new inclusion of the second step: python-exercises

Machine Learning for Economics and Finance/python-exercises/randomly_split_train_test_NOTE.txt

@@ -0,0 +1,21 @@
+import numpy as np
+
+# set seed
+np.random.seed(1)
+
+# Number of observations in the dataset
+n = len(default_data)
+
+# Randomly shuffle the indices of the dataset
+indices = np.random.permutation(n)
+
+# Compute training and validation sample sizes
+nT = int(0.7 * n)  # Training sample size
+
+# Split the dataset based on shuffled indices
+n_train = indices[:nT]   # First 70% for training
+n_test = indices[nT:]    # Remaining 30% for validation
+
+# Create training and validation datasets
+train_data = default_data.iloc[n_train]
+test_data = default_data.iloc[n_test]