Spotify Song Popularity Analysis with Audio Features and Statistical Code

A dataset and analysis materials for a research project investigating the relationship between Spotify audio features and song popularity. The deposit includes analysis code, documentation, and supporting materials intended to support transparency and reproducibility. The work was authored by Krisha Dua and deposited on Harvard Dataverse in July 2026.

Use Cases

• Predicting song popularity based on audio features like danceability and energy mentioned in the description.
• Conducting statistical analysis on the relationship between valence (musical positiveness) and listener engagement.
• Reproducing or extending the data visualization and analysis of Spotify's acousticness and speechiness metrics.

Strengths

• Includes the full analysis code and documentation for the research project, supporting reproducibility.
• Focuses on eight specific Spotify audio features: danceability, energy, valence, acousticness, speechiness, loudness, tempo, and duration.

Limitations

• The original data may not be included due to licensing; users may need to download it separately from Kaggle.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Original Spotify dataset was obtained from Kaggle.

Collection Method

Likely gathered via Spotify's Web API, as implied by the standard audio features.

Freshness

Last updated 2026-07-06 13:57:12; freshness should be verified.