Implementation of Naive Bayes method for Decision Support

Naive Bayes use case studies:
For example, we will take a decision for a loan using Naive Bayes for a new customer, before we do that, we need a supporting base for it. The model that created from Naive Bayes method was the one which would be the basis (for decision support). This model created from the customer data that already existed. We take some of variables from the data, for example: character, occupation, salary, and age.

Customer Data that has been given Loan Decision
and we will give a decision for Putu using Naive Bayes

Calculate the probability of class using this formula

Pm = probability of class
nm = number of records
N = total number of records
M = number of class

For example, from the data, there are 10 records of customer, 6 customers have got 'accept' loan decision, and 4 others are 'denied'. We will calculate for the variable of character.

Valuation for Variable of Character

1. 'Accept' for character with value of 1 = 5 customers
2. 'Accept' for character with value of 2 = 1 customers
3. 'Denied' for character with value of 1 = 1 customers
4. 'Denied' for character with value of 2 = 3 customers

Calculate each probability, for example

Probability for Accept Character 1