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 Mishra, Anchal; Chandan; Shetty, Chetan; Malhotra, Amit; Maheshwari, Abhishek; Basha, Md Shaik Amzad Title A name given to the resource Balancing the Cart: Evaluating Imbalance-Aware Machine-Learning Pipelines for Predicting E-Commerce Purchases 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 IEEE 6th Global Conference for Advancement in Technology, GCAT 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/GCAT66372.2025.11368362" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/GCAT66372.2025.11368362</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034265209?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034265209?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 Mishra A., Institute of Management Studies, Ghaziabad, India; Chandan, Symbiosis International (Deemed University), Symbiosis Centre for Management Studies, Bengaluru, India; Shetty C., Dayanand Sagar College of Arts, Science and Commerce, Bengaluru, India; Malhotra A., School of Commerce, Finance and Accountancy, Christ (Deemed to be University), Ghaziabad, India; Maheshwari A., School of Commerce, Finance and Accountancy, Christ (Deemed to be University), Ghaziabad, India; Basha M.S.A., Gandhi Institute of Technology and Managemet (Deemed to be University), Gitam School of Business, Bangalore, India Description An account of the resource We present a comprehensive investigation into predicting purchase conversions in e-commerce sessions, addressing the challenges of severe class imbalance and complex user behavior signals. Using a real-world dataset of 12,330 user sessions described by 24 features (interaction counts, durations, bounce/exit rates, page values, temporal and device metadata), we first conduct exploratory analysis to reveal seasonal peaks in conversion and strong correlations between page value metrics and purchase likelihood. To mitigate the low positive-class rate (10.8%), we embed SMOTE oversampling within our training pipelines, ensuring balanced learning for all classifiers. We then perform a head-to-head comparison of twelve algorithmsranging from linear and generative methods (Logistic Regression, LDA, Gaussian NB), instance-based learners (KNN, SVM), bagging ensembles (Random Forest, Extra Trees, AdaBoost), gradient boosters (XGBoost, LightGBM, CatBoost), to a feed-forward neural network (MLP). Evaluation on a stratified 80/20 holdout set uses overall accuracy plus precision, recall, and F1-score for the purchase class, alongside ROC AUC. Our results demonstrate that ensemble tree methods dramatically outperform simpler models: LightGBM achieves the highest F1 (0.694) and ROC AUC (0.924), with Extra Trees closely following (F1 0.678, AUC 0.926). Simpler classifiers, despite SMOTE, lag markedly in recall and F1, underscoring the importance of powerful nonlinear learners. These findings establish a new benchmark for imbalance-aware conversion prediction and recommend SMOTE-augmented gradient boosting and randomized tree ensembles as the methods of choice for future research and practical deployments. 2025 IEEE. Subject The topic of the resource E-Commerce Conversion Prediction; Ensemble Learning; Gradient Boosting (LightGBM); Imbalanced Classification; Purchase Intent Modeling; Randomized Trees (Extra Trees); SMOTE Oversampling Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151458-7; 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