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j450h1/gist:8acfb5e941b4427ebe67

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gistfile1.txt
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fire up graphlab create"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import graphlab"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load some house sales data\n",
"\n",
"Dataset is from house sales in King County, the region where the city of Seattle, WA is located."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO] \u001b[1;32m1448324177 : INFO: (initialize_globals_from_environment:282): Setting configuration variable GRAPHLAB_FILEIO_ALTERNATIVE_SSL_CERT_FILE to /usr/local/lib/python2.7/dist-packages/certifi/cacert.pem\n",
"\u001b[0m\u001b[1;32m1448324177 : INFO: (initialize_globals_from_environment:282): Setting configuration variable GRAPHLAB_FILEIO_ALTERNATIVE_SSL_CERT_DIR to \n",
"\u001b[0mThis non-commercial license of GraphLab Create is assigned to j450h1@gmail.com and will expire on November 24, 2016. For commercial licensing options, visit https://dato.com/buy/.\n",
"\n",
"[INFO] Start server at: ipc:///tmp/graphlab_server-1501 - Server binary: /usr/local/lib/python2.7/dist-packages/graphlab/unity_server - Server log: /tmp/graphlab_server_1448324177.log\n",
"[INFO] GraphLab Server Version: 1.7.1\n"
]
}
],
"source": [
"sales = graphlab.SFrame('home_data.gl/')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bedrooms</th>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98003</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.30972002</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1060</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1963</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98198</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.40949984</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1050</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">730</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1960</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98146</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.51229381</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1890</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2003</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98038</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.36840673</td>\n",
" </tr>\n",
"</table>\n",
"<table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">long</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_living15</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_lot15</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.25677536</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1340.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5650.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.3188624</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1690.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7639.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.23319601</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2720.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8062.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.39318505</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1360.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.04490059</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1800.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7503.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.00528655</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4760.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">101930.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.32704857</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2238.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6819.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.31457273</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1650.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9711.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.33659507</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1780.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8113.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.0308176</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2390.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7570.0</td>\n",
" </tr>\n",
"</table>\n",
"[21613 rows x 21 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tid\tstr\n",
"\tdate\tdatetime\n",
"\tprice\tint\n",
"\tbedrooms\tstr\n",
"\tbathrooms\tstr\n",
"\tsqft_living\tint\n",
"\tsqft_lot\tint\n",
"\tfloors\tstr\n",
"\twaterfront\tint\n",
"\tview\tint\n",
"\tcondition\tint\n",
"\tgrade\tint\n",
"\tsqft_above\tint\n",
"\tsqft_basement\tint\n",
"\tyr_built\tint\n",
"\tyr_renovated\tint\n",
"\tzipcode\tstr\n",
"\tlat\tfloat\n",
"\tlong\tfloat\n",
"\tsqft_living15\tfloat\n",
"\tsqft_lot15\tfloat\n",
"\n",
"Rows: 21613\n",
"\n",
"Data:\n",
"+------------+---------------------------+---------+----------+-----------+-------------+\n",
"| id | date | price | bedrooms | bathrooms | sqft_living |\n",
"+------------+---------------------------+---------+----------+-----------+-------------+\n",
"| 7129300520 | 2014-10-13 00:00:00+00:00 | 221900 | 3 | 1 | 1180 |\n",
"| 6414100192 | 2014-12-09 00:00:00+00:00 | 538000 | 3 | 2.25 | 2570 |\n",
"| 5631500400 | 2015-02-25 00:00:00+00:00 | 180000 | 2 | 1 | 770 |\n",
"| 2487200875 | 2014-12-09 00:00:00+00:00 | 604000 | 4 | 3 | 1960 |\n",
"| 1954400510 | 2015-02-18 00:00:00+00:00 | 510000 | 3 | 2 | 1680 |\n",
"| 7237550310 | 2014-05-12 00:00:00+00:00 | 1225000 | 4 | 4.5 | 5420 |\n",
"| 1321400060 | 2014-06-27 00:00:00+00:00 | 257500 | 3 | 2.25 | 1715 |\n",
"| 2008000270 | 2015-01-15 00:00:00+00:00 | 291850 | 3 | 1.5 | 1060 |\n",
"| 2414600126 | 2015-04-15 00:00:00+00:00 | 229500 | 3 | 1 | 1780 |\n",
"| 3793500160 | 2015-03-12 00:00:00+00:00 | 323000 | 3 | 2.5 | 1890 |\n",
"+------------+---------------------------+---------+----------+-----------+-------------+\n",
"+----------+--------+------------+------+-----------+-------+------------+---------------+\n",
"| sqft_lot | floors | waterfront | view | condition | grade | sqft_above | sqft_basement |\n",
"+----------+--------+------------+------+-----------+-------+------------+---------------+\n",
"| 5650 | 1 | 0 | 0 | 3 | 7 | 1180 | 0 |\n",
"| 7242 | 2 | 0 | 0 | 3 | 7 | 2170 | 400 |\n",
"| 10000 | 1 | 0 | 0 | 3 | 6 | 770 | 0 |\n",
"| 5000 | 1 | 0 | 0 | 5 | 7 | 1050 | 910 |\n",
"| 8080 | 1 | 0 | 0 | 3 | 8 | 1680 | 0 |\n",
"| 101930 | 1 | 0 | 0 | 3 | 11 | 3890 | 1530 |\n",
"| 6819 | 2 | 0 | 0 | 3 | 7 | 1715 | 0 |\n",
"| 9711 | 1 | 0 | 0 | 3 | 7 | 1060 | 0 |\n",
"| 7470 | 1 | 0 | 0 | 3 | 7 | 1050 | 730 |\n",
"| 6560 | 2 | 0 | 0 | 3 | 7 | 1890 | 0 |\n",
"+----------+--------+------------+------+-----------+-------+------------+---------------+\n",
"+----------+--------------+---------+-------------+---------------+---------------+-----+\n",
"| yr_built | yr_renovated | zipcode | lat | long | sqft_living15 | ... |\n",
"+----------+--------------+---------+-------------+---------------+---------------+-----+\n",
"| 1955 | 0 | 98178 | 47.51123398 | -122.25677536 | 1340.0 | ... |\n",
"| 1951 | 1991 | 98125 | 47.72102274 | -122.3188624 | 1690.0 | ... |\n",
"| 1933 | 0 | 98028 | 47.73792661 | -122.23319601 | 2720.0 | ... |\n",
"| 1965 | 0 | 98136 | 47.52082 | -122.39318505 | 1360.0 | ... |\n",
"| 1987 | 0 | 98074 | 47.61681228 | -122.04490059 | 1800.0 | ... |\n",
"| 2001 | 0 | 98053 | 47.65611835 | -122.00528655 | 4760.0 | ... |\n",
"| 1995 | 0 | 98003 | 47.30972002 | -122.32704857 | 2238.0 | ... |\n",
"| 1963 | 0 | 98198 | 47.40949984 | -122.31457273 | 1650.0 | ... |\n",
"| 1960 | 0 | 98146 | 47.51229381 | -122.33659507 | 1780.0 | ... |\n",
"| 2003 | 0 | 98038 | 47.36840673 | -122.0308176 | 2390.0 | ... |\n",
"+----------+--------------+---------+-------------+---------------+---------------+-----+\n",
"[21613 rows x 21 columns]\n",
"Note: Only the head of the SFrame is printed.\n",
"You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sales"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploring the data for housing sales "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The house price is correlated with the number of square feet of living space."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
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519000], [940, 329000], [1760, 1150000], [3750, 1245000], [1830, 250000], [1250, 459500], [740, 178500], [2290, 975000], [1640, 645000], [1750, 322000], [810, 170000], [2450, 450000]], \"columns\": [{\"dtype\": \"str\", \"name\": \"id\"}, {\"dtype\": \"datetime\", \"name\": \"date\"}, {\"dtype\": \"int\", \"name\": \"price\"}, {\"dtype\": \"str\", \"name\": \"bedrooms\"}, {\"dtype\": \"str\", \"name\": \"bathrooms\"}, {\"dtype\": \"int\", \"name\": \"sqft_living\"}, {\"dtype\": \"int\", \"name\": \"sqft_lot\"}, {\"dtype\": \"str\", \"name\": \"floors\"}, {\"dtype\": \"int\", \"name\": \"waterfront\"}, {\"dtype\": \"int\", \"name\": \"view\"}, {\"dtype\": \"int\", \"name\": \"condition\"}, {\"dtype\": \"int\", \"name\": \"grade\"}, {\"dtype\": \"int\", \"name\": \"sqft_above\"}, {\"dtype\": \"int\", \"name\": \"sqft_basement\"}, {\"dtype\": \"int\", \"name\": \"yr_built\"}, {\"dtype\": \"int\", \"name\": \"yr_renovated\"}, {\"dtype\": \"str\", \"name\": \"zipcode\"}, {\"dtype\": \"float\", \"name\": \"lat\"}, {\"dtype\": \"float\", \"name\": \"long\"}, {\"dtype\": \"float\", \"name\": \"sqft_living15\"}, {\"dtype\": \"float\", \"name\": \"sqft_lot15\"}]}, e);\n",
