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 Theres, Abidha Maria; George, Jossy; Kumar, Pawan Title A name given to the resource A Machine Learning Approach to Modelling Sales Performance Based on Social Media Analysis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1827 LNNS;pp.243-256 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-18138-1_21" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-18138-1_21</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038095719?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038095719?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 Theres A.M., CHRIST (Deemed to be University), Karnataka, Bengaluru, India; George J., CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Kumar P., CHRIST (Deemed to be University), Karnataka, Bengaluru, India Description An account of the resource By address the strategic challenges faced while maximising the advertising effectiveness, this paper examines the predictive relationship between different advertising sales platform and the brand sales performance. Four different supervised machine learning models - Random Forest, XGBoost, LightGBM, and a voting classifier ensemble model are applied to categorise the brand sales performance. The models are trained on a multi-platform advertising dataset. This research focuses on both the accuracy and interpretability of the data to make it easier identifying which platform affects the brand sales the most, which is quite different from the existing approaches that concentrates on finding predictive accuracy. This study aims to help marketers and businesses to make better and well informed data-driven decisions for marketing strategies by finding the most effective advertising platform. Each of the models were evaluated using Classification metrics which includes Accuracy, Precision, Recall and F1 score, along with confusion matrix. Future scope of the paper includes merging the models in real time systems and also by expanding it to carefully examine different time periods and customer groups. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Advertising Analytics; Brand Sales Prediction; Classification.; Ensemble Learning; Feature Importance; LightGBM; Random Forest; Social Media Marketing; Voting Classifier; XGBoost Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303218137-4; 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