Spotify Recommendation System

The Spotify Recommendation System aims to predict the likelihood that a user will enjoy a specific song. Given the vast library of tracks available on Spotify, users often struggle to discover new music that aligns with their tastes, leading to decision fatigue and a less engaging user experience. Traditional methods of music discovery, such as curated playlists and manual searches, fall short in providing a personalized listening experience tailored to individual preferences. The challenge lies in accurately analyzing a user's past listening behavior and the intrinsic properties of songs to generate a list of recommended tracks. The system must effectively utilize the diverse features available in the Spotify dataset, such as acousticness, danceability, energy, instrumentalness, liveness, loudness, speechiness, tempo, and valence, to inform its recommendations. The goal is to create a scalable and efficient recommendation engine that not only enhances user satisfaction and engagement but also continuously learns and adapts to evolving user preferences. 

The objective of our project is to develop a music recommendation system for Spotify that enhances user experience and engagement. The system aims to provide personalized song recommendations tailored to individual user preferences. The goal is to address the challenge of music discovery by recommending songs that similar users have enjoyed and by analyzing the attributes of songs to make relevant suggestions. Through this approach, the project aims to increase user satisfaction, encourage prolonged interaction with the platform, and ultimately improve the overall value proposition of Spotify's music streaming service.