Exploring Audio Features, classifying into moods and building a Machine Learning Approach. In this article, an author tries to analyze not only his but also a fiancée’s preference to determine what the data has to say about this.
A few weeks ago I came across with an interesting article titled “A Music Taste Analysis Using Spotify API and Python”. In this article, an author tries to analyze not only his but also a fiancée’s preference to determine what the data has to say about this. Thus, he compares two different profiles in terms of music features, which are provided by Spotify’s API, simultaneously. While reading it, I was curious to not only analyze my own preference but also to play with my Spotify data. Therefore, I wanted to cluster my saved songs on Spotify into separate playlists that would represent a specific mood I have while listening to them.
So in order to accomplish my task I broke down my goal into few separate tasks:
Initially, it is worth mentioning that likewise Twitter, Slack, and Facebook Spotify offers an API for developers to explore their music database and get insights into our listening habits. It provides a large variety of features; however, I used 8 features describing a song.
For more information on different features provided: **[Get Audio Features for a Track | Spotify for Developers**](https://developer.spotify.com/documentation/web-api/reference/tracks/get-audio-features/)
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