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PUBG is an online video game that has become very popular among the youths inrecent years. Final rank, which indicates the performance of a player, is oneof the most important feature for this game. This paper focuses on predictingthe final rank of the players based on their skills and abilities. In thispaper we have used different machine learning algorithms to predict the finalrank 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 forthe model. Out of these models GBR and LGBM have given the best result with theaccuracy of 91.63% and 91.26% respectively for 14 features and the accuracy of90.54% and 90.01% for 8 features. Although the accuracy of the models with 14features is slightly better than 8 features, the empirical time taken by 8features 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 theperformance of all the ML models. Therefore, we conclude that 8 is the optimalnumber of features that can be used to predict the final rank of a player inPUBG with high accuracy and low run-time.

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