" });\n",
" })();\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"graphlab.canvas.set_target('ipynb')\n",
"sales.show(view=\"Scatter Plot\", x=\"sqft_living\", y=\"price\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create a simple regression model of sqft_living to price"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Split data into training and testing. \n",
"We use seed=0 so that everyone running this notebook gets the same results. In practice, you may set a random seed (or let GraphLab Create pick a random seed for you). "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_data,test_data = sales.random_split(.8,seed=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build the regression model using only sqft_living as a feature"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PROGRESS: Linear regression:\n",
"PROGRESS: --------------------------------------------------------\n",
"PROGRESS: Number of examples : 17384\n",
"PROGRESS: Number of features : 1\n",
"PROGRESS: Number of unpacked features : 1\n",
"PROGRESS: Number of coefficients : 2\n",
"PROGRESS: Starting Newton Method\n",
"PROGRESS: --------------------------------------------------------\n",
"PROGRESS: +-----------+----------+--------------+--------------------+---------------+\n",
"PROGRESS: | Iteration | Passes | Elapsed Time | Training-max_error | Training-rmse |\n",
"PROGRESS: +-----------+----------+--------------+--------------------+---------------+\n",
"PROGRESS: | 1 | 2 | 1.003877 | 4349521.926170 | 262943.613754 |\n",
"PROGRESS: +-----------+----------+--------------+--------------------+---------------+\n",
"PROGRESS: SUCCESS: Optimal solution found.\n",
"PROGRESS:\n"
]
}
],
"source": [
"sqft_model = graphlab.linear_regression.create(train_data, target='price', features=['sqft_living'],validation_set=None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaluate the simple model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"543054.042563\n"
]
}
],
"source": [
"print test_data['price'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'max_error': 4143550.8825285956, 'rmse': 255191.0287052738}\n"
]
}
],
"source": [
"print sqft_model.evaluate(test_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"RMSE of about \\$255,170!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Let's show what our predictions look like"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Matplotlib is a Python plotting library that is also useful for plotting. You can install it with:\n",
"\n",
"'pip install matplotlib'"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fef16b610d0>,\n",
" <matplotlib.lines.Line2D at 0x7fef27677d10>]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fef16bcd5d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(test_data['sqft_living'],test_data['price'],'.',\n",
" test_data['sqft_living'],sqft_model.predict(test_data),'-')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Above: blue dots are original data, green line is the prediction from the simple regression.\n",
"\n",
"Below: we can view the learned regression coefficients. "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">index</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">value</th>\n",
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" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">(intercept)</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-47114.0206702</td>\n",
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],
"text/plain": [
"Columns:\n",
"\tname\tstr\n",
"\tindex\tstr\n",
"\tvalue\tfloat\n",
"\n",
"Rows: 2\n",
"\n",
"Data:\n",
"+-------------+-------+----------------+\n",
"| name | index | value |\n",
"+-------------+-------+----------------+\n",
"| (intercept) | None | -47114.0206702 |\n",
"| sqft_living | None | 281.957850166 |\n",
"+-------------+-------+----------------+\n",
"[2 rows x 3 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sqft_model.get('coefficients')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore other features in the data\n",
"\n",
"To build a more elaborate model, we will explore using more features."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"my_features = ['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'zipcode']"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
