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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Year Project Report |
| Language: | English English |
| Published: |
Universiti Malaysia Sarawak, (UNIMAS)
2023
|
| Subjects: | |
| 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!
|
| Summary: | 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 |
|---|