Prediction of Player Churn in Mobile Games Using Classification Algorithms

Year 2025, Issue: 4, 20 - 29, 09.01.2026

İlkim Ecem Emre , Selin Evrim Çotul

https://doi.org/10.26650/JODA.1742874

Abstract

This study aims to predict player churn in a mobile game using machine learning algorithms. A behavioural dataset from Kaggle was used, and five key features were extracted through feature engineering: success rate, average duration, help usage, number of levels played, and remaining step rate. These features were used as inputs for the three classification models. Random Forest (RF), XGBoost, and Logistic Regression (LR) algorithms were used for model development for the prediction. The model performances were evaluated based on the evaluation metrics. Among all the models, RF achieved the highest overall accuracy (0.70) and strong recall for churned users (0.84). XGBoost showed the highest recall for churn (0.90). LR offered a balanced performance. The most influential predic tors were avg_reststep, level_count, and avg_duration. The findings showed the usefulness of behavioural features and machine learning algorithms in early churn detection. These results can support game developers in designing targeted interventions to retain users and reduce churn.

Keywords

Churn Rate , Mobile Games , Classification Mdels , Machine Learning , Player Churn

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