Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Predicting Player Engagement in Online Gaming: A Machine Learning Approach Subject The topic of the resource Behavior; Machine Learning models; Online Gaming; Player Engagement Description An account of the resource The aim of this research is to make precise forecasts on player participation in online game using state-of-the-art machine learning algorithms. Player engagement plays a crucial element in determining the success of online games because it affects player retention, satisfaction and monetization. By understanding and predicting engagement levels, game developers and marketers can enhance the gaming experience and develop strategies to keep players invested. This research involves a comprehensive analysis of player behavior data from an online gaming platform. The dataset includes various demographic and behavioral features such as age, gender, location, game genre, playtime hours, in-game purchases, game difficulty, sessions per week, average session duration, player level, achievements unlocked, and engagement level. The data was preprocessed through handling missing values, normalizing numerical features, and encoding categorical variables. Exploratory Data Analysis (EDA) was conducted to understand the distribution and relationships between different features. Multiple machine learning models were evaluated to predict player engagement levels, including Random Forest, Gradient Boosting, XGBoost, and Support Vector Machine (SVM). These models were then compared through the accuracy, precision, recall, and F1-score metrics. In the comparison, XGBoost emerged as the best model. Since it is the best-performing model, we can make the feature importance analysis to identify the best factors for predicting engagement in the next step. The XGBoost model achieved the highest accuracy of 91%, demonstrating superior precision, recall, and F1-scores across all engagement levels (High, Medium, Low). Ensemble methods like XGBoost, Gradient Boosting, and Random Forest outperformed the SVM model, highlighting their effectiveness in handling complex datasets. 2024 IEEE. Creator An entity primarily responsible for making the resource Manju Usha Sree T.; Sasikumar P.; Ayesha S.; Amzad Basha M.S.; Martha Sucharitha M. Source A related resource from which the described resource is derived Proceedings of NKCon 2024 - 3rd Edition of IEEE NKSS's Flagship International Conference: Digital Transformation: Unleashing the Power of Information Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/NKCon62728.2024.10774748" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/NKCon62728.2024.10774748</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215295742&amp;doi=10.1109%2FNKCon62728.2024.10774748&amp;partnerID=40&amp;md5=da6eafd1a38351897115404278edead7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215295742&amp;doi=10.1109%2fNKCon62728.2024.10774748&amp;partnerID=40&amp;md5=da6eafd1a38351897115404278edead7</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835036456-9 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Manju Usha Sree T., Master of Business Administration Mallareddy Engineering College (Autonomous), Secunderabad, India; Sasikumar P., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Ayesha S., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Amzad Basha M.S., Gitam (Deemed to Be University), Gitam School of Business, Bengaluru, India; Martha Sucharitha M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India