[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121690-en":3,"doc-seo-121690-105":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},121690,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","NFT Artifact Prediction using Machine Learning - Master’s Project","NFT Prediction Systems deliver actionable insights about digital artifacts to support investors and collectors in making purchase decisions. This master’s project develops a web application that takes user input, recommends suitable NFTs, and trains a machine learning model to predict cost. The workflow includes data collection, preprocessing, analysis, and filtering of large multi-source datasets. The model is trained from NFT title and description keyword occurrences plus parameters such as price, and evaluates accuracy by varying user input.","San Jose State University  \nSJSU ScholarWorks  \n\n| Master's Projects | Master's Theses and Graduate Research |\n| --- | --- |\n| Spring 2023\u003Cbr>NFT Artifact Prediction using Machine Learning\u003Cbr>Rishabh Pandey\u003Cbr>San Jose State University\u003Cbr>Follow this and additional works at: [https://scholarworks.sjsu.edu/etd_projects](https://scholarworks.sjsu.edu/etd_projects)\u003Cbr> Part of the Information Security Commons, and the Other Computer Sciences Commons |  |\n\nRecommended Citation  \nPandey, Rishabh, \"NFT Artifact Prediction using Machine Learning\" (2023) . Master 's Projects. 1241. DOI: [https://doi.org/10.31979/etd.3whn-9vqm](https://doi.org/10.31979/etd.3whn-9vqm)  \n[https://scholarworks.sjsu.edu/etd_projects/1241](https://scholarworks.sjsu.edu/etd_projects/1241)  \nThis Master's Project is brought to you for free and open access by the Master's Theses and Graduate Research at SJSU ScholarWorks. It has been accepted for inclusion in Master's Projects by an authorized administrator of SJSU ScholarWorks. For more information, please contact [scholarworks@sjsu.edu](scholarworks@sjsu.edu).  \nNFT Artifact Prediction using Machine Learning  \nA Project Report  \nPresented to  \nThe Faculty of the Department of Computer Science San José State University  \nIn Partial Fulfillment  \nOf the Requirements for the Class  \nDegree of Master of Science  \nBy  \nRishabh Pandey  \nMay 2023  \n© 2023  \nRishabh Pandey  \nALL RIGHTS RESERVED  \nNFT Artifact Prediction Using Machine Learning  \nBy Rishabh Pandey  \nAPPROVED FOR THE DEPARTMENT OF COMPUTER SCIENCE  \nSAN JOSÉ STATE UNIVERSITY  \n2023  \nProf. William Andreopoulos  \nProf Robert Chun  \nProf Nada Attar  \nDepartment of Computer Science  \nDepartment of Computer Science  \nDepartment of Computer Science  \nACKNOWLEDGEMENT  \nI want to express my heartfelt gratitude to my project advisor Prof William Andreopoulos for his continuous support during the creation and execution of this master’s project over the past year. I am indebted to him for showing patience and willingness to help me navigate the challenges and complexities of the project. The periodic feedback and suggestions have been incredibly valuable in shaping the direction of this project and improving its overall quality. I would also like to appreciate other faculties in the Department of Computer Science at San José State University for their efforts and guidance while teaching the advanced computer science courses. Lastly, I would like to thank my family and friends for their constant care and encouragement during my journey of completing the Master of Science in Computer Science degree at San Jose State University.  \nABSTRACT  \nNFT Prediction Systems are web applications that provide their users with valuable insights about the artifact. These insights are useful for investors and collectors to make better decisions about their purchases. This project builds upon the same concept of prediction by developing a web application to dynamically provide recommendations based on user input and training an ML model to predict their cost. Preliminary work for the prediction system involved data collection , pre-processing, analysis, and filtering of large datasets from diverse sources. The project focused on the development of a userfriendly UI to enable seamless categorization of search results generated by the Machine Learning model. The ML model serves as the backbone of the prediction system. It is trained using the occurrences of keywords in the NFT description and title alongside other parameters such as price. The model adapts according to the user input and provides an output that can help the user select the appropriate NFT. Experiments were conducted to determine the accuracy of the prediction system by altering the user input and analyzing the resulting outcome.  \nKeywords – NFT (Non-Fungible Token), UI (User Interface) , ML (Machine Learning)  \nTABLE OF CONTENTS  \nI INTRODUCTION 1  \nII LITERATURE REVIEW 3  \nIII METHODOLOGY 7  \nA. Data Collection ","cbCaim03KLkYPI3t","https://ap.wps.com/l/cbCaim03KLkYPI3t","pdf",2964045,1,51,"English","en",105,"# Introduction\n## Literature Review\n## Methodology\n## Data Collection\n## Data Processing\n## Machine Learning Techniques\n## Results and Discussion\n## Challenges and Future Work\n## References\n## Appendix\n## List of Figures","[{\"question\":\"What is the goal of the NFT Artifact Prediction system in this project?\",\"answer\":\"The project aims to predict NFT artifact cost and provide recommendations to help users select appropriate NFTs based on their input.\"},{\"question\":\"How is the machine learning model trained?\",\"answer\":\"The model is trained using occurrences of keywords in NFT descriptions and titles along with parameters such as price, then adapts outputs according to user input.\"},{\"question\":\"What parts of the workflow were emphasized during development?\",\"answer\":\"Development emphasized data collection, preprocessing, analysis, and filtering of large datasets, as well as building a user-friendly UI to categorize and display results from the model.\"}]","NFT Artifact Prediction using Machine Learning - 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