Feat: Add K-Nearest Neighbors (KNN) classification algorithm (#7562)

Implements the K-Nearest Neighbors (KNN) classification algorithm from scratch using native Java utility structures. 

Key Features:
- Computes spatial vector variance using the Euclidean Distance metric calculation.
- Leverages a custom internal data layout to manage feature/label pairs cleanly.
- Processes classification matching via a frequency-based majority vote resolution.
- Includes a standalone execution test routine within the class main entry channel.

Author: Rajat-Builds

src/main/java/com/thealgorithms/machinelearning/machinelearning/KNN.java

@@ -0,0 +1,74 @@
+package com.thealgorithms.machinelearning;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Comparator;
+import java.util.List;
+
+public class KNN {
+    public static class DataPoint {
+        double[] features;
+        String label;
+
+        public DataPoint(double[] features, String label) {
+            this.features = features;
+            this.label = label;
+        }
+    }
+
+    private static class DistancePair {
+        double distance;
+        String label;
+
+        public DistancePair(double distance, String label) {
+            this.distance = distance;
+            this.label = label;
+        }
+    }
+
+    public static double calculateEuclideanDistance(double[] point1, double[] point2) {
+        double sum = 0.0;
+        for (int i = 0; i < point1.length; i++) {
+            sum += Math.pow(point1[i] - point2[i], 2);
+        }
+        return Math.sqrt(sum);
+    }
+
+    public static String classify(List<DataPoint> dataset, double[] queryPoint, int k) {
+        List<DistancePair> distances = new ArrayList<>();
+
+        for (DataPoint p : dataset) {
+            double dist = calculateEuclideanDistance(p.features, queryPoint);
+            distances.add(new DistancePair(dist, p.label));
+        }
+
+        distances.sort(Comparator.comparingDouble(d -> d.distance));
+
+        List<String> topKLabels = new ArrayList<>();
+        for (int i = 0; i < Math.min(k, distances.size()); i++) {
+            topKLabels.add(distances.get(i).label);
+        }
+
+        String bestLabel = null;
+        int maxCount = -1;
+        for (String label : topKLabels) {
+            int count = Collections.frequency(topKLabels, label);
+            if (count > maxCount) {
+                maxCount = count;
+                bestLabel = label;
+            }
+        }
+        return bestLabel;
+    }
+
+    public static void main(String[] args) {
+        List<DataPoint> trainData = new ArrayList<>();
+        trainData.add(new DataPoint(new double[]{1.0, 2.0}, "ClassA"));
+        trainData.add(new DataPoint(new double[]{2.0, 3.0}, "ClassA"));
+        trainData.add(new DataPoint(new double[]{7.0, 8.0}, "ClassB"));
+
+        double[] target = new double[]{1.5, 2.5};
+        String prediction = classify(trainData, target, 3);
+        System.out.println("Predicted Category: " + prediction);
+    }
+}