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zero entries in sparse data. In our case, we’ll use StandardScaler to scale our hypothetical dataset, resulting in a DataFrame named scaled_features. This prepares our data for dimensionality reduction with PCA and clustering with KMeans. scaler = StandardScaler() scaled_features =