Identify highly correlated features

Using the data in house_sales_df, you will practice identifying features that have high correlation. High correlation among features indicates redundant information and can cause problems in modeling such as multicollinearity in regression models. You will determine which of the highly correlated features to remove. A correlation matrix will help you identify highly correlated features.

The tidyverse and corrr packages have been loaded for you.

This exercise is part of the course

Dimensionality Reduction in R

View Course

Exercise instructions

Create a correlation plot with the correlations printed on the plot.

Hands-on interactive exercise

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

# Create a correlation plot of the house sales
house_sales_df %>% 
  ___() %>% 
  ___() %>% 
  ___(print_cor = ___) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

Edit and Run Code