[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117396-en":3,"doc-seo-117396-105":30,"detail-sidebar-cat-0-en-105":83},{"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},117396,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Cryptocurrency Price Prediction Using Machine Learning","Machine learning models have become a key focus for forecasting cryptocurrency prices amid persistent volatility and difficulty of reliable real-time prediction. Prior research surveys recurrent neural networks, deep learning architectures, Bayesian regression, k-nearest neighbor, and support vector machine methods for assets such as Bitcoin, Ethereum, Dogecoin, and Litecoin, alongside related work on NFT sale predictability, gold price prediction, and silver forecasting. The study emphasizes high-dimensional time-series features, feature engineering, and comparisons across statistical models and machine learning techniques, while addressing gaps in applying these approaches to a broader set of cryptocurrencies and incorporating factors like liquidity, exchange dynamics, market trends, trade networks, and visual features.","Cryptocurrency Price Prediction Using Machine  \nLearning  \nDhruva Parag Kawli Department of Computer Engineering St. John College of Engineering and Management, Palghar[dhruvakawli123@gmail.com](dhruvakawli123@gmail.com)  \nGaurav Arun Telange Department of Computer Engineering St. John College of Engineering and Management, Palghar [gauravtelange786@gmail.com](gauravtelange786@gmail.com)  \nAditi Sunil Chaudhari Department of Computer Engineering St. John College of Engineering and Management, Palghar[aditi1d2801@gmail.com](aditi1d2801@gmail.com)  \nAbira Banik Department of AI/ML St. John College of Engineering and Management, Palghar[abirab@sjcem.edu.in](abirab@sjcem.edu.in)  \nPrajakta Dnyaneshwar Ingale Department of Computer Engineering St. John College of Engineering and Management, Palghar [prajaktaingale641@gmail.com](prajaktaingale641@gmail.com)  \nAbstract—The application of machine learning algorithms in predicting cryptocurrency prices has gained significant attention in recent years. Researchers have explored various approaches such as recurrent neural networks, deep learning neural networks, Bayesian regression, k-nearest neighbor, support vector machine, and other algorithms to forecast the prices of cryptocurrencies like Bitcoin, Ethereum, Dogecoin and Litecoin. This paper will draw on established literature on price prediction using machine learning, including studies on NFT sales predictability, NFT sale price fluctuations prediction, gold price prediction, and silver price forecasting. The research paper has focused on utilizing high-dimensional features, time-series analysis, as well as the comparison of different statistical models and machine learning algorithms. Additionally, the prediction models have incorporated factors such as market liquidity, exchange market dynamics. While the literature acknowledges the potential of machine learning in cryptocurrency price prediction, gold, silver and NFT’s there is a recognized gap in the application of these techniques across a broader range of cryptocurrencies. The proposed methodology will integrate various machine learning models and statistical methods to predict the prices of cryptocurrencies, gold, silver, and NFTs, taking into account factors such as market trends, trade networks and visual features. Furthermore, the studies emphasize the importance of feature engineering, sample dimension engineering, and the use of various machine learning techniques to enhance the accuracy and stability of cryptocurrency price predictions. As the cryptocurrency market continues to expand, there is a need for further research to develop robust machine learning models that can effectively forecast the prices of diverse cryptocurrencies, contributing to the advancement of this field.  \nKeywords: Price Prediction, Random Forest, Long Short-Term Memory (LSTM)  \nI. INTRODUCTION  \nThe use of machine learning for price prediction in various asset classes, including cryptocurrencies, gold, silver, and nonfungible tokens, has gained significant attention in recent years. The challenges in real-time price prediction for cryptocurrencies due to their deterministic nature emphasized the use of machine learning algorithms to predict and forecast cryptocurrency  \nprices, aiming to facilitate trading activities acknowledged the difficulty in predicting cryptocurrency prices due to their high volatility.  \nCryptocurrency, gold, silver and Non-Fungible Token (NFT) are distinct assets which having unique features [15] . Gold is often seen as a hedge against economic uncertainty, while silver serves both investment and industrial purposes. Non-Fungible Tokens (NFTs) are indivisible and have gained popularity in the digital world for proving ownership. Cryptocurrency was created in 2009, that was Bitcoin and since then, thousands of another cryptocurrency was created such as Ethereum, Litecoin, Dogecoin, etc. cryptocurrency are relatively unpredictable compared to traditional financial instru","cbCaih4f6DOp08QF","https://ap.wps.com/l/cbCaih4f6DOp08QF","pdf",1878183,1,5,"English","en",105,"# Introduction\n# Literature Survey\n## Random forest regression and related algorithms\n## Biases and expert feedback\n## Feature selection and factor interactions\n## Price action challenges in cryptocurrency markets\n## Gold price prediction focus","[{\"question\":\"What gap in existing literature motivates the paper?\",\"answer\":\"The paper highlights a recognized gap in applying machine learning techniques across a broader range of cryptocurrencies beyond commonly studied assets, and notes limitations in deeper exploration such as biases, expert feedback contributions, and factor-by-factor reasoning.\"}]","Cryptocurrency Price Prediction Using Machine Learning | PDF",1785675650,13,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"cryptocurrency-price-prediction-using-machine-learning","",{"@graph":36,"@context":77},[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/cryptocurrency-price-prediction-using-machine-learning/117396/",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","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"What gap in existing literature motivates the paper?","Question",{"text":75,"@type":76},"The paper highlights a recognized gap in applying machine learning techniques across a broader range of cryptocurrencies beyond commonly studied assets, and notes limitations in deeper exploration such as biases, expert feedback contributions, and factor-by-factor reasoning.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,101,106,111,114,119,122,126],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":21,"slug":129},19,"General","general"]