This machine learning project looks at implementing the KMeans clustering algorithm on the wine quality dataset. The elbow method and the silhouette method are used to find the optimum number of clusters. The Kelbow visualizer is also used to select the optimum value for the number of clusters. The dataset used for this project is available on kaggle and on UCI ML repository. For a complete video explanation of this project, check out this video on my youtube channel by clicking on the following link. https://youtu.be/m2HyLz7E2Vg
DataSet Links: Wine quality dataset on Kaggle: https://www.kaggle.com/uciml/red-wine-quality-cortez-et-al-2009
Wine Quality dataset on UCI ML Repository: https://archive.ics.uci.edu/ml/datasets/wine+quality