diff --git a/examples/ml/knn.kg b/examples/ml/knn.kg
new file mode 100644
index 0000000..35141d0
--- /dev/null
+++ b/examples/ml/knn.kg
@@ -0,0 +1,20 @@
+:" euclidean distance "
+ed::{[d];d::x-y;({+/x}'d*d)^0.5}
+
+:" compute knn between point x and data y "
+knn::{[distances];distances::ed(y;z);x#<distances}
+
+:" test data "
+data::[[1 2] [2 3] [3 4] [5 5] [1 4] [2 5]]
+
+:" test point "
+tp::[3 3]
+
+:" number of neighbors "
+k::3
+
+:" Find the k-nearest neighbors "
+neighbors::knn(k;tp;data)
+
+:" Output the indices of the nearest neighbors "
+.d("Indices of the k-nearest neighbors: ");.p(neighbors)
diff --git a/examples/ml/knn.py b/examples/ml/knn.py
new file mode 100644
index 0000000..b0c3717
--- /dev/null
+++ b/examples/ml/knn.py
@@ -0,0 +1,24 @@
+import numpy as np
+
+def euclidean_distance(a, b):
+    return np.sqrt(np.sum((a - b) ** 2))
+
+def knn(k, point, data):
+    distances = np.array([euclidean_distance(point, d) for d in data])
+    sorted_indices = np.argsort(distances)
+    return sorted_indices[:k]
+
+# Sample Data
+data = np.array([[1, 2], [2, 3], [3, 4], [5, 5], [1, 4], [2, 5]])
+
+# Test Point
+test_point = np.array([3, 3])
+
+# Number of Neighbors
+k = 3
+
+# Find the k-nearest neighbors
+neighbors = knn(k, test_point, data)
+
+# Output the indices of the nearest neighbors
+print("Indices of the k-nearest neighbors:", neighbors)