Combined, each prediction will be closer to the true class than individual predictions.RFC = RandomForestClassifier()RFC.fit(X_train, y_train)y_pred = RFC.predict(X_test)Accuracy = accuracy_score(y_test, y_pred)Confusion_matrix = confusion_matrix(y_test, y_pred)print('Accuracy = '+str(Accuracy))print(Confusion_matrix)Accuracy = 0.5302013422818792[[41 31] [39 38]]Again, slightly over 50% correct and just a bit better than our decision tree, but still not great.So yeah, none of these models are all that great or reliable..But that’s the point of experimenting while in school, right?.Being curious about something, checking it out, finding yourself crashing and burning with each consecutive model, and learning from it.I did this small project to learn a bit more about how to implement many predictive models to a binary classification problem..The models weren’t as accurate or reliable as one would hope (as predicted!), but I got a bit more experience iterating through different classification models along the way..I think if these models were too accurate, the appeal of the show would be gone!.Thanks for sticking with me.. More details