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master's thesis
Prediktivna analiza upotrebom metode linearne regresije
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2015. urn:nbn:hr:168:581385
Paprić, Karmen
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Paprić, K. (2015). Prediktivna analiza upotrebom metode linearne regresije (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:581385

Paprić, Karmen. "Prediktivna analiza upotrebom metode linearne regresije." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2015. https://urn.nsk.hr/urn:nbn:hr:168:581385

Paprić, Karmen. "Prediktivna analiza upotrebom metode linearne regresije." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2015. https://urn.nsk.hr/urn:nbn:hr:168:581385

Paprić, K. (2015). 'Prediktivna analiza upotrebom metode linearne regresije', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 12 January 2025, https://urn.nsk.hr/urn:nbn:hr:168:581385

Paprić K. Prediktivna analiza upotrebom metode linearne regresije [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2015 [cited 2025 January 12] Available at: https://urn.nsk.hr/urn:nbn:hr:168:581385

K. Paprić, "Prediktivna analiza upotrebom metode linearne regresije", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2015. Available at: https://urn.nsk.hr/urn:nbn:hr:168:581385

Metadata
TitlePrediktivna analiza upotrebom metode linearne regresije
Title (english)Predictive analysis using linear regression
AuthorKarmen Paprić
MentorMarko Banek (mentor)
Committee memberMarko Banek (predsjednik povjerenstva)
Committee memberZoran Skočir (član povjerenstva)
Committee memberBoris Vrdoljak (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2015-07-03, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Abstract
Da bi dubinska analiza podataka bila što uspješnija potrebno je prvo kvalitetno upoznati i pripremiti podatke. Po potrebi podaci se transformiraju. Prediktivna analiza podataka se koristi za predviđanje vjerojatnih događaja u budućnosti s nekom razinom pouzdanosti. Predviđanje je zapravo samo sugestija onoga što se može dogoditi, ali nikako i garancija da će se baš to doista i dogoditi. Čim je više povijesnih podataka dostupno za izgradnju modela, to će model biti precizniji i kvalitetniji. Na studijskom primjeru regresije prikazani su koraci od upoznavanja s podacima do izvođenje linearne regresije nad konkretnim podatkovnim skupom. Izvedena je transformacija atributa, provjerena korelacija među varijablama te dan grafički prikaz varijabli histogramima. Za provjeru ovisnosti izlazne varijable o ulaznim korišten je t-test te je konačno izvedena linearna regresija nad podacima. Na kraju je automatskom metodom za odabir varijabli za regresiju prikazan konačni model.
Abstract (english)
In order to ensure a successful data mining analysis, the first need is to have a good knowledge of the data and to prepare them for analysis. If it is necessary, data can be transformed. Predictive analysis is used to predict the event in the future with some confidence level. Prediction is actually only a suggestion of what can happen, but not a guarantee that this will really happen. The more historical data is available for the construction of the model, the more precise the resulting model will be. In this thesis we preformed regression on case study data, preforming all steps of data mining analysis, from getting an initial view aimed at understanding the data to creating the final linear regression model on a the data. Attributes were transformed, correlation between variables was tested and graphical presentation of variables with histograms was provided. In order to check the output variables dependence on the input we used t-test, and finally performed a linear regression. The final regression model was constructed using automated method for variable selection.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:581385
Study programmeTitle: Information and Communication Technology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije (magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije)
Type of resourceText
File originBorn digital
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Created on2018-02-06 11:04:18