# Naive bayes classifier in Data Mining

**Step 1. Calculate P(C _{i})**

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

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

**Step 2. Calculate P(X|C _{i}) 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|C****i****) :**

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|C**

**i**

**)*P(**

**C**

**i**

**) :**

P(X|buys_computer = “no”) * P(buys_computer = “no”) = 0.007

P(X|buys_computer = “yes”) * P(buys_computer = “yes”) = 0.028

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