notebook.community

Edit and run

Fire up GraphLab Create





In [1]:



    


import graphlab

Load some house sales data





In [2]:



    


sf = graphlab.SFrame('home_data.gl/')



    


















    






This non-commercial license of GraphLab Create for academic use is assigned to sudhanshu.shekhar.iitd@gmail.com and will expire on September 18, 2017.










    






[INFO] graphlab.cython.cy_server: GraphLab Create v2.1 started. Logging: /tmp/graphlab_server_1474241627.log

















In [3]:



    


sales = sf



    











In [4]:



    


sales



    


















    
Out[4]:








    

        id
        date
        price
        bedrooms
        bathrooms
        sqft_living
        sqft_lot
        floors
        waterfront
    

    

        7129300520
        2014-10-13 00:00:00+00:00
        221900
        3
        1
        1180
        5650
        1
        0
    

    

        6414100192
        2014-12-09 00:00:00+00:00
        538000
        3
        2.25
        2570
        7242
        2
        0
    

    

        5631500400
        2015-02-25 00:00:00+00:00
        180000
        2
        1
        770
        10000
        1
        0
    

    

        2487200875
        2014-12-09 00:00:00+00:00
        604000
        4
        3
        1960
        5000
        1
        0
    

    

        1954400510
        2015-02-18 00:00:00+00:00
        510000
        3
        2
        1680
        8080
        1
        0
    

    

        7237550310
        2014-05-12 00:00:00+00:00
        1225000
        4
        4.5
        5420
        101930
        1
        0
    

    

        1321400060
        2014-06-27 00:00:00+00:00
        257500
        3
        2.25
        1715
        6819
        2
        0
    

    

        2008000270
        2015-01-15 00:00:00+00:00
        291850
        3
        1.5
        1060
        9711
        1
        0
    

    

        2414600126
        2015-04-15 00:00:00+00:00
        229500
        3
        1
        1780
        7470
        1
        0
    

    

        3793500160
        2015-03-12 00:00:00+00:00
        323000
        3
        2.5
        1890
        6560
        2
        0
    





    

        view
        condition
        grade
        sqft_above
        sqft_basement
        yr_built
        yr_renovated
        zipcode
        lat
    

    

        0
        3
        7
        1180
        0
        1955
        0
        98178
        47.51123398
    

    

        0
        3
        7
        2170
        400
        1951
        1991
        98125
        47.72102274
    

    

        0
        3
        6
        770
        0
        1933
        0
        98028
        47.73792661
    

    

        0
        5
        7
        1050
        910
        1965
        0
        98136
        47.52082
    

    

        0
        3
        8
        1680
        0
        1987
        0
        98074
        47.61681228
    

    

        0
        3
        11
        3890
        1530
        2001
        0
        98053
        47.65611835
    

    

        0
        3
        7
        1715
        0
        1995
        0
        98003
        47.30972002
    

    

        0
        3
        7
        1060
        0
        1963
        0
        98198
        47.40949984
    

    

        0
        3
        7
        1050
        730
        1960
        0
        98146
        47.51229381
    

    

        0
        3
        7
        1890
        0
        2003
        0
        98038
        47.36840673
    





    

        long
        sqft_living15
        sqft_lot15
    

    

        -122.25677536
        1340.0
        5650.0
    

    

        -122.3188624
        1690.0
        7639.0
    

    

        -122.23319601
        2720.0
        8062.0
    

    

        -122.39318505
        1360.0
        5000.0
    

    

        -122.04490059
        1800.0
        7503.0
    

    

        -122.00528655
        4760.0
        101930.0
    

    

        -122.32704857
        2238.0
        6819.0
    

    

        -122.31457273
        1650.0
        9711.0
    

    

        -122.33659507
        1780.0
        8113.0
    

    

        -122.0308176
        2390.0
        7570.0
    



[21613 rows x 21 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.

Exploring the data for housing





In [5]:



    


sales.show()



    


















    






Canvas is accessible via web browser at the URL: http://localhost:54562/index.html
Opening Canvas in default web browser.

















In [6]:



    


graphlab.canvas.set_target('ipynb')



    











In [7]:



    


sales.show(view='Scatter Plot', x='sqft_living', y='price')

Create a simple regression model for sqft_living v/s price





In [9]:



    


train_dataset, test_dataset = sales.random_split(.8, seed=0)

Build the regression model





In [10]:



    


sqft_model = graphlab.linear_regression.create(train_dataset, target='price', features=['sqft_living'])



    


















    






PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
          You can set ``validation_set=None`` to disable validation tracking.











    







Linear regression:










    







--------------------------------------------------------










    







Number of examples          : 16550










    







Number of features          : 1










    







Number of unpacked features : 1










    







Number of coefficients    : 2










    







Starting Newton Method










    







--------------------------------------------------------










    







+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+










    







| Iteration | Passes   | Elapsed Time | Training-max_error | Validation-max_error | Training-rmse | Validation-rmse |










    







+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+










    







| 1         | 2        | 1.020379     | 4350333.447369     | 1768744.090955       | 263220.266380 | 257393.722223   |










    







+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+










    







SUCCESS: Optimal solution found.










