{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\gujianflsgj\\Anaconda2\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "import json\n", "import numpy as np\n", "from statsmodels.regression.linear_model import OLS\n", "import pandas as pd\n", "import scipy as sp\n", "from sklearn import preprocessing\n", "from sklearn.cross_validation import KFold\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n", "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA\n", "from sklearn.neighbors import KNeighborsClassifier as KNN\n", "from sklearn.tree import DecisionTreeClassifier as DecisionTree\n", "from sklearn.ensemble import RandomForestClassifier as RandomForest\n", "from sklearn.svm import SVC\n", "from sklearn.cross_validation import train_test_split\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import itertools as it\n", "%matplotlib inline\n", "from sklearn.linear_model import LinearRegression as Lin_Reg\n", "from sklearn.linear_model import Ridge as Ridge_Reg\n", "from sklearn.linear_model import Lasso as Lasso_Reg\n", "from sklearn.preprocessing import StandardScaler as Standardize\n", "\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import matplotlib.cm as cmx\n", "import matplotlib.colors as colors\n", "from sklearn.neighbors import KNeighborsRegressor as KNN\n", "from sklearn.cross_validation import train_test_split as sk_split" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "theDf = pd.read_csv('[dataFinal]/_housingPriceZillowMergeFinal.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['pixelPlant' 'pixelPole' 'pixelLake' 'pixelRoad' 'pixelGrass' 'pixelWall'\n", " 'pixelCar' 'propertiesAsses' 'pixelSea' 'numCraigslistHouse' 'pixelRiver'\n", " 'pixelBus' 'pixelCeiling' 'pixelPath' 'pixelBuilding' 'crime' 'pixelFence'\n", " 'walkSchool' 'walkMbta' 'energySiteEUI' 'pixelPerson' 'pixelTree'\n", " 'pixelVan' 'walkPark' 'walkUniversity' 'pixelSidewalk' 'pixelGround'\n", " 'pixelMountain' 'pixelPalmTree' 'pixelHouse' 'pixelBridge' 'pixelSign'\n", " 'pixelRailing' 'pixelField' 'pixelWindow' 'pixelGrandstand'\n", " 'numCraigslistRoom' 'pixelSky' 'longitude' 'latitude' 'bathrooms'\n", " 'last_sold_price' 'zestimate_amount' 'prices' 'property_size' 'zip'\n", " 'zestimate_valuation_range_high' 'long' 'tax_year'\n", " 'zestimate_value_change' 'status' 'zestimate_percentile' 'bedrooms'\n", " 'zestimate_last_updated' 'zillow_id' 'address' 'lat' 'last_sold_date'\n", " 'tax_value' 'zillow' 'year_built' 'zestimate_valuationRange_low'\n", " 'graph_data_link' 'home_size' 'home_detail_link' 'home_type' 'property'\n", " 'map_this_home_link']\n", "(727, 68)\n" ] } ], "source": [ "print theDf.columns.values\n", "print theDf.shape" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pixelPlant float64\n", "pixelPole float64\n", "pixelLake float64\n", "pixelRoad float64\n", "pixelGrass float64\n", "pixelWall float64\n", "pixelCar float64\n", "propertiesAsses int64\n", "pixelSea float64\n", "numCraigslistHouse int64\n", "pixelRiver float64\n", "pixelBus float64\n", "pixelCeiling float64\n", "pixelPath float64\n", "pixelBuilding float64\n", "crime int64\n", "pixelFence int64\n", "walkSchool int64\n", "walkMbta int64\n", "energySiteEUI float64\n", "pixelPerson float64\n", "pixelTree float64\n", "pixelVan float64\n", "walkPark int64\n", "walkUniversity int64\n", "pixelSidewalk float64\n", "pixelGround int64\n", "pixelMountain float64\n", "pixelPalmTree float64\n", "pixelHouse float64\n", "pixelBridge float64\n", "pixelSign float64\n", "pixelRailing float64\n", "pixelField float64\n", "pixelWindow float64\n", "pixelGrandstand float64\n", "numCraigslistRoom int64\n", "pixelSky float64\n", "longitude float64\n", "latitude float64\n", "bathrooms float64\n", "last_sold_price float64\n", "zestimate_amount float64\n", "prices object\n", "property_size float64\n", "zip int64\n", "zestimate_valuation_range_high float64\n", "long float64\n", "tax_year float64\n", "zestimate_value_change float64\n", "status object\n", "zestimate_percentile int64\n", "bedrooms float64\n", "zestimate_last_updated object\n", "zillow_id int64\n", "address object\n", "lat float64\n", "last_sold_date object\n", "tax_value float64\n", "zillow int64\n", "year_built float64\n", "zestimate_valuationRange_low float64\n", "graph_data_link object\n", "home_size float64\n", "home_detail_link object\n", "home_type object\n", "property int64\n", "map_this_home_link object\n" ] } ], "source": [ "for col in theDf.columns:\n", " print col,theDf[col].dtype" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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pixelPlantpixelPolepixelLakepixelRoadpixelGrasspixelWallpixelCarpropertiesAssespixelSeanumCraigslistHouse...tax_valuezillowyear_builtzestimate_valuationRange_lowgraph_data_linkhome_sizehome_detail_linkhome_typepropertymap_this_home_link
00.00.01741.9507.5420.0000.9201.177471740460.000...NaN0NaN728068.0NaN1400.0http://www.zillow.com/homedetails/321-Dorchest...Condominium0http://www.zillow.com/homes/2096497652_zpid/
10.00.59740.6001.5700.0000.0259.659129370000.000...499300.001885.0692745.0http://www.zillow.com/homedetails/54-Grampian-...2423.0http://www.zillow.com/homedetails/54-Grampian-...SingleFamily0http://www.zillow.com/homes/59113320_zpid/
20.00.12712.4803.1400.0000.4272.55542942680.000...NaN02016.0695682.0NaN2800.0http://www.zillow.com/homedetails/129-Gardner-...SingleFamily0http://www.zillow.com/homes/2096677074_zpid/
30.00.0000.0000.0000.0000.0000.000156015370.000...NaN01925.0458252.0NaN1260.0http://www.zillow.com/homedetails/7-9-Herberts...Condominium0http://www.zillow.com/homes/2096675064_zpid/
40.00.05513.3302.2050.0000.0498.027173147000.000...448800.001900.0665224.0http://www.zillow.com/homedetails/31-Maple-St-...1847.0http://www.zillow.com/homedetails/31-Maple-St-...SingleFamily0http://www.zillow.com/homes/59155302_zpid/
50.00.02448.58010.9600.0000.0010.979469376270.000...509100.002006.0743110.0http://www.zillow.com/homedetails/255-Northamp...1057.0http://www.zillow.com/homedetails/255-Northamp...Condominium0http://www.zillow.com/homes/87796106_zpid/
60.00.0000.0000.0000.0000.0000.000223097900.000...183100.001910.0326026.0http://www.zillow.com/homedetails/43-Parkvale-...578.0http://www.zillow.com/homedetails/43-Parkvale-...Condominium0http://www.zillow.com/homes/59088144_zpid/
70.00.00729.6904.2350.0000.2274.43792180200.000...NaN01920.0254898.0NaN1000.0http://www.zillow.com/homedetails/53-Torrey-St...Condominium0http://www.zillow.com/homes/2096823174_zpid/
80.00.00015.2400.0860.0000.0010.440104219200.000...NaN01900.0438629.0NaN1400.0http://www.zillow.com/homedetails/31-Houghton-...Condominium0http://www.zillow.com/homes/2096881020_zpid/
90.00.00051.3902.5350.0000.0446.958123274000.000...156300.001991.0252014.0http://www.zillow.com/homedetails/10-Arbutus-S...1520.0http://www.zillow.com/homedetails/10-Arbutus-S...SingleFamily0http://www.zillow.com/homes/59102139_zpid/
100.00.0000.0000.0000.0000.0000.000186813000.000...242000.001890.0407830.0http://www.zillow.com/homedetails/181-Glenway-...2684.0http://www.zillow.com/homedetails/181-Glenway-...SingleFamily0http://www.zillow.com/homes/59112097_zpid/
110.00.00729.6904.2350.0000.2274.43792180200.000...NaN01920.0254898.0NaN1000.0http://www.zillow.com/homedetails/53-Torrey-St...Condominium0http://www.zillow.com/homes/2096896512_zpid/
120.00.7712.4750.0741.3490.0001.106175651000.010...1783000.001884.02628337.0http://www.zillow.com/homedetails/242-Beacon-S...2267.0http://www.zillow.com/homedetails/242-Beacon-S...Condominium0http://www.zillow.com/homes/59170501_zpid/
130.00.0000.0000.0000.0000.0000.000110355000.000...NaN01940.0401920.0NaN1370.0http://www.zillow.com/homedetails/23-Goethe-St...Condominium0http://www.zillow.com/homes/2096807696_zpid/
140.00.0000.0000.0000.0000.0000.000139442000.000...426577.001925.0598091.0http://www.zillow.com/homedetails/100-Maple-St...16857.0http://www.zillow.com/homedetails/100-Maple-St...SingleFamily0http://www.zillow.com/homes/90139732_zpid/
150.00.0000.0000.0000.0000.0000.000209818600.000...NaN01899.0776495.0NaN1916.0http://www.zillow.com/homedetails/801-Centre-S...Condominium0http://www.zillow.com/homes/2097055956_zpid/
160.00.00047.9804.0420.0000.0000.152427586070.000...1132500.002004.01594559.0http://www.zillow.com/homedetails/505-Tremont-...1364.0http://www.zillow.com/homedetails/505-Tremont-...Condominium0http://www.zillow.com/homes/67710271_zpid/
170.00.31432.8304.0540.0001.5824.447184060000.000...212600.001905.0362862.0http://www.zillow.com/homedetails/16-Chelmsfor...1061.0http://www.zillow.com/homedetails/16-Chelmsfor...Condominium0http://www.zillow.com/homes/81854555_zpid/
180.00.0000.0000.0000.0000.0000.000226553040.000...450000.001950.0343486.0http://www.zillow.com/homedetails/1391-Hyde-Pa...1570.0http://www.zillow.com/homedetails/1391-Hyde-Pa...Condominium0http://www.zillow.com/homes/81855097_zpid/
190.00.00089.4703.7150.0000.7430.0002696777240.000...NaN02016.0846440.0NaN923.0http://www.zillow.com/homedetails/1-Franklin-S...Condominium0http://www.zillow.com/homes/2097180167_zpid/
200.00.03262.0903.8160.0000.0383.715127432040.000...NaN01900.0439798.0NaN1400.0http://www.zillow.com/homedetails/29-Houghton-...Condominium0http://www.zillow.com/homes/2096881071_zpid/
210.00.04055.9208.5550.0000.2820.175148937570.001...NaN02016.0674316.0NaN1700.0http://www.zillow.com/homedetails/28-Woodward-...Condominium0http://www.zillow.com/homes/2107330626_zpid/
220.00.0000.0000.0000.0000.0000.000171185000.000...382100.001901.0590871.0http://www.zillow.com/homedetails/12-Mosgrove-...1950.0http://www.zillow.com/homedetails/12-Mosgrove-...Condominium0http://www.zillow.com/homes/59144888_zpid/
230.00.0000.1830.0000.0000.0010.000245929000.000...630200.001999.0747983.0http://www.zillow.com/homedetails/235-Victory-...2286.0http://www.zillow.com/homedetails/235-Victory-...Condominium0http://www.zillow.com/homes/56605009_zpid/
240.00.0000.1830.0000.0000.0010.000276378850.000...NaN01904.0378836.0NaN850.0http://www.zillow.com/homedetails/29-Brainerd-...Condominium0http://www.zillow.com/homes/2097576158_zpid/
250.00.07143.5405.4340.0001.4342.297223577990.000...NaN02016.0NaNNaN1603.0http://www.zillow.com/homedetails/5-Atwood-Sq-...Townhouse0http://www.zillow.com/homes/2096375498_zpid/
260.00.01617.7407.5380.0002.9085.515112877000.000...368900.001925.0503991.0http://www.zillow.com/homedetails/60-Sanborn-A...1692.0http://www.zillow.com/homedetails/60-Sanborn-A...SingleFamily0http://www.zillow.com/homes/59158025_zpid/
270.00.0000.0000.0000.0000.0000.000250608250.000...NaN0NaNNaNNaN1296.0http://www.zillow.com/homedetails/188-Brooklin...Condominium0http://www.zillow.com/homes/2096368520_zpid/
280.00.00170.2304.0770.0000.0000.218238847280.000...NaN02015.0NaNNaN850.0http://www.zillow.com/homedetails/51-Benningto...Condominium0http://www.zillow.com/homes/2096368919_zpid/
290.00.00170.2304.0770.0000.0000.218238847280.000...NaN02016.0NaNNaN1225.0http://www.zillow.com/homedetails/51-Benningto...Condominium0http://www.zillow.com/homes/2096368818_zpid/
..................................................................
6970.00.02448.58010.9600.0000.0010.979469376270.000...509100.002006.0743110.0http://www.zillow.com/homedetails/255-Northamp...1057.0http://www.zillow.com/homedetails/255-Northamp...Condominium0http://www.zillow.com/homes/87796106_zpid/
6980.00.0000.0000.0000.0000.0000.000110355000.000...NaN01940.0401920.0NaN1370.0http://www.zillow.com/homedetails/23-Goethe-St...Condominium0http://www.zillow.com/homes/2096807696_zpid/
6990.00.0000.0000.0000.0000.0000.000139442000.000...426577.001925.0598091.0http://www.zillow.com/homedetails/100-Maple-St...16857.0http://www.zillow.com/homedetails/100-Maple-St...SingleFamily0http://www.zillow.com/homes/90139732_zpid/
7000.00.00047.9804.0420.0000.0000.152427586070.000...1132500.002004.01594559.0http://www.zillow.com/homedetails/505-Tremont-...1364.0http://www.zillow.com/homedetails/505-Tremont-...Condominium0http://www.zillow.com/homes/67710271_zpid/
7010.00.03262.0903.8160.0000.0383.715127432040.000...NaN01900.0439798.0NaN1400.0http://www.zillow.com/homedetails/29-Houghton-...Condominium0http://www.zillow.com/homes/2096881071_zpid/
7020.00.04055.9208.5550.0000.2820.175148937570.001...NaN02016.0674316.0NaN1700.0http://www.zillow.com/homedetails/28-Woodward-...Condominium0http://www.zillow.com/homes/2107330626_zpid/
7030.00.0000.0000.0000.0000.0000.000171185000.000...382100.001901.0590871.0http://www.zillow.com/homedetails/12-Mosgrove-...1950.0http://www.zillow.com/homedetails/12-Mosgrove-...Condominium0http://www.zillow.com/homes/59144888_zpid/
7040.00.07143.5405.4340.0001.4342.297223577990.000...NaN02016.0NaNNaN1603.0http://www.zillow.com/homedetails/5-Atwood-Sq-...Townhouse0http://www.zillow.com/homes/2096375498_zpid/
7050.00.01741.9507.5420.0000.9201.177471740460.000...NaN0NaN879040.0NaN2400.0http://www.zillow.com/homedetails/321-Dorchest...Condominium0http://www.zillow.com/homes/2096497571_zpid/
7060.00.59740.6001.5700.0000.0259.659129370000.000...499300.001885.0692745.0http://www.zillow.com/homedetails/54-Grampian-...2423.0http://www.zillow.com/homedetails/54-Grampian-...SingleFamily0http://www.zillow.com/homes/59113320_zpid/
7070.00.12712.4803.1400.0000.4272.55542942680.000...NaN02016.0695682.0NaN2800.0http://www.zillow.com/homedetails/129-Gardner-...SingleFamily0http://www.zillow.com/homes/2096677074_zpid/
7080.00.0000.0000.0000.0000.0000.000156015370.000...NaN01925.0458252.0NaN1260.0http://www.zillow.com/homedetails/7-9-Herberts...Condominium0http://www.zillow.com/homes/2096675064_zpid/
7090.00.05513.3302.2050.0000.0498.027173147000.000...448800.001900.0665224.0http://www.zillow.com/homedetails/31-Maple-St-...1847.0http://www.zillow.com/homedetails/31-Maple-St-...SingleFamily0http://www.zillow.com/homes/59155302_zpid/
7100.00.0000.0000.0000.0000.0000.000223097900.000...183100.001910.0326026.0http://www.zillow.com/homedetails/43-Parkvale-...578.0http://www.zillow.com/homedetails/43-Parkvale-...Condominium0http://www.zillow.com/homes/59088144_zpid/
7110.00.00015.2400.0860.0000.0010.440104219200.000...NaN01900.0438629.0NaN1400.0http://www.zillow.com/homedetails/31-Houghton-...Condominium0http://www.zillow.com/homes/2096881020_zpid/
7120.00.0000.0000.0000.0000.0000.000110355000.000...NaN01940.0401920.0NaN1370.0http://www.zillow.com/homedetails/23-Goethe-St...Condominium0http://www.zillow.com/homes/2096807696_zpid/
7130.00.0000.0000.0000.0000.0000.000139442000.000...426577.001925.0598091.0http://www.zillow.com/homedetails/100-Maple-St...16857.0http://www.zillow.com/homedetails/100-Maple-St...SingleFamily0http://www.zillow.com/homes/90139732_zpid/
7140.00.0000.0000.0000.0000.0000.000226553040.000...450000.001950.0343486.0http://www.zillow.com/homedetails/1391-Hyde-Pa...1570.0http://www.zillow.com/homedetails/1391-Hyde-Pa...Condominium0http://www.zillow.com/homes/81855097_zpid/
7150.00.04055.9208.5550.0000.2820.175148937570.001...NaN02016.0674316.0NaN1700.0http://www.zillow.com/homedetails/28-Woodward-...Condominium0http://www.zillow.com/homes/2107330626_zpid/
7160.00.0000.0000.0000.0000.0000.000171185000.000...382100.001901.0590871.0http://www.zillow.com/homedetails/12-Mosgrove-...1950.0http://www.zillow.com/homedetails/12-Mosgrove-...Condominium0http://www.zillow.com/homes/59144888_zpid/
7170.00.07143.5405.4340.0001.4342.297223577990.000...NaN02016.0NaNNaN1603.0http://www.zillow.com/homedetails/5-Atwood-Sq-...Townhouse0http://www.zillow.com/homes/2096375498_zpid/
7180.00.12712.4803.1400.0000.4272.55542942680.000...NaN02016.0695682.0NaN2800.0http://www.zillow.com/homedetails/129-Gardner-...SingleFamily0http://www.zillow.com/homes/2096677074_zpid/
7190.00.0000.0000.0000.0000.0000.000156015370.000...NaN01925.0458252.0NaN1260.0http://www.zillow.com/homedetails/7-9-Herberts...Condominium0http://www.zillow.com/homes/2096675064_zpid/
7200.00.02448.58010.9600.0000.0010.979469376270.000...509100.002006.0743110.0http://www.zillow.com/homedetails/255-Northamp...1057.0http://www.zillow.com/homedetails/255-Northamp...Condominium0http://www.zillow.com/homes/87796106_zpid/
7210.00.0000.0000.0000.0000.0000.000223097900.000...183100.001910.0326026.0http://www.zillow.com/homedetails/43-Parkvale-...578.0http://www.zillow.com/homedetails/43-Parkvale-...Condominium0http://www.zillow.com/homes/59088144_zpid/
7220.00.0000.0000.0000.0000.0000.000139442000.000...426577.001925.0598091.0http://www.zillow.com/homedetails/100-Maple-St...16857.0http://www.zillow.com/homedetails/100-Maple-St...SingleFamily0http://www.zillow.com/homes/90139732_zpid/
7230.00.00047.9804.0420.0000.0000.152427586070.000...1132500.002004.01594559.0http://www.zillow.com/homedetails/505-Tremont-...1364.0http://www.zillow.com/homedetails/505-Tremont-...Condominium0http://www.zillow.com/homes/67710271_zpid/
7240.00.0000.0000.0000.0000.0000.000226553040.000...450000.001950.0343486.0http://www.zillow.com/homedetails/1391-Hyde-Pa...1570.0http://www.zillow.com/homedetails/1391-Hyde-Pa...Condominium0http://www.zillow.com/homes/81855097_zpid/
7250.00.00089.4703.7150.0000.7430.0002696777240.000...NaN02016.0846440.0NaN923.0http://www.zillow.com/homedetails/1-Franklin-S...Condominium0http://www.zillow.com/homes/2097180167_zpid/
7260.00.04055.9208.5550.0000.2820.175148937570.001...NaN02016.0674316.0NaN1700.0http://www.zillow.com/homedetails/28-Woodward-...Condominium0http://www.zillow.com/homes/2107330626_zpid/
\n", "

727 rows × 68 columns

\n", "
" ], "text/plain": [ " pixelPlant pixelPole pixelLake pixelRoad pixelGrass pixelWall \\\n", "0 0.0 0.017 41.950 7.542 0.000 0.920 \n", "1 0.0 0.597 40.600 1.570 0.000 0.025 \n", "2 0.0 0.127 12.480 3.140 0.000 0.427 \n", "3 0.0 0.000 0.000 0.000 0.000 0.000 \n", "4 0.0 0.055 13.330 2.205 0.000 0.049 \n", "5 0.0 0.024 48.580 10.960 0.000 0.001 \n", "6 0.0 0.000 0.000 0.000 0.000 0.000 \n", "7 0.0 0.007 29.690 4.235 0.000 0.227 \n", "8 0.0 0.000 15.240 0.086 0.000 0.001 \n", "9 0.0 0.000 51.390 2.535 0.000 0.044 \n", "10 0.0 0.000 0.000 0.000 0.000 0.000 \n", "11 0.0 0.007 29.690 4.235 0.000 0.227 \n", "12 0.0 0.771 2.475 0.074 1.349 0.000 \n", "13 0.0 0.000 0.000 0.000 0.000 0.000 \n", "14 0.0 0.000 0.000 0.000 0.000 0.000 \n", "15 0.0 0.000 0.000 0.000 0.000 0.000 \n", "16 0.0 0.000 47.980 4.042 0.000 0.000 \n", "17 0.0 0.314 32.830 4.054 0.000 1.582 \n", "18 0.0 0.000 0.000 0.000 0.000 0.000 \n", "19 0.0 0.000 89.470 3.715 0.000 0.743 \n", "20 0.0 0.032 62.090 3.816 0.000 0.038 \n", "21 0.0 0.040 55.920 8.555 0.000 0.282 \n", "22 0.0 0.000 0.000 0.000 0.000 0.000 \n", "23 0.0 0.000 0.183 0.000 0.000 0.001 \n", "24 0.0 0.000 0.183 0.000 0.000 0.001 \n", "25 0.0 0.071 43.540 5.434 0.000 1.434 \n", "26 0.0 0.016 17.740 7.538 0.000 2.908 \n", "27 0.0 0.000 0.000 0.000 0.000 0.000 \n", "28 0.0 0.001 70.230 4.077 0.000 0.000 \n", "29 0.0 0.001 70.230 4.077 0.000 0.000 \n", ".. ... ... ... ... ... ... \n", "697 0.0 0.024 48.580 10.960 0.000 0.001 \n", "698 0.0 0.000 0.000 0.000 0.000 0.000 \n", "699 0.0 0.000 0.000 0.000 0.000 0.000 \n", "700 0.0 0.000 47.980 4.042 0.000 0.000 \n", "701 0.0 0.032 62.090 3.816 0.000 0.038 \n", "702 0.0 0.040 55.920 8.555 0.000 0.282 \n", "703 0.0 0.000 0.000 0.000 0.000 0.000 \n", "704 0.0 0.071 43.540 5.434 0.000 1.434 \n", "705 0.0 0.017 41.950 7.542 0.000 0.920 \n", "706 0.0 0.597 40.600 1.570 0.000 0.025 \n", "707 0.0 0.127 12.480 3.140 0.000 0.427 \n", "708 0.0 0.000 0.000 0.000 0.000 0.000 \n", "709 0.0 0.055 13.330 2.205 0.000 0.049 \n", "710 0.0 0.000 0.000 0.000 0.000 0.000 \n", "711 0.0 0.000 15.240 0.086 0.000 0.001 \n", "712 0.0 0.000 0.000 0.000 0.000 0.000 \n", "713 0.0 0.000 0.000 0.000 0.000 0.000 \n", "714 0.0 0.000 0.000 0.000 0.000 0.000 \n", "715 0.0 0.040 55.920 8.555 0.000 0.282 \n", "716 0.0 0.000 0.000 0.000 0.000 0.000 \n", "717 0.0 0.071 43.540 5.434 0.000 1.434 \n", "718 0.0 0.127 12.480 3.140 0.000 0.427 \n", "719 0.0 0.000 0.000 0.000 0.000 0.000 \n", "720 0.0 0.024 48.580 10.960 0.000 0.001 \n", "721 0.0 0.000 0.000 0.000 0.000 0.000 \n", "722 0.0 0.000 0.000 0.000 0.000 0.000 \n", "723 0.0 0.000 47.980 4.042 0.000 0.000 \n", "724 0.0 0.000 0.000 0.000 0.000 0.000 \n", "725 0.0 0.000 89.470 3.715 0.000 0.743 \n", "726 0.0 0.040 55.920 8.555 0.000 0.282 \n", "\n", " pixelCar propertiesAsses pixelSea numCraigslistHouse \\\n", "0 1.177 47174046 0.00 0 \n", "1 9.659 12937000 0.00 0 \n", "2 2.555 4294268 0.00 0 \n", "3 0.000 15601537 0.00 0 \n", "4 8.027 17314700 0.00 0 \n", "5 0.979 46937627 0.00 0 \n", "6 0.000 22309790 0.00 0 \n", "7 4.437 9218020 0.00 0 \n", "8 0.440 10421920 0.00 0 \n", "9 6.958 12327400 0.00 0 \n", "10 0.000 18681300 0.00 0 \n", "11 4.437 9218020 0.00 0 \n", "12 1.106 17565100 0.01 0 \n", "13 0.000 11035500 0.00 0 \n", "14 0.000 13944200 0.00 0 \n", "15 0.000 20981860 0.00 0 \n", "16 0.152 42758607 0.00 0 \n", "17 4.447 18406000 0.00 0 \n", "18 0.000 22655304 0.00 0 \n", "19 0.000 269677724 0.00 0 \n", "20 3.715 12743204 0.00 0 \n", "21 0.175 14893757 0.00 1 \n", "22 0.000 17118500 0.00 0 \n", "23 0.000 24592900 0.00 0 \n", "24 0.000 27637885 0.00 0 \n", "25 2.297 22357799 0.00 0 \n", "26 5.515 11287700 0.00 0 \n", "27 0.000 25060825 0.00 0 \n", "28 0.218 23884728 0.00 0 \n", "29 0.218 23884728 0.00 0 \n", ".. ... ... ... ... \n", "697 0.979 46937627 0.00 0 \n", "698 0.000 11035500 0.00 0 \n", "699 0.000 13944200 0.00 0 \n", "700 0.152 42758607 0.00 0 \n", "701 3.715 12743204 0.00 0 \n", "702 0.175 14893757 0.00 1 \n", "703 0.000 17118500 0.00 0 \n", "704 2.297 22357799 0.00 0 \n", "705 1.177 47174046 0.00 0 \n", "706 9.659 12937000 0.00 0 \n", "707 2.555 4294268 0.00 0 \n", "708 0.000 15601537 0.00 0 \n", "709 8.027 17314700 0.00 0 \n", "710 0.000 22309790 0.00 0 \n", "711 0.440 10421920 0.00 0 \n", "712 0.000 11035500 0.00 0 \n", "713 0.000 13944200 0.00 0 \n", "714 0.000 22655304 0.00 0 \n", "715 0.175 14893757 0.00 1 \n", "716 0.000 17118500 0.00 0 \n", "717 2.297 22357799 0.00 0 \n", "718 2.555 4294268 0.00 0 \n", "719 0.000 15601537 0.00 0 \n", "720 0.979 46937627 0.00 0 \n", "721 0.000 22309790 0.00 0 \n", "722 0.000 13944200 0.00 0 \n", "723 0.152 42758607 0.00 0 \n", "724 0.000 22655304 0.00 0 \n", "725 0.000 269677724 0.00 0 \n", "726 0.175 14893757 0.00 1 \n", "\n", " ... tax_value zillow \\\n", "0 ... NaN 0 \n", "1 ... 499300.0 0 \n", "2 ... NaN 0 \n", "3 ... NaN 0 \n", "4 ... 448800.0 0 \n", "5 ... 509100.0 0 \n", "6 ... 183100.0 0 \n", "7 ... NaN 0 \n", "8 ... NaN 0 \n", "9 ... 156300.0 0 \n", "10 ... 242000.0 0 \n", "11 ... NaN 0 \n", "12 ... 1783000.0 0 \n", "13 ... NaN 0 \n", "14 ... 426577.0 0 \n", "15 ... NaN 0 \n", "16 ... 1132500.0 0 \n", "17 ... 212600.0 0 \n", "18 ... 450000.0 0 \n", "19 ... NaN 0 \n", "20 ... NaN 0 \n", "21 ... NaN 0 \n", "22 ... 382100.0 0 \n", "23 ... 630200.0 0 \n", "24 ... NaN 0 \n", "25 ... NaN 0 \n", "26 ... 368900.0 0 \n", "27 ... NaN 0 \n", "28 ... NaN 0 \n", "29 ... NaN 0 \n", ".. ... ... ... \n", "697 ... 509100.0 0 \n", "698 ... NaN 0 \n", "699 ... 426577.0 0 \n", "700 ... 1132500.0 0 \n", "701 ... NaN 0 \n", "702 ... NaN 0 \n", "703 ... 382100.0 0 \n", "704 ... NaN 0 \n", "705 ... NaN 0 \n", "706 ... 499300.0 0 \n", "707 ... NaN 0 \n", "708 ... NaN 0 \n", "709 ... 448800.0 0 \n", "710 ... 183100.0 0 \n", "711 ... NaN 0 \n", "712 ... NaN 0 \n", "713 ... 426577.0 0 \n", "714 ... 450000.0 0 \n", "715 ... NaN 0 \n", "716 ... 382100.0 0 \n", "717 ... NaN 0 \n", "718 ... NaN 0 \n", "719 ... NaN 0 \n", "720 ... 509100.0 0 \n", "721 ... 183100.0 0 \n", "722 ... 426577.0 0 \n", "723 ... 1132500.0 0 \n", "724 ... 450000.0 0 \n", "725 ... NaN 0 \n", "726 ... NaN 0 \n", "\n", " year_built zestimate_valuationRange_low \\\n", "0 NaN 728068.0 \n", "1 1885.0 692745.0 \n", "2 2016.0 695682.0 \n", "3 1925.0 458252.0 \n", "4 1900.0 665224.0 \n", "5 2006.0 743110.0 \n", "6 1910.0 326026.0 \n", "7 1920.0 254898.0 \n", "8 1900.0 438629.0 \n", "9 1991.0 252014.0 \n", "10 1890.0 407830.0 \n", "11 1920.0 254898.0 \n", "12 1884.0 2628337.0 \n", "13 1940.0 401920.0 \n", "14 1925.0 598091.0 \n", "15 1899.0 776495.0 \n", "16 2004.0 1594559.0 \n", "17 1905.0 362862.0 \n", "18 1950.0 343486.0 \n", "19 2016.0 846440.0 \n", "20 1900.0 439798.0 \n", "21 2016.0 674316.0 \n", "22 1901.0 590871.0 \n", "23 1999.0 747983.0 \n", "24 1904.0 378836.0 \n", "25 2016.0 NaN \n", "26 1925.0 503991.0 \n", "27 NaN NaN \n", "28 2015.0 NaN \n", "29 2016.0 NaN \n", ".. ... ... \n", "697 2006.0 743110.0 \n", "698 1940.0 401920.0 \n", "699 1925.0 598091.0 \n", "700 2004.0 1594559.0 \n", "701 1900.0 439798.0 \n", "702 2016.0 674316.0 \n", "703 1901.0 590871.0 \n", "704 2016.0 NaN \n", "705 NaN 879040.0 \n", "706 1885.0 692745.0 \n", "707 2016.0 695682.0 \n", "708 1925.0 458252.0 \n", "709 1900.0 665224.0 \n", "710 1910.0 326026.0 \n", "711 1900.0 438629.0 \n", "712 1940.0 401920.0 \n", "713 1925.0 598091.0 \n", "714 1950.0 343486.0 \n", "715 2016.0 674316.0 \n", "716 1901.0 590871.0 \n", "717 2016.0 NaN \n", "718 2016.0 695682.0 \n", "719 1925.0 458252.0 \n", "720 2006.0 743110.0 \n", "721 1910.0 326026.0 \n", "722 1925.0 598091.0 \n", "723 2004.0 1594559.0 \n", "724 1950.0 343486.0 \n", "725 2016.0 846440.0 \n", "726 2016.0 674316.0 \n", "\n", " graph_data_link home_size \\\n", "0 NaN 1400.0 \n", "1 http://www.zillow.com/homedetails/54-Grampian-... 2423.0 \n", "2 NaN 2800.0 \n", "3 NaN 1260.0 \n", "4 http://www.zillow.com/homedetails/31-Maple-St-... 1847.0 \n", "5 http://www.zillow.com/homedetails/255-Northamp... 1057.0 \n", "6 http://www.zillow.com/homedetails/43-Parkvale-... 578.0 \n", "7 NaN 1000.0 \n", "8 NaN 1400.0 \n", "9 http://www.zillow.com/homedetails/10-Arbutus-S... 1520.0 \n", "10 http://www.zillow.com/homedetails/181-Glenway-... 2684.0 \n", "11 NaN 1000.0 \n", "12 http://www.zillow.com/homedetails/242-Beacon-S... 2267.0 \n", "13 NaN 1370.0 \n", "14 http://www.zillow.com/homedetails/100-Maple-St... 16857.0 \n", "15 NaN 1916.0 \n", "16 http://www.zillow.com/homedetails/505-Tremont-... 1364.0 \n", "17 http://www.zillow.com/homedetails/16-Chelmsfor... 1061.0 \n", "18 http://www.zillow.com/homedetails/1391-Hyde-Pa... 1570.0 \n", "19 NaN 923.0 \n", "20 NaN 1400.0 \n", "21 NaN 1700.0 \n", "22 http://www.zillow.com/homedetails/12-Mosgrove-... 1950.0 \n", "23 http://www.zillow.com/homedetails/235-Victory-... 2286.0 \n", "24 NaN 850.0 \n", "25 NaN 1603.0 \n", "26 http://www.zillow.com/homedetails/60-Sanborn-A... 1692.0 \n", "27 NaN 1296.0 \n", "28 NaN 850.0 \n", "29 NaN 1225.0 \n", ".. ... ... \n", "697 http://www.zillow.com/homedetails/255-Northamp... 1057.0 \n", "698 NaN 1370.0 \n", "699 http://www.zillow.com/homedetails/100-Maple-St... 16857.0 \n", "700 http://www.zillow.com/homedetails/505-Tremont-... 1364.0 \n", "701 NaN 1400.0 \n", "702 NaN 1700.0 \n", "703 http://www.zillow.com/homedetails/12-Mosgrove-... 1950.0 \n", "704 NaN 1603.0 \n", "705 NaN 2400.0 \n", "706 http://www.zillow.com/homedetails/54-Grampian-... 2423.0 \n", "707 NaN 2800.0 \n", "708 NaN 1260.0 \n", "709 http://www.zillow.com/homedetails/31-Maple-St-... 1847.0 \n", "710 http://www.zillow.com/homedetails/43-Parkvale-... 578.0 \n", "711 NaN 1400.0 \n", "712 NaN 1370.0 \n", "713 http://www.zillow.com/homedetails/100-Maple-St... 16857.0 \n", "714 http://www.zillow.com/homedetails/1391-Hyde-Pa... 1570.0 \n", "715 NaN 1700.0 \n", "716 http://www.zillow.com/homedetails/12-Mosgrove-... 1950.0 \n", "717 NaN 1603.0 \n", "718 NaN 2800.0 \n", "719 NaN 1260.0 \n", "720 http://www.zillow.com/homedetails/255-Northamp... 1057.0 \n", "721 http://www.zillow.com/homedetails/43-Parkvale-... 