The tidymodels packages provides a better way to filter no- and near-zero-variance features with its step_zv() and step_nzv() functions, respectively. These recipe steps identify low-variance features by examining the number of unique values and the ratio of the frequency of the most common values in each feature. This approach is more robust than the simple variance cutoff we used previously.

In addition, you will use the step_scale() recipe step to normalize the variance of the features. Remember it's always a good idea to normalize the data to make variances across features comparable.

The house_sales_df is available for you to use. The target variable is price. The tidyverse and tidymodels packages have also been loaded for you.

Selecting based on correlation with other features

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