unfortunatelyfor legal reasonsthis dataset is no longer available (although the data was anonymousthere were concerns that it might be possible to discover who the clients were and reveal the private details of movie rentalshoweverwe can use an academic dataset with similar characteristics this data comes from grouplensa research laboratory at the university of minnesota machine learning in the real world much has been written about the netflix prize and you may learn lot reading up on it (this book will have given you enough to start to understand the issuesthe techniques that won were mix of advanced machine learning with lot of work in the preprocessing of the data for examplesome users like to rate everything very highlyothers are always more negativeif you do not account for this in preprocessingyour model will suffer other not so obvious normalizations were also necessary for good resulthow old the film ishow many ratings did it receiveand so on good algorithms are good thingbut you always need to "get your hands dirtyand tune your methods to the properties of the data you have in front of you we can formulate this as regression problem and apply the methods that we learned in this it is not good fit for classification approach we could certainly attempt to learn the five class classifiersone class for each possible grade there are two problems with this approacherrors are not all the same for examplemistaking -star movie for -star one is not as serious of mistake as mistaking -star movie for -star one intermediate values make sense even if our inputs are only integer valuesit is perfectly meaningful to say that the prediction is we can see that this is different prediction than these two factors together mean that classification is not good fit to the problem the regression framework is more meaningful we have two choiceswe can build movie-specific or user-specific models in our casewe are going to first build user-specific models this means thatfor each userwe take the moviesit has rated as our target variable the inputs are the ratings of other old users this will give high value to users who are similar to our user (or negative value to users who like more or less the same movies that our user dislikesthe system is just an application of what we have developed so far you will find copy of the dataset and code to load it into python on the book' companion website there you will also find pointers to more informationincluding the original movielens website