Zillow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# List files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/home/sravya/data/Kaggle/zillow/zillow_data_dictionary.xlsx',\n",
      " '/home/sravya/data/Kaggle/zillow/sample_submission.csv',\n",
      " '/home/sravya/data/Kaggle/zillow/__MACOSX',\n",
      " '/home/sravya/data/Kaggle/zillow/properties_2016.csv',\n",
      " '/home/sravya/data/Kaggle/zillow/train_2016_v2.csv']\n"
     ]
    }
   ],
   "source": [
    "PATH='/home/sravya/data/Kaggle/zillow/'\n",
    "import glob\n",
    "import pprint\n",
    "pprint.pprint(glob.glob(PATH+'*'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Load from CSV using pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sravya/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (22,32,34,49,55) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "props = pd.read_csv('/home/sravya/data/Kaggle/zillow/properties_2016.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parcelid</th>\n",
       "      <th>airconditioningtypeid</th>\n",
       "      <th>architecturalstyletypeid</th>\n",
       "      <th>basementsqft</th>\n",
       "      <th>bathroomcnt</th>\n",
       "      <th>bedroomcnt</th>\n",
       "      <th>buildingclasstypeid</th>\n",
       "      <th>buildingqualitytypeid</th>\n",
       "      <th>calculatedbathnbr</th>\n",
       "      <th>decktypeid</th>\n",
       "      <th>...</th>\n",
       "      <th>numberofstories</th>\n",
       "      <th>fireplaceflag</th>\n",
       "      <th>structuretaxvaluedollarcnt</th>\n",
       "      <th>taxvaluedollarcnt</th>\n",
       "      <th>assessmentyear</th>\n",
       "      <th>landtaxvaluedollarcnt</th>\n",
       "      <th>taxamount</th>\n",
       "      <th>taxdelinquencyflag</th>\n",
       "      <th>taxdelinquencyyear</th>\n",
       "      <th>censustractandblock</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>193796.0</td>\n",
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       "      <td>2015.0</td>\n",
       "      <td>239695.0</td>\n",
       "      <td>5725.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   parcelid  airconditioningtypeid  architecturalstyletypeid  basementsqft  \\\n",
       "0  10754147                    NaN                       NaN           NaN   \n",
       "1  10759547                    NaN                       NaN           NaN   \n",
       "2  10843547                    NaN                       NaN           NaN   \n",
       "3  10859147                    NaN                       NaN           NaN   \n",
       "4  10879947                    NaN                       NaN           NaN   \n",
       "\n",
       "   bathroomcnt  bedroomcnt  buildingclasstypeid  buildingqualitytypeid  \\\n",
       "0          0.0         0.0                  NaN                    NaN   \n",
       "1          0.0         0.0                  NaN                    NaN   \n",
       "2          0.0         0.0                  NaN                    NaN   \n",
       "3          0.0         0.0                  3.0                    7.0   \n",
       "4          0.0         0.0                  4.0                    NaN   \n",
       "\n",
       "   calculatedbathnbr  decktypeid         ...           numberofstories  \\\n",
       "0                NaN         NaN         ...                       NaN   \n",
       "1                NaN         NaN         ...                       NaN   \n",
       "2                NaN         NaN         ...                       NaN   \n",
       "3                NaN         NaN         ...                       1.0   \n",
       "4                NaN         NaN         ...                       NaN   \n",
       "\n",
       "   fireplaceflag  structuretaxvaluedollarcnt  taxvaluedollarcnt  \\\n",
       "0            NaN                         NaN                9.0   \n",
       "1            NaN                         NaN            27516.0   \n",
       "2            NaN                    650756.0          1413387.0   \n",
       "3            NaN                    571346.0          1156834.0   \n",
       "4            NaN                    193796.0           433491.0   \n",
       "\n",
       "   assessmentyear  landtaxvaluedollarcnt  taxamount  taxdelinquencyflag  \\\n",
       "0          2015.0                    9.0        NaN                 NaN   \n",
       "1          2015.0                27516.0        NaN                 NaN   \n",
       "2          2015.0               762631.0   20800.37                 NaN   \n",
       "3          2015.0               585488.0   14557.57                 NaN   \n",
       "4          2015.0               239695.0    5725.17                 NaN   \n",
       "\n",
       "   taxdelinquencyyear  censustractandblock  \n",
       "0                 NaN                  NaN  \n",
       "1                 NaN                  NaN  \n",
       "2                 NaN                  NaN  \n",
       "3                 NaN                  NaN  \n",
       "4                 NaN                  NaN  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
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       "      <th>parcelid</th>\n",
       "      <th>logerror</th>\n",
       "      <th>transactiondate</th>\n",
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       "      <th>0</th>\n",
       "      <td>11016594</td>\n",
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       "      <td>2016-01-01</td>\n",
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       "      <td>-0.1684</td>\n",
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       "      <th>2</th>\n",
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       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
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       "      <td>2016-01-02</td>\n",
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       "      <td>2016-01-02</td>\n",
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      "text/plain": [
       "   parcelid  logerror transactiondate\n",
       "0  11016594    0.0276      2016-01-01\n",
       "1  14366692   -0.1684      2016-01-01\n",
       "2  12098116   -0.0040      2016-01-01\n",
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      ]
     },
     "execution_count": 6,
     "metadata": {},
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    }
   ],
   "source": [
    "train = pd.read_csv('/home/sravya/data/Kaggle/zillow/train_2016_v2.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ParcelId</th>\n",
       "      <th>201610</th>\n",
       "      <th>201611</th>\n",
       "      <th>201612</th>\n",
       "      <th>201710</th>\n",
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      "text/plain": [
       "   ParcelId  201610  201611  201612  201710  201711  201712\n",
       "0  10754147       0       0       0       0       0       0\n",
       "1  10759547       0       0       0       0       0       0\n",
       "2  10843547       0       0       0       0       0       0\n",
       "3  10859147       0       0       0       0       0       0\n",
       "4  10879947       0       0       0       0       0       0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_submission = pd.read_csv('/home/sravya/data/Kaggle/zillow/sample_submission.csv')\n",
    "sample_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "hidden": true
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'airconditioningtypeid'</td>\n",
       "      <td>Type of cooling system present in the home (i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>'architecturalstyletypeid'</td>\n",
       "      <td>Architectural style of the home (i.e. ranch, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>'basementsqft'</td>\n",
       "      <td>Finished living area below or partially below...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>'bathroomcnt'</td>\n",
       "      <td>Number of bathrooms in home including fractio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>'bedroomcnt'</td>\n",
       "      <td>Number of bedrooms in home</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                      Feature  \\\n",
       "0     'airconditioningtypeid'   \n",
       "1  'architecturalstyletypeid'   \n",
       "2              'basementsqft'   \n",
       "3               'bathroomcnt'   \n",
       "4                'bedroomcnt'   \n",
       "\n",
       "                                         Description  \n",
       "0   Type of cooling system present in the home (i...  \n",
       "1   Architectural style of the home (i.e. ranch, ...  \n",
       "2   Finished living area below or partially below...  \n",
       "3   Number of bathrooms in home including fractio...  \n",
