Business Intelligence and Analytics

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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.

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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)

 
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