578.0 \n", "722 http://www.zillow.com/homedetails/100-Maple-St... 16857.0 \n", "723 http://www.zillow.com/homedetails/505-Tremont-... 1364.0 \n", "724 http://www.zillow.com/homedetails/1391-Hyde-Pa... 1570.0 \n", "725 NaN 923.0 \n", "726 NaN 1700.0 \n", "\n", " home_detail_link home_type \\\n", "0 http://www.zillow.com/homedetails/321-Dorchest... Condominium \n", "1 http://www.zillow.com/homedetails/54-Grampian-... SingleFamily \n", "2 http://www.zillow.com/homedetails/129-Gardner-... SingleFamily \n", "3 http://www.zillow.com/homedetails/7-9-Herberts... Condominium \n", "4 http://www.zillow.com/homedetails/31-Maple-St-... SingleFamily \n", "5 http://www.zillow.com/homedetails/255-Northamp... Condominium \n", "6 http://www.zillow.com/homedetails/43-Parkvale-... Condominium \n", "7 http://www.zillow.com/homedetails/53-Torrey-St... Condominium \n", "8 http://www.zillow.com/homedetails/31-Houghton-... Condominium \n", "9 http://www.zillow.com/homedetails/10-Arbutus-S... SingleFamily \n", "10 http://www.zillow.com/homedetails/181-Glenway-... SingleFamily \n", "11 http://www.zillow.com/homedetails/53-Torrey-St... Condominium \n", "12 http://www.zillow.com/homedetails/242-Beacon-S... Condominium \n", "13 http://www.zillow.com/homedetails/23-Goethe-St... Condominium \n", "14 http://www.zillow.com/homedetails/100-Maple-St... SingleFamily \n", "15 http://www.zillow.com/homedetails/801-Centre-S... Condominium \n", "16 http://www.zillow.com/homedetails/505-Tremont-... Condominium \n", "17 http://www.zillow.com/homedetails/16-Chelmsfor... Condominium \n", "18 http://www.zillow.com/homedetails/1391-Hyde-Pa... Condominium \n", "19 http://www.zillow.com/homedetails/1-Franklin-S... Condominium \n", "20 http://www.zillow.com/homedetails/29-Houghton-... Condominium \n", "21 http://www.zillow.com/homedetails/28-Woodward-... Condominium \n", "22 http://www.zillow.com/homedetails/12-Mosgrove-... Condominium \n", "23 http://www.zillow.com/homedetails/235-Victory-... Condominium \n", "24 http://www.zillow.com/homedetails/29-Brainerd-... Condominium \n", "25 http://www.zillow.com/homedetails/5-Atwood-Sq-... Townhouse \n", "26 http://www.zillow.com/homedetails/60-Sanborn-A... SingleFamily \n", "27 http://www.zillow.com/homedetails/188-Brooklin... Condominium \n", "28 http://www.zillow.com/homedetails/51-Benningto... Condominium \n", "29 http://www.zillow.com/homedetails/51-Benningto... Condominium \n", ".. ... ... \n", "697 http://www.zillow.com/homedetails/255-Northamp... Condominium \n", "698 http://www.zillow.com/homedetails/23-Goethe-St... Condominium \n", "699 http://www.zillow.com/homedetails/100-Maple-St... SingleFamily \n", "700 http://www.zillow.com/homedetails/505-Tremont-... Condominium \n", "701 http://www.zillow.com/homedetails/29-Houghton-... Condominium \n", "702 http://www.zillow.com/homedetails/28-Woodward-... Condominium \n", "703 http://www.zillow.com/homedetails/12-Mosgrove-... Condominium \n", "704 http://www.zillow.com/homedetails/5-Atwood-Sq-... Townhouse \n", "705 http://www.zillow.com/homedetails/321-Dorchest... Condominium \n", "706 http://www.zillow.com/homedetails/54-Grampian-... SingleFamily \n", "707 http://www.zillow.com/homedetails/129-Gardner-... SingleFamily \n", "708 http://www.zillow.com/homedetails/7-9-Herberts... Condominium \n", "709 http://www.zillow.com/homedetails/31-Maple-St-... SingleFamily \n", "710 http://www.zillow.com/homedetails/43-Parkvale-... Condominium \n", "711 http://www.zillow.com/homedetails/31-Houghton-... Condominium \n", "712 http://www.zillow.com/homedetails/23-Goethe-St... Condominium \n", "713 http://www.zillow.com/homedetails/100-Maple-St... SingleFamily \n", "714 http://www.zillow.com/homedetails/1391-Hyde-Pa... Condominium \n", "715 http://www.zillow.com/homedetails/28-Woodward-... Condominium \n", "716 http://www.zillow.com/homedetails/12-Mosgrove-... Condominium \n", "717 http://www.zillow.com/homedetails/5-Atwood-Sq-... Townhouse \n", "718 http://www.zillow.com/homedetails/129-Gardner-... SingleFamily \n", "719 http://www.zillow.com/homedetails/7-9-Herberts... Condominium \n", "720 http://www.zillow.com/homedetails/255-Northamp... Condominium \n", "721 http://www.zillow.com/homedetails/43-Parkvale-... Condominium \n", "722 http://www.zillow.com/homedetails/100-Maple-St... SingleFamily \n", "723 http://www.zillow.com/homedetails/505-Tremont-... Condominium \n", "724 http://www.zillow.com/homedetails/1391-Hyde-Pa... Condominium \n", "725 http://www.zillow.com/homedetails/1-Franklin-S... Condominium \n", "726 http://www.zillow.com/homedetails/28-Woodward-... Condominium \n", "\n", " property map_this_home_link \n", "0 0 http://www.zillow.com/homes/2096497652_zpid/ \n", "1 0 http://www.zillow.com/homes/59113320_zpid/ \n", "2 0 http://www.zillow.com/homes/2096677074_zpid/ \n", "3 0 http://www.zillow.com/homes/2096675064_zpid/ \n", "4 0 http://www.zillow.com/homes/59155302_zpid/ \n", "5 0 http://www.zillow.com/homes/87796106_zpid/ \n", "6 0 http://www.zillow.com/homes/59088144_zpid/ \n", "7 0 http://www.zillow.com/homes/2096823174_zpid/ \n", "8 0 http://www.zillow.com/homes/2096881020_zpid/ \n", "9 0 http://www.zillow.com/homes/59102139_zpid/ \n", "10 0 http://www.zillow.com/homes/59112097_zpid/ \n", "11 0 http://www.zillow.com/homes/2096896512_zpid/ \n", "12 0 http://www.zillow.com/homes/59170501_zpid/ \n", "13 0 http://www.zillow.com/homes/2096807696_zpid/ \n", "14 0 http://www.zillow.com/homes/90139732_zpid/ \n", "15 0 http://www.zillow.com/homes/2097055956_zpid/ \n", "16 0 http://www.zillow.com/homes/67710271_zpid/ \n", "17 0 http://www.zillow.com/homes/81854555_zpid/ \n", "18 0 http://www.zillow.com/homes/81855097_zpid/ \n", "19 0 http://www.zillow.com/homes/2097180167_zpid/ \n", "20 0 http://www.zillow.com/homes/2096881071_zpid/ \n", "21 0 http://www.zillow.com/homes/2107330626_zpid/ \n", "22 0 http://www.zillow.com/homes/59144888_zpid/ \n", "23 0 http://www.zillow.com/homes/56605009_zpid/ \n", "24 0 http://www.zillow.com/homes/2097576158_zpid/ \n", "25 0 http://www.zillow.com/homes/2096375498_zpid/ \n", "26 0 http://www.zillow.com/homes/59158025_zpid/ \n", "27 0 http://www.zillow.com/homes/2096368520_zpid/ \n", "28 0 http://www.zillow.com/homes/2096368919_zpid/ \n", "29 0 http://www.zillow.com/homes/2096368818_zpid/ \n", ".. ... ... \n", "697 0 http://www.zillow.com/homes/87796106_zpid/ \n", "698 0 http://www.zillow.com/homes/2096807696_zpid/ \n", "699 0 http://www.zillow.com/homes/90139732_zpid/ \n", "700 0 http://www.zillow.com/homes/67710271_zpid/ \n", "701 0 http://www.zillow.com/homes/2096881071_zpid/ \n", "702 0 http://www.zillow.com/homes/2107330626_zpid/ \n", "703 0 http://www.zillow.com/homes/59144888_zpid/ \n", "704 0 http://www.zillow.com/homes/2096375498_zpid/ \n", "705 0 http://www.zillow.com/homes/2096497571_zpid/ \n", "706 0 http://www.zillow.com/homes/59113320_zpid/ \n", "707 0 http://www.zillow.com/homes/2096677074_zpid/ \n", "708 0 http://www.zillow.com/homes/2096675064_zpid/ \n", "709 0 http://www.zillow.com/homes/59155302_zpid/ \n", "710 0 http://www.zillow.com/homes/59088144_zpid/ \n", "711 0 http://www.zillow.com/homes/2096881020_zpid/ \n", "712 0 http://www.zillow.com/homes/2096807696_zpid/ \n", "713 0 http://www.zillow.com/homes/90139732_zpid/ \n", "714 0 http://www.zillow.com/homes/81855097_zpid/ \n", "715 0 http://www.zillow.com/homes/2107330626_zpid/ \n", "716 0 http://www.zillow.com/homes/59144888_zpid/ \n", "717 0 http://www.zillow.com/homes/2096375498_zpid/ \n", "718 0 http://www.zillow.com/homes/2096677074_zpid/ \n", "719 0 http://www.zillow.com/homes/2096675064_zpid/ \n", "720 0 http://www.zillow.com/homes/87796106_zpid/ \n", "721 0 http://www.zillow.com/homes/59088144_zpid/ \n", "722 0 http://www.zillow.com/homes/90139732_zpid/ \n", "723 0 http://www.zillow.com/homes/67710271_zpid/ \n", "724 0 http://www.zillow.com/homes/81855097_zpid/ \n", "725 0 http://www.zillow.com/homes/2097180167_zpid/ \n", "726 0 http://www.zillow.com/homes/2107330626_zpid/ \n", "\n", "[727 rows x 68 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "theDf" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\gujianflsgj\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:5: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n" ] }, { "data": { "text/html": [ "
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pixelPlantpixelPolepixelLakepixelRoadpixelGrasspixelWallpixelCarpropertiesAssespixelSeanumCraigslistHouse...tax_valuezillowyear_builtzestimate_valuationRange_lowgraph_data_linkhome_sizehome_detail_linkhome_typepropertymap_this_home_link
00.00.01741.957.5420.00.9201.177471740460.00...NaN0NaN728068.0NaN1400.0http://www.zillow.com/homedetails/321-Dorchest...Condominium0http://www.zillow.com/homes/2096497652_zpid/
10.00.59740.601.5700.00.0259.659129370000.00...499300.001885.0692745.0http://www.zillow.com/homedetails/54-Grampian-...2423.0http://www.zillow.com/homedetails/54-Grampian-...SingleFamily0http://www.zillow.com/homes/59113320_zpid/
20.00.12712.483.1400.00.4272.55542942680.00...NaN02016.0695682.0NaN2800.0http://www.zillow.com/homedetails/129-Gardner-...SingleFamily0http://www.zillow.com/homes/2096677074_zpid/
30.00.0000.000.0000.00.0000.000156015370.00...NaN01925.0458252.0NaN1260.0http://www.zillow.com/homedetails/7-9-Herberts...Condominium0http://www.zillow.com/homes/2096675064_zpid/
40.00.05513.332.2050.00.0498.027173147000.00...448800.001900.0665224.0http://www.zillow.com/homedetails/31-Maple-St-...1847.0http://www.zillow.com/homedetails/31-Maple-St-...SingleFamily0http://www.zillow.com/homes/59155302_zpid/
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5 rows × 68 columns

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" ], "text/plain": [ " pixelPlant pixelPole pixelLake pixelRoad pixelGrass pixelWall \\\n", "0 0.0 0.017 41.95 7.542 0.0 0.920 \n", "1 0.0 0.597 40.60 1.570 0.0 0.025 \n", "2 0.0 0.127 12.48 3.140 0.0 0.427 \n", "3 0.0 0.000 0.00 0.000 0.0 0.000 \n", "4 0.0 0.055 13.33 2.205 0.0 0.049 \n", "\n", " pixelCar propertiesAsses pixelSea numCraigslistHouse \\\n", "0 1.177 47174046 0.0 0 \n", "1 9.659 12937000 0.0 0 \n", "2 2.555 4294268 0.0 0 \n", "3 0.000 15601537 0.0 0 \n", "4 8.027 17314700 0.0 0 \n", "\n", " ... tax_value zillow \\\n", "0 ... NaN 0 \n", "1 ... 499300.0 0 \n", "2 ... NaN 0 \n", "3 ... NaN 0 \n", "4 ... 448800.0 0 \n", "\n", " year_built zestimate_valuationRange_low \\\n", "0 NaN 728068.0 \n", "1 1885.0 692745.0 \n", "2 2016.0 695682.0 \n", "3 1925.0 458252.0 \n", "4 1900.0 665224.0 \n", "\n", " graph_data_link home_size \\\n", "0 NaN 1400.0 \n", "1 http://www.zillow.com/homedetails/54-Grampian-... 2423.0 \n", "2 NaN 2800.0 \n", "3 NaN 1260.0 \n", "4 http://www.zillow.com/homedetails/31-Maple-St-... 1847.0 \n", "\n", " home_detail_link home_type property \\\n", "0 http://www.zillow.com/homedetails/321-Dorchest... Condominium 0 \n", "1 http://www.zillow.com/homedetails/54-Grampian-... SingleFamily 0 \n", "2 http://www.zillow.com/homedetails/129-Gardner-... SingleFamily 0 \n", "3 http://www.zillow.com/homedetails/7-9-Herberts... Condominium 0 \n", "4 http://www.zillow.com/homedetails/31-Maple-St-... SingleFamily 0 \n", "\n", " map_this_home_link \n", "0 http://www.zillow.com/homes/2096497652_zpid/ \n", "1 http://www.zillow.com/homes/59113320_zpid/ \n", "2 http://www.zillow.com/homes/2096677074_zpid/ \n", "3 http://www.zillow.com/homes/2096675064_zpid/ \n", "4 http://www.zillow.com/homes/59155302_zpid/ \n", "\n", "[5 rows x 68 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# price values listed as string with a commoa inside, which is \n", "#theDf['prices'].replace(regex=True,inplace=True,to_replace=r',',value=r'')\n", "\n", "#pd.to_numeric(theDf['prices'],errors='raise')\n", "data = theDf.convert_objects(convert_numeric=True)\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(727, 51)\n" ] }, { "data": { "text/html": [ "
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pixelPlantpixelPolepixelLakepixelRoadpixelGrasspixelWallpixelCarpixelSeanumCraigslistHousepixelRiver...pricesproperty_sizezipstatusbedroomszillow_idlast_sold_dateyear_builthome_sizehome_type
00.00.01741.957.5420.00.9201.1770.000.000...849000.0NaN2127Condo For Sale4.02096497652NaNNaN1400.0Condominium
10.00.59740.601.5700.00.0259.6590.000.000...899000.06534.02125House For Sale5.05911332012/11/19981885.02423.0SingleFamily
20.00.12712.483.1400.00.4272.5550.000.004...849000.06969.02132House For Sale4.02096677074NaN2016.02800.0SingleFamily
30.00.0000.000.0000.00.0000.0000.000.000...529000.09999.02130Condo For Sale3.02096675064NaN1925.01260.0Condominium
40.00.05513.332.2050.00.0498.0270.000.210...739900.04356.02132House For Sale3.059155302NaN1900.01847.0SingleFamily
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5 rows × 51 columns

\n", "
" ], "text/plain": [ " pixelPlant pixelPole pixelLake pixelRoad pixelGrass pixelWall \\\n", "0 0.0 0.017 41.95 7.542 0.0 0.920 \n", "1 0.0 0.597 40.60 1.570 0.0 0.025 \n", "2 0.0 0.127 12.48 3.140 0.0 0.427 \n", "3 0.0 0.000 0.00 0.000 0.0 0.000 \n", "4 0.0 0.055 13.33 2.205 0.0 0.049 \n", "\n", " pixelCar pixelSea numCraigslistHouse pixelRiver ... prices \\\n", "0 1.177 0.0 0 0.000 ... 849000.0 \n", "1 9.659 0.0 0 0.000 ... 899000.0 \n", "2 2.555 0.0 0 0.004 ... 849000.0 \n", "3 0.000 0.0 0 0.000 ... 529000.0 \n", "4 8.027 0.0 0 0.210 ... 739900.0 \n", "\n", " property_size zip status bedrooms zillow_id last_sold_date \\\n", "0 NaN 2127 Condo For Sale 4.0 2096497652 NaN \n", "1 6534.0 2125 House For Sale 5.0 59113320 12/11/1998 \n", "2 6969.0 2132 House For Sale 4.0 2096677074 NaN \n", "3 9999.0 2130 Condo For Sale 3.0 2096675064 NaN \n", "4 4356.0 2132 House For Sale 3.0 59155302 NaN \n", "\n", " year_built home_size home_type \n", "0 NaN 1400.0 Condominium \n", "1 1885.0 2423.0 SingleFamily \n", "2 2016.0 2800.0 SingleFamily \n", "3 1925.0 1260.0 Condominium \n", "4 1900.0 1847.0 SingleFamily \n", "\n", "[5 rows x 51 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "### dropping useless or repeated information\n", "zestimate = pd.concat([data['zestimate_amount'],data['zestimate_valuation_range_high'],data['zestimate_valuationRange_low'],data['zestimate_value_change'],data['zestimate_percentile']],axis=1)\n", "data = data.drop('graph_data_link',axis = 1) \n", "data = data.drop('home_detail_link',axis = 1) \n", "data = data.drop('map_this_home_link',axis = 1)\n", "data = data.drop('zestimate_last_updated',axis = 1)\n", "data = data.drop('propertiesAsses',axis = 1)\n", "data = data.drop('zestimate_amount',axis = 1)\n", "data = data.drop('zestimate_valuation_range_high',axis = 1)\n", "data = data.drop('zestimate_valuationRange_low',axis = 1)\n", "data = data.drop('zestimate_value_change',axis = 1)\n", "data = data.drop('zestimate_percentile',axis = 1)\n", "data = data.drop('address',axis = 1)\n", "data = data.drop('tax_value',1)\n", "data = data.drop('tax_year',1)\n", "data = data.drop('lat',1)\n", "data = data.drop('long',1)\n", "data = data.drop('zillow',1)\n", "data = data.drop('property',1)\n", "print data.shape\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index([u'pixelPlant', u'pixelPole', u'pixelLake', u'pixelRoad', u'pixelGrass',\n", " u'pixelWall', u'pixelCar', u'pixelSea', u'numCraigslistHouse',\n", " u'pixelRiver', u'pixelBus', u'pixelCeiling', u'pixelPath',\n", " u'pixelBuilding', u'crime', u'pixelFence', u'walkSchool', u'walkMbta',\n", " u'energySiteEUI', u'pixelPerson', u'pixelTree', u'pixelVan',\n", " u'walkPark', u'walkUniversity', u'pixelSidewalk', u'pixelGround',\n", " u'pixelMountain', u'pixelPalmTree', u'pixelHouse', u'pixelBridge',\n", " u'pixelSign', u'pixelRailing', u'pixelField', u'pixelWindow',\n", " u'pixelGrandstand', u'numCraigslistRoom', u'pixelSky', u'longitude',\n", " u'latitude', u'bathrooms', u'last_sold_price', u'prices',\n", " u'property_size', u'zip', u'status', u'bedrooms', u'zillow_id',\n", " u'last_sold_date', u'year_built', u'home_size', u'home_type'],\n", " dtype='object')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.columns \n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ID = data['zillow_id'].copy() #recourding test data ID for future output\n", "data = data.drop('zillow_id',axis=1)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data['last_sold_price'] = data['last_sold_price'].isnull()\n", "data = data.drop('last_sold_date',1)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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pixelPlantpixelPolepixelLakepixelRoadpixelGrasspixelWallpixelCarpixelSeanumCraigslistHousepixelRiver...bathroomslast_sold_pricepricesproperty_sizezipstatusbedroomsyear_builthome_sizehome_type
00.00.01741.957.5420.00.9201.1770.000.000...2.0True849000.0NaN2127Condo For Sale4.0NaN1400.0Condominium
10.00.59740.601.5700.00.0259.6590.000.000...2.0False899000.06534.02125House For Sale5.01885.02423.0SingleFamily
20.00.12712.483.1400.00.4272.5550.000.004...3.0True849000.06969.02132House For Sale4.02016.02800.0SingleFamily
30.00.0000.000.0000.00.0000.0000.000.000...2.0True529000.09999.02130Condo For Sale3.01925.01260.0Condominium
40.00.05513.332.2050.00.0498.0270.000.210...2.0True739900.04356.02132House For Sale3.01900.01847.0SingleFamily
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5 rows × 49 columns

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" ], "text/plain": [ " pixelPlant pixelPole pixelLake pixelRoad pixelGrass pixelWall \\\n", "0 0.0 0.017 41.95 7.542 0.0 0.920 \n", "1 0.0 0.597 40.60 1.570 0.0 0.025 \n", "2 0.0 0.127 12.48 3.140 0.0 0.427 \n", "3 0.0 0.000 0.00 0.000 0.0 0.000 \n", "4 0.0 0.055 13.33 2.205 0.0 0.049 \n", "\n", " pixelCar pixelSea numCraigslistHouse pixelRiver ... \\\n", "0 1.177 0.0 0 0.000 ... \n", "1 9.659 0.0 0 0.000 ... \n", "2 2.555 0.0 0 0.004 ... \n", "3 0.000 0.0 0 0.000 ... \n", "4 8.027 0.0 0 0.210 ... \n", "\n", " bathrooms last_sold_price prices property_size zip status \\\n", "0 2.0 True 849000.0 NaN 2127 Condo For Sale \n", "1 2.0 False 899000.0 6534.0 2125 House For Sale \n", "2 3.0 True 849000.0 6969.0 2132 House For Sale \n", "3 2.0 True 529000.0 9999.0 2130 Condo For Sale \n", "4 2.0 True 739900.0 4356.0 2132 House For Sale \n", "\n", " bedrooms year_built home_size home_type \n", "0 4.0 NaN 1400.0 Condominium \n", "1 5.0 1885.0 2423.0 SingleFamily \n", "2 4.0 2016.0 2800.0 SingleFamily \n", "3 3.0 1925.0 1260.0 Condominium \n", "4 3.0 1900.0 1847.0 SingleFamily \n", "\n", "[5 rows x 49 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#base cleaning for missing value \n", "data = data.replace('None',np.nan)\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pixelPlant 0\n", "pixelPole 0\n", "pixelLake 0\n", "pixelRoad 0\n", "pixelGrass 0\n", "pixelWall 0\n", "pixelCar 0\n", "pixelSea 0\n", "numCraigslistHouse 0\n", "pixelRiver 0\n", "pixelBus 0\n", "pixelCeiling 0\n", "pixelPath 0\n", "pixelBuilding 0\n", "crime 0\n", "pixelFence 0\n", "walkSchool 0\n", "walkMbta 0\n", "energySiteEUI 0\n", "pixelPerson 0\n", "pixelTree 0\n", "pixelVan 0\n", "walkPark 0\n", "walkUniversity 0\n", "pixelSidewalk 0\n", "pixelGround 0\n", "pixelMountain 0\n", "pixelPalmTree 0\n", "pixelHouse 0\n", "pixelBridge 0\n", "pixelSign 0\n", "pixelRailing 0\n", "pixelField 0\n", "pixelWindow 0\n", "pixelGrandstand 0\n", "numCraigslistRoom 0\n", "pixelSky 0\n", "longitude 0\n", "latitude 0\n", "bathrooms 28\n", "last_sold_price 0\n", "prices 9\n", "property_size 327\n", "zip 0\n", "status 0\n", "bedrooms 23\n", "year_built 55\n", "home_size 14\n", "home_type 0\n" ] } ], "source": [ "# checking missing values for each features\n", "for col in data.columns:\n", " print col,len(data[data[col].isnull()])\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#there is a lot of missing values in propoerty area, we will drop the column first " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## knn learning to filling in missing values for built year # after milestones " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pixelPlant float64\n", "pixelPole float64\n", "pixelLake float64\n", "pixelRoad float64\n", "pixelGrass float64\n", "pixelWall float64\n", "pixelCar float64\n", "pixelSea float64\n", "numCraigslistHouse int64\n", "pixelRiver float64\n", "pixelBus float64\n", "pixelCeiling float64\n", "pixelPath float64\n", "pixelBuilding float64\n", "crime int64\n", "pixelFence int64\n", "walkSchool int64\n", "walkMbta int64\n", "energySiteEUI float64\n", "pixelPerson float64\n", "pixelTree float64\n", "pixelVan float64\n", "walkPark int64\n", "walkUniversity int64\n", "pixelSidewalk float64\n", "pixelGround int64\n", "pixelMountain float64\n", "pixelPalmTree float64\n", "pixelHouse float64\n", "pixelBridge float64\n", "pixelSign float64\n", "pixelRailing float64\n", "pixelField float64\n", "pixelWindow float64\n", "pixelGrandstand float64\n", "numCraigslistRoom int64\n", "pixelSky float64\n", "longitude float64\n", "latitude float64\n", "bathrooms float64\n", "last_sold_price bool\n", "prices float64\n", "property_size float64\n", "zip int64\n", "status object\n", "bedrooms float64\n", "year_built float64\n", "home_size float64\n", "home_type object\n", "['numCraigslistHouse', 'crime', 'pixelFence', 'walkSchool', 'walkMbta', 'walkPark', 'walkUniversity', 'pixelGround', 'numCraigslistRoom', 'zip']\n", "['status', 'home_type']\n" ] } ], "source": [ "# Encoding if we want to implement a decision tree or svm \n", "to_float = []\n", "to_encode = []\n", "for col in data.columns:\n", " if data[col].dtype =='object':\n", " to_encode.append(col);\n", " if data[col].dtype =='int64':\n", " to_float.append(col);\n", " print col,data[col].dtype\n", " \n", "print to_float\n", "print to_encode" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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pixelPlantpixelPolepixelLakepixelRoadpixelGrasspixelWallpixelCarpixelSeanumCraigslistHousepixelRiver...bathroomslast_sold_pricepricesproperty_sizezipstatusbedroomsyear_builthome_sizehome_type
00.00.01741.957.5420.00.9201.1770.00.00.000...2.0True849000.0NaN2127.0Condo For Sale4.0NaN1400.0Condominium
10.00.59740.601.5700.00.0259.6590.00.00.000...2.0False899000.06534.02125.0House For Sale5.01885.02423.0SingleFamily
20.00.12712.483.1400.00.4272.5550.00.00.004...3.0True849000.06969.02132.0House For Sale4.02016.02800.0SingleFamily
30.00.0000.000.0000.00.0000.0000.00.00.000...2.0True529000.09999.02130.0Condo For Sale3.01925.01260.0Condominium
40.00.05513.332.2050.00.0498.0270.00.00.210...2.0True739900.04356.02132.0House For Sale3.01900.01847.0SingleFamily
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5 rows × 49 columns

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" ], "text/plain": [ " pixelPlant pixelPole pixelLake pixelRoad pixelGrass pixelWall \\\n", "0 0.0 0.017 41.95 7.542 0.0 0.920 \n", "1 0.0 0.597 40.60 1.570 0.0 0.025 \n", "2 0.0 0.127 12.48 3.140 0.0 0.427 \n", "3 0.0 0.000 0.00 0.000 0.0 0.000 \n", "4 0.0 0.055 13.33 2.205 0.0 0.049 \n", "\n", " pixelCar pixelSea numCraigslistHouse pixelRiver ... \\\n", "0 1.177 0.0 0.0 0.000 ... \n", "1 9.659 0.0 0.0 0.000 ... \n", "2 2.555 0.0 0.0 0.004 ... \n", "3 0.000 0.0 0.0 0.000 ... \n", "4 8.027 0.0 0.0 0.210 ... \n", "\n", " bathrooms last_sold_price prices property_size zip \\\n", "0 2.0 True 849000.0 NaN 2127.0 \n", "1 2.0 False 899000.0 6534.0 2125.0 \n", "2 3.0 True 849000.0 6969.0 2132.0 \n", "3 2.0 True 529000.0 9999.0 2130.0 \n", "4 2.0 True 739900.0 4356.0 2132.0 \n", "\n", " status bedrooms year_built home_size home_type \n", "0 Condo For Sale 4.0 NaN 1400.0 Condominium \n", "1 House For Sale 5.0 1885.0 2423.0 SingleFamily \n", "2 House For Sale 4.0 2016.0 2800.0 SingleFamily \n", "3 Condo For Sale 3.0 1925.0 1260.0 Condominium \n", "4 House For Sale 3.0 1900.0 1847.0 SingleFamily \n", "\n", "[5 rows x 49 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for feature_name in to_float:\n", " data[feature_name] = data[feature_name].astype(float)\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def encode_categorical(array):\n", " if not array.dtype == np.dtype('float64'):\n", " return preprocessing.LabelEncoder().fit_transform(array) \n", " else:\n", " return array\n", " \n", "# Categorical columns for use in one-hot encoder\n", "categorical = (data.dtypes.values != np.dtype('float64'))\n", "\n", "# Encode all labels\n", "data = data.apply(encode_categorical)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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pixelPlantpixelPolepixelLakepixelRoadpixelGrasspixelWallpixelCarpixelSeanumCraigslistHousepixelRiver...bathroomslast_sold_pricepricesproperty_sizezipstatusbedroomsyear_builthome_sizehome_type
00.0000.01741.9507.5420.0000.9201.1770.000.00.000...2.01849000.01364.002127.044.01982.001400.01
10.0000.59740.6001.5700.0000.0259.6590.000.00.000...2.00899000.06534.002125.085.01885.002423.06
20.0000.12712.4803.1400.0000.4272.5550.000.00.004...3.01849000.06969.002132.084.02016.002800.06
30.0000.0000.0000.0000.0000.0000.0000.000.00.000...2.01529000.09999.002130.043.01925.001260.01
40.0000.05513.3302.2050.0000.0498.0270.000.00.210...2.01739900.04356.002132.083.01900.001847.06
50.0000.02448.58010.9600.0000.0010.9790.000.00.000...1.00675000.01057.002118.042.02006.001057.01
60.0000.0000.0000.0000.0000.0000.0000.000.00.000...1.00339900.0578.002134.041.01910.00578.01
70.0000.00729.6904.2350.0000.2274.4370.000.00.000...1.01339000.06697.752124.042.01920.001000.01
80.0000.00015.2400.0860.0000.0010.4400.000.00.000...2.01479000.02092.252122.043.01900.001400.01
90.0000.00051.3902.5350.0000.0446.9580.000.00.000...2.00284999.02613.002124.083.01991.001520.06
100.0000.0000.0000.0000.0000.0000.0000.000.00.000...1.01399000.05662.002121.085.01890.002684.06
110.0000.00729.6904.2350.0000.2274.4370.000.00.000...1.01345000.06697.752124.042.01920.001000.01
120.0000.7712.4750.0741.3490.0001.1060.010.010.420...3.002495000.02267.002116.043.01884.002267.01
130.0000.0000.0000.0000.0000.0000.0000.000.00.000...2.01399900.02418.752132.043.01940.001370.01
150.0000.0000.0000.0000.0000.0000.0000.000.00.000...2.01669000.01916.002130.043.01899.001916.01
160.0000.00047.9804.0420.0000.0000.1520.000.00.000...2.001540000.01364.002116.041.02004.001364.01
170.0000.31432.8304.0540.0001.5824.4470.000.00.000...1.00389000.02496.252122.042.01905.001061.01
180.0000.0000.0000.0000.0000.0000.0000.000.00.000...1.00399500.03484.002136.041.01950.001570.01
190.0000.00089.4703.7150.0000.7430.0000.000.00.000...1.001300000.01042.002108.041.02016.00923.01
200.0000.03262.0903.8160.0000.0383.7150.000.00.000...2.01479000.02092.252122.043.01900.001400.01
210.0000.04055.9208.5550.0000.2820.1750.001.00.000...3.01875000.06860.002127.043.02016.001700.01
220.0000.0000.0000.0000.0000.0000.0000.000.00.000...2.00549500.05530.002131.043.01901.001950.01
230.0000.0000.1830.0000.0000.0010.0000.000.00.000...2.00769000.04377.752122.042.01999.002286.01
240.0000.0000.1830.0000.0000.0010.0000.000.00.000...1.01549000.01076.002134.052.01904.00850.01
250.0000.07143.5405.4340.0001.4342.2970.000.00.000...2.51749000.03375.252130.0123.02016.001603.07
260.0000.01617.7407.5380.0002.9085.5150.000.00.010...2.01425000.05662.002132.084.01925.001692.06
270.0000.0000.0000.0000.0000.0000.0000.000.00.000...2.011913600.05000.002215.042.01912.501296.01
280.0000.00170.2304.0770.0000.0000.2180.000.00.000...1.01299000.02197.002128.042.02015.00850.01
290.0000.00170.2304.0770.0000.0000.2180.000.00.000...2.01424900.03073.752128.044.02016.001225.01
301.7830.00066.2201.2710.0008.9610.2390.000.00.000...2.011195000.08697.002127.0112.01954.251250.01
..................................................................
