python-scikit-learnClustering example

from sklearn import datasets, cluster

X, y = datasets.load_iris(return_X_y=True)
model = cluster.KMeans(n_clusters = 3)
model.fit(X)

clusters = model.predict(X)ctrl + cgithub

`from sklearn import` import module from scikit-learn	`load_iris` loads Iris dataset
`.KMeans(` create KMeans clustering model	`n_clusters` number of clusters we want to see
`.fit(` train model with a given features and target variable dataset	`.predict(` predict target variable based on given features dataset

Usage example

from sklearn import datasets, cluster

X, y = datasets.load_iris(return_X_y=True)
model = cluster.KMeans(n_clusters = 3)
model.fit(X)

clusters = model.predict(X)
print(clusters)

output

[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2 0 0 0 0 2 0 0 0 0
 0 0 2 2 0 0 0 0 2 0 2 0 2 0 0 2 2 0 0 0 0 0 2 0 0 0 0 2 0 0 0 2 0 0 0 2 0
 0 2]

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python-scikit-learnClustering example

Usage example

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