Published Paper Details:

Predicting wine quality has emerged as a crucial topic of interest for producers and consumers alike, with the goal of ensuring production excellence and increasing market value. Relying on human expertise, traditional techniques of evaluating quality are time-consuming, subjective, and resource-intensive. Using publicly accessible datasets from the UCI Machine Learning Repository, we use Machine Learning (ML) approaches to forecast wine quality based on physicochemical properties in order to overcome these difficulties. Models like Logistic Regression, Decision Tree, Random Forest, and XGBoost are all part of our methodology and are assessed based on performance indicators like accuracy. By identifying the most pertinent qualities, feature selection reduces model complexity and boosts efficiency. Predicting wine quality has emerged as a crucial field of With XGBoost producing better results, our investigation demonstrates the strong predictive ability of ensemble methods and shows how they might be used to expedite quality assurance procedures in the wine business. This study demonstrates how ML-driven approaches can revolutionize conventional methods by providing a quicker, more accurate, and more affordable substitute for predicting wine quality. Predicting the quality of wine has become a crucial aspect of Our research demonstrates the strong predictive ability of ensemble approaches, with XGBoost producing better outcomes and showing promise for streamlining wine industry quality assurance procedures. This research highlights how machine learning (ML)-driven approaches can revolutionize conventional methods by providing a quicker, more accurate, and more affordable substitute for wine quality assessment.