I have a dataset with employee data with around 9500 rows, and have to predict if the target is 0 or 1. Some of my features are the department of an employee, gender, salary, review_score(numerical), average_number_of_hours per month, bonus(1 or 0), number of projects an employee is involved in, and tenure.
I have a question if number of projects (3,4,5,6) and tenure(2,3,4,5,6,7,8,9,10,11,12) should be treated as 'categories' rather than numerical values. I can make them ordinal.
However, I am not sure about treating tenure (the number of years an employee has been with the company) as 'category' because there are too many values.
I will be using linear/logistic algorithms to predict the target '1', and will also be attempting to find the best features.
Can somebody explain to me if 'tenure' and 'the number of projects' should be treated as numerical or categorical here and why? Is there a generally accepted limit on the maximum number in a category.