Question One: Business Intelligence and Data Mining _________(5 marks)

Explain briefly in your own words the concept of business intelligence. What is the difference between business intelligence and data mining? (ANSWER SHOULD NOT EXCEED 5 LINES).

Question Two: Data mining core concepts _________________(5 marks)

Describe in your own words two core ideas of data mining. (ANSWER SHOULD NOT EXCEED 6 LINES).

Question Three: Nave Bayes Classifier (15 marks)

Naive Bayes is a classification technique based on Bayes Theorem with an assumption of independence among predictors. A Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

For the question below, consider the following training data for the Naive Bayes Classifier.

Experience over 5 years	Major	ILETS score	Employable/not employable
Yes	Engineering	Medium	Employable
Yes	BIT	Low	not employable
No	Management	Medium	not employable
No	BIT	High	Employable
Yes	Management	Medium	Employable
Yes	Management	Low	not employable
No	BIT	Low	not employable
Yes	Engineering	Medium	not employable
Yes	BIT	High	Employable
No	Management	Medium	not employable
Yes	Engineering	Medium	?

The human resources department at company B is seeking a higher efficiency in selecting suitable employees. To do so, nave Bayes technique was employed to classify a new job candidate whether she/he is employable or not employable.

The goal is the classify a job candidate for employment. This candidate has the following predictors: Experience over 5 years, his major is Engineering, and his ILETS score is medium.

1. Compute the probability of hiring the target candidate using the Exact Bayes calculations. (2.5 marks, 0.25 for each value)
2. Compute the probability of hiring the target candidate using the naive Bayes technique. To find that, youll have to calculate:
   1. Conditional probabilities of the employable class, and the prior probability of the class employable.
   2. Conditional probabilities of not employable class, and the prior probability of the class not employable.
   3. Conclude the value of the nave Bayes probability

P (employable | experience = yes, major = engineering, ILETS score = medium)

3. Which class is the candidate is classified in (employable / not employable)? Justify your answer.