Grapes to Grades: Machine Learning Insights into Grape Nectar Quality

In the present days Wine is a popular and luxurious beverage worldwide, particularly in Europe and Russia. The wine industry heavily relies on wine quality certification, which can have a significant impact on the human body and is expensive. Wine quality is predicted based on various traits that change over time, refining over time. Various ML algorithms including SVM, Regression algorithms, Random Forest Algorithm, KNN, Linear Regression & Decision Tree Algorithm are utilized to assess wine quality. Random Forest Algorithm has the optimal accuracy score at 97.44%. This study contributes to the broader discourse on machine learning applications in viticulture and offers practical insights for stakeholders within the wine industry. It accentuates the criticality of model selection in achieving accurate and reliable wine quality prediction and providing accurate data related to wine quality.