ボストン市の住宅価格を調べてみる

scikit-learnのデータを用いてボストン市の住宅価格を調べてみます。今回は回帰の分類となります。犯罪発生数や住居の平均部屋数などの10数個のデータより、ボストン市の住宅価格を求めます。全学習データは506件ですが、そのうち学習用に8割、残り2割を評価用に用います。

使用するデータ

全データ :506件 (学習用：404件)
属性データ：14件

CRIM	人口 1 人当たりの犯罪発生数
ZN	25,000 平方フィート以上の住居区画の占める割合
INDUS	小売業以外の商業が占める面積の割合
CHAS	チャールズ川によるダミー変数 (1: 川の周辺, 0: それ以外)
NOX	NOx の濃度
RM	住居の平均部屋数
AGE	1940 年より前に建てられた物件の割合
DIS	5つのボストン市の雇用施設からの距離
RAD	環状高速道路へのアクセスしやすさ
TAX	$10,000 ドルあたりの不動産税率の総計
PTRATIO	町毎の児童と教師の比率
B	町毎の黒人 (Bk) の比率を次の式で表したもの。 1000(Bk – 0.63)^2
LSTAT	給与の低い職業に従事する人口の割合 (%)
MEDV	所有者が占有している家屋の$ 1000単位の中央値

ソースコードの流れ

１、準備作業
２、学習用データの読み込み
３、学習用データの確認
４、学習用データの分割
５、学習
６、評価

ソースコード

ソースコードです。ソースコードの詳細は後で説明します。

#準備作業
from sklearn import linear_model
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split

#学習用データの読み込み
data = load_boston()

#学習用データの確認
print(data.DESCR)

import pandas as pd
names=pd.DataFrame(data=data.data,columns=data.feature_names)
print(names)

print(data.target)

#学習用データの分割
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=0)

X_train.shape

X_test.shape

#学習
clf = linear_model.LinearRegression()
clf.fit(X_train,y_train)

#評価
test=pd.DataFrame(data=X_test,columns=data.feature_names)
print(test)

print(clf.predict(X_test))

#準備作業

from sklearn import linear_model

from sklearn.datasets import load_boston

from sklearn.model_selection import train_test_split

#学習用データの読み込み

data = load_boston()

#学習用データの確認

print(data.DESCR)

import pandas as pd

names=pd.DataFrame(data=data.data,columns=data.feature_names)

print(names)

print(data.target)

#学習用データの分割

X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=0)

X_train.shape

X_test.shape

#学習

clf = linear_model.LinearRegression()

clf.fit(X_train,y_train)

#評価

test=pd.DataFrame(data=X_test,columns=data.feature_names)

print(test)

print(clf.predict(X_test))

ソースコードの詳細

準備作業

from sklearn import linear_model
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split

from sklearn import linear_model

from sklearn.datasets import load_boston

from sklearn.model_selection import train_test_split

scikit-learnからロンドンの住宅価格データをインポートします。今回は線形回帰モデルを利用するのでlinear_modelをインポートします。またデータを分割するためにtrain_test_splitについてもインポートします。

学習用データの読み込み

data = load_boston()

1	data = load_boston()

先ほどインポートした住宅価格データを読み込みます。

学習用データの確認

print(data.DESCR)

1	print(data.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.

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.

データの詳細をも見込んでいます。今回のデータが14個の属性と506個のデータを持っていることが分かります。

import pandas as pd
names=pd.DataFrame(data=data.data,columns=data.feature_names)
print(names)

import pandas as pd

names=pd.DataFrame(data=data.data,columns=data.feature_names)

print(names)

