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In [1]:



    


% matplotlib inline

import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import sklearn



    











In [2]:



    


from sklearn import datasets
boston = datasets.load_boston()



    











In [3]:



    


boston.keys()



    


















    
Out[3]:








['data', 'feature_names', 'DESCR', 'target']

















In [5]:



    


boston.data.shape



    


















    
Out[5]:








(506, 13)

















In [6]:



    


boston.feature_names



    


















    
Out[6]:








array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',
       'TAX', 'PTRATIO', 'B', 'LSTAT'], 
      dtype='|S7')

















In [8]:



    


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 [9]:



    


df = pd.DataFrame(boston.data)



    











In [11]:



    


df.head()



    


















    
Out[11]:










  
    

      
      0
      1
      2
      3
      4
      5
      6
      7
      8
      9
      10
      11
      12
    

  
  
    

      0
      0.00632
      18
      2.31
      0
      0.538
      6.575
      65.2
      4.0900
      1
      296
      15.3
      396.90
      4.98
    

    

      1
      0.02731
      0
      7.07
      0
      0.469
      6.421
      78.9
      4.9671
      2
      242
      17.8
      396.90
      9.14
    

    

      2
      0.02729
      0
      7.07
      0
      0.469
      7.185
      61.1
      4.9671
      2
      242
      17.8
      392.83
      4.03
    

    

      3
      0.03237
      0
      2.18
      0
      0.458
      6.998
      45.8
      6.0622
      3
      222
      18.7
      394.63
      2.94
    

    

      4
      0.06905
      0
      2.18
      0
      0.458
      7.147
      54.2
      6.0622
      3
      222
      18.7
      396.90
      5.33
    

  




















In [12]:



    


df.columns = boston.feature_names



    











In [13]:



    


df.head()



    


















    
Out[13]:










  
    

      
      CRIM
      ZN
      INDUS
      CHAS
      NOX
      RM
      AGE
      DIS
      RAD
      TAX
      PTRATIO
      B
      LSTAT
    

  
  
    

      0
      0.00632
      18
      2.31
      0
      0.538
      6.575
      65.2
      4.0900
      1
      296
      15.3
      396.90
      4.98
    

    

      1
      0.02731
      0
      7.07
      0
      0.469
      6.421
      78.9
      4.9671
      2
      242
      17.8
      396.90
      9.14
    

    

      2
      0.02729
      0
      7.07
      0
      0.469
      7.185
      61.1
      4.9671
      2
      242
      17.8
      392.83
      4.03
    

    

      3
      0.03237
      0
      2.18
      0
      0.458
      6.998
      45.8
      6.0622
      3
      222
      18.7
      394.63
      2.94
    

    

      4
      0.06905
      0
      2.18
      0
      0.458
      7.147
      54.2
      6.0622
      3
      222
      18.7
      396.90
      5.33
    

  




















In [14]:



    


boston.target[:5]



    


















    
Out[14]:








array([ 24. ,  21.6,  34.7,  33.4,  36.2])

















In [18]:



    


from sklearn import linear_model
lm = linear_model.LinearRegression()



    











In [20]:



    


X = df

Important functions: fit(), predict() and score()

  • model.fit(): Train the model
  • model.predict(): Predect an outcome
  • model.score: Calculate how well the predection is




In [22]:



    


lm.fit(X, boston.target)



    


















    
Out[22]:








LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

















In [23]:



    


lm.intercept_



    


















    
Out[23]:








36.491103280361635

















In [25]:



    


lm.coef_



    


















    
Out[25]:








array([ -1.07170557e-01,   4.63952195e-02,   2.08602395e-02,
         2.68856140e+00,  -1.77957587e+01,   3.80475246e+00,
         7.51061703e-04,  -1.47575880e+00,   3.05655038e-01,
        -1.23293463e-02,  -9.53463555e-01,   9.39251272e-03,
        -5.25466633e-01])

















In [27]:



    


pd.DataFrame(zip(X.columns, lm.coef_), columns=['features', 'coeffecients'])



    


















    
Out[27]:










  
    

      
      features
      coeffecients
    

  
  
    

      0
      CRIM
      -0.107171
    

    

      1
      ZN
      0.046395
    

    

      2
      INDUS
      0.020860
    

    

      3
      CHAS
      2.688561
    

    

      4
      NOX
      -17.795759
    

    

      5
      RM
      3.804752
    

    

      6
      AGE
      0.000751
    

    

      7
      DIS
      -1.475759
    

    

      8
      RAD
      0.305655
    

    

      9
      TAX
      -0.012329
    

    

      10
      PTRATIO
      -0.953464
    

    

      11
      B
      0.009393
    

    

      12
      LSTAT
      -0.525467
    

  




















In [31]:



    


plt.scatter(df.RM, boston.target)
plt.xlabel("Avg num of rooms")
plt.ylabel("Housing price")



    


















    
Out[31]:








<matplotlib.text.Text at 0x7f79397b2b50>










    
























In [30]:



    


lm.predict(X)[:5]



    


















    
Out[30]:








array([ 30.00821269,  25.0298606 ,  30.5702317 ,  28.60814055,  27.94288232])

















In [33]:



    


plt.scatter(boston.target, lm.predict(X))
plt.xlabel("Price")
plt.ylabel("Predicted Price")



    


















    
Out[33]:








<matplotlib.text.Text at 0x7f793756e090>

Content source: obulpathi/datascience

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