diff --git a/machine_learning/decision_tree.py b/machine_learning/decision_tree.py
index 72970431c3fc..b4df64796bb1 100644
--- a/machine_learning/decision_tree.py
+++ b/machine_learning/decision_tree.py
@@ -146,14 +146,13 @@ def predict(self, x):
         """
         if self.prediction is not None:
             return self.prediction
-        elif self.left or self.right is not None:
+        elif self.left is not None and self.right is not None:
             if x >= self.decision_boundary:
                 return self.right.predict(x)
             else:
                 return self.left.predict(x)
         else:
-            print("Error: Decision tree not yet trained")
-            return None
+            raise ValueError("Decision tree not yet trained")
 
 
 class TestDecisionTree:
@@ -201,4 +200,4 @@ def main():
     main()
     import doctest
 
-    doctest.testmod(name="mean_squarred_error", verbose=True)
+    doctest.testmod(name="mean_squared_error", verbose=True)
diff --git a/maths/monte_carlo.py b/maths/monte_carlo.py
index d174a0b188a2..5eb176238ffb 100644
--- a/maths/monte_carlo.py
+++ b/maths/monte_carlo.py
@@ -8,7 +8,7 @@
 from statistics import mean
 
 
-def pi_estimator(iterations: int):
+def pi_estimator(iterations: int) -> None:
     """
     An implementation of the Monte Carlo method used to find pi.
     1. Draw a 2x2 square centred at (0,0).