The presented model allows surface and materials researchers to more easily obtain spin coated polystyrene thin films of a desired thickness. Existing methods of predicting thickness of spin coated polystyrene samples are based on equations backed by experimental data, which relate final coverage thickness to a variety of variables, such as viscosity, that need to be measured before spin coating. Thus, in addition to ease of use, the machine-learning model circumvents the use of a mathematical model to translate collected data to predicted thicknesses. This new approach is a more direct reflection of the real world and removes the potential mathematical error intrinsic to complex multi-variable equations. We're able to bypass the need of several previously determined