Predict And Associate Tourist Preference Patterns, The Case Of Ministry Of Culture And Tourism Of Ethiopia

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dc.contributor.advisorTeferra Abate (Phd), Solomon
dc.contributor.authorHailemichael Hailu, Dawit
dc.date.accessioned2018-11-10T11:14:49Z
dc.date.accessioned2023-11-18T12:43:52Z
dc.date.available2018-11-10T11:14:49Z
dc.date.available2023-11-18T12:43:52Z
dc.date.issued2015-06-05
dc.description.abstractTourism is one of the largest and rapidly growing industries in Ethiopia. It has a vital influence on economic development of a country. The degree of tourist attraction of Ethiopia to the others east African countries is, not strong enough to penetrate the tourism market. The sector faces immense challenges, because of its intangible nature and lack of understanding tourist preference. This study attempts to identify the major determinants factors to predict tourist preference in Ethiopia. This research used the CRISP data mining methodology and 10484 tourist data were taken from Ministry of Culture and Tourism of Ethiopia for experiment that was collected from 2008 - 2012. Experiments conducted using the J48 decision tree and Naïve Bayes algorithm for classification and the Apriori algorithm of association rule in Waikato environment for knowledge analysis (WEKA). J48 decision tree algorithm with the overall model accuracy of 94.8 % has offered interesting rules. The results of this study have showed that the data mining techniques are valuable for predicating tourist preference.en_US
dc.identifier.urihttp://etd.aau.edu.et/handle/12345678/14122
dc.language.isoenen_US
dc.publisherAddis Ababa Universityen_US
dc.subjectTourism, Data mining, Classification, and Associationen_US
dc.subjectTourism, Data mining, Classification, and Associationen_US
dc.titlePredict And Associate Tourist Preference Patterns, The Case Of Ministry Of Culture And Tourism Of Ethiopiaen_US
dc.typeThesisen_US

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