Prediction of the final rank of Players in PUBG with the optimal number of features

TL;DR

This study predicts PUBG players' final ranks using machine learning, identifying that using 8 features offers a good balance of high accuracy and reduced computational time.

Contribution

It determines the optimal number of features (8) for predicting player ranks with high accuracy and efficiency, comparing multiple ML algorithms.

Findings

GBR and LGBM achieved over 91% accuracy with 14 features.

Reducing features to 8 maintains high accuracy (~90%) while decreasing runtime.

Using fewer than 8 features significantly reduces model performance.

Abstract

PUBG is an online video game that has become very popular among the youths in recent years. Final rank, which indicates the performance of a player, is one of the most important feature for this game. This paper focuses on predicting the final rank of the players based on their skills and abilities. In this paper we have used different machine learning algorithms to predict the final rank of the players on a dataset obtained from kaggle which has 29 features. Using the correlation heatmap,we have varied the number of features used for the model. Out of these models GBR and LGBM have given the best result with the accuracy of 91.63% and 91.26% respectively for 14 features and the accuracy of 90.54% and 90.01% for 8 features. Although the accuracy of the models with 14 features is slightly better than 8 features, the empirical time taken by 8 features is 1.4x lesser than 14 features for LGBM and 1.5x lesser for GBR. Furthermore, reducing the number of features any more significantly hampers the performance of all the ML models. Therefore, we conclude that 8 is the optimal number of features that can be used to predict the final rank of a player in PUBG with high accuracy and low run-time.