CLASSIFICATION OF WINE QUALITY PREDICTION WITH RANDOM FOREST

The primary objective of this project is to determine whether a wine is of good or bad quality. Traditionally, wine tasting has relied on human judgment based on sensory perception. However, with the advancement of technology, modern industries are increasingly adopting automated solutions across various domains. Predicting wine quality is particularly challenging because taste remains one of the most complex and least understood human senses. An accurate prediction system can significantly aid in the wine evaluation process, which is currently dominated by subjective assessments from human tasters. Introducing an automated model can enhance both the speed and consistency of quality assessments by serving as a decision support tool. Additionally, incorporating feature selection techniques can help identify the most influential factors affecting wine quality. This insight can guide adjustments in the production process to enhance the final product. In this project, a Random Forest Classifier is employed as the classification model.