KMeans

    dataset = pd.read_csv('test.csv')
    X = dataset.iloc[:,:].values

    wcss = []
    for i in range(1,11):
        kmeans = KMeans(n_clusters = i, init= 'k-means++', max_iter=300, n_init=10, random_state=0)
        kmeans.fit(X)
        wcss.append(kmeans.inertia_)
    plt.plot(range(1,11),wcss)
    #plt.show()
    kmeans = KMeans(n_clusters =3, init= 'k-means++', max_iter=300, n_init=10, random_state=0)
    y_kmeans = kmeans.fit_predict(X)
    plt.scatter(X[y_kmeans==0,0],X[y_kmeans==0,1], s=100, c='red', label='One')
    plt.scatter(X[y_kmeans==1,0],X[y_kmeans==1,1], s=100, c='green', label='Two')
    plt.scatter(X[y_kmeans==2,0],X[y_kmeans==2,1], s=100, c='cyan', label='Three')
    plt.scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],s=300, c='yellow',label='centroids')