Music Recommendation System

This repository contains a Python-based Music Recommendation System built using Jupyter Notebook, Streamlit, and Spotipy. The system recommends music based on similarity metrics calculated from a dataset of songs and artists.

Features

• Song Recommendation: Input a song name and get recommendations based on similarity.
• Interactive UI: Built using Streamlit for easy interaction and visualization.
• Integration with Spotify API: Utilizes Spotipy to fetch album covers and enhance user experience.

Installation

1. Clone the repository:

   git clone https://github.com/your-username/music-recommendation-system.git
   cd music-recommendation-system
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Spotify API Credentials:

• Obtain Spotify API credentials from Spotify for Developers. Update config.py with your CLIENT_ID and CLIENT_SECRET.

Usage

1. Run the Streamlit app:

   streamlit run app.py
   

2. Open your web browser and navigate to http://localhost:8501 (or the URL provided by Streamlit).

3. Select a song from the dropdown menu or enter the song name directly.

4. Click on "Show Recommendation" to see recommended songs and their album covers.

File Structure

• app.py: Main application file using Streamlit.
• config.py: Configuration file for Spotify API credentials.
• notebooks/: Jupyter Notebooks for data preprocessing and similarity calculations.
• data/: Directory for dataset files (e.g., df.pkl, similarity.pkl).

Contributing

Contributions are welcome! Please fork the repository and create a pull request with your proposed changes.

• Fork the project.
• Create your feature branch (git checkout -b feature/AmazingFeature).
• Commit your changes (git commit -m 'Add some AmazingFeature').
• Push to the branch (git push origin feature/AmazingFeature).
• Open a pull request.