Load the zoo data set of 101 animals described by 16 attributes.Compute pairwise similarities (distances) by using the Euclidean distance.In Hierarchical Clustering, select the desired number of clusters. We suggest 4 to 6. Remember to use Ward linkage as it works best.Now inspect the selected clusters by setting the variable to Type and the Subgroups to Cluster. You can do it also the other way around.gAN9cQAoWBIAAABjb250cm9sQXJlYVZpc2libGVxAYhYDAAAAHJlY2VudF9wYXRoc3ECXXEDKGNv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{'autocommit': True, 'axis': 0, 'controlAreaVisible': True, 'metric_idx': 0, 'normalized_dist': True, 'savedWidgetGeometry': b'\x01\xd9\xd0\xcb\x00\x02\x00\x00\x00\x00\x02\xce\x00\x00\x01z\x00\x00\x03\xae\x00\x00\x02|\x00\x00\x02\xce\x00\x00\x01\x90\x00\x00\x03\xae\x00\x00\x02|\x00\x00\x00\x00\x00\x00\x00\x00\x06\x90', '__version__': 2}gAN9cQAoWBcAAABhbm5vdGF0aW9uX2lmX2VudW1lcmF0ZXEBWAsAAABFbnVtZXJhdGlvbnECWBMA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gAN9cQAoWAcAAABjb21wYXJlcQFLAlgSAAAAY29udHJvbEFyZWFWaXNpYmxlcQKIWBMAAABvcmRl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