https://github.com/npatta01/udacity-mlnd-plagirism-detector

https://github.com/npatta01/udacity-mlnd-plagirism-detector

Last synced: 14 days ago
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• Host: GitHub
• URL: https://github.com/npatta01/udacity-mlnd-plagirism-detector
• Owner: npatta01
• Created: 2019-08-21T23:15:15.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-08-21T23:15:39.000Z (over 5 years ago)
• Last Synced: 2024-12-07T07:04:32.179Z (18 days ago)
• Language: Jupyter Notebook
• Size: 444 KB
• Stars: 1
• Watchers: 2
• Forks: 4
• Open Issues: 0
• 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.

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).