In this unit, you'll import Matplotlib into the notebook you've been working with and configure the notebook to support inline Matplotlib output.

1. Switch back to the Azure notebook that you created in the previous section. If you closed the notebook, you can sign back into the Microsoft Azure Notebooks portal , open your notebook, and use the Cell -> Run All to rerun the all of the cells in the notebook after opening it.
2. Execute the following statements in a new cell at the end of the notebook. Ignore any warning messages that are displayed related to font caching:

%matplotlib inline import matplotlib.pyplot as plt import seaborn as sns sns.set()

The first statement is one of several magic commands supported by the Python kernel that you selected when you created the notebook. It enables Jupyter to render Matplotlib output in a notebook without making repeated calls to show. And it must appear before any references to Matplotlib itself. The final statement configures Seaborn to enhance the output from Matplotlib.

3. To see Matplotlib at work, execute the following code in a new cell to plot the ROC curve for the machine-learning model you built in the previous lab:

from sklearn.metrics import roc_curve fpr, tpr, _ = roc_curve(test_y, probabilities[:, 1]) plt.plot(fpr, tpr) plt.plot([0, 1], [0, 1], color='grey', lw=1, linestyle='--') plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate')

4. Confirm that you see the following output:


The dotted line in the middle of the graph represents a 50-50 chance of obtaining a correct answer. The blue curve represents the accuracy of your model. More importantly, the fact that this chart appears at all demonstrates that you can use Matplotlib in a Jupyter notebook.

The reason you built a machine-learning model is to predict whether a flight will arrive on time or late. In this exercise, you'll write a Python function that calls the machine-learning model you built in the previous lab to compute the likelihood that a flight will be on time. Then you'll use the function to analyze several flights.

1. Enter the following function definition in a new cell, and then run the cell.

def predict_delay(departure_date_time, origin, destination): from datetime import datetime try: departure_date_time_parsed = datetime.strptime(departure_date_time, '%d/%m/%Y %H:%M:%S') except ValueError as e: return 'Error parsing date/time - {}'.format(e) month = departure_date_time_parsed.month day = departure_date_time_parsed.day day_of_week = departure_date_time_parsed.isoweekday() hour = departure_date_time_parsed.hour origin = origin.upper() destination = destination.upper() input = [{'MONTH': month, 'DAY': day, 'DAY_OF_WEEK': day_of_week, 'CRS_DEP_TIME': hour, 'ORIGIN_ATL': 1 if origin == 'ATL' else 0, 'ORIGIN_DTW': 1 if origin == 'DTW' else 0, 'ORIGIN_JFK': 1 if origin == 'JFK' else 0, 'ORIGIN_MSP': 1 if origin == 'MSP' else 0, 'ORIGIN_SEA': 1 if origin == 'SEA' else 0, 'DEST_ATL': 1 if destination == 'ATL' else 0, 'DEST_DTW': 1 if destination == 'DTW' else 0, 'DEST_JFK': 1 if destination == 'JFK' else 0, 'DEST_MSP': 1 if destination == 'MSP' else 0, 'DEST_SEA': 1 if destination == 'SEA' else 0 }] return model.predict_proba(pd.DataFrame(input))[0][0]

This function takes as input a date and time, an origin airport code, and a destination airport code, and returns a value between 0.0 and 1.0 indicating the probability that the flight will arrive at its destination on time. It uses the machine-learning model you built in the previous lab to compute the probability. And to call the model, it passes a DataFrame containing the input values to predict_proba. The structure of the DataFrame exactly matches the structure of the DataFrame we used earlier.

2. Use the code below to compute the probability that a flight from New York to Atlanta on the evening of October 1 will arrive on time. The year you enter is irrelevant because it isn't used by the model.

predict_delay('1/10/2018 21:45:00', 'JFK', 'ATL')

Confirm that the output shows that the likelihood of an on-time arrival is 60%:


3. Modify the code to compute the probability that the same flight a day later will arrive on time:

predict_delay('2/10/2018 21:45:00', 'JFK', 'ATL')

How likely is this flight to arrive on time? If your travel plans were flexible, would you consider postponing your trip for one day?

4. Now modify the code to compute the probability that a morning flight the same day from Atlanta to Seattle will arrive on time:

predict_delay('2/10/2018 10:00:00', 'ATL', 'SEA')

Is this flight likely to arrive on time?

You now have an easy way to predict, with a single line of code, whether a flight is likely to be on time or late. Feel free to experiment with other dates, times, origins, and destinations. But keep in mind that the results are only meaningful for the airport codes ATL, DTW, JFK, MSP, and SEA because those are the only airport codes the model was trained with.

1. Execute the following code to plot the probability of on-time arrivals for an evening flight from JFK to ATL over a range of days:

import numpy as np labels = ('Oct 1', 'Oct 2', 'Oct 3', 'Oct 4', 'Oct 5', 'Oct 6', 'Oct 7') values = (predict_delay('1/10/2018 21:45:00', 'JFK', 'ATL'), predict_delay('2/10/2018 21:45:00', 'JFK', 'ATL'), predict_delay('3/10/2018 21:45:00', 'JFK', 'ATL'), predict_delay('4/10/2018 21:45:00', 'JFK', 'ATL'), predict_delay('5/10/2018 21:45:00', 'JFK', 'ATL'), predict_delay('6/10/2018 21:45:00', 'JFK', 'ATL'), predict_delay('7/10/2018 21:45:00', 'JFK', 'ATL')) alabels = np.arange(len(labels)) plt.bar(alabels, values, align='center', alpha=0.5) plt.xticks(alabels, labels) plt.ylabel('Probability of On-Time Arrival') plt.ylim((0.0, 1.0))

2. Confirm that the output looks like this:

3. Modify the code to produce a similar chart for flights leaving JFK for MSP at 1:00 p.m. on April 10 through April 16. How does the output compare to the output in the previous step?
4. On your own, write code to graph the probability that flights leaving SEA for ATL at 9:00 a.m., noon, 3:00 p.m., 6:00 p.m., and 9:00 p.m. on January 30 will arrive on time. Confirm that the output matches this:


If you are new to Matplotlib and would like to learn more about it, you will find an excellent tutorial at https://www.labri.fr/perso/nrougier/teaching/matplotlib/. There is much more to Matplotlib than what was shown here, which is one reason why it is so popular in the Python community.