{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Driven Modeling\n",
    "### (Theme of this semester: CODING AS LITERACY)\n",
    "<hr style=\"height:3px;border:none;color:stillblue;background-color:black;\" />\n",
    "### PhD seminar series at Chair for Computer Aided Architectural Design (CAAD), ETH Zurich\n",
    "\n",
    "[Vahid Moosavi](https://vahidmoosavi.com)\n",
    "<hr style=\"height:1px;border:none;color:stillblue;background-color:black;\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>\n",
    "\n",
    "# 13th Session \n",
    "<hr style=\"height:1px;border:none;color:stillblue;background-color:steelblue;\" />\n",
    "<span style=\"color:steelblue;font-size: 1em;\">14 March 2017</span>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Driven Modeling in Practice: Real Estate Market "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Topics to be discussed \n",
    "* Main Elements of full stack development of data driven project.\n",
    "* Today: Real Estate Project\n",
    "\n",
    "\n",
    "* **Waht are the key questions in real estate market?**\n",
    "* **Where and How to get the data?**\n",
    "* **Data Wrangling: Big Data is always messy**\n",
    "* **Machine Learning Part**\n",
    "* **Deploying the model on a server**\n",
    "\n",
    "![](images/RealEstate.png)\n",
    "\n",
    "\n",
    "<hr style=\"height:1px;border:none;color:stillblue;background-color:steelblue;\" />\n",
    "<hr style=\"height:1px;border:none;color:stillblue;background-color:steelblue;\" />\n",
    "### <a></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data collection for this project\n",
    "* web Crawling of Real Estate Search Portals: Homegate.ch, Immoscout.ch, ...\n",
    "    * https://www.crummy.com/software/BeautifulSoup/\n",
    "* Available open source data sets including demographics, transport data, education, environment,...\n",
    "* Open Street Map: https://osmbuildings.org/?lat=40.71098&lon=-74.00276&zoom=15&rotation=0&tilt=30\n",
    "* GeoAdmin.ch : https://map.geo.admin.ch/\n",
    "* Mapzen: http://tangrams.github.io/gui-demo/\n",
    "* Mapbox Studio: https://www.mapbox.com/mapbox-studio/\n",
    "* Data Driven Styling: https://www.mapbox.com/blog/population-inspector/?utm_source=nov-newsletter&utm_medium=email&utm_content=blog-track-pop&utm_campaign=nov-newsletter\n",
    "* Google Geocoding APIs: https://console.developers.google.com/apis/library?project=googcse20150904"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    " ### Data resampling and synthetic data generation\n",
    "     * We will talk about it in another session on resampling and bootstraping\n",
    " ### Geocoding of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import datetime\n",
    "import pandas as pd\n",
    "# import pandas.io.data\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "import sys\n",
    "import sompylib.sompy as SOM# from pandas import Series, DataFrame\n",
    "\n",
    "from ipywidgets import interact, HTML, FloatSlider\n",
    "\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic analysis of the cleaned data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Address</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Floor</th>\n",
       "      <th>Living space</th>\n",
       "      <th>Floor space</th>\n",
       "      <th>Room height</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Year built</th>\n",
       "      <th>Last renovation</th>\n",
       "      <th>Net rent</th>\n",
       "      <th>Additional expenses</th>\n",
       "      <th>Rent</th>\n",
       "      <th>Available</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>URL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>106322589.0</td>\n",
       "      <td>Reutlingerstrasse 10/14 5000 Aarau</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2017-02-22</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>930</td>\n",
       "      <td>160</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>immediately</td>\n",
       "      <td>47.382578</td>\n",
       "      <td>8.051152</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>106250068.0</td>\n",
       "      <td>Rue de Monthoux 40 1201 Genève</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2017-02-22</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350</td>\n",
       "      <td>120</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>immediately</td>\n",
       "      <td>46.210998</td>\n",
       "      <td>6.147436</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106330306.0</td>\n",
       "      <td>Rue de Berne 29bis 1201 Genève</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2017-02-22</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>immediately</td>\n",
       "      <td>46.210495</td>\n",
       "      <td>6.145934</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>106181244.0</td>\n",
       "      <td>Rue de Monthoux 40 1201 Genève</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2017-02-22</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1400</td>\n",
       "      <td>120</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>immediately</td>\n",
       "      <td>46.210998</td>\n",
       "      <td>6.147436</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>105931148.0</td>\n",
       "      <td>Rue de Bâle 16 1201 Genève</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2017-02-22</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1650.0</td>\n",
       "      <td>immediately</td>\n",
       "      <td>46.212934</td>\n",
       "      <td>6.149217</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID                             Address     ZIP        Date  \\\n",
       "0  106322589.0  Reutlingerstrasse 10/14 5000 Aarau  5000.0  2017-02-22   \n",
       "1  106250068.0      Rue de Monthoux 40 1201 Genève  1200.0  2017-02-22   \n",
       "2  106330306.0      Rue de Berne 29bis 1201 Genève  1200.0  2017-02-22   \n",
       "3  106181244.0      Rue de Monthoux 40 1201 Genève  1200.0  2017-02-22   \n",
       "4  105931148.0          Rue de Bâle 16 1201 Genève  1200.0  2017-02-22   \n",
       "\n",
       "        Type  Rooms Floor  Living space  Floor space Room height  Volume  \\\n",
       "0  Apartment    2.0    1.          42.0          NaN         NaN     NaN   \n",
       "1  Apartment    2.0    2.           NaN          NaN         NaN     NaN   \n",
       "2  Apartment    3.0    4.           NaN          NaN         NaN     NaN   \n",
       "3  Apartment    2.0    2.           NaN          NaN         NaN     NaN   \n",
       "4  Apartment    3.0    4.           NaN          NaN         NaN     NaN   \n",
       "\n",
       "   Year built  Last renovation Net rent Additional expenses    Rent  \\\n",
       "0      1983.0              NaN      930                 160  1090.0   \n",
       "1         NaN              NaN     1350                 120  1470.0   \n",
       "2         NaN              NaN     1500                 NaN  1500.0   \n",
       "3         NaN              NaN     1400                 120  1520.0   \n",
       "4         NaN              NaN      NaN                 NaN  1650.0   \n",
       "\n",
       "     Available        lat       lng  URL  \n",
       "0  immediately  47.382578  8.051152  NaN  \n",
       "1  immediately  46.210998  6.147436  NaN  \n",
       "2  immediately  46.210495  6.145934  NaN  \n",
       "3  immediately  46.210998  6.147436  NaN  \n",
       "4  immediately  46.212934  6.149217  NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path ='./Data/Real_Estate_Market/All_Listing_latlng_2017_2_23.txt'\n",
    "listing_with_latlong = pd.read_csv(path)\n",
    "listing_with_latlong.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(158171, 20)\n",
      "(50766, 8)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print listing_with_latlong.shape \n",
    "#Kinds of features we are interested in\n",
    "cols = ['ID', 'ZIP', 'Date','Type','Rooms','Floor',\n",
    "       'Living space','Floor space','Room height','Volume',\n",
    "       'Year built','Last renovation','Net rent','Additional expenses',\n",
    "       'Rent','lng','lat']\n",
    "#         'Available','lng','lat','Public transport','Shopping','Kindergarten','Primary school','Secondary school','Motorway']\n",
    "listing = listing_with_latlong[cols]\n",
    "\n",
    "#Types of listing we are interested in\n",
    "Types = ['Apartment','Attic compartment','Attic flat','Bachelor flat','Bifamiliar house','Cellar compartment','Chalet',\n",
    "       'Duplex','Farm house','Granny flat','Home','Roof flat','Row house','Rustic house','Single house','Studio','Terrace flat',\n",
    "       'Terrace house','Villa']\n",
    "Types = ['Apartment']\n",
    "id_type = []\n",
    "for i in range(listing.shape[0]):\n",
    "    if listing['Type'].values[i] in Types:\n",
    "        id_type.append(i)\n",
    "listing = listing.ix[id_type]\n",
    "\n",
    "cols = ['Rent','ZIP',\n",
    "        'Type',\n",
    "        'Rooms','Year built','Last renovation',\n",
    "       'Living space',\n",
    "        'Floor',\n",
    "       'lng','lat']\n",
    "listing = listing[cols]\n",
    "\n",
    "\n",
    "try:\n",
    "    ind_by = listing['Floor']=='GF' \n",
    "    listing['Floor'].ix[ind_by] =0\n",
    "    ind_by = listing['Floor']=='Underground' \n",
    "    listing['Floor'].ix[ind_by] =-1\n",
    "except:\n",
    "    print 'floor is not in the columns'\n",
    "\n",
    "listing['Rent'] = listing['Rent'].values[:].astype(float) \n",
    "\n",
    "#Remove outliers based on a global statistics, calculated previously\n",
    "# \n",
    "Supply_stat = listing.describe(percentiles=[.001,.01,.2,.5,.99,.995,.999])\n",
    "for f in ['Rooms','Living space','Rent']:\n",
    "    mx = Supply_stat[f].ix['99.9%']\n",
    "    ind = listing[f]>mx\n",
    "    listing[f].ix[ind]=mx\n",
    "    mn = Supply_stat[f].ix['0.1%']\n",
    "    ind = listing[f]<mn\n",
    "    listing[f].ix[ind]=mn\n",
    "\n",
    "\n",
    "#For Zurich\n",
    "# listing.index = range(listing.shape[0])\n",
    "# sel = [i for i in listing.index.values[:] if listing.ZIP.values[i] in Zurich_Zip.zip.values[:]]\n",
    "# listing = listing.ix[sel]  \n",
    "\n",
    "# eps = .2\n",
    "# ind0 = listing['lat'].values[:]>(Zlatbound[1]+eps)\n",
    "# ind1 = listing['lat'].values[:]<(Zlatbound[0]-eps)\n",
    "# ind2 = listing['lng'].values[:]>(Zlngbound[1]+eps)\n",
    "# ind3 = listing['lng'].values[:]<(Zlngbound[0]-eps)\n",
    "# indremove = (ind0 + ind1 + ind2 + ind3)\n",
    "# indkeep = [not i for i in indremove]\n",
    "# print indremove.sum()\n",
    "# listing = listing.ix[indkeep]\n",
    "\n",
    "listing = listing[['Rent','ZIP','Rooms','Year built','Living space','Floor','lng','lat']]    \n",
    "listing = listing.dropna()\n",
    "listing.index = range(listing.shape[0])\n",
