{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. [What would you find in this project?](#what-would-you-find-in-this-project)\n",
    "2. [Exploratory Data Analysis](#ExploratoryDataAnalysis)\n",
    "    1. [Import data](#Importdata)\n",
    "    2. [A First round of feature engineering](#FFE)\n",
    "    3. [EDA for numerical features](#EDAN)\n",
    "    4. [EDA for categorical features](#EDAC)\n",
    "    3. [Seed questions](#SEED)\n",
    "    3. [Summary of EDA](#SUMMARY)\n",
    "3. [Machine Learning](#ML)\n",
    "    1. [Outliers](#OUTLIERS)\n",
    "    2. [Transform categorical variables to numerical](#TRANSFORMCAT)\n",
    "    3. [Input missing values](#NULL)\n",
    "    4. [Split in training and test dataset. Compare both](#SPLIT)\n",
    "    5. [Performance Metrics](#METRIC)\n",
    "    6. [Models](#Models)\n",
    "        1. [SVM](#SVM)\n",
    "        2. [Decision Forests](#DF)\n",
    "        3. [Linear Regressions](#LR)\n",
    "        4. [KNN](#KNN)\n",
    "        5. [Neural Network](#NN)\n",
    "        6. [First Model Comparison](#1MC)\n",
    "        7. [PCA](#PCA)\n",
    "        8. [Best Model](#BEST)\n",
    "            1. [Interpretation](#INTERPRET)\n",
    "        9. [Bonus: Pycaret](#PYCARET)\n",
    "4. [Production](#PRODUCTION)\n",
    "    1. [Get insights with models output](#NEWINSIGHTS)\n",
    "    2. [Share model results with the company](#SHARE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id=\"what-would-you-find-in-this-project\"></a>\n",
    "# What would you find in this project?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of this project is to deal with a **real world regression problem and approach it with a business concern**: from explaining the insights we can get just from the data, find a ML model that would fit on the regression problem and finally put into production the model and the insights we can get from it.\n",
    "\n",
    "For this project I've chosen [this](https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data) Kaggle dataset. This dataset contains information of different houses in Iowa and the goal of this project is to find a model that able to **predict the sale price of a house**. [Predicting the market price](https://www.future-processing.com/blog/the-importance-of-price-prediction/#what-benefits-do-businesses-see-from-price-prediction) of an element (in this case a house) gives to any business and important and powerful information to take advantage on. In the case of the real state business, one of these huge advantages could be improve to the profit margin and increase the turnover.\n",
    "\n",
    "So, in the following sections we will put ourselves in the shoes of a real state company, which want to understand which useful insights we can get from their historical data, train a model able to predict the price of a house and take advantage of this to increase the turnover."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='ExploratoryDataAnalysis'></a>\n",
    "# Exploratory Data Analysis\n",
    "\n",
    "In any ML project, the [first step](https://www.bitstrapped.com/blog/exploratory-data-analysis-accelerates-machine-learning) that should be done is an exploratory data analysis, not only because doing this will help us to understand better the data we're working with, but also let us know if there are problems in the data we're working on prior trying to model it, let us know that the model we've trained is reliable and finally is a first step to bring useful insights without even have to train a model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='Importdata'></a>\n",
    "## Import data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HBL9ScMUoyWX"
   },
   "source": [
    "First of all, we'll import (and install) all necessary libraries we'll need in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Gr6-odQimjh-",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\rojol\\anaconda3\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pip install keras-tuner\n",
    "#pip install xgboost\n",
    "#pip install pycaret\n",
    "#pip install catboost\n",
    "#pip install lightgbm\n",
    "#pip install tensorflow\n",
    "#pip install keras\n",
    "#pip install scikeras\n",
    "#pip install imbalanced-learn==0.11.0 \n",
    "#pip install shap\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score,  GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from xgboost import XGBRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "from catboost import CatBoostRegressor\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from keras_tuner.tuners import RandomSearch, Hyperband\n",
    "import keras_tuner as kt\n",
    "from scipy.stats import zscore\n",
    "import shap\n",
    "import pickle\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') #For a cleaner lecture\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iHhYjYREpr6O"
   },
   "source": [
    "Now, we can import the data and take a look to the first rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "6DuoFHXcnmPR"
   },
   "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>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>RL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>14115</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>10</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>143000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>75.0</td>\n",
       "      <td>10084</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>307000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10382</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shed</td>\n",
       "      <td>350</td>\n",
       "      <td>11</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>50</td>\n",
       "      <td>RM</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6120</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>129900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>190</td>\n",
       "      <td>RL</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7420</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>118000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "5   6          50       RL         85.0    14115   Pave   NaN      IR1   \n",
       "6   7          20       RL         75.0    10084   Pave   NaN      Reg   \n",
       "7   8          60       RL          NaN    10382   Pave   NaN      IR1   \n",
       "8   9          50       RM         51.0     6120   Pave   NaN      Reg   \n",
       "9  10         190       RL         50.0     7420   Pave   NaN      Reg   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC  Fence MiscFeature MiscVal  \\\n",
       "0         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "2         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "3         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "4         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "5         Lvl    AllPub  ...        0    NaN  MnPrv        Shed     700   \n",
       "6         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "7         Lvl    AllPub  ...        0    NaN    NaN        Shed     350   \n",
       "8         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "9         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "\n",
       "  MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0      2   2008        WD         Normal     208500  \n",
       "1      5   2007        WD         Normal     181500  \n",
       "2      9   2008        WD         Normal     223500  \n",
       "3      2   2006        WD        Abnorml     140000  \n",
       "4     12   2008        WD         Normal     250000  \n",
       "5     10   2009        WD         Normal     143000  \n",
       "6      8   2007        WD         Normal     307000  \n",
       "7     11   2009        WD         Normal     200000  \n",
       "8      4   2008        WD        Abnorml     129900  \n",
       "9      1   2008        WD         Normal     118000  \n",
       "\n",
       "[10 rows x 81 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('train.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6xG0pWvonzKP"
   },
   "source": [
    "Let's take a look to the features of our dataset, the type of each features inferred from pandas and the count of not null values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "udgS9PYXp0jw",
    "outputId": "00e44d1a-0be4-4273-e97c-baebb1fbb39d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             1460 non-null   int64  \n",
      " 1   MSSubClass     1460 non-null   int64  \n",
      " 2   MSZoning       1460 non-null   object \n",
      " 3   LotFrontage    1201 non-null   float64\n",
      " 4   LotArea        1460 non-null   int64  \n",
      " 5   Street         1460 non-null   object \n",
      " 6   Alley          91 non-null     object \n",
      " 7   LotShape       1460 non-null   object \n",
      " 8   LandContour    1460 non-null   object \n",
      " 9   Utilities      1460 non-null   object \n",
      " 10  LotConfig      1460 non-null   object \n",
      " 11  LandSlope      1460 non-null   object \n",
      " 12  Neighborhood   1460 non-null   object \n",
      " 13  Condition1     1460 non-null   object \n",
      " 14  Condition2     1460 non-null   object \n",
      " 15  BldgType       1460 non-null   object \n",
      " 16  HouseStyle     1460 non-null   object \n",
      " 17  OverallQual    1460 non-null   int64  \n",
      " 18  OverallCond    1460 non-null   int64  \n",
      " 19  YearBuilt      1460 non-null   int64  \n",
      " 20  YearRemodAdd   1460 non-null   int64  \n",
      " 21  RoofStyle      1460 non-null   object \n",
      " 22  RoofMatl       1460 non-null   object \n",
      " 23  Exterior1st    1460 non-null   object \n",
      " 24  Exterior2nd    1460 non-null   object \n",
      " 25  MasVnrType     1452 non-null   object \n",
      " 26  MasVnrArea     1452 non-null   float64\n",
      " 27  ExterQual      1460 non-null   object \n",
      " 28  ExterCond      1460 non-null   object \n",
      " 29  Foundation     1460 non-null   object \n",
      " 30  BsmtQual       1423 non-null   object \n",
      " 31  BsmtCond       1423 non-null   object \n",
      " 32  BsmtExposure   1422 non-null   object \n",
      " 33  BsmtFinType1   1423 non-null   object \n",
      " 34  BsmtFinSF1     1460 non-null   int64  \n",
      " 35  BsmtFinType2   1422 non-null   object \n",
      " 36  BsmtFinSF2     1460 non-null   int64  \n",
      " 37  BsmtUnfSF      1460 non-null   int64  \n",
      " 38  TotalBsmtSF    1460 non-null   int64  \n",
      " 39  Heating        1460 non-null   object \n",
      " 40  HeatingQC      1460 non-null   object \n",
      " 41  CentralAir     1460 non-null   object \n",
      " 42  Electrical     1459 non-null   object \n",
      " 43  1stFlrSF       1460 non-null   int64  \n",
      " 44  2ndFlrSF       1460 non-null   int64  \n",
      " 45  LowQualFinSF   1460 non-null   int64  \n",
      " 46  GrLivArea      1460 non-null   int64  \n",
      " 47  BsmtFullBath   1460 non-null   int64  \n",
      " 48  BsmtHalfBath   1460 non-null   int64  \n",
      " 49  FullBath       1460 non-null   int64  \n",
      " 50  HalfBath       1460 non-null   int64  \n",
      " 51  BedroomAbvGr   1460 non-null   int64  \n",
      " 52  KitchenAbvGr   1460 non-null   int64  \n",
      " 53  KitchenQual    1460 non-null   object \n",
      " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
      " 55  Functional     1460 non-null   object \n",
      " 56  Fireplaces     1460 non-null   int64  \n",
      " 57  FireplaceQu    770 non-null    object \n",
      " 58  GarageType     1379 non-null   object \n",
      " 59  GarageYrBlt    1379 non-null   float64\n",
      " 60  GarageFinish   1379 non-null   object \n",
      " 61  GarageCars     1460 non-null   int64  \n",
      " 62  GarageArea     1460 non-null   int64  \n",
      " 63  GarageQual     1379 non-null   object \n",
      " 64  GarageCond     1379 non-null   object \n",
      " 65  PavedDrive     1460 non-null   object \n",
      " 66  WoodDeckSF     1460 non-null   int64  \n",
      " 67  OpenPorchSF    1460 non-null   int64  \n",
      " 68  EnclosedPorch  1460 non-null   int64  \n",
      " 69  3SsnPorch      1460 non-null   int64  \n",
      " 70  ScreenPorch    1460 non-null   int64  \n",
      " 71  PoolArea       1460 non-null   int64  \n",
      " 72  PoolQC         7 non-null      object \n",
      " 73  Fence          281 non-null    object \n",
      " 74  MiscFeature    54 non-null     object \n",
      " 75  MiscVal        1460 non-null   int64  \n",
      " 76  MoSold         1460 non-null   int64  \n",
      " 77  YrSold         1460 non-null   int64  \n",
      " 78  SaleType       1460 non-null   object \n",
      " 79  SaleCondition  1460 non-null   object \n",
      " 80  SalePrice      1460 non-null   int64  \n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above we have a dataset with 1460 rows and 81 columns. [Here](https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data?select=data_description.txt) you can see a description of each feature, get from the kaggle dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The feature id is a unique identifier for each house, so it doesn't have any relevance to get any insight from it or for training the model, that's why we'll delete this from the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.drop(columns = ['Id'], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qaD0oLXQud1x"
   },
   "source": [
    "Another important thing is to **check how many null data** we have on the dataset. In case we only have a few nulls or to much nulls, we just can delete those records/feature, but otherwise we'll have to infer the missing data. Here you can see all the features with the total of null values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BzhPwFLnuhBv",
    "outputId": "4b9826c3-aaf1-4d09-a91f-36f1d9c26e98"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_nulls</th>\n",
       "      <th>perc_null</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PoolQC</th>\n",
       "      <td>1453.0</td>\n",
       "      <td>99.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature</th>\n",
       "      <td>1406.0</td>\n",
       "      <td>96.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>1369.0</td>\n",
       "      <td>93.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>1179.0</td>\n",
       "      <td>80.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <td>690.0</td>\n",
       "      <td>47.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>259.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType</th>\n",
       "      <td>81.0</td>\n",
       "      <td>5.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>81.0</td>\n",
       "      <td>5.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish</th>\n",
       "      <td>81.0</td>\n",
       "      <td>5.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual</th>\n",
       "      <td>81.0</td>\n",
       "      <td>5.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond</th>\n",
       "      <td>81.0</td>\n",
       "      <td>5.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure</th>\n",
       "      <td>38.0</td>\n",
       "      <td>2.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <td>38.0</td>\n",
       "      <td>2.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <td>37.0</td>\n",
       "      <td>2.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond</th>\n",
       "      <td>37.0</td>\n",
       "      <td>2.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>37.0</td>\n",
       "      <td>2.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count_nulls  perc_null\n",
       "PoolQC             1453.0      99.52\n",
       "MiscFeature        1406.0      96.30\n",
       "Alley              1369.0      93.77\n",
       "Fence              1179.0      80.75\n",
       "FireplaceQu         690.0      47.26\n",
       "LotFrontage         259.0      17.74\n",
       "GarageType           81.0       5.55\n",
       "GarageYrBlt          81.0       5.55\n",
       "GarageFinish         81.0       5.55\n",
       "GarageQual           81.0       5.55\n",
       "GarageCond           81.0       5.55\n",
       "BsmtExposure         38.0       2.60\n",
       "BsmtFinType2         38.0       2.60\n",
       "BsmtFinType1         37.0       2.53\n",
       "BsmtCond             37.0       2.53\n",
       "BsmtQual             37.0       2.53\n",
       "MasVnrArea            8.0       0.55\n",
       "MasVnrType            8.0       0.55\n",
       "Electrical            1.0       0.07"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_count = df.isna().sum()\n",
    "null_count = null_count.where(null_count > 0).dropna().sort_values(ascending = False)\n",
    "total_null_values = null_count.to_frame(name=\"count_nulls\")\n",
    "perc_null_values = (round(100*null_count/len(df),2)).to_frame(name=\"perc_null\")\n",
    "df_null_values = pd.concat([total_null_values, perc_null_values], axis=1)\n",
    "df_null_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HaTBrdFEu3x4"
   },
   "source": [
    "There are 4 features with more than 80% missing data: PoolQC, MiscFeature, Alley and Fence. It's way to much null data to try estimate its value so we'll delete these columns from the dataset. Anyway, if we could have access to the original data could be **good to know why there's missing so much data** for these columns and try to fill those values from the source.    \n",
    "\n",
    "There is *no consensus on how many null values a feature should have to eliminate it*, but you can find out in some sources (like [this](https://towardsdatascience.com/7-ways-to-handle-missing-values-in-machine-learning-1a6326adf79e) one) that from 50% of null in a feature, you can remove the whole feature from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.drop(columns = ['PoolQC', 'MiscFeature', 'Alley', 'Fence'], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='FFE'></a>\n",
    "## A First round of feature engineering "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking a look info the description of each feature, we can realise that there are some ordinal features that are defined as object. Let's do a numerical transformation to these features in order to work in an easier way with them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 76 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   MSSubClass     1460 non-null   int64  \n",
      " 1   MSZoning       1460 non-null   object \n",
      " 2   LotFrontage    1201 non-null   float64\n",
      " 3   LotArea        1460 non-null   int64  \n",
      " 4   Street         1460 non-null   object \n",
      " 5   LotShape       1460 non-null   object \n",
      " 6   LandContour    1460 non-null   object \n",
      " 7   Utilities      1460 non-null   object \n",
      " 8   LotConfig      1460 non-null   object \n",
      " 9   LandSlope      1460 non-null   object \n",
      " 10  Neighborhood   1460 non-null   object \n",
      " 11  Condition1     1460 non-null   object \n",
      " 12  Condition2     1460 non-null   object \n",
      " 13  BldgType       1460 non-null   object \n",
      " 14  HouseStyle     1460 non-null   object \n",
      " 15  OverallQual    1460 non-null   int64  \n",
      " 16  OverallCond    1460 non-null   int64  \n",
      " 17  YearBuilt      1460 non-null   int64  \n",
      " 18  YearRemodAdd   1460 non-null   int64  \n",
      " 19  RoofStyle      1460 non-null   object \n",
      " 20  RoofMatl       1460 non-null   object \n",
      " 21  Exterior1st    1460 non-null   object \n",
      " 22  Exterior2nd    1460 non-null   object \n",
      " 23  MasVnrType     1452 non-null   object \n",
      " 24  MasVnrArea     1452 non-null   float64\n",
      " 25  ExterQual      1460 non-null   int64  \n",
      " 26  ExterCond      1460 non-null   int64  \n",
      " 27  Foundation     1460 non-null   object \n",
      " 28  BsmtQual       1423 non-null   float64\n",
      " 29  BsmtCond       1423 non-null   float64\n",
      " 30  BsmtExposure   1422 non-null   float64\n",
      " 31  BsmtFinType1   1423 non-null   float64\n",
      " 32  BsmtFinSF1     1460 non-null   int64  \n",
      " 33  BsmtFinType2   1422 non-null   float64\n",
      " 34  BsmtFinSF2     1460 non-null   int64  \n",
      " 35  BsmtUnfSF      1460 non-null   int64  \n",
      " 36  TotalBsmtSF    1460 non-null   int64  \n",
      " 37  Heating        1460 non-null   object \n",
      " 38  HeatingQC      1460 non-null   int64  \n",
      " 39  CentralAir     1460 non-null   int64  \n",
      " 40  Electrical     1459 non-null   object \n",
      " 41  1stFlrSF       1460 non-null   int64  \n",
      " 42  2ndFlrSF       1460 non-null   int64  \n",
      " 43  LowQualFinSF   1460 non-null   int64  \n",
      " 44  GrLivArea      1460 non-null   int64  \n",
      " 45  BsmtFullBath   1460 non-null   int64  \n",
      " 46  BsmtHalfBath   1460 non-null   int64  \n",
      " 47  FullBath       1460 non-null   int64  \n",
      " 48  HalfBath       1460 non-null   int64  \n",
      " 49  BedroomAbvGr   1460 non-null   int64  \n",
      " 50  KitchenAbvGr   1460 non-null   int64  \n",
      " 51  KitchenQual    1460 non-null   int64  \n",
      " 52  TotRmsAbvGrd   1460 non-null   int64  \n",
      " 53  Functional     1460 non-null   object \n",
      " 54  Fireplaces     1460 non-null   int64  \n",
      " 55  FireplaceQu    770 non-null    float64\n",
      " 56  GarageType     1379 non-null   object \n",
      " 57  GarageYrBlt    1379 non-null   float64\n",
      " 58  GarageFinish   1379 non-null   float64\n",
      " 59  GarageCars     1460 non-null   int64  \n",
      " 60  GarageArea     1460 non-null   int64  \n",
      " 61  GarageQual     1379 non-null   float64\n",
      " 62  GarageCond     1379 non-null   float64\n",
      " 63  PavedDrive     1460 non-null   int64  \n",
      " 64  WoodDeckSF     1460 non-null   int64  \n",
      " 65  OpenPorchSF    1460 non-null   int64  \n",
      " 66  EnclosedPorch  1460 non-null   int64  \n",
      " 67  3SsnPorch      1460 non-null   int64  \n",
      " 68  ScreenPorch    1460 non-null   int64  \n",
      " 69  PoolArea       1460 non-null   int64  \n",
      " 70  MiscVal        1460 non-null   int64  \n",
      " 71  MoSold         1460 non-null   int64  \n",
      " 72  YrSold         1460 non-null   int64  \n",
      " 73  SaleType       1460 non-null   object \n",
      " 74  SaleCondition  1460 non-null   object \n",
      " 75  SalePrice      1460 non-null   int64  \n",
      "dtypes: float64(12), int64(40), object(24)\n",
      "memory usage: 867.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Making Dictionaries of ordinal features\n",
    "ExterQual_map = {  #Evaluates the quality of the material on the exterior \n",
    "    'Po' :    1,\n",
    "    'Fa' :    2,\n",
    "    'TA' :    3,\n",
    "    'Gd' :    4,\n",
    "    'Ex' :    5\n",
    "    }\n",
    "\n",
    "ExterCond_map = ExterQual_map # Evaluates the present condition of the material on the exterior\n",
    "\n",
    "BsmtQual_map = ExterQual_map #Evaluates the height of the basement\n",
    "BsmtQual_map['NA'] = -1\n",
    "\n",
    "BsmtCond_map = BsmtQual_map #Evaluates the general condition of the basement\n",
    "\n",
    "BsmtExposure_map = {  #Refers to walkout or garden level walls\n",
    "    'NA' :    -1,\n",
    "    'No' :    0,\n",
    "    'Mn' :    1,\n",
    "    'Av' :    2,\n",
    "    'Gd' :    3\n",
    "    }\n",
    "\n",
    "BsmtFinType1_map = {  #Rating of basement finished area\n",
    "    'NA' :    -1,\n",
    "    'Unf' :    0,\n",
    "    'LwQ' :    1,\n",
    "    'Rec' :    2,\n",
    "    'BLQ' :    3,\n",
    "    'ALQ' :    4,\n",
    "    'GLQ' :    5\n",
    "    }\n",
    "\n",
    "BsmtFinType2_map = BsmtFinType1_map #Rating of basement finished area (if multiple types)\n",
    "\n",
    "HeatingQC_map = ExterQual_map #Heating quality and condition\n",
    "\n",
    "CentralAir_map = {  #Central air conditioning\n",
    "   'Y': 1,\n",
    "   'N': 0\n",
    "}\n",
    "\n",
    "KitchenQual_map = ExterQual_map #Kitchen quality\n",
    "\n",
    "FireplaceQu_map = BsmtQual_map #Fireplace quality\n",
    "\n",
    "GarageFinish_map = { #Interior finish of the garage\n",
    "    'NA': -1,\n",
    "    'Unf': 0,\n",
    "    'RFn': 1,\n",
    "    'Fin': 2\n",
    "}\n",
    "\n",
    "GarageQual_map = BsmtQual_map #Garage quality\n",
    "\n",
    "GarageCond_map = BsmtQual_map #Garage condition\n",
    "\n",
    "PavedDrive_map = {  #Paved driveway\n",
    "    'N': 0,\n",
    "    'P': 1,\n",
    "    'Y': 2\n",
    "}\n",
    "\n",
    "# Transforming Categorical features into numerical features\n",
    "df_first_transform = df.copy()\n",
    "df_first_transform.loc[:,'ExterQual'] = df_first_transform['ExterQual'].map(ExterQual_map)\n",
    "df_first_transform.loc[:,'ExterCond'] = df_first_transform['ExterCond'].map(ExterCond_map)\n",
    "df_first_transform.loc[:,'BsmtQual'] = df_first_transform['BsmtQual'].map(BsmtQual_map)\n",
    "df_first_transform.loc[:,'BsmtCond'] = df_first_transform['BsmtCond'].map(BsmtCond_map)\n",
    "df_first_transform.loc[:,'BsmtExposure'] = df_first_transform['BsmtExposure'].map(BsmtExposure_map)\n",
    "df_first_transform.loc[:,'BsmtFinType1'] = df_first_transform['BsmtFinType1'].map(BsmtFinType1_map)\n",
    "df_first_transform.loc[:,'BsmtFinType2'] = df_first_transform['BsmtFinType2'].map(BsmtFinType2_map)\n",
    "df_first_transform.loc[:,'HeatingQC'] = df_first_transform['HeatingQC'].map(HeatingQC_map)\n",
    "df_first_transform.loc[:,'CentralAir'] = df_first_transform['CentralAir'].map(CentralAir_map)\n",
    "df_first_transform.loc[:,'KitchenQual'] = df_first_transform['KitchenQual'].map(KitchenQual_map)\n",
    "df_first_transform.loc[:,'FireplaceQu'] = df_first_transform['FireplaceQu'].map(FireplaceQu_map)\n",
    "df_first_transform.loc[:,'GarageFinish'] = df_first_transform['GarageFinish'].map(GarageFinish_map)\n",
    "df_first_transform.loc[:,'GarageQual'] = df_first_transform['GarageQual'].map(GarageQual_map)\n",
    "df_first_transform.loc[:,'GarageCond'] = df_first_transform['GarageCond'].map(GarageCond_map)\n",
    "df_first_transform.loc[:,'PavedDrive'] = df_first_transform['PavedDrive'].map(PavedDrive_map)\n",
    "\n",
    "df_first_transform.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, the ordinal features has been changed to a float64 type (due there are also null values). Finally, let's **add two more important features**: total square feet of the house and the total of bathrooms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_first_transform['TotalSF'] = df_first_transform['TotalBsmtSF'] + df_first_transform['GrLivArea']\n",
    "df_first_transform['Total_Bathrooms'] = df_first_transform['FullBath'] + df_first_transform['HalfBath'] + df_first_transform['BsmtFullBath'] + df_first_transform['BsmtHalfBath']\n",
    "\n",
    "# Code for transforming the dataset. We'll be using it later.\n",
    "def feature_engineering_sale_price_dataset(df):\n",
    "    df_first_transform = df.copy()\n",
    "    df_first_transform.drop(columns = ['PoolQC', 'MiscFeature', 'Alley', 'Fence'], inplace = True)\n",
    "    df_first_transform.loc[:,'ExterQual'] = df_first_transform['ExterQual'].map(ExterQual_map)\n",
