Naive Bayes Classifier

Naive Bayes classifiers are quick and easy to code in Python and are very efficient.

Naive Bayes classifiers are based on Bayes' Theorem and assume independence among predictors $($hence the "Naive" terminology$).$ Not only are Naive Bayes classifiers handy and straightforward in pinch, but they also outperform many other methods without the need for advanced feature engineering of the data.

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Using scikit$-$learn, write Naive Bayes classifier in Python. It can be single or multiple features. Submit the classifier in the form of an executable Python script alongside basic instructions for testing.

Your Naive Bayes classification script should allow you to do the following:

$1.$ Calculate the posterior probability by converting the dataset into frequency table.

$2.$ Create $"$Likelihood$"$ table by finding relevant probabilities.

$3.$ Calculate the posterior probability for each class.

$4.$ Correct Zero Probability errors using Laplacian correction.

Your classifier may use Gaussian, Multinomial, or Bernoulli model, depending on your chosen function. Your classifier must properly display its probability prediction based on its input data.

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Check out scikit$-$learn and its documentation at the following website:

scikit$-$learn