Hello all,
the last of the third choice is not a quantitative numerical value … especially if you are thinking that this one is not part of the numerical columns proposed for question 5.
we can transform it into a quantitative with a small transformation for sure but this is not a formal quantitative 
I agree!
This is indeed a tricky question. As I cannot spoil it for other students with my answer I will just point to the Using numerical and categorical variables together notebook, where we mention
Sometimes
objectdata type could contain other types of information, such as dates that were not properly formatted (strings) and yet relate to a quantity of elapsed time.
As also implied by the explanation displayed after you have submitted your response, properly formatted dates require no extra transformation (I insist on the word “relate” as already containing the same numerical information) and can be directly used when training a model.
About the numerical_features of question 5, this is cherry-picking of some yet not all the available numerical features in the dataset. The historical reason is that the selected subset of columns did not contain missing values (now none of the columns do).
ok I get it, this is all about interpretation and feeling about it
Regarding the way to use this quantitative numerical value, is it better to use it directly as an integer (with the good transformation if needed ) or one has to transform it into a “delta” ? the first one will go bigger as the second will go smaller. Don’t know if you understand it, but I don’t want to spoil it too much
Indeed, you can let the feature as-is here. The predictive model can handle the delta through an intercept. Later you will see that you might need to normalize this feature if other features to be considered in the modeling do not have the same range of values.