  We estimate a statistical model to predict the superconducting critical
temperature based on the features extracted from the superconductor's chemical
formula. The statistical model gives reasonable out-of-sample predictions: $\pm
9.5$ K based on root-mean-squared-error. Features extracted based on thermal
conductivity, atomic radius, valence, electron affinity, and atomic mass
contribute the most to the model's predictive accuracy. It is crucial to note
that our model does not predict whether a material is a superconductor or not,
it only gives predictions for superconductors.
