

In [1]:

    
%config InlineBackend.figure_format = 'png'

Scikit-Learn 패키지의 샘플 데이터 - 회귀 분석용

보스턴 집값

`load_boston()`

Target
- Boston, Owner's House Price, 1978
- 506 Towns' Median Prices (unit: USD 1,000)
Feature
- CRIM: 범죄율
- INDUS: 비소매상업지역 면적 비율
- NOX: 일산화질소 농도
- RM: 주택당 방 수
- LSTAT: 인구 중 하위 계층 비율
- B: 인구 중 흑인 비율
- PTRATIO: 학생/교사 비율
- ZN: 25,000 평방피트를 초과 거주지역 비율
- CHAS: 찰스강의 경계에 위치한 경우는 1, 아니면 0
- AGE: 1940년 이전에 건축된 소유주택의 비율
- RAD: 방사형 고속도로까지의 거리
- DIS: 직업센터의 거리
- TAX: 재산세율



In [2]:

    
from sklearn.datasets import load_boston
boston = load_boston()
print(boston.DESCR)









    



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)



In [3]:

    
dfX = pd.DataFrame(boston.data, columns=boston.feature_names)
dfy = pd.DataFrame(boston.target, columns=["MEDV"])
df = pd.concat([dfX, dfy], axis=1)
df.tail()



In [4]:

    
df.describe()



In [5]:

    
cols = ["LSTAT", "NOX", "RM", "MEDV"]
sns.pairplot(df[cols])
plt.show()

당뇨병

`load_diabetes()`

http://www4.stat.ncsu.edu/~boos/var.select/diabetes.html
consists of 10 physiological variables measure on 442 patients
- age
- sex
- body mass index (bmi)
- average blood pressure (bp)
- six blood serum measurements
  - tc, ldl, hdl, tch, ltg, glu
indication of disease progression after one year



In [6]:

    
from sklearn.datasets import load_diabetes
diabetes = load_diabetes()
df = pd.concat([pd.DataFrame(diabetes.data, columns=["x%d" % (i + 1) for i in range(diabetes.data.shape[1])]),
                pd.DataFrame(diabetes.target, columns=["target"])],
               axis=1)
df.tail()



In [7]:

    
df.describe()









    Out[7]:






  
    
      
      x1
      x2
      x3
      x4
      x5
      x6
      x7
      x8
      x9
      x10
      target
    
  
  
    
      count
      4.420000e+02
      4.420000e+02
      4.420000e+02
      4.420000e+02
      4.420000e+02
      4.420000e+02
      4.420000e+02
      4.420000e+02
      4.420000e+02
      4.420000e+02
      442.000000
    
    
      mean
      -3.634285e-16
      1.308343e-16
      -8.045349e-16
      1.281655e-16
      -8.835316e-17
      1.327024e-16
      -4.574646e-16
      3.777301e-16
      -3.830854e-16
      -3.412882e-16
      152.133484
    
    
      std
      4.761905e-02
      4.761905e-02
      4.761905e-02
      4.761905e-02
      4.761905e-02
      4.761905e-02
      4.761905e-02
      4.761905e-02
      4.761905e-02
      4.761905e-02
      77.093005
    
    
      min
      -1.072256e-01
      -4.464164e-02
      -9.027530e-02
      -1.123996e-01
      -1.267807e-01
      -1.156131e-01
      -1.023071e-01
      -7.639450e-02
      -1.260974e-01
      -1.377672e-01
      25.000000
    
    
      25%
      -3.729927e-02
      -4.464164e-02
      -3.422907e-02
      -3.665645e-02
      -3.424784e-02
      -3.035840e-02
      -3.511716e-02
      -3.949338e-02
      -3.324879e-02
      -3.317903e-02
      87.000000
    
    
      50%
      5.383060e-03
      -4.464164e-02
      -7.283766e-03
      -5.670611e-03
      -4.320866e-03
      -3.819065e-03
      -6.584468e-03
      -2.592262e-03
      -1.947634e-03
      -1.077698e-03
      140.500000
    
    
      75%
      3.807591e-02
      5.068012e-02
      3.124802e-02
      3.564384e-02
      2.835801e-02
      2.984439e-02
      2.931150e-02
      3.430886e-02
      3.243323e-02
      2.791705e-02
      211.500000
    
    
      max
      1.107267e-01
      5.068012e-02
      1.705552e-01
      1.320442e-01
      1.539137e-01
      1.987880e-01
      1.811791e-01
      1.852344e-01
      1.335990e-01
      1.356118e-01
      346.000000



In [8]:

    
sns.pairplot(df.ix[:,:4])
plt.show()

linnerud 체력 검사

`load_linnerud()`

신체 조건 및 운동 능력
복수의 target을 가지는 regression dataset



In [9]:

    
from sklearn.datasets import load_linnerud
linnerud = load_linnerud()
print(linnerud.DESCR)









    



Linnerrud dataset

Notes
-----
Data Set Characteristics:
    :Number of Instances: 20
    :Number of Attributes: 3
    :Missing Attribute Values: None

The Linnerud dataset constains two small dataset:

- *exercise*: A list containing the following components: exercise data with
  20 observations on 3 exercise variables: Weight, Waist and Pulse.

- *physiological*: Data frame with 20 observations on 3 physiological variables:
   Chins, Situps and Jumps.

References
----------
  * http://rgm2.lab.nig.ac.jp/RGM2/func.php?rd_id=mixOmics:linnerud
  * Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic.



