Common pitfalls in Random Forest preprocessing?

Hi all, I'm a fairly new data scientist working on a regression project with Random Forest and can't seem to get models that perform well. I know the data may just not be able to yield strong models, but based on preliminary projects, I'd really expect a better outcome. Considering that I'm pretty new to my DS position, I'm thinking that the poor outcome may be from mistakes or lack of knowledge on my end.

For preprocessing, so far I've: …

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