    "    df_first_transform.loc[:,'ExterCond'] = df_first_transform['ExterCond'].map(ExterCond_map)\n",
    "    df_first_transform.loc[:,'BsmtQual'] = df_first_transform['BsmtQual'].map(BsmtQual_map)\n",
    "    df_first_transform.loc[:,'BsmtCond'] = df_first_transform['BsmtCond'].map(BsmtCond_map)\n",
    "    df_first_transform.loc[:,'BsmtExposure'] = df_first_transform['BsmtExposure'].map(BsmtExposure_map)\n",
    "    df_first_transform.loc[:,'BsmtFinType1'] = df_first_transform['BsmtFinType1'].map(BsmtFinType1_map)\n",
    "    df_first_transform.loc[:,'BsmtFinType2'] = df_first_transform['BsmtFinType2'].map(BsmtFinType2_map)\n",
    "    df_first_transform.loc[:,'HeatingQC'] = df_first_transform['HeatingQC'].map(HeatingQC_map)\n",
    "    df_first_transform.loc[:,'CentralAir'] = df_first_transform['CentralAir'].map(CentralAir_map)\n",
    "    df_first_transform.loc[:,'KitchenQual'] = df_first_transform['KitchenQual'].map(KitchenQual_map)\n",
    "    df_first_transform.loc[:,'FireplaceQu'] = df_first_transform['FireplaceQu'].map(FireplaceQu_map)\n",
    "    df_first_transform.loc[:,'GarageFinish'] = df_first_transform['GarageFinish'].map(GarageFinish_map)\n",
    "    df_first_transform.loc[:,'GarageQual'] = df_first_transform['GarageQual'].map(GarageQual_map)\n",
    "    df_first_transform.loc[:,'GarageCond'] = df_first_transform['GarageCond'].map(GarageCond_map)\n",
    "    df_first_transform.loc[:,'PavedDrive'] = df_first_transform['PavedDrive'].map(PavedDrive_map)\n",
    "    df_first_transform['TotalSF'] = df_first_transform['TotalBsmtSF'] + df_first_transform['GrLivArea']\n",
    "    df_first_transform['Total_Bathrooms'] = df_first_transform['FullBath'] + df_first_transform['HalfBath'] + df_first_transform['BsmtFullBath'] + df_first_transform['BsmtHalfBath']\n",
    "    return df_first_transform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='EDAN'></a>\n",
    "## Exploratory Data Analysis of SalePrice for numerical features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset contains too many features to explain them by themselves and their relationship to each other to extract information one at a time. That's why we'll try to filter the important features from our predictive feature SalePrice and then we could extract more detailed analysis only from them and get useful insights.\n",
    "\n",
    "This first feature filter we'll do it using the **spearman correlation** (because we're dealing with ordinal and continous variables). We'll only do the exploratory data analysis with features with the greatest correlation with our SalePrice feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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c99Ojx+x8qgCQX6zDLM96wM7rCgAFCvbbwLKYP1BfWcz/fpgPdluNTBXcGKnZ7OfEUo9/ktIHu44I/NTVuYWc46iAn3O3SMXPH6nDS6Otzz+FWeo/Y5Y/LObnqRXZsic90BHePFX3S2acGz50tHjtTAwKuHUEXXZuz3wRP/dnYSH7WBVM+K+dkTN7f40T2HlOAeBRIXs7tR7y2yGux3//CkXsOoUq/vs7S2Afi8o8/vNe3zqbWV6sxYQGBhWQed5UxM9ly8vLa6TiP+/ZIvZzlqfFsSqYStjlRvzPAAOre9w6pHr4z16pZTl+/Dhat24NsVgMW1tbTJo0CUVFJSfgwMBAHD9+HEuXLoUgCBAEAcnJyerHrl+/Hj169MCoUaOwZcsW5OdrJtz29vZGcHAwQkJCYGVlBR8fHwDA1atX0bVrV0gkEtjY2GDQoEF48uSfBOgHDx7ERx99BHNzc1haWqJHjx5ISkqq/CeDEEIIIe+FSqWs1KWmoU7ta+7fv49u3brB09MTcXFxWLlyJdatW4fZs2cDAJYuXQovLy8MHz4caWlpSEtLg719ycxYKpUK69evx8CBA+Hi4gIHBwfs3Lmz1DY2btwIfX19REdHY9WqVcjKykKnTp3QvHlzxMbG4uDBg3j48CH69u2rfkxeXh7GjRuH2NhYREZGQiQS4bPPPoOyBv58QAghhBDQ8INyouEHr1mxYgXs7e2xfPlyCIIAFxcXPHjwAKGhoZg6dSrMzMygr68PIyMjSKVSjccePXoUz58/V199HThwINatW4dBgwZp1HN0dMT8+fPV/8+ePRvNmzfH3Llz1esiIiJgb2+PmzdvwsnJCb1799aIERERAWtra1y/fh2NGzcuc18UCgUUCs2ftItVxdAR2D8tEkIIIYRUN3Sl9jXx8fHw8vKC8Mqc023btkVubi7u3WOPu4mIiIC/vz90dUu+KwQEBCA6OrrUMIGWLVtq/B8XF4djx45BIpGoFxcXFwBQPzYxMREBAQFo2LAhTE1NIZPJAJSM4X2T8PBwmJmZaSw3shO1eyIIIYQQ8n6plJW71DDUqa0gmZmZ2LNnD1asWAFdXV3o6uqibt26KCoqQkREhEZdY2Njjf9zc3Ph5+cHuVyusSQmJqJ9+5Ibsvz8/JCZmYk1a9bg7NmzOHv2LICSG83eJCwsDNnZ2RqLi5ljBe85IYQQQsj7R8MPXuPq6opdu3ZBpVKpr9ZGR0fDxMQEdnZ2AAB9fX0Uv3ZH/ObNm2FnZ4e9e/dqrD98+DAWLVqEmTNnQken7J/9W7RogV27dkEmk6mv8r4qIyMDCQkJWLNmDdq1awcAOHXqFHdfxGIxxGKxxjoaekAIIYRUEzRNbrn8p6/UZmdnl7o6OmLECKSmpuLrr7/GjRs38Mcff2DatGkYN24cRKKSp0smk+Hs2bNITk7GkydPoFQqsW7dOvTp0weNGzfWWIYOHYonT57g4MGDb2zH6NGjkZmZiYCAAJw/fx5JSUk4dOgQgoKCUFxcjFq1asHS0hK//PILbt26hb///hvjxo37t54mQgghhJAq7z99pTYqKgrNmzfXWDd06FAcOHAAEyZMQLNmzWBhYYGhQ4di8uTJ6jrjx4/HkCFD4Obmhvz8fMTGxiIuLg5r1qwptQ0zMzN8/PHHWLduHbp3715mO+rUqYPo6GiEhobik08+gUKhQP369eHr6wuRSARBELB161aMGTMGjRs3hrOzM5YtWwZvb+9y7/PEIv73mJxp67l1eHlorfet48a43uo7ZvnAXz/lxsD9O8zi4stXuSH0Bn7FLJd34T8fsvUDuHVUgZuZ5br6/PFNrrLHzPIn6eycjQAQZcjOy6qnRZ7LUS7s8eU69RtyY7SayD/9PNvHHgOu0ib3p0cWszzuIj8fssijBbtc4Od2vqvH3t8O47twYxj8dohbJ+lvc2Z5rB4/B2nfXwcxy4v/+oMbQ0fKbkez9o+4MQyG92VXSHvzPQVqtevw6zxOY4c4eIUbwkrJPhYtbfm5jPWMOLl92/H3JWUDO0YDLa5lFSr5uZt1OPmsL4v5MTwU7GPxuYgfQ3/4NGZ5cUIMN4aRlTW3zntTA8e9Vqb/bKd2w4YN2LBhwxvLz50798YyJycnxMRovlFUjCTxBw4cUP8dFRVVZh1HR0fs3r37jTE6d+6M69eva71NQgghhJD/kv9sp5YQQgghpEqrgblkKxN1agkhhBBCqiIaflAu/+kbxQghhBBCSM1AV2oJIYQQQqoiGn5QLnSllhBCCCGEVHvUqeXw9vZGSEiI+n+ZTIYlS5a8t/a8ThCEUhM+EEIIIaT6U6mKK3Wpad57pzY1NRVffvkl6tSpA319fdSvXx9jx45FRkbG+26a1i5duoQvvvgCNjY2MDAwgKOjI4YPH46bN2++76YRQgghhPwnCKr3mOz09u3b8PLygpOTE2bPno0GDRrg2rVrmDBhAgoKCnDmzBlYWFhUyrYLCwuhp8dP7Ozt7Q0PDw/11VmZTIaQkBD11dv9+/ejd+/e8PHxwZgxY9CoUSM8evQIO3bsQGpqKrZt21Yp7X9JEATs2bMHvXr10qr+T/YDuXWK+PnsIeEM87muW8SNMT92LrO8uXt/boxv9ByY5Ve1aEc22HV+bPeUG6PnMf6TNrWQfSxfEutzY/C+hdbm7y4c8JxfieOQniGzPFUo4MZorBRz67QpYrd1p74WMRTsZ81Yi2koTxqyY8i0mNSkJXKZ5Yu0mMH6QyV/co3aRexT+l19/rGqw/lUKNTiHGHJeVp1tfjkWalKZZa76ltxYxRqcfe4ucD+LOio0GIyAs5Haao+/xgx4jT1Pu+FAdCd855JLzbgxjBR8U8kaTrs81U9pYIbI0XEfv9aFvPfm6cN2G+chwJ/shEdsA/o5cmV+znO8kK+v1LjG3j0qNT4/7b3eqV29OjR0NfXx+HDh9GhQwfUq1cPXbt2xdGjR3H//n18//33+O6779CmTZtSj23WrBlmzpyp/n/t2rVwdXWFgYEBXFxcsGLFCnVZcnIyBEHAtm3b0KFDBxgYGGDz5s3IyMhAQEAA6tatCyMjIzRp0gRbtmzRuv3Pnz9HUFAQunXrhn379qFz585o0KAB2rRpg4ULF2L16tXqusePH0fr1q0hFotha2uLSZMmoajonxOHt7c3xowZg4kTJ8LCwgJSqRTTp0/X2F5iYiLat28PAwMDuLm54ciRI1q3lRBCCCGkJntv2Q8yMzNx6NAhzJkzB4aGmld9pFIpBgwYgG3btuHkyZMIDw9HUlISGjVqBAC4du0aLl++jF27dgEANm/ejKlTp2L58uVo3rw5Ll26hOHDh8PY2BhDhgxRx500aRIWLVqE5s2bw8DAAC9evEDLli0RGhoKU1NT/Pnnnxg0aBAaNWqE1q1bc/fh0KFDePLkCSZOnFhmubm5OQDg/v376NatGwIDA/Hrr7/ixo0bGD58OAwMDDQ6rhs3bsS4ceNw9uxZxMTEIDAwEG3btkWXLl2gVCrx+eefw8bGBmfPnkV2drbGWF9CCCGE1DCU/aBc3lunNjExESqVCq6urmWWu7q64unTp7C2tkazZs3w+++/Y8qUKQBKOrFt2rSBg0PJT8/Tpk3DokWL8PnnnwMAGjRogOvXr2P16tUandqQkBB1nZfGjx+v/vvrr7/GoUOHsH37dq06tYmJJfPSu7i4MOutWLEC9vb2WL58OQRBgIuLCx48eIDQ0FBMnToVIlHJBfOmTZti2rSSeawdHR2xfPlyREZGokuXLjh69Chu3LiBQ4cOoU6dkvm/586di65du75xuwqFAgqF5k9Ahapi6Ala/M5JCCGEkPeLJl8ol/d+o5g2Q3oHDBiA33//XV1/y5YtGDBgAAAgLy8PSUlJGDp0KCQSiXqZPXs2kpKSNOK0atVK4//i4mLMmjULTZo0gYWFBSQSCQ4dOoSUlJQKazsAxMfHw8vLC4Lwz7idtm3bIjc3F/fu3VOva9q0qcbjbG1t8ejRI3UMe3t7dYcWALy8vJjbDQ8Ph5mZmcZyJOeaVm0mhBBCCKlO3lun1sHBAYIgID4+vszy+Ph41KpVC9bW1ggICEBCQgIuXryI06dPIzU1Ff7+/gCA3NySmy/WrFkDuVyuXq5evYozZ85oxDQ2Ntb4f8GCBVi6dClCQ0Nx7NgxyOVy+Pj4oKCAf5MLADg5OQEAbty4Ua59f5PXb1wTBAHKd/jpISwsDNnZ2RpLF1P3d20mIYQQQv4NyuLKXWqY99aptbS0RJcuXbBixQrk5+drlKWnp2Pz5s3w9/eHIAiws7NDhw4dsHnzZmzevBldunRB7dq1AQA2NjaoU6cObt++DQcHB42lQYMGzDZER0ejZ8+eGDhwIJo1a4aGDRuWKw3XJ598AisrK8yfP7/M8qysLAAlQyliYmI0ruxGR0fDxMQEdnZ2Wm3L1dUVqampSEtLU697vdP+OrFYDFNTU42Fhh4QQgghpCZ6r8MPli9fDoVCAR8fH5w4cQKpqak4ePAgunTpgrp162LOnDnqugMGDMDWrVuxY8cO9dCDl2bMmIHw8HAsW7YMN2/exJUrV7B+/XosXryYuX1HR0ccOXIEp0+fRnx8PEaOHImHDx9q3X5jY2OsXbsWf/75Jz799FMcPXoUycnJiI2NxcSJE/G///0PAPDVV18hNTUVX3/9NW7cuIE//vgD06ZNw7hx49TjaXk6d+4MJycnDBkyBHFxcTh58iS+//57rdtKCCGEkGpGpazcpYZ5bzeKASWdytjYWEybNg19+/ZFZmYmpFIpevXqhWnTpmnkqO3Tpw+Cg4Oho6NTKifrsGHDYGRkhAULFmDChAkwNjZGkyZNuNkBJk+ejNu3b8PHxwdGRkYYMWIEevXqhezsbK33oWfPnjh9+jTCw8PRv39/5OTkwN7eHp06dcLs2bMBAHXr1sWBAwcwYcIENGvWDBYWFhg6dCgmT56s9XZEIhH27NmDoUOHonXr1pDJZFi2bBl8fX21jgEADQv4Pze4SZ9w6zx6bMIsH/jrp9wYvDy0l679zo1RfPcys7xo60ZuDN2+7Ny9i/rs5sY4cmEqt86DrqOZ5c3q8vMp6pqz37IZcv6V+E051sxyPU7ORgDoJbDfI40GGzPLAUCwMefWKYxNZ5Z/l8l/zqLP1mGXc3LQAsD4JveZ5Y8T+Pt7NJ+dpzhilj03RvbaGG6dv5LYv/5kCPxzwPSfPZnlqRMOc2PwqFT842zgqfXM8qLzWuTw1GYI16MH7OKfLnJDqJTs/enSnp9TueDOM2a52NmcG+PDTY+Y5bPA/gUTAK5okf/ZlpMPOUafnw/XoYD92jzW4XdRJm3yYZYXHdjLjaHj1Ihbh1QP77VTCwD169fHhg0buPXMzc3x4sWLN5b3798f/fuX3UmSyWRl3tRlYWHBnWI2KipK4//k5ORSdVq1aqVOL/YmHTp0wLlz57TeDoBSbXNycsLJkyc11r3HuTMIIYQQUpkopVe5vPfsB4QQQgghhLyr936llhBCCCGElKEGjnutTNSpJYQQQgipimj4QbnQ8ANCCCGEEFLt0ZVaQgghhJCqiK7Ulgtdqa0EMpkMS5Yseec43t7e3LRkFbUtQgghhJDqTOtOrUqlQufOneHjUzon3IoVK2Bubo579+5VaOOAklRXgiCoF2tra3Tr1g1Xrlyp8G1VtvDwcOjo6GDBggXvuymEEEIIqeJUquJKXWoarYcfCIKA9evXo0mTJli9ejVGjhwJALhz5w4mTpyIlStXaj3lq7YKC/9JrJ6QkABTU1M8ePAAEyZMQPfu3XHr1i3o6+tX6DYrU0REBCZOnIiIiAhMmDDhvbQhX4sZzDIy+Ink84s5Sf7v3+HG+EbPgVnOm1gBAHTqN2WWZ0ZncWPU8s1glrd8wX/jF535g1vnaYYRs1wQ5XFjKFPZr9+dR7W4MYoMORW0yH1sYMie9EBxJY1ZDgCG7q7cOoXp7C+vhc/4x7OJqohZblvMTzQPzi+AWbn8RPMSzlwDytvJ3BiZ9/nvTQXnKRFpMbmGUMuGWa6nz39PPM1kH++yFlncGMrHd5nlqmv8c4SqkD9Bhyozh1me8Zj/vBdyzolFR/nvb0Mzdgw9aT6zHABaGrAnGzHM5/+cbaDiv6/+jQzplsXs9y4AqB6yjxHlI/ZrCwBQ3NC2SaSKK9fwA3t7eyxduhTjx4/HnTt3oFKpMHToUHzyySdo3rw5unbtColEAhsbGwwaNAhPnvwzM9XBgwfx0UcfwdzcHJaWlujRoweSkpLU5cnJyRAEAdu2bUOHDh1gYGCAzZs3q8tr164NqVSKFi1aICQkBKmpqbhx458D8dSpU2jXrh0MDQ1hb2+PMWPGIC/vn5OITCbD7NmzMXjwYEgkEtSvXx/79u3D48eP0bNnT0gkEjRt2hSxsbEa+7xr1y64u7tDLBZDJpNh0aJFGuWPHj2Cn58fDA0N0aBBA402v+r48ePIz8/HzJkzkZOTg9OnT2uU5+Xlqdtma2tbajvl2RYhhBBCagClsnKXGqbcY2qHDBmCjz/+GF9++SWWL1+Oq1evYvXq1ejUqROaN2+O2NhYHDx4EA8fPkTfvn3Vj8vLy8O4ceMQGxuLyMhIiEQifPbZZ1C+9qROmjQJY8eORXx8fJlDHbKzs7F161YAUF+lTUpKgq+vL3r37o3Lly9j27ZtOHXqFIKDgzUe++OPP6Jt27a4dOkSunfvjkGDBmHw4MEYOHAgLl68iEaNGmHw4MHqWbouXLiAvn37ol+/frhy5QqmT5+OKVOmaMyAFhgYiNTUVBw7dgw7d+7EihUr8OhR6WkK161bh4CAAOjp6SEgIADr1q3TKJ8wYQKOHz+OP/74A4cPH0ZUVBQuXtScmlHbbRFCCCGE/Ne8VfaDX375Be7u7jhx4gR27dqF1atXo3nz5pg7d666TkREBOzt7XHz5k04OTmhd+/eGjEiIiJgbW2N69evo3Hjxur1ISEh+Pzzz9X/JyQkAIB6aMPLq6+ffvopXFxcAJSMVR0wYID6pipHR0csW7YMHTp0wMqVK2FgUPLTYLdu3dTDJqZOnYqVK1fC09MTX3zxBQAgNDQUXl5eePjwIaRSKRYvXoyPP/4YU6ZMAVAyTe3169exYMECBAYG4ubNm/jrr79w7tw5eHqWzJW+bt06uLpq/qyak5ODnTt3IiamZN72gQMHol27dli6dCkkEglyc3Oxbt06bNq0CR9//DEAYOPGjRrDObTd1qsUCgUUCoXGukJVMfQEztABQgghhLx/NPlCubxV9oPatWtj5MiRcHV1Ra9evRAXF4djx45BIpGol5cdzpdDDBITExEQEICGDRvC1NQUMpkMAJCSkqIRu1WrVmVu8+TJk7hw4QI2bNgAJycnrFq1Sl0WFxeHDRs2aGzfx8cHSqUSd+78M7azadN/xl/a2JSMGWvSpEmpdS+vfsbHx6Nt27Ya7Wjbti0SExNRXFyM+Ph46OrqomXLlupyFxcXmJubazxmy5YtaNSoEZo1awYA8PDwQP369bFt2zb1c1RQUIA2bdqoH2NhYQFnZ2f1/9pu61Xh4eEwMzPTWPbkXntjfUIIIYRUITT8oFzeOk+trq4udHVLHp6bmws/Pz/MmzevVD1bW1sAgJ+fH+rXr481a9agTp06UCqVaNy4MQoKCjTqGxuXPSC/QYMGMDc3h7OzMx49egR/f3+cOHFCvf2RI0dizJgxpR5Xr1499d96enrqvwVBeOO614dEvKt169bh2rVr6ufr5TYiIiIwdOjQCt3Wq8LCwjBu3DiNdX86jai07RFCCCGEvC8VMvlCixYtsGvXLshkMo2O20sZGRlISEjAmjVr0K5dOwAlN3a9rdGjRyM8PBx79uzBZ599hhYtWuD69etwcGDfTV9erq6uiI6O1lgXHR0NJycn6OjowMXFBUVFRbhw4YJ6SEBCQgKysrLU9a9cuYLY2FhERUXBwsJCvT4zMxPe3t64ceMGGjVqBD09PZw9e1bdCX/69Clu3ryJDh06AIBW23qdWCyGWKx5ZzcNPSCEEEKqCRp+UC4VMvnC6NGjkZmZiYCAAJw/fx5JSUk4dOgQgoKCUFxcjFq1asHS0hK//PILbt26hb///rvUFcTyMDIywvDhwzFt2jSoVCqEhobi9OnTCA4OhlwuR2JiIv74449SN4qV17fffovIyEjMmjULN2/exMaNG7F8+XKMHz8eAODs7AxfX1+MHDkSZ8+exYULFzBs2DAYGv6TM2ndunVo3bo12rdvj8aNG6uX9u3bw9PTE+vWrYNEIsHQoUMxYcIE/P3337h69SoCAwMheiX9ljbbIoQQQgj5r6qQK7V16tRBdHQ0QkND8cknn0ChUKB+/frw9fWFSCSCIAjYunUrxowZg8aNG8PZ2RnLli2Dt7f3W28zODgYixcvxo4dO9C3b18cP34c33//Pdq1aweVSoVGjRrB39//nfarRYsW2L59O6ZOnYpZs2bB1tYWM2fORGBgoLrO+vXrMWzYMHTo0AE2NjaYPXu2+saygoICbNq0CaGhoWXG7927NxYtWoS5c+diwYIF6mEcJiYm+Pbbb5Gdna1Rn7UtbelpkYO0nvNTbp2sB+zOdPHlq9wYV3X1mOVFWzdyY/Dy0FrvW8csB4D80JHM8vt67JydAKD4/S9unYwXtZnlBjn8nIymVuw8lW6O/GwY8XfrMsu1+ab7INOEWW7Lf8rwYNZpbp3iYvZxJv2IfxWjOJG9R1qkuoWOOfsXDn0dft5W6wJ2HcWlB/x26LFzvwJAyxfsfKiCip9z9fn8X5jlEvahDAAwsihgll86I+XGaDF5BrNc0OXn3NWtZ86to3rGfl8VK/kXD0QC+9yqZ8A/RvKy2HnXiy7wzxHm4JxXuRGAuoX8Wnki9nvCpAIuMD7jbAMAlHHsXMUiK/a5CgCK07O5dd6bGjjutTIJKpUWvRxSY/wh7c+t4+V+n1uH16mt04v/wTllC/vkO+sLBbMcAHL+hU7tjkP8Hlrvtvzn7EIUuydQ1+wZNwavUyto0UHbVQGdWrcC9mvT8jP+vjw6xT/1FBezOy3adGrP72NPSHHOgH0cAsCoj9gdztsn+B+caQXsDqnXB/xO7eMEfqc25xl7IoiLAv+9+UXLVGZ58XNuCCg5/aKr17Xo1H70kFn+b3Vqb/zfu/8iVtuG/55Q5LOvM4kN+Z3NJZlWzPIu/PkbtMLr1Gbq8F8bmyL2+7dQ4MfoNow9uYaqgP+c8Tq1ZhsjuTEqS/7hFZUa3/CTryo1/r+tQq7UEkIIIYSQCkZjasulQsbUEkIIIYQQ8j5Rp5YQQgghpCqqYnlqf/75Z8hkMhgYGKBNmzY4d+4cs35WVhZGjx4NW1tbiMViODk54cCBA2/7bHDR8ANCCCGEkKqoCt0otm3bNowbNw6rVq1CmzZtsGTJEvj4+CAhIQG1a5e+Z6SgoABdunRB7dq1sXPnTtStWxd3795lThr1rqhTSwghhBBCmBYvXozhw4cjKCgIALBq1Sr8+eefiIiIwKRJk0rVj4iIQGZmJk6fPq2e6OrlbLKVhYYfEEIIIYRURSplpS4KhQI5OTkai0JROrtNQUEBLly4gM6dO6vXiUQidO7cGTExMWU2fd++ffDy8sLo0aNhY2ODxo0bY+7cuSgu5qe3e1vUqa3GvL29ERIS8r6bQQghhJBqKDw8HGZmZhpLeHh4qXpPnjxBcXExbGw0U1za2NggPT29zNi3b9/Gzp07UVxcjAMHDmDKlClYtGgRZs+eXSn7AlTz4QeBgYHYuLF0gn4fHx8cPHiQ+3hvb294eHhgyZIlFdam06dPY/bs2YiJiUF+fj4cHR0RFBSEsWPHQkfn/U9Ra6hFepCcNHaeSwAoULAPHb2B/Nx32VtWMst1+w7kxqjlm8Es5+WgBQDDeauZ5UZ/8Se4EI8ewq1jG7uDWS6p9YIbg5eHNu2uKTdGug77GNBV8XNDfub4hFkusrbmxqgT5syt83DBGWa5qoB/PNvVymGW38q15MbQc7Njlgsn+cnb7+iz3/8du3pwYxikXuPWuZJtwSy/LeZfJTGeN5NZnhc2lRtDxX7a4dG67A/CVxmO/x+zXHmFfZMKAAhG/Ny+qgz2eQT/x86XCwDFnPeNSIefl9nMhn0OMG7Dfm0BQLmNXa7Nh/4jHX6tWpyrbff0+J93RpzPo8cifjt0erFzr6vkp7gxRFaZ3DrvTSWPqQ0LCys1w6tYLK6Q2EqlErVr18Yvv/wCHR0dtGzZEvfv38eCBQswbdq0CtnG66p1pxYAfH19sX79eo11FfWCaKugoAD6+vrYs2cP+vbti6CgIBw7dgzm5uY4evQoJk6ciJiYGGzfvh2CFsmkCSGEEEIqm1gs1qrPZGVlBR0dHTx8qPkF7+HDh5BKy55IxdbWFnp6ehoX9FxdXZGenq7uN1W0aj/8QCwWQyqVaiy1atVCVFQU9PX1cfLkSXXd+fPno3bt2nj48CECAwNx/PhxLF26FIIgQBAEJCcnAwCuXr2Krl27QiKRwMbGBoMGDcKTJ/9cmfL29kZwcDBCQkJgZWUFHx8f5OXlYfjw4fj000/xyy+/wMPDAzKZDMOGDcPGjRuxc+dObN++HQAQFRUFQRCQlZWljimXyzXakJGRgYCAANStWxdGRkZo0qQJtmzZUunPJyGEEEKqiEoeU6stfX19tGzZEpGR/8yuplQqERkZCS8vrzIf07ZtW9y6dQvKV64237x5E7a2tpXSoQVqQKf2TV6ONx00aBCys7Nx6dIlTJkyBWvXroWNjQ2WLl0KLy8vDB8+HGlpaUhLS4O9vT2ysrLQqVMnNG/eHLGxsTh48CAePnyIvn37asTfuHEj9PX1ER0djVWrVuHw4cPIyMjA+PHjS7XFz88PTk5O5eqUvnjxAi1btsSff/6Jq1evYsSIERg0aBA3J9yryhoAXqCqvAHahBBCCKmZxo0bhzVr1mDjxo2Ij4/HqFGjkJeXp86GMHjwYISFhanrjxo1CpmZmRg7dixu3ryJP//8E3PnzsXo0aMrrY3VfvjB/v37IZFINNZ99913+O677zB79mwcOXIEI0aMwNWrVzFkyBB8+umnAAAzMzPo6+vDyMhI49L58uXL0bx5c8ydO1e9LiIiAvb29rh58yacnJwAAI6Ojpg/f766zt69ewGUXFovi4uLC27evKn1ftWtW1ejg/z111/j0KFD2L59O1q3bq1VjPDwcMyYMUNj3UAjdwySNNa6HYQQQgh5T6pQnlp/f388fvwYU6dORXp6Ojw8PHDw4EH1zWMpKSkQif65Vmpvb49Dhw7hm2++QdOmTVG3bl2MHTsWoaGhldbGat+p7dixI1au1LzhyMKiZDC9vr4+Nm/ejKZNm6J+/fr48ccfufHi4uJw7NixUh1lAEhKSlJ3alu2bFnm41WqN98MUJ7L7cXFxZg7dy62b9+O+/fvo6CgAAqFAkZa3PTwUlkDwE84DNX68YQQQgghLwUHByM4OLjMsqioqFLrvLy8cOYM+4bfilTtO7XGxsZwcHB4Y/np06cBAJmZmcjMzISxsTEzXm5uLvz8/DBv3rxSZba2thrbfZWjoyMAID4+Hh9++GGpx8bHx8PDwwMA1N9kXu0AFxYWatRfsGABli5diiVLlqBJkyYwNjZGSEgICgoKmO1/VVkDwPWF95+BgRBCCCFaKMe4V1KDx9QCJVdWv/nmG6xZswZt2rTBkCFDNAYs6+vrl0oC3KJFC1y7dg0ymQwODg4aC6tD7OPjAwsLCyxatKhU2b59+5CYmIjAwEAAgPX/T3eUlpamriOXyzUeEx0djZ49e2LgwIFo1qwZGjZsWK7hC4QQQgip5pTKyl1qmGrfqVUoFEhPT9dYXiYJHjhwIHx8fBAUFIT169fj8uXLGp1OmUyGs2fPIjk5GU+ePIFSqcTo0aORmZmJgIAAnD9/HklJSTh06BCCgoKYs2AYGxtj9erV+OOPPzBixAhcvnwZycnJWLduHQIDAzF8+HB069YNAODg4AB7e3tMnz4diYmJ+PPPP0t1hh0dHXHkyBGcPn0a8fHxGDlyZKlUGoQQQgghpISgYg0CreLeNPmCs7Mz+vfvj1WrVuHKlSuwtCxJrr57924EBATg3LlzaNasGW7evIkhQ4YgLi4O+fn5uHPnDmQyGRITExEaGopjx45BoVCgfv368PX1xeLFiyEIAnPShpMnT2LOnDmIiYlBTk5J9vF58+Zh4sSJGvWio6MxatQoJCYmwtPTE2PGjMEXX3yhbkNmZia+/PJLREZGwsjICCNGjEBKSgqys7PVN6W9zeQR6+z4ExpokSMclpzE29ki/jAHv4/TmOXLT5Sd++5VLV+w23Ffjz/CxkjJ3uE+l2dxY/zYkp+MvptOFrP8ygszbgzTYvY3awPwv3nzamjzTTdBX49ZbqTFBQBpURG3jonArnNP4OdX1OOc4nJ0+Husx3lPWBbz98Vchz106Bb44+UVWrw4VkXsJz9dlx+kfiF7f55qMZGMIed99ViLdqRxTkYWSn7eb2MtjkXeOaCWkp81ppZu6alFX5VTxL+n4gXn3afQIs+5Htj7Ugh+DG3OAfqcn8WfafEZYMB5bxZqsb8iTox8EX9vDDlXLD9P/50bo7Lkb2dPhPKuDPvyP7uqk2rdqa3qXrx4gZ49eyI1NRXHjx9XDzt4n6hTWxp1ajVRp7asGOxy6tSWRp1aTdSpLY06tXzUqS2faj/8oCozMDDAH3/8gcGDB+PEiRPvuzmEEEIIqU5Uqspdaphqn/2gqjMwMMCkSZPedzMIIYQQQmo06tQSQgghhFRFNTBDQWWi4QeEEEIIIaTaoyu1hBBCCCFVEV2pLRe6UluNeXt7IyQk5H03gxBCCCGVQaWs3KWGqfZXal/PVWthYQFPT0/Mnz8fTZs2rZRtTp8+HXv37i01CxhQMi3v7NmzERMTg/z8fDg6OiIoKAhjx46FjhZpcCpbM+Ry65iavuDWURazvw/J1g/gxvD1X88sP3KBn2qk6MwfzHLF739xY4hHD2GWa5Ou65sL/LQr9zuPZJZ3bfOAG4Mn8yo71RYA/Jrz7qnluhY+Z5a7D+DfVSuYsaesBoAnu5+wt+PIT7V0J8aUWf43TLgxRvbJYZY/+Is/ffXhXCtm+dDw+twYj5fw51CPTLNllqfo8j/IBi9pzCx/ujiSG0NHn30M5GXwU1zV3fE9s1x56W9uDGhx3lWlpDDLby3nT3yjq8N+Xu3rPuXGEHPemnr12ccyAAzayX7ehxTwj/c8LdJg8XIDpuvx03HZF/BSevGb8dluP2a5Mpp/rAq1zPkbItVCjbhS6+vri7S0NKSlpSEyMhK6urro0aPHv96OPXv2oEOHDrCzs8OxY8dw48YNjB07FrNnz0a/fv1AKYEJIYQQojWaJrdcakSnViwWQyqVQiqVwsPDA5MmTUJqaioeP36MgoICBAcHw9bWFgYGBqhfvz7Cw8PVjxUEAatXr0aPHj1gZGQEV1dXxMTE4NatW/D29oaxsTE+/PBDJCUlAQA2bNiAGTNmIC4uDoIgQBAEbNiwAXl5eRg+fDg+/fRT/PLLL/Dw8IBMJsOwYcOwceNG7Ny5E9u3bwcAREVFQRAEZGVlqdshl8shCAKSk5MBABkZGQgICEDdunVhZGSEJk2aYMuWLf/ac0oIIYQQUp3UiE7tq3Jzc7Fp0yY4ODjA0tISy5Ytw759+7B9+3YkJCRg8+bNkMlkGo+ZNWsWBg8eDLlcDhcXF/Tv3x8jR45EWFgYYmNjoVKpEBwcDADw9/fHt99+C3d3d/XVYX9/fxw+fBgZGRkYP358qTb5+fnBycmpXJ3SFy9eoGXLlvjzzz9x9epVjBgxAoMGDcK5c+e0jqFQKJCTk6OxFKj4P9cSQgghpAqgyRfKpdqPqQWA/fv3QyKRAADy8vJga2uL/fv3QyQSISUlBY6Ojvjoo48gCALq1y89bi0oKAh9+/YFAISGhsLLywtTpkyBj48PAGDs2LEICgoCABgaGkIikUBXVxdS6T/TuN68eRMA4OrqWmYbXVxc1HW0UbduXY0O8tdff41Dhw5h+/btaN26tVYxwsPDMWPGDI11w02cMcLURet2EEIIIYRUBzXiSm3Hjh0hl8shl8tx7tw5+Pj4oGvXrrh79y4CAwMhl8vh7OyMMWPG4PDhw6Ue/+oNZTY2NgCAJk2aaKx78eIFcnLYN4sAYI6b1dfn3xjxUnFxMWbNmoUmTZrAwsICEokEhw4dQgrnhoZXhYWFITs7W2MJNHHU+vGEEEIIeY9oTG251IhOrbGxMRwcHODg4ABPT0+sXbsWeXl5WLNmDVq0aIE7d+5g1qxZyM/PR9++fdGnTx+Nx+vp/XPHuCAIb1ynZBwAjo4lncX4+Pgyy+Pj4+Hk5AQAEP3/O0tf7QAXFhZq1F+wYAGWLl2K0NBQHDt2DHK5HD4+Pigo4N9l/ZJYLIapqanGoi+8/wwMhBBCCCEVrUZ0al8nCAJEIhHy8/MBAKampvD398eaNWuwbds27Nq1C5mZmW8dX19fH8XFmmNTfXx8YGFhgUWLFpWqv2/fPiQmJiIwMBAAYG1dkrclLS1NXef19GDR0dHo2bMnBg4ciGbNmqFhw4blGr5ACCGEkGqOrtSWS40YU6tQKJCeng4AePr0KZYvX47c3Fz4+flh8eLFsLW1RfPmzSESibBjxw5IpVKYm5u/9fZkMhnu3LkDuVwOOzs7mJiYwNjYGKtXr0a/fv0wYsQIBAcHw9TUFJGRkZgwYQKGDx+Obt26AQAcHBxgb2+P6dOnY86cObh582apzrCjoyN27tyJ06dPo1atWli8eDEePnwINze3t243IYQQQqqRGjhBQmWqEZ3agwcPwta2JOG4iYkJXFxcsGPHDnh7eyMxMRHz589HYmIidHR04OnpiQMHDqiHALyN3r17Y/fu3ejYsSOysrKwfv16BAYGok+fPjh27BjmzJmDdu3aqcfgzps3DxMnTlQ/Xk9PD1u2bMGoUaPQtGlTeHp6Yvbs2fjiiy/UdSZPnozbt2/Dx8cHRkZGGDFiBHr16oXs7Oy3bjcAJBfzE95bZPLH/uaDPYxBFbiZG2NqoQWz/EHX0dwYTzOMmOUZL2pzY9jG7mCWd9PhZwDnTawAAHWPrmaWn28ygRvDSL+QWZ71QsyN0UVQcOvwXNVlP++qzezJGQBArPuMW8eCPV8B5FGcCgAei9jHs4g/XwVObWbvr5HAf96dVezX7vLEsocuvSq3mL+/JiL2Hc11OROnAMCtCWeZ5Un57AkeAMCEk2nlhRY/FBZ9Fs4sz8vjP+96uvyML0ol+z2e9cKAG0Oixx4alnmff+59etOQXSGaGwJtxOy25nOOD20pBPZzVqeQvx0x5+77+zr8YyTtfxHM8nsPzbgxLI3TmOVuQ7khSBUhqGhGgErz4sUL9OzZE6mpqTh+/Lh62MH7tNOWP9OXhZL94QvwO7UNTfmd77QcCbO8UV3+EBF+p5bzIQHAVsKeZU2p4ndqJab8jmJV6dTqCe/+lr+hy/7gdFdq06nldzYsrPKY5Xfu1+LG4HVq72kx85GroohZbiTw90WhYn9Am+rw33e5xfwe+FMR+1rFI13+/rbTZb9/k/L5s1JVRKfW0YI9C1d16tQa6LOPIQB4msc/X/Gc5HRq62vR2VSCf4zocroO2lw2MuL8/H1bn38PSA8L9mxv2nVq85nlbkl/cmNUlue/fFOp8Y1G/Fip8f9tNXJMbVVhYGCAP/74A4MHD8aJEyfed3MIIYQQQmqsGjH8oCozMDDApEmT3nczCCGEEFLd1MCbuSoTXaklhBBCCCHVHl2pJYQQQgipiij7QbnQlVpCCCGEEFLtvXOnNjAwEIIgqBdLS0v4+vri8uXLFdG+Mk2fPh0eHh6l1stkMo22vFx++OGHSmsLIYQQQkilUKoqd6lhKmT4ga+vL9avXw8ASE9Px+TJk9GjRw+kpKRURPhymTlzJoYPH66xzsSEn36mKisoKIC+Pj93rDa0SddVx4yd4goAjDgprHT1+T+ZHFCw89Q2q8tvqyBip3wyyOGn0ZHUesEsP5nKz8nZtc0Dbh1eyi7PKwu4MfJGsxMmKgv4qdTOnebvD88dXfbr26tJBjdG6mV+qp17D8yZ5Y09HnFjLEmoyyy3KeanLzIVsY9Flw+ecGNcOC1lluuI+O+Zq7r8lF68I95Qi88xCyn7fXX7Dv+cykuV91zB3xdTa/Z708SSn0pPpMd/XgXO5Z0nV/mpttIK2OkFTQr55yL3luzj+foFflpI3surTZpaQy1+8tbn1Hmiy+9e6HDSJWozqbu5A/u9KdLJ0iJKFUY3ipVLhQw/EIvFkEqlkEql8PDwwKRJk5CamorHjx+joKAAwcHBsLW1hYGBAerXr4/w8H8SaguCgNWrV6NHjx4wMjKCq6srYmJicOvWLXh7e8PY2BgffvghkpKSAAAbNmzAjBkzEBcXp74Su2HDBnU8ExMTdVteLsbGJUmvZ86ciTp16iAj458P3O7du6Njx45Q/v8DRxAErFy5El27doWhoSEaNmyInTt3auzvlStX0KlTJxgaGsLS0hIjRoxAbu4/J/CoqCi0bt0axsbGMDc3R9u2bXH37l0AJVe2e/XqpREvJCQE3t7e6v+9vb0RHByMkJAQWFlZwcfHBwBw9epVdO3aFRKJBDY2Nhg0aBCePOF/mBJCCCGE1HQVPqY2NzcXmzZtgoODAywtLbFs2TLs27cP27dvR0JCAjZv3gyZTKbxmFmzZmHw4MGQy+VwcXFB//79MXLkSISFhSE2NhYqlQrBwcEAAH9/f3z77bdwd3dHWloa0tLS4O/vr1Xbvv/+e8hkMgwbNgwA8PPPP+P06dPYuHGjxgxjU6ZMQe/evREXF4cBAwagX79+iI8vme0nLy8PPj4+qFWrFs6fP48dO3bg6NGj6vYVFRWhV69e6NChAy5fvoyYmBiMGDECAmf2lddt3LgR+vr6iI6OxqpVq5CVlYVOnTqhefPmiI2NxcGDB/Hw4UP07du3XHEJIYQQUk0olZW71DAVMvxg//79kEhKZofKy8uDra0t9u/fD5FIhJSUFDg6OuKjjz6CIAioX79+qccHBQWpO2ehoaHw8vLClClT1Fcox44di6CgIACAoaEhJBIJdHV1IZWW/jkvNDQUkydP1lj3119/oV27dtDR0cGmTZvUV5OXLVuGtWvXol69ehr1v/jiC3XHd9asWThy5Ah++uknrFixAr///jtevHiBX3/9VX0FePny5fDz88O8efOgp6eH7Oxs9OjRA40aNQIAuLq6lvs5dXR0xPz589X/z549G82bN8fcuXPV6yIiImBvb4+bN2/CycmpVAyFQgGFQvPnuQJVMfQFbX7UIYQQQgipPirkSm3Hjh0hl8shl8tx7tw5+Pj4oGvXrrh79y4CAwMhl8vh7OyMMWPG4PDhw6Ue37RpU/XfNjY2AIAmTZporHvx4gVycnK4bZkwYYK6LS+XVq1aqcsbNmyIhQsXYt68efj000/Rv3//UjG8vLxK/f/ySm18fDyaNWum7tACQNu2baFUKpGQkAALCwsEBgbCx8cHfn5+WLp0KdLS2PNKl6Vly5Ya/8fFxeHYsWOQSCTqxcXFBQDUQzNeFx4eDjMzM41lS96NcreFEEIIIe+BSlW5Sw1TIZ1aY2NjODg4wMHBAZ6enli7di3y8vKwZs0atGjRAnfu3MGsWbOQn5+Pvn37ok+fPhqP19P754aBlz/Tl7VOqcWlcisrK3VbXi6GhpqD/E+cOAEdHR0kJyejqIg/eL+81q9fj5iYGHz44YfYtm0bnJyccObMGQCASCSC6rUDqbCw9ED3VzvNQMmwDj8/v1Id9sTERLRv377MdoSFhSE7O1tjCTB2qaC9JIQQQgipOiolT60gCBCJRMjPzwcAmJqawt/fH2vWrMG2bduwa9cuZGZmvnV8fX19FBcXv9Vjt23bht27dyMqKgopKSmYNWtWqTovO6Cv/v9yCIGrqyvi4uKQl/fP3cHR0dEQiURwdnZWr2vevDnCwsJw+vRpNG7cGL///jsAwNrautSVW7lczm13ixYtcO3aNchkslKd9tc7wC+JxWKYmppqLDT0gBBCCKkmaExtuVRIp1ahUCA9PR3p6emIj4/H119/rb6yuHjxYmzZsgU3btzAzZs3sWPHDkilUpibm7/19mQyGe7cuQO5XI4nT55ojBt99uyZui0vl5fDFu7du4dRo0Zh3rx5+Oijj7B+/XrMnTu3VCd2x44diIiIwM2bNzFt2jScO3dOfSPYgAEDYGBggCFDhuDq1as4duwYvv76awwaNAg2Nja4c+cOwsLCEBMTg7t37+Lw4cNITExUd4o7deqE2NhY/Prrr0hMTMS0adNw9epV7j6PHj0amZmZCAgIwPnz55GUlIRDhw4hKCjorTv4hBBCCCE1RYV0ag8ePAhbW1vY2tqiTZs26qwA3t7eMDExwfz589GqVSt4enoiOTkZBw4c0Mg2UF69e/eGr68vOnbsCGtra2zZskVdNnXqVHVbXi4TJ06ESqVCYGAgWrdure6g+vj4YNSoURg4cKBGSq4ZM2Zg69ataNq0KX799Vds2bIFbm5uAAAjIyMcOnQImZmZ8PT0RJ8+ffDxxx9j+fLl6vIbN26gd+/ecHJywogRIzB69GiMHDlSvc0pU6Zg4sSJ8PT0xLNnzzB48GDuPtepUwfR0dEoLi7GJ598giZNmiAkJATm5ubv9FwSQgghpIqiyRfKRVC9PsDzP04QBOzZs6dULtmaYq+09I1xr6st8BOa5ynZiTNcZY+5MXak1WGWf9mBf4Pd0yvsDr2+EX/MNC/x+sVEdtJ8AGjbKZ1bJ/k0O2G97INn3BjGP69jVyjkv3YnPKZz6/D0eyFnlt/4iP+cPUliJ6sH+BMSGJrxJ+hY84A92YQJJwE8ALgr2MdRh19acGOcHnGBWW6qW8CNkazkP2fXOPO0SLTY38GOqczy3TftuTFag308X1XyJ3D4pNE9bh2ewnz+kCt9Y/avXY/u8ttaVMw+kUjr8W90fp7FfvGePRNzY0QI7IkieuZzQ+CpiJ8YyZDz03WuFhdbnovYx2L9Iv57okVndq729PMG3Bh2PdnPq2TBHm6MyvJ8wZeVGt9oQkSlxv+30SU+QgghhBBS7VVInlpCCCGEEFLBauAQgcpEndrX0GgMQgghhJDqhzq1hBBCCCFVkKoGpt2qTDSmlhBCCCGEVHt0pZYQQgghpCqiMbXlQldq34IgCNi7d+/7bgYhhBBCCPn/qtSV2sDAQGzcuFH9v4WFBTw9PTF//nw0bdq0UrY5ffp07N27t9RUtTKZDHfv3tVYV7duXdy7dw9paWmoVatWubazZs0aLF++HElJSdDV1UWDBg3Qt29fhIWFqdsxY8aMUo87cuQIOnfujGvXrmHq1Km4cOEC7t69ix9//BEhISHlagMAxIv532OcdF9w61jqscf5PEmXcGPU5qSQzZDz80veecR+HdwcH3FjpN01ZZYbgD+mKfOqHrdO1gt2LkRlQTY3BjcPrR4/j+ULzndZPfCvDDzNz2WWi5tacWOkxPJPPw2ts5jlxg25IZCZxj7Qaqn4r50O5zkRbBtwYxgI55jlqcXs/KIAUMDJ6wkAZpyXT8EPgSfJ7PevoMXFI/Na7ISo+U/5uV9v3rRmltvbZHFjFBfyz3l6huw8tcVK/pMmcJ4UZTE/hopzqnmm4CQhBmBiwD5vFmpxPrNS8vPDFnLOIyItrpnpcY6jHIF/jlC9YO+PufQ5NwaKq1RXSBPvoCAaqtyVWl9fX6SlpSEtLQ2RkZHQ1dVFjx493ktbZs6cqW5LWloaLl26BACQSqUQi/mdh5ciIiIQEhKCMWPGQC6XIzo6GhMnTtSYxQwA3N3dNbaXlpaG9u3bAwCeP3+Ohg0b4ocffoBUyk9sTwghhJBqjmYUK5cq16kVi8WQSqWQSqXw8PDApEmTkJqaisePH6OgoADBwcGwtbWFgYEB6tevj/DwcPVjBUHA6tWr0aNHDxgZGcHV1RUxMTG4desWvL29YWxsjA8//BBJSUkAgA0bNmDGjBmIi4uDIAgQBAEbNmxQxzMxMVG3RSqVwtraWr2dl8MPkpOTIQgCdu/ejY4dO8LIyAjNmjVDTEyMOs6+ffvQt29fDB06FA4ODnB3d0dAQADmzJmjse+6uroa25NKpdDXL/lm7unpiQULFqBfv37l6lATQgghhPwXVLlO7atyc3OxadMmODg4wNLSEsuWLcO+ffuwfft2JCQkYPPmzZDJZBqPmTVrFgYPHgy5XA4XFxf0798fI0eORFhYGGJjY6FSqRAcHAwA8Pf3x7fffqtxhdTf3/+t2vr9999j/PjxkMvlcHJyQkBAAIqKSn72lEqlOHPmTKnhDIQQQgghb6RUVu5Sw1S5Tu3+/fshkUggkUhgYmKCffv2Ydu2bRCJREhJSYGjoyM++ugj1K9fHx999BECAgI0Hh8UFIS+ffvCyckJoaGhSE5OxoABA+Dj4wNXV1eMHTsWUVFRAABDQ0NIJBKNK6SGhv+MawsNDVW3RSKRYNmyZW9s9/jx49G9e3c4OTlhxowZuHv3Lm7dugUAmDZtGszNzSGTyeDs7IzAwEBs374dytcOqCtXrmhsr3Xr1u/0XCoUCuTk5GgsRSr22DFCCCGEkOqoynVqO3bsCLlcDrlcjnPnzsHHxwddu3bF3bt3ERgYCLlcDmdnZ4wZMwaHDx8u9fhXbyizsbEBADRp0kRj3YsXL5CTk8Nty4QJE9RtkcvlGDx48BvrvrpdW1tbAMCjR4/U/8fExODKlSsYO3YsioqKMGTIEPj6+mp0bJ2dnTW2t2vXLm4bWcLDw2FmZqaxRGVfe6eYhBBCCPmX0JjacqlynVpjY2M4ODjAwcEBnp6eWLt2LfLy8rBmzRq0aNECd+7cwaxZs5Cfn4++ffuiT58+Go/X0/vnTmZBEN647vWrpGWxsrJSt8XBwQHm5uZvrKvNNho3boyvvvoKmzZtwpEjR3DkyBEcP35cXa6vr6+xPXt7e24bWcLCwpCdna2xeJu5v1NMQgghhJCqqArnsSghCAJEIhHy80vSw5iamsLf3x/+/v7o06cPfH19kZmZCQsLi7eKr6+vj+Lif/8neTc3NwBAXl5epW1DLBaXuqlMV+CnySKEEEJIFUApvcqlynVqFQoF0tPTAQBPnz7F8uXLkZubCz8/PyxevBi2trZo3rw5RCIRduzYAalUyryCyiOTyXDnzh3I5XLY2dnBxMSkwrMLjBo1CnXq1EGnTp1gZ2eHtLQ0zJ49G9bW1vDy8tIqRkFBAa5fv67++/79+5DL5ZBIJHBwcKjQ9hJCCCGEVDdVrlN78OBB9ZhUExMTuLi4YMeOHfD29kZiYiLmz5+PxMRE6OjowNPTEwcOHIBI9PajKHr37q1Ox5WVlYX169cjMDCwgvamROfOnREREYGVK1ciIyMDVlZW8PLyQmRkJCwtLbWK8eDBAzRv3lz9/8KFC7Fw4UJ06NBBfeObNoy0GEKTkstPiq7PSeAdZchPaN8d7KTYm3LYidcBoIiTrz7+bl1ujHQd9r50AP9K/q9atLWLwJ444dxpW24MPY/pzHLexAoA8Mm1Odw6PN1bjGGWn4rgv/7nOUniAUD1mD25xu2j/APazoDdljqF/BiJ+uwYtv02MssBwEifvb8JggE3Ri0tLtrYc/bnuRYTOBxQsZ93Iy0mcLjymD0BR6oBf2eSdNkfUbJMG24MbX6Hs8hil9ct5kx6AuAZZ6KAnDv8iyWFKvb79zrnOASAtvnsyUYydPgf+9ZaPGm8CUmMlfwgeZzXl7cNANh2zo5ZbqTFe0Zyk13p88X8GJWmBo57rUyCSqWiZ+w/ZGm9gdw6zgrOVF+ooE5tAXvGoUN6/BmWijgfrrW1mMWH26nN55+cow34HxRdFOwPxmcqfgzebF//Vqe2D6dTO0rBn1FOm05tmxfs517Q4kPvEqdT26CAH+OxLvs4aq+XxY1RWMTe34MC/8ukNp1aq6J379Q+4rw02nw5rlvIbux5fh8eBZzXV1bMP4a06tRyKlVEp9YM/PNqRXRqGxWwt/NUq04tv628DqcCWhxnnE6tpRZDA+/rsY8BrTq1xZxObfrv/CCVJDesd6XGl4S/2w3pVU2Vu1GMEEIIIYSQ8qJOLSGEEEJIVVTFUnr9/PPPkMlkMDAwQJs2bXDu3DmtHrd161YIgoBevXqVe5vlQZ1aQgghhBDCtG3bNowbNw7Tpk3DxYsX0axZM/j4+Khz8r9JcnIyxo8fj3bt2lV6G6lTSwghhBBSFVXyldqyZh5VvOH+j8WLF2P48OEICgqCm5sbVq1aBSMjI0RERLyx+cXFxRgwYABmzJiBhg0bVtazpEadWkIIIYSQ/6CyZh4NDw8vVa+goAAXLlxA586d1etEIhE6d+6MmJiYN8afOXMmateujaFDh1ZK+19X5VJ6VQeCIGDPnj2VPjaEEEIIIf9hlTz5QlhYGMaNG6exrqxc/U+ePEFxcTFsbDTT6NnY2ODGjRtlxj516hTWrVsHuVxeYe3lqVJXagMDAyEIgnqxtLSEr68vLl++XGnbnD59Ojw8PEqtl8lkGm0RBAF2diX58NLS0tC1a9dybWfNmjVo1qwZJBIJzM3N0bx5c41vQ9OnTy+1PUEQcPToUfXj27Vrh1q1aqFWrVro3Lmz1gO0X6XUYhFptaiYi54K3IVHDwJ30VWBuWizL7oqgbloE6Oq0IOKu1QEfUGHvUDJXbR5XgWomIuOFgvveNeGjoq9iAQVd+ERtFlU/IWnUOAvOmAv2uCdI0QQuIsOZ9HuGOIvvLZWBN6xrN3C3xcelRZLRbS1WBC4C++1U0LgLrzPGbFSxV3+jXNmVSUWi2FqaqqxVMQEVM+ePcOgQYOwZs0aWFmxc1ZXpCp3pdbX1xfr168HAKSnp2Py5Mno0aMHUlJS/vW2zJw5E8OHD1f/r6NTcjqXSqXlihMREYGQkBAsW7YMHTp0gEKhwOXLl3H16lWNeu7u7upO7Esvp/+NiopCQEAAPvzwQxgYGGDevHn45JNPcO3aNdSty59ggBBCCCHVTBWZfMHKygo6Ojp4+PChxvqHDx+W2SdKSkpCcnIy/Pz81OuUypLLCLq6ukhISECjRo0qvJ1V6SITgJJvDVKpFFKpFB4eHpg0aRJSU1Px+PFjFBQUIDg4GLa2tjAwMED9+vU1rnYKgoDVq1ejR48eMDIygqurK2JiYnDr1i14e3vD2NgYH374IZKSkgAAGzZswIwZMxAXF6e+MrphwwZ1PBMTE3VbpFIprK2t1dvZu3cvgJK7+gRBUM9KZmRkhGbNmmmMMdm3bx/69u2LoUOHwsHBAe7u7ggICMCcOZpJ8HV1dTW2J5VKoa+vDwDYvHkzvvrqK3h4eMDFxQVr166FUqlEZGRkZbwMhBBCCHnPVEpVpS7a0tfXR8uWLTX6HC/7IF5eXqXqu7i44MqVK5DL5erl008/RceOHSGXy2Fvb18hz8/rqlyn9lW5ubnYtGkTHBwcYGlpiWXLlmHfvn3Yvn07EhISsHnzZshkMo3HzJo1C4MHD4ZcLoeLiwv69++PkSNHIiwsDLGxsVCpVAgODgYA+Pv749tvv4W7uzvS0tKQlpYGf3//t2rr999/j/Hjx0Mul8PJyQkBAQEoKiqZlUUqleLMmTO4e/fuOz0fr3r+/DkKCwvVV3IJIYQQQirLuHHjsGbNGmzcuBHx8fEYNWoU8vLyEBQUBAAYPHgwwsLCAAAGBgZo3LixxmJubg4TExM0btxYfcGuolW54Qf79++HRFIyxWZeXh5sbW2xf/9+iEQipKSkwNHRER999BEEQUD9+vVLPT4oKAh9+/YFAISGhsLLywtTpkyBj48PAGDs2LHqF8DQ0BASiUR9hfR1oaGhmDx5svr/uXPnYsyYsqcHHT9+PLp37w4AmDFjBtzd3XHr1i24uLhg2rRp+PzzzyGTyeDk5AQvLy9069YNffr0gUj0z/eKK1euqPcdANzc3N44bjY0NBR16tTRuBPxdQqFolRqjiJVMXQFbUfFEUIIIeS9qSLDD4CSC4GPHz/G1KlTkZ6eDg8PDxw8eFB981hKSopGn+Z9qHKd2o4dO2LlypUAgKdPn2LFihXo2rUrzp07h8DAQHTp0gXOzs7w9fVFjx498Mknn2g8vmnTpuq/Xz7RTZo00Vj34sUL5OTkwNTUlNmWCRMmIDAwUP0/a7Dzq9u1tbUFADx69AguLi6wtbVFTEwMrl69ihMnTuD06dMYMmQI1q5di4MHD6oPAmdnZ+zbt08d502DtX/44Qds3boVUVFRMDB48wTq4eHhmDFjhsa6T0ybwNes6RseQQghhBBStuDgYPWv3a+LiopiPvbV4Z2Vpcp1ao2NjeHg4KD+f+3atTAzM8OaNWswe/Zs3LlzB3/99ReOHj2Kvn37onPnzti5c6e6vp6envpvQRDeuO7lgGUWKysrjbawaLONl5fgv/rqK/zvf/9Du3btcPz4cXTs2BFAyZgV3vYWLlyIH374AUePHtXoSJelrFQdq91HarU/hBBCCHnPtOirkH9UuU7t6wRBgEgkQn5+PgDA1NQU/v7+8Pf3R58+feDr64vMzMy3Hluqr6+P4uLiimyyVtzc3ACUDLHQ1vz58zFnzhwcOnQIrVq14tYXi8WlrvbS0ANCCCGE1ERVrlOrUCiQnp4OoGT4wfLly5Gbmws/Pz8sXrwYtra2aN68OUQiEXbs2AGpVApzc/O33p5MJsOdO3cgl8thZ2cHExOTCsnR9qpRo0ahTp066NSpE+zs7JCWlobZs2fD2tq6zLsGyzJv3jxMnToVv//+O2Qymfo5kkgkGuNwCSGEEFJDVKExtdVBlevUHjx4UD0m1cTEBC4uLtixYwe8vb2RmJiI+fPnIzExETo6OvD09MSBAwfeaWBy79691em4srKysH79eo1xtBWhc+fOiIiIwMqVK5GRkQErKyt4eXkhMjISlpaWWsVYuXIlCgoK0KdPH43106ZNw/Tp07VuS64W2dmt9PP5cQrZdy6OcrnHjbHihh2zvJeQzY1hYFjILH+QacKN8ZnjE2b5nrv8PMBdC59z61zVNWKW39Hl/8y0+ukFZvnT/FxujO4tyr7Z8SV9La7m/37hR2b582+GM8sBoP4l/unnUgE7afcH9dK4MVTJ1szyZ1rsby9H9vH84ZVn3Bgjjd2Y5UEN+O+ZqAT+seguecosv5xbixujnUkGs9y8Dv8ccf1qbWb5V/Uec2M8f8o+z9x/zL4vAgCsTPjvzdzn7AsZxwyMuTEknLfvAxX/ONPnnJ4/NsjkxrhbwH5OjLT4OfuRjh63TgFnpociLWaCqFPI/pU0XY//nHmbs48j67b8dvx0kH2e8WOWkqqkSnVqN2zYwBxIPHz4cI3JEF6nUmmeEWQyWal13t7eGuvEYrHGmNyXkpOTtdpOWdswNzfXWNe7d2/07t37jfGAkhnFWJ1TVnsIIYQQUgPRldpyqdJ5agkhhBBCCNFGlbpSSwghhBBCSrz+SzBho04tIYQQQkhVRMMPyoWGHxBCCCGEkGqPrtQSQgghhFRFdKW2XP5zV2oFQcDevXvfdzMIIYQQQkgFem+dWkEQmAsvvZUgCJDL5RXaDl1dXdSrVw/jxo2DQqF459gsMpkMS5YsKbV+zZo1aNasGSQSCczNzdG8eXOEh4ery6dPn17m83X06NFKbS8hhBBC/l0qpapSl5rmvQ0/SEv7J2n6tm3bMHXqVCQkJKjX/ZuzZK1fvx6+vr4oLCxEXFwcgoKCYGxsjFmzZv1rbQCAiIgIhISEYNmyZejQoQMUCgUuX76Mq1evatRzd3cv1YnVdprgU0p+8u6Q3locFgXsJP869RtyQ6QmsGM0GsxPeK64wk6+b2vDDQGRNTs5v1EEP4b7AP7JQbWZnQS+VxN2wnsA+EYpZZaLm7KTiAPAqQh2YnV98JOz8yZXMPpxDTeGaUAQt04bw3Rmuc2fa7kxvm4xllneHmbcGB+7s4/FWAt+pvnDp9nHiIE9P9H8nTv87VjksCccUejyY9Tpyp70IOcM/0u/R2f28Zx1jZ/g37IFOzm/lS5/ghYdhzrcOoKRIbO8eN4j/nY4E9vUqcdvK4/YlD+l+5oE9uQLvvn897eNsoBb55mK/TlxW5//OaLkHIpiflNRZ15XZrlO4/bcGF9ljuNviFQL7+1KrVQqVS9mZmYQBEH9f+3atbF48WLY2dlBLBbDw8MDBw8eVD+2QYMGAIDmzZtDEAR4e3sDAM6fP48uXbrAysoKZmZm6NChAy5evMhti7m5OaRSKezt7dGjRw/07NlT43FxcXHo2LEjTExMYGpqipYtWyI2NhZAyYQR5ubm2L9/P5ydnWFkZIQ+ffrg+fPn2LhxI2QyGWrVqoUxY8aguLjkhOTt7Y27d+/im2++UV9pBYB9+/ahb9++GDp0KBwcHODu7o6AgADMmTNHo726uroaz59UKoW+PvtDiBBCCCHVjFJVuUsNUyXH1C5duhSLFi3CwoULcfnyZfj4+ODTTz9FYmIiAODcuXMAgKNHjyItLQ27d+8GADx79gxDhgzBqVOncObMGTg6OqJbt2549ow/heVLN2/exN9//402bdqo1w0YMAB2dnY4f/48Lly4gEmTJkFP75+rDM+fP8eyZcuwdetWHDx4EFFRUfjss89w4MABHDhwAL/99htWr16tnrls9+7dsLOzw8yZM5GWlqa+ai2VSnHmzBncvXv33Z5AQgghhJD/mCqZ/WDhwoUIDQ1Fv379AADz5s3DsWPHsGTJEvz888+w/v8/F1taWkIq/efn2E6dOmnE+eWXX2Bubo7jx4+jR48eb9xeQEAAdHR0UFRUBIVCgR49eiAsLExdnpKSggkTJsDFxQUA4OjoqPH4wsJCrFy5Eo0aNQIA9OnTB7/99hsePnwIiUQCNzc3dOzYEceOHYO/vz8sLCygo6MDExMTjfZPmzYNn3/+OWQyGZycnODl5YVu3bqhT58+EIn++f5x5coVjeEZbm5u6o7+qxQKRamxwUqVEiKhSn6XIYQQQsirtBiCQf5R5Xo3OTk5ePDgAdq2bauxvm3btoiPj2c+9uHDhxg+fDgcHR1hZmYGU1NT5ObmIiUlhfm4H3/8EXK5HHFxcdi/fz9u3ryJQYMGqcvHjRuHYcOGoXPnzvjhhx+QlJSk8XgjIyN1hxYAbGxsIJPJNDqeNjY2ePSIPS7L1tYWMTExuHLlCsaOHYuioiIMGTIEvr6+UCr/ObKdnZ0hl8vVy65du8qMFx4eDjMzM43ldk5SmXUJIYQQUrXQjWLlU+U6te9iyJAhkMvlWLp0KU6fPg25XA5LS0sUFLAHvUulUjg4OMDZ2Rndu3fHjBkzsG3bNty6dQtAScaBa9euoXv37vj777/h5uaGPXv2qB//6lAEoCSjQlnrXu2YsjRu3BhfffUVNm3ahCNHjuDIkSM4fvy4ulxfXx8ODg7qxd7evsw4YWFhyM7O1lgamjYqsy4hhBBCSHVW5Tq1pqamqFOnDqKjozXWR0dHw83NDQDUN0W9vPHq1TpjxoxBt27d4O7uDrFYjCdPnpS7DTo6JXch5+fnq9c5OTnhm2++weHDh/H5559j/fr15Y77Kn19/VLtL8vLfc7Lyyv3NsRiMUxNTTUWGnpACCGEVBN0o1i5VMkxtRMmTMC0adPQqFEjeHh4YP369ZDL5di8eTMAoHbt2jA0NMTBgwdhZ2cHAwMDmJmZwdHREb/99htatWqFnJwcTJgwAYaG7FQtAJCVlYX09HQolUokJiZi5syZcHJygqurK/Lz8zFhwgT06dMHDRo0wL1793D+/Hn07t37nfZRJpPhxIkT6NevH8RiMaysrDBq1CjUqVMHnTp1gp2dHdLS0jB79mxYW1vDy8vrnbZHCCGEEFKTVclO7ZgxY5CdnY1vv/0Wjx49gpubG/bt26e+QUtXVxfLli3DzJkzMXXqVLRr1w5RUVFYt24dRowYgRYtWsDe3h5z587F+PHjudsLCirJl/kyrVj79u0xd+5c6OrqQqlUIiMjA4MHD8bDhw9hZWWFzz//HDNmzHinfZw5cyZGjhyJRo0aQaFQQKVSoXPnzoiIiMDKlSuRkZEBKysreHl5ITIyEpaWlu+0vZcCi/n5bK9se8GtU6QyYJa3msg/tBorxcxywcacG8PQ3ZVZ/mDWaW6MOmHOzHJpUSI3hmDGz6kr1mVn4Ui9zM+XamhYyCxPieU/7+cN2PlQReDnS61/ib0dbXLQmm/h/9qRM4QdJ7Xj/7gxpoL9xTaO/4MJCu6wfylJuMjPD5ylw07KKejzf0XxUfEzuVzTYeeplesVcWN80cyNWa6IvMCNkXWGnWbQ1CqfWQ4A+u1bMMuLb97mxii+9YBbpyiTPUTteTE/4bWBiH0g5WW9e9pFozr8c7OE8/7VBfscAgDXddnnd4D/M6+eFhcBC8F+T+iptMj/ffsmszx/+/9xYwgifu7m94ZuFCsXQaXS4qghNcaWOgO4deqrtOnUsk9prSaac2OsWcL+UBseyo8hWLEnTtCuU9uaWX58HL9T2+5rfmfy5i/sDonASd4OaNGpfcpOvA4AZwzYbdVmgEo/E/awHlNb/jFUEZ3a7BT2FyMAyM7mdWrZnUAA8PNIZZZr06m9osNuR4DPQ26MpCNG3DrXitj7c1Gf36mdM0PGLE9fzO/U5ue+e6fWcsS7d2qVT/hfBHidWvnpd+/UWpmXfwjZ66zd+O+rHy/UZZZ75/M7tUl6/A447zyhTV/Msohdq1Dgdzb9vmNfDCiMTWCWA4DqBbsd5tuOcWNUliz/jpUa/33uW2WokldqCSGEEEL+62pihoLKRHcNEUIIIYSQao+u1BJCCCGEVEU0prZcqFNLCCGEEFIF0fCD8qHhB4QQQgghpNqjTu17IJPJsGTJknI9Zvr06fDw8KiU9hBCCCGkClJW8lLDVPtObXp6Or7++ms0bNgQYrEY9vb28PPzQ2RkZIVtw9vbGyEhIRUW703Cw8Oho6ODBQsWlCobP358he4TIYQQQkhNUq3H1CYnJ6Nt27YwNzfHggUL0KRJExQWFuLQoUMYPXo0bty48a+1RaVSobi4GLq6b/+URkREYOLEiYiIiMCECRM0yiQSCSQSyRsfW1BQoJ4+mCVZj5/3z4GTOxAAdDjpjZ/t4+d2bVPEngiiMDadG6Mw/QqzvLiYP6PcwwVnmOUmAn9ShCe7+dMxW3BSmd57YM6NITFWMMsbWmdxY6ge12KWC+CP4bpUwN6ZNob8146XgxYATDeyc9k+/jCYG4OXt1WhxVf7W5fYk5/o6/BncLAvZOeHjf+Lny/XUJ+fY9RMyW7LIxU7JysA5KxnvycS0qXcGM7SDGb5/gd1uDH6bD/PLFfk8CcKURbxz3kqFfu8bWeRw41hZsPOIWtgz29r2ll23uXr0VpM8mHAPs7ytZhcpS7nWAWAAs6U6/kVMKGBUosQBafimeUP4/gTSRiZsN8T5vxmVBpVDbyaWpmq9ZXar776CoIg4Ny5c+jduzecnJzg7u6OcePG4cyZkpNyVlYWhg0bBmtra5iamqJTp06Ii4tTx3j5s/5vv/0GmUwGMzMz9OvXD8+elSTsDgwMxPHjx7F06VIIggBBEJCcnIyoqCgIgoC//voLLVu2hFgsxqlTp5CUlISePXvCxsYGEokEnp6eOHr0KHdfjh8/jvz8fMycORM5OTk4fVpz0oDXhx8EBgaiV69emDNnDurUqQNnZ/asWIQQQgghNVm17dRmZmbi4MGDGD16NIyNS09Ram5uDgD44osv8OjRI/z111+4cOECWrRogY8//hiZmZnquklJSdi7dy/279+P/fv34/jx4/jhhx8AAEuXLoWXlxeGDx+OtLQ0pKWlwd7eXv3YSZMm4YcffkB8fDyaNm2K3NxcdOvWDZGRkbh06RJ8fX3h5+eHlJQU5v6sW7cOAQEB0NPTQ0BAANatW8d9DiIjI5GQkIAjR45g//792jxthBBCCKkuaExtuVTb4Qe3bt2CSqWCi4vLG+ucOnUK586dw6NHjyAWl/yss3DhQuzduxc7d+7EiBEjAABKpRIbNmyAiUnJz3+DBg1CZGQk5syZAzMzM+jr68PIyAhSaemf3GbOnIkuXbqo/7ewsECzZs3U/8+aNQt79uzBvn37EBxc9k+lOTk52LlzJ2JiYgAAAwcORLt27bB06VLmkANjY2OsXbv2jcMOFAoFFArNn6uLVMXQFfg/PxFCCCGEVCfV9kqtijOmEwDi4uKQm5sLS0tL9ZhUiUSCO3fuICkpSV1PJpOpO7QAYGtri0ePHmnVjlatWmn8n5ubi/Hjx8PV1RXm5uaQSCSIj49nXqndsmULGjVqpO4Me3h4oH79+ti2bRtz202aNGGOow0PD4eZmZnGEpV9Tav9IoQQQsj7pVJW7lLTVNsrtY6OjhAEgXkzWG5uLmxtbREVFVWq7OXwBADQ09PTKBMEAUqldq/260Mfxo8fjyNHjmDhwoVwcHCAoaEh+vTpg4KCNw9EX7duHa5du6Zxk5lSqURERASGDh2q9bZfFxYWhnHjxmmsW9p4JPMxhBBCCKkiamDHszJV206thYUFfHx88PPPP2PMmDGlOnhZWVlo0aIF0tPToaurC5lM9tbb0tfXR3Ex/+5mAIiOjkZgYCA+++wzACUd6+Tk5DfWv3LlCmJjYxEVFQULi3+yAWRmZsLb2xs3btxgDrFgEYvF6mEXL9HQA0IIIYTURNV2+AEA/PzzzyguLkbr1q2xa9cuJCYmIj4+HsuWLYOXlxc6d+4MLy8v9OrVC4cPH0ZycjJOnz6N77//HrGxsVpvRyaT4ezZs0hOTsaTJ0+YV3EdHR2xe/duyOVyxMXFoX///sz669atQ+vWrdG+fXs0btxYvbRv3x6enp5a3TBGCCGEkJqHhh+UT7W9UgsADRs2xMWLFzFnzhx8++23SEtLg7W1NVq2bImVK1dCEAQcOHAA33//PYKCgvD48WNIpVK0b98eNjY2Wm9n/PjxGDJkCNzc3JCfn487d+68se7ixYvx5Zdf4sMPP4SVlRVCQ0ORk1N2jsOCggJs2rQJoaGhZZb37t0bixYtwty5c7VuK88F8PMtjhlqxA+kzz50VHn8BIM7k9l5db/L5OfkLHzG/l4m/Yj/rlUVsOucfcjOHQkA7o78K/nyKHaOycYe/HHcvLycxg25IXD7KHs8uo4WeWo/qJfGLLf5cy03RmrH/3Hr8PLQNjq9nBtjcssQZnnXYlNujCbfs3OqZm3mj1W/d5v9vnLrlc+N8cMBdo5hAOhn+IxZPiqffzwbNGC/v9u6ZHFjCBL2/n5y6N2Pd8Pa/PeduHV9bh3B9M035ALAtR/YxzsAZGaz99fqUR43hrEZOw+1qRX/GLG4zz5WDcDPQSvR4Z9784vZr81TgX+cWarYr1+uwO+iiP27MMsbzPuEGyNn5LfcOqR6qNadWqDkpq7ly5dj+fKyP9xMTEywbNkyLFu2rMzy6dOnY/r06RrrQkJCNGYQc3JyUmcmeEkmk5V5s5pMJsPff/+tsW706NEa/786HOHJkzcn7Z84cSImTpxYZjs3bNjwxscRQgghpPqriVdTK1O1Hn5ACCGEEEIIUAOu1BJCCCGE1ER0pbZ86EotIYQQQgip9uhKLSGEEEJIVaTi33RN/kGdWkIIIYSQKoiGH5QPDT94C1FRURAEAVlZWVrV37Bhg8YMZoQQQgghpGLV6E5teHg4PD09YWJigtq1a6NXr15ISEio8O287OS+vkyePLlccR4/foxRo0ahXr16EIvFkEql8PHxQXR0tLqOTCYrtR07O7uK3iVCCCGEvGcqpVCpS01To4cfHD9+HKNHj4anpyeKiorw3Xff4ZNPPsH169dLTatbERISEmBq+k8id4mEndD7pYKCAujr66N3794oKCjAxo0b0bBhQzx8+BCRkZHIyMjQqD9z5kwMHz5c/b+OjvZT3/46jp2YGwCi5z7l1rHUZyd4r++RxY3RRsHujEef5bfVRMVOJF6cyP/eZleLPSGFXhn5iF93J4afwP+xiD3ZxJKEutwYupzxVZlp/MTqdgZ6zHJtfu1SJVszy79uMZYbYyoMuXWuFZkwy3kTKwDAlgtLmOVR7mHcGIJ7C2b5ggdvzjf9UjMdzrHImdAEACavbc+to7rN/uIePeUBN8aDU+xjVbbQmxsjbfJRZvnpjNrcGHaP2JMRGKdoMZHAdf5rk52Xyyy/Bf7nRSGnf6B4xp/UxiWDvb8OsgxmOQB88II9ocF9Xfb7HwAknIkVAMAM7O0YaHHerIhf1vM3HmJX+I1TDsDQW1YBLSFVQY3u1B48eFDj/w0bNqB27dq4cOEC2rcv+XAQBAFr1qzBn3/+iUOHDqFu3bpYtGgRPv30U/XjDhw4gJCQEKSmpuKDDz7AkCFDytxe7dq1tRpmMH36dOzduxfBwcGYM2cO7t69i8zMTJw8eRJRUVHo0KEDAKB+/fpo3bp1qcebmJhAKpVq+zQQQgghpBqiMbXlU6OHH7wuOzsbAGBhYaGxfsaMGejbty8uX76Mbt26YcCAAcjMzAQApKam4vPPP4efnx/kcjmGDRuGSZMmvXNbbt26hV27dmH37t2Qy+WQSCSQSCTYu3cvFAr2t3VCCCGEEKLpP9OpVSqVCAkJQdu2bdG4cWONssDAQAQEBMDBwQFz585Fbm4uzp07BwBYuXIlGjVqhEWLFsHZ2RkDBgxAYGBgmduws7NTd04lEkmpYQOvKigowK+//ormzZujadOm0NXVxYYNG7Bx40aYm5ujbdu2+O6773D58uVSjw0NDdXYzpumAFYoFMjJydFYFEX8udIJIYQQ8v6pVEKlLjXNf6ZTO3r0aFy9ehVbt24tVda0aVP138bGxjA1NcWjR48AAPHx8WjTpo1GfS8vrzK3cfLkScjlcvVSq1atN7anfv36sLbWHJfYu3dvPHjwAPv27YOvry+ioqLQokULbNiwQaPehAkTNLYzePDgMrcRHh4OMzMzjWXhkUtvbBMhhBBCSHVVo8fUvhQcHIz9+/fjxIkTZWYK0NPTHDgvCAKUyvIPZGnQoIHWqbvedKOagYEBunTpgi5dumDKlCkYNmwYpk2bpnF12MrKCg4ODtxthIWFYdy4cRrritd8q1X7CCGEEPJ+0Zja8qnRV2pVKhWCg4OxZ88e/P3332jQoEG5Y7i6uqqHIrx05syZimoil5ubG/Ly8t7qsWKxGKamphqLWFf7TAmEEEIIeX8opVf51OhO7ejRo7Fp0yb8/vvvMDExQXp6OtLT05Gfn691jP/9739ITEzEhAkTkJCQgN9//73UcICKkJGRgU6dOmHTpk24fPky7ty5gx07dmD+/Pno2bNnhW+PEEIIIaQmqdHDD1auXAkA8Pb21li/fv36N97s9bp69eph165d+Oabb/DTTz+hdevWmDt3Lr788ssKbatEIkGbNm3w448/IikpCYWFhbC3t8fw4cPx3XffVdh2YsP5eQ7v64q5dR4Ws3Mdxl1k5xcFABslO8dktCH/O5dtMbutz7T42nYr15JZLtbi4vbf4O+viJMe0qaY/625gFOlloqfg7JOIT9/JM8zgf2ktIcZN0acFvcsKjivX9difn5gXh5a72vh3BgnODEc9fnPux7naT+wjb8vhdtPcetIOEOnFCL+af92vgWz/M6Ii9wY9/XYeWjrFvNzzCZzzkW5Iv65SvWCn2NWn3OcWRe9+2/Ad3h5igEk6bH350mKLTeGhJM/1qyYvy/PRPy2mnNeP95xCADZIvZ5RJvbmn+/aM8styvkt+PxefaWhvNTWVcaLdL9klcIKhU9Zf8lJ6RfcOvc1uF/UOhyjhpeInIAsCmqiE4tu442nVoLzplTrMU7JEOLji+vKbznFOB3arX5lloRnVrehBR3eL0EALW06dRWwP7WKyxklldEpzZBi06tGWd/dbU4FRcK/DcWt1OrRYwsHXYd+0J+h/S+HvvVqatFjEe67Bi5Wry/tTna9TmVKqRTq8V7wpyzGbNi/t5IlOwDTSHw26FNp7ZOcQGzPI/zxRcAnnPa8lzEP1bzOE3VqlOryw4y/N4mbozKktLq40qNXy82slLj/9tq9PADQgghhJDqqqqNqf35558hk8lgYGCANm3alLrn6FVr1qxBu3btUKtWLdSqVQudO3dm1q8I1KklhBBCCCFM27Ztw7hx4zBt2jRcvHgRzZo1g4+PjzoF6uuioqIQEBCAY8eOISYmBvb29vjkk09w//79SmsjdWoJIYQQQqqgyr5SW+YkTW+Y1XTx4sUYPnw4goKC4ObmhlWrVsHIyAgRERFl1t+8eTO++uoreHh4wMXFBWvXroVSqURkZOUNeaBOLSGEEELIf1BZkzSFh5e+z6CgoAAXLlxA586d1etEIhE6d+6MmJgYrbb1/PlzFBYWwsKCfSPqu6jR2Q8IIYQQQqqryr6Vv6xJmsTi0jeLP3nyBMXFxbCxsdFYb2Njgxs3bmi1rdDQUNSpU0ejY1zRqFPLMH36dOzduxdyufx9N4UQQggh/zGVPUGCWCwusxNb0X744Qds3boVUVFRMDAwqLTt1IjhB+np6Rg7diwcHBxgYGAAGxsbtG3bFitXrsTz58/f+Ljk5GQIgvDGTuv48ePfeuyHi4sLxGIx0tPT3+rxhBBCCCFVgZWVFXR0dPDw4UON9Q8fPoRUKmU+duHChfjhhx9w+PBhNG3atDKbWf2v1N6+fRtt27aFubk55s6diyZNmkAsFuPKlSv45ZdfULduXXz66aelHlfIyV0JlEyIIJFIyt2mU6dOIT8/H3369MHGjRsRGhrKrF9QUAB9ff1yb+dt1JNmces0/4ifc1Nka8Uu92jBjTFzNDu1x/gmWtwhyUlBqGPOz5Wo52bHLN/7Mz/P4cg+Odw6pzYbMctNRfxjMkfJfm10tMjKmcjJqaqjxc9dvRzvMcs/ducnvC+4w5/++dYl9sQYTb6vw40huLOPRV4OWgBoz8ll+7TJFG6MXE7Ozd47unFjFEce4NYRNWRPBx4fdpUbwznIkFmuP2YuN0bhpnnM8tvL+O/vNq7P2O2wZ7cTAHQ8nLl1kMs+FmMWsNsBAAqwX9/WBey8rgDQsEEms9zsI/6kJpu3sN97doX8BNH1lS+4dQo518QeaDElu2tR2TckvZQM/hXE4ZH/Y5aLLPjniCL5YW6d90WlqhpT2err66Nly5aIjIxEr169AEB901dwcPAbHzd//nzMmTMHhw4dQqtWrSq9ndX+Su1XX30FXV1dxMbGom/fvnB1dUXDhg3Rs2dP/Pnnn/Dz8wMACIKAlStX4tNPP4WxsTHmzJnDjT19+nR4eHgAAA4fPgwDAwNkZWVp1Bk7diw6deqksW7dunXo378/Bg0aVOZdgTKZDLNmzcLgwYNhamqKESNGACjpDLdr1w6Ghoawt7fHmDFjkJf3z8n2t99+Q6tWrWBiYgKpVIr+/fu/MZUGIYQQQkhFGTduHNasWYONGzciPj4eo0aNQl5eHoKCggAAgwcPRljYPxcJ5s2bhylTpiAiIgIymQzp6elIT09Hbm5upbWxWndqMzIycPjwYYwePRrGxmV/OxVemT1n+vTp+Oyzz3DlypVyT3P78ccfw9zcHLt27VKvKy4uxrZt2zBgwAD1umfPnmHHjh0YOHAgunTpguzsbJw8ebJUvIULF6JZs2a4dOkSpkyZgqSkJPj6+qJ37964fPkytm3bhlOnTml8AyosLMSsWbMQFxeHvXv3Ijk5WevpfgkhhBBSvaiUlbuUh7+/PxYuXIipU6fCw8MDcrkcBw8eVN88lpKSgrS0NHX9lStXoqCgAH369IGtra16WbhwYUU+RRqq9fCDW7duQaVSwdlZ8+clKysrvHhR8vPJ6NGjMW9eyc9g/fv3V3+jAErG1GpLR0cH/fr1w++//46hQ4cCACIjI5GVlYXevXur623duhWOjo5wd3cHAPTr1w/r1q1Du3btNOJ16tQJ3377rfr/YcOGYcCAAQgJCQEAODo6YtmyZejQoQNWrlwJAwMDjY54w4YNsWzZMnh6eiI3N7fMYRIKhaJUvjmFUgmxFlMgEkIIIYS8Kjg4+I3DDaKiojT+L08fq6LUyN7NuXPnIJfL4e7urtGpe9fxHAMGDEBUVBQePHgAoCSxcPfu3WFubq6uExERgYEDB6r/HzhwIHbs2IFnzzTHZL3elri4OGzYsEE9jlcikcDHxwdKpRJ37twBAFy4cAF+fn6oV68eTExM0KFDBwAl347KUlb+uZWP7rzTc0AIIYSQf4dSJVTqUtNU606tg4MDBEFAQkKCxvqGDRvCwcEBhoaaNxG8aYiCtjw9PdGoUSNs3boV+fn52LNnj8bQg+vXr+PMmTOYOHEidHV1oauriw8++ADPnz/H1q1bmW3Jzc3FyJEjIZfL1UtcXBwSExPRqFEj5OXlwcfHB6ampti8eTPOnz+PPXv2ACi50awsYWFhyM7O1lhG1WbfREIIIYQQUh1V6+EHlpaW6NKlC5YvX46vv/76nTut2hgwYAA2b94MOzs7iEQidO/eXV22bt06tG/fHj///LPGY9avX49169Zh+PDhb4zbokULXL9+HQ4ODmWWX7lyBRkZGfjhhx9gb28PAIiNjWW2taz8c5k09IAQQgipFqpK9oPqotr3cFasWIGioiK0atUK27ZtQ3x8PBISErBp0ybcuHEDOjr8tCIJCQkaV0jlcvkbU34NGDAAFy9exJw5c9CnTx91p7GwsBC//fYbAgIC0LhxY41l2LBhOHv2LK5du/bGNoSGhuL06dMIDg6GXC5HYmIi/vjjD/XYlXr16kFfXx8//fQTbt++jX379mHWrFlv8YwRQgghpDpQKYVKXWqaan2lFgAaNWqES5cuYe7cuQgLC8O9e/cgFovh5uaG8ePH46uvvuLG6NevX6l1qampZdZ1cHBA69atce7cOSxZskS9ft++fcjIyMBnn31W6jGurq5wdXXFunXrsHjx4jLjNm3aFMePH8f333+Pdu3aQaVSoVGjRvD39wcAWFtbY8OGDfjuu++wbNkytGjRAgsXLiwzBy9L5BMbbh2nnfwchToCO6+jSDjBjSETsfO2Pk7gX3nPymXPTKKvw8/JKJzMZpZbFvNzYT74i5+D0khg51x0+eAJN4ZhYHdmuWDLH15i228js1wk8BPVfniFnbcz1oJ/sky4yM51DPBfv6zNb/6i+NKCB+zn1ZGTtxfg56HteYX/BXNqq8nM8g39/uLG0NPiqs0DnWRmuSVMuDEMt7DzpUaun8qN8YGKnbbnqsqcG8PpIvs9U+tmPjdG0aHb3DomZuxznoBa/BgC+1iV6PPzUOc+Ze/v7d/4F2kKOIdzvha/1ilV/PeEMdj7a6rkn0du67D3176I/5w9HsQ+Fp9l8mevqjfEgl2hMzsXLqk6qn2nFgBsbW3x008/4aeffnpjHVUZEyjLZLIy1780ffp0TJ8+vdT6s2fPllrXu3dvFBe/+U1+/fp19d9vuiPQ09MThw+/OQl0QEAAAgICNNax2k8IIYSQ6os+4sun2g8/IIQQQgghpEZcqSWEEEIIqWlq4rjXykRXagkhhBBCSLVHV2oJIYQQQqqgmjhBQmWiK7WEEEIIIaTao06tlgRBwN69e9X/37hxAx988AEMDAzg4eFR6dvfsGGDxnS8hBBCCKnZVCqhUpeapsI7tYGBgRAEQb1YWlrC19cXly9fruhNqU2fPr3MjqVMJtPIJcurXx7Tpk2DsbExEhISEBkZCQAa+62rq4t69eph3LhxUCgU5Yr9pnYTQgghhJCyVcqYWl9fX6xfvx4AkJ6ejsmTJ6NHjx5ISUmpjM29F0lJSejevTvq16+vsX79+vXw9fVFYWEh4uLiEBQUBGNj4yoz+5dpMT/p3QPOJAEAYKZkJ96+q8c/tFqCnZz9aD4nITYACeeLpnUBf/KFO/rshOatdJ5zYxzO5U8k4KxiJxK/cFrKjSE6fYFZbiCc48Yw4uyvNkYauzHLD5/mH2dZOvyrBPaFRczye7fZE3gAQDMd9nd3PS3yQOaK2G3lTawAADNjZzPLIzz4ExqItGir5wv2Mf9Il//ePPOc/d6T8JuBh0XsSUue6/NjnNJlxzDL50+MItbiORM9NmOWSwQlPwhHFm9WBABiBXt/UrU4r0o4Ta2lZL+nAOCFFte7ngnstkiU/OfMlPPaJOvxn7P01DrMcm3SvOavZk8m04L/9q40lKe2fCpl+IFYLIZUKoVUKoWHhwcmTZqE1NRUPH78GAUFBQgODoatrS0MDAxQv359hIeHqx8rCAJWr16NHj16wMjICK6uroiJicGtW7fg7e0NY2NjfPjhh0hKSgJQ8rP8jBkzEBcXp75KumHDhnK19/z58+jSpQusrKxgZmaGDh064OLFi2+sLwgCLly4gJkzZ0IQBI0JGszNzSGVSmFvb48ePXqgZ8+eGrGSkpLQs2dP2NjYQCKRwNPTE0ePHlWXe3t74+7du/jmm2/U+/OqQ4cOwdXVFRKJBL6+vkhLSyvXvhJCCCGkelCqhEpdappKH1Obm5uLTZs2wcHBAZaWlli2bBn27duH7du3IyEhAZs3b4ZMJtN4zKxZszB48GDI5XK4uLigf//+GDlyJMLCwhAbGwuVSoXg4GAAgL+/P7799lu4u7sjLS0NaWlp6qlltfXs2TMMGTIEp06dwpkzZ+Do6Ihu3brh2bOyv72lpaXB3d0d3377LdLS0jB+/Pgy6928eRN///032rRpo/F8dOvWDZGRkbh06RJ8fX3h5+envoq9e/du2NnZYebMmer9een58+dYuHAhfvvtN5w4cQIpKSlv3DYhhBBCyH9JpQw/2L9/PySSkh+o8vLyYGtri/3790MkEiElJQWOjo746KOPIAhCqZ/vASAoKAh9+/YFAISGhsLLywtTpkyBj48PAGDs2LEICgoCABgaGkIikUBXVxdSaemfb0NDQzF5suZvBwUFBXBz++fn006dOmmU//LLLzA3N8fx48fRo0ePUjGlUil0dXUhkUhKbTMgIAA6OjooKiqCQqFAjx49EBYWpi5v1qwZmjVrpv5/1qxZ2LNnD/bt24fg4GBYWFhAR0cHJiYmpWIXFhZi1apVaNSoEQAgODgYM2fOLNW+lxQKRanxvIWqYugJ7/7zMyGEEEIqV028masyVcqV2o4dO0Iul0Mul+PcuXPw8fFB165dcffuXQQGBkIul8PZ2RljxozB4cOHSz2+adOm6r9tbGwAAE2aNNFY9+LFC+Tk5HDbMmHCBHVbXi7/+9//NOo8fPgQw4cPh6OjI8zMzGBqaorc3Ny3GgP8448/Qi6XIy4uDvv378fNmzcxaNAgdXlubi7Gjx8PV1dXmJubQyKRID4+XqttGRkZqTu0AGBra4tHjx69sX54eDjMzMw0lj2518q9T4QQQgghVV2lXKk1NjaGg4OD+v+1a9fCzMwMa9aswezZs3Hnzh389ddfOHr0KPr27YvOnTtj586d6vp6rwwOfzmmtKx1Si0GoltZWWm0BQAsLDRvghgyZAgyMjKwdOlS1K9fH2KxGF5eXigoKCjHXpeQSqXq7Tk7O+PZs2cICAjA7Nmz4eDggPHjx+PIkSNYuHAhHBwcYGhoiD59+mi1Lb3XBs0LggAVYxR5WFgYxo0bp7Fuv9OIcu8TIYQQQv59dKNY+fwrM4oJggCRSIT8/HwAgKmpKfz9/eHv748+ffrA19cXmZmZpTqb2tLX10dxMf8u9zeJjo7GihUr0K1bNwBAamoqnjx58tbxXqWjU/JT/8t9j46ORmBgID777DMAJVduk5OTNR7zrvvzklgshlismcmAhh4QQgghpCaqlE6tQqFAeno6AODp06dYvnw5cnNz4efnh8WLF8PW1hbNmzeHSCTCjh07IJVK32liAZlMhjt37kAul8POzg4mJialOnMsjo6O+O2339CqVSvk5ORgwoQJMDTkp4opS1ZWFtLT06FUKpGYmIiZM2fCyckJrq6u6m3t3r0bfn5+EAQBU6ZMKXXFWSaT4cSJE+jXrx/EYjGsrPjpogghhBBSs9TEDAWVqVLG1B48eBC2trawtbVFmzZtcP78eezYsQPe3t4wMTHB/Pnz0apVK3h6eiI5ORkHDhyASPT2Tenduzd8fX3RsWNHWFtbY8uWLeV6/Lp16/D06VO0aNECgwYNwpgxY1C7du23aktQUBBsbW1hZ2eHgIAAuLu746+//oLu/88NuXjxYtSqVQsffvgh/Pz84OPjgxYtWmjEmDlzJpKTk9GoUSNYW1u/VTsIIYQQQv5LBBVrUCapce4068Kto6vPH6uso8euU2s8fztffsOeKCBilis3hvJ2MrNccekBN4ZBVw9m+e6ZGdwYveeXzuLxussT45nlOiL+8867Eza1mP8LQ4KY/QVSm+sCQQ3uMcsN7PnDXAR9/hfZ+L9MmOVuvfK5MaDP/kHqwDZTboieO7sxyzf0+4sbQ+Ccab+UvzmTyUuJbb7m1qn70QtmeepJ/jHScCT7y7TwWhrGsuSu/ZtZfu4yO2k+AHRawN6OMpl/g62ojg23jjL9zTfcAkACJzk/ALwoYh9nJgb8+yYspHnMctPm/F8gZx8wZ5a31+Itk67Hf//aciZG0WaiCMdC9nOSpsOfoePzzd7sCgXs9wMAQJe9HcOP39+9KOfrflap8T3v76nU+P+2f2VMLSGEEEIIKR8aflA+lT75AiGEEEIIIZWNrtQSQgghhFRBND60fOhKLSGEEEIIqfboSi0hhBBCSBVEY2rLh67Uasnb2xshISHq/58/f47evXvD1NQUgiAgKyurUrefnJwMQRAgl8srdTuEEEIIIdVRje/UBgYGolevXqXWR0VFvVNndOPGjTh58iROnz6NtLQ0mJmZwdvbG4IgqBcbGxt88cUXuHv3boW0mRBCCCH/HSqVUKlLTUPDD95SUlISXF1d0bhxY431w4cPx8yZM6FSqXD37l2EhIRg4MCBOHny5HtqqSZTO36uxISL/BnMeDkXDX47xI3xodKeWZ69NoYbI/O+MbNcR8+IG8Mg9RqzXCGScmM8XnKGWye3mP28XtXV48aoU8i+baBAxD9J1eKkw+XlUwWAqIS6zPI7d/jt8FHxc38a6hcyy384UIsbY/La9szywu2nuDGKIw8wy/W0+HAQcZ5XbXLQOp79iVsne1AQs/xgkTk3Ru/f2XmILT/g539+kcX+eLkh5udCdZl/kVluaMo/n6lUadw6OU8MmOWPCvi5jPU4t/ToFfDzUD9IMWOW60syuTGMVezp5nO1uJRVr5D9vgOAFwI7kL4W55GHOvxzHk/Br1uZ5S/usPPpAoBp6Ofv3A5SNdT4K7XayMjIQEBAAOrWrQsjIyM0adKEOSuZt7c3Fi1ahBMnTkAQBHh7e6vLjIyMIJVKYWtriw8++ADBwcG4ePGfE3NxcTGGDh2KBg0awNDQEM7Ozli6dKm6fPr06di4cSP++OMP9RXfqKgodfnt27fRsWNHGBkZoVmzZoiJ4Xf8CCGEEFL9KCt5qWnoSi2AFy9eoGXLlggNDYWpqSn+/PNPDBo0CI0aNULr1q1L1d+9ezcmTZqEq1evYvfu3dDXL3s2kszMTGzfvh1t2rRRr1MqlbCzs8OOHTtgaWmJ06dPY8SIEbC1tUXfvn0xfvx4xMfHIycnB+vXrwcAWFhY4MGDkisj33//PRYuXAhHR0d8//33CAgIwK1bt9TT8BJCCCGkZlBpNccjeek/0RPav38/JBKJxrri4mL133Xr1sX48ePV/3/99dc4dOgQtm/fXman1sLCAkZGRtDX14dUqvnT9IoVK7B27VqoVCo8f/4cTk5OOHTon5/i9fT0MGPGDPX/DRo0QExMDLZv346+fftCIpHA0NAQCoWiVGwAGD9+PLp37w4AmDFjBtzd3XHr1i24uLiUqqtQKKBQKDTXFSsh1qEL9IQQQgipWf4TvZuOHTtCLpdrLGvXrlWXFxcXY9asWWjSpAksLCwgkUhw6NAhpKTw5xV/3YABAyCXyxEXF4dTp07BwcEBn3zyCZ49+2f84M8//4yWLVvC2toaEokEv/zyi9bbatq0qfpvW1tbAMCjR2XPWx4eHg4zMzONZUlS+feJEEIIIf8+papyl5rmP3Gl1tjYGA4ODhrr7t3750aIBQsWYOnSpViyZAmaNGkCY2NjhISEoKCAfxPC68zMzNTbcnBwwLp162Bra4tt27Zh2LBh2Lp1K8aPH49FixbBy8sLJiYmWLBgAc6ePatVfD29fwbWC0LJzxJKZdkjY8LCwjBu3DiNdbl9u5d7nwghhBBCqrr/RKeWJzo6Gj179sTAgQMBlHQSb968CTc3t3eOraNTcodvfn6+elsffvghvvrqK3WdpKQkjcfo6+trDI94W2KxGGKxWGNdIQ09IIQQQqoFJY2pLRfq4QBwdHTEkSNHcPr0acTHx2PkyJF4+PDhW8V6/vw50tPTkZ6ejri4OIwaNQoGBgb45JNP1NuKjY3FoUOHcPPmTUyZMgXnz5/XiCGTyXD58mUkJCTgyZMnKNQivQohhBBCyH8ZdWoBTJ48GS1atICPjw+8vb0hlUrfevKDNWvWwNbWFra2tujYsSOePHmCAwcOwNnZGQAwcuRIfP755/D390ebNm2QkZGhcdUWKMl16+zsjFatWsHa2hrR0dHvuouEEEIIqWZUECp1qWkElUpVA4cKkzf526Yvt06Lzk+4dQRd9psh6W8JsxwAEovYdZ5rMZGAgvO1rKUyjxvjrpI9QYOeFm+RTC2GdZhwRuXf1ePv7zPOzAhmWkwCYM+ZwEEb7pKnzPJ7OSbcGNokXjdTsofhyAxzuTEahTkwyw9P5f8q47OwAbN84ffJ3BieL9j74uX7mBuj8BE/kbzZb+uZ5XfafcUsB4DrT9kJ/H339uTGSAzYxCyPLWBPNADw39/SQn6mTTvd59w6vJmVUosNuTGKBXaMIi36D1bF7NfXRIf/q12qit1WBaedAFBLi+FvxmC3NVPgv78lKvbr90CLVJWf1L/PLDf9gH8uUiRmM8st/+84N0ZlibTxr9T4Hz/cVqnx/200ppYQQgghpAqqiRMkVCbq1BJCCCGEVEE1cYhAZaIxtYQQQgghpNqjK7WEEEIIIVUQDT8oH7pSSwghhBBCqr330qn19vZGSEjIe49RUaKioiAIArKyst53UwghhBBSQygrealpKqVTGxgYWCrP686dO2FgYIBFixZh9+7dmDVrlrpMJpNhyZIlldGUChMTEwMdHR10716508weO3YMPXr0gLW1NQwMDNCoUSP4+/vjxIkTlbpdQgghhJDq7F8ZU7t27VqMHj0aq1atQlBQ0L+xyQq3bt06fP3111i3bh0ePHiAOnXqVPg2VqxYgeDgYAwaNAjbtm1Do0aNkJ2djWPHjuGbb77BhQsXynxccXExBEGASMT/jtK0BT8n55G/7bh18jk5ZGP1+PkU7TgxMgR+rkQR585QQWXMjXFbzN5OwyIdbowUXf533rrF7NfHUIv0sbw7YRVa3CjLy/9bqEWMy7m12O3g5DEGALkeP+fqI1UBs3xUvphZDgDRUx4wyxUi/mkwPuwqs9wS/FyYjzg5N1NP8nOhHiwy59b5lJOHtsHJFdwYP7f6jlneauxqbowdKltmuVSLSyrZnDq3DPhvmsaF/JzZvCgFWnxSPuO0NUvEP0eYqti5XQu1+Mj2eMHeTp4u/3x2T49fR6Jk1zHl5JgG+DlzaxXzn7MlD6TM8s6b+cfIYx1TZvkAboTKQ9kPyqfShx/Mnz8fX3/9NbZu3aru0L46dMDb2xt3797FN998A0EQILxykP8/9u48rqb8/wP467bd9m6rkpQWKWRkG5MlS0qW7MtEZRtjN7Yw9iGyMybGVtaxDBpfjK1kCSlkSSqJbFkiWlS37vn90a87rup8ThTh/fS4j4d7Pp/zPss99/a5n/s5709kZCRcXV2hqakJfX19uLu749Wr/5K+y2QyTJkyBQYGBjA1NcWcOXMUtp2RkYGhQ4fC2NgYurq6aNu2La5duyYvnzNnDr777jts27YNVlZW0NPTQ79+/ZCZmakQJysrC7t378aIESPQqVMnhISElHqskZGRcHJygrq6Or7//nvcvFn0h/DNmzfQ0NDAv//+q1D/wIED0NHRQU5ODlJTUzF+/HiMHz8eW7ZsQdu2bWFpaQknJyeMGzcOMTEx8vVCQkIgkUhw8OBBODo6QiwWIzU1VdgLQgghhBDyFarURq2/vz9+++03HDp0CN27dy+1zv79+1GjRg3MmzcPT548wZMnTwAAsbGxaNeuHRwdHXHhwgWcO3cOXbp0QeE7M51s2bIFWlpaiIqKwuLFizFv3jycOHFCXt67d288e/YM//77Ly5fvgxnZ2e0a9cOL1++lNdJTk5GaGgoDh06hEOHDuH06dNYtGiRwj7u2bMHderUgb29PQYMGIDNmzejtInYJk+ejGXLliE6OhrGxsbo0qULpFIpdHV10blzZ+zcuVOh/o4dO9CtWzdoampi3759kEqlmDJlSqnnSfTeN9qcnBwEBgZi48aNiIuLg4mJSanrEUIIIeTLJBNV7uNrU2nDD/7991/8888/CAsLQ9u2bcusZ2BgAGVlZejo6MDU9L+fERYvXozGjRsjKOi/n8rq1q2rsK6TkxNmz54NALCzs8OaNWsQFhYGNzc3nDt3DpcuXcKzZ88gFhf9RLl06VKEhobi77//xk8//QSgqLc3JCQEOjpFPx8OHDgQYWFhWLBggXw7mzZtwoABAwAAHh4eeP36NU6fPg1XV1eF/Zk9ezbc3NwAFDW4a9SogQMHDqBPnz7w9vbGwIEDkZOTA01NTbx58waHDx/GgQMHAACJiYnQ1dVVOAf79u2Dr6+v/PmFCxdQv359AIBUKkVQUBAaNGhQ5rnNy8tDXl6e4jKZDGIBwxQIIYQQ8nnJaPhBuVRa68bJyQlWVlaYPXs2srLY87O/r7inlrWNd5mZmeHZs2cAgGvXriErKwuGhobQ1taWP1JSUpCcnCxfx8rKSt6gfT8GACQkJODSpUvo378/AEBFRQV9+/bFpk2bSuxP8+bN5f83MDCAvb094uPjAQCenp5QVVXFwYMHARQ1WHV1ddG+fXv5Ou/3xrq7uyM2NhaHDx9Gdna2Qi+1mppaieN/38KFC6Gnp6fwWJVMwxQIIYQQ8vWptJ5ac3Nz/P3332jTpg08PDzw77//KjQeWTQ02DdNqKoqDqoXiUSQyYoGlmdlZcHMzAwREREl1pNIJIJiAEW9tAUFBQo3hnEcB7FYjDVr1kBPT0/I4UBNTQ29evXCzp070a9fP+zcuRN9+/aFyv/fPGJnZ4fXr18jLS1N3lurra0NW1tbeZ13aWholGgEv2/atGmYMGGCwrLM3pWbvYEQQgghFUPA/cPkHZX6O7SlpSVOnz6NtLQ0eHh4lLgBq5iamppCLyRQ1AsbFhb2wdt2dnZGWloaVFRUYGtrq/AwMjISFKOgoABbt27FsmXLEBsbK39cu3YN1atXx19//aVQ/+LFi/L/v3r1ComJiXBwcJAv8/b2xtGjRxEXF4fw8HB4e/93T2WvXr2gqqqKwMDADz7m94nFYujq6io8aOgBIYQQQr5Gld7CsbCwQEREBJ49ewZ3d3e8efOmRB0rKyucOXMGjx49wosXLwAU9TJGR0dj5MiRuH79Om7fvo21a9fKy1nat2+P5s2bo1u3bjh+/Dju3buH8+fP49dff1XIJMDn0KFDePXqFYYMGYJ69eopPHr27FliCMK8efMQFhaGmzdvws/PD0ZGRgr5elu1agVTU1N4e3ujVq1aaNasmbysZs2aWLZsGVatWgVfX1+cOnUK9+7dw5UrV7B69WoAgLIyO80KIYQQQr4ONPlC+XySbrsaNWogIiICL168KLVhO2/ePNy7dw82NjYwNjYGANSuXRvHjx/HtWvX0LRpUzRv3hz//PNPqT/Fl0YkEuHIkSNo1aoVBg0ahNq1a6Nfv364f/8+qlWrJijGpk2b0L59+1KHGPTs2RMxMTG4fv26fNmiRYswbtw4NGrUCGlpafjf//4HNTU1hX3q378/rl27ptBLW2zMmDE4fvw4nj9/jl69esHOzg6enp5ISUnB0aNH5TeJEUIIIYQQRSKutNxU5KsVavojs049g3RmHamUv9e41taBzBh/9D3MWz4yyJkZQ6TP/wUlZ/F6ZgytwHm85adar2XGaP1HPWadO5OjeMsNTLOZMVQ1+ROav7jHTjR/RMo/cYKQ3wM6afH/YlK9oxpvOQAoN3Bk1nkTfJG3XL0W+0vu43P8+3LmrQEzxoDB/BNFpP5V8heo913M4d9O37H8ifcB4OnOh8w6154a85afUmf3zyyNCeAtfyNgEh1ZLn+5qhG7T0Xcn/8eANmN67zlAKBUjT3cjMvO4S2/vfQJM4a0kP+dY1b9NTOGpiH/dSZ24J8kAAACQvnvXWn5lv36ZyuxPwUMZPz7Gq3Ovp4b5fJP0pMhYGKULv904y2XRZ9mxkAO/+uvOXkzO0Yl+duscqd+6PVkR7nq//HHH1iyZAnS0tLQoEED/P7772jatGmZ9ffu3YuZM2fi3r17sLOzQ2BgIDw9PT92t8tEAywJIYQQQgiv3bt3Y8KECZg9ezauXLmCBg0awN3dXSFj1LvOnz+P/v37Y8iQIbh69Sq6deuGbt26ySemqgzUqCWEEEIIqYK4Sn6Ux/LlyzFs2DAMGjQIjo6OWLduHTQ1NbF5c+k92atWrYKHhwcmT54MBwcH/Pbbb3B2dsaaNWvKuWXhqFFLCCGEEFIFVfaNYnl5eXjz5o3C4/1JmwAgPz8fly9fVsitr6SkhPbt2+PChQul7vuFCxcU6gNF+ffLql8RqFFLCCGEEPINKm2SpoULF5ao9+LFCxQWFpa40b5atWpIS0srNXZaWlq56leESpt8gRBCCCGEfDhZJc+SW9okTWKxuHI3WomoUUsIIYQQ8g0Si8WCGrFGRkZQVlbG06dPFZY/ffpUPgvq+0xNTctVvyLQ8AOBrKyssHLlys+9GyXMmTMH33333efeDUIIIYRUMBlElfoQSk1NDY0aNVKY6VUmkyEsLAzNmzcvdZ3mzZuXmBn2xIkTZdavCB/UUysS8Z+I2bNnY86cOaWW3bt3D7Vq1cLVq1cVGmNz5szB3LlzARQNPq5evTo6duyIRYsWwcCAnUeyItSpUwcpKSm4f/9+pX2TSEtLw8KFC3H48GE8fPgQenp6sLW1xYABA+Dr6wtNTc1K2W6xOroZzDrVXNnfdUQ66rzlhf/+w4whFfFffg8mH2fGUFXjz9uqbcIMgexps3jLXynXYMZ4tZw9pXPyWzPe8rsp/PklAeCJKv9rIxJwO6tmBfycJan+lrf8zcWSNxq8Ly/sMrNOQhr/+9ClTgYzhtVSV97ylJ+uMGOojeXP2xoWzH8NAQArg7DIyooZw/D7x8w6HoO8eMsbj/uTGYOVh1Y3OJgZ463/cN7yx2fZeUzFt47xlmsZ5TNjaNR/xawjS+fPU5oqZU/YU8D4u5j6SIMZo/4b/n01VmPnQ7aXlpws6F2ZSuwPAE2OncuWVaM6fxpbAICU0ajKErCvXDz/54hSk9bMGLLzJ5l1CDBhwgT4+vqicePGaNq0KVauXIns7GwM+v/PCx8fH5ibm8vH5I4bNw6tW7fGsmXL0KlTJ+zatQsxMTFYv56dP/5DfVCj9smT/xJR7969G7NmzUJCQoJ8mbY2OwF8aerWrYuTJ0+isLAQ8fHxGDx4MF6/fo3du3d/ULzyOHfuHN6+fYtevXphy5Yt8Pf3r/Bt3L17Fy4uLpBIJAgICED9+vUhFotx48YNrF+/Hubm5ujatWup60qlUqiqsv8IEEIIIeTrUJVmx+rbty+eP3+OWbNmIS0tDd999x2OHj0qvxksNTUVSkr/dbz88MMP2LlzJ2bMmIHp06fDzs4OoaGhqFePPVnRh/qg4Qempqbyh56eHkQikfy5iYkJli9fjho1akAsFssPulitWrUAAA0bNoRIJIKrq6u8TEVFBaampjA3N0f79u3Ru3dvnDhxQmHbIpEIf/75Jzp37gxNTU04ODjgwoULuHPnDlxdXaGlpYUffvgBycnJ8nWuXbuGNm3aQEdHB7q6umjUqBFiYmIU4m7atAk//vgjBg4cWGbOtczMTPTv3x9aWlowNzfHH3/8IS/78ccf0bdvX4X6UqkURkZG2Lp1KwBg5MiRUFFRQUxMDPr06QMHBwdYW1vDy8sLhw8fRpcuXRSOc+3atejatSu0tLSwYMECAEVT8VarVg06OjoYMmQIcnMZ0/YQQgghhFSA0aNH4/79+8jLy0NUVBSaNWsmL4uIiEBISIhC/d69eyMhIQF5eXm4efNmpc4mBlTCmNpVq1Zh2bJlWLp0Ka5fvw53d3d07doVSUlJAIBLly4BAE6ePIknT55g//79pca5d+8ejh07BjW1klNc/vbbb/Dx8UFsbCzq1KmDH3/8EcOHD8e0adMQExMDjuMwevRoeX1vb2/UqFED0dHRuHz5MqZOnarQ65mZmYm9e/diwIABcHNzw+vXr3H27NkS212yZAkaNGiAq1evYurUqRg3bpy80e3t7Y3//e9/yMrKktc/duwYcnJy0L17d6Snp+P48eMYNWoUtLS0Sj3m94d1zJkzB927d8eNGzcwePBg7NmzB3PmzEFAQABiYmJgZmaGoKCgUmMRQggh5MsmE1Xu42tT4dkPli5dCn9/f/Tr1w8AEBgYiFOnTmHlypX4448/YGxcNC+5oaFhiXGrN27cgLa2NgoLC+U9kMuXLy+xjUGDBqFPnz4AAH9/fzRv3hwzZ86Eu7s7gKJxHIPeGROWmpqKyZMno06dOgAAOzs7hXi7du2CnZ0d6tatCwDo168fNm3ahJYtWyrUc3FxwdSpUwEAtWvXRmRkJFasWAE3Nze4u7tDS0sLBw4cwMCBAwEAO3fuRNeuXaGjo4OoqChwHAd7e3uFmEZGRvJjHTVqFAIDA+VlP/74o8Jx9OvXD0OGDMGQIUMAAPPnz8fJkyfL7K3Ny8srkUQ5X1YINQHzehNCCCHk82KPbibvqtCe2jdv3uDx48dwcXFRWO7i4oL4+Hjm+vb29oiNjUV0dDT8/f3h7u6OMWPGlKjn5OQk/3/xWI769esrLMvNzcWbN0WD6idMmIChQ4eiffv2WLRokcLQBADYvHkzBgwYIH8+YMAA7N27F5mZmQr13r9jr3nz5vLjUlFRQZ8+fbBjxw4AQHZ2Nv755x94e3vzHvOlS5cQGxuLunXrlmiANm7cWOF5fHy8Qld/afv0rtKSKq9/dZd3fwghhBBCvkRVKqWXmpoabG1tUa9ePSxatAjKysryjAjvenfoQPFP9qUtk8mKvuPMmTMHcXFx6NSpE8LDw+Ho6IgDBw4AAG7duoWLFy9iypQpUFFRgYqKCr7//nvk5ORg165d5dp/b29vhIWF4dmzZwgNDYWGhgY8PDwAALa2thCJRAo31AGAtbU1bG1toaFR8s7YsoYpCDVt2jS8fv1a4fGTvvVHxSSEEELIp8FV8uNrU6GNWl1dXVSvXh2RkZEKyyMjI+Ho6AgA8jGyhYX8qZgAYMaMGVi6dCkeP2ansWGpXbs2fvnlFxw/fhw9evRA8P+npNm0aRNatWqFa9euITY2Vv6YMGECNm3apBDj4sWLJZ47ODjIn//www+wsLDA7t27sWPHDvTu3Vve2DY0NISbmxvWrFmD7OzsDzoGBwcHREVF8e7Tu8RiMXR1dRUeNPSAEEIIIV+jCu+pnTx5MgIDA7F7924kJCRg6tSpiI2Nxbhx4wAAJiYm0NDQwNGjR/H06VO8fv26zFjNmzeHk5MTAgL4c0Tyefv2LUaPHo2IiAjcv38fkZGRiI6OhoODA6RSKbZt24b+/fujXr16Co+hQ4ciKioKcXFx8liRkZFYvHgxEhMT8ccff2Dv3r3y4yr2448/Yt26dThx4kSJoQdBQUEoKChA48aNsXv3bsTHxyMhIQHbt2/H7du3oazM3+AcN24cNm/ejODgYCQmJmL27NkK+0cIIYSQrwfdKFY+FX6j2NixY/H69WtMnDgRz549g6OjIw4ePCi/OUtFRQWrV6/GvHnzMGvWLLRs2RIRERFlxvvll1/g5+cHf39/WFhYlHt/lJWVkZ6eDh8fHzx9+hRGRkbo0aMH5s6di4MHDyI9PR3du3cvsZ6DgwMcHBywadMm+c1qEydORExMDObOnQtdXV0sX75cfnNaMW9vbyxYsACWlpYlxhbb2Njg6tWrCAgIwLRp0/Dw4UOIxWI4Ojpi0qRJGDlyJO+x9O3bF8nJyZgyZQpyc3PRs2dPjBgxAseO8Scof9ftNxJmHclNds+4pj1/T7uyKXs7huzOeqZXL/knq9A0YCdn5xj5zDVk7B9plNXYdXQ4/gM2087iLQcAi3z+t6xEn39SBAC48dyIt1xJwI9St27yz2rxXft0ZoyMiyUzm7zP3pQ/jkibPVnJkxn8idUfqbJn6JBuD+Qt/55jv3ZPC/iT72dtDGfGyM1gf2RnHN/OW76X458EBADG6D7kLWdNrAAAGoH8kzzcqD+TGcP0Df8kHvVsXzBjFD5n/zImfcb/3jRXZr+vcgv5OyV0xOzPIk1d/uPNSWO//jmMrirjAvatR/aGL5l1nrzknyxGlWPva4X0qkn5z6ss6hQzREH8vYrYE1IFiDiO+xqHVZAyhJr+yKzzfW0hjVr+BomQRu3OYP4YrXXYf7CysvjnrDa3zWDGEDE+Wc/HmTNjNK/DPmc34/lnJRLSqM1lNWoln6ZRq8PxTxckpFH7+CJ7hiUNbf4/WMZu7Ebts2P85yTsFbtR6z2Jf3z7raVPecsB4KmU/3ibOrGvIUGN2hf852Qvp8uMMcaOv1Errsn+QsJq1IYKadRyjEatC/szQqTObjqxGrX3brFntayIRq2BMX8DnDFpGQDgf2n8X1os89m9CRXRqH2oxP/ZDAD6jGGIT1TY13u/ufzvX+4tO5d7wc1k3nLdP4V3HFW0DTUGsCt9hGEP+b8Ef2mq1I1ihBBCCCGEfIgKH35ACCGEEEI+HuWpLR9q1BJCCCGEVEHcV3gzV2Wi4QeEEEIIIeSLRz21hBBCCCFVEA0/KJ8q31Pr6uqK8ePHf5JtiUQihIaGfpJtEUIIIYSQilNlemr9/PywZcuWEsujoqIUZu362hQWFmL16tXYvHkzkpKSoKGhge+//x4zZswokee2IqSpsr/HXE5g57HUSuBP6dSg1TNmDBWuBm85J2AwkZVzBm/51YumzBjfNU3jLX+uwj5n2ensFEe5jO+QOXmqvOUAcIvT5i1/+4o/zQ4APFDn/+6vJOC77siaz3nLM+LYx6JrxE4/duhxdd7yDsfY19n5dP6UP+aF/NcyANxd/Yi3/CYnYcbIYV0i1/mPFQBui9kzAhowMjaZCujKUDXir/T4LPv1ZeWh7XbjN2aMh+348+EmXGCn2tLTYqd0ymOkyrsuYk9ZLmP8NS2UqTNjmD7kf38L6bXTrIBJIxPS2ec1XYV/Q/dV2KkBGzGuVVUBCUczQq7ylhfksS/4w4w0aD+zd6PSUE9t+VSpnloPDw88efJE4dGoUSPo6JT9hzo/n537r6riOA79+vXDvHnzMG7cOMTHxyMiIgIWFhZwdXWlXmNCCCGEEIGqVKNWLBbD1NRU4dGuXTuF4QdWVlb47bff4OPjA11dXfz0008AgHPnzqFly5bQ0NCAhYUFxo4di+zs7BLr9e/fH1paWjA3N8cff/zBuz/+/v6oXbs2NDU1YW1tjZkzZ0IqlSrU+d///ocmTZpAXV0dRkZGCrOT5eXlYdKkSTA3N4eWlhaaNWumMHvanj178Pfff2Pr1q0YOnQoatWqhQYNGmD9+vXo2rUrhg4dKj8GPz8/dOvWTWHb48ePh6uraznOMCGEEEK+FFwlP742VapRK9TSpUvRoEEDXL16FTNnzkRycjI8PDzQs2dPXL9+Hbt378a5c+cwevRohfWWLFkiX2/q1KkYN24cTpw4UeZ2dHR0EBISglu3bmHVqlXYsGEDVqxYIS8/fPgwunfvDk9PT1y9ehVhYWFo2rSpvHz06NG4cOECdu3ahevXr6N3797w8PBAUlISAGDnzp2oXbs2unTpUmLbEydORHp6Ou/+EUIIIYSQIlVmTC0AHDp0CNra/40p6tixY6n12rZti4kTJ8qfDx06FN7e3vIeXTs7O6xevRqtW7fG2rVroa5eNJbJxcUFU6dOBQDUrl0bkZGRWLFiBdzc3ErdzowZM+T/t7KywqRJk7Br1y5MmTIFALBgwQL069cPc+fOlddr0KABACA1NRXBwcFITU1F9epFY+UmTZqEo0ePIjg4GAEBAUhMTCxzvHDx8sTExDLOFlteXh7y8hSnmZRyhVAVVcCgK0IIIYRUKhnlqS2XKtWobdOmDdauXSt/rqWlhf79+5eo17hxY4Xn165dw/Xr17Fjxw75Mo7jIJPJkJKSIm8gNm/eXGG95s2bY+XKlWXuz+7du7F69WokJycjKysLBQUF0NX9b9702NhYDBs2rNR1b9y4gcLCQtSuXVtheV5eHgwNDRX2k4+aGvsGpLIsXLhQocENAJ106qOzntMHxySEEELIp0E3ipVPlWrUamlpwdbWVlC9d2VlZWH48OEYO3Zsibo1a9b8oH25cOECvL29MXfuXLi7u0NPTw+7du3CsmXL5HU0NDTKXD8rKwvKysq4fPkylJUVe0aLe6Pt7OwQHx9f6vrFy4sbxUpKSiUawO+P733ftGnTMGHCBIVlWxz57yYmhBBCCPkSValG7YdydnbGrVu3mA3iixcvlnhe1s//58+fh6WlJX799Vf5svv37yvUcXJyQlhYGAYNGlRi/YYNG6KwsBDPnj1Dy5YtS91G//798eOPP+J///tfiXG1y5YtQ/Xq1eVDI4yNjXHz5k2FOrGxsVBVLTu1jlgshlgsVlhGQw8IIYSQLwP11JbPF3mj2Pv8/f1x/vx5jB49GrGxsUhKSsI///xT4kaxyMhILF68GImJifjjjz+wd+9ejBs3rtSYdnZ2SE1Nxa5du5CcnIzVq1fjwIEDCnVmz56Nv/76C7Nnz0Z8fDxu3LiBwMBAAEU9rN7e3vDx8cH+/fuRkpKCS5cuYeHChTh8+DAAoF+/fujWrRt8fX2xadMm3Lt3D9evX8fw4cNx6NAhbN++Xd5obdu2LWJiYrB161YkJSVh9uzZJRq5hBBCCCHfqq+ip9bJyQmnT5/Gr7/+ipYtW4LjONjY2KBv374K9SZOnIiYmBjMnTsXurq6WL58Odzd3UuN2bVrV/zyyy8YPXo08vLy0KlTJ8ycORNz5syR13F1dcXevXvx22+/YdGiRdDV1UWrVq3k5cHBwZg/fz4mTpyIR48ewcjICN9//z06d+4MoGgGs71792LlypVYsWIFRo4cifz8fBgYGODq1atwdHSUx3J3d8fMmTMxZcoU5ObmYvDgwfDx8cGNGzfKda4cpXnMOpqq/MMaAOCZlD+RuPqwPswYayN385YPOBfMjCF7fp+33HnGXN5yANCYxJ9a+0m/Q8wY5nt/ZdYp6L6Qt1zXmJ0kvgYyeMsTE42ZMZJV+N/2Qvryc17xj/U2dGZkVQeg1sqZWafXnmje8rw37L2t8Yz/mr+nIuYtB4BmDpm85bWvsGOcUyl7uBIAtF1ixYxRZ/EVZp0Tr/mvgdcCujLE/Tvxl986xoxh+ob/vLMmVgCAGmF/8m/j6CZmDJhZsutkv+EPsZz9GZD7hv99paHP/lxVYtxCoV5XwozRczd/7vYRIn1mjEQx+yKxkPLfD6Iv4A6nXBH/dpQFJJ2STOG/VguOhjFjDLSour9gfo1ptypTlWnUhoSElLr83byuAHDv3r1S6zVp0gTHjx/n3Yauri727NlTZvn7Y1YXL16MxYsXKyx7f8reHj16oEePHqXGU1VVxdy5c0vcrPUuFRUVTJo0CZMmTQIAXLlyBe3bt0dwcDCWLFmiUJcVixBCCCHkW/VVDD/4mjg7OyMsLAxaWlpITk7+3LtDCCGEkM9EJqrcx9emyvTUkv80bNgQDRs2/Ny7QQghhBDyxfhmGrVlDVsghBBCCKmKKPtB+dDwA0IIIYQQ8sX7ZnpqCSGEEEK+JJT9oHyop7YC+fn5oVu3buVaRyQSITQ0tMLrEkIIIeTLJgNXqY+vzSdt1KalpWHcuHGwtbWFuro6qlWrBhcXF6xduxY5OTmfclc+2Jw5cyASiUo8Tp48iVWrVpWZmqwsT548QceOHStnZwkhhBBCvhGfbPjB3bt34eLiAolEgoCAANSvXx9isRg3btzA+vXrYW5ujq5du5Y7bmFhIUQiEZSUPl37vG7dujh58qTCMgMDA6ipMTJnl8LU1LSidkuQTI79ktexf8aso/+Uf/IFPEllxnBQM+ItL4hmJzzn4q7zlotU2DlLZDcu8ZYbCMh7IrsazqyTnc2foF/HkD0xhkiJ/5u1RbUMZgyrl9V4y4W8kx491+UtN1J5zYxRmHiXWYc1uYKGCXuSB63UAt7yLCX2xAlqFvwTJ+gnvmXG0HvLH0N2j/2e0dDlT6wPAKYv+G8tuaPO7p2R3eB/X2kZsfejnu0L3vKECwbMGKzJFVQ8hjBj5K+bxawje5zOW/7qsSY7RiH/5wQn4HNE05D/vBamZzNjWChLeMtV89mvfy0BddQ5/utMKmBK9reMv9ti7uN7EpWM9Jh1RDzTzX9udKNY+XyyluDIkSOhoqKCmJgY9OnTBw4ODrC2toaXlxcOHz6MLl26AACWL1+O+vXrQ0tLCxYWFhg5ciSysrLkcUJCQiCRSHDw4EE4OjpCLBYjNTUV0dHRcHNzg5GREfT09NC6dWtcuaI4+87t27fRokULqKurw9HRESdPnizxk/6DBw/Qp08fSCQSGBgYwMvLq0TmBBUVFZiamio81NTUSgw/cHV1xdixYzFlyhQYGBjA1NRUYUYyQHFIQX5+PkaPHg0zMzOoq6vD0tISCxcqzkL14sULdO/eHZqamrCzs8PBgwc/7AUhhBBCCPmKfJJGbXp6Oo4fP45Ro0ZBS0ur1DoiUdG3WCUlJaxevRpxcXHYsmULwsPDMWXKFIW6OTk5CAwMxMaNGxEXFwcTExNkZmbC19cX586dw8WLF2FnZwdPT09kZhZNb1lYWIhu3bpBU1MTUVFRWL9+PX79VXFqU6lUCnd3d+jo6ODs2bOIjIyEtrY2PDw8kJ/P7pkozZYtW6ClpYWoqCgsXrwY8+bNw4kTJ0qtu3r1ahw8eBB79uxBQkICduzYASsrK4U6c+fORZ8+fXD9+nV4enrC29sbL1++/KB9I4QQQkjVxVXy42vzSYYf3LlzBxzHwd7eXmG5kZERcnOL5rsfNWoUAgMDFaahtbKywvz58/Hzzz8jKChIvlwqlSIoKAgNGjSQL2vbtq1C7PXr10MikeD06dPo3LkzTpw4geTkZERERMh/8l+wYAHc3Nzk6+zevRsymQwbN26UN7KDg4MhkUgQERGBDh06AABu3LgBbW1t+XqOjo64dKn0n7CdnJwwe/ZsAICdnR3WrFmDsLAwhe0WS01NhZ2dHVq0aAGRSARLy5Jzlvv5+aF///4AgICAAKxevRqXLl2Ch4dHibp5eXnIy1P8SVvKFUJVwM9ChBBCCCFfks+a/eDSpUuIjY1F3bp15Y2vkydPol27djA3N4eOjg4GDhyI9PR0hRvJ1NTU4OTkpBDr6dOnGDZsGOzs7KCnpwddXV1kZWUhNbVonFpCQgIsLCwUxrA2bdpUIca1a9dw584d6OjoQFtbG9ra2jAwMEBubq7ClLX29vaIjY2VP/bt21fmMb6/n2ZmZnj2rPQxq35+foiNjYW9vT3Gjh2L48eP88bT0tKCrq5umfEWLlwIPT09hcee7Ftl7ishhBBCqg5ZJT++Np+kp9bW1hYikQgJCQkKy62trQEAGhpFN1Dcu3cPnTt3xogRI7BgwQIYGBjg3LlzGDJkCPLz86GpqSmvX9yTWszX1xfp6elYtWoVLC0tIRaL0bx583ING8jKykKjRo2wY8eOEmXGxsby/6upqcHW1lZQTNX3BqCLRCLIZKVfSs7OzkhJScG///6LkydPok+fPmjfvj3+/vvvD4o3bdo0TJgwQWFZuO1QQftNCCGEEPIl+SSNWkNDQ7i5uWHNmjUYM2ZMmeNqL1++DJlMhmXLlsmzGezZs0fQNiIjIxEUFARPT08ARTd8vXjx39239vb2ePDgAZ4+fYpq1Yru/o6OjlaI4ezsjN27d8PExAS6uvx3d1cWXV1d9O3bF3379kWvXr3g4eGBly9fwsCAfafw+8RiMcRixTu7aegBIYQQ8mUQkDSDvOOTDT8ICgpCQUEBGjdujN27dyM+Ph4JCQnYvn07bt++DWVlZdja2kIqleL333/H3bt3sW3bNqxbt05QfDs7O2zbtg3x8fGIioqCt7e3vAcYANzc3GBjYwNfX19cv34dkZGRmDFjBoD/blLz9vaGkZERvLy8cPbsWaSkpCAiIgJjx47Fw4cPK/6kvGf58uX466+/cPv2bSQmJmLv3r0wNTWFRCKp9G0TQgghpGqhyRfK55PlqbWxscHVq1cREBCAadOm4eHDhxCLxXB0dMSkSZMwcuRIaGpqYvny5QgMDMS0adPQqlUrLFy4ED4+Psz4mzZtwk8//QRnZ2dYWFggICAAkyZNkpcrKysjNDQUQ4cORZMmTWBtbY0lS5agS5cuUFcvyrmqqamJM2fOwN/fHz169EBmZibMzc3Rrl27T9Jzq6Ojg8WLFyMpKQnKyspo0qQJjhw5UqE5eAtE7K996tbsvJ3imoz8oCbVmTGkXAJ/hTKGVbyLk0p5y1VqSpgxRJr8OSi1hAw8Umb3gKuq8J8zJVX2hvKz+N+yhVL2tcLK7CrkcI10+CdLUbZlv/6Fdx4z68gK+K9XcdOSN1O+T/sWf75ULrf0X47epfydPW95wTF2zl0x4++HUnX+/MEAwHFPmHVqqPC/NvWk2rzlAKBUjT+HtEb9V8wYhc/5c6rqaeUyY8CM//UVkoNW7ed57O0wFBwdy6yTncufp9zIKou3HAAYqV8FUcbHd+2pCcgPW8jYTo6AP1s18/lzSD9RZTdRRDVq85Yr5bOvM+7Fc2Yd8mX4ZI1aoOgmqd9//x2///57mXV++eUX/PLLLwrLBg4cKP+/n58f/Pz8SqzXsGHDEsMJevXqpfC8Tp06OHfunPx5ZGQkACiMjzU1NcWWLVvK3L85c+aUyDVb7P3ZxCIiIkrUeX+aW+6dD49hw4Zh2LBhZW6bK+WDJiMjo8z6hBBCCPlyfX19qZXrkzZqP7cDBw5AW1sbdnZ2uHPnDsaNGwcXFxfY2Nh87l0jhBBCCCEf4Ztq1GZmZsLf3x+pqakwMjJC+/btsWzZss+9W4QQQgghJXyNabcq0zfVqPXx8RE0PpcQQgghhHxZvqlGLSGEEELIl+JrzFBQmT7rjGKEEEIIIYRUBOqpJYQQQgipgqiftnyqTE9tWloaxo0bB1tbW6irq6NatWpwcXHB2rVrkZPDn3exqnn48CHU1NRQr169z70rhBBCCPlCySr58bWpEj21d+/ehYuLCyQSCQICAlC/fn2IxWLcuHED69evh7m5Obp27VruuIWFhRCJRBU6eYEQISEh6NOnD86cOYOoqCg0a9aMt75UKoWqquon2bdnKuxzIdLgTyIOAKICRgr/5+wk8RIR45ifsZPzcy/f8JdnvmXHSE/nLdeUsb8rc6mpzDoyxnyHIgGXqZoW/3lX1WBNrQAYZPCXKwnoG8jK4Z+gQ6SpwVsOAAUv85l1OI7/I0qky55I4HU2f9J7NSEfD1n8Ewno6LETvCs91+Mtl6U9Y8Z480KdWYfj+K8zIT0/XDZ/R4Isnd3RIH3Gfy3m5Qv485PN//6WPeZ/71YUHYmA1/c1/5kVNLEC47WDgM8iTXz8VOhCPgOUGRM0iGUCJoJhTAakKuBi5bJe8lcQMIkPXvNfZ+TLUSV6akeOHAkVFRXExMSgT58+cHBwgLW1Nby8vHD48GF06dIFQNE0svXr14eWlhYsLCwwcuRIZGX99wcrJCQEEokEBw8ehKOjI8RiMVJTUxEdHQ03NzcYGRlBT08PrVu3xpUrVxT24fbt22jRogXU1dXh6OiIkydPQiQSKUyW8ODBA/Tp0wcSiQQGBgbw8vLCvXv3FOJwHIfg4GAMHDgQP/74IzZt2qRQfu/ePYhEIuzevRutW7eGuro6duzYAQDYuHEjHBwcoK6ujjp16iAoKEhhXX9/f9SuXRuampqwtrbGzJkzIWXMqEUIIYSQLxNNk1s+n71Rm56ejuPHj2PUqFHQ0ip9ukrR/3+bU1JSwurVqxEXF4ctW7YgPDwcU6ZMUaibk5ODwMBAbNy4EXFxcTAxMUFmZiZ8fX1x7tw5XLx4EXZ2dvD09ERmZiaAoh7dbt26QVNTE1FRUVi/fj1+/fVXhbhSqRTu7u7Q0dHB2bNnERkZCW1tbXh4eCA//78ep1OnTiEnJwft27fHgAEDsGvXLmRnl+zlmTp1KsaNG4f4+Hi4u7tjx44dmDVrFhYsWID4+HgEBARg5syZCrOb6ejoICQkBLdu3cKqVauwYcMGrFix4sNOPCGEEELIV+SzDz+4c+cOOI6Dvb3i3OpGRkbIzS36yWfUqFEIDAzE+PHj5eVWVlaYP38+fv75Z4UeTalUiqCgIDRo0EC+rG3btgqx169fD4lEgtOnT6Nz5844ceIEkpOTERERAVNTUwDAggUL4ObmJl9n9+7dkMlk2Lhxo7yRHRwcDIlEgoiICHTo0AEAsGnTJvTr1w/KysqoV68erK2tsXfv3hJT+44fPx49evSQP589ezaWLVsmX1arVi3cunULf/75J3x9fQEAM2bMUDj+SZMmYdeuXSUa9sXy8vKQl5ensEzKFUJV9PE/TxFCCCGkcn19famV67P31Jbl0qVLiI2NRd26deUNs5MnT6Jdu3YwNzeHjo4OBg4ciPT0dIUbydTU1ODk5KQQ6+nTpxg2bBjs7Oygp6cHXV1dZGVlIfX/x0EmJCTAwsJC3qAFgKZNmyrEuHbtGu7cuQMdHR1oa2tDW1sbBgYGyM3NRXJyMgAgIyMD+/fvx4ABA+TrDRgwoMQQBABo3Lix/P/Z2dlITk7GkCFD5LG1tbUxf/58eWygqGHt4uICU1NTaGtrY8aMGfJjKM3ChQuhp6en8Pg3M67sk04IIYQQ8oX67D21tra2EIlESEhIUFhubW0NANDQKLrh5N69e+jcuTNGjBiBBQsWwMDAAOfOncOQIUOQn58PTU1NeX3Re4PPfX19kZ6ejlWrVsHS0hJisRjNmzdXGDbAkpWVhUaNGsnHv77L2NgYALBz507k5uYq3BjGcRxkMhkSExNRu3Zt+fJ3h1oUjwvesGFDiZvKlJWLelUvXLgAb29vzJ07F+7u7tDT08OuXbt4p/mdNm0aJkyYoLBsu8NwoYdMCCGEkM/oa8xQUJk+e6PW0NAQbm5uWLNmDcaMGVPmuNrLly9DJpNh2bJl8mwGe/bsEbSNyMhIBAUFwdPTE0DRDV8vXryQl9vb2+PBgwd4+vQpqlWrBgCIjo5WiOHs7Izdu3fDxMQEurq6pW5n06ZNmDhxYomhBiNHjsTmzZuxaNGiUterVq0aqlevjrt378Lb27vUOufPn4elpaXCWN/79+/zHrdYLIZYrHiXOg09IIQQQsjXqEoMPwgKCkJBQQEaN26M3bt3Iz4+HgkJCdi+fTtu374NZWVl2NraQiqV4vfff8fdu3exbds2rFu3TlB8Ozs7bNu2DfHx8YiKioK3t7e8BxgA3NzcYGNjA19fX1y/fh2RkZHy8avFvb7e3t4wMjKCl5cXzp49i5SUFERERGDs2LF4+PAhYmNjceXKFQwdOhT16tVTePTv3x9btmxBQUFBmfs4d+5cLFy4EKtXr0ZiYiJu3LiB4OBgLF++XH4Mqamp2LVrF5KTk7F69WocOHDgQ085IYQQQqo4rpL/VZaXL1/C29sburq6kEgkGDJkiEK2qtLqjxkzBvb29tDQ0EDNmjUxduxYvH79ulzbFXEcI9ncJ/LkyRMEBATg8OHDePjwIcRiMRwdHdG7d2+MHDkSmpqaWLFiBZYsWYKMjAy0atUK3t7e8PHxwatXryCRSBASEoLx48cjIyNDIfbVq1fx008/4ebNm7CwsEBAQAAmTZqE8ePHy28+u337NoYOHYro6GhYW1tjyZIl6NKlC44ePQp3d3cARRNE+Pv748iRI8jMzIS5uTnatWuHpUuX4tdff0V4eDji4kqOWU1LS4O5uTkOHDgAJycn1KpVC1evXsV3332nUG/nzp1YsmQJbt26BS0tLdSvXx/jx49H9+7dAQBTpkzB5s2bkZeXh06dOuH777/HnDlzShwvn7/NSu8Jflc1Lo9ZR8rxfx8y0WLnsYx/y5+3s6nZU2aM9Oel9+wXKxSQK5HlVQF/TlYAMNXgz2MKABm5/DlGC1g5KgEYa/Ln3S1k5MIt2g/28bBcUueP0UrKfv1zCtk/FNUw4M8f+foNOx9uAsd/jWgKyGOpx/HnXBUJ+OOQIeI/XitV9jX0LJ99vDmMhMfPBeSqbiLK5C1PlfKfUwAwV+a/Vq+L2DE62z7kLX/1WJMZo6CAfbysPLTVj69nb+doyXsn3vUkMJq3HACSn+nzlusqs1M4Hlbjv0a+y2VfqzJG/lgA0JXxvyceq7Df3xqMvLtCfldsZMKf3/nJs9J/WX2Xtph/KKLTvf8J2JPKMdqqb6XGX3Nvd6XE7dixI548eYI///wTUqkUgwYNQpMmTbBz585S69+8eROzZ8+Gn58fHB0dcf/+ffz8889wcnLC33//LXi7VaZRW9VERkaiRYsWuHPnDmxsbD737lQYatSWHzVqS6JGrSJq1JZEjVpF1KgtiRq1bF9iozY+Ph6Ojo6Ijo6W3xR/9OhReHp64uHDh6hevbqgOHv37sWAAQOQnZ0NFQHXE1AFxtRWFQcOHIC2tjbs7Oxw584djBs3Di4uLl9Vg5YQQgghX47KniChtNSfpd2PUx4XLlyARCJRyPLUvn17KCkpISoqSv7rM8vr16+hq6sruEELVJExtVVBZmYmRo0ahTp16sDPzw9NmjTBP//887l3ixBCCCGkUpSW+nPhwoUfFTMtLQ0mJiYKy1RUVGBgYIC0tDRBMV68eIHffvsNP/30U7m2TT21/8/Hxwc+Pj6fezcIIYQQQgBU/uQLpaX+LKuXdurUqQgMDOSNFx8f/9H79ObNG3Tq1AmOjo6YM2dOudalRi0hhBBCyDeoPEMNSktZ+j5ra2uYmpri2TPFsc4FBQV4+fKlwiRXpcnMzISHhwd0dHRw4MABqKqqCtq3YtSoJYQQQgipgip7TG15GBsbyyeb4tO8eXNkZGTg8uXLaNSoEQAgPDwcMpmsxART73rz5g3c3d0hFotx8OBBqKvz31xdGhpT+wXz8/NDt27dPvduEEIIIYQAABwcHODh4YFhw4bh0qVLiIyMxOjRo9GvXz955oNHjx6hTp06uHTpEoCiBm2HDh2QnZ2NTZs24c2bN0hLS0NaWhoKC/kzbbzrszVq09LSMG7cONja2kJdXR3VqlWDi4sL1q5di5wcdjqgquLNmzf49ddfUadOHairq8PU1BTt27fH/v37QdnSCCGEEPKhZJX8qCw7duxAnTp10K5dO3h6eqJFixZYv/6/1HhSqRQJCQny9t6VK1cQFRWFGzduwNbWFmZmZvLHgwcPBG/3sww/uHv3LlxcXCCRSBAQEID69etDLBbjxo0bWL9+PczNzdG1a9dyxy0sLIRIJJJPo1vZMjIy0KJFC7x+/Rrz589HkyZNoKKigtOnT2PKlClo27YtJBLJJ9kXoTKV2PkHjQoE5Dpl5Nw0EpAvVZnR6OcExJAW8mcyVBKxv1gUMvLD6quw8/aqKLM/HrRV+XMhPskXkHOzkP/aFgk43kzGayeENuNwlQXsh7oS+9u3XjX+/KEvX7PPmZR9GTHlgT+Ijkh4T0JZcgvYr4uqgJ8iCxk5RjMFfDxKC/jfVwUC8pjmMt6bMgGXYe4b/kqyQvZ+ZOeqMesoveY/r6wctACg4jGEt1zvr3PMGPqv+D9r3gq4RjLBfy2+VWLHkAjoGVNmXItajBy0AMD8UyOgXyg/j/94WJ/vVV1lzvpVmQwMDMqcaAEArKysFDr+XF1dK6Qj8LP01I4cORIqKiqIiYlBnz594ODgAGtra3h5eeHw4cPo0qULAGD58uWoX78+tLS0YGFhgZEjRypMsxYSEgKJRIKDBw/C0dERYrEYqampiI6OhpubG4yMjKCnp4fWrVvjypUrCvtw+/ZttGjRAurq6nB0dMTJkychEokQGhoqr/PgwQP06dMHEokEBgYG8PLywr179+Tl06dPx7179xAVFQVfX184Ojqidu3aGDZsGGJjY6GtrQ0AePXqFXx8fKCvrw9NTU107NgRSUlJJY7j2LFjcHBwgLa2Njw8PPDkyRN5ncLCQkyYMAESiQSGhoaYMmUK9QQTQgghhPy/T96oTU9Px/HjxzFq1ChoaZU+o4zo/3sBlJSUsHr1asTFxWHLli0IDw/HlClTFOrm5OQgMDAQGzduRFxcHExMTJCZmQlfX1+cO3cOFy9ehJ2dHTw9PZGZWTRLTmFhIbp16wZNTU1ERUVh/fr1+PXXXxXiSqVSuLu7Q0dHB2fPnkVkZKS8sZmfnw+ZTIZdu3bB29u71NkxtLW15QmD/fz8EBMTg4MHD+LChQvgOA6enp6QSv+bHSYnJwdLly7Ftm3bcObMGaSmpmLSpEny8mXLliEkJASbN2/GuXPn8PLlSxw4cOADXgFCCCGEfAm+1OEHn8snH35w584dcBwHe3t7heVGRkbIzS36qXHUqFEIDAzE+PHj5eVWVlaYP38+fv75ZwQFBcmXS6VSBAUFoUGDBvJlbdu2VYi9fv16SCQSnD59Gp07d8aJEyeQnJyMiIgIeXqJBQsWwM3NTb7O7t27IZPJsHHjRnkjOzg4GBKJBBEREfjuu+/w6tUr1KlTh/d4k5KScPDgQURGRuKHH34AUDTWxMLCAqGhoejdu7f8ONatWyefwWz06NGYN2+ePM7KlSsxbdo09OjRAwCwbt06HDt2jHfbpc0UIuUKoSoSMvkgIYQQQsiXo8pkP7h06RJiY2NRt25deUPs5MmTaNeuHczNzaGjo4OBAwciPT1d4UYyNTU1ODk5KcR6+vQphg0bBjs7O+jp6UFXVxdZWVlITU0FACQkJMDCwkIhX1rTpk0VYly7dg137tyBjo4OtLW1oa2tDQMDA+Tm5iI5OVnwT//x8fFQUVFRSGNhaGgIe3t7hSTFmpqaClPympmZyfO8vX79Gk+ePFGIoaKiojAFXWlKmynkcGacoP0mhBBCyOfFVfK/r80n76m1tbWFSCRCQkKCwnJra2sAgIaGBgDg3r176Ny5M0aMGIEFCxbAwMAA586dw5AhQ5Cfnw9NTU15fdF7Ny34+voiPT0dq1atgqWlJcRiMZo3b478fP4bdd6VlZWFRo0aYceOHSXKjI2NoaOjA4lEgtu3b5fr+MvyfoJhkUj00WNmS5spZFed4R8VkxBCCCGkKvrkPbWGhoZwc3PDmjVrkJ2dXWa9y5cvQyaTYdmyZfj+++9Ru3ZtPH78WNA2IiMjMXbsWHh6eqJu3boQi8V48eKFvNze3h4PHjzA06dP5cuio6MVYjg7OyMpKQkmJiawtbVVeOjp6UFJSQn9+vXDjh07St2vrKwsFBQUwMHBAQUFBYiKipKXpaenIyEhAY6OjoKOR09PD2ZmZgoxCgoKcPnyZd71xGIxdHV1FR409IAQQgj5MtCY2vL5LMMPgoKCUFBQgMaNG2P37t2Ij49HQkICtm/fjtu3b0NZWRm2traQSqX4/fffcffuXWzbtg3r1q0TFN/Ozg7btm1DfHw8oqKi4O3tLe8BBgA3NzfY2NjA19cX169fR2RkJGbMmAHgv5vUvL29YWRkBC8vL5w9exYpKSmIiIjA2LFj8fDhQwBF43AtLCzQrFkzbN26Fbdu3UJSUhI2b96Mhg0bIisrC3Z2dvDy8sKwYcNw7tw5XLt2DQMGDIC5uTm8vLwEn7Nx48Zh0aJFCA0Nxe3btzFy5EhkZGQIXp8QQggh5Gv2WfLU2tjY4OrVqwgICMC0adPw8OFDiMViODo6YtKkSRg5ciQ0NTWxfPlyBAYGYtq0aWjVqhUWLlwIHx8fZvxNmzbhp59+grOzMywsLBAQEKCQSUBZWRmhoaEYOnQomjRpAmtrayxZsgRdunSRT8umqamJM2fOwN/fHz169EBmZibMzc3Rrl076OrqAijKw3bx4kUsWrQI8+fPx/3796Gvr4/69etjyZIl0NPTA1B0g9m4cePQuXNn5Ofno1WrVjhy5Ei55jSeOHEinjx5Al9fXygpKWHw4MHo3r07Xr9+XZ5Tj7cCvsYYab1l1pFl8+f+MzQruxe+2KlHerzlbq3Y81EXnOTfjqo6O9+ikjL/MI/EVCNmDAvzV8w6Lx+Vnu2jmI60gBnDtOYb3nIheTvfpPCfV5GAcVaPOf4e/+o12ddldgY7f6i6Bf92jJ6xr7O8TP5ctinK7DdFU8bQJW01KW85AGTk87/fddTZw6NU89l9K884/o/1DCV2DLPq/K9f6iMN3nIA0BHzH0+hjD0FpoY+/3kVksvayCqLWYdjnJIngdH8FcDOQ6u7JZgZQ9n5F95yIb1Qzzn+864lY0dh5aAFgEzGr375AnIZiysgLaWRDf9kTaK77G1oSYQPTfzUZJS6s1xEHCU7BVA0ZKFFixa4c+eOwg1bX5sgiwHMOq1UM5h1nmXzNxTsrV/wlgPA349KpkJ71+Bu7P14epL/j96natTWtX/KrMNq1L5gnFMAsLPlP69CGrXJKYa85UIatbdU+BsknS3YQ4WENGqrNeZ/fZ9fZn8vP5nJP1c54/sZAKBpPv8kEEIatQ8Zk2tYabAbX7n57OO9w/FvJ06N/fr+ZMR/PUc9MuUtBwA7tUze8osyXWaMHrX5ZxLKSWdfQ5qG7AYLq1Gb/YL9BVuvJv/ECUIatTcZjdocxhcjAFgr5j+YXnnsLxPqrBMCIFfE3zjOFjAJUkU0ats4P+QtT7/L/gLGatSaXwgv1z5VpAGWPSo1/vb7+ys1/qf2WXpqq4IDBw5AW1sbdnZ2uHPnDsaNGwcXF5evukFLCCGEEPK1+mYbtZmZmfD390dqaiqMjIzQvn17LFu27HPvFiGEEEIIAED2FabdqkzfbKPWx8dH0PhcQgghhBBS9X2zjVpCCCGEkKrsa5wgoTJVmRnFCCGEEEII+VDfXKPW1dUV48eP/yzbFolECA0N/SzbJoQQQsiXhSZfKJ9P0qhdt24ddHR0UFDwXx7OrKwsqKqqwtXVVaFuREQERCIRkpOTP8WuwdXVFSKRCCKRCGKxGObm5ujSpQv27//0aS5SUlLw448/onr16lBXV0eNGjXg5eWlMBVv8b6++2jRosUn31dCCCGEkKrkk4ypbdOmDbKyshATE4Pvv/8eAHD27FmYmpoiKioKubm58kkPTp06hZo1a37S1FrDhg3DvHnzUFBQgIcPH+LAgQPo168f/Pz8sH79+k+yD1KpFG5ubrC3t8f+/fthZmaGhw8f4t9//y0xc1hwcDA8PDzkz9XU2Lkai5lL2d/N3srYuRAtjfmTs6tqsvPDajJ2JT+FP88lAGjo8ScAF5ILVa8afw7SXAHf/cT8qVABAK8S+fMl1m30jBkj4z5/jkkB6SUh5fiPR0ieWgGpTivEkyj+/KBaevy5QQGgTjp/nWRVdg5S61ovecuzXrFjiPP4X38DU/ZEEo9T+ScsAQCjQv5JPHQ59vtb05A/Rv037MlGNHX5z7vpQ21mDCXG27cictAWVeJPVpz8TJ8ZQv8V//GyJlYAgHpXVvCWs/LYAoC5iD9PcYaAyUbSBdSpn8eYLEbAeVdl5KktEDCBg2p1/veeQQF7MiEldorwz4ayH5TPJ+mptbe3h5mZGSIiIuTLIiIi4OXlhVq1auHixYsKy9u0aYO8vDyMHTsWJiYmUFdXR4sWLRAdrTiry+nTp9G0aVOIxWKYmZlh6tSpCr3B2dnZ8PHxgba2NszMzMpM2aWpqQlTU1PUqFED33//PQIDA/Hnn39iw4YNOHnypLzegwcP0KdPH0gkEhgYGMDLywv37t1TiLV582bUrVtXvk+jR48u87zMnj0bZmZmuH79OuLi4pCcnIygoCB8//33sLS0hIuLC+bPny//IlBMIpHA1NRU/jAwMChzG4QQQgj5MnGV/O9r88nG1LZp0wanTp2SPz916hRcXV3RunVr+fK3b98iKioKbdq0wZQpU7Bv3z5s2bIFV65cga2tLdzd3fHyZVGPyaNHj+Dp6YkmTZrg2rVrWLt2LTZt2oT58+fLtzF58mScPn0a//zzD44fP46IiAhcuXJF0P76+vpCX19fPgxBKpXC3d0dOjo6OHv2LCIjI6GtrQ0PDw/k//8UmmvXrsWoUaPw008/4caNGzh48CBsbW1LxOY4DmPGjMHWrVtx9uxZODk5wdjYGEpKSvj7779RWMju5SSEEEIIIf/5ZCm92rRpg/Hjx6OgoABv377F1atX0bp1a0ilUqxbtw4AcOHCBeTl5cHV1RXDhg1DSEgIOnbsCADYsGEDTpw4gU2bNmHy5MkICgqChYUF1qxZA5FIhDp16uDx48fw9/fHrFmzkJOTg02bNmH79u1o164dAGDLli2oUaOGoP1VUlJC7dq15T2xu3fvhkwmw8aNGyH6/59EgoODIZFIEBERgQ4dOmD+/PmYOHEixo0bJ4/TpEkThbgFBQUYMGAArl69inPnzsHc3BwAYG5ujtWrV2PKlCmYO3cuGjdujDZt2sDb2xvW1tYKMfr37w9l5f9+dt++fTu6detW4hjy8vKQl6f4k5iUK4QqY85uQgghhHx+X+PNXJXpk/XUurq6Ijs7G9HR0Th79ixq164NY2NjtG7dWj6uNiIiAtbW1nj9+jWkUilcXFzk66uqqqJp06aIj48HAMTHx6N58+byBiYAuLi4ICsrCw8fPkRycjLy8/PRrFkzebmBgQHs7e0F7zPHcfL4165dw507d6CjowNtbW1oa2vDwMAAubm5SE5OxrNnz/D48WN5A7osv/zyC6KionDmzBl5g7bYqFGjkJaWhh07dqB58+bYu3cv6tatixMnTijUW7FiBWJjY+UPNze3Ure1cOFC6OnpKTz+zr4l+PgJIYQQQr4Un6yn1tbWFjVq1MCpU6fw6tUrtG7dGgBQvXp1WFhY4Pz58zh16hTatm37qXaJV2FhIZKSkuQ9rVlZWWjUqBF27NhRom7x0AEh3Nzc8Ndff+HYsWPw9vYuUa6jo4MuXbqgS5cumD9/Ptzd3TF//nyFhqupqWmpwxreN23aNEyYMEFh2XG7YYL2kxBCCCGfF8e4mY4o+qR5atu0aYOIiAhEREQopPJq1aoV/v33X1y6dAlt2rSBjY0N1NTUEBkZKa8jlUoRHR0NR0dHAICDgwMuXLig8IJHRkZCR0cHNWrUgI2NDVRVVREVFSUvf/XqFRITEwXt65YtW/Dq1Sv07NkTAODs7IykpCSYmJjA1tZW4aGnpwcdHR1YWVkhLCyMN27Xrl2xc+dODB06FLt27eKtWzysIjubfVd0acRiMXR1dRUeNPSAEEIIIV+jT96oPXfuHGJjY+U9tQDQunVr/Pnnn8jPz0ebNm2gpaWFESNGYPLkyTh69Chu3bqFYcOGIScnB0OGDAEAjBw5Eg8ePMCYMWNw+/Zt/PPPP5g9ezYmTJgAJSUlaGtrY8iQIZg8eTLCw8Nx8+ZN+Pn5ldqjmpOTg7S0NDx8+BAXL16Ev78/fv75Z4wYMQJt2rQBAHh7e8PIyAheXl44e/YsUlJSEBERgbFjx+Lhw4cAgDlz5mDZsmVYvXo1kpKScOXKFfz+++8ltte9e3ds27YNgwYNwt9//w0AiI2NhZeXF/7++2/cunULd+7cwaZNm7B582Z4eXlV+GtBCCGEkKpNBq5SH1+bTzb8AChq1L59+xZ16tRBtWrV5Mtbt26NzMxMeeovAFi0aBFkMhkGDhyIzMxMNG7cGMeOHYO+flG+QHNzcxw5cgSTJ09GgwYNYGBggCFDhmDGjBnyuEuWLEFWVha6dOkCHR0dTJw4Ea9fl8yvumHDBmzYsAFqamowNDREo0aNsHv3bnTv3l1eR1NTE2fOnIG/vz969OiBzMxMmJubo127dtDV1QVQlDEhNzcXK1aswKRJk2BkZIRevXqVei569eolPz4lJSW0atUKVlZWmDt3Lu7duweRSCR//ssv7NyEQikJuIj19XKYdQoL+fMHarWszozx6A7/vojtJcwYqqb8OQgLLjNyKQLQasafEi3vtoBciZa6zDqI5C++dZmd7FZHzJ+XMzOPnZf3lhp/nlL20QLt1Pnztop1BeQprs6fHxgAbkUa8ZbrGrFzUNpapfOWv0g1Y8bQa8GfH/buNvYvIA9U+T9umzdk57pV0+Y/7wCQHGfIWy4V8LEvduC/no3V3jBj5KTxb0fIDTDqdSW85YXpH/YrVgky/s8i3UQpM8TbAv7jFdKDxMpDy8pjCwCFjX/lLZcUss+8toz9KSBlfFK8EpDr1rKA//Pstir780zFwZK3XNkkgxmDy2f/nSBfhk/aqLWysip1fIilpWWJ5erq6li9ejVWr15dZrzWrVvj0qVLZZZra2tj27Zt2LZtm3zZ5MmTFeq8mzuXxdTUFFu2bOGtM3z4cAwfPrzUsvePsU+fPujTp4/8+apVq5j7QONrCCGEkG8DZT8on0/aqCWEEEIIIcJ8jRMkVKZPOqaWEEIIIYSQykA9tYQQQgghVdDXeDNXZaKeWkIIIYQQ8sWjnlpCCCGEkCqIbg4vnyrfU+vn54du3bp98/sQEREBkUiEjIyMz7ofhBBCCCFV0Uc3av38/CASiUo8PDw8KmL/qqR3j1lNTQ22traYN28eCgoo1x0hhBBCKoaskh9fmwoZfuDh4YHg4GCFZWIxO5H4l6z4mPPy8nDkyBGMGjUKqqqqmDZtWrljFRYWQiQSlTrbWUXLE7G3EZrFngRAnfGLSGoIO/l+7wL+SR5+2P6MGaOROv8kDxLwTzQAALLd/OXtBQzUH/g3u04zsTpvuZAfmZ4wpjnWUWdPAuDy9uO/fN3P50/OvyGBPRmFNtj7mqHOv68Gj9iTfHyfy38taoN9re74S4u3PJ99mUGb8Rdk/hEJM4YWxz9RCAA4MC6k73LZf8oCQnV4y+2l/JNRAEAO46NGU8CM3T138yfnt1CWMGMoC5hORJNxLeqosXc2k3EdPef4jwUAzEWavOWsiRUAYGHMAt7yrd/NYsZ4I+BPUQ0pf6VqAjp5UlX4J1cwLmB/Ko5bncFbXpNjT+CQyrhENixhhiBVRIW0osRiMUxNTRUexTN/iUQibNy4Ed27d4empibs7Oxw8OBBhfXj4uLQuXNn6OrqQkdHBy1btkRycnKp28rLy8PYsWNhYmICdXV1tGjRAtHR0fLyV69ewdvbG8bGxtDQ0ICdnZ1Cg/vBgwfo06cPJBIJDAwM4OXlhXv37snLCwsLMWHCBEgkEhgaGmLKlCmljmkpPmZLS0uMGDEC7du3lx/Xq1ev4OPjA319fWhqaqJjx45ISkqSrxsSEgKJRIKDBw/C0dERYrEYqampyMvLg7+/PywsLCAWi2Fra4tNmzYpbPfy5cto3LgxNDU18cMPPyAhIUHgq0QIIYSQLwlXyf++Np9kTO3cuXPRp08fXL9+HZ6envD29sbLl0XTPT569AitWrWCWCxGeHg4Ll++jMGDB5f5U/6UKVOwb98+bNmyBVeuXIGtrS3c3d3l8WbOnIlbt27h33//RXx8PNauXQsjo6KpNqVSKdzd3aGjo4OzZ88iMjIS2tra8PDwQH5+0bfoZcuWISQkBJs3b8a5c+fw8uVLHDhwgHmMGhoa8hh+fn6IiYnBwYMHceHCBXAcB09PT0il/021mJOTg8DAQGzcuBFxcXEwMTGBj48P/vrrL6xevRrx8fH4888/oa2trbCdX3/9FcuWLUNMTAxUVFQwePDgcr4ahBBCCCFfnwoZfnDo0KESja/p06dj+vTpAIoaef379wcABAQEYPXq1bh06RI8PDzwxx9/QE9PD7t27YKqatFveLVr1y51O9nZ2Vi7di1CQkLQsWNHAMCGDRtw4sQJbNq0CZMnT0ZqaioaNmyIxo0bAyiamrfY7t27IZPJsHHjRohERb83BAcHQyKRICIiAh06dMDKlSsxbdo09OjRAwCwbt06HDt2rMxj5zgOYWFhOHbsGMaMGYOkpCQcPHgQkZGR+OGHHwAAO3bsgIWFBUJDQ9G7d28ARQ3soKAgNGjQAACQmJiIPXv24MSJE2jfvj0AwNrausT2FixYgNatWwMApk6dik6dOiE3Nxfq6iV/2s7Ly0NeXp7CMilXCFXGT9iEEEII+fwoT235VEijtk2bNli7dq3CMgOD/8Z+OTk5yf+vpaUFXV1dPHtWNF4yNjYWLVu2lDdo+SQnJ0MqlcLFxUW+TFVVFU2bNkV8fDwAYMSIEejZsyeuXLmCDh06oFu3bvLG5bVr13Dnzh3o6CiOF8vNzUVycjJev36NJ0+eoFmzZvIyFRUVNG7cuMQQhOKGvFQqhUwmw48//og5c+YgLCwMKioqCjEMDQ1hb28v30cAUFNTUzgvsbGxUFZWljdYy/LuOmZmZgCAZ8+eoWbNmiXqLly4EHPnzlVY1lurHvroOJWoSwghhJCqhVJ6lU+FNGq1tLRga2tbZvn7DVaRSASZrOhmBQ0NjYrYBbmOHTvi/v37OHLkCE6cOIF27dph1KhRWLp0KbKystCoUSPs2LGjxHrGxuybo95V3JBXU1ND9erVoaJSvlOpoaEh7y0ufi7Eu+eyeP3ic/m+adOmYcKECQrLDtf+qVz7SQghhBDyJfjseWqdnJxw9uxZhfGmZbGxsYGamhoiIyPly6RSKaKjo+Ho6ChfZmxsDF9fX2zfvh0rV67E+vXrAQDOzs5ISkqCiYkJbG1tFR56enrQ09ODmZkZoqKi5LEKCgpw+fLlEvtS3JCvWbOmQoPWwcEBBQUFCjHS09ORkJCgsI/vq1+/PmQyGU6fPs08D0KJxWLo6uoqPGjoASGEEPJlkIGr1MfXpkIatXl5eUhLS1N4vHjxQtC6o0ePxps3b9CvXz/ExMQgKSkJ27ZtK/Wufi0tLYwYMQKTJ0/G0aNHcevWLQwbNgw5OTkYMmQIAGDWrFn4559/cOfOHcTFxeHQoUNwcHAAAHh7e8PIyAheXl44e/YsUlJSEBERgbFjx+Lhw4cAgHHjxmHRokUIDQ3F7du3MXLkyHJNeGBnZwcvLy8MGzYM586dw7Vr1zBgwACYm5vDy8urzPWsrKzg6+uLwYMHIzQ0VL5ve/bsEbxtQgghhJBvVYU0ao8ePQozMzOFR4sWLQSta2hoiPDwcGRlZaF169Zo1KgRNmzYUOYY20WLFqFnz54YOHAgnJ2dcefOHRw7dkyeQkxNTQ3Tpk2Dk5MTWrVqBWVlZezatQsAoKmpiTNnzqBmzZro0aMHHBwcMGTIEOTm5kJXtyiv5sSJEzFw4ED4+vqiefPm0NHRQffu3ct1PoKDg9GoUSN07twZzZs3B8dxOHLkCHPc8Nq1a9GrVy+MHDkSderUwbBhw5CdnV2ubRNCCCHk60ApvcpHxNEo5G9KsPkAZp2aBeyhICy5Ar4vyRgJr2UCkqZrcPyJ5IVMM8AaDZ0lYMIKId8O3yrxH4+SgHeikYz/iKQCzlm6Mv8RC/lA0CxjHHcxbcbrAgAqAuazectIiq8uIMYjFf4vk3qFAvaV8TH5VsDEKfqM107IeyZLwHakIv5roIB9icCEkTg/U4k9jEmbcY0IwToW1U/05yuPsR8A+xrQEnA+MpT5Y0gEXKvpKvwxfGLnMWMImaBBU8Z/7lUEvDSszxEh7ysDGf/fq/wK6LvzeLrro2N8KNca7Ss1fsTDk5Ua/1OrkBvFCCGEEEJIxZJRv2O5fPYbxQghhBBCCPlY1FNLCCGEEFIFUT9t+VCjlhBCCCGkCvoa025VJhp+QAghhBBCvnjUU/sF8fPzQ0ZGBkJDQz/3rhBCCCGkklFPbfl81p7a58+fY8SIEahZsybEYjFMTU3h7u6uMGNYVSUSieQPPT09uLi4IDw8/HPvFiGEEELIN+mz9tT27NkT+fn52LJlC6ytrfH06VOEhYUhPT293LEKCwshEomgJCCvXUUJDg6Gh4cHXrx4gV9//RWdO3fGzZs3YW1tXe5Y+fn5UFNTq4S9VCRkklwhuU51lfhzA0pl/LlBAUCLK+Qtv6EmZsZQ5/hfb3MpO1PtM0beViH5NrMr4Lpj5dwFgFdK/PtqJMtnxjDmP+0QCegZeKbM//pWE7Aft1TUmXVYr5+2MjunsnYh/1WfKeC1s5Tl8pbLOPb1zspDm6bKfnfWFDCdOCsv50MB28lm5KHVFHCt2hu+5C1PSDdgxkgU8x9LrXz2taomICWSEuOalwnIUysp5H9jKQt4X6Uz8tRqs5J7A3jDuJyF5KAVksv2cL0ZvOVCshQXVEAeYk1l/s8ILQH7kV5Y+X97PxRNJVA+n62nNiMjA2fPnkVgYCDatGkDS0tLNG3aFNOmTUPXrl3ldYYPH45q1apBXV0d9erVw6FDhwAAISEhkEgkOHjwIBwdHSEWi5Gamoq8vDxMmjQJ5ubm0NLSQrNmzRAREaGw7XPnzqFly5bQ0NCAhYUFxo4dqzBzl5WVFQICAjB48GDo6OigZs2aWL9+fYljkEgkMDU1Rb169bB27Vq8ffsWJ06cAACcPn0aTZs2hVgshpmZGaZOnYqCdxKau7q6YvTo0Rg/fjyMjIzg7u4OAIiLi0Pnzp2hq6sLHR0dtGzZEsnJyQrbXbp0KczMzGBoaIhRo0ZBKuAPHSGEEELI1+yzNWq1tbWhra2N0NBQ5OXllSiXyWTo2LEjIiMjsX37dty6dQuLFi2CsvJ/PQg5OTkIDAzExo0bERcXBxMTE4wePRoXLlzArl27cP36dfTu3RseHh5ISkoCACQnJ8PDwwM9e/bE9evXsXv3bpw7dw6jR49W2P6yZcvQuHFjXL16FSNHjsSIESOQkJBQ5vFoaGgAKOpxffToETw9PdGkSRNcu3YNa9euxaZNmzB//nyFdbZs2QI1NTVERkZi3bp1ePToEVq1agWxWIzw8HBcvnwZgwcPVmgMnzp1CsnJyTh16hS2bNmCkJAQhISElPv8E0IIIaRqk4Gr1MfX5rMNP1BRUUFISAiGDRuGdevWwdnZGa1bt0a/fv3g5OSEkydP4tKlS4iPj0ft2rUBoMTP+lKpFEFBQWjQoAEAIDU1FcHBwUhNTUX16tUBAJMmTcLRo0cRHByMgIAALFy4EN7e3hg/fjwAwM7ODqtXr0br1q2xdu1aqKsX/STq6emJkSNHAgD8/f2xYsUKnDp1Cvb29iWOJScnBzNmzICysjJat26NoKAgWFhYYM2aNRCJRKhTpw4eP34Mf39/zJo1Sz5Ews7ODosXL5bHmT59OvT09LBr1y6oqhb9nFl87MX09fWxZs0aKCsro06dOujUqRPCwsIwbNiwEvuVl5dX4guDlCuEqkjIIARCCCGEkC/HZ71RrGfPnnj8+DEOHjwIDw8PREREwNnZGSEhIYiNjUWNGjVKNOrepaamBicnJ/nzGzduoLCwELVr15b3BGtra+P06dPyn/CvXbuGkJAQhXJ3d3fIZDKkpKTIY70bVyQSwdTUFM+ePVPYfv/+/aGtrQ0dHR3s27cPmzZtgpOTE+Lj49G8eXOI3hkv5OLigqysLDx8+FC+rFGjRgrxYmNj0bJlS3mDtjR169ZV6K02MzMrsV/FFi5cCD09PYXHocy4MmMTQgghpOrgKvnf1+azp/RSV1eHm5sb3NzcMHPmTAwdOhSzZ8/GpEmTmOtqaGgoNByzsrKgrKyMy5cvKzT8gKLhDsV1hg8fjrFjx5aIV7NmTfn/329YikQiyN67YWjFihVo37499PT0YGxszD7Y92hpKQ5hLx7CwEfIfhWbNm0aJkyYoLBsb53h5dxLQgghhHwOdKNY+Xz2Ru37HB0dERoaCicnJzx8+BCJiYm8vbXvatiwIQoLC/Hs2TO0bNmy1DrOzs64desWbG1tP3pfTU1NS43j4OCAffv2geM4eaM7MjISOjo6qFGjRpnxnJycsGXLFkilUt7eWqHEYjHEYsUMAjT0gBBCCCFfo882/CA9PR1t27bF9u3bcf36daSkpGDv3r1YvHgxvLy80Lp1a7Rq1Qo9e/bEiRMnkJKSgn///RdHjx4tM2bt2rXh7e0NHx8f7N+/HykpKbh06RIWLlyIw4cPAygaH3v+/HmMHj0asbGxSEpKwj///FPiRrGPMXLkSDx48ABjxozB7du38c8//2D27NmYMGECb8qx0aNH482bN+jXrx9iYmKQlJSEbdu28d6gRgghhJCv05d6o9jLly/h7e0NXV1dSCQSDBkyBFlZWYLW5TgOHTt2hEgkKvdkU581+0GzZs2wYsUKtGrVCvXq1cPMmTMxbNgwrFmzBgCwb98+NGnSBP3794ejoyOmTJmCQkYuwODgYPj4+GDixImwt7dHt27dEB0dLR9a4OTkhNOnTyMxMREtW7ZEw4YNMWvWLPmNZRXB3NwcR44cwaVLl9CgQQP8/PPPGDJkCGbM4M/rZ2hoiPDwcGRlZaF169Zo1KgRNmzYUCG9toQQQgghn4K3tzfi4uJw4sQJHDp0CGfOnMFPP/0kaN2VK1cqDC0tDxFHAza+KYer9WfW0RV9fN7bXI49zOEFY9IDVQFXZkVcvJqMyRWETEaQL2J/P8xjvEl1ZIxZEQBwjIkx1ASkPGftqZDjva/Cn6zcrIA96cUjVfboJ8MC/uORcOxrtZBxxEKOtyJkiljXO/u1KxTwQa/D8Z/7R8rsSU1MC/nPq5DE+uoi/us5RZk9+QYr+b6QiVEKBUwmo8Y490LG6bEmV8gUMPSLNamFkIlxchiTiRQIaCtoyNjviU435/OWn6g7nRnjhQr/mc0R0O1mk89/rQqZ9IKl7dM9Hx3jQzU0danU+Bfvh5fIklTa0MXyiI+Ph6OjI6Kjo9G4cWMAwNGjR+Hp6YmHDx/ydiLGxsaic+fOiImJgZmZGQ4cOIBu3boJ3vZnzX5ACCGEEEI+j9KyJC1cuPCjYl64cAESiUTeoAWA9u3bQ0lJCVFRUWWul5OTgx9//BF//PEHTE1NP2jbVe5GMUIIIYQQgkqfIKG0LEkf00sLAGlpaTAxMVFYpqKiAgMDA6SlpZW53i+//IIffvgBXl5eH7xtatQSQgghhHyDyjPUYOrUqQgMDOStEx8f/0H7cfDgQYSHh+Pq1asftH4xatQSQgghhFRBVWmChIkTJ8LPz4+3jrW1damTVRUUFODly5dlDisIDw9HcnIyJBKJwvKePXuiZcuWiIiIELSP1KglhBBCCKmCZFXoXn5jY2NBE001b94cGRkZuHz5snzm1PDwcMhkMjRr1qzUdaZOnYqhQ4cqLKtfvz5WrFiBLl26CN5HulGsnFxdXTF+/PjPvRsAgHv37kEkEiE2NvZz7wohhBBCCBwcHODh4YFhw4bh0qVLiIyMxOjRo9GvXz955oNHjx6hTp06uHTpEoCiyazq1aun8ACKZnqtVauW4G1ToxaAn58fRCIRfv755xJlo0aNgkgkkne579+/H7/99ttHbe/p06dQVVXFrl27Si0fMmQInJ2dP2obhBBCCPmycZX8r7Ls2LEDderUQbt27eDp6YkWLVpg/fr18nKpVIqEhATk5ORU6HZp+MH/s7CwwK5du7BixQpoaGgAAHJzc7Fz5075xA0AYGBg8NHbqlatGjp16oTNmzejX79+CmXZ2dnYs2cPFi1a9NHbKc0zFXauxIJCdiJDTY4/B+V1MXvCiO/zc3nLL6ix81iy6AhIqPlQlf+cVJey3/hpquxzxorDytkIAGJG/kglAd9TtRj5cAsF5Nxl5bq8q8Y+FiF5iN8q8W/olYh9g4N6BeQ6fcx43+gKyOvJ2s4DAXl71QScMynj/asrIB9ytDr/+7c6Ow0xVDn+47mvwj4YfRn/sUgF5H4VkutULGPlMmbTYlwD+UKSyTMuxVfK7IOpxsgRna3EPmdC8hCz8tC6xQUwY/xTfyZvuZB810lq/NeqkGPRqDq/8H81DAwMsHPnzjLLrayswJom4UOmUaCe2v/n7OwMCwsL7N+/X75s//79qFmzJho2bChf9v7wg6CgINjZ2UFdXR3VqlVDr1695GUymQyLFy+Gra0txGIxatasiQULFgAo6o0NCwtDamqqwn7s3bsXBQUF8Pb2xtGjR9GiRQtIJBIYGhqic+fOSE5OrqQzQAghhJCqRMZxlfr42lCj9h2DBw9GcHCw/PnmzZsxaNCgMuvHxMRg7NixmDdvHhISEnD06FG0atVKXj5t2jQsWrQIM2fOxK1bt7Bz505Uq1YNAODp6Ylq1aohJCREIWZwcDB69OgBiUSC7OxsTJgwATExMQgLC4OSkhK6d+8OmYCeJUIIIYSQbwkNP3jHgAEDMG3aNNy/fx8AEBkZiV27dpWZSiI1NRVaWlro3LkzdHR0YGlpKe/VzczMxKpVq7BmzRr4+voCAGxsbNCiRQsAgLKyMnx9fRESEoKZM2dCJBIhOTkZZ8+exYkTJwAUpbJ41+bNm2FsbIxbt27JB1HzycvLKzH9nZQrhKqAn+wIIYQQ8nlVpZReXwLqqX2HsbExOnXqhJCQEAQHB6NTp04wMjIqs76bmxssLS1hbW2NgQMHYseOHfJBz/Hx8cjLy0O7du3KXH/w4MFISUnBqVOnABT10lpZWaFt27YAgKSkJPTv3x/W1tbQ1dWFlZUVAJQYslCW0qa/O5wZJ2hdQgghhJAvCTVq3zN48GCEhIRgy5YtGDx4MG9dHR0dXLlyBX/99RfMzMwwa9YsNGjQABkZGfKbzfjY2dmhZcuWCA4Ohkwmw9atWzFo0CCI/v+Ggi5duuDly5fYsGEDoqKi5HMm5+fnCzqWadOm4fXr1wqPTjp1Ba1LCCGEkM+LxtSWDzVq3+Ph4YH8/HxIpVK4u7sz66uoqKB9+/ZYvHgxrl+/jnv37iE8PBx2dnbQ0NBAWFgY7/pDhgzBvn37sG/fPjx69EieOiw9PR0JCQmYMWMG2rVrBwcHB7x69apcxyIWi6Grq6vwoKEHhBBCyJfhS03p9bnQmNr3KCsry+cuVlbmbwAeOnQId+/eRatWraCvr48jR45AJpPB3t4e6urq8Pf3x5QpU6CmpgYXFxc8f/4ccXFxGDJkiDxG7969MXbsWAwfPhwdOnSAhYUFAEBfXx+GhoZYv349zMzMkJqaiqlTp1begRNCCCGEfMGoUVsKXV1dQfUkEgn279+POXPmIDc3F3Z2dvjrr79Qt27RT/wzZ86EiooKZs2ahcePH8PMzKzEBA+ampro168f1q9frzDcQUlJCbt27cLYsWNRr1492NvbY/Xq1XB1da2w4ySEEEJI1fU1DhGoTCLuQ7Lbki9WqOmPzDpCEo1LGYnE9WXspNnPlPm/U2kISGhfETQ5/hRpbwVMRsBOZw+IGW+1PAHJ2V8zkq8LmdCANXGCkDFJhgX8R8zImQ8AkAq60vhpM147gJ18/bWAZPRmHP849rvK7EkgjBnnTEPAVfRUmT2piWEhfxwh15k647wKee1Y15GQxIS5jPfeWyX21apfyP4sKmSckxwBnwGsa17Ie1ODka5RyDWSqqLGW866DgGgoAI+izQFfH573eCfnXNW4xnMGG5v+V9fFRF7PzIZE4V0evoXM0ZlsTGq3NlFk19cqdT4nxr11BJCCCGEVEFf47jXykQ3ihFCCCGEkC8e9dQSQgghhFRBnIAhVuQ/1FNLCCGEEEK+eNRTSwghhBBSBcloTG25UKOWEEIIIaQKogRV5UPDD6qwkJAQSCQS3jpz5szBd99990n2hxBCCCGkqqKeWh4cx8HNzQ3Kyso4duyYQllQUBCmT5+OmzdvokaNGiXWzcnJwW+//YY9e/bg0aNH0NHRgaOjIyZMmAAvL69PdQglZDByCwKAloD8gtlKjLyOSux8mqYF/PkFnzPy2AKAISMHZaaAHKTPlfi3w8ovCwBSASlXHzHOvZAJjOvk8+dLfSNinzNlxs9ZMgE5SNNU+fdWLODeBlUB55WV+zNLwPGyCMkxfA/8eWgtCqTsGKr87wl1WcVMYf1Yhf+c6BeyX5wMxnsii/H+F0JI3lbWtSrkvflElX2NsPZFSM7ViviVmJUf9rYqfw5aADAu4N8RIbl9hbw3cxhhzBif7wA7D+28mPnMGDsazGLWYRHyN+9zoeEH5UONWh4ikQjBwcGoX78+/vzzTwwfPhwAkJKSgilTpmDt2rUlGrSFhYUQiUT4+eefERUVhd9//x2Ojo5IT0/H+fPnkZ6e/jkOhRBCCCHkq0bDDxgsLCywatUqTJo0CSkpKeA4DkOGDEGHDh0wcOBA+RCBgwcPwtHREWKxGKmpqTh48CCmT58OT09PWFlZoVGjRhgzZozCVLivXr2Cj48P9PX1oampiY4dOyIpKYl3fxYtWoRq1apBR0cHQ4YMQW5ubmWfAkIIIYR8BhzHVerja0ONWgF8fX3Rrl07DB48GGvWrMHNmzfx559/ystzcnIQGBiIjRs3Ii4uDiYmJjA1NcWRI0eQmZlZZlw/Pz/ExMTg4MGDuHDhAjiOg6enJ6TS0n/K3LNnD+bMmYOAgADExMTAzMwMQUFBFX68hBBCCCFfGhp+IND69etRt25dnDlzBvv27YOxsbG8TCqVIigoCA0aNFCo7+3tDUNDQzRo0AAtWrRAr1694OLiAgBISkrCwYMHERkZiR9++AEAsGPHDlhYWCA0NBS9e/cusQ8rV67EkCFDMGTIEADA/PnzcfLkyTJ7a/Py8pCXl6ewTMoVQlVUMWP3CCGEEFJ5ZF9hb2plop5agUxMTDB8+HA4ODigW7duCmVqampwcnJSWNaqVSvcvXsXYWFh6NWrF+Li4tCyZUv89ttvAID4+HioqKigWbNm8nUMDQ1hb2+P+Pj4UvchPj5eoT4ANG/evMx9XrhwIfT09BQehzPjynPYhBBCCCFfBGrUloOKigpUSrmzWENDA6JS7lpVVVVFy5Yt4e/vj+PHj2PevHn47bffkM+4g72iTJs2Da9fv1Z4dNKp+0m2TQghhJCPw1Xyv68NNWo/IUdHRxQUFCA3NxcODg4oKChAVFSUvDw9PR0JCQlwdHQsdX0HBweF+gBw8eLFMrcnFouhq6ur8KChB4QQQsiXgW4UKx8aU1tJXF1d0b9/fzRu3BiGhoa4desWpk+fjjZt2sgbmF5eXhg2bBj+/PNP6OjoYOrUqTA3Ny8zj+24cePg5+eHxo0bw8XFBTt27EBcXBysra0/8dERQgghhFQt1KitJO7u7tiyZQumT5+OnJwcVK9eHZ07d8asWf8lig4ODsa4cePQuXNn5Ofno1WrVjhy5AhUy0jS3rdvXyQnJ2PKlCnIzc1Fz549MWLEiBITQ/DRFpBk2sOPnSZMpKvNW642bDYzxoJGM3nLp253Z8bgnt7nLZddu86ModztR97yI532MGN039+FWefJz5t5yyW27AT+InX+H1e4XHZi/d2XSk4W8i4hSfFdJc95y6sHdmTG4O4mMuvknyt9fHkxcV83Zoy3W/jfHzuvWDBjDAv7mbf8+UB2Avi0B9V5y3vscGXGyN+6i1knO4F/OomVj02ZMebt6c5bzsVfZsaAlH+YVUbIVWYIyZRO7O0wiGrUZtbhsl7ylqeN2cGMkZ/H/+fUyCaHGUO1Ov8kHyoOlswY41Zn8Jb3f8ueOENTmT1xgmY+/2QiSWrsCXjc3vJfI0