Abstract:

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 beused in unseen data to achieve the desired task.

ML has been widely used invarious 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 Random Forest(RF), Support Vector Machine (SVM), extreme Gradient Boosting(XGBoost), and multi-layer K-Nearest Neighbor(KNN),logistic regression,Decision tree,navie bayes to predict the wine quality.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.

Block Diagram