ItemConference Paper

Leveraging Machine Learning to Predict Revenue-Generating Sessions in E-Commerce Platforms

Title
Leveraging Machine Learning to Predict Revenue-Generating Sessions in E-Commerce Platforms
Creator
Yadav, Sangeeta; Ranka, Monica; Ojha, Shikha; Malhotra, Amit; Basha, Md Shaik Amzad
Description
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.
Source
2025 International Conference on Information, Implementation, and Innovation in Technology, I2ITCON 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Customer Behavior Analysis; Online Shopping Behavior; Predictive Analytics; Purchase Intention Prediction
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833153482-0;
Format
online
Language
English
Type
Conference paper

Citation

Yadav, Sangeeta; Ranka, Monica; Ojha, Shikha; Malhotra, Amit; Basha, Md Shaik Amzad, “Leveraging Machine Learning to Predict Revenue-Generating Sessions in E-Commerce Platforms,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25849.

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