       "4                        Number of bedrooms in home   "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary = pd.read_excel('/home/sravya/data/Kaggle/zillow/zillow_data_dictionary.xlsx')\n",
    "dictionary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generic exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2985217, 58)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>parcelid</th>\n",
       "      <th>airconditioningtypeid</th>\n",
       "      <th>architecturalstyletypeid</th>\n",
       "      <th>basementsqft</th>\n",
       "      <th>bathroomcnt</th>\n",
       "      <th>bedroomcnt</th>\n",
       "      <th>buildingclasstypeid</th>\n",
       "      <th>buildingqualitytypeid</th>\n",
       "      <th>calculatedbathnbr</th>\n",
       "      <th>decktypeid</th>\n",
       "      <th>...</th>\n",
       "      <th>yardbuildingsqft26</th>\n",
       "      <th>yearbuilt</th>\n",
       "      <th>numberofstories</th>\n",
       "      <th>structuretaxvaluedollarcnt</th>\n",
       "      <th>taxvaluedollarcnt</th>\n",
       "      <th>assessmentyear</th>\n",
       "      <th>landtaxvaluedollarcnt</th>\n",
       "      <th>taxamount</th>\n",
       "      <th>taxdelinquencyyear</th>\n",
       "      <th>censustractandblock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.985217e+06</td>\n",
       "      <td>811519.000000</td>\n",
       "      <td>6061.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>2.973755e+06</td>\n",
       "      <td>2.973767e+06</td>\n",
       "      <td>12629.000000</td>\n",
       "      <td>1.938488e+06</td>\n",
       "      <td>2.856305e+06</td>\n",
       "      <td>17096.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2647.000000</td>\n",
       "      <td>2.925289e+06</td>\n",
       "      <td>682069.000000</td>\n",
       "      <td>2.930235e+06</td>\n",
       "      <td>2.942667e+06</td>\n",
       "      <td>2.973778e+06</td>\n",
       "      <td>2.917484e+06</td>\n",
       "      <td>2.953967e+06</td>\n",
       "      <td>56464.000000</td>\n",
       "      <td>2.910091e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.332586e+07</td>\n",
       "      <td>1.931166</td>\n",
       "      <td>7.202607</td>\n",
       "      <td>646.883292</td>\n",
       "      <td>2.209143e+00</td>\n",
       "      <td>3.088949e+00</td>\n",
       "      <td>3.725948</td>\n",
       "      <td>5.784787e+00</td>\n",
       "      <td>2.299263e+00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>278.296562</td>\n",
       "      <td>1.964262e+03</td>\n",
       "      <td>1.401464</td>\n",
       "      <td>1.708836e+05</td>\n",
       "      <td>4.204790e+05</td>\n",
       "      <td>2.014999e+03</td>\n",
       "      <td>2.524780e+05</td>\n",
       "      <td>5.377607e+03</td>\n",
       "      <td>13.892409</td>\n",
       "      <td>6.048431e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.909966e+06</td>\n",
       "      <td>3.148587</td>\n",
       "      <td>2.436290</td>\n",
       "      <td>538.793473</td>\n",
       "      <td>1.077754e+00</td>\n",
       "      <td>1.275859e+00</td>\n",
       "      <td>0.501700</td>\n",
       "      <td>1.805352e+00</td>\n",
       "      <td>1.000736e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>369.731508</td>\n",
       "      <td>2.344132e+01</td>\n",
       "      <td>0.539076</td>\n",
       "      <td>4.020683e+05</td>\n",
       "      <td>7.263467e+05</td>\n",
       "      <td>3.683161e-02</td>\n",
       "      <td>4.450132e+05</td>\n",
       "      <td>9.183107e+03</td>\n",
       "      <td>2.581006</td>\n",
       "      <td>3.249035e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.071172e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.801000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+03</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.340000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.164371e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>272.000000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>1.950000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.480000e+04</td>\n",
       "      <td>1.796750e+05</td>\n",
       "      <td>2.015000e+03</td>\n",
       "      <td>7.483600e+04</td>\n",
       "      <td>2.461070e+03</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>6.037400e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.254509e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>534.000000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>1.963000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.225900e+05</td>\n",
       "      <td>3.060860e+05</td>\n",
       "      <td>2.015000e+03</td>\n",
       "      <td>1.670420e+05</td>\n",
       "      <td>3.991780e+03</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>6.037572e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.409712e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>847.250000</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>1.981000e+03</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.968890e+05</td>\n",
       "      <td>4.880000e+05</td>\n",
       "      <td>2.015000e+03</td>\n",
       "      <td>3.069180e+05</td>\n",
       "      <td>6.201005e+03</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>6.059042e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.696019e+08</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>8516.000000</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6141.000000</td>\n",
       "      <td>2.015000e+03</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>2.514860e+08</td>\n",
       "      <td>2.827860e+08</td>\n",
       "      <td>2.016000e+03</td>\n",
       "      <td>9.024622e+07</td>\n",
       "      <td>3.458861e+06</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>4.830301e+14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           parcelid  airconditioningtypeid  architecturalstyletypeid  \\\n",
       "count  2.985217e+06          811519.000000               6061.000000   \n",
       "mean   1.332586e+07               1.931166                  7.202607   \n",
       "std    7.909966e+06               3.148587                  2.436290   \n",
       "min    1.071172e+07               1.000000                  2.000000   \n",
       "25%    1.164371e+07               1.000000                  7.000000   \n",
       "50%    1.254509e+07               1.000000                  7.000000   \n",
       "75%    1.409712e+07               1.000000                  7.000000   \n",
       "max    1.696019e+08              13.000000                 27.000000   \n",
       "\n",
       "       basementsqft   bathroomcnt    bedroomcnt  buildingclasstypeid  \\\n",
       "count   1628.000000  2.973755e+06  2.973767e+06         12629.000000   \n",
       "mean     646.883292  2.209143e+00  3.088949e+00             3.725948   \n",
       "std      538.793473  1.077754e+00  1.275859e+00             0.501700   \n",
       "min       20.000000  0.000000e+00  0.000000e+00             1.000000   \n",
       "25%      272.000000  2.000000e+00  2.000000e+00             3.000000   \n",
       "50%      534.000000  2.000000e+00  3.000000e+00             4.000000   \n",
       "75%      847.250000  3.000000e+00  4.000000e+00             4.000000   \n",
       "max     8516.000000  2.000000e+01  2.000000e+01             5.000000   \n",
       "\n",
       "       buildingqualitytypeid  calculatedbathnbr  decktypeid  \\\n",
       "count           1.938488e+06       2.856305e+06     17096.0   \n",
       "mean            5.784787e+00       2.299263e+00        66.0   \n",
       "std             1.805352e+00       1.000736e+00         0.0   \n",
       "min             1.000000e+00       1.000000e+00        66.0   \n",
       "25%             4.000000e+00       2.000000e+00        66.0   \n",
       "50%             7.000000e+00       2.000000e+00        66.0   \n",
       "75%             7.000000e+00       3.000000e+00        66.0   \n",
       "max             1.200000e+01       2.000000e+01        66.0   \n",
       "\n",
       "              ...           yardbuildingsqft26     yearbuilt  numberofstories  \\\n",
       "count         ...                  2647.000000  2.925289e+06    682069.000000   \n",
       "mean          ...                   278.296562  1.964262e+03         1.401464   \n",
       "std           ...                   369.731508  2.344132e+01         0.539076   \n",
       "min           ...                    10.000000  1.801000e+03         1.000000   \n",
       "25%           ...                    96.000000  1.950000e+03         1.000000   \n",
       "50%           ...                   168.000000  1.963000e+03         1.000000   \n",
       "75%           ...                   320.000000  1.981000e+03         2.000000   \n",
       "max           ...                  6141.000000  2.015000e+03        41.000000   \n",
       "\n",
