Predicting Delayed Flights (Boosting). The file FlightDelays.csv contains information on all commercial flights departing the Washington, DC area and arriving at 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 info a categorical variable. Bin the scheduled departure time into eight bins (in Python use function pd.cut() from the pandas package). Partition the data into training (60%) and validation (40%).

Run a boosted classification tree for delay. With the exception of setting n_estimators=500 and random_state=1, use default setting for the DecisionTreeClassifier and the AdaBoostClassifier.