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Paper ID:IJSREM64003

Gaming Behavior Engagement Level Prediction

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM64003

Volume:

10 |

5 |

May 2026

Pages:

1-6

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM64003

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

TBV

TEJESH B V

MASTER OF COMPUTER APPLICATIONS

BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY DAVANAGERE

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JKR

JYOTHIKA K R

MASTER OF COMPUTER APPLICATIONS

BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY DAVANAGERE

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Mentors

JKR

JYOTHIKA K R

MASTER OF COMPUTER APPLICATIONS

BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY DAVANAGERE

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Abstract

By analyzing players interactions and behavioral patterns gaming behavior engagement level prediction seeks to determine how engaged a player is in a gaming environment. The system classifies player engagement into low medium and high levels by using machine learning techniques to evaluate player progress in-game activities session habits demographics and gameplay statistics. Platform administrators and game developers can more fully comprehend user behavior and design features that enhance player satisfaction retention and experience by employing a predictive approach. To create an intricate prediction model the system combines feature engineering data preprocessing and categorization methods such as Random Forest and Logistic Regression. Additionally, players can submit their data and receive engagement projections immediately through an easy-to-use web application. The platform is made more efficient by admin features like query handling FAQ control and user management. This project is crucial in how data-driven insights can enhance gaming and help game developers make decisions.

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Cite This Article

TEJESH B V, JYOTHIKA K R, JYOTHIKA K R, "Gaming Behavior Engagement Level Prediction," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-6, May 2026. https://doi.org/10.55041/IJSREM64003

Full Article/Conclusion

An efficient method for analyzing and forecasting a player's engagement based on their gaming patterns is the Gaming Behavior Engagement Level Prediction System. The system can provide precise and timely predictions through using Random Forest together with other machine learning techniques and logistic regression. Smooth administration, user assistance, and ease of interaction are guaranteed by the system's modular design, which includes separate admin and user features. The platform is dependable, scalable, and secure thanks to strong preprocessing, model training, and testing techniques. All things considered, the method not only aids in comprehending player engagement but furthermore offers helpful details for enhancing the user experience on gaming platforms.investment, . strong . political will, and a culture of collaboration across all city departments. We must also be mindful of the ethical implications of collecting and using large amounts of citizen data, ensuring that privacy is protected and that all residents.

References

1. Predicting Online Gaming Behaviour Using Machine Learning Techniques; Nurul Rismayanti; Indonesian Journal of Data and Science, Vol 5, Issue 2, July 2024; DOI: 10.56705/ijodas.v5i2.166; ISSN (E-ISSN): 2715-9936 2. Game-Based Learning Prediction Model Construction: Toward Validated Stealth Assessment Implementation; W. Lu; Journal of Learning Analytics, Vol 12, Issue   1,   March   2025;   DOI: 10.18608/jla.2025.8105;    E-ISSN:    2381- 4244. 3. Predicting Game Difficulty and Engagement Using AI Players; Author(s): Shaghayegh  Roohi, Christian Guckelsberger, Asko Relas, Henri Heiskanen, Jari Takatalo, Perttu Hämäläinen; Publication: Proceedings of the ACM on Human-Computer Interaction, Vol 5 (CHIPLAY), Article 231, Sept 2021; DOI: 10.1145/3474658; E-ISSN: 2573-0142 4. Predicting Game Engagement and Difficulty Using AI Players; Shaghayegh Roohi, Christian Guckelsberger, Asko Relas, Henri Heiskanen, Jari Takatalo & Perttu Hämäläinen; arXiv preprint arXiv:2107.12061; DOI: 10.1145/3474658 (published in Proc. ACM Hum.-Comput. Interact. Vol 5 (CHI PLAY), Article 231) 5. Predicting Video Game Addiction Through the Dimensions of Consumer Video Game Engagement: Quantitative and Cross-sectional Study; Amir Zaib Abbasi, Umair Rehman, Zahra Afaq, Mir Abdur Rafeh, Helmut Hlavacs, Mohammed A. Mamun, Muhammad Umair Shah; JMIR Serious Games, Vol 9, Issue 4 (2021); DOI: 10.2196/30310; E-ISSN: 2291-9279

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Publication Date

6/11/2026

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