AnEX.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection and Feature Engineering with Python\n",
    "\n",
    "#### Python libraries\n",
    " - Pandas https://pandas.pydata.org/docs/getting_started/overview.html\n",
    " - Seaborn https://seaborn.pydata.org/\n",
    " - Mathplotlib https://matplotlib.org/stable/tutorials/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read data from stored location\n",
    "\n",
    "Pandas will search the current working directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/sharonmorris/Documents'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('/Users/sharonmorris/Documents/AnEx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Assign data to value\n",
    "df = pd.read_csv('Ames_Housing_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>526301100</td>\n",
       "      <td>20</td>\n",
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       "      <td>141.0</td>\n",
       "      <td>31770</td>\n",
       "      <td>Pave</td>\n",
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       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>526350040</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>526351010</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>526353030</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>93.0</td>\n",
       "      <td>11160</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>244000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>527105010</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>189900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
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      "text/plain": [
       "         PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street Alley  \\\n",
       "0  526301100           20        RL         141.0     31770   Pave   NaN   \n",
       "1  526350040           20        RH          80.0     11622   Pave   NaN   \n",
       "2  526351010           20        RL          81.0     14267   Pave   NaN   \n",
       "3  526353030           20        RL          93.0     11160   Pave   NaN   \n",
       "4  527105010           60        RL          74.0     13830   Pave   NaN   \n",
       "\n",
       "  Lot Shape Land Contour Utilities  ... Pool Area Pool QC  Fence Misc Feature  \\\n",
       "0       IR1          Lvl    AllPub  ...         0     NaN    NaN          NaN   \n",
       "1       Reg          Lvl    AllPub  ...         0     NaN  MnPrv          NaN   \n",
       "2       IR1          Lvl    AllPub  ...         0     NaN    NaN         Gar2   \n",
       "3       Reg          Lvl    AllPub  ...         0     NaN    NaN          NaN   \n",
       "4       IR1          Lvl    AllPub  ...         0     NaN  MnPrv          NaN   \n",
       "\n",
       "  Misc Val Mo Sold Yr Sold  Sale Type  Sale Condition  SalePrice  \n",
       "0        0       5    2010        WD           Normal     215000  \n",
       "1        0       6    2010        WD           Normal     105000  \n",
       "2    12500       6    2010        WD           Normal     172000  \n",
       "3        0       4    2010        WD           Normal     244000  \n",
       "4        0       3    2010        WD           Normal     189900  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
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       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
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       "      <th>Yr Sold</th>\n",
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       "      <th>Sale Condition</th>\n",
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       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>132000</td>\n",
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       "    <tr>\n",
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       "      <td>77.0</td>\n",
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       "      <td>74.0</td>\n",
       "      <td>9627</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Lvl</td>\n",
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       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>188000</td>\n",
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      ],
      "text/plain": [
       "            PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street Alley  \\\n",
       "2925  923275080           80        RL          37.0      7937   Pave   NaN   \n",
       "2926  923276100           20        RL           NaN      8885   Pave   NaN   \n",
       "2927  923400125           85        RL          62.0     10441   Pave   NaN   \n",
       "2928  924100070           20        RL          77.0     10010   Pave   NaN   \n",
       "2929  924151050           60        RL          74.0      9627   Pave   NaN   \n",
       "\n",
       "     Lot Shape Land Contour Utilities  ... Pool Area Pool QC  Fence  \\\n",
       "2925       IR1          Lvl    AllPub  ...         0     NaN  GdPrv   \n",
       "2926       IR1          Low    AllPub  ...         0     NaN  MnPrv   \n",
       "2927       Reg          Lvl    AllPub  ...         0     NaN  MnPrv   \n",
       "2928       Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "2929       Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "\n",
       "     Misc Feature Misc Val Mo Sold Yr Sold  Sale Type  Sale Condition  \\\n",
       "2925          NaN        0       3    2006        WD           Normal   \n",
       "2926          NaN        0       6    2006        WD           Normal   \n",
       "2927         Shed      700       7    2006        WD           Normal   \n",
       "2928          NaN        0       4    2006        WD           Normal   \n",
       "2929          NaN        0      11    2006        WD           Normal   \n",
       "\n",
       "      SalePrice  \n",
       "2925     142500  \n",
       "2926     131000  \n",
       "2927     132000  \n",
       "2928     170000  \n",
       "2929     188000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
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     "execution_count": 13,
     "metadata": {},
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   "source": [
    "df.tail()"
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis\n",
    "\n",
    "Exploratory Data Analysis\n",
    "\n",
    "Is a crucial step that allows for the determination whether selected features are appropriate for the chosen model and whether there are correlations among variables. After EDA going back to pre-processing the data may be required.\n",
    "\n",
    "- Description of data\n",
    "- Handling missing data\n",
    "- Handling outliers\n",
    "- Understanding relationships and new insights through plots\n",
    "\n",
    "\n",
    "\n",
    "![image.png](attachment:41ac5daf-651f-4b29-9ce5-c0411bb5097a.png)\n",
    "(Source: TowardsAI.net) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Set Description\n",
    "\n",
    "VARIABLE DESCRIPTIONS:\n",
    "Tab characters are used to separate variables in the data file. The data has 82 columns which include 23 nominal, 23 ordinal, 14 discrete, and 20 continuous variables (and 2 additional observation identifiers).\n",
    "http://jse.amstat.org/v19n3/decock/DataDocumentation.txt\n",
    "\n",
    "Order (Discrete): Observation number\n",
    "\n",
    "PID (Nominal): Parcel identification number  - can be used with city web site for parcel review. \n",
    "\n",
    "MS SubClass (Nominal): Identifies the type of dwelling involved in the sale.\t\n",
    "\n",
    "       020\t1-STORY 1946 & NEWER ALL STYLES\n",
    "       030\t1-STORY 1945 & OLDER\n",
    "       040\t1-STORY W/FINISHED ATTIC ALL AGES\n",
    "       045\t1-1/2 STORY - UNFINISHED ALL AGES\n",
    "       050\t1-1/2 STORY FINISHED ALL AGES\n",
    "       060\t2-STORY 1946 & NEWER\n",
    "       070\t2-STORY 1945 & OLDER\n",
    "       075\t2-1/2 STORY ALL AGES\n",
    "       080\tSPLIT OR MULTI-LEVEL\n",
    "       085\tSPLIT FOYER\n",
    "       090\tDUPLEX - ALL STYLES AND AGES\n",
    "       120\t1-STORY PUD (Planned Unit Development) - 1946 & NEWER\n",
    "       150\t1-1/2 STORY PUD - ALL AGES\n",
    "       160\t2-STORY PUD - 1946 & NEWER\n",
    "       180\tPUD - MULTILEVEL - INCL SPLIT LEV/FOYER\n",
    "       190\t2 FAMILY CONVERSION - ALL STYLES AND AGES\n",
    "\n",
    "MS Zoning (Nominal): Identifies the general zoning classification of the sale.\n",
    "\t\t\n",
    "       A\tAgriculture\n",
    "       C\tCommercial\n",
    "       FV\tFloating Village Residential\n",
    "       I\tIndustrial\n",
    "       RH\tResidential High Density\n",
    "       RL\tResidential Low Density\n",
    "       RP\tResidential Low Density Park \n",
    "       RM\tResidential Medium Density\n",
    "\t\n",
    "Lot Frontage (Continuous): Linear feet of street connected to property\n",
    "\n",
    "Lot Area (Continuous): Lot size in square feet\n",
    "\n",
    "Street (Nominal): Type of road access to property\n",
    "\n",
    "       Grvl\tGravel\t\n",
    "       Pave\tPaved\n",
    "       \t\n",
    "Alley (Nominal): Type of alley access to property\n",
    "\n",
    "       Grvl\tGravel\n",
    "       Pave\tPaved\n",
    "       NA \tNo alley access\n",
    "\t\t\n",
    "Lot Shape (Ordinal): General shape of property\n",
    "\n",
    "       Reg\tRegular\t\n",
    "       IR1\tSlightly irregular\n",
    "       IR2\tModerately Irregular\n",
    "       IR3\tIrregular"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identify Data Types\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2930 entries, 0 to 2929\n",
      "Data columns (total 81 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   PID              2930 non-null   int64  \n",
      " 1   MS SubClass      2930 non-null   int64  \n",
      " 2   MS Zoning        2930 non-null   object \n",
      " 3   Lot Frontage     2440 non-null   float64\n",
      " 4   Lot Area         2930 non-null   int64  \n",
      " 5   Street           2930 non-null   object \n",
      " 6   Alley            198 non-null    object \n",
      " 7   Lot Shape        2930 non-null   object \n",
      " 8   Land Contour     2930 non-null   object \n",
      " 9   Utilities        2930 non-null   object \n",
      " 10  Lot Config       2930 non-null   object \n",
      " 11  Land Slope       2930 non-null   object \n",
      " 12  Neighborhood     2930 non-null   object \n",
      " 13  Condition 1      2930 non-null   object \n",
      " 14  Condition 2      2930 non-null   object \n",
      " 15  Bldg Type        2930 non-null   object \n",
      " 16  House Style      2930 non-null   object \n",
      " 17  Overall Qual     2930 non-null   int64  \n",
      " 18  Overall Cond     2930 non-null   int64  \n",
      " 19  Year Built       2930 non-null   int64  \n",
      " 20  Year Remod/Add   2930 non-null   int64  \n",
      " 21  Roof Style       2930 non-null   object \n",
      " 22  Roof Matl        2930 non-null   object \n",
      " 23  Exterior 1st     2930 non-null   object \n",
      " 24  Exterior 2nd     2930 non-null   object \n",
      " 25  Mas Vnr Type     2907 non-null   object \n",
      " 26  Mas Vnr Area     2907 non-null   float64\n",
      " 27  Exter Qual       2930 non-null   object \n",
      " 28  Exter Cond       2930 non-null   object \n",
      " 29  Foundation       2930 non-null   object \n",
      " 30  Bsmt Qual        2850 non-null   object \n",
      " 31  Bsmt Cond        2850 non-null   object \n",
      " 32  Bsmt Exposure    2847 non-null   object \n",
      " 33  BsmtFin Type 1   2850 non-null   object \n",
      " 34  BsmtFin SF 1     2929 non-null   float64\n",
      " 35  BsmtFin Type 2   2849 non-null   object \n",
      " 36  BsmtFin SF 2     2929 non-null   float64\n",
      " 37  Bsmt Unf SF      2929 non-null   float64\n",
      " 38  Total Bsmt SF    2929 non-null   float64\n",
      " 39  Heating          2930 non-null   object \n",
      " 40  Heating QC       2930 non-null   object \n",
      " 41  Central Air      2930 non-null   object \n",
      " 42  Electrical       2929 non-null   object \n",
      " 43  1st Flr SF       2930 non-null   int64  \n",
      " 44  2nd Flr SF       2930 non-null   int64  \n",
      " 45  Low Qual Fin SF  2930 non-null   int64  \n",
      " 46  Gr Liv Area      2930 non-null   int64  \n",
      " 47  Bsmt Full Bath   2928 non-null   float64\n",
      " 48  Bsmt Half Bath   2928 non-null   float64\n",
      " 49  Full Bath        2930 non-null   int64  \n",
      " 50  Half Bath        2930 non-null   int64  \n",
      " 51  Bedroom AbvGr    2930 non-null   int64  \n",
      " 52  Kitchen AbvGr    2930 non-null   int64  \n",
      " 53  Kitchen Qual     2930 non-null   object \n",
      " 54  TotRms AbvGrd    2930 non-null   int64  \n",
      " 55  Functional       2930 non-null   object \n",
      " 56  Fireplaces       2930 non-null   int64  \n",
      " 57  Fireplace Qu     1508 non-null   object \n",
      " 58  Garage Type      2773 non-null   object \n",
      " 59  Garage Yr Blt    2771 non-null   float64\n",
      " 60  Garage Finish    2771 non-null   object \n",
      " 61  Garage Cars      2929 non-null   float64\n",
      " 62  Garage Area      2929 non-null   float64\n",
      " 63  Garage Qual      2771 non-null   object \n",
      " 64  Garage Cond      2771 non-null   object \n",
      " 65  Paved Drive      2930 non-null   object \n",
      " 66  Wood Deck SF     2930 non-null   int64  \n",
      " 67  Open Porch SF    2930 non-null   int64  \n",
      " 68  Enclosed Porch   2930 non-null   int64  \n",
      " 69  3Ssn Porch       2930 non-null   int64  \n",
      " 70  Screen Porch     2930 non-null   int64  \n",
      " 71  Pool Area        2930 non-null   int64  \n",
      " 72  Pool QC          13 non-null     object \n",
      " 73  Fence            572 non-null    object \n",
      " 74  Misc Feature     106 non-null    object \n",
      " 75  Misc Val         2930 non-null   int64  \n",
      " 76  Mo Sold          2930 non-null   int64  \n",
      " 77  Yr Sold          2930 non-null   int64  \n",
      " 78  Sale Type        2930 non-null   object \n",
      " 79  Sale Condition   2930 non-null   object \n",
      " 80  SalePrice        2930 non-null   int64  \n",
      "dtypes: float64(11), int64(27), object(43)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PID                 int64\n",
       "MS SubClass         int64\n",
       "MS Zoning          object\n",
       "Lot Frontage      float64\n",
       "Lot Area            int64\n",
       "                   ...   \n",
       "Mo Sold             int64\n",
       "Yr Sold             int64\n",
       "Sale Type          object\n",
       "Sale Condition     object\n",
       "SalePrice           int64\n",
       "Length: 81, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View data type of each variable/feature\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine Shape of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2930, 81)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>526301100</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>141.0</td>\n",
       "      <td>31770</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>526350040</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>526351010</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>526353030</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>93.0</td>\n",
       "      <td>11160</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>244000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>527105010</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>189900</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>2925</th>\n",
       "      <td>923275080</td>\n",
       "      <td>80</td>\n",
       "      <td>RL</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7937</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>142500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2926</th>\n",
       "      <td>923276100</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8885</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Low</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>131000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2927</th>\n",
       "      <td>923400125</td>\n",
       "      <td>85</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>132000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2928</th>\n",
       "      <td>924100070</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>77.0</td>\n",
       "      <td>10010</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2929</th>\n",
       "      <td>924151050</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>188000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2930 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street Alley  \\\n",
       "0     526301100           20        RL         141.0     31770   Pave   NaN   \n",
       "1     526350040           20        RH          80.0     11622   Pave   NaN   \n",
       "2     526351010           20        RL          81.0     14267   Pave   NaN   \n",
       "3     526353030           20        RL          93.0     11160   Pave   NaN   \n",
       "4     527105010           60        RL          74.0     13830   Pave   NaN   \n",
       "...         ...          ...       ...           ...       ...    ...   ...   \n",
       "2925  923275080           80        RL          37.0      7937   Pave   NaN   \n",
       "2926  923276100           20        RL           NaN      8885   Pave   NaN   \n",
       "2927  923400125           85        RL          62.0     10441   Pave   NaN   \n",
       "2928  924100070           20        RL          77.0     10010   Pave   NaN   \n",
       "2929  924151050           60        RL          74.0      9627   Pave   NaN   \n",
       "\n",
       "     Lot Shape Land Contour Utilities  ... Pool Area Pool QC  Fence  \\\n",
       "0          IR1          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "1          Reg          Lvl    AllPub  ...         0     NaN  MnPrv   \n",
       "2          IR1          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "3          Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "4          IR1          Lvl    AllPub  ...         0     NaN  MnPrv   \n",
       "...        ...          ...       ...  ...       ...     ...    ...   \n",
       "2925       IR1          Lvl    AllPub  ...         0     NaN  GdPrv   \n",
       "2926       IR1          Low    AllPub  ...         0     NaN  MnPrv   \n",
       "2927       Reg          Lvl    AllPub  ...         0     NaN  MnPrv   \n",
       "2928       Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "2929       Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "\n",
       "     Misc Feature Misc Val Mo Sold Yr Sold  Sale Type  Sale Condition  \\\n",
       "0             NaN        0       5    2010        WD           Normal   \n",
       "1             NaN        0       6    2010        WD           Normal   \n",
       "2            Gar2    12500       6    2010        WD           Normal   \n",
       "3             NaN        0       4    2010        WD           Normal   \n",
       "4             NaN        0       3    2010        WD           Normal   \n",
       "...           ...      ...     ...     ...        ...             ...   \n",
       "2925          NaN        0       3    2006        WD           Normal   \n",
       "2926          NaN        0       6    2006        WD           Normal   \n",
       "2927         Shed      700       7    2006        WD           Normal   \n",
       "2928          NaN        0       4    2006        WD           Normal   \n",
       "2929          NaN        0      11    2006        WD           Normal   \n",
       "\n",
       "      SalePrice  \n",
       "0        215000  \n",
       "1        105000  \n",
       "2        172000  \n",
       "3        244000  \n",
       "4        189900  \n",
       "...         ...  \n",
       "2925     142500  \n",
       "2926     131000  \n",
       "2927     132000  \n",
       "2928     170000  \n",
       "2929     188000  \n",
       "\n",
       "[2930 rows x 81 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary of numeric variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\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>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Overall Qual</th>\n",
       "      <th>Overall Cond</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>Year Remod/Add</th>\n",
       "      <th>Mas Vnr Area</th>\n",
       "      <th>BsmtFin SF 1</th>\n",
       "      <th>...</th>\n",
       "      <th>Wood Deck SF</th>\n",
       "      <th>Open Porch SF</th>\n",
       "      <th>Enclosed Porch</th>\n",
       "      <th>3Ssn Porch</th>\n",
       "      <th>Screen Porch</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.930000e+03</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2440.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2907.000000</td>\n",
       "      <td>2929.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "      <td>2930.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.144645e+08</td>\n",
       "      <td>57.387372</td>\n",
       "      <td>69.224590</td>\n",
       "      <td>10147.921843</td>\n",
       "      <td>6.094881</td>\n",
       "      <td>5.563140</td>\n",
       "      <td>1971.356314</td>\n",
       "      <td>1984.266553</td>\n",
       "      <td>101.896801</td>\n",
       "      <td>442.629566</td>\n",
       "      <td>...</td>\n",
       "      <td>93.751877</td>\n",
       "      <td>47.533447</td>\n",
       "      <td>23.011604</td>\n",
       "      <td>2.592491</td>\n",
       "      <td>16.002048</td>\n",
       "      <td>2.243345</td>\n",
       "      <td>50.635154</td>\n",
       "      <td>6.216041</td>\n",
       "      <td>2007.790444</td>\n",
       "      <td>180796.060068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.887308e+08</td>\n",
       "      <td>42.638025</td>\n",
       "      <td>23.365335</td>\n",
       "      <td>7880.017759</td>\n",
       "      <td>1.411026</td>\n",
       "      <td>1.111537</td>\n",
       "      <td>30.245361</td>\n",
       "      <td>20.860286</td>\n",
       "      <td>179.112611</td>\n",
       "      <td>455.590839</td>\n",
       "      <td>...</td>\n",
       "      <td>126.361562</td>\n",
       "      <td>67.483400</td>\n",
       "      <td>64.139059</td>\n",
       "      <td>25.141331</td>\n",
       "      <td>56.087370</td>\n",
       "      <td>35.597181</td>\n",
       "      <td>566.344288</td>\n",
       "      <td>2.714492</td>\n",
       "      <td>1.316613</td>\n",
       "      <td>79886.692357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.263011e+08</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1950.000000</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>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>12789.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.284770e+08</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>7440.250000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1954.000000</td>\n",
       "      <td>1965.000000</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>4.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>129500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.354536e+08</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>9436.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.000000</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>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>160000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.071811e+08</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11555.250000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>734.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>70.000000</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>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>213500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.007100e+09</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>215245.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5644.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1424.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>1012.000000</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                PID  MS SubClass  Lot Frontage       Lot Area  Overall Qual  \\\n",
       "count  2.930000e+03  2930.000000   2440.000000    2930.000000   2930.000000   \n",
       "mean   7.144645e+08    57.387372     69.224590   10147.921843      6.094881   \n",
       "std    1.887308e+08    42.638025     23.365335    7880.017759      1.411026   \n",
       "min    5.263011e+08    20.000000     21.000000    1300.000000      1.000000   \n",
       "25%    5.284770e+08    20.000000     58.000000    7440.250000      5.000000   \n",
       "50%    5.354536e+08    50.000000     68.000000    9436.500000      6.000000   \n",
       "75%    9.071811e+08    70.000000     80.000000   11555.250000      7.000000   \n",
       "max    1.007100e+09   190.000000    313.000000  215245.000000     10.000000   \n",
       "\n",
       "       Overall Cond   Year Built  Year Remod/Add  Mas Vnr Area  BsmtFin SF 1  \\\n",
       "count   2930.000000  2930.000000     2930.000000   2907.000000   2929.000000   \n",
       "mean       5.563140  1971.356314     1984.266553    101.896801    442.629566   \n",
       "std        1.111537    30.245361       20.860286    179.112611    455.590839   \n",
       "min        1.000000  1872.000000     1950.000000      0.000000      0.000000   \n",
       "25%        5.000000  1954.000000     1965.000000      0.000000      0.000000   \n",
       "50%        5.000000  1973.000000     1993.000000      0.000000    370.000000   \n",
       "75%        6.000000  2001.000000     2004.000000    164.000000    734.000000   \n",
       "max        9.000000  2010.000000     2010.000000   1600.000000   5644.000000   \n",
       "\n",
       "       ...  Wood Deck SF  Open Porch SF  Enclosed Porch   3Ssn Porch  \\\n",
       "count  ...   2930.000000    2930.000000     2930.000000  2930.000000   \n",
       "mean   ...     93.751877      47.533447       23.011604     2.592491   \n",
       "std    ...    126.361562      67.483400       64.139059    25.141331   \n",
       "min    ...      0.000000       0.000000        0.000000     0.000000   \n",
       "25%    ...      0.000000       0.000000        0.000000     0.000000   \n",
       "50%    ...      0.000000      27.000000        0.000000     0.000000   \n",
       "75%    ...    168.000000      70.000000        0.000000     0.000000   \n",
       "max    ...   1424.000000     742.000000     1012.000000   508.000000   \n",
       "\n",
       "       Screen Porch    Pool Area      Misc Val      Mo Sold      Yr Sold  \\\n",
       "count   2930.000000  2930.000000   2930.000000  2930.000000  2930.000000   \n",
       "mean      16.002048     2.243345     50.635154     6.216041  2007.790444   \n",
       "std       56.087370    35.597181    566.344288     2.714492     1.316613   \n",
       "min        0.000000     0.000000      0.000000     1.000000  2006.000000   \n",
       "25%        0.000000     0.000000      0.000000     4.000000  2007.000000   \n",
       "50%        0.000000     0.000000      0.000000     6.000000  2008.000000   \n",
       "75%        0.000000     0.000000      0.000000     8.000000  2009.000000   \n",
       "max      576.000000   800.000000  17000.000000    12.000000  2010.000000   \n",
       "\n",
       "           SalePrice  \n",
       "count    2930.000000  \n",
       "mean   180796.060068  \n",
       "std     79886.692357  \n",
       "min     12789.000000  \n",
       "25%    129500.000000  \n",
       "50%    160000.000000  \n",
       "75%    213500.000000  \n",
       "max    755000.000000  \n",
       "\n",
       "[8 rows x 38 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PID</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>7.144645e+08</td>\n",
       "      <td>1.887308e+08</td>\n",
       "      <td>526301100.0</td>\n",
       "      <td>5.284770e+08</td>\n",
       "      <td>535453620.0</td>\n",
       "      <td>9.071811e+08</td>\n",
