I am currently working on a ML problem where the features used for modelling are sourced from different places/providers. It is very unlikely to find the features from all the different sources to be present for the same subject. So my data count looks something like the following:
|Feat Group 1||Feat Group 2||Feat Group 3||No of Records|
|Missing||NOT MISSING||NOT MISSING||200|
|NOT MISSING||Missing||NOT MISSING||200|
|NOT MISSING||NOT MISSING||Missing||200|
|NOT MISSING||NOT MISSING||NOT MISSING||100|
I have modelled using the "usual" imputation strategies like single value imputation but I am unsure if this is the best way to handle this scenario.
Any other strategies that I could use here?