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The guide describes replication steps, including the selected deep learning and time-series methods (GRU, LSTM, RNN, Prophet, and Prophet-GRU), and explains why GRU achieved the best accuracy with lower error. It specifies the required hardware and software stack, dataset source structure, preprocessing workflow, and the package configurations used in Jupyter Notebook.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/bitcoin-price-prediction-a-machine-learning-approach-using-opening-and-closing-data-configuration-manual/291134/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/bitcoin-price-prediction-a-machine-learning-approach-using-opening-and-closing-data-configuration-manual/291134.png","ImageObject",300,407,{"name":92,"@type":93},"Theodora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":56},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Which machine learning models are compared for Bitcoin opening and closing price prediction?","Question",{"text":112,"@type":113},"The project compares GRU, LSTM, RNN, Prophet, and Prophet-GRU, and reports that GRU surpasses the others in accuracy and error minimization.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What hardware and software environment is required to run the experiments?",{"text":117,"@type":113},"The setup uses local Windows operating systems with CPU/GPU in Jupyter Notebook, with recommended NVIDIA RTX 2060+ GPU, Python 3.11, and supporting tools such as Jupyter Notebook or VS Code.",{"name":119,"@type":110,"acceptedAnswer":120},"Where is the dataset obtained and what key fields does it include?",{"text":121,"@type":113},"The dataset is downloaded from Kaggle and contains minute-level Bitcoin OHLC prices plus trading volumes in BTC and USD, including a timestamp for each record.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},291134,1789643533,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":56,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","Configuration Manual  \nMSc Research Project Data Analytics  \nSahithi Dornala Student ID: x23241551  \nSchool of Computing National College of Ireland  \nSupervisor: Furqan Rustam  \nNational College of Ireland MSc Project Submission Sheet School of Computing  \nStudent Name: Sahithi Dornala  \nStudent ID: X23241551  \nProgramme: MSc Data Analytics Year: 2024-25  \nModule: MSc Research Project  \nLecturer: Furqan Rustam  \nSubmission Due  \nDate: 12/12/2024  \nProject Title: Bitcoin Price Prediction: A Machine Learning Approach Using Opening and Closing Data  \nWord Count: 1067 Page Count: 11  \nI hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author's written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \nSignature: Sahithi Dornala  \nDate: 12/12/2024  \nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) | □NA |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □NA |\n| You must ensure that you retain a HARD COPY of the project, both for your own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □NA |\n\nAssignments that are submitted to the Programme Coordinator Office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nConfiguration Manual  \nSahithi Dornala  \nStudent ID: x23241551  \n1. Introduction  \nThis manual outlines a step-by-step replication guide for predicting and forecasting the opening and closing prices of Bitcoin using advanced machine learning models. The experiment compares several models, including GRU, LSTM, RNN, Prophet, and Prophet-GRU, whereby GRU surpasses others in accuracy and error minimization. This document lists the packages and configurations of software for the experimental environment so that it can replicate similar results.  \n2. Deployment Environment  \nThe environment used for this research is the local windows operating systems with GPU and CPU on Jupyter Notebook. However, the hardware and the software specification details are mentioned below here:  \n2.1 Hardware Specification  \n• Processor: Intel Core i7 or equivalent  \n• RAM: 16 GB or higher  \n• GPU: NVIDIA RTX 2060 or higher (recommended for faster training of deep learning models) .  \n2.2 Software Specification  \n• Operating System: Windows 10/11, macOS, or Linux-based OS  \n• Programming Language: Python 3.11  \n• IDE: Jupyter Notebook or VS Code (with Python extension)  \nFigure 1: Python Version  \n2.1.1 Integrated Development Environment (IDE): Jupyter Notebook  \n2.2 Python Libraries Required  \nFigure 3 shows the list of the Python Libraries required necessary for the execution of thecode. This mentioned python libraries can be installed using the pip command.  \n• pandas: For data manipulation and analysis.  \n• NumPy: For numerical operations, especially array operations.  \nData Visualization Libraries:  \n• Seaborn: For statistical data visualization.  \n• Matplotlib: For creating static, animated, and interactive visualizations. Machine Learning Libraries:  \n• scikit-learn: For various machine learning algorithms, including:  \no MinMaxScaler for feature scaling  \no r2_score and mean_squared_error for model evaluation  \n• TensorFlow: For building and training deep learning models, specifically:  \no Sequential model for creating a sequential model  \no Dense layer for ful","cbCaih4hr6627jB6","https://ap.wps.com/l/cbCaih4hr6627jB6","pdf",821250,11,"English","# 1. Introduction\n# 2. Deployment Environment\n## 2.1 Hardware Specification\n## 2.2 Software Specification\n# 3. Data Source\n# 4. Project Code Files\n# 5. Data Preparation\n## 5.1 Data Loading\n## 5.2 Data Pre-processing\n## 5.3 EDA and Plotting graphs\n# 6. Model Building","[{\"question\":\"Which machine learning models are compared for Bitcoin opening and closing price prediction?\",\"answer\":\"The project compares GRU, LSTM, RNN, Prophet, and Prophet-GRU, and reports that GRU surpasses the others in accuracy and error minimization.\"},{\"question\":\"What hardware and software environment is required to run the experiments?\",\"answer\":\"The setup uses local Windows operating systems with CPU/GPU in Jupyter Notebook, with recommended NVIDIA RTX 2060+ GPU, Python 3.11, and supporting tools such as Jupyter Notebook or VS Code.\"},{\"question\":\"Where is the dataset obtained and what key fields does it include?\",\"answer\":\"The dataset is downloaded from Kaggle and contains minute-level Bitcoin OHLC prices plus trading volumes in BTC and USD, including a timestamp for each record.\"}]","Bitcoin Price Prediction - A Machine Learning Approach Using Opening and Closing Data - Configuration Manual | PDF",28]