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In [1]:



    


import graphlab

Load some house sales data





In [2]:



    


sales = graphlab.SFrame("home_data.gl/")



    


















    






[INFO] This non-commercial license of GraphLab Create is assigned to eroicaleo@yahoo.comand will expire on September 28, 2016. For commercial licensing options, visit https://dato.com/buy/.

[INFO] Start server at: ipc:///tmp/graphlab_server-2112 - Server binary: /Users/yang/anaconda/lib/python2.7/site-packages/graphlab/unity_server - Server log: /tmp/graphlab_server_1443945542.log
[INFO] GraphLab Server Version: 1.6.1

















In [3]:



    


sales



    


















    
Out[3]:








    

        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 sales





In [6]:



    


graphlab.canvas.set_target('ipynb')
sales.show(view="Scatter Plot", x="sqft_living", y="price")

Create a simple regression model of sqft_living to price





In [7]:



    


train_data, test_data = sales.random_split(.8, seed=0)

Build the regression model





In [8]:



    


sqft_model = graphlab.linear_regression.create(train_data, 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.

PROGRESS: Linear regression:
PROGRESS: --------------------------------------------------------
PROGRESS: Number of examples          : 16529
PROGRESS: Number of features          : 1
PROGRESS: Number of unpacked features : 1
PROGRESS: Number of coefficients    : 2
PROGRESS: Starting Newton Method
PROGRESS: --------------------------------------------------------
PROGRESS: +-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+
PROGRESS: | Iteration | Passes   | Elapsed Time | Training-max_error | Validation-max_error | Training-rmse | Validation-rmse |
PROGRESS: +-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+
PROGRESS: | 1         | 2        | 1.034375     | 4350131.032643     | 2041911.113660       | 263925.873913 | 243176.138579   |
PROGRESS: +-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+

Evaluate the simple model





In [10]:



    


print test_data['price'].mean()



    


















    






543054.042563

















In [13]:



    


print sqft_model.evaluate(test_data)



    


















    






{'max_error': 4144017.2534299586, 'rmse': 255188.79520236902}

Show the prediction like





In [16]:



    


import matplotlib.pyplot as plt
%matplotlib inline



    











In [17]:



    


plt.plot(test_data['sqft_living'], test_data['price'], '.',
        test_data['sqft_living'], sqft_model.predict(test_data), '_')



    


















    
Out[17]:








[<matplotlib.lines.Line2D at 0x111ebd510>,
 <matplotlib.lines.Line2D at 0x10a01e8d0>]










    
























In [21]:



    


sqft_model.get('coefficients')
# sqft_model.list_fields()



    


















    
Out[21]:








    

        name
        index
        value
    

    

        (intercept)
        None
        -46926.84768
    

    

        sqft_living
        None
        281.891768883
    



[2 rows x 3 columns]

Explore other features in the data





In [26]:



    


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



    











In [27]:



    


sales[my_features].show()



    


















    



























In [28]:



    


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

Build a regression model with more features





In [29]:



    


my_features_model = graphlab.linear_regression.create(train_data, 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.

PROGRESS: Linear regression:
PROGRESS: --------------------------------------------------------
PROGRESS: Number of examples          : 16531
PROGRESS: Number of features          : 6
PROGRESS: Number of unpacked features : 6
PROGRESS: Number of coefficients    : 115
PROGRESS: Starting Newton Method
PROGRESS: --------------------------------------------------------
PROGRESS: +-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+
PROGRESS: | Iteration | Passes   | Elapsed Time | Training-max_error | Validation-max_error | Training-rmse | Validation-rmse |
PROGRESS: +-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+
PROGRESS: | 1         | 2        | 0.032130     | 3764064.890994     | 2244828.026264       | 182474.608921 | 179513.335747   |
PROGRESS: +-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+

















In [30]:



    


print my_features



    


















    






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

















In [34]:



    


print my_features_model.evaluate(test_data)
print sqft_model.evaluate(test_data)



    


















    






{'max_error': 3538468.141033254, 'rmse': 178164.9092413636}
{'max_error': 4144017.2534299586, 'rmse': 255188.79520236902}

Apply learned models to predict prices of 3 houses





In [35]:



    


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



    











In [40]:



    


print house1



    


















    






+------------+---------------------------+--------+----------+-----------+-------------+
|     id     |            date           | price  | bedrooms | bathrooms | sqft_living |
+------------+---------------------------+--------+----------+-----------+-------------+
| 5309101200 | 2014-06-05 00:00:00+00:00 | 620000 |    4     |    2.25   |     2400    |
+------------+---------------------------+--------+----------+-----------+-------------+
+----------+--------+------------+------+-----------+-------+------------+---------------+
| sqft_lot | floors | waterfront | view | condition | grade | sqft_above | sqft_basement |
+----------+--------+------------+------+-----------+-------+------------+---------------+
|   5350   |  1.5   |     0      |  0   |     4     |   7   |    1460    |      940      |
+----------+--------+------------+------+-----------+-------+------------+---------------+
+----------+--------------+---------+-------------+---------------+---------------+-----+
| yr_built | yr_renovated | zipcode |     lat     |      long     | sqft_living15 | ... |
+----------+--------------+---------+-------------+---------------+---------------+-----+
|   1929   |      0       |  98117  | 47.67632376 | -122.37010126 |     1250.0    | ... |
+----------+--------------+---------+-------------+---------------+---------------+-----+
[? rows x 21 columns]
Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.
You can use len(sf) to force materialization.

















In [42]:



    


type(house1), type(sales)



    


















    
Out[42]:








(graphlab.data_structures.sframe.SFrame,
 graphlab.data_structures.sframe.SFrame)

















In [43]:



    


print house1['price']



    


















    






[620000, ... ]

















In [44]:



    


print sqft_model.predict(house1)



    


















    






[629613.397638509]

















In [45]:



    


print my_features_model.predict(house1)



    


















    






[719156.552359593]

Prediction for the fancier house





In [48]:



    


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



    











In [49]:



    


print house2



    


















    






+------------+---------------------------+---------+----------+-----------+-------------+
|     id     |            date           |  price  | bedrooms | bathrooms | sqft_living |
+------------+---------------------------+---------+----------+-----------+-------------+
| 1925069082 | 2015-05-11 00:00:00+00:00 | 2200000 |    5     |    4.25   |     4640    |
+------------+---------------------------+---------+----------+-----------+-------------+
+----------+--------+------------+------+-----------+-------+------------+---------------+
| sqft_lot | floors | waterfront | view | condition | grade | sqft_above | sqft_basement |
+----------+--------+------------+------+-----------+-------+------------+---------------+
|  22703   |   2    |     1      |  4   |     5     |   8   |    2860    |      1780     |
+----------+--------+------------+------+-----------+-------+------------+---------------+
+----------+--------------+---------+-------------+---------------+---------------+-----+
| yr_built | yr_renovated | zipcode |     lat     |      long     | sqft_living15 | ... |
+----------+--------------+---------+-------------+---------------+---------------+-----+
|   1952   |      0       |  98052  | 47.63925783 | -122.09722322 |     3140.0    | ... |
+----------+--------------+---------+-------------+---------------+---------------+-----+
[? rows x 21 columns]
Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.
You can use len(sf) to force materialization.

















In [50]:



    


print sqft_model.predict(house2)



    


















    






[1261050.9599357897]

















In [51]:



    


print my_features_model.predict(house2)



    


















    






[1434803.5734414714]

Last fancy





In [ ]:

Content source: eroicaleo/MachineLearningUW

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