Question
Please help. I have everything right up to question 6 CG Q0 # Read in the data as tlmrk. tlmrk
Please help. I have everything right up to question 6
CG Q0 # Read in the data as tlmrk.
tlmrk <- read.csv("telemarketing.csv", strings = T)
# CG Q1 # Use the nrow() function to find the nummber of customer records.
nrow(tlmrk)
# CG Q2 # How many customers in this dataset subscribed to a term deposit product?
sum(tlmrk$subscribe==1)
# CG Q3 # Find the percent of customers that subscribed to a term deposit product.
sum(tlmrk$subscribe==1) / nrow(tlmrk)*100
# CG Q4a # Fit a logistic regression model for subscribe modeled by all other possible
########## variables in the data set plus the variable durmin squared using the code below.
fit <- glm(subscribe ~ . + I(durmin^2), data=tlmrk, family="binomial")
# CG Q4b # Use length() combined with the coef() function to find how many
########## coefficients were estimated by glm().
length(coef(fit))
# CG Q5 # Find the R-squared for the fitted regression by referring to the
######### deviances from summary.glm (use the code from youe lecture examples).
1 - (fit$deviance / fit$null.deviance)
# CG Q6a # Customer 27 in our dataset did not end up subscribing to the term deposit.
########## Let's use our fitted model to see what it would have predicted for this customer.
########## First, create an object called "nd" that that you will pass to the predict()
########## function in the newdata argument.
# CG Q6b # Use your fitted logistic regression and your "newdata" in the predict()
########## function to make a prediction of the probability the customer would subscribe.
predict(fit, newdata=nd, type="response")
# CG Q7 # Use the following code to create a confusion matrix and calculate the PPV.
rule <- 1/5 # classification rule
yhat <- as.numeric(fit$fitted>rule) # classify subscription status based on your rule
table(yhat, actualSubscriptionStatus=tlmrk$subscribe) # confusion matrix
# Now, use the confusion matrix to calculate the sensitivity (recall).
# CG Q8 # Now, for a classification rule of 1/12, find the PPV (precision).
######### You should submit 4 separate lines of code.
# CG Q9 # Calculate the specificity for a rule of 1/3 using 4 lines of code again.
######### Name the rule object rule3 and your predicted subscription status yhat3.
Step by Step Solution
There are 3 Steps involved in it
Step: 1
Get Instant Access to Expert-Tailored Solutions
See step-by-step solutions with expert insights and AI powered tools for academic success
Step: 2
Step: 3
Ace Your Homework with AI
Get the answers you need in no time with our AI-driven, step-by-step assistance
Get Started