anushakuppahally / taylor-swift-data

Used SQL and Python to analyze Taylor Swift Spotify data

taylor-swift-data

Used SQL and Python to analyze Spotify data related to all of Taylor Swift's songs from deluxe albums and their characteristics.

The following albums are included: Taylor Swift (2006), Fearless (Taylor's Version) (2021), Speak Now (Deluxe Package) (2010), Red (Deluxe Edition) (2012), 1989 (Deluxe) (2014), reputation (2017), Lover (2019), folklore (deluxe version) (2020), evermore (deluxe version) (2020)

Used a dataset from Kaggle that included each song and characteristics: https://www.kaggle.com/thespacefreak/taylor-swift-spotify-data

Here is a description of each column (from the dataset):

name: Name of song

album: Name of album

artist: Name of artist/s involved

release date : Release date of album

length: Song length in milliseconds

popularity: Percent popularity of the song based on Spotify's algorithm (possibly the number of stream at a certain period of time)

danceability: How suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity

acousticness: How acoustic a song is

energy: A perceptual measure of intensity and activity

instrumentalness: The amount of vocals in the song

liveness: Probability that the song was recorded with a live audience

loudness: Tendency of music to be recorded at steadily higher volumes

speechiness: Presence of spoken words in a track (if the speechiness of a song is above 0.66, it is probably made of spoken words, a score between 0.33 and 0.66 is a song that may contain both music and words, and a score below 0.33 means the song does not have any speech)

valence: A measure of how happy or sad the song sounds

tempo: Beats per minute