Algorithms for Data Science: Please provide a code/syntax that WORKS on PYCHARM as well it prints because I have been getting the wrong code from other tutors thanks.

 

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Using your k-means/medians algorithm as a template, create a new python implementation for k- medoids. Your method should take as input: a dataset x, an initial set of medoids and a distance matrix. [[2, 3], [1, 2], [1, 3], [4, 5], [7, 3], [5, 2], [3, 1]] Q1: Compute the distance matrix of dataset x al Q2: Find the medoid of dataset x Q3: Find the resulting clusters using k-medoids on dataset x, using euclidean distance, k = 2, and initial random medoids of: c = [[2, 3], [1, 2]] Submit your python script and the script output for the above 3 questions. *You will need supporting methods to "compute distance matrix" and "calculate medoid" Your k-medoids method should take as input 3 parameters: dataset, initial medoids and a distance matrix. Using your k-means/medians algorithm as a template, create a new python implementation for k- medoids. Your method should take as input: a dataset x, an initial set of medoids and a distance matrix. [[2, 3], [1, 2], [1, 3], [4, 5], [7, 3], [5, 2], [3, 1]] Q1: Compute the distance matrix of dataset x al Q2: Find the medoid of dataset x Q3: Find the resulting clusters using k-medoids on dataset x, using euclidean distance, k = 2, and initial random medoids of: c = [[2, 3], [1, 2]] Submit your python script and the script output for the above 3 questions. *You will need supporting methods to "compute distance matrix" and "calculate medoid" Your k-medoids method should take as input 3 parameters: dataset, initial medoids and a distance matrix.