Tag Archives: Scikit-learn

PUBG Final Placement Prediction

PlayerUnknown’s BattleGround (PUBG) is one of the most popular first-person shooting games in the world. It was introduced the world in March 2017, within 2 years, it has accumulated over 40-million sales over the world. The goal of this study is to 1) predict the PUBG Final Placement in solo mode; 2) find the winning pattern to master the game; 3) derived some applicable pattern from the game to use in the real world. The method used in the study is use the processed data and feature selected data to cross test eight models, Lasso Regression, Ridge Regression, Regression Tree, Random Forest, Bagging (RT), Gradient boosting, Ada Boosting and Neural Network to find the most predictive one. The result demonstrates that the Neural Network model is the most predictive one in predicting PUBG Final Placement with RMSE 0.0577 and explained variance 0.9619. The findings in this study suggests Neural Network model is the best predictive model for PUBG game, and winning strategy is applicable in both game and life.

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