This Streamlit app aims to provide valuable insights for enhancing user experience, improving song recommendations, and optimizing playlist curation.

Analyzing Spotify’s Top Streamed Songs of 2024

Introduction

Music streaming has transformed the way we listen to and discover music. For a music streaming service, understanding user preferences and trends is crucial to enhancing user experience, improving song recommendations, and optimizing playlist curation. Our latest project, the “Spotify 2024 Top Streamed Songs Analysis” app, aims to uncover the trends and characteristics of the most streamed songs on Spotify in 2024. This blog post will walk you through the features of this app and how it can provide valuable insights for marketing and curation teams.

Check out the live app: Spotify Analysis App

Project Goals

The primary goals of this project are:

1. Analyze trends in song releases.
2. Determine the impact of artists on streaming counts.
3. Assess the influence of playlist inclusion on streaming counts.
4. Investigate external factors affecting song popularity.
5. Provide an interactive and engaging user experience with advanced visualizations and machine learning integration.

Features of the App

1. Elegant and Impressive Design

• Wide Layout: Optimized for larger screens to display more information.
• Consistent Color Scheme: Uses a clean and modern design.
• Built-in Components: Utilizes Streamlit’s built-in components for a polished look.
• Animations and Transitions: Adds a dynamic feel to the app.

2. Features and Components

a. Sidebar

• Includes filters for year, artist, and date range.

b. Trend Analysis Tab

• Interactive time series chart showing song releases over time.
• Heatmap of song releases by month and day of the week.
• Placeholder for seasonal trend analysis with decomposition plots.

c. Artist Impact Tab

• Bar chart of top artists by average streams.
• Scatter plot of artist popularity vs. stream counts.

d. Playlist Influence Tab

• Correlation heatmap between playlist features and stream counts.
• Bar chart of top playlists by contribution to streams.

e. External Factors Tab

• Correlation analysis between external factors and stream counts.
• Box plots comparing explicit vs. non-explicit content streams.
• Scatter plot of Shazam counts vs. stream counts.

f. Popularity Predictor Tab

• Simple prediction model for song popularity based on its features.
• Users can input song characteristics and get a predicted stream count.

g. Recommendation System Tab

• Users can upload their own Spotify playlist for analysis.
• Recommendation system based on the analysis of the uploaded playlist.

3. Interactive Elements

• Tooltips for detailed information on hover.
• Sliders or multi-select dropdowns for filtering data.
• Clickable elements that update other visualizations.

4. Performance Optimization

• Caching for data loading and heavy computations.
• Lazy loading for charts and graphs.

5. Responsive Design

• Ensures the app looks good on both desktop and mobile devices.
• Uses Streamlit’s column layout for better organization on different screen sizes.

Technical Implementation

Data Loading and Preprocessing

The dataset contains detailed information about the top streamed songs on Spotify in 2024. It includes attributes such as track name, artist, release date, streaming counts, playlist inclusions, Shazam counts, SiriusXM spins, and whether the track is explicit.

Machine Learning Integration

A RandomForestRegressor model is trained to predict song popularity based on its features. Users can input song characteristics and get a predicted stream count through the “Popularity Predictor” tab.

User Interaction and Recommendations

The app allows users to upload their own Spotify playlist for analysis. Based on the analysis, the app provides song recommendations to enhance user engagement and discoverability.

Performance and Responsiveness

The app uses caching for data loading and heavy computations to ensure smooth performance. It also adapts well to different screen sizes, making it accessible on both desktop and mobile devices.

Acknowledgment

Special thanks to Data in Motion for providing the dataset as part of their weekly challenges. Their valuable resources and support made this project possible.

Conclusion

The “Spotify 2024 Top Streamed Songs Analysis” app is a powerful tool for analyzing music trends and characteristics. By providing actionable insights, this app helps marketing and curation teams improve user experience, song recommendations, and playlist curation.

Explore the app: Spotify Analysis App

For more details about the project and to view the code, visit our GitHub repository.

Connect with Me

• GitHub: Mohammed-Mebarek-Mecheter
• LinkedIn: mohammed-mecheter
• Portfolio: Mebarek

Thank you for reading, and feel free to reach out with any questions or feedback!