I'm working with a dataset with a number of potential predictors like :
Age : continuous
Number of children : discrete and numerical
Marital Situation : Categorical ( Married/Single/Divorced.. )
Id_User : Categorical ( an id of the user who conducted the first interview with this person )
I'm stopping at four potential predictors, there are more, but for the sake of shortness, these would be enough to ask my question.
Question : Continuous features are easy to deal with, normalize, and feed it to the model, what about categorical and independant ?
Note : I get that categorical features that follow a certain pattern can be encoded as integers and fed to the model, but what if those categorical features have no meaning as integers ( 1 for single, 2 for married , 3 for divorced ; for the model that treats it as a quantitative predictor it doesn't make sense to feed it to it like that)
Any ways to deal with these different types of features?