RandomForestRegressor-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "from sklearn import tree\n",
    "import os\n",
    "import xlrd\n",
    "import pandas as pd\n",
    "import seaborn\n",
    "%matplotlib inline\n",
    "import csv\n",
    "from csv import reader\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib.lines import Line2D\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy.stats import pearsonr\n",
    "import pickle\n",
    "RF_SEED = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\coset\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3071: DtypeWarning: Columns (5) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parcel_ID</th>\n",
       "      <th>Parcel_Acreage</th>\n",
       "      <th>Property_Street_Address</th>\n",
       "      <th>Property_City</th>\n",
       "      <th>Property_State</th>\n",
       "      <th>Property_Zip</th>\n",
       "      <th>Parcel_Vacancy</th>\n",
       "      <th>Conveyance_Date</th>\n",
       "      <th>Property_Sales_Price</th>\n",
       "      <th>Seller_City</th>\n",
       "      <th>...</th>\n",
       "      <th>Buyer_ZIP</th>\n",
       "      <th>Primary_Residence</th>\n",
       "      <th>Assessed_Value_Land</th>\n",
       "      <th>Assessed_Value_Improvement</th>\n",
       "      <th>Total_Assessed_Value</th>\n",
       "      <th>Property_Class_Code</th>\n",
       "      <th>Class_Code_General</th>\n",
       "      <th>Class_Code_Translated</th>\n",
       "      <th>County_ID</th>\n",
       "      <th>County_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4574368</td>\n",
       "      <td>0.169</td>\n",
       "      <td>3909 MARIAH DRIVE                             ...</td>\n",
       "      <td>NEW ALBANY</td>\n",
       "      <td>IN</td>\n",
       "      <td>41750</td>\n",
       "      <td>N</td>\n",
       "      <td>6/2/2020</td>\n",
       "      <td>179900.0</td>\n",
       "      <td>NEW ALBANY</td>\n",
       "      <td>...</td>\n",
       "      <td>47150</td>\n",
       "      <td>Y</td>\n",
       "      <td>43800.0</td>\n",
       "      <td>138500.0</td>\n",
       "      <td>182300.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>500</td>\n",
       "      <td>Residential</td>\n",
       "      <td>22</td>\n",
       "      <td>Floyd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4515880</td>\n",
       "      <td>0.134</td>\n",
       "      <td>4110 E 10TH STREET</td>\n",
       "      <td>INDIAAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>42601</td>\n",
       "      <td>N</td>\n",
       "      <td>10/19/2020</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>Petaluma</td>\n",
       "      <td>...</td>\n",
       "      <td>46074</td>\n",
       "      <td>N</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>88500.0</td>\n",
       "      <td>93500.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>500</td>\n",
       "      <td>Residential</td>\n",
       "      <td>49</td>\n",
       "      <td>Marion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4454504</td>\n",
       "      <td>0.172</td>\n",
       "      <td>5404 OAK HARBOR CT</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>42637</td>\n",
       "      <td>N</td>\n",
       "      <td>6/18/2020</td>\n",
       "      <td>195000.0</td>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>...</td>\n",
       "      <td>46237</td>\n",
       "      <td>Y</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>158000.0</td>\n",
       "      <td>188000.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>500</td>\n",
       "      <td>Residential</td>\n",
       "      <td>49</td>\n",
       "      <td>Marion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4421719</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6516 EMERALD HILL COURT #310</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>42637</td>\n",
       "      <td>N</td>\n",
       "      <td>5/1/2020</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>...</td>\n",
       "      <td>42637</td>\n",
       "      <td>Y</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>77900.0</td>\n",
       "      <td>88300.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>500</td>\n",
       "      <td>Residential</td>\n",
       "      <td>49</td>\n",
       "      <td>Marion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4531964</td>\n",
       "      <td>2.155</td>\n",
       "      <td>6335 Intech Commons Drive</td>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>IN</td>\n",
       "      <td>42678</td>\n",
       "      <td>N</td>\n",
       "      <td>12/16/2020</td>\n",
       "      <td>1785000.0</td>\n",
       "      <td>MIAMI BEACH</td>\n",
       "      <td>...</td>\n",
       "      <td>75093</td>\n",
       "      <td>N</td>\n",
       "      <td>845000.0</td>\n",
       "      <td>957900.0</td>\n",
       "      <td>1802900.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>400</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>49</td>\n",
       "      <td>Marion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184184</th>\n",
       "      <td>4666351</td>\n",
       "      <td>4000.000</td>\n",
       "      <td>301 S COBB St                                 ...</td>\n",
       "      <td>PINE VILLAGE</td>\n",
       "      <td>IN</td>\n",
       "      <td>47975</td>\n",
       "      <td>N</td>\n",
       "      <td>5/19/2020</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>WILLIAMSPORT</td>\n",
       "      <td>...</td>\n",
       "      <td>47975</td>\n",
       "      <td>Y</td>\n",
       "      <td>13200.0</td>\n",
       "      <td>51300.0</td>\n",
       "      <td>64500.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>500</td>\n",
       "      <td>Residential</td>\n",
       "      <td>86</td>\n",
       "      <td>Warren</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184185</th>\n",
       "      <td>4666542</td>\n",
       "      <td>2000.000</td>\n",
       "      <td>405 E Fifth St                                ...</td>\n",
       "      <td>West Lebanon</td>\n",
       "      <td>IN</td>\n",
       "      <td>47991</td>\n",
       "      <td>N</td>\n",
       "      <td>11/4/2020</td>\n",
       "      <td>78000.0</td>\n",
       "      <td>LAFAYETTE</td>\n",
       "      <td>...</td>\n",
       "      <td>47991</td>\n",
       "      <td>Y</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>41100.0</td>\n",
       "      <td>46700.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>500</td>\n",
       "      <td>Residential</td>\n",
       "      <td>86</td>\n",
       "      <td>Warren</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184186</th>\n",
       "      <td>4666463</td>\n",
       "      <td>2700.000</td>\n",
       "      <td>120 W Third                                   ...</td>\n",
       "      <td>Williamsport</td>\n",
       "      <td>IN</td>\n",
       "      <td>47993</td>\n",
       "      <td>N</td>\n",
       "      <td>7/20/2020</td>\n",
       "      <td>11250.0</td>\n",
       "      <td>WEST LEBANON</td>\n",
       "      <td>...</td>\n",
       "      <td>47906</td>\n",
       "      <td>N</td>\n",
       "      <td>8600.0</td>\n",
       "      <td>58600.0</td>\n",
       "      <td>67200.0</td>\n",
       "      <td>511.0</td>\n",
       "      <td>500</td>\n",
       "      <td>Residential</td>\n",
       "      <td>86</td>\n",
       "      <td>Warren</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184187</th>\n",
       "      <td>4666265</td>\n",
       "      <td>1564.000</td>\n",
       "      <td>206 Sycamore Dr                               ...</td>\n",
       "      <td>Williamsport</td>\n",
       "      <td>IN</td>\n",
       "      <td>47993</td>\n",
       "      <td>N</td>\n",
       "      <td>1/31/2020</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>LAFAYETTE</td>\n",
       "      <td>...</td>\n",
       "      <td>47993</td>\n",
       "      <td>Y</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>9200.0</td>\n",
       "      <td>599.0</td>\n",
       "      <td>500</td>\n",
       "      <td>Residential</td>\n",
       "      <td>86</td>\n",
       "      <td>Warren</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184188</th>\n",
       "      <td>4666350</td>\n",
       "      <td>9700.000</td>\n",
       "      <td>304 Lincoln St                                ...</td>\n",
       "      <td>Williamsport</td>\n",
       "      <td>IN</td>\n",
       "      <td>47993</td>\n",
       "      <td>N</td>\n",
       "      <td>6/15/2020</td>\n",
       "      <td>184000.0</td>\n",
       "      <td>WILLIAMSPORT</td>\n",
       "      <td>...</td>\n",
       "      <td>47993</td>\n",
       "      <td>Y</td>\n",
       "      <td>12700.0</td>\n",
       "      <td>93000.0</td>\n",
       "      <td>105700.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>500</td>\n",
       "      <td>Residential</td>\n",
       "      <td>86</td>\n",
       "      <td>Warren</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184189 rows × 25 columns</p>\n",
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      ],
      "text/plain": [
       "        Parcel_ID  Parcel_Acreage  \\\n",
       "0         4574368           0.169   \n",
       "1         4515880           0.134   \n",
       "2         4454504           0.172   \n",
       "3         4421719             NaN   \n",
       "4         4531964           2.155   \n",
       "...           ...             ...   \n",
       "184184    4666351        4000.000   \n",
       "184185    4666542        2000.000   \n",
       "184186    4666463        2700.000   \n",
       "184187    4666265        1564.000   \n",
       "184188    4666350        9700.000   \n",
       "\n",
       "                                  Property_Street_Address  \\\n",
       "0       3909 MARIAH DRIVE                             ...   \n",
       "1                                      4110 E 10TH STREET   \n",
       "2                                      5404 OAK HARBOR CT   \n",
       "3                            6516 EMERALD HILL COURT #310   \n",
       "4                               6335 Intech Commons Drive   \n",
       "...                                                   ...   \n",
       "184184  301 S COBB St                                 ...   \n",
       "184185  405 E Fifth St                                ...   \n",
       "184186  120 W Third                                   ...   \n",
       "184187  206 Sycamore Dr                               ...   \n",
       "184188  304 Lincoln St                                ...   \n",
       "\n",
       "                         Property_City Property_State Property_Zip  \\\n",
       "0       NEW ALBANY                                 IN        41750   \n",
       "1                          INDIAAPOLIS             IN        42601   \n",
       "2                         INDIANAPOLIS             IN        42637   \n",
       "3                         INDIANAPOLIS             IN        42637   \n",
       "4                         Indianapolis             IN        42678   \n",
       "...                                ...            ...          ...   \n",
       "184184  PINE VILLAGE                               IN        47975   \n",
       "184185  West Lebanon                               IN        47991   \n",
       "184186  Williamsport                               IN        47993   \n",
       "184187  Williamsport                               IN        47993   \n",
       "184188  Williamsport                               IN        47993   \n",
       "\n",
       "       Parcel_Vacancy Conveyance_Date  Property_Sales_Price  \\\n",
       "0                   N        6/2/2020              179900.0   \n",
       "1                   N      10/19/2020               48000.0   \n",
       "2                   N       6/18/2020              195000.0   \n",
       "3                   N        5/1/2020              120000.0   \n",
       "4                   N      12/16/2020             1785000.0   \n",
       "...               ...             ...                   ...   \n",
       "184184              N       5/19/2020               90000.0   \n",
       "184185              N       11/4/2020               78000.0   \n",
       "184186              N       7/20/2020               11250.0   \n",
       "184187              N       1/31/2020              200000.0   \n",
       "184188              N       6/15/2020              184000.0   \n",
       "\n",
       "                           Seller_City  ... Buyer_ZIP Primary_Residence  \\\n",
       "0       NEW ALBANY                      ...     47150                 Y   \n",
       "1                             Petaluma  ...     46074                 N   \n",
       "2                         Indianapolis  ...     46237                 Y   \n",
       "3                             Franklin  ...     42637                 Y   \n",
       "4                         MIAMI BEACH   ...     75093                 N   \n",
       "...                                ...  ...       ...               ...   \n",
       "184184  WILLIAMSPORT                    ...     47975                 Y   \n",
       "184185  LAFAYETTE                       ...     47991                 Y   \n",
       "184186  WEST LEBANON                    ...     47906                 N   \n",
       "184187  LAFAYETTE                       ...     47993                 Y   \n",
       "184188  WILLIAMSPORT                    ...     47993                 Y   \n",
       "\n",
       "       Assessed_Value_Land Assessed_Value_Improvement Total_Assessed_Value  \\\n",
       "0                  43800.0                   138500.0             182300.0   \n",
       "1                   5000.0                    88500.0              93500.0   \n",
       "2                  30000.0                   158000.0             188000.0   \n",
       "3                  10400.0                    77900.0              88300.0   \n",
       "4                 845000.0                   957900.0            1802900.0   \n",
       "...                    ...                        ...                  ...   \n",
       "184184             13200.0                    51300.0              64500.0   \n",
       "184185              5600.0                    41100.0              46700.0   \n",
       "184186              8600.0                    58600.0              67200.0   \n",
       "184187              7500.0                     1700.0               9200.0   \n",
       "184188             12700.0                    93000.0             105700.0   \n",
       "\n",
       "       Property_Class_Code Class_Code_General  Class_Code_Translated  \\\n",
       "0                    510.0                500            Residential   \n",
       "1                    510.0                500            Residential   \n",
       "2                    520.0                500            Residential   \n",
       "3                    550.0                500            Residential   \n",
