Authors: Mateo Gomez, Jiawei Wu, Yucong Chen, Qinmiao Wei
Wine quality assessment, a complex interplay of various chemical and sensory factors, has long intrigued oenophiles and industry professionals alike. The intrinsic challenge lies in discerning the nuanced relationship between a wine’s chemical composition and the subjective evaluations of experts. In this research, we embark on a comprehensive exploration aimed at developing an accurate forecast model for predicting wine quality.
Our endeavor to predict wine quality stands at the intersection of domain knowledge, statistical rigor, and machine learning innovation. By combining these elements, we aim to contribute to the evolving landscape of predictive modeling in oenology, offering valuable insights for both researchers and practitioners in the field.
For the full project writeup, please see the attached pdf.
Please refer to EDA and Modeling for coding parts.