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 Book Chapter 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/. Title A name given to the resource A Comparative Analysis of Machine Learning Algorithms for Image Classification: Evaluating Performance Subject The topic of the resource AdaBoost; Artificial Neural Networks (ANN); Decision tree; Image classification; k-Nearest Neighbour (kNN); Logistic regression; Machine learning; Nae Bayes; Random forest; Support Vector Machine (SVM) Description An account of the resource Image classification plays a crucial role in various applications, and selecting the most effective machine learning algorithm is essential for achieving accurate results. In this study, we conducted a comparative analysis of several well-known supervised machine learning techniques, including logistic regression, support vector machine (SVM), k-nearest neighbours (kNN), nae Bayes, decision trees, random forest, AdaBoost, and artificial neural networks (ANN). To assess the performance of these algorithms, we utilised different fonts of the English alphabet as our dataset and performed the analysis using the R programming language. We evaluated the algorithms based on standard performance criteria, such as the area under the Receiver Operating Characteristic curve (ROC), accuracy, F1 score, precision, and recall. Our research findings demonstrated that the classification performance varied depending on the training size of the dataset. Notably, as the training size increased, neural networks exhibited superior performance compared to other machine learning techniques. Consequently, we conclude that neural networks and SVM are the most effective algorithms for image classification based on our study. By conducting this comprehensive analysis, we contribute valuable insights into selecting appropriate machine learning algorithms for image classification tasks. Our findings emphasise the significance of considering the training dataset size and highlight the advantages of neural networks and SVM in achieving high classification accuracy. This study provides valuable guidance for practitioners and researchers in choosing the most suitable machine learning algorithm for image classification, considering their specific requirements and dataset characteristics. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. Creator An entity primarily responsible for making the resource Kapse M.; Elangovan N.; Lalkiya M.; Deshpande A. Source A related resource from which the described resource is derived Data-Driven Decision Making, pp. 59-75. Publisher An entity responsible for making the resource available Springer Nature 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/10.1007/978-981-97-2902-9_3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-2902-9_3</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215969257&amp;doi=10.1007%2F978-981-97-2902-9_3&amp;partnerID=40&amp;md5=4cb4e5f6c90b9e4b06296fea5bee18e7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215969257&amp;doi=10.1007%2f978-981-97-2902-9_3&amp;partnerID=40&amp;md5=4cb4e5f6c90b9e4b06296fea5bee18e7</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-981972902-9; 978-981972901-2 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 Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kapse M., Symbiosis Centre for Management and Human Resource Development, Symbiosis International (Deemed University), Pune, India; Elangovan N., School of Business and Management, CHRIST (Deemed to Be University), Bangalore, India; Lalkiya M., Bangalore, India; Deshpande A., Indira School of Business Studies, PGDM, Pune, India