As a subfield of Artificial Intelligence (AI), Machine Learning (ML) aims
to understand the structure of the data and fit it into models, which later can
be used in unseen data to achieve the desired task. ML has been widely used in
various sectors such as in Businesses, Medicine, Astrophysics, and many other
scientific problems. Inspired by the success of ML in different sectors, here,
we use it to predict the wine quality based on the various parameters. Among
various ML models, we compare the performance of Ridge Regression (RR), Support
Vector Machine (SVM), Gradient Boosting Regressor (GBR), and multi-layer Artificial
Neural Network (ANN) to predict the wine quality. Multiple parameters that
determine the wine quality are analyzed. Our analysis shows that GBR surpasses all
other models’ performance with MSE, R, and MAPE of 0.3741, 0.6057, and 0.0873
respectively. This work demonstrates, how
statistical analysis can be used to identify the components that mainly control
the wine quality prior to the production. This will help wine manufacturer to
control the quality prior to the wine production.
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