K-Means Clustering is an Unsupervised Learning algorithm, which groups the unlabeled dataset into different clusters. It is an iterative algorithm that divides the unlabeled dataset into k different clusters in such a way that each dataset belongs only one group that has similar properties. Unsupervised Learning means that the observations given in the data set are unlabeled, there is no outcome to be predicted. We are going to use a Wine data set to cluster different types of wines. The elbow method and the silhouette method are used to find the optimum number of clusters. Our goal is to try to group similar observations together and determine the number of possible clusters (it may differ from 3). This would help us make predictions and reduce dimensionality. 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.
Dataset : https://www.kaggle.com/datasets/uciml/red-wine-quality-cortez-et-al-2009