    "print listing.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10cfe6d10>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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zpm0CADB1eVPy3b1ruiVFY8aLPHqUDcGwdSouXjSp6DFlp/7Lq71w/bo5DndY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\nzLwBJ20hIAEAR8OsAhLHVSyYEAscuEK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R06fN4mcSpGZ3+McY6+RWOQe7u6YDdP9+lrHg\nz2oRy4TY3jZTZt66FX+i+8IL5j544YX8dqRmW5TNsnHP1d27+UUi7RASm9ET4hYczZt5xdYbKVOM\nsNXK2hc7n6HgQrdriqimru+z96v9zL74Ylp7p8G2wQZ9Utp/nMzzjEQA5ozWmqXBRUT2RESvrKxo\nAMD8Gwy07vXM61Hndj/L6vWy9/Z62ff7/cm2aw0GWrfb49uvYm3NbGdtbfT79hhC21fK/EypcNvc\nY+x04vvJ20feNlPOn3+uQ0u/b9YNtTdF3jZjP89b2u387cauVdG5Wl0Nb7PXM9u191JoWVhIPx9V\n2d8b7n6Pw+8QAEfXysqKFhEtInt6DvrFdiFDAgCAHE1NGVi3uutAbG+bJ9j+OH13FoFJpvRzZyj4\n4hcnq6lhZ7PwU+Tz6kjYfWs9ft78uga2JoF96u0+/U4taLm1ZbIyfMPh6HSsblv8ugmWO4VpqGbB\nu+/mt8UVyrq4ejXLlLDd65RZL0RM1opS4/UpRMy9E7tW7vX3s0kWFuKFOW/eNLNtvPKKOSeh4pdu\n5sxwaIpettv11juwvzfcbJ/z5+P383GpubC4aK6bWycEAPJQQ6Jh1JAAADTB1kWwtQSmEVAZDs2U\njW7RQBGTsu8XmkwRG4JQ9KeKrYewulp9WsHNTdNBfuyx8ePx2eNbXMw6t1VrBPi1JHq98WOwtRFC\n50cpkVdfHb++9tq8+67IuXPliitubuYHP+y+2u34dLGhdn7wg1kgodXKb1fecJ7r10fPvcsO+/DP\nhy3eatmv7efE7jP1eMo4fXp8lhDfcai54N/LdDGA+UINCQAAMHX2yfLennnd35/OrCAiZrsPHphO\nYK+Xfb+OTp1fbyIv48N2cvNqQBTZ3TUdwcuXi9e1neUrV7Lv3bhhnrSXzUhxp8fUejyzot0WefZZ\nc+yhdmgdvr5bW6aexv5++Zke8tZ3n/Lv72ftttkxeUGlT3wiW99tl32q7i6xbVy/bq5xKBghIvK+\n94WDbwcHo5kk1vZ2tr8nnhh9T11ZRm5djFCmiMjxqLng3oex4wQAHwEJAADmXKxjFPq+HT6wv2/S\n6lOGD1TR7WbFPi9dMk/w7TSeVafztJ2YhQWRb/om8/Uv/II5hrwpV939xZ7sp9raCqf5i5iOs9tp\n3NoaTcl/8GDy4I8/lOOVV8wQBDsLiBu8ePXV0esb6tCHhr6kDA8IDSexbtwY36YtkpkXjDp/Plzs\nMhZccLnnOe8a593rdhjF5z+fHf/WljmP3a6ZocX9LNnClHVcUxsMcYNYLhsQO8rThNrhSoNB2uw2\nACAiFLVsehGKWgIASooVSlxcNN9fXMy+11QRPXc/y8vT3X7RMflFD+sQKqQYKw7Z75timJ3OdM55\nSmFVW5TTPQexgqOtVtq1swUnUxa/bSnvCbXRXewxLS+PFr8sc2+E2IKX7XZ4e2XOexPmpR2+wWD0\nGgGYb/Na1JIaEg2jhgQAoKzh0Dyl3d4eTUd3n4Tb/85tLYSNjcmftm5uirz+unlyfPlyfN8io7UF\n6uBv32YmxI5pfd08ua9ayyGlDXm1Meo871WErseLL2btXV7O6hgsLY3Wycj7UzCvnkSIu62i9y4v\nmyloY+w95dcmCMm7NrbGiLWwkJ+VMW9/Gts6F7aWyLxw62+IzN95AzCKGhIAAMxQ3bNONCk204df\nZ0FE5Fd+xXTM3nhj8qr9r79uUvAfPDAdQjvTgsj4sIZp1akQMcMH8tLZl5ZMMKLTGQ1G1H3N84Yj\nXLtmzrs768gkbUh9n13Pt7MzWo/g3r3sXkipk2Ht7o7XA1hejnc+3fbu7mZ5ByF+MMKfpWVnxwSa\nQsEItx6E1vlFVP0ZOfKCEfZYu10T5FlaMp+j0FCTpqTO3NI0t/5G6qwrADBm1ikaJ20RhmwAwNTk\npTbHhj3Mu7Lp2pOksvs6nfFtuew5bbeL97G8rEuldpdZP9Y+d2hClXMQG2YQ0u+b89Dvj36/6n2X\n+j67nrssLGTH6w6JaLeL2xsTGhYTG26xujr+/jLDPtx7PnXIiG2TPwRDa9Me93vLy9lwJ/+8FLU3\ntC4AHAXzOmSDDAkAwLGRV4Ru3p4ypjwBt6nqN28Wp6xbi4vj36uaveA/SfefgtpzmjKtqH0afu+e\nearqzsrh29zM1v/614vbaQsw+oUY3YwGew56veL9W27micj4E3xXrChh0X23tJQ9fXfvhe1t85T+\nnXfy7xG3kKDtNj98OD68RimTMTEcmmkoP/c5c92eey4tE8Mt4GlfYxkrt26NZtOImHYVPUW318jN\nCPKvQRFb1NV18+Zo2+/eFfn0p7On+1qPZ0DYtvr31DPPlGsPAKDArCMiJ20RMiQA4LfVXaxtXou/\nhaQ8AfeffqfwnypPej5spoItXhd6+m33m7cvu52U47FFB8scd4h9Mt5qZe2KbdctmuhKzSSwhS3L\ntjt0rdzX1OwK//xabgFHrUfvqV5v8swhP/sgJTPHfY+9NkX3aWqGRco9lpp5Ya+5mwnSRIbVUfo9\nBuDomNcMiZk34KQtBCQAION2jmId3eNqMNC62zXDIrrd0c6H7QS76ecLC/Ht+J2XssMjitrpD90I\nSenYFqX1W24HMDazRVW2M+zv322bH5RIERtCUMQ9t34QokzHNLZvP9DizwhSR+fXD8RUOQ9FUgII\nvV5aACk0zCW0tFrZe5oMErjti33uAaAsAhIs5oQTkACA3zaN6RqPEr8jZzs7boaA27EKrVt3bQy/\n45XaySz7lDtVLIBQt4WFye7FujriVTu+sX3790fV+6Woo1+UuTCpou2XOWf+7x3/2rvBMBs49IOG\noW3WFbA4qr8Xy9YlAdAsAhIs5oQTkACAEU11OOdBrAhg6MlsqIPpPw3Wuv4nt36HdZpPvVPEnliX\n4Z73WPaD2wlcXDTfs9kLKVkabjsn6ZBVvZ5ucMs9Rn97Vbbv37exdufd01UzXdz2hvZRZ+fXZhZ1\nOuPDZoqOo+7AoA2SHKUMCX94EID5QkCCxZxwAhIAcOzEOnl+J86vjVBm8bdn9xWrfVCF3ZY7S8Os\nAxJ+jQKrTMc6tSZFXnZIU7pds79ut7l9FvHPXywIUBRwq8Lv6Nd5v6ewGRJFx0PdBzIkgHk3rwEJ\nZtkAAGBCodk9/FkLhkMzy8EkBoNsRgU7i4KdVSA0u0BZdhuPHhXPmjFtdhaST3xCpHX410rL+asl\nb0YVn3veFxbi67mzO/j8WRhc6+vmZ+vrxW2pi22PnSmiLvZYFheze9i/b69dC793d9fcmzG2rYuL\no+23S6s1/rnxZym5ft2EA65fL39sVquV7bPdjq83HJr769Kl8dk2fHn3znGwuSly6lT4/rdiM80A\nQK5ZR0RO2iJkSADAsRIbY+4/VXVTud1ilVWXaWZIuNsK7VupyfeV0o68FPgqdQN6vSwVPmUIQeg6\nhY69zkyK1OPyMxb8oU+hNvkzcYSKnsbuWa3Tn4BPem+77ZvGE/eUzA13eIh7zWeRNVNVnVkbbpbM\nSc4CAY6yec2QmHkDTtpCQAIAjofQNJZuJ9cPSIT+iE+dfjC0NDH9oNbhTvm0+Z3tKp2qUOfZLyKa\n2tF1a3qEAhLTGkaQV1Ax797wizTaOgShdf02F92zRe2b5J6OLa1W+j0Q64S730+5n/3Pr1XnDDbT\nVmddi6anPgVQPwISLOaEE5AAgEZMa0x3UefJ3X+nYzpTKR3fsp20SYoUuh1WpeZvzLfbwa9aUyFU\nYM8/h2WK781ifLz7VDpU0DO1SKrIeC0OP7hl75/FRfM9W9gzTyhAViXg4E8rW3XROuuEt9ujnwn3\n+ylBFzcgUffUs02p+3cgdTKAo42ABIs54QQkAKARdVe9t4o6VnVx2x+bdcNfr2r766yKX2fHfTDI\njj0WkIh1kurMkJiVvMBBbB2R+DSWeUuVWVtCHfvQfRrKJppWQGIwCBcxdb+f8lkpu/5JcJSyQwCM\nm9eABEUtAQDHkl8Mb1K9Xn4BQa1F7t+vZ18iIl/8onm9eVPk4GD0Z8vL2deh47Rt9YswxgrSTVps\n03Xtmsj+frzwYRk7O+bY221TWFBkvBDi+fPmHF28mL2v1RK5etW047nnsu8fHGTFCTsds840CkNO\nyhb0dK+zSFbE0/58OBRZWxt//6NH4e3627Pa7ez+KVOc0S02Glrftu3u3fztPHgwHl6wBVzX1vIL\nT/q2tkSefDL8/VdeSfudsLkp8uKLIs88U+/vkKNsc1Pk3j3z9b178/m5AXBEzToictIWIUMCAI6c\nUGq8+1R3GlMQhp4Ah9L2U95rMwFCmRZutkWRlFoJocyEqqneoffFno67GRShJ/8hZc9BHSnrKdtw\ns17sUIvl5ex99t7rdOotkGrbVrVuR+j4/KyUvOyGou1Wyfyo8vkMDfc56WJTFgM4OuY1Q2LmDThp\nCwEJAJhfsc7iLFL9UzqOsQ6jXy/AdqxCqfGxYptFwwPKdFbrHD4TOi8LC6Ptca9XajHIsvvOk3d9\nYufCDeTkvb/uopG24KXbNr/zmdKhjxXgjN3H7r/tzCAp7LmpEpSw7/WDOP4QhFnUC5l3sXol1JQA\njg4CEizmhBOQAIC55Xd0bEfF78jXLfRkOlSE0HZiUzv4fscq1tH1Mx9i20+ZjjPv+Ko+cfeDQamd\nRbvfvIKOdgnNZOFLvQfc85d6Dcpuu2gpqtsQCti492Fq8cdQu9x7I5QhMQn/3qwaoMg7X26QBpnY\nuTuqRT+Bk4aABIs54QQkAGBuxaYEnHZ1eVvR3w6pcDtbTbSnzPar7Dtv+soifmcz5Wl9mdknQgGf\nmFin2g4lsB3ZvCkmYx3yvJ+723O3GRqCEws2xRZ7vkLDbOyMGynnpswUpZPIu/8mDUrQyc5nA3uh\n4RtkkwDzj4AEiznhBCQAYK75T03LSKmxEGIDEp1OWs2EumtWlGl3lYBE7Ol5ilhQJk9svHtomSRY\nYuW1LTUgEbsG7pP70EwYKdseDOK1HLTOfqZU+L111M0omi2lDu45dI/RXt+84EMoowRhsUBb6P4B\nMD/mNSDBLBsAgIm4Ff+PA/dP7KLZAXw3boy+pvrIR8xMAt/7veMzHKyuxvdTl+vXzfFev1687sWL\n47NaFNneFul2zVJ2xoKtLTPjwsKC+XdoVgmfnTVEKZF+P3wOXW+/bRZ73odDkdOnzZJyX9u22dcq\nYtfAzmwgEp4Jw84aIhI/N1tb4zO1WGfPZu1+4onwe/17cnNT5NSp+KwtoW185jNmxgo7W8o0uOfQ\n/Rzb6xsLSdy/P/5Zd88rRm1tmc+Vzzx3A4BylOa3R6OUUnsisrKysiJ7e3uzbg4ATOzsWdNB7fVM\nx2VebW6aqSg3NkR2d6ezj/V1EyxYW0vr3Fup57Dq9ut0+rTpvHW7ppN3lLTb4x1z/5wPhyIf/3jW\nuZr0vvanRlxeFvnoR829+MwzInfumEBDbJrNbtcEJZaXxzvNw2EWGLp0KX+qzl5P5Nat+D7e9778\ndrhOnTJTqrbbIu+9V7z+Uef/7rCfw8VFkU9/Ou2cHUf+va1UPPAFYPbOnDkjt2/fFhG5rbU+M+v2\nWGRIAAAmcufO6Os88J/gttsiV6+aTtTVq9Pbb5lMA9f2tukwFmUPVN1+nS5dmvxJ96yyavb3R7MI\nlBo/5zs7o096y2Z0+AYDc74GA7Pdr389uxdv3DCBqJ2d+Ps/9Snz/k99KvuePX8XLmRBiqJOcSwY\nYa+lnwWRZ2PDfKZsJkqqo5pN9frr2e+Ozc0sQ+nhw/xrF3JUz0EKP0ABACnIkGgYGRIAjpPNzdEO\n/rz8l2Kf4MYsLIhcuTJ/TzaHQ9PBSX1SfVRNmlVjswasfr9c9kveeXYzJDodk84fe//Nm+bfeU+G\n/WN1O21ra8UZEu769vNlt6lU9r1+P//YFxdFHj0a/V7TmTa23SIirVb+Z3SeLC2JPHhgvlZK5EMf\nyoISRec9tq3YvXWUhLJu5uX/AADjyJAAABw7165lXy8vz64dPvsEN+bRI5GXXmquPalsJ7fsU9ej\nJjUjJMYNRoiY+3B/f/R+zBOqi+D+7NVXTfsuXw6/3w1GiOR3wvxjtbUJOh0TDCiTmWDZfbv7fe21\n+PrD4Xgwot9vPtPGvd5HKbXfvw+uXzfXVETkzTfLbevdd0dfj7KbN8O1JACgDAISAIDKbMe/3y9f\nAHKadneLn776HbQiZQv5VTFpR/2ocAMCsRT2vNR2P/hVdQhBSvtCnnpq9N95qer+tu7fzwoppkgt\nmBkqSGn5AS6t40/1NzfN8dhlfT28XpWhB1tbJjNCJHs9CmwRx3Zb5PnnzfdSPqvr69k5tF+///1m\nO+fONdP2advdNRlLItkrAJTBkI2GMWQDAJpRNJ55bU3kYx9LHyLRRCG/kzJkw3KH/PjDN4qGddRR\npNQWJxQxHf6HD8Pr+Wn2k94LkxYn9e/tdlvklVfi98xwKHL+fPbevOyE0OfGHRqi1Ohwi0mKftqU\n/1bLzMJxXO75vAKiIsdvWEMTBYMBTI4hGwAATJGbwZASjLh+vdwQibqfwoeclCEbljvEwn/SXPQE\nenfXBANsB6jbzZ7qpz6pdadPzcuYsfUD7Osk90K7XX162BgbjIhlLdipU3s9MxylLLcDrfVo9lHV\nbJ6lpazTfnAg8sIL1bYzbzY3w8EIW0w1Zdrao+bNN809UXb4CgCIEJAAABwTthL+66/H1+n3Tafs\nYx8z/y4zRMLvAE/Ds8+aDuuzz05vH/PEHfLjPx0vGjbhc+tK+DUmYtzOYdGQCGs4HL8XhkMzHaoN\niCwtxd/vZiesroa37wcV3CEUIlkdCpHRc5cX0Eo9n6EOcyzAp1S1rIbNzSy4Y/lZG00MkZqGUB0T\nW69j1jPkTMtXvmJeb948etcLwOwRkAAAHAuPPz76GnL1qvmj+fx50+Er2+mdttCTxuM8TWCZII+b\nARGqa+DWlUgtsGo7iVrHh2v4Qp39nZ3RGip+Z9vl1k64dcsExNyAQ0qWzOXL2VSizz2X3R911CCx\n58StXXFwMBoEEcmGcVTpgKYUHy1bqLRp7XZ2zdzP5saG+V6nk0312vQwhuHQ3Ge2fadPT/f3hxsA\nnOa0ygCOJwISAIBjwXbSYjMj+PI6fG4xuiaFOpQnbRhHyObmaKfnxo3xLIS7d7PgghscqDugE+rs\nb2+PDhPxO+8uv9iqn97v3wOhdruBNPf+qDPAduWKaceVK+bfthinXWxgpUrAIDTUxS9y2cQQqUm4\nGR0vvph9vbtrfnb//uwCnTs7o8Ns7t6d7u+PUKYPAKQiIAEAx9hRTXuuwnbGYvwOz7PPxmd3qHuM\nf6pQh/KozLxhgzhKZVMiTmpx0Wwv9NQ1LwvBVUdAxw0w2Owa19aWyNtvZ531ohk03Kk//fvSvQfa\n7awYpUh4isVp3R9FwY1JAgbPPTf6737fBGrc4FETQ6Qm4V63/f1yM0z4QbKmgmbT4k6BCwClaa1Z\nGlxEZE9E9MrKigaAaRkMtO71tFbKdJHa7Vm3qDndrvsc1yy9nvmZ/z33Z5b9vojWa2vNtv2o6vfH\nz3kd/G26S6eTtg37WRgM6muHf89MYjDItttq5e93Ev2++T3Q76e1adJzlqfTCR9X7DM5b+z5WV6u\ndn3844wdd+g6rK1l+8u7lvZ3f9F6dWm3T97/NcBRs7KyokVEi8ienoN+sV3IkACAY+jCBfPUSh+m\n7e7vF888cZzZp4Nu98Gm2b/zzuiTSfvEeTAw4+mPcw2Huvhp++12PdsNFZq04/KLshCsOoYx+IUe\n63zavLWV3ZPucA7/fssbBlJkODRZJvv75nV9Pf+envYwoa99Lfz9o5INZM/Pk09m9UpS65aIjB9n\n7LhD18HN2sobLnNwkN1XTWSZzPsQGwDzS2n71yoaoZTaE5GVlZUV2dvbm3VzABxTseDDSfiVPxya\nP+C3t/M7oe456vXiwz3OnjWdgrx1TrrNTdM5euYZkTt3is/9UeTeL0Wfo17P1IZYXU1PZ19aMsNQ\nOh0TbGm1xqfbrMrew6F2hu7p1M+Qb3MzG15jjyO23rVrpvM6r0My8rjn58d/3AQJ7FTC09qPvQ7r\n61lQot8/mucPwGycOXNGbt++LSJyW2t9ZtbtsQhINIyABIBpa7fHp9Cz+JWfcTuYg4HIJz9pCicu\nL48XRazSOcPx4t8vqcGulM/c4qLIo0ej73G3Eep4DocmE+rdd0XOncvvmA6Ho7UoREx20KVL9d7T\np06NZnnU9ftmnj+DRdcJ+P/Zu//oyNK7vvOfp6qlmZY8tlWdgYx7LE1YzhIcCGewRLJtYmDJCd7D\nGmcTomlVCPnhLEc9TIYkvQuym6xtlsYKbHPwZGZUB45jw56oZ8QGYp8layAnG4a4N4l67QPekGRz\nWKz2/Eg8tNr2oJ6ZVkvP/vHUw33q6lbVrar7q6rer3N0qiRV3XtVqh/3+T7f5/sFqqKqAQmWbADA\nBDGmezBilJTvqstiWYXv4hB2c5Cq1xoU5bt4MX2x2DTPyTAYIXUOcufmkge5m5suo+L4WHr2yFoC\nfQAAIABJREFU2c62qPElM+vrJwti3nffaM/ppNfcww93HndW/NKFxx6r9tKpIlte+naxWRWQBYCy\nEJAAgAnRrUWlMYOtuR9Hw6x5D+tJhPcbZC14kjxqTvhuE7Oz2W2zTONel+PVV10mQJqWl2FmQtIg\nstdj0Ot1G9YbuPfezkBaUlAyHtQYtU5D0mvu5Zfd5dJStu83GxsuyHJ0VK32t/HWs1JxrYJ9u9h4\n21gAGDcEJABgQiS1qFxa6p4xMUlGLYYXFrIMl2sMY5jgSKvlagYYk9w+0M+gx2fSx1XeRRO7abWi\n4M6gA8etreg5cv78YAX8/P82aRDZ7TEIsyR85kP4sw9/OLoeb4EabyXq+eKcy8ujZ/wkvebybEH6\n5JPVK3iZ1Hq2qFbBi4udl6FxD/gBmC7UkCgYNSQA5CUsdub1W+teVVVeM97PMMfuCxp68Y/msMaA\nMS6LI+v1/0Xq9Rjl+b+PF3fc2sp+X2Fhx27COiXx+hFeWHAyqSZFUuHaIk7p/P/HP469lolNuvjr\nVsqnuOWgKMQLIElVa0gQkCgYAQkAeQoHKTMz0p075R3LKIo6oQ4fr1qtsyBfkeKFSJM+muMD0Ekd\nbOT5v2+1pMcfdwGA5eXOAF5Wwbt4d4xu4oUrQz4TwB9PlQISSYPwaT+VHLSIad6KDOgO01EGQDmq\nGpBgyQYATJCwLsK4BiOk9KnfWaYmlzXL22q5GgBet3T78OeNRrVS17OUV9q/5AZnd+6410d8Fjur\n5SNpB6RJBTGNccUg4wPJ8HUtJT/fuz1vehnm9fPaa53fdwuqTJP4/6dsRRbipZYFgFERkAAAVE7a\nE+rHH3ezco8/Pvg+0nRIKILvlrC05AY03bI0nnoqqmFw8+b4LtfoZ5TB1KAD7JmZ6HoWAZCk+h/d\nJC3rsNY9Fy5c6P38TAqePPVU+n2H2xm0lscjj0TXFxbKCeRRI6GaCE4BGAYBCQDA2Bql2GOaDglF\nSJsRQPvR/sIB9spK/+KVPlvC2t6Pa7OZrs1nvGXsKOIBi1ZLOnPGfZ07d7LdY7/nRVKHj2GyUba3\no8fsXe9K3/40S2UVRcVJBCEAjIqABACMkTSDrHFQr0ddA0aZ5Qy7Bgwq3iEhqVp9EdIGGpIGlP75\n4H9njHtsx92wbU7DAbavD5FF14OdnXRtPodtGbuwcDLAEPf4464Q5v6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9L8LB1he+IF28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6PuCwAgHwQkAEyF69c7L9MYdDA/TNAjr4CBP5aPfvRksc0stFouoDA/P1hgwdvfd19pAzEXLkQF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\nqsW+plrsa5quXbt2sWvXrnHbDQ0NtXZ3Z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iRJkqTqTEhIkiRJkqTqTEhIkiRJkqTq/j9B\nNPfaTVD65AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115bc46d0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 356,
       "width": 530
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(listing.lng,listing.lat,'.',markersize=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rent</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Year built</th>\n",