       "4                    425.0                400             Commercial   \n",
       "...                    ...                ...                    ...   \n",
       "184184               510.0                500            Residential   \n",
       "184185               510.0                500            Residential   \n",
       "184186               511.0                500            Residential   \n",
       "184187               599.0                500            Residential   \n",
       "184188               510.0                500            Residential   \n",
       "\n",
       "        County_ID  County_Name  \n",
       "0              22        Floyd  \n",
       "1              49       Marion  \n",
       "2              49       Marion  \n",
       "3              49       Marion  \n",
       "4              49       Marion  \n",
       "...           ...          ...  \n",
       "184184         86       Warren  \n",
       "184185         86       Warren  \n",
       "184186         86       Warren  \n",
       "184187         86       Warren  \n",
       "184188         86       Warren  \n",
       "\n",
       "[184189 rows x 25 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the csv file into a pandas DataFrame\n",
    "\n",
    "Property = pd.read_csv('Resources/Property.csv')\n",
    "Property"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "184189"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Property)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180360"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop NA's from DF\n",
    "pd.DataFrame(Property).dropna()\n",
    "Property.head()\n",
    "Property_df = Property[~Property.isin([np.nan, np.inf, -np.inf]).any(1)]\n",
    "len(Property_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Property_Sales_Price</th>\n",
       "      <th>Class_Code_Translated</th>\n",
       "      <th>Total_Assessed_Value</th>\n",
       "      <th>Parcel_Acreage</th>\n",
       "      <th>Parcel_Vacancy</th>\n",
       "      <th>Property_Zip</th>\n",
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       "      <th>0</th>\n",
       "      <td>179900.0</td>\n",
       "      <td>Residential</td>\n",
       "      <td>182300.0</td>\n",
       "      <td>0.169</td>\n",
       "      <td>N</td>\n",
       "      <td>41750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48000.0</td>\n",
       "      <td>Residential</td>\n",
       "      <td>93500.0</td>\n",
       "      <td>0.134</td>\n",
       "      <td>N</td>\n",
       "      <td>42601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>195000.0</td>\n",
       "      <td>Residential</td>\n",
       "      <td>188000.0</td>\n",
       "      <td>0.172</td>\n",
       "      <td>N</td>\n",
       "      <td>42637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1785000.0</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>1802900.0</td>\n",
       "      <td>2.155</td>\n",
       "      <td>N</td>\n",
       "      <td>42678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>257575.0</td>\n",
       "      <td>Residential</td>\n",
       "      <td>210900.0</td>\n",
       "      <td>0.201</td>\n",
       "      <td>N</td>\n",
       "      <td>45228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Property_Sales_Price Class_Code_Translated  Total_Assessed_Value  \\\n",
       "0              179900.0           Residential              182300.0   \n",
       "1               48000.0           Residential               93500.0   \n",
       "2              195000.0           Residential              188000.0   \n",
       "4             1785000.0            Commercial             1802900.0   \n",
       "5              257575.0           Residential              210900.0   \n",
       "\n",
       "   Parcel_Acreage Parcel_Vacancy Property_Zip  \n",
       "0           0.169              N        41750  \n",
       "1           0.134              N        42601  \n",
       "2           0.172              N        42637  \n",
       "4           2.155              N        42678  \n",
       "5           0.201              N        45228  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select 5 Columns of DF\n",
    "#Property_df=Property_df[['Property_Sales_Price','Total_Assessed_Value','Parcel_Acreage','Parcel_Vacancy','Class_Code_Translated']]\n",
    "Property_df=Property_df[['Property_Sales_Price','Class_Code_Translated','Total_Assessed_Value','Parcel_Acreage','Parcel_Vacancy', 'Property_Zip' ]]\n",
    "Property_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_col = ['Property_Sales_Price','Total_Assessed_Value', 'Parcel_Acreage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(646, 6)\n",
      "(179715, 6)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# outliers\n",
    "print(Property_df[(np.abs(stats.zscore(Property_df[num_col])) > 3)].shape)\n",
    "# good data\n",
    "print(Property_df[(np.abs(stats.zscore(Property_df[num_col])) < 3).all(axis=1)].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>index</th>\n",
       "      <th>Property_Sales_Price</th>\n",
       "      <th>Class_Code_Translated</th>\n",
       "      <th>Total_Assessed_Value</th>\n",
       "      <th>Parcel_Acreage</th>\n",
       "      <th>Parcel_Vacancy</th>\n",
       "      <th>Property_Zip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>179900.0</td>\n",
       "      <td>Residential</td>\n",
       "      <td>182300.0</td>\n",
       "      <td>0.169</td>\n",
       "      <td>N</td>\n",
       "      <td>41750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>Residential</td>\n",
       "      <td>93500.0</td>\n",
       "      <td>0.134</td>\n",
       "      <td>N</td>\n",
       "      <td>42601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>195000.0</td>\n",
       "      <td>Residential</td>\n",
       "      <td>188000.0</td>\n",
       "      <td>0.172</td>\n",
       "      <td>N</td>\n",
       "      <td>42637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1785000.0</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>1802900.0</td>\n",
       "      <td>2.155</td>\n",
       "      <td>N</td>\n",
       "      <td>42678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>257575.0</td>\n",
       "      <td>Residential</td>\n",
       "      <td>210900.0</td>\n",
       "      <td>0.201</td>\n",
       "      <td>N</td>\n",
       "      <td>45228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179710</th>\n",
       "      <td>184184</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>Residential</td>\n",
       "      <td>64500.0</td>\n",
       "      <td>4000.000</td>\n",
       "      <td>N</td>\n",
       "      <td>47975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179711</th>\n",
       "      <td>184185</td>\n",
       "      <td>78000.0</td>\n",
       "      <td>Residential</td>\n",
       "      <td>46700.0</td>\n",
       "      <td>2000.000</td>\n",
       "      <td>N</td>\n",
       "      <td>47991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179712</th>\n",
       "      <td>184186</td>\n",
       "      <td>11250.0</td>\n",
       "      <td>Residential</td>\n",
       "      <td>67200.0</td>\n",
       "      <td>2700.000</td>\n",
       "      <td>N</td>\n",
       "      <td>47993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179713</th>\n",
       "      <td>184187</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>Residential</td>\n",
       "      <td>9200.0</td>\n",
       "      <td>1564.000</td>\n",
       "      <td>N</td>\n",
       "      <td>47993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179714</th>\n",
       "      <td>184188</td>\n",
       "      <td>184000.0</td>\n",
       "      <td>Residential</td>\n",
       "      <td>105700.0</td>\n",
       "      <td>9700.000</td>\n",
       "      <td>N</td>\n",
       "      <td>47993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179715 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index  Property_Sales_Price Class_Code_Translated  \\\n",
       "0            0              179900.0           Residential   \n",
       "1            1               48000.0           Residential   \n",
       "2            2              195000.0           Residential   \n",
       "3            4             1785000.0            Commercial   \n",
       "4            5              257575.0           Residential   \n",
       "...        ...                   ...                   ...   \n",
       "179710  184184               90000.0           Residential   \n",
       "179711  184185               78000.0           Residential   \n",
       "179712  184186               11250.0           Residential   \n",
       "179713  184187              200000.0           Residential   \n",
       "179714  184188              184000.0           Residential   \n",
       "\n",
       "        Total_Assessed_Value  Parcel_Acreage Parcel_Vacancy Property_Zip  \n",
       "0                   182300.0           0.169              N        41750  \n",
       "1                    93500.0           0.134              N        42601  \n",
       "2                   188000.0           0.172              N        42637  \n",
       "3                  1802900.0           2.155              N        42678  \n",
       "4                   210900.0           0.201              N        45228  \n",
       "...                      ...             ...            ...          ...  \n",
       "179710               64500.0        4000.000              N        47975  \n",
       "179711               46700.0        2000.000              N        47991  \n",
       "179712               67200.0        2700.000              N        47993  \n",
       "179713                9200.0        1564.000              N        47993  \n",
       "179714              105700.0        9700.000              N        47993  \n",
       "\n",
       "[179715 rows x 7 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Git Rid of Outliers\n",
    "Property_df = Property_df[(np.abs(stats.zscore(Property_df[num_col])) < 3).all(axis=1)].reset_index()\n",
    "Property_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>...</th>\n",
       "      <th>Property_Zip_47989</th>\n",
       "      <th>Property_Zip_47990</th>\n",
       "      <th>Property_Zip_47991</th>\n",
       "      <th>Property_Zip_47992</th>\n",
       "      <th>Property_Zip_47993</th>\n",
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       "      <th>Property_Zip_47995</th>\n",
       "      <th>Property_Zip_47997</th>\n",
       "      <th>Property_Zip_48448</th>\n",
       "      <th>Property_Zip_`4790</th>\n",
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       "      <td>1802900.0</td>\n",
       "      <td>2.155</td>\n",
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       "      <td>0</td>\n",
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       "      <td>257575.0</td>\n",
       "      <td>210900.0</td>\n",
       "      <td>0.201</td>\n",
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       "      <td>0</td>\n",
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       "      <th>179712</th>\n",
       "      <td>184186</td>\n",
       "      <td>11250.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179714</th>\n",
       "      <td>184188</td>\n",
       "      <td>184000.0</td>\n",
       "      <td>105700.0</td>\n",
       "      <td>9700.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179715 rows × 990 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index  Property_Sales_Price  Total_Assessed_Value  Parcel_Acreage  \\\n",
       "0            0              179900.0              182300.0           0.169   \n",
       "1            1               48000.0               93500.0           0.134   \n",
       "2            2              195000.0              188000.0           0.172   \n",
       "3            4             1785000.0             1802900.0           2.155   \n",
       "4            5              257575.0              210900.0           0.201   \n",
       "...        ...                   ...                   ...             ...   \n",
       "179710  184184               90000.0               64500.0        4000.000   \n",
       "179711  184185               78000.0               46700.0        2000.000   \n",
       "179712  184186               11250.0               67200.0        2700.000   \n",
       "179713  184187              200000.0                9200.0        1564.000   \n",
       "179714  184188              184000.0              105700.0        9700.000   \n",
       "\n",
       "        Parcel_Vacancy_N  Parcel_Vacancy_Y  \\\n",
       "0                      1                 0   \n",
       "1                      1                 0   \n",
       "2                      1                 0   \n",
       "3                      1                 0   \n",
       "4                      1                 0   \n",
       "...                  ...               ...   \n",
       "179710                 1                 0   \n",
       "179711                 1                 0   \n",
       "179712                 1                 0   \n",
       "179713                 1                 0   \n",
       "179714                 1                 0   \n",
       "\n",
       "        Class_Code_Translated_Agricultural  Class_Code_Translated_Commercial  \\\n",
       "0                                        0                                 0   \n",
       "1                                        0                                 0   \n",
       "2                                        0                                 0   \n",
       "3                                        0                                 1   \n",
       "4                                        0                                 0   \n",
       "...                                    ...                               ...   \n",
       "179710                                   0                                 0   \n",
       "179711                                   0                                 0   \n",
       "179712                                   0                                 0   \n",
       "179713                                   0                                 0   \n",
       "179714                                   0                                 0   \n",
       "\n",
       "        Class_Code_Translated_Exempt  Class_Code_Translated_Industrial  ...  \\\n",
       "0                                  0                                 0  ...   \n",
       "1                                  0                                 0  ...   \n",
       "2                                  0                                 0  ...   \n",
       "3                                  0                                 0  ...   \n",
       "4                                  0                                 0  ...   \n",
       "...                              ...                               ...  ...   \n",
