Editor/Reviewer loginFrequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
IJCATR Volume 8 Issue 9
Wine Quality Prediction using Machine Learning Algorithms
Devika Pawar, Aakanksha Mahajan, Sachin Bhoithe
10.7753/IJCATR0809.1010
keywords : Machine Learning, Classification,Random Forest, SVM,Prediction.
Wine classification is a difficult task since taste is the least understood of the human senses. A good wine quality prediction can be very useful in the certification phase, since currently the sensory analysis is performed by human tasters, being clearly a subjective approach. An automatic predictive system can be integrated into a decision support system, helping the speed and quality of the performance. Furthermore, a feature selection process can help to analyze the impact of the analytical tests. If it is concluded that several input variables are highly relevant to predict the wine quality, since in the production process some variables can be controlled, this information can be used to improve the wine quality. Classification models used here are 1) Random Forest 2) Stochastic Gradient Descent 3) SVC 4)Logistic Regression.
@artical{d892019ijcatr08091010,
Title = "Wine Quality Prediction using Machine Learning Algorithms",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "8",
Issue ="9",
Pages ="385 - 388",
Year = "2019",
Authors ="Devika Pawar, Aakanksha Mahajan, Sachin Bhoithe"}
Classification models used here are 1) Random Forest 2) Stochastic Gradient Descent 3) SVC.
We were able to achieve maximum accuracy using random forest of 88%. Stochastic gradient descent giving an accuracy of 81% .SVC has an accuracy of 85% and logistic regression of 86%.
Label Encoding is used to convert the labels into numeric form so as to convert it into the machine-readable form.
We are splitting our dataset in a way such that all of the wine qualities are represented proportionally equally in both training and testing dataset.