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Spotify-Famous-Music-Dashboard

  1. Business Request:

This dataset contains a comprehensive list of the most popular songs on Spotify up to July 2023. The goal is to analyze musical data to uncover valuable insights such as song details (Track Name, Artist, Release Date, etc.), musical attributes (Danceability, Liveness, Energy, etc.), and the energy level each song delivers. These insights can support decision-making for music labels aiming to grow in the industry.

  1. Main Idea

This project utilizes ChatGPT (for web data extraction), PowerPoint, and BI tools like Power BI and DAX to build an interactive dashboard. The dashboard enables analysis of stream counts and musical statistics to identify top-performing songs and measure the percentage difference between their stream counts and the average across all tracks.

Key variables include:

track_name: Name of the song

artist(s)_name: Name of the artist(s) of the song

artist_count: Number of artists contributing to the song

released_year / released_month / released_day: The time 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

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	

image_url: Link that leads to the album cover of the song
  1. Tools and Techniques Used

Tools:

  • Python (requests library)

  • Power BI (Bravo Extension, DAX)

Techniques:

  • Data Retrieval & Extraction:
  • Retrieved album cover URLs for each song using Spotify’s API and Python.

Exploratory Data Analysis (EDA):

  • Created an automatic Date table using Bravo in Power BI
  • Visualized song attributes and statistics using: HTML Visual for album covers, Deneb Visual for unit charts and heatmaps
  • Built DAX metrics such as: Top Song Streams, Average Streams per Year, Top Song vs Average

About

This project analyzes a dataset of the most popular songs on Spotify up to July 2023 to uncover key musical insights. Using Python (requests library), album cover URLs were retrieved via Spotify’s API. Power BI, Bravo extension, and DAX were used to build an interactive dashboard that visualizes stream counts, song attributes, and release trends.

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