Using python solve the solve the following:Predicting Delayed Flights. The file FlightDelays.csv contains information on all commercial flights departing the Washington, DC area and arriving in New York during January $2004.$ For each flight, there is information on the departure and arrival airports, the distance of the route, the scheduled time and date of the flight, and so on$.$ The variable that we are trying to predict is whether or not a flight is delayed. A delay is defined as an arrival that is at least $15$ minutes later than scheduled.

Data Preprocessing. Transform variable day of week $($DAY$\_$WEEK$)$ into a categorical variable.

Bin the scheduled departure time into eight bins. Use these and all other columns as predictors $($excluding DAY$\_$OF$\_$MONTH$).$ Partition the data into training $(60\%)$ and validation $(40\%)$ sets.

a$.$ Fit a classification tree to the flight delay variable using all the relevant predictors. Do not

include DEP$\_$TIME $($actual departure time$)$ in the model because it is unknown at the time of

prediction $($unless we are generating our predictions of delays after the plane takes off, which

is unlikely$).$ Use a tree with maximum depth $8$ and minimum impurity decrease $=0\mathrm{.}01.$

Express the resulting tree as a set of rules.

b$.$ If you needed to fly between DCA and EWR on a Monday at $7$:$00$ AM$,$ would you be able to

use this tree? What other information would you need? Is it available in practice? What

information is redundant?

c$.$ Fit the same tree as in $($a$),$ this time excluding the Weather predictor. Display the

resulting tree.