I'm trying to do some non-linear predictions and am going to use a random forest with Sk-learn. However my question has to do with data pre-processing.I'm planning on using minmaxscaler to normalize the temperature, wind, and humidity data...however I have two other variables class, and month. Class is a category data feature which I'm going to use OneHotEncoder to pre-process. However do I want to normalize or do anything with the month data? (Normalize on scale of 0 to 1? Or leave 1-12?) What might be the best approach?Thanks!
Machine Learning - Data Pre-Processing
machine learning
No.  You can leave the month as is.  It shouldn't matter.  For a random forest, there's no value in normalizing it; it won't make the classifier any more or less accurate.(The same is actually true for temperature, wind, and humidity data, too: there's no harm in normalizing them, but also no benefit, if you're using a random forest classifier.)