回归模型与房价预测 

import pandas as pd
pd.DataFrame(boston.data)

 

from sklearn.datasets import load_boston
boston=load_boston()
boston.keys()

dict_keys(['data', 'target', 'feature_names', 'DESCR'])

0123456789101112
0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1.0 296.0 15.3 396.90 4.98
1 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2.0 242.0 17.8 396.90 9.14
2 0.02729 0.0 7.07 0.0 0.469 7.185 61.1 4.9671 2.0 242.0 17.8 392.83 4.03
3 0.03237 0.0 2.18 0.0 0.458 6.998 45.8 6.0622 3.0 222.0 18.7 394.63 2.94
4 0.06905 0.0 2.18 0.0 0.458 7.147 54.2 6.0622 3.0 222.0 18.7 396.90 5.33
5 0.02985 0.0 2.18 0.0 0.458 6.430 58.7 6.0622 3.0 222.0 18.7 394.12 5.21
6 0.08829 12.5 7.87 0.0 0.524 6.012 66.6 5.5605 5.0 311.0 15.2 395.60 12.43
7 0.14455 12.5 7.87 0.0 0.524 6.172 96.1 5.9505 5.0 311.0 15.2 396.90 19.15
8 0.21124 12.5 7.87 0.0 0.524 5.631 100.0 6.0821 5.0 311.0 15.2 386.63 29.93
9 0.17004 12.5 7.87 0.0 0.524 6.004 85.9 6.5921 5.0 311.0 15.2 386.71 17.10
10 0.22489 12.5 7.87 0.0 0.524 6.377 94.3 6.3467 5.0 311.0 15.2 392.52 20.45
11 0.11747 12.5 7.87 0.0 0.524 6.009 82.9 6.2267 5.0 311.0 15.2 396.90 13.27
12 0.09378 12.5 7.87 0.0 0.524 5.889 39.0 5.4509 5.0 311.0 15.2 390.50 15.71
13 0.62976 0.0 8.14 0.0 0.538 5.949 61.8 4.7075 4.0 307.0 21.0 396.90 8.26
14 0.63796 0.0 8.14 0.0 0.538 6.096 84.5 4.4619 4.0 307.0 21.0 380.02 10.26
15 0.62739 0.0 8.14 0.0 0.538 5.834 56.5 4.4986 4.0 307.0 21.0 395.62 8.47
16 1.05393 0.0 8.14 0.0 0.538 5.935 29.3 4.4986 4.0 307.0 21.0 386.85 6.58
17 0.78420 0.0 8.14 0.0 0.538 5.990 81.7 4.2579 4.0 307.0 21.0 386.75 14.67
18 0.80271 0.0 8.14 0.0 0.538 5.456 36.6 3.7965 4.0 307.0 21.0 288.99 11.69
19 0.72580 0.0 8.14 0.0 0.538 5.727 69.5 3.7965 4.0 307.0 21.0 390.95 11.28
20 1.25179 0.0 8.14 0.0 0.538 5.570 98.1 3.7979 4.0 307.0 21.0 376.57 21.02
21 0.85204 0.0 8.14 0.0 0.538 5.965 89.2 4.0123 4.0 307.0 21.0 392.53 13.83
22 1.23247 0.0 8.14 0.0 0.538 6.142 91.7 3.9769 4.0 307.0 21.0 396.90 18.72
23 0.98843 0.0 8.14 0.0 0.538 5.813 100.0 4.0952 4.0 307.0 21.0 394.54 19.88
24 0.75026 0.0 8.14 0.0 0.538 5.924 94.1 4.3996 4.0 307.0 21.0 394.33 16.30
25 0.84054 0.0 8.14 0.0 0.538 5.599 85.7 4.4546 4.0 307.0 21.0 303.42 16.51
26 0.67191 0.0 8.14 0.0 0.538 5.813 90.3 4.6820 4.0 307.0 21.0 376.88 14.81
27 0.95577 0.0 8.14 0.0 0.538 6.047 88.8 4.4534 4.0 307.0 21.0 306.38 17.28
28 0.77299 0.0 8.14 0.0 0.538 6.495 94.4 4.4547 4.0 307.0 21.0 387.94 12.80
29 1.00245 0.0 8.14 0.0 0.538 6.674 87.3 4.2390 4.0 307.0 21.0 380.23 11.98
... ... ... ... ... ... ... ... ... ... ... ... ... ...
476 4.87141 0.0 18.10 0.0 0.614 6.484 93.6 2.3053 24.0 666.0 20.2 396.21 18.68
477 15.02340 0.0 18.10 0.0 0.614 5.304 97.3 2.1007 24.0 666.0 20.2 349.48 24.91
478 10.23300 0.0 18.10 0.0 0.614 6.185 96.7 2.1705 24.0 666.0 20.2 379.70 18.03
479 14.33370 0.0 18.10 0.0 0.614 6.229 88.0 1.9512 24.0 666.0 20.2 383.32 13.11
480 5.82401 0.0 18.10 0.0 0.532 6.242 64.7 3.4242 24.0 666.0 20.2 396.90 10.74
481 5.70818 0.0 18.10 0.0 0.532 6.750 74.9 3.3317 24.0 666.0 20.2 393.07 7.74
482 5.73116 0.0 18.10 0.0 0.532 7.061 77.0 3.4106 24.0 666.0 20.2 395.28 7.01
483 2.81838 0.0 18.10 0.0 0.532 5.762 40.3 4.0983 24.0 666.0 20.2 392.92 10.42
484 2.37857 0.0 18.10 0.0 0.583 5.871 41.9 3.7240 24.0 666.0 20.2 370.73 13.34
485 3.67367 0.0 18.10 0.0 0.583 6.312 51.9 3.9917 24.0 666.0 20.2 388.62 10.58
486 5.69175 0.0 18.10 0.0 0.583 6.114 79.8 3.5459 24.0 666.0 20.2 392.68 14.98
487 4.83567 0.0 18.10 0.0 0.583 5.905 53.2 3.1523 24.0 666.0 20.2 388.22 11.45
488 0.15086 0.0 27.74 0.0 0.609 5.454 92.7 1.8209 4.0 711.0 20.1 395.09 18.06
489 0.18337 0.0 27.74 0.0 0.609 5.414 98.3 1.7554 4.0 711.0 20.1 344.05 23.97
490 0.20746 0.0 27.74 0.0 0.609 5.093 98.0 1.8226 4.0 711.0 20.1 318.43 29.68
491 0.10574 0.0 27.74 0.0 0.609 5.983 98.8 1.8681 4.0 711.0 20.1 390.11 18.07
492 0.11132 0.0 27.74 0.0 0.609 5.983 83.5 2.1099 4.0 711.0 20.1 396.90 13.35
493 0.17331 0.0 9.69 0.0 0.585 5.707 54.0 2.3817 6.0 391.0 19.2 396.90 12.01
494 0.27957 0.0 9.69 0.0 0.585 5.926 42.6 2.3817 6.0 391.0 19.2 396.90 13.59
495 0.17899 0.0 9.69 0.0 0.585 5.670 28.8 2.7986 6.0 391.0 19.2 393.29 17.60
496 0.28960 0.0 9.69 0.0 0.585 5.390 72.9 2.7986 6.0 391.0 19.2 396.90 21.14
497 0.26838 0.0 9.69 0.0 0.585 5.794 70.6 2.8927 6.0 391.0 19.2 396.90 14.10
498 0.23912 0.0 9.69 0.0 0.585 6.019 65.3 2.4091 6.0 391.0 19.2 396.90 12.92
499 0.17783 0.0 9.69 0.0 0.585 5.569 73.5 2.3999 6.0 391.0 19.2 395.77 15.10
500 0.22438 0.0 9.69 0.0 0.585 6.027 79.7 2.4982 6.0 391.0 19.2 396.90 14.33
501 0.06263 0.0 11.93 0.0 0.573 6.593 69.1 2.4786 1.0 273.0 21.0 391.99 9.67
502 0.04527 0.0 11.93 0.0 0.573 6.120 76.7 2.2875 1.0 273.0 21.0 396.90 9.08
503 0.06076 0.0 11.93 0.0 0.573 6.976 91.0 2.1675 1.0 273.0 21.0 396.90 5.64
504 0.10959 0.0 11.93 0.0 0.573 6.794 89.3 2.3889 1.0 273.0 21.0 393.45 6.48
505 0.04741 0.0 11.93 0.0 0.573 6.030 80.8 2.5050 1.0 273.0 21.0 396.90 7.88

