house_price_prediction_2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Parishri07/Linear_Regression_model/blob/main/house_price_prediction_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "6ad3cddc",
      "metadata": {
        "id": "6ad3cddc"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "6d445bdc",
      "metadata": {
        "id": "6d445bdc",
        "outputId": "f683a634-0f56-456c-8fb5-a705151206cd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/datasets/_openml.py:968: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
            "  warn(\n"
          ]
        }
      ],
      "source": [
        "from sklearn.datasets import fetch_openml\n",
        "df = fetch_openml(name=\"house_prices\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "4c8bdc6e",
      "metadata": {
        "scrolled": false,
        "id": "4c8bdc6e",
        "outputId": "fa8ed865-a7d0-47cb-cd52-91cfc12df7ca",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'data':         Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
              " 0        1          60       RL         65.0     8450   Pave  None      Reg   \n",
              " 1        2          20       RL         80.0     9600   Pave  None      Reg   \n",
              " 2        3          60       RL         68.0    11250   Pave  None      IR1   \n",
              " 3        4          70       RL         60.0     9550   Pave  None      IR1   \n",
              " 4        5          60       RL         84.0    14260   Pave  None      IR1   \n",
              " ...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
              " 1455  1456          60       RL         62.0     7917   Pave  None      Reg   \n",
              " 1456  1457          20       RL         85.0    13175   Pave  None      Reg   \n",
              " 1457  1458          70       RL         66.0     9042   Pave  None      Reg   \n",
              " 1458  1459          20       RL         68.0     9717   Pave  None      Reg   \n",
              " 1459  1460          20       RL         75.0     9937   Pave  None      Reg   \n",
              " \n",
              "      LandContour Utilities  ... ScreenPorch PoolArea PoolQC  Fence  \\\n",
              " 0            Lvl    AllPub  ...           0        0   None   None   \n",
              " 1            Lvl    AllPub  ...           0        0   None   None   \n",
              " 2            Lvl    AllPub  ...           0        0   None   None   \n",
              " 3            Lvl    AllPub  ...           0        0   None   None   \n",
              " 4            Lvl    AllPub  ...           0        0   None   None   \n",
              " ...          ...       ...  ...         ...      ...    ...    ...   \n",
              " 1455         Lvl    AllPub  ...           0        0   None   None   \n",
              " 1456         Lvl    AllPub  ...           0        0   None  MnPrv   \n",
              " 1457         Lvl    AllPub  ...           0        0   None  GdPrv   \n",
              " 1458         Lvl    AllPub  ...           0        0   None   None   \n",
              " 1459         Lvl    AllPub  ...           0        0   None   None   \n",
              " \n",
              "      MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
              " 0           None       0      2    2008        WD         Normal  \n",
              " 1           None       0      5    2007        WD         Normal  \n",
              " 2           None       0      9    2008        WD         Normal  \n",
              " 3           None       0      2    2006        WD        Abnorml  \n",
              " 4           None       0     12    2008        WD         Normal  \n",
              " ...          ...     ...    ...     ...       ...            ...  \n",
              " 1455        None       0      8    2007        WD         Normal  \n",
              " 1456        None       0      2    2010        WD         Normal  \n",
              " 1457        Shed    2500      5    2010        WD         Normal  \n",
              " 1458        None       0      4    2010        WD         Normal  \n",
              " 1459        None       0      6    2008        WD         Normal  \n",
              " \n",
              " [1460 rows x 80 columns],\n",
              " 'target': 0       208500\n",
              " 1       181500\n",
              " 2       223500\n",
              " 3       140000\n",
              " 4       250000\n",
              "          ...  \n",
              " 1455    175000\n",
              " 1456    210000\n",
              " 1457    266500\n",
              " 1458    142125\n",
              " 1459    147500\n",
              " Name: SalePrice, Length: 1460, dtype: int64,\n",
              " 'frame':         Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
              " 0        1          60       RL         65.0     8450   Pave  None      Reg   \n",
              " 1        2          20       RL         80.0     9600   Pave  None      Reg   \n",
              " 2        3          60       RL         68.0    11250   Pave  None      IR1   \n",
              " 3        4          70       RL         60.0     9550   Pave  None      IR1   \n",
              " 4        5          60       RL         84.0    14260   Pave  None      IR1   \n",
              " ...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
              " 1455  1456          60       RL         62.0     7917   Pave  None      Reg   \n",
              " 1456  1457          20       RL         85.0    13175   Pave  None      Reg   \n",
              " 1457  1458          70       RL         66.0     9042   Pave  None      Reg   \n",
              " 1458  1459          20       RL         68.0     9717   Pave  None      Reg   \n",
              " 1459  1460          20       RL         75.0     9937   Pave  None      Reg   \n",
              " \n",
              "      LandContour Utilities  ... PoolArea PoolQC  Fence MiscFeature MiscVal  \\\n",
              " 0            Lvl    AllPub  ...        0   None   None        None       0   \n",
              " 1            Lvl    AllPub  ...        0   None   None        None       0   \n",
              " 2            Lvl    AllPub  ...        0   None   None        None       0   \n",
              " 3            Lvl    AllPub  ...        0   None   None        None       0   \n",
              " 4            Lvl    AllPub  ...        0   None   None        None       0   \n",
              " ...          ...       ...  ...      ...    ...    ...         ...     ...   \n",
              " 1455         Lvl    AllPub  ...        0   None   None        None       0   \n",
              " 1456         Lvl    AllPub  ...        0   None  MnPrv        None       0   \n",
              " 1457         Lvl    AllPub  ...        0   None  GdPrv        Shed    2500   \n",
              " 1458         Lvl    AllPub  ...        0   None   None        None       0   \n",
              " 1459         Lvl    AllPub  ...        0   None   None        None       0   \n",
              " \n",
              "      MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
              " 0         2   2008        WD         Normal     208500  \n",
              " 1         5   2007        WD         Normal     181500  \n",
              " 2         9   2008        WD         Normal     223500  \n",
              " 3         2   2006        WD        Abnorml     140000  \n",
              " 4        12   2008        WD         Normal     250000  \n",
              " ...     ...    ...       ...            ...        ...  \n",
              " 1455      8   2007        WD         Normal     175000  \n",
              " 1456      2   2010        WD         Normal     210000  \n",
              " 1457      5   2010        WD         Normal     266500  \n",
              " 1458      4   2010        WD         Normal     142125  \n",
              " 1459      6   2008        WD         Normal     147500  \n",
              " \n",
              " [1460 rows x 81 columns],\n",
              " 'categories': None,\n",
              " 'feature_names': ['Id',\n",
              "  'MSSubClass',\n",
              "  'MSZoning',\n",
              "  'LotFrontage',\n",
              "  'LotArea',\n",
              "  'Street',\n",
              "  'Alley',\n",
              "  'LotShape',\n",
              "  'LandContour',\n",
              "  'Utilities',\n",
              "  'LotConfig',\n",
              "  'LandSlope',\n",
              "  'Neighborhood',\n",
              "  'Condition1',\n",
              "  'Condition2',\n",
              "  'BldgType',\n",
              "  'HouseStyle',\n",
              "  'OverallQual',\n",
              "  'OverallCond',\n",
              "  'YearBuilt',\n",
              "  'YearRemodAdd',\n",
              "  'RoofStyle',\n",
              "  'RoofMatl',\n",
              "  'Exterior1st',\n",
              "  'Exterior2nd',\n",
              "  'MasVnrType',\n",
              "  'MasVnrArea',\n",
              "  'ExterQual',\n",
              "  'ExterCond',\n",
              "  'Foundation',\n",
              "  'BsmtQual',\n",
              "  'BsmtCond',\n",
              "  'BsmtExposure',\n",
              "  'BsmtFinType1',\n",
              "  'BsmtFinSF1',\n",
              "  'BsmtFinType2',\n",
              "  'BsmtFinSF2',\n",
              "  'BsmtUnfSF',\n",
              "  'TotalBsmtSF',\n",
              "  'Heating',\n",
              "  'HeatingQC',\n",
              "  'CentralAir',\n",
              "  'Electrical',\n",
              "  '1stFlrSF',\n",
              "  '2ndFlrSF',\n",
              "  'LowQualFinSF',\n",
              "  'GrLivArea',\n",
              "  'BsmtFullBath',\n",
              "  'BsmtHalfBath',\n",
              "  'FullBath',\n",
              "  'HalfBath',\n",
              "  'BedroomAbvGr',\n",
              "  'KitchenAbvGr',\n",
              "  'KitchenQual',\n",
              "  'TotRmsAbvGrd',\n",
              "  'Functional',\n",
              "  'Fireplaces',\n",
              "  'FireplaceQu',\n",
              "  'GarageType',\n",
              "  'GarageYrBlt',\n",
              "  'GarageFinish',\n",
              "  'GarageCars',\n",
              "  'GarageArea',\n",
              "  'GarageQual',\n",
              "  'GarageCond',\n",
              "  'PavedDrive',\n",
              "  'WoodDeckSF',\n",
              "  'OpenPorchSF',\n",
              "  'EnclosedPorch',\n",
              "  '3SsnPorch',\n",
              "  'ScreenPorch',\n",
              "  'PoolArea',\n",
              "  'PoolQC',\n",
              "  'Fence',\n",
              "  'MiscFeature',\n",
              "  'MiscVal',\n",
              "  'MoSold',\n",
              "  'YrSold',\n",
              "  'SaleType',\n",
              "  'SaleCondition'],\n",
              " 'target_names': ['SalePrice'],\n",
              " 'DESCR': \"Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.\\n\\nWith 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.\\n\\nMSSubClass: Identifies the type of dwelling involved in the sale.\\t\\n\\n        20\\t1-STORY 1946 & NEWER ALL STYLES\\n        30\\t1-STORY 1945 & OLDER\\n        40\\t1-STORY W/FINISHED ATTIC ALL AGES\\n        45\\t1-1/2 STORY - UNFINISHED ALL AGES\\n        50\\t1-1/2 STORY FINISHED ALL AGES\\n        60\\t2-STORY 1946 & NEWER\\n        70\\t2-STORY 1945 & OLDER\\n        75\\t2-1/2 STORY ALL AGES\\n        80\\tSPLIT OR MULTI-LEVEL\\n        85\\tSPLIT FOYER\\n        90\\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\\nMSZoning: 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\\nLotFrontage: Linear feet of street connected to property\\n\\nLotArea: Lot size in square feet\\n\\nStreet: Type of road access to property\\n\\n       Grvl\\tGravel\\t\\n       Pave\\tPaved\\n       \\t\\nAlley: Type of alley access to property\\n\\n       Grvl\\tGravel\\n       Pave\\tPaved\\n       NA \\tNo alley access\\n\\t\\t\\nLotShape: General shape of property\\n\\n       Reg\\tRegular\\t\\n       IR1\\tSlightly irregular\\n       IR2\\tModerately Irregular\\n       IR3\\tIrregular\\n       \\nLandContour: Flatness of the property\\n\\n       Lvl\\tNear Flat/Level\\t\\n       Bnk\\tBanked - Quick and significant rise from street grade to building\\n       HLS\\tHillside - Significant slope from side to side\\n       Low\\tDepression\\n\\t\\t\\nUtilities: Type of utilities available\\n\\t\\t\\n       AllPub\\tAll public Utilities (E,G,W,& S)\\t\\n       NoSewr\\tElectricity, Gas, and Water (Septic Tank)\\n       NoSeWa\\tElectricity and Gas Only\\n       ELO\\tElectricity only\\t\\n\\t\\nLotConfig: Lot configuration\\n\\n       Inside\\tInside lot\\n       Corner\\tCorner lot\\n       CulDSac\\tCul-de-sac\\n       FR2\\tFrontage on 2 sides of property\\n       FR3\\tFrontage on 3 sides of property\\n\\t\\nLandSlope: Slope of property\\n\\t\\t\\n       Gtl\\tGentle slope\\n       Mod\\tModerate Slope\\t\\n       Sev\\tSevere Slope\\n\\t\\nNeighborhood: Physical locations within Ames city limits\\n\\n       Blmngtn\\tBloomington Heights\\n       Blueste\\tBluestem\\n       BrDale\\tBriardale\\n       BrkSide\\tBrookside\\n       ClearCr\\tClear Creek\\n       CollgCr\\tCollege Creek\\n       Crawfor\\tCrawford\\n       Edwards\\tEdwards\\n       Gilbert\\tGilbert\\n       IDOTRR\\tIowa DOT and Rail Road\\n       MeadowV\\tMeadow Village\\n       Mitchel\\tMitchell\\n       Names\\tNorth Ames\\n       NoRidge\\tNorthridge\\n       NPkVill\\tNorthpark Villa\\n       NridgHt\\tNorthridge Heights\\n       NWAmes\\tNorthwest Ames\\n       OldTown\\tOld Town\\n       SWISU\\tSouth & West of Iowa State University\\n       Sawyer\\tSawyer\\n       SawyerW\\tSawyer West\\n       Somerst\\tSomerset\\n       StoneBr\\tStone Brook\\n       Timber\\tTimberland\\n       Veenker\\tVeenker\\n\\t\\t\\t\\nCondition1: Proximity to various conditions\\n\\t\\n       Artery\\tAdjacent to arterial street\\n       Feedr\\tAdjacent to feeder street\\t\\n       Norm\\tNormal\\t\\n       RRNn\\tWithin 200' of North-South Railroad\\n       RRAn\\tAdjacent to North-South Railroad\\n       PosN\\tNear positive off-site feature--park, greenbelt, etc.\\n       PosA\\tAdjacent to postive off-site feature\\n       RRNe\\tWithin 200' of East-West Railroad\\n       RRAe\\tAdjacent to East-West Railroad\\n\\t\\nCondition2: Proximity to various conditions (if more than one is present)\\n\\t\\t\\n       Artery\\tAdjacent to arterial street\\n       Feedr\\tAdjacent to feeder street\\t\\n       Norm\\tNormal\\t\\n       RRNn\\tWithin 200' of North-South Railroad\\n       RRAn\\tAdjacent to North-South Railroad\\n       PosN\\tNear positive off-site feature--park, greenbelt, etc.\\n       PosA\\tAdjacent to postive off-site feature\\n       RRNe\\tWithin 200' of East-West Railroad\\n       RRAe\\tAdjacent to East-West Railroad\\n\\t\\nBldgType: Type of dwelling\\n\\t\\t\\n       1Fam\\tSingle-family Detached\\t\\n       2FmCon\\tTwo-family Conversion; originally built as one-family dwelling\\n       Duplx\\tDuplex\\n       TwnhsE\\tTownhouse End Unit\\n       TwnhsI\\tTownhouse Inside Unit\\n\\t\\nHouseStyle: Style of dwelling\\n\\t\\n       1Story\\tOne story\\n       1.5Fin\\tOne and one-half story: 2nd level finished\\n       1.5Unf\\tOne and one-half story: 2nd level unfinished\\n       2Story\\tTwo story\\n       2.5Fin\\tTwo and one-half story: 2nd level finished\\n       2.5Unf\\tTwo and one-half story: 2nd level unfinished\\n       SFoyer\\tSplit Foyer\\n       SLvl\\tSplit Level\\n\\t\\nOverallQual: Rates the overall material and finish of the house\\n\\n       10\\tVery Excellent\\n       9\\tExcellent\\n       8\\tVery Good\\n       7\\tGood\\n       6\\tAbove Average\\n       5\\tAverage\\n       4\\tBelow Average\\n       3\\tFair\\n       2\\tPoor\\n       1\\tVery Poor\\n\\t\\nOverallCond: Rates the overall condition of the house\\n\\n       10\\tVery Excellent\\n       9\\tExcellent\\n       8\\tVery Good\\n       7\\tGood\\n       6\\tAbove Average\\t\\n       5\\tAverage\\n       4\\tBelow Average\\t\\n       3\\tFair\\n       2\\tPoor\\n       1\\tVery Poor\\n\\t\\t\\nYearBuilt: Original construction date\\n\\nYearRemodAdd: Remodel date (same as construction date if no remodeling or additions)\\n\\nRoofStyle: Type of roof\\n\\n       Flat\\tFlat\\n       Gable\\tGable\\n       Gambrel\\tGabrel (Barn)\\n       Hip\\tHip\\n       Mansard\\tMansard\\n       Shed\\tShed\\n\\t\\t\\nRoofMatl: Roof material\\n\\n       ClyTile\\tClay or Tile\\n       CompShg\\tStandard (Composite) Shingle\\n       Membran\\tMembrane\\n       Metal\\tMetal\\n       Roll\\tRoll\\n       Tar&Grv\\tGravel & Tar\\n       WdShake\\tWood Shakes\\n       WdShngl\\tWood Shingles\\n\\t\\t\\nExterior1st: Exterior covering on house\\n\\n       AsbShng\\tAsbestos Shingles\\n       AsphShn\\tAsphalt Shingles\\n       BrkComm\\tBrick Common\\n       BrkFace\\tBrick Face\\n       CBlock\\tCinder Block\\n       CemntBd\\tCement Board\\n       HdBoard\\tHard Board\\n       ImStucc\\tImitation Stucco\\n       MetalSd\\tMetal Siding\\n       Other\\tOther\\n       Plywood\\tPlywood\\n       PreCast\\tPreCast\\t\\n       Stone\\tStone\\n       Stucco\\tStucco\\n       VinylSd\\tVinyl Siding\\n       Wd Sdng\\tWood Siding\\n       WdShing\\tWood Shingles\\n\\t\\nExterior2nd: Exterior covering on house (if more than one material)\\n\\n       AsbShng\\tAsbestos Shingles\\n       AsphShn\\tAsphalt Shingles\\n       BrkComm\\tBrick Common\\n       BrkFace\\tBrick Face\\n       CBlock\\tCinder Block\\n       CemntBd\\tCement Board\\n       HdBoard\\tHard Board\\n       ImStucc\\tImitation Stucco\\n       MetalSd\\tMetal Siding\\n       Other\\tOther\\n       Plywood\\tPlywood\\n       PreCast\\tPreCast\\n       Stone\\tStone\\n       Stucco\\tStucco\\n       VinylSd\\tVinyl Siding\\n       Wd Sdng\\tWood Siding\\n       WdShing\\tWood Shingles\\n\\t\\nMasVnrType: Masonry veneer type\\n\\n       BrkCmn\\tBrick Common\\n       BrkFace\\tBrick Face\\n       CBlock\\tCinder Block\\n       None\\tNone\\n       Stone\\tStone\\n\\t\\nMasVnrArea: Masonry veneer area in square feet\\n\\nExterQual: Evaluates the quality of the material on the exterior \\n\\t\\t\\n       Ex\\tExcellent\\n       Gd\\tGood\\n       TA\\tAverage/Typical\\n       Fa\\tFair\\n       Po\\tPoor\\n\\t\\t\\nExterCond: Evaluates the present condition of the material on the exterior\\n\\t\\t\\n       Ex\\tExcellent\\n       Gd\\tGood\\n       TA\\tAverage/Typical\\n       Fa\\tFair\\n       Po\\tPoor\\n\\t\\t\\nFoundation: Type of foundation\\n\\t\\t\\n       BrkTil\\tBrick & Tile\\n       CBlock\\tCinder Block\\n       PConc\\tPoured Contrete\\t\\n       Slab\\tSlab\\n       Stone\\tStone\\n       Wood\\tWood\\n\\t\\t\\nBsmtQual: Evaluates the height of the basement\\n\\n       Ex\\tExcellent (100+ inches)\\t\\n       Gd\\tGood (90-99 inches)\\n       TA\\tTypical (80-89 inches)\\n       Fa\\tFair (70-79 inches)\\n       Po\\tPoor (<70 inches\\n       NA\\tNo Basement\\n\\t\\t\\nBsmtCond: Evaluates the general condition of the basement\\n\\n       Ex\\tExcellent\\n       Gd\\tGood\\n       TA\\tTypical - slight dampness allowed\\n       Fa\\tFair - dampness or some cracking or settling\\n       Po\\tPoor - Severe cracking, settling, or wetness\\n       NA\\tNo Basement\\n\\t\\nBsmtExposure: Refers to walkout or garden level walls\\n\\n       Gd\\tGood Exposure\\n       Av\\tAverage Exposure (split levels or foyers typically score average or above)\\t\\n       Mn\\tMimimum Exposure\\n       No\\tNo Exposure\\n       NA\\tNo Basement\\n\\t\\nBsmtFinType1: Rating of basement finished area\\n\\n       GLQ\\tGood Living Quarters\\n       ALQ\\tAverage Living Quarters\\n       BLQ\\tBelow Average Living Quarters\\t\\n       Rec\\tAverage Rec Room\\n       LwQ\\tLow Quality\\n       Unf\\tUnfinshed\\n       NA\\tNo Basement\\n\\t\\t\\nBsmtFinSF1: Type 1 finished square feet\\n\\nBsmtFinType2: Rating of basement finished area (if multiple types)\\n\\n       GLQ\\tGood Living Quarters\\n       ALQ\\tAverage Living Quarters\\n       BLQ\\tBelow Average Living Quarters\\t\\n       Rec\\tAverage Rec Room\\n       LwQ\\tLow Quality\\n       Unf\\tUnfinshed\\n       NA\\tNo Basement\\n\\nBsmtFinSF2: Type 2 finished square feet\\n\\nBsmtUnfSF: Unfinished square feet of basement area\\n\\nTotalBsmtSF: Total square feet of basement area\\n\\nHeating: Type of heating\\n\\t\\t\\n       Floor\\tFloor Furnace\\n       GasA\\tGas forced warm air furnace\\n       GasW\\tGas hot water or steam heat\\n       Grav\\tGravity furnace\\t\\n       OthW\\tHot water or steam heat other than gas\\n       Wall\\tWall furnace\\n\\t\\t\\nHeatingQC: Heating quality and condition\\n\\n       Ex\\tExcellent\\n       Gd\\tGood\\n       TA\\tAverage/Typical\\n       Fa\\tFair\\n       Po\\tPoor\\n\\t\\t\\nCentralAir: Central air conditioning\\n\\n       N\\tNo\\n       Y\\tYes\\n\\t\\t\\nElectrical: Electrical system\\n\\n       SBrkr\\tStandard Circuit Breakers & Romex\\n       FuseA\\tFuse Box over 60 AMP and all Romex wiring (Average)\\t\\n       FuseF\\t60 AMP Fuse Box and mostly Romex wiring (Fair)\\n       FuseP\\t60 AMP Fuse Box and mostly knob & tube wiring (poor)\\n       Mix\\tMixed\\n\\t\\t\\n1stFlrSF: First Floor square feet\\n \\n2ndFlrSF: Second floor square feet\\n\\nLowQualFinSF: Low quality finished square feet (all floors)\\n\\nGrLivArea: Above grade (ground) living area square feet\\n\\nBsmtFullBath: Basement full bathrooms\\n\\nBsmtHalfBath: Basement half bathrooms\\n\\nFullBath: Full bathrooms above grade\\n\\nHalfBath: Half baths above grade\\n\\nBedroom: Bedrooms above grade (does NOT include basement bedrooms)\\n\\nKitchen: Kitchens above grade\\n\\nKitchenQual: Kitchen quality\\n\\n       Ex\\tExcellent\\n       Gd\\tGood\\n       TA\\tTypical/Average\\n       Fa\\tFair\\n       Po\\tPoor\\n       \\t\\nTotRmsAbvGrd: Total rooms above grade (does not include bathrooms)\\n\\nFunctional: Home functionality (Assume typical unless deductions are warranted)\\n\\n       Typ\\tTypical Functionality\\n       Min1\\tMinor Deductions 1\\n       Min2\\tMinor Deductions 2\\n       Mod\\tModerate Deductions\\n       Maj1\\tMajor Deductions 1\\n       Maj2\\tMajor Deductions 2\\n       Sev\\tSeverely Damaged\\n       Sal\\tSalvage only\\n\\t\\t\\nFireplaces: Number of fireplaces\\n\\nFireplaceQu: Fireplace quality\\n\\n       Ex\\tExcellent - Exceptional Masonry Fireplace\\n       Gd\\tGood - Masonry Fireplace in main level\\n       TA\\tAverage - Prefabricated Fireplace in main living area or Masonry Fireplace in basement\\n       Fa\\tFair - Prefabricated Fireplace in basement\\n       Po\\tPoor - Ben Franklin Stove\\n       NA\\tNo Fireplace\\n\\t\\t\\nGarageType: Garage location\\n\\t\\t\\n       2Types\\tMore than one type of garage\\n       Attchd\\tAttached to home\\n       Basment\\tBasement Garage\\n       BuiltIn\\tBuilt-In (Garage part of house - typically has room above garage)\\n       CarPort\\tCar Port\\n       Detchd\\tDetached from home\\n       NA\\tNo Garage\\n\\t\\t\\nGarageYrBlt: Year garage was built\\n\\t\\t\\nGarageFinish: Interior finish of the garage\\n\\n       Fin\\tFinished\\n       RFn\\tRough Finished\\t\\n       Unf\\tUnfinished\\n       NA\\tNo Garage\\n\\t\\t\\nGarageCars: Size of garage in car capacity\\n\\nGarageArea: Size of garage in square feet\\n\\nGarageQual: Garage quality\\n\\n       Ex\\tExcellent\\n       Gd\\tGood\\n       TA\\tTypical/Average\\n       Fa\\tFair\\n       Po\\tPoor\\n       NA\\tNo Garage\\n\\t\\t\\nGarageCond: Garage condition\\n\\n       Ex\\tExcellent\\n       Gd\\tGood\\n       TA\\tTypical/Average\\n       Fa\\tFair\\n       Po\\tPoor\\n       NA\\tNo Garage\\n\\t\\t\\nPavedDrive: Paved driveway\\n\\n       Y\\tPaved \\n       P\\tPartial Pavement\\n       N\\tDirt/Gravel\\n\\t\\t\\nWoodDeckSF: Wood deck area in square feet\\n\\nOpenPorchSF: Open porch area in square feet\\n\\nEnclosedPorch: Enclosed porch area in square feet\\n\\n3SsnPorch: Three season porch area in square feet\\n\\nScreenPorch: Screen porch area in square feet\\n\\nPoolArea: Pool area in square feet\\n\\nPoolQC: Pool quality\\n\\t\\t\\n       Ex\\tExcellent\\n       Gd\\tGood\\n       TA\\tAverage/Typical\\n       Fa\\tFair\\n       NA\\tNo Pool\\n\\t\\t\\nFence: Fence quality\\n\\t\\t\\n       GdPrv\\tGood Privacy\\n       MnPrv\\tMinimum Privacy\\n       GdWo\\tGood Wood\\n       MnWw\\tMinimum Wood/Wire\\n       NA\\tNo Fence\\n\\t\\nMiscFeature: Miscellaneous feature not covered in other categories\\n\\t\\t\\n       Elev\\tElevator\\n       Gar2\\t2nd Garage (if not described in garage section)\\n       Othr\\tOther\\n       Shed\\tShed (over 100 SF)\\n       TenC\\tTennis Court\\n       NA\\tNone\\n\\t\\t\\nMiscVal: $Value of miscellaneous feature\\n\\nMoSold: Month Sold (MM)\\n\\nYrSold: Year Sold (YYYY)\\n\\nSaleType: Type of sale\\n\\t\\t\\n       WD \\tWarranty Deed - Conventional\\n       CWD\\tWarranty Deed - Cash\\n       VWD\\tWarranty Deed - VA Loan\\n       New\\tHome just constructed and sold\\n       COD\\tCourt Officer Deed/Estate\\n       Con\\tContract 15% Down payment regular terms\\n       ConLw\\tContract Low Down payment and low interest\\n       ConLI\\tContract Low Interest\\n       ConLD\\tContract Low Down\\n       Oth\\tOther\\n\\t\\t\\nSaleCondition: Condition of sale\\n\\n       Normal\\tNormal Sale\\n       Abnorml\\tAbnormal Sale -  trade, foreclosure, short sale\\n       AdjLand\\tAdjoining Land Purchase\\n       Alloca\\tAllocation - two linked properties with separate deeds, typically condo with a garage unit\\t\\n       Family\\tSale between family members\\n       Partial\\tHome was not completed when last assessed (associated with New Homes)\\n\\nDownloaded from openml.org.\",\n",
              " 'details': {'id': '42165',\n",
              "  'name': 'house_prices',\n",
              "  'version': '1',\n",
              "  'description_version': '1',\n",
              "  'format': 'arff',\n",
              "  'creator': 'OkCupid',\n",
              "  'collection_date': 'NA',\n",
              "  'upload_date': '2019-10-04T12:57:32',\n",
              "  'language': 'English',\n",
              "  'licence': 'NA',\n",
              "  'url': 'https://api.openml.org/data/v1/download/21754539/house_prices.arff',\n",
              "  'parquet_url': 'https://openml1.win.tue.nl/datasets/0004/42165/dataset_42165.pq',\n",
              "  'file_id': '21754539',\n",
              "  'default_target_attribute': 'SalePrice',\n",
              "  'version_label': '0.1',\n",
              "  'tag': 'Economics',\n",
              "  'visibility': 'public',\n",
              "  'original_data_url': 'https://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview',\n",
              "  'paper_url': 'https://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview',\n",
              "  'minio_url': 'https://openml1.win.tue.nl/datasets/0004/42165/dataset_42165.pq',\n",
              "  'status': 'active',\n",
              "  'processing_date': '2019-10-04 12:58:02',\n",
              "  'md5_checksum': 'd5ca59f8d02b1b1c127034392c0f995f'},\n",
              " 'url': 'https://www.openml.org/d/42165'}"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e9ef5b20",
      "metadata": {
        "id": "e9ef5b20"
      },
      "source": [
        "***Preparing the dataset***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "d3a9f7aa",
      "metadata": {
        "id": "d3a9f7aa",
        "outputId": "486a6705-25c1-49c9-d0a1-3499a3a5fed2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
              "0   1          60       RL         65.0     8450   Pave  None      Reg   \n",
              "1   2          20       RL         80.0     9600   Pave  None      Reg   \n",
              "2   3          60       RL         68.0    11250   Pave  None      IR1   \n",
              "3   4          70       RL         60.0     9550   Pave  None      IR1   \n",
              "4   5          60       RL         84.0    14260   Pave  None      IR1   \n",
              "\n",
              "  LandContour Utilities  ... ScreenPorch PoolArea PoolQC Fence MiscFeature  \\\n",
              "0         Lvl    AllPub  ...           0        0   None  None        None   \n",
              "1         Lvl    AllPub  ...           0        0   None  None        None   \n",
              "2         Lvl    AllPub  ...           0        0   None  None        None   \n",
              "3         Lvl    AllPub  ...           0        0   None  None        None   \n",
              "4         Lvl    AllPub  ...           0        0   None  None        None   \n",
              "\n",
              "  MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
              "0       0      2    2008        WD         Normal  \n",
              "1       0      5    2007        WD         Normal  \n",
              "2       0      9    2008        WD         Normal  \n",
              "3       0      2    2006        WD        Abnorml  \n",
              "4       0     12    2008        WD         Normal  \n",
              "\n",
              "[5 rows x 80 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
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              "      <th>...</th>\n",
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              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>Pave</td>\n",
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              "      <td>0</td>\n",
              "      <td>None</td>\n",
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              "      <td>None</td>\n",
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              "      <td>2</td>\n",
              "      <td>2008</td>\n",
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              "      <td>Normal</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>Pave</td>\n",
              "      <td>None</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>Pave</td>\n",
              "      <td>None</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>Pave</td>\n",
              "      <td>None</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
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              "      <td>Abnorml</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>Pave</td>\n",
              "      <td>None</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 80 columns</p>\n",
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    {
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      "id": "5b2d29cd",
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        "colab": {
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          "height": 255
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          "data": {
            "text/plain": [
              "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
              "0   1          60       RL         65.0     8450   Pave  None      Reg   \n",
              "1   2          20       RL         80.0     9600   Pave  None      Reg   \n",
              "2   3          60       RL         68.0    11250   Pave  None      IR1   \n",
              "3   4          70       RL         60.0     9550   Pave  None      IR1   \n",
              "4   5          60       RL         84.0    14260   Pave  None      IR1   \n",
              "\n",
              "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
              "0         Lvl    AllPub  ...        0   None  None        None       0      2   \n",
              "1         Lvl    AllPub  ...        0   None  None        None       0      5   \n",
              "2         Lvl    AllPub  ...        0   None  None        None       0      9   \n",
              "3         Lvl    AllPub  ...        0   None  None        None       0      2   \n",
              "4         Lvl    AllPub  ...        0   None  None        None       0     12   \n",
              "\n",
              "  YrSold  SaleType  SaleCondition   Price  \n",
              "0   2008        WD         Normal  208500  \n",
              "1   2007        WD         Normal  181500  \n",
              "2   2008        WD         Normal  223500  \n",
              "3   2006        WD        Abnorml  140000  \n",
              "4   2008        WD         Normal  250000  \n",
              "\n",
              "[5 rows x 81 columns]"
            ],
            "text/html": [
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              "  <div id=\"df-b60fdec7-a117-4df8-b494-923e569db664\" class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
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              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>...</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>Pave</td>\n",
              "      <td>None</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>208500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>Pave</td>\n",
              "      <td>None</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>181500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>Pave</td>\n",
              "      <td>None</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>223500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>Pave</td>\n",
              "      <td>None</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>Pave</td>\n",
              "      <td>None</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
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              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 81 columns</p>\n",
              "</div>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3f61ae48-108e-4f7e-ab84-0e83cc2e6dad button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataset"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "dataset['Price']=df.target\n",
