![]() ![]() # speechiness acousticness instrumentalness liveness valence tempo # playlist_genre playlist_subgenre danceability energy key loudness mode # track_album_release_date playlist_name playlist_id # 5: Someone You Loved (Future Humans Remix) # 5: Someone You Loved - Future Humans Remix Lewis Capaldi # 4: Call You Mine - Keanu Silva Remix The Chainsmokers # 3: All the Time - Don Diablo Remix Zara Larsson ![]() # 2: Memories - Dillon Francis Remix Maroon 5 # 1: I Don't Care (with Justin Bieber) - Loud Luxury Remix Ed Sheeran # "duration_ms" #displaying the first 6 observations. # "track_album_release_date" "playlist_name" We then check the variable names in the dataframe using names() function which returns the column names.įirst 6 observartions are displayed using the head() function. Data importing is done using the fread() from data.table package which is then stored in a data frame called songs. Original dataset extracted via this package has 23 variables as listed below. ![]() # this is used to generate decision trees which will help in classifying the data in to genres. # this is used to genrate random forest(data mining technique) to model the data and classify the results. ![]() # it is a collection of multiple packages used to clean, visualise,model, and to communicate the data. # this package is used to get basic statistics of numerical variables using basicStats function Recommendations for new songs from different genres/languages which have similar features such as danceability, energy, valence etc can be made to a user depending on their listening history and preferences.ĭata.table tidyverse randomForest rpart # this package is used to read data files, is faster than readr package Consumer can make use of the analysis to put songs in to broader genres or categories which traditionally may not belong the same genre but have similar features and that can be used to create custom playlists suitable for a particular occasion or mood. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |