This project implements a Song Recommender System designed to suggest songs to users based on their listening habits. Using K-means clustering, the system groups songs by their attributes and recommends songs that align with the user's preferences. With very high accuracy, this system provides reliable and engaging music recommendations.
- Music Streaming Platforms: Enhance user engagement by recommending songs tailored to individual tastes.
- Marketing for Artists: Promote tracks to users likely to appreciate them based on listening habits.
- Music Curation: Automate playlist creation for various moods or genres.
- User Retention: Increase user satisfaction and loyalty by offering personalized music experiences.
- Increased Revenue: Encourages subscriptions or song purchases by showcasing relevant music.
Contributions are welcome! Feel free to fork the repository, propose enhancements.