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The goal of this project is to design a classifier to use for sentiment analysis of product reviews. Our training set 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.
Implemented and compared three types of linear classifiers to use for sentiment analysis of Amazon product reviews.
Naive Bayes and Averaged Perceptron on IMDB dataset for review classification
This Repository consists of the solutions to various tasks of this course offered by MIT on edX
Linear Regression with L2 Regularization, Online, Average, and Polynomial Kernel Perceptron for Optical Character Recognition, Decision Tree Ensemble, Random Forest, AdaBoost
MITx - MicroMasters Program on Statistics and Data Science - Machine Learning with Python - First Project
ML.NET Meetup presentation + source code