Journal of Advances in Developmental Research

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Player Behavior Prediction for IN-Game Purchases using Machine Learning

Author(s) K. Narasimha Reddy, G. Nikhil Kumar, K. Anitha, J. Mounika, Dr M.C Bhanu Prasad
Country India
Abstract Nowadays, the gaming industries collects the large amount of players data during the gameplay. This project mostly focuses on the prediction of player behavior for in-game purchases using the Machine Learning. By monitoring and observing the player activities such as his play time, game progress and previous purchase history, the system can predict the player whether he is gonna make it in-game purchases. This project can analyze the player behavior based on his in-game purchases and how active and how much time he is spending in the game. Machine learning algorithms like Random Forest algorithm, Support Vector Machine, Gradient Boosting and Long Short-Term Memory networks. These algorithms make sure the prediction is very accurate and helps developers to understand the player preferences and improve in the game purchases. In this project, the machine learning models identify the players movements in the game such as how much time he is spending in the game, how active he is in the game, what his movements are during the game. By analyzing these details, it predicts the behaviour of the player and predicts how likely he is gonna do the in-game purchases. These predictions are mainly done by using the machine learning models.
Keywords Random Forest Algorithm, Support Vector Machine (SVM), Gradient Boosting, Long Short-Term Memory (LSTM), Machine learning, Purchase, Player behaviour, Prediction.
Field Engineering
Published In Volume 17, Issue 1, January-June 2026
Published On 2026-04-04
Cite This Player Behavior Prediction for IN-Game Purchases using Machine Learning - K. Narasimha Reddy, G. Nikhil Kumar, K. Anitha, J. Mounika, Dr M.C Bhanu Prasad - IJAIDR Volume 17, Issue 1, January-June 2026.
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