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.
-
Notifications
You must be signed in to change notification settings - Fork 0
Tola-adelase/wine-quality
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Folders and files
Name | Name | Last commit message | Last commit date | |
---|---|---|---|---|
Repository files navigation
About
The Prediction and Classification of Wine Quality
Topics
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published