input: Write a python function to implement the K-means clustering algorithm and visualize the clusters.
  assert len(kmeans_clustering(3, 'https://raw.githubusercontent.com/krishnaik06/KNN-K-means/master/Dataset/dataset.csv')) == 3
output: import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn.cluster import KMeans
  import requests
  
  def kmeans_clustering(k, url):
      # Load data from URL
      response = requests.get(url)
      data = pd.read_csv(pd.compat.StringIO(response.text))
      
      # Assuming the dataset has two features for simplicity
      X = data.iloc[:, :2].values
      
      # K-means clustering
      kmeans = KMeans(n_clusters=k, random_state=0)
      y_kmeans = kmeans.fit_predict(X)
      
      # Plotting the clusters
      plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis')
      
      centers = kmeans.cluster_centers_
      plt.scatter(centers[:, 0], centers[:, 1], c='red', s=200, alpha=0.75)
      plt.xlabel('Feature 1')
      plt.ylabel('Feature 2')
      plt.title('K-means Clustering')
      plt.show()
      
      return [X[y_kmeans == i] for i in range(k)]