In [10]:

    
df = pd.concat([pd.DataFrame(linnerud.data, columns=linnerud.feature_names),
                pd.DataFrame(linnerud.target, columns=linnerud.target_names)],
               axis=1)
df.tail()



In [11]:

    
sns.pairplot(df.ix[:,:4])
plt.show()

	CRIM	INDUS	NOX	RM	AGE	DIS	RAD	TAX	PTRATIO	B	LSTAT	MEDV
501	0.06263	11.93	0.573	6.593	69.1	2.4786	1.0	273.0	21.0	391.99	9.67	22.4
502	0.04527	11.93	0.573	6.120	76.7	2.2875	1.0	273.0	21.0	396.90	9.08	20.6
503	0.06076	11.93	0.573	6.976	91.0	2.1675	1.0	273.0	21.0	396.90	5.64	23.9
504	0.10959	11.93	0.573	6.794	89.3	2.3889	1.0	273.0	21.0	393.45	6.48	22.0
505	0.04741	11.93	0.573	6.030	80.8	2.5050	1.0	273.0	21.0	396.90	7.88	11.9

	CRIM	ZN	INDUS	CHAS	NOX	RM	AGE	DIS	RAD	TAX	PTRATIO	B	LSTAT	MEDV
count	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000
mean	3.593761	11.363636	11.136779	0.069170	0.554695	6.284634	68.574901	3.795043	9.549407	408.237154	18.455534	356.674032	12.653063	22.532806
std	8.596783	23.322453	6.860353	0.253994	0.115878	0.702617	28.148861	2.105710	8.707259	168.537116	2.164946	91.294864	7.141062	9.197104
min	0.006320	0.000000	0.460000	0.000000	0.385000	3.561000	2.900000	1.129600	1.000000	187.000000	12.600000	0.320000	1.730000	5.000000
25%	0.082045	0.000000	5.190000	0.000000	0.449000	5.885500	45.025000	2.100175	4.000000	279.000000	17.400000	375.377500	6.950000	17.025000
50%	0.256510	0.000000	9.690000	0.000000	0.538000	6.208500	77.500000	3.207450	5.000000	330.000000	19.050000	391.440000	11.360000	21.200000
75%	3.647423	12.500000	18.100000	0.000000	0.624000	6.623500	94.075000	5.188425	24.000000	666.000000	20.200000	396.225000	16.955000	25.000000
max	88.976200	100.000000	27.740000	1.000000	0.871000	8.780000	100.000000	12.126500	24.000000	711.000000	22.000000	396.900000	37.970000	50.000000

	x1	x2	x3	x4	x5	x6	x7	x8	x9	x10	target
437	0.041708	0.050680	0.019662	0.059744	-0.005697	-0.002566	-0.028674	-0.002592	0.031193	0.007207	178.0
438	-0.005515	0.050680	-0.015906	-0.067642	0.049341	0.079165	-0.028674	0.034309	-0.018118	0.044485	104.0
439	0.041708	0.050680	-0.015906	0.017282	-0.037344	-0.013840	-0.024993	-0.011080	-0.046879	0.015491	132.0
440	-0.045472	-0.044642	0.039062	0.001215	0.016318	0.015283	-0.028674	0.026560	0.044528	-0.025930	220.0
441	-0.045472	-0.044642	-0.073030	-0.081414	0.083740	0.027809	0.173816	-0.039493	-0.004220	0.003064	57.0

	Chins	Situps	Jumps	Weight	Waist	Pulse
15	12.0	210.0	120.0	202.0	37.0	62.0
16	4.0	60.0	25.0	176.0	37.0	54.0
17	11.0	230.0	80.0	157.0	32.0	52.0
18	15.0	225.0	73.0	156.0	33.0	54.0
19	2.0	110.0	43.0	138.0	33.0	68.0

	x1	x2	x3	x4	x5	x6	x7	x8	x9	x10	target
count	4.420000e+02	4.420000e+02	4.420000e+02	4.420000e+02	4.420000e+02	4.420000e+02	4.420000e+02	4.420000e+02	4.420000e+02	4.420000e+02	442.000000
mean	-3.634285e-16	1.308343e-16	-8.045349e-16	1.281655e-16	-8.835316e-17	1.327024e-16	-4.574646e-16	3.777301e-16	-3.830854e-16	-3.412882e-16	152.133484
std	4.761905e-02	4.761905e-02	4.761905e-02	4.761905e-02	4.761905e-02	4.761905e-02	4.761905e-02	4.761905e-02	4.761905e-02	4.761905e-02	77.093005
min	-1.072256e-01	-4.464164e-02	-9.027530e-02	-1.123996e-01	-1.267807e-01	-1.156131e-01	-1.023071e-01	-7.639450e-02	-1.260974e-01	-1.377672e-01	25.000000
25%	-3.729927e-02	-4.464164e-02	-3.422907e-02	-3.665645e-02	-3.424784e-02	-3.035840e-02	-3.511716e-02	-3.949338e-02	-3.324879e-02	-3.317903e-02	87.000000
50%	5.383060e-03	-4.464164e-02	-7.283766e-03	-5.670611e-03	-4.320866e-03	-3.819065e-03	-6.584468e-03	-2.592262e-03	-1.947634e-03	-1.077698e-03	140.500000
75%	3.807591e-02	5.068012e-02	3.124802e-02	3.564384e-02	2.835801e-02	2.984439e-02	2.931150e-02	3.430886e-02	3.243323e-02	2.791705e-02	211.500000
max	1.107267e-01	5.068012e-02	1.705552e-01	1.320442e-01	1.539137e-01	1.987880e-01	1.811791e-01	1.852344e-01	1.335990e-01	1.356118e-01	346.000000