Analysis of ‘Spotify Recommendation’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/bricevergnou/spotify-recommendation on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Spotify Recommandation

( You can check how I used this dataset on my github repository )

I am basically a HUGE fan of music ( mostly French rap though with some exceptions but I love music ). And someday , while browsing stuff on Internet , I found the Spotify's API . I knew I had to use it when I found out you could get information like danceability about your favorite songs just with their id's.

https://user-images.githubusercontent.com/86613710/127216769-745ac143-7456-4464-bbe3-adc53872c133.png" alt="image">

Once I saw that , my machine learning instincts forced me to work on this project.

1. Data Collection

1.1 Playlist creation

I collected 100 liked songs and 95 disliked songs

For those I like , I made a playlist of my favorite 100 songs. It is mainly French Rap , sometimes American rap , rock or electro music.

For those I dislike , I collected songs from various kind of music so the model will have a broader view of what I don't like

There is : - 25 metal songs ( Cannibal Corps ) - 20 " I don't like " rap songs ( PNL ) - 25 classical songs - 25 Disco songs

I didn't include any Pop song because I'm kinda neutral about it

1.2 Getting the ID's

1. From the Spotify's API "Get a playlist's Items" , I turned the playlists into json formatted data which cointains the ID and the name of each track ( ids/yes.py and ids/no.py ). NB : on the website , specify "items(track(id,name))" in the fields format , to avoid being overwhelmed by useless data.

2. With a script ( ids/ids_to_data.py ) , I turned the json data into a long string with each ID separated with a comma.

1.3 Getting the statistics

Now I just had to enter the strings into the Spotify API "Get Audio Features from several tracks" and get my data files ( data/good.json and data/dislike.json )

2. Data features

From Spotify's API documentation :

• acousticness : A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.
• danceability : Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.
• duration_ms : The duration of the track in milliseconds.
• energy : Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.
• instrumentalness : Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.
• key : The key the track is in. Integers map to pitches using standard Pitch Class notation . E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on.
• liveness : Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.
• loudness : The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typical range between -60 and 0 db.
• mode : Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0.
• speechiness : Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.
• tempo : The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.
• time_signature : An estimated overall time signature of a track. The time signature (meter) is a notational convention to specify how many beats are in each bar (or measure).
• valence : A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).

And the variable that has to be predicted :

• liked : 1 for liked songs , 0 for disliked songs

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Spotify Song Attributes’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/geomack/spotifyclassification on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

A dataset of 2017 songs with attributes from Spotify's API. Each song is labeled "1" meaning I like it and "0" for songs I don't like. I used this to data to see if I could build a classifier that could predict whether or not I would like a song.

I wrote an article about the project I used this data for. It includes code on how to grab this data from the Spotipy API wrapper and the methods behind my modeling. https://opendatascience.com/blog/a-machine-learning-deep-dive-into-my-spotify-data/

Content

Each row represents a song.

There are 16 columns. 13 of which are song attributes, one column for song name, one for artist, and a column called "target" which is the label for the song.

Here are the 13 track attributes: acousticness, danceability, duration_ms, energy, instrumentalness, key, liveness, loudness, mode, speechiness, tempo, time_signature, valence.

Information on what those traits mean can be found here: https://developer.spotify.com/web-api/get-audio-features/

Acknowledgements

I would like to thank Spotify for providing this readily accessible data.

Inspiration

I'm a music lover who's curious about why I love the music that I love.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Spotify Past Decades Songs Attributes’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/cnic92/spotify-past-decades-songs-50s10s on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Why do we like some songs more than others? Is there something about a song that pleases out subconscious, making us listening to it on repeat? To understand this I collected various attributes from a selection of songs available in the Spotify's playlist "All out ..s" starting from the 50s up to the newly ended 10s. Can you find the secret sauce to make a song popular?

