Filtered the dataset to keep only the numerical features, then calculated covariance between selected feature pairs to examine how the variables change together.
LDA — Selected the numerical features and used Customer_Segment as the class label, applied Linear Discriminant Analysis, and visualized the first two discriminant dimensions to compare class separation.
PCA — Selected the numerical features, computed the principal components using PCA Compute, applied the PCA transformation to the data, and visualized the first two components using a scatter plot.
To use this workflow in KNIME, download it from the below URL and open it in KNIME:
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