问K means聚类方法调用生成回溯错误："k means is not defined“

EN

kmeans.fit(scaled_features) #SyntaxError: invalid syntax
NameError: name 'kmeans' is not defined

kmeans.cluster_centers_
NameError: name 'kmeans' is not defined

kmeans.inertia_
    ^
SyntaxError: invalid syntax

import matplotlib.pyplot as plt
#from kneed import KneeLocator
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler

import matplotlib.pyplot as plt
#from kneed import KneeLocator
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler

#generate some data with make_blob()
features, true_labels = make_blobs(
n_samples=200,
centers=3,
cluster_std=2.75,
random_state=42
)

#show the features
features[:5]
#first 5 elements for each variable
true_labels[:5]

#scale the features
scaler = StandardScaler()
scaled_features = scaler.fit_transform(features)

#show the features
scaled_features[:5]

kmeans = KMeans(
     init="random",
     n_clusters=3,
     n_init=10,
     max_iter=300,
     random_state=42

kmeans.fit(scaled_features) #SyntaxError: invalid syntax

#The lowest SSE value
kmeans.inertia_ #SyntaxError: invalid syntax; kmeans not defined

#Final locations of the centroid
kmeans.cluster_centers_

kmeans.n_iter_

kmeans.labels_[:5]