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 Book Chapter Faculty Publications- Book Chapter 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 Ruben, V. Muthu; VijayaKumar, R.; Kumar, T. K. Sateesh Title A name given to the resource Optimizing Machine Learning for Product Category Prediction in Digital Wallet Transactions: A Case Study of Feature-Driven Performance 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 Studies in Systems, Decision and Control;Volume;584;pp.115-121 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-031-85898-7_12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-85898-7_12</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105017615078?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017615078?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 Ruben V.M., Christ University (Deemed to Be University), Bangalore, India; VijayaKumar R., Kristu Jayanti College (Autonomous), Bangalore, India; Kumar T.K.S., Kristu Jayanti College (Autonomous), Bangalore, India Description An account of the resource The Digital Wallet transactions is one of the rapid phenomena in the application of technology. There were various studies which explored to application and sophistication of this digital wallet transactions. Based on the secondary data, the researcher developed a model for classifications using machine learning algorithms in Jupyter notebook (Python IDE). In the current study the performance of the machine learning model for classification is conducted on product categories in digital wallet transaction using many features such as product amount transaction fees cashback and encode categorical variables merchant name product name and payment methods. The test results of the classification model show and oral accuracy of the model at 92% with Precision recall and F1 scores averaging up to 0.92. It is noticeable that some of the features such as gas bill electricity bill showed weaker performance suggesting the need for further engineering and model tuning. This provide the deep understanding on how the transactions related features contribute to predicting the accuracy and highlights the potential for improving classification models for financial technology and its applications. The study also provides future directions and implications for the model refinement focusing on improving miss classification in categories. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. Subject The topic of the resource Classification model; Digital wallet transactions; Feature engineering; Financial technology; Machine learning; Precision and recall; Product category prediction; Transaction features Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21984182; Language A language of the resource English Type The nature or genre of the resource Book chapter 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