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Naive bayes

Naive bayes theorem:

Naive bayes theorm uses bayes theorm for conditional probability with a naive assumption that the features are not correlated to each other and tries to find conditional probability of target variable given the probabilities of features. We will use titanic survival dataset here and using naive bayes classifier find out the survival probability of titanic travellers. We use sklearn library and python for this beginners machine learning tutorial. GaussianNB is the classifier we use to train our model. There are other classifiers such as MultinomialNB


Naive Bayes Part 2:
we will build email spam classifier using naive bayes algorithm. We will use sklearn CountVectorizer to convert email text into a matrix of numbers and then use sklearn MultinomialNB classifier to train our model. The model score with this approach comes out to be very high (around 98%). Sklearn pipeline allows us to handle pre processing transformations easily with its convenient api. In the end there is an exercise where you need to classify sklearn wine dataset using naive bayes.


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