Using the R-Program:

The Bank data set contains the following variables: > names (Bank) [1] "age" "job" "marital" "education" "default" [6] "housing" "loan" "contact" "month" "day_of_week" [11] "duration" "campaign" "pdays" "previous" "poutcome" [16] "emp.var.rate" "cons.price.idx" "cons.conf.idx" "euribor 3m" "nr.employed" [21] "y" Among the variables, y is a binary variable taking either "yes" or "no" depending on whether the client subscribed a term deposit. The data is randomly divided into training and test sets, and train contains the observation indices of the training set. Using the training set, perform LDA with y as the response and all other variables as predictors. Predict y for the clients in the test set. Construct a confusion matrix for the test set. The Bank data set contains the following variables: > names (Bank) [1] "age" "job" "marital" "education" "default" [6] "housing" "loan" "contact" "month" "day_of_week" [11] "duration" "campaign" "pdays" "previous" "poutcome" [16] "emp.var.rate" "cons.price.idx" "cons.conf.idx" "euribor 3m" "nr.employed" [21] "y" Among the variables, y is a binary variable taking either "yes" or "no" depending on whether the client subscribed a term deposit. The data is randomly divided into training and test sets, and train contains the observation indices of the training set. Using the training set, perform LDA with y as the response and all other variables as predictors. Predict y for the clients in the test set. Construct a confusion matrix for the test set