506 rows × 13 columns

data=boston.data
x=data[:,6]
y=boston.target

from sklearn.linear_model import LinearRegression
LineR=LinearRegression()
LineR.fit(x.reshape(-1,1),y)
w=LineR.coef_
b=LineR.intercept_
print(w,b)

import matplotlib.pyplot as pl
pl.scatter(x,y)
pl.plot(x,w*x+b,'g')
pl.show()
 
[-0.12316272] 30.97867776261804
 
 

 

 

xx=data[:,6].reshape(-1,1)
pl.scatter(xx,y)
pl.show()

from sklearn.preprocessing import PolynomialFeatures
p=PolynomialFeatures()
p.fit(xx)
p.transform(xx)

 
Out[51]:
array([[1.00000e+00, 6.52000e+01, 4.25104e+03],
       [1.00000e+00, 7.89000e+01, 6.22521e+03],
       [1.00000e+00, 6.11000e+01, 3.73321e+03],
       ...,
       [1.00000e+00, 9.10000e+01, 8.28100e+03],
       [1.00000e+00, 8.93000e+01, 7.97449e+03],
       [1.00000e+00, 8.08000e+01, 6.52864e+03]])
x_poly=p.transform(xx)
x_poly

lrp=LinearRegression()
lrp.fit(x_poly,y)
y_poly=lrp.predict(x_poly)
pl.scatter(xx,y)
pl.plot(xx,w*xx+b,'r')
pl.scatter(xx,y_poly)
pl.show()
lrp.coef_

 

 

array([ 0.        ,  0.06919309, -0.00159822])

posted @ 2018-12-10 11:40  梁柏钧  阅读(134)  评论(0)  编辑  收藏  举报