Algorithm 1: Naive bayes (NB)

Step

Processes involved

1

Start

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Input: Training_Dataset(T)

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F = (f₁, f₂, f₃, ..., f_n) // the predictor variables for testing items

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Output: Class of testing items

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Compute mean and standard deviation of predictor variables in each class

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Repeat this step

(a) Compute probabilities required for the Bayesian theorem for Exiting employees

(b) Compute posterior probability of all those are not leaving the organization

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Compute the likelihood of each class(first and second class)

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Get the greatest likelihood

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Return