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

A machine learning project implementing three different linear classification algorithms for sentiment analysis of text reviews. The project compares the performance of Perceptron, Average Perceptron, and Pegasos algorithms on movie review data.

Implementation of Natural Language Processing algorithms using Python3.

ML.NET Meetup presentation + source code

Linear Regression with L2 Regularization, Online, Average, and Polynomial Kernel Perceptron for Optical Character Recognition, Decision Tree Ensemble, Random Forest, AdaBoost