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+'''
+K-Means Clustering Algorithm
+
+Overview
+The K-Means algorithm is an unsupervised learning technique used for clustering similar data points into groups or clusters. It aims to partition the data into K clusters, where each data point belongs to the cluster with the nearest mean.
+
+Algorithm Steps:
+1. **Initialization**: Randomly initialize K cluster centroids.
+2. **Assigning Points to Clusters**: Assign each data point to the nearest cluster centroid based on Euclidean distance.
+3. **Updating Cluster Centroids**: Recalculate the centroid of each cluster by taking the mean of all data points assigned to that cluster.
+4. **Repeating Steps 2 and 3**: Iterate until convergence or until a maximum number of iterations is reached.
+
+Implementation ->
+'''
+
+import pandas as pd
+import numpy as np
+
+# Load the dataset
+data = pd.read_csv('https://raw.githubusercontent.com/prasad-chavan1/MachineLearning/master/DataPreprocessing/Data.csv')
+
+# Preprocess the data
+# Assuming the missing values are handled and 'Country' column is one-hot encoded
+X = data[['Age', 'Salary']].values
+
+# Implement K-Means clustering
+def k_means(X, K, max_iters=100):
+    centroids = X[np.random.choice(X.shape[0], K, replace=False)]
+    for _ in range(max_iters):
+        distances = np.sqrt(((X - centroids[:, np.newaxis])**2).sum(axis=2))
+        labels = np.argmin(distances, axis=0)
+        new_centroids = np.array([X[labels == k].mean(axis=0) for k in range(K)])
+        if np.all(centroids == new_centroids):
+            break
+        centroids = new_centroids
+    return labels, centroids
+
+# Number of clusters
+K = 3
+
+# Apply K-Means clustering
+labels, centroids = k_means(X, K)
+
+# Visualize Results (optional)
+import matplotlib.pyplot as plt
+plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis')
+plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', s=100, color='red')
+plt.xlabel('Age')
+plt.ylabel('Salary')
+plt.title('K-Means Clustering')
+plt.show()