We'd like you to build a library recommendation engine for R programmers, who usually refer to libraries as packages. We think that you can help neophyte R programmers by letting them know which packages are most likely to be installed by the average R user and what measurable properties of the packages themselves are able to predict this information. To train your algorithm, we're providing a data set that contains approximately 150,40 rows of data describing installation information for 1760 packages for 55 users of R. For each package, we've provided a variety of predictors derived from the rich metadata that is available for every R package. Your task is to model the behavior of the sample users for this training set of 1865 packages well enough that your predictions will generalize to a test data set, containing 51,012 rows.