Naive Bayes Classifier Implementation | Machine Learning

In this video, we are going to see the intuition of SMS classification problems using the Naive Bayes algorithm using Sklearn module in Python.

The Naive Bayes algorithm is widely utilized to address issues in academia, government, and business thanks to a recent surge in popularity. A Naive Bayes classifier combines various useful characteristics in real-world machine learning. We'll explain the underlying assumptions later on. 

The two popular Naive Bayes classifiers are: Multinomial Naive Bayes and Bernoulli Naive Bayes.

Steps involved in Naive Bayes classifiers:

1) Load the dataset
2) Split X and y into training and testing sets
3) Train the model on the training set
4) Make predictions on the testing set
5) Compare actual response values with predicted response values