Uporaba metoda strojnog učenja za analizu podataka iz turističkog sektora

undergraduate thesis

• Title (english): Analyzing Data from the Tourist Sector Domain Using Machine Learning Methods
• Author: Mateo Elez
• Mentor: Damir Pintar (mentor)
• Committee member: Damir Pintar (predsjednik povjerenstva)
• Committee member: Ana Šarčević (član povjerenstva)
• Committee member: Luka Humski (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2022-07-12, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Turistički sektor može polučiti velike benefite od kvalitetne primjene analize podataka. Kvalitetan uvid u dinamiku rezervacija smještaja, potražnju i potrebe klijenata može ponuđaču turističkih usluga dati mogućnost donošenja poslovnih odluka koje će s jedne strane dodatno podići kvalitetu ponude i zadovoljstvo klijenata, a s druge rezultirati većim profitima. Zadatak ovog rada jest prikupiti podatkovni skup povezan uz turistički sektor, provesti eksploratornu analizu nad tim podacima i razviti prediktivni model za odabrane zavisne varijable. Konačno rješenje potrebno je realizirati u obliku programske skripte koje će implementirati razvijene modele i evaluirati njihovu učinkovitost uz komparativnu analizu korištenih metoda.

Abstract (english)

The tourism sector can benefit from the quality application of data analysis. Quality insight into the dynamics of accommodation reservations, demand and customer needs can give the tourism service provider the opportunity to make business decisions that will on the one hand, further raise the quality of the offer and customer satisfaction, and on the other hand, result higher profits. The task of this paper is to collect a data set related to the tourism sector, to implement exploratory analysis of these data and develop a predictive model for selected dependent variables. The final solution needs to be implemented in the form of a program script that will implement the developed models and evaluate their effectiveness with comparative analysis of used methods.

• Keywords: eksploratorna analiza; analiza podataka; strojno učenje; logistička regresija; naivni Bayesov klasifikator; stablo odluke
• Keywords (english): exploratory analysis; data analysis; machine learning; logistic regression; naive Bayes classifier; decision tree
• Language: croatian
• URN:NBN: urn:nbn:hr:168:016983
• Study programme: Title: Computing; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-15 10:05:18