This is the second deployment project which is part of the MLE Nanodegree. 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. In this project, a plagiarism detector is developed 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.

This project is broken down into three main notebooks:

1. Load in the corpus of plagiarism text data.
2. Explore the existing data features and the data distribution.

1. Clean and pre-process the text data.
2. Define features for comparing the similarity of an answer text and a source text, and extract similarity features.
3. Select "good" features, by analyzing the correlations between different features.
4. Create train/test .csv files that hold the relevant features and class labels for train/test data points.

1. Upload train/test feature data to S3.
2. Define a binary classification model and a training script.
3. Train the model and deploy it using SageMaker.
4. Evaluate deployed classifier.

1. AWS Account
2. Experience with model development on AWS SageMaker
3. Familiarity AWS S3

cd SageMaker
git clone https://github.com/rohanjn98/plagiarism-detection-sagemaker.git
exit