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.
Este exercício faz parte do curso
Dimensionality Reduction in R
Instruções do exercício
- Create a correlation plot with the correlations printed on the plot.
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
# Create a correlation plot of the house sales
house_sales_df %>%
___() %>%
___() %>%
___(print_cor = ___) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))