Uncorking the Code: Random Forests Decipher the Secrets of Wine Quality

Abstract From Barrel to Algorithm: Demystifying Red Wine Quality with Feature-Rich Random Forests. Traditionally shrouded in subjectivity, red wine quality assessment is now poised for a data-driven revolution. This paper delves into the intricate world of chemical and physical signatures, employing Random Forests to decipher their relationship with expert quality scores. Our model, exceeding 93% accuracy, not only predicts quality but also illuminates hidden patterns, revealing how acidity, citric acid, and other features influence the final product. This discovery opens doors for optimized grape selection, tailored vinification processes, and informed consumer choices. By bridging the gap between terroir and technology, we pave the way for a future where wine quality is not just tasted, but precisely predicted. Keywords: Wine quality prediction, Random Forests, feature engineering, chemical and physical properties, model interpret- ability.