Milestone-2-Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Milestone-2: Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook corresponds to the second stage of the Machine Learning final project, as part of the Copernicus Master in Digital Earth, in the Data Science track at UBS by Candela Sol PELLIZA & Rajeswari PARASA.\n",
    "\n",
    "The project is focused on properties' price prediction for the city of Ames, Iowa, Us. The present Notebook contains two parts: \n",
    "1. The first part corresponds to some updates and corrections in the preprocessing and splitting of data that was proposed in the previous stage, ending with the updated, ready-to-work training and test sets.\n",
    "2. The second part corresponds to the specific tasks of regression, following the requirements of milestone 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PART 1: UPDATES ON PREPROCESSING STEPS FROM MILESTONE 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The observations we got from the milestone-1 notebook, mainly regarding the splitting of data and some minor preprocessing steps, made us consider that we needed to change the way we were splitting the data into train and test. Considering that this step was performed at the very beginning of the previous notebook, with the purpose of avoiding data leakage in the preprocessing, redoing the train-test split implies also redoing the preprocessing steps.\n",
    "\n",
    "Having this situation in mind, this first part of the notebook will present in detail the new changes we decided to include, in terms of both data splitting and preprocessing. However, for the rest of preprocessing steps, as they were already explained and justified in the previous notebook, they will be added here to include the whole workflow, but not explained again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.a. Loading libraries and importing the original dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.compose import make_column_selector as selector\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from scipy.stats import pearsonr\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib.ticker as ticker\n",
    "from matplotlib.ticker import ScalarFormatter\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting pandas to show all the columns\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>BsmtFin.Type.2</th>\n",
       "      <th>BsmtFin.SF.2</th>\n",
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       "      <th>Yr.Sold</th>\n",
       "      <th>Sale.Type</th>\n",
       "      <th>Sale.Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>112.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Attchd</td>\n",
       "      <td>1958.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>36</td>\n",
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       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
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       "      <th>3</th>\n",
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       "      <td>2110</td>\n",
       "      <td>244000</td>\n",
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       "      <td>RL</td>\n",
       "      <td>93.0</td>\n",
       "      <td>11160</td>\n",
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       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
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       "      <td>BrkFace</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
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       "      <td>1065.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>2110.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>2110</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>Typ</td>\n",
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       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>522.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>527105010</td>\n",
       "      <td>1629</td>\n",
       "      <td>189900</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1997</td>\n",
       "      <td>1998</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>791.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>928</td>\n",
       "      <td>701</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>212</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Order        PID  area   price  MS.SubClass MS.Zoning  Lot.Frontage  \\\n",
       "0      1  526301100  1656  215000           20        RL         141.0   \n",
       "1      2  526350040   896  105000           20        RH          80.0   \n",
       "2      3  526351010  1329  172000           20        RL          81.0   \n",
       "3      4  526353030  2110  244000           20        RL          93.0   \n",
       "4      5  527105010  1629  189900           60        RL          74.0   \n",
       "\n",
       "   Lot.Area Street Alley Lot.Shape Land.Contour Utilities Lot.Config  \\\n",
       "0     31770   Pave   NaN       IR1          Lvl    AllPub     Corner   \n",
       "1     11622   Pave   NaN       Reg          Lvl    AllPub     Inside   \n",
       "2     14267   Pave   NaN       IR1          Lvl    AllPub     Corner   \n",
       "3     11160   Pave   NaN       Reg          Lvl    AllPub     Corner   \n",
       "4     13830   Pave   NaN       IR1          Lvl    AllPub     Inside   \n",
       "\n",
       "  Land.Slope Neighborhood Condition.1 Condition.2 Bldg.Type House.Style  \\\n",
       "0        Gtl        NAmes        Norm        Norm      1Fam      1Story   \n",
       "1        Gtl        NAmes       Feedr        Norm      1Fam      1Story   \n",
       "2        Gtl        NAmes        Norm        Norm      1Fam      1Story   \n",
       "3        Gtl        NAmes        Norm        Norm      1Fam      1Story   \n",
       "4        Gtl      Gilbert        Norm        Norm      1Fam      2Story   \n",
       "\n",
       "   Overall.Qual  Overall.Cond  Year.Built  Year.Remod.Add Roof.Style  \\\n",
       "0             6             5        1960            1960        Hip   \n",
       "1             5             6        1961            1961      Gable   \n",
       "2             6             6        1958            1958        Hip   \n",
       "3             7             5        1968            1968        Hip   \n",
       "4             5             5        1997            1998      Gable   \n",
       "\n",
       "  Roof.Matl Exterior.1st Exterior.2nd Mas.Vnr.Type  Mas.Vnr.Area Exter.Qual  \\\n",
       "0   CompShg      BrkFace      Plywood        Stone         112.0         TA   \n",
       "1   CompShg      VinylSd      VinylSd          NaN           0.0         TA   \n",
       "2   CompShg      Wd Sdng      Wd Sdng      BrkFace         108.0         TA   \n",
       "3   CompShg      BrkFace      BrkFace          NaN           0.0         Gd   \n",
       "4   CompShg      VinylSd      VinylSd          NaN           0.0         TA   \n",
       "\n",
       "  Exter.Cond Foundation Bsmt.Qual Bsmt.Cond Bsmt.Exposure BsmtFin.Type.1  \\\n",
       "0         TA     CBlock        TA        Gd            Gd            BLQ   \n",
       "1         TA     CBlock        TA        TA            No            Rec   \n",
       "2         TA     CBlock        TA        TA            No            ALQ   \n",
       "3         TA     CBlock        TA        TA            No            ALQ   \n",
       "4         TA      PConc        Gd        TA            No            GLQ   \n",
       "\n",
       "   BsmtFin.SF.1 BsmtFin.Type.2  BsmtFin.SF.2  Bsmt.Unf.SF  Total.Bsmt.SF  \\\n",
       "0         639.0            Unf           0.0        441.0         1080.0   \n",
       "1         468.0            LwQ         144.0        270.0          882.0   \n",
       "2         923.0            Unf           0.0        406.0         1329.0   \n",
       "3        1065.0            Unf           0.0       1045.0         2110.0   \n",
       "4         791.0            Unf           0.0        137.0          928.0   \n",
       "\n",
       "  Heating Heating.QC Central.Air Electrical  X1st.Flr.SF  X2nd.Flr.SF  \\\n",
       "0    GasA         Fa           Y      SBrkr         1656            0   \n",
       "1    GasA         TA           Y      SBrkr          896            0   \n",
       "2    GasA         TA           Y      SBrkr         1329            0   \n",
       "3    GasA         Ex           Y      SBrkr         2110            0   \n",
       "4    GasA         Gd           Y      SBrkr          928          701   \n",
       "\n",
       "   Low.Qual.Fin.SF  Bsmt.Full.Bath  Bsmt.Half.Bath  Full.Bath  Half.Bath  \\\n",
       "0                0             1.0             0.0          1          0   \n",
       "1                0             0.0             0.0          1          0   \n",
       "2                0             0.0             0.0          1          1   \n",
       "3                0             1.0             0.0          2          1   \n",
       "4                0             0.0             0.0          2          1   \n",
       "\n",
       "   Bedroom.AbvGr  Kitchen.AbvGr Kitchen.Qual  TotRms.AbvGrd Functional  \\\n",
       "0              3              1           TA              7        Typ   \n",
       "1              2              1           TA              5        Typ   \n",
       "2              3              1           Gd              6        Typ   \n",
       "3              3              1           Ex              8        Typ   \n",
       "4              3              1           TA              6        Typ   \n",
       "\n",
       "   Fireplaces Fireplace.Qu Garage.Type  Garage.Yr.Blt Garage.Finish  \\\n",
       "0           2           Gd      Attchd         1960.0           Fin   \n",
       "1           0          NaN      Attchd         1961.0           Unf   \n",
       "2           0          NaN      Attchd         1958.0           Unf   \n",
       "3           2           TA      Attchd         1968.0           Fin   \n",
       "4           1           TA      Attchd         1997.0           Fin   \n",
       "\n",
       "   Garage.Cars  Garage.Area Garage.Qual Garage.Cond Paved.Drive  Wood.Deck.SF  \\\n",
       "0          2.0        528.0          TA          TA           P           210   \n",
       "1          1.0        730.0          TA          TA           Y           140   \n",
       "2          1.0        312.0          TA          TA           Y           393   \n",
       "3          2.0        522.0          TA          TA           Y             0   \n",
       "4          2.0        482.0          TA          TA           Y           212   \n",
       "\n",
       "   Open.Porch.SF  Enclosed.Porch  X3Ssn.Porch  Screen.Porch  Pool.Area  \\\n",
       "0             62               0            0             0          0   \n",
       "1              0               0            0           120          0   \n",
       "2             36               0            0             0          0   \n",
       "3              0               0            0             0          0   \n",
       "4             34               0            0             0          0   \n",
       "\n",
       "  Pool.QC  Fence Misc.Feature  Misc.Val  Mo.Sold  Yr.Sold Sale.Type  \\\n",
       "0     NaN    NaN          NaN         0        5     2010       WD    \n",
       "1     NaN  MnPrv          NaN         0        6     2010       WD    \n",
       "2     NaN    NaN         Gar2     12500        6     2010       WD    \n",
       "3     NaN    NaN          NaN         0        4     2010       WD    \n",
       "4     NaN  MnPrv          NaN         0        3     2010       WD    \n",
       "\n",
       "  Sale.Condition  \n",
       "0         Normal  \n",
       "1         Normal  \n",
       "2         Normal  \n",
       "3         Normal  \n",
       "4         Normal  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing  and visualizing the dataset\n",
    "data = pd.read_csv('OpenData/Ames.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.b. First preprocessing steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a dictionary with the old and new variable's names\n",
    "RenameMapping = {\n",
    "    'area': 'BldgArea',\n",
    "    'price': 'SoldPrice',\n",
    "    'MS.SubClass': 'MSSubClass',\n",
    "    'MS.Zoning': 'MSZoning',\n",
    "    'Lot.Frontage': 'LotFrontage',\n",
    "    'Lot.Area': 'LotArea',\n",
    "    'Lot.Shape': 'LotShape',\n",
    "    'Land.Contour': 'LandContour',\n",
    "    'Lot.Config': 'LotConfig',\n",
    "    'Land.Slope': 'LandSlope',\n",
    "    'Condition.1': 'Condition1',\n",
    "    'Condition.2': 'Condition2',\n",
    "    'Bldg.Type': 'BldgType',\n",
    "    'House.Style': 'HouseStyle',\n",
    "    'Overall.Qual': 'OverallQual',\n",
    "    'Overall.Cond': 'OverallCond',\n",
    "    'Year.Built': 'YearBuilt',\n",
    "    'Year.Remod.Add': 'YearRemodAdd',\n",
    "    'Roof.Style': 'RoofStyle',\n",
    "    'Roof.Matl': 'RoofMatl',\n",
    "    'Exterior.1st': 'Exterior1st',\n",
    "    'Exterior.2nd': 'Exterior2nd',\n",
    "    'Mas.Vnr.Type': 'MasVnrType',\n",
    "    'Mas.Vnr.Area': 'MasVnrArea',\n",
    "    'Exter.Qual': 'ExterQual',\n",
    "    'Exter.Cond': 'ExterCond',\n",
    "    'Bsmt.Qual': 'BsmtQual',\n",
    "    'Bsmt.Cond': 'BsmtCond',\n",
    "    'Bsmt.Exposure': 'BsmtExposure',\n",
    "    'BsmtFin.Type.1': 'BsmtFinType1',\n",
    "    'BsmtFin.SF.1': 'BsmtFinSF1',\n",
    "    'BsmtFin.Type.2': 'BsmtFinType2',\n",
    "    'BsmtFin.SF.2': 'BsmtFinSF2',\n",
    "    'Bsmt.Unf.SF': 'BsmtUnfSF',\n",
    "    'Total.Bsmt.SF': 'TotalBsmtSF',\n",
    "    'Heating.QC': 'HeatingQual',\n",
    "    'Central.Air': 'CentralAir',\n",
    "    '1st.Flr.SF': '1stFlrSF',\n",
    "    '2nd.Flr.SF': '2ndFlrSF',\n",
    "    'Low.Qual.Fin.SF': 'LowQualFinSF',\n",
    "    'Bsmt.Full.Bath': 'BsmtFullBath',\n",
    "    'Bsmt.Half.Bath': 'BsmtHalfBath',\n",
    "    'Full.Bath': 'FullBath',\n",
    "    'Half.Bath': 'HalfBath',\n",
    "    'Kitchen.Qual': 'KitchenQual',\n",
    "    'TotRms.AbvGrd': 'TotRmsAbvGrd',\n",
    "    'Fireplaces': 'Fireplaces',\n",
    "    'Fireplace.Qu': 'FireplaceQu',\n",
    "    'Garage.Type': 'GarageType',\n",
    "    'Garage.Yr.Blt': 'GarageYrBlt',\n",
    "    'Garage.Finish': 'GarageFinish',\n",
    "    'Garage.Cars': 'GarageCars',\n",
    "    'Garage.Area': 'GarageArea',\n",
    "    'Garage.Qual': 'GarageQual',\n",
    "    'Garage.Cond': 'GarageCond',\n",
    "    'Paved.Drive': 'PavedDrive',\n",
    "    'Wood.Deck.SF': 'WoodDeckSF',\n",
    "    'Open.Porch.SF': 'OpenPorchSF',\n",
    "    'Enclosed.Porch': 'EnclosedPorchSF',\n",
    "    '3Ssn.Porch': '3SsnPorchSF',\n",
    "    'Screen.Porch': 'ScreenPorchSF',\n",
    "    'Pool.Area': 'PoolArea',\n",
    "    'Pool.QC': 'PoolQual',\n",
    "    'Misc.Feature': 'MiscFeature',\n",
    "    'Misc.Val': 'MiscVal',\n",
    "    'Mo.Sold': 'MoSold',\n",
    "    'Yr.Sold': 'YrSold',\n",
    "    'Sale.Type': 'SaleType', \n",
    "    'Sale.Condition': 'SaleCondition',\n",
    "    'X1st.Flr.SF': 'X1FloorSF',\n",
    "    'X2nd.Flr.SF': 'X2FloorSF',\n",
    "    'X3Ssn.Porch': '3SsnPorchSF',\n",
    "    'Kitchen.AbvGr': 'KitchenAbvGr',\n",
    "    'Bedroom.AbvGr': 'BedroomAbvGr',\n",
    "    }\n",
    "\n",
    "#Applying the name change\n",
    "data.rename(columns=RenameMapping, inplace=True)\n",
    "\n",
    "#Dropping 'Order' column\n",
    "data = data.drop('Order', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.c. Data Split into Train and Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After reviewing the train & test data split performed in milestone 1, we realised that the unbalanced spatial distribution of samples had not been taken into account properly. The spatial attribute of our data is given by the \"neighborhood\" variable.\n",
    "The barplot below shows the amount of samples belonging to each neighborhood in the original dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Neighborhood'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Neighborhood'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The barplot gives meaningful information about two things. The first one is that the neighborhoods \"GrnHill\" and \"Landmrk\" have very few samples (1 and 2 respectively). The second one is that the neighborhoods' samples distribution is clearly unbalanced.\n",
    "\n",
    "To solve the first issue, the rows corresponding to the mentioned neighborhoods will be dropped, given that such a small number can not be representative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Landmrk samples: 1\n",
      "GrnHill samples: 2\n"
     ]
    }
   ],
   "source": [
    "# Counting the number of samples\n",
    "landmrk_smp = len(data[data['Neighborhood'] == 'Landmrk'])\n",
    "print(f'Landmrk samples: {landmrk_smp}')\n",
    "\n",
    "GrnHill_smp = len(data[data['Neighborhood'] == 'GrnHill'])\n",
    "print(f'GrnHill samples: {GrnHill_smp}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the lines for Landmrk and GrnHill neighborhoods\n",
    "neighb_todrop = ['Landmrk', 'GrnHill']\n",
    "data = data[~data['Neighborhood'].isin(neighb_todrop)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to handle the unbalanced neighborhoods' samples sdistribution. Considering that location is a key aspect on defining the price of a property, and taking into account the spatial autocorrelation effect, it is crucial to have a balanced distribution of samples from the different neighborhoods in our training and test datasets. This is specially important for the generalization of the model to unseen data, and also to take into account the influence of the spatial aspect.\n",
    "\n",
    "When checking how the random split used in the previous notebook was handling this issue, we realised that the distribution was not balanced, specially for the classes with few samples. This can be confirmed in the graph below, which shows the neighborhood samples distribution in training and test dataset for the random technique previously adopted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Divide data into train and test following the previously adopted technique\n",
    "data_train_prev, data_test_prev = train_test_split(data, test_size=0.2, random_state=33)\n",
    "\n",
    "# We use test_size =0.2 to indicate 20% of the data is used for testing and 80% for training\n",
    "# We use random_state = 33 to ensure that the same random split is reproduced every time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------Proportions of samples from neighborhoods---------\n",
      "   Neighborhood  train_prop %  test_prop %\n",
      "0         NAmes          15.1         15.2\n",
      "1       CollgCr           9.5          7.5\n",
      "2       OldTown           8.5          7.0\n",
      "3       Edwards           6.5          7.2\n",
      "4       NridgHt           5.6          6.1\n",
      "5       Gilbert           5.5          6.1\n",
      "6       Somerst           5.5          9.2\n",
      "7        Sawyer           5.3          4.4\n",
      "8        NWAmes           4.8          3.2\n",
      "9       SawyerW           4.2          4.6\n",
      "10      Mitchel           4.1          2.9\n",
      "11      BrkSide           3.7          3.6\n",
      "12      Crawfor           3.5          3.8\n",
      "13       IDOTRR           3.1          3.6\n",
      "14      NoRidge           2.5          2.0\n",
      "15       Timber           2.1          4.1\n",
      "16      StoneBr           1.8          1.4\n",
      "17      ClearCr           1.7          0.9\n",
      "18        SWISU           1.6          1.9\n",
      "19      MeadowV           1.3          1.0\n",
      "20       BrDale           1.0          1.2\n",
      "21      Blmngtn           1.0          0.9\n",
      "22      Veenker           0.9          0.5\n",
      "23      NPkVill           0.7          1.0\n",
      "24      Blueste           0.3          0.3\n",
      "25       Greens           0.3          0.3\n"
     ]
    }
   ],
   "source": [
    "# Function to plot double bar graphs to compare the distribution of neighborhoods in train and test sets\n",
    "def samples_neighb(data_train, data_test, technique):\n",
    "    # Count samples by neighborhood in train and test sets\n",
    "    train_counts = data_train['Neighborhood'].value_counts().reset_index()\n",
    "    test_counts = data_test['Neighborhood'].value_counts().reset_index()\n",
    "\n",
    "    # Merge the counts based on the 'Neighborhood' column\n",
    "    combined_counts = pd.merge(train_counts, test_counts, on='Neighborhood', how='outer')\n",
    "\n",
    "    # Plot\n",
    "    ax=combined_counts.plot(x='Neighborhood', kind='bar', color=['blue', 'orange'], alpha=0.7)\n",
    "    plt.xlabel('Neighborhood')\n",
    "    plt.ylabel('Count')\n",
    "    plt.title(f'Comparison of Train and Test Samples by Neighborhood {technique} splitting')\n",
    "    plt.legend([\"Train\", \"Test\"])\n",
    "    plt.show()\n",
    "\n",
    "    # Propotions as percentages\n",
    "    combined_counts['train_prop %'] = round(combined_counts['count_x'] *100/ combined_counts['count_x'].sum(),1)\n",
    "    combined_counts['test_prop %'] = round(combined_counts['count_y']*100 / combined_counts['count_y'].sum(),1)\n",
    "    print('----------Proportions of samples from neighborhoods---------')\n",
    "    print(combined_counts[['Neighborhood', 'train_prop %', 'test_prop %']])\n",
    "\n",
    "\n",
    "samples_neighb(data_train_prev, data_test_prev, technique='random')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To guarantee a balanced distribution of neighborhoods' samples between training and test datasets, we will perform an stratified split for the neighborhood variable, by means of the \"stratify\" parameter in the scikitlearn train_test_split() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Divide data into train and test with stratified split\n",
    "data_train, data_test= train_test_split(data, test_size=0.2, random_state=33, stratify=data['Neighborhood'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot below shows the distribution of neighborhoods' samples in training and test data after the stratified split, with a proportional amount of samples for each neighborhood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------Proportions of samples from neighborhoods---------\n",
      "   Neighborhood  train_prop %  test_prop %\n",
      "0         NAmes          15.1         15.2\n",
      "1       CollgCr           9.1          9.0\n",
      "2       OldTown           8.2          8.2\n",
      "3       Edwards           6.6          6.7\n",
      "4       Somerst           6.2          6.1\n",
      "5       NridgHt           5.7          5.6\n",
      "6       Gilbert           5.6          5.6\n",
      "7        Sawyer           5.2          5.1\n",
      "8        NWAmes           4.5          4.4\n",
      "9       SawyerW           4.3          4.3\n",
      "10      Mitchel           3.9          3.9\n",
      "11      BrkSide           3.7          3.8\n",
      "12      Crawfor           3.5          3.6\n",
      "13       IDOTRR           3.2          3.2\n",
      "14       Timber           2.5          2.4\n",
      "15      NoRidge           2.4          2.4\n",
      "16      StoneBr           1.8          1.7\n",
      "17        SWISU           1.6          1.7\n",
      "18      ClearCr           1.5          1.5\n",
      "19      MeadowV           1.3          1.2\n",
      "20       BrDale           1.0          1.0\n",
      "21      Blmngtn           0.9          1.0\n",
      "22      Veenker           0.8          0.9\n",
      "23      NPkVill           0.8          0.9\n",
      "24      Blueste           0.3          0.3\n",
      "25       Greens           0.3          0.2\n"
     ]
    }
   ],
   "source": [
    "samples_neighb(data_train, data_test, technique='stratified')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.d. Post data-splitting Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following blocks of code contain all the preprocessing steps that the dataset requires, such as variables encoding and NA values handling, which were described in detail in the Milestone-1 notebook. For more information on the reasons, steps and parameters, refer to this notebook [## COMPLETE]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.d.1. Variables Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordinal = ['LotShape', 'Utilities', 'LandSlope', 'ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'HeatingQual', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageFinish', 'GarageQual', 'GarageCond', 'PoolQual', 'Fence', 'PavedDrive' ]\n",
    "binary = ['Street', 'CentralAir']\n",
    "nominal = ['MSSubClass', 'MSZoning', 'LandContour', 'LotConfig', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'Foundation', 'Heating', 'Electrical', 'GarageType', 'MiscFeature', 'SaleType', 'SaleCondition']\n",
    "other = ['Alley']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encoding Ordinal variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping dictionary\n",
    "variable_mappings = {\n",
    "    'LotShape': {'Reg': 4, 'IR1': 3, 'IR2': 2, 'IR3': 1},\n",
    "    'Utilities': {'AllPub': 4, 'NoSewr': 3, 'NoSeWa': 2, 'ELO': 1},\n",
    "    'LandSlope': {'Gtl': 1, 'Mod': 2, 'Sev': 3},\n",
    "    'ExterQual': {'Ex': 5, 'Gd': 4, 'Ta': 3, 'Fa': 2, 'Po': 1},\n",
    "    'ExterCond': {'Ex': 5, 'Gd': 4, 'Ta': 3, 'Fa': 2, 'Po': 1},\n",
    "    'BsmtQual': {'Ex': 6, 'Gd': 5, 'Ta': 4, 'Fa': 3, 'Po': 1, 'NA': 0},\n",
    "    'BsmtCond': {'Ex': 6, 'Gd': 5, 'Ta': 4, 'Fa': 3, 'Po': 1, 'NA': 0},\n",
    "    'BsmtExposure': {'Ex': 5, 'Gd': 4, 'Ta': 3, 'Fa': 2, 'Po': 1},\n",
    "    'BsmtFinType1': {'GLQ': 6, 'ALQ': 5, 'BLQ': 4, 'Rec': 3, 'Lwq': 2, 'Unf': 1, 'Na': 0},\n",
    "    'BsmtFinType2': {'GLQ': 6, 'ALQ': 5, 'BLQ': 4, 'Rec': 3, 'Lwq': 2, 'Unf': 1, 'Na': 0},\n",
    "    'HeatingQual': {'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa': 2, 'Po': 1},\n",
    "    'KitchenQual': {'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa': 2, 'Po': 1},\n",
    "    'Functional': {'Typ': 8, 'Min1': 7, 'Min2': 6, 'Mod': 5, 'Maj1': 4, 'Maj2': 3, 'Sev': 2, 'Sal': 1},\n",
    "    'FireplaceQu': {'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa': 2, 'Po': 1, 'NA': 0},\n",
    "    'GarageFinish': {'Fin': 3, 'RFn': 2, 'Unf': 1, 'NA': 0},\n",
    "    'GarageQual': {'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa': 2, 'Po': 1, 'NA': 0},\n",
    "    'GarageCond': {'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa': 2, 'Po': 1, 'NA': 0},\n",
    "    'PoolQual': {'Ex': 4, 'Gd': 3, 'TA': 2, 'Fa': 1, 'Na': 0},\n",
    "    'Fence': {'GdPrv': 4, 'MnPrv': 3, 'GdWo': 2, 'MnWw': 1, 'NA': 0},\n",
    "    'PavedDrive': {'N': 0, 'P': 1, 'Y': 2}\n",
    "}\n",
    "\n",
    "# List of columns to map\n",
    "columns_to_map = variable_mappings.keys()\n",
    "\n",
    "def apply_mappings(data):\n",
    "    for column in columns_to_map:\n",
    "        data[column] = data[column].map(variable_mappings[column])\n",
    "\n",
    "# Applying changes to training dataset\n",
    "apply_mappings(data_train)\n",
    "\n",
    "# Applying changes to test dataset\n",
    "apply_mappings(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate through the columns in the 'ordinal' list and fill NA values with 0\n",
    "\n",
    "#Applying changes to training dataset\n",
    "for column in ordinal:\n",
    "    data_train[column].fillna(0, inplace=True)\n",
    "\n",
    "#Applying changes to test dataset\n",
    "for column in ordinal:\n",
    "    data_test[column].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encoding binary variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert binary variables into numerical\n",
    "\n",
    "Street = {'Grvl': 0, 'Pave': 1}\n",
    "CentralAir = {'N': 0, 'Y': 1}\n",
    "\n",
    "#Applying changes to training dataset\n",
    "data_train['Street'] = data_train['Street'].map(Street)\n",
    "data_train['CentralAir'] = data_train['CentralAir'].map(CentralAir)\n",
    "\n",
    "#Applying changes to test dataset\n",
    "\n",
    "data_test['Street'] = data_test['Street'].map(Street)\n",
    "data_test['CentralAir'] = data_test['CentralAir'].map(CentralAir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Encode\n",
    "Alley = {'Grvl': 1, 'Pave': 1, 'NA': 0}\n",
    "\n",
    "#Applying changes to training dataset\n",
    "\n",
    "data_train['Alley'] = data_train['Alley'].map(Alley)\n",
    "\n",
    "#Fill NA\n",
    "data_train['Alley'].fillna(0, inplace=True)\n",
    "\n",
    "#Applying changes to test dataset\n",
    "\n",
    "data_test['Alley'] = data_test['Alley'].map(Alley)\n",
    "\n",
    "#Fill NA\n",
    "data_test['Alley'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.d.2. Handling NA Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filling NAs on training\n",
    "data_train['MiscFeature'].fillna('None', inplace=True)\n",
    "data_train['GarageType'].fillna('None', inplace=True)\n",
    "\n",
    "#Filling NAs on test\n",
    "data_test['MiscFeature'].fillna('None', inplace=True)\n",
    "data_test['GarageType'].fillna('None', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rows with NaN values in specific columns in training\n",
    "columns_to_check = ['MasVnrType' , 'BsmtHalfBath', 'BsmtFullBath', 'GarageCars', 'Electrical', 'GarageArea']\n",
    "data_train.dropna(subset=columns_to_check, inplace=True)\n",
    "\n",
    "# Drop rows with NaN values in specific columns in test\n",
    "data_test.dropna(subset=columns_to_check, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## LotFrontage VARIABLE\n",
    "\n",
    "#Fill Na values with column mean - training\n",
    "mean_LotFrontage_train = data_train['LotFrontage'].mean()\n",
    "data_train['LotFrontage'].fillna(mean_LotFrontage_train, inplace=True)\n",
    "\n",
    "#Fill Na values with column mean - test\n",
    "mean_LotFrontage_test = data_test['LotFrontage'].mean()\n",
    "data_test['LotFrontage'].fillna(mean_LotFrontage_test, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "## GarageYrBlt VARIABLE\n",
    "\n",
    "#Filling GarageYrBlt NA values with YearBuilt in training\n",
    "data_train['GarageYrBlt'].fillna(data_train['YearBuilt'], inplace=True)\n",
    "\n",
    "#Filling GarageYrBlt NA values with YearBuilt in test\n",
    "data_test['GarageYrBlt'].fillna(data_test['YearBuilt'], inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.d.3. Encoding Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform one-hot encoding (returns True/False values) in training\n",
    "data_train = pd.get_dummies(data_train, columns=nominal, prefix=nominal, dtype=int)\n",
    "\n",
    "# Perform one-hot encoding (returns True/False values) in test\n",
    "data_test = pd.get_dummies(data_test, columns=nominal, prefix=nominal, dtype=int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.e. Columns dropping\n",
    "\n",
    "In the previous milestone, we observed that, the correlation matrix has two rows with `nan` values for all cells. Upon inspection of the corresponding features, we realised that the issue was cause because the columns had same value for all samples (because of being columns deried from onehot encoding). This columns are insignificant for the analysis, so we decide to drop them from both the training and test datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LowQualFinSF']\n"
     ]
    }
   ],
   "source": [
    "# List of colunmns with only 1 value for all rows in test\n",
    "def singlevalue_columns(data):\n",
    "    # Create a list of single-valued columns\n",
    "    singlevalue_cols = []\n",
    "    for i in data.columns:\n",
    "        if data[i].nunique() <2:\n",
    "            singlevalue_cols.append(i)\n",
    "    #print(f'Columns with only 1 value: {singlevalue_cols}')\n",
    "    return singlevalue_cols\n",
    "\n",
    "drop_cols = singlevalue_columns(data_train)\n",
    "print(drop_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping columns in train dataset\n",
    "data_train.drop(drop_cols, axis=1, inplace=True)\n",
    "\n",
    "# Dropping columns in test dataset\n",
    "data_test.drop(drop_cols, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.f. Treatment for missing columns between train and test\n",
    "\n",
    "We are finished with column preprocessing at this stage for both train and test datasets. Since preprocessing is carried out separately for train and test datasets, we need to check if the number of columns match. This is important for being able to apply our train model to the test dataset afterwards.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(923, 204)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(231, 185)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7  features from test data do not appear in train data\n",
      "These are  ['Condition2_RRAn', 'RoofMatl_ClyTile', 'Heating_Wall', 'MiscFeature_Othr', 'MiscFeature_TenC', 'SaleType_Con', 'SaleType_ConLw']\n"
     ]
    }
   ],
   "source": [
    "# Columns from test dataset not present in train dataset\n",
    "test_cols_drop = [col for col in data_test.columns if col not in data_train.columns]\n",
    "print(len(test_cols_drop), ' features from test data do not appear in train data')\n",
    "print('These are ', test_cols_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26  features from train data do not appear in test data\n",
      "These are  ['MSSubClass_70', 'MSSubClass_75', 'MSSubClass_190', 'LotConfig_FR3', 'Neighborhood_MeadowV', 'Condition1_RRAe', 'Condition1_RRNn', 'Condition2_Feedr', 'Condition2_PosA', 'Condition2_PosN', 'BldgType_2fmCon', 'HouseStyle_2.5Unf', 'RoofStyle_Gambrel', 'RoofStyle_Shed', 'RoofMatl_Roll', 'RoofMatl_WdShngl', 'Exterior1st_BrkFace', 'Exterior2nd_ImStucc', 'Exterior2nd_Other', 'MasVnrType_CBlock', 'Foundation_Wood', 'MiscFeature_Elev', 'SaleType_ConLI', 'SaleType_Oth', 'SaleType_VWD', 'SaleCondition_AdjLand']\n"
     ]
    }
   ],
   "source": [
    "# Columns from train dataset not present in test dataset\n",
    "train_cols_add = [col for col in data_train.columns if col not in data_test.columns]\n",
    "print(len(train_cols_add), ' features from train data do not appear in test data')\n",
    "print('These are ', train_cols_add)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the columns in the dataset are not matching, having each dataset some columns that are no present on the other one. Following a procedure that avoids data leakage, we need to consider as valid all, and only, the columns that are present in our train dataset after the preprocessing. \n",
    "\n",
    "This means that we will perform a two step procedure:\n",
    "\n",
    "The columns that are present in the test dataset but not in the train set will be eliminated from the test set. This is beause the test set is considered as unseen data, for which we shouldnt have knowledge about this other variables existence.\n",
    "\n",
    "For the columns that are present in the training dataset but not in the test one, we will recreate them in the test dataset, at the same position, filling all the rows with zeros. The addition of these columns will be neutral in affecting the test dataset, but will make it easier for handling numpy arrays with the same dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(231, 204)\n"
     ]
    }
   ],
   "source": [
    "# Treatment for missing columns:\n",
    "\n",
    "# A) For columns missing in train data, drop them from test data. \n",
    "data_test.drop(test_cols_drop, axis=1, inplace=True)\n",
    "\n",
    "# B) For columns missing in test data, add them to test data and fill them with 0s.\n",
    "for c in train_cols_add:\n",
    "    data_test[c] = 0\n",
    "\n",
    "data_test = data_test[data_train.columns]\n",
    "\n",
    "print(data_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(923, 204)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The columns in both train and test datasets, now match. We are now ready to export them to csv files for further analysis in the next milestone."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.g. Processed datasets exporting\n",
    "After processing both training and test datasets, we export them as csv files. It is improtant to notice that the normalization step that will be performed later should also be done for the testing set when running the models.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exporting training datset\n",
    "data_train.to_csv('OpenData/ames_train_clean.csv', index=False)\n",
    "\n",
    "# Exporting test datset\n",
    "data_test.to_csv('OpenData/ames_test_clean.csv', index=False)"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    },
    "image-3.png": {
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"
    },
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.h. Target Variable Distribution, Outliers and Evaluation Metric\n",
    "\n",
    "During the exploatory analysis of the data performed in the previous notebok, we identify two main issues in the distribution of our data:\n",
    "\n",
    "1- **The target variable is right skewed and not normally distributed. This can be seen in the histogram plot and the QQ plot below.**\n",
    "\n",
    "![image-2.png](attachment:image-2.png) ![image-3.png](attachment:image-3.png)\n",
    "\n",
    "2- **There are outliers in the data (as can be seen in the graph below plotted between SoldPrice and BldgArea.)** \n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "Due to these characteristics, we discussed in the previous notebook the possbility of log transforming the data, and eliminating the outliers, for obtaining a normally distributed dataset. However, after reconciling our observations from the EDA and the feedback received from the mentors, we realised that doing these steps were redundant with the proposition of Root Mean Squared Logarithic Error (RMSLE) as the main metric.\n",
    "\n",
    "Based on literature that we encountered on the RMSLE metric, is robust to outliers ([Saxena, 2019](https://medium.com/analytics-vidhya/root-mean-square-log-error-rmse-vs-rmlse-935c6cc1802a); [Sagi, 2023](https://help.pecan.ai/en/articles/6456388-model-performance-metrics-for-regression-models#Modelperformancemetricsforregressionmodels-RootMeanSquaredLogarithmicError(RMSLE))) and is suitable for non-normally distributed or skewed data [(Hussain, 2022)](https://techwithshadab.medium.com/root-mean-squared-error-root-mean-squared-logarithmic-error-70e645efcccb). This is because RMSLE measures the relative distance of the predicted value from the actual value, unlike RMSE which measures the absolute distance. Due to this, RMSE can be heavily affected by the outliers, the range of the target variable and the distribution of the target variable.\n",
    "\n",
    "Following this criteria, the dataset will be kept without dropping any outlier or applying log transformation, considering these characteristics when measuring the model accuracy with RMSLE.\n",
    "\n",
    "Moreover, we will keep taking into account the other two auxiliary metrics previously defined:\n",
    "\n",
    "- RSME given that it provides a good measure of how far the predicted values are from the groundtruth values, providing the error value in the same unit scale as the target variable. Being our prediction in a currency unit, this allows an easy interpretability of the results. \n",
    "\n",
    "- R2 in order to allow for a standardised comparison. It gives a measure of how much of the variance in the data is explained by the model, and allows comparing the results with other models in a range from 0-1. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. MILESTONE 2: REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_log_error, mean_squared_error, r2_score, make_scorer\n",
    "from sklearn.model_selection import cross_val_score, KFold, cross_validate\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from math import sqrt\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.a. Importing data and transforming to Numpy Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import train and test datasets in csv format\n",
    "data_train = pd.read_csv('OpenData/ames_train_clean.csv')\n",
    "data_test = pd.read_csv('OpenData/ames_test_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(923, 204)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(231, 204)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After importing the datasets in csv format, we transform them to numpy array format. We will also store the names of the columns in a separate vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating numpy arrays for X and Y\n",
    "X_train = data_train.drop(columns = ['PID', 'SoldPrice']).values\n",
    "X_test = data_test.drop(columns = ['PID', 'SoldPrice']).values\n",
    "X_names = data_train.drop(columns = ['PID', 'SoldPrice']).columns.tolist()\n",
    "\n",
    "Y_train = data_train['SoldPrice'].values.reshape(-1,1)\n",
    "Y_test = data_test['SoldPrice'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['BldgArea', 'LotFrontage', 'LotArea', 'Street', 'Alley', 'LotShape', 'Utilities', 'LandSlope', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1', 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'HeatingQual', 'CentralAir', 'X1FloorSF', 'X2FloorSF', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual', 'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual', 'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorchSF', '3SsnPorchSF', 'ScreenPorchSF', 'PoolArea', 'PoolQual', 'Fence', 'MiscVal', 'MoSold', 'YrSold', 'MSSubClass_20', 'MSSubClass_30', 'MSSubClass_50', 'MSSubClass_60', 'MSSubClass_70', 'MSSubClass_75', 'MSSubClass_80', 'MSSubClass_85', 'MSSubClass_90', 'MSSubClass_120', 'MSSubClass_160', 'MSSubClass_180', 'MSSubClass_190', 'MSZoning_FV', 'MSZoning_RH', 'MSZoning_RL', 'MSZoning_RM', 'LandContour_Bnk', 'LandContour_HLS', 'LandContour_Low', 'LandContour_Lvl', 'LotConfig_Corner', 'LotConfig_CulDSac', 'LotConfig_FR2', 'LotConfig_FR3', 'LotConfig_Inside', 'Neighborhood_Blmngtn', 'Neighborhood_BrDale', 'Neighborhood_BrkSide', 'Neighborhood_ClearCr', 'Neighborhood_CollgCr', 'Neighborhood_Crawfor', 'Neighborhood_Edwards', 'Neighborhood_Gilbert', 'Neighborhood_IDOTRR', 'Neighborhood_MeadowV', 'Neighborhood_Mitchel', 'Neighborhood_NAmes', 'Neighborhood_NWAmes', 'Neighborhood_NoRidge', 'Neighborhood_NridgHt', 'Neighborhood_OldTown', 'Neighborhood_SWISU', 'Neighborhood_Sawyer', 'Neighborhood_SawyerW', 'Neighborhood_Somerst', 'Neighborhood_StoneBr', 'Neighborhood_Timber', 'Neighborhood_Veenker', 'Condition1_Artery', 'Condition1_Feedr', 'Condition1_Norm', 'Condition1_PosA', 'Condition1_PosN', 'Condition1_RRAe', 'Condition1_RRAn', 'Condition1_RRNe', 'Condition1_RRNn', 'Condition2_Feedr', 'Condition2_Norm', 'Condition2_PosA', 'Condition2_PosN', 'BldgType_1Fam', 'BldgType_2fmCon', 'BldgType_Duplex', 'BldgType_Twnhs', 'BldgType_TwnhsE', 'HouseStyle_1.5Fin', 'HouseStyle_1Story', 'HouseStyle_2.5Unf', 'HouseStyle_2Story', 'HouseStyle_SFoyer', 'HouseStyle_SLvl', 'RoofStyle_Flat', 'RoofStyle_Gable', 'RoofStyle_Gambrel', 'RoofStyle_Hip', 'RoofStyle_Mansard', 'RoofStyle_Shed', 'RoofMatl_CompShg', 'RoofMatl_Roll', 'RoofMatl_Tar&Grv', 'RoofMatl_WdShake', 'RoofMatl_WdShngl', 'Exterior1st_AsbShng', 'Exterior1st_BrkFace', 'Exterior1st_CemntBd', 'Exterior1st_HdBoard', 'Exterior1st_MetalSd', 'Exterior1st_Plywood', 'Exterior1st_Stucco', 'Exterior1st_VinylSd', 'Exterior1st_Wd Sdng', 'Exterior1st_WdShing', 'Exterior2nd_AsbShng', 'Exterior2nd_AsphShn', 'Exterior2nd_CmentBd', 'Exterior2nd_HdBoard', 'Exterior2nd_ImStucc', 'Exterior2nd_MetalSd', 'Exterior2nd_Other', 'Exterior2nd_Plywood', 'Exterior2nd_Stucco', 'Exterior2nd_VinylSd', 'Exterior2nd_Wd Sdng', 'Exterior2nd_Wd Shng', 'MasVnrType_BrkCmn', 'MasVnrType_BrkFace', 'MasVnrType_CBlock', 'MasVnrType_Stone', 'Foundation_BrkTil', 'Foundation_CBlock', 'Foundation_PConc', 'Foundation_Slab', 'Foundation_Wood', 'Heating_GasA', 'Heating_GasW', 'Electrical_FuseA', 'Electrical_FuseF', 'Electrical_SBrkr', 'GarageType_2Types', 'GarageType_Attchd', 'GarageType_Basment', 'GarageType_BuiltIn', 'GarageType_CarPort', 'GarageType_Detchd', 'GarageType_None', 'MiscFeature_Elev', 'MiscFeature_Gar2', 'MiscFeature_None', 'MiscFeature_Shed', 'SaleType_COD', 'SaleType_CWD', 'SaleType_ConLD', 'SaleType_ConLI', 'SaleType_New', 'SaleType_Oth', 'SaleType_VWD', 'SaleType_WD ', 'SaleCondition_Abnorml', 'SaleCondition_AdjLand', 'SaleCondition_Alloca', 'SaleCondition_Family', 'SaleCondition_Normal', 'SaleCondition_Partial']\n"
