our choice of can be very important you want to make sure it' small enough that you don' go too far and start picking up irrelevant neighborsbut it has to be big enough to enclose enough data points to get meaningful sample sooften you'll have to use train/test or similar technique to actually determine what the right value of is for given dataset butat the end of the dayyou have to just start with your intuition and work from there that' all there is to itit' just that simple soit is very simple technique all you're doing is literally taking the nearest neighbors on scatter plotand letting them all vote on classification it does qualify as supervised learning because it is using the training data of set of known pointsthat isknown classificationsto inform the classification of new point but let' do something little bit more complicated with it and actually play around with moviesjust based on their metadata let' see if we can actually figure out the nearest neighbors of movie based on just the intrinsic values of those moviesfor examplethe ratings for itthe genre information for itin theorywe could recreate something similar to customers who watched this item also watched (the above image is screenshot from amazonjust using -nearest neighbors andi can take it one step furtheronce identify the movies that are similar to given movie based on the -nearest neighbors algorithmi can let them all vote on predicted rating for that movie that' what we're going to do in our next example so you now have the concepts of knnk-nearest neighbors let' go ahead and apply that to an example of actually finding movies that are similar to each other and using those nearest neighbor movies to predict the rating for another movie we haven' seen before