the final datasetmerged with the triplets dataframe looks similar to the depiction in figure - this will form the starting dataframe for our exploratory data analysis figure - play counts dataset merged with songs metadata visual analysis before we start developing various recommendation engineslet' do some visual analysis of our dataset we will try to see what the different trends are regarding the songsalbumsand releases most popular songs the first information that we can plot for our data concerns the popularity of the different songs in the dataset we will try to determine the top songs in our dataset slight modification of this popularity will also serve as our most basic recommendation engine the following code snippet gives us the most popular songs from our dataset in [ ]import matplotlib pyplot as pltplt rcdefaults(import numpy as np import matplotlib pyplot as plt popular_songs triplet_dataset_sub_song_merged[['title','listen_count']groupby('title'sum(reset_index(popular_songs_top_ popular_songs sort_values('listen_count'ascending=falsehead( = objects (list(popular_songs_top_ ['title'])y_pos np arange(len(objects)performance list(popular_songs_top_ ['listen_count']plt bar(y_posperformancealign='center'alpha= plt xticks(y_posobjectsrotation='vertical'plt ylabel('item count'plt title('most popular songs'plt show(