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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.  \n\n| Signature: | Saran Raj Srinivasan |\n| --- | --- |\n| Date: | 31st January 2024 |\n\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 foryour 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  \nSaran Raj Srinivasan  \nx22149066  \n1 Introduction  \nThis document provides a detailed step by step information related to the environment needed to execute this research project ”Comparative Analysis of Batch and Online Machine Learning Techniques for Fraud Detection: A Case Study on Real European Credit Card Transactions”. The complete project is performed using Python libraries in Jupyter notebook and Google Colab. This manual explain the system configuration used for the execution with the information about the data set along with tools and setups required to run the project.  \n2 System Specification  \nThis section gives basic hardware and operating system requirements for this project:  \n2.1 Hardware  \nThis project is executed with following hardware as default configuration on current laptop:  \nLaptop: HP Laptop 14s-fq1xxx  \nProcessor: AMD Ryzen 3 5300U with Radeon Graphics, 2600 Mhz, 4 Core(s), 8 Logical Processor(s)  \nRAM: 24GB  \nStorage: 500GB  \n2.2 Software  \nThe below software is configured on the current laptop used:  \nOperating System: Windows 11  \nJupyter Notebook: Version 6.5.2  \nPython: Version 3.11.1  \nGoogle Colab Cloud: Used for executing Python code via the browser.  \nRuntime Type: Python 3  \nHardware Accelerator: T4GPU  \nPython Package Upgrades: ‘bigframes‘: Upgraded from 0.12.0 to 0.13.0  \nPython Package Inclusions: ‘transformers‘: Version 4.35.2 ‘google-generativeai‘: Version 0.2.2  \n3 Development Environment  \nPython scripting language is used to develop this research project and below are the details of the environments utilized:  \n3.1 Anaconda  \nFigure 1: Anaconda  \n3.2 Jupyter notebook  \nFigure 2: Jupyter Notebook  \n4 Implementation  \n4.1 Python Libraries  \nThe following packages and libraries are utilized develop this research project, The pip command can be used to install new libraries if development requires them.  \nNumPy Pandas Matplotlib Seaborn  \nScikit-learn (Sklearn) missingpy  \nFigure 3: Importing libraries and package  \n4.2 Dataset Details  \nThe detailed ins","cbCaipnvIFrgwGgy","https://ap.wps.com/l/cbCaipnvIFrgwGgy","pdf",1680350,"English","# 1 Introduction\n# 2 System Specification\n## 2.1 Hardware\n## 2.2 Software\n# 3 Development Environment\n## 3.1 Anaconda\n## 3.2 Jupyter notebook\n# 4 Implementation\n## 4.1 Python Libraries\n## 4.2 Dataset Details\n## 4.3 EDA, Preparation and Modelling","[{\"question\":\"What is the document’s purpose?\",\"answer\":\"It provides step-by-step information for configuring the environment needed to execute the research project and describes the dataset, tools, and setups required to run it.\"},{\"question\":\"What system requirements are specified?\",\"answer\":\"Hardware requirements include the laptop model, AMD Ryzen 3 5300U, 24GB RAM, and 500GB storage. Software requirements cover Windows 11, Jupyter Notebook 6.5.2, Python 3.11.1, and Google Colab with Python 3 runtime and a T4GPU accelerator.\"},{\"question\":\"Which modelling approaches are covered in the manual?\",\"answer\":\"It outlines modelling workflows for batch methods (e.g., Random Forest, Gradient Boosting LGBM, Ensemble AdaBoost) and online methods using batch-wise input (e.g., Random Forest and XGBoost), along with evaluation metrics and result plots.\"}]","Configuration Manual - MSc Research Project - Configuration | PDF",25]