       "      <th>Living space</th>\n",
       "      <th>Floor</th>\n",
       "      <th>lng</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1090.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.</td>\n",
       "      <td>8.051152</td>\n",
       "      <td>47.382578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1923.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.</td>\n",
       "      <td>6.150230</td>\n",
       "      <td>46.213396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2425.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1.</td>\n",
       "      <td>6.148701</td>\n",
       "      <td>46.210094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2480.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>5.</td>\n",
       "      <td>6.148480</td>\n",
       "      <td>46.210668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1430.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1945.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.</td>\n",
       "      <td>6.147548</td>\n",
       "      <td>46.210081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rent     ZIP  Rooms  Year built  Living space Floor       lng        lat\n",
       "0  1090.0  5000.0    2.0      1983.0          42.0    1.  8.051152  47.382578\n",
       "1  1923.0  1200.0    4.0      1930.0          72.0    1.  6.150230  46.213396\n",
       "2  2425.0  1200.0    4.0      1983.0          76.0    1.  6.148701  46.210094\n",
       "3  2480.0  1200.0    4.0      1910.0          68.0    5.  6.148480  46.210668\n",
       "4  1430.0  1200.0    2.0      1945.0          32.0    3.  6.147548  46.210081"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rent</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Year built</th>\n",
       "      <th>Last renovation</th>\n",
       "      <th>Living space</th>\n",
       "      <th>lng</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>130582.000000</td>\n",
       "      <td>132403.000000</td>\n",
       "      <td>130944.000000</td>\n",
       "      <td>59741.000000</td>\n",
       "      <td>23185.000000</td>\n",
       "      <td>112647.000000</td>\n",
       "      <td>132403.000000</td>\n",
       "      <td>132403.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1916.145319</td>\n",
       "      <td>5528.768381</td>\n",
       "      <td>3.489651</td>\n",
       "      <td>1983.713781</td>\n",
       "      <td>2011.122967</td>\n",
       "      <td>86.835735</td>\n",
       "      <td>7.993818</td>\n",
       "      <td>47.091962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>956.898994</td>\n",
       "      <td>2856.183542</td>\n",
       "      <td>1.134819</td>\n",
       "      <td>45.390784</td>\n",
       "      <td>15.048527</td>\n",
       "      <td>43.262236</td>\n",
       "      <td>0.907196</td>\n",
       "      <td>0.459413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1016.000000</td>\n",
       "      <td>1014.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>5.972127</td>\n",
       "      <td>45.821860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1%</th>\n",
       "      <td>457.905000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1960.368000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>6.040631</td>\n",
       "      <td>45.837649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1%</th>\n",
       "      <td>700.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1842.000000</td>\n",
       "      <td>1986.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>6.122570</td>\n",
       "      <td>46.000216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>1320.000000</td>\n",
       "      <td>1814.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1963.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>7.061325</td>\n",
       "      <td>46.607187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1730.000000</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>8.294367</td>\n",
       "      <td>47.285629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>5500.000000</td>\n",
       "      <td>9500.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>9.566651</td>\n",
       "      <td>47.696539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99.5%</th>\n",
       "      <td>6570.950000</td>\n",
       "      <td>9545.000000</td>\n",
       "      <td>6.642500</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>9.680681</td>\n",
       "      <td>47.706840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99.9%</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9630.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>280.708000</td>\n",
       "      <td>9.881999</td>\n",
       "      <td>47.726667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25680.000000</td>\n",
       "      <td>9658.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>2089.000000</td>\n",
       "      <td>8090.000000</td>\n",
       "      <td>10.342745</td>\n",
       "      <td>47.783855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Rent            ZIP          Rooms    Year built  \\\n",
       "count  130582.000000  132403.000000  130944.000000  59741.000000   \n",
       "mean     1916.145319    5528.768381       3.489651   1983.713781   \n",
       "std       956.898994    2856.183542       1.134819     45.390784   \n",
       "min       200.000000    1000.000000       1.000000   1016.000000   \n",
       "0.1%      457.905000    1000.000000       1.000000   1500.000000   \n",
       "1%        700.000000    1000.000000       1.000000   1842.000000   \n",
       "20%      1320.000000    1814.000000       2.500000   1963.000000   \n",
       "50%      1730.000000    6000.000000       3.500000   1994.000000   \n",
       "99%      5500.000000    9500.000000       6.500000   2017.000000   \n",
       "99.5%    6570.950000    9545.000000       6.642500   2017.000000   \n",
       "99.9%   10000.000000    9630.000000       8.000000   2017.000000   \n",
       "max     25680.000000    9658.000000      13.000000   2019.000000   \n",
       "\n",
       "       Last renovation   Living space            lng            lat  \n",
       "count     23185.000000  112647.000000  132403.000000  132403.000000  \n",
       "mean       2011.122967      86.835735       7.993818      47.091962  \n",
       "std          15.048527      43.262236       0.907196       0.459413  \n",
       "min        1014.000000      11.000000       5.972127      45.821860  \n",
       "0.1%       1960.368000      15.000000       6.040631      45.837649  \n",
       "1%         1986.000000      25.000000       6.122570      46.000216  \n",
       "20%        2008.000000      60.000000       7.061325      46.607187  \n",
       "50%        2014.000000      83.000000       8.294367      47.285629  \n",
       "99%        2017.000000     190.000000       9.566651      47.696539  \n",
       "99.5%      2017.000000     216.000000       9.680681      47.706840  \n",
       "99.9%      2017.000000     280.708000       9.881999      47.726667  \n",
       "max        2089.000000    8090.000000      10.342745      47.783855  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Supply_stat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def hist1d_Supply_query(Area,Min_Rooms,Min_Size,Max_Rent):\n",
    "    Supply_Threshold=1\n",
    "    Max_Size=500\n",
    "    Max_Rooms=11\n",
    "    Min_Rent=100\n",
    "    q = Area\n",
    "\n",
    "    fig = plt.figure(figsize=(12,3))\n",
    "    font = {'size'   : 8}\n",
    "    plt.rc('font', **font)\n",
    "    ind_q_Supply = (listing['ZIP']==q)\n",
    "    q_data_Supply = listing.ix[ind_q_Supply]\n",
    "    \n",
    "    \n",
    "    import itertools\n",
    "    import numpy as np    \n",
    "    \n",
    "    if Min_Rooms >= Max_Rooms:\n",
    "            Max_Rooms = Min_Rooms\n",
    "    if Min_Size>=Max_Size:\n",
    "            Max_Size= Min_Size\n",
    "    if Min_Rent>=Max_Rent:\n",
    "            Max_Rent= Min_Rent\n",
    "            \n",
    "    def check_search(df):\n",
    "        \n",
    "        amn = df['Rooms']>=Min_Rooms\n",
    "        amx = df['Rooms']<=Max_Rooms\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        bmn = df['Living space']>=Min_Size\n",
    "        bmx = df['Living space']<=Max_Size\n",
    "        \n",
    "        \n",
    "        cmn = df['Rent']>=Min_Rent\n",
    "        cmx = df['Rent']<=Max_Rent\n",
    "        \n",
    "        return amn&bmn&cmn&amx&bmx&cmx\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "        \n",
    "    if q_data_Supply.shape[0]>=1:\n",
    "    \n",
    "        rooms = []\n",
    "        sizes =[]\n",
    "        prices = []\n",
    "        print \"**************************************************************\"\n",
    "        print 'Number of unique Supply Ads: {}.'.format(q_data_Supply.shape[0])\n",
    "        \n",
    "            \n",
    "        ax = plt.subplot(1,3,1)\n",
    "        plt.title('Supply: Room')\n",
    "        \n",
    "        rooms = q_data_Supply['Rooms'].dropna().values[:]\n",
    "        room_bin = range(int(Supply_stat['Rooms'].ix['1%']),int(Supply_stat['Rooms'].ix['99.5%']+1))\n",
    "        a = plt.hist(rooms,bins=room_bin,alpha=1,color='white',linewidth=.5,edgecolor='black',rwidth=1,normed=False)\n",
    "        ax.yaxis.grid(True)\n",
    "        \n",
    "        ax = plt.subplot(1,3,2)\n",
    "        plt.title('Supply: Size')\n",
    "    \n",
    "        mn = int(Supply_stat['Living space'].ix['1%'])\n",
    "        mx = int(Supply_stat['Living space'].ix['99.5%'])\n",
    "        R = mx-mn\n",
    "            # stp = int(R/30)\n",
    "        stp = 20\n",
    "        size_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        \n",
    "        sizes = q_data_Supply['Living space'].dropna().values[:]\n",
    "        \n",
    "        a = plt.hist(sizes,bins=size_bin,alpha=1,color='white',linewidth=.5,edgecolor='black',normed=False)\n",
    "        ax.yaxis.grid(True)\n",
    "    \n",
    "        ax = plt.subplot(1,3,3)\n",
    "        plt.title('Supply: Price')\n",
    "        \n",
    "        mn = int(Supply_stat['Rent'].ix['1%'])\n",
    "        mx = int(Supply_stat['Rent'].ix['99.5%'])\n",
    "        R = mx-mn\n",
    "        stp = 200\n",
    "        price_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        prices = q_data_Supply['Rent'].dropna().values[:]\n",
    "        a = plt.hist(prices,bins=price_bin,alpha=1,color='white',linewidth=.5,edgecolor='black',normed=False)\n",
    "    \n",
    "        ax.yaxis.grid(True)\n",
    "        plt.tight_layout()\n",
    "        \n",
    "        mn = int(Supply_stat['Rent'].ix['1%'])\n",
    "        mx = int(Supply_stat['Rent'].ix['99.5%'])\n",
    "        plt.xlim(mn,mx)\n",
    "        plt.title('Relative Supply Distributions: Price')\n",
    "        \n",
    "        \n",
