Predicting Credit Card Defaulters

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Overview

Banks and credit card clients often have a high risk in that they don't know if an indvidual will default on their credit card payment or not. The 10 features they have collected are:

• CUSTID: Unique Customer ID
• LIMIT_BAL: Maximum Spending Limit for the customer
• SEX: Sex of the customer. Some records have M and F to indicate sex. Some records have 1 ( Male) and 2 (Female)
• education: Education Level of the customer. The values are 1 (Graduate), 2 (University), 3 (High School) and 4 (Others)
• MARRIAGE: Marital Status of the customer. The values are 1 (Single), 2 ( Married) and 3 ( Others)
• AGE: Age of customer
• PAY_1 to PAY_6: Payment status for the last 6 months, one column for each month. This indicates the number of months (delay) the customer took to pay that month’s bill
• BILL_AMT1 to BILL_AMT6: The Billed amount for credit card for each of the last 6 months.
• PAY_AMT1 to PAY_AMT6: The actual amount the customer paid for each of the last 6 months
• DEFAULTED: Whether the customer defaulted or not on the 7th month. The values are 0 (did not default) and 1 (defaulted)

Questions to be answered:

1. Loading and processing the data : Do Data cleansing and making it to be enhanced to answer the required questions.
2. Do the Analysis : Perform the basic analysis in spark
3. Use of spark machine learning lib : build models using pyspark.ml