[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117754-en":3,"doc-seo-117754-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117754,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Machine Learning Approach for Prediction of the Online User Intention for a Product Purchase - Research on SGD and Random Forest","Machine learning is presented as self-improving computation in e-commerce, trained on large datasets to discover patterns, relationships, and anomalies and to build predictive mathematical models. The work applies classification-based machine learning to forecast online shoppers’ purchasing intention on a store website using the UC Irvine online shoppers purchasing intention dataset. Two methods are evaluated: Stochastic Gradient Descent (SGD) and Random Forest, with Random Forest achieving the highest F1-score of 0.90.","Machine Learning Approach for Prediction of the Online User Intention for a Product Purchase  \nDr. Anubhav Kumar1, Dr. Dileep Kumar M2, Víctor Daniel Jiménez Macedo3, B R Mohan4, Achyutha Prasad N5  \n1Department of Computer Science and Engineering  \nUnited Institute of Technology  \nPrayagraj, India  \n[Email: kanubhav914@gmail.com](Email: kanubhav914@gmail.com),  \n[anubhavkrprasad@gmail.com](anubhavkrprasad@gmail.com)  \n2Professor Strategy, Faculty of Management Sciences  \nNile University of Nigeria, Abuja.  \nEmail:  \n[Dileep.KM@nileuniversity.edu.ng](Dileep.KM@nileuniversity.edu.ng)  \n3Mechanical Engineer Faculty,  \nMichoacan University of Saint Nicholas of Hidalgo,  \n[Email:victor.daniel.jimenez@umich.mx](Email:victor.daniel.jimenez@umich.mx)  \n4Computer Science and Engineering,  \nEast West Institute of Technology, Bangalore, India  \n[Email: mohan.bangalore77@gmail.com](Email: mohan.bangalore77@gmail.com)  \n5Department of Computer Science and Engineering,  \nEast West Institute of Technology, Bangalore, India,  \n[Email : achyuth001@gmail.com](Email : achyuth001@gmail.com)  \nAbstract-- The deployment of self-learning computer algorithms that can automatically enhance their performance via experience is referred to as machine learning in ecommerce and is a crucial trend of the retail digital transformation. Machine learning algorithms can be unambiguously trained by analysing big datasets, identifying repeating patterns, relationships, and anomalies among all of this data, and creating mathematical models resembling such associations. These models are improved when the algorithms analyse ever-increasing amounts of data, providing us with useful insights into specific ecommerce-related events and the links between all the variables that underlie them. A tool that has been quite effective in studying current affairs, predicting future trends, and making data-driven decisions. The present work investigates the implementation of machine learning algorithms to predict the user intention for purchasing a product on a specific store's website. An Online Shoppers Purchasing Intention data set from the UC Irvine Machine Learning Repository was used for this investigation. In this study, two classification-based machine learning algorithms i.e. Stochastic Gradient Descent (SGD) algorithm and Random Forest algorithm were used. SGD algorithm was used for first time in prediction of the online user intention. The results showed that the Random Forest resulted in the highest F1-Score of 0.90 in contrast to the Stochastic Gradient Descent algorithm.  \nKeywords-machine learning, SGD algorithm, random forest, user intention  \nI. INTRODUCTION  \nAll of the client data can be used by computers thanks to machine learning. It adheres to the pre-programmed instructions while also changing or adapting in response to new situations. Data causes algorithms to adapt and display previously unprogrammed characteristics [1-3] . If a digital assistant could read and understand context, it might be able to scan emails and extract the important content. This learning comes with the ability to predict future client behaviour as a built-in capability. As a result, you may be more proactive and responsive to the needs of your clients. Deep learning is a part of machine learning. A three-layer artificial neural network is essentially what it is. Uncertain predictions can be made by one-layer neural networks. Accuracy and optimization can be enhanced by  \nincorporating additional layers [4–10] . Machine learning is helpful ina variety of fields and has the ability to develop throughout time. [11- 15] .  \nIt is difficult to create prosthetic controllers that are clever enough to understand user intent across users. Methods for anticipating a user's locomotor mode can be developed using machine learning techniques. In the most recent state-of-the-art for subject dependent models, linear discriminant analysis (LDA) provides the standard answer and has been utilized in ","cbCaia1gfL7gsJho","https://ap.wps.com/l/cbCaia1gfL7gsJho","pdf",401647,1,9,"English","en",105,"# Abstract\n# Keywords\n# Introduction","[{\"question\":\"What problem does the document address in e-commerce?\",\"answer\":\"It investigates how machine learning models can predict the online user’s intention to purchase a product on a specific store website based on available shopper data.\"},{\"question\":\"Which dataset is used for the study?\",\"answer\":\"The study uses the Online Shoppers Purchasing Intention dataset from the UC Irvine Machine Learning Repository.\"},{\"question\":\"How do the two classification algorithms compare in performance?\",\"answer\":\"The Random Forest model achieved the highest F1-score of 0.90, outperforming Stochastic Gradient Descent (SGD).\"}]","Machine Learning Approach for Prediction of the Online User Intention for a Product Purchase - 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