Spotify Song Popularity and Artist Rankings Analysis

Project Overview: This project provides a comprehensive analysis of Spotify data to uncover factors influencing song popularity and artist rankings, offering insights for artists looking to optimize their reach.

Objectives:

  • Determine factors significantly impacting artist rankings and song popularity.
  • Analyze correlations between song attributes (e.g., danceability, energy) and popularity.
  • Offer strategic recommendations for artists and Spotify to enhance performance and engagement.

Methodology: Using Spotify's API, Excel, and Tableau, data preprocessing and transformation were applied to create popularity bins, rank artists, and perform correlation analysis on song attributes like danceability and energy.

Key Findings:

  • Top Artists by Popularity: Developed a ranking system highlighting popular artists and current musical preferences.
  • Correlation Analysis: Identified that high danceability and energy levels correlate with popularity.
  • Danceability and Mood: Songs with high danceability and positive mood resonated more with listeners, leading to higher popularity scores.
  • Trend Analysis: Showcased evolving listener preferences over the past decade, helping predict future trends.

Limitations and Recommendations: The lack of listener demographics limits understanding of popularity impact, suggesting artists focus on high-valence, danceable songs and experiment with longer durations.

Project Impact: Enhances understanding of Spotify’s ranking algorithms, supporting strategic decisions for artists and potentially helping Spotify improve its recommendation engine.

You can find the full project on my GitHub: GitHub Link