       "       structuretaxvaluedollarcnt  taxvaluedollarcnt  assessmentyear  \\\n",
       "count                2.930235e+06       2.942667e+06    2.973778e+06   \n",
       "mean                 1.708836e+05       4.204790e+05    2.014999e+03   \n",
       "std                  4.020683e+05       7.263467e+05    3.683161e-02   \n",
       "min                  1.000000e+00       1.000000e+00    2.000000e+03   \n",
       "25%                  7.480000e+04       1.796750e+05    2.015000e+03   \n",
       "50%                  1.225900e+05       3.060860e+05    2.015000e+03   \n",
       "75%                  1.968890e+05       4.880000e+05    2.015000e+03   \n",
       "max                  2.514860e+08       2.827860e+08    2.016000e+03   \n",
       "\n",
       "       landtaxvaluedollarcnt     taxamount  taxdelinquencyyear  \\\n",
       "count           2.917484e+06  2.953967e+06        56464.000000   \n",
       "mean            2.524780e+05  5.377607e+03           13.892409   \n",
       "std             4.450132e+05  9.183107e+03            2.581006   \n",
       "min             1.000000e+00  1.340000e+00            0.000000   \n",
       "25%             7.483600e+04  2.461070e+03           14.000000   \n",
       "50%             1.670420e+05  3.991780e+03           14.000000   \n",
       "75%             3.069180e+05  6.201005e+03           15.000000   \n",
       "max             9.024622e+07  3.458861e+06           99.000000   \n",
       "\n",
       "       censustractandblock  \n",
       "count         2.910091e+06  \n",
       "mean          6.048431e+13  \n",
       "std           3.249035e+11  \n",
       "min          -1.000000e+00  \n",
       "25%           6.037400e+13  \n",
       "50%           6.037572e+13  \n",
       "75%           6.059042e+13  \n",
       "max           4.830301e+14  \n",
       "\n",
       "[8 rows x 53 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parcelid</th>\n",
       "      <th>airconditioningtypeid</th>\n",
       "      <th>architecturalstyletypeid</th>\n",
       "      <th>basementsqft</th>\n",
       "      <th>bathroomcnt</th>\n",
       "      <th>bedroomcnt</th>\n",
       "      <th>buildingclasstypeid</th>\n",
       "      <th>buildingqualitytypeid</th>\n",
       "      <th>calculatedbathnbr</th>\n",
       "      <th>decktypeid</th>\n",
       "      <th>...</th>\n",
       "      <th>yardbuildingsqft26</th>\n",
       "      <th>yearbuilt</th>\n",
       "      <th>numberofstories</th>\n",
       "      <th>structuretaxvaluedollarcnt</th>\n",
       "      <th>taxvaluedollarcnt</th>\n",
       "      <th>assessmentyear</th>\n",
       "      <th>landtaxvaluedollarcnt</th>\n",
       "      <th>taxamount</th>\n",
       "      <th>taxdelinquencyyear</th>\n",
       "      <th>censustractandblock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2985217</td>\n",
       "      <td>811519</td>\n",
       "      <td>6061</td>\n",
       "      <td>1628</td>\n",
       "      <td>2973755</td>\n",
       "      <td>2973767</td>\n",
       "      <td>12629</td>\n",
       "      <td>1938488</td>\n",
       "      <td>2856305</td>\n",
       "      <td>17096</td>\n",
       "      <td>...</td>\n",
       "      <td>2647</td>\n",
       "      <td>2925289</td>\n",
       "      <td>682069</td>\n",
       "      <td>2930235</td>\n",
       "      <td>2942667</td>\n",
       "      <td>2973778</td>\n",
       "      <td>2917484</td>\n",
       "      <td>2953967</td>\n",
       "      <td>56464</td>\n",
       "      <td>2910091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13325858</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>647</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>278</td>\n",
       "      <td>1964</td>\n",
       "      <td>1</td>\n",
       "      <td>170884</td>\n",
       "      <td>420479</td>\n",
       "      <td>2015</td>\n",
       "      <td>252478</td>\n",
       "      <td>5378</td>\n",
       "      <td>14</td>\n",
       "      <td>60484312212574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7909966</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>539</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>370</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>402068</td>\n",
       "      <td>726347</td>\n",
       "      <td>0</td>\n",
       "      <td>445013</td>\n",
       "      <td>9183</td>\n",
       "      <td>3</td>\n",
       "      <td>324903454737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10711725</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>1801</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11643707</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>96</td>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>74800</td>\n",
       "      <td>179675</td>\n",
       "      <td>2015</td>\n",
       "      <td>74836</td>\n",
       "      <td>2461</td>\n",
       "      <td>14</td>\n",
       "      <td>60374002041015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12545094</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>534</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>168</td>\n",
       "      <td>1963</td>\n",
       "      <td>1</td>\n",
       "      <td>122590</td>\n",
       "      <td>306086</td>\n",
       "      <td>2015</td>\n",
       "      <td>167042</td>\n",
       "      <td>3992</td>\n",
       "      <td>14</td>\n",
       "      <td>60375715022011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14097122</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>847</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>320</td>\n",
       "      <td>1981</td>\n",
       "      <td>2</td>\n",
       "      <td>196889</td>\n",
       "      <td>488000</td>\n",
       "      <td>2015</td>\n",
       "      <td>306918</td>\n",
       "      <td>6201</td>\n",
       "      <td>15</td>\n",
       "      <td>60590423191014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>169601949</td>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>8516</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>6141</td>\n",
       "      <td>2015</td>\n",
       "      <td>41</td>\n",
       "      <td>251486000</td>\n",
       "      <td>282786000</td>\n",
       "      <td>2016</td>\n",
       "      <td>90246219</td>\n",
       "      <td>3458861</td>\n",
       "      <td>99</td>\n",
       "      <td>483030105084015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       parcelid  airconditioningtypeid  architecturalstyletypeid  \\\n",
       "count   2985217                 811519                      6061   \n",
       "mean   13325858                      2                         7   \n",
       "std     7909966                      3                         2   \n",
       "min    10711725                      1                         2   \n",
       "25%    11643707                      1                         7   \n",
       "50%    12545094                      1                         7   \n",
       "75%    14097122                      1                         7   \n",
       "max   169601949                     13                        27   \n",
       "\n",
       "       basementsqft  bathroomcnt  bedroomcnt  buildingclasstypeid  \\\n",
       "count          1628      2973755     2973767                12629   \n",
       "mean            647            2           3                    4   \n",
       "std             539            1           1                    1   \n",
       "min              20            0           0                    1   \n",
       "25%             272            2           2                    3   \n",
       "50%             534            2           3                    4   \n",
       "75%             847            3           4                    4   \n",
       "max            8516           20          20                    5   \n",
       "\n",
       "       buildingqualitytypeid  calculatedbathnbr  decktypeid  \\\n",
       "count                1938488            2856305       17096   \n",
       "mean                       6                  2          66   \n",
       "std                        2                  1           0   \n",
       "min                        1                  1          66   \n",
       "25%                        4                  2          66   \n",
       "50%                        7                  2          66   \n",
       "75%                        7                  3          66   \n",
       "max                       12                 20          66   \n",
       "\n",
       "              ...           yardbuildingsqft26  yearbuilt  numberofstories  \\\n",
       "count         ...                         2647    2925289           682069   \n",
       "mean          ...                          278       1964                1   \n",
       "std           ...                          370         23                1   \n",
