from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score

X, y_true = make_blobs(n_samples=300, n_features=2,
                        centers=4, cluster_std=0.8,
                        random_state=42)

kmeans = KMeans(n_clusters=4, init='k-means++',
                 max_iter=300, n_init=10, random_state=42)
kmeans.fit(X)
labels = kmeans.labels_
centroids = kmeans.cluster_centers_

wcss, silhouette_scores = [], []
for i in range(1, 11):
    km = KMeans(n_clusters=i, init='k-means++',
                max_iter=300, n_init=10, random_state=42)
    km.fit(X)
    wcss.append(km.inertia_)