diff --git a/chapter12/loaves.py b/chapter12/loaves.py
new file mode 100644
index 0000000..ef94fa7
--- /dev/null
+++ b/chapter12/loaves.py
@@ -0,0 +1,53 @@
+import logging
+from dataclasses import dataclass
+
+import numpy as np
+
+logging.basicConfig(
+    level=logging.DEBUG,
+    format="%(asctime)s %(levelname)s %(message)s",
+    datefmt="%H:%M:%S",
+)
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class Point:
+    identifier: str
+    weather: int
+    is_weekend: bool
+    is_game_on: bool
+    sold: int = 0
+    distance: int = 0
+
+    @property
+    def array(self):
+        return np.array([self.weather, int(self.is_weekend), int(self.is_game_on)])
+
+
+def knn(point: Point, neighbours):
+    for neighbour in neighbours:
+        neighbour.distance = np.linalg.norm(point.array - neighbour.array)
+        logger.debug(f"{neighbour.identifier}: {neighbour.distance}")
+
+    average = 0
+    for n in sorted(neighbours, key=lambda x: x.distance)[:K]:
+        average += n.sold
+    return average / K
+
+
+neighbours = [
+    Point("A", 5, True, False, 300),  # A
+    Point("B", 3, True, True, 225),  # B
+    Point("C", 1, True, False, 75),  # C
+    Point("D", 4, False, True, 200),  # D
+    Point("E", 4, False, False, 150),  # E
+    Point("F", 2, False, False, 50),  # F
+]
+
+point = Point("T", 4, True, False)
+K = 4
+average_distance = knn(point, neighbours)
+
+logger.debug(average_distance)
+print(f"Number of loaves to make: {int(round(average_distance, 0))}")