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@article{187067,
        author = {Mv Karthikeya and Dr.T.V.Nagalakshmi and D. Nanda Kishore and Vishnu Vardhan and A. Narendrasai and B Sharath reddy},
        title = {Predictive Modeling of Wine Quality Using Advanced Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {6},
        pages = {3366-3368},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187067},
        abstract = {Wine quality prediction based on physicochemical properties is a classical problem that has gained significant attention in machine learning. This paper investigates wine quality prediction using the UCI Wine Quality dataset. We compare several supervised learning techniques Logistic Regression, Support Vector Machines (SVM), Random Forests, and Gradient Boosting (XGBoost) to classify wines into three categories: Low, Medium, and High, based on eleven measurable properties such as alcohol content, pH, and acidity. Preprocessing includes normalization, outlier handling, and treatment for class imbalance. Experimental results demonstrate that ensemble learning models outperform linear classifiers; XGBoost achieved approximately 81% accuracy on an 80/20 stratified split. We report feature importance, use interpretable AI approaches (SHAP), and discuss deployment using Flask to show real-world implementation. The framework is reproducible and suitable for academic and industrial applications.},
        keywords = {Wine quality, Machine learning, Random Forest, XGBoost, classification, Flask deployment, UCI dataset},
        month = {February},
        }