data mining for business analytics

Assignment #5 (Due on 4/25/18 at the beginning of class.)

Submission on blackboard is required. Must be a MS Word document. No late assignments

accepted!

Description

The supplied data set contains past records for applications for loans. Your job is to try to

predict whether an applicant has good credit (RESPONSE = 1). Using techniques that you have

learned during this course, create a data model with a high accuracy rate. You may have to do

some data visualization and data exploration to help determine the best predictors for your

model. Document the steps taken to do your analysis. Provide screenshots and your reasoning.

You will not be graded solely on your final model but also on your methodology.

Variables

Variable Name Description Var

Type

Code Description

OBS# Observation No. Cat

CHK_ACCT Checking account status Cat 0 : < $0

1: 0 < ...< $200

2 : => $200

3: no checking account

DURATION Duration of credit in months Num

HISTORY Credit history Cat 0: no credits taken

1: all credits at this bank paid back duly

2: existing credits paid back duly till now

3: delay in paying off in the past

4: critical account

NEW_CAR Purpose of credit Binary car (new) 0: No, 1: Yes

USED_CAR Purpose of credit Binary car (used) 0: No, 1: Yes

FURNITURE Purpose of credit Binary furniture/equipment 0: No, 1: Yes

RADIO/TV Purpose of credit Binary radio/television 0: No, 1: Yes

EDUCATION Purpose of credit Binary education 0: No, 1: Yes

RETRAINING Purpose of credit Binary retraining 0: No, 1: Yes

AMOUNT Credit amount Num

SAV_ACCT Average balance in savings account Cat 0 : < $100

1 : $100 <= ... < $500

2 : $500 <= ... < $1,000

3 : => $1,000

4 : unknown/ no savings account

EMPLOYMENT Present employment since Cat 0 : unemployed

1: < 1 year

2 : 1 <= ... < 4 years

3 : 4 <=... < 7 years

4 : >= 7 years

INSTALL_RATE Installment rate as % of disposable income Num

MALE_DIV Applicant is male and divorced Binary 0: No, 1: Yes

MALE_SINGLE Applicant is male and single Binary 0: No, 1: Yes

MALE_MAR_WID Applicant is male and married or a

widower

Binary 0: No, 1: Yes

CO-APPLICANT Application has a co-applicant Binary 0: No, 1: Yes

GUARANTOR Applicant has a guarantor Binary 0: No, 1: Yes

PRESENT_RESIDENT Present resident since - years Cat 0: <= 1 year

1<…<=2 years

2<…<=3 years

3:>4years

REAL_ESTATE Applicant owns real estate Binary 0: No, 1: Yes

PROP_UNKN_NONE Applicant owns no property (or unknown) Binary 0: No, 1: Yes

AGE Age in years Num

OTHER_INSTALL Applicant has other installment plan credit Binary 0: No, 1: Yes

RENT Applicant rents Binary 0: No, 1: Yes

OWN_RES Applicant owns residence Binary 0: No, 1: Yes

NUM_CREDITS Number of existing credits at this bank Num

JOB Nature of job Cat 0 : unemployed/ unskilled - non-resident

1 : unskilled - resident

2 : skilled employee / official

3 : management/ self-employed/highly

qualified employee/ officer

NUM_DEPENDENTS Number of people for whom liable to

provide maintenance

Num

TELEPHONE Applicant has phone in his or her name Binary 0: No, 1: Yes

FOREIGN Foreign worker Binary 0: No, 1: Yes

RESPONSE Credit rating is good Binary 0: No, 1: Yes