Exercise $2$: Before diving deeper into the data, we should stop and create a training and a test set.

Since we are trying to predict whether an individual earns over $$50$K$,$ save the income column as a Series named income$\_$label.

Drop the income column from the census$\_$income DataFrame and save the remaining columns as a DataFrame named income$\_$features.

Utilize Scikit$-$learn's train$\_$test$\_$split function, employing the income$\_$features and income$\_$label variables, to partition the data into a training set and a test set. Allocate $80\%$ of the instances for training and $20\%$ for testing. Set the random$\_$state to $42$ to ensure reproducibility of our results. Assign the DataFrames the following names: X$\_$train, X$\_$test, y$\_$train, and y$\_$test.