    "    else:\n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Not enough Supply this area with the zip code {}.'.format(Area)\n",
    "        return\n",
    "    \n",
    "    \n",
    "    q_data_Supply_bounded = q_data_Supply.ix[check_search(q_data_Supply)]\n",
    "    \n",
    "    if q_data_Supply_bounded.shape[0]>=1:\n",
    "    \n",
    "        rooms = []\n",
    "        sizes =[]\n",
    "        prices = []\n",
    "        print \"**************************************************************\"\n",
    "        print 'Number of unique Supply Ads based on your query: {}.'.format(q_data_Supply_bounded.shape[0])\n",
    "        \n",
    "            \n",
    "        ax = plt.subplot(1,3,1)\n",
    "        plt.title('No. of rooms')\n",
    "        \n",
    "        rooms = q_data_Supply_bounded['Rooms'].dropna().values[:]\n",
    "        room_bin = range(int(Supply_stat['Rooms'].ix['1%']),int(Supply_stat['Rooms'].ix['99.5%']+1))\n",
    "        a = plt.hist(rooms,bins=room_bin,alpha=1,color='red',linewidth=.5,edgecolor='black',rwidth=1,normed=False)\n",
    "        ax.yaxis.grid(True)\n",
    "        \n",
    "        ax = plt.subplot(1,3,2)\n",
    "        plt.title('Living space (m^2)')\n",
    "    \n",
    "        mn = int(Supply_stat['Living space'].ix['1%'])\n",
    "        mx = int(Supply_stat['Living space'].ix['99.5%'])\n",
    "        R = mx-mn\n",
    "            # stp = int(R/30)\n",
    "        stp = 20\n",
    "        size_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        \n",
    "        sizes = q_data_Supply_bounded['Living space'].dropna().values[:]\n",
    "        \n",
    "        a = plt.hist(sizes,bins=size_bin,alpha=1,color='red',linewidth=.5,edgecolor='black',normed=False)\n",
    "        ax.yaxis.grid(True)\n",
    "    \n",
    "        ax = plt.subplot(1,3,3)\n",
    "        plt.title('Monthly rent')\n",
    "        \n",
    "        mn = int(Supply_stat['Rent'].ix['1%'])\n",
    "        mx = int(Supply_stat['Rent'].ix['99.5%'])\n",
    "        R = mx-mn\n",
    "        stp = 200\n",
    "        price_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        prices = q_data_Supply_bounded['Rent'].dropna().values[:]\n",
    "        a = plt.hist(prices,bins=price_bin,alpha=1,color='red',linewidth=.5,edgecolor='black',normed=False)\n",
    "    \n",
    "        ax.yaxis.grid(True)\n",
    "        plt.tight_layout()\n",
    "\n",
    "        \n",
    "    else:\n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Based on your query there are not enough Supply for this area with the zip code {}.'.format(Area)\n",
    "        return\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**************************************************************\n",
      "Number of unique Supply Ads: 148.\n",
      "**************************************************************\n",
      "Number of unique Supply Ads based on your query: 41.\n"
     ]
    },
    {
     "data": {
      "image/png": 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B/lDFt7H6+cvAszPzFZk5VHFIkiSNZTnIpVXd+o1nrgEubSp7G3D5IOIa7LqO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\nJEmSJEmSJEmSaphgJUmSJEm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f872c10>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 292,
       "width": 1196
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(hist1d_Supply_query,Area=(8001,8008,1),Min_Rooms=(1,6,1),Min_Size=(10,100,10),Max_Rent=(1000,6000,100));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def spatial_query_supply(Min_Rooms,Min_Size,Max_Rent,whattoplot='percentofcoverage'):\n",
    "    \n",
    "    Supply_Threshold=1\n",
    "    Max_Size=500\n",
    "    Max_Rooms=11\n",
    "    Min_Rent=100\n",
    "\n",
    "    \n",
    "    cmapname=\"RdYlBu_r\"\n",
    "#     cmapname=\"B\"\n",
    "    import itertools\n",
    "    import numpy as np    \n",
    "    \n",
    "    if Min_Rooms >= Max_Rooms:\n",
    "            Max_Rooms = Min_Rooms\n",
    "    if Min_Size>=Max_Size:\n",
    "            Max_Size= Min_Size\n",
    "    if Min_Rent>=Max_Rent:\n",
    "            Max_Rent= Min_Rent\n",
    "            \n",
    "    def check_search(df):\n",
    "        \n",
    "        amn = df['Rooms'].values[:]>=Min_Rooms\n",
    "        amx = df['Rooms'].values[:]<=Max_Rooms\n",
    "        \n",
    "        \n",
    "        \n",
    "        bmn = df['Living space'].values[:]>=Min_Size\n",
    "        bmx = df['Living space'].values[:]<=Max_Size\n",
    "        \n",
    "        \n",
    "        cmn = df['Rent'].values[:]>=Min_Rent\n",
    "        cmx = df['Rent'].values[:]<=Max_Rent\n",
    "        \n",
    "        return (amn*bmn*cmn*amx*bmx*cmx).sum()\n",
    "    \n",
    "   \n",
    "    Complete_data_zip = listing.copy()\n",
    "    Complete_data_zip.index = Complete_data_zip['ZIP']\n",
    "    geo_info = []\n",
    "    total_demand = []\n",
    "    percentofchange = []\n",
    "    \n",
    "    \n",
    "    long_lat_zip_all = Complete_data_zip.groupby(by='ZIP')['ZIP','lng','lat'].first()\n",
    "    \n",
    "    zip_GB = Complete_data_zip.groupby(by='ZIP')\n",
    "    long_lat_zip_specific = zip_GB['ZIP','lng','lat'].first()\n",
    "    \n",
    "    ind_zip = zip_GB.size()>Supply_Threshold\n",
    "\n",
    "    long_lat_zip_sel = long_lat_zip_specific.ix[ind_zip]\n",
    "    \n",
    "    total_specific_supply=zip_GB.size()[ind_zip]\n",
    "    total_supply = total_specific_supply.values[:]\n",
    "    \n",
    "    \n",
    "    total_interest_in_property1 = Complete_data_zip.ix[ind_zip].groupby(by='ZIP').apply(check_search)\n",
    "    \n",
    "    total_cases = total_interest_in_property1.values[:]\n",
    "    percentofcoverage = 100*total_cases/(total_specific_supply.values[:]).astype(float)\n",
    "    \n",
    "    \n",
    "    \n",
    "    #To Plot\n",
    "    fig = plt.figure(figsize=(12,8))\n",
    "    \n",
    "    if whattoplot=='total_cases':\n",
    "        \n",
    "    \n",
    "        ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "        md = np.median(total_cases)\n",
    "        sd = np.std(total_cases)\n",
    "        mn = md-2*sd\n",
    "        mx= md+2.5*sd\n",
    "        mn = np.min(total_cases)\n",
    "    \n",
    "        sc = plt.scatter(long_lat_zip_sel.lng,long_lat_zip_sel.lat,c=total_cases,s=20,vmin=mn,vmax=mx,marker='o',edgecolor='None', cmap=cmapname ,alpha=1)\n",
    "    \n",
    "\n",
    "    \n",
    "        ticklabels = np.round(np.linspace(mn,mx,5),decimals=3).astype(int).astype(str)\n",
    "        ticklabels[-1]=\">\"+ticklabels[-1]\n",
    "    \n",
    "        cbar = plt.colorbar(sc,ticks=np.round(np.linspace(mn,mx,5),decimals=3).astype(int),shrink=0.4)\n",
    "        cbar.ax.set_yticklabels(ticklabels)\n",
    "    \n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        plt.title(\"Total number of available cases\")\n",
    "        plt.axis('off')\n",
    "    \n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    if whattoplot=='percentofcoverage':\n",
    "        ax = fig.add_subplot(1,1,1)\n",
    "        md = np.median(percentofcoverage)\n",
    "        sd = np.std(percentofcoverage)\n",
    "        \n",
    "        mn = np.min(percentofcoverage)\n",
    "        mx = np.max(percentofcoverage)\n",
    "    \n",
    "        sc = plt.scatter(long_lat_zip_sel.lng,long_lat_zip_sel.lat,c=percentofcoverage,s=20,vmin=mn,vmax=mx,marker='o',edgecolor='None', cmap=cmapname ,alpha=1)\n",
    "    \n",
    "\n",
    "    \n",
    "        ticklabels = np.round(np.linspace(mn,mx,5),decimals=3).astype(int).astype(str)\n",
    "        ticklabels[-1]=ticklabels[-1]\n",
    "    \n",
    "        cbar = plt.colorbar(sc,ticks=np.round(np.linspace(mn,mx,5),decimals=3).astype(int),shrink=0.4)\n",
    "        cbar.ax.set_yticklabels(ticklabels)\n",
    "    \n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        plt.title(\"percent of coverage\")\n",
    "        plt.axis('off')\n",
    "        \n",
    "    \n",
    "    plt.tight_layout()\n",
    "    font = {'size'   : 12}\n",
    "    plt.rc('font', **font)\n",
    "    plt.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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KwYwLMfPb1FY64RhNh94LdAWeAlbt+WDhfrtzkxslYBgh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HKCLJxbrBqpjniUUtRUuOnSJ5aE9jasqckkXhZ+w7ylZ2f0vZV4t++GmBhsF41B69QxUGg2\nkN2w9MHF9a9/1AdI9zv/ZOXk7BeAOxNm5DWEu2/FtVkOIBnY1+65ZflBwt7GWEQO3N+grdeCnXAq\nxzHA9RYL7dsn47FYWrVLaSWmUzTpuUf5H/4C+Eeg+PqpYwfis8DtbfSy8+OtNOxpm1ehe3UwdnFn\nuY7NGOlYl78xxPMxlSzibfTibfSiNFVojbVNvPXn065ZV9Xl0kCxO+O78Eb/8znj5485enfYtTgL\n0AGjjYoZ/0fwRIlWcXmFZe9kpyUHbE3TLTZpRXFN+ZcY1UlCW7MStXt/ZxTdq4dacHcDzwc47iKC\ndhZEtbRn+RqjLcls1emK+uAXGF68/mQ3RlLNf6+cvrKXUqqP0tTZSqkc4GHg73W17pVRdouuaUaT\nmHbxUXRJicfiVz0kId5O545xuo5+SJIE3HMn3AHcgfEd9fWQe+6E/wJTrOPnNACnRDJu3PBem7We\nXfvEHNMLZTO+gg1bd1Oz4seaujWbYnGHL5RRuWZHmT01YQ4wWxszc10k9xeBpS5eW1iUkzkUOB+4\nFqMikg58BzwHvJu6eG3EVUx+C7LTkh/OKyzbCNxJ2xYSK4D7s9OSTbdP+p2LtEVYpPFCCCGEEEIc\nUkrXw1bUFEIIIYQQQhzhnMqRAKQA9cB2M6XzxYGpvmnUTcCTEVyyOm7a0hMiuUfppSctx2+neSC6\nV0fXQbMYC8XxFx5H9OA0U/fwllfhXvEdtssuRuvaJWhcw8r11DvfDbCxvpUPgXMTZuQ1+p+ouDbL\nCvwJYze67w7mAuBZ4Nl2zy1rtejsVI5XgFwzTwPol6u7NvtdbwdeBC5rPqZpkBAP0dHGrv2W41Ea\nnY/tQO9z0rDF2QCjjUbj3rqvojrFjrDkvlwd6Mb1U8f+E/h788+lq4qp/tnUBux8YKBjXX7AV7T0\n0pO6YLQYsJkZDHj4zJ/H/wDMDheoeT1c/r2TjrVh16pScnVXabigvMIyBWwE2mSZBPFWdlrymGAn\ni2vKU4FPgd5BR9j8IzxwB6q2puWQsqhQrTj+lbLg8zYJL97lt5yP0SLDjL0c1e80FR1TpjpdURws\nSC9+th1wOZADxAGlwDzgLdXtOvfVz3+Ri1FBwx7oeotF0T45mu5d26FUyGoZjcDZN5zQ62OT8w/L\nPXfCE8Cfw4QtBkbrlQ3F+LWeCUb164Elo0/Q8w0/l1A2fSl6bdhcK4DMrm+sPmRVNcTvU15h2TFA\nBsafct9kpyV//ytP6Tclf2j6QIwEG7Ouz9hQ8L/W2kUIIYQQQvyGRVh2UgghhBBCCHEkcSrHCU7l\ncAK7gZ+AQmCjUzludSqHlDo+NFIijO8cSXBTVYnWiRKahqVLByypKai4/W0ilKZaEiWwWbAP6Gb6\nPlpSPKpf75CJEt6SvdTPWxwuUQLgDOCv/gcrrs2KBt7F2InvX+o9HXgC+LDi2qw4v3N3AWYyD/KA\nLQGOz8UnUQLA64V9FVBSCnUNik49o+nWL5a+QxJI6WKjYWsF1a69VK7bxe53N1G+vPC4koWuW0Pc\nu0PzA3etm+qtpivVZwBnBTvZ6dVVO4FFJsfyYiSc3GwqWLOwPmVIuLC1ZhIlmmRiPlEC4Ny8wjL/\n97pFt9ikIuAEPn7PTa1fjkpFObyzAP59a6tEiRB04P5AiRJN3gXMLoo+oaVN/j5MosS1wHbgKeBs\n4DSMCjSvAT/PeGfJHcAcgiRKAHg8OugqXKIEGIk0zz/zxZawgWa4504YRfhECTCSQK7HaMETlkrr\nEjJRAiCqdwpJf8oyMxy0bmMixEGRnZa8Pjst2ZmdljxPEiUil7Gh4HvgE5PhlRi/B4UQQgghhDhi\nSBsOIYQQQgghfqOcynEz8Djgv2B2FPAocJVTOU7P1V1Fh31y/9sCVho4iPGjmx+o+BhizjiB6Oxh\nWDokAqC7PTSsK6D2/VU0FmxpuUiLjUJZg3aZCMh6dHopIXaIN3y61sgyMOe6ysnZDybMyHP7HHsG\nODPMdacBL9ZPHTsFSK8ua7BpVnWP160nmrjnKGAmcGXzAadyZAMXBbtA16H3wDiSOu7/63DDrhoa\ndgVcgP/7jguGv9T1jdWBFocrmx9U/7zPSFswbxLwfojzU4CTgHBlQm4/8+fx7YDwvVea/NAxnT9s\nXY4WPANmutmxtNrqzt6YoLkPgVgwWssE/U50i00q2TFr2nrd+cIwBgyBmFioqoDvv0a52xQuafYU\ncDLGZ7kKeA94NmXB55uCzn3k417v8lvOxUi4CV7JwljYuz/Uk9KLn51MiNdN10ndui/mQdr+rm6j\ndE8NKZ3iiIoK+13ug5F0E+pzZNaNEcRej9FWyL91QRuao5epAe39uxLVrwsNG3eGC43sF1wQTuU4\nEXAAHmB9ru6KZFe8EKKta4CVQMB+Xk28wJUZGwqqDs+UhBBCCCGEMEeSJYQQQgghhPgNcirHRcB/\nwoSlA+87lePYXN0VdJVRRGwJ8EAE8ZEuZrYD0FLak3TXlVg6t291Ulkt2I8fSFRmBtXzPqD23RWA\nkUQRKZWSEnKLvvvLbyMZrhvwB+ADgIprs7pjtCQIPYdoC5b20Rfrun6hUkpzfbwbrzuiLjJXOJXj\n/VzdtaDp5+tDBcclaK0SJcLQgOsw+tr7e4emahruqoi/Xj1Dnez06qrtpZeedArgxEgC8FcO3NXp\n1VUzGD79nEhu3GiJoiE6hui6gG/9p8ArZsbZccHw6O4Dhv2j8Lr7Irk9+CSZhPCcqqsdxrovzIy3\noctrX9wU6SQAtJGPb/Euv2U4cCtGAovvQt964GngRW3k40E/kHrxsx0wktaC+r40gd01dtNVIHbv\nraFblwQzoefxC5Ml3HMnxAHnRnCJg1jr19S49+BTXcWfSmmPio02PWjMqQ4zyRIu0wMG4FSOCcqi\n/goM8D2+oN3gde7K+jtzddeHv2R8IX4vnMqRCFwMpGK0f/vkmCHqFIyqToESqQqBGzM2FLx9+GYp\nhBBCCCGEOdKGQwghhBBCiN+me0zGDQLGHsqJ/N7ETVu6HvjcZLgXiLQ3915sVhL/OrFNooQvpWnE\n//FsooYb3S306gbcu0y3ggAoIzquLthJ3etFrzLV7sCXb0+PSYRJ0NcSorClJWCJj0IppTXUuNm6\ntjzSewLc4PP49FCBHbrYIh074EKyfcqiVcAGAGWJuBtC2OyKTq+u2tbp1VWnYCw8PYXRmsMJXA10\n7/TqqubPVX2kN8+6dxBR8W3emsXAubm6q8HkMFPjXOszLRV7I7n1J9lpyRUm4pzAZpNjhqz6EI42\n8vFSbeTjd2As+p2EkfAzUBv5+LHayMdnhUqUaDIJCJkVULAnPqI5VVaZfQs4GK2WkomwYoOyaHEY\nFXCCJ74kmUr2aGFLDZp30ayg6xurzZb6b8OpHPcDL+sefYD/OXdl/bFo6oO3ep9kphWJEL9bTuWI\ndSrHM0Ax8ALwD4zk0ZXrv9bnr/9avxsYjtFiywk8D1wA9JZECSGEEEIIcaSSyhJCCCGEEEL8xjiV\n4yRgcASXXAfMP0TT+b26GVgOxISJeyRu2lKzi77NXrefOPgha9eOplbg48aMpGG1UUW+bs0W4s8d\nYvY+LynN0gOjHH0bStPAaoHIKlbU+jxusyjZany7BWuXWJTa/zS3rdsXaVWJZqc5laNbru4qBmJD\nBVqsESc2hFrpvg5YZu8YE+5z4G+V2cBOr676CvjK/7hTOaIAG8f/eS1QQ5jn3ax/l1p6DkmgyzPD\n3FsX/LS3KL9qVcnm2vtzdddas3PaccHwZOBK5XGT9PkH7DnzMrOXmmrx0fWN1bU7Lhh+NvAR0CNQ\njK7r7NtVX11RUt+pq8mb6+XzYjGST2KBHSrpsq+bz2kjH6/HfBKUr/PDBTR6ItunontNfwf2RDRw\nYAdSEr/KNnn+qsYZ404B7sNIKPJ9kjtUu/gCIMv0iFrY7+UjRTmZltTFayMuoeNUjguAu0IGeXVq\nisqeWJp13rpRy975LNJ7CPG/zqkcMcCHBK52BDAEeG/91/plubrrL4dvZkIIIYQQQvwyUllCCCGE\nEEKI356hhzhehBE3bemXwJlASZAQD8aO99ALdAF0enXVpthzTzFdIsLaqyvWfmkA1G3YhrvYVGWG\nIuBR4LlQQRZHb7PTAGgAfHd+h1zxtSRHt0qUAKgp+0XdYjo1/T9kLf/Gem+k45YGO2Gfsmg1cHps\nWsJ2S4zpvQg6kVcbAYzFKqdyXO1Ujq8wKkpU3frlkxvP+en9nxLrzX1kLhpmVIKwp8Rae4/snHLy\npd3GXHDXUY/X3p2TGsFUcmlKFOrw8ULsxT+bueZd4HWzN+j6xuofq8sbRlSU1u9wN+5/z3Rdp2Zf\nIyWbq6koqY8Dpq8+Kj1Qm5T915TP66KXz3sC2I7xGX0f2KCXz/tWL593rV4+L+IMGh9hSygkRUf2\nuY6NtprIHQBgQfiQ0Kzj55QTQfIOsAtYA2CbPP8b2+T5o4E+GC13rgHOAXpo3Tq9EMk8PCWBC47o\nXh1Pg2edp94zFXAX5WTWFuVkvl6Uk/kHs2Pb2kWbqsSku71Y7NZnzY4rxO/MfQRPlGhmBeY4laPz\nYZiPEEIIIYQQB4UkSwghhBBCCPHbE+nC3i9ZCBRBxE1b+hnQE5gILAW+w1hEvL+2rD7jx4+KFv34\nUdGw9RnpXUKNE4ile0pUJPHWtKZ1iUYP++asorEwZGuETcAflOP2HU3zXh0sMGpEoNbjQb2eMCNv\nl8/P3wSN1BRaQtt2GFrk7Sx8Ne+QnxMqqKS4Ea/5nfsAr4Y6aZ+yaKXSVK/olFhTVROA5x3r8iOt\nNoJTObphvFcvAMf6nOqYsffHwVd+M4e+ZZtCjtG/Sy05Q/Yn02jdWtb5TwU+q707x+xntSWLxlJX\nQ4+n/0rMT98GjvR6Sfh6ZTlwUXZackRVAfZsq72hfEdd1+L8Sopdlez4sZLtP1Sye2sN9dWthrp/\n9VHpRwcaQy+fdxTG6/ZnIMnv9CDgWeAVvXzegf77SNg+JMO7l6Ep85+59F7JHNU5gZiokN0xvr7h\nhF4rTQ8amtnPLsB/rePntMr+sE2ev9U2ef4c2+T5L9gmz19smzzfjdE2xnTli/qC7Uvwa+vhdXt/\n9jZ6dXSOxWgXAkbLk7HA0qKcTGdRTmbIvjpO5ejXWFF3jNl5lKz4acDPcx8K2xNEiN8Tp3LEAleZ\nDI8G/nQIpyOEEEIIIcRBJW04hBBCCCGE+O0pOMTxwqS4aUvrgJeb/mN9RnpP4BaMpInEpjDP+oz0\nxcDjx+QXLDczrjK5rdznipZHek0D+2Z+iq1vZ2KG9cbSJVHX4u17lEX7DqN/+OvKcXuDcVmWjr5s\nNEa7gzaLzZaBfbEM6ofnu43hJrAH+LvfsReBfwJtEj+UTWtTVQKgfc9Iu1m0KMjVXc2ZAs8CtwIB\nF1Eb6nR272gkpbupfJQajOcRkn3KInfqFG5wHZuxCXiM4AlKc4EbzdzYl1M57BjVEAImBABYdQ/n\n/bSY+ekXUpzQrc359K61PDl+C9G2/Yv2WoLdN6QXRrWRCSam5G5176p99Jp2G7U9HZSddDYNnbqj\ndC/RhT+RvPI9okqLN3d9Y3W9iXFbrD4qPRqY1HLD0BVBFHA9MNn3oF4+z4JR0SJgKw8flwEujM9r\npOYTtN2EAouV9vGKY7pW8FVxYuAwH7HRVtI6J6BpirQOcWwpraLB3ea578Oo5HCwzMd4388ME5cP\nPOZZeMVwjPYjCRjJIgssF7/0g2+g6j65Xt8+40ngXybuX9Bu7PHn1uT9EINRyt/uafAMQ+ehMNdd\nhvEdvTpETE8T92/hqW4gunO7sRhJSb9L+ranegLjgBSgGnhf9fi/L37dWYlf2Xm0TTYLZQLw70M0\nFyGEEEIIIQ4qpesH1A9WCCGEEEII8StxKocCfgT6mrzkqlzdNesQTkkA6zPSjwOWAB2DhOjAX47J\nL3gy7GD6si+BYWbvXXbv87hdWwOdqgQu6vTqqg/D3C8BY7H5OoxF82bLvOWVz1ff9cTFGLu5AykC\nzkuYkbfB/0TFtVlPAzf4H1d2C1E927WdhldnyQMbqSmPuB3Hn3N117TmH5zKMQF4iSDVFKPsisyR\nCXX2GC06xJge4NKub6x+LZKJuI7NGICxaH8Z0AFjMfc9YLpjXf7ySMZq5lSOy4HZpoL7dubtAaMp\n2huFpsGAbrVcOGwvIxwVWP0KFehuLw2fb/M9VA+kxdy/OGjrEYAdFwy/AKNygFkzur6x+voI4ll9\nVPp5wNsRXFI2fFNBe98DnuKXLvLsKluo1zeiJcRg7R7sqwkYCT+pKumyukjmqRc/G4fR3mN/JoTS\nICoGLLaWpKCqeo2H8zrrOyqigmZC2awa2cen0SFx/8dyX00DxWW1vmHrgYnbdlbuxkgSGIrxOXcB\nLzw85ujQ5UWCcM+dEIvxnbk4SMgqbNq/lKbuB44LcH4Z8CfLxS+13F/fPkPD+NyOD3HrrUC26j65\npdpKUU6mtel426yftnTAkbp4bcCMrk96D5qyfUvjYybGaXH2urtvTj7m8vC/p//H6Nue6oqRbHYu\nbX93rgduUj3+b8Vhn5j41TmV42bgPxFcUpGru8JnhwkhhBBCCHEEkGQJIYQQQgghfoOcyvEnjCoB\n4WwD0nN1V23YSHHA1mekdwB+wNiJG4oOjD4mv+Cd0FHLrsbkzmZd17/fnfu3v2IkO5wM2DEWGl8E\nZnZ6dVXIhW+/+2pAfyAW2IXK2t58qnJy9vEYSQAnYlSL2ALMAhYmzMgLWDWg4tosGzAPuLDVCQVR\nfZJQAdpubF1bztpXt7c5HsIXQLb/Z9ypHOcBDwED/OKrgVn9M6xLE1Oi7rZGacPssRblV+liE3Bz\n1zdWvxvJRPy5js3QHOvyQ5ZEMMOpHJ8DJ5gKVnDOf08koVts2FBvVT2N63f4H54Uc//ikNU0dlww\n3ILxGetuak5wdNc3Vn9nMhaA1UelX4GJqh5+LMM3FXhLLz0pCZiiYu236TX1LeUzLN06ED3yaKJP\nGRSsessfVdJlzoHd4TQAACAASURBVAjviV787GjgdcCCZoHoeJRqm6dT3aCxcEMyn2+Nx+P3qeja\nMZZj0lNIal3tA13XvZt2Vb3W6PEWAwu27axcAzwJXEPbaqE68Bow6eExR1dxANxzJwzE+J4fA1gw\nEvOeV3aLB6NtT6gPVglwsuXil35qmdD2GQqjQsj/YVSNaFaG8f4+orpP9m3fQ1FO5oVNz8Osx1MX\nr53if3Db6cec4W7U31271mNzVzeYGiih3/+zd+fhUVXnA8e/Z2aSyUYIS9gUwqYTVBQQpYAiEbUV\nVJa6YKyiVGvFrQ1Waal7Xav86lKsVXGpjqgVxAVRhEQsIEoBQSWj7CJbWELInsyc3x8ngclklnuz\nQLDv53l4TOa+99yTZDJx7nnP+3Zg1Df3XOyIG/G2jesf9WoSJZZQN1kuVAUwSnW7ecFhmZRoMbzK\n8xvgWRun7MzWPtstyIQQQgghhDgSJFlCCCGEEEKIo5RXeR4F/hAlZBtwbrb2fRslRjSBlX0ybwce\nsRi+pP/a/KFRI3RuEvAN0Reuav0KlfWaxWsfdkXXZzmA8RxK5gDA1SlplzPVHTa55PvP9rD63R1m\n6Te6j4DLsrVvf6QAr/IMB84A3EqxtWtX2jsd6hqgV22McnAgqXWcr02XhM9dcY4PgI86z17WIt4s\n11SSqSZClYxwhkw5iW5ndYwZV7VuD4HtB0Ifnpz4wNxpsc7dPnbQNZhkmVhe7zx7WbaFuDqW9cq0\nu2BeOmh9fnLB+CGdgIVAn0iB8f160uq681HOet/Se1Xa5ffYnSuA3vaPkcB0EltnKEf9H5VGUaY6\nU0kKSwuOZevOEsorq3G5HHRpn0yr5KhtYcae3bXNO3e8s0ZhWmZEqv5QazFwziNj+tqqkhGJ/62r\nXcBG4FgL4Uudl7w0JNwB/eMzfYvmfjWkeOG351cXFB2LxoFJxngeWJCxYJUG2Dpy4OOYdkZWLT12\n7vI619xybn8nsAHo9oPrWDbP+87SQP0fHVfR57bz0lFZ9X4xfsr0lqfexbRaiGUP0FV1u1kSMP+H\neJWnF/A9kVtMhXo1W/ustHQSQgghhBDiiAvdhSCEEEIIIYQ4SmRr3+01O85vBc4KOrQPU079sWzt\n23Yk5vY/6Dc2Yoes7JN5Uv+1+ZF32qusUnTuLzA7uaMtUN7VkhMlAFKfzQ0AXsBbdH1WZ6ANsN+Z\n6k4A/ktw+4Iax53Zjo7HJbNh6T62rCjUVWUBBQSA/cAOzGLws9natzzW9bO1Lw/IW9ojMxF4Hzg7\nNEYHaFWyr2pgyb4qN3Dv4I35LSJRooYDG4kSAIHQsgVh6Eo/gV1hiw8UWrlG59nLXtw+dlB7TJJQ\npAW0OZiqAg2xANPCJHaJDKO2Wss7REmUAKhctYHS2UtIvviM0EN+WzMMorr8dq4unPmQUuofoceK\nVC8OOHoSUG4CWuOOr6JXV1sV6mvLTVxM7EQJMElJNwN/tXORKEZjLVECYLD/rasHOC95aUXwg5tH\n9EsG7gXGhsT3By4DVmwe0W90xoJVW4E4m/MLl2lyAdANoHO7Sra3TaZyb0nUQVod35HuV/7s1f/B\nRIkewCiL4e0wyW92q76Io1i29q33Ks/HwM8tnvJMc85HCCGEEEKIpmTrhosQQgghhBCiZcnWvtnZ\n2jcc6IlJmBgEHJOtfTktOVHCqzxJXuUZ5VWeK7zKM9KrPIlHek4NtbJPZjxBVQosirqYC4DK8gED\ngYeA4FYaAeBD4OeorPttXveISn02d3vqs7nfpj6b+6N78qz1mAW6fWFjOyXQb2znPRfd32cwEJet\nfc5s7WubrX0nZGvfdVYSJUI8TZhEiRB9gTdtjtussrXPj2l5YZkr0TUv2nFd5afqm53gr5cTUgl8\nYPU6nWcv+ytmsfufQG3mRe3z80JgXOfZyxpU3WDQ+vxCYKaNU/5eMH7IcMxrYExln31NoKQcSopg\n/14o2gv7dqfotQ/bXag/SClVLzFkr+Mk9jv7EFAm38GhFOE7gES1pea/N9o457c1lSiawuWNid88\nop8LmE39RIlgA4DczSP6tce0+LEjXPyY2g9cO3dx+uTBJHRoFXGA1MxODH//xo2JHVOn2Lz2T8F4\n7N0fvKK5JiJatDs49DofjTdb+5Y092SEEEIIIYRoKlJZQgghhBBCiKOM3jVjIKan/EBMT/nvL995\nxz+BearDxJa0I74er/KkAXcDVwNpQYf2epXnReD+aC0VWqiGfM+tnaOydgJ/QufeA2RidphvRWVt\nb8A1j4jql3/VHbgesxjqBHzAP10TXv3KPXnW4orHx/XFPJ+vBWp7R2wHngP+4Z48a3v25MbNYWmP\nzE6A1ZLgWUt7ZA4cvDHfbjJGc3oesJoY882xg9NHYr6ntwCe2gM6oAnsLsG/pRBdVh3u3FmJD8zd\naWdinWcv+wrz871++9hBSUB559nLYpe2sOZO4Fyga7QgV7Lr7X5TBizZ++66VyyN6nSQPCITVbiD\nkMyF24EJeu3D96k+U6Y3YL49gj8pVZ0ocXSvF5TqdlBYbvlblH921zZL73hnTVvqVhCKpSfQD1hp\n45xI0m3Gh7bXuRTzc4ylN+Zn8BgmSSxqb5Ig4drBpNb5LHcJZ949nN0lbtbPWMqB73ehHIo2/bty\n3G+H0fnUznsSe3cYgsrabfGaPyWxe/bUFbZ9kvhpy9a+r7zK83NM9Z5Irwn/wvwtF0IIIYQQ4qgh\nyRJCCCGEEEIcJfSuGSmYdgahfcVPxOyiXaN3zbhQdZhoaxf64eJVnnQgDzghzOG2wGTgPK/yZGVr\n357DObfG6L82v2pln8x8TDKDVWtsXURlVQKrbZ1zGHmVJwnzMyzN1r69ANUv/yoeU4r7auruWh4B\nTKp++VcfA5e7J8/6EfhzxePj7saUeNfAHvfkWU214E7NHOxUDLgOaEnJEs8BtxGmZUkY0xy/mK6B\n6cD0wLxJg6s3F/5OF1dcGjhQAVURv61bgT80ZpKdZy8rbcz5oQatz9+2rFfmWZiqBKfUC3BAh9M7\n0vX8br8ENiae0M5Z/v0+dOSvERyK1MsHEd8r4vp/R+Dveu3D7VWfKffZnHKdDJRiR4+wQa3dTvaX\nB6xmWT1Z89+0qFHhNeSccKL3r6gvdPf5JBvnXuOvqL7T6Xa9BlxjIT4fqFdJpbLc70CDUuCMd+Bw\nKMpm55GSnMDQW04nvk8GKi4O/659FM9bQoF324fd5q/cYWOePyV2f75N+nsujh7Z2rfEqzzdgWzM\n39VjgXLgU+CZbO1bdeRmJ4QQQgghRMNIsoQQQgghhBBHAb1rRhwwh+htBPoCuXrXjJ+pDhN3HZ6Z\n2eIlfKJEsL7AK1jvn95SPAv8n8XYvP5r833NOZnDxas8Q4GbgXHUJCN4lWe5Uvxj3LOnXuiMd4yO\ncvp5wILql381zDXh1QPuybP8QHM9b4+3EuSMd5DWO5XEdgnnVTw+7g4g1z151hfNNCfLsrVvp1d5\nxgLvAclRQp/I1r46u+wdv5i+NB6Wlk0d+SVwT4TzvwQuTXxg7tammnNTGbQ+fyPQb1mvzCxgoruN\ne1h8G3e3lK4ppJ+WTnxrd21o18TebYjvlMyBpdsIhK+cQeLgXtESJYLdq9c+PE/1mWLn578U87tA\nNQlUqHZhg+Kdio7JTnaU+GON9xLwj5qPC23Mo1ZDzgnnQ+B8m/EAbB7RLxUYauPc9sDpmNeVTGBw\nlNjtwEXHzl1+MDvmqxMyrwNuxSQRGgriE50ktHLhLCnnwKxF4cZ6y8Ycf2rmAX+yEf9h7BDxU5Wt\nfaWYakfPH+m5CCGEEEII0RSU1i26Sq8QQgghhBAC0LtmXAG8ajF8muowsZGNC5qWV3lOBr6ycUqf\nbO3Lb675NLWVfTJbY6pFRG0XAASAX/Rfmz+/+WfVvLzKcxdwb6Tj7Y9L4YzfH0d8Uswc/XtcE16N\nOE5TWNoj8zmilAZ3xCmOGdqJdp7WOOIcoYeXA39wT56V14xTtKTm9+jPwFjqbn74CpiWrX1R21CU\nTR2ZCkzAtHNIwFSTeCnxgbmfN8+MD9l9xVAncAFwGWZBvBh4H3i9/WuLy6yM4fdeNRF4IVZcdVEF\nRZ/+ELbZTZtbz8GZlmR12q+oPlMmWA1m/xvnAPMBKkllp2tY1PDSqgB7yvyUV9eb6Bbgb8Dfzu7a\n5uDBO95ZkwsMtzibDUDvR8b0bfRNH/9bV6cC24ieqFNrE9DLeclLAYDNI/p1rjnXjpEZC1Z9uHXk\nwETM8/066pb9LwPeAO46du7yHwC+OiFTYdpxXB1pUOWAlHZuXPH1fse3AD27zV8ZM3sFoOzOURnA\neKATpsrCx4n3f/CplXNbKr3lqdWYZMVYqoFuqtvNR00rKCGEEEIIIYSIRpIlhBBCCCGEOAroXTMW\nA0Mshu8DjlEdJlpagDwcvMrzBHCLjVMey9a+RrUEONxW9sk8AfgYOCZCiB/4Tf+1+TMiHD9qeJXn\nN5hqGlF1PDGVs/7giRX2I9DdNeHV8KUAmsDSHpk3YNpS1OOIUxw3pjvJHROjDVEFjHNPnvV+c8zP\nLq/ydAFOBeKBTdna998jPKWodl8xdDDwOpAR5vA+4Kb2ry32RhvD771KAd8Dvaxc88CX26naXre7\ngKtbW9KuOcPSnGuUAymqzxRLi+jsf0NhkiVGVJHCDtdwS6dVVAcor9ZUVux7PF2v39fFsWmNQwW+\nV52uXRscd8c7ay7GegWE2x8Z0/evlq7vX56MqeLgAja7nQPrtaPwv3X19RyqcoHWmvLNRRz4ZjfV\n+ypM9Yb2idqV6r693bSPH6uN2zyiXyJQhL3KpgMzFqw6+JzeOnJgPHAOJmGiGMg9du7yvcEnfHVC\n5m1AzK9XOaB1xwSUQ9U+VAGc123+yrDlJoKV3TmqI+Z170LqthYC+Aa4NfH+DxbEGqcl0lueGg58\nhHlNieZO1e3mvzT/jIQQQgghhBDi8JBkCSGEEEIIIVo4vWtGCnDA5mlZqsPEvGaYToN4leddzAKT\nVbOyte+XzTWfprSyT6YLaI3ZYZwK3IjZCd2pJqQCs8D5RP+1+cub9OI6tw9mp3ltlYD3UFnl0U4J\n5OXEYxbak4FdjuHTVtu5pFd5nJjd48daic/6Uybpx7eKFTbYNeHVZqtusLRHZivM7vaU0GNdz+pE\net+2VoYpBjLck2ftjRkpDtp9xdDTgVwgWjkHDUxo/9rif0UK8HuvOheTjGRJVUEpB5bWLWgQn9mZ\n1MtOszpErTaqzxTr7Sz2v5EKzNEwfLvzbPwqdhWLOP8+UivWlCT4f4xXNe1saiwBHledrp0FcMc7\naxQwE7g0xpD/Ac55ZEzfimhBFf7l3YHJwFWY1y4w1W/mAtPczoG5wfH+t66+AZhWXVyZsOu99VTt\njpiPtwC4uOei1YUAm0f0exO4JMaca/mAPhkLVlm+WfXVCZkuzGtSpES1OpLS4nAnuwDWARO7zV/5\nWaxzyu4c1QFYDPSOElYFjE28/4MPrMyjpdFbnvoFJqkpLcxhP3Cv6nbz/Yd3VkIIIYQQQgjRvOrV\nHhRCCCGEEEK0OFZKn4eyXGf+MLFbNaCqWWbRhFb2yRy4sk/mS5hElt2YZIl/A2uBHphFtT5Ah/5r\n869s0kQJnTsInZsLfIupmDANeBP4EZ37MDq33u7gQF5O20BezgPAD5hF2PnAV4G8nK8DeTm/DeTl\nWH1/eAEWEyUA1i/cZSUsNXZIww3emH8AeCz0cUecg7aecOuCYaUQpcS/iOg5Yr8eKWD67iuGRsuq\nOcnORZ2t6m+Q11X2XoYCKD3XdcVxs9cVDJ29riDaIvkhrS8rAs5VcHGS3r4+Vri7ejvppfNJ9P+Y\nHJIoAaaa0Nt6x/N3A9S01LgCeJrwr5EBzOvALx4Z07diW2lhu22lhX/YVlq4cltp4dZtpYW+baWF\n/7ettPC4Cv/ygZgWMzdR9/fPgfkdX1DhX/674MGdl7z0zL6l2/pu967dHSVRAmAEMHfDsJMTaj5/\nOtb3Ich0O4kSNc7HYqIEQPmB6l3Az4HjrSRK1Pg70RMlwPz8vGV3joqZHdYSqW43z8O8tl8PfIr5\nW/YlcB+Qsfv2158vGD/k/ILxQy4oGD8kZskgIYQQQgghhDga2CmDKIQQQgghhDgyCoFKYpfHDmZp\nhfow+hwYayN+WXNNpCms7JN5O/AwZpE32Bk1/xYCo/uvzS9u8ovr3HOBdzHVJEK1Be4ABqJzR6Gy\nKgACeTldMLv7jw9zzonAM8C5gbycyxzDp8VaUR5gZ7qFW0qthB2Oag33AR2BG2ofSOvVCme8rT0E\nV2ISUxql5JZz+gATgK