I have a dataset that looks something like this;
ID | Location | Job_title | blue_jumper | red_jumper | yellow_jumper | green_jumper | Target(purple_jumper)
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B7372 | Rome | Builder | 2 | 1 | 0 | 9 | 1
D9823 | London | Lawyer | 0 | 1 | 8 | 3 | 0
E8718 | Rome | Teacher | 3 | 0 | 2 | 3 | 1
etc etc.....
What I would like to do is to use this information to predict whether a person will have a Purple jumper (Target 1 or 0)
Things to note about this dataset I think are the following;
- I have an ID that relates to the individual
- I have a number of catergorical features
- I have some information relating to features that are of the same type (jumpers) but differing by some aspect (colour)
- These features are of the same type as the Target (e.g jumpers)
- The target is binary (e.g. I am not looking to predict how many Purple jumpers a person has, just whether they have one or not)
As the Target is binary I know I could use a classification method but I have decided to use Multiple Linear Regression. I like this algorithm because it gives me a measure out that is equivalent to how much like a 1 or 0 my record is.
I have generated dummies for my categorical features but what I am struggling with is whether or not to scale my other features in a situation like this.