Identify the association between different types of songsĪs a music aficionado, find the songs that were off your radar Several univariate and multivariate analyses are done across variables post data preparation to substantiate the findings / results obtained by the above approaches The Spotify_data used for this analysis is a contiguous dataset of over 30,000 songs with 12 audio features for each track, including confidence measures like acousticness,liveness, speechiness and instrumentalness, perceptual measures like energy, loudness, danceablity and valence (positiveness), and descriptors like duration,key, tempo and mode.Īn overview of the genres and the artistsĪn analysis of the track popularity and how it extends aross genresīased on the preferences of the user, build a simple system that gives out related songs based on genre and / or artist As a music afficionado, this prompted me to dig deeper in to the spotify songs database to discover interesting trends regarding the songs, their artists and hopefully help people discover new music. With over 36% market share among online music subscribers and having a base of over 100 million subscribers, Spotify occupies the top spot. Spotify, Apple music, Google play etc are some of the most popular of these. From the vintage gramaphones through mix tapes through radio, our listening medium has now reached online music-streaming platforms. Over the centuries since Shakespeare,music has continued to be an indispensable aspect of our daily life.Not a day passes by without you humming a tune or listening to one.Our sources of listening to them have also evolved over time.
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