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undergraduate thesis
Sustav za automatizirano određivanje tipa osobnosti
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:044322
Horvat, Dora
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Horvat, D. (2021). Sustav za automatizirano određivanje tipa osobnosti (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:044322

Horvat, Dora. "Sustav za automatizirano određivanje tipa osobnosti." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:044322

Horvat, Dora. "Sustav za automatizirano određivanje tipa osobnosti." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:044322

Horvat, D. (2021). 'Sustav za automatizirano određivanje tipa osobnosti', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 19 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:044322

Horvat D. Sustav za automatizirano određivanje tipa osobnosti [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 June 19] Available at: https://urn.nsk.hr/urn:nbn:hr:168:044322

D. Horvat, "Sustav za automatizirano određivanje tipa osobnosti", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:044322

Metadata
TitleSustav za automatizirano određivanje tipa osobnosti
Title (english)System for automatized personality type detection
AuthorDora Horvat
MentorIvica Botički (mentor)
Committee memberIvica Botički (predsjednik povjerenstva)
Committee memberMirjana Domazet-Lošo (član povjerenstva)
Committee memberTomislav Jagušt (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-06, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U ovom je radu primjenom nadziranog strojnog učenja razvijen sustav za predviđanje Myers-Briggs tipa osobnosti, jednog od danas najkorištenijih modela osobnosti, na temelju internetskih objava korisnika. Pri razvoju modela binarnih klasifikatora korišteni su algoritmi logističke regresije, slučajnih šuma i naivnog Bayesova klasifikatora te su se kroz određene mjere procjene uspoređivale performanse modela, kako bi se na kraju izabrao jedan algoritam čiji je model ugrađen u izrađeno grafičko sučelje za previđanje tipa osobnosti. Implementacija modela izrađena je u programskom jeziku Python korištenjem poznatih knjižnica za obradu teksta i strojno učenje općenito.
Abstract (english)
In this paper a system for predicting the Myers-Briggs personality type, one of the most used personality models today, based on supervised machine learning was developed. The developed prediction model is based on online users’ posts. Logistic regression, random noise and naive Bayesian classifier algorithms were used in the development of a binary classifier model. Performance of the model was compared via evaluation measures, and a specific algorithm was chosen, whose model was then implemented as a UI for predicting personality types. Python programming language and well-known libraries for word processing and machine learning were used as part of the machine learning process.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:044322
Study programmeTitle: 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 resourceText
File originBorn digital
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Created on2023-01-14 20:34:54