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Strange Spider answered on July 21, 2021 Popularity 10/10 Helpfulness 8/10

answer kmeans sklearn

related sklearn kmeans clustering

related kmeans sklearn

kmeans sklearn

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from sklearn.cluster import KMeans
import numpy as np
X = np.array([[1, 2], [1, 4], [1, 0],
              [10, 2], [10, 4], [10, 0]])
kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
kmeans.labels_

kmeans.predict([[0, 0], [12, 3]])

kmeans.cluster_centers_

Popularity 10/10 Helpfulness 8/10 Language python

Source: scikit-learn.org

Tags: k-means python

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Contributed on Jul 21 2021

Strange Spider

0 Answers Avg Quality 2/10

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sklearn kmeans clustering

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>>> from sklearn.cluster import KMeans
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
...               [10, 2], [10, 4], [10, 0]])
>>> kmeans = KMeans(n_clusters=2, random_state=0, n_init="auto").fit(X)
>>> kmeans.labels_
array([1, 1, 1, 0, 0, 0], dtype=int32)
>>> kmeans.predict([[0, 0], [12, 3]])
array([1, 0], dtype=int32)
>>> kmeans.cluster_centers_
array([[10.,  2.],
       [ 1.,  2.]])

Popularity 8/10 Helpfulness 5/10 Language python

Source: scikit-learn.org

Tags: k-means python p

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Contributed on Jul 13 2023

Lonely Louse

0 Answers Avg Quality 2/10

kmeans sklearn

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kmean_model = KMeans(init='k-means++',n_clusters = 8,max_iter = 200,n_init=10) #here we are using the KMEANS class and configuring it's parameters such as
# initializor , total number of clusters to apply, maximum iterations and no of times to run the algorithm with different centroid seeds
kmean_model.fit(df)

print(kmean_model.cluster_centers_)
predict = kmean_model.predict(df)

plt.scatter(df.iloc[:,2],df.iloc[:,3],c = predict,cmap = 'viridis')

Popularity 9/10 Helpfulness 4/10 Language python

Source: Grepper

Tags: k-means

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Contributed on Apr 26 2022

Muhammad Usman Shakeel

0 Answers Avg Quality 2/10

kmeans sklearn

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kmeans sklearn

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