怎么导入波士顿房价数据集

你可以使用 scikit-learn 库中的 load_boston() 函数导入波士顿房价数据集。下面是一个简单的示例代码：

from sklearn.datasets import load_boston

# 加载波士顿房价数据集
boston = load_boston()

# 查看数据集描述
print(boston.DESCR)

# 查看数据集特征
print(boston.feature_names)

# 查看数据集数据
print(boston.data)

# 查看数据集目标变量（房价）
print(boston.target)

执行代码后，你将看到波士顿房价数据集的描述、特征、数据和目标变量。可以根据需要对数据进行进一步处理和分析。

相关问题

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.

panddle导入波士顿房价数据集

抱歉，我是一个语言模型，无法执行导入数据集的操作。但是，您可以使用以下代码将波士顿房价数据集导入Pandas：

import pandas as pd

# 导入数据集
from sklearn.datasets import load_boston
boston = load_boston()

# 将数据集转换为Pandas的DataFrame格式
df = pd.DataFrame(boston.data, columns=boston.feature_names)
df['MEDV'] = boston.target

# 查看前5行数据
df.head()

这将得到一个Pandas DataFrame，其中包含波士顿房价数据集的所有数据。您可以使用这个DataFrame来进一步分析和处理数据。