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波士顿房价预测

作者:互联网

波士顿房价预测

import numpy as np
import matplotlib
from sklearn import  linear_model
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error

1.获取数据

1.1通过load_boston()获取数据

boston = load_boston()
特征含义

CRIM:城镇人均犯罪率。

ZN:住宅用地超过 25000 sq.ft. 的比例。

INDUS:城镇非零售商用土地的比例。

CHAS:查理斯河空变量(如果边界是河流,则为1;否则为0)。

NOX:一氧化氮浓度。

RM:住宅平均房间数。

AGE:1940 年之前建成的自用房屋比例。

DIS:到波士顿五个中心区域的加权距离。

RAD:辐射性公路的接近指数。

TAX:每 10000 美元的全值财产税率。

PTRATIO:城镇师生比例。

B:1000(Bk-0.63)^ 2,其中 Bk 指代城镇中黑人的比例。

LSTAT:人口中地位低下者的比例。

MEDV:自住房的平均房价,以千美元计。

# 数据描述
print(boston.DESCR)
.. _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.   
     
.. topic:: 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.
# 查看数据
print(boston)
{'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,
        4.9800e+00],
       [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,
        9.1400e+00],
       [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,
        4.0300e+00],
       ...,
       [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,
        5.6400e+00],
       [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,
        6.4800e+00],
       [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,
        7.8800e+00]]), 'target': array([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]), 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',
       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7'), 'DESCR': ".. _boston_dataset:\n\nBoston house prices dataset\n---------------------------\n\n**Data Set Characteristics:**  \n\n    :Number of Instances: 506 \n\n    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n\n    :Attribute Information (in order):\n        - CRIM     per capita crime rate by town\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n        - INDUS    proportion of non-retail business acres per town\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n        - NOX      nitric oxides concentration (parts per 10 million)\n        - RM       average number of rooms per dwelling\n        - AGE      proportion of owner-occupied units built prior to 1940\n        - DIS      weighted distances to five Boston employment centres\n        - RAD      index of accessibility to radial highways\n        - TAX      full-value property-tax rate per $10,000\n        - PTRATIO  pupil-teacher ratio by town\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n        - LSTAT    % lower status of the population\n        - MEDV     Median value of owner-occupied homes in $1000's\n\n    :Missing Attribute Values: None\n\n    :Creator: Harrison, D. and Rubinfeld, D.L.\n\nThis is a copy of UCI ML housing dataset.\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n\n\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\nprices and the demand for clean air', J. Environ. Economics & Management,\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\npages 244-261 of the latter.\n\nThe Boston house-price data has been used in many machine learning papers that address regression\nproblems.   \n     \n.. topic:: References\n\n   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n   - 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.\n", 'filename': '/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/sklearn/datasets/data/boston_house_prices.csv'}

取特征X和标签y

X = boston.data
y = boston.target

1.2 从文件读取

使用pandas读取。

import pandas as pd
df = pd.read_excel('data/boston.xls')
df
Unnamed: 0CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATprice
000.0063218.02.3100.5386.57565.24.0900129615.3396.904.9824.0
110.027310.07.0700.4696.42178.94.9671224217.8396.909.1421.6
220.027290.07.0700.4697.18561.14.9671224217.8392.834.0334.7
330.032370.02.1800.4586.99845.86.0622322218.7394.632.9433.4
440.069050.02.1800.4587.14754.26.0622322218.7396.905.3336.2
................................................
5015010.062630.011.9300.5736.59369.12.4786127321.0391.999.6722.4
5025020.045270.011.9300.5736.12076.72.2875127321.0396.909.0820.6
5035030.060760.011.9300.5736.97691.02.1675127321.0396.905.6423.9
5045040.109590.011.9300.5736.79489.32.3889127321.0393.456.4822.0
5055050.047410.011.9300.5736.03080.82.5050127321.0396.907.8811.9

506 rows × 15 columns

取特征X和标签y

X = df[df.columns[0:-1]]
y = df[df.columns[-1]]