         CRIM    ZN  INDUS  CHAS    NOX     RM    AGE     DIS   RAD    TAX  \
0     0.00632  18.0   2.31   0.0  0.538  6.575   65.2  4.0900   1.0  296.0   
1     0.02731   0.0   7.07   0.0  0.469  6.421   78.9  4.9671   2.0  242.0   
2     0.02729   0.0   7.07   0.0  0.469  7.185   61.1  4.9671   2.0  242.0   
3     0.03237   0.0   2.18   0.0  0.458  6.998   45.8  6.0622   3.0  222.0   
4     0.06905   0.0   2.18   0.0  0.458  7.147   54.2  6.0622   3.0  222.0   
5     0.02985   0.0   2.18   0.0  0.458  6.430   58.7  6.0622   3.0  222.0   
6     0.08829  12.5   7.87   0.0  0.524  6.012   66.6  5.5605   5.0  311.0   
7     0.14455  12.5   7.87   0.0  0.524  6.172   96.1  5.9505   5.0  311.0   
8     0.21124  12.5   7.87   0.0  0.524  5.631  100.0  6.0821   5.0  311.0   
9     0.17004  12.5   7.87   0.0  0.524  6.004   85.9  6.5921   5.0  311.0   
10    0.22489  12.5   7.87   0.0  0.524  6.377   94.3  6.3467   5.0  311.0   
11    0.11747  12.5   7.87   0.0  0.524  6.009   82.9  6.2267   5.0  311.0   
12    0.09378  12.5   7.87   0.0  0.524  5.889   39.0  5.4509   5.0  311.0   
13    0.62976   0.0   8.14   0.0  0.538  5.949   61.8  4.7075   4.0  307.0   
14    0.63796   0.0   8.14   0.0  0.538  6.096   84.5  4.4619   4.0  307.0   
15    0.62739   0.0   8.14   0.0  0.538  5.834   56.5  4.4986   4.0  307.0   
16    1.05393   0.0   8.14   0.0  0.538  5.935   29.3  4.4986   4.0  307.0   
17    0.78420   0.0   8.14   0.0  0.538  5.990   81.7  4.2579   4.0  307.0   
18    0.80271   0.0   8.14   0.0  0.538  5.456   36.6  3.7965   4.0  307.0   
19    0.72580   0.0   8.14   0.0  0.538  5.727   69.5  3.7965   4.0  307.0   
20    1.25179   0.0   8.14   0.0  0.538  5.570   98.1  3.7979   4.0  307.0   
21    0.85204   0.0   8.14   0.0  0.538  5.965   89.2  4.0123   4.0  307.0   
22    1.23247   0.0   8.14   0.0  0.538  6.142   91.7  3.9769   4.0  307.0   
23    0.98843   0.0   8.14   0.0  0.538  5.813  100.0  4.0952   4.0  307.0   
24    0.75026   0.0   8.14   0.0  0.538  5.924   94.1  4.3996   4.0  307.0   
25    0.84054   0.0   8.14   0.0  0.538  5.599   85.7  4.4546   4.0  307.0   
26    0.67191   0.0   8.14   0.0  0.538  5.813   90.3  4.6820   4.0  307.0   
27    0.95577   0.0   8.14   0.0  0.538  6.047   88.8  4.4534   4.0  307.0   
28    0.77299   0.0   8.14   0.0  0.538  6.495   94.4  4.4547   4.0  307.0   
29    1.00245   0.0   8.14   0.0  0.538  6.674   87.3  4.2390   4.0  307.0   
..        ...   ...    ...   ...    ...    ...    ...     ...   ...    ...   
476   4.87141   0.0  18.10   0.0  0.614  6.484   93.6  2.3053  24.0  666.0   
477  15.02340   0.0  18.10   0.0  0.614  5.304   97.3  2.1007  24.0  666.0   
478  10.23300   0.0  18.10   0.0  0.614  6.185   96.7  2.1705  24.0  666.0   
479  14.33370   0.0  18.10   0.0  0.614  6.229   88.0  1.9512  24.0  666.0   
480   5.82401   0.0  18.10   0.0  0.532  6.242   64.7  3.4242  24.0  666.0   
481   5.70818   0.0  18.10   0.0  0.532  6.750   74.9  3.3317  24.0  666.0   
482   5.73116   0.0  18.10   0.0  0.532  7.061   77.0  3.4106  24.0  666.0   
483   2.81838   0.0  18.10   0.0  0.532  5.762   40.3  4.0983  24.0  666.0   
484   2.37857   0.0  18.10   0.0  0.583  5.871   41.9  3.7240  24.0  666.0   
485   3.67367   0.0  18.10   0.0  0.583  6.312   51.9  3.9917  24.0  666.0   
486   5.69175   0.0  18.10   0.0  0.583  6.114   79.8  3.5459  24.0  666.0   
487   4.83567   0.0  18.10   0.0  0.583  5.905   53.2  3.1523  24.0  666.0   
488   0.15086   0.0  27.74   0.0  0.609  5.454   92.7  1.8209   4.0  711.0   
489   0.18337   0.0  27.74   0.0  0.609  5.414   98.3  1.7554   4.0  711.0   
490   0.20746   0.0  27.74   0.0  0.609  5.093   98.0  1.8226   4.0  711.0   
491   0.10574   0.0  27.74   0.0  0.609  5.983   98.8  1.8681   4.0  711.0   
492   0.11132   0.0  27.74   0.0  0.609  5.983   83.5  2.1099   4.0  711.0   
493   0.17331   0.0   9.69   0.0  0.585  5.707   54.0  2.3817   6.0  391.0   
494   0.27957   0.0   9.69   0.0  0.585  5.926   42.6  2.3817   6.0  391.0   
495   0.17899   0.0   9.69   0.0  0.585  5.670   28.8  2.7986   6.0  391.0   
496   0.28960   0.0   9.69   0.0  0.585  5.390   72.9  2.7986   6.0  391.0   
497   0.26838   0.0   9.69   0.0  0.585  5.794   70.6  2.8927   6.0  391.0   
498   0.23912   0.0   9.69   0.0  0.585  6.019   65.3  2.4091   6.0  391.0   
499   0.17783   0.0   9.69   0.0  0.585  5.569   73.5  2.3999   6.0  391.0   
500   0.22438   0.0   9.69   0.0  0.585  6.027   79.7  2.4982   6.0  391.0   
501   0.06263   0.0  11.93   0.0  0.573  6.593   69.1  2.4786   1.0  273.0   
502   0.04527   0.0  11.93   0.0  0.573  6.120   76.7  2.2875   1.0  273.0   
503   0.06076   0.0  11.93   0.0  0.573  6.976   91.0  2.1675   1.0  273.0   
504   0.10959   0.0  11.93   0.0  0.573  6.794   89.3  2.3889   1.0  273.0   
505   0.04741   0.0  11.93   0.0  0.573  6.030   80.8  2.5050   1.0  273.0   

     PTRATIO       B  LSTAT  
0       15.3  396.90   4.98  
1       17.8  396.90   9.14  
2       17.8  392.83   4.03  
3       18.7  394.63   2.94  
4       18.7  396.90   5.33  
5       18.7  394.12   5.21  
6       15.2  395.60  12.43  
7       15.2  396.90  19.15  
8       15.2  386.63  29.93  
9       15.2  386.71  17.10  
10      15.2  392.52  20.45  
11      15.2  396.90  13.27  
12      15.2  390.50  15.71  
13      21.0  396.90   8.26  
14      21.0  380.02  10.26  
15      21.0  395.62   8.47  
16      21.0  386.85   6.58  
17      21.0  386.75  14.67  
18      21.0  288.99  11.69  
19      21.0  390.95  11.28  
20      21.0  376.57  21.02  
21      21.0  392.53  13.83  
22      21.0  396.90  18.72  
23      21.0  394.54  19.88  
24      21.0  394.33  16.30  
25      21.0  303.42  16.51  
26      21.0  376.88  14.81  
27      21.0  306.38  17.28  
28      21.0  387.94  12.80  
29      21.0  380.23  11.98  
..       ...     ...    ...  
476     20.2  396.21  18.68  
477     20.2  349.48  24.91  
478     20.2  379.70  18.03  
479     20.2  383.32  13.11  
480     20.2  396.90  10.74  
481     20.2  393.07   7.74  
482     20.2  395.28   7.01  
483     20.2  392.92  10.42  
484     20.2  370.73  13.34  
485     20.2  388.62  10.58  
486     20.2  392.68  14.98  
487     20.2  388.22  11.45  
488     20.1  395.09  18.06  
489     20.1  344.05  23.97  
490     20.1  318.43  29.68  
491     20.1  390.11  18.07  
492     20.1  396.90  13.35  
493     19.2  396.90  12.01  
494     19.2  396.90  13.59  
495     19.2  393.29  17.60  
496     19.2  396.90  21.14  
497     19.2  396.90  14.10  
498     19.2  396.90  12.92  
499     19.2  395.77  15.10  
500     19.2  396.90  14.33  
501     21.0  391.99   9.67  
502     21.0  396.90   9.08  
503     21.0  396.90   5.64  
504     21.0  393.45   6.48  
505     21.0  396.90   7.88  

[506 rows x 13 columns]

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CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \

0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1.0 296.0

1 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2.0 242.0

2 0.02729 0.0 7.07 0.0 0.469 7.185 61.1 4.9671 2.0 242.0

3 0.03237 0.0 2.18 0.0 0.458 6.998 45.8 6.0622 3.0 222.0

4 0.06905 0.0 2.18 0.0 0.458 7.147 54.2 6.0622 3.0 222.0

5 0.02985 0.0 2.18 0.0 0.458 6.430 58.7 6.0622 3.0 222.0

6 0.08829 12.5 7.87 0.0 0.524 6.012 66.6 5.5605 5.0 311.0

7 0.14455 12.5 7.87 0.0 0.524 6.172 96.1 5.9505 5.0 311.0

8 0.21124 12.5 7.87 0.0 0.524 5.631 100.0 6.0821 5.0 311.0

9 0.17004 12.5 7.87 0.0 0.524 6.004 85.9 6.5921 5.0 311.0

10 0.22489 12.5 7.87 0.0 0.524 6.377 94.3 6.3467 5.0 311.0

11 0.11747 12.5 7.87 0.0 0.524 6.009 82.9 6.2267 5.0 311.0

12 0.09378 12.5 7.87 0.0 0.524 5.889 39.0 5.4509 5.0 311.0

13 0.62976 0.0 8.14 0.0 0.538 5.949 61.8 4.7075 4.0 307.0

14 0.63796 0.0 8.14 0.0 0.538 6.096 84.5 4.4619 4.0 307.0

15 0.62739 0.0 8.14 0.0 0.538 5.834 56.5 4.4986 4.0 307.0

16 1.05393 0.0 8.14 0.0 0.538 5.935 29.3 4.4986 4.0 307.0

17 0.78420 0.0 8.14 0.0 0.538 5.990 81.7 4.2579 4.0 307.0

18 0.80271 0.0 8.14 0.0 0.538 5.456 36.6 3.7965 4.0 307.0

19 0.72580 0.0 8.14 0.0 0.538 5.727 69.5 3.7965 4.0 307.0

20 1.25179 0.0 8.14 0.0 0.538 5.570 98.1 3.7979 4.0 307.0

21 0.85204 0.0 8.14 0.0 0.538 5.965 89.2 4.0123 4.0 307.0

22 1.23247 0.0 8.14 0.0 0.538 6.142 91.7 3.9769 4.0 307.0

23 0.98843 0.0 8.14 0.0 0.538 5.813 100.0 4.0952 4.0 307.0

24 0.75026 0.0 8.14 0.0 0.538 5.924 94.1 4.3996 4.0 307.0

25 0.84054 0.0 8.14 0.0 0.538 5.599 85.7 4.4546 4.0 307.0

26 0.67191 0.0 8.14 0.0 0.538 5.813 90.3 4.6820 4.0 307.0

27 0.95577 0.0 8.14 0.0 0.538 6.047 88.8 4.4534 4.0 307.0

28 0.77299 0.0 8.14 0.0 0.538 6.495 94.4 4.4547 4.0 307.0

29 1.00245 0.0 8.14 0.0 0.538 6.674 87.3 4.2390 4.0 307.0

.. ... ... ... ... ... ... ... ... ... ...

476 4.87141 0.0 18.10 0.0 0.614 6.484 93.6 2.3053 24.0 666.0

477 15.02340 0.0 18.10 0.0 0.614 5.304 97.3 2.1007 24.0 666.0

478 10.23300 0.0 18.10 0.0 0.614 6.185 96.7 2.1705 24.0 666.0

479 14.33370 0.0 18.10 0.0 0.614 6.229 88.0 1.9512 24.0 666.0

480 5.82401 0.0 18.10 0.0 0.532 6.242 64.7 3.4242 24.0 666.0

481 5.70818 0.0 18.10 0.0 0.532 6.750 74.9 3.3317 24.0 666.0

482 5.73116 0.0 18.10 0.0 0.532 7.061 77.0 3.4106 24.0 666.0

483 2.81838 0.0 18.10 0.0 0.532 5.762 40.3 4.0983 24.0 666.0

484 2.37857 0.0 18.10 0.0 0.583 5.871 41.9 3.7240 24.0 666.0

485 3.67367 0.0 18.10 0.0 0.583 6.312 51.9 3.9917 24.0 666.0

486 5.69175 0.0 18.10 0.0 0.583 6.114 79.8 3.5459 24.0 666.0

487 4.83567 0.0 18.10 0.0 0.583 5.905 53.2 3.1523 24.0 666.0

488 0.15086 0.0 27.74 0.0 0.609 5.454 92.7 1.8209 4.0 711.0

489 0.18337 0.0 27.74 0.0 0.609 5.414 98.3 1.7554 4.0 711.0

490 0.20746 0.0 27.74 0.0 0.609 5.093 98.0 1.8226 4.0 711.0

491 0.10574 0.0 27.74 0.0 0.609 5.983 98.8 1.8681 4.0 711.0

492 0.11132 0.0 27.74 0.0 0.609 5.983 83.5 2.1099 4.0 711.0

493 0.17331 0.0 9.69 0.0 0.585 5.707 54.0 2.3817 6.0 391.0

494 0.27957 0.0 9.69 0.0 0.585 5.926 42.6 2.3817 6.0 391.0

495 0.17899 0.0 9.69 0.0 0.585 5.670 28.8 2.7986 6.0 391.0

496 0.28960 0.0 9.69 0.0 0.585 5.390 72.9 2.7986 6.0 391.0

497 0.26838 0.0 9.69 0.0 0.585 5.794 70.6 2.8927 6.0 391.0

498 0.23912 0.0 9.69 0.0 0.585 6.019 65.3 2.4091 6.0 391.0

499 0.17783 0.0 9.69 0.0 0.585 5.569 73.5 2.3999 6.0 391.0

500 0.22438 0.0 9.69 0.0 0.585 6.027 79.7 2.4982 6.0 391.0

501 0.06263 0.0 11.93 0.0 0.573 6.593 69.1 2.4786 1.0 273.0

502 0.04527 0.0 11.93 0.0 0.573 6.120 76.7 2.2875 1.0 273.0

503 0.06076 0.0 11.93 0.0 0.573 6.976 91.0 2.1675 1.0 273.0

504 0.10959 0.0 11.93 0.0 0.573 6.794 89.3 2.3889 1.0 273.0

505 0.04741 0.0 11.93 0.0 0.573 6.030 80.8 2.5050 1.0 273.0

PTRATIO B LSTAT

0 15.3 396.90 4.98

1 17.8 396.90 9.14

2 17.8 392.83 4.03

3 18.7 394.63 2.94

4 18.7 396.90 5.33

5 18.7 394.12 5.21

6 15.2 395.60 12.43

7 15.2 396.90 19.15

8 15.2 386.63 29.93

9 15.2 386.71 17.10

10 15.2 392.52 20.45

11 15.2 396.90 13.27

12 15.2 390.50 15.71

13 21.0 396.90 8.26

14 21.0 380.02 10.26

15 21.0 395.62 8.47

16 21.0 386.85 6.58

17 21.0 386.75 14.67

18 21.0 288.99 11.69

19 21.0 390.95 11.28

20 21.0 376.57 21.02

21 21.0 392.53 13.83

22 21.0 396.90 18.72

23 21.0 394.54 19.88

24 21.0 394.33 16.30

25 21.0 303.42 16.51

26 21.0 376.88 14.81

27 21.0 306.38 17.28

28 21.0 387.94 12.80

29 21.0 380.23 11.98

.. ... ... ...