       "179710                             0                                 0  ...   \n",
       "179711                             0                                 0  ...   \n",
       "179712                             0                                 0  ...   \n",
       "179713                             0                                 0  ...   \n",
       "179714                             0                                 0  ...   \n",
       "\n",
       "        Property_Zip_47989  Property_Zip_47990  Property_Zip_47991  \\\n",
       "0                        0                   0                   0   \n",
       "1                        0                   0                   0   \n",
       "2                        0                   0                   0   \n",
       "3                        0                   0                   0   \n",
       "4                        0                   0                   0   \n",
       "...                    ...                 ...                 ...   \n",
       "179710                   0                   0                   0   \n",
       "179711                   0                   0                   1   \n",
       "179712                   0                   0                   0   \n",
       "179713                   0                   0                   0   \n",
       "179714                   0                   0                   0   \n",
       "\n",
       "        Property_Zip_47992  Property_Zip_47993  Property_Zip_47994  \\\n",
       "0                        0                   0                   0   \n",
       "1                        0                   0                   0   \n",
       "2                        0                   0                   0   \n",
       "3                        0                   0                   0   \n",
       "4                        0                   0                   0   \n",
       "...                    ...                 ...                 ...   \n",
       "179710                   0                   0                   0   \n",
       "179711                   0                   0                   0   \n",
       "179712                   0                   1                   0   \n",
       "179713                   0                   1                   0   \n",
       "179714                   0                   1                   0   \n",
       "\n",
       "        Property_Zip_47995  Property_Zip_47997  Property_Zip_48448  \\\n",
       "0                        0                   0                   0   \n",
       "1                        0                   0                   0   \n",
       "2                        0                   0                   0   \n",
       "3                        0                   0                   0   \n",
       "4                        0                   0                   0   \n",
       "...                    ...                 ...                 ...   \n",
       "179710                   0                   0                   0   \n",
       "179711                   0                   0                   0   \n",
       "179712                   0                   0                   0   \n",
       "179713                   0                   0                   0   \n",
       "179714                   0                   0                   0   \n",
       "\n",
       "        Property_Zip_`4790  \n",
       "0                        0  \n",
       "1                        0  \n",
       "2                        0  \n",
       "3                        0  \n",
       "4                        0  \n",
       "...                    ...  \n",
       "179710                   0  \n",
       "179711                   0  \n",
       "179712                   0  \n",
       "179713                   0  \n",
       "179714                   0  \n",
       "\n",
       "[179715 rows x 990 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#One Hot encoding for non Numerical \n",
    "Property_df = pd.get_dummies(data = Property_df, columns=['Parcel_Vacancy','Class_Code_Translated','Property_Zip'])\n",
    "Property_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Property_Sales_Price</th>\n",
       "      <th>Total_Assessed_Value</th>\n",
       "      <th>Parcel_Acreage</th>\n",
       "      <th>Parcel_Vacancy_N</th>\n",
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       "      <th>Property_Zip_47992</th>\n",
       "      <th>Property_Zip_47993</th>\n",
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       "      <td>184188</td>\n",
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       "<p>179715 rows × 989 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index  Property_Sales_Price  Total_Assessed_Value  Parcel_Acreage  \\\n",
       "0            0              179900.0              182300.0           0.169   \n",
       "1            1               48000.0               93500.0           0.134   \n",
       "2            2              195000.0              188000.0           0.172   \n",
       "3            4             1785000.0             1802900.0           2.155   \n",
       "4            5              257575.0              210900.0           0.201   \n",
       "...        ...                   ...                   ...             ...   \n",
       "179710  184184               90000.0               64500.0        4000.000   \n",
       "179711  184185               78000.0               46700.0        2000.000   \n",
       "179712  184186               11250.0               67200.0        2700.000   \n",
       "179713  184187              200000.0                9200.0        1564.000   \n",
       "179714  184188              184000.0              105700.0        9700.000   \n",
       "\n",
       "        Parcel_Vacancy_N  Parcel_Vacancy_Y  \\\n",
       "0                      1                 0   \n",
       "1                      1                 0   \n",
       "2                      1                 0   \n",
       "3                      1                 0   \n",
       "4                      1                 0   \n",
       "...                  ...               ...   \n",
       "179710                 1                 0   \n",
       "179711                 1                 0   \n",
       "179712                 1                 0   \n",
       "179713                 1                 0   \n",
       "179714                 1                 0   \n",
       "\n",
       "        Class_Code_Translated_Agricultural  Class_Code_Translated_Commercial  \\\n",
       "0                                        0                                 0   \n",
       "1                                        0                                 0   \n",
       "2                                        0                                 0   \n",
       "3                                        0                                 1   \n",
       "4                                        0                                 0   \n",
       "...                                    ...                               ...   \n",
       "179710                                   0                                 0   \n",
       "179711                                   0                                 0   \n",
       "179712                                   0                                 0   \n",
       "179713                                   0                                 0   \n",
       "179714                                   0                                 0   \n",
       "\n",
       "        Class_Code_Translated_Exempt  Class_Code_Translated_Industrial  ...  \\\n",
       "0                                  0                                 0  ...   \n",
       "1                                  0                                 0  ...   \n",
       "2                                  0                                 0  ...   \n",
       "3                                  0                                 0  ...   \n",
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       "179714                             0                                 0  ...   \n",
       "\n",
       "        Property_Zip_47989  Property_Zip_47990  Property_Zip_47991  \\\n",
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       "4                        0                   0                   0   \n",
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       "\n",
       "        Property_Zip_47992  Property_Zip_47993  Property_Zip_47994  \\\n",
       "0                        0                   0                   0   \n",
       "1                        0                   0                   0   \n",
       "2                        0                   0                   0   \n",
       "3                        0                   0                   0   \n",
       "4                        0                   0                   0   \n",
       "...                    ...                 ...                 ...   \n",
       "179710                   0                   0                   0   \n",
       "179711                   0                   0                   0   \n",
       "179712                   0                   1                   0   \n",
       "179713                   0                   1                   0   \n",
       "179714                   0                   1                   0   \n",
       "\n",
       "        Property_Zip_47995  Property_Zip_47997  Property_Zip_48448  \\\n",
       "0                        0                   0                   0   \n",
       "1                        0                   0                   0   \n",
       "2                        0                   0                   0   \n",
       "3                        0                   0                   0   \n",
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       "...                    ...                 ...                 ...   \n",
       "179710                   0                   0                   0   \n",
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       "179712                   0                   0                   0   \n",
       "179713                   0                   0                   0   \n",
       "179714                   0                   0                   0   \n",
       "\n",
       "        Property_Zip_`4790  \n",
       "0                        0  \n",
       "1                        0  \n",
       "2                        0  \n",
       "3                        0  \n",
       "4                        0  \n",
       "...                    ...  \n",
       "179710                   0  \n",
       "179711                   0  \n",
       "179712                   0  \n",
       "179713                   0  \n",
       "179714                   0  \n",
       "\n",
       "[179715 rows x 989 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = Property_df.loc[:, Property_df.columns != 'Class_Code_Translated_Locally Assessed']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Property_Sales_Price</th>\n",
       "      <th>Total_Assessed_Value</th>\n",
       "      <th>Parcel_Acreage</th>\n",
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       "<p>179715 rows × 989 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index  Property_Sales_Price  Total_Assessed_Value  Parcel_Acreage  \\\n",
       "0            0              179900.0              182300.0           0.169   \n",
       "1            1               48000.0               93500.0           0.134   \n",
       "2            2              195000.0              188000.0           0.172   \n",
       "3            4             1785000.0             1802900.0           2.155   \n",
       "4            5              257575.0              210900.0           0.201   \n",
       "...        ...                   ...                   ...             ...   \n",
       "179710  184184               90000.0               64500.0        4000.000   \n",
       "179711  184185               78000.0               46700.0        2000.000   \n",
       "179712  184186               11250.0               67200.0        2700.000   \n",
       "179713  184187              200000.0                9200.0        1564.000   \n",
       "179714  184188              184000.0              105700.0        9700.000   \n",
       "\n",
       "        Parcel_Vacancy_N  Parcel_Vacancy_Y  \\\n",
       "0                      1                 0   \n",
       "1                      1                 0   \n",
       "2                      1                 0   \n",
       "3                      1                 0   \n",
       "4                      1                 0   \n",
       "...                  ...               ...   \n",
       "179710                 1                 0   \n",
       "179711                 1                 0   \n",
       "179712                 1                 0   \n",
       "179713                 1                 0   \n",
       "179714                 1                 0   \n",
       "\n",
       "        Class_Code_Translated_Agricultural  Class_Code_Translated_Commercial  \\\n",
       "0                                        0                                 0   \n",
       "1                                        0                                 0   \n",
       "2                                        0                                 0   \n",
       "3                                        0                                 1   \n",
       "4                                        0                                 0   \n",
       "...                                    ...                               ...   \n",
       "179710                                   0                                 0   \n",
       "179711                                   0                                 0   \n",
       "179712                                   0                                 0   \n",
       "179713                                   0                                 0   \n",
       "179714                                   0                                 0   \n",
       "\n",
       "        Class_Code_Translated_Exempt  Class_Code_Translated_Industrial  ...  \\\n",
       "0                                  0                                 0  ...   \n",
       "1                                  0                                 0  ...   \n",
       "2                                  0                                 0  ...   \n",
       "3                                  0                                 0  ...   \n",
       "4                                  0                                 0  ...   \n",
       "...                              ...                               ...  ...   \n",
       "179710                             0                                 0  ...   \n",
       "179711                             0                                 0  ...   \n",
