Spotify Tracks Dataset

📂 Dataset Overview Each row in the dataset represents a single Spotify track, complete with metadata and audio analysis features. With tracks from diverse genres, this dataset offers a rich resource for music analysis.

Key Features: 125+ Genres: Wide variety of musical styles. Audio Features: Detailed data on track characteristics. Track Metadata: Information on artists, albums, and tracks. 📑 Column Descriptions

1. track_id: Unique Spotify identifier for the track.
2. artists: Names of the artists performing the track (multiple artists separated by a semicolon ;).
3. album_name: Title of the album where the track appears.
4. track_name: Name of the track.
5. popularity: Popularity score (0–100), higher values indicate more recent and frequent plays.
6. duration_ms: Length of the track in milliseconds.
7. explicit: Whether the track contains explicit lyrics (true or false).
8. danceability: Score (0.0–1.0) indicating how suitable a track is for dancing.
9. energy: Score (0.0–1.0) measuring intensity and activity.
10. key: Musical key of the track (0 = C, 1 = C♯/D♭, ..., 11 = B; -1 = no key detected).
11. loudness: Overall loudness of the track in decibels (dB).
12. mode: Musical mode of the track: 1 = major, 0 = minor.
13. speechiness: Score (0.0–1.0) detecting spoken words in the track. Higher values indicate more speech-like content.
14. acousticness: Confidence (0.0–1.0) that the track is acoustic.
15. instrumentalness: Score (0.0–1.0) predicting the likelihood of no vocals in the track.
16. liveness: Score (0.0–1.0) detecting the presence of an audience. Values > 0.8 suggest live performances.
17. valence: Score (0.0–1.0) indicating the positivity of the track’s mood.
18. tempo: Speed of the track in beats per minute (BPM).
19. time_signature: Estimated beats per measure (values range from 3 to 7).
20. track_genre: The genre of the track.

🔍 How to Use the Dataset Load the Dataset: Use Python libraries like pandas to load the data: import pandas as pd df = pd.read_csv('spotify_tracks.csv') Explore Features: Analyze columns like danceability, energy, or valence for trends.

Perform Genre Analysis: Identify patterns across genres using track_genre and popularity.

Build Machine Learning Models: Use audio features for tasks like genre classification or popularity prediction.

• Potential Applications Music Recommendations: Create systems to suggest tracks based on user preferences. Genre Studies: Analyze unique patterns within and across genres. Hit Prediction: Explore what makes a track popular. Music Trends: Gain insights into evolving musical styles.
• Notes and Limitations Popularity scores may not reflect the current state of Spotify streams. Some tracks could belong to multiple genres due to overlapping classifications. Explicitness is binary and does not capture variations in explicit content. Contributions and insights are welcome! Share your analyses, visualizations, or suggestions to enhance the dataset’s utility. 🎉