Second project of the Udacity Machine Learning Engineer Nanodegree program where a Plagiarism Detector is created using custom similarity features amongst source and answer file such as containment and longest common subsequence. Further, trained and deployed the model on Amazon Sagemaker.

This repository contains code and associated files for deploying a plagiarism detector using AWS SageMaker.

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.

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).