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 a 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 a 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:

Calculate the posterior probability by converting the dataset into a frequency table.

Create a "Likelihood" table by finding relevant probabilities.

Calculate the posterior probability for each class.

Correct Zero Probability errors using Laplacian correction.

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

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

scikit$-$learnLinks to an external site.

There is simple implementation of a Naive Bayes classifier in Python using scikit$-$learn. The script includes the necessary functions to calculate posterior probabilities, create likelihood tables, handle zero probability errors with Laplace correction, and make predictions. We will use the Gaussian Naive Bayes model for this example.