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SUBMIT YOUR IDEAThank you, @GeneR, for a neat challenge. It helped me to practice Predictive tools that I am just starting to learn.
Lot of solutions didn't seem to have considered multi-collinearity in predictor variables (top 10)
for instance R and R_G are highly co-related although they both are significant. It makes sense to use only 1 f these variables to avoid multi-collinearity issue. Similarly, it makes sense to remove 'PA' as well for the same reason.
Difficult.
Isn't it necessary to normalize the variables before applying a model?