Exercise

# Correlated variables

In this exercise, you will inspect the dataset with respect to correlated variables. It is important to remove them before applying a binary classifier, especially in the case of logistic regression. When two or more variables are highly correlated you should remove all except for one.

First, we will use the `corrplot()`

function in the `corrplot`

package to visualize the correlations.
In the correlation plot, blue represents a positive correlation and red a negative correlation.
A darker color indicates a higher correlation.
Finally, you will remove the highly correlated variables from the data set.

Instructions 1/2

**undefined XP**

- Load the
`corrplot`

package. - Generate a correlation matrix,
`M`

, using the function`cor()`

. The function takes a subset of the dataset as an argument. - Visualize the correlation between the variables using
`corrplot()`

and`M`

.