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Fire up graphlab create





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 alireza.smr@gmail.comand will expire on September 24, 2016. For commercial licensing options, visit https://dato.com/buy/.

[INFO] Start server at: ipc:///tmp/graphlab_server-93286 - Server binary: /Users/alireza/.graphlab/anaconda/lib/python2.7/site-packages/graphlab/unity_server - Server log: /tmp/graphlab_server_1446642743.log
[INFO] GraphLab Server Version: 1.6.1

















In [1]:



    


sales



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-d019a263fce9> in <module>()
----> 1 sales

NameError: name 'sales' is not defined

Exploring the data for housing sales





In [8]:



    


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



    


















    






Canvas is accessible via web browser at the URL: http://localhost:59848/index.html
Opening Canvas in default web browser.

Create Simple Regression Model of sqft_living to price





In [5]:



    


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



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-5-afaf3c3bb6d9> in <module>()
----> 1 train_data,test_data = sales.random_split(.8,seed=0)

NameError: name 'sales' is not defined
















In [6]:



    


import graphlab



    











In [7]:



    


sales = graphlab.SFrame('home_data.gl')



    


















    






[INFO] This non-commercial license of GraphLab Create is assigned to alireza.smr@gmail.com and will expire on September 24, 2016. For commercial licensing options, visit https://dato.com/buy/.

[INFO] Start server at: ipc:///tmp/graphlab_server-4709 - Server binary: /Users/alireza/.graphlab/anaconda/lib/python2.7/site-packages/graphlab/unity_server - Server log: /tmp/graphlab_server_1446902901.log
[INFO] GraphLab Server Version: 1.6.1

















In [8]:



    


sales



    


















    
Out[8]:








    

        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.


















In [9]:



    


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

Build the regression model





In [10]:



    


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          : 16503
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        | 1.023398     | 4357231.530617     | 3146023.385001       | 261524.626678 | 288253.590973   |
PROGRESS: +-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+

Evaluate Simple Model





In [12]:



    


print test_data['price'].mean()



    


















    






543054.042563

















In [14]:



    


print sqft_model.evaluate(test_data)



    


















    






{'max_error': 4149587.1862886357, 'rmse': 255174.30834723086}

Visualizing the Prediction





In [15]:



    


import matplotlib.pyplot as plt
%matplotlib inline



    











In [16]:



    


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



    


















    
Out[16]:








[<matplotlib.lines.Line2D at 0x106007d50>,
 <matplotlib.lines.Line2D at 0x112701710>]










    
























In [17]:



    


sqft_model.get('coefficients')



    


















    
Out[17]:








    

        name
        index
        value
    

    

        (intercept)
        None
        -45488.7763763
    

    

        sqft_living
        None
        281.183173922
    



[2 rows x 3 columns]

Explore other features in the data





In [18]:



    


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



    











In [21]:



    


sales[features].show()



    


















    






Canvas is accessible via web browser at the URL: http://localhost:59740/index.html
Opening Canvas in default web browser.

















In [22]:



    


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



    


















    






Canvas is updated and available in a tab in the default browser.

Build Regression Model w/ More Features





In [23]:



    


my_features_model= graphlab.linear_regression.create(train_data,target='price',features=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          : 16536
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        | 0.041302     | 3750846.574966     | 2491462.643862       | 181383.824914 | 195917.626553   |
PROGRESS: +-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+

















In [27]:



    


print features



    


















    






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

















In [29]:



    


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



    


















    






{'max_error': 4149587.1862886357, 'rmse': 255174.30834723086}
{'max_error': 3460731.684507328, 'rmse': 179169.8862436952}

Apply learned models to prices of 3 houses





In [30]:



    


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



    











In [31]:



    


house1



    


















    
Out[31]:








    

        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 len(sf) to force materialization.


















In [32]:



    


print house1['price']



    


















    






[620000, ... ]

















In [34]:



    


print sqft_model.predict(house1)



    


















    






[629350.8410362888]

















In [35]:



    


print my_features_model.predict(house1)



    


















    






[722466.6864358183]

















In [36]:



    


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



    











In [37]:



    


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 [38]:



    


print house2['price']



    


















    






[2200000, ... ]

















In [39]:



    


print sqft_model.predict(house2)



    


















    






[1259201.150621358]

















In [40]:



    


print my_features_model.predict(house2)



    


















    






[1436501.026331065]

















In [46]:



    


expensiveHouses = sales[sales['zipcode']=='98039']



    











In [47]:



    


print expensiveHouses



    


















    






