Developed a machine learning model to classify audio tracks into 10 distinct music genres using extracted tabular features from .wav files. The system achieved 77.5% accuracy on the test dataset, demonstrating strong performance on a challenging multi-class classification task. The project involved audio feature engineering, model selection, and evaluation, showcasing skills in signal processing, supervised learning, and model deployment readiness.