BIT405 Business Intelligence Assignment

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Reference no: EM132596138

BIT405 Business Intelligence - Emirates College of Technology

Question One: Business Intelligence and Data Mining

Explain briefly in your own words the concept of business intelligence. What is the difference between business intelligence and data mining?

Question Two: Data mining core concepts
Describe in your own words two core ideas of data mining.

Question Three: Naïve Bayes Classifier
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 datafor 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, naïve 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. Compute the probability of hiring the target candidate using the naive Bayes technique. To find that, you'll have to calculate:
a. Conditional probabilities of the employable class, and the prior probability of the class employable.
b. Conditional probabilities of notemployable class, and the prior probability of the class not employable.
c. Conclude the value of the naïve 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.

Reference no: EM132596138

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