6940.0000.0000.0000.0000.0000.0000.0000.000.00.000...2.00549500.05530.002131.043.01901.001950.01
6950.0000.12712.4803.1400.0000.4272.5550.000.00.004...3.01849000.06969.002132.084.02016.002800.06
6960.0000.0000.0000.0000.0000.0000.0000.000.00.000...2.01529000.09999.002130.043.01925.001260.01
6970.0000.02448.58010.9600.0000.0010.9790.000.00.000...1.00675000.01057.002118.042.02006.001057.01
6980.0000.0000.0000.0000.0000.0000.0000.000.00.000...2.01399900.02418.752132.043.01940.001370.01
7000.0000.00047.9804.0420.0000.0000.1520.000.00.000...2.001540000.01364.002116.041.02004.001364.01
7010.0000.03262.0903.8160.0000.0383.7150.000.00.000...2.01479000.02092.252122.043.01900.001400.01
7020.0000.04055.9208.5550.0000.2820.1750.001.00.000...3.01875000.06860.002127.043.02016.001700.01
7030.0000.0000.0000.0000.0000.0000.0000.000.00.000...2.00549500.05530.002131.043.01901.001950.01
7040.0000.07143.5405.4340.0001.4342.2970.000.00.000...2.51749000.03375.252130.0123.02016.001603.07
7050.0000.01741.9507.5420.0000.9201.1770.000.00.000...3.01849000.04032.752127.044.01982.002400.01
7060.0000.59740.6001.5700.0000.0259.6590.000.00.000...2.00899000.06534.002125.085.01885.002423.06
7070.0000.12712.4803.1400.0000.4272.5550.000.00.004...3.01849000.06969.002132.084.02016.002800.06
7080.0000.0000.0000.0000.0000.0000.0000.000.00.000...2.01529000.09999.002130.043.01925.001260.01
7090.0000.05513.3302.2050.0000.0498.0270.000.00.210...2.01739900.04356.002132.083.01900.001847.06
7100.0000.0000.0000.0000.0000.0000.0000.000.00.000...1.00339900.0578.002134.041.01910.00578.01
7110.0000.00015.2400.0860.0000.0010.4400.000.00.000...2.01479000.02092.252122.043.01900.001400.01
7120.0000.0000.0000.0000.0000.0000.0000.000.00.000...2.01399900.02418.752132.043.01940.001370.01
7140.0000.0000.0000.0000.0000.0000.0000.000.00.000...1.00399500.03484.002136.041.01950.001570.01
7150.0000.04055.9208.5550.0000.2820.1750.001.00.000...3.01875000.06860.002127.043.02016.001700.01
7160.0000.0000.0000.0000.0000.0000.0000.000.00.000...2.00549500.05530.002131.043.01901.001950.01
7170.0000.07143.5405.4340.0001.4342.2970.000.00.000...2.51749000.03375.252130.0123.02016.001603.07
7180.0000.12712.4803.1400.0000.4272.5550.000.00.004...3.01849000.06969.002132.084.02016.002800.06
7190.0000.0000.0000.0000.0000.0000.0000.000.00.000...2.01529000.09999.002130.043.01925.001260.01
7200.0000.02448.58010.9600.0000.0010.9790.000.00.000...1.00675000.01057.002118.042.02006.001057.01
7210.0000.0000.0000.0000.0000.0000.0000.000.00.000...1.00339900.0578.002134.041.01910.00578.01
7230.0000.00047.9804.0420.0000.0000.1520.000.00.000...2.001540000.01364.002116.041.02004.001364.01
7240.0000.0000.0000.0000.0000.0000.0000.000.00.000...1.00399500.03484.002136.041.01950.001570.01
7250.0000.00089.4703.7150.0000.7430.0000.000.00.000...1.001300000.01042.002108.041.02016.00923.01
7260.0000.04055.9208.5550.0000.2820.1750.001.00.000...3.01875000.06860.002127.043.02016.001700.01
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689 rows × 49 columns

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" ], "text/plain": [ " pixelPlant pixelPole pixelLake pixelRoad pixelGrass pixelWall \\\n", "0 0.000 0.017 41.950 7.542 0.000 0.920 \n", "1 0.000 0.597 40.600 1.570 0.000 0.025 \n", "2 0.000 0.127 12.480 3.140 0.000 0.427 \n", "3 0.000 0.000 0.000 0.000 0.000 0.000 \n", "4 0.000 0.055 13.330 2.205 0.000 0.049 \n", "5 0.000 0.024 48.580 10.960 0.000 0.001 \n", "6 0.000 0.000 0.000 0.000 0.000 0.000 \n", "7 0.000 0.007 29.690 4.235 0.000 0.227 \n", "8 0.000 0.000 15.240 0.086 0.000 0.001 \n", "9 0.000 0.000 51.390 2.535 0.000 0.044 \n", "10 0.000 0.000 0.000 0.000 0.000 0.000 \n", "11 0.000 0.007 29.690 4.235 0.000 0.227 \n", "12 0.000 0.771 2.475 0.074 1.349 0.000 \n", "13 0.000 0.000 0.000 0.000 0.000 0.000 \n", "15 0.000 0.000 0.000 0.000 0.000 0.000 \n", "16 0.000 0.000 47.980 4.042 0.000 0.000 \n", "17 0.000 0.314 32.830 4.054 0.000 1.582 \n", "18 0.000 0.000 0.000 0.000 0.000 0.000 \n", "19 0.000 0.000 89.470 3.715 0.000 0.743 \n", "20 0.000 0.032 62.090 3.816 0.000 0.038 \n", "21 0.000 0.040 55.920 8.555 0.000 0.282 \n", "22 0.000 0.000 0.000 0.000 0.000 0.000 \n", "23 0.000 0.000 0.183 0.000 0.000 0.001 \n", "24 0.000 0.000 0.183 0.000 0.000 0.001 \n", "25 0.000 0.071 43.540 5.434 0.000 1.434 \n", "26 0.000 0.016 17.740 7.538 0.000 2.908 \n", "27 0.000 0.000 0.000 0.000 0.000 0.000 \n", "28 0.000 0.001 70.230 4.077 0.000 0.000 \n", "29 0.000 0.001 70.230 4.077 0.000 0.000 \n", "30 1.783 0.000 66.220 1.271 0.000 8.961 \n", ".. ... ... ... ... ... ... \n", "694 0.000 0.000 0.000 0.000 0.000 0.000 \n", "695 0.000 0.127 12.480 3.140 0.000 0.427 \n", "696 0.000 0.000 0.000 0.000 0.000 0.000 \n", "697 0.000 0.024 48.580 10.960 0.000 0.001 \n", "698 0.000 0.000 0.000 0.000 0.000 0.000 \n", "700 0.000 0.000 47.980 4.042 0.000 0.000 \n", "701 0.000 0.032 62.090 3.816 0.000 0.038 \n", "702 0.000 0.040 55.920 8.555 0.000 0.282 \n", "703 0.000 0.000 0.000 0.000 0.000 0.000 \n", "704 0.000 0.071 43.540 5.434 0.000 1.434 \n", "705 0.000 0.017 41.950 7.542 0.000 0.920 \n", "706 0.000 0.597 40.600 1.570 0.000 0.025 \n", "707 0.000 0.127 12.480 3.140 0.000 0.427 \n", "708 0.000 0.000 0.000 0.000 0.000 0.000 \n", "709 0.000 0.055 13.330 2.205 0.000 0.049 \n", "710 0.000 0.000 0.000 0.000 0.000 0.000 \n", "711 0.000 0.000 15.240 0.086 0.000 0.001 \n", "712 0.000 0.000 0.000 0.000 0.000 0.000 \n", "714 0.000 0.000 0.000 0.000 0.000 0.000 \n", "715 0.000 0.040 55.920 8.555 0.000 0.282 \n", "716 0.000 0.000 0.000 0.000 0.000 0.000 \n", "717 0.000 0.071 43.540 5.434 0.000 1.434 \n", "718 0.000 0.127 12.480 3.140 0.000 0.427 \n", "719 0.000 0.000 0.000 0.000 0.000 0.000 \n", "720 0.000 0.024 48.580 10.960 0.000 0.001 \n", "721 0.000 0.000 0.000 0.000 0.000 0.000 \n", "723 0.000 0.000 47.980 4.042 0.000 0.000 \n", "724 0.000 0.000 0.000 0.000 0.000 0.000 \n", "725 0.000 0.000 89.470 3.715 0.000 0.743 \n", "726 0.000 0.040 55.920 8.555 0.000 0.282 \n", "\n", " pixelCar pixelSea numCraigslistHouse pixelRiver ... bathrooms \\\n", "0 1.177 0.00 0.0 0.000 ... 2.0 \n", "1 9.659 0.00 0.0 0.000 ... 2.0 \n", "2 2.555 0.00 0.0 0.004 ... 3.0 \n", "3 0.000 0.00 0.0 0.000 ... 2.0 \n", "4 8.027 0.00 0.0 0.210 ... 2.0 \n", "5 0.979 0.00 0.0 0.000 ... 1.0 \n", "6 0.000 0.00 0.0 0.000 ... 1.0 \n", "7 4.437 0.00 0.0 0.000 ... 1.0 \n", "8 0.440 0.00 0.0 0.000 ... 2.0 \n", "9 6.958 0.00 0.0 0.000 ... 2.0 \n", "10 0.000 0.00 0.0 0.000 ... 1.0 \n", "11 4.437 0.00 0.0 0.000 ... 1.0 \n", "12 1.106 0.01 0.0 10.420 ... 3.0 \n", "13 0.000 0.00 0.0 0.000 ... 2.0 \n", "15 0.000 0.00 0.0 0.000 ... 2.0 \n", "16 0.152 0.00 0.0 0.000 ... 2.0 \n", "17 4.447 0.00 0.0 0.000 ... 1.0 \n", "18 0.000 0.00 0.0 0.000 ... 1.0 \n", "19 0.000 0.00 0.0 0.000 ... 1.0 \n", "20 3.715 0.00 0.0 0.000 ... 2.0 \n", "21 0.175 0.00 1.0 0.000 ... 3.0 \n", "22 0.000 0.00 0.0 0.000 ... 2.0 \n", "23 0.000 0.00 0.0 0.000 ... 2.0 \n", "24 0.000 0.00 0.0 0.000 ... 1.0 \n", "25 2.297 0.00 0.0 0.000 ... 2.5 \n", "26 5.515 0.00 0.0 0.010 ... 2.0 \n", "27 0.000 0.00 0.0 0.000 ... 2.0 \n", "28 0.218 0.00 0.0 0.000 ... 1.0 \n", "29 0.218 0.00 0.0 0.000 ... 2.0 \n", "30 0.239 0.00 0.0 0.000 ... 2.0 \n", ".. ... ... ... ... ... ... \n", "694 0.000 0.00 0.0 0.000 ... 2.0 \n", "695 2.555 0.00 0.0 0.004 ... 3.0 \n", "696 0.000 0.00 0.0 0.000 ... 2.0 \n", "697 0.979 0.00 0.0 0.000 ... 1.0 \n", "698 0.000 0.00 0.0 0.000 ... 2.0 \n", "700 0.152 0.00 0.0 0.000 ... 2.0 \n", "701 3.715 0.00 0.0 0.000 ... 2.0 \n", "702 0.175 0.00 1.0 0.000 ... 3.0 \n", "703 0.000 0.00 0.0 0.000 ... 2.0 \n", "704 2.297 0.00 0.0 0.000 ... 2.5 \n", "705 1.177 0.00 0.0 0.000 ... 3.0 \n", "706 9.659 0.00 0.0 0.000 ... 2.0 \n", "707 2.555 0.00 0.0 0.004 ... 3.0 \n", "708 0.000 0.00 0.0 0.000 ... 2.0 \n", "709 8.027 0.00 0.0 0.210 ... 2.0 \n", "710 0.000 0.00 0.0 0.000 ... 1.0 \n", "711 0.440 0.00 0.0 0.000 ... 2.0 \n", "712 0.000 0.00 0.0 0.000 ... 2.0 \n", "714 0.000 0.00 0.0 0.000 ... 1.0 \n", "715 0.175 0.00 1.0 0.000 ... 3.0 \n", "716 0.000 0.00 0.0 0.000 ... 2.0 \n", "717 2.297 0.00 0.0 0.000 ... 2.5 \n", "718 2.555 0.00 0.0 0.004 ... 3.0 \n", "719 0.000 0.00 0.0 0.000 ... 2.0 \n", "720 0.979 0.00 0.0 0.000 ... 1.0 \n", "721 0.000 0.00 0.0 0.000 ... 1.0 \n", "723 0.152 0.00 0.0 0.000 ... 2.0 \n", "724 0.000 0.00 0.0 0.000 ... 1.0 \n", "725 0.000 0.00 0.0 0.000 ... 1.0 \n", "726 0.175 0.00 1.0 0.000 ... 3.0 \n", "\n", " last_sold_price prices property_size zip status bedrooms \\\n", "0 1 849000.0 1364.00 2127.0 4 4.0 \n", "1 0 899000.0 6534.00 2125.0 8 5.0 \n", "2 1 849000.0 6969.00 2132.0 8 4.0 \n", "3 1 529000.0 9999.00 2130.0 4 3.0 \n", "4 1 739900.0 4356.00 2132.0 8 3.0 \n", "5 0 675000.0 1057.00 2118.0 4 2.0 \n", "6 0 339900.0 578.00 2134.0 4 1.0 \n", "7 1 339000.0 6697.75 2124.0 4 2.0 \n", "8 1 479000.0 2092.25 2122.0 4 3.0 \n", "9 0 284999.0 2613.00 2124.0 8 3.0 \n", "10 1 399000.0 5662.00 2121.0 8 5.0 \n", "11 1 345000.0 6697.75 2124.0 4 2.0 \n", "12 0 2495000.0 2267.00 2116.0 4 3.0 \n", "13 1 399900.0 2418.75 2132.0 4 3.0 \n", "15 1 669000.0 1916.00 2130.0 4 3.0 \n", "16 0 1540000.0 1364.00 2116.0 4 1.0 \n", "17 0 389000.0 2496.25 2122.0 4 2.0 \n", "18 0 399500.0 3484.00 2136.0 4 1.0 \n", "19 0 1300000.0 1042.00 2108.0 4 1.0 \n", "20 1 479000.0 2092.25 2122.0 4 3.0 \n", "21 1 875000.0 6860.00 2127.0 4 3.0 \n", "22 0 549500.0 5530.00 2131.0 4 3.0 \n", "23 0 769000.0 4377.75 2122.0 4 2.0 \n", "24 1 549000.0 1076.00 2134.0 5 2.0 \n", "25 1 749000.0 3375.25 2130.0 12 3.0 \n", "26 1 425000.0 5662.00 2132.0 8 4.0 \n", "27 1 1913600.0 5000.00 2215.0 4 2.0 \n", "28 1 299000.0 2197.00 2128.0 4 2.0 \n", "29 1 424900.0 3073.75 2128.0 4 4.0 \n", "30 1 1195000.0 8697.00 2127.0 11 2.0 \n", ".. ... ... ... ... ... ... \n", "694 0 549500.0 5530.00 2131.0 4 3.0 \n", "695 1 849000.0 6969.00 2132.0 8 4.0 \n", "696 1 529000.0 9999.00 2130.0 4 3.0 \n", "697 0 675000.0 1057.00 2118.0 4 2.0 \n", "698 1 399900.0 2418.75 2132.0 4 3.0 \n", "700 0 1540000.0 1364.00 2116.0 4 1.0 \n", "701 1 479000.0 2092.25 2122.0 4 3.0 \n", "702 1 875000.0 6860.00 2127.0 4 3.0 \n", "703 0 549500.0 5530.00 2131.0 4 3.0 \n", "704 1 749000.0 3375.25 2130.0 12 3.0 \n", "705 1 849000.0 4032.75 2127.0 4 4.0 \n", "706 0 899000.0 6534.00 2125.0 8 5.0 \n", "707 1 849000.0 6969.00 2132.0 8 4.0 \n", "708 1 529000.0 9999.00 2130.0 4 3.0 \n", "709 1 739900.0 4356.00 2132.0 8 3.0 \n", "710 0 339900.0 578.00 2134.0 4 1.0 \n", "711 1 479000.0 2092.25 2122.0 4 3.0 \n", "712 1 399900.0 2418.75 2132.0 4 3.0 \n", "714 0 399500.0 3484.00 2136.0 4 1.0 \n", "715 1 875000.0 6860.00 2127.0 4 3.0 \n", "716 0 549500.0 5530.00 2131.0 4 3.0 \n", "717 1 749000.0 3375.25 2130.0 12 3.0 \n", "718 1 849000.0 6969.00 2132.0 8 4.0 \n", "719 1 529000.0 9999.00 2130.0 4 3.0 \n", "720 0 675000.0 1057.00 2118.0 4 2.0 \n", "721 0 339900.0 578.00 2134.0 4 1.0 \n", "723 0 1540000.0 1364.00 2116.0 4 1.0 \n", "724 0 399500.0 3484.00 2136.0 4 1.0 \n", "725 0 1300000.0 1042.00 2108.0 4 1.0 \n", "726 1 875000.0 6860.00 2127.0 4 3.0 \n", "\n", " year_built home_size home_type \n", "0 1982.00 1400.0 1 \n", "1 1885.00 2423.0 6 \n", "2 2016.00 2800.0 6 \n", "3 1925.00 1260.0 1 \n", "4 1900.00 1847.0 6 \n", "5 2006.00 1057.0 1 \n", "6 1910.00 578.0 1 \n", "7 1920.00 1000.0 1 \n", "8 1900.00 1400.0 1 \n", "9 1991.00 1520.0 6 \n", "10 1890.00 2684.0 6 \n", "11 1920.00 1000.0 1 \n", "12 1884.00 2267.0 1 \n", "13 1940.00 1370.0 1 \n", "15 1899.00 1916.0 1 \n", "16 2004.00 1364.0 1 \n", "17 1905.00 1061.0 1 \n", "18 1950.00 1570.0 1 \n", "19 2016.00 923.0 1 \n", "20 1900.00 1400.0 1 \n", "21 2016.00 1700.0 1 \n", "22 1901.00 1950.0 1 \n", "23 1999.00 2286.0 1 \n", "24 1904.00 850.0 1 \n", "25 2016.00 1603.0 7 \n", "26 1925.00 1692.0 6 \n", "27 1912.50 1296.0 1 \n", "28 2015.00 850.0 1 \n", "29 2016.00 1225.0 1 \n", "30 1954.25 1250.0 1 \n", ".. ... ... ... \n", "694 1901.00 1950.0 1 \n", "695 2016.00 2800.0 6 \n", "696 1925.00 1260.0 1 \n", "697 2006.00 1057.0 1 \n", "698 1940.00 1370.0 1 \n", "700 2004.00 1364.0 1 \n", "701 1900.00 1400.0 1 \n", "702 2016.00 1700.0 1 \n", "703 1901.00 1950.0 1 \n", "704 2016.00 1603.0 7 \n", "705 1982.00 2400.0 1 \n", "706 1885.00 2423.0 6 \n", "707 2016.00 2800.0 6 \n", "708 1925.00 1260.0 1 \n", "709 1900.00 1847.0 6 \n", "710 1910.00 578.0 1 \n", "711 1900.00 1400.0 1 \n", "712 1940.00 1370.0 1 \n", "714 1950.00 1570.0 1 \n", "715 2016.00 1700.0 1 \n", "716 1901.00 1950.0 1 \n", "717 2016.00 1603.0 7 \n", "718 2016.00 2800.0 6 \n", "719 1925.00 1260.0 1 \n", "720 2006.00 1057.0 1 \n", "721 1910.00 578.0 1 \n", "723 2004.00 1364.0 1 \n", "724 1950.00 1570.0 1 \n", "725 2016.00 923.0 1 \n", "726 2016.00 1700.0 1 \n", "\n", "[689 rows x 49 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# filling missing values of land property size \n", "k = 4\n", "knntest = data[data['year_built'].isnull()]\n", "knntrain = data[data['year_built'].isnull()==False]\n", "xknn_train = knntrain[['longitude','latitude']].values\n", "yknn_train = knntrain['year_built'].values\n", "xknntest = knntest[['longitude','latitude']].values\n", "\n", "neighbours = KNN(n_neighbors=k)\n", "neighbours.fit(xknn_train, yknn_train)\n", "yknn_test = neighbours.predict(xknntest)\n", "\n", "data.set_value( data['year_built'].isnull(),'year_built',yknn_test)\n", "\n", "# drop a couple cases that have missing values for bathrooms and bedrooms \n", "\n", "data.dropna(axis=0,subset=['bedrooms','prices','bathrooms','home_size'],inplace=True)\n", "data.shape\n", "\n", "# filling missing valuees of property_size \n", "k = 4\n", "knntest = data[data['property_size'].isnull()]\n", "knntrain = data[data['property_size'].isnull()==False]\n", "xknn_train = knntrain[['longitude','latitude','bedrooms','year_built','home_size','home_type','bathrooms']].values\n", "yknn_train = knntrain['property_size'].values\n", "xknntest = knntest[['longitude','latitude','bedrooms','year_built','home_size','home_type','bathrooms']].values\n", "\n", "neighbours = KNN(n_neighbors=k)\n", "neighbours.fit(xknn_train, yknn_train)\n", "yknn_test = neighbours.predict(xknntest)\n", "\n", "data.set_value( data['property_size'].isnull(),'property_size',yknn_test)\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#changing the bulit year to the age of the building, to make it more interpretable\n", "data['year_built'] = 2017-data['year_built']" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(689, 49)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# get x and y , get ready for modle building " ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "count 689.000000\n", "mean 58.960784\n", "std 41.707208\n", "min 9.441111\n", "25% 33.314021\n", "50% 43.910256\n", "75% 76.384365\n", "max 447.941889\n", "Name: prices, dtype: float64\n", "\n" ] } ], "source": [ "y = data['prices'].values/data['home_size']/10\n", "data.set_value(data.index.values,'prices', y)\n", "prices= data['prices']\n", "\n", "print prices.describe()\n", "print prices.median\n", "High = data[data['prices']>=76]\n", "Mid = data[(data['prices']<76) & (data['prices']>33)]\n", "Low = data[data['prices']<33]\n", "\n", "\n", "HousingPriceforNJ =data[['prices','longitude','latitude']]\n", "HousingPriceforNJ.to_csv('HousingPriceForNJ.csv',index=False)\n", "\n", "data = data.drop('prices',1)\n", "data = data.drop('home_size',1)\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(y,bins=50)\n", "plt.xlabel('housing price')\n", "plt.ylabel('frequency')\n", "plt.title('Histogram of Boston Housing Price (Dollars/Square Meter)')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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NGzfimWeewQMPPICLLrooMp9hhmeSknNSoGiF1XwCF6G0Xq9jcnIShUIBpVKJ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\n3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TPP+Fi3TqesTNLc7OHBBz185zthcnOjSCnjIsTj8aT8PBICzj/fYulSC8NQjT9SmZ5e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\n0VXtn/zEabWafCTwq8yp8eN+1ayxFZ7WhiulktdNO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/oIHnrIoLraZOZMyeWXe6QWpf3k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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plotting boston data\n", "fig = plt.figure(figsize=(30,10))\n", "\n", "#Project onto axes: 1, 2, 3\n", "ax1 = fig.add_subplot(1, 3, 1, projection='3d')\n", "\n", "ax1.scatter(data['longitude'], data['latitude'],y, color='b', label='Training Set Data')\n", "\n", "ax1.set_xlabel('Longitude')\n", "ax1.set_ylabel('Latitude')\n", "ax1.set_zlabel('housing prices')\n", "ax1.set_title('Boston Housing Prices')\n", "ax1.legend(loc='lower left')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = data.drop('pixelGrass',axis = 1) \n", "data = data.drop('pixelFence',axis = 1) \n", "data['pixelRiver'] = data['pixelRiver'] + data['pixelLake'] +data['pixelSea']\n", "data.rename(columns={'pixelRiver':'pixelWater'}, inplace=True)\n", "data = data.drop('pixelLake',1)\n", "data = data.drop('pixelSea',1)\n", "\n", "x = data.values" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# checking correlation among all the features , doesn't seem like they are correlated \n", "corr_matrix = np.corrcoef(x.T)\n", "fig,ax = plt.subplots(1,1,figsize=(6,6))\n", "ax.pcolor(corr_matrix)\n", "ax.set_title('Heatmap of correlation matrix')\n", "plt.show" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index([u'pixelPlant', u'pixelPole', u'pixelRoad', u'pixelWall', u'pixelCar',\n", " u'numCraigslistHouse', u'pixelWater', u'pixelBus', u'pixelCeiling',\n", " u'pixelPath', u'pixelBuilding', u'crime', u'walkSchool', u'walkMbta',\n", " u'energySiteEUI', u'pixelPerson', u'pixelTree', u'pixelVan',\n", " u'walkPark', u'walkUniversity', u'pixelSidewalk', u'pixelGround',\n", " u'pixelMountain', u'pixelPalmTree', u'pixelHouse', u'pixelBridge',\n", " u'pixelSign', u'pixelRailing', u'pixelField', u'pixelWindow',\n", " u'pixelGrandstand', u'numCraigslistRoom', u'pixelSky', u'longitude',\n", " u'latitude', u'bathrooms', u'last_sold_price', u'property_size', u'zip',\n", " u'status', u'bedrooms', u'year_built', u'home_type'],\n", " dtype='object')" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.columns" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(43L,)\n" ] }, { "data": { "text/plain": [ "43" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print data.columns.shape\n", "len(data.columns)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\gujianflsgj\\Anaconda2\\lib\\site-packages\\matplotlib\\pyplot.py:516: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", " max_open_warning, RuntimeWarning)\n" ] }, { "data": { "image/png": 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PUF3msPViJotLu7T/3O1UdXO30458j8kXM5Ha5W6nqps9hDY9udvpCK0XM3G3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\nGAiSJAD+P1YpRKl2UGYNAAAAAElFTkSuQmCC\n", 