       "min           ...                           10       1801                1   \n",
       "25%           ...                           96       1950                1   \n",
       "50%           ...                          168       1963                1   \n",
       "75%           ...                          320       1981                2   \n",
       "max           ...                         6141       2015               41   \n",
       "\n",
       "       structuretaxvaluedollarcnt  taxvaluedollarcnt  assessmentyear  \\\n",
       "count                     2930235            2942667         2973778   \n",
       "mean                       170884             420479            2015   \n",
       "std                        402068             726347               0   \n",
       "min                             1                  1            2000   \n",
       "25%                         74800             179675            2015   \n",
       "50%                        122590             306086            2015   \n",
       "75%                        196889             488000            2015   \n",
       "max                     251486000          282786000            2016   \n",
       "\n",
       "       landtaxvaluedollarcnt  taxamount  taxdelinquencyyear  \\\n",
       "count                2917484    2953967               56464   \n",
       "mean                  252478       5378                  14   \n",
       "std                   445013       9183                   3   \n",
       "min                        1          1                   0   \n",
       "25%                    74836       2461                  14   \n",
       "50%                   167042       3992                  14   \n",
       "75%                   306918       6201                  15   \n",
       "max                 90246219    3458861                  99   \n",
       "\n",
       "       censustractandblock  \n",
       "count              2910091  \n",
       "mean        60484312212574  \n",
       "std           324903454737  \n",
       "min                     -1  \n",
       "25%         60374002041015  \n",
       "50%         60375715022011  \n",
       "75%         60590423191014  \n",
       "max        483030105084015  \n",
       "\n",
       "[8 rows x 53 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('precision', 6)\n",
    "pd.set_option('display.float_format', lambda x: '%.0f' % x)\n",
    "props.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   202717\n",
       "mean      1381\n",
       "std        633\n",
       "min          3\n",
       "25%       1012\n",
       "50%       1283\n",
       "75%       1615\n",
       "max      31303\n",
       "Name: finishedfloor1squarefeet, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props['finishedfloor1squarefeet'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   2973780\n",
       "mean       6048\n",
       "std          20\n",
       "min        6037\n",
       "25%        6037\n",
       "50%        6037\n",
       "75%        6059\n",
       "max        6111\n",
       "Name: fips, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props['fips'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     5163\n",
       "unique       1\n",
       "top       True\n",
       "freq      5163\n",
       "Name: fireplaceflag, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props['fireplaceflag'].describe()\n",
    "# Are some of the missing values false? or all?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     69014\n",
       "unique        1\n",
       "top        True\n",
       "freq      69014\n",
       "Name: hashottuborspa, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props['hashottuborspa'].describe()\n",
    "# Are some of the missing values false? or all?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   1806401\n",
       "mean          4\n",
       "std           3\n",
       "min           1\n",
       "25%           2\n",
       "50%           2\n",
       "75%           7\n",
       "max          24\n",
       "Name: heatingorsystemtypeid, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props['heatingorsystemtypeid'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   36939\n",
       "mean        1\n",
       "std         0\n",
       "min         1\n",
       "25%         1\n",
       "50%         1\n",
       "75%         1\n",
       "max         1\n",
       "Name: pooltypeid10, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props['pooltypeid10'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   32075\n",
       "mean        1\n",
       "std         0\n",
       "min         1\n",
       "25%         1\n",
       "50%         1\n",
       "75%         1\n",
       "max         1\n",
       "Name: pooltypeid2, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props['pooltypeid2'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   485459\n",
       "mean         1\n",
       "std          0\n",
       "min          1\n",
       "25%          1\n",
       "50%          1\n",
       "75%          1\n",
       "max          1\n",
       "Name: pooltypeid7, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props['pooltypeid7'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     2972940\n",
       "unique        240\n",
       "top          0100\n",
       "freq      1153896\n",
       "Name: propertycountylandusecode, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props['propertycountylandusecode'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   2973780\n",
       "mean        260\n",
       "std          16\n",
       "min          31\n",
       "25%         261\n",
       "50%         261\n",
       "75%         261\n",
       "max         275\n",
       "Name: propertylandusetypeid, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props['propertylandusetypeid'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     1978629\n",
       "unique       5638\n",
       "top          LAR1\n",
       "freq       275029\n",
       "Name: propertyzoningdesc, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props['propertyzoningdesc'].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2973780\n",
       "mean    60483450\n",
       "std       200812\n",
       "min     60371011\n",
       "25%     60373203\n",
       "50%     60375712\n",
       "75%     60590423\n",
       "max     61110091\n",
       "Name: rawcensustractandblock, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props['rawcensustractandblock'].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   1156402\n",
       "mean     193476\n",
       "std      165713\n",
       "min        6952\n",
       "25%       46736\n",
       "50%      118920\n",
       "75%      274800\n",
       "max      764167\n",
       "Name: regionidneighborhood, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props['regionidneighborhood'].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   80355\n",
       "mean      320\n",
       "std       233\n",
       "min        10\n",
       "25%       190\n",
       "50%       270\n",
       "75%       390\n",
       "max      7983\n",
       "Name: yardbuildingsqft17, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props['yardbuildingsqft17'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     56462\n",
       "unique        1\n",
       "top           Y\n",
       "freq      56462\n",
       "Name: taxdelinquencyflag, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props['taxdelinquencyflag'].describe()\n",
    "# Are some of the missing values false? or all?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Data types of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "parcelid                          int64\n",
       "airconditioningtypeid           float64\n",
       "architecturalstyletypeid        float64\n",
       "basementsqft                    float64\n",
       "bathroomcnt                     float64\n",
       "bedroomcnt                      float64\n",
       "buildingclasstypeid             float64\n",
       "buildingqualitytypeid           float64\n",
       "calculatedbathnbr               float64\n",
       "decktypeid                      float64\n",
       "finishedfloor1squarefeet        float64\n",
       "calculatedfinishedsquarefeet    float64\n",
       "finishedsquarefeet12            float64\n",
       "finishedsquarefeet13            float64\n",
       "finishedsquarefeet15            float64\n",
       "finishedsquarefeet50            float64\n",
       "finishedsquarefeet6             float64\n",
       "fips                            float64\n",
       "fireplacecnt                    float64\n",
       "fullbathcnt                     float64\n",
       "garagecarcnt                    float64\n",
