# How to aggregate features to a group level as a feature in machine learning model?

I am building a model to predict some behavior at a household level. I could roll up income or number of cars etc so that I can take everyone into consideration. But how can I roll up something like age? I don't know if it makes sense to add up ages or education. Is there a cleaver way to still use those features at a aggregated level?

I guess adding up the ages won't makes sense, since the number 30 means that there is one person of 20 years and one of 10, or a bunch of 5 years old?
What you could do is have number_of_adults_in_household and number_of_children_in_household. This way you still aggregate the ages but keep more information.
Education might be a more difficult feature to aggregate. My educated suggestion would be to have highest_education_degree_in_household.