Optimizing Machine Learning Models for Real-Time Detection of Fake Product Reviews on E-commerce - Submission and Configuration Manual

Sep 17, 2026

Uploaded by Theodora

MSc Research Project submission and configuration manual for the MSc in Data Analytics programme (2025). It certifies the student’s referenced research work, provides submission checklist items, and records project and student details. The document specifies hardware and software requirements for the development environment, including Python 3.9+, Windows/MacOS/Linux, and running in Jupyter Notebook via Anaconda Navigator. It also guides steps to download a Kaggle fake reviews dataset and set up and run the provided notebook (Research.ipynb), followed by reference entries.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Hardware and Software Configuration

Hardware Requirements

Software Requirements

Development Environment

IDE/Editor and Environment Manager

Download Fake Reviews Dataset from Kaggle

Jupyter Notebook Setup

Install Anaconda

Launching Jupyter Notebook

Open Notebook & Run

Run all cells

References

Configuration Manual MSc Research Project MSc in Data Analytics Chollety Manoj Kumar Student ID: X23227541 School of Computing National College of Ireland Supervisor: Jorge Basilio National College of Ireland MSc Project Submission Sheet School of Computing Student Name: Chollety Manoj Kumar Student ID: X23227541 Programme: MSc in Data Analytics Year: 2025 Module: MSc Research Project Lecturer: Jorge Basilio Submission Due Date: 14 September 2025 Project Title: Optimizing Machine Learning Models for Real-Time Detection of Fake Product Reviews on E-commerce Word Count: 268 Page Count: 5 I 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. ALL 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. Signature: Chollety Manoj Kumar Date: 14-09-2025 PLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST | Attach a completed copy of this sheet to each project (including multiple copies) | □ | | --- | --- | | Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ | | 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. | □ | Assignments that are submitted to the Programme Coordinator Office must be placed into the assignment box located outside the office. | Office Use Only | | | --- | --- | | Signature: | | | Date: | | | Penalty Applied (if applicable): | | Configuration Manual Chollety Manoj Kumar Student ID: X23227541 1. Hardware and Software Configuration Hardware Requirements: Minimum 8 GB RAM (Recommended 16 GB or higher) At least 2 CPU cores (Intel i5 or equalivant) 5 GB available disk space Internet (for package installation and data) Software Requirements: Operating System: Windows 10/11, MacOS X or Linux Python Version: 3.9 or higher Running in the Jupyter Notebook ( Anaconda | Standalone) 2. Development Environment Note: This project has been developed and tested on IDE/Editor: Jupyter Notebook (Anaconda Navigator) Environment Manager: Anaconda (version 2023. x) Notebook Name: Research.ipynb Execution Mode: Local (Using Jupyter Notebook on Desktop) 3 . Download Fake Reviews Dataset from Kaggle You can access the dataset using the following link Figure 1. Fake Reviews Dataset 4. Jupyter Notebook Setup 4.1 Install Anaconda Anaconda comes bundled with Jupyter Notebook, Python, and useful libraries. 1. Download Anaconda from [https://www.anaconda.com/products/distribution](https://www.anaconda.com/products/distribution) [2](2). Install and launch Anaconda Navigator Figure 2. Anaconda Navigator 4.2 Launching Jupyter Notebook 1. From Anaconda Navigator, open Jupyter Notebook 2. Navigate to the folder where your .ipynb file is stored 3. Open the notebook (e.g. Research.ipynb) Figure 3. Jupyter Notebook File View 5. Open Notebook & Run 1. Launch Research.ipynb 2. Ensure the dataset file is loaded correctly: Figure 4: Reading Dataset Figure 5. Research.ipynb File 5.1 Run all cells: Run each cell in sequence using Kernel → Restart & Run All 6. References Kaggle. (2022) . Fake Reviews Dataset. Available at: [https://www.kaggle.com/datasets/mexwell/fake-reviews-dataset](https://www.kaggle.com/datasets/mexwell/fake-reviews-dataset) (Accessed: 7 August 2025) . Anaconda Inc. (2023) . Anaconda Distribution. Available at: [https://www.anaconda.com/](https://www.anaconda.com/) (Accessed: 7 Au

FAQs

What are the minimum hardware and software requirements for this project setup?

The manual specifies at least 8 GB RAM, a minimum of 2 CPU cores, 5 GB available disk space, and an internet connection. It also requires Windows 10/11, macOS X or Linux, and Python 3.9+ running in Jupyter Notebook via Anaconda or standalone.

How does the manual instruct students to set up the development environment?

It states the project is developed and tested on Jupyter Notebook (Anaconda Navigator), uses Anaconda as the environment manager (version 2023.x), and uses the notebook named Research.ipynb with local execution on the desktop.

Where is the dataset obtained and how should the notebook be run?

The fake reviews dataset is downloaded from Kaggle. Students then launch and run Research.ipynb, ensuring the dataset loads correctly and executing all cells using Kernel → Restart & Run All.

Sep 17, 2026

Uploaded by Theodora