https://github.com/adeboyeml/project_plagiarism_detection
The aim of this project is to create / build a plagiarism detector that can distinguish between a plagiarized and non-plagiarized text.
https://github.com/adeboyeml/project_plagiarism_detection
Last synced: 11 months ago
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The aim of this project is to create / build a plagiarism detector that can distinguish between a plagiarized and non-plagiarized text.
- Host: GitHub
- URL: https://github.com/adeboyeml/project_plagiarism_detection
- Owner: AdeboyeML
- Created: 2019-11-25T21:49:16.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-11-25T22:33:36.000Z (over 6 years ago)
- Last Synced: 2025-06-13T17:43:50.049Z (about 1 year ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 416 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Plagiarism Project, Machine Learning Deployment
This repository contains code and associated files for deploying a plagiarism detector using AWS SageMaker.
## Project Overview
In this project, you will be tasked with building a plagiarism detector that examines a text file and performs binary classification; labeling that file as either *plagiarized* or *not*, depending on how similar that text file is to a provided source text. Detecting plagiarism is an active area of research; the task is non-trivial and the differences between paraphrased answers and original work are often not so obvious.
## Libraries and Frameworks
The libraries and machine learning frameworks utilized for this projects includes: **Pandas, Numpy, Pytorch, and Amazon SageMaker.**
This project will be broken down into three main notebooks:
**Notebook 1: Data Exploration**
* Load in the corpus of plagiarism text data.
* Explore the existing data features and the data distribution.
* This first notebook is **not** required in your final project submission.
**Notebook 2: Feature Engineering**
* Clean and pre-process the text data.
* Define features for comparing the similarity of an answer text and a source text, and extract similarity features.
* Select "good" features, by analyzing the correlations between different features.
* Create train/test `.csv` files that hold the relevant features and class labels for train/test data points.
**Notebook 3: Train and Deploy Your Model in SageMaker**
* Upload your train/test feature data to S3.
* Define a binary classification model and a training script.
* Train your model and deploy it using SageMaker.
* Evaluate your deployed classifier.
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Please see the [README](https://github.com/udacity/ML_SageMaker_Studies/tree/master/README.md) in the root directory for instructions on setting up a SageMaker notebook and downloading the project files (as well as the other notebooks).