Welcome to our Spotify Insights project! 🎶 Here, we delve into the heart of music, offering a detailed exploration of the hottest songs of 2023. Our dataset, conatins musical information from Spotify, unveils the essence of each track, from beats per minute to danceability percentages.
This project not only explores the depths of 2023's chart-toppers but also leverages Python and the Spotify Web APIs.Here Python script is used that interacts with the Spotify Web API and a pandas DataFrame to retrieve track details including the cover URL for a list of tracks provided in an Excel file.Using a pandas DataFrame, we retrieve intricate track details, from beats and danceability to album cover In this project, we meticulously calculate average streams per year, dissect streams by track and artist names, and analyze trends based on release dates, top songs, their danceability, valence, energy levels, and instrumental content🎵📊🔍
The dataset for this project contains a comprehensive list of the most famous songs of 2023 as listed on Spotify. The dataset offers a wealth of features beyond what is typically available in similar datasets. It provides insights into each song's attributes, popularity, and presence on various music platforms. The dataset includes track name, artist(s) name, release date, Spotify playlists and charts, streaming statistics, Apple Music presence, Deezer presence, Shazam charts, and various audio features.
- track_name: Name of the song
- artist_name: Name of the artist of the song
- artist_count: Number of artists contributing to the song
- released_year: Year when the song was released
- released_month: Month when the song was released
- released_day: Day of the month when the song was released
- in_spotify_playlists: Number of Spotify playlists the song is included in
- in_spotify_charts: Presence and rank of the song on Spotify charts
- streams: Total number of streams on Spotify
- in_apple_playlists: Number of Apple Music playlists the song is included in
- in_apple_charts: Presence and rank of the song on Apple Music charts
- in_deezer_playlists: Number of Deezer playlists the song is included in
- in_deezer_charts: Presence and rank of the song on Deezer charts
- in_shazam_charts: Presence and rank of the song on Shazam charts
- bpm: Beats per minute, a measure of song tempo
- key: Key of the song
- mode: Mode of the song (major or minor)
- danceability_%: Percentage indicating how suitable the song is for dancing
- valence_%: Positivity of the song's musical content
- energy_%: Perceived energy level of the song
- acousticness_%: Amount of acoustic sound in the song
- instrumentalness_%: Amount of instrumental content in the song
- liveness_%: Presence of live performance elements
- speechiness_%: Amount of spoken words in the song
Power BI (Business Intelligence Tool) Power Query Editor DAX Language Data Modeling.
🌐Imported data from MS Excel to Power BI
Adding Cover-URl column using Spotify Web API
🪄Data transformation in Power Query editor
⚒️Made measures important for Dashboarding
📊Setup Dashboard background
Set-up Dashboard Theme >Format Visuals
Overall rating visualization
Dashboard formatting
- Link to pdf of Dashboard ->Spotify Streaming Report 2023 pdf
Contributions to enhance the dashboard or address specific Revenue Analytics challenges are welcome! Please create issues or pull requests to collaborate on improving the dashboard's functionality and visualization.
I've shared all the necessary files, datasets, and workbooks above. Please feel free to utilize these resources for your upcoming projects. If you find value in this project and dashboard, consider giving it a star or simply let me know. Your feedback would be greatly appreciated!