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 [ ]: