[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124446-en":3,"doc-seo-124446-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},124446,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Behavioural Insights into Online Shoppers’ Purchase Intention Using Machine Learning Models","Evolving e-commerce dynamics make user behavior analysis essential for improving customer experience and optimizing conversion rates. This study predicts whether users make a purchase during a session using session-based behavioral signals and demographic data. Leveraging the “Online Shoppers Purchasing Intention” dataset from the UCI Machine Learning Repository, it applies preprocessing, exploratory analysis, feature selection, and supervised models including Logistic Regression, Random Forest, and Support Vector Machines, evaluated with accuracy, precision, recall, and F1-score.","Behavioural Insights into Online Shoppers’ Purchase Intention Using Machine Learning Models  \nSamuel-Soma M. Ajibade 1,* , Anthonia Oluwatosin Adediran2 , Kayode A. Akintoye3 , Olumide Simeon Ogunnusi4 , Muhammed Basheer Jasser5 , and Olamide Emmanuel Ayodele6  \n1Faculty of Engineering and Technology, Sunway University, Selangor, Malaysia  \n2Department of Real Estate, Faculty of Built Environment, Universiti Malaya, Kuala Lumpur, Malaysia 3Department of Computer Science, The Federal Polytechnic Ado Ekiti, Ekiti State, Nigeria 4Department of Computer Science, The Federal Polytechnic Ado Ekiti, Ekiti State, Nigeria 5Faculty of Engineering and Technology, Sunway University, Selangor, Malaysia  \n6Department of Banking and Finance, Ekiti State University, Ekiti State, Ado Ekiti, Nigeria  \nAbstract: With evolving dynamics in e-commerce, user behavior analysis on e-commerce websites has grown more crucial in customer experience improvement and conversion rate optimization. Predictive analytics is instrumental in deriving hidden patterns from user behavior and enabling data-informed decisions. However, amidst vast amounts of web traffic data, the majority of online retailers struggle to identify actionable behavioral cues that accurately predict buying intent on a consistent basis. This study addresses the issue of accurately predicting purchasing intention based on session-based user behavior and demographic data. By examining the \"Online Shoppers Purchasing Intention\"dataset available at the UCI Machine Learning Repository, this project aims to predict whether or not a user will make a purchase during a session. Using Python as the primary tool, the study employs data preprocessing, exploratory data analysis, feature selection, and machine learning algorithms like Logistic Regression, Random Forest, and Support Vector Machines. The performance of these algorithms is evaluated using accuracy, precision, recall, and F1-score.  \nPreliminary results show that page value, bounce rate, and visit month have a significant influence on purchase likelihood. The results highlight the importance of behavioral data in predicting e-commerce outcomes. The results can be utilized to inform strategic planning in UX design, online marketing, and inventory management.  \nKeywords: Predictive analytics, E-commerce behavior, Purchasing intention, Machine learning, Web analytics.  \n1. INTRODUCTION  \nAs online shopping has increased, e-commerce sites have become major sources of global retail traffic. The online trend has produced huge behavioral data from online shoppers with the potential of mining patterns for business decision-making [1, 2] . Predictive analytics, which employs historical data for forecasting future outcomes, is particularly useful in understanding what fuels a customer's purchase intent [3] . Past studies have applied machine learning models toe-commerce data for user action and purchasing intent prediction [4] . For instance, Gkikas et al. demonstrated that Random Forest and Gradient Boosting improve the performance of prediction in online retail environments [5] . Sakar et al. also predicted conversion outcomes from user sessions with page value and exit rate being key characteristics [6] . These papers enhance the argument for validity in predictive modeling from session-based behavioral data and area foundation for the current study.  \n*Address correspondence to this author at the Faculty of Engineering and Technology, Sunway University, Selangor, Malaysia;  \nE-mail: [samuelma@sunway.edu.my](samuelma@sunway.edu.my)  \nWeb analytics tools track a wide variety of features like browsing time, product browsing, referral medium, and engagement time [5] . These parameters can be used to allow businesses to streamline user experience and enhance conversion rates [7] . In this study, we focus on the use of machine learning models to predict whether a user will convert based on session behavior. While e-commerce companies are stuck with ric","cbCaiuvKOVD9Od6O","https://ap.wps.com/l/cbCaiuvKOVD9Od6O","pdf",652796,1,13,"English","en",105,"# Introduction\n## Predictive analytics and session behavior\n## Dataset and study scope\n# Methods\n## Data preprocessing and exploratory analysis\n## Feature selection and machine learning models\n## Evaluation metrics\n# Results and Implications\n## Influential behavioral factors","[{\"question\":\"What does the study aim to predict in online shopping sessions?\",\"answer\":\"The study predicts whether a user will make a purchase during a session, using session-based user behavior together with demographic data.\"},{\"question\":\"Which machine learning models are used to build the predictive system?\",\"answer\":\"The project uses Logistic Regression, Random Forest, and Support Vector Machines, after data preprocessing, exploratory analysis, and feature selection.\"},{\"question\":\"Which factors show significant influence on purchase likelihood in the preliminary results?\",\"answer\":\"Preliminary results indicate that page value, bounce rate, and visit month significantly influence the likelihood of purchase.\"}]","Behavioural Insights into Online Shoppers’ Purchase Intention Using Machine Learning Models | 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