🎵 Music Popularity Analysis (Spotify Dataset Visualization)

🔗 Live Demo:
https://cs-aashishjayapuram7.github.io/CSCE567-Final-Project/

This project is an interactive data visualization dashboard built using Tableau to analyze how musical characteristics differ between highly popular and less popular songs on Spotify. It explores how features such as energy, danceability, tempo, valence, speechiness, and loudness vary across genres and evolve over time.

The goal of this project is to uncover patterns that help explain what makes a song popular and how musical trends shift across genres and years.

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📊 Dataset

Source: Kaggle (Spotify Dataset collected via Spotify API)

The dataset combines:

• Descriptive Features: Track metadata such as artist, album, release date, genre, and popularity score.
• Audio Features: Spotify’s musical attributes derived from signal processing.

Key Features Used

Audio Features

• Energy – Intensity and activity of the track
• Danceability – Suitability for dancing
• Tempo – Speed of the song (BPM)
• Loudness – Overall volume (dB)
• Liveness – Probability of a live recording
• Valence – Musical positivity (happy vs sad)
• Speechiness – Presence of spoken words
• Instrumentalness – Likelihood of no vocals
• Acousticness – Confidence a track is acoustic
• Duration_ms – Length of the track

Descriptive Features

• Track name, artist, album, release date
• Playlist genre and subgenre
• Track popularity score (0–100)

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🎯 Project Objectives

• Compare high vs low popularity songs using core musical features
• Identify genre-based musical characteristics
• Explore how music trends change over time
• Provide interactive visualizations that enable filtering by genre and year
• Practice real-world data storytelling using Tableau

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📈 Visualizations Included

1. Average Energy by Genre
   Compare energy levels across genres for popular and less popular songs.

2. Tempo and Liveness by Genre
   Dual-axis line chart showing tempo and live-performance likelihood.

3. Duration and Valence by Genre
   Explore song length and emotional tone by genre.

4. Speechiness and Popularity by Genre
   Relationship between spoken words and popularity.

5. Energy & Danceability by Genre (Treemap)
   Identify genres with the most energetic and danceable tracks.

6. Changes Over Time
   Trends in energy, danceability, loudness, and speechiness across years.

All charts are interactive:

• Hover for detailed values
• Filter by genre and year
• Compare popular vs less popular tracks dynamically

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🛠 Tools & Technologies

• Tableau – Data visualization and dashboard creation
• GitHub Pages – Project hosting/deployment
• Spotify API – Original data collection (via Kaggle)
• HTML/CSS (minimal) – Page structure and navigation

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🚀 How to Use

1. Visit the live site:
   👉 https://cs-aashishjayapuram7.github.io/CSCE567-Final-Project/
2. Use the navigation bar to explore different visualization topics.
3. Apply filters inside charts to customize insights.
4. Hover over visual elements to see exact values and trends.

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📚 Academic Context

This project was created as a final visualization project for CSCE 567 - Visualization Tools and demonstrates:

• Exploratory data analysis
• Data storytelling
• Interactive dashboard design
• Analytical reasoning using real-world datasets

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👨‍💻 Author

Aashish Jayapuram
University of South Carolina

© 2025 – Music Popularity Analysis Project