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mdiTQ6e4nmdm6sDEbwvW+A6x39x9FPKevX79+6H5HRwcdHR1lae9o3vWud/HF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\nUOCXIAj8qAug7HS4POeDnhk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I5Ux1zE70HrWesTxeQJrq/Zp9hrVMXaMFg2fLfv5s9O+fAxcnjt8F/HaFa07P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\n7m5mz5vZGcAB4HeBz1e64EQ/jIiITM5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tsykoKKWuro6SkpKsxiYiI0tNTQ01NTUDOsaAG5DNbF+gxd13mFkcWA3cDJwCvOHut/TS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\nFtLe3srXv/5p6us384lPnNt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5TkReEJG77PYZIvKUiPxARIZEZLrvOXeIyMsi8qKIXFJpGoiIqHpEVSs/iMg0Vf0vEWkF\n8F0AtwG4GsDPVfVzInI7gBmqul5EzgCwFcC5AE4G8DSABRqQEBEJ2kxERHmICFRVyn1+VZqSVPW/\n7MMEgDYACmAFgMfs9scAXGUfLwewQ1WPquoYgJcBnFeNdETJ+Pg49uzZg/Hx8XonhYjIUZXAICIt\nIvIcgNcBfFNV9wDoVdUDAKCqrwM4we4+G8Crvqfvt9umjO3bB9HXtxAXX7wafX0LsX37YL2TREQ0\noVo1huOqugSmaeg8ETkTptbg7FaN14q68fFxDAzcgkOHhnHw4LM4dGgYAwO3sOZARA2jrZoHU9X/\nFJFdAN4P4ICI9KrqARE5EUDa7rYfwCm+p51stwW6++67Jx4vW7YMy5Ytq2aSa25sbAzxeD8OHVps\ntyxGLNaHsbEx9PT01DVtRBRNu3btwq5du6p2vIo7n0XktwAcUdWDIpICMATgswDeA+ANVb0/pPP5\nnTBNSN/EFOp8Hh8fR1/fQhw6NAxgMYC9SKUuxL59LzEwEFFVVNr5XI0awywAj4lIC0zT1KCqfk1E\nngHwhIjcCGAfgGsAQFVHReQJAKMAjgC4pely/zx6enqwefNDGBi4ELFYH44c2YfNmx9iUCCihlGV\n4aqTpRlrDJ7x8XGMjY2hv7+fQYGIqqrSGgMDAxFRk2mIeQxERNQ8GBiIiMjBwEBERA4GBiIicjAw\nEBGRg4GBiIgcDAxERORgYCAiIgcDAxERORgYiIjIwcBAREQOBgYiInIwMBARkYOBgYiIHAwMRETk\nYGAgIiIHAwMRETkYGIiIyMHAQEREDgaGOhsfH8eePXswPj5e76QQEQFgYKir7dsH0de3EBdfvBp9\nfQuxfftgvZNERARR1XqnIZSIaCOnrxLj4+Po61uIQ4eGASwGsBep1IXYt+8l9PT01Dt5RBRhIgJV\nlXKfzxpDnYyNjSEe74cJCgCwGLFYH8bGxuqXKCIiMDDUTX9/Pw4fHgOw127ZiyNH9qG/v79+iSIi\nAgND3fT09GDz5oeQSl2Irq6lSKUuxObND7EZiYjqjn0MdTY+Po6xsTH09/czKBBRVVTax8DAQETU\nZNj5TEREVcXAQEREDgYGIiJyMDAQEZGDgYGIiBwMDERE5GBgICIiBwMDERE5GBiIiMjBwEA0yXgz\nJooaBgZqeFHOWHkzJooirpVEDW379kEMDNyCeNwsU75580NYtWplvZNVFN6MieqFayVR0xofH8fA\nwC04dGgYBw8+i0OHhjEwcEtkag68GRNFFQMDNayoZ6y8GRNFFQMDNayoZ6y8GRNFFfsYqKF5fQyx\nWB+OHNkXqT4GD2/GRLXGG/VQ02PGSlQaBoaIYmZHzYKf5cbDUUkRxLHt1Cy2bx/EnDmn4sIL/whz\n5pzKz3KTqLjGICInA/gigF4AxwE8oqobRWQGgEEAfQDGAFyjqgftc+4AcCOAowDWqOpTIcduuhoD\nx7ZTlOSrDYyPj2P27Hk4cqQNwFwAP0YsdgT79/+In+U6a4Qaw1EAa1X1TADvAvDHIrIQwHoAT6vq\naQC+BeAOm+AzAFwD4HQAlwF4SETKPoGoifoQTJo6CtVsn3vuORw5cgzALgDPAtiFI0eO47nnnqtD\naqmaKg4Mqvq6qn7fPn4TwIsATgawAsBjdrfHAFxlHy8HsENVj6rqGICXAZxXaTqiIupDMGlqKH5y\n4UnwF3KAWbVNKE2KqvYxiEg/gHMAPAOgV1UPACZ4ADjB7jYbwKu+p+2326YEjm2nKCimZrtkyRLE\n4+PwF3Li8Z9hyZIlNU0rVV9btQ4kIh0A/g6mz+BNEcnuHCirs+Duu++eeLxs2TIsW7as3CQ2jFWr\nVuKii97LkRxTRBRH7bg1W9MXll2z7enpwZYtn8fAwIVoaTkZx4+/hs2bPx+Zc2wmu3btwq5du6p3\nQFWt+AcmwHwDJih4216EqTUAwIkAXrSP1wO43bffNwC8M+S4ShRl27bt0FRqpk6fvlRTqZm6bduO\neiepaF7au7qW5E17Op3WkZERTafTNU4hhbF5Z9l5elXmMYjIFwH8TFXX+rbdD+ANVb1fRG4HMENV\n19vO560A3gnThPRNAAs0ICHNOCqJpo5mGIEWxdoOVT4qqeKmJBG5AMAfAnhBRJ6DaTK6E8D9AJ4Q\nkRsB7IMZiQRVHRWRJwCMAjgC4Bbm/lNXM2c8Xjv9oUO57fRROdeenp7IpJWqhzOf66SZM8RiRfle\nC8VohhoDRVMjzGOgEnHmc/TvtVAMjkCjqGKNocZYijT27NmDiy9ejYMHn53Y1tW1FE8//Xmce+65\ndUxZ9bF2SLVW9z4GKk0ztDtXQzHDIZsF2+kpatiUVGOc+WywmYWocbEpqQ6a4eYz1cJmFqLq4/0Y\nIooZIhFNFgYGIiJycLgqERFVFQMDEYUaHx/Hnj17mmp+CRXGwEBUoqmSWXIiZmWi/DlhYCAqwVTJ\nLKfCzPTJFPXPCTufiYo0lWatT6WZ6dXWCJ8Tdj4T1chUul83J2KWrxk+JwwMREWaSpklZ6aXrxk+\nJ2xKIirBVJu1zomY5an354QT3IhqjJklFaOenxMGBiIicrDzmYioAXEeAxERTeA8hknEpiQiihrO\nYyAiIgfnMRARkaMZ5jEwMNRJlDumqonXgZpNM0wOZB9DHXiTX+JxU7Jo9klSYXgdqJlxHsMkacbA\nkOmY+hKAdgC/Rip1dVMuxJZPI3TQTVWcoNf82PkcMaYDqhvA1QBWA7gaql2R6piqBnO+s+HvoANO\nmnLXodaiPowyStatW4e+vj6sW7eu3kkpGWsMNfbiiy/ijDPeAeAZeCVl4HyMjj6L008/vb6JqyFe\nh9qbzFoaayGu1tYUjh8XACcDeA2trcdw9OhbNXt91hgi5s0330QqNR/+knIqNQ9vvvlmPZNVc+Y6\nnAjgQgBLAVyIZLJ3yl2HWpqsYZSshbjWrVtng8IzAP4dwDM4dqw1UjUH1hhqbHx8HCefvACHD38F\nXh9DPH4VXnvt5SlV0mJfi6sWJe7JqDHUs6+oUWspfX19eOWVBExQ8CzAnDmHsW/fvpqkgTWGCDp6\n9DCAywFcB+DymlYxG0VmSN/V6Or6OFKpqyM3pK9aalXiLmcYZaHhxPWazNXItZTLL78cwGvwz2MA\n9tvtEaGqDftjktdchoaGFJimwPMKqP09TYeGhuqdtLoYHR3VLVu26OjoaL2TUhfpdFpTqZnO5yGV\nmqnpdHpSX3NkZKTga2zbtkNTqZk6ffpSTaVm6rZtOwKPVY/01/o1SzEyMqJAiwIpBebb3y06MjJS\nszTYvLPsvJc1hro4CcAsAHvs71n1TU6dbN8+iHe8491Ys2Yj3vGOdzdUqa9W6lHi7unpwbnnnluw\npjAwcAsOHRrGwYPP4tChYQwM3JJTc6jHZK5GX3Kiv78fsVg7gGMAXgFwDLHYtEjNfK57rSDfD5qw\nxpBOp7W1tV2BLgUWKNClra3TGqa0UyuNXuqrlUa9DiMjIzp9+lKbJvPT1bUktNRbbC2kGhr1mnnS\n6bTG49Od9MXj02uaPrDGED3Hjx8H0AqgE0Arjh9vrg72YjRaqa9eS3M06vIJpa73U0wtpFoa9Zp5\nxsbGkErNg/+znUy+vWFqNEWpJKpM9g+asMYwODgY2McwODhY76TVVD1Lfdml22La0qv9mqX+vx68\n69LVtURTqZm6adPDDZXGRrxmqo1Ro0GFNYa6Z/55E9eEgeHOO+9UYJ5TRQfm6Z133lnvpNVcdsYz\nGRly2Gt6QWDTpocn/Utci8AzWbzM17tOUTyHeqjHZ9uPgSFi7rvvPjtKwV9jSOl9991X76TVRb3b\nphOJLu3sXFJ0W3o5r5lMdiuwVYF0w7WHF6MRSsBRVM8aTaWBgX0MNfbBD34QwBEA5wNYYH8fsdun\nnlq2TQf3a8zB4cM/RjXWzg/qp/j85x/Bb35zGMADABYCeLFqfSm16hdptP6gqKjlZ7vqKokqk/2D\nJqwxqKreeuttCsQVmKFAXG+99bZ6J2lKCCv5es0klVT7g5qLgl4PmKHJZHfFpchaNk+xxhA9YFNS\n9GzbtkOTyRmaSi3SZHJG07XXNmqnoGp4228laQ7LOIeGhnKGfALz9N57K2s2rEdGXe82cyoNA0PE\nZL7UwwqMKDDcVKUvLwNpbz+7YTOQageusDH/Q0NDRWfgpaSp1DkG1dLIAZ9cDAwRMzIyoqnU2xWY\nqcBSBWZqMtlf0+nyk6WciT1eZjM6OhrZTCdfCb6Ykra3T1vbCQpAL7/8irJfj0iVgSFyRkdH7agk\nr8bwZQUSunv37nonrWJmHaj5OU0nYetAeRliKnWWAilNpeaWVMtopBKs6TdKqZnNnnL6jfKlM5PJ\np5znt7TE874em3YoHwaGiBkZGdFYbI6tMZxlM4NZmkh01/XLvWLFCm1vb9cVK1aUfYxSFggM7pid\nWXTTWiP102TO5csKbFHgy0WX4EdGRmxNIXcI89q1ayeOH1SraqTASI2FgSFiMjWG8jLEyQC0OqVV\nc5uO0qXTaY3FOhSYrsCpCkzXWKwj8JyC2smBxQoM5bSXZ2eA6XRa29o6nWvY1tZZtwyyUPNgoRoD\nAHvt/ddivs6ZM8dXq3q7rVWdxRoCFVRpYOA8hhp74YUXYFZXde91DLTXZWz4VVddBSAO/92mgKTd\nXpqenh58/OMDAA4DUACH8fGP3xg4jjtoLR7ghwCuwaFDL0/MIwhad394eBhHj54A/zU8evQEDA8P\nl5xmoPL5AB0dHTh06KcAhgE8C2AYv/nNAXR0dBS8b0BPTw8uv/wKhK3fb1Y4/RIOHfolgGdw6NDe\n0JVOiaqmkqji/QDYDOAAgL2+bTMAPAXgBwCGAEz3/e8OAC8DeBHAJXmOO0nxtH7CZj6X0vxQTe3t\n7YGl1fb29pKPVWqnqFcaNkuEzFBgh9NhHTaCy1zD3CarjRs3lpzmaswHMDWGs5xrmEotKmlUUktL\nXP3r97e2xn21qhFbE8kcvxajkCi60CA1hi8AuDRr23oAT6vqaQC+ZYMBROQMANcAOB3AZQAeEpGy\nb0EXNfPnzwfQBf+9joHpiMc/XJcVIi+66CIElVbN9tJK06XOkF21aiW+8pXtaG9vgyk/rIR/JUrz\nvG4AVwNYDeBqqHbZa3gUwDKYa7gMwNGJNBer2HsOFGJqN/vhXsOfAEDR1+PYsbewdu3NmDPnMNau\nvRlHj77lq1X9GsAYqjE7m6golUQV/w+APrg1hpcA9NrHJwJ4yT5eD+B2335fB/DOkGNORjCtq0z7\neKYU3NraUdc7mAGi7t2mTB9DqaXpcoZR5ntOcH9MSkdHR+0ooKQCpyiQLGv2eDXnAwSNEirmehTq\nQPaOm0z22z6GRXXrY6h1Zzc718uHRul8DggMb2T9/w37+y8BfMi3/VEA/y3kmJNwyepv27YdGo93\naix2ksZiHbp+/R06NDSUk2EMDQ3lbJ8s2aOSyh0rX84wyrDnhDXReBl3pbcFrfZ8gKCMLN/1KDbw\n1nOuh3eNN2x4oKarq1ajiW8qB5YoBYafKwODqqreeusadUcBxRSYr/H4dN22bYdu27ZDY7FO246e\n2V5LlZSmy/lCBj2n3Iy7lNfPzNReXHGGF/a61Ty3Wsp8Tk+1v++3aR3WRKIrb0CejCVGSjlWlJc6\nr4ZGDgwvZjUlvWgfZzclfSNfU9Jdd9018TM8PDwZ17CmwppHgFEFntdkstsu0zyjrplGo2RcpdZA\nSs0QzHyIbm1vP02TyfLnkpT6uvVa1qJY4cOqH1agW4E5Go93hc7kjsc7tK3tBI3HO0q+ppVem0b5\n7NbS8PCwk1c2UmDoB/CC7+/7vQAA4HYAn7WPzwDwHMwYybkwYxQl5JiTdBnrZ8uWLbYEpr6fBWom\nRqkmk/M1mVyg2aNQ2tsX1zzTaJTZtcWWPt0MIa3A1rwrmVYrAynnOKOjo5pIdFc98yrmWhWzT/Dn\n9Cxbi51hP58zcuapmHkZ3girTI241NpjJe9LowfdWmiIwABgG8wwjLcAvALgIzDDVZ+GGW7yFIBu\n3/532IAw5YarVlpjaNYOwGq8TiZD2KGZyWbTQlczzeyfVjMQIF1WBlJqRpSZtDZX/R3K9957X0Xn\nX0ytpdg/xFP6AAAgAElEQVSaTfDnNKmFZrY/+OCDgZ/vBx98sKxzKadQMhVrDNkaIjBM1k8zBgZV\n1bPOOkfdUUAtCsxz+hja2toVSCjQ72xvxnbTap1XOp32BdVhLbR6rZmp3Zm3BFzs65ayiqq777C2\ntk7TZLK74o7WYkZAlTJKKnv9p0svfb8G3ZbWHxiuvPJKu08m2ALz9MorryzrnMotLDRKbbdeGBgi\nxpTEYuofagm06SOPPDLxBfA+1NOmLdZEols3bXq4aUtB1T6ve++9T4FZWszqtel06avBBjGDBTps\naXpe3sECubWLtK8UXrj5K0wxtZZC+wQFaP/Ir3zDhz033XSTLdDMUNP01KlATG+66aaSzqcaOCqJ\ngSEy1qxZE/jlWrNmjaqWdtOXKLWbhn1Jq90eXEzmVc3XdoOLydhjsfB1m3Lf36225lhc81e+dFRS\nYyjm+Wb48FybziWBQfeRRx7RzAimmQqcrUBKP/rRj5V0PlQZBoaIMVXt3CUovKp24eUVhjVqN/jJ\n11RU7RpDobkPftV47WKWGs8Oiv5mjmSy29ecVdk1KNR8kk6n9d5779NksjtwzkihIBm2RIk/neZ6\neMEjcz6JROW3M6XiMTBEzD333BNYor3nnntUNX+JN3tceRTuFV1M5lvN9uBSm4cqfe1CS42HBUV/\nsDDNX25w8WfKpTSJhO2bnY7sju5ig2Qxwcf0j53tnE9n5zmRqd02AwaGiDEZSUyzh/N5GUlYdb2U\nBdkaSbHNNdVqDzYdyh22BL5Ew4ZUVuu+Bvler9jMNt9+hWpbpQ/jLT/TL/Z1N216OKdwU+2JiZQf\nA0PEmBpBXE2n3Bz7Oz7RBh5WXY9qH0OtO80LDUGdjJFdmUlypzqT5IptnhkZGdFNmx7OyZTLDRjh\n1yQ3HdmZcaXLjHg2bXpYE4lu7ew8Z1ImJlJ+DAwRY2oMJ6qZPXqq/d3rtEmXuyBbo6rl0MHdu3fn\nNCWV0sFarqDSbqHXy84MN2162DlGWIZeau0xrHnNC0be699665qqZc7pdPFrfZX7vpRaw6h2jaSR\nazgMDBEzODho26Qzt4EEpung4KCzX9CHrtHGZlej7buaMn0wsxRIaTx+elEdrENDQ5OWtrD3rJJR\nRENDQ9rZuaTo2mM6ndbWVm/GsmnuamlJ2Tkfw5q593jpzT/5znkylwcpZ+mTatZIGr2Gw8AQMSYw\nZPcxtOUEhjCNUkpptC9Gbqf9sAIJ3b1798Q+QRltLNaZt9ReDUHvWbGZYVBgKbUN39RS5yiwUYFB\n9SadxWInaWaIbLtmT14rp6mynNJ/qc+Z7P0n4xxrjYEhYkxgyB11VGxgaASN+MUIW4Nqy5Ytzn7Z\nQ0Wzm1iAlHZ2Tv59lQtdQ38w8TfLjI6O2ud58wTOUCChGzY8EPpaH/jAVequkvoh+zs7kFZeYyh3\nbkgpteFSX6Pac2WisBYTA0PEPPDAAxq0ZMADD4R/sRtNI34xiqkxeLxMN6hDH1hs35fJD3ZhmWF2\nbczf9p9IdNtRa6qZlU4XaCIRvDJs2PDnlpaEJpOLss69V4EuNc1N03T16ptLPv9KCg3F1Ia9IGma\nwVhjCMPAEDHr1q3TzJIBS+3vhN588831TlrRGvGLkU6ntaUlqf4+BmBewWGX2edhSuHpmgW7oKGz\nbpqGAzN20ydQ+D0IrknN1w9/+IaAc59hA8OAAknt7CyvL2uy+sL8ATMen66xWEfRr5G97lOlc4Aa\nrb8vGwNDxDz++OOBX/RYrL3hPlz5FDPRqZZ9IZlazG4FgkclBfHOo7PTW9jw/qKeN/nn4WXiIzkZ\nezJ5ppp+Knd70CSysBrDhg0P2Il109SbL2OW5Zibs38512EyRgCFdcQXP+ppWKu5akCj9PcFYWCI\nGFOCm6/AWjUdgmvVNC3dE7lJQGFpqUfHdObLv1Wz72VRqOSfby5BraXT3v0Meu1nYzgno04kunXa\ntAVqmpEKLztx4403qTvY4UOaSs3U0dFR2ySz1daSntdYrEM7Os4peP0KfQ6rPZS0kubLRmz6nGwM\nDBFjagytWV9UUWBn2UP0Gi1YTEYzUzHn6E00y16iopTXr9YEr3Ll3uRG9NZbb3MClhfAgE/b4HCW\nAindtOnhwGOOjIxoe/vpaoZHjzoZY3bNL3PswkuYhAX+Uu9lnT2fImj/SvsuGq3pc7IxMETM0qVL\nA6v2wLvK6kAr5ktVS5NROiulBpJOm7WHyin5V1rT2blzpw4MDOjOnTtLep5n9erVgZ+N1atXO4Fx\n27Yd2tLijSqaq0BCBwY+GnrcUkZA+a9D0PUr5ljFZMKZJryzim66qqRdv9H7BKqNgSFiOjo6bGlw\n1FeCm68ASh6i19l5jiYSXQ1VEqr2qJRazorNfp14fLpeeuml+vjjjxd8/qJFS5yS/llnnVNyWnp7\nezVo5d3e3l7nWOXcE7zUjDEszWGfwy1btkw8p/hVWp9X0+Z/prN/2Gq4pVzLaj83ahgYIsbUGLwJ\nbt648jY966yz8j7P/TKZdf/j8S5b4gr/EtZDOaWzsNJ6uTWQUjOBoNcxfT+nKJDSU07pD33uzp07\nA0v6O3fuLKkWkq/G4E9ne/tpWs49wYsdDlqo7yB3NFNm7kcxTVHutc5//4xqZuYMDAwMDWvFihWB\nX4QVK1YUfK65U1inmjb0+RqLddmVPRunxuAp9kvojUsPy0zKqTGU0yQUnOHNUK9TFkiF1hwGBgYC\nS/oXXPDuktJuOp5F3du+Sk4NqpwaQzGKvW6FRnJt2PCAJhJd2tGxKHTEmltj6NOgm/9UcxBDo83U\nn2wMDBFTTHNBmLDmjqAbr0SB92U1JeDw+xGUUgOppCnLe522ttNs8N3hvEfLly93XmdkZERHR0f1\nwQcfDAz2bW3T7JpGwSu9ZsuUpDMj1sKWySj2VqLFKvW6pdNp3bJlS06NNZmca1dVXTJxW9og3rXu\n6Fhkr92w+oeSZmZ4Vx782PnMwNDwimkuCDM4OKjJ5AL1JmB5GehkLgI3WXKbxtwScPbNdYqtgVTa\n+Z1Op/Uzn/mMmntxu++RV2PwMjVzp7iUmpnI3kgzr6R/mXZ0LNKWFm/xOjOZsbV1Wug5jI6OaiJR\n3Ixer6ZVzDh+VdWVK1dqV1eXrly5sqLr5n8fipmMF9TJPTo66vwOGiZczUEMHK7KwBAJQc0FhWRW\nDp2nZojifRql23tmy/2y7rAl4MUadHOdYlWrdHjKKf3Oe9Tbe5Ju2bJFd+/eHdDk5E2eiivwYQV2\nqukD6ggsBAQNhc0Em7k22JhmmOw7rQWdb6GACbSpOwS2pazrFnQXuE2bHp64F0U83qHZt1XNHhab\nSr3dnp+7HlX2eVSzlM8aAwNDw9u9e7eazmco0GJ/twWu6eMJnr06TYFk2VP7690RF9ym363AkBZq\ncikke6G8QplrmMcff1yXL1+uF110qWYGCyQ0Hj/dyfxM2/iIAovs+2KaeMwSHYUX9gsqebe1tWsi\n0aXt7WeHNp8V026+cuXKwOAUVHModZgqME3b2to1FjPpNPevzh0ll2kWGtZilvEoJj2l4nBVBoaG\ndsMNN2juWklxvfbaa0Ofc+e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TEdO7mzt3LuEjaiJbFQQjmDt3Lm+88Qap1ImERuczgHlUVtbwxhtvFOI0RKiu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\n8wQ8ygAAAABJRU5ErkJggg==\n", 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STWvrvLBkHsXFVTQ3N5NMJnNaNhEZmZqammhqahr0dgbdqWxmxwDt7r7XzBLAauDbwBnA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\nuz86mDKIiEg8YmkyEhGRwpd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bslq1alXn7bq6Ourq6vIZck5aWlooK6uhvX1+uGU+sVg1LS0tVFVVDXs8IiLpGhsbaWxs\nHNIxhtyBbGZHAvvdfY+ZJYCNwE3AWcAr7r66lw7kMwiahx6iyDuQk8kk1dVzaG/fDMwHHieRWERr\n61NKBiJSdAbTgZyPmsExwFozG0PQ7LTB3R8ws0eBe8zsKqAVWALg7s1mdg/QDOwHlhdFid+Hqqoq\n6uvXsHTpImKxavbvb6W+fo0SgYiMGnkZWlooxVIzSEkmk7S0tFBTU6NEICJFazA1AyUDEZFRJrJ5\nBiIiMrIpGYiIiJKBiIgoGYiICEoGIiKCkoGIiKBkICIiKBmIiAhKBiIigpKBiIigZCAiIigZiIgI\nSgYiIoKSgYiIoGQgIiIoGYiICEoGIiKCkoGIiKBkICIiKBkMSDKZZMuWLSSTyahDERHJKyWDHDU0\nbKC6eg6LFy+junoODQ0bog5JRCRvzN2jjqFXZubFEF8ymaS6eg7t7ZuB+cDjJBKLaG19iqqqqqjD\nExHpxsxwdxvIc1QzyEFLSwtlZTUEiQBgPrFYNS0tLdEFJSKSR0oGOaipqaGjowV4PNzyOPv3t1JT\nUxNdUCIieaRkkIOqqirq69eQSCyisnIBicQi6uvXqIlIREYN9RkMQDKZpKWlhZqaGiUCESlag+kz\nUDIQERll1IEsIiKDomQgIiJKBiIiomQgIiIoGYiICEoGIiKCkoGIiKBkICIiKBmIiAhKBkOmC96I\nyGigZDAAmQW/LngjIqOF1ibKUUPDBpYuXU5ZWbCc9c0338SnP71CF7wRkaKjheoKJNuVzsrLz6Ks\n7DjeeOPxzv0qKxewadM3OP300yOLVUREC9UVSLYrnQU1hFZ0wRsRGQ2UDHKQ7UpnBw5s55Zb/kUX\nvBGRUUHNRDlK9RnEYtXs399Kff0aLr30Yl3wRkSKjvoMCkwFv4iMBOozKLCXX36Z5uZmXn755ahD\nERHJKyWDHH3iE59i3rx38NGP3si8ee/gE5/4ZNQhiYjkzZCbicxsGvAdYApwCPimu99qZpOADUA1\n0AIscfc94XNWAlcBB4BPuvuDvRy7KJqJtm3bxrx57wAeACqAvcAHaG5+jLlz50YbnIhIhqiaiQ4A\nn3H3k4B3An9vZnOAFcAmd58NPAysDIOcBywB5gLnAWvMbEBBD7dNmzYBE4APA8vC35XhdhGRkW/I\nycDdX3L334W33wS2AdOAC4C14W5rgQvD2+cD6939gLu3AM8CC4caRyElEglgD7AZeCz8/Xq4XURk\n5Mtrn4GZ1QCnAY8CU9x9NwQJAzgq3G0qsCPtabvCbUVr+vTpwLGkTzqDY8PtIiIjX2m+DmRm44Dv\nEfQBvGlmmY39g2r8X7VqVefturo66urqBhvioNXW1lJW9jIdHY+TWo6irOxlamtrNdxURCLX2NhI\nY2PjkI6Rl3kGZlYK/Bj4ibvfEm7bBtS5+24zOxrY7O5zzWwF4O6+Otzvp8D17v7rLMctig5k6Jp0\nNmbMNA4d2kl9/RqAbovXpSaiiYhEKbJJZ2b2HeBld/9M2rbVwCvuvtrMrgMmufuKsAP5buAMguah\nh4ATspX6xZQMoPukM6DH4nVatVREisFgksGQm4nM7F3A5cATZraVoDno88Bq4B4zuwpoJRhBhLs3\nm9k9QDOwH1heVCV+jlKL17W3d/UjxGLVtLS0KBmIyIgz5GTg7r8ASnp5+JxenvNl4MtDfe3h1NVM\ndByHDu3g5ptvSlu8LqgZaNVSERmptDZRDpLJJNOmnUBHx8/p6kB+L7fe+lU+/ekVPRavExGJUiTN\nRIeDrVu30tFRRfrQ0o6OI5kxo5rW1qc0mkhERjwlg5y9QHqTELwIQFVVlZKAiIx4aibKQTKZZOrU\n49m/PwbUAC3EYvvZtes5JQIRKTpawrpAqqqqWLv2TuJxp6JiL/G4s3btnUoEIjJqqGYwAJptLCIj\nga50JiIiaiYSEZHBUTIYgGQyyZYtW0gmk1GHIiKSV0oGOWpo2EB19RwWLbqa6uo5NDRsiDok6YMS\nt8jAqM8gB73NQN6581l1JBeh1NIhWk1WDlfqMyiQ3mYgb926NcqwJItkMsnSpctpb9/Mnj2P0d6+\nmaVLl6uGINIPJYOcpWYgQ/oMZCkuqdVk0xN3ajVZEemdkkEOamtricXGAHXAAqCOWGwMtbW10QYm\nPdTU1KStJgtaTVYkN0oGOdAM5JGjqqqK+vo1JBKLqKxcQCKxiPr6NfpbifRDHcgDoBnII4f+VnI4\n0wxkERHRaKJC09h1ERmtlAxylJp0tnjxMk06E5FRR81EOUgmk1RXz6G9fTOpSWeJxCJaW59Se7SI\nFB01ExWIxq6LyGinZJADjV0XkdFOySAHqbHr8fhZxOMnEI+fpbHrIjKqKBnk6Je//BX79r3Nvn3G\nvn1v88tf/jLqkERE8kYdyDnYtm0b8+a9A3iUVAcynElz82PMnTs32uBERDKoA7lAmpqagKnA94DZ\n4e9jw+0iIiOfkkEOSktLgeeBfwE8/P0cb731VqRxiYjki5JBDm677TagjKCZ6Jnwd5zly6/V5DMR\nGRXUZ5CDKVOm0NY2gSARpJwAdJBIvMm99zZQW1ur0UUiUhTUZ1Ag5513HrCT7he32QVcSXv7JD70\noWu1RIWIjGhKBjk48cQTgX3AmQQ1gjPDRy4CXmHv3v/V5RVFZERTMsjB3r17geOBfwDeBA4RjC46\nE/gPILg+spaoEJGRSskgB0ceeSTBNZAvIrj28U+BHZSUGJCaZ6AlKkRk5FIyyEF7eztwADgDmA6c\nBxzioov+SpdXFJFRoTTqAEaCRCJBkDfLgQnA68A+zjjjDG677Ta2bt0KQG1tbXRBiogMgWoGOWht\nbSX4qH4OPBH+HkNrayubNj3MhRdeypIlKzWiSERGLCWDHLz00kvAsaRfzwCOpbW1lauuWkZ7+23s\n2fNTjSgSkRFLySAHM2fOJOg4Tp9n8CKvvPIq+/Z1AF8B5gDbNKJIREYkzUDOQbBq6WlABVADtAB7\nKSmJcfDgL+layfQ9xONj2L79GXUki0hkBjMDWR3IOTjyyCMxK8H9IPAGcBAYw8GDR9G96aiKT33q\nYiUCERlx1EyUg61bt+I+keDjKgl/TyCYe9C96WjRorOiCVJEZAiUDHLw2muvAa8Bfwm0h79fxwyg\nDlgA1BGLjdHwUhEZkZQMcrBz506gA/g+wVyD7wP7uOyyJcTjTkXFXuJxZ+3aOwHYsmWLRhSJyIii\nZJCD22+/nWzXM/jBD77H9u3PsHnzf7F9e7C89fTpsznrrKuYPn225hyIyIih0UQ5SCQS7Nt3HD2v\nZ/A8cIimpl8zbtw45s8/gwMHHiE1uqi09N288ML/qUNZRIaVrmdQINOmTSP79QzqgTgLF57Daae9\nkwMHxpI+uujAgaPYvHnz8AcsIjJAeUkGZlZvZrvN7PG0bZPM7EEze9rMNprZhLTHVprZs2a2zczO\nzUcMhRSsWpp5PYM64EqCpayho+PnBGsWNYbPCkYX7d69e3iDFREZhHzVDL4NvC9j2wpgk7vPBh4G\nVgKY2TxgCcHaz+cBa8xsQNWZ4VZeXg5MIRhJ9DzBNQweoKuGcB6pJSrgfFKji+AA55xzTgQRi4gM\nTF6Sgbs/AryasfkCYG14ey1wYXj7fGC9ux9w9xbgWWBhPuIolLPPPhvYA/yQ4MI2f0dXDWEfsJ6g\nj6CNYNTRy0A711yzjLlz52Y/qIhIESnkDOSj3H03gLu/ZGZHhdunAr9K228XqbaWIjV79myCJHAZ\nMBP4I/BHjj76aPbsSRCLLWD//lbq67/JaafNp6mpiYULFyoRiMiIMZzLUUQ/LGiQdu3aRVCJepSu\ndYjO5NVXyzHbx+c+dxEf//jVnaOGlAREZKQpZDLYbWZT3H23mR0NtIXbdwHHpe03LdyW1apVqzpv\n19XVUVdXl/9I+7FhwwayLWH99tsfBi7nS19axMc/fvWwxyUiAtDY2EhjY+OQjpG3eQZmVgP8yN1P\nCe+vBl5x99Vmdh0wyd1XhB3IdxNcQ3Iq8BBwQrYJBcUyz2Dy5Mm8+urbBK1bqZrBO4Fa4BEqKxew\nadM3OP3006MMU0QEiHDVUjNbRzB85ggz2w5cD9wEfNfMrgJaCUYQ4e7NZnYP0AzsB5YXRYnfh1gs\nBuwleIs1BEtYHyAYXfQ4+/e3UlNTE1F0IiJDpxnIOaiurmb79tR8gQkEI4tqgO3E4+V861u3c+ml\nF0cWn4hIOl3PoED27dtHUBMoAyoJrmnwR+A47rvvG5x7btHPmxMR6ZOWo8jBG2+8QddCdc+Gv8uA\nHRx33HF9PVVEZERQMshBMEG652gigDfffHNAx0omk1riWkSKjpJBDo499ljgRTKvagY+oI7jhoYN\nVFfPYfHiZVRXz9ES1yJSNJQMcjB9+nSCPoM60tcdOumkYHJZLmf6yWSSpUuX096+mT17HqO9fTNL\nly5XDUFEioKSQQ4qKysJlqNoB5Lh70PE44mcz/RbWlooK6shvakpFqumpaWlsMGLiORAo4lyMH78\neOAIgiQwhqDzeAKPPfZ74De0twcT0ZYuXcRpp83nzTffpKampttFbWpqaujoaCFoYgr21/wEESkW\nqhnkIGjKeR24D/he+HsvQS49JtxrPgcPTqG29sysNYWqqirq69eQSCyisnIBicQi6uvX6CpoA6QO\neJHC0KSzHJx88sk8+eSzQAVdM5D3AkcBqwlWMw0Wr4N1BKt1P04isYjW1qe6FfjJZJKWlpYeNQfp\nX0PDBpYuXU5ZWVDLqq9fo8l+IlkMZtKZkkEOghnI5cAvCBJBDfCu8HaCYFnrVqCcoNZwOpCkouLd\n/OAHt2lSWh4kk0mqq+fQ3r6ZVDNbtmQrIroGcsGUlJQQXAP5yXDLk+F9CGoG3wC+D7xGUGPYAMxm\n795DXHjhpRpCmgfqgBcpLNUMcnDcccexc+eLBB3H0wgSQQfBCKNyghW5d3LuuXX87Ge/4O23D5C+\nwqnOYIdONQOR3KlmUCClpaUEhf6jwDPh73KC6/X8hGBF7gfYvPkRguQ1DZ3B5pc64EUKSzWDHMye\nPZtnnjlIsGLp74DTCFYu3U5QQ4Bg/kEN8N/AhwGdwRaCOuBF+qeaQYGccsopBJ3F2wg6i7cBzxHM\nSk4tUfEQwXpFdcAaYBFwIuXlZ+kMVkSKnmoGOUgkEuzbZ2ReAxnaicXGkUicQEfH8xw65HR0/Dzc\np5Hy8gvYuvVRXRM5TzS0VCQ3GlpauDiAE+g+tPTPgT+SSEzm3nsbqK2tZdOmh1m6dDmxWDX797fy\nsY9dxs6d21myZAmXX345MLhmDjWNqANZZCDUTFRQ24HZwLLw93aglFismkmTJgEwa9bxPPbYI2za\n9A0mThzHmjX13H//Nq644mqmT58xqFVLtdJpkAgeeOABSkunoo750UUzyouIuxftTxBe9CoqKhwS\nDr938PB3wmGSl5dP9K9+9V89kZjsEyYs8ERisi9ffk2W/eNeVjah27ZEYrK3tbX1+rptbW2eSEwe\n0HNGm3Xr1nsiMdnHj68NP9PVh+1nMdqk/rap/5t169ZHHdKoEZadAytvB/qE4fwplmQQi8UcTggL\nodTPLAc8Hp/eo+AfM6bCYUbavm0OR3lJyczwdpNDm1dW1npTU1Ovr9vU1OQTJizo9rr9PWc0yZYM\nIeHjxp2ct8Kjra3Nm5qalFSGmU50CmswyUDNRDkIVhbdCTQCW8Lfu4Dp7Nv3PeBE0psvystnAC8Q\ndDQHs5FhPAcP7gRmkWpq2rv36T5XLe2+0ikcbiudZpt1PH78bL7+9X+gtfWpIXceqwkuOppRXoQG\nmj2G84ciqRl88YtfdLCwBnBC+Nscvuiw0aHnGU5V1RSHUoex4WNtDpN6nOU2Nzf3+drr1q33eHyi\nV1Sc6PH4xMOqKl3Is0edmUZLn39hoZpBYdx2221AnO4zkOPA9cBK4G3gTMaPryWRWMS7330GyeTr\nBPMOjiE4+2kBZtD9OsrTaGpq6vf1zcYAifB3dIa7s6+Qs451ZhotzSgvQgPNHsP5Q5HUDIBe+wxS\nZzWlpRV+6623+o9+9KO0PoT02kDfNYNU23Vzc3NnG/ZwnT0tW7bMp0yZ4suWLesWS/rrRNnZl492\n/cxj6MxfgTvqAAASZUlEQVS0OKjPpjBQB3JhBIV+ttFEJeH99Q5jvaLi1HDE0DFpSWN92FQ0K/zd\n1dT0kY9c7O5dBW0icYpDwhOJGZ5ITPYbbrjRx48/pbPDOR8dyJn/fFCW0fxV0qPQH+kFZ2+JLLW9\nsrJWo1lkVFEyKJDp06en9RnMSuszMIfmrGf8sDntftxLS1Pbmh3+2cvLK3s9+w/6IDZ7aWlqSOup\n4bbVQyqEMwvFs88+p5ckd0W3Qn/jxo0jdlRTf4lMZ6YyGg0mGegayDmLA5OBPwJTgVeAQ5SXL+bt\nt4+ge1/AscD7genATkpLx3P99Z/iS1/6cOfs5Pr6O6iqqmLLli2UldWE11FOPb8a6ODAASdzCYyb\nb75lUO2qyWSSpUuX096+ufOazf/zP6cSzKxOf+2pwM8778di1QAj9vrNqb6B9M831TdQVVXV+SNy\nuFMHcg4OHDhA0En8CkHh+QrQDhzg+OMrCYaZdg3/DIaVfhX4ArCOWOwgH//41bS2PsWmTd/oNiwy\n2/DR4KppzxEkla5CbOzYWcyYERTOA+3MzdZhWlJyJMGQ2fTX3gW8t/N+R8fzANx8802RdfYNpeP6\ncB+eW0w027jIDbQqMZw/FEkzUe8zkGekNRlNdqgNf0/x8vLKzrboG264sc9miK4+g5MdEh6P13g8\nPrHHjGUY6/H4RL/mmmsH3Jnb3Nzs5eWV3ZqvguaT0ozmr5KwH6HEocTLyiZ0vs6KFSt948aNw9qk\nko+Oa/UNRE+zjYcX6jMoDDPznqOJ5ocdu+nt7E1hYTvWv/a1r/mHPvRhLy+fkPUfINvolszRRKl/\nIJgZ9kusz9on0V8/wu233+Hl5ZUej8/tTDbp8aSPJurZoRzrkYyG6x+5t/b+1atX+3ve8x5fs2bN\ngI6lvoFojPQBCCORkkGBlJSUZKkZTPbUCJ+uYaa1DpPcLOFQ7l0Tzrr/A6SfJUGZV1ZO8GXLlmUt\nsDZu3OgVFbPTXsvDgropp87c22+/o0cndHn5RG9ubu7xekEyyFYD+kx4v9bh7mH7R862HAfEuyWr\nyZOrCh7HQCjp9JTt7zh+/Gl+11135fVzGu7Pvpj/1koGBRIU9KmEkGpOuSyjwMTHjj3Ry8snhGfX\ndzv0HIGzcePGtLOk7gUbWI9aRG/r82TWDB555BG/6667us1obmtr8/LyiT2S2LhxJ/sNN9zYo9o+\nZcqULDWgWQ7TuyXA4RpJ1PO935g1WQ2khtDb6+Tjn7rQTSHFXPj0pbfv8Pjxp+TtcxruZqhib/ZS\nMiiQIBmMDQvgVFNQwqGmsxC/9tpPelNTk994440eNOu0ebZlKrqGaX6mj7PwYN9Uk9Htt9/RbUmK\nVJ9Bqg188eLzwuee6JDwa6651t2DM7Jgtc/0wn2+l5WNy1ptv/LKKzNi+qEHzUTm6c1U+VwSor/C\nLb29P0jIPZPVe97znkG/VuY/9e233zGoArfQTSFDLXwKnUj6O37X6rOneb5Xnx3uZqiR0OylZFAg\nQTKY6cEcgbvC3zPD7eYwxTds2OBNTU3+zW9+07uah9aHhehMLyubkDGBq7ez8CkObR6Pz/Dy8ok+\nYcICj8XGeyxW6RUVp3abCNbU1OSPPPJI1qSSagYKXis9icX9ggsu9Hh8TrfXTp3tm6U6lCd5Zq0l\nnx2wAyncUu919erVg6oZ9PZa+TxjLeQKs9n+jgMpfAp9Fpvr8dva2vyuu+4KJ1Lm73Ma7tV9R8Jq\nwkoGBRIU+rFuZ9/BKBwcjnKzMo/HJ3lFxakej09ys/KwMK11mOBjxpR3a75Zt269d3XUZtYMjnSY\nGD6efRmL9ILgrrvuCmNKTyon+F133eXu7tdc88mMuMvDRNb72dmMGTOyxrZ48eKCLRJXVjbBH3nk\nkX6fO3lylac31/XXZ9DXWVz2PomugQEDKXALebbY1NTkicTxHtQ0FzhM9ni8JqfCp9BnsQM9fiHi\nUc2gJyWDAgkK/VQ7fXozEQ5lXlIyrtsXo6RkXDh6Z1avo2/a2tq8Zz9E95E7Xdc+6N7Uk34W0tzc\nnLXg7l4zSH9sUnjcVPKp8Xh8UrcYKysrPVutpbKyMi+fZ/ZCeJZDeWcTV1/WrFmT82iivs7iep/9\nPbilP/I5hDW92aWvv/FQ3n8+DOb4hRjqO9zDh4t9uLKSQYEEhf4x3c7M4Ohwe+pMO71gm9nZh5B5\ntpA+hPSGG24ME8AYh8oexwg6oVOjgXo/C7nmmms9vUknvc8gWO8o/bi13jUS6RSHmN94443dYrz4\n4ouzFj4XX3xxXj7PvpbgyLWQG8prpX9++W7LzkfbfGazS9DZ3/3vmEicPCJrBunPy3cfhkYTdVEy\nKJCumkFmk06Zd9USup99l5VVdpsz4J4+uex476oRTHL416zHLysb50ENYXVYWM53SPjtt9/RI8bm\n5uYeo4myn1Gm1wwmO9T4hg0bsrznMd691jImr5/punXrw0l1s8I41ntmE1c+X6uvs7jUP/Xtt98R\n+dleb4VrPD6xx7aB9hkU6n0V+1ny4UjJoECCZDAr4wx7lgf9Bu5Bp+9Y75qBvN7Ly2s6O4BTo1S6\nOgEzz4pTw1FT/QyTHMr985//vFdUnBruFzQZjR07r7Ozur/CIKgZzPCu2dGV4evM99Scg9LS8b0e\n5+KLL/bKysq81QgyBZ3f5d59Ub/81gxScj2Li/psr7dml9RQ4MEWuFGPJpLhpWRQIGPGpM6SM2sG\nlWm3CQvYZVlrC+XlE9OWo17g6ddChpMcpmZsm+k33nhjluaUsQ5xTyT6H/GSbRRKaWmFx2LjPB6f\n06OvoNCyFRi9NXEdrvpqdlGBe/gY6t9ayaBA3v3ud3v2JazHedd6Pt2HYWa28Y4ff1ra2kDjw7P/\nBZ21gGACWs8Owu5LUkwMXzP35oJsVfgoCpW+hh9mNnEd7oWeml0Ob/kYCqxkUCCXXHJJWOCT9lPi\ncLLDlb3UGmIedAC3dRbaqcljmctUxGKVfumlV3RLNulnyF1LUmz0bLOa++tIjLpwHUgnY7HP7Bwu\nUf/NJBr56vBXMiiQK664IuvZP1wXNu/0dknM4Opmsdi4zkItKNhPzVqgZ+sEds9s7um9CWHjxo0F\nW1V0KIVTrsMPR8L4bZFCytdQYCWDAjnllFN6Ofs/Ji1JZD52RdYCbbAFXuqMOR4PlsBIJE7uPHPu\nPjKne/LJh3wshZDLe+7tH2Hjxo06S5bDgmoGRZ4MjjjiiD7O/k/x7P0JvWf2wbYJp89RSBWOvU0s\ni8cnFmy28GC+nLm852yvFYuNV7ORHFby0WekZFAgl19+eS9n/5ek3Y+FTUYX9dg3W+GZrzbhpqam\nHs1OUOsVFSfmZZZpPmew5vKe0/8Rsl3gR81GcjiIYjSRBc8rTmbmxRDftm3bmDfvJILrIE8luDTk\nPmA3kLr046xw26uMGXOI0tJy4vHjw+sdr+m8zGW+JZNJqqvn0N6+ma5rJdcRjzvbtz8z5EtTZjt+\nIrGI1tanCnbZy2QySUtLC6+++ipLlqxkz57HOh+rrFzApk3f4PTTTy/Ia4uMBmaGu9tAnlNaqGD6\nY2bvB/6N4DrM9e6+OqpY+vP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iMr4pGIiIiIKBiIgoGIiICAoGIiKCgoGIiKBgICIiKBiIiAgKBiIigoKBiIigYCAiIigY\niIgICgYiIoKCgYiIoGAgIiIoGIiICAoGIiKCgoGIiKBgICIiKBiULJFIsHXrVhKJRLWzIiJSNAWD\nEmzYsJG2thNZtmw1bW0nsmHDxmpnSUSkKObu1c7DiMzMazV/iUSCtrYT6evrBBYDT9LcvJSdO5+m\ntbW12tkTkUnMzHB3K+Q9qhkUqaenh8bGdkIgAFhMQ0MbPT091cuUiEiRFAyK1N7ezoEDPcCTMeVJ\n+vt30t7eXr1MiYgUScGgSK2traxdezvNzUuZOfNUmpuXsnbt7WoiEpFxSX0GJUokEvT09NDe3q5A\nICI1oZg+AwUDEZEJRh3IIiJSFAUDERFRMBAREQUDERFBwUBERFAwEBERFAxERAQFAxERQcFARERQ\nMJBIm/SITG4KBqJNeqSqdCNSG7Q20SSnTXqkmjZs2MiqVWtobAxLwq9dezsXX3xRtbM17mltIimY\nNumRakkkEqxatYa+vk727XuCvr5OVq1aoxpClSgYTHLapEeqRTcitUXBYJLTJj1SLboRqS3qMxBA\nm/RIdST7DBoa2ujv36k+gzLR5jYiMu7oRqT8FAykaJPlP+Rk+Z4yuWk0kRRlw4aNzJ+/gKVLL2P+\n/AUTdp6B5lOIjKzkmoGZzQO+CcwGDgF3uvttZnY4sBFoA3qAC919X3zPdcAVwEHgU+6+aYRzq2aQ\nh1LudhOJBHPnHkd/fz1wLPAbGhr62bPn+Ql156z5FDKZVKtmcBD4tLufBPwB8FdmdiJwLfCou78b\n+BFwXczkIuBCYCFwLnC7mRWUaUkp9W5327Zt9PcPAF3AE0AX/f2H2LZt2xjktno0jFFkdCUHA3f/\nnbv/PD5/E9gBzANWAOvjy9YDH4nPzwfudfeD7t4DPAucUWo+JqPyTdp5J+mFJBxd3ozWAA1jFBld\nWfsMzKwdeC/wODDb3fdCCBjAUfFlc4FdaW/bE9OkQOW4212yZAmNjQnSC8nGxpdYsmRJWfNabZpP\nITK6+nKdyMxmAN8h9AG8aWZDG/uLavy/6aabBp93dHTQ0dFRbBYnnMy73dAOXujdbmtrK+vW3cEV\nV5wFHAm8xD//8x0TspC8+OKLOPvsD2k0kUw4XV1ddHV1lXSOsgQDM6snBIJvufv3YvJeM5vt7nvN\nbA7QG9P3AMekvX1eTMsqPRhIpuTd7qpVSzMm7RRTyJlNYcqU6Rw69MoY5LR2tLa2KgjIhDP0Rvkf\n/uEfCj5HWeYZmNk3gZfc/dNpaV8AXnH3L5jZZ4DD3f3a2IF8N/A+QvPQZuCEbMOGNJooP6WOJtIo\nG5GJpZjRRCXXDMzs/cDHgKfMbBuhOeh64AvAfWZ2BbCTMIIId99uZvcB24F+YI1K/NHlKuxLudtN\n9jv09Q3vd1AwEJk8NAO5xo31eu+qGYhMPJqBPMFUYr331tZWVq26FDgTWACcyapVlyoQiEwyCgY1\nrBITpRKJBGvX3gU8TOjKeZi1a+/SBiMik4yCQQ2rxESpVMDpAE4HOjQzV2QSUjCoYZWYKKWZuSIC\n6kAeF8Z62WVtMCIysWg/Ayma1vkXmTgUDERERENLRUSkOAoGE0AikWDr1q0aDioiRVMwGGNjXVBr\nK0eRypjoN10KBmNorAvqSsxQFpHJcdOlDuQxUok1f7Zu3cqyZavZt++JwbSZM0/l0Ufv4PTTTy/L\nZ4hMduNx/S51INeQSiwloQljImNvsuyfrWAwRipRUGsrR5GxN1luutRMNIYqNbNXE8ZExtZ4m6Wv\nSWc1SAW1yMQwnv4vKxiIiIg6kEVExormGYiITHKaZ1BlaiYSkWrTPAMREdE8AxERmTzzDBQMxoFK\ndFxN9M4xkWJNlsmd6jOoccnJLo2N4e5kLCa7VOIzRMY7zTOooskeDCrRcZX6jPuB6cBbNDevHPYZ\n4+k/gshkpw7kCSZ0UM0lveMK3lnWjqtwrlnASmA1sBL3mRmfMRmG1Ynkcs0119DW1sY111xT7ayM\nCdUMatiOHTtYtOg04HGSNQM4k+3bn2DhwoVl/oyHSdYM4LzBzxiPw+pEyq2urplDhwyYB+ymrm6A\ngwffrna2RqSawQTz5ptv0tw8B1gKnAospalpNm+++WZZP6OhoZX0mkFDw5GDnzFZhtWJjOSaa66J\ngeBx4D+BxxkYqJtwNQQFgyobbRRPGLq2D7gfuAO4H7PXyzqkbcaMGfT3J4BO4Amgk/7+l5gxY8Zg\nHmppWJ1GPUmlfec73yHUCNKba+fG9IlDwaCKcrXFp4a0rWTmzL+kuXll1iFtpRSQu3btAt7J0H6J\nkF5bw+rUdyHVcN555wG7Sb8hgj0xfQJx95r9CdmbmHp7e725+QiHXzi4wy+8ufkI7+3tHfba7du3\n+7p163z79u3Djt1zz73e3HyEt7Sc6s3NR/g999xbUD42btzo0JyRD2j2jRs3Dstvd3d31vxVQiHX\nS6Scuru7HabE/yfHx8cp3t3dXe2sjSiWnQWVt6oZVEm+bfEbNmzktNM+wKc+dRunnfaBjLvhRCLB\nqlVr6OvrZN++J+jr62TVqjUF1RBmzZoFtJDeLwEzY3pKa2srp59+etU6jdV3MZyazCqjvb2dhobp\nwADwAjBAQ8M0zUCW8sinLT5XYV+OAnLJkiU0NvaR3i/R2LifJUuWlPYFy6zW+i6qbcOGjcyfv4AP\nfvBjzJ+/QE1mY8xsCrAVeBvYilldlXNUfgoGVZJPW3yuwj7fAnK0O8jW1lbWrbuD5uaVTJ/+cZqb\nV7Ju3R01N2y0lvouqi2RSHDZZavYv9/Yv/8w9u83LrvsCtUQxkhPTw/NzceR/v+wqeldE69WWmi7\nUiV/mMB9BkmjtcXn006e7DOYPn1x1j6DfPsUKtEnUI7PGC/5HEuhn2fakH6eacP6eaQ8xmN/FUX0\nGVS9wB81c5MgGOSSLMxnzlwyYmHf1DTLp09/tzc1zco4Xkt/xKV2dFfKeMjn9ddf73Bc/DdN/hzn\n119/fbWzNmHl+n9YaxQMJqiR7lRzFfbd3d3e0nJqRqExc+aSrKMgxvJuuJaC0mjGSz5vueWWrCPA\nbrnllmpnbUKr9RpjumKCgfoMqiyfESEjjeQpV59Cucbvj/RdxstIoPGSzwsuuADoB84EToiP/TFd\nxkq1R9SNuUKjRyV/mOA1g1Lb8wvpUxipeluuu+HRvst4ueMeL/l0d7/66k86THU4ymGqX331J6ud\nJakhqJmo8oqtOuZb8OQKGPm0ZY6Wx0Kakkr5LuOlzXW85NN99MmIMrkpGFRYaiTPewouOPIphFOF\nbKdDt0Nn1oBRSltmOe6G8w0oGk0kUhkKBhXU29vrjY0tGYVoY2NL3gVIPoVwd3e3Nze/y6HRod6h\n0Zua2ss+Db7Uu+FyNa/kKoTHw0gfkVqgYFBBjzzySFynpDfetfc6HOePPPJI3ucI7b7NDic4NA9r\n992+fbtDXcZrwMakWaDUu+FSA0qugn48tefnQ7UPGUsKBhUUgsFUh8MdTo2PU4cFgxUrVvj06dN9\nxYoVGempwu0Bh3UODwwr3FauXJl1COHKlSsr8h0LNZb9J6GWdEpGU1Rz88ljslhYru9Ralt9uWo4\nCigyEgWDCgp37cML6vQCIttdfVKqCeiIGEyOGNYE1NraGt/raT/He2tra9m/TzULllSfQ6qWNbTP\nIZ/rXQ65Cuqrr/5UzMeCrLW5XMo5emukyYbjjYJa+SkYVFB3d7c3Ni7MKKgbGxcOFmArVqzIWngl\nawj5FG6rV6/O+prVq1eX9btU+061t7fXGxoOy6hlNTTMyDhPWIJhdgyeS+Lj7LIuwZCroC5HQCrX\n6K1c12u8UD/Q2KhaMADWAnuBJ9PSDgc2Ac8AjwAtaceuA54FdgDLRznv2F2tEuUqGKZPn571rn76\n9Onunn+zh1m9p6+jblZf1u9RiXkG+eQhV2f8bbfd5mE9nk5PjqyCaX7bbbcVlM/R5Cqo161bF2sE\n6f+mJ/i6desK+q6lXu/QRDl8baJC+qtqQb6j5aRwxQSDcs1A/r/AHw1JuxZ41N3fDfwoBgDMbBFw\nIbAQOBe43cwK2ri5FuTan/jss88m2+5IIT25peUeoIuwNG4X8Nths4MPHepn9erLmT37DVavvpxD\nh/rL+j3KMeu21H0V8lkVMly3g8CfAH8ZHw8OXs9yyDVj+4wzzgB2kflvujum56d8q68O3Z3u6ALf\nX33h33cW6ftvu8+suRnfk0ah0WOkH6CNzJrB08Ds+HwO8HR8fi3wmbTX/QB43wjnHKO4Wbp87mrA\nPHN3JMs4R6ntz+VQyXkGI3W85puHMPqqyeEYh6airle+w1dHGhWVawRYufKR672lDGuuFZXqB5qM\nqGafQZZg8MqQ46/Exy8Bl6SlfwP4kxHOOTZXqkxyLR/tns9ootyFcK6Co9TRL5WYZ5Ar8KU6RBeM\n2iFaykiefJuycn1GruOV6BDN52+vFuSa/V6pEWKTTa0Hg5d9AgaDYkd05DOCJvkZ+SxHkXv0y+h3\nsuWaZ5CtcMrnDrCU2dz5KNfyH7lUskO01kfhTLa5I7Wk1oLBjiHNRDvi86HNRD8crZnoxhtvHPzp\n7Owco0tXuFL/kPMZEZLrMyox+iVfowXGXB2vlSgUClv+o/h/UxVuQaHBdzysBVXLOjs7M8rKageD\nduCptN+/kCz0gc8An4/PFwHbgEbgWOA5QmP6uKoZlDpEMJ9231yfket4WPd++IimbOvel2d9o07P\n1n+yZcuWrEFpy5YtZbmW+XyPfPp4Ss1HOb/HeFcre2lMVsUEg7KMJjKze4B/BxaY2Qtm9hfA54Fl\nZvYM8Ifxd9x9O3AfsB14GFgTMz+upEaedJEcDVTIBu35jKDJNbol1/G33nqLbCOaQnpKqfsZpEaF\nXAB8DLiA9FEh3d3dwADQQRh51QEMxPTCruVo+z+M9j1aW1tZteoy4DzgUuA8Vq26NGMUT777P4yk\n1PdPJIVciwm/T8B4UWj0qOQPNVwzcHdftuzcjPb45cvPzfu9hVSjR+tYveeee33q1Jne1DTfp06d\nmXE8TNRq8cwRTS0ZE7XK0bQRmqOGLs3RONgc9eEPf9jDNo3bPSy9sd3hOP/whz88eI58RlaVsmdC\nvmPay7XGkpo9dC2qCc1Arpx82+NHqwLnM0wxBIPDvbn5ZG9qOryg5RFShfTM+BkzMwpp9/KM6Mi1\nQfuVV16ZNVhceeWVg9coV0DK9Zp8mtRyLf+Rft1KWXtIzR4puhbVoWBQQfnMRs3vTnbkO9Vc/Qq5\nAlIopIcfT68ZlKOTOcwOHr5Be3J28J133hk/I/VdodnvvPNOd8+vfTnXa8rVmT6elkdQQSsjUTCo\noFyFS6l3su7py2RnFrLJZQdyBaRQSA/vQE5fwiF8xhwfuuZPIUsb5LoW4TOOzrgrT/+MctQM3Edv\nlsinBjSeRgONp6AlladgUGHLlyf7DEJ7fHqfQa7CJ5+CJ1fzS65COJ+74dQ6N52evubP0GCQ6y40\n1eQVrsXw5qr85hmM1r5cyhaf5Ri9VSvGU9CS6lAwqKDUPIEWh3c7tGTMEyhHAZi5Z8ISz7Znwmj9\nDr29vT5lSlPG8SlTmobdcTc0zMj4jKHzHfK9C92yZYvfcMMNg0NGk/Ltl8in2aO0lVFH/57jpZAd\nL0FLqkfBoIJyrRwZCsBjM5pfsnVYjla4pQqwFg/NQS1ZC+qRRhOlCuEtDjc4bMlaCI82YqkcM3fz\nPcdYtoHnO+O7XGsPjaXxErSkehQMKihXe36+QxnzWTdopMI+9RnZd0vLNeQz3UgFcbna2kdrRkp+\nz7HcrKWwfonR/81qgYZtymgUDCoo1QadKjiGtkHnXgEzv7H1U6fO8qlTF/nUqbOG3fk3NMzOOEd9\nfetgQZ3PaKJc8mnuynXXnatJLZ+lOcoh17/HeGt+0WgiGYmCQYXlswjcXXfd5eeff77fddddGen5\nFLK9vb1eX39Yxmvq6w/Le5mHsBzF8CGf2ZajGElmwDlhWMBJ5nO0wjxXk1olN2vJ1Syn5heZCIoJ\nBuXa3GbSSSQSfP3r64DHgf8EHufrX1+fsUzCKaecyqWXfpzvf38bl176cRYvXjJ4LCzFcAyZG5TM\nG1yiAaA0rRiNAAAYqElEQVSzs5ODB4/KeM3Bg0fR2dkJwM9+9jNg3pBzzI3pcPzxxwO/JXM5it/G\n9PzMmDGD/v7XCSuH3A08zMGDbzJjxoyM15lNISwn8QTQhVndkDPl2owlv81aRluOIh+jLX1Qvo1n\nRMYfBYMibdu2jQMHWkkvwA4cOJJt27YB8NBDD/HLX24HmoFWoJmnntrOQw89BKTvmvVdYH18zNw1\na+/evcCLZBbmL8Z0aG5uJtvaQyEdli5dSlgT6EzghPh4MKbnJ+zodjxhPaHTgQ6am48b3NENcq+z\ntGTJEhobExn5bGx8iSVLluR1PKnUNZQgdzC5+OKL2LnzaR599A527nyaiy++qODPEBmXCq1KVPKH\nGm4mytUef8kll2Rt+rjkkksGz3HyyUsyml9OOeW9GZ8RmpIaMl4D9UNmGA893jCYh3z6NXLJt+M1\n1xj+XJux5DpejiYcTdSSyQL1GVROrpm7X/ziF7O213/xi1909/z6DFJ9AqnCPL1PIDWiKXMBuPTh\nreXoEM3V8ZrPGP7k64rdsa0cS4arP0Ami2KCgZqJinTMMccA+4D7gTvi4+sxHS677DLMMpt4zF7k\nsssuA+DRRx8F5jK0vT+kB5s2bSL0CZwUU04C5sb09OaVzwE3AJ/LaF4pZBnhq666ijlz5nDVVVcN\nO5ar6aSnp4dp0xYAz8Rr8QzNzScUvLH5aO35pS4P3dPTQ2NjO+nXu6GhTZuviyQVGj0q+UMN1wxC\nE83w2cHpTTR1ddM8DKdc5NDidXXTBu9EU4u3ZdYMkou3uSdrBsPnCaTP8B3eTFSXkc98xqNDY8Y5\nzOoLuhaFrBtUShNNKWPr82nKEpkoUDNR5aRW6kyNrU9fqTPVhJN5PHM45ewRm5nck8tJTI9NQDc4\nrPMpU6YPFmCrV6/OGlBWr16dkdfRml/yPUcuoxXU5WyiGcvlKEQmimKCgZqJinTssccShm3+Kqb8\nCvhtTIfXXnsN2DPseEiHAwcOAK8ztJkppAc9PT0cOjQFuArYAFzFoUM22LTx7W9/m9CM1EAYkdQA\nzI3pKaM1v9x///1kG54a0lNKGYVTziaaYnfFKldTlshEpWBQpJdffhloImyj+LH4ODWmw6xZs9KO\nXxofG2M6bN26ldRWkZfGx5aYHvz0pz8FDpI+lwEGYjqccsopQA9wGqHf4DSgJ6bn54Mf/CDZhqeG\n9CDfIZ0jFdS1sB1kKg8vEobIvjhpt6QUyUbBoEi//vWvgf1kFtRvx/RkB3Py+DPx8cBgB/Py5cuB\nV+LZmuPjKzE9+OEPf0i2u/aQDrNnzwbqh3xGQ0zPz2c/+9mYz/S5CPtjeqgRrFq1hr6++9m372v0\n9d3PqlVrCpr0FfYfvjSeewFw5rD9h8eaJpSJjE7BoEgPP/www2fNvjOmp0/WOpqwyfvRGZO1FixY\nwJQp9cBjwM+Bx5gypYEFCxYMfsY555xDtrv2kA4/+tGPyBYsQnp+Qj5PJjRFHQCuorn5pMF8pja7\nXwmsBlaSvtl9PhKJBGvX3kX6LOa1a+8aFlBKnV2ciyaUiYxMwaBIixYtItvs4JCebJZ4HpgDvA+Y\nw4EDzw82S/T09DB16rtIL8inTj02o5A97rjjgENk3rUfiun5NfEA7Nixg/Xr17Njx45h3yPkZw9w\nObAzPv52MJ8zZsygr+9FoJOw1EQn+/fvHbYcBYxcmIfvNJf0WczwzozvWo7Zxfkots9BZMIrtMe5\nkj/U8GiiMGmsMQ4dPTk+Ng7ZZaxuyLBPK2gXstTw1WlxtNG0YcNXwTx9aWiwIUtHf8qhyeEYh6YR\nV0YdafZvvhvTjDZ0tNQtQkWkMGg0UeUsXLiQk08+CXibMPnsbU45ZRELFy4E4MILLwQayexTaIrp\noXmmoaEVWAqcCiyloeHIjDV/Xn/9dULl7T+Al+PjlJgOL730EqHPoA94Pj7WxfRQI/jyl79G+vpI\nX/7y17LWENwPAW/Hx5T29nYGBl4gvfYxMLAro+M11a/Qyb59T9DX15nRrxCaouYAZwHvBs6iqWl2\nRlOUJoSJVJeCQZF27NjBL3/5NPAT4AXgJzz11DODBW14HN6enzweVgNNAB8iDFH9EP39L2U0v/T1\n9RGaV1L9DjA3psMDDzxAGE76APDP8bExpieP15G+mijUDx6HVEG+f/8DvPXWt9i//4FhHcTuA4SC\n/D3AWfH3lFyFeXt7OwcPvgQ4IUA6AwMvDQaUWhhtNBmNdR+NjC8KBkUKy0YML+yTy0kceeSRZGvP\nD+nEu+IDwL8CM+Pj/oyawdlnn01Y2fQE4LL4+EJMT5oJrAJui4+HDR4J/8mPJjOYHJ3xnz9XB3FP\nTw/19UfFV78NQF3dkRl37fkU5qHV79+Ap4B/w90Gj2mkT+VVqo9Gxg8FgyIdPHiQbIV9SIc1a9aQ\nbchmSIevfOUrZGtGCulBCBxG+GeaHh9tMKAsXryY0ESV6tyF12M6sd9lN6FpZnV83JXsjwFydxCn\njj8GPA08NqwDOdfQ0W3btnHw4GyG7suQXO4bwkifzZu/z1//9X9h8+bva6TPGMrVrCeTk4JBkerr\n6wmF+XsIBfZ7gMaYDmZGmHR2NmE+wdlAU0zPPYcAiJvYZG4aA3WDm9uECW7Dz5Gc+BZMAf4F+Fp8\nzNx0JjUENnWO9CGwuY5DvkNHh26y82JGPj7xib/mAx9Yxs0338sHPrCMT3ziU8jYUB+NZKNgUKTQ\nVPMmoXP2hPj4+mATzjPPPAO0AD8mFNg/BlpiOlxwwQVkq1mE9CBsYnM0Q3cAS25uEzbCGX6O5AY5\nIfC0kN4EBDMHAxKkDy3tIjQldZE+tDR1PHO3tPQmoFTh0kFy6Gh64RIm2h2Mx0+NjwcHJ+CFju6v\nkz557stfvjNrR7eUTn00ko2CQZGuu+46sjXzhHQ499xzgVcJO429HR9fienw1a9+NaanNyO9HdOD\nEFiGb1uZDDgLFy7k6quvJL155uqrrxwc0TRt2jSyNSOF9CA08VxG+rIZ6U086e3506e/J2t7fq7C\nZdeuXYRmrj7gpfjYHNOTW4AOr+GkbwEq5aM+GslGwaBIDz74INlmIId06O3tJVze7wLfio91MT05\n2qiOUDA+Fx+nZNwNL1y4kOXLOwhNUHXAe1i+vGOwsAf40pduZcuWzdxww8Vs2bKZL33p1sFj73jH\nO7Lk8eiYHoQmnm+RfleebXbwSENPIXfhEhbn2w/8gLAg3w+AA4OL9oU9mYfXcArZq1kKo9nYMpSC\nQZEOHTpEthnIIR1+/OPQLBQWoLssPs6M6clhn4cI/Qrz4uPAsGGfYSObZuA4oJlNm36YUVBv2LCR\nZcvO59ZbH2LZsvMzRoXs37+fbG31IT3o6emJezmvB9qA9Rw4kBotlBp6+hhvvfU0+/c/lrWzcbTC\nJSzON3wGcnLRvrfeeiteq9Q8BJgZ0zNpOGT5aDa2pFMwKEk/6U00YahoEEbsvBp/a4qPrw6O5Nm8\neTNhwlgzcFR8bIjpwd/8zd+QrSkqpOdeRK6pqYkw9DQ1sQ1mxvRg7969DAw8B3wVmAp8lYGBZwf7\nJQrpbBypcMlvw/vktUou2vf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Vs09Nqonc/Vfh0y6gA3DgImB9uH09cHH4/ELgXnd/y92HgaeA02qRDpFaGx0dZdu2bYyO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\nLeD0Asccr2slMWp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VDFKx5911EsK6/1MIM3gvj8f9E9sPYL/99gNqgBcI2yK+AMyN6fmr\nrq5mxYo7mTDBmTRpBxMmOCtW3FkS/wgiaautraWrq43ca0N3d3vJbPuqYJCwj3/84wzWSRrS83PY\nYYcROnibCE1QTcDbMT1/J598MqF/4zVgQTxujunJWLLkAjZu/B1NTd9n48bfaVtEGTP69ohexJQp\nJ5LJLCqpPaLVTJSwF154gbDM9E3As4S9fq+J6fkJK5/u2QSVxIqo0LuqaAUhgM0m1Ggs8VVFq6ur\nS+YfQKSQliy5gNNP/whtbW3U1taW1P+BgkHCwsibw4Gv5KTeSnt7/ssuzJo1i74mqOPpbYIK6fmr\nq6ujqmo8XV23EJaJ+ABVVVepTV8kQaV6M6RmooSNHz+ewSZWhfT8LFq0iIoKJ3ftoIoKZ9GiRXnn\nDeFDetllFxOGfK4EvsRll11ckh9cEUmWJTW7NA1m5qVcvsFUVVXR3T2JsMbPHEJg2I/Kyh10dXXl\nnX9j4yo+//nLMJuG+1t897v/J7F292w2S03NPDo7lxP6JqaSyTTQ3r5eAUGkjJgZ7j6s1THVTJSw\n6upqXn31D4Q763BBhQsTu5im2ebY1tZGmNTWQO/QT/cpJTP0TUTSo2aihH36059msMXeQnoyqqur\nWbBgQeIX6MmTJ9PZ+Rq5o5V27tzC5MmTE30fESk9RQsGZvZxM1tvZr8zs2uKVY6kffGLXwQqCVtT\nborHyphe2rZv304mcwS5o5UymcPZvn17MYslIgVQlGBgZhXA3wMfI+zhuMTM5hWjLEmbP38+S5de\nDhhQDRhLl17O/Pnzi1yy9xcmvwycMPdqyUyKEZH0FKUD2cxOAW5w9zPj19cC7u43DXhd2XUg92pt\nbWXNmjUsXLiwLAJBr8bGVTQ0XEFlZQ3d3e0sX367JoaJlJmRdCAXKxicB3zM3S+LX38WWOjuVw54\nXdkGg3KWzWZLclKMiOwbjSaSRJTqpBgRSU+xgsFmYG7O13PYy2puy5Yte+95fX099fX1aZZLRKTs\nNDc309zcnFcexWomGkdYEvOjhNXQ1gBL3L11wOvUTCQiMkxl00zk7rvNbCnwCGFE0/KBgUBERApH\ny1GIiIwyI6kZaAayiIgoGIiIiIKBiIigYCAiIigYiIgICgYiIoKCgYiIoGAgIiIoGIiICAoGIiKC\ngoGIiKBgICIiKBiIiAgKBiIigoKBiIigYCAiIigYiIgICgYiIoKCgYiIoGAgIiIoGIiICAoGIiKC\ngoGIiKBgICIiKBiIiAgKBiIigoKBiIigYCAiIigYiIgICgYiIoKCgYiIoGAgIiIoGIiICAoGIiKC\ngoGIiKBgICIiKBiIiAgKBiIigoKBiIigYCAiIigYiIgICgYiIoKCgYiIoGAgIiIoGIiICAoGIiKC\ngoGIiJBnMDCzPzez35jZbjM7ccD3rjOzDWbWamZn5KSfaGbPmdnvzOx/5fP+IiKSjHxrBs8D5wKP\n5yaa2XzgfGA+cCZwu5lZ/PY/Ag3ufhRwlJl9LM8ylKzm5uZiF2HEyrnsoPIXm8pffvIKBu7+grtv\nAGzAtz4F3OPuu9y9DdgALDSzg4D93X1tfN33gHPyKUMpK+cPVDmXHVT+YlP5y09afQazgVdyvt4c\n02YDm3LSN8U0EREpovHv9wIzexSYlZsEOPA1d//ntAomIiKFY+6efyZmTcDV7v6r+PW1gLv7TfHr\nh4AbgHagyd3nx/TPAKe5+5f2km/+hRMRGYPcfWDz/ZDet2YwDLlv/FPgbjP7FqEZ6Ahgjbu7mW0z\ns4XAWuAvgFv3luFwfxkRERmZfIeWnmNmrwCnAA+Y2c8A3L0FuBdoAR4ErvC+KsiXgeXA74AN7v5Q\nPmUQEZH8JdJMJCIi5a2kZyCb2TfipLVfm9mPzGxKscu0L8zs42a2Pk6su6bY5RkOM5tjZo+Z2W/N\n7Hkzu7LYZRouM6sws1+Z2U+LXZaRMLOpZvaD+Nn/rZmdXOwy7Ssz+8s4EfU5M7vbzKqKXaahmNly\nM9tiZs/lpE03s0fM7AUze9jMphazjEPZS/lHdN0s6WAAPAIc6+4fJMxVuK7I5XlfZlYB/D3wMeBY\nYImZzStuqYZlF/BVdz8W+BDw5TIrP8BVhCbKcnUL8GAcaHEC0Frk8uwTMzsE+ApworsfT+iT/Exx\nS/W+vkv4X811LbDa3Y8GHqO0rzuDlX9E182SDgbuvtrde+KXTwJzilmefbSQ0BfS7u7dwD2ESXhl\nwd1fd/dfx+fbCReispkLYmZzgLOAO4tdlpGId3H/1t2/CxAnbr5d5GINxzhgkpmNByYCrxa5PENy\n9yeAPwxI/hSwIj5fQQlPjB2s/CO9bpZ0MBjgUuBnxS7EPhg44a5sJ9aZWS3wQeCp4pZkWL4F/BVh\nLkw5OgzYambfjU1d/8fMMsUu1L5w91eBm4GNhImmb7n76uKWakQOdPctEG6OgAOLXJ587PN1s+jB\nwMweje2LvY/n4/Hf5bzma0C3u68sYlHHFDObDPwQuCrWEEqemX0C2BJrNsaey6SUg/HAicA/uPuJ\nwDuEZouSZ2bTCHfVNcAhwGQzu7C4pUpEWd5YDPe6meQ8gxFx98VDfd/MLiFU+z9SkALlbzMwN+fr\nOTGtbMQq/g+Bf3L3+4tdnmH4MHC2mZ0FZID9zex77v4XRS7XcGwCXnH3p+PXPwTKZRDC6cBL7v4m\ngJn9GPhToNxu4raY2Sx33xLXU+sodoGGayTXzaLXDIZiZh8nVPnPdvd3i12efbQWOMLMauJIis8Q\nJuGVk+8ALe5+S7ELMhzufr27z3X3DxDO+2NlFgiIzROvmNlRMemjlE9n+EbgFDObEFcp/ijl0fk9\nsBb5U+CS+PxioNRviPqVf6TXzZKeZ2BmG4Aq4I2Y9KS7X1HEIu2T+Me4hRBsl7v714tcpH1mZh8G\nfkFYntzj4/pymxxoZqcRlkg5u9hlGS4zO4HQAV4JvAR83t23FbdU+8bMbiAE4m5gHfAf4kCKkmRm\nK4F64ABgC2HZnPuAHwCHEpbQOd/d3ypWGYeyl/JfzwiumyUdDEREpDBKuplIREQKQ8FAREQUDERE\nRMFARERQMBARERQMREQEBQMREUHBQEREgP8fJTPsZbse/AQAAAAASUVORK5CYII=\n", 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IiMRSkBARkVgKEiIiEktBQkREYilIiIhILAUJERGJpSAhIiKxFCRERCSWgoSIiMRSkBAR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\niXoLbQWuK3V6YtJ4OnCAqPfV5nDczwU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Hjh3jI8pEyjXbg8HPXcHAh4aGHJqzRgc1+aZNH3H3/EMRYWVYiyg9887OTKPX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\n1q1bPeh7iBaaW+hwrN9yyy0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c06nZgsFjsWaix8Kf481Ef1esmejGG2+c/BoeHq7TrWu8Uj9I9957n6dSc72j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\nfC6o1UuwLgAAAABJRU5ErkJggg==\n"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FBhERiVFgEBGRGAUGERGJUWAQEZEYBQYREYlRYBARkRgFBhERiVFgEBGRGAUGERGJUWAQ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\nbeLE47yxcWrWL/3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2sWbNOhoawjjwG2+8jiVLTqStra2m8y1SKRQYRtnzzz8PJICDwOvA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\nLKrLOwAAAABJRU5ErkJggg==\n", 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evR/IZOgzqNQM5L8F/ijv2DrgcXc/Cfh+FAAws5OBy4BFwIXABjMrbwjUJLFg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\nd90KGermnPb+whvlTPepU+d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hQymrZiOsUCj4IoVWEh6YViayTuNass7E69N0Dhs3bqSxcQXQ/yUNfMmDsaGm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\n", 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3v7e3t8eMBprmDQ0Lhl16rtZO2ui+h1JDE5kIRhIMyrnt5R4zm+7ue8zsSKAn\nPL4bmJV13szwWKxbbrml//mSJUtYsmRJ6VM6iO9///sEk7WfJLO5zVL6+r7O3/zNBcA32bdvCdEe\nyKecspAbbvh4wfnuR7Bw4alAA5Mm/Z4NGx7k8ssvLdireP36e5k375hh7y28adMWrrpqJTU10zl4\ncA9/93f39W+KE7cN5sc+tpSvfW0TyeQc3ngju3N8Jn193cOaQRx3D8PdkKec3A8Bb4Q/RSaWjo4O\nOjo6RneR4UaPYg+CzuBns36/g7BvgPgO5CQwh3HQgbxixQovnDewyIMx+vPCn97fHr1hw4aCMf7B\nsMtorkLU71Dr27dv91SqyWGbB+P+H/G6uuZh1wwGGy46UKdx/udA2teuXTfkEn41N8VUc9pEyoUK\nDi3dCPwrMN/MXjazDwO3A+ea2QvA2eHvuHsX8CjQBXwTuC5MfNW66KKLgFfJHbq5C/hPgkrN7v7j\nfX27OP300/PWHdoJvAgkCGoLL4Y/E7ztbWfzxhtHAssIhqHeyR/+sJ+vfvWxYe0tnFk/qYNouGj2\n+km5G9cHadq//2cA3H337aTTS2lsXEQi8cckEjV84QvfHvIwzO7ubmprZwBvECy4N/bDR4vJDG09\nCthBsLd0daRNpKoMN3qM5YMqqRm4u0+d2hKW5uf29xlA2hOJYNhpXV1bTnt01E7d2HiKp1LNvmDB\nQs+sb9QePk5wqHfoCkv08UMzh9Ip297e7rlLYLvD3JwlsKNtNevr53ttbb0nk1MK+g+CbTeHV4ou\n1j9SDaXvUk+wExkP0E5n5fHcc8/x29/+lmAg0f7wZy/wBfr6dgFP4v4aTz21nXPOeQc7duzgnHPe\nwVNPbefzn//vPP30v3LmmW8lqF0cA1wMfAT4GdAEdBK0mMX3D5x22mkFNYL8iVQ7d/6YoIaSXRv5\nObNmzSKXcfDgJA4cOMT+/f+7f4Lbxz62FoBU6pjYdBTT29sbvvdJghVdtwG3cPfdt5dlueyRKOUE\nO5GJqpwdyBPG3XffTaaJJ+oQPgP4F2A5sJBUag5f/epj/NVf3Rl2/P4f3A8yefJ89u/v5oMffB9g\n4XU68q4zjyAw7Ow/HrcEdLQcdUNDQ0Fn8Cc/+XZSqRbeeGMp0Z4LdXXT2bt3b/97P/Sha9i//38T\nNOdcTX6mD2Q1JRVPR7aoGSa7o7ux8S0sXnzKcL/msuju7iadnsv+/Zn01dUdM2BHvMhhabhVibF8\nUCXNRG99GxcGAAAbuElEQVR729tiOpDnOZwUdh5v87q65piO2Klhs9CPwuGbR4dNFblNOfX18z2R\naPBkckrRiVnZw0JTqWZPp+fkXCdojoo6ooM0ZTfV5DYj9YTNOYXNQcOdIFbtHbTVnj6RckAL1ZXH\nqlWrPG45CsBhvkPa3//+SwtG62RGHAWZdU3NZIcmz5352+Tt7e0D9g/EZWjB52/LyeCyJ5LlZ+RB\nMJicdY07+vtAksncbTyHO1+gkjOMh6La0ydSagoGZRLsZ5A/LLQm7PwNSv6D1QzS6Wnh6p65awgl\nEg39waCYzs5OT6dPygk0yWSwZ0B+BheXkUcdzIlEg+euX1Tv8AWvq2vuP3+kE8eqdcJZpNrTJ1JK\nCgZlEuxnkAwzz+nhzwaHBf0l/6amRX7rrbf1l0CTySmeSDTkZNaZjWByd0yrr3/LgCXWYCG6wprJ\n1q1bB11+Irt5KZFo8NraBg/WL2p22Nyf9vb29v70a98BkeLGQ8FCwaBMgiaWIzxYCG5emJHe4dDo\nwRDRTPt89h9K/h9NsBHMFIdH+msM+bWHuD+woGYwJ2znX+QwzROJWZ5KNQ2Yccc1L6VSTVl9C+7w\nmNfU1HkiUZ/XjKS2dZF842WjJgWDMgk2j5nssDqsERzvmbWKgj6DgVYJjZx77vmemaswObzG5v6m\nn+zVNPODSrBRzTaPOofj+gzyM+5is46jGkBt7XTPXW/piIJzS726ZyUXrKv20pxUt0zhKvP/sFoL\nTAoGZRIEg0SYYc4Jm4xym20GWyrigQceiG3qgcf6f08mp+SM6MmeEJbb3j/F4chBM+6BRtIETV8D\np6fUf+iVKlWNl9KcVLeghn5MWENf7DDN6+raqnI5dAWDMgmCQXbG+YjnbzNZLDOO9jRIJmd5/PDU\nyZ7dmRy3JlEq1eyNjSd5pq/hfWGtonjNIG6p6uyMcMOGDWGtJjs9x4bpmVvyTLNSQzw1tFRKpVjf\n3UB9dpUykmCgSWdD8OqrrwJHk1nfZhGZbSbjJ2dFq3ju2zeNYLLZfwc+mfOeYMbwo8B0oI10+l10\ndnYWTOJ64403ceDAT8PP/P+BfyaYqHYB0AL08pnP/M/+SVT5K4jeffftLF58Cm1tbf3nnH766cAr\neel5lWSyhk996iquuebqkk7KipucNtgqrOP5c2Xi2bt3L+n0vJy/pWh71wlhuNFjLB9USc1gzZo1\nYUk8s75Nptno2II+g/h5AdMc/qsX9hnUe9BvEDQTdXV1eSLRlPfeqf07kRWWTJodZvuGDRuKfnZc\nSbinp8cnTarLuQezVNlKOaoZyHg3nv6W0NpE5bFo0SKC/Qw6iNa3CVbyuAN4BPgm69c/3L95fdwm\n8MESEX9EsK7RZ4BugjWJksCHgbfhfhCAQ4f6gDOBxcBS4AvU1MwOA+TMvOvOBvaEJf34z45bX6i7\nu5vGxhPC+/kE8BSNjSeUrZTT0tIyrFVYx/vnysQz4f+Whhs9xvJBldQMgvb1eR6MJpod/pzrsCG2\nzyC+ZjDZk8kGTyZPyGunX+DBKJ4Gr6trC/dCOCks8WcPQU07fNbzl5GAtK9Y8V/70zqcmsFYlnJW\nrlzp06dP9+XLl2s0kYxr4+FvCXUgl8fDDz/smRnI0TBMc7inaEaavQRCXd1UX7nyWr/55r/Ma+aJ\nloSI5i4kffv27WEmfUeY8S8Mz0mGTVQNHowmmue1tY3+uc/dmZPWjRs3hyOPgo7gaKmJ/G01i61D\nNNo/9Lj3Z0ZfRd9dTVX/RxIZ7xQMymTx4sUxbfXB2kSNjacMOOkrGk0UlPbTWZn8CTHXnOzt7e39\nmXRDwwJPpZq8trY+77wpnkw2FLTv55b2M7umRSOKgiUt0p5Oz4nN/IczBHPr1q2+YsUK37p1a/+x\nuPevXLmyyHeX1BBPkTJRMCiThoaGsFTbFTYNdTnM82Qy6e3t7UXXFsrNnDs92PzFw4w6anryrEdm\nM5ook25vb49ZAG+u33rrbQWfFzfJLLOaaX5n9mOeSjX1B5Senp5wY5tM01SxZqMFCxbllPRPOumU\nos1OLS0tHj+kdnrZO98efvhhX7ZsmT/88MNl+wyRaqRgUCZBzSAaPTTfo/2LjzrqaE+lmryx8aTY\nknRu5py/bPRjsSXmgUv7mUw2bk7B0OYo9Hgwca7ZYb6nUs2+ceNmv/XW28KmpcVhOjfHzp0ImswK\n033XXXfFzna+5JJLitQMVpdlhnNk5sw5OQFr1qy2snyOSDVSMCiTCy+8sEiGhkOrF9vqsTAjD/oI\notJ6sGVmZr2hYrMZB1qCOb9pZtWq63PODWYvZ2/7OKXgXurqmr2ubmre/U31urrmgj6GSZNSsSX9\ns846KzZoBRN1zHNXfDUv51T+YgFLNQQ5XCgYlMn06dOLNnUEmfk2h6CNPz8zz8/I77vv/rxS/DYf\nyjonXV1dBSuUxi9E1+zbt2+PWdcof/Z0pqZQXz/f6+tPzru/uf7+91+aE2iCjukNsRltMtkQO9s5\nUzvKHol1rKdSTWXrM1i2bFnsv9eyZcvK8nki1UbBoEyKd4KuDkv1wS5iyWT8RuvFRugMZdOVqBM6\nnZ7mjY2LPJls8uXLl/t5553n9fX1btacl+nlZrSF/Qg9nj+BzqyuIKjU1U0N+xByO7iD9/+XvJL+\n+f1NPnErtcYFrHJO4VfNQA53CgZlVNjUkQhL9Y0e7F421xOJ4qXduJK9e/FAkT0SKbO09OYwE88f\n5jopK9Ob5vlLaudmxvF9FVdccWXONc8++9xw74Xc2kKmg7ne4RqH7T7YHIVK7DQ2a1Zbzr+X+gzk\ncKJgUCbBCp8JD/oILPx5lGfG/w88cWvVqht8KMtdZy8ulwkCj4S1j6gD+j1Fain1HnX85k+CizLj\nxsZTwvvIb0I5NmwC2ua5S2Tnfk6wTEYUMKL5DoscJseObsq/t2Ijr8o1iUejieRwpWBQJh/60IcK\nmlaCzDDKNNvDzDoYypndbzCUlQ67urr8mmuuDUcmRcM2V4Wf1eOZ2cjRSJ+4/gs8exXT/KaYKMMt\nnPgWpKe+fkHeNReFaUj3z6W47777w2GqjeFnZeYyLF++3KdPn+4rV66M/Q6LzWHQ8tIipadgUCaX\nXXaZ5+8CFvy+Lvx5kkcjihKJppz28k9/+tOxJfFoYblMrSFq8rneMwvQRc070d7J0dDPuJrBn3hm\nZNJkr6mZHpu5bty42c1SYXALhl9eddXVRRbW6/GGhgW+YcOG/nuKm+Gcv2ifWW3B5jzFRhqNl4W/\nRMYTBYMyufPOOz1//4Lg9/yZwWmvra3Pmc07eXKUyeeet3379qK1hmBS20KH28IayOTw86JMd5IX\nDtXMzDoOmm8KJ4719PRkDTM9xaHRa2rSOekNPmeqRyupFlvXKGryWb58eZF7SPaX9m+99bbYOQgb\nNmyIPV6Nm4WIjCcKBmVy1113hRncezy33X5OXoA40evr53t7e3vezOOWnJJzbW2Ld3Z2DrDBzGcc\n0j55cpTZx22qszxMS1MYlJo9s47R/R4NHU0kju3PXIO9nAtrONmznqNO66F29g487Db4jLq6ZtUM\nRMbQSIKBlrAegp07dwL7gSeAN4U//0BmgxvCny9x4MAvAbKWkW4DDgIbCZaK3kgicZC2tra8DWai\na7xCInEH733vu9m//xfALIJNda4G1gE/C8/bAGwjWEp7DrAFeJBgo5xngeOAq+nre5V//udvZd3N\n0eQugX0Ur732Gjt27ADgk59cx8sv/4Qnnvgiu3Y9z+WXXzrgd3PxxRcDr+bdw27gg/2fkUzOYd26\nNTlL/65bt4Y3v/nNE2pJ4N7eXnbs2NG/lLnIuDLc6DGWD6qkZjBvXn4JPWoKqfHsGcQw3deuXTfg\nzOP80vaqVdc7EDb94DU1DV5Xd2J4/U950Fkbt2LqQo/6KYJjf+nwhaympExao72Ve3p6vLa2Mec1\ns8meSNR7ff3xnko1+X333T/s7yeTvvxmq9zSfnbNI7vDeDwsCTwYdYRLNUHNROWRTCaLNIVEI3ii\n4ZiZJpfslUeTyQa//vobcoZVRhlg4fLOiayA0xybuWeWwojWSZrqmR3UUp7fv9HQcHJ/Zht0/k71\nYDZwTZhx14Tve4tDOjYg5GfYq1ev9tmzZ/vKlSvDwHdleM0rPZFoiJ1XMJ52ihqOiXpfMn4pGJTJ\nnDnRomf5GfKbw5+neLSh/aWXXurTpk3z5cuXh+sCNXm08X0i0eAbN27O6qydVuS6q8PfZzvUFglE\nNR60/9eEgeHK8LVtBdesq2v29vZ237Jli0+evDC8bvGaRirVHLs3Q1TqLQxglpO+pqZF3t7eXlDa\nj1tVdSJ0GE/U+5LxS8GgTM4880yPX2ztNIc2h5ZwklhtTCaZu/hbIlHvNTUN4e+zi2T0s8PX67I+\nN65mkN98lAzPaXOY7On0gnDv5Cn9exlkRiLlX+9hj4aU1tdn1lgqLPVeWeT9V/b/HnUO5weDiVqC\nnqj3JeOXgkGZTJoUZaAbHD7tmcXacKjz66+/wU88cUGRTHJlVkZ/vOfOAF5d5D3TPGgiWhV+RtKD\npp1F4c9keDzhwfIS+ZnyVE+lmnzLli0xq5HWFAlAy8LnCz2VysyVKCz1FgtgmY1+opVT4/oF4haz\nmwgqseSGSDEKBmUSZLzHhqXnZeHPIAN8z3suCmsFFMkkp+Vl1o957r4GUZ9Ado3juvCcxVnXzd6P\noMELJ6plMuVEIlhB9JprrvXC4a9RE1GxmkGmz6Cnp8cfeOABr61t8MFqBpMmNfunP/3prG07t3nU\nl5JINOYEh2jl1olWcp4IHeEyMSgYlEl8k0ywRlFmd7Bi7f81HnTopj1Yz8g9mNAVLSvR5EHTzaTw\n3FrPLHXhDsflXTd+CeloAbqTTz6lv/SdCTB3ZJ07zeHIvAA0yYNd2KbkTJoLRh5FaU95ZtRUbd77\ng++msXGRp1LNnkhES3tHy2ekvBRNKMpsRYZGwaBMgtFEaS9cyA2vq5vtwdpEzV44xLI2LMm3e13d\nzLxMfJtnOmKzh6DWOcwKr7c5PJ7dX1GsmSfhkCiyUU3aYYHDZK+tfbOnUk3+p3/6Hj/ppJM8lZrd\nn0Zo94aGBd7e3l7kGl/waGZzECAJl9DOv6/8YBUtfR2kN65zdbCMXkM3RYZOwaBMgozvqLzS7pHh\n8bQHI3GmeDATuN4zNYnc0vBVV13tubWLes/dFzl7R7LsxfAm92e+QZCIqxm0OfR4KnWMFy6dcbLD\n+x3qPJFodUh7Xd0CT6Wavba2PgxCzWG66nzt2nWeSp2Yd41o2GqU9pSnUk1+zz335C113RkTrKKl\nrzPfRXamHy2AV2z70IHWNlJNQaSQgkGZZDL9uBE9uceiSWfFOhS3b9/uiUS9B809zWGm/yOHLR6/\nGF4yDArR5LJtHj/JKwogiTAw5ac12n8hf0G6qAaTCUK1tfUFk9OC5qykB0tr1PUPky3MqLcVfFfJ\n5JSiS1zcd9/94fkne7HtQ+OGbqbTCzyValJNQSSGgkGZBJn+vLzSbjTpLHMsf/nquKaP3Ixtc5jR\nR6uIxpWot3jU1l9Xd1xYgj7FM/sqFAakzCN7lvLk8FoL8z5jYfjZ23KC0IUXXuyZiWxRUKrxdevW\nFexJkB/48vdhLjbLuKenx1Op/N3UCrcPjasZZAJj8LuGcopkKBiUybRpxTqHczPidHpa7OYtkZUr\nV3pLS4vnbojzWFbGlp/hRauPBpl2KtXkn/vcnf1NKqlUU5HmnM94sKx2Z9b7o1VW82sf0zwYcdSe\ndf5c37JlS7jUdSI8J+FmqaL3Frfd5WBNOJ2dnTG7qeUObY1kB5xUqtnT6dxRUtUwyUsd3FItFAzK\n5KMf/ajHTTpbsmRpfwaVTE7xRKLBp0xZ7HV1zX7rrbflZApxs3br6+d7MtngdXXRxjLZo4zyRwGl\n/XOfu3PQJpkgs++KCSxRwImaZfLXNpriUTNRTc3k/qaumprJ4TXneTI5ZUjNMUPNFIuV+IutjxRd\ntxpXO1UHt1QTBYMyufbaaz1o3yfrMcWvvfba/rX9M5vHR/sUz+vPFFauXBlbszCb5nV1zeEGMZkM\nPqoBRFtVplLN/WPz89vO6+rawqaWuWEaG/LSEQwNTSRmZ73vfoeUT558gqfT08IMP35hu6FkutmZ\n/3AzxewtOaP7HIpqmuSlGchSbRQMyiTYz6BwnsFdd93l7tn9ANE+xbmZQtA0VHzZibiF3eL2DO7p\n6ckKHMFGNolEY7jm0PzwWKZ2kUw2+TXXXOtbt24taJuvq2v2LVu2xIwGyjS5DGXNnezMvzCwDS1T\nHGnzSrU0y2htIqk2CgZlcskll8SW7C+55BJ3zy4ZRvsUR5lCj9fXz/dzzz039v3RUhXRwm7RI5o0\nll+6zqw6Gu1+FjRZXXFF/qzgoHbR1dXVn1kHbexpT6cX5HTyNjaeVJC27CWnByrxFr7+iOd3tB8O\nmaJqBlJtFAzKZMaMGbEl+xkzZvSfs3Hj5rCpKGpyyTQXBZvIxy1019mfcUQBICil5/YXRBlL0OEa\n7becH1g+FR4Pdju77777Y4d9plJNWUtGZE92i99vYaDmmMIScY/nd1AfLpliNTVbiSgYlMnq1fEL\nyq1evTrnvOzNW3IzxW2eWURutkfr+zQ0LMgJBIWjfHpyStfBUMwmz0xUix7HemYkUGf/0MxizRdx\new/nb3yff19xzTFxJeJiexkcDqql2UpEwaBMenp6Ykv2xf7Tt7e3e319boYddfRGmWT2Ym1xmXZQ\nws/UHKLPykzSGnzM/UAzd0vVrBFXIlamKFJZ4yoYAOcBzwM/AW4sck7pv6URyGTWn/Jgd7FPDdgW\nPtzlE4oNsYxqDvml62D5huaCJaPjSuPFmi9K2ayhzF+kuoybYABMAn4KtAIJ4BnguJjzyvE9DdtI\nOgiHm9nmnz/YMs/DmeQ1UDOPMnGRiWckwcCC940tMzsDuNndzw9/Xxsm/o6887wS6YuzadMWVqy4\njkSilb6+Xaxffy+XX37pgO/p7e2lu7ubtrY2WlpaBv2M4Z4vIhLHzHB3G9Z7KhQMLgHe5e7/Lfz9\nSuB0d78+77yqCQagzFpExoeRBIPaciVmImppaVEQEJEJqVLBYDcwO+v3meGxArfcckv/8yVLlrBk\nyZJypktEZNzp6Oigo6NjVNeoVDNRDfACcDbwC6ATuNzdn8s7r6qaiURExoNx00zk7gfNbBXwOMHI\novX5gUBERMZORWoGQ6WagYjI8I2kZjCpXIkREZHxQ8FAREQUDERERMFARERQMBARERQMREQEBQMR\nEUHBQEREUDAQEREUDEREBAUDERFBwUBERFAwEBERFAxERAQFAxERQcFARERQMBARERQMREQEBQMR\nEUHBQEREUDAQEREUDEREBAUDERFBwUBERFAwEBERFAxERAQFAxERQcFARERQMBARERQMREQEBQMR\nEUHBQEREUDAQEREUDEREBAUDERFBwUBERFAwEBERFAxERAQFAxERQcFARERQMBARERQMREQEBQMR\nEUHBQEREUDAQEREUDEREBAUDERFhlMHAzN5nZj82s4NmtjjvtZvM7EUze87M3pl1fLGZ7TSzn5jZ\n/zuazxcRkdIYbc3gWeBi4HvZB83seOADwPHA+cC9Zmbhy18AVrj7fGC+mb1rlGkYUx0dHZVOQgGl\naeiqMV1K09AoTeU1qmDg7i+4+4uA5b10IbDZ3Q+4ezfwInC6mR0JNLr7jvC8LwMXjSYNY60a//GV\npqGrxnQpTUOjNJVXufoMZgCvZP2+Ozw2A3g16/ir4TEREamg2sFOMLPvANOzDwEOfMLdt5YrYSIi\nMnbM3Ud/EbNtwBp3/2H4+1rA3f2O8PdvAzcDu4Bt7n58ePwy4O3ufm2R644+cSIihyF3z2++H9Cg\nNYNhyP7gbwCPmNndBM1A84BOd3cze93MTgd2AH8O3FPsgsO9GRERGZnRDi29yMxeAc4A/snMvgXg\n7l3Ao0AX8E3gOs9UQT4CrAd+Arzo7t8eTRpERGT0StJMJCIi41tVz0A2s8+Gk9aeMbOvmllTBdNy\nnpk9H06Wu7FS6chmZjPN7Ltm9u9m9qyZXV/pNEXMbJKZ/dDMvlHptACY2RQz+/vw7+nfzeytVZCm\nj4WTNnea2SNmlqxQOtab2R4z25l1bKqZPW5mL5hZu5lNqYI0VTQ/iEtT1mtrzOyQmU2rhjSZ2UfD\n7+pZM7t9KNeq6mAAPA6c6O6nEMxVuKkSiTCzScDfAO8CTgQuN7PjKpGWPAeA1e5+InAm8JEqSRfA\nDQTNhNXir4FvhoMXTgaeq2RizOxo4KPAYndfSNB/d1mFkvMlgr/tbGuBJ9z9LcB3Gfv/e3FpqnR+\nEJcmzGwmcC7BAJmxVpAmM1sCvAc4yd1PAv6foVyoqoOBuz/h7ofCX58EZlYoKacT9G/scvc+YDPB\nxLqKcvdfuvsz4fO9BBlcxedthP85LgA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NNx6ls3MzK1ZcrpKCiJS8ikIezN1/b2YtwAeAnWY20913mtmhQEfcbQdwRMbHZsdted1w\nww09rxsbG2lsbCxkkguuvb2dqqoGOjsXxi0Lqaysp729nbq6uqKmTUT2Ty0tLbS0tIz6OKNuVDaz\ng4Eud3/DzJLAJuAW4FTgt+5+az+NyicTqooeYD9qVE6lUtTXz6ezczOwEHicZHIp27c/qYAgIuNi\npI3KhSghHAbcZWZlhCqoZnf/sZk9AtxjZsuB7cAFAO7eZmb3AG1AF3D5hMv1B1BXV0dT0xpWrFhK\nZWU9XV3baWpao2AgIiWvIN1Ox8pELCGkpVIp2tvbaWhoUDAQkXE10hKCAoKIyH6mqOMQRERk4lNA\nEBERQAFBREQiBQQREQEUEEREJFJAEBERQAFBREQiBQQREQEUEEREJFJAEBERQAFBREQiBQQREQEU\nEEREJFJAEBERQAFBREQiBQQREQEUEEREJFJAEBERQAFBREQiBYQiSKVSbNmyhVQqVeykiIj0UEAY\nZxs2NFNfP5/TT19Jff18NmxoLnaSREQAMHcvdhr6ZWZeyukbrlQqRX39fDo7NwMLgcdJJpeyffuT\n1NXVFTt5IrKfMDPc3Yb7OZUQxlF7eztVVQ2EYACwkMrKetrb24uXKBGRSAFhHDU0NLBnTzvweNzy\nOF1d22loaCheokREIgWEcVRXV0dT0xqSyaVMmbKYZHIpTU1rVF0kIiVBbQhFkEqlaG9vp6GhQcFA\nRApupG0ICggiIvsZNSqLiMioKCCIiAiggCAiIpECgoiIAAoIIiISKSCIiAiggCAiIpECgoiIAAoI\nIiISKSCMMS2GIyIThQLCGNJiOIWhoCoyPjSX0RjRYjiFsWFDMytWXE5VVZg6vKlpDcuWXVjsZImU\nNM1lVGK0GM7opVIpVqy4nM7OzbzxxqN0dm5mxYrLVVIQGSMKCGNEi+GMnoKqyPhSQBgjWgxn9BRU\nRcaX2hDGmBbDGZ10G0JlZT1dXdvVhiAyBFogR/ZbCqoiw6OAUGLGMhNTBikiA1EvoxIyluMPNmxo\n5sgjj2bp0o9z5JFHa2yDiBTMqEsIZjYb+BYwE+gG7nD328xsOtAM1APtwAXu/kb8zLXAcmAvcKW7\n39/PsSdcCWE44w+Ge6efSqWYNWsuXV0VwBzgOSoru9ix41mVFESkRzFLCHuBq9z97cA7gc+a2Xxg\nNfCgux8DPARcGxN6LHABsAA4E1hjZsNOeKkaalfJkZQitm7dSlfXPqAFeBRooaurm61btxbyK4jI\nAWrUAcHdX3H3X8bXu4BtwGzgHOCuuNtdwLnx9dnARnff6+7twFPAktGmo1QMpavk6AZcHU5msIHD\nCph6ETmQFbQNwcwagBOBR4CZ7r4TQtAADom7zQJeyPjYjrhtvzCU8QcjHXC1aNEiqqpSZAabqqpX\nWbRoUeG/iIgccCoKdSAzqwW+S2gT2GVmuZX/I2oMuOGGG3peNzY20tjYONIkjptlyy7ktNPeR3t7\nO7W1tezatYtUKtUTFLJLEaGdYSgDrurq6li37uusWLGUsrLZdHe/SFPT19V+IHKAa2lpoaWlZfQH\ncvdRPwiB5aeEYJDeto1QSgA4FNgWX68GrsnY76fAyf0c1yey9es3ejI5w6dOXezJ5Axfv35jn/em\nTFnU573BdHR0eGtrq3d0dIxFskVkgot557Dz8oKMQzCzbwGvuvtVGdtuBX7r7rea2TXAdHdfHRuV\n7wZOJlQVPQAc5XkSMhF7GaUNpbeRxhOIyFgYaS+jUVcZmdm7gY8CT5jZVkLV0BeAW4F7zGw5sJ3Q\nswh3bzOze4A2oAu4fMLm+gNItxN0dvZtJ0hn/nV1dSMKBAokIjIWNFJ5jGzbto1Fi97FW2/9jEKu\nh6D1AURkMJq6ooSkM22YSmfnKySTc4GXRp15a9EdERmKolUZSbbMMQYh026hu/sctm59hAULFozq\n2EOphhIRGSnNZVRgfccYNJJIzGXXrl2jPrbWBxCRsaSAUGBjmWlr0R0RGUtqQxgDY72oi3oZichA\n1KhcYpRpi0ixKCCIiAigBXJERGSUFBCKKJVKsWXLliFOey0iMrYUEMZRZgAYy2U2FWhEZCTUhjBO\nMqeceOutZ+nudvbs+XcKPeJYU1uIiBqVS1jfKSfWA9cTFosLpkxZzIMPfp2TTjqpgOfR1BYiByI1\nKpewvqOXTwdeotCD10a6EpuICCggjIu+o5dfprKyrOAjjjW1hYiMhia3GwfpKSdWrFiaMXr5Gz3L\nbBZq8Fr+82hqCxEZGrUhjKPxGr2sUdIiBzY1KouICKBGZcmgcQgiMhIKCPuZsRzwJiL7N1UZ7Uc0\nDkFEQFVGgsYhiMjoKCDsRzQOQURGQwFhjIxlw25/x9YSmyIyGmpDGANjOcHcUI6tcQgiBzaNQygR\nvQ273wNqgDdJJs8vSMPugdZorMAmMjJqVC4RoQF3GnA+sBI4H/cpBWnYDceYRWajMRze59hXX301\n9fX1XH311aM+Z7Go+6zI+FMJocC2bdvGsce+A/gx6RICnEVb26MsWLCgQMd+hHQJAU7JOnZ5eZLu\nbgNmAy9SXr6PvXvfGtV5x9uBVhISKTSVEErErl27qKysI7OEUFl5MLt27SrIsZPJQ4GlwGJgKdXV\nM3uOffXVV8dg8AjwG+AR9u0rn3AlBXWfFSkOBYQCq62tpasrBWwGHgU209X1KrW1taM+dug++gbQ\nBFwJNGH2+55upd/97ncJJYPMKqVZNDdPrOoWdZ8VKQ4FhAJ74YUXgMPJrecP27MNt2tqXV0dK1Z8\nHLgY+BJwMStWfKynGuWss84CXiQzI4Ud7NiRmlB18Oo+K1Ik7l6yj5C8iaW5udkh6fCYg8fnpDc3\nN2ftt379Rk8mZ/jUqYs9mZzh69dvHPTYHR0dnkzOyDp2MjnDOzo63N29tbXVoSyef158TvbZb6Lo\n6Ojw1tbWCZdukWKLeeew81yVEAps2rRpwFRCPf8J8XlK3B6kUilWrLiczs7NvPHGo3R2bmbFissH\nLSkM1suooaGBysoaoAtoBz4H/JGJWgdfV1fHSSedpJKByDhRQCiwRYsWUV7+B2AP8Dqwh/LyP7Bo\n0aKefUbaaFpbW0tn59NkVgl1dj6T1T5hVgY8AEwBPtyzn+rgRWQwWkJzDHR3dwPVQB2wi+7u3UDv\nQKva2tqMRtPQrXIoGXa6l1Fn51KgHtie1cuovb2dZHIue/Y0AmsIpZODSCRSNDWt1Z22iAxIAaHA\nNm/eTOj+20I6s3d/J3/7t1/ijju+RUVFPXv2PMdHP/oRNmwY3trHvb2MekdBm53fE0iye+dcCMwk\nkTiHrVsfGfUYCBHZ/ykgFNhjjz0GHEZ2Pf8hrFnTxN69D5MOEnfeeQpf+crfcuqp/2PIUzOke9+s\nWHF+3kDy4IMPsXfvHuCdwGFUVb3KN795u4KBiAyJRioX2Be/+EW+9KV/JHs08RIqK99GV1dbxp4n\nkEi088ILTw+7KiffHD/Zo3sPAx6guvqzPP/8b1RVJHKA0UjlEnHCCScAVcApwFHxOUFXVzvZ4wNe\npLLyyBH1/MnX+2br1q2UldURgkEdcDFVVXMmXM+iodCa0SJjQwGhwJYuXUpFRTewHvjr+FwBXE4I\nDumuqNewb99LBen5s2FDM+eeu4w339wHHAM0s7/2LNKkdyJjaCSDF8brwQQcmObuvmrVFRmDw2Y4\nbHRwTySO8MrKWq+tPW7Ig9EGk2+wGkzy6uppBTl+KRlsYJ4UlgYGTlxoYFppSKVSNDV9B/ga8DJh\n3qELgccpK3uTxx5r5aGH7mT79icLsmhOvjENNTXzuO++Zk477X37VdWKJr0bP+mS2NKll6okdgBR\nQCiw3vUQrgLeRph36HASiVNpalrDggULOOmkk7jllluGtGZBvvryzG35JoLr7n6R557bvt9VrWjS\nu/GRSqW45JLL6OzczJtv/pLOzs1ccsll+82NhQxgJMWK8XowAauM2traYnXRZofW+Jz0H/7whz3F\n77Ky6rjPUQ5JLy+vynusfPMd5dvWW0UVjrd8+aX7bdVK+vtPmbKoYNVukm3Tpk2xutMzHnN906ZN\nxU6aDBEjrDIqeqY/YOImYEBobW31ysojY9vBYocZXlZ2sIM5EB99J7+76qqrso6Tr768unpa3oy+\nunqaw70O6xzu9URiik+evCjrH3rKlEXe2to6YNonSp3xREnnRBUCwqQ+7VIKCBPHSAOCqowKLN96\nCN3drxOmsjgKKCffmgVhLYNevfXlhwFbgMMoLz+EsrIjsj5bVjYb90nACuC2+DyVPXueYzhVK6Xc\neye32kyT3o2tRYsWUVlZBryX0GvtvVRWlmXNxyX7qZFEkfF6MAFLCGH667kZd+cX5pQI1g25hFBZ\nOdlheixpTPeKipo+JYSqqqkOiT7H+8pX/n7IVSul3HtnJNOEy+itWnVlVjXkqlVXFDtJMgwUs8qI\n0JVmJ/B4xrbpwP3Ar4FNwNSM964FngK2AWcMcNwxu2Bj5bbbbsspbtfEfyrPeNRkrVlgVtnnOB0d\nHTGzz87816693ZPJGZ5MHueQ9Kqq+ni+jT3HTyaP66lSGUrVSmtrq0+dunjYVUxjrTdQbe5pjymV\nQLU/K+UbBBmakQaEQlUZfRN4f8621cCD7n4M8FAMApjZscAFwALgTGCNmQ17iHWpOu2004C9hOL2\nUcCb9F3FrDvu8zTQxc6dO/ocJz1zaWb1UHX121i8+EQeffRhurufB34ce938J/AZIBWP/1LPtBZD\nqVop1d47vT22etendp+ibqZjTN17D1wFCQju/jBh8v9M5wB3xdd3AefG12cDG919r7u3E0oKSwqR\njlKwYMECVq1aCewGnicslrOb7KksdlNTM4fq6mmsX/+dPhl2KpXi9ddf5623niVfJr1r1y6qq+cB\njfG9hcAMamreM6LlJkt1ycqw/sPLZLbH7N69syDrU0v/SvUGQcbeWDYqH+LuOwHc/RXgkLh9FpC5\nwPCOuG2/8a53vYuKinJgH2GhnGpCqeBpoJvq6qmsXv1JfvGL/8e8eW/L6t+dbty94IJr6e52Kivf\nTU3N8VRWvpsbb/widXV1ef9hk8nX+dd//ecRD3hbtuxCtm9/kgcf/HreYxRj/qCw/sM8Mu9Uk8m5\nPes/yNgo1RsEGQcjqWfK9yCs2JLZhvDbnPdfi8//DFycsf0bwHn9HLPANWtjL4wzSK9lfFR8Tjgs\njHX91/a0B1RXT8tqLM1Xd1teXutQ3adxL93YWlOz0JPJGb527e1j1hWzWA27qssuLnXvnbgYYRvC\nWK6HsNPMZrr7TjM7FOiI23cAR2TsNztuy+uGG27oed3Y2EhjY2PhU1pAmzdvprsbsqe/PgXYSFjr\n+BRgDnv2HAzcyO7dFwOPs2LFUr7//Q1UVTXQ2dl7R7xv30zgRsKI58f56ldP4fLLVwLg3g28RVfX\nW1xxxV/F1dLaaWpaU5BpMSB7/eeQrpDW005735jfMfau/zC8hYSkMOrq6nStJ4iWlhZaWlpGf6CR\nRJF8D6ABeCLj51uBa+Lra4Bb4utjga2EOaLnEOpRrJ9jjlUAHTOhl1Fur6KjYndTjyWFybHk0Oaw\nyWGT19Ye55s2bco7UR10ZBxrnt98880Z+3XErqljcxddCj2QdKcqMjwUs4RgZusJLZwHmdnzwPXA\nLcC/mNlyYDuhZxHu3mZm9wBthFvmy+MX2C+EXkbXkLlecuhltCTj9UGYvYz7EkLbwuHs2rWD557b\n3nNH7H4Yu3c/S2iHeJmwxsHjwA7efPPNWJI4DPgxIRbn7xGSu5DOcGW3Vwx9/edC0p2qyDgZSRQZ\nrwcTsITg7j57dkPWOAMoiyWDGQ63ekVFrVdU1DpMdbg73uU/5tXV033Tpk3e1tbmS5Ysie/PdJgW\njzXNYabfcccdGYPWjvfcgW7pNoVC1fuX2vxBKjGIDAzNZVQaHn744diIXOtwRHyu8vLyST5p0rGe\nTM7w1au/4OXlB8XqoMXeu2bCXK+pOcaTyRl+wgknxsbk3AbqSr/jjjtyBq3d6pD0yZNPzAoGhaxG\nKpVMuLcx/YSSCE4ipUgBoURccsklfe7YIekXXXSRt7a2+tq1t8fSQe7kYdNjiaAj9i6qiQGl77G+\n8IUv9Kn2iIRlAAAZZUlEQVTXr609ztetW9eTcYf3OzyM8O0oiZHHo9Xf6O1iBymRUjPSgKDJ7Qqs\ns7MTOJzsyesOZ/fu3bz++uv85V9ew969Xwfm5+wzA/g8oa1gIVVVRwKvEDpkZU+EV1NT02ccwr59\nL3HWWWf1jFP44x+fIkxMthI4hs7Opyb8wKKtW7eyZ0+4PsFC9uw5mK1btxYzWSL7DQWEAjv55JMJ\njcCZU1W8zI9+dD/nnXcFu3cfBJwOtOfs8xLwroyfdwJGGMOXud8OTj311AEHDr366qvxsy2EEb4t\nmJWP1VceZy+Re21FpDDGchzCAWnWrFmEnkGNhN4/7cA+9u793+zdez5wNCETWxP3mQG8hFk3icR5\nlJcfQXf3izQ1reGZZ57huuuuJ4xdmAXsoKKilqqqKpYtu5DTTntfn15EGzY086lPfZqurkPJnQep\nvb19QvfWSU/L3NXVSPraalpmkcJRCWFMOKE76ZvxGcIEbXXAWkIG/0XCHEcvAf9ERcUc9u3bw759\nv6O7ey/PPPMM55//Iaqra4G/Aa4A1lNZua+n6id38rr0ILK33roPeJX9bS6auro6LrtsOdBJ+H6d\nXHbZ8gkd5ERKiQLCmJhOqLIhPjvwAGE20gWEIPEK8A+EIRon09X1HF1dlezefTBvvVXOddfdyOLF\n7+K88/6Mysq/IZn8KsnkCpqa1gDknVeod5bKRkIJZClwdM96ziPNOLdt28Zdd93Ftm3bRvT5Qkml\nUjQ1fQf4CfA94Cc0NX1Ha/2KFMpIWqLH68EE7GUUFsjJXFP5uowxCZM8rIVQFZ8neVhMZ1Ke3kST\nHN6X1eXULOGrVl3ZM76gunqa33TTzT29bPrO/bPZE4kp3tbW1iedQ+1G2rtQytE+moVSCtFttRRG\nTYtMBKjbaWkIAaHKw/QU9Xm7jcLb4vsVbjbF8y+i09DPZyvi642xq+q8rP74QxlENtTJ6tra2vKm\nIV+AGUihJsfTZHciQ6OAUCLCAuW1MSM9wrOX03QPI5ZbHS72MPBsUixN5JvD6AjPnsdooYdBbw97\nGLWcP2Mc6G68v0y1ubnZN23alPWZyy77TJ5AdZSvW7duyNej0Jl4qY2aFilFCgglIoxUTlcZbfIw\n2Cwzo58RM/Skw9ccjomZ/sb43ryY6SdjMJke30t/Nl1yOHpEVSf5ql3g0BiA5nlV1dSeqbgTiSmj\nLiGMRTVPW1ubr1u3btglFZEDxUgDghqVC+zpp58mNCqfT1w1lDCxXXq1tA8QJnidTuhptI8wgAzg\ne5SXv0Ro638k7tcCLAdOJUyatxNYz0h7EfVdXKcFeIOwDOdT7Nnz76xYcTlbt26lqqqe0CB+CqG7\n7CmYdXPwwQcP+XoUevWtDRuaecc73sOVV97GO97xHjZsaB7RcUQkj5FEkfF6MKFLCLl1/82x1JD0\nMGFd30bkiopaLy9POpyQcwc/1+GwWBU1M25LlyiO8kRi2rCqTjIX1wmlkeOzzldTs9A3bdoUSwgn\neJime118nus33XTzsK5Joap51IYgMjSoyqg0hDaE3HaD4zy0G7hPmnS8V1XNdJjfJ9NPJGY5zPG+\n7Qk4WHyu9MF6Ea1cudJnzpzpK1eu7DedHR0dGZl+/vUU1q69PU/gmu7V1dOGnQmrl5HI+FFAKBEh\nIOSbuC5MWpdITPOysnzdTKfHO/DpHmYvneGhfaHcs2c7tXhXn72kZlro4ZTZVbWi5718mfL69Ru9\nsjI92d7cnjaEtCuuuDKe7zhPz8o6kky4EAFBJQSRoVFAKBEdHR1ulowZ+yKHKQ5VXl39doekV1TM\nihl2OtOfEzP9v8moCprkcHgsEeSrfpofSxybszLElStX5t1/5cqVA3b9TJcWcnsZpT8TGrqnOtw+\noky4kGsyq5eRyOAUEEpE6Ltf5WG8QHl8rvCqqsneO1jthFhiWJJz939mzMSnOXzCw8I6ud0+5zkc\nlLfKZObMmXn3r6urG/addb67cZjk1dXDa68Yi7v6wbrVlsK6DSLFNNKAoF5GBdba2kroOVQJvC0+\n78OsDng7YVK2ZwnLST9B6E30m/jcArybMN/RRwhTXLxI7mynoddSCljPnj3P9fTY+dCHPpR3//e+\n971xSov8y2zm0zsNRu9namrmcd99zSxbduGQr0e+4/R37lQqlXdKjly5czilbdjQTH39fE4/fSX1\n9fPVA0nGxVD/bieEkUSR8XowAUsIF154YT/VPOnG29vj68v6ufv/RKw2muxhfIB59nKc5uXlkzx3\n3IB7uDvOt39bW1tBSggjubMf6nFGW62k9gUphkJWhxYSqjIqDXV1df1k9LNiZpXw0AvpO/0EjirP\nHr282eGjDnUOeCIx33vbILIzvt5eOFc5HOlwVU+V0kjq3teuvd0TiWk9S3OO9I99sHMXIjNXDyQZ\nb6V8E6KAUCI++MEP9pPRf9RDt9NjvXckcro76SHx52NiQEh3W80cvZwdBML2jqyMr78/0La2Nm9t\nbe15HsofbDoTnzz5eE8kpvjatbeP6roMVLdfiMy8lP85Zf9UyjchCgglInQ77VttE3oRPeZVVel5\njiq8b3fSpE+ZMiMnoGz2MPZgTk6pIz0nUnbGd8YZZ2ad+/jjTxx2kXa8M9dCnU89kGQ89f7dbvZ8\nvf6KSQGhRPzwhz+MGTgeegmlB5NVu1nCTzxxkYfZTfOVIpKeSEzz3i6pC3OqkTKDRMJD99NJPSOH\ne2cn7f0D7f25/4w29+69GHc+hRzNrF5GMl4KNT18oSkglIjPfOYzMbOe7rA4Pic8TGSXzAgS+doZ\niM8eq4M+6qHralksYUz10NCc9DBQbKpXVNT0ZH4333yz9x0lPdfDJHt9M/aOjg6/6aabvbp6WlYJ\nor879txxCoWmzFwmklKuphxpQFC30wKrqqoCyslc4D78PAOYRu8y1vm6k0KYvO5x4EjgXwldVxPA\nDfEYbxC6qD4B/DtlZbnLYr9M30Xon+35OT2xXLqL5nXXfZPdu4033vg8nZ2bWbHicgCamtaQTC5l\nypTFVFW9l71793DBBdeOaXfO/rqTipSi4XSpnjBGEkXG68EELCGEBXLy3aV/2XunlTjB87czbPYw\ngV1/I5QrfKBpr3sHxaVHSU/3MEp6WlZVTP5BZ6GROrcEsWnTJq+u7n/tBZEDlUoIMqiuri7gJeBs\n4KD4/BJhqusKwt39L+OjC3gGeAdQDcwklAgAZpN55wGzCFNm9z/t9YIFC1i1aiVhEfoU0MmqVSt5\n/vnf8OCDX2f79idZtuzCvHc2UA88kHW8uro6pk+fTiLxNvaruyCRAqirq8sqSSeTS0e1dnlJGEkU\nGa8HE7CEcN5553m+CemOP/74PCWH9PTTbR56EV3mUO0nn3xKPyWEqQ7neWZX1MxGrHQd/MMPP5x3\nAZn0HX9zc3PeaSmguk+jWCndBamNQUpRKf5dokbl0jBv3ry8mfmcOXO8rKw2TyZ/hMNf9FQfVVZO\n8VWrrshTpVSVEWASHkY735s1VXUiMcUnTz7ek8kZftNNN2f9ga5de3vWCOfy8kleWTklBqlpDjc7\nbPaqqql9luJcu/b2onfnHOs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5CgQzKzOzDcALwN3uvh440N23Abj7C8CUsPtU4JmUp28N20aN5uY11NXN4rTTLqGubhbN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\nQHqb4llEpBAKFQhbiZbUSpo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AmtyuD6jk5W527NhR07iG0t+GkAbOQHUbgq8SzA5cqnbdRrprYqqOlS4udXWs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\n", 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jhLhYRnjbaggjevewefNm6uoWMnfuaQwaNIrW1peoq/u3fX6x98UV3mpqanIm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\nzIYDdwFXxppD55ENJTfSwcz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trb1TdoCqErXOxybdjS6uknGTpMHEWs00mex8dTCrrc3hCIe2qs5qm+o0m6i6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\naZmZuKah0DTarq6ugkGlrW1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uKoL+g/3A5e5+X4X9quUxiRXOFD+cIGBsJajYtxLceCkKFg3A6yltUTwHHKSm\nBg4enEHQnfU7IEOwPPsugmXWc+Fz8eDzLmAm8ETfHpualnD//d9g6dKlI3LOIqNhIs0w/3vgvUXb\n1gD3u/uxwI+AqwDM7HiCzueFBB3Rt5nZiJ6kjE+F98joIshFRC2DeiA+i/wDlLvjX7D9cA4e/GuC\n4LAe+LvwfY+H+6kHriKVqiVorQQr7sJngV8X7HOwo69EprqqBA933wa8ULT5LGBj+HgjcHb4+Exg\ns7vvd/cu4DHglGqUQyaWwlngrUA05wOCFXHjS4pcTZDjiN/xb2+4/TmCVsQfEaxVdT3wMMuWvZtM\nZh8NDa8nl7uRjRvvZN26W8hkushmDwX+irq6BuAt5HInFiyz3tPTw/bt23WnQJEKRjLncai77wZw\n91+b2aHh9jnAT2Kv2xVukykmukfGypXLw1VwezF7J9nsUfT27mTlyotZv345tbWvp7f3KW655Rt8\n8Ytf5KWXHgemEeRA/oCgS2tj+JPvlvrpT5ezY8cDvPLKK30J7kceeYRrrrkSgHe9612k02kaGhoK\nXhPPw+zb18X69bf1LcVe/NqhUNJdJoVqjfklyG08HPv9t0XP/yb892+AC2Lb7wQ+VGGfQxrjLBNL\nd3e3d3R0eGdnp7e1tXlbW1vf/IlgDsgMb2xc7NnsjHDeRn6uBTQ6ZB2OdpjmsLlv7kdT02Lv6Ojo\nO87q1ZeH7w9eW1OTK1lipNx8jlSq0XO5WZ7LHeWQ81zuRM/lZvnatdcnnuehJU5kNDAK8zxGMng8\nAswOHx8GPBI+XgNcGXvdvwJvrrBPv/baa/t+2tvbq/jxynhSrlItrcjvCit+j/0cHW6PgslMh+6S\nSXydnZ1lAs9Mr6trKAgAHR0dPn36ktj+u8Og1O4QL8u3HVKeStUPOgBooqGMlPb29oK6cqIFj1bg\n57Hfb4yCBMGSpjeEj48nuGVbmiBD+jjhqK8y+6zuJyzjUqVKta2trUJFHg8A08Lt8WAyp+SqfsOG\nDeGM83jgOdlhjre1tfVTlmg2e4dDVJaoBRPNYE8NKgCUBqbS1pFINYxG8KhKwtzM7gZ+DCwws6fM\n7I+BG4DTzOxR4D3h77h7J3AP0An8ALg0PFmZhAaTeC4cdQWwiNra1/Poo4/GEuoQJMYPECTL54f/\nWrid8HW/IZer48Ybr+PUU9/dd4xTTjmF0tFajxIsyZ5XfK/ybPYzpNM9BAn8LoLZ7d8g6G39PwQ5\nljq+9a1g65eYAAAgAElEQVRvDfhZFA4QCMqg0V0yYY10dBrOD2p5TGgD9e/Hcx2FV/s3OuS8sXGx\np1INnko1ObSG+Y2bHb7uUOvB+lSbw+6kxWErpLbvvdG6WG1tbb5mzdVulg1bCsc7NDl8ySHnnZ2d\nJWWPytbd3d13Htlsq0ONB2tjneAww+F2h6P9Ax/4QKLPJFomXjkPGQlMpG6rESmcgseENVD/fnFg\niRZDbGg4oSQ3UVs7LQwM0XNHhv9GXVjdYfdSJvxpLwhCQbdTzmG1w7yw0l/sMMtTqbkDdht1d3d7\nW1ubf/azUXfVSWHAivaf8osvvjjRZxMFJpGRoOCh4DFh9de/XymwbNu2za+55hqvrz9hgDzHrDBA\npGOBoCGsyBeEz9/uhQnuh8LXFifNc/3eDCoKcsFNq3JhwIiX43iHjN9xxx2j+OmK9G80gofuYS4j\nIt+/vxXYDmzt698vl+Nwn8573vMBvva1e3n11V8BXw2f+yHBsiTxNalayM9A/z35XMgDBHmMduAL\nBAsoxt93CHBU0bY5vOc9Z7Bp05aSc4jfACpY0fcBgnEgPeF7owUaD2fevHkF7xvMBENNRJQJbaSj\n03B+UMtjQsvPq1jg8XtnlLY82su2CBoaTvBsdoan09PLtjxyuVl+2WWXO6S8dAjvkWX2ma3Yiik3\nZLZc6wkWeTDy6qFw/1/ydHp6xe64SjkNzfeQkYS6rRQ8Jqr+ch7d3d2+du31ns3O8KamxZ7JNHk2\ne0JBJd3YeLJv2LDBu7u7fd262z2f6M6FeYucr1t3u7u7b9u2rUygmO5Bcr3J8xMI0/7mN7/NM5kZ\nHgyzneXRpMJyQ2bLnUNhDuXQAYJi+XkcA302yofIcCl4KHhMWJVyHtHd/qIr7rVrr/ebbrq5pPKP\nV7odHR3e2HhieMW/zWGD53ILCuZnrF59WUHFbpbzYIRW1oNken7i4LZt2zyTafJ8Yj3Y3tnZWVJx\nRy2EhoaTwv1fEgakb4flybdaBjuPY7CfjVojMlQKHgoeE1alq+tgiZH8tmx2Rvi6G8OWwKKCVkXh\nvqLXBBV5XV3h7O7Ozk7fsGGDb9u2LTzOn3uQTM9X0o2NJ3tHR0fJkNlzz13h2eyMglvfRoHk2mu/\n7LW1WU+nj/RgNFf8FrjdXl+/oG9JleIutlSqqWC5lSSfjWafy1ApeCh4TGh3373Zs9kZXl+/wLPZ\nGb527fUlV9z19Qu8vv6kvooYOryh4YSSq/V811Xh8iKZTGnlnL+y7/bSEVf5wBTvPgsCwsywGys/\nz6S2tsGDUV1HhYHjtFg5Nofvmd8XcFKphnDb4vDftNfXL/RMpsnXrbu9r4US3Zc9Cl7lPhvNPpeh\nUvBQ8JjQoqv7+vqTPJeb5dde++Wwcm0v0/Lo/4q7o6PDp01bVFC5BvM+Mn37j1ohhVf2UdBZ5NHc\njHh+oTSnMaNMwGkKt0ddV9Mc6r04+R4s4HhiXxAM/m2JvTfjqVSTNzYu9kxmht900819rRuteyXV\npOCh4DFhVU42B6OgUqm5fRX+YGZdV95fe9nKNh+4FoTHjCrz/BV9+dFUCxyOLdpWvPhizuGIcHs+\nUDQ2nhzmUsqNIusOWyLlW0FRmeMtNeU8ZKhGI3honoeMiHJzOYKbOG0BHqC393l++MPvcf75Kzj/\n/BU8+OA2br31ch58cBvnn7+i7D6vvvoKUqm3E6xr9VaC+5Iv69t/KtVCV1cXAOefv4KdO3/BP//z\n35BK/YbgtrTNwMPs2fMrXnjhBfbt28drrz1OMBcFgjWndgJPUbgG1nPAabHzmANcAuwmuHXuKuBY\nXnvtcW655a/I5ZbT2LgYeB/BvJLDCZZxm1PyeVx++RcK5nmY1QC58F+RcWyko9NwflDLY8Iq31KY\n5fkVcI/xDRs2uPvAcx6Kn1+z5irfsmXLoIfFBsubFOYhstmFDjmvrZ3rkPNsttVzuVn+wQ+e43C4\n59fLmuVBrqN4Jd/G8Ce/PZrvEeU1glFkmdixS2eoR/kddVtJNaFuKwWPiax0mGu84gwWJByo0uzv\n+cF0d7W1tXmQDI/nIeIT/YJJgqlUk3d2dsbu+9Hu0VDcIGHeGHZTRUn1aKl27/spTnB3d3eHizoW\nd7Ud71H+JQo4Wq5dqmk0gofaxpPIJZdcwmGHHcYll1wy1kUB6OuO+uhH34aZA18mWkp99eqLWbhw\nYdnurXj3U/75jQTLkmzsez7qmrr//m+wc+cvKnZ3wbMEXU9Lw3+fIbj9TH6pk97eWdx///0ccsgh\nrF59MXAG8NHw3w8D/wjUECx/soKgG+tZ+ltevauri7q6Fgq7ql5PsLT764EbcD8AlF+ufe/eJ2lo\naBjUZy0y6kY6Og3nB7U8Bi24Oo7foKh2zLs8ogRw8eq32eyMQbUsouejZdbz52aDPrfg6j8aPnty\n2a6jaGn2XO5Ez2Rm+Lp1t/fNGfnCF74Yay3EhwrfGH7m0xyO9nR6et/dD6NuqPIz33MeTHQMWkHx\n1kXUksrlTgjLc6QmC8qQoG4rBY/BWLVqVYVKKj1mFU8+KNzl+TvwednumP66nyqd26pVqwZdlrvv\n3uypVKPDnFiQPTr8d27Z/UejoII7EB7msNLhi2Gwia+blQ+I0dyN6dOXeDo93Wtrcx7kT6Z7fomU\njAdLw5cGSvdgomO52e9jfSEgE4uCh4LHgILKFQ/We/LYz3yH2WNW8fQ3Ua9SYrvcmk6zZ8/20tvH\nzvfm5uZBl6Wzs9Pr6ho8v0zJt722NuupVL1ns/O8OHcBR3ttbc67u7v9qKMWFLV63uzBQoyFa3E1\nNJxUNEw3Wl9rRvhvfokUyHkmc2TZ4bjKfUg1jEbwUM5jjAy0HPdglus2y7Bu3UaCIbA9BH30EPSb\n7wI+WpA/GE35PvzngNsIhtTOJ5dbzvr1t9Hc3Fzw+ubmZpYuXVqy/YMf/CClt4/dRU/Pi2WXUY/r\n6enhL/7iLzn++EXs338Iwef0HWAlBw7Mo6YmxX//7x8NbzMb3/9vOXAA1q1bxxNPPE2wFPsvw38f\nJlgOvrBM+/Z1kU63UJjfOAo4n2CI8AXhv0HeY+/e/bz22l5+/OMfF3zXg7lVrZZyl3FhpKPTcH6Y\nZC2P4qUpBjs0tVzXU+Wuqnl9XVZj3eUR746KlicZSlnM6rxwNduBzy06dvCeqKvqDSWfWX5Z92me\nH5q72eFoX758edlWT5CD+ZLH1+K66aabywxNnunQGbY+irdHrZCMZ7MzC77r/rrxtJS7DAbqtpo8\nwSN/R7oTy1Zgg00gRyp15wAO6VG7R/ZAS4hXa4nxc845J6y0Pz9gd07hZxif2d3hwTIhXrCPtra2\nMCcS71qa5qlUfdkAfeqpp3smM8Pr60/oW7PKvTBYptPTPZVqCJPf+cR68O/m8DhtFf8vlPvcNBdE\nBkvBY5IEj8I/+vIVWFQJtrW1xRYKDH6ilWDj+kskj9Y9IUbzKjhJxVmYN+jwfMK+cv4lGBk20/Nz\nOd7pwZpWFLR6crnGvouAeOCIlzO+XlVHR4d3dnZ6W1ub33zzzR4sER+tDnxiLJgE33V9/aK+hR6L\nv8P+8iHR6LDOzs4R+w5k4lD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7bzKzfYDesNvzwAGJw+aGbWVdc801A6/b29tpb2+vZpJFpIKenh6y2Tb6+xeELQvIZFrp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\nQ9i/26HcGIp48sA4U42fxLs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KZbNZmpqup6NjDbt3P0FHxxqamq5XCUFEBqW6lBtz9z1m1gJ8CNhpZvXuvtPMTgba42rb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\ntbl3IBARkaMnlZLBQKlkICI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H1RASFq6M2AF8DGgAMkBHWV0xUaQUvfzyy3Q3QjCUlwclhIS9+eabQD2hZvAs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\n", 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dUw9/rRdVh6jYBCwCHgBOBDYRcWhT6xqNjo4Og0A6gNmH0GDHH3888BzVKSCO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\n/wENrjuzUBJRhQAAAABJRU5ErkJggg==\n", 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ykMRNnjyZA5sLXojlhank0V6jccU9WpYuvYTnn/8F69d/i+ef/0XR8wFqa2u5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\ngpKBiIigZCAiIigZiIgISgY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6gWQySV1dXUnzJiKjo7W1ldbW1mF9xrAbkM1sNtDj7m+bWQJ4BPgGcAbwprvfmaUB+Q8I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\nW8+1BvK8efMI9dvpE9Vtj+n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Mzbx6Dgw/zPv/D+3vPwVwo2/7ZwD8Zsg+q3DK6kNuqOIlzo16wr64UY4QiXJZ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\nNAqFzUQ7neMKG3HlNeOETeQ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i in range(len(data.columns)):\n", " \n", " fig,ax = plt.subplots(1,1,figsize=(6,6))\n", " ax.scatter(data[data.columns[i]],y)\n", " ax.set_title('housing prices vs.'