A Machine Learning Model to Predict Booking Outcomes for Expedia
Provided Data Description
Three data sets were provided: the training set from 2013 and 2014, the test set from 2015, the data set containing features extracted from hotel reviews.
The training and test datasets contained information on customer behavior, such as what customers searched for, whether they clicked or booked, and whether a search result was a travel package.
The booking outcome was defined as a hotel cluster. Hotel clusters were formed using Expedia’s in-house algorithm, which combined hotels with the same characteristics into one cluster, based on the hotels’ historical data.
The competitors were asked to predict booking outcomes for a user event, described by user search data and other attributes.