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Clustering Demystified
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Course Content

Clustering Demystified

Clustering Demystified

IntroductionImport Necessary Libraries and DataPreliminary AnalysisData PreparationDeclare Feature Vector and Target VariableConvert Categorical Variable into IntegersFeature ScalingK-Means Model with 2 ClustersCheck the QualityHow many Clusters?K-Means Model with 4 Clusters

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Now that we have trained our clustering algorithm we have to evaluate its performances to assess the results.

Methods description

  • .labels_: This attribute of the KMeans object contains the cluster labels assigned to each data point after fitting the KMeans algorithm to the data;
  • y == labels: This is a comparison operation that checks element-wise equality between two arrays y and labels, resulting in a boolean array;
  • correct_labels: This variable stores the count of data points that were correctly labeled, i.e., assigned to the correct cluster.

Task

  1. Compare the correct_labels with the predicted ones.
  2. Print the results.

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Now that we have trained our clustering algorithm we have to evaluate its performances to assess the results.

Methods description

  • .labels_: This attribute of the KMeans object contains the cluster labels assigned to each data point after fitting the KMeans algorithm to the data;
  • y == labels: This is a comparison operation that checks element-wise equality between two arrays y and labels, resulting in a boolean array;
  • correct_labels: This variable stores the count of data points that were correctly labeled, i.e., assigned to the correct cluster.

Task

  1. Compare the correct_labels with the predicted ones.
  2. Print the results.

Mark tasks as Completed
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 9
AVAILABLE TO ULTIMATE ONLY
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