2、选择合适的机器学习模型

该问题是房价预测问题,线性回归能很好的应用于预测问题,因此我们选择使用线性回归模型

model = linear_model.Ridge(alpha=0.1)
model.fit(X,y)
y_hat = model.predict(X)

3、训练模型(使用交叉验证选择合适的参数)

# 切分数据集
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)
from sklearn.model_selection import GridSearchCV
ridge_model = linear_model.Ridge()
# 可选参数范围
param = {'alpha':[0.01,0.03,0.05,0.07,0.1,0.5,0.8,1],'normalize':[True,False]}
# cv=5 5折交叉验证
gsearch = GridSearchCV(estimator=ridge_model,param_grid=param,cv=5,scoring='neg_mean_squared_error')
gsearch.fit(X_train,y_train)
GridSearchCV(cv=5, error_score=nan,
             estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True,
                             max_iter=None, normalize=False, random_state=None,
                             solver='auto', tol=0.001),
             iid='deprecated', n_jobs=None,
             param_grid={'alpha': [0.01, 0.03, 0.05, 0.07, 0.1, 0.5, 0.8, 1],
                         'normalize': [True, False]},
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring='neg_mean_squared_error', verbose=0)
# 最优参数
gsearch.best_params_,gsearch.best_score_
({'alpha': 0.03, 'normalize': True}, -26.79889044849392)

4、模型评价

final_model = linear_model.Ridge(alpha=0.03,normalize=True)
final_model.fit(X_train,y_train)
y_train_hat = final_model.predict(X_train)
y_test_hat = final_model.predict(X_test)
print("train-MSE=",mean_squared_error(y_train,y_train_hat))
print("test-MSE=",mean_squared_error(y_test,y_test_hat))
train-MSE= 23.97025486039045
test-MSE= 14.309867058504892

5、上线部署使用

5.1 保存模型

from sklearn.externals import joblib
joblib.dump(final_model,"house_train_model.m")
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.
  warnings.warn(msg, category=FutureWarning)





['house_train_model.m']

5.2 模型读取

load_model = joblib.load("house_train_model.m")
load_model.predict(X_test)
array([30.86303512, 32.79968797, 18.13455905, 20.40093887, 22.55648762,
       18.52481884,  5.79687417, 21.9629842 ,  9.0068248 ,  9.96212273,
       17.65398317, 30.25054533, 25.10003805, 17.40790836, 21.33936858,
       33.59055899, 16.86450916, 19.61200224,  6.91328576, 25.14956329,
       29.29708671, 15.44101851, 38.50687743, 15.56660779, 28.75792533,
       14.87142875, 26.98223834, 15.26065778, 18.16696527, 28.15512538,
       24.97284918, 21.69102163, 32.29555697, 20.20588182, 20.21130528,
       19.51947782, 26.97609243, 17.12827828, 22.2803063 , 22.64121736,
        8.72866157, 22.17943575, 28.66894552, 22.05452734, 18.08105446,
       27.06372036, 29.40518658, 20.53498735, 34.30239592, 25.2630965 ,
       17.91569653, 16.47077539, 24.93826934, 17.02902032, 28.46656821,
       19.43795752, 31.32818684, 39.21693664, 10.21290457, 29.7576716 ,
       18.44035523, 21.47856043, 15.61795029, 27.61939858, 32.41498347,
       23.21414905, 13.9004624 , 21.08777079, 21.59304958, 19.0168253 ,
       16.66463116, 34.61710439, 20.45793411, 23.45252405, 23.49866486,
       25.68604367, 22.90430613, 18.7968677 , 21.45816043, 25.57413156,
       27.04579564, 14.41884812, 14.45022443, 13.35822713, 13.6005322 ,
       27.78762317, 23.48920868,  6.76428576, 22.03442767, 20.09361292,
       21.33547403, 36.87517116, 37.20038258, 31.35625611, 25.66105111,
       23.32712931, 35.89108294, 16.60310795, 19.15123475, 22.65298319,
       24.98301704, 36.00402402])

标签:00,01,预测,房价,50,train,test,model,波士顿
来源: https://blog.csdn.net/qq_39827677/article/details/118461614