this gives us another %bub'sbnf that tells usfor examplenpwjf@jehad ratings (which is measure of its popularitythat ishow many people actually watched it and rated it)and mean review score of so people watched npwjf@jeand they gave it an average review of which is pretty good nowthe raw number of ratings isn' that useful to us mean don' know if means it' popular or not soto normalize thatwhat we're going to do is basically measure that against the maximum and minimum number of ratings for each movie we can do that using the mbnceb function sowe can apply function to an entire %bub'sbnf this way what we're going to do is use the oqnjo and oqnby functions to find the maximum number of ratings and the minimum number of ratings found in the entire dataset sowe'll take the most popular movie and the least popular movie and find the range thereand normalize everything against that rangenpwjf/vn bujoht qe%bub'sbnf npwjf spqfsujft npwjf/psnbmj[fe/vn bujoht npwjf/vn bujohtbqqmz mbnceby oqnjo oqnby oqnjo npwjf/psnbmj[fe/vn bujohtifbe what this gives uswhen we run itis the followingthis is basically measure of popularity for each movieon scale of to soa score of here would mean that nobody watched itit' the least popular movieand score of would mean that everybody watchedit' the most popular movieor more specificallythe movie that the most people watched sowe have measure of movie popularity now that we can use for our distance metric