Content

This data repo contains 7 datasets (.csv files), each representing a Spotify's "All out ..s" type of playlist. Those playlists collect the most popular/iconic songs from the decade. For each song, a set of attributes have been reported in order to perform some data analysis. The attributes have been scraped from this amazing website. In particular, according to the website the attributes are:

• top genre: genre of the song
• year: year of the song (due to re-releases, the year might not correspond to the release year of the original song)
• bpm(beats per minute): beats per minute
• nrgy(energy): energy of a song, the higher the value the more energetic the song is
• dnce(danceability): the higher the value, the easier it is to dance to this song.
• dB(loudness): the higher the value, the louder the song.
• live(liveness): the higher the value, the more likely the song is a live recording.
• val(valence): the higher the value, the more positive mood for the song.
• dur(duration): the duration of the song.
• acous(acousticness): the higher the value the more acoustic the song is.
• spch(speechiness): the higher the value the more spoken word the song contains.
• pop(popularity): the higher the value the more popular the song is.

Acknowledgements

I got inspired by the top-notch work by Leonardo Henrique in this dataset. Thanks to him I discovered this website, from which all the data collected here have been scraped.

--- Original source retains full ownership of the source dataset ---

The dataset is composed by different musial genres and for each kind we have different features that caracterize it. Reference https://developer.spotify.com/documentation/web-api/reference/#endpoint-get-audio-features

• Acousticness: A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.
• Danceability: Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.
• Duration_ms: The duration of the track in milliseconds.
• Energy: Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.
• Instrumentalness: Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.
• Key: The key the track is in. Integers map to pitches using standard Pitch Class notation. E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on.
• Liveness: Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.
• Loudness: The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typical range between -60 and 0 db.
• Mode: Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0.
• Popularity: The popularity of the track. The value will be between 0 and 100, with 100 being the most popular. The popularity is calculated by algorithm and is based, in the most part, on the total number of plays the track has had and how recent those plays are. Note: When applying track relinking via the market parameter, it is expected to find relinked tracks with popularities that do not match min_*, max_*and target_* popularities. These relinked tracks are accurate replacements for unplayable tracks with the expected popularity scores. Original, non-relinked tracks are available via the linked_from attribute of the relinked track response.
• Speechiness: Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.
• Tempo: The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.
• Valence: A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).

Analysis of ‘Spotify Top 2020 Songs’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/heminp16/spotify-top-2020-songs on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The top 50 songs from the year 2020, totaling 50 songs in the dataset.

Content

The attributes were scraped from this website. Top Genre - genre of the song Year - release year of the song BPM – Beats Per Minute - The tempo of the song. Energy - The energy of a song; the higher the value, the more energetic. Danceability – Describes how suitable a track is for dancing; the higher the value, the easier it is to dance. Loudness (dB) – The loudness level in decibels, higher the value, the louder the song Liveness - the higher the value, the more likely the song is a live recording. Valence - A measure of musical positiveness of the track. The tracks with the highest number give a sense of positive moods. Duration (sec) - The duration of the song in seconds. Acousticness - A measure of how acoustic the track is. Speechiness - The higher the value that tells how many spoken words were in the track. Popularity - The higher the value, the more popular the song is.

Acknowledgements

I got inspired by Leonardo Henrique in this dataset, and found the amazing website that helped me scrap this data!

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Spotify Top 2018 & 2019 Songs’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/heminp16/spotify-top-2018-2019-songs on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The top 50 songs from the years 2018 and 2019, totaling 100 songs in the dataset. Note: Better Now by Post Malone repeats twice, as it was popular in both 2018 and 2019.

Content

The attributes were scraped from this website. Top Genre - genre of the song Year - release year of the song BPM – Beats Per Minute - The tempo of the song. Energy - The energy of a song; the higher the value, the more energetic. Danceability – Describes how suitable a track is for dancing; the higher the value, the easier it is to dance. Loudness (dB) – The loudness level in decibels, higher the value, the louder the song Liveness - the higher the value, the more likely the song is a live recording. Valence - A measure of musical positiveness of the track. The tracks with the highest number give a sense of positive moods. Duration (sec) - The duration of the song in seconds. Acousticness - A measure of how acoustic the track is. Speechiness - The higher the value that tells how many spoken words were in the track. Popularity - The higher the value, the more popular the song is.

Acknowledgements

I got inspired by Leonardo Henrique in this dataset, and found the amazing website that helped me scrap this data!

--- Original source retains full ownership of the source dataset ---