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[\"bedrooms\", \"bathrooms\", \"sqft_living\", \"sqft_lot\", \"floors\", \"zipcode\"], \"view\": null}, \"view_components\": [\"Summary\", \"Table\", \"Bar Chart\", \"BoxWhisker Plot\", \"Line Chart\", \"Scatter Plot\", \"Heat Map\", \"Plots\"], \"type\": \"SFrame\", \"columns\": [{\"dtype\": \"str\", \"name\": \"bedrooms\"}, {\"dtype\": \"str\", \"name\": \"bathrooms\"}, {\"dtype\": \"int\", \"name\": \"sqft_living\"}, {\"dtype\": \"int\", \"name\": \"sqft_lot\"}, {\"dtype\": \"str\", \"name\": \"floors\"}, {\"dtype\": \"str\", \"name\": \"zipcode\"}], \"column_identifiers\": [\"bathrooms\", \"bedrooms\", \"zipcode\", \"sqft_living\", \"floors\", \"sqft_lot\"]}, \"columns\": [{\"dtype\": \"str\", \"name\": \"bedrooms\"}, {\"dtype\": \"str\", \"name\": \"bathrooms\"}, {\"dtype\": \"int\", \"name\": \"sqft_living\"}, {\"dtype\": \"int\", \"name\": \"sqft_lot\"}, {\"dtype\": \"str\", \"name\": \"floors\"}, {\"dtype\": \"str\", \"name\": \"zipcode\"}]}, e);\n",
" });\n",
" })();\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sales[my_features].show()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" type: \"text/css\",\n",
" href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
"}));\n",
"$(\"head\").append($(\"<link/>\").attr({\n",
" rel: \"stylesheet\",\n",
" type: \"text/css\",\n",
" href: \"//dato.com/files/canvas/1.7.1/css/canvas.css\"\n",
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"\n",
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" var e = null;\n",
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" if (typeof requirejs !== 'undefined') {\n",
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" requirejs.config({waitSeconds: 0});\n",
" }\n",
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2160000.0, 3200000.0]]], \"omitted\": 0}, \"columns\": [{\"dtype\": \"str\", \"name\": \"id\"}, {\"dtype\": \"datetime\", \"name\": \"date\"}, {\"dtype\": \"int\", \"name\": \"price\"}, {\"dtype\": \"str\", \"name\": \"bedrooms\"}, {\"dtype\": \"str\", \"name\": \"bathrooms\"}, {\"dtype\": \"int\", \"name\": \"sqft_living\"}, {\"dtype\": \"int\", \"name\": \"sqft_lot\"}, {\"dtype\": \"str\", \"name\": \"floors\"}, {\"dtype\": \"int\", \"name\": \"waterfront\"}, {\"dtype\": \"int\", \"name\": \"view\"}, {\"dtype\": \"int\", \"name\": \"condition\"}, {\"dtype\": \"int\", \"name\": \"grade\"}, {\"dtype\": \"int\", \"name\": \"sqft_above\"}, {\"dtype\": \"int\", \"name\": \"sqft_basement\"}, {\"dtype\": \"int\", \"name\": \"yr_built\"}, {\"dtype\": \"int\", \"name\": \"yr_renovated\"}, {\"dtype\": \"str\", \"name\": \"zipcode\"}, {\"dtype\": \"float\", \"name\": \"lat\"}, {\"dtype\": \"float\", \"name\": \"long\"}, {\"dtype\": \"float\", \"name\": \"sqft_living15\"}, {\"dtype\": \"float\", \"name\": \"sqft_lot15\"}]}, e);\n",
" });\n",
" })();\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sales.show(view='BoxWhisker Plot', x='zipcode', y='price')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pull the bar at the bottom to view more of the data. \n",
"\n",
"98039 is the most expensive zip code."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Build a regression model with more features"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PROGRESS: Linear regression:\n",
"PROGRESS: --------------------------------------------------------\n",
"PROGRESS: Number of examples : 17384\n",
"PROGRESS: Number of features : 6\n",
"PROGRESS: Number of unpacked features : 6\n",
"PROGRESS: Number of coefficients : 115\n",
"PROGRESS: Starting Newton Method\n",
"PROGRESS: --------------------------------------------------------\n",
"PROGRESS: +-----------+----------+--------------+--------------------+---------------+\n",
"PROGRESS: | Iteration | Passes | Elapsed Time | Training-max_error | Training-rmse |\n",
"PROGRESS: +-----------+----------+--------------+--------------------+---------------+\n",
"PROGRESS: | 1 | 2 | 0.037983 | 3763208.270524 | 181908.848367 |\n",
"PROGRESS: +-----------+----------+--------------+--------------------+---------------+\n",
"PROGRESS: SUCCESS: Optimal solution found.\n",
"PROGRESS:\n"
]
}
],
"source": [
"my_features_model = graphlab.linear_regression.create(train_data,target='price',features=my_features,validation_set=None)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'zipcode']\n"
]
}
],
"source": [
"print my_features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comparing the results of the simple model with adding more features"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'max_error': 4143550.8825285956, 'rmse': 255191.0287052738}\n",
"{'max_error': 3486584.5093818563, 'rmse': 179542.4333126908}\n"
