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.
• The features explored were Containment Score and LCS score . The Containment Score was calculated on n-grams where n ~ range(1, 11)
• Select "good" features, by analyzing the correlations between different features. The features with the lease correlation was chosen to train the model.
• 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 train/test feature data to S3.
• Define a binary classification model and a training script.
• I tried out SVM and Random Forest models and RF seemed to perform better
• Train Randon Forest model and deploy it using SageMaker.
• The accuracy of the model turned out to be 96%

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Please see the README in the root directory for instructions on setting up a SageMaker notebook and downloading the project files (as well as the other notebooks).