$10\mathrm{.}7$ LAB: Random Forests for Regression and Classification

You will use the same mpg$.$ csv dataset for the following tasks.

Load the mpg$.$ csv dataset.

Create a feature dataframe, $x,$ using weight, model$\_$year, and horsepower features.

Create a target dataframe, $Y,$ using the mpg feature.

$Y$ will contain the continuous mpg values for the regression model.

For the classification model, create a new column mpg$\_$classes in $Y$ that categorizes mpg values as low: $(0-17),$ medium: $(1729),$ and high:$(29$ and above$).$

Initialize a Random Forest Regressor with: n$\_$estimators$=100,$ random$\_$state$=100,$ max$\_$depth$=5,$ min$\_$samples$\_$leaf$=5.$

Fit this model on the $x$ and the mpg column in y $.$

Print Feature importances and Mean Squared Error $($MSE$).$

Initialize a Random Forest Classifier with the same parameters as for the regression model.

Fit this model on the x and the mpg$\_$classes column in y $.$

Print Feature importances and classification accuray

$*$ i am posting the whole question for full reference i really only need help with creating the new column for Y for the classification model which is the code shown at the bottom of the screenshot