[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120400-en":3,"doc-seo-120400-105":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},120400,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Credit Card Fraud Detection Using Machine Learning - Thesis","Credit card fraud detection using machine learning focuses on identifying fraudulent transactions by building and evaluating predictive models from transaction attributes. The work presents a structured approach including data sourcing, data preparation, and preprocessing, followed by model development and comparison. Key modeling techniques include K-Nearest Neighbor, Logistic Regression, and Support Vector Machine. Experimental analysis examines relationships between attributes and fraud likelihood, supporting feature understanding and improving classification performance for fraud detection.","CALIFORNIA STATE UNIVERSITY, NORTHRIDGE  \nCredit Card Fraud Detection Using Machine Learning  \nA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in  \nComputer Science  \nBy  \nShivang Himmatbhai Kothari  \nDecember 2024  \nCopyright by Shivang Himmatbhai Kothari, 2024  \nii  \nThe thesis of Shivang Himmatbhai Kothari is approved:  \nDr. George Taehyung Wang Date  \nDr. Kyle Dewey  \nDate  \nDr. Robert Mcllhenny, Chair  \nDate  \nCalifornia State University, Northridge  \nAcknowledgments  \nI would like to thank Dr. Robert Mcllhenny, my thesis advisor, for all his helpful advice, steadfast support, and perceptive criticism during my research. The success of this thesis has been largely attributed to his knowledge and dedication to academic achievement, and his mentoring has significantly aided in my development on both a professional and personal level.  \nAdditionally, I would like to express my gratitude to Dr. George Taehyung Wang and Dr. Kyle Dewey, who served on my thesis committee, for their insightful inquiries, helpful critiques, and support. Their criticism has been extremely helpful in honing my work and raising the standard of my research.  \nI would especially like to thank my family, who have been my pillar of love, support, and encouragement, and for their faith in me. Lastly, I want to express my gratitude to all of my friends, peers, and coworkers who have helped and encouraged me along the way. Their company has been a vital component of my academic experience and has enhanced the process.  \nWithout the combined support and encouragement of everyone listed above, my thesis would not have been feasible. I hope this work represents their faith in me, and I am incredibly grateful for all the time, effort, and resources they have generously spent on me.  \nTable of Contents  \nCopyright................................................................................................................................................................... ii  \nSignature page........................................................................................................................................................... iii  \nAcknowledgments...................................................................................................................................................... iv  \nList of Abbreviations.................................................................................................................................................. vi  \nList of Figures........................................................................................................................................................... vii  \nList of Tables........................................................................................................................................................... viii  \nAbstract..................................................................................................................................................................... ix  \nChapter 1................................................................................................................................................................... 1  \n1. 1 Introduction............................................................................................................................................. 1  \n1.2 Project Goals............................................................................................................................................ 1  \nChapter 2: Literature Review........................................................................................................................................ 2  \n2. 1 Introduction............................................................................................................................................. 2  \n2.2 Literature Review................................................................","cbCaikBTmbkpBhek","https://ap.wps.com/l/cbCaikBTmbkpBhek","pdf",1908860,1,40,"English","en",105,"# Acknowledgments\n# Table of Contents\n# Abstract\n# Chapter 1 - Introduction\n## Project Goals\n# Chapter 2: Literature Review\n## Introduction\n## Literature Review\n# Chapter 3: Project Description\n## Introduction\n## Data Source\n# Chapter 4: Data Analysis\n## Data Preparation\n## Data Preprocessing\n## Data Modeling\n## K-Nearest Neighbor (KNN)\n## Logistic Regression (L.R.)\n## Support Vector Machine (SVM)","[{\"question\":\"What is the main purpose of the thesis?\",\"answer\":\"The thesis aims to detect credit card fraud using machine learning models trained on transaction data and evaluated through structured analysis.\"},{\"question\":\"Which machine learning methods are used for modeling?\",\"answer\":\"The modeling section includes K-Nearest Neighbor (KNN), Logistic Regression (L.R.), and Support Vector Machine (SVM).\"},{\"question\":\"How does the thesis handle data before modeling?\",\"answer\":\"It covers data preparation and data preprocessing steps, including examining correlations between attributes and organizing the dataset for model training.\"}]","Credit Card Fraud Detection Using Machine Learning - 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