Reference no: EM13719070

1. Describe the role of Business Intelligence in an organization.

2. Understand the data mining process and its related issues.

3. Create, evaluate and apply different intelligence models.

Personal Loan Acceptance contains data on 5000 customers of Universal Bank. The data include customer demographic information (age, income, etc.), the customer's relationship with the bank (mortgage, securities account, etc.), and the customer response to the last personal loan campaign (Personal Loan).

Among these 5000 customers, only 480 (= 9.6%) accepted the personal loan that was offered to them in the earlier campaign.

In this exercise we focus on two predictors:

Online (whether or not the customer is an active user of online banking services) and Credit Card (abbreviated CC below) (does the customer hold a credit card issued by the bank), and the outcome Personal Loan (abbreviated Loan below).

Partition the data into training (60%) and validation (40%) sets.

Table 1 gives the pivot table for the training data with Online as a column variable, CC as a row variable, and Loan as a secondary row variable. The values inside the cells should convey the count (how many records are in that cell).

Count of ID		Online
				Grand
CreditCard	Personal Loan	0	1	Total
0	0	769	1163	1932
	1	71	129	200
0 Total		840	1292	2132
1	0	321	461	782
		36	50	86
1 Total		357	511	868
Grand
Total		1197	1803	3000

Table 1 : The pivot table for the training data

1) Consider the task of classifying a customer that owns a bank credit card and is actively using online banking services. Looking at the pivot table, what is the probability that this customer will accept the loan offer? (This is the probability of loan acceptance (Loan=1) conditional on having a bank credit card (CC=1) and being an active user of online banking services (Online=1)).

2) Given the two separate pivot tables for the training data. Table 2 has Loan (rows) as a function of Online (columns) and Table 3 has Loan (rows) as a function of CC.

Count of ID	Online
		Grand
Personal Loan	0                                    1	Total
0	1090                         1624	2714
1	107                             179	286
Grand Total	1197                         1803	3000

Table 2: Pivot tables for the training data:  Loan (rows) as a function of Online (columns)

Count of ID	CreditCard
Personal Loan	0                                     1	Grand Total
0	1932                                             782	2714
1	200                               86	286
Grand Total	2132                           868	3000

Table 3: Pivot tables for the training data: Loan (rows) as a function of CC.

Compute the following quantities (P(A|B) means "the probability of A given B"):

3) Use the quantities computed above to compute the naive Bayes probability:

4) Compare this value with the one obtained from the crossed pivot table in  question 1). Which is a more accurate estimate?

5) Given the following table 4 as an output of XLMiner using KNN technique, what is a choice of k that balances between overfitting and ignoring the predictor information? Justify your answer.

Value of	% Error Traini	% Error Validatio
1	Ch • CO 0/ CO CO O Ti Q ui 6 co	10 2,1
		11                                 35
		8 95
		10 20
		9.                                  I
:		9.                                  a
		8.8
:		8. -1

Table 4: Prior class probabilities