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 train set that is randomly populated via the train$\_$indices

train$\_$set $=[]$

###

### YOUR CODE HERE

###

folds.append$(($train$\_$set, test$\_$set$))$

return folds

Split the csv file into kfolds for cross validation