Difference between business intelligence and data mining

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Reference no: EM132603437

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: EM132603437

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