A Machine Learning Based Approach for Wine Quality Prediction

Y. Harsha Vardhan Reddy; M.Naga raju; D. Venkatesh; K.Partha Sarthi Reddy; P. Shobha Rani

A Machine Learning Based Approach for Wine Quality Prediction

Author(s)	Ms. Y. Harsha Vardhan Reddy, M.Naga raju, D. Venkatesh, K.Partha Sarthi Reddy, P. Shobha Rani
Country	India
Abstract	The main goal of this project is to predict wine quality whether it is good or bad. For centuries tasting has been done by humans and they have always predicted on the basis of sensory organs. But in recent times the industries are adopting newer technologies and applying them in all kinds of areas. But, still there are many areas in which human expertise is needed like product quality assurance. Nowadays, it becomes an expensive process as the demand of product is growing over the time. Therefore, this project searches different machine learning techniques such as MLP classifier, Decision Tree classifier, Support Vector Machines (SVM) for product quality assurance. These techniques do quality assurance process with the help of available characteristics of product and automate the process by minimizing human interference. The Red Wine Quality Project aims to analyse and predict the quality of red wines based on a range of chemical and sensory factors. By examining key attributes such as alcohol content, acidity, sugar levels, pH, and others, the project seeks to develop an accurate model to predict wine quality. This can help winemakers optimize production processes and provide consumers with reliable quality evaluations. Using machine learning techniques like regression and classification models, the project strives to uncover hidden relationships between wine features and their quality ratings.
Keywords	MLP classifier, Decision Tree classifier, Support Vector Machines (SVM), Machine Learning.
Field	Engineering
Published In	Volume 7, Issue 4, April 2026
Published On	2026-04-04

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A Machine Learning Based Approach for Wine Quality Prediction

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