I desperately need help with this question. Please provide a detailed answer in clear steps. Thank you in advance.

Consider the following table:

$\backslash$table$[[\backslash$table$[[$Credit$],[$History$]],$Debt,$\backslash$table$[[$Marital$],[$Status$]],$Income,$\backslash$table$[[$Accept Loan$],[$Application$]]],[$bad$,$high,married,$$30$k$,$No$],[$bad$,$low,single,$$25$k$,$No$],[$good$,$high,divorced,$$35$k$,$No$],[$bad$,$high,divorced,$$45$k$,$No$],[$good$,$low,single,$$48$k$,$Yes$],[$good$,$high,married,$$55$k$,$Yes$],[$good$,$high,single,$$70$k$,$Yes$],[$good$,$low,married,$$38$k$,$Yes$],[$bad$,$high,divorced,$$70$k$,$No$],[$bad$,$low,married,$$90$k$,$Yes$]]$

a$.$ Build an NB classifier to predict the label of each class row of the above table

b$.$ Given the following records for a set of employees:

$\backslash$table$[[\backslash$table$[[$Credit$],[$History$]],$Debt,$\backslash$table$[[$Marital$],[$Status$]],$Income$],[$good$,$high,married,$$80$k$],[$bad$,$low,divorced,$$45$k$],[$bad$,$high,single,$$65$k$],[$good$,$low,divorced,$$40$k$]]$

Predict whether to accept loan applications of those employees having the above data

c$.$ Implement an NB classier using any programming language you prefer. You can also reuse any public domain code.