【ML】KMeans 实践（基于sklearn）

读取数据

import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/xclara/xclara.csv')
data.head()

输出：

	V1	V2
0	2.072345	-3.241693
1	17.936710	15.784810
2	1.083576	7.319176
3	11.120670	14.406780
4	23.711550	2.557729

可视化数据（观察规律）

from matplotlib import pyplot as plt
V1 = data.loc[:,'V1']
V2 = data.loc[:,'V2']
plt.scatter(V1,V2)

训练

from sklearn.cluster import KMeans
KM = KMeans(n_clusters=3)
KM.fit(data)

预测 + 可视化

predict = KM.predict(data)

plt.scatter(V1[predict==0],V2[predict==0])
plt.scatter(V1[predict==1],V2[predict==1])
plt.scatter(V1[predict==2],V2[predict==2])
plt.show()