+data.columns[i])\n", " ax1.set_xlim([-0.1,1])\n", " plt.savefig(\"pic%d\"%i, transparent = True)\n", "\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best subset by exhaustive search:\n", "[0, 3, 6, 8, 9, 10, 11, 12, 13, 14]\n", "['pixelPlant' 'pixelWall' 'pixelWater' 'pixelCeiling' 'pixelPath'\n", " 'pixelBuilding' 'crime' 'walkSchool' 'walkMbta' 'energySiteEUI']\n" ] } ], "source": [ "min_bic = 1e10 # set some initial large value for min BIC score\n", "best_subset = [] # best subset of predictors\n", "\n", "# Create all possible subsets of the set of 10 predictors\n", "predictor_set = set(range(15)) # predictor set = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}\n", "\n", "# Repeat for every possible size of subset\n", "for size_k in range(15): \n", " # Create all possible subsets of size 'size', \n", " # using the 'combination' function from the 'itertools' library\n", " subsets_of_size_k = it.combinations(predictor_set, size_k + 1) \n", " \n", " max_r_squared = -1e10 # set some initial small value for max R^2 score\n", " best_k_subset = [] # best subset of predictors of size k\n", " \n", " # Iterate over all subsets of our predictor set\n", " for predictor_subset in subsets_of_size_k: \n", " # Use only a subset of predictors in the training data\n", " x_subset = x[:, predictor_subset]\n", " # Add a column of ones\n", " x_subset = np.hstack((x_subset, np.ones((x_subset.shape[0], 1))))\n", " \n", " # Fit and evaluate R^2\n", " model = OLS(y, x_subset)\n", " results = model.fit()\n", " r_squared = results.rsquared\n", " \n", " # Update max R^2 and best predictor subset of size k\n", " # If current predictor subset has a higher R^2 score than that of the best subset \n", " # we've found so far, remember the current predictor subset as the best!\n", " if(r_squared > max_r_squared): \n", " max_r_squared = r_squared\n", " best_k_subset = predictor_subset[:]\n", " \n", "\n", " # Use only the best subset of size k for the predictors\n", " x_subset = x[:, best_k_subset]\n", " \n", " # Fit and evaluate BIC of the best subset of size k\n", " model = OLS(y, x_subset)\n", " results = model.fit()\n", " bic = results.bic\n", " \n", " # Update minimum BIC and best predictor subset\n", " # If current predictor has a lower BIC score than that of the best subset \n", " # we've found so far, remember the current predictor as the best!\n", " if(bic < min_bic): \n", " min_bic = bic\n", " best_subset = best_k_subset[:]\n", " \n", "print('Best subset by exhaustive search:')\n", "print sorted(best_subset)\n", "exhaust = sorted(best_subset)\n", "print data.columns.values[sorted(best_subset)]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Step-wise forward subset selection:\n", "[3, 8, 9, 12, 13, 14, 18, 25, 29, 30, 34, 35, 40]\n", "['pixelWall' 'pixelCeiling' 'pixelPath' 'walkSchool' 'walkMbta'\n", " 'energySiteEUI' 'walkPark' 'pixelBridge' 'pixelWindow' 'pixelGrandstand'\n", " 'latitude' 'bathrooms' 'bedrooms']\n" ] } ], "source": [ "### Step-wise Forward Selection\n", "d = x.shape[1] # total no. of predictors\n", "\n", "# Keep track of current set of chosen predictors, and the remaining set of predictors\n", "current_predictors = [] \n", "remaining_predictors = range(d)\n", "\n", "# Set some initial large value for min BIC score for all possible subsets\n", "global_min_bic = 1e10 \n", "\n", "# Keep track of the best subset of predictors\n", "best_subset = [] \n", "\n", "# Iterate over all possible subset sizes, 0 predictors to d predictors\n", "for size in range(d): \n", " max_r_squared = -1e10 # set some initial small value for max R^2\n", " best_predictor = -1 # set some throwaway initial number for the best predictor to add\n", " bic_with_best_predictor = 1e10 # set some initial large value for BIC score \n", " \n", " # Iterate over all remaining predictors to find best predictor to add\n", " for i in remaining_predictors:\n", " # Make copy of current set of predictors\n", " temp = current_predictors[:]\n", " # Add predictor 'i'\n", " temp.append(i)\n", " \n", " # Use only a subset of predictors in the training data\n", " x_subset = x[:, temp]\n", " # Add a column of ones\n", " x_subset = np.hstack((x_subset, np.ones((x_subset.shape[0], 1))))\n", " \n", " # Fit and evaluate R^2\n", " model = OLS(y, x_subset)\n", " results = model.fit()\n", " r_squared = results.rsquared\n", " \n", " # Check if we get a higher R^2 value than than current max R^2, if so, update\n", " if(r_squared > max_r_squared):\n", " max_r_squared = r_squared\n", " best_predictor = i\n", " bic_with_best_predictor = results.bic\n", " \n", " # Remove best predictor from remaining list, and add best predictor to current list\n", " remaining_predictors.remove(best_predictor)\n", " current_predictors.append(best_predictor)\n", " \n", " # Check if BIC for with the predictor we just added is lower than \n", " # the global minimum across all subset of predictors\n", " if(bic_with_best_predictor < global_min_bic):\n", " best_subset = current_predictors[:]\n", " global_min_bic = bic_with_best_predictor\n", " \n", "print 'Step-wise forward subset selection:'\n", "print sorted(best_subset) # add 1 as indices start from 0\n", "forward = data.columns.values[sorted(best_subset)]\n", "print forward" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Step-wise backward subset selection:\n", "[3, 8, 9, 12, 13, 14, 18, 25, 30, 34, 35, 40]\n", "['pixelWall' 'pixelCeiling' 'pixelPath' 'walkSchool' 'walkMbta'\n", " 'energySiteEUI' 'walkPark' 'pixelBridge' 'pixelGrandstand' 'latitude'\n", " 'bathrooms' 'bedrooms']\n" ] } ], "source": [ "### Step-wise Backward Selection\n", "d = x.shape[1] # total no. of predictors\n", "\n", "# Keep track of current set of chosen predictors\n", "current_predictors = range(d)\n", "\n", "# First, fit and evaluate BIC using all 'd' number of predictors\n", "model = OLS(y, x)\n", "results = model.fit()\n", "bic_all = results.bic\n", "\n", "# Set the minimum BIC score, initially, to the BIC score using all 'd' predictors\n", "global_min_bic = bic_all\n", "# Keep track of the best subset of predictors\n", "best_subset = [] \n", "\n", "# Iterate over all possible subset sizes, d predictors to 1 predictor\n", "for size in range(d - 1, 0, -1): # stop before 0 to avoid choosing an empty set of predictors\n", " max_r_squared = -1e10 # set some initial small value for max R^2\n", " worst_predictor = -1 # set some throwaway initial number for the worst predictor to remove\n", " bic_without_worst_predictor = 1e10 # set some initial large value for min BIC score \n", " \n", " # Iterate over current set of predictors (for potential elimination)\n", " for i in current_predictors:\n", " # Create copy of current predictors, and remove predictor 'i'\n", " temp = current_predictors[:]\n", " temp.remove(i)\n", " \n", " # Use only a subset of predictors in the training data\n", " x_subset = x[:, temp]\n", " # Add a column of ones\n", " x_subset = np.hstack((x_subset, np.ones((x_subset.shape[0], 1))))\n", " \n", " # Fit and evaluate R^2\n", " model = OLS(y, x_subset)\n", " results = model.fit()\n", " r_squared = results.rsquared\n", " \n", " # Check if we get a higher R^2 value than than current max R^2, if so, update\n", " if(r_squared > max_r_squared):\n", " max_r_squared = r_squared\n", " worst_predictor = i\n", " bic_without_worst_predictor = results.bic\n", " \n", " # Remove worst predictor from current set of predictors\n", " current_predictors.remove(worst_predictor)\n", " \n", " # Check if BIC for the predictor we just removed is lower than \n", " # the global minimum across all subset of predictors\n", " if(bic_without_worst_predictor < global_min_bic):\n", " best_subset = current_predictors[:]\n", " global_min_bic = bic_without_worst_predictor\n", " \n", "print 'Step-wise backward subset selection:'\n", "print sorted(best_subset)\n", "backward = data.columns.values[sorted(best_subset)]\n", "print backward" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['pixelWall', 'pixelCeiling', 'pixelPath', 'walkSchool', 'walkMbta',\n", " 'energySiteEUI', 'walkPark', 'pixelBridge', 'pixelGrandstand',\n", " 'latitude', 'bathrooms', 'bedrooms'], dtype=object)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.columns.values[sorted(best_subset)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using BIC selection criterial with exhaustive search, watking distance , distance to school, bathrooms numbers, latitude , home size, and home type are the releveant features " ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Plain Regression: R^2 score on training set 0.316326357898\n", "Plain Regression: R^2 score on test set 0.390477980533\n", "[ -8.86686784e+01 4.34318178e+02 3.10418622e+00 -6.93186846e+00\n", " -9.17424434e-07 -2.87754641e-01 2.54193714e+00 -3.32795469e+00\n", " -2.75095526e-03 -4.95002385e+00]\n" ] } ], "source": [ "# base linear regression \n", "base=['longitude', 'latitude',\n", " 'bathrooms', 'last_sold_price', 'property_size', 'zip', 'status',\n", " 'bedrooms', 'year_built', 'home_type']\n", "xlinear = data[base].values\n", "n = xlinear.shape[0]\n", "n_train = int(np.round(n * 0.4))\n", "\n", "# First 40% train, remaining test\n", "xlinear_train = xlinear[:n_train, :]\n", "y_train = y[:n_train]\n", "xlinear_test = xlinear[n_train:, :]\n", "y_test = y[n_train:]\n", "reg = Lin_Reg() #automatically fits intercept (adds column of one's) for you\n", "reg.fit(xlinear_train, y_train)\n", "ylinearpred = reg.predict(xlinear_test)\n", "train_r_squared_plain = reg.score(xlinear_train, y_train)\n", "test_r_squared_plain = reg.score(xlinear_test, y_test)\n", "\n", "print 'Plain Regression: R^2 score on training set', train_r_squared_plain\n", "print 'Plain Regression: R^2 score on test set', test_r_squared_plain\n", "print reg.coef_" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## regular linear regression " ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Split data into train and test\n", "# First 40% train, remaining test\n", "xlinear = data[backward].values\n", "xlinear_train = xlinear[:n_train, :]\n", "xlinear_test = xlinear[n_train:, :]" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x_train = x[:n_train, :]\n", "x_test = x[n_train:, :]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Plain Regression: R^2 score on training set 0.554806605344\n", "Plain Regression: R^2 score on test set 0.552280768531\n", "['pixelWall' 'pixelCeiling' 'pixelPath' 'walkSchool' 'walkMbta'\n", " 'energySiteEUI' 'walkPark' 'pixelBridge' 'pixelGrandstand' 'latitude'\n", " 'bathrooms' 'bedrooms']\n", "[ 1.66975864e+00 -5.08047923e+00 1.02931553e+01 8.37755472e-01\n", " -4.70824163e-01 -2.65410930e-03 9.89936107e-01 -9.55759953e+00\n", " 4.06865061e+01 3.34295897e+02 2.07474792e+00 -4.47986540e+00]\n" ] } ], "source": [ "# Fit plain regression on train set, evaluate on train and test sets\n", "reg = Lin_Reg() #automatically fits intercept (adds column of one's) for you\n", "reg.fit(xlinear_train, y_train)\n", "ylinearpred = reg.predict(xlinear_test)\n", "train_r_squared_plain = reg.score(xlinear_train, y_train)\n", "test_r_squared_plain = reg.score(xlinear_test, y_test)\n", "\n", "print 'Plain Regression: R^2 score on training set', train_r_squared_plain\n", "print 'Plain Regression: R^2 score on test set', test_r_squared_plain\n", "print backward\n", "print reg.coef_" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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fNflJswVb7BOHAVi1nAxNRO0pl8vWhMHp06fjlFNOwSmnnAIAE4JyJBLBJz7x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\n2toycvekNxzx7NCJgYjnbBSJwx2dTXcHxUxgtDuKDJTkqLTH40lsf3c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tDCzjioULTWbO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\nmuwzXBc1A2cAwdJfbMZlLuG2/8tgSuYiJ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hLy8iQrVwrOOcfHG29ESaS2aM0awX336eTk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\n1eOzzz7TXyciIoKOHTtiY2ODo6MjkyZNKjb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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plotting boston data\n", "fig = plt.figure(figsize=(30,10))\n", "\n", "#Project onto axes: 1, 2, 3\n", "ax1 = fig.add_subplot(1, 3, 1, projection='3d')\n", "\n", "ax1.scatter(data['longitude'][n_train:].values,data['latitude'][n_train:].values,y_test,c='b', color='b',label='Original Test Set')\n", "ax1.scatter(data['longitude'][n_train:],data['latitude'][n_train:],ylinearpred,c='red',color = 'red',label='Predicted Price')\n", "ax1.set_xlabel('Longitude')\n", "ax1.set_ylabel('Latitude')\n", "ax1.set_zlabel('housing prices')\n", "ax1.set_title('Boston Housing Prices')\n", "ax1.legend(loc='lower left')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Lasso:\n", "Coefficients: [ 1.78022984 0.76351372 -1.62028417 3.85289307 1.19614439 0. 0.\n", " -0.16021163 -3.09622089 2.86537383 -0.03692707 1.54431305\n", " 9.99467568 4.29210187 -1.21071298 0.09701096 0. 1.49519728\n", " 6.15427688 0. 0. 0. -0.23433414 0. -0.\n", " -2.08276022 -0. -0. -1.34738636 0.19296128\n", " 5.31385155 -0. 0. -0. 11.50284928\n", " 5.42092042 0. 0. -0.87417761 1.74624793\n", " -8.45279138 -0. -1.42791519]\n", "Predictors with non-zero coefficients: [0, 1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 22, 25, 28, 29, 30, 34, 35, 38, 39, 40, 42]\n", "['pixelPlant' 'pixelPole' 'pixelRoad' 'pixelWall' 'pixelCar' 'pixelBus'\n", " 'pixelCeiling' 'pixelPath' 'pixelBuilding' 'crime' 'walkSchool' 'walkMbta'\n", " 'energySiteEUI' 'pixelPerson' 'pixelVan' 'walkPark' 'pixelMountain'\n", " 'pixelBridge' 'pixelField' 'pixelWindow' 'pixelGrandstand' 'latitude'\n", " 'bathrooms' 'zip' 'status' 'bedrooms' 'home_type']\n" ] } ], "source": [ "x_std = Standardize(with_mean=False).fit_transform(x)\n", "\n", "# Lasso regression\n", "reg = Lasso_Reg(alpha =1)\n", "reg.fit(x_std, y)\n", "coefficients = reg.coef_\n", "\n", "print 'Lasso:'\n", "print 'Coefficients:', coefficients\n", "print 'Predictors with non-zero coefficients:', [i for i, item in enumerate(coefficients) if abs(item) > 0]\n", "print data.columns.values[[i for i, item in enumerate(coefficients) if abs(item) > 0]]" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Lasso Regression: max R^2 score on training set 0.63298542706\n", "Lasso Regression: max R^2 score on test set 0.533951380935\n" ] }, { "data": { "image/png": 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+JSUllJSUpHWfmW5EbwTMJmhEXwZMBUa6+6yEMgOAO4BhQDPgP8CZwAIgz903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\nHlp8wN1vrLJ+CPAcMC9c9LS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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# tune regularizaiton parameters\n", "\n", "# Store test R-squared for different regression parameters\n", "max_pow_of_10 = 7 # maximum power of 10\n", "min_pow_of_10 = -3 # minimum power of 10\n", "num_params = max_pow_of_10 - min_pow_of_10 + 1\n", "\n", "train_r_squared = []\n", "test_r_squared = []\n", "\n", "#standardize x_train and y_train\n", "std = Standardize(with_mean=False)\n", "x_train_std = std.fit_transform(x_train)\n", "x_test_std = x_test / std.scale_ \n", "\n", "for i in range(min_pow_of_10, max_pow_of_10 + 1): \n", " # Fit ridge regression on train set\n", " reg = Lasso_Reg(alpha = 10**i)\n", " reg.fit(x_train_std, y_train)\n", " \n", " # Evaluate train & test performance\n", " train_r_squared.append(reg.score(x_train_std, y_train))\n", " test_r_squared.append(reg.score(x_test_std, y_test))\n", " \n", "# Plot train an test R-squared as a function parameter value\n", "fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n", "\n", "ax.semilogx(10.0**np.arange(min_pow_of_10, max_pow_of_10 + 1), \n", " train_r_squared, \n", " c='b', \n", " label='Lasso: Train')\n", "ax.semilogx(10.0**np.arange(min_pow_of_10, max_pow_of_10 + 1), \n", " test_r_squared, \n", " c='r', \n", " label='Lasso: Test')\n", "ax.axhline(y=test_r_squared_plain, \n", " c='g', \n", " label='Plain Regression')\n", "\n", "ax.set_xlabel('Regularization parameter')\n", "ax.set_ylabel('R^2 score')\n", "ax.set_ylim((test_r_squared_plain - 0.2, 0.8))\n", "ax.set_title('Comparison of R^2 Score')\n", "ax.legend(loc='best')\n", "\n", "print 'Lasso Regression: max R^2 score on training set', max(train_r_squared)\n", "print 'Lasso Regression: max R^2 score on test set', max(test_r_squared)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ridge:\n", "Coefficients: [ 1.52969298 2.29213497 -2.42188672 5.31383223 1.92563376\n", " 0.54345318 -1.18607724 -0.95832993 -6.82493919 3.53947267\n", " -1.47858632 2.83548732 9.43789854 5.56754656 -3.96066808\n", " 1.92563376 -1.15601065 1.71357325 6.54673171 0.70230854\n", " 1.9336431 0. -1.2053437 -0.26665896 -1.47599769\n", " -3.10929521 1.01643824 -0.56669797 -3.0005008 1.94453138\n", " 8.74845762 -0.14141461 0.2827675 -1.05692459 12.21418469\n", " 6.39028537 0.28957751 0.19573106 -0.7571994 3.56798651\n", " -8.4690419 -1.03686992 -3.70092368]\n", "Predictors with non-zero coefficients: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42]\n" ] } ], "source": [ "# Ridge regression: Fit and evaluate \n", "reg = Ridge_Reg(alpha = 10)\n", "reg.fit(x_std, y)\n", "coefficients = reg.coef_\n", "\n", "print 'Ridge:'\n", "print 'Coefficients:', coefficients\n", "print 