6YVg95wJvJT34StRLA0Wb3FUOHAidbDE/BfH/Dtkuh/u9aVCrOUYSpUlBb\nSsJfvXXfPO0PZCmnI2ryRiXxrHecyEbHCZWVKuGL2sdnryv4AnhqbO/0V6NevPVl1cDbB0pK5qP1\nYpQKm+jhDJTQruw/OIjZ5eMeveP5b1Sna//9yJi+1cDNd7yz5gHg10A/TIKDD3j+kTF9NwBsKy28\nBHgZCO0vc7zCf6vWqkwpHe37oIBpFf7l69zOgQfbzxQt3zERaB9rwsBgzM/zuYwFqxZtHtHvIeCP\nMc55H5OUYJe1RJYaAb92dZu/0nKVkrI7Rx1DtL9ftc9Mk+KRSvTncYumut1cAvyz5h8ABeOHDACe\nAEYTdB+xYPyQz4C/ps9c8t7hnqcQQgghhBBCNBVJlhBCCCGEEKKFUx0mVuhdM94GLrd4Sr7qMLFp\n2z003gzMYrXbQmwp8GLzTqfhVvbJvBp4JEbY2Zhy+Rc06cV1bhtM9YpwiRLBRmC+33fUfD5Hw/F7\nko6jKOEYNA4SqwvpUPwNrsDB9flxwIPA7THGdtqaciBmyAbgv3bGbIjBG/M1MGlpj8w5mFYpo+KS\nXXarLXZuzBxKbjmnE2YB+7yQQxOBaSW3nPPH5Cc/eb4x12hhxjQgPtIi89d2BlJOx3/TZy45u2D8\nkPaYRIw97V9edECvffgpTDWFsMpIYrHrfIpVGtR/vTod+NfsdQUjgIlje6dHrYDQNTm56IeSknOB\nOTXn1pFctQ6H9aI7t2F+9wF4ZEzfHcAD4QK3lRaOwrRTCPu76nYWqRiJErUUMAWTxMCGYSfHYxI0\nrLoBU1mEjAWr/rR5RL+dwFQgPSSuFLM4f3vGglVRM0cC717XuWYOJ9fM79u4tgnVVXujdh+qy4Gj\n/L4LnwbaAPuAWQl3vbcwyhmXEvq9dChwKnAolDLZElprqA6AX1/FUZosEapg/JBRRP6bcyZwZsH4\nIX9Mn7nk4cM7MyGEEEIIIUQwpZQDsxnmNGBgzX9P5lAFwzyt9dkNHPtszIaPn2Gq+lVgWsF+BLyg\ntfY1YMxMzL2Qn2Mq3LmBH4GlwCta62jv0ZqU0tpudUMhhBBCCCHE4aZ3zRiK6UNvxS2qw8SnmnM+\nDeFVngeAP1kIvTdb++5p5uk0yMo+mQ7M4n6GxVN+1n9tftNVydC5OcDjFqP3AccEPn1v8I6Uvgs2\ntT2L8rg2dQIcgUo6F62k154Ftbvbi4EujuHTDkQa1Ks8V2EW/C3pfEprzvx9uIIWB/3BNeHVei0y\nmtvSHpnJJ03ofVt8q/h7bJy2yT15Vo+GXK/klnPSMe1PYu2C/13yk588YWXMsqkjewHDMDcVtgIf\nJT4wt8W0sNl9xdBngd/YOOWL9q8tHjQ3zaMANbLQdzDVxu+9SgHrgJ5WBtJVgTtcE159tN7jax/u\niKl0073eMSDPNZr9ykrhBO4e2zv9PiuBP5SUKOAc4LeYm1ca+OaYA2+crQi0iXpyXX1Vp2ujJo1s\nKy1UmCoTx0WKSYvfhFPZaklykts58JsNw04ehPne2dGx56LVBysdbR7RLx64GFOhxgl8B3gzFqza\nH22QwLvXxQFPAtcSsvGnZHORf8MzX1lO4krqkEjXs+t1EsoHrkm46716X1/ZnaMewiSNGE6Fiot8\nOR3QVcqheidMnbOlzteQlxMP/AJzc7Ec+NQxfNoGq/M+3ArGD+mBSVKyklhzkVSYEEIIIYQQ4shQ\nSo0GXqP+/7sHJwF8ajdZQinVCpMAf2mYMWtr7FUBd2utLSdQK6X+BNzNoUSO0DHBbAC4Xmvd9BVb\nQ9jdRSOEEEIIIYQ4AlSHiYuBey2EzqHl7mj9M7Hn9gTWvs7Dwqs8aV7lOd6rPF1rHhqF9UQJgElN\nPKUJNmLbAKPXtx3xWH7HMfUSJQACjnh+TBvEms6XETBvD1OA7BjjvoVpDWNJz+Ghm8jryMUsgB52\ngzfml8S3in/b5mmfNOKSf8Vau4BpJbecEzUho2zqyFPLpo6cB3yPqdryDPAesLls6sg7y6aObClV\nJPdZDfRXa378vjR5bprnK6AaqJ6b5vl2bprnd3PTPGnO7Fc08JCVsbQ/gC6teqTqmcv+UfXMZXVW\ntVWfKTuB4cBXoeftUsdYTZQAuHX2uoJYFV4A6JqcrLsmJ8/vmpz8y67JyZldk5P7HHvg9WtsJkpA\nYqvL0LmnoXOjtSQ5lyiJEqDtJkoA9Kn5r5VF81DJwZ9kLFhVmbFglTdjwarbMhas+n3GglXPWEiU\ncABvYpJN6j23kzNSnQmdk+udF4lDB/CX1fseZAILy++7cHCYU0oOnRw9UQJAOVQc8GH5A6PjAAJ5\nOXGBvJx7gC0c+hs9A1gXyMv5MJCX09/y5A+vG7H+M7+tOScihBBCCCGEiCoN8//uOuQf2GxpWUsp\n5QJmYxIlasdbA7yCeX+2reaxOOBBpdSdFse9D/gL5r2drhnnzZpxvw661uXAWzUVM5qVJEsIIYQQ\nQghxlFAdJt6DWXzfFubwAcxi7MWqw8SoZcyPlGzt09nadyNmJ/xMoLLmUAXgBc7I1r7fZWvfES9/\n51WeLK/yzAZ2Y3Zpb/EqT/6BYn2jzaEGNvHUusYOOaSgrPWpP6T9LOZC3L6kXvyQNqT2U0+02Gzt\nKwMsVS5JaO3a3eWUtJIwh6qBl/Nf8k388p7lf1jaI/P1pT0yZy7tkfnnpT0yG9Xqwg735FlfA5/Z\nOKVBiUglt5zTDrjMYrgDsygcVtnUkWcDizClKkNvenTGtF+Z1UISJt6yElRW7OfrRfv48bvSEzFl\nQh2Yr60P8H/At3PTPP2c2a88j2kVE5H2a3TJweIa19ecX4fqM2Uz0B/TDuVNYAWwLN8xYL2lr8po\ni6mQ0FD2X6fdyX8GvgDy0bm3oHPDrdqPaMScIql9Td5p8zw/5jW0sbKJ0dKl47nWcticcQqnEw5s\nCJvvlQi8Wn7fhaH3yj48+JH1zj0nAOMCeTlxmASJu4GOITEKU2nis0BezllWBz4cCsYPcQLX2Dhl\nWMH4IVFLCAUbMGh6+wGDpt8+YND0dwYMmv7BgEHTpw8YNP1U+zMVTcWrPHKPWAghhBDi6KYx79ne\nx7z/GEnjNqfchWkxqzGV8cZrrU/RWl+jtb4cU63xr0HXvkcpdWa0AZVSIzAbqWrfYz4KdNdaX14z\n7smY938VNTHnYa1CbaO0hJsnQgghhBBCCItUh4nP6F0zngNGA6diyph/D8xUHSY2e2m6ppCtfZ9R\ns0DtVZ7EmsX3FsOrPPdg3liG8lSU42mVYmu4pn7PVW4neMuB9EFYTMLf1noA3QoXo9BWFnHvwexe\nHx8lZlP5/uqzlUPtxVTE6E9N2f2S7aX/+vbZb6dgSvDHBZ1zGXD30h6ZLwI3D96YX2Fp8o1zG5CH\nWSiNZoZ78qyVDbzGhYClKgQ1LgHuCH2wbOrINsAsYu/2vhBzA+Ke4Af15if6YCqO7Ae+VRm3Nmti\nUvvXFv939xVDvwBOjxRTVRnA98V+KssCkULAJIF8NDfNc9rIQt/U6teuXIxfv4VTJSll8kV0QKMr\n/VDpr1toFG6qeuayv8Xd8EaddgeqzxQNzK/5B8C+dQXf2vwS+8QOCU91urZU73j+W8yiekwaxY6q\nfrRx7SDReeB4TBWeEejcX6KygsskxHgeK/yBOJwOW91a1gD0XLT62w3DTl6J+V224t2ei1ZHbOlj\nQ8wktdQT2tHmxLbs+2ZvxBhnnCK5bTxKKSr2lOGv8ON018s36YlJRDqYIJF4/wf/Lbtz1Jc41GnK\nYWtT1m+BU4DzY8QlA7MDeTndHcOnFdm5QDNqg0kIsqMX5jU9ogGDpivgfszrrjvk8A0DBk1fBIxf\nsWzSdpvXFjZ5lccJXIBJwj0LiPcqz3ZMm61/ZGvflmjnCyGEEEKIFuVDIENrvTX4QaXUzxoymFIq\nHfg9h95d36q1rrMZQmtdDUxRSmVg7uXUVoM8I8rQwZsfXtda/zE0QGv9hlIqDVNBE+A2pdR0rXXk\nN3uNJMkSQgghhBBCHGVUh4nVwNs1/45qLTBR4lrCJ0oAUGlrfREAOzvVrfiU6AkKdeyvTI64qKmr\nqimfl0f5Ox8RKNiDSkxgRb94up/s3JA+PPq42doX8CpPNrAQuBnoG3R4L/Ai8Gi29u2qeezgboal\nPTIdmAX/0RGGdwHXAV2W9sgcPXhjfpNVSvEqT+easc/DJBzsAF4bdbdndEIr10wiLw4+D9zQiEvb\nXXSMFH8N0NriGDeUTR35YMJvfh7AVFiYRN2F+e/05ieeAZ5RGbc2Z1LKROA/mLKg9ezaVB4rUaJW\nByAH+J0uqqwGklCga5IlCETM+1CYn90fLFzDbvJIY5NN/oHFnT5lzmOooD07ytrSKWEtia4igIsw\nN6OCv7aYi8zlgdYkO+oWfNAaNA40Tg4VLNEoAt9qXJuDQp8GXrAy55rYRgm8e10XwNINPkdlFa3a\nx1NR6qey7FDSjDNOEZ/kJD7RSW1yDRoq9paR1Dls9tulBFeTMG5BsYi6yV0xJqROwlRKsaINJqnM\nUtWew6Ahr7tWznkC8zcjkmHApwMGTR+yYtmkpqhKIsLwKk8rTEnl0Eo0XYA/Ajle5bkqW/vePOyT\nE0IIIYQQVq5G9gAAIABJREFUtmmtd8WOsmUCJqlbA99prZ+PEns7ZsOHAxislDpFa12v7aVSaiBw\nWs2nAWBKpAG11s8qpXIwm3RaAVdi3ks0CymxJoQQQgghhBAcLEE9NVpMURH4/bbWR59r1KTq+7vV\nQH9A/QdU2JXAyhVfUzB0LPtv/DMV8z+jatW3VC5dwffPfM78Gxb/zas89bL7Q9W0VXkuW/tOBvph\ndmMPA47J1r7bghIlQl1F5ESJYKOwVwY+Kq/y3AlsAe4FhmJ2x58PvPrBvb5X5j343XjMovrSmrjv\nMDsZ+ronz7rOPXlWdfiRLbG7WzxSvJ3vRwcS48cAH2AWrUMrGByPaVExX29+wl69FBvav7b4G+BM\nYHXoMa01u7aU2VmUnTA3zZMEDDcDYJIkIidK1Bpucfx6N3SaOD7US8C6WEEBnBTGnVTzmYNdFcej\n9cEKB9ejc1sFhb+GufEUUYW/FX59aO+M1hAgDk0chzqgKMCBxnUC8HVp9ZreNeEv1lwjlod6Llq9\n0EJc+Dk+Pi6l4vFx11Rt2jfVv7eUQGnsTLVAZQBnnIOk1nG07ugmtaOb1p3ctGrvxp3kOpQoUUNX\nR3ze1EtWSrz/g89xOv5h52twtE12hxsriiZ7vWus9JlL9mHhuRmkihi/DwMGTR9M9ESJWsdhXqdF\nM/Aqj8Ik20Zr2eMGXvMqz7mHZ1ZCCCGEEKKFCW6D+FK0QK31D5iNNLXGxhhTA5+EVsEI42ULYzYJ\nSZYQQgghhBBCCON8TM/FiLSGfWFb3Yf1HfBu46YUQmX9B1OVIZYKpfQUwuz0rVqTz74rbiGwoyDS\nufHAg17l+bPVaWVr31fZ2vdxtvZ9lq19sVqFxCypH2SSjdiIvMpzN3AfkasrdirZW/XO27d981/3\n5FlD3JNnZbgnz/K4J8+a5J486+smmMJcwE6yxZwIj3ezc9H4n/fPwVTRiOZMzAJ4s2n/2uKv27+2\n+JSaa/0deAN4ft+Oyt9UVeh6fRCiSAMysdfSBBvxdhbDdwDv2JxHHarTtQcwSUYRF6UDOClwn0ml\ns/2hx3QcJdXtaj9tRVC1mS5JaVuI/PwBQOPkQFUXAtoZMIkS8cS4PdQbmF9avSa956LVGpPwdB+w\nL0zsduDmnotWN6ivbMXj49wVj4+bBmwDZlAVmBTYU4b/xyKqNhcSKK6MeK4j4dCvt1IKh0PVS5Co\nEx8X8WsOm6ykHMrW67lKirdbGcHW7/dh8KyN2NnpM5fsjBFj57X/ygGDpreKHSYaYARgJQnCBfyl\nmecihBBCCCFaGKWUm7rV/fIsnJYb9PHZEWKyGjimAoYopaxX+bNJkiWEEEIIIYQQwjjFSlBBARw4\nEHMn+1bggv5rm66FRJAriJ4wUQyMdTiyFgPzQw8W3T0NXVJq5Tr3eJWna8OmGN7SHpndgIE2Tum/\ntEdmr8Zc06s8GcBdFkKTaKayjslPfrINewvr0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33Bf0h+AJ4GHm+hFbSEEEIIIZqVUmo5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LCCGEEEIIcZJQe2b3QC+cdUP3qf8KeMlIG19YG+LbOiCYYWrnzC5Gx4nfN/E0G8Vp2HsD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\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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e4b4250>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 784,
       "width": 1061
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(spatial_query_supply,whattoplot=(['total_cases','percentofcoverage']),Min_Rooms=(1,6,1),Min_Size=(10,100,10),Max_Rent=(1000,5000,100));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Automated Property Evaluation\n",
    "### More of Machine Learning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rent</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Year built</th>\n",
       "      <th>Living space</th>\n",
       "      <th>Floor</th>\n",
       "      <th>lng</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1090.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.</td>\n",
       "      <td>8.051152</td>\n",
       "      <td>47.382578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1923.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.</td>\n",
       "      <td>6.150230</td>\n",
       "      <td>46.213396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2425.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1.</td>\n",
       "      <td>6.148701</td>\n",
       "      <td>46.210094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2480.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>5.</td>\n",
       "      <td>6.148480</td>\n",
       "      <td>46.210668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1430.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1945.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.</td>\n",
       "      <td>6.147548</td>\n",
       "      <td>46.210081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rent     ZIP  Rooms  Year built  Living space Floor       lng        lat\n",
       "0  1090.0  5000.0    2.0      1983.0          42.0    1.  8.051152  47.382578\n",
       "1  1923.0  1200.0    4.0      1930.0          72.0    1.  6.150230  46.213396\n",
       "2  2425.0  1200.0    4.0      1983.0          76.0    1.  6.148701  46.210094\n",
       "3  2480.0  1200.0    4.0      1910.0          68.0    5.  6.148480  46.210668\n",
       "4  1430.0  1200.0    2.0      1945.0          32.0    3.  6.147548  46.210081"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mat_all = listing.copy()\n",
    "listing.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Out of sample prediction\n",
    "## Train test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor 4.52321129457\n",
      "BaggingRegressor 4.2736214953\n",
      "RandomForestRegressor 4.28208694203\n",
      "ExtraTreesRegressor 3.8951019887\n",
      "XGBoost 7.83118977952\n",
      "Polynomial_degree:2 with dimensions: 36  12.0564957511\n",
      "Polynomial_degree:3 with dimensions: 120  11.1831634915\n",
      "Polynomial_degree:4 with dimensions: 330  10.3103958899\n",
      "Polynomial_degree:5 with dimensions: 792  9.63010094713\n"
     ]
    }
   ],
   "source": [
    "sel_cols = range(1,Mat_all.shape[1])\n",
    "X = Mat_all.values[:,sel_cols]\n",
    "y = Mat_all.values[:,0]\n",
    "\n",
    "\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.333)\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn import linear_model\n",
    "\n",
    "def my_custom_loss_func(ground_truth, predictions):\n",
    "    return np.median(100*np.abs(predictions-ground_truth)/ground_truth)\n",
    "\n",
    "\n",
    "n_estimators = 200\n",
    "regr = DecisionTreeRegressor()\n",
    "regr.fit(X_train,y_train)\n",
    "preds = regr.predict(X_test)\n",
    "print 'DecisionTreeRegressor', my_custom_loss_func(y_test,preds)\n",
    "\n",
    "\n",
    "regr = BaggingRegressor(DecisionTreeRegressor(), n_estimators=n_estimators,n_jobs=-1)\n",
    "regr.fit(X_train,y_train)\n",
    "preds = regr.predict(X_test)\n",
    "print 'BaggingRegressor', my_custom_loss_func(y_test,preds)\n",
    "\n",
    "\n",
    "regr = RandomForestRegressor(n_estimators=n_estimators, max_depth=None,min_samples_split=2, random_state=0,n_jobs=-1)\n",
    "regr.fit(X_train,y_train)\n",
    "preds = regr.predict(X_test)\n",
    "print 'RandomForestRegressor', my_custom_loss_func(y_test,preds)\n",
    "\n",
    "regr = ExtraTreesRegressor(n_estimators=n_estimators, max_depth=None,min_samples_split=2, random_state=0,n_jobs=-1)\n",
    "regr.fit(X_train,y_train)\n",
    "preds = regr.predict(X_test)\n",
    "print 'ExtraTreesRegressor', my_custom_loss_func(y_test,preds)\n",
    "\n",
    "\n",
