconductor discovery, Meredig et al. states that "novel materials discovery would be enabled by running a model against a large database of candidate compounds and simply ranking them by predicted T c " , which is what is done in this implementation. For this specific implementation, a composition-based approach is used to test the limits of this algorithm by predicting a single property: a material's critical temperature.