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Split the csv dataset into k folds for cross validation. def k _ fold _ cross _ validation ( dataset , k ) : n

Split the csv dataset into k folds for cross validation.
def k_fold_cross_validation(dataset, k):
n = len(dataset) # Length of the dataset
fold_size = n // k # Divide the length into smaller folds
folds =[] # Empty list of folds
# Shuffle the dataset
shuffled_dataset = dataset.copy()
random.shuffle(shuffled_dataset)
for i in range(k):
# Assign a start and end variables in respect to the fold size
###
### YOUR CODE HERE
###
# Generate all the test indices for the current fold
test_indices =[]
###
### YOUR CODE HERE
###
# Generate all the train indices for the all other folds
train_indices =[]
###
### YOUR CODE HERE
###
# Create a test set that is randomly populated via the test_indices
test_set =[]
###
### YOUR CODE HERE
###
# Create a test set that is randomly populated via the train_indices
train_set =[]
###
### YOUR CODE HERE
###
folds.append((train_set, test_set))
return folds

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