       "      <td>1.007100e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS SubClass</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>5.738737e+01</td>\n",
       "      <td>4.263802e+01</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7.000000e+01</td>\n",
       "      <td>1.900000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lot Frontage</th>\n",
       "      <td>2440.0</td>\n",
       "      <td>6.922459e+01</td>\n",
       "      <td>2.336533e+01</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.800000e+01</td>\n",
       "      <td>68.0</td>\n",
       "      <td>8.000000e+01</td>\n",
       "      <td>3.130000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lot Area</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>1.014792e+04</td>\n",
       "      <td>7.880018e+03</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>7.440250e+03</td>\n",
       "      <td>9436.5</td>\n",
       "      <td>1.155525e+04</td>\n",
       "      <td>2.152450e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overall Qual</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>6.094881e+00</td>\n",
       "      <td>1.411026e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overall Cond</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>5.563140e+00</td>\n",
       "      <td>1.111537e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year Built</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>1.971356e+03</td>\n",
       "      <td>3.024536e+01</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>1.954000e+03</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>2.001000e+03</td>\n",
       "      <td>2.010000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year Remod/Add</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>1.984267e+03</td>\n",
       "      <td>2.086029e+01</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1.965000e+03</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>2.004000e+03</td>\n",
       "      <td>2.010000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mas Vnr Area</th>\n",
       "      <td>2907.0</td>\n",
       "      <td>1.018968e+02</td>\n",
       "      <td>1.791126e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.640000e+02</td>\n",
       "      <td>1.600000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFin SF 1</th>\n",
       "      <td>2929.0</td>\n",
       "      <td>4.426296e+02</td>\n",
       "      <td>4.555908e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>370.0</td>\n",
       "      <td>7.340000e+02</td>\n",
       "      <td>5.644000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFin SF 2</th>\n",
       "      <td>2929.0</td>\n",
       "      <td>4.972243e+01</td>\n",
       "      <td>1.691685e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.526000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bsmt Unf SF</th>\n",
       "      <td>2929.0</td>\n",
       "      <td>5.592625e+02</td>\n",
       "      <td>4.394942e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.190000e+02</td>\n",
       "      <td>466.0</td>\n",
       "      <td>8.020000e+02</td>\n",
       "      <td>2.336000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Bsmt SF</th>\n",
       "      <td>2929.0</td>\n",
       "      <td>1.051615e+03</td>\n",
       "      <td>4.406151e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.930000e+02</td>\n",
       "      <td>990.0</td>\n",
       "      <td>1.302000e+03</td>\n",
       "      <td>6.110000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1st Flr SF</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>1.159558e+03</td>\n",
       "      <td>3.918909e+02</td>\n",
       "      <td>334.0</td>\n",
       "      <td>8.762500e+02</td>\n",
       "      <td>1084.0</td>\n",
       "      <td>1.384000e+03</td>\n",
       "      <td>5.095000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2nd Flr SF</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>3.354560e+02</td>\n",
       "      <td>4.283957e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.037500e+02</td>\n",
       "      <td>2.065000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low Qual Fin SF</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>4.676792e+00</td>\n",
       "      <td>4.631051e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.064000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gr Liv Area</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>1.499690e+03</td>\n",
       "      <td>5.055089e+02</td>\n",
       "      <td>334.0</td>\n",
       "      <td>1.126000e+03</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>1.742750e+03</td>\n",
       "      <td>5.642000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bsmt Full Bath</th>\n",
       "      <td>2928.0</td>\n",
       "      <td>4.313525e-01</td>\n",
       "      <td>5.248202e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bsmt Half Bath</th>\n",
       "      <td>2928.0</td>\n",
       "      <td>6.113388e-02</td>\n",
       "      <td>2.452536e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Full Bath</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>1.566553e+00</td>\n",
       "      <td>5.529406e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Half Bath</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>3.795222e-01</td>\n",
       "      <td>5.026293e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bedroom AbvGr</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>2.854266e+00</td>\n",
       "      <td>8.277311e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>8.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kitchen AbvGr</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>1.044369e+00</td>\n",
       "      <td>2.140762e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotRms AbvGrd</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>6.443003e+00</td>\n",
       "      <td>1.572964e+00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>1.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fireplaces</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>5.993174e-01</td>\n",
       "      <td>6.479209e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garage Yr Blt</th>\n",
       "      <td>2771.0</td>\n",
       "      <td>1.978132e+03</td>\n",
       "      <td>2.552841e+01</td>\n",
       "      <td>1895.0</td>\n",
       "      <td>1.960000e+03</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>2.002000e+03</td>\n",
       "      <td>2.207000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garage Cars</th>\n",
       "      <td>2929.0</td>\n",
       "      <td>1.766815e+00</td>\n",
       "      <td>7.605664e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garage Area</th>\n",
       "      <td>2929.0</td>\n",
       "      <td>4.728197e+02</td>\n",
       "      <td>2.150465e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.200000e+02</td>\n",
       "      <td>480.0</td>\n",
       "      <td>5.760000e+02</td>\n",
       "      <td>1.488000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wood Deck SF</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>9.375188e+01</td>\n",
       "      <td>1.263616e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.680000e+02</td>\n",
       "      <td>1.424000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Open Porch SF</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>4.753345e+01</td>\n",
       "      <td>6.748340e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.000000e+01</td>\n",
       "      <td>7.420000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enclosed Porch</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>2.301160e+01</td>\n",
       "      <td>6.413906e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.012000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3Ssn Porch</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>2.592491e+00</td>\n",
       "      <td>2.514133e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.080000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Screen Porch</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>1.600205e+01</td>\n",
       "      <td>5.608737e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.760000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pool Area</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>2.243345e+00</td>\n",
       "      <td>3.559718e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Misc Val</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>5.063515e+01</td>\n",
       "      <td>5.663443e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.700000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mo Sold</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>6.216041e+00</td>\n",
       "      <td>2.714492e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yr Sold</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>2.007790e+03</td>\n",
       "      <td>1.316613e+00</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2.007000e+03</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2.009000e+03</td>\n",
       "      <td>2.010000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>1.807961e+05</td>\n",
       "      <td>7.988669e+04</td>\n",
       "      <td>12789.0</td>\n",
       "      <td>1.295000e+05</td>\n",
       "      <td>160000.0</td>\n",
       "      <td>2.135000e+05</td>\n",
       "      <td>7.550000e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count          mean           std          min  \\\n",
       "PID              2930.0  7.144645e+08  1.887308e+08  526301100.0   \n",
       "MS SubClass      2930.0  5.738737e+01  4.263802e+01         20.0   \n",
       "Lot Frontage     2440.0  6.922459e+01  2.336533e+01         21.0   \n",
       "Lot Area         2930.0  1.014792e+04  7.880018e+03       1300.0   \n",
       "Overall Qual     2930.0  6.094881e+00  1.411026e+00          1.0   \n",
       "Overall Cond     2930.0  5.563140e+00  1.111537e+00          1.0   \n",
       "Year Built       2930.0  1.971356e+03  3.024536e+01       1872.0   \n",
       "Year Remod/Add   2930.0  1.984267e+03  2.086029e+01       1950.0   \n",
       "Mas Vnr Area     2907.0  1.018968e+02  1.791126e+02          0.0   \n",
       "BsmtFin SF 1     2929.0  4.426296e+02  4.555908e+02          0.0   \n",
       "BsmtFin SF 2     2929.0  4.972243e+01  1.691685e+02          0.0   \n",
       "Bsmt Unf SF      2929.0  5.592625e+02  4.394942e+02          0.0   \n",
       "Total Bsmt SF    2929.0  1.051615e+03  4.406151e+02          0.0   \n",
       "1st Flr SF       2930.0  1.159558e+03  3.918909e+02        334.0   \n",
       "2nd Flr SF       2930.0  3.354560e+02  4.283957e+02          0.0   \n",
       "Low Qual Fin SF  2930.0  4.676792e+00  4.631051e+01          0.0   \n",
       "Gr Liv Area      2930.0  1.499690e+03  5.055089e+02        334.0   \n",
       "Bsmt Full Bath   2928.0  4.313525e-01  5.248202e-01          0.0   \n",
       "Bsmt Half Bath   2928.0  6.113388e-02  2.452536e-01          0.0   \n",
       "Full Bath        2930.0  1.566553e+00  5.529406e-01          0.0   \n",
       "Half Bath        2930.0  3.795222e-01  5.026293e-01          0.0   \n",
       "Bedroom AbvGr    2930.0  2.854266e+00  8.277311e-01          0.0   \n",
       "Kitchen AbvGr    2930.0  1.044369e+00  2.140762e-01          0.0   \n",
       "TotRms AbvGrd    2930.0  6.443003e+00  1.572964e+00          2.0   \n",
       "Fireplaces       2930.0  5.993174e-01  6.479209e-01          0.0   \n",
       "Garage Yr Blt    2771.0  1.978132e+03  2.552841e+01       1895.0   \n",
       "Garage Cars      2929.0  1.766815e+00  7.605664e-01          0.0   \n",
       "Garage Area      2929.0  4.728197e+02  2.150465e+02          0.0   \n",
       "Wood Deck SF     2930.0  9.375188e+01  1.263616e+02          0.0   \n",
       "Open Porch SF    2930.0  4.753345e+01  6.748340e+01          0.0   \n",
       "Enclosed Porch   2930.0  2.301160e+01  6.413906e+01          0.0   \n",
       "3Ssn Porch       2930.0  2.592491e+00  2.514133e+01          0.0   \n",
       "Screen Porch     2930.0  1.600205e+01  5.608737e+01          0.0   \n",
       "Pool Area        2930.0  2.243345e+00  3.559718e+01          0.0   \n",
       "Misc Val         2930.0  5.063515e+01  5.663443e+02          0.0   \n",
       "Mo Sold          2930.0  6.216041e+00  2.714492e+00          1.0   \n",
       "Yr Sold          2930.0  2.007790e+03  1.316613e+00       2006.0   \n",
       "SalePrice        2930.0  1.807961e+05  7.988669e+04      12789.0   \n",
       "\n",
       "                          25%          50%           75%           max  \n",
       "PID              5.284770e+08  535453620.0  9.071811e+08  1.007100e+09  \n",
       "MS SubClass      2.000000e+01         50.0  7.000000e+01  1.900000e+02  \n",
       "Lot Frontage     5.800000e+01         68.0  8.000000e+01  3.130000e+02  \n",
       "Lot Area         7.440250e+03       9436.5  1.155525e+04  2.152450e+05  \n",
       "Overall Qual     5.000000e+00          6.0  7.000000e+00  1.000000e+01  \n",
       "Overall Cond     5.000000e+00          5.0  6.000000e+00  9.000000e+00  \n",
       "Year Built       1.954000e+03       1973.0  2.001000e+03  2.010000e+03  \n",
       "Year Remod/Add   1.965000e+03       1993.0  2.004000e+03  2.010000e+03  \n",
       "Mas Vnr Area     0.000000e+00          0.0  1.640000e+02  1.600000e+03  \n",
       "BsmtFin SF 1     0.000000e+00        370.0  7.340000e+02  5.644000e+03  \n",
       "BsmtFin SF 2     0.000000e+00          0.0  0.000000e+00  1.526000e+03  \n",
       "Bsmt Unf SF      2.190000e+02        466.0  8.020000e+02  2.336000e+03  \n",
       "Total Bsmt SF    7.930000e+02        990.0  1.302000e+03  6.110000e+03  \n",
       "1st Flr SF       8.762500e+02       1084.0  1.384000e+03  5.095000e+03  \n",
       "2nd Flr SF       0.000000e+00          0.0  7.037500e+02  2.065000e+03  \n",
       "Low Qual Fin SF  0.000000e+00          0.0  0.000000e+00  1.064000e+03  \n",
       "Gr Liv Area      1.126000e+03       1442.0  1.742750e+03  5.642000e+03  \n",
       "Bsmt Full Bath   0.000000e+00          0.0  1.000000e+00  3.000000e+00  \n",
       "Bsmt Half Bath   0.000000e+00          0.0  0.000000e+00  2.000000e+00  \n",
       "Full Bath        1.000000e+00          2.0  2.000000e+00  4.000000e+00  \n",
       "Half Bath        0.000000e+00          0.0  1.000000e+00  2.000000e+00  \n",
       "Bedroom AbvGr    2.000000e+00          3.0  3.000000e+00  8.000000e+00  \n",
       "Kitchen AbvGr    1.000000e+00          1.0  1.000000e+00  3.000000e+00  \n",
       "TotRms AbvGrd    5.000000e+00          6.0  7.000000e+00  1.500000e+01  \n",
       "Fireplaces       0.000000e+00          1.0  1.000000e+00  4.000000e+00  \n",
       "Garage Yr Blt    1.960000e+03       1979.0  2.002000e+03  2.207000e+03  \n",
       "Garage Cars      1.000000e+00          2.0  2.000000e+00  5.000000e+00  \n",
       "Garage Area      3.200000e+02        480.0  5.760000e+02  1.488000e+03  \n",
       "Wood Deck SF     0.000000e+00          0.0  1.680000e+02  1.424000e+03  \n",
       "Open Porch SF    0.000000e+00         27.0  7.000000e+01  7.420000e+02  \n",
       "Enclosed Porch   0.000000e+00          0.0  0.000000e+00  1.012000e+03  \n",
       "3Ssn Porch       0.000000e+00          0.0  0.000000e+00  5.080000e+02  \n",
       "Screen Porch     0.000000e+00          0.0  0.000000e+00  5.760000e+02  \n",
       "Pool Area        0.000000e+00          0.0  0.000000e+00  8.000000e+02  \n",
       "Misc Val         0.000000e+00          0.0  0.000000e+00  1.700000e+04  \n",
       "Mo Sold          4.000000e+00          6.0  8.000000e+00  1.200000e+01  \n",
       "Yr Sold          2.007000e+03       2008.0  2.009000e+03  2.010000e+03  \n",
       "SalePrice        1.295000e+05     160000.0  2.135000e+05  7.550000e+05  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180796.0600682594"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SalePrice'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12789"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SalePrice'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "755000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SalePrice'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SalePrice'].idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PID               902207130\n",
       "MS SubClass              30\n",
       "MS Zoning                RM\n",
       "Lot Frontage             68\n",
       "Lot Area               9656\n",
       "                    ...    \n",
       "Mo Sold                   6\n",
       "Yr Sold                2010\n",
       "Sale Type               WD \n",
       "Sale Condition      Abnorml\n",
       "SalePrice             12789\n",
       "Name: 181, Length: 81, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[181]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['TA', nan, 'Fa', 'Gd', 'Ex', 'Po'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Garage Cond'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TA    2665\n",
       "Fa      74\n",
       "Gd      15\n",
       "Po      14\n",
       "Ex       3\n",
       "Name: Garage Cond, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Garage Cond'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>902207130</td>\n",
       "      <td>30</td>\n",
       "      <td>RM</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9656</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>12789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>904300150</td>\n",
       "      <td>50</td>\n",
       "      <td>RL</td>\n",
       "      <td>72.0</td>\n",
       "      <td>10632</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>COD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>107500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>904352190</td>\n",
       "      <td>190</td>\n",
       "      <td>RH</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6048</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>COD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>114900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>905475510</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11200</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>COD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>114000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>908102300</td>\n",
       "      <td>50</td>\n",
       "      <td>RL</td>\n",
       "      <td>89.0</td>\n",
       "      <td>11792</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>64000</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>2843</th>\n",
       "      <td>909101330</td>\n",
       "      <td>30</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>8088</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>ConLD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2844</th>\n",
       "      <td>909103020</td>\n",
       "      <td>50</td>\n",
       "      <td>RL</td>\n",
       "      <td>76.0</td>\n",
       "      <td>11388</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Low</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>121000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2850</th>\n",
       "      <td>909250120</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>96.0</td>\n",
       "      <td>13132</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>138887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2861</th>\n",
       "      <td>909279040</td>\n",
       "      <td>30</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>137500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2914</th>\n",
       "      <td>923226290</td>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1484</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>79400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street Alley  \\\n",
       "181   902207130           30        RM          68.0      9656   Pave   NaN   \n",
       "210   904300150           50        RL          72.0     10632   Pave   NaN   \n",
       "214   904352190          190        RH          60.0      6048   Pave   NaN   \n",
       "242   905475510           20        RL           NaN     11200   Pave   NaN   \n",
       "277   908102300           50        RL          89.0     11792   Pave   NaN   \n",
       "...         ...          ...       ...           ...       ...    ...   ...   \n",
       "2843  909101330           30        RL          60.0      8088   Pave  Grvl   \n",
       "2844  909103020           50        RL          76.0     11388   Pave   NaN   \n",
       "2850  909250120           70        RL          96.0     13132   Pave   NaN   \n",
       "2861  909279040           30        RL          80.0     11600   Pave   NaN   \n",
       "2914  923226290          160        RM          21.0      1484   Pave   NaN   \n",
       "\n",
       "     Lot Shape Land Contour Utilities  ... Pool Area Pool QC Fence  \\\n",
       "181        Reg          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "210        IR1          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "214        Reg          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "242        IR1          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "277        Reg          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "...        ...          ...       ...  ...       ...     ...   ...   \n",
       "2843       Reg          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "2844       Reg          Low    AllPub  ...         0     NaN   NaN   \n",
       "2850       Reg          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "2861       Reg          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "2914       Reg          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "\n",
       "     Misc Feature Misc Val Mo Sold Yr Sold  Sale Type  Sale Condition  \\\n",
       "181           NaN        0       6    2010        WD          Abnorml   \n",
       "210           NaN        0       1    2010        COD          Normal   \n",
       "214           NaN        0       6    2010        COD          Normal   \n",
       "242           NaN        0       6    2010        COD          Normal   \n",
       "277           NaN        0       6    2010        WD          Abnorml   \n",
       "...           ...      ...     ...     ...        ...             ...   \n",
       "2843          NaN        0       2    2006      ConLD          Normal   \n",
       "2844          NaN        0       9    2006        WD           Normal   \n",
       "2850          NaN        0       7    2006        WD           Normal   \n",
       "2861          NaN        0       7    2006        WD           Normal   \n",
       "2914          NaN        0       5    2006        WD           Normal   \n",
       "\n",
       "      SalePrice  \n",
       "181       12789  \n",
       "210      107500  \n",
       "214      114900  \n",
       "242      114000  \n",
       "277       64000  \n",
       "...         ...  \n",
       "2843      35000  \n",
       "2844     121000  \n",
       "2850     138887  \n",
       "2861     137500  \n",
       "2914      79400  \n",
       "\n",
       "[74 rows x 81 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Garage Cond']=='Fa']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
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       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>18</th>\n",
       "      <td>527276150</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>527402200</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>149900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>527403020</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
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       "      <td>70.0</td>\n",
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       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>126000</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>2915</th>\n",
       "      <td>923227100</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13384</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>923251180</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>131000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>923275080</td>\n",
       "      <td>80</td>\n",
       "      <td>RL</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7937</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>142500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2926</th>\n",
       "      <td>923276100</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8885</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Low</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>131000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2927</th>\n",
       "      <td>923400125</td>\n",
       "      <td>85</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>132000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>965 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street Alley  \\\n",
       "18    527276150           20        RL         140.0     19138   Pave   NaN   \n",
       "23    527402200           20        RL           NaN     11241   Pave   NaN   \n",
       "24    527402250           20        RL           NaN     12537   Pave   NaN   \n",
       "25    527403020           20        RL          65.0      8450   Pave   NaN   \n",
       "26    527404120           20        RL          70.0      8400   Pave   NaN   \n",
       "...         ...          ...       ...           ...       ...    ...   ...   \n",
       "2915  923227100           20        RL          80.0     13384   Pave   NaN   \n",
       "2924  923251180           20        RL         160.0     20000   Pave   NaN   \n",
       "2925  923275080           80        RL          37.0      7937   Pave   NaN   \n",
       "2926  923276100           20        RL           NaN      8885   Pave   NaN   \n",
       "2927  923400125           85        RL          62.0     10441   Pave   NaN   \n",
       "\n",
       "     Lot Shape Land Contour Utilities  ... Pool Area Pool QC  Fence  \\\n",
       "18         Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "23         IR1          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "24         IR1          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "25         Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "26         Reg          Lvl    AllPub  ...         0     NaN  MnPrv   \n",
       "...        ...          ...       ...  ...       ...     ...    ...   \n",
       "2915       Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "2924       Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "2925       IR1          Lvl    AllPub  ...         0     NaN  GdPrv   \n",
       "2926       IR1          Low    AllPub  ...         0     NaN  MnPrv   \n",
       "2927       Reg          Lvl    AllPub  ...         0     NaN  MnPrv   \n",
       "\n",
       "     Misc Feature Misc Val Mo Sold Yr Sold  Sale Type  Sale Condition  \\\n",
       "18            NaN        0       6    2010        WD           Normal   \n",
       "23           Shed      700       3    2010        WD           Normal   \n",
       "24            NaN        0       4    2010        WD           Normal   \n",
       "25            NaN        0       7    2010        WD           Normal   \n",
       "26            NaN        0       4    2010        WD           Normal   \n",
       "...           ...      ...     ...     ...        ...             ...   \n",
       "2915          NaN        0       5    2006        WD           Normal   \n",
       "2924          NaN        0       9    2006        WD          Abnorml   \n",
       "2925          NaN        0       3    2006        WD           Normal   \n",
       "2926          NaN        0       6    2006        WD           Normal   \n",
       "2927         Shed      700       7    2006        WD           Normal   \n",
       "\n",
       "      SalePrice  \n",
       "18       141000  \n",
       "23       149000  \n",
       "24       149900  \n",
       "25       142000  \n",
       "26       126000  \n",
       "...         ...  \n",
       "2915     140000  \n",
       "2924     131000  \n",
       "2925     142500  \n",
