• Mr. E. Ramesh Reddy
• Ch. Giripriya

This project implements a music recommendation system using the K-Nearest Neighbors (KNN) algorithm, the Spotify API, and the Streamlit framework. The system provides personalized music recommendations based on user input. The Spotify API is utilized to fetch song details, including album cover URLs, enhancing the visual appeal of the recommendations. Users can select a song from a dataset loaded from a CSV file, and the system identifies similar tracks by the same artist using KNN. Streamlit serves as the front-end interface, allowing users to interact seamlessly with the recommendation system. It displays recommended songs along with their album covers, providing an engaging user experience. The application demonstrates the integration of machine learning, API usage, and web application frameworks to deliver real-time, personalized music suggestions.

Music Recommendation System, K-Nearest Neighbors (KNN), Spotify API, Streamlit Framework, Personalized Recommendations, User Interaction, Real-Time Suggestions, Album Cover Retrieval.

"Music Recommendation System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.105-108, June-2024, Available :http://www.jetir.org/papers/JETIRGL06019.pdf

"Music Recommendation System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp105-108, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06019.pdf