ImVvC+No9Zh6Uw9eZHx6gsNPlC+VCjthzmzJmDOXPmKCzz8/ODn59fibrTpk3DtGnTeOPp6+tj69atZZaXFnv69OmYPn26wrLAwEDe7RBCCCGEfO2oUUsIIYQQUgV9jeNeKxPdKEYIIYQQQr541FNLCCGEEFIF0eQL5UM9tYQQQggh5ItHPbWEEEIIIVUQjaktH+qp/YxEIhFCQ0M/924QQgghpAqSgavUx9eGemqBUqe4fdfs2bNLpPIqdu/ePdSqVQtXr17Fd99991H7cfr0acydOxexsbHIzc2Fubk5fvjhB2zYsAFqamqIiIhAmzZtSqz366+/Yv78+YK2kaPEzlEo0lBn19HU4C0vTLjAjPFUxJ+XteBIKDOG7Nkb3nIlIx1mDC72HG/5WyX2dz9ZZBizzsOnerzlSsoZzBhvs9R4yyWm7FyYmoxUtmIBuYyNXfjLleu1YsZ4u+d/zDpPr/Ffi7UCOzBjYBt/ntoaUnZuXyUD/hyzmS/Z7xnmWc1n54fOTWHnD9X9Xpe3vP0O9usriz7NW67UpDU7RtQp3vKCPPb7quAo//tKyYj/PQUASgLOK2T818CTZ/znFAAKOf7PVtFd9nk3KHjLW65sksGMUZPj/4zIB38eYwDQYtYAlBlXNPtdBaiIqkajintwm7+Cc9dPsyPko1GjFsCTJ0/k/9+9ezdmzZqFhIQE+TJtbf6JBirCrVu34OHhgTFjxmD16tXQ0NBAUlIS9u3bh8JCxQ+hhIQE6Or+9yH7KfaPEEIIIZ8WDT8oHxp+AMDU1FT+0NPTg0gkkj83MTHB8uXLUaNGDYjFYnz33Xc4evSofN1atWoBABo2bAiRSARXV1cAQHR0NNzc3GBkZAQ9PT20bt0aV65cKXMfjh8/DlNTUyxevBj16tWDjY0NPDw8sGHDBmhoKPaKmpiYKOwzNWoJIYQQ8q2jRi3DqlWrsGzZMixduhTXr1+Hu7s7unbtiqSkJADApUuXAAAnT57EkydPsH//fgBAZmYmfH19ce7cOVy8eBF2dnbw9PREZmZmqdsxNTXFkydPcObMmU9zYIQQQgip0mQcV6mPrw0NP2BYunQp/P390a9fPwBFU9KeOnUKK1euxB9//AFjY2MAgKGhIUxN/5tTvW3btgpx1q9fD4lEgtOnT6Nz584lttO7d28cO3YMrVu3hqmpKb7//nu0a9cOPj4+CkMNAKBGjRoKz+/fvw9DQ8MSMfPy8pCXl6ewTMoVQlWkXI4zQAghhBBS9VFPLY83b97g8ePHcHFRvCvGxcUF8fHxvOs+ffoUw4YNg52dHfT09KCrq4usrCykpqaWWl9ZWRnBwcF4+PAhFi9eDHNzcwQEBKBu3boKY34B4OzZs4iNjZU/9PX1S425cOFC6OnpKTwOZcaV4wwQQggh5HPhKvnf14YatZXE19cXsbGxWLVqFc6fP4/Y2FgYGhoiPz+fdz1zc3MMHDgQa9asQVxcHHJzc7Fu3TqFOrVq1YKtra38oVTG3fnTpk3D69evFR6ddepW2DESQgghhFQVNPyAh66uLqpXr47IyEi0bv1fCpvIyEg0bdoUAKCmVpQ+5f0MBZGRkQgKCoKnpycA4MGDB3jx4kW5tq+vrw8zMzNkZ2d/0P6LxWKIxWKFZTT0gBBCCPkyfI3jXisTNWoZJk+ejNmzZ8PGxgbfffcdgoODERsbix07dgAoykSgoaGBo0ePokaNGlBXV4eenh7s7Oywbds2NG7cGG/evMHkyZNLZDF4159//onY2Fh0794dNjY2yM3NxdatWxEXF4fff//9Ux0uIYQQQsgXiRq1DGPHjsXr168xceJEPHv2DI6Ojjh48CDs7OwAACoqKli9ejXmzZuHWbNmoWXLloiIiMCmTZvw008/wdnZGRYWFggICMCkSZPK3E7Tpk1x7tw5/Pzzz3j8+DG0tbVRt25dhIaGKvQSfyy3Wo+YdTL+ZU/QoG70kLdc08iYGUMZ/NtRrm3DjIE8/qTZhWmvmSGUjF7ylmswErMDgEhfwqxjqPWEWYelhpeYv0Ih+y2tnch/PKoCxln9ftSIt3zkywnMGCIBE4Fo6vAP13kzfCIzhoarFW/582h2MvqC2OO85TV9DZgx3v5ZeuYTORX+pPkAoOvfg1knJ2gfb/lzZfZEAsjhn8RDdv4kM0RB/D3e8sNpZswYAy34f1kSqaoyY3AvnjPr4DX/JC7aYv7rUAgtCTuGkiZ/OZfPnnwjlfG2agL2r3XphexrUZ0xvYKGgA7GNJi3dAAAijBJREFUTI7/80pLwEQwhak3ecuZEysAUHHpxazzuVCe2vKhRu17/Pz84OfnJ3+upKSE2bNnY/bs2WWuM3ToUAwdOlRhWcOGDREdHa2wrFcvxTfOuxdrw4YNsW3bNt59c3V1pQucEEII+UZ8jTdzVSa6UYwQQgghhHzxqKeWEEIIIaQKol9ny4d6agkhhBBCyBePemoJIYQQQqog6qktH+qpJYQQQgghXzzqqSWEEEIIqYKon7Z8qKeWEEIIIYR8+TjyTcvNzeVmz57N5ebmUoyvLEZV2heK8fXGqEr7QjG+3hhVbV9I1STiOBqF/C178+YN9PT08Pr1a+jqCphpiGJ8MTGq0r5QjK83RlXaF4rx9caoavtCqiYafkAIIYQQQr541KglhBBCCCFfPGrUEkIIIYSQLx41ar9xYrEYs2fPhlgsphhfWYyqtC8U4+uNUZX2hWJ8vTGq2r6QqoluFCOEEEIIIV886qklhBBCCCFfPGrUEkIIIYSQLx41agkhhBBCyBePGrWEEEIIIeSLR41aQkiFCQ8PR0FBwefeDUIIId8gyn5AviirV68WXHfs2LGVuCekNMrKynjy5AlMTEwAAN9//z327dsHc3PzcseSSqWoU6cODh06BAcHh4re1W8Wx3F48OABTExMoK6uLni9hg0bQiQSCap75cqVD9098g2ryGts1qxZmDp1KjQ1NQEAr169gr6+/kfvI6naVD73DpBvy8GDBwXX7dq1a4llK1asUHj+/Plz5OTkQCKRAAAyMjKgqakJExOTL7pRe+fOHSQnJ6NVq1bQ0NAAx3GCP+w/p/e/I8fFxSEvL++DYqmqqiI3N/eD96VmzZq4evUqDA0NAQBr1qyBj4/PR833npOTg9TUVOTn5yssd3Jy+uCYnxrHcbC1tUVcXBzs7OwEr9etWzf5/3NzcxEUFARHR0c0b94cAHDx4kXExcVh5MiRFb3LvDIyMnDp0iU8e/YMMplMoczHx0dQjMLCQqxYsQJ79uwp9fV9+fJlqev16NFD8H7u37+/zLJWrVrh4MGD8s+xgwcPws3NDRoaGoLjlyY/P7/U81KzZk3e9QoLCxESEoKwsLBS1w8PD/+o/SpLRV5jCxYswOjRo+WNWktLS8TGxsLa2rpS9p1UDdSo/QZt2bIFRkZG6NSpEwBgypQpWL9+PRwdHfHXX3/B0tJScKxbt26V+kegtAYpoPihBQAikUihIfRuw62wsLDE+ikpKfL/79y5E0FBQdi0aRPs7e0BAAkJCRg2bBiGDx/O3PcJEyYw6xRbvnx5pcV4V3p6Ovr27Yvw8HCIRCIkJSXB2toaQ4YMgb6+PpYtWyZ4e+9LTk7GsGHDmH+Qnjx5grCwMBgYGKB9+/ZQU1OTl2VnZ2PZsmWYNWvWB+9HeYwaNQqBgYHYuHEjVFTK93H18OFDhWto+vTp8PT0/KBG7fPnzzFo0CD8+++/pZaXdq2W5unTp5g0aZK8sfD+lwChcWQyGe7cuVNqg6NVq1a86yopKcHOzg7p6enlatTOnj1b/v+hQ4di7Nix+O2330rUefDggeCYbdq04f2yxrpW//e//8Hb2xtZWVnQ1dVViCUSiQQ3aufOnYuNGzdi4sSJmDFjBn799Vfcu3cPoaGhvNe6np6e/P8cx+HAgQPQ09ND48aNAQCXL19GRkYGs/F77tw5hc/QAQMGfFQDLCkpCYMHD8b58+cVlhd/OWZdZ+PGjUNISAg6deqEevXqlesL9fXr1wXXff/LYEVeY++/t+hH6W8DDT/4Btnb22Pt2rVo27YtLly4gPbt22PFihU4dOgQVFRUeHsUit29exfdu3fHjRs3FBqmxR9+Qv44nzx5Ev7+/ggICJB/E79w4QJmzJiBgIAAuLm58a5vY2ODv//+Gw0bNlRYfvnyZfTq1UuhAVyaNm3aMPcRKDqmsv64VkSMd/n4+ODZs2fYuHEjHBwccO3aNVhbW+PYsWOYMGEC4uLiBG2vNNeuXYOzszPvaxMdHY0OHTpAJpNBKpXC3NwcoaGhqFu3LoCiRln16tXLjKGsrIy0tDQYGxsDAHR1dXHt2jXUqlXrg/a5e/fuCAsLg7a2NurXrw8tLS2Fcr5rVUlJCWlpafKhEDo6OvLzWV7e3t64f/8+Vq5cCVdXVxw4cABPnz7F/PnzsWzZMvkXRJaOHTsiNTUVo0ePhpmZWYnGgpeXFzPGxYsX8eOPP+L+/fsl/lALabAARY3BxYsXY+3atahXr56gfX+Xnp4eYmJiSjSKk5KS0LhxY7x+/VpQnF9++UXhuVQqRWxsLG7evAlfX1+sWrWKd/3atWvD09MTAQEB8h65D2FjY4PVq1ejU6dO0NHRQWxsrHzZxYsXsXPnTmYMf39/vHz5EuvWrYOysjKAos/BkSNHQldXF0uWLClz3Yq8VgHAxcUFKioqmDp1aqnXWYMGDXjXNzIywtatW+Hp6VnubSspKZXorHhXcRnrWv3Ya6yizyn5MlBP7TfowYMHsLW1BQCEhoaiZ8+e+Omnn+Di4gJXV1dBMcaNG4datWohLCwMtWrVwqVLl5Ceno6JEydi6dKlgmKMHz8e69atQ4sWLeTL3N3doampiZ9++gnx8fG86z958qTUm5IKCwvx9OlT5vZPnTolaD8rO8a7jh8/jmPHjqFGjRoKy+3s7HD//n3edVnjjR89esTc/vTp09G9e3ds3LgR2dnZ8Pf3R+vWrXHixIkSXx5Kw3Ec2rVrJ+9VzcnJQZcuXRR6ewHhYy4lEgl69uwpqG5lCg8Pxz///IPGjRtDSUkJlpaWcHNzg66uLhYuXCi4UXvu3DmcPXsW33333Qfvy88//4zGjRvj8OHDpTZYhPDx8UFOTg4aNGgANTW1Ej9zl/VzezENDQ1ERkaWaHBERkaWa5zu+8OJis2ZMwdZWVnM9R89eoSxY8d+VIMWANLS0lC/fn0AgLa2trzB1LlzZ8ycOVNQjM2bN+PcuXPyBi1Q9CVvwoQJ+OGHH3gbtRUtNjYWly9fRp06dT5ofTU1NfnfiPJidSYI9bHXmEgkQmZmJtTV1eWN6KysLLx580ah3scMRyJVDzVqv0Ha2tpIT09HzZo1cfz4cflP6Orq6nj79q2gGBcuXEB4eDiMjIygpKQEJSUltGjRAgsXLsTYsWNx9epVZozk5GT5GLJ36enp4d69e8z127Vrh+HDh2Pjxo1wdnYGUNRLO2LECLRv317QcVQ12dnZpf6BfvnyJXOu8vHjx8PMzKxEA7LY+0NESnP58mX88ccfUFJSgo6ODoKCglCzZk20a9cOx44dY47Fe/fnQ0BYzyOf4ODgj1p/48aN0NbWBgAUFBQgJCQERkZGCnWEjL3Ozs6W9/jo6+vj+fPnqF27NurXr1+um6IsLCw++mfQpKQk/P333x/c6ACAlStXftQ+jB8/HiNGjMCVK1fQtGlTAEBUVBQ2b94suBHIZ8CAAWjatCnzC7K7uztiYmI+uvetRo0aePLkCWrWrAkbGxscP34czs7OiI6OZr7vihUUFOD27dvyoVDFbt++XWKISGmOHTsmH84gk8kQFhaGmzdvKtQpa1jX+xwdHfHixQtBdUszceJErFq1CmvWrCn3l6byDF/j87HXGMdxqF27tsLzd7+YCx2KQb4s1Kj9Brm5uWHo0KFo2LAhEhMT5T8xxcXFwcrKSlCMwsJC6OjoACj6qerx48ewt7eHpaUlEhISBMVo0qQJJkyYgG3btqFatWoAin7enjx5svxDjM/mzZvh6+uLxo0bQ1VVFUDRHxZ3d3ds3LhR0D68KyYmpswbRYQMyaiIGC1btsTWrVvl48hEIhFkMhkWL17MHOpgaWmJwMBA9OnTp9Ty2NhYNGrUiLkP79+cNXXqVKioqKBDhw7YvHkz77rvN2o/p5o1a2LDhg3y56ampti2bZtCHZFIJKhRa29vj4SEBFhZWaFBgwb4888/YWVlhXXr1sHMzEzwPq1cuRJTp06Vr/8hmjVrhjt37nxUo9bX1/eD1wWKrglra2usWrUK27dvBwA4ODggODi4zOuvPC5cuFBmb9y7N5t26tQJkydPxq1bt1C/fn3550AxoY3A4mEuzZo1w5gxYzBgwABs2rQJqampJYZIlGXQoEEYMmQIkpOTFRphixYtwqBBg5jrv/+avH9fAKsB9m4PZGBgIKZMmYKAgIBSz0tpvZPvj/sNDw/Hv//+i7p165ZYX+jnYbHy3nsBfPw1VtG/opEvAzVqv0F//PEHZsyYgQcPHmDfvn3yu8MvX76M/v37C4pRr149+VjJZs2aYfHixVBTU8P69esF95ps3rwZ3bt3R82aNWFhYQGgaGiEnZ0dQkNDmesbGxvjyJEjSExMxO3btwEAderUUfh2LtSuXbvg4+MDd3d3HD9+HB06dEBiYiKePn2K7t27f7IYixcvRrt27RATE4P8/HxMmTIFcXFxePnyJSIjI3nXbdSoES5fvlzmBz7fOLdi9erVw/nz50vcwDFp0iTIZDLB18f7Tp8+jezsbDRv3pyZVsfZ2RlhYWHQ19dnpvjh6yUV0tsv1Lhx4/DkyRMARQ13Dw8P7NixA2pqaggJCeFdV19fX+EYsrOzYWNjA01NzRKNhbJ+9n/35psxY8Zg4sSJ8p/M349RnkwMcXFxCg0lZWVl+fjpshQUFCAgIACDBw/+6Abs+w0pjuPw5MkTxMTElNkb9/7NpgAwb968EsvK0wu3aNEi+f/79u2LmjVr4sKFC7Czs0OXLl0ExVi6dClMTU2xbNky+bViZmaGyZMnY+LEibzrCunJZZFIJArXWfFQoHfx9U6+e9MbAMGfWXw+9t6LPn36fPA11rp16w9aj3zZ6EYx8kGOHTuG7Oxs9OjRA3fu3EHnzp2RmJgIQ0ND7N69G23bthUUh+M4nDhxQt4odXBwQPv27T95+ionJycMHz4co0aNkt9QUKtWLQwfPhxmZmaYO3fuJ4kBAK9fv8aaNWtw7do1ZGVlwdnZGaNGjWL2CN66dQs5OTnyO6/fJ5VK8fjxY96fBzdu3IjTp0+X6NEsFhgYiHXr1pU5bi4wMBBZWVnynmaO49CxY0ccP34cAGBiYoKwsDDehtPcuXMxefJkaGpqMs/Z5+oZzsnJwe3bt1GzZs0Swxnet2XLFsFxy+pBraibb86ePYsJEyYgOjoaQNHNMzk5OQqNjWPHjjGH72hra+PmzZsf3Ntc7P0eTCUlJRgbG6Nt27bo0KHDR8X+nIp7TT/leM3Tp08LrvupGnxdunSBsrIyNm7cWOq9Fy1btqy0bRcUFKCwsFBh+MjTp0+xbt06ZGdno2vXrgr3c5CvAzVqv0FHjx6Ftra2/A39xx9/YMOGDXB0dMQff/zxwQmqX758WaJXqqJVdAqtYlpaWvLhF4aGhoiIiED9+vURHx+Ptm3bynteKjOGVCqFh4cH1q1bV65US1WJs7Mz/P390bdvXwDA3r174evrixMnTsDBwQE+Pj7Q1NTEnj17PnpbhYWFCjflvO/ChQtIT09H586d5cu2bt2K2bNnIzs7G926dcPvv/8ueMwkUDQuOSUlBTY2NuVOMfYxWDcJvovvS0v//v3RvHlz+ZALHR0dHD58GJaWluA4DqtXr8b9+/exb98+3m14eXmhR48eHz2MoTJkZGSUOlafZdu2bfIvbBcuXIClpSVWrlyJWrVqffTYcJbExERkZGQoDLsKCwvD/Pnz5dfq9OnTK3Uf3pWSkoKCgoJSMw+oqqoK/jJjZGSE8PBwODk5QU9PD5cuXYK9vT3Cw8MxceLEEvdelOfvB+tmxkGDBkFNTQ1//vknACAzMxN169ZFbm4uzMzMcOvWLfzzzz8flOGBVF00/OAbNHnyZAQGBgIAbty4gYkTJ2LChAk4deoUJkyYUK6bc96dJMDAwKDcN8GEhYWVmeC7tPGb738IXrlyBQUFBfKbMxITE6GsrCxo7Oi79PX1kZmZCQAwNzfHzZs3Ub9+fWRkZCAnJ+eTxFBVVS1Xjsf3hYeHo1WrVh/V2PrYGCkpKQo/fx85cgS9evWCi4sLAGDGjBno3bv3B+8fUPQab9q0CVu3buX9ojBv3jy4urrKG7U3btzAkCFD4OfnBwcHByxZsgTVq1fHnDlzmNvMycnBmDFj5L2uiYmJsLa2xpgxY2Bubo6pU6cK2vf3Z1wrlp6eDhMTkzJ7Wd9tqJ45cwY//PBDideooKAA58+f523UxsTE4Ndff1VYVqNGDfk6AwcOFJTJoWPHjpg6dSpu3LiBRo0alUi1JnQs69u3b3HixAkkJiZCTU0N9vb2aN++Pe+XlXcFBgbCyspK/iWqd+/e2LdvH8zMzHDkyBFm6qpia9euxaxZszB+/HgsWLBA/jpIJBKsXLlSUKP2Y3IQ+/v7o379+vJGbUpKCrp06YKWLVvCyckJCxcuhKamJsaPHy/oeIKDg6GtrV3ivbZ3717k5OQwv4z4+flh8ODBJRq1UVFR2LhxIyIiIgTtR3nvvfjYGxjfFRkZiTVr1sifb926FYWFhUhKSoKenh78/f2xZMkSatR+bTjyzdHS0uJSUlI4juO42bNncz179uQ4juMuX77MVatWTVCMFy9ecG3btuVEIhGnpKTEJScncxzHcYMGDeImTJggKMacOXM4JSUlrmnTppyXlxfXrVs3hQfLsmXLuC5dunAvX76UL3v58iXn5eXFLV26VNA+FOvfvz+3bNkyjuM4bt68eZyxsTE3dOhQztLSkuvevfsnizF+/HjO39+/XPteTElJiXv69Kn8ebNmzbiHDx9+0hja2trya4HjOM7e3p5bu3at/Pn9+/c5dXX1cu0Tx3FcdnY2t3nzZq5FixacsrIy16xZM27x4sW865iamnLR0dHy59OnT+dcXFzkz/fs2cM5ODgI2v7YsWO5Ro0acWfPnuW0tLTkxxgaGsp99913go9DJBIpnN9ijx49Enxe3n+Nir148YJTUlLiXVddXZ1LTU2VP9+3bx+XnZ0tf37v3j1OTU2NuQ8ikajMB2sfiv3zzz+csbFxifVr1KjBnT59Wl7v7t27ZcawsrLiIiMjOY7juOPHj3MSiYQ7duwYN2TIEM7NzU3QfnAcxzk4OHAHDhzgOE7xGr5x4wZnaGgoKIaHhwfn6OjIBQUFcQcOHOBCQ0MVHnxq1KjBnT9/Xv78t99+4xo0aCB/vnHjRoXnLHZ2dlx4eHiJ5REREVzt2rWZ6+vo6HBJSUklliclJXF6enqC96NFixby89q/f3/Ow8ODO3fuHOfj48PVrVtXcJwPoampqXDtdO/enRszZoz8eVxcHGdsbFyp+0A+Peqp/QapqanJew5Pnjwpn3XHwMCgRA6/svzyyy9QVVVFamoqHBwc5Mv79u2LCRMmCJr5at26dQgJCcHAgQM/4CiAZcuW4fjx4wrDJfT19TF//nx06NCBeXPGu9asWSO/6//XX3+Fqqoqzp8/j549e2LGjBmfLEZBQQE2b96MkydPltoDxjekgquAKWo/NoaNjQ3OnDkDa2trpKamIjExUWGGq4cPH8pvTBTi4sWL2LhxI/bu3YuaNWsiPj4ep06dEjQW79WrV/KsGkDRmMOOHTvKnzdp0kTw7FehoaHYvXs3vv/+e4WfR+vWrYvk5GTm+sU5hEUikUKaMaCoN+vMmTOCc4pyZUyZnJ6eXuJ6eZ+Ojg6Sk5PlN2a+f6NWSkqKoHGgH3tj0/nz59GrVy907doVEydOlH+G3Lp1C8uWLYO7uzuuXr2K4OBgaGlplTmrV1pamvxYDh06hD59+qBDhw6wsrJCs2bNBO9PSkpKqXmYxWIxsrOzBcX4mBzEL168UMhNferUKYUb1FxdXcv1eZaamlrqhCeWlpZITU1lrl+c4/V9r1+/LlcKrBkzZsjP37x589C5c2e0bNlSfu8FS2FhIUJDQ+U5y+vWrYuuXbsK6sl/P0XlxYsXFXIFq6urC8qFTL4s1Kj9BrVo0QITJkyAi4sLLl26JP9wSUxMLJH0vywfM0lAsfz8fPzwww/l2/l3vHnzBs+fPy+x/Pnz56V+IPMxMDCQ/19JSUnwz8kVHePmzZvynLuJiYkKZZ/65rkPMWrUKIwePRpnz57FxYsX0bx5czg6OsrLw8PDBU3isGzZMmzevBmvX79G//79cebMGTRo0ACqqqqCG8XVqlVDSkoKLCwskJ+fjytXrijceJaZmVkic0BZnj9/XmLIAFCUyUDI61I8yQDHcQozTgFFXzKL04PxKW6AikQi+Pn5KYwFLiwsxPXr15nvp2bNmmHr1q1lTrISEhJSrsbgh5o/fz4GDRokH+9Y7IcffsAPP/yA4cOHo2XLluA4DmFhYWXG0dfXx4MHD2BhYYGjR49i/vz5AIrOc3kaX7Vq1UJsbGyJoRtHjx5V+NLO52NyEBsYGODJkyewsLCATCZDTEyMwv0D+fn55YptYmKC69evlxj7eu3aNUHvn1atWmHhwoX466+/FGZHW7hwoaCbqxo3boyhQ4fixx9/lH9JsrW1xe3btwXfe3Hnzh14enri0aNH8uFlCxcuhIWFBQ4fPgwbGxve9b/77jts27YNCxcuxNmzZ/H06VOFG5iTk5NRvXp15rGQL8zn6yQmn8v9+/e5Tp06cU5OTtzGjRvly8ePH6/w8wwfbW1tLjExUf7/4p/roqOjOQMDA0ExpkyZws2bN6+ce/+fgQMHclZWVty+ffu4Bw8ecA8ePOD+/vtvrlatWpyPj0+5Yt2/f5/38alifAwlJSXu2bNn8uc6Ojq8P91WVoxNmzZx3bp1437++WfuyZMnCmUjRozg9u3bx4yhrKzMTZ8+nSsoKFBYrqKiwsXFxQnaj59//plr3rw5d+bMGW7ChAmcoaEhl5eXJy/fvn0717hxY0GxWrZsya1evZrjuKLrvficjB49mnN3dxcUg+M4ztXVVWG4THn4+flxfn5+nEgk4vr27St/7ufnx/30009cQEAA9/z5c94Y4eHhnJKSEjdp0iSFIQxPnz7lJkyYwCkrK3NhYWFlrp+Tk8P973//kz+fOnUq98svv8gfkyZN4t6+fcs8Fn19fe769etlll+7do0TiUTc1atXeeOMGjWKs7S05Nq3b88ZGhpymZmZHMdx3F9//cU1bNiQuR/FNmzYwJmbm3O7du3itLS0uL/++oubP3++/P9CHDt2jOvQoYN8aFd5/Pjjj1znzp251NRUbtmyZZy2tjaXlZUlL//77785JycnwfGmTJnCWVpacuHh4VxBQQFXUFDAhYWFcZaWltzEiROZ69+8eZMzNDTkbGxs5NeYjY0NZ2xszN24cYO5/uDBgzkdHR1OU1OTGzhwIHfq1CnB+16sY8eOnIeHB5eeni5f9uLFC87Dw4Pz9PRkrh8REcFpaGhw1tbWnIaGBjd48GCF8hEjRpT77wSp+qhRSz5Ix44duRkzZnAc998f+cLCQq53797yMbosY8eO5SQSCdeqVStu9OjRCn8cf/nlF+b62dnZ3IgRIzixWMwpKSlxSkpKnJqaGjdixAiFPwhCFI8FLOvxqWJ8DJFIxNWvX59r2LAh17BhQ05ZWZmrW7eu/Hnxo7JjVISAgADOzs6Os7Cw4KZMmSL/Q1qeRu3z58+5li1bciKRiNPR0eH279+vUN62bVtu+vTpgmKdPXuW09bW5n7++WdOXV2dGzduHOfm5sZpaWlxMTEx5Tu4jzRnzpxyX9/v+uOPPzg1NTVOSUmJk0gknL6+vvy98/vvv/Ouu3btWq5z587y59ra2lyzZs04V1dXztXVlTM1NeWWL1/O3Ad1dXXu3r17ZZbfu3dP0Bjj/Px8bsmSJdzYsWO5K1euyJcvX76c27BhA3P9d23fvp2ztbWVj+01NzdX+NLPIpFI5OdVW1ub09fXV3jwSUlJkW9bRUWFCwoKUij38vLixo8fL3hf8vLyuD59+nAikYhTVVXlVFVVOWVlZW7QoEEKX+z4PHr0iJs2bRrn6enJ9ezZk5s7d65CA5MlOzubCw4O5lq3bs0pKSlxNjY23IIFCwSP0dfU1Cz1i09sbCynpaUlKMatW7e4lStXcrt27eIKCwsVyv7880/mlyby5aGUXt+43NzcErO8CBlTd/PmTbRr1w7Ozs4IDw9H165dFSYJYP00BIB3hiyRSITw8HD2AaDoJ+DicY02NjbQ0tJipnt637Vr1xSeS6VSXL16FcuXL8eCBQtKjD2srBht2rTh/VmO75wIzYPLl9u1ImLg/9o776gosuftPzPkDCooKogoIqgoBswZBXPOmHURDHzRRXENq5hd45oDCpiQNbu4GAEFsyQDKCAIrmIAQSWoQL1/8E7/GCb1BFHX/pwzR6d7bvXtmaa77r1VT0HxLP+KREZGYt++fUxZ2IcPHyIyMpJRU2BDXl4e9PX1Ra6HnJwc6OvrSywrXJGnT59i1apVQvrBgqx1eXj+/DnOnDkjtsKSPDJ0ypCRkYHjx48jOTkZQFnY0NChQ5n4VEl07NgRc+fOZeI9BXrMgoIrBw8exLZt23Djxg2pdhwcHODt7S2x0ta+ffuwadMmpdRA2FJcXIzDhw/DxcUF1atXR0FBAT5+/Cg23EQasvSIZSkOFBcX4+HDhzA1NRVZFo+Pj0ft2rVZhQ4QETIzM2Fqaornz58jLi4OOjo6aNKkCasStl9DWjA1NRX79+/HgQMH8OLFC/Ts2ROTJ0+Wek+sUqUK/v77b5GQmujoaPTr10+mpBfHzwnn1P6E5OfnY968eQgJCUF2drbIfrYOh6JFAr4mbOWe2BIaGoo//viDtYSNsjYqluT88uUL4uLi8ODBA4wfPx6bN29WuB+VCZ/PR1ZWlohj8OLFC9SrV08ogYMNHz58wOHDh7Fv3z7cu3cPTk5OGDp0qEzd4i9fvkBHRwdxcXFo3Lix3OchsOHu7o5FixaJTb6Rh8uXL6N///6wtrZGUlISGjdujPT0dBARM0Bkw7FjxySWY5ZWZU1AUVGRxDK00jA3N8eNGzeYWE1TU1PcuXOHef/kyRO0atUKeXl5Uu1s3LgRy5cvx4EDB0QklUJDQzF+/Hj89ttvYn/fM2fOoFevXtDQ0BAqmSsOttJiurq6SExMZOX0fU0ePHgg8To9deqU2GpqFSktLYW2tjYePnyosFNqamqK69evq1wvm4hw/PhxuLu7Izc3V+qzZty4cYiJiYG/v79Q2eGpU6eiRYsWUiv5ybouysP2GuH4QfiGs8Qc3whPT0+ys7OjY8eOkY6ODu3bt4+WLVtGtWvXpoMHD8ps//nzZ+rWrRsTU6sKBDGxiqCI3BNbkpOTSVdX95vb+P3331nFwokjIiKCQkNDFY7llMfG5s2bafPmzcTn82nFihXM+82bN9OGDRto4MCBcklgiSMhIYG8vLxYy/HUrVuX4uLilDqmoaGh3LHF4mjVqhUtXryYiP4vFv3Dhw/Uv39/kSVnSWzevJn09fVpxowZpKmpSe7u7uTs7ExGRkaswykMDAxo/PjxdOHCBZFlWWloa2tTUlKSxP2JiYmkpaUl005JSQkNHTqUeDweNWzYkAYNGkQDBw4kW1tb4vP5NGjQIIn9Ki+LpgppMSKizp07M9JTylBcXEzHjh2jZcuW0bJly+jEiRMiceHSqFmzptjr7NixY3LdQ+zt7enGjRusP18RZaQFJREeHk7jxo0jPT09MjIyInd3d6mff/fuHfXv3594PB5pamoyoR0DBw6k3NxcqW2lXReKXiMcPwacU/sTYmFhwQTul9cjDAoKol69erGyUa1aNaWd2pKSElq6dCkZGhoycadGRkbk5+fH6kF748YNmjx5MhkaGlLjxo1JTU2Nrl69qlBf8vLyhF65ubmUmJhII0aMYK0PqQobkkhOTpYZl7d69WomzpmIqLS0lFxcXJgbePXq1enBgwdf1YaVlRVZWVkRj8cjCwsL5r2VlRU1aNCAevbsSTdv3mR51tL5/Pkzq8/t3buXevfuLVc8YEXGjRvHKlZUFvr6+pSSkkJEZTGYgu8yLi6O6tSpw8qGra0tHT58mLEnSNJctGgRTZ8+nZWNEydO0NChQ0lHR4dq1KhBXl5eQpq+kqhfvz4dO3ZM4v6jR49SvXr1WPWBiCg4OJgGDBhAdnZ2ZGdnR/3792edmKVKjh49StbW1rRlyxa6fv06xcfHC73YkJycTDY2NqSrq8vEnuvq6pKtrS3zm8ti8eLFZG1tLZRgGRwcTLq6uhQSEsL6fM6cOUMdOnRgldQljhkzZpChoSG1aNGCfvnlF7nzHQRkZmbSsmXLqF69esTj8ahTp04UGBhIBQUFrG0kJyfTmTNn6MyZM2K1czk4ysOFH/yE6Ovr49GjR7C0tETt2rVx4sQJODk5IS0tDU2aNGGl3eft7Q0tLS2sXr1a4X7Mnz8f/v7+WLp0KRMjGRUVhSVLlmDq1KlYsWKF2HYV5Z7c3NwYuaf4+HghCSm28Pl8kVhWIoKFhQWCg4PRtm3bSrEhiQMHDmDevHl48eKFxM+ookStqsrcdu3aFSdOnFCo5LJA01UWPB4PM2fOlPk5R0dHpKSk4MuXL6hTp46Iliub5frly5dj/fr16N69u1j9YEHZWVnUqFED4eHhsLOzg729PVavXo3+/fsjPj4e7du3Z/W3V36p3MzMDBcvXkTTpk2RnJyMNm3aiA0pksSHDx9w7NgxHDlyBFeuXIG1tTXc3Nwk6sJ6eXnh0qVLuHfvnkj4QmFhIVq2bAlnZ+cfJkxGAJ/PF9nG4/EYTWA2IVm9e/cGEeHQoUOMvF92djbc3NzA5/MRGhrKqi8zZ85EeHg4rl69irCwMEyZMgUHDhzAkCFDWJ+PiYkJCgoKUFxcDE1NTejo6AjtlxWPqmy+Q0hICPbt24fLly/DzMwM48ePx6RJk1C/fn3W51CR4uJiFBUVCWk8c3BUhHNqf0IcHBywZcsWdO7cGc7OzmjWrBnWrVuHP//8E2vXrsXz589l2pg5cyaCgoJgY2Mjd5EAATVr1sTOnTtFYppOnz4NT09P/Pvvv2LbqaurY968efDz8xNK/lHGqY2MjBR6z+fzYWpqivr167MuGasKGxUTJ4gIL1++xN27d7Fo0SKpCVomJia4fv06o6s5ceJElJSUICgoCECZ+PiwYcOkFhxQhQ1lYRu3yuPx8PTpU5mfk5X8JivpTVaf2PYDAAYOHIg+ffpg6tSp+PXXX3H69GlMmDCBGQBcunRJpg1ra2scP34cjo6OaNmyJaZOnQp3d3dcuHABI0eOVDiB5tGjRxgzZgwSEhIkOnGvXr1Cs2bNoKmpiRkzZqBBgwYAgMePH2Pr1q0oLi5GbGysUNELWQiSiJ4+fYpNmzbBzMwM//zzDywtLdGoUSOZ7eUttS0OWdrabGJt9fT0cPPmTZHEQXkGLALGjBmDO3fu4N9//8Xhw4dZlektj7JJa8qiqamJPn36YPLkyejdu7fYQYMkzp49i+zsbEyYMIHZtmLFCixbtgzFxcXo1q0bjh49KteAOT8/H5GRkWJj0NkOSDl+DLjiCz8hEydORHx8PDp37gxfX1/069cPW7duxZcvX1hnX0srEsCWnJwcsVWUGjZsKPXBvGzZMiaTdtSoURg7dqzCSUACOnfurFR7VdkwMjISes/n82Fraws/Pz/07NlTatvi4mIhQf4bN24I1YqvWbMm3r59+9VtAGXO+LFjxxAeHi7W2Thx4oTEtmlpaTLtywMbp1UWqurThg0bGOdm6dKl+PjxI44ePQobGxvWf3vdunXDmTNn4OjoiIkTJ8Lb2xvHjh3D3bt3WSlslKeoqAhnzpzB4cOHERYWhurVq8PHx0fi56tXr47r16/Dw8MDvr6+TEEAHo+HHj16YPv27XI5tIIqb+3bt8fVq1exfPlymJmZIT4+Hv7+/jh27JjU9kuXLoWfnx9atmwJc3NzhQuUSHJaS0tLce7cOVZOrZaWltiiLx8/fpSqsCEuqWnw4MG4du0aRo0aBR6Px3yGbVKTKp1WwSQH28I8gjbyqkcI2LBhA4YOHcq8v379OhYvXgw/Pz/Y2dlhwYIFWLZsGeu/l9jYWPTu3RsFBQXIz89HlSpV8PbtW+jq6sLMzIxzav9rfKOwB47viPT0dDp+/Djr2DFV4eTkJLbYw4wZM6h169Yy20dERNC4ceNIV1eXHBwcSE1NjaKiohTqS0hICA0aNIjRZB0xYgSFhYVVug1laNq0Ke3fv5+IygpB8Hg8IU3X6OhoqlWr1le3QVSmQaylpUWurq40fvx4oUIBEyZMkP/kvlMePXqkcAKfopSUlNCXL1+Y90eOHKGZM2fSn3/+yVqDNCwsjMaNG0eGhoZUpUoV+uWXXygyMlKufmRnZ9OtW7fo1q1bCscrt2nThtavX09EwvHBt27dYnWd1ahRg4KCghQ6tjSSk5Np/vz5ZG5uTurq6qzajB07lho1akQ3b96k0tJSKi0tpRs3blDjxo1p/PjxEtt9raQmZZLWlM13ELBy5Ury9/cX2e7v70+rV68W28bU1FRIc9jb21uowEloaCjVr1+fdR86d+5MU6dOpZKSEuYay8jIoE6dOrEqBMPxY8E5tRwqo7S0lM6dO8e6+EJERATp6emRnZ0dTZo0iSZNmkR2dnakr68vV8LX+/fvaefOneTk5ERqamrUtm1b5kEpi5KSEkak3NbWlgYMGEADBgygBg0aEJ/Pp2nTphFRWSWbiuL9qrQhICMjQ0gF4tatW+Tl5UW7du2SeS67d+8mPT09mjRpEtnb21O7du2E9i9btkxIOP9r2SAqqxgVGhoq83NsyMzMpG3bttG8efNYJ6wIigqwecnLx48fae/evdS2bVvi8XjUqFEjhc8tNTWVHjx4IJejoAp0dHRo2LBhdOrUKdYJd+U5cOAA5efnK90PPT09Jtu/vFOblpbGSkWhSpUqrJOwZFFQUECBgYHUsWNH4vP51LlzZ9qxYwdlZWWxaq9Mtr6qUTZpzdfXl0xNTWn79u1Msty2bdvI1NSUtcIGEVGdOnUoOjpaZPvNmzfJyspKbBttbW2hCoytWrUSUrNJT0+XSwnCyMiIUewwMjKiR48eMX2wtbVlbYfjx4ALP/hJYJt8A8gfY5SWloZ9+/YhICAAb968gbOzM6t2nTt3xpMnT7Bt2zYkJSUBKFt28/T0lKsmt4GBAdzd3eHu7o779+/D398fq1evlqlhCgCbN2/GpUuXcObMGfTt21do35kzZzBx4kTUq1cPAQEBGDdu3FezIWD06NH45ZdfMHbsWGRlZcHZ2RmNGzfGoUOHkJWVJTGBBwCmTp0KNTU1nD17Fp06dRJZdn/x4oVEsXtV2gDKwigEgvzKIEvXVRKbNm1S+tgViY6Ohr+/P0JCQlBYWAhvb2/s27dPbAhNRb58+YLly5cjJiYGbdq0ga+vL9zc3JiEO1tbW5w7d47Re2VLfn4+jh49isLCQvTs2ZO1ruirV69gYGAg17HK4+3tjWnTpqF///5wc3ODi4uLXMVOBBgbG+Ply5ciMcuxsbGoVauWzPZTpkzB4cOHsWjRIrmPLeDOnTvYu3cvgoODUa9ePYwZMwbXr1/H9u3b5YrPNzY2xunTp5GSkoLExEQAgJ2dnVLJUYoya9Ys1KtXDzdv3hRJWps1a5bMpLXAwEDs3btXKNzBwcEBtWrVgqenp8Qk3opkZWWJ1S03NTWVqCNeq1YtJCYmwtLSEh8/fkR8fDw2btzI7M/Ozoauri6r4wNluRaCmF4zMzNkZGTAzs4ORkZGXzU3gOMb8a29ao7Koby0krRX3bp1WdkrKiqigwcPUteuXUlDQ4P4fD5t2LCB8vLyvvKZsIPt7FOTJk3ELo8J2Lt3L/H5fHJ1dZW4tKsKGwKMjY2ZWYXNmzczM6Xnz59n/dt8DwQEBNDIkSPlku4Rhyp0XZXh1atXtGbNGrK1taUaNWqQt7c33blzR65yvUREs2fPJlNTU5oyZQpZW1tT//79ydbWloKDgykkJISaNGlCo0ePlmrj2bNn1KlTJ9LX1ydnZ2d69uwZNWjQgFme1tXVlSuEoLi4mP766y/y8/MjPz8/+uuvv4TCGqTx5csXOnv2LI0ePZr09PTI1NSUPD09xc7KSWPOnDnUoUMHevnyJSMvGBUVRdbW1rRkyRKZ7ZUttd2kSROqU6cOzZ8/X0iqTt7fNy8vT+xse0lJiVz3xJkzZ9LmzZtFtm/ZsoW8vLxY21G2xKyWlhY9fvxYZHtSUhKr8sUC6tevTwcOHBDZHhQUJPF+5uvrSw0bNqSgoCAaOXIkWVpaCoVN7Nq1i9q3b8+6Dz169KBDhw4REdGUKVPIycmJDh48SC4uLuTk5MTaDsePAefUcsjF3bt3ycPDg4yNjally5a0efNmysrKkush8OTJExo5cqTYm31ubi6NGjWKWYasSHkxf2mvP//8k1VfKi51VSQ9PZ34fL5UZ1QVNgTo6elRWloaERH169ePiTt79uyZXA+TkpISevz4MV27do0iIyOFXpVho6CggFxcXEhfX58aN27MLIEKXmxRRtd10aJFQkvkihSf0NbWJjc3NwoLCxNyWuR1eiwtLZlwjMePHxOPx6Nz584x+yMiImTGkA4bNozatGlDBw8epP79+1PDhg2pT58+lJWVRa9fv6YhQ4ZQ165dWfXnwYMHVLduXaHlaT09PbKyspJb2zQ/P58OHjxIvXv3Jk1NTbK2tmbd9tOnTzRlyhRSV1cnHo/HDJDd3NxYxX926dJF4ovNd6GpqUljx46lCxcuUGlpKbNdnt/3xIkTZGNjIzYc4+PHj9SgQQM6c+YMK1s1a9aku3fvimy/d+8eqxhjASYmJmIHGFFRUaxCbpTNdxCwZs0aqlq1Ku3bt4/S09MpPT2d/P39qWrVqrRy5UqxbQoKCmjs2LFkbGxMDRs2FAlF69Kli8R4XHHcuXOHrly5QkRlg1QXFxcyMDCg5s2bU2xsLGs7HD8GnFP7k6HsjIKamhr973//E6kqJM9DYOrUqeTj4yNx/9y5c5k41IpUnFnW09MjHo/HxEfyeDzS09NjPatpYmIiNUEuISGBjI2Nv7oNAU5OTjRv3jy6evUqaWtrM5Wwbty4wfqhduPGDapbty7x+XyFk02UtTFs2DCqVq0aTZs2jX7//XdasmSJ0Ist1atXZ2Lg7Ozs6PTp00TEbsaJz+czlaeIygqNSBosScLW1pasrKzot99+o8TERGa7vE6turo6PX/+nHmvra0tVLzkxYsXpKamJtVG9erV6datW0RUlqTF4/Ho+vXrzP64uDiqWrUqq/60adOG+vXrJ+To5+TkUP/+/alt27asbJTnzZs3tGXLFmrUqJFCVZoyMjIoNDSUjh49qtJKhbJ4/vw5LV++nOrVq0c1a9akOXPmUExMDGloaLD+fXv06EF79uyRuN/f35969uzJypaWlpbYAgPJycmsYowFKJq0JkBV+Q6lpaU0d+5c0tbWZhLOdHV1aenSpTLbpaenK73Sw/HzwTm1PxGqmFHo2bMnGRgY0OjRo+mff/5hZjfkecg3aNCAbt++LXH/3bt3qUGDBjLtHDp0iNq3by/kYCclJVHHjh1ZlfslIurdu7dEB5qIyN3dXWaVNVXYEBAeHk7GxsbE5/Np4sSJzPb58+fToEGDWNlo2rQpDRs2jB49ekTv3r2j3NxcoVdl2NDV1aVr166xOpY0BgwYQLt37yaisqXq+vXr0/Lly6l58+bUvXt3qW3Ll1MlEk5EkoeoqCiaOHEi6evrU/PmzWnDhg2krq7OONtskNWXrKwsmc4gj8cTSlrS09OT24YAbW1tsZXh7t+/z3pFQDBD26tXL9LU1KR69erRwoULhZz/H4nLly/TmDFjSEdHh3g8Hvn4+Ihdgq+Iubm51EpXycnJZG5uzqoPjRo1oi1btohs//PPP8nOzo6VDSLVJK39+++/tGDBAho8eDANHjyYFixYQP/++y/rPpTnw4cPdPv2bbp//z4VFRXJ/HxJSQlpaGioZIDTtWtXevfuncj2vLw81isbHD8OnFP7E6GqGYWMjAxaunQpWVlZUfXq1WnWrFlyPeS1tbUpPT1d4v709HTS0dGRacfa2lpI+kXA3bt3JWbWViQ6Opo0NDRo2LBhdOvWLaa87Y0bN2jo0KGkoaEhUyZMFTbKU1xcLLJUnpaWJuQUSUNXV1fpcpLK2rC1tVWJRFxqaipj5+PHj+Tu7k5NmjShwYMHS72GiFTn1Ar48OED7d69m1E96NKlC+3evZtev34tsy2Px6OgoCA6ffo0nT59mnR1dWn37t3M+8DAQFZOrbKOsQAHBwe6fPmyyPbLly9T48aNZbYfMWIEE0s7ffp0oRljeRg8eLDYpeQ1a9bQ0KFDxbYZNGgQs6o0aNAgqS9FyM3NpW3btlGLFi2Ix+NRkyZNpH5eW1tbqiP/6NEj1gMFf39/0tHRocWLF1NERARFRETQokWLmOtFXhQpMZuWlka7d++mrVu3KlxmVxJ5eXl08uRJVs8Ke3t7unHjhtLHrPh3I+DVq1es5do4fhw4p/YnQpUzCgIuXLhAo0aNIm1tbbKxsaH58+fTvXv3pLapXr262AeqgEuXLlH16tVlHltHR0fsjO+tW7dYOcUCTpw4QdWqVWOWxwSvqlWrSq1zr2ob5Xn9+jVdu3aNrl27xsppKk/Xrl3pn3/+kfuYqrTx999/k4uLCxMfrAjFxcUUGRkpdpaFDXw+n1JSUphBhoGBAcXHx1NeXp7QSxEE+rRmZmasHoyq0CHl8Xjk7u7OJEFpamrSpEmTmPfu7u5SbZQ/59DQUGrUqBH99ddflJmZSZmZmfTXX39RkyZNWEmxjR49mkJDQ1nrnkqiWrVqYhOaEhISyMzMTGybCRMm0Pv375n/S3spS2xsrNjY0vI0bNhQbDKUgKCgILmko7Zv3061atVirou6detSYGAgq7YlJSW0evVqateuHbVs2ZLmzZsn1xL+lStXSFdXlzm2hoaG1HOTxbBhw5iZ54KCArKxsSENDQ1SV1eXeV88c+YMdejQQWHHWiBFxuPxKDw8nHkfHx9PMTExtHLlSplx+Rw/HlyZ3J8IHR0dxMbGSpQgSkxMRPPmzVFYWCi37Xfv3uHgwYPYt2+f1DKbADB8+HB8+fIFJ0+eFLt/wIAB0NTUxF9//SX1mP369cO///6LvXv3MvJO9+7dwy+//IJatWqJrdQjiYKCAly4cIGpjmZjYwMXFxe5pGNUYSM/P58pQSyowqWmpoZx48Zhy5YtrGydPHkSCxcuhI+PD5o0aQINDQ2h/Q4ODl/dRvna87q6uiLt2ZZy1dbWRmJiIuvSueXh8/lCFaaISOx7adeqLIqLi3HmzBm5K3kpQpcuXVhVzAoPDxe7Xdz3AYDZVv69Mt+JPOjo6CAuLg62trZC25OSkuDo6CjxXnTlyhV06tSJdflpNhQXFyMiIgKpqakYPXo0DAwM8OLFCxgaGkJfX19iuwULFuDgwYO4ffu2SDW1rKwstG7dGm5ubqxlsAS8efMGOjo6Uo9dkWXLlmHJkiVwdnaGjo4Ozp8/j1GjRrEuF9yhQwdUq1YNO3bsgLa2NhYuXIiTJ0/ixYsXcvVdQI0aNXD+/Hk0bdoUhw8fxu+//474+HgEBgZi9+7diI2Nldi2/D1EU1MTOjo6Qvtl3UPKX+/i3BwdHR1s2bIFkyZNUuDMOL5XOKf2J0JQYtDNzU3s/gMHDmDFihWMZqw0MjIyYGFhIfKQJSLExsZK1RCNjY1F27Zt0bdvX8ydO5d5oCUlJWHt2rUIDQ3F9evXpdoAym7648ePR1hYGOM0FRcXw8XFBQEBAazLNH758gWurq7YuXMna51PcQQFBWHEiBFCZWYB4PPnzwgODpapUQsA7u7uuHTpErZu3Yr27dsDAKKiojBr1iz06NEDO3bskGlDXJ11Ho8nlxOnrA1V1Z5v2bIl1qxZg+7du7P6fHkiIyNZfY5NeWNnZ2e4ublh8ODBMDQ0lKsfzZs3x+XLl2FiYgI/Pz/8+uuvcg10AOD9+/dyH7cibL8PgN13cvnyZWzcuFFIk/V///sfa51qAHByckLfvn1F9JeXLFmCs2fP4t69e2Lbqamp4eXLl8zfeJs2bXD8+HFW2rbiePbsGVxdXZGRkYFPnz7hyZMnsLa2hpeXFz5//iz17+7Dhw9o27YtMjIy4ObmJnQ/O3ToECwsLHDz5k3WusCKOtdA2UD6119/hbu7OwDg0qVL6NOnDwoLC8X+TVfE2NgY169fZ/R5CwoKYGhoiFevXqFq1aqs+l8eHR0dPHnyBBYWFhg3bhxq1qyJ1atXIyMjA/b29kzJ6PK8ePECNWvWREBAgNRBnKx7yLNnz0BEsLa2xu3bt2Fqasrs09TUhJmZmULayhzfOd9kfpjjm/Dbb7+RpaWl2Ao5L1++JEtLS9bVYipmlgt4+/Ytq7i+s2fPkqmpqchyvampKZPhzpbHjx/TqVOn6PTp06wSO8RRrVo1pZMSlP1OiIiqVq1K4eHhItuvXLlC1apVY2VDIJ0j6VVZNlTBP//8Q82aNaOzZ8/SixcvVBI6oAizZs2iGjVqkI6ODg0dOlSuSlza2tpMlThJ14gs+Hw+E4YiKfFFVbBZ7t22bRupq6vTyJEjGRm9UaNGkYaGBm3dupX1sc6cOUPq6uo0btw4CggIoICAABo7diypq6vTyZMnJbZTdbz0gAEDyM3NjT59+iRkKzw8nFVJ1tzcXPLw8KAqVaowS/cmJibk4eEhl5Rceno6NWzYkHR1dUlNTY3px6xZs8jd3V1me01NTcrIyBDapqWlJVSlUBri4k+V+W5tbGzo6NGj9PHjRzI1NWXCzqQpdRgbGzO6shwc8sLN1P5EqHJGgc/n49WrV0KjX6BsdGxvb4/8/HyZNgoLCxEWFoaUlBQQERo0aICePXvKPYsFiC6lyou3tze0tLSwevVqhdoDkr+T+Ph4dO3aldWSu66uLu7duwc7Ozuh7Q8fPoSTkxOr7/V7ICYmBhoaGmjSpAkA4PTp09i/fz/s7e2xZMkSaGpqsrJTfnZJntCB9+/fs+4r2xnQ0tJSXLp0CYcPH8bJkyehpqaGoUOHYsyYMVJnNtu2bQt9fX106NABS5cuxa+//ipxxk1SxTgjIyPcvHkTdnZ2Eq8zZfjw4QOOHDmCvXv34t69ezJn4mvXrg1fX1/MmDFDaPu2bduwcuVK/Pvvv6yPHRoaipUrVyIuLg46OjpwcHDA77//LvU75fP5yMrKYmZqDQwMEB8fr3AVu6pVq+L69euwtbUVspWeng57e3sUFBSwskNEePv2LYgIpqamct+PBg4cCAMDA/j7+6Nq1apMPyIiIjB16lQkJydLba+mpoasrCyha8PAwAAJCQmsQnj4fD4CAwNhZGTEbBs1ahQ2bdokFFpRvtKYNLZv3w4vLy/o6+ujTp06iImJAZ/Px5YtW3DixAmxoTLbt2/HvHnz4Orqil27djEV0ZQhNTUVmzZtYlYV7O3t4eXlhXr16iltm+P7gnNqfzLy8vIwf/58HD16FO/evQNQtuQ0cuRIrFixAiYmJlLbC0rPbt68GVOnThVyQEtKSnDr1i2oqakhOjr6651EOYKCgvDHH38wN/sGDRrAx8cHY8eOlcuOII7VxsYGLVq0gJ6entD+DRs2SGzr6OgIHo+H+Ph4NGrUSCjOr6SkBGlpaXB1dWVKokqje/fuqFq1KoKCgqCtrQ2gzPkfP348cnJycOnSJbHtzpw5g169ekFDQ0NmLLGkB5IqbAho1aoVfH19MWTIEDx9+hT29vYYPHgw7ty5gz59+rAuYytryVyS41MxflQaisSPFhUV4ezZs1ixYgXu378v1cbjx4/x+++/IzU1FTExMbC3txcbC8rj8RATEyPWxpAhQxAdHQ07OztERkaiXbt2EgcGV65cYX0eV69ehb+/P44fP46aNWti8ODBGDJkCFq1aiW1nb6+PuLi4kRKwCYnJ8PR0VHssrIqqei8GRoaIj4+XqHYa6AsfjM6Ohr29vZCTm1UVBSGDBmCV69esbKjTOgAoLxzzefz0atXL6EQqLNnz6Jbt25C97QTJ05IbC8LeWOu7969i8zMTPTo0YP5DkJDQ2FsbMyEWFUkLS0NkydPxqNHj7Bnzx7069eP9fEqcv78efTv3x/NmjVjjhcdHY34+HicPXsWPXr0UNg2x/cH59T+pCg6o9C1a1cAZc5G27ZthR6smpqasLKywq+//ioxNvXPP/9k3cdZs2ZJ3b9hwwYsWrQIM2bMEIo/3bZtG5YvXw5vb2/WxxKclzh4PJ5UR2Hp0qXMv3PmzBF6eAm+kyFDhrCanXzw4AFcXFzw6dMnNG3aFEDZTK+2tjbOnz+PRo0aiW1XfuZK2oNJ2gNJFTYEGBkZISYmBvXq1cOaNWtw5coVnD9/HtHR0Rg5ciTrmuvSYrczMzNhaWkptl15Zzg9PR2+vr6YMGEC2rZtCwC4ceMGAgMDsWrVKtbxvQKysrIQHByMgwcPIiYmBk5OTrh58yarthVnGNlSWFiIwMBApKamYv369SIDyvJs3LhRZv8DAgLg7++P9+/fY/jw4di5cyfi4+OZWEpZjB49Go6OjvDx8RHavm7dOty9exfBwcHsTkxB+Hw+GjduzAwOEhIS0LBhQ5G/MUmDhIqMGDECRkZG2L17NzOzaWpqigEDBsDS0hL79++XaUNaXO6nT5+wc+dOmTaUda4nTpzI6nzZnM/3wNatW+Ht7Q07OzuRgSDb39bR0REuLi4iq3C+vr64cOECazscPwacU/sT8/r1azx+/BgAYGtrK9eDduLEidi8ebPcyStsZ1J4PB6ePn0q09bSpUtFErACAwOxZMkSpKWlydU3ZQkMDMSIESOYGVZFKSgowKFDh5iEPTs7O4wZM0Yk+/d7xtDQEPfu3YONjQ169OiBvn37wsvLCxkZGbC1tWWtsFExIUhAdnY2zMzMWM0Yde/eHVOmTMGoUaOEth8+fBi7d+9GRESETBvv37/H8ePHcfjwYURERMDa2hpjxozBmDFjKn0Js2vXrjh58iSMjY3lbtuvXz9cvXoVffr0wZgxY+Dq6go1NTVoaGjIdGrLD0jfv3+PdevWoX379sxA4ebNm4iOjsacOXOwcOFCVv0pKSnBxo0bERISgoyMDHz+/Flov6SQHcFAUha///47q889f/4cLi4uICIkJyejZcuWSE5ORrVq1XD16lVW90ZlQwcA1TjX35rZs2dj2bJl0NPTY1b2JCFtBezZs2eYOHEiHjx4AHd3dxGnlu1vq62tjfv374tMtDx58gQODg4oKipiZYfjx4Bzan9C3r9/j+nTpyM4OJhxCtTU1DBixAhs27ZNKJ6KDc+fPwdQFmdXmWhra+PBgwdil0CbNGmi0M0qJSUFqamp6NSpE3R0dERkoNjy8eNHRpJLgLLZ67KwtLREbGwsk6W8detWjBs3Tq7jqsIGAHTr1g0WFhZwdnZmlhHr16+PyMhIjB8/Hunp6azsqCJ2W1dXF/Hx8WIfas2aNWMVL6mjowMTExOMGDECY8aMQcuWLVn1H4Bc0nJsYxUVRV1dHbNmzYKHh4fQ98HGqVXlgFTA4sWLsXfvXsYRXrBgAdLT03Hq1CksXrxY5mqNKikuLkZwcDASEhLw8eNHNG/eXK7BpCriclXhXKuK5ORkhIeH4/Xr1yL3Mkmx34DwoEvaChggWX5uz549mDNnDpydnbFr1y6l4sctLCywYcMGDBs2TGh7SEgIfv31V2RkZChsm+P7Q3Uifxw/DFOnTkVsbCz+/vtvoeVYLy8vuLu7s1o6LC0txfLly7F+/Xomfs7AwABz5szBggULWMVmKUv9+vUREhKC3377TWj70aNH5Zbmys7OxvDhwxEeHg4ej4fk5GRYW1tj8uTJMDExwfr162XaSEtLw4wZMxARESHkUMtKaqrI48ePsWXLFiGppBkzZkjUFxbw/PlzoWP89ttv6N27t1wOqSpsAMCmTZswZswYnDp1CgsWLGAGHseOHUO7du1kthfM8PB4PCxatEhs7HazZs1Y9cXCwgJ79uzB2rVrhbbv3bsXFhYWrGycOXMG3bt3V+i6HjhwIKvPSbtGZM14lUfa7FdUVBT8/f3RokUL2NnZYezYsRg5ciQru19j5ePQoUPYs2cP+vTpgyVLlmDUqFGoV68eHBwccPPmTVZOrbJxrALU1dUlyh2yobS0VOzv9/z5c9ZyXrVr10Z8fLyQcz158mRWzrU8WsmSYmoF7NmzBx4eHqhWrRpq1KghNLDn8XhSndryjqokp1Uarq6uuH37NjOgVhSBfN7UqVPxyy+/4OnTp8y9Jzo6GmvWrJHr74rjx4Cbqf0J0dPTw/nz59GhQweh7deuXYOrqyur2a/58+fD398fS5cuFYpnXbJkCaZOnSpRaFxVD2cAOH78OEaMGAFnZ2ehBIDLly8jJCQEgwYNYn2scePG4fXr19i7dy/s7OyYWZbz589j9uzZePjwoUwb7du3BxHBy8sL1atXF5nhZaP9efz4cYwcORItW7YUWta9c+cOgoODMWTIEIltVZERruqs8ooUFRUxy93SUDZ2uzznzp3DkCFDUL9+fbRu3RoAcPv2bSQnJ+P48ePo3bu3EmdUOcia8RIgK/5bQH5+Po4ePYp9+/bh9u3bKCkpwYYNGzBp0iTWDpg4EhMT4e/vj3Xr1rH6vJ6eHhITE2FpaQlzc3OEhoaiefPmePr0KRwdHZGXlye1vSriWAHJs+k8Hg/a2tqoX7++zJnqbx06UD6elohw8uRJGBkZMasK9+7dQ25uLgYPHiyzL3Xq1IGnpyfmzZunUF/YFDTg8Xjw9/cX2d6jRw/s379f6ZU/QeiSqakpNm3ahPXr1zNFJGrWrAkfHx/MmjVLYcUcju8Tzqn9CbG0tERoaCgjtyQgISEBvXv3ZsIJpFGzZk3s3LlTZLn09OnT8PT0lCjpo+qH871790QE4OfMmQNHR0dWxxFQvvJNeUfu6dOncHBwYJXNra+vj3v37olUR5KHevXqYcyYMfDz8xPa/vvvv+PgwYNITU2V2PZ7dmqfPn2KwsJCRpKKLYrGblfk+fPn2L59u1Cc8rRp06TO1JYvmiBQuJDEj5xs8vjxY/j7++PAgQPIzc1Fjx495AqZyM/PR3BwMPz9/XHz5k3Y29vjwYMHrNra2toiKCgIrVu3RocOHdC3b1/4+vri6NGjmDlzJl6/fi21vSriWIH/U8uo+DgsX3CkQ4cOOHXqlESFGFWEDly5cgUnTpxAeno6eDwerK2tMWTIEHTq1InVeQiYN28ecnJysHPnTqbAQElJCTw9PWFoaIg//vhDantDQ0PExcUp/HfP5/NRp04dODo6iq3mJUBSVUlVIC4p88OHDwCg1MCN4/uGCz/4CVm4cCFmz56NAwcOoEaNGgDKMqJ9fHywaNEiVjZycnLELoc3bNhQqh6rIstR0mjRogUOHjyotJ38/Hyx2eQ5OTkiFcIk0apVK2RmZirl1L58+VLskpubm5vMBxFQtqQuWHItLi5GQEAAqlWrJvQZWUu6ytj48uULli9fjpiYGLRp0wa+vr5wc3Nj5MxsbW1x7tw5WFlZyTwXQHVZ2rVr18bKlSvlajNgwADmt2cbQsCG/Px8REZGik2MqswYUgG2trZYu3YtVq1ahbNnz7IuqRodHQ1/f3+EhISgsLAQ3t7e2Ldvn8wwmfIMGjQIly9fRuvWrTFz5ky4ubnB398fGRkZrNRLrl27huvXr4uoHlhZWcmllXvx4kUsWLAAK1asgJOTE4Cy2fxFixZh4cKFMDIygru7O3799Vexs4uAcqEDADBt2jTs3r0bJiYmaNCgAYgI169fx9atW+Hp6YktW7awPp99+/YhKipKqGKWmpoaZs+ejXbt2sm8lwwbNgwXLlzAtGnTWB+zPB4eHjhy5AjS0tIwceJEuLm5qURvVl4qDkQ5Z/a/DzdT+5NQcaYpOTkZnz59YiSRMjIyoKWlBRsbG1azTq1bt0br1q1FJLpmzpyJ27dv49atW6o9ASm8fv1abDKDg4MDaxu9e/dGixYtsGzZMmbpsE6dOhg5ciRKS0tx7NgxmTZSU1Mxbdo0uLm5oXHjxiJL7Gz607t3bwwbNkxEmmf//v0IDg7G+fPnJba1srKSuZQmK4lHWRtz5szBgQMHMGDAAFy5cgWNGzfG48ePsXTpUvD5fCxbtgxNmjTBoUOHpB5DQH5+PlavXo3Lly+L/Y3ZJiTl5ubi9u3bYm0oE7dXXFyM169fo2bNmqw+Hxsbi969e6OgoAD5+fmoUqUK3r59C11dXZiZmbE+n7t370pUDJAVL6kMr1+/RkBAAPbt24e8vDyMGjUKo0ePRtu2beWSBJPEjRs3cOPGDdjY2LDSJlWVvmzjxo2xe/dukXjv6Oho/PLLL3j48CEuXbqESZMmfZXEopMnT2LkyJHYtWsXxo8fz/wNlpaWIiAgAB4eHvjrr79YJxKamJggICAAAwYMENp++vRpTJgwgdEol8SqVauwYcMG9OnTB02aNBG5l7EZfH369AknTpzAvn37cP36dfTp0weTJ09Gz549K2XJn8/nw8jISOax2BTF4fhx4JzanwS2EjgAO6mUyMhI9OnTB5aWlkLJZpmZmTh37hw6duzI6ljKPJzv3buH8ePHIzExUeyyoTwC4Q8ePED37t3RvHlzXLlyBf3798fDhw+Rk5OD6OhoVrJNN2/exOjRo4Uy+8svX7Lpz86dO7F48WIMHz4cbdq0Yez+9ddfWLp0qZDz9LUz5RWhTp062LFjB3r37o0nT56gYcOGCA0NRa9evQCUXTdjxoxhFeIClFUzioyMxNixY2Fubi7ygPLy8pJp4+zZsxgzZgw+fvwIQ0NDkaQXZR5q8fHxaN68OetrrUuXLmjQoAF27twJIyMjxMfHQ0NDA25ubvDy8mKV7BMcHIxx48bBxcUFFy5cQM+ePfHkyRO8evUKgwYNYjW7LW2wwOPxJIa56OjoYOjQoXBzc0OPHj2YUBI26glfA1XFsero6ODOnTto3Lix0Pb79+/DyckJhYWFePbsGezs7CSqGCgTl9u/f380atQIq1atErt/3rx5SEpKwunTp1mdz+zZsxEUFITffvuNmXm+desWVq9ejbFjx8rMV5AWPyyPuoWAZ8+eISAgAEFBQSguLsbDhw/lSuJTBD6fj02bNslU85FXp5rj+4ZzajkU5sWLF9i2bZtQnOIvv/yC5cuXY/fu3TLbK/twbtq0KerVq4d58+aJTcyqU6eOXOeTl5eHrVu3Ij4+npH0mT59OszNzVm1t7e3h52dHebOnatwf9jGm0pzkoOCgjBixAiRsInPnz8z37ksFLWhoaGB9PR01KpVC0CZs5CQkMAkdL18+RIWFhYoLi6W2QegrNpdaGioxMpDbGjQoAF69+6NlStXKlSCWRryOrXGxsa4desWbG1tYWxsjBs3bsDOzg63bt3C+PHjmb8laTg4OMDd3R3Tp09nZifr1q0Ld3d3mJubsxrAKjpYaNiwIT59+oTRo0dj7NixTKiBPE6tKiXOVCWB1aFDBxgYGCAoKIiRj3rz5g3GjRuH/Px8XL16FZcuXcL06dMZbe+KKBOXW7t2bZw4cYJxQCty69YtDBkyhPVgsLS0FOvWrcPmzZvx8uVLAIC5uTm8vLwwZ84cobCEyiAzMxP79+9HQEAAPn/+jKSkpEpxahUpdMLxg0McHCokLi6O+Hw+q882adKEtm7dSkRE+vr6lJqaSqWlpTR16lRavHixzPb6+vqUnJysVH8FPHv2jEpLSyXuY4Ourq7K+qMMfD6fXr16JbL97du3rH8bRW3weDyhdoLfVUBWVhbrPhARWVlZ0aNHj1h/Xhy6urpCfVAl8lzvRETVqlWjJ0+eEBGRjY0NhYWFERFRYmIi6erqsrKhq6tLaWlpRERUpUoVSkhIICKiR48eUY0aNVjZMDIyoqioKNb9Lk9UVBRNnDiR9PX1qXnz5rRhwwZSV1dn/TvxeDxWL7bf65cvX+jAgQPk4+NDHh4etGfPHiooKJDrnJKSksjW1pY0NTWpXr16VK9ePdLU1KSGDRvS48ePiYjo5MmTFBQUJNHGpUuXqHXr1nTp0iV6//49vX//ni5dukRt27al0NBQioqKokaNGtGkSZNE2mppadG///4r0fbz589JW1tbrnMSkJeXR3l5eQq1/fTpEyUlJdGXL1/kbltUVESHDx8mZ2dn0tbWpqFDh1JoaCiVlJQo1Bd5kXQP4/hvwyWK/YQIZhQkIc+yvTKkpqaiT58+AMpkmvLz88Hj8eDt7Y1u3brJnHHq3r074uPjRYovKELdunUlVq6qW7cuq++kW7duKuuPOAoKCljNNJKEghHPnz9nXVhDGRvnz59nPlNaWorLly8z2fC5ubmsji9g2bJlWLx4MQIDAxWeZXVxccHdu3dVJkumDI6Ojrhz5w5sbGzQuXNnLF68GG/fvsWBAwdElr4lYWJiwmRx16pVCw8ePECTJk2Qm5vLSuBfYEPRxJ327dujffv2+PPPP3HkyBHs37+fyawfPXo0Bg4cKFUsv2JMs7Ioqy8LlCXLPXr0CBcuXMCTJ0+YbeVDLGQlC3p5eYnE5Xbv3h3a2tpMXO6mTZvEyl19/vxZqsydurq6SHiWLCrq9wJgrd9bUFCAmTNnIjAwEAAYqbSZM2eiVq1a8PX1ldre09MTwcHBsLCwwKRJk3DkyBGRZNOvDXGL0D8lnFP7E1JRRuXLly+IjY1FYGCgXLG3yqLsw3nv3r0YP348Hjx4IDYxS56YU0lO3MePH1mXve3Xrx+8vb1x//59sckVbPrTvXt3BAUFMcv3Am7duoWxY8cyD1xxCJIBeTweunfvLlRWsqSkBGlpaXB1dZV6fFXYqBij5u7uLvReniSR9evXIzU1FdWrV4eVlZXId8omqbFPnz7w8fHBo0eP5P5dEhISpNqWtBQtiZUrVzLX/IoVKzBu3Dimuhdb1YFOnTrh4sWLaNKkCYYNGwYvLy9cuXIFFy9eRPfu3VnZUMVgQV9fH1OnTsXUqVORmJiIvXv3YuHChfD09MSXL18UsikvqtCXFcDn8+Hq6irz+pZEamqqWOk5Q0NDJgbVxsYGb9++Fdu+YpGR8rAdrAioqN/bo0cPGBgYYM2aNaz0e+fPn4/4+HhEREQIfR/Ozs5YsmSJTKd2586dsLS0hLW1NSIjIxEZGSn2c18zqVHVgyeOHwPOqf0JqZgRCwBDhw5Fo0aNcPToUUyePLlS+qHsw/nGjRuIjo7GP//8I7KPbWKWKitXCeRvKmrMytMfbW1tODg4YPv27RgxYgRKS0vh5+eHlStXwtPTU2pbwUxSXFwcXFxchGZjBAULpBVvUIUNVT9IVCGlNXXqVACK/S7NmjUTGycpaCtpMCSJ8uV1zczMEBYWxrqtgK1btzIV6xYsWAANDQ1cv34dQ4YMwcKFC1nZUMVgoTx2dnZYv3491qxZI1fMLABcvnxZRGv6f//7H5ydnWW2HThwoNL6sgKUlVpr0aIFfHx8ROJy586di1atWgEoU50Rp43cqVMnmQMkebRqvby80LJlS8THxzMlr4EyCTXB34M0Tp06haNHj6JNmzZC13ejRo2kamULGDduHFfUgOObwCWKcTCwKTQgKzs7NzcXkZGRrBy4nJwcFBUVoWbNmigtLcXatWtx/fp12NjYYOHChTIfQlZWVujbty8WLVqE6tWryzyeOFRZuUpVbNu2DXPnzsWAAQOQnp6OZ8+eYf/+/ejZsyer9oGBgRgxYgTrGeavZeO/wLNnz1h9Tt6kREV4//49q8+xKVQha0VGlgJKTEwMNDQ0mAIup0+fxv79+2Fvb48lS5aI6MZKYvv27fDy8sLQoUOFKugdO3YMGzduxPTp06W2v3z5Mit92datW0vUlwVUI7X2+PFjDBgwAGlpaYzjmpmZCWtra5w+fRoNGjTAqVOn8OHDB4wdO5bV96MoVatWxfXr12FrayskdZaeng57e3uZM7+6urp48OABrK2thdrHx8ejU6dOMiu9cXB8KzinlgMAUFhYiPnz5+Off/6ROmNQUT9VErKUC4qLi3H48GG4uLgo7JAaGBggLi6OldyWLFRVuaoiubm5MDY2lrvd/PnzsWbNGqirqyMiIkJEP5MtHz9+FJk9lfcc2dpQZVZ7eXJzc3Hs2DGkpqbCx8cHVapUQUxMDKpXry4SpvE1KC4uxsqVKzFp0iSFS3fKqkgmQNoMqaxYeAGVERPfqlUr+Pr6YsiQIXj69CkaNWqEQYMG4c6dO+jTpw82bdrEyk7t2rXh6+uLGTNmCG3ftm0bVq5cKbOAgqr0ZVUhtQaUrVRIi8utLJTV7+3UqROGDRuGmTNnMlJpdevWxcyZM5GcnKzQCgMHR2XAObU/ISYmJkIPRyLChw8foKuri4MHD1aa/qmuri4SExMVnuUaP348OnbsiClTpijdlzdv3khMbhHEyMpizZo1sLKywogRIwCUVeU5fvw4zM3Nce7cOTRt2lSmjXfv3mHKlCm4fPky/vjjD0RGRuLUqVNYu3atzPADAWlpaZgxYwYiIiKYZWoAcunlKmJDFXJkFUlISICzszOMjIyQnp6Ox48fw9raGgsXLkRGRgaCgoKkti8sLMS9e/dQpUoVEbmpoqIihISEsJI409fXx4MHD1hXQqtI+ZlRIsKqVaswbdo0kWQtaTOk5eMSiQi9e/fG3r17RRz7zp07s+qTMoMFIyMjxMTEoF69elizZg2uXLmC8+fPIzo6GiNHjkRmZiarPujr6yMuLk4kuTI5ORmOjo4yy1OrQl8WUI3UmqIIQqDYIEtfVoCy+r1RUVHo1asX3NzcEBAQAHd3dzx69AjXr19HZGQkWrRowbrPHByVCRdT+xNScRaFz+fD1NQUrVu3lrnkr0qcnJwQFxensFPboEEDzJ8/H1FRUQpXvRHQpEkT+Pv7M2oMAtatW4dFixahsLBQpo2dO3cylbIuXryIS5cuISwsDCEhIfDx8cGFCxdk2mjcuDHq1q2L2NhY1K1bF1OnTsXRo0fh6emJ0NBQhIaGyrTh5uYGIsK+ffvE6uWyQREbXyMxY/bs2ZgwYQLWrl0rVOKyd+/eTEa3JJ48eYKePXsiIyODia0MDg5mdIfz8vIwceJEVk5t9+7dERkZqbBTW9FZXb9+Pby8vORSZKjorKqpqaFNmzYKqTpUHCxMnToVVapUwYkTJ1gNFoiI+b0vXbqEvn37AgAsLCwkJkKJo3///jh58iR8fHyEtp8+fZqxKQ1l4ljLo6GhwQzKzMzMkJGRATs7OxgZGbF20AHF4nJjY2OF3sfExKC4uJgpt/3kyROoqanJ5UiuX78eLi4usLe3R1FREUaPHs3o9x45ckRm+w4dOiAuLg6rV69GkyZNcOHCBTRv3hw3btxgNcDn4PhmVKZ+GAdHeY4ePUrW1ta0ZcsWun79OsXHxwu9ZGFlZSXxVbduXbn6smbNGtLS0qJp06ZRQUEBPX/+nLp160ampqZ04sQJVja0tbUpIyODiIhmzZpFv/zyCxERPX78mIyNjVnZ8PPzE6vjmJmZSc7Ozqxs6OnpUVJSEqvPfk0bqsDQ0JBSUlKISFjzNj09nbS0tKS2HThwIPXp04fevHlDycnJ1KdPH6pbty6jOyyPZu6OHTuoRo0aNGfOHDp8+DCdPn1a6CUvFfV7FUEZG927dycfHx8RO9HR0VSnTh2Z7bt27Urjxo2joKAg0tDQYPSZIyIiWLUXsGzZMjIyMqLevXvTsmXLaNmyZdSnTx8yNjamZcuW0ebNm5mXOFShL0tE1KNHDzp06BAREU2ZMoWcnJzo4MGD5OLiQk5OTqzOJSYmhmrUqEGGhoakpqZGpqamxOPxSE9Pj/X9aP369dSvXz/KyclhtuXk5NCAAQNo3bp1rGwI+PLlCx08eFAp/V4Ojh8Nzqn9iXjz5g2lp6cLbXvw4AFNmDCBhg0bxtzUKwtJguvyCK+rkpiYGGrUqBHVr1+fqlSpQr169aKXL1+ybm9ubk7R0dFERNSgQQMKCQkhorIHr4GBwVfpszi6dOlCFy9e/OY2Pn78SKGhobRjxw4h50SSgyIOU1NTiomJISJh5+vChQtUu3ZtqW3NzMyYwgRERKWlpTRt2jSytLSk1NRUuZxaVRQJKM+3dmqVGSwQEcXHx1Pjxo3J0NCQlixZwmyfMWMGjRo1inU/pA1M2Q5SS0pK6J9//mGurbCwMLkF/u/cuUNXrlwhIqJXr16Ri4sLGRgYUPPmzSkuLo6Vjc6dO9PUqVOppKSE+U4zMjKoU6dOdPz4cVY2atasSQ8ePBDZfv/+fTI3N2d/QiogJSWFFixYQKNGjWKKGJw7d05s/zg4vhc4p/YnYuTIkTR79mzm/atXr8jExIQaNWpE/fv3Jw0NDZkzGqokPT1d6ostylS9Kc/79+9pxIgRpK6uTurq6hQQECBX++nTp1OdOnXI2dmZqlatSh8+fCAioiNHjpCjo6PUtmvWrBGaRYmKiqKioiKhvnl4eLDqR0pKCjk7O1NAQADdvXtX7hlwVdhQxawVEdHkyZNp4MCB9PnzZ9LX16enT5/Ss2fPyNHRkby8vKS2NTAwEFvlavr06VS7dm26evXqNxk8EanOqX369KlCbZUZLEijsLCQPn/+rHD7b0FpaSk9e/aMCgsLlbJjZGTErG4YGRkx197NmzfJ1taWlQ19fX0KDw8X2X7lyhXS19dn3ZeAgAD6+++/mfc+Pj5kZGREbdu2ZXVvjYiIIB0dHXJ2diZNTU3m+li1ahUNGTKEdT84OCobLqb2J+LmzZsICAhg3gcFBaFKlSqIi4uDuro61q1bh23btn11uRkBysogKVv1pjzR0dFwc3NDlSpVkJCQgOjoaMycORPnzp3Dzp07WcUab9y4EVZWVsjMzMTatWsZjdeXL1/KTPKaP38+JkyYAB0dHQBAr169EBcXx8RLFhQUYNeuXdi+fbvMfrx58wapqalCShXldTvZJGkpa8Pb2xv9+vVjsslv3rwplE3OlvXr12Po0KEwMzNDYWEhOnfujKysLLRp0wYrVqyQ2rZhw4a4e/cu7OzshLZv3boVADsFhsLCQly+fJmJ75w/fz4+ffrE7FdXV4efn59M6bM///xT6H1xcTECAgJEqixJiwOvmIFfVFSEadOmQU9PT2g7G0H7/v37w8/PDyEhIQDKftuMjAzMmzdPppZxRcSpY0irjiWOz58/Iy0tDfXq1RMq+MEGZfVliQj169fHw4cPlZLuU0Vc7qBBgzBx4kSsX7+ekSi7desWfHx8WCswAGVFPnbs2AGgTM9769at2LRpE/7++294e3vLvEZ8fX2xfPlyzJ49WyiWvVu3bszfDwfHd8k3dqo5KhFtbW2hUXqvXr2YuDqistjPKlWqVGqfgoKCqF27dmRubs70bePGjXTq1CmZbWfNmkUtWrSga9eukZ6eHjObcOrUKWrWrJlc/dDU1KR58+YJzTKlpKRQmzZtqFatWqxsvH37lvl/RkYGLVq0iH799VeKjIyU2ZbH4wnVKa84kyfPUrmdnR0NHjyYbt68SWlpaQrNgCtrQxWzVuWJioqibdu20Zo1a1iHRaxcuZJ69eolcb+HhwfxeDypNnbs2EF9+/Zl3uvr61Pr1q2pS5cu1KVLF6pRowatX79eZl+UXWInIpowYQKrFxtyc3PJ2dmZjI2NSU1NjSwsLEhDQ4M6depEHz9+lNn+6dOn1Lt3b9LV1SU+n8+85A3HyM/Pp0mTJpGamhqpqakx1/yMGTNo1apVMturakXA3t6ebty4wfrz4lBFXG5+fj55eHiQlpYW851qamqSh4cHq99FgI6ODhM7PnfuXBo7diwRlYWbVatWTWZ7PT09ZhWg/L0oLS2NVXgKB8e3gnNqfyLMzMyE4sOqVq1Kx44dY94/efKE9PT0Kq0/27dvp2rVqtHy5ctJR0eHuXHu37+funTpIrO9paUl8yAqf+NNTk6WO4Y1IiJC7PaSkhLy8/OT2jYhIYHq1KlDfD6fbG1tKTY2lqpXr076+vrMw/bkyZNSbajSqdXV1WUSdxRFWRvVqlWjJ0+eEBGRjY0NhYWFERFRYmIi6erqymx/+fJlsrOzo7y8PJF9ubm5ZG9vT1evXlW4f2zp0KEDnTlzhnlf8Xc5cOAAtWnT5qv342tx7do1uQcLRETt2rWjtm3bUnBwMIWHh1NERITQiy3KDkxVEcdKRHTmzBnq0KED3b9/n3WbiqgiLlfAx48fmVAfeZxZAeXDS5o1a8aElaWkpLC6x9eqVYvJDyh/zZ84cYKsra3l7g8HR2XBObU/Ef3796dJkyZRSUkJ/fXXX6SpqSmUZfv3339Tw4YNK60/dnZ2jLNX/sZ5//59qlq1qsz25R3h8u3j4uLI0NCQVR969epFubm5zPtVq1bRu3fvmPdv374lOzs7qTZcXV2pb9++FBUVRe7u7lSrVi3mey4pKSFPT09q3bq1VBuqdGr79u0rNFhRBGVtKDtr1a9fP9qwYYPE/Zs3b6aBAwcq3D+21KhRg9LS0pj31apVE3r/+PFj1tcaEVFgYKBQrLSAT58+UWBgoDJdrVRUpY6h7MBUVSsCxsbGpKmpSXw+n7S1tcnExEToJQtVxeWWJzMzkzIzMxVqO3r0aGrevDlNnjyZdHV1mVWk06dPU6NGjWS2nzNnDnXo0IFevnxJBgYGlJycTFFRUWRtbS2UGMjB8b3BxdT+RCxbtgzdu3fHwYMHUVxcjN9++00oVjQ4OJi1cLsqSEtLg6Ojo8h2LS0t5Ofny2zfsmVLhIaGYubMmQDAaKnu3buXKbkpi/PnzwvFSK5cuRLDhw9nqoAVFxfLrMl+584dXLlyBQ4ODmjatCl2794NT09PJr5u5syZaNOmjcy+7N27l4nDrRhz+eHDB1bnAwD9+vWDt7c3UzSiYnwjm1hSZW2sXLmS6fOKFSswbtw4eHh4wMbGRmq5UgHx8fFYs2aNxP09e/bEunXrZNpRltzcXKHr482bN0L7S0tLhfbLYuLEiXB1dYWZmZnQ9g8fPrDWzFUVly9fxuXLl/H69WuRmNh9+/ZJbduqVStkZmYyWqqK8ubNG5HvAiiLk2WjjawqfVm2FdAkQSqKyy0tLcXy5cuxfv16pvCEgYEB5syZgwULFrAucLJt2zYsXLgQmZmZOH78OKpWrQoAuHfvHkaNGiWz/cqVKzF9+nRYWFigpKQE9vb2KCkpwejRo7Fw4UKFz4+D42vDObU/EQ4ODkhMTER0dDRq1KiB1q1bC+0fOXKkSNWlr0ndunXFFl8ICwsTSe4Rx8qVK9GrVy88evQIxcXF2Lx5s1DVGzZQhYJ6Fd+zIScnBzVq1ABQViFJT09PaLBgYmIi0ym1tLTEnj17mPc1atTAgQMHRD7DhmnTpgEA/Pz8RPaxTRRT1kbLli2Z/5uZmcldVvPVq1dSk43U1dVFHMyvQe3atfHgwQOJzltCQoJcpXPp/yfaVeT58+cwMjJSuJ/ysnTpUvj5+aFly5YwNzeXu0DH3r17MW3aNPz7779o3LixyG/l4ODAyo6yA1NHR0fcuXMHNjY26Ny5MxYvXoy3b9/iwIEDIlXGpDF+/HjWnxUHn8+HjY0NsrOzlXJqFyxYAH9/f6xevRrt27cHUFbda8mSJSgqKpKZHCnA2NhYbEJX+cp2kiAiZGVl4c8//8TixYtx//59fPz4EY6OjkqdGwdHpfBN54k5fmr27NlDtWrVouDgYNLT06MjR47Q8uXLmf+zISUlhaZMmUKtWrUiOzs7GjNmjJA2qSxUsezP4/Ho9evXQjbKSy3JEzrwX6Fr165CYRwC8vLyqGvXrjLbW1tbS41DPn78uNwFNhRh1qxZZG9vL3ZZuaCggOzt7WnWrFky7TRr1owcHR2Jz+dTkyZNyNHRkXk5ODiQgYEBDRs27Gucglhq1KihlHzfjRs3qG7dukprTF+7do309fVp2rRppK2tTV5eXtSjRw/S09Oju3fvymyvyjhWAYWFhZSXlyf0YoMq4nLNzc3FFvM4deoU1axZUy5b7969o3Xr1tHkyZNp8uTJtGHDBqFQK0mUlJSQhoYGExPPwfEjwSNSYGqK44fkxo0byM7OFio/GRQUhN9//x35+fkYOHAgtmzZAi0trUrr06FDh7BkyRKkpqYCAGrWrImlS5di8uTJStk9duwYhg4dKvNzampqyMrKYkpsCuqk161bF0DZjGHNmjWlzkzy+Xz06tWL+d7Onj2Lbt26MVJLnz59QlhYGKsZ0qCgIIwYMULkN/j8+TOCg4MVXp7Ozc1lQioURR4bfD4fWVlZIkvLr1+/Rq1atfDlyxep7WfOnImIiAjcuXNHRC6rsLAQTk5O6Nq1q4hUlqp59eoVmjVrBk1NTcyYMQMNGjQAADx+/Bhbt25FcXExYmNjUb16dal2BDNkS5cuxZw5c5gwEwDQ1NSElZUVhgwZAk1Nza93MuWoWrUqbt++jXr16inU3t7eHnZ2dpg7d67YMsryyPU9ffoUq1atQnx8PD5+/IjmzZtj3rx5MsuxEhEyMzNhZmYmU1JNFvn5+Zg3bx5CQkKQnZ0tsp/N366JiQkKCgpQXFwMTU1NRp5PQE5Ojkwb2traSEhIYK4zAY8fP0azZs1YlesGgLt378LFxQU6OjqMNNidO3dQWFjIlLyVRqNGjeDv788qbIqD43uCc2p/Inr16oUuXbpg3rx5AID79++jefPmmDBhAuzs7PDHH3/A3d0dS5YsqfS+FRQU4OPHj2Lj68RRXFyMpKQkaGpqCj0ATp8+jcWLFyMpKYlVrKMqHNLyWq7S2L9/v8zPqKmp4eXLlyLfQ3Z2NszMzFg9XNesWQMrKyuMGDECADBs2DAcP34c5ubmOHfuHJo2bfrVbCQkJAAAmjVrhitXrqBKlSrMvpKSEoSFhWHXrl1IT0+XevxXr16hefPmUFNTw4wZM5jl/6SkJGzbtg0lJSWIiYmR6UyqgrS0NHh4eODixYtMeAqPx0OPHj2wfft2RkuYDYGBgRgxYoTSTpiyzJs3D/r6+li0aJFC7fX09BAfH4/69esr3IcvX77A3d0dixYtYgaR8lBaWgptbW2l41gBYPr06QgPD8eyZcswduxYbNu2Df/++y927dqF1atXY8yYMTJtCPSyJcEmxKF169Zo3bq1yGBt5syZuHPnDm7evCnTBgB07NgR9evXx549exjd3+LiYkyZMgVPnz7F1atXpbY/e/Ys1q5dix07dsgVxsHB8a3hnNqfCHNzc5w9e5aJd1ywYAEiIyMRFRUFAPjrr7/w+++/49GjR5XSn+XLl2PMmDFyP9AePHiAvn37MokgAwYMwI4dOzB8+HA8ePAAU6dOxYwZM1jFOqrSIVUFfD4fr169YmaOBcTHx6Nr166sZnvq1q2LQ4cOoV27drh48SKGDx+Oo0ePIiQkBBkZGbhw4cJXs8Hn85lZO3G3Fh0dHWzZsgWTJk2S2Ydnz57Bw8MD58+fF3ImXVxcsG3bNoUcIWXIyclBSkoKAKB+/fpCDrsiiCtaYGhoqJRNacyePZv5f2lpKQIDA+Hg4AAHBweRmNgNGzZItdWvXz9MmDBB7kINFTEyMkJcXJzCv6WqZhQtLS0RFBSELl26wNDQEDExMahfvz4OHDiAI0eO4Ny5c0rZZ0tkZCT69OkDS0tLJqb4xo0byMzMxLlz59CxY0dWdnR0dBAbG4uGDRsKbX/06BFatmyJgoICqe1VMevMwfEt4BLFfiLevXsnNLMVGRmJXr16Me8FGc2VhcCJbt26Ndzc3DB8+HCRCkvimDdvHurXr4+tW7fiyJEjOHLkCBITEzF58mSEhYWJ3IClUVnOqiwcHR3B4/HA4/HQvXt3oapKJSUlSEtLg6urKytbWVlZsLCwAAD8/fffGD58OHr27AkrKyuR5EBV20hLSwMRwdraGrdv3xZyzjU1NWFmZgY1NTVWfahTpw7OnTuHd+/eISUlBUQEGxsbVtXdvgZVqlRhlnIVJS0tDTNmzEBERASKioqY7SRHtTdFiY2NFXrfrFkzAGWDxPKwSRpThcIGAAwcOBCnTp2Ct7c3q89XZPXq1fDx8VF6RjEnJ4eZcTc0NGSctg4dOsDDw0Nue0VFRSLVzdgMWDp37ownT55g27ZtSEpKAlBWSc7T0xM1a9ZkfXxDQ0NkZGSIOLWZmZlCFcIkoawaBAfHt4Jzan8iqlevjrS0NFhYWODz58+IiYkRyob98OGD3OUtlSE+Ph4PHz7EoUOHsG7dOvzvf/9Djx49MGbMGAwcOBC6urpi2925cwcXLlxAs2bN0LFjRxw5cgS//fZbpZX3/RoMHDgQABAXFwcXFxeJMZdsMDExQWZmJiwsLBAWFobly5cDKHOc2DpNitoQxFJWnIFUBhMTE7Rq1Upl9r4lbm5uICLs27dPbCzq1yQ8PFxltlShsAEANjY28PPzQ3R0NFq0aCFS8ldWmdtx48ahoKAATZs2VWpG0draGmlpabC0tETDhg0REhICJycnnD17lnUcuSricoGyvAK2KgeSGDFiBCZPnox169ahXbt2AMpKgfv4+LCS9FJWDYKD41vBhR/8RHh4eDD6n6dOnUJgYCBevHjBJKccOnQImzZtwp07d75J/6Kjo3H48GH89ddfKCoqwvv378V+rmISkoGBAWJiYv4TcjOqiLmcMWMG/v77b9jY2CA2Nhbp6enQ19dHcHAw1q5di5iYmEqxkZqaik2bNiExMRFAWXKRl5eXwslJ/wX09fVx7949pfVdlSUvLw8lJSUiIRQ5OTlQV1f/qmEQ5ZEWdsDj8fD06VOp7VURxwoAGzduhJqaGmbNmoVLly6hX79+ICJ8+fIFGzZsgJeXl0wbqojLBcoSMv39/Zm/m0aNGmHSpElySb59/vwZPj4+2LlzJ4qLiwGUafp6eHhg9erVMpOBz507BzU1Nbi4uAhtv3DhAkpKSoRW+Dg4visqWW2B4xvy5s0b6tixI/F4PDIwMBApI9mtWzf67bffvlHviGJjY2nOnDlUq1Yt0tbWlvg5Pp9PKSkplJeXR7m5uWRgYEDx8fEKyfB8z3z48EGhc/r8+TP98ccfNGvWLKZUJhHRhg0baM+ePZViIywsjDQ1NcnJyYm8vb3J29ubnJycSEtLiy5cuMCqD/9FunTpIlc52q+Fq6srbdu2TWT7jh07qFevXkrZfv78uVLtvwfS09Pp+PHjFB8fz7qNhYUFhYeHExExVbiIiIKCglh/p3fu3KEqVapQrVq1aNCgQTRo0CCqXbs2Va1ale7duyf3eeTn51NCQgIlJCRQfn4+63ZNmjSh0NBQke3//PMPOTg4yN0PDo7Kgpup/QnJy8uDvr6+SGxjTk4ODAwMKjUEIS0tDYcPH8bhw4fx+PFjdO7cGaNHj8bQoUMlzkyUT0YCRAXtqRLiE78Wqoi5zM7OZioIZWZmYs+ePSgsLES/fv3QqVMnVv1Q1oajoyNcXFywevVqoe2+vr64cOECq5ne/yKpqamYNm0a3NzclCpaoCxVqlRBdHS0SJGTpKQktG/fXuzyuSyysrKwYsUK+Pv7y0xE+hooEsdaWFiIy5cvMzKH8+fPF1JNUVdXh5+fH6uVE319fTx69AiWlpaoXbs2Tpw4AScnJ6SlpaFJkyZMhTBpKKtaoCp0dHSQmJgIKysroe3p6elo1KgRq4qPHBzfAi6m9ieCTcY5ILtEpqpo06YNbt++jaZNm2LixIkYNWoUatWqJbOdKmMDvzeUibm8f/8++vXrh8zMTNjY2CA4OBiurq7Iz88Hn8/Hxo0bcezYMSZ+92vZAIDExESEhISIbJ80adJPnYTy5s0bpKamCqlu8Hi8Sh+Iffr0iVmWLs+XL1+kaqG+e/cOnp6euHjxIjQ1NeHr64sZM2ZgyZIlWLduHRwcHGQmX86ePRvLli2Dnp6ekCKDOGSpMCgbxxoYGIjQ0FDGqd26dSsaNWrExOYmJSXB3NycVSKbKuJy7969K+TQAmWO9dy5c4Wq9Ilj8ODBrI4BACdOnJC638jICE+fPhVxalNSUkTinjk4vic4p/YnIiAgAHXq1IGjo6NC5WBVTffu3bF//34mQ56N8gFQliFcXFyMw4cPw8XFpVK0SiuL+Ph4hWMu586diyZNmuDQoUM4cOAA+vbtiz59+jDld2fOnInVq1dLdUhVYQMATE1NERcXJxLnHBcXx1qL+L/IpEmT4OjoiCNHjlR6olh5nJycsHv3bmzZskVo+86dO9GiRQuJ7Xx9fXH9+nVMmDAB58+fh7e3N8LCwsDn83HlyhVW0lqxsbFISkqCo6OjiCJDedh8N3PnzkV4eDh27NghNo5VFocOHcLcuXOFth0+fJhRQjh48CC2bdvGyqmdOHEi4uPj0blzZ/j6+qJfv37YunUrE5fLBmVUC1RZZnnAgAH43//+h5MnTzIx8CkpKZgzZw5rZQsOjm/Ct4t84KhsPD09ycTEhJo1a0abN2+m7Ozsb9aXd+/ekYeHB1WtWpX4fD7x+XyqWrUqTZ8+XWx5VXHo6OhQenr61+1oJaNMzGXVqlWZGMAPHz4Qj8cTKjWamJhIRkZGX9XG0qVLKT8/n5YuXUrGxsa0evVqunr1Kl29epVWrVpFxsbG5Ofnp9D5/RfQ1dVlYi2/JVFRUaStrU0dO3akJUuW0JIlS6hjx46kra1NV69eldjOwsKCLl++TEREaWlpxOPxaP78+XIfn8/nC5WnHj58OGVlZcltR9k41ho1alBaWhrzvlq1akLvHz9+TIaGhnL3i0ixuNyZM2dS7dq1KTg4mDIyMigjI4OOHDlCtWvXJi8vL4X6oQi5ubnUpk0bUldXJysrK7KysiI1NTWJ5a85OL4XOKf2J6OoqIgOHz5Mzs7OpKurS8OGDaOwsDAqLS2ttD5kZ2dTgwYNSE9Pj3755RfauHEjbdy4kaZOnUp6enrUsGFDysnJkWmnc+fOdPLkya/f4UokJSWFnJ2dKSAggO7evUvx8fFCL2nweDwhR0FfX59SU1OZ91lZWcTn87+qDYGzUlpaShs2bKBatWoRj8cjHo9HtWrVok2bNlXqtfa90bdvXzp27Ni37gYRlSVmjh49muzt7alFixY0ceJEevLkidQ2ampq9OLFC+a9jo4OPXz4UO5jV7zODAwMhK4ztujp6dGzZ8+IiKhWrVp069YtIiJ6+vQp6enpyWyvra1NSUlJEvcnJiaSlpaWVBsFBQV09uxZ5r2vry+THOnt7U0+Pj5UWFgo1cbTp0+JiOjTp080a9Ys0tTUJD6fTzwej7S0tOh///sfFRUVyTyf8vbE/ZZPnjwRctqlUVpaSufPn6e1a9fSli1bpA52ODi+F7jwg58MLS0tjBo1CqNGjcKzZ88QEBAAT09PFBcX4+HDh0L6qF8LPz8/aGpqIjU1VSR0wM/PDz179oSfnx82btwo1Y6npyfmzJmD58+fi9W4rKykG1WibMxlxSVbRZa3lbFB5Sp/eXt7w9vbGx8+fAAAVqLv/3VUVbRAFTRr1gyHDh2Sqw0RCcV7qqmpyVXsRJpdRVA2jrV27dp48OCBxHCfhIQEmZUJVRGXW69ePdSpUwddu3ZF165dkZKSgtzcXGafJM1uSUyYMAGTJk0SCf+5desW9u7di4iICLHtbty4gezsbPTt2xc8Hg89e/bEy5cv8fvvv6OgoAADBw7Eli1bZEqCcXB8Kzj1g5+YzMxM7N+/HwEBAfj8+TOSkpIqxam1srLCrl27RDQQBYSFhWHatGlIT0+XaofP54ts+xZJN6rE3t4ednZ2mDt3rtiYS0FxA3Hw+Xz06tWLeeCcPXsW3bp1Y5z9T58+ISwsTOr3oqwNSWV+OcoQd80KqMxrVk1NDS9fvhSJb87OzoaZmZnU37dx48aMY5uQkICGDRsyWtcCZKlbqKmpISsri7lODAwMkJCQIHe5XGX1Zb28vHDp0iXcu3dPROGgsLAQLVu2hLOzMzZv3izRRseOHTF37lz069ePOZf4+HiRuNwbN25ItBEREcG8bt26hc+fP8Pa2hrdunVDt27d0KVLF7lyB8qX+i1PSkoKWrZsyTjMFenVqxe6dOmCefPmAShLHG3RogXGjx8POzs7/PHHH3B3d8eSJUtY94WDozLhnNqfjE+fPuHEiRPYt28foqKi0LdvX0ycOBGurq5SH7iqREtLC6mpqRJnQJ4/f4769esLSVqJ49mzZ1L3S3MAv1f09PQQHx8v8jBiQ/nZXWlIy05X1gafz4eRkZHM2V2udvy3pWIBEwEvXrxAvXr1JCoglK9AKI3ff/9d5vGlDZ4EyMrSr8izZ89w79491K9fn9VKzatXr9CsWTNoampixowZaNCgAQDg8ePH2Lp1K4qLixEbGyvVoTQ3N8eNGzcYpQBTU1PcuXOHef/kyRO0atUKeXl5rM6hqKgI169fZ5zc27dv48uXL2jYsCEePnzIyoaRkREiIiLg6OgotP3evXvo0qULs3oi7lzOnj3LKC0sWLAAkZGRiIqKAvB/pc0fPXrEqh8cHJUNF37wE+Hp6Yng4GBYWFhg0qRJOHLkCGvFAVVSrVo1pKenS3Rq09LSRCodieNHdFpl0a1bN4WdWllSSpVlY+nSpSrNxP6vk5uby1rySVn+/PNPAGWzwnv37hVamSkpKcHVq1dFMu/LI8tZZUvFSl9ubm5ytZelL3vz5k1W+rLVq1fH9evX4eHhAV9fX6HwmR49emD79u0yZ0hzc3OFjv3mzRuh/aWlpUL7ZaGtrY1u3bqhQ4cO6Nq1K/755x/s2rULSUlJrG106tQJq1atwpEjRxg98pKSEqxatQodOnSQ2O7du3dC5xsZGSlUPaxVq1bIzMxk3Q8OjsqGm6n9ieDz+bC0tISjo6PUmTR5Z0fkZdKkSUhNTWW0Lsvz6dMnuLi4wNramrVe7qNHj5CRkSEivP4jSs/s3r0by5cvx6RJk755zKUiSJoB5ChjzZo1sLKywogRIwAAw4YNw/Hjx2Fubo5z586hadOmX/X4guX9Z8+eoXbt2kIFWDQ1NWFlZQU/Pz+0bt1aqp1u3brhxIkTIs74+/fvMXDgQFy5ckXlfS/Pzp07ERoairNnzwIoW/KvGMc6d+5cVlJcAnJycpCSkgIAqF+/PquBNQDY2Nhg9erVGDJkiNj9ISEh+O233xjbkvj8+TNu3ryJ8PBwJgzBwsICnTp1QqdOndC5c2dYWlqy6tOjR4/QqVMnGBsbo2PHjgCAa9eu4f3797hy5QoaN24stl2dOnVw4MABdOrUCZ8/f4axsTHOnj2L7t27AygLR+jcuTO30sLx3cI5tT8REyZMYJX0o4rZOmk8f/4cLVu2hJaWFqZPn46GDRuCiJCYmIjt27fj06dPuHv3LiwsLKTaefr0KQYNGoT79+8zsbTA/yU2/Ygxtd9LzKWiSIrV5Cijbt26OHToENq1a4eLFy9i+PDhOHr0KEJCQpCRkYELFy5USj+6du2KEydOwMTERKH2kgYvr1+/Rq1atfDlyxdVdFMiqohjVRWqiMvt1q0bbt26hbp166Jz587o2LEjOnfuDHNzc4X79eLFC2zduhXx8fHQ0dGBg4MDZsyYIdVZ9/DwQHx8PNasWYNTp04hMDAQL168YCYfDh06hE2bNuHOnTsK94uD42vCObUc34S0tDR4enriwoULIkt+W7duZbX83q9fP6ipqWHv3r2oW7cubt++jezsbMyZMwfr1q1jZig4Kg9uplY6Ojo6ePLkCSwsLODl5YWioiLs2rULT548QevWrfHu3btv3UWpJCQkAChTTrhy5YqQg1RSUoKwsDDs2rVLZpKnsqg6jlUZVBGXq6GhAXNzcwwcOBBdunRB586dmTLVlcnbt28xePBgREVFQV9fH4GBgRg0aBCzv3v37mjTpg1WrFhR6X3j4GAD59RyfFPevXuH5ORkAPIt+QFlsblXrlyBg4MDjIyMcPv2bdja2uLKlSuYM2eO1GpFPxKVGXPJ8XWpWbMmjh07hnbt2sHW1hbLly/HsGHD8PjxY7Rq1Qrv37//asdWRXlaPp/PrISIe3To6Ohgy5YtrEtyK4qOjg7i4uIkSnElJSWhWbNmMpNNVUVaWho8PDxw8eJFsXG5ghlkSeTn5+PatWuIiIhAeHg44uLi0KBBA3Tu3JlxcuVVFMnNzcXt27fx+vVrlJaWCu0bN26c1LZ5eXnQ19cXCk8BykI09PX1RcLGODi+F7hEMY5viomJCZycnBRqW1JSwmifVqtWDS9evICtrS3q1KmDx48fq7Kblca3jrnk+LoMHjwYo0ePho2NDbKzs5kknNjYWIWSA+UhNjaWCQuQNuCTlBkPlDlvRARra2vcvn1byNHS1NSEmZmZiCP0NVCFvqwqqVu3LsLCwhSOy9XT04OrqytcXV0BlP0GUVFRCA8Px9q1azFmzBjY2NjgwYMHrOydPXsWY8aMwcePH2FoaCgUdsbj8WQ6tZISPeWZdODg+BZwTi3HD0vjxo0RHx+PunXronXr1li7di00NTWxe/dumTMj3ys7d+5kBPEvXryIS5cuISwsDCEhIfDx8am0mEuOr8PGjRthZWWFzMxMrF27llEfePnyJTw9Pb/qscPDw7Fx40Z4e3sjPDxc7Gc+fPjAOFbiECiOVJz5q2x69+6NxYsXo0+fPmLjWJcuXYo+ffpUer+qVKmi8CC9PHp6eqhSpQqqVKkCExMTqKurIzExkXX7OXPmYNKkSVi5cqXchRs4OH5kuPADjh+W8+fPIz8/H4MHD0ZKSgr69u2LJ0+eoGrVqjh69Ci6dev2rbsoNz96zCWHdLKzs5lYyczMTOzZsweFhYXo168fOnXq9NWPr6Ojg127domdqcvPz4eLiwvevn3LSj4qNTUVmzZtYpwte3t7eHl5oV69eirvd0VUEcf6PVFaWoq7d+8y4QfR0dHIz89HrVq1mCpjXbt2ZS1jqKenh/v37/+wg3sODoWppHK8HByVQnZ2NpWWln7rbiiMubk5RUdHExFRgwYNKCQkhIiIkpKSyMDA4Ft2jUMJEhISqE6dOsTn88nW1pZiY2OpevXqpK+vT4aGhqSmpkYnT5786v3466+/SFtbm06fPi20/cOHD9S+fXuysbGhf//9V6adsLAw0tTUJCcnJ/L29iZvb29ycnIiLS0tunDhwtfqvhBPnz4lFxcX4vP5xOPxiMfjEZ/PJxcXF0pNTa2UPqgKAwMD4vP5VLNmTRozZgzt3buXUlJSFLY3aNAgOnr0qAp7yMHxY8DN1HL88KSkpCA1NRWdOnWCjo4OUyb3R2TGjBn4+++/YWNjg9jYWKSnp0NfXx/BwcFYu3atzPKjHN8nvXr1grq6Onx9fXHgwAH8/fffcHFxwZ49ewAAM2fOxL1793Dz5s2v3pe9e/fCy8sLoaGh6NKlC/Lz8+Hq6oqsrCxERkaiZs2aMm04OjrCxcUFq1evFtru6+uLCxcuVOp1qmgc6/fErl270LVrV2bGWVn8/f3h5+eHiRMn/pB61xwcisI5tRw/LNnZ2Rg+fDjCw8PB4/GQnJwMa2trTJo0CSYmJli/fv237qLcfPnyBZs3b0ZmZiYmTJjAlLncuHEjDAwMMGXKlG/cQw5FKK/UIUjeuXPnDlq0aAGgLFu/TZs2yM3NrZT+rF27FitWrMDp06exePFi/Pvvv4iMjGSdXKWtrY379+/DxsZGaPuTJ0/g4OBQaaoDHOL50fWuOTgUhUsU4/hh8fb2hoaGBjIyMmBnZ8dsHzFiBGbPnv1DOrXv37/Hr7/+CqAs5nLx4sWVGnPJ8XXIyclBjRo1AAD6+vrQ09MTKnxgYmIiVXVA1cydOxc5OTno3r07rKysEBERIZdagKmpKeLi4kSc2ri4OE6j+DvgWyfycXB8KzinluOH5cKFCzh//rzIw9jGxgbPnj37Rr1SjPv376Nfv37IzMyEjY0NgoOD4erqivz8fPD5fGzcuBHHjh3DwIEDv3VXORSkYkjMtwiRGTx4sNB7DQ0NVKtWDV5eXkLbJZXK9vPzw6+//oqpU6fil19+wdOnT9GuXTsAQHR0NNasWSNTA5fj69G7d28cOXKEkeRavXo1pk2bxuhcZ2dno2PHjnj06NE37CUHx9eDCz/g+GExMDBATEwMbGxshMpk3r17Fy4uLsjOzv7WXWTN9xRzyaF6+Hw+evXqBS0tLQBlOqLdunWDnp4eAODTp08ICwv76svCEydOZPU5SaWyBWWQTU1NsWnTJqxfvx4vXrwAUFZYwsfHB7NmzfphY9p/dCqWqTY0NERcXByjgvDq1SvUrFmTCz/g+M/CObUcPxwvXrxAzZo10bt3b7Ro0QLLli2DgYEBEhISUKdOHYwcORKlpaU4duzYt+4qa763mEsO1aKsM/m9IK4MsiBsQlAIhePbUfH3KT/YBzinluO/Dxd+wPHD0ahRI2zbtg1//PEHunXrhrt37+Lz58+YO3cuHj58iJycHERHR3/rbsrF9xZzyaFavndnVR4qzsJyziwHB8f3AufUcvxwrFixAu7u7nB1dcWjR4+wc+dOGBgY4OPHjxg8eDCmT58Oc3Pzb91NufkeYi45OGTRoEEDmddmTk5OJfWGozw8Ho+7j3D81HBOLccPh6enJ3r16oXJkyejUaNG2L17NxYsWPCtu6U0EyZMYGIui4qKMG3aNKGYSw6O74GlS5cyiUgc3xdExN1HOH5quJhajh+arVu3wtvbG3Z2dlBXFx6j/UiFCv4rMZcc/23ExdRyfD9w9xGOnx1uppbjh+XZs2c4ceIETExMMGDAABGn9keCe8hw/AhwS9nfN9x9hONn58f1Ajh+avbs2YM5c+bA2dkZDx8+hKmp6bfuEgfHfx5uYY+Dg+N7hnNqOX44XF1dcfv2bWzduhXjxo371t3h4Php4CpVcXBwfM9wTi3HD0dJSQkSEhLkKuvJwcHBwcHB8d+GSxTj4ODg4ODg4OD44eF/6w5wcHBwcHBwcHBwKAvn1HJwcHBwcHBwcPzwcE4tBwcHBwcHBwfHDw/n1HJwcHBwcHBwcPzwcE4tBwcHBwcHBwfHDw/n1HJwcHBwcHBwcPzwcE4tBwcHBwcHBwfHDw/n1HJwcHBwcHBwcPzw/D8UImcDd1AfgQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = df_first_transform.corr(method='spearman')\n",
    "f, ax = plt.subplots(figsize=(8, 6))\n",
    "sns.heatmap(corrmat, vmax=.8, square=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This first filter, only contains numerical features. We'll deal later with the categorical features of the dataset. For now, we'll keep those features with a correlation at least 0.6 (positive or negative), from that value we can assume that [exists a strong correlation](https://www.statstutor.ac.uk/resources/uploaded/spearmans.pdf).\n",
    "\n",
    "Also, we'll sort the correlation matrix in order that the most correlated features will appear on the top of the heatmap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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grmlmQsbjuFzrWIzsScKuv4jfeoDUW2E8PXSK6PnrMRvQERSKt9FtkV/ZWYWX1LRkyRLs7e3R1dXFxcVFOd2em/T0dGbMmIGDgwO6urpUr15dZQ0YYNq0aTnOJK1YMffp21eRgaWAGBkZYWNjQ0BAgEp+QEAAlStXBkBbWxsgx5lbAQEBDB8+nNatW1OlShV0dHSIiYlRa/8lSpSgZMmSJCcnv1b/vby8SEhIUEn9Tcu9VltFXnoGKddvY1C/+j95CgUGn9Tg+aWbuVZR6OmQnZWtmvn3N1gZWEQufH198fT0ZOrUqVy8eJHq1avj6upKdHR0ruUnTZrE8uXL+emnn7hx4wYDBw6kQ4cOXLp0SaXc3zMff6cTJ06o3TdZYylAY8aMYerUqTg4OFCjRg3Wrl1LYGAgGzduBF6coaWnp8f+/fspVaoUurq6GBsb4+TkxIYNG6hTpw6JiYmMGTMmx9ld/7Z8+XICAwPp0KEDDg4OpKSk8Msvv3D9+nV++umn1+q7jo4OOjo6KnnaCs3XaktA7Jod2MzzJOVaCM+v3MK0dzs09HSI//0QACXnfUtGVCyP568DIOmvs5h+04HUG6E8vxyMdhkbLEb2JOmvs8oBRqGvi3YZG+U+tEtZoVOpHJnxT8mIePzWj7HIek+mrHx8fOjXrx99+vQBYNmyZezdu5c1a9Ywfvz4HOU3bNjAxIkTad26NQCDBg3i8OHDzJ8/n19//VVZTktLS2UN+HXIwFKAhg8fTkJCAt9++y3R0dFUrlyZ3bt34+TkBLz4D1u0aBEzZsxgypQpNGrUCH9/f1avXk3//v2pVasWdnZ2zJkzh9GjR790P/Xq1ePEiRMMHDiQR48eYWhoSJUqVdi5cyeNGzd+W4crXuHpvmNEmxphMaInmhYlSA26Q3jfKWTGxgNQzMZCZeojZukmyM7GYlQvtKzMyIxL4OlfZ3nss15ZRq+qE2U2zlW+tprYH4D47YeIGPfj2zkwUahSU1NJTU1VycvtS19aWhoXLlzAy8tLmaehoUHz5s05dSr309FTU1PR1dVVydPT08sRkYSEhGBjY4Ouri7169fH29ub0qVLq3Uciuzs7Oy8i4miKMip9bvughCFrlLIPrXKPz+8LO9Cr2nuiUimT5+ukjd16tQcd/R49OgRtra2nDx5kvr1/7kDw9ixYzl69ChnzpzJ0Xa3bt24fPkyO3fuxMHBAT8/P9q1a0dmZqZyMPvzzz9JSkqiQoUKREREMH36dB4+fMi1a9coXrx4vo9DIhYhhHhPeHl54enpqZL332jldS1cuJB+/fpRsWJFFAoFDg4O9OnThzVr1ijLtGrVSvlztWrVcHFxoUyZMmzZsoW+ffvme18ysAghhDoKcY0lt2mv3Jibm6OpqUlUVJRKflRU1EvXRywsLNi5cycpKSnExsZiY2PD+PHjKVfu5SfpmJiYUL58eW7fvq3WcchZYUII8YHR1tamdu3a+Pn5KfOysrLw8/NTmRrLja6uLra2tmRkZPD777/Trl27l5ZNSkoiNDSUkiVLqtU/iViEEEId78mDvjw9PXF3d6dOnTrUq1ePBQsWkJycrDxLrFevXtja2uLt7Q3AmTNnePjwITVq1ODhw4dMmzaNrKwsxo4dq2xz9OjRuLm5UaZMGR49esTUqVPR1NSka1f1LtSVgUUIIdTxnpxu3LlzZx4/fsyUKVOIjIxU3vTWysoKgPDwcJULolNSUpg0aRJ37tzB0NCQ1q1bs2HDBkxMTJRlHjx4QNeuXYmNjcXCwoKGDRty+vRp5e2o8kvOChMvJWeFiaJA7bPC/lxUSD0BvVbDC63tt0kiFiGEUMd7ErG8z2TxXgghRIGSiEUIIdTxnizev88kYhFCCFGgJGIRQgh1yBpLniRiEUIIUaAkYhEvlZEht80XIgdZY8mTDCxCCKEOmQrLk0yFCSGEKFASsQghhDpkKixPErEIIYQoUBKxCCGEOmSNJU8SsQghhChQErEIIYQ6JGLJk0QsQgghCpRELEIIoQ55hFWeZGARQgh1yFRYnmQqTAghRIGSgaWQNGnShJEjRypf29vbs2DBgvemP6LwmfZsTYXjq6hy83ccdvyAXnWnV5Y369OW8n4/UyVoGxUC1lBykgcK7WLK7fr1qlBm1WQqnl6H890/MGrxv8I+BJGbrKzCSx+JD3ZguX//Pt988w02NjZoa2tTpkwZRowYQWxs7LvuWr6dPHmS1q1bU6JECXR1dXF2dsbHx4fMzMx33TXxhoy/aEjJiR5EL9zE7TYjSQm6S9n1M9A0M869fNvGWI9zJ2rhZm41H8zD8T9h3KYh1mN6Kcto6OmSEnSXR1OWva3DEOK1fJADy507d6hTpw4hISFs2rSJ27dvs2zZMvz8/Khfvz5xcXGFtu/09PQCaWfHjh00btyYUqVKceTIEW7evMmIESOYNWsWXbp0IVsWCD9o5h7teeJ7gCfb/Ei9fZ+HE5eS9TwV044tci1vULsiz84HkbD7KOkPo0k6fon4P46hV728skzS0QtEzf+VxIOn39ZhiNxkZxVe+kh8kAPLkCFD0NbW5uDBgzRu3JjSpUvTqlUrDh8+zMOHD5k4cSITJkzAxcUlR93q1aszY8YM5etVq1ZRqVIldHV1qVixIkuXLlVuCwsLQ6FQ4OvrS+PGjdHV1WXjxo3ExsbStWtXbG1t0dfXx9nZmU2bNuW7/8nJyfTr14+2bduyYsUKatSogb29PR4eHqxfv55t27axZcsWAPz9/VEoFMTHxyvrBwYGolAoCAsLA3jj/oiCpSimhV5VR5JOXP4nMzubpIBA9GtVyLVO8oWb6Dk7KKfLitlZUbxJHZ76n38bXRaiQH1wZ4XFxcVx4MABZs+ejZ6enso2a2trunfvjq+vL8ePH8fb25vQ0FAcHBwAuH79OleuXOH3338HYOPGjUyZMoXFixdTs2ZNLl26RL9+/TAwMMDd3V3Z7vjx45k/fz41a9ZEV1eXlJQUateuzbhx4zAyMmLv3r307NkTBwcH6tWrl+cxHDx4kNjYWEaPHp1jm5ubG+XLl2fTpk107tw5X+/Jm/ZHFCzNEkYotDTJiHmikp8RE4+OQ6lc6yTsPoqWqRHltsxFoVCgKKZF7K/7eLx069voslDHR7QWUlg+uIElJCSE7OxsKlWqlOv2SpUq8eTJEywsLKhevTq//fYbkydPBl4MJC4uLjg6OgIwdepU5s+fz5dffglA2bJluXHjBsuXL1cZWEaOHKks87d/DwrDhg3jwIEDbNmyJV8f5Ldu3VL2NTcVK1ZUlskPW1vbN+oPQGpqKqmpqSp5admZaCvkYV9vg4FLVSwGd+TRlGU8CwxGp0xJSk7pj+WwOKJ/8n3X3RNCLR/kVBiQrzWI7t2789tvvynLb9q0ie7duwMvpqNCQ0Pp27cvhoaGyjRr1ixCQ0NV2qlTp47K68zMTGbOnImzszOmpqYYGhpy4MABwsPDC+wYtLW1891OQfTH29sbY2NjlbQq/na+64t/ZD5JJDsjEy3zEir5WuYmZDx+kmsdq297EL/jCE98D5IafI/Eg6eJ+uEXLAZ1BIXibXRb5Fd2duGlj8QHN7A4OjqiUCgICgrKdXtQUBAlSpTAwsKCrl27EhwczMWLFzl58iT3799XTi8lJSUBsHLlSgIDA5Xp2rVrnD6tujhqYGCg8vr7779n4cKFjBs3jiNHjhAYGIirqytpaWn5OgYnJydlX192DOXLv1i01dB48V/070HovycQvGl/ALy8vEhISFBJHiaO+a4v/pGdnsHza7cxaFDtn0yFAsNPqvPsYnCudTR0dXJMsWRnZinrCvEh+eCmwszMzGjRogVLly5l1KhRKusskZGRbNy4kV69eqFQKChVqhSNGzdm48aNPH/+nBYtWmBpaQmAlZUVNjY23LlzRxnF5FdAQADt2rWjR48eAGRlZXHr1i0qV66cr/qurq6Ympoyf/58PvnkE5Vtu3fvJiQkRHnNi4WFBQARERGUKPHiG3BgYGCB9gdAR0cHHR0dlTyZBnt9Mat2Umr+KJ5fuc3zy7cw+6YdGvq6PNl2GIBS80eRHhlL1Pe/AJDodxbzvu15fv0OzwJvoWNfEivP7iT6nVUOOBr6umiXKancRzE7K3QrlSUzIYn0R4/f/kEWVbLGkqcPbmABWLx4MZ988gmurq7MmjWLsmXLcv36dcaMGYOtrS2zZ89Wlu3evTtTp04lLS2NH3/8UaWd6dOnM3z4cIyNjWnZsiWpqamcP3+eJ0+e4Onp+dL9Ozk5sW3bNk6ePEmJEiXw8fEhKioq3x/kBgYGLF++nC5dutC/f3+GDh2KkZERfn5+jBkzhn79+tG6dWvgRYRmZ2fHtGnTmD17Nrdu3WL+/PkF2h9R8BL2nkDLzBgrz+5omZcgJegOd3tPJSMmHoBiNhaQ9U8UGr3YF7Kzsfq2B8WszciITeTpX2eJ/H6DsoyesyPlNnsrX9tM9gDgyTY/HoxZ8FaOSyADSz58kAOLk5MT58+fZ+rUqXTq1Im4uDisra1p3749U6dOxdTUVFn266+/ZujQoWhqatK+fXuVdjw8PNDX1+f7779nzJgxGBgY4OzsnOcV6pMmTeLOnTu4urqir69P//79ad++PQkJCfk+hq+//pojR44we/ZsGjVqRGJiIgBz585l7NixynLFihVj06ZNDBo0iGrVqlG3bl1mzZpFx44dC7Q/ouDF/rKX2F/25rrtbtcJqhmZWUQv2kz0os0vbS/5zDWulnUryC4KUSgU2XIl3nshJSWFdu3acf/+fY4ePaqcAnuX5ENMFAXOd/9Qq/zzVS+fzXhTeh4+hdb22/TBLd5/rHR1ddm1axe9evXi2LFj77o7Qgjx2j7IqbCPla6uLuPHj3/X3RBCvEJ2lkzy5EUiFiGEEAVKIhYhhFCHnBWWJ4lYhBBCFCiJWIQQQh0f0e3tC4sMLEIIoQ5ZvM+TTIUJIYQoUBKxCCGEOmTxPk8SsQghhChQErEIIYQ6JGLJk0QsQgghCpRELOKlfs8yetddEKLQOatbQe7bmyeJWIQQQhQoiViEEEIdssaSJxlYhBBCHXKBZJ5kKkwIIUSBkoFFCCHUkZ1VeElNS5Yswd7eHl1dXVxcXDh79uxLy6anpzNjxgwcHBzQ1dWlevXq7N+//43afBkZWIQQ4gPk6+uLp6cnU6dO5eLFi1SvXh1XV1eio6NzLT9p0iSWL1/OTz/9xI0bNxg4cCAdOnTg0qVLr93my8gz78VLTSvT/V13QYhCN+3eRrXKP5vbp5B6Avrj1ua7rIuLC3Xr1mXx4sUAZGVlYWdnx7Bhw3J9Eq2NjQ0TJ05kyJAhyryvvvoKPT09fv3119dq82UkYhFCiA9MWloaFy5coHnz5so8DQ0NmjdvzqlTp3Ktk5qaiq6urkqenp4eJ06ceO02X0bOChNCCDVkF+LpxqmpqaSmpqrk6ejooKOjo5IXExNDZmYmVlZWKvlWVlbcvHkz17ZdXV3x8fHh008/xcHBAT8/P7Zv305mZuZrt/kyErEIIcR7wtvbG2NjY5Xk7e1dIG0vXLgQJycnKlasiLa2NkOHDqVPnz5oaBT8MCADSwGZNm0aNWrUeNfdEEIUtqzsQkteXl4kJCSoJC8vrxxdMDc3R1NTk6ioKJX8qKgorK2tc+22hYUFO3fuJDk5mXv37nHz5k0MDQ0pV67ca7f5MkVuKiwyMhJvb2/27t3LgwcPMDY2xtHRkR49euDu7o6+vn6u9cLCwihbtiyXLl3KdQAZPXo0w4YNe60+VaxYkbt373Lv3j21/wPF+61urxY06P8FhhbGRAaF8+fU9Ty8fCfXsr03T8S+fuUc+bf+usRvfX4AoMnIL6nqVh8jG1My0zOJuHoXv++38DAwtFCPQ/xLIT6aOLdpr9xoa2tTu3Zt/Pz8aN++PfBiod3Pz4+hQ4e+sq6uri62trakp6fz+++/06lTpzdu87+K1MBy584dGjRogImJCXPmzMHZ2RkdHR2uXr3KihUrsLW1pW3btjnqpaen59m2oaEhhoaGavfpxIkTPH/+nK+//pr169czbty4V5ZPS0tDW1tb7f2It69Km//hOqk7eyau4WFgKP/7piU9NoxncdPRJMcm5ijvO2ABmtr//EnqmRgyaL83N/b+cx1B7N1I9k1Zx5PwaLR0tanv0YqeG8azqLEnz+KevpXjEu8HT09P3N3dqVOnDvXq1WPBggUkJyfTp8+Ls9Z69eqFra2tcirtzJkzPHz4kBo1avDw4UOmTZtGVlYWY8eOzXeb+VWkpsIGDx6MlpYW58+fp1OnTlSqVIly5crRrl079u7di5ubGwAKhYKff/6Ztm3bYmBgwOzZs/Ns+99TYQcPHkRXV5f4+HiVMiNGjKBZs2YqeatXr6Zbt2707NmTNWvW5GjX3t6emTNn0qtXL4yMjOjfvz/wYkBq1KgRenp62NnZMXz4cJKTk5X1NmzYQJ06dShevDjW1tZ069ZN7XPRxZup79GKi5uPELj1GI9DHrJnwhrSn6dSs1PjXMs/T0gm6XGCMjk0cib9eRrX955Rlrm66yR3Aq7z5P5jHoc85MDMjega6WNVqfTbOixRiFNh6ujcuTM//PADU6ZMoUaNGgQGBrJ//37l4nt4eDgRERHK8ikpKUyaNInKlSvToUMHbG1tOXHiBCYmJvluM7+KzMASGxvLwYMHGTJkCAYGBrmWUSgUyp+nTZtGhw4duHr1Kt98841a+/rss88wMTHh999/V+ZlZmbi6+tL9+7/XBvy9OlTtm7dSo8ePWjRogUJCQkcP348R3s//PAD1atX59KlS0yePJnQ0FBatmzJV199xZUrV/D19eXEiRMq4Wp6ejozZ87k8uXL7Ny5k7CwMHr37q3WcYjXp1lMExvnstw5cU2Zl52dzZ0T1yhVyylfbdTs3IRrf5wi/Xlqrts1i2lSu1tTUhKSibpxr0D6LT4sQ4cO5d69e6SmpnLmzBlcXFyU2/z9/Vm3bp3ydePGjblx4wYpKSnExMTwyy+/YGNjo1ab+VVkpsJu375NdnY2FSpUUMk3NzcnJSUFgCFDhjB37lwAunXrphL+hYWF5XtfmpqadOnShd9++42+ffsC4OfnR3x8PF999ZWy3ObNm3FycqJKlSoAdOnShdWrV9OoUSOV9po1a8a3336rfO3h4UH37t0ZOXIkAE5OTixatIjGjRvz888/o6urqzIYlitXjkWLFlG3bl2SkpJea8pOqEe/RHE0tDRJiklQyU+OScTcIecf83/ZVi+HVUU7do9dkWNb+WY1+XrxUIrpafM0Op5fenzHsydJBdZ3kQe5u3GeikzE8jJnz54lMDCQKlWqqJw/XqdOnTdqt3v37vj7+/Po0SMANm7cyBdffKESdq5Zs4YePXooX/fo0YOtW7fy9KnqXPl/+3L58mXWrVunXNcxNDTE1dWVrKws7t69C8CFCxdwc3OjdOnSFC9enMaNX0y/hIeH59rf1NRUEhMTVVJGduYbvQfi9dXs3ISooPBcF/rvnrrBslYTWP3ldG4fvULHpcMwMJOHson3R5EZWBwdHVEoFAQHB6vklytXDkdHR/T09FTyXzZdll9169bFwcGBzZs38/z5c3bs2KEyDXbjxg1Onz7N2LFj0dLSQktLi//97388e/aMzZs3v7IvSUlJDBgwgMDAQGW6fPkyISEhODg4kJycjKurK0ZGRmzcuJFz586xY8cO4MXif25yO3/+RML1N3oPirJnT56SlZGJobmxSr6BuRFJjxNeUuuFYno6VHWrz0Vf/1y3pz9PJe5eFA8u3Wb32JVkZWRRs3OTAuq5yNN7ssbyPisyA4uZmRktWrRg8eLFKovchal79+5s3LiRP/74Aw0NDb744gvlttWrV/Ppp59y+fJllQHC09OT1atXv7LdWrVqcePGDRwdHXMkbW1tbt68SWxsLN999x2NGjWiYsWKeS7c53b+fEPjKgXyPhRFmemZPLp6l7IN/nkPFQoF5RpU5cHFkFfWrfKFC1raWlzZEZCvfSk0FGhpF5lZbfEBKFK/jUuXLqVBgwbUqVOHadOmUa1aNTQ0NDh37hw3b96kdu3aebbx34gHUK6R/Ff37t2ZNm0as2fP5uuvv1aen56ens6GDRuYMWMGVatWVanj4eGBj48P169ff2m748aN43//+x9Dhw7Fw8MDAwMDbty4waFDh1i8eDGlS5dGW1ubn376iYEDB3Lt2jVmzpz5yuPK7fx5LYXmK+uIVzu16k86zB/Aoyt3eXj5xenGxfR1uLT1KAAdfAaSGPkEv3m+KvVqdm7MzYMXeB6vum5STE+HT4e2I/jwRZ5Gx6NfwpB67i0wsiqhcuaYKGSFeB3Lx6JIDSwODg5cunSJOXPm4OXlxYMHD9DR0aFy5cqMHj2awYMH59lGly5dcuTdv38/17KOjo7Uq1ePs2fPsmDBAmX+7t27iY2NpUOHDjnqVKpUiUqVKrF69Wp8fHxybbdatWocPXqUiRMn0qhRI7Kzs3FwcKBz587Aiyts161bx4QJE1i0aBG1atXihx9+yPUaHVF4ru85jYFZcZp6fv3iAskb9/i111ySY15cw2JsY0b2f6Y/zMqVpEy9ivzSPedtPLKzsjB3tKH6143QL1Gc5/FJPLx8hzUdZ/I45OFbOSbBRzVlVVjktvnipeS2+aIoUPe2+ckTOxZST8Bg9tZCa/ttKlIRixBCvKnCvLvxx6LILN4LIYR4OyRiEUIIdcgaS54kYhFCCFGgJGIRQgh1SMSSJ4lYhBBCFCiJWIQQQh1ygWSeZGARQgh1yFRYnmQqTAghRIGSiEUIIdTw39vwiJwkYhFCCFGgJGIRQgh1SMSSJxlYxEtdRx53K4RQnwwsQgihDrkJZZ5kjUUIIUSBkohFCCHUIWsseZKBRQgh1CEDS55kKkwIIUSBkohFCCHUIE9zz5tELEIIIQqURCxCCKEOWWPJk0QsQgghCpRELEIIoQ6JWPL03kcsCoWCnTt3vutusG7dOkxMTN51N4QQ4r2X74FFoVC8Mk2bNu2ldcPCwlAoFAQGBhZAl/PXRy0tLUqXLo2npyepqalqtWNvb8+CBQsKp5OiyHDt1ZolJ1awMXgrc3Z+j2N1p1eW1zcyoO/MAaw4t5bfbm1j4ZGl1GxaW7ld10CP3lP6sjRgJRuDtzBr+1wcqjkW9mGI/8jOyi609LHI91RYRESE8mdfX1+mTJlCcHCwMs/Q0LBge/aa1q5dS8uWLUlPT+fy5cv06dMHAwMDZs6c+db7kpaWhra29lvfr3j3PmnTEPdJ37Bi4s/cDrzFF9+4MXHDNEY0HUxibEKO8lrFtJj863QSYxOYP2gucZFxWNhakJyYrCwzaO5Q7CqU5qdRP/IkKo5GHZowZeMMRjUfSlxU3Fs8uiLuIxoACku+IxZra2tlMjY2RqFQKF9bWlri4+NDqVKl0NHRoUaNGuzfv19Zt2zZsgDUrFkThUJBkyZNADh37hwtWrTA3NwcY2NjGjduzMWLF9/ogExMTLC2tsbOzo42bdrQrl07lTZDQ0Np164dVlZWGBoaUrduXQ4fPqzc3qRJE+7du8eoUaOU0c+/HThwgEqVKmFoaEjLli1VBtzevXvTvn17Zs+ejY2NDRUqVADg6tWrNGvWDD09PczMzOjfvz9JSf/cOTgrK4sZM2a89P37O+LbsmULjRo1Qk9Pj7p163Lr1i3OnTtHnTp1MDQ0pFWrVjx+/FhZz9/fn3r16mFgYICJiQkNGjTg3r17b/T+ivxp49EOv80H8d/qx4OQ+6yY8DNpz1Np1ql5ruWbdmqOoYkh8/rNIfj8TR4/iObGmevcCwoDQFtHG5dW9fnVex1BZ28QeS+SrQs2E3kvgs97tnqLRyZE3gpkjWXhwoXMnz+fH374gStXruDq6krbtm0JCQkB4OzZswAcPnyYiIgItm/fDsDTp09xd3fnxIkTnD59GicnJ1q3bs3Tp08LolvcunWLv/76CxcXF2VeUlISrVu3xs/Pj0uXLtGyZUvc3NwIDw8HYPv27ZQqVYoZM2YQERGhMnA8e/aMH374gQ0bNnDs2DHCw8MZPXq0yj79/PwIDg7m0KFD7Nmzh+TkZFxdXSlRogTnzp1j69atHD58mKFDh+b7/fvb1KlTmTRpEhcvXkRLS4tu3boxduxYFi5cyPHjx7l9+zZTpkwBICMjg/bt29O4cWOuXLnCqVOn6N+/f46BUhQ8rWJalHN24MqJy8q87Oxsrpy4TPlaFXKtU6dFXW5dDMZj5gBWnl/P/IOL6DDkazQ0XvyJamhpoqmlSVpqukq9tJQ0KtapVHgHI3LKKsT0kSiQs8J++OEHxo0bR5cuXQCYO3cuR44cYcGCBSxZsgQLCwsAzMzMsLa2VtZr1qyZSjsrVqzAxMSEo0eP0qZNm9fqS9euXdHU1CQjI4PU1FTatGmDl5eXcnv16tWpXr268vXMmTPZsWMHu3fvZujQoZiamqKpqUnx4sVV+gqQnp7OsmXLcHBwAGDo0KHMmDFDpYyBgQGrVq1SToGtXLmSlJQUfvnlFwwMDABYvHgxbm5uzJ07Fysrqzzfv7+NHj0aV1dXAEaMGEHXrl3x8/OjQYMGAPTt25d169YBkJiYSEJCAm3atFH2t1Il+QB6G4qXMEJTS5OEmHiV/ISYeGwdSuVax8rOmqr1LTmx6yjevWdgbV8Sj1kD0NTSZNtCX1KSnxN84SZfD+vEw5AHJMTE06BdI8rXqkBkWORbOCoh8u+NI5bExEQePXqk/HD7W4MGDQgKCnpl3aioKPr164eTkxPGxsYYGRmRlJSkjB5ex48//khgYCCXL19mz5493Lp1i549eyq3JyUlMXr0aCpVqoSJiQmGhoYEBQXla5/6+vrKD2mAkiVLEh0drVLG2dlZZV0lKCiI6tWrKwcVePHeZGVlERwcrNb7V61aNeXPVlZWyv39O+/v/piamtK7d29cXV1xc3Nj4cKFKtHXf6WmppKYmKiSMrMz83xPRMFQaChIjE1g+fil3LkWysk9J9i+eCuf92ipLPPTyB9RKBQvFvdDttG6dxtO7D5OVvZH9FX3AyCL93l7p6cbu7u7ExgYyMKFCzl58iSBgYGYmZmRlpb22m1aW1vj6OhIhQoV+OKLL5g+fTq+vr7cvn0bePGtf8eOHcyZM4fjx48TGBiIs7NzvvZZrFgxldcKhSLHfYP+PYAUtH/v/+8prf/mZf3rIURr167l1KlTfPLJJ/j6+lK+fHlOnz6da9ve3t4YGxurpJsJIbmWFa/29EkimRmZGJubqOQbm5sQ//hJrnXio5/w6O4jlf+/B7cfUMLSFK1iLyYWosIjmdp5Ij0qdmJg/b54tRuDlpYW0eFRhXYsQryONx5YjIyMsLGxISAgQCU/ICCAypUrAyi/wWdmZuYoM3z4cFq3bk2VKlXQ0dEhJibmTbukQlNTE4Dnz58r99m7d286dOiAs7Mz1tbWhIWFqdTR1tbO0dfXValSJS5fvkxy8j9n9wQEBKChoUGFChXy9f69iZo1a+Ll5cXJkyepWrUqv/32W67lvLy8SEhIUEkVjV99eqzIXUZ6BneuhuLc4J8IU6FQ4NygGrcuBuda5+b5IKzLWKusgdmUtSEuKo6M9AyVsqnPU4mPfoKBkQHVP63BuYNnCudARO6ysgsvfSQKJGIZM2YMc+fOxdfXl+DgYMaPH09gYCAjRowAwNLSEj09Pfbv309UVBQJCS9Ot3RycmLDhg0EBQVx5swZunfvjp6e3hv1JT4+nsjISB49esTRo0eZMWMG5cuXV64vODk5sX37duV0Wbdu3VS+JcKL61iOHTvGw4cP33ig6969O7q6uri7u3Pt2jWOHDnCsGHD6Nmzp3I6K6/373XcvXsXLy8vTp06xb179zh48CAhISEvXWfR0dHByMhIJWkqNF97/0XdnlW7+KzL5zT+qim2jqXoN3sgOvq6HNn64gzEoT4j6Tb2nynag7/ux9CkOH2meVCyrA21mtWmw5COHPhln7JM9U9rUqNxTSztLKnWsDrTNs/iYehDjmz1e+vHJ8SrFMji/fDhw0lISODbb78lOjqaypUrs3v3bpycXnzj1dLSYtGiRcyYMYMpU6bQqFEj/P39Wb16Nf3796dWrVrY2dkxZ86cHGdZqatPnz4AytOhP/30U+bMmYOW1otD9fHx4ZtvvuGTTz7B3NyccePGkZiYqNLGjBkzGDBgAA4ODqSmpr7RbbL19fU5cOAAI0aMoG7duujr6/PVV1/h4+OjLJPX+/e6+7158ybr168nNjaWkiVLMmTIEAYMGPDabYr8O7nnBEZmRnT27IaJRQnCbtxldq/pJMS8+FJlbmNO9r++0MRGxDC71zTcJ/flh/0LiYuKZd/aP9j183ZlGf3i+nQb1xMza3OSEp5y5s9TbPr+VzIzZC3srZIlrTwpsuXhAuIlOpZp9667IESh23pvl1rl4zs3LaSegInvkUJr+22Sm1AKIYQaPqaztwrLe38Tyr/NmTMHQ0PDXFOrVnLlsRDiLZELJPP0wUQsAwcOpFOnTrlue9MFfyGEEAXngxlYTE1NMTU1fdfdEEIUcTIVlrcPZipMCCHEh0EGFiGEUMd7tMayZMkS7O3t0dXVxcXFRXnD35dZsGABFSpUQE9PDzs7O0aNGkVKSopy+7Rp03I8a6tixYpq9+uDmQoTQgjxD19fXzw9PVm2bBkuLi4sWLAAV1dXgoODsbS0zFH+t99+Y/z48axZs4ZPPvmEW7du0bt3bxQKhcp1dVWqVFF5lMjf1wCqQwYWIYRQw/tyz08fHx/69eunvCh82bJl7N27lzVr1jB+/Pgc5U+ePEmDBg3o1q0b8OIOI127duXMGdVbAmlpaeW4s7u6ZCpMCCE+MGlpaVy4cIHmzf95cJyGhgbNmzfn1KlTudb55JNPuHDhgnK67M6dO+zbt4/WrVurlAsJCcHGxoZy5crRvXv317rbvEQsQgihjkKMWFJTU0lNTVXJ09HRQUdHRyUvJiaGzMxM5f0G/2ZlZcXNmzdzbbtbt27ExMTQsGFDsrOzycjIYODAgUyYMEFZxsXFhXXr1lGhQgUiIiKYPn06jRo14tq1axQvXjzfxyERixBCqCE7q/BSbo+v8Pb2LpB++/v7M2fOHJYuXcrFixfZvn07e/fuZebMmcoyrVq1omPHjlSrVg1XV1f27dtHfHw8W7ZsUWtfErEIIcR7wsvLC09PT5W8/0YrAObm5mhqahIVpfosnqioqJeuj0yePJmePXvi4eEBvHhIYHJyMv3792fixInKx2D/m4mJCeXLl1c+zyq/JGIRQgh1FOLpxrk9viK3gUVbW5vatWvj5/fPIxOysrLw8/Ojfv36uXb72bNnOQaPv59X9bJ7ESclJREaGkrJkiXzfl/+RSIWIYT4AHl6euLu7k6dOnWoV68eCxYsIDk5WXmWWK9evbC1tVVOpbm5ueHj40PNmjVxcXHh9u3bTJ48GTc3N+UAM3r0aNzc3ChTpgyPHj1i6tSpaGpq0rVrV7X6JgOLEEKo4X053bhz5848fvyYKVOmEBkZSY0aNdi/f79yQT88PFwlQpk0aRIKhYJJkybx8OFDLCwscHNzY/bs2coyDx48oGvXrsTGxmJhYUHDhg05ffo0FhYWavVNnsciXkqexyKKAnWfx/K4ReNC6glYHDpaaG2/TRKxCCGEGt6XiOV9Jov3QgghCpRELEIIoQaJWPImA4sQQqgjW/Gue/Dek6kwIYQQBUoiFiGEUINMheVNIhYhhBAFSiIWIYRQQ3aWrLHkRSIWIYQQBeqDHVgiIyMZMWIEjo6O6OrqYmVlRYMGDfj555959uzZu+5eviUmJjJx4kQqVqyIrq4u1tbWNG/enO3bt7/0xnDiw+DaqzVLTqxgY/BW5uz8HsfqTq8sr29kQN+ZA1hxbi2/3drGwiNLqdm0tnK7roEevaf0ZWnASjYGb2HW9rk4VHMs7MMQ/1GYt83/WHyQU2F37tyhQYMGmJiYMGfOHJydndHR0eHq1ausWLECW1tb2rZtq3a7mZmZKBSKXG8fXRji4+Np2LAhCQkJzJo1i7p166KlpcXRo0cZO3YszZo1w8TERO12s7OzyczMfK1nVYuC8UmbhrhP+oYVE3/mduAtvvjGjYkbpjGi6WASYxNylNcqpsXkX6eTGJvA/EFziYuMw8LWguTEZGWZQXOHYlehND+N+pEnUXE06tCEKRtnMKr5UOKi4t7i0Qnxah9kxDJ48GC0tLQ4f/48nTp1olKlSpQrV4527dqxd+9e3NzcgBfPhHZ2dsbAwAA7OzsGDx5MUlKSsp1169ZhYmLC7t27qVy5Mjo6OoSHh3Pu3DlatGiBubk5xsbGNG7cmIsXL6r04ebNmzRs2BBdXV0qV67M4cOHUSgU7Ny5U1nm/v37dOrUCRMTE0xNTWnXrh1hYWHK7RMmTCAsLIwzZ87g7u5O5cqVKV++PP369SMwMBBDQ0MANmzYQJ06dShevDjW1tZ069aN6OhoZTv+/v4oFAr+/PNPateujY6ODidOnODy5cs0bdqU4sWLY2RkRO3atTl//nwh/I+I/2rj0Q6/zQfx3+rHg5D7rJjwM2nPU2nWqXmu5Zt2ao6hiSHz+s0h+PxNHj+I5saZ69wLCgNAW0cbl1b1+dV7HUFnbxB5L5KtCzYTeS+Cz3u2eotHJrKzFYWWPhYf3MASGxvLwYMHGTJkCAYGBrmWUShe/AdpaGiwaNEirl+/zvr16/nrr78YO3asStlnz54xd+5cVq1axfXr17G0tOTp06e4u7tz4sQJTp8+jZOTE61bt+bp06fAi8imffv26Ovrc+bMGVasWMHEiRNV2k1PT8fV1ZXixYtz/PhxAgICMDQ0pGXLlqSlpZGVlcXmzZvp3r07NjY2OY7B0NBQGXGkp6czc+ZMLl++zM6dOwkLC6N379456owfP57vvvuOoKAgqlWrRvfu3SlVqhTnzp3jwoULjB8/nmLFiqn9ngv1aBXTopyzA1dOXFbmZWdnc+XEZcrXqpBrnTot6nLrYjAeMwew8vx65h9cRIchXyujZw0tTTS1NElLTVepl5aSRsU6lQrvYEQOMhWWtw9uruT27dtkZ2dToYLqH6i5uTkpKSkADBkyhLlz5zJy5Ejldnt7e2bNmsXAgQNZunSpMj89PZ2lS5dSvXp1ZV6zZs1U2l6xYgUmJiYcPXqUNm3acOjQIUJDQ/H391c+rW327Nm0aNFCWcfX15esrCxWrVqlHOjWrl2LiYkJ/v7+1KhRgydPnlCxYsU8j/mbb75R/lyuXDkWLVpE3bp1SUpKUkY1ADNmzFDpQ3h4OGPGjFHuw8np1XP8omAUL2GEppYmCTHxKvkJMfHYOpTKtY6VnTVV61tyYtdRvHvPwNq+JB6zBqCppcm2hb6kJD8n+MJNvh7WiYchD0iIiadBu0aUr1WByLDIt3BUQuTfBxexvMzZs2cJDAykSpUqpKamAnD48GE+++wzbG1tKV68OD179iQ2NlZlcV9bW5tq1aqptBUVFUW/fv1wcnLC2NgYIyMjkpKSCA8PByA4OBg7OzuVR4DWq1dPpY3Lly9z+/ZtihcvjqGhIYaGhpiampKSkkJoaKhaC/MXLlzAzc2N0qVLU7x4cRo3fnHb7r/787c6deqovPb09MTDw4PmzZvz3XffERoa+tJ9pKamkpiYqJIyszPz3UfxZhQaChJjE1g+fil3roVycs8Jti/eyuc9WirL/DTyRxQKxYvF/ZBttO7dhhO7j5P1MX3V/QBkZykKLX0sPriBxdHREYVCQXBwsEp+uXLlcHR0RE9PD4CwsDDatGlDtWrV+P3337lw4QJLliwBIC0tTVlPT09PGVH8zd3dncDAQBYuXMjJkycJDAzEzMxMpV5ekpKSqF27NoGBgSrp1q1bdOvWDQsLC0xMTLh58+Yr20lOTsbV1RUjIyM2btzIuXPn2LFjR47jAHJMDU6bNo3r16/zxRdf8Ndff1G5cmVl3f/y9vbG2NhYJd1MCMn38Yp/PH2SSGZGJsbmJir5xuYmxD9+kmud+OgnPLr7iKysfwaJB7cfUMLSFK1iLyYWosIjmdp5Ij0qdmJg/b54tRuDlpYW0eFRubYpxLvywQ0sZmZmtGjRgsWLF5OcnPzSchcuXCArK4v58+fzv//9j/Lly/Po0aN87SMgIIDhw4fTunVrqlSpgo6ODjExMcrtFSpU4P79+0RF/fMHfe7cOZU2atWqRUhICJaWljg6OqokY2NjNDQ06NKlCxs3bsy1X0lJSWRkZHDz5k1iY2P57rvvaNSoERUrVlRZuM9L+fLlGTVqFAcPHuTLL79k7dq1uZbz8vIiISFBJVU0lqmz15GRnsGdq6E4N/gnElYoFDg3qMati8G51rl5PgjrMtYqX3JsytoQFxVHRnqGStnU56nERz/BwMiA6p/W4NzBM4VzICJX2dmFlz4WH9zAArB06VIyMjKoU6cOvr6+BAUFERwczK+//srNmzfR1NTE0dGR9PR0fvrpJ+7cucOGDRtYtmxZvtp3cnJiw4YNBAUFcebMGbp3766MhABatGiBg4MD7u7uXLlyhYCAACZNmgT8c+JA9+7dMTc3p127dhw/fpy7d+/i7+/P8OHDefDgAfBiXcbOzg4XFxd++eUXbty4QUhICGvWrKFmzZokJSVRunRptLW1lcexe/duZs6cmecxPH/+nKFDh+Lv78+9e/cICAjg3LlzVKqU+0Kvjo4ORkZGKklToZmv90vktGfVLj7r8jmNv2qKrWMp+s0eiI6+Lke2HgZgqM9Iuo3tqSx/8Nf9GJoUp880D0qWtaFWs9p0GNKRA7/sU5ap/mlNajSuiaWdJdUaVmfa5lk8DH3Ika1+b/34hHiVD27xHsDBwYFLly4xZ84cvLy8ePDgATo6OlSuXJnRo0czePBg9PX18fHxYe7cuXh5efHpp5/i7e1Nr1698mx/9erV9O/fn1q1amFnZ8ecOXMYPXq0crumpiY7d+7Ew8ODunXrUq5cOb7//nvc3NzQ1dUFQF9fn2PHjjFu3Di+/PJLnj59iq2tLZ999hlGRkYAmJqacvr0ab777jtmzZrFvXv3KFGiBM7Oznz//fcYGxujUChYt24dEyZMYNGiRdSqVYsffvghz+t0NDU1iY2NpVevXkRFRWFubs6XX37J9OnT3+CdF/l1cs8JjMyM6OzZDROLEoTduMvsXtNJiHlxDYu5jTnZ/5r2io2IYXavabhP7ssP+xcSFxXLvrV/sOvn7coy+sX16TauJ2bW5iQlPOXMn6fY9P2vZGbIWtjb9DGthRQWeeZ9AQkICKBhw4bcvn0bBweHd92dAiHPvBdFgbrPvL9XK/drkQpCmYuHC63tt+mDjFjeBzt27MDQ0BAnJydu377NiBEjaNCgwUczqAghcicRS95kYHlNT58+Zdy4cYSHh2Nubk7z5s2ZP3/+u+6WEKKQyRxP3mRgeU29evXK13qNEEIUNTKwCCGEGmQqLG8f5OnGQggh3l8SsQghhBo+prsQFxaJWIQQQhQoiViEEEINcs/PvEnEIoQQokBJxCKEEGrIkjWWPMnAIoQQapDF+7zJVJgQQogCJRGLEEKoQS6QzJsMLOKl0uTRxEKI1yADixBCqEFuQpk3WWMRQghRoCRiEUIINcgaS94kYhFCCFGgJGIRQgg1yAWSeZOBRQgh1CAXSOZNpsKEEEIUKIlYhBBCDXK6cd4kYhFCCFGgJGIRQgg1yOJ93iRiKSKaNGnCyJEj33U3hBBFQJEZWHr37o1CociRWrZsma/6hfHBfPLkSVq3bk2JEiXQ1dXF2dkZHx8fMjPlHl0fg9a9vmBFwGq23trO97vm41S9/CvLGxgZMGDmQNae/4VtITtY6r+c2k3rKLdraGjQ7dserDixii23fmfZ8ZV0Gt6lsA9D/Ed2tqLQ0seiSE2FtWzZkrVr16rk6ejovNU+pKWloa2tzY4dO+jUqRN9+vThyJEjmJiYcPjwYcaOHcupU6fYsmULCsXH84tW1DR0a8Q3kz34ecISbgUG49a3HdN+ncHgJgNIiE3IUV6rmBbTN84kISaBuQO9iYuMxcLWkuTEZGWZLwd9RauerVjg+SP3b4XjWM2J4T+M4NnTZPas/eNtHp4Qr1RkIhZ4MYhYW1urpBIlSuDv74+2tjbHjx9Xlp03bx6WlpZERUXRu3dvjh49ysKFC5WRTlhYGADXrl2jVatWGBoaYmVlRc+ePYmJiVG206RJE4YOHcrIkSMxNzfH1dWV5ORk+vXrR9u2bVmxYgU1atTA3t4eDw8P1q9fz7Zt29iyZQsA/v7+KBQK4uPjlW0GBgaq9CE2NpauXbtia2uLvr4+zs7ObNq0qdDfT/Fy7Tzac3DTAfy2HuZ+yH1+9lpC6vNUmndukWv55p1bYGhSnDn9ZnHzfBDRD6K5fuYaYUF3lWUq1qnEmYNnuPDXeaIfRHNyXwCXjl3KMxISBSs7u/DSx6JIDSwv8/c0V8+ePUlISODSpUtMnjyZVatWYWVlxcKFC6lfvz79+vUjIiKCiIgI7OzsiI+Pp1mzZtSsWZPz58+zf/9+oqKi6NSpk0r769evR1tbm4CAAJYtW8bBgweJjY1l9OjROfri5uZG+fLl1RoYUlJSqF27Nnv37uXatWv079+fnj17cvbs2Td+b4T6tIpp4eDsyOUTgcq87OxsLp8IpEKtirnWqdvcheALNxkwaxDrL2xg0aElfD2kIxoa//yJ3jwfRLUG1bEpawOAfaWyVK5bmYv+Fwr1eISqrGxFoSV1LVmyBHt7e3R1dXFxccnzb37BggVUqFABPT097OzsGDVqFCkpKW/UZm6K1FTYnj17MDQ0VMmbMGECEyZMYNasWRw6dIj+/ftz7do13N3dadu2LQDGxsZoa2ujr6+PtbW1su7ixYupWbMmc+bMUeatWbMGOzs7bt26RfnyL75JOjk5MW/ePGWZnTt3AlCpUqVc+1mxYkVu3bqV7+OytbVVGaSGDRvGgQMH2LJlC/Xq1ctXG6mpqaSmpqrkZWZnoqnQzHc/xAtGpkZoamkSHxOvkh8fE08ph1K51rEubYXlJ9U4utOfGb2nUdLehgGzBqFZTAvfBS++ZPy+dBv6xfVZcmQZWZlZaGhq8Ov3Gzi607+Qj0i8j3x9ffH09GTZsmW4uLiwYMECXF1dCQ4OxtLSMkf53377jfHjx7NmzRo++eQTbt26pVx79vHxea02X6ZIDSxNmzbl559/VskzNTUFQFtbm40bN1KtWjXKlCnDjz/+mGd7ly9f5siRIzkGK4DQ0FDlwFK7du1c62e/IvbV1tbOc/9/y8zMZM6cOWzZsoWHDx+SlpZGamoq+vr6+W7D29ub6dOnq+SVN3KiorFMs7wNCg0NEmLjWTp+MVlZWYReDcXUyowOA79UDiwN2zSicfsm+Az7gfBb9yhbpRx9p/YjLiqWI9v+esdHUHS8L4vsPj4+9OvXjz59+gCwbNky9u7dy5o1axg/fnyO8idPnqRBgwZ069YNAHt7e7p27cqZM2deu82XKVIDi4GBAY6Oji/dfvLkSQDi4uKIi4vDwMDgle0lJSXh5ubG3Llzc2wrWbKkyn7/zcnJCYCgoCA++eSTHHWDgoKoUaMGgHIq5N+DUHp6ukr577//noULF7JgwQKcnZ0xMDBg5MiRpKWlvbL//+bl5YWnp6dKXrcqnfNdX/wjMS6RzIxMTMxNVPJNzE148vhJrnWeRMeRmZFJVlaWMu/B7fuYWpqiVUyLjPQMek/sw+9Lt3H8j2MA3Au+h4WtJV8P7igDy0cit5kDHR2dHCcZpaWlceHCBby8vJR5GhoaNG/enFOnTuXa9ieffMKvv/7K2bNnqVevHnfu3GHfvn307Nnztdt8GVlj+X+hoaGMGjWKlStX4uLigru7u8ofuba2do7TgGvVqsX169ext7fH0dFRJb1qUHJ1dcXU1JT58+fn2LZ7925CQkLo3bs3ABYWFgBEREQoywQGBqrUCQgIoF27dvTo0YPq1atTrlw5tabS4MUvr5GRkUqSabDXk5GeQejV21RrUF2Zp1AoqNagOsEXb+ZaJ+h8ENZlSqqcCWhTzpa4qFgy0jMA0NbTUfmdBMjKykKhIX/Gb1NhrrF4e3tjbGyskry9vXP0ISYmhszMTKysrFTyraysiIyMzLXf3bp1Y8aMGTRs2JBixYrh4OBAkyZNmDBhwmu3+TJF6jcyNTWVyMhIlfT3m9mjRw9cXV3p06cPa9eu5cqVKyof/Pb29pw5c4awsDBiYmLIyspiyJAhxMXF0bVrV86dO0doaCgHDhygT58+r7wWxcDAgOXLl7Nr1y769+/PlStXCAsLY/Xq1fTu3Zt+/frRunVrABwdHbGzs2PatGmEhISwd+/eHAOSk5MThw4d4uTJkwQFBTFgwACioqIK500U+bJr1U4+7+pK06+bUcqxFAPnDEZXX5fDWw4DMPJHT3qOc1eW379hH8VNiuMxrT82ZW2o3awOHYd0ZN/6vcoy5w6fpeOwztRuVgfLUpb8z7U+7Tzac/qAet8mxfvLy8uLhIQElfTvCOJN+Pv7M2fOHJYuXcrFixfZvn07e/fuZebMmQXS/r8Vqamw/fv3q0xRAVSoUIFu3bpx79499uzZA7yYxlqxYgVdu3bl888/p3r16owePRp3d3cqV67M8+fPuXv3Lvb29gQEBDBu3Dg+//xzUlNTKVOmDC1btlQ5myc3X3/9NUeOHGH27Nk0atSIxMREAObOncvYsWOV5YoVK8amTZsYNGgQ1apVo27dusyaNYuOHTsqy0yaNIk7d+7g6uqKvr4+/fv3p3379iQk5LxeQrwdJ/44jpGpMd08e1DCogR3b9xhes8pJPz/gr65jYVK9BETEcO0nlPoO8WDhQcWExsVyx9rdrP959+VZVZOWU630T0YOGswxubGxEXFcWDjn/gu3Py2D69IK8yzgnOb9sqNubk5mpqaOb5ARkVFqZxg9G+TJ0+mZ8+eeHh4AODs7ExycjL9+/dn4sSJr9XmyyiyX7WCLN6alJQU2rVrx/379zl69KhyCuxdale6zbvughCFblf4HrXKn7b5spB6Av97tD3fZV1cXKhXrx4//fQT8GJatHTp0gwdOjTXhfbatWvTvHlzlTXhTZs20bdvX54+fYqmpqbabb5MkYpY3me6urrs2rWLBQsWcOzYMb766qt33SUhRC7el5tQenp64u7uTp06dahXrx4LFiwgOTlZeUZXr169sLW1Va7RuLm54ePjQ82aNXFxceH27dtMnjwZNzc3NDU189VmfsnA8h7R1dVV61uBEOLte19ON+7cuTOPHz9mypQpREZGUqNGDfbv369cfA8PD1eZkp80aRIKhYJJkybx8OFDLCwscHNzY/bs2fluM79kKky8lEyFiaJA3amwAOuvC6kn0CByW6G1/TZJxCKEEGrIyrtIkVekTjcWQghR+CRiEUIINWTzfqyxvM8kYhFCCFGgJGIRQgg1ZMnpTnmSiEUIIUSBkohFCCHUkCVrLHmSiEUIIUSBkohFCCHUIGeF5U0GFvFSydnpeRcSooiRCyTzJlNhQgghCpRELEIIoQaZCsubRCxCCCEKlEQsQgihBlljyZtELEIIIQqURCxCCKEGiVjyJhGLEEKIAiURixBCqEHOCsubDCxCCKGGLBlX8iRTYUIIIQqURCxCCKEGubtx3j6aiKVJkyaMHDnynbfxPpo2bRo1atR4190octq6u/HryfXsC/mDn3YvpEKNCq8sb2BkwLBZQ/A9/xv7bv/BuqOrqde0rnL7ryfXc/j+gRxp2KwhhX0oQqjlgxlYevfuTfv27VXytm3bhq6uLvPnz2f79u3MnDlTuc3e3p4FCxa83U6q4f79+3zzzTfY2Nigra1NmTJlGDFiBLGxse+6a6IANHFrzMDJ/dmwYCMDWw/hzo07fLdhNiZmxrmW1yqmxbzfvLEuZcWMgbPo08QDn3ELiIn85/dhSJvhdKzVRZnGdh0PwLE9x9/KMYkXsgsxfSw+2KmwVatWMWTIEJYtW0afPn3edXfUcufOHerXr0/58uXZtGkTZcuW5fr164wZM4Y///yT06dPY2pq+q67Kd7AV/2+ZN+m/RzYchCABV6LcPmsHi07u7J56ZYc5Vt2dqW4SXGGtx9FZkYmAFEPolTKJMQlqLzuMrgzD8Mecfn0lUI6CiFezwcTsfzbvHnzGDZsGJs3b1YOKv+exmrSpAn37t1j1KhRKBQKFIp/5kQDAgJo0qQJ+vr6lChRAldXV548eaLcnpWVxdixYzE1NcXa2ppp06ap7Ds+Ph4PDw8sLCwwMjKiWbNmXL58Wbn972mnDRs2YG9vj7GxMV26dOHp06fKMkOGDEFbW5uDBw/SuHFjSpcuTatWrTh8+DAPHz5k4sSJyrIKhYKdO3eq9MHExIR169YpX48bN47y5cujr69PuXLlmDx5Munpcsv7d0WrmBblnZ24eOKiMi87O5uLxy9RuXblXOvUb/E/blwIYvisoWy9uJmVh5fTdWgXNDRy/xPVKqZF8y+bsd/3QKEcg3i5rEJMH4sPbmAZN24cM2fOZM+ePXTo0CHXMtu3b6dUqVLMmDGDiIgIIiIiAAgMDOSzzz6jcuXKnDp1ihMnTuDm5kZmZqay7vr16zEwMODMmTPMmzePGTNmcOjQIeX2jh07Eh0dzZ9//smFCxeoVasWn332GXFxccoyoaGh7Ny5kz179rBnzx6OHj3Kd999B0BcXBwHDhxg8ODB6OnpqfTb2tqa7t274+vrS3Z2/gPj4sWLs27dOm7cuMHChQtZuXIlP/74Y77ri4JlbGqEppYmTx7Hq+Q/iXlCCYsSudYpWbokn7ZuhIamBhPcJ7Fx4W907P8V3Yd3zbV8A9dPMDQy5ODWgwXdfSHe2Ac1Ffbnn3+ya9cu/Pz8aNas2UvLmZqaoqmpSfHixbG2tlbmz5s3jzp16rB06VJlXpUqVVTqVqtWjalTpwLg5OTE4sWL8fPzo0WLFpw4cYKzZ88SHR2Njo4OAD/88AM7d+5k27Zt9O/fH3gR9axbt47ixYsD0LNnT/z8/Jg9ezYhISFkZ2dTqVKlXPteqVIlnjx5wuPHj7G0tMzX+zJp0iTlz/b29owePZrNmzczduzYfNUHSE1NJTU1VSUvKzsLDcUH993jg6ShoSA+Np4fxy0kKyuLkKu3MbM2o9OAr9mwYGOO8q26uHL2yDlio+JyaU0UpiyFnBWWlw/qU6NatWrY29szdepUkpKS1K7/d8SS1z7+rWTJkkRHRwNw+fJlkpKSMDMzw9DQUJnu3r1LaGioso69vb1yUPlvG3/LKyLR1tbO1zEB+Pr60qBBA6ytrTE0NGTSpEmEh4fnuz6At7c3xsbGKiks8Y5abYgXEuISyczIpISFiUp+CfMSPHn8JNc6sdFxPLjzkKysfyZEwkPCMbMyQ6uY6vc/S1tLajasyZ+b9xd430XeZPE+bx/UwGJra4u/vz8PHz6kZcuWKusW+fHfqafcFCtWTOW1QqFQ/rEnJSVRsmRJAgMDVVJwcDBjxozJVxuOjo4oFAqCgoJy3X9QUBAWFhaYmJgo6/53EPr3+smpU6fo3r07rVu3Zs+ePVy6dImJEyeSlpaW57H+m5eXFwkJCSrJ3qicWm2IFzLSM7h1NYRaDWoq8xQKBTUb1uDGhRu51rl+/gY29iVV1gNLlStFTFQsGekZKmVbdvqc+Jh4TvudKZwDEOINfVADC0CZMmU4evQokZGRrxxctLW1VdZO4EU04ufn99r7rlWrFpGRkWhpaeHo6KiSzM3N89WGmZkZLVq0YOnSpTx//lxlW2RkJBs3bqR3797KPAsLC+UaEUBISAjPnj1Tvj558iRlypRh4sSJ1KlTBycnJ+7du6f2seno6GBkZKSSZBrs9f2+cjutu7aixdfNKe1ox4g5w9DV02X//58lNu7HMfQd98/ZjH/8sofiJsUZMn0QtmVtcWlWj25Du7B7/R8q7SoUClw7fc6hbYfJyvyYlns/HLJ4n7cP8pPDzs4Of39/oqOjcXV1JTExMUcZe3t7jh07xsOHD4mJiQFefCs/d+4cgwcP5sqVK9y8eZOff/5ZuT0vzZs3p379+rRv356DBw8SFhbGyZMnmThxIufPn893/xcvXkxqaiqurq4cO3aM+/fvs3//flq0aEH58uWZMmWKsmyzZs1YvHgxly5d4vz58wwcOFAlInJyciI8PJzNmzcTGhrKokWL2LFjR777IgqH/x9HWT5rJb2/7cWy/UtxqOKAV8+JxMfEA2Bpa4Gp1T+nlD+OeMz4HhOpUL08Kw8uY8iMQWxfs5PNS3xV2q3VqCZWpaz4U84GE++xD2rx/t9KlSqFv78/TZs2xdXVNcfC84wZMxgwYAAODg6kpqaSnZ1N+fLlOXjwIBMmTKBevXro6enh4uJC1665n3nzXwqFgn379jFx4kT69OnD48ePsba25tNPP8XKyirffXdycuLcuXNMmzaNTp06ER0dTXZ2Nl9++SUbNmxAX19fWXb+/Pn06dOHRo0aYWNjw8KFC7lw4YJye9u2bRk1ahRDhw4lNTWVL774gsmTJ+c4TVq8fbvW72bX+t25bvu2U84TK4IuBjGs3chXtnnh2EWa27kWRPfEa5KbUOZNka3Oea2i0EydOhUfHx8OHTrE//73v3fdHQD5ABNFwuH76kV/m2y6F1JPoOujnGcAfog+2IjlYzN9+nTs7e05ffo09erVe+mFcUKId0tuQpk3GVjeIx/arWmEECI3MrAIIYQaZO0gbzKwCCGEGmTxPm8ykS+EEKJAScQihBBq+JguZCwsErEIIYQoUBKxCCGEGmTxPm8SsQghhChQErEIIYQa5KywvEnEIoQQokBJxCKEEGqQs8LyJgOLeKnnWel5FxKiiJGBJW8yFSaEEKJAycAihBBqyFYUXlLXkiVLsLe3R1dXFxcXF86ePfvSsk2aNEGhUORIX3zxhbJM7969c2xv2bKl2v2SqTAhhPgA+fr64unpybJly3BxcWHBggW4uroSHByMpaVljvLbt28nLS1N+To2Npbq1avTsWNHlXItW7Zk7dq1ytc6Ojpq900iFiGEUMP78sx7Hx8f+vXrR58+fahcuTLLli1DX1+fNWvW5Fre1NQUa2trZTp06BD6+vo5BhYdHR2VciVKlFCzZzKwCCHEeyM1NZXExESV9N/HrgOkpaVx4cIFmjdvrszT0NCgefPmnDp1Kl/7Wr16NV26dMHAwEAl39/fH0tLSypUqMCgQYOIjY1V+zhkYBFCCDUUZsTi7e2NsbGxSvL29s7Rh5iYGDIzM7GyslLJt7KyIjIyMs9jOHv2LNeuXcPDw0Mlv2XLlvzyyy/4+fkxd+5cjh49SqtWrcjMzMznu/OCrLEIIcR7wsvLC09PT5W811njyMvq1atxdnamXr16KvldunRR/uzs7Ey1atVwcHDA39+fzz77LN/tS8QihBBqyC7EpKOjg5GRkUrKbWAxNzdHU1OTqKgolfyoqCisra1f2f/k5GQ2b95M37598zzWcuXKYW5uzu3bt/Ms+28ysAghhBqyFIWX8ktbW5vatWvj5+f3T7+ysvDz86N+/fqvrLt161ZSU1Pp0aNHnvt58OABsbGxlCxZMv+dQwYWIYT4IHl6erJy5UrWr19PUFAQgwYNIjk5mT59+gDQq1cvvLy8ctRbvXo17du3x8zMTCU/KSmJMWPGcPr0acLCwvDz86Ndu3Y4Ojri6uqqVt9kjaWIaNKkCTVq1GDBggXvuitCfNDel1u6dO7cmcePHzNlyhQiIyOpUaMG+/fvVy7oh4eHo6GhGjsEBwdz4sQJDh48mKM9TU1Nrly5wvr164mPj8fGxobPP/+cmTNnqr3OU6Qilv9eVWpmZkbLli25cuVKoe1z2rRp1KhRI9dtJ0+epHXr1pQoUQJdXV2cnZ3x8fFR+wwM8X760r0d207/xl+h+1nxxxIq1aj4yvKGRgZ4zh7OrotbOXJnP5uOr6d+MxeVMubW5kxZ5MW+azv46/af/HJ4FRWrlS/MwxDvsaFDh3Lv3j1SU1M5c+YMLi7//L74+/uzbt06lfIVKlQgOzubFi1a5GhLT0+PAwcOEB0dTVpaGmFhYaxYsSLHmWf5UaQGFnhxOl1ERAQRERH4+fmhpaVFmzZt3no/duzYQePGjSlVqhRHjhzh5s2bjBgxglmzZtGlSxeys+U5dR+yz9o2YdjUQazx+YVvWg7g9o1QfDbOxcTMJNfyWsW0WLDpe0raWTOp/zS6furO3DHzeRz5WFmmuLEhy3YuIiMjk297eNG9aR8Wz1jG04Skt3RUAt6fCyTfZ0VuYPn3VaU1atRg/Pjx3L9/n8ePH5OWlsbQoUMpWbIkurq6lClTRuUccoVCwfLly2nTpg36+vpUqlSJU6dOcfv2bZo0aYKBgQGffPIJoaGhAKxbt47p06dz+fJlZZS0bt06kpOT6devH23btmXFihXUqFEDe3t7PDw8WL9+Pdu2bWPLli3Ai28dCoWC+Ph4ZT8CAwNRKBSEhYUBL27N0LVrV2xtbdHX18fZ2ZlNmza9tfdU5NS5X0f++G0f+7bsJyzkHt+P/5HU56m06dIq1/JturTCyMSI8d9M5ur560Q+iCLw9BVu37ijLNN9cFeiH0Uzx3MeQYE3ibgfydlj53l479HbOiwh8qXIDSz/lpSUxK+//oqjoyNmZmYsWrSI3bt3s2XLFoKDg9m4cSP29vYqdWbOnEmvXr0IDAykYsWKdOvWjQEDBuDl5cX58+fJzs5m6NChwIs50G+//ZYqVaooo6TOnTtz8OBBYmNjGT16dI4+ubm5Ub58ebUGhpSUFGrXrs3evXu5du0a/fv3p2fPnq+8IZ0oPFrFtKhQrTznjl9Q5mVnZ3P+xAWq1q6ca52GLT7h2oXrfDt7BH8EbmOD32p6DeumMkfe8PP63Lxyi5nLp7Ln8u+sPbAct25f5NqeKDyFebrxx6LILd7v2bMHQ0ND4MX53CVLlmTPnj1oaGgQHh6Ok5MTDRs2RKFQUKZMmRz1+/TpQ6dOnQAYN24c9evXZ/LkycqzJkaMGKE8K0NPTw9DQ0O0tLRUzi2/desWAJUqVcq1jxUrVlSWyQ9bW1uVQWrYsGEcOHCALVu25LgA6mVSU1Nz3DoiKzsLDUWR/u7xWkxMjdHS0iQu5olKftzjJ5R2KJ1rHZsyJanVoCYHdxxmdE8vSpW15ds5I9DU0mLtj7+8KFPahvY92+K7ciu/LNpIpRoVGDVjKBnp6fy5NedirBDvSpH71GjatCmBgYEEBgZy9uxZXF1dadWqFffu3aN3794EBgZSoUIFhg8fnuuZE9WqVVP+/PeilrOzs0peSkoKiYmJefblVeso2tra+T6mzMxMZs6cibOzM6amphgaGnLgwAHCw8Pz3UZut5J48PRevuuLN6PQUPAk9gnzxvoQfDUEv93+rF+0kfY93ZRlNDQU3LoWwvLvVhNy/Ta7N+5l9297VcqIwvc+XMfyvityA4uBgQGOjo44OjpSt25dVq1aRXJyMitXrqRWrVrcvXuXmTNn8vz5czp16sTXX3+tUr9YsWLKnxUKxUvzsrJevhTn5OQEQFBQUK7bg4KCKF/+xZk+f0+F/HsQSk9XfbLj999/z8KFCxk3bhxHjhwhMDAQV1dXlVtk58XLy4uEhASVVKp4zohN5C0+LoGMjExMzVXvCmtqUYK4x3G51omNiuP+nQcqvzf3QsIxtzJDq9iLiYXY6DjCboWp1Au7HY6Vjfpn7YjXJ4v3eStyA8t/KRQKNDQ0eP78OQBGRkZ07tyZlStX4uvry++//05cXO4fBvmhra2d4/RhV1dXTE1NmT9/fo7yu3fvJiQkhN69ewNgYWEBQEREhLJMYGCgSp2AgADatWtHjx49qF69OuXKlVNrKg1yv5WETIO9noz0DIKv3KJOw1rKPIVCQe2Gtbh24Uauda6ev0Ype1vlFxMAu3KliImMISM9A4Ar565R2sFOpV7pcqWIfKh6Ww8h3rUi98mRmppKZGQkkZGRBAUFMWzYMJKSknBzc8PHx4dNmzZx8+ZNbt26xdatW7G2tsbExOS192dvb8/du3cJDAwkJiaG1NRUDAwMWL58Obt27aJ///5cuXKFsLAwVq9eTe/evenXrx+tW7cGwNHRETs7O6ZNm0ZISAh79+7NMSA5OTlx6NAhTp48SVBQEAMGDMhxDyHxdvmu3Ipbty9o1fFzyjiWZvR3I9HV02Wv734AJi0cz8Dx/9xZdscvuzEyKc7IGUOxK1eK+p+50GtYN35fv+tfbW6jSq3K9BrWDVt7G1q0b0bb7l+wfd3Ot314RZos3uetyC3e79+/X3nfm+LFi1OxYkW2bt1KkyZNCAkJYd68eYSEhKCpqUndunXZt29fjqtX1fHVV1+xfft2mjZtSnx8PGvXrqV37958/fXXHDlyhNmzZ9OoUSPlmszcuXMZO3assn6xYsXYtGkTgwYNolq1atStW5dZs2apPJxn0qRJ3LlzB1dXV/T19enfvz/t27cnISHhtfst3ozfbn9MTE3wGN0HU4sShFwP5dse43jy/wv6VjaWZP9r2iv60WNGdR/HiGmDWX9oFTGRMWxdvZ1fl2xWlrl5ORgvjykMHO9B75G9iLgfwcKpSzm4wy/H/oV4lxTZciXeeyElJYV27dpx//59jh49qpwCe5ca2DZ7110QotAFPPxLrfKzy3QvpJ7AxHsbC63tt6nITYW9r3R1ddm1axe9evXi2LFj77o7Qgjx2orcVNj7TFdXl/Hjx7/rbgghXuFjOnursEjEIoQQokBJxCKEEGqQRem8ycAihBBqkKmwvMlUmBBCiAIlEYsQQqjhY7qnV2GRiEUIIUSBkohFCCHUkCXL93mSiEUIIUSBkohFCCHUIPFK3iRiEUIIUaAkYhEv9SwzNe9CQhQxch1L3iRiEUIIUaAkYhFCCDXIWWF5k4FFCCHUIMNK3mQqTAghRIGSiEUIIdQgi/d5k4hFCCFEgZKIRQgh1CCL93mTiEUIIUSBkohFCCHUIPFK3iRiEUIIUaBkYPkIKBQKdu7cCUBYWBgKhYLAwMB32ichPlZZhZg+FkV+YMnOzqZ58+a4urrm2LZ06VJMTEx48OBBge/X398fhUKhTHp6elSpUoUVK1ao3VZERAStWrV65X7i4+PfsMdCXZ36fMnec9s4HfYXv+xbQZWalV5Z3tDIkPHenhy8vIsz946wM2ATDT+rr9w+YPQ3XIoMUEnbj/9W2Ich/iO7EP99LIr8GotCoWDt2rU4OzuzfPlyBgwYAMDdu3cZO3YsP//8M6VKlSrQfaanpyt/Dg4OxsjIiOfPn/PHH38waNAgHBwc+Oyzz/LdnrW1dYH2T7y5z9t9xrfThjF73Pdcu3iDbv06sXSTD+0bduVJTHyO8lrFtFi2ZQFxMU8Y4zGJ6MjH2JSy5mlCkkq52zfvMLDjCOXrzMzMwj4UIdRW5CMWADs7OxYuXMjo0aO5e/cu2dnZ9O3bl88//5yaNWvSqlUrDA0NsbKyomfPnsTExCjr7t+/n4YNG2JiYoKZmRlt2rQhNDRUuf3vqSlfX18aN26Mrq4uGzduVG63tLTE2tqasmXLMnz4cMqWLcvFixeV2+3t7VmwYIFKf2vUqMG0adOUr/89FfZvYWFhNG3aFIASJUqgUCjo3bv3m71ZIl96DOjM9o1/sHvzPu7cCmP22O9JeZ5K+y5tci3fvmsbjEyM8Ow9nsvnrhJxP5ILpwK5deO2SrnMjExiH8cpU3xcwts4HPEvMhWWNxlY/p+7uzufffYZ33zzDYsXL+batWssX76cZs2aUbNmTc6fP8/+/fuJioqiU6dOynrJycl4enpy/vx5/Pz80NDQoEOHDmRlqf6ajB8/nhEjRhAUFJTrtFt2djb79+8nPDwcFxeXAjkmOzs7fv/9d+BFZBQREcHChQsLpG3xclrFtKhUrQJnjp1T5mVnZ3Pm+Hmq1amaa53Grg25cv4a472/5fDVP9jqv4FvhvdCQ0P1T7R0uVIcDNzFH2e2MHvJVKxtrQr1WIR4HUV+KuzfVqxYQZUqVTh27Bi///47y5cvp2bNmsyZM0dZZs2aNdjZ2XHr1i3Kly/PV199pdLGmjVrsLCw4MaNG1St+s+HyMiRI/nyyy+Vr4ODgwGU02ypqalkZWUxY8YMPv300wI5Hk1NTUxNTYEXkZGJiUmBtCterYSpCVpaWsQ9jlPJj30ch71j6Vzr2Ja2oW6DWvy5/SDDuo/GrmwpvLy/RauYJivmrwXg2sUbTBkxm3u3wzG3MmPAt9+wZtdSvm7ck2fJzwr9uMQLcoFk3mRg+RdLS0sGDBjAzp07ad++PRs3buTIkSMYGhrmKBsaGkr58uUJCQlhypQpnDlzhpiYGGWkEh4erjKw1KlTJ9d9Hj9+nOLFi5OamsrZs2cZOnQopqamDBo0qHAO8iVSU1NJTVV9sFdWdhYaCglq3wYNDQVxMU+YOXoeWVlZBF0JxtLanF6DuykHloC/TivLhwSFcvXiDfad/53P2zZj56Y976rrQuQgA8t/aGlpoaX14m1JSkrCzc2NuXPn5ihXsmRJANzc3ChTpgwrV67ExsaGrKwsqlatSlpamkp5AwODXPdXtmxZZSRRpUoVzpw5w+zZs5UDi4aGBtnZqt+Q/r34X1C8vb2ZPn26Sp6VQSlKGub+DVu83JO4eDIyMjC1MFXJN7MwJTY6Ltc6MdGxZKRnqEyh3g25h4WVOVrFtMhIz8hRJykxifA797ErW7Anl4hXk3glb/J19BVq1arF9evXsbe3x9HRUSUZGBgQGxtLcHAwkyZN4rPPPqNSpUo8efLkjfapqanJ8+fPla8tLCyIiIhQvk5MTOTu3bv5bk9bWxvI++whLy8vEhISVJKVgXxgvY6M9AyCrgTj0uifKFWhUFCvYW2unL+Wa53As1exK1sKhUKhzCtdzo7HkTG5DioAevp6lCpjS0xUTK7bhXhXZGB5hSFDhhAXF0fXrl05d+4coaGhHDhwgD59+pCZmUmJEiUwMzNjxYoV3L59m7/++gtPT0+19hEdHU1kZCT37t1j69atbNiwgXbt2im3N2vWjA0bNnD8+HGuXr2Ku7s7mpqa+W6/TJkyKBQK9uzZw+PHj0lKSsq1nI6ODkZGRipJpsFe36/LfenQ3Q23Tq0o61SGCXNHo6evy67NewGY+dMkhk0YqCy/df0OjEyMGDtrJKXL2dGweX36juiF79rflWVGTR1C7fo1KGlnTfU6VfFZ601WVib7dx5+68dXlGWRXWjpYyFTYa9gY2NDQEAA48aN4/PPPyc1NZUyZcrQsmVLNDQ0UCgUbN68meHDh1O1alUqVKjAokWLaNKkSb73UaFCBeDFFJydnR0DBgxQOZXYy8uLu3fv0qZNG4yNjZk5c6ZaEYutrS3Tp09n/Pjx9OnTh169erFu3bp81xev5+AuP0qYmTBorAdmFqYEXw9hSNdviYt5EdFa21qRlfXPB0nUo2iGdBnFtzNGsOWv9URHxvDbyq2sW/yrsoxVSUu8f56OcQkjnsTGE3j2Cr1aD+BJbPzbPrwi7WM6LbiwKLL/O4EvxP+rad3gXXdBiEJ3KTJArfL97DsWUk9gZdjWQmv7bZKIRQgh1PAx3XqlsMgkuhBCiAIlA4sQQqjhfbqly5IlS7C3t0dXVxcXFxfOnj370rJNmjRRufHt3+mLL75QlsnOzmbKlCmULFkSPT09mjdvTkhIiNr9koFFCCE+QL6+vnh6ejJ16lQuXrxI9erVcXV1JTo6Otfy27dvJyIiQpmuXbuGpqYmHTv+s2Y0b948Fi1axLJlyzhz5gwGBga4urqSkpKiVt9kYBFCCDW8L7fN9/HxoV+/fvTp04fKlSuzbNky9PX1WbNmTa7lTU1Nsba2VqZDhw6hr6+vHFiys7NZsGABkyZNol27dlSrVo1ffvmFR48e5XqT21eRgUUIId4TqampJCYmqqT/3moJIC0tjQsXLtC8eXNlnoaGBs2bN+fUqVP52tfq1avp0qWL8q4gd+/eJTIyUqVNY2NjXFxc8t2msi9qlRZCiCKuMNdYvL29MTY2Vkne3t45+hATE0NmZiZWVqp3t7aysiIyMjLPYzh79izXrl3Dw8NDmfd3vddt89/kdGMhhFBDViFe+ufl5ZXj7h06OjoFvp/Vq1fj7OxMvXr1CrxtkIhFCCHeG7ndWim3gcXc3BxNTU2ioqJU8qOiovJ8omxycjKbN2+mb9++Kvl/13udNv9LBhYhhFBDdiGm/NLW1qZ27dr4+fkp87KysvDz86N+/fqvrLt161ZSU1Pp0aOHSn7ZsmWxtrZWaTMxMZEzZ87k2eZ/yVSYEEJ8gDw9PXF3d6dOnTrUq1ePBQsWkJycTJ8+fQDo1asXtra2OdZoVq9eTfv27TEzM1PJVygUjBw5klmzZuHk5ETZsmWZPHkyNjY2tG/fXq2+ycAihBBqeF/uQty5c2ceP37MlClTiIyMpEaNGuzfv1+5+B4eHp7j0dbBwcGcOHGCgwcP5trm2LFjSU5Opn///sTHx9OwYUP279+Prq6uWn2Tm1CKl5KbUIqiQN2bUHYr06GQegK/3dtRaG2/TRKxiJfKlBuEC5GD3IQyb7J