ABSTRACT
Great gains can be obtained in the efficiency of processes that have
a large number of variables that influence their results through the
use of machine learning techniques. This work introduces the use
of machine learning techniques to obtain a better understanding of
the factors that most influence the cost of corporate travel, as well
as the construction of a behavioral suggestion model for decreasing
this value. The optimization of this process can result in direct
savings to companies that performed corporate travel. The final
results of these implementations reached the following accuracy:
prediction of air tickets with an ????2 value of 0.8 with a multiple
linear regression model and an ????2 value of 0.74 with an artificial
neural network model.