Xg3-3-3-1

from sklearn.datasets import load_wine

ds_wine = load_wine()

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ds_wine.data

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ds_wine.data.shape

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Xg3-3-3-2

X = ds_wine.data[:, [0,12]]

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X

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Xg3-3-3-3

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
scaler.fit(X)
X_std = scaler.transform(X)

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X_std

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Xg3-3-3-4

from sklearn.cluster import KMeans

kmns = KMeans(n_clusters=3)

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Xg3-3-3-5

kmns.fit(X_std)

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Xg3-3-3-6

kmns.labels_

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Xg3-3-3-7

import matplotlib.pyplot as plt

plt.scatter(X_std[:,0], X_std[:,1], c=kmns.labels_)
plt.show()

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Xg3-3-3-8

import matplotlib.pyplot as plt

plt.scatter(X[:,0], X[:,1], c=kmns.labels_)
plt.show()

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Xg3-3-3-9

import matplotlib.pyplot as plt

plt.scatter(X_std[:,0], X_std[:,1], c=kmns.labels_)
plt.scatter(kmns.cluster_centers_[:,0],
    kmns.cluster_centers_[:,1], s=200, marker='*', c='red')
plt.show()

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Xg3-3-3-10

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
scaler.fit(ds_wine.data)
X_std = scaler.transform(ds_wine.data)

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Xg3-3-3-11

from sklearn.decomposition import PCA

pca = PCA(n_components=2)
X_pca = pca.fit_transform(X_std)

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X_pca

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Xg3-3-3-12

from sklearn.cluster import KMeans

kmns = KMeans(n_clusters=3)
kmns.fit(X_pca)

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kmns.labels_

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Xg3-3-3-13

import matplotlib.pyplot as plt

plt.scatter(X_pca[:,0], X_pca[:,1], c=kmns.labels_)
plt.show()

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