if you really want to do more involved exercise you can actually try to apply it to train/testto actually find the value of that most optimally can predict the rating of the given movie based on knn andyou can use different distance metricsi kind of made that up toosoplay around the distance metricmaybe you can use different sources of informationor weigh things differently it might be an interesting thing to do maybepopularity isn' really as important as the genre informationor maybe it' the other way around see what impact that has on your results too sogo ahead and mess with these algorithmsmess with the code and run with itand see what you can getandif you do find significant way of improving on thisshare that with your classmates that is knn in actionsoa very simple concept but it can be actually pretty powerful sothere you have itsimilar movies just based on the genre and popularity and nothing else works out surprisingly wellandwe used the concept of knn to actually use those nearest neighbors to predict rating for new movieand that actually worked out pretty well too sothat' knn in actionvery simple technique but often it works out pretty darn gooddimensionality reduction and principal component analysis alrighttime to get all trippywe're going to talking about higher dimensionsand dimensionality reduction sounds scarythere is some fancy math involvedbut conceptually it' not as hard to grasp as you might think solet' talk about dimensionality reduction and principal component analysis next very dramatic soundingusually when people talk about thisthey're talking about technique called principal component analysis or pcaand specific technique called singular value decomposition or svd so pca and svd are the topics of this section let' dive into itdimensionality reduction sowhat is the curse of dimensionalitywella lot of problems can be thought of having many different dimensions sofor examplewhen we were doing movie recommendationswe had attributes of various moviesand every individual movie could be thought of as its own dimension in that data space