anywaythose are some ideas on how to go back and improve upon the results of this recommender engine that we wrote soplease feel free to tinker around with itsee if you can improve upon it however you wish toand have some fun with it this is actually very interesting part of the bookso hope you enjoy itsummary sogo give it trysee if you can improve on our initial results there there' some simple ideas there to try to make those recommendations betterand some much more complicated ones too nowthere' no right or wrong answeri' not going to ask you to turn in your workand ' not going to review your work you knowyou decide to play around with it and get some familiarity with itand experimentand see what results you get that' the whole point just to get you more familiar with using python for this sort of thingand get more familiar with the concepts behind item-based collaborative filtering we've looked at different recommender systems in this we ruled out user-based collaborative filtering system and dove straight in to an item-based system we then used various functions from pandas to generate and refine our resultsand hope you've seen the power of pandas here in the next we'll take look at more advanced data mining and machine learning techniques including -nearest neighbors look forward to explaining those to you and seeing how they can be useful