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undergraduate thesis
Predviđanje kraja interesa korisnika za igru putem strojnog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. urn:nbn:hr:168:468943
Bačić, Krešimir
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Bačić, K. (2020). Predviđanje kraja interesa korisnika za igru putem strojnog učenja (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:468943

Bačić, Krešimir. "Predviđanje kraja interesa korisnika za igru putem strojnog učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:468943

Bačić, Krešimir. "Predviđanje kraja interesa korisnika za igru putem strojnog učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:468943

Bačić, K. (2020). 'Predviđanje kraja interesa korisnika za igru putem strojnog učenja', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 25 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:468943

Bačić K. Predviđanje kraja interesa korisnika za igru putem strojnog učenja [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2020 [cited 2024 June 25] Available at: https://urn.nsk.hr/urn:nbn:hr:168:468943

K. Bačić, "Predviđanje kraja interesa korisnika za igru putem strojnog učenja", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:168:468943

Metadata
TitlePredviđanje kraja interesa korisnika za igru putem strojnog učenja
Title (english)Predicting user churn in a game by using machine learning
AuthorKrešimir Bačić
MentorMirko Sužnjević (mentor)
Committee memberMirko Sužnjević (predsjednik povjerenstva)
Committee memberIrena Oršolić (član povjerenstva)
Committee memberDemijan Grgić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2020-07-03, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Podaci o zabilježenim događajima korisnika jedne free to play mobilne igre dohvaćaju se iz lokalne MongoDB baze i prolaze postupak pretprocesiranja tijekom kojeg se pokušavaju zaobići nedostaci skupa podataka. Podaci obrađeni u pretprocesiranju koriste se za izgradnju korisničkog profila - prethodno definiranog seta karakteristika značajnih za strojno učenje kojima se opisuje svaki korisnik. Izabrani korisnički profil sastoji se od broja sjednica, prosječne duljine sjednice, prosječnog broja događaja po sjednici, brojeva otključanih, započetih, prekinutih i uspješno riješenih poglavlja, broja otključanih i broja riješenih priča, pogledanih videa, neuspjelih verifikacija, broja neuspješnih kupovina zbog manjka resursa te količinu potrošenog novca za svaki od 3 perioda: od početka igranja do 72. sata, od 21.6 do 72 i od 50.4 do 72 sata od prvog zabilježenog događaja. Korisničkim se profilima dodjeljuje i klasa, a 80% ih se koristi za treniranje modela dok preostalih 20% služi za evaluaciju. Prije početka treniranja podaci se skaliraju min-maks algoritmom s rasponom [0, 1], a koristi se algoritam strojnog učenja slučajne šume. Rezultati se provjeravaju 30-strukom unakrsnom validacijom.
Abstract (english)
User data collected from the userbase of a free to play mobile game is retrieved from a local MongoDB database and is preprocessed in an effort to circumvent the dataset's imperfections. Preprocessed data is then used in building user profiles – predefined sets of properties relevant to machine learning, each describing a single user. The user profile template used in this thesis consists of session count, average session length, average event count per session, chapter unlock, start, drop and completion counts, story unlock and completion counts, number of promotional videos watched, failed verification count, number of times the player didn't have enough resources to complete a purchase and money spent for each of the 3 observed periods: between start of the first play session and the 72nd hour, between 21.6 and 72 hours and between 50.4 and 72 hours of playtime. User profiles are assigned a class and 80% are used in training the machine learning model, while the remaining 20% is left for model evaluation. Before training the model on calculated profiles, the data is scaled using the min-max algorithm with a range of [0, 1], while random forest is the chosen machine learning algorithm. Results are evaluated using 30-fold cross-validation.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:468943
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 on2023-01-13 14:24:19