Classifications on wine informatics using PCA, LDA, and supervised machine learning techniques
by Swarna Prabha Jena; Bijay Kumar Paikaray; Jitendra Pramanik; Rishi Thapa; Abhaya Kumar Samal
International Journal of Work Innovation (IJWI), Vol. 4, No. 1, 2023

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.

Online publication date: Thu, 20-Apr-2023

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