Develop models that predict customers probability of


Dataset -

  • Consumer credit usage data that contains credit line granted to a customer, their payment and balance history, demographics and their default status.
  • Data Dictionary (below): Contains definition of all the attributes on .csv file.

Questions -

1. Develop model(s) that predict customers' probability of default and outline the methodology used. It could include (but not limited to) the following:

  • Data cleansing steps
  • Feature engineering and selection
  • Rationale behind choosing the modeling technique(s)
  • Model selection criteria
  • Model diagnostics

2. Summarize key drivers and their relationship with default rate (Y).

3. Perform model validation.

4. Based on your findings what recommendations would you make to the business.

Delivery method - Please submit the following via email:

  • Runnable code,
  • Any spreadsheets or data frames used and
  • List of packages used
  • Create a few slides to describe the process, share the findings.

Data Dictionary -

X1: Amount of the given credit : it includes both the individual consumer credit and his/her family (supplementary) credit.

X2: Gender (1 = male; 2 = female).

X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others).

X4: Marital status (1 = married; 2 = single; 3 = others).

X5: Age (year).

X6 - X11: History of past payment. Past monthly payment records (from April to September, 2005) as follows: X6 = the repayment status in September, 2005; X7 = the repayment status in August, 2005; . . .;X11 = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; . . .; 8 = payment delay for eight months; 9 = payment delay for nine months and above.

X12-X17: Amount of bill statement. X12 = amount of bill statement in September, 2005; X13 = amount of bill statement in August, 2005; . . .; X17 = amount of bill statement in April, 2005.

X18-X23: Amount of previous payment. X18 = amount paid in September, 2005; X19 = amount paid in August, 2005; . . .;X23 = amount paid in April, 2005.

Y = default (Yes = 1, No = 0)

Attachment:- Assignment Files.rar

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Database Management System: Develop models that predict customers probability of
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