476 20.2 396.21 18.68

477 20.2 349.48 24.91

478 20.2 379.70 18.03

479 20.2 383.32 13.11

480 20.2 396.90 10.74

481 20.2 393.07 7.74

482 20.2 395.28 7.01

483 20.2 392.92 10.42

484 20.2 370.73 13.34

485 20.2 388.62 10.58

486 20.2 392.68 14.98

487 20.2 388.22 11.45

488 20.1 395.09 18.06

489 20.1 344.05 23.97

490 20.1 318.43 29.68

491 20.1 390.11 18.07

492 20.1 396.90 13.35

493 19.2 396.90 12.01

494 19.2 396.90 13.59

495 19.2 393.29 17.60

496 19.2 396.90 21.14

497 19.2 396.90 14.10

498 19.2 396.90 12.92

499 19.2 395.77 15.10

500 19.2 396.90 14.33

501 21.0 391.99 9.67

502 21.0 396.90 9.08

503 21.0 396.90 5.64

504 21.0 393.45 6.48

505 21.0 396.90 7.88

[506 rows x 13 columns]

pandasを用いてロンドンの住宅データを表形式にしています。

print(data.target)

1	print(data.target)

[ 24.   21.6  34.7  33.4  36.2  28.7  22.9  27.1  16.5  18.9  15.   18.9
  21.7  20.4  18.2  19.9  23.1  17.5  20.2  18.2  13.6  19.6  15.2  14.5
  15.6  13.9  16.6  14.8  18.4  21.   12.7  14.5  13.2  13.1  13.5  18.9
  20.   21.   24.7  30.8  34.9  26.6  25.3  24.7  21.2  19.3  20.   16.6
  14.4  19.4  19.7  20.5  25.   23.4  18.9  35.4  24.7  31.6  23.3  19.6
  18.7  16.   22.2  25.   33.   23.5  19.4  22.   17.4  20.9  24.2  21.7
  22.8  23.4  24.1  21.4  20.   20.8  21.2  20.3  28.   23.9  24.8  22.9
  23.9  26.6  22.5  22.2  23.6  28.7  22.6  22.   22.9  25.   20.6  28.4
  21.4  38.7  43.8  33.2  27.5  26.5  18.6  19.3  20.1  19.5  19.5  20.4
  19.8  19.4  21.7  22.8  18.8  18.7  18.5  18.3  21.2  19.2  20.4  19.3
  22.   20.3  20.5  17.3  18.8  21.4  15.7  16.2  18.   14.3  19.2  19.6
  23.   18.4  15.6  18.1  17.4  17.1  13.3  17.8  14.   14.4  13.4  15.6
  11.8  13.8  15.6  14.6  17.8  15.4  21.5  19.6  15.3  19.4  17.   15.6
  13.1  41.3  24.3  23.3  27.   50.   50.   50.   22.7  25.   50.   23.8
  23.8  22.3  17.4  19.1  23.1  23.6  22.6  29.4  23.2  24.6  29.9  37.2
  39.8  36.2  37.9  32.5  26.4  29.6  50.   32.   29.8  34.9  37.   30.5
  36.4  31.1  29.1  50.   33.3  30.3  34.6  34.9  32.9  24.1  42.3  48.5
  50.   22.6  24.4  22.5  24.4  20.   21.7  19.3  22.4  28.1  23.7  25.
  23.3  28.7  21.5  23.   26.7  21.7  27.5  30.1  44.8  50.   37.6  31.6
  46.7  31.5  24.3  31.7  41.7  48.3  29.   24.   25.1  31.5  23.7  23.3
  22.   20.1  22.2  23.7  17.6  18.5  24.3  20.5  24.5  26.2  24.4  24.8
  29.6  42.8  21.9  20.9  44.   50.   36.   30.1  33.8  43.1  48.8  31.
  36.5  22.8  30.7  50.   43.5  20.7  21.1  25.2  24.4  35.2  32.4  32.
  33.2  33.1  29.1  35.1  45.4  35.4  46.   50.   32.2  22.   20.1  23.2
  22.3  24.8  28.5  37.3  27.9  23.9  21.7  28.6  27.1  20.3  22.5  29.
  24.8  22.   26.4  33.1  36.1  28.4  33.4  28.2  22.8  20.3  16.1  22.1
  19.4  21.6  23.8  16.2  17.8  19.8  23.1  21.   23.8  23.1  20.4  18.5
  25.   24.6  23.   22.2  19.3  22.6  19.8  17.1  19.4  22.2  20.7  21.1
  19.5  18.5  20.6  19.   18.7  32.7  16.5  23.9  31.2  17.5  17.2  23.1
  24.5  26.6  22.9  24.1  18.6  30.1  18.2  20.6  17.8  21.7  22.7  22.6
  25.   19.9  20.8  16.8  21.9  27.5  21.9  23.1  50.   50.   50.   50.
  50.   13.8  13.8  15.   13.9  13.3  13.1  10.2  10.4  10.9  11.3  12.3
   8.8   7.2  10.5   7.4  10.2  11.5  15.1  23.2   9.7  13.8  12.7  13.1
  12.5   8.5   5.    6.3   5.6   7.2  12.1   8.3   8.5   5.   11.9  27.9
  17.2  27.5  15.   17.2  17.9  16.3   7.    7.2   7.5  10.4   8.8   8.4
  16.7  14.2  20.8  13.4  11.7   8.3  10.2  10.9  11.    9.5  14.5  14.1
  16.1  14.3  11.7  13.4   9.6   8.7   8.4  12.8  10.5  17.1  18.4  15.4
  10.8  11.8  14.9  12.6  14.1  13.   13.4  15.2  16.1  17.8  14.9  14.1
  12.7  13.5  14.9  20.   16.4  17.7  19.5  20.2  21.4  19.9  19.   19.1
  19.1  20.1  19.9  19.6  23.2  29.8  13.8  13.3  16.7  12.   14.6  21.4
  23.   23.7  25.   21.8  20.6  21.2  19.1  20.6  15.2   7.    8.1  13.6
  20.1  21.8  24.5  23.1  19.7  18.3  21.2  17.5  16.8  22.4  20.6  23.9
  22.   11.9]