+------------+---------------------------+---------+----------+-----------+-------------+
|     id     |            date           |  price  | bedrooms | bathrooms | sqft_living |
+------------+---------------------------+---------+----------+-----------+-------------+
| 3625049014 | 2014-08-29 00:00:00+00:00 | 2950000 |    4     |    3.5    |     4860    |
| 2540700110 | 2015-02-12 00:00:00+00:00 | 1905000 |    4     |    3.5    |     4210    |
| 3262300940 | 2014-11-07 00:00:00+00:00 |  875000 |    3     |     1     |     1220    |
| 3262300940 | 2015-02-10 00:00:00+00:00 |  940000 |    3     |     1     |     1220    |
| 6447300265 | 2014-10-14 00:00:00+00:00 | 4000000 |    4     |    5.5    |     7080    |
| 2470100110 | 2014-08-04 00:00:00+00:00 | 5570000 |    5     |    5.75   |     9200    |
| 2210500019 | 2015-03-24 00:00:00+00:00 |  937500 |    3     |     1     |     1320    |
| 6447300345 | 2015-04-06 00:00:00+00:00 | 1160000 |    4     |     3     |     2680    |
| 6447300225 | 2014-11-06 00:00:00+00:00 | 1880000 |    3     |    2.75   |     2620    |
| 2525049148 | 2014-10-07 00:00:00+00:00 | 3418800 |    5     |     5     |     5450    |
+------------+---------------------------+---------+----------+-----------+-------------+
+----------+--------+------------+------+-----------+-------+------------+---------------+
| sqft_lot | floors | waterfront | view | condition | grade | sqft_above | sqft_basement |
+----------+--------+------------+------+-----------+-------+------------+---------------+
|  23885   |   2    |     0      |  0   |     3     |   12  |    4860    |       0       |
|  18564   |   2    |     0      |  0   |     3     |   11  |    4210    |       0       |
|   8119   |   1    |     0      |  0   |     4     |   7   |    1220    |       0       |
|   8119   |   1    |     0      |  0   |     4     |   7   |    1220    |       0       |
|  16573   |   2    |     0      |  0   |     3     |   12  |    5760    |      1320     |
|  35069   |   2    |     0      |  0   |     3     |   13  |    6200    |      3000     |
|   8500   |   1    |     0      |  0   |     4     |   7   |    1320    |       0       |
|  15438   |   2    |     0      |  2   |     3     |   8   |    2680    |       0       |
|  17919   |   1    |     0      |  1   |     4     |   9   |    2620    |       0       |
|  20412   |   2    |     0      |  0   |     3     |   11  |    5450    |       0       |
+----------+--------+------------+------+-----------+-------+------------+---------------+
+----------+--------------+---------+-------------+---------------+---------------+-----+
| yr_built | yr_renovated | zipcode |     lat     |      long     | sqft_living15 | ... |
+----------+--------------+---------+-------------+---------------+---------------+-----+
|   1996   |      0       |  98039  | 47.61717049 | -122.23040939 |     3580.0    | ... |
|   2001   |      0       |  98039  | 47.62060082 |  -122.2245047 |     3520.0    | ... |
|   1955   |      0       |  98039  | 47.63281908 | -122.23554392 |     1910.0    | ... |
|   1955   |      0       |  98039  | 47.63281908 | -122.23554392 |     1910.0    | ... |
|   2008   |      0       |  98039  | 47.61512031 | -122.22420058 |     3140.0    | ... |
|   2001   |      0       |  98039  | 47.62888314 | -122.23346379 |     3560.0    | ... |
|   1954   |      0       |  98039  | 47.61872888 | -122.22643371 |     2790.0    | ... |
|   1902   |     1956     |  98039  | 47.61089438 | -122.22582388 |     4480.0    | ... |
|   1949   |      0       |  98039  | 47.61435052 | -122.22772057 |     3400.0    | ... |
|   2014   |      0       |  98039  | 47.62087993 | -122.23726918 |     3160.0    | ... |
+----------+--------------+---------+-------------+---------------+---------------+-----+
[? 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 [48]:



    


print expensiveHouses['price'].mean()



    


















    






2160606.6

















In [63]:



    


fraction_finder = sales[(sales['sqft_living'] >= 2000) & (sales['sqft_living'] <= 4000)]
fraction_finder.show()



    


















    






Canvas is updated and available in a tab in the default browser.

Building Advance Model





In [55]:



    


advanced_features = [
'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'zipcode',
'condition', # condition of house				
'grade', # measure of quality of construction				
'waterfront', # waterfront property				
'view', # type of view				
'sqft_above', # square feet above ground				
'sqft_basement', # square feet in basement				
'yr_built', # the year built				
'yr_renovated', # the year renovated				
'lat', 'long', # the lat-long of the parcel				
'sqft_living15', # average sq.ft. of 15 nearest neighbors 				
'sqft_lot15', # average lot size of 15 nearest neighbors 
]



    











In [56]:



    


print advanced_features



    


















    






['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'zipcode', 'condition', 'grade', 'waterfront', 'view', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'lat', 'long', 'sqft_living15', 'sqft_lot15']

















In [58]:



    


my_advance_model= graphlab.linear_regression.create(train_data,target='price', features = advanced_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          : 16476
PROGRESS: Number of features          : 18
PROGRESS: Number of unpacked features : 18
PROGRESS: Number of coefficients    : 127
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        | 0.031650     | 3468187.219168     | 2197643.599194       | 154092.671887 | 165367.609769   |
PROGRESS: +-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+

















In [59]:



    


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



    


















    






{'max_error': 4149587.1862886357, 'rmse': 255174.30834723086}
{'max_error': 3460731.684507328, 'rmse': 179169.8862436952}
{'max_error': 3553748.8054397926, 'rmse': 156678.87771855685}

















In [ ]:

Content source: alirezasamar/SeattleHousesPrice

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