Model-based predictions were implemented to classify the red wine quality when knowing a set of its attributes. Classical supervised machine learning (ML) classifiers were implemented, namely the Logistic Regression, Support Vector Machine (SVM), Random Forest Classifier (RFC). Deep learning models classifiers were implemented as well. The main purpose of this paper is to provide a comparison of the different models' behaviour when predicting a red wine quality from objective analytical tests that were made available from the proposed dataset and as well as to investigate its relevance for the application of prediction models within the scientific community.

Requirements:

Python 3.8 or above.

Tensorflow 2 was used to train deep learning modules.

numpy

seaborn

sklearn

pandas

pickle

matplotlib

tqdm