Description

Problem 1
Include transaction costs of 0.1% (or 10 basis points) for each transaction. Discuss the results.

Problem 2
Use the data set Default.csv which has 7,000 observations on the following 4 variables:

• default – A factor with levels No and Yes indicating whether the customer defaulted on their debt
• student – A factor with levels No and Yes indicating whether the customer is a student
• balance – The average balance that the customer has remaining on their credit card after making their monthly payment
• income – Income of customer

Apply logistic regression, linear discriminant analysis, quadratic discriminant analysis and Knearest neighbor classification methods to predict customers that are likely to default in DefaultPredict.csv dataset. Please use several values of K in the KNN classification method such that you can minimize the errors. Compare the errors for all the methods and draw conclusions.

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