How to compensate for class imbalance in prediction model?
There is a lot of information and techniques for rare event or imbalanced classes. Sorry to post links but (https://stats.stackexchange.com/search?q=rare+event and https://stats.stackexchange.com/search?q=imbalanced) but I do not want to duplicate all of the other work.

In my experiences, often I left the data set alone. I usually got good results. If that is the true ratio, then my model should know that. Sometimes I downsampled the majority class. I played with SMOTE (https://arxiv.org/pdf/1106.1813.pdf) before as well.

For your questions above, yes - changing the data with weights or over/unsampling may bias the results.  Need to check. If you downsample, make sure you are not tossing out signal from the majority class.