I am currently working on a dataset having 10 features and one continuous target variable. One of the features is 'Country' , in which there are seven unique values [Argentina ,Denmark , France...etc].
Now , the continuous target variable is sales of a given product in that particular country for a given month in a given year.
It has been given in the problem statement , that the Sales have been taken in the local currency of that country , so now I have values on different scales and I am not sure how to deal with them.
When I grouped the data as per the different countries , (using pandas's groupby function) , I got at least 1000 observations for each country. So maybe I could train a model separately for each country ? All kind of approaches will be appreciated.