I'm trying to apply machine learning to pharmaceutical manufacturing to predict whether batches of drug products manufactured are good or not. for the sake of relatability, let's use coffee brewing as an analogous process. Let's imagine that I'm trying to predict the acidity of the coffee that I've brewed.
The dataset that I have contains features such as temperature of water, stirring speed and pressure that are constantly measured (say..on a per second basis) over a variable amount of time (the first cup may be brewed in 5 minutes, the second in 10 etc).
What kind of preprocessing should I perform on such a multidimensional dataset? One stumbling block is that for each observation, the duration is different, which may complicate dimension reduction? Once preprocessed, is there any specific model that would suit the task at hand? I'm looking at something like a regression but alternatively, classifiers seem to be fine as well if I split the acidity(pH) into "<5.5" or ">5.5"?
I hope to get some general directions and if you can paste a few links to texts or examples that'll be good! Also, I'm more familiar with python and scikit learn, so if you can point me in the right section in the documentation that'll be great too!