analyzing music trends and recommendations recommendation engines are probably one of the most popular and well known machine learning applications lot of people who don' belong to the machine learning community often assume that recommendation engines are its only use although we know that machine learning has vast subspace where recommendation engines are just one of the candidatesthere is no denying the popularity of recommendation engines one of the reasons for their popularity is their ubiquitous natureanyone who is onlinein any wayhas been in touch with recommendation engine in some form or the other they are used for recommending products on ecommerce sitestravel destinations on travel portalsongs/videos on streaming sitesrestaurants on food aggregator portalsetc long list underlines their universal application the popularity of recommendation engines stems from two very important points about themthey are easy to implementrecommendation engines are easy to integrate in an already existing workflow all we need is to collect some data regarding our user trends and patternswhich normally can be extracted from the businesstransactional database they workthis statement is equally true for all the other machine learning solutions that we have discussed but an important distinction comes from the fact that they have very limited downside for exampleconsider travel portalwhich suggests set of most popular locations from its dataset the recommendation system can be trivial one but its mere presence in front of the user is likely to generate user interest the firm can definitely gain by working sophisticated recommendation engine but even very simple one is guaranteed to pay some dividends with minimal investments this point makes them very attractive proposition this studies how we can use transactional data to develop different types of recommendation engines we will learn about an auxiliary dataset of very interesting dataset"the million song datasetwe will go through user listening history and then use it to develop multiple recommendation engines with varying levels of sophistication (cdipanjan sarkarraghav bali and tushar sharma sarkar et al practical machine learning with python