Here’s a hint to figure out how to interpret the confusion matrix. After splitti

Here’s a hint to figure out how to interpret the confusion matrix. After splitting the data into train and test, execute this code. (Be sure to comment this out before turning in your project!) If the number of mines and rocks in the test data differ, you can look at your confusion matrix and easily determine which row is for rocks and which is for mines.
rocks = 0 # initialize counters
mines = 0
for obj in y_test: # for all of the objects in the test set
if obj == 2: # mines are class 2, rocks are class 1
mines += 1 # increment the appropriate counter
else:
rocks += 1
print(“rocks”,rocks,” mines”,mines) # print the results
..
i shared a link for the the data to use as I’m unable to upload it.?
https://drive.google.com/file/d/1YrWWoSpUZruCL02LmVVB6HDYP56SsUiq