Use Rstudio please!

answer questions A and B at the bottom of the page.

personal_loan.csv is the dataset used

2. Predicting personal loan acceptance using Logistics Regression Universal Bank is a relatively young bank growing rapidly in terms of overall customer acquisition. The majority of these customers are liability customers (depositats) with varying sizes of relationship with the bank. The customer hase of act customers betower) is quite small, and the hank is interested in expanding this base rapidly to bring = more nan business. In particular, it wants to explore ways of converting its liabits customers to personal Inan customers (while retaining them as depositors) A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department o devise smanter campaigns with better target marketing. The goal is to use Logistics Regression to predict whether a new customer will accept a loan offer. This will serve as the basis for the design of a new campaign The file personal loan.csv contains data on 5000 cm O customers. The desenption of the variables is given below Variable fil Description Customer age in years Experience in years Income in thousands of dollars Spending on credit cards Family size Education (undergraduate, advanced Value of house more in thousands of dollars 11f customer has securities account with bank, otherwise 1 if customer has certificate of deposit account with bank, otherwise 1ifcustomer ses Internet banking facilities, otherwise | Hot_ personal Homework 3 lif customer uses credit card and by the bank, otherwise accept if customer accepted the loan reject otherwise a) Partition the data into training (20%) and validation (30) sets. Runa logistics regression on the training data. Choose one numeric variable and one hinary variable in the model results and interpret their coctficients in odds (1 po Instruction: Ser seed to 30) b) Report the confusion matris, miscarification rate overall accuracy, specificity and sensitivity measures for the validation data (Dipe 2. Predicting personal loan acceptance using Logistics Regression Universal Bank is a relatively young bank growing rapidly in terms of overall customer acquisition. The majority of these customers are liability customers (depositats) with varying sizes of relationship with the bank. The customer hase of act customers betower) is quite small, and the hank is interested in expanding this base rapidly to bring = more nan business. In particular, it wants to explore ways of converting its liabits customers to personal Inan customers (while retaining them as depositors) A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department o devise smanter campaigns with better target marketing. The goal is to use Logistics Regression to predict whether a new customer will accept a loan offer. This will serve as the basis for the design of a new campaign The file personal loan.csv contains data on 5000 cm O customers. The desenption of the variables is given below Variable fil Description Customer age in years Experience in years Income in thousands of dollars Spending on credit cards Family size Education (undergraduate, advanced Value of house more in thousands of dollars 11f customer has securities account with bank, otherwise 1 if customer has certificate of deposit account with bank, otherwise 1ifcustomer ses Internet banking facilities, otherwise | Hot_ personal Homework 3 lif customer uses credit card and by the bank, otherwise accept if customer accepted the loan reject otherwise a) Partition the data into training (20%) and validation (30) sets. Runa logistics regression on the training data. Choose one numeric variable and one hinary variable in the model results and interpret their coctficients in odds (1 po Instruction: Ser seed to 30) b) Report the confusion matris, miscarification rate overall accuracy, specificity and sensitivity measures for the validation data (Dipe