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 Faculty Publications 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/. Creator An entity primarily responsible for making the resource Tyagi, Tamanna; Naveen, J.; Rao, B Satyam; Dhabuwala, Prince Title A name given to the resource Machine Learning Approaches for Predicting Player Position in Football Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of 2025 IEEE International Conference on Contemporary Computing and Communications, InC4 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/InC465408.2025.11256390" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/InC465408.2025.11256390</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030339993?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030339993?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Tyagi T., Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Bengaluru, India; Naveen J., Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Bengaluru, India; Rao B.S., Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Bengaluru, India; Dhabuwala P., Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Bengaluru, India Description An account of the resource This paper presents a comparative study of different machine learning algorithms, including K- Nearest Neighbor (KNN), Random Forest, Gradient Boosting, XG Boost, Support Vector Machine, Voting Classifier and Logistic regression to develop a Player Position Prediction System in football. Initially, the study utilized a modified dataset containing 18434 records, focusing on simplicity for ease of analysis. Through experimentation, it was found that Logistic regression provided a strong balance between efficiency and scalability, making them ideal for rapid decision-making in environments with limited features. In contrast, Support Vector Machine, XGboost and voting classifier excelled in offering more detailed, feature-rich analyses, which are particularly beneficial when handling complex data. Building on these findings, the plan is to apply the same algorithms to improve the system's overall accuracy and efficiency. By leveraging the strengths of each approach, the aim is to create a scalable, effective recommendation system tailored for real-world applications in the car industry. This study highlights the importance of choosing the right algorithm based on the tradeoffs between computational efficiency and the depth of analysis required in recommendation systems. 2025 IEEE. Subject The topic of the resource Gradient Boosting; KNN; Logistic Regression; Random Forest; Support Vector Machine; Voting Classifier; XG Boost Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152118-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online