Spotify Content-Based Recommendation System and Data Visualization

This is a web application that provides users with personalized Spotify music recommendations based on their listening history and music preferences. Using a combination of TensorFlow for machine learning and the Spotify API for data collection, the Streamlit application displays engaging visualizations of popular and user-specific music trends. The application is built with a Taipy or Streamlit GUI for an interactive and user-friendly experience.

Demo

Demo.mp4

Features

• Global Spotify Statistics: View the most-streamed songs, trending artists, and popular albums.
• User-Specific Insights: Discover your most listened-to songs, favorite artists, and recent listening trends.
• Content-Based Recommendations: Get personalized song recommendations based on your listening history using a content-based recommendation model built with TensorFlow.
• Interactive UI: Explore data through a visually appealing and interactive web interface.

Tech Stack

• Frontend: Streamlit for an interactive and responsive user interface.
• Machine Learning: TensorFlow for building a content-based recommendation model.
• Backend:
  • APIs: Spotify API for collecting user listening data.
  • Visualization: plotly for interactive visualizations.
• Data Visualization: Matplotlib, Seaborn, and Pandas for statistical visualization.

Setup

1. Clone the repository git clone https://github.com/kailinxGitHub/Spotify-UnWrapped-Spotify-Content-Based-Recommendation-System-and-Data-Visualization.git
2. Create a .env file in the root directory with the following Spotify API credentials:

SPOTIPY_CLIENT_ID='your_client_id'
SPOTIPY_CLIENT_SECRET='your_client_secret'
SPOTIPY_REDIRECT_URI='http://localhost:8501'

You can obtain these credentials by:

1. Going to Spotify Developer Dashboard

2. Creating a new application

3. Copying the Client ID and Client Secret

4. Adding http://localhost:8501 to the Redirect URIs in your app settings

5. Conda environment conda env create -f environment.yml

6. Activate the environment conda activate SpotifyUnWrapped

7. Run the Streamlit application streamlit run app.py

8. Open the Streamlit app in your browser at http://localhost:8501

Usage

• Home: The Welcoming Page
• Explore: Explore global music trends and statistics.
• User Profile: Log in with your Spotify account to view your personal listening stats.
• Recommendations: Discover new music tailored to your taste based on song features.

License

This project is licensed under the MIT License.

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Contributing

Contributions are welcome! Please submit a pull request for any improvements or suggestions.

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Acknowledgments

• Top 100 Spotify Songs History
• Spotify API
• TensorFlow
• Get Playlist Data
• For the SpotiPy page
• for the SpotifyAPI class