       "179712                             0                                 0  ...   \n",
       "179713                             0                                 0  ...   \n",
       "179714                             0                                 0  ...   \n",
       "\n",
       "        Property_Zip_47989  Property_Zip_47990  Property_Zip_47991  \\\n",
       "0                        0                   0                   0   \n",
       "1                        0                   0                   0   \n",
       "2                        0                   0                   0   \n",
       "3                        0                   0                   0   \n",
       "4                        0                   0                   0   \n",
       "...                    ...                 ...                 ...   \n",
       "179710                   0                   0                   0   \n",
       "179711                   0                   0                   1   \n",
       "179712                   0                   0                   0   \n",
       "179713                   0                   0                   0   \n",
       "179714                   0                   0                   0   \n",
       "\n",
       "        Property_Zip_47992  Property_Zip_47993  Property_Zip_47994  \\\n",
       "0                        0                   0                   0   \n",
       "1                        0                   0                   0   \n",
       "2                        0                   0                   0   \n",
       "3                        0                   0                   0   \n",
       "4                        0                   0                   0   \n",
       "...                    ...                 ...                 ...   \n",
       "179710                   0                   0                   0   \n",
       "179711                   0                   0                   0   \n",
       "179712                   0                   1                   0   \n",
       "179713                   0                   1                   0   \n",
       "179714                   0                   1                   0   \n",
       "\n",
       "        Property_Zip_47995  Property_Zip_47997  Property_Zip_48448  \\\n",
       "0                        0                   0                   0   \n",
       "1                        0                   0                   0   \n",
       "2                        0                   0                   0   \n",
       "3                        0                   0                   0   \n",
       "4                        0                   0                   0   \n",
       "...                    ...                 ...                 ...   \n",
       "179710                   0                   0                   0   \n",
       "179711                   0                   0                   0   \n",
       "179712                   0                   0                   0   \n",
       "179713                   0                   0                   0   \n",
       "179714                   0                   0                   0   \n",
       "\n",
       "        Property_Zip_`4790  \n",
       "0                        0  \n",
       "1                        0  \n",
       "2                        0  \n",
       "3                        0  \n",
       "4                        0  \n",
       "...                    ...  \n",
       "179710                   0  \n",
       "179711                   0  \n",
       "179712                   0  \n",
       "179713                   0  \n",
       "179714                   0  \n",
       "\n",
       "[179715 rows x 989 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = Property_df.loc[:, Property_df.columns != 'Class_Code_Translated_Mineral']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\coset\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3990: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n"
     ]
    }
   ],
   "source": [
    "#Git Rid of Index\n",
    "df.drop(['index'],axis=1,inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[df['Total_Assessed_Value']>0,:]\n",
    "df = df.loc[df['Parcel_Acreage']>0,:]\n",
    "#len(new_df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Build"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Seperate X and Y\n",
    "y_data = df['Property_Sales_Price']\n",
    "\n",
    "x_data = df.loc[:, df.columns != 'Property_Sales_Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0          179900.0\n",
      "1           48000.0\n",
      "2          195000.0\n",
      "3         1785000.0\n",
      "4          257575.0\n",
      "            ...    \n",
      "179710      90000.0\n",
      "179711      78000.0\n",
      "179712      11250.0\n",
      "179713     200000.0\n",
      "179714     184000.0\n",
      "Name: Property_Sales_Price, Length: 171928, dtype: float64         Total_Assessed_Value  Parcel_Acreage  Parcel_Vacancy_N  \\\n",
      "0                   182300.0           0.169                 1   \n",
      "1                    93500.0           0.134                 1   \n",
      "2                   188000.0           0.172                 1   \n",
      "3                  1802900.0           2.155                 1   \n",
      "4                   210900.0           0.201                 1   \n",
      "...                      ...             ...               ...   \n",
      "179710               64500.0        4000.000                 1   \n",
      "179711               46700.0        2000.000                 1   \n",
      "179712               67200.0        2700.000                 1   \n",
      "179713                9200.0        1564.000                 1   \n",
      "179714              105700.0        9700.000                 1   \n",
      "\n",
      "        Parcel_Vacancy_Y  Class_Code_Translated_Agricultural  \\\n",
      "0                      0                                   0   \n",
      "1                      0                                   0   \n",
      "2                      0                                   0   \n",
      "3                      0                                   0   \n",
      "4                      0                                   0   \n",
      "...                  ...                                 ...   \n",
      "179710                 0                                   0   \n",
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      "179713                 0                                   0   \n",
      "179714                 0                                   0   \n",
      "\n",
      "        Class_Code_Translated_Commercial  Class_Code_Translated_Exempt  \\\n",
      "0                                      0                             0   \n",
      "1                                      0                             0   \n",
      "2                                      0                             0   \n",
      "3                                      1                             0   \n",
      "4                                      0                             0   \n",
      "...                                  ...                           ...   \n",
      "179710                                 0                             0   \n",
      "179711                                 0                             0   \n",
      "179712                                 0                             0   \n",
      "179713                                 0                             0   \n",
      "179714                                 0                             0   \n",
      "\n",
      "        Class_Code_Translated_Industrial  \\\n",
      "0                                      0   \n",
      "1                                      0   \n",
      "2                                      0   \n",
      "3                                      0   \n",
      "4                                      0   \n",
      "...                                  ...   \n",
      "179710                                 0   \n",
      "179711                                 0   \n",
      "179712                                 0   \n",
      "179713                                 0   \n",
      "179714                                 0   \n",
      "\n",
      "        Class_Code_Translated_Locally Assessed  \\\n",
      "0                                            0   \n",
      "1                                            0   \n",
      "2                                            0   \n",
      "3                                            0   \n",
      "4                                            0   \n",
      "...                                        ...   \n",
      "179710                                       0   \n",
      "179711                                       0   \n",
      "179712                                       0   \n",
      "179713                                       0   \n",
      "179714                                       0   \n",
      "\n",
      "        Class_Code_Translated_Residential  ...  Property_Zip_47989  \\\n",
      "0                                       1  ...                   0   \n",
      "1                                       1  ...                   0   \n",
      "2                                       1  ...                   0   \n",
      "3                                       0  ...                   0   \n",
      "4                                       1  ...                   0   \n",
      "...                                   ...  ...                 ...   \n",
      "179710                                  1  ...                   0   \n",
      "179711                                  1  ...                   0   \n",
      "179712                                  1  ...                   0   \n",
      "179713                                  1  ...                   0   \n",
      "179714                                  1  ...                   0   \n",
      "\n",
      "        Property_Zip_47990  Property_Zip_47991  Property_Zip_47992  \\\n",
      "0                        0                   0                   0   \n",
      "1                        0                   0                   0   \n",
      "2                        0                   0                   0   \n",
      "3                        0                   0                   0   \n",
      "4                        0                   0                   0   \n",
      "...                    ...                 ...                 ...   \n",
      "179710                   0                   0                   0   \n",
      "179711                   0                   1                   0   \n",
      "179712                   0                   0                   0   \n",
      "179713                   0                   0                   0   \n",
      "179714                   0                   0                   0   \n",
      "\n",
      "        Property_Zip_47993  Property_Zip_47994  Property_Zip_47995  \\\n",
      "0                        0                   0                   0   \n",
      "1                        0                   0                   0   \n",
      "2                        0                   0                   0   \n",
      "3                        0                   0                   0   \n",
      "4                        0                   0                   0   \n",
      "...                    ...                 ...                 ...   \n",
      "179710                   0                   0                   0   \n",
      "179711                   0                   0                   0   \n",
      "179712                   1                   0                   0   \n",
      "179713                   1                   0                   0   \n",
      "179714                   1                   0                   0   \n",
      "\n",
      "        Property_Zip_47997  Property_Zip_48448  Property_Zip_`4790  \n",
      "0                        0                   0                   0  \n",
      "1                        0                   0                   0  \n",
      "2                        0                   0                   0  \n",
      "3                        0                   0                   0  \n",
      "4                        0                   0                   0  \n",
      "...                    ...                 ...                 ...  \n",
      "179710                   0                   0                   0  \n",
      "179711                   0                   0                   0  \n",
      "179712                   0                   0                   0  \n",
      "179713                   0                   0                   0  \n",
      "179714                   0                   0                   0  \n",
      "\n",
      "[171928 rows x 987 columns] Index(['Total_Assessed_Value', 'Parcel_Acreage', 'Parcel_Vacancy_N',\n",
      "       'Parcel_Vacancy_Y', 'Class_Code_Translated_Agricultural',\n",
      "       'Class_Code_Translated_Commercial', 'Class_Code_Translated_Exempt',\n",
      "       'Class_Code_Translated_Industrial',\n",
      "       'Class_Code_Translated_Locally Assessed',\n",
      "       'Class_Code_Translated_Residential',\n",
      "       ...\n",
      "       'Property_Zip_47989', 'Property_Zip_47990', 'Property_Zip_47991',\n",
      "       'Property_Zip_47992', 'Property_Zip_47993', 'Property_Zip_47994',\n",
      "       'Property_Zip_47995', 'Property_Zip_47997', 'Property_Zip_48448',\n",
      "       'Property_Zip_`4790'],\n",
      "      dtype='object', length=987)\n"
     ]
    }
   ],
   "source": [
    "    data = df.loc[:, df.columns !='Property_Sales_Price'] \n",
    "    print( df['Property_Sales_Price'], data, data.columns )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(171928, 987)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 171928 entries, 0 to 179714\n",
      "Columns: 987 entries, Total_Assessed_Value to Property_Zip_`4790\n",
      "dtypes: float64(2), uint8(985)\n",
      "memory usage: 165.4 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Assessed_Value</th>\n",
       "      <th>Parcel_Acreage</th>\n",
       "      <th>Parcel_Vacancy_N</th>\n",
       "      <th>Parcel_Vacancy_Y</th>\n",
       "      <th>Class_Code_Translated_Agricultural</th>\n",
       "      <th>Class_Code_Translated_Commercial</th>\n",
       "      <th>Class_Code_Translated_Exempt</th>\n",
       "      <th>Class_Code_Translated_Industrial</th>\n",
       "      <th>Class_Code_Translated_Locally Assessed</th>\n",
       "      <th>Class_Code_Translated_Residential</th>\n",
       "      <th>...</th>\n",
       "      <th>Property_Zip_47989</th>\n",
       "      <th>Property_Zip_47990</th>\n",
       "      <th>Property_Zip_47991</th>\n",
       "      <th>Property_Zip_47992</th>\n",
       "      <th>Property_Zip_47993</th>\n",
