R GLM Regression Model - Graph outcome incorrect & Error Rate code needed

Question

For my homework, I am working with a dataset titled Default. I split my data into training and test sets, and ran a logistic regression for the relationship of default1 and the other 3 predictors(income (continuous), balance(continuous), student(0/1)). I am supposed to plot the regression model, but it keeps showing a straight horizontal line on the graph and I don't think that's correct.

How can I graph multiple predictors with a singular binary outcome using my Default_train_logistic glm? Also, how can I obtain those coefficients and error rates of the model?

TIA!

set.seed(1234)
Default$subsample <- runif(nrow(Default))
Default$test <- ifelse(Default$subsample < 0.80, "train", "test")
Default_train <- filter(Default, test == "train")
Default_test <- filter(Default, test == "test")

###Q1 Part B: b. Construct a logistic regression to predict if an individual will default based on all of the provided predictors, and visualize your final predicted model.
#Immediately after loading data, I created default1 to use default as a numerical binary variable for logistic regression.

Default_train_logistic <- glm(default1 ~ ., data = Default_train %>% select(-test), family = "binomial")
summary(Default_train_logistic)

plot(Default_train_logistic)

G1 <- ggplot(Default_train_logistic, aes(balance + income + student1, default1)) +
  geom_point() +
  geom_smooth(method = "glm",
              method.args = list(family = "binomial"),
              se = FALSE)
print(G1)