Please use this identifier to cite or link to this item: https://repository.ldufk.edu.ua/handle/34606048/22085
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dc.contributor.authorTkach, Viktoriya-
dc.contributor.authorPavlenchyk, Anatolii-
dc.contributor.authorSadchenko, Оlena-
dc.contributor.authorNikola, Svetlana-
dc.contributor.authorDrozdova, Valeriia-
dc.contributor.authorDavydenko, Iryna-
dc.contributor.authorТкач, Вікторія-
dc.contributor.authorПавленчик, Анатолій-
dc.contributor.authorСадченко, Олена-
dc.contributor.authorНікола, Світлана-
dc.contributor.authorДроздова, Валерія-
dc.contributor.authorДавиденко, Ірина-
dc.date.accessioned2019-09-02T10:51:03Z-
dc.date.available2019-09-02T10:51:03Z-
dc.date.issued2019-
dc.identifier.citationModelling Buying Demand in the Tourism Industry based on Machine Training Methods / Viktoriya Tkach, Anatolii Pavlenchyk, Оlena Sadchenko, Svetlana Nikola, Valeriia Drozdova, Iryna Davydenko // International Journal of Recent Technology and Engineering. - 2019. - Vol. 8, is. 2. - P. 744-747. (Scopus)uk_UA
dc.identifier.urihttp://repository.ldufk.edu.ua/handle/34606048/22085-
dc.description.abstractThe business processes of companies in the tourism industry lend themselves well to formalization and, consequently, computer automation. This study focuses on the process of creating a demand forecast model for a travel agent based on data mining algorithms. The program code was developed in the Anaconda development environment, which allows to process the initial data and to give the prediction results for two indicators of MAE and program accuracy. The program code is designed to improve the performance of the entire system by selecting the correct functionsuk_UA
dc.language.isoenuk_UA
dc.subjectBox-Jenkins modeluk_UA
dc.subjectbuying demanduk_UA
dc.subjectmachine traininguk_UA
dc.subjecttourism industryuk_UA
dc.titleModelling Buying Demand in the Tourism Industry based on Machine Training Methodsuk_UA
dc.typeArticleuk_UA
Appears in Collections:Наукові праці професорсько-викладацького складу ЛДУФК в базах даних Scopus, WoS, Tomson Reuters



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