]
}
],
"source": [
"print sqft_model.evaluate(test_data)\n",
"print my_features_model.evaluate(test_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The RMSE goes down from \\$255,170 to \\$179,508 with more features."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Apply learned models to predict prices of 3 houses"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first house we will use is considered an \"average\" house in Seattle. "
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"house1 = sales[sales['id']=='5309101200']"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">id</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">date</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">price</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bedrooms</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bathrooms</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_living</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_lot</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">floors</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">waterfront</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5309101200</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2014-06-05 00:00:00+00:00</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.5</td>\n",
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"</table>\n",
"<table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">view</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">condition</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">grade</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_above</th>\n",
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" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">yr_renovated</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">zipcode</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">lat</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1929</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98117</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.67632376</td>\n",
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"</table>\n",
"<table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">long</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_living15</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_lot15</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.37010126</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1250.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4880.0</td>\n",
" </tr>\n",
"</table>\n",
"[? rows x 21 columns]<br/>Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.<br/>You can use len(sf) to force materialization.\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tid\tstr\n",
"\tdate\tdatetime\n",
"\tprice\tint\n",
"\tbedrooms\tstr\n",
"\tbathrooms\tstr\n",
"\tsqft_living\tint\n",
"\tsqft_lot\tint\n",
"\tfloors\tstr\n",
"\twaterfront\tint\n",
"\tview\tint\n",
"\tcondition\tint\n",
"\tgrade\tint\n",
"\tsqft_above\tint\n",
"\tsqft_basement\tint\n",
"\tyr_built\tint\n",
"\tyr_renovated\tint\n",
"\tzipcode\tstr\n",
"\tlat\tfloat\n",
"\tlong\tfloat\n",
"\tsqft_living15\tfloat\n",
"\tsqft_lot15\tfloat\n",
"\n",
"Rows: Unknown\n",
"\n",
"Data:\n",
"+------------+---------------------------+--------+----------+-----------+-------------+\n",
"| id | date | price | bedrooms | bathrooms | sqft_living |\n",
"+------------+---------------------------+--------+----------+-----------+-------------+\n",
"| 5309101200 | 2014-06-05 00:00:00+00:00 | 620000 | 4 | 2.25 | 2400 |\n",
"+------------+---------------------------+--------+----------+-----------+-------------+\n",
"+----------+--------+------------+------+-----------+-------+------------+---------------+\n",
"| sqft_lot | floors | waterfront | view | condition | grade | sqft_above | sqft_basement |\n",
"+----------+--------+------------+------+-----------+-------+------------+---------------+\n",
"| 5350 | 1.5 | 0 | 0 | 4 | 7 | 1460 | 940 |\n",
"+----------+--------+------------+------+-----------+-------+------------+---------------+\n",
"+----------+--------------+---------+-------------+---------------+---------------+-----+\n",
"| yr_built | yr_renovated | zipcode | lat | long | sqft_living15 | ... |\n",
"+----------+--------------+---------+-------------+---------------+---------------+-----+\n",
"| 1929 | 0 | 98117 | 47.67632376 | -122.37010126 | 1250.0 | ... |\n",
"+----------+--------------+---------+-------------+---------------+---------------+-----+\n",
"[? rows x 21 columns]\n",
"Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.\n",
"You can use len(sf) to force materialization."
