Spotify Recommendation System 🎵

This project automates the creation of a personalized Spotify playlist by scraping Billboard Hot 100 songs for a specific date, adding them to a playlist, and recommending additional songs based on audio features. It integrates web scraping and Spotify API authentication to curate and enhance playlists based on user input.

Features

• Scrapes the Billboard Hot 100 chart for a user-specified date.
• Authenticates with Spotify API to search for and add songs from the Billboard list.
• Automatically creates a private Spotify playlist with the selected songs.
• Extracts audio features (danceability, energy, tempo, valence) for the playlist songs.
• Uses K-Nearest Neighbors (KNN) to recommend similar songs based on their audio features.
• Adds the recommended songs to the created playlist.

Requirements

• Python 3.x
• Spotipy (pip install spotipy)
• Beautiful Soup (pip install beautifulsoup4)
• Requests (pip install requests)
• Scikit-learn (pip install scikit-learn)
• Pandas (pip install pandas)

Installation

1. Clone the Repository:

git clone https://github.com/Dhwanil25/Spotify_Recommendation_System.git
cd Spotify_Recommendation_System

2. Install the required libraries:

pip install -r requirements.txt

Setup

3. Set up your Spotify Developer Account:

• Go to the Spotify Developer Dashboard and create a new application.
• Copy the Client ID and Client Secret to the script.

4. Update the CLIENT_ID and CLIENT_SECRET in the script with your credentials:

CLIENT_ID = "your_client_id_here"
CLIENT_SECRET = "your_client_secret_here"

Usage

1. Run the script:

python Spotify_Recommendation_System.py

2. Enter the date in the format YYYY-MM-DD when prompted. This date corresponds to the Billboard Hot 100 chart for that period:

Which era of music would you like to explore? Enter the date in the format YYYY-MM-DD to uncover the top songs from that time! 2022-01-01

3. The app will:

• Scrape the Billboard Hot 100 songs from the specified date.
• Create a private playlist in your Spotify account.
• Add the top songs and recommend similar tracks based on audio features.

Technologies Used

• Python: Main programming language.
• Spotipy: For interacting with the Spotify API.
• BeautifulSoup: For web scraping Billboard's Hot 100.
• Scikit-learn: For using the K-Nearest Neighbors (KNN) algorithm to recommend similar songs.
• Pandas & NumPy: For data manipulation and analysis.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

Dhwanil Mori

Feel free to contribute by submitting pull requests or raising issues!