       "garagetotalsqft                 float64\n",
       "hashottuborspa                   object\n",
       "heatingorsystemtypeid           float64\n",
       "latitude                        float64\n",
       "longitude                       float64\n",
       "lotsizesquarefeet               float64\n",
       "poolcnt                         float64\n",
       "poolsizesum                     float64\n",
       "pooltypeid10                    float64\n",
       "pooltypeid2                     float64\n",
       "pooltypeid7                     float64\n",
       "propertycountylandusecode        object\n",
       "propertylandusetypeid           float64\n",
       "propertyzoningdesc               object\n",
       "rawcensustractandblock          float64\n",
       "regionidcity                    float64\n",
       "regionidcounty                  float64\n",
       "regionidneighborhood            float64\n",
       "regionidzip                     float64\n",
       "roomcnt                         float64\n",
       "storytypeid                     float64\n",
       "threequarterbathnbr             float64\n",
       "typeconstructiontypeid          float64\n",
       "unitcnt                         float64\n",
       "yardbuildingsqft17              float64\n",
       "yardbuildingsqft26              float64\n",
       "yearbuilt                       float64\n",
       "numberofstories                 float64\n",
       "fireplaceflag                    object\n",
       "structuretaxvaluedollarcnt      float64\n",
       "taxvaluedollarcnt               float64\n",
       "assessmentyear                  float64\n",
       "landtaxvaluedollarcnt           float64\n",
       "taxamount                       float64\n",
       "taxdelinquencyflag               object\n",
       "taxdelinquencyyear              float64\n",
       "censustractandblock             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing values analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "missing_counts = props.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "parcelid                              0\n",
       "airconditioningtypeid           2173698\n",
       "architecturalstyletypeid        2979156\n",
       "basementsqft                    2983589\n",
       "bathroomcnt                       11462\n",
       "bedroomcnt                        11450\n",
       "buildingclasstypeid             2972588\n",
       "buildingqualitytypeid           1046729\n",
       "calculatedbathnbr                128912\n",
       "decktypeid                      2968121\n",
       "finishedfloor1squarefeet        2782500\n",
       "calculatedfinishedsquarefeet      55565\n",
       "finishedsquarefeet12             276033\n",
       "finishedsquarefeet13            2977545\n",
       "finishedsquarefeet15            2794419\n",
       "finishedsquarefeet50            2782500\n",
       "finishedsquarefeet6             2963216\n",
       "fips                              11437\n",
       "fireplacecnt                    2672580\n",
       "fullbathcnt                      128912\n",
       "garagecarcnt                    2101950\n",
       "garagetotalsqft                 2101950\n",
       "hashottuborspa                  2916203\n",
       "heatingorsystemtypeid           1178816\n",
       "latitude                          11437\n",
       "longitude                         11437\n",
       "lotsizesquarefeet                276099\n",
       "poolcnt                         2467683\n",
       "poolsizesum                     2957257\n",
       "pooltypeid10                    2948278\n",
       "pooltypeid2                     2953142\n",
       "pooltypeid7                     2499758\n",
       "propertycountylandusecode         12277\n",
       "propertylandusetypeid             11437\n",
       "propertyzoningdesc              1006588\n",
       "rawcensustractandblock            11437\n",
       "regionidcity                      62845\n",
       "regionidcounty                    11437\n",
       "regionidneighborhood            1828815\n",
       "regionidzip                       13980\n",
       "roomcnt                           11475\n",
       "storytypeid                     2983593\n",
       "threequarterbathnbr             2673586\n",
       "typeconstructiontypeid          2978470\n",
       "unitcnt                         1007727\n",
       "yardbuildingsqft17              2904862\n",
       "yardbuildingsqft26              2982570\n",
       "yearbuilt                         59928\n",
       "numberofstories                 2303148\n",
       "fireplaceflag                   2980054\n",
       "structuretaxvaluedollarcnt        54982\n",
       "taxvaluedollarcnt                 42550\n",
       "assessmentyear                    11439\n",
       "landtaxvaluedollarcnt             67733\n",
       "taxamount                         31250\n",
       "taxdelinquencyflag              2928755\n",
       "taxdelinquencyyear              2928753\n",
       "censustractandblock               75126\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(missing_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OkkwiIqKzJJOIiOgsySQiIjpLMomIiM6STCIiorMkk4iI6CzJJCIiOksyiYiIzpJMIiKi\nsySTiIjoLMkkIiI6SzKJiIjOkkwiIqKzJJOIiOgsySQiIjpLMomIiM46JRNJN0m6WtIVkubXsg0l\nnSvpx/V2g1ouSZ+QtFDSVZKeMfQ8B9TzfyzpgKHy7evzL6yP1WR1REREP1bElcnOtre1Pa8eHwac\nZ3sucF49BtgDmFt/DgaOgZIYgCOAHYBnAUcMJYdj6rmDx+0+RR0REdGDmWjm2hM4qf5+ErDXUPnJ\nLi4C1pf0OGA34Fzbd9i+EzgX2L3et67tC20bOHnMc41XR0RE9KBrMjHwTUkLJB1cyx5r+xaAevuY\nWr4x8LOhxy6qZZOVLxqnfLI6IiKiB6t1fPxzbN8s6THAuZJ+NMm5GqfMy1E+bTXBHQyw2WabLctD\nIyJiGXS6MrF9c729Ffgypc/jF7WJinp7az19EbDp0MM3AW6eonyTccqZpI6x8R1re57teXPmzFne\nf2ZERExhuZOJpLUlrTP4HdgV+CFwFjAYkXUAcGb9/Sxg/zqqa0fgrtpEdQ6wq6QNasf7rsA59b7F\nknaso7j2H/Nc49URERE96NLM9Vjgy3W07mrAqba/IelS4HRJBwE/Bfap558NvAhYCPwGOBDA9h2S\n3g9cWs97n+076u+HACcCawFfrz8AH5qgjoiI6MFyJxPbNwBPH6f8l8Dzxyk3cOgEz3U8cPw45fOB\nbaZbR0RE9CMz4CMiorMkk4iI6CzJJCIiOksyiYiIzpJMIiKisySTiIjoLMkkIiI6SzKJiIjOkkwi\nIqKzJJOIiOgsySQiIjpLMomIiM6STCIiorMkk4iI6CzJJCIiOksyiYiIzpJMIiKisySTiIjoLMkk\nIiI6SzKJiIjOkkwiIqKz1foOICJittn8sK91fo6bPvTiFRDJ6MiVSUREdJZkEhERnSWZREREZ0km\nERHR2axOJpJ2l3S9pIWSDus7noiIldWsTSaSVgU+CewBbA3sJ2nrfqOKiFg5zdpkAjwLWGj7Btv3\nAacBe/YcU0TESmk2zzPZGPjZ0PEiYIeeYomHqVGZTzAqcURMRLb7jmG5SNoH2M32G+rx64Fn2X7r\n0DkHAwfXw62A6ztWuxFwe8fn6GoUYoDRiGMUYoDRiGMUYoDRiGMUYoDRiGNFxPAE23OmOmk2X5ks\nAjYdOt4EuHn4BNvHAseuqAolzbc9b0U932yNYVTiGIUYRiWOUYhhVOIYhRhGJY6WMczmPpNLgbmS\ntpC0OrAvcFbPMUVErJRm7ZWJ7fslvQU4B1gVON72NT2HFRGxUpq1yQTA9tnA2Q2rXGFNZh2MQgww\nGnGMQgwwGnGMQgwwGnGMQgwwGnE0i2HWdsBHRMTomM19JhERMSKSTCIiorMkk4hZStIa0ymLmadi\n06nPfPhKn8kEJL1isvttf6lVLH0bpddC0odtv3OqsgZxnGf7+VOVzXAMl9l+xlRlD2eSJv232r6s\nYSwLbG/fqr5x6u/1tZjVo7lm2Evr7WOAZwPfrsc7A98BZvwDVNJfTXa/7Y/OdAxV76/FkBcCYxPH\nHuOUzQhJawKPBDaStAGgete6wOMbxfAHlOWE1pK03ZgYHtkihhrH1cCE30ZtP61BGEfV2zWBecCV\nlNfjacDFwE4NYhi4SNIzbV/asM5hvb4WSSYTsH0ggKSvAlvbvqUeP46yWnEL69TbrYBnsmRS5kuB\n7zaKYSReC0mHAG8GtpR01dBd6wDfbxFD9UbgHZTEsYAlH+R30+59sRvwZ5RVH4a/UCwG3t0oBoCX\n1NtD6+0p9fa1wG9aBGB7ZwBJpwEH2766Hm8D/HWLGIbsDLxR0k+AX1PeG26UVHt/LdLMNQVJP7S9\nzdDxKsBVw2UNYvgm8Erbi+vxOsB/2N69VQy13t5ei/oN/E7gg8Dw3jWLbd8x0/WPE89bbR/dut4x\nMbzS9hf7jKHG8X3bz5mqbIZjuML2tlOVzXAMTxiv3PZPWsVQ4+jltciVydS+I+kc4POUS/p9gfMb\nx7AZcN/Q8X3A5o1jgH5fi8/Y3l7SY1r/cY7H9tGSnk35f1htqPzkhmF8VdJrxonhfQ1jAFhb0k62\nLwCor8vajWO4TtJngM9S3puvA65rGYDtn9R+i51qDN9v2WczpJfXIlcm01A7oP+kHn7X9pcb1384\n8Crgy5Q3x8uB023/Y8s4aiy9vBaSLge+ArwB+NjY+xv2Hw3iOQXYErgCeGBJGH5bwxi+AdxFaW4b\nxIDtoyZ80MzEsT1wPLBeLfoV8OeNO7/XBA4BnluLvgscY/uehjH8A7APS/oQ96K0IHygVQw1jl5e\niySTWaJ+4xn+EL+8z3hak7QV5Y/zHcCnx95v+72N47mO0n/U2x/Q2GbHvklal/KZclffsfShvie2\nG3xoS1oLuMz2U/qNrI00c01A0gW2d5K0mKVHrAw61dZtEMO6tu+WtCFwU/0Z3Ldhq76CUXgtbF8P\nfFjSVba/PtP1TcMPgT8Abukxhh9I+qNBR2trkl5n+7NjRx1KZUxCi6tFSafbftVEI8tadX5XN1FG\nUg2uANYA/qdV5X2/FkkmE7C9U71dZ6pzZ9CplBEzCyhvDo25fWKLIEbktRh4iqQJv+k1bO7aCLhW\n0iXAvUP1v6xR/VDa5v9M0o01hqajh1jSL9Ln++Lt9fYlk541gyQdTfl7vBe4RtK59fiFwAUNQ+n1\ntUgz1wTq1cCE+hhBNAok7QTMtX2CpI2AdWzf2LD+Uxl/mPTPoF1zl6Q/Ha/c9n+1qL/GMBKjh0ZF\nfT3m2v5WbWJabTACcobrPWCy+22fNNMxjNXHa5FkMoH6bW9wFTCWbTe5KqixiDJ2fwvb75e0GfAH\nti9pFUON4wjKZKitbD9J0uMpHYwth4COyjDpLYBbxrSPP9b2TQ1j2BG4ZsxrsbXti1vFUOt9EnAM\n5d+/jaSnAS9r2fEs6S8oW3RvaHtLSXOBT7dckWBU9PVaZG2uCdjewvYT6+3Yn2aJpPoU8MfAa+rx\nYtpNkBv2cuBllAlZ2L6Z9k0cozJM+j+A3w8dP1DLWjoG+L+h41/Xstb+HXgX8DsA21dRho23dCjw\nHMrkUWz/mLJiQzOSniPpXEn/LekGSTdKuqFlDFUvr0X6TKYwIlcFO9h+Rh0ei+07VbYqbu0+25Zk\nAEmt5xJAmWV9iaThYdLNmxEozQYPJjXb9/Xwf6Lh0WS2fy+pj7/pR9q+ZNDxXt3fOIZ76/8BAPV1\naN3schzwl4wZqt2DXl6LXJlMbRSuCn4naVXqG0LSHJb+VtzK6ZL+DVi/Xkp/i/KttBnbRwIHUmbD\n/wo40PYHW8ZQ3Sbpwc52SXsCtzeO4QZJb5P0iPrzdqCPb8K3S9qSJe/PvWk/yu2/JL2bsl7ZCylX\nif/ZOIa7bH/d9q22fzn4aRwD9PRapM9kCqqrsEq63PZ2texK209vGMNrgVcD2wMnAnsDf2e7dbMK\n9c25az38pu1zG9e/JbDI9r2SnkdZxO5k27/qIY7PUdboEmUAwP62FzaM4THAJ4BdKB/k5wHvsH1r\nqxhqHE+kbA/7bEqSvxF4bcuBACpL+xxEeW8KOIeyakKzDzhJHwJWpUxaHB7h13QWfF+vRZLJFCRd\nTPkjubQmlTmUD9HtGsfxZGDQgfZt202XihiK4w+AZ1E+vC61/b+N67+CMghgc+AblG9cW9l+Ucs4\nhuJ5FOXvaMZHDY262uy5Sl+vRW1mfDLlvXn9cDNko/rHW1rItndpGUeNpflrkT6TqX2CsozJYyQd\nSb0q6CGOR1K+9RhYq4f6kfQG4B8oS9ALOFrS+2wf3zCM39u+X2VZl4+7rJHVfDUASesD+1PXxRqa\nqNdyOZUtgLfy0LW5Ws51QdKjgSOoa1JJugB4X8smHkkvpqyM8D+U9+YWkt7YcoKr66q9fevrtciV\nyTQMXRUIOK/1VYGWrPnzxRpDX2v+XA88e/AhUT9EfmB7q4YxXAz8C3A48FLbN6qHZUUk/QC4CLia\nof6rlnMKJF1J6fQdG0OzuS41jnMpc30+W4teCzzP9gsaxvAj4CWDZsbaDPk1209uGMMawCvpeeHN\nvl6LXJlMYWgs/yfr8TqSdmg8ln8/ll7z50PAZUDTZAIsogxAGFhMnSzY0IHAm4AjayLZgiUfYi2t\naXvSzcsauMf2J3qOAcp8hvcPHX9A0l6NY7h1TH/VDUDTviPgTJYsvHnvFOfOpF5eiySTqR0DDG+H\n+etxymbaTfS45s+QnwMXSzqT0ty2J2WY7l9Bs6VMXjjclFQTym8b1DvWKXVE21dZurO15coIH68T\nSb9Jjx2+wPmS9gVOr8d7A19rHMM1ks6uMZhyJX9pbQ5ttbX0Jq0nz06gl9cizVxT0PgbzVzlhgvI\nSfoKZQmRsWv+3Art2unrB9eE3GApE42/7/mDI+1akXQocCRlePLgj6j1yggfBF5P+WIxaOZq3uGr\nsgDo2iyZW7EqdWIr7RZFPWGSu237zxvEcCxwtHtaeHMojl5eiySTKUj6EmWf88HM4jcDO9tudhmv\nEVn7R9I2tn/Yoq5x6t6PMtdnJ+B7Q3etAzzQsn2+xvM/lMmkreeWDMfwI+BprUctjSI1XEV7nLoH\nq/SuBsylNCv1sfDmIJ5eXos0c03tTZQRXX/HkrH8BzeO4ZfA2bb7mKg47NN1yOGJwKmN53b8gDIR\nbiNgePOnxcBV4z5iZl1Do33OJ3ElsD7t+waWIukMyuZY3+jxPXpxHTZ+AvD1lvNL6HHF4gn08lrk\nymQSddb522w/ZGe/xnF8ljIL/4vACX3NMamxPInSCb4PcAlwou1v9hVPX+pyLk+lbFs83F/Rcmjw\ndyiTNi+lv2XwkfQCyntiR8ps6xNt/6hxDAJeAPw5ZR7UF2oc/90whlNsv36qsgZx9PJaJJlMQdJ3\nbD9vBOJYlzKq60DKFdIJwOf7mCBWk+xelCu2uymX8+9u0clZOxE/TFm4TtBug64xcYzb9Nh4aHDv\ny+APk7Qe5T16OGWU378Dn7X9u8Zx7EwZ4bc25ertMNsXNqh3qf68+ndyte2tZ7ruSWJq9lokmUyh\nTlRcj5LdB52KfYyYQWX/kNdRtq69DvhD4BO2j25U/9MoyezFlMEAx9m+TGUp+gttj7u/xgqOYSFl\nfklvV2dDsawOPKkeXt/6Q7PG8FjK4AyAS1ovpTIUx6Mp783XAzdTlprZCfijFl/GxtT/C8r8m7OA\nbSlzsraYwbrfBbybMpl40PQpyorWx9p+10zVPUE8vbwWSSZTGIUlElQWFDwQ2JKyau5Jtm+V9Ejg\nuhYf4jWO71K+bZ5h+7dj7nu97VMaxPB9N9w/ZZI4nkdZrfgmygfHpsABtr/bMIZXAf9EGSAi4E+A\nv7F9RqsYahxfoizdcQqlOeWWofvm257XIIb/rvWfYHvRmPveafvDDWL4YOvEMUEcvbwWSSazgKST\nKQu1PeSDStLzbZ/XKI532P6XMWVvt/3xFvXX+j5O2Xv9KyzdT9BiHsFwHAuA17jsTT/oS/q87e0b\nxnAlZd7NrfV4DvAtN1yEtNb7Ittnjylbw3aziXuSXmX79DFl+7jxYqiSNqCM6FpzUNbyC0aNoZfX\nIslkGupaN09l6TdIsyUSJH3Y9junKmsQR+9zPCYYQ99kHsGYOB4y16iH+UdX2/6joeNVgCuHyxrF\nMd774iFlK0EMb6Dsw74JcAVlQMKFPcz76eW1yNDgKUj6NGWRxZ2Bz1Bm9zbdLpcySXFs4thjnLIZ\nMTTHYwtJZw3dtQ5l2HIztg9sWd8k5ks6jtKcAGU9qgWNY/iGpHOAz9fjVwNnT3L+CqWygvTGlH0z\ntoMHt7hel/I30yKGPYAXARtLGl5aZl3ab9D1dkr/1UW2d1ZZ02/GJ/IO9P1aJJlM7dm2n1a/db5X\n0lGU/QpmnKRDKJMkt5Q0PJdiHeD7LWKoRmaOh0Zgv/HqEMr2qG+jfIh+l7KRWjO2/6aObtupxnCs\n7S83DGE34M8o38SPYkkyWUzpkG7hZmA+ZTvp4WS+mLLrYUv32L5H0qCZ70eSmi2CSs+vRZq5piDp\nYts7SLoIeAXlm/gPbc9tUPd6wAbAB4HDhu5a3ONs3ycAc21/S9JalO1rmw1PlvRfwN8A/+Ylm5U1\nXzW41rspKqwCAAAWJklEQVQ6sBVL9ozoazTXYH+ZXkZzSXql7S+2rndMDC+lrIzb28TeOvfoQMpo\ny10oG4U9wo332pH0TNuXjil7qe0Z3W0x2/ZO7asqe1d8hJLtbwJOa1Gx7bts30T5Brx46AdJj2gR\nwzCVhQ3PAP6tFm1C6Qhv6ZG2xzYztm7OGIzm+jHwr5Qrkv+W9NzGMbyK0uS6N/AqysznvVvGUG0i\naV0Vn5F0maRdp37YCvVq4MeSPiLpKY3rBsD2y23/yvZ7gL+nDMltvXoywL9JGu5L248WezDZzs8k\nP5Sx439F2SDrS5TLxTUbx3ATZRG92ylXRg9QloO/DNi+YRxXAKsDlw+VXd34tfg6ZYj0ZfV4b8qS\nEa3fFwsoOzwOjp8ELGgcw5XAY4aO51A64Fu/FlfW290o8xmePvj/aRzHusAbKfvMXEhZ9midxjE8\ng9L0+VbgGa1fgxrDE+tnw1OAv6CsZbfeTNebK5OpnUQZyfUJ4GjKf9DJjWP4BvAi2xvZfjSl8/10\nSn9Ky3b6ez20qKCk1ViyYm4rh1KujJ4s6eeUJoVDGscApfni+sGBy1IVra8WV/HSzVq/pJ/WhkFf\nyYsocxuuHCprxvbdlCWHTgMeB7wcuEzSW1vUr7KJ3UnAoyn9iydIar4rq+0bgH0pr8XewK6275rp\netNnMgVJV3rMuP3xymY4hodM/BqUaZwl8mcwjo9Qllzfn/LN683AtbYPb1H/mFj63m/8eEoiHR7N\ntZobjjaT9E+UtbmGR3Nd5fZDxk+gjOragnJVsirwHbedc9P7xF5J17H0JnZrUa7QmjS7acnqxQOP\noWzWdS+AZ3jYekZzTe1ySTvavghA0g60HUkFcIekd7Kkr+bVwJ0qa/+07HA8DDiIsk3sGynDUD/T\nsH40AnuvVxnNtcRBlKU6brD9G0kbUj7YW9ob+JjHTBCs8bSag3QT/W5i1+vqxbkymUL9trEV8NNa\ntBllXazf02ivApU1uY5gyYfGBZTx63cBm3npLTof1jQae6+vSvnm+7pWdU4QwzluvI/LBLE8B7jC\n9q8lvY7Sb/Bx2z9pGENvE3slHU25ItiMcTaxs73vTMcwJp7BVuODwTrrAFt7hrcaTzKZQh0KO6GW\nfzB9GefyeSktEupQLE1nNU8SxzmUBSd725iqTiB9fYv28CniuIrSvPU0ShPTccArbI+7qvEMxTDe\nrO8mKxJoRDavG5B0OaXz3/V4FWD+TP/dpJlrCqOQLOpEvb+mNu0Myt1umYZR2vxnFPZeh9Kk8f36\ngT68mvRHG8ZwD3C1pHPHxNC6ye9+25a0J+WK5LipPmBXlFGY2Ns6WUyDPHSVYPv3dbDMjEoymR3+\nA/g0pX/igSnOXeEGCVXSWyj7U7TcYXGs+ygr5R7O0N7rlOGQLd1cf1ahfHD14Wv1p2+LVZZhfx3w\n3NoE12pk26mU4eK9TewdpSv36gZJb2PprcZvmOlK08w1C0ha0HJkzCRxfIAy5PAyyjat57jxG0gj\nsPd6LK2u0fUa4FLb35O0GfA82zM+hF7Surbvrp3+D9EioQw1hR9ab4dH+P3GDReFrfE8hjKVYReW\nbDX+Ds/w6ghJJrOApPdQ9vn+Mv027aAyfGpXymideZT5LsfZbjJqpTYr7Wu71/3X+2x6HMFvwr2R\n9FXbL5F0I+U1GZ7fYtvNrlg1zl4745U9XKWZa3YYtD//zVBZH0071Lbx/wX+l7KMyQbAGZLOtf23\nDUJ4ALhCZdOyXvZer/psehz0YY37TbhxLIPRQ4MJvatT5pn8n+31Zrpu2y+ptzO2k+IyWFvSTrYv\nAJD0bMp2uU2pp8VQc2US01bbYQ+gLOvyGeArtn9XR4v82PaWDWLofe/1GkfvTY+j8k1Y0nxK8+d/\nUK5W96csBjrjKwdLmnSEkhtury1pe0rz73qUL3t3AX/eMoYaRy+LoebKZIRJ2sX2t+vEtIdw490F\nKUtEvGLsCLc6WqTJiC/bJ9WZxZsNL2fSylDb/H9KejP9Nj2OxDdhANsLJa1q+wHKMiI/aFT1UZPc\nZ0q/QRO2FwBPl7Qu5Yt6X0O2H2n7ksGE3ir7mazk/hT4NvDSce4zjfZVGbLF2EQi6RTbr7d9XYsA\nVJYa/2dKc8oWkrYF3mf7ZS3qpyzwONw232fT40HA8SpbFTz4Tbhh/QO/UVmO/4q65M4tNEpqtndu\nUc90qGwH8I/A423vIWlr4I9tH9c4lNslbUntV1NZSfqWma40zVwxbWMnhtUhoFfb3rphDAso3za/\nM3QJv9T2tY3iWHOwBtNkZY1i6fWbcB3NdCtlOPBfUpp5PtV6ZQZJ2wBbs/T22s0WZZX0deAE4HDb\nT69zOy7v4b35ROBY4NmUPVVuBF4703PmcmUyC0haA3glDx051GTIYZ1D8G7K9qx3D4opcz6ObRHD\nkPtt3zXmEr6Pb0Q/oCwbMlXZjBmVb8JDH1K/peE2tcMkHQE8j5JMzqasrH0BbVf43sj26fXvBdv3\nS2o2OEPSXw0dng2cT5kH9WvK58eMTqhNMpkdzqQ0YSxgqH2+FdsfBD4o6YO239W6/jF+KOk1wKqS\n5lIWWmzVPj8S+54POZH6Tbge/zfwBcpyJjNuxIYo701Z0uVy2wfWRNt0EVLg15IezZLmpR0pf7et\nDCbPbkVZI+xMyvvz9ZSFSGdUksnssInt3fsOgrLr5Np9LuhHWfr+cEpS/TxwDvD+hvUP73s+/E2v\n5b7nA71+E2a0ltn5bR0Icn9t9ruV9kPn/4qyOdiWkr5P2axsn1aV234vgKRvUtbmGiz0+B7KSLsZ\nlWQyO/xA0h/ZvrrnOI6hjFZ5OvC3lG/AJ1MGCjRRJyseDhxe+2zWbtlPUYcgn6QR2Pecnr8JDy2z\nswVwi5fex+OxreKo5qtsT/DvlCv4/6NsadzSNZS/ha0oVwTX089mZZtRmqAH7qM0kc+odMDPApKu\nBf6Q0pF2L+WN6tYznQcd8Co7yv28LujXdBVfSacCb6JMFFxA6ez9qO1/ahXDUCwvpuzCOdzh22zp\njDrH4ugawzWUb8J7275q0geu+DjmA892XUG5juz6vu1ntoxjKJ7NgXV7eB3GW7m4+SrXkg4HXkUZ\ntm7KjpNfqM3VMyZXJrPDHn0HUPW5oN/A1nUtptdSOhnfSUkqTZOJpE9T+kh2prTN7037b8LXUj4w\nfkNpZvsKpd+ktdU8tBS/7ftqQmlG0nPHK/OYzbJmqO5R6kfD9pF1ZNmf1KIDbV8+0/UmmcwCtn9S\nm5YGb47vueyz3dqrKQv6HWT7f1UW9Gt9RfAISY8A9gL+tc7A7+Py+tm2n6ayZ8Z7JR1F+3k/JwN3\nU0Z0AexHWVqlWTt9dZukl9k+C0BlKfrWC3EOz/dZE3gW5UtGi0mLo9SPBjw487/tzPs0c40+SW8H\n/oIlH1Yvp2zRenR/UfWjLunyTuBK4MWU9uHP2v6TSR+44uO42PYOki4CXgH8Evih7bkNY7jS9tOn\nKmsQx5bA54DHU76V/wzYv/U8kzExbQp8xPZ+DeschX603iSZzAIqm/78se1f1+O1gQtb9ZlIWsz4\nQ0AHfTfrtohjIpJWsz3jy0WMqfPvKf0VuwCfrMWfsf33DWM4Efi07Yvq8Q7AAbbf3CqGMfE8ivKZ\nsriP+sfEIuCqHiYM9tqP1qc0c80OYumVaR9g6aW2Z5TtvjZ/Gtd4f7BA6z/YfwYOoTQ9Xgh8jyWb\nEc2oofkdjwD2l/TTevwESj9KU2Mmy1EnlN4FLLB9RaMYBvuwQxlBtS3l6rWZEelH602SyexwAnCx\npC/X471oNDFtPCqb7wx/8/ppw7pH5Q/2JEqb+Cfq8X6UPoxXNah7lOZ3QFkpeB7wn/X4xcClwJsk\n/YftjzSIYf7Q7/cDn7fdZNveIaPQj9abNHPNEnUY6E6UK5LvthidMU4ML6Os0vp4yqSwJwDX2X5q\nwxiuGvqDfVptWvmS7V1bxVDjGIn+ilEg6Rzglbb/rx4/CjiD0re3oOXabX0ahX60PuXKZIRp6S1J\nb6o/g/s2bLzcOZSZ5jsC37K9naSdKd/IW/ptvf2NpMdT/mD72Bjpckk7jumvaP1NeFSMnST3O+AJ\ntn8rqcnyP5Ms7dJyTtZX68TJf6KMpDLtl3TpTZLJaDuV0qQxWPZ8QPSz0+LvbP9S0iqSVrF9vqQP\nN45h8Af7EcrrAg3/YEetv2JEnApcJOnMevxS4PN1oEir1+Tr9XbsrpPNNk2zPVjW54uSvgqs2ddK\nzn1IM1dMm6RvUfprPkjZKOtW4Jm2n90whrVY0vFtasd3qyVVVJZbn1DjdcpGhqR5wHMoX3QusD1/\nioes6Pp723VSE2xeN+D2m9j1IslkFpB0nu3nT1XWII61Kc1Mq1C++a0HfM72LxvGcDql4/uztWg/\nYH3bLTq+YwJ1NYTHsvQWCS0HZlwBvMVL7zr5KdvbNqj7hEnutu0+NixrLslkhElakzJy6XzKXg3D\nyzR83fZTGsayKnCO7Re0qnOCONLxPWIkvRU4AvgFS4atN107Tkvvvw7wK3rYf31llj6T0fZG4B2U\n0VPDfxR3s2SiXBO2H5D0G0nr9dwOnI7v0fN2YKuWV6hjeQT2X68LoD5EJi1G72x/HPi4pLeOyNIp\n9wBXSzqXsnsbALbfNtMVp+N7pP2MtptAPcQoTJxk6G+CMg/rJcB1jeruXZq5RpikXWx/e6IOvtYd\ne5IOmCCOGR8xk47v0SXpOMoeHl9jaCdQ2zO6TeyYGE5l/ImTTwZaTZwcG9MawFm2d2tddx9yZTLa\n/hT4NmWo5Vim8ezaFkljkrqTLEbXT+vP6vWnD4+m7C44mDh5BGXi5HMpQ8ibJxNKf2fr4fu9yZVJ\nTJukGxlnYpjtleYPJkaTpOuApw9t0LUGcIXtp0i63PZ2DWIYnji5KmWzsvfZ/teZrnsU5MpkhI1t\nBx6rZTNCNW/o9zUp+2Zs2DiGGBGS/sX2OyT9J+N/yXhZw3BGYeLk8Jpp9wO/aL2adZ9yZTLC6qU6\nlPboZwJn1eOXUtbnekMvgQ2RdIHtnfqOI9qTtL3tBZL+dLz7bf9X43j6nji5I3DNYAn+ukbZU21f\n3DKOviSZzAKSvklZSG/wJl2H0qm4e+M4hveyXoVypXJI5njEKBiBiZOXU/ptXI9XAea78R7wfUkz\n1+wwdiG9+4DNe4jjqKHf7wdupM2S6zGCJllcEYDGkxbHnTgJNIuB8uX8wdfD9u8lrTSfsSvNP3SW\nOwW4pO5nYsrS3if3EMdBtm8YLpDUx4q9MRoGfQSH1tuxiyy21PvESeAGlW2lB5ukvRm4YZLzH1bS\nzDVL1OUiBn0Tfe1nctnYS3ZJC2xv3zqWGB19LrI4VN/5wAv77PCum8Z9grKVs4HzgHfYvrWvmFrK\nlcnscQVwC/X/TNJmrdqDJT2Zsk3uemMmUK7L0lvnxsppbUk7jVlkce3GMdwAfEdSbxMna9LYt1V9\noybJZBYYgfbgrShNGuuz9ATKxcBfNIohRtdBwPGSllpksXEMvU2cHLP//EO0WG5oFKSZaxaQtBDY\noef2YCT9se0L+4whRlefiyz2aaJlhgb6XDmipVyZzA69L6RXvUnSdbZ/BSBpA+ColWW/hljaRJNq\n6yKLTZqYRmHi5MqSLKaSZDI79N4eXD1tkEhq/XdKmvFlKmJkrdN3ACwZQfbPvUYBSJoDvBPYmqG+\nRNu79BZUQ0kms8MoLKQHsIqkDWzfCSBpQ/IeWmnZfu8IxLCg3jadbT+BzwFfoKxY/CbgAOC2XiNq\nKH0ms0id+e7Byqg91L8/8C7KaqymTFg80vYpkz4wHpYk/a3tj0zUAd14n5txNZ44ucD29pKuGtQr\n6b9sj7vczMNNvlXOApK2oVzOb1iPbwf2t31NyzhsnyxpPmUcvYBX2M7GVCuvwcZPTdfAGmOUJk7+\nrt7eIunFwM3AJo1j6E2uTGYBST8ADrd9fj1+HvCPtp/dQyw7AXNtn1DbiB9l+8bWcUQMG5GJky8B\nvgdsChxNmYf1Htv/OekDHyZyZTI7rD1IJAC2v1OX1m6qrmI8jzLv5ATKFrqfpazUGiupOvt8vGau\nlh3PozBxch/KasU/BHaufYr/zJLdHx/Wkkxmhxsk/T1LLuFfR1lksbWXA9sBlwHYvrn248TK7a+H\nfl8TeCVlIdCWRmHi5NjRjnesTKMdk0xmhz8H3suSbXq/CxzYQxz32bakwRLbza+OYvQMRlQN+b6k\npqOragxP73ni5Eo92nGl+YfOZvXNOQpLMpwu6d+A9SX9BSXJfabnmKJn9UNzYLDPzR80qrv3iZND\njgJ+IGmp0Y4N6+9VksksIOlcYJ8xM89Ps71byzhs/7OkFwJ3U/pN/sH2uS1jiJG0gCV9JvcDN1Ga\nnVoYmWbWlX20Y0ZzzQKSLre93VRlDeL4sO13TlUWKxdJa1H27tiJklS+Bxxj+55eA4umkkxmAUkL\ngJcPlpyX9ATgy623A51gP5MHJ2jFyknS6ZSr1c/Vov2ADWzv06Du3idORpFmrtnhcOCCoU7N5wIH\nt6pc0iGUb55PlHTV0F3rAN9vFUeMrK1sP33o+HxJVzaqexQmTga5Mhl5Kj2JmwC/BXaktMVeaPv2\nhjGsB2wAfBA4bOiuxbbvaBVHjCZJJwKftn1RPd4BOMD2m3sNLJpKMpkF+t4aV9K6tu8eM2rnQUko\nK6ehdbEeQRmQ8dN6/ATgWtvbNIxlFCZOrtTSzDU7XCTpmbYv7an+UylrIA1G7WjoPgNP7COo6N1L\npj6lmVGYOLlSy5XJLCDpWuBJwE+AX1O37U3Hd8TEVqYVe0dBrkxmhz36DmBA0saUZowH3zu2v9tf\nRBH9TpyMIslkhA36KoDFfccCZU4J8GrgWuCBWmzK8i4Rfepz4mSQZq6RJumrtl8i6UaW7lwcNHM1\n7auQdD1lMbt7pzw5oqFMnOxfksksUS/j57L03tJNF9OT9HXKsi697PQYMZE+J05GkWauWUDSG4C3\nU+abXEGZb/ID4PmN6h/MLv4NcIWk84AHr04yyzhGQJ8TJ4Mkk9ni7cAzgYts7yzpyZQl6VsZzC5e\nAJzVsN6I6bpc0o5jJk5mdYaGkkxmh3ts3yMJSWvY/pGkrVpVbvskeHD/kntsP1CPVwXWaBVHxFhj\nJk7uL2mpiZN9xraySTKZHRZJWh/4CnCupDuBm3uI4zzgBcCgz2Qt4JtA873oI6pRmji5UksH/Cwj\n6U+B9YBv2L6vcd1X2N52qrKIWPnkymSWaT2Ca4xfS3qG7csAJG1PWYAyIlZyuTKJaZM0D/gCS5rY\nHge8epw9wCNiJZMrk5gWSasAqwNPpqwQK+BHtn/Xa2ARMRJyZRLTJulC23/cdxwRMXpW6TuAmFW+\nKemVdcOuiIgH5cokpk3SYmBtykJ697BkjbB1ew0sInqXPpOYNtvrjLdGWEREkklMW99rhEXE6Eqf\nSSyLwRphP7G9M7AdcHu/IUXEKEgyiWVxz2B/iMEaYZRhwhGxkkszVyyLUVkjLCJGTEZzxXLpc42w\niBg9SSYREdFZ+kwiIqKzJJOIiOgsySQiIjpLMomIiM6STCIiorP/D2nBo73Blkz1AAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f675df743c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f675df742b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing_counts[:10].plot.bar()\n",
    "plt.figure(figsize=(100,100)) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "for c in props.columns:\n",
    "    props[c]=props[c].fillna(-1)\n",
    "    if props[c].dtype == 'object':\n",
    "        lbl = LabelEncoder()\n",
    "        lbl.fit(list(props[c].values))\n",
    "        props[c] = lbl.transform(list(props[c].values))\n",
    "# Understand LabelEncoder"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "type(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_df = train.merge(props, how='left', on='parcelid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_df.logerror)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3LIAkB42uJUnSXDVsqFyV5E/pPbb+14Avsfs/2CVJeoEb9u6vP2y/Tf8k8Grgv1fV5pF2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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f675df74128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df.logerror.plot.hist(bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8503162862"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[train_df.logerror <-0.4].parcelid.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10091118029"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[train_df.logerror > 0.5].parcelid.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# drop out ouliers\n",
    "train_df=train_df[ train_df.logerror > -0.4 ]\n",
    "train_df=train_df[ train_df.logerror < 0.5 ]\n",
    "#train_df=train_df[ train_df.logerror > -0.4 ]\n",
    "#train_df=train_df[ train_df.logerror < 0.418 ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare training examples, labels and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_df['transactiondate'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2016-01-01'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['transactiondate'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train = train_df[\"logerror\"].values.astype(np.float32)\n",
    "x_train=train_df.drop(['parcelid', 'logerror','transactiondate'], axis=1)\n",
    "#x_train=train_df.drop(['parcelid', 'logerror'], axis=1)\n",
    "x_test = props.drop(['parcelid'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "seed = 7\n",
    "test_size = 0.33\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=test_size, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((59495, 57), (59495,), (29304, 57), (29304,), (2985217, 57))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, y_train.shape, x_valid.shape, y_valid.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_mean = np.mean(y_train)\n",
    "xgb_params = {\n",
    "    #'eta': 0.037,\n",
    "    'max_depth': 5,\n",
    "    'subsample': 0.80,\n",
    "    'objective': 'reg:linear',\n",
    "   # 'eval_metric': 'mae',\n",
    "    #'lambda': 0.8,   \n",
    "    #'alpha': 0.4, \n",
    "    'base_score': y_mean,\n",
    "    'silent': 1\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-mae:0.054744\tvalidation_1-mae:0.054043\n",
      "[1]\tvalidation_0-mae:0.054637\tvalidation_1-mae:0.053969\n",
      "[2]\tvalidation_0-mae:0.054549\tvalidation_1-mae:0.053905\n",
      "[3]\tvalidation_0-mae:0.054463\tvalidation_1-mae:0.053855\n",
      "[4]\tvalidation_0-mae:0.054401\tvalidation_1-mae:0.053814\n",
      "[5]\tvalidation_0-mae:0.054332\tvalidation_1-mae:0.053789\n",
      "[6]\tvalidation_0-mae:0.054283\tvalidation_1-mae:0.053761\n",
      "[7]\tvalidation_0-mae:0.054246\tvalidation_1-mae:0.053757\n",
      "[8]\tvalidation_0-mae:0.054196\tvalidation_1-mae:0.053731\n",
      "[9]\tvalidation_0-mae:0.054152\tvalidation_1-mae:0.053709\n",
      "[10]\tvalidation_0-mae:0.054112\tvalidation_1-mae:0.053699\n",
      "[11]\tvalidation_0-mae:0.054086\tvalidation_1-mae:0.053692\n",
      "[12]\tvalidation_0-mae:0.054052\tvalidation_1-mae:0.053681\n",
      "[13]\tvalidation_0-mae:0.05402\tvalidation_1-mae:0.053668\n",
      "[14]\tvalidation_0-mae:0.05399\tvalidation_1-mae:0.053671\n",
      "[15]\tvalidation_0-mae:0.053964\tvalidation_1-mae:0.053663\n",
      "[16]\tvalidation_0-mae:0.053936\tvalidation_1-mae:0.053656\n",
      "[17]\tvalidation_0-mae:0.053918\tvalidation_1-mae:0.053649\n",
      "[18]\tvalidation_0-mae:0.053894\tvalidation_1-mae:0.05365\n",
      "[19]\tvalidation_0-mae:0.05387\tvalidation_1-mae:0.053644\n",
      "[20]\tvalidation_0-mae:0.053833\tvalidation_1-mae:0.053627\n",
      "[21]\tvalidation_0-mae:0.05381\tvalidation_1-mae:0.053621\n",
      "[22]\tvalidation_0-mae:0.053791\tvalidation_1-mae:0.053612\n",
      "[23]\tvalidation_0-mae:0.053773\tvalidation_1-mae:0.053613\n",
      "[24]\tvalidation_0-mae:0.053756\tvalidation_1-mae:0.053605\n",
      "[25]\tvalidation_0-mae:0.053736\tvalidation_1-mae:0.053601\n",
      "[26]\tvalidation_0-mae:0.053719\tvalidation_1-mae:0.0536\n",
      "[27]\tvalidation_0-mae:0.053695\tvalidation_1-mae:0.053595\n",
      "[28]\tvalidation_0-mae:0.053679\tvalidation_1-mae:0.053598\n",
      "[29]\tvalidation_0-mae:0.053663\tvalidation_1-mae:0.053596\n",
      "[30]\tvalidation_0-mae:0.053653\tvalidation_1-mae:0.053591\n",
      "[31]\tvalidation_0-mae:0.053632\tvalidation_1-mae:0.053587\n",
      "[32]\tvalidation_0-mae:0.053614\tvalidation_1-mae:0.053584\n",
      "[33]\tvalidation_0-mae:0.0536\tvalidation_1-mae:0.053585\n",
      "[34]\tvalidation_0-mae:0.053579\tvalidation_1-mae:0.053581\n",
      "[35]\tvalidation_0-mae:0.053555\tvalidation_1-mae:0.053575\n",
      "[36]\tvalidation_0-mae:0.053533\tvalidation_1-mae:0.053579\n",
      "[37]\tvalidation_0-mae:0.053508\tvalidation_1-mae:0.053572\n",
      "[38]\tvalidation_0-mae:0.053494\tvalidation_1-mae:0.053568\n",
      "[39]\tvalidation_0-mae:0.053484\tvalidation_1-mae:0.053567\n",
      "[40]\tvalidation_0-mae:0.053467\tvalidation_1-mae:0.053569\n",
      "[41]\tvalidation_0-mae:0.053454\tvalidation_1-mae:0.053573\n",
      "[42]\tvalidation_0-mae:0.053443\tvalidation_1-mae:0.053567\n",
      "[43]\tvalidation_0-mae:0.053429\tvalidation_1-mae:0.053571\n",
      "[44]\tvalidation_0-mae:0.053413\tvalidation_1-mae:0.053575\n",
      "[45]\tvalidation_0-mae:0.053402\tvalidation_1-mae:0.053578\n",
      "[46]\tvalidation_0-mae:0.053386\tvalidation_1-mae:0.053582\n",
      "[47]\tvalidation_0-mae:0.053371\tvalidation_1-mae:0.053587\n",
      "[48]\tvalidation_0-mae:0.053351\tvalidation_1-mae:0.053577\n",
      "[49]\tvalidation_0-mae:0.053332\tvalidation_1-mae:0.053574\n",
      "[50]\tvalidation_0-mae:0.053327\tvalidation_1-mae:0.053578\n",
      "[51]\tvalidation_0-mae:0.053311\tvalidation_1-mae:0.053568\n",
      "[52]\tvalidation_0-mae:0.053292\tvalidation_1-mae:0.053568\n",
      "[53]\tvalidation_0-mae:0.053285\tvalidation_1-mae:0.053565\n",
      "[54]\tvalidation_0-mae:0.053275\tvalidation_1-mae:0.05356\n",
      "[55]\tvalidation_0-mae:0.053267\tvalidation_1-mae:0.053564\n",
      "[56]\tvalidation_0-mae:0.053251\tvalidation_1-mae:0.053554\n",
      "[57]\tvalidation_0-mae:0.053237\tvalidation_1-mae:0.053562\n",
      "[58]\tvalidation_0-mae:0.053225\tvalidation_1-mae:0.053568\n",
      "[59]\tvalidation_0-mae:0.053213\tvalidation_1-mae:0.053564\n",
      "[60]\tvalidation_0-mae:0.053202\tvalidation_1-mae:0.053568\n",
      "[61]\tvalidation_0-mae:0.053187\tvalidation_1-mae:0.053568\n",
      "[62]\tvalidation_0-mae:0.053176\tvalidation_1-mae:0.05357\n",
      "[63]\tvalidation_0-mae:0.053167\tvalidation_1-mae:0.05357\n",
