Predicting Wine Quality with High Accuracy using Machine Learning Models

Singh, Bhupinder (2024) Predicting Wine Quality with High Accuracy using Machine Learning Models. Masters thesis, Dublin, National College of Ireland.

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Abstract

This Research is about Predicting the wine Quality using a dataset which is having chemical Properties, sensory feedback, environmental factors and Aging data in it. The main objective of this search is to predict wine Quality with high accuracy using different machine learning models. A Comprehensive approach was taken to produce results, like Data collection then data processing then feature engineering, then EDA and then finally implementing different machine learning models like Random Forest, Gradient Boosting, XGBoost, SVM and stacking ensemble. Different approaches were taken while implementing these models to get the optimal results, techniques like feature selection then hyper parameter tunning were used for each model separately. Also, Cross validation techniques was used to ensure the reliability of these machine learning models. Then to evaluate the performance of each model different evaluation metrics like Accuracy, Precision, Recall, F1-score were used. The Random Forest (Tunned) got 87.10% accuracy, Gradient Boosting (Tunned) got 86.13% accuracy, XGBoost (Tunned) got 87.61% accuracy, SVM tunned got 73.01% accuracy and the best model was stacking ensemble which got accuracy of 88.47%. These results show the effectiveness of these machine learning algorithms in predicting wine quality with great number of wine features together. The methodology of this research is robust and could be used in wine making industry to predict wine quality with use of machine learning techniques, as this would improve accuracy in wine quality prediction and would improve the overall wine quality.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Tomer, Vikas UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Food Industry > Beverage industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	26 Aug 2025 10:49
Last Modified:	26 Aug 2025 10:49
URI:	https://norma.ncirl.ie/id/eprint/8634

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