This project is part of the course Machine Learning with Python: from Linear Models to Deep Learning.
Design a classifier to use for sentiment analysis of product reviews with a training set that consists of reviews written by Amazon customers for various food products.
The reviews, originally given on a 5 point scale, have been adjusted to a +1 or -1 scale, representing a positive or negative review, respectively.
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Implement and compare three types of linear classifiers: the perceptron algorithm, the average perceptron algorithm, and the Pegasos algorithm.
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Use your classifiers on the food review dataset, using some simple text features.
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Experiment with additional features and explore their impact on classifier performance.
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project1.py contains various useful functions and templates to implement learning algorithms
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main.py is a script skeleton where these functions are called to run the experiments
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utils.py contains utility functions implemented by MITx staff
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test.py is a script which runs tests to debug learning algorithms