     ]
    }
   ],
   "source": [
    "print(X_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.5190e+03, 4.2000e+01, 2.6178e+04, ..., 0.0000e+00, 1.0000e+00,\n",
       "        0.0000e+00],\n",
       "       [1.7280e+03, 9.8000e+01, 1.3260e+04, ..., 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00],\n",
       "       [1.0980e+03, 7.4000e+01, 7.3900e+03, ..., 0.0000e+00, 1.0000e+00,\n",
       "        0.0000e+00],\n",
       "       ...,\n",
       "       [2.3380e+03, 5.2000e+01, 5.1974e+04, ..., 0.0000e+00, 0.0000e+00,\n",
       "        1.0000e+00],\n",
       "       [1.0730e+03, 7.4000e+01, 9.9810e+03, ..., 0.0000e+00, 1.0000e+00,\n",
       "        0.0000e+00],\n",
       "       [2.8140e+03, 7.4000e+01, 9.7710e+03, ..., 0.0000e+00, 1.0000e+00,\n",
       "        0.0000e+00]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.52400000e+03, 7.59562842e+01, 2.99800000e+03, ...,\n",
       "        0.00000000e+00, 1.00000000e+00, 0.00000000e+00],\n",
       "       [2.02000000e+03, 9.40000000e+01, 1.22200000e+04, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 1.00000000e+00],\n",
       "       [1.96000000e+03, 9.50000000e+01, 1.36180000e+04, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 1.00000000e+00],\n",
       "       ...,\n",
       "       [1.49400000e+03, 3.90000000e+01, 1.54100000e+04, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [2.19800000e+03, 8.40000000e+01, 1.42600000e+04, ...,\n",
       "        0.00000000e+00, 1.00000000e+00, 0.00000000e+00],\n",
       "       [1.72600000e+03, 7.90000000e+01, 9.41600000e+03, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "array([[166000],\n",
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      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.b. Standardization\n",
    "As we concluded in the visual analysis from the previous notebook, the variables have different scales. This can be a problem for linear regression, as the variables with higher scale will have a higher impact on the model. To avoid this problem, we will standardize the variables. This will bring all the variables to the same scale, and hence the variables will have equal impact on the model.\n",
    "\n",
    "For this purpose, we use the sckit function StandardScaler(). It is important to notice that this function includes a statement for authomatic handling the standardisation of variables with standard deviation = 0, replacing the std for 1. This is important given the fact that, as we added 0 columns in the test dataset, if we dont handle this issue, it would throw an error. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "X_train = StandardScaler().fit_transform(X_train)\n",
    "X_test = StandardScaler().fit_transform(X_test)\n",
    "\n",
    "# Stack ones to X matrices to account for the intercept\n",
    "X_train = np.hstack((np.ones((X_train.shape[0],1)),X_train))\n",
    "X_test = np.hstack((np.ones((X_test.shape[0],1)),X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  1.62061741, -1.48895632, ..., -0.11952286,\n",
       "         0.49220027, -0.37538608],\n",
       "       [ 1.        ,  0.11620038,  1.11671724, ..., -0.11952286,\n",
       "        -2.03169333, -0.37538608],\n",
       "       [ 1.        , -1.08200787,  0.        , ..., -0.11952286,\n",
       "         0.49220027, -0.37538608],\n",
       "       ...,\n",
       "       [ 1.        ,  1.27637028, -1.02365747, ..., -0.11952286,\n",
       "        -2.03169333,  2.66392403],\n",
       "       [ 1.        , -1.12955582,  0.        , ..., -0.11952286,\n",
       "         0.49220027, -0.37538608],\n",
       "       [ 1.        ,  2.18168318,  0.        , ..., -0.11952286,\n",
       "         0.49220027, -0.37538608]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00000000e+00, -2.40384636e-01, -5.48281015e-16, ...,\n",
       "        -9.34538627e-02,  5.52344771e-01, -4.50693909e-01],\n",
       "       [ 1.00000000e+00,  6.82831603e-01,  6.96159874e-01, ...,\n",
       "        -9.34538627e-02, -1.81046342e+00,  2.21880078e+00],\n",
       "       [ 1.00000000e+00,  5.71152219e-01,  7.34741721e-01, ...,\n",
       "        -9.34538627e-02, -1.81046342e+00,  2.21880078e+00],\n",
       "       ...,\n",
       "       [ 1.00000000e+00, -2.96224328e-01, -1.42584168e+00, ...,\n",
       "        -9.34538627e-02, -1.81046342e+00, -4.50693909e-01],\n",
       "       [ 1.00000000e+00,  1.01414711e+00,  3.10341410e-01, ...,\n",
       "        -9.34538627e-02,  5.52344771e-01, -4.50693909e-01],\n",
       "       [ 1.00000000e+00,  1.35602623e-01,  1.17432177e-01, ...,\n",
       "        -9.34538627e-02, -1.81046342e+00,  2.21880078e+00]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.c. Basic Regression model: Multiple Linear Regression\n",
    "\n",
    "Based on the exploratory data analysis performed in the previous milestone, we will begin with fitting Multiple Linear Regression model (MLR) to our data and understand its performance. Since we observe notable values for Pearson's correlation coefficient between the target variable and the features, we expect the multivariate linear regression model to be a good model to start with for establishing a multivariate relationship between the them. \n",
    "\n",
    "However, we do bear in mind that a linear regression model is sensitive to outliers and skewness of the target variable (which have not been treated with log transformation currently). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by defining functions for calculating the selected metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for calculating RMSLE\n",
    "def rmsle(y_test, y_pred):\n",
    "    \"\"\"\n",
    "    This function calculates the RMSLE between the predicted and the actual values\n",
    "    -------------------------------------------\n",
    "    Parameters:\n",
    "    y_test (array): Array of actual values\n",
    "    y_pred (array): Array of predicted values\n",
    "    \n",
    "    Returns:\n",
    "    float: RMSLE value\n",
    "    \"\"\"\n",
    "    return mean_squared_log_error(y_test, y_pred, squared=False)\n",
    "# Define RMSLE as a metric for cross_validate\n",
    "rmsle_scorer = make_scorer(rmsle)\n",
    "\n",
    "# Define a custom scorer for R2\n",
    "r2_scorer = make_scorer(r2_score)\n",
    "\n",
    "# Function for calculating RMSE\n",
    "def rmse(y_test, y_pred):\n",
    "    \"\"\"\n",
    "    This function calculates the RMSE between the predicted and the actual values\n",
    "    -------------------------------------------\n",
    "    Parameters:\n",
    "    y_test (array): Array of actual values\n",
    "    y_pred (array): Array of predicted values\n",
    "\n",
    "    Returns:\n",
    "    float: RMSE value\n",
    "    \"\"\"\n",
    "    \n",
    "    return mean_squared_error(y_test, y_pred, squared=False)\n",
    "# Define a custom scorer for RMSE\n",
    "rmse_scorer = make_scorer(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After, we define functions for printing the graphics and errors results when testing the models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define functions for printing model's predictions and scores\n",
    "def plot_Yt_Yp(y_test, y_pred, set_limit=None):\n",
    "    \"\"\"\n",
    "    Plots the actual vs predicted values\n",
    "    ------------------------------------\n",
    "    Parameters:\n",
    "    y_test: actual values\n",
    "    y_pred: predicted values\n",
    "\n",
    "    Returns:\n",
    "    None\n",
    "    \"\"\"\n",
    "\n",
    "    plt.scatter(y_test, y_pred, alpha=0.3, label= 'Property Sold')\n",
    "    plt.xlabel('Actual Sold Price')\n",
    "    plt.ylabel('Predicted Sold Price')\n",
    "    plt.title('Actual vs Predicted Sold Price')\n",
    "    # Set axis limits if set_limit parameter is provided\n",
    "    if set_limit:\n",
    "        plt.axis(set_limit)\n",
    "    #plot x=y line\n",
    "    plt.plot(np.linspace(np.min(y_test), np.max(y_test), 1000), np.linspace(np.min(y_test), np.max(y_test) , 1000), color='red', label= 'x=y line')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "def print_eval_metrics(y_test,y_pred):\n",
    "\n",
    "    \"\"\"\n",
    "    Prints the evaluation metrics\n",
    "    -----------------------------\n",
    "    Parameters:\n",
    "    y_test: actual values\n",
    "    y_pred: predicted values\n",
    "\n",
    "    Returns:\n",
    "    None\n",
    "    \"\"\"\n",
    "    try:\n",
    "        # try block to catch errors in case -ve log computations are attempted\n",
    "        print('RMSLE:', rmsle(y_test, y_pred))\n",
    "    except:\n",
    "        print('RMSLE: cannot be calculated')\n",
    "\n",
    "    print('R2:', r2_score(y_test, y_pred))\n",
    "\n",
    "    print('RMSE:', rmse(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.c.1. MLR performance on training data - considering whole train data\n",
    "\n",
    "The first approach consists on training the model on the training dataset, and asses the prediction performance over the same data. For this purpose we use the LinearRegression() function from ScikitLearn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSLE: 0.12206663830964438\n",
      "R2: 0.923319333246195\n",
      "RMSE: 25516.990372154785\n"
     ]
    }
   ],
   "source": [
    "# Calculate simple regression model on the whole training data and evaluate on training data\n",
    "basic_mlr = LinearRegression().fit(X_train, Y_train)\n",
    "Y_pred = basic_mlr.predict(X_train)\n",
    "plot_Yt_Yp(Y_train, Y_pred)\n",
    "print_eval_metrics(Y_train, Y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that the fitting of the model for the training data is really good (RMSLE = 0.1, R2 = 0.92). However, this is not a good metric for the model, considering that we are evaluating the prediction performance for dataset that was used for training the model. We now need to evaluate the generalization ability of the model on unseen data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.c.2. MLR performance on training data - considering 5-fold cross validation\n",
    "A second approach, still using only the training data, is testing the model using cross validation. In this case, we are setting the fold splits to 5 (a standard common partition). The objective is to analyze how he model is performing when testing on unseen validation data inside the training dataset. \n",
    "\n",
    "For this purpose, we use the cross_validate function from SciKitLearn. In a first attempt, we try to analyze the three metrics previously defined. Hwever, when trying to calculate the RMSLE we get an error (check the commented scoring = {} line). The reason of this will be further analyzed, but for now we will keep the R2 and RMSE metrics for this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'fit_time': array([0.04338074, 0.03689885, 0.031914  , 0.03292561, 0.03191447]), 'score_time': array([0.00498724, 0.00199533, 0.00099754, 0.00199461, 0.00199533]), 'test_R2': array([-7.84485878e+21, -5.38355524e+22, -9.12249509e+19, -2.29949871e+22,\n",
      "       -9.36347464e+22]), 'test_RMSE': array([7.86964588e+15, 2.00434202e+16, 8.80279601e+14, 1.34835698e+16,\n",
      "       3.14829637e+16]), 'test_RMSLE': array([       nan,        nan, 3.16746218,        nan,        nan])}\n",
      "Mean RMSE 1.4751975815309152e+16\n",
      "Mean R2 -3.5680273917722633e+22\n",
      "Mean RMSLE: nan\n"
     ]
    }
   ],
   "source": [
    "# Uncomment the line below for checking RMSLE error\n",
    "scoring = {'R2': r2_scorer, 'RMSE': rmse_scorer, 'RMSLE': rmsle_scorer}\n",
    "#scoring = {'R2': r2_scorer, 'RMSE': rmse_scorer}\n",
    "\n",
    "# Calculate model accuracy using crossval\n",
    "# fix the splits for cross validation: shuffle = True because random_state is set, otherwise error is thrown\n",
    "kf = KFold(n_splits=5, random_state=33, shuffle=True)\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "cv_results = cross_validate(basic_mlr, X_train, Y_train, scoring= scoring, cv=kf)\n",
    "print(cv_results)\n",
    "\n",
    "print('Mean RMSE', np.mean(cv_results['test_RMSE']))\n",
    "print('Mean R2', np.mean(cv_results['test_R2']))\n",
    "\n",
    "try:\n",
    "    print('Mean RMSLE:', np.mean(cv_results['test_RMSLE']))\n",
    "except:\n",
    "    print('Mean RMSLE: cannot be calculated')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations and conclusions:**\n",
    "- RMSE is very large (+ve infinity)\n",
    "- R2 score is very low (-ve infinity) \n",
    "- RMSLE cannot be generated (as seen as 'nan')- because the model is predicting negative values for the target value, and consequently it is mathematically not possible to get the Root Mean Squared Logarithic Error.\n",
    "\n",
    "Furthermore, as we saw in earlier step, a very high R2 score (0.92) is observed when the model is tested on the training data itself. All of these observations, confirm that the **model is overfitting** on the training data and is highly complex."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.d. Adding a Regularization Term - ElasticNet Model\n",
    "The previous results of the MLR (sections 2.c.1 and 2.c.2) justify the inclusion of a regularisation term. Between the existing options, Ridge, Lasso and Elasticnet, we consider Elasticnet as the most appropriate option for our case, given that it combines the benefits from both Ridge and Lasso. This will be able to solve two important problems of our model:\n",
    "\n",
    "1. Our model is clearly overfitting, is complex, and considering too many variables. The EDA performed at the first stage showed that the dataset has many variables with really low correlation values with the target variable. Lasso can force less a coefficient of 0 on less important terms, allowing an automatic feature selection.\n",
    "\n",
    "2. The EDA also exposed that a lot of explanatory variables are higly correlated among them. Ridge technique obliges less important terms to take values closer to zero (and not exactly 0), minimizing their influence on the model, and grouping correlated varibales to share the weights. \n",
    "\n",
    "With the inclusion of an Elasticnet regression, we can address both issues at the same time, expecting a better model performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.d.1. Tuning EasticNet regularization hyperparameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given that test data is unseen, we cannot use it to finetune our regularisation hyperparameter. So we rely on cross validation scores to choose the best regularisation hyperparameter. We will use 5-fold cross validation.\n",
    "\n",
    "`ElasticNet()` function from ScikitLearn is used to fit the model. The function uses the parameter `alpha` as the regularisation hyperparameter to initiate an ElasticNet regressor. By default, the function uses `l1_ratio` = `0.5`, which means that the penalty is equally distributed between Lasso (L1) and Ridge (L2) regularisation strategies. We will use the `cross_val_score` function from SciKitLearn to perform cross validation and obtain cross validation scores for each fold. We will use the mean of the cross validation scores to choose the best regularisation hyperparameter.\n",
    "The scoring metric is specified in the crossval score, establishing rmsle, which was selected as our main metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum RMSLE 0.14334777037026797 \n",
      "Alpha value at minimum RMSLE (from alphas tested) 3.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Since alpha is a positive value, we need to test a range of positive values to find the optimal one\n",
    "alpha_elast = [1e-15, 1e-10, 1e-8,1e-5,1e-4, 1e-3,1e-2,0.5,1,1.5, 2,3,4, 5, 10, 20, 30, 40]\n",
    "mean_cv = []\n",
    "# kfolds: remember that the variable 'kf' is already defined above\n",
    "\n",
    "for i in alpha_elast:\n",
    "    # Set up each model with a different alpha value iteratively\n",
    "    warnings.filterwarnings('ignore') #ignore warnings\n",
    "    elasticnet_mlr = ElasticNet(alpha= i).fit(X_train, Y_train)\n",
    "\n",
    "    # Perform cross validation on the same splits each time\n",
    "    cv_scores = cross_val_score(elasticnet_mlr, X_train, Y_train, cv=kf, scoring=rmsle_scorer)\n",
    "    \n",
    "    mean_cv.append(np.mean(cv_scores))\n",
    "    \n",
    "\n",
    "# drop entries with mean_cv = nan. This is necessary to print non-nan minimum value for mean score\n",
    "dict = {'alpha': alpha_elast, 'mean_cv': mean_cv}\n",
    "df = pd.DataFrame(dict).dropna()\n",
    "alpha_elast = df['alpha'].tolist()\n",
    "mean_cv = df['mean_cv'].tolist()\n",
    "# print(alpha_elast,mean_cv)\n",
    "\n",
    "print('Minimum RMSLE', min(mean_cv), '\\n'\n",
    "      'Alpha value at minimum RMSLE (from alphas tested)', alpha_elast[mean_cv.index(min(mean_cv))])\n",
    "plt.plot(alpha_elast, mean_cv)\n",
    "plt.scatter(alpha_elast, mean_cv)\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('RMSLE')\n",
    "plt.title('RMSLE vs alpha values in ElasticNet')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen from the text and graph outputs above, highest average cross validation score is obtained for alpha in the range of 2 to 3, from the list of alphas that we tested. To get a better approximation, we can zoom into a range of 2 and 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum RMSLE 0.14 \n",
      "Alpha value at minimum RMSLE (from alphas tested) 2.67\n"
     ]
    },
    {
     "data": {
      "image/png": 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u3LhxkMlkWLp0aYXjEQRB8/8rNzcXpaWlWus7deoEqVSKoqKiWhyxfqjs/9v3339f4U4CtXmfWlpaAkCtgvP48eM1l9v/qbqet7/3Rv1znGNQUBB8fHywevXqSnu37927p/l3XWol/cSeKCIAr732GiZMmIAvv/wSL730UqVtlEplre6D9vLLL6OgoABPPPEE2rdvj+LiYkRERGDHjh3w9PSsMBdRXFxcpT0GAQEBGDlyJACgtLQUW7ZsqfT1nnjiCc0v46pMmjQJH3/8McLDw9GpUyd06NBBa/2wYcPg4uKC3r17w9nZGVevXsUnn3yCkSNHVjv2a9SoUdi1axeeeOIJjBw5EgkJCdiwYQP8/PyqvTz6dwkJCRgzZgyGDx+OyMhIbNmyBVOnTtW6hFZbo0aNwjfffAOlUgk/Pz9ERkbi999/1xpXU5OazlV9jnny5Mn497//jSeeeAKvvPIKCgoKsH79evj6+moNoPbx8cHy5cuxePFiJCYm4vHHH4e1tTUSEhKwe/duvPDCC1i4cCEOHz6MuXPnYsKECfD19UVpaSm++eYbyGSyOo/1aSjHjx+v9I+Rzp07o3PnzpVuM2rUKCxbtgyzZs1Cr169cOnSJWzduhXe3t5a7WrzPg0KCgIAvP7665g8eTJMTU0xevToSn9GXnvtNezcuRMTJkzAM888g6CgIGRlZWHPnj3YsGFDte/B8rFR0dHRWsulUik+++wzPPbYY+jYsSNmzZoFNzc3pKSk4MiRI7CxscHevXvrXCvpKRG+EUgkivIpDs6ePVthnUqlEnx8fAQfHx+htLRUEATtKQ6qUtkUB/v27ROeeeYZoX379oKVlZUgl8uFNm3aCC+//LKQlpamtb2Hh0eVXwkvn1KhuikOUMuvR6vVasHd3V0AICxfvrzC+o0bNwr9+vUT7O3tBTMzM8HHx0d47bXXhJycnBr3++677woeHh6CmZmZEBAQIPz888+VfpUfVUxxEBMTIzz55JOCtbW10KJFC2Hu3LnCw4cPK2wbFhZW4fX/+ZX17OxsYdasWYKDg4NgZWUlhIaGCrGxsRXa1XRM1Z2r+hyzIJRNgeHv7y/I5XKhXbt2wpYtWypMcVDuhx9+EPr06SNYWloKlpaWQvv27YWwsDDh2rVrgiAIQnx8vPDMM88IPj4+gkKhEOzs7ISBAwcKv//+e43HWdUUB6tWrarQtrLj+Keapjj4+/aVTXHwf//3f4Krq6tgbm4u9O7dW4iMjBT69+8v9O/fX9Outu/Tt99+W3BzcxOkUqnWz0hl74PMzExh7ty5gpubmyCXy4VWrVoJM2bMEDIyMmo8L+W/U/C3KQ7KnT9/Xhg3bpymVg8PD2HixInCoUOHalUrGQaJIDTiaEEioiq89dZbWLp0Ke7du1dh3AsRkSHgmCgiIiIiHTBEEREREemAIYqIiIhIBxwTRURERKQD9kQRERER6YAhioiIiEgHnGyzEanVaty5cwfW1tac3p+IiMhACIKAvLw8tGzZElJp1f1NDFGN6M6dO3B3dxe7DCIiItJBcnIyWrVqVeV6hqhGVH4bguTkZNjY2IhcDREREdVGbm4u3N3dq73tFcAQ1ajKL+HZ2NgwRBERERmYmobicGA5ERERkQ4YooiIiIh0wBBFREREpAOGKCIiIiIdMEQRERER6YAhioiIiEgHDFFEREREOmCIIiIiItIBQxQRERGRDjhjuYFRqQWcSchCel4hnKwVCPayg0zKmxsTERE1NYYoA7L/ciqW7o1Bak6hZpmrUoHw0X4Y7u8qYmVERETNDy/nGYj9l1Mxe8s5rQAFAHdzCjF7yznsv5wqUmVERETNE0OUAVCpBSzdGwOhknXly5bujYFKXVkLIiIiagwMUQbgTEJWhR6ovxMApOYU4kxCVtMVRURE1MwxRBmA9LyqA5Qu7YiIiKj+GKIMgJO1okHbERERUf0xRBmAYC87uCoVqGoiAwnKvqUX7GXXlGURERE1awxRBkAmlSB8tB8AVBqkBADho/04XxQREVETYogyEMP9XbF+eiBclBUv2YV2dOY8UURERE2Mk20akOH+rhjq56KZsTw56yFW/3YNJ+MykVNQAqWFqdglEhERNRsMUQZGJpUgxMceAKBWC/j54h3E3s3D15GJeHlwW5GrIyIiaj54Oc+ASaUSzB7gAwD44mQCCopLRa6IiIio+WCIMnAjO7nCw94C2QUl+PZMstjlEBERNRsMUQbORCbFS/3LeqM2/RGPolKVyBURERE1DwxRRmBcoBucbcxwN7cQu8+liF0OERFRs8AQZQTMTGR4vq83AGD9sZsoValFroiIiMj4MUQZiSnBrdHCwhS3Mgvw6+W7YpdDRERk9BiijISlmQlm9fYCAKw7EgdBEESuiIiIyLgxRBmRGSGesJTLEHs3D4dj08Uuh4iIyKgxRBkRpYUppod4AAA+YW8UERFRo2KIMjLP9vGC3ESK80n3cSo+S+xyiIiIjBZDlJFxslZgUjd3AMC6o3EiV0NERGS8GKKM0Av9vCGTSnD8RgYuJN8XuxwiIiKjxBBlhNztLDC2a0sA7I0iIiJqLAxRRmrOAB9IJMCBK2m4kZYndjlERERGhyHKSLVxskaonwsAYP3RmyJXQ0REZHwYoozYnIFlNyb+6cIdJGcViFwNERGRcWGIMmKdW9mib1sHqNQCNv7B3igiIqKGxBBl5OYMaAMA+O7P20jPLRS5GiIiIuPBEGXkenrbIbC1LYpL1fj8RILY5RARERkNhigjJ5FIEDawrDdqy6lbyCkoEbkiIiIi48AQ1QwMau+E9i7WyC9W4avIRLHLISIiMgoMUc2ARCLBnEe9UV+cTEB+UanIFRERERk+hqhmYmQnV3jaW+B+QQm+PZMkdjlEREQGjyGqmZBJJXipf9m8UZuOx6OoVCVyRURERIaNIaoZeSLQDS42CqTlFmHXuRSxyyEiIjJoDFHNiJmJDM/38wYAbDh2E6UqtcgVERERGS6GqGZmSrA7WliY4lZmAX65lCp2OURERAaLIaqZsZCb4JneXgCAdUduQq0WRK6IiIjIMDFENUNPh3jCyswE19LycDg2XexyiIiIDBJDVDOktDDF9J4eAIBPjsRBENgbRUREVFd6EaLWrl0LT09PKBQK9OjRA2fOnKmy7ZUrVzB+/Hh4enpCIpFgzZo11e575cqVkEgkmD9/fqXrBUHAY489BolEgh9//FFrXVJSEkaOHAkLCws4OTnhtddeQ2mpcUxU+WwfL5iZSBGdfB+R8Zlil0NERGRwRA9RO3bswIIFCxAeHo5z586hS5cuCA0NRXp65ZeZCgoK4O3tjZUrV8LFxaXafZ89exYbN25E586dq2yzZs0aSCSSCstVKhVGjhyJ4uJiRERE4KuvvsKXX36JJUuW1O0A9ZSjtRkmdXcHUDY2ioiIiOpG9BD1/vvv4/nnn8esWbPg5+eHDRs2wMLCAl988UWl7bt3745Vq1Zh8uTJMDMzq3K/Dx48wLRp07Bp0ya0aNGi0jbR0dF47733Kn2t3377DTExMdiyZQu6du2Kxx57DG+//TbWrl2L4uJi3Q5Wz7zQzxsmUglOxGUgOvm+2OUQEREZFFFDVHFxMaKiojBkyBDNMqlUiiFDhiAyMrJe+w4LC8PIkSO19v13BQUFmDp1KtauXVtpj1ZkZCQ6deoEZ2dnzbLQ0FDk5ubiypUrle6zqKgIubm5Wg991qqFBcZ2dQMArDsSJ3I1REREhkXUEJWRkQGVSqUVVADA2dkZd+/e1Xm/27dvx7lz57BixYoq27z66qvo1asXxo4dW+n6u3fvVlpX+brKrFixAkqlUvNwd3fX8QiazuwB3pBIgN9i0nA9LU/scoiIiAyG6JfzGlpycjLmzZuHrVu3QqFQVNpmz549OHz4cI2D0utq8eLFyMnJ0TySk5MbdP+NoY2TNYZ3LOuJW3+UY6OIiIhqS9QQ5eDgAJlMhrS0NK3laWlpNQ4ar0pUVBTS09MRGBgIExMTmJiY4NixY/joo49gYmIClUqFw4cP4+bNm7C1tdW0AYDx48djwIABAAAXF5dK6ypfVxkzMzPY2NhoPQzBnAFtAAB7LtxBUmaByNUQEREZBlFDlFwuR1BQEA4dOqRZplarcejQIYSEhOi0z8GDB+PSpUuIjo7WPLp164Zp06YhOjoaMpkMixYtwsWLF7XaAMAHH3yAzZs3AwBCQkJw6dIlrW8JHjx4EDY2NvDz89P9oPVQp1ZK9PN1hEotYOMf7I0iIiKqDROxC1iwYAFmzJiBbt26ITg4GGvWrEF+fj5mzZoFAHj66afh5uamGd9UXFyMmJgYzb9TUlIQHR0NKysrtGnTBtbW1vD399d6DUtLS9jb22uWu7i4VNqb1Lp1a3h5ld0SZdiwYfDz88NTTz2F//3vf7h79y7eeOMNhIWFVfutQEMVNsAHf1y/h+//vI15g9vCyabyS6FERERURvQxUZMmTcLq1auxZMkSdO3aFdHR0di/f79mEHdSUhJSU/+6Ue6dO3cQEBCAgIAApKamYvXq1QgICMBzzz3XoHXJZDL8/PPPkMlkCAkJwfTp0/H0009j2bJlDfo6+iLYyw7dPFqgWKXGZycSxC6HiIhI70kE3vOj0eTm5kKpVCInJ8cgxkcdiU3HrC/PwkIuQ8SiQbC1kItdEhERUZOr7ee36D1RpD8GtHNEB1cbFBSr8GVEotjlEBER6TWGKNKQSCQIG+gDANh8MhEPiozjPoFERESNgSGKtDzm7wovB0vkPCzBt6eTxC6HiIhIbzFEkRaZVILZ/ct6ozYdj0dhiUrkioiIiPQTQxRV8HiAG1yVCqTnFeGHc7fFLoeIiEgvMURRBXITKV7o5w0A2HDsJkpVapErIiIi0j8MUVSpyd1bw85SjuSsh/j5YmrNGxARETUzDFFUKXO5DM/2KZu9fd3ROKjVnE6MiIjo7xiiqErTe3rA2swE19Me4PeraTVvQERE1IwwRFGVlOameCrEAwCw9uhNcHJ7IiKivzBEUbWe6eMFMxMpLiTfR8TNTLHLISIi0hsMUVQtByszTAluDQBYeyRO5GqIiIj0B0MU1ej5ft4wkUoQcTMT55KyxS6HiIhILzBEUY3cbM3xRIAbAGDdkZsiV0NERKQfGKKoVl4a4AOJBPj9ahpi7+aKXQ4REZHoGKKoVnwcrTDC3xUAsP4oe6OIiIgYoqjWZg8ouzHx3gt3cCszX+RqiIiIxMUQRbXm76bEgHaOUAvAhmPxYpdDREQkKoYoqpOwgW0AAD9E3UZabqHI1RAREYmHIYrqpLunHYI97VCsUuOz4+yNIiKi5oshiups9sCysVFbTychO79Y5GqIiIjEwRBFdTbA1xF+rjYoKFbhy4hEscshIiISBUMU1ZlEItGMjfoyIhEPikpFroiIiKjpMUSRTob7u8DbwRI5D0uw7fQtscshIiJqcgxRpBOZVIKXHs0btel4AgpLVCJXRERE1LQYokhnj3d1Q0ulAvfyirAz6rbY5RARETUphijSmdxEihf6eQMANhy7iVKVWuSKiIiImg5DFNXLpO6tYW8px+3sh9h78Y7Y5RARETUZhiiqF3O5DM/08QIArDtyE2q1IHJFRERETYMhiurtqRAPWJuZ4Eb6Axy8miZ2OURERE2CIYrqzUZhiqd7eQAA1h2JgyCwN4qIiIwfQxQ1iFm9vaAwleLC7RycjMsUuxwiIqJGxxBFDcLBygyTu7cGAKw9EidyNURERI2PIYoazAv9vGEilSAyPhNRt7LFLoeIiKhRMURRg2lpa45xgW4AgPVH2RtFRETGjSGKGtRL/X0gkQC/X03H1dRcscshIiJqNAxR1KC8Ha0wopMrAGD90ZsiV0NERNR4GKKowc15dGPiny/eQWJGvsjVEBERNQ6GKGpwHVsqMbCdI9QCsPEP9kYREZFxYoiiRhE2sA0AYGfUbdzNKRS5GiIioobHEEWNopunHYK97FCiErDpeLzY5RARETU4hihqNOW9UdtOJyErv1jkaoiIiBoWQxQ1mn5tHeDvZoOHJSp8eTJB7HKIiIgaFEMUNRqJRIKwAWW9UV9GJCKvsETkioiIiBoOQxQ1qtCOLvBxtERuYSm2nk4SuxwiIqIGwxBFjUoqlWD2o96oz44noLBEJXJFREREDYMhihrd2K4t4WZrjowHRfj+z2SxyyEiImoQDFHU6ExlUrzY3xsAsOFYPEpUapErIiIiqj+GKGoSE7u5w8FKjpT7D7En+o7Y5RAREdUbQxQ1CYWpDM/2KeuNWnc0Dmq1IHJFRERE9cMQRU1mes/WsFaY4Oa9fPwWc1fscoiIiOqFIYqajLXCFDN7eQIA1h65CUFgbxQRERkuhihqUrN6e8HcVIZLKTn49I94/BSdgsibmVDx8h4RERkYE7ELoObFzlKOEB97HI5Nx4p9sZrlrkoFwkf7Ybi/q4jVERER1R57oqhJ7b+cisOx6RWW380pxOwt57D/cqoIVREREdUdQxQ1GZVawNK9MZWuK7+Yt3RvDC/tERGRQWCIoiZzJiELqTmFVa4XAKTmFOJMQlbTFUVERKQjhihqMul5VQcoXdoRERGJiSGKmoyTtaJB2xEREYmJIYqaTLCXHVyVCkiqaeOqVCDYy67JaiIiItIVQxQ1GZlUgvDRfgBQZZAKH+0HmbS6mEVERKQfGKKoSQ33d8X66YFwUVa8ZCcB4GZr0fRFERER6UAi8N4bjSY3NxdKpRI5OTmwsbERuxy9olILOJOQhfS8QjhZK/DtmSTsuXAHXdxtsXt2L0jZG0VERCKp7ec3ZywnUcikEoT42Gue+zha4nBsOi4k38eOP5MxJbi1iNURERHVjJfzSC842Sjw6lBfAMB/98ciK79Y5IqIiIiqxxBFemNGiAfau1jjfkEJVh2IrXkDIiIiEYkeotauXQtPT08oFAr06NEDZ86cqbLtlStXMH78eHh6ekIikWDNmjXV7nvlypWQSCSYP3++1vIXX3wRPj4+MDc3h6OjI8aOHYvYWO0P7bNnz2Lw4MGwtbVFixYtEBoaigsXLuh6mFQLJjIplo31BwBsP5uM80nZIldERERUNVFD1I4dO7BgwQKEh4fj3Llz6NKlC0JDQ5GeXvEGtQBQUFAAb29vrFy5Ei4uLtXu++zZs9i4cSM6d+5cYV1QUBA2b96Mq1ev4sCBAxAEAcOGDYNKpQIAPHjwAMOHD0fr1q1x+vRpnDhxAtbW1ggNDUVJSUn9D5yqFOxlh3GBbhAE4M2fLvM+ekREpLdE/XZejx490L17d3zyyScAALVaDXd3d7z88stYtGhRtdt6enpi/vz5FXqZgLIQFBgYiHXr1mH58uXo2rVrtb1WFy9eRJcuXRAXFwcfHx/8+eef6N69O5KSkuDu7g4AuHTpEjp37owbN26gTZs2tTo+fjtPN/fyijDovaPIKyzF22M74qkQT7FLIiKiZqS2n9+i9UQVFxcjKioKQ4YM+asYqRRDhgxBZGRkvfYdFhaGkSNHau27Kvn5+di8eTO8vLw0galdu3awt7fH559/juLiYjx8+BCff/45OnToAE9Pz3rVRjVztDbDwmHtAACrDlxDxoMikSsiIiKqSLQQlZGRAZVKBWdnZ63lzs7OuHv3rs773b59O86dO4cVK1ZU227dunWwsrKClZUV9u3bh4MHD0IulwMArK2tcfToUWzZsgXm5uawsrLC/v37sW/fPpiYVD0rRFFREXJzc7UepJvpPT3QsaUNcgtLsXIfB5kTEZH+EX1geUNKTk7GvHnzsHXrVigU1d/Edtq0aTh//jyOHTsGX19fTJw4EYWFhQCAhw8f4tlnn0Xv3r1x6tQpnDx5Ev7+/hg5ciQePnxY5T5XrFgBpVKpeZT3bFHdyaQSvP142SDznVG38WdilsgVERERaRMtRDk4OEAmkyEtLU1reVpaWo2DxqsSFRWF9PR0BAYGwsTEBCYmJjh27Bg++ugjmJiYaAaOA4BSqUTbtm3Rr18/7Ny5E7Gxsdi9ezcAYNu2bUhMTMTmzZvRvXt39OzZE9u2bUNCQgJ++umnKl9/8eLFyMnJ0TySk5N1Og4qE9i6BSZ1Kwuib/x4GaUqtcgVERER/UW0ECWXyxEUFIRDhw5plqnVahw6dAghISE67XPw4MG4dOkSoqOjNY9u3bph2rRpiI6Ohkwmq3Q7QRAgCAKKisrG3hQUFEAqlUIi+evWI+XP1eqqP8jNzMxgY2Oj9aD6+fdj7aE0N0Xs3Tx8HXlL7HKIiIg0RL2ct2DBAmzatAlfffUVrl69itmzZyM/Px+zZs0CADz99NNYvHixpn1xcbEmHBUXFyMlJQXR0dGIi4sDUDaWyd/fX+thaWkJe3t7+PuXXRqKj4/HihUrEBUVhaSkJERERGDChAkwNzfHiBEjAABDhw5FdnY2wsLCcPXqVVy5cgWzZs2CiYkJBg4c2MRnqXmzs5TjX8PLBpl/cPA60nMLRa6IiIiojKghatKkSVi9ejWWLFmCrl27Ijo6Gvv379cMNk9KSkJqaqqm/Z07dxAQEICAgACkpqZi9erVCAgIwHPPPVfr11QoFDh+/DhGjBiBNm3aYNKkSbC2tkZERAScnJwAAO3bt8fevXtx8eJFhISEoG/fvrhz5w72798PV1fXhj0JVKPJ3VujSysl8opK8e6vV8Uuh4iICIDI80QZO84T1XAu3r6PsWtPQhCA7S/0RE9v+5o3IiIi0oHezxNFVBedW9lianBrAMCSny6jhIPMiYhIZAxRZDBeC20HO0s5rqc9wJcnE8Uuh4iImjmGKDIYthZyLBreHgCw5vfruJvDQeZERCQehigyKE8GtUJga1vkF6uw/JcYscshIqJmjCGKDIr00UzmUgnw88VUnIzLELskIiJqphiiyOB0bKnE0yGeAIA3f7qMolJV9RsQERE1AoYoMkivDvWFg5UZ4u/l4/MTCWKXQ0REzRBDFBkkpbkp/jOibJD5x4fikHK/6htDExERNQaGKDJYTwS4IdjTDg9LVHh7LweZExFR02KIIoMlkUiw7PGOkEkl2H/lLo5eSxe7JCIiakYYosigtXexwcxengCA8D1XUFjCQeZERNQ0GKLI4M0f0hZO1ma4lVmAT/+IF7scIiJqJhiiyOBZK0zxxig/AMDaI3FIzioQuSIiImoOGKLIKIzu7IoQb3sUlaqxdO8VscshIqJmgCGKjIJEIsHbj3eEiVSC36+m4/eYNLFLIiIiI8cQRUajjZM1nu3rBQB4ay8HmRMRUeNiiCKj8sqgtnBVKnA7+yHWHYkTuxwiIjJiDFFkVCzNTPDmo0HmG47FIzEjX+SKiIjIWDFEkdF5zN8Ffds6oFilRvieKxAEQeySiIjICDFEkdGRSCRYNtYfcpkUx67fw4ErHGROREQNjyGKjJKXgyVe6OcNAFi29woKiktFroiIiIwNQxQZrbCBbeBma447OYX45DAHmRMRUcNiiCKjZS6XIXx02SDzTcfjEZf+QOSKiIjImDBEkVEb6ueMQe2dUKISEL7nMgeZExFRg6lTiPLz80NWVpbm+Zw5c5CRkaF5np6eDgsLi4arjqieJBIJwkf7QW4ixcm4TPxyKVXskoiIyEjUKUTFxsaitPSvAbpbtmxBbm6u5rkgCCgsLGy46ogagIe9JeYM8AEAvP1zDB4UcZA5ERHVX70u51V2aUQikdRnl0SN4qX+PmhtZ4G03CJ8dOiG2OUQEZER4JgoahYUpjIsHdMRAPDFiQRcT8sTuSIiIjJ0dQpREomkQk8Te57IUAxs74Rhfs4oVQt480cOMiciovoxqUtjQRAwePBgmJiUbfbw4UOMHj0acrkcALTGSxHpoyWj/fDHjXs4nZCFn6Lv4PEAN7FLIiIiA1WnEBUeHq71fOzYsRXajB8/vn4VETWiVi0s8PKgtlh14Bre+fUqBnVwgo3CVOyyiIjIAEkEXtNoNLm5uVAqlcjJyYGNjY3Y5dAjRaUqPLbmOOIz8jGrtyfCR3cUuyQiItIjtf38btCB5RcvXtRc2iPSV2YmMrz1aJD5VxGJiLmTW8MWREREFTVoiBIEgeOiyCD083XEiE4uUAvAkp8uQ61mhywREdVNg09xwG/rkaF4c5QfLOQy/HkrGz+cuy12OUREZGA4TxQ1W65Kc7wyuC0AYOW+WOQUlIhcERERGZI6hajc3NxqH3l5nMCQDMszvb3QxskKmfnFWP3bNbHLISIiA1KnKQ5sbW2rvVwnCAIv55FBkZtIsWxsR0zddBpbTt/CxG7u6NRKKXZZRERkAOoUoo4cOdJYdRCJppePA8Z0aYk9F+7gzZ8uY9fsXpBK+ccAERFVr04hqn///o1VB5GoXh/ZAYdj0xGdfB/f/ZmMycGtxS6JiIj0XJ3GRJWWlqKoqEhrWVpaGpYuXYp//etfOHHiRIMWR9RUnG0UmD+kbJD5f/fHIju/WOSKiIhI39UpRD3//PN45ZVXNM/z8vLQvXt3rF27FgcOHMDAgQPx66+/NniRRE1hZi9PtHexRnZBCf53IFbscoiISM/VKUSdPHlS6954X3/9NVQqFW7cuIELFy5gwYIFWLVqVYMXSdQUTGRSLBvrDwDYfjYZ55OyRa6IiIj0WZ1CVEpKCtq2bat5fujQIYwfPx5KZdm3mWbMmIErV640bIVETSjYyw7jAt0gCMCbP12GijOZExFRFeoUohQKBR4+fKh5furUKfTo0UNr/YMHDxquOiIRLH6sA6wVJrickottZ5LELoeIiPRUnUJU165d8c033wAAjh8/jrS0NAwaNEiz/ubNm2jZsmXDVkjUxBytzbBwWDsAwKr9sch4UFTDFkRE1BzVKUQtWbIEH374IXx8fBAaGoqZM2fC1dVVs3737t3o3bt3gxdJ1NSm9/RAx5Y2yC0sxX/3cZA5ERFVVOd5oqKiovDbb7/BxcUFEyZM0FrftWtXBAcHN2iBRGKQSSVYNtYf49dH4Puo25jU3R3dPO3ELouIiPSIRBAEjpxtJLm5uVAqlcjJyYGNjY3Y5ZAO/r3zInb8mYwOrjbYO7c3TGS8ZzcRkbGr7ed3nXqi/vjjj1q169evX112S6S3/jW8HfZfuYurqbn45tQtzOrtJXZJRESkJ+rUEyWVSjU3GK5qM4lEApVK1TDVGTj2RBmHradv4fXdl2FtZoJD/9cfTjYKsUsiIqJGVNvP7zpdm2jRogXc3d3x5ptv4saNG8jOzq7wyMrKqnfxRPpkcvfW6NxKibyiUqzgIHMiInqkTiEqNTUV//3vfxEZGYlOnTrh2WefRUREBGxsbKBUKjUPImMik0qw/HF/SCTA7vMpOBWfKXZJRESkB+oUouRyOSZNmoQDBw4gNjYWnTt3xty5c+Hu7o7XX38dpaWljVUnkag6t7LF1ODWAIAlP11GiUotckVERCQ2nb9q1Lp1ayxZsgS///47fH19sXLlSuTm5jZkbUR65bXQdrCzlON62gN8eTJR7HKIiEhkOoWooqIibNu2DUOGDIG/vz8cHBzwyy+/wM6O8+iQ8bK1kGPR8PYAgDW/X8fdnEKRKyIiIjHVKUSdOXMGs2fPhouLC1atWoUxY8YgOTkZ3333HYYPH95YNRLpjSeDWiGwtS3yi1VY/kuM2OUQEZGI6jzFQevWrTFjxgwEBQVV2W7MmDENUpyh4xQHxulySg7GfHICagHY+lwP9G7jIHZJRETUgGr7+V3nEFUTzhP1F4Yo4xX+02V8FXkLPo6WeGt0R2QVFMPJWoFgLzvIpBKxyyMionpolBBVGwUFBbCwsGjIXRoshijjlfOwBH3+exh5hdrfSHVVKhA+2g/D/V2r2JKIiPRdo0y2WZ2ioiK8//778Pb2bqhdEumtyJsZFQIUANzNKcTsLeew/3KqCFUREVFTqlOIKioqwuLFi9GtWzf06tULP/74IwDgiy++gJeXFz744AO8+uqrjVEnkd5QqQUs3Vv5oPLybt2le2OgUvPe3kRExqxONyBesmQJNm7ciCFDhiAiIgITJkzArFmzcOrUKbz//vuYMGECZDJZY9VKpBfOJGQhtZrpDQQAqTmFOJOQhRAf+6YrjIiImlSdQtT333+Pr7/+GmPGjMHly5fRuXNnlJaW4sKFC5obExMZu/S82s0PVdt2RERkmOp0Oe/27duaqQ38/f1hZmaGV199lQGKmhUna0WDtiMiIsNUpxClUqkgl8s1z01MTGBlZdXgRRHps2AvO7gqFajuTwcXGzMEe3EGfyIiY1any3mCIGDmzJkwMzMDABQWFuKll16CpaWlVrtdu3Y1XIVEekYmlSB8tB9mbzkHCf4aTP53PbztOV8UEZGRq1NP1IwZM+Dk5ASlUgmlUonp06ejZcuWmuflj7pYu3YtPD09oVAo0KNHD5w5c6bKtleuXMH48ePh6ekJiUSCNWvWVLvvlStXQiKRYP78+VrLX3zxRfj4+MDc3ByOjo4YO3YsYmNjK2z/5ZdfonPnzlAoFHByckJYWFidjo2M13B/V6yfHggXpfYlO6V52d8ley7cwZHYdDFKIyKiJlKnnqjNmzc36Ivv2LEDCxYswIYNG9CjRw+sWbMGoaGhuHbtGpycnCq0LygogLe3NyZMmFDjVApnz57Fxo0b0blz5wrrgoKCMG3aNLRu3RpZWVl46623MGzYMCQkJGi+Xfj+++/jvffew6pVq9CjRw/k5+cjMTGxQY6bjMNwf1cM9XPBmYQspOcVamYsf/Ony9h2OgmvbD+PvXP7wNPBsuadERGRwWnwGcvrokePHujevTs++eQTAIBarYa7uztefvllLFq0qNptPT09MX/+/Aq9TADw4MEDBAYGYt26dVi+fDm6du1aba/VxYsX0aVLF8TFxcHHxwfZ2dlwc3PD3r17MXjwYJ2PjzOWN0/FpWpM2XQKUbey4etshV1zesPKrE5/rxARkYiafMbyuiouLkZUVBSGDBnyVzFSKYYMGYLIyMh67TssLAwjR47U2ndV8vPzsXnzZnh5ecHd3R0AcPDgQajVaqSkpKBDhw5o1aoVJk6ciOTk5Gr3VVRUhNzcXK0HNT9yEynWTwuEk7UZrqc9wGvfX4CIf6sQEVEjES1EZWRkQKVSwdnZWWu5s7Mz7t69q/N+t2/fjnPnzmHFihXVtlu3bh2srKxgZWWFffv24eDBg5pvHsbHx0OtVuPdd9/FmjVrsHPnTmRlZWHo0KEoLi6ucp8rVqzQGhtWHsqo+XGyUWD99ECYyiTYd/ku1h+7KXZJRETUwEQLUY0hOTkZ8+bNw9atW6FQVD9Hz7Rp03D+/HkcO3YMvr6+mDhxIgoLyyZHVKvVKCkpwUcffYTQ0FD07NkT3377LW7cuIEjR45Uuc/FixcjJydH86ip54qMW5CHHZaO8QcArDpwDUevcaA5EZExES1EOTg4QCaTIS0tTWt5WloaXFxcdNpnVFQU0tPTERgYCBMTE5iYmODYsWP46KOPYGJiApVKpWmrVCrRtm1b9OvXDzt37kRsbCx2794NAHB1dQUA+Pn5ado7OjrCwcEBSUlJVb6+mZkZbGxstB7UvE3t0RpTgt0hCMAr357Hrcx8sUsiIqIGIlqIksvlCAoKwqFDhzTL1Go1Dh06hJCQEJ32OXjwYFy6dAnR0dGaR7du3TBt2jRER0dXeV8/QRAgCAKKiooAAL179wYAXLt2TdMmKysLGRkZ8PDw0Kk2ar7eGtMRAa1tkVtYihe+jkJ+UanYJRERUQMQ9StDCxYswIwZM9CtWzcEBwdjzZo1yM/Px6xZswAATz/9NNzc3DTjm4qLixETE6P5d0pKCqKjo2FlZYU2bdrA2toa/v7+Wq9haWkJe3t7zfL4+Hjs2LEDw4YNg6OjI27fvo2VK1fC3NwcI0aMAAD4+vpi7NixmDdvHj799FPY2Nhg8eLFaN++PQYOHNhUp4eMhJmJDBumB2HUxydwLS0P/9p5EZ9MDeDtkoiIDJyoY6ImTZqE1atXY8mSJejatSuio6Oxf/9+zWDzpKQkpKamatrfuXMHAQEBCAgIQGpqKlavXo2AgAA899xztX5NhUKB48ePY8SIEWjTpg0mTZoEa2trREREaM1N9fXXX6NHjx4YOXIk+vfvD1NTU+zfvx+mpqYNdwKo2XC2UWD9tLKB5r9cSsXGP+LFLomIiOpJ1HmijB3niaJ/2nLqFt748TKkEuDLWcHo5+sodklERPQPej9PFFFzNK1Ha0zq5g61ALz87XkkZRaIXRIREemIIYqoCUkkEix7vCO6utsi52EJXvjmTxQUc6A5EZEhYogiamLlA80drMwQe7dsoDmvqhMRGR6GKCIRuCgVWDctECZSCX6+mIpNxznQnIjI0DBEEYkk2MsO4aPLJnRduS8Wx2/cE7kiIiKqC4YoIhFN7+mBCUGtNAPNk7M40JyIyFAwRBGJSCKR4O3H/dGllRL3C0rwwjdReFisqnlDIiISHUMUkcgUpjKsnx4EBys5rqbm4t8/cKA5EZEhYIgi0gMtbc2xdmrZQPM9F+7g8xMJYpdEREQ1YIgi0hM9vO3x5qiygebv/noVJ+MyRK6IiIiqwxBFpEeeDvHA+MCygeZzt53jQHMiIj3GEEWkRyQSCd55wh+dWymRXVCCFznQnIhIbzFEEekZhWnZjOb2lnLEpOZi8S4ONCci0kcMUUR6qKWtOT6ZGgiZVIIfo+/gi5OJYpdERET/wBBFpKdCfOzxxsgOAMoGmkfc5EBzIiJ9whBFpMdm9vLEuEA3qNQC5m47j9vZHGhORKQvGKKI9JhEIsG7T3SCv5sNsvKL8dKWKBSWcKA5EZE+YIgi0nPlA83tLOW4nJKLxbsucaA5EZEeYIgiMgCtWljgk6kBkEkl2H0+BV9GJIpdEhFRs8cQRWQgevk44D8jygaaL//lKiJvZopcERFR88YQRWRAnuntice7tnw00PwcUu4/FLskIqJmiyGKyIBIJBKsGNcZHVvaIDO/GC99w4HmRERiYYgiMjDm8rKB5i0sTHEpJQev777MgeZERCJgiCIyQO52FvhkaiCkEuCHc7fxdeQtsUsiImp2GKKIDFTvNn8NNH/75xicjudAcyKipsQQRWTAnu3jhbFdW6JULWDO1nO4w4HmRERNhiGKyIBJJBKsHNcZHVzLBprP5ozmRERNhiGKyMCZy2X49Kkg2FqY4sLtHLz5IweaExE1BYYoIiPgbmeBT6aUDTT/Puo2tpziQHMiosbGEEVkJPq0dcCix9oDAJbujcGZhCyRKyIiMm4MUURG5Pm+3hjdpXygeRRSczjQnIiosTBEERkRiUSC/47vhPYu1sh4UIyXtpxDUSkHmhMRNQaGKCIjYyE3wadPdYPS3BQXku9jyY9XONCciKgRMEQRGaHW9hb4eEoApBJgx5/J2Ho6SeySiIiMDkMUkZHq5+uIfw0vH2h+BX8mcqA5EVFDYogiMmIv9vPGyM6uKFEJeGnLOdzNKRS7JCIio8EQRWTEJBIJVj3Z+dFA8yLM3hrFgeZERA2EIYrIyFnITbDxqSDYKExwPuk+3tpzReySiIiMAkMUUTPgYW+Jj6YEQCIBvj2TjG0caE5EVG8MUUTNxIB2TngttB0AIHzPZUTd4kBzIqL6YIgiakZm9/fBiE4umoHmabkcaE5EpCuGKKJmpGygeRf4OlvhXl4RZm+JQnGpWuyyiIgMEkMUUTNjaVY2o7mNwgTnku7jrb0caE5EpAuGKKJmyNPBEh8+Gmi+7XQSvj2TBJVaQOTNTPwUnYLIm5lQqXmrGCKi6kgE3lSr0eTm5kKpVCInJwc2NjZil0NUwdojcVh14BpMpBIozU2RmV+sWeeqVCB8tB+G+7uKWCERUdOr7ec3e6KImrE5A3wQ4G6LUrWgFaAA4G5OIWZvOYf9l1NFqo6ISL8xRBE1Y2oBuJPzsNJ15V3US/fG8NIeEVElGKKImrEzCVlIyy2qcr0AIDWnEGcSOKcUEdE/MUQRNWPpebWbJ6q27YiImhOGKKJmzMla0aDtiIiaE4YoomYs2MsOrkoFJNW0cbCSI9jLrslqIiIyFAxRRM2YTCpB+Gg/AKgySOUVliLiZkbTFUVEZCAYooiaueH+rlg/PRAuSu1Lds42ZmjnbIWiUjVmbT6LXedui1QhEZF+MhG7ACIS33B/Vwz1c8GZhCyk5xXCyVqBYC87lKrVeO37i9hz4Q4WfHcBd3MLMbu/DySS6i4AEhE1DwxRRASg7NJeiI/9P5bJsGZSV7goFfj0j3j8b/813M0pRPjojpBJGaSIqHnj5TwiqpZUKsF/RnTAklF+kEiAryNvYc7WKBSWqMQujYhIVAxRRFQrz/TxwidTAiGXSXHgShqmfXYa9wuKa96QiMhIMUQRUa2N7OyKr58Nho3CBFG3sjF+fQRuZxeIXRYRkSgYooioTnp622Pn7F5wVSpw814+xq2LwJU7OWKXRUTU5BiiiKjOfJ2tsWtOL7RztkZ6XhEmbTyFEzc4lxQRNS8MUUSkE1elOb57KQQ9ve3woKgUMzefwY/nU8Qui4ioyTBEEZHOlOam+OqZYIzq7IpStYD5O6Kx4dhNCIIgdmlERI2OIYqI6sXMRIaPJgfg+b5eAICV+2KxdG8MVGoGKSIybgxRRFRvUqkEr4/0wxsjOwAAvoxIRNjWc5xLioiMGkMUETWY5/p64+MpAZDLpNh/5S6e+pxzSRGR8WKIIqIGNbpLS3z1TDCsFSY4m5iNJzdEIuX+Q7HLIiJqcAxRRNTgQnzssfOlXnCxUSAu/QHGrTuJmDu5YpdFRNSg9CJErV27Fp6enlAoFOjRowfOnDlTZdsrV65g/Pjx8PT0hEQiwZo1a6rd98qVKyGRSDB//nyt5S+++CJ8fHxgbm4OR0dHjB07FrGxsZXuIzMzE61atYJEIsH9+/freHREzVM7F2vsDiubSyottwgTN0biZBznkiIi4yF6iNqxYwcWLFiA8PBwnDt3Dl26dEFoaCjS09MrbV9QUABvb2+sXLkSLi4u1e777Nmz2LhxIzp37lxhXVBQEDZv3oyrV6/iwIEDEAQBw4YNg0pVcSDss88+W+k+iKh65XNJ9fD6ay6pn6I5lxQRGQfRQ9T777+P559/HrNmzYKfnx82bNgACwsLfPHFF5W27969O1atWoXJkyfDzMysyv0+ePAA06ZNw6ZNm9CiRYsK61944QX069cPnp6eCAwMxPLly5GcnIzExEStduvXr8f9+/excOHCeh0nUXOlNDfF188GY2RnV5SoBMzbHo2NnEuKiIyAqCGquLgYUVFRGDJkiGaZVCrFkCFDEBkZWa99h4WFYeTIkVr7rkp+fj42b94MLy8vuLu7a5bHxMRg2bJl+PrrryGV1nyqioqKkJubq/UgorK5pD6eHIBn+5TNJbWCc0kRkREQNURlZGRApVLB2dlZa7mzszPu3r2r8363b9+Oc+fOYcWKFdW2W7duHaysrGBlZYV9+/bh4MGDkMvlAMoC0ZQpU7Bq1Sq0bt26Vq+7YsUKKJVKzePvgYyouZNKJXhzlPZcUi9/y7mkiMhwiX45r6ElJydj3rx52Lp1KxQKRbVtp02bhvPnz+PYsWPw9fXFxIkTUVhYCABYvHgxOnTogOnTp9f6tRcvXoycnBzNIzk5uV7HQmSMnuvrjY8ezSX166W7ePrzM8gpKBG7LCKiOhM1RDk4OEAmkyEtLU1reVpaWo2DxqsSFRWF9PR0BAYGwsTEBCYmJjh27Bg++ugjmJiYaA0cVyqVaNu2Lfr164edO3ciNjYWu3fvBgAcPnwY33//vWYfgwcP1tQcHh5e6WubmZnBxsZG60FEFY3p0hJfPtMd1mYmOJOYhSc3RHAuKSIyOKKGKLlcjqCgIBw6dEizTK1W49ChQwgJCdFpn4MHD8alS5cQHR2teXTr1g3Tpk1DdHQ0ZDJZpdsJggBBEFBUVAQA+OGHH3DhwgXNPj777DMAwPHjxxEWFqZTbUT0l14+Dvh+dghcbBS48WguqaupHEdIRIbDROwCFixYgBkzZqBbt24IDg7GmjVrkJ+fj1mzZgEAnn76abi5uWnGNxUXFyMmJkbz75SUFERHR8PKygpt2rSBtbU1/P39tV7D0tIS9vb2muXx8fHYsWMHhg0bBkdHR9y+fRsrV66Eubk5RowYAQDw8fHR2kdGRtn8Nh06dICtrW2jnQ+i5qS9iw12zemFmZvP4HraA0zcEImNTwehl4+D2KUREdVI9DFRkyZNwurVq7FkyRJ07doV0dHR2L9/v2aweVJSElJTUzXt79y5g4CAAAQEBCA1NRWrV69GQEAAnnvuuVq/pkKhwPHjxzFixAi0adMGkyZNgrW1NSIiIuDk5NTgx0hEVWtpa47vX+yFYC875BWVYsYXZ7Dnwh2xyyIiqpFE4GQtjSY3NxdKpRI5OTkcH0VUg8ISFf7vuwv45VLZH02vj+iA5/p6QSKRiFwZETU3tf38Fr0niogIABSmMnw8JQCzensCAN759Sre/vkq1JxLioj0FEMUEekNqVSCJaP88PqIsrmkvjiZgJe3n+dcUkSklxiiiEivSCQSPN/PGx9O7gpTmQS/XEzF019wLiki0j8MUUSkl8Z2dcNXs4LL5pJKyMKEjRG4w7mkiEiPMEQRkd7q1cYB370UAmcbM1xPe4Bx6yIQe5dzSRGRfmCIIiK91sHVBrvm9EZbJyvczS3EhPWRiLyZKXZZREQMUUSk/9xszfH9SyEI9vxrLqm9nEuKiETGEEVEBsHWQo6vnw3GiE4uKFap8fK35/HZ8XixyyKiZowhiogMRtlcUoGY2csTALD8l6t4++cYziVF1Myo1AIib2bip+gURN7MhEqk3wGi3zuPiKguZFIJwkf7oaWtAu/+GovPTyTgbm4h3p/YBWYmMqjUAs4kZCE9rxBO1goEe9lBJuWs50TGYv/lVCzdG4PUnELNMlelAuGj/TDc37VJa+FtXxoRb/tC1Lh+ik7Bwu8voEQloIeXHSZ1b4VVB67rxS9XImp4+y+nYvaWc/hncCn/M2n99MAG+Vmv7ec3Q1QjYogianwn4zLw4jdReFBUWun6hv7lSkTiUKkF9PnvYa0/kv5OAsBFqcCJfw+qd+8z751HRM1C7zYO2P5CT1T1O7P8r8Sle2NEGzdBRPV3JiGrygAFlP2sp+YU4kxCVpPVxBBFRAYvr7AU1eUjMX65ElHDSs+rOkDp0q4hMEQRkcHTx1+uRNRw1GoB8ffya9XWyVrRyNX8hd/OIyKDV9tfmk35y5WI6k8QBByOTcd7v11HTGr1t3wqHxMV7GXXNMWBPVFEZASCvezgqlSgpqGkR66lI7ewpElqIqL6ORmXgXHrI/DsV38iJjUXVmYmGNnJBQAq/KyXPw8f7dekU5rw23mNiN/OI2o65V99BlDh689/Z2cpx7zBbTG1R2uYyvh3JJG+ibqVhdUHriMyvuwemQpTKWb08sRL/XzQwlLeJPNEcYoDPcAQRdS0qvrlumRUB5iZyvDur7GIS38AAPB2sMSix9pjqJ8zJBJOxkkktsspOXjvt2s4cu0eAEAuk2Jqj9aYM8AHTjbal+Ibe1Jdhig9wBBF1PSq++VaqlJj+9lkfHDwOjLziwEAPbzs8PrIDujcylbEqomarxtpeXj/4HXsu3wXQNldCSYEtcLLg9vCzdZclJoYovQAQxSRfsorLMGGYzfx2fEEFJWqAQBPBLhhYWg70X5pEzU3tzLzseb3G/gxOgWCAEgkwNguLTFviC+8HCxFrY0hSg8wRBHpt5T7D/HegWvYdT4FACA3keLZPl6YM8AH1gpTkasjMk537j/Ex4dv4Ls/b2smwB3e0QWvDvVFOxdrkasrwxClBxiiiAzDpds5WP5LDE4/mozT3lKO+UPaYnIwB58TNZR7eUVYdzQOW08loVhV1gPc39cRC4e1Q6dWSpGr08YQpQcYoogMhyAI+P1qOlbsu6qZ1M/H0RKLH+uAwR2cOPicSEf3C4qx4Vg8vopIxMMSFYCysYgLQ9uhu2fTzelUFwxReoAhisjwlKjU+PZMEtb8fgNZjwafh3jb4/WRHeDvpl9/LRPps7zCEnxxIhGfHY9H3qMbhHdxt8Vrw9qhdxt7vf7DhCFKDzBEERmu3MISrDtyE1+cTEDxo8Hn4x4NPm/JwedEVXpYrMLXkYnYcOwmsgvKJrdt72KNhcPaGUyvLkOUHmCIIjJ8t7MLsPrANfwYfQcAYGYixXN9vTB7QBtYmfHOWUTlikpV2H4mGZ8cicO9vCIAgLejJRYM9cUIf1dIm3Am8fpiiNIDDFFExuPi7ftY/stVnHk0+NzBSo75Q3wxubs7TDj4nJqxUpUaP5y7jY8OxSHl/kMAQKsW5pg3uC2eCHAzyJ8Phig9wBBFZFwEQcDBmDSs3BeL+IyywedtnKzwnxHtMbCdYVymIGooarWAvRfvYM3vN5Dw6OfB2cYMcwe1xaRu7pCbGF54KscQpQcYooiMU4lKjW2nk7Dm9+uaMR+9fMoGn3dsycHnZNwEQcCBK2n44OB1XEvLA1B2T8o5A3wwvacHFKYykSusP4YoPcAQRWTcch6WYN3ROGw+kYhilRoSCTAuoBUWhvrCVcnB52RcBEHAHzcy8N5v13Dxdg4AwFphghf7eWNmby+jGiPIEKUHGKKImofkrAKsOnANey6UDT5XmErxfF9vvNjfx6g+WKj5Oh2fifd+u44ziWVjAi3kMjzT2wvP9/WG0sL4ZvdniNIDDFFEzcv5pGy8++tVnE3MBgA4WJlhwVBfTOzWyiAH1xJFJ9/He79dw/EbGQDKbo30dE8PvDTABw5WZiJX13gYovQAQxRR81M2XuQuVu6LRWJmAQDA19kKi0d0wABfRw4+J4NwNTUX7/12Hb9fTQMAmEglmBzsjrkD28JFqRC5usbHEKUHGKKImq/iUjW2nr6FDw/dwP1Hg8/7tHHAf0Z0gF9L/j4gcajUAs4kZCE9rxBO1goEe9lB9rf5m27ee4APDl7HzxdTAQBSCTAusBXmDW4LdzsLscpucgxReoAhiohyCkqw9mgcvjz51+DzJwNbYWFoOzjbGP9f9KQ/9l9OxdK9MUjNKdQsc1UqED7aDx1bKvHRoRv44dxtqB+lglGdXTF/iC/aOFmJVLF4GKL0AEMUEZVLyizA/w7Eav7CNzeV4fl+3nixnzcsOficGtn+y6mYveUcqvrAl0klUD1KT0M6OGHB0HbNuseUIUoPMEQR0T+dS8rGO79cRdStssHnjtZm+L+hvpjQzV3rskpNl12IakulFtDnv4e1eqAq07uNPRYOa4eA1i2aqDL9xRClBxiiiKgygiBg/+W7WLk/FrceDT5v52yN/4zsgP6+jtVedhnu7ypW2WSgIm9mYsqmUzW2+/b5ngjxsW+CivRfbT+/2YdMRNTEJBIJHuvkisEdnPHNqVv46NANXEvLw4wvzsDP1RoxqXkVtrmbU4jZW85h/fRABimqtdvZBdh1/nat2qbnVd9TRRUxRBERiURuIsWzfbwwPtANnxyOw1eRiZUGKAAQAEgALN0bg6F+Lry0R5UqLFHhTEIWjl2/h6PX0nHzXn6tt3Wy5hcd6oohiohIZLYWcrwxyg/+bkrM3xFdZTsBQGpOIc4kZPGyC2kkZuRrQlNkfCYKS9SadTKpBAHutrh2Nw95RaWVbi8B4KIsG3dHdcMQRUSkJ2o7D+evl+7Ay8GyWUx6SBU9LFbhVHwmjl5Lx7Hr9zSTupZztjFDf19HDGjnhN5tHKA0N9V8Ow+A1jf0yt9y4aP92LupA4YoIiI9UdvLKd+cSsI3p5Lg5WCJnt726OlthxAfe16OMVKCIODmvb96m04nZKG49K/eJhOpBN08W2BAOyf093VEexfrCjPjD/d3xfrpgRW+sODCLyzUC7+d14j47Twiqovyr6LfzSmscj4fS7kMXo6WiLmTq5kUsZyPoyVCfOwfBSt7o763mbHLLypFxM2/eptuZz/UWu9ma47+7RzR39cRvds41PpG15w6o3Y4xYEeYIgiorqq6bJL+bfzch6W4GxCFiLjMxF5MxNX7+bin7/NfZ2tEPIoUPXwtoedpbxJjoHqThAEXE97oAlNZxOzUKL663+oXCZFD2+7R5fpHOHjaMX7MDYihig9wBBFRLrQZZ6o+wXFOJ2QhcibmTgVn4nYuxW/5dfexfqvnioveygtTBvtGKhmuYUliIjLwNFr93Ds+r0Kk2G2trPAgHZloamntz0s5ByB01QYovQAQxQR6aq+l12y8otxOj4TkfFloep62gOt9RIJ4Odqg57e9gjxtkewtx1sFAxVjUkQBMSk5mpC07lb2Sj92zVZMxMpQnzsNYPCPe0t2NskEoYoPcAQRUT6IuNBEU49uvR3Kj6zwvxBUgng76bUXP7r7mVX63E2VLX7BcU4fiMDx66XBad7eUVa670dLTWhqYeXHRSmMpEqpb9jiNIDDFFEpK/Scwsf9VJl4VR8JhIytEOVTCpBJzdlWU+Vjz26ebRo1jdKrm3PoFot4PKdHBy9VvZNuujk+1pfALCQy9DLxx792zmhf1tHtLa3aMKjoNpiiNIDDFFEZChScx7iVHwmTt0sG6yelKU995CJVIIu7rZl0yl4OyDIowXM5TX3mhjDt8FqGqOW+aBI09v0x/V7yMwv1tre19lK09vUzbMFzEzY26TvGKL0AEMUERmqlPsPNZf+Im9mIuW+9lfsTWUSBLi3QE9vO/T0sUdg6xYVLkUZw42Uy78tWdUHpYe9BZKyCrS+GWllZoLebewxoJ0T+vk6ws3WvElqpYbDEKUHGKKIyFgkZxX8FariMyt8k0xuIkVga1vNQPX0vEK88m10hfDxz6kampJKLaC4VF32UD16PHpeolKj6G/rSkrVKCxR4Y0fL+P+w5Ia993B1QYDHs3bFOTRAqYyaRMcETUWhig9wBBFRMZIEATcyizQBKrIm5lI/8eA6ZrYWcrx/sQufwWbRyGm5G/BRhNuKllWHoC0tlGVrxc064pLVWXPVWqo/jk7aQNZOzUQIzsbRs8a1U5tP7+b7yhBIiLSiUQigaeDJTwdLDE5uDUEQUB8Rr7m0t8fNzKQW0PvTVZ+MWZuPttEFVdObiKFmUwKuYkUpo/+KzeRQi6TwtREigeFJRW+xViZUrW6xjZknBiiiIioXiQSCXwcreDjaIVpPTzw4/nbmL/jQo3btbRVwNHKTDu8/C3MmJWHm78tM5WVLa+sfXkoMjXR3kbz778FJlOZpMY5mCJvZmLKplM1HgfvWdh8MUQREVGDcrap3UDq9yZ0RYiPfSNXo7tgLzu4KhVV3stQgrIb+AZ72TV1aaQnOPKNiIgaVHn4qKqfR4Kyb+npe/iQSSUIH+0HABWOpfx5+Gg/g5uygRoOQxQRETUoYwofw/1dsX56IFyU2pfsXJQKUb5hSPqF385rRPx2HhE1Z8YwT1Q5Y5g0lGqPUxzoAYYoImruGD7IEHGKAyIiEp1MKtHrweNE9cExUUREREQ6YIgiIiIi0gFDFBEREZEOGKKIiIiIdMAQRURERKQDvQhRa9euhaenJxQKBXr06IEzZ85U2fbKlSsYP348PD09IZFIsGbNmmr3vXLlSkgkEsyfP19r+YsvvggfHx+Ym5vD0dERY8eORWxsrGb9hQsXMGXKFLi7u8Pc3BwdOnTAhx9+WJ/DJCIiIiMieojasWMHFixYgPDwcJw7dw5dunRBaGgo0tPTK21fUFAAb29vrFy5Ei4uLtXu++zZs9i4cSM6d+5cYV1QUBA2b96Mq1ev4sCBAxAEAcOGDYNKpQIAREVFwcnJCVu2bMGVK1fw+uuvY/Hixfjkk0/qf9BERERk8ESfbLNHjx7o3r27Jpyo1Wq4u7vj5ZdfxqJFi6rd1tPTE/Pnz6/QywQADx48QGBgINatW4fly5eja9eu1fZaXbx4EV26dEFcXBx8fHwqbRMWFoarV6/i8OHDtTo2TrZJRERkeGr7+S1qT1RxcTGioqIwZMgQzTKpVIohQ4YgMjKyXvsOCwvDyJEjtfZdlfz8fGzevBleXl5wd3evsl1OTg7s7Kq+YWZRURFyc3O1HkRERGScRJ2xPCMjAyqVCs7OzlrLnZ2dtcYn1dX27dtx7tw5nD17ttp269atw7/+9S/k5+ejXbt2OHjwIORyeaVtIyIisGPHDvzyyy9V7m/FihVYunRpheUMU0RERIaj/HO7pot1Rnfbl+TkZMybNw8HDx6EQqGotu20adMwdOhQpKamYvXq1Zg4cSJOnjxZYbvLly9j7NixCA8Px7Bhw6rc3+LFi7FgwQLN85SUFPj5+VXbu0VERET6KS8vD0qlssr1ooYoBwcHyGQypKWlaS1PS0urcdB4VaKiopCeno7AwEDNMpVKhT/++AOffPIJioqKIJPJAABKpRJKpRJt27ZFz5490aJFC+zevRtTpkzRbBsTE4PBgwfjhRdewBtvvFHta5uZmcHMzEzz3MrKCsnJybC2toZE0nA33MzNzYW7uzuSk5M51qoR8Tw3HZ7rpsHz3DR4nptGY55nQRCQl5eHli1bVttO1BAll8sRFBSEQ4cO4fHHHwdQNrD80KFDmDt3rk77HDx4MC5duqS1bNasWWjfvj3+/e9/awLUPwmCAEEQUFRUpFl25coVDBo0CDNmzMA777xT51qkUilatWpV5+1qy8bGhj+gTYDnuenwXDcNnuemwfPcNBrrPFfXA1VO9Mt5CxYswIwZM9CtWzcEBwdjzZo1yM/Px6xZswAATz/9NNzc3LBixQoAZYPRY2JiNP9OSUlBdHQ0rKys0KZNG1hbW8Pf31/rNSwtLWFvb69ZHh8fjx07dmDYsGFwdHTE7du3sXLlSpibm2PEiBEAyi7hDRo0CKGhoViwYAHu3r0LAJDJZHB0dGySc0NERET6S/QQNWnSJNy7dw9LlizB3bt30bVrV+zfv18z2DwpKQlS6V9fIrxz5w4CAgI0z1evXo3Vq1ejf//+OHr0aK1eU6FQ4Pjx41izZg2ys7Ph7OyMfv36ISIiAk5OTgCAnTt34t69e9iyZQu2bNmi2dbDwwOJiYn1P3AiIiIyaKLPE0V1V1RUhBUrVmDx4sVaY7CoYfE8Nx2e66bB89w0eJ6bhj6cZ4YoIiIiIh2IftsXIiIiIkPEEEVERESkA4YoIiIiIh0wRBERERHpgCFKz6xYsQLdu3eHtbU1nJyc8Pjjj+PatWs1bvf999+jffv2UCgU6NSpE3799dcmqNZw6XKeN23ahL59+6JFixZo0aIFhgwZgjNnzjRRxYZL1/d0ue3bt0MikWgm5KXK6Xqe79+/j7CwMLi6usLMzAy+vr78/VENXc/zmjVr0K5dO5ibm8Pd3R2vvvoqCgsLm6Biw7R+/Xp07txZM5FmSEgI9u3bV+02YnwOMkTpmWPHjiEsLAynTp3CwYMHUVJSgmHDhiE/P7/KbSIiIjBlyhQ8++yzOH/+PB5//HE8/vjjuHz5chNWblh0Oc9Hjx7FlClTcOTIEURGRsLd3R3Dhg1DSkpKE1ZueHQ51+USExOxcOFC9O3btwkqNWy6nOfi4mIMHToUiYmJ2LlzJ65du4ZNmzbBzc2tCSs3LLqc523btmHRokUIDw/H1atX8fnnn2PHjh34z3/+04SVG5ZWrVph5cqViIqKwp9//olBgwZh7NixuHLlSqXtRfscFEivpaenCwCEY8eOVdlm4sSJwsiRI7WW9ejRQ3jxxRcbuzyjUZvz/E+lpaWCtbW18NVXXzViZcantue6tLRU6NWrl/DZZ58JM2bMEMaOHds0BRqJ2pzn9evXC97e3kJxcXETVmZcanOew8LChEGDBmktW7BggdC7d+/GLs+otGjRQvjss88qXSfW5yB7ovRcTk4OAMDOzq7KNpGRkRgyZIjWstDQUERGRjZqbcakNuf5nwoKClBSUlKnbaj253rZsmVwcnLCs88+2xRlGZ3anOc9e/YgJCQEYWFhcHZ2hr+/P959912oVKqmKtPg1eY89+rVC1FRUZrL//Hx8fj11181txmj6qlUKmzfvh35+fkICQmptI1Yn4Oi3/aFqqZWqzF//nz07t27wv0A/+7u3bua2+SUc3Z21tzvj6pX2/P8T//+97/RsmXLCj+4VLXanusTJ07g888/R3R0dNMVZ0Rqe57j4+Nx+PBhTJs2Db/++ivi4uIwZ84clJSUIDw8vAkrNky1Pc9Tp05FRkYG+vTpA0EQUFpaipdeeomX82pw6dIlhISEoLCwEFZWVti9ezf8/PwqbSvW5yBDlB4LCwvD5cuXceLECbFLMWq6nOeVK1di+/btOHr0KBQKRSNWZ1xqc67z8vLw1FNPYdOmTXBwcGjC6oxHbd/TarUaTk5O+PTTTyGTyRAUFISUlBSsWrWKIaoWanuejx49infffRfr1q1Djx49EBcXh3nz5uHtt9/Gm2++2UTVGp527dohOjoaOTk52LlzJ2bMmIFjx45VGaRE0agXC0lnYWFhQqtWrYT4+Pga27q7uwsffPCB1rIlS5YInTt3bqTqjEddznO5VatWCUqlUjh79mwjVmZ8anuuz58/LwAQZDKZ5iGRSASJRCLIZDIhLi6uiSo2THV5T/fr108YPHiw1rJff/1VACAUFRU1VolGoS7nuU+fPsLChQu1ln3zzTeCubm5oFKpGqtEozN48GDhhRdeqHSdWJ+DHBOlZwRBwNy5c7F7924cPnwYXl5eNW4TEhKCQ4cOaS07ePBgldeOSbfzDAD/+9//8Pbbb2P//v3o1q1bI1dpHOp6rtu3b49Lly4hOjpa8xgzZgwGDhyI6OhouLu7N1HlhkWX93Tv3r0RFxcHtVqtWXb9+nW4urpCLpc3ZrkGS5fzXFBQAKlU++NWJpNp9ke1o1arUVRUVOk60T4HGzWiUZ3Nnj1bUCqVwtGjR4XU1FTNo6CgQNPmqaeeEhYtWqR5fvLkScHExERYvXq1cPXqVSE8PFwwNTUVLl26JMYhGARdzvPKlSsFuVwu7Ny5U2ubvLw8MQ7BYOhyrv+J386rmS7nOSkpSbC2thbmzp0rXLt2Tfj5558FJycnYfny5WIcgkHQ5TyHh4cL1tbWwrfffivEx8cLv/32m+Dj4yNMnDhRjEMwCIsWLRKOHTsmJCQkCBcvXhQWLVokSCQS4bfffhMEQX8+Bxmi9AyASh+bN2/WtOnfv78wY8YMre2+++47wdfXV5DL5ULHjh2FX375pWkLNzC6nGcPD49KtwkPD2/y+g2Jru/pv2OIqpmu5zkiIkLo0aOHYGZmJnh7ewvvvPOOUFpa2rTFGxBdznNJSYnw1ltvCT4+PoJCoRDc3d2FOXPmCNnZ2U1ev6F45plnBA8PD0EulwuOjo7C4MGDNQFKEPTnc1AiCOxLJCIiIqorjokiIiIi0gFDFBEREZEOGKKIiIiIdMAQRURERKQDhigiIiIiHTBEEREREemAIYqIiIhIBwxRRER/k5iYCIlEgujo6Fpv8+WXX8LW1rbRaiIi/cQQRURERKQDhigiIiIiHTBEEVGzs3//fvTp0we2trawt7fHqFGjcPPmzUrbHj16FBKJBL/88gs6d+4MhUKBnj174vLlyxXaHjhwAB06dICVlRWGDx+O1NRUzbqzZ89i6NChcHBwgFKpRP/+/XHu3LlGO0YianwMUUTU7OTn52PBggX4888/cejQIUilUjzxxBNQq9VVbvPaa6/hvffew9mzZ+Ho6IjRo0ejpKREs76goACrV6/GN998gz/++ANJSUlYuHChZn1eXh5mzJiBEydO4NSpU2jbti1GjBiBvLy8Rj1WImo8JmIXQETU1MaPH6/1/IsvvoCjoyNiYmJgZWVV6Tbh4eEYOnQoAOCrr75Cq1atsHv3bkycOBEAUFJSgg0bNsDHxwcAMHfuXCxbtkyz/aBBg7T29+mnn8LW1hbHjh3DqFGjGuzYiKjpsCeKiJqdGzduYMqUKfD29oaNjQ08PT0BAElJSVVuExISovm3nZ0d2rVrh6tXr2qWWVhYaAIUALi6uiI9PV3zPC0tDc8//zzatm0LpVIJGxsbPHjwoNrXJCL9xp4oImp2Ro8eDQ8PD2zatAktW7aEWq2Gv78/iouLdd6nqamp1nOJRAJBEDTPZ8yYgczMTHz44Yfw8PCAmZkZQkJC6vWaRCQuhigialYyMzNx7do1bNq0CX379gUAnDhxosbtTp06hdatWwMAsrOzcf36dXTo0KHWr3vy5EmsW7cOI0aMAAAkJycjIyNDhyMgIn3BEEVEzUqLFi1gb2+PTz/9FK6urkhKSsKiRYtq3G7ZsmWwt7eHs7MzXn/9dTg4OODxxx+v9eu2bdsW33zzDbp164bc3Fy89tprMDc3r8eREJHYOCaKiJoVqVSK7du3IyoqCv7+/nj11VexatWqGrdbuXIl5s2bh6CgINy9exd79+6FXC6v9et+/vnnyM7ORmBgIJ566im88sorcHJyqs+hEJHIJMLfL9oTEZGWo0ePYuDAgcjOzuatXYhIC3uiiIiIiHTAEEVERESkA17OIyIiItIBe6KIiIiIdMAQRURERKQDhigiIiIiHTBEEREREemAIYqIiIhIBwxRRERERDpgiCIiIiLSAUMUERERkQ4YooiIiIh08P9vlipS2qoz2QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha_elast = np.linspace(2, 3, 10, endpoint=True)\n",
