Analyzed Spotify data encompassing 38,000+ songs to develop a predictive model, achieving
87.3% accuracy in genre classification using Random Forest
(F1 score: 0.88) and 96% accuracy in hit prediction.
Conducted data pre-processing, explored dimensionality reduction techniques like PCA,
t-SNE, and UMAP, applied machine learning algorithms, and created
visualizations to uncover patterns in music genre and popularity trends.