Ted Rogers School of Information Technology Management
ITM 618: Business Intelligence and Analytics
Assignment #2

Due on Sunday November 22, 2020

➢ The dataset (CreditData.csv) classifies customers as “approved” or “not approved” (Yes
or No) (i.e., target class).
➢ The target class is in the 21st column and its name is “Approved”.
➢ Number of Attributes for Classification: 20 (7 numerical, 13 categorical).
➢ The task should be developed using R (and in RStudio).

Tasks:
1- Divide data into two datasets
• 80% as training data
• 20% as test data
Note: Use this link to learn how to divide one dataset into training and test data:
https://rpubs.com/ID_Tech/S1

2- Build a classification model based on the training data to predict if a new customer is
approved or not.
• You can use Regression or Decision Tree (or both to learn more!).

3- Test the model on the test data.

4- Explain the model that you build and report its accuracy (precision).
• If you use decision tree, draw the tree.
• If you use regression, report the parameters and weight values.

Deliverables:
1- Source code (copy the R source code in a .txt file and upload .txt file in D2L)
2- The answer to question 4 as a PDF file.

Dataset Description:
Here is the attribute description for the dataset:

Attribute 1: (qualitative)
Status of existing checking account
• A11: balance = $0
• A12: balance ≤ $200K
• A13: balance > $200K
• A14: no checking account

Attribute 2: (numerical)
Duration of bank membership in month

Attribute 3: (qualitative)
Credit history
• A30: no credits taken/all credits paid back duly
• A31: all credits at this bank paid back duly
• A32: existing credits paid back duly till now
• A33: delay in paying off in the past
• A34: critical account/other credits existing (not at this bank)

Attribute 4: (qualitative)
Purpose of applying for a loan
• A40: car (new)
• A41: car (used)
• A42: furniture/equipment
• A43: radio/television
• A44: domestic appliances
• A45: repairs
• A46: education
• A47: vacation
• A48: retraining
• A49: business
• A410: others

Attribute 5: (numerical)
Credit amount

Attribute 6: (qualitative)
Savings account/bonds
• A61: value < $10K
• A62: $10K ≤ value < $50K
• A63: $50K ≤ value < $100K
• A64: value ≥ $100K
• A65: unknown/ no savings account

Attribute 7: (qualitative)
Present employment since
• A71: unemployed
• A72: employment period < 1 year
• A73: 1 ≤ employment period < 4 years
• A74: 4 ≤ employment period < 7 years
• A75: employment period ≥ 7 years
Attribute 8: (numerical)
Installment rate in percentage of disposable income

Attribute 9: (qualitative)
Personal status and sex
• A91: male and married/divorced/separated
• A92: female and married/divorced/separated
• A93: male and single
• A94: female and single

Attribute 10: (qualitative)
Other debtors / guarantors
• A101: none
• A102: co-applicant
• A103: guarantor

Attribute 11: (numerical)
Present residence since how many year ago

Attribute 12: (qualitative)
Property
• A121: real estate
• A122: if not A121: building society savings agreement/life insurance
• A123: if not A121/A122: car or other, not in attribute 6
• A124: unknown/no property

Attribute 13: (numerical)
Age in years

Attribute 14: (qualitative)
Other installment plans
• A141: bank
• A142: stores
• A143: none

Attribute 15: (qualitative)
Housing
• A151: rent
• A152: own
• A153: for free

Attribute 16: (numerical)
Number of existing credits at this bank

Attribute 17: (qualitative)
Job
• A171: unemployed/unskilled - non-resident
• A172: unskilled - resident
• A173: skilled employee/official
• A174: management/self-employed/highly qualified employee/officer

Attribute 18: (numerical)
Number of people being liable to provide maintenance for

Attribute 19: (qualitative)
Telephone
• A191: none
• A192: yes, registered under the customer’s name

Attribute 20: (qualitative)
Foreign worker
• A201: yes
• A202: no

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