This project is the Spotify Song Recommender application built using Streamlit, Spotipy, and Tableau Public. The application allows users to enter a song name and receive a cluster-based song recommendation using audio features retrieved from the Spotify API.
- Song Classification: Utilizes K-Means Clustering based on audio features of songs.
- Spotify Song Recommendations: Provides song recommendations from the same cluster using Spotify’s API.
Python: Main programming language for the application. Streamlit: For building the web application. Spotipy: A lightweight Python library for accessing the Spotify Web API. Pandas: For data manipulation and analysis. Scikit-learn: For implementing the K-Means clustering model. Tableau Public: For creating and displaying visualizations.
Spotify Credentials: Ensure you have your Spotify credentials stored in a config file.
Execute the following command in your terminal: streamlit run main.py This will launch the app in your default browser.
Home: Overview of the application and its functionalities. Songs: Enter a song name and receive a recommendation from the same cluster. Songs Tab Enter a song name in the input field. The app will retrieve the song’s audio features and classify it into a cluster. A recommendation from the same cluster will be displayed along with the original song.
Tableau Dashboard: Gnod Clustering Project Dashboard
This application leverages Spotify music data to recommend songs based on shared characteristics, providing an engaging user experience.