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 Yadav, Sangeeta; Ranka, Monica; Ojha, Shikha; Malhotra, Amit; Basha, Md Shaik Amzad Title A name given to the resource Leveraging Machine Learning to Predict Revenue-Generating Sessions in E-Commerce Platforms 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 2025 International Conference on Information, Implementation, and Innovation in Technology, I2ITCON 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/I2ITCON65200.2025.11210485" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/I2ITCON65200.2025.11210485</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105026287123?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105026287123?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 Yadav S., New Delhi Institute of Management, Ndim, New Delhi, India; Ranka M., Dayananda Sagar College of Arts, Science and Commerce, Bangalore, India; Ojha S., Ifim college, Bangalore, India; Malhotra A., School of Commerce, Finance and Accountancy, Christ (Deemed to be University), Ghaziabad, India; Basha M.S.A., Gitam School of Business, Gandhi Institute of Technology and Management (Deemed to be University), Hyderabad, India Description An account of the resource Due to the rapid growth of e commerce, develops effective predictive models of online shopper behavior has become important. The goal of this study is to use dataset of online shopping sessions to predict purchase intentions based on session characteristics, user behavior and site metrics. This research aims to apply machine learning and deep learning models to predict online purchasing intentions to assist businesses to improve their strategies of maximizing conversion rates. Using a dataset having numerical and categorical features, features like page views, session duration, bounce rates etc., and the presence of some special days near the user session, we used. We evaluated nine models, including the traditional methods: Logistic Regression, Decision Tree, Naive Bayes, ensemble methods: Random Forest, Gradient Boosting, XGBoost, and more advanced ones like Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Neural Networks. Then, key metrics including Accuracy, Precision, Recall, F1 Score and ROC AUC were used to asses each model. We find that ensemble models perform best (ROC AUC = 0.9245) with Gradient Boosting performing best, with XGBoost and Random Forest close behind. With a competitive ROC AUC of 0.9000, neural networks showed strong potential, but fell slightly behind in recall compared with ensemble methods. Logistic Regression and Decision Tree were simpler models that did not achieve as strongly in predictive accuracy as more complex model; however they provided a baseline insight. Through this analysis, ensemble models and deep learning showed to be very efficient to predict online purchase intentions and provide actionable insights to optimize e-commerce platforms. 2025 IEEE. Subject The topic of the resource Customer Behavior Analysis; Online Shopping Behavior; Predictive Analytics; Purchase Intention Prediction Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153482-0; 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