Naive bayes classification is an effective algorithm to predict the category/class of data points based on a training data set, and it works on Bayes theorem of probability to predict the class of unknown data sets. Utilizing this algorithm, we assume independency among predictors which is a strongly simplifying yet affective assumption.
In this project we have been given a training data set from which we have to evaluate each word's probability of appearance in each class based on its experienced frequency. Note that the given dataset includes reviews which are written in Persian and thus the part of the code which deals with the stopwords does not apply to other languages.