Which PreProcessing method should be used?

I have a dataset that consists of a poisson distribution, a exponential distribution, categorical variables, and my target variable is a numerical bimodal variable. This is a regression model. I wanted to clarify the type of model best to use and any transformations required for my variables.

Model 1. Random Forest. Since RFs are scale invariant, no transformations are required for either target and independent variables.

Model 2. Gaussian Regression. Categorical Variables would be One Hot Encoded. The Poisson and Exponential Variables could be transformed with a Box-Cox Transformer. How would I transform my target bimodal variable? I already tried abs(y-mean(y)), which when tested was not normal. Some things I have read, say to fit to a Gaussian Mixture Model, but I do not understand what they mean because I have only used GMMs with clustering.

Is there a different type of model that may work better here?

• indicate a small part of your data and purpose of your model/key hypothesis? – Subhash C. Davar Jan 27 at 0:02
• I am trying to think of more generally what the steps are and see if my process is correct. I am not looking for specific variable feedback based on an industry standard. Essentially I am predicting a dollar value. – Jack Armstrong Jan 27 at 0:12
• without knowing the nature of data/variables, and purpose of model, it is not feasible to work out processes required. – Subhash C. Davar Jan 27 at 0:24
• The nature of the data would be their distributions and the purpose of the model is to predict some dollar value. If you had some data that followed a lognormal distribution, you could use a box-cox to shape it into normal. Those concepts should transcend the type of data. I can understand since there is no definitions of the data, harder to discuss transformations, but that is why I am asking if I am on the right path generally. – Jack Armstrong Jan 27 at 1:42
• numerical bimodal variable - shall appreciate a description of this variable. – Subhash C. Davar Feb 27 at 10:34