        "dataset.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "1185effe",
      "metadata": {
        "id": "1185effe",
        "outputId": "a4551c83-6c27-413f-bb28-b3ffc060c6bc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1460 entries, 0 to 1459\n",
            "Data columns (total 81 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Id             1460 non-null   int64  \n",
            " 1   MSSubClass     1460 non-null   int64  \n",
            " 2   MSZoning       1460 non-null   object \n",
            " 3   LotFrontage    1201 non-null   float64\n",
            " 4   LotArea        1460 non-null   int64  \n",
            " 5   Street         1460 non-null   object \n",
            " 6   Alley          91 non-null     object \n",
            " 7   LotShape       1460 non-null   object \n",
            " 8   LandContour    1460 non-null   object \n",
            " 9   Utilities      1460 non-null   object \n",
            " 10  LotConfig      1460 non-null   object \n",
            " 11  LandSlope      1460 non-null   object \n",
            " 12  Neighborhood   1460 non-null   object \n",
            " 13  Condition1     1460 non-null   object \n",
            " 14  Condition2     1460 non-null   object \n",
            " 15  BldgType       1460 non-null   object \n",
            " 16  HouseStyle     1460 non-null   object \n",
            " 17  OverallQual    1460 non-null   int64  \n",
            " 18  OverallCond    1460 non-null   int64  \n",
            " 19  YearBuilt      1460 non-null   int64  \n",
            " 20  YearRemodAdd   1460 non-null   int64  \n",
            " 21  RoofStyle      1460 non-null   object \n",
            " 22  RoofMatl       1460 non-null   object \n",
            " 23  Exterior1st    1460 non-null   object \n",
            " 24  Exterior2nd    1460 non-null   object \n",
            " 25  MasVnrType     1452 non-null   object \n",
            " 26  MasVnrArea     1452 non-null   float64\n",
            " 27  ExterQual      1460 non-null   object \n",
            " 28  ExterCond      1460 non-null   object \n",
            " 29  Foundation     1460 non-null   object \n",
            " 30  BsmtQual       1423 non-null   object \n",
            " 31  BsmtCond       1423 non-null   object \n",
            " 32  BsmtExposure   1422 non-null   object \n",
            " 33  BsmtFinType1   1423 non-null   object \n",
            " 34  BsmtFinSF1     1460 non-null   int64  \n",
            " 35  BsmtFinType2   1422 non-null   object \n",
            " 36  BsmtFinSF2     1460 non-null   int64  \n",
            " 37  BsmtUnfSF      1460 non-null   int64  \n",
            " 38  TotalBsmtSF    1460 non-null   int64  \n",
            " 39  Heating        1460 non-null   object \n",
            " 40  HeatingQC      1460 non-null   object \n",
            " 41  CentralAir     1460 non-null   object \n",
            " 42  Electrical     1459 non-null   object \n",
            " 43  1stFlrSF       1460 non-null   int64  \n",
            " 44  2ndFlrSF       1460 non-null   int64  \n",
            " 45  LowQualFinSF   1460 non-null   int64  \n",
            " 46  GrLivArea      1460 non-null   int64  \n",
            " 47  BsmtFullBath   1460 non-null   int64  \n",
            " 48  BsmtHalfBath   1460 non-null   int64  \n",
            " 49  FullBath       1460 non-null   int64  \n",
            " 50  HalfBath       1460 non-null   int64  \n",
            " 51  BedroomAbvGr   1460 non-null   int64  \n",
            " 52  KitchenAbvGr   1460 non-null   int64  \n",
            " 53  KitchenQual    1460 non-null   object \n",
            " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
            " 55  Functional     1460 non-null   object \n",
            " 56  Fireplaces     1460 non-null   int64  \n",
            " 57  FireplaceQu    770 non-null    object \n",
            " 58  GarageType     1379 non-null   object \n",
            " 59  GarageYrBlt    1379 non-null   float64\n",
            " 60  GarageFinish   1379 non-null   object \n",
            " 61  GarageCars     1460 non-null   int64  \n",
            " 62  GarageArea     1460 non-null   int64  \n",
            " 63  GarageQual     1379 non-null   object \n",
            " 64  GarageCond     1379 non-null   object \n",
            " 65  PavedDrive     1460 non-null   object \n",
            " 66  WoodDeckSF     1460 non-null   int64  \n",
            " 67  OpenPorchSF    1460 non-null   int64  \n",
            " 68  EnclosedPorch  1460 non-null   int64  \n",
            " 69  3SsnPorch      1460 non-null   int64  \n",
            " 70  ScreenPorch    1460 non-null   int64  \n",
            " 71  PoolArea       1460 non-null   int64  \n",
            " 72  PoolQC         7 non-null      object \n",
            " 73  Fence          281 non-null    object \n",
            " 74  MiscFeature    54 non-null     object \n",
            " 75  MiscVal        1460 non-null   int64  \n",
            " 76  MoSold         1460 non-null   int64  \n",
            " 77  YrSold         1460 non-null   int64  \n",
            " 78  SaleType       1460 non-null   object \n",
            " 79  SaleCondition  1460 non-null   object \n",
            " 80  Price          1460 non-null   int64  \n",
            "dtypes: float64(3), int64(35), object(43)\n",
            "memory usage: 924.0+ KB\n"
          ]
        }
      ],
      "source": [
        "dataset.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "825e1665",
      "metadata": {
        "id": "825e1665",
        "outputId": "639a547a-fa22-49a6-a57d-f42c2d9cf681",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Id   MSSubClass  LotFrontage        LotArea  OverallQual  \\\n",
              "count  1460.000000  1460.000000  1201.000000    1460.000000  1460.000000   \n",
              "mean    730.500000    56.897260    70.049958   10516.828082     6.099315   \n",
              "std     421.610009    42.300571    24.284752    9981.264932     1.382997   \n",
              "min       1.000000    20.000000    21.000000    1300.000000     1.000000   \n",
              "25%     365.750000    20.000000    59.000000    7553.500000     5.000000   \n",
              "50%     730.500000    50.000000    69.000000    9478.500000     6.000000   \n",
              "75%    1095.250000    70.000000    80.000000   11601.500000     7.000000   \n",
              "max    1460.000000   190.000000   313.000000  215245.000000    10.000000   \n",
              "\n",
              "       OverallCond    YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1  ...  \\\n",
              "count  1460.000000  1460.000000   1460.000000  1452.000000  1460.000000  ...   \n",
              "mean      5.575342  1971.267808   1984.865753   103.685262   443.639726  ...   \n",
              "std       1.112799    30.202904     20.645407   181.066207   456.098091  ...   \n",
              "min       1.000000  1872.000000   1950.000000     0.000000     0.000000  ...   \n",
              "25%       5.000000  1954.000000   1967.000000     0.000000     0.000000  ...   \n",
              "50%       5.000000  1973.000000   1994.000000     0.000000   383.500000  ...   \n",
              "75%       6.000000  2000.000000   2004.000000   166.000000   712.250000  ...   \n",
              "max       9.000000  2010.000000   2010.000000  1600.000000  5644.000000  ...   \n",
              "\n",
              "        WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  ScreenPorch  \\\n",
              "count  1460.000000  1460.000000    1460.000000  1460.000000  1460.000000   \n",
              "mean     94.244521    46.660274      21.954110     3.409589    15.060959   \n",
              "std     125.338794    66.256028      61.119149    29.317331    55.757415   \n",
              "min       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
              "25%       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
              "50%       0.000000    25.000000       0.000000     0.000000     0.000000   \n",
              "75%     168.000000    68.000000       0.000000     0.000000     0.000000   \n",
              "max     857.000000   547.000000     552.000000   508.000000   480.000000   \n",
              "\n",
              "          PoolArea       MiscVal       MoSold       YrSold          Price  \n",
              "count  1460.000000   1460.000000  1460.000000  1460.000000    1460.000000  \n",
              "mean      2.758904     43.489041     6.321918  2007.815753  180921.195890  \n",
              "std      40.177307    496.123024     2.703626     1.328095   79442.502883  \n",
              "min       0.000000      0.000000     1.000000  2006.000000   34900.000000  \n",
              "25%       0.000000      0.000000     5.000000  2007.000000  129975.000000  \n",
              "50%       0.000000      0.000000     6.000000  2008.000000  163000.000000  \n",
              "75%       0.000000      0.000000     8.000000  2009.000000  214000.000000  \n",
              "max     738.000000  15500.000000    12.000000  2010.000000  755000.000000  \n",
              "\n",
              "[8 rows x 38 columns]"
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>...</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1201.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1452.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>730.500000</td>\n",
              "      <td>56.897260</td>\n",
              "      <td>70.049958</td>\n",
              "      <td>10516.828082</td>\n",
              "      <td>6.099315</td>\n",
              "      <td>5.575342</td>\n",
              "      <td>1971.267808</td>\n",
              "      <td>1984.865753</td>\n",
              "      <td>103.685262</td>\n",
              "      <td>443.639726</td>\n",
              "      <td>...</td>\n",
              "      <td>94.244521</td>\n",
              "      <td>46.660274</td>\n",
              "      <td>21.954110</td>\n",
              "      <td>3.409589</td>\n",
              "      <td>15.060959</td>\n",
              "      <td>2.758904</td>\n",
              "      <td>43.489041</td>\n",
              "      <td>6.321918</td>\n",
              "      <td>2007.815753</td>\n",
              "      <td>180921.195890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>421.610009</td>\n",
              "      <td>42.300571</td>\n",
              "      <td>24.284752</td>\n",
              "      <td>9981.264932</td>\n",
              "      <td>1.382997</td>\n",
              "      <td>1.112799</td>\n",
              "      <td>30.202904</td>\n",
              "      <td>20.645407</td>\n",
              "      <td>181.066207</td>\n",
              "      <td>456.098091</td>\n",
              "      <td>...</td>\n",
              "      <td>125.338794</td>\n",
              "      <td>66.256028</td>\n",
              "      <td>61.119149</td>\n",
              "      <td>29.317331</td>\n",
              "      <td>55.757415</td>\n",
              "      <td>40.177307</td>\n",
              "      <td>496.123024</td>\n",
              "      <td>2.703626</td>\n",
              "      <td>1.328095</td>\n",
              "      <td>79442.502883</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</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>34900.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>365.750000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>7553.500000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1954.000000</td>\n",
              "      <td>1967.000000</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>5.000000</td>\n",
              "      <td>2007.000000</td>\n",
              "      <td>129975.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>730.500000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>9478.500000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1973.000000</td>\n",
              "      <td>1994.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>383.500000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>25.000000</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>163000.000000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>1095.250000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>11601.500000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2004.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>712.250000</td>\n",
              "      <td>...</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>68.000000</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>214000.000000</td>\n",
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              "      <td>190.000000</td>\n",
              "      <td>313.000000</td>\n",
              "      <td>215245.000000</td>\n",
              "      <td>10.000000</td>\n",
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              "      <td>2010.000000</td>\n",
              "      <td>1600.000000</td>\n",
              "      <td>5644.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>857.000000</td>\n",
              "      <td>547.000000</td>\n",
              "      <td>552.000000</td>\n",
              "      <td>508.000000</td>\n",
              "      <td>480.000000</td>\n",
              "      <td>738.000000</td>\n",
              "      <td>15500.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>755000.000000</td>\n",
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              "</table>\n",
              "<p>8 rows × 38 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
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          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "dataset.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1f7f0dd5",
      "metadata": {
        "id": "1f7f0dd5"
      },
      "source": [
        "***Checking the missing values***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "93871782",
      "metadata": {
        "id": "93871782",
        "outputId": "62eebdcb-c1cf-4a10-aa70-da4d86c2142f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "         Id  MSSubClass  MSZoning  LotFrontage  LotArea  Street  Alley  \\\n",
              "0     False       False     False        False    False   False   True   \n",
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              "[1460 rows x 81 columns]"
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f3b3431d-2e08-4329-8938-ee05861578f6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f3b3431d-2e08-4329-8938-ee05861578f6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9acd5a59-daa8-4dd0-9d6b-81866a453c71\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9acd5a59-daa8-4dd0-9d6b-81866a453c71')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9acd5a59-daa8-4dd0-9d6b-81866a453c71 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "dataset.isnull()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "c8142602",
      "metadata": {
        "scrolled": true,
        "id": "c8142602",
        "outputId": "a08bce41-f8cc-427a-b4f6-5c00ba42f6ba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                 0\n",
              "MSSubClass         0\n",
              "MSZoning           0\n",
              "LotFrontage      259\n",
              "LotArea            0\n",
              "                ... \n",
              "MoSold             0\n",
              "YrSold             0\n",
              "SaleType           0\n",
              "SaleCondition      0\n",
              "Price              0\n",
              "Length: 81, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "dataset.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "7fffc3c3",
      "metadata": {
        "id": "7fffc3c3",
        "outputId": "a98ee9dd-75ba-4ac4-b5a6-3d321fad4170",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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A9ASQjDHKn0QIIUpCY/6IsiksiJkwYQIOHz6Ms2fPonr16tz2+Ph4rFy5ErGxsXB1dQUA1K9fH+fOncOqVauwZs0aqeU1a9YMOTk5SExMhJOTU5H98+fPR0hIiMQ2kVgPIjWDMr8XIdMOEEIIIYr0LU2xVnoW68zMTAD5WakLU1NTk5mhGgBiYmIgFothYWEhdX9wcLBE6w0AGJvWKc1bKIICFUIIKRldK1VDZQxWZBE8iBk/fjy2bt2KAwcOcFmsAcDQ0BA6OjqoU6cOatasiTFjxmDx4sUwNTXF/v37ceLECRw+fBgAcOnSJVy5cgXu7u7Q19fHpUuXEBAQgMGDB8PY2Fjq62ppaRXpZqKuJEIIUR7qTlIN31JLjNKzWAPA48ePERQUhPPnzyM9PR01a9bElClTMGTIEADAjRs38MMPP+DBgwfIysqCg4MDhgwZgsDAQKnjYWShBJCEEEIqK0oASVmslY6CGEJIZUXXN+WS9/+7Iv19+AYxlDuJByEH9qrih4UQQhRJyG4Mea+h8r42XaMrFmqJUbKKFAkTQghRXdSdpIQVewkhhBBCFIG6k3igdWIIIYQQ1SN4S8zq1atRr149GBgYwMDAAC1atMC///5b5DjGGDp16gSRSIT9+/dz22/duoUBAwbAxsYGOjo6cHZ2xh9//CF0NeWiY91G5oMQQkg+yp1ElE3wlpjq1atjwYIFqFWrFhhj2LhxI7p164abN29yK/QCwLJly6ROx75+/TosLCywZcsW2NjY4OLFixg9ejTU1NQwYcIEoatLCCGEkApK8CDG19dX4vncuXOxevVqXL58mQtiYmJisGTJEkRHR8PKykrieH9/f4nnjo6OuHTpEvbu3UtBDCGEEEI4Ch0Tk5ubi127diEjIwMtWrQAkJ92YODAgVi1ahUsLS15lfPhwweYmJgosqrFojExhBBSMroeqrbK+PdRSBBz584dtGjRAv/99x/09PSwb98+uLi4AAACAgLQsmVLdOvWjVdZFy9exI4dO3DkyJFij1NkFuvK+IcnhBCh0RISqq0y/n0UEsQ4OTkhJiYGHz58wO7du+Hn54czZ84gLi4OkZGRuHnzJq9yYmNj0a1bN8yaNQuenp7FHqvILNaEEEJKVpF/DEnFpJTF7jw8PFCjRg3o6Ohg+fLlEhmsc3NzIRaL0aZNG0RFRXHb7927B3d3d4wcORJz584t8TWktcQYm9YRpCVGyO6kyhgJE0IIUT5a7E5J68Tk5eUhKysLISEhGDlypMQ+Nzc3LF26VGJA8N27d9G+fXv4+fnxCmAAymJNCCGEfGsED2KCg4PRqVMn2Nra4tOnT9i6dSuioqJw7NgxWFpaSh3Ma2trCwcHBwD5XUjt27eHl5cXAgMDkZqaCgBQU1ODubm50NXlRRWjVEIIUTXU0kyUTfAg5tWrVxg6dChSUlJgaGiIevXq4dixY+jYsSOv83fv3o3Xr19jy5Yt2LJlC7fdzs4OiYmJQleXEEKIQChYIcpGCSCVjO5UCCGVFV3flIvGxFDuJEIIIQJRxR9DUrlREENIBaXonDSyfpBo8UdCiKqgIIYHumgTVVRenz36zBNZlJHsUahlLYQqn5QvwYOY1atXY/Xq1dwgXFdXV8ycOROdOnUCAMTHx2PKlCk4f/48srKy4O3tjRUrVqBq1apcGTdu3MC0adNw7do1qKmpoVevXggLC4Oenp7Q1eWFPtSEEFIyVbxWqmKdiHDEJR8in4Is1tevX0d0dDTat2+Pbt264e7du8jIyICnpydEIhEiIyNx4cIFfPnyBb6+vsjLywMAJCcnw8PDAzVr1sSVK1dw9OhR3L17F8OGDRO6qoQQQgT0Ofmc1AchiqKU2UkmJiZYtGgRbGxs0KlTJ7x79w4GBvnpAD58+ABjY2McP34cHh4eWLduHWbMmIGUlBRuZd87d+6gXr16ePz4MWrWrMn7dWl2EiGEKA91JykXzU5Schbr+Ph4iEQiiZV1tbW1IRaLcf78eXh4eCArKwuampoSqQl0dHQAAOfPn5criFFFqvhhIYQQRSrP6963eM0tzaD8ikqpWazNzc2hq6uLadOmYd68eWCMISgoCLm5uUhJSQEAtG/fHoGBgVi0aBEmT56MjIwMBAUFAQB3TEVGLTGEEEIUqTIGK7IIPiYG+P8s1leuXMG4cePg5+eHe/fuwdzcHLt27cKhQ4egp6cHQ0NDvH//Ho0aNeJaXlxdXbFx40YsWbIEVapUgaWlJRwcHFC1alWJ1pmvZWVl4ePHjxKPSryOHyGEEPLNU0hLjKamJtft07hxY1y7dg1//PEH1q5dC09PT8THx+PNmzdQV1eHkZERLC0t4ejoyJ0/cOBADBw4EC9fvoSuri5EIhHCwsIkjvna/PnzERISIrFNJNaDSM1AEW+REEIIUUnUnSSwgizWhZmZmQEAIiMj8erVK3Tt2rXIeQXTrtevXw9tbe1i8y8FBwcjMDBQYpuxaZ2yVp0QQgipUCpjsCKLUrNYA0BERAScnZ1hbm6OS5cuYfLkyQgICICTkxNXxsqVK9GyZUvo6enhxIkTmDp1KhYsWAAjIyOZr6ulpSUxYBgARCKR0G+PEEIIISpC6VmsHz58iODgYKSlpcHe3h6//vorAgICJMq4evUqZs2ahfT0dNSpUwdr167FkCFDhK4qIYQQAdEEBaJslMVayWh2EiGksqLrm3LROjEKmp1ECCGEEKJolACSB0oASQghJaPrIVE2CmJ4oC8mIYSUjLqTiLIpPIhZsGABgoODMXnyZCxbtgwA0K5dO5w5c0biuDFjxmDNmjUS2zZs2ICwsDA8evQIBgYG6NOnD1atWqXoKhNCCBGQkFN+KXcSKUyhQcy1a9ewdu1a1KtXr8i+UaNGYc6cOdzzKlWqSOwPCwvDkiVLsGjRIjRr1gwZGRlITExUZHUJIYSUgSoGAKpYJ0Wjxe4EkJ6ejkGDBuGvv/7Cb7/9VmR/QUoBad69e4fp06fj0KFD6NChA7ddWjBECCGEkP9XGYMVWRQ2O2n8+PHw8fGBh4eH1P3/+9//YGZmhrp16yI4OBiZmZncvhMnTiAvLw8vXryAs7Mzqlevjr59++LZs2eKqi4hhBBCKhiFtMRs374dN27cwLVr16TuHzhwIOzs7GBtbY3bt29j2rRpePjwIfbu3QsAePLkCfLy8jBv3jz88ccfMDQ0xPTp09GxY0fcvn0bmpqaiqg2IYSQMqCBvUTZBA9inj17hsmTJ+PEiRPQ1taWeszo0aO5f7u5ucHKygodOnRAfHw8atSogby8PGRnZ2P58uXw9PQEAGzbtg2WlpY4ffo0vLy8ipSZlZVVJD8TY4xSDxBCCCGVlOBBzPXr1/Hq1Ss0atSI25abm4uzZ89i5cqVyMrKgpqamsQ5zZo1AwDExcWhRo0asLKyAgC4uLhwx5ibm8PMzAxJSUlSX1eRWayFXCeG7kgIIZUVXd9UAw3sLYMOHTrgzp07EtuGDx+OOnXqYNq0aUUCGACIiYkBAC54adWqFYD8PEvVq1cHAKSlpeHNmzews7OT+rqKzGIt5BeTmlsJIZUVXd+IsgkexOjr66Nu3boS23R1dWFqaoq6desiPj4eW7duRefOnWFqaorbt28jICAAbdu25WYf1a5dG926dcPkyZOxbt06GBgYIDg4GHXq1IG7u7vU11VkFmtasZcQQkpG10PVUBlbXGRR+oq9mpqaOHnyJJYtW4aMjAzY2NigV69emD59usRxmzZtQkBAAHx8fCAWi/H999/j6NGj0NDQUHaV6YtJCCE8UEsMUTbKYq1k9CUnhBAiBMpiTVmsCSGEEFJBURBDCCGEkAqJghhCCCGEVEhKH9hLCCGkcqIxf0TZBG+JmT17NkQikcSjTp3/X69l3bp1aNeuHQwMDCASifD+/fsiZXTt2hW2trbQ1taGlZUVhgwZguTkZKGrSgghREA61m2kPghRFIW0xLi6uuLkyZP//yLq//8ymZmZ8Pb2hre3N4KDg6We7+7ujl9++QVWVlZ48eIFpkyZgt69e+PixYuKqK5S0ReaEFJZUUuMaqAVe8taqLo6LC0tpe778ccfAQBRUVEyzw8ICOD+bWdnh6CgIHTv3h3Z2dnlsk6MkOhLTgghRJEqY7Aii0KCmMePH8Pa2hra2tpo0aIF5s+fD1tb21KVlZaWhv/9739o2bJlhQ9gAApWCCGEEKEIHsQ0a9YMGzZsgJOTE1JSUhASEoI2bdogNjYW+vr6vMuZNm0aVq5ciczMTDRv3hyHDx8WuqrlglpiCCGEKNK31J2k8BV7379/Dzs7O4SFhWHEiBHc9qioKLi7u+Pdu3cwMjIqct6bN2+QlpaGp0+fIiQkBIaGhjh8+LDMfEhZWVnIysqS2GZsWkew/ElCoSCGEFJZ0fVNuWjFXiVMsTYyMkLt2rURFxcn13lmZmYwMzND7dq14ezsDBsbG1y+fBktWrSQevz8+fMREhIisU0k1oNIzaDUdSeEEMKfKv4YkspN4YvdpaenIz4+HlZWVqUuIy8vDwCKtLQUFhwcjA8fPkg8RGL+3VeEEEIIqVgEb4mZMmUKfH19YWdnh+TkZMyaNQtqamoYMGAAACA1NRWpqalcy8ydO3egr68PW1tbmJiY4MqVK7h27Rpat24NY2NjxMfHY8aMGahRo4bMVhgA0NLSgpaWlsQ2VetKAuhOhRBCCBGK4EHM8+fPMWDAALx9+xbm5uZo3bo1Ll++DHNzcwDAmjVrJLp92rZtCwCIiIjAsGHDUKVKFezduxezZs1CRkYGrKys4O3tjenTpxcJUpSluH5HeYMS6jOuXOTtk6a/MyGECEfhA3vLk7pmtfKuQhEUxBChKHqmQWlmONDn+NumjNkvQt04ClV+eaKBvZQ7iRe6aBNVVF6fPfrME3mV52fmW/y8fktTrCmI4eFb/BIQQoi86FqpGipjsCKLwmcnEUIIIYQoglKzWCcmJhbZV/DYtWsXV0ZSUhJ8fHxQpUoVWFhYYOrUqcjJyRG6qoQQQgT0Ofmc1AdRrm8pm7hSs1jb2NggJSVF4th169Zh0aJF6NSpEwAgNzcXPj4+sLS0xMWLF5GSkoKhQ4dCQ0MD8+bNU0R1CSGECKCy/lBWNN9S4KjULNZqampFtu/btw99+/aFnp4eAOD48eO4d+8eTp48iapVq6JBgwYIDQ3FtGnTMHv2bGhqaiqiyoQQQkilQAN7y4hvFuvr168jJiYGq1at4rZdunQJbm5uqFq1KrfNy8sL48aNw927d9GwYUNFVLlYNDuJEEJIRVEZgxVZBB8TU5DF+ujRo1i9ejUSEhLQpk0bfPr0qcix4eHhcHZ2RsuWLbltqampEgEMAO55amqq0NUlhBBCSAUleEtMwdgWAKhXrx6aNWsGOzs77Ny5UyKL9efPn7F161bMmDFDkNeVlsWaMSZI6gFqbSGEEEJUT7llsd69ezcyMzMxdOhQie2Wlpa4evWqxLaXL19y+2ShLNaEEFK+aEVyomzllsU6PDwcXbt25XIqFWjRogXu3LmDV69ecdtOnDgBAwMDuLi4yHwdymJNCCHl61ua2ktUg9KzWANAXFwczp49i3/++afI+Z6ennBxccGQIUPw+++/IzU1FdOnT8f48eOLTQCpyCzWNLCXEEIIUT1Kz2INAOvXr0f16tXh6elZ5Hw1NTUcPnwY48aNQ4sWLaCrqws/Pz/MmTNH6KryRoEKIYSUjLqTiLJRFmsloy85IYQQIVAWa0oASQghRCB0k0aUjYIYJaMvMyGEECIMCmKUjO5UCCGVFV3HiLIpfIo1IYQQQogiKCSIefHiBQYPHgxTU1Po6OjAzc0N0dHRAIDs7GxMmzYNbm5u0NXVhbW1NYYOHYrk5GTu/KioKIhEIqmPa9euKaLKhBBCCKlgBO9OevfuHVq1agV3d3f8+++/MDc3x+PHj2FsbAwAyMzMxI0bNzBjxgzUr18f7969w+TJk9G1a1cu0GnZsiVSUlIkyp0xYwZOnTqFJk2aCF3lEtE6MYQQQojqETyIWbhwIWxsbBAREcFtc3Bw4P5taGiIEydOSJyzcuVKfPfdd0hKSoKtrS00NTUlUgxkZ2fjwIEDmDhxomAL2BFCCCGkYhM8iDl48CC8vLzQp08fnDlzBtWqVcMPP/yAUaNGyTznw4cPEIlEMDIyklnm27dvMXz4cKGrywu1thBCCCGqR/Ag5smTJ1i9ejUCAwPxyy+/4Nq1a5g0aRI0NTXh5+dX5Pj//vsP06ZNw4ABA2BgID1ZY3h4OLy8vFC9enWZr6vILNbUnUQIIYSoHsGDmLy8PDRp0gTz5s0DADRs2BCxsbFYs2ZNkSAmOzsbffv2BWMMq1evllre8+fPcezYMezcubPY11VkFmsKVAghhBDVI/jsJCsrqyLZpp2dnZGUlCSxrSCAefr0KZelWpqIiAiYmpqia9euxb4uZbEmhBBCvi2Ct8S0atUKDx8+lNj26NEj2NnZcc8LApjHjx/j9OnTMDU1lVoWYwwREREYOnQoNDQ0in1dRWaxFhK16hBCKiuhcvkUR95rqLx1omt0xSJ4EBMQEICWLVti3rx56Nu3L65evYp169Zh3bp1APIDmN69e+PGjRs4fPgwcnNzkZqaCgAwMTGBpqYmV1ZkZCQSEhIwcuRIoatZbmjFXkJIZaWK1zFVrBMRjkKyWB8+fBjBwcF4/PgxHBwcEBgYyM1OSkxMlJhyXdjp06fRrl077vnAgQPx9OlTXLhwoVT1oCzWhBCiPHR9Uy55/78r0t+HbxZrhQQxqoKCGEIIIZWVUN13qvj7wzeIoQSQhBBCSCUib0tMRUZBDCGEEEFQS7NqqIzBiiwUxBBCCCGVyLfUEqP0LNYAkJ6ejgkTJqB69erQ0dGBi4sL1qxZI7Usxhg6deoEkUiE/fv3K6K6hBBCCKmAlJ7FGgACAwMRGRmJLVu2wN7eHsePH8cPP/wAa2vrIovaLVu2TCXXeyGEECKJuo1UQ2VscZFF6VmsAeDixYvw8/PjplOPHj0aa9euxdWrVyWCmJiYGCxZsgTR0dGwsrISuqqEEEIERGNiiLIJ3p108OBBNGnSBH369IGFhQUaNmyIv/76S+KYli1b4uDBg3jx4gUYYzh9+jQePXoET09P7pjMzEwMHDgQq1atgqWlpdDVJIQQIjAd6zZSH4QoSrlksV6xYgVGjx6N6tWrQ11dHWKxGH/99Rfatm3LlVOw8m+3bt2ErqLcKIs1IYSUjFpiiLKVSxbrFStW4PLlyzh48CDs7Oxw9uxZjB8/HtbW1vDw8MDBgwcRGRmJmzdv8n7drKwsZGVlSWxjjAkynoa+gIQQUjK6VhJlEzyIkZXFes+ePQCAz58/45dffsG+ffvg4+MDAKhXrx5iYmKwePFieHh4IDIyEvHx8TAyMpIop1evXmjTpg2ioqKKvO78+fMREhIisU0k1oNITXp2bHlQSwwhhJSMWmKIsik9i3V2djays7MhFksOx1FTU0NeXh4AICgoqEjSRzc3NyxduhS+vr5SXzc4OBiBgYES24xN65TpvRSgLyAhhJSMrpVE2ZSexdrAwADff/89pk6dCh0dHdjZ2eHMmTPYtGkTwsLCAACWlpZSB/Pa2trKTB6ppaUFLS0tiW00NZsQQgipvAQPYpo2bYp9+/YhODgYc+bMgYODA5YtW4ZBgwZxx2zfvh3BwcEYNGgQ0tLSYGdnh7lz52Ls2LFCV4cQQgj5pnxLK/ZSFmsloz5jQgghQqAs1pQ7iRca2EsIIYSoHgpieKBAhRBCSqaM7gp5r8fy1omu9xULBTE8UEsMIYSUXnleJ+kaXblREMMDfQkIIaRkdK0kyiZ47iR7e3uIRKIij/Hjx0scxxhDp06dIBKJsH//fol9kyZNQuPGjaGlpYUGDRoIXUVCCCEK8Dn5nNQHIYoieEvMtWvXkJubyz2PjY1Fx44d0adPH4njli1bVuw6Lv7+/rhy5Qpu374tdBXlRt1JhBBSMroeEmUTPIgxNzeXeL5gwQLUqFED33//PbctJiYGS5YsQXR0NKysrIqUsXz5cgDA69evVSKIoS8mIYQQonoUOibmy5cv2LJlCwIDA7lWl8zMTAwcOBCrVq2SuiqvKqKWGEIIKRmtg0WUTaFBzP79+/H+/XsMGzaM21aQlqBbt26CvhZlsSaEENUk5LgYmmJNClNoEBMeHo5OnTrB2toaAHDw4EFERkbi5s2bgr+WIrNYE0IIKT2aYq1c31LaAYUFMU+fPsXJkyexd+9ebltkZCTi4+NhZGQkcWyvXr3Qpk0bREVFlfr1FJnFmrqTCCGkZHQ9VA2VMViRRWFBTEREBCwsLODj48NtCwoKwsiRIyWOc3Nzw9KlS+Hr61um11NkFmshv5j0JSeEVFa0Yi9RNoUEMXl5eYiIiICfnx/U1f//JSwtLaUO5rW1tYWDgwP3PC4uDunp6UhNTcXnz58RExMDAHBxcYGmpqYiqqw0NPCNEPKtoe4koigKCWJOnjyJpKQk+Pv7l+r8kSNH4syZM9zzhg0bAgASEhJgb28vRBXLDX2hCCGEEGEoJIjx9PQEY4zXsdKOK8vYGFVHLTGEEEKIMCh3EiGEEEHQzZhq+Jb+DhTE8ECzkwghpGTU0qwavqXBzBTE8FCR/8CEEKIsdK0kyqb0LNbx8fHo0aMHzM3NYWBggL59++Lly5cSZTx69AjdunWDmZkZDAwM0Lp1a5w+fVroqhJCCCGVjo51G6mPykipWawzMjLg6emJ+vXrIzIyEgAwY8YM+Pr64vLlyxCL82OqLl26oFatWoiMjISOjg6WLVuGLl26ID4+vsLkWyKEkG8NdSephm9psTsR4zuNqJR+/PFHHD58GI8fP8aJEyfQqVMnvHv3DgYG+ekAPnz4AGNjYxw/fhweHh548+YNzM3NcfbsWbRpk//B//TpEwwMDHDixAl4eHjwfm11zWqCvAchx8TQl5wQQogQhApWVPH3J+fLC17HKTWLdVZWFkQikcTKutra2hCLxTh//jw8PDxgamoKJycnbNq0CY0aNYKWlhbWrl0LCwsLNG7cWJHVJYQQUgZ0k0aUTalZrJs3bw5dXV1MmzYN8+bNA2MMQUFByM3NRUpKCoD8VAEnT55E9+7doa+vD7FYDAsLCxw9ehTGxsaKrK5M9AUkhBBCVI/gA3sL+zqLtbm5OXbt2oVDhw5BT08PhoaGeP/+PRo1asSNh2GMYfz48bCwsMC5c+dw9epVdO/eHb6+vlygI01WVhY+fvwo8VBwTxkhhBBCypFSs1gD+av5xsfH482bN1BXV4eRkREsLS3h6OgIID/T9eHDhyXGzfz55584ceIENm7ciKCgIKmvN3/+fISEhEhsE4n1IFIzUMC7I4QQQkh5U1hLjLQs1oWZmZnByMgIkZGRePXqFbp27QoAyMzMzK+YWLJqYrEYeXl5Ml8vODgYHz58kHiIxPoCvRtCCCGEqBqlZrEG8oMbZ2dnmJub49KlS5g8eTICAgLg5OQEAGjRogWMjY3h5+eHmTNnQkdHB3/99RcSEhJkBkQAoKWlJTFgGMgfX0MIIYR8S2SN46yMU6+VnsX64cOHCA4ORlpaGuzt7fHrr78iICCA229mZoajR4/i119/Rfv27ZGdnQ1XV1ccOHAA9evXV0R1CSGEkErjW5olpvB1YsoTrRNDCCGksqJ1Yih3Ei9C/oFV8cNCCCFCoJs0omwUxCgZfckJIZUVXceIslEQo2T0JSeEVFZ0k0aUTfAp1rm5uZgxYwYcHBygo6ODGjVqIDQ0VGLhuWHDhhXJcu3t7S1RjrRs2AsWLBC6ukr3Ofmc1AchhBBC5CN4S8zChQuxevVqbNy4Ea6uroiOjsbw4cNhaGiISZMmccd5e3sjIiKCe/719GgAmDNnDkaNGsU919endV8IIYQQkk/wIObixYvo1q0bt6aLvb09tm3bhqtXr0ocp6WlBUtLy2LL0tfXL/GYioaaVQkhlRVd34iyCd6d1LJlS5w6dQqPHj0CANy6dQvnz59Hp06dJI6LioqChYUFnJycMG7cOLx9+7ZIWQsWLICpqSkaNmyIRYsWIScnR+jq8iKrC6g03UDUnUQIIYQIQ/CWmKCgIHz8+BF16tSBmpoacnNzMXfuXAwaNIg7xtvbGz179oSDgwPi4+Pxyy+/oFOnTrh06RLU1NQAAJMmTUKjRo1gYmKCixcvIjg4GCkpKQgLCxO6yiWiuwtCCCFE9Qi+2N327dsxdepULFq0CK6uroiJicGPP/6IsLAw+Pn5ST3nyZMnqFGjBk6ePIkOHTpIPWb9+vUYM2YM0tPTpY6fycrKQlZWlsQ2Y9M6gqQeoMXuCCGEqBp5f08q0u9PuS12N3XqVAQFBaF///4AADc3Nzx9+hTz58+XGcQ4OjrCzMwMcXFxMoOYZs2aIScnB4mJiVyepcIoizUhhJSvivQjSSoHwYOYzMzMIhmo1dTUis1A/fz5c7x9+xZWVlYyj4mJiYFYLIaFhYXU/cHBwQgMDJTYZmxaR46ay0ZfQEIIKRldK4myCR7E+Pr6Yu7cubC1tYWrqytu3ryJsLAwLhlkeno6QkJC0KtXL1haWiI+Ph4///wzatasCS8vLwDApUuXcOXKFbi7u0NfXx+XLl1CQEAABg8eDGNjY6mvS1msCSGkfFFLjGr4liaLCD4m5tOnT5gxYwb27duHV69ewdraGgMGDMDMmTOhqamJz58/o3v37rh58ybev38Pa2treHp6IjQ0FFWrVgUA3LhxAz/88AMePHiArKwsODg4YMiQIQgMDJQ6HkYWSgBJKjNFX6jk7Vcv7hxCiPAoASRlsVY6CmIIIZUVXd+Uiwb2Uu4kQgghpFL5lrqTBF/sjhBCCCFEGaglhhBCCKlESjOeraKiIIYQQoggVHFsxbeoMgYrsiikO+nTp0/48ccfYWdnBx0dHbRs2RLXrl3j9u/duxeenp4wNTWFSCRCTEyMxPlpaWmYOHEinJycoKOjA1tbW0yaNAkfPnxQRHUJIYQIgHLDqQYd6zZSH5WRQlpiRo4cidjYWGzevBnW1tbYsmULPDw8cO/ePVSrVg0ZGRlo3bo1+vbti1GjRhU5Pzk5GcnJyVi8eDFcXFzw9OlTjB07FsnJydi9e7ciqqw0lfWDRAghRDV8S4Gj4FOsP3/+DH19fRw4cAA+Pj7c9saNG6NTp0747bffuG2JiYlwcHDAzZs30aBBg2LL3bVrFwYPHoyMjAyoq/OLvWidGEIIUR66vikXrROjgJaYnJwc5ObmQltbW2K7jo4Ozp8/X+pyP3z4AAMDA94BDCGEEPIt+pYG9go+JkZfXx8tWrRAaGgokpOTkZubiy1btuDSpUtISUkpVZlv3rxBaGgoRo8eLXBtCSGEEFJRKaRZY/PmzfD390e1atWgpqaGRo0aYcCAAbh+/brcZX38+BE+Pj5wcXHB7NmzZR6XlZWFrKwsiW2MMUHyJ6liUxshhBAij8r4W6aQIKZGjRo4c+YMMjIy8PHjR1hZWaFfv35wdHSUq5xPnz7B29sb+vr62LdvHzQ0NGQeO3/+fISEhEhsE4n1IFIzKNV7IIQQQioiebuNKnJwo9AVe3V1dWFlZYV3797h2LFj6NatG+9zP378CE9PT2hqauLgwYNFxth8LTg4GB8+fJB4iMT6ZX0LhBBCCFFRCmmJOXbsGBhjcHJyQlxcHKZOnYo6depg+PDhAPLXgUlKSkJycjIA4OHDhwAAS0tLWFpacgFMZmYmtmzZgo8fP+Ljx48AAHNzc6ipqRV5TS0trSIZroXoSiKEEMJPRb6jJxWTQoKYDx8+IDg4GM+fP4eJiQl69eqFuXPnct1BBw8e5AIaAOjfvz8AYNasWZg9ezZu3LiBK1euAABq1qwpUXZCQgLs7e0VUW2ZhJxiTQghhBBhCL5OjCoRap0YIdE6CoQQQoRA68RQ7iRCCCECoZs0omwUxPBA3UmEEFIyuh4SZaMghgf6YhJCSMmoJYYoW7lksQaA+/fvo2vXrjA0NISuri6aNm2KpKQkbn98fDx69OgBc3NzGBgYoG/fvnj58qUiqksIIUQA31L2ZKIayiWLdXx8PFq3bo0RI0YgJCQEBgYGuHv3LrcWTEZGBjw9PVG/fn1ERkYCAGbMmAFfX19cvnwZYrFCl7chhBBSCtQSoxq+pdxJ5ZLFun///tDQ0MDmzZullnH8+HF06tQJ7969g4FB/oq7Hz58gLGxMY4fPw4PDw9edaHZSYQQojx0fVMump2kgO6kkrJY5+Xl4ciRI6hduza8vLxgYWGBZs2aYf/+/dyxWVlZEIlEEovXaWtrQywWlykTNiGEEEIqD6VnsX716hXS09OxYMECeHt74/jx4+jRowd69uyJM2fOAACaN28OXV1dTJs2DZmZmcjIyMCUKVOQm5tb6kzYhBBCCKlclJ7FOi8vDwDQrVs3BAQEAAAaNGiAixcvYs2aNfj+++9hbm6OXbt2Ydy4cVi+fDnEYjEGDBiARo0ayRwPo8gs1jTFmhBCCFE9Ss9ibWZmBnV1dbi4uEic4+zsLNFV5Onpifj4eLx58wbq6uowMjKCpaWlzEzYisxiTYEKIYQQonoUuk6Mrq4udHV1uSzWv//+OzQ1NdG0aVMu6WOBR48ewc7OrkgZZmZmAIDIyEi8evUKXbt2lfpawcHBCAwMlNhmbFpHkPdBLTGEEEKI6imXLNZTp05Fv3790LZtW7i7u+Po0aM4dOgQoqKiuDIiIiLg7OwMc3NzXLp0CZMnT0ZAQACcnJykvqYis1hToEIIIaSi+JamWJdLFusePXpgzZo1mD9/PiZNmgQnJyfs2bMHrVu35sp4+PAhgoODkZaWBnt7e/z666/cGBplo5YYQggpGV0PVUNlDFZkoSzWSkbrKBBCCBECrRNDuZOUThU/LIQQQkhFREGMklFLDCGksqLrm2qgMTGEEEIIqZAqY7AiCwUxPNDAXkIIIUT1yJ124OzZs/D19YW1tTVEIpFEziMgf5XcmTNnwsrKCjo6OvDw8MDjx4+5/YmJiRgxYgQcHBygo6ODGjVqYNasWfjy5UuRchYvXozatWtDS0sL1apVw9y5c0v3LstIVnp5CmAIIYSQ8iN3S0xGRgbq168Pf39/9OzZs8j+33//HcuXL8fGjRvh4OCAGTNmwMvLC/fu3YO2tjYePHiAvLw8rF27FjVr1kRsbCxGjRqFjIwMLF68mCtn8uTJOH78OBYvXgw3NzekpaUhLS2tbO+WkEpE0U3GpelXp8CeSCPkZ1Xez5i8r02f4YqlTFOsRSIR9u3bh+7duwPIbz2xtrbGTz/9hClTpgDIXzOmatWq2LBhA/r37y+1nEWLFmH16tV48uQJAOD+/fuoV68eYmNjZS5uxwdNsSaEEFJZ0RRrgbNYJyQkIDU1FR4eHtw2Q0NDNGvWDJcuXZJ53ocPH2BiYsI9P3ToEBwdHXH48GE4ODjA3t4eI0eOLLeWmM/J52Q+CCGEEFI+BB3Ym5qaCgCoWrWqxPaqVaty+74WFxeHFStWSHQlPXnyBE+fPsWuXbuwadMm5ObmIiAgAL1790ZkZKTUchSZxVoVo1RCCFE11NJMlK1cZye9ePEC3t7e6NOnD0aNGsVtz8vLQ1ZWFjZt2oTatWsDAMLDw9G4cWM8fPhQaheTIrNYE0IIKRkFK0TZBA1iLC0tAQAvX76ElZUVt/3ly5do0KCBxLHJyclwd3dHy5YtsW7dOol9VlZWUFdX5wIYAHB2dgYAJCUlSQ1iKIs1IYSUL2V0sdPAXlKYoEGMg4MDLC0tcerUKS5o+fjxI65cuYJx48Zxx7148QLu7u5o3LgxIiIiIBZLDs1p1aoVcnJyEB8fjxo1agAAHj16BACws7OT+tqUxZoQQsqXKl4rVbFOikYr9hYjPT0dcXFx3POEhATExMTAxMQEtra2+PHHH/Hbb7+hVq1a3BRra2trbgbTixcv0K5dO9jZ2WHx4sV4/fo1V1ZBS46HhwcaNWoEf39/LFu2DHl5eRg/fjw6duwo0TpDCCFE9dEUa+WqjMGKLHIHMdHR0XB3d+eeF3Th+Pn5YcOGDfj555+RkZGB0aNH4/3792jdujWOHj0KbW1tAMCJEycQFxeHuLg4VK9eXaLsgtneYrEYhw4dwsSJE9G2bVvo6uqiU6dOWLJkSanfKCGEkPJRnoFBZQhKiGxlWidG1dE6MYQQQiorWieGcicRQggRCN2kEWWjIIYQQoggKFghyiboir2EEEIIIcqi9CzWQP506W7dusHMzAwGBgZo3bo1Tp8+ze1/+/YtvL29YW1tDS0tLdjY2GDChAn4+PFj6d6lCqFs2ISQyorSsxBlkzuIKchivWrVKqn7C7JYr1mzBleuXIGuri68vLzw33//ccd06dIFOTk5iIyMxPXr11G/fn106dKFS00gFovRrVs3HDx4EI8ePcKGDRtw8uRJjB07tpRvU3XQl5wQUlnRTRpRNqVnsX7z5g3Mzc1x9uxZtGmT/+H+9OkTDAwMcOLECYnkkYUtX74cixYtwrNnz3jXj2YnEUKI8tD1TblodlI5ZLE2NTWFk5MTNm3ahIyMDOTk5GDt2rWwsLBA48aNpZabnJyMvXv34vvvvxeyuoQQQgipwJSexVokEuHkyZPo3r079PX1IRaLYWFhgaNHj8LY2FjivAEDBuDAgQP4/PkzfH198ffffwtZXUIIIQJSxTt6UrkpfXYSYwzjx4+HhYUFzp07h6tXr6J79+7w9fVFSkqKxLFLly7FjRs3cODAAcTHxxdJ8FhYVlYWPn78KPGoxOv4EUIIId88pWexjoyMxOHDh/Hu3TsYGBgAAP7880+cOHECGzduRFBQkER5lpaWqFOnDkxMTNCmTRvMmDFDouwC8+fPR0hIiMQ2kVgPIjWDMr8vymJNCCElozExRNmUnsU6MzMTAIpkrhaLxcjLy5NZdsG+rKwsqfuDg4OLtNQYm9Yp1fv4Gn0BiSpS9Ky20mTCpe8KIUSZlJ7FukWLFjA2Noafnx9mzpwJHR0d/PXXX0hISICPjw8A4J9//sHLly/RtGlT6Onp4e7du5g6dSpatWoFe3t7qfXS0tKClpaWxDaRSCTv2yOkwiivgIECFUKIqlB6FmszMzMcPXoUv/76K9q3b4/s7Gy4urriwIEDqF+/PgBwgU1AQACysrJgY2ODnj17SnQ1KRPdeRJCCCGqh7JYKxn1GRNCKiu6vikXrRNDCSAJIYQIRBV/DEnlRgkgCSGEEFIhUUsMIYQQQVB3ElE2aokhhBBCSIUkdxBz9uxZ+Pr6wtraGiKRCPv375fYv3fvXnh6esLU1BQikQgxMTEyy2KMoVOnTlLLSUpKgo+PD6pUqQILCwtMnToVOTk58laXEEIIIZWU3N1JGRkZqF+/Pvz9/dGzZ0+p+1u3bo2+ffti1KhRxZa1bNkyqWu55ObmwsfHB5aWlrh48SJSUlIwdOhQaGhoYN68efJWmRBCiBJQtxFRNrmDmE6dOqFTp04y9w8ZMgQAkJiYWGw5MTExWLJkCaKjo4ukETh+/Dju3buHkydPomrVqmjQoAFCQ0Mxbdo0zJ49G5qamvJWW2XQl5wQUlnRmBiibOUysDczMxMDBw7EqlWruHxLhV26dAlubm4S2bC9vLwwbtw43L17Fw0bNlRmdQVFX3JCSGVF1zGibOUSxAQEBKBly5bo1q2b1P2pqakSAQwA7nlqaqrUc7KysorkVWKMUeoBQghREkXn8wLkD5TkrRMFYhWL0oOYgwcPIjIyEjdv3hS0XMpiTQgh5UsVr4eqWCciHKUHMZGRkYiPj4eRkZHE9l69eqFNmzaIioqCpaUlrl69KrH/5cuXACC1+wmgLNaEEELIt0bpQUxQUBBGjhwpsc3NzQ1Lly6Fr68vgPxM13PnzsWrV69gYWEBADhx4gQMDAzg4uIitVzKYk0IIeWLxvwRZZM7iElPT0dcXBz3PCEhATExMTAxMYGtrS3S0tKQlJSE5ORkAMDDhw8B5LegFH58zdbWFg4ODgAAT09PuLi4YMiQIfj999+RmpqK6dOnY/z48UUCFWWg7iRCCCFE9cgdxERHR8Pd3Z17XtCF4+fnhw0bNuDgwYMYPnw4t79///4AgFmzZmH27Nm8XkNNTQ2HDx/GuHHj0KJFC+jq6sLPzw9z5syRt7qCoECFEEJKRtdKomwixhgr70ooirpmNUHKEbIlhppbCSGECEGo2WCq+PuT8+UFr+MoASQPqvgHJoQQVUM3aUTZKIghhBAiCApWiLJREEMIIUShhFwEjxa7K5ms96CMxQiVTe4g5uzZs1i0aBGuX7+OlJQU7Nu3D927d+f27927F2vWrMH169eRlpaGmzdvokGDBhJlxMfHY8qUKTh//jyysrLg7e2NFStWcKvyRkVFSQweLuzq1ato2rSpvNUmhBCiYKrYnVQZghIim1jeEwqyWK9atUrm/tatW2PhwoUy93t6ekIkEiEyMhIXLlzAly9f4Ovri7y8PABAy5YtkZKSIvEYOXIkHBwc0KRJE3mrXGafk8/JfBBCCCGkfCg9i/WFCxeQmJiImzdvwsAgPyXAxo0bYWxsjMjISHh4eEBTU1NiLZns7GwcOHAAEydOLJcF7CiSJ4SQktG1UjV8SzfYSh8Tk5WVBZFIJLFonba2NsRiMc6fPw8PD48i5xw8eBBv376VWH+GEEKIalHF7iRSuSk9iGnevDl0dXUxbdo0zJs3D4wxBAUFITc3FykpKVLPCQ8Ph5eXF6pXr67k2gqPvsyEEEIUiQb2KpC5uTl27dqFcePGYfny5RCLxRgwYAAaNWoEsbjoEJ3nz5/j2LFj2LlzZ7HlZmVlISsrS2IbY0zl8ifRnQohpLKi65hqqIzBiizlMsXa09MT8fHxePPmDdTV1WFkZARLS0s4OjoWOTYiIgKmpqbo2rVrsWXOnz8fISEhEttEYj2I1AwErTshhBDp6CZNtVXGv0O5rhNjZmYGAIiMjMSrV6+KBCqMMURERGDo0KHQ0NAotqzg4GAuj1MBY9M6wlaYEEIIUXHydidV5OBG6VmsgfzWFWdnZ5ibm+PSpUuYPHkyAgIC4OTkJPFakZGRSEhIwMiRI0usl5aWVpEM16rWlUQIIYQoGnUnFUOILNYPHz5EcHAw0tLSYG9vj19//RUBAQFFXis8PBwtW7ZEnTrUokIIIYQQSZTFWskqY3MeKR+KvtsqzQwH+hx/2+j6plyUxZpyJxFSYZXXhUcVL3iEkG8TBTGEEEIEQQEuUTa5cycRQgghhKgCQbNYZ2dnY/r06fjnn3/w5MkTGBoawsPDAwsWLIC1tTVXhr29PZ4+fSpR7vz58xEUFFTk9eLi4tCwYUOoqanh/fv38laXkEqLxsQQVUNjYoiyyR3EFGSx9vf3R8+ePSX2ZWZm4saNG5gxYwbq16+Pd+/eYfLkyejatSuio6Mljp0zZw5GjRrFPdfX1y/yWtnZ2RgwYADatGmDixcvyltVQio1GhNDCPnWCZrF2tDQECdOnJDYtnLlSnz33XdISkqCra0tt11fX18iU7U006dPR506ddChQwcKYgghhBAiQeEDez98+ACRSAQjIyOJ7QsWLEBoaChsbW0xcOBABAQEQF39/6sTGRmJXbt2ISYmBnv37lV0NYtFzeeEEFIyuh4SZVNoEPPff/9h2rRpGDBgAAwM/j+H0aRJk9CoUSOYmJjg4sWLCA4ORkpKCsLCwgAAb9++xbBhw7BlyxaJ88qLkF9M+pITQgghwlBYEJOdnY2+ffuCMYbVq1dL7Cuc46hevXrQ1NTEmDFjMH/+fGhpaWHUqFEYOHAg2rZty/v1KIs1IYSUL7q+qYZv6f9bIUFMQQDz9OlTREZGltia0qxZM+Tk5CAxMRFOTk6IjIzEwYMHsXjxYgD5wUheXh7U1dWxbt06+Pv7FymDslgTQkj5+pZ+PFWZvDMXK/LfTfAgpiCAefz4MU6fPg1TU9MSz4mJiYFYLIaFhQUA4NKlS8jNzeX2HzhwAAsXLsTFixdRrZr0VAKUxZoQQsoXtcQQZRM0i7WVlRV69+6NGzdu4PDhw8jNzUVqaioAwMTEBJqamrh06RKuXLkCd3d36Ovr49KlSwgICMDgwYNhbGwMAHB2dpZ4zejoaIjFYtStW1dmvSpKFmv6MhNCKiu6vqmG0qzxVFEJmsV69uzZOHjwIACgQYMGEuedPn0a7dq1g5aWFrZv347Zs2cjKysLDg4OCAgIKNKKUlnRnQohhBBFqozBiiyUxZoHIadYUxBDCCFECJTFmhJA8qKKf2BCCFE1dJNGlI0SQBJCCCGkQqKWGEIIIQol5BgNobrwhSqflC8KYnigtAOEEFJ65XmdpGt05SZ3d9LZs2fh6+sLa2triEQi7N+/X2L/7NmzUadOHejq6sLY2BgeHh64cuWKxDFz585Fy5YtUaVKlSI5lQpMmjQJjRs3hpaWVpGZTsqmY91G5oMQQggh5UPuICYjIwP169fHqlWrpO6vXbs2Vq5ciTt37uD8+fOwt7eHp6cnXr9+zR3z5csX9OnTB+PGjSv2tfz9/dGvXz95q0gIIYR8s76lm265u5M6deqETp06ydw/cOBAiedhYWEIDw/H7du30aFDBwDg0gNs2LBBZjnLly8HALx+/Rq3b9+Wt5qEEEIIqeQUOibmy5cvWLduHQwNDVG/fn1FvhQhhBBC8G0tdqeQIObw4cPo378/MjMzYWVlhRMnTsDMzEwRL0UIIYSQb5RCghh3d3fExMTgzZs3+Ouvv9C3b19cuXKFS/CoCFlZWcjKypLYxhhTyfxJhBBCCCk7hSx2p6uri5o1a6J58+YIDw+Huro6wsPDFfFSnPnz58PQ0FDiwfI+KfQ1CSGE/L9vaUApUQ1KWScmLy+vSCuJ0IKDg4skkTQ2raPQ1ySEEPL/KO0AUTa5g5j09HTExcVxzxMSEhATEwMTExOYmppi7ty56Nq1K6ysrPDmzRusWrUKL168QJ8+fbhzkpKSkJaWhqSkJOTm5iImJgYAULNmTejp6QEA4uLikJ6ejtTUVHz+/Jk7xsXFBZqamkXqpaWlBS0tLYlt1JVECCHlj1bsJYoidxbrqKgouLu7F9nu5+eHNWvWYODAgbhy5QrevHkDU1NTNG3aFNOnT0fTpk25Y4cNG4aNGzcWKeP06dNo164dAKBdu3Y4c+ZMkWMSEhJgb2/Pq65CZbEWEt2pEEIIEYK8vycV6feHbxZruYOYioSCGEIIUR66vimXUC1cqvj34RvEUBZrQgghhFRIFMQQQgghpEKiLNY8UBZrQggpGV0PibKVSxZrADhy5AiaNWsGHR0dGBsbo3v37hL7r127hg4dOsDIyAjGxsbw8vLCrVu35K0uIYQQQiopuVtiCrJY+/v7o2fPnkX2F2SxdnR0xOfPn7F06VJ4enoiLi4O5ubmAIA9e/Zg1KhRmDdvHtq3b4+cnBzExsZyZaSnp8Pb2xtdu3bFn3/+iZycHMyaNQteXl549uwZNDQ0yvCW5Ud3F4QQUjIa2EuUrUyzk0QiEfbt21ekFaWwjx8/wtDQECdPnkSHDh2Qk5MDe3t7hISEYMSIEVLPiY6ORtOmTZGUlAQbGxsAwJ07d1CvXj08fvwYNWvW5FU/oWYnCdmdRF9yQgghQqDZSQoe2Csti/WNGzfw4sULiMViNGzYEFZWVujUqZNES4yTkxNMTU0RHh6OL1++4PPnzwgPD4ezszPvNWIIIYQo1+fkc1IfhCiK0rNYP3nyBED+2JmwsDDY29tjyZIlaNeuHR49egQTExPo6+sjKioK3bt3R2hoKACgVq1aOHbsGNTVaSwyIYSoIlW8oyeVm9KzWOfl5QEAfv31V/Tq1QsAEBERgerVq2PXrl0YM2YMPn/+jBEjRqBVq1bYtm0bcnNzsXjxYvj4+ODatWvQ0dEp8pqKzGJNX0xCCCkZdZcTZVN6FmsrKysA+TmQCmhpacHR0RFJSUkAgK1btyIxMRERERFo2rQpmjdvjq1btyIhIQEHDhyQ+pqUxZoQQgj5tig9i3Xjxo2hpaWFhw8fonXr1gCA7OxsJCYmws7ODgCQmZkJsVgs0YpS8LygJedrlMWaEELKF7W4EGVTehZrAwMDjB07FrNmzYKNjQ3s7OywaNEiAOCO6dixI6ZOnYrx48dj4sSJyMvLw4IFC6Curi41+SRAWawJIaS8UXcSUTa5g5jo6GiJQKKg9aMgi/WDBw+wceNGiSzW586dg6urK3fOokWLoK6ujiFDhuDz589o1qwZIiMjYWxsDACoU6cODh06hJCQELRo0YKbyXT06FGuO4oQQggh3zbKYq1kdKdCCCGEFI/vOjE0X5kQQogg6CZNuSrzYnd8URBDCCFEEBX5x5BUTBTEEEIIEQS1xKgGWf/flXH1ZApiCCGECIKCFdVQGYMVWeRe7O7s2bPw9fWFtbU1RCIR9u/fL/PYsWPHQiQSYdmyZdy2xMREjBgxAg4ODtDR0UGNGjUwa9YsfPnyReIYkUhU5HH58mV5q0sIIURJKHeSatCxbiP1URnJ3RKTkZGB+vXrw9/fHz179pR53L59+3D58mVYW1tLbH/w4AHy8vKwdu1a1KxZE7GxsRg1ahQyMjKwePFiiWNPnjwpMTXb1NRU3uoSQggh35RvKXCUO4jp1KkTOnXqVOwxL168wMSJE3Hs2DH4+PhI7PP29oa3tzf33NHREQ8fPsTq1auLBDGmpqawtLSUt4qEEELKQWW92yeqS/AxMXl5eRgyZAimTp0q0YpSnA8fPsDExKTI9q5du+K///5D7dq18fPPP6Nr165CV5eX4qJa+tISQggh5UPwIGbhwoVQV1fHpEmTeB0fFxeHFStWSLTC6OnpYcmSJWjVqhXEYjH27NmD7t27Y//+/TIDGcpiTQghhHxbBA1irl+/jj/++AM3btzgFTy8ePEC3t7e6NOnD0aNGsVtNzMzk0jm2LRpUyQnJ2PRokUyg5j58+cjJCREYptIrAeRmkEp3w0hhBB50BRromyCBjHnzp3Dq1evYGtry23Lzc3FTz/9hGXLliExMZHbnpycDHd3d7Rs2RLr1q0rsexmzZrhxIkTMvcrMos1dScRQkjJ6HpIlE3QIGbIkCHw8PCQ2Obl5YUhQ4Zg+PDh3LYXL17A3d0djRs3RkREBMTikmd6x8TEFJv8UZFZrOmLSQghhKgeuYOY9PR0xMXFcc8TEhIQExMDExMT2NraFpkGraGhAUtLSzg5OQHID2DatWsHOzs7LF68GK9fv+aOLZiJtHHjRmhqaqJhw4YAgL1792L9+vX4+++/5X+HhBBCCKmU5A5ioqOj4e7uzj0v6MLx8/PDhg0bSjz/xIkTiIuLQ1xcHKpXry6xr3BC7dDQUDx9+hTq6uqoU6cOduzYgd69e8tbXUFQdxIhhBCiekSscORQyahrVivvKhRBA98IIZUVXd+UqzJnsc758oLXcZQ7iRBCiEIJuYKsvD+48r62Kv6gE9nkzp1ECCGEEKIKqCWGEEKIIFSxFUMV66Ro39R7Zt+A//77j82aNYv9999/KlOWqpWjinWi90Z1UrVyVLFOlfm9qWKd6L0pv6zifBNBzIcPHxgA9uHDB5UpS9XKUcU60XujOqlaOapYp8r83lSxTvTelF9WcWhMDCGEEEIqJApiCCGEEFIhURBDCCGEkArpmwhitLS0MGvWrCK5lcqzLFUrRxXrRO+N6qRq5ahinSrze1PFOtF7U35ZxanUK/YSQgghpPL6JlpiCCGEEFL5UBBDCCGEkAqJghhCCCGEVEgUxBBCCCGkQqIghhBCCCEVEiWAVJKIiAj069cPVapUKe+qEEIqOMYYnj17BgsLC2hra8t1bsOGDSESiXgde+PGjdJUjxD8999/cn82S4OmWMuptH+YqlWr4vPnz+jTpw9GjBiBli1blqkez549g0gkQvXq1QEAV69exdatW+Hi4oLRo0eXqezSev/+PXbv3o34+HhMnToVJiYmuHHjBqpWrYpq1arJPO/gwYO8X6Nr165y1+vLly9ISEhAjRo1oK5OcTuRT15eHsRi1Wq0zsvLg7a2Nu7evYtatWrJdW5ISAj37//++w9//vknXFxc0KJFCwDA5cuXcffuXfzwww+YP3++oPXm6/3797h69SpevXqFvLw8iX1Dhw4t9lxjY2PeQVpaWlqx+9u2bYuDBw/CyMgIQP61qmPHjtDR0eFV/rcmLy8Pc+fOxZo1a/Dy5Us8evQIjo6OmDFjBuzt7TFixAjBX7PSBTGBgYG8jw0LC+N1nBB/mJycHBw6dAgbNmzAv//+C0dHRwwfPhx+fn6wtLTkXecCbdq0wejRozFkyBCkpqbCyckJrq6uePz4MSZOnIiZM2fKPFcR/0e3b9+Gh4cHDA0NkZiYiIcPH8LR0RHTp09HUlISNm3aJPPcr38gRCIRCn8sC1+QcnNzedc9MzMTEydOxMaNGwGA+7tNnDgR1apVQ1BQkMxzhbwQCvn/rYi/nSy3bt1Co0aNeP2fp6Sk4NSpUzAxMYGHhwc0NTW5fRkZGViyZEmxn8nCbt++LXW7SCSCtrY2bG1ti11Aq2fPnrxeBwD27t1b4jFqampISUmBhYUFAGDq1KkIDg6GiYkJ79cp8PLlS0yZMgWnTp3Cq1ev8PXlV57Pt6urK8LDw9G8eXO561Fg5MiRsLKyQmhoqMT2WbNm4dmzZ1i/fr1c5bm7uxf7vYmMjCyxjEOHDmHQoEFIT0+HgYGBRHkikajE71vB950PPz+/YveLxWKkpqZyf3sDAwPExMTA0dGR92tI8/jxY5w+fVpqkMbne5KcnIywsDDMnDkTBgYGEvs+fPiA3377DVOmTEHVqlVlliHreyZNvXr1eB03Z84cbNy4EXPmzMGoUaMQGxsLR0dH7NixA8uWLcOlS5d4vyZfle629ObNmxLPb9y4gZycHDg5OQHI/yFTU1ND48aNeZf522+/YePGjfj9998xatQobnvdunWxbNkyXkGMuro6evTogR49euDly5fYsmULNm7ciBkzZsDb2xsjRoyAr68v7zu+2NhYfPfddwCAnTt3om7durhw4QKOHz+OsWPHFvtFUMT/UWBgIIYNG4bff/8d+vr63PbOnTtj4MCBxZ5b+Et88uRJTJs2DfPmzePuDC9duoTp06dj3rx5vOsDAMHBwbh16xaioqLg7e3Nbffw8MDs2bOLDWKWLVvG/fvt27f47bff4OXlJVGnY8eOYcaMGSXW4+v/b1n4BE1ClsUHn3uca9euwdPTE3l5ecjOzka1atWwf/9+uLq6AgDS09MREhLCO4hp0KBBsfXX0NBAv379sHbtWqmtooaGhrxeh6+v/w/Wrl2LcePGlSqIGTZsGJKSkjBjxgxYWVmV6e+0YMECTJ06FatXr0bdunVLVcauXbsQHR1dZPvgwYPRpEkTuYOYBg0aSDzPzs5GTEwMYmNjSwwYCvz000/w9/fHvHnzStX9zvd1SkOIe/6//voL48aNg5mZGSwtLYsEaXy+J2FhYfj48WORAAbI//x/+vQJYWFhWLhwocwyCr5nst5TwT6RSMQ7uN60aRPWrVuHDh06YOzYsdz2+vXr48GDB7zKkJtCc2SXsyVLljBfX1+WlpbGbUtLS2PdunVjixcv5l1OjRo12MmTJxljjOnp6bH4+HjGGGP3799nRkZGparb5cuX2ejRo5mWlhazt7dnhoaGzN7enp0+fZrX+bq6uiwhIYExxpivry9bsGABY4yxp0+fMm1tbd71EOr/yMDAgMXFxTHGJP+PEhMTmZaWFu9yXF1d2blz54psP3v2LKtTpw7vchhjzNbWll26dKlInR4/fsz09fV5l9OzZ0+2YsWKIttXrFjBunXrJledVEmPHj2KfbRv356JxeISy/Hw8GDDhw9nubm57OPHj2zcuHHM1NSU3bhxgzHGWGpqKq9yCuzfv585OTmxv//+m92+fZvdvn2b/f3338zZ2Zlt376dbdmyhVWvXp399NNPpX7v8hCJROzly5fc88KfJXnp6emxmzdvClIvIyMjpqmpycRiMdPW1mbGxsYSDz6qVq3KIiIiimyPiIhgFhYWgtSTMcZmzZrF++9VpUqVUv//Fufz58/sw4cPEo+SCPm3L2Bra8tdr0tL1nWywIULF5iLi0uxZSQmJvJ+8KWtrc0dX/j/6u7du0xXV5d3OfKodC0xhS1ZsgTHjx+HsbExt83Y2Bi//fYbPD098dNPP/Eq58WLF6hZs2aR7QV3nny9fPkSmzdvRkREBJ48eYLu3bvj8OHD8PDwQEZGBubMmQM/Pz88ffq0xLJcXV2xZs0a+Pj44MSJE1xzcHJyMkxNTXnXSaj/Iy0tLXz8+LHI9kePHsHc3Jx3feLj47n+58IKuqnk8fr1a64ZuLCMjAy57oCPHTsm9Y7G29u72NYcVXfo0CF07NhRZpMz37uv69evY9WqVRCLxdDX18eff/4JW1tbdOjQAceOHYOtra1c9Zo7dy7++OMPeHl5cdvc3NxQvXp1zJgxA1evXoWuri5++uknLF68WK6yy5uNjY0gd/OAZGthaf34448YN24cbty4wbXsXrlyBevXr+fVysjX4MGD8d133/H6e3l5eSE6OrrMXTZA/nd92rRp2LlzJ96+fVtkP5/P+LFjx7jWvby8PJw6dQqxsbESx8gzVu/du3fo06cP7+OlSUhIKPZ7Vb169RKvl3Z2dmWqgzQuLi44d+5ckbJ3796Nhg0bCv56QCXsTirs48ePeP36dZHtr1+/xqdPn3iXI8QfxtfXF8eOHUPt2rUxatQoDB06VKI5uuCivGjRIl7lLVy4ED169MCiRYvg5+eH+vXrA8gfeFZwMeJDqP+jrl27Ys6cOdi5cyeA/KbIpKQkTJs2Db169eJdTtOmTREYGIjNmzdzP64vX77E1KlT5XpfANCkSRMcOXIEEydO5OoEAH///TfXLcSHqakpDhw4UCSgO3DggFwBY4Ho6Gjs3LkTSUlJ+PLli8Q+PmM0hCrL2dkZvXr1ktkdGhMTg8OHD/Oqx3///SfxPCgoCOrq6vD09JS7S+LOnTtSL7B2dna4c+cOgPym8JSUFF7l7d69W+b/Ed/ZNzNnzuS6Nr58+YK5c+cW6bbiMwZp2bJlCAoKwtq1a2Fvb8/rtWURotskKCgIjo6O+OOPP7BlyxYA+Z+LiIgI9O3bt8zlF7h06VKxEyIKD+738fHB1KlTce/ePbi5uUFDQ0PiWHkChp9//hmnT5/G6tWrMWTIEKxatQovXrzA2rVrsWDBAl5lfP3/PGbMGInn8nS3AECfPn24bv/S0tHRQWJiosxAJjExsVSDj+/duyf1e8L3/3zmzJnw8/PDixcvkJeXh7179+Lhw4fYtGkT72uJ3BTSvqMihgwZwuzt7dmePXvYs2fP2LNnz9ju3buZg4MDGzp0KO9y9u/fzwwNDdmCBQtYlSpV2KJFi9jIkSOZpqYmO378OK8y/P392cWLF4s9Ji8vT66mu5ycHIluIMYYS0hIkGj+LIlQ/0fv379nHh4ezMjIiKmpqTEbGxumoaHB2rZty9LT03mX8/jxY1a3bl2mqanJatSowWrUqME0NTWZq6sre/z4Me9yGGPs3LlzTE9Pj40dO5Zpa2uzyZMns44dOzJdXV0WHR3Nu5yIiAimpqbGunTpwkJDQ1loaCjr0qULU1dXl9oUX5xt27YxDQ0N1qVLF6apqcm6dOnCateuzQwNDdmwYcOUWtawYcPYDz/8IHP/vXv3mL29fYnltGnThq1evVrqvoULFzItLS25upMaNGjA/Pz8WFZWFrfty5cvzM/PjzVo0IAxxtj58+d51e2PP/5genp6bMKECUxTU5ONGTOGeXh4MENDQ/bLL7/wqs/333/P2rVrV+zD3d1d5vlGRkYS3TwFXUB6enql6gL6WmxsLLt16xb3iI2N5XVednY2CwkJYc+ePSvV60rzdZdk9+7dWbNmzZiamhqbPXu2zPNEIhGvhzyfI8YYs7Gx4bro9fX1uWvIpk2bWKdOnUr9PuX1xx9/cI958+YxMzMz5ufnxxYvXiyx748//uBVXufOndnIkSNl7h8xYoRc7y8+Pp7Vq1eP+z8u/P8t7//52bNnmYeHBzM3N2c6OjqsVatW7NixY3KVIY9KHcRkZGSwcePGcRdRkUjENDU12bhx4+T6YWVM2D/M58+fS3Xe17Kzs9mJEyfYmjVr2MePHxljjL148YJ9+vSJdxlf/x+JxeJS/x8xlh84rFq1ii1cuJCdOHFC7vMZyw/mjh07xn2pjx8/zvLy8kpVVlxcHBs5ciRr2rQpc3Z2ZoMGDWK3b9+Wu5zLly+zgQMHsoYNG7KGDRuygQMHssuXL8tdjpubG1u5ciVj7P/7jPPy8tioUaPYzJkzlVrWf//9xzIyMuR+D1/766+/2ODBg2XuX7BgAa+Ao8CFCxeYqakpMzc3Zx06dGAdOnRgFhYWzNTUlBvjtGnTJvb777+XWJaTkxPbunUrY0yyj37GjBls/PjxvOtUFhs2bOD94OPs2bOsSZMm3HM9Pb0iPzx8v3uFx9YJYdiwYRIPf39/Nm3aNIX+iBVHV1eXPX36lDHGWLVq1diVK1cYY4w9efJEYWM0pLG3t+f1cHBw4FVeZGQkU1NTYz/99BNLTU3ltqemprLAwECmpqbGTp06xbt+Xbp0Yd26dWOvX79menp67N69e+zcuXPsu+++Y2fPnpX7/SpTpQ5iCqSnp3N3KaX5YRZCbm4umzNnDrO2tmZqamrcxXT69Ons77//lru8xMREVqdOHValShWJ8iZNmsTGjBkjd3mq8H/0LahSpQr3o2FiYsIFVPfu3WOWlpblVpaq+fjxI1u9ejULCAhgAQEBEoG6PHR0dLjWTXNzcxYTE8MYY+zRo0fMxMSkTHXMzs6W64ZBKP3795e4Y9fT02NnzpxhiYmJLCEhgQUEBLCePXvyKqtr1668g6fy9O7du1Kd5+bmxqKiohhjjHXo0IEbXPzHH3+watWqlXj+w4cPucCnwMmTJ1m7du1Y06ZN2dy5c0tVLyGsWbOGu/ksaO0Ti8VMS0uL/fnnn3KVZWpqym7dusUYy5+k8eDBA8YYY6dOneJaP+Vx7do1tmnTJrZp0ya5Wr1Lo1KOieG7ToS84w/KQohp2oVNnjwZTZo0wa1btyTGZfTo0UOifL50dXW5MTq6urpynw8Ap06dwtKlS3H//n0A+X3rP/74Izw8POQup2Adja/XUChpfIW0wcWySJueKEteXh7i4uKk1qlt27a8yzE2NubGGlWrVg2xsbFwc3PD+/fvkZmZybscocuKj49HREQE4uPj8ccff8DCwgL//vsvbG1tuanSskRGRqJt27aCLiSor69fpjEDBSwtLZGWlgY7OzvY2tri8uXLqF+/PhISEngPsD106BDevn2LYcOGcdvmzp2L0NBQ5OTkoH379tixY4fE4HhZvl5zpsDbt29hYWHBa2xFdHQ0fv31V4lt1atX58YRDRkyBD4+PjzeGdCpUycEBQXhzp07aNy4cZHvvryLS37+/BknTpzAo0ePoKmpCScnJ3h4eEBNTY13GQsXLoS9vT369esHIH8MyZ49e2BlZYV//vmHG//Hx/Dhw3Hr1i18//33CAoKgq+vL1auXIns7GxeY5imTZsGNzc3bjxeQkICfH190aZNG9SrVw/z589HlSpV8OOPP/Kuk1DGjBmDLl26YOfOnYiLiwNjDLVr10bv3r25RVD5ys3N5ZbGMDMzQ3JyMpycnGBnZ4eHDx/yLuf58+cYMGAALly4wE3QeP/+PVq2bInt27fLXS9eFBoilZOvmzRlPfj6uk+74GFiYsKsra1Z27Zt2fr164stQ+hp2iYmJly0XLi8hIQEpqOjw7uc3NxcFhISwgwMDLjuJENDQzZnzhyWm5vLu5xVq1YxdXV17i7xjz/+YAMGDGAaGhpclwcfs2fPZmKxmH333XesW7durHv37hKPkhTux5X1kLdv/dKlS8zBwUGiyb60ffQDBgxgS5YsYYwxNmfOHGZubs5GjhzJ7OzsWI8ePcqlrKioKKajo8M8PDyYpqYm91maP38+69WrV4nni8ViiXFYzZo1Y8+fP5frvXzt0aNHbO3atSw0NJSFhIRIPOQxYsQIbizGypUrufdpZGTE/P39eZXRrl07ic/whQsXmFgsZr/99hvbs2cPq1OnDgsICOBV1tdTdgu8ePGC99II2traLCkpiXu+Z88eiW7BxMREpqmpybs+Qo0/OXDgADM3Ny9STvXq1dmZM2e44548eVJsOfb29uzChQuMMcaOHz/OjIyM2LFjx9iIESNYx44d5arT1xITE9mePXu4VoeSVK9eXWIsY2hoKKtfvz73/O+//5Z4zkfPnj2lTrFeuHAh6927t1xlCaV169Zs3759jLH864q3tzc7f/48Gzp0KHN1deVdjpeXF2vWrBn328QYYw8ePGAtWrRgXl5eQlebMfaNdCeVVVhYGDM1NWWDBw9my5cvZ8uXL2eDBw9mZmZmbO7cuWzkyJFMS0uLrVu3TmYZQs+fNzIyYnfv3i1S3rlz5+Ra3yEoKIiZm5uzP//8k+tOWrVqFTM3N+c98JGx/P5maWuprFy5kllbW/Mux9LSkm3atIn38V+Liori/eCrfv36rE+fPuzevXvs3bt37P379xIPebx9+5a9ePGCMZYfQM6fP5/5+vqywMDAIoO0lVVW8+bNuWCo8GfpypUrvJrchV5LY926dUxNTY1VrVqV1a9fnzVo0IB7NGzYUK6ycnNzWXZ2Nvd827ZtbOLEiWz58uUSA4eLY25uzq15wxhjAQEBEhfkI0eOsJo1axZbRkFgLxaL2dy5cyUGcoaFhbHu3bvzbrY3Nzcvdj2p06dPMzMzM15lCeXChQtMQ0OD9erVi128eJG9e/eOvXv3jl24cIH17NmTaWtrs/v377Off/65xEC0cJA2adIkNnr0aMZYftdOadflKq2vA8b27duz6dOnc8/j4uKYoaGhXGWamZlJHZd3+/Ztudfm2blzJ+vRowdzdXVlDRs2ZP369WNHjx6VqwzGGDt69Cjbs2cPYyx/coWTkxMTiUTMzMxMrrE12traEt+VAtHR0XLdXMuDghgeevbsKXX2xZo1a7i+5+XLl7O6devKLKNRo0Zs8+bNjDHJi3xISAhr3bq13HXq27cvGzVqFFfekydP2KdPn1j79u3lamWysrJiBw4cKLJ9//79cgUfurq6UmcPPXr0SK4gzcTEhFs0T1VUqVJF7plRFYmuri53d/x1qx6fhQqFDmKEWAxMSNra2tzgUMYYa9q0qcSg4sTERFalSpViyygYuCkSiZiNjY3EYM7atWszT09P3gPFu3TpwoYPHy5zv5+fH/Px8eFVllA6derEBRvSjB49mpmZmTFTU1NuXJIsVlZWXEtM7dq12c6dOxlj+Xf08ixSyRgr0oonb6uetbU1NyYmNzeXGRgYsMOHD3P77927xwwMDOSqk7a2tkRLRYH79+/zbo3Lzc1lffv2ZSKRiDk5ObFu3bqxbt26sdq1azOxWMzGjh3LGGPszZs3bO/evTLLady4MVu9erXUhf/evn0r94SKWrVqFRlDxFj+DVGNGjXkKosvCmJ4kPUD/fjxY+4HOi4urtgLmRDTtAt79uwZc3FxYc7OzkxdXZ01b96cmZqaMicnJ7mmWGtpabGHDx8W2f7gwQO5Vv4dMGCA1NkiixYtYv369eNdzs8//8zmzJnD+/ji2Nvbs5CQEIkfoNJwd3dn//77ryB1evr0abGP8iirWrVq3I9G4QBk7969zNHRscTzxWIxe/XqFfdcX1+/xC6D4ujr6wu2Yuv69eu5H8HCdu7cyXtAa40aNbi720+fPjFNTU12/vx5bv/169d5t3y0a9dO7ha3r0VGRjKxWMymTJki8V1/+fIl75kpmZmZ7NChQ9zzoKAgbhB1QEAAmzJlilyzKI2NjYud9Xfr1i0mEol4rVY8fvx4Zmdnxzw8PJipqSk3eHrbtm1yt8QVbsVr0KABc3V1ZVWqVGEGBga8yho4cCDr0qULS0pKYkuWLGF6enoSEx92797N6tWrJ1edmjZtKjWAmjVrFmvUqBGvMsLCwpiJiYnE37DAgQMHmImJCVu0aBFzdXVlCxculFmOv78/09fXZ1WqVGFDhgzhvWK8LPv372ffffcdu3btGrft2rVrrHnz5lx3ldAoiOHBxsaGhYWFFdkeFhbGbGxsGGP5X9KqVasWW47Q8+ezs7PZ5s2b2dSpU9m4cePYX3/9xTIzM+Uq47vvvmMTJ04ssn3ChAmsWbNmvMsJDQ1lhoaGrHPnztxaKj4+PszIyIiFhobyXgdh0qRJzMjIiLVt25ZNmDBB4sLKd9xBgaVLl7L69eszNTU15uHhwbZt28b+++8/ucpgLP/H3MXFhUVERLDo6GiJNTn49q0XKGnMTnmU9dNPP7HWrVuzlJQUbi2N8+fPM0dHx2LX9ihcDzc3N276uZqaGte8XfjBl7+/v8x1Z+RVq1YtFhkZWWR7VFQUq127Nq8ygoKCWJ06ddimTZtY//79ma2tLcvJyeH2r127lrVq1UqQ+vK1atUqbr2ZwjNTNDU1pXbrfm316tWsS5cu3HM9PT3WrFkzbt0bS0tLqdc8WQp3l0uTmJjI+6boy5cvbNGiRWzSpEkSXRNhYWHsr7/+4l0nWT58+MB69OjBq9s6ISGB1axZk4lEIqaurl5k1k+3bt3Yjz/+KNfrHzx4kKmrq7OhQ4dyU+uHDBnC1NXVef/Qu7m5sfDwcJn7//77byYWi5m3t3eJ3aYZGRksIiKCff/990wsFrMaNWqwuXPnlmpcW+FUGJqamhL/FmJNJGkqXRZrRShI2NW5c2dulPq1a9fwzz//YM2aNRgxYgSWLFmCq1evYseOHeVcW/mcOXMGPj4+sLW1lUhu+OzZM/zzzz9o06YNr3IcHBx4HScSifDkyROZ+93d3Ys9l08W3K/duHEDGzZswLZt25Cbm4uBAwfC398fjRo14nW+tKScpUmOBuRnhi4sOzsbN2/eRFhYGObOnStXBmahyvry5QvGjx+PDRs2IDc3F+rq6tz/04YNG0qcWRISEsLrdWbNmsXruPnz5yMsLAw+Pj5SV2ydNGkSr3IAQFtbGw8ePCiyOm5iYiKcnZ3x+fPnEsv4/PkzxowZg0OHDsHS0hLr1q2T+F64u7vD29sb06ZN41Wn58+f4+DBg1JXRpUn83hSUhL27NmDx48fAwBq1aqF3r17w8bGpsRz27Rpg59//hm+vr4A8meD3bp1i1vqf8uWLVi1ahXvrMP16tVDQEAAhg8fLnX/+vXrsWzZMrkyJyvSnTt34OvryyuVSU5ODu7evQtzc3NYW1tL7Lt16xaqV68u98rdR44cwbx58xATEwMdHR3Uq1cPs2bNwvfff8/rfB0dHTx8+FDmir1Pnz6Fo6MjPn/+LJFRviQFsxQ3b96M5ORkeHp6YsSIEbyvJRs2bOCd0kWoRJ0UxPB04cIFrFy5kptu5uTkhIkTJ6Jly5ZKq8PBgwfRqVMnaGhoSCzTLY08UyOTk5OxatUqLsuos7MzfvjhhyJf2IouOzsbf/75J6ZNm4bs7Gy4ublh0qRJGD58eLFfvJJyWQmRg+TIkSNYtGgRoqKiyq2spKQkxMbGIj09HQ0bNkStWrXKXJfSKC4gLikI/pqtrS1WrlxZ5Ptw4MABjB8/Hs+fPy91PUvj1KlT6Nq1KxwdHfHgwQPUrVsXiYmJYIyhUaNGcgXp//33X7FL+RfHysoKly5d4oI7c3NzXLt2jXv+6NEjNG3aFB8+fOBV3tKlS/Hbb79h8+bN6Ny5s8S+I0eOwM/PD7/88gsCAwOlnq+oa5ss58+fh6+vL969e8fr+NjYWJmZwvfv34/u3bvzKicnJwfz5s2Dv79/maYbm5iYICoqCvXq1ZO6/86dO2jbti3v9/c1xhj27NmDMWPG4P3793LdqCmdYG06pAhZU7OlPfgoPIBSyKmRQpDWZF9WBWkQyurLly9sx44dzNvbm6mpqbFWrVqx9evXszlz5rCqVauyAQMGCFDbsnn8+HGJg0OVUVZeXl6pV0f+WlRUFDty5EiZx4CUxc8//8zs7OxYZGQky8nJYTk5OezUqVPMzs5OaVmwC2vatCm3mnLB+KNPnz6xrl27yr1Amb6+PvPz82PHjx+XazkExmQPLi1w//59ubLP5+bmst69ezORSMTq1KnDpRxwcnJiYrGY9ejRo9g6Kura9vWS/suWLWPTpk1j1tbWcn3vra2tpY7z2r17t9zfNSFWSe7cuTM3eFeaMWPGlDqtwunTp9nQoUOZrq4uMzQ0lGvx1LZt27KNGzfKPayhLCiIkZM86dyFXm5cUd69e8cWL17MRowYwUaMGMHCwsLknjqsqanJHB0dWWhoqMSURHkJtW4NY/kDLidMmMAtYf/TTz+x+/fvSxxz586dEvvqnz17JnVl1i9fvkisf8HH15+d9+/fs/v377N+/frJvd6EkGX9/fffzNXVlevHdnV15T3+YMGCBRLTTvPy8piXlxf3w1O1alXe+XyElpWVxc3i0NDQYBoaGkxNTY0NHz6c9xTrAqmpqWzw4MHMysqKqamplWoMkp6eHjf7zsjIiPt/iYmJYXZ2dnLVZ+/evax3795MR0eHWVpassmTJ0sMqCxOzZo12e7du2Xu37FjR6lmk2zfvp1169aNOTs7M2dnZ9a1a1e2bds2ucsRytfL+js6OrJmzZqx4OBguVaAnjlzJnN0dGQpKSnctu3bt7MqVapIHTheHCFWSS6Y0t6nTx925coV7vt/6dIl1rt3b6ahoSEx+Lwkz549Y6GhoaxGjRpMJBKVOhiZPHkyMzc3ZwYGBmzkyJFcmhBFoiCGh4yMDDZ+/Hhmbm5e5sGYQtm4caPUQapZWVls48aNvMu5du0aMzExYdWqVeOStlWvXp2Zmpqy69ev8y7n9evXLCwsjNWvX5+pq6szT09PtmPHDrl/KIRat4ax/FkzXl5ebOfOnezLly9Sj0lPT5c5JT05OZk1bdqUicVipqamxoYMGSIRzKSmpgoyGFckEjFbW9sSE4QqqqwZM2YwXV1dFhQUxA4cOMAOHDjAgoKCmJ6eHpsxY0aJ5zds2JBt376de75z506mo6PDzp8/z96+fct8fHxYnz59ii0jICCAm/Xx9WDusgzuLvDw4UO2c+dOdujQIbmSrBbm7e3NXFxc2J9//sn27dvH9u/fL/Hgo2rVquzevXuMMcacnZ255Q1iYmJKncvn48ePbP369axjx45MTU2N1apVq8Tpw5MmTWIuLi5SZyBlZmYyFxcXNmnSpFLVp7KaMGECc3V1ZW/fvmX/+9//mI6OTrGBoCyrV69mlpaW7KeffmJbt27lvnMFD7727t3LzMzMilwDTE1Neddrx44dzMvLi6mrqzNra2sWHBxc5uUksrOz2Z49e1jXrl2ZhoYGc3Z2ZosWLZLI8SQkGhPDw/jx43H69GmEhoZKTec+aNAgqecpagl8QJjly4H8AX41a9bEX3/9xS0bn5OTg5EjR+LJkyc4e/asXPUC8gfSRkREYNu2bQCAgQMHYsSIEbyWC7e2tsaaNWukjmH44Ycf8OLFC971ePr0aZnGq/j5+eHhw4dYuXIl3r9/j6CgIIhEIhw/fhzGxsZ4+fIlrKysiqQhKM6ZM2cknovFYpibm6NmzZpyL9svVFnm5uZYvnw5BgwYILF927ZtmDhxIt68eVPs+cbGxrh48SKcnZ0B5C/1npubi02bNgEALl++jD59+uDZs2cyy3B3d8e+fftgZGRU7OBuADh9+jSftyU4fX19nDt3Dg0aNCh1Gd27d4ePjw9GjRqFKVOm4MCBAxg2bBj27t0LY2NjnDx5skx1vHfvHgYNGoTbt28Xew14+fIlGjRoAE1NTUyYMAG1a9cGAO7znpOTg5s3b6Jq1apy16FgcOiTJ0+wbNkyuVJYFChL6pGvvX//HnFxcdDU1ISDgwO3vH5pDBo0CNeuXcOLFy+wdetWdOvWTe4ypE0UKCDvRIHMzEwcO3ZMYnC3l5cXqlSpwut8TU1N+Pj4YMSIEejcuXOxdSuNV69eYd26dZg7dy5yc3PRuXNnTJo0Ce3btxfuRRQSGlUypU3nrogl8AuXXXhtjgIxMTFyTV8rWEnza3fv3i3TCosvXrxgs2bNYlpaWkxXV5epqamx1q1bl9itINS6NYwx5uDgwN68eVNk+7t373hliy280BVj+VmffX19WYMGDdjbt29L1RKjigwNDdmjR4+KbH/48CGv1Ui/XtzOyclJYor006dP5f7blYWiWnWcnZ2lrkYqj/j4eG5afnp6OhszZgxzc3NjPXv2LHUL0efPn9mOHTtYt27dmJaWFrO1tWXTpk0r8bwnT54wLy+vIhmwvby8Sr1OT1lTWDBW9tQjBRISEljnzp0luv40NTVZ//79JVoFZC278HULyYEDB9ju3buZjY0NGzFiRKlaT1SNPGuKyevKlSts7NixzMjIiNna2rKZM2eyESNGMB0dHUHHo1XKBJBCS0tL46YeGhgYIC0tDQDQunVrjBs3TuZ5fO8Y79y5w7suDRs2hEgkgkgkQocOHSTuuHNzc5GQkABvb2/e5RkYGCApKQl16tSR2P7s2TO571iys7Nx4MABrF+/HidOnECTJk2wcuVKDBgwAK9fv8b06dPRp08f3Lt3T2YZ9evXx8qVK7F8+XKJ7StXrpQr8RuQP41W2l1NVlYWrxadDx8+SCT109LSwt69e9GnTx+4u7tjy5YtctUHAHbt2oVt27ZxCfJq166N4cOHw8vLq9zKGjJkCFavXl1keu+6detktjIWVqNGDZw9exaOjo5ISkrCo0ePJJJiPn/+XK4pqP7+/vjjjz+KfP4yMjIwceLEEu/Eb968iezsbAD5rYKyZp7xnQpaYNmyZQgKCsLatWuLTNnmq+A6AuQnWl2zZk2pygGAY8eOYevWrdi/fz/U1dXRu3dvHD9+nHdCUgcHBxw9ehRpaWmIi4sDANSsWZNLBFsaQUFB+O233xAYGCjx92vfvj1WrlzJq4w1a9Zgw4YNGDJkSKnr8ezZMzRv3hwaGhoIDQ3lWgnv3buH1atXo3nz5rh58ybOnj2L+/fvS50eX9yMo/Xr13OfQ3lbT8rq62tjcUpajqCgJX/+/PmoWrUq/P39JfavX78er1+/LnH5gLNnz6Jly5ZIS0vD5s2bERERgcePH8PX1xfbtm2Dl5cX930bNmwYvL29sXjxYt7vo1iChUOVWFnTuUvz8eNHtnbtWm7MBV+zZ89ms2fPZiKRiE2ZMoV7Pnv2bDZv3jy2detWucahTJw4kVWvXp1t376dJSUlsaSkJLZt2zZWvXp1Nnny5BLPd3d3Z+/eveMG0JqYmLDJkyezO3fuFDk2JSWFiUSiYsuLiopiurq6zNnZmfn7+zN/f3/m7OzM9PT02NmzZ3m9p4K7I5FIxDZt2iRxx7R37142fvx4Xgudubm5Se1bzs7OZt27d2e2tra8/3ZCLRMudFmM5ff1GxgYMFdXV25wd926dZmBgUGRBQelWbduHdPV1WX+/v7MxcWFtWzZUmJ/aGioxMJqJfk6oWSB169fMzU1Nd7lCK3wQl56enqCLN4VHx/PYmNj5R60zhhjOjo6rE+fPmz//v0yx3yVZPPmzRLJI8uqrCksGBMm9Yi/vz9r27atzDE/bdu2Za1bt2ba2tq8xzMJKSoqinXp0oXVqFGD1ahRg/n6+vK+vn09WFnWg09rcwE7Oztu1e7CLl++zOzt7Us8v+A7q6GhwerUqcN+//13qT0FjOVPSGjXrh3vupWEghgewsLCuJVmT5w4wbS1tZmWlhYTi8Vs2bJlcpV15swZbvparVq12LRp09jVq1flrtOGDRvkWhZclqysLDZp0iTu4iwWi5mWlhb78ccfea1uW/Dhbd++Pdu6dWux52RnZ/NKvPjixQv2yy+/sJ49e7KePXuyX3/9lUt0yEfhpvGvp2hqamqy2rVrS12u+2s///wz8/T0lPleunbtyjuIEWqZcKHLYoxxq7SW9HB3d5dZRnh4OOvevTsbO3asxAwOxhgbN24cl1yuOAUzLEQiEYuLi5OYeZWWlsY2btzIrKysSiynwJcvX5iamprUgLo0yjLD8MuXL2zmzJmsS5cu7LfffmM5OTmsf//+3HfO2dlZ7mm38syukcXMzIzp6uqyAQMGsCNHjkisRFwaZU1hwZgwqUesra3ZuXPnZO4/c+YME4lExa56qyibN29m6urqrG/fvtzU7759+zINDQ32v//9T+n1YSy/G1/aFPL4+Hi58qfxDcSEREFMKcibzj0lJYXNnz+f1axZk1lYWLAJEyYwdXV1Lgu1KsjIyGC3b99mt2/fluvO7Ovkf6rE3t6evX79utTnZ2dnFzuFPjs7m/c4BiGXCReyLFVS0hgyNTU19ttvv8lVpoODQ4kJB5UhMDCQmZubs5EjRzJHR0fWtWtX5uTkxLZv38527tzJ3Nzc2MCBA+UuNycnh+3atYvNmTOHzZkzh+3atUsia3dJsrOz2aFDh9jAgQOZrq4uMzc3Zz/88IPUu3I+yprCgjFhUo9oamoWu8bUs2fPmIaGBq+yGMtvsZaWMmXFihW8WqwLq1OnjtSUDkuWLGF16tSRq6zCyrLGU82aNbkExYVt2rSJV4uOrDGaykBBDA9lmc7cpUsXZmBgwAYMGMAOHz7M3emUJogxNjbmfpRLWkhPWUQiETt9+nSRfELy5hd69OgR69+/v9Sg4f3792zAgAGCJQWUx5cvX5ijoyM3Lba0vs6E/LXExEQmFot5BR1ClsVYfpJEoRanys3NZQ8fPmTnzp1jZ86ckXiUJCoqip0+fZqJRCK2d+9eFhUVxT0uXrwoV2tcgb///pt17tyZvX37tjRvR+Lz+PW6PHzXi2IsPzP3kSNHGGP5A6ZFIhH7559/uP1RUVFyd03HxsYyBwcHVqVKFS4/la6uLrO3ty9V61NGRgbbsmUL69y5M7fuk7yysrLYyJEjmbq6Orc2j1gsZoMHD+bdylPa1sDC7Ozsis1L9++//8q1Lo+1tTWLjo4usv369ety/900NTVlJhSWZ4HBAhs3bmR169ZlWlpaTEtLi7m5ufHKC1XYwoULmampKVu/fj1LTExkiYmJLDw8nJmamrJ58+aVeL5IJGKdO3fmlumQ9VAEGtjLw/Dhw+Ht7V1kOvOnT58wfPhwDB06VOa5//77LyZNmoRx48aVeRn3pUuXcoPlli5dKvfARGkyMjKwYMECmdMZ+Szx3qFDBzApM/XlyS+0aNEi2NjYSJ1qbmhoCBsbGyxatAirV68usT6XLl3C27dv0aVLF27bpk2bMGvWLGRkZKB79+5YsWIFtLS0SixLQ0MD//33X4nHlURHRwfv37+Xmevk48ePMDAw4JXnRMiygPzBmJMnT0afPn0wYsSIUqfSuHz5MgYOHIinT58W+Tzw+QwU5I1JSEiAjY2NINM9V65cibi4OFhbW8POzg66uroS+2/cuFHs+cbGxtxSBkZGRlK/c3w+48nJydzA9Nq1a0NLSws1a9bk9teuXRupqanyvDWMHDkSdevWxfXr17kB6O/evcOwYcMwevRoXLx4Ua7yqlSpAi8vL7x79w5Pnz7F/fv35TofyJ+y+9dff2HmzJm4c+dOqVJYCDGFvnv37pgyZQpOnToFc3NziX2vXr3CtGnTeKcKAPKXrjA0NCyy3cDAoMQlCL5mY2ODU6dOSfz9AeDkyZO88l4VFhYWhhkzZmDChAlo1aoVgPyUCmPHjsWbN28QEBDAq5ypU6fi7du3+OGHH7h8Xtra2pg2bRqCg4N5laGvrw8dHR256i8ECmJ4KLhIfe358+dSP9iFnT9/HuHh4WjcuDGcnZ0xZMgQ9O/fv1T1KJwwa9iwYaUq42sjR47EmTNnMGTIEFhZWZUqMLpy5UqRC4W8zpw5U+xsn759+2LgwIG8ypozZw7atWvHBTF37tzBiBEjMGzYMDg7O2PRokWwtrbG7NmzeZU3fvx4LFy4EH///bfca7kUaNGiBVavXi0zCFu1ahWXgFOZZQHAixcvcOjQIWzYsAHt2rWDo6Mjhg8fDj8/P1haWvIuZ+zYsWjSpAmOHDlS6s8S8P+5qDIzM6UmSZSVL0aabt26lSnYj4yM5GbrlOXHNTc3VyKRpbq6ukRiTbFYLPVGoDgxMTGIjo6WmEFnbGyMuXPnomnTprzLyczMxL59+/C///0Pp06dgo2NDQYMGIDdu3fLVZ/CbGxs5P5BFtKsWbPwzz//oEaNGhg8eDDq1KkDxhju37+PrVu3wtLSEjNnzuRdXs2aNXH06FFMmDBBYvu///4rMeOMj59++gmTJk1CTEwMd8Nw4cIFbNiwAX/88YdcZa1YsQKrV6+WuJHu2rUrXF1dMXv2bN5BjEgkwsKFCzFjxgzcv38fOjo6qFWrFq8bvQLLly8vcqOvFApp36kkGjRowBo2bMjEYjFzc3PjmmwbNmzI6tWrx/T19UtcibRAeno6Cw8PZ61ateKaWJctWybX4LySmrP5Nm0XZmhoKNfy1F8TakyMtrZ2seNLEhMTea9bY2lpKbH8+i+//MJatWrFPd+5cydzdnbmXbfu3bszfX19ZmVlxTw9PUvVRCrkMuFCLzleWGpqKlu8eDFzc3NjGhoazNfXl+3fv5/X7JkqVaqUebVPxhh79eoV8/HxkTk2RtmGDBki8T2NiYmRezbQ1zPlqlSpwtatW8c937hxo9zvrV69euzUqVNFtp86dYrVrVuXVxn9+vXjxsKMHz9e7lWjv9azZ0+2YMGCItsXLlzIevfuLfO8Hj16cNctobok0tLS2NixY5mxsTE3sN/Y2JiNGTNG6vpRxQkPD2c6Ojps5syZXBfnjBkzuL+jvPbu3ctatWrFTExMmImJCWvVqlWpZklpaWlJ/c49evSoVF1TBT58+MD27dvHuxtd1oxCZaAgphhCT2cu8ODBAzZ16lRmaWnJtLW1ma+vL6/zFLF4nr29fZnGewgVxFStWlXqBbnAyZMnWdWqVXmVpaWlJZG/qVWrVhIDQhMSEpienh7vug0bNqzYB19CLBOuiLK+dvnyZTZ69GimpaXF7O3tmaGhIbO3t+cWfJTF3d2d/fvvv2V6bcYYGzhwIGvVqhW7du0a09XVZcePH2ebN29mTk5O7PDhw3KVVdYFDxkreoHW19eXe3xWcUkN5UluWPhG5ciRI8zV1ZXt2rWLS5a6a9cu5ubmxo2/KcnAgQMFmZVUwMzMjN2+fbvI9tu3bzMLCwuZ5w0bNowLFIX6vhXIy8tjL1++ZC9fvixTctM///yTVatWjft7OTg4yJXipaAujx49YrGxsXINwJbF1dWVzZ07t8j20NBQ3oEsY4z16dOHrVixgjGWPwW9Vq1aTENDg6mrq/O6npTnBA8KYngQajrz13Jycti+fft4BzGFBzqW9OBr8+bNrHfv3qVeK6Jdu3bs3bt3pTq3sD59+hS7GmfXrl2LvZMrzNbWlhtImpWVxXR0dNjJkye5/bdv31bq4OfCMjIy2L59+9jChQvZwoUL2d69e0v9fy9kWampqWzRokXMxcWFaWtrs/79+7MTJ04wxvJbEX/++Wdma2tbbBl79+5lLi4uLCIigkVHR8s9uLuApaUlt1Kyvr4+t4LzgQMHJFrU+JB1cU1NTeU9O+XrMr5eoViZvr6RKRwAff28PMjKjn3//v0SV20+deqUID/sX8vOzmYnTpxga9as4QKlFy9eSE3qyserV69Kde6TJ09Y3bp1ub+Vra0t74SdsuzevZupqakxLy8vboZaQS4kPmtEFahatSo3i+9///sfq1mzJsvIyGB//vkna9CgQYnnR0VFFfnbKeI3UxoKYuSQlZXFnj17xp4+fSrxqGgKuskKHvr6+kxPT4/VrVtXYnvDhg2VVqcbN24wLS0t1qtXL3blyhX2/v179v79e3b58mXWs2dPpqWlxTsh5dixY1mLFi3Y2bNnWWBgIDM1NZVoMduyZQtr0qSJ3HV89eoVO3fuHDt37lypphN++fKFtW/fXuoS/6VR1iSgBa0UXbp0YRoaGszV1ZUtXbpU6kyely9flrhQoazWBXl/VPX19bk1U2xtbbmusSdPnvDuUhRqwcOC91WWIKZhw4YsLS2NMcZYSEhImRaXU8SNzMmTJ5mPjw9zdHRkjo6OzMfHhwtg5dW0aVOpySdnzZrFGjVqVOy5X7d4NWvWjD1//rxU9SiQmJjI6tSpw6pUqcLU1NS4v9ukSZPYmDFj5CqrrMFQr169WJ06ddjWrVvZ3r17WcuWLQW5xkZHR7NBgwaxRo0asUaNGrFBgwbJnR5DW1uba70eMmQIl7bi6dOnciUmzc3NZXPmzGHW1tYS/9/Tp09nf//9t1x14osG9vLw+PFj+Pv7Fxntz3jOvBHK7du3UbduXYjFYty+fbvYY4sb/CjPqHy+jI2NpQ6gFIlE0NbWRs2aNTFs2DAMHz5c6vkNGzbE7t274e/vj3379knsMzU1xc6dO9GoUSNedQkNDUXPnj3x/fffQ09PDxs3bpSYqbN+/Xp4enryfm8Fy91v2rSJm72lpqaGoUOHYsWKFbyTrWloaJT4d5NHWWbNAfkJMnNzc2FhYYEzZ84UOxjY3NwcCQkJxZZX0n6+nJyc8PDhQ9jb26N+/frcMv9r1qyBlZUVrzIKPuMikUhiQDyQ/3ewt7fHkiVLeNfp3r173OwhxhgePHiA9PR0iWNkfefu37+PjIwMGBsbIyQkBGPHjuX9mflawQyuksTGxvI67s8//8TkyZPRu3dvTJ48GUD+LLPOnTtj6dKlGD9+vFz1mzFjBnr27In4+Hguyd+pU6ewbds27Nq1q9hz2VcDm+/evYusrCy5Xv9rkydPRpMmTXDr1i2J1Bc9evTAqFGjeJfz9OlTeHt7IykpCVlZWejYsSP09fWxcOFCZGVl8Uofcf78eezevRutW7cGADRv3hzVq1dHRkZGkVlz8mjcuHGpUqAUZmNjg0uXLsHExARHjx7F9u3bAeTPdtPW1uZdzm+//YaNGzfi999/l/j/rVu3LpYtW4YRI0aUqZ5SKSQ0qmRatmzJ2rZty/755x928+ZNFhMTI/FQlsJ3hLJWpJXnrjc7O5uFhIQUuygUX2FhYczU1JQNHjyYLV++nC1fvpwNHjyYmZmZsblz57KRI0cyLS2tEgfBZWZmsr1797Lff/+dLVy4kO3bt0+uO9dbt25xg1Dfv38vta//7du3co1lGj16NHN0dGT//POPxHiEGjVqcEv88/Xjjz/ySs7HR1mTgKrqQoWbN29mERERjLH8u8yCsT/a2tps+/btcpVV1gUPGSv5u1bSd6558+bMw8ODG183depUFhISIvVRFqVJZVKtWjVuLERhK1euZNbW1qWqx+HDh1nLli1ZlSpVmKmpKXN3d+fVMqSIbjsTExOue+vrNAjyJLjt1q0bGzx4MMvKypIo5/Tp06xmzZq8yhCJRBKJJxmTTNNQWjk5OWz37t0sNDSUhYaGsr1798o9xmnVqlVMXV2dGRkZsfr163PX0OXLl8uVIqBGjRpc133h/6f79+8zIyMjuerEl4gxOef1fYN0dXVx/fr1IkkSle3p06ewtbWFSCTC06dPiz22YJpqSfT19XHnzp1SJ7Qr0KtXL3Ts2BFjx46V2L527VocP34ce/bswYoVK7Bu3Tq5El7KS01NjVvXw9HREdeuXZMr+aA0ZmZm2L17N9q1ayex/fTp0+jbty9ev37Nu6yCFp1atWqhcePGRe7Avk7CKE1BEtBbt27B1dVVZhLQnTt3FluOWCzGxo0bS1wmoGvXrjL3HTx4EJ06dYKGhgYOHjxY6nKKk5mZiQcPHsDW1hZmZmalKqMsSvquFZD1nXv48CFmzZqF+Ph43LhxAy4uLlKn6otEohLXrZHm7NmzCA8Px549e2BtbY2ePXuiV69evKZZ6+npISYmpsiaJY8fP0bDhg2LtDYpkpqaGlJTU7nlGgwMDHDr1i04ODiUukxjY2NcuHABLi4u0NfXx61bt+Do6Ijz58+jV69eePnyJa9yTE1NcfHiRTg5OUmUk5iYCBcXF2RmZvJ6f48ePZJYjqJ69eo4f/68xPVX2lpZssTFxcHHxwfPnz+Hk5MTgPzPm42NDY4cOYIaNWrwLis6OhrPnj1Dx44doaenBwA4cuQIjIyMuDVoSqKjo4MHDx7Azs5O4v/p3r17+O677xTyeaLuJB5cXFzkXtBIEQpfJJ8+fYqWLVsWuRjm5OTg4sWLvIOY9u3b48yZM2UOYo4dO4aFCxcW2d6hQwf89NNPAIDOnTsjKCioyDFCZmU1MjJCQkICLCwskJiYWGTxvtLIzMxE1apVi2y3sLDgdfEqLDY2lusWe/TokcQ+vuuZFHSVxMTEwMvLi7vgAPmLjdnb26NXr168yvq6q+VrJXWXdu/eHampqbCwsCi2m7I03a5fvnxBQkICatSowbsr8WuTJk1CzZo1i3xuChbBW7ZsWYll8P0uyeLk5MQ1z4vFYpw6darM62mkpqZiw4YNCA8Px8ePH9G3b19kZWVh//79cHFx4V1O165dsW/fPkydOlVi+4EDByQWi1QGxhg6dOjAXdMyMzPh6+tbZNFGeQI9T09PLFu2DOvWrQOQ/zlMT0/HrFmz0LlzZ97l5OXlSf38Pn/+vEi2dVkYY6hdu3aRbQ0bNuT+Le/3ZNKkSXB0dOS6goD8hfkGDx6MSZMm4ciRI7zLatKkCZo0aSKxzcfHh/f5QP5v5blz54p8Z3bv3s29T6FRSwwPkZGRmD59OubNmwc3NzeJRasA+SJnoRRucSjs7du3sLCw4P1FWLNmDUJCQjBo0CCpLQN8755tbW0REBBQZHGlpUuXYunSpUhKSsLt27fh6elZZGVSvndaIpGoxBWER48ejU2bNsHKygpJSUmoXr26xKJihfFZjRjID8RMTU2xadMmrn/48+fP8PPzQ1paGk6ePMmrHKFt3LgR/fr1k6vPujCxWMwFIKokMzMTEydOxMaNGwHkB3uOjo6YOHEiqlWrJjUQlqVatWo4ePAgGjduLLH9xo0b6Nq1K54/f17s+fKMYZJnEb6y8PX1xdmzZ+Hj44NBgwbB29sbampq0NDQwK1bt0oMYgrfNHz8+BGLFy9Gq1atuDFRly9fxoULF/DTTz9h+vTpctUtNzcXS5cuxc6dO6UuVJiWlibz3JCQEF6vMWvWLN71ef78Oby8vMAYw+PHj9GkSRM8fvwYZmZmOHv2LO/Pfr9+/WBoaIh169ZBX18ft2/fhrm5Obp16wZbW1tERESUWMaZM2d4vRbfcU9Afi/B5cuX4ebmJrH91q1baNWqVbEtH4GBgQgNDYWuri4CAwOLfR0+LcRAfvDr5+eH4OBgzJkzByEhIXj48CE2bdqEw4cPo2PHjrzKkQcFMTwULH/+9Z1yaSJnIev08uXLIivlPnr0CE2aNMHHjx95lyOLPO/tr7/+wrhx49C5c2d89913AIBr167hn3/+wZo1azBixAgsWbIEV69exY4dO3iVWVpHjx5FXFwcJk2ahDlz5si8UyoYyFiS2NhYeHl5ISsri1s6/tatW9DW1saxY8fg6uoqdx3j4uIQHx+Ptm3bQkdHR+aq0Hylp6cXaXUqKbiWFQjLy9bWFjdv3uS67VauXImhQ4eWOrifPHkyLly4gGXLlsHb2xu3b9+Go6MjDhw4gNmzZ+PmzZu8y9LW1kZsbGyR7pK4uDjUrVu3xJQSYrGYS59RnOK+KyV1sxXG56ZBXV1daioTvkGMkDcNX5s5cyb+/vtvLgD69ddfkZiYiP3792PmzJkltqQqQk5ODrZv347bt28jPT0djRo1wqBBg+RaIl+oYEhoJiYmOHz4cJFUIRcuXICvr2+xQaO7uzv27dsHIyMjuLu7F/s68qxWfe7cOcyZMwe3bt3i/r9nzpwp12QKeVAQw0NJEbQ8kXNZ9ezZE0B+xOvt7S2xLHRubi5u374NJycnHD16VGl1KnDhwgWsXLkSDx8+BJDfjD5x4sRS5+Ipq+HDh2P58uW8m3uLk5mZif/973948OABAMDZ2VnuCyGQ31LWt29fnD59GiKRCI8fP4ajoyP8/f1hbGws14yZhIQETJgwAVFRURI/xnyDa6FaYr4ux8DAADExMXIvx17Azs4OO3bsQPPmzSX61ePi4tCoUSPeATqQPyti7NixRZaLL1iu/d69e8Wez3c8TEG9peGbA4rvTcPly5cRHh6OHTt2SKQysbKy4hXEKFKNGjWwfPly+Pj4QF9fHzExMdy2y5cvY+vWrbzKycnJQVRUFOLj4zFw4EDo6+sjOTkZBgYGEt2nyiREMFQgLy8PcXFxUvPVtW3blnc5Q4cOxY0bNxAeHs7dPF65cgWjRo1C48aNsWHDBrnrVtHQmBgelBmklKRgECZjrEjCLU1NTTRv3lyuqYNCatWqFe8BYIWV1JRZGN9mTQASTbwF3QbVq1fnX7FCqlSpIsj/a0BAADQ0NJCUlARnZ2due79+/RAYGChXEDN48GAwxrB+/XpUrVpV7pYcPz8/hSRsK+t90evXr6UGVhkZGXK/x8DAQEyYMAGvX7+WmPK7ZMkSpYyHASDIuKzCmjdvjubNm2PZsmXYsWMH1q9fj8DAQOTl5eHEiROwsbEpc+B+//59hIeHY/HixXKdl5qaynVt6Onp4cOHDwCALl26YMaMGbzKEGI6cwFZrWCFl37g2zKlrq6OwYMH835tWcqaKLWw5cuXY9iwYRLjI3NyctC1a1deeZj8/f1LPEYkEiE8PJx3nZSNghie3r9/j/DwcC6zq6urK/z9/Uuc2SG0gh9me3t7TJ06tdTrTRR25swZLF68mHtvLi4umDp1Ktq0aVPsefLcERfXtcC3e0DeH7C8vDz89ttvWLJkCdc3rK+vj59++gm//vprsXfIipp1c/z4cRw7dqxIMFWrVi257vqB/C6t69evc7MS5FU4yBPqzlAIBUkkJ06cCOD//+5///23XIktgfyLdFZWFubOnYvQ0FAA+d+dr5PmyePevXtSx3uUdvZVaenq6sLf3x/+/v54+PAhwsPDsWDBAgQFBaFjx45ydWMB+UHi9u3bER4ejsuXL8PFxUXuIKZ69epISUmBra0tatSogePHj6NRo0a4du0a72SCQq3tAuQPPJfWHViwTSQSoXXr1ti/f79EIs3/a+/c42JM////mso0nSnSWQ6pkLJ8y7nDonJe67AOm8opkVjrzC7JokXbYp0tsbQWG7aVs5BYJLWSSJTTbopVUWl6//7oN/en6TDdM3N30v18PO7Ho7mn+5r3XDP33O/7ut7X61We8+fP4+jRo3j8+DEEAgHatGmDzz//XKFzgwuj1JKSEnz//fc4fvw4ioqKMHz4cEycOBECgQC2trYVpk+rYs+ePWjVqhW6dOmi8M1HVRphlSFrekthamTh9kfGjRs3SF9fn0xNTRkTMjMzMzIwMGCtIssVTZs2pWbNmlXYLC0tacCAAXT69Gm52tu3bx+pqanR6NGjKSwsjMLCwmj06NHUpEkT+uWXX2Qey8bLqa5M+4iIFi5cSC1atKCffvqJkb7fvHkztWjRghYvXizz2PKaPMr43ZRFW1ubUewtq6Mg+Y7Jg4uLi8LKqmWJi4uj1q1bV6qFwub9CQQCWrVqFfP9EYlEtGzZMuaxZGPL5cuXSVtbm/z8/EgkElFgYCD179+ftLS06ObNmwq/T0Xl4iWkpaVR586dK+jGyPsdz8vLo6ioKNqyZYvCfVQV8lqZEBFduXKFfHx8SEtLi1RUVGju3Ll07949hV5/wYIFjJdPREQEqampUbt27UgoFLLWR+JK24WoVI3YycmJzp49S2/fvqW3b9/S2bNnqUePHhQVFUVXrlyhjh07kq+vb5VtTJs2jQQCAenr61P37t3JycmJ9PX1SUVFhWbOnClXPETcGKUGBQWRiooKDRgwgIYNG0YikYh8fHzkbsff35+aNWtGDg4OFBYWVqlad3Xs2bOH9VYT8EkMC3r37k3e3t5S3hAfPnygiRMnUp8+fWo1lqq+HD/88AN9+eWXJBQK6fjx46zbs7GxoQ0bNlTYv379erKxsZF5bFmJ8z179pCRkREtXLiQkXdfuHAhGRsb19iXtzqMjY3p2LFjFfZHRkYqLOSlLJ6enrR06VIiKv2BfvToEYnFYho1ahR9/vnncrX18OFD6tevH+3Zs0cpryJ7e3saNWoUJScn0+vXrxnLB8lWHa1atSJLS0uZG1uzxbLvbfLkyfR///d/ZGtrS+PHj6/UWJANXHnnDB48mIYNG0ZZWVmkra1NycnJdPnyZXJ0dKRLly6xaiM+Pp6MjIxIV1eXVFVVqUWLFiQQCEhLS0vuPlKGf/75h9auXUvW1tZkZGREc+bMoRs3bpCamhrdvXuXs9e5evUqrV+/Xq7fpKZNmzIxlE1iLl++LNNEsjI6duxIsbGxFfZfuXKFOnToQEREZ86cIXNz80qPP3r0KAmFQvr555+lzCPFYjHt2rWLhEJhpb8xsuDCKLVdu3a0detW5vGZM2dIKBSycpsvT0FBAR04cID69etHmpqaNGrUKIqOjlbKLLM24ZMYFohEokrvTO7evSv3nUFNs379eurRowfr/xcKhZXeFTx48EAuK3c3Nzc6cOBAhf2//PILOTs7s26HqHRUYt68eTRmzBhm5EuyyYO6ujpjHliWlJSUas3oJHDtd5SUlESGhobk4eFBQqGQRo4cSba2ttSyZUt6+PChXG1JRlCU9Sri4s6QC8o6NMva5IFL7xwDAwMmOdTV1WVGC86dO8fKJI+IyNnZmaZMmUJisZi5QGdkZFDfvn3pyJEjcsXz/v17CgkJIU9PT+ratauU51l1PkUikYgmTJhA0dHRUhc+rpMYRRg9ejRNmTKFiP6X6Ofm5pKbm5vcLtYikYiSkpIq7E9MTGR+Ax4/flzl7/iQIUNo4cKFVbY/f/58Gjp0qFwxcWGUKhQKGa8jCerq6kqrrz9+/JiWL19Obdq0IQsLC6VGLt+/f6/UucsWviaGBbq6usjIyKig2JuZmcnJyhcuGTx4MIKDg1n/v7m5Oc6dO1dhDvXs2bMwNzdn3U5cXFylBXfdunXD5MmTWbcTEREBLy8vuLu74/Tp0xgwYABSU1Pxzz//4LPPPmPdDgDY29tj06ZNFcT0Nm3axCyVrg6u/Y46deqE1NRUbNq0CTo6OsjLy8OIESMwY8YM1r5AEnx9fdGlSxccPHhQocJeCU5OTnj48CHrefSqCA8Px5gxYyrUPhQVFTGfqyyaNm0q8z2QApIGXNZXiMVi5nxv3rw5nj9/Dmtra7Rq1YpZkVcdCQkJ2LZtG1RUVKCqqorCwkK0adMGISEhmDhxIrP6kA2TJk3C6dOnMXLkSDg6Osr1+bdq1QpXrlyBhYUFWrVqpZQaOddLyNevXw93d3d06NABBQUFGDduHLOc+eDBg3LF1rVrV8ybNw/h4eGMHEVWVhbmz5/PKBo/ePCgyt+6+Ph4mVo5EnVkeZD8f9mi2rI1Omy+38XFxRX0oZo0aYIPHz7IFUt5ykoKKCIdkp+fjwULFuDQoUPIzs6u8HyNyJHUSGr0kREQEEBmZmYUERFBGRkZlJGRQQcPHiQzMzMKDAys6/CkSExMpJYtW7L+/59++omEQiH5+flReHg4hYeH07Rp00hdXV1quLI62rdvT/Pmzauwf968eazdgomI7OzsaNOmTUT0v6HkkpISmjJlCn3zzTes2yEqne7S0tIiW1tb8vX1JV9fX7K1tSVtbW3Ww/9E3PodPXnypMphWnkd0bkaQeHizpCooguxhFevXrEaGSo7PXnhwgXS0NCgX375RWF3ZiJu6yt69+5Nv//+OxERjR07ljw8POjKlSvk5eVFHTt2ZNVG8+bNmVE9Kysrio6OJqJSbxlNTU254tHV1WUcvhVBUgujra1Nn3zyCW3YsIHU1NQoOTlZrnZk1YwpWj/24cMH2rdvH82bN4+mT59OO3bsoHfv3sn7FiklJYWsra1JKBRS27ZtqW3btiQUCsnGxoYZpf39998pPDy80uPV1dXp2bNnVbb/9OlT1qO6Eh4/fixzY4NAIKCBAwdKjVKrqanRgAED5B65LjudJBKJaOTIkRQVFaXQ1JS/vz/Z2trS4cOHSUNDg3bv3k0rV64kMzMz2r9/v9ztsYFPYlhQWFhIs2bNIqFQyBTxqaur0+zZs6mgoKCuw5MiMDCQ3N3d5Trm6NGj1KtXL9LX1yd9fX3q1asXRUZGytVGVFQUiUQi6tSpE02aNIkmTZpEdnZ2JBKJKCoqinU7mpqalJ6eTkSlFyBJHURycjIZGRnJFRNRae3D4sWLacSIETRixAhasmSJzB+lypg5cybp6upS165daerUqTRnzhypTR6UvdCXZfDgwXT48GG5jqkMRY0NK2tHGUPK8nBhAMhlfUV0dDQz5fPgwQOytrYmgUBAzZs3p3PnzrFqo3///kzB/OTJk8nR0ZH2799P7u7u5OjoKFc8tra2ciWZVZGbm0vbt2+nHj16kEAgIBcXF9q+fXuln2VDRCwW08mTJ5ni6fJTaLKo6jst4eXLl3WycMHb25vVVh3Tp0+nZs2aUefOnemHH35Q2izV3NycLly4QEREOjo6zE1WeHg4eXp6KtV2VfBid9UgFosRGxsLOzs7qKurIy0tDUCpqBMXy5vlpSpNlf/++w/x8fFITU3FpUuXKsis1waZmZnYsmWLlCCcn5+fXNNSZmZmOHnyJOzs7NC5c2csWrQIY8eORVxcHDw8PBjdidpElpqlQCDA+fPnWbdVldLykydP0KFDB+Tn57Nua/v27QgODoavr2+ldhhsl/wqaybKlSFlecoK3SkKF3LxssjJyZFrienNmzeRm5sLV1dX/Pvvv/Dy8sLVq1dhZWWF3bt3s57mBICTJ0/ixx9/xNatWznRswFK9WF27tyJ/fv3IycnR+npCUXgUttFWVRUVDB16tQqf+vfvXuHHTt2yDVNsnfvXjRv3pzxJZo/fz62b9+ODh064ODBg5x9lmxQUVGBhYUFcw5XxdGjR1m1p62tjeTkZFhYWMDMzAxHjx6Fo6Mj0tPTYWdnVyMGkHwSwwKRSIR79+7V2okji6ouqLq6urC2tsb06dMVivPmzZtSOjF1kQQBwLhx49CtWzfG12Pjxo0YNmwYzpw5g08++YT1ySShvuj7SJLPsLAwTJkyRepHUSwW4/r161BVVUVsbCzrNrmyjFAWiefNihUrMHfu3CoNKcsb+VUHF0lMfZWL54KsrCyMHj0aly5dgqamZoUkVhlNjuLiYhw/flyuGh0J586dQ2hoKHPO2draYvbs2ejXrx+r46uyepBX20VCfn4+YmJiKtX2qc4GwcXFhVWCKo8sv7W1NbZs2QI3NzfExcXh008/xQ8//IA//vgDampqcv/GKYO3tzer98c22e/cuTM2btwIZ2dn9OvXDw4ODli3bh1+/PFHhISEVOtVpgh8EsOCbt26Ye3atfj000/rOhTOefr0KcaOHYvY2Fg0bdoUQOmFv2fPnoiIiJCpcFsT5ng5OTkoKCiAiYkJSkpKEBISwtypLl26lNWPloSbN2/C3d0dGhoaUn5O79+/Z0S45EEZvyNJ8hkTE4MePXpIXdAlF/qvv/5aygunJqkJMT9lDSnLIxk5UfbmgSu5eFdXV5mftzwjclzQr18/ZGRkYNKkSZUWdlfnUA6UFq42adKEUdk9duwYfv75Z3To0AHLly+XO/H86aefEBgYiJEjR0oZSh4+fBihoaGYMWNGtW2cO3cOS5YswapVq5jz9q+//sKyZcuwdOlS6OnpYdq0aXBycqpWSfb27dsYOHAg3r17h/z8fOjr6+PVq1fQ1NSEoaGh3N5QXKCpqYmUlBRYWFhgwYIFePHiBcLDw3H37l24uLggKyur1mPiitDQUKiqqmLWrFk4e/YshgwZAiLChw8fsGHDBtZ+dXJRI5NUHxknT54kBwcHOnHiBD1//rxWlo3VFu7u7uTk5MQUPxKVFsP16NGj2tqa8qJfyhb0ffjwgfbu3UsvX75U6j1J4Erf59WrV+Tm5sa8F0ldhY+PD3311VdyxeTt7V2j35nXr1+z+r+aEvOTkJubK/d5Un45fWWFivIus+eS2bNnS20zZsygXr16kZ6eHs2aNUvmsQ4ODlJLoKva5EFDQ4MSEhKUeUvUrVs3pq4qLS2NRCIRjR07ltq1a6fQogVTU1PauHFjhf2bNm1irc2krLZLWbhc0s4VLVq0oPj4eCIq/V5IioofPnxIWlpadRJTTfH48WM6cuQIJ7VbVcEvsWbBwIEDAZTekZa926E6dLHmipiYGFy9elVKut7a2hobN26s1nYgPT2d01jU1NTg5+fHDEMry82bN7Fjxw6pGg01NTXMnz8f3bp1Y90Ol35HISEhVVowJCUlMXfEbFi7di0sLS0xZswYAMCoUaNw5MgRGBsb488//5RZX1HWWoArbx9lDSnLT/Ep6lPD9ZJfCaGhoZXuX758ebVz/cOHD2f+JiKsXr0afn5+0NfXZ/365bGxscH79+8VPh4odb13cHAAAPz222/o27cvDhw4gNjYWHzxxRes/KXK8ubNG3h4eFTYP2DAACxYsIBVG2lpaZWeI7q6uszIiZWVFV69elVtW8osaa8pT7f+/ftj8uTJ6NKlC1JTU5nry927d2Fpacm6nfpEXFwcsrOzMXjwYGZfeHg4vv32W+Tn52P48OHYuHEja+sJeeCTGBbIM9/Z0DA3N6+0eE8sFsPExETmsWUL0LKzsxkdjszMTOzYsQPv37/H0KFDq02GyuLo6IiEhAROitu40vfh0u/Izs4Ou3btYor6JKxbtw7Lli2T66K0detW/PLLLwCAM2fO4OzZs4iOjsahQ4cwb948nD59WubxFhYWuH37NvO5bdq0CV5eXjJ9rmShrCGlskW2EsomDLLg6gZkwoQJcHR0lOkz9O2330o9Xr9+PQIDA5Wq91mzZg3mzp2LVatWVVrYzeZzJCImiT179ixzETI3N2eVJJRn6NCh+P333zFv3jyp/ceOHZO6wMlCWW2XsjRp0oSpHTM0NGRuRPT09JCZmSnz2PKebvHx8SguLmZu+FJTU6Gqqip3/eDmzZuxdOlSZGZm4siRI8z5d+vWLYwdO1autuoLQUFBcHFxYT7jpKQkTJo0Cd7e3ujQoQNCQkJgYmKC5cuXc//iNTbG8xHw5ZdfMjLlRKVLRYuKiuowIu6JjIwkR0dHunHjBrPvxo0b1L17d0YTQxaJiYnUqlUrUlFRIWtra7p9+za1bNmStLW1GWl1Nu1I+PXXX6lNmza0ceNGunr1qsKaJURV6/uYmppWO/xfFi79jtauXUvq6urk5+dH7969o6dPn5Kbmxu1aNGCjh49KldbIpGIUe2cNWsWTZ06lYiI7t+/T02bNq32+LJTSkSlSyKVWdKspaUlNS3ZWAgPDydjY2O5juFi+Xh57ybJJs80oKurK3l5eVF4eDg1adKEWRJ78eJFatWqldwxrVy5kvT09GjgwIG0cuVKWrlyJQ0aNIiaNm1KK1euZOUTpay2S1m4WtK+fv16GjJkCOXk5DD7cnJyaNiwYbRu3TrW7XysGBkZSV1DFi9eTL169WIeHzp0iGxtbWvktfkkRgblNT2U/ZGvL5Q3kZTo3wiFQqm/2Wh7eHh40ODBg+nKlSs0bdo0MjU1JV9fXxKLxSQWi8nf35+cnJxYx8aVZglRRX0fgUBA6urqNGfOHLmEs7j0OyIq9c/p2LEjtWvXjvT19cnT05NevHghdzvGxsZM7UD79u3p0KFDRFR6EdDR0an2+PJJjLIXVq4MKbnA09NTyvdp9erVUvVCr169kvtHtXxtzvDhw8nJyYlUVVVp+fLlcrXFRRJTXgRQEVHAO3fuUKdOnUhXV1fqPcycOZPGjh0rd0zVeWix9