How to Build a Credit Approval Prediction Model Using R

Dividing the Data into Training and Test Datasets

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
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  
74: 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