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Business calculation for hotel booking dataset

Previously, you were introduced to the challenge of predicting booking cancellations. Here, you will work with the actual Hotel Booking dataset, where a model predicts booking cancellations based on the customer's country of origin, time between booking and arrival, required parking spaces, and the chosen hotel.

The reference and analysis sets have already been loaded for you. Here are the first two rows:

  country  lead_time  parking_spaces       hotel  y_pred  y_pred_proba  is_canceled  timestamp
0  FRA     120        0               City Hotel  0       0.239983      0           2016-05-01
1  ITA     120        1               City Hotel  0       0.003965      0           2016-05-01

Your task is to check the model's monetary value and ROC AUC performance.

This exercise is part of the course

Monitoring Machine Learning in Python

View Course

Exercise instructions

  • Initialize a custom threshold with 0 as the lower value and 150,000 as the upper value.
  • Specify the business value and roc_auc metric for monitoring.
  • Set TN to 0, FP to -100, FN to -200, and TP to 1500 in business_value_matrix.
  • Assign custom threshold to the business value metric.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Custom business value thresholds
ct = ConstantThreshold(____=____, ____=____)
# Intialize the performance calculator
calc = PerformanceCalculator(problem_type='classification_binary',
			y_pred_proba='y_pred_proba',
  			timestamp_column_name="timestamp", 		
  			y_pred='y_pred',
  			y_true='is_canceled',
            chunk_period='m',
  			metrics=[____, ____],
  			business_value_matrix = [[____, ____],[____, ____]],
  			thresholds={____: ____})
calc = calc.fit(reference)
calc_res = calc.calculate(analysis)
calc_res.filter(period='analysis').plot().show()
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