       "2926     131000  \n",
       "2927     132000  \n",
       "\n",
       "[965 rows x 81 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['SalePrice'].between(107000, 150000, inclusive=True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1767</th>\n",
       "      <td>528351010</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>104.0</td>\n",
       "      <td>21535</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>755000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1760</th>\n",
       "      <td>528320050</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>15623</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>555</td>\n",
       "      <td>Ex</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>745000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2445</th>\n",
       "      <td>528320060</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>118.0</td>\n",
       "      <td>35760</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1063</th>\n",
       "      <td>528164060</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>106.0</td>\n",
       "      <td>12720</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>144</td>\n",
       "      <td>Ex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>615000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>528150070</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>100.0</td>\n",
       "      <td>12919</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>611657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>528110020</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>105.0</td>\n",
       "      <td>13693</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637</th>\n",
       "      <td>527216080</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>52.0</td>\n",
       "      <td>51974</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2007</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>591587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2450</th>\n",
       "      <td>528360050</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>114.0</td>\n",
       "      <td>17242</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>584500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>528110090</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>107.0</td>\n",
       "      <td>13891</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2009</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>582933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2332</th>\n",
       "      <td>527212030</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>16056</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>556581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street Alley  \\\n",
       "1767  528351010           60        RL         104.0     21535   Pave   NaN   \n",
       "1760  528320050           60        RL         160.0     15623   Pave   NaN   \n",
       "2445  528320060           60        RL         118.0     35760   Pave   NaN   \n",
       "1063  528164060           20        RL         106.0     12720   Pave   NaN   \n",
       "44    528150070           20        RL         100.0     12919   Pave   NaN   \n",
       "432   528110020           20        RL         105.0     13693   Pave   NaN   \n",
       "1637  527216080           20        RL          52.0     51974   Pave   NaN   \n",
       "2450  528360050           60        RL         114.0     17242   Pave   NaN   \n",
       "433   528110090           60        RL         107.0     13891   Pave   NaN   \n",
       "2332  527212030           60        RL          85.0     16056   Pave   NaN   \n",
       "\n",
       "     Lot Shape Land Contour Utilities  ... Pool Area Pool QC  Fence  \\\n",
       "1767       IR1          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "1760       IR1          Lvl    AllPub  ...       555      Ex  MnPrv   \n",
       "2445       IR1          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "1063       Reg          HLS    AllPub  ...       144      Ex    NaN   \n",
       "44         IR1          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "432        Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "1637       IR1          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "2450       IR1          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "433        Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "2332       IR1          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "\n",
       "     Misc Feature Misc Val Mo Sold Yr Sold  Sale Type  Sale Condition  \\\n",
       "1767          NaN        0       1    2007        WD           Normal   \n",
       "1760          NaN        0       7    2007        WD          Abnorml   \n",
       "2445          NaN        0       7    2006        WD           Normal   \n",
       "1063          NaN        0       2    2008        WD           Normal   \n",
       "44            NaN        0       3    2010        New         Partial   \n",
       "432           NaN        0       3    2009        WD           Normal   \n",
       "1637          NaN        0       6    2007        New         Partial   \n",
       "2450          NaN        0       5    2006        WD           Normal   \n",
       "433           NaN        0       1    2009        New         Partial   \n",
       "2332          NaN        0       7    2006        New         Partial   \n",
       "\n",
       "      SalePrice  \n",
       "1767     755000  \n",
       "1760     745000  \n",
       "2445     625000  \n",
       "1063     615000  \n",
       "44       611657  \n",
       "432      610000  \n",
       "1637     591587  \n",
       "2450     584500  \n",
       "433      582933  \n",
       "2332     556581  \n",
       "\n",
       "[10 rows x 81 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter by top 10 sales price\n",
    "df.nlargest(10, 'SalePrice')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dealing with missing values\n",
    "\n",
    "In the real-world data rarely do not require cleaning. Missing data can occur during data extraction or collection. Missing values must be handled carefully because the technique(s) used may reduce the quality of model performance. Thus, the approach to dealing with missing data is largely dependent on the nature of our data and the missing values. \n",
    "\n",
    "Python machine learning estimators (SKLearn) expect numeric values and that all have a meaning.\n",
    "\n",
    "#### Some approaches for dealing with missing data\n",
    "\n",
    "- Drop NULL or missing values\n",
    "- Fill Missing Values\n",
    "- Predict Missing values with an ML Algorithm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>526301100</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>141.0</td>\n",
       "      <td>31770</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>526351010</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>526353030</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>93.0</td>\n",
       "      <td>11160</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>244000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>527105030</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>195500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>527127150</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4920</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>213500</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>2922</th>\n",
       "      <td>923230040</td>\n",
       "      <td>90</td>\n",
       "      <td>RL</td>\n",
       "      <td>63.0</td>\n",
       "      <td>9297</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Family</td>\n",
       "      <td>188000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>923250060</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>17400</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Low</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>923251180</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>131000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2928</th>\n",
       "      <td>924100070</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>77.0</td>\n",
       "      <td>10010</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2929</th>\n",
       "      <td>924151050</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>188000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2358 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street Alley  \\\n",
       "0     526301100           20        RL         141.0     31770   Pave   NaN   \n",
       "2     526351010           20        RL          81.0     14267   Pave   NaN   \n",
       "3     526353030           20        RL          93.0     11160   Pave   NaN   \n",
       "5     527105030           60        RL          78.0      9978   Pave   NaN   \n",
       "6     527127150          120        RL          41.0      4920   Pave   NaN   \n",
       "...         ...          ...       ...           ...       ...    ...   ...   \n",
       "2922  923230040           90        RL          63.0      9297   Pave   NaN   \n",
       "2923  923250060           20        RL          80.0     17400   Pave   NaN   \n",
       "2924  923251180           20        RL         160.0     20000   Pave   NaN   \n",
       "2928  924100070           20        RL          77.0     10010   Pave   NaN   \n",
       "2929  924151050           60        RL          74.0      9627   Pave   NaN   \n",
       "\n",
       "     Lot Shape Land Contour Utilities  ... Pool Area Pool QC Fence  \\\n",
       "0          IR1          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "2          IR1          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "3          Reg          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "5          IR1          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "6          Reg          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "...        ...          ...       ...  ...       ...     ...   ...   \n",
       "2922       Reg          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "2923       Reg          Low    AllPub  ...         0     NaN   NaN   \n",
       "2924       Reg          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "2928       Reg          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "2929       Reg          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "\n",
       "     Misc Feature Misc Val Mo Sold Yr Sold  Sale Type  Sale Condition  \\\n",
       "0             NaN        0       5    2010        WD           Normal   \n",
       "2            Gar2    12500       6    2010        WD           Normal   \n",
       "3             NaN        0       4    2010        WD           Normal   \n",
       "5             NaN        0       6    2010        WD           Normal   \n",
       "6             NaN        0       4    2010        WD           Normal   \n",
       "...           ...      ...     ...     ...        ...             ...   \n",
       "2922          NaN        0       7    2006        WD           Family   \n",
       "2923          NaN        0       5    2006        WD           Normal   \n",
       "2924          NaN        0       9    2006        WD          Abnorml   \n",
       "2928          NaN        0       4    2006        WD           Normal   \n",
       "2929          NaN        0      11    2006        WD           Normal   \n",
       "\n",
       "      SalePrice  \n",
       "0        215000  \n",
       "2        172000  \n",
       "3        244000  \n",
       "5        195500  \n",
       "6        213500  \n",
       "...         ...  \n",
       "2922     188000  \n",
       "2923     160000  \n",
       "2924     131000  \n",
       "2928     170000  \n",
       "2929     188000  \n",
       "\n",
       "[2358 rows x 81 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Fence'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PID                 0\n",
       "MS SubClass         0\n",
       "MS Zoning           0\n",
       "Lot Frontage      490\n",
       "Lot Area            0\n",
       "                 ... \n",
       "Mo Sold             0\n",
       "Yr Sold             0\n",
       "Sale Type           0\n",
       "Sale Condition      0\n",
       "SalePrice           0\n",
       "Length: 81, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method dropna in module pandas.core.frame:\n",
      "\n",
      "dropna(axis=0, how='any', thresh=None, subset=None, inplace=False) method of pandas.core.frame.DataFrame instance\n",
      "    Remove missing values.\n",
      "    \n",
      "    See the :ref:`User Guide <missing_data>` for more on which values are\n",
      "    considered missing, and how to work with missing data.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    axis : {0 or 'index', 1 or 'columns'}, default 0\n",
      "        Determine if rows or columns which contain missing values are\n",
      "        removed.\n",
      "    \n",
      "        * 0, or 'index' : Drop rows which contain missing values.\n",
      "        * 1, or 'columns' : Drop columns which contain missing value.\n",
      "    \n",
      "        .. versionchanged:: 1.0.0\n",
      "    \n",
      "           Pass tuple or list to drop on multiple axes.\n",
      "           Only a single axis is allowed.\n",
      "    \n",
      "    how : {'any', 'all'}, default 'any'\n",
      "        Determine if row or column is removed from DataFrame, when we have\n",
      "        at least one NA or all NA.\n",
      "    \n",
      "        * 'any' : If any NA values are present, drop that row or column.\n",
      "        * 'all' : If all values are NA, drop that row or column.\n",
      "    \n",
      "    thresh : int, optional\n",
      "        Require that many non-NA values.\n",
      "    subset : array-like, optional\n",
      "        Labels along other axis to consider, e.g. if you are dropping rows\n",
      "        these would be a list of columns to include.\n",
      "    inplace : bool, default False\n",
      "        If True, do operation inplace and return None.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    DataFrame\n",
      "        DataFrame with NA entries dropped from it.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    DataFrame.isna: Indicate missing values.\n",
      "    DataFrame.notna : Indicate existing (non-missing) values.\n",
      "    DataFrame.fillna : Replace missing values.\n",
      "    Series.dropna : Drop missing values.\n",
      "    Index.dropna : Drop missing indices.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> df = pd.DataFrame({\"name\": ['Alfred', 'Batman', 'Catwoman'],\n",
      "    ...                    \"toy\": [np.nan, 'Batmobile', 'Bullwhip'],\n",
      "    ...                    \"born\": [pd.NaT, pd.Timestamp(\"1940-04-25\"),\n",
      "    ...                             pd.NaT]})\n",
      "    >>> df\n",
      "           name        toy       born\n",
      "    0    Alfred        NaN        NaT\n",
      "    1    Batman  Batmobile 1940-04-25\n",
      "    2  Catwoman   Bullwhip        NaT\n",
      "    \n",
      "    Drop the rows where at least one element is missing.\n",
      "    \n",
      "    >>> df.dropna()\n",
      "         name        toy       born\n",
      "    1  Batman  Batmobile 1940-04-25\n",
      "    \n",
      "    Drop the columns where at least one element is missing.\n",
      "    \n",
      "    >>> df.dropna(axis='columns')\n",
      "           name\n",
      "    0    Alfred\n",
      "    1    Batman\n",
      "    2  Catwoman\n",
      "    \n",
      "    Drop the rows where all elements are missing.\n",
      "    \n",
      "    >>> df.dropna(how='all')\n",
      "           name        toy       born\n",
      "    0    Alfred        NaN        NaT\n",
      "    1    Batman  Batmobile 1940-04-25\n",
      "    2  Catwoman   Bullwhip        NaT\n",
      "    \n",
      "    Keep only the rows with at least 2 non-NA values.\n",
      "    \n",
      "    >>> df.dropna(thresh=2)\n",
      "           name        toy       born\n",
      "    1    Batman  Batmobile 1940-04-25\n",
      "    2  Catwoman   Bullwhip        NaT\n",
      "    \n",
      "    Define in which columns to look for missing values.\n",
      "    \n",
      "    >>> df.dropna(subset=['name', 'born'])\n",
      "           name        toy       born\n",
      "    1    Batman  Batmobile 1940-04-25\n",
      "    \n",
      "    Keep the DataFrame with valid entries in the same variable.\n",
      "    \n",
      "    >>> df.dropna(inplace=True)\n",
      "    >>> df\n",
      "         name        toy       born\n",
      "    1  Batman  Batmobile 1940-04-25\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(df.dropna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2930, 81)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PID, MS SubClass, MS Zoning, Lot Frontage, Lot Area, Street, Alley, Lot Shape, Land Contour, Utilities, Lot Config, Land Slope, Neighborhood, Condition 1, Condition 2, Bldg Type, House Style, Overall Qual, Overall Cond, Year Built, Year Remod/Add, Roof Style, Roof Matl, Exterior 1st, Exterior 2nd, Mas Vnr Type, Mas Vnr Area, Exter Qual, Exter Cond, Foundation, Bsmt Qual, Bsmt Cond, Bsmt Exposure, BsmtFin Type 1, BsmtFin SF 1, BsmtFin Type 2, BsmtFin SF 2, Bsmt Unf SF, Total Bsmt SF, Heating, Heating QC, Central Air, Electrical, 1st Flr SF, 2nd Flr SF, Low Qual Fin SF, Gr Liv Area, Bsmt Full Bath, Bsmt Half Bath, Full Bath, Half Bath, Bedroom AbvGr, Kitchen AbvGr, Kitchen Qual, TotRms AbvGrd, Functional, Fireplaces, Fireplace Qu, Garage Type, Garage Yr Blt, Garage Finish, Garage Cars, Garage Area, Garage Qual, Garage Cond, Paved Drive, Wood Deck SF, Open Porch SF, Enclosed Porch, 3Ssn Porch, Screen Porch, Pool Area, Pool QC, Fence, Misc Feature, Misc Val, Mo Sold, Yr Sold, Sale Type, Sale Condition, SalePrice]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 81 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Each record has missing values -- no rows were returned \n",
    "df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Using dropna() with threshold parameter defines the number of NA values that are required"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>526301100</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>141.0</td>\n",
       "      <td>31770</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>526350040</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>526351010</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>526353030</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>93.0</td>\n",
       "      <td>11160</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>244000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>527105010</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>189900</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>2925</th>\n",
       "      <td>923275080</td>\n",
       "      <td>80</td>\n",
       "      <td>RL</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7937</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>142500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2926</th>\n",
       "      <td>923276100</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8885</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Low</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>131000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2927</th>\n",
       "      <td>923400125</td>\n",
       "      <td>85</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>132000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2928</th>\n",
       "      <td>924100070</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>77.0</td>\n",
       "      <td>10010</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2929</th>\n",
       "      <td>924151050</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>188000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2930 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street Alley  \\\n",
       "0     526301100           20        RL         141.0     31770   Pave   NaN   \n",
       "1     526350040           20        RH          80.0     11622   Pave   NaN   \n",
       "2     526351010           20        RL          81.0     14267   Pave   NaN   \n",
       "3     526353030           20        RL          93.0     11160   Pave   NaN   \n",
       "4     527105010           60        RL          74.0     13830   Pave   NaN   \n",
       "...         ...          ...       ...           ...       ...    ...   ...   \n",
       "2925  923275080           80        RL          37.0      7937   Pave   NaN   \n",
       "2926  923276100           20        RL           NaN      8885   Pave   NaN   \n",
       "2927  923400125           85        RL          62.0     10441   Pave   NaN   \n",
       "2928  924100070           20        RL          77.0     10010   Pave   NaN   \n",
       "2929  924151050           60        RL          74.0      9627   Pave   NaN   \n",
       "\n",
       "     Lot Shape Land Contour Utilities  ... Pool Area Pool QC  Fence  \\\n",
       "0          IR1          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "1          Reg          Lvl    AllPub  ...         0     NaN  MnPrv   \n",
       "2          IR1          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "3          Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "4          IR1          Lvl    AllPub  ...         0     NaN  MnPrv   \n",
       "...        ...          ...       ...  ...       ...     ...    ...   \n",
       "2925       IR1          Lvl    AllPub  ...         0     NaN  GdPrv   \n",
       "2926       IR1          Low    AllPub  ...         0     NaN  MnPrv   \n",
       "2927       Reg          Lvl    AllPub  ...         0     NaN  MnPrv   \n",
       "2928       Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "2929       Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "\n",
       "     Misc Feature Misc Val Mo Sold Yr Sold  Sale Type  Sale Condition  \\\n",
       "0             NaN        0       5    2010        WD           Normal   \n",
       "1             NaN        0       6    2010        WD           Normal   \n",
       "2            Gar2    12500       6    2010        WD           Normal   \n",
       "3             NaN        0       4    2010        WD           Normal   \n",
       "4             NaN        0       3    2010        WD           Normal   \n",
       "...           ...      ...     ...     ...        ...             ...   \n",
       "2925          NaN        0       3    2006        WD           Normal   \n",
       "2926          NaN        0       6    2006        WD           Normal   \n",
       "2927         Shed      700       7    2006        WD           Normal   \n",
       "2928          NaN        0       4    2006        WD           Normal   \n",
       "2929          NaN        0      11    2006        WD           Normal   \n",
       "\n",
       "      SalePrice  \n",
       "0        215000  \n",
       "1        105000  \n",
       "2        172000  \n",
       "3        244000  \n",
       "4        189900  \n",
       "...         ...  \n",
       "2925     142500  \n",
       "2926     131000  \n",
       "2927     132000  \n",
       "2928     170000  \n",
       "2929     188000  \n",
       "\n",
       "[2930 rows x 81 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(thresh=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fill missing values\n",
    "\n",
    "This is a common approach of handling missing values. Missing values of a feature are replaced with a test statistic like mean, median or mode. Let’s suppose we have a missing value of age in the boston data set. Then the below code will fill the missing value with the 30."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([141.,  80.,  81.,  93.,  74.,  78.,  41.,  43.,  39.,  60.,  75.,\n",
       "        nan,  63.,  85.,  47., 152.,  88., 140., 105.,  65.,  70.,  26.,\n",
       "        21.,  53.,  24., 102.,  98.,  83.,  94.,  95.,  90.,  79., 100.,\n",
       "        44., 110.,  61.,  36.,  67., 108.,  59.,  92.,  58.,  56.,  73.,\n",
       "        72.,  84.,  76.,  50.,  55.,  68., 107.,  25.,  30.,  57.,  40.,\n",
       "        77., 120., 137.,  87., 119.,  64.,  96.,  71.,  69.,  52.,  51.,\n",
       "        54.,  86., 124.,  82.,  38.,  48.,  89.,  66.,  45.,  35., 129.,\n",
       "        31.,  42.,  28.,  99., 104.,  97., 103.,  34., 117., 149., 122.,\n",
       "        62., 174., 106., 112.,  32., 115., 128.,  91.,  33., 121., 144.,\n",
       "       130., 109., 150., 113., 125., 101.,  46., 114., 135., 136.,  37.,\n",
       "        22., 313.,  49., 123., 160., 195., 118., 134., 182., 116., 138.,\n",
       "       155., 126., 200., 168., 111., 131., 153., 133.])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Lot Frontage'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Lot Frontage'] = df['Lot Frontage'].fillna(df['Lot Frontage'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([141.        ,  80.        ,  81.        ,  93.        ,\n",
       "        74.        ,  78.        ,  41.        ,  43.        ,\n",
       "        39.        ,  60.        ,  75.        ,  69.22459016,\n",
       "        63.        ,  85.        ,  47.        , 152.        ,\n",
       "        88.        , 140.        , 105.        ,  65.        ,\n",
       "        70.        ,  26.        ,  21.        ,  53.        ,\n",
       "        24.        , 102.        ,  98.        ,  83.        ,\n",
       "        94.        ,  95.        ,  90.        ,  79.        ,\n",
       "       100.        ,  44.        , 110.        ,  61.        ,\n",
       "        36.        ,  67.        , 108.        ,  59.        ,\n",
       "        92.        ,  58.        ,  56.        ,  73.        ,\n",
       "        72.        ,  84.        ,  76.        ,  50.        ,\n",
       "        55.        ,  68.        , 107.        ,  25.        ,\n",
       "        30.        ,  57.        ,  40.        ,  77.        ,\n",
       "       120.        , 137.        ,  87.        , 119.        ,\n",
       "        64.        ,  96.        ,  71.        ,  69.        ,\n",
       "        52.        ,  51.        ,  54.        ,  86.        ,\n",
       "       124.        ,  82.        ,  38.        ,  48.        ,\n",
       "        89.        ,  66.        ,  45.        ,  35.        ,\n",
       "       129.        ,  31.        ,  42.        ,  28.        ,\n",
       "        99.        , 104.        ,  97.        , 103.        ,\n",
       "        34.        , 117.        , 149.        , 122.        ,\n",
       "        62.        , 174.        , 106.        , 112.        ,\n",
       "        32.        , 115.        , 128.        ,  91.        ,\n",
       "        33.        , 121.        , 144.        , 130.        ,\n",
       "       109.        , 150.        , 113.        , 125.        ,\n",
       "       101.        ,  46.        , 114.        , 135.        ,\n",
       "       136.        ,  37.        ,  22.        , 313.        ,\n",
       "        49.        , 123.        , 160.        , 195.        ,\n",
       "       118.        , 134.        , 182.        , 116.        ,\n",
       "       138.        , 155.        , 126.        , 200.        ,\n",
       "       168.        , 111.        , 131.        , 153.        ,\n",
       "       133.        ])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Lot Frontage'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imput missing data based on other features\n",
    "\n",
    "Other options for dealing with missing data\n",
    "https://scikit-learn.org/stable/modules/impute.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>Lot Frontage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Blmngtn</th>\n",
       "      <td>53.278454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blueste</th>\n",
       "      <td>27.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BrDale</th>\n",
       "      <td>21.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BrkSide</th>\n",
