13.1 Acceptance of Consumer 1.0m Universal Bank has begun a program to encourage its existing mtstnrners to borrow 1iris a consumer loan program. The hank has promoted the loan to 5000 customers. of whom 430 accepted the o'er. The data are available in El: Urrr'wraulmlrorr'. 1111: hank now wants to Islamic-p a model to predict which (Instillllm have the greatest probability of accepting the loan. to reduce pmrrlutiun costs and send the offer only to a subset of its customers. We 1will develop several models. then combine them in an ensemble. The models we will use are (1) logistic regression. (2} litnearest neighbors with it = 3. and (3) classication treu. Preproceru the data as alltrws: ' Zip code can be ignored. It Partition the data: {rm-i training. 40% validation. -. Fit mode-la to the date For (1} logistic regression. {2} h-neuest neighbors with l: 3., and (3} classication trees. Use Personal Low as the outcome variable. Report the validation confusion matrix for each of the dues: models. I). Create a data frame with the actual outcome. predicted outcome. and each of the three models- Report the First '10 rows of this data ame- c. Add two columns to this data ame liar (I) a majority vote of predicted outcomes. and [2} the average ofthe predicted probabilities. Using the classications generated by these two methods derive a connion matrix for each method and report the overall accuracy. t1. CULIJPIIE tilt: onus tum fur the lion: individual methods and the term CIIIICLLIIJII: methods