Classifications on wine informatics using PCA, LDA, and supervised machine learning techniques. (16th April 2023)

• Record Type: Journal Article
• Authors: Jena, Swarna Prabha; Paikaray, Bijay Kumar; Pramanik, Jitendra; Thapa, Rishi; Samal, Abhaya Kumar
• Is Part Of: International journal of work innovation. Volume 4:Number 1(2023)
• Page Start: 58
• Page End: 73
• Publication Date: 2023-04-16
• Subjects: classification -- machine learning -- decision tree -- principle component analysis -- PCA -- linear discriminant analysis -- LDA -- Gaussian naive Bayes -- GNB -- K-nearest neighbour -- KNN; 658.314
• Languages: English
• ISSNs: 2043-9032

Abstract

Proving the quality of a food product is challenging for any country. Every country recommends using products whose quality has been assured. A similar thing applies to the wine industry. To promote their products, wine industries acquire quality certifications through expert assessments. It is an expensive and time-consuming process. This paper explores the usage of machine algorithms like principle component analysis (PCA), linear discriminant analysis (LDA), random forest (RF), Gaussian naive Bayes (GNB), decision trees (DT), K-nearest neighbour (KNN), logistic regression (LR), and gradient boost (GB) for classifying the wine data into three main categories. The experimental work provides a comparative study of the accuracy of all classifiers is discussed in detail.