Create a data frame called Actual_ vs_predicted by placing the Personal Loan column value and the predicted probability

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

 

#### Table 10.2

bank.df <- read.csv(”D:/R_Exercises/UniversalBank.csv”)

head’:bank.df,3)

bank.df <- bank.df[ , -CCI, 5)] # Drop ID and zip code columns.

head’bank.df,3)

# treat Education as categorical (R will create duimny variables with the Factor function)

g bank.dfSEducation <- factorbank.dfSEducation, levels = c(1, 2, 3),

lo labels c(”Undergrad”, “Graduate”, “Advanced/Professional’

 head’:bank.df, 3)

 6’ partition data

 set.seed(2)

train.index <- sample(c(1:dim(bank.df) [1]), dim(bankdf)[1]*06)

 train.df <- bank.dfLtrain.index, ]

 valid.df <- bank.df{ train.index}

 1 run logistic regression

1 use g 1mO (genera 1 linear model) with family “binomial” to fit a log is t;

 logit.reg <- glmPersonal.Loan .- ., data — train.df, family — “bin

 options(scipen 999) 6’ suppresses scientific notations in cregrssion.

sunanary(logit.reg)

 #101 Table 10.3

 1 use predict () with type “response” to compute predicted probabilities for the va 1 idat ion set records

 logit.reg.pred <- predict(loqit.reg, valid.dfL, -8], type = “response”)

headlogit.reg.pred 6’ shows the predicted probabilities of a Personal Loan for each validation set record

WRITE ADDITIONAL COGE ON )VUR OIiW 70 ACCOAFL ISH STEPS DESCRIBED BELOW:

 1. Create a data frame called Actual_ vs_predicted by placing the Personal Loan column value and the predicted probability value in the logic.reg .pred variable side by side

 2. Using the round () function, round the probability values in the second column above

 3. Create misclassification (or Conñision) matrix using the table function

 4. Calculate the misclassification error using the mean()function

Attachments:

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