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.
Cet exercice fait partie du cours
Dimensionality Reduction in R
Instructions
- Create a correlation plot with the correlations printed on the plot.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Create a correlation plot of the house sales
house_sales_df %>%
___() %>%
___() %>%
___(print_cor = ___) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))