AzureML-Part-B-Clustering on Wine Dataset
To group the wine dataset using clustering technique, specifically k-means clustering.
Clustering:
Our goal in this exercise is to group the wines into clusters to find natural groups of objects where in
objects within groups are similar and objects within different groups are dissimilar. That is grouping the
wines into clusters which have similar physical and chemical properties.
k-Means algorithm has been used to build a clustering model with 2 clusters (k= 2). All the physical and
chemical properties have been used to group the wines (except the quality rating ).
