ITM 618: Business Intelligence and Analytics
The dataset (CreditData.csv) classifies customers as “approved” or “not approved” (i.e., target class). The target class is in the 21st column and its name is “Approved”. Value of 1 means approved and value of 2 means not approved.
Number of Attributes for Classification: 20 (7 numerical, 13 categorical).
The task should be developed using R (and in RStudio).
1- Divide data into two datasets
75% as training data
25% 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.
1. Source code (copy the R source code in a .txt file)
2. The answer to question 4 as a PDF file.
Here are 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
account/other credits existing (not at this bank)