    "\n",
    "regr = xgb.XGBRegressor(max_depth=3, learning_rate=0.1, n_estimators=n_estimators, silent=True, objective='reg:linear', nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, seed=0, missing=None).fit(X_train,y_train)\n",
    "preds = regr.predict(X_test)\n",
    "print 'XGBoost', my_custom_loss_func(y_test,preds)\n",
    "\n",
    "\n",
    "degree = 2\n",
    "poly = PolynomialFeatures(degree=degree)\n",
    "X_train_ = poly.fit_transform(X_train)\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X_train_, y_train)\n",
    "\n",
    "X_test_ = poly.fit_transform(X_test)\n",
    "preds = regr.predict(X_test_)\n",
    "print 'Polynomial_degree:{} with dimensions: {} '.format(degree,X_train_.shape[1]), my_custom_loss_func(y_test,preds)\n",
    "\n",
    "\n",
    "degree = 3\n",
    "poly = PolynomialFeatures(degree=degree)\n",
    "X_train_ = poly.fit_transform(X_train)\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X_train_, y_train)\n",
    "\n",
    "X_test_ = poly.fit_transform(X_test)\n",
    "preds = regr.predict(X_test_)\n",
    "print 'Polynomial_degree:{} with dimensions: {} '.format(degree,X_train_.shape[1]), my_custom_loss_func(y_test,preds)\n",
    "\n",
    "degree = 4\n",
    "poly = PolynomialFeatures(degree=degree)\n",
    "X_train_ = poly.fit_transform(X_train)\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X_train_, y_train)\n",
    "\n",
    "X_test_ = poly.fit_transform(X_test)\n",
    "preds = regr.predict(X_test_)\n",
    "print 'Polynomial_degree:{} with dimensions: {} '.format(degree,X_train_.shape[1]), my_custom_loss_func(y_test,preds)\n",
    "\n",
    "degree = 5\n",
    "poly = PolynomialFeatures(degree=degree)\n",
    "X_train_ = poly.fit_transform(X_train)\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X_train_, y_train)\n",
    "\n",
    "X_test_ = poly.fit_transform(X_test)\n",
    "preds = regr.predict(X_test_)\n",
    "print 'Polynomial_degree:{} with dimensions: {} '.format(degree,X_train_.shape[1]), my_custom_loss_func(y_test,preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This method of out of sample prediction is data demanding \n",
    "## Further, it might be overfitted to test data\n",
    "#### There is a better way than this..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross Validation\n",
    "## K-fold "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor 5.95544130249\n",
      "BaggingRegressor 5.10758708709\n",
      "RandomForestRegressor 5.09783107701\n",
      "ExtraTreesRegressor 4.923125\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.metrics import make_scorer\n",
    "def my_custom_loss_func(ground_truth, predictions):\n",
    "    return np.median(-100*np.abs(predictions-ground_truth)/ground_truth)\n",
    "\n",
    "loss = make_scorer(my_custom_loss_func, greater_is_better=False)\n",
    "\n",
    "# loss = 'neg_median_absolute_error'\n",
    "# loss = None\n",
    "\n",
    "\n",
    "sel_cols = range(1,Mat_all.shape[1])\n",
    "X = Mat_all.values[:,sel_cols]\n",
    "y = Mat_all.values[:,0]\n",
    "\n",
    "n_estimators = 100\n",
    "\n",
    "regr = DecisionTreeRegressor()\n",
    "scores = cross_val_score(regr, X, y,cv=3,scoring=loss)\n",
    "print 'DecisionTreeRegressor',  np.median(scores)                             \n",
    "\n",
    "regr = BaggingRegressor(DecisionTreeRegressor(), n_estimators=n_estimators,n_jobs=-1)\n",
    "scores = cross_val_score(regr, X, y,cv=3,scoring=loss)\n",
    "print 'BaggingRegressor', np.median(scores)  \n",
    "\n",
    "regr = RandomForestRegressor(n_estimators=n_estimators, max_depth=None,min_samples_split=2, random_state=0,n_jobs=-1)\n",
    "scores = cross_val_score(regr, X, y,cv=3,scoring=loss)\n",
    "print 'RandomForestRegressor', np.median(scores)                               \n",
    "\n",
    "regr = ExtraTreesRegressor(n_estimators=n_estimators, max_depth=None,min_samples_split=2, random_state=0,n_jobs=-1)\n",
    "scores = cross_val_score(regr, X, y,cv=3,scoring=loss)\n",
    "print 'ExtraTreesRegressor', np.median(scores)  \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grid Search with Cross Validation\n",
    "## Meta-Parameter optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost 6.94599430833 {'n_estimators': 1000, 'max_depth': 3}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.metrics import make_scorer\n",
    "def my_custom_loss_func(ground_truth, predictions):\n",
    "    return np.median(-100*np.abs(predictions-ground_truth)/ground_truth)\n",
    "\n",
    "loss = make_scorer(my_custom_loss_func, greater_is_better=False)\n",
    "\n",
    "# loss = 'neg_median_absolute_error'\n",
    "# loss = None\n",
    "\n",
    "\n",
    "sel_cols = range(1,Mat_all.shape[1])\n",
    "X = Mat_all.values[:,sel_cols]\n",
    "y = Mat_all.values[:,0]\n",
    "\n",
    "n_estimators = 50\n",
    "\n",
    "# model = DecisionTreeRegressor()\n",
    "# regr = GridSearchCV(model,param_grid={'min_samples_split':[2,10]},cv=3,scoring=loss)\n",
    "# regr.fit(X,y)\n",
    "# print 'DecisionTreeRegressor',regr.best_score_  ,regr.best_params_\n",
    "\n",
    "# model = BaggingRegressor(DecisionTreeRegressor(), n_estimators=n_estimators,n_jobs=-1)\n",
    "# regr = GridSearchCV(model,param_grid={'n_estimators':[50,100]},cv=3,scoring=loss)\n",
    "# regr.fit(X,y)\n",
    "# print 'BaggingRegressor', regr.best_score_  ,regr.best_params_\n",
    "\n",
    "# model = RandomForestRegressor(n_estimators=n_estimators, max_depth=None,min_samples_split=2, random_state=0,n_jobs=-1)\n",
    "# regr = GridSearchCV(model,param_grid={'n_estimators':[50,100],'min_samples_split':[2,10]},cv=3,scoring=loss)\n",
    "# regr.fit(X,y)\n",
    "# print 'RandomForestRegressor',  regr.best_score_  ,regr.best_params_                             \n",
    "\n",
    "# model = ExtraTreesRegressor(n_estimators=n_estimators, max_depth=None,min_samples_split=2, random_state=0,n_jobs=-1)\n",
    "# regr = GridSearchCV(model,param_grid={'n_estimators':[50,100],'min_samples_split':[2,10]},cv=3,scoring=loss)\n",
    "# regr.fit(X,y)\n",
    "# print 'ExtraTreesRegressor',  regr.best_score_  ,regr.best_params_\n",
    "\n",
    "\n",
    "n_estimators = 2000\n",
    "model = xgb.XGBRegressor(max_depth=X_train.shape[1], learning_rate=0.1, n_estimators=n_estimators, silent=True, objective='reg:linear', nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, seed=0, missing=None)\n",
    "regr = GridSearchCV(model,param_grid={'max_depth':[3,X_train.shape[1]],'n_estimators':[1000,2000]},cv=3,scoring=loss)\n",
    "regr.fit(X,y)\n",
    "print 'XGBoost',  regr.best_score_  ,regr.best_params_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of Error matters a lot!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor 4.7197640118\n",
      "BaggingRegressor 4.37518983269\n",
      "RandomForestRegressor 4.36536281179\n",
      "ExtraTreesRegressor 3.96771929825\n",
      "XGBoost 4.12200187416\n",
      "Polynomial_degree:2 with dimensions: 36  11.8858901582\n",
      "Polynomial_degree:3 with dimensions: 120  11.0282923547\n",
      "Polynomial_degree:4 with dimensions: 330  10.2554469462\n"
     ]
    }
   ],
   "source": [
    "sel_cols = range(1,Mat_all.shape[1])\n",
    "X = Mat_all.values[:,sel_cols]\n",
    "y = Mat_all.values[:,0]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn import linear_model\n",
    "\n",
    "def my_custom_loss_func(ground_truth, predictions):\n",
    "    return 100*(predictions-ground_truth)/ground_truth\n",
    "\n",
    "ERRS = pd.DataFrame()\n",
    "\n",
    "n_estimators = 200\n",
    "regr = DecisionTreeRegressor()\n",
    "regr.fit(X_train,y_train)\n",
    "preds = regr.predict(X_test)\n",
    "print 'DecisionTreeRegressor', np.median(np.abs(my_custom_loss_func(y_test,preds)))\n",
    "ERRS['DecisionTreeRegressor'] = my_custom_loss_func(y_test,preds)\n",
    "\n",
    "\n",
    "regr = BaggingRegressor(DecisionTreeRegressor(), n_estimators=n_estimators,n_jobs=-1)\n",
    "regr.fit(X_train,y_train)\n",
    "preds = regr.predict(X_test)\n",
    "print 'BaggingRegressor', np.median(np.abs(my_custom_loss_func(y_test,preds)))\n",
    "ERRS['BaggingRegressor'] = my_custom_loss_func(y_test,preds)\n",
    "\n",
    "regr = RandomForestRegressor(n_estimators=n_estimators, max_depth=None,min_samples_split=2, random_state=0,n_jobs=-1)\n",
    "regr.fit(X_train,y_train)\n",
    "preds = regr.predict(X_test)\n",
    "print 'RandomForestRegressor', np.median(np.abs(my_custom_loss_func(y_test,preds)))\n",
    "ERRS['RandomForestRegressor'] = my_custom_loss_func(y_test,preds)\n",
    "\n",
    "regr = ExtraTreesRegressor(n_estimators=n_estimators, max_depth=None,min_samples_split=2, random_state=0,n_jobs=-1)\n",
    "regr.fit(X_train,y_train)\n",
    "preds = regr.predict(X_test)\n",
    "print 'ExtraTreesRegressor', np.median(np.abs(my_custom_loss_func(y_test,preds)))\n",
    "ERRS['ExtraTreesRegressor'] = my_custom_loss_func(y_test,preds)\n",
    "\n",
    "\n",
    "\n",
    "n_estimators = 2000\n",
    "regr = xgb.XGBRegressor(max_depth=X_train.shape[1], learning_rate=0.1, n_estimators=n_estimators, silent=True, objective='reg:linear', nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, seed=0, missing=None)\n",
    "regr.fit(X_train,y_train,)\n",
    "preds = regr.predict(X_test)\n",
    "print 'XGBoost', np.median(np.abs(my_custom_loss_func(y_test,preds)))\n",
    "ERRS['XGBoost'] = my_custom_loss_func(y_test,preds)\n",
    "\n",
    "degree = 2\n",
    "poly = PolynomialFeatures(degree=degree)\n",
    "X_train_ = poly.fit_transform(X_train)\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X_train_, y_train)\n",
    "\n",
    "X_test_ = poly.fit_transform(X_test)\n",
    "preds = regr.predict(X_test_)\n",
    "print 'Polynomial_degree:{} with dimensions: {} '.format(degree,X_train_.shape[1]), np.median(np.abs(my_custom_loss_func(y_test,preds)))\n",
    "ERRS['Polynomial_degree:{} with dimensions: {} '.format(degree,X_train_.shape[1])] = my_custom_loss_func(y_test,preds)\n",
    "\n",
    "degree = 3\n",
    "poly = PolynomialFeatures(degree=degree)\n",
    "X_train_ = poly.fit_transform(X_train)\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X_train_, y_train)\n",
    "\n",
    "X_test_ = poly.fit_transform(X_test)\n",
    "preds = regr.predict(X_test_)\n",
    "print 'Polynomial_degree:{} with dimensions: {} '.format(degree,X_train_.shape[1]),np.median(np.abs(my_custom_loss_func(y_test,preds)))\n",
    "ERRS['Polynomial_degree:{} with dimensions: {} '.format(degree,X_train_.shape[1])] = my_custom_loss_func(y_test,preds)\n",
    "\n",
    "degree = 4\n",
    "poly = PolynomialFeatures(degree=degree)\n",
    "X_train_ = poly.fit_transform(X_train)\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X_train_, y_train)\n",
    "\n",
    "X_test_ = poly.fit_transform(X_test)\n",
    "preds = regr.predict(X_test_)\n",
    "print 'Polynomial_degree:{} with dimensions: {} '.format(degree,X_train_.shape[1]), np.median(np.abs(my_custom_loss_func(y_test,preds)))\n",