'Predictors with non-zero coefficients:', [i for i, item in enumerate(coefficients) if abs(item) > 0]" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ridge Regression: max R^2 score on training set 0.6329854815\n", "Ridge Regression: max R^2 score on test set 0.510849003858\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# tune regularizaiton parameters\n", "\n", "# Store test R-squared for different regression parameters\n", "max_pow_of_10 = 7 # maximum power of 10\n", "min_pow_of_10 = -7 # minimum power of 10\n", "num_params = max_pow_of_10 - min_pow_of_10 + 1\n", "\n", "train_r_squared = []\n", "test_r_squared = []\n", "\n", "#standardize x_train and y_train\n", "std = Standardize(with_mean=False)\n", "x_train_std = std.fit_transform(x_train)\n", "x_test_std = x_test / std.scale_ \n", "\n", "for i in range(min_pow_of_10, max_pow_of_10 + 1): \n", " # Fit ridge regression on train set\n", " reg = Ridge_Reg(alpha = 10**i)\n", " reg.fit(x_train_std, y_train)\n", " \n", " # Evaluate train & test performance\n", " train_r_squared.append(reg.score(x_train_std, y_train))\n", " test_r_squared.append(reg.score(x_test_std, y_test))\n", " \n", "# Plot train an test R-squared as a function parameter value\n", "fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n", "\n", "ax.semilogx(10.0**np.arange(min_pow_of_10, max_pow_of_10 + 1), \n", " train_r_squared, \n", " c='b', \n", " label='Ridge: Train')\n", "ax.semilogx(10.0**np.arange(min_pow_of_10, max_pow_of_10 + 1), \n", " test_r_squared, \n", " c='r', \n", " label='Ridge: Test')\n", "ax.axhline(y=test_r_squared_plain, \n", " c='g', \n", " label='Plain Regression')\n", "\n", "ax.set_xlabel('Regularization parameter')\n", "ax.set_ylabel('R^2 score')\n", "ax.set_ylim((test_r_squared_plain - 0.2, 1.2))\n", "ax.set_title('Comparison of R^2 Score')\n", "ax.legend(loc='best')\n", "\n", "print 'Ridge Regression: max R^2 score on training set', max(train_r_squared)\n", "print 'Ridge Regression: max R^2 score on test set', max(test_r_squared)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ridge regression: Test R^2 score for CV choice 0.506296900204\n", "Ridge regression: Max Test R^2 score 0.510849003858\n", "Plain regression: Test R^2 score: 0.552280768531\n" ] }, { "data": { "image/png": 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f2fgukmmc84kkm8ePlFQqkfTq5ev+Nm4MLSbJHLFUbb0EbDGz9sADwK7AM0mN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\nuOKKZIclCaJEIkmnebWkeCb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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#-------- k_fold_r_squared\n", "# A function for k-fold cross validation with Ridge regression\n", "# Input: \n", "# x_train (n x d array of predictors in training data)\n", "# y_train (n x 1 array of response variable vals in training data)\n", "# num_folds (no. of folds for CV)\n", "# param_val (regularization parameter value)\n", "# Return: \n", "# average R^2 value across folds\n", "\n", "def k_fold_r_squared(x_train, y_train, num_folds, param_val):\n", " n_train = x_train.shape[0]\n", " n = int(np.round(n_train * 1. / num_folds)) # points per fold\n", "\n", " # Iterate over folds\n", " cv_r_squared = 0\n", " \n", " for fold in range(1, num_folds + 1):\n", " # Take k-1 folds for training \n", " x_first_half = x_train[:n * (fold - 1), :]\n", " x_second_half = x_train[n * fold + 1:, :]\n", " x_train_cv = np.concatenate((x_first_half, x_second_half), axis=0)\n", " \n", " y_first_half = y_train[:n * (fold - 1)]\n", " y_second_half = y_train[n * fold + 1:]\n", " y_train_cv = np.concatenate((y_first_half, y_second_half), axis=0)\n", " \n", " # Take the middle fold for testing\n", " x_test_cv = x_train[1 + n * (fold - 1):n * fold, :]\n", " y_test_cv = y_train[1 + n * (fold - 1):n * fold]\n", "\n", " # Fit ridge regression model with parameter value on CV train set, and evaluate CV test performance\n", " reg = Ridge_Reg(alpha = param_val)\n", " reg.fit(x_train_cv, y_train_cv)\n", " r_squared = reg.score(x_test_cv, y_test_cv)\n", " \n", " # Cummulative R^2 value across folds\n", " cv_r_squared += r_squared\n", "\n", " # Return average R^2 value across folds\n", " return cv_r_squared * 1.0 / num_folds\n", "\n", "# Store test & CV R^2 values for different regression parameter values\n", "# Range: 10^-7, ... 10^7\n", "max_pow_of_10 = 7\n", "min_pow_of_10 = -3\n", "num_params = max_pow_of_10 - min_pow_of_10 + 1\n", "\n", "test_r_squared = []\n", "cv_r_squared = []\n", "\n", "# Iterate over various parameter values\n", "for power_of_10 in range(min_pow_of_10, max_pow_of_10+1):\n", " \n", " #standardize x_train and y_train\n", " std = Standardize(with_mean=False)\n", " x_train_std = std.fit_transform(x_train)\n", " x_test_std = x_test / std.scale_ \n", " \n", " # Fit regression model on train set, and evaluate test R^2\n", " reg = Ridge_Reg(alpha=10**power_of_10)\n", " reg.fit(x_train_std, y_train)\n", " test_r_squared.append(reg.score(x_test_std, y_test))\n", " \n", " # Evaluate 5-fold CV R^2\n", " cv_r_squared.append(k_fold_r_squared(x_train_std, y_train, 5, 10**power_of_10))\n", "\n", "# Plot CV and test R^2 values as a function of parameter value\n", "fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n", "\n", "ax.semilogx(10.0**np.arange(min_pow_of_10, max_pow_of_10 + 1), \n", " cv_r_squared, \n", " c='b', \n", " label='5-fold CV')\n", "ax.semilogx(10.0**np.arange(min_pow_of_10, max_pow_of_10 + 1), \n", " test_r_squared, \n", " c='r', \n", " label='Test')\n", "\n", "ax.set_xlabel('Regularization parameter')\n", "ax.set_ylabel('R^2 score')\n", "ax.set_title('Comparison of R^2 Score')\n", "ax.legend(loc='lower right')\n", "\n", "# Best CV parameter value\n", "best_cv_param = np.argmax(cv_r_squared)\n", "\n", "# Print R^2 for best CV parameter, max R^2 across all parameters, and R^2 for plain regression\n", "print 'Ridge regression: Test R^2 score for CV choice', test_r_squared[best_cv_param]\n", "print 'Ridge regression: Max Test R^2 score', max(test_r_squared)\n", "print 'Plain regression: Test R^2 score:', test_r_squared_plain\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tree" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\gujianflsgj\\Anaconda2\\lib\\site-packages\\sklearn\\grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", " DeprecationWarning)\n" ] } ], "source": [ "import statsmodels.api as sm\n", "from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier, export_graphviz\n", "from sklearn.ensemble import RandomForestRegressor\n", "\n", "#import seaborn as sns\n", "#sns.set_style(\"whitegrid\")\n", "#sns.set_context(\"poster\")\n", "\n", "# special matplotlib argument for improved plots\n", "from matplotlib import rcParams\n", "from IPython.display import Image\n", "#import pydotplus\n", "\n", "\n", "\n", "from sklearn.grid_search import GridSearchCV\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.metrics import confusion_matrix\n", "\n", "from sklearn import tree" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.585502733075\n" ] } ], "source": [ "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n", "\n", "max_depth =5\n", "regr_rf = RandomForestRegressor(max_depth=max_depth, random_state=2)\n", "regr_rf.fit(x_train, y_train)\n", "y_rf = regr_rf.predict(x_test)\n", "\n", "score = regr_rf.score(x_test,y_test)\n", "print score" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (, line 1)", "output_type": "error", "traceback": [ "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m pip install treeinterpreter\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "pip install treeinterpreter" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_get_tree_paths(regr_rf,3, depth=5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# class sklearn.ensemble.ExtraTreesRegressor(n_estimators=10, criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_split=1e-07, bootstrap=False, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False)[source]¶\n", "import sklearn.BaseForest as regr_rf\n", "# from sklearn.ensemble import regr_rf\n", "\n", "# regr_rf.decision_path(x_train)\n", "\n", "#regr_rf.decision_path(x_train)\n", "\n", "BaseForest\n", "\n", "# decision_path\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.datasets import make_blobs\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.ensemble import ExtraTreesClassifier\n", "from sklearn.tree import DecisionTreeClassifier" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# COLOR STUFF \n", "from matplotlib.colors import ListedColormap\n", "# cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n", "cmap_light = ListedColormap(['#FFAAAA', '#AAAAFF'])\n", "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n", "cm = plt.cm.RdBu\n", "cm_bright = ListedColormap(['#FF0000', '#0000FF'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# A generic function to do CV\n", "def cv_optimize(clf, parameters, X, y, n_jobs=1, n_folds=5, score_func=None):\n", " if score_func:\n", " gs = GridSearchCV(clf, param_grid=parameters, cv=n_folds, n_jobs=n_jobs, scoring=score_func)\n", " else:\n", " gs = GridSearchCV(clf, param_grid=parameters, n_jobs=n_jobs, cv=n_folds)\n", " gs.fit(X, y)\n", "\n", " best = gs.best_estimator_\n", " return best\n", "\n", "def do_classify(clf, parameters, indf, featurenames, targetname, target1val, mask=None, reuse_split=None, score_func=None, n_folds=5, n_jobs=1):\n", " subdf=indf[featurenames]\n", " X=subdf.values\n", " y=(indf[targetname].values==target1val)*1\n", " if mask !=None:\n", " print \"using mask\"\n", " Xtrain, Xtest, ytrain, ytest = X[mask], X[~mask], y[mask], y[~mask]\n", " if reuse_split !=None:\n", " print \"using reuse split\"\n", " Xtrain, Xtest, ytrain, ytest = reuse_split['Xtrain'], reuse_split['Xtest'], reuse_split['ytrain'], reuse_split['ytest']\n", " if parameters:\n", " clf = cv_optimize(clf, parameters, Xtrain, ytrain, n_jobs=n_jobs, n_folds=n_folds, score_func=score_func)\n", " clf=clf.fit(Xtrain, ytrain)\n", " training_accuracy = clf.score(Xtrain, ytrain)\n", " test_accuracy = clf.score(Xtest, ytest)\n", " print \"############# based on standard predict ################\"\n", " print \"Accuracy on training data: %0.2f\" % (training_accuracy)\n", " print \"Accuracy on test data: %0.2f\" % (test_accuracy)\n", " print confusion_matrix(ytest, clf.predict(Xtest))\n", " print \"########################################################\"\n", " return clf, Xtrain, ytrain, Xtest, ytest\n", "\n", "def plot_2tree(ax, Xtr, Xte, ytr, yte, clf, plot_train = True, plot_test = True, lab = ['Feature 1', 'Feature 2'], mesh=True, colorscale=cmap_light, cdiscrete=cmap_bold, alpha=0.3, psize=10, zfunc=False):\n", " # Create a meshgrid as our test data\n", " plt.figure(figsize=(15,10))\n", " plot_step= 0.05\n", " xmin, xmax= Xtr[:,0].min(), Xtr[:,0].max()\n", " ymin, ymax= Xtr[:,1].min(), Xtr[:,1].max()\n", " xx, yy = np.meshgrid(np.arange(xmin, xmax, plot_step), np.arange(ymin, ymax, plot_step) )\n", "\n", " # Re-cast every coordinate in the meshgrid as a 2D point\n", " Xplot= np.c_[xx.ravel(), yy.ravel()]\n", "\n", "\n", " # Predict the class\n", " Z = clf.predict( Xplot )\n", "\n", " # Re-shape the results\n", " Z= Z.reshape( xx.shape )\n", " cs = plt.contourf(xx, yy, Z, cmap= cmap_light, alpha=0.3)\n", " \n", " # Overlay training samples\n", " if (plot_train == True):\n", " plt.scatter(Xtr[:, 0], Xtr[:, 1], c=ytr-1, cmap=cmap_bold, alpha=alpha,edgecolor=\"k\") \n", " # and testing points\n", " if (plot_test == True):\n", " plt.scatter(Xte[:, 0], Xte[:, 1], c=yte-1, cmap=cmap_bold, alpha=alpha, marker=\"s\")\n", "\n", " plt.xlabel(lab[0])\n", " plt.ylabel(lab[1])\n", " plt.title(\"Boundary for decision tree classifier\",fontsize=7.5)\n", " \n", "# This function creates images of tree models using pydotplus\n", "# https://github.com/JWarmenhoven/ISLR-python\n", "def print_tree(estimator, features, class_names=None, filled=True):\n", " tree = estimator\n", " names = features\n", " color = filled\n", " classn = class_names\n", " \n", " dot_data = StringIO.StringIO()\n", " export_graphviz(estimator, out_file=dot_data, feature_names=features, proportion=True, class_names=classn, filled=filled)\n", " graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n", " return(graph)\n", "\n", "# Print decision tree model 'dt'\n", "def display_dt(dt):\n", " dummy_io = StringIO.StringIO() \n", " tree.export_graphviz(dt, out_file = dummy_io, proportion=True) \n", " print dummy_io.getvalue()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Tree interpreter" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# -*- coding: utf-8 -*-\n", "import numpy as np\n", "from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier, _tree\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.ensemble import RandomForestClassifier\n", "from distutils.version import LooseVersion\n", "import sklearn\n", "if LooseVersion(sklearn.__version__) < LooseVersion(\"0.17\"):\n", " raise Exception(\"treeinterpreter requires scikit-learn 0.17 or later\")\n", "\n", "\n", "def _get_tree_paths(tree, node_id, depth=0):\n", " \"\"\"\n", " Returns all paths through the tree as list of node_ids\n", " \"\"\"\n", " if node_id == _tree.TREE_LEAF:\n", " raise ValueError(\"Invalid node_id %s\" % _tree.TREE_LEAF)\n", "\n", " left_child = tree.children_left[node_id]\n", " right_child = tree.children_right[node_id]\n", "\n", " if left_child != _tree.TREE_LEAF:\n", " left_paths = _get_tree_paths(tree, left_child, depth=depth + 1)\n", " right_paths = _get_tree_paths(tree, right_child, depth=depth + 1)\n", "\n", " for path in left_paths:\n", " path.append(node_id)\n", " for path in right_paths:\n", " path.append(node_id)\n", " paths = left_paths + right_paths\n", " else:\n", " paths = [[node_id]]\n", " return paths\n", "\n", "\n", "def _predict_tree(model, X, joint_contribution=False):\n", " \"\"\"\n", " For a given DecisionTreeRegressor or DecisionTreeClassifier,\n", " returns a triple of [prediction, bias and feature_contributions], such\n", " that prediction ≈ bias + feature_contributions.\n", " \"\"\"\n", " leaves = model.apply(X)\n", " paths = _get_tree_paths(model.tree_, 0)\n", "\n", " for path in paths:\n", " path.reverse()\n", "\n", " leaf_to_path = {}\n", " #map leaves to paths\n", " for path in paths:\n", " leaf_to_path[path[-1]] = path \n", " \n", " # remove the single-dimensional inner arrays\n", " values = model.tree_.value.squeeze()\n", " # reshape if squeezed into a single float\n", " if len(values.shape) == 0:\n", " values = np.array([values])\n", " if type(model) == DecisionTreeRegressor:\n", " biases = np.full(X.shape[0], values[paths[0][0]])\n", " line_shape = X.shape[1]\n", " elif type(model) == DecisionTreeClassifier:\n", " # scikit stores category counts, we turn them into probabilities\n", " normalizer = values.sum(axis=1)[:, np.newaxis]\n", " normalizer[normalizer == 0.0] = 1.0\n", " values /= normalizer\n", "\n", " biases = np.tile(values[paths[0][0]], (X.shape[0], 1))\n", " line_shape = (X.shape[1], model.n_classes_)\n", " direct_prediction = values[leaves]\n", " \n", " \n", " #make into python list, accessing values will be faster\n", " values_list = list(values)\n", " feature_index = list(model.tree_.feature)\n", " \n", " contributions = []\n", " if joint_contribution:\n", " for row, leaf in enumerate(leaves):\n", " path = leaf_to_path[leaf]\n", " \n", " \n", " path_features = set()\n", " contributions.append({})\n", " for i in range(len(path) - 1):\n", " path_features.add(feature_index[path[i]])\n", " contrib = values_list[path[i+1]] - \\\n", " values_list[path[i]]\n", " #path_features.sort()\n", " contributions[row][tuple(sorted(path_features))] = \\\n", " contributions[row].get(tuple(sorted(path_features)), 0) + contrib\n", " return direct_prediction, biases, contributions\n", " \n", " else:\n", "\n", " for row, leaf in enumerate(leaves):\n", " for path in paths:\n", " if leaf == path[-1]:\n", " break\n", " \n", " contribs = np.zeros(line_shape)\n", " for i in range(len(path) - 1):\n", " \n", " contrib = values_list[path[i+1]] - \\\n", " values_list[path[i]]\n", " contribs[feature_index[path[i]]] += contrib\n", " contributions.append(contribs)\n", " \n", " return direct_prediction, biases, np.array(contributions)\n", "\n", "\n", "def _predict_forest(model, X, joint_contribution=False):\n", " \"\"\"\n", " For a given RandomForestRegressor or RandomForestClassifier,\n", " returns a triple of [prediction, bias and feature_contributions], such\n", " that prediction ≈ bias + feature_contributions.\n", " \"\"\"\n", " biases = []\n", " contributions = []\n", " predictions = []\n", "\n", " \n", " if joint_contribution:\n", " \n", " for tree in model.estimators_:\n", " pred, bias, contribution = _predict_tree(tree, X, joint_contribution=joint_contribution)\n", "\n", " biases.append(bias)\n", " contributions.append(contribution)\n", " predictions.append(pred)\n", " \n", " \n", " total_contributions = []\n", " \n", " for i in range(len(X)):\n", " contr = {}\n", " for j, dct in enumerate(contributions):\n", " for k in set(dct[i]).union(set(contr.keys())):\n", " contr[k] = (contr.get(k, 0)*j + dct[i].get(k,0) ) / (j+1)\n", "\n", " total_contributions.append(contr) \n", " \n", " for i, item in enumerate(contribution):\n", " total_contributions[i]\n", " sm = sum([v for v in contribution[i].values()])\n", " \n", "\n", " \n", " return (np.mean(predictions, axis=0), np.mean(biases, axis=0),\n", " total_contributions)\n", " else:\n", " for tree in model.estimators_:\n", " pred, bias, contribution = _predict_tree(tree, X)\n", "\n", " biases.append(bias)\n", " contributions.append(contribution)\n", " predictions.append(pred)\n", " \n", " \n", " return (np.mean(predictions, axis=0), np.mean(biases, axis=0),\n", " np.mean(contributions, axis=0))\n", "\n", "\n", "def predict(model, X, joint_contribution=False):\n", " \"\"\" Returns a triple (prediction, bias, feature_contributions), such\n", " that prediction ≈ bias + feature_contributions.\n", " Parameters\n", " ----------\n", " model : DecisionTreeRegressor, DecisionTreeClassifier or\n", " RandomForestRegressor, RandomForestClassifier\n", " Scikit-learn model on which the prediction should be decomposed.\n", " X : array-like, shape = (n_samples, n_features)\n", " Test samples.\n", " \n", " joint_contribution : boolean\n", " Specifies if contributions are given individually from each feature,\n", " or jointly over them\n", " Returns\n", " -------\n", " decomposed prediction : triple of\n", " * prediction, shape = (n_samples) for regression and (n_samples, n_classes)\n", " for classification\n", " * bias, shape = (n_samples) for regression and (n_samples, n_classes) for\n", " classification\n", " * contributions, If joint_contribution is False then returns and array of \n", " shape = (n_samples, n_features) for regression or\n", " shape = (n_samples, n_features, n_classes) for classification, denoting\n", " contribution from each feature.\n", " If joint_contribution is True, then shape is array of size n_samples,\n", " where each array element is a dict from a tuple of feature indices to\n", " to a value denoting the contribution from that feature tuple.\n", " \"\"\"\n", " # Only single out response variable supported,\n", " if model.n_outputs_ > 1:\n", " raise ValueError(\"Multilabel classification trees not supported\")\n", "\n", " if (type(model) == DecisionTreeRegressor or\n", " type(model) == DecisionTreeClassifier):\n", " return _predict_tree(model, X, joint_contribution=joint_contribution)\n", " elif (type(model) == RandomForestRegressor or\n", " type(model) == RandomForestClassifier):\n", " return _predict_forest(model, X, joint_contribution=joint_contribution)\n", " else:\n", " raise ValueError(\"Wrong model type. Base learner needs to be \\\n", " DecisionTreeClassifier or DecisionTreeRegressor.\")" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }