Classification of tourist routes through deep learning

Abstract

Introduction. Currently deep learning or deep learning has applications of all kinds, tourism is not the exception, data mining has allowed optimizing processes within the tourism industry such as tourist demand, knowing the preference of people's tourist routes allows optimizing resources and propose improvements within this sector. Target. Determine if tourist routes can be classified by means of deep learning or deep learning tools. Methodology. The research design was qualitative, techniques such as the interview were used, for these two hypotheses are proposed, the first has to do with the relationship between the type of climate of the tourist destination and the preference of tourists, the second hypothesis is the verification of the conformation of tourist clusters based on the preference of the people. As verification tools, direct verification and the Weka program were used with the SimpleKMeans clusters option that allows the identification of tourists' preferences based on the data mining of 31 people. Results. The results indicate that the largest number of people interviewed prefer tourist destinations in hot climates, however, this was not a determining parameter in the formation of clusters. Conclusion. The study determined that it is possible to form clusters for the classification of tourist routes based on people's preferences.