Contains the following notebooks along with the necessary scripts:

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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Notebook 3 has two approaches:

1. Uses sklearn to create classification model
2. Uses pytorch to create classification model