波士顿房价预测

妖狐艹你老母 2022-10-12 05:54 182阅读 0赞

# 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

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>Unnamed: 0</th> 
   <th>CRIM</th> 
   <th>ZN</th> 
   <th>INDUS</th> 
   <th>CHAS</th> 
   <th>NOX</th> 
   <th>RM</th> 
   <th>AGE</th> 
   <th>DIS</th> 
   <th>RAD</th> 
   <th>TAX</th> 
   <th>PTRATIO</th> 
   <th>B</th> 
   <th>LSTAT</th> 
   <th>price</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>0</td> 
   <td>0.00632</td> 
   <td>18.0</td> 
   <td>2.31</td> 
   <td>0</td> 
   <td>0.538</td> 
   <td>6.575</td> 
   <td>65.2</td> 
   <td>4.0900</td> 
   <td>1</td> 
   <td>296</td> 
   <td>15.3</td> 
   <td>396.90</td> 
   <td>4.98</td> 
   <td>24.0</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>1</td> 
   <td>0.02731</td> 
   <td>0.0</td> 
   <td>7.07</td> 
   <td>0</td> 
   <td>0.469</td> 
   <td>6.421</td> 
   <td>78.9</td> 
   <td>4.9671</td> 
   <td>2</td> 
   <td>242</td> 
   <td>17.8</td> 
   <td>396.90</td> 
   <td>9.14</td> 
   <td>21.6</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>2</td> 
   <td>0.02729</td> 
   <td>0.0</td> 
   <td>7.07</td> 
   <td>0</td> 
   <td>0.469</td> 
   <td>7.185</td> 
   <td>61.1</td> 
   <td>4.9671</td> 
   <td>2</td> 
   <td>242</td> 
   <td>17.8</td> 
   <td>392.83</td> 
   <td>4.03</td> 
   <td>34.7</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>3</td> 
   <td>0.03237</td> 
   <td>0.0</td> 
   <td>2.18</td> 
   <td>0</td> 
   <td>0.458</td> 
   <td>6.998</td> 
   <td>45.8</td> 
   <td>6.0622</td> 
   <td>3</td> 
   <td>222</td> 
   <td>18.7</td> 
   <td>394.63</td> 
   <td>2.94</td> 
   <td>33.4</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>4</td> 
   <td>0.06905</td> 
   <td>0.0</td> 
   <td>2.18</td> 
   <td>0</td> 
   <td>0.458</td> 
   <td>7.147</td> 
   <td>54.2</td> 
   <td>6.0622</td> 
   <td>3</td> 
   <td>222</td> 
   <td>18.7</td> 
   <td>396.90</td> 
   <td>5.33</td> 
   <td>36.2</td> 
  </tr> 
  <tr> 
   <th>...</th> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
  </tr> 
  <tr> 
   <th>501</th> 
   <td>501</td> 
   <td>0.06263</td> 
   <td>0.0</td> 
   <td>11.93</td> 
   <td>0</td> 
   <td>0.573</td> 
   <td>6.593</td> 
   <td>69.1</td> 
   <td>2.4786</td> 
   <td>1</td> 
   <td>273</td> 
   <td>21.0</td> 
   <td>391.99</td> 
   <td>9.67</td> 
   <td>22.4</td> 
  </tr> 
  <tr> 
   <th>502</th> 
   <td>502</td> 
   <td>0.04527</td> 
   <td>0.0</td> 
   <td>11.93</td> 
   <td>0</td> 
   <td>0.573</td> 
   <td>6.120</td> 
   <td>76.7</td> 
   <td>2.2875</td> 
   <td>1</td> 
   <td>273</td> 
   <td>21.0</td> 
   <td>396.90</td> 
   <td>9.08</td> 
   <td>20.6</td> 
  </tr> 
  <tr> 
   <th>503</th> 
   <td>503</td> 
   <td>0.06076</td> 
   <td>0.0</td> 
   <td>11.93</td> 
   <td>0</td> 
   <td>0.573</td> 
   <td>6.976</td> 
   <td>91.0</td> 
   <td>2.1675</td> 
   <td>1</td> 
   <td>273</td> 
   <td>21.0</td> 
   <td>396.90</td> 
   <td>5.64</td> 
   <td>23.9</td> 
  </tr> 
  <tr> 
   <th>504</th> 
   <td>504</td> 
   <td>0.10959</td> 
   <td>0.0</td> 
   <td>11.93</td> 
   <td>0</td> 
   <td>0.573</td> 
   <td>6.794</td> 
   <td>89.3</td> 
   <td>2.3889</td> 
   <td>1</td> 
   <td>273</td> 
   <td>21.0</td> 
   <td>393.45</td> 
   <td>6.48</td> 
   <td>22.0</td> 
  </tr> 
  <tr> 
   <th>505</th> 
   <td>505</td> 
   <td>0.04741</td> 
   <td>0.0</td> 
   <td>11.93</td> 
   <td>0</td> 
   <td>0.573</td> 
   <td>6.030</td> 
   <td>80.8</td> 
   <td>2.5050</td> 
   <td>1</td> 
   <td>273</td> 
   <td>21.0</td> 
   <td>396.90</td> 
   <td>7.88</td> 
   <td>11.9</td> 
  </tr> 
 </tbody> 
</table>

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,
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