How to know if classification model is predicting 1 or 0
You can find the model predictions here:
model1.predict(X_test)

array([0, 0, 0, ..., 0, 0, 0], dtype=int64)

In a binary (good/bad or 1/0) model, predict_proba is the probability of 0 or 1 , the value that is calculated by your model, which is used along with a cutoff (which is 0.5 in the sklearn implementation and cannot be changed) to determine if the final prediction is 0 or 1
model1.predict_proba(X_test)[:,0] # probability the answer is 0

array([0.94009529, 0.96378774, 0.98951049, ..., 0.67607543, 0.97599932,
   0.82838031])


model1.predict_proba(X_test)[:,1] # probability the answer is 1

array([0.05990471, 0.03621226, 0.01048951, ..., 0.32392457, 0.02400068,
   0.17161969])

since this is a binary model, both should add up to one.
The accuracy score is how many predictions were correct.
(correct_pred_0 + correct_pred_1)/total_predictions
If you remove the [:,1], you get the entire array:
model1.predict_proba(X_test)

        #col 1       col 2

array([[0.94009529, 0.05990471],
       [0.96378774, 0.03621226],
       [0.98951049, 0.01048951],
       ...,
       [0.67607543, 0.32392457],
       [0.97599932, 0.02400068],
       [0.82838031, 0.17161969]])

First column is the probability for 0 and the second column is the probability for 1
you can check the order of the classes using this:
model1.classes_, 

for binomial, the default is like this:
array([0, 1], dtype=int64)