# 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

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**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”) **** **