dJSRtulLDdu3KDz588TUalflLu7O+no6NAnn3wiVz2RiYkJ/f333xX2JyUlsU5gs7KyKojZ/f333+Tt7U2jRo2q1nC3PqOuri5lg9CrVy8KDg5mHqenp5O2tnaNvDY/nSQDKrdwq/zjhoq889yyuHHjBs6fP4/OnTvD3t4e27dvh7+/PzOEGxAQgO7du7Nuj8s6G6FQiLCwMKxevVpK32fLli1o3bo1Xr58yaqdkJAQfPrpp7h58yaKioowf/583L17Fzk5OXItiZbQrl07dOrUCUeOHAFQWltjZGQkdzsjRozAuHHjYGVlhezsbHh6egIoHQZX1kJAEXbu3Ak/Pz88e/YMnTp1qjC9wXaFGhdER0ejsLCQefzdd99h9OjRzAq84uJi1lYBEsrX7KioqMDa2hpBQUEYMGCA0jHLi7Ozs9JtdO7cGUlJSRX2f//991BVVZW7Pa7OXxUVFXh4eFRaX8MWIoKhoSE6deoEoHQ6KTo6WqG23r59W+mqoaysLOTm5rJqIyAgACYmJkwN3b///os+ffrAxMQEbdu2hbe3N8RiMb788kuFYqxLWrZsifT0dJibm6OoqAjx8fGM9AIA5ObmVvg94Ao+iWmEsFl6yZacnBzmAqytrQ0tLS2pZdDNmjVjfZID1QursaGwsBDLly/HmTNnoK6ujnnz5mH48OH4+eef4eHhAVVVVcyZM4d1e1z6HcXGxmLChAnQ19dHYmIiYmNjERAQgD///BNbt26Vawl5aGgoLC0tkZmZiZCQEEaf5cWLF/D392fVxs6dO5njiouLsWfPHjRv3lzqf6rT0pCQlZWFtLQ0+Pj4MPvk9YSpKbi4AVGmZqe8f5eyfS2BSx2kvLy8CkXeil54ioqKkJ6ejrZt20oV1rNFGW0XCUSEdu3a4e7du0pLF3z22Wfw8fHB+vXrmWXf169fx7x581hr6Vy7dg179uxhHoeHh0NfXx8JCQlQU1PDunXrsHnz5gaZxAwcOBALFy7E2rVrERkZCU1NTalayMTERLRt27ZmXrxGxnc+EgQCAV24cIGpx9DS0qKoqCil6jTqI2KxmO7fv0+XL1+mmJgYqa06ystyS6ZbJCgiyx0eHk49e/YkY2NjZvg1NDSUtRXC/PnzSU9Pjz7//HMyNjYmNTU1mjJlCtnZ2dHBgwepuLhYrni49DsSCoW0YMECqdqqhw8fUvfu3cnU1FSutl69esX8nZGRQcuWLaOvv/6a1edGRNSqVSulh/3LYmtrSyNGjKBr165Renq6Qp4wXFHdVJki38t3797RsWPH6Pvvv2emONh+l7iYYimPpCbL1NSUmeIyMzMjAwMDunXrFqs2Hj16RAMHDiRNTU2F62rKkp+fT76+vqSqqirlGj5z5kxavXo1qzbi4+PJyMiIqalr0aIFCQQC0tLSkruPOnToQHFxcXK/j/Lk5+fT9OnTSV1dnekjoVBI06dPp7y8PFZtiEQiqfPA09NTym/u/v37ctfY1ReysrKoT58+JBAISEdHp0J9n5ubGy1evLhGXptPYmQgSwdFmRO9PhEXF0etW7eu9H2yeW/ljcjKa3sMHDhQrj766aefqHnz5hQcHEwaGhrMj+DPP/9MLi4urNpo3bo1HTt2jIhK56wFAgH5+PhUmYhUB5d+R1XVKojFYgoKCmLVBtfF1FzBlSElF6ioqHCaXB87doy5mJbdzMzMpJLGsq9R03Chg9SzZ0/q0aMHRURE0IULF5Qy2yQqLTDv2rUrXb58mbS0tJjzNzIykhwcHFi1waW2y/Hjx6l3796UlJQk93upjLy8PObmlW3yIsHQ0FCqDsfAwEDK+yw1NbXB68S8efOm0sQ+OzubCgsLa+Q1+SRGBtW5jdbFHSbX2Nvb06hRoyg5OZlev35Nb968kdqqgysjMgm2trbMBbjs3XNSUhIZGBiwaqNJkyb09OlT5rFIJGKMJBWhKhO4x48fs3Ye5rLQlOti6r1791ZqZFpYWEh79+5l3Q5XhpRcwGVyHRsbS02aNKHPP/+crl69Sq9fv6bXr19TbGwsjRgxgkQiEd27d4/mz59PK1askNkWV31NRMzrlufu3busHbq5XlFmYWHBjHyUPX8fPHjAqticiEhPT4+JSU9Pj3HVvnbtGllbW8sVT9OmTZnCfpFIJLWgQRFTUiKizMxMyszMlPu4oUOHMufpb7/9RkKhUGq10x9//EE2NjYKxdSY4WtiZCCpz8jIyIC5uXmluhcZGRm1HRanPHjwAIcPH1a4EJQrbQ8J6enp6NKlS4X96urqrM0RxWKxlFy6mpqalJ8PWyRiVwKBAMuWLavU70giFFYdp06d4qzQlOtiah8fH3h4eFTwEcrNzYWPjw+8vLxYtTNkyBDMmTOHEe1T1JCSC8rXfVUmnMf2fQUHB8PHxwfbtm2T2t+zZ0/07NkT06ZNQ58+fUBEOHfunMy2uOprgBsdpP/7v/9DZmamlNilMmRlZVXqR5Wfn89aN0gZbZfycLWIoaSkBMHBwVi/fj0jbKijo4O5c+diyZIlMn3MJKxcuRKffvop9u/fj+LiYixevFiqBi4iIoKTYu3GBp/EsKB169Z48eJFhZMzOzsbrVu3btCKvU5OTnj48GGdrGapjNatW1cqdhcdHS2llisLIoK3tzejDllQUAA/Pz9oaWlJ/V91RmsSsSsiQlJSUgW/I3t7e3z99desY5L1WB64LqamKjygnj59KleBqJ+fH4BS4avy1HZhL5fJ9bVr17B27doqn58xYwZ27NiB+Pj4al2oueproHRV26RJk7Bu3Tr07NkTQGnh+Lx581iLpnG9oqxbt26IiopCQEAAADDvdefOnYyXUnV06dIFN27cgJWVFZydnfHNN9/g1atX2LdvH7PSiC1cLWJYsmQJdu3ahTVr1qBXr14AgCtXrmD58uUoKCjAqlWrqm2jc+fOuHfvHmJjY2FkZAQnJyep57/44gt06NCBk3gbE3wSw4Kqfnjy8vI4M7urTcoaNwYEBGDu3Ll4+fJlpXfPtbksFigd/ZgxYwYKCgpARPjrr79w8OBBrF69Gjt37mTVBpu7cDZIlJp9fHwQFhamsJJtTVD++yivOi5QerEQCAQQCAT49NNPpVaRiMVipKeny7XElSv7gvrG+/fvZX72enp6UFdXlzkqx3VfA6UqzwKBAF5eXiguLgZQOooxffp0rFmzhlUbXK8o++677+Dp6Ynk5GQUFxcjLCwMycnJuHr1KmJiYli3IUnCV61aBS8vL0yfPh1WVlbYvXu3XPGUpaCgoMJKJ7bn9N69e7Fz506p0cTOnTvD1NQU/v7+rJIYoLS/c3JymJGvlJQUhIWFobCwEOPHj1fa7LQxwicxMuByOqE+4eDgUMHq3tfXl/m7LpfFTp48GRoaGli6dCnevXuHcePGwcTEBGFhYfjiiy9YtcH1FBcXfkeSC1j5fYoia6Sp7LSVLCTy/AkJCXB3d5eacpM4a3/++ecKxwiULgGWTJk1VKysrHD+/HmpC31Zzp07V+0S3pro66p0kMr+TlWHr68vunTpgoMHDypkFVGe3r17486dO1i9ejXs7OwYt/i4uDhW5wlxqO0ClE5jLViwAIcOHUJ2dnaF59n+vuXk5FSYtgNK/atycnJYtREdHY1hw4ZBW1sb7969w++//w4vLy/Y29ujpKQE7u7uOH36NNzc3Fi1x1OKgJQZ1/7IcXV1BVBqktijR48K0wmWlpb4+uuvldYgqG3k8fvhQrdFUd69e4e8vLxK59hrEyMjI6X9jlRUVODp6ckkHidOnICbm5tU4hEdHc3qR7Wqi2l52CZze/fuxZgxY5QeVVTGkLI+ExoaiuDgYOzbt48x65MQFRWFiRMnYvHixawMA7nq6/I8ffoUACr4e1WHlpYW7ty5w8l08ocPHzBt2jQsW7ZM4RGFkpISiEQiTrRdgNKpvgsXLmDlypX48ssvsXnzZjx79gzbtm3DmjVrMH78eFbtODk5wcnJqYLeT0BAAG7cuIFr165V20bPnj3h5uaG4OBgREREwN/fH9OnT2dGcRYtWoRbt25V63nGU446KCZucHh7e9N///1X12E0ClauXFmry1TZwIXfEderuGqK3Nxc+u+//6Q2tlhaWjI2CKdPn6amTZvSqVOnaNKkSdS/f/+aCrnGEYvFNHLkSBIIBGRjY8NYDlhbW5OKigp99tlnCkniK9PXkrhWrFhBurq6jHaJnp4eBQUFsY6H6xVlurq6Sp+/XGm7EBGZm5vThQsXiKjUNkYiARAeHk6enp6s27l48SJpaWmRra0t+fr6kq+vL9na2pK2tjZdunSJVRu6urrM64vFYlJTU6P4+Hjm+aSkJGrZsiXrmHhK4Udi5ETRO576yvHjxyvdLxAIIBKJ0K5du1qdp7W3t8fff/8NJycnTJgwAaNHj66galoX3L59G19++SUKCwuRk5MDJycn7N69WyG7gPpGeno6Zs6ciYsXL6KgoIDZT3JOKWpoaCA1NRXm5uYIDAxEQUEBtm3bhtTUVDg5OeH169c19RZqhV9//RUHDx5EamoqgNJpprFjx7Ke5gS462ug9M59165dWLFiRYVi0ylTprCq09i+fTuCg4Ph6+vLyYqyiRMnwsHBQS5F7PKcOHECISEh2LJli9yFvOXR1tZGcnIyLCwsYGZmhqNHj8LR0RHp6emws7NjVhqx4fnz59i8eTNSUlIAALa2tvD394eJiQmr4/X09BAfH88o1+ro6ODOnTuMk/mTJ09gY2Mjl5M9D18TwwoultfVV4YPH16hPgaQrovp3bs3IiMj5ZLEV5Q7d+7g7t27+OWXX7Bu3TrMnj0b/fv3x/jx4zF8+HC55vu5hCu/o/rIhAkTQETYvXu3UnURzZo1Q2ZmJszNzREdHY3g4GAApRfohryCT8KYMWOYqTJF4aqvAW6KTbleUWZlZYWgoCDExsaia9euFVYEsrEM8PLywrt372Bvbw+hUAgNDQ2p59nWoABAmzZtkJ6eDgsLC9jY2ODQoUNwdHTEiRMn5K7VMjExYV3AWxmWlpZ48OABk8TExcXBwsKCeT4jI0NuGxMeviaGFVzc8dRXzp07hyVLlmDVqlWMJ8hff/2FZcuWYenSpdDT08O0adPg5OSEXbt21Xp8sbGxOHDgAH777TcUFBTg7du3dRKDxO9o//79iI2NxVdffQVPT0+5/Y7qI9ra2rh165bSWiEzZ87EH3/8ASsrK9y+fRuPHz+GtrY2IiIiEBISgvj4eI4irhu8vLzg6uoKZ2dn5u5ZXrjqawAQiURITExE+/btpfbfv38fDg4OdXJHL2vUViAQ4NGjR9W2sXfvXpnPy7NsOjQ0FKqqqpg1axbOnj2LIUOGgIjw4cMHbNiwAYGBgazbUtanauvWrTA3N69QWydh8eLF+Pfff1mvwuT5/9TNLFbDwtjYmJGxL0tkZCSZmJjUQUTc0bFjR6aOoSxXrlyhDh06EBHRmTNnyNzcvLZDIyKi27dv09y5c8nU1JREIlGdxMCl31F9xMXFhc6cOaN0O0VFRfT999/TrFmzpOb6N2zYQDt27FC6/bpm0qRJZGVlxdgNjB8/nnbs2EGpqams2+Cqr4mIHB0dKSAgoML+mTNnyqXYXBVlVa8/Fh4/fkxHjhyR2/OOC58qnpqBH4lhQX284+EKDQ0N3Lhxo8Lcc1JSEhwdHfH+/Xs8efIEtra2ePfuXa3ElJ6ejgMHDuDAgQO4f/8+nJ2dMW7cOIwcOVIhd15liYmJqVRJs6SkBKtWrcKyZctqPSYuSUtLg5+fHyZMmKCU4Fl2djYMDAwAlKrG7tixA+/fv8eQIUPQt29fzuOuK549e4ZLly4hJiYGMTExSE1NhbGxMVMvJwuu+hoo/V4OGjQIFhYWjJBcXFwcMjMz8eeff0q5CMvDy5cvsWrVKuzatavWzvmqUFTb5f379zh37hwGDx4MoHQ0vaz0gJqaGoKCglivEuvTpw/atWuHHTt2MBo/xcXFmDx5Mh49eoRLly6xfUs8XFPXWVRDQNYdj6OjYx1ExB29evUiDw8PKW+gf//9lzw8PBgTuTNnzlD79u1rJR4nJycSCATk4OBA33//fZ3eDXLpd1SfkZiAKmpwWl8NKWuK/Px8OnXqFC1cuJC6d+9OQqGQtbmhsn1dnmfPntHixYtpxIgRNGLECFqyZAk9e/as2uNycnLoiy++IAMDAzI2NqawsDASi8W0bNky0tDQICcnJ4qIiGAVw5w5cxgzxDlz5sjc2JCXl0czZsygFi1aSDlrSzY2bNmyhQYPHsw81tbWJicnJ3JxcSEXFxcyMjKiDRs2sGqLiBufKp6agU9iWMDF8rr6SkpKCllbW5NQKKS2bdtS27ZtSSgUko2NDd2/f5+IiH7//XcKDw+vlXgWL15MycnJlJWVRVlZWbXymlVR3r1aR0eHMbQjkt8Jub5ia2tLI0aMoGvXrlF6errcBqdcG1LWVxYtWkQ9evQgkUhEXbp0odmzZ1NkZKSUiV91KNvXEoqKisjNzU2uqayyTJ06lSwsLGju3LnUqVMnUlFRIU9PTxo0aJDcy5tdXFzo5s2bJBaLmSShss3V1ZVVe/7+/mRra0uHDx8mDQ0N2r17N61cuZLMzMxo//79rNro3bs3HT9+nHlc1oySiGjfvn3UvXt31u/R0NCQTp06VWF/dHQ0GRoasm6Hh3v4JIYlld3xPHnyhKZMmVLXoSmNWCymkydPUlhYGIWFhVF0dLRCuhfK8vr1a5o+fToZGBgwd10GBgY0Y8YMqRGQ2kIgEEglMeV/CD+WJEZTU5PRr1AEAwMDpsYgNzeXBAIB3bx5k3n+3r17pKenp2yYdY5AICBDQ0NavXo1k+DLi7J9XZbmzZsrnMSYm5vTuXPniIgoPT2dBAIBLVq0SOFYyif8o0ePppcvXyocm7LaLkZGRpSens48bt68udTj+/fvk66uLuuYAgICyMzMjCIiIigjI4MyMjLo4MGDZGZmRoGBgazb4eEePolRgoSEhI/iIlYfyM7Opvbt25OWlhZNnTqVQkNDKTQ0lKZMmUJaWlpkY2Mj1x0vFzSWJEZZwbPG0k8JCQkUFhZGn332GTVv3pxMTExo7NixtG3bNtZJDZficrNnz6YFCxYodKyqqio9f/6ceayhoUF3795VOJby34Hyo5byoKWlRU+ePCEiIlNTU7p+/ToRET169Ii0tLRYtSESiSglJaXK5+/du0fq6urVtiMR7issLKRZs2aRUChkpv/U1dVp9uzZVFBQwComnpqB14lphPz444+YOnUqRCJRBRnt8rDRdeCCoKAgCIVCpKWloWXLlhWeGzBgAIKCghAaGlor8QDc+x3VV4YMGYI5c+YwPlCKCJ41hn6yt7eHvb09c07cuXMHoaGhmDFjBkpKSlhpqnDR1xKKi4uxe/dunD17tlJNlg0bNlR5LBFJmVCqqqpW0GNRBlJivQgX2i5mZmb4+++/q1zKnpiYyEqwtG3btmjVqhVcXV3h6uqKhw8f4s2bN8xzdaVbxfM/+NVJSnDnzh188sknDU7Iq3Xr1rh58yYMDAw40XXgAktLS2zbtg3u7u6VPh8dHQ0/Pz88fvy4VuIBuPU7qs/IEmtkI3jWWPqJiHD79m1cvHgRFy9exJUrV/D27Vt07twZzs7OrBJsZfu6LBJvt6raOn/+vMw4OnXqxCQyiYmJsLGxkfKHA8Ba20dVVRUvX75EixYtAJSKgSYmJiqk9s2FtktgYCDOnj2LW7duVViB9P79e3Tr1g39+vVDWFiYzHYkn/XFixdx/fp1FBUVoU2bNnBzc4ObmxtcXFwq3HTx1C58EqMEDTWJqY+oq6sjLS2tyrujp0+fol27dlJS7TUN10aLHyuNpZ+aNWuGvLw82Nvbw9nZGS4uLujTp0+DdOlesWIFq//79ttvWf1fdYmshKNHj8oXKErl+G/duoV27dqxXoL+zz//wMHBAUKhEDNnzmTkMe7fv49NmzahuLgYt2/flisBKSgowNWrV5mk5q+//sKHDx9gY2ODu3fvyv2+eLiBT2JkMGLECJnPv3nzBjExMR9FElNUVIT09HS0bdtWapi5tjA1NcWvv/6K3r17V/r85cuXMWbMGDx//ryWI2ucvHnzpkFenGuSqKgo9OnTh5VOiTx8DH3NRSLLtbZLeno6pk+fjjNnzjDTWwKBAP3798dPP/2ksOpyUVERYmNjcfLkSWzbtg15eXkfxTWgocInMTJoDHeY7969Q0BAACP1nZqaijZt2iAgIACmpqZYuHBhrcTh6+uLtLQ0nDlzpsKQdmFhIdzd3dGmTRvs3r27VuJpTKxduxaWlpaML9CoUaNw5MgRGBsb488//4S9vX0dR1j/UNQIVtm+ru7GqixsRj3c3Nxw9OjRCknU27dvMXz4cJlTUlyzdetWREVF4cSJEwBKp6Q6duzI1OqkpKRg/vz5cptL5uTk4OHDhwBKPdD09fXlOr6oqAjXrl3DhQsXmGklc3Nz9O3bF3379oWzs7OUBxJPLVMn5cQ89YZZs2ZR165d6fLly6SlpcWsKIiMjGQt4MUFmZmZ1LJlS7KwsKC1a9fSsWPHKDIyklavXk3m5uZkaGhIGRkZtRZPY8LS0pKxnjh9+jQ1bdqUTp06RZMmTaL+/fvXcXT1B7FYTCtWrCBdXV1GAkBPT4+CgoJYSxIo29fe3t7MNnHiRNLV1SVzc3NGCt/CwoJ0dXXJ29ubVTzlVxVJ+Oeff0hNTY1VG1zBtbYLF7i6upKmpiZ17NiR/P396eDBg1KrunjqHn51UiMnMjISv/76K7p37y61oqRjx45IS0urtTjMzMwQFxcHf39/LFq0qMLw76ZNm2Bubl5r8TQmXr58yfTtH3/8gdGjR2PAgAGwtLSEk5NTHUdXf1iyZAl27dqFNWvWVDCCLSgoYGUEq2xflx31XbBgAUaPHo2tW7dCVVUVACAWi+Hv71/tlFdiYiLzd3JyMl6+fMk8FovFiI6OhqmpabXxcMnDhw9hZ2fHPBaJRFKF0I6OjpgxY0atxnT58mUYGxszRbzOzs6MtQZPPaGusyieukVDQ4O52yl755OQkCCXGBSX5OTk0PXr1+n69euUnZ1dJzE0JoyNjZnRgfbt29OhQ4eIqFTNWUdHpy5Dq1dwYQTLZV83b968Ui2UlJQU0tfXl3msxOZAonlSftPU1KRdu3bJFY+ycKXtwiV5eXl08uRJWrBgATk6OpJQKKROnTrRjBkz6LfffpOya+GpG/iRmEZOt27dEBUVhYCAAAD/0/fYuXMnYypX2zRr1gyOjo518tqNkREjRmDcuHGwsrJCdnY2PD09AQC3b99Gu3bt6ji6+kNOTg5sbGwq7LexsUFOTg6rNrjs6+LiYqSkpFTQQklJSUFJSYnMY9PT00FEaNOmDf766y9maTQACIVCGBoaMqM7tQVX2i5coqWlBQ8PD3h4eAAAcnNzceXKFVy4cAEhISEYP348rKys8Pfff9dqXDz/g09iGjnfffcdPD09kZycjOLiYoSFhSE5ORlXr15FTExMXYfHUwuEhobC0tISmZmZCAkJgba2NgDgxYsX8Pf3r+Po6g/29vbYtGlTBYHITZs2sV76y2Vf+/j4YNKkSUhLS2OS/uvXr2PNmjXVLkpo1aoVAFSb7NQmAwcOxDfffINBgwZVqu2yYsUKDBo0qI6iK0VLSwv6+vrQ19dHs2bNoKamhnv37tVpTI0dfnUSD9LS0rBmzRrcuXMHeXl5+OSTT7BgwQKp+Wmej5fs7Gxmnj8zMxM7duzA+/fvMWTIEPTt27eOo6s/xMTEYNCgQbCwsGBGKePi4pCZmYk///wTffr0qbYNLvu6pKQE69atQ1hYGF68eAEAMDY2RmBgIObOnct6JCUtLQ0//PADczHu0KEDAgMD0bZtW7niUZaa0HZRlpKSEty8eRMXL17EhQsXEBsbi/z8fJiamjIqvq6urkxSyFP78EkMD08jJSkpCUOGDEFmZiasrKwQEREBDw8P5OfnQ0VFBfn5+Th8+DCGDx9e16HWG54/f47NmzcjJSUFAGBra4upU6ciODgY27dvr/K4mu7rt2/fAoDcGjanTp3C0KFD4eDgwBQrx8bG4s6dOzhx4gT69++vUDyKUlPaLoqiq6uL/Px8GBkZMQmLi4tLrSd4PFXDJzGNFBUVlWr9bQQCAYqLi2spIp7axtPTE2pqali4cCH27duHP/74A+7u7tixYwcAICAgALdu3cK1a9fqONL6DRvl7prq6+LiYly8eBFpaWkYN24cdHR08Pz5c+jq6jJTVbLo0qUL3N3dsWbNGqn9CxcuxOnTp1nbDnCNstouXLFt2za4uroyo0I89Q8+iWmkHDt2rMrn4uLi8OOPP6KkpKRWZf55apfmzZvj/Pnz6Ny5M/Ly8qCrq4sbN26ga9euAEoLRLt3784Y3vFUDpskpib6+smTJ/Dw8EBGRgYKCwsZocrAwEAUFhZi69at1bYhEomQlJQEKysrqf2pqano3Lkzf/7z1Hv4wt5GyrBhwyrsu3//PhYuXIgTJ05g/PjxCAoKqoPIeGqLnJwcGBkZAQC0tbWhpaWFZs2aMc83a9YMubm5dRXeR0VN9HVgYCC6deuGO3fuSGmXfPbZZ5gyZQqrNlq0aIGEhIQKSUxCQgIMDQ3lioeHpy7gkxgePH/+HN9++y327t0Ld3d3JCQkoFOnTnUdFk8tUH5KsbopRh7F4bqvL1++jKtXr1aw6bC0tMSzZ89kHhsUFISvv/4aU6ZMwdSpU/Ho0SP07NkTQGlNzNq1a/HVV18pFR8PT23AJzGNmP/++w/fffcdNm7cCAcHB5w7d47VCguejwdvb2/GebigoAB+fn6M83BZ873GDBsjWDZw3dclJSWVTmE9ffoUOjo6Mo9dsWIF/Pz8sGzZMujo6GD9+vVYtGgRAMDExATLly/HrFmz5I6Jh6e24WtiGikhISFYu3YtjIyM8N1331U6vcTzcdMYDE65gIt+qom+HjNmDPT09LB9+3bo6OggMTERLVq0wLBhw2BhYSGzLRUVFbx8+VJqykgynVVdAsTDU5/gk5hGioqKCjQ0NNCvXz+ZehJsnHB5eHhqn6dPn8Ld3R1EhAcPHqBbt2548OABDAwMcPnyZZk1LSoqKvjnn3+klHp5eBoifBLTSPH29mY1J9/Y78J5eOozxcXFiIiIQGJiIiNUOX78eGhoaMg8TkVFBXp6etX+BrC1U+DhqSv4JIaHh4enAVKV+u/QoUOrrW1TUVHBDz/8AD09PZn/N3HiRM7i5eGpCfgkhoeHh6cBwYX6b2U1MTw8DRGVug6Ah4eHh4c98+fPh52dHS5dugQXFxcMHjwYgwYNwn///YfXr19j2rRpFRR4y8Mvpef5WOBHYnh4eHgaEFyo//IjMTwfC7xODA8PD08Dggv135KSkhqNkYentuCnk3h4eHgaGLzSMg9PKfxIDA8PD08Dg1da5uEpha+J4eHh4WlA8ErLPDz/g09ieHh4eHh4eBokfE0MDw8PDw8PT4OET2J4eHh4eHh4GiR8EsPDw8PDw8PTIOGTGB4eHh4eHp4GCZ/E8PDw8PDw8DRI+CSGh4eHh4eHp0HCJzE8PDw8PDw8DRI+ieHh4eHh4eFpkPw/EWK1TIzCHwkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "sns.heatmap(dataset.isnull(),cbar=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "85c38503",
      "metadata": {
        "id": "85c38503"
      },
      "outputs": [],
      "source": [
        "dataset = dataset.dropna(axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "16da24e7",
      "metadata": {
        "id": "16da24e7",
        "outputId": "f0e212df-5e4e-4947-f716-d5da74b0f91f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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btWqF06dPw9HREVu3bkVQUJDS9v3798fVq1cRGRlZoPNyijUREZHueW9lB/LKysrCxo0bkZKSgnr16gHIKTvQuXNnLF68GA4ODhodJykpCSVLlizOrhIREZGOKZYg5uLFi6hXrx7evHkDc3NzbN26FT4+PgCAoUOHon79+mjTpo1Gxzp27Bg2bNiAXbt2qdxPUQFIIQTrJxEREX2kiiWI8fLyQnR0NJKSkrBp0yYEBwfj4MGDiImJQWRkJM6dO6fRcS5duoQ2bdpgwoQJ8PPzU7nvjBkzMGnSJLl1Mj1zyPQtC/08iIiI6MP1TsbENG/eHB4eHjAxMcHChQvlKlhnZWVBT08PjRo1QlRUlLT+ypUraNKkCXr37o1p06apPYeiKzE2thV4JYaIiEjHfBBjYnJlZ2cjLS0NkyZNQu/eveW2Va5cGfPmzUNgYKC07vLly2jatCmCg4M1CmAAwMjIKF9xSAYwREREHy+tBzEhISEICAiAs7MzXr16hXXr1iEqKgq7d++Gg4ODwsG8zs7OcHNzA5BzC6lp06bw9/fHsGHDkJiYCADQ19eHnZ2dtrtLREREOkrrQcyjR4/QrVs3PHjwAFZWVqhSpQp2796NL7/8UqP2mzZtwuPHj7F27VqsXbtWWu/i4oL4+Hhtd5eIiIh01DsZE/O+ME8MERGR7tF0TAxrJxEREZFOYhBDREREOolBDBEREemkd17FOjY2Fm3btoWdnR0sLS3RsWNHPHz4UO4YZ8+exZdffglra2vY2tqib9++SE5O1nZXiYiISIdpPYjJrWJ95swZnD59Gk2bNkWbNm1w+fJlpKSkwM/PDzKZDJGRkTh69CjS09MRGBiI7OxsAMD9+/fRvHlzeHp64uTJk4iIiMDly5fRvXt3bXeViIiIdNg7rWJdtmxZBAQE4Pnz57C0zCkHkJSUBBsbG+zZswfNmzfHsmXLMG7cODx48EDK7Hvx4kVUqVIFN2/ehKenp8bn5ewkIiIi3fNBzE7KysrC+vXrpSrWaWlpkMlkcpl1jY2NoaenhyNHjgDIKR9gaGgoV5rAxMQEAKR9iIiIiIoliLl48SLMzc1hZGSE7777TqpiXbduXZiZmWHUqFFITU1FSkoKRowYgaysLDx48AAA0LRpUyQmJmLWrFlIT0/H8+fPMXr0aACQ9lEkLS0NL1++lFs+4hQ4REREn7xiCWJyq1ifPHkS33//PYKDg3HlyhXY2dlh48aN2LFjB8zNzWFlZYUXL16gevXq0pWXihUrYtWqVZgzZw5MTU3h4OAANzc3lC5dWu7qzNtmzJgBKysruUVkvyqOp0dEREQfgHdaxfr333+X1j158gQGBgawtraGg4MDhg8fjpEjR8q1e/jwIczMzCCTyWBpaYn169ejQ4cOCs/BKtZEREQfhw+yinVepUqVAgBERkbi0aNHaN26db52pUuXBgCsWLECxsbGKusvsYo1ERHRp+WdVrEGgJUrV8Lb2xt2dnY4fvw4Bg8ejKFDh8LLy0s6xqJFi1C/fn2Ym5tj7969GDlyJGbOnAlra2ttd5eIiIh01DuvYn39+nWEhITg2bNncHV1xZgxYzB06FC5Y/z333+YMGECkpOTUaFCBfz+++/o2rWrtrtKREREOoxVrImIiOiD8kHkiSEiIiIqLgxiiIiISCcxiCEiIiKdVOxBzMyZMyGTyTBkyBBpXePGjSGTyeSW7777Ll/bsLAwVKlSBcbGxrC3t0f//v2Lu7tERESkI4o1T8ypU6fw+++/o0qVKvm29enTB5MnT5Yem5qaym2fO3cu5syZg1mzZqFOnTpISUlBfHx8cXaXiIiIdEixBTHJycn49ttv8ccff2Dq1Kn5tueWFFDk+fPnGDt2LHbs2IFmzZpJ6xUFQ0RERPRpKrbbSf3790fLli3RvHlzhdv//PNPlCpVCpUqVUJISAhSU1OlbXv37kV2djbu3bsHb29vlClTBh07dsSdO3eKq7tERESkY4rlSsz69etx9uxZnDp1SuH2zp07w8XFBU5OTrhw4QJGjRqF69evY8uWLQCAW7duITs7G9OnT8eCBQtgZWWFsWPH4ssvv8SFCxdgaGiY75iKaicJIVh6gIiI6COl9SDmzp07GDx4MPbu3QtjY2OF+/Tt21f6uXLlynB0dESzZs0QGxsLDw8PZGdnIyMjAwsXLoSfnx8A4K+//oKDgwMOHDgAf3//fMecMWMGJk2aJLdOpmcOmb6lFp8dERERfSi0fjvpzJkzePToEapXrw4DAwMYGBjg4MGDWLhwIQwMDJCVlZWvTZ06dQAAMTExAABHR0cAgI+Pj7SPnZ0dSpUqhYSEBIXnDQkJQVJSktwi07PQ9tMjIiKiD4TWr8Q0a9YMFy9elFvXo0cPVKhQAaNGjYK+vn6+NtHR0QD+L3hp0KABgJw6S2XKlAEAPHv2DE+ePIGLi4vC87KKNRER0aflndROaty4MapVq4b58+cjNjYW69atw1dffQVbW1tcuHABQ4cORZkyZXDw4EGpTVBQEGJiYrBs2TJYWloiJCQEt27dQnR0NEqUKKHReVk7iYiISPd8sLWTDA0NsW/fPvj5+aFChQoYPnw42rdvjx07dsjtt3r1atSpUwctW7bEF198gRIlSiAiIkLjAIaIiIg+bqxiTURERB+UD/ZKDBEREZE2MIghIiIincQghoiIiHQSgxgiIiLSSVoPYiZOnAiZTCa3VKhQQdq+bNkyNG7cGJaWlpDJZHjx4kW+Y7Ru3RrOzs4wNjaGo6Mjunbtivv372u7q0RERKTDiuVKTMWKFfHgwQNpOXLkiLQtNTUVLVq0wE8//aS0fZMmTfD333/j+vXr2Lx5M2JjY/H1118XR1eJiIhIRxVLAUgDAwM4ODgo3DZkyBAAQFRUlNL2Q4cOlX52cXHB6NGjERQUhIyMDOaJISIiIgDFdCXm5s2bcHJygru7O7799lul9Y408ezZM/z555+oX78+AxgiIiKSaD2IqVOnDsLCwhAREYElS5YgLi4OjRo1wqtXrwp0nFGjRsHMzAy2trZISEjA9u3bVe6flpaGly9fyi0fcR4/IiKiT57Wg5iAgAB06NABVapUgb+/P/755x+8ePECf//9d4GOM3LkSJw7dw579uyBvr4+unXrpjIomTFjBqysrOQWkV2wwImIiIh0R7GMicnL2toa5cuXR0xMTIHalSpVCqVKlUL58uXh7e2NsmXL4sSJE6hXr57C/UNCQjBs2DC5dTa2FRTuS0RERLqv2PPEJCcnIzY2Fo6OjoU+RnZ2NoCcW0bKGBkZwdLSUm6RyWSFPicRERF92LR+JWbEiBEIDAyEi4sL7t+/jwkTJkBfXx+dOnUCACQmJiIxMVG6MnPx4kVYWFjA2dkZJUuWxMmTJ3Hq1Ck0bNgQNjY2iI2Nxbhx4+Dh4aH0KgwRERF9erR+Jebu3bvo1KkTvLy80LFjR9ja2uLEiROws7MDACxduhS+vr7o06cPAODzzz+Hr68vwsPDAQCmpqbYsmULmjVrBi8vL/Tq1QtVqlTBwYMHYWRkpO3uEhERkY6SiY94Co+B4WfvuwtERERUQJnp9zTaj7WTiIiISCcxiCEiIiKdxCCGiIiIdNI7rWIdHx+fb1vusnHjRukYCQkJaNmyJUxNTWFvb4+RI0ciMzNT210lIiIiHVYsye4qVqyIffv2/d9JDHJOU7ZsWTx48EBu32XLlmHWrFkICAgAAGRlZaFly5ZwcHDAsWPH8ODBA3Tr1g0lSpTA9OnTi6O7REREpIPeaRVrfX39fOu3bt2Kjh07wtzcHACwZ88eXLlyBfv27UPp0qVRrVo1TJkyBaNGjcLEiRNhaGhYHF0mIiIiHfNeq1ifOXMG0dHR6NWrl7Tu+PHjqFy5MkqXLi2t8/f3x8uXL3H58uXi6C4RERHpIK1ficmtYu3l5YUHDx5g0qRJaNSoES5dugQLCwu5fUNDQ+Ht7Y369etL6xITE+UCGADS48TERKXnTUtLy1eWQAjB0gNEREQfqfdWxfr169dYt26d3FWYomAVayIiok9LsU+xVlbFetOmTUhNTUW3bt3k1js4OODhw4dy63IfKxpnkyskJARJSUlyi0zPQun+REREpNveWxXr0NBQtG7dWqqplKtevXq4ePEiHj16JK3bu3cvLC0t4ePjo/Q8rGJNRET0adF6EDNixAgcPHgQ8fHxOHbsGNq2bStXxRoAYmJicOjQIfTu3Ttfez8/P/j4+KBr1644f/48du/ejbFjx6J///4sAElEREQSrQ/sza1i/fTpU9jZ2aFhw4ZyVawBYMWKFShTpgz8/PzytdfX18fOnTvx/fffo169ejAzM0NwcDAmT56s7a4SERGRDmMVayIiIvqgsIo1ERERfdQYxBAREZFOYhBDREREOolBDBEREemkYgli7t27hy5dusDW1hYmJiaoXLkyTp8+DQDIyMjAqFGjULlyZZiZmcHJyQndunXD/fv3pfZRUVGQyWQKl1OnThVHl4mIiEjHaH120vPnz+Hr64smTZrg+++/h52dHW7evAkPDw94eHggKSkJX3/9Nfr06YOqVavi+fPnGDx4MLKysqRAJz09Hc+ePZM77rhx47B//37ExsZqnMSOs5OIiIh0j6azk7QexIwePRpHjx7F4cOHNW5z6tQp1K5dG7dv34azs3O+7RkZGfjss88wcOBAjBs3TuPjMoghIiLSPe9tinV4eDhq1qyJDh06wN7eHr6+vvjjjz9UtklKSoJMJoO1tbXSYz59+hQ9evTQdneJiIhIR2n9SoyxsTEAYNiwYejQoQNOnTqFwYMHY+nSpQgODs63/5s3b9CgQQNUqFABf/75p8JjfvXVVwCAf/75R+l509LSkJaWJrfOxrYC6ycRERHpmPd2O8nQ0BA1a9bEsWPHpHWDBg3CqVOncPz4cbl9MzIy0L59e9y9exdRUVGwtLTMd7y7d+/CxcUFf//9N9q3b6/0vBMnTsSkSZPk1sn0zKGnn/+YRERE9OF6b7eTHB0d81Wb9vb2RkJCgty6jIwMdOzYEbdv35aqVCuycuVK2NraonXr1irPGxISgqSkJLlFpmdRtCdDREREHyytF4Bs0KABrl+/Lrfuxo0bcHFxkR7nBjA3b97EgQMHYGtrq/BYQgisXLkS3bp1Q4kSJVSe18jIKF+Va95KIiIi+nhpPYgZOnQo6tevj+nTp6Njx47477//sGzZMixbtgxATgDz9ddf4+zZs9i5cyeysrKQmJgIAChZsiQMDQ2lY0VGRiIuLg69e/fWdjeJiIhIxxVLFeudO3ciJCQEN2/ehJubG4YNG4Y+ffoAAOLj4+Hm5qaw3YEDB9C4cWPpcefOnXH79m0cPXq0UP3gFGsiIiLd894G9n5IGMQQERHpnvc2sJeIiIjoXWAQQ0RERDqJQQwRERHppHdexRoAkpOTMWDAAJQpUwYmJibw8fHB0qVLFR5LCIGAgADIZDJs27atOLpLREREOkjrU6yfP3+OBg0aoEmTJvj333+lKtY2NjbSPsOGDUNkZCTWrl0LV1dX7NmzBz/88AOcnJzyJbWbP38+870QERFRPloPYn7++WeULVsWK1eulNa9PaX62LFjCA4OlqZT9+3bF7///jv+++8/uSAmOjoac+bMwenTp+Ho6KjtrhIREZEOey9VrOvXr4/w8HDcu3cPQggcOHAAN27cgJ+fn7RPamoqOnfujMWLF8PBwUHb3SQiIiIdp/Ug5tatW1iyZAnKlSuH3bt34/vvv8egQYOwatUqaZ9ff/0VPj4+KFOmDAwNDdGiRQssXrwYn3/+ubRPbubfNm3aaHTetLQ0vHz5Um75iFPgEBERffK0fjspOzsbNWvWxPTp0wEAvr6+uHTpEpYuXYrg4GAAOUHMiRMnEB4eDhcXFxw6dAj9+/eHk5MTmjdvjvDwcERGRuLcuXMan3fGjBkKq1jLWMWaiIjoo6T1jL0uLi748ssvsXz5cmndkiVLMHXqVNy7dw+vX7+GlZUVtm7dipYtW0r79O7dG3fv3kVERASGDBmChQsXQk/v/y4UZWVlQU9PD40aNUJUVFS+86alpSEtLU1unY1tBQ4KJiIi0jGaZux951WsMzIykJGRIRegAIC+vj6ys7MBAKNHj85X9LFy5cqYN28eAgMDFZ6XVayJiIg+Le+8irWlpSW++OILjBw5EiYmJnBxccHBgwexevVqzJ07FwDg4OCgcDCvs7Oz0uKRRERE9Gl551WsASAxMREhISHYs2cPnj17BhcXF/Tt2xdDhw5VevVEJpNh69atCAoK0rgfLABJRESke1jFGgxiiIiIdBGrWBMREdFHjUEMERER6SQGMURERKSTGMQQERGRTtJ6EOPq6gqZTJZv6d+/v9x+QggEBARAJpNh27ZtctsGDRqEGjVqwMjICNWqVdN2F4mIiOgjoPU8MadOnUJWVpb0+NKlS/jyyy/RoUMHuf3mz5+vMhldz549cfLkSVy4cEHbXSQiIqKPgNaDGDs7O7nHM2fOhIeHB7744gtpXXR0NObMmYPTp0/D0dEx3zEWLlwIAHj8+DGDGCIiIlKoWMfEpKenY+3atejZs6d01SU1NRWdO3fG4sWLFWblJSIiItKE1q/E5LVt2za8ePEC3bt3l9blliVo06aNVs+lqACkEIL1k4iIiD5SxXolJjQ0FAEBAXBycgIAhIeHIzIyEvPnz9f6uWbMmAErKyu5RWS/0vp5iIiI6MNQbEHM7du3sW/fPrlq1JGRkYiNjYW1tTUMDAxgYJBzIah9+/Zo3Lhxkc4XEhKCpKQkuUWmZ1GkYxIREdGHq9huJ61cuRL29vZo2bKltG706NFyQQ0AVK5cGfPmzUNgYGCRzmdkZAQjIyO5dbyVRERE9PEqliAmOzsbK1euRHBwsHS1BQAcHBwUDuZ1dnaGm5ub9DgmJgbJyclITEzE69evER0dDQDw8fGBoaFhcXSZiIiIdEyxBDH79u1DQkICevbsWaj2vXv3xsGDB6XHvr6+AIC4uDi4urpqo4tERESk42RCCPG+O1FcDAw/e99dICIiogLKTL+n0X6snUREREQ6iUEMERER6SQGMURERKST3nkV69jYWLRt2xZ2dnawtLREx44d8fDhQ7lj3LhxA23atEGpUqVgaWmJhg0b4sCBA9ruKhEREekwrQcxp06dwoMHD6Rl7969AIAOHTogJSUFfn5+kMlkiIyMxNGjR5Geno7AwEBkZ2dLx2jVqhUyMzMRGRmJM2fOoGrVqmjVqhUSExO13V0iIiLSUcU+O2nIkCHYuXMnbt68ib179yIgIADPnz+HpaUlACApKQk2NjbYs2cPmjdvjidPnsDOzg6HDh1Co0aNAACvXr2CpaUl9u7di+bNm2t8bs5OIiIi0j0fxOykt6tYp6WlQSaTyWXWNTY2hp6eHo4cOQIAsLW1hZeXF1avXo2UlBRkZmbi999/h729PWrUqFGc3SUiIiId8k6rWNetWxdmZmYYNWoUpk+fDiEERo8ejaysLDx48ABATqmAffv2ISgoCBYWFtDT04O9vT0iIiJgY2Oj9FysYk1ERPRpeadVrO3s7LBx40bs2LED5ubmsLKywosXL1C9enXo6eV0RQiB/v37w97eHocPH8Z///2HoKAgBAYGSoGOIqxiTURE9GkptjExt2/fhru7O7Zs2YI2bdrk2/7kyRMYGBjA2toaDg4OGD58OEaOHIn9+/fDz89PbtwMAJQrVw69evXC6NGjFZ5P0ZUYG9sKvBJDRESkYzQdE/NOq1jnVapUKQBAZGQkHj16hNatWwMAUlNTAUC6MpNLT09PbgbT21jFmoiI6NPyTqtYAznBjbe3N+zs7HD8+HEMHjwYQ4cOhZeXFwCgXr16sLGxQXBwMMaPHw8TExP88ccfiIuLUxoQERER0afnnVexvn79OkJCQvDs2TO4urpizJgxGDp0qLS9VKlSiIiIwJgxY9C0aVNkZGSgYsWK2L59O6pWrVoc3SUiIiIdxCrWRERE9EH5IPLEEBERERUXBjFERESkkxjEEBERkU7SehCTlZWFcePGwc3NDSYmJvDw8MCUKVOQd+hN9+7d81W5btGihdxxFFXDnjlzpra7S0RERDpK67OTfv75ZyxZsgSrVq1CxYoVcfr0afTo0QNWVlYYNGiQtF+LFi2wcuVK6fHbOV4AYPLkyejTp4/02MLCQtvdJSIiIh2l9SDm2LFjaNOmjZTTxdXVFX/99Rf+++8/uf2MjIzg4OCg8lgWFhZq9yEiIqJPk9ZvJ9WvXx/79+/HjRs3AADnz5/HkSNHEBAQILdfVFQU7O3t4eXlhe+//x5Pnz7Nd6yZM2fC1tYWvr6+mDVrFjIzM7XdXSIiItJRWr8SM3r0aLx8+RIVKlSAvr4+srKyMG3aNHz77bfSPi1atEC7du3g5uaG2NhY/PTTTwgICMDx48ehr68PABg0aBCqV6+OkiVL4tixYwgJCcGDBw8wd+5chedlFWsiIqJPi9aT3a1fvx4jR47ErFmzULFiRURHR2PIkCGYO3cugoODFba5desWPDw8sG/fPjRr1kzhPitWrEC/fv2QnJyscPzMxIkTMWnSJLl1Mj1z6Olb5tuXiIiIPlyaJrvTehBTtmxZjB49Gv3795fWTZ06FWvXrsW1a9eUtrOzs8PUqVPRr18/hdsvX76MSpUq4dq1a1KdpbxYxZqIiOjj8N6qWKempuarQK2vr6+yAvXdu3fx9OlTODo6Kt0nOjoaenp6sLe3V7idVayJiIg+LVoPYgIDAzFt2jQ4OzujYsWKOHfuHObOnSsVg0xOTsakSZPQvn17ODg4IDY2Fj/++CM8PT3h7+8PADh+/DhOnjyJJk2awMLCAsePH8fQoUPRpUsX2NjYaLvLREREpIO0fjvp1atXGDduHLZu3YpHjx7ByckJnTp1wvjx42FoaIjXr18jKCgI586dw4sXL+Dk5AQ/Pz9MmTIFpUuXBgCcPXsWP/zwA65du4a0tDS4ubmha9euGDZsmMLxMMqwACQREZHueW9jYj4kDGKIiIh0D6tYExER0UeNQQwRERHpJAYxREREpJMYxBAREZFOKpYg5tWrVxgyZAhcXFxgYmKC+vXr49SpU9L2LVu2wM/PD7a2tpDJZIiOjpZr/+zZMwwcOBBeXl4wMTGBs7MzBg0ahKSkpOLoLhEREemgYglievfujb1792LNmjW4ePEi/Pz80Lx5c9y7lzPaOCUlBQ0bNsTPP/+ssP39+/dx//59zJ49G5cuXUJYWBgiIiLQq1ev4uguERER6SCtT7F+/fo1LCwssH37drRs2VJaX6NGDQQEBGDq1KnSuvj4eLi5ueHcuXOoVq2ayuNu3LgRXbp0QUpKCgwMNMvRxynWREREuue9TbHOzMxEVlYWjI2N5dabmJjgyJEjhT5uUlISLC0tNQ5giIiI6OOm9YjAwsIC9erVw5QpU+Dt7Y3SpUvjr7/+wvHjx+Hp6VmoYz558gRTpkxB3759le6jqACkEIL1k4iIiD5SxTImZs2aNRBC4LPPPoORkREWLlyITp065SsMqYmXL1+iZcuW8PHxwcSJE5XuN2PGDFhZWcktIvtVEZ4FERERfciKJYjx8PDAwYMHkZycjDt37uC///5DRkYG3N3dC3ScV69eoUWLFrCwsMDWrVtRokQJpfuGhIQgKSlJbpHpWRT1qRAREdEHqlgHmJiZmcHMzAzPnz/H7t278csvv2jc9uXLl/D394eRkRHCw8PzjbF5m5GRUb7ikLyVRERE9PEqliBm9+7dEELAy8sLMTExGDlyJCpUqIAePXoAyMkDk5CQgPv37wMArl+/DgBwcHCAg4MDXr58CT8/P6SmpmLt2rV4+fIlXr58CQCws7ODvr5+cXSbiIiIdEixBDFJSUkICQnB3bt3UbJkSbRv3x7Tpk2TbgeFh4dLAQ0AfPPNNwCACRMmYOLEiTh79ixOnjwJAPkGA8fFxcHV1bU4uk1EREQ6ROt5Yj4kzBNDRESke95bnhgiIiKid4FBDBEREekkBjFERESkk95LFWsAuHr1Klq3bg0rKyuYmZmhVq1aSEhIkLbHxsaibdu2sLOzg6WlJTp27IiHDx8WR3eJiIhIB72XKtaxsbFo2LAhKlSogKioKFy4cAHjxo2TcsGkpKTAz88PMpkMkZGROHr0KNLT0xEYGIjs7Ozi6DIRERHpmPdSxfqbb75BiRIlsGbNGoXH2LNnDwICAvD8+XNYWloCyJm2bWNjgz179qB58+Ya9YWzk4iIiHTPB1vFOjs7G7t27UL58uXh7+8Pe3t71KlTB9u2bZP2TUtLg0wmk8vAa2xsDD09vSJVwiYiIqKPh9aDmLxVrO/fv4+srCysXbsWx48fx4MHD/Do0SMkJydj5syZaNGiBfbs2YO2bduiXbt2OHjwIACgbt26MDMzw6hRo5CamoqUlBSMGDECWVlZePDggcLzpqWlSZl9c5ePOAUOERHRJ++dV7HOHdPSpk0bDB06FNWqVcPo0aPRqlUrLF26FEBOaYGNGzdix44dMDc3h5WVFV68eIHq1asrrYTNKtZERESflndexbpUqVIwMDCAj4+PXBtvb2+52Ul+fn6IjY3Fo0eP8OTJE6xZswb37t1TWgmbVayJiIg+Le+8irWhoSFq1aolFX3MdePGDbi4uOQ7RqlSpQAAkZGRePToEVq3bq3wXKxiTURE9Gl5L1WsR44cif/973/4/PPP0aRJE0RERGDHjh2IioqSjrFy5Up4e3vDzs4Ox48fx+DBgzF06FB4eXkVR5eJiIhIx7yXKtZt27bF0qVLMWPGDAwaNAheXl7YvHkzGjZsKB3j+vXrCAkJwbNnz+Dq6ooxY8Zg6NChxdFdIiIi0kGsYk1EREQfFFaxJiIioo8agxgiIiLSSQxiiIiISCcxiCEiIiKdVOAg5tChQwgMDISTkxNkMplczSMAEEJg/PjxcHR0hImJCZo3b46bN29K2+Pj49GrVy+4ubnBxMQEHh4emDBhAtLT0/MdZ/bs2ShfvjyMjIzw2WefYdq0aYV7lkRERPTRKXAQk5KSgqpVq2Lx4sUKt//yyy9YuHAhli5dipMnT8LMzAz+/v548+YNAODatWvIzs7G77//jsuXL2PevHlYunQpfvrpJ7njDB48GMuXL8fs2bNx7do1hIeHo3bt2oV4ikRERPQxKtIUa5lMhq1btyIoKAhAztUTJycnDB8+HCNGjACQkzOmdOnSCAsLwzfffKPwOLNmzcKSJUtw69YtAMDVq1dRpUoVXLp0qUjJ7TjFmoiISPe8lynWcXFxSExMRPPmzaV1VlZWqFOnDo4fP660XVJSEkqWLCk93rFjB9zd3bFz5064ubnB1dUVvXv3xrNnz7TZXSIiItJhWg1iEhMTAQClS5eWW1+6dGlp29tiYmLw66+/ol+/ftK6W7du4fbt29i4cSNWr16NsLAwnDlzBl9//bXSc6elpeHly5dyy0ecx4+IiOiT915nJ927dw8tWrRAhw4d0KdPH2l9dnY20tLSsHr1ajRq1AiNGzdGaGgoDhw4kK9wZK4ZM2bAyspKbhHZr97VUyEiIqJ3TKtBjIODAwDg4cOHcusfPnwobct1//59NGnSBPXr18eyZcvktjk6OsLAwADly5eX1nl7ewMAEhISFJ47JCQESUlJcotMz6LIz4mIiIg+TFoNYtzc3ODg4ID9+/dL616+fImTJ0+iXr160rp79+6hcePGqFGjBlauXAk9PfluNGjQAJmZmYiNjZXW3bhxAwDg4uKi8NxGRkawtLSUW2QymTafHhEREX1AClzFOjk5GTExMdLjuLg4REdHo2TJknB2dsaQIUMwdepUlCtXDm5ubhg3bhycnJykGUy5AYyLiwtmz56Nx48fS8fKvVrTvHlzVK9eHT179sT8+fORnZ2N/v3748svv5S7OkNERESfrgJPsY6KikKTJk3yrQ8ODkZYWBiEEJgwYQKWLVuGFy9eoGHDhvjtt9+k4CMsLAw9evRQeOy8Xbl//z4GDhyIPXv2wMzMDAEBAZgzZ47cLCZ1OMWaiIhI92g6xbpIeWI+dAxiiIiIdM97yRNDRERE9K4wiCEiIiKdxCCGiIiIdNI7r2IN5EyXbtOmDUqVKgVLS0s0bNgQBw4ckLY/ffoULVq0gJOTE4yMjFC2bFkMGDAAL1++LNyzJCIioo/OO69iDQCtWrVCZmYmIiMjcebMGVStWhWtWrWSShPo6emhTZs2CA8Px40bNxAWFoZ9+/bhu+++K+TTJCIioo/NO69i/eTJE9jZ2eHQoUNo1KgRAODVq1ewtLTE3r175YpH5rVw4ULMmjULd+7c0bh/nJ1ERESkez7YKta2trbw8vLC6tWrkZKSgszMTPz++++wt7dHjRo1FB73/v372LJlC7744gttdpeIiIh0WIEz9qqiSRVrmUyGffv2ISgoCBYWFtDT04O9vT0iIiJgY2Mj165Tp07Yvn07Xr9+jcDAQCxfvlzpudPS0pCWlia3TgjB0gNEREQfqXc+O0kIgf79+8Pe3h6HDx/Gf//9h6CgIAQGBuLBgwdy+86bNw9nz57F9u3bERsbi2HDhik9LqtYExERfVq0Oibm1q1b8PDwwLlz51CtWjVpvy+++ALVqlXDggULsH//fvj5+eH58+ewtLSU9ilXrhx69eqF0aNHKzzXkSNH0KhRI9y/fx+Ojo75tiu6EmNjW4FXYoiIiHTMexkTo0kV69TU1JwTv1W5Wk9PD9nZ2UqPnbvt7UAlF6tYExERfVreeRXrevXqwcbGBsHBwRg/fjxMTEzwxx9/IC4uDi1btgQA/PPPP3j48CFq1aoFc3NzXL58GSNHjkSDBg3g6uqqlSdOREREuq3AQczp06flqljnjlPJrWL9448/IiUlBX379pWqWEdERMDY2BgAUKpUKURERGDMmDFo2rQpMjIyULFiRWzfvh1Vq1YFACmwGTp0KNLS0lC2bFm0a9dO6a0mIiIi+vSwijURERF9UFjFmoiIiD5qDGKIiIhIJzGIISIiIp3EIIaIiIh0UoGDmEOHDiEwMBBOTk6QyWTYtm2b3PYtW7bAz88Ptra2kMlkiI6OVnosIQQCAgIUHichIQEtW7aEqakp7O3tMXLkSGRmZha0u0RERPSRKnAQk5KSgqpVq2Lx4sVKtzds2BA///yz2mPNnz9fYUK6rKwstGzZEunp6Th27BhWrVqFsLAwjB8/vqDdJSIioo9UgfPEBAQEICAgQOn2rl27AgDi4+NVHic6Ohpz5szB6dOn85UR2LNnD65cuYJ9+/ahdOnSqFatGqZMmYJRo0Zh4sSJMDQ0LGi3iYiI6CPzXsbEpKamonPnzli8eDEcHBzybT9+/DgqV64sVw3b398fL1++xOXLl99lV4mIiOgDVeArMdowdOhQ1K9fH23atFG4PTExUS6AASA9TkxMVNhGUQFIIQTrJxEREX2k3vmVmPDwcERGRmL+/PlaPe6MGTNgZWUlt4jsV1o9BxEREX043nkQExkZidjYWFhbW8PAwAAGBjkXg9q3b4/GjRsDABwcHPDw4UO5drmPFd1+AoCQkBAkJSXJLTI9i+J7IkRERPRevfPbSaNHj0bv3r3l1lWuXBnz5s1DYGAggJxK19OmTcOjR49gb28PANi7dy8sLS3h4+Oj8LhGRkYwMjKSW8dbSURERB+vAgcxycnJiImJkR7HxcUhOjoaJUuWhLOzM549e4aEhATcv38fAHD9+nUAOVdQ8i5vc3Z2hpubGwDAz88PPj4+6Nq1K3755RckJiZi7Nix6N+/f75AhYiIiD5NBb6ddPr0afj6+sLX1xcAMGzYMPj6+ko5XMLDw+Hr64uWLVsCAL755hv4+vpi6dKlGp9DX18fO3fuhL6+PurVq4cuXbqgW7dumDx5ckG7S0RERB8pmRBCvO9OFBcDw8/edxeIiIiogDLT72m0H2snERERkU5iEENEREQ6iUEMERER6aT3UsU6NjYWbdu2hZ2dHSwtLdGxY0e5vDBRUVGQyWQKl1OnThX4SRIREdHH551XsU5JSYGfnx9kMhkiIyNx9OhRpKenIzAwENnZ2QCA+vXr48GDB3JL79694ebmhpo1axa0y0RERPQReudVrI8ePYr4+HicO3cOlpaWAIBVq1bBxsYGkZGRaN68OQwNDeVyyWRkZGD79u0YOHAgE9gRERERgPcwJiYtLQ0ymUwuaZ2xsTH09PRw5MgRhW3Cw8Px9OlT9OjR4111k4iIiD5w7zyIqVu3LszMzDBq1CikpqYiJSUFI0aMQFZWFh48eKCwTWhoKPz9/VGmTBmlx01LS8PLly/llo84BQ4REdEn750HMXZ2dti4cSN27NgBc3NzWFlZ4cWLF6hevTr09PJ35+7du9i9ezd69eql8risYk1ERPRpeecFIIGc2kixsbF48uQJDAwMYG1tDQcHB7i7u+fbd+XKlbC1tUXr1q1VHjMkJATDhg2TW2djW0Gr/SYiIqIPx3sJYnKVKlUKABAZGYlHjx7lC1SEEFi5ciW6deuGEiVKqDwWq1gTERF9Wt55FWsg5+qKt7c37OzscPz4cQwePBhDhw6Fl5eX3LkiIyMRFxeH3r17F/oJEhER0cepwAUgo6Ki0KRJk3zrg4ODERYWhrCwMIWziCZMmICJEycCAEaPHo2wsDA8e/YMrq6u+O677zB06NB8V046d+6M27dv4+jRowXpooQFIImIiHSPpgUgWcWaiIiIPiisYk1EREQfNQYxREREpJMYxBAREZFO0moV64yMDIwaNQqVK1eGmZkZnJyc0K1bN2mmUi5XV9d81alnzpyp8HwxMTGwsLCAtbV1QbtKREREHzGtVrFOTU3F2bNnMW7cOJw9exZbtmzB9evXFSaqmzx5slyV6oEDB+bbJyMjA506dUKjRo0K2k0iIiL6yGm1irWVlRX27t0rt27RokWoXbs2EhIS4OzsLK23sLCQq1StyNixY1GhQgU0a9YMx44dK2hXiYiI6CNW7GNikpKSIJPJ8t0OmjlzJmxtbeHr64tZs2YhMzNTbntkZCQ2btyo8IoPERERUbGWHXjz5g1GjRqFTp06wdLSUlo/aNAgVK9eHSVLlsSxY8cQEhKCBw8eYO7cuQCAp0+fonv37li7dq1cOyIiIqJcxRbEZGRkoGPHjhBCYMmSJXLb8hZqrFKlCgwNDdGvXz/MmDEDRkZG6NOnDzp37ozPP/9c4/OlpaUhLS1Nbp0QgvWTiIiIPlLFcjspN4C5ffs29u7dq/ZqSp06dZCZmYn4+HgAObeSZs+eDQMDAxgYGKBXr15ISkqCgYEBVqxYofAYM2bMgJWVldwisl9p+6kRERHRB0LrV2JyA5ibN2/iwIEDsLW1VdsmOjoaenp6sLe3BwAcP34cWVlZ0vbt27fj559/xrFjx/DZZ4pLCYSEhMhd4QEAG9sKRXgmRERE9CHTahVrR0dHfP311zh79ix27tyJrKwsJCYmAgBKliwJQ0NDHD9+HCdPnkSTJk1gYWGB48ePY+jQoejSpQtsbGwAAN7e3nLnPH36NPT09FCpUiWl/TIyMoKRkZHcOt5KIiIi+ngVOIg5ffq0XBXr3KsfwcHBmDhxIsLDwwEA1apVk2t34MABNG7cGEZGRli/fj0mTpyItLQ0uLm5YejQofmuohARERGpwirWRERE9EFhFWsiIiL6qDGIISIiIp3EIIaIiIh0EoMYIiIi0kkFDmIOHTqEwMBAODk5QSaTYdu2bXLbJ06ciAoVKsDMzAw2NjZo3rw5Tp48KbfPtGnTUL9+fZiamuarqZRr0KBBqFGjBoyMjPLNdCIiIiIqcBCTkpKCqlWrKi3MWL58eSxatAgXL17EkSNH4OrqCj8/Pzx+/FjaJz09HR06dMD333+v8lw9e/bE//73v4J2kYiIiD4BRZpiLZPJsHXrVgQFBSnd5+XLl7CyssK+ffvQrFkzuW1hYWEYMmQIXrx4obT9xIkTsW3bNkRHRxe4f5xiTUREpHs+iCnW6enpWLZsGaysrFC1atXiPBURERF9YoqlivXOnTvxzTffIDU1FY6Ojti7dy9KlSpVHKeSsIo1ERHRp6VYrsQ0adIE0dHROHbsGFq0aIGOHTvi0aNHxXEqCatYExERfVqKJYgxMzODp6cn6tati9DQUBgYGCA0NLQ4TiUJCQlBUlKS3CLTsyjWcxIREdH7Uyy3k96WnZ2d71aPtrGKNRER0aelwEFMcnIyYmJipMdxcXGIjo5GyZIlYWtri2nTpqF169ZwdHTEkydPsHjxYty7dw8dOnSQ2iQkJODZs2dISEhAVlaWNPPI09MT5ubmAICYmBgkJycjMTERr1+/lvbx8fGBoaFhEZ4yERERfQwKPMU6KioKTZo0ybc+ODgYS5cuRefOnXHy5Ek8efIEtra2qFWrFsaOHYtatWpJ+3bv3h2rVq3Kd4wDBw6gcePGAIDGjRvj4MGD+faJi4uDq6urRn3lFGsiIiLdo+kU6yLlifnQMYghIiLSPR9EnhgiIiKi4sIghoiIiHQSgxgiIiLSSe+lijUA7Nq1C3Xq1IGJiQlsbGzy1V86deoUmjVrBmtra9jY2MDf3x/nz58vaHeJiIjoI/Veqlhv3rwZXbt2RY8ePXD+/HkcPXoUnTt3lrYnJyejRYsWcHZ2xsmTJ3HkyBFYWFjA398fGRkZhXiaRERE9LF551WsMzMz4erqikmTJqFXr14K25w+fRq1atVCQkICypYtCwC4ePEiqlSpgps3b8LT01Oj/nF2EhERke75IGYnKapiffbsWdy7dw96enrw9fWFo6MjAgICcOnSJamdl5cXbG1tERoaivT0dLx+/RqhoaHw9vbWOEcMERERfdyKJYjZuXMnzM3NYWxsjHnz5slVsb516xaAnLEzY8eOxc6dO2FjY4PGjRvj2bNnAAALCwtERUVh7dq1MDExgbm5OSIiIvDvv//CwEBxkuG0tDS8fPlSbvmIU+AQERF98t55Fevs7GwAwJgxY9C+fXvUqFEDK1euhEwmw8aNGwEAr1+/Rq9evdCgQQOcOHECR48eRaVKldCyZUu8fv1a4TlZxZqIiOjT8s6rWDs6OgLIqYGUy8jICO7u7khISAAArFu3DvHx8Vi5ciVq1aqFunXrYt26dYiLi8P27dsVnpNVrImIiD4t7yRPTN4q1jVq1ICRkRGuX78ubc/IyEB8fDxcXFwAAKmpqdDT05OrQp37OPdKztuMjIxgaWkpt7CKNRER0cerwEFMcnIyoqOjparSuVWsExISkJKSgp9++gknTpzA7du3cebMGfTs2VOuirWlpSW+++47TJgwAXv27MH169fx/fffA4C0z5dffonnz5+jf//+uHr1Ki5fvowePXrAwMBAYfFJIiIi+vQoHiWrwunTp+UCiWHDhgH4vyrW165dw6pVq+SqWB8+fBgVK1aU2syaNQsGBgbo2rUrXr9+jTp16iAyMhI2NjYAgAoVKmDHjh2YNGkS6tWrJ81kioiIkG5HERER0aeNVayJiIjog/JB5IkhIiIiKi4MYoiIiEgnMYghIiIincQghoiIiHRSgYOYQ4cOITAwEE5OTpDJZNi2bZvSfb/77jvIZDLMnz9fWhcfH49evXrBzc0NJiYm8PDwwIQJE5Ceni63j0wmy7ecOHGioN0lIiKij1SBp1inpKSgatWq6NmzJ9q1a6d0v61bt+LEiRNwcnKSW3/t2jVkZ2fj999/h6enJy5duoQ+ffogJSUFs2fPltt33759clOzbW1tC9pdIiIi+kgVOIgJCAhAQECAyn3u3buHgQMHYvfu3WjZsqXcthYtWqBFixbSY3d3d1y/fh1LlizJF8TY2trCwcGhoF0kIiKiT4DWx8RkZ2eja9euGDlypNxVFFWSkpJQsmTJfOtbt24Ne3t7NGzYEOHh4druKhEREemwAl+JUefnn3+GgYEBBg0apNH+MTEx+PXXX+Wuwpibm2POnDlo0KAB9PT0sHnzZgQFBWHbtm1o3bq1wuOkpaVJ9ZlyCSFYP4mIiOgjpdUg5syZM1iwYAHOnj2rUfBw7949tGjRAh06dECfPn2k9aVKlZLKGQBArVq1cP/+fcyaNUtpEDNjxgxMmjRJbp1MzxwyfctCPhsiIiL6kGn1dtLhw4fx6NEjODs7w8DAAAYGBrh9+zaGDx8OV1dXuX3v37+PJk2aoH79+li2bJnaY9epUwcxMTFKt4eEhCApKUlukelZFPUpERER0QdKq1diunbtiubNm8ut8/f3R9euXdGjRw9p3b1799CkSRPUqFEDK1euhJ6e+lgqOjpaZfFHIyMjGBkZya3jrSQiIqKPV4GDmOTkZLkrInFxcYiOjkbJkiXh7Oycbxp0iRIl4ODgAC8vLwA5AUzjxo3h4uKC2bNn4/Hjx9K+uTORVq1aBUNDQ/j6+gIAtmzZghUrVmD58uUFf4ZERET0USpwEHP69Gk0adJEepw7diU4OBhhYWFq2+/duxcxMTGIiYlBmTJl5LblLag9ZcoU3L59GwYGBqhQoQI2bNiAr7/+uqDdJSIioo+UTOSNHD4yBoafve8uEBERUQFlpt/TaD/WTiIiIiKdxCCGiIiIdBKDGCIiItJN4hPx5s0bMWHCBPHmzRu2Z3u2Z3u2Z3u215H2qnwyQUxSUpIAIJKSktie7dme7dme7dleR9qrwttJREREpJMYxBAREZFOYhBDREREOumTCWKMjIwwYcKEfPWV2J7t2Z7t2Z7t2f7Dba/KR52xl4iIiD5en8yVGCIiIvq4MIghIiIincQghoiIiHQSgxgiIiLSSQxiiIiISCcZvO8OfMzc3d1x6tQp2Nrayq1/8eIFqlevjlu3br2nnpEiCxcu1HjfQYMGFWNPqKgyMjJQoUIF7Ny5E97e3u+7OzpHCIE7d+7A3t4exsbGGrXx9fWFTCbTaN+zZ88WpXtUDHT19WMQU4zi4+ORlZWVb31aWhru3bunsm1mZiamT5+Onj17okyZMsXVxWLzzz//YMuWLShZsiR69uyJChUqSNueP3+O9u3bIzIyUu1xMjMzERUVhdjYWHTu3BkWFha4f/8+LC0tYW5unm//8PBwjfvYunVrucfz5s2Te/z48WOkpqbC2toaQE7waWpqCnt7ewYxH7gSJUrgzZs3hWrr7OyMc+fOSV8+Fi1ahG7dusHS0rJQx0tNTUVCQgLS09Pl1lepUqVQx3sXhBDw9PTE5cuXUa5cOY3aBAUFST+/efMGv/32G3x8fFCvXj0AwIkTJ3D58mX88MMPxdHlfLKysjBv3jz8/fffCn//z549U9ju888/R3h4uPR/Hx4eji+//BImJiaF6kd6ejoePXqE7OxsufXOzs5q+x8WFob9+/crbK/J+2dBaPv1e/jwIUaMGCH1/+1sLoo+Gwvjo8sTM2zYMI33nTt3rsrtbdu2VRiZymQyGBsbw9PTE507d4aXl5fc9twP0qCgIKxatQpWVlbStqysLOzfvx979+7F9evXVZ7fwsICFy9ehKurq4bPSJ6NjY3a/nfv3h09evSQtmnj97du3Tp069YNLVq0QFJSEk6fPo3ly5fj22+/BZDzx+3k5KT2j/j27dto0aIFEhISkJaWhhs3bsDd3R2DBw9GWloali5dmq+Nnp78HVKZTCb3z5P396Hq/OvWrcNvv/2G0NBQ6fW9fv06+vTpg379+knP5W3t2rVT+Zzy2rJlS751yl4zRRS9CRcliNNUbGws+vTpo/RNdPz48Rg9ejRMTU0B5AStNjY2BT7PhQsXFK7P/ft1dnZWmTxr+vTpuHHjBpYvXw4DA82/r+np6SExMRH29vYAAEtLS0RHR8Pd3b1A/X/8+DF69OiBf//9V+F2dX//2vgQyM7ORkxMjMIPwc8//1xl24oVKyI0NBR169ZVe5639e7dG46OjpgyZYrc+gkTJuDOnTtYsWKF2mNERETA3NwcDRs2BAAsXrwYf/zxB3x8fLB48WK1f1Pjx4/H8uXLMXz4cIwdOxZjxoxBfHw8tm3bhvHjxyv9IqKt1//mzZvo2bMnjh07JrdeCAGZTKb29RswYADCwsLQsmVLODo65ntfePtLF6D8f0YRVUG0Nl6/gIAAJCQkYMCAAQr736ZNG437qpLWS0q+Z40bN5ZbLC0thampqfD19RW+vr7CzMxMWFpaiiZNmqg9VnBwsLCyshIuLi6iXbt2ol27dsLV1VVYW1uLjh07Ci8vL2FkZCSOHDki104mkwmZTCb09PSkn3MXQ0NDUb58ebFjxw6152/durUICwsr9O9i7ty5wtbWVnTp0kUsXLhQLFy4UHTp0kWUKlVKTJs2TfTu3VsYGRmJZcuWSW208furVq2aWLBggfR4w4YNwszMTCxfvlwIIURiYqLQ09NT2/82bdqILl26iLS0NGFubi5iY2OFEEIcOHBAeHp6qm2/d+9eUb16dRERESGSkpJEUlKSiIiIEDVr1hR79uxR2dbd3V2cPXs23/rTp08LV1dXpe26d++u8aJIWFiYtMyZM0fY2NiIb775RixYsEAsWLBAfPPNN8LGxkbMnTtXYfu3/97e/hvU09OTlsKKjo5W2V5PT088fPhQemxhYSG9dgXxdn/fXoyMjES3bt3E69evFbYPCgoSFhYWwtHRUfj5+Ym2bdvKLarOm7f/ef/2CqJz586iQYMG4tSpU8LMzEzs2bNHrFmzRnh5eYmdO3eqbd+iRQvh4+MjfvvtN7F161axbds2uUWd48ePCzc3N4XvQ5q8/uHh4aJhw4bi4sWLGj3fvCwtLcWNGzfyrb9x44awtLTU6BiVKlUSu3btEkIIceHCBWFkZCRCQkJE3bp1lf7/5OXu7i79ns3NzUVMTIwQQogFCxaITp06KW2nrde/fv364vPPPxf//POPOHfunIiOjpZb1LG1tZWev6aUfe68/X6g7vXXxutnbm4uzp07V6D+F8ZHdzvpwIED0s9z586FhYUFVq1aJUXtz58/R48ePdCoUSO1x3JwcEDnzp2xaNEi6Rt+dnY2Bg8eDAsLC6xfvx7fffcdRo0ahSNHjkjtcr/xuLm54dSpUyhVqlShnktAQABGjx6NixcvokaNGjAzM5Pbru6b9JEjRzB16lR89913cut///137NmzB5s3b0aVKlWwcOFC9OnTB4B2fn83b95EYGCg9Lhjx46ws7ND69atkZGRgbZt22r0/A8fPoxjx47B0NBQbr2rq6va23EAMGTIECxdulT6JgcA/v7+MDU1Rd++fXH16lWlbR88eIDMzMx867OysvDw4UOl7VauXKm2X6oEBwdLP7dv3x6TJ0/GgAEDpHWDBg3CokWLsG/fPgwdOjRf+7zftvft24dRo0Zh+vTp0iXh48ePY+zYsZg+fbrSPqgbG6Tudy/eumLw9mNNbd26FaNGjcLIkSNRu3ZtAMB///2HOXPmYMKECcjMzMTo0aMxduxYzJ49O197a2trtG/fvlDn1obIyEhs374dNWvWhJ6eHlxcXPDll1/C0tISM2bMQMuWLVW2P3LkCA4fPoxq1aoV6vzfffcdatasiV27din8JqxOt27dkJqaiqpVq8LQ0DDf7RRlt2MAwMTEBEePHs13K+ro0aMaj7GJi4uDj48PAGDz5s1o1aoVpk+fjrNnz+Krr75S2z4xMRGVK1cGAJibmyMpKQkA0KpVK4wbN06jPhRFdHQ0zpw5I3crvSAMDQ3h6elZoDZxcXGFOtfbtPH6lS1bttD/+wVS7GHSe+Tk5CQuXbqUb/3FixeFo6Oj2valSpUS169fz7f++vXrwtbWVgiR8w3ByspK7bGUfVtURVk0rek3KTMzM3Hz5s1862/evCnMzMyEEELExMQIU1NThe0L+/tzdHQUx48fz7c+KipKmJubizFjxmjUf2tra3H58mUhhPy3ocOHDwt7e3u17Y2NjRV+izx//rwwNjZW2bZVq1bC19dXnDlzRlp3+vRpUb16dREYGKj23EII0aRJE/H8+fN865OSkjS6EqjJ66dKxYoVxeHDh/OtP3TokKhQoYLSdjKZTDg5OQlXV1eFi5OTk8rXT1vfZGvVqiUiIiLyrY+IiBC1atUSQgixdetW4e7uXuBjqyKTycS0adOkq1/GxsZi3Lhx0uPcRR0LCwsRFxcnhBDC2dlZumJ769YtYWJiora9t7e3wquBmjI1NVX496OpvFcFFS2qzJgxQxgbG4uBAweKNWvWiDVr1ogBAwYIU1NTMWPGDI3Ob2NjI/3/N2jQQPz+++9CCCHi4uI0+v2VL19enDhxQmqfe97169cLOzs7pe1kMplYvXq12L59u9i+fbswNTUVy5Ytkx7nLurUrFlT4f+fpmbPni1++OEHkZ2dXehjFJY2Xr/du3cLPz8/6X+guHzUQYy5ubk4cOBAvvWRkZHC3NxcbXtra2uFf6zbt28X1tbWQoicy2u5P78tKytLTJ48WTg5OQl9fX3pjXzs2LHSrZXiVLZsWYW3HebOnSvKli0rhMj5QC9durTC9oX9/bVp00aMHz9e4bYDBw4IMzMzjYKYjh07ij59+kh9uXXrlnj16pVo2rSpRpeTGzVqJL788kuRmJgorUtMTBR+fn7i888/V9n20aNHIiAgQLoFaGhoKPT09ERAQIDcB7Qqb3+Y53r48KEwMDBQ297Z2VnMnj073/rZs2cLZ2dnte0LG8S5urqKDRs2KN1+7tw5tbeTYmJiRFJSknjx4oWwsLAQ58+fl27p5S6a9P/q1av51l+9elXqv6YfaAXh4uKiNIDLXdzc3NQep2bNmlIQFhgYKLp27Sru3r0rfvzxR40Cr6J+CDRp0kT8+++/hWqrDRs2bBD169cXNjY2wsbGRtSvX1/l39XbAgMDhb+/v5g8ebIoUaKEuHv3rhAi5/dSrlw5te1HjRolpk2bJoTICVwMDAyEp6enMDQ0FKNGjVLaTtWXR3VfIvP+fe/fv1/Uq1dPHDhwQDx58kSjv/+3b3laWVkJNzc30apVK41vh77t8uXL4t9//y1wEFbU18/a2lp63zQ3N5eOk7toy0d3Oymvtm3bokePHpgzZ450OfrkyZMYOXKkRgMwu3btil69euGnn35CrVq1AACnTp3C9OnT0a1bNwDAwYMHUbFiRYXtp06dilWrVuGXX36RbtcAQKVKlTB//nz06tWrqE9RpXHjxuH777/HgQMHpOd/6tQp/PPPP9Kg2L179+KLL75Q2L6wv7+hQ4fmG8yWq3HjxtixYwdWr16ttv9z5syBv78/fHx88ObNG3Tu3Bk3b95EqVKl8Ndff6ltv2LFCrRt2xbOzs4oW7YsAODOnTsoV64ctm3bprKtnZ0d/vnnH9y4cQPXrl0DAFSoUAHly5dXe968g+uuXLmCxMRE6XFWVhYiIiLw2WefqT3OpEmT0Lt3b0RFRaFOnToAcn7/ERER+OOPP9S2r1WrFoYNG4Y1a9agdOnSAHIGi+a9PaNIjRo1cObMGXTs2FHh9rcHS79NCCH3exJCwNfXV+6xJgMbK1SogJkzZ2LZsmXSLcWMjAzMnDlTukR/79496bkBQPXq1bF//37Y2NionTKqbJpofHy8yn5pavDgwXjw4AGAnAGRLVq0wJ9//glDQ0OEhYUpbPP2wO6UlBR4eHjA1NQUJUqUkNtX0e2cvH97AwcOxPDhw6XbKm+313R21OXLl+VeK319faXveYD8zEplf0OaWLRoEX744Qds2rQJS5Yskf5n/v33X7Ro0UJt+5kzZ0o//+9//4OzszOOHz+OcuXKyd3uftvbA6ALwtraWu71E0KgWbNmcvuo+vvPOwkEgMa33hW5desW2rZti4sXL8r9z+b2T93/X8eOHYv0+s2fP7/QbQvio5udlFdqaipGjBiBFStWICMjA0IIlChRAr169cKsWbPyjTF5W1ZWFmbOnIlFixZJ4yBKly6NgQMHYtSoUdDX10dCQgL09PQUToP29PTE77//jmbNmsHCwgLnz5+Hu7s7rl27hnr16uH58+cqzz958mSV28ePH6/mN5BzD3PRokXSTCgvLy8MHDgQ9evXV9v27d8fABgYGGj8+9OGzMxMrF+/HhcuXEBycjKqV6+Ob7/9VuPpjkII7N27VwpEvL290bx58wKPDygIPT096fiK/r1MTEzw66+/omfPnmqPdfLkSSxcuFAav+Pt7Y1BgwZJQY0qMTExaNu2LW7cuKEwiFN2v/3KlStITU1FzZo1FW7PyMjA/fv34eLionD7wYMH1fYNgNLgOdexY8fQunVr6OnpSR+4Fy9eRFZWFnbu3Im6detizZo1SExMxMiRIwHkBH4jR46EqakpJk2apPL4EyZM0Kif2pKamopr167B2dlZ6Ti5VatWaXy8vOOncuX+7Sl7W8/dpiqIPHz4MIYNG4ZTp04ByJklmZqaKvchuHv3bjRv3lxp38zNzXHp0qVCz6zUVZr+7QPq//6LKjAwEPr6+li+fDnc3Nzw33//4enTpxg+fDhmz56t0bhQXfBRBzG5UlJSEBsbCwDw8PAo1Ifvy5cvAaBAuSJMTExw7do1uLi4yAUxV65cQe3atZGcnKyyfd5vr0DOh0dcXBwMDAzg4eHxzhIOFeX3V5Qpnu+atqbn3759G0IIuLu747///oOdnZ20zdDQEPb29tDX1y9SXzX1PoI4bXr16hX+/PNP3LhxA0BOEJ6bL6gosrKylL4Gx48fx9OnT9GqVStp3erVqzFhwgSkpKQgKCgIv/76q8rp3Xmlp6cjLi4OHh4eBZrqXRi3b9/WeF9lQWinTp1Qr149aQqyhYUFdu3aBRcXFwghsHDhQty+fRubN29Weuw2bdqgXbt2CgOtgoiNjcXKlSsRGxuLBQsWwN7eHv/++y+cnZ1VXg3KtWbNGixduhRxcXE4fvw4XFxcMH/+fLi5uSmd4nvjxg28ePFC7mrl/v37MXXqVOn1/+mnn4r0vDQRFxeHzMzMfINrb968iRIlSqgNEEuVKoXIyEhUqVIFVlZW+O+//+Dl5YXIyEgMHz4c586dk9u/qOkdFMnKysK2bdukL2EVK1ZE69attfr+91HeTtI0V4eiPB3KFCbRlY+PDw4fPpzvzWLTpk35AhRF3v4jA3KCqe7duyu9zPjy5Uupr7mBlzKaPiczMzOULFlS+llTJ06cQOfOnaUP9byUfRPUdp6T/fv3K00W9Xaug7d/32fPnkVmZqaUJ+bGjRvQ19dHjRo1VJ4z9/UuymXpXEUNAmUyGfz8/ODn56fxOSMjI/H5558X+gM3MzMTWVlZch/yDx8+xNKlS5GSkoLWrVvLzRhTxcLCIt/suqK4ceMGQkNDsXr1aulWz9smT56Mxo0bS0HMxYsX0atXL3Tv3h3e3t6YNWsWnJycMHHiRJXnSk1NxcCBA6WrK7l5jgYOHIjPPvsMo0ePVtleX18fDx48kPKV5Hr69Cns7e0V/v/kfa85dOgQ6tevn+91zMzMxLFjx5QGMadPn8aYMWPk1pUpU0bav2vXrmpnVhV1ZiWQc1UjICAADRo0wKFDhzBt2jTY29vj/PnzCA0NxaZNm1S2X7JkCcaPH48hQ4Zg2rRp0u/L2toa8+fPVxrEjBo1CpUrV5aCmLi4OAQGBqJRo0aoUqUKZsyYAVNTUwwZMkTl+VeuXAlzc3N06NBBbv3GjRuRmpqqNsDr3r07evbsmS+IOXnyJJYvX46oqCiV7bOysqRgv1SpUrh//z68vLzg4uKiMEeZtm//xMTE4KuvvsK9e/ek99AZM2agbNmy2LVrFzw8PLRzIq2NrvmAFCVPR16JiYmiS5cuwtHRUejr6+fLU6HOtm3bhJWVlZg5c6YwNTUVs2bNEr179xaGhoZq85SocuHCBeHi4qJwW94cHcrybGg6uykrK0tMmjRJWFpaSm2trKzE5MmTRVZWltr2VatWFR06dBBXrlwRz58/Fy9evJBbFNFkUJ2m/Z84caLQ09MTtWvXFm3atBFBQUFyiypz5swRgYGB4tmzZ9K6Z8+eiTZt2igcbKtKYQfWFTXPhxBC7Nu3T4SEhIhevXqJHj16yC3KvJ3npU6dOtKgSk10795d9O3bV3r88uVLUbZsWWFnZyeqVKkiDAwMNM5/cePGDfH777+LKVOmiEmTJsktmkpJSRErVqwQDRs2FPr6+qJOnTril19+Ubq/g4ODOHXqlPT4p59+Eg0aNJAe//3338Lb21vteQcNGiRq1KghDh8+LMzMzKSB/du2bRPVqlVT217ZwPB79+6pnV0nRP7XMdeTJ09U/v0YGxuLhIQE6fHmzZtFSkqK9Dg+Pl4YGhqq7XtR/neFEKJu3bpizpw5Qgj5GW4nT54Un332mdr23t7eYuvWrfnaX7x4UZpdqkiZMmXEsWPHpMdTpkwRVatWlR4vX75c7rEy5cqVE5GRkfnWR0VFifLly6ttb2FhoXR2oiYzYhs2bCg9/06dOokWLVqII0eOiG7duomKFSuqbV9UAQEBokWLFuLp06fSuidPnogWLVqIr776Smvn+SivxBQ1V0eu7t27IyEhAePGjStUnoU2bdpgx44dmDx5MszMzDB+/HhUr14dO3bswJdfflnofiUlJUk5D94WGRkpXTXJm/OlMMaMGYPQ0FDMnDkTDRo0AJCTu2LixIl48+YNpk2bprL9zZs3sWnTpgLlOtDG1YtcS5cuRVhYGLp27VrgtnPmzMGePXvksoLa2Nhg6tSp8PPzw/Dhw9Ueo6gD64qa52PSpEmYPHkyatasWaD24q2rZpcvX0ZaWprG580dh5Vr9erVyMrKws2bN2FlZYVRo0Zh1qxZanN9/PHHH/j+++9RqlQpODg4yPVfJpOpHRN24sQJLF++HBs3boSzszOuXr2KAwcOqB0L8Pz5c7nBwrlXBHLVqlULd+7cUXkMANi2bRs2bNiAunXryvW9YsWK0u1ZRXLz9MhkMixfvlyuvEZWVhYOHTqkUe4R8f/Hvrzt6dOnKq+oWlhYIDY2VhpH9faV7bi4OLVXcbXxf3zx4kWsW7cu33p7e3s8efJEbfu4uDiFV7yNjIyQkpKitN2TJ0/kxjgeOHBAbiBw48aNNfr/T0hIgJubW771Li4uSEhIUNteJpPh1atX+dYnJSVplK157Nix0vOcPHkyWrVqhUaNGsHW1hYbNmxQ276ot4IOHjyIEydOSJ9HAGBrayv3eaIVWguHPkLvKuOgMm/npZg/f74YNWqUcHJyUppxsm3bttL0vVWrVok3b94U+vyOjo4Krxhs27ZNODk5qW1f1CmehcktklfJkiWlLJ0FVdTp+ULk5Jpp06aNePz4sTA3NxdXrlwRhw8fFrVr1xaHDh1S276oeT4cHBzE6tWrC9yuqHleTE1Nxa1bt6THbdu2FQMHDpQeX758WWWejlzOzs5i5syZGp831+zZs4WPj4/47LPPxIgRI6TsqAYGBlLeEXXnPXjwoBBCiLS0NGFiYiL27dsnbb9w4YJGU0RNTEyk31ve32F0dLTKrKe507hlMpkoW7as3NTu8uXLCz8/Pyn/iSK5U3D19PTEV199JTctt3Xr1sLV1VX4+/srbd+qVSuVV+qCg4NFy5Yt1T39Ivvss8/E0aNHhRDyv78tW7ZoNEXd29tbymyct/3ChQuFr6+v0nZOTk7i5MmTQoicq9GWlpZyGZavXLmiUdbasmXLKn3/1ORKUqtWrUSHDh1EZmamtC4zM1O0b99etGjRQmm7GjVqiCVLliicxv306VON8s7cvHlTlCtXTi5bu6mpqfDy8tL4PdXGxkZ6/fI6cuSIVqdYM4hRoajJpoQQ4vnz5+KPP/4QISEh0mW1M2fOaHR5/u3cFO7u7qJOnToiJCREvHz5UmGbEiVKiPv37wshlF9O1pSRkZHCZH/Xrl3T6HL2li1bhI+Pj1i5cqU4ffq0OH/+vNyijkwmE40bNxZr1qwpVLLAH3/8UUyePLnA7YQQomvXrsLV1VVs3rxZ3LlzR9y5c0ds2rRJuLm5iW7duml0DFtbW+l5WlpaimvXrgkhhNi/f79GtxOKGgQWNojT09MTjx49kh5bWFjIBSWanDdvsODo6CjWrl0rPY6NjdUot0thyxXo6+uLn376Se7NXwjNg5jvvvtO1KtXTxw6dEgMGzZM2NrairS0NGn72rVrRc2aNdUep1GjRmLhwoVCiP/LcySEEAMGDFAZRORq3Lix3O1MTeXeLpfJZOJ///uf3C30vn37iunTp4vHjx8rbR8ZGSn09PTEiBEj5N4/Hj58KIYNGyb09fXF/v37FbZNTU2VK6kyevRoMXToUGkZMWKExv/Lw4cPFw0bNhQPHjyQbq0cOXJEuLu7i4kTJ6pt/8cff4jPPvtMrF+/XpiZmYm//vpLTJ06VfpZmc6dO4tWrVqJhIQEMWfOHGFubi6Sk5Ol7Zs2bRJVqlRRe/4ff/xRuLi4iMjISJGZmSkyMzPF/v37hYuLixg+fLja9pcuXRK2trbCw8NDev08PDyEnZ2dylIQPXv2FBYWFsLU1FR07dpV4ZcxdbRxK6hr166iYsWK4sSJEyI7O1tkZ2eL48ePi0qVKong4OAC90kZBjEqFDXZ1Pnz54WdnZ3w9PQUBgYG0hvymDFjRNeuXbXY0/9TuXJlERwcLMLCwoRMJhO//vqrWLVqlcJFndq1a8t9g841YMAAUadOHbXti1K7Q4icpGqDBg0SdnZ2wsrKSvTt21f6hqSJQYMGCWtra/H555+LAQMGyL2ZDh06VGXblJQU8f333wsjIyNpPJChoaH4/vvv5d7QVLG2tpY+uNzd3aX74zExMRp9iBc1CCxsECeTyUTlypWlb2D6+vqiYsWK0uPcRZmmTZuK0aNHCyFysgPr6elJgbUQQuzZs0d4eHio7UfPnj3FkiVLCtz/6dOni3LlyomyZcuKH3/8UXrD1zSIefz4sWjUqJGQyWTCwsJCbNmyRW5706ZNxU8//aT2OIcPHxbm5ubiu+++E8bGxmLw4MHiyy+/FGZmZuL06dMFfl4FNXHiRI3/Vt+2ePFiKVGZtbW1sLGxkf4Hfv31V6XtlixZIlq1aiU9Njc3F3Xq1JFqsTk4OCit+/W2tLQ00bt3b2FgYCBkMpkoUaKE0NPTE126dMkXoCqzdu1a4enpKb3/fPbZZ2oTjcbFxUltDAwMxG+//Sa3vU2bNmLIkCEa9b9jx45S30uUKCH09fVFjx495IJiVe7duydCQkLEV199Jdq3by8mTZokF1gok5KSIlauXCm++OILoaenJzw8PMS0adM0HttmamoqLly4kG99dHS0RtnChcj5At+6det8CUODgoKUjoksjE9iinVh2djYIDU1FZmZmRonm8qrefPmqF69On755Re5KdbHjh1D586dC5RU6+7duwCgMB9NXseOHcOwYcMQGxuLZ8+ewcLCQmkla3X9P3jwIFq2bAlnZ2e52jt37tzBP//8o3ZsgbrpnspmR7wtMzMT4eHhCAsLQ0REBMqXL4+ePXuia9euctOX39akSROl22QymUal7BVNL1c1PTevRo0aYfjw4QgKCkLnzp3x/PlzjB07FsuWLcOZM2dw6dIlle3frsid22+hYbK4wYMHY/Xq1ahSpQqqVKmS7+9X2TRxdflVcinLs5I7hsTR0REPHjxAp06dEBoaKm3/4YcfkJKSojYnyowZMzB37ly0bNlSYbI2ZVWI8/ZjxYoV0risy5cv4+DBgxrfj09KSoK5uXm+1/rZs2cwNzfPV9NLkVu3bmHGjBk4f/68lOcod/aLJu7evYvw8HAkJCQgPT1dbpuqaf7akJCQgM2bN+PmzZsAgHLlyuHrr7+Wxsoo0qhRI/z444/SGJK873sAsHbtWixevBjHjx/XuB937tzBxYsXkZycDF9f33yzdRTJzMzEunXr4O/vj9KlSyM1NRXJycn5Znqpan/58mXY2dnByclJbtv58+dRpkwZ2NraKm0vhMCdO3dgZ2eHu3fvIjo6GiYmJqhcubJG73sZGRlo0aIFli5dqtHzVSV3mvqaNWtw//59+Pn5oVevXipn8ZYsWRI7d+7Ml0/s6NGjCAwM1HiKNZAzNjJvioeC1oNSh0GMCureZNVNkbOyssLZs2fh4eEh9898+/ZteHl54c2bNyrbZ2dnY+rUqZgzZ46UU8bCwgLDhw/HmDFjFH7I5fV2SfnCuH//PhYvXiz3R/jDDz/k+8d+F9LS0vDbb78hJCQE6enpMDQ0RMeOHfHzzz/D0dGx2M+vyfTcvHbv3o2UlBS0a9cOMTExaNWqFW7cuCENrGvatKnK9kUNArURxBXW1atXsWfPHjg4OKBDhw5yf6vLli1D7dq11RY2VDQoMpdMJsOtW7c06surV6+wbt06rFixAmfOnEHt2rXx9ddfq8wLlJGRARMTE0RHR6NSpUoaneft9v369cO4ceNUPg9V9u/fj9atW0sJMitVqoT4+HgIIVC9enWNXr9Nmzbh77//VhgEqcsz9ebNG42L/eVydHTE8ePHpRwmdnZ2OHXqlPT4xo0bqFWrltKJCdpkamqKq1evavxl6W2XLl1S+tpv27YNQUFBSttmZ2fD2NgYly9fLnQQYmdnh2PHjhU5iMklhMDmzZvRr18/vHjxQuWXoG7duuHs2bMIDQ2Vy9bep08f1KhRQ2nG6fdCa9d0KB87OztpTE3egWV79uwRZcqUUdt+9OjRws7OTvz222/SLYTFixcLOzs7jS5nx8fHv5fiYXnFxMSIAQMGiGbNmolmzZqJgQMHFnicxqlTp8T3338vbGxsRJkyZcSYMWPErVu3xKFDh0SzZs2kYoCq5I5rKaiCTs9VR9OBdR+iqKgosWvXLpXjNHx9faXtkyZNkpua+yG4cOGCGDx4sEYDi93c3KRBwYVhaWlZoLFEb6tVq5ZUgyz3/ePVq1eidevW+W5xKLJgwQJhbm4uBgwYIAwNDUW/fv1E8+bNhZWVlUbvHxYWFiI4OFjs2bNHo5QKQuRMz84d+6XI1atXhZGRkUbHateuncKB3T///LP4+uuv1bb/4osvpCnGheHk5KTw9du0aZPSorl5+fj4KCyEq6khQ4aorPFUEAcOHBDdunUTZmZmwsrKSvTr10/l/oW9FTR06FDpFubbt+8Lcju/IBjEvCXviO63C3YVtIBdr169RFBQkEhPT5cG9t2+fVv4+vqKwYMHq21f1NlBQuT8Me7evVusWbOmwGNictvPnj1b9OrVS/Tq1UvMnTtX4/uZERERwtDQUNSuXVv6w61du7YwMjLSKE/OnDlzRKVKlUSJEiVEmzZtxI4dO/K9md65c0fo6+srbF+UPDfHjx8XvXr1EpaWlqJSpUpCX19foxlF2nTnzh3x6tWrfOvT09Ol2TMFOZamQdzMmTPF2LFjpcfZ2dnC399fGldQunRphdXNhcj5EMs9z9sDhD8k6enpavdZvny5+OqrrzQag6BIt27dNB7/oYi5ubkU8FtbW0u/8+joaKV5ovLy8vIS69atk46V+yVq3Lhxon///mrbb9myRXz99dfCxMREODg4iMGDB8vlz1HE09NTbNq0Sen2DRs2aDQeSgghSpUqpXBcxoULFzSqYr9hwwbh7u4ufv31V3Hs2LECjykbP368cHd3Fw8ePJDWrV+/Xpiamoq///5bbfvw8HDRsGFDlYNwVRkwYICwtLQUNWrUEH379i1wEHDnzh0xZcoU4eHhIWQymfj888/FqlWrRGpqqsZ9uHnzpggPDxfh4eEazZRs3LixeP78ufSzqkVbeDvpLXmzZOatgZOX0HBMQlJSEr7++mucPn0ar169gpOTExITE1GvXj38888/arPfGhsb48KFC/mKDl6/fh3VqlXD69evVbbfsWMHvv32WyQnJ8PS0jJfng119zVPnz4Nf39/mJiYyBWQfP36Nfbs2YPq1aurbO/r6wt/f3+5QmwAMHr0aOzZs0ft5exy5cqhZ8+e6N69u9LbRenp6fjrr78U3toLCQlBaGgoJk2alC/PTZ8+fRTmuZkzZw5WrFiBpKQkdOrUCV26dEHVqlVRokQJnD9/Hj4+Pir7DECjmkhA/ozBuR48eIA2bdrgzJkzkMlk6Ny5M3777TcpX8jDhw/h5OSk9u+vsLcjc8dt/O9//wOQk2E0ODgYe/fuhbe3N7p16wZTU1P8/fff+drWq1cP5ubmaNiwISZNmoQRI0bI5TnJS1Gel2HDhmHKlCkwMzNTWwZC0ZiQ3Bwr6shkMgwcOFDlPr6+voiJiUFGRgZcXFzy/b+q+/vN/d03a9ZMYdZadWN6HBwccODAAXh7e8PHxwczZ85E69atcf78eTRo0EBt2ZK8t1Ps7e2xd+9eVK1aFTdv3kTdunXx9OlTle1zvXr1Cps2bcJff/2FyMhIuLu7o0uXLgpfv8GDB2Pfvn04c+ZMvltRr1+/Rs2aNdG8eXMsWLBA7Xlzb+flZnvNde3aNfj6+qp9/yvqmDIgp4jmgQMHcOjQIURERKB3795Ys2YN2rdvr7Zt3jGVhoaG+eq9qXv/Lezt4L///hsrVqzA/v37YW9vj+DgYPTs2bNIY1EyMzPx5s0bpf/L7xODmLfkDvwzMDBQW8xL0wJeR48elRvYp6pwWl516tRBnTp18r0xDxw4EKdOncKJEydUti9fvjy++uorTJ8+HaamphqdM69GjRrB09MTf/zxh5S6PDMzE71798atW7dw6NAhle2NjY1x8eLFfPd0b9y4gSpVqqgdExQfHw9nZ+d8b0bi/w+ac3Z2VtneyckJS5cuzZfifPv27fjhhx9w7969fG0MDAwwatQoTJ48WW5AZ0GCGD09Pbi4uMDX11dlteetW7cqXB8cHIzr169j0aJFePHiBUaPHg2ZTCYl33v48CEcHR3VJhQrTBAH5Lz5Hjt2DN7e3gCAHj16ICsrS6o8fuLECXTo0EFhwrfr169jwoQJiI2NxdmzZ+Hj46OwfIFMJlMYBDRp0gRbt26FtbW1yjdxQHEyR03Hn2gypqaoBSSLOqYnKCgILVu2RJ8+fTBixAhs374d3bt3x5YtW2BjY4N9+/apbO/u7o7NmzfD19cXNWvWRJ8+fdCvXz/s2bMH33zzTYEGZ+a6cuUKvv32W1y4cEFhEPDw4UNUq1YNhoaGGDBggPQFLPfvOTMzE+fOnZNLJqhM7dq10apVq3zB0sSJE7Fjxw6cOXNGZXttTSz49ttvcerUKdy7dw/r1q1TWq7gbUUdU1lYhoaGaNmyJXr16oWvvvpK7djJvHbs2IGnT5+ie/fu0rpp06ZhypQpyMzMRNOmTbFhwwa5JKDK9OzZEwsWLMhX5ywlJQUDBw5U+iWuwLR2TYfkpKenC319/UJfShQiZwyCmZmZ8Pb2Fj179hQ9e/YU3t7ewtzcXONkaUVJGGdsbCyuXr2ab/3ly5c1miJcpkwZhZddN2zYIMqWLau2fWHTpucqTJ6bok7PFUKIH374QdjY2Ihq1aqJBQsWFPh2RN5kW0II8ebNGxEYGCiqVasmnj59KhITEzV6/oW9Hfl2cjsvLy+5qc63b9/WKE+QsrT5pJnY2FjptkdycrLo16+fqFy5smjXrp2Ij49X275Xr15SPpVFixYJExMT0bx5c2FtbS169uypcT9ev34tNmzYINq0aSOMjIyEs7OzyrEat27dEv7+/nLlMvT09IS/v3+B3o/Cw8OFgYGB6NatmwgLCxNhYWGia9euwsDAoEhjXbKysuRy2eT1dmmQ7du3i02bNomyZcuKXr16FahsiDYV5HZwUf7nGjduLBYtWiQ9Pnr0qNDT0xNTp04VmzdvFhUqVNB4PIuy9+/Hjx8rHQJQGAxi1CjKmJKiDgwUIidPwE8//STatWsn2rVrJ8aMGSPu3bunUdu2bduKDRs2FPrc9vb2Yvfu3fnWR0REaHRPetKkScLa2lrMnDlTHDp0SBw6dEjMmDFDWFtba5S/RNmHYHx8vEYD64qS5yYqKkp069ZNmJqaiipVqgh9fX1x5MgRtefM9ebNG7Fu3TrRvHlzYWpqKjp06CAiIiI0GtRrZmYmbty4IbcuIyNDBAUFiSpVqogLFy4UWxAnRE7Nq5UrVwohcgIWmUwmF8AdPXpUo4yjRdWjRw+FSR2Tk5NVZpT90F25ckWjZGdFlZWVJTIyMqTHf/31lxg4cKBYuHChRnlKIiIiRLdu3YSlpaUoWbKk6Nu3b4HGYj19+lScPHlSnDx5stDjinbu3Cnq168vTE1Nha2trWjSpImIiooq1LFu3rwpQkJChKOjozAwMFC4jzZrtwmRk2F306ZNYsqUKWLKlCliy5YtGue4KWrtuunTp4vQ0NB860NDQ5Vmws47GUWInMG5eRMz7tq1S3h6eqo8b1JSknjx4oWQyWQiJiZGbhzps2fPxKpVq4Sjo6Pa/muKQYwK4eHhwsLCQshkMmFlZSWsra2lRZO0yUUdGHj79m2lH3q3b9/W6PzOzs5iwoQJYtOmTQUuQDhw4EBRpkwZsX79epGQkCASEhLEX3/9JcqUKaPRwOTs7Gwxd+5c8dlnn8klm5o/f77KD/PcgWt6enqiX79+coPZBg0aJOrUqSPq16+v9vxFvZIlRE7xwqVLl4ratWsLfX19Ua9ePakonabi4+PFxIkThbu7u3B2dlY4WDevypUrKxwcmRvIODs7a/QmWtggbtmyZcLMzEz07NlT+Pj45PtdT5kyRS6hmSpFKeCojW9yd+7cEYsXLxajRo3SaGBk7v+2JktBJCcni+XLl4t69eoJmUxWqAJ8sbGx4tKlSxrPFCoqExMT0aFDB7Ft2zaNBkLntWbNmg9iZlpqaqpYtWqVaNSokdDT0xNffPGFWLJkiUhMTCz2cxc1dX9RZ6e6uLgoTPt/4sQJ4erqqrCNsbGx3GdLrVq15GZjavIFUlnh4dxFX19fTJ06VW3/NcUxMSoUdUxJUQcG5h1knNfTp09hb2+vdmCaqnuhmgxsS09Px8iRI7F06VJkZmYCyBkb8v3332PmzJkwMjJS2T6v3EJmb98fVSR3LMTBgwdRr149uaRihoaGcHV1xYgRIzTKn6DNPDcXL15EaGgo1q1bh0ePHmnc7s6dO1i5ciXCwsKQnp6Oa9euqRwgN2rUKERHR2P37t35tmVmZqJ9+/bYuXOn2tevKMkKV6xYgR07dsDBwQETJkyAg4ODtO2HH35A8+bNVSbLAtQXcFT29//y5UsIIWBjY4ObN2/KJTTMysrCjh07MHr0aNy/f1/l+QuTZ0XdOIa8NBnTcPToUYSGhuLvv//G69evMXToUPTu3VtlAceMjAxMnToVZ8+eRd26dTF69Gh06dJFGkjt5eWFf/75R8q9oomUlBRs2LABr1+/hp+fn0b/O69evdLo/1UROzs7vH79Gq1bt0aXLl3g7++vceFAbTh16hSWL1+O9evXw8PDA99++y1GjRqFCxcuaDSuTRu++uorCCHw559/SkUQnz59ii5dukBPTw+7du1S2b4wY/ryMjY2xtWrV/ONzbp16xZ8fHwUjkn09PTE4sWL4e/vj+TkZNja2iIyMlIaU3f27Fn4+/vj8ePHSs978OBBCCHQtGlTbN68Wa4ApKGhIVxcXLSbZ0xr4dBHqKhjSiZMmCAmTpyodFFHJpMpnKKq6e0UbUlJSREXLlwQFy5ceKffrrp3767RVPZ3TZNvpXlvJxkbG4uvv/5a7Nq1S6Nv0RkZGSqfd0ZGhkZjIoQo2u3IoipsAUdtfZMrap6Vwnr48KH4+eefhZeXl3BwcBBDhw4Vp06d0nhc1bBhw4SdnZ3o3bu3cHd3F61btxZeXl5i/fr14u+//xaVK1cWnTt3Vtr+9u3b4vPPPxfm5uaiefPm4vbt26J8+fLS1VBTU1ONbwtlZmaKjRs3ismTJ4vJkyeLjRs3yt2iUiYjI0Ps2LFDdO7cWZiZmQk7Ozvxww8/KLwyoOrcs2bNErVq1RKlS5fW+EpY5cqVhYuLiwgJCZFLBVCQcW1C5FyJXrBgQb71v/76q0ZXoouaur+otes8PT3FmjVr8q1fvXq1cHNzU9hm9OjRokKFCmL16tXim2++Ec7OznK3v37//XfRoEEDtecW4t3lKWMQo0JRx5QUlrZup7wPeZOdVatWLV+9HU1q7ygTHx8vLl++rDYQuHHjhvjmm28UBgIvXrwQnTp1Uhicvl01XNmSW9RPmdzEfFWqVBHz589XWWxPmfT0dOHu7i6uXLlS4LbalJWVJa5fvy4OHz4sDh48KLeoU9gCjlFRUeLAgQNCJpOJLVu2iKioKGk5duyYxkFYYfOsjBs3Ti5YL2gRRmNjY9GlSxcREREh97dakCrau3btEkIIcf36dSGTycQ///wjbY+KilI5JqlDhw6ibt26Yu3ataJ169aiQoUKomXLliIxMVE8evRItG/fXjRp0kRtPy5duiTc3NzkboeYmZkJV1fXAk1YSElJEWvXrhVfffWVMDQ01KgCtRA5r4Ojo6OYPXu2MDY2FlOmTBG9evUStra2CoOLXIaGhqJr165iz549ch+iBQ1inJycFNa4OnPmjEZjwopaxbmotet+/vlnYWtrK1asWCHi4+NFfHy8CA0NFba2tmL69OkK26SmpoquXbsKa2trUaFChXy33Rs3bqzyi8n58+elv/m38/IUNE+PphjEqFDUMSVubm7iyZMn+dY/f/5caSQsxP8lCZLJZKJ+/fpyCYL8/PxE37598w38VCYqKkq0atVKeHh4CA8PDxEYGKjxeJDk5GQxduxYUa9ePeHh4SHc3NzkFkUmTpwofQAU9kpUaGhovnEnffr0kb6Je3t7i4SEBKXt+/TpI0aOHKl0+48//ii+++67fOvfrhpuZmYmZDKZ9M1PJpMJMzMzla+dEDlXElxcXERQUJBo27at0kUdJyenQgUxhQ3i3nb8+HHh5uYmN8ukIAMbC1vAMVd8fHyRxn+ULl1a+v15e3tL/7Pqvgm/PRanoMGYl5eXcHV1FT/99JPc7D5NP0QNDAzkCvUZGxvL/b/fv39f5Zig0qVLS7Pbnj59KmQymTh27Ji0PTo6Wtja2qrtR926dUVgYKBcEPfs2TPRunVrUa9ePbXt83r8+LH49ddfRcWKFTUeFOvu7i527twphJAPSBcsWCA6deqktN3du3fF1KlThYeHh3BychLDhw8XZ8+eFSVKlChQEGNkZKQwwdvNmzc1yjpc1CrORR3Tl52dLX788UdhbGwsvXeampqqHY+WnZ0t4uPjC5QUL1feyRh5C/4WdmC0JhjEqFDU0enKZtckJiaKEiVKqG1f1Nspa9asEQYGBqJjx47SVYSOHTuKEiVKiD///FNt+2+++UY4OjqKH3/8UcybN0/Mnz9fbikuderUEStWrJAe//vvv8LAwECsXbtWnDlzRtSrV0/06tVLafvy5cuL//77T+n206dPi/Lly6vsw59//ikaNGggl0L92rVrolGjRmLt2rUq2wYHB4vu3burXdSZNm2aCA4O1ujyfV6FDeLeVrVqVdGhQwdx5coV8fz5c/HixQu5RZ3p06eLUqVKieDgYDF79ux8V7Q0lZKSIq5evVrgb3Jt2rQRy5YtE0IIMXz4cOHp6SmmTp0qqlevLpo1a6a03dv/t29POdfEkSNHRI8ePYS5ubmoXr26mDt3rjAwMNAoKFV3fnVT7GUymdzAVTMzswK1z2VsbKwwM/PFixc1up2RewUmICBAGBoaCg8PDzF27FiFaRsUMTU1lQaZOjg4iDNnzgghcgY4W1paanSM/fv3i2+//VaYmJgImUwmRo4cqfAWjSIVK1ZUWLF74cKFwtvbW217bVRxvnfvnhgzZkyRbge/evVK/Pfff+LixYvizZs3avfPysoSJUqU0PiLcl55byHlXv1RtmgLg5hikHulRiaTidWrV8tdvdmyZYvo37+/2g9RbahQoYLCtOdz5swRFSpUUNveysqqQNOK31bYK1ElS5aUu5f83Xffifbt20uPDxw4oHR0vRA5b76q/kni4+PV5rlxd3eXm2qY6/Tp0yrPff78eY2nUKoTFBQkLCwshKOjo/Dz89P4So42gjghcj5ENEk1rszbV7byLuquZgkhxKNHj0TLli2Vjo1Rp7B5VrQRxOR69eqVWLZsmTQrqXHjxmLZsmUqyzG8/b5hamoqli1bJj1etWqV2iCmKEFQripVqoj9+/fnW79//35RqVIllW3/97//SWNh+vfvL3clSFPly5cXJ06cEEII0aBBAzFjxgwhRE7qf01qX+X14sULsXjxYlGjRg0hk8lE5cqV1bYJDQ0VJiYmYvz48dLtzHHjxkmvh6YKmrpfCCHi4uLEsmXLxKJFi4qUayyvpKQksXXrVo0C6aLWfXqXGMQUg7xXa96+gmNoaCjKly+vNNlSXoW5nZOXoaFhkS6Hurq6FmlMRmGvRJmYmMh9yFSpUkXum7u6ZGulS5dW+Oaba9++faJ06dIq+25iYqIwEDh58qTKAChvvSBlQZymCnslRxtBnBBCNGnSRPz777+F6rs2dO7cWTRo0ECcOnVKmJmZiT179og1a9YILy8v6TaDMpmZmeLgwYNSHZeC0NPTk/JbvHjxQlhYWIjz588XuHba23Lzw9jb2yvNUyKEZrlK1AUxecfSGRoaip49e0qP+/Xrp7R93ue3a9cuUbFiRbFx40Yp2drGjRtF5cqVpTE7ynTu3Fns2rWrSAH9qFGjxLRp04QQOYGLgYGB8PT0FIaGhkUqjHju3DmFY00U+e233+RSRLi5uanNEZaVlSVmzpwp6tevL2rWrClGjRpVoFszkZGRwtTUVDpniRIlFA7QVadDhw7SlaTU1FRRrlw5UaJECWFgYKCyvpUQha/7pChZoLJFWxjEqFGUMSWurq6FGtSZq6i3czw8PMTSpUvzrV+yZInahEVC5NyO+vrrrws8I6moV6IqVKggNm/eLIT4v5wgeQfYnTx5UmUQ0qFDBxEUFKR0e+vWrdVWwW3VqpXw9fWVLmELkXMFo3r16iIwMFBpu5IlS0rfHpXNLitu2gjihMgpAOjj4yNWrlwpTp8+XWwD85RxcHCQxnZYWFhItwG2b9+u0QwJIyOjQlWRfnt2lLLHhZWRkSH9fReHL774Qm3xPWUF+BQ917zrimNMg6aOHTsm5syZI8LDwzVuk5GRIfbu3SuWLl0qJU68d++e2lxNb3v06JHGbSZPniz09PSEn5+faNOmjTA2Ni5QcsYGDRqINm3aiPv374tnz56JH374oVDJ4UqXLi0lW/3zzz+Fp6enSElJEb/99puoVq2ayrbW1tbS7S9jY2ONZ4YpCrbffqzplVRNMU+MCmvXrkWPHj3Qrl07aZ780aNHsXXrVoSFhaFz584K2x0/fhxPnz5Fq1atpHWrV6/GhAkTkJKSgqCgIPz6669q86xYW1tj165d0rkLasmSJRgyZAh69uyJ+vXrS/0PCwvDggUL0K9fv3xtfH195fJ5xMTEQAgBV1dXlChRQm5fZXk+cvPT5BZby6tEiRJwdXXFnDlz5H4/ec2cORMLFizADz/8gMjISDx+/BiXLl2Sts+fPx87d+5UWjvm3LlzqFevHlq1aoUff/xRKiB37do1/PLLL9i1axeOHTumsoDl48ePERwcjIiICOl5Z2Zmwt/fH2FhYfly9+Tq27cvVq9eDUdHRyQkJKBMmTJK82Ooq52Tty/Xr18HkJMjJG/eFEU6duyIjIwMpbWZ2rRpA0NDQ2zcuFHlcQpbQE9Z/hgrKyuUL18evXv3VvscAMDS0hIXLlyAq6srXFxcsG7dOjRo0ABxcXGoWLEiUlNTVbavWbMmfv75ZzRr1kztufJSVzMtl7raac2bN0eXLl3Qrl07WFpaanTM6tWrY//+/bCxscHkyZMxYsSIAuWoevnypcbnUkTT5w6of/779+/HvHnzcPXqVQA5OZqGDBmice24orp9+zZatGiBhIQEpKWl4caNG3B3d8fgwYORnp6OJUuWqD1GZmYmoqKiEBsbi86dO8PCwgL379+HpaWl0lxP5cqVw4gRI6T313379qFly5Z4/fq1RnWMrK2tcezYMSmfTWpqKiwtLfHw4UPY2tpq/PxNTExw48YNlC1bFt26dYOTkxNmzpyJhIQE+Pj4KCwgev/+fTg5OSEsLExh8eNcmuRI2rdvH0aNGoXp06fL5akaO3Yspk+fji+//FLj56IKgxgVvL290bdvXwwdOlRu/dy5c/HHH39I/5xva9GiBZo0aYJRo0YByEmSVr16dXTv3h3e3t6YNWsW+vXrh4kTJ6o8v5ubG/755x+pEF9hbN26FXPmzJF7Ixk5cqTSImbqit7lpUkBvFOnTqFUqVKadxg51Zdzi7w5ODhg7ty5cr+DDh06oEWLFujVq5fSY+zcuRM9e/bMV6nX1tYWy5cvz5dASpkbN27g6tWrkMlkqFChQr6K4opEREQgJiYGgwYNwuTJk5UmDBs8eLDK4+QWSlu9erVU7FFfXx/dunXDr7/+qvTDTRtBHFD4Ano9evRQuP7Fixc4f/48Xrx4gUOHDqFSpUoqj1+rVi1MnToV/v7+aN26NaytrTFjxgwsXLgQmzZtQmxsrMr2ERERCAkJwZQpUxRWkS7Kh70mBg8ejL///htJSUlo2bIlunTpgq+++irfl4G8TExMcPPmTSn4VZTsUhV9fX0kJibCzs4OTZs2xZYtW2Btba2FZyPv0qVLKl+/3377DYMHD8bXX38tfYCdOHECmzZtwrx589C/f3+F7cLDwzXug7r/4aCgIFhYWCA0NBS2trY4f/483N3dERUVhT59+uDmzZsq26sKgtLS0rB06VKF7YyMjBATE4OyZctK64yNjRETE4MyZcqofV56enpITEyUe90tLCyk/muqfPnymDp1Klq2bAk3NzesX78eTZs2xfnz59GsWTM8efIkXxsbGxssXrxY6Rf0gqhUqRKWLl2Khg0byq0/fPgw+vbtq/Tzs8C0dk3nI1TYMSUODg7i1KlT0uOffvpJ7vL333//rdHo9sLeztGGjIwMMWnSJI2Ljn2IUlNTxZYtW8Qvv/wifv75Z7F169ZC/S5zp0cWVPfu3RXW/tFU3759hbu7u/jnn3/kxil4eHionV20Y8cOYWdnl28wrJ2d3TsvXpdXVlaW6Nmzp0ZlC9asWSPVcDp9+rQoVaqUdHl7/fr1atsru4yt6nbI2+NeVC2aPt/du3eL4OBgYWlpKWxsbESfPn2U1v+pW7euaN68uZg4caI0m+btcg2qyjZYWlpK49i0fTvz5cuX4vfffxe1atVSezvgs88+UzizZ9GiRSqLj2qzdlHJkiWl2YV5BzfHxcVpNCasTZs2okuXLiItLU2u/YEDB1Tejs87Li6Xubm5xrc2Fd2Gf3twtyb/w4sXLxYGBgbC2tpaVK1aVUpXsHDhQqW3ExcvXizMzc3F119/XehyObmMjY0Vjqk5f/68RrPbNMUrMSp4enpi5MiR+W67LF26FHPmzFEayRsbG+PmzZtSJN6wYUMEBARgzJgxAID4+HhUrlxZSsWvjK+vL2JjYwt8O+f58+dYu3YtgoOD833bTEpKwurVqxVue5uFhQUuXrxYoPTmCxcuRN++fWFsbIyFCxeq3HfQoEFqj/fixQvpW/fIkSNRsmRJnD17FqVLl8Znn32mcb8KY/Xq1Zg1a5b0OpcvXx4jR45E165dC3ysu3fvAoBG38RylSpVCps2bULjxo3l1h84cAAdO3ZUmfobAF6/fo3du3fj5s2bEEKgfPny8PPzU3l7Ijw8HAEBAShRooTab8WaXs162/nz5xEQEKC2bMDbUlNTce3aNTg7O2t0dU/drRFFt0P09PRUXkbPS13Zh7e9efMGO3bswLRp03Dx4kWF7a9fv44JEyYgNjYWZ8+ehY+PDwwMDPLtp6xsQ/v27XH06FF4e3vj4MGDqF+/vlzZjrwUlV1Q5NChQwgNDcXmzZvh5OSEdu3aoX379qhVq5bSNubm5oiOjoanp6fc+ps3b8LX11fhrQxts7GxwdGjR+Hj4yN3JePIkSNo3749Hj58qLK9ra0tjh07Bi8vL7n28fHx8PHxUXo7U09PDwEBAXLDBXbs2IGmTZvKXQ3csmWL0vbqaFI2BgBOnz6NO3fu4Msvv5Ruf+3atQvW1tZKhynExcWhV69euHLlCv744w8EBgaqPY8in3/+OYyNjbFmzRqULl0aAPDw4UN069YNb968KdCtS1Xy/3eQZPjw4Rg0aBCio6MVjilRpnTp0oiLi0PZsmWRnp6Os2fPyt2mefXqlcpLyrmCgoIK1e9FixbhwoULGDhwYL5tVlZWOHz4MF6+fCkFVco0bdoUBw8eLFAQM2/ePHz77bcwNjbGvHnzlO4nk8nUBjEXLlxAs2bNYG1tjfj4ePTp0wclS5bEli1bkJCQgNWrV+droy5wykvV+efOnYtx48ZhwIAB0j/7kSNH8N133+HJkyf5bjEqkp2djalTp2LOnDnSm7aFhQWGDx+OMWPGqH2zSk1Nlf7587K3t1c7HiQjIwOtWrXC0qVLC/R3FBQUJF3KVtVO0zdRRczMzNT2P6/09HTExcXBw8ND7S2wvNzc3FC2bNl8QYkQAnfu3FHY5sCBA9LP8fHxGD16NLp37y53T3/VqlWYMWOGxv0AgMTERKxfvx5r167FhQsXULt2bYX7eXl5Yf369QByPsz2799foNtJa9euxapVqxAbG4uDBw+iYsWKhar7lpiYiLCwMISGhuLly5fo2LEj0tLSsG3bNo1qD7Vu3Rpbt27FyJEj5dZv375d6Vg4bfPz88P8+fOxbNkyADl/s8nJyZgwYQK++uorte2zs7MV/o3fvXtXZU0pReNFunTponG/c28da0PNmjVRs2ZNuXUtW7ZU2cbNzQ2RkZFYtGgR2rVrB29v73yBtLq6f0BO/bW2bdvC2dlZ+kJ/584dlCtXDtu2bSvYE1FFa9d0PlJbtmwRDRo0ECVLlhQlS5YUDRo0ENu2bVPZ5rvvvhP16tUThw4dEsOGDRO2trYiLS1N2r527VpRs2bNYutz1apVxb59+5Ru37dvn9rR6ULkzGJycHAQw4cPF+vWrSu2KXLKNGvWTEralvdy7tGjR5WmjVeVm6QgeUpcXV0VTqUMCwtTmScmr6JWoW3atKno0KGDeP36tbQuNTVVdOjQQWWytlylSpUqVMKq4vbbb7+JWrVqqd0vJSVF9OzZU+jr6wt9fX3p9R8wYICUM0QVZVWwnzx5otHtiKZNm4p169blW//nn3+KL774Qm37pKQksWLFCtG8eXNhYGAgypcvLyZNmqRRBWNtaNy4caGmmLdq1UpYWlqKTp06iZ07d0rTpNVlHM6byHDKlCnCyspKfPXVV2LKlCliypQpomXLlsLa2lr8v/bOPK6m/P/jr5u0L0JSaVFZSjV2GaRCi32y7ypLkhprhrElqWwxzFhiFCaMdYydISRbKAoVpeyULCEt798f/e75drvb6d7bYuY8H4/7eHTPuZ/z+dzbvee8z+fzfr9eS5cuZT2WM2fOUJ8+fcjCwoIsLCyoT58+dPr0aVZtc3JyyMbGhqytrUlZWZkcHByoQYMG1KJFC5Hfi4oMHTqUJk6cSET/Ww768OEDubi4sBKrrAmmT59OHz9+ZP6W9JBEVlYWOTs7k76+Pv3888+V9v3jU1paSidPnmS+FxWtIBQBt5xUBbx58waenp64dOkStLS0EB0djR9++IHZ36NHDzg4OGDZsmWsjpeYmMgkQbVq1Qpt2rSR+HptbW2kpKTA1NRU5P7s7GzY2tri/fv3Eo8jrwu2vOjq6uLmzZuwtLQUmM59/PgxWrRoIdKFVVGoqanh7t27IqfD7ezsWPUtrwvt3bt34ebmhsLCQnz33XcAypZi1NTUcPLkSbRq1Upi++nTp0NVVRVhYWFSx1oeU1NT3Lp1i6mEWL9+PcaOHcs6EVbcMtS7d++QmJiIqKgoREVFYfjw4RKPExgYiPj4eERGRsLd3R3JycmwsLDA4cOHsXjxYty6dUtieyUlJbx8+VKoEurx48ewsbFBQUGBxPYaGhpISkoScnxOS0tD69atpc4mqaurQ09PD8OGDcOoUaOE7ohFocjEVllRVlZGQEAApkyZIvDe69ati6SkJLEzMRXdksXB4/FYVebJmhxcnuLiYuzevRvJycn4+PEj2rZti1GjRkFdXV1q2ydPnsDNzQ1EhPT0dLRv3x7p6elo2LAhLly4UKkZMllJT0/HuXPn8OrVK6EZmoULFwq93tnZGQcPHkS9evXg7Ows8djlZx3Ls2XLFsycORM9e/bEpk2bWFUS1iTccpIIli5dipEjR8LS0lLk/vfv3+PHH3/Etm3bRO7nf8nfvXsHLS0toRLbP//8U2x5XnlevXqF4cOH4/z580yFQX5+PpydnbF7926xX646derg2bNnYoOYZ8+esVp3lWVac8aMGaxfu3r1aon7VVVVRQZaaWlpVf7DsrKywt69ezFv3jyB7Xv27BG6qIkjLy8PLVu2FNresmVL5OXlSW1va2uL9PR07Nq1C/fv3wcAjBgxgvVJuLi4GNu2bcOZM2dEVueI+/yfPHkiEKDOmzcPvXv3Zh3EiFuG0tbWRosWLVgFMABw6NAh7NmzBw4ODgJLQq1atZJYmcT/DvJ4PCxYsEBgOaWkpARXr15F69atpfZvYmKCLVu2ICIiQmB7VFSUQOWJOP766y/06NGD1W+ND9ulP3E3EYr4/V26dAlbt25Fu3btYG1tjTFjxrD6f2VmZrLumw2hoaFYs2YN/P39mW0BAQHo0qULQkNDWQUxysrKlVrKKU+TJk2QlJQkEAT5+PhI/P2JkxcQhbicGD5btmzBlClT0LBhQzRu3FjgN8Dj8UQGMeUDE3FBiiTc3d1x7do15sZFXs6ePYuzZ8+KDMLEXT8rCxfEiGDRokWIjIzEnj17RGoafP78GdHR0VL/Cbq6uiK3169fn9U4pk2bhg8fPiAlJYUpMU5NTcW4ceMQEBCA2NhYke3atGmDQ4cOwcHBQeT+gwcPSp3NkZWKd8c3b95EcXExU+ablpaGOnXqoF27dlKP1b9/fwQHB2Pv3r0Ayn642dnZCAoKwqBBg0S2UVQQtWTJEgwbNgwXLlwQ0Ag6e/YsMx5pfPfdd1i/fr1Qns769euZmRVpaGhoYOLEiaxeW5G7d+8yOSRpaWkC+9gmrwIQ0vqRhqLW9F+/fi3ybregoEDi+PnfQSLCnTt3BBJbVVRU8N1332HWrFlS+1+zZg0GDRqE48ePo1OnTgCAa9euIT09Hfv375faXhYdDHk/O2mzU3wkfX4ODg5wcHBgzoHbtm3DjBkzUFpaitOnT8PExERiTogk7t27h61bt2LlypVSX5ufnw93d3eh7a6urox8hSTEzWrxeDyoqanByspK6uxRZYOg8ud8IsLBgwehq6vLzMIlJiYiPz+fVbATEhKCZcuWsXqv5fH29pb6Gh6Ph61btwptLykpQXJycqUKEMSxZMkSBAcHo3379jA0NKzUOacycMtJIlBSUsL48eOxc+dOhIeHCyVxvnz5EkZGRtWynHLmzBmhKoBr167B1dUV+fn5Itvt378fw4cPx5o1azBlyhRmJqikpAS//vorZs6ciT/++AODBw+WOoa4uDisXLmSWc6ysbHB7Nmz0a1bN6ltV69ejfPnzyM6Ohp6enoAyiqnvLy80K1bN8ycOVNi+3fv3mHw4MG4ceMGPnz4ACMjI7x48QKdO3fGsWPHhGYWAEidQuXD4/GkVmckJiYKiXXNnDmTdQAYFxeHPn36wNTUVCAxNCcnB8eOHRP5GVZHdZA0KupUyKJRAZRVdw0bNkxI1PHr16/YvXu31Ds9R0dHDBkyBNOmTYO2tjaSk5PRtGlTTJs2Denp6Thx4oTE9l5eXli7dq1cejBPnjzBr7/+ysyEWVtbw9fXV+xMTHmxuorCkRVhkxxZW3jw4AG2bt2KHTt2ID8/H7169WK99FVQUIDdu3dj69atuHLlCmxsbATEK8UxcuRItGnTRig5eOXKlbhx4waTAC0OfqVZxUtcecHGrl274tChQ8z5qTz//PMPDhw4gKysLPB4PFhYWGDQoEFwdHRk8a6BoKAg5OXlYePGjQLnYD8/P+jo6GDFihUS2+vo6OD27duV/t0pKSnBzMwMbdq0kXgDIk4MU1EYGhoiIiJCpmrOysAFMSLgi0ydPn0akyZNwpAhQ7B582bmjq66ghhtbW1cvHhRaOr71q1b6N69u8Sclvnz52P58uXQ1tZmfgSPHj3Cx48fMXv2bFZ5ErIqFvMxNjbGqVOnhHI37t69C1dXV9YltvHx8UhKSmLWtKtL8VMRPHv2DBs2bBC4CPr5+cHIyEjk68sHEIrKScrIyMDDhw/h6OgIdXV15gQuDiUlJYSEhDBLnkFBQZg9e7ZQWbO06jJxYm25ublo1KiR1PFfunQJHh4eGD16NLZv347JkycjNTUVly9fRlxcHKvZvOpmyZIlmD17NjQ0NKQKR0oTiwTKAoC4uDhkZ2fj69evAvvYSBQompKSEhw5cgTbtm2TGsTEx8dj69at2Lt3Lz5//ozp06djwoQJIpdYRRESEoKVK1eiS5cuAjkx8fHxmDlzpkBwKuqzOHv2LObPn49ly5Yx1WDXrl3DggUL8PPPP0NXVxeTJ09Gp06dhGYlfH19sXnzZujp6aF58+ZMXkx+fj78/Pzwyy+/SB2/vr4+Ll26xMxC83nw4AG+//57ISHOivj4+KBDhw7w9fWV2ld5pk6ditjYWJiZmcHLywujR49mPfuvSBo0aIBr166JTctQFFwQI4LyF5LExER4enrC0NAQBw8ehKGhYbUFMQMGDEB+fj5iY2OZi97Tp08xatQo6OnpSY2kr127hl27djHWAc2bN8fIkSPFlndWRFbFYj7a2to4cuSISJ2T/v37S9XJEUV+fn6VKJCK49WrVyLXc+3t7attDLKSm5uLoUOH4ty5c+DxeEhPT4eFhQW8vb2hp6eHVatWiWxnbm4udeqXTXKmuMTapKQkODs7s8oLevjwIcLCwgSC2KCgINjZ2UltW1BQgLCwMLFr8mySS/Pz83Ht2jWR7WXNGSguLsarV6/EBrJ8bt26hd69e+PTp08oKChA/fr18ebNG2hoaKBRo0asxn/jxg3s3btXZBAkLSdDFl69eoXt27dj27ZtePfuHUaMGIGRI0eic+fOEpOCRSFvorCtrS02b97MyGPwiY+Px6RJk5CSkoIzZ87A29sb2dnZzP6DBw9i+PDh2LRpE8aNG8f8FkpLS7F9+3ZMmTIFf/75p9SZUD09PWzfvl1IHf3w4cMYP3483r59K7H98uXLsXr1avTp0wd2dnZCshySgtjCwkIcOHAA27Ztw+XLl9GnTx/4+PjA1dW1ypZ1KhIUFAQtLS0sWLCgSvvhghgRVJxOf/XqFQYPHoyHDx/iwIEDMDc3r5YgJicnB/3790dKSopAnb2trS3++usvqeuWfO8eUXf02dnZYhN/+aiqqiIlJUWoQicjIwO2trZSK3TGjh2LixcvYtWqVUzgdPXqVWY5Kjo6WmL78PBwmJubY9iwYQDKPIH279+Pxo0b49ixY6zySmQ9iScmJmLcuHG4d++eyOlotv/7/Px8bN26VaC6zNvbW2y+FJ+ioiK4u7tj48aNrBOJKzJ27Fi8evUKUVFRsLa2ZpaETp48iRkzZiAlJUWm40qDv4ySlJSEVq1aCWhMlJSUIDMzE+7u7mJzi6RVzfGRtkw0YsQIxMXFYcyYMSLX5KXZPhw5cgSjRo3Cx48foaOjI5RYySYIE0VSUhLatm0r9Tvk5OSE5s2bY+PGjdDV1UVSUhLq1q2L0aNHIzAwUGpeBX/Jzs3NDadOnYKrqyvS0tLw8uVL/PDDD/j9998ltpcUBPJ4PJHJ1erq6hg8eDBGjx6NXr16MeceaZVNVYG6ujquX78uZI9w584ddOzYEZ8/f8bjx49hbW0tUGnWv39/tGrVSqwWUFBQEO7fv4/Dhw9L7H/GjBmIiYnBvHnzBM5/YWFhGDNmjNTCBklBHNsKL6CsGm/79u2IiYlBcXExUlJSWBWWyEtgYCBiYmJgb28Pe3t7oSBM2vtnjUILtv8liNKXKCoqIl9fX1JTU6PQ0NBqc3EtLS2lU6dO0bp162jdunWsNRKI5NfJkNcFu6CggKZMmUKqqqqM5LuKigpNmTKF0TKQhLm5OcXHxxMR0alTp6hevXp08uRJ8vHxoV69ekltHxsbS3Xr1qW+ffuSiooK9e3bl5o3b066urpSdR7s7e3phx9+oCtXrlBmZiZlZWUJPNhw/fp1ql+/PhkbG9MPP/xAP/zwAzVp0oQaNGgg4I4tDnl1Xsq72JbX2Xn48CFpampKbR8dHU1fvnwR2l5YWChSQ4cPX0uCx+PRrFmzBPQlQkND6Y8//hDQTapIRZuAig+2svO6urp06dIlqa8TR7NmzSgwMFDhth+3b99mPX6+bL6uri5jJ3DlyhVq0aKF1PZ2dna0fv16Ivrf/7+0tJQmTpxICxculNp++PDhZGhoSHPmzKE1a9ZQZGSkwEMULVq0IHNzc5o3bx7du3eP2S5NY0YShYWFdP/+fSoqKqpUuy5dupC7u7uABcCrV6/I3d2dunXrRkREp0+fpubNmwu0MzY2ZtzTRXHlyhUyNjaW2n9JSQmFh4eTkZERY5dgZGRE4eHhjPZOdZCdnU1Lliyhpk2bkrGxcaUdvGVFkoO6s7OzwvrhghgR8Hg8sWJImzZtYi7KtR1x7yMrK4s0NDSktv/1119JRUWFfH19KSYmhmJiYmjy5MmkqqoqMrgRx8ePHxmxNzbBCx81NTXKzs4mIqKAgACaNGkSERE9ePCA6tWrJ7W9PCdxLS0tkb5ZlaFr1640fvx4gZNvUVERjRs3jjmJSuLHH3+koKAgmfvX0tJigqDyQQw/uJKGvEHw9u3bBYT62HL+/Hnmce7cOVJXV6ddu3YJbBfnPVQec3Nz5sIvCxoaGsxnpkjYBjHlg9hmzZrRiRMniIjo3r17rH6/GhoalJmZSURlPkLJyclERJSamkqNGzeW2l7WIPDSpUvk5eVFWlpa1LZtW1q9ejUpKytX+n8hr9jh/fv3qUWLFqSiokKWlpZkaWlJKioq1LJlS3rw4AERER08eJBiYmIE2qmqqtLTp0/FHvfJkyeV9v6pjN9WRWQJ4r58+UJ//PEH9ezZk9TU1Gjw4MF09OhRxj/p3wQXxIhg8eLFEu+++D/SquLs2bNkbW0t8kufn59PNjY2dOHCBbHt+YqMSkpKNHnyZAGVxoCAAOrUqRN9//33rMYii2KxojA0NGRmYpo3b0579+4lorKTk7a2ttT28pzEBwwYQPv27ZNj9GVBWPm7UT4pKSmsDOj8/f1JR0eH2rVrR5MmTaqU4iYRkYeHB/38889E9D/F0ZKSEhoyZAgNGjRIantxBoK3b98mPT09qe3L8+HDB5kMFPljlyWYkNdA9YcffqA9e/bI1FYSbIOYXr160a5du4iIaMKECdSxY0fauXMnubm5UceOHaW2NzY2Zr7zdnZ2jPrw5cuXSUdHR2p7eYPADx8+0ObNm6lz587E4/HIycmJNm/ezNqUMiAggNq1a0cXL14kTU1N5jtw6NAhVorjRGWzIcePH2cUY0+cOCH1Qi7NOPPFixesb2KLioro9OnTtHHjRsYM9unTp6xmQ2QN4qZMmUJ6enpkb29PkZGR9Pr1a1ZjrUpycnKqzEyY04kRgbSqgS5duog1z1IEkZGRmDhxosg1f35G/erVq8WWOStKJwMAfvjhBwG14cogb2Klp6cnRo4ciWbNmiE3NxceHh4Ayt5fxTwdUejp6THJw8bGxrh79y7s7OyQn58vVW01KioK48aNw927d2Frayu0nsumvFlHRwfZ2dlC1Rg5OTmsdDbk1XmJiIhAjx49cOPGDXz9+hVz5sxBSkoK8vLyEB8fL7YdP6eFx+OhR48eYnNapJGZmQl/f3+cP39eIH+K/r86qqpzylatWoWHDx/CwMCgUgaqfPr06YPZs2cjNTVVZGKluO9AcnKyxOM+ePCAxejLxN74399ly5Zh7NixjIouG6EwR0dHnD59GnZ2dhgyZAgCAwPxzz//4PTp0+jRo4fU9kuXLsXChQsRHR0tk/+SlpYWJk6ciIkTJ+LevXuIiorCzz//DD8/PxQVFUltL6vYYXmUlJTg7u7O6vtanooiieVh6/v1+PFjuLu7Izs7G4WFhejVqxe0tbURHh6OwsJCbNy4UWL7n376CUlJSTh//rzA+Hv27InFixdj7ty5Ittt3LgRpqamsLCwQFxcnFijxapI7C6PvN5xbOGCGBEkJCQgNzdXwKgsJiYGixYtQkFBAQYOHIhffvlFSP9CUSQlJSE8PFzsfldXV4liUXylRkXoZABlybHldWLYlrZOmDBBYmKlNNasWQNzc3Pk5OQgIiKCSUZ7/vw5/Pz8pLaX5ySekJCA+Ph4HD9+XGgf2wvwsGHD4OPjg5UrVwoYiM6aNYuVAqosipvlsbW1RVpaGtavXw9tbW18/PgRnp6emDp1KgwNDcW246vG3r59G25ubgJJgCoqKjA3NxcrNlie0aNHg4iwbds2GBgYVFtVBB9ZDVT58EUGg4ODhfZJ+g60bt1apD4Jvx1JKXHnU96moFGjRlJ1cSqyfv16JnicP38+6tati8uXL2PQoEH4+eefpbaXNwgsj7W1NVatWoXw8HDW+jKyih1WfG1lS9QdHR2lBppstGICAwPRvn17JCUlMRYeQNmNIRsBS1mDuLFjx1b7b00U8+fPx9atWxEWFiZgort48WJ8+fKFte2OVKpkfucbx93dncLCwpjnycnJpKysTBMmTKBVq1ZR48aNadGiRVXWv6qqqsR8jPT09EqvycoynZeTk0Ndu3YlHo9Henp6pKenRzwej7p06cLqWPImVspLbm4us7ZdUlJCy5cvp379+tGMGTMoLy9PYlszMzOaOnUqvXjxQub+CwsLKSAggFRUVJiEVFVVVZo+fTp9+vSJ9XHS09PpxIkTTBu2BmqPHz8W+9rHjx9LbS9rTgsfTU1NJjFVHvhLYd8KFZPAxT2qiorLduIe0qho+lcZE8DExERmKYuobAlowIAB9NNPP0lM6i5Pt27daN26dUQk+B3w9/cnNzc3qe1v3rxJjRs3Jh0dHapTpw7p6+sTj8cjTU1NqQawiqB+/frM97/8kmhmZiar5WR1dXWmTfn2t2/fZrUcWNMYGhqKNAo+dOgQGRkZKawfLogRQePGjen69evM83nz5lGXLl2Y53v37iVra+sq69/CwoIOHjwodv/+/ftZ/QhLSkpoyZIlpKOjw1R26OrqUnBwMKsELzc3N+rUqZPAhej+/fvUuXNnVicRedfUo6OjJT4kUVRURNHR0TIHIVpaWgpzGy4oKKDk5GRKTk6mgoICWrlyJRkYGEht9+bNG3JxcWGqcfgnMS8vL5oxY4bU9vIm5pZHlpwWJyenSlXT8eFXcvEfysrK5OrqKrSdDW/fvqUtW7bQ3LlzKTc3l4jKLrBPnjyp9LgqQ1FRES1ZskSmPIDWrVtTmzZtpD7EIa26i/+oStq3b8/klD18+JDU1NRoxIgRZGVlRYGBgayOcfHiRdLS0mKqQgMDA6lXr16kqalJN27ckNq+e/fuNHHiRCopKWGCgOzsbHJ0dKT9+/fL8/ZYUa9ePaYiq3wQcvHiRWrUqJHU9vIGcTWNqqoqk0Bdnvv371f6JlwS3HKSCN6+fQsDAwPmeVxcHJOPAQAdOnRATk5OlfXfu3dvLFiwAO7u7lBTUxPY9/nzZyxatEhgqUsc8k7nxcXF4fLlywKKky1atMAvv/zCynZA3jX1ijoeRUVF+PTpE1RUVKChoSFRbExZWRm+vr5SBfnE4enpiXPnzsmkNllYWIjFixfj9OnTUFVVxezZszFw4ED8/vvvcHd3R506dYQEBEUxffp01K1bF9nZ2Yx3FlC2TDVjxgyxYnV8SMyyxcePH4W+V6KQN6clKioKvr6+ePr0qci8InGCgRU1dGQ18EtOTkbPnj2hq6uLrKwsTJw4EfXr18eBAweQnZ2NmJgYsW0/f/6MxMRE1K9fX0jb5MuXL9i7d6/U719ERIRMgnjll8GICMuXL4evry9r1dXyy5BEhN69eyMqKgrGxsaVHkt+fj727duHhw8fYvbs2ahfvz5u3rwJAwMDicfjO30DZYa3jo6O+OOPPxAfH4/hw4cjMjJSat9du3ZFUlISli9fDjs7O5w6dQpt27ZFQkICK7HD27dvY9OmTVBSUkKdOnVQWFgICwsLREREYNy4cSJ1dhRpYOvq6orIyEhs3rwZQNlS4sePH7Fo0SL07t1b6vFDQ0Ph4eGB1NRUFBcXY+3atQKK1bUdRXjHsUJh4dC/CFNTU4qLiyOisiUBdXV1OnPmDLM/OTm50tUZleHFixdkZGREJiYmFB4eTocOHaJDhw5RWFgYmZiYkJGREasZBnmn85o1ayZSL+Hq1atkaWkptX3r1q1JW1ubtLS0yNbWlvWdpCTS0tKoR48eTLmpJLp37y5zJVVISAg1bNiQxo0bRytXrmSqG/gPScyZM4d0dXVp0KBBZGhoSMrKyjRx4kSys7Oj2NhY1hoRsuq8KKo67fvvv6fOnTvT7t276dy5c5UucU5ISKCmTZsyGhn8GQK2Oi/y0qNHD5o9ezYRCX5+8fHxZGZmJrbdgwcPyMzMjBmno6MjPXv2jNnPtjqlf//+tH37dvneBMlenSVv+6SkJNLX1ycrKytSVlZmjjF//nwaM2aMxLba2tpMeXjPnj0ZXZnHjx+zugv/+vUreXl5ybWMKEuJekU9Ex0dHdLQ0GDOWZqamqSjo8NK5yQnJ4dsbGzI2tqalJWVycHBgRo0aEAtWrQQK+FRkYyMDJowYQJ16NCBrK2tadSoUQLLdLWZ8+fPk6amJllbW5O3tzd5e3uTtbU1aWlpSayurSzcTIwIevfujblz5yI8PByHDh2ChoaGwMxDcnJylfpBGBgY4PLly5gyZQp++uknJkGQx+PBzc0NGzZsEJgpEkdeXp5In5KWLVuyUhtdsWIFpk2bhg0bNjBJhjdu3EBgYCArF1p5EytF0axZM4SFhWH06NGMH5E4/Pz8MGPGDOTk5KBdu3ZChpGSrAOioqKgpaUlMrufx+NJlPz+888/ERMTg/79++Pu3buwt7dHcXExkpKSKpVwV1BQIHIGKy8vT2JSuaKq05KSkpCYmCjk/cIWb29vtGnTBrGxsTWS2Hv9+nVs2rRJaLuxsTFevHghtl1QUBBsbW1x48YN5Ofn48cff0SXLl1w/vx5qSrX5fHw8MDcuXNx584dkd+/qjLwVBQzZszA+PHjERERIVBN17t3b6m+ae3bt0dISAh69uyJuLg4/PbbbwDKZvfYnLvq1q2L/fv3yyVZ36ZNG1y/fh3NmjVD9+7dsXDhQrx58wY7duwQUvHlU34Wa/Xq1dDW1hZrYCuNJk2aICkpCXv27GFsM3x8fDBq1Cioq6uzeg+WlpbYsmULq9fWNrp37460tDQB7zhPT0+J3nEyobBw6F/E69evqVu3bsTj8UhbW1to/dTFxYXmzZtXLWPJy8uja9eu0dWrV6Umo1akY8eONG3aNKHt/v7+YnUm6tWrxyTx6unpMUmpKioqAn9X5UyUNG7dusVKJ6b8DEB1zgTUrVtXIOdCTU1NprsneXVexo8fL7PAFpHsOS18NDQ05BYMlAd9fX26efMmEQnORpw6dYqaNGkitl2jRo0E/l+lpaXk6+tLpqam9PDhQ9YzMaK+f+W/h2ypqZkYHR0dJi+s/DGysrJIVVVVYtukpCSytbUlHR0dgSRgf39/GjFiBKv+x44dS6tXr670uPlcv36d/vnnHyIievnyJbm5uZG2tja1bduWmeGUhJGREd29e1do+507d8jQ0FDmcVWGjIwMmj9/Po0YMYKZvTl27JjIcf1X4WZiRNCwYUNcuHAB7969g5aWFmOjzufPP/9kpfOhCPT09NChQweZ2kZERKBPnz44c+YM4wKbkJCAnJwcHDt2TGQbNmvVlSUxMVHAO6hNmzas2lUsxSQiPH/+HOvXr2el05OZmVn5wVbg69evyMzMhKWlpYBeiiRKSkoEZj+UlZVl8iqRVeelfHtx5fV37tyRmlcga04LHxcXFyQlJbHS9KkK+vfvj+DgYMajicfjITs7G0FBQRJLxD9//izwv+bxePjtt9/g7++P7t27448//mDVf0VdpJpEllkwVVVVkT5WaWlpQqaeFbG3t8edO3eEtq9YsULofCqOZs2aITg4GPHx8SJnsiTNhhIRGjVqxMy4yFKi/v79e7x+/Vpo++vXr1mZ10ZHR6Nhw4bo06cPAGDOnDnYvHkzbGxsGJdpSfBzMbt06YILFy4gJCQEjRo1QlJSErZu3Yp9+/ZV6v1UF+np6Vi4cCE2bdokdP559+4dpkyZgpCQEFhYWCikP84AUgTe3t6sXsdGcKqySDN1Kw8bsaJnz54JTOfxnalDQkKYhLOq4tWrVxg+fDjOnz/POE/n5+fD2dkZu3fvlnoirCiGxOPxoK+vDxcXF6xatUqi1om8fPr0CdOmTWNMKtPS0mBhYYFp06bB2NhYrNAUf9weHh7Mks+RI0fg4uIidBJm8/979+4d1q9fL+DiLE3nhU/jxo2xdetW5iTKZ+XKlViwYAE+f/4ssf2VK1cwcuRIZGVlMdvK65xIS+zdvHkzQkJC4O3tXSmxOEXx7t07DB48GDdu3MCHDx9gZGSEFy9ewMHBAcePHxf6f/Dp2LEjpk2bhjFjxgjt8/f3x65du/D+/Xux7//z5884e/Ysk3z/008/obCwkNmvrKyM4OBgscnVFRMhg4KCMHv2bDRs2FBgu7iLeMVziKzfvwkTJiA3Nxd79+5F/fr1kZycjDp16mDgwIFwdHRkfcPz8eNHoYCOjXaVPAaIpaWlUFNTQ0pKilwGqvIY2LZo0QK//fYbXFxckJCQgB49eiAyMhJ///03lJWVpX7+nTt3xpAhQzBjxgxoa2szBq7Xrl2Dp6cnnjx5ItP7qmomTZqEevXqISIiQuT+oKAgvH//nllilBcuiBGBkpISzMzM0KZNG5GCVXwOHjyo8L69vLyYv4kIBw8ehK6uLpOTkpiYiPz8fHh6ekp1oRUHWxddoOxkkJGRIVJxV5rg07Bhw/Do0SPExMQw1TWpqakYN24crKysEBsbK9P4K8OOHTuwceNGZGZmIiEhAWZmZoiMjETTpk0xYMAAse0CAwMRHx+PyMhIuLu7Izk5GRYWFjh8+DAWL17M5J2Iovz/UBLS/n/Z2dkwMTEReRfNxoU8IiICCxcuhJeXF1avXo28vDyMHTsWd+7cwaZNm6QqMdvY2MDa2hpz5swRmdMi7U5SkiJndSj28omPjxcIAnv27Cnx9cuXL8fFixfFzlb6+flh48aNYmdaNm7ciKNHj+LIkSMAylRKW7VqxeRB3L9/H7NnzxZbCSPp4s1H0kVcUd8/cUFg586dcezYMbFBIFDzas1A2azv1q1b4eDgIFP7T58+YdasWdi2bRujMKysrAwfHx+sWLFC4vsHAA0NDdy/fx+mpqYICgrC8+fPERMTg5SUFDg5OYmc5SmPlpYW7ty5g6ZNmwoEMVlZWWjZsqXA51qbaNGiBXbu3Cl2BSExMREjR45krVwtlZpax6rN+Pn5kZ6eHrVu3ZrWrl3L6EtUN3PmzKEJEyYIVLMUFxfTpEmTaNasWTIfl613C7+6hJ9HUtk1fR0dHbp27ZrQ9qtXr5Kurm6lxlxaWspa5I3Pr7/+Sg0bNqSQkBAB4ajff/+dnJycJLY1NTWlhIQEIhLMB0hPT2eVj6MIFKHzcvPmTWrVqhVZWVlR/fr1ycPDg54/f86qbU3ntMiKvN5j8tK1a1f666+/mOcVc1J27NhBDg4OVda/orl48SJt2LCBwsPDWedIyVvZpgj++usv6tq1K925c0eu48hqYFs+J6t169aM0WRGRgYrF3ljY2PGO678d+jAgQNkYWFR2bdRbaipqUkUc8zKymIl9scWLogRQ3kXUA0NDRoyZAidOHGi0hdSeWjYsKFIxdP79++zciEWB9sg5rvvvqMhQ4ZQamoqvX37lvLz8wUe0tDS0qJbt24Jbb958ybrQCA6OppsbW1JVVWVVFVVyc7OTsh1VhzW1taMaGD5k8CdO3eoQYMGEtvWBrVMcUZ0bF3IiYjev39Pw4YNI2VlZVJWVq5UyW/fvn3lNsGsyNu3bxV6PFH069dPYkLo2rVraeDAgVXWf+PGjRnjUaKy33H55w8ePGD1HYqOjqYvX74IbS8sLJQq9ljTyKrWPH36dCZQqGh4WlkD1Hr16jHFCGpqagIFC5UtTJBF8XzkyJHUtm1b8vHxIQ0NDXrz5g0RER0+fJhatWoltf3MmTOpa9eu9Pz5c9LW1qb09HS6dOkSWVhYSFVMrkkMDAzo7NmzYvefOXOGldgnW7jEXjGoqqpixIgRGDFiBB4/fozt27fDz88PxcXFSElJkSlRs7IUFxfj/v37QiWu9+/fr5akwfT0dOzbt0/mxEwXFxcEBgYiNjaWKal7+vQppk+fzsqAbvXq1ViwYAH8/f0FxPp8fX3x5s0bqYJxmZmZIpOIVVVVUVBQILFt+/btcfToUUybNg3A/xIjo6KimCTpqoK/zMDj8YSM6EpKSnD16lVGSEwS8fHxGD16NJPPEB8fj2nTpuHYsWPYuHEjUzYqjn79+mH69OlMEnBlc1rCw8Nhbm6OYcOGAQCGDBmC/fv3w9DQEMeOHVOs4FU55PUek5f8/HyBHJiKywalpaUC+8Xh5eUFd3d3If+gDx8+wMvLSyYhvcpy9uxZsQauknIC+YKglS3Pv3XrFu7fv482bdpIXLJlk6gsb5GCvAaGGzZswM8//4ycnBzs37+f8U9KTEzEiBEjpPYfGhqKqVOnwsTEBCUlJbCxsUFJSQlGjhzJyvuqpnB0dMQvv/wCFxcXkfvXrVvHqkSdNQoLh/7FZGdn05IlS6hp06ZkbGzMykZdEUyfPp0aNGhAq1atoosXL9LFixdp5cqV1LBhQ4l3IhXl2Ss+nJ2dWc3EODs70/Hjx2Uef3Z2NrVu3Zrq1q1LFhYWZGFhQXXr1qU2bdqwuqsxNzcXece5fft2Mjc3l9re2tqaEbsrP5uybt06qWJ78kqeywNfaIvH49H3338vIL7l6upKkyZNYkS8JKGiokJBQUH09etXZltGRgY5ODiQsbGx1Pbylgibm5sz0+GnTp2ievXq0cmTJ8nHx4d69eoltb2sVIX3WGWwsrKSOIO1Z88eVmKR4mbibt++XS0SB4sXLyYlJSXq2LEjDRgwgAYOHCjwkERGRgb17NmTtm/fTjdu3GCWY/gPSVRcRh06dKhcHmayMnfuXNLX16dff/2VGfeGDRtIX1+/yiU2SktL6fHjx/Tp0yfKzs6mo0eP0p49e1j97muamzdvkqqqKg0aNIiuXr3KzNxfuXKFPD09SVVVlRITExXWHxfEiKH8cpKamhoNHjyYjh49yspzSFGUlJRQeHg4GRkZMRcPIyMjCg8Pl6j6On78eFYPUZQ/0Rw4cIBsbGzo999/r/SJiE9paSmdOnWK1q1bR+vWrauU7oi4i1FaWppUnQoioi1btpCxsTHt3r2bNDU1KTY2lkJCQpi/pVHTapny6ryIyz0oKSmh4OBgmY/LFjU1NcrOziYiooCAAJo0aRIRlS2n1KtXr8r6VZT3mKwEBASQjY2NSPPMT58+kY2NDQUEBIhtz/dOUlJSIjs7OwGVa3t7e9LW1qYhQ4ZU2fj5NG7cmPXSbUXkUWvm8XgCQYy2trZcOjlERJ8/f66095ciDAzfvn1LK1euJB8fH/Lx8aHVq1ezWoovKSmhunXrfhNBiyiOHDlC+vr6Qn5d+vr6Ij9TeeCCGBFMmTKF9PT0yN7eniIjI+n169c1PSTWPzx5KX+ykVUsTlGJla1ataJly5YJbV+6dCnZ2tqyej87d+4kKysrZvzGxsYUFRXFqq04/vzzT7nas0XUXTgfScGUh4eHwIly+fLlArkob968kdnAtDI5LYaGhsxMTPPmzWnv3r1EVJbTVZXJ0f7+/mRrays2iLC1tRUpAqkoXrx4QY0bNyZTU1OKiIhgbEPCw8PJxMSEDA0NJc4s8F2ieTwezZo1S8A5OjQ0lP744w/WTtDyUL9+fZlNUK2trcnT05OuXLlCmZmZlXLwrhjEyCrW9/HjR5o6darIiymbmUR5DQyvX79O9evXJ2NjY2YWvEmTJtSgQQNWMxE2NjZMccG3yKdPn+jAgQMUERFB4eHhdPDgQSooKFB4P1wQIwIej0dmZmY0cOBAicsy/0YqnmwkPcShqMTKffv2UZ06dcjNzY2Cg4MpODiY3NzcSFlZmQ4cOFCp91VQUMDar6SoqIju3LkjdAI7dOgQ2dvbk4qKSqX6lhUDAwP6+++/hbavWLFC4km04nR8xTtZtoqzYWFhtHv3bub54MGDmdlANoqnU6dOJTMzM+rZsyc1aNCAWYaNjY2V2TuLDYryHpOHR48ekZubm8ANgZKSErm5ubG+IG/fvl1kIFZdzJkzR+YZO3kq25SUlAQC+PIOzpXBz8+PrK2tad++faSurk7btm2jpUuXUpMmTWjnzp1S20tSPO/UqZPU9l27dqXx48dTUVERs62oqIjGjRtH3bp1k9peUdVVtYGq/B5zOjEiGD9+PKvEMVl1Wtjy8uVLzJo1i0msq/ivqi6djcpiZmaGEydOCDgvl+f+/ftwdXVFdna21GMlJiZizZo1jOKvtbU1Zs6cyUr1NyQkBKNGjWKlu8Hn7t276Nu3L+NSPmDAAPz2228YOnQo7t69i4kTJ8Lf3x9NmjRhfUxZkVXnRUlJCS9evGASQstrTABl3ysjIyOp35+mTZti165d+P7773H69GkMHToUe/bswd69e5GdnY1Tp05JbF9UVIS1a9ciJycH48ePZ/5na9asgba2NiZMmFDZj4Q1jx8/xpQpU3Dy5EmR3mOV+U7IQ15eHjIyMgAAVlZWrJ2oKyKrYFxlKa9dU1paiujoaNjb28Pe3l4osVuSi3O/fv0wfvx4icrI4lCUWKSpqSliYmLg5OQEHR0d3Lx5E1ZWVtixYwdiY2PF6gDxiYuLQ58+fWBqaipS8Vxacqq6ujpu3bol5F+XmpqK9u3b49OnTxLb6+np4dOnTyguLoaKioqQ3xIb/7uapKSkBKGhodi4cSNevnzJCIYuWLAA5ubm8PHxUUg/XHWSCLZv317TQwBQFkxlZ2djwYIFMDQ0rHYDvYqy/3x4PB7U1NRgZWUl8mLw8uVLoRNeeZSVlSUKPZWXOm/WrBl+/fVXka+RdhL/888/sWjRInTq1AmjR4/G0KFDhVRPKxIUFAQrKyusX78esbGxiI2Nxb179+Dj44MTJ06wNm5TBHPmzEGvXr0wZswY2NvbIy8vD506dUJycjIaN25c5f2/ePECJiYmAIC///4bQ4cOhaurK8zNzdGpUyep7d+/f88YTebk5GDhwoX4/Pkz+vXrJ1UoUV7MzMxw7NgxvH37FhkZGSAiNGvWTGpFlqKpX78+o/ZaWWpCMK5iRRC/Cu7u3bsC26Wdi+SpbBs3bpzA89GjR0sbtkjy8vKYwF1HR4e56Hft2hVTpkyR2l5eA0MdHR1kZ2cLBTE5OTmsbGuqwgKmOlm2bBmio6MRERGBiRMnMtttbW0RGRmpsCCGW06qxYjTWakuxOXHlM+LcXR0FDKmlDexkn9saQ823L17l3766Sdq2rQp1a1bl3r37k27du0Suzarr6/PfOb5+fnE4/FkTm5UBLLovEibjme7nCRrTktycjKZmZmRkpIStWjRgm7dukUGBgakpaVFOjo6VKdOHYnfD44yaoNgnKwoyvxSHuzs7JjPqUePHjRz5kwiKlvOZlOdJy/Tpk2jJk2a0O7duyk7O5uys7MpNjaWmjRpQoGBgVXef01jaWlJZ86cISLBvKZ79+4pNLGfC2JqMdbW1oziY01w5swZ6tSpE505c4bev39P79+/pzNnzlDnzp3p6NGjdOnSJWrVqhV5e3sLtJM3sbL8ifrcuXOkrq5Ou3btkvskfunSJfLz8yN9fX2xF2FRSYU1VSFw6dIlMjc3p7Zt21Jqaipt2bKFtLW1aejQoRIdzXk8HvXu3ZvJ3VJWViZXV1fmee/evVldSGTNaXF3d6e+ffvSpUuXaPLkyWRsbEze3t5UUlJCJSUl5Ofnxyqn4L+OrIJxiiI/P1+kWnlubm61FBnIy+rVq2nt2rVERHT69GlSU1MjVVVVUlJSosjISFbHkLW6iKhMlDAgIIAR3FNSUiJVVVX68ccfRYoYVuTo0aN04sQJoe0nT56kY8eOsRpDTVJeubd8EJOSksJKsZgtXBBTizl58iS5uroKqH1WJ61atWLuxMtz6dIlsrGxIaKyk4OJiYnAfkUnVspanVCRW7du0cyZM8nY2FhsYqySkhJlZGTQu3fvKD8/n7S1tSkpKanS5ZmKQFadF3lK7Mvz9etXWrFiBQUEBAgE06tXr6YtW7aIbdegQQOmBP/Dhw/E4/EEtHXu3btXaduJ/yJOTk6VkiRQNO7u7rRhwwah7b/99ht5eHjIfNwnT57IMyyZycrKov3797OWh5C3uohPQUEBJScnU3JycqWqc+zs7Ojo0aNC248fP0729vasj1NTtG3blnbs2EFEgufwJUuWUNeuXRXWDxfE1GLKy2ZraWnJJZstC2pqaiIz45OTk5kgQJwPRlZWFnl4eAhVZ3h4eFS60kCeIObRo0cUEhJCNjY2VKdOHXJxcaGoqCixd1MVl7LEPa8OalrnhS+TTlQmXLhgwQKaNWsWxcXFSWwnrUSW7XLWfx15BOMUgZ6eHqWmpgptv3fvnky2J8+fPyd/f3+F+uaI4tOnT3TkyBHm+dy5cwXsCmbPns2qWkbe6iJ5UVNTE3kDm5mZydp2pCY5dOgQ6erqUlhYGGloaNCKFStowoQJpKKiQqdOnVJYP1xiby2mphO72rVrh9mzZyMmJgb6+voAyiTU58yZwziUpqenM8mf5akNiZUODg64du0avvvuO3h5eWHEiBEwNjaW2ObcuXPVNDrx9O7dG7GxsejevTsAICwsDL6+vqhXrx4A4O3bt4iNjcWCBQuqpP87d+6gX79+yMnJQbNmzbB79264u7ujoKAASkpKWLNmDfbt24eBAweKPUbFxM/qTkr/N/D69Ws8fPhQwJWax+NVmxN0YWEhiouLhbYXFRXh8+fPItu8ffsWfn5+OH36NFRUVDB37lz4+/tj8eLFWLlyJezt7au8qjM6OhpHjx5F3759AQDr168XchE3NDSUalty48YNbNmyBcrK/7tMKisrY86cOWjfvr3INp6enqzHKa26SldXF48ePYK5ubnA9oyMDKkO2rWBAQMG4MiRIwgODoampiYWLlyItm3b4siRI+jVq5fC+uFKrDnE8uDBAwwYMACZmZlMoJKTkwMLCwscPnwYzZs3x6FDh/DhwweMGTOmysahra2N5OTkSpfFzp8/H6NHj2YCMGmVSXyKi4vxxx9/wM3NDQYGBpUer7zUqVMHz58/Z0qkdXR0cPv27UqXSMuKh4cHlJWVMXfuXOzYsQN///033NzcsGXLFgDAtGnTkJiYiCtXrohsL61EtrCwECdOnKi1EgG1BRsbG1hbW2POnDkwMDAQCgTNzMyqtH9nZ2fY2tril19+Edg+depUJCcn4+LFi0JtJk+ejBMnTmDIkCE4efIkUlNT4ebmBiUlJfz8889wcHCo0jEDQLdu3TBnzhz069cPgLDEwM6dO7FhwwYkJCRIPI6BgQF27NgBV1dXge0nT57E2LFj8fLlS6E25QNOaUgL5iZPnoyEhAQcPHgQlpaWAMoCmEGDBqFDhw6Iiopi3de/GS6I+Ub48uULvn79KrCtKnQiKlJaWopTp04hLS0NANCiRQv06tVLqvmZPFS8m5FFJyI/Px/z5s3D3r178fbtWwBlugvDhw9HSEgIM6shDg0NDdy7d6/KLxSiUJTOi6w0bNgQ//zzD+zt7fHx40fo6Ojg+vXraNeuHYCyO1kHBwfk5+eLbM/2RF7Vd+TfOpqamkhKSpLZgFVe4uPj0bNnT3To0IExbD179iyuX7+OU6dOidRJMTU1xfbt2+Hi4oKsrCxYWFhg7ty5CA0NrbZxGxoaIiEhgZnB0NfXx/Xr15nnaWlp6NChA969eyfxOAEBATh48CBWrlyJ77//HkDZZzJ79mwMGjSoymfK3717B3d3d9y4cYPRpcrJyYGjoyMOHDgg9Rz2X4FbTqrFFBQUICgoCHv37kVubq7Q/uq4k1VSUoK7uzvc3d2rvC8+urq6As8rqxORl5eHzp074+nTpxg1ahQjupeamort27fj7NmzuHz5ssSlrY4dO+LWrVs1EsTUNHl5eYwOjZaWFjQ1NQU+Kz09PXz48EFsey44UQwuLi41GsR06dIFCQkJWLFiBfbu3Qt1dXXY29tj69ataNasmcg2z549Y35v5ubmUFNTk1nnRVbkdRHPzMxE06ZNsXLlSvB4PIwdOxbFxcUgIqioqGDKlCkICwuTOo7MzEwUFxcLfVbp6emoW7eu0DJRRXR1dXH58mWcPn0aSUlJUFdXx3fffadYB2gFo6enx3rpWFFifVwQU4uZM2cOzp07h99++w1jxozBhg0b8PTpU2zatInVj0gW1q1bh0mTJkFNTQ3r1q2T+NqAgIAqGYO8F8Hg4GCoqKjg4cOHQstBwcHBcHV1RXBwMNasWSP2GH5+fpg5cyaePHmCdu3aCc0C2dvbyzVGSfB4vBrPKanp/jnkE4xTFK1bt8auXbtYv56IBHJI6tSpU60CkQDQpEkT3L17Fy1atBC5Pzk5WaLitqWlJczMzODs7AxnZ2dkZGQws46WlpbQ0NBgNY7x48fD29tbKIi5evUqoqKicP78eZHtEhISkJubi759+4LH48HV1RXPnz/HokWL8OnTJwwcOBC//PILs1xbm6iJPE5uOakWI69stiw0bdoUN27cQIMGDSTmoPB4PDx69Ejh/SsCc3NzbNq0CW5ubiL3nzhxAr6+vsjKyhJ7DFHLZdWVVFnTOSU13T9HGZKWbKsjsbdibhaf3NxcNGrUSGT/SkpKsLW1ZQKZ5ORktGzZEioqKgKvu3nzZpWNOzAwEGfOnEFiYiLU1NQE9n3+/Bnt27dHz549sXbtWpHtz58/zzyuXr2Kr1+/wsLCAi4uLnBxcYGTkxOrXLny5+zyZGRkoH379mKXYz08PODk5ISgoCAAZYn27dq1w7hx42BtbY0VK1Zg8uTJWLx4sfQP4z8AF8TUYrS0tJCamgpTU1M0adIEBw4cQMeOHZGZmQk7Ozt8/PixpodYK1FVVcXDhw/F3m09efIEVlZWAlLuFXn8+LHEPqpymammc0pqun+O2kHF3Cw+z549g6WlpcgKpSVLlrA69qJFixQyRlG8fPkSrVu3hoqKCvz9/dG8eXMAZYUK69evR3FxMW7dusUqEPny5QsuX77MBDXXrl1DUVERWrZsiZSUFIltdXV1cf78eSGft8TERDg5OYldkjU0NMSRI0eYCqj58+cjLi4Oly5dAvA/O5XU1FSp468tVGVOJ7ecVIuxsLBAZmYmTE1N0bJlS+zduxcdO3bEkSNHhPJGqpKvX78iMzMTlpaWAlPFtZWGDRsiKytLbBCTmZkp1YivJnNhajo4qOn+OcSTn59f5Qmd/GVkHo+HqKgoaGlpMftKSkpw4cIFIT8gPlUZnLDFwMAAly9fxpQpUzB37lwBA9BevXrh119/ZV11qKamBhcXF3Tt2hXOzs44fvw4Nm3axHgpScLR0RHLly9HbGws6tSpA6Ds81u+fDm6du0qtt3bt28FxhcXFwcPDw/meYcOHRiD2tpMteV0KkxxhkPhKEI2Wx4KCgrI29ub6tSpQ3Xq1GEEy/z9/Wn58uVV3r+seHl5kaOjIxUWFgrt+/LlC3Xv3p28vLxYHSslJYWOHz9Ohw8fFnhwcFQ1YWFhtHv3bub54MGDicfjkZGREd2+fbvK+jU3Nydzc3Pi8XhkYmLCPDc3N6fmzZuTq6srXblyReIxnJ2d6e3bt0Lb3717R87OzlU0cmFyc3Pp6tWrdPXqVZEWCuIoLCykuLg4Wrx4MTk5OZG6ujo1b96cJkyYQDExMfT48WOpx0hJSaEGDRqQpaUlo5JtaWlJ+vr6IkVE+ZiamjKCkoWFhaSurs54EBGViY1Wh9ipvPj5+ZG1tTXt27eP1NXVadu2bbR06VJq0qQJ7dy5U2H9cEHMNwRfNjsuLo4mTpxY5f0FBARQu3bt6OLFi6SpqckEMYcOHaLWrVtXef+ykpOTQwYGBmRqakrh4eF0+PBhOnToEC1fvpxMTEyoUaNGlJ2dLfEYDx8+JHt7eyETzMqYT3JwyIO5uTlj+3Hq1CmqV68enTx5knx8fKhXr15V3r+Tk5NEjy5JVFRt5vPy5UtSVlaWd2hVirOzM2loaFCrVq3Iz8+PYmNj6dmzZzId6+nTp/TTTz9R7969adCgQbRkyRKpwZSvry917tyZLly4QDNmzKAGDRoI3JDt3LmT2rdvL9N4qhMTExM6d+4cERFpa2tTeno6ERHFxMTIZVtRES6I+Qa5fft2tVxITU1NKSEhgYgEpePT09MluhjXBh49ekTu7u5CAYibmxvzY5JE3759acCAAfT69WvS0tKi1NRUunjxInXs2JEuXLhQDe+A47+OmpoaE2wHBATQpEmTiIjowYMHCnUBViR8SwQej0fnzp0TsEm4efMmhYaGkpmZWU0PUyLKyspkYmJC06ZNo/379wvYb1QHr1+/pm7duhGPxyNtbW06cOCAwH4XFxeaN29etY5JFjQ1NZkZK2NjY7p69SoRlZ2bFWkAWfsTHDhqjNevXwsl9QFla521veS2adOmOH78ON6+fYv09HQAgJWVldRcGD4JCQn4559/0LBhQygpKUFJSQldu3bF8uXLERAQgFu3blXl8Dk4oKenh5ycHJiYmODEiRMICQkBUFbGXFWVSTNmzMDSpUuhqamJGTNmSHzt6tWrhba1bt2akQhwcXER2q+uri6kAFzbyM/Px8WLF3H+/HmEh4djxIgRaN68Obp37w4nJyd0796dUQFnc6xr167h1atXKC0tFdg3duxYkW0aNmyICxcu4N27d9DS0mLyafj8+eefAnlKtRVJOZ2KzOvighgOsbRv3x5Hjx7FtGnTAPxPKyQqKgqdO3euyaGxRk9PDx07dqx0u5KSEmhrawMoO6k8e/YMLVq0gJmZGR48eKDoYXJwCOHp6YmRI0eiWbNmyM3NZZI7b926VWUCeLdu3UJRURHztzjEVdZkZmaCiGBhYYFr164JXOxVVFTQqFEjoYtybUNTU1NA4PPDhw+4dOkSzp07h4iICIwaNQrNmjXD3bt3JR7nyJEjGDVqFKN6Xf7Gjy+iJwlxxRtsb8RqGi8vLyQlJaF79+6YO3cu+vXrh/Xr16OoqEhkACwrXBDDIZbQ0FB4eHggNTUVxcXFWLt2LVJTU3H58mXExcXV9PCqFFtbWyQlJaFp06bo1KkTIiIioKKigs2bNzPy/xwcVcmaNWtgbm6OnJwcREREMHffz58/h5+fX5X0ee7cOaxZswbTp08Xa4b64cMHsQre/Kq+irMO3zKampqoX78+6tevDz09PSgrK+PevXtS282cORPe3t4IDQ1lLZD3b6K8wWbPnj1x//59JCYmwsrKSqFioZxOTC1EmhNqfn4+4uLiqkVs7OHDhwgLC0NSUhI+fvyItm3bIigoCHZ2dlXed01y8uRJFBQUwNPTExkZGejbty/S0tLQoEED7NmzR+RUOQeHIsnNzUWDBg0AlHnmbNmyBZ8/f0a/fv3g6OhYZf2qq6tj06ZNImcKCgoK4Obmhjdv3kgtM3748CEiIyOZC76NjQ0CAwMZM8PaSmlpKW7cuIHz58/j3LlziI+PR0FBAYyNjRkVX2dnZ6kyDJqamrhz585/7qanvOIwn5iYGCxatAgFBQUKVxzmgphaCCc2VjvJy8urlDcIB4cs3LlzB/369UNOTg6aNWuG3bt3w93dHQUFBVBSUkJBQQH27duHgQMHVkn/+/btw5gxY7Bnzx4Ba4OPHz/C3d0dr169wvnz52FkZCT2GCdPnkT//v3RunVrdOnSBUCZeWJSUhKOHDmCXr16VcnYFYGOjg4KCgrQuHFjJmBxcnKqdPDl6emJ4cOHY+jQoVU00tqJKMXhtm3bYvz48bCxsUFERIRCFYe5IIZDCCUlJakXah6Ph+Li4moaUc2RkZGBhw8fwtHREerq6oztAAdHVeHh4QFlZWXMnTsXO3bswN9//w03Nzds2bIFADBt2jQkJibiypUrVTaGqKgoBAYG4ujRo3ByckJBQQHc3d3x4sULxMXFSQxgAKBNmzZwc3MT8nibO3cuTp06VaW2A/KyadMmODs7M0q/srJ161YEBwfDy8urxryvaoLqVhzmghgOIQ4fPix2X0JCAtatW4fS0lKJsv3fOrm5uRg6dCjOnTsHHo+H9PR0WFhYwNvbG3p6eli1alVND5HjX0rDhg3xzz//wN7enkkKvX79Otq1awcAuH//PhwcHMR67yiKiIgILFu2DIcPH8bChQvx9OlTxMXFSTRP5KOmpoY7d+4ImR+mpaXB3t7+X33u4FPT3lc1hZqaGtLT02FiYgIA6Nq1Kzw8PDB//nwAQFZWFuzs7MQmh1cWLrGXQ4gBAwYIbXvw4AHmzp3LZNwHBwfXwMiqj+nTp6Nu3brIzs6GtbU1s33YsGGYMWMGF8RwVBl5eXlo3LgxgDL/NE1NTejp6TH79fT0FHYBkMScOXOQl5eHHj16wNzcHOfPn2cVwACAvr4+bt++LRTE3L59W6Rsw7+Rf1Nyc2UwMDBAZmYmTExM8PXrV9y8eVPAU+vDhw9Cs1LywAUxHBJ59uwZFi1ahOjoaLi5ueH27duwtbWt6WFVOadOncLJkyeFTtrNmjWTag7JwSEvFZcsq3MJs2JhQd26ddGwYUMEBgYKbD9w4IBQ2+DgYMyaNQsTJ07EpEmT8OjRI3z//fcAynJiwsPDperPfOv07t0bsbGxTIl0WFgYfH19GW2U3NxcdOvW7ZsycKwMvXv3xty5cxEeHo5Dhw5BQ0MD3bp1Y/YnJycrNLmbC2I4RPLu3TuEhobil19+QevWrXH27FmBL+K/nYKCApFlkXl5eQrLqufgEMf48eOZ79mXL1/g6+sLTU1NAEBhYWGV9l1Rn2TEiBGs2y5ZsgS+vr5YsGABtLW1sWrVKvz0008AACMjIyxevBgBAQEKHW9t4+TJkwL/o9DQUAwdOpQJYoqLi//VWlNLly6Fp6cnunfvDi0tLURHR0NFRYXZv23bNri6uiqsPy4nhkOIiIgIhIeHo3HjxggNDRW5vPRv5dmzZzAyMkLv3r3Rrl07LF26FNra2khOToaZmRmGDx+O0tJS7Nu3r6aHyvEv5VuuTlRSUsKLFy8Eloz4S1988ch/OxU/A21tbSQlJTGl1i9fvoSRkdG/NieGjzjF4by8PGhpaQkENvLABTEcQigpKUFdXR09e/aUqK4pajr5W0dPTw8bNmzAd999BxcXF7Rt2xb//PMP+vfvj5SUFOTl5SE+Pr7Wa11wcNQESkpKePnyJWtZ/n8jXBBTvXDLSRxCjB079j9bRrxs2TJMnjwZ7u7uSE1NxcaNG6GtrY2PHz/C09MTU6dOhaGhYU0Pk4Oj1tK8eXOp54+8vLxqGk31w/eOqriNo2rgZmI4OCqQmZkJHx8fpKamYvPmzf9aPQcODkWjpKSEyMhIsb4/fMaNG1dNI6p+lJSU4OHhweQ0HTlyBC4uLgI5TSdOnOBmYhQEF8RwcIhh/fr1mD59OqytraGsLDhpWZvFujg4agpROTH/Nb7lnKZvEW45iYNDBI8fP8aBAwegp6eHAQMGCAUxHBwcwnDLJlxwUt1wZ2YOjgps2bIFM2fORM+ePZGSkvKfTlLk4KgM3MQ+R3XDBTEcHOVwd3fHtWvXsH79epEuvhwcHOL5r6rUctQcXBDDwVGOkpISJCcns5ZX5+Dg4OCoObjEXg4ODg4ODo5vEvE2mxwcHBwcHBwctRguiOHg4ODg4OD4JuGCGA4ODg4ODo5vEi6I4eDg4ODg4Pgm4YIYDg4ODg4Ojm8SLojh4ODg4ODg+CbhghgODg4ODg6ObxIuiOHg4ODg4OD4Jvk/880t0hfLkz8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.heatmap(dataset.isnull(),cbar=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aba9a276",
      "metadata": {
        "id": "aba9a276"
      },
      "source": [
        "***Removing the string values***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "d5cbc0ed",
      "metadata": {
        "id": "d5cbc0ed",
        "outputId": "9172608d-7287-40a5-f17f-3f5ae316d595",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "could not convert string to float: 'RL'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-c187c74d1e71>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mcorr\u001b[0;34m(self, method, min_periods, numeric_only)\u001b[0m\n\u001b[1;32m  10052\u001b[0m         \u001b[0mcols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10053\u001b[0m         \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m> 10054\u001b[0;31m         \u001b[0mmat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m  10055\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10056\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"pearson\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mto_numpy\u001b[0;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[1;32m   1836\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1837\u001b[0m             \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1839\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1840\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mas_array\u001b[0;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[1;32m   1730\u001b[0m                 \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriteable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1731\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1732\u001b[0;31m             \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interleave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1733\u001b[0m             \u001b[0;31m# The underlying data was copied within _interleave, so no need\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m             \u001b[0;31m# to further copy if copy=True or setting na_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36m_interleave\u001b[0;34m(self, dtype, na_value)\u001b[0m\n\u001b[1;32m   1792\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1793\u001b[0m                 \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1794\u001b[0;31m             \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1795\u001b[0m             \u001b[0mitemmask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1796\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'RL'"
          ]
        }
      ],
      "source": [
        "dataset.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "30a7ee00",
      "metadata": {
        "id": "30a7ee00"
      },
      "outputs": [],
      "source": [
        "dataset = dataset.applymap(lambda x: np.nan if isinstance(x, str) else x) #To replace all string data in a DataFrame with np.nan (missing value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "ee409eaf",
      "metadata": {
        "id": "ee409eaf",
        "outputId": "432627ca-2b70-4f57-f077-698b16760955",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.heatmap(dataset.isnull(),cbar=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "bf9692bf",
      "metadata": {
        "id": "bf9692bf",
        "outputId": "431b35bc-a648-4bf0-b650-1605af17d9b0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                  0\n",
              "MSSubClass          0\n",
              "MSZoning         1460\n",
              "LotArea             0\n",
              "Street           1460\n",
              "                 ... \n",
              "MoSold              0\n",
              "YrSold              0\n",
              "SaleType         1460\n",
              "SaleCondition    1460\n",
              "Price               0\n",
              "Length: 62, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "dataset.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "884bf6d4",
      "metadata": {
        "scrolled": true,
        "id": "884bf6d4"
      },
      "outputs": [],
      "source": [
        "dataset = dataset.dropna(axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "0d185c8a",
      "metadata": {
        "id": "0d185c8a",
        "outputId": "2230cd19-a76c-4e8a-a0d6-a354fb31bf61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.heatmap(dataset.isnull(),cbar=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "0833244f",
      "metadata": {
        "id": "0833244f",
        "outputId": "e5fe39ca-238d-4ba7-eb96-93ae44663da3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Id  MSSubClass   LotArea  OverallQual  OverallCond  \\\n",
              "Id             1.000000    0.011156 -0.033226    -0.028365     0.012609   \n",
              "MSSubClass     0.011156    1.000000 -0.139781     0.032628    -0.059316   \n",
              "LotArea       -0.033226   -0.139781  1.000000     0.105806    -0.005636   \n",
              "OverallQual   -0.028365    0.032628  0.105806     1.000000    -0.091932   \n",
              "OverallCond    0.012609   -0.059316 -0.005636    -0.091932     1.000000   \n",
              "YearBuilt     -0.012713    0.027850  0.014228     0.572323    -0.375983   \n",
              "YearRemodAdd  -0.021998    0.040581  0.013788     0.550684     0.073741   \n",
              "BsmtFinSF1    -0.005024   -0.069836  0.214103     0.239666    -0.046231   \n",
              "BsmtFinSF2    -0.005968   -0.065649  0.111170    -0.059119     0.040229   \n",
              "BsmtUnfSF     -0.007940   -0.140759 -0.002618     0.308159    -0.136841   \n",
              "TotalBsmtSF   -0.015415   -0.238518  0.260833     0.537808    -0.171098   \n",
              "1stFlrSF       0.010496   -0.251758  0.299475     0.476224    -0.144203   \n",
              "2ndFlrSF       0.005590    0.307886  0.050986     0.295493     0.028942   \n",
              "LowQualFinSF  -0.044230    0.046474  0.004779    -0.030429     0.025494   \n",
              "GrLivArea      0.008273    0.074853  0.263116     0.593007    -0.079686   \n",
              "BsmtFullBath   0.002289    0.003491  0.158155     0.111098    -0.054942   \n",
              "BsmtHalfBath  -0.020155   -0.002333  0.048046    -0.040150     0.117821   \n",
              "FullBath       0.005587    0.131608  0.126031     0.550600    -0.194149   \n",
              "HalfBath       0.006784    0.177354  0.014259     0.273458    -0.060769   \n",
              "BedroomAbvGr   0.037719   -0.023438  0.119690     0.101676     0.012980   \n",
              "KitchenAbvGr   0.002951    0.281721 -0.017784    -0.183882    -0.087001   \n",
              "TotRmsAbvGrd   0.027239    0.040380  0.190015     0.427452    -0.057583   \n",
              "Fireplaces    -0.019772   -0.045569  0.271364     0.396765    -0.023820   \n",
              "GarageCars     0.016570   -0.040110  0.154871     0.600671    -0.185758   \n",
              "GarageArea     0.017634   -0.098672  0.180403     0.562022    -0.151521   \n",
              "WoodDeckSF    -0.029643   -0.012579  0.171698     0.238923    -0.003334   \n",
              "OpenPorchSF   -0.000477   -0.006100  0.084774     0.308819    -0.032589   \n",
              "EnclosedPorch  0.002889   -0.012037 -0.018340    -0.113937     0.070356   \n",
              "3SsnPorch     -0.046635   -0.043825  0.020423     0.030371     0.025504   \n",
              "ScreenPorch    0.001330   -0.026030  0.043160     0.064886     0.054811   \n",
              "PoolArea       0.057044    0.008283  0.077672     0.065166    -0.001985   \n",
              "MiscVal       -0.006242   -0.007683  0.038068    -0.031406     0.068777   \n",
              "MoSold         0.021172   -0.013585  0.001205     0.070815    -0.003511   \n",
              "YrSold         0.000712   -0.021407 -0.014261    -0.027347     0.043950   \n",
              "Price         -0.021917   -0.084284  0.263843     0.790982    -0.077856   \n",
              "\n",
              "               YearBuilt  YearRemodAdd  BsmtFinSF1  BsmtFinSF2  BsmtUnfSF  \\\n",
              "Id             -0.012713     -0.021998   -0.005024   -0.005968  -0.007940   \n",
              "MSSubClass      0.027850      0.040581   -0.069836   -0.065649  -0.140759   \n",
              "LotArea         0.014228      0.013788    0.214103    0.111170  -0.002618   \n",
              "OverallQual     0.572323      0.550684    0.239666   -0.059119   0.308159   \n",
              "OverallCond    -0.375983      0.073741   -0.046231    0.040229  -0.136841   \n",
              "YearBuilt       1.000000      0.592855    0.249503   -0.049107   0.149040   \n",
              "YearRemodAdd    0.592855      1.000000    0.128451   -0.067759   0.181133   \n",
              "BsmtFinSF1      0.249503      0.128451    1.000000   -0.050117  -0.495251   \n",
              "BsmtFinSF2     -0.049107     -0.067759   -0.050117    1.000000  -0.209294   \n",
              "BsmtUnfSF       0.149040      0.181133   -0.495251   -0.209294   1.000000   \n",
              "TotalBsmtSF     0.391452      0.291066    0.522396    0.104810   0.415360   \n",
              "1stFlrSF        0.281986      0.240379    0.445863    0.097117   0.317987   \n",
              "2ndFlrSF        0.010308      0.140024   -0.137079   -0.099260   0.004469   \n",
              "LowQualFinSF   -0.183784     -0.062419   -0.064503    0.014807   0.028167   \n",
              "GrLivArea       0.199010      0.287389    0.208171   -0.009640   0.240257   \n",
              "BsmtFullBath    0.187599      0.119470    0.649212    0.158678  -0.422900   \n",
              "BsmtHalfBath   -0.038162     -0.012337    0.067418    0.070948  -0.095804   \n",
              "FullBath        0.468271      0.439046    0.058543   -0.076444   0.288886   \n",
              "HalfBath        0.242656      0.183331    0.004262   -0.032148  -0.041118   \n",
              "BedroomAbvGr   -0.070651     -0.040581   -0.107355   -0.015728   0.166643   \n",
              "KitchenAbvGr   -0.174800     -0.149598   -0.081007   -0.040751   0.030086   \n",
              "TotRmsAbvGrd    0.095589      0.191740    0.044316   -0.035227   0.250647   \n",
              "Fireplaces      0.147716      0.112581    0.260011    0.046921   0.051575   \n",
              "GarageCars      0.537850      0.420622    0.224054   -0.038264   0.214175   \n",
              "GarageArea      0.478954      0.371600    0.296970   -0.018227   0.183303   \n",
              "WoodDeckSF      0.224880      0.205726    0.204306    0.067898  -0.005316   \n",
              "OpenPorchSF     0.188686      0.226298    0.111761    0.003093   0.129005   \n",
              "EnclosedPorch  -0.387268     -0.193919   -0.102303    0.036543  -0.002538   \n",
              "3SsnPorch       0.031355      0.045286    0.026451   -0.029993   0.020764   \n",
              "ScreenPorch    -0.050364     -0.038740    0.062021    0.088871  -0.012579   \n",
              "PoolArea        0.004950      0.005829    0.140491    0.041709  -0.035092   \n",
              "MiscVal        -0.034383     -0.010286    0.003571    0.004940  -0.023837   \n",
              "MoSold          0.012398      0.021490   -0.015727   -0.015211   0.034888   \n",
              "YrSold         -0.013618      0.035743    0.014359    0.031706  -0.041258   \n",
              "Price           0.522897      0.507101    0.386420   -0.011378   0.214479   \n",
              "\n",
              "               ...  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  \\\n",
              "Id             ...   -0.029643    -0.000477       0.002889  -0.046635   \n",
              "MSSubClass     ...   -0.012579    -0.006100      -0.012037  -0.043825   \n",
              "LotArea        ...    0.171698     0.084774      -0.018340   0.020423   \n",
              "OverallQual    ...    0.238923     0.308819      -0.113937   0.030371   \n",
              "OverallCond    ...   -0.003334    -0.032589       0.070356   0.025504   \n",
              "YearBuilt      ...    0.224880     0.188686      -0.387268   0.031355   \n",
              "YearRemodAdd   ...    0.205726     0.226298      -0.193919   0.045286   \n",
              "BsmtFinSF1     ...    0.204306     0.111761      -0.102303   0.026451   \n",
              "BsmtFinSF2     ...    0.067898     0.003093       0.036543  -0.029993   \n",
              "BsmtUnfSF      ...   -0.005316     0.129005      -0.002538   0.020764   \n",
              "TotalBsmtSF    ...    0.232019     0.247264      -0.095478   0.037384   \n",
              "1stFlrSF       ...    0.235459     0.211671      -0.065292   0.056104   \n",
              "2ndFlrSF       ...    0.092165     0.208026       0.061989  -0.024358   \n",
              "LowQualFinSF   ...   -0.025444     0.018251       0.061081  -0.004296   \n",
              "GrLivArea      ...    0.247433     0.330224       0.009113   0.020643   \n",
              "BsmtFullBath   ...    0.175315     0.067341      -0.049911  -0.000106   \n",
              "BsmtHalfBath   ...    0.040161    -0.025324      -0.008555   0.035114   \n",
              "FullBath       ...    0.187703     0.259977      -0.115093   0.035353   \n",
              "HalfBath       ...    0.108080     0.199740      -0.095317  -0.004972   \n",
              "BedroomAbvGr   ...    0.046854     0.093810       0.041570  -0.024478   \n",
              "KitchenAbvGr   ...   -0.090130    -0.070091       0.037312  -0.024600   \n",
              "TotRmsAbvGrd   ...    0.165984     0.234192       0.004151  -0.006683   \n",
              "Fireplaces     ...    0.200019     0.169405      -0.024822   0.011257   \n",
              "GarageCars     ...    0.226342     0.213569      -0.151434   0.035765   \n",
              "GarageArea     ...    0.224666     0.241435      -0.121777   0.035087   \n",
              "WoodDeckSF     ...    1.000000     0.058661      -0.125989  -0.032771   \n",
              "OpenPorchSF    ...    0.058661     1.000000      -0.093079  -0.005842   \n",
              "EnclosedPorch  ...   -0.125989    -0.093079       1.000000  -0.037305   \n",
              "3SsnPorch      ...   -0.032771    -0.005842      -0.037305   1.000000   \n",
              "ScreenPorch    ...   -0.074181     0.074304      -0.082864  -0.031436   \n",
              "PoolArea       ...    0.073378     0.060762       0.054203  -0.007992   \n",
              "MiscVal        ...   -0.009551    -0.018584       0.018361   0.000354   \n",
              "MoSold         ...    0.021011     0.071255      -0.028887   0.029474   \n",
              "YrSold         ...    0.022270    -0.057619      -0.009916   0.018645   \n",
              "Price          ...    0.324413     0.315856      -0.128578   0.044584   \n",
              "\n",
              "               ScreenPorch  PoolArea   MiscVal    MoSold    YrSold     Price  \n",
              "Id                0.001330  0.057044 -0.006242  0.021172  0.000712 -0.021917  \n",
              "MSSubClass       -0.026030  0.008283 -0.007683 -0.013585 -0.021407 -0.084284  \n",
              "LotArea           0.043160  0.077672  0.038068  0.001205 -0.014261  0.263843  \n",
              "OverallQual       0.064886  0.065166 -0.031406  0.070815 -0.027347  0.790982  \n",
              "OverallCond       0.054811 -0.001985  0.068777 -0.003511  0.043950 -0.077856  \n",
              "YearBuilt        -0.050364  0.004950 -0.034383  0.012398 -0.013618  0.522897  \n",
              "YearRemodAdd     -0.038740  0.005829 -0.010286  0.021490  0.035743  0.507101  \n",
              "BsmtFinSF1        0.062021  0.140491  0.003571 -0.015727  0.014359  0.386420  \n",
              "BsmtFinSF2        0.088871  0.041709  0.004940 -0.015211  0.031706 -0.011378  \n",
              "BsmtUnfSF        -0.012579 -0.035092 -0.023837  0.034888 -0.041258  0.214479  \n",
              "TotalBsmtSF       0.084489  0.126053 -0.018479  0.013196 -0.014969  0.613581  \n",
              "1stFlrSF          0.088758  0.131525 -0.021096  0.031372 -0.013604  0.605852  \n",
              "2ndFlrSF          0.040606  0.081487  0.016197  0.035164 -0.028700  0.319334  \n",
              "LowQualFinSF      0.026799  0.062157 -0.003793 -0.022174 -0.028921 -0.025606  \n",
              "GrLivArea         0.101510  0.170205 -0.002416  0.050240 -0.036526  0.708624  \n",
              "BsmtFullBath      0.023148  0.067616 -0.023047 -0.025361  0.067049  0.227122  \n",
              "BsmtHalfBath      0.032121  0.020025 -0.007367  0.032873 -0.046524 -0.016844  \n",
              "FullBath         -0.008106  0.049604 -0.014290  0.055872 -0.019669  0.560664  \n",
              "HalfBath          0.072426  0.022381  0.001290 -0.009050 -0.010269  0.284108  \n",
              "BedroomAbvGr      0.044300  0.070703  0.007767  0.046544 -0.036014  0.168213  \n",
              "KitchenAbvGr     -0.051613 -0.014525  0.062341  0.026589  0.031687 -0.135907  \n",
              "TotRmsAbvGrd      0.059383  0.083757  0.024763  0.036907 -0.034516  0.533723  \n",
              "Fireplaces        0.184530  0.095074  0.001409  0.046357 -0.024096  0.466929  \n",
              "GarageCars        0.050494  0.020934 -0.043080  0.040522 -0.039117  0.640409  \n",
              "GarageArea        0.051412  0.061047 -0.027400  0.027974 -0.027378  0.623431  \n",
              "WoodDeckSF       -0.074181  0.073378 -0.009551  0.021011  0.022270  0.324413  \n",
              "OpenPorchSF       0.074304  0.060762 -0.018584  0.071255 -0.057619  0.315856  \n",
              "EnclosedPorch    -0.082864  0.054203  0.018361 -0.028887 -0.009916 -0.128578  \n",
              "3SsnPorch        -0.031436 -0.007992  0.000354  0.029474  0.018645  0.044584  \n",
              "ScreenPorch       1.000000  0.051307  0.031946  0.023217  0.010694  0.111447  \n",
              "PoolArea          0.051307  1.000000  0.029669 -0.033737 -0.059689  0.092404  \n",
              "MiscVal           0.031946  0.029669  1.000000 -0.006495  0.004906 -0.021190  \n",
              "MoSold            0.023217 -0.033737 -0.006495  1.000000 -0.145721  0.046432  \n",
              "YrSold            0.010694 -0.059689  0.004906 -0.145721  1.000000 -0.028923  \n",
              "Price             0.111447  0.092404 -0.021190  0.046432 -0.028923  1.000000  \n",
              "\n",
              "[35 rows x 35 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>...</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Id</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.011156</td>\n",
              "      <td>-0.033226</td>\n",
              "      <td>-0.028365</td>\n",
              "      <td>0.012609</td>\n",
              "      <td>-0.012713</td>\n",
              "      <td>-0.021998</td>\n",
              "      <td>-0.005024</td>\n",
              "      <td>-0.005968</td>\n",
              "      <td>-0.007940</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.029643</td>\n",
              "      <td>-0.000477</td>\n",
              "      <td>0.002889</td>\n",
              "      <td>-0.046635</td>\n",
              "      <td>0.001330</td>\n",
              "      <td>0.057044</td>\n",
              "      <td>-0.006242</td>\n",
              "      <td>0.021172</td>\n",
              "      <td>0.000712</td>\n",
              "      <td>-0.021917</td>\n",
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              "    <tr>\n",
              "      <th>MSSubClass</th>\n",
              "      <td>0.011156</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.139781</td>\n",
              "      <td>0.032628</td>\n",
              "      <td>-0.059316</td>\n",
              "      <td>0.027850</td>\n",
              "      <td>0.040581</td>\n",
              "      <td>-0.069836</td>\n",
              "      <td>-0.065649</td>\n",
              "      <td>-0.140759</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.012579</td>\n",
              "      <td>-0.006100</td>\n",
              "      <td>-0.012037</td>\n",
              "      <td>-0.043825</td>\n",
              "      <td>-0.026030</td>\n",
              "      <td>0.008283</td>\n",
              "      <td>-0.007683</td>\n",
              "      <td>-0.013585</td>\n",
              "      <td>-0.021407</td>\n",
              "      <td>-0.084284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LotArea</th>\n",
              "      <td>-0.033226</td>\n",
              "      <td>-0.139781</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.105806</td>\n",
              "      <td>-0.005636</td>\n",
              "      <td>0.014228</td>\n",
              "      <td>0.013788</td>\n",
              "      <td>0.214103</td>\n",
              "      <td>0.111170</td>\n",
              "      <td>-0.002618</td>\n",
              "      <td>...</td>\n",
              "      <td>0.171698</td>\n",
              "      <td>0.084774</td>\n",
              "      <td>-0.018340</td>\n",
              "      <td>0.020423</td>\n",
              "      <td>0.043160</td>\n",
              "      <td>0.077672</td>\n",
              "      <td>0.038068</td>\n",
              "      <td>0.001205</td>\n",
              "      <td>-0.014261</td>\n",
              "      <td>0.263843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OverallQual</th>\n",
              "      <td>-0.028365</td>\n",
              "      <td>0.032628</td>\n",
