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In this project, I have trained a Random Forest Classifier on documents containing hashed data. I have created a Flask app which takes in an input document and tries to classify it based on the target labels available at training time. I have achieved an accuracy of ~84%.

The used the given shuffled data to train the classifier and saves the tf-idf-vectorizer and the trained model so that they can be used later for testing/classifying live samples.

If running the file locally, it starts a flask app deployed locally at and this app can handle both GET queries and also POST requests.

The model is deployed and can be tested at

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