  Online travel agencies (OTA's) advertise their website offers on meta-search
bidding engines. The problem of predicting the number of clicks a hotel would
receive for a given bid amount is an important step in the management of an
OTA's advertisement campaign on a meta-search engine, because bid times number
of clicks defines the cost to be generated. Various regressors are ensembled in
this work to improve click prediction performance. Following the preprocessing
procedures, the feature set is divided into train and test groups depending on
the logging date of the samples. The data collection is then subjected to
feature elimination via utilizing XGBoost, which significantly reduces the
dimension of features. The optimum hyper-parameters are then found by applying
Bayesian hyperparameter optimization to XGBoost, LightGBM, and SGD models. The
different trained models are tested separately as well as combined to form
ensemble models. Four alternative ensemble solutions have been suggested. The
same test set is used to test both individual and ensemble models, and the
results of 46 model combinations demonstrate that stack ensemble models yield
the desired R2 score of all. In conclusion, the ensemble model improves the
prediction performance by about 10%.
