Do I always need to exec. a normalization/z-trans. to compare data each other and apply a ML model?
First of all, I am a beginner in using RM and data science techniques. Therefore, please be patient with me. I got the attached NBA data set from Kaggle I am using for a university project work / exam.
In general, do I always need to execute a normalization (z-transformation) to compare data each other within my data set, e.g. NBA statistics in my data set > columns L - Q and W - AB, and apply a machine learning model, e.g. naive bayes or linear/logistic regression?
Is an outlier detection a real machine learning model or more a technique to filter out outliers? At which number of detected outliers is it advantageous to apply an outlier detection, e.g. 10 or more detected outliers?
I would be very grateful if someone could help me.