Wine Quality Classification System

Implemented a multi-class classification model to predict wine quality scores based on physicochemical attributes. The project involved feature analysis, class distribution evaluation, and supervised learning to assess how chemical properties influence quality perception. The model provides interpretable insights into feature impact while highlighting challenges related to class imbalance. This project showcases the application of machine learning in quality control, product evaluation, and data-driven decision-making within the food and beverage industry.