Spotify music recommendation system using EDA

This system utilizes Spotify song data from 2019 to 2022 to uncover trends in “song popularity” through detailed visualizations, achieved via Exploratory Data Analysis (EDA) to identify pertinent features. By applying K-means clustering, the system groups genres based on their audio characteristics, highlighting genre similarities and enabling nuanced song recommendations. Leveraging SpotiPy, a Python library for accessing Spotify’s music library API, the system analyzes users’ listening histories to recommend songs with similar audio features, aligning with their preferences. Regular updates with new data ensure the recommendations remain current and relevant.