Naive Bayes Algorithm - kumar-brar/Natural-Language-Processing-NLP- GitHub Wiki

Machine Learning explores the study and construction of algorithms that can learn from and make predictions on data. Among Classification Algorithms, Naive Bayes along with Regression is one of the most popular and powerful algorithms.

Real world examples where Naïve-Bayes Algorithm is used :

• News Categorization : for example dividing news into different categories like Sport, Politics, Business, Technology and Science, Entertainment etc.
• E-Mail Spam Detection
• Face Recognition
• Sentiment Analysis
• Medical Diagnosis
• Digit Recognition
• Weather Prediction

The Math behind the Naive-Bayes Algorithm : P(A|B) = [P(B|A)* P(A)]/P(B)

With these definitions: P(A) is the prior probability of A occurring independently. P(B) is the prior probability of B occurring independently. P(A|B) is the posterior probability that A occurs given B. P(B|A) is the likelihood probability of B occurring, given A.

Note: Here, in my repository, I have made use of movie_review data to classify reviews of movies into either 'positive' or 'negative'.