For this reason, boosting is referred to as an ensemble method. In this example, boosting techniques are used to determine whether a customer will cancel their hotel booking or not. Hotel cancellations represent the response (or dependent) variable, where 1 cancel, 0 follow through with booking. The relevant features to be included as the x variable in the boosting models are identified by the ExtraTreesClassifier. The three features identified by the ExtraTreesClassifier (excluding variables deemed to be theoretically irrelevant) are lead time, country and deposit type. The following boosting techniques are used in predicting hotel cancellations.

As you can imagine, the cancellation rate for bookings in the online booking industry is quite high. Once the reservation has been cancelled, there is almost nothing to be done. This creates discomfort for many institutions and creates a desire to take precautions. Therefore, predicting reservations that can be cancelled and preventing these cancellations will create a surplus value for the institutions. In this article, I will try to explain how future cancelled reservations can be predicted in advance by machine learning methods.

Hotel cancellations can cause issues for many businesses in the industry. Not only is there the lost revenue as a result of the customer canceling, but this can also cause difficulty in coordinating bookings and adjusting revenue management practices. Data analytics can help to overcome this issue, in terms of identifying the customers who are most likely to cancel – allowing a hotel chain to adjust its marketing strategy accordingly. To investigate how machine learning can aid in this task, the ExtraTreesClassifer, logistic regression, and support vector machine models were employed in Python to determine whether cancellations can be accurately predicted with this model. For this example, both hotels are based in Portugal.