Wine is an alcoholic drink, which is produced by fermenting grapes in a controlled environment. Different varieties ofgrapes and strains of yeasts are major factors in different styles of wine. Wine has been produced for thousands of years and has a long history and culture in many regions of the world. Wine can be classified into different types, such as red, white, rosé, sparkling, dessert, etc. based on the color, sweetness, carbonation, and other factors. The global wine market is expected to growfrom $340 billion in 2021 to $456 billion in 2028. As wine production is part of such a huge industry, quality checks involved consists of sophisticated procedures, which include the participation of distinguished wine quality checking officials, called Wine Sommeliers. These Sommeliers require years of rigorous training and practice to give a valid verdict about the quality and classification of different wines.

1.
P.
Cortez
,
A.
Cerdeira
,
F.
Almeida
,
T.
Matos
and
J.
Reis
. Modeling wine preferences by data mining from physicochemical properties. In
Decision Support Systems
,
Elsevier
,
47
(
4
):
547
553
,
2009
.
2.
Gupta
,
Mohit
&
Chandrasekaran
,
Vanmathi
. (
2021
).
A Study and Analysis of Machine Learning Techniques in Predicting Wine Quality
.
International Journal of Recent Technology and Engineering.
10
.
314
321
. .
3.
Del Pino-García
R.
,
González-SanJosé
M.L.
,
Rivero-Pérez
M.D.
,
García-Lomillo
J.
,
Muñiz
P.
The effects of heat treatmentonthe phenolic composition and antioxidant capacity of red wine pomace seasonings
,
Food Chem.
2017
;
221
:
1723
1732
. doi: .
4.
Cavallini
G.
,
Straniero
S.
,
Donati
A.
,
Bergamini
E.
Resveratrol requires red wine polyphenols for optimum antioxidant activity,
J. Nutr. Health Aging.
2016
;
20
:
540
545
. doi: .
5.
Majumder
,
P.
(
2020
).
Gaussian Naive Bayes
.
OpenGenus IQ: Computing Expertise & Legacy.
https://iq.opengenus.org/gaussian-naive-bayes.
6.
Decision Tree Algorithm in Machine Learning - Javatpoint. (n.d
.). www.javatpoint.com. https://www.javatpoint.com/machine-learning-decision-tree-classification-algorithm.
7.
Machine Learning Random Forest Algorithm - Javatpoint. (n.d
.). www.javatpoint.com. https://www.javatpoint.com/machine-learning-random-forest-algorithm.
8.
Logistic Regression in Machine Learning - Javatpoint. (n.d.-b)
. www.javatpoint.com. https://www.javatpoint.com/logistic-regression-in-machine-learning.
9.
K-Nearest Neighbor(KNN) Algorithm for Machine Learning - Javatpoint. (n.d
.). www.javatpoint.com. https://www.javatpoint.com/k-nearest-neighbor-algorithm-for-machine-learning.
10.
Geeksfor
Geeks
. (
2023
).
Gradient Boosting in ML
.
GeeksforGeeks.
https://www.geeksforgeeks.org/ml-gradient-boosting/
11.
Masui
,
T.
(
2022
, February 12).
All You Need to Know about Gradient Boosting Algorithm−Part 1. Regression
.
Medium.
https://towardsdatascience.com/all-you-need-to-know-about-gradient-boosting-algorithm-part-1-regression-2520a34a502
12.
Support Vector Machine (SVM) Algorithm - Javatpoint. (n.d
.). www.javatpoint.com. https://www.javatpoint.com/machine-learning-support-vector-machine-algorithm.
13.
Ghoneim
,
S.
(
2021
, December 9).
Accuracy, Recall, Precision, F-Score & Specificity, which to optimize on?
Medium.
https://towardsdatascience.com/accuracy-recall-precision-f-score-specificity-which-to-optimize-on-867d3f11124.
14.
Selvaraj
,
N.
(n.d.).
Hyperparameter Tuning Using Grid Search and Random Search in Python - KDnuggets
.
KDnuggets.
https://www.kdnuggets.com/2022/10/hyperparameter-tuning-grid-search-random-search-python.html.
This content is only available via PDF.
You do not currently have access to this content.