    "mean_cv = []\n",
    "\n",
    "for i in alpha_elast:\n",
    "    # Set up each model. \n",
    "    warnings.filterwarnings('ignore') #ignore warnings\n",
    "    elasticnet_mlr = ElasticNet(alpha= i).fit(X_train, Y_train)\n",
    "    \n",
    "    # Perform cross validation on the same splits each time\n",
    "    cv_scores = cross_val_score(elasticnet_mlr, X_train, Y_train, cv=kf, scoring=rmsle_scorer)\n",
    "    mean_cv.append(np.mean(cv_scores))\n",
    "    min_cv = round(min(mean_cv), 2)\n",
    "    alpha_min_cv = round(alpha_elast[mean_cv.index(min(mean_cv))], 2)\n",
    "\n",
    "print('Minimum RMSLE', min_cv, '\\n'\n",
    "      'Alpha value at minimum RMSLE (from alphas tested)', alpha_min_cv)\n",
    "plt.plot(alpha_elast, mean_cv)\n",
    "plt.scatter(alpha_elast, mean_cv)\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('RMSLE')\n",
    "plt.title('RMSLE vs alpha values in ElasticNet')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new alpha where the RMSLE is minimum is `2.67`, so we decide to take 2.67 as the alpha value for our ElasticNet model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.d.2. Training and testing ElasticNet\n",
    "Once we have tuned the alpha parameter, we will train our ElasticNet model, and test its performance on the train dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSLE: 0.12403706263458687\n",
      "R2: 0.869527723607678\n",
      "RMSE: 33284.77529417477\n"
     ]
    }
   ],
   "source": [
    "elasticnet_mlr = ElasticNet(alpha= 2.67).fit(X_train, Y_train)\n",
    "Y_pred = elasticnet_mlr.predict(X_train)\n",
    "plot_Yt_Yp(Y_train, Y_pred)\n",
    "print_eval_metrics(Y_train, Y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that the RMSLE in ElasticNet (0.124) metric is similar to the one obtained in the the MLR (0.122). But we can see that R2 score on the train data has dropped from (MLR) 0.92 to (ElasticNet) 0.86. But this tradeoff is expected when trying to reduce the overfit. Considering that this model was already tuned with CV and a good result was obtained, we can expect a better generalization performance when testing with the unseen test dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.e. Commenting on the explanatory variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will comment on the explanatory variables by looking at the coefficients generated by both the MLR and ElasticNet models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.e.1 Using basic MLR model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients of explantory variables [-4.68354532e+13 -9.95911730e+16  2.84447357e+03  3.19194530e+03\n",
      "  1.84093717e+03 -3.01570194e+03  1.47015520e+03  3.41448476e+03\n",
      " -2.33511030e+02  1.70917291e+04  5.93824814e+03  9.69912203e+02\n",
      " -3.45011283e+03  4.51364014e+03 -2.43018557e+02 -7.63114922e+01\n",
      " -8.81647936e+02 -1.18341260e+03  3.92835897e+03 -3.12465677e+02\n",
      "  6.23283800e+15  1.90665794e+03  1.96573574e+15  6.10749248e+15\n",
      " -5.65608038e+15  4.93128569e+02  9.25764071e+02  7.80776616e+16\n",
      "  9.02545956e+16  1.52524492e+03  7.97636388e+02 -1.20007798e+03\n",
      "  8.51167323e+02 -2.15100000e+03 -9.96406250e+02  7.70600000e+03\n",
      "  1.26800000e+03  3.75300000e+03  4.97200000e+03 -2.66200000e+03\n",
      "  4.10450000e+03  3.60000000e+02 -2.88000000e+02  6.09200000e+03\n",
      "  6.96000000e+02  1.83400000e+03 -5.64000000e+02  1.60800000e+03\n",
      "  6.16000000e+02  7.06000000e+02 -9.45000000e+01  3.12200000e+03\n",
      " -8.43500000e+02  3.95900000e+03  7.42000000e+02  3.52000000e+02\n",
      " -2.03800000e+03 -1.01000000e+03 -6.81255940e+15 -9.04445796e+14\n",
      " -2.23513092e+15 -6.07189976e+15 -6.40235285e+14 -4.52960419e+14\n",
      " -3.28698979e+15 -1.55971269e+15  2.02027720e+15 -4.10465965e+15\n",
      " -3.17472084e+15 -7.83699075e+14 -1.19451484e+15  6.68373630e+15\n",
      "  1.80508761e+15  1.07780773e+16  8.76948899e+15 -8.74096454e+13\n",
      " -1.81835002e+14 -1.06760973e+14 -2.23536015e+14 -5.55321202e+15\n",
      " -4.05151840e+15 -2.43545297e+15 -1.50379499e+15 -6.77510616e+15\n",
      "  1.26398282e+15  1.32133649e+15  5.56821513e+14  9.19860598e+14\n",
      "  2.77849734e+15  1.45384728e+15  1.61908168e+15  1.57391121e+15\n",
      "  4.82483866e+14  3.94160470e+14  1.40252674e+15  3.15224041e+15\n",
      "  2.34400643e+15  1.91875877e+15  2.92002553e+15  7.85748924e+14\n",
      "  4.82483866e+14  1.64112369e+15  1.66282283e+15  2.21444944e+15\n",
      "  1.29301032e+15  1.66282283e+15  6.81221816e+14  1.84934654e+15\n",
      "  3.16429169e+15  5.32011638e+15  2.00824599e+15  2.48132362e+15\n",
      "  1.47849988e+15  1.84934654e+15  5.60647639e+14  1.25092387e+15\n",
      "  8.07548831e+15  2.27540586e+16  1.39719774e+16  1.61246792e+16\n",
      "  1.94295167e+15  3.90832941e+14 -3.62470315e+15  1.05830191e+15\n",
      "  1.51819467e+15 -6.22473942e+14 -1.87309826e+15 -1.74937571e+14\n",
      " -1.76815526e+15 -6.22473942e+14 -9.34666830e+14  6.39514667e+14\n",
      "  4.46189044e+15  3.47577323e+15  4.41438603e+15  6.39514667e+14\n",
      "  8.33332897e+14 -3.67692500e+15 -9.56668948e+14 -2.69557710e+15\n",
      " -1.91022255e+15 -1.35220031e+15 -2.39425021e+15 -2.39425021e+15\n",
      " -8.56496005e+15 -1.71062604e+16 -1.37351824e+16 -1.09141339e+16\n",
      " -2.76313908e+15 -2.09470868e+16 -1.11362387e+16 -4.56466338e+15\n",
      " -1.46397883e+15 -4.19124561e+15 -6.33356776e+15 -1.23412687e+16\n",
      " -3.09374374e+15 -1.02007382e+16 -1.03575120e+15 -9.10377378e+15\n",
      " -2.31098075e+15 -1.56475330e+16 -8.22484077e+15 -4.23329511e+15\n",
      "  8.30172947e+15  2.47140215e+16  1.41036102e+15  2.38666001e+16\n",
      " -1.63903738e+14 -7.39206069e+14 -7.44760437e+14 -1.21382307e+14\n",
      " -4.96890343e+13 -2.67008442e+14 -2.67008442e+14 -1.02045814e+16\n",
      " -6.66177128e+15 -1.21107228e+16  3.87830456e+14  2.54256291e+15\n",
      "  7.46522176e+14  1.74273835e+15  2.74535792e+14  1.86686443e+15\n",
      "  6.95739294e+14 -5.51448268e+14 -5.51448268e+14 -2.72111871e+15\n",
      " -2.61291023e+15 -6.44400729e+15 -2.60756775e+15 -3.19012825e+15\n",
      " -2.25944821e+15 -1.30115664e+16 -1.30591019e+15 -1.30591019e+15\n",
      " -1.47617868e+16 -1.17348715e+16 -2.53378427e+15 -4.37910256e+15\n",
      " -6.42121476e+15 -2.15901412e+16 -1.79288119e+16]\n"
     ]
    }
   ],
   "source": [
    "# Using the .coef_ attribute of the model to get the coefficients of the explanatory variables\n",
    "print('Coefficients of explantory variables',basic_mlr.coef_[0])\n",
    "# .coeff_ returns a 2D array, so we need to select the first element of the array which contains the coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('BldgArea', -9.95911730444114e+16), ('X2FloorSF', 9.025459559385973e+16), ('X1FloorSF', 7.807766161653653e+16), ('MasVnrType_BrkFace', 2.4714021475833332e+16), ('MasVnrType_Stone', 2.386660009850087e+16), ('Condition2_Norm', 2.2754058631474452e+16), ('SaleCondition_Normal', -2.1590141198490156e+16), ('Exterior1st_VinylSd', -2.0947086773487456e+16), ('SaleCondition_Partial', -1.7928811850646292e+16), ('Exterior1st_HdBoard', -1.710626039322694e+16), ('Condition2_PosN', 1.612467921964309e+16), ('Exterior2nd_VinylSd', -1.5647533012470304e+16), ('SaleType_WD ', -1.4761786780317904e+16), ('Condition2_PosA', 1.3971977362617574e+16), ('Exterior1st_MetalSd', -1.3735182443348664e+16), ('SaleType_New', -1.301156636775968e+16), ('Exterior2nd_HdBoard', -1.2341268707035932e+16), ('Electrical_SBrkr', -1.2110722812397214e+16), ('SaleCondition_Abnorml', -1.1734871491846084e+16), ('Exterior1st_Wd Sdng', -1.1136238658510542e+16), ('Exterior1st_Plywood', -1.0914133903099602e+16), ('MSZoning_RL', 1.0778077260295266e+16), ('Electrical_FuseA', -1.020458136933156e+16), ('Exterior2nd_MetalSd', -1.0200738172019952e+16), ('Exterior2nd_Plywood', -9103773776688316.0), ('MSZoning_RM', 8769488992641758.0), ('Exterior1st_CemntBd', -8564960048078940.0), ('MasVnrType_BrkCmn', 8301729466252748.0), ('Exterior2nd_Wd Sdng', -8224840767287636.0), ('Condition2_Feedr', 8075488310505130.0), ('MSSubClass_20', -6812559400160492.0), ('LotConfig_Inside', -6775106158599604.0), ('MSZoning_FV', 6683736302342733.0), ('Electrical_FuseF', -6661771279037488.0), ('SaleType_COD', -6444007285417401.0), ('SaleCondition_Family', -6421214762738519.0), ('Exterior2nd_CmentBd', -6333567757735184.0), ('BsmtFinSF1', 6232838001912892.0), ('BsmtUnfSF', 6107492482442728.0), ('MSSubClass_60', -6071899759822032.0), ('TotalBsmtSF', -5656080380986465.0), ('LotConfig_Corner', -5553212017241418.0), ('Condition1_Norm', 5320116376531069.0), ('Exterior1st_WdShing', -4564663376717128.0), ('RoofStyle_Gable', 4461890435919236.0), ('RoofStyle_Hip', 4414386028398740.0), ('SaleCondition_Alloca', -4379102555713921.0), ('Exterior2nd_Wd Shng', -4233295106763436.0), ('Exterior2nd_AsphShn', -4191245613583104.0), ('MSSubClass_120', -4104659650915589.0), ('LotConfig_CulDSac', -4051518399219406.0), ('RoofMatl_CompShg', -3676924997208024.0), ('BldgType_Duplex', -3624703151767818.0), ('RoofStyle_Gambrel', 3475773231760392.0), ('MSSubClass_80', -3286989790642766.5), ('SaleType_ConLD', -3190128250939902.0), ('MSSubClass_160', -3174720835505836.0), ('Condition1_Feedr', 3164291685655976.0), ('Neighborhood_NAmes', 3152240413553167.0), ('Exterior2nd_ImStucc', -3093743740939605.0), ('Neighborhood_NridgHt', 2920025533117471.0), ('Neighborhood_CollgCr', 2778497343077456.0), ('Exterior1st_Stucco', -2763139080898627.0), ('MiscFeature_None', -2721118712246408.0), ('RoofMatl_Tar&Grv', -2695577097807704.0), ('MiscFeature_Shed', -2612910228061920.0), ('SaleType_CWD', -2607567751936980.0), ('GarageType_Attchd', 2542562913942488.5), ('SaleCondition_AdjLand', -2533784272545814.0), ('Condition1_PosN', 2481323620985121.5), ('LotConfig_FR2', -2435452965148674.5), ('Exterior1st_BrkFace', -2394250214974227.0), ('Exterior1st_AsbShng', -2394250214972821.0), ('Neighborhood_NWAmes', 2344006430345336.0), ('Exterior2nd_Stucco', -2310980749546286.0), ('SaleType_ConLI', -2259448210117474.0), ('MSSubClass_50', -2235130915512926.0), ('Neighborhood_Somerst', 2214449436858124.5), ('MSSubClass_90', 2020277195641185.2), ('Condition1_PosA', 2008245993279152.0), ('BsmtFinSF2', 1965735739122236.5), ('BldgType_1Fam', 1942951674044792.5), ('Neighborhood_NoRidge', 1918758773671775.0), ('RoofMatl_WdShake', -1910222554927109.5), ('HouseStyle_1Story', -1873098256539082.0), ('GarageType_Detchd', 1866864426330782.5), ('Condition1_RRAn', 1849346537815084.2), ('Condition1_Artery', 1849346537814102.5), ('MSZoning_RH', 1805087605759778.0), ('HouseStyle_2Story', -1768155255539484.0), ('GarageType_BuiltIn', 1742738350278055.0), ('Neighborhood_Timber', 1662822826299240.0), ('Neighborhood_SawyerW', 1662822826296994.0), ('Neighborhood_Sawyer', 1641123691063411.0), ('Neighborhood_Edwards', 1619081683601417.0), ('Neighborhood_Gilbert', 1573911207508493.8), ('MSSubClass_85', -1559712685694517.0), ('BldgType_TwnhsE', 1518194674659715.0), ('LotConfig_FR3', -1503794986201194.0), ('Condition1_RRAe', 1478499880251471.0), ('Exterior2nd_AsbShng', -1463978834634010.0), ('Neighborhood_Crawfor', 1453847279016657.5), ('MasVnrType_CBlock', 1410361022998272.0), ('Neighborhood_Mitchel', 1402526740220892.0), ('RoofMatl_WdShngl', -1352200307174548.0), ('Neighborhood_BrDale', 1321336486212142.0), ('SaleType_Oth', -1305910189767296.5), ('SaleType_VWD', -1305910189763850.0), ('Neighborhood_StoneBr', 1293010318192218.5), ('Neighborhood_Blmngtn', 1263982819163855.0), ('Condition1_RRNn', 1250923869577583.0), ('MSSubClass_190', -1194514840582136.0), ('BldgType_Twnhs', 1058301911598152.0), ('Exterior2nd_Other', -1035751201069055.0), ('RoofMatl_Roll', -956668948424891.5), ('HouseStyle_SLvl', -934666829711761.0), ('Neighborhood_ClearCr', 919860598067184.0), ('MSSubClass_30', -904445795681766.0), ('RoofStyle_Shed', 833332897353114.0), ('Neighborhood_OldTown', 785748924374324.0), ('MSSubClass_180', -783699074620355.0), ('GarageType_Basment', 746522176228332.0), ('Foundation_PConc', -744760437266180.0), ('Foundation_CBlock', -739206068526979.5), ('GarageType_None', 695739293679763.0), ('Neighborhood_Veenker', 681221815939921.0), ('MSSubClass_70', -640235285462524.0), ('RoofStyle_Flat', 639514667419556.0), ('RoofStyle_Mansard', 639514667416646.5), ('HouseStyle_1.5Fin', -622473941585012.2), ('HouseStyle_SFoyer', -622473941581050.0), ('Condition1_RRNe', 560647638719773.5), ('Neighborhood_BrkSide', 556821512942773.0), ('MiscFeature_Elev', -551448267607228.0), ('MiscFeature_Gar2', -551448267590078.0), ('Neighborhood_SWISU', 482483866367240.0), ('Neighborhood_IDOTRR', 482483866367065.5), ('MSSubClass_75', -452960418687440.0), ('Neighborhood_MeadowV', 394160470413616.0), ('BldgType_2fmCon', 390832941347930.0), ('GarageType_2Types', 387830456117717.5), ('GarageType_CarPort', 274535791939984.0), ('Heating_GasA', -267008442213442.0), ('Heating_GasW', -267008442212618.0), ('LandContour_Lvl', -223536015461912.0), ('LandContour_HLS', -181835001553813.0), ('HouseStyle_2.5Unf', -174937571312273.0), ('Foundation_BrkTil', -163903737683943.5), ('Foundation_Slab', -121382307341165.25), ('LandContour_Low', -106760973082798.0), ('LandContour_Bnk', -87409645428740.0), ('Foundation_Wood', -49689034278554.5), ('OverallQual', 17091.72905234334), ('KitchenQual', 7706.0), ('GarageArea', 6092.0), ('OverallCond', 5938.248143216419), ('Fireplaces', 4972.0), ('MasVnrArea', 4513.640138426428), ('GarageYrBlt', 4104.5), ('PoolQual', 3959.0), ('BsmtExposure', 3928.3589730664116), ('Functional', 3753.0), ('YearRemodAdd', -3450.112830829713), ('Utilities', 3414.484760346857), ('LotArea', 3191.9452974886904), ('ScreenPorchSF', 3122.0), ('Alley', -3015.7019445400847), ('LotFrontage', 2844.4735734252936), ('FireplaceQu', -2662.0), ('BedroomAbvGr', -2151.0), ('MoSold', -2038.0), ('BsmtFinType2', 1906.6579394325217), ('Street', 1840.9371694051852), ('GarageCond', 1834.0), ('WoodDeckSF', 1608.0), ('BsmtFullBath', 1525.2449226860044), ('LotShape', 1470.1552036441608), ('TotRmsAbvGrd', 1268.0), ('FullBath', -1200.0779818218011), ('BsmtCond', -1183.4125966773418), ('YrSold', -1010.0), ('KitchenAbvGr', -996.40625), ('YearBuilt', 969.912202743988), ('CentralAir', 925.7640711580734), ('BsmtQual', -881.6479357068279), ('HalfBath', 851.1673233181336), ('PoolArea', -843.5), ('BsmtHalfBath', 797.6363879245928), ('Fence', 742.0), ('EnclosedPorchSF', 706.0), ('GarageQual', 696.0), ('OpenPorchSF', 616.0), ('PavedDrive', -564.0), ('HeatingQual', 493.12856899985127), ('GarageFinish', 360.0), ('MiscVal', 352.0), ('BsmtFinType1', -312.4656772396602), ('GarageCars', -288.0), ('ExterQual', -243.01855656016232), ('LandSlope', -233.51102973857087), ('3SsnPorchSF', -94.5), ('ExterCond', -76.31149221886108)]\n"
     ]
    }
   ],
   "source": [
    "# create a dictionary of the coefficients and feature names\n",
    "coef_dict1 = {}\n",
    "for feat, coef in zip(X_names, basic_mlr.coef_[0][1:]): # skipping the first coefficient that corresponds to intercept\n",
    "    coef_dict1[feat] = coef\n",
    "\n",
    "# sort dictionary by the absolute value of the coefficient (magnitude)\n",
    "coef_dict1 = sorted(coef_dict1.items(), key=lambda x: abs(x[1]), reverse=True)\n",
    "print(coef_dict1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the basic MLR model sets very high coefficients to the Variables. As per this model, `BldgArea`, `X2FloorSF` and `X1FloorSF` are the most important features, and have been given the highest coefficients.This follows what was analyzed in the pearson correlation with the target variable in the EDA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.e.2 Using ElasticNet model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients of explanatory variables [    0.          6241.38857966  3241.95569359  2504.42887322\n",
      "   231.99696237  -936.666419   -1028.80874531   599.08703736\n",
      "   678.0310961   7059.88515725  1002.41035761  2128.84643652\n",
      "  2053.98376249  5218.4466057   3198.03338133   170.23213282\n",
      "  2586.36682473   -40.30004209  3710.59277485  2095.27521023\n",
      "  4617.26468809  1132.58487095   933.16956399   -18.63846215\n",
      "  5391.65159922  1797.34528345    65.05149833  5223.33269672\n",
      "  2367.64554208  2761.88500754  -337.31019487  2519.73660876\n",
      "  2011.76548208  1154.99983304  -824.67774639  5212.56541505\n",
      "  4206.41815322  1004.62118114  3426.67723629  3129.27840343\n",
      "  2162.64120916  1663.21852165  4022.40844978  4646.83661226\n",
      "   186.80738023  -110.51412857  -204.96993591  2133.59972811\n",
      "   437.30139174   278.55096538  -235.68833132  2207.07546121\n",
      "    88.72707137  2687.89204388  -497.67910546 -2721.50286846\n",
      "  -651.08001237  -213.45837206   292.04462312   105.75963748\n",
      "   380.64697704  1391.26034529   115.13165445   -37.44197093\n",
      "  -606.17815744   -69.42363367  -515.17826133 -1315.29353194\n",
      "  -718.04266379    57.36605537  -359.44692414    89.35476941\n",
      "  -130.23446611   867.46881904 -1108.23793944 -2658.07621796\n",
      "  2142.82923093   -81.70402583  -663.61272053  -323.09731555\n",
      "  1517.90056271  -111.75440306  -441.17474465  -502.98099051\n",
      " -1139.36268048   452.93219224  -316.21340485  -109.47037983\n",
      " -1733.30629925   964.98348514 -2318.25933295 -1433.24394598\n",
      "   -20.93096071   -73.8303222   -892.76825256 -1790.33398361\n",
      " -1364.900825    3552.43339723  4379.89501885  -558.25960469\n",
      "   203.99896891  -618.0724612  -1281.9553536    184.41673753\n",
      "  4986.58027716  -995.40415856  -930.76866321  -557.50182675\n",
      " -1010.5446755    624.38421527   949.45584866   615.04661598\n",
      "  -673.4056554   -402.32169246  -123.67136211  -572.89800741\n",
      "   -43.82324766  -450.23520438  2273.32603304 -1310.49360589\n",
      "  1635.60110365  -191.30847987  -515.1809192   -873.07638113\n",
      " -1136.77963376   517.51931929  -712.00879468   -32.63057795\n",
      "   881.72454832   -16.14022827  -567.61955777  -449.86267609\n",
      " -2193.79144975   268.35966676  2462.68734814 -1063.8190838\n",
      "  -622.55715469  -838.55723808    17.45126617  -198.20937389\n",
      "  -340.5109461   3148.4480529   -139.2395465    331.19757971\n",
      "  1419.40705117  -616.8128907    762.45276753  -857.87426776\n",
      "   403.37553986   176.11958056  -656.35605142  -147.12462198\n",
      "  -446.95368384   268.35929769  1059.00078018  -609.95144389\n",
      "   449.74640511   724.78357272  -269.88729294  -820.5190935\n",
      "   173.64882855   557.86515985  -702.88208305  -753.6195709\n",
      "  -310.96641773  -643.76916057  -622.55795471   825.43068341\n",
      "   407.92085703 -1873.06923705  1819.02786195  -358.08493079\n",
      "   127.91265707  -201.58068028   201.58044987  -100.66999784\n",
      "  -389.21982635   299.31809257 -1008.79576326  -131.0955612\n",
      "  -572.20791825  1009.83861528    44.66113498  -397.9937605\n",
      "   180.25589971 -4140.60183382   883.74026053   706.01300292\n",
      "   -47.94003111  -639.47047734   392.50345845  -238.95517968\n",
      "   423.91417041  1742.36042025 -1215.31024529  -186.78412989\n",
      " -1214.55178356  -781.07460042   152.30194077    25.11032599\n",
      " -1489.49918431  -594.90628548  1734.26806328]\n"
     ]
    }
   ],
   "source": [
    "# where alpha is the optimal value of alpha found above\n",
