I am fairly new to data science. I am working on use-case of predicting sales demand using linear regression based on product no and store no as predictor. There can be many stores and products with numeric values. Do I need to standardize or scales these variables/predictors if theirs values are numeric, unbounded and at different scale? I believe if I try to use interaction term I will have standardize it?
1 Answers
Since these are categorical features, before using linear models you should encode this correctly to create a reasonable model. If you can encode these categorical features to give them linear correlation, then you can standardize it otherwise it wouldn't make sense. If you use tree-based models then you don't have to encode since they are able to discover nonlinear relationships.
Edit-note: You can try to use methods of mean-encodings. Methods like CV loop, Expanding mean, etc.
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