帮我写一个python的波士顿房价k-means聚类分析

以下是一个基本的Python波士顿房价K-means聚类分析的实现：

首先，我们需要导入必要的库和数据集：

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets import load_boston

boston = load_boston()
df = pd.DataFrame(boston.data, columns=boston.feature_names)

然后，我们需要选择要使用的特征并对其进行标准化：

X = df[['RM', 'LSTAT']]
from sklearn.preprocessing import StandardScaler
X = StandardScaler().fit_transform(X)

接下来，我们需要确定要使用的聚类数：

kmeans = KMeans(n_clusters=3)
kmeans.fit(X)

然后，我们可以使用matplotlib库可视化聚类结果：

plt.scatter(X[:,0], X[:,1], c=kmeans.labels_)
plt.xlabel('Average Number of Rooms per Dwelling')
plt.ylabel('% Lower Status of the Population')
plt.title('K-means Clustering (k=3) of Boston Housing Data')
plt.show()

完整的代码如下所示：

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets import load_boston

boston = load_boston()
df = pd.DataFrame(boston.data, columns=boston.feature_names)

X = df[['RM', 'LSTAT']]
from sklearn.preprocessing import StandardScaler
X = StandardScaler().fit_transform(X)

kmeans = KMeans(n_clusters=3)
kmeans.fit(X)

plt.scatter(X[:,0], X[:,1], c=kmeans.labels_)
plt.xlabel('Average Number of Rooms per Dwelling')
plt.ylabel('% Lower Status of the Population')
plt.title('K-means Clustering (k=3) of Boston Housing Data')
plt.show()