    "print('Coefficients of explanatory variables',elasticnet_mlr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('OverallQual', 7059.885157250567), ('BldgArea', 6241.388579659397), ('TotalBsmtSF', 5391.651599218554), ('X1FloorSF', 5223.332696719026), ('MasVnrArea', 5218.446605696034), ('KitchenQual', 5212.565415046286), ('Neighborhood_StoneBr', 4986.580277155378), ('GarageArea', 4646.836612264841), ('BsmtFinSF1', 4617.264688086146), ('Neighborhood_NridgHt', 4379.895018853271), ('TotRmsAbvGrd', 4206.418153221087), ('MiscFeature_Elev', -4140.601833819825), ('GarageCars', 4022.408449776146), ('BsmtExposure', 3710.5927748546133), ('Neighborhood_NoRidge', 3552.433397228771), ('Fireplaces', 3426.6772362859783), ('LotFrontage', 3241.9556935949263), ('ExterQual', 3198.033381330467), ('RoofMatl_WdShngl', 3148.4480528986574), ('FireplaceQu', 3129.2784034257147), ('BsmtFullBath', 2761.885007541484), ('MiscVal', -2721.502868460807), ('PoolQual', 2687.8920438842374), ('LandContour_Bnk', -2658.076217960781), ('BsmtQual', 2586.366824732989), ('FullBath', 2519.736608762087), ('LotArea', 2504.4288732212226), ('RoofStyle_Hip', 2462.6873481416856), ('X2FloorSF', 2367.6455420778525), ('Neighborhood_Edwards', -2318.2593329474566), ('Condition2_PosA', 2273.326033044703), ('ScreenPorchSF', 2207.075461210447), ('RoofStyle_Gable', -2193.7914497493703), ('GarageYrBlt', 2162.6412091627217), ('LandContour_HLS', 2142.8292309321737), ('WoodDeckSF', 2133.599728110384), ('YearBuilt', 2128.8464365199125), ('BsmtFinType1', 2095.275210226852), ('YearRemodAdd', 2053.9837624883244), ('HalfBath', 2011.7654820785579), ('Foundation_CBlock', -1873.0692370455713), ('Foundation_PConc', 1819.0278619475898), ('HeatingQual', 1797.345283454648), ('Neighborhood_NAmes', -1790.333983607699), ('SaleType_New', 1742.3604202499846), ('SaleCondition_Partial', 1734.2680632827971), ('Neighborhood_CollgCr', -1733.3062992503997), ('GarageFinish', 1663.2185216491234), ('BldgType_1Fam', 1635.6011036531913), ('LotConfig_CulDSac', 1517.900562713674), ('SaleCondition_Family', -1489.4991843104337), ('Neighborhood_Gilbert', -1433.2439459826244), ('Exterior1st_CemntBd', 1419.4070511673904), ('MSSubClass_60', 1391.260345290358), ('Neighborhood_NWAmes', -1364.900825001147), ('MSSubClass_120', -1315.2935319350047), ('Condition2_PosN', -1310.4936058926526), ('Neighborhood_SawyerW', -1281.9553535951281), ('SaleType_Oth', -1215.3102452931792), ('SaleType_WD ', -1214.551783557643), ('BedroomAbvGr', 1154.9998330359624), ('Neighborhood_Blmngtn', -1139.3626804835149), ('BldgType_TwnhsE', -1136.7796337581635), ('BsmtFinType2', 1132.5848709455088), ('MSZoning_RM', -1108.237939438584), ('RoofStyle_Mansard', -1063.8190837975394), ('Exterior2nd_CmentBd', 1059.0007801824768), ('LotShape', -1028.8087453100818), ('Condition1_Feedr', -1010.5446755047775), ('GarageType_BuiltIn', 1009.8386152843866), ('GarageType_2Types', -1008.7957632628694), ('Functional', 1004.6211811396284), ('OverallCond', 1002.410357613913), ('Neighborhood_Timber', -995.4041585583278), ('Neighborhood_Crawfor', 964.9834851398275), ('Condition1_PosA', 949.4558486634389), ('Alley', -936.6664189991914), ('BsmtFinSF2', 933.1695639854505), ('Neighborhood_Veenker', -930.7686632096546), ('Neighborhood_Mitchel', -892.7682525584017), ('MiscFeature_Gar2', 883.7402605339252), ('HouseStyle_2Story', 881.7245483162776), ('BldgType_Twnhs', -873.0763811265815), ('MSZoning_RL', 867.4688190448558), ('Exterior1st_Plywood', -857.874267759199), ('RoofMatl_CompShg', -838.5572380840142), ('MasVnrType_Stone', 825.4306834112112), ('KitchenAbvGr', -824.6777463908123), ('Exterior2nd_Plywood', -820.519093504461), ('SaleCondition_Abnorml', -781.0746004154599), ('Exterior1st_MetalSd', 762.4527675270277), ('Exterior2nd_Wd Shng', -753.6195709044765), ('Exterior2nd_MetalSd', 724.7835727199111), ('MSSubClass_160', -718.0426637916912), ('HouseStyle_1Story', -712.0087946790726), ('MiscFeature_None', 706.0130029197463), ('Exterior2nd_Wd Sdng', -702.8820830451356), ('LandSlope', 678.0310960967299), ('Condition1_RRAe', -673.4056554029161), ('LandContour_Lvl', -663.6127205310099), ('Exterior1st_Wd Sdng', -656.3560514197128), ('MoSold', -651.0800123709921), ('MasVnrType_BrkFace', -643.7691605743557), ('SaleType_COD', -639.470477343719), ('Condition1_Norm', 624.384215272418), ('MasVnrType_CBlock', -622.5579547072464), ('RoofStyle_Shed', -622.5571546919698), ('Neighborhood_Sawyer', -618.0724612022148), ('Exterior1st_HdBoard', -616.8128907019609), ('Condition1_PosN', 615.0466159815154), ('Exterior2nd_HdBoard', -609.9514438940099), ('MSSubClass_80', -606.1781574362113), ('Utilities', 599.0870373605435), ('SaleCondition_Normal', -594.9062854811302), ('Condition1_RRNn', -572.8980074147934), ('GarageType_Basment', -572.2079182507808), ('HouseStyle_SLvl', -567.6195577680295), ('Neighborhood_OldTown', -558.2596046870185), ('Exterior2nd_VinylSd', 557.8651598469754), ('Condition1_Artery', -557.501826750842), ('HouseStyle_1.5Fin', 517.5193192875918), ('BldgType_Duplex', -515.1809191994362), ('MSSubClass_90', -515.1782613260767), ('LotConfig_Inside', -502.9809905050709), ('Fence', -497.6791054558564), ('Neighborhood_BrDale', 452.93219223861365), ('Condition2_Norm', -450.23520438313744), ('RoofStyle_Flat', -449.8626760938404), ('Exterior2nd_ImStucc', 449.746405110114), ('Exterior2nd_AsbShng', -446.9536838425169), ('LotConfig_FR3', -441.1747446456852), ('OpenPorchSF', 437.30139173617823), ('SaleType_ConLI', 423.9141704116332), ('Foundation_BrkTil', 407.92085703280264), ('Exterior1st_Stucco', 403.3755398578549), ('Condition1_RRAn', -402.3216924637265), ('GarageType_Detchd', -397.9937604982475), ('SaleType_CWD', 392.5034584464036), ('Electrical_FuseF', -389.21982635234383), ('MSSubClass_50', 380.64697703632635), ('MSSubClass_190', -359.44692413606083), ('Foundation_Slab', -358.0849307853805), ('RoofMatl_WdShake', -340.510946101769), ('BsmtHalfBath', -337.3101948662352), ('Exterior1st_BrkFace', 331.1975797138395), ('LotConfig_Corner', -323.09731555326147), ('Neighborhood_BrkSide', -316.2134048531142), ('MasVnrType_BrkCmn', -310.9664177286605), ('Electrical_SBrkr', 299.31809256592726), ('MSSubClass_20', 292.04462312311347), ('EnclosedPorchSF', 278.5509653762687), ('Exterior2nd_Other', -269.8872929418742), ('RoofStyle_Gambrel', 268.3596667587248), ('Exterior2nd_AsphShn', 268.3592976945317), ('SaleType_ConLD', -238.95517967547715), ('3SsnPorchSF', -235.68833131841475), ('Street', 231.99696236821822), ('YrSold', -213.45837206218877), ('PavedDrive', -204.96993591365924), ('Neighborhood_SWISU', 203.9989689093147), ('Heating_GasA', -201.58068028402974), ('Heating_GasW', 201.58044986854952), ('RoofMatl_Tar&Grv', -198.20937388713392), ('BldgType_2fmCon', -191.30847987087847), ('GarageQual', 186.8073802283798), ('SaleType_VWD', -186.78412989296658), ('Neighborhood_Somerst', 184.416737531665), ('GarageType_None', 180.25589971462725), ('Exterior1st_VinylSd', 176.11958055928895), ('Exterior2nd_Stucco', 173.648828552596), ('ExterCond', 170.23213282005932), ('SaleCondition_AdjLand', 152.30194077069268), ('Exterior1st_WdShing', -147.12462198079544), ('Exterior1st_AsbShng', -139.2395464989598), ('GarageType_Attchd', -131.09556119836594), ('MSZoning_RH', -130.23446610689126), ('Foundation_Wood', 127.91265707040847), ('Condition1_RRNe', -123.67136210755139), ('MSSubClass_70', 115.13165444735924), ('LotConfig_FR2', -111.75440306233205), ('GarageCond', -110.51412857068983), ('Neighborhood_ClearCr', -109.47037982672146), ('MSSubClass_30', 105.75963747721265), ('Electrical_FuseA', -100.66999783516002), ('MSZoning_FV', 89.35476941296422), ('PoolArea', 88.72707137340336), ('LandContour_Low', -81.70402583193393), ('Neighborhood_MeadowV', -73.83032219600977), ('MSSubClass_85', -69.42363367069495), ('CentralAir', 65.05149833440187), ('MSSubClass_180', 57.36605537015282), ('MiscFeature_Shed', -47.94003111445254), ('GarageType_CarPort', 44.661134975840724), ('Condition2_Feedr', -43.823247657575884), ('BsmtCond', -40.30004209297724), ('MSSubClass_75', -37.44197093168006), ('HouseStyle_2.5Unf', -32.63057794727178), ('SaleCondition_Alloca', 25.110325992036994), ('Neighborhood_IDOTRR', -20.93096070937639), ('BsmtUnfSF', -18.638462145991042), ('RoofMatl_Roll', 17.451266166130434), ('HouseStyle_SFoyer', -16.140228272537325)]\n"
     ]
    }
   ],
   "source": [
    "# create a dictionary of the coefficients and feature names\n",
    "coef_dict2 = {}\n",
    "for feat, coef in zip(X_names, elasticnet_mlr.coef_[1:]): # skipping the first coefficient that corresponds to intercept\n",
    "    coef_dict2[feat] = coef\n",
    "    \n",
    "# sort dictionary by the absolute value of the coefficient (magnitude)\n",
    "coef_dict2 = sorted(coef_dict2.items(), key=lambda x: abs(x[1]), reverse=True)\n",
    "print(coef_dict2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the ElasticNet model sets smaller coefficients compared to the basic MLR model. As per this model, `OverallQual`, `BldgArea` and `TotalBsmtSF` are the most important features. This is because they have been given the highest coefficients. In our EDA, we had seen that the these features had high correlation with the target variable. Hence, it is not surprising that they are given high coefficients by the ElasticNet model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.e.3 Comparing the coefficients of the two models\n",
    "We now combine the coefficients from both models into a dataframe, and compare them side by side in a table and in a bar chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coefficient_Basic</th>\n",
       "      <th>Coefficient_ElasticNet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variable</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>1.709173e+04</td>\n",
       "      <td>7059.885157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BldgArea</th>\n",
       "      <td>-9.959117e+16</td>\n",
       "      <td>6241.388580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <td>-5.656080e+15</td>\n",
       "      <td>5391.651599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X1FloorSF</th>\n",
       "      <td>7.807766e+16</td>\n",
       "      <td>5223.332697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>4.513640e+03</td>\n",
       "      <td>5218.446606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenQual</th>\n",
       "      <td>7.706000e+03</td>\n",
       "      <td>5212.565415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_StoneBr</th>\n",
       "      <td>1.293010e+15</td>\n",
       "      <td>4986.580277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageArea</th>\n",
       "      <td>6.092000e+03</td>\n",
       "      <td>4646.836612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <td>6.232838e+15</td>\n",
       "      <td>4617.264688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_NridgHt</th>\n",
       "      <td>2.920026e+15</td>\n",
       "      <td>4379.895019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Coefficient_Basic  Coefficient_ElasticNet\n",
       "Variable                                                       \n",
       "OverallQual                1.709173e+04             7059.885157\n",
       "BldgArea                  -9.959117e+16             6241.388580\n",
       "TotalBsmtSF               -5.656080e+15             5391.651599\n",
       "X1FloorSF                  7.807766e+16             5223.332697\n",
       "MasVnrArea                 4.513640e+03             5218.446606\n",
       "KitchenQual                7.706000e+03             5212.565415\n",
       "Neighborhood_StoneBr       1.293010e+15             4986.580277\n",
       "GarageArea                 6.092000e+03             4646.836612\n",
       "BsmtFinSF1                 6.232838e+15             4617.264688\n",
       "Neighborhood_NridgHt       2.920026e+15             4379.895019"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plotting top 10 variables from ElasticNet and comparing their coefficients in both models\n",
    "elast_coeff = pd.DataFrame(coef_dict1, columns=['Variable', 'Coefficient_Basic'])\n",
    "basic_coeff = pd.DataFrame(coef_dict2, columns=['Variable', 'Coefficient_ElasticNet'])\n",
    "\n",
    "# Merge the counts based on the 'Neighborhood' column\n",
    "combined = pd.merge(elast_coeff, basic_coeff, on='Variable', how='outer')\n",
    "\n",
    "# sort the dataframe by the absolute value of the coefficient in the elastic net model\n",
    "combined = combined.reindex(combined['Coefficient_ElasticNet'].abs().sort_values(ascending=False).index)\n",
    "combined = combined.set_index('Variable')\n",
    "\n",
    "combined.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting as subplots because the scales are very different - some bar heights are visually nullified in dual bar chart \n",
    "\n",
    "fig, axes = plt.subplots(1,2, figsize=(20,5))\n",
    "combined['Coefficient_Basic'].head(10).plot(kind='bar', alpha=0.7, ax=axes[0], color = 'blue')\n",
    "combined['Coefficient_ElasticNet'].head(10).plot(kind='bar', alpha=0.7, ax=axes[1], color = 'orange' )\n",
    "axes[0].set_title('Coefficients of explanatory variables in Basic MLR')\n",
    "axes[1].set_title('Coefficients of explanatory variables in Elastic Net')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the coefficients from the ElasticNet model are much smaller than the coefficients from the basic MLR model. If we look at the chart on the left for coefficients from Basic MLR, we see that there are exponential differences among some of the coefficients (some bars are completely nullified!). However, in the ElasticNet model, the coeffiecients are more comparable among the variables. This is because the ElasticNet model has a regularisation term, which penalises the high coefficients."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Evaluation of the test performance of the models\n",
    "\n",
    "In this section, we will evaluate the test performance of the both basic MLR and ElasticNet models. We will use the test dataset that we had created in the first section of this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.a. Test performance of basic MLR model\n",
    "We first apply the basic MLR model on the test dataset fitted previously (section 2.c). We then calculate the three evaluation metrics, namely, RMSE, R2 and RMSLE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j+f3vf09LSwsdHR1omsaLXvQi/vu//3tCm4X+/n6+9a1v8fznP59kMjnlfRzuGOdCPa+Ppmm84hWv4M4775wy0BocHJyXsQpxKEdlBmomu3fv5rbbbmP37t21rsrvec97+N///V9uu+02Pvaxj9X1d5555hnuv/9+wuEwd999N0NDQ7ztbW9jeHiY2267bS4fgjjID37wA/L5PH/913895ffPOeccOjo6uP3227niiit473vfy/e//31e9apXcd1113HGGWcwMjLCD37wA2699VZOPfVU1q1bRzqd5tZbbyWRSBCLxTj77LNZu3Ytb3rTm/j+97/Pi1/8Yl796lezfft2vvnNb7Ju3boJ9/vSl76Uu+66i5e//OVceuml7Nixg1tvvZUTTjiBQqEw68d7xRVX8LrXvY7Pf/7zXHLJJZM6bZ9wwglceOGFnHHGGbS2tvLggw/y/e9/v1Z3M1fWrVvHzTffzAc+8AF27tzJZZddRiKRYMeOHdx999285S1v4T3veQ+GYXDzzTfz1re+lRe84AVcccUV7Nixg9tuu62u2p3/+I//4PnPfz7Pec5zeMtb3sLatWvZuXMnP/zhD2tH75xxxhkA3HDDDVx55ZUYhsHLXvayeRvjo48+ymtf+1pe8pKXcN5559Ha2kpvby9f//rX6evr4zOf+UwtwL755pv52c9+xvOf/3ze9ra3oes6X/ziF7Esi09+8pPT3sfhjnGu1PP6fPzjH+fee+/l7LPP5s1vfjMnnHACIyMjPPTQQ/z85z9nZGRkwcYvRM1CbP1bTIDg7rvvrn19zz33BEAQi8Um/NN1PXj1q1896fevvvrq4G/+5m8m3f6Xf/mXQTgcDjKZTO22O++8M1AURbbgzrOXvexlQTgcDorF4rQ/c8011wSGYdS2TQ8PDwdvf/vbg+7u7iAUCgUrV64Mrr766gnbqv/7v/87OOGEEwJd1ydt1f63f/u3oLu7OzBNMzj33HODBx98cFIbA9/3g4997GNBT09PYJpmcPrppwf33HNPcPXVVwc9PT0TxkcdbQzG5HK5IBKJBEDwzW9+c9L3b7755uC5z31ukE6ng0gkEmzcuDH46Ec/Gti2PePfHdsm/73vfW/GnxtrYzA4ODjl9++8887g+c9/fu3/rY0bNwbXX399sHnz5gk/9/nPfz5Yu3ZtYJpmcOaZZwa//vWvJz2HU22TD4IgeOKJJ4KXv/zlQTqdDsLhcHDccccFH/zgByf8zE033RR0d3cHqqpOamnQzDFOpb+/P/j4xz8eXHDBBcHy5csDXdeDlpaW4AUveEHw/e9/f9LPP/TQQ8Ell1wSxOPxIBqNBhdddFHwu9/9bsLPHNzG4HDHGATVNgaHankx9hr867/+67Tfm83r09/fH1x//fXBqlWrAsMwgq6uruDiiy8OvvSlLx1y3ELMByUIDspTH2UUReHuu++uLXN85zvf4aqrruLJJ5+cVEQaj8fp6uqacNs111xDJpOZtJPv6quv5re//S3btm2r3fb0009zwgknsGXLlkk7Y4QQQgixdMgS3kFOP/10PM9jYGCg1mF6Ns4991y+973vUSgUajt3tmzZgqqqrFy5slnDFUIIIcQCOCqLyAuFAo888khtvX3Hjh088sgj7N69mw0bNnDVVVfxhje8gbvuuosdO3bwpz/9iVtuuYUf/vCHtb/x1FNP8cgjjzAyMkI2m53w9wBe+9rX0tbWxrXXXstTTz3Fr3/9a9773vdy3XXXNW27rxBCCCEWxlG5hHfffffVulGPd/XVV/O1r30Nx3G4+eab+cY3vkFvby/t7e2cc8453HjjjZx88slAtSXBWKO68cY/nZs2beId73gHv/3tb2lra+PVr341N998swRQQgghxBK3pAKo3t5e/umf/okf//jHlEoljj32WG677bZJR0gIIYQQQsylJVMDNTo6yrnnnstFF13Ej3/8Yzo6Oti6dSstLS0LPTQhhBBCHGWWTAbq/e9/P7/97W/5zW9+s9BDEUIIIcRRbskEUCeccAKXXHIJe/fu5Ve/+hXd3d287W1v481vfvO0v2NZ1oTuymMn3re1tdV1MrsQQgghFl4QBOTzeVasWIGqLpL9bwvTfqpxpmkGpmkGH/jAB4KHHnoo+OIXvxiEw+Hga1/72rS/M9bQT/7JP/kn/+Sf/JN/S//fnj175jHymNmSyUCFQiHOPPNMfve739Vue+c738kDDzww7dlhB2egstksq1evZs+ePdOeHyWEEEKIxSWXy7Fq1SoymQypVGqhhwMsoSLy5cuXc8IJJ0y47fjjj+fOO++c9ndM08Q0zUm3J5NJCaCEEEKIJWYxld8skoXEQzv33HPZvHnzhNu2bNlCT0/PAo1ICCGEEEerJRNA/cM//AN/+MMf+NjHPsa2bdv41re+xZe+9CWuv/76hR6aEEIIIY4ySyaAOuuss7j77rv59re/zUknncRNN93EZz7zGa666qqFHpoQQgghjjJLpoi8GXK5HKlUimw2KzVQQgghxBKxGOfvJZOBEkIIIYRYLCSAEkIIIYRokARQQgghhBANkgBKCCGEEKJBEkAJIYQQQjRIAighhBBCiAZJACWEEEII0SAJoIQQQgghGiQBlBBCCCFEgySAEkIIIYRokARQQgghhBANkgBKCCGEEKJBEkAJIYQQQjRIAighhBBCiAZJACWEEEII0SAJoIQQQgghGiQBlBBCCCFEgySAEkIIIYRokARQQgghhBANkgBKCCGEEKJBEkAJIYQQQjRIAighhBBCiAZJACWEEEII0SAJoIQQQgghGiQBlBBCCCFEgySAEkIIIYRokARQQgghhBANkgBKCCGEEKJBEkAJIYQQQjRIAighhBBCiAZJACWEEEII0SAJoIQQQgghGiQBlBBCCCFEgySAEkIIIYRokARQQgghhBANkgBKCCGEEKJBEkAJIYQQQjRIAighhBBCiAZJACWEEEII0SB9oQcghFi8giAgU3KwXB9TV0lHDRRFWehhCSHEgpMASggxpYFchSd6c/RmStieT0hT6U5HOak7SWcyvNDDE0KIBSUBlBBikoFchfs2D5It23QmwoQNjYrjsX0wz1DB4sLjOiSIEkIc1aQGSggxQRAEPNGbI1u2WdMWI2bqaKpCzNRZ0xYjW7Z5ojdHEAQLPVQhhFgwEkAJISbIlBx6MyU6E+FJ9U6KotCZCNObKZEpOQs0QiGEWHgSQAkhJrBcH9vzCRvalN8PGxq252O5/jyPTAghFg8JoIQQE5i6SkhTqTjelN+vOB4hTcXU5fIhhDh6yRVQCDFBOmrQnY4ykK9MqnMKgoCBfIXudJR01FigEQohxMKTAEoIMYGiKJzUnSQVCbFzuEjRcvH8gKLlsnO4SCoa4qTupPSDEkIc1aSNgRBiks5kmAuP66j1gRoqWoQ0lXUdCekDJYQQSAAlhJhGZzLMRQlTOpELIcQUluwS3sc//nEUReHv//7vF3ooQhyxFEWhJRaiKxWmJRaS4EkIIQ5YkgHUAw88wBe/+EVOOeWUhR6KEEIIIY5CSy6AKhQKXHXVVfznf/4nLS0tCz0cIYQQQhyFllwAdf3113PppZfywhe+cKGHIoQQQoij1JIqIr/jjjt46KGHeOCBB+r6ecuysCyr9nUul5uroQkhhBDiKLJkMlB79uzhXe96F7fffjvhcH1bqG+55RZSqVTt36pVq+Z4lEIIIYQ4GijBEjlS/f/9v//Hy1/+cjTt2fO5PM9DURRUVcWyrAnfg6kzUKtWrSKbzZJMJudt7EIIIYSYvVwuRyqVWlTz95JZwrv44ot5/PHHJ9x27bXXsnHjRv7pn/5pUvAEYJompmnO1xDFEhIEgfQ3EkIIMWtLJoBKJBKcdNJJE26LxWK0tbVNul2ImQzkKrUO27bnE9JUutNR6bAthBCibksmgBKiGQZyFe7bPEi2bNOZCBM2NCqOx/bBPEMFiwuP65AgSgghxCEt6QDqvvvuW+ghHDGOhiWtIAh4ojdHtmyzpi1We3wxU2dNKMbO4SJP9Oa4KGEecY9dCCFEcy3pAEo0x9GypJUpOfRmSnQmwpMCJEVR6EyE6c2UyJQcWmKhBRqlEEKIpUACqKPc0bSkZbk+tucTNiZvOAAIGxpDRQvL9ed5ZEIIIZaaJdMHSjTfwUtaMVNHU5XqklZbjGzZ5oneHEuk08UhmbpKSFOpON6U3684HiFNxdTlfwshhBAzk5niKNbIktaRIB016E5HGchXJgWFQRAwkK/QnY6SjhoLNEIhhBBLhQRQR7F6lrRszz9ilrQUReGk7iSpSIidw0WKlovnBxQtl53DRVLRECd1J6WAXAghxCFJDdRRbPySVsyc/FY4Epe0OpNhLjyuo1Y0P1S0CGkq6zoSR1zRvBBCiLkjAdRRbGxJa/tgnjWh2ITMy9iS1rqOxBG3pNWZDHNRwjzi2zYIIYSYOxJAHcXGlrSGChY7h4sTduEN5CtH9JKWoijSqkAIIcSsSQB1lJMlLSGEEKJxEkAJWdISQgghGiQBlABkSUsIIYRoxJGzvUoIIYQQYp5IACWEEEII0SAJoIQQQgghGiQ1UEIsMkEQSEG/EEIschJACbGIDOQqtZYStucT0lS601FpKTFLEowKIeaKBFBCLBIDuQr3bR4kW7YnNDXdPphnqGBx4XEdEkQ1QIJRIcRckhooIRaBIAh4ojdHtmyzpi1GzNTRVIWYqbOmLUa2bPNEb44gCBZ6qEvCWDC6fTBPMmywMh0lGTbYPpjnvs2DDOQqCz1EIcQSJwGUEItApuTQmynRmQhPWmJSFIXORJjeTIlMyVmgES4dEowKIeaDBFBCLAKW62N7PmFDm/L7YUPD9nws15/nkS09EowKIeaDBFBCLAKmrhLSVCqON+X3K45HSFMxdflf9lAkGBVCzAe5GguxCKSjBt3pKAP5yqSlpSAIGMhX6E5HSUeNBRrh0iHBqBBiPsgVRIhFQFEUTupOkoqE2DlcpGi5eH5A0XLZOVwkFQ1xUndStuDXQYJRIcR8kDYGQiwSnckwFx7XUdt6P1S0CGkq6zoSsvW+AWPB6FDBYudwcUJLiIF8RYJRIURTSAAlxCLSmQxzUcKU5o+HSYJRIcRckwBKiEVGURRaYqGFHsaSJ8GoEGIuSQAlhDikpXokigSjQoi5IgGUEGJGciSKEEJMJgGUEGJacj6fEEJMTdoYCCGmJEeiCCHE9CSAEkJMSY5EEUKI6UkAJYSYkhyJIoQQ05MASggxJTkSRQghpidXPiHElORIFCGEmJ4EUEKIKcn5fEIIMT1pYyCEmJYcibJ0LdXmp0IsFRJACSFmJEeiLD3S/FSIuScBlBDikORIlKVDmp8KMT+kBkoIIY4Q0vxUiPkjAZQQQhwhpPmpEPNHAighhDhCSPNTIeaP1EAJIeaU7AabP+Obn8bMyZd3aX4qRPNIACWEmDOyG2x+jTU/3T6YZ00oNiFQHWt+uq4jIc1PhWgCCaCEEHNCdoPNv7Hmp0MFi53DxQnP+0C+siDNTyUDKY5UEkAJIZru4N1gYxNmzNRZE4qxc7jIE705LkqYMpk22WJqfioZSHEkkwBKCNF0jewGk/5SzbcYmp9KBlIc6aSSUAjRdLIbbOGNNT/tSoVpiYXmfdlO+lGJI50EUEKIphu/G2wqzd4NFgQBo0Wb/dkKo0VbJuYFtlD9qOR9IOaTLOEJIZpuPneDSZ3N4lNPBnKoaDU1AynvAzHfZv3xb9u2bfzkJz+hXC4DSKQvhKgZ2w2WioTYOVykaLl4fkDRctk5XGzabrCxOpvtg3mSYYOV6SjJsMH2wTz3bR5kIFdp0iMSjZjvDKS8D8RCaPjdOzw8zAtf+EI2bNjAX/3VX7Fv3z4A3vjGN/KP//iPTR+gmH+SBhfNMLYbbF1HglzFYW+mRK7isK4jwYUbDr+AWOpsFq+xDORAvjLp+R/LQHano03JQMr7QCyUhgOof/iHf0DXdXbv3k00Gq3dfsUVV/C///u/TR3ceLfccgtnnXUWiUSCzs5OLrvsMjZv3jxn93e0GshVuHfTIPc81scPH+/jnsf6uHeTfIITs9OZDHPRxg5eesoKLj15BS89ZQUXbWzO7is5923xmq8MJMj7QCychmugfvrTn/KTn/yElStXTrh9/fr17Nq1q2kDO9ivfvUrrr/+es466yxc1+Wf//mfedGLXsRTTz1FLBabs/s9msi2YzEXxnaDNdvBdTYBAUXLw/F8DE0lbKjYRdnpt1Dmqx/VQtRbCQGzCKCKxeKEzNOYkZERTNNsyqCmcnB262tf+xqdnZ38+c9/5vzzz5+z+z1aSONDMVfmqhP1+Dob1/fZNVxmuGjh+j66qhIL6aSjhpz7toDmox+VnP8nFkrDAdR5553HN77xDW666Sag+unS930++clPctFFFzV9gNPJZrMAtLa2TvszlmVhWVbt61wuN+fjWqqk8aGYC3O5M2qszubRvRkyRZuS45KOhDB0A9vx2D5YYHVrFNudupBZzI+5ykCOkfP/xEJpOID65Cc/ycUXX8yDDz6Ibdu8733v48knn2RkZITf/va3czHGSXzf5+///u8599xzOemkk6b9uVtuuYUbb7xxXsa01EkafHFayueIzfWSsKIonLgiwW+2DrI3U2ZtWxRDU7Fdn6zlsrIlQiKs82Rfns7k5A8G4siwGM//E0eHhgOok046iS1btvDZz36WRCJBoVDg8ssv5/rrr2f58uVzMcZJrr/+ep544gnuv//+GX/uAx/4AO9+97trX+dyOVatWjXXw1uSJA0+Nw4nADpU9mYxB1fztSQc0jU6EiEMTaFoe+QtF11T6UqG6WmtBlSSOT3yLabz/8TRY1aNNFOpFDfccEOzx1KXt7/97dxzzz38+te/nlTIfjDTNOe0LutIImnw5juc5atDZW9O6k6yP2st2qaB87UkbLk+pqHxnNVxKo6P4/sYqkrM1FAUBc8PJHN6lFgM5/+Jo0vDAdRtt91GPB7nVa961YTbv/e971Eqlbj66qubNrjxgiDgHe94B3fffTf33Xcfa9eunZP7OVpJGry5Dmf5aqrsTRAEBAGkIgZbB/I81ZdlRUuEZYt0t+R8LQmPZU4t1ycelszp0W6u662EGK/hq8ott9xCe3v7pNs7Ozv52Mc+1pRBTeX666/nm9/8Jt/61rdIJBLs37+f/fv31zqhi8M3140PjxaH29jv4OxNpmTzWG+WB3aN8ODOUZ7uy/JYb5aQpi7apoHz1Yl6Phs2CiHEeA1noHbv3j1l9qenp4fdu3c3ZVBT+cIXvgDAhRdeOOH22267jWuuuWbO7vdoI2nww3e4y1fjszeZUjUgKjouqXB1S74XgO36PNmXJW7qpKOhKf92Omos2Os4n0vCK1sibB8s8PS+HKvbokQMXTKnQog513AA1dnZyWOPPcaaNWsm3P7oo4/S1tbWrHFNstCfqI8mkgY/PIe7fDWWvSnbLrtGShQdl854tdi6aLkEAbREDWwvYNdIiVTk2cBo7G/3Zko8vNtesBqp+VgSHl9jVrQdBnMWe0ZKpKMh2hMhju1IcPLKlGROhRBzouEA6jWveQ3vfOc7SSQStQaWv/rVr3jXu97FlVde2fQBCrHUHO6OxrHszeO9GYYLNqnwswGSqkDJdmmJhkiENYaLNkXLq9X/VBwPy/F5YMcoru8vaEf5udwZdXCNmalpFCse+7IFRssOpqGBJJ2EWFQW887h2Wg4gLrpppvYuXMnF198Mbpe/XXf93nDG94wpzVQQiwVh7t8NZa92T5YYCCfpac1in/gwrN7uIDlegwVLPwgAEXh2I4Y8bBOEAT05ytYroeuwtr2+GG3DzjcC95cLAkfXGOWLTs8ta+6zHlMe4zRso3t+mwfLDBcsBdFUb0QR7u5bKq7UBoOoEKhEN/5zne46aabePTRR4lEIpx88sn09PTMxfiEWHKasXzVmQxzwYYOdg0XyVZc7ILFQM5C11ROXJFktOSSKzs4ns+mfXkMTaHiVC9Krq6yLBk57PYBzbrgTbUkfDiB2fgaM2DSMmerolC0XY6PJxgqWHIEkRAL7Eg9Z3VWfaAANmzYwIYNG5o5FiGOGM1Yvlq/LM5FGzt5ojdLpmSjUC2YVhSVZNhh62CBuKIzXLTYvL/ARRs7WZYM88cdw4fdPmAuL3iHG5iNrzErWh4jxYnLnIau4loOrh/IEURCLLAj+ZzVugKod7/73dx0003EYrEJnb2n8u///u9NGZgQi92hsiiHu3ylKAond6fYPVzi6X05OhMmQaBguR5Fx+PYjjjHtMeBgIrrcfqqNIqiHHZH+bm84DUjMBtfY+b4Pq7nEwo/uxzquNXDhA1NlSOIhFhgR/I5q3UFUA8//DCO4wDw0EMPTXvRXGrRoxCzVW8W5XB3NHYmw5y1tpXN/TlcD4aLVu2oktUt1aNKKq5HzqoGaV2p8GG3D5irC16zArPxNWbtcRNdU6sHBisKruczUrLpaY0RMzVKljTSFGIhHcnnrNYVQN177721/77vvvvmaixCLAkzZVEGCxVOX9VCMmI0bZdJdzrCCcuT1ayKrmKoKo7nsXu0xEjRpmS5OH7AAztHOOeYtsOuvzrcC950mblmBWbja8wG8xUI4Ol9eYLAp+j4RAyNjoRJtmQzWnKa1m/qSNtBJMR8OJLPWW2oBspxHCKRCI888ggnnXTSXI1JiEVrpixKi2fwp50ZHt+bZU17FFPXmrLLJB01WNkSq2aVEtVdZ0/25avNNU2digMrEhH2HwjsLjyuY1b1V2MBQrbk4HoBZcclbk4OPGa64M2UmfMDmvZJdKzG7P6tQzy8O8NI2UZXFZYnI7QlDIbyFvdmLZ6zOt2URppH4g4iIebDkXzOakMBlGEYrF69Gs+b+ngGIY5002VRqkeo5KnYDhVAVeJoisK2gdxhF12Pz7jsGCowVLApWA7piEG24pAIhziuK04ybDy7DLaxg4s2dtSdMRkfIFiux56REtsHizx3TZp09NkDuWe64B2qvum0VelZfxKdKvvTkTBJRQ2OX57kjJ4W+vMWRdvD83xipoHn+7REQ3QkDu9A8eke17bBPLuGi5y1tpXudEQyUkJM4Ug+Z7XhXXg33HAD//zP/8x//dd/0draOhdjEmLRmmp5KyBg13CZoWIF3wvYn7cIGCUZMWiNhihYpcPeZTKWcfnD9hEe681haApl12d5KkpPW4RUpLrkdfAyWD01SpMDhDAhTeNPO4a5d/MQ5xzTQmciMuMFr576pj0jJVakIzwzWGjok+h02Z+VLRH6MmV6Dpw32NMWo2hVC8sNVQUC8pZ7WMWp0z0u1/fJl1229Od4rHeUde1xVrfGOfuYFpalIrO6LyGOVHPZVHchNRxAffazn2Xbtm2sWLGCnp4eYrHYhO8/9NBDTRucEM3SrPqVqdbzi5bH3tESubKL5fqEDZWOhImqKOzPV9AVhU37s5y+Ot2EgvIWdo0U6UiYhA2NmKmhjGu5PX4ZrJ7HPF2AsCId4YINnTy4a4TN+wvYbkBIn/6CV099U1+2zNlr2xgu2HV/Ep0pq7V9sEDRdmr9oBRFqXVkB/D8gOGSfVjFqVM9rmzZ5vG9OYaKFo4XsGe4guUEPN6X4+E9o7zqzJWcsCI16/sU4kh0JJ6z2nAA9Td/8zdL+gGLo0899Sv1BlhTrefbrsdgvoLrB2gqtMZMooYGioKpq/TnKuweKVFxDn/pO2xopKMGEUObcRksV3Z4qu/QNTszBT4tsRB/cUwb/fkKzz+2nc5keNrnpd7C82TEqPuT6KGyWk/vyzGUtym3usTDjdVq1evgx/VsttGiZLlUXA9NU2iPm4Q0hR3DJb734F7+9oKQZKKEOMiRds5qwwHURz7ykTkYhhBzo56+Q0DdBcJTredXHJ+87aEB6UiIjrgJByZ7RVGIhjRGSk5TAqh6CjLb4yYP7x4lV3HoSJh4PhQshyd6MwwWKlx0XGftcR0q8ImEdHRNJRWdeTmwkZ02LbFQXZ9ED5XVWt0WZSBvsWukxAnLJy8pNqM49eDHVbQ8hgoVHNfH8nxiIR3b9TH0as+ptW1R9mbK/HHHCC87dYV82BTiCFb3R7Niscjf/d3f0d3dTUdHB1deeSWDg4NzOTYhDsvBGYyYqaOpSjWD0RYjW7a5f+sQ924eYPtgnmTYYGU6SjJssH0wz32bBxnIVSb93bH1/HUdCXIVh+GihQqEDZVVrRE0VaFouVQcr7q13naJm/q0QUojjydTcuhMhtBVlR1DBYqWi+cHFC2XncNFUhGDIIBcxaElavDMYImHdo/y1L4c+3MV/rwrw2+3DREEATAxQJhKvVmcscBuIF+p/e3x4x7IV+hOR2vBzNgn0a5UmJZYaMpAo+J4ZMo2ZcejUHEn/d2IodORMImGdHYOFyc/F00oTj34cTmeT8l2KTseEV2jZHvEwwbhA89PyNCIhDT2jJbJlJxZ368QYvGrOwP1wQ9+kP/6r//iqquuIhwO8+1vf5u3vOUt3H333XM5PiFm7VAZjI64yYO7RulMmBw/LoNRT2PH8ev5AzkLz4c9w0W2DRYI/ABQgABFrS7vrOuIHlYAdfAypOX4WK5Hb6aEaWi1ZbCVLZEDR7moPNGbp2g7pCMhDN3AcX0G8hV+vWWQE1ek2NCVaNoW42bvtBnIVXhgZ7X+6hm1SNTUaY2F6GmNko5WM2EVx6M1FuLstW3sHS3PSXHqwY8rZuoEAZQsF1dXiRg6nePeH45bzeb5frAkGwMKIepXdwB19913c9ttt/GqV70KgDe84Q2cc845uK6Lrs/6SD0h5syhlqe8oNrZe/2yOIqiEBBUd3F5PoZWLQSfqbHjWBYlHTU4pi/KY3szVBwPRVEwtABQIYBCxWV5KjzrpaTpliH78xUMVZ2wjb4/Z2G7PqNFh6LtsCwRri0nmoZGdyrClsECj+/N1h53swKfZu20GXu8mbLNilSE4WKFiK6yP1chX3Y5qTtJKmLUgrv1y+KsXxafs+LUCY9rtISqqBQPBG/dLdFnlyyDgEzZpiVqkj7QSFUIceSqO/LZu3cv5557bu3rM844A8Mw6OvrY/Xq1XMyOCEOx6HqcgoVBwKImwbZss2u4TLDRQvXr56l1hIJYehKXQfvPtWXZ7hgAVTPYNN1OpImmqJQtj0OWn2q20yF1GsPZMkGchYnrqgGOaau4ng+u0eKxE2diutXl5cO/J7tB7REDIaKlVpg2Mwtxoe702b8413bFqMtFuLxvT7ZikMqbJApO2zpz9MeD5GOmROCu7ksTh3/uE5bneZbf9xNf66CpoAfBDiuT6ZsEw3pREIa3S3RJdkYUAhRv7oDKN/3MYyJFwRd16Wppli0Zlqe8gOf/dkKkZDOYK7CvlyFku1OWO7aM1pEVRRyZYeu1NRBxECuwo8e38+2wQLpqEHmwPEhgwWLgXy1E/Zz17ZRtGfXj6jR40+qOwJttvTnaY2GMAyVRNigI24SDWlkKw7LktU6rfGBYTO3GB/OTpuDH28qEuLklclacOsFAX3ZCietSHPOutaGd1EejrHH1RIL8XpV4XsP7mVvpkwkpBE2NFqi5oHgKbJkGwMKIepXdwAVBAEXX3zxhOW6UqnEy172MkKhZy+W0gdKLBbTLU8N5Ms83pvHdlx84KdP9RMJaWxYFsc8sNwX0lVCuoqqKOwZKdWWu8Yby5bsHS1RsBwMTWVFOkoAWI7HcMliIGdx2iqFgu3NqiamkXPpBnIVfrVlCE2FWFjHDQJCKIwULXIlh1TEoC1u0pk0URQmLTEthi3GUz3eahBlULQ8Ko7HYN7irLUtteBpIY5ZOWFFir+9IMQfd4ywZ7SM7wekIwbdLXK8ixBHi7oDqA9/+MOTbvubv/mbpg5GiGYbW556fG+WbQN59oyW6c2USZgaZ/S04gfQN1omU7bZPlDkmM4YmqIwWLQwVY21HbFp66AyJYfe0RKOF+AF0GLqhPTqxG/qGhFDY7Bo8WRfnvWd8VnVxNTbHiCkKTyyJ0u2bHPmmhY8Hzb1ZwmCABWFvO2SjBqcsDxBttK8A3abbbrHq6AQN3UUqpnFsQCrnjYVcxXMLEtFeNmpK46oxoBCiPodVgAlxFKRLdtsGyiwqT9H2fZZkQqza6REdzrCinQE1/PZl6/w9L48YaMa6KTCsHu4jI/Pc3paJwVQluuTKTvYnkdn3KRguxiaWptANU0lpKrsHily5pqWhgOWIAgIgoBoSGfXcJGNyxOoijrh+2OF1EBt6UtVVE5YkcD1fEbLFvGQAQrYjse+XIXl6cW7xNTIrsB6jo853CN0DmUxZO0WwnwsmQqx2Mn2OTHBkXZhHMhV+J9H+3hodwbf94mbBm0xlbLj81hvhuGCjX5gx10kpPPMcJHWmEF3KkJI1yhYDgN5hwd2jNCZMCdkM0xdRT1QS7Q8FWHPSIlsxSFq6BiqQtnxsDyftB7imI7JS4CHGvfYstRw0WLXUIm9mQondyemPJfO9oIJS1+pSIhTVqVqtUO251G0PVamo5y3oX3RLjE1sitwtGg3VB8mmmMhlkyFWIwkgBI1R9qFMQgCHu/Nsrm/QMRQiJth8rZLNKQTDUG2ZDNUrJCOhMiUA0oHGl8uS4QJh3QIAsqOx/rOBI7v80RvjgvjIbIHzrwLaQor02Ee35uhLQqr26IMFizyFYeS7VOyfboSJievTNKdrv9Yj4OXpToTYVqjJk/2ZXloV4Y17Q6tsdCEXXKjRXvS0tf42qFs2abi+Fy0sYPWuDlXT3lT1LsrsJH6MNEcC7lkKsRiIwGUABbXhbFZWbBMyWHbQAE/CGiJmgSApii4XoChq8TCRq2Vge9D72gZXVUoOz5eYB8IRgzWtEfRVZVN+3MUKi7Zil0LMGMhjVTEYMdwibVtUVanI+Rtg9Giw8pWlaRp0JWK1pbjDvU4Zjrctytp8vT+PCvTUS7a2DGhg/d0S18KCrGQxmDeZ31nYslkYurZFdjI8THi8C2GJVMhFhMJoMSiujA2Iws2FoDtGS0xUrRRqAZMqgKJsMFo0SKthdBUBQUFVVVoixk8ssdFU2Hz/hyGrtKVDHP88giaqjKQr/Dongxlx+XYjsSEZaXlBw6N3ZMpEzU0IoZOOqqTLXvYjs2e0SI/fNyt63FkSg57R4vEQjqZsoOhqsRMDUVRUFWVNW0xchUHRVEmvBbN7gS+GByqvqhZXdRFfRptqSHEkU4CKLFoLozNyIKND8AyZZudQwUKlktIV2mJmnQmTMpW9Yy1kKbhE2C5HlsHSkRDGsd0xGmJhQj8gNGSzW+3DZMM6+zLlcmWXVa1RnF9H03VJwSYZ69tRUVh21CebMllqGCTCOuc1J1mWTJc9+PozZR5al8OFRUv8NE1dcIRJjMtSzWzIeZScCQGjYuZLJkKMVFdAdR//Md/1P0H3/nOd856MGJhzOWFsd7luLEsWKZk0ZEIY3s+QQAxU2NNW31ZsIMDsI64Sa7k8sDOYTbty3PKSpWYadDTHqU/V6EvU8YPIKQpdCZM1nXEyNsuCVOnZHuUbI+hgo3n+1Qcn+VJk0zJ4vG9ASevTJKKhMYFmGW60xHipsHu4TKW67MuESYa0p49wPgQ2byBXIUHdoxUDwxOmKRNE9vzJxxhYmjqhGWpg5/fjoTJRRs7jqiNADNpNGg80jZJzCdZMhVioroCqE9/+tMTvh4cHKRUKpFOpwHIZDJEo1E6OzslgFqCZrowBkHAcMGiYnuUbbeuOp4xjSzHZUoOm/ZnyZQcdo2Ucb2J2ZdDZcGmW4Y8ritBpmTzRF+Wp/flOWFFAlCqO+/iJmvaY5i6yrqOOK5f/RsD+QrZskvF9WiNGQwVbAxNZVVLjERYpz9fYddwmZNXGigoWK7HU/tylB2X5akIsbBePZeuYFGwPE7qTpKOhmbM5o2N3/GrtUr7c2WS4Wq/I1NXGShY7BoukQhrHNuZJB01pn1+T1yRqPWjOhrU20X9SNskcThmE0jKkqkQE9UVQO3YsaP239/61rf4/Oc/z1e+8hWOO+44ADZv3syb3/xm3vrWt87NKMWcmu7CmCnZ7BousWUgT0s0xP1bh9gxVKprwml0Oa66dJUnrKukoyFCYWNC9uWE5Ulsz582CzbVMmQQBOiqysblSYIAdo4U2TVcJqQrtMVMLljfwYauBH/cMUwkpKOp1SWhzfvz7BwuoSoKvgrxsHbgXLzqmXLpSIjhokXR8oiZGtsGijiezzHt1SDMDwLi4RAJqAY+IyVSkeoENV02b2z8yxJh2mI++YpLf75y4GgZlYiusmUgzznHtHFSd5LBvDXl8/vo3gy/2TpIRyKEaWhHTZBwqHqpxbRJYqHNNpCUJVMhJmq4BuqDH/wg3//+92vBE8Bxxx3Hpz/9aV75yldy1VVXNXWAYu5NdWG0HJ+H94wykLfoTJqcviqNqWtsH8wzWKhw+qoWkgdOnD/402ujRelBEPDMYAHX80knzNpSYlh9NvuybTDPypbItMsDBy9DZko2u0aqReSu52PoKitbIrzw+C7WdyboSFTPNMuUnAnZt3Q0xPplcQYLFZLhEEEQ4Po+8QMH2Zq6iqGruJaD4/kUKgG7R4r0tEaJmzpF20NXVRzXxzQ0UmGDkaJN0fKIh/VplznGj19T9Qnnv7mWg6ootERDnLWmhY6Eyb2bBic9v67vkyna7M2UMTSF56yOY7n+URkkjLeYNkkstMMNJI+2OjshZtJwALVv3z5c1510u+d59Pf3N2VQYv6NvzDuHS3y1L4cmZLDCcuTrGmPkopUP923eAZ/2pnh8b1Z1rRHMXVt0qfXRovSMyWHbMlhdWuM0ZJVDYLGtuErCilTZ9dIiZNWpKddHhi/DOl41Z5NRcclFTYIhQ0KlsNoyWcwb/Hcta21XkhTZd9CmkbMNAhpCtmKy/JUlFWtEZ7szTFQsIgcOCPP8Xz2jFgYmsqxHQkURSFmarTFTPZlSyzTw4Q0lXzFwfH9GZc5Dl5GHd/DyfF8bNcjX3ExVI2dQ0V6Rw/KthGwa7hMyXFZ2xalaHtUHJ94+OgLEg62WDZJLLRmBZLNPHhaiKWs4Wq/iy++mLe+9a0TDg3+85//zN/93d/xwhe+sKmDE/OrMxnmoo0dnLe+g1WtMc7f0MEpq1K14ClbtnmiN4/luFiOR0vUJBk22D6Y577NgwzkKkB9Renjl+Ms18f2fY7tjBELGfTnK1iOhx8EWI7HaLlag3RMZ3Tai/RYINSfK7NruETRcemMV7NZqsKkhphBEADPZt9SkRA7h4sULZewUe3vtGO4RDSk09MWoeXA8kRXIkx/3iIIwPMCju2McfzyBOaB418UFHraIrXHUbAcNKWakdo5XJx2mWNs/AP5yrNjq53/FvDo3hx7R8v8ZusAdz60lwd2jTBctAmo/mzR8hguWqQjIUK6huv5OL5fe4zjg4SjTaPvxzFBEDBatNmfrTBatGuvy2w1++81qpFA8lDGlky7UuEJ/ciEOJo0nIH66le/ytVXX82ZZ56JYVQ/RbuuyyWXXMKXv/zlpg9QzC9FUYiEdMKGSlvMRGFihqNoO3SnIgyXbPwD57S1x02eGSrwh+0aLz21q+HdOqauYqjVBper2yLsy6oULQfPqtYwtUargZChqowW7Sk/7Y4FQruGi2wZyLAsUW2caTvVlgXjG2IenG04eFnCcj1MXaUtFsLUVTRVwfMDDE0lEdY455g2zlrTQnc6Siqic9/moQkZrGr2KMnOoRJbB/Kkowau78+4zDFdfclAvswfnhlFIWDNigSZksuu0RJb9ufpGy1z6qo0J6xI4gfVJTxDN7DdagG+oT77+eho3mI+m91jzS44XwwF7NKGQIjmajiA6ujo4Ec/+hFbtmxh06ZNAGzcuJENGzY0fXBiYUw14YzPcNh+gK6plCyP3QfqjEqWy46hEigBZ69tbWi3ju16DBdtNu3Pkwob6LpKzNTpSobRVOXADjeP+7cNEdKnn3g6k2HOWtvC5v48jh8wXLTQVZXlqSg9bRFSkRCeH0w5SYwtS2ztL/B4bxbPB01VGCrYPLhzlI6ESWssxLGdyUn3PVXgo6sqiYjOOce0ceaaVrrTkUMuc0yqLylY7BgqEjc1jlsWZ/dIhaLtsCxu4nb47B4ps2l/DtcPOKYjjq6q2I5H1nLpSoaJmc9OlItli/lCtBFodPdYswvOF0sBe6OBpLR8EGJms26kuWHDBgmajlBTTTiO5+P6PrqmM1S0SZg6zwwVKDkeqbBBwqxu7982UKTi+HXv1hnIVfjVliFAoTUawvY8wko10zSYtyhUHExD5+RjkrVDdGeaeLrTUU5cnkTTFAxNxdAOdPI+kEmbKZAYzFs8sidDtmzTlayOuWy77BqpLuWdvaaV9oSJ7QUTMmHTFdYeO4vC2vH1JQO5Cq7v05kw2TFUzf4tS4RBUehKRihYHoWKS3+uQtzUiRoazwwVWdkSoaf12eXOxbLFfKGyMI3sHptNndBMgcZiKmBvJJBcDBkzIRa7ugKod7/73XX/wX//93+f9WDE4jDVhKMqCp4PfdkK6XB1Ei45Hp3x6oXfcjyiIZ217VGGChb7sxYXbGjnyb78tLt1xk8uJ61IkmuN1HaeKUrA7uESUVPjkhPbaIlVL9pjE8+OoQJ/2D7CmWvSWG51WSJsaKQiOt0tByaJtukniVREZ7Ro1ya9VESfcqKLhw1OWJ7k8d4sP3xiH+0xE9ufPKE0Ulh7qE/2Y/Ul1oGlOD9Qatk/xk3Ax7TFeGaogKYoPDNYYF1ngtWtURJhHUNT8fxg0WwxX+gsTL27xxotOD9UoLGYCtjrDSSna5FxtO/mFOJgdQVQDz/8cF1/TNK7R46DJxzb9QnrKpoCa9tjbB0skAofmPiDgEzZZnkqSjysoyoKvZkSp69Oz9gV++DJZfzOs2zJwXICQrqKoU18m2bLDkMFmwd2jnLv5n4cPyBuaqxujbKxK0VXymSwUOHpfTlSUYO4aaCp1QxTKhqiK2Vy3+ahCZNeKmKwd7REdzoKQKHi4vg+hqrieNXde6Mlm2UbwqxMRqecUA7Viwgay8KMLbkULKdW3zSeoausao2ysSvJcNHir05aQTqqzxi0LoTFkoWpJ8htpE6onqDQD1hUdUeHCiSna5FxNLZ8EOJQ6gqg7r333rkeh1iEDp5wcmWHh3ePsjdTomRVjzyxxhVp97RFUJjYLHKmoGKqyWps55nj+ZghFXyltpsMqv2dnujNMZi3GCpYGLpKeyxEyfbYM1Km4vikhgw0RWEgb/H0/hwE0BY3OaOnheO6ErXJfPykt22gyDNDBUKqRtZyav2jNFUhV3EJfEhGqpmdeo9mOdjYhJspWcRNg1hIx/cDtg3kpvxkP7bk8kRvBk1Rar2loBqUZCsOXckwybCOokBn0qQlFqplPhZL7cpiy8KM3cdUmcB664RCmsIje7KHDDROW5VadMefzBRIjhbtRfNaCbHYHdZhwnv37gVg5cqVTRmMWHzGTzhdqTDpqMEfto+wY6hEf75CNKRPKNKG+ieFmSYrQ1MJAgWFoLabLAgCdo2UKNoOjuehKArLU2Eihk4iHDBQsChYDtsGCiTCOuesbcVHoVBxyJQdLMdj8/78lJPe2vYoj+0d5ddbB+hMhavtAMIGuYpDb6ZMWFcxjTCG9uxjamRCGcvC7B0tEQRMPK4mGqJglSYFYmNLLoOFCnszFQbyFbpTEWy/GjzFDJ3VLVEGC9aE+qZ6MmHzyXJ9LNfD9XVGSzaGeqAu7cDjXIjdXzMdg1NPnRBQV6Bx2qrUojr+5OCgcVnSnHUGToijXcMBlO/73Hzzzfzbv/0bhUIBgEQiwT/+4z9yww03oKpykOSRrDMZ5qWndoESsG2gyNr26rJdrd1BA5PCTEWt0VC1WaWiVP8bqjsBR4o2pqEyWLTpjJtEDlzoFUUhFdbZNlAkYqhoqoqiqCRNDU1RiIQ0nhkskq84nLW2ddKkFzc1TF1j53CR9Z3x2gSiayoxXSVbcXHiAbHQxPd3vRPK2Fl/g3kLNwhqDT5tz2d/voKuKGzan+X01ekJwU9nMsxFx3US0lR+vWWQLYMFWiIGy5IROpMmmbK94PVNh5IrO+wcKrHZyaOqyoQzDtPR0LxnYQ619FZPnZDtBXUFGrYXLJrjT+pZPpYDg4WoX8MB1A033MBXvvIVPv7xj3PuuecCcP/99/ORj3yESqXCRz/60aYPUiwuqqpyzjFtVByfoYKFeuCMt0YnhUMVtR63LH4gW1P9pF9xPUqWi+V56Go1+wTP3ocfQK7i0JWM4/k+w0WL7UPPLscVKi77cxWO7UwQC+nVLt8H6pwCAiIhjWhIpz9vsVytHtnieT5Ft3oMi6GpFG2fuPns5FHvhFJxqi0fXC9gWfLZrMXYcTX9uQq7R0pUHG/S73Ymw1x2ejcnrkjx+N4sQ8UKmloNLhe6vulQBnIVHt4zWgs4uuPVDNrYGYcnrkiSKdvzloWppx6rng0Qo0W77kCjJRZa8ONP6i3ilwODhahfwwHU17/+db785S/z13/917XbTjnlFLq7u3nb294mAdRRollnYh3q7wC172XKNo4f0B4LE9b1CctpQC34UFWFiuOzdaCANy7bo2sK2wbz1WW6ZBjPC1AVMPRqEGO5HmvaIrQnIpRsF9dy0BSFlekofhCga+B4z2aaGplQKo5HwfJonaYJaDSkMVJypgygxn5mQ1eC9cvii6q+aSZjwUqu7PDcNWme6M0zeGA3YXssRF+2woO7RnhOT8u8ZWHqrcc61AaIRgONhTz+pNEi/sWSMVvqpI/Wka/hAGpkZISNGzdOun3jxo2MjIw0ZVBi8Tr4onDhce1ky+5hXSQONbmMfa/ieDywc6SWvdifK7NMr/ZFCoKAou2RMDXyFYcABU1l4nlxPgQBPNmbY+dQka5kmJaYSVpTGSrY7B4pcfqqNGetSVOyfRzPx9Cqu/Ae2jXKaMnG8fxZtQcIG9qBw4ZdkmGj1o7gwJNK0XaJm/q0S0JjFlt900zGBysxU+fklcq4A5L9Wp+u01e1zEkWZqoJrJEan5me69kEGgv12jVaxC8HBh8+6aN1dGg4gDr11FP57Gc/y3/8x39MuP2zn/0sp556atMGJhafmS4KXanDuygcarIa+945x7Rx3+ZBipaHrqrsy5WJhXRKtkfUUPEjJpbj1g7kHZswihWXLf15QCFsqBCApqpkSjYFyyUR1ogaOtmyCwHExy3LBEFAR8KkM2nieQF7M6WGJ5SwUW2zsGekWnyfjoQw9OoZeZmyTUjXWNUSPWQAtZQcHKwcfECyqihkSjbJSPOXg6Z7r65siTStxmepBBqzKQyXA4Nnb6F7non503AA9clPfpJLL72Un//85/zFX/wFAL///e/Zs2cPP/rRj5o+QNFcs00rz+dFYaYxjp+0Nu3PVY+SKTnETY1VrVGWpyLsGCry1L4crbEQfhBgux5bB/O4QUBrLERINxkq2Li+j6Yp5CsuqYjBhRva2TZY5On91Sac4zMKK1ujXLC+nZCuTWi+mS277M9WDvlcpqMGG7uSWK6P7weMlGxcy0FXVbqSEVRVYWNX8oiqLZmqIHmsTQVA0XIJ6c0vSJ7pvTqYr3ZtH8hXmlLjsxQCjdkWhi+lbOdisVh6non50XAAdcEFF7BlyxY+97nP1c7Cu/zyy3nb297GihUrmj5A0TyzTSvP50WhnjGOTVqnr05TcTwqjlfrRJ6OGmztLzBStMlWXDTbxfMCdFVhRSrCYMECqo0zl6fCmLqGpoEfKHSlo7gBrExHyVWcGTMK1Ul6qO7ncvyST6Zsszxd7e7uBwEFyyV9mLUli7HeYiEKkut5r7bFTJIRo2k1Pos90JDC8PmzmHqeibk3qz5QK1askGLxJeZwMkjzdVFoZIwzTVrrl8W5aGMnT/Zm6UqF6c9Z7M9XGC3Z9GfLVNyAsK7iej5RUydm6uhKtV9UayzERRs7qsfTTBOMzPa5PHjJp+x5sz4z7+DnbTHWW4zvYzVdV/hmFyTX817NVRzOXtvG3tHyol56axYpDJ8/0kfr6FJ3ADU0NESxWKSnp6d225NPPsmnPvUpisUil112Ga997WvnZJDi8BxuBmk+LgrNzHIpisLJ3SmGCza9mTJ7RkoUKi4hVcHxgurSnaJScXyiJgwXLEBhb6bMXxzTTkssNO19HO44m73ksxTqLQxVnbIr/LnHtjd9bPW+V5MRg4uW0I7Gek2XiVwq9VpLnfTROrrUHUC94x3vYMWKFfzbv/0bAAMDA5x33nmsWLGCdevWcc011+B5Hq9//evnbLBidg43gzQfF4VmZ7k6k2Eu2NDOt/60m6LlEA/p9GUrhA2NWEhFUTUs16NQdtG06sTreD4nrkhMuv/xk1LZdukdPbxxNmvJZy6XVpuxJDg+uDuzpwUvoNYV3p6jT+CNvFcX+9Jbow6ViWxW8L4Yl4sXC1kuPbrUHUD94Q9/4Gtf+1rt62984xu0trbyyCOPoOs6n/rUp/jc5z4nAdQidLgZpPm4KMw0xoAA1w8YKtgM5Cp1X7BDukZbLMQFGzrZPVJkf66C7fkomorjuHge5Cou3ekIJ3en6EyECekT7//gSalie+wZLfOc1ekpJ+j5TNFPF3QGQUDRqgYKWweq57G1xs26/24zlgSnC+5SEYMV6WDOimmP1gms3kzk4QaNs3lvHE0BlyyXHl3qDqD279/PmjVral//8pe/5PLLL0fXq3/ir//6r7nllluaPkBx+A43gzQfF4Xpxpgt2+waLtOXLVO0HKIhld5Mpa7J3HJ9HD8gbCgMFxxCukLMNKtn0KkKFcVHVVTOXNPC6atb6M2UJwQ+U01KwwWLx3qzPLw7w5lrWmrn/9X7XDbTVEFnpmSza6TESNHGdnwKtkN7PMx56+tbLmvWkuBCFdMu1AS2kEHCfG3ymM17Y7HW580lWS49etR9lU8mk2QymdrXf/rTnzj77LNrXyuKgmVZTR3cVD73uc+xZs0awuEwZ599Nn/605/m/D6XurFP5QP5CkEQTPje2Kfy7nR0xk/lYxeFdR0JchWHvZkSuYrDuo4EF26YeOEMgoDRos3+bIXRoj3pPusdY7Zs8/jeHH2ZIo7ns649wbJEmO2Dee7bPMhArjLj3zR1lZCqsm2giOW5dMTDdMZDtMfDdCTCrGqNkI7o+L4yKfA5eFKKmTqaqtCRMNnQmWAgb7FzqETAs4+t3ueyWUxdxVAVhgsWoyWb3tESj+/Nsj9XIWJoJCM6MdOgd7RU1/M13WOOmTpr2mJkyzZP9OZmfD3HXvs9o9XgyDSmvsSEDQ3b8+ckU9fIe7UZBnIV7t00yD2P9fHDx/u457E+7t106Oe7WRoJVmdrNu+NsYBr+2CeZNhgZTpKMmzU/f/vUtaZDHPRxg5eesoKLj15BS89ZQUXbVz4ekTRXHVnoM455xz+4z/+g//8z//krrvuIp/P84IXvKD2/S1btrBq1ao5GeSY73znO7z73e/m1ltv5eyzz+Yzn/kMl1xyCZs3b6azs3NO73spa9an8npqKGb7ifPgMXYkTHYMlhgtWYR0lZaIwZr2KPGwQczU6/pUnY4apKIGv90+RHcqTMny2ZstoxAQBGC5AemIzmC+xO5hjZO607XAZ7pJSVEUetqiDOYrPNmXJWpqdCbCaAoMFuZmZ9l0bNdjuGizaX+epKkzkLcoux49rVGCICBbtlmRjrFxeYJdw6VDPl+HmzUa/9pnyjab+wvkKy7HdcXnPVM3X/2ZFkMR/3xs8mj0vSH9kBZ/ewtx+Oq+et1000384Ac/IBKJcMUVV/C+972PlpaW2vfvuOMOLrjggjkZ5Jh///d/581vfjPXXnstJ5xwArfeeivRaJSvfvWrc3q/R4JmfSofuyh0pcKTdqvV+4lzugzV+DH25yy2D+YJaSpdqQgndSdJR0O1MYy/YAdBwHC+wgPPjPDbrYM8M5DH96tHcRzTEUfXVPbnbPIVF9v1qDgBrhcQ1qt9mDYPFPECJgQ+4yelgGqvptEDXcsDIGLoZMsOf9oxws+f3s+Du0Zpi5lzkuGYykCuwq+2DAEKrdEQo2WHvkyJvtEyf3xmmN9uH2Kk6NAS1VEVta4sRD0T8XRZo4Nf+/UdCVakwmwfLPDYnizZsl372fnK1M30Xm2GZmTsmmH88vdUZgpW680WN/remI+smBALre4M1CmnnMLTTz/Nb3/7W7q6uiYs3wFceeWVnHDCCU0f4Bjbtvnzn//MBz7wgdptqqrywhe+kN///vdT/o5lWROWFXO53JyNbymYy0/l9X7iPDEIaqfcz7RTqDtdPdB3dWuMZFifNMaxT9W9mRL//fAo920ZYCBv4fsBiYjBaSvTvPw53XSnIxzfFefxvVls36ctalKyPQIgamoYqoLq+rTHQ3Qkni20HpuUBvJlBvNO9fw238dxA7JlB0ODte0xTlmZIgCyJQfHn90n/EbrZ8Y/1yetSLI3orN9oMBwyUFTwPOrjUHzpsPT+/IkwgaJsHHILMRsa+Wme+03LEtguT57R8uYhsZzetJYjn/EFNMulqaJsy2cbyRb3Oh7Q/ohiaNBQ40029vb+Zu/+Zspv3fppZc2ZUDTGRoawvM8li1bNuH2ZcuW1TqiH+yWW27hxhtvnNNxLTVzlVauZzLZtD/HruEiru8fcqdQZ9KkPW6iq8qUk2zF8bAcnx880scfnhnGC6A9HoIARsoO920ZZF+2zN9duI5VLXEe3ZvlxK4Euq6hKqAAnh8wUrJYlohAwISJLh01iJs6P396gIih0BI10TWdZwYLDOQr1cmmJcrydAQFhRWp2e0sm82S5/jnGgX2jlZ3FyZMnYihoakKnh+gKArPDBeIh3VO6U4dcslsthPxdK99Ohri5O4Upq7Sly2T6NdJR40jpph2sQQJs1mib3TpsdH3hvRDEkeDI/rd+4EPfIBsNlv7t2fPnoUe0hHrUJOJaagHzq2z6lruOFThe3++Qtl2eWpfFoBl8RAl22e4aON5AY7n8mRfjrse6mVNe7U9Qdn1MFSFsKGhKgpF26U1Fmb9sji2P3lpKghAoRqIBIDl+OQrLlFDRVVgXP34rJYlZltkO/65LlRctg/mMQ2VjoSJooBpaChq9cw5TVHZPlBg53DxkEtmYxNxKhJi53CRQsUhW3boHS3x1L4cyYgxZdZoptc+HQ3xnNUtHNcV54LjOhZdMe1sNjyMOZyls2ZrdJNHo0uPB783ipaL5wcULZedw8VJQVozNq4IsdjN6iiXhdDe3o6mafT390+4vb+/n66uril/xzRNTLP+/jdi9g71iXOkWK0fOqU7Vddyx0yfqvvzFVwvYKhQIVtyiId1hoo2jhdgGtUMk64qjJZs/vDMMKeuTLOqJULR8ijaLp4VoKsqy1NRetoi6KpaWz4bkyk5FG2X565tY7BgMVK0yZUdKq5PVypMOhKi4voULa92OG4jGYfDKbId/1xnyw65iktbLEQA2G5AoeKgKQqqqhAxVPZlKyiqUvdGgQuP6+D+rUM8uGuU4aJV6x6+ujU6q9fecn3SkRCrWqKLqqj2cLfYL7aeU/Uu0c926bGR7fnSD0kcDZZMABUKhTjjjDP4xS9+wWWXXQaA7/v84he/4O1vf/vCDk4ccjLZn7WImxqt00ygUwUfU12wLcfHcj1sL+CZoSLDRYeS5aGqCsnIs5NFSFcxdYWhgsWPHu8jFTXIllxWtkZYno7QFjOJmRoEsHO4OGmiG8uqrExHWZ4KU7Q8MmW7OilFDEKGxnDRwvGeHW8jGYfDqZ8Z/1zr6ljbhWrmqSMRoi/roylQcVw8vxqUndXT2lDWx/F9OhMm65fFJ5xfd9/mwcNe3lkMmrF7bjEGCfUs0R/O0mMjdZTSD0kc6ZZMAAXw7ne/m6uvvpozzzyT5z73uXzmM5+hWCxy7bXXLvTQjnqHmkxaYwZho3pAr65NDjCmCz7GX7B7MyUe2DGKpgQkTJV4yICgRKbsETV1Il5ASFdqf69Q8VBUAIWNy1JsGcizY6hE0fJIrNZRYNqJ7uCsSjysEzM1hos2+3MVUoCuqhjas32jGgkUDmcSG/9c92XLRA2NbNkmrVSzYsuTYbpSEUKaQn+uQlvcZGNXYsr7ObiAPRXReaI3R67scPzyic9JLDR1+4jFGEjMpJlb7JdikNCMxrr1ZhLnq52EEAuhrgCqkd1ryWRy1oM5lCuuuILBwUE+9KEPsX//fk477TT+93//d1JhuVgYM00mJ65I8GRfflZZCkVRSEcNHt5tM1qyCQIYLlWouB62H1BxPQwVSraKoY31cbKx/YBjWmMkwjrxiM4ZPS3sGi6xZSDPQ7sznLg8Oe1EN11WpT1msj9TZstAnuOWJQkbKkXLbThQONxJbOy5fnxvlj0j1Rolx7dYlgizPBXB0BRGSxaqqnJGT8sh+zaNLWGlIgZ7R0t0p6Nztryz0Obi3MWlFCTMd8ZQ+iGJI1VdAVQ6na77YuB5UxdUNsvb3/52WbJbxGaaTBRFqStLMdW2/kzJYdP+LIN5CzcISIUN1i9LkC87bB0okrM8fGxCmkLR9inaHq2xEKtbo4QMDUNViYd1UhGD5akwoyWb569vZ017bMr39sFZFVPXGMhZ9OfK7M9VcDyfkZLN5v15WmOhhgOFZkxinckwLzjeZFVrlB8+3seu4RK6qlB0HAJbQVU1nrM6zrnHtk96jNMtYW0bKPLMUIH2WJjYFOWDzVreWUhzsXtuvoOEwzk6ZqllDIVYrOoKoO69997af+/cuZP3v//9XHPNNfzFX/wFAL///e/5+te/LmfhCWD6yaSeLMV0hb0dCYPdIyVcL2BZcixzoHHGmlZcP2D3SIm85aEqNhFDpzNhcurKNKgKrbFQtd7pwNja4iYlx8V2A/pz1rQT0PiC6t9sHSRvuaSjIU5d2UJn0mC05BIN6Zy9to31y+INTzgrWyJsHyzw1L4cPa1RIiG94UlMURQ2dCVIR3t4vDfLtoECJdsjGtI4tjNePST5oKBupiWste1RtvTn2DaY58xoCyXbx/F9DFUlGlIZLthUbI+y7RIEwZQZnMWebVjqW+ybcb7cUsoYCrFYKUGDbXIvvvhi3vSmN/Ga17xmwu3f+ta3+NKXvsR9993XzPE1VS6XI5VKkc1m53SpUcxsbOv4YN4GAjoSJi2xUK1I+eCsyEC+gu36PLInw/JUmORBx4IULZfH9owyVHB4/vo2VrZE2DZQwA2gJRKa0MUcoC9TZkt/nlWtUXRNmXECCoKAX24a4MneLF2pajuEmKmhUM2UjRWgX7SxY8pgZ6pMwWDeqk1cI0WbwXy12Wt7IkRbzJz1Yav1ZiVGizb3PNZH8sCxOBP/hs9vtg6zdSDPuo44fhDg+QGOF2C7PgXLoSsV4cTlSbpbluahsEEQcO+mavuI8QHk2PcO9ZoupOkyhwP5CqlIqOGjYxbyEGQhGrEY5++Gi8h///vfc+utt066/cwzz+RNb3pTUwYljmzjA4ixT9Ar0hGyJWfawt7H9o5iudU2BMmwAROKmzWWpSPEwjorUhFCukYkpON4PieuSJKKGBQqLo7vU7RcHtgxTCwcYlnSJGLoU+6+GptYBnIW2wYKrG6LEjefXU4LCCjaHiFdZetAntNWpWiNT1zzmipTEDd1hgs2AQGdiTCdiTA9bS67h0tEDiObBfVnf6ZbwsqWbXYNlxnIl9k5VKQ/V2FNW5SWqMm+bJmhgkVr1GB9R4xkxJjX896aaakuYc3F+XJLIWMoxGLVcAC1atUq/vM//5NPfvKTE27/8pe/POeHCYulb7pP0E/2ZtkxVOT01dXzFccCHkNViZkay1NRNu0vEAQK/fkK6UgIQ1dxXJ9M2SasqXR2xNm4PEF3S5TnH9vOI3sy7BktsXWguiOvZDvsHi2jqQov6WmpBUQzHTczVLDYsj/Puo4EazuipCKhWqAxXLSwPY9C2aU9ZnLehvZaIDHV4yw7Lr/bPkLJcrhgQ2ct+xM3DY5fnmTncJG9o2XWL4vP6Wsw1RJWtmzz+N4cRdvBdgNaYyE6EmFGija7R8uEdY2e1uqkvT9ns6o1SnvcZMdQiT88M8xLT1mOqi7OJa+pLMYlrENlgxbL0TFCiKqGA6hPf/rTvOIVr+DHP/5x7Ty8P/3pT2zdupU777yz6QMUC69Zaf6ZPkF3pcI81ptlx2CRwbzFSMnG9aotD1qjIVqiBpGQRiSk0hKpHp7rWg6OG5Ar2+w/UMu0P1uhPW5yRk8L3S0RNu3Psy9bJhLSUFWFqKERM3V2j1QzDanIxAOKDz5uJhrS6Rsts2e0SMFy6WmLsGu4TNF2SEdC1ToZH/ZmSrUeSR0Jk8d7s+zPlulKhQkA9UA7BV0FTVPZPVqa8DzO1QQ41Wt3cAE7CrXHlAjrbBsosrIlwrGdcTIlhwd3jaIoYHs+tuvzp53D7B4pEjN1AgKeGSpAoHDOusZ6TTUy5rnIBi2movd66poWy9ExQoiqhgOov/qrv2LLli184QtfqJ1B97KXvYy//du/lQzUEagZBatjZvoEHdI1TEPj8d4M3S1ROhImobBBpmzz0J5RrAPLZa5Xrcc5pj2OpsJDuzP0ZsqEDY0NyxJEQxpDBYufP9VPPKKzLBHmlJVduAeOnXhqX472aIjBosWu4TInrzRQqI5l7LiZjkSIU7rTtR2BXekI+7NlChWbh3ZbhA2VZYlqYDRQsOhKRzi+K8GukRJP9OZYmQ5z76YBKq5Pb7aMrqq0xUxSUR0vCGiLhRgp2tUu5uFn/xds9gQ402s3fgkrZuoMFipEDI3+nA1AImJgudXn2nEDbLfa7TxsqAzkLWyvREfcZE1blHzFZetgjorrHfZyXjPfb/UYW8IaC9pm2lQwV+pt6jkXxe9SAyXE7M2qkeaqVav42Mc+1uyxiEWmGd2ax5vpE3Q0VM3kZCsOJ0Z0woZG0XLpz1oEvk/Z9ViWjHDWmhSP7Mny8J5RlIADTTpN1nfGiYerS3KrWjR2jRTZur9AMlw9FFhRFIwDjS9tPyAdCTFctCYcxTLVcTOKotDTGiVfdhnIl9mXrXDCiiQV1ydbcYgZOj2tUVRVrWWwHt+bYX+uQk9rFNPQcFyffdkSA3kVxw1QQuB61d1t4zVz91c9r93YEtbT+3MMFSziIQ3X8/GDgL2jZQZyFvmKgxv4hPXq+YGjpWqA1Z0OU7Y99ucs2hMmx7THGSpYDdfgNDrmuQii5jtoG6+RuqZm929ayMctxJFgVlfq3/zmN7zuda/jec97Hr29vQD813/9F/fff39TBycWzmwOHD2UmQ5fLVk+ilrt7zRadqg4LgM5i4LtAgqt0RCW57JloISqQK5ks2e0hK4p9LRGasETAIpCwjRwA5/hQjXTAxA7cJRMtuKgawqu79eOYpnpuJn0gaLijkSYsuORLbmUHY+uZHjCDr+xDFbedmpZNlVRMA2NZYkwnle9v6GChaaqGONqhpp5wGq9r11HwuSijR381UldHNtRLVxPmBpdyTAq1YvDaMnCcX2KtkvZdsmUbZIRA1PXiBo6g3mLWEgjbuoNH6Y81ZgzJYv2uInt+ZQPtGOo5/0220OBZ3ugc7M0UtfU6IG+M1noxy3EkaDhDNSdd97J61//eq666ioeeughLKu6BTubzfKxj32MH/3oR00fpJh/c1GwOtMnaNvzqDg+J69KEQ8Z7MuW2ZerLs3FQjoVx2FLf4G+TJmWSAhdU7E9j8CCfVkL09AnLGuYhkYQBIyUrGr3cgJcP6A9bpIrO/RlKxiaiqootU7i0x03EwTVw4eXJUy6khFOWJFgRSpabWcw7jGMZbBOXpFkf95if66CqavVn1EU0tEQQ4UKRcs7kIWrtgho9u6vRl+7nrYoIV0lW3bZuCxOwvHZPRzUzh4s2R6KovDMYBFUWJFScLyAou2ia7AsEaZoe1Qcj0zJmTJArmfMm/ZnyZQcdo2Un61/i4XoaY3O+H6bKpOyIhVhVWv0QLA39dLUXOxqa1SjdU3NKH5fDI9biCNBwwHUzTffzK233sob3vAG7rjjjtrt5557LjfffHNTBycWzuEUrE5XVzHT9vF92QoJU+eY9hhdqQjt8RCW6xMzdQZyZfrzNiqwPBlB01RGSzYl26ctqlO0HfZmynSnwuiaiuf59GbKFC2PQrnEz5x9hHSNZMQgETYwNZUgCAjrKpmSTUif/riZTMlm10iJ4YLNQK4MSsDO4RId8TCKotceb8Fy2TZQxNCgNRbCNLTqsl/BIhU2CGkqXhCQtzxOXJGqLgtaLsMlu+m7vxp97bJlF1NX6UyYDBRtUmGD1rjB3kyJku1haCphQyMR0ckUHfZlbToTCqmIjqqq9OcsdgwXKdkujhfwwI5RzlmnNPRYejNlntqXJ6yrpKMhQmED2/PZn6uQL7ucsDyJ7flYrj/h/ZUrOzy8e5Rcxam9n/pzFX761H5s12dNe4zWWGjKpanFsKttNnVNh1v8vhgetxBHgoYDqM2bN3P++edPuj2VSpHJZJoxJrEIzLZg9VB1FdN9gj5pRYrVrVGGChYEkIqGSISrPZMKlofj+rRETUyjmvVJhTUGFIXRso1Wgf5shZGChaEplGwP2/OJHugFlSk7RIyAAAhpGvuzFVoiOi89ZTlr2uPTHjdj6hrbBgpkKw74Aa1xk7ZYiO0DBe7b3M/Zx7Rj6ipP9mXZNVJdWtRUhYf3ZNiwrBoQ7RqpNsvMVxx8P6ArGebSk5ezfll8zop3p3vtAgKKlke2bON6ASGten+W62MaGqevamFPphos7stViIY01rZXm2lGDJXjupLsy1bYmymRMDV0VaHi+oyWKoR1DcvxaY+H2Jctct/m+gvKgyDgmcECrueTTpi1wC+sapi6ykDBYttgnpUtEXJlh6f6Dry/XJ8dQ0Ucz+fMnlZipk6mZPPMYBE/CPCDagF8IqxPWUe1GHa1zbau6XD6Ny2Gxy3EkaDhAKqrq4tt27axZs2aCbfff//9HHPMMc0al1hgs7mw11sEPN0n6LFO5DuHi3Qkqk0u+3JZNAV0TaElVg0yKrZLX87CD2C4aOMGAboCPtW6nZLjEQ3pdKV0EmETU9PIW26tNufkFSlUVaFk+yxLTlymGH9I772bB9ifq5AwNVzA9QJGSnZ111rR4r4t/eQrLgXLIxUx6EqalG2fp/blsFyfk7tTnNKdomh52F41y3bSilStUWYzPt3X06ZAUZRa76qhQoWBvEVXMszDuzOcvDJVC7hMQ+WU7hQDOYuy47KmNYauKjwzVG3hEADd6SgFy2X3aJnWWIhoSCNX8dieL6Fr1cm3YPkU7dK0y0AHjzkIArIlh9WtMUZLVnViH1fEnzJ1do2UWN0anZBtcv2AipvH8Xye7Mtx4ooku0dLFB2XzkQY2/UZLdtAnDVtk5emFsORLgvR1HMxPG4hjgQNB1BvfvObede73sVXv/pVFEWhr6+P3//+97znPe/hgx/84FyMUSyARi/sjdZVKIpSOyTYcn0yJYeOhDkhO6UoAYEPRkglFdbwgoDhgkV/3sL3PDzfx9AUYnp1XPmKixcExAyNRNhAVVTWtMYwDY2K61OyXBzPZ11HHFVVpl2m6EyGOX21wrbBAl2pMHtHy+i+P6F5Z9Fy2Ly/QGs8xBmr0yQjBo4bHFhy8tk+WMDUVZ6zugVFgWzZYXk6wskrU02bDOttUxA2VLb2F8mWbVAUVqSqPZ6eGSowXLS5YEP7swFXW4yQUZ1gk2EDVYFkxCAVMfB9sAOXZclqx3U/8Bkp+qBARyLE8mQEQ1fZn6+gKwqb9mc5fXV6wvM71Zijhs5IyebYzhhP9fmTGqWOlu3q7kkvoOw4tffXaMlGU6EjHmaoaLOlP0/RdkmFq4G2oavVXmGej6Lok5ammr2rbbbmu6nnYnncQix1DQdQ73//+/F9n4svvphSqcT555+PaZq85z3v4R3veMdcjFEskEYu7GN1FR0Jk6Lt4Xg+hqbWCq0Pnrxmmvwv2thx4BiVChFDZ6RY4dHeLH37i9hedWkmpKs4XkDCNIiYGlFDw/ECyq5LW8wkGtIoWC4+1WAwYmiYmspw0cINApKGPuMyhe0F6KpCvuzj+T7LEuFaViSkq/h+NdPVY+oHduEpmAb0tEarv+/69GXKJCI66Uho2slwtn146m1T8Hhvlns3VTNpnYkwbfFqUXY6Gqqd+/ZkX54TVySe7QsV0tEUlYLlUHY82uMmJ61IomsqzoGdhMMFi6f351ACha5U9f7Hxm3qKv25CrtHShMKyqcb887hIjuHirTGDE5emax1eXct50APrfCB19ujKxmp3Y+hqehqtTdYKmwwVLAIqLaoAHBcH/1A6wqYvDS1mI50mc+mnovpcR8NpNfWkavhAEpRFG644Qbe+973sm3bNgqFAieccALx+NwePyEWRr0Xdsv1GSna9GctRss2ru/XGkj2tEWIm0Zt8qp3qS8dNThmX45nBgvEQxpDAdWWBArkKm61/WU4oDUeoiMZxvV9ekd9wgcyF64XULGry3lQ7aSta9X2AYdapjB1FdcP2J+v0BIJTTh7r+L6jJYdIno1oKg44+pJFIWuZJiC5dISM7hgQyerWqJTPmez7cNTd7ZvYwenr0qzbSDP+s44qUhows7B8YHt6avTtWB572gRD5/9GYuelihr2mOkosaEA5Tb4iYKCm3xEJEDGT7PD9BUhbCuEg1pjIzbkTfTmI/vStCXKfN4b54XHt/BySuTFK1qEK6rCoMFi7aY+ezy3gExU6MtZrIvW6IjZhIceEy25xNWVDJlm+UHdkvC9AXZi+VIl/k8l24xPe4jmfTaOrI1HEBdd911/J//839IJBKccMIJtduLxSLveMc7+OpXv9rUAYqFV8+FPVd22DlULd7tTIQxdKPWQDJXdljXGSWkqYQ0hUf2ZOta6gMIgmpzzX1Zq1oErlcncccDz4eyU20JYOoaIV/B0FTKjo+uVv9mwXZoDUIEVP9OVzJMNKSya6Q04zJFOmrQHjd5YOcIHfGJj931fCqORzxiEBxoRTCeoatUih4xM1oLnqar95