从 sklearn 数据集库导入波士顿房价数据集( boston ）

在Python的sklearn库中，你可以直接使用`load_boston()`函数来导入波士顿房价数据集。这个数据集包含了马萨诸塞州波士顿地区的房屋价格信息，包括诸如犯罪率、人口密度、距离就业中心的距离等多个特征，目的是预测每个区域的房屋价值。

以下是导入数据的代码示例：

from sklearn.datasets import load_boston

# 加载波士顿房价数据集
boston_dataset = load_boston()
boston = boston_dataset.data  # 获取特征数据
target = boston_dataset.target  # 获取目标变量，即房价

# 查看数据集的一些基本信息
print("Boston Housing dataset:")
print("Features: ", boston_dataset.feature_names)
print("Shape of data: ", boston.shape)
print("Target variable: ", target.shape)

在这个数据集中，`boston`是一个二维数组，每一行代表一个样本，每列对应一个特征。而`target`则是对应的房屋价格标签。

相关问题

我如何从sklearn导入并加载波士顿房价数据集？

在Python中，如果你想要从scikit-learn库导入并加载波士顿房价数据集，可以按照以下几个步骤操作：

首先，你需要确保已经安装了`scikit-learn`库。如果没有，可以使用pip安装：

pip install scikit-learn

然后，你可以通过下面的代码来加载波士顿房价数据集：

from sklearn.datasets import load_boston

# 加载数据集
boston = load_boston()

`load_boston()`函数会返回一个字典，包含了数据集的各种信息，如特征数组、目标变量数组、特征名称等。你可以分别访问这些内容，例如：

# 特征数据（输入）
X = boston.data

# 目标变量（房价）
y = boston.target

# 数据集描述
print(boston.DESCR)

如果你想查看特征名，可以这样做：

# 特征名称
feature_names = boston.feature_names

这就是加载波士顿房价数据的基本过程。

sklearn导入波士顿房价数据集

sklearn中自带了波士顿房价数据集，可以通过以下代码导入：

from sklearn.datasets import load_boston

boston = load_boston()

X = boston.data # 特征矩阵
y = boston.target # 目标向量

其中，X是一个13维的特征矩阵，y是一个样本数量为506的目标向量。可以通过以下代码查看数据集的描述：

print(boston.DESCR)

输出结果如下：

.. _boston_dataset:

Boston house prices dataset
---------------------------

**Data Set Characteristics:**  

    :Number of Instances: 506 

    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.

    :Attribute Information (in order):
        - CRIM     per capita crime rate by town
        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.
        - INDUS    proportion of non-retail business acres per town
        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
        - NOX      nitric oxides concentration (parts per 10 million)
        - RM       average number of rooms per dwelling
        - AGE      proportion of owner-occupied units built prior to 1940
        - DIS      weighted distances to five Boston employment centres
        - RAD      index of accessibility to radial highways
        - TAX      full-value property-tax rate per $10,000
        - PTRATIO  pupil-teacher ratio by town
        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
        - LSTAT    % lower status of the population
        - MEDV     Median value of owner-occupied homes in $1000's

    :Missing Attribute Values: None

    :Creator: Harrison, D. and Rubinfeld, D.L.

This is a copy of UCI ML housing dataset.
https://archive.ics.uci.edu/ml/machine-learning-databases/housing/


This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics ...', Wiley, 1980.   N.B. Various transformations are used in the table on pages 244-261 of the latter.

The Boston house-price data has been used in many machine learning papers that address regression problems.
     
**References**
- Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.
- Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.