Project Overview

This project is a Spotify song recommender application built using Streamlit, Spotipy, and Tableau Public. It allows users to:

• Enter a song name to receive a cluster-based song recommendation using audio features retrieved from the Spotify API.
• View a Tableau dashboard visualization showcasing Spotify-related data.

Key Features

• Song classification using K-Means Clustering based on song audio features.
• Spotify song recommendations from the same cluster using Spotify’s API.
• Integration of a Tableau Public dashboard for visual insights.

Technologies Used

• Python: Main programming language for the application.
• Streamlit: For building the web application.
• Spotipy: A lightweight Python library for the Spotify Web API.
• Pandas: For data manipulation and analysis.
• Scikit-learn: For K-Means clustering model.
• Pillow (PIL): To load and display images in the app.
• Joblib: For loading saved machine learning models.
• Tableau Public: For creating and displaying visualizations.

How to Run

1. Running the Streamlit App Run the Streamlit app using the following command:

streamlit run main.py

This will launch the app in your default browser. You can choose between two options from the sidebar:

• MusicMaster: Enter a song and receive a recommendation from the same cluster.
• Visuals: View a Tableau dashboard visualization related to Spotify.

2. Spotify Song Recommender In the MusicMaster section:

• 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.

3. Tableau Dashboard Visualization In the Visuals section:

• View a static image of the Spotify dashboard created with Tableau Public.
• You can also click a link to view the full interactive Tableau dashboard in a new tab.

What you would need

• Spotify credential in a config file