This document contains information about a student named Sanket V. Butoliya analyzing a credit card default payment data set using SAS Enterprise Miner. The data set contains 5000 instances and 24 attributes related to customers' credit limit, payment history, bill amounts, demographic information and whether they defaulted on payments. The document discusses importing the data, partitioning it, comparing different models for predicting defaults, and achieving a baseline accuracy of 77.76%

1. Name – Sanket V. Butoliya
UID – U95365115
Major - Business Analytics & Information Systems
SAS® Enterprise Miner™ -
Default Credit Card Payment

2. Attributes
Information
Description Values
LIMIT_BAL Amount of the given credit Numeric
SEX Gender 1 = male; 2 = female
Education Education Status
1 = graduate school; 2 =
university; 3 = high school; 4 =
others
Marital status Married or not
1 = married; 2 = single; 3 =
others
Age Age of person Numeric
PAY_XX History of past payment
-1 = pay duly; 1 = payment
delay for one month; 2 =
payment delay for two months;
BILL_AMT XX Amount of bill statement Numeric
PAY_AMT XX Amount of previous payment Numeric
Default
payment next
month
Will customer default? Yes = 1, No = 0
Predicted attribute = Default
payment next
month
Number of Instances = 5000
Number of Attributes = 24
Missing Attribute Values = None
Data set Data Import Stats & Partition Models Comparison

3. Data set Data Import Stats & Partition Models Comparison

4. Data set Data Import Stats & Partition Models Comparison

5. Data set Data Import Stats & Partition Models Comparison
Baseline
Accuracy
77.76 %

6. Data set Data Import Stats & Partition Models Comparison

7. Data set Data Import Stats & Partition Models Comparison

8. Data set Data Import Stats & Partition Models Comparison

9. Data set Data Import Stats & Partition Models Comparison

10. Data set Data Import Stats & Partition Models Comparison

11. Thank You…