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It details dataset sourcing from Yahoo Finance stock prices, preprocessing steps (missing values, feature engineering, normalization, train-test split), model development with Random Forest regression and hyperparameter tuning, evaluation metrics, model serialization, and cloud deployment considerations including S3 encryption and supporting AWS access configuration.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/msc-research-project-enhancing-predictive-analysis-through-machine-learning-models-in-cloud-computing-environments/290703/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/msc-research-project-enhancing-predictive-analysis-through-machine-learning-models-in-cloud-computing-environments/290703.png","ImageObject",300,407,{"name":42,"@type":43},"Levi","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-28","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",12,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What environment setup is required before implementing the thesis project?","Question",{"text":63,"@type":64},"The document specifies minimum local hardware (8 GB RAM, 50 GB disk space, modern processor) and AWS infrastructure using EC2, S3, Lambda, and API Gateway. It also requires Python 3.9+ with supporting libraries such as pandas, numpy, scikit-learn, boto3, and optionally matplotlib.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How is the stock dataset prepared for model training?",{"text":68,"@type":64},"The dataset uses stock price data from Yahoo Finance including opening, closing, high, low, adjusted close, and volume. It is downloaded and stored in an S3 bucket, then preprocessed using Python and pandas with missing-value handling, feature engineering (daily return, moving average), normalization, and an 80/20 train-test split.",{"name":70,"@type":61,"acceptedAnswer":71},"How is the predictive model developed, evaluated, and prepared for deployment?",{"text":72,"@type":64},"A Random Forest Regressor predicts stock prices with hyperparameter tuning via GridSearchCV. Model performance is evaluated using MSE and R² on the test set, and the trained model is serialized using joblib and uploaded to S3 for deployment.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290703,1789642397,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":97,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":102,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":80,"read_time":144,"preview_page_count":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Configuration Manual  \nMSc Research Project MSc in Cloud Computing  \nAbhishek Goud Chathurpally Student ID: 22236783  \nSchool of Computing National College of Ireland  \nSupervisor: Shreyas Setlur Arun  \nNational College of Ireland  \nMSc Project Submission Sheet  \nSchool of Computing  \nStudent Name: Abhishek Goud Chathurpally  \nStudent ID: 22236783  \nProgramme: MSc in Cloud Computing Year: 2014-2025  \nModule: MSc Research Project  \nLecturer: Shreyas Setlur Arun  \nSubmission Due  \nDate: 29/01/2025  \nProject Title: Enhancing Predictive Analysis through Machine Learning Models in Cloud Computing Environments  \nWord Count: 1190 Page Count: 6  \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: Abhishek Goud Chathurpally  \nDate: 28/01/2025  \nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\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. | □ |\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  \nAbhishek Goud Chathurpally  \nStudent ID: 22236783  \n1 Environment Setup  \nConfiguring the environment for preprocessing, training of the models, and deployment on cloud infrastructure is the first step in implementation of this thesis project. On this part, it gives an overview of what you’ll need to prepare before installing to your local or cloud environment  \n1.1 Hardware Requirements :  \n􀁸 Local Machine: Minimum system requirements are so that initial data preprocessing and model experimentation can be preformed using system with at least 8 GB RAM, 50 GB of free disk space, and a modern processor (e.g., Intel i5 or AMD Ryzen 5) .  \n􀁸 AWS Cloud : Run on scalable infrastructure using Amazon Web Services. EC2, S3, Lambda, and API Gateway are what they need. Make it so that you can access your AWS Management console.  \n1.2 Software Requirements :  \n􀁸 Python: For performing any of the programming task, install Python 3.9 or later. To manage dependencies, it’s best to setup a virtual environment  \n􀁸 Libraries: Install the following Python libraries using pip install  \no pandas: Used for (data) manipulation and preprocessing.  \no numPy: For numerical computations.  \no scikit-learn: For developing evaluation of machine learning models.  \no jilbab: For model serialization.  \no boto3: To use AWS services inside of Python scripts.  \no matplotlib (optional): For exploratory data analysis if you have any data visualization tasks  \n1.3 Setting Up AWS :  \n􀁸 AWS Account: For the initial experimentation, always use the AWS Free Tier.  \n􀁸 AWS CLI: AWS Command Line Interface (CLI) CLI command line tool is installed on your local machine to automate cloud operations. You can use aws configure to configure your credentials. You will need  \no AWS Access Key ID  \no AWS Secret Access Key  \no Default region (e.g., us-east-1)  \nList available S3 buckets with the command and test the connection.  \n“aws s3 ls”  \n􀁸 IAM Roles : Permissions for IAM roles  \no An EC2 instance role with ","cbCaibQW70pNmD6c","https://ap.wps.com/l/cbCaibQW70pNmD6c","pdf",668189,"English","# 1 Environment Setup\n## 1.1 Hardware Requirements\n## 1.2 Software Requirements\n## 1.3 Setting Up AWS\n# 2 Data Preprocessing and Model Development\n## 2.1 Dataset\n## 2.2 Data Preprocessing\n## 2.3 Model Training\n## 2.4 Model Serialization\n# 3 Cloud Deployment\n## 3.1 AWS S3","[{\"question\":\"What environment setup is required before implementing the thesis project?\",\"answer\":\"The document specifies minimum local hardware (8 GB RAM, 50 GB disk space, modern processor) and AWS infrastructure using EC2, S3, Lambda, and API Gateway. It also requires Python 3.9+ with supporting libraries such as pandas, numpy, scikit-learn, boto3, and optionally matplotlib.\"},{\"question\":\"How is the stock dataset prepared for model training?\",\"answer\":\"The dataset uses stock price data from Yahoo Finance including opening, closing, high, low, adjusted close, and volume. It is downloaded and stored in an S3 bucket, then preprocessed using Python and pandas with missing-value handling, feature engineering (daily return, moving average), normalization, and an 80/20 train-test split.\"},{\"question\":\"How is the predictive model developed, evaluated, and prepared for deployment?\",\"answer\":\"A Random Forest Regressor predicts stock prices with hyperparameter tuning via GridSearchCV. Model performance is evaluated using MSE and R² on the test set, and the trained model is serialized using joblib and uploaded to S3 for deployment.\"}]","MSc Research Project - Enhancing Predictive Analysis through Machine Learning Models in Cloud Computing Environments | PDF",15]