Authors :
B. Jhansi Vazaram; D. Shiva Sankar; M. Lokesh; M. Mallikarjuna
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/mthjd8tp
Scribd :
https://tinyurl.com/5t4h534f
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR2134
Abstract :
The objective of this study aimed to create a
model to forecast the quality of red wine by examining its
physicochemical attributes. Various factors affect the
precision of quality prediction in red wine analysis. This
paper presents a computational intelligence approach
employing machine learning methods. Specifically, the
Random Forest Classifier, Naive Bayes Algorithm, and
Support Vector Machine were applied. Using these
machine learning techniques and the provided
information, it becomes possible to predict the quality of
a given red wine sample.
Keywords :
Red Wine, Naive Bayes Algorithm,Support Vector Machine, Quality Prediction and Random Forest Classifier.
The objective of this study aimed to create a
model to forecast the quality of red wine by examining its
physicochemical attributes. Various factors affect the
precision of quality prediction in red wine analysis. This
paper presents a computational intelligence approach
employing machine learning methods. Specifically, the
Random Forest Classifier, Naive Bayes Algorithm, and
Support Vector Machine were applied. Using these
machine learning techniques and the provided
information, it becomes possible to predict the quality of
a given red wine sample.
Keywords :
Red Wine, Naive Bayes Algorithm,Support Vector Machine, Quality Prediction and Random Forest Classifier.
