We will plot the same histogram as we did for my playlist so we can compare them later.
Distribution of music styles in the top 100 songs of 2018By observing the histogram we can see that tracks in the top 100 charts are:Very high in danceability and energy but low in liveness, speechiness and acousticness (we can already see some signs that my playlist isn’t as cool as the top 100 ????).
For example, Drake’s “In My Feelings” from our dataset, is highly danceable and also has a relatively high energy value.
Finally, I decided to plot a radar chart of the top 100 songs and superimpose the audio features of my playlist for easy comparison.
The Top 100 songs are in blue while my songs are in orange.
ConclusionSo I think I got the answers to both my questions at the start of this post.
I got to see what my music looks like and there seems to be a DNA for hit songs.
The audio features from my playlist are a bit similar to the top 100 songs but I have more acoustic songs and a few live songs.
Want to make a hit song?.Make sure it is danceable, has a lot of energy and a bit of valence (positivity, feel-good vibes).
I am quite happy with the results but I want to build on this in another post.
You can get the code to the entire project on GitHub.
Here are my recommended next steps:See how to use your playlist to determine your personalities and recommend adverts that you might like.
Use Machine Learning Clustering algorithm, K-Means, to see which songs are similar to yours and you can use that to eventually discover new songs that you might like.
Use Machine Learning to predict “Popularity” of songs based on their audio featuresThanks to Alvin Chung, Ashrith and John Koh for their helpful articles on this subject.
Spotify and Spotipy, thanks for the awesome API and library!.