Adaboost: Universal Bank Dataset The Universal Bank dataset has 14 variables and 5000 records. Use “Personal.Loan” as target variable.

1. Import the data into R library(vegan) library(dummies) library(ada) attr = c('id', 'age', 'exp', 'inc', 'zip','family', 'ccavg', 'edu', 'mortgage','loan', 'securities', 'cd', 'online', 'cc')

data = read.csv(file = "UniversalBank.csv", header = TRUE, col.names = attr) 2. Drop ID & ZIP Code, exp drop_Attr = c("id", "zip", "exp") attr = setdiff(attr, drop_Attr) data = data[, attr] 3. Using domain knowledge separate categorical and numeric attributes. Convert them into appropriate types. cat_Data <- data.frame(sapply(data[,cat_Attr], as.factor)) num_Data <- data.frame(sapply(data[,num_Attr], as.numeric)) data = cbind(num_Data, cat_Data) 4. Standardize numerical data using range method cla_Data = decostand(data[,ind_Num_Attr], "range") 5. Convert all categorical attributes into numeric

edu = dummy(data$edu) family = dummy(data$family) cla_Data = cbind(cla_Data, edu, family) ind_Cat_Attr = setdiff(ind_Cat_Attr, c("edu","family")) rm(edu, family)

cla_Data = cbind(cla_Data, sapply(data[,ind_Cat_Attr],as.numeric)) rm(ind_Cat_Attr) ind_Attr = names(cla_Data) 6. Append the target attribute cla_Data = cbind(cla_Data, loan=data[,"loan"]) 7. Split the data into train, test data sets set.seed(123) train_RowIDs = sample(1:nrow(cla_Data), nrow(cla_Data)*0.6) train_Data = cla_Data[train_RowIDs,] test_Data = cla_Data[-train_RowIDs,] 8.Build the classification model using Adaboost: model = ada(x = train_Data[,ind_Attr],y = train_Data$loan,iter=20, loss="logistic") 9. Predict the values using model on train and test data sets, and calculate accuracy.

pred_Train = predict(model, train_Data[,ind_Attr])

cm_Train = table(train_Data$loan, pred_Train) accu_Train= sum(diag(cm_Train))/sum(cm_Train) rm(pred_Train, cm_Train)

pred_Test = predict(model, test_Data[,ind_Attr])

cm_Test = table(test_Data$loan, pred_Test) accu_Test= sum(diag(cm_Test))/sum(cm_Test) rm(pred_Test, cm_Test)