The following are contained: Python code to generate features to input into machine learning models for superconducting critical temperatures, as well as the code to implement the machine learning models. Chemical compositions, critical temperatues, and pressures at which the critcial temperatures were measured ("0" indicates ambient pressure, "1" indicates applied pressure) of materials in our cleaned SuperCon data set. Critical temeprature predictions, weight coefficients, and feature-weight products for SuperCon materials at implicit pressure and ambient pressure (made only for those samples with pressures of "0") Chemical compositions, identifiers, energies above convex hulls, band gaps, and machine learning features for samples in Materials Project. Critical temperature predictions, weight coefficients, and feature-weight products for samples in Materials Project at implicit pressure and ambient pressure.