       "      <td>57.406664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ClearCr</th>\n",
       "      <td>77.827049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CollgCr</th>\n",
       "      <td>70.964628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crawfor</th>\n",
       "      <td>69.810600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edwards</th>\n",
       "      <td>67.100997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gilbert</th>\n",
       "      <td>72.576533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greens</th>\n",
       "      <td>44.528074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GrnHill</th>\n",
       "      <td>69.224590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDOTRR</th>\n",
       "      <td>62.691909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Landmrk</th>\n",
       "      <td>69.224590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MeadowV</th>\n",
       "      <td>30.321577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mitchel</th>\n",
       "      <td>73.898159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAmes</th>\n",
       "      <td>74.291811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NPkVill</th>\n",
       "      <td>31.715182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NWAmes</th>\n",
       "      <td>77.201001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NoRidge</th>\n",
       "      <td>86.265043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NridgHt</th>\n",
       "      <td>83.913697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OldTown</th>\n",
       "      <td>62.088895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SWISU</th>\n",
       "      <td>59.914549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sawyer</th>\n",
       "      <td>72.681479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SawyerW</th>\n",
       "      <td>70.450138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Somerst</th>\n",
       "      <td>65.063142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StoneBr</th>\n",
       "      <td>62.865156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timber</th>\n",
       "      <td>78.671790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Veenker</th>\n",
       "      <td>71.074863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Lot Frontage\n",
       "Neighborhood              \n",
       "Blmngtn          53.278454\n",
       "Blueste          27.300000\n",
       "BrDale           21.500000\n",
       "BrkSide          57.406664\n",
       "ClearCr          77.827049\n",
       "CollgCr          70.964628\n",
       "Crawfor          69.810600\n",
       "Edwards          67.100997\n",
       "Gilbert          72.576533\n",
       "Greens           44.528074\n",
       "GrnHill          69.224590\n",
       "IDOTRR           62.691909\n",
       "Landmrk          69.224590\n",
       "MeadowV          30.321577\n",
       "Mitchel          73.898159\n",
       "NAmes            74.291811\n",
       "NPkVill          31.715182\n",
       "NWAmes           77.201001\n",
       "NoRidge          86.265043\n",
       "NridgHt          83.913697\n",
       "OldTown          62.088895\n",
       "SWISU            59.914549\n",
       "Sawyer           72.681479\n",
       "SawyerW          70.450138\n",
       "Somerst          65.063142\n",
       "StoneBr          62.865156\n",
       "Timber           78.671790\n",
       "Veenker          71.074863"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Neighborhood')[['Lot Frontage']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize data\n",
    "\n",
    "#### Matplotlib\n",
    "- Matplotlib is a visualization library that can create 2D graphics. Matplotlib has many default features and allows customization.\n",
    "\n",
    "https://matplotlib.org/stable/index.html\n",
    "\n",
    "\n",
    "#### Seaborn\n",
    "- Seaborn is a visualization library built on Matplotlib. Seaborn allows for less customization that Matplotlib and requires less code.\n",
    "\n",
    "https://seaborn.pydata.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SalePrice', ylabel='Count'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram\n",
    "sns.histplot(data=df, x='SalePrice', kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SalePrice', ylabel='Count'>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x750 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5), dpi=150)\n",
    "\n",
    "sns.histplot(data=df, x='SalePrice', kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SalePrice'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x750 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Boxplot\n",
    "\n",
    "plt.figure(figsize=(8,5), dpi=150)\n",
    "\n",
    "sns.boxenplot(data=df, x='SalePrice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180796"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df['SalePrice'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      2930.000000\n",
       "mean     180796.060068\n",
       "std       79886.692357\n",
       "min       12789.000000\n",
       "25%      129500.000000\n",
       "50%      160000.000000\n",
       "75%      213500.000000\n",
       "max      755000.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SalePrice'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>902207130</td>\n",
       "      <td>30</td>\n",
       "      <td>RM</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9656</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>12789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>902205010</td>\n",
       "      <td>30</td>\n",
       "      <td>RM</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5925</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>902205030</td>\n",
       "      <td>50</td>\n",
       "      <td>RM</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5925</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdWo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>37900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>902477120</td>\n",
       "      <td>30</td>\n",
       "      <td>C (all)</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7879</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdWo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>34900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>902477130</td>\n",
       "      <td>30</td>\n",
       "      <td>C (all)</td>\n",
       "      <td>72.0</td>\n",
       "      <td>9392</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>44000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>902205070</td>\n",
       "      <td>20</td>\n",
       "      <td>RM</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7311</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>46500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1553</th>\n",
       "      <td>910251050</td>\n",
       "      <td>20</td>\n",
       "      <td>A (agr)</td>\n",
       "      <td>80.0</td>\n",
       "      <td>14584</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Low</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>13100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1555</th>\n",
       "      <td>911102170</td>\n",
       "      <td>70</td>\n",
       "      <td>C (all)</td>\n",
       "      <td>50.0</td>\n",
       "      <td>8500</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>534450090</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Low</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>39300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2843</th>\n",
       "      <td>909101330</td>\n",
       "      <td>30</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>8088</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>ConLD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2880</th>\n",
       "      <td>911175430</td>\n",
       "      <td>20</td>\n",
       "      <td>C (all)</td>\n",
       "      <td>50.0</td>\n",
       "      <td>9000</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>35311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street Alley  \\\n",
       "181   902207130           30        RM          68.0      9656   Pave   NaN   \n",
       "708   902205010           30        RM          50.0      5925   Pave   NaN   \n",
       "709   902205030           50        RM          50.0      5925   Pave   NaN   \n",
       "726   902477120           30   C (all)          60.0      7879   Pave   NaN   \n",
       "727   902477130           30   C (all)          72.0      9392   Pave   NaN   \n",
       "1302  902205070           20        RM          62.0      7311   Pave   NaN   \n",
       "1553  910251050           20   A (agr)          80.0     14584   Pave   NaN   \n",
       "1555  911102170           70   C (all)          50.0      8500   Pave  Pave   \n",
       "1901  534450090           20        RL          50.0      5000   Pave   NaN   \n",
       "2843  909101330           30        RL          60.0      8088   Pave  Grvl   \n",
       "2880  911175430           20   C (all)          50.0      9000   Pave   NaN   \n",
       "\n",
       "     Lot Shape Land Contour Utilities  ... Pool Area Pool QC  Fence  \\\n",
       "181        Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "708        Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "709        Reg          Lvl    AllPub  ...         0     NaN   GdWo   \n",
       "726        Reg          Lvl    AllPub  ...         0     NaN   GdWo   \n",
       "727        Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "1302       Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "1553       Reg          Low    AllPub  ...         0     NaN    NaN   \n",
       "1555       Reg          Lvl    AllPub  ...         0     NaN  MnPrv   \n",
       "1901       Reg          Low    AllPub  ...         0     NaN    NaN   \n",
       "2843       Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "2880       Reg          Lvl    AllPub  ...         0     NaN    NaN   \n",
       "\n",
       "     Misc Feature Misc Val Mo Sold Yr Sold  Sale Type  Sale Condition  \\\n",
       "181           NaN        0       6    2010        WD          Abnorml   \n",
       "708           NaN        0      10    2009        WD           Normal   \n",
       "709           NaN        0       5    2009        WD          Abnorml   \n",
       "726           NaN        0      11    2009        WD          Abnorml   \n",
       "727           NaN        0       9    2009        WD          Abnorml   \n",
       "1302          NaN        0       3    2008        WD          Abnorml   \n",
       "1553          NaN        0       2    2008        WD          Abnorml   \n",
       "1555          NaN        0       7    2008        WD           Normal   \n",
       "1901          NaN        0       1    2007        WD           Normal   \n",
       "2843          NaN        0       2    2006      ConLD          Normal   \n",
       "2880          NaN        0      10    2006        WD          Abnorml   \n",
       "\n",
       "      SalePrice  \n",
       "181       12789  \n",
       "708       45000  \n",
       "709       37900  \n",
       "726       34900  \n",
       "727       44000  \n",
       "1302      46500  \n",
       "1553      13100  \n",
       "1555      40000  \n",
       "1901      39300  \n",
       "2843      35000  \n",
       "2880      35311  \n",
       "\n",
       "[11 rows x 81 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['SalePrice'] < 50000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with Correlated features\n",
    "•\tDepending on the use case highly correlated variable maybe problematic\n",
    "\n",
    "Here is a link to explaining various scenarios\n",
    "https://datascience.stackexchange.com/questions/24452/in-supervised-learning-why-is-it-bad-to-have-correlated-features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kXuWFknpL2lfSxvXdkpmZmZmZWedZGNHQzTqmaYe3SLq/heKTIuJCSZ8nrZByepH4eAZYn9TjYAZwSPlkohExXtKjwBaS/gs8ASwArmrU0IuIiKLdtwM/BvaRNJY0ZOTjwHzgSxHxeobLXQV8B3iomKNjNvB0RPy+KHuCtMztc5LuAfoCOwJjSb06PlYeLCKek/Rj4PfAnZJuByYX7V5Iei325P0eNUTEfEn7ADeSVqM5qngNppKGw6xPWjVmH1IyyMzMzMzMrMs4DdFcmjbpAWzdQtkwgIi4VdKWwE+AnYCPAJNIy9X+KiKernLufqQP7Z8grTTSg9QjpGHzTUTEY5I+CvwU2I00XORd0rKuv42I/2a61LGkyUX3Ag4kvR/uAH4fEXMlfQL4NfBpYA/gVeBPwC+B62q0/Q+SJgA/JD2HU4EbgGOAfxfVJlecM07SpsCRpOTGdkW7JgJ3knrj3EIHPPVEvm9NQy85NVssgL+P2T5rvF//c8dssX630yXZYgHMnJYv1oaDe+cLltmU+YOyxpswb3rrldpgypx8Xw8bLNer9UptMPbNatMBtc/cV2ZkiwWw4fJZw1Wd2bm9XpvzcMZosGzv/q1XqtPkKXl/1RjWq1+2WD1yvgjAOkstmzXe0n3zxZqx8IV8weD9acgzWGv5vC/EYzPmtl6pTtMW5PueBNCXfO9fgFV654u3YGG2UAD0yvge6bdU3vfI2n2XyRpv1rx8P1cH9c97r70z/rr0RtWPTksm975oLk2X9IiINn0niIgngEPaUP850gfvtrZrNHUOOannHiLiZeBrrdUr6o6nhd+fI2IUMKrK8Rmknh7fqXHeO8A3a4Qd0cL1LgAuKD9WDJHZlNTD5rka1zqu2MzMzMzMzLol5zyay+I8p4d1AUlrSVq24tgA0jwgQ4ELI2JBlzTOzMzMzMzMlihN19PDur0DgFGSHiQNC1qONIfKUGA8aZ4SMzMzMzOzpuThLc3FSQ/L7VbSMJZtSMkOAS8DZwG/i4i3uq5pZmZmZmZmHeOUR3Nx0sOyioj/AQd1dTvMzMzMzMw6g3t6NBcnPczMzMzMzMzq5JxHc3HSw8zMzMzMzKxOC530aCpOepiZmZmZmZnVKdzVo6k46WFmZmZmZmZWp4Vd3QBrEyc9zMzMzMzMzOrknh7NxUkPMzMzMzMzszp5To/m0qOrG2BmZmZmZmZm1hnc08PMzMzMzMysTh7e0lyc9DAzMzMzMzOrk4e3NBcnPWyJsPNRG2SLdfNJT2WLBfD8pNOzxvvdTpdki3XMbX/JFgvgwR8dlS3WHRt9NVssgJi5IFusufc9kS0WwNRJ12aNt8mayhbrlMffzBYLYOOdjskWa+Erb2eLBXDps//IGu+ojwzLFmvDTX6YLRbApGtPyhZrpRX6ZIsF8OTaI7PFuuT+k7PFAvj0MitmjbfWFktnizVk/ObZYgGs0DPf6/qfDXfIFgtg2Fb5fr1daeq8bLEAHrnx11njTZyVbw2JfT49NFssgMfum5stVq+e2UIBsMqAvCP8+/XNF2vm7HyxAJbO2LYvnHFivmBNzjmP5tLUc3pIGirp/yQ9LGmKpJmSnpP0d0kbdXX7cpM0QlJIGl1xfGRxfFQ7YvaX9P8k3SdpsqTZkl6SdI6k7XK1vb0kjSnubXhXt8XMzMzMzGxhREM365imTXpI+iTwLPBTYBXgDuAaYB7wVWCspB91XQu7P0mbAk8DvwM2AO4HrgAmA58H7pL0J0lN+z4xMzMzMzPLKaKxm3VMUw5vkbQlcC3QCzgW+ENEzC8r/wxwDvBbSTMj4tSuaWn3JWkN4HZgEHAq8KOImFVWvjVwIfBtUiLp+13QTDMzMzMzs27Fc3o0l6b7C74kAWcBvYGfR8Tx5QkPgIi4DtibNNzqd8UHfPug00kJjzMi4qjyhAdARPwH2BWYBXy3SIKYmZmZmZkt0SKioZt1TNMlPYBPk4ZivEoallFVRNwJXAz0Bb4FIKlX2bwVg6qdJ2mrYg6Je6qU7SnpxrIYzxRzigyoUve9uSgkHSLpfknTJE0pq7O7pDMlPSVpqqQZkh6R9GNJeWd++2DbNgQ+BcwG/l+tehHxDPAnQMD3KmKMl1T1K7CFuUeGFfOH3CHpVUlzJb0u6bKi946ZmZmZmVm35uEtzaUZkx6fKfYXR0RrU2qfV+w/DVDUvxjoA+xX45xDiv255QclnQhcBWwPPE4aXtObNKfIGEn9a8Q7FjgbmEuac+TxsrIzgM8B7wI3AHcBqwG/Bq6TlHmu6vd8utjfEBGtLW9Qeg53K3rZdMRepETVysBjpPlDXgP2Ae6RtGtHgkt6otbWwXabmZmZmZkBaXhLIzfrmGZMemxa7B+so26pzocl9Sr+X0pmHFJZuZiw8wBgPnBR2fEDSHNaPAxsEBE7RMR+wLrA34HNgVE12vBFYKeI2D4iDo6I8hVRvgGsFBHbRMQBEbEbsAYpObITaTLRzrBpsa/nOXyclLBZFlizg9e9B9gkItaNiN2Ke/4osBtpKNJfMyRWzMzMzMzMzIDmTHoMKfZv1lH3rWLfAxhc/P9u4CVghKSVK+rvBAwDboyISWXHf1zsD46I8aWDRc+Ro4DXga/UWOXkjIi4o1rjIuKKiJhRcWwa7w8l2auFe+uIup/DiFgAlHqDdGgB94h4LCIerXL8RlIPnLWBdi81HBEb1to60GwzMzMzM7P3RIM365hmXL1FFft66kLxfomIkHQ+8CPgIOCPZXUWGdoiaQVgE+CpiHi68gIRMVvSA8AepJ4flXWuarGB0rqkITvrAP1JCZpSu9dt6dwOaMtzWF6vw8NtirlKdgO2ApYnDREC2LjYr0sa+mJmZmZmZtbtLPREG02lGZMek4D1gBXqqLt8sQ/gnbLj55KSHp+nSHoUH8b3BWYAV5bVLa38skGtiTvLDGXRpMfL1SoWwzj+QOrVUSv5sEwr12uvUi+WVp9DSUsBy1Wc1y6SNiYlgYa3UK2z7tnMzMzMzKzDmjXnIakvac7Jg4HVST36byCtijqhjbF2I4162JI0FcI7wH+BkyLi1pzt7qhmTHo8AnycNI/G2a3U3bzYP1E+6WlEPC7pUeCjktaPiHHA7qQX65yImFkWo9S7YSJwUyvXm1zl2OwadQ8kzRMyAfgucB/wVkTMk9QbmEP9PTHa6hFSwmfz1iqShpv0BmYCL9YZf5FhPkWS5yJSwuO0YnsBmF70vvkN6QvQc3qYmZmZmVm31YxJjyLhcSvwMdJn2ytJn80OB/aQtG1EPF9nrO8DJ5I6F9xDWll1LdJn6t0lHRERp2W/iXZqxqTH9cA3gf0l/bCVFVxKw1VuqFJ2LvCRos7PqbFqCykpAfB6RIxsV4ur26fYHxER11SUrZXxOtVcD5wAfErS4FZWcCk9LzdHxPyy43MBJA2IiOkV56xWJc76xfZARBxRpbyz79nMzMzMzKzDmnR4y49JCY/7gF1Ln+HKEhhnAju0FkTS8sBvSZ8Hd46Iu8vK9iPN1XiipHOqfE7sEs04kel1pCEkqwDH1KokaXtgf9KL8ZcqVc4jZaYOkTSQlJV6E7ilvFLRzedp4COSOrp6SbnSkJFXqpQdkPE6i4iIx4Gbgb6k5EdVkj4EfLt4eFJF8cRi/6Eqp1ZberZ0v4t0m5K0HLBLC002MzMzMzPrFiIau3VUsZLpd4qH3ypPRkTEH4FHge0l1TMSYGvSSIDbyhMeRaxLi1j9gA93vOV5NF1Pj4hYKGkkcCfwS0nzgD8Uq4wAIOnTpB4bAn5UvuJKWZwJku4kZbN+R0oA/KOiN0PJr0hDaS6V9MUiafAeSWsDO0TEmW24lWdIH/S/JunbEentLOkTwA/bEKe9vk5agvfLkqYDx0bErFKhpK2BC4Glgd9XWYHmDmB74FhJB5Wef0mHkiaIrfQcsBDYSdK6EfFsUb8vaajL4CrnZPOvXz3eeqU6jdi4w/O5fsBR69QzPU39Zk7LF+vBHx2VLxiw+fGnZIu13uuLLATUIRd96Yys8bbZvlfrler01vghrVdqg/FtGrHZst/uvUq+YEDvgZdmi7VwhQWtV2qDx2cNyxpvcrUBke20/7DR+YIBkzccmC3W2BcWZosF8LWv3ZYt1hs9V8oWC+D1CXn/8jfl5VojZNvu8G0zfuEDU97Kd68f3Xe9bLEAptx9Z75g/WHejJY6FbfNxivk/T6yarW+te106y1z8gUD+vfqviOWl8v3LQ6At6fmi7Vs/3yxACZPyRfrw+ecmC8YwF4fyxuvgZqwo8d2wCDg+Yh4uEr5JaRREHsCD7YSq95vFi2NJmioZuzpQUTcD3wWmAocD7wm6QpJF0p6ktQbZFngZxFR2UOhXGkoyzcqHlde7xxSj4jNgLGS/ifpIkk3SHqK9IH+yDbexqmkSVO/CTwu6fwiCXMHKQnQqSLiRWBn4DXSBDQTJV1TtONB4H7S5DZ/Jc21UekvpCWB9weelHSxpLHAaGCRT7YR8SZwBjAQeKS41sXAeNJSwaNz3p9ZV8qZ8DAzs66TM+FhZouPhdHYLYNNiv1DNcofqqjXkv8B75L+mL1deYGkfUnJk3sj4rn2NLQzNGXSAyAibiAtb/pr0lCLnUiJkL7AP4HNIuJXrYS5mPczVc9HxH9auN4xpCTBVcCqwN6kJMhM4PfAl9rY/mdIM91eTVr15bPAAODrEdGInh5ExIOklXCOJQ3h+Tipl8ZHiyrfi4hvlfeiKTv3DVJPj2uAYcCnSW/+Xai9TO8RwA9IE6LuDHyCNJxoC+ClPHdlZmZmZmbWeaLB/yQ9UWurs8mrF/ta3f4mVNSrfe8RU4CvFA/vlHSXpAsk/YfUY+QG3p+/sltouuEt5SLiLeCnxdae86eQkiT11r8NqKtfbUSMqKPOU6RkR7WyRfoERsQYqqxuEhGjaWdPiWI81/HFBoCk75B6ovxY0jW1ZvEtVr3Zs0boau1cQFoi+I9V6o8qtspzRrR4A2ZmZmZmZtaSAcV+Zo3yGRX1WhQRl0h6mzQdQnlvjzdIn5czDuLtuKbt6WGdJyL+REqCrADcICnvpBNmZmZmZmZNqtETmUbEhrW2Optc+oN0rcEybZqER9IPSAtj3EkazjKg2N9HGgVxYVvidbam7ulhnScijpX0KmnozbakdZzNzMzMzMyWaE04kWlpqYNaU+X2K/atLjEraQfgD6R5QD4XEaUZyx+TtD9pzo/9JO0aETd1oM3ZOOlhNUXEn7u6DWZmZmZmZt1JpslFG+nlYr9qjfJVK+q15IvF/rKyhAeQpjOQdBlp7ssRgJMeZmZmZmZmZs2kCXt6PFLsP1qjvHT80TpilRIktRZrLh0fXEeshnDSw8zMzMzMzKxO0XxZj3tIK22uLWmziHi4onz/Yn9NHbFeL/Zb1CjfstiPb1MLO5EnMjUzMzMzMzOrU6MnMu14e2MuUJq64M+S3pvbQ9L3SZOQ3h0R/ys7/m1J4yT9tiLcFcX+85I+sJKnpL2AQ4CFwOUdb3ke7ulhZmZmZmZmVqfm6+gBwK+ATwIfA56VdBewBrA1aYnZwyvqDwXWA4ZVHL8CuBj4HHCVpAeAF4E1eb/3x08i4ulOuId2cU8PMzMzMzMzszo1W0+P1OaYDewI/B8wE9gbGA6cBWwWEc/VGSeAA4Evk5asXQfYp4h1HfDpiPhNnlbn4Z4eZmZmZmZmZnVq0p4eRMQs4OfF1lrdUcCoGmUBnFls3Z57epiZmZmZmZnZYsk9PczMzMzMzMzqtLBZu3osoZz0sCXCN884KFusq4+6OFssgGenzs0ab8PBvbPFumOjr2aLBbDe6/Us/V2fASt9JFssgJf2OCJbrJv+fXK2WABzmJY13hHrD80W65xrp2eLBTCrz+bZYk2c9kC2WAD7rdw3a7yNts33tfrjy17KFgugT/TMFuvLG6yQLRbAaVd9NFusJ14cnS0WwNGbrpg13oDl8/2adsn1M7PFAnht7qx8wfrn/X4+5vnB2WItnDglWyyAZyb+M2u8pV/P9x457cqfZIsFcN03TssWa8iQbKEAmPpu3niDBuSLNXtOvlgAg5bJF+uyXoflCwZ8Nmu0xnLOo7l4eEudJF0mKST9rpV6FxX1TmxU29pD0vCinZXbHEkvSPq7pOEZrzemiD+84vh4Sf62YWZmZmZmTaEZJzJdkjnpUb9vAe8CP5BU9c9MxTrFnyMt2fOzBratI2aQZuwtbTcD/YGvAg9L2rDRDSpLyIxp9LXNzMzMzMxaEg3erGM8vKVOETFR0v8DTgfOkLRlRMwvlUtaBvhr8fDrEZG3H2nnmRQRI8sPSOoPXAnsDPyWPL3Pvgj0A17NEMvMzMzMzKxLuPdFc3FPj7b5BzAG2BQ4uqLseGBV4KyIuLmxzcorImYApbWVt88U8+WIGBcR83LEMzMzMzMz6woe3tJcnPRog2I94q8Bs4FfSFoXQNK2wDeAN4Hvl+pLWl7SHyQ9LWm2pHckXS9pkUSCkoMlXSDpGUkzJE2T9F9J35S0yGslaVQxDGSkpK0kXSNpcnFs0w7e7hvF/gO9gVobelLeporjVef0qHY+aXgQwA4V842MbvttmJmZmZmZ5RMRDd2sY5z0aKOIeBb4JdAX+IekPsA/Sc/lURHxNoCk9YGHgR8APYHrgEeBnYDbJR1SEboPcB6wKyl5cjXwH2BD4C/AmS00a3vgbmA4cBNwJ7Cwg7daWkLhqQ7GaauxwKXF/9/gg/ON3N3gtpiZmZmZmX2Ae3o0F8/p0T6/Bw4AdiAlGD4MXBMRFwBI6glcDKwCHAX8qeglgqTNSJOF/l3SLRHxZhFzPrBfEee9NUwlLU9KmBwm6cyIuLNKew4HjomIEzp6Y5KGFPd1AmnenOM7GrMtIuIKSWNJz8W4yvlGWiLpiVplj15/bscbZ2ZmZmZmSzwnIpqLe3q0QzGB6VeABcBWwDTgiLIqewIbAedHxKlR1icpIh4G/o+0Qsqh5TEj4rLyhEdx/C3g2OLhXjWa9DgpEdMea5QPIQEmkXpaTAd2i4hLWz7dzMzMzMzMrHtyT492iogHJV0O7A+cHhETyop3KfZX1Di9NExjy8qCYi6OXYE1SKudCFimKF63Rryro/2DvWYAl5Q9XhpYB/gocLKkAyLi8XbGbqiIqLm87mM3nOd8rJmZmZmZdZh7ejQXJz06ZkbFvmR4sb9Q0oUtnD+09B9JvYHRwMEt1F+mxvGXWzinNYssWVu051PANcBtktaNiHc7cA0zMzMzM7PFgpMezcVJj87Rs9hfT5qUtJZxZf//Pinh8TjwQ+Ah4J2ImCfpQ8DTpF4f1czuWHMXFRE3SrqC1JPli8Cf6jzVQ6bMzMzMzGyx5aRHc3HSo3OUhrqcFhFX1XnOPsX+4CrDSdbK06w2Ky0du17ZsdKcIwNqnLNa5zXHzMzMzMysa3kZ2ebiv8p3jluK/d5tOGe5Yv9KlbIDOtSa9islW8qH70wC5gFrSvpA0qwYorNDhuuWEitOypmZmZmZWfcSDd6sQ5z06ByXkIaujJR0jKRe5YWSekvaV9LGZYefKfbfqKhbGl7SUMWcHnsXD68rHS9Wl7kfGAx8q6x+L+AkYM0Mly8lVtYulv81MzMzMzPrFiIau1nH+C/pnSAi5kvaB7gROB44StKjwFTS8I/1gUGkIS2PFaedAOwGHC/pc6QkyLrAFsAfgKM7qblDJY0ue1y+egvAXyPijopzfkm6t5MlHQi8DmxOWm3mLOCwjjQoIuZKuoG09O8jkh4i9f64JyL+1Z6Yb159dUea9AELM3/jWaZn9/0yjJkLssa76EtnZIv10h5HtF6pDY775gbZYn3x7OnZYgGs1HPprPH61Rqc1g7vLpjbeqU2eGPavdliLVDlHNMdc+cbteaSbp+Vxud7IfrGkGyxAFbvtTBbrF69s4UCIGbMyxar38K8f/uZkfdLn6E9ak3l1XbPzpmaLRbAqkv1zxbr3rumZYsFMOfRca1XqtOMaRNar9QGPWtOz9Y+08n3PfjtO6/MFgtgdr4vVXpk/jNtr16t12mLBRl/XVqY79svAO9k/PKKGXl/5jczJyKaS/f9tNXkImJcsfzskaTkxnakiUgnAncCl/P+MBgi4k5J2wG/BjYDPkRKiOxHmtS0s5Ie/flgkmIB8DZwA/CPiLis8oSIuEXSZ4FfkJIjM0j3cgwwMlO7vkJK9uwCHEKaHHYpoF1JDzMzMzMzsxyc9GguTnp0QLHU68gWyt8Bjiu2euLdD+xco3iRPw1ExChgVD2xq5w7vlrMNpx/LXBtlaKqbYqIETXiDK9x/E26YFiPmZmZmZlZS5z0aC6e08PMzMzMzMzMFkvu6WFmZmZmZmZWJ/f0aC5OepiZmZmZmZnVy1mPpuKkh5mZmZmZmVmdnPNoLk56mJmZmZmZmdXJSY/m4qSHmZmZmZmZWZ2c9GguTnqYmZmZmZmZ1ctJj6bipIeZmZmZmZlZndzTo7k46WFmZmZmZmZWp3DWo6k46WFmZmZmZmZWL+c8moqTHmZmZmZmZmZ1ckeP5tKjqxtgZmZmZmZmZtYZ3NPDlgj91hiaLday/SZkiwVw7aS3ssabMn9Qtlhz73siWyyAbbbvlS3WTf8+OVssgC+ePT1brH/fd262WACf2+ZLWeNNnZIv1vA+/fMFA5aK9bLGy2nZhXm/HnJaacCmWeMtnPNgtlhv5f0Wx+Nv/DtbrMHqky0WwLjX8v7pb+l+c7PFmp75z5JPzp+aL9hffpIvFrCCPpwt1lI98r5HBvfomzVe/x75fpV/+9E3ssUCWLp3vlhPjc8XC2ClZfPGmzIjX6zBA/LFAlgm4+vArHkZgzU59/RoKt2qp4ekKNu2baHeAWX1xjewibXac0LRljPqqLuGpIWSZkka2Ij2tUbSMfU872ZmZmZmZku6iMZu1jHdKulR4fMtlB3asFbU5+xiv5+k1lL4hwACroyIjH8m6ZDy5/MLXdYKMzMzMzOzbs5Jj+bSHZMec4AngQMlLdJnT9IQYDfgoUY3rJaIeAx4BFgW2KOV6qVkzjmd2qg6SdoM2Ah4ndRR6wBJ+cYgmJmZmZmZLU6c9Wgq3THpAXAuMBT4VJWyA4FedJOkQZlSb4+aPVQkbQpsCLwF3NCANtWj1LPjdOAuYAjwma5rjpmZmZmZWfflnEdz6c5Jj6D6MJZDgenAldVOVHKwpAskPSNphqRpkv4r6ZuSFrnn4pyDJN0p6XVJsyW9IukWSd+qs83nAQuAz0harkad0v1cGBHzy64/pphPY7ikvSXdX7T7bUnnS1q1SptHF+eMkPQpSbdLmlIcG1RPgyX1BA4uHp7D+4mkqsOHivZF0d6Bkk6U9KKkeZJOLqu3vKQ/SHq6eC7fkXS9pO2rxGzz62VmZmZmZtZlosGbdUi3/EAZES8B9wCflfTeHMaS1gS2BS4DZtY4vQ8pAbEr8CZwNfAfUg+LvwBnVjnnt8D5wKakYTOXAc8BmwA/rLPNE4Fbgd7A5yrLiw/vBxUPz64sL3wTuJQ058cNpOTOQcBtkpaucc4hwPVA/2L/P+r/0tgFWAn4T0Q8B1xMGl60ZyuJk6WBO4DDgbHAVcA7AJLWBx4GfgD0BK4DHgV2Am6XdEhFrPa8XmZmZmZmZl3CPT2aS3desvYcYDtgX6C0Pl2pB0JL60HOB/YDromI99Z6k7Q86QP4YZLOjIg7i+N9ge8C44HNI+LtsnOWAj7WhjafTfrwfijw94qyHYFVgGcj4r81zv8msEtE3FZcvx9wc9GGg6meAPgqcFBEXNiGdpaUhracAxARUyRdS3rO9wf+WeO8rYD7gLUiYkrpYNFz5GLSfR4F/CkifZkWc4fcDPxd0i0R8WZxWpter5ZIqrme5L2/+3Zrp5uZmZmZmbXOiYim0i17ehQuAubywTkyPk+acPPWWidFxPyIuKz8A3Rx/C3g2OLhXmVFA0m9DR4pT3iUxWr1w3aZy0m9M7aTtEZFWek+avXyADiplPAorj8TOLF4uMjQkMK17Ul4FD1o9iYlHS4oKyoNcWltFZcjyxMehT1Jk6KeHxGnlhIeABHxMPB/pB4ph5Ydb+vrZWZmZmZm1mUioqGbdUy37ekREe9Iuo401GIlYDVgPVJiYIGkFs8vJg3dFVgD6EcaMrJMUbxu2XXelDQB2F3SD4FzI+K1drZ5hqTLSQmDQ0jDZkq9SfYrqrXUS+WmKseeKfbDapxzVTuaStGefqQeFpPKjl8LvA18QtIaxVCjShMj4oEqx3cp9lfUuObdxX7LyoJ6X6+WRMSGtcruO+E7/m5hZmZmZma2hOm2SY/COaTeCAcBa5Ydq0lSb2A070/QWc0yFY8PI/V2OAE4QdKLwJ3AeRFRLRHRkrNJSY/PUyQ9SD0gBgL3RMQLLZw7ocqx6cW+T41zXm5j+0o+MLSlJCLmSroY+DrpHn7ThmsOL/YXSmqp98nQ0n/a+XqZmZmZmZl1Df85tal096THNcAU4IvAysBTEfFQK+d8n/QB+nHSJKQPAe9ExDxJHwKeJvUieE9E3CZpHWAPYDdgB1Ii5DBJF0XEgW1o863Aa8CGkjaNiLG8P5yjtWV22/PlM7utJ0hahTTHCMDRkr5TUWWFYn8o1ZMeta7Zs9hfT5qUtJZxZf9v8+tlZmZmZmbWZZz0aCrdOukREXMkXQJ8pTh0ah2n7VPsD46IxyvK1mrhWlNJq4icByBpG9KknAdIGh0R19fZ5oWSzgOOBj4v6WVSImUuaZ6S7uDzvD+fyxYt1NtA0uYR8WCdcUs9VU6LiHqH3bTr9TIzMzMzM+sKsbCrW2Bt0Z0nMi35NzAZmETL82GULFfsX6lSdkC9F42I+3l/0tGN6z2vUDrvYNLQnN7AdZUTpXahUs+T3SNC1Tbe7+FxaI0Y1dxS7PduwzlZXi8zMzMzM7OGiAZv1iHdPukREXdFxNCIWL7GpJqVShN/fqP8oKT9ScNkqDi+uqSRxfKw5cf78P4QkDbNmxERjwKPkpZu/WVxuKVVWxqmmDB0Y1Ii6eYWqp5f7A8ulqKtxyWkoSsjJR0jqVfFtXtL2ldSeRKpTa+XmZmZmZlZV/LqLc2lWw9vaacTSMNJjpf0OdKH6nVJwzj+QBp2Um4w8C/gL5IeIA3R6A98DFge+C9wWTvacTbwe2AIaV6Sa9sRozOUJjC9JCLm1aoUEY9LegLYkLQqyw2tBY6I+ZL2AW4EjgeOkvQoMJW0+s76wCDSkJbHitPa+nq1y5v/fTVHGACGLJ8tFAA9Xs47ZcmEedNbr1SnqZPyvm3fGj8kW6w5TMsWC2Clnktni/W5bb6ULRbAxfefmTXenUccmS3WD2/7c7ZYAD/90f+yxVrwwsRssQC+8Il830cA3nxmZrZYr0+/I1ssgPW/MSpbrInn5n2P7NKv1mJmbbfW8nm//66xfr1/I6jP7Gn5+k/vOXho65Xa4Lq3p2aL1Z+av460yxt6Mluspectmy0WwEJmZY03eKlB2WItmJ/3g9W7GW912ry8Ywlmv533a3/+wnzP3eABef8mPTXfr4XMnVJtzYUllPMQTaXb9/Roq4i4E9gOuI00J8QepPk09gP+UuWU50kfrMcAqwP7Ah8HxgNHAiMiYm47mnIusKD4/8URMacdMbIqemyUVkm5oI5TSnW+0GKtMhExDtgUGEWazHQ7YHdSAulO4HDeHwbTntfLzMzMzMys63h4S1PpVj09irkk6q37OjVW9Sjm49i5xqmVK7dMA04stmwiYiJ1Pr8RMaKFsvFUuc+IGAmMbGObFpBWwam3/q+AX7XWlirnvQMcV2z1XKfu18vMzMzMzKwrecRJc+lWSQ8zMzMzMzOzbs1Zj6ay2A1vMTMzMzMzMzMD9/QwMzMzMzMzq587ejQVJz3MzMzMzMzM6uTRLc3FSQ8zMzMzMzOzejnp0VSc9DAzMzMzMzOrl7t6NBUnPczMzMzMzMzq5ZxHU3HSw8zMzMzMzKxOsbCrW2Bt4aSHmZmZmZmZWb3c06Op9OjqBpiZmZmZmZk1jYjGbplI6ivpOEnPSJot6TVJZ0patZ3x1pH0D0nji3hvSbpX0g+zNToDJz3MzMzMzMzM6hUN3jKQ1Be4Ffg5MAC4EngFOBx4SNLabYy3D/AY8GVgMnA58DCwJvD1PK3Ow8NbzMzMzMzMzBZvPwY+BtwH7BoR0wEkfR84ETgT2KGeQJI2AS4ApgG7RMTdZWU9gI/mbXrHKLrxcjuSqjVuHvAGcCdwfEQ81thWdYykUcAvWqm2Y0SMKe7/pYgY3slt6gt8B/gcsD7QB5hEyvzdA1wZEXeW1R8B3N5K2OMiYlQd194c2BPYGVgbGAK8CdwBnBARj7btbqr782b7ZHujr7BSrkjJuOfyxpsyJ9/X9CZrKlssgFcm5os1bGi+WAD9BuSLNXVKvlgAA5bJG2/7v52aLdarV5+YLRbABSe/mC1W3ncvjNi6Z9Z4E17I97Xaq1e2UABc88K0bLFW7d0vWyyAHTfN9zpMfzdbqE7RI2N/3J6Z/8z1RMb376br5/1qnfJ2vlhz5uaLBfDMpAVZ4314xXxfD+/OyBYKgOWXyxfrxdfzxQLok/nrYUjGn9M5v+4B5s7LF0uZf7AeOfby3D+qG+ZL372poR+izzx51w49V5J6kT5jDQI+GhEPV5Q/AnwE2CIiHqwj3p3AJ4A9I+KajrStEZqlp8dZZf9fFtgcOATYX9JuEdHaB/CGkTSGlCFbMyLGt1D1EWBsjbLM39prk7QccBuwKTCHlPmbCAwkPc9bAxuSkkyV3gBuqBF6bB3XXgp4oHg4CfgvMBPYDPg8cICkQyLikvruxszMzMzMrHN1544DNWxHSng8X5nwKFxCSnrsCbSY9JC0ASnh8UwzJDygSZIeETGy/HGRqToD+AJwCukFajZX1NETYgNSz5bO9EtSwuMBYI+IeKNUIEmkL5Ba3ZPGVb427fAf4P+A6yPS4k9Fl6hfAj8BzpQ0JiImdfA6ZmZmZmZmHdd0OQ82KfYP1Sh/qKJeS3Yu9jcXIwYOBLYgPSuPAhdFxNT2NrQzNEXSo1JEzCuGiXwB2FjSoIiY0rWtyi8ixjXgMvsW+x+VJzyK6wdwV7FlFxHzgW2qHF8o6WfAfqThNrvzwd4+ZmZmZmZmXaP5kh6rF/sJNconVNRryYbFfhapd/96FeW/lbRf+fQIXa2ZV28p/4D+geSNpCGSfiPpCUnTJb1bLMvzb0lbVdSNYomdpST9TNJzkmZJekrS4WX1dpJ0u6Spkt4pYg0pKx9ezMFRmvzlxSJ21JibpFWltlUcG1EcHy1psKS/SZooaY6kxyV9qY2XWb7Yv9WeNnaWIuFSmq9l5a5si5mZmZmZ2XsWRkO34nNt1a3OFpdmr5tZo3xGRb2WlGbs+S4wmPRH9EGk5Md5wFDgCknD6mxbp2vKnh6FzYv9pPKhD5IGAPcD6wDPAjcWRasDBwMvkOaOqHQR8EnSnBbPk5IXZ6YRHkwDzifNw3EzaZ6LLwBrStq++IA+ndQbYTdgReDS4lhnGVS0dVnS/QwAtgfOkNQjIv5ZZ5wJFMsKSfp2dK8BamsV+4bNcWJmZmZmZtai7vSJqT6liVBrtbwtE6WWZlBeCjg0Im4qHr8LfF7SusCWwLeAn7a1oZ2h6ZIekpYFtgL+XBz6TUWV/UkJjz9FxJEV564ArFAl7BqkxMaHI2JCUXdH0gSfvwZ6AwdFxKVF2UDgXtJ8FyOA24vEy8hiItMVgaNbmci0o/YiJVYOi4gZRbv2Aq4AfgbUm/T4J+kevwnsJOlKUjLl3ojost4fkrYjJbbmUnuyVDMzMzMzs8Zq8N+JI2LD1mu1qLQsW/8a5aWl1ur5o30p1qtlCY9y/yIlPUbU3bpO1hTDWyqGiUwBbiL1dDgkIk6qqF5KatxWGSci3oyIx2tc5shSwqOoeztpQpdhwLWlhEdRNhX4e/GwrrWMq/hF+X2VbSfXef5U4GulhEfRritJQ0JWlzS8zji/A/4IzCfNn3EMKXHyhqT/STq4hXN3qHEPY+u8dlVFUunM4uFJEVHXQqcZun2ZmZmZmZm1LBq8ddzLxX7VGuWrVtRryfhi/1Ir5dU6G3SJZunpUT6JZR9Sz4ytgRMkvRYRd5SVl5bY+Y2k+cAtETG7lfhzgTuqHH+BtHLJzVXKni/27R2rVGvJ2mpDb6p5ICKqrUT/DLBx0a7xrQWJiAXAD4pky+dIQ2S2Ks7fAjhP0jYRcVSV02stWVvPF0tVknqSxoKtS3ouft7eWGZmZmZmZtk13/CWR4p9rVU5S8cfrSNWacnbwTXKS/NeduZUD23SFEmPasuiStqMlKi4UdIGEfFiUfdWSSeRJla5Gphb9Dy4CTijxpCT10vLpVYo9aJ4tYWyPvXfyQfUs2RtS2rNvFt6c7WpXRHxCqnHxx8BJG0CjAL2Bo6UdFFE3FNxWo4layv9nbRay9PA7hExt94TW+r29efN9mm+b01mZmZmZmYddw9pzo21JW0WEQ9XlO9f7K+pI9atpM/Ca0tarfgcWW5Esa+1PG7DNcXwlmqKF+p00of7b1eUfR/YgDRUYwxpWZ2fAk9L2rtauNYu18HmdoZObVNEPEJaMvZ/xaHdO/N6AJJ+D3wJeAXYpXyCWjMzMzMzs+4gIhq6ZWjvXN6fE/PPkt6b20PS94GPAHdHxP/Kjn9b0jhJv62INRP4E9AL+FtFrN2Aw0ifVf9ON9G0SY/Ci8W+cm1gIuLpiDghIj5FWjbnaNKEpKc3sH1Nrej9UlpfeWhnXkvSsaTX6E1SwqMyY2hmZmZmZtb1mm9OD4BfAf8BPgY8K+lCSfcDJwKTgcMr6g8lfc6uNp3DcaTeI7sXsS6XdA9wLWk0yU8jot5pGzpdsyc9SkuazmipUkTMjogTgYnACsUqLp2lNByjKYYOqViTtwVrF/vXOrENXyOtwjMF+FREPN1Z1zIzMzMzM+uQhdHYLYNinssdgf8DZpKmMRhOmj9zs4h4ro2xdgJ+QvoM92nS6IrbgT0ionKF1S7VFB/Mqynm9Pha8fC6suN7k+bouL9K/RVJS+y804lNKyUH1gPqfuN0oXslnQZcXHRVAt5LhowkLY0bwOWdcXFJ+wN/I81F8pmIGNsZ1zEzMzMzM8uiO05+UIeImEVaKKLVxSKK+SdHtVA+l/SH626V4KimKZIekkaXPexNWr1lG1JPlauBs8vKRwBHSXqVNLPsVGBlYLui/s8iYl4nNvcq0jim8yTdRJowhoj4SidesyM2AEaTxmM9SJogtT+wEbBmUednxRwfWRU9bs4lvS4vAl+X9PUqVa+IiCtyX9/MzMzMzKzNmjTpsaRqiqQHKYlQspDUheZOUrJjdMXKK6OB+by/9OqywOuk3iAnRcSYzmxoRFwm6XvAV4E9eX8Vle6a9Nge+AywM2m40OaASEOBzgNOi4i7Ouna/UhJLEjL7G5co9544IqOXGjl1VsbxVO/fsvl/bIZP3Zm65XaYIPlemWLdcrjb2aLBfDbvVfJFuuca/OugvXugroXCmrV8D79W6/UBj+87c+tV2qDV68+MVusVfb8QbZYAK+e+s9ssV6edne2WABrPTcwa7y3M76Fr3nn9XzBgPNv/GO2WE+e8IdssQCOuzXfvX57g7yjXc99qtpK8u231cBaqwG23Q3TJmaLBXD4qtWGeLfPvx+Ymi0WwAKqLcjXPr3VM1ssgP02WCZrvDuezfd3vE1WyPf7A8CcOflirT88XyyA+Zn//Dl7dr5YM2bliwUwNN+3Ec59Mm9n+SOzRmuwDJOLWuN066RHRLT5k2oxPGJsjmsUy7GOrFE2hpQcqFZ2MnByjbJRtNBNqLW2tXTdonwkNdpco/6jpPWYj2/DOS22oQ1xxueIY2ZmZmZm1jDOeTSVbp30MDMzMzMzM+tW3NOjqTjpYWZmZmZmZlavfKPorAGafclaMzMzMzMzM7Oq3NPDzMzMzMzMrF4e3tJUnPQwMzMzMzMzq5dzHk3FSQ8zMzMzMzOzermnR1Nx0sPMzMzMzMysXs55NBUnPczMzMzMzMzqtdBZj2bipIeZmZmZmZlZncLDW5qKkx5mZmZmZmZm9XLOo6k46WFmZmZmZmZWL/f0aCpOepiZmZmZmZnVyzmPptKjqxtgZmZmZmZmZtYZurSnh6S25sheiojhndGWehVtrrsdkkYBv6hSNA0YB5wH/Dki5udqY1eT1Bf4DvA5YH2gDzAJeAW4B7gyIu4sqz8CuL2VsMdFxKj2tmnoBgPbe+oillpm6WyxACZc0ydrvLFvPpst1sY7HZMtFkDvgZdmizWrz+bZYgG8Me3ebLGWivWyxQL46Y/+lzXecv97MVusV0/9Z7ZYAH+8+SvZYl0y7lPZYgFs9sCZWeONu3lytljr9jkwWyyAL+16fLZY+6yS9+8r/WNotlh9+mYLBcB2yw3OGm/NlfPFuunxXvmCAZe+omyxltKK2WIBLD/0o9li9Rqa7/0G8NfHT88ab0Gbf5Wubfc1Mr7hgKefzte2lybn/bO68r19AZi3MF+sVQfmbdysWflirf7Vn+YL1uy8ektT6erhLWdVObYdsDbwCDC2omxSW4K3NUHRycrvpyewOvBxYEtgN0mfiYiM3zLbrywJcVZEjGzjucsBtwGbAnOA+4CJwEBgc2BrYEPgziqnvwHcUCP02La0w8zMzMzMrFN4To+m0qVJj2ofqCWNJiU9rujIX/a7oUXuR9JmpJ4PnwL2Bi5rfLOy+yUp4fEAsEdEvFEqkCRSUqvWn2DGtTXJYmZmZmZm1lDOeTQVz+nRhSLiYeCS4uH2XdmWjPYt9j8qT3gARHJXRJzSBe0yMzMzMzPruIjGbtYhTZX0kDRE0u8lPStptqS3Jd0gadeKeiPL5gtZQ1KUbWPK6m0q6QRJD0p6S9IcSS9I+qukvAMbayslBhbpdSPpU5JulDShaNtrku6W9IuKeqOKexspaXNJ10uaUjw/F0latajXv3j+xhfP3+OS9q+INZr359c4rOK5G1XH/Sxf7N9qw3NgZmZmZmbWHJz0aCpNk/SQtArwX+BooDdwBfAw8EngRknfK6v+HO/PFzKj+H9pK58z4kfA90lzbNwDXAcIOAJ4oEGJj9JsjE+VH5T0DVJbdyjKLgWeAIYDo2rE2pp0H6sBtwCTSZOJ3ippWVIy43DgMdJcGx8GLpJUPtvf3cCNxf+f54PP3dg67mdCsf96MZzFzMzMzMxs8bGwwZt1SFdPZNoWpwFrAWcDX46IeQCStiN9SP+9pFsj4tGIuBu4W9JhwKQW5on4O/C9iJhYOiCpB/BT4DjgV8CXct+IpJ6kxMQ3gR1Jq5qcXVHtR8BUYJOIGF92roARNUJ/g3Q/Jxd1e5ESOZ8E7gXeBNaNiHeK8i8D/wR+TJHoiIh/SnqONM/I3e2YY+OfwK+Le9tJ0pWkBMu9EeHeH2ZmZmZm1tzc+6KpNEVPD0lrAXuQkgBHlhIeAEWC4zRSb41vtiVuRNxWnvAoji2MiF8CrwJ7dbTtZX5RGiYCzAdeBH4IXABsGxFTK+qvALxYnvAo2hcRUWt51ztLCY+i7jzg1OLhesBXSwmPwmjSijjbFgmSHH4H/JF0j+sDx5B65bwh6X+SDm7h3B0qhtOUtrH1XFjSE7W2Dt6TmZmZmZlZ4uEtTaVZenpsV+yvi4gpVcrPJg1T+URbA0saAnwW2AgYREqeAPQCBksaHBFvtzVuFZVL8C4PbEYafjJL0hERMaes/EFgO0nHA/+IiOfruMbNVY69UOzHR8Rz5QURsUDSeGALYChpadkOiYgFwA8knUy6t+2BrYBhxXXOk7RNRBxV5fRaS9a+3NF2mZmZmZmZZeFERFNplqRHaW6N8TXKS8fbNAdH0evg78CAFqotA+RIelRbsrY38Ffgy6SeEV8rK/4WqYfEMcAxkl4D7iKt9nJZRFQb3fVqlWMzWigrL+/T+i3ULyJeIfX4+COApE1Ic5HsDRwp6aKIuKfitA4tWRsRG9Yqu/NHI/2dyczMzMzMbAnTFMNbytT64BqtlC9C0hqk4R19gO8C6wL9IkIRIdI8FJAmNu0UETEX+B6p3V+SNKis7FHSRKP7AP8ApgMHAhcDdxQJk0VCtnS5TM1ul4h4BNgP+F9xaPcubI6ZmZmZmVn7RIM365BmSXq8VuzXrFE+vNi3ZXjGZ0irwJwaEadExHMRMausfK22NbF9ImIaaV6NnsA6FWWzI+KKiPhaRKwHbAg8Shru8+VGtC+nonfKncXDoV3ZFjMzMzMzs3ZZGI3drEOaJelxd7Hfvbw3RJlDi/1dFcfnUXsIz3LF/pXKAknbAyu2sY3tImkZ3k8AzGipbkQ8CfyleLhxJzZrbrFv8/CnOpapXbvYv9ZiLTMzMzMzs24oIhq6Wcc0RdIjIl4AriXNr3FK+UojkrYFjgAWkObHKPcasGKNRMkzxf5QSf3L4q1CWg2m0xVDVE4iDaF5ERhXHO8n6cjKdhfL6e5aPOzMyT1LCYn12nHuvZIOk9Sv/KCSw0kr4gRweQfbaGZmZmZm1nhevaWpNMtEpgBfJ/Xk+CJpadP7SCugjCANDflBMQ9GuauA7wAPSboXmA08HRG/L8qeIK0o8pyke4C+wI6kVVbuBT6Wsf17Sxpe9ngoafWWlYGZwJfi/TReb+AU4PeSHiJN1Nq7aOvqpBVZTs/Ytg+IiPGSHgW2kPRf0vO0ALgqIq5q5fQNSHOl/E3Sg8AEoD9pdZzS8KSfFXN8mJmZmZmZNRcnIppK0yQ9IuJVSVsCx5JWANmXlCy4FTgxIm6qctqxpF4Ue5EmAV0KuAP4fUTMlfQJ4NfAp4E9SCuc/An4JXBd5lvYpNhK5pCG1pwO/KFiOdnppNVbdi7O+QhpyMlLpElN/1xj6d6c9gN+T1oGeHNSr6AJpGRRS7YnzZeyM2lelM1Jr8FE4DzgtIioHIbU6WZPntV6pTotOyzvdCQrrLpR1nhzX2lxlFSbLHwlx8JFZfFWWJAt1sRpD2SLBbBA+Z633Ba80OHVpD8g5+zML0+7u/VKbXDJuE9li7X/+qtliwXw2vjlWq/UBvPnT84Wq/emeaehmvbmBdliScOyxQIY2KtNC7W1aMDgN7PFAnj9ybmtV2qDGS/1zBZr1b5bZYsFsFSvfq1XqtNzM6v9Ctd+K2iLfMF65O4UnXd+/JzR5s3J+yFufrV1Bttp5oKMwYAZC/L9PgIwoGe+j1Sz5uZ9j/RbOl+smOcP+u/xPBtNpdslPYolS0fWKJsMHF1s9cSaQerp8Z0a5e8A36xx+oga57TpO1GxTO2oNp4znzRUp3K4TpuvERHjaeFnYkSMqHH8OdLKMW1S9LZ5FDi+DeeMoRNXyTEzMzMzM8vGOY+m0u2SHmZmZmZmZmbdloe3NBUnPczMzMzMzMzq5aRHU2mK1VvMzMzMzMzMzNrKPT3MzMzMzMzM6uWeHk3FSQ8zMzMzMzOzOoVXb2kqTnqYmZmZmZmZ1cs9PZqKkx5mZmZmZmZm9XLSo6k46WFmZmZmZmZWLyc9moqTHmZmZmZmZmb1ctKjqTjpYWZmZmZmZlYnT2TaXJz0MDMzMzMzM6tXLOzqFlgbOOlhZmZmZmZmVi8Pb2kqPbq6AWZmZmZmZmZmncE9PSpI6gfsCuwJbAkMB3oCzwGXAn+MiOkNaMcI4HbgrIgYWec544E1WqjyUkQMr6wfEWpnM2u1Yz3gaGAnYFVgDvAmMA64AzgnIiaW1R8NHNZK2DUjYnx72/Tsg3Pbe+oizr70kWyxAPZZ8fms8TZcPl+sS5/9R75gwOOzhmWLtd/KfbPFArjzjWWyxVp24RPZYgF84ROvZo03bWLPbLHWem5gtlgAmz1wZrZYr41fLlssgJV3OzJrvLtPzxev97oDssUCOHHw2tlivT5uZrZYAH2nDM4W6/Jhe2eLBbDww5OzxlvuuYuyxRq0+87ZYgFMuvzEbLF2XHqlbLEAnnzz6myxVnhn6WyxALbtn/GHNPDEzHezxXry2WyhAFhz5XyxBk7N93MLYOqsvPGG5PsVgrnz8sUC6JHxT9xjz/pFvmAAJ+6SN14juadHU3HSY1GHAKVPek8ANwADgY8BxwEHS9ohIt7sovbV41KgWmJmUmdfWNKngcuAvsAE4GZgKrA6sDOwO/AKcEGV0+8hJZeq6fREk5mZmZmZWWvCSY+m4qTHouYCfwNOioj3ct6ShgHXApsBJ5OSI93V0R3pFdFekpYGziIlPEYBv46I+WXlywAHkJIh1fwzIkZ3cjPNzMzMzMzaz0mPpuKkR4WI+Dfw7yrHJ0r6FnAvsK+k3hGRb8zE4mE7YHnglYg4rrIwIqYBZzS8VWZmZmZmZrks9OotzcQTmbZNaTKHPsCQ8gJJ4yVF8f+vSHpU0ixJr0s6XdKgagElDZd0vqTJkqZLulfS7p16Fy0o2hOSxkgaKOlESS9Kmifp5FZOLw1U7fRhNGZmZmZmZl0hIhq6Wcc46dE2axX7ecDb1SpIOgH4C2keixsAAV8DrpKkirprA/8BDiIlCq4mvSZXk4aBdKWlSZOOHg6MBa4C3mnlnNKwlY0lfazzmmZmZmZmZtZFIhq7WYd4eEvbHFXsb4iIOTXqHApsHRFjASQNBe4DPgHsCNxWVvevwArF/jsRsbA45yu8P5lqV9mK1O61ImJKnefcAzwFbADcKelG4FbgfuCBzh4OJKnmkhl/3mzvzry0mZmZmZktKZyIaCru6VEnSZ8Bvkzq5fGzFqr+rJTwAIiISaSJUQG2L4u3Nmlp3HeA/1dKeBTn/JM0d0h7vVgMUancNm1jnCPbkPAgIhYAewD/JS3z+xngRFIyZIqk8yR9qIUQ/6rR7u+2sd1mZmZmZmadImJhQzfrGPf0qIOkDYBzSENVfhgRj7RQ/aYqx54p9sPKjn282F8XETOqnHMBaZnc9qi1ZG3VITk1TIyIB9p64Yh4Adha0g6kBMi2wEdJw2UOBj4r6dMRcVeV02stWftkndfesFbZXz66j9OxZmZmZmbWce7p0VSc9GiFpFVJc3MsB/wxIk5p5ZRqy7GWEhB9yo6tXOxfrhGn1vF65FiytiPXJyLuIM0JUlrKdg/gD8DqwBmS1otFZ+XxkrVmZmZmZmaWjZMeLSjm47iZ9EH9X8DRrZ1T5YN8zfClU9rXuk43O1egiJgFXCzpKeAxYN1ie6bFE83MzMzMzLoZDzlpLp7TowZJywDXA+sDlwFfbUNCox6vFfs1apSvnvFa3UJEPA5MLh4O7cq2mJmZmZmZtYtXb2kq7ulRhaQ+wJXAFsCNwMHFJJ053VPsPyOpf5V5PQ7KfL1OJ0ktJYYkLQcMLh6+VquemZmZmZlZt+WeHk3FPT0qSOoJnE9aXvYuYN/OWGo1Ip4jLee6HHC8pPdeC0mH0/5JTLvSnpIulLRtZUGR8PgXaVjPwxnmHDEzMzMzM2u4iGjoZh3jnh6L+jawT/H/ScBfJVWrd3SxHG1HHEHq8fFtYBdJDwFrAlsDpwHf6GD8RusBHAAcIGki8DDwLrAisBUwgDS85fBGN+yNKfli7bfSgHzBgD69s4aj6ru1nY76yLDWK7XB5Mmt16nXRtvmfeJWGp/3dc3pzWdmZo03+a18sd6utk5UB4y7Od+bZP78jG844O7Tj8wa74DLT80W63c7fStbLIBX1+2ZLdaMzO+REUPqWtCrLss8nC8WwJMv5/3FdOVB+b6jT7nutGyxAD6zxsBssQYumy0UANv0HpQ13vx5+WJNn5YvFsA685bLFuu5SXn/cj1zZr6/rVb/Vbz9lsr8Z9+c7ct9r3Mz/un2qA+vmC9Ys2vSnh6S+gLHklbVXJ20uucNwM8jotqCHPXGXRd4FOgL3BgRu2VobjZOeiyq/KfHPjVrwShSUqTdIuJZSdsAvwV2AfYiTfS5NzCV5kt63EBapWU3YBvSUrVDgZnA06ShQqdExJtd1kIzMzOzOuVMeJjZ4qMZe18UCY9bSSMKJpKmcxhO+oP0HpK2jYjn2xn+dD64Umm34qRHhYgYRUpotPW84S2UjaHGH+Aj4gXgwBqntinX21Ib6q1fDDtpV445ImYD1xZbW84bCYxszzXNzMzMzMwaqjl7evyYlPC4D9g1IqYDSPo+cCJwJrBDW4NK+jJpaoi/A1/L1tqMPKeHmZmZmZmZWZ2abU4PSb2A7xQPv1VKeBT38kfS0JTtJW3exrgrAL8HbiHNi9ktOelhZmZmZmZmVqdgYUO3DLYDBgHPR8TDVcovKfZ7tjHuqcDSpLkquy0nPczMzMzMzMwWX5sU+4dqlD9UUa9Vkj5DmqbhN8XKpN2W5/QwMzMzMzMzq1eD5/SQ9EStsojYsI4Qqxf7Wiu0TKio11p7+gN/JS1W8bt6zulKTnqYmZmZmZmZ1akJV28ZUOxn1iifUVGvNb8C1gB2ioiMCyN3Dic9zMzMzMzMzOoUDe7pUWdvjpaUVuesla2pe/VOSVuQJkX9d0Tc3sF2NYSTHmZmZmZmZmb1ar4la6cV+/41yvsV++k1ygGQtBTwD+Bd4Og8Tet8TnqYmZmZmZmZ1Slqdpjotl4u9qvWKF+1ol4tqwKbAq8DF0sf6CAyqNhvJWkMMD0i9mhrQzuDkx5mZmZmZmZmdWr08JYMHin2H61RXjr+aJ3xViq2apYDdiD1BukWvGStmZmZmZmZWd0WNnjrsHtISYi1JW1WpXz/Yn9NS0EiYnxEqNoG7FhUu7E4NihHw3Nw0sPMzMzMzMysThHR0C1De+cCfy4e/rlYchYASd8HPgLcHRH/Kzv+bUnjJP22ww3oYh7eYmZmZmZmZlanJhzeAmmZ2U8CHwOelXQXadnZrYHJwOEV9YcC6wHDGtnIzuCkhy0RttwyX6eme/+zIFssgCffGZ413mtzHs4Wa8NNfpgtFsD+w0Zni/Xjy17KFgugbwzJFmulAZtmiwXw+vQ7ssb74rrLZ4t1zTuvZ4sFsG6fA7PF6r3pWtliAfRet96l6+vzu52+lS3WMbf9JVssgNN3OzJbrA+tni0UAHds/c1ssWZeMTZbLIDeG+S92bfGnp0t1qZ//0m2WAD3/PTBbLHGPXJ+tlgAG374a/mCZfgLa7nHXzgta7xBsUa2WHuvNjdbLID5GX9dGv923g+Yb8zNe69rzOmTLVb/3nWvHFqXYUPzxXpo/+/mCwYcljWatSYiZkvaETgWOATYG3gHOAv4WUS80oXN61RL3PAWSf0kfUfSTZImSporaZqkJyX9S9KekrI+L5LGSApJI+qoO6KoOzpnG1q55t+Kay6QVGtGXzMzMzMzsyVesLChW7Z2R8yKiJ9HxDoR0SciVoqIkdUSHhExqpibY2SdsccU9XfL1uBMlqieHpK2BS4BVgZmA/8FXgP6AusCI4vtUWCTLmlkg0nqDRxQPOxByvqd0HUtMjMzMzMz68aac3jLEmuJSXpI2hS4DegDHA/8JiKmVdQZDnyfRcczNdJ/gQ1o3BI/uwODgYmk8VqH4qSHmZmZmZlZVUHeoW/WuZaI4S2SBJxD6tHx44g4tjLhAe8twXMkMKLBTSxvw8yIGBcRExt0yS8U+58A44GNJS0RvVzMzMzMzMzaKmJhQzfrmCUi6QF8BtgQeIk6ejFExAdm55I0vJjzYoykgZJOlPSipHmSTs7Z0Gpzekj6U3HsGy2c90RR50NtuNZypJ4es0jDfs4rig5trW2SVpL0T0kTJM2X9N2yesMlnS5pvKQ5kt6SdImkj1SJ2VfSlyVdKekFSbMkTZF0p6SD6r0XMzMzMzOzRmjWOT2WVEtK0uPTxf6S6FiqbGngDtLwl7HAVaQZbzvbucX+89UKi6E7Hwb+FxHPtCHugUBv4Kqi50tpmvhDWpnMdXngf6SEyX3A9cDMoi3bAY8AXwOmk56jZ4F9gfuLGYPLDQf+SVoq6WXgStJzuw1wvqRRbbgfMzMzMzOzThUN/mcds6TM6VEartHRtTy3In3IXysipnQwVt0i4n5JzwMfl7RGRFSu1XlIsT+XtikNbTmnuM44SQ8BHwV2Am6pcd5ngMuBQyJidumgpIHAxaTk0Oci4pKysk8C1wJnS1orIkprhb0FfAq4pTwhJWlN0hwsP5M0OiLGt3Yzkp6oVXbVAfu2drqZmZmZmVmrPOSkuSwpPT1KK1RPqlZYDNeo3LauEevIRiY8ypwHCDi4/GAxX8lBwALgwnqDSVoL+BjpObmhrOicYv+FRU563xzgO+UJj8KXgJWAP5QnPAAi4hbgr8AqwB5lxydHxE2VPXAi4kXg16T36J713peZmZmZmVnnWtjgzTpiSenpoWJfq2/QYVWO3QD8p+LYxIh4IFur2uZc4GekXh3Hlx3/BLAacFNEvN6GeKWkxgURMb/s+PnA74F9JR0RETOrnPtQRLxa5fguxf6KGte8G/gusCVwWXlBMSxmBCkp0pf0mg0ritdt6UZKImLDWmVXH7if+4WZmZmZmVmHechJc1lSkh6TgPV4v8fHB0REKSlCMYFotSQIpDknukREPC3pQWBzSRtHxGNFUXuHtpQmKz2n/GBEvC7pFtKQk715f3LTcrWeh+HF/j+pA0pN770OkpYlJUB2aqH+Mi0FMzMzMzMzM6tmSUl6PAJ8nDRXRbUP8fWqHM7RaOcCm5MSHcdK6gXsT1p95fJ6g0jaFlineHhilQTFasX+UKo/X7Weh57F/mKKiU1rKO9B8ztSwuNO4OfA48CUiFggaVfgRt7vqWNmZmZmZtalPKdHc1lSkh7XA98E9pf0/zq4gktXuoA09OQQST8m9cYYAlxYrL5Sr/L5Oj7eQr1dJa0YEW/UGXcCqUfNryLi0TrP2Yc0H8lnI+LdirK16oxhZmZmZmbWEF5GtrksKROZXgc8BawBHNPFbWm3iJgI3A6sTkpWtHloS9E75IDi4UYRoWobqYdHT9IkqfUqrfaydxvOWQ6YViXhQVk7zczMzMzMugUvWdtcloikR9Gz4wukVUd+Lem3khaZJ0LSGsCHGt2+NiolOL4GfBZ4mw+uvtKa3Um9Qx6NiJpLvJJ6lUDLq7hUOp20BO2PJR2uinEzkvpL+qKkVcsOPwMMknRgRd3vATu24dpmZmZmZmadLmJhQzfrmCVleAsR8aCkTwKXAD8Cvivpv8BrwNLAqsBmpETQE8DYTmjGXyVNrVF2R0TU0wvlMuBvvJ+MODsi5rWhDaXzzm+l3o3AO6SJUzeIiKdaCxwR70jaB7gKOBP4haTHScmm1YENgP6k53lCcdpvSZOpXiDpW8XxTYD1gZOA77Xh3mp6e2K+bxYrLJN3ipHJc5/PGm/Z3v2zxZp07UnZYgFM3nBgtlh9omfrldpg9V753iML5zyYLRbA+t8YlTXeNf84vvVKdTr/xj9miwXwpV3ztW3amxe0XqkNThy8dtZ4r66b7z18+m5HZosF8PUbTs0Wa9yJP8wWC2CbO/6aLVavFbKFAmC1tR/OGu/5t3tli9X3xBOyxQJ459VqHTTbZ82e+X5uAbw77uxssWYunN96pTYYTJ+s8T7Sf062WCuulPf3m/Ev5Yu1bJ+8f6cdsnTfrPGWG5A1XFavvpUv1t3H/SRfMIAj784br4Hc+6K5LDFJD4CIuFvS2sBXgT2BjYBtSR/KJ5B6UVwMXBcRCzqhCRu0UFbXcrMRMVXS1cDnikN1T8wqaRCppwe835Oj1nXmSroM+DJpQtO6vstFxD2SNga+X1xrJ9KcHa8B15CSNk+W1T9X0juk5Xg3BTYGHiDNwSIyJT3MzMzMzMzycO+LZrJEJT0AImIGcHKx1XvOeDqwgkhEjGhD3TGtXSsi2jXXRURMAepObUfEV4CvtKVtRb3XgKOLrZ7rXEead6Uar9xiZmZmZmbdhnt6NJclLulhZmZmZmZm1l5evaW5OOlhZmZmZmZmVqcI9/RoJkvE6i1mZmZmZmZmtuRxTw8zMzMzMzOzOnl4S3Nx0sPMzMzMzMysbh7e0kyc9DAzMzMzMzOrk3t6NBcnPczMzMzMzMzq5CVrm4uTHmZmZmZmZmZ1ctKjuTjpYWZmZmZmZlY3Jz2aiZMeZmZmZmZmZnXynB7NxUkPMzMzMzMzszp5eEtzcdLDzMzMzMzMrE5OejQXRfgFs8XfqZvuk+2N3rNHrkhJnz55402eki/WSivkiwUw9oV8XQE3Xj3vC9Grd75Yb72VLxbAxGl5v0/PmJ/vddjt472yxQJ4/ql8bZOyhQJghRXzxps6JV+sfv3zxQIYtl6+b0zr/+D32WIBjPn6kdli9cz8p5+Jr+WNt8qq+WLNm5svFsDLr+aLteYa+WIBzJmVL9b8+fliAbw6KW+84cPyxZqV8XkDmDojX6x5C/LFApg5L2+8ARl/h+id+fvSwAH5YvXOeJ8AI++6PPNP6sbZZvAnG/oh+v63b2na56o7yPKpQVJU2eZKekXSuZI2znGd7kLS+OIeh7dQZ2RRZ3SG640uYo2oUraLpLslTSs993XEG1Pl9VogaZKkGyV9tqNtrldZW4Y36ppmZmZmZma2ZMg9vOWssv8vC2wOHALsL2m3iLg98/XaTdIYYAdgzYgY37WtaR9JqwOXA72BW4A32xjiRuD14v99gQ2AXYFdJf00In6doY3jgTUiwtlJMzMzMzNreh7e0lyyJj0iYmT5Y0m9gDOALwCnAB/JeT3jk0B/4P8i4uftOP/4iBhTfkDS14HTgF9IOiMiXq96ppmZmZmZ2RLJSY9mknl2gg+KiHnAqOLhxpIGdeb1lkCl0b4v5AoYEacDLwO9gG1yxTUzMzMzM1scRIP/Wcd0atKj8EbZ/z/Qs0TSEEm/kfSEpOmS3pX0jKR/S9qqom4Uc2ksJelnkp6TNEvSU5IOL6u3k6TbJU2V9E4Ra0hZ+fBi3osdikMvls9t0Qn3/wGSBkn6TjF3xkuS5kiaLOkGSbvUGWNE0dbjikP/KruHURmaWRomU/l6DZP0/yTdIenVYt6W1yVdJmnLGm1co3hcPn/I+Br3tbek+yXNkPS2pPMlZZzGzczMzMzMrGOc9GgujViydvNiPyki3puzWtIA4H5gHeBZ0vwSAKsDB5N6L/y3SryLSMM67gOeJyUvzlSapn8acD7wCHAzsDVpaM2akraPtFTNdNLcI7sBKwKXFscaZRvgVOAV4JniPlbn/bk0vhIRZ7YS43XSPWwKbALcAzxXlI3tSOMkLQN8qHj4VEXxXsDvims9BkwlvX77AHtI2iMibqpo4/6kITjl871Um7v8m8APgAeAG4AtgYOAzSVtEhGZ5xQ3MzMzMzNrDycimkmnJT0kLQtsBfy5OPSbiir7kz4w/ykijqw4dwWg2mKZa5ASGx+OiAlF3R2B24Bfkyb0PCgiLi3KBgL3AtsBI4Dbi8TLyGIi0xWBoxs8kenTwMcj4t7yg5I2I93HSZIuioiaiZiIGEe6h1GkpMc/I2J0RxolqS+wHvBbYCBwVUQ8UVHtHmCTiHi04txPAVcBf5W0biSlNo4A+lfO91LFN4FdIuK2ImY/UuLqY6QkWGuJICRVtvc9p2yyd2unm5mZmZmZtcq9L5pL1uEtFcNEpgA3AYOAQyLipIrqpaTGbZVxIuLNiHi8xmWOLCU8irq3Aw8Bw4BrSwmPomwq8Pfi4Q7k94GhMRX3/69qJ0TEi5UJj+L4w8BfSAmHHTuhrdXcXtbeWaReIp8EfgkcUKWNj1UmPIrjNwIXA2sDG7WzLSeVEh5FzJnAicXD7dsZ08zMzMzMLLNo8GYd0ZlL1vYh9czYGjhB0msRcUdZ+YPF/jeS5gO3RMTsVuLPBe6ocvwF4KOkngGVni/2w1prfDu0NDRmHeDj1Qok9QR2JvViWIm0XCzAuhX7zla+ZG0PYGXS8JvvA5NJw3A+QFIf0tCgrYDlSb1rADYu9uuShr601U1Vjj1T7Ot67SJiw1plp266j79bmJmZmZlZh0XnTwVpGXXqkrXw3rCNO4AbJW0QES8WdW+VdBLwXeBqYK6ksaQPv2fUGHLyekQsrHJ8RrF/tYWyPvXfSd1qDo2RNJIqSY9iYs5rSMNSalkmR+PqUG3J2uVJc2qcImlSRJxXVrYxaRjL8BZitrftE6ocKyWUOuO1MzMzMzMzawcnPZpJp6/eUgzbOJ30wfXbFWXfBzYAjgHGABsCPwWelrR3tXCtXa6DzW2Ef5ISHpeResEMAnpGhICvF3XUNU2DiHgL+Hnx8Ael40ozxV5ESnicRppEdSDQo2j7b0tV23vpdp5nZmZmZmZmVlUjlqwFeLHYr1dZEBFPR8QJEfEpYChwNGnIxOkNalvDSOoP7EJaxveAiPhvRLxb1ntlra5r3QdUe73WL7YHIuKIiHgkIqYVK+JA92m7mZmZmZlZp/GMHs2lUUmP0gfiGS1ViojZEXEiMBFYoVjFpbPMLfaNWLa3ZFnScz4xIhaUF0hairT0a3dQ7fVartgvMgxF0nKkZE41c4s6jXyezczMzMzMOoeisZt1SKcnPYo5Pb5WPLyu7PjekrapUX9F0tK073Ri014r9ov0PulEbwLvAhtJem++j2Ji0xOADzWwLVUVc3ocVzy8rqzoOWAhsJOkdcvq9yUNdxlcI2RXPM9mZmZmZmadIhr8zzom61/fJY0ue9ibtHrLNqTkytXA2WXlI4CjJL0KPAxMJa0esl1R/2cRMS9n+ypcBRwGnCfpJlIygoj4SmddMCLmSzoB+DVwh6TbgLdJc3usSFqy9luddf0qflRMuArpOR8GbAv0J6168+NSxYh4U9IZwFeBR4q2zwI+AfQERgOlWOWuIi0XfKuk20m9RyZFxI864X5q2njH/tli/eO8ydliAbw1f1bWeMN69csW68m1R2aLBfC1ry2yQnW7nXbVR7PFAogZ+b7dPP7Gv7PFAtilX97Fp3bctGe2WMfd+nrrldqgfwzNFmtgr5WzxQLoO6VWbrd9Rgx5MlusO7b+ZrZYANvc8ddssV7/+pHZYgGMOH2RhcXa7dsfPzxbLIA5rJo1Xs83XskWa+jHjsgWC2DcpBNbr1Sn9YblbVvMz/f9fNbE8dliAUyfcXfWeFe8OzNbrF/vulq2WABPP7Kg9Up1GpR5iv8p0/LGG5jv10zeejdfLIClM36aumf1L+YLRvUPDk3DvS+aSu4hB4eV/X8hMAW4k5TsGF2x8spoYD6wPWn502VJy6deB5xUuapIbhFxmaTvkT7E78n7K4R0WtKjuO5vJE0grVrzcVLi4G7S5KF5P8W17lMVj6eTlom9CvhjREytKD8CGAd8mbTk7rvALcBPgFq/OZ5KGhpzMLAf0At4CWho0sPMzMzMzCwH975oLlmSHsXqHW09ZywwNsc1iqVyR9YoG0ONFUUi4mTg5HrbUHbe8DrqjCYldqqV/Ruo9qfgR6udU+v+ImIUMKq1tlQ5b0RbzynOWwD8sdgqVW1LRMwHflZsbWpLsRxwl61kY2ZmZmZmVkn+iNJUPLmkmZmZmZmZWZ08uqW5NGr1FjMzMzMzMzOzhnJPDzMzMzMzM7M6uadHc3FPDzMzMzMzMzNbLLmnh5mZmZmZmVmdPI1pc3FPDzMzMzMzMzNbLLmnh5mZmZmZmVmdvGRtc3FPDzMzMzMzMzNbLLmnh5mZmZmZmVmdvHpLc3HSw8zMzMzMzKxOHtzSXJz0MDMzMzMzM6ub0x7NxEkPMzMzMzMzszo55dFcnPRoBymN4oqImu93SaOAXwDHRcSoDl5vDLADsGZEjK8oOxg4GtgAWBp4KSKGtxJvPLBGxeEFwCTgfuDEiLirI22uV6ktLT2XOcTCfAPv3lkwJ1ssgB2GLJc1Xo+Mz+Ql95+cLxjwRs+VssV64sXR2WIB9FuYb17nweqTLRbAWsvn/fKY/m6+WN/eYIV8wYA+ffPFGjD4zXzBgMuH7Z013jIPP5kt1swrxmaLBdAr48vaM/NvGt/++OHZYv35nn9liwWw97afyxrvyI2GZYt19r2XZYsF0Dfjr5DPPHZatlgAC8n3M39wj4zflIBVe/XPGi/m5os1e+qCfMGAgcvki/Xm2/liASyzdN54U2fkizV4QL5YAAsX5ov15K0n5wsGwD6Z4zWOkx7NxUmPJiZpS+AcYDZwEzCFlLio16XA9OL//YGPAHsBn5X0xYg4J0MbgzoSMWZmZmZmZma5OenR3PYkLTv8nYg4sx3nH13ec0SSgF8DxwKnSLowIuZlaamZmZmZmdliQE3a10NSX9JnvYOB1YG3gRuAn0fEhDpjDAI+A+wBbEoaQbAQeBI4D/hrd/sMma8/t3WFVYv9CzmCRUQAo4D5wGBgwxxxzczMzMzMFhdq8JalzSnhcSvwc2AAcCXwCnA48JCktesMdTRwLnAgMBO4GvgvsAlwMnCbpH6Zmp2Fkx4NJmmYpP8n6Q5Jr0qaK+l1SZcVw1XqiTGyGDZSGth8u6QotpEdaV9EzCUNk4GKnkCS1pE0StJ9RZvnSpog6d+SPlSjjQBrlLUvijlKqt3XVyQ9KmlWEf/0IpNoZmZmZmZm7fdj4GPAfcCHIuLAiNga+AGwPFDvyIHpwG+A1SNii4g4KCJ2BjYGXga2A36avfUd4KRH4+0F/A5YGXgMuAJ4jTSTzz2Sdq0jxnPAWcDzxeMbi8dnFWXtJmkNYCgwryx+yVdIk7MOBB4ArgKmAl8A/ifpI1XaCDCjrH1nkbpQVV73BOAvRbwbSEnNrwFXFcNuzMzMzMzMupwa/K/D7ZV6Ad8pHn4rIkrzOhIRfwQeBbaXtHlrsSLi+Ij4SUS8WnH8WeBHxcODO9zojDynR+PdA2wSEY+WH5T0KVIS4a+S1i2GmlQVEXcDd0saDawNHB8RYzrSKEmliUxPLg79LSLeqah2BfCPiPhAMkTS4aTM4MnAThVtPAyYFBEjW2nCocDWETG2iDmUlIX8BLAjcFs7bsvMzMzMzCyrJvyL7HbAIOD5iHi4SvklpM+CewIPduA6jxT7lTsQIzsnPTqgbPhG3SLisRrHb5R0MfB5YCNSL5DO9mKVThTTgSOBP1cWRMT91YJExL8kfRkYIWnZiGjPgpg/KyU8ipiTJP0NOBHYnjqSHpKeqFV265GHtKNJZmZmZmZmH9SEE5luUuwfqlH+UEW99lqr2L/ewThZOenRMWe1ULYpNd40kvoAuwFbkcZP9S6KNi7269KYpEf5krVLkSZG3ZY0hOUN4KLKEyQNIGUANyVNdtqrKBpGSnquTe0vppbcVOXYM2WxzczMzMzMulyjUx4t/XE3IupZfGL1Yl9rhZYJFfXa66hif2UH42TlpEcHtDRkQ9IoqiQ9JG1MGsYyvIXQy3SwafX6wJK1AJKGA3cCF0h6NSLuKSvbCbiAlKippb1tr/YFWErI9KknQEtf8Lcd9fk298oxMzMzMzOr1IRTDg4o9jNrlM+oqNdmkr4BfJK0KMbx7Y3TGTyRaQMVE3JeREp4nEbqLTEQ6BERAn5bqtoV7QMokiC/K9rw/dLxoofHRaSEx/8BHwb6837bzy9Vbed1nZQwMzMzM7Nur9FL1kbEhrW2NjQZoNZnrg59/pS0A3BKEf9LEfFaR+Ll5p4ejbV+sT0QEUdUKV+ryrGu8GKxX6/s2CeAIcClEfHzKud0l7abmZmZmZnZ+6YV+/41yvsV++k1ymsqVvC8gjRlw5ERcXmbW9fJnPRorOWK/SJDOSQtB+zS2ObUVEpgzCg7Vmr7K5WVJa0DfLRGrHn4fWZmZmZmZouJphvcAi8X+1VrlK9aUa8uktYGbiStDDMqIv7UrtZ1Mg9vaazngIXATpLWLR2U1Jc03GVwVzWsrC3Dgf9XPLyurKg0qei+kpYvqz8IOIP3JzSt9BqwYlHPzMzMzMysqanB/zIoLSVb6w/VpeOP1htQ0srAzcBKwCkRcVz7m9e5/Bf4BoqINyWdAXwVeETSbcAs0tCRnsBoYGQDm/QHSdVWb+kN/Je0XCwAEfGApJtJvVGekTSmKBoBTCLN0LtXlWtcBXwHeEjSvcBs4OmI+H32uzEzMzMzM+tkTdjT4x7gXWBtSZtFxMMV5fsX+2vqCVaMUrgRWBP4F/C9XA3tDE56NN4RwDjgy8DOpDffLcBPgMMb3Jb9yv4fpLFeD5EmLP1rRMypqL8XqZ0HAJ8G3iSt5vJTyhIkFY4lfV/YCziQ9J67A2ho0uOxMbUmKm67fdZeNlssgH88l3cZ63WWyte+Ty+zYrZYAK9PyDdf7dGb5m3bjDaPYKxt3Gt55+VdY/2eWeO9MX5BtljnPvV2tlgA2y2Xr8Pb60/OzRYLYOGHJ2eN9+TL+d4nvTfo6ApzH7Ta2pW/C7Xf2NtnZYsFMKdmz9y223vbz2WLBXDFfRdnjbfvtgdki7X50b/JFgtgmdNOyRZr4zXzfnx44LmF2WL17Zm3bYOXzhtv4Mx8P/MfeypbKADWXi1frH51reNXv2l5vy2xzNL5Yr01NV8sgFWG5ou11lJ98wVrcpl6XzRMRMyV9GfSZ7k/S9o1ImYASPo+8BHg7oj4X+kcSd8Gvg1cHhHHlh3vRxoRsBHpc+NXu/uiFE56tEOxWklrdUYBo6ocXwD8sdgq1TpnRI1rjKQdPUMiYnhbzynOm0VKcPy0SnHVthRfTN8ptja1JSLG0JSJVDMzMzMzW1z1aM6PKL8iLSn7MeBZSXcBawBbA5NZ9A/wQ0kLWwyrOP5rYBtgATAfOKPaEr7FZ9VuwUkPMzMzMzMzszpV+Yzf7UXEbEk7knriHwLsDbwDnAX8LCIWWbCihtICFz2LOLWMbF9L83PSw8zMzMzMzKxOzTa8paTouf/zYmut7iiqj0IYSTdKaNTDSQ8zMzMzMzOzOjVnymPJ5aSHmZmZmZmZWZ2atafHkqpHVzfAzMzMzMzMzKwzuKeHmZmZmZmZWZ3cz6O5OOlhZmZmZmZmVqcezbh8yxLMSQ8zMzMzMzOzOjnl0Vyc9DAzMzMzMzOrkycybS5OepiZmZmZmZnVySmP5uKkh5mZmZmZmVmdPKdHc3HSw8zMzMzMzKxOTnk0Fyc9zMzMzMzMzOrkOT2aS0OSHpICICIWeXdIWgq4ANgPeBbYMSJelTQS+BdwXESM6qR2DQdeBO6IiBGdcY1GkPQ34BvAQmCNiJhQo94I4HbgrIgY2aC2bQ58FdgBWBlYGngbeBy4FTg3Il7u7HZMmBLZYt0y8/VssQAOX21Y1nhL980Xa60tls4XDJjy8uxssQYsn/fb19Ae+X54Ld1vbrZYALOnLcwar0ePfLG2Gjg4XzBgzZXzxZrxUs98wYDlnrsoa7yVB+V7z7019uxssQCef7tXtlirrJotFAA933glW6wjN8r7/XffbQ/IGu+y+/K95/bf5rBssQB+usty2WItmJfvZzTAR9fM902ud8afqQB9++X9oLTmnHyxxr+ULxbA7Fn5Yq2wQr5YAFPfzRtv2ox8sfpk/nQ2aUq+WIftuky+YE0u469S1gBd+npJ6gVcSEp4PA2MiIhXWzlntKQoPsAv8ST1Bkq/ZfUADunC5rxHUi9JpwMPAF8H+pASLpeQEh5bAr8BnpN0YJc11MzMzMzMzBZbXTa8pSzhsQ8wjtTDo/xP6JcD9wOTuqB5zWR3YDAwERgGHAqc0KUtSv4NHER6bb8aEXeXFxY9fPYGRgFrNrpxZmZmZmZm7SFPZNpUuqSnR5HwuJiU8HiS1MPjA2MGIuLdiBgXEU56tOwLxf4nwHhgY0mbdF1zQNIBpITHa8AnKhMeABExPyIuATYHrmlwE83MzMzMzNpFDd6sYxqe9CiGY1wC7EUa5rBjRLxRpd7IYhjLqLJjAZQGpd5elJe24RXnf1rSNZLelDRH0suSrpC0e412LS3peEkvFfWfk3SMaqTxJC0v6Q+SnpY0W9I7kq6XtH2VuiOKNo6WNFjS3yRNLK7zuKQv1ffsLRJ3OVJPj1mk5/S8oujQOs4dVrTnDUmzJD0k6YsVdVaQNF/Sq5KqvlckHVDc27llh39Y7H/eWtIqIuZExOMVMd8bwiTpU5JulzSlODaotXszMzMzMzPrLGrwP+uYhg5vKRIelwJ7AI8CO7exJ8dZwHbA2sCNQHnvkOll1zkR+D6wALgPmECaRHNHYBBwbUXc3sBNwIbAf4GnSBNvHg8sA/y04j7WB24BVgGeB64DhgA7AbtK+kJEnMeiBhXtWba4zgBge+AMST0i4p/1PhGFA4u2XxgR0ySdDfwYOETSMRFRa/bDwaShQ32AMcBypOfmLElrRsRxABHxpqRbgE8V5bdWiVWaQ+RcSMkgYAvSpKoXt/F+qsX+CmlekOtJr3ve2c7MzMzMzMzawBOZNpdGz+lxGalnwljgkxExuS0nR8RISaNJH36Pj4gxlXUkHUpKeEwAdo+IR8vK+gNbVwm9LXAX8KFSEkbSFqQExfckHR8R04vjPUkf5lcBjgL+FBFRlG0G3Az8XdItEfFmxXX2IiV9DouIGcU5ewFXAD8D2pr0KA1tOQcgIsZJegj4KCkBc0uN8/Ys2rlPWTu2BG4Dfi7pyogYW9Q9l5T0OISKpEfR6+LTpHlXbioOf6TYvxARU9t4P5W+ChwUERd2MI6ZmZmZmVkWntOjuTQ6SbU76S/1X2hrwqMNflzsv1ue8ACIiBkRcVuVcxYCXynvdRIRpd4F/Ug9F0r2BDYCzo+IU0sJj+Kch4H/A/pTfYjJVOBrpURDcc6VwGPA6pVDdFoiaS3gY6SEww1lRecU+y8sctL7AvhORTv+B/yF9J44oqzu5cBMYD9JfSri7M/7PU3mF8eGFvuqPXgk7V0MXynfflujnde2JeEh6YlaW70xzMzMzMzMWuI5PZpLo5Me95BetwskDckdXNLKwAbA5Ii4tA2njo+IZ6ocLx0bVnZsl2J/RY1YpUk7t6xS9kBEvF3ndVpTSmpcUJZwADifNKxnX0n9apz7cEQ8XeX4+cV+u9KBoofLVaQhOZXzoXxgaEuh9HVZaxjKpqR5Wcq3fWrUvarGcTMzMzMzsy7RAzV0s47pip4eD5HmzrhR0sDM8Vcr9s+38bwJNY6X5gkp7+EwvNhfWDGRahQTrT5QlA9lUW25TmtKPUnOKT9YrIJzC2m+kL1rnPtSjePji/3KFcdLSY3Plw4UCaYdgBcj4r6yuqUeHtXun4gYFRGKCNH6UrUvt1JeGXvDWltb4piZmZmZmdUiNXazjmnonB4R8a6kTwF3UCxVKmm3iJiZ+1KdWL9nsb8eqJyzo9y4Dl6nJknbAusUD0+sMqaslPw5lPdXdOmIG0nJjN0lLRsR75KWpO3BB3t5ADxS7NeWNLCD83rM7sC5ZmZmZmZm2XlFlebS6IlMiYhJknYhTRz6CeAySZ+NiLkZwr9S7NdpsVbHlHprnBYRXTX8ony+jo+3UG9XSStWWRJ4jRr1S8dfKz8YEfMkXUya62M/4EzeH9pyXkXdtyQ9SEpqfQ44o4X2mZmZmZmZmXWaLlltJyJeA3YGXiWtDHJ+sSpKPUrJkUUSNkXcp4AhkvbN0dYqSiui7N1J8VskqRdwQPFwo9JQkcqNlIzoSeqRUWlTSR+qcvzgYn9PlbJSj45DinM3Bx6KiKeq1P1Dsf+lpKrDXMzMzMzMzJpRjwZv1jFd9hxGxHjgk8BbwL7Amapv7Z9SL4T1apQfX+xPlvSBuRwk9Ze0UzuaW+4S0tCVkZKOKZIQ5dfoLWlfSRt38Dq17A4MAR6NiJZWJbmg2FdbxaUHcGr5RKeSNge+RVrJ5vQq59xLmvNjR+AHxbHKoS0ARMQFpGV9VwbukrRdZZ0iydXR18LMzMzMzKyhPKdHc2n48JZyETFO0q7A7cAXgWnAt1s57Wrg56S5LHbh/Ykzj4mIyRHxb0lbFnEekXQvaUjKysBmwMNAtWVr623zfEn7kOa5OB44StKjpOVoVwPWBwaRViR5rL3XaUEpiXF+i7VS+94BNpe0QUWPjGuAjwDPS7qTtDLLTkAv4FcR8WBlsIgISeeRlgT+Gik5ckFlvTKHkiZoPZyU+HgReJS0/O2KwIeBlYB5QN3L0pqZmZmZmXUlz+nRXLo06QEQEWMlfQa4GfiWpGkRcWwL9R+UdCipt8GuwNJF0a+AyUWd70i6lTQHxZbA1sDrwK3APzK0eZykTYEjScmN7UhLtU4E7gQu5/1hMNlIGsT7y8a2lHAgIuZKugz4MikB8ZOy4snAtsDvSMOLBgJPAidHxOgWwp5LSnoAjCmGE9W8PvAlSacDXwG2Jw1p6kNKxjwB/AU4KyJeqRUnly02yPeNacj4tqws3LprJgzIGm/GwheyxRoyfvNssQAO37bWAkZtd8n1eec/fnZOR+bc/aDpkWXO4vfsOTjvKLG1V88X64ZpE/MFA256vFfrleq0at+tssUCGLT7zlnjTbnutGyxNv37T1qv1AZ9TzwhW6x5OWbsKjP0Y0dki3X2vZdliwWw+dG/yRpv/20OyxbrkvvPyhYL4HPbfClbrBV75v2eOZtVssWatWBKtlgAQ3rm/dk1YeH01ivV6eBhK2WLBXn/Ov1Svl8fAJgyO+97bqmMN9sn86ezVVfIF+uCVz6bLxjwmazRGstDTppLQ5IexRwTLZXfR1pitfzYaGB0jfrn0cqqJBFxBXBFK3XGQ+00XUSMAkbVKHsHOK7YWhQRY1q5zkhgZB1xpgB9W6tXVv8rpIRDrXZUG/rSUrwnaeE+apzzH+A/bTxnJHU8H2ZmZmZmZo1W36wM1l10eU8PMzMzMzMzs2bhlEdzcdLDzMzMzMzMrE49nPVoKk56mJmZmZmZmdXJE5k2Fyc9zMzMzMzMzOrklEdz8cSzZmZmZmZmZrZYck8PMzMzMzMzszp5To/m4qSHmZmZmZmZWZ2c82guTnqYmZmZmZmZ1Uly2qOZOOlhZmZmZmZmVienPJqLkx5mZmZmZmZmdfKcHs3FSQ8zMzMzMzOzOjnn0Vyc9DAzMzMzMzOrk6f0aC5OepiZmZmZmZnVqUdXN8DaxEmPGiRFK1XuiIgRkkYBvwAOj4jRnd6wNuru7WuUfsvm+9a06soLs8UCeGvys1nj0TNfqBV69skXDJjyVmtfVvV7be6sbLEAVl2qf7ZYT86fmi0WwHVv54230/xlssU6fNVh2WIBXPpKvj+dLNWrX7ZYAJMuPzFrvM+sMTBbrHt++mC2WADvvPputlgfH7xstlgA4yblex36Zv41aJnTTska76e7LJct1ue2+VK2WAAX339mtlgn7vztbLEAHpwxOVusuczMFgvgtQWzs8ZbpeeAbLF698oWCoB3M/7oumdS3p+Dw/vm+5kPsO6QfL98zZyTLRQAszO+5WKpufmCNTmv3tJcnPRo3Vk1jo9raCvMzMzMzMzMrE2c9GhFRIxspcqfgQuAiZ3fGjMzMzMzM+tK7ufRXJz06KCImARM6up2mJmZmZmZWefz6Jbm4jlYOkjSKEkhaWTF8THF8eGSDpF0v6RpkqaU1ZGkwyTdKWmKpFmSHpV0tKRFRlZKGl/ElKSjJD0pabakVyWdKmlQG9q9TtH2+yS9LmmupAmS/i3pQy2cN1TSbyU9LmlG0e6xkn4taUhF3bbe3xBJv5H0hKTpkt6V9EzRpq3qvTczMzMzM7PO0qPBm3WMn8POdyxwNjAXuAZ4HEBSD+BCYDSwCfAAcCOwPPB74IqiTjV/KupMAK4kTV35HeAOSfXOUPgV0gSnA4trXwVMBb4A/E/SRypPkPRhYCzwI2AwcAMwBugD/BjYuKxum+5P0gDgftLz1auoewvwLnDw/2fvvuPlqqo2jv+ehITeQ+9Negeld0R6VxSkqSgWQFR8UVRUVCwIoqgoUqRJFQSRDkKo0nsn1NAJJSGFZL1/7D1kMpm5d+69Z87MJM83n/lM7in7rJk7M3fOOnuvDWzX5OMyMzMzMzNrGancmw2Mh7e03r7AFhHx35rl3wb2BK4B9o6I1wEkzQqcC+wIHAycVKfNzwPrR8TdeZ/ZSMmPLYAfA4c3EdclwF8j4unqhZIOAE4FTsjtVZbPAFwELAIcBxwZEROq1q8JvD6Ax7cHsCzw+4g4pCam+YH5e3tAkh5utO5fn96tt93NzMzMzMx65UREd3FPj17k4ST1bnM12cTfahMeOYHwHeA94HOVhABARIwGvgSMA77coM0/VBIeeZ/3ga8DAXxBUq/zjEbE7bUJj7z8NOAWYDNJ1fMM7gasADwAHFGd8Mj73RsRLw7g8VWSGtfXiem1iHiot8dkZmZmZmbWair5ZgPjnh69azRlbbMTVf+rzrI1gWHAf3Ih1ClExKuSngRWkTRzRHxQs8k/6uzzqKT7gTWA1YD/9RZY7iGyY95nHtKwEoCFSO+vZYB78rKt8v1fI2JSL0335/FVkjg/l/QhcG1E9Glm8YhYudG6yz6ze/SlLTMzMzMzs3rc06O7OOnRiyamrO3N83WWLZnvt5XU28n4PMBLNcuea7DtCFICY+HegpK0BSl5Ml8Pm1XXB1ks30/VO6SOJfN9048vIq6TdDxwGHAZMF7SfcDVpN4yI5o4rpmZmZmZWUsNctKjqzjp0Xr1eisMzvdPArf2sv+4Phyrqbdf7uFxPjAv8FNSjY3ngA8iIiSdQyoeWq+9ZnpM9OvxRcThkk4Gdga2BDYEPg4cIekzEXFJE8c2MzMzMzNrGec8uouTHu3xYr5/qJ89SZYAHqyzfPF8/3Iv+29MSnhcFBE/rLN+6TrLXsj3yzYRX78fX0Q8DvwK+JWkmYCvAb8BTiYVXzUzMzMzMzNriguZtsf/SFOxbi5pjn7s/5naBZJWIA1teY9UbLQnc+f7F2pXSFoWWKvOPtfm+y9KvY5iG+jjAyAixkbEccBIYP48i4uZmZmZmVnbeMra7uKkRxtExDhS74W5gIskLVG7jaTVJE2V3Mi+nqeIrWw7K/B7Uk+rU3P7PXki3+8m6aOaHnlGmr8xuaBptYvzfqsDx+YZWqrjXUPSov19fJJ2kbRene3WBBYgJXPe7uVxmZmZmZmZtZSTHt3Fw1va5+fASqTaGY9LuodU9HQYaXjJUsClwHl19j0LuEPS9aQeFZsACwIPAz/q7cARcZeka4CtgSck3ZhXbQa8kY+7c80+H0raHbgGOALYR9KtpNfQ8sCKwOZMHtrS18e3GXCopJeAe4F3SQVZNyIl535QO01uX8y/yXL93XUq1x37aGFtAWw1y4KFtrf0fMV9Mt6x8qaFtQWw1m7LF9fYrKsV1xZw683vFdbW2sDdJ32/sPZmpd8v/brWWKG418jf73q3sLYAZtAChbX11JirC2sLYPOZi32vzjFn79s067H7zy2uMWCpwbMW19ZUae+BWX6hgwtr64kH/1xYWwCrLlXsN9OJE4qbeGyBwcVOYnbcll8vrK1vXfeHwtoC+MFv6o0AHoD3ivsMfvyqXxbWFsDIie8X1tYGR3yqsLYArj9meGFt7b1WvzsO1/XuqEKbY1Jv8xr2wfhi/+QTBb71B81f4B+uLuc8RHdx0qNN8rSvn5N0EfBFYJ18e4NUVPQM6kxNm30DeDbvtxTwFnASKTHwTpMh7Ax8H/g0sC3wWj7eUcBxDWJ+SNIawHeAnUjT3Y7J8R5D1bCafjy+04EPSQmcjwNzAq8AVwDHR8SNTT4us7YrMuFhZmZtVGDCw8ymHe590V2c9GggIpp6KUfE0cDRdZZv1uT+FwEX9SE0IiJIiYm6yYkm4/uAlOA4qs5u++dbvfZeBb6db83E2tTji4j7gPuaadPMzMzMzKxdPGVtd3HSw8zMzMzMzKxJ7unRXVzI1MzMzMzMzKxJKvlWWNzSTJJ+LOkJSWMlvSzp1MqEFH1say5JJ0h6TtK4fP+7PDlGR3HSw8zMzMzMzKxJ3Th7i6SZgOuAHwKzkSaVeAE4ALhH0jJ9aGte4E7gUFJdxktIs20eAvwvr+8YTnp0kYhYstlaI2ZmZmZmZla8bkx6AN8DNgBuAz4WEZ+JiE8A3wLmA07tQ1vHA8sBFwPL57ZWAX4PLAv8trCoC+Ckh5mZmZmZmdk0StIQ0gygAF+LiI/mu46I35Jm4dxE0tpNtLUgsDcwAfhqRHxYtfo7wOvA3pIWKCr+gXLSw8zMzMzMzKxJXdjTYyNgLuDpiLi3zvoL8/2OTbS1LSmPcFOe2fMjETEOuAwYnLfrCE56mJmZmZmZmTWpC5Meq+f7exqsv6dmu7LaKoWnrDUzMzMzMzNrUhdOWbt4vn+xwfoXa7Yrq61SOOlhZmZmZmZm1qSycx6SHm60LiJWbqKJ2fL9mAbrR9dsV1ZbpXDSw8zMzMzMzKxJXdjToxJx9LK+7LZK4aSHmZmZmZmZWZPKTno02ZujJ+/l+1kbrJ8l37/fYH2r2iqFkx5mZmZmZmZmTerCnh7P5/tFG6xftGa7stoqhZMeNl2IsWMLa2utZYqd9OjPDzQaDtc/D44eX1hbC3282I+IUcNvKqytG5+ep7C2AMY98Fhhbc2vlQprC+BVPVJoe6PeKq6tiUwqrjFgvmFrFdbW/FqnsLYAHnntskLbW2/oXIW1tfJKBxXWFsA7j51ZWFvjPiisKQDiwwmFtTWpYc/c/rnrqWLfD2stVdzfm7EsUlhbAHePfrOwtn7wmwcLawvgp99etbC2Hi3yAxP43tXFvkZmiSGFtfXaVXcU1lbRnh5RbHszFDx/5dxzFNdWgV/jAJirwNiGrlHsd69u1oVJj/vzfaMvWpXlD5TcVim6dspaSbNI+oakqyWNlDRe0nuSHpF0mqQdJXXt4yuTpBUlnSjpIUnvSBon6SVJ/5K0r6Sh7Y7RzMzMzMysE3ThlLW3AO8Ay0has876PfL95U20dSUwCdhY0vzVKyTNCOyY1/+n/+EWqyuTApLWB54ETgQ2Bp4ALgKuJT3B+wP/Au5tU4hdQ9KPgQeBbwCzAzcAFwPPAp8CzgCKvcxsZmZmZmZmpYiI8cAf8o9/kPRRPQ5JhwOrAcMj4n9Vy78u6TFJv6hpayRwLjAU+KOk6q7hvwLmA86JiFda82j6ruuGt0haA7gemBE4Fvh5RLxXs82SwOHAAWXH100kHQN8H3gVODAirqhZPzfwbeA7bQjPzMzMzMys43Th8BaAY4CtgA2AJyXdDCwBfAJ4k6nPnYcBywML1WnrMGA9YHfgMUl3ASsDqwBPA99sQfz91lU9PSQJOAuYCfheRBxZm/AAiIgREXEIsFnJIXYNSesC3wM+ADavTXgARMTbEfF9YIuy4zMzMzMzM+tEXTi8hYgYC2wO/BQYA+wCLEnq2b9mRDzVh7beANYFfk/q8bErMCepN8nH8/qO0VVJD2A7UgbpOVLXmR5FxN21yyRtL+lUSY9KelfSaEn3S/peHoNUu/3+kkLS0ZI+Jukfkl6VNEnSLnmbZfP62yS9kuuLvCjp75I+1ig+SVtKuinH8KakiyQtl9sKSfvX2Wc2ST+U9KCkMfkx/LcSSx98izSH8okR8WhPG0bE8JoYNpb0B0kPSHpb0ge569OxkuaqE/Nm+fGcLmlBSafk5+dDSYflbYZI+rKkOyW9kR/bCEmXS9qrj4/NzMzMzMysJbox6QEQER9ExA8jYtmImDEiFoyI/SPihTrbHh0Rioj9G7T1dkQcEhGL57YWj4hvRESxFaAL0G3DW7bN9xdGRH/LX/+NNKfww6RaFnMAHwd+Bmwp6ZMRMbHOfssD/yN1/bkBmBuolJD/IvBdUu2Lu4CxwErA54GdJW0cEVNUr5W0O3A+KfF0C/ACsA5wJ6keyVQkLUAa2rMS8BJwDWke5PWBf0o6MiKO7e0JyAVeP5V/PKe37ev4NbAG8BCThxqtRXoOdpC0XkTUm5d5PtJzOAMwnNRjpzJ1yZnAZ4A3gFvz8kVINVtmA/7RjzjNzMzMzMwK1aXDW6Zb3Zb0WD3fD6RA6VeAayJidGWBpNlJJ/87AHsDf6+z316k7jqH1UmKXAL8NSKerl4o6QDgVOAEqoaISJoT+Asp4fHpiLggLx8M/An4UoPYTyMlPH4FHBURE/J+SwNXA8dIuqI2wVLH0qTuR+NIyZ+++glwW0S8XfWYZiQVlj2IVE/lJ3X22w74J/C53L2qsu+SpITH/4BNatbNTEqw9EpSw8dy228ObaYJMzMzMzOzHjnp0V26bXjLsHxfd4xQHj5Re/tE9TYRcUl1wiMve4/JxVZ2bnDs14Hv1usFEhG31yY88vLTSL04NsuJjoo9gXmAqyoJj7z9RFLh0KnqlOQCrtuSekH8XyXhkfd7hjRcZTCp10lv5s33bzfo1dKjiLiiOuGRl40jFbT5kMbP4TjgG9VJjawy1dGttetyF6zb+hqjmZmZmZlZK3Tr8JbpVbf19Kj8yqPB+v3qLLsSuGOKRqTlSL0OliUNdRlU1fZyDdq+NiLGNFiHpNlIcxKvQUpoDMmrFsptLwPck5dtkO8voEZEvCvpalIl3Gpb5/tLI6Le46/U3Vi3UYzV4TaxTc8NSIuQHu8KpCFClQTaeBo/h/dExEt1lj8GjAYOyL01Lo6IN/saU0Ss3Gjd7ccd1ug1Y2ZmZmZm1jQnIrpLtyU93iDV1hhWb2VEfPTyk3Q6NUmQPPvLb0i9Ohq9VGdvsPz5RkFJ2oJUc2K+RtvUtLtwvp+qYEwPx1oy3/9S0i97OE7d56ZGpafM3JIG97W3R57L+RekSr19Ufc5zImeL5GG/PwFOFnS46TaKX+PiNv7eBwzMzMzM7OWcNKju3Tb8Jb78/1a/dz/M6R6Ey8Be5AKZQ7NyZLKzC2NXsK1QzLSxqmHx/mkhMdPSTU3ZgUG5XbP7aHdRr0P6m07ON/fTJpWqNHt0gZtVnsGeIf0mBv2jqgbmLQecBxpqtv9ScmYmXJlXwEje9i97nMIEBHnkmqNfAm4kNRb5mDgNkm9ztRjZmZmZmZWBg9v6S7d1tPjP8BXgT0kHdGPGVx2zfcHR8TlNeuW7mdMG5NqZFwUET+ss75eu5XEwOIN2lyszrIX8/2FEXFi30KcUkRMknQV8Gngc0BvhU+rVZ7DoyLijOoVuejoggOI63XgFOCU3CtnG+A84DuSTo+IR/rbtpmZmZmZmU1/uq2nxxXAo8ASpOlR+2rufF9vWMmn+xlTwzYlLUv9Xim35vs96uwzB5Prd1S7Nt/v0vcQ6/otqafJIZJW7GlDSRtU/djTc7gnBdQLAYjkSuDfedEqRbRrZmZmZmY2ICr5ZgPSVUmP3LPj86RZQH4m6Rd5utkpSFoC+FidJp7I9wflngSV7TcGvtPPsCpt7ibpo5oekuYC/sbkgqbVLgDeBj4lafeqfQYBvyQVBp1CrmtxHbC5pOPzsBqq95X0SUkbNRN0RNxBmvp2ZuB6SdvVbiNpTkk/JtXWqH28X5A0pGrblXLsfSZpTUm7VbeXl88NVGbfaVhTxczMzMzMrCwe3tJdum14CxFxt6StSHUf/g84TNKdwMukE/hFgTVJCZ2Hgfuqdj+RVIfiq6RpZB8g1fXYiFSn4tv9iOcuSdeQemc8IenGvGozUsHQS6mZwjUiRkn6Cqnex4WShpN6TqxDmr71LGAf0kwo1fYGriZNDbuvpPtIU+kuQirwOh+pSOtwmnMkaYrZI4F/S3oOuJdUr2NRUsJhKPBk1T6nkabH3RF4XNL/SPU3NgUuAT5O6onTF0sAFwHvSLoLeAWYizR0aA7gnwMtZjrx/YYT7/TZXPMVmyucY/BChbb33sQne9+oSQu+O6H3jfpgAsW1N2nkqMLaAhj93ou9b9SkGQbN2PtGfTDzhDl736gPxtV+sgzAUA3ufaM+GDKsmVrMTRpU7Ht1/rdnLrS9D4t8e9Wd1Kv/xkz6sLC2PiyuKQA+GDmisLbmGTRTYW0BzDS42G+mQwsM74OJo4prDBhPcX9Xea/YvzWPjnqrsLZWnGuewtoCUMGXbMdS3Bts/Jg+1bXv1YcFNvdBsS8RZij4su/M44pra0jBsU0s8PcQEz0ZYoUTEd2lq3p6VETEcNIUsN8kDRVZgTTF65akIqJnAzsBq0fEY1X7PUGa0vUy0iwnOwGzAV+OiP729ICU1PgZKQGxLbA2aTaX9YBRDR7D+cCnSAmKtfN+j5ASDZWCn2/W7PNqbvNwUiJiXdJwl0VJyYqvkRImTclDSI4CVgNOAsaQnsM9SM/vVaTky8pV+7yZj3sOKSGyEynp8kPgs80eu8btwFHA3aTkzZ6kBNADpBl4+jv0yMzMzMzMrFDu6dFduq6nR0VEjAZOyLe+7Pco6US93rqpXlIRcTpwei9tfkA6aT+qzur9863eftcA11Qvy0Nc1s8/3t/gWMfnWyEi4mHg633Y/kVSr5N6lqyz/Y30MBotIl4hJY1+1mwMZmZmZmZm7eBERHfp2qRHt5O0CDAhIl6rWjYEOIbUs+L6nAwwMzMzMzOzDuGkR3dx0qN9NgbOknQP8BxpWM7qwMLAW8A32hibmZmZmZmZ1eOkR1dx0qN97gbOJCU/ViTVx3gZ+Avwi4gY0b7QzMzMzMzMrB739OguTnq0SUQ8CRzQ7jjMzMzMzMyseU56dJeunL3FzMzMzMzMzKw37ulhZmZmZmZm1iT39OguTnqYmZmZmZmZNclJj+7ipIeZmZmZmZlZs5z06CpOepiZmZmZmZk1yT09uouTHmZmZmZmZmZNctKjuzjpYWZmZmZmZtYkJz26i5MeZmZmZmZmZs1y1qOrOOlh04Vn//t6YW2NGV1YUwAsNENxsQHMxCyFtXX/VT8rrC2AVedfqLC2nhh5SmFtAQwusCLVPINmKqwtgEl8UGh7T7wxY2Ft7b7i7IW1BfDHh04usLViv5CsP+t8hbb3/nvFtfXQM38urjFgHop7jbz0RmFNAfD+6OGFtbXokFkLawtgnpmLfc3NNEtx7c07eExhbQG8PHFsYW09ftUvC2sL4HtXTyqsLRX8OXLxbecX2t7fdziksLZmGFrsY529uK8jLLZIcW0B3P54ca8RgIhBhbU1V4HPG8CECcW19cSff19cYwA/36rY9krknEd3Ke4dWoektSSFpOcarJ9B0ui8ze8abLN9Xn9XK2PtC0k35piWbHL7/fP21bfRkl7Obf1S0sotDruZ+I4uqL1FJB0v6VFJH+TH+