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Jupyter Notebook Python
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Project Overview

In this project, I build 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. 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.

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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