I'm new to machine learning and I'm working on a project work related to SVM.
I'm now a little confuse about pre-processing, and in particular with in the management of a possible grouping of data according to the centre of origin (i.e. data source).
My dataset is of different patients from two different hospitals. In a first analysis of the data, I found that the data from centre B does not have 4 of the 20 total predictors, which instead centre A has.
I am wondering how to consider that the data are grouped (= clustered) by centre, in the training of the model through SVM.
Also, despite this grouping, will it be necessary to impute the missing data, which in this case also includes the data that centre B did not collect compared to centre A?
EDIT: I think that I wasn't clear enough, and I apologize. By "centre" I mean that part of the data comes from a different source, namely a "hospital A", while the rest comes from a "hospital B". However, this is explained within the dataset through a variable called "centre". The data coming from hospital B, however, do not have all the variables present in the data of hospital A. I was therefore wondering how this information could be included in my analysis; or should the data be grouped by hospital? But how will I then train the model via SVM?