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 Machine Learning Based Spam E-Mail Detection Using Logistic Regression Algorithm Subject The topic of the resource E-Mail; Logistic Regression; Machine Learning; Nae Bayes; Spam detection; Support Vector Machines Description An account of the resource The rise of spam mail, or junk mail, has emerged as a significant nuisance in the modern digital landscape. This surge not only inundates user's email inboxes but also exposes them to security threats, including malicious content and phishing attempts. To tackle this escalating problem, the proposed machine learning-based strategy that employs Logistic Regression for accurate spam mail prediction. This research is creating an effective and precise spam classification model that effectively discerns between legitimate and spam emails. To achieve this, we harness a meticulously labeled dataset of emails, each classified as either spam or non-spam. This model is to apply preprocessing techniques to extract pertinent features from the email content, encompassing word frequencies, email header data, and other pertinent textual attributes. The choice of Logistic Regression as the foundational classification algorithm is rooted in its simplicity, ease of interpretation, and appropriateness for binary classification tasks. To process train the model using the annotated dataset, refining its hyper parameters to optimize its performance. By incorporating feature engineering and dimensionality reduction methodologies, bolster the model's capacity to generalize effectively to unseen data. Our evaluation methodology encompasses rigorous experiments and comprehensive performance contrasts with other well-regarded machine learning algorithms tailored for spam classification. The assessment criteria encompass accuracy, precision, recall, and the F1 score, offering a holistic appraisal of the model's efficacy. Furthermore, we scrutinize the model's resilience against diverse forms of spam emails, in addition to its capacity to generalize to new data instances. This model is to findings conclusively demonstrated that our Logistic Regression-driven spam mail prediction model achieves a competitive performance standing when juxtaposed with cutting-edge methodologies. The model adeptly identifies and sieves out spam emails, thereby cultivating a more trustworthy and secure email environment for users. The interpretability of the model lends valuable insights into the pivotal features contributing to spam detection, thereby aiding in the identification of emerging spam patterns. 2023 IEEE. Creator An entity primarily responsible for making the resource Livia Shreenithi S.A.; Yougandar S.V.; Jayapandian N. Source A related resource from which the described resource is derived 3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023 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 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTBIG59752.2023.10455970" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG59752.2023.10455970</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189241095&amp;doi=10.1109%2FICTBIG59752.2023.10455970&amp;partnerID=40&amp;md5=872eacfb907b5a86092fd0818d138365" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189241095&amp;doi=10.1109%2fICTBIG59752.2023.10455970&amp;partnerID=40&amp;md5=872eacfb907b5a86092fd0818d138365</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835034327-4 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 Livia Shreenithi S.A., Christ (Deemed to Be University), Department of Cse, Bangalore, India; Yougandar S.V., Christ (Deemed to Be University), Department of Cse, Bangalore, India; Jayapandian N., Christ (Deemed to Be University), Department of Cse, Bangalore, India