I think the answer for this question is yes, but I'm still wondering how to do this.
Here's the thing, I have a dataset with several products, their characteristics and the price a customer paid for the product. I want to know which characteristic make the product more prone to be sold.
So, I would have something like this:
id color size material price sold A1 yellow big plastic 200 0 A2 yellow medium wood 30 1 C1 blue medium wood 200 0 B1 purple small plastic 10 1 D1 yellow medium plastic 110 0 C2 pueplw big wood 140 1 A3 yellow small wood 50 1 A1 yellow medium wood 100 1
And I want to know if the product being yellow increase the probability of selling it and if it does, if it's more important than the material.
Is that something I could do with random forest and other models? And is there anything I should look at that we usually don't look when running them for prediction?
I was wondering this because I'm used to work with these for prediction. Before that, I've only worked with logistic regression and IV models. I ran a random forest on this dataset and got the feature importance, but I'm not 100% sure about that.