Create a zero-variance filter

house_sales_df contains ten continuous variables describing house sales in King County, California. Examples of these variables include square footage, number of rooms, and sales price. You will need to reduce the dimensionality to make the dataset easier to work with and reduce the training time when creating models.

Let's get started with creating a zero-variance filter. The tidyverse package has been loaded for you.

This exercise is part of the course

Dimensionality Reduction in R

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Exercise instructions

Create a zero-variance filter using summarize() and filter() and store it in zero_var_filter.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create zero-variance filter
___ <- ___ %>% 
  ___(across(everything(), ~ ___(___, ___ = ___))) %>% 
  pivot_longer(everything(), names_to = "feature", values_to = "variance") %>% 
  ___(___ == ___) %>% 
  pull(feature)

zero_var_filter

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