       "      <th>Property_Zip_47994</th>\n",
       "      <th>Property_Zip_47995</th>\n",
       "      <th>Property_Zip_47997</th>\n",
       "      <th>Property_Zip_48448</th>\n",
       "      <th>Property_Zip_`4790</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.719280e+05</td>\n",
       "      <td>171928.000000</td>\n",
       "      <td>171928.000000</td>\n",
       "      <td>171928.000000</td>\n",
       "      <td>171928.000000</td>\n",
       "      <td>171928.000000</td>\n",
       "      <td>171928.000000</td>\n",
       "      <td>171928.000000</td>\n",
       "      <td>171928.000000</td>\n",
       "      <td>171928.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>171928.000000</td>\n",
       "      <td>171928.000000</td>\n",
       "      <td>171928.000000</td>\n",
       "      <td>171928.000000</td>\n",
       "      <td>171928.000000</td>\n",
       "      <td>171928.000000</td>\n",
       "      <td>171928.000000</td>\n",
       "      <td>171928.000000</td>\n",
       "      <td>171928.0</td>\n",
       "      <td>171928.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.471466e+05</td>\n",
       "      <td>2867.616932</td>\n",
       "      <td>0.894130</td>\n",
       "      <td>0.105870</td>\n",
       "      <td>0.060037</td>\n",
       "      <td>0.036736</td>\n",
       "      <td>0.005491</td>\n",
       "      <td>0.004793</td>\n",
       "      <td>0.000326</td>\n",
       "      <td>0.892606</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>0.000558</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.574944e+05</td>\n",
       "      <td>21266.499256</td>\n",
       "      <td>0.307672</td>\n",
       "      <td>0.307672</td>\n",
       "      <td>0.237556</td>\n",
       "      <td>0.188114</td>\n",
       "      <td>0.073896</td>\n",
       "      <td>0.069063</td>\n",
       "      <td>0.018045</td>\n",
       "      <td>0.309614</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004823</td>\n",
       "      <td>0.003411</td>\n",
       "      <td>0.009340</td>\n",
       "      <td>0.013853</td>\n",
       "      <td>0.023623</td>\n",
       "      <td>0.003411</td>\n",
       "      <td>0.017221</td>\n",
       "      <td>0.003411</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.000000e+01</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.060000e+04</td>\n",
       "      <td>0.179000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.090000e+05</td>\n",
       "      <td>0.345000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.827000e+05</td>\n",
       "      <td>4.725250</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.526210e+07</td>\n",
       "      <td>417500.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 987 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total_Assessed_Value  Parcel_Acreage  Parcel_Vacancy_N  \\\n",
       "count          1.719280e+05   171928.000000     171928.000000   \n",
       "mean           1.471466e+05     2867.616932          0.894130   \n",
       "std            3.574944e+05    21266.499256          0.307672   \n",
       "min            9.000000e+01        0.001000          0.000000   \n",
       "25%            5.060000e+04        0.179000          1.000000   \n",
       "50%            1.090000e+05        0.345000          1.000000   \n",
       "75%            1.827000e+05        4.725250          1.000000   \n",
       "max            3.526210e+07   417500.000000          1.000000   \n",
       "\n",
       "       Parcel_Vacancy_Y  Class_Code_Translated_Agricultural  \\\n",
       "count     171928.000000                       171928.000000   \n",
       "mean           0.105870                            0.060037   \n",
       "std            0.307672                            0.237556   \n",
       "min            0.000000                            0.000000   \n",
       "25%            0.000000                            0.000000   \n",
       "50%            0.000000                            0.000000   \n",
       "75%            0.000000                            0.000000   \n",
       "max            1.000000                            1.000000   \n",
       "\n",
       "       Class_Code_Translated_Commercial  Class_Code_Translated_Exempt  \\\n",
       "count                     171928.000000                 171928.000000   \n",
       "mean                           0.036736                      0.005491   \n",
       "std                            0.188114                      0.073896   \n",
       "min                            0.000000                      0.000000   \n",
       "25%                            0.000000                      0.000000   \n",
       "50%                            0.000000                      0.000000   \n",
       "75%                            0.000000                      0.000000   \n",
       "max                            1.000000                      1.000000   \n",
       "\n",
       "       Class_Code_Translated_Industrial  \\\n",
       "count                     171928.000000   \n",
       "mean                           0.004793   \n",
       "std                            0.069063   \n",
       "min                            0.000000   \n",
       "25%                            0.000000   \n",
       "50%                            0.000000   \n",
       "75%                            0.000000   \n",
       "max                            1.000000   \n",
       "\n",
       "       Class_Code_Translated_Locally Assessed  \\\n",
       "count                           171928.000000   \n",
       "mean                                 0.000326   \n",
       "std                                  0.018045   \n",
       "min                                  0.000000   \n",
       "25%                                  0.000000   \n",
       "50%                                  0.000000   \n",
       "75%                                  0.000000   \n",
       "max                                  1.000000   \n",
       "\n",
       "       Class_Code_Translated_Residential  ...  Property_Zip_47989  \\\n",
       "count                      171928.000000  ...       171928.000000   \n",
       "mean                            0.892606  ...            0.000023   \n",
       "std                             0.309614  ...            0.004823   \n",
       "min                             0.000000  ...            0.000000   \n",
       "25%                             1.000000  ...            0.000000   \n",
       "50%                             1.000000  ...            0.000000   \n",
       "75%                             1.000000  ...            0.000000   \n",
       "max                             1.000000  ...            1.000000   \n",
       "\n",
       "       Property_Zip_47990  Property_Zip_47991  Property_Zip_47992  \\\n",
       "count       171928.000000       171928.000000       171928.000000   \n",
       "mean             0.000012            0.000087            0.000192   \n",
       "std              0.003411            0.009340            0.013853   \n",
       "min              0.000000            0.000000            0.000000   \n",
       "25%              0.000000            0.000000            0.000000   \n",
       "50%              0.000000            0.000000            0.000000   \n",
       "75%              0.000000            0.000000            0.000000   \n",
       "max              1.000000            1.000000            1.000000   \n",
       "\n",
       "       Property_Zip_47993  Property_Zip_47994  Property_Zip_47995  \\\n",
       "count       171928.000000       171928.000000       171928.000000   \n",
       "mean             0.000558            0.000012            0.000297   \n",
       "std              0.023623            0.003411            0.017221   \n",
       "min              0.000000            0.000000            0.000000   \n",
       "25%              0.000000            0.000000            0.000000   \n",
       "50%              0.000000            0.000000            0.000000   \n",
       "75%              0.000000            0.000000            0.000000   \n",
       "max              1.000000            1.000000            1.000000   \n",
       "\n",
       "       Property_Zip_47997  Property_Zip_48448  Property_Zip_`4790  \n",
       "count       171928.000000            171928.0       171928.000000  \n",
       "mean             0.000012                 0.0            0.000006  \n",
       "std              0.003411                 0.0            0.002412  \n",
       "min              0.000000                 0.0            0.000000  \n",
       "25%              0.000000                 0.0            0.000000  \n",
       "50%              0.000000                 0.0            0.000000  \n",
       "75%              0.000000                 0.0            0.000000  \n",
       "max              1.000000                 0.0            1.000000  \n",
       "\n",
       "[8 rows x 987 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-130-f9246d1dec9c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#to see pairwise relationships on our dataset we will check pairplot from seaborn library\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mseaborn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36mpairplot\u001b[1;34m(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, height, aspect, corner, dropna, plot_kws, diag_kws, grid_kws, size)\u001b[0m\n\u001b[0;32m   2082\u001b[0m     \u001b[1;31m# Set up the PairGrid\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2083\u001b[0m     \u001b[0mgrid_kws\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"diag_sharey\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdiag_kind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"hist\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2084\u001b[1;33m     grid = PairGrid(data, vars=vars, x_vars=x_vars, y_vars=y_vars, hue=hue,\n\u001b[0m\u001b[0;32m   2085\u001b[0m                     \u001b[0mhue_order\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhue_order\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcorner\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcorner\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2086\u001b[0m                     height=height, aspect=aspect, dropna=dropna, **grid_kws)\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, hue, hue_order, palette, hue_kws, vars, x_vars, y_vars, corner, diag_sharey, height, aspect, layout_pad, despine, dropna, size)\u001b[0m\n\u001b[0;32m   1285\u001b[0m         \u001b[0mfigsize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_vars\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mheight\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0maspect\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_vars\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1286\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1287\u001b[1;33m         fig, axes = plt.subplots(len(y_vars), len(x_vars),\n\u001b[0m\u001b[0;32m   1288\u001b[0m                                  \u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1289\u001b[0m                                  \u001b[0msharex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"col\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msharey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"row\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36msubplots\u001b[1;34m(nrows, ncols, sharex, sharey, squeeze, subplot_kw, gridspec_kw, **fig_kw)\u001b[0m\n\u001b[0;32m   1176\u001b[0m     \"\"\"\n\u001b[0;32m   1177\u001b[0m     \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mfig_kw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1178\u001b[1;33m     axs = fig.subplots(nrows=nrows, ncols=ncols, sharex=sharex, sharey=sharey,\n\u001b[0m\u001b[0;32m   1179\u001b[0m                        \u001b[0msqueeze\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msubplot_kw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msubplot_kw\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1180\u001b[0m                        gridspec_kw=gridspec_kw)\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36msubplots\u001b[1;34m(self, nrows, ncols, sharex, sharey, squeeze, subplot_kw, gridspec_kw)\u001b[0m\n\u001b[0;32m   1574\u001b[0m                 \u001b[0msubplot_kw\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"sharex\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mshared_with\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msharex\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1575\u001b[0m                 \u001b[0msubplot_kw\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"sharey\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mshared_with\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msharey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1576\u001b[1;33m                 \u001b[0maxarr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msubplot_kw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1577\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1578\u001b[0m         \u001b[1;31m# turn off redundant tick labeling\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36madd_subplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1419\u001b[0m             \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msubplot_class_factory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprojection_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1420\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1421\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_axes_internal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1422\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1423\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_add_axes_internal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36m_add_axes_internal\u001b[1;34m(self, key, ax)\u001b[0m\n\u001b[0;32m   1424\u001b[0m         \u001b[1;34m\"\"\"Private helper for `add_axes` and `add_subplot`.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1425\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1426\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msca\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1427\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_remove_method\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_remove_ax\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1428\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36msca\u001b[1;34m(self, a)\u001b[0m\n\u001b[0;32m   1959\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0msca\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1960\u001b[0m         \u001b[1;34m\"\"\"Set the current axes to be a and return a.