The dataset (CreditData.csv) classifies customers as “approved” or “not approved” (i.e., target class).
business
Description
ITM 618:
Business Intelligence and Analytics
Assignment #2
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).
Tasks:
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.
Deliverables:
1. Source code (copy the R source code in a .txt file)
2. The answer to question 4 as a PDF file.
Dataset
Description:
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
A34: critical
account/other credits existing (not at this bank)
