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Machine Learning - K - Means Clustering Objective: To design and evaluate the performance of a k - means clustering model on a synthetic dataset.
Machine Learning KMeans Clustering
Objective:
To design and evaluate the performance of a kmeans clustering model on a synthetic dataset.
Instructions:
Generate the synthetic dataset as follows:
makeblobsnsamples centers nfeatures randomstate
Find the optimal number of centroids according to the Elbow method. Justify your
selection.
Find the optimal number of centroids according to the Silhouette method. Justify your
selection. Which method is more accurate?
Fit a kMeans model, with k Visualize all clusters found by kMeans. Report the
inertia of the model. Analyze and discuss your plot.
Evaluate the accuracy of labels estimated by kMeans Hint: you may use the
classificationreport method Analyze the classification report and draw your
conclusions for the designed model.
Note Include the code and explanation
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