      "[64]\tvalidation_0-mae:0.053152\tvalidation_1-mae:0.053565\n",
      "[65]\tvalidation_0-mae:0.053134\tvalidation_1-mae:0.053563\n",
      "[66]\tvalidation_0-mae:0.05313\tvalidation_1-mae:0.053562\n",
      "[67]\tvalidation_0-mae:0.053116\tvalidation_1-mae:0.053561\n",
      "[68]\tvalidation_0-mae:0.053102\tvalidation_1-mae:0.053562\n",
      "[69]\tvalidation_0-mae:0.053086\tvalidation_1-mae:0.053559\n",
      "[70]\tvalidation_0-mae:0.053074\tvalidation_1-mae:0.05356\n",
      "[71]\tvalidation_0-mae:0.053061\tvalidation_1-mae:0.053567\n",
      "[72]\tvalidation_0-mae:0.053036\tvalidation_1-mae:0.053564\n",
      "[73]\tvalidation_0-mae:0.053031\tvalidation_1-mae:0.053565\n",
      "[74]\tvalidation_0-mae:0.053019\tvalidation_1-mae:0.053571\n",
      "[75]\tvalidation_0-mae:0.053\tvalidation_1-mae:0.053565\n",
      "[76]\tvalidation_0-mae:0.052992\tvalidation_1-mae:0.053568\n",
      "[77]\tvalidation_0-mae:0.052982\tvalidation_1-mae:0.05357\n",
      "[78]\tvalidation_0-mae:0.052975\tvalidation_1-mae:0.053573\n",
      "[79]\tvalidation_0-mae:0.052956\tvalidation_1-mae:0.053571\n",
      "[80]\tvalidation_0-mae:0.052944\tvalidation_1-mae:0.053568\n",
      "[81]\tvalidation_0-mae:0.052929\tvalidation_1-mae:0.053572\n",
      "[82]\tvalidation_0-mae:0.052924\tvalidation_1-mae:0.053573\n",
      "[83]\tvalidation_0-mae:0.052899\tvalidation_1-mae:0.053577\n",
      "[84]\tvalidation_0-mae:0.052888\tvalidation_1-mae:0.053584\n",
      "[85]\tvalidation_0-mae:0.052861\tvalidation_1-mae:0.053581\n",
      "[86]\tvalidation_0-mae:0.052844\tvalidation_1-mae:0.053586\n",
      "[87]\tvalidation_0-mae:0.052826\tvalidation_1-mae:0.05359\n",
      "[88]\tvalidation_0-mae:0.052814\tvalidation_1-mae:0.053586\n",
      "[89]\tvalidation_0-mae:0.052799\tvalidation_1-mae:0.053594\n",
      "[90]\tvalidation_0-mae:0.052783\tvalidation_1-mae:0.053593\n",
      "[91]\tvalidation_0-mae:0.052777\tvalidation_1-mae:0.053593\n",
      "[92]\tvalidation_0-mae:0.052765\tvalidation_1-mae:0.053593\n",
      "[93]\tvalidation_0-mae:0.052756\tvalidation_1-mae:0.053595\n",
      "[94]\tvalidation_0-mae:0.052743\tvalidation_1-mae:0.053595\n",
      "[95]\tvalidation_0-mae:0.052733\tvalidation_1-mae:0.053598\n",
      "[96]\tvalidation_0-mae:0.052717\tvalidation_1-mae:0.053601\n",
      "[97]\tvalidation_0-mae:0.052708\tvalidation_1-mae:0.053603\n",
      "[98]\tvalidation_0-mae:0.052695\tvalidation_1-mae:0.053606\n",
      "[99]\tvalidation_0-mae:0.052689\tvalidation_1-mae:0.053606\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBModel(base_score=0.0094006034, colsample_bylevel=1, colsample_bytree=1,\n",
       "     gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
       "     min_child_weight=1, missing=None, n_estimators=100, nthread=-1,\n",
       "     objective='reg:linear', reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "     seed=0, silent=1, subsample=0.8)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = xgb.XGBModel(**xgb_params)\n",
    "clf.fit(x_train, y_train,\n",
    "        eval_set=[(x_train, y_train), (x_valid, y_valid)], \n",
    "        eval_metric='mae',\n",
    "        verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#dtest = xgb.DMatrix(x_test)\n",
    "xgb_pred = clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.02469542,  0.01942533,  0.04795396, ...,  0.17271759,\n",
       "        0.17271759,  0.17271759], dtype=float32)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.float_format', lambda x: '%.4f' % x)\n",
    "xgb_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write to submission file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred=[]\n",
    "\n",
    "for i,predict in enumerate(xgb_pred):\n",
    "    y_pred.append(str(round(predict,4)))\n",
    "y_pred=np.array(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['-0.0247', '0.0194', '0.048', ..., '0.1727', '0.1727', '0.1727'], \n",
       "      dtype='<U7')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Writing results to disk ...\n"
     ]
    }
   ],
   "source": [
    "output = pd.DataFrame({'ParcelId': props['parcelid'].astype(np.int32),\n",
    "        '201610': y_pred, '201611': y_pred, '201612': y_pred,\n",
    "        '201710': y_pred, '201711': y_pred, '201712': y_pred})\n",
    "# set col 'ParceID' to first col\n",
    "cols = output.columns.tolist()\n",
    "cols = cols[-1:] + cols[:-1]\n",
    "output = output[cols]\n",
    "from datetime import datetime\n",
    "\n",
    "print( \"\\nWriting results to disk ...\" )\n",
    "output.to_csv('sub{}.csv'.format(datetime.now().strftime('%Y%m%d_%H%M%S')), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ExecutableNotFound",
     "evalue": "failed to execute ['dot', '-Tpng'], make sure the Graphviz executables are on your systems' PATH",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/home/sravya/anaconda3/lib/python3.6/site-packages/graphviz/backend.py\u001b[0m in \u001b[0;36mpipe\u001b[0;34m(engine, format, data, quiet)\u001b[0m\n\u001b[1;32m    153\u001b[0m             \u001b[0mstdout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPIPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPIPE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m             startupinfo=STARTUPINFO)\n\u001b[0m\u001b[1;32m    155\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sravya/anaconda3/lib/python3.6/subprocess.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, encoding, errors)\u001b[0m\n\u001b[1;32m    706\u001b[0m                                 \u001b[0merrread\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrwrite\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 707\u001b[0;31m                                 restore_signals, start_new_session)\n\u001b[0m\u001b[1;32m    708\u001b[0m         \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sravya/anaconda3/lib/python3.6/subprocess.py\u001b[0m in \u001b[0;36m_execute_child\u001b[0;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, restore_signals, start_new_session)\u001b[0m\n\u001b[1;32m   1325\u001b[0m                                 \u001b[0merr_msg\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m': '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mrepr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_executable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1326\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno_num\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1327\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'dot'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mExecutableNotFound\u001b[0m                        Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-55-06628ebc07a2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mxgboost\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplot_tree\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplot_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sravya/anaconda3/lib/python3.6/site-packages/xgboost/plotting.py\u001b[0m in \u001b[0;36mplot_tree\u001b[0;34m(booster, num_trees, rankdir, ax, **kwargs)\u001b[0m\n\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m     \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 248\u001b[0;31m     \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    249\u001b[0m     \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m     \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sravya/anaconda3/lib/python3.6/site-packages/graphviz/files.py\u001b[0m in \u001b[0;36mpipe\u001b[0;34m(self, format)\u001b[0m\n\u001b[1;32m    118\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtext_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_encoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m         \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sravya/anaconda3/lib/python3.6/site-packages/graphviz/backend.py\u001b[0m in \u001b[0;36mpipe\u001b[0;34m(engine, format, data, quiet)\u001b[0m\n\u001b[1;32m    155\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrno\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0merrno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENOENT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 157\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mExecutableNotFound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    158\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pragma: no cover\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m             \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mExecutableNotFound\u001b[0m: failed to execute ['dot', '-Tpng'], make sure the Graphviz executables are on your systems' PATH"
     ]
    }
   ],
   "source": [
    "from xgboost import plot_tree\n",
    "plot_tree(clf)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
   "outputs": [],
   "source": []
  }
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