qykayX9QNKyNdsfXef5qb1tVkRsZmZmZmZmA6FB5d5sYFrd0+N+4F1gcUmLR8TzNevXho8uS2/coI2N8v3NLYivbE8DlUtUQ4FhwJrApsARks4GvhoR77YpvgGTtBZwLTA38AbwX+AtYEHgE8CWwATg2Dq73w/c16DpV4qO1czMzMzMrK/c06O7tDTpERETJd0GbENKapxds0kloXE/sLqkOeqc8E9LSY/hEbF/9QJJArYHfg/sDSwqaeuIKLAzWqnOICU8/gocEhEf9X+VNCOwMzCuwb6XRMTRLY/QzMzMzMzMpgtldJapJCs2qrNuI+AD4A85lvWrV0oaCqyTfyxuEG8HieRyUi+Il0m9Pg5ub1T9I2k5YBVST44pEh4AETEuIs6PiEvbEqCZmZmZmdlAqeSbDUiZSY96w1c2AP4H3NBgm3WBmYDHI+K1ykJJ80r6taQnJY2V9JakKyV9slEQklaSdLakkZLGS3pJ0t8lLd/DPgdJejAf4yVJv5c0ZzMPuq/y4/th/rFuVSpJO0q6StKbOaYnJP1U0mwNth8i6auSbpE0StKYvM9fJa3STFySviVpkqRHJPVWRqpS5e/92oSHmZmZmZnZtEAq92YDU0bS407ScIaVJM1TWZiTDfOThnw8TarZUJv0mGpoSz7xvhP4NqkuxiXAvcBWwFWSvlkbgKQtgbuAz5F6U1wEvAZ8HrhL0lQJGUm/AU4GlgOuB24jDT+5AQosaz+l84FJwDKSFquJ5zjgX8AmwEPAv0mP/yjgRkmz1mw/K3AdcBKwGnArcBkwCtgf2KO3YCT9HPgN6bnbOCJe6mWXF/P93JI+3Vv7ZmZmZmZm3UZSqTcbmJYnPfIV/7tIHXM2rFpVSWjcku9vBT6eh7TUblNdz+PPwNLAmcCyEbFXRGwJbAaMAX4tabXKxvnk/2xgZuDgiFg7Ij4bEWsChwOzAefkehOVfTYAvkUqwLl2RGwXEXuQEiCDgfX69WT0IiLeA57JP65YFc+nc6z3AitGxKYRsXuO5y+kgrBH1zT3O1IS6QZgyfwYPhMRHweWJCVA6pI0SNLJwJGkhM8WEfFmE/E/D1yTfzwvz0xzpKTNJRU8AdfUJD3c6NbqY5uZmZmZ2XTCw1u6SlkT4NQb4rIREKRkB6Tkx0zkGh65wOcG1ftLWhrYgTQjzCHVxT4jYjgpITIY+GrVcT4NLADcHBF/rg4qIo4H7gYWBXatWvWVfH9cRDxctf2bwHeafdD99Ea+n7tq2ffy/WcjYkRVPBOAQ0m9ZL4opQmNJC1E6s3xAbBvbcIiIl6KiLvrHTwnnf4BHAT8E9guIt7vQ/yfA67M/98U+DkpcTJK0uWS1u1h3x81mK72hD4c38zMzMzMrGU8vKW7lJ30qC5muhHwUESMyj/fUrPNSsA8wEsR8WzNuiuq9qt2Zr6vTq5U/l87c0zFWXX2qRzn/NqNI+JqUg+QVqm8rANA0vzA6sCjEfF4nXgqPWnmIvX8ANiclPy5IiJerN2nB7MClwN7AqcBe0ZEo5lW6oqINyJiW1Ly6hhSwuNdYAhplppbexj6cj9p9pfa251NHnvlRre+PAYzMzMzM7NGnPToLi2dsrbKLaRaFetImhmYHViW1DOj4h5Sz4SNgV9Rf2jLwvl+RIPjVJYvXLWsv/sE8EKDfZ4nJWRaYVi+ryRWlsj3K0qKJvZ9HKjUA3m6j8c+jPSauAL4QkT0dryGck+SuwEkDQG2INUHWQU4WdIVdXqQeMpaMzMzMzPrbIOciegmpSQ9IuIdSQ+Seix8Apg3rxpetc0ESf8DNsxDW+olPT7avNGheljf2wl8v0/wiyJpDlK9EoBH8v3gfD8SuLqXJmrrbvT1Mf2HVCj1k8DuwIV93L+uPAznKkn3kRIxc5GGLvX2eMzMzMzMzDqKe190l7J6ekBKXqxOSmZUkh631GxzC+mke2XqJz1ezvdLNTjGkvl+ZB/2qfSkqN5nZG5rMeCpOvss3qCtgfo0aXjLExFRibsyPOWViNi/yXYqPVSW7ePx7wF+RkpGnCtpYkT8s49tNBQRr0p6jFR4dVhv25uZmZmZmXUaJz26S1k1PWDKYqYbkWp1jKjZppIE+Qwp6TCKND1rRaVnyPaS5qpzjH1qjlX9/70bxLV3zXbVx9mzdmNJW9OCoS25dseP84+/qyzPNTkeB1aT1ChxU+tGYCKwXZ7it2kRcQewDWmo0XmSdmp2X/Uyn5KkwUxOTL3cw6ZmZmZmZmZmA9aOpMcGwBpUDW2pcitpSMbX88/Dq+tKRMQzwL9JNUF+l2tFACBpfeBg0sn+H6vaPB94FdhY0kHVB5N0CLAuqTdFdY+Gk/P94ZKqp46dh1RvpDBKtgNuJ9USuZ40DW21Y0jDXC6StEqdNpaRdGDl59xL5O+kaXpPz3FXb7+wpLUaxRQRtwOfAsYBF0javsmHs5qkqyRtXZlJpuqYswB/IPXyeQW4rck2zczMzMzMOocrmXaV0oa3RMRISU8Dy+RFtUNbiIi3JT1KmrkF6tfz+HJevi+wqaTbgPmAzUiJgW9FxANVbY6WtDdwGamA5kHAE8AKwJrAaOBz1bOURMTwPE3qYcC9kq4l9XzYglTE9HZgvX48DRtJOj3/fygpAbAWk4d6nAl8LSI+rN4pIs6StCpwBHCfpHuBZ4E5SMNzViDNfHJq1W6H5uVbASMk3Qy8T+ppsRZpGMs9jQKNiFslbUuq83GRpF0i4spG22ci1QP5JPCmpLtJU/AOI83mMg+Tp9Ht06wwAzV0xuLaGt2XCXyb8OaHxT4ViwydpbC2Rn4wqbC2ABZdrPdtmjXzK8V+fL3P+MLamnVQsbHNM8Nchba30gKDe9+oSf99ckLvG/XBxF7rNTev6K8ID495p9D2lp0wd+8bNWmuWKL3jfpgtVmL+1xacqHCmgLgknfGFNZWFPe2B2COMXMW2t5SBf55eHFSsX+8Fhk8W2FtjZxYbGyzxJDeN2rSWD7sfaM++PsOhxTa3r6Xn1hYWzd/rdjYBhV4afWp54prC2D2IcVe9/1gYnF/u2b+sNi/XvMW2D99YoHflbqd8xDdpcyaHpCSFZWkR72eHpCSISs12iYiXpK0LnAksAuwGzAGuA44Lk8pW7vPdXmf75MSF6uRTsbPAo6pNxUscDhpWMnXga1JRULPz8e9pJfH2cgyTH78H5CG7zxCSqL8PSIebrRjRHxX0lU5nvVJ9VHeJvVS+TXwj5rt35O0Oan3y97ApqRzgBdJyZELegs2J3+2J83m8k9JO0XENT3s8hCwJamXyMak2izzk3qMjCAldX5XNQWxmZmZmZlZd3HSo6uUmvSIiAOAA3rZ5iDgoF62eRP4dr41e+yHgc/1YfsgTan75zqrN2u2ndzW6cDpfdmnQTvXk4a/NLv9OOCEfOtpu9NpEF9E3AQ0dTkn91DpU4x5v6OBo/uyj5mZmZmZWTv0UsrQOkzZPT3MzMzMzMzMupbKrIxpA+akh5mZmZmZmVmz3NGjqzjpYWZmZmZmZtYkD2/pLk56mJmZmZmZmTXLOY+u4qSHmZmZmZmZWZPc06O7uASLmZmZmZmZmU2T3NPDzMzMzMzMrFnu6NFVnPQwMzMzMzMza5KnrO0uTnqYmZmZmZmZNcs1PbqKkx5mZmZmZmZmTXLOo7s46WFmZmZmZmbWLGc9uoqTHmZmZmZmZmZN8pS13UURMfBGpGYaOSMi9h/wwZokaX/gNODHEXF0WcctmqQbgU2BpSJiRBPb70963NUmAK8BNwO/joh7io2y/yQdDfwIOCAiTm/VcXaeb6uBv9CzHZaZvaimAHh/dKHNMXFScW2ttv7Q4hoDrrt2XGFtHXDWwYW1BfDWTZcW19YDrxbWFsDEDwt7+QLwyEPFtTfLjIU1BcCiSxT3JWLCuIKftycLbY53JxT3Zl163mIrqi2wYHFtvfVGcW0BLL784MLaGvvuxMLaAnjw0UKbY+YhxbU1pLinDYChBca2wREbFdcY8NpVdxTW1vgxxb5GZhha7InSqFeLi2/jk04srC2Aaw88pLC2Zp61sKYAuPXhAr8sAcsU+Bk8y8yFNQXAuOK+erHMasVeL9/2jAu6NnNw/aF7F/sloxdb/O7srn2uOkHRPT3O6GHd8IKPZT17msnP+WzAOsBewO6S9oiIf7UtMjMzMzMzsy7lnh7dpdCkR5k9OaxXw6t/H5KGAH8Evgj8WdKVETG+XcGZmZmZmZl1Jec8uopnGJ5ORMQE4DDgfWAh4ONtDcjMzMzMzMysxdqW9JAUkkZIGizpCElPSBon6QVJv5RUd6S4pFklHSnpHknvSXpf0iOSTpC0RJPHnkXSDyQ9JOkDSe9IuknSXg22n1fSzyU9nI/3To7375KmSh5Imk/SbyQ9LmmspLcl/UfSJj3EdJCkB/P2L0n6vaQ5m3k8zYqI0cAT+cfFah7fryU9mY//lqQrJX2yQayV391QST+U9Fj+3V1StU2ff0+SVpX0r/x8jZb0X0kbFPgUmJmZmZmZDYikUm82MJ0we8vZwA7AncDjwMbAEcAiwD7VG0paCLgWWAl4C7ieVKRzWeAQ4D7g9J4OJml24AZgbeB14HJgVmALYGNJ60XEYVXbzwbcno/xJHBVXrU48FngmRx7ZfsVcoyLkOpqXAHMm9v/pKTPR8Q5NTH9BvgWMC4/pjHA3sCGeVmRKlU4x+VjLwLcBCwNPA9cAswHbAVsI+nwiDi+TjuD8rabAP8FHgDezG325/e0DnAS8CJwXd52E+A6SetGxEMDedBmZmZmZmaFcCKiq7Q76bEE6QR/lcrMJJKWAu4G9pb0o4h4umr7M0kn0ucCX8o9F8j7LQc0U5v856SEx7XArhHxft5/BdLJ+6GSro6IK/L2e5BOwH8fEVOUoZY0PzB/1c+DgQtICY9D8z6R160JXAP8RdK1EfFaXr4BKeHxFrBJRDycl89LShas18RjaoqklUjJDUhJCoA/52VnAl/Iw2CQtBEpwfNrSddFxAM1zS1GSpwsHxEv1azrz+/pa8B3I+JXVdseTxqScwSwb98erZmZmZmZWfE0HReJyOevR5HOU4cCjwAnRURPk5rUa2dtYEdgS2AZUkeB10jn5L+qc/7Zb4X+uvKwh0a3XRrs9o3qqVgj4lngrPzjxlVtf5z0hLxCzYl03u/JiHisl/hmBb4ATAK+Wkl45P0fA47JP1YnNypJjetr24uI12p6IOwIrAKcGxEnVhIeedt7gZ+SepVU92D5Sr4/rpLwyNu/CXynp8fTrDzUZEvgYlLC4dqIeErS0qReNu8Ch1QSHvn4w0kJkcHAVxs0fWRtwmMAv6fh1QmPrPL7aDgsqObYDze6NbO/mZmZmZlZr6Rybx1C0q6kUQKfIl1EvxJYDjhd0m/70M4MwF3Aj4AVgHuBf5Euqu8N3CVpj6LiLnPK2ufrLJsA3FhneaXuxEJVy7bK92fXnkj3wdrAzMDtEfFknfVnAicCG0pSTlrcndf9XNKHpITB2Abtb53vL2mwvjKF7LpVyyqT059fu3FEXC3pLWCeBu31ZD9J+9VZfhfw+ZpjXxERo+pseyZwOFXJp+rwgMvqLO/v7+nqqQ4Q8aakN5nydWBmZmZmZtY+HZSIKIukuYHTSBfFd4+Ii/PyBUjnud+UdFlE3NBkk3eQOgX8JyIm5bYGAT8Bvg+cKunGiHhjoLG3e8rakRExsc7ySg+M6mKmlcKbT9N/C+f7EfVWRsQoSe8AcwJzAO9ExHVVwywuA8ZLuo90kv636l4qwJL5/jxJ5/UQx7CamAJ4ocG2z9O/pMfTTE6yTABeBW4Grqm8qOjl+ahavnCdda9FRL16I/39Pb3YYPn7pK5OvYqIlRut23m+raLROjMzMzMzs2ZNhzkPgC+SzpMvrSQ8ACLiVUlHkEYVHE6qn9mjiPiQOmUcImKSpB8Au5N6gGxPzx0rmtLumh79OREt4uS1mTaqh6YcLulkYGfS0I0NSVO+HiHpMxFxSd60UqviP6TxSI30OAynIMP7kIRq9HxED+sb9Xbprc2itjczMzMzMyvfoOky67FDvr+wzrp/k84Pt5I0Uw8jI3oVESHpQVLSo97F9z5rd9KjLyo9IZYdQBsv5/ul6q1UmiJ2TmA08F71uoh4HPgV8CtJM5EKb/4GOJnJw1kqvRX+HBH/ajKmkaQeIosBT9VZv3iT7fRHj88Hk3uujOxDm0X8nszMzMzMzDrSdDqN7Gr5/p7aFRExXtJDpBk5lwfuH+CxKpNvvDLAdoCCC5m22LX5fm9Js/SzjbuBD4CP51lEalUKjA6vLkJaKyLGRsRxpGTA/HkWl+oYd+lDTJUhKHvWrpC0Nf0b2tLXY28vaa466yvPx819aLOI35OZmZmZmVlnKrmQabsnbJA0BzBX/rFRWYLK8gFdtM+ziK4NjCcVSh2wrkl6RMSdpPFBCwIn155QS1o2TzvbUxujgVNJj/ukPJtLZf+PkabeAfh91fJdJE013ihPQbsAqUfI23nxhaShK/tL+q6kITX7DJW0m6RVqxafnO8Pl7Ri1bbzkHqWtExEPEPqijQ78LvqeCWtDxwMTAT+2Ic2B/x7MjMzMzMzs44xW9X/xzTYpjKJxWwN1vcqJ1dOzT8eHxF9GXHQUKHDWySd3sPq5yPihwM8xOdJU8fuA2wraTipSOeywOrAgfReL+NIUtGUrYFnJP2XNI3sFsBMwIkR8e+q7TcDDpX0EmkqnXdJY4s2IiVPflCZ6jUiPszT+FwFHJv3eyDvsxhpXNJcwK7Ag3mf4ZJOIBVKvVfStaTeKFuQipjeTp0iLwX6Mqknx77AppJuA+bLj3sw8K1+zJFcxO/JzMzMzMys85Q8vKWnCRuaJelCYJU+7rZvvqjdzAMe0JMiaTBwDmkK3DuBgeYOPlJ0TY96U6RW3M8AA4+IlyStC3wT2AP4JPAhqY7ECaQT7d7aeE/SpsC3gM8AO5G6ztwF/DEizq3Z5fR8jE1IxUvnJI0tuoKUfbqxpv3HJK0BHEJKbmxEegGMJM1p/E8mDwGpOBx4HPg6KRnzJmkK2yNpPP1tIaqe0yNJw3J2I2XvrgOOi4ipppLtQ5v9/j0VbcdlZy+srSFDet+mL8a/U2x7Qwb3vk2zHrxtfHGNAbMOKe4PxBVf+XNhbQGMnVBcWzMPLa4tgHc+KLa9Recrrq1x9eZwGoDHHy+upvGHk3rfpi+WKqSU1mRjxhTX2fLDevOgDcCI54ptr0iP31/cg52juD8NACyzWO/b9MXYAt/7RX8/f+fd4tq6/pjhvW/UJkW/t2YveODvoAL7bF974CHFNQZsdeqJhbV1zQHFxrbCAsV2dp9U4N+btwr+Xji4wId63x0fFtcYsG2hrZWrS0t6LEmqt9EXlU+t92qW1fsrUNn2/TrrmvEX0mwtjwPbR0RhJyLqoXSF2TTjlPV3LeyFXnTS45XXi22vyKRH0R8PowvMocw2Y+/b9IWTHv1TdNLjndG9b9OsopMeiy1QbHtjGnUO7YeiT8zGFpvvLFSRn3FFJz1mnKnY9qaXpEcnfxOdnpIeMxX8+u3kpMfYfs8rUV+RSY/RBcdWZNJjQsHvhyMf+2d3pg6A4Ud/qdSPro2O/mvbnytJo0gdAFaOiEfqrP8fqZDpGhHRp0Kmkn4NfJt0kXzDiHihl136pGtqepiZmZmZmZm1m1Jx0dJuHaKSyFirdkWuDbkKMI7UU6Npko4kJTxeA7YuOuEBTnqYmZmZmZmZNa/k2Vs6RKXu5R511u1Aqo95XUQ03V9J0kHAz4FRwDYR0aeESbOc9DAzMzMzMzNr1vSZ9DiFVMtjZ0m7VRZKmp/Js47+tnYnSY/l2yI1y/cA/kSqAbJdRNzXqsCLLmRqZmZmZmZmNs3SoI5JRJQmIt6SdCBpwo0L8yyobwBbkWYoPTEirquza6V46keVEXOi5GxSJ4xngS9L+nKdfS+JiEsGGruTHmZmZmZmZmbN6pzeF6WKiIskbQIcBawHDAUeBU6KiNP60NQseV+AVfOtnhEUMJupkx5mZmZmZmZmzZpOkx4AEXELfZhxOCKmerIiYgRQ2pPomh5mZmZmZmZmNk1yTw8zMzMzMzOzJnXQNLLWBCc9zMzMzMzMzJo1HRYy7WZOepiZmZmZmZk1yz09uoqTHmZmZmZmZmbNctKjqzjpYWZmZmZmZtYk1/ToLk56mJmZmZmZmTVrkCdB7SYdn/SQtDXwNWA9YB7gPeBV4F7gRuCMiBjftgA7jKQlgWdrFk8C3gbuAU6OiIvKjqsRSZsBN5B+j/u3NZgmDS74XfPGmCi0vVlmKC7zPGRwYU0Vbt55i22vyL9dj44ori2A9yZMKrS9Ca8U92BXWLKwpgB47s3i3g9jJhb7vM3xbrFviCIvEo14q9jHOueMxb1GZij4e+FcsxfX1mtvFdcWwCwzFtve/PMX19ZzLxbXFsAtb7xbWFt7rzVHYW0BPD2iuLY+mFBcWwCLLVJse089V1xbyy9TXFsA1xxwSGFtbX3aiYW1BXDOzsXFBjDT0EKbK9QsMxXX1r0jC35DdDP39OgqHZ30kPRj4If5x4eAW4CJwPLAZ4HPAZcBr7QlwM42Grgw/38IsCKwNbC1pF9GxP+1LTIzMzMzM7Mu5eEt3aVjkx6S1iElPMYDu0bEFTXrFwG+BIxrQ3jd4I3anhOS9gdOA46QdE5EPNCOwMzMzMzMzLqWkx5dpZMHI+2a78+vTXgARMRLEXF0RLxdclxdKyJOJw0lEbBTe6MxMzMzMzMza61OTnrMl+9f7+uOkoZJ+oWkhySNljRK0n2SfiZp3qrtTpcUkjaTtI2kG/K2IWmuqu12lHSVpDcljZX0hKSfSpqtwfGHSjpU0v8kvZdjuFPSF1SnL1Q+3ghJgyUdkdsfJ+kFSb+UVOQo4Xvz/WJVx59B0jck3S3p/Xy7U9LBkqYaxC7pxhzzkpI+J+n2/DhHVW0jSXtLuq7qeXtG0jmSNqwXmKR5JP1J0sj8+B+SdGCBj93MzMzMzGxgpHJvNiAdO7wFqJTc2l3SLyKiqeSHpJWAq4FFgJHAlcBgUh2Q7wHXkAqgVvsc8EXgLuA/wDJA5PaOAw4HxgJ3Am8AawNHAdtK2jQiRlcdf9bcxsZ52+GkQqLrA6cA6wJfaRD+2cAO+TiP5zaOyI9ln2YefxMqJeDG5XgHA5cC2wHvAtfm9VsAfyTVANkjIupVyTuS9LzdAlxOTqTkNv8B7JGPM5z0XCxO6sEzPu9TbS7gNmBO0uOfDdgE+JukQRFxykAetJmZmZmZWSE8e0tX6eSkx9mkk+rFgackXQLcTDoxfiQipirxL2kG4CJSkuA44MiImFC1fk3q9xz5ErBXRJxX096nSQmPe4HdImJEXj4E+ANwEHA08J2q3X5NSlacCXw1It7P+8xHKrr6ZUmXRcS/a2JYAhgDrFJ1nKWAu4G9Jf0oIp5u8Fw1RdJMpGKmAJV6HoeREh4PAltFxGt524VIQ2F2JSVp/linyX2BLSLivzXLjyQlPB4EdoyIj2qLS5qHVFS11s6k391+lSSSpJ2BS4AfkBJGZmZmZmZmbeVCpt2lY1NU+QR/Z+BlYA7SCfZfSbO4vCLpV9VDULLdgBVIJ/RHVCc8cpv3RkS9Sdv+XZvwyL6X7z9bSUTkdiYAh5JmjfmipEEAkuYn9Xx4FvhSJeGR93kd+HL+sXJf6xs1x3kWOCv/uHGDfXolaYikVUmzuSwJvAlckFdX5uw6rJLwyMceyeRkTqN5vf5Wm/CQNBT4FqmnzIHVCY/c7lsRUdvLA1Ivk4Oqe81ExKWkxMnieSre3h7nw41uve1rZmZmZmbWFA9v6Sodm/QAiIirgaWBT5MSHg+QhorMTzoh/1/uQVGxVb7/a4PhGI38q3ZBTmCsDjwaEY/XiW0saTjMXMByefGmpOlhr4yIqWaViYj7gfdIQ1xqTWDqYTcAT+T7hXp7EDWWyHU3gjSc5AFge+BVYOeIeEfS4qSeNK9ExPV12rgcGAUsX/M8V0z1vAHrkJ6TeyLirj7Ee1dEvFVneX8fv5mZmZmZWfE0qNybDUgnD28BICcPLsi3yjCR/UnDSpYFfk4angKTi3P2dRjI83WWLZHvV8yJg54MI9XgWDL/fLCkg3vYfuY6y0ZGxMQ6yyu9RfpazHQ0qWcHwERS8uIe4J8RMSYvXzjfj6jXQESEpOdISYyFmXpoUL3nrb+/g3o9cKAPjz8iVm607pT1d+3td2hmZmZmZtYrDXLvi27S8UmPWnmYyK8lfQD8ntR7YarN+tjs2DrLKrOWjCQVRu3JmzX73MvkmhnNKvqk/I2I2L/AY9fbpt7z1pc2B7K9mZmZmZlZ+TzkpKt0XdKjyo35fljVshfy/bIFtF/pefBKH5IHlX1ujIjDC4ih1V7O90v1sM3i+X5kk20W+TswMzMzMzPrLB5y0lU69rel3kviLpPvX65aVplu9YtN7N+jXPD0cWC1PItKM24gDSXZIU/b2tEi4nnSEJUFJW1Ru17S9sDcwOPNThlMqnMyClhL0tpFxWpmZmZmZtYRXMi0q3Rs0gP4aZ6hZaqEg6TlSFPSAlxctepiUuHL1YFj8xS21futIWnRPsRwDGnIykWSVqkTxzKSDqz8HBEvAaeTCpueKWlYnX02kLRdH2Jotd/n++Ori5VKWpA0/W71Nr2KiPHA8YCAv0larHq9pHkkbTiwkM3MzMzMzMx618nDW2YjTQv7bUmPA4+SZjhZHPg4KWFzN/Djyg4R8aGk3YFrgCOAfSTdSnqcywMrApvTuGjmFCLirDzV6xHAfZLuJU1HOwep0OkKwP3AqVW7HUKaceazpB4f95F6oyxIGvKxCPA74Iq+PR0tczywBbAt8KSk60kJiy2B2YFLgD/1sc2fA2sCu+Q2bwbeIP3u1gLOA+pNW2tmZmZmZtbR5OEtXaWTkx7HkJIa25B6bmxKSjaMAv5LmpnklNyz4CMR8ZCkNUhT2u4E7AiMAZ7LbfapwGhEfFfSVcDXgfVzLG+TEie/Bv5Rs/0YSZ8E9gM+D6wGfAJ4jTSjye+Ac/sSQytFxERJOwFfJc2Ks01e9QhwGnByH6f/rU4+7QccSEpSDSXVBbkIOLmY6Js3bqoJhDujLYCHR79baHvLzDR7YW0tMluxH+hzz1FcW+++U1xbAEOGFNfWgnMW1xbA2LeK7dY4Y4Gf/B9OKK4tKLYH5+iJ9SbE6r93Pyh21OIMBb69Xh0/vveN+mDemWcqrK0xBb9GRr1XXFuz15tLbQDe+6DY9or8nBs1tth64UvONGthbb07qrCmgGLfW0W2BXD74336StWr2YcUF+CtDxcb2woLFBfbOTsfUlhbAJ+79MRC27tw9+LiW3D+wpoCYIYC/+YPe7OTTx1L5tlbukrHvnIj4g3gzHzr676vAt/Ot5622590ot9be9cD1/fh+B8Cf8u3ZrZv+K6JiNNJQ2aaPfYIUk+NpuV4T8y3ZrbfrIltJpGSJqf1st2N9BBvs78jMzMzMzOzUrinR1fp2KSHmZmZmZmZWacZ4JwZVjInPczMzMzMzMya5aRHV3HSw8zMzMzMzKxZHt7SVZz0MDMzMzMzM2vWICc9uomTHmZmZmZmZmZNck2P7uKkh5mZmZmZmVmzPLylq/i3ZWZmZmZmZmbTJPf0MDMzMzMzM2uWh7d0FSc9zMzMzMzMzJokD2/pKk56mJmZmZmZmTVrkHt6dBMnPczMzMzMzMya5Z4eXcVJDzMzMzMzM7MmeXhLd3HSw8zMzMzMzKxZLmTaVZz0KImkqPpxg4i4rcF2nwbOyz8+FxFLVq1bEngW+G9EbNaaSHsmaW5gJDAUWDIinu9l+1OBA4CfR8T3+3G8oOZ56I8iP5dmmqm4tgDWm3vOQtv7YEL0vlGTZin4sb71bnFtzTVbcW0BTJxYXFujRhfXFsCHk4r7nQLMO3txb4ixYwtrCoAJk4pra7bBxf6Jm3f2Qpsr9HNpiXEzFtcYMHeB769BBb8f5pi1uLbeLTi22Wcutr33CoxvhoK/oC837+DC2ppU4PseYO45imtr5nHFtQUQUezV4Q8mFvf3YZl5i42tyN/rTEOLawvgwt0PKbS9PS46sbC2Lt6z2NiKtOT8PtH/iHt6dBX/ttpj7x7W7VNaFP0QEW8D/wYEfK6nbSXNBOyWfzyrxaGZmZmZmZm1nDSo1JsNjJ/Bco0DHgE+I2mqS5CS5gU+BdzTYP+XgBWBfVsWYXPOzPe9JWh2BOYE7o6IR1sbkpmZmZmZWQkGqdybDYiTHuU7GxgGbFNn3WeAITToFREREyLisd6GlJTgCuBNYGVJq/ewXaVHy5k9bGNmZmZmZmbWEk56lO9sIKjfS2If4H3g0no7SlpSUki6sc66bSRdJelFSeMkvSxpuKQfNWhrW0mXS3otb/+8pEskbd/bA4iI8cD5VTHXa38eYFtgIvCPvGwNSb+SdLek1/Nxn5H0R0kL93ZcMzMzMzOzttOgcm82IH4GSxYRzwG3ADtJ+qhUnKSlgPWBi4ExfWlT0leAK4FNgUeBi4CHgSWBo+tsfxypt8angMfzMZ8FNge+0+RhK703Pqv6A832JBU7vToiXs3L/g84HBhMeg6uINUGORi4y4kPMzMzMzPrdK7p0V08e0t7nAVsRCry+fe8rNJj4ux+tPd/wLvA6hExorJQkoDNqjeUtA8p8fAisH1EPFC1blbgE80cMCJuk/QUsGw+xvU1m1QeT/VQnb8A34yIkVXHHAQcBfwYOAY4sJnjm5mZmZmZtYWnrO0qThu1x/nAeKacxWVv4BXgun60Nz/wbHXCAyCSG2q2/V6+P6w64ZG3Hx0RtcmLnlQSGlPMRiNpCWBD0lCdS6rav7464ZGXTYqIn5CKtO7ch2NPRdLDjW4DadfMzMzMzOwjHt7SVdzTow0i4m1JVwA7SloQWAxYHjg+Iiaq75nDu4GNJB0L/DUinq63UR4+siLwZkRc1P9H8JEzScNndpf0tYgYm5fvTRq2cnFETDFUJ89QsxOwCjAXaagLpAKu80iaJyLeKiA2MzMzMzOzwnnISXdx0qN9zgJ2AfYClqpa1h9fI/Wo+C7wXUkvAzcDF5ISD5Pydovl+7pJkb6KiGck3QpsAOyQjwcNZm2R9FnSEJfZaGx2oF9Jj4hYudG6P669a/SnTTMzMzMzsyk46dFV/Ntqn8uBUcC+pKlqH42Ie/rTUB6mshKwK/BX0rCSzwAXAP+VNLR2l37GXE8lsbEPgKQ1cywvU1XnIw95OR2YETgMWA6YJSIUEQJuq2xaYGxmZmZmZmaFklTqzQbGSY82iYhxpJ4RawIL0P9eHpX2xkbEJRFxUEQsD6wMPEAqmPqFvNkL+X7ZgRyrxnmk+iTbSpqbyb08zqnqYQKwHWk2lxMj4ncR8VREfFC1fukCYzIzMzMzM2sN1/ToKn4G2+vvwJvAG/Rv1paGIuIR4KT846p52cukKW3nlbRbQcd5G/g3KaGxF/DZvKo2iTN3vn+hZjmSNiElfszMzMzMzDqaBg0q9WYD42ewjSLi5ogYFhHzRcRz/WlD0iySDpE0V83yQcAn84/PV606Nt+fIGnlmn1mlbRFP8KoDHH5KbAw8GBE3F+zzRP5fp88NW7lmIsAf+7HMc3MzMzMzMx65EKm3W8o8Dvg15LuAUbkZesAiwPPACdXNo6Iv0taF/g6cH8uRPoiKVmxJnAvVbU4mvRvUvHRefPP9Ybq/At4OMf1lKRbgJmAzYH7gEpB1JaYY85i23u7wPll5pi5uLYA5pq1uHF/Y8b2vk1fzDlr79s0a+y44toCmDSp922aNetQmLG2ks4AzDNbsfnpIi8YjP6g9236YtE5inv9fjC+2DGw4ycU2hxFDtGddWjnjvcdWvA3jdffKba9eXoqrd1Hr79bXFsAMxb43BXZFsCYAj+Di35vjR5fbHtDCvzMnGuW4toCmPnD4t77sxT8feStgt+rRVpw/mLbu3jPQwpra7cLTiysLYDHTziisLbuvK7gL4bdzHU2uop7enS/90mzt1wOzEeaDnYL4G3gB8DaeQjKRyLiG6Sip9eRpo7dnTSDzHXAL/saQESMB87PP04CzmmwzcbAn4CxpNleVgR+D2wNFPyVp3WKTHjYtKnIhIeZtU6RCQ+bNhWZ8DCzaYhrenQV9/QoSZ6hpNltX6HOLCYRMaJ2eUR8CPwx3/oSzyWkaW4LEREHAwf3ss3bwFcbrN6swT5Oo5qZmZmZWceQExFdxUkPMzMzMzMzs2Y56dFVnPQwMzMzMzMza5Jc06OrOOlhZmZmZmZm1iz39OgqTnqYmZmZmZmZNck1PbqLkx5mZmZmZmZmzfLwlq7iFJWZmZmZmZlZk6RBpd46iaQNJF0h6S1J70u6U9J+BbV9qqTIt/WKaBPc08PMzMzMzMzMeiFpV+ACUueJm4A3gC2B0yWtHhGHD6DtzYEDgAAK7UrjpIeZmZmZmZlZszqs90UZJM0NnAYMBnaPiIvz8gWA4cA3JV0WETf0o+2ZgJOBh4F3gA0KCxwPbzEzMzMzMzNrnlTurTN8EZgTuLSS8ACIiFeBI/KP/e3p8QNgWeArwISBBFmPe3qYmZmZmZmZNanT6myUZId8f2Gddf8GxgJbSZopIsY226ikVYDvAKdGxHC1IMkzXf62zMzMzMzMzPpFg8q9dYbV8v09tSsiYjzwEDATsHyzDSplj/5KGtJyRC+b95t7epiZmZmZmZk1qRW9ETqZpDmAufKPLzbY7EVgHWBx4P4mm/4asB6wX0S8NZAYe+Kkh00X3nijuLZmmbm4tgA+/LDY9oYOLa6tmWcqri2AN0cV19ZcsxfXFsDb7xXX1uwF/g4A3n2/2PaGFPjJP2ye4toC+OCD4toq+r06qOALLePHF9fWQsOKawvgpdeLa2vBgl8jMxc40nfSpOLaAlik4N/DG6OKa2vR+YtrC2Bs0x2XexdRXFsAc81RXFsTJxbXFsCEgkeqz1vg++u9Av8OAgwu8DNzloK/j8zQwWdAj59Q7MXu5Q/7VWFtPX//IYW11fVK7n0h6eFG6yJi5RJCmK3q/2MabDO6zrYNSVoU+BlwY0T8fQCx9apj+soYSLo4z0n8y162Oz9vd1yBx15X0jmSXpA0XtIoSY9LukjSNyTNWcAxQtKIPu5zY95vyYEe38zMzMzMbKBU8r9CYpYulPRYH28f/+ghN/O09M1JwIzAwX3cr886OM85XfoasAXwLUnnRcRU46Uk7QjsCTxLqnI7YJK+APyFlAR7CvgP8AGwNLATsBvwP+D2Io5nZmZmZmbWtUru6VFQb44l6UO9jWyWfP9ezbJ3e9i21z7KknYnnWf+NCIe62NMfeakRweJiJGSjiDNUfw3SetGxEeDHyTNDvwx//jliGjUtahpkhYhZdlEmobo1IjJHU0lDQM+D4wa6LHMzMzMzMy6XTfO3hIR6wxg33clvUOasnZR4JE6my2a759voskd8/3WkjapWbdGvv+jpHeBP0REvRljmuakR+f5K/BZYDPg28CxVeuOJb2YzoiIawo63nakbkXDI+JvtSsj4g3g+IKOZWZmZmZmZt3nfmATYC1qkh6ShgCrAOOAx/vQ5no9rFsz31/Sh/bq6r4U1TQu97I4iDTP8Y8kLQcgaX3gK8BrwOHV+0jaLNe9OF3SgpJOkfSipA8lHdbLIefL930uWydpMUknS3pO0jhJr+W6JOv2o62DJD0oaayklyT9vog6ImZmZmZmZoWSyr11hn/n+z3qrNuBNF3tdRHRa6nriNg/IlTvBvw3b7Z+XnbCQAN30qMDRcSTwE9IL5y/SpoROIX0+zq0h+l85iPV3tgeuI1Um6O3ITCVKYe2rCRYmiFpVdIczQflY1wMPAnsCtwqac8+tPUb0pCe5YDrc+x7AzeQeqGYmZmZmZl1Bg0q99YZTiHV8thZ0m6VhZLmByrTBP22dqeqoqiLlBPm1Dy8pXP9Gvg0sClwE7AScHlE/KOHfbYD/gl8rpkMW3YpqZfHfMADki4HbiQlHu6PiKkma1OamPpsYBjwC+D7lTogkvYAziPVJLkpIl7t6eCSNgC+BbwFbBIRD+fl85ISID11eaptq+FUTsevukuzzZiZmZmZmTWkzul9UZqIeEvSgcD5wIWS/gu8AWwFzAWcGBHX1dm1Ujx1SCmB1tExaSObUi5g+kVgIvBxUsXc3qbzGQd8ow8JDyLiHeBTpLFXM5G6K/0BuBt4U9KfJS1cs9tmwKrkGWSqC5/mIjOXALMDBzQRwlfy/XGVhEdu503gO80+DjMzMzMzs3Ko5FtniIiLSHU9riIVHN0OeBo4MCIObWNoPXLSo4NFxN2knhsAJ0fEiz1tD9wTES/14zj3ACuThsX8AbgLmECqzvtl4F5J1dMbbZzvz6vXEwQ4s2a7nmyU78+vE9fVpB4gTYmIlRvdmm3DzMzMzMysR9Pn8BYAIuKWiNg2IuaOiFkjYp2IOK2H7Sv1OkY02f5mefvbi4rZw1s63+ia+540Mz1QXTl5cUW+IWkO0vCaY4H5ScmQrfPmlZ4fIxo0V1le20OknoWBAF5osP55YJ4m2jEzMzMzM2u56XF4Szdz0mPa0vSwlt5ExLvAKZJeAS4DNpc0S0RUF0aN+ns3vd7MzMzMzKy7OOnRVTqrr4x1ohvz/WBSgRqAl/P9Ug32WSLfj2yi/ZGkgWqLNVi/eBNtmJmZmZmZlWT6rOnRrZz0mM6p975Zy+T78aTqvAA35/vPSBpcZ599arbryfB8P9UUt5K2xkNbzMzMzMysg0iDSr3ZwPgZtIMlnSxpldoVedaWP+cf/x0R4/P/bwQeJPX0+El14kTSLsBuwPvA6U0c/+R8f7ikFavamYfJ8z2bmZmZmZmZ9ZlrethQ4CDgIEnPkpIZY4BFgE/k9c8CH01BFBEhaW/gBuB7wK6S7iMNRdkQ+JA0bdErvR08IoZLOgE4jDRLzLXAB8AWpCKmtwPrDfRBrv+FZXrfqEkvXvdMYW0B/PW2OQpt79V4vLC2Pv+34wprC2Cls4pr7+Ih+xXWFkCMHt/7Rs36YEJxbQHjR/U2cVPfrD/k1sLaOvuRtwtrC2DxLx1VWFsxodiyQved8aNC2zt0pQUKa+uePQ4rrC2A4T/+fmFtfWPB+QprC+CWxfctrK1HrjuhsLYAlp5hpkLb2++TsxfW1j9e2KmwtgBihuI+MwfNP2dhbQEMXaO4TqIxsdjPkSf+/PtC25tIcb+Ho3acrbC2AO6748PC2rp3ZLF/V4e9Wewp0JLzFzf84M7rCivRB8Dz9x9SWFtbn3ZiYW11Pw856SZOetipwIvAp4C1gfWBuYH3gLuBfwEnRcR71TtFxIOS1gKOyvvuAbwDXAL8IiLu7EMMhwOPA18nzRDzJmkK2yNze2ZmZmZmZp3BhUy7ipMeHS4i9gf272WbG+lnujHP0nJhvvV13+dJvUSa3b5ujBERpGE0f66zerO+xmVmZmZmZtYqnrK2uzjpYWZmZmZmZtY0Jz26iZMeZmZmZmZmZs1yzqOrOOlhZmZmZmZm1jRnPbqJkx5mZmZmZmZmTZKTHl3FSQ8zMzMzMzOzZrmQaVdx0sPMzMzMzMysaU56dJNB7Q7AzMzMzMzMzKwV3NPDzMzMzMzMrGnu6dFNnPQwMzMzMzMza5JTHt3FSQ8zMzMzMzOzZrmQaVdx0sPMzMzMzMysaU56dBMXMjUzMzMzMzOzaZJ7epiZmZmZmZk1zT09uokiot0xmHUESQ8DRMTK7Y6llmPrv06Oz7H1j2Prn06ODTo7PsfWP46tfxxb/3VyfI7NrH08vMXMzMzMzMzMpklOepiZmZmZmZnZNMlJDzMzMzMzMzObJjnpYWZmZmZmZmbTJCc9zMzMzMzMzGya5KSHmZmZmZmZmU2TPGWtmZmZmZmZmU2T3NPDzMzMzMzMzKZJTnqYmZmZmZmZ2TTJSQ8zMzMzMzMzmyY56WFmZmZmZmZm0yQnPczMzMzMzMxsmuSkh5mZmZmZmZlNk5z0MDMzMzMzM7NpkpMeZmZmZh1O0hySZm93HGZmZt3GSQ8zmyZIOlXSgU1st7+kU8uIyaYdkhaXNE+747Dp2ijg6nYHMS2RtIKkPSR9vKTjbSLpY2Ucy8zMJpuh3QGY2dQkDQP2AdYFhgHXRcSv8rpVgKWBayNiTIvjWHwg+0fE80XF0oT9831vCY0Ngf2AXhMkRZO0EbAzsBwwO6A6m0VEbFlCLPMA2wMLA68AV0bEqz1svzOwekT8pIUx/RC4LyL+1apjDMCzwOnAF2pXSDoEeCQiri07qJ7k9++O9P56m+oxtZqkGYHPApsACwEzNti0lPdDLUmb0VxsZT537wDPlHi8aYKkzwBfBo6MiDuqlh8LfKfq54uBz0TEpBaGcyNwGvU/R34L3BkR/2jh8adJ+bOup/cqEXFTeRF1ji77HmfWMk562HRN0gzAksDcQABvASNa/KWnt5j2Av4CzEo6SQngpapNlgMuJJ3kn9nicEbk4/dH0JmfMUOBiWUeUJKAv5GSLZUTz2DKk9DKz/19vvsSz26k5FB1V/nx+Uv3jyLiwzq77QLsC7Qs6QEcTUosTJX0kHQPcGlE/LiFx++JqJ80ADiBFHfHJD1yAukHTNmjs/q1V/k5qHMC1kqSFgGuI32WNXpOK1r+fqgmaU7gUmBjmoutzOfuXmCZEo9XuJzQfzsiyvwM3gdYE7ivKo5PAEcA7wL/BtYDdiMl4s5ucTyNXleHkT5HOibpIWkl0vNUSQAObbBpRETpf+9z784fAM2c2A9uYRwDSUZGRLTyfT2Cae97nFmf+YVs0yVJOwCHkK76z1Szeoykm4HfRcRVJce1MXAW8DZwFHAL8L+azS4nXfHbjdYnPW6i5JOOVsrJh7WA10s+9FdISaq7gCOBg4FdgeVJvXY+A3we+C3wx1YGImlV4FxgCPAY8GCOYW3g/4DNJe0QEW+1Mo5+WIOqkxZrLF/ZPpr0ZfdnwJ7A1sA2TH69bUZ6vV3WhhB/DXwMuBU4DngCeL8NcdTzS9IJ3lPAn+i82K6QtEdEXNjuYOqRtA6wHXBhRDxStXwn4GRgfuBdST+IiD+UFNYqwAMRMa5q2b6kv22fjoirJc1N6s11EK1PenQFSeuTErkz50Vv0jnvBSQdAJySf3yQ9r5Xl2zTcZtR73vcjKREH6SLfZXeHIsDlWGctwPjMJtGOOlh0xVJQ0hXuD/H5Kst40hJBpF6fMwKfArYRtIZwJdKvCp1JDAB2Coi7s8xT7FBREyQ9BiwcquDiYjNWn2MgZB0fc2iT9VZVjED6SrpgrQ+WVRrf2A0sG1EvClpH4CIeBJ4ErhK0hXAeaQTwedaGMsRpITHMRHxw8rCPPTmVNIXoZslbR0RL7cwDmudrwLjgc0j4rn8uyUirsnrT5b0TeBXwCVtiG8b0pfsrSJibBuO35OdgVeB9Tow8fcB6STvPEmXkxJWzwN1n8M2def/BrAXVclbSUsA55N6CYwEFgB+J+n+iLi5hJjmB26rWbY58FpEXA0QEW/nix1rlxBPt/gFKeFxAunvRae9Hw4HPgR2j4h2JG8/EhEdWyOx9nucpDmAG4AHgO9U/V2orN+alGCdkfRd2Gya4KSHTW+OBfYmXQ04gdSN9NGICPioJ8BKpC6uh5CGI7wOfLek+NYDbq8kPHrwArBqCfF0us2q/h+khMaCPWw/gdRT5tstjKmeFYHbIuLN/HPl9Ta4klCLiAsl3Z1ju7SFsWwKPFWd8MjHHy5pbVIvkO2AW3Li46kWxmKtsRpwa0RUkmcffb5VPusi4nhJXyD1KCv7i+2MwPUdmPAAmBP4Twee4EGqB1EZBrcjsEMv27esO38P1iPV5XmjatkXSAmPb+XX3ZrAHcA3gTKSHmOAWSo/5HpGywMX1Gw3inThw5J1SL/Lw9sdSAPLATe1O+HRhY4BlgCWi4i3a1dGxDX5u8gTwM+Br5ccn1lLOOlh0418telQ4A1g44h4vHabfELwMHCUpLNIX8gOl/SHiHihhDBnJnUh7c0cTEPDTgZgqXwvUoG/C6kqTFdjPPBGREwoI7Aag0ivu4pKAdq5a5Y/SSou2krz0yCpEhHv5W7ofwUOAG6StE1EPNjimKxYM5KK01ZUkgtzkXq1VdxPe67kPQgs2objNuNJYL52B9HA3+n8z/0FSFeQq32SdKHhJICIuFfScNKQtTI8A6wvaUj+/N8tL6+dCWdB4LWSYuoG40nDvDrVW3TQcJsushsp6TxVwqMiIt7KvWZ3wUkPm0Y46WHTk/1JJ8eH1Ut41IqIxyQdRhoKsR8pO95qz5Gu0jaUi6+uRpu/jEhamZ5nhSAi/t7KGKquZCPpx8C91cs6yEtMeZJXiXFNoLpr6cdI3XVb6U2qrnrWykV8vyDpLeBbwA2SWp2I6RbLStq3H+ta/l6oMZIpezxVCiGvDAyvWr4o7ekN8EvgQkmbRsR/23D8nvwe+IOkVTst2RcR+7c7hiZM8XrKs/SsAdwQEeOrVr3M5JoCrXYqqT7LTZJuIyV036cq+Zv/rq5DqrvUamvkQsN9XRcR8dNWBVXHcDq7R+mlwM6SZo6ID9odTE8kzUXP35XKnCFlXnr4DlBl5ryt2TTBSQ+bnmxAqtTel8ro/yBdndqoJRFN7XLgm5K+FhEnNdjmcNIJzZ9LimkKkrYijdfuqdp4ZVaI0k702jirRzPuAbaWNEOeGeVq0onfryV9FniRVOx0bdKsFq30NPDx3jaKiO/kxMfPSPE+2+K4Knqqy9LTujKmNt0w3+rZqId1UOJ7gdSTovp3fCPpPfkTSTtFxPuSPk2aoaS21kHh6kyZeA+pgOllecaga0jvgbq9GMo8IYiIUyQtB/xH0lHANRHxUm/72UeeY8oT5a1IQ1tqP9fmIBXkLsPfSDU8Pg18glRf6UtVww0hDReaE2j0+VKkNWjcy6XeuuqZvcpMenwPuF3StyLiuBKP26zvkX6vp0v6ckSManM8U5C0IOli2c5MLg5aT9kzpDwFbCFphYh4rN4GklYgvXefLDEus5ZSHt5rNs2T9DzwZF9PjCRdC3wsIgY013mTxxpGmqFiIeAc0pWM84ErSEmOXUg9Vl4A1oiIsr40VuJbhzSjTJDGQ6+ab8eSkiBbkYZsnA483+GJiNLkxMbZwG4RcUledg6p4F/1h/BEYKOIuLOFsRxNmuJv+4i4sontDyIluQQQEa2c9m8gU0VHi2M7bSD7R8QBRcXSm1yr46/AJyPi2rzsOtIJwofAe6ShLtDk62CA8UyifkKjmSmaWzoVpqRGRarbHls3kvRzUg2sE0nFEn9Bqp+xYi7cXNnuBeCliCirt0dliOv8wGMR8V7NujVIdQ5uj4hXWxjDjwayfyv/pjboqbYuqTDyzfSenGxpYlfSqXUWzw3sRLqgdReN44uIKG16aUkLkWbeW5jU024IkwvqLk0aBhb55wkRsXmJsR1E+j75JvAb0ne5yvDtxUizfR0ODAMOjoi/lBWbWSs56WHTDUnvApdHxOf6uN+5pBODOVoT2VTHW4H0R2hlprzCQ/7/Y8CuzQzRaUFsF5ESL5/Kxa5OA/atnGzmLpwnkwqMrtPKOig9nKw0o/STldzN+8NK4dI8k9C3SM/n3KSiYb9q9WwGktYifTm8PSI2aHKfPUhJmxlanFjYdCD7d+BQibbIr7UlgdcrBTlzxf5fMeXr7RcRcU4J8dzIAGpRtPKEQNIIBhbbUr1v1X+S9iTVLro8ppwC9uek32U9/4iIn7QyrkZykdA7SEnwyt+v30bEt6u2+QTpZO83EXFECTEdAoyJiFN63Xg61ktykgbrKutbmnSGzk6K15J0Emlq+h9GxDF1vittQhpy9RawddlFnSX9hpTYqPxOq79jVu6Pj4hvlRmXWSs56WHTjfwH8/SIOLCP+03xx6oMkgaRrl5sRTp5GUy6gnEtcGGUN4VubVwjSVfn1sk/T/Xc5BOuEcDVEbFfC2MZQQefrHQySYMB+vI6kjQfMEuH1kwxm+ZIWozUFf1xYO3qIsz5s7fR5+t4YJl2DcuRNCuwB+nK9t0RcX3N+p1JifHTIqK26Gkr4pkAXBERO7f6WN0s9wIcyN/Ulvbs7KakuKSn8zGXyT/X+660COm9/buI+H5ZsVUdf31SYmZDUo8USPWghgMnR8QtZcdk1krulmnWgXIxyUvyrZPMA1R/cRgP6UtuRIwGiIhxkm4Gtm5lIBGxZCvbn5b1J2kWEa+3IpZpjaRlSd2CXy65OJ1Ne/YmfU87usGsU0HqKl9tS+AUYF/S0JLS5b8FZ/Sw/lJaOy13rVeYPINRx5M0C6kG2TBSwdfbGvz+CxURR7f6GAPRZT35FgH+XfVzpYfnjBExDiAiXpJ0A6nWTOlJj4i4jRJqOpl1Cic9bHrTU2X0hvu0IpAu9TqpAF31z5C+eFfPdDAzqSicVck1W/YhjZMeBlwXEb/K61YhPY/XRsSYxq1M3/LVqe2ZfELwzzJm2ZC0ALAp8GJE3Fpn/YakgonLVS27BzggIh5qdXz15BkpdmDy6+2OiDg1r1s4L3skF9ctM65ngAsi4ru9bPcL4NOVq6VlyIVyr6y8L3vY7tvAdhGxRQvD2ZJUq+CSRhvU9rySdDqpeOKWtCnpUS33/JsHGFcZatUGVwHbShoaU84gUzpJywC7A49GxGV11u9BGiI6V9XikZL2re0xYx3tXaacqWVUvl+ENIVyxdi8zMxazEkPm96sQd+SGLU1NVoqj/NsxnhSEaqnotwxak8x5awtd5Keny+T53LPV7q3YMo/7NM9SXsBfwFmZfJrqrr7+XLAhaRCtWeWHV8nkLQxcCTwn4j4fZ31xwOHVH4kPYc/lPSDiGj1Cd7nSTPufBGYIumRZ/u4kjQNoEjvzXnIs/FIWjki3mhxfFPIXcHPJH2hrjxXQ0jTd0I6KT6ddJXxojJjIw3Zm6+J7Yblbcu0GWl4Xm+WJyXBWmkV4H+5519TImKSpEdINaHaRtLBpL8Lq5Bef2cAB+Z1nyYVcf5udXHTFvo+8EngbEmHRMTIEo7ZyGeBHwOfqV2RC4WfQ/puPppUv2tJ0tCDf+XPkdKGF+bhVZuT6j890WCb5Ukz4lwfES+WFVs+9gakz+O/5l4LPW1zckTcUWJ4zzPlZ1cl8b0d8Icc2yykoSXtfD2aTTec9LDpSTfMJHIjfUuwjJb0D+D7JQ0/uAI4VtIq+er1laQpCg+WtC6p7sgWwEykq94tUzUN5ksRMbHOtJg9KnPoQT6ZPwt4GziKNAPO/2o2u5w0heNuTKdJD9LUftuQpsmdQk4aHZp//B9wEykBtzNwjKSbWjwGeVPSVbnz66w7mpTMeg7YISIezkV9TydNhXkI0NceZv0maVXSe3Uw8DvS6+2Cms0uIs3KszvlJz2aNSvQ8m79/TQTaSacVpoHeK3BuktonJx5lfKmWZ9C7l30T9LJ3XjgEVLio9qjpCKs95B6pbTaL4D7SZ+t2+ceWM9Tf8hLq2f52BR4n/rDe35E+l7+IKm45Wu5vtdJpATSIaTi12U5HPgGKcHXyIfAacBvge+UEVSVr5FeR4f3sM2jpATbYFKB3bJcDxwmaYE8G9C/SIms3+Rk0oukXp8LkAqatkzuWddfUWZPO7NWciFTsw6SuybPRSpiOgm4l/TlTKSpxNYEBgGXkYaQrEG6GvoM8ImIeLPF8S1IOskcHhEP52Wrkk4EK1+MJpESHl9pZS+UXJh2ErBSRDzRQ+X5ekqdvUXSFaQrZutFxP152VSFdSXdCgyLiI+VFVsnkXQbsHhETNXdV9KdpJ4TlwB7VF5bkg4gvd7Oioh6Uy4WFduTwMiI2KRm+RBS1+WZSEMxLqpaNxfp/ftYRHy8VbHVifUCYFfSlLXX52X1Xm93ArNHxIplxdYolpr1g0ifJ9cD75X5fmim4HWeCedeYHArawtJGkW6gr5bH/f7J7BFRJQ+xFDSt4Bfk/5GfSmfuNd77T0NvBIRG5YQU19m/WjpLB/5BPSZiNiqZvnMpM+RGcizo9Wsq0zxu3qrYqsT68OkYUlr9bLdPcCQiFi1nMg+Ou7TpM/kHhN8koYDC0bEsuVEBpJWB/6P1MPkxrzss6QE0VAm9yJ+mDRN/TstjGUgs94QEYOKisWsndzTw6yzHAHcTiqAdWhETJGhl7QU6crtasB6wHuk8b975317HCM/UBHxSj5e9bIHgRWVptqdmzTkpoxeJzeRvjiMqfm5E61H6iJ8fy/bvQCU+sWxwywG3F27MNdCWTv/eHR1Mi0iTpN0FKnwXyvNT/0rheuQEpBjSL11PhIRo6qSNWXalPR6660GwPO0uOBwhaaeYno/Sb3N7iTSkLCWqnMldA9JmzXYfAbS1dkZyN3UW+hl0md9X62W922HfUnd9feKiA962O4ZoKxkW8umPO6H+akZHpd9gjT87B1Ssu8jEfGBpLuA9Vsf3hQWB/7TxHZPkYYPlW1hmuu98TzpglHLSPopcGZlGFD+W//Z6m0i4lxJt5B6QVWmDf9Xq4vUOmlhljjpYdZZjiW9L3evV3AtIp6VtCfwJHBsRBwg6SukIQE70eKkR08i4rGSj7dZTz93mJlJdR56Mwedm7gpwzBSAbhaG5BOgF9uULT0IdL0zq00I1MW8a1YL9/fU6nKX+NVUq2PMs3BlPViGpmR1O27DC8w+bW9OClJ1KjOyXjSSfu/gBNbH9oUY+8DmC3f6pnA5NiObG1Y3AR8SdL6jWoW1MoFdZcC/trSyBpbjlQItqeEB6Tf/bAS4um0WT8GkYYt1ap8jtzVYHatN0jv1zJV6gD1ZgjtOZ8YTXOvoWHkmeZa6PvA93Jy6kzgvHoXf/Kw2j+3OBYzq8NJD5tu9GPWlmoRET8tLJjGtiN1Z274BzrSlLC3ANvmn0dLupc2jeG2pjxHL1ds81j41UhXzaZX44CF6iyvXOG8s8F+o2l9suhFYHVJg2oKS26Zj93opHROGp/ct8pImruKvgrptdly1cNAcnfrC3oaQlKm6iuhzQxvKdFfgIOAv0raOCLe7mljSfOQkh1Bmra2HcbROGFUbXFSr4bpzQhg7TozyWxD+r3V6wUC6cS9UX2XVnkS2EjSzI2SWHnozUbA06VGltxPim/RRkVUJS0KbEzjvx1FOZ00pHBdUu+/30q6ilTL69KI6Jopk82mVU562PTkaCaPo2xW9ewtZSQ95qC5KxfzMuVV57cpoYeApFN73+ojrS4I100uB74p6WsRcVKDbQ4HFqTFV4Fy8u++iPhXK4/TT48B60saVjPbyc6k1/dNDfZblNZXwL+BNPvE98jFFyWtRzpZgZqhLVXWJCVMynQ18EVJu0bEP+ttIGl/YAngN2UGlm0OvNKG4zbjADok8RgR90j6K/Al4P783r0wIt6v3k7SbMCewE9IXf5Pi4jaQslleRBYV9K8jWpM5aLTa1EzjGM6cRWpIOnvJB0aEePzNLWbkj7jLqndQZJIQ+TqzqDSQheSikr/RdJBtYkPSTORhrvOQzk9smqdSvosuUzSgRFxb018a5LqPQ1l8qxVLRERB+YZi3YkFSj9FGlq9e2A9yVdRKo71TGveUnzkT7vNiZ9bgTp7+hNwBkRUXaSzaylXMjUphuSfjSQ/SOi5bO/SPofsDqwWUTUveIjaX3SH6X7ImLdvGw4sHBELN3i+JopiPVRoqiVBeFqSepTEcuI+HurYqmVa1LcR+rFcA6pcv/5pBk2/kyqQL8/aQjAGiUUNat7JTsXpLu0jNd6PZK+Q5oW9k5SEbg3SLMWfI00rGDpiHipZp+Z83bDI2IbWkTSx0i/wxlJv6c3SPVXhgB3RMRU4+1zUuRW4PcRcWjt+hbGugTwQI7116TX252k19wxpNfb90g9ZFbNtXqsA+UeYGeSpjgNUvHmZ0ivvyAlyZchDZsQaZaevSOi1TPLNIp3P1KxxquAz0XE29WfOTlBczGph9QejZJyLYhrFlJieWfSEJzZG2za0iLXkhYmDcebk/SZ9j6pvoNIU3VvX2efrUiJzF9FxP+1KrY6x52FNFPWCqQk5dmkHh0BLEuqJbYgKRmzbm0yrqQYLyDNQDWJNBtQdXxrkd4X/4yI3UuOa27SdOD7MHl4ZpCGxp0NnN1gqGZZ8e1OSgjNztQXAoNUL+7AiLi47NjMWsVJD7MOIunTwD+AD0jdJS9k8lj4xYE9SCfHMwGfjYjz80wCr5L+sH+uxfFt2mDVIFIRym1IX85PAC4rcyx1H2ZvKT0hA5ALvV4ArMyUPYgqMT0G7BoRj7c4jp6SHm3t2i9pRlIh39WZ8ncp4LiImGpKxJzsOh34v4j4VYvj2xY4gyl7Yz0KbB8RI+ps/w/SF9/tI6KZgoCFkbQR6fW2AFO/LwS8Tnq9NepOXwpJKzP5JLRuL7wyE5TVJM1OSij0FFuj3kdFx/Jp0pSgjYri3g38JiLOKyOenkg6n/S36l1gOOlq9xOkk/3NSSf550TEPiXFMydwM+mzdyKpvsMspKvaCzL5d/scQEQs1eJ41iJ9jqxctfga0t/0t+psfyWpUOgWlZlAyqI0Y9tZpKnoYcq/WZB6wH0+ItpSODfP9PQ9UkJrrprVo4DjgZ83qJNSityzaR9Skqgy7DBIvaLOBM4t8/mTtA4pGT+I1LPoTCZPf70E8HnSUJ2JwIYRcVdZsZm1kpMeZh1G0mGkgqaVac2mWE26OvS9iDgub78U6cTquk744yRpH1JX0q3KOiHIxz2a+kmPSkJmU1KBv9OB59rRmyF/QduJVHRzSVIRyReBa0nd1lv+xayTkx45hrlIQ8l2BeYjPT+nAL+sqaVR2f5W0hfJjSPioRLim4k0hr0S2y314srb7kh6/f2npzo9rZJP2r9A/dfbyRExquyYqmLbCvgjKanQcDPak6BchZS43YxehkO2IbZhpNo/w0ixvQHcXzMcrK3ycIzvAN9m6uGa75CGVP08SvoCKukXpCLfJwPfJPWu+3xEDM7v50+T/ubeREo8lBXXsuTPkYh4oYftViP9rh9q18m7pHVJnyOL5UUvANe2cRjVFJSmDl+HKeO7K1o8M0pfSVqDlADZi8lDSiZGxNASY7iI1Ntvz0Y9OSTtQuqRdXFE7FFWbGat5KSHTXckbUf6wF+MVHTtAdIY6GfbGVe1nMj4AqmA48J58UhSdv60iGhH0bCm5cKqb0fEFr1uXJL8pegE0hXIdXMV9bKO/VvS81FGXZjeYunopIcNnKRDgDER0a5ilj3KVxpvIX3hv4A0TGhV0onnMqSTq7lJCcrny0xQSlqO1KV/jhzjQqRk6T+ApUld5mcgzd4yKiIOKCu2biNpMKmmzZJMTrj9r+wEoKRHSb11loqICZJOA/atTlhJWgm4FzgqIn5dZnydStLFwMiI+Fq7Y6lH0lvAgxHRqAdqR8o9jz5LSu7PS/lDgV8FnoiIjXvZ7mbgYxGxQDmRmbWWC5nadEXS2aQMO0y+grcj8G1Je3VKccecgDmq3XEMwJOkQl4dI3/ZPZRUXOxYoKVDgWp8nVRXwawMx5HqxXRk0oM01esMwKci4pp8ErpqRHwfPurtczKwA+nqbZmOIp0gHxARZ+TYloqIvXNsy5LGwq/E5GlGSyHpTNJQg2sa9S7qJLlXwl351k5LkHolVK76T4KUCK8si4hHJP0X2I9UB8fSsKRL2h1ED2ag/CLR/SJpKOnzbB/SzHtDSd9B3yLVWirTnEAzF32eJ81GYzZNGNT7JmbTBklfIGXXJ5KuIB5Cmlv9dlKNjL/nDLwNQB7CsRr5i2UnycX97ga2LvnQL+LPWyvPK0AnT5G4AXBvRFxTb2UedrMv6TPkmBLjglS74NGIOKPeyoh4ilQMcz7KmdGr2t6kZNZISb+T9ImSj98rSXNL2iQX7Gy0zSJ5m7lKCmssqVdnxbv5fsGa7d4i9eqx5Flg1nYH0YOHgUXaHURPJG2mNAPTK6RebbuQkh2XALsBC0XEV0sO6xVgjSa2W4POnWXLrM/8JdymJ/uRvkRvGxFfiIg/RMQvImJDUlGx2Ul/hDqGpLkkLSZp8Xq3dsdXTdIsebzquaTChKUVMe2jBSn/i9w/gU1zjYVO8ClJ19feelqXb9e1NWpr1lXARvnqYieahymnhR0PIOmj92VEjCMVnyw7QTk/8EjVzxPgo1ouwEdJmRtJV27LtBdwGelK7TeAWyU9JeloScuXHEsj3yIVt5yvh22G5W0OKyMgUn2HJap+fizffzQsQmmWnHWButPsTqfOJf3dqk0OdYrfkz7nNmp3INUkrSrpl5KeB64jDVWeC7gNOBhYMCJ2j4hL2lRz5CpgBUk/zfV3pqDkGNKsPVeWHp1Zi7imh003JL0NPBwRU/2BzOO4HwdOiIjDSw9uylgWJF3d3Jl0ctBIS6fWq0dSM0XUKjNDbBwRT7Q4pKblHihfA34H3BkRpXVNz8mO/5KmCD0kIu4t69h1YhlID5zSi0pa30lagFSX4g7S621km0OagqQXgQciYrv88zGkIS9rVE/jKOky0owVpSUpJY0k1Z3YKf98LKko50rVMyvlegefiohZyoqt6thzAnuShuhtQrqAFaRebGcD50WbpiHO9ZyGRsTKvWz3MDA2IhrNRlNkTH8ADiRdVX8n90J5BhhD6u35IunEdEfSrDKfb3VM3SDXwbqENP3r/wGXd1Jh0Hzh53uk2UZOISUEn6dBL7dW1vGStCjp/bgPk2flESm5exZwZqfUjcux3kv6fvkMaXjNCNJnyFKkGfiWIiUA14qIrhhCZNYbJz1supFP2M+KiP3qrBsEfAicGhFfLD24yXEsRDpZWRh4CRhCuvJ4G6mIXmX6yduACRGxecnxjaDxtLDjScVW/wucFBGvlRUXQFVPhXpmI/0Rn4cU/45lTiGaY5sZ+EQ+/kgafzmLiNiyhbEMqOhblDgNsfWPpFNJV9q3I3Xrv4eeX29fKDE8JN1IOgFdPv+8E+nk6o8R8fW8bFngfuCZiFi1xNhuBWavHFPSXsA5wI8rBVWVZlB5AngtIlYoK7Z68t+Mz5JOuNbKiycCN0TEJ9sQz9v52D32msxJo00ionZ2l1bEtBGpTsePI+LKvOxw0iwy1VOwvgKsE22afrXTSHqGybOfQXquXqPx50hPMzEVTpOnqa+e/r2Rll4kkvRhjkOkZMH5pETH7a065kBIWpWUIF0lL6qdivhBYO8yZkQzK4uTHjbdUC8zU/S2vgySTiJ1f/xhRBxTW2Ve0ibAn0hjj7eOiE4et1+qJnowTCDVb/lJRJQ6TKOPvSvcm8IGpNNfb5KOIBUTXi0iHsrDcB4HFicVvXyRVFtjDuBbEXFCibEdDfwAWDYins1Dbp4lzbJwYY5ttxzrzyLih2XF1pvcY/FbwEG06XNE0vukKZr37GW784Ed2tFTpiqGT5Cmxp6blMQ6LSLealc8naavvQIjotQh8zl52vRJTCsvEkkaC1wOnAlc0Uk9YnoiaTNgYybPEvgycHNE3NimkMxaxrO3mHWWTwHPRkTd4n0RcZOkT5JOEH5A6pprSU8F6MYDb7Txi4iL401j1EHTENdRag+wfvg78A75hCUixufeHueT6iqsS6q/dAppOFqZzgRmJPWUeTYiRufeHueThpRUXAP8rOTY6srD53Yn9fZo9+/+WWB9SYPz7C1TUZrKdgOam0GiZSLiDtIQsLaRtFCzw88kbVlmwr7sJEZfRcRm7Y6hyoK51k9XycmNG9schlkp3NPDphtVXSH7o5T6Gflqwb8jYvf88ynAAcAsubBfZbvLgBUiYrlWx9QgziVJxegGA2+QTg46brYW6x9J65Om9h1GuvLzz+paCyXG8UPgvuhlKmlJOwJrRsRPyokMJI0HLu3tirb1jaQVSFfen4qI19sdT0Xu8bExuVdARNzd5niGkN6je+f7GUld00eQhuOcHRGPtiGuXwBHAL+JiO822KZSJ+W4iDiizPg6jaSHgI16O2GWtA1wcZn1bWz6kos1z0W6QPRhm8MxK1xHZ3HNWkD9vJX1XnmXyWMqAUbl+9pp2cbWWdZSktaVdI6kt4CnSVfIbiV1C35H0kWdVkW9m+SK6Z+UdGaLj7OxpCskfaPB+uOB4aTCkgcBPwLulXRkK+Nq4GjSFH+92YkUZ5m6fhpiSctL6qieKhHxWETc1kkJD4CIGB0RV0bEue1MeEjaXGkKzFeBi0g9PN4H/kw6eV46Io5qR8Ij+w2pZtG3Jd0j6auStsmfbV+VdA8p4fEKqc5G4SQNkfSkpLGSNmhi+/Xzto/mXihlWhH4j6SGw3wkbUuqedNMIfHplqR5lWZOWUXSvO2Op1NIml3SxyV9rMH65SRdRfr++RLwnqQLJHX0dMBmfdXVX9jM+iIiBg3kVlKYzwNLVv1cKSK1XWVB/nK0IemLZSkk/YZUPHUv0pWACaQv3a/l/89KGhv9X0m/kCZPgyZpWL4SX7r8x/5rkk7McW3cjjh6ImlNSceRvmz8h9RFvZV2BrYhFbesjWUv4FBS4u0u4Djg0vzzMZI2bHFs/TWYNByiTJ02DXFTJM0n6RuS7iRNzfq9dsdkzZH0AnAtaaaRocB5pBlHFo6Ir0XEre2MDyAi3gQ+SUqGr0GaVvQK0mfb7/OyJ4FPtjCxtRewDKm3Sa/PSUTcBvwS+BhTDmEqwxGkAteX5N47U5C0A+mzZgJV3wPKlJMIf5T0oKQ3Jb2R/3+SpFV6b6Hl8X1F0iOk7yP3kQogvybpYUlfaWtwnWE/0ve3bWtXKBVCvhnYilTyQKReY7sDN6pqGnGzrhcRvvnmW4fcgF+R6k8skH+eh5R9H0v6UvYNUg+LicAfSorpd6QTytHk4oPAoKr1g4HVc+yjc2wn5nVLk+qP/LBFsa1IugL2Vn6ebgd2yetWJl1NnJjjn1gdW5t/z4uRpgB8qCq+SaSK6d9v8bFvA15qsO7OHM9F5OGPefkBOb6/l/w8TSLNqNTbdneQZtIoM7bZSYmjm0lDa9r6muol1plIJ4KX58+XymvuTeCvJcYxP6lXzm7AijXr9iLNXPUuKaF7SuVzsKTYBufPtrWAOWvWrQ9cADyQ3z9HkYYclv17/JCUPNgHmLXdr6smns89gZNJSY8r8v/3BAa3+NiX5r+Zc/ZhnznyPv9sw3P18/x+vKDmc3dn0uxL7wIbtOn3eGjNZ0btbTxwaJtiG0QqLlyJ7S3SVKz35s+2yt/9C6uf1+ntlh//OGCeOuv+lJ+n10jD5GYlfZ+7LT9332l3/L75VtTNNT3MOoik1UknwydHrp4t6bPAaaQre5Xp2R4mdWV+p8XxbEg6qRsBbBMRT/ay/ceAq4FFgW+SriLPD3wjIv5YcGxLkL7czMmUQ4ImkQrC/g5YjpQUeZaUgNmF9EVpt4i4tMh4eiNpDmAP4POk2gCVoVMB/AI4NyIeLiGOF4F7ImKnmuXDSL13ANaImhoekp4m1bZZtsXxnVr14/7AU6ThNvXMACwPrANcErkWThnUQdMQNyJpC9LrbTfStM2V19u5wD+Aq6Kk4r55eNSPSNNwV1wKfAb4CnACU76Pg/S7Xzsi3m9xbJ8jfV7MkxdNIE2fe7ikPUnPV3VvvyAl2jYt6/nLcc4XHTbspxPlz7hnImKTPu73X2DpiFis140LJulk4EvA3yLiS5J2J73uPiD97S196lNJWwNXAWNIw6fOJH0XCFKP1M+T3ruzkHrulD0r2leAP5IurHwnIi6vWb89aQjV8sBXI+LkMuPrFJIeA0ZFxHo1yweRkkNzAF+KiFOr1i0MPAPcFREetmzThnZnXXzzzbfeb6TpEb9CqrOwOzCkpOOeScr2r9uHfT7B5Cssk0jTOrYitpNz+9eQZixYCdiXdPL5RD7+1jX7bJP3ubyk528wqfv5eaQvjpXn5A3SFZbHgYklv5bGAmfVWb5Tju3FBvtdCowuIb7qq4iNri7W3u4jnayU+Tw2E9dHj6PEuFYl9Qp7oer5m0DqITCi7NdbjmnHqudiBKlH0agc349IV7LvInXfX5F0xfHuvP4HLY5to6rnaTzpimfltfc14HXgOeCrpO7hXyMltyYCh5T9XPrW1O90LKmQa1/3OxsY26aYRZohaCJwGenK/CjgE218Hv+T3xMNe5mQekFNIE3TWnZ8d+TnqGGPMGBB0kxRd7TreWz3DXgbOKfO8jXzZ91YYLY66/9LKmra9sfgm29F3DxlrVkXiIjnSVdayrYxcG9E/K/ZHSLijlysbk1SD4+TWhTbVqSeCTtFxAd52SOS3id157w7Iq6pie2qHNs6LYoJAEmfIF0F+zQwL+kL7TjS2OyzSDP0TJB0M9DSnhN1jAMWqrN8/Xx/Z4P9RtP/2Y/6ojLlpoDrgStJJ/H1jAdejojnSoirVsdMQ5yvyn2ONOxhVSb3mLiH9Ho7NyJeza+30q9ikxIFAXwlIv4KIGk20knekaTf4zaR6kEAPJprjjxD6uLfymKr3yI9Xz8Hfpzfl0uQEpU/J/WwWzt/BpNj/zfwGKmXyoktjK0uSasBB5MSNgvnxS+TekT9JSLuLTumenK9m2VIQ8FUb5uIuKkFhx5L6oXVVzNTv6dWy0VESNqb1HNxe9LJ/DZ9+dvbAh8H/hs91EWJiNsk3Ui62FG2lYBrIuLVRhtExCuSrgO2Li+sjjML9d8Plb/590f93nQvAevVWW7WlZz0MOsgucv8lRHxq162+zawXURs0eKQFiDN0NJXT5DqHLQq4QFp9pqrqxIeFTfm+6ca7PcUaex+K93G5ATBzaQTzwuixcORmvQYsL6kYRHxRtXynUkxNzoJWZQSiudGxH8r/5d0BnBz9bJO0aZESyPPM3m41AjSlKVnRsTj7QyqytrAA5WEB0BEvJ+HvNwL3FSV8Kisf13SbbT+ZGo94PGIOKrq2M9J+iZwC3BjdcIjrx+RY1ujxbFNRdIPgB+SepFVm5tUx+iLkn4WEUeXHVtFLm55ArAZDZIdVVoxW8oLpKR7X61JmpWpZZSm4e7J/aTE703AtnnmloqIiDJnW5qF1NOpN6/nbTtV6eP4JW0HfBv4SeShynW22Rz4AfDLiLiqheG8SkoQ1dqM9Nw0Gjo1C6mXiNk0wUkPs86yGemkpTfLA5u2NJJkNOkqXV/NzuTpdltlKGmYyBQi4q08eUxtMqTiA1rzRbueV0i9Fa7vkIQHpF4w6wKXS/o/0nP4ZWAFUjflC2p3kDQz6cS1UW2NloiIA8o8XhcbRPry+jLwY+DCiBjd3pCmMDdpGFqtx/L9yw32G0mqRdJKw6if6Ls/3zc6CX6R1BOuNJI+T/r9vg+cRKr5MCKvXoLU2+erwA8kPR0RLZ3+ukGMy5E+J+YgJY0WIvWK+geprtJapO+e/6J1fyNuAL4maZtmTyZzcmEJ4A8tiqniaCbX5urJTvlWLWhtr6daL5AS5IMjou50uZJmIPUYeKHEuCoeBzaXNG9t0rQi16raIm9bpi+Rkmh39LDNHaT3wxdJtVNaZTjwGUn7R8TpAJKWBXbI6//TYL9VSb09zKYJTnqYdaeZSJX8W+0JYENJM0bEuGZ2kDQTqdv1Y71tOw07EtgbWIVUs+BHkm4n9fg4LyLeamNsJ5JOjj4O1BaeOzEi6n3J2ZPUPbbUQnXVJA0mnaDO2Gib2ivy05HKrBgLA6cCJ0m6lFSj4MqIKHs631qDqDNsICLG5QRlo/jKiHswKbk7hYgYk2Nr9Dn7Ib2fuBbtMFJicvOIuLtm3YPAkZIuJPXOO4xUk6lsR5GS3gdExBmSTgOWioi94aOTrb+Rrjy3quv8H0nDf06VtHlEPNHTxpKWzzFNJNVaaqVuSuReShr+dYqkQyPi3eqVuTj370g1x45rQ3xn5ONfK+mw2h6BkjYDjicl4E4vOba1gPvq9ET9SP6MuZcWD7cFfksaanuKpANIPXO2JH2PfIw6CRdJK5OSlae0ODaz0jjpYdZl8heNDShhqAFp5pNjSbOLHN7kPr8gfcn4Z4ti6ngR8Uvgl5LWIBVX3Yt0NWw94ARJ/yGdkDY8gW9hbONyt9qfArsC85GuWp9C49oZXyEVg7uilCCr5BkEvkd6/ob0sGnQwr9pkupe6WxSRETLYouIgyUdQrpy93lSQdDPkl53b0g6l/R6s+62InBDnYTHRyLi7jxMsoyegPVsATwaEWfUWxkRT0namVSv5afA14sOICIek/Rz0tCBeyT9jtQr5uGISFVDU0ZrZVIC+BBSV/5jIuLRouOpia3u89KhfkGa+WlfYBdJVzB59palSLVH5iD9Ln/Rhvj+SJqpbVvgekmv1MS3ICkxeUXetkwL0FzPyJdpcd2MiLhL0pdJvZiqe6e9Dnyu8p6o8Y18f2UrYzMrk6esNWszSc9U/bgkqevyVMM2shlIf0xnAP4QEYe2OLZZSL09FiKdFH83IkY12HYu4FfAF0h/yJePiDEtjG0SjZ+rJUhXb+utGwbMGhFlDXGpTA23NenL486kL9jVH77bAdc26kI8vcrTNp5H6inwBukLbcNhGxGxeaN1BcQyggGMDY+I0gqf5vfiXqQESKVYXXXsK0dEaT2x8nv1SlICtdaNPaw7kjQVZsveqzm2C0nj72uN6GHdcaSpr8v8HHmFlPT4bC/b/QPYLCIWLCeyKY49Drg0Ij6df/4L6W/CrBExtmq7i4G1ImLJFsZyDOk1VDGOyTUK5mbKpPOvIqJ6W+OjIsknkxIc9fwb+HJENBqi1lL5b+s3SYmr2iLNzwO/B44vu7ebpFeBxyKix+RjniZ55YgYVkJMC5O+a1QudFxa23unatuDSUOI/9agyKlZ13HSw6zN8pfuit7G+k4gJRT+BRzZyqRCRe6tcAPpis5Y0gnK3aSpHQXMT6r3sA2pu+S7wBatnkGg5nnrqyjzZKWapFmBPUgnpJsxuR7Dm6R6Gue1aEaDriPpQdLV7YOA0ztgmEbXkbQUKdm2D2kmDUivtwdIBU/Pa/WwoPxebfRlQ72tKyHpMZBkVplJj9OBTwLLNOo2n+vvPE2a1WK/smKrOv5I4H8RsVP++VjgO8BK1YV1c9LjUxHR0gKYktYhJa22Ic2MUu0dUtf+30ZEo1mrWioPI9gduKzR30xJa5Kmfb6g1T1RGsmfI1PNFhQRz7YjnnokLUZVfBHRjjojlVguJ71X14yIhxtssxKpdtC1EbFtvW3MrDhOeph1kPwF/PSIOLDdsVSTtDjwF9IfcZj6JKGSqLmGdNVnRAkxLTGQ/Tth9o185WWffFslL57UyqEQ3UTSB8CtEbFlu2OZFkhaj5QA2ZM0lTKU8HrLU1oOJLHgHjyApAVJMy08SpoO/Kma9cuSavasCKwfEa+UFVtVDLcCs0fEqvnnvUjJtR9HxI/zsmGkHoSvRcQKJcUlUiHVyhTibwDPNOjaX5rcE2Y/YMmIqDtkVdJCwHPAKRHx1RJimoFU82kxUu+YByLimZ73Kk+eHWgnJsd3P6l483ttDaxKLoz7b1Jvk8Mi4pKa9buQZjhaDNg1Iv5Vcohm0x0nPcw6iKT9gKci4pZ2x1JPvjKxLWmqxuovj/cD/2l0RcN6J2l1cv2PiFik3fF0AknPkr5w79zuWKYl+aRme9LrbbuImLnNIVkdkk6ts3he0lX/SaTpfivJ2yVIn8uDgMuBNyLiCyWEOQVJR5NqaSwbEc/mnm3PkuK+kNStfjdS8cufRURvU7hO0yQ9DrwdET3WdciFsOeMiBVbHM8OwJ9JQ1qrXQPsGxGvtfL4vcm1Wo5g8hTdlZOYN4CdI6LR9Kulk3QcaehNpSfn0/n/yzL5+9PvWz1M2cwSJz3MzDqIpEEexpFI+jXpKuiyjcYelxjLM6Tu5d+ts2414K2IaDS9aceSNGd0znTKVqUbh/BJWoY0Bec/K0NGJG0BnA/MU7XpNcBO0eSsYNMqSaOBfzVZp2X7iOjPFPLNxrImqSfREFK9rCdIM/EsTUqm/a+35EwrSdqNlDiDNOT2XlJ8W5PqoY0kDf2aaqaodpG0D6kQd22PpkeBY6MN00qbTa+c9DDrQLmA6Dqkqy09TdH599KCMitZfh/cCLwHfLGd48d7GnqWZ3Y5vR1X1m3aJWlAM7BEzRSe7ZR7fGxMKiD6RE8z0ExPJL0N3BIRO/Sy3eXAJhExRwtjOYdUAPmvwDcrNcNyD8+LgeVIdViuaVUMvcR3FbAVaTrkv1ctnxG4iNQLdZ+IOLcd8fUkD1GqFFp9odFQJjNrHSc9zDqMpJ+QukT2VOCt5QX+zDpBnqL5VuBjpNk0XqJ+DYZoZe2PXpIeHVmLx8w6m6ThwKrAEj3MjDY36bPvsYj4RAtjeYFULH252pnEJG1J6p1zTLuGJEl6HXguItaps25V0jDb4yLiO6UHZ2Ydz8XyzDqIpCOAo4APSeOynyB1MzWb7khaBLiWlPAQaSz0sg02dwbfDJC0POmK9w/aHYv16hzgD8AFkvat7QGQewicAcwGnN3iWOYnTWNab+r02/L9Ai2OoSdzk2aPq6cyq81c5YTSP7me0hdIia7ngZM9vNCsHE56mHWWLwEfABtHxD3tDsaszX4LLE9KfJwIPAOMbmtEZh1I0nykoQmfJ00hDqmgaKuPu+9A9vcQTf5C+r1tCTwp6QqmLHi5HanX563An1ocS6WWx1QiYkyaAIchLY6hJ4NIPVGmEhEf5vg6overpB8CPwK2qAwzyzMIXQ9syOQirF+UtE6ZNaskHQKMiYhTyjqmWSdw0sOssywGXO+Eh7VDnpq4tzoyN5UXEVsBTwLbNrj6aDbdkjQTsAtpyutPkk74BLxNqsFQhtPpXy+ryknfdJ30yCfrnyIldfcD9qjZZCJwGnBoRNQ94beOtDXwUk1dnd2AjYAHgJOAbfKyrwG/KDG244ArACc9bLripIdZZ3ml3QF0qzyDxqSIeKjdsdTKdSkiIt5rdyz1SDqQdFV48SY2L/NK2iDg/g5KeMyWE0N9XUdEPN+imGw6k2dD+TzphGk2JicQzgX+AVxV4gnyT/DQsgHJBUO/KOkoYHOqCl4CN5Zc9HKjBlMl97Y+SirkvIekzRqsix7WR0Qs06KY6lkaqP0usgcpxr0i4jFJp5Cmc96TcpMerwAdM8ONWVlcyNSsg0g6ljTd33IR8Xa74+kmuZjkjRGxRbtjqZVjuyMi1m93LLUkHQD8Lf/4IL3UkYmIA8qIC0DSf4BFImK1so7ZQyyT6P/JXUREaRcZJC0bEU+Vdbze1M5uk7t+3xcR/2pvZFNPRZyHazwVEbe2N7Ip5UKN+wCfAxYmJTomkoZ+rQgs5sLWPZO0Cunv67rAMFL9iiPyug1Jw4LOioi32hdl+3T6FMmdHl81SWOBCyNin6plLwLvRcSKVcsuBDaPiHlLjO0U0kw3S0XE+LKOa9Zu7ulh1lmOBjYALpb0xYh4us3xdJO3gE6dBu4dUj2KTnQ4qXDu7hFxWbuDqfF94BZJ34iI37c5lufpnivaT0i6g1T48PyIfSl24AAAQ8tJREFUeK3N8SjfKo4mDYtoe9IDWBKYr+rn0/Ot7UkPSQuTkhz7kAofVp7De4CzgHMj4lVJNzO5d4DVkYuEH8Pk771BSnxUzAIcD4wDTi43uskkLUeK682IeKLkw5eW0O6npdodQB+8TtXniqSlScnKv9RsNx4YWmJckP6ufhI4W9Ihnj7XphdOeph1litIXfo3BB6TNAJ4kTZM0QkfXZHtr4iInxYWTO9uJ50YdKJ7gTK71vbFcsBNHZjwAFiFNJ79BEm7kq5qN5qytqVFESNiyVa13QKPAp8APg78VtK1pATIJRHRjkKw75NmhuhEH9C5Mz48z+SE0QjSTB9nRsTj7QyqWZKGAKuRTvYAXgYeKLs2haSdgWNJBUK/BdxCOimtdi3wBqlGSqlJD0kzkxKBX2Tya/EM4MC8/gDgG8CBEXFfq+KIiDNa1XYRIuK5dsfQB48AG0taLCJeIBWpD9J3vGpLUv7Fml+QpvfdDdhe0j2kz5p6Q17KGrZk1nIe3mLWQfrYfbOs7qTBlFdpm1V2d9J1geHA9yLiuLKO2wxJ25C+7HwmIi5sdzzVJL0M3BkRu7Q7llp1Xn+N/mCJkl9vnU7S6sDewGdIvQCCdIJ/KSkBclVZtVIkDQfWIX3ZfpbUk2I4TRbSa2UyS9LdpCmRv55ju5E0LeaxTcbWssK+Va//kaSrsxfWS1rlnh4bdMrrX9KswI9JU3POUbP6PdJwuqPLqnEk6UZgLWC1iBiRl00iDbk6sGq7m0jD6UpLUOfn6gbS0JpXgbuAHapjk7QMqaDzLyPiyLJis/7Lf/P/Q+rl+SywBqm350qVISWS5iT9zi+LiD1LjK2jvmealcVJD7MOImmJvmzf6isfkvYbyP5lXjnKY/E3BfYn1aa4nMZXL0qdKlHSJqQT0C/muC7rJbbSZkiR9CdgZ2CZiPigrOM2Q9LR9GFISUT8uHXRdC9JG5Nef7sD85Ke07eA80hDJG5p8fG3Js0mMiuTk1jN/F5bnsyS9FngTCYn1pqNDYAWx/YnUpHDefj/9u4zXJKyWvv4/yZKziAiMIAgimFAkCAZVETJiAgKiAEzR0FQj/gq6BFFQTCiKKAMcsiSEckZJYPkHATJyBwc0v1+WNXs3k33DjNTT9fes37X1dfs7q6mFjt0V61az1qvTVqdZfuVarvGJD2qE7kLiAoPiCvK9xLf16WIkz+IJo/r2n6mQEzPAFfYfn/bY92SHn8CtrA9Z90xte1zX+BbxLKH3W1P6RHbP4DJtlcrFVuaNpI+C3ydWOZyNfAF2ze2Pf8l4GDg87Z/XTCu9UazfccEmpTGrEx6pJTGhR5VKd3e4IpXBYyiYiGeLBvbAsDlxMnJbrafLrXvVJakWYBNiD4RmxF9DIo0WZW0JDGCeEmilP864gR+WHUnsyStSnw/liSSpncSSyCGVXdj32qJyIeIaS2bEuv/TSzF+BORADmQ5iQ9DiaWYpwHfNH2rR3Prwj8DNgQ+Lnt3QvENBn4i+2t2h7rllj4K7Cq7fnrjqltn7cBsxLNy18eIrYzgYm2Fy8VW6pXtaxpNuC5Bk0oS2ncyqRHSmlcaHJVgKQjGF1sJSek/J5YR74F8CxRXj1UH5lc3zvGVZMqPkOcSBcvX+52UtcUDY9tfmB74ufWmgTV/ne6UmeSobRqQsVMwJsco1i7bTMnkVh6xfYbC8R0A/Eet0yvxEK1zOQ+4Hbba9UdU1tszwOntS9v6JH0+F+iCuV1pWJL44OkA4B9bOeY2jRDy0amKTWQpEWITurrEE3gWuu6LwKObMA0hsax/Z1+x9CL7V36HcMQdmn7ej5gqOa4Jtbp91VVsfBJonHt/cChJcrkxzJJKxEVHjsQywxELK/qxwSVDYBH+rDfkfgEcULeOFUV1q+BX0taBtiJmOzS6kFxc3WCfzTwv7bv70OYCxENc7smPABs/1/VP2PzQjEdT1QX7Qd8s8c2+wELEEu+SprM4CkyvSwDPFFzLGl82gPYUtJuts/rdzAp9UtWeqTUMJK2IRq9zcNrG4iaaAS3q+0TC8Ty+2l4eVYFjAFNXt9bTQ/6f8CGrf1KEtEzYG0G+i/cRZSlP1sqtrGgWlLyUSLR0Rp5+jLROHEScGKpZpKpPpLWIBIgHyaSDhBVFMUvbEm6GXjA9ibDbHcmsJTtlQrENBdwFbAisWzpFOBHxEWE44mJLRsSS/zWsD2l7pjaYjubeC9bsZry0a0KZSWq5WC2ty0VWxo5SecRn0U7236wuj9Sdo2T+CQdC2xLxHcEsEcuY00zokx6pNQg1dryy4jy4JOJBnv3Vk8vTZQ1b0WcuLzH9t9rjmc0Xb47ZdfvNE2qBo1L216q7bFtgOOAG4BfAO8nRu/9t+0f9CXQhpH0GaJx6XsYGHl6NZHoOMZ20SqLqpHvVKt5QspSw2/VW5+qKbqqKqA+SCRANrU9Rx9i2B34IZE8uK7HNhOBK4Gv2z6oUFyLEid8m/Da/koCzgV2LF1FKWkzorfN1cBHbd/ZnvSompufSDSA3cj2BSXjSyPT1rfrLbZvb9qElOr37JfAEkSV3e62j6tznyk1TSY9UmoQSScQV50+3KuSQ9KWxEHQiXVf9RltFUCnfnT9lrQ20Z9iebpXy0DNV1Z6xDU7cdV9XWBxYPYemxaPrakkPQTc1DF14U/AdlT9C6rKj3uAJ22v0qdQG6XtgPsuYqnDJNu39zmeqT7YqHlCyrTEVqQJ7NSQNF+/lnxJ+hmx7OZnxHKR1pSxpYkRyl8EjrL95T7E9k7gvcAEYGaif9FfbV9ZOpa2mH4CfIX4PbwFeAuxnPURYgrOLOS42kZrm7z3kO2XmjaJD0DS3ERCcjfiuOhUYnLMw3XvO6UmyKRHSg0i6VGikdo6w2x3MbCC7cXKRNZ81cnv74Cdee1VPDrul57esgRxJXF5uidh2mWFTEXSf4DjbX+s7bEHgX/bfkvbY8cDG9heqMt/ZoYj6RAi0dG3E7l2o23k26nOxr6SLmDaYttg+kUz9klqTaEYavRvr+cam0Sqm6TtiH4j7+h46jbge7YnFYhhTFY9Vf1t1mH4iwn7lYuquSStCRxGJNeeBQ4lest0ZXvfQqGlVKtMeqTUINVJ3gm2dxxmu0nANtnJfYCkzxHLHf4OfAP4HLEUaEVgWeIK48eJ8Y6/LHFlpS22o4mpC5cBPwFuB57rtX2dsUm6mzjh2Nj2PdX9kbLt5YbfbPqQ9ADwj1alh6RliSaTv7H92bbtjgY2sz1PjbFkf5uUhiHpXqYtibTM9Itm7KmamC9NVYVi+6GC+x5TVU+SZiNO3lvHS0NdUMiLCW2q791FwLsZJjmZ37c0XsyQGfWUGuwRYu3ucCbS3OkH/bILcbXiA7afkPQxANt3AHcAZ0s6gyi3voyBkusS3k9MGdm4AWPjJlT/ztpxv4n+Aawjacmqyd+niQO0Mzq2m0CUg9dplx6Ptw4YuzUdbj3eiKk3KdXN9oR+x9BJ0k4j3PQFYkLK9f2akGb7MeCxfuybOAnuPAGeHVij+vpJ4nMMYgLUgtXXVwDFmr+22ZdYRvUUcBTDXExIQdJywG+B1Yj+cCcxRKVHSuNFJj1SapazgU9J2g/4tjtKsaolHPsR1Qu/7UN8rRh2ZGR9M4pVBRClmpfbbo31M4CkmW2/XAV0vKSrgT2J5nGlzA6c14CEB7ZnGup+wxxIrL+/QdI9RLLvbuCs1gaS5gNWIdYn16nbMobtiIqiy4BjGHxC8FFgTeBXwLF1BlZVoRj4pu1HR1mVklUoabw7glFWMEg6F/iy7VtriWjwvpYgPr8WIOJ8kqhwK3phw/b6HXHNS0x6ugH4mu1zOp5/L9EjYnaiQWxpOwBPA6uUrNzsZpQVk52KHCtJmok49vl/wBzERKBP2b6m7n2n1AS5vCWlBpH0RuBa4grK3cTJ0r3EgdAyxBKNZYirUavYfrBwfLMBpxPj/XqVkr7aR6PkCbWk54BTbO9Q3f85cUK6mO3H27abBHzQ9vwFY7scwPaapfY5RCzrAo/0s7HlaEj6LPB1YBFiwsEXbN/Y9vyXgIOJhmy/LhjXpsToy8/Y7ppkkPQJIjm5he3Ta4yl0ZMDepE0J5FMGi552pe1+NWo0KFiw/YfigY1xlQNnBcEpth+sk8xfIeoBtuJGPn+FyJBKWBJIrE6LzEtbQqRrFwJeJwYhT3de1VImpn4fPoy0OuE9zbive23tqdlktpUqXoD7QAsb/upHtssSFRYHGP7i4Xj+w9wtu0tSu63Ryyt9+DhenZ1U2J6y0Si59lE4nd8X+CA1gWhlGYEmfRIqWEkvZ0YLfm26qHO8vkbidF6N/Uhtm8A3yeuqn8V+DZRXvo6Bvpm7An8yvbehWO7DXjU9rrV/a8B+wObtF+hkvQ3YBnbCxeMbUvgeGLkYPGJNh2xvDoOsctzBwJX2T6mfGRTR9IcwGzAcyUP4CRdSnyGrjXMdpcBDLfdNMbSmrJ0pe3/jHbqUj9+JyXtAhxEnGy++jCDr8j3ZU25pI2J8Y5DXX3N9e5DqHos7UZ8jgk4svWeUzXt3B7Yu1p+WHcsbwKuIi4ifM32vzuenwf4EfH5tTrRN2h/4GvAr21/fjrHMw9Rabge8b15hbjI8Xh1f2Hi87T193AusJXtoksQqqbRl9nebpjtjgXWsv3GMpG9ut/bgAdsb1xyvz1iaSU9riGW2vwZeH6kr7f9aE2hIekHwB5Edf8FRKL+zrr2l1JTZdIjpYaStD7RkfwN1UMPAxfbvqBPISHpOuCNwNK2J0s6HNip/cBf0jpESWzPK+A1xfYn4ord6x0j495JVM3cQCw1eBD4LFGOe67t9xaMbSngC8SVvQOBc6p4ur4B19kFf5ikR8/n0mCS/k1UFo2k6fDmdTZZHWuqpMLZwDNE8+ENiKvrnyUSDVsRFRY/B662fWTB2FYFLiX+No8D3l7d9q9i25hYhnAEcL/t75aKbSyQNAvRI2BTokfGHUTi44i2pMfbgeuJJZzfKxDT8cTPcMXOJaNt2wi4lRiRvU31/3EvUaEyXZceSPojsUT0YeLCwbG2n+vYZh4iCfNd4PXAH1zjFKMecT5PfFZ+aJjtTiX6Vc1RJrJX97sX8C1ifPkDJffdJZbtiJ/pJkRy4TngBOIC1nm9fu8KxfYK8V67l+2+LItOqQky6ZFSg0g6Efin7S/0O5ZuqiUkF9netLr/O6LB42ztV9klXQjMZXvVgrF9lDjA2Nr2ydVjrakp7W90LwNr276qYGztpa/DvenW2gV/PCQ9qrXmbwIm276tTzE8DjzmttG5XbYR0Yx1kZKVRSMlaX+idH2bwvs9k0hQvsv29Z3J06r0/wDgM8AaJavaJJ0AbElVIdYltvmJEY/rE0sf+nKyVSVShxrRie2LykUUJO1B/OxOBT5t+1/d3lck3UUss3tPgZgeJ5ZBjCRBuYmr0deSTgc2nJ4n85LeCtwE3EP8bg/ZtFTSokSj0KWBt9m+ZXrFMhxJNxKJvlV69TaRtCJxceEO253jduuObybiM//twBeBC/uZXKhiWpA45vgY0QDWRNP5o4kx4tf1IaYTiGWh2fw+zdCykWlKzbIpcHK/gxjCy8Rc95ZWue0iDJ4m8xCwWamgAGz/qUoavdT28M5EpceWxNXZ24EflUx4VLp1xU89VMuB1iU+o24B/mj7OUmzAj8DdiVGOrYayO1q++LCYZ4DbFete9/b9qBS5mrZzf7ACtTcyHQarEeMLCxtNeAK29d3e9L2y5L2JP5uvwuUTMqsBVzrjqaNbbE9rZgGci/wPeI9phhJuwL7EM1yh9OPpTc7EZOUtu/8m+hwN9G8s4TXEb07hrMkg5NIk4lqlelpJ+Kz4EvDJTwAqqTRF4HTiJHr35zO8QzlZ8CvgYsl/ZiofGol+ZYEPkwsc52NqMoqrbVEY2liCdCLkv5J989aT++KnW6qvjW/BH4paQLxM9uBWF7yVUm3EL1jji6VMC2d1E6pqTLpkVKz3APM1e8ghvAQgw+2WwcdazA4WfMO+jA6zvaUjvsvEiee+5eOpSOO9fu5/7GiusJ/KjHiFwYqY/aStBZxkvmJ6rHHibXvywFnSHqn7WnpoD9aXyca+n4B2EExDvn+KraliQTmAsT4yW8UjGssmJuBSTcA/4Eo6W/1W7D9iqQrgY0Kx7Yg0N7j5IUqtrlaPRVsT5F0MVGtUkzVGPew6u6NNHNE5/LAWcMkPGDg77eEa4D3SNra9ondNpC0NbA20J48XRqY3r0WVgcet905drsn22dU1SpFG2Hb/o2kFYjExv9Ut84eYwIOsv2bkrFVJnTcn434mTWC7XuJaXv7SVqNWP7yEeL7+BVi2VJKqZBMeqTULH8C9pT0+oaWIl4BbCNpjuqg9gyiGeHBkqYQfSo+Q1zBq3uEaGNJmrtzjXYakS8Sa6KfJxq/PgqsSiwl+DFxZfF3wFeqyo+5gZ8SlR97EAmIImzfJ6k1kva9RDlzp3OBz1UHv2nAIww+4W29161ATOhpWZAYrVjSYwxurtq6Gr8skWhomQOYr1RQla8SlWzb2G7q++sUIqk1nKWIPgMlfJfoIXOcpLOI95YHiBP4pYBtifedl4mpFkhahBiFPb2n86xALAcZrWsYaG5ejO09q+URnwPew0CPsX8ClwCH2r60dFxVbE0et97pPqK66WFgMWAsxZ7SuJBJj5Sa5QfElaALJX0dOK2qVmiKE4APECd5p9i+U9JPiasWp1XbiCgLLjq9pUXSwsQJ6GrEidW5tn9UPfc24uTlr7b/r8YwHpV0MrHe+Cz3YdzgMCZK+vZUPGfXOz50B+Kkbq32pQ+SfkRMBXqYSCK8VAXzXDUlYhOiwWRRVWXJ+yUty8AJgao4L7V9V+mYxohbgRXb7l9GfN/2lvQR264qezYkGl6WdCeDp7ZcVcW2G5GUa00D2ZA4iSlpeaKnUlMTHhCJodUkLWT7iW4bVP1IVgHOKxGQ7XMlfQT4DfH5tUlnSMBTwGdtn1s9NivxOXI109d8RJXLaD0BzD99QxkZ25cDl/dj32OZYiT31kSFx0bEcrNniGqtHHWdUmGZ9EipWW4jrgAsSVyNsqR/UZV/dyiyRrVjh6cTzfPaH9tDMQZ2Swb6ZhziAqMIO0nanjiwnYuBpREPtW2yPPF93YVYV1uXOYiJMdsDj0k6BjjK9t9r3OdoTKxuI32uvQlrnUmPFYlkQeeJ7i+IpMe1rYTHq4HZL0q6lvLLINpjuJvyJ8Bj2enA+yStYfsKoiLmBqJ3x0PVuvy3Ee+FPy0c2xnA/pLeVjVQPYu4Svu5qkT9QSLh8Tqi6qikJ2necpZOvwMOB46StIPtp9qfrKqzDiOWIhzW5fW1sH2CpHOIarG1GFyxcBlwnO1n2rZ/GPjfGkKZk6nrE/IC5aue0ihVzVXfTyTMNid+3i8SF4WOIi5kTe8+MSmlEcikR0rNMqHjvhgD6z5tHwMc088YqlG5RxFX7L5FjJ38W8dmpxFXWram3qTHBOLqzo7AW4EvA1+SdAdxhefoPi55aPKIzXkY3OuhpdXwrVfjv8cZYopFapw/EMnRf8Kr/Ts+SJwwb0y85z1DNB0+qg+xPUPVu8D2C5I2J5rRrlbdXiFO2A8uHNufgS3alhc2ju0jq5/ltsA9ki6pnlqrGh27AZEcP9r2SYVje5b4HSudrBrTGlI9OVR8HyAqsVrxHWX7k23PvRf4cZXIqiuG1Rno2bEI8f5xKXFMcqztp+vad0ppZHJkbUppxCQ9Cdxoe71+x9KpaiS5ATEG8PrqsW6jEi8DFra9QqG43kl0cP8IsAQDjeAuIQ6IjssDojC143Q7x4qWJGl9YtLMUOND3ToIrymGdafypb8CVuzH962XqiR8PuBfbhuD3QTVeM4FgDtHMnmjhv0vQCwzuB7YranvG9Wo5q8R1VmdzUqfIfrz/E+/x4v2Q/U+difx/j8a6wDLlv5b7VE9eWTrfVjSVlTVk7brvJDQK75fEgkPAf8mEudHtMW3JpF82NP2gTXG0RpL/w9iWeukUtNZUkojk0mPlPqoGn14p+3L+h3LSEh6FjjV9o79jqVTlZC53vYGbY91S3r8L7Cp7XkKxyciKfMxotJkXuIg6QWi3P8o4PSG9XApaiwlPSTNR1x5X4eBSQa9uM7Y2g64R/1Sao4tTT+Sfk/0ddiCGB3+d2K5Ta8RnbUl2kaimsa0MlH5NjMR69/6Wd4vaR6iZ8s89Pi7tX1Rjfuflv5ORf9Wq+rJ84nqyf0YqJ5sTyrMSjScvtD2VqViq/a9K1FxdSWRBLyhx2f+A8Rx1gY9/lPTI5bWe/CU4bbtwrabPLUvpXEhl7ek1F9HVLfXJD0knQicZ/vnhWMays1EtUITzUE0extOK9lQVHVV8zzgvKr55uZEAuT9RBJkK+BpScfa/lypuCS9TBwkDnmCJOm3wCds1/25MTVNVifWGE8vPyQqPO4kKib6OT70IvrwO52K26Xt6/kYuo+NgeJJj/ZqwKpS5+/Vra+qZRg/JSZBDZekrDOx8Ika/9vT2zeIfhQbt1VPDtqg6ql0K7BS+fDYjehz86FeTXMrdxJLcOomotdPSqmBMumRUnNtCTzd5xg6/Qz4g6S1bY+2PLdu9wHvGGoDSbNU29xZJKIebE8BjiNGKC5ANDzdF1iIGPlbLOlBHKgNdxLQvm3dJjL1TVZL2oK4wrmG7ScL73sQ2+v3c//DqSoUplbdS4N6JdhGou5pRp1qu1I9Hc1CVHQ0hqTlieUk8xLVCosDyxB9qJYlJsnMApxCzZ+5to+s878/na0BXNGlsXSnB4C3F4in00rA+cMkPCBGYq9RZyBjbHxuSjOkTHqklEbjEqKc9GxJhwGnEo0nu02XwXa3ppR1OQ34iqQv2P5Fj22+SjRJ/HW5sHqT9A6i+dlHgQWrhxvVx6DNfExd6e5oNLnJaqf5gDP7nfAYI3YZ4rlWsqpXQq3uioXvMJA467ZvujxXaprR4J3aF5ba1zRoYjXgt4jlLJ+oGq0eDizTWqZZjSD+HdF0utaT4zGm0dWTREPhkSQb3gBMrjmWlFLDZdIjpTQa9zJwwP/F6taLKfsesz9RMXGIpDWIfgsAi0r6EFE5swuRpDmkYFyDSFoS2IFIdqzEwAnVDcREmUkFYliq46G5uzzWMgvwZuB9wF11xmV7LCU97iC69Kfh9SrpF/B7Ipnar4kavX7nBHwbuI6B95I0vCZWA24I3NKrysL2nZK2IMZO78fQn2szkqZXT94KrCppzl6TYyQtRFQHXl0ysJRS82TSI6U0Go3pHSDpAGAf2/8BsP24pI2JZSM7EokFgA9UNxEHSVvZfqZwrPMBHyZ6eKzNwJKSh4GjgT/avrFgSPcy+Oe4TXUbioDf1hXQGPQz4OeS3l74ZzfmDFXSXy19ubNfZf9DJdqqpS/XNS0Z1/ARok2sBlyUmHrT8iKApNe1fX48LekC4ENk0qOl6dWTk4CDgF9I+rTtl9qfrJqHHwLMTb0j6lNKY0AmPVJKI9aw3gF7AFtK2s32eQC2b61GxG4ObMzgqQF/BY4vNQZT0mzEAfSOwKbAbETi4DngJOIg7Nw+jW1sT16tB/yLSAh18wKRnDnF9kkFYhsTbB9W9Qo4U9K3gHNsP9TvuNL41mOEaPvv3fJUI0Tpz4nevTSvGvBJBjeYbC1JWxq4rWPbRQvEM1Y0vXryl0SyfmdgbUlnV4+/Q9KPic/fFYgG4mOpl0pKqQY5sjalPqrGnN1JXB3rtMsQz0EDRhL2k6RjgW2JA+cjgD1sP93PmFqqSSfbEH0fRPTpOJc4CTmpT1dguxpqFGzqrZp6AyNrouoCU2/GpCb//jUttqaPEK32fwGjqAasc4xoi6TLgHlsv726vz1RYffdVhVPVT1zO/Av2yvWHdNYIWlFonpyJV7bNLq9erIzeVQqvjmBHxN9f2btePpl4tjgy7afLxxaSqlhMumRUh9VB9VTy7brHK03rOpK98LAE7Zv78P+NyOu9ixBdGjf3fZxpePo1PZzvZ5IdBxt+5E+htSTpKWB50bQAT+1kXQvozu5W6a+aMaupiUW2jUtNklnEBNc1mgbIfqaGKuT/IVtr9CfSJtF0neAfYA32b5H0lzAPcS0rOOJSsCtgaWA79uelok+446kmWhA9eRQJC1CVC1OYCC+820/3M+4UkrNkVeeUuqvXg3+GkvSHMTEg08B81cPHwm0rjR+AvgSsKvt6+qMxfapks4HfgjsBhwjaUfg830+2DmA6NNxUx9jGBHb9/U7hrHI9oR+x5BmOE0fIdpUfwRmJxoP32N7clXtcSzRa6nlHOD7fYiv0Wy/Apxc3RrJ9mNEAiullLrKpEdKfdSv5n1Tq7pCdj7wLqKE+nRi3Wy7i4hJDB8hJh/UyvZzwBckHUU00NscWF/SoQwxps72vjXGtHdd/+26SFqPuFK2OHGC0M0MvaRqrJB0IPCU7WLjVFMRTR8h2ki27wK+0fHYeVWV2zrAAsDttnPCxyhUS4KeakKlB/S/8jSl1GyZ9EgpjcbewKpEI73dbU/pXKJj+y5JtxKlsN/o8t+ohe3LJa1MJF3eDezZY9PWmuTakh5jiaQFicaqrakyQzGxdjo12xdp0JjVIcYhtww1MrnUhI+xoHEjRCXtVH15ku1/t90fEdt/qCGsQSRtDrxo+8yOfU8Gzqp7/x2xDPe3MKSSfwuSViWacB9v+x9tj28OHEo0fX1W0j62f14qro4YG1N5mlJqtkx6pJRG4yNEd/4vDHN15z5gYomAWiQtR4xUXY1oYHYSQ1R6pFcdRFztvJlIZt1Fft+GVJ1YLgQ8afvFYbZdEJi78In7g8BMBfc3nHvpXXlghh6ZXOuEj7aGtL32vbOknXs9X7hBbRNHiB5BfJ+uAP7ddn84reRz7UkP4rPgL8CZw21YwL1MfRVOqWk3LV8iprf8svVAVR1zLDGN7J/AYsDBkq63fXHB2BpZeZpSaq5MeqSURmMp4LQRlLM+S5QM165qsrYn8P+I8u/rgE/ZvqbE/seBzYiT5DWrpUKph6qc+6dE08PZgRclnQl82/aNPV72E+DjlP28PYk4WZ/H9r8L7reX+2nucovhqpvqeu3UaOII0X2Jn+3jHfeb5DFi4k0TtI8Lb5md6NcCMU63lSBdCliw+voKYErt0Q22BnCd7cfbHvskkfDYw/ZBVXXllcBXgKJJDxpceZpSap6c3pJSGjFJjwM3to8Z7DE94CpgCdtL1BzPROIqzkTigHBf4ICmrDEeCyQ9A/zF9oeH3bj+WO6ehpfb9nLTLZgO1VXFvwFv5rUnuy8Ae3Yr8ZZ0OLBTyUlLkuYBLiQqdr5s+9pS+071avoI0SaSdAxxcry8G3bQK2leolphZuBrts/peP69RKNuAxvYfrZgbE8D57R/Nki6AngrMR3oheqx84AJtpctFVu139uIMbXLtz7zexyPnAlMtL14yfhSSs2SlR4ppdG4Glhb0pK2H+i2gaSVgJWpuaeApB8AexDvYxcAn7FdZB37OHMNsHS/g6hM6HcAQ/gqsCJwLfAFYhzxssB/EevHD5a0lO29+hbhgD8TScD3AH+X9E/i6vF/umxr2xuVDC5NPdu3SnonDRkhWp3wnmn7gC7PzQu8YLvb711J+xAJy4Mkfb0B8bT7HvH+u7zt11Sj2D5H0tXA7cD/EP16ShmUqJU0O3GB4fxWwqPyMAOVKiU1rvI0pdRcmfRIKY3Gz4H3AidK+mhnkqFa7/sHop9A3Y3N9gaeAfay/dua9zWe7QecJWlL2yf3MxDbTepD0Wkb4uB5U9uPVo/dDHxa0knAUcAekhYgEnD9vKK8ftvXAt5Q3bpp1JXvNLyGjRBdn+hT0c1TRI+Pfjc//ihwBlWPCkl/ZegkYMmpR1sD53VLeLQF9GSVXNqSskmP+xg8+nhjYmnLuR3bzUt8Fpc2mZjWMpxlGNnUo5TSOJZJj5TSiNk+VdJBxPrd2yTdQpw0vU/S34mpAbMAP7R9Qc3hnEQ0VH2k5v3UStL7iQPL+4ETbb9Ucv/V6MYdgN9UXfn/CjxEj5Nh2xeVjK9B3gRc1JbweJXtMyS9h5gEsSswn6QdSv8s2yzTp/2m1E6U73vSzXcYWAq0KLDDENuaSASXshAw5wi2m6PatqRTgL2rz/zzgR8Ar/DaKs6ViQRJaY2pPE0pNV8mPVJqEEnfJhqHnTLMdpsBK9suPnbV9h6SrgS+ycD4xNaV5NuA79meVCCOXtMeGkfS54lmqzvZvqTt8T8B27VteqWkDWyXblg3N/AisHN1G0qx3hQN8zJR6dGV7VuqxMc5RFXInyX15XfUdj9OQFIhkuYkelQsTjTB7KrEONgx4hP9DmAIdwIbSlrR9q3dNqj6uGwM3FE0Mvgx8GFgd+DLRNLoQNuvxiFpdWAJ4E+FY4NmVZ6mlBouG5mm1CDdmnD12O63xNz5vp6ASlqEWI88M/Cg7Yf6GU9TSTqHSBAtXpWmtxrUnU2sxf8jsCHwbuBLtn/Z679VQ2y7EM1gRfT3uJshRtba7tsJhKT5gXnocfW4zrGwkq4nPjPfMcx2CxEVH6sQ0wyeALbs999qGh8k7UtU2g1VHSBimUbtv3NDfWaN9PNsRibpM8R44SeIJMNxQKtqYUki6fBVYhnH52z/pnB8cwHbEhUyV9s+r+P5LYglTofbvqFkbNX+f0L8PRi4BXgLMUr3EQZXnubklpRmcFnpkdLYNDNRZtpXth8jxgGmob0ZuKmV8KjsQByobWv7qqpJ3P3ATkCxpAewF9H0ctMCS5JGTdLriWZ/WzAwvrEbU+9n2t+BXSQta7vnlBnbT0jagCgNX58+98yQtBq9l7tcZ/v2kvGkqSdpL+BbwEvAaURzyxwzPYbZ/o2kFYjExv9Ut/ZpPK1/Dyqd8KjimwwcOcTzf6aPS0eaUnmaUmq+THqkNDatRDSJK0rSxUTDxuNsP1l6/2PYIkBnL4x1gfttXwVge4qky4C1Csc2AbiwoQmPxYmpC28g+ow8RlxxvJyYnLIYcYJwObE8p06nEmXyX2WYZoK2n5O0CXAM0Xyw1sSHJBFTZZYB1u8YUft5IpHWzU2SJjZtjGfq6dPA88A6tq/pdzBp+rC9p6QTgM8RE5daTYf/CVwCHGr70n7F164ah23bfU+2VdOBbPtY4NisPE0pDSWTHin1maTfdzy0dpfHWmYhqgZWpT+d+99DnJQfIuksYBJwSsNGADbR08D8rTvVyfwyxHrjdpOJ/holNfnA8FvECcC3bX9P0uFEX5T3AEhaF/gVkVT4QM2xnE2cdL4w3IYAtl+QtC2RIKl7XOIHiauch3QkPFrEa6/WrkCMmfwAMdkiNd+SxKSPpiU8dpbUrReQh3gO4oS1yHFolRjckagYW57ey+Rse7kSMXXs9HIieds4VQL3y8Tn/9zVY5OJ5Xs/s31Wn0J7GrgSWBOy8jSlNLRMeqTUf7u0fW1iSsSbhnnNDcDX6gpoCKsTB44fBjYDPgRMlnQikQA5t2MJRwp3EMms+Ww/Q3wPTfR+aPdGYi1ySX8gRq0ubvufhfc9nE2Ae2x/r9uTti+S9D6ijHkf4L/rCsT280Tvk9G85hXgkHoiGmRr4vfpwN6hDO7FUjX5u4v4Wy6a9JD0MtHrYchRplXvok+UOjEeA5o6qWpqJ7QUmewiaTbgdKJvUq99eojnZliSDiQamba+N63RtPMRCdNNJB1s+6t9CO8ZogdVSikNa6Z+B5BSYoPq1jogO6vtsc7be4BlbE8cqq9AXWz/zfZ/ESfn7yWuHr9MlM+fBTwk6aCqj0Aa8EtgXuDqKkH0feKK1GmtDSTNQVTw/KNwbP8DnAucVVVONMkSwHVt918GqPqfAFCVMJ/P4Ck4M5rVgZtH08i1mvByLdE8t7TRjDLNE9EBxwDvllR35dCI2Z5pWm6FwtwD2Ih4v12eaBxtYvLNW4iRtpOBAwrGNIikWSVtJ+lnkk6QdHz19XaSZu1TTB8B/ov4rPoysIDtBWwvQFQufgn4F7C7pH68/14LFK/KSSmNTXn1JKU+s31h62tJRwIXtz/WRFUPgHOBcyV9lqj42AHYlGq8naQ7bb+5j2E2hu1jJE0kvjfLEhNbdu5YF70dMZHhvNf+F2rVGj+4NHC+pBeIK8rd+jyULv1+lsEnvU9X/y7B4Ct8/6kem1EtSYzK7WaoBMODRLK1qeYjmuym8B1ieeGJkj5l+64+xzNWfAR4EtjB9uRqqgy2XySqxPaVdD7x/neb7V7LS2tRjbo+mriY0Pm3+nngAUk72L6sZFzVvv8DrNvZ8Nj2s8Avqslk11XbHls4vh8CZ0ja1vbxhfedUhpjcmRtSmm6qZqc/RD4LIVGJo4lVYXCvNXa487nliSmk9xVsklc6wRgpEpeCZX0d2Am26tU93chlpjsbvvn1WNzAncCz/djLX4TSJoCHG97x1G+7mhga9uvqyeyQftaqu3uvcDxwJ49Nm/1LvoT8IDtt9cbXTNJ6pYAnY3oYfAK8X18kN4Jyo3qi27skPQccJHtTav7vyOWlc5m++W27S4E5rK9asHYViAmQ80NXE00Cr+3enpp4GNEBeC/gVVt39HlP1NXbE8Dl9j+0DDbnQasbXv+EnG17XddYqnop4gqnlOJCWhde4zZ7mwmnlKagWSlR0oNVJWzbgWsQzRyNNHJ/WLgpOoKVWNUB247VLfWiWdeoe1gewo9Gq3ZfgB4oGxEZZMYU+E84L8kLWb7UWIM7GTgx1WS6EHipGAxoqHpjOoJBiY+jMYbKDcF6l4Gn5xvU92GIuC3dQU0Bqw/xHMzE++1vRJ9eUVrwMtE1VjL5OrfRRjcJ+UholdVSf9NJDy+YvvgLs8fIunLwE+rbXcpFxqzMfC9GsrkatvSLmCgF0urx9hQ8iJMSjOwTHqk1DANLnUdRNLrge2JKy2rELGaGM06CTiuf9GlcWISsXTjLcCjtp+UtBtwONHIt3XAezM1NjEdA24kGuXOZXskJylImpvo51FqFOZFDJyIr0f0Ari1x7YvAA8Tk6FOKhBbUy3T7wDGiYeA9kqjO6t/12DwFLR3AKVHsW4EXNsj4QGA7UOqCTgblwsLiEbH60ma0/b/ddugqrRbr9q2tD+Qyb2U0ghl0iOlBqkqJs5k+FLXMyUVLXWt4puXuDq7A3EVcibipPMG4gT16KqxZOpC0tqMbGRiY8rSJS0MPNVeBl6K7euBj3Y89idJlxL9YxYAbidOjhtV/VTYaURj4W8QY35H4ptEI8dT6wqqne31W19XS6rOtL1riX2PVVWz2TTtrgC2kTRHNYXpDOAg4OBqadiDwGeI5GqRv4c2iwAj6eF1K7BSzbF0Ohb4LtFD5vOdzdMlLQf8gvh/+Hnh2LC9S+l9ppTGruzpkVKDVI1MP07vUlfaSl3/UPpDX9L/ESdKAu4j1txPsn1zyTjGGkkielHszECio3NEYut+0V4oklYlEgjH2/5H2+ObA4cCixKl4fu0+mikZqkm/9xJLPP5hu0Dhtl+b2Jqz6PActWJYDHVuNznbD9Rcr9pxiTpg8BhwG62T6ke+wnwFQYqBUQs01jV9m0FY3sYeMj2kBPPJP0NWML21CxjmyrV+8rFRCXny8BVDCxTW4aoFJuZ6EmyXun3kZRSGo1MeqTUIJIeJMr43zXMdlcDi9l+Y5nIXt3vE8TVn0m2Lym577FM0ueIK2J/J67Gf47o2bIiMc3lI0Sy60DglyWv8FaJtu2JA+rHq8eWJqYazEb0klmMOClY3/bFBWM7DzjL9o+G2W5PYFPbTZ5EUitJaxITlWYnEpLHEyMdHydOUhYmTl62JarGXgA26vcyuTRykjYlmr/ua/uCHttsAOwD/ND22QXDG3MkbQ9syUDF2CF9qJ6cRLz/ftZ21/41kj5NJKCPtv2xwvHNDfwA2BWYo+Pp54HfE4nW0suCqEY3vx240/bDPbZZguh7c4PtpwuGl1JqmEx6pNQgI53CUB0obVNi6kLHfmex/VLJfY4Hkq4E3gpMsP2EpMOBndorOiRtC/wvMU3jzwVjuw142vbqbY/tSyyT2MP2QZJWBq4ETrO9dcHYXgGOGG4ZhKTfArvO6NOCqrHIRxG/a70+3AXcAnzc9jWFQuseiLQe0Q9gcSJZ041tf7JcVM0l6SRiWeEbel1Vr3osPAycY/vDBcNLU0HSW4hk+OuIvjdHM7iaYkeiofnzwGq2b+lTnHMC72KgYfLDwNW9en0Uiul7xEWEVaqlkN22eSdwDbCf7e8UDC+l1DCZ9EipQZpc6tqx/5mJ8aqmT/0exhJJzwJX2H5fdf/3xFKXzpGJVwFTbK9TMLan6ThBknQFceK8sO0XqsfOI5I2yxaMbaRJjz8C29nudeI8Q6kqAj4AvJOo8BBR8XE90TPoDPfxw1/SgsBJwNp072vTLkdfVyTdB9xte4Nhtjuf+FvNRqhjgKSNiJ5Yi/LaZKWIZWg72u42wniGJela4jN0yF4nkm4G/jNcBW1KaXzLRqYpNcv5wPaSPj1Mqeu7iCtCxVQjQr9AnEy9lWhiCuDqoOJMYmnG/SXjGiNmIk46W1pXxxboePwO4IOlgqoMOqGUNDswETi/lfCoPExMO2iUqrnuWsQynATYPoNo1thUBxFXr28GfkNMfhjR1JkZ3GLASJYVNvJvtQkkrUbvqTjX2b69ZDwAts+VtCywHQNj6iF+jhcDx/azoqLBJhDHTMO5DVi33lBSSk2XSY+UmuV7xBrjX0vagd6lrv8HfL9UUJK+CPyQKMHtvDIrYl3t24DdJe1t+5BSsY0RDxEjiFtaPTtWBs5pe3wFoPTyofuIn1/LxkQvj3M7tpsXeKbuYCTd3fHQtpLW77H5LMSJ4Cz0YXpAmmqbERMz1uxHL4Ax7BkGv4/08kZm4CRS1Tj6WuIzc33b17Y9/Xlgpx4vvUnSxH5UQVVJjSOqW19I+vY0vNy295tuwYzMrESD1eG8BMxZcywppYbLpEdKDWL7lmpqxiRirXvn1Yn2Utcia3vbJj0AHEckYq4GHqviWYSoPNmRGGd7kKS5bP+gRHxjxDXAe9t6ovyFSCIdIOmjxAngZ4nvY2eyoW6nAHtLOoi4avYD4BWgs6/Iygwka+o0oe1rE+Ob5+6x7YvE1dBTiLXdaWyYGbgyEx6j9jfgfZJW6jUxS9JbicqnvxaNrFk+CLyDaEx6bZfnBRzZ8dgKRHXMB2h2lVSdvsNrp4qNlIHSSY97gDUlzdxriW21FHctICtQU5rBZdIjpYZpUqmrpJWI6pOngK16TO54sLr9WdK6wMnAvpJOtX1TiTjHgFOICS0fAk62fb2kY4iu/e3fo5eA/y4c24+BDwO7A18mDngPbJ9iIGl1YAliRHGtbLeWTY24p0cac64hJsik0fkFMV76dEn/Zfvk9iclbUmMM58J+FXp4Bpka+Ik/MAez9v2J9ofqCZW3UW8FxZPekh6G1GF0vrMN7Fk7yLgV4U+Sz8x/CaNchqwF3FRZu8e23yfaJT8k1JBpZSaKRuZpjQGSfowsHjdy0gkHQp8Cvig7bNG+JoPAKcDv7H92TrjG0uqXhkvta5ISZoV2IPBIxN/VHIkbFtscxGjTBclOvKf1/H8FsTUiMNt31Awrp2JcYSXltpnqp+kDYGziOazJ/c5nDFF0k+ArxAnxU8QJ+oG3gQsRCQtf2Z7974F2WdVj6mXbb+jy3OvmZzV9tzfgDmHa4w5vUnaHTiAqIDqVmXxEvA12weXjKvpJC1ENGdevPr3MAb/PXyKaOb8CDDR9mN9CjWl1ACZ9EhpDJJ0OfDuuqcaSLoDeGG0B4HVQefstt9UT2QppbGsGtH8S+Jq7V+JvjddD0hsX1QwtMaT9DHgm8CKHU/dAuxv+4/lo2qOalrWOba36fLcEUTSY6Yuz50EbGh7vvqjfHWf7wXOJvp0/Rr4IwN9vCYAHyeWPs4JvM926eWPjVYt5zoBeDPdJ9/cDmzTazlYSmnGkUmPlMaggkmPycCptrcf5euOATazPVc9kaUZSUNKv9N0JGkXovT89cNtmyNru5O0OLBkdfcB2znBCJA0BTje9o6jfN3RwNa2X1dPZF33eSawEdFw9bIe26xJvNedY3vTgrEtCWxAjFvvOtVG0puB1YHzbD9YKraOGGYmljRtTNvfA5FMPbFXv4+U0owle3qklIbyAjD7VLxudqLJZGqwatzrLLaf7Hh8aeCLxNWzycDpto/qQ4hDlX4vCKwEfFpSln6PIVXC43fEz/Ma4G5m4GkjIyVpKeC51t9rleR4TaJD0gLAPDPw+PAnGOiFNRpvIPpXlfRu4MJeCQ8A25dLuoBILpT0VeBLxOdALy8BhxP9U75WIqhOVVLjuOqWUkpdZdIjpTSUVnf0mWy/MpIXSJoJWJM4kZkhdRm7Ohq2vdx0C6YLSesBB1ONqpX0OPB924dUzWjPIhJXrSTDdpK2sb1VnXF1ifO9wEEMX/p9oKSbsvR7zNgLmAJsavuCPscyltxDjDT95DDb/YhoSjmjHuPdCKxdTREbUTJN0txEAqJ0/6A5iUlow3mM8mNX3wfcYPuuXhvYvkvS9cAm9CnpkVJKI/GaNY0ppdTmdGIk7R6jeM2e1WtOqyWisWECMZ1iwlTealMtFTmbGOkoBsYOHyTpI8BRRJXOT4AvVP8+B2xeNRYt6avElcT32d7T9vW2n7H9rO0bbH+NODB/hdH9jqb+mkBc3b6gz3GMNa2/15FuO6M6DZiD0Y2x/iaR6D21loh6e4Bq7GqvDSTNQlxIeKBYVGEp4M4RbHcnA8tKaiNpFkmLSurac0XSQpIOlfSgpP9IulvSAZLmqTu2lFLzZdIjpTSUnwLPAj+Q9I1hDsxmlvRN4AfAM0QlwYzuamLSwrJEh/mR3KamLHs0vg7MRlRNLAfMTUxmuYP4eS8OrGt7L9u/qhIL6xPVFTvVHFunEZV+AxdQvvQ7Tb2H+h3AOLcw8Hy/g+ijw4hlP1+XNGz1gaS9iZGnjwK/rTm2Tn8mEuSHVcsNO2Obt4ppKWIcfEkGZh3BdrNSpqpoF+Ln+prJRFUi5DJiYssbiM+4CUTi/K9V4iilNAPLRqYpjUGlGplW+9qYuHI2K3GychxxMv8v4mriosC7iJGnSxBX5jez/Ze6Y2sqSdsBOxIlv7MQlRInAJOIhm99e+OVdD9RGbFce4O3tikCl9leu8vrLgHebHuRgrE+D5xke4dhtjsa2Mr2HGUiS9NC0j5EZc5bsvnm0KrlZi0XEEvP9u+x+SxE/4UDgZtsr1ZvdM1VNf88l6jeuA84HrgWeJw4mV8YWIX43Fqa6F+10VAJ1priXBD4G3GC/ixwBgNL+JYBPgjMSywXXc12sZ4jkq4mki1L2e6aRJM0B3A/8LDtd9Ycz1HA9sCSne8bkn5ILK+ZTCT2LyDG1v6E+D5+3vahdcaXUmq2THqk1EeSpqmreKmpBpJWA44kRiR2e9NolVLfBuxi+8oScTVddUC7PfAxYA3ie/cIcDQwyfZ1fYhpCnBa5zjH6ori08Cfuk09kDQJ2M72SK78TReSbieSbW/q1YG/uoJ3B/Ci7RVKxZamXlUxdixxUvKlHEnbm6RXGHjPFT3G+ra/pNpmR9vH1Blb00maSCzXeyu9v28iRv1+3PY1hUIbHID0BuBQIsHRzenAbrYfLhcVSPoGMWFpEvCZzsSHpNcBvyES/N+xvV/N8dwMvGB75S7PPUIs09zL9k/aHl+e+PleYHvjOuNLKTVbJj1S6qPqgHZqufQoR0kfADYFJgILEQeMjwPXA2cCZ/SziqHJJE0gGm/uQFyNNXEw9kfgaNtF1mtXv3NH2N51lM8dDuxU8ndO0gFERcCRwO62n+14fl5iGdVOwE9s71UqtjT12hr9Ll39+wKRDOz23lF7Y98mk3QEA9+XnYn+Cb2abb4APEyMGe/LCXwTSdoU+ADwTqLCo5GfW5KWAdZmYInjw8Altu/pUzxzElUoKxJ/n5OAu4jfxzcRyY7XA7cTVSjP1RzP48BfbW/f8fhbgZuAl4HX236i4/nLiMrGxeqML6XUbJn0SCmlwqrKmR2BjxDLgx6z/fpC+25s0qM6GT7O9t7V/caWfqepN9pkr+3sP8bQf59pbJB0FXARcAmR0Hi8zyENSdLriWqZDauH2quOAM4nqmRqr0KpqhRPsf3hjsc/SfQ9ub5HFcgxxPLH2euOMaXUXNnYJ6WUyruPOFF/GFiMbCrdMoEoUQbA9pOS1mGg9PujXV7TKv3OhMcYkUmMqZPft3FhVaIH1lcAJN0GXNy62b6vj7G9hu1HgI2rRP3GDExpeYCouvhbwXCeBLotYVyHSMb0WlY7K5E0TynNwLLSI6WUCqhKhbcmKjw2AmYmptwcD/zB9iWF4mjvETBqNVd6DFVp0qjS75RSGq1qqc3a1W1V4HXVU6335IcYnAS5uXiQDSXpZGAzYFPbZ1ePLUws+ZqH6Dl1QpfX/YPo+VRro9WUUrNl0iOllGoiaSbg/UQj082BOYEXiWUaRxENRV8oHFNj+8hk+X6CV09knurVvHZGIenbxMnwL6qqp2+P4uWuu7FkmjaSZgVWYyAJshawYPV06+D8aWIpzMVEkveKwmE2RtuEsSlEI+THgG2I3kAPAcvafrHjNROIPiSTbJceuZ5SapBMeqSU0nQmaXUGenYsQhzAXkokOo61/XT/omuuTHrMGCStSjREPt72P9oe35xYyrQoUY6+j+2f9yfK/murynqL7dvb7mvoVwJ9aHSdpl3VlHNtYsnGe4glf60Dddsuviy9qrJbB1icGAHcTZEkm6TvAvu09kn8LfwH2Nz2X7ts3xpl+3Hbk+qOL6XUXJn0SCml6azt5OQfRMf7SaWms4xl1fftOuDkqXm97X2nZzypHpKOJEY5L9Fq5ChpaWLk9WzAP4leNwLWt31xv2LtJ0k7V1+eaPvfbfdHxPaRNYSVCpG0EpE4/yIwP4UTWZJmAw4jEvgwdLKtWGyS3gVsRVxQeJD4fL27x7b7AXMD+9l+skR8KaVmyqRHSilNZ21JjylT8XLbnms6hzQmTEO/EZFXtseMqnnj07ZXb3tsX+BbwB62D5K0MtGY8DTbW/cp1JSKkDQL0eOjvcpjAar3NmK8+aW2dysY0/7AXsBTRJXi7UDPsbSZZEspNVlOb0kppXqIgSZ1aeTuIpYCpfFrMeCGjsfeR5xQ/QLA9rWSLgEmlg2tOSQtbvufU/na79v+7+kdU5o+JM0NrEkkONYB3k18Xgh4nhjVfWl1u6xPSyJ3IHqKrNK0qTIppTRamfRIKaXpLEdLTpNLsqfHuDeoIkfS7ERy4/yOxr4PA2sUjKtpzpe0nu1HR/MiST8DPg9k0qNhJB1EJDneycDfwSPAmQwkOa6x/VJ/IhxkUeDsTHiklMaDTHqklFJKqaT7gLe33d+Y6OVxbsd28xJjnWdUKwDnSdrA9r+G21iSgN8DOzPEMoTUV7sTy1WuB34OXNCrH0UD3AfMkEstU0rjT16NTCmllFJJpwDLSzqomtjyI+AV4M8d261MnHjNqE4E3kIkPhYZasOqJ8SxRMLjaWK5UGqe54klLBOBQ4DDJO0r6f2S5u1rZK/1O+DdkpbsdyAppTStspFpSimlRsiRtTMGSQsSTUqXY2Ds5IG292zbZnXgcuDHtvfqS6B9JmlmIpGxFXATsGFr2k3Hdq8jEiSbAI8B77N9fclY08hUP9N3EQ1L1wbWIpaRuLrdRCxxuYRoXHp/n0JF0kzE9LG3ExNkLnSeNKSUxqhMeqSUUmqETHrMOCTNBWxLnPBdbfu8jue3ANYHDrfd2fR0htFWwbEl0fx1w/bRm1VDzNOAdYGHgI1t39aHUNNUkrQCg6e2vKl6ysTP9BKqREjJZJak1rKbpat/XyTGSXc7cbDt5YoEllJKUyGTHimllFJKDVUlPo4HNgeuAzay/ZSkBYCziVGnd1ePz8jLgcaFainTOgxUg7yT6MFn28V68VVJ6BHLBt4ppSbLN6iUUkoppYaqJnlsC5xK9II4R9JbgQuJhMc/gHUy4TFuPAs8Xt2eBKYQS8BUMgjbM43mVjK2lFIaraz0SCmllFIRVbPGWdqXaFSPL030DXgzMBk43fZRfQixsSTNSvTu+CDR+HUm4Brg/baf6GdsaepJmp9Y1tKq7lgVmLX1dPXvXcDFufQvpZSmTiY9UkoppVQrSesBBzMwqvZx4Pu2D5G0LnAWMDsDJ3kGTrG9VfFgG6wj8XEJ8EHb/+5vVGk0JL2RgQTHOsBbGVzJ8QpwI3Bx62b7kT6EmlJK40YmPVJKKaVUG0lvA/4OzNbxlIEdgAOA+YBDgXuAZYHPAHMDu9o+sly0zSHp5Wl4edH+D2lkJN0DLNW6W/07hfj7aCU5LrX9bB9i+z3RLPX3XZ7bHLjf9nVdnvsu8CHb76o/ypRSmjqZ9EgppZRSbSQdRSQ3/gh8B3iUKOH/DZHsWBhYtX0yhaSVgb8RYzI3Kh1zE4y2kWSn7LPQPNXP9FliHHMryXGV7Sl9DYyhp2cN89zhwE62Zy4QZkopTZW8CpBSSimlOq0L3E9UbbSqFy6S9CVi+shlnaM4bV8r6QrgHWVDbY5MWoxL7wKutz1NCa2UUkqjkx+oKaWUUqrTYsDVbQmPliurf3tNHbkPmL+uoFIqzfa1mfBIKaXyMumRUkoppTrNCjzT+WBb34Jepf0vkMcpKaWUUppGeTCRUkoppZRSSimlcSmTHimllFJKKaWUUhqXMumRUkoppbrtLOnlzhsxtrbXczv1OeaUUkopjQM5sjallFJKtZnG0avOUZgp1a/6O53qk4L8O00pNVmOrE0ppZRSbXL0akpjhqbydXkFNaXUaFnpkVJKKaWUUkoppXEpr76klFJKKaWUUkppXMqkR0oppZRSSimllMalTHqklFJKKaWUUkppXMqkR0oppZRSSimllMalTHqklFJKKaWUUkppXMqkR0oppZRSSimllMalTHqklFJKKaWUUkppXMqkR0oppZRSSimllMalTHqklFJKKaWUUkppXMqkR0oppZRSSimllMalTHqklFJKKaWUUkppXMqkR0oppZRSSimllMalTHqklFJKKaWUUkppXMqkR0oppZRSSimllMal/w/oklCZWTY2EgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x750 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5), dpi=150)\n",
