Execute the following steps to split the dataset into training and test sets.

1. Import the function from sklearn:

from sklearn.model_selection import train_test_split

2. Split the data into training and test sets:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

3. Split the data into training and test sets without shuffling:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)

4. Split the data into training and test sets with stratification:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)

5. Verify that the ratio of the target is preserved:

y_train.value_counts(normalize=True)
y_test.value_counts(normalize=True)

In both sets, the percentage of defaults is ~22.12%.