OOP design pattern for kmeans clustering (python)

I am trying to figure out a appropriate design pattern for my problem.

I have a single data source for many malls that I am querying and then applying preprocessing to convert them into desired features.

Then I am filtering these features for each of the mall and then applying kmeans to each of these malls separately.

I have written my code for a single mall for now by filtering by features for this mall.

I wanted to ask for some direction for a appropriate design pattern to implement my kmeans across all the malls by training my kmeans separately for each mall.

Also should I preprocess all the features for all malls together in a separate Preprocess class or should I do it globally and pass it into my class structures as an input?

I am a data scientist but don't have much background in OOP patterns. Kindly forgive me if it's a relatively trivial problem.