    "\n",
    "sns.heatmap(df.corr(), cmap=plt.cm.twilight_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PID               -0.246521\n",
       "Enclosed Porch    -0.128787\n",
       "Kitchen AbvGr     -0.119814\n",
       "Overall Cond      -0.101697\n",
       "MS SubClass       -0.085092\n",
       "Low Qual Fin SF   -0.037660\n",
       "Bsmt Half Bath    -0.035835\n",
       "Yr Sold           -0.030569\n",
       "Misc Val          -0.015691\n",
       "BsmtFin SF 2       0.005891\n",
       "3Ssn Porch         0.032225\n",
       "Mo Sold            0.035259\n",
       "Pool Area          0.068403\n",
       "Screen Porch       0.112151\n",
       "Bedroom AbvGr      0.143913\n",
       "Bsmt Unf SF        0.182855\n",
       "Lot Area           0.266549\n",
       "2nd Flr SF         0.269373\n",
       "Bsmt Full Bath     0.276050\n",
       "Half Bath          0.285056\n",
       "Open Porch SF      0.312951\n",
       "Wood Deck SF       0.327143\n",
       "Lot Frontage       0.357318\n",
       "BsmtFin SF 1       0.432914\n",
       "Fireplaces         0.474558\n",
       "TotRms AbvGrd      0.495474\n",
       "Mas Vnr Area       0.508285\n",
       "Garage Yr Blt      0.526965\n",
       "Year Remod/Add     0.532974\n",
       "Full Bath          0.545604\n",
       "Year Built         0.558426\n",
       "1st Flr SF         0.621676\n",
       "Total Bsmt SF      0.632280\n",
       "Garage Area        0.640401\n",
       "Garage Cars        0.647877\n",
       "Gr Liv Area        0.706780\n",
       "Overall Qual       0.799262\n",
       "SalePrice          1.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View correllation of all features with the target variable\n",
    "df.corr()['SalePrice'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gr Liv Area', ylabel='SalePrice'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x750 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5), dpi=150)\n",
    "\n",
    "sns.scatterplot(x='Gr Liv Area',y='SalePrice',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1182</th>\n",
       "      <td>533350090</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24572</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Family</td>\n",
       "      <td>150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>908154235</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>313.0</td>\n",
       "      <td>63887</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR3</td>\n",
       "      <td>Bnk</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>480</td>\n",
       "      <td>Gd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2180</th>\n",
       "      <td>908154195</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>128.0</td>\n",
       "      <td>39290</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Bnk</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elev</td>\n",
       "      <td>17000</td>\n",
       "      <td>10</td>\n",
       "      <td>2007</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>183850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2181</th>\n",
       "      <td>908154205</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>130.0</td>\n",
       "      <td>40094</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Bnk</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2007</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>184750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street Alley  \\\n",
       "1182  533350090           60        RL           NaN     24572   Pave   NaN   \n",
       "1498  908154235           60        RL         313.0     63887   Pave   NaN   \n",
       "2180  908154195           20        RL         128.0     39290   Pave   NaN   \n",
       "2181  908154205           60        RL         130.0     40094   Pave   NaN   \n",
       "\n",
       "     Lot Shape Land Contour Utilities  ... Pool Area Pool QC Fence  \\\n",
       "1182       IR1          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "1498       IR3          Bnk    AllPub  ...       480      Gd   NaN   \n",
       "2180       IR1          Bnk    AllPub  ...         0     NaN   NaN   \n",
       "2181       IR1          Bnk    AllPub  ...         0     NaN   NaN   \n",
       "\n",
       "     Misc Feature Misc Val Mo Sold Yr Sold  Sale Type  Sale Condition  \\\n",
       "1182          NaN        0       6    2008        WD           Family   \n",
       "1498          NaN        0       1    2008        New         Partial   \n",
       "2180         Elev    17000      10    2007        New         Partial   \n",
       "2181          NaN        0      10    2007        New         Partial   \n",
       "\n",
       "      SalePrice  \n",
       "1182     150000  \n",
       "1498     160000  \n",
       "2180     183850  \n",
       "2181     184750  \n",
       "\n",
       "[4 rows x 81 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Overall Qual']>8) & (df['SalePrice']<200000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>908154235</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>313.0</td>\n",
       "      <td>63887</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR3</td>\n",
       "      <td>Bnk</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>480</td>\n",
       "      <td>Gd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2180</th>\n",
       "      <td>908154195</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>128.0</td>\n",
       "      <td>39290</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Bnk</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elev</td>\n",
       "      <td>17000</td>\n",
       "      <td>10</td>\n",
       "      <td>2007</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>183850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2181</th>\n",
       "      <td>908154205</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>130.0</td>\n",
       "      <td>40094</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Bnk</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2007</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>184750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street Alley  \\\n",
       "1498  908154235           60        RL         313.0     63887   Pave   NaN   \n",
       "2180  908154195           20        RL         128.0     39290   Pave   NaN   \n",
       "2181  908154205           60        RL         130.0     40094   Pave   NaN   \n",
       "\n",
       "     Lot Shape Land Contour Utilities  ... Pool Area Pool QC Fence  \\\n",
       "1498       IR3          Bnk    AllPub  ...       480      Gd   NaN   \n",
       "2180       IR1          Bnk    AllPub  ...         0     NaN   NaN   \n",
       "2181       IR1          Bnk    AllPub  ...         0     NaN   NaN   \n",
       "\n",
       "     Misc Feature Misc Val Mo Sold Yr Sold  Sale Type  Sale Condition  \\\n",
       "1498          NaN        0       1    2008        New         Partial   \n",
       "2180         Elev    17000      10    2007        New         Partial   \n",
       "2181          NaN        0      10    2007        New         Partial   \n",
       "\n",
       "      SalePrice  \n",
       "1498     160000  \n",
       "2180     183850  \n",
       "2181     184750  \n",
       "\n",
       "[3 rows x 81 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Gr Liv Area']>4000) & (df['SalePrice']<400000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "lvdrop = df[(df['Gr Liv Area']>4000) & (df['SalePrice']<400000)].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(lvdrop,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PID, MS SubClass, MS Zoning, Lot Frontage, Lot Area, Street, Alley, Lot Shape, Land Contour, Utilities, Lot Config, Land Slope, Neighborhood, Condition 1, Condition 2, Bldg Type, House Style, Overall Qual, Overall Cond, Year Built, Year Remod/Add, Roof Style, Roof Matl, Exterior 1st, Exterior 2nd, Mas Vnr Type, Mas Vnr Area, Exter Qual, Exter Cond, Foundation, Bsmt Qual, Bsmt Cond, Bsmt Exposure, BsmtFin Type 1, BsmtFin SF 1, BsmtFin Type 2, BsmtFin SF 2, Bsmt Unf SF, Total Bsmt SF, Heating, Heating QC, Central Air, Electrical, 1st Flr SF, 2nd Flr SF, Low Qual Fin SF, Gr Liv Area, Bsmt Full Bath, Bsmt Half Bath, Full Bath, Half Bath, Bedroom AbvGr, Kitchen AbvGr, Kitchen Qual, TotRms AbvGrd, Functional, Fireplaces, Fireplace Qu, Garage Type, Garage Yr Blt, Garage Finish, Garage Cars, Garage Area, Garage Qual, Garage Cond, Paved Drive, Wood Deck SF, Open Porch SF, Enclosed Porch, 3Ssn Porch, Screen Porch, Pool Area, Pool QC, Fence, Misc Feature, Misc Val, Mo Sold, Yr Sold, Sale Type, Sale Condition, SalePrice]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 81 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Gr Liv Area']>4000) & (df['SalePrice']<400000)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dealing with categorical data\n",
    "Categorical variables have 2 or more categories but there is no ordering but have no intrinsic ordering to the categories (e.g., yes, no).\n",
    "\n",
    "Link to additional information for dealing with categorical data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 20,  60, 120,  50,  85, 160,  80,  30,  90, 190,  45,  70,  75,\n",
       "        40, 180, 150])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MS SubClass'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MS SubClass\n",
    "    20    1-STORY 1946 & NEWER ALL STYLES /n\n",
    "    30    1-STORY 1945 & OLDER\n",
    "    40    1-STORY W/FINISHED ATTIC ALL AGES\n",
    "    45    1-1/2 STORY - UNFINISHED ALL AGES\n",
    "    50    1-1/2 STORY FINISHED ALL AGES\n",
    "    60    2-STORY 1946 & NEWER\n",
    "    70    2-STORY 1945 & OLDER\n",
    "    75    2-1/2 STORY ALL AGES\n",
    "    80    SPLIT OR MULTI-LEVEL\n",
    "    85    SPLIT FOYER\n",
    "    90    DUPLEX - ALL STYLES AND AGES\n",
    "    120    1-STORY PUD (Planned Unit Development) - 1946 & NEWER\n",
    "    150    1-1/2 STORY PUD - ALL AGES\n",
    "    160    2-STORY PUD - 1946 & NEWER\n",
    "    180    PUD - MULTILEVEL - INCL SPLIT LEV/FOYER\n",
    "    190    2 FAMILY CONVERSION - ALL STYLES AND AGES\n",
    "    \n",
    "- The numeric values do have any relationship to each other nor does it relate to any order."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create dummy variables\n",
    "\n",
    "- Dummy variables are a set of binary (0 or 1) variables that each represent a single class from a categorical feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Select all object data types in the dataset\n",
    "\n",
    "df_objs = df.select_dtypes(include='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2927 entries, 0 to 2929\n",
      "Data columns (total 43 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   MS Zoning       2927 non-null   object\n",
      " 1   Street          2927 non-null   object\n",
      " 2   Alley           198 non-null    object\n",
      " 3   Lot Shape       2927 non-null   object\n",
      " 4   Land Contour    2927 non-null   object\n",
      " 5   Utilities       2927 non-null   object\n",
      " 6   Lot Config      2927 non-null   object\n",
      " 7   Land Slope      2927 non-null   object\n",
      " 8   Neighborhood    2927 non-null   object\n",
      " 9   Condition 1     2927 non-null   object\n",
      " 10  Condition 2     2927 non-null   object\n",
      " 11  Bldg Type       2927 non-null   object\n",
      " 12  House Style     2927 non-null   object\n",
      " 13  Roof Style      2927 non-null   object\n",
      " 14  Roof Matl       2927 non-null   object\n",
      " 15  Exterior 1st    2927 non-null   object\n",
      " 16  Exterior 2nd    2927 non-null   object\n",
      " 17  Mas Vnr Type    2904 non-null   object\n",
      " 18  Exter Qual      2927 non-null   object\n",
      " 19  Exter Cond      2927 non-null   object\n",
      " 20  Foundation      2927 non-null   object\n",
      " 21  Bsmt Qual       2847 non-null   object\n",
      " 22  Bsmt Cond       2847 non-null   object\n",
      " 23  Bsmt Exposure   2844 non-null   object\n",
      " 24  BsmtFin Type 1  2847 non-null   object\n",
      " 25  BsmtFin Type 2  2846 non-null   object\n",
      " 26  Heating         2927 non-null   object\n",
      " 27  Heating QC      2927 non-null   object\n",
      " 28  Central Air     2927 non-null   object\n",
      " 29  Electrical      2926 non-null   object\n",
      " 30  Kitchen Qual    2927 non-null   object\n",
      " 31  Functional      2927 non-null   object\n",
      " 32  Fireplace Qu    1505 non-null   object\n",
      " 33  Garage Type     2770 non-null   object\n",
      " 34  Garage Finish   2768 non-null   object\n",
      " 35  Garage Qual     2768 non-null   object\n",
      " 36  Garage Cond     2768 non-null   object\n",
      " 37  Paved Drive     2927 non-null   object\n",
      " 38  Pool QC         12 non-null     object\n",
      " 39  Fence           572 non-null    object\n",
      " 40  Misc Feature    105 non-null    object\n",
      " 41  Sale Type       2927 non-null   object\n",
      " 42  Sale Condition  2927 non-null   object\n",
      "dtypes: object(43)\n",
      "memory usage: 1006.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_objs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_num = df.select_dtypes(exclude='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2927 entries, 0 to 2929\n",
      "Data columns (total 38 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   PID              2927 non-null   int64  \n",
      " 1   MS SubClass      2927 non-null   int64  \n",
      " 2   Lot Frontage     2437 non-null   float64\n",
      " 3   Lot Area         2927 non-null   int64  \n",
      " 4   Overall Qual     2927 non-null   int64  \n",
      " 5   Overall Cond     2927 non-null   int64  \n",
      " 6   Year Built       2927 non-null   int64  \n",
      " 7   Year Remod/Add   2927 non-null   int64  \n",
      " 8   Mas Vnr Area     2904 non-null   float64\n",
      " 9   BsmtFin SF 1     2926 non-null   float64\n",
      " 10  BsmtFin SF 2     2926 non-null   float64\n",
      " 11  Bsmt Unf SF      2926 non-null   float64\n",
      " 12  Total Bsmt SF    2926 non-null   float64\n",
      " 13  1st Flr SF       2927 non-null   int64  \n",
      " 14  2nd Flr SF       2927 non-null   int64  \n",
      " 15  Low Qual Fin SF  2927 non-null   int64  \n",
      " 16  Gr Liv Area      2927 non-null   int64  \n",
      " 17  Bsmt Full Bath   2925 non-null   float64\n",
      " 18  Bsmt Half Bath   2925 non-null   float64\n",
      " 19  Full Bath        2927 non-null   int64  \n",
      " 20  Half Bath        2927 non-null   int64  \n",
      " 21  Bedroom AbvGr    2927 non-null   int64  \n",
      " 22  Kitchen AbvGr    2927 non-null   int64  \n",
      " 23  TotRms AbvGrd    2927 non-null   int64  \n",
      " 24  Fireplaces       2927 non-null   int64  \n",
      " 25  Garage Yr Blt    2768 non-null   float64\n",
      " 26  Garage Cars      2926 non-null   float64\n",
      " 27  Garage Area      2926 non-null   float64\n",
      " 28  Wood Deck SF     2927 non-null   int64  \n",
      " 29  Open Porch SF    2927 non-null   int64  \n",
      " 30  Enclosed Porch   2927 non-null   int64  \n",
      " 31  3Ssn Porch       2927 non-null   int64  \n",
      " 32  Screen Porch     2927 non-null   int64  \n",
      " 33  Pool Area        2927 non-null   int64  \n",
      " 34  Misc Val         2927 non-null   int64  \n",
      " 35  Mo Sold          2927 non-null   int64  \n",
      " 36  Yr Sold          2927 non-null   int64  \n",
      " 37  SalePrice        2927 non-null   int64  \n",
      "dtypes: float64(11), int64(27)\n",
      "memory usage: 891.8 KB\n"
     ]
    }
   ],
   "source": [
    "df_num.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Covert to dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_objs = pd.get_dummies(df_objs,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>MS Zoning_FV</th>\n",
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       "      <th>Street_Pave</th>\n",
       "      <th>Alley_Pave</th>\n",
       "      <th>Lot Shape_IR2</th>\n",
       "      <th>Lot Shape_IR3</th>\n",
       "      <th>...</th>\n",
       "      <th>Sale Type_ConLw</th>\n",
       "      <th>Sale Type_New</th>\n",
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       "      <th>Sale Type_VWD</th>\n",
       "      <th>Sale Type_WD</th>\n",
       "      <th>Sale Condition_AdjLand</th>\n",
       "      <th>Sale Condition_Alloca</th>\n",
       "      <th>Sale Condition_Family</th>\n",
       "      <th>Sale Condition_Normal</th>\n",
       "      <th>Sale Condition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 223 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MS Zoning_C (all)  MS Zoning_FV  MS Zoning_I (all)  MS Zoning_RH  \\\n",
       "0                  0             0                  0             0   \n",
       "1                  0             0                  0             1   \n",
       "2                  0             0                  0             0   \n",
       "3                  0             0                  0             0   \n",
       "4                  0             0                  0             0   \n",
       "\n",
       "   MS Zoning_RL  MS Zoning_RM  Street_Pave  Alley_Pave  Lot Shape_IR2  \\\n",
       "0             1             0            1           0              0   \n",
       "1             0             0            1           0              0   \n",
       "2             1             0            1           0              0   \n",
       "3             1             0            1           0              0   \n",
       "4             1             0            1           0              0   \n",
       "\n",
       "   Lot Shape_IR3  ...  Sale Type_ConLw  Sale Type_New  Sale Type_Oth  \\\n",
       "0              0  ...                0              0              0   \n",
       "1              0  ...                0              0              0   \n",
       "2              0  ...                0              0              0   \n",
       "3              0  ...                0              0              0   \n",
       "4              0  ...                0              0              0   \n",
       "\n",
       "   Sale Type_VWD  Sale Type_WD   Sale Condition_AdjLand  \\\n",
       "0              0              1                       0   \n",
       "1              0              1                       0   \n",
       "2              0              1                       0   \n",
       "3              0              1                       0   \n",
       "4              0              1                       0   \n",
       "\n",
       "   Sale Condition_Alloca  Sale Condition_Family  Sale Condition_Normal  \\\n",
       "0                      0                      0                      1   \n",
       "1                      0                      0                      1   \n",
       "2                      0                      0                      1   \n",
       "3                      0                      0                      1   \n",
       "4                      0                      0                      1   \n",
       "\n",
       "   Sale Condition_Partial  \n",
       "0                       0  \n",
       "1                       0  \n",
       "2                       0  \n",
       "3                       0  \n",
       "4                       0  \n",
       "\n",
       "[5 rows x 223 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_objs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Concatenate the objects and numbers dataframes back together\n",
    "\n",
    "concat_df = pd.concat([df_num,df_objs],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Overall Qual</th>\n",
       "      <th>Overall Cond</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>Year Remod/Add</th>\n",
       "      <th>Mas Vnr Area</th>\n",
       "      <th>BsmtFin SF 1</th>\n",
       "      <th>...</th>\n",
       "      <th>Sale Type_ConLw</th>\n",
       "      <th>Sale Type_New</th>\n",
       "      <th>Sale Type_Oth</th>\n",
       "      <th>Sale Type_VWD</th>\n",
       "      <th>Sale Type_WD</th>\n",
       "      <th>Sale Condition_AdjLand</th>\n",
       "      <th>Sale Condition_Alloca</th>\n",
       "      <th>Sale Condition_Family</th>\n",
       "      <th>Sale Condition_Normal</th>\n",
       "      <th>Sale Condition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>526301100</td>\n",
       "      <td>20</td>\n",
       "      <td>141.0</td>\n",
       "      <td>31770</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1960</td>\n",
       "      <td>1960</td>\n",
       "      <td>112.0</td>\n",
       "      <td>639.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>526350040</td>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1961</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>526351010</td>\n",
       "      <td>20</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1958</td>\n",
       "      <td>1958</td>\n",
       "      <td>108.0</td>\n",
       "      <td>923.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>526353030</td>\n",
       "      <td>20</td>\n",
       "      <td>93.0</td>\n",
       "      <td>11160</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1968</td>\n",
       "      <td>1968</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>527105010</td>\n",
       "      <td>60</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1997</td>\n",
       "      <td>1998</td>\n",
       "      <td>0.0</td>\n",
       "      <td>791.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 261 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         PID  MS SubClass  Lot Frontage  Lot Area  Overall Qual  Overall Cond  \\\n",
       "0  526301100           20         141.0     31770             6             5   \n",
       "1  526350040           20          80.0     11622             5             6   \n",
       "2  526351010           20          81.0     14267             6             6   \n",
       "3  526353030           20          93.0     11160             7             5   \n",
       "4  527105010           60          74.0     13830             5             5   \n",
       "\n",
       "   Year Built  Year Remod/Add  Mas Vnr Area  BsmtFin SF 1  ...  \\\n",
       "0        1960            1960         112.0         639.0  ...   \n",
       "1        1961            1961           0.0         468.0  ...   \n",
       "2        1958            1958         108.0         923.0  ...   \n",
       "3        1968            1968           0.0        1065.0  ...   \n",
       "4        1997            1998           0.0         791.0  ...   \n",
       "\n",
       "   Sale Type_ConLw  Sale Type_New  Sale Type_Oth  Sale Type_VWD  \\\n",
       "0                0              0              0              0   \n",
       "1                0              0              0              0   \n",
       "2                0              0              0              0   \n",
       "3                0              0              0              0   \n",
       "4                0              0              0              0   \n",
       "\n",
       "   Sale Type_WD   Sale Condition_AdjLand  Sale Condition_Alloca  \\\n",
       "0              1                       0                      0   \n",
       "1              1                       0                      0   \n",
       "2              1                       0                      0   \n",
       "3              1                       0                      0   \n",
       "4              1                       0                      0   \n",
       "\n",
       "   Sale Condition_Family  Sale Condition_Normal  Sale Condition_Partial  \n",
       "0                      0                      1                       0  \n",
       "1                      0                      1                       0  \n",
       "2                      0                      1                       0  \n",
       "3                      0                      1                       0  \n",
       "4                      0                      1                       0  \n",
       "\n",
       "[5 rows x 261 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "The dataset may require the removal of more columns -- it is important to examine correlation among the variables and determine whether others should be removed based on the use case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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