We will use a credit default dataset (default of credit card clients.xlsx) for this exercise. Here comes the data set information: Source: https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients Attribute Information: This dataset has a binary variable, default payment (Yes , No 0), as the response variable The following 23 variables as explanatory variables X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. *X2: Gender (-male; 2- female). X3: Education(-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. We tracked the 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, 200:5 The measurement scale for the repayment status is: -1-pay duly-payment delay for one month; 2-payment delay for two months;..;8-payment delay for eight months payment delay for nine months and above X 12- 17: Amount of bill statement (NT dollar). X12-amount of bill statement in September, 2005; X 13 = amount of bill statement in August, 2005; X17-amount of bill statement in April, 2005 X18-X23: Amount of previous payment (NT dollar). X18-amount paid in September, 2005; X19-amount paid in August, 2005;...;X23-amount paid in April, 2005 . Question Explore the data set, and then use a neural network to model this classification problem (using partition 80:20). Identify the significant impact factors. Can you conclude any insight from this modeling? If you are a marketing manager of this credit card company, what will you do? Share R script We will use a credit default dataset (default of credit card clients.xlsx) for this exercise. Here comes the data set information: Source: https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients Attribute Information: This dataset has a binary variable, default payment (Yes , No 0), as the response variable The following 23 variables as explanatory variables X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. *X2: Gender (-male; 2- female). X3: Education(-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. We tracked the 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, 200:5 The measurement scale for the repayment status is: -1-pay duly-payment delay for one month; 2-payment delay for two months;..;8-payment delay for eight months payment delay for nine months and above X 12- 17: Amount of bill statement (NT dollar). X12-amount of bill statement in September, 2005; X 13 = amount of bill statement in August, 2005; X17-amount of bill statement in April, 2005 X18-X23: Amount of previous payment (NT dollar). X18-amount paid in September, 2005; X19-amount paid in August, 2005;...;X23-amount paid in April, 2005 . Question Explore the data set, and then use a neural network to model this classification problem (using partition 80:20). Identify the significant impact factors. Can you conclude any insight from this modeling? If you are a marketing manager of this credit card company, what will you do? Share R script
