| _version_ | 1866901164667174912 |
|---|---|
| author | Ajala, Theophilus Bamise |
| author_facet | Ajala, Theophilus Bamise |
| contents | <p><span>The main aim of this project is to predict wine quality by using K-Nearest Neighbor (KNN) machine learning model. The user opens the GUI application, supplies the values of the wine features, </span><span>the entered information serves as the dataset for the red wine quality prediction system, which utilizes it to accurately forecast outcomes based on the specified range. </span><span>The output of the KNN algorithm has been estimated using various evaluation metrics. </span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15836112 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Prediction of Wine Quality using KNN Machine Learning Model Ajala, Theophilus Bamise <p><span>The main aim of this project is to predict wine quality by using K-Nearest Neighbor (KNN) machine learning model. The user opens the GUI application, supplies the values of the wine features, </span><span>the entered information serves as the dataset for the red wine quality prediction system, which utilizes it to accurately forecast outcomes based on the specified range. </span><span>The output of the KNN algorithm has been estimated using various evaluation metrics. </span></p> |
| title | Prediction of Wine Quality using KNN Machine Learning Model |
| url | https://doi.org/10.5281/zenodo.15836112 |