With months of historic location and temperature data captured, this blog covers how to train a machine learning (ML) model to predict where my cat would go throughout her day. For the impatient, you can skip directly to the prediction web-app here. With some inexpensive hardware (and a cat ambivalent to data privacy concerns) I wanted to see if I could train a machine learning (ML) model to predict where Snowy the cat would go throughout her day. Home based location and temperature tracking allowed me to build up an extensive history of which room she used for her favourite sleeping spots. I had a theory that with sufficient data collected from her past I'd be able to train an ML model to predict where she was likely to go in the future.

We look at whether machine learning can predict the final objective function value of a difficult combinatorial optimisation problem from the input. Our context is the pattern reduction problem, one industrially important but difficult aspect of the cutting stock problem. Machine learning appears to have higher prediction accuracy than a na\"ive model, reducing mean absolute percentage error (MAPE) from 12.0% to 8.7%.