  The goal of this paper was to predict the placement in the multiplayer game
PUBG (playerunknown battleground). In the game, up to one hundred players
parachutes onto an island and scavenge for weapons and equipment to kill
others, while avoiding getting killed themselves. The available safe area of
the game map decreases in size over time, directing surviving players into
tighter areas to force encounters. The last player or team standing wins the
round. In this paper specifically, we have tried to predict the placement of
the player in the ultimate survival test. The data set has been taken from
Kaggle. Entire dataset has 29 attributes which are categories to 1
label(winPlacePerc), training set has 4.5 million instances and testing set has
1.9 million. winPlacePerc is continuous category, which makes it harder to
predict the survival of the fittest. To overcome this problem, we have applied
multiple machine learning models to find the optimum prediction. Model consists
of LightGBM Regression (Light Gradient Boosting Machine Regression), MultiLayer
Perceptron, M5P (improvement on C4.5) and Random Forest. To measure the error
rate, Mean Absolute Error has been used. With the final prediction we have
achieved MAE of 0.02047, 0.065, 0.0592 and 0634 respectively.
