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Exercise

Create a missing values filter

The zero-variance filter only removes some of the low-information features. Features may also contain little to no information because they have a high number of missing values. In this exercise, you'll create a missing values filter. You'll take an extreme approach and remove any feature with at least one missing value, which means you could remove features with significant information.

house_sales_df is available on the console and tidyverse package has been loaded for you.

Instructions

100 XP
  • Create a missing values filter using summarize(), across(), sum(), and is.na() to remove features with zero or more missing values and store it in na_filter.