Exercise $3$: Before diving deeper into the data, we should stop and create a training and a test set.

Since we are trying to predict the price of an apartment, save the price column as a Series named price$\_$target.

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

Utilize Scikit$-$learn's train$\_$test$\_$split function, employing the apartments$\_$features and price$\_$target 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 resulting DataFrames the following names: X$\_$train, X$\_$test, y$\_$train, and y$\_$test.