    
































Evaluate the simple model
















In [11]:



    


print test_dataset['price'].mean()



    


















    






543054.042563

























In [12]:



    


print sqft_model.evaluate(test_dataset)



    


















    






{'max_error': 4144226.701603405, 'rmse': 255192.49630115367}

























Let's show what our predictions look like
















In [16]:



    


import matplotlib.pyplot as plt
%matplotlib inline



    



















In [17]:



    


plt.plot(test_dataset['sqft_living'], test_dataset['price'], '.',
        test_dataset['sqft_living'], sqft_model.predict(test_dataset), '-'
        )



    


















    
Out[17]:








[<matplotlib.lines.Line2D at 0x124f495d0>,
 <matplotlib.lines.Line2D at 0x124f49690>]










    
































In [19]:



    


sqft_model.get('coefficients')



    


















    
Out[19]:








    

        name
        index
        value
        stderr
    

    

        (intercept)
        None
        -47168.499925
        5041.52919943
    

    

        sqft_living
        None
        281.895025108
        2.21598508346
    



[2 rows x 4 columns]



























Explore other data
















In [21]:



    


my_features = ['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'zipcode']



    



















In [22]:



    


sales[my_features].show()



    


















    



































In [24]:



    


sales.show(view='BoxWhisker Plot', x='zipcode', y='price')



    


















    



































Build a regression model with more features
















In [25]:



    


my_features_model = graphlab.linear_regression.create(train_dataset, target='price', features=my_features)



    


















    






PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
          You can set ``validation_set=None`` to disable validation tracking.











    







Linear regression:










    







--------------------------------------------------------










    







Number of examples          : 16482










    







Number of features          : 6










    







Number of unpacked features : 6










    







Number of coefficients    : 115










    







Starting Newton Method










    







--------------------------------------------------------










    







+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+










    







| Iteration | Passes   | Elapsed Time | Training-max_error | Validation-max_error | Training-rmse | Validation-rmse |










    







+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+










    







| 1         | 2        | 0.036841     | 3757587.679293     | 2583304.420120       | 181683.836387 | 190587.117671   |










    







+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+










    







SUCCESS: Optimal solution found.










    
































In [26]:



    


print sqft_model.evaluate(test_dataset)
print my_features_model.evaluate(test_dataset)



    


















    






{'max_error': 4144226.701603405, 'rmse': 255192.49630115367}
{'max_error': 3480086.29817522, 'rmse': 179394.03976785974}

























Apply learned model to 3 different houses of the dataset
















In [27]:



    


house1 = sales[sales['id'] == '5309101200']



    



















In [28]:



    


house1



    


















    
Out[28]:








    

        id
        date
        price
        bedrooms
        bathrooms
        sqft_living
        sqft_lot
        floors
        waterfront
    

    

        5309101200
        2014-06-05 00:00:00+00:00
        620000
        4
        2.25
        2400
        5350
        1.5
        0
    





    

        view
        condition
        grade
        sqft_above
        sqft_basement
        yr_built
        yr_renovated
        zipcode
        lat
    

    

        0
        4
        7
        1460
        940
        1929
        0
        98117
        47.67632376
    





    

        long
        sqft_living15
        sqft_lot15
    

    

        -122.37010126
        1250.0
        4880.0
    



[? rows x 21 columns]
Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.
You can use sf.materialize() to force materialization.


























 Testing HTML 
















In [29]:



    


print house1['price']



    


















    






[620000, ... ]

























In [30]:



    


print sqft_model.predict(house1)



    


















    






[629379.5603350642]

























In [32]:



    


print my_features_model.predict(house1)



    


















    






[721234.7128401375]

























Prediction for a second fancier house
















In [33]:



    


house2 = sales[sales['id'] == '1925069082']



    



















In [34]:



    


house2



    


















    
Out[34]:








    

        id
        date
        price
        bedrooms
        bathrooms
        sqft_living
        sqft_lot
        floors
        waterfront
    

    

        1925069082
        2015-05-11 00:00:00+00:00
        2200000
        5
        4.25
        4640
        22703
        2
        1
    





    

        view
        condition
        grade
        sqft_above
        sqft_basement
        yr_built
        yr_renovated
        zipcode
        lat
    

    

        4
        5
        8
        2860
        1780
        1952
        0
        98052
        47.63925783
    





    

        long
        sqft_living15
        sqft_lot15
    

    

        -122.09722322
        3140.0
        14200.0
    



[? rows x 21 columns]
Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.
You can use sf.materialize() to force materialization.


























In [35]:



    


print house2['price']



    


















    






[2200000, ... ]

























In [36]:



    


print sqft_model.predict(house2)



    


















    






[1260824.4165777648]

























In [37]:



    


print my_features_model.predict(house2)



    


















    






[1440187.4614511132]

























Even more fancier house
















In [38]:



    


# it was Bill Gates house



    



















In [39]:



    


house_temp = sales[['id']=='5309101200']



    



















In [40]:



    


house_temp



    


















    
Out[40]:








{'bathrooms': '1',
 'bedrooms': '3',
 'condition': 3,
 'date': datetime.datetime(2014, 10, 13, 0, 0, tzinfo=GMT +0.0),
 'floors': '1',
 'grade': 7,
 'id': '7129300520',
 'lat': 47.51123398,
 'long': -122.25677536,
 'price': 221900,
 'sqft_above': 1180,
 'sqft_basement': 0,
 'sqft_living': 1180,
 'sqft_living15': 1340.0,
 'sqft_lot': 5650,
 'sqft_lot15': 5650.0,
 'view': 0,
 'waterfront': 0,
 'yr_built': 1955,
 'yr_renovated': 0,
 'zipcode': '98178'}

























In [ ]:



    






    
















    


    

        

            

            

                Content source: zomansud/coursera
            

            
Similar notebooks:

            

            
                notebook.community |
                gallery |
                about