I'm building a model that fills the missing values from a Dataframe that contains the number of visitors for different stores, each day:
I'm using the IterativeImputer class from scikit-learn, this method of imputation puts aside one column at each step and train an estimator on the other columns to predict the column that was put aside.
My question is: should I transpose my dataframe or not?
If I keep my dataframe (1 line = 1 day, 1 column = 1 store), this means that we can completely predict the number of visitors in a store in a particular day just by looking at the other stores.
But if I transpose my dataframe (1 line = 1 store, 1 line = 1 day), this means that we can predict the number of visitors just by looking at the history of one store.
I guess one simple way to check if I should transpose is comparing the RMSE of the two methods but I wanted some explanation instead of "method A works better, move along."