I am working on a Machine Learning regression problem, with a data-set where I have data from a period of several years. From the "date" feature, I extracted the week number (0-53). Next I am doing 2 things:
1) One hot encoding: Splitting this categorical "week number" feature into 53 binary features, where each feature indicates whether the data points belong to that particular week number or not.
2) I am also using the cyclic variable (week number) as a continuous variable to predict my outcome. First I am converting this feature, however, to the distance from week 1 (so week 2 and 53 don't represent drastically different time points)
My question is, am I making this too complicated without increasing potential improvements in my model outcome? Does including the continuous variable actually provide my model with valuable information that is not already covered in the categorical feature extraction? Thank you in advance