Which preprocessing step will meet these requirements?

Explanations:

While using the built-in PCA algorithm can perform dimensionality reduction, it does not address the necessity of scaling the features before PCA. PCA is sensitive to the scale of the data; therefore, scaling is essential to ensure that all features contribute equally to the analysis.

This option includes scaling the data with a Min Max Scaler transformation before applying PCA. Scaling the data is crucial in PCA as it standardizes the feature ranges, ensuring that PCA accurately reflects the correlations between features, leading to a better and more effective dimensionality reduction.

Although this option scales the data (using Standard Scaler), it incorrectly suggests reducing dimensionality by removing features with the highest correlation. Instead, PCA is intended to retain components that explain the most variance, so the correct approach is to utilize PCA directly after scaling without manually removing correlated features.

This option incorrectly suggests removing features with the lowest correlation, which is not aligned with the purpose of PCA. Moreover, while it mentions scaling with Min Max Scaler, it still fails to appropriately handle the correlation feature selection and does not effectively utilize PCA’s capability to reduce dimensionality based on variance rather than correlation.