pN08hGdlHZXoCuqXSlImjq5E+947MyXalqsLy1P4LnQ748wp7RMnnbYzBnsSwVouL4pKIhju9K8NjeLENFm5GiQ95yagFUwjTwCUiGjVrAM9OYVVXlxBUpHt49yqZ9eXraYkQMDYVqo9R0NMTJ3Sn+uGN4Qt2OgkJPW7WwvDdbxtBUUpEQgwULQ4V4OERPW6QW+E23NLWYjnSZrdlkOY6Ex72YLVRjWDF/Gg6gvv71r/Pxj3+cRCIx4fZyucw3vvENCaCOQkEQsGekVF3aOrC8pow1kNTD7M+VebzX45ITlwHUPfkD5Cs2erWNEjFTx3J9fB90VUHRwPED9mXKdMRDOD6kIsaB/k8Op65MEtZ19oyWQFFIhQ064ia7Rkp1LVP0tFaDjl3DRVakwvgoVByP0ZJDzNQxNJWS7XFwXGI7HmXbY1VLBNv1uHdTZlKPolzFmXUfnkZ2UTVaMDyYt3hkTwbX9znnmDYGchb9uTKb+nP0ZnTO39DBuce2Y2gqrTGDHQN5ym5AW9wgYupYrsfukRIRQ2FNW7Q2xkONeVkyTE97lO6WKLmKMykj0pEw2TtanlS3k4qEOKk7wZ92eoQ0hXhYZaQIqqqxriNK3DRqfb5mes3nM/vTbIeT5VjKj3sxk15bR4e6A6hcrtoFOAgC8vk84fCz/2N6nsePfvQjOjs752SQYnHLlBz6smVOXJHimcHihN5Htufj+KD6Hqtaothe0NAW6v68RcHy6UqGyZYdIoZGoAUEgULFC/ACn+Giw0C+gqFXi8dLtsvq1ihr2mK4Pozlh1rjBorCIZcpxiakvaNFwrrK9sEiu4dLKGo1w5UwNVJhg6zl0Bo1yFnVLtEhTcV2PXYMV3eMreuI8astQ5M+gT7Rl2XnUJHn9KRn1YdnfFAUNbXactdYzdn4oKiRguGpLvrLU2GKVry2izAZNuhImARBgKGpGCGN9oRBwXYp2C6aorCqJULWcg5khPRJY54ukGuLmVx0XAeKokyZEZmubme05HBmT5rTVrWQjBjkyg57Rkv0ZcrszZSmXJo6UupSJMuxOEmvraND3QFUOp2u7Z7asGHDpO8risKNN97Y1MGJpWEsu7AyHSUa0ib0PtI1lVXpKLpOrSt0IxkR3w+oOA6hQCdAoSNhkiu7WK6HFviE1GqRd1+mQmcyTEhTWb8swSuf082KdBTL9Wv9jmwvOORkefCEdPKKNH2ZMsNFm3Q0xLEdMUxdZcdwCd8LWJYKEzd1ChWPYcei5Hisbovyyud005+zp/wEujwV5vHeLP1Zm65UpHaY8ZhD9eEZC4oe3ZvB96utFcZqzlqjIVRV4dSV6drjrLdgeLRoT7roK4py4MBjHVPX6MuWa9lBU6++tm7g0xoPoSoKfhBQcattHcwD3c0b2fHWEgtN+9rUW7fTlQrP2Gdrys7l6QirWmbuXL7YzJTl6DGiPL0/z2+2DHHRxo4Zn1fRfNJr6+hQdwB17733EgQBL3jBC7jzzjtpbW2tfS8UCtHT08OKFSvmZJBicRsfFKWjIVIRo5oV8f0D570F5C132ozIWINH2/XYn61wYneqVqfS0xrnF/4gluUQC+nomkoyolCyFFzPwdA0NCUgHjY4bVWa01e1cvYxLSxLRSaMcXzGIVNypj3a4/HeLPuzZbpSYfwgIGs5LEtF6EyY9OUq7MtZrGqNcvqqFkq2Szys0xYNkbUcVMKsaolx9jEthHSNh3ZnpvwEGtI0WiIG+/MVjrXitcOMxxyqwF1RFLpSJj9+vMRIyaE7HSYRCVGyXZ7al6M1anDJictq91tv4NHoRX98882Roo3tetV6q2SEVekoedtpaMfbiSsSh8wK1Vu3M93S1FQZm4F8mZ882Y/jevS0R2mLmUui0Hd8lgOgUHFxfJ+S5TFUsNifLbNpX46hosX6TikOn0/Sa+voUHcAdcEFFwCwY8cOVq9eLZ9mRM1UQVE1a0Ft19bYMtHBE2nYUOnP2uzPV8iUbBKmzsqWKIN5qzpZbmznh4/3sWl/joiuEqjVA3p1DdoTIRzfpzsd5dTuFJeesoINXYlZ73bb2l/gl0/3k7Nctg4WIAjIlV1WtkZIR0K0xkwKtsuJy5N0JkxKdrWr93nrO4iE9AmT+f5sZdpgJGZqLEtG2NSfw3Y9GN8tvI4+PGMHHy9PR+hKRRgp2YyWbHRN5YTlSRQF9mctjl8eTAiiDhV4NHrRH998c3zAHDM1SraH5dW/460rZfJkX76uOp7Z1u1MlbHJlm22D5TwfQ8fcF1ImFN3Ll9sxgJey/HZNlioZn3LDv15C11TWN0SJR7RCRvqkng8RxLptXV0aLiI/Je//CXxeJxXvepVE27/3ve+R6lU4uqrr27a4MTS0GhfmbGJ9Lfbhvj1lkHylktLxGDjsiSdSZPhYnWH2oXHddCVjnLVOav5159sYbBQ7QIe0lVCuobjByxLhDl7TSuJiEFncnK2p94akYFchXse6+OJvhzRkIaqKFiOx3DRwvF8Ni7XSEQMbN+vFclXMzIBkZBOV2ripDRTMKIoCp1Jk96Mzv5sBVPXGurDM5Z5WNcRJxrSpgxepqqvOFTg0ehFf3zzzbGAebqfHTNVIGe73pS1Ys2e9A+uSwkI2DVcpmg7dCUjB9pTTN+5fLExdRXL8djWX6j2NjN1Rg/sCA2CgN5MmfZ4qNq+IqQt+sdzJJFeW0eHhvOHt9xyC+3t7ZNu7+zs5GMf+1hTBiWWnrGgaF1HglzFYW+mRK7isK4jwYUbJk+AHQmTZNhgTXuMF52wjHOP7eDUVSlWpCOsaYuRLds80VvduPDik5bz+nNW09MWOxDcgKlrHNsR5+KNnRh6NVtx8GR9cMYhZupoqlLdCTPuPnzf5/6tQzy9L4eqKJgHDh6Ohw0UVPZly+weKVWXqMY1ZpwpDT8WjAzkKwRBMGlclutx3voOTuxO1fV8jTd+qW0s29cSDREP67XAbuzg3UaMXfRTkRA7h4sULRfPDyhaLjuHixMu+o387FT30xIL0ZWq9vp6si9/yNfo4OdwNg5eoixa1QA5faDPV0hTcT0fx/en3BG6WARBwGjRpmy7ZMoO/bkKnbHqYyhaLqmIQTpcbZ3heAFRQ6Voe4R0la0DeUaL9kI/hKNCo9dEsfQ0nIHavXs3a9eunXR7T08Pu3fvbsqgxNLUSF+ZsZ17Y5PmeFPtUrn0lBVEQhq7hktETZ1UWCcZ1hks2NNO1vXuhNk5VOSh3aNEQzor0iqZskMUjWhIoyVmMFCw6MuUiRoqa9oTxEztkGn4ej6BPn99Ox2z6MMzl/UVjTRYbEYzxvncrXTw7sVsySFXdqrPUxBMaLQKi7PQd/xydKbksGOwiOv57BotkTQNXD/A8AOyjks6auD5Pg/szFByXGzPo1B2aY+ZnLehXSbweSC9to5sDQdQnZ2dPPbYY5MOE3700Udpa2tr1rjEElVvfUqjBcudyTAv2LhsQi1T3vJmnKzrvY++bIXhosXyVIQggIrtk604RA2dVMQgb7kM5i3WtMZY2RKmZNWXhu9ImJy2Kl0tTM9V0FWFkD45wGg0MJjr+opGLvqHO0GMf42CIJi0HNnMIObg3Yv7smV6M2VGChbpWAgVhTUdsRk7ly+kg5ejDU0lHq4uaZdtnyCoHr0TENAaM4kZKs8Ml1BVheXJSPVx+LA3U6otkUsQNfek19aRq+EA6jWveQ3vfOc7SSQSnH/++QD86le/4l3vehdXXnll0wcojkyzyaI0OlnXex8hTYUAFCBq6qxuizJYsMhXqt21k6aG5xl0psLkLRfL9evuJdWbKWG5HhCQjpicvDLF+mXxw/oEOh/1FY1c9A9nghh7jfpzFQYLFiNFG/dAJqg1FqIjbjYtiDl49+KKtMmyRJiBXJk9IyUSpsHpq6vtWhZboe9UBfABEA3ptEVVspZDOhyiM2EyUnJYmTLZNljC9QOWJatnCQ4ULLrSEY7vSrBrpLTo66GOlF5d4sjVcAB10003sXPnTi6++GJ0vfrrvu/zhje8QWqgRN1mm0VpZLKu9z7WdcZoi5sMFSxWtWjETJ1YqHq+m+v57M9VOK7L5A3nrCZqHrpP0OTC9XAtuHlkT4Z01DjsT/5L9SyzgyfFVEQnFtL55aZ+IiGNVCREKGxgez77s2V2DBZ4wcZlTQliptq9aOgKuq4RVgPiYZ2RokNrzGGwYC2qQt+pljrHnwWYChuUHI8NnXGCoMSejMW+XJlliTBKAAMFi5ih09MaRVXVRd/IUc6QE0tBwwFUKBTiO9/5DjfddBOPPvookUiEk08+mZ6enrkYnzhCzVcWZew+dgwXiZt6rdljwXJJH7iP1pjJGT0t/PzpAfbnyrRETQxdRQEKloOmqpy5poW1HYfOHM3nEQ5Lrb5iugaWuYpDcOC8OgVAqWYDgyAgQKFZD2e63Ytl22Uwb7MvV2H7UJ5ERGN9Z3JRTdZTLUePPwtwtGzj+9XeXMd0xBjeaVW/1jXKbrWTf09rlHS0GiwtxvquMdJdXSwVDQdQYzZs2DBlR3Ih6jUfWZTOZJiTupP85Mn9PLY3i+V6mLrGuo4Yzz/22ULac49tZ6Ros7m/wGjZQVGqx8WoqsZzVsc599j2ugKT+T7CYanUV0w3KT7Zm2XHUJETV8SpODBctHAtB11VWZGO0ZEwKFhuU56vqXYvArREQyxPR1hTjrF7pMSFGzqn7Ce2kKZbjk5FQpy8Msnm/QX6smUGCxXSkRAvPL6TbQMF2uNmtY2BqU14PIutvmuMnCEnlpK6Aqh3v/vd3HTTTcRiMd797nfP+LP//u//3pSBiaPDXGdRxrIesZDG845pQ1UVfD+gYDk80ZujPW7SmQzTmQzzslNXsKY3y7aBAiXbIxrSOLYzzsndqbqDOTnCYbKZJsWuVJjHerOUrIBTViUp2bEJ5/r5B4qem/F8zdibCwVdVWiPh6bsJ7bQZlqOToYN2hPVbOpZa1oJGxqpiM59m4fYPpifFDwttvqu8eQMObGU1BVAPfzwwziOU/vv6Sy2i45YGuYqizJ+4l7bPnH5rSMwJ32a7UyGeUHC5DmrW2YdzMkRDpPNNCmGdI10NER/rkzJik9oyAlQcdymPV9LuTv0oZa809EQ5xzTNiHQX4qNHOUDiFhK6gqg7r333in/W4jFbDafZg83mFvKk/RcmWlSjJkaXYlw9Vgbz2P8JanZz9dS7w7d6JL3UtxoIB9AxFIy6xooIRa7qSbusYOLHc9HVRRst/GO3TNZ6pP0XDjU0tmyVIjejM6+WRxr06ilGFSM1+iS91LbaCAfQMRSUlcAdfnll9f9B++6665ZD0aIZjp44s6WbXYNl6uFyr6P50NYV8mVnQln2R1u/5mlPkk326EmxYrjc/6GDpJhg75sec6fr6UWVBys0SzpUtloAPIBRCwtdQVQqVSq9t9BEHD33XeTSqU488wzAfjzn/9MJpNpKNASYq6Nn7hbPIMnevMUbYd0JISu6fRlK2gKPLx7tNabqVn9Z5b6JN1M9UyK5x47u2NtDmdMSyWoONrIBxCxVChBg6d0/tM//RMjIyPceuutaFp1acTzPN72treRTCb513/91zkZaDPkcjlSqRTZbJZkMrnQwxHzYCBX4d7NA/x5VwbLcelORbD9gGzFRldU1nXEKNkeJ3anOGlFkl9tGZq01X4gXyEVCUn/mcPUny3zxx0j7Bkt4/sB6YhBd8vCN0eUjteLk7wuYrzFOH83HEB1dHRw//33c9xxx024ffPmzTzvec9jeHi4qQNspsX4Aoi5t2V/nm/8fieW46GqCo7n43gBhqZi6AqeD6amsLErScX1Jmy1h+qFfOdwkXUdCS7a2CEX8VkYy+ztHS2SrTioKKxqiXH2MS0sS0UWfFzS8VqIxW0xzt8Nb2VwXZdNmzZNun3Tpk34vmwtFYtPMmKwpj3KOevaOa4rQUjTMHWFtliItphJKqyzZ7TM/duHCBvqIXfsicaMNdHcPpgnFQmxoTPJypYo/fkyv9oyxECusuDjSoYNVqajJMMG2wfz3Ld5cMHGJYRYGhrehXfttdfyxje+ke3bt/Pc5z4XgD/+8Y98/OMf59prr236AIU4XKauYuoamgqZkosX+HQlI4ydEaIoCq2xECNFm/6sTVcqgsLEIEr6z8zOYu0svVjHJYRYOhoOoD71qU/R1dXFv/3bv7Fv3z4Ali9fznvf+17+8R//sekDFOJwjRWTP9GbYejAURdjwVMQBGQrDu0JE11V2J+vcKwVJ24e3NBR+s/MxmLtLL1YxyWEWDoang1UVeV973sfvb29ZDIZMpkMvb29vO9976sVlQuxmIztAouEdAbyFl4Q4PtBtUD8wCn1x3XG6UpGyJRsbNeb8Ptj/We601HpP9OgejpL215ze3Et5XEJIZaOWX2cdl2Xn//853z729+ufXrr6+ujUCg0dXBCNEtnMswFGzroSobJlx2GixZlx6PrwGHDLTGTzqRJwtTZn61QtFw8P6BouewcLkr/mVka34trKguV2Vus4xJCLB0NL+Ht2rWLF7/4xezevRvLsvjLv/xLEokEn/jEJ7Asi1tvvXUuxinEYVu/LM5Fx3XyRF+W5akwIU2rHbQaBAGW63He+g5SUYO+zNw3dDwaLNbO0ot1XEKIpaPhAOpd73oXZ555Jo8++ihtbW2121/+8pfz5je/uamDE6KZFEXh5JUphov2gV5PGn4AFdutNXR8/vr5beh4pFusnaUX67iEEEtHwwHUb37zG373u98RCk0srFyzZg29vb1NG5gQc6HeLsdSONw8i7Wz9GIdlxBiaWg4gPJ9H8+bXDewd+9eEolEUwYlxFxq1jEr0im5fov1aJvFOi4hxOLXcAD1ohe9iM985jN86UtfAqqp8EKhwIc//GH+6q/+qukDFGIuHO5ZaNLBunGL9fy5xTouIcTi1vBRLnv27OHFL34xQRCwdetWzjzzTLZu3Up7ezu//vWv6ezsnKuxHrbF2Ar+SHSkZ2bGOljLmXlCCDE/FuP83XAGatWqVTz66KN85zvf4dFHH6VQKPDGN76Rq666ikhk4c60EovDkZ6ZkQ7WQgghoMEAynEcNm7cyD333MNVV13FVVddNVfjEkvQdJmZ7YN5hgrWEZGZkQ7WQgghoMFGmoZhUKnM/wGbO3fu5I1vfCNr164lEomwbt06PvzhD2Pb9ryPRUzt4MxMzNTRVKWamWmLkS3bPNGbo8EV40VHOlgLIYSAWXQiv/766/nEJz6B67pzMZ4pbdq0Cd/3+eIXv8iTTz7Jpz/9aW699Vb++Z//ed7GIGbWSGZmKZMO1kIIIWAWNVAPPPAAv/jFL/jpT3/KySefTCwWm/D9u+66q2mDG/PiF7+YF7/4xbWvjznmGDZv3swXvvAFPvWpTzX9/kTj6snMDBWtOcvMNFq4PttCd+lgLYQQAmYRQKXTaV7xilfMxVgaks1maW1tXehhiAPGZ2Zi5uS31VxmZhotXD+cQnfpYC2EEAJmEUDddtttczGOhmzbto3/+3//7yGzT5ZlYVlW7etcLjfXQztqLVRmptHC9WYUuksHayGEEHWnA3zf5xOf+ATnnnsuZ511Fu9///spl8uHdefvf//7URRlxn+bNm2a8Du9vb28+MUv5lWvetUhz9675ZZbSKVStX+rVq06rPGK6Y1lZlKREDuHixQtF88PKFouO4eLc5KZabRwvZmF7p3JMBdt7OClp6zg0pNX8NJTVnDRxqW/y1AIIUR96m6kedNNN/GRj3yEF77whUQiEX7yk5/wmte8hq9+9auzvvPBwUGGh4dn/Jljjjmmdu5eX18fF154Ieeccw5f+9rXUNWZ47+pMlCrVq1aVI24jjTz2QdqtGhzz2N9JMPGlMuGRcslV3F46SkraImFGv55IYQQi8OSbqT5jW98g89//vO89a1vBeDnP/85l156KV/+8pcPGchMp6Ojg46Ojrp+tre3l4suuogzzjiD2267ra77NE0T0zRnNTYxO/N5tlijhesLXeguhBDiyFF3ALV79+4JZ9298IUvRFEU+vr6WLly5ZwMbkxvby8XXnghPT09fOpTn2JwcLD2va6urjm9b9G4+TpbrNHC9YUsdBdCCHFkqTuAcl2XcHjiEoxhGDjO3Pf1+dnPfsa2bdvYtm3bpGBtqTdmFLPXaOG6tCAQQgjRLHXXQKmqykte8pIJS2L/8z//wwte8IIJvaDmog9UsyzGNVRxeGY82Dca4sIN9e3Cm+7nhRBCLLzFOH/XnYG6+uqrJ932ute9rqmDEaJRjbYUkBYEQgghmqHuDNSRYDFGsKI55qsTuRBCiPm3GOfvhhtpCrEYNVq4Pl+F7kIIIY5Mst1ICCGEEKJBEkAJIYQQQjRIAighhBBCiAZJDZQQdZCicyGEEONJACXEIczn+X5CCCGWBgmghJjBdI03tw/mGSpYXHicNN4UQoijkdRACTGNIAh4ojdHtmyzpi1GzNTRVIWYqbOmLUa2bPNEb06OExJCiKOQBFBCTGO0aLN1IE9IVynaHgHPBkqKotCZCNObKZEpzf15kEIIIRYXWcITYgoDuQq/2TLEI7tHiUd0QppGW8ykpy1CKlJtwBk2NIaKFpbrL/BohRBCzDfJQAlxkLG6p72ZEnFTJ2EaRA2NfdkSj++tLukBVByPkKZi6vK/kRBCHG3kyi/EOOPrno7vStCVjpC3XEK6yrJEmKLtsGu4jB/4DOQrdKejpKPGQg9bCCHEPJMlPCHGyZQcejMlOhNhVFWlpzVKvuwyULBIhQ2SYYO+bBldha50hJO6k9IPSgghjkKSgRJiHMv1sT2fsKEBkI6GOKk7SVcyTNnxyJVdipZDd0uUCzdICwMhhDhaSQZKiHFMXSWkqVQcj5hZ/d8jHQ2RihgULY9s2abielx0XAetcXOBRyuEEGKhSAZKiHHSUYPudJSBfGVCfydFUYiZGrbns74zSUsstICjFEIIsdAkgBJiHEVROKk7SSoSYudwkaLl4vkBRctl53CR1IElPal7EkKIo5ss4QlxkM5kmAuP66idfzdUtAhpKus6EnL+nRBCCEACKCGm1JkMc1HCJFNysFwfU1dJRw3JPAkhhAAkgBJiWoqiSK2TEEKIKUkNlBBCCCFEgySAEkIIIYRokARQQgghhBANkgBKCCGEEKJBEkAJIYQQQjRIAighhBBCiAZJACWEEEII0SAJoIQQQgghGiQBlBBCCCFEgySAEkIIIYRokARQQgghhBANkgBKCCGEEKJBEkAJIYQQQjRIAighhBBCiAZJACWEEEII0SAJoIQQQgghGiQBlBBCCCFEgySAEkIIIYRokARQQgghhBANkgBKCCGEEKJBEkAJIYQQQjRIAighhBBCiAZJACWEEEII0SAJoIQQQgghGiQBlBBCCCFEgySAEkIIIYRokARQQgghhBANkgBKCCGEEKJBEkAJIYQQQjRoyQVQlmVx2mmnoSgKjzzyyEIPRwghhBBHoSUXQP3/9u48KIo77QP4d2CYAYQZUG7l8EAJKioSWYxRV3CJuolarlcRCxNjFqPRbIzX5jVYqUp08cwaNdldje5uSqMmEoxitFQ8KJWIgCIsHqhYImBALg9k4Hn/sOzKBNBpg47I91PVVU7/nu5+5rGLfqqvmTt3Lnx8fKydBhEREbViLaqBSk5Oxt69e7Fs2TJrp0JEREStmNbaCViquLgYU6dORWJiIhwdHS1apqamBjU1NcrnysrKJ5UeERERtSIt4gyUiGDy5MmIi4tDWFiYxcstXrwYRqNRmXx9fZ9glkRERNRaWLWBmj9/PjQazUOn//3vf1i9ejWqqqqwYMECVetfsGABKioqlOnq1atP6JsQERFRa6IREbHWxm/cuIHS0tKHxnTq1Anjxo3Dzp07odFolPl1dXWwtbVFTEwMNm3aZNH2KisrYTQaUVFRAYPB8JtyJyIioqfjWTx+W7WBslRBQYHZ/UuFhYWIjo7G9u3bER4ejg4dOli0nmfxP4CIiIge7lk8freIm8j9/PzMPjs5OQEAOnfubHHzRERERNRcWsRN5ERERETPkhZxBurXAgIC0AKuPBIREdFzimegiIiIiFRiA0VERESkEhsoIiIiIpXYQBERERGp1CJvIqffTkRQfrsWNaZ66LU2cHG0M3tRKRERETWNDVQrVFJ5F9nXKnGt/Dbu1dVDZ2uD9i6O6NHeAA+DvbXTIyIieuaxgWplSirvIiXvBiru3IOHsz3s7Wxxt7YOF29U4efqGgzu5s4mioiI6BF4D1QrIiLIvlaJijv3ENCuDdrotbC10aCNXouAdm1Qcecesq9V8h1bREREj8AGqhUpv12La+W34eFs3+B+J41GAw9ne1wrv43y27VWypCIiKhlYAPVitSY6nGvrh72draNjtvb2eJeXT1qTPVPOTMiIqKWhQ1UK6LX2kBna4O7tXWNjt+trYPO1gZ6LXcLIiKih+GRshVxcbRDexdHlFTdbXCfk4igpOou2rs4wsXRzkoZEhERtQxsoFoRjUaDHu0NMDrocLn0Fm7VmFBXL7hVY8Ll0lswOurQo72B74MiIiJ6BL7GoJXxMNhjcDd35T1QP9+qgc7WBp3dnfkeKCIiIguxgWqFPAz2+L2znm8iJyIiekxsoFopjUYD1zY6a6dBRETUIvEeKCIiIiKV2EARERERqcQGioiIiEglNlBEREREKrGBIiIiIlKJDRQRERGRSmygiIiIiFRiA0VERESkEhsoIiIiIpXYQBERERGpxAaKiIiISCU2UEREREQqsYEiIiIiUokNFBEREZFKbKCIiIiIVGIDRURERKQSGygiIiIildhAEREREanEBoqIiIhIJTZQRERERCqxgSIiIiJSiQ0UERERkUpsoIiIiIhUYgNFREREpBIbKCIiIiKV2EARERERqcQGioiIiEglNlBEREREKrGBIiIiIlKJDRQRERGRSmygiIiIiFRiA0VERESkEhsoIiIiIpXYQBERERGp1KIaqF27diE8PBwODg5wdXXFqFGjrJ0SERERtUJaaydgqW+//RZTp07Fp59+iiFDhsBkMiE7O9vaaREREVEr1CIaKJPJhFmzZmHp0qWYMmWKMj84ONiKWREREVFr1SIu4Z06dQrXrl2DjY0N+vTpA29vbwwbNoxnoIiIiMgqWsQZqPz8fADAokWLsGLFCgQEBGD58uUYPHgwzp07h7Zt2za6XE1NDWpqapTPFRUVAIDKysonnzQRERE1iwfHbRGxcia/IFY0b948AfDQKTc3V77++msBIF9++aWy7N27d8XNzU2++OKLJtcfHx//yPVz4sSJEydOnFrGdPHixafRnlhEI2K9du7GjRsoLS19aEynTp2QmpqKIUOG4MiRIxgwYIAyFh4ejqioKHzyySeNLvvrM1Dl5eXw9/dHQUEBjEZj83yJVqiyshK+vr64evUqDAaDtdNp0VjL5sNaNg/Wsfmwls2noqICfn5+uHnzJlxcXKydDgArX8Jzd3eHu7v7I+P69u0LvV6PvLw8pYGqra3F5cuX4e/v3+Ryer0eer2+wXyj0ciduRkYDAbWsZmwls2HtWwerGPzYS2bj43Ns3Prdou4B8pgMCAuLg7x8fHw9fWFv78/li5dCgAYO3aslbMjIiKi1qZFNFAAsHTpUmi1WkyaNAl37txBeHg4Dhw4AFdXV2unRkRERK1Mi2mg7OzssGzZMixbtuyx16HX6xEfH9/oZT2yHOvYfFjL5sNaNg/Wsfmwls3nWaylVW8iJyIiImqJnp27sYiIiIhaCDZQRERERCqxgSIiIiJSiQ0UERERkUqtpoFas2YNAgICYG9vj/DwcKSlpVk7pSfq8OHDePXVV+Hj4wONRoPExESzcRHBRx99BG9vbzg4OCAqKgrnz583iykrK0NMTAwMBgNcXFwwZcoUVFdXm8WcPn0aL7/8Muzt7eHr64uEhIQGuWzbtg1BQUGwt7dHz549sXv3btW5WMvixYvx4osvwtnZGR4eHhg1ahTy8vLMYu7evYvp06ejXbt2cHJywpgxY1BcXGwWU1BQgBEjRsDR0REeHh6YM2cOTCaTWUxKSgpCQ0Oh1+vRpUsXbNy4sUE+j9qPLcnFWtatW4eQkBDlpYIRERFITk5WxlnHx7NkyRJoNBq89957yjzW0jKLFi2CRqMxm4KCgpRx1tFy165dw+uvv4527drBwcEBPXv2xMmTJ5Xx5/KYY71fkXl6tmzZIjqdTjZs2CBnz56VqVOniouLixQXF1s7tSdm9+7d8uGHH8p3330nAGTHjh1m40uWLBGj0SiJiYmSlZUlr732mnTs2FHu3LmjxLzyyivSq1cvOX78uBw5ckS6dOkiEydOVMYrKirE09NTYmJiJDs7WzZv3iwODg5mv1mYmpoqtra2kpCQIDk5OfJ///d/YmdnJ2fOnFGVi7VER0fLV199JdnZ2ZKZmSnDhw8XPz8/qa6uVmLi4uLE19dX9u/fLydPnpTf/e530r9/f2XcZDJJjx49JCoqSjIyMmT37t3i5uYmCxYsUGLy8/PF0dFR3n//fcnJyZHVq1eLra2t7NmzR4mxZD9+VC7WlJSUJLt27ZJz585JXl6e/PWvfxU7OzvJzs4WEdbxcaSlpUlAQICEhITIrFmzlPmspWXi4+Ole/fucv36dWW6ceOGMs46WqasrEz8/f1l8uTJcuLECcnPz5cff/xRLly4oMQ8j8ecVtFA9evXT6ZPn658rqurEx8fH1m8eLEVs3p6ft1A1dfXi5eXlyxdulSZV15eLnq9XjZv3iwiIjk5OQJAfvrpJyUmOTlZNBqNXLt2TURE1q5dK66urlJTU6PEzJs3T7p166Z8HjdunIwYMcIsn/DwcPnzn/9scS7PkpKSEgEghw4dEpH7udrZ2cm2bduUmNzcXAEgx44dE5H7zayNjY0UFRUpMevWrRODwaDUbu7cudK9e3ezbY0fP16io6OVz4/ajy3J5Vnj6uoq//rXv1jHx1BVVSWBgYGyb98+GTRokNJAsZaWi4+Pl169ejU6xjpabt68eTJgwIAmx5/XY85zfwnv3r17SE9PR1RUlDLPxsYGUVFROHbsmBUzs55Lly6hqKjIrCZGoxHh4eFKTY4dOwYXFxeEhYUpMVFRUbCxscGJEyeUmIEDB0Kn0ykx0dHRyMvLw82bN5WYX27nQcyD7ViSy7OkoqICANC2bVsAQHp6Ompra83yDwoKgp+fn1kte/bsCU9PTyUmOjoalZWVOHv2rBLzsDpZsh9bksuzoq6uDlu2bMGtW7cQERHBOj6G6dOnY8SIEQ2+L2upzvnz5+Hj44NOnTohJiYGBQUFAFhHNZKSkhAWFoaxY8fCw8MDffr0wT//+U9l/Hk95jz3DdTPP/+Muro6sx0cADw9PVFUVGSlrKzrwfd+WE2Kiorg4eFhNq7VatG2bVuzmMbW8cttNBXzy/FH5fKsqK+vx3vvvYeXXnoJPXr0AHA/f51O1+DXwX/9HR+3TpWVlbhz545F+7EluVjbmTNn4OTkBL1ej7i4OOzYsQPBwcGso0pbtmzBqVOnsHjx4gZjrKXlwsPDsXHjRuzZswfr1q3DpUuX8PLLL6Oqqop1VCE/Px/r1q1DYGAgfvzxR0ybNg0zZ87Epk2bADy/x5wW81MuRNY2ffp0ZGdn4+jRo9ZOpcXq1q0bMjMzUVFRge3btyM2NhaHDh2ydlotytWrVzFr1izs27cP9vb21k6nRRs2bJjy75CQEISHh8Pf3x9bt26Fg4ODFTNrWerr6xEWFoZPP/0UANCnTx9kZ2fjiy++QGxsrJWze3Ke+zNQbm5usLW1bfC0QnFxMby8vKyUlXU9+N4Pq4mXlxdKSkrMxk0mE8rKysxiGlvHL7fRVMwvxx+Vy7NgxowZ+OGHH3Dw4EF06NBBme/l5YV79+6hvLzcLP7X3/Fx62QwGODg4GDRfmxJLtam0+nQpUsX9O3bF4sXL0avXr3w2WefsY4qpKeno6SkBKGhodBqtdBqtTh06BD+/ve/Q6vVwtPTk7V8TC4uLujatSsuXLjAfVIFb29vBAcHm8174YUXlMuhz+sx57lvoHQ6Hfr27Yv9+/cr8+rr67F//35ERERYMTPr6dixI7y8vMxqUllZiRMnTig1iYiIQHl5OdLT05WYAwcOoL6+HuHh4UrM4cOHUVtbq8Ts27cP3bp1g6urqxLzy+08iHmwHUtysSYRwYwZM7Bjxw4cOHAAHTt2NBvv27cv7OzszPLPy8tDQUGBWS3PnDlj9sdh3759MBgMyh+dR9XJkv3YklyeNfX19aipqWEdVYiMjMSZM2eQmZmpTGFhYYiJiVH+zVo+nurqaly8eBHe3t7cJ1V46aWXGrze5dy5c/D39wfwHB9zVN1y3kJt2bJF9Hq9bNy4UXJycuTtt98WFxcXsycnnjdVVVWSkZEhGRkZAkBWrFghGRkZcuXKFRG5/xini4uLfP/993L69GkZOXJko4+U9unTR06cOCFHjx6VwMBAs0dKy8vLxdPTUyZNmiTZ2dmyZcsWcXR0bPBIqVarlWXLlklubq7Ex8c3+kjpo3KxlmnTponRaJSUlBSzR51v376txMTFxYmfn58cOHBATp48KRERERIREaGMP3jU+Q9/+INkZmbKnj17xN3dvdFHnefMmSO5ubmyZs2aRh91ftR+/KhcrGn+/Ply6NAhuXTpkpw+fVrmz58vGo1G9u7dKyKs42/xy6fwRFhLS82ePVtSUlLk0qVLkpqaKlFRUeLm5iYlJSUiwjpaKi0tTbRarXzyySdy/vx5+frrr8XR0VH++9//KjHP4zGnVTRQIiKrV68WPz8/0el00q9fPzl+/Li1U3qiDh48KAAaTLGxsSJy/1HOhQsXiqenp+j1eomMjJS8vDyzdZSWlsrEiRPFyclJDAaDvPHGG1JVVWUWk5WVJQMGDBC9Xi/t27eXJUuWNMhl69at0rVrV9HpdNK9e3fZtWuX2bgluVhLYzUEIF999ZUSc+fOHXnnnXfE1dVVHB0dZfTo0XL9+nWz9Vy+fFmGDRsmDg4O4ubmJrNnz5ba2lqzmIMHD0rv3r1Fp9NJp06dzLbxwKP2Y0tysZY333xT/P39RafTibu7u0RGRirNkwjr+Fv8uoFiLS0zfvx48fb2Fp1OJ+3bt5fx48ebvbuIdbTczp07pUePHqLX6yUoKEj+8Y9/mI0/j8ccjYiIunNWRERERK3bc38PFBEREVFzYwNFREREpBIbKCIiIiKV2EARERERqcQGioiIiEglNlBEREREKrGBIiIiIlKJDRQRPRM0Gg0SExOf6DYWLVqE3r17PzRm8uTJGDVqlFW2TUQtBxsoolbm2LFjsLW1xYgRI1QvGxAQgFWrVjV/Uha4ceMGpk2bBj8/P+j1enh5eSE6OhqpqalPNY+UlBRoNBpl8vT0xJgxY5Cfn//Q5T744IMGv9FFRC0XGyiiVmb9+vV49913cfjwYRQWFlo7HYuNGTMGGRkZ2LRpE86dO4ekpCQMHjwYpaWlVsknLy8PhYWF2LZtG86ePYtXX30VdXV1DeJEBCaTCU5OTmjXrp0VMiWiJ4ENFFErUl1djW+++QbTpk3DiBEjsHHjxgYxO3fuxIsvvgh7e3u4ublh9OjRAIDBgwfjypUr+Mtf/qKcfQEavzS1atUqBAQEKJ9/+uknDB06FG5ubjAajRg0aBBOnTplcd7l5eU4cuQI/va3v+H3v/89/P390a9fPyxYsACvvfaaEldQUICRI0fCyckJBoMB48aNQ3FxcZPrraurw/vvvw8XFxe0a9cOc+fOhaW/buXh4QFvb28MHDgQH330EXJycnDhwgXlDFVycjL69u0LvV6Po0ePNlqnDRs2oHv37tDr9fD29saMGTPMvvNbb70Fd3d3GAwGDBkyBFlZWRbXjIieLDZQRK3I1q1bERQUhG7duuH111/Hhg0bzBqGXbt2YfTo0Rg+fDgyMjKwf/9+9OvXDwDw3XffoUOHDvj4449x/fp1XL9+3eLtVlVVITY2FkePHsXx48cRGBiI4cOHo6qqyqLlnZyc4OTkhMTERNTU1DQaU19fj5EjR6KsrAyHDh3Cvn37kJ+fj/Hjxze53uXLl2Pjxo3YsGEDjh49irKyMuzYscPi7/WAg4MDAODevXvKvPnz52PJkiXIzc1FSEhIg2XWrVuH6dOn4+2338aZM2eQlJSELl26KONjx45FSUkJkpOTkZ6ejtDQUERGRqKsrEx1fkT0BKj++WEiarH69+8vq1atEhGR2tpacXNzk4MHDyrjEREREhMT0+Ty/v7+snLlSrN58fHx0qtXL7N5K1euFH9//ybXU1dXJ87OzrJz505lHgDZsWNHk8ts375dXF1dxd7eXvr37y8LFiyQrKwsZXzv3r1ia2srBQUFyryzZ88KAElLS2s0V29vb0lISFA+19bWSocOHWTkyJFN5nHw4EEBIDdv3hQRkcLCQunfv7+0b99eampqlPHExESz5X69bR8fH/nwww8b3caRI0fEYDDI3bt3zeZ37txZvvzyyyZzI6Knh2egiFqJvLw8pKWlYeLEiQAArVaL8ePHY/369UpMZmYmIiMjm33bxcXFmDp1KgIDA2E0GmEwGFBdXY2CggKL1zFmzBgUFhYiKSkJr7zyClJSUhAaGqpchszNzYWvry98fX2VZYKDg+Hi4oLc3NwG66uoqMD169cRHh6uzNNqtQgLC7Monw4dOqBNmzbw8fHBrVu38O2330Kn0ynjD1tPSUkJCgsLm6x1VlYWqqur0a5dO+Xsm5OTEy5duoSLFy9alB8RPVlaaydARE/H+vXrYTKZ4OPjo8wTEej1enz++ecwGo3KpSg1bGxsGtw3VFtba/Y5NjYWpaWl+Oyzz+Dv7w+9Xo+IiAizS16WsLe3x9ChQzF06FAsXLgQb731FuLj4zF58mTVef9WR44cgcFggIeHB5ydnRuMt2nTpsllH1Xn6upqeHt7IyUlpcGYi4uL2lSJ6AngGSiiVsBkMuHf//43li9fjszMTGXKysqCj48PNm/eDAAICQl56KP2Op2uwZNm7u7uKCoqMmuiMjMzzWJSU1Mxc+ZMDB8+XLlp+ueff/7N3ys4OBi3bt0CALzwwgu4evUqrl69qozn5OSgvLwcwcHBDZY1Go3w9vbGiRMnlHkmkwnp6ekWbbtjx47o3Llzo83Tozg7OyMgIKDJWoeGhqKoqAharRZdunQxm9zc3FRvj4iaH89AEbUCP/zwA27evIkpU6bAaDSajY0ZMwbr169HXFwc4uPjERkZic6dO2PChAkwmUzYvXs35s2bB+D+e6AOHz6MCRMmQK/Xw83NDYMHD8aNGzeQkJCAP/3pT9izZw+Sk5NhMBiUbQQGBuI///kPwsLCUFlZiTlz5qg621VaWoqxY8fizTffREhICJydnXHy5EkkJCRg5MiRAICoqCj07NkTMTExWLVqFUwmE9555x0MGjSoyctps2bNwpIlSxAYGIigoCCsWLEC5eXlKqv7eBYtWoS4uDh4eHhg2LBhqKqqQmpqKt59911ERUUhIiICo0aNQkJCArp27YrCwkLlJn9LLzMS0ZPDM1BErcD69esRFRXVoHkC7jdQJ0+exOnTpzF48GBs27YNSUlJ6N27N4YMGYK0tDQl9uOPP8bly5fRuXNnuLu7A7h/5mft2rVYs2YNevXqhbS0NHzwwQcNtn/z5k2EhoZi0qRJmDlzJjw8PCzO38nJCeHh4Vi5ciUGDhyIHj16YOHChZg6dSo+//xzAPffZP7999/D1dUVAwcORFRUFDp16oRvvvmmyfXOnj0bkyZNQmxsLCIiIuDs7Ky8tuFJi42NxapVq7B27Vp0794df/zjH3H+/Hnlu+zevRsDBw7EG2+8ga5du2LChAm4cuUKPD09n0p+RPRwGvn1zQtERERE9FA8A0VERESkEhsoIiIiIpXYQBERERGpxAaKiIiISCU2UEREREQqsYEiIiIiUokNFBEREZFKbKCIiIiIVGIDRURERKQSGygiIiIildhAEREREanEBoqIiIhIpf8HpQLsnW45QFEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSLE: cannot be calculated\n",
      "R2: -8.318860667658792e+22\n",
      "RMSE: 2.6104883570526824e+16\n"
     ]
    }
   ],
   "source": [
    "# Apply the basic MLR model on the test data\n",
    "Y_pred= basic_mlr.predict(X_test)\n",
    "plot_Yt_Yp(Y_test, Y_pred, set_limit=(0, 600000, -60000000000000000, 60000000000000000))\n",
    "print_eval_metrics(Y_test, Y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that the basic linear regression model performs poorly on the test dataset, with a negative R2 score and a high RMSE value. The RMSLE presents the same issue as in the **cross validation implementation** (section 2.c.2). We can clearly observe this bad performance in the graph and also notice the prediction of negative values.\n",
    "\n",
    "There are two interesting elements to observe from these results:\n",
    "1. The model is predicting negative values, when the ground truth target value has only positive values (and not even values close to zero). Moreover the scale of the predcited values is totally different from the scale of true values\n",
    "2. The model presents a really good performance when testing on the same training data, but a really bad performance when testing on unseen data.\n",
    "\n",
    "While this only concludes that the model fitted for Multiple Linear Regression is too complex, and overfitting for the training data, with a poor ability of generalization on unseen data - we would like to reiterate that we were already able to make these conclusions from cross validation fairly accurately."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.b. Test performance of ElasticNet model\n",
    "We now apply the ElasticNet model on the test dataset tuned previously (section 2.d). We then calculate the three evaluation metrics, namely, RMSE, R2 and RMSLE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSLE: 0.16236722580427698\n",
      "R2: 0.7657033201006015\n",
      "RMSE: 43809.964636868935\n"
     ]
    }
   ],
   "source": [
    "# Evaluate test performance\n",
    "Y_pred = elasticnet_mlr.predict(X_test)\n",
    "plot_Yt_Yp(Y_test, Y_pred)\n",
    "print_eval_metrics(Y_test, Y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the RMSLE is 0.16,  is achieved on the test data which is a good score (i.e. closer to zero). This is a significant improvement from the basic MLR that we fitted earlier and a considerably good score. The other metrics also indicate a good performance, with an R2 of 0.75 and an RMSE that is in a reasonable scale considering the scale of the target variable. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.c. Configuration of the best model\n",
    "\n",
    "After analyzing the performance of the two developed models, we can conclude that the **configuration** of the best model is the ElasticNet model with alpha = 2.67. This model has an RMSLE score of 0.16, R2 of 0.76 and an RMSE of 43809.96 on the test data.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "\"Applied Machine Learning\" [Web page]. (n.d.). Andreas C. Müller. https://amueller.github.io/aml/index.html\n",
    "\n",
    "\"Cross-Validation with Linear Regression\" [Web log post]. (n.d.). Kaggle. https://www.kaggle.com/code/jnikhilsai/cross-validation-with-linear-regression\n",
    "\n",
    "\"Good Train-Test Split: An Approach to Better Accuracy\" [Web log post]. (2019, October 22). Medium. https://code.likeagirl.io/good-train-test-split-an-approach-to-better-accuracy-91427584b614\n",
    "\n",
    "\"Root Mean Squared Error (RMSE) vs. RMSLE\" [Web log post]. (n.d.). Analytics Vidhya. https://medium.com/analytics-vidhya/root-mean-square-log-error-rmse-vs-rmlse-935c6cc1802a\n",
    "\n",
    "\"Root Mean Squared Logarithmic Error (RMSLE)\" [Web page]. (n.d.). Pecan AI Help Center. https://help.pecan.ai/en/articles/6456388-model-performance-metrics-for-regression-models#Modelperformancemetricsforregressionmodels-RootMeanSquaredLogarithmicError(RMSLE)\n",
    "\n",
    "Shadab, M. (n.d.). \"Root Mean Squared Error (RMSE) & Root Mean Squared Logarithmic Error.\" Medium. https://techwithshadab.medium.com/root-mean-squared-error-root-mean-squared-logarithmic-error-70e645efcccb"
   ]
  }
 ],
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