Manhattan Distance Demystified: A Beginners guide to Real-World Applications

Manhattan distance, a metric used to calculate distances in grid-like structures, is an important metric in distance calculation. In this document I will be sharing details around the same.

I will also share real-world applications of Manhattan distance, including optimizing delivery routes, image recognition, and customer segmentation, highlighting its versatility and practical significance in data science and machine learning. Key contents of the document:

1)   What is Manhattan Distance? (Page 1)
a)   Definition and comparison to Euclidean distance.
2)   Formula (Page 2)
a)   Mathematical representation for 2D and higher dimensions.
3)   Calculation Examples (Page 4)
a)   Illustrative examples in 2D and higher-dimensional spaces.
4)   Applications
a)   Use Case 1: Efficient Delivery Routing for Grocery Services (Page 5)
i)   Objective, challenge, and solution.
ii)   Example with customer locations and distance calculations. (Page 6)
iii)   Benefits of optimized routing.
b)   Use Case 2: Image Recognition and Processing (Page 7)
i)   Objective, challenge, and solution.
ii)   Example with image pixel values and distance calculation. (Page 8)
iii)   Benefits of efficient image recognition.
c)   Use Case 3: Customer Segmentation in E-commerce (Page 9)
i)   Objective, challenge, and solution.
ii)   Example with customer purchase data and distance calculations. (Page 10)
iii)   Benefits of targeted marketing through segmentation.
5)   Summary (Page 11)
a)   Versatility of Manhattan distance across domains.
b)   Value in data science and machine learning.
c)   Practical solutions to real-world problems.

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