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1961\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbubble\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1962\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axobservers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1963\u001b[0m             \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mbubble\u001b[1;34m(self, a)\u001b[0m\n\u001b[0;32m    107\u001b[0m         \u001b[0mstack\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mto\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mtop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    108\u001b[0m         \"\"\"\n\u001b[1;32m--> 109\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbubble\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_entry_from_axes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    110\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    111\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\__init__.py\u001b[0m in \u001b[0;36mbubble\u001b[1;34m(self, o)\u001b[0m\n\u001b[0;32m    644\u001b[0m                 \u001b[0mbubbles\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthiso\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    645\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 646\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthiso\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    647\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mbubbles\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    648\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\__init__.py\u001b[0m in \u001b[0;36mpush\u001b[1;34m(self, o)\u001b[0m\n\u001b[0;32m    605\u001b[0m         \u001b[1;33m*\u001b[0m\u001b[0mo\u001b[0m\u001b[1;33m*\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mreturned\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    606\u001b[0m         \"\"\"\n\u001b[1;32m--> 607\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_elements\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_elements\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_pos\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mo\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    608\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_pos\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_elements\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    609\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in callback <function flush_figures at 0x000002527AB20670> (for post_execute):\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipykernel\\pylab\\backend_inline.py\u001b[0m in \u001b[0;36mflush_figures\u001b[1;34m()\u001b[0m\n\u001b[0;32m    119\u001b[0m         \u001b[1;31m# ignore the tracking, just draw and close all figures\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    120\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 121\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    122\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    123\u001b[0m             \u001b[1;31m# safely show traceback if in IPython, else raise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipykernel\\pylab\\backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[1;34m(close, block)\u001b[0m\n\u001b[0;32m     41\u001b[0m             display(\n\u001b[0;32m     42\u001b[0m                 \u001b[0mfigure_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 43\u001b[1;33m                 \u001b[0mmetadata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     44\u001b[0m             )\n\u001b[0;32m     45\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipykernel\\pylab\\backend_inline.py\u001b[0m in \u001b[0;36m_fetch_figure_metadata\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    178\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_facecolor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    179\u001b[0m         \u001b[1;31m# the background is transparent\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 180\u001b[1;33m         ticksLight = _is_light([label.get_color()\n\u001b[0m\u001b[0;32m    181\u001b[0m                                 \u001b[1;32mfor\u001b[0m \u001b[0maxes\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    182\u001b[0m                                 \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipykernel\\pylab\\backend_inline.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    181\u001b[0m                                 \u001b[1;32mfor\u001b[0m \u001b[0maxes\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    182\u001b[0m                                 \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 183\u001b[1;33m                                 for label in axis.get_ticklabels()])\n\u001b[0m\u001b[0;32m    184\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mticksLight\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mticksLight\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mticksLight\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    185\u001b[0m             \u001b[1;31m# there are one or more tick labels, all with the same lightness\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_ticklabels\u001b[1;34m(self, minor, which)\u001b[0m\n\u001b[0;32m   1318\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mminor\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1319\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_minorticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1320\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_majorticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1321\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1322\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_majorticklines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_majorticklabels\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1274\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_majorticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1275\u001b[0m         \u001b[1;34m'Return a list of Text instances for the major ticklabels.'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1276\u001b[1;33m         \u001b[0mticks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1277\u001b[0m         \u001b[0mlabels1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mtick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtick\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mticks\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_visible\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1278\u001b[0m         \u001b[0mlabels2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mtick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtick\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mticks\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_visible\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_major_ticks\u001b[1;34m(self, numticks)\u001b[0m\n\u001b[0;32m   1429\u001b[0m         \u001b[1;34m'Get the tick instances; grow as necessary.'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1430\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnumticks\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1431\u001b[1;33m             \u001b[0mnumticks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_majorticklocs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1432\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1433\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajorTicks\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mnumticks\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_majorticklocs\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1346\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_majorticklocs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1347\u001b[0m         \u001b[1;34m\"\"\"Get the array of major tick locations in data coordinates.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1348\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1349\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1350\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_minorticklocs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\ticker.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   2201\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2202\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2203\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2204\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2205\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\ticker.py\u001b[0m in \u001b[0;36mtick_values\u001b[1;34m(self, vmin, vmax)\u001b[0m\n\u001b[0;32m   2209\u001b[0m         vmin, vmax = mtransforms.nonsingular(\n\u001b[0;32m   2210\u001b[0m             vmin, vmax, expander=1e-13, tiny=1e-14)\n\u001b[1;32m-> 2211\u001b[1;33m         \u001b[0mlocs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_raw_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2212\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2213\u001b[0m         \u001b[0mprune\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prune\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\ticker.py\u001b[0m in \u001b[0;36m_raw_ticks\u001b[1;34m(self, vmin, vmax)\u001b[0m\n\u001b[0;32m   2148\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_nbins\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'auto'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2149\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2150\u001b[1;33m                 nbins = np.clip(self.axis.get_tick_space(),\n\u001b[0m\u001b[0;32m   2151\u001b[0m                                 max(1, self._min_n_ticks - 1), 9)\n\u001b[0;32m   2152\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_tick_space\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   2188\u001b[0m         \u001b[0mends\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransAxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2189\u001b[0m         \u001b[0mlength\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mends\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mends\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m72\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2190\u001b[1;33m         \u001b[0mtick\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tick\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2191\u001b[0m         \u001b[1;31m# There is a heuristic here that the aspect ratio of tick text\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2192\u001b[0m         \u001b[1;31m# is no more than 3:1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m_get_tick\u001b[1;34m(self, major)\u001b[0m\n\u001b[0;32m   1941\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1942\u001b[0m             \u001b[0mtick_kw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_minor_tick_kw\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1943\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mXTick\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmajor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mtick_kw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1944\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1945\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, axes, loc, label, size, width, color, tickdir, pad, labelsize, labelcolor, zorder, gridOn, tick1On, tick2On, label1On, label2On, major, labelrotation, grid_color, grid_linestyle, grid_linewidth, grid_alpha, **kw)\u001b[0m\n\u001b[0;32m    155\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_tickdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtickdir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    156\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 157\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtick1line\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tick1line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    158\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtick2line\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tick2line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    159\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgridline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_gridline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m_get_tick1line\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    460\u001b[0m         \u001b[1;34m'Get the default line2D instance'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    461\u001b[0m         \u001b[1;31m# x in data coords, y in axes coords\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 462\u001b[1;33m         l = mlines.Line2D(xdata=(0,), ydata=(0,), color=self._color,\n\u001b[0m\u001b[0;32m    463\u001b[0m                           \u001b[0mlinestyle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'None'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tickmarkers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    464\u001b[0m                           \u001b[0mmarkersize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\lines.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[0;32m    376\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_color\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    377\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_color\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 378\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_marker\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMarkerStyle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmarker\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfillstyle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    379\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    380\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_markevery\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\markers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, marker, fillstyle)\u001b[0m\n\u001b[0;32m    223\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_marker_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_fillstyle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfillstyle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 225\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_marker\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmarker\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    226\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    227\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_recache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\markers.py\u001b[0m in \u001b[0;36mset_marker\u001b[1;34m(self, marker)\u001b[0m\n\u001b[0;32m    294\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    295\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_marker\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmarker\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 296\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_recache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    297\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    298\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\markers.py\u001b[0m in \u001b[0;36m_recache\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    236\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_capstyle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'butt'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    237\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filled\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 238\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_marker_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    240\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__bool__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\markers.py\u001b[0m in \u001b[0;36m_set_tickdown\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    732\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    733\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_set_tickdown\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 734\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_transform\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAffine2D\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    735\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_snap_threshold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    736\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filled\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\transforms.py\u001b[0m in \u001b[0;36mscale\u001b[1;34m(self, sx, sy)\u001b[0m\n\u001b[0;32m   1999\u001b[0m         scale_mtx = np.array(\n\u001b[0;32m   2000\u001b[0m             [[sx, 0.0, 0.0], [0.0, sy, 0.0], [0.0, 0.0, 1.0]], float)\n\u001b[1;32m-> 2001\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mtx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscale_mtx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mtx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2002\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minvalidate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2003\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mdot\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "#to see pairwise relationships on our dataset we will check pairplot from seaborn library\n",