[ 24. 21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 15. 18.9

21.7 20.4 18.2 19.9 23.1 17.5 20.2 18.2 13.6 19.6 15.2 14.5

15.6 13.9 16.6 14.8 18.4 21. 12.7 14.5 13.2 13.1 13.5 18.9

20. 21. 24.7 30.8 34.9 26.6 25.3 24.7 21.2 19.3 20. 16.6

14.4 19.4 19.7 20.5 25. 23.4 18.9 35.4 24.7 31.6 23.3 19.6

18.7 16. 22.2 25. 33. 23.5 19.4 22. 17.4 20.9 24.2 21.7

22.8 23.4 24.1 21.4 20. 20.8 21.2 20.3 28. 23.9 24.8 22.9

23.9 26.6 22.5 22.2 23.6 28.7 22.6 22. 22.9 25. 20.6 28.4

21.4 38.7 43.8 33.2 27.5 26.5 18.6 19.3 20.1 19.5 19.5 20.4

19.8 19.4 21.7 22.8 18.8 18.7 18.5 18.3 21.2 19.2 20.4 19.3

22. 20.3 20.5 17.3 18.8 21.4 15.7 16.2 18. 14.3 19.2 19.6

23. 18.4 15.6 18.1 17.4 17.1 13.3 17.8 14. 14.4 13.4 15.6

11.8 13.8 15.6 14.6 17.8 15.4 21.5 19.6 15.3 19.4 17. 15.6

13.1 41.3 24.3 23.3 27. 50. 50. 50. 22.7 25. 50. 23.8

23.8 22.3 17.4 19.1 23.1 23.6 22.6 29.4 23.2 24.6 29.9 37.2

39.8 36.2 37.9 32.5 26.4 29.6 50. 32. 29.8 34.9 37. 30.5

36.4 31.1 29.1 50. 33.3 30.3 34.6 34.9 32.9 24.1 42.3 48.5

50. 22.6 24.4 22.5 24.4 20. 21.7 19.3 22.4 28.1 23.7 25.

23.3 28.7 21.5 23. 26.7 21.7 27.5 30.1 44.8 50. 37.6 31.6

46.7 31.5 24.3 31.7 41.7 48.3 29. 24. 25.1 31.5 23.7 23.3

22. 20.1 22.2 23.7 17.6 18.5 24.3 20.5 24.5 26.2 24.4 24.8

29.6 42.8 21.9 20.9 44. 50. 36. 30.1 33.8 43.1 48.8 31.

36.5 22.8 30.7 50. 43.5 20.7 21.1 25.2 24.4 35.2 32.4 32.

33.2 33.1 29.1 35.1 45.4 35.4 46. 50. 32.2 22. 20.1 23.2

22.3 24.8 28.5 37.3 27.9 23.9 21.7 28.6 27.1 20.3 22.5 29.

24.8 22. 26.4 33.1 36.1 28.4 33.4 28.2 22.8 20.3 16.1 22.1

19.4 21.6 23.8 16.2 17.8 19.8 23.1 21. 23.8 23.1 20.4 18.5

25. 24.6 23. 22.2 19.3 22.6 19.8 17.1 19.4 22.2 20.7 21.1

19.5 18.5 20.6 19. 18.7 32.7 16.5 23.9 31.2 17.5 17.2 23.1

24.5 26.6 22.9 24.1 18.6 30.1 18.2 20.6 17.8 21.7 22.7 22.6

25. 19.9 20.8 16.8 21.9 27.5 21.9 23.1 50. 50. 50. 50.

50. 13.8 13.8 15. 13.9 13.3 13.1 10.2 10.4 10.9 11.3 12.3

8.8 7.2 10.5 7.4 10.2 11.5 15.1 23.2 9.7 13.8 12.7 13.1

12.5 8.5 5. 6.3 5.6 7.2 12.1 8.3 8.5 5. 11.9 27.9

17.2 27.5 15. 17.2 17.9 16.3 7. 7.2 7.5 10.4 8.8 8.4

16.7 14.2 20.8 13.4 11.7 8.3 10.2 10.9 11. 9.5 14.5 14.1

16.1 14.3 11.7 13.4 9.6 8.7 8.4 12.8 10.5 17.1 18.4 15.4

10.8 11.8 14.9 12.6 14.1 13. 13.4 15.2 16.1 17.8 14.9 14.1

12.7 13.5 14.9 20. 16.4 17.7 19.5 20.2 21.4 19.9 19. 19.1

19.1 20.1 19.9 19.6 23.2 29.8 13.8 13.3 16.7 12. 14.6 21.4

23. 23.7 25. 21.8 20.6 21.2 19.1 20.6 15.2 7. 8.1 13.6

20.1 21.8 24.5 23.1 19.7 18.3 21.2 17.5 16.8 22.4 20.6 23.9

22. 11.9]

住宅価格のデータです。単位は$1000です。

学習用データの分割

X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=0)

1	X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=0)

データを学習用8割、評価用2割に分割しています。

X_train.shape

1	X_train.shape

(404, 13)

(404, 13)

学習用が404件となりました。

X_test.shape

1	X_test.shape

(102, 13)

(102, 13)

評価用が102件となりました。

学習

clf = linear_model.LinearRegression()
clf.fit(X_train,y_train)

1 2	clf = linear_model.LinearRegression() clf.fit(X_train,y_train)

線形回帰を用いて学習していきます。

評価

test=pd.DataFrame(data=X_test,columns=data.feature_names)
print(test)

1 2	test=pd.DataFrame(data=X_test,columns=data.feature_names) print(test)

