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June 2023
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Spotify music analyzer2/12/2023 The resulting dataset is made of 15 columns and 1074 songs, of which 563 come from my playlist, and 511 from hers (from now on I will refer to my friend as she or her). Once I had the basic information of the songs, including their Spotify ID, I was able to get the audio features of them using the same script. The data of the music was obtained using a Python script I wrote that fetches all the playlists of a user, and all the songs of a particular playlist. Python was used to obtain the data using the library Spotipy, and to train the machine learning model using scikit-learn. I will give a more detailed explanation of the features later. These audio features represent characteristics about a song, such as how acoustic and loud it is. The principal tool used in this project is the audio features component of the Spotify API service. Lastly, I trained a machine learning model with the purpose of predicting if a song would be more suitable for my playlist or hers. Moreover, to make the problem more interesting, I compared each of these three characteristics against my friend’s songs. In this article I will share the findings of this experiment, in which I analyzed my Spotify songs to see if they are indeed varied, instrumental, and boring. However, her comment gave me a nice idea: let’s check what the data says about this. I’ll admit that my music taste might be a bit weird- for example, I could start the day listening to Kendrick Lamar, then switch to the Inception soundtrack, followed by some Spanish salsa. I laughed at that comment because it is not the first time I have heard that. After a couple of songs she interrupted the conversation to tell me: “Your music taste is interesting…your playlist has a lot of variety, instrumental songs, and some of them are boring”. Is my Spotify music boring? An analysis involving music, data, and machine learningĪ couple of days ago I was chatting with a friend, while listening to my Spotify saved songs.
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