Update Part 1 - Introduction to Machine Learning with scikit-learn.md

Part 1 - Introduction to Machine Learning with scikit-learn.md

@@ -67,9 +67,14 @@ Thankfully, scikit-learn gives us a method for automatically splitting up our fu
 
 ```
 from sklearn.model_selection import train_test_split
-X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.50, random_state=42)
-# Note: random_state=42 seeds the random value with 42, meaning that everyone that runs this code will have the same accuracy.
-# Machine learning algorithms have a degree of randomness to them, which can be mitigated by using the same random seed.
+X_train, X_test, y_train, y_test = train_test_split(digits.data, 
+                                                    digits.target, 
+                                                    test_size=0.50, 
+                                                    random_state=42)
+# Note: random_state=42 seeds the random value with 42, meaning that 
+# everyone that runs this code will have the same accuracy.
+# Machine learning algorithms have a degree of randomness to them, 
+# which can be mitigated by using the same random seed.
 # Disregard this if you don't know what that means.
 ```