{"id":12453,"date":"2023-10-11T15:01:54","date_gmt":"2023-10-11T09:31:54","guid":{"rendered":"https:\/\/skillioma.com\/learn\/courses\/ai-ml-nascomm-fsp\/lesson\/distances-minkowski-manhattan-euclidian-2\/"},"modified":"2024-02-02T16:16:40","modified_gmt":"2024-02-02T10:46:40","slug":"distances-minkowski-manhattan-euclidian-2","status":"publish","type":"lesson","link":"https:\/\/skillioma.com\/learn\/courses\/ai-ml-and-data-science-foundation-nascomm-fsp\/lesson\/distances-minkowski-manhattan-euclidian-2\/","title":{"rendered":"Distances – Minkowski \/ Manhattan \/ Euclidian"},"content":{"rendered":"

The Minkowski, Manhattan, and Euclidean distances are all ways to measure the distance between points in a space. Let’s dive deeper into each of these.<\/span>
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Minkowski Distance:<\/b><\/h4>\n

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Manhattan Distance (L1 norm):<\/b><\/h4>\n

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Also known as the “city block” or “taxicab” distance, it’s the distance between two points in a grid-based path (like Manhattan street grids) and is computed as:<\/span>
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Euclidean Distance (L2 norm):<\/b><\/h4>\n

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The most common metric for continuous quantities. It’s the “ordinary” straight-line distance between two points in Euclidean space.<\/span>
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Business Scenarios:<\/b>
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E-commerce Recommendations:<\/b> If you want to suggest products to a user based on their browsing history, you might use distance metrics to find products that are “close” to what they’ve looked at or purchased in the past.<\/span><\/p>\n

Real Estate Pricing:<\/b> When determining the price of a house, you might consider the Euclidean distance to key landmarks or Manhattan distance if you’re considering city blocks to essential facilities like schools, hospitals, etc.<\/span><\/p>\n

Supply Chain Optimization:<\/b> If you’re routing delivery trucks, Manhattan distance might be more appropriate in urban settings where you’re constrained by a grid of streets.<\/span><\/p>\n

Fraud Detection in Banking:<\/b> By examining transactions in a multi-dimensional space (where dimensions could be amount, frequency, merchant category, etc.), you might identify unusual (i.e., distant from typical) transactions using a distance metric.<\/span><\/p>\n

Clustering Customers for Market Segmentation:<\/b> Using clustering algorithms like K-means, which inherently use distance metrics, can help in segmenting customers based on their purchase behaviour, demographic information, etc.<\/span><\/p>\n

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When choosing a distance metric for a business problem, consider the nature of your data and what “distance” would mean contextually. For instance, in a city with a grid-like structure, Manhattan distance might be more representative of actual distances people must travel, while in a more continuous space, Euclidean might be more appropriate.<\/span><\/p>\n","protected":false},"comment_status":"open","ping_status":"closed","template":"","class_list":["post-12453","lesson","type-lesson","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/skillioma.com\/learn\/wp-json\/wp\/v2\/lesson\/12453","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/skillioma.com\/learn\/wp-json\/wp\/v2\/lesson"}],"about":[{"href":"https:\/\/skillioma.com\/learn\/wp-json\/wp\/v2\/types\/lesson"}],"replies":[{"embeddable":true,"href":"https:\/\/skillioma.com\/learn\/wp-json\/wp\/v2\/comments?post=12453"}],"wp:attachment":[{"href":"https:\/\/skillioma.com\/learn\/wp-json\/wp\/v2\/media?parent=12453"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}