we can see multiple statistical measures like countaveragestandard deviationand quartiles for each of the numeric features in this output try plotting their distributions if possiblec ounts raw numeric measures can also indicate countsfrequencies and occurrences of specific attributes let' look at sample of data from the million-song datasetwhich depicts counts or frequencies of songs that have been heard by various users in [ ]popsong_df pd read_csv('datasets/song_views csv'encoding='utf- 'popsong_df head( figure - song listen counts as numeric feature we can see that the listen_count field in the data depicted in figure - can be directly used as count/frequency based numeric feature binarization often raw numeric frequencies or counts are not necessary in building models especially with regard to methods applied in building recommender engines for example if want to know if person is interested or has listened to particular songi do not need to know the total number of times he/she has listened to the same song am more concerned about the various songs he/she has listened to in this casea binary feature is preferred as opposed to count based feature we can binarize our listen_count field from our earlier dataset in the following way in [ ]watched np array(popsong_df['listen_count']watched[watched > popsong_df['watched'watched you can also use scikit-learn' binarizer class here from its preprocessing module to perform the same task instead of numpy arraysas depicted in the following code