    "ERRS['Polynomial_degree:{} with dimensions: {} '.format(degree,X_train_.shape[1])] = my_custom_loss_func(y_test,preds)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DecisionTreeRegressor</th>\n",
       "      <th>BaggingRegressor</th>\n",
       "      <th>RandomForestRegressor</th>\n",
       "      <th>ExtraTreesRegressor</th>\n",
       "      <th>XGBoost</th>\n",
       "      <th>Polynomial_degree:2 with dimensions: 36</th>\n",
       "      <th>Polynomial_degree:3 with dimensions: 120</th>\n",
       "      <th>Polynomial_degree:4 with dimensions: 330</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.55172</td>\n",
       "      <td>7.45086</td>\n",
       "      <td>7.82931</td>\n",
       "      <td>6.55172</td>\n",
       "      <td>6.74156</td>\n",
       "      <td>7.92854</td>\n",
       "      <td>7.24409</td>\n",
       "      <td>23.0881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.276</td>\n",
       "      <td>13.5364</td>\n",
       "      <td>15.0812</td>\n",
       "      <td>25.3918</td>\n",
       "      <td>18.0784</td>\n",
       "      <td>29.6592</td>\n",
       "      <td>34.0707</td>\n",
       "      <td>36.622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.106186</td>\n",
       "      <td>8.60285</td>\n",
       "      <td>-10.2302</td>\n",
       "      <td>-13.7669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.83871</td>\n",
       "      <td>31.8898</td>\n",
       "      <td>33.3501</td>\n",
       "      <td>34.205</td>\n",
       "      <td>13.8054</td>\n",
       "      <td>50.051</td>\n",
       "      <td>22.4718</td>\n",
       "      <td>30.2563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-26.1438</td>\n",
       "      <td>21.5672</td>\n",
       "      <td>8.87081</td>\n",
       "      <td>10.402</td>\n",
       "      <td>3.3972</td>\n",
       "      <td>47.5559</td>\n",
       "      <td>55.5207</td>\n",
       "      <td>23.8037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  DecisionTreeRegressor BaggingRegressor RandomForestRegressor  \\\n",
       "0               6.55172          7.45086               7.82931   \n",
       "1                37.276          13.5364               15.0812   \n",
       "2                     0                0                     0   \n",
       "3               4.83871          31.8898               33.3501   \n",
       "4              -26.1438          21.5672               8.87081   \n",
       "\n",
       "  ExtraTreesRegressor   XGBoost Polynomial_degree:2 with dimensions: 36   \\\n",
       "0             6.55172   6.74156                                  7.92854   \n",
       "1             25.3918   18.0784                                  29.6592   \n",
       "2                   0 -0.106186                                  8.60285   \n",
       "3              34.205   13.8054                                   50.051   \n",
       "4              10.402    3.3972                                  47.5559   \n",
       "\n",
       "  Polynomial_degree:3 with dimensions: 120   \\\n",
       "0                                   7.24409   \n",
       "1                                   34.0707   \n",
       "2                                  -10.2302   \n",
       "3                                   22.4718   \n",
       "4                                   55.5207   \n",
       "\n",
       "  Polynomial_degree:4 with dimensions: 330   \n",
       "0                                   23.0881  \n",
       "1                                    36.622  \n",
       "2                                  -13.7669  \n",
       "3                                   30.2563  \n",
       "4                                   23.8037  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ERRS.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EkiRJkrQ0+vLYYzlZ/ZUU83VNAtdm5mMRcXNEnAYQETcAtwMviYivRsQbys0/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mAAAg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\nWkv4BQAAAEBrCb8AAAAAaK3/Hzig7Df7QlmMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115d92110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "font = {'family' : 'normal',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 7}\n",
    "fig = plt.figure(figsize=(15,15))\n",
    "for i in range(ERRS.shape[1]):\n",
    "    plt.subplot(3,3,i+1)\n",
    "    plt.hist(ERRS.values[:,i],bins=500,normed=True,color='black',alpha=.5)\n",
    "    plt.title(ERRS.columns[i])\n",
    "    plt.rc('font', **font)\n",
    "    plt.xlim(-50,50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking at the error distribution more quantitatively "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>median</th>\n",
       "      <th>1</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>15</th>\n",
       "      <th>20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DecisionTreeRegressor</th>\n",
       "      <td>4.71976</td>\n",
       "      <td>27.9116</td>\n",
       "      <td>51.2582</td>\n",
       "      <td>67.8928</td>\n",
       "      <td>78.7512</td>\n",
       "      <td>85.8571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BaggingRegressor</th>\n",
       "      <td>4.37519</td>\n",
       "      <td>18.0726</td>\n",
       "      <td>54.3261</td>\n",
       "      <td>75.7128</td>\n",
       "      <td>86.2412</td>\n",
       "      <td>91.8206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestRegressor</th>\n",
       "      <td>4.36536</td>\n",
       "      <td>18.0824</td>\n",
       "      <td>54.1981</td>\n",
       "      <td>75.8359</td>\n",
       "      <td>86.2904</td>\n",
       "      <td>91.7959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExtraTreesRegressor</th>\n",
       "      <td>3.96772</td>\n",
       "      <td>27.7589</td>\n",
       "      <td>55.8773</td>\n",
       "      <td>75.1957</td>\n",
       "      <td>85.8325</td>\n",
       "      <td>91.5152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost</th>\n",
       "      <td>4.122</td>\n",
       "      <td>16.2752</td>\n",
       "      <td>56.3353</td>\n",
       "      <td>78.1849</td>\n",
       "      <td>88.3094</td>\n",
       "      <td>93.5047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Polynomial_degree:2 with dimensions: 36</th>\n",
       "      <td>11.8859</td>\n",
       "      <td>4.52061</td>\n",
       "      <td>22.7803</td>\n",
       "      <td>43.4136</td>\n",
       "      <td>59.8316</td>\n",
       "      <td>71.4532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Polynomial_degree:3 with dimensions: 120</th>\n",
       "      <td>11.0283</td>\n",
       "      <td>4.73728</td>\n",
       "      <td>24.5088</td>\n",
       "      <td>46.0728</td>\n",
       "      <td>62.8798</td>\n",
       "      <td>74.7476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Polynomial_degree:4 with dimensions: 330</th>\n",
       "      <td>10.2554</td>\n",
       "      <td>5.47594</td>\n",
       "      <td>26.8725</td>\n",
       "      <td>48.8551</td>\n",
       "      <td>66.2038</td>\n",
       "      <td>78.0421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            median        1        5       10  \\\n",
       "DecisionTreeRegressor                      4.71976  27.9116  51.2582  67.8928   \n",
       "BaggingRegressor                           4.37519  18.0726  54.3261  75.7128   \n",
       "RandomForestRegressor                      4.36536  18.0824  54.1981  75.8359   \n",
       "ExtraTreesRegressor                        3.96772  27.7589  55.8773  75.1957   \n",
       "XGBoost                                      4.122  16.2752  56.3353  78.1849   \n",
       "Polynomial_degree:2 with dimensions: 36    11.8859  4.52061  22.7803  43.4136   \n",
       "Polynomial_degree:3 with dimensions: 120   11.0283  4.73728  24.5088  46.0728   \n",
       "Polynomial_degree:4 with dimensions: 330   10.2554  5.47594  26.8725  48.8551   \n",
       "\n",
       "                                                15       20  \n",
       "DecisionTreeRegressor                      78.7512  85.8571  \n",
       "BaggingRegressor                           86.2412  91.8206  \n",
       "RandomForestRegressor                      86.2904  91.7959  \n",
       "ExtraTreesRegressor                        85.8325  91.5152  \n",
       "XGBoost                                    88.3094  93.5047  \n",
       "Polynomial_degree:2 with dimensions: 36    59.8316  71.4532  \n",
       "Polynomial_degree:3 with dimensions: 120   62.8798  74.7476  \n",
       "Polynomial_degree:4 with dimensions: 330   66.2038  78.0421  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quality = [1,5,10,15,20]\n",
    "percentiles = pd.DataFrame(data= np.zeros((len(quality),ERRS.shape[1])),index=quality,columns=ERRS.columns)\n",
    "for percent in quality:\n",
    "    for m in range(ERRS.shape[1]):\n",
    "        a = np.abs(ERRS.values[:,m])\n",
    "        n = float(a.shape[0])\n",
    "        percentiles.ix[percent,m]= 100*a[a<=percent].shape[0]/n\n",
    "percentiles\n",
    "med_error = pd.DataFrame(data=np.median(np.abs(ERRS.values[:]),axis=0)[np.newaxis,:],columns=ERRS.columns,index=['median'])\n",
    "med_error\n",
    "percentiles = pd.concat((med_error,percentiles))\n",
    "percentiles.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other possible use cases\n",
    "* ** Spatial Price Sensitivity Analysis for developers and constructors**\n",
    "* **Trend analysis and forecasting for banks**\n",
    "* ** Estimating the dynamics of different region**\n",
    "* **Personalized real estate search for end users**\n",
    "* ** Many other use cases for other stakeholders: Banks, Real Estate Agents, Mortgage lenders, Movers,...**\n",
    "\n",
    "* ** Further: Using Machine Learning for Data Collection**\n",
    "    * Logging users interaction\n",
    "    * Receiving the feedbacks from users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Deploying the trained model on a web application"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **We need to serve the trained model like an API.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# from sklearn.externals import joblib\n",
    "\n",
    "# # Dump the trained model\n",
    "# joblib.dump(regr, 'model.pkl')\n",
    "\n",
    "# # Load it like this on the server\n",
    "# regr = joblib.load('model.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **To do so, we need a \"Web Framework\"** \n",
    "    * <a href=http://flask.pocoo.org/> Flask is a nice, minimal and easy one!</a>\n",
    "* **Further, we need a host and server which works with Flask.**\n",
    "    * <a href=https://www.pythonanywhere.com/> https://www.pythonanywhere.com/</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a style=\"color:red;font-size: 3em;\" href=http://www.keylead.ch/> I put them together here in a complete web application!</a>"
   ]
  }
 ],
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