         CRIM    ZN  INDUS  CHAS    NOX     RM    AGE     DIS   RAD    TAX  \
0     0.06724   0.0   3.24   0.0  0.460  6.333   17.2  5.2146   4.0  430.0   
1     9.23230   0.0  18.10   0.0  0.631  6.216  100.0  1.1691  24.0  666.0   
2     0.11425   0.0  13.89   1.0  0.550  6.373   92.4  3.3633   5.0  276.0   
3    24.80170   0.0  18.10   0.0  0.693  5.349   96.0  1.7028  24.0  666.0   
4     0.05646   0.0  12.83   0.0  0.437  6.232   53.7  5.0141   5.0  398.0   
5     0.62739   0.0   8.14   0.0  0.538  5.834   56.5  4.4986   4.0  307.0   
6     4.83567   0.0  18.10   0.0  0.583  5.905   53.2  3.1523  24.0  666.0   
7     0.06151   0.0   5.19   0.0  0.515  5.968   58.5  4.8122   5.0  224.0   
8     2.63548   0.0   9.90   0.0  0.544  4.973   37.8  2.5194   4.0  304.0   
9     0.22876   0.0   8.56   0.0  0.520  6.405   85.4  2.7147   5.0  384.0   
10   73.53410   0.0  18.10   0.0  0.679  5.957  100.0  1.8026  24.0  666.0   
11   14.05070   0.0  18.10   0.0  0.597  6.657  100.0  1.5275  24.0  666.0   
12    6.28807   0.0  18.10   0.0  0.740  6.341   96.4  2.0720  24.0  666.0   
13   24.39380   0.0  18.10   0.0  0.700  4.652  100.0  1.4672  24.0  666.0   
14    1.83377   0.0  19.58   1.0  0.605  7.802   98.2  2.0407   5.0  403.0   
15    0.05561  70.0   2.24   0.0  0.400  7.041   10.0  7.8278   5.0  358.0   
16    5.82401   0.0  18.10   0.0  0.532  6.242   64.7  3.4242  24.0  666.0   
17    0.04011  80.0   1.52   0.0  0.404  7.287   34.1  7.3090   2.0  329.0   
18    0.06664   0.0   4.05   0.0  0.510  6.546   33.1  3.1323   5.0  296.0   
19    0.08014   0.0   5.96   0.0  0.499  5.850   41.5  3.9342   5.0  279.0   
20    0.16760   0.0   7.38   0.0  0.493  6.426   52.3  4.5404   5.0  287.0   
21    2.31390   0.0  19.58   0.0  0.605  5.880   97.3  2.3887   5.0  403.0   
22    0.13117   0.0   8.56   0.0  0.520  6.127   85.2  2.1224   5.0  384.0   
23    0.07978  40.0   6.41   0.0  0.447  6.482   32.1  4.1403   4.0  254.0   
24    0.17142   0.0   6.91   0.0  0.448  5.682   33.8  5.1004   3.0  233.0   
25   13.52220   0.0  18.10   0.0  0.631  3.863  100.0  1.5106  24.0  666.0   
26    0.85204   0.0   8.14   0.0  0.538  5.965   89.2  4.0123   4.0  307.0   
27    2.14918   0.0  19.58   0.0  0.871  5.709   98.5  1.6232   5.0  403.0   
28    0.12083   0.0   2.89   0.0  0.445  8.069   76.0  3.4952   2.0  276.0   
29    0.22212   0.0  10.01   0.0  0.547  6.092   95.4  2.5480   6.0  432.0   
..        ...   ...    ...   ...    ...    ...    ...     ...   ...    ...   
72    0.03584  80.0   3.37   0.0  0.398  6.290   17.8  6.6115   4.0  337.0   
73    0.03049  55.0   3.78   0.0  0.484  6.874   28.1  6.4654   5.0  370.0   
74    5.70818   0.0  18.10   0.0  0.532  6.750   74.9  3.3317  24.0  666.0   
75   22.59710   0.0  18.10   0.0  0.700  5.000   89.5  1.5184  24.0  666.0   
76    0.33147   0.0   6.20   0.0  0.507  8.247   70.4  3.6519   8.0  307.0   
77    0.22969   0.0  10.59   0.0  0.489  6.326   52.5  4.3549   4.0  277.0   
78    0.04684   0.0   3.41   0.0  0.489  6.417   66.1  3.0923   2.0  270.0   
79    0.26838   0.0   9.69   0.0  0.585  5.794   70.6  2.8927   6.0  391.0   
80    0.09252  30.0   4.93   0.0  0.428  6.606   42.2  6.1899   6.0  300.0   
81    0.35233   0.0  21.89   0.0  0.624  6.454   98.4  1.8498   4.0  437.0   
82   11.95110   0.0  18.10   0.0  0.659  5.608  100.0  1.2852  24.0  666.0   
83    0.31533   0.0   6.20   0.0  0.504  8.266   78.3  2.8944   8.0  307.0   
84    0.52693   0.0   6.20   0.0  0.504  8.725   83.0  2.8944   8.0  307.0   
85    0.30347   0.0   7.38   0.0  0.493  6.312   28.9  5.4159   5.0  287.0   
86    0.11504   0.0   2.89   0.0  0.445  6.163   69.6  3.4952   2.0  276.0   
87   12.24720   0.0  18.10   0.0  0.584  5.837   59.7  1.9976  24.0  666.0   
88    1.42502   0.0  19.58   0.0  0.871  6.510  100.0  1.7659   5.0  403.0   
89    5.29305   0.0  18.10   0.0  0.700  6.051   82.5  2.1678  24.0  666.0   
90    0.01360  75.0   4.00   0.0  0.410  5.888   47.6  7.3197   3.0  469.0   
91   11.16040   0.0  18.10   0.0  0.740  6.629   94.6  2.1247  24.0  666.0   
92    0.04819  80.0   3.64   0.0  0.392  6.108   32.0  9.2203   1.0  315.0   
93    0.04417  70.0   2.24   0.0  0.400  6.871   47.4  7.8278   5.0  358.0   
94    0.04741   0.0  11.93   0.0  0.573  6.030   80.8  2.5050   1.0  273.0   
95    0.33983  22.0   5.86   0.0  0.431  6.108   34.9  8.0555   7.0  330.0   
96   18.49820   0.0  18.10   0.0  0.668  4.138  100.0  1.1370  24.0  666.0   
97    0.02055  85.0   0.74   0.0  0.410  6.383   35.7  9.1876   2.0  313.0   
98    4.75237   0.0  18.10   0.0  0.713  6.525   86.5  2.4358  24.0  666.0   
99    0.14932  25.0   5.13   0.0  0.453  5.741   66.2  7.2254   8.0  284.0   
100   0.14052   0.0  10.59   0.0  0.489  6.375   32.3  3.9454   4.0  277.0   
101   0.12802   0.0   8.56   0.0  0.520  6.474   97.1  2.4329   5.0  384.0   

     PTRATIO       B  LSTAT  
0       16.9  375.21   7.34  
1       20.2  366.15   9.53  
2       16.4  393.74  10.50  
3       20.2  396.90  19.77  
4       18.7  386.40  12.34  
5       21.0  395.62   8.47  
6       20.2  388.22  11.45  
7       20.2  396.90   9.29  
8       18.4  350.45  12.64  
9       20.9   70.80  10.63  
10      20.2   16.45  20.62  
11      20.2   35.05  21.22  
12      20.2  318.01  17.79  
13      20.2  396.90  28.28  
14      14.7  389.61   1.92  
15      14.8  371.58   4.74  
16      20.2  396.90  10.74  
17      12.6  396.90   4.08  
18      16.6  390.96   5.33  
19      19.2  396.90   8.77  
20      19.6  396.90   7.20  
21      14.7  348.13  12.03  
22      20.9  387.69  14.09  
23      17.6  396.90   7.19  
24      17.9  396.90  10.21  
25      20.2  131.42  13.33  
26      21.0  392.53  13.83  
27      14.7  261.95  15.79  
28      18.0  396.90   4.21  
29      17.8  396.90  17.09  
..       ...     ...    ...  
72      16.1  396.90   4.67  
73      17.6  387.97   4.61  
74      20.2  393.07   7.74  
75      20.2  396.90  31.99  
76      17.4  378.95   3.95  
77      18.6  394.87  10.97  
78      17.8  392.18   8.81  
79      19.2  396.90  14.10  
80      16.6  383.78   7.37  
81      21.2  394.08  14.59  
82      20.2  332.09  12.13  
83      17.4  385.05   4.14  
84      17.4  382.00   4.63  
85      19.6  396.90   6.15  
86      18.0  391.83  11.34  
87      20.2   24.65  15.69  
88      14.7  364.31   7.39  
89      20.2  378.38  18.76  
90      21.1  396.90  14.80  
91      20.2  109.85  23.27  
92      16.4  392.89   6.57  
93      14.8  390.86   6.07  
94      21.0  396.90   7.88  
95      19.1  390.18   9.16  
96      20.2  396.90  37.97  
97      17.3  396.90   5.77  
98      20.2   50.92  18.13  
99      19.7  395.11  13.15  
100     18.6  385.81   9.38  
101     20.9  395.24  12.27  

[102 rows x 13 columns]

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \

0 0.06724 0.0 3.24 0.0 0.460 6.333 17.2 5.2146 4.0 430.0

1 9.23230 0.0 18.10 0.0 0.631 6.216 100.0 1.1691 24.0 666.0

2 0.11425 0.0 13.89 1.0 0.550 6.373 92.4 3.3633 5.0 276.0

3 24.80170 0.0 18.10 0.0 0.693 5.349 96.0 1.7028 24.0 666.0

4 0.05646 0.0 12.83 0.0 0.437 6.232 53.7 5.0141 5.0 398.0

5 0.62739 0.0 8.14 0.0 0.538 5.834 56.5 4.4986 4.0 307.0

6 4.83567 0.0 18.10 0.0 0.583 5.905 53.2 3.1523 24.0 666.0

7 0.06151 0.0 5.19 0.0 0.515 5.968 58.5 4.8122 5.0 224.0

8 2.63548 0.0 9.90 0.0 0.544 4.973 37.8 2.5194 4.0 304.0

9 0.22876 0.0 8.56 0.0 0.520 6.405 85.4 2.7147 5.0 384.0

10 73.53410 0.0 18.10 0.0 0.679 5.957 100.0 1.8026 24.0 666.0

11 14.05070 0.0 18.10 0.0 0.597 6.657 100.0 1.5275 24.0 666.0

12 6.28807 0.0 18.10 0.0 0.740 6.341 96.4 2.0720 24.0 666.0

13 24.39380 0.0 18.10 0.0 0.700 4.652 100.0 1.4672 24.0 666.0

14 1.83377 0.0 19.58 1.0 0.605 7.802 98.2 2.0407 5.0 403.0

15 0.05561 70.0 2.24 0.0 0.400 7.041 10.0 7.8278 5.0 358.0

16 5.82401 0.0 18.10 0.0 0.532 6.242 64.7 3.4242 24.0 666.0

17 0.04011 80.0 1.52 0.0 0.404 7.287 34.1 7.3090 2.0 329.0

18 0.06664 0.0 4.05 0.0 0.510 6.546 33.1 3.1323 5.0 296.0

19 0.08014 0.0 5.96 0.0 0.499 5.850 41.5 3.9342 5.0 279.0

20 0.16760 0.0 7.38 0.0 0.493 6.426 52.3 4.5404 5.0 287.0

21 2.31390 0.0 19.58 0.0 0.605 5.880 97.3 2.3887 5.0 403.0

22 0.13117 0.0 8.56 0.0 0.520 6.127 85.2 2.1224 5.0 384.0

23 0.07978 40.0 6.41 0.0 0.447 6.482 32.1 4.1403 4.0 254.0

24 0.17142 0.0 6.91 0.0 0.448 5.682 33.8 5.1004 3.0 233.0

25 13.52220 0.0 18.10 0.0 0.631 3.863 100.0 1.5106 24.0 666.0

26 0.85204 0.0 8.14 0.0 0.538 5.965 89.2 4.0123 4.0 307.0

27 2.14918 0.0 19.58 0.0 0.871 5.709 98.5 1.6232 5.0 403.0

28 0.12083 0.0 2.89 0.0 0.445 8.069 76.0 3.4952 2.0 276.0

29 0.22212 0.0 10.01 0.0 0.547 6.092 95.4 2.5480 6.0 432.0

.. ... ... ... ... ... ... ... ... ... ...