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"house1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"http://info.kingcounty.gov/Assessor/eRealProperty/MediaHandler.aspx?Media=2916871\">"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[620000, ... ]\n"
]
}
],
"source": [
"print house1['price']"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[629584.8197281545]\n"
]
}
],
"source": [
"print sqft_model.predict(house1)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[721918.9333272739]\n"
]
}
],
"source": [
"print my_features_model.predict(house1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case, the model with more features provides a worse prediction than the simpler model with only 1 feature. However, on average, the model with more features is better."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prediction for a second, fancier house\n",
"\n",
"We will now examine the predictions for a fancier house."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"house2 = sales[sales['id']=='1925069082']"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">id</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">date</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">price</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bedrooms</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bathrooms</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_living</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_lot</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">floors</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">waterfront</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1925069082</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2015-05-11 00:00:00+00:00</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2200000</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.25</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4640</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">22703</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
"</table>\n",
"<table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">view</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">condition</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">grade</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_above</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_basement</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">yr_built</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">yr_renovated</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">zipcode</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">lat</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2860</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1780</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1952</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98052</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.63925783</td>\n",
" </tr>\n",
"</table>\n",
"<table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">long</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_living15</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_lot15</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.09722322</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3140.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">14200.0</td>\n",
" </tr>\n",
"</table>\n",
"[? rows x 21 columns]<br/>Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.<br/>You can use len(sf) to force materialization.\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tid\tstr\n",
"\tdate\tdatetime\n",
"\tprice\tint\n",
"\tbedrooms\tstr\n",
"\tbathrooms\tstr\n",
"\tsqft_living\tint\n",
"\tsqft_lot\tint\n",
"\tfloors\tstr\n",
"\twaterfront\tint\n",
"\tview\tint\n",
"\tcondition\tint\n",
"\tgrade\tint\n",
"\tsqft_above\tint\n",
"\tsqft_basement\tint\n",
"\tyr_built\tint\n",
"\tyr_renovated\tint\n",
"\tzipcode\tstr\n",
"\tlat\tfloat\n",
"\tlong\tfloat\n",
"\tsqft_living15\tfloat\n",
"\tsqft_lot15\tfloat\n",
"\n",
"Rows: Unknown\n",
"\n",
"Data:\n",
"+------------+---------------------------+---------+----------+-----------+-------------+\n",
"| id | date | price | bedrooms | bathrooms | sqft_living |\n",
"+------------+---------------------------+---------+----------+-----------+-------------+\n",
"| 1925069082 | 2015-05-11 00:00:00+00:00 | 2200000 | 5 | 4.25 | 4640 |\n",
"+------------+---------------------------+---------+----------+-----------+-------------+\n",
"+----------+--------+------------+------+-----------+-------+------------+---------------+\n",
"| sqft_lot | floors | waterfront | view | condition | grade | sqft_above | sqft_basement |\n",
"+----------+--------+------------+------+-----------+-------+------------+---------------+\n",
"| 22703 | 2 | 1 | 4 | 5 | 8 | 2860 | 1780 |\n",
"+----------+--------+------------+------+-----------+-------+------------+---------------+\n",
"+----------+--------------+---------+-------------+---------------+---------------+-----+\n",
"| yr_built | yr_renovated | zipcode | lat | long | sqft_living15 | ... |\n",
"+----------+--------------+---------+-------------+---------------+---------------+-----+\n",
"| 1952 | 0 | 98052 | 47.63925783 | -122.09722322 | 3140.0 | ... |\n",
"+----------+--------------+---------+-------------+---------------+---------------+-----+\n",
"[? rows x 21 columns]\n",
"Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.\n",
"You can use len(sf) to force materialization."
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"house2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://ssl.cdn-redfin.com/photo/1/bigphoto/302/734302_0.jpg\">"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1261170.4040999676]\n"
]
}
],
"source": [
"print sqft_model.predict(house2)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1446472.4690775052]\n"
]
}
],
"source": [
"print my_features_model.predict(house2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case, the model with more features provides a better prediction. This behavior is expected here, because this house is more differentiated by features that go beyond its square feet of living space, especially the fact that it's a waterfront house. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Last house, super fancy\n",
"\n",
"Our last house is a very large one owned by a famous Seattleite."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bill_gates = {'bedrooms':[8], \n",
" 'bathrooms':[25], \n",
" 'sqft_living':[50000], \n",
" 'sqft_lot':[225000],\n",
" 'floors':[4], \n",
" 'zipcode':['98039'], \n",
" 'condition':[10], \n",
" 'grade':[10],\n",
" 'waterfront':[1],\n",
" 'view':[4],\n",
" 'sqft_above':[37500],\n",
" 'sqft_basement':[12500],\n",
" 'yr_built':[1994],\n",
" 'yr_renovated':[2010],\n",
" 'lat':[47.627606],\n",
" 'long':[-122.242054],\n",
" 'sqft_living15':[5000],\n",
" 'sqft_lot15':[40000]}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/d/d9/Bill_gates%27_house.jpg/2560px-Bill_gates%27_house.jpg\">"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[13749825.525718132]\n"
]
}
],
"source": [
"print my_features_model.predict(graphlab.SFrame(bill_gates))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"The model predicts a price of over $13M for this house! But we expect the house to cost much more. (There are very few samples in the dataset of houses that are this fancy, so we don't expect the model to capture a perfect prediction here.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
@j450h1
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j450h1 commented Nov 23, 2015

test

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