    "seaborn.pairplot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create correlation matrix\n",
    "correlations = data.corr()\n",
    "indx=correlations.index\n",
    "\n",
    "#plot this correlation for clear visualisation\n",
    "plt.figure(figsize=(26,22))\n",
    "#annot = True , dsiplays text over the cells.\n",
    "#cmap = \"YlGnBu\" is nothing but adjustment of colors for our heatmap\n",
    "seaborn.heatmap(data[indx].corr(),annot=True,cmap=\"YlGnBu\")\n",
    "#amount of darkness shows how our features are correalated with each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def split_data_train_model(labels, data):\n",
    "    labels = labels.values.reshape(-1,1)\n",
    "    Xscaler = StandardScaler()\n",
    "    Xscaler = Xscaler.fit(data)\n",
    "    Xscaled = Xscaler.transform(data)\n",
    "    Yscaler = StandardScaler()\n",
    "    Yscaler = Yscaler.fit(labels)\n",
    "    Yscaled = Yscaler.transform(labels)\n",
    "    # 20% examples in test data\n",
    "    train, test, train_labels, test_labels = train_test_split(Xscaled,\n",
    "                                                              Yscaled,\n",
    "                                                              test_size=0.25,\n",
    "                                                              random_state=RF_SEED)\n",
    "    print(train)\n",
    "    print(train_labels)\n",
    "# training data fit\n",
    "    regressor = RandomForestRegressor(n_estimators = 100, random_state = RF_SEED, max_depth=5)\n",
    "    regressor = regressor.fit(train, train_labels)\n",
    "    regressor.score(train, train_labels)\n",
    "    return test, test_labels, regressor, Xscaler, Yscaler "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.34398456e-01 -1.34448797e-01  3.44100966e-01 ... -3.41070547e-03\n",
      "   0.00000000e+00 -2.41172596e-03]\n",
      " [ 2.13579987e-01 -1.34830996e-01  3.44100966e-01 ... -3.41070547e-03\n",
      "   0.00000000e+00 -2.41172596e-03]\n",
      " [-9.07612880e-02 -1.34835792e-01  3.44100966e-01 ... -3.41070547e-03\n",
      "   0.00000000e+00 -2.41172596e-03]\n",
      " ...\n",
      " [-2.45169729e-01 -1.34835886e-01  3.44100966e-01 ... -3.41070547e-03\n",
      "   0.00000000e+00 -2.41172596e-03]\n",
      " [-2.77338154e-01 -1.34820369e-01  3.44100966e-01 ... -3.41070547e-03\n",
      "   0.00000000e+00 -2.41172596e-03]\n",
      " [-3.67130020e-01  8.17585850e+00 -2.90612378e+00 ... -3.41070547e-03\n",
      "   0.00000000e+00 -2.41172596e-03]]\n",
      "[[-0.06357317]\n",
      " [ 0.07208747]\n",
      " [-0.08545911]\n",
      " ...\n",
      " [-0.18346306]\n",
      " [-0.11748339]\n",
      " [-0.27680015]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-150-cfd0aa8e37a9>:18: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  regressor = regressor.fit(train, train_labels)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.21192221 -0.05888015 -0.17687073 ... -0.07691865  0.2201111\n",
      "  0.22367264]\n",
      "[[ 0.31862613]\n",
      " [-0.07564262]\n",
      " [-0.32668722]\n",
      " ...\n",
      " [-0.2059927 ]\n",
      " [-0.02076685]\n",
      " [ 0.25586498]]\n"
     ]
    }
   ],
   "source": [
    "x_test, y_test, regressor, Xscaler, Yscaler = split_data_train_model(y_data, x_data)\n",
    " \n",
    "predictions_test = regressor.predict(x_test)\n",
    "print(predictions_test)\n",
    "print(y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the correlation between real answer and prediction\n",
    "#correlation_test = round(pearsonr(predictions_test, x_test_labels)[0], 5)\n",
    "#correlation_train = round(pearsonr(predictions_train, x_test_labels)[0], 5)\n",
    "#print(correlation_test)\n",
    "#print(correlation_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. Total_Assessed_Value (0.7665496560144001)\n",
      "2. Parcel_Acreage (0.023041780093659807)\n",
      "3. Parcel_Vacancy_N (0.008173844484498453)\n",
      "4. Parcel_Vacancy_Y (0.005369347345920151)\n",
      "5. Class_Code_Translated_Agricultural (0.0)\n",
      "6. Class_Code_Translated_Commercial (0.0033615097438252084)\n",
      "7. Class_Code_Translated_Exempt (0.007561992911196941)\n",
      "8. Class_Code_Translated_Industrial (0.007384251437174532)\n",
      "9. Class_Code_Translated_Locally Assessed (0.0)\n",
      "10. Class_Code_Translated_Residential (0.01253094170800235)\n",
      "11. Property_Zip_41750 (0.0)\n",
      "12. Property_Zip_42601 (0.0)\n",
      "13. Property_Zip_42637 (0.0)\n",
      "14. Property_Zip_42678 (0.0)\n",
      "15. Property_Zip_45228 (0.0)\n",
      "16. Property_Zip_46001 (0.0)\n",
      "17. Property_Zip_46011 (0.0)\n",
      "18. Property_Zip_46012 (0.0)\n",
      "19. Property_Zip_46013 (0.0)\n",
      "20. Property_Zip_46014 (0.0)\n",
      "21. Property_Zip_46015 (0.0)\n",
      "22. Property_Zip_46016 (0.00034474074901412645)\n",
      "23. Property_Zip_46017 (0.0)\n",
      "24. Property_Zip_46018 (0.0)\n",
      "25. Property_Zip_46030 (0.0)\n",
      "26. Property_Zip_46031 (0.0)\n",
      "27. Property_Zip_46032 (0.00033070855752661746)\n",
      "28. Property_Zip_46033 (0.0)\n",
      "29. Property_Zip_46034 (0.0)\n",
      "30. Property_Zip_46035 (0.0)\n",
      "31. Property_Zip_46036 (0.0)\n",
      "32. Property_Zip_46037 (0.0)\n",
      "33. Property_Zip_46038 (0.0)\n",
      "34. Property_Zip_46039 (0.0)\n",
      "35. Property_Zip_46040 (0.0)\n",
      "36. Property_Zip_46041 (0.0)\n",
      "37. Property_Zip_46044 (0.0)\n",
      "38. Property_Zip_46045 (0.0)\n",
      "39. Property_Zip_46047 (0.0)\n",
      "40. Property_Zip_46048 (0.0)\n",
      "41. Property_Zip_46049 (0.0)\n",
      "42. Property_Zip_46050 (0.0)\n",
      "43. Property_Zip_46051 (0.0)\n",
      "44. Property_Zip_46052 (0.0001580241178942011)\n",
      "45. Property_Zip_46055 (0.0)\n",
      "46. Property_Zip_46056 (0.0)\n",
      "47. Property_Zip_46057 (0.0)\n",
      "48. Property_Zip_46058 (0.0)\n",
      "49. Property_Zip_46060 (0.0)\n",
      "50. Property_Zip_46062 (0.0002294765001846619)\n",
      "51. Property_Zip_46063 (0.0)\n",
      "52. Property_Zip_46064 (0.0)\n",
      "53. Property_Zip_46065 (0.0)\n",
      "54. Property_Zip_46068 (0.0)\n",
      "55. Property_Zip_46069 (0.0)\n",
      "56. Property_Zip_46070 (0.0)\n",
      "57. Property_Zip_46071 (0.0)\n",
      "58. Property_Zip_46072 (0.0)\n",
      "59. Property_Zip_46074 (4.042002203759889e-05)\n",
      "60. Property_Zip_46075 (0.023990052667665334)\n",
      "61. Property_Zip_46076 (0.0)\n",
      "62. Property_Zip_46077 (0.0)\n",
      "63. Property_Zip_46102 (0.0)\n",
      "64. Property_Zip_46103 (0.0)\n",
      "65. Property_Zip_46104 (0.0)\n",
      "66. Property_Zip_46105 (0.0)\n",
      "67. Property_Zip_46106 (0.0)\n",
      "68. Property_Zip_46107 (0.0)\n",
      "69. Property_Zip_46110 (0.0)\n",
      "70. Property_Zip_46111 (0.0)\n",
      "71. Property_Zip_46112 (0.0029483912342692783)\n",
      "72. Property_Zip_46113 (0.0)\n",
      "73. Property_Zip_46115 (0.0)\n",
      "74. Property_Zip_46117 (0.0)\n",
      "75. Property_Zip_46118 (0.0)\n",
      "76. Property_Zip_46120 (0.0)\n",
      "77. Property_Zip_46121 (0.0)\n",
      "78. Property_Zip_46122 (0.0)\n",
      "79. Property_Zip_46123 (7.876820868600274e-09)\n",
      "80. Property_Zip_46124 (0.0)\n",
      "81. Property_Zip_46126 (0.0)\n",
      "82. Property_Zip_46127 (0.0)\n",
      "83. Property_Zip_46128 (0.0)\n",
      "84. Property_Zip_46129 (0.0)\n",
      "85. Property_Zip_46130 (0.0)\n",
      "86. Property_Zip_46131 (4.128489773182449e-05)\n",
      "87. Property_Zip_46133 (0.0)\n",
      "88. Property_Zip_46135 (0.0)\n",
      "89. Property_Zip_46140 (0.012229572736699444)\n",
      "90. Property_Zip_46141 (0.0)\n",
      "91. Property_Zip_46142 (0.0)\n",
      "92. Property_Zip_46143 (0.0017094048864193146)\n",
      "93. Property_Zip_46144 (0.0)\n",
      "94. Property_Zip_46146 (0.0)\n",
      "95. Property_Zip_46147 (0.0)\n",
      "96. Property_Zip_46148 (0.0)\n",
      "97. Property_Zip_46149 (0.0)\n",
      "98. Property_Zip_46150 (0.0)\n",
      "99. Property_Zip_46151 (0.0)\n",
      "100. Property_Zip_46154 (0.0)\n",
      "101. Property_Zip_46155 (0.0)\n",
      "102. Property_Zip_46156 (0.0)\n",
      "103. Property_Zip_46157 (0.0)\n",
      "104. Property_Zip_46158 (0.0)\n",
      "105. Property_Zip_46160 (0.0)\n",
      "106. Property_Zip_46161 (0.0)\n",
      "107. Property_Zip_46162 (0.0)\n",
      "108. Property_Zip_46163 (0.0)\n",
      "109. Property_Zip_46164 (0.0)\n",
      "110. Property_Zip_46165 (0.0)\n",
      "111. Property_Zip_46166 (0.0)\n",
      "112. Property_Zip_46167 (0.0)\n",
      "113. Property_Zip_46168 (0.0042973870880429685)\n",
      "114. Property_Zip_46171 (0.0)\n",
      "115. Property_Zip_46172 (0.0)\n",
      "116. Property_Zip_46173 (0.0)\n",
      "117. Property_Zip_46175 (0.0)\n",
      "118. Property_Zip_46176 (0.00019429182109087013)\n",
      "119. Property_Zip_46180 (0.0)\n",
      "120. Property_Zip_46181 (0.0)\n",
      "121. Property_Zip_46182 (0.0)\n",
      "122. Property_Zip_46183 (0.0)\n",
      "123. Property_Zip_46184 (0.0)\n",
      "124. Property_Zip_46186 (0.0)\n",
      "125. Property_Zip_46200 (0.0)\n",
      "126. Property_Zip_46201 (0.0)\n",
      "127. Property_Zip_46202 (0.0010176575895188117)\n",
      "128. Property_Zip_46203 (0.0)\n",
      "129. Property_Zip_46204 (0.0007806933189798114)\n",
      "130. Property_Zip_46205 (0.028764504530203776)\n",
      "131. Property_Zip_46206 (0.0)\n",
      "132. Property_Zip_46208 (0.0)\n",
      "133. Property_Zip_46211 (0.0)\n",
      "134. Property_Zip_46214 (0.0)\n",
      "135. Property_Zip_46216 (0.0)\n",
      "136. Property_Zip_46217 (0.0004830392266288932)\n",
      "137. Property_Zip_46218 (0.0)\n",
      "138. Property_Zip_46219 (0.0)\n",
      "139. Property_Zip_46220 (0.0)\n",
      "140. Property_Zip_46221 (0.0)\n",
      "141. Property_Zip_46222 (0.0)\n",
      "142. Property_Zip_46224 (0.0)\n",
      "143. Property_Zip_46225 (0.0005788856002443489)\n",
      "144. Property_Zip_46226 (0.0)\n",
      "145. Property_Zip_46227 (0.00047110348165856357)\n",
      "146. Property_Zip_46228 (0.0)\n",
      "147. Property_Zip_46229 (0.0003327774257617267)\n",
      "148. Property_Zip_46231 (0.0)\n",
      "149. Property_Zip_46233 (0.0)\n",
      "150. Property_Zip_46234 (0.0)\n",
      "151. Property_Zip_46235 (0.0035305653212020253)\n",
      "152. Property_Zip_46236 (0.0)\n",
      "153. Property_Zip_46237 (0.0)\n",
      "154. Property_Zip_46239 (0.0)\n",
      "155. Property_Zip_46240 (0.0004335825451551654)\n",
      "156. Property_Zip_46241 (0.0)\n",
      "157. Property_Zip_46245 (0.0)\n",
      "158. Property_Zip_46250 (0.0)\n",
      "159. Property_Zip_46253 (1.4633708529949096e-07)\n",
      "160. Property_Zip_46254 (0.0014926686913550057)\n",
      "161. Property_Zip_46256 (1.5578623982094942e-06)\n",
      "162. Property_Zip_46259 (0.0)\n",
      "163. Property_Zip_46260 (0.0)\n",
      "164. Property_Zip_46266 (0.0)\n",
      "165. Property_Zip_46268 (0.006430970943921357)\n",
      "166. Property_Zip_46278 (0.0)\n",
      "167. Property_Zip_46280 (0.0)\n",
      "168. Property_Zip_46288 (0.0)\n",
      "169. Property_Zip_46290 (0.0)\n",
      "170. Property_Zip_46292 (0.0)\n",
      "171. Property_Zip_46301 (0.0)\n",
      "172. Property_Zip_46302 (0.0)\n",
      "173. Property_Zip_46303 (0.0)\n",
      "174. Property_Zip_46304 (0.0)\n",
      "175. Property_Zip_46307 (0.0)\n",
      "176. Property_Zip_46310 (0.0)\n",
      "177. Property_Zip_46311 (0.0)\n",
      "178. Property_Zip_46312 (0.00014337954033954235)\n",
      "179. Property_Zip_46319 (0.0)\n",
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      "596. Property_Zip_47385 (0.0)\n",
      "597. Property_Zip_47386 (0.0)\n",
      "598. Property_Zip_47387 (0.0)\n",
      "599. Property_Zip_47388 (0.0)\n",
      "600. Property_Zip_47390 (0.0)\n",
      "601. Property_Zip_47392 (0.0)\n",
      "602. Property_Zip_47393 (0.0)\n",
      "603. Property_Zip_47394 (0.0)\n",
      "604. Property_Zip_47396 (0.0)\n",
      "605. Property_Zip_47401 (4.8971854704000625e-05)\n",
      "606. Property_Zip_47402 (0.0)\n",
      "607. Property_Zip_47403 (0.00018520127623869388)\n",
      "608. Property_Zip_47404 (0.0)\n",
      "609. Property_Zip_47408 (0.0)\n",
      "610. Property_Zip_47420 (0.0)\n",
      "611. Property_Zip_47421 (0.0)\n",
      "612. Property_Zip_47424 (0.0)\n",
      "613. Property_Zip_47427 (0.0)\n",
      "614. Property_Zip_47429 (0.0)\n",
      "615. Property_Zip_47430 (0.0)\n",
      "616. Property_Zip_47431 (0.0)\n",
      "617. Property_Zip_47432 (0.0)\n",
      "618. Property_Zip_47433 (0.0)\n",
      "619. Property_Zip_47434 (0.0)\n",
      "620. Property_Zip_47435 (0.0)\n",
      "621. Property_Zip_47436 (0.0)\n",
      "622. Property_Zip_47437 (0.0)\n",
      "623. Property_Zip_47438 (0.0)\n",
      "624. Property_Zip_47439 (0.0)\n",
      "625. Property_Zip_47441 (0.0)\n",
      "626. Property_Zip_47443 (0.0)\n",
      "627. Property_Zip_47446 (0.0)\n",
      "628. Property_Zip_47448 (4.82248918609233e-06)\n",
      "629. Property_Zip_47449 (0.0)\n",
      "630. Property_Zip_47451 (0.0)\n",
      "631. Property_Zip_47452 (0.0)\n",
      "632. Property_Zip_47453 (0.0)\n",
      "633. Property_Zip_47454 (0.0)\n",
      "634. Property_Zip_47455 (0.0)\n",
      "635. Property_Zip_47456 (0.0)\n",
      "636. Property_Zip_47457 (0.0)\n",
