https://github.com/delosreyesjohnpaul/thesis-similarity-checker-svm-v2

This system is a Plagiarism Checker built with Flask. It uses a Support Vector Machine (SVM) model to detect plagiarism in user-submitted text. If plagiarism is found, the system calculates a plagiarism percentage using cosine similarity. The result and percentage are displayed on a simple web interface.
https://github.com/delosreyesjohnpaul/thesis-similarity-checker-svm-v2

cosine-similarity flask plagiarism-detection python svm

Last synced: about 2 months ago
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This system is a Plagiarism Checker built with Flask. It uses a Support Vector Machine (SVM) model to detect plagiarism in user-submitted text. If plagiarism is found, the system calculates a plagiarism percentage using cosine similarity. The result and percentage are displayed on a simple web interface.

• Host: GitHub
• URL: https://github.com/delosreyesjohnpaul/thesis-similarity-checker-svm-v2
• Owner: delosreyesjohnpaul
• License: mit
• Created: 2024-09-09T13:00:33.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-10-17T04:40:07.000Z (2 months ago)
• Last Synced: 2024-10-31T05:04:09.328Z (about 2 months ago)
• Topics: cosine-similarity, flask, plagiarism-detection, python, svm
• Language: Python
• Homepage:
• Size: 92.7 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Thesis Similarity Checker SVM V2

This repository contains a project for checking the similarity of thesis documents using Support Vector Machine (SVM) models. The project is designed to assist in identifying and analyzing similarities between academic theses.

## Features

- **SVM-based Similarity Checking**: Utilizes Support Vector Machine models to perform document similarity checks.

- **Document Preprocessing**: Includes functions for text preprocessing such as tokenization, stemming, and stop-word removal.

- **Similarity Metrics**: Implements various similarity metrics to evaluate the similarity between documents.

- **Extensive Testing**: Contains unit tests to ensure the accuracy and reliability of the similarity checking process.

## Installation

To install and set up the project, follow these steps:

1. Clone the repository:

    ```sh

    git clone https://github.com/delosreyesjohnpaul/Thesis-Similarity-Checker-SVM-V2.git

    cd Thesis-Similarity-Checker-SVM-V2

    ```

2. Set up a virtual environment and install dependencies:

    ```sh

    python -m venv venv

    source venv/bin/activate  # On Windows use `venv\Scripts\activate`

    pip install -r requirements.txt

    ```

## Usage

To use the similarity checker, you can run the main script with your documents as input. Example usage:

```sh

python main.py --input1 document1.txt --input2 document2.txt