I'm working on a dataset of Uber Rides from Kaggle. Of the important variables there are pickup and drop-off coordinates, passenger count, datetime of pickup, distance and the final price. I'm currently in the exploration phase and just about to begin feature engineering. When I'm plotting the different potential correlations, some of them just feel odd to plot fare against something. For example, fare vs passenger count or fare vs hour doesn't make much sense to me, as the average fare also depends on how far people usually go. Price/km on the other hand seems to have more sense when plotting. Price/km vs year can show yearly price increase, price/km vs hour shows how the hour affects the pricing of the trip and so on.
Example: price/km vs hour, makes sense
My question is, would it make sense to change the predicted variable from fare to price/km and then multiply by the trip distance? Obviously if I predict one I have to remove the other from the dataset, but does it matter which one I predict?