              "      <td>0.105806</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.091932</td>\n",
              "      <td>0.572323</td>\n",
              "      <td>0.550684</td>\n",
              "      <td>0.239666</td>\n",
              "      <td>-0.059119</td>\n",
              "      <td>0.308159</td>\n",
              "      <td>...</td>\n",
              "      <td>0.238923</td>\n",
              "      <td>0.308819</td>\n",
              "      <td>-0.113937</td>\n",
              "      <td>0.030371</td>\n",
              "      <td>0.064886</td>\n",
              "      <td>0.065166</td>\n",
              "      <td>-0.031406</td>\n",
              "      <td>0.070815</td>\n",
              "      <td>-0.027347</td>\n",
              "      <td>0.790982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OverallCond</th>\n",
              "      <td>0.012609</td>\n",
              "      <td>-0.059316</td>\n",
              "      <td>-0.005636</td>\n",
              "      <td>-0.091932</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.375983</td>\n",
              "      <td>0.073741</td>\n",
              "      <td>-0.046231</td>\n",
              "      <td>0.040229</td>\n",
              "      <td>-0.136841</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.003334</td>\n",
              "      <td>-0.032589</td>\n",
              "      <td>0.070356</td>\n",
              "      <td>0.025504</td>\n",
              "      <td>0.054811</td>\n",
              "      <td>-0.001985</td>\n",
              "      <td>0.068777</td>\n",
              "      <td>-0.003511</td>\n",
              "      <td>0.043950</td>\n",
              "      <td>-0.077856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YearBuilt</th>\n",
              "      <td>-0.012713</td>\n",
              "      <td>0.027850</td>\n",
              "      <td>0.014228</td>\n",
              "      <td>0.572323</td>\n",
              "      <td>-0.375983</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.592855</td>\n",
              "      <td>0.249503</td>\n",
              "      <td>-0.049107</td>\n",
              "      <td>0.149040</td>\n",
              "      <td>...</td>\n",
              "      <td>0.224880</td>\n",
              "      <td>0.188686</td>\n",
              "      <td>-0.387268</td>\n",
              "      <td>0.031355</td>\n",
              "      <td>-0.050364</td>\n",
              "      <td>0.004950</td>\n",
              "      <td>-0.034383</td>\n",
              "      <td>0.012398</td>\n",
              "      <td>-0.013618</td>\n",
              "      <td>0.522897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <td>-0.021998</td>\n",
              "      <td>0.040581</td>\n",
              "      <td>0.013788</td>\n",
              "      <td>0.550684</td>\n",
              "      <td>0.073741</td>\n",
              "      <td>0.592855</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.128451</td>\n",
              "      <td>-0.067759</td>\n",
              "      <td>0.181133</td>\n",
              "      <td>...</td>\n",
              "      <td>0.205726</td>\n",
              "      <td>0.226298</td>\n",
              "      <td>-0.193919</td>\n",
              "      <td>0.045286</td>\n",
              "      <td>-0.038740</td>\n",
              "      <td>0.005829</td>\n",
              "      <td>-0.010286</td>\n",
              "      <td>0.021490</td>\n",
              "      <td>0.035743</td>\n",
              "      <td>0.507101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <td>-0.005024</td>\n",
              "      <td>-0.069836</td>\n",
              "      <td>0.214103</td>\n",
              "      <td>0.239666</td>\n",
              "      <td>-0.046231</td>\n",
              "      <td>0.249503</td>\n",
              "      <td>0.128451</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.050117</td>\n",
              "      <td>-0.495251</td>\n",
              "      <td>...</td>\n",
              "      <td>0.204306</td>\n",
              "      <td>0.111761</td>\n",
              "      <td>-0.102303</td>\n",
              "      <td>0.026451</td>\n",
              "      <td>0.062021</td>\n",
              "      <td>0.140491</td>\n",
              "      <td>0.003571</td>\n",
              "      <td>-0.015727</td>\n",
              "      <td>0.014359</td>\n",
              "      <td>0.386420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <td>-0.005968</td>\n",
              "      <td>-0.065649</td>\n",
              "      <td>0.111170</td>\n",
              "      <td>-0.059119</td>\n",
              "      <td>0.040229</td>\n",
              "      <td>-0.049107</td>\n",
              "      <td>-0.067759</td>\n",
              "      <td>-0.050117</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.209294</td>\n",
              "      <td>...</td>\n",
              "      <td>0.067898</td>\n",
              "      <td>0.003093</td>\n",
              "      <td>0.036543</td>\n",
              "      <td>-0.029993</td>\n",
              "      <td>0.088871</td>\n",
              "      <td>0.041709</td>\n",
              "      <td>0.004940</td>\n",
              "      <td>-0.015211</td>\n",
              "      <td>0.031706</td>\n",
              "      <td>-0.011378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <td>-0.007940</td>\n",
              "      <td>-0.140759</td>\n",
              "      <td>-0.002618</td>\n",
              "      <td>0.308159</td>\n",
              "      <td>-0.136841</td>\n",
              "      <td>0.149040</td>\n",
              "      <td>0.181133</td>\n",
              "      <td>-0.495251</td>\n",
              "      <td>-0.209294</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.005316</td>\n",
              "      <td>0.129005</td>\n",
              "      <td>-0.002538</td>\n",
              "      <td>0.020764</td>\n",
              "      <td>-0.012579</td>\n",
              "      <td>-0.035092</td>\n",
              "      <td>-0.023837</td>\n",
              "      <td>0.034888</td>\n",
              "      <td>-0.041258</td>\n",
              "      <td>0.214479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <td>-0.015415</td>\n",
              "      <td>-0.238518</td>\n",
              "      <td>0.260833</td>\n",
              "      <td>0.537808</td>\n",
              "      <td>-0.171098</td>\n",
              "      <td>0.391452</td>\n",
              "      <td>0.291066</td>\n",
              "      <td>0.522396</td>\n",
              "      <td>0.104810</td>\n",
              "      <td>0.415360</td>\n",
              "      <td>...</td>\n",
              "      <td>0.232019</td>\n",
              "      <td>0.247264</td>\n",
              "      <td>-0.095478</td>\n",
              "      <td>0.037384</td>\n",
              "      <td>0.084489</td>\n",
              "      <td>0.126053</td>\n",
              "      <td>-0.018479</td>\n",
              "      <td>0.013196</td>\n",
              "      <td>-0.014969</td>\n",
              "      <td>0.613581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1stFlrSF</th>\n",
              "      <td>0.010496</td>\n",
              "      <td>-0.251758</td>\n",
              "      <td>0.299475</td>\n",
              "      <td>0.476224</td>\n",
              "      <td>-0.144203</td>\n",
              "      <td>0.281986</td>\n",
              "      <td>0.240379</td>\n",
              "      <td>0.445863</td>\n",
              "      <td>0.097117</td>\n",
              "      <td>0.317987</td>\n",
              "      <td>...</td>\n",
              "      <td>0.235459</td>\n",
              "      <td>0.211671</td>\n",
              "      <td>-0.065292</td>\n",
              "      <td>0.056104</td>\n",
              "      <td>0.088758</td>\n",
              "      <td>0.131525</td>\n",
              "      <td>-0.021096</td>\n",
              "      <td>0.031372</td>\n",
              "      <td>-0.013604</td>\n",
              "      <td>0.605852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <td>0.005590</td>\n",
              "      <td>0.307886</td>\n",
              "      <td>0.050986</td>\n",
              "      <td>0.295493</td>\n",
              "      <td>0.028942</td>\n",
              "      <td>0.010308</td>\n",
              "      <td>0.140024</td>\n",
              "      <td>-0.137079</td>\n",
              "      <td>-0.099260</td>\n",
              "      <td>0.004469</td>\n",
              "      <td>...</td>\n",
              "      <td>0.092165</td>\n",
              "      <td>0.208026</td>\n",
              "      <td>0.061989</td>\n",
              "      <td>-0.024358</td>\n",
              "      <td>0.040606</td>\n",
              "      <td>0.081487</td>\n",
              "      <td>0.016197</td>\n",
              "      <td>0.035164</td>\n",
              "      <td>-0.028700</td>\n",
              "      <td>0.319334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <td>-0.044230</td>\n",
              "      <td>0.046474</td>\n",
              "      <td>0.004779</td>\n",
              "      <td>-0.030429</td>\n",
              "      <td>0.025494</td>\n",
              "      <td>-0.183784</td>\n",
              "      <td>-0.062419</td>\n",
              "      <td>-0.064503</td>\n",
              "      <td>0.014807</td>\n",
              "      <td>0.028167</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.025444</td>\n",
              "      <td>0.018251</td>\n",
              "      <td>0.061081</td>\n",
              "      <td>-0.004296</td>\n",
              "      <td>0.026799</td>\n",
              "      <td>0.062157</td>\n",
              "      <td>-0.003793</td>\n",
              "      <td>-0.022174</td>\n",
              "      <td>-0.028921</td>\n",
              "      <td>-0.025606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GrLivArea</th>\n",
              "      <td>0.008273</td>\n",
              "      <td>0.074853</td>\n",
              "      <td>0.263116</td>\n",
              "      <td>0.593007</td>\n",
              "      <td>-0.079686</td>\n",
              "      <td>0.199010</td>\n",
              "      <td>0.287389</td>\n",
              "      <td>0.208171</td>\n",
              "      <td>-0.009640</td>\n",
              "      <td>0.240257</td>\n",
              "      <td>...</td>\n",
              "      <td>0.247433</td>\n",
              "      <td>0.330224</td>\n",
              "      <td>0.009113</td>\n",
              "      <td>0.020643</td>\n",
              "      <td>0.101510</td>\n",
              "      <td>0.170205</td>\n",
              "      <td>-0.002416</td>\n",
              "      <td>0.050240</td>\n",
              "      <td>-0.036526</td>\n",
              "      <td>0.708624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <td>0.002289</td>\n",
              "      <td>0.003491</td>\n",
              "      <td>0.158155</td>\n",
              "      <td>0.111098</td>\n",
              "      <td>-0.054942</td>\n",
              "      <td>0.187599</td>\n",
              "      <td>0.119470</td>\n",
              "      <td>0.649212</td>\n",
              "      <td>0.158678</td>\n",
              "      <td>-0.422900</td>\n",
              "      <td>...</td>\n",
              "      <td>0.175315</td>\n",
              "      <td>0.067341</td>\n",
              "      <td>-0.049911</td>\n",
              "      <td>-0.000106</td>\n",
              "      <td>0.023148</td>\n",
              "      <td>0.067616</td>\n",
              "      <td>-0.023047</td>\n",
              "      <td>-0.025361</td>\n",
              "      <td>0.067049</td>\n",
              "      <td>0.227122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <td>-0.020155</td>\n",
              "      <td>-0.002333</td>\n",
              "      <td>0.048046</td>\n",
              "      <td>-0.040150</td>\n",
              "      <td>0.117821</td>\n",
              "      <td>-0.038162</td>\n",
              "      <td>-0.012337</td>\n",
              "      <td>0.067418</td>\n",
              "      <td>0.070948</td>\n",
              "      <td>-0.095804</td>\n",
              "      <td>...</td>\n",
              "      <td>0.040161</td>\n",
              "      <td>-0.025324</td>\n",
              "      <td>-0.008555</td>\n",
              "      <td>0.035114</td>\n",
              "      <td>0.032121</td>\n",
              "      <td>0.020025</td>\n",
              "      <td>-0.007367</td>\n",
              "      <td>0.032873</td>\n",
              "      <td>-0.046524</td>\n",
              "      <td>-0.016844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FullBath</th>\n",
              "      <td>0.005587</td>\n",
              "      <td>0.131608</td>\n",
              "      <td>0.126031</td>\n",
              "      <td>0.550600</td>\n",
              "      <td>-0.194149</td>\n",
              "      <td>0.468271</td>\n",
              "      <td>0.439046</td>\n",
              "      <td>0.058543</td>\n",
              "      <td>-0.076444</td>\n",
              "      <td>0.288886</td>\n",
              "      <td>...</td>\n",
              "      <td>0.187703</td>\n",
              "      <td>0.259977</td>\n",
              "      <td>-0.115093</td>\n",
              "      <td>0.035353</td>\n",
              "      <td>-0.008106</td>\n",
              "      <td>0.049604</td>\n",
              "      <td>-0.014290</td>\n",
              "      <td>0.055872</td>\n",
              "      <td>-0.019669</td>\n",
              "      <td>0.560664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HalfBath</th>\n",
              "      <td>0.006784</td>\n",
              "      <td>0.177354</td>\n",
              "      <td>0.014259</td>\n",
              "      <td>0.273458</td>\n",
              "      <td>-0.060769</td>\n",
              "      <td>0.242656</td>\n",
              "      <td>0.183331</td>\n",
              "      <td>0.004262</td>\n",
              "      <td>-0.032148</td>\n",
              "      <td>-0.041118</td>\n",
              "      <td>...</td>\n",
              "      <td>0.108080</td>\n",
              "      <td>0.199740</td>\n",
              "      <td>-0.095317</td>\n",
              "      <td>-0.004972</td>\n",
              "      <td>0.072426</td>\n",
              "      <td>0.022381</td>\n",
              "      <td>0.001290</td>\n",
              "      <td>-0.009050</td>\n",
              "      <td>-0.010269</td>\n",
              "      <td>0.284108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <td>0.037719</td>\n",
              "      <td>-0.023438</td>\n",
              "      <td>0.119690</td>\n",
              "      <td>0.101676</td>\n",
              "      <td>0.012980</td>\n",
              "      <td>-0.070651</td>\n",
              "      <td>-0.040581</td>\n",
              "      <td>-0.107355</td>\n",
              "      <td>-0.015728</td>\n",
              "      <td>0.166643</td>\n",
              "      <td>...</td>\n",
              "      <td>0.046854</td>\n",
              "      <td>0.093810</td>\n",
              "      <td>0.041570</td>\n",
              "      <td>-0.024478</td>\n",
              "      <td>0.044300</td>\n",
              "      <td>0.070703</td>\n",
              "      <td>0.007767</td>\n",
              "      <td>0.046544</td>\n",
              "      <td>-0.036014</td>\n",
              "      <td>0.168213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <td>0.002951</td>\n",
              "      <td>0.281721</td>\n",
              "      <td>-0.017784</td>\n",
              "      <td>-0.183882</td>\n",
              "      <td>-0.087001</td>\n",
              "      <td>-0.174800</td>\n",
              "      <td>-0.149598</td>\n",
              "      <td>-0.081007</td>\n",
              "      <td>-0.040751</td>\n",
              "      <td>0.030086</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.090130</td>\n",
              "      <td>-0.070091</td>\n",
              "      <td>0.037312</td>\n",
              "      <td>-0.024600</td>\n",
              "      <td>-0.051613</td>\n",
              "      <td>-0.014525</td>\n",
              "      <td>0.062341</td>\n",
              "      <td>0.026589</td>\n",
              "      <td>0.031687</td>\n",
              "      <td>-0.135907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <td>0.027239</td>\n",
              "      <td>0.040380</td>\n",
              "      <td>0.190015</td>\n",
              "      <td>0.427452</td>\n",
              "      <td>-0.057583</td>\n",
              "      <td>0.095589</td>\n",
              "      <td>0.191740</td>\n",
              "      <td>0.044316</td>\n",
              "      <td>-0.035227</td>\n",
              "      <td>0.250647</td>\n",
              "      <td>...</td>\n",
              "      <td>0.165984</td>\n",
              "      <td>0.234192</td>\n",
              "      <td>0.004151</td>\n",
              "      <td>-0.006683</td>\n",
              "      <td>0.059383</td>\n",
              "      <td>0.083757</td>\n",
              "      <td>0.024763</td>\n",
              "      <td>0.036907</td>\n",
              "      <td>-0.034516</td>\n",
              "      <td>0.533723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fireplaces</th>\n",
              "      <td>-0.019772</td>\n",
              "      <td>-0.045569</td>\n",
              "      <td>0.271364</td>\n",
              "      <td>0.396765</td>\n",
              "      <td>-0.023820</td>\n",
              "      <td>0.147716</td>\n",
              "      <td>0.112581</td>\n",
              "      <td>0.260011</td>\n",
              "      <td>0.046921</td>\n",
              "      <td>0.051575</td>\n",
              "      <td>...</td>\n",
              "      <td>0.200019</td>\n",
              "      <td>0.169405</td>\n",
              "      <td>-0.024822</td>\n",
              "      <td>0.011257</td>\n",
              "      <td>0.184530</td>\n",
              "      <td>0.095074</td>\n",
              "      <td>0.001409</td>\n",
              "      <td>0.046357</td>\n",
              "      <td>-0.024096</td>\n",
              "      <td>0.466929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageCars</th>\n",
              "      <td>0.016570</td>\n",
              "      <td>-0.040110</td>\n",
              "      <td>0.154871</td>\n",
              "      <td>0.600671</td>\n",
              "      <td>-0.185758</td>\n",
              "      <td>0.537850</td>\n",
              "      <td>0.420622</td>\n",
              "      <td>0.224054</td>\n",
              "      <td>-0.038264</td>\n",
              "      <td>0.214175</td>\n",
              "      <td>...</td>\n",
              "      <td>0.226342</td>\n",
              "      <td>0.213569</td>\n",
              "      <td>-0.151434</td>\n",
              "      <td>0.035765</td>\n",
              "      <td>0.050494</td>\n",
              "      <td>0.020934</td>\n",
              "      <td>-0.043080</td>\n",
              "      <td>0.040522</td>\n",
              "      <td>-0.039117</td>\n",
              "      <td>0.640409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageArea</th>\n",
              "      <td>0.017634</td>\n",
              "      <td>-0.098672</td>\n",
              "      <td>0.180403</td>\n",
              "      <td>0.562022</td>\n",
              "      <td>-0.151521</td>\n",
              "      <td>0.478954</td>\n",
              "      <td>0.371600</td>\n",
              "      <td>0.296970</td>\n",
              "      <td>-0.018227</td>\n",
              "      <td>0.183303</td>\n",
              "      <td>...</td>\n",
              "      <td>0.224666</td>\n",
              "      <td>0.241435</td>\n",
              "      <td>-0.121777</td>\n",
              "      <td>0.035087</td>\n",
              "      <td>0.051412</td>\n",
              "      <td>0.061047</td>\n",
              "      <td>-0.027400</td>\n",
              "      <td>0.027974</td>\n",
              "      <td>-0.027378</td>\n",
              "      <td>0.623431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <td>-0.029643</td>\n",
              "      <td>-0.012579</td>\n",
              "      <td>0.171698</td>\n",
              "      <td>0.238923</td>\n",
              "      <td>-0.003334</td>\n",
              "      <td>0.224880</td>\n",
              "      <td>0.205726</td>\n",
              "      <td>0.204306</td>\n",
              "      <td>0.067898</td>\n",
              "      <td>-0.005316</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.058661</td>\n",
              "      <td>-0.125989</td>\n",
              "      <td>-0.032771</td>\n",
              "      <td>-0.074181</td>\n",
              "      <td>0.073378</td>\n",
              "      <td>-0.009551</td>\n",
              "      <td>0.021011</td>\n",
              "      <td>0.022270</td>\n",
              "      <td>0.324413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <td>-0.000477</td>\n",
              "      <td>-0.006100</td>\n",
              "      <td>0.084774</td>\n",
              "      <td>0.308819</td>\n",
              "      <td>-0.032589</td>\n",
              "      <td>0.188686</td>\n",
              "      <td>0.226298</td>\n",
              "      <td>0.111761</td>\n",
              "      <td>0.003093</td>\n",
              "      <td>0.129005</td>\n",
              "      <td>...</td>\n",
              "      <td>0.058661</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.093079</td>\n",
              "      <td>-0.005842</td>\n",
              "      <td>0.074304</td>\n",
              "      <td>0.060762</td>\n",
              "      <td>-0.018584</td>\n",
              "      <td>0.071255</td>\n",
              "      <td>-0.057619</td>\n",
              "      <td>0.315856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <td>0.002889</td>\n",
              "      <td>-0.012037</td>\n",
              "      <td>-0.018340</td>\n",
              "      <td>-0.113937</td>\n",
              "      <td>0.070356</td>\n",
              "      <td>-0.387268</td>\n",
              "      <td>-0.193919</td>\n",
              "      <td>-0.102303</td>\n",
              "      <td>0.036543</td>\n",
              "      <td>-0.002538</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.125989</td>\n",
              "      <td>-0.093079</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.037305</td>\n",
              "      <td>-0.082864</td>\n",
              "      <td>0.054203</td>\n",
              "      <td>0.018361</td>\n",
              "      <td>-0.028887</td>\n",
              "      <td>-0.009916</td>\n",
              "      <td>-0.128578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3SsnPorch</th>\n",
              "      <td>-0.046635</td>\n",
              "      <td>-0.043825</td>\n",
              "      <td>0.020423</td>\n",
              "      <td>0.030371</td>\n",
              "      <td>0.025504</td>\n",
              "      <td>0.031355</td>\n",
              "      <td>0.045286</td>\n",
              "      <td>0.026451</td>\n",
              "      <td>-0.029993</td>\n",
              "      <td>0.020764</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.032771</td>\n",
              "      <td>-0.005842</td>\n",
              "      <td>-0.037305</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.031436</td>\n",
              "      <td>-0.007992</td>\n",
              "      <td>0.000354</td>\n",
              "      <td>0.029474</td>\n",
              "      <td>0.018645</td>\n",
              "      <td>0.044584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ScreenPorch</th>\n",
              "      <td>0.001330</td>\n",
              "      <td>-0.026030</td>\n",
              "      <td>0.043160</td>\n",
              "      <td>0.064886</td>\n",
              "      <td>0.054811</td>\n",
              "      <td>-0.050364</td>\n",
              "      <td>-0.038740</td>\n",
              "      <td>0.062021</td>\n",
              "      <td>0.088871</td>\n",
              "      <td>-0.012579</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.074181</td>\n",
              "      <td>0.074304</td>\n",
              "      <td>-0.082864</td>\n",
              "      <td>-0.031436</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.051307</td>\n",
              "      <td>0.031946</td>\n",
              "      <td>0.023217</td>\n",
              "      <td>0.010694</td>\n",
              "      <td>0.111447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PoolArea</th>\n",
              "      <td>0.057044</td>\n",
              "      <td>0.008283</td>\n",
              "      <td>0.077672</td>\n",
              "      <td>0.065166</td>\n",
              "      <td>-0.001985</td>\n",
              "      <td>0.004950</td>\n",
              "      <td>0.005829</td>\n",
              "      <td>0.140491</td>\n",
              "      <td>0.041709</td>\n",
              "      <td>-0.035092</td>\n",
              "      <td>...</td>\n",
              "      <td>0.073378</td>\n",
              "      <td>0.060762</td>\n",
              "      <td>0.054203</td>\n",
              "      <td>-0.007992</td>\n",
              "      <td>0.051307</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.029669</td>\n",
              "      <td>-0.033737</td>\n",
              "      <td>-0.059689</td>\n",
              "      <td>0.092404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MiscVal</th>\n",
              "      <td>-0.006242</td>\n",
              "      <td>-0.007683</td>\n",
              "      <td>0.038068</td>\n",
              "      <td>-0.031406</td>\n",
              "      <td>0.068777</td>\n",
              "      <td>-0.034383</td>\n",
              "      <td>-0.010286</td>\n",
              "      <td>0.003571</td>\n",
              "      <td>0.004940</td>\n",
              "      <td>-0.023837</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.009551</td>\n",
              "      <td>-0.018584</td>\n",
              "      <td>0.018361</td>\n",
              "      <td>0.000354</td>\n",
              "      <td>0.031946</td>\n",
              "      <td>0.029669</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.006495</td>\n",
              "      <td>0.004906</td>\n",
              "      <td>-0.021190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MoSold</th>\n",
              "      <td>0.021172</td>\n",
              "      <td>-0.013585</td>\n",
              "      <td>0.001205</td>\n",
              "      <td>0.070815</td>\n",
              "      <td>-0.003511</td>\n",
              "      <td>0.012398</td>\n",
              "      <td>0.021490</td>\n",
              "      <td>-0.015727</td>\n",
              "      <td>-0.015211</td>\n",
              "      <td>0.034888</td>\n",
              "      <td>...</td>\n",
              "      <td>0.021011</td>\n",
              "      <td>0.071255</td>\n",
              "      <td>-0.028887</td>\n",
              "      <td>0.029474</td>\n",
              "      <td>0.023217</td>\n",
              "      <td>-0.033737</td>\n",
              "      <td>-0.006495</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.145721</td>\n",
              "      <td>0.046432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YrSold</th>\n",
              "      <td>0.000712</td>\n",
              "      <td>-0.021407</td>\n",
              "      <td>-0.014261</td>\n",
              "      <td>-0.027347</td>\n",
              "      <td>0.043950</td>\n",
              "      <td>-0.013618</td>\n",
              "      <td>0.035743</td>\n",
              "      <td>0.014359</td>\n",
              "      <td>0.031706</td>\n",
              "      <td>-0.041258</td>\n",
              "      <td>...</td>\n",
              "      <td>0.022270</td>\n",
              "      <td>-0.057619</td>\n",
              "      <td>-0.009916</td>\n",
              "      <td>0.018645</td>\n",
              "      <td>0.010694</td>\n",
              "      <td>-0.059689</td>\n",
              "      <td>0.004906</td>\n",
              "      <td>-0.145721</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.028923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Price</th>\n",
              "      <td>-0.021917</td>\n",
              "      <td>-0.084284</td>\n",
              "      <td>0.263843</td>\n",
              "      <td>0.790982</td>\n",
              "      <td>-0.077856</td>\n",
              "      <td>0.522897</td>\n",
              "      <td>0.507101</td>\n",
              "      <td>0.386420</td>\n",
              "      <td>-0.011378</td>\n",
              "      <td>0.214479</td>\n",
              "      <td>...</td>\n",
              "      <td>0.324413</td>\n",
              "      <td>0.315856</td>\n",
              "      <td>-0.128578</td>\n",
              "      <td>0.044584</td>\n",
              "      <td>0.111447</td>\n",
              "      <td>0.092404</td>\n",
              "      <td>-0.021190</td>\n",
              "      <td>0.046432</td>\n",
              "      <td>-0.028923</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>35 rows × 35 columns</p>\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "dataset.corr()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "11bf7f0c",
      "metadata": {
        "id": "11bf7f0c"
      },
      "source": [
        "***Separating the independent and dependent variables***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "9862afcb",
      "metadata": {
        "id": "9862afcb"
      },
      "outputs": [],
      "source": [
        "X=dataset.iloc[:,:-1]\n",
        "Y=dataset.iloc[:,-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "857f0bb2",
      "metadata": {
        "id": "857f0bb2",
        "outputId": "99487ba6-18fb-43b0-a3b8-319b4941f22a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Id  MSSubClass  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
              "0        1          60     8450            7            5       2003   \n",
              "1        2          20     9600            6            8       1976   \n",
              "2        3          60    11250            7            5       2001   \n",
              "3        4          70     9550            7            5       1915   \n",
              "4        5          60    14260            8            5       2000   \n",
              "...    ...         ...      ...          ...          ...        ...   \n",
              "1455  1456          60     7917            6            5       1999   \n",
              "1456  1457          20    13175            6            6       1978   \n",
              "1457  1458          70     9042            7            9       1941   \n",
              "1458  1459          20     9717            5            6       1950   \n",
              "1459  1460          20     9937            5            6       1965   \n",
              "\n",
              "      YearRemodAdd  BsmtFinSF1  BsmtFinSF2  BsmtUnfSF  ...  GarageArea  \\\n",
              "0             2003         706           0        150  ...         548   \n",
              "1             1976         978           0        284  ...         460   \n",
              "2             2002         486           0        434  ...         608   \n",
              "3             1970         216           0        540  ...         642   \n",
              "4             2000         655           0        490  ...         836   \n",
              "...            ...         ...         ...        ...  ...         ...   \n",
              "1455          2000           0           0        953  ...         460   \n",
              "1456          1988         790         163        589  ...         500   \n",
              "1457          2006         275           0        877  ...         252   \n",
              "1458          1996          49        1029          0  ...         240   \n",
              "1459          1965         830         290        136  ...         276   \n",
              "\n",
              "      WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  \\\n",
              "0              0           61              0          0            0   \n",
              "1            298            0              0          0            0   \n",
              "2              0           42              0          0            0   \n",
              "3              0           35            272          0            0   \n",
              "4            192           84              0          0            0   \n",
              "...          ...          ...            ...        ...          ...   \n",
              "1455           0           40              0          0            0   \n",
              "1456         349            0              0          0            0   \n",
              "1457           0           60              0          0            0   \n",
              "1458         366            0            112          0            0   \n",
              "1459         736           68              0          0            0   \n",
              "\n",
              "      PoolArea  MiscVal  MoSold  YrSold  \n",
              "0            0        0       2    2008  \n",
              "1            0        0       5    2007  \n",
              "2            0        0       9    2008  \n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "        fill: #D2E3FC;\n",
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              "\n",