72 0.03584 80.0 3.37 0.0 0.398 6.290 17.8 6.6115 4.0 337.0

73 0.03049 55.0 3.78 0.0 0.484 6.874 28.1 6.4654 5.0 370.0

74 5.70818 0.0 18.10 0.0 0.532 6.750 74.9 3.3317 24.0 666.0

75 22.59710 0.0 18.10 0.0 0.700 5.000 89.5 1.5184 24.0 666.0

76 0.33147 0.0 6.20 0.0 0.507 8.247 70.4 3.6519 8.0 307.0

77 0.22969 0.0 10.59 0.0 0.489 6.326 52.5 4.3549 4.0 277.0

78 0.04684 0.0 3.41 0.0 0.489 6.417 66.1 3.0923 2.0 270.0

79 0.26838 0.0 9.69 0.0 0.585 5.794 70.6 2.8927 6.0 391.0

80 0.09252 30.0 4.93 0.0 0.428 6.606 42.2 6.1899 6.0 300.0

81 0.35233 0.0 21.89 0.0 0.624 6.454 98.4 1.8498 4.0 437.0

82 11.95110 0.0 18.10 0.0 0.659 5.608 100.0 1.2852 24.0 666.0

83 0.31533 0.0 6.20 0.0 0.504 8.266 78.3 2.8944 8.0 307.0

84 0.52693 0.0 6.20 0.0 0.504 8.725 83.0 2.8944 8.0 307.0

85 0.30347 0.0 7.38 0.0 0.493 6.312 28.9 5.4159 5.0 287.0

86 0.11504 0.0 2.89 0.0 0.445 6.163 69.6 3.4952 2.0 276.0

87 12.24720 0.0 18.10 0.0 0.584 5.837 59.7 1.9976 24.0 666.0

88 1.42502 0.0 19.58 0.0 0.871 6.510 100.0 1.7659 5.0 403.0

89 5.29305 0.0 18.10 0.0 0.700 6.051 82.5 2.1678 24.0 666.0

90 0.01360 75.0 4.00 0.0 0.410 5.888 47.6 7.3197 3.0 469.0

91 11.16040 0.0 18.10 0.0 0.740 6.629 94.6 2.1247 24.0 666.0

92 0.04819 80.0 3.64 0.0 0.392 6.108 32.0 9.2203 1.0 315.0

93 0.04417 70.0 2.24 0.0 0.400 6.871 47.4 7.8278 5.0 358.0

94 0.04741 0.0 11.93 0.0 0.573 6.030 80.8 2.5050 1.0 273.0

95 0.33983 22.0 5.86 0.0 0.431 6.108 34.9 8.0555 7.0 330.0

96 18.49820 0.0 18.10 0.0 0.668 4.138 100.0 1.1370 24.0 666.0

97 0.02055 85.0 0.74 0.0 0.410 6.383 35.7 9.1876 2.0 313.0

98 4.75237 0.0 18.10 0.0 0.713 6.525 86.5 2.4358 24.0 666.0

99 0.14932 25.0 5.13 0.0 0.453 5.741 66.2 7.2254 8.0 284.0

100 0.14052 0.0 10.59 0.0 0.489 6.375 32.3 3.9454 4.0 277.0

101 0.12802 0.0 8.56 0.0 0.520 6.474 97.1 2.4329 5.0 384.0

PTRATIO B LSTAT

0 16.9 375.21 7.34

1 20.2 366.15 9.53

2 16.4 393.74 10.50

3 20.2 396.90 19.77

4 18.7 386.40 12.34

5 21.0 395.62 8.47

6 20.2 388.22 11.45

7 20.2 396.90 9.29

8 18.4 350.45 12.64

9 20.9 70.80 10.63

10 20.2 16.45 20.62

11 20.2 35.05 21.22

12 20.2 318.01 17.79

13 20.2 396.90 28.28

14 14.7 389.61 1.92

15 14.8 371.58 4.74

16 20.2 396.90 10.74

17 12.6 396.90 4.08

18 16.6 390.96 5.33

19 19.2 396.90 8.77

20 19.6 396.90 7.20

21 14.7 348.13 12.03

22 20.9 387.69 14.09

23 17.6 396.90 7.19

24 17.9 396.90 10.21

25 20.2 131.42 13.33

26 21.0 392.53 13.83

27 14.7 261.95 15.79

28 18.0 396.90 4.21

29 17.8 396.90 17.09

.. ... ... ...

72 16.1 396.90 4.67

73 17.6 387.97 4.61

74 20.2 393.07 7.74

75 20.2 396.90 31.99

76 17.4 378.95 3.95

77 18.6 394.87 10.97

78 17.8 392.18 8.81

79 19.2 396.90 14.10

80 16.6 383.78 7.37

81 21.2 394.08 14.59

82 20.2 332.09 12.13

83 17.4 385.05 4.14

84 17.4 382.00 4.63

85 19.6 396.90 6.15

86 18.0 391.83 11.34

87 20.2 24.65 15.69

88 14.7 364.31 7.39

89 20.2 378.38 18.76

90 21.1 396.90 14.80

91 20.2 109.85 23.27

92 16.4 392.89 6.57

93 14.8 390.86 6.07

94 21.0 396.90 7.88

95 19.1 390.18 9.16

96 20.2 396.90 37.97

97 17.3 396.90 5.77

98 20.2 50.92 18.13

99 19.7 395.11 13.15

100 18.6 385.81 9.38

101 20.9 395.24 12.27

[102 rows x 13 columns]

評価用の用いる住宅データを表示しています。

print(clf.predict(X_test))

1	print(clf.predict(X_test))

[ 24.89012999  23.72488246  29.37213304  12.14010251  21.4468654
  19.28645278  20.4963729   21.361896    18.90187947  19.89240314
   5.14887233  16.34684109  17.06012498   5.60903056  40.0046213
  32.49427341  22.46081666  36.85586503  30.86579318  23.15478003
  24.77656022  24.67996181  20.59378189  30.35624965  22.42640026
  10.22873821  17.64814177  18.26038473  35.5307741   20.96125278
  18.3033109   17.78873855  19.96636449  24.06489726  29.10613394
  19.26824306  11.16302351  24.57838036  17.55060167  15.4775377
  26.21443038  20.86590644  22.31521746  15.60756369  23.00542683
  25.17799668  20.1224094   22.89324789  10.03421509  24.28328238
  20.90741758  17.34683545  24.52357489  29.93973985  13.41445583
  21.72648406  20.79476218  15.49582771  14.00076106  22.18730294
  17.7328223   21.58942     32.9052782   31.11274689  17.74095677
  32.76792649  18.69693213  19.78338311  19.00577539  22.90129865
  22.96388226  24.0302524   30.73473079  28.82862993  25.90146307
   5.23941707  36.71283639  23.77448475  27.27134636  19.29485488
  28.62428371  19.17839754  18.97551342  37.81623842  39.20883283
  23.71409982  24.93482847  15.8506365   26.09648351  16.67799923
  15.83315513  13.0653193   24.72280719  31.25443519  22.17141029
  20.25167573   0.59633964  25.44521598  15.52175986  17.93778172
  25.30617772  22.37221992]