Music Analysis and Predictive Recommendation System Model

We introduce a music review and suggestion platform that makes machine learning to be a core point of the user experience improvement. By means of the song metadata and audio characteristics available in the CSV file, the system uses unsupervised learning methods such as K-Means clustering with PCA and t-SNE in order to group the songs. Instead of traditional collaborative or content-based filtering, our clusterization mechanism avoids cold-start and scalability issues, while it also leads to the more accurate recommendations. The method of evaluating performance includes Precision, Recall, Silhouette Score, and Davies-Bouldin Index. The latter takes the values 0.82, 0.83, 0.61, and 0.77 respectively. From the further study perspective, deep learning will be the main contender to explore for better feature identification and signaling in real-time as well. The system design is focused on high-quality, and user-centered music recommendation as it also supports fairness and scalability.