The Music Dataset, loaded and parsed as CSV.

This is music data from 1923-2023 from Spotify. It includes the title of the song (Track), the name of the performing artist or band (Artist),the year the track was released (Year), the length of the track in milliseconds (Duration),the musical time signature (Time_Signature), how suitable the track is for dancing (Danceability),how intense and active the track is (Energy), the key of the track(Key),the loudness of the track in decibels (Loudness), if the track is major or minor (Mode),how much spoken words are in the track (Speechiness), how acoustic a track is(Acousticness),how likely the track is instrumental (Instrumentalness), how present a live audience is in the track (Liveness),how musically positive the track is (Valence), the speed of the track in beats per minute (Tempo),how popular the track is (Popularity), the genre of the track(Genre).

Challenge

• Find 3 datasets on a topic you are interested in
• For each of those, fork this viz and replace the data
• Parse the quantitative attributes into numbers
• Update README.md to indicate the original data source

5 Tasks:

• Identify correlations between danceability and energy for different genres
• Explore the distribution of popularity scores across different years to see if older tracks are more popular
• Examine trends in tempo and loudness over time to identify any differences in musical styles
• Filter tracks with high acousticness and see how it correlates with instrumentalness
• Compare average track durations by genre to see if certain genres tend to have longer or shorter tracks