      "637. Property_Zip_47458 (0.0)\n",
      "638. Property_Zip_47459 (0.0)\n",
      "639. Property_Zip_47460 (0.0)\n",
      "640. Property_Zip_47462 (0.0)\n",
      "641. Property_Zip_47464 (0.0)\n",
      "642. Property_Zip_47465 (0.0)\n",
      "643. Property_Zip_47467 (0.0)\n",
      "644. Property_Zip_47468 (0.0)\n",
      "645. Property_Zip_47469 (0.0)\n",
      "646. Property_Zip_47470 (0.0)\n",
      "647. Property_Zip_47471 (0.0)\n",
      "648. Property_Zip_47474 (0.0)\n",
      "649. Property_Zip_47501 (0.0)\n",
      "650. Property_Zip_47512 (0.0)\n",
      "651. Property_Zip_47513 (0.0)\n",
      "652. Property_Zip_47514 (0.0)\n",
      "653. Property_Zip_47515 (0.0)\n",
      "654. Property_Zip_47516 (0.0)\n",
      "655. Property_Zip_47519 (0.0)\n",
      "656. Property_Zip_47520 (0.0)\n",
      "657. Property_Zip_47521 (0.0)\n",
      "658. Property_Zip_47522 (0.0)\n",
      "659. Property_Zip_47523 (0.0)\n",
      "660. Property_Zip_47524 (0.0)\n",
      "661. Property_Zip_47525 (0.0)\n",
      "662. Property_Zip_47527 (0.0)\n",
      "663. Property_Zip_47528 (0.0)\n",
      "664. Property_Zip_47529 (0.0)\n",
      "665. Property_Zip_47531 (0.0)\n",
      "666. Property_Zip_47532 (0.0)\n",
      "667. Property_Zip_47535 (0.0)\n",
      "668. Property_Zip_47536 (0.0)\n",
      "669. Property_Zip_47537 (0.0)\n",
      "670. Property_Zip_47541 (0.0)\n",
      "671. Property_Zip_47542 (0.0)\n",
      "672. Property_Zip_47546 (0.0)\n",
      "673. Property_Zip_47550 (0.0)\n",
      "674. Property_Zip_47551 (0.0)\n",
      "675. Property_Zip_47552 (0.0)\n",
      "676. Property_Zip_47553 (0.0)\n",
      "677. Property_Zip_47556 (0.0)\n",
      "678. Property_Zip_47557 (0.0)\n",
      "679. Property_Zip_47558 (0.0)\n",
      "680. Property_Zip_47561 (0.0)\n",
      "681. Property_Zip_47562 (0.0)\n",
      "682. Property_Zip_47564 (0.0)\n",
      "683. Property_Zip_47567 (0.0)\n",
      "684. Property_Zip_47568 (0.0)\n",
      "685. Property_Zip_47574 (0.0)\n",
      "686. Property_Zip_47575 (0.0)\n",
      "687. Property_Zip_47576 (0.0)\n",
      "688. Property_Zip_47577 (0.0)\n",
      "689. Property_Zip_47578 (0.0)\n",
      "690. Property_Zip_47579 (0.0)\n",
      "691. Property_Zip_47580 (0.0)\n",
      "692. Property_Zip_47581 (0.0)\n",
      "693. Property_Zip_47584 (0.0)\n",
      "694. Property_Zip_47585 (0.0)\n",
      "695. Property_Zip_47586 (0.0)\n",
      "696. Property_Zip_47588 (0.0)\n",
      "697. Property_Zip_47590 (0.0)\n",
      "698. Property_Zip_47591 (0.0)\n",
      "699. Property_Zip_47592 (0.0)\n",
      "700. Property_Zip_47596 (0.0)\n",
      "701. Property_Zip_47597 (0.0)\n",
      "702. Property_Zip_47598 (0.0)\n",
      "703. Property_Zip_47601 (0.0)\n",
      "704. Property_Zip_47610 (0.0)\n",
      "705. Property_Zip_47611 (0.0)\n",
      "706. Property_Zip_47612 (0.0)\n",
      "707. Property_Zip_47613 (0.0)\n",
      "708. Property_Zip_47615 (0.0)\n",
      "709. Property_Zip_47616 (0.0)\n",
      "710. Property_Zip_47617 (0.0)\n",
      "711. Property_Zip_47619 (0.0)\n",
      "712. Property_Zip_47620 (0.0)\n",
      "713. Property_Zip_47630 (0.0)\n",
      "714. Property_Zip_47631 (0.0)\n",
      "715. Property_Zip_47633 (0.0)\n",
      "716. Property_Zip_47634 (0.0)\n",
      "717. Property_Zip_47635 (0.0)\n",
      "718. Property_Zip_47637 (0.0)\n",
      "719. Property_Zip_47638 (0.0)\n",
      "720. Property_Zip_47639 (0.0)\n",
      "721. Property_Zip_47640 (0.0)\n",
      "722. Property_Zip_47646 (0.0)\n",
      "723. Property_Zip_47647 (0.0)\n",
      "724. Property_Zip_47648 (0.0)\n",
      "725. Property_Zip_47649 (0.0)\n",
      "726. Property_Zip_47650 (0.0)\n",
      "727. Property_Zip_47654 (0.0)\n",
      "728. Property_Zip_47660 (0.0)\n",
      "729. Property_Zip_47665 (0.0)\n",
      "730. Property_Zip_47666 (0.0)\n",
      "731. Property_Zip_47670 (0.0)\n",
      "732. Property_Zip_47683 (0.0)\n",
      "733. Property_Zip_47684 (0.0)\n",
      "734. Property_Zip_47708 (0.0)\n",
      "735. Property_Zip_47710 (0.0)\n",
      "736. Property_Zip_47711 (0.0)\n",
      "737. Property_Zip_47712 (0.0)\n",
      "738. Property_Zip_46056 (0.0)\n",
      "739. Property_Zip_46072 (0.0)\n",
      "740. Property_Zip_46077 (0.0)\n",
      "741. Property_Zip_46104 (0.0)\n",
      "742. Property_Zip_46107 (0.0)\n",
      "743. Property_Zip_46113 (0.0)\n",
      "744. Property_Zip_46118 (0.0)\n",
      "745. Property_Zip_46121 (0.0)\n",
      "746. Property_Zip_46123 (0.0)\n",
      "747. Property_Zip_46124 (0.0)\n",
      "748. Property_Zip_46142 (0.0)\n",
      "749. Property_Zip_46148 (0.0)\n",
      "750. Property_Zip_46160 (0.0)\n",
      "751. Property_Zip_46164 (0.0)\n",
      "752. Property_Zip_46167 (0.0)\n",
      "753. Property_Zip_46201 (0.0)\n",
      "754. Property_Zip_46202 (0.0)\n",
      "755. Property_Zip_46203 (0.0)\n",
      "756. Property_Zip_46205 (0.0)\n",
      "757. Property_Zip_46208 (0.0)\n",
      "758. Property_Zip_46214 (0.0)\n",
      "759. Property_Zip_46216 (0.0)\n",
      "760. Property_Zip_46217 (0.0)\n",
      "761. Property_Zip_46218 (0.0)\n",
      "762. Property_Zip_46219 (0.0)\n",
      "763. Property_Zip_46220 (0.0)\n",
      "764. Property_Zip_46221 (0.0)\n",
      "765. Property_Zip_46222 (0.0)\n",
      "766. Property_Zip_46224 (0.0)\n",
      "767. Property_Zip_46225 (0.0)\n",
      "768. Property_Zip_46226 (0.0)\n",
      "769. Property_Zip_46227 (0.0)\n",
      "770. Property_Zip_46228 (0.0)\n",
      "771. Property_Zip_46229 (0.0)\n",
      "772. Property_Zip_46231 (0.0)\n",
      "773. Property_Zip_46234 (0.0)\n",
      "774. Property_Zip_46235 (0.0)\n",
      "775. Property_Zip_46236 (0.0)\n",
      "776. Property_Zip_46237 (0.0)\n",
      "777. Property_Zip_46239 (0.0)\n",
      "778. Property_Zip_46240 (0.0)\n",
      "779. Property_Zip_46241 (0.0)\n",
      "780. Property_Zip_46250 (0.0)\n",
      "781. Property_Zip_46254 (0.0)\n",
      "782. Property_Zip_46256 (0.0)\n",
      "783. Property_Zip_46259 (0.0)\n",
      "784. Property_Zip_46260 (0.0)\n",
      "785. Property_Zip_46268 (0.0)\n",
      "786. Property_Zip_46278 (0.0)\n",
      "787. Property_Zip_46360 (0.0)\n",
      "788. Property_Zip_46383 (0.0)\n",
      "789. Property_Zip_46385 (0.0)\n",
      "790. Property_Zip_46394 (0.0)\n",
      "791. Property_Zip_46405 (0.0)\n",
      "792. Property_Zip_46507 (0.0)\n",
      "793. Property_Zip_46554 (0.0)\n",
      "794. Property_Zip_46580 (0.0)\n",
      "795. Property_Zip_46613 (0.0)\n",
      "796. Property_Zip_46617 (0.0)\n",
      "797. Property_Zip_46637 (0.0)\n",
      "798. Property_Zip_46795 (0.0)\n",
      "799. Property_Zip_46913 (0.0)\n",
      "800. Property_Zip_46919 (0.0)\n",
      "801. Property_Zip_46920 (0.0)\n",
      "802. Property_Zip_46928 (0.0)\n",
      "803. Property_Zip_46933 (0.0)\n",
      "804. Property_Zip_46938 (0.0)\n",
      "805. Property_Zip_46952 (0.0)\n",
      "806. Property_Zip_46953 (0.0)\n",
      "807. Property_Zip_46986 (0.0)\n",
      "808. Property_Zip_46989 (0.0)\n",
      "809. Property_Zip_46991 (0.0)\n",
      "810. Property_Zip_46992 (0.0)\n",
      "811. Property_Zip_47001 (0.0)\n",
      "812. Property_Zip_47012 (0.0)\n",
      "813. Property_Zip_47018 (0.0)\n",
      "814. Property_Zip_47022 (0.0)\n",
      "815. Property_Zip_47025 (0.0)\n",
      "816. Property_Zip_47031 (0.0)\n",
      "817. Property_Zip_47032 (0.0)\n",
      "818. Property_Zip_47041 (0.0)\n",
      "819. Property_Zip_47060 (0.0)\n",
      "820. Property_Zip_47112 (0.0)\n",
      "821. Property_Zip_47117 (0.0)\n",
      "822. Property_Zip_47130 (0.0)\n",
      "823. Property_Zip_47135 (0.0)\n",
      "824. Property_Zip_47136 (0.0)\n",
      "825. Property_Zip_47142 (0.0)\n",
      "826. Property_Zip_47161 (0.0)\n",
      "827. Property_Zip_47167 (0.0)\n",
      "828. Property_Zip_47201 (0.0)\n",
      "829. Property_Zip_47202 (0.0)\n",
      "830. Property_Zip_47203 (0.0)\n",
      "831. Property_Zip_47223 (0.0)\n",
      "832. Property_Zip_47227 (0.0)\n",
      "833. Property_Zip_47230 (0.0)\n",
      "834. Property_Zip_47231 (0.0)\n",
      "835. Property_Zip_47232 (0.0)\n",
      "836. Property_Zip_47240 (0.0)\n",
      "837. Property_Zip_47244 (0.0)\n",
      "838. Property_Zip_47246 (0.0)\n",
      "839. Property_Zip_47265 (0.0)\n",
      "840. Property_Zip_47270 (0.0)\n",
      "841. Property_Zip_47273 (0.0)\n",
      "842. Property_Zip_47274 (0.0)\n",
      "843. Property_Zip_47282 (0.0)\n",
      "844. Property_Zip_47302 (0.0)\n",
      "845. Property_Zip_47303 (0.0)\n",
      "846. Property_Zip_47304 (0.0)\n",
      "847. Property_Zip_47305 (0.0)\n",
      "848. Property_Zip_47320 (0.0)\n",
      "849. Property_Zip_47330 (0.0)\n",
      "850. Property_Zip_47338 (0.0)\n",
      "851. Property_Zip_47342 (0.0)\n",
      "852. Property_Zip_47348 (0.0)\n",
      "853. Property_Zip_47356 (0.0)\n",
      "854. Property_Zip_47362 (0.0)\n",
      "855. Property_Zip_47383 (0.0)\n",
      "856. Property_Zip_47401 (0.0)\n",
      "857. Property_Zip_47403 (0.0)\n",
      "858. Property_Zip_47404 (0.0)\n",
      "859. Property_Zip_47448 (0.0)\n",
      "860. Property_Zip_47494 (0.0)\n",
      "861. Property_Zip_47581 (0.0)\n",
      "862. Property_Zip_47633 (0.0)\n",
      "863. Property_Zip_47639 (0.0)\n",
      "864. Property_Zip_47648 (0.0)\n",
      "865. Property_Zip_47708 (0.0)\n",
      "866. Property_Zip_47710 (2.959170202573588e-06)\n",
      "867. Property_Zip_47711 (0.0)\n",
      "868. Property_Zip_47712 (0.0)\n",
      "869. Property_Zip_47713 (0.0)\n",
      "870. Property_Zip_47714 (0.0)\n",
      "871. Property_Zip_47715 (0.0)\n",
      "872. Property_Zip_47716 (0.0)\n",
      "873. Property_Zip_47719 (0.0)\n",
      "874. Property_Zip_47720 (0.0)\n",
      "875. Property_Zip_47725 (0.0)\n",
      "876. Property_Zip_47800 (0.0)\n",
      "877. Property_Zip_47802 (0.0)\n",
      "878. Property_Zip_47803 (0.0)\n",
      "879. Property_Zip_47804 (0.0010193760359307298)\n",
      "880. Property_Zip_47805 (0.0)\n",
      "881. Property_Zip_47807 (0.0)\n",
      "882. Property_Zip_47808 (0.0)\n",
      "883. Property_Zip_47831 (0.0)\n",
      "884. Property_Zip_47832 (0.0)\n",
      "885. Property_Zip_47833 (0.0)\n",
      "886. Property_Zip_47834 (0.0)\n",
      "887. Property_Zip_47836 (0.0)\n",
      "888. Property_Zip_47837 (0.0)\n",
      "889. Property_Zip_47838 (0.0)\n",
      "890. Property_Zip_47840 (0.0)\n",
      "891. Property_Zip_47841 (0.0)\n",
      "892. Property_Zip_47842 (0.0)\n",
      "893. Property_Zip_47845 (0.0)\n",
      "894. Property_Zip_47846 (0.0)\n",
      "895. Property_Zip_47847 (0.0)\n",
      "896. Property_Zip_47848 (0.0)\n",
      "897. Property_Zip_47849 (0.0)\n",
      "898. Property_Zip_47850 (0.0)\n",
      "899. Property_Zip_47851 (0.0)\n",
      "900. Property_Zip_47852 (0.0)\n",
      "901. Property_Zip_47853 (0.0)\n",
      "902. Property_Zip_47854 (0.0)\n",
      "903. Property_Zip_47855 (0.0)\n",
      "904. Property_Zip_47857 (0.0014650859302434143)\n",
      "905. Property_Zip_47858 (0.0)\n",
      "906. Property_Zip_47859 (0.0)\n",
      "907. Property_Zip_47860 (0.0)\n",
      "908. Property_Zip_47861 (0.0)\n",
      "909. Property_Zip_47862 (0.0)\n",
      "910. Property_Zip_47863 (0.0)\n",
      "911. Property_Zip_47865 (0.0)\n",
      "912. Property_Zip_47866 (0.0)\n",
      "913. Property_Zip_47868 (0.0)\n",
      "914. Property_Zip_47869 (0.0)\n",
      "915. Property_Zip_47871 (0.0)\n",
      "916. Property_Zip_47872 (0.0)\n",
      "917. Property_Zip_47874 (0.0)\n",
      "918. Property_Zip_47875 (0.0)\n",
      "919. Property_Zip_47876 (0.0)\n",
      "920. Property_Zip_47878 (0.0)\n",
      "921. Property_Zip_47879 (0.0)\n",
      "922. Property_Zip_47881 (0.0)\n",
      "923. Property_Zip_47882 (0.0)\n",
      "924. Property_Zip_47884 (0.0)\n",
      "925. Property_Zip_47885 (0.0)\n",
      "926. Property_Zip_47901 (0.0)\n",
      "927. Property_Zip_47902 (0.0)\n",
      "928. Property_Zip_47904 (0.0)\n",
      "929. Property_Zip_47905 (0.00018019272885217905)\n",
      "930. Property_Zip_47906 (0.0)\n",
      "931. Property_Zip_47909 (0.0)\n",
      "932. Property_Zip_47917 (0.0)\n",
      "933. Property_Zip_47918 (0.0)\n",
      "934. Property_Zip_47920 (0.0)\n",
      "935. Property_Zip_47921 (0.0)\n",
      "936. Property_Zip_47922 (0.0)\n",
      "937. Property_Zip_47923 (0.0)\n",
      "938. Property_Zip_47924 (0.0)\n",
      "939. Property_Zip_47925 (0.0)\n",
      "940. Property_Zip_47926 (0.0)\n",
      "941. Property_Zip_47928 (0.0)\n",
      "942. Property_Zip_47929 (0.0)\n",
      "943. Property_Zip_47930 (0.0)\n",
      "944. Property_Zip_47932 (0.0)\n",
      "945. Property_Zip_47941 (0.0)\n",
      "946. Property_Zip_47942 (0.0)\n",
      "947. Property_Zip_47943 (0.0)\n",
      "948. Property_Zip_47944 (0.0)\n",
      "949. Property_Zip_47946 (0.0)\n",
      "950. Property_Zip_47948 (0.0)\n",
      "951. Property_Zip_47949 (0.0)\n",
      "952. Property_Zip_47950 (0.0)\n",
      "953. Property_Zip_47951 (0.0)\n",
      "954. Property_Zip_47952 (0.0)\n",
      "955. Property_Zip_47954 (0.0)\n",
      "956. Property_Zip_47957 (0.0)\n",
      "957. Property_Zip_47958 (0.0)\n",
      "958. Property_Zip_47959 (0.0)\n",
      "959. Property_Zip_47960 (0.0)\n",
      "960. Property_Zip_47962 (0.0)\n",
      "961. Property_Zip_47963 (2.0861805080269654e-05)\n",
      "962. Property_Zip_47964 (0.0)\n",
      "963. Property_Zip_47966 (0.0)\n",
      "964. Property_Zip_47968 (0.0)\n",
      "965. Property_Zip_47969 (0.0)\n",
      "966. Property_Zip_47970 (0.0)\n",
      "967. Property_Zip_47971 (0.0)\n",
      "968. Property_Zip_47974 (0.0)\n",
      "969. Property_Zip_47975 (0.0)\n",
      "970. Property_Zip_47977 (0.0)\n",
      "971. Property_Zip_47978 (0.0)\n",
      "972. Property_Zip_47980 (0.0)\n",
      "973. Property_Zip_47981 (0.0)\n",
      "974. Property_Zip_47982 (0.0)\n",
      "975. Property_Zip_47983 (0.0)\n",
      "976. Property_Zip_47986 (0.0)\n",
      "977. Property_Zip_47987 (0.0)\n",
      "978. Property_Zip_47989 (0.0)\n",
      "979. Property_Zip_47990 (0.0)\n",
      "980. Property_Zip_47991 (0.0)\n",
      "981. Property_Zip_47992 (0.0)\n",
      "982. Property_Zip_47993 (0.0)\n",
      "983. Property_Zip_47994 (0.0)\n",
      "984. Property_Zip_47995 (0.0)\n",
      "985. Property_Zip_47997 (0.0)\n",
      "986. Property_Zip_48448 (0.0)\n",
      "987. Property_Zip_`4790 (0.0)\n"
     ]
    }
   ],
   "source": [
    "features_importance = regressor.feature_importances_\n",
    " \n",
    "print(\"Feature ranking:\")\n",
    "for i, data_class in enumerate(feature_names):\n",
    "    print(\"{}. {} ({})\".format(i + 1, data_class, features_importance[i]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6357135705501051\n"
     ]
    }
   ],
   "source": [
    "print(regressor.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pickle.dump(regressor, open('model.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(Xscaler, open('Xscaler.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(Yscaler, open('Yscaler.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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