Cover Image

Predicting Cheaters in PlayerUnknown’s Battlegrounds (PUBG) using Random Forest Algorithm

PlayerUnknown's Battlegrounds (PUBG) has become a massively popular online video game, attracting a significant number of players of all ages. However, the prevalence of cheating presents an important challenge to maintain a fair gaming environment. This study aims to address this issue b...

Full description

Saved in:
Bibliographic Details
Main Author: Nurin Alya, Haris
Format: Final Year Project Report
Language:English
English
Published: Universiti Malaysia Sarawak, (UNIMAS) 2023
Subjects:
Q Science (General)
T Technology (General)
Online Access:http://ir.unimas.my/id/eprint/44066/1/Nurin%20Alya%20Binti%20Haris%20%2824pgs%29.pdf
http://ir.unimas.my/id/eprint/44066/2/Nurin%20Alya%20Binti%20Haris%20%28fulltext%29.pdf
http://ir.unimas.my/id/eprint/44066/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.unimas.ir.44066
record_format eprints
spelling my.unimas.ir.440662024-01-11T04:04:05Z http://ir.unimas.my/id/eprint/44066/ Predicting Cheaters in PlayerUnknown’s Battlegrounds (PUBG) using Random Forest Algorithm Nurin Alya, Haris Q Science (General) T Technology (General) PlayerUnknown's Battlegrounds (PUBG) has become a massively popular online video game, attracting a significant number of players of all ages. However, the prevalence of cheating presents an important challenge to maintain a fair gaming environment. This study aims to address this issue by developing a reliable prediction model to identify potential cheaters in PUBG matches. The research methodology involves the collection of a comprehensive data set from Kaggle containing a variety of gameplay features. Patterns and relationships between input variables and cheating behaviours are analysed through the application of supervised learning techniques, specifically a classification model. The primary goal is to use the Random Forest algorithm, an effective machine learning technique, to predict instances of cheating based on the behavioural patterns of participants. Utilising the selected features, the Random Forest model is then trained to produce a robust prediction model. The evaluation of the prediction model demonstrates its accuracy, precision, recall, and F1 score, demonstrating its capacity to identify potential cheaters in PUBG encounters. The developed prediction model obtains an impressive accuracy of 95.7%, demonstrating its reliability in distinguishing cheaters from non-cheaters. The outcomes of this research contribute to the advancement of cheat detection mechanisms in online gaming. The developed prediction model can be integrated into existing systems to improve cheat detection capabilities and promote an enjoyable and fair gaming experience for all PUBG participants Universiti Malaysia Sarawak, (UNIMAS) 2023 Final Year Project Report NonPeerReviewed text en http://ir.unimas.my/id/eprint/44066/1/Nurin%20Alya%20Binti%20Haris%20%2824pgs%29.pdf text en http://ir.unimas.my/id/eprint/44066/2/Nurin%20Alya%20Binti%20Haris%20%28fulltext%29.pdf Nurin Alya, Haris (2023) Predicting Cheaters in PlayerUnknown’s Battlegrounds (PUBG) using Random Forest Algorithm. [Final Year Project Report] (Unpublished)
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
English
topic Q Science (General)
T Technology (General)
spellingShingle Q Science (General)
T Technology (General)
Nurin Alya, Haris
Predicting Cheaters in PlayerUnknown’s Battlegrounds (PUBG) using Random Forest Algorithm
description PlayerUnknown's Battlegrounds (PUBG) has become a massively popular online video game, attracting a significant number of players of all ages. However, the prevalence of cheating presents an important challenge to maintain a fair gaming environment. This study aims to address this issue by developing a reliable prediction model to identify potential cheaters in PUBG matches. The research methodology involves the collection of a comprehensive data set from Kaggle containing a variety of gameplay features. Patterns and relationships between input variables and cheating behaviours are analysed through the application of supervised learning techniques, specifically a classification model. The primary goal is to use the Random Forest algorithm, an effective machine learning technique, to predict instances of cheating based on the behavioural patterns of participants. Utilising the selected features, the Random Forest model is then trained to produce a robust prediction model. The evaluation of the prediction model demonstrates its accuracy, precision, recall, and F1 score, demonstrating its capacity to identify potential cheaters in PUBG encounters. The developed prediction model obtains an impressive accuracy of 95.7%, demonstrating its reliability in distinguishing cheaters from non-cheaters. The outcomes of this research contribute to the advancement of cheat detection mechanisms in online gaming. The developed prediction model can be integrated into existing systems to improve cheat detection capabilities and promote an enjoyable and fair gaming experience for all PUBG participants
format Final Year Project Report
author Nurin Alya, Haris
author_facet Nurin Alya, Haris
author_sort Nurin Alya, Haris
title Predicting Cheaters in PlayerUnknown’s Battlegrounds (PUBG) using Random Forest Algorithm
title_short Predicting Cheaters in PlayerUnknown’s Battlegrounds (PUBG) using Random Forest Algorithm
title_full Predicting Cheaters in PlayerUnknown’s Battlegrounds (PUBG) using Random Forest Algorithm
title_fullStr Predicting Cheaters in PlayerUnknown’s Battlegrounds (PUBG) using Random Forest Algorithm
title_full_unstemmed Predicting Cheaters in PlayerUnknown’s Battlegrounds (PUBG) using Random Forest Algorithm
title_sort predicting cheaters in playerunknown’s battlegrounds (pubg) using random forest algorithm
publisher Universiti Malaysia Sarawak, (UNIMAS)
publishDate 2023
url http://ir.unimas.my/id/eprint/44066/1/Nurin%20Alya%20Binti%20Haris%20%2824pgs%29.pdf
http://ir.unimas.my/id/eprint/44066/2/Nurin%20Alya%20Binti%20Haris%20%28fulltext%29.pdf
http://ir.unimas.my/id/eprint/44066/
_version_ 1789430357101117440
score 13.211869

Similar Items