波士顿住宅街数据模型问题

余小c真的很强

从网上找的机器学习例题
#boston住宅数据读入

from sklearn.datasets import load_boston
boston = load_boston()

print(boston.DESCR)

这是输出：

Boston House Prices dataset
===========================

Notes
------
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.
http://archive.ics.uci.edu/ml/datasets/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.
   - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)

#用pandas模块的dataframe读入boston住宅街的数据

import pandas as pd
df = pd.DataFrame(boston.data,columns=boston.feature_naMSE)
df['MEDV'] = boston.target  #目标变量读入
x = df.RM.to_frame()
y = df.MEDV

报错信息：
KeyError                                  Traceback (most recent call last)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\__init__.py in __getattr__(self, key)
     60         try:
---> 61             return self[key]
     62         except KeyError:

KeyError: 'feature_naMSE'

During handling of the above exception, another exception occurred:

AttributeError                            Traceback (most recent call last)
<ipython-input-11-1277f379165e> in <module>()
      1 import pandas as pd
----> 2 df = pd.DataFrame(boston.data,columns=boston.feature_naMSE)
      3 df['MEDV'] = boston.target  #目标变量读入
      4 x = df.RM.to_frame()
      5 y = df.MEDV

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\__init__.py in __getattr__(self, key)
     61             return self[key]
     62         except KeyError:
---> 63             raise AttributeError(key)
     64
     65     def __setstate__(self, state):

AttributeError: feature_naMSE
新手上路，求大佬指教，另外有什么好的机器学习方法吗

余小c真的很强

听过一天的自学最终找到了问题所在

import pandas as pd
df = pd.DataFrame(boston.data,columns=boston.feature_naMSE)
df['MEDV'] = boston.target  #目标变量读入
x = df.RM.to_frame()
y = df.MEDV

其中的feature_naMSE应该是key中的feature_name
自学还是有好处的

java2python

高大上