# naive bayes classifier tutorial in data mining

## Naive bayes classifier in Data Mining

Step 1. Calculate P(Ci)

P(buys_computer = “no”) = 5/14= 0.357.

P(buys_computer = “yes”)  = 9/14 = 0.643.

Step 2. Calculate P(X|Ci) for all classes

P(age = “<= 30” | buys_computer = “no”) = 3/5 = 0.6.

P(age = “<=30” | buys_computer = “yes”)  = 2/9 = 0.222.

P(income = “medium” | buys_computer = “no”) = 2/5 = 0.4.

P(income = “medium” | buys_computer = “yes”) = 4/9 = 0.444

P(student = “yes” | buys_computer = “no”) = 1/5 = 0.2

P(student = “yes” | buys_computer = “yes) = 6/9 = 0.667

P(credit_rating = “fair” | buys_computer = “no”) = 2/5 = 0.4

P(credit_rating = “fair” | buys_computer = “yes”) = 6/9 = 0.667

Step 3. Select the scenario against which you want to classify.

X = (age <= 30 , income = medium, student = yes, credit_rating = fair)

Step 4: Calculate P(X|Ci) :

P(X|buys_computer = “no”) = 0.6 x 0.4 x 0.2 x 0.4 = 0.019

P(X|buys_computer = “yes”) = 0.222 x 0.444 x 0.667 x 0.667 = 0.044

Step 5: Calculate C P(X|Ci)*P(Ci) :

Therefore,  X belongs to class (“buys_computer = yes”)

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