In this project, we will explore the importance of various metrics that are used for measuring distances in machine learning. There are many ways of computing the distances between points including euclidean, cityblock, minkowski, hamming, cosine and jaccard.
We will be exploring a cars dataset to look into how K-Means clustering and Agglomerative Hierarchical clustering is impacted by using euclidean or cosine distance.