# The Winning Solution to the IEEE CIG 2017 Game Data Mining Competition

## TL;DR

This paper presents the winning machine learning solutions for predicting player churn and remaining lifetime in a large online game, using LSTM and survival ensemble models, demonstrating robustness and scalability in real-world scenarios.

## Contribution

The authors introduce a novel combination of LSTM and survival ensemble models for churn prediction, effectively handling censored data and adapting to changing business models.

## Key findings

- Models accurately predicted player churn and lifetime.
- Solutions were robust to business model changes.
- Methods scaled efficiently to large datasets.

## Abstract

Machine learning competitions such as those organized by Kaggle or KDD represent a useful benchmark for data science research. In this work, we present our winning solution to the Game Data Mining competition hosted at the 2017 IEEE Conference on Computational Intelligence and Games (CIG 2017). The contest consisted of two tracks, and participants (more than 250, belonging to both industry and academia) were to predict which players would stop playing the game, as well as their remaining lifetime. The data were provided by a major worldwide video game company, NCSoft, and came from their successful massively multiplayer online game Blade and Soul. Here, we describe the long short-term memory approach and conditional inference survival ensemble model that made us win both tracks of the contest, as well as the validation procedure that we followed in order to prevent overfitting. In particular, choosing a survival method able to deal with censored data was crucial to accurately predict the moment in which each player would leave the game, as censoring is inherent in churn. The selected models proved to be robust against evolving conditions---since there was a change in the business model of the game (from subscription-based to free-to-play) between the two sample datasets provided---and efficient in terms of time cost. Thanks to these features and also to their a ability to scale to large datasets, our models could be readily implemented in real business settings.