CREDITDATA.CSV REFERENCE TABLE ON EXCEL:

https://courses.ryerson.ca/d2l/common/viewFile.d2lfile/Database/MTY5MTQ2ODA/CreditData.csv?ou=539945

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, create the confusion matrix, and report its accuracy,

precision, and recall.

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)

Note: D2L may not let you upload a file with .R extension

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