diff --git a/build.zig b/build.zig
index fcfc589..9d04405 100644
--- a/build.zig
+++ b/build.zig
@@ -208,6 +208,14 @@ pub fn build(b: *std.Build) void {
             .name = "X25519+Kyber768Draft00.zig",
             .category = "web/tls",
         });
+    // Machine Learning
+    if (std.mem.eql(u8, op, "machine_learning/k_means_clustering.zig"))
+        buildAlgorithm(b, .{
+            .optimize = optimize,
+            .target = target,
+            .name = "k_means_clustering.zig",
+            .category = "machine_learning",
+        });
 }
 
 fn buildAlgorithm(b: *std.Build, info: BInfo) void {
diff --git a/machine_learning/k_means_clustering.zig b/machine_learning/k_means_clustering.zig
new file mode 100644
index 0000000..4348ee0
--- /dev/null
+++ b/machine_learning/k_means_clustering.zig
@@ -0,0 +1,134 @@
+const KMeansError = error{SmallK};
+
+const Point2D = struct {
+    x: f32,
+    y: f32,
+    const zero: Point2D = .{ .x = 0, .y = 0 };
+    fn eq(self: Point2D, other: Point2D) bool {
+        return self.x == other.x and self.y == other.y;
+    }
+    fn add(self: Point2D, other: Point2D) Point2D {
+        return .{ .x = self.x + other.x, .y = self.y + other.y };
+    }
+    fn div(self: Point2D, scalar: f32) Point2D {
+        return .{ .x = self.x / scalar, .y = self.y / scalar };
+    }
+};
+const Cluster = struct {
+    point: Point2D,
+    count: usize,
+    const zero: Cluster = .{ .point = .zero, .count = 0 };
+    fn eq(self: Cluster, other: Cluster) bool {
+        return self.point.eq(other.point) and self.count == other.count;
+    }
+};
+
+fn distanceSquared(a: Point2D, b: Point2D) f32 {
+    const y = a.y - b.y;
+    const x = a.x - b.x;
+    return x * x + y * y;
+}
+
+fn calculateNearest(point: Point2D, clusters: anytype) usize {
+    var min_distance = distanceSquared(point, clusters[0].point);
+    var closest_cluster_idx: usize = 0;
+    for (1..clusters.len) |cluster_idx| {
+        const distance = distanceSquared(clusters[cluster_idx].point, point);
+        if (distance < min_distance) {
+            min_distance = distance;
+            closest_cluster_idx = cluster_idx;
+        }
+    }
+    return closest_cluster_idx;
+}
+
+pub fn KMeans(data: []const Point2D, comptime k: usize) ![k]Cluster {
+    if (data.len < k) {
+        return KMeansError.SmallK;
+    }
+    // assign clusters to different data points
+    var old_clusters: [k]Cluster = undefined;
+    for (0..old_clusters.len) |i| {
+        old_clusters[i].point = data[i];
+        old_clusters[i].count = 0;
+    }
+    while (true) {
+        var new_clusters: [k]Cluster = .{Cluster.zero} ** k;
+        for (data) |point| {
+            const cluster_idx = calculateNearest(point, old_clusters);
+            const new = &new_clusters[cluster_idx];
+            new.point = new.point.add(point);
+            new.count += 1;
+        }
+        for (&new_clusters) |*cluster| {
+            const count_as_f32: f32 = @floatFromInt(cluster.count);
+            cluster.point = cluster.point.div(count_as_f32);
+        }
+        check_equal: {
+            for (old_clusters, new_clusters) |old, new| {
+                if (!old.eq(new)) {
+                    break :check_equal;
+                }
+            }
+            return new_clusters;
+        }
+        old_clusters = new_clusters;
+    }
+}
+
+const std = @import("std");
+const expectEqual = std.testing.expectEqual;
+test "Kmeans" {
+    try expectEqual(
+        [_]Cluster{
+            .{ .point = .{ .x = 34.0, .y = 34.0 }, .count = 1 },
+        },
+        try KMeans(
+            &[_]Point2D{
+                .{ .x = 34.0, .y = 34.0 },
+            },
+            1,
+        ),
+    );
+    try expectEqual(
+        [_]Cluster{
+            .{ .point = .{ .x = 33.0, .y = 33.0 }, .count = 2 },
+        },
+        try KMeans(
+            &[_]Point2D{
+                .{ .x = 33.0, .y = 33.0 },
+                .{ .x = 33.0, .y = 33.0 },
+            },
+            1,
+        ),
+    );
+    try expectEqual(
+        [_]Cluster{
+            .{ .point = .{ .x = 33.0, .y = 34.0 }, .count = 2 },
+        },
+        try KMeans(
+            &[_]Point2D{
+                .{ .x = 32.0, .y = 33.0 },
+                .{ .x = 34.0, .y = 35.0 },
+            },
+            1,
+        ),
+    );
+    try expectEqual(
+        [_]Cluster{
+            .{ .point = .{ .x = 0.0, .y = 0.5 }, .count = 3 },
+            .{ .point = .{ .x = 2.0, .y = 0.5 }, .count = 3 },
+        },
+        try KMeans(
+            &[_]Point2D{
+                .{ .x = 0.0, .y = 1.0 },
+                .{ .x = 2.0, .y = 1.0 },
+                .{ .x = 0.0, .y = 0.5 },
+                .{ .x = 0.0, .y = 0.0 },
+                .{ .x = 2.0, .y = 0.5 },
+                .{ .x = 2.0, .y = 0.0 },
+            },
+            2,
+        ),
+    );
+}
diff --git a/runall.cmd b/runall.cmd
index 8a7980d..262fccd 100644
--- a/runall.cmd
+++ b/runall.cmd
@@ -49,4 +49,7 @@ rem Web
 %ZIG_TEST% -Dalgorithm=web/httpServer %Args%
 %ZIG_TEST% -Dalgorithm=web/tls1_3 %Args%
 
+rem Machine Learning
+%ZIG_TEST% -Dalgorithm=machine_learning/k_means_clustering.zig %Args%
+
 rem Add more...
diff --git a/runall.sh b/runall.sh
index cb9c831..552af45 100755
--- a/runall.sh
+++ b/runall.sh
@@ -49,4 +49,6 @@ $ZIG_TEST -Dalgorithm=web/httpClient $Args
 $ZIG_TEST -Dalgorithm=web/httpServer $Args
 $ZIG_TEST -Dalgorithm=web/tls1_3 $Args
 
+## Machine Learning
+$ZIG_TEST -Dalgorithm=machine_learning/k_means_clustering.zig $Args
 ## Add more...