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              "        background-color: #434B5C;\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "      buttonEl.style.display =\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "28420348",
      "metadata": {
        "id": "28420348",
        "outputId": "72565fb6-58d5-4108-ccd2-c4fec2d01026",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       208500\n",
              "1       181500\n",
              "2       223500\n",
              "3       140000\n",
              "4       250000\n",
              "         ...  \n",
              "1455    175000\n",
              "1456    210000\n",
              "1457    266500\n",
              "1458    142125\n",
              "1459    147500\n",
              "Name: Price, Length: 1460, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "Y"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0fc2e007",
      "metadata": {
        "id": "0fc2e007"
      },
      "source": [
        "***Train and test split***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "eaec6880",
      "metadata": {
        "id": "eaec6880"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.3, random_state = 42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "ec896bbd",
      "metadata": {
        "id": "ec896bbd",
        "outputId": "5e608791-037d-4fd5-e7ca-be995aba045b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Id  MSSubClass  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
              "135    136          20    10400            7            6       1970   \n",
              "1452  1453         180     3675            5            5       2005   \n",
              "762    763          60     8640            7            5       2009   \n",
              "932    933          20    11670            9            5       2006   \n",
              "435    436          60    10667            7            6       1996   \n",
              "...    ...         ...      ...          ...          ...        ...   \n",
              "1095  1096          20     9317            6            5       2006   \n",
              "1130  1131          50     7804            4            3       1928   \n",
              "1294  1295          20     8172            5            7       1955   \n",
              "860    861          50     7642            7            8       1918   \n",
              "1126  1127         120     3684            7            5       2007   \n",
              "\n",
              "      YearRemodAdd  BsmtFinSF1  BsmtFinSF2  BsmtUnfSF  ...  GarageArea  \\\n",
              "135           1970           0           0       1304  ...         530   \n",
              "1452          2005         547           0          0  ...         525   \n",
              "762           2009          24           0        732  ...         614   \n",
              "932           2006           0           0       1905  ...         788   \n",
              "435           1996         385         344         70  ...         550   \n",
              "...            ...         ...         ...        ...  ...         ...   \n",
              "1095          2006          24           0       1290  ...         440   \n",
              "1130          1950         622           0        500  ...         576   \n",
              "1294          1990         167           0        697  ...         572   \n",
              "860           1998           0           0        912  ...         216   \n",
              "1126          2007           0           0       1373  ...         660   \n",
              "\n",
              "      WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  \\\n",
              "135           98            0              0          0            0   \n",
              "1452           0           28              0          0            0   \n",
              "762          169           45              0          0            0   \n",
              "932            0          191              0          0            0   \n",
              "435          158           61              0          0            0   \n",
              "...          ...          ...            ...        ...          ...   \n",
              "1095           0           22              0          0            0   \n",
              "1130         431           44              0          0            0   \n",
              "1294           0            0              0          0            0   \n",
              "860            0          240              0          0            0   \n",
              "1126         143           20              0          0            0   \n",
              "\n",
              "      PoolArea  MiscVal  MoSold  YrSold  \n",
              "135          0        0       5    2008  \n",
              "1452         0        0       5    2006  \n",
              "762          0        0       6    2010  \n",
              "932          0        0       3    2007  \n",
              "435          0        0       4    2009  \n",
              "...        ...      ...     ...     ...  \n",
              "1095         0        0       3    2007  \n",
              "1130         0        0      12    2009  \n",
              "1294         0        0       4    2006  \n",
              "860          0        0       6    2007  \n",
              "1126         0        0       6    2009  \n",
              "\n",
              "[1022 rows x 34 columns]"
            ],
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              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>LotArea</th>\n",
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              "      <th>135</th>\n",
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              "      <th>1452</th>\n",
              "      <td>1453</td>\n",
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              "      <td>5</td>\n",
              "      <td>2006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>762</th>\n",
              "      <td>763</td>\n",
              "      <td>60</td>\n",
              "      <td>8640</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2009</td>\n",
              "      <td>2009</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>732</td>\n",
              "      <td>...</td>\n",
              "      <td>614</td>\n",
              "      <td>169</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>932</th>\n",
              "      <td>933</td>\n",
              "      <td>20</td>\n",
              "      <td>11670</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>2006</td>\n",
              "      <td>2006</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1905</td>\n",
              "      <td>...</td>\n",
              "      <td>788</td>\n",
              "      <td>0</td>\n",
              "      <td>191</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2007</td>\n",
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              "      <th>435</th>\n",
              "      <td>436</td>\n",
              "      <td>60</td>\n",
              "      <td>10667</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>1996</td>\n",
              "      <td>1996</td>\n",
              "      <td>385</td>\n",
              "      <td>344</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>550</td>\n",
              "      <td>158</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>2009</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <th>1095</th>\n",
              "      <td>1096</td>\n",
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              "      <td>9317</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
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              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1130</th>\n",
              "      <td>1131</td>\n",
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              "      <td>2009</td>\n",
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              "    <tr>\n",
              "      <th>1294</th>\n",
              "      <td>1295</td>\n",
              "      <td>20</td>\n",
              "      <td>8172</td>\n",
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              "      <td>7</td>\n",
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              "      <td>167</td>\n",
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              "      <td>861</td>\n",
              "      <td>50</td>\n",
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              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>1918</td>\n",
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              "    <tr>\n",
              "      <th>1126</th>\n",
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              "      <td>5</td>\n",
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              "      <td>2007</td>\n",
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              "      <td>2009</td>\n",
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              "</table>\n",
              "<p>1022 rows × 34 columns</p>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5dfa16d3-b35c-4214-a669-20f9abc7b8f0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b355c50b-ce57-43e0-8c11-f8b1bc4e98f3')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b355c50b-ce57-43e0-8c11-f8b1bc4e98f3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_99726553-30db-45ee-9cea-e516480b1497\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('x_train')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_99726553-30db-45ee-9cea-e516480b1497 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('x_train');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x_train"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "x_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "157c62ad",
      "metadata": {
        "id": "157c62ad",
        "outputId": "883f38f0-ac9e-4b82-ae23-231d9719e532",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Id  MSSubClass  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
              "892    893          20     8414            6            8       1963   \n",
              "1105  1106          60    12256            8            5       1994   \n",
              "413    414          30     8960            5            6       1927   \n",
              "522    523          50     5000            6            7       1947   \n",
              "1036  1037          20    12898            9            5       2007   \n",
              "...    ...         ...      ...          ...          ...        ...   \n",
              "331    332          20     8176            5            6       1958   \n",
              "323    324          20     5820            3            8       1955   \n",
              "650    651          60     8125            7            6       2007   \n",
              "439    440          50    12354            6            8       1920   \n",
              "798    799          60    13518            9            5       2008   \n",
              "\n",
              "      YearRemodAdd  BsmtFinSF1  BsmtFinSF2  BsmtUnfSF  ...  GarageArea  \\\n",
              "892           2003         663           0        396  ...         264   \n",
              "1105          1995        1032           0        431  ...         712   \n",
              "413           1950           0           0       1008  ...         360   \n",
              "522           1950         399           0        605  ...         420   \n",
              "1036          2008        1022           0        598  ...         912   \n",
              "...            ...         ...         ...        ...  ...         ...   \n",
              "331           1992         846           0        210  ...         308   \n",
              "323           2005         256           0        906  ...         220   \n",
              "650           2007           0           0        813  ...         562   \n",
              "439           2000           0           0        684  ...         528   \n",
              "798           2009           0           0       1926  ...         820   \n",
              "\n",
              "      WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  \\\n",
              "892          192            0              0          0            0   \n",
              "1105         186           32              0          0            0   \n",
              "413            0            0            130          0            0   \n",
              "522            0           24             36          0            0   \n",
              "1036         228            0              0          0            0   \n",
              "...          ...          ...            ...        ...          ...   \n",
              "331            0            0              0          0            0   \n",
              "323          142           98              0          0            0   \n",
              "650            0            0              0          0            0   \n",
              "439            0           46              0          0            0   \n",
              "798          144           78              0          0            0   \n",
              "\n",
              "      PoolArea  MiscVal  MoSold  YrSold  \n",
              "892          0        0       2    2006  \n",
              "1105         0        0       4    2010  \n",
              "413          0        0       3    2010  \n",
              "522          0        0      10    2006  \n",
              "1036         0        0       9    2009  \n",
              "...        ...      ...     ...     ...  \n",
              "331          0        0       8    2007  \n",
              "323          0        0       7    2006  \n",
              "650          0        0       5    2008  \n",
              "439          0      800       8    2009  \n",
              "798          0        0       7    2009  \n",
              "\n",
              "[438 rows x 34 columns]"
            ],
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              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>2006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>650</th>\n",
              "      <td>651</td>\n",
              "      <td>60</td>\n",
              "      <td>8125</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>2007</td>\n",
              "      <td>2007</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>813</td>\n",
              "      <td>...</td>\n",
              "      <td>562</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>439</th>\n",
              "      <td>440</td>\n",
              "      <td>50</td>\n",
              "      <td>12354</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1920</td>\n",
              "      <td>2000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>684</td>\n",
              "      <td>...</td>\n",
              "      <td>528</td>\n",
              "      <td>0</td>\n",
              "      <td>46</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>800</td>\n",
              "      <td>8</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>798</th>\n",
              "      <td>799</td>\n",
              "      <td>60</td>\n",
              "      <td>13518</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>2008</td>\n",
              "      <td>2009</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1926</td>\n",
              "      <td>...</td>\n",
              "      <td>820</td>\n",
              "      <td>144</td>\n",
              "      <td>78</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>438 rows × 34 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-80e12a5e-1baa-4ead-81d7-4151eb88f10b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-80e12a5e-1baa-4ead-81d7-4151eb88f10b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-80e12a5e-1baa-4ead-81d7-4151eb88f10b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b3edea96-66ec-4459-b5a5-8f5d8fc549c2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b3edea96-66ec-4459-b5a5-8f5d8fc549c2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b3edea96-66ec-4459-b5a5-8f5d8fc549c2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_d18b71bc-7638-45c8-908a-af8f37ba07a4\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('x_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_d18b71bc-7638-45c8-908a-af8f37ba07a4 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('x_test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x_test"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "x_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "a3997bc2",
      "metadata": {
        "id": "a3997bc2",
        "outputId": "fd2e4dfa-e817-4103-fcb5-4be958aa00c6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "135     174000\n",
              "1452    145000\n",
              "762     215200\n",
              "932     320000\n",
              "435     212000\n",
              "         ...  \n",
              "1095    176432\n",
              "1130    135000\n",
              "1294    115000\n",
              "860     189950\n",
              "1126    174000\n",
              "Name: Price, Length: 1022, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "y_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "6f3c4024",
      "metadata": {
        "id": "6f3c4024",
        "outputId": "15b6f565-bdfe-482a-bc7e-9fd344f941ce",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "892     154500\n",
              "1105    325000\n",
              "413     115000\n",
              "522     159000\n",
              "1036    315500\n",
              "         ...  \n",
              "331     139000\n",
              "323     126175\n",
              "650     205950\n",
              "439     110000\n",
              "798     485000\n",
              "Name: Price, Length: 438, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "y_test\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "c0635142",
      "metadata": {
        "id": "c0635142",
        "outputId": "63cb1424-6396-48b5-b3eb-a89d95cc9e8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Price')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.scatter(dataset['OverallQual'], dataset['Price'])\n",
        "plt.xlabel('Overall Quality')\n",
        "plt.ylabel('Price')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "b1e29678",
      "metadata": {
        "id": "b1e29678",
        "outputId": "5b201d42-59dc-4e6f-d099-b30c23bf05f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='OverallQual', ylabel='Price'>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.regplot(x='OverallQual', y='Price', data=dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "757113c0",
      "metadata": {
        "id": "757113c0"
      },
      "source": [
        "***Standarizing the dataset***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "6e4b45e7",
      "metadata": {
        "id": "6e4b45e7"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler() #values of features in dataset are calculated using different units so we need to make it one standard"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "59e0594b",
      "metadata": {
        "id": "59e0594b"
      },
      "outputs": [],
      "source": [
        "x_train = scaler.fit_transform(x_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "8f8cc2bb",
      "metadata": {
        "id": "8f8cc2bb",
        "outputId": "8edfd3b0-a15e-4847-cb7d-4545706dd2b8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.40100022, -0.86894959, -0.03050433, ..., -0.12298707,\n",
              "        -0.51494368,  0.14029267],\n",
              "       [ 1.6930766 ,  2.88261838, -0.62436478, ..., -0.12298707,\n",
              "        -0.51494368, -1.36896114],\n",
              "       [ 0.07203407,  0.0689424 , -0.18592357, ..., -0.12298707,\n",
              "        -0.13741682,  1.64954649],\n",
              "       ...,\n",
              "       [ 1.32188135, -0.86894959, -0.22725096, ..., -0.12298707,\n",
              "        -0.89247054, -1.36896114],\n",
              "       [ 0.3022691 , -0.1655306 , -0.27405334, ..., -0.12298707,\n",
              "        -0.13741682, -0.61433423],\n",
              "       [ 0.92719274,  1.47578039, -0.62357003, ..., -0.12298707,\n",
              "        -0.13741682,  0.89491958]])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "x_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "631373e6",
      "metadata": {
        "id": "631373e6"
      },
      "outputs": [],
      "source": [
        "x_test = scaler.transform(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "01acad4e",
      "metadata": {
        "id": "01acad4e",
        "outputId": "1a5fd5ea-732d-4d1c-c22c-7801c988f900",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.37744788, -0.86894959, -0.20588081, ..., -0.12298707,\n",
              "        -1.64752425, -1.36896114],\n",
              "       [ 0.87785666,  0.0689424 ,  0.13339233, ..., -0.12298707,\n",
              "        -0.89247054,  1.64954649],\n",
              "       [-0.74788454, -0.63447659, -0.15766552, ..., -0.12298707,\n",
              "        -1.2699974 ,  1.64954649],\n",
              "       ...,\n",
              "       [-0.19109167,  0.0689424 , -0.23140136, ..., -0.12298707,\n",
              "        -0.51494368,  0.14029267],\n",
              "       [-0.68680177, -0.1655306 ,  0.14204635, ...,  2.2441087 ,\n",
              "         0.61763689,  0.89491958],\n",
              "       [ 0.15661021,  0.0689424 ,  0.24483499, ..., -0.12298707,\n",
              "         0.24011004,  0.89491958]])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "x_test"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fe9dc698",
      "metadata": {
        "id": "fe9dc698"
      },
      "source": [
        "***Model Training***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "44822fe8",
      "metadata": {
        "id": "44822fe8",
        "outputId": "14d3d5c5-a804-45c3-bde4-c00a0f8b6279",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regression = LinearRegression()\n",
        "regression.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "1998139c",
      "metadata": {
        "id": "1998139c",
        "outputId": "09dabd1d-bd0a-4eb3-c15e-dfd03b5b8461",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-9.58748752e+02 -8.57552154e+03  4.46870305e+03  2.57854404e+04\n",
            "  3.26204807e+03  9.90409570e+03  3.74500338e+03  4.66708007e+16\n",
            "  1.53425192e+16  4.52536806e+16 -4.53976493e+16 -3.99003099e+16\n",
            " -4.51829019e+16 -4.91130779e+15  5.43856027e+16  6.20766340e+03\n",
            "  2.73240483e+01  2.36403629e+03 -8.43367027e+02 -7.77046849e+03\n",
            " -1.99398800e+03  8.71058506e+03  3.14087451e+03  1.07079321e+04\n",
            " -1.11674523e+03  2.98471808e+03 -1.27536511e+03  2.49649397e-01\n",
            "  1.42057226e+03  4.11176139e+03 -1.13127185e+03 -7.22524744e+02\n",
            " -1.13402417e+02 -2.02025934e+02]\n"
          ]
        }
      ],
      "source": [
        "print(regression.coef_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "287b0ede",
      "metadata": {
        "id": "287b0ede",
        "outputId": "3e036f59-7e9f-4cb4-9309-4536f5194e0f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "181336.2082325432\n"
          ]
        }
      ],
      "source": [
        "print(regression.intercept_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "a16b0fa6",
      "metadata": {
        "id": "a16b0fa6",
        "outputId": "b2ef27fe-2a54-4140-8d5e-4b818643641a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'copy_X': True, 'fit_intercept': True, 'n_jobs': None, 'positive': False}"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "regression.get_params()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5ae7aac3",
      "metadata": {
        "id": "5ae7aac3"
      },
      "source": [
        "***Prediction with the test data***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "93f82125",
      "metadata": {
        "id": "93f82125"
      },
      "outputs": [],
      "source": [
        "reg_pred=regression.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "560cd39a",
      "metadata": {
        "id": "560cd39a",
        "outputId": "1b6174b0-c324-4649-85bc-77d443e3a808",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([153579.41046946, 308044.90327782, 116814.11625398, 181248.40140604,\n",
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              "       216091.60311655, 136276.0147936 , 118812.18569816, 233882.22477068,\n",
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              "        90997.96062234, 163651.60093573, 287745.01416813, 118958.06449482,\n",
              "       135779.39441592, 222682.00741511, 120073.37270791, 149118.15588087,\n",
              "       151000.57341702, 111682.928933  , 114438.40792215, 166275.64708838,\n",
              "       267394.22137907, 186047.16963382, 284941.90701672, 234688.89315977,\n",
              "       193101.71507913,  60529.76891097, 137660.1476355 ,  93765.37307175,\n",
              "       145365.64720825, 135739.50457865, 195333.40332385, 200118.0693985 ,\n",
              "       216097.24967811,  71137.47786983, 225447.88263976, 112333.02667948,\n",
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              "       167047.03279808,  97979.11867126, 261691.13648364, 166772.80985009,\n",
              "       104721.5423402 , 151652.35773229, 221154.05006012, 254834.51247251,\n",
              "       202621.52133092, 121636.74688266, 108234.81829641, 136542.17147589,\n",
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              "       169920.66123618,  86750.90989146,  98085.70390578, 183237.68276914,\n",
              "       136823.71434034, 108544.14552038, 260789.41151518, 153244.3673361 ,\n",
              "       220308.13859058, 166043.25781496, 258475.83285884, 128016.65568451,\n",
              "       134017.80210914, 266604.26568033, 234512.00333574, 367846.41941605,\n",
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              "       137922.72842796,  92955.69256177, 177697.76791588, 195283.52493493,\n",
              "       144601.62943827,  72696.65050603, 141238.28710331, 146179.45627807,\n",
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              "       286528.77455119, 183823.49871288, 124213.42074982, 258296.2434233 ,\n",
              "       288959.75207858, 223576.87665256, 178794.6015701 , 157668.30894691,\n",
              "       103123.16934518, 201026.50315955, 344977.46009873, 225555.87622484,\n",
              "       231718.70866991,  71607.28224905,  96344.56940148, 136683.00691039,\n",
              "       186558.32899962, 257168.69431582, 213149.52799412, 138071.26042882,\n",
              "       218394.96783699,  68181.92396732, 195339.61138653, 108947.97349293,\n",
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              "       244703.67096795, 220439.78657398, 103764.26546828,  98860.93502664,\n",
              "       140643.53054347, 186864.25531485,  73316.83645504, 157799.36277748,\n",
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              "       211626.2755276 , 241120.94443269, 165071.69414265, 216506.55431665,\n",
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              "        81802.98508272, 218058.97144505, 127785.23142079, 189393.66424817,\n",
              "       212319.78604731, 165394.85363506, 253967.13862235,  62210.72937602,\n",
              "       207575.4387964 , 125332.21328633, 118831.49416853,  92443.61783438,\n",
              "       213302.63455906, 178409.06853854, 132629.38995291, 219106.53253204,\n",
              "       196299.52571609,  77322.70170095, 161486.54979435, 155143.29044241,\n",
              "       125885.59960522, 254077.27450325, 171947.32613579, 121425.35866734,\n",
              "       138540.45411496,  58223.70851304,  99136.24720584, 200098.0887507 ,\n",
              "       209322.39773047, 135659.32662903,  93566.35272121, 198036.71796786,\n",
              "       238538.97942474, 334969.64369965, 313976.82735991, 118120.1241033 ,\n",
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              "       253401.83741456, 193733.30033437, 254205.15967836, 114286.00089036,\n",
              "       112266.75362988,  80082.03144049, 216696.51509714, 313049.91044874,\n",
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              "       129763.61937236, 143202.2699229 , 115239.63485111, 192735.60422756,\n",
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              "       204841.66183134, 206612.38407182,  97251.40097015, 224187.74959633,\n",
              "       150510.79185539, 167750.74287418, 209236.5875074 , 151885.60683604,\n",
              "       119325.71079903, 247423.40346393, 149938.1786926 , 156592.77436558,\n",
              "       268478.64333966, 197777.77387146, 114073.28375557, 178594.76953196,\n",
              "       133089.83184527, 310469.84394804, 160854.70794411, 360799.15550995,\n",
              "       137850.6803285 , 182210.99570069, 180416.6568153 , 144086.88267268,\n",
              "       184479.13875094, 326589.55631157,  97162.83251151, 168045.43191862,\n",
              "       124134.68718689,  76042.76646866, 214974.47701715, 133574.37842806,\n",
              "       235923.82122755, 211178.43703156, 242733.08485268, 231822.56478107,\n",
              "        60475.05669946, 291051.32366844, 122618.4320653 , 323442.71501453,\n",
              "       106270.63278056, 321764.69953101, 270064.15434562, 125128.39765706,\n",
              "       235117.93339369, 232987.80475372, 213298.87440659, 183656.69168977,\n",
              "       242632.82697121,  84154.72356066, 215423.26405562, 202089.13566409,\n",
              "       212490.7929299 , 225749.70033948, 102472.52438509, 140317.20342571,\n",
              "       185350.01208505, 134551.15996291, 104479.16179583, 209912.69025453,\n",
              "       145540.05057153, 354314.70838434])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "reg_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "de2a5346",
      "metadata": {
        "id": "de2a5346",
        "outputId": "da1e873e-3ebd-4375-d19a-d38939856b11",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "892     154500\n",
              "1105    325000\n",
              "413     115000\n",
              "522     159000\n",
              "1036    315500\n",
              "         ...  \n",
              "331     139000\n",
              "323     126175\n",
              "650     205950\n",
              "439     110000\n",
              "798     485000\n",
              "Name: Price, Length: 438, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "ax1 = sns.distplot(y_test, hist=False, color=\"r\", label=\"Actual Value\")\n",
        "sns.distplot(reg_pred, hist=False, color=\"b\", label=\"Fitted Values\" , ax=ax1)"
      ],
      "metadata": {
        "id": "AX6dQ67oSuFS",
        "outputId": "6061d5ae-4682-4bcd-8888-06ee732e0249",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 864
        }
      },
      "id": "AX6dQ67oSuFS",
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-44-3118da9133fd>:3: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  ax1 = sns.distplot(y_test, hist=False, color=\"r\", label=\"Actual Value\")\n",
            "<ipython-input-44-3118da9133fd>:4: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(reg_pred, hist=False, color=\"b\", label=\"Fitted Values\" , ax=ax1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Price', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "01bb74d5",
      "metadata": {
        "id": "01bb74d5"
      },
      "outputs": [],
      "source": [
        "plt.scatter(y_test, reg_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bfcca797",
      "metadata": {
        "id": "bfcca797"
      },
      "outputs": [],
      "source": [
        "residuals = y_test - reg_pred\n",
        "residuals"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "610d063c",
      "metadata": {
        "id": "610d063c"
      },
      "outputs": [],
      "source": [
        "sns.displot(residuals, kind = 'kde')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c9a7cc49",
      "metadata": {
        "id": "c9a7cc49"
      },
      "outputs": [],
      "source": [
        "plt.scatter(reg_pred, residuals)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6b98fc7b",
      "metadata": {
        "id": "6b98fc7b"
      },
      "source": [
        "***Performance metrics***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "79a8b7a4",
      "metadata": {
        "id": "79a8b7a4"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "print(mean_absolute_error(y_test, reg_pred))\n",
        "print(mean_squared_error(y_test, reg_pred)) #Cost Function\n",
        "print(np.sqrt(mean_squared_error(y_test, reg_pred)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "532f5d4d",
      "metadata": {
        "id": "532f5d4d"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import r2_score\n",
        "score = r2_score(y_test, reg_pred)\n",
        "print(score)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0ade8829",
      "metadata": {
        "id": "0ade8829"
      },
      "outputs": [],
      "source": [
        "#adjusted R square\n",
        "1 - (1-score)*(len(y_test)-1) / (len(y_test) - x_test.shape[1] - 1)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.4"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}