When looking at both model 1 and model 2 to see the effectiveness they have on achieving the goal of predicting the rotten tomatoes score of a show on a given streaming service, model 2 has proven to be the clear winner. Model 1 has the issue of needing the ID to achieve an accurate prediction. The model needing the ID score prevents trying to make an objective conclusion as to what the score can be without the need of knowing what show we are basing it out of. This causes an issue of trying to guess an ID in order to just get a prediction. Another problem that can come from model 1 is the fact that entering a large ID number, lets say 4890, can automatically conflate the prediction into a low rotten tomatoes score compared to an ID number of 45. However, in model 2 we don't have this problem since the decision tree allows use to decide from a true and false questions on whether the show is on that streaming service or not. These questions lead to a final prediction that shows the average score based on the samples that were collected.