Chapter 14: exercise 1
From data to decisions
(0 = no, 1 = yes) Has the customer defaulted on his/her loan?
Status of existing checking account
duration in months
Duration of the loan
How have the customer paid loans in the past?
Purpose of the loan
How much is the loan?
Does the customer have a savings account and for how much?
Installment (payment) rate as a percentage of disposable income
personal status and gender
Marital status and gender
Are there other debtors and guarantors (co-signers)?
present resident since
Length of residence at current address (years)
What property does the customer own?
age in years
other installment plans
What other loans does the customer have?
# of existing credits at this bank
Other loans at this bank?
What job skills and status does the customer have?
# of people responsible for
How many dependents rely on this customer?
Does the customer own a phone?
Is this customer a foreign worker?
Build a classification model that classifies customers based on their default risk. Then answer the following questions.
1. What is the most important factor in predicting whether a customer will default? Does this make sense; is it logical that this is the most important factor? Why or why not?
2. What is the least important factor in predicting whether a customer will default? Does this make sense; is it logical that this is the least important factor? Why or why not?
3. What are the most important influencers for default prediction? Explain.
4. What data did you use for training and what data did you use for validation?
5. Show how well your model works on the validation data. Describe the model confidence.
6. Describe the confusion matrix. Explain the risk/loss for the bank when there are false positives and false negatives.
7. Can this model confidence be improved? If yes, how? What is the drawback of overfitting the training data?
8. Show the decision tree that classifies customers.
9. Show one recommendation for a specific customer and the variable values you chose for that customer.
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