实现代码：
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_boston
# 加载波士顿房价数据集
boston_dataset = load_boston()
x = boston_dataset.data
y = boston_dataset.target
# 打印所有属性名并提示用户输入
print("Available attributes:")
for i, feature_name in enumerate(boston_dataset.feature_names):
print(f"{i}: {feature_name}")
# 提示用户输入属性序号
attribute_index = int(input("Enter the attribute index you want to analyze with
house prices: "))
# 检查用户输入是否有效
if 0 <= attribute_index < len(boston_dataset.feature_names):
# 使用 plt.scatter 函数绘制散点图
plt.scatter(x[:, attribute_index], y, color='blue', label='House Prices')
# 设置图表的标题和坐标轴标签
plt.title(f'Scatter plot of House Prices vs {boston_dataset.feature_names
[attribute_index]}')
plt.xlabel(boston_dataset.feature_names[attribute_index])
plt.ylabel('House Prices ($)')
# 添加图例
plt.legend()
# 显示图表
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