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.

Это упражнение является частью курса

Dimensionality Reduction in R

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Инструкции к упражнению

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

Интерактивное практическое упражнение

Попробуйте выполнить это упражнение, дополнив этот пример кода.

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

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