figure - distribution of play counts for users given the nature of the datawe can perform large variety of cool visualizationsfor exampleplotting how the top tracks of artists are playedthe play count distribution on yearly basisetc but we believe by now you are sufficiently skilled in both the art of asking pertinent questions and answering them through visualizations so we will conclude our exploratory data analysis and move on to the major focus of the which is development of recommendation engines but feel free to try out additional analysis and visualizations on this data and if you find out something coolas alwaysfeel free to send across pull request to the book' code repositoryrecommendation engines the work of recommendation engine is succinctly captured in its name--all it needs to do is make recommendations but we must admit that this description is deceptively simple recommendation engines are way of modeling and rearranging information available about user preferences and then using this information to provide informed recommendations on the basis of that information the basis of the recommendation engine is always the recorded interaction between the users and products for examplea movie recommendation engine will be based on the ratings provided to different movies by the usersa news article recommender will take into account the articles the user has read in pastetc this section uses the user-song play count dataset to uncover different ways in which we can recommend new tracks to different users we will start with very basic system and try to evolve linearly into sophisticated recommendation system before we go into building those systemswe will examine their utility and the various types of recommendation engines