Nice, thanks for sharing.
I like your structure a lot.
Using models prediction (with no claim history) as an offset as you did is a clever way to use the same model for everyone, renewal and new businesses.
Thinking about it, I wonder. xgboost has a smart way to deal with NA, for each new branch, the NA can go either way optimizing the cost function. So the NA are used in the model. Knowing that, for your data with no claim info, were the predictions from (2) * (3) identical to predictions from (4)? Or did they differ by a multiplicative constant?
I also checked my residuals for manufacture_year and the difference between -11 and -12 is surprisingly stable. I mean, exposures are still relatively low, so it could be random, but it is intriguing! I wonder if we observe the same for the 40K policies not in training data (probably since your models performed well!)
Talking about these policies, I would have loved to have a different feedback for new businesses and renewals.
This game is addictive, even after it’s over, we have a lot of unanswered questions!
Congrats again, thanks for sharing.