I competed in this year's edition of the Utah Data Competition, a local data science competition. The semiconductor yield prediction problem seemed simple enough on the surface, but it ultimately proved to be quite challenging. I attempted several modeling approaches but eventually converged on two that received most of my attention: a modified ridge regression with special cost function, and an approximate Kalman filter (my dynamic systems background creeping in!). An IPython notebook with code for both approaches is available on Github for download or can be viewed directly with nbviewer. A shorter summary of the notebook is available as slides, too.