[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121546-en":3,"doc-seo-121546-105":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":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},121546,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Leveraging Machine Learning to Predict E-commerce Shopping Behaviour and Enhance Recommendations","This study explored how machine learning techniques could predict e-commerce shopping behaviour and enhance product recommendations. The research analysed data from user interactions, purchase histories, and sentiment analysis to develop effective models. It involved thorough data preparation, including handling missing values, processing text with TF-IDF, and applying SMOTE to balance the data. Several algorithms were tested, and Logistic Regression/AdaBoost performed best for behaviour prediction, supporting practical, real-world personalization, satisfaction, and sales improvement.","Leveraging Machine Learning to Predict E-commerce Shopping Behaviour and Enhance Recommendations  \nSheikh Fuad Ahmed  \n20000816  \nDissertation submitted in partial fulfilment of the requirements  \nfor the degree of  \nMaster of Science in Business Analyticsat Dublin Business School  \nSupervisor: Dr Vivek Kshirsagar  \nMay 2024  \nDECLARATION  \nI Sheikh Fuad Ahmed hereby declare that the research work titled \"Leveraging Machine Learning to Predict E-commerce Shopping Behaviour and Enhance Recommendations\" is entirely my own effort and represents the outcome of my intellectual endeavours, except where otherwise stated and duly acknowledged through references. This research project is submitted  \nin partial fulfilment of the requirements for the MSc in Business Analytics program at Dublin Business School.  \nSigned: Sheikh Fuad Ahmed  \nStudent ID: 20000816  \nDate: 19th May, 2024  \nACKNOWLEDGEMENT  \nI would like to express my deep and sincere gratitude to my dissertation supervisor Dr. Vivek Kshirsagar for his guidance, support, and invaluable feedback. It has been a privilege and a pleasure to work under his supervision.  \nI am also very grateful to Dublin Business School for providing me with all the necessary tools and resources to complete this project and further my studies in this course.  \nThank You  \nABSTRACT  \nThis study explored how machine learning techniques could predict e-commerce shopping behaviour and enhance product recommendations. The research analysed data from user interactions, purchase histories, and sentiment analysis to develop effective models. It involved thorough data preparation, including handling missing values, processing text with TFIDF, and applying SMOTE to balance the data. Various models were tested such as Logistic Regression, AdaBoost, Random Forest, Naive Bayes, XGBoost, and Linear Support Vector Machine. The results indicated that Logistic Regression and AdaBoost were most effective for predicting shopping behaviour while Logistic Regression and Linear Support Vector Classifier excelled in sentiment analysis. These models achieved high accuracy, precision, and recall, demonstrating their practicality for real-world e-commerce applications. Implementing these models allowed e-commerce platforms to offer personalized recommendations, enhance customer satisfaction and increase sales. This study demonstrated the significant potential of machine learning to improve e-commerce strategies and enrich the overall shopping experience.  \nTable of Contents  \n1. Introduction ........................................................................................................................ 7  \n1.1 Background ...................................................................................................................... 7  \n1.2 Research Question ............................................................................................................ 8  \n1.3 Research Objective........................................................................................................... 8  \n2. Literature Review ............................................................................................................... 8  \n3. Methodology..................................................................................................................... 13  \n3.1 Business Understanding ................................................................................................. 14  \nObjectives and Requirements ........................................................................................... 14  \nData Mining Problem Definition ...................................................................................... 14  \nPreliminary Plan ............................................................................................................... 15  \n3.2 Data Understanding ........................................................................................................ 15  \nCollect I","cbCaigkANv9ad5Qr","https://ap.wps.com/l/cbCaigkANv9ad5Qr","pdf",574741,1,40,"English","en",105,"# 1. Introduction\n## 1.1 Background\n## 1.2 Research Question\n## 1.3 Research Objective\n# 2. Literature Review\n# 3. Methodology\n## 3.1 Business Understanding\n## 3.2 Data Understanding\n## 3.3 Data Preparation\n## 3.4 Modeling\n## 3.5 Evaluation","[{\"question\":\"What data sources were used to predict e-commerce shopping behaviour and improve recommendations?\",\"answer\":\"The study used user interaction data, purchase histories, and sentiment analysis signals. These inputs supported model development for both behaviour prediction and recommendation enhancement.\"},{\"question\":\"Which machine learning models performed best for predicting shopping behaviour and for sentiment analysis?\",\"answer\":\"Logistic Regression and AdaBoost were most effective for predicting shopping behaviour. For sentiment analysis, Logistic Regression and Linear Support Vector Classifier performed best.\"},{\"question\":\"What steps in data preparation were used before modeling?\",\"answer\":\"Data preparation included handling missing values, processing text with TF-IDF, and applying SMOTE to balance the dataset. These steps improved model training quality and stability.\"}]","Leveraging Machine Learning to Predict E-commerce Shopping Behaviour and Enhance Recommendations | PDF",1785736185,101,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"leveraging-machine-learning-to-predict-e-commerce-shopping-behaviour-and-enhance-recommendations","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/leveraging-machine-learning-to-predict-e-commerce-shopping-behaviour-and-enhance-recommendations/121546/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What data sources were used to predict e-commerce shopping behaviour and improve recommendations?","Question",{"text":75,"@type":76},"The study used user interaction data, purchase histories, and sentiment analysis signals. These inputs supported model development for both behaviour prediction and recommendation enhancement.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models performed best for predicting shopping behaviour and for sentiment analysis?",{"text":80,"@type":76},"Logistic Regression and AdaBoost were most effective for predicting shopping behaviour. For sentiment analysis, Logistic Regression and Linear Support Vector Classifier performed best.",{"name":82,"@type":73,"acceptedAnswer":83},"What steps in data preparation were used before modeling?",{"text":84,"@type":76},"Data preparation included handling missing values, processing text with TF-IDF, and applying SMOTE to balance the dataset. 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