In this repository i will show you how i did Spam Classifier with naive Bayes classifier
Github link to repository: https://github.com/MadoDoctor/Spam-Classifier
In this Machine Learning we worked with packages:
- NumPy;
- Pandas;
- re.
In statistics, naive Bayes classifier are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naïve) independence assumptions between the features. Bayes' theorem is stated mathematically as the following equation:
- is a conditional probability: the probability of event A occurring given that B is true. It is also called the posterior probability of A given B.
- is also a conditional probability: the probability of event B occurring given that A is true. It can also be interpreted as the likelihood of A given a fixed B because
.
and
are the probabilities of observing A and B respectively without any given conditions; they are known as the marginal probability or prior probability.
A and B must be different events.
To learn more about Bayes' theorem follow the link: https://en.wikipedia.org/wiki/Bayes%27_theorem
- The number of
words repeated in spam messages.
- Total number of words in spam messages.
- The total number of unique words in all messages.
- Anti-aliasing parameter.