wine-quality

In this project, different Machine Learning techniques were used on a wine dataset, to class and predict a wine preference and quality based on the physicochemical data. The dataset used is related to red Vinho Verde wine samples, from the north of Portugal. The red wine dataset was used in this project, which contains 1143 instances with 13 attributes features of physicochemical data such as volatile acidity, residual sugar, sulphates, pH, and density. In this paper, three classification techniques are implemented: Support Vector Machine, Logistic Regression, k-Nearest Neighbors (kNN) algorithms were used. Their performance was obtained, evaluated, compared, and discussed based on their scores and findings. All these were carried out using Jupyter Notebook utilizing python programming language with machine learning repositories and libraries.