This project is a song recommendation engine that utilizes the Spotify API and the K-nearest neighbors algorithm (KNN) to recommend similar songs based on the user's top tracks.

• Fetches the user's top tracks from their Spotify account.
• Extracts audio features of the user's top tracks using the Spotify API.
• Trains a KNN model using the extracted audio features.
• Fetches a song catalog playlist from Spotify.
• Calculates the similarity between the user's top tracks and songs in the catalog using the KNN model.
• Ranks and displays the top recommended songs.

1. Create a Spotify Developer account and set up a new application to obtain the required client ID and client secret.
2. Update the client_id and client_secret variables in the code with your own credentials.
3. Run the script and follow the authentication prompts to grant access to the Spotify API.

1. Run the script using python song-recommendation-engine.py.
2. The script will retrieve your top tracks and calculate the most similar songs from the provided catalog playlist.
3. The top recommended songs will be displayed in the console.

• You can modify the limit parameter when fetching the user's top tracks to specify the number of tracks to consider.
• Update the playlist_id variable to target a different Spotify playlist for the song catalog.

This project is licensed under the MIT License.