4L4QQokBJxCKEEGqQOD5vMrAIIYQa3pfF+/eZTIUJIYQoUBKxCCGEGmTxPm8SsQghhChQErEIIYQaZPE+bxKxCCGEKFASsQghhBrkZiV5k4hFCCFEgZKIRQgh1CDXseTtg4xYIiMjGTFiBI6Ojujq6mJlZUWDBg34+eefefbs2bvunloePHiAtrY2VatWfdddEQWsc5+v+PPcds6F+bNx3yqq1qz8yvLFjQyZ4D0av8t/cP7eUXYH+NLws3+egzFodF+uRJ5SSbuOby7swxD/UZiPJv5YfHARy507d2jQoAEmJibMmTMHZ2dndHR0uHr1KitWrMDW1pa2bduq3W5mZiYKhSLHbaYL27p16+jUqRPHjh3jzJkzuLi4vLJ8eno6xYoVe0u9E6/Ltd1njJk2nJnj5nH14nV69OvMsk0/0rZhF+JinuQor1VMi+VbFhIX84RvPSYSHRlNyVIleZrwVKXc7Zuh9Os4XPk6MzOz0I9FCHV9cBHL4MGD0dLS4vz583Tq1IlKlSpRrlw52rVrx969e3FzcwPAx8cHZ2dnDAwMsLOzY/DgwSQlJSnbWbduHSYmJuzevZvKlSujo6NDeHg4586do0WLFpibm2NsbEzjxo25ePGiSh9u3rxJw4YN0dXVpXLlyhw+fBiFQsHOnTuVZe7fv0+nTp0wMTHB1NSUdu3aERYWptJOdnY2a9eupWfPnnTr1o3Vq1erbA8LC0OhUODr60vjxo3R1dVl48aNAKxatYpKlSqhq6tLxYoVWbp0qUrdcePGUb58efT19SlXrhyTJ08mPT39Td9+kU+9BnTl94272bV5L3duhTFz7DyeP0+lfZc2uZbv0NUNYxMjRvYeR+C5Kzy6H8mFU5e4deO2SrmMjExiH8cpU3xcwts4HPEv2YX472PxQQ0ssbGxHDx4kCFDhmBgYJBrGYVCAYCGhgaLFi3i+vXrrF+/nr/++ouxY8eqlH327Blz585l1apVXL9+HUtLS54+fYq7uzsnTpzg9OnTODk50bp1a54+ffHNMTMzk/bt26Ovr8+ZM2dYsWIFEydOVGk3PT0dV1dXihcvzvHjxwkICMDQ0JCWLVuSlpamLHfkyBGePXtG8+bN6dGjB5s3byY5OTnHMY0fP54RI0YQFBSEq6srGzduZMqUKcyePZugoCDmzJnD5MmTWb9+vbJO8eLFWbduHTdu3GDhwoWsXLmSH3/88fXeeKEWrWJaVKpWgdPHzinzsrOzOXP8HNXr5D7l2cS1IZfPX2OC92iOXN3Ldv9f8RjuniOCLlPOjsOBu9l3ZhveS6ZhbWtVqMcixOv4oKbCbt++TXZ2NhUqVFDJNzc3JyUlBYAhQ4Ywd+5cRo4cqdxub2/PrFmzGDhwoMo3+/T0dJYuXUr16tWVec2aNVNpe8WKFZiYmHD06FHatGnDoUOHCA0Nxd/fH2trawBmz55NixYtlHV8fX3Jyspi1apVyoFu7dq1mJiY4O/vz+effw68eERoly5d0NTUpGrVqpQrV46tW7fSu3dvlT6MHDmSL7/8Uvl66tSpzJ8/X5lXtmxZbty4wfLly3F3dwdg0qRJKsc/evRoNm/enGNw/VtqaiqpqakqeVnZWWgoPqjvHu+FEqYmaGlpEfs4TiU/9nEcZR3L5FqnVGlb6jWozd7tBxnc3ZPSZUsx0XsMWsU0WTZ/DQBXL15n0ohZhN2+h4WVOQO/7cu6XT/zZeMePEv+sNYWP2SyeJ+3D2pgeZmzZ8+SlZVF9+7dlR+Ohw8fxtvbm5s3b5KYmEhGRgYpKSk8e/YMfX19ALS1talWrZpKW1FRUUyaNAl/f3+io6PJzMzk2bNnhIeHAy8e7WlnZ6ccVADq1aun0sbly5e5ffs2xYsXV8lPSUkhNDQUgPj4eLZv386JEyeU23v06MHq1atzDCx16tRR/pycnExoaCh9+/alX79+yvyMjAyMjY2Vr319fVm0aBGhoaEkJSWRkZGBkZHRS99Db29vpk+frpJnaWCLlaHdS+uIgqPQUBAX84QZo78jKyuLoCvBWFpb0Htwd+XAcuKv08ryIUGhXL14nf3nd+Da9jN2bPrjXXVdiBw+qIHF0dERhUJBcHCwSn65cuUA0NPTA16sTbRp04ZBgwYxe/ZsTE1NOXHiBH379iUtLU05sOjp6Skjir+5u7sTGxvLwoULKVOmDDo6OtSvX19lCisvSUlJ1K5dW7ke8m8WFhYA/Pbbb6SkpKgs1mdnZ5OVlcWtW7coX768Mv/f035/rxOtXLkyx0K/pqYmAKdOnaJ79+5Mnz4dV1dXjI2N2bx5M/Pnz39pn728vPD09FTJ+8SpxUtKi1d5EhdPRkYGZhamKvlmFqbERMfmWicmOpaM9Ayysv45N+huSBgWVuZoFdMiIz0jR52niUncuxOOXdlSBXsA4pXkAsm8fVDzHGZmZrRo0YLFixfnuhbxtwsXLpCVlcX8+fP53//+R/ny5Xn06FG+9hEQEMDw4cNp3bo1VapUQUdHh5iYGOX2ChUqcP/+faKiopR5586dU2mjVq1ahISEYGlpiaOjo0r6O6pYvXo13377LYGBgcp0+fJlGjVqxJo1a17aPysrK2xsbLhz506OtsuWLQvAyZMnKVOmDBMnTqROnTo4OTlx7969Vx63jo4ORkZGKkmmwV5PRnoGQVeCcWn0T6SpUChwaViHy+ev5Von8OwV7MqWUvmiU6ZcaaIjH+c6qADo6ethV6YUMVExuW4X4l354D45li5dSkZGBnXq1MHX15egoCCCg4P59ddfuXnzJpqamjg6OpKens5PP/3EnTt32LBhA8uWLctX+05OTmzYsIGgoCDOnDlD9+7dlZEQQIsWLXBwcMDd3Z0rV64QEBCgXM/4+0Ohe/fumJub065dO44fP87du3fx9/dn+PDhPHjwgMDAQC5evIiHhwdVq1ZVSV27dmX9+vVkZOT+YQIwffp0vL29WbRoEbdu3eLq1ausXbsWHx8f5TGEh4ezefNmQkNDWbRoETt27Hjdt1y8hl+Wb+Kr7m1p26k1ZZ3KMGnuWPT0ddm5eQ8As3+awvAJg5Tlfddvx9jEiHGzRlGmnB2Nmn+Cxwh3fNf+rizz7dRh1K5fExs7a6rXcWbB2u/IzMrkz52H3vrxFWVyHUvePriBxcHBgUuXLtG8eXO8vLyoXr06derU4aeffmL06NHMnDmT6tWr4+Pjw9y5c6latSobN27E29s7X+2vXr2aJ0+eUKtWLXr27Mnw4cOxtLRUbtfU1GTnzp0kJSVRt25dPDw8lGeF6erqAqCvr8+xY8coXbo0X375JZUqVaJv376kpKRgZGTE6tWrqVy5MhUrVsyx/w4dOhAdHc2+ffte2kcPDw9WrVrF2rVrcXZ2pnHjxqxbt04ZsbRt25ZRo0YxdOhQatSowcmTJ5k8eXK+32Px5g7s8mP+jMUMHuvB1sO/ULGqE4O6jlJew2Jta4WFlZmyfNSjaAZ2GUnVGpXY9tcGxs8axcaVW1j90wZlGcuSFsz9eTq7T/jyw4pZxD9JoEfrfjyJjX/bh1ekyenGeVNky4ThGwsICKBhw4bcvn0bBweHd92dAlPNun7ehYT4wF2JPKVW+c/tWhZST+Dg/f2F1vbb9EEt3r8vduzYgaGhIU5OTty+fZsRI0bQoEGDj2pQEULkTk43zpsMLK/h6dOnjBs3jvDwcMzNzWnevPkrz7gSQoiiRAaW19CrVy969er1rrshhHgHZPUgbx/c4r0QQoj3m0QsQgihBlljyZtELEIIIQqURCxCCKGGj+l6k8IiA4sQQqghSxbv8yRTYUIIIQqURCxCCKEGiVfyJhGLEEKIAiURixBCqEFON86bDCzipZ6my+NuhRDqk4FFCCHUIBFL3mSNRQghRIGSiEUIIdQgN6HMm0QsQgghCpRELEIIoQZZY8mbDCxCCKEGuVdY3mQqTAghRIGSiEUIIdQgi/d5k4hFCCFEgZKIRQgh1CCL93mTiKUQNGnShJEjRypf29vbs2DBgreyb39/fxQKBfHx8W9lf0II8V8ysLxE7969USgUOdLt27ffuG17e3tle5qamtjY2NC3b1+ePHmiVjv/HcDE+6Vn384cv7SPmw/PsuPgr1SvVfWV5YsbFWfGPC/OXD/MzUfn+OvMbpo0b5hr2YEjvuFu7GUmzx5TGF0Xr5CdnV1oSV1LlizB3t4eXV1dXFxcOHv27CvLx8fHM2TIEEqWLImOjg7ly5dn3759yu3Tpk3L8ZlXsWJFtfslU2Gv0LJlS9auXauSZ2FhUSBtz5gxg379+pGZmcmtW7fo378/w4cPZ8OGDQXSvni3vmjvysSZo5k0ehaBF67yzYDurN/6M5+5tCM2Ji5H+WLFtNiwfRmxj+MY3Gc0kRHRlLIrSWLC0xxlq9WsQjf3rwm6Fvw2DkW8p3x9ffH09GTZsmW4uLiwYMECXF1dCQ4OxtLSMkf5tLQ0WrRogaWlJdu2bcPW1pZ79+5hYmKiUq5KlSocPnxY+VpLS/1hQiKWV9DR0cHa2lol9e3bl/bt26uUGzlyJE2aNFGr7eLFi2NtbY2trS1NmzbF3d2dixcvKrfHxsbStWtXbG1t0dfXx9nZmU2bNim39+7dm6NHj7Jw4ULlN4uwsDDl9gsXLlCnTh309fX55JNPCA6WD6G3yWNwT3w3bGfbb7u4HXyHid/O4vnzFDp2b59r+Y7dO2BiYsyAnqO4cDaQh/cfcebkBYKu31Ipp2+gx4Jl3niNmk5CfOJbOBLxX1lkF1pSh4+PD/369aNPnz5UrlyZZcuWoa+vz5o1a3Itv2bNGuLi4ti5cycNGjTA3t6exo0bU716dZVyWlpaKp955ubmar9HMrC8Bx4+fMgff/yBi4uLMi8lJYXatWuzd+9erl27Rv/+/enZs6cy1F24cCH169enX79+REREEBERgZ2dnbL+xIkTmT9/PufPn0dLS4tvvvnmrR9XUVWsmBZVq1fixNHTyrzs7GwCjp6mVt1qudZp3rIxl85fYcY8L84F/cX+E78zeFRfNDRU/0RnzJvAX4eOEXD0TKEeg3i57EL8l5qaSmJiokpKTU3N0Ye0tDQuXLhA8+bNlXkaGho0b96cU6dO5drv3bt3U79+fYYMGYKVlRVVq1Zlzpw5ZGZmqpQLCQnBxsaGcuXK0b17d8LDw9V+j2RgeYU9e/ZgaGioTB07diywtseNG4ehoSF6enqUKlUKhUKBj4+PcrutrS2jR4+mRo0alCtXjmHDhtGyZUu2bNkCgLGxMdra2ujr6yu/WWhqairrz549m8aNG1O5cmXGjx/PyZMnSUlJKbD+i5crYVYCLS0tYqJjVfJjomOxsMz9219p+1K0cmuOhqYmfboM4acfVuAxuBdDv+2nLNOmQ0uqVKvEvJmLCrX/4t3x9vbG2NhYJXl7e+coFxMTQ2ZmJlZWVir5VlZWREZG5tr2nTt32LZtG5mZmezbt4/Jkyczf/58Zs2apSzj4uLCunXr2L9/Pz///DN3796lUaNGPH2ac0r2VWSN5RWaNm3Kzz//rHxtYGCAl5dXgbQ9ZswYevfuTXZ2Nvfv32fChAl88cUXHDt2DE1NTTIzM5kzZw5btmzh4cOHpKWlkZqair6+fr7ar1btn2/GJUuWBCA6OprSpUvnWj41NTXHN6Ps7CwUCvnu8TZoKDSIiYljwqgZZGVlce1yENYlLek/1J1F3y+npI0VU+eMpedXA0hLTXvX3S3SsgrxAkkvLy88PT1V8nR0dAqk7aysLCwtLVmxYgWamprUrl2bhw8f8v333zN16lQAWrVqpSxfrVo1XFxcKFOmDFu2bKFv37753pcMLK9gYGCAo6OjSp6GhkaOszfS09PVbtvc3FzZtpOTEwsWLKB+/focOXKE5s2b8/3337Nw4UIWLFiAs7MzBgYGjBw5krS0/H2oFCtWTPmzQqEAXvxivYy3tzfTp09XyTPWtaSEvrW6h1bkPYl9QkZGBuaWZir55pZmPI6OybVOdNRj0jMyVP6Pbt+6g6W1xYuptRqVMbc0448jm5XbtbS0qPdJbXp5dKFCybqv/P8VHwYdHZ18DSTm5uZoamoSFRWlkh8VFYW1de5/syVLlqRYsWIqMxuVKlUiMjKStLQ0tLW1c9QxMTGhfPnyap8NK19H1WRhYUFERIRKXmBg4Bu3+/d/9vPnzwEICAigXbt29OjRg+rVq1OuXDlu3VJdyNXW1s4xP/q6vLy8SEhIUEkmejnPLBF5S0/P4NrlIBp8+s+amUKh4JNPXbh47kqudc6fDcS+rJ3ySwBAWYcyREVGk56ewcljZ3Bt8BVfNO6sTJcvXWPXtn180bizDCpvUWGuseSXtrY2tWvXxs/PT5mXlZWFn58f9evXz7VOgwYNuH37tsrvyq1btyhZsmSugwpAUlISoaGhylmP/JKBRU3NmjXj/Pnz/PLLL4SEhDB16lSuXbumdjtPnz4lMjKSiIgIzp49y5gxY7CwsOCTTz4BXkQxhw4d4uTJkwQFBTFgwIAc307s7e05c+YMYWFhxMTEvNGHi46ODkZGRipJpsFe36qlG+jS80u+7OKGQ/myzPphEvr6emz7bScA85fOYszk4cryG9dswbiEMVO9x1HWoQxNWzRiyCgPNqz2BSA56Rm3bt5WSc+Tn/MkLp5bN9/82irx4fH09GTlypWsX7+eoKAgBg0aRHJyMn369AGgV69eKlP3gwYNIi4ujhEjRnDr1i327t3LnDlzGDJkiLLM6NGjOXr0KGFhYZw8eZIOHTqgqalJ165d1eqbTIWpydXVlcmTJzN27FhSUlL45ptv6NWrF1evXlWrnSlTpjBlyhTgRRRUt25dDh48iJnZi+mTSZMmcefOHVxdXdHX16d///60b9+ehIQEZRujR4/G3d2dypUr8/z5c+7evVtwByreyN6dBzAzL4Hn+MGYW5oTdC2Y3p0GE/P4xTUsNrbWKl8EIh5F4f71ICbPHsOfx7YSGRHN2hUbWbZw7ct2Id6RwlxjUUfnzp15/PgxU6ZMITIykho1arB//37lgn54eLjKWYV2dnYcOHCAUaNGUa1aNWxtbRkxYgTjxo1Tlnnw4AFdu3YlNjYWCwsLGjZsyOnTp9W+fk+RLbfqFC9R1qx63oWE+MDdjb2sVvlKlvUKqScQFP3qK+c/FBKxCCGEGuRBX3mTgUUIIdTwvkyFvc9kdVYIIUSBkohFCCHUIFNheZOIRQghRIGSiEUIIdQgayx5k4hFCCFEgZKIRQgh1CBrLHmTiEUIIUSBkohFCCHUkJ0tN/zMiwwsQgihBnUfIVwUyVSYEEKIAiURixBCqEHu25s3GVjES91/mvvTDoUQ4lVkYBFCCDXIGkveZI1FCCFEgZKIRQgh1CBrLHmTiEUIIUSBkohFCCHUIDehzJsMLEIIoQa5V1jeZCpMCCFEgZKIRQgh1CCL93mTiEUIIUSBkohFCCHUIBdI5k0ilpdQKBTs3LnzXXdD/F979x4VZbkvcPw73AZkYBAQRg4IxE0Rb5gauRUwXZqSFlLq8oR327gL06JknQMICmhbRVEz8wJssp1pZsb2uLygphgnRMBLbjVL3SqCcVJEXQPEe/5wNctRBNHBpP37rPWu5Tzvc/m9rzC/eZ7nXYwQos156hOLSqVq8pg7d+4D2547dw6VSkVpaalJ47CwsKBTp07Mnj0bvV7/2H03xcvLi6VLl95XvmbNGnr06IFGo8HBwYFevXqRnp5uOD937txG79fu3btbNV7RtJg/T+CH04XUVJ/l0MGv6fNszybra7X2ZC5L5V/nj3Dzxo98f+IALw4b9GSCFY1SFKXVjj+Kp34prLy83PDvjRs3kpiYyKlTpwxlGo3micWSlZXFsGHDqKuro6ysjEmTJmFra8u8efOeWAwA69ev5+233yYzM5PQ0FD0ej1Hjx7l+PHjRvW6du16XyJxdHR8kqGKu7z66kgW/TWJGX+Zw3dFJcS+NZXt/9hAYNBArl6tuq++paUlO/7n71ytrGLM2OlcunwFz07uXLte/TtEL8TDe+pnLDqdznBotVpUKpXhtYuLC0uWLMHd3R21Wk3Pnj3ZsWOHoa23tzcAvXr1QqVSERYWBkBRURFDhgzB2dkZrVZLaGgoR44caTYWBwcHdDodHh4eREREMGrUKKN2ZWVlhIeHY2dnh729Pb179+bw4cMAZGdn4+DgQF5eHgEBAbRr146oqChu3bpFTk4OXl5etG/fntjYWH799VcAwsLCOH/+PLNmzTLMOAC2bdvGa6+9xpQpU/D19aVr166MGzeO1NRUo3gtLCyM7p9Op8PKyurR/zPEY5k1cxpr131Kzt8+5+TJM8z4yxxu3brNpIljG60/aeJYHNs7EBk1mUPfHub8+Yt8c6CQo0e/f8KRi7s1KEqrHX8UT31iacqyZctYvHgxixYt4ujRowwdOpSRI0dy5swZAL777jsAdu/eTXl5OVu2bAHgxo0bTJgwgYMHD1JYWIifnx/Dhw/nxo0bDz326dOnyc/Pp1+/foay8ePH4+7uTlFREcXFxcyZMwdLS0vD+Vu3bpGZmclnn33Gjh072LdvH6+88grbt29n+/bt5Obmsnr1ajZv3gzAli1bcHd3JyUlhfLycsPsTafTUVhYyPnz5x/vBoonxtLSkuDg7uzJP2AoUxSFPfkHee653o22eSliCIX/W8zyzFQu/auU0pI9zHn/LczM2vSvbZsnS2HNe+qXwpqyaNEi3n//fcaOvfOJb+HChezdu5elS5eycuVKOnToAICTkxM6nc7QbtAg4zXqjz/+GAcHB/bv309ERMQDxxs3bhzm5ubU19ej1+uJiIggPj7ecP7ChQvExcXRuXNnAPz8/Iza19XVsWrVKnx8fACIiooiNzeXiooKNBoNgYGBhIeHs3fvXsaMGYOjoyPm5ubY2dkZxZ+UlERkZCReXl74+/sTEhLC8OHDiYqKMnrTOXbsmNFSYWBgoCHZiifL2dkRCwsLKiuMv+OmsvIqnQN8Gm3j/Ywn4Z79+fTvX/LSyNfx8fVmRWYalpYWzJuf8STCFuKRtNmPPtXV1Vy+fJn+/fsblffv35+TJ0822baiooJp06bh5+eHVqvF3t6empoaLly40GS7jIwMSktLKSsrIy8vj9OnT/P6668bzs+ePZupU6cyePBgFixYwNmzZ43at2vXzpBUAFxdXfHy8jJ683d1daWysrLJODp27Mi3337LsWPHmDlzJvX19UyYMIFhw4bR0NBgqBcQEEBpaanh+OKLLx7Yp16vp7q62uj4I32CaovMzMyorKzizzHvcaTkGJs2bSN9QSbTp73efGPRahpQWu34o2izieVxTJgwgdLSUpYtW8ahQ4coLS3FycmJ2traJtvpdDp8fX0JCAhgxIgRJCcns3HjRn744QfgzpNYJ06cYMSIEeTn5xMYGMiXX35paH/3shjcedKssbK7k0NTgoKCmDFjBp988gm7du1i165d7N+/33DeysoKX19fw+Hh4fHAvtLT09FqtUaH0vDwS4OiaT///H/U19fj4upsVO7i0oErFVcbbXOlvIIzZ340+nn45z/P0LGj630/N0I8TdpsYrG3t8fNzY2CggKj8oKCAgIDAwEMG9W/bYbfXSc2Npbhw4fTtWtX1Go1P//c8q/hNTc3B+D27duGMn9/f2bNmsXOnTuJjIwkKyurxf3ezcrK6r74G/PbNd+8efORxomPj+f69etGh8rM7pH6Everq6vjyJGjDAr/k6FMpVIxKPxPFBYWN9rm0LeH8fHxMjy0AeDn9wyXL1+hrq6u1WMWjZM9lua16T2WuLg4kpKS8PHxoWfPnmRlZVFaWsqGDRsAcHFxwcbGhh07duDu7o61tTVarRY/Pz9yc3N59tlnqa6uJi4uDhsbm2bHu3btGleuXKGhoYEzZ86QkpKCv78/Xbp04fbt28TFxREVFYW3tzcXL16kqKiI0aNHP9Y1enl58c033zB27FjUajXOzs7ExMTg5ubGoEGDcHd3p7y8nPnz59OhQwdCQkIeaRy1Wo1arTYqu/sNTTy+jGVryFqXQfGRoxQVlRD71jRsbW3IztkIQNb6ZVy+XM5//fcCAD5a/TdmxEwkY0kKKz/Mws/Xmznvv8WKlet/z8sQolltOrHExsZy/fp13nnnHSorKwkMDGTbtm2GTXMLCwsyMzNJSUkhMTGRAQMGsG/fPtatW8f06dMJDg7Gw8ODtLQ03n333WbHmzRpEoDhkeeBAweSlpaGhYUFDQ0NVFVVER0dTUVFBc7OzkRGRpKcnPxY15iSksIbb7yBj48Per0eRVEYPHgw69evZ9WqVVRVVeHs7ExISAh79uzBycnpscYTrWfTpm10cHZkbuK76HQdKCs7wYiI/6Sy8s5suZOHm9Gy18WLlxk+YjyLF82lpHgXly5dYfmKdXzw15W/1yUI5PtYHoZK+SPNv4RJWVj9x+8dghCtrr72Uovqa9p5t1IkUHPrp1br+0lq0zMWIYR40uSLvponiUUIIVpAlsKa12afChNCCPF0khmLEEK0gGxLN09mLEIIIUxKZixCCNECsnnfPJmxCCGEMCmZsQghRAvIHkvzZMYihBDCpGTGIoQQLSAzluZJYhFCiBaQtNI8WQoTQghhUvJHKIV4Suj1etLT04mPj7/vKwyEaEsksQjxlKiurkar1XL9+nXs7e1/73CEeGSyFCaEEMKkJLEIIYQwKUksQgghTEoSixBPCbVaTVJSkmzcizZPNu+FEEKYlMxYhBBCmJQkFiGEECYliUUIIYRJSWIRohVkZ2fj4ODQ6uOcO3cOlUpFaWlpq48lxMOSxCJEI65evUpMTAydOnVCrVaj0+kYOnQoBQUFrTaml5cXKpUKlUqFra0twcHBbNq0qck2Hh4elJeXExQU1GpxCdFSkliEaMTo0aMpKSkhJyeH06dPs23bNsLCwqiqqmrVcVNSUigvL6ekpIQ+ffowZswYDh061Gjd2tpazM3N0el0WFjIHyoXTw9JLELc49q1axw4cICFCxcSHh6Op6cnffv2JT4+npEjRwKwZMkSunXrhq2tLR4eHsyYMYOampom+/3qq68IDg7G2tqaZ555huTkZOrr643q2NnZodPp8Pf3Z+XKldjY2PD1118Dd2Y08+bNIzo6Gnt7e6ZPn97oUtiJEyeIiIjA3t4eOzs7BgwYwNmzZw3n165dS5cuXbC2tqZz5858+OGHJrpzQtwhiUWIe2g0GjQaDVu3bkWv1zdax8zMjMzMTE6cOEFOTg75+fm89957D+zzwIEDREdHM3PmTL7//ntWr15NdnY2qampD2xjYWGBpaUltbW1hrJFixbRo0cPSkpKSEhIuK/NpUuXGDhwIGq1mvz8fIqLi5k8ebIhgW3YsIHExERSU1M5efIkaWlpJCQkkJOT87C3R4jmKUKI+2zevFlp3769Ym1trTz//PNKfHy8UlZW9sD6mzZtUpycnAyvs7KyFK1Wa3j9wgsvKGlpaUZtcnNzlY4dOxpee3p6KhkZGYqiKIper1fS0tIUQMnLyzOcf/nll436+OmnnxRAKSkpURRFUeLj4xVvb2+ltra20Th9fHyUTz/91Khs3rx5SkhIyAOvTYiWksQixAPcvn1b2blzp5KSkqKEhIQo5ubmSlZWlqIoirJr1y5l0KBBipubm6LRaBRra2sFUG7evKkoyv2JxdnZWbG2tlZsbW0Nx71tPD09FSsrK8XW1lYxNzdXtFqtsmDBAkMfnp6eyvz5841ivDexvPjii0p0dHSj11NTU6MAio2NjVEcarVacXFxMdFdE0JRZMdPiAewtrZmyJAhDBkyhISEBKZOnUpSUhJhYWFEREQQExNDamoqjo6OHDx4kClTplBbW0u7du3u66umpobk5GQiIyMbHec3cXFxTJw4EY1Gg6urKyqVyqiura1tkzHb2Ng88Nxve0Br1qyhX79+RufMzc2b7FeIlpDEIsRDCgwMZOvWrRQXF9PQ0MDixYsxM7uzTfn555832TY4OJhTp07h6+vbZD1nZ+dm6zSle/fu5OTkUFdXh6WlpdE5V1dX3Nzc+PHHHxk/fvwjjyFEcySxCHGPqqoqXn31VSZPnkz37t2xs7Pj8OHDfPDBB4waNQpfX1/q6upYvnw5L730EgUFBXz00UdN9pmYmEhERASdOnUiKioKMzMzysrKOH78OPPnzzdZ7G+++SbLly9n7NixxMfHo9VqKSwspG/fvgQEBJCcnExsbCxarZZhw4ah1+s5fPgwv/zyC7NnzzZZHOLfmzwVJsQ9NBoN/fr1IyMjg4EDBxIUFERCQgLTpk1jxYoV9OjRgyVLlrBw4UKCgoLYsGED6enpTfY5dOhQ8vLy2LlzJ3369OG5554jIyMDT09Pk8bu5OREfn4+NTU1hIaG0rt3b9asWWOYvUydOpW1a9eSlZVFt27dCA0NJTs7G29vb5PGIf69yZ/NF0IIYVIyYxFCCGFSkliEEEKYlCQWIYQQJiWJRQghhElJYhFCCGFSkliEEEKYlCQWIYQQJiWJRQghhElJYhFCCGFSkliEEEKYlCQWIYQQJiWJRQghhEn9P4OX0xUibkg/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 300x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_corr_features = corrmat.index[abs(corrmat[\"SalePrice\"])>=0.6]\n",
    "plt.figure(figsize=(3,6))\n",
    "g = sns.heatmap(df_first_transform[top_corr_features].corr(method='spearman')[['SalePrice']].sort_values('SalePrice', ascending=False),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, there are 2 features very strongly correlated with SalesPrice:\n",
    "- **OverallQual** (Rates the overall material and finish of the house)\n",
    "- **TotalSF** (Living area square feet inc.)\n",
    "\n",
    "and 10 features strongly correlated with SalesPrice: \n",
    "- **GrLivArea** (Above grade (ground) living area square feet)\n",
    "- **Total_Bathrooms** (Total of bathrooms inc. basement)\n",
    "- **Full Bath** (Total of Full bathrooms above grade)\n",
    "- **GarageCars** (Size of garage in car capacity)\n",
    "- **GarageArea** (Size of garage in square feet)\n",
    "- **ExterQual** (Evaluates the quality of the material on the exterior)\n",
    "- **KitchenQual** (Kitchen quality)\n",
    "- **BsmtQual** (Evaluates the height of the basement)\n",
    "- **YearBuilt** (Original construction date)\n",
    "- **TotalBsmtSF** (Total square feet of basement area)\n",
    "\n",
    "In addition, in the correlation matrix doesn't appear any high negative correlation, so this means that a high value of any of these features also has a high value for sale price and low value of any of these features also has a low value for sale price.\n",
    "\n",
    "Finally, we can get the firsts insights from this correlation matrix:\n",
    "- At first place, for greater **material qualities** of the house (specially for the exterior, the kitchen and the basement) we can expect higher house prices.\n",
    "- In second place, also we can expect higher price for larger **total square feet** of the house (specially for the living ground and the basement)\n",
    "- Finally,  the **total number of bathrooms** and the **year construction** of the house also are positively correlated with the price sale.\n",
    "\n",
    "Note that in the previous correlation doesn't appear any feature about the condition of the house (overall, material of the exterior, basement) and that's interesting because it doesn't seem that the actual condition of the house is highly related to the sale price, but it does the qualities (as we've seen above). \n",
    "\n",
    "Let's check this plotting the relation of these features with the Sales Price feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1800 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_corr_features_excl_salesprice = top_corr_features.drop('SalePrice')\n",
    "fig, ax = plt.subplots(round(len(top_corr_features_excl_salesprice) / 3), 3, figsize = (18, 18))\n",
    "for i, ax in enumerate(fig.axes):\n",
    "    if i < len(top_corr_features_excl_salesprice) - 1:\n",
    "        sns.regplot(x=top_corr_features_excl_salesprice[i],y='SalePrice', data=df_first_transform[top_corr_features], ax=ax, line_kws={'color': '#000000'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, each scatterplot has a positive slope in the linear regression (black line), so, as we said before, **for greater values of each feature, the greater value for the sale price**.\n",
    "\n",
    "Let's get some more insights. Let's plot the histogram of these previous features and the main statistical metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot: title={'center': 'OverallQual'}>,\n",
       "        <AxesSubplot: title={'center': 'YearBuilt'}>,\n",
       "        <AxesSubplot: title={'center': 'ExterQual'}>,\n",
       "        <AxesSubplot: title={'center': 'BsmtQual'}>],\n",
       "       [<AxesSubplot: title={'center': 'TotalBsmtSF'}>,\n",
       "        <AxesSubplot: title={'center': 'GrLivArea'}>,\n",
       "        <AxesSubplot: title={'center': 'FullBath'}>,\n",
       "        <AxesSubplot: title={'center': 'KitchenQual'}>],\n",
       "       [<AxesSubplot: title={'center': 'GarageCars'}>,\n",
       "        <AxesSubplot: title={'center': 'GarageArea'}>,\n",
       "        <AxesSubplot: title={'center': 'SalePrice'}>,\n",
       "        <AxesSubplot: title={'center': 'TotalSF'}>],\n",
       "       [<AxesSubplot: title={'center': 'Total_Bathrooms'}>,\n",
       "        <AxesSubplot: >, <AxesSubplot: >, <AxesSubplot: >]], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x2000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_first_transform[top_corr_features].hist(figsize=(16, 20), bins=50, xlabelsize=8, ylabelsize=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "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>OverallQual</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>TotalSF</th>\n",
       "      <th>Total_Bathrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.00000</td>\n",
       "      <td>1423.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.099315</td>\n",
       "      <td>1971.267808</td>\n",
       "      <td>3.39589</td>\n",
       "      <td>3.579761</td>\n",
       "      <td>1057.429452</td>\n",
       "      <td>1515.463699</td>\n",
       "      <td>1.565068</td>\n",
       "      <td>3.511644</td>\n",
       "      <td>1.767123</td>\n",
       "      <td>472.980137</td>\n",
       "      <td>180921.195890</td>\n",
       "      <td>2572.893151</td>\n",
       "      <td>2.430822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.382997</td>\n",
       "      <td>30.202904</td>\n",
       "      <td>0.57428</td>\n",
       "      <td>0.680602</td>\n",
       "      <td>438.705324</td>\n",
       "      <td>525.480383</td>\n",
       "      <td>0.550916</td>\n",
       "      <td>0.663760</td>\n",
       "      <td>0.747315</td>\n",
       "      <td>213.804841</td>\n",
       "      <td>79442.502883</td>\n",
       "      <td>823.598492</td>\n",
       "      <td>0.922647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>334.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34900.000000</td>\n",
       "      <td>334.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1954.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>795.750000</td>\n",
       "      <td>1129.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>334.500000</td>\n",
       "      <td>129975.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>991.500000</td>\n",
       "      <td>1464.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>163000.000000</td>\n",
       "      <td>2479.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1298.250000</td>\n",
       "      <td>1776.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>214000.000000</td>\n",
       "      <td>3008.500000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6110.000000</td>\n",
       "      <td>5642.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1418.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "      <td>11752.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       OverallQual    YearBuilt   ExterQual     BsmtQual  TotalBsmtSF  \\\n",
       "count  1460.000000  1460.000000  1460.00000  1423.000000  1460.000000   \n",
       "mean      6.099315  1971.267808     3.39589     3.579761  1057.429452   \n",
       "std       1.382997    30.202904     0.57428     0.680602   438.705324   \n",
       "min       1.000000  1872.000000     2.00000     2.000000     0.000000   \n",
       "25%       5.000000  1954.000000     3.00000     3.000000   795.750000   \n",
       "50%       6.000000  1973.000000     3.00000     4.000000   991.500000   \n",
       "75%       7.000000  2000.000000     4.00000     4.000000  1298.250000   \n",
       "max      10.000000  2010.000000     5.00000     5.000000  6110.000000   \n",
       "\n",
       "         GrLivArea     FullBath  KitchenQual   GarageCars   GarageArea  \\\n",
       "count  1460.000000  1460.000000  1460.000000  1460.000000  1460.000000   \n",
       "mean   1515.463699     1.565068     3.511644     1.767123   472.980137   \n",
       "std     525.480383     0.550916     0.663760     0.747315   213.804841   \n",
       "min     334.000000     0.000000     2.000000     0.000000     0.000000   \n",
       "25%    1129.500000     1.000000     3.000000     1.000000   334.500000   \n",
       "50%    1464.000000     2.000000     3.000000     2.000000   480.000000   \n",
       "75%    1776.750000     2.000000     4.000000     2.000000   576.000000   \n",
       "max    5642.000000     3.000000     5.000000     4.000000  1418.000000   \n",
       "\n",
       "           SalePrice       TotalSF  Total_Bathrooms  \n",
       "count    1460.000000   1460.000000      1460.000000  \n",
       "mean   180921.195890   2572.893151         2.430822  \n",
       "std     79442.502883    823.598492         0.922647  \n",
       "min     34900.000000    334.000000         1.000000  \n",
       "25%    129975.000000   2014.000000         2.000000  \n",
       "50%    163000.000000   2479.000000         2.000000  \n",
       "75%    214000.000000   3008.500000         3.000000  \n",
       "max    755000.000000  11752.000000         6.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_first_transform[top_corr_features].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the histogram and the principal statistical metrics of the main numerical features, we can tell the big picture of the houses that have been sold:\n",
    "1. The **quality** of the houses sold are **medium-high**. They score 5-7 over 10 for the overall quality and 3-4 over 5 for exterior material and basement qualities.\n",
    "2. We can appreciate that that houses that have been sold are from the **last years of construction**.\n",
    "3. The size of the houses are around the **2000 and 3000 sq**. \n",
    "4. The number of bathrooms are 2-3 and the garage has a capacity for 2 cars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='EDAC'></a>\n",
    "## Exploratory Data Analysis of SalePrice for categorical features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we've done before, let's pick a few important categorical features for the price sale and let's try to get some insights of them. This time the criterion I have chosen has been my own knowledge of what characteristics might be important in predicting the value of a house. From the data_description file I've picked these 5 features:\n",
    "\n",
    "- **MSZoning** (Identifies the general zoning classification of the sale.)\n",
    "- **Neighborhood** (Physical locations within Ames city limits)\n",
    "- **BldgType** (Type of dwelling)\n",
    "- **HouseStyle** (Style of dwelling)\n",
    "- **Foundation** (Type of foundation)\n",
    "\n",
    "Let's get a detailed view of each one:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### MSZoning\n",
    "\n",
    "Acronym and values:\n",
    "- A: Agriculture\n",
    "- C: Commercial\n",
    "- FV: Floating Village Residential\n",
    "- I: Industrial\n",
    "- RH: Residential High Density\n",
    "- RL: Residential Low Density\n",
    "- RP: Residential Low Density Park \n",
    "- RM: Residential Medium Density\n",
    "\n",
    "Let's start doing a histogram to check the main category:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='MSZoning', ylabel='Count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,4))\n",
    "sns.histplot(df_first_transform[\"MSZoning\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if there are price difference between them using a boxplot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='SalePrice', ylabel='MSZoning'>"
      ]
     },
     "execution_count": 15,
     "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": [
    "sns.boxplot(data=df_first_transform, y=\"MSZoning\", x=\"SalePrice\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, the majority of houses sold are in a residential zone with low density. It seems that from more expensive to less expensive we have the following zones: floating village, residential zone with low density, then very similar we have residential zone with medium and high density and finally we have commercial zones. Although the floating village is, in general, more expensive that the low density residential, we can notice than there are quite a lot outliers for high prices in this type of residential."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Neighborhood\n",
    "\n",
    "As we've done previously, let's plot a histogram to check if there are top neighborhood:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_first_transform[\"Neighborhood\"].hist(figsize=(20, 5), bins=50, xlabelsize=8, ylabelsize=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='SalePrice', ylabel='Neighborhood'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9/pHBYJCXl5eysrKuupyXl5fefPNNdevWTf369dPPP/981eWbNm0qf39/Wz+CBQUGBqpBgwbatm2brSwnJ8fuNbR27drJaDTq+PHjCg0NtfvQKsVecHCwoqOjbT/YDQaDoqOj1bBhQydHBqA0/ngvF2QwGNShQ4ci73NHngE8NwAAAADg6tw2+WexWHTq1CmdOnVK+/fvV1xcnC5cuKA77rjjmut6e3vr7bffVqdOndS/f3+dOnVKUv5IvlOnTlVKSoqOHTum3bt3a/To0crOzi702u4VDz/8sObNm6c1a9bowIEDGj9+vM6fP2+bX6NGDU2ePFmPPvqoVq5cqSNHjmjXrl1KSEjQypUry+VYeJJhw4YpKChIkhQUFFSlB2gA3Nkf7+WC35955pli73NHngE8NwAAAACgeG6b/Fu7dq0aNGigBg0a6MYbb9Q333yj999/XxERESVa38fHR++++67at2+v/v376/Tp0woPD9fRo0c1cuRItWnTRrfeeqtOnTqldevWqXXr1kVu57HHHtNf//pX3X///erVq5dq1KihIUOG2C3z7LPP6umnn1Z8fLzatm2rQYMG6dNPP1WzZs3Kehg8jslkUlxcnOrVq6e4uLgq1T8b4EkK3ssTJ07UxIkTbfd1YGBgsfe5I88AnhsAAAAAUDyD9crIFHBpmZmZCgwMVEZGhmrWrOnscNyC1WqV2WyWlJ8coP+v32VlZSkqKkqS1Ga0QV6+zjs2edlWHVhmrbBYCm4/OTm5SvX5xz0AAAAAAJ6pNHkitx3wA7gWg8FQpRI9wB9xDwAAAAAASP4BVVxejnRlNFyn1J9tLfJ7uW0/p9w3CQAAAACA2yD5B1RxB1e5zpv/B9+UnJmIBAAAAADA07jtgB8AAAAAAAAAro6Wf0AVZDKZlJyc7OwwJOUPSmGxWCRJRqOxQgelYBRYAAAAAEBVQ/IPqIJcbSAIf39/Z4cAAAAAAIBHIvkHwKVYrVaZzeYSL1vRrQZNJlOFtkYEAAAAAKAikfwD4FLMZrOioqKcHYZNcnKyS7WSBAAAAACgNBjwAwAAAAAAAPBQtPwD4LK8RjWUfIt/5daanSfr8pOSJMOoBjL4ltPfM7Ktylv+c/lsCwAAAAAAJyL5B8B1+RqumdCz/v9/Db5e5Zb8syqvXLYDAAAAAICzkfwDPEjBwTIYqKLq4LwDAAAAAIpDn3+AB7kyWEZUVFSJR8yF++O8AwAAAACKQ/IPKEZqaqpGjBih1NRUl6ynsuKD5+NaAgAAAADP5fbJvzNnzujBBx9U48aNZTQaVb9+fQ0cOFBbtmxxdmhwY2azWQkJCTp9+rQSEhIqrDWVo/VUVnzwfFxLAAAAAODZ3D7595e//EW7d+/WypUrdfDgQX388ceKiIhQenq6s0MrEavVqpycHGeHgT9ITEy0XUPp6elKSkpyqXoqKz54Pq4lAAAAAPBsbp38O3/+vDZt2qT58+erX79+atKkiXr06KFp06bpzjvvlCS98MILCgsLU/Xq1RUSEqLx48frwoULkvITb3Xr1tUHH3xg22bnzp3VoEED2/TmzZtlNBp16dIljR49WrfffrtdDNnZ2apXr57eeOMNSVJeXp7i4+PVrFkz+fn5qVOnTnbbT0lJkcFg0Oeff65u3brJaDRq8+bNFXaMUHonTpxQUlKSrNb8cWStVquSkpJ04sQJl6inpOuZzWZlZWW53adgy7Mr+1jZCtbrDsfR0dZ6lXWtAwAAAACcx61H+w0ICFBAQIDWrFmjnj17ymg0FlrGy8tLixYtUrNmzXT06FGNHz9eU6dO1ZIlS2QwGNS3b1+lpKTorrvu0rlz57R//375+fnpwIEDatOmjTZu3KgbbrhB/v7+io2NVd++fXXy5ElbgvCTTz7RpUuXFB0dLUmKj4/XW2+9paVLl6ply5b66quvNGLECNWtW1fh4eG2uJ544gk9//zzat68uWrXrl0obovFIovFYpvOzMws78OHIlitVi1evLjY8rlz55bLSKqO1nOt9aZMmWIru3JNurUcq1TNSfX+P3c7jmazWX5+ftdcrrKudQAAAACAc7l1yz8fHx+tWLFCK1euVK1atXTTTTfp73//u/bu3Wtb5pFHHlG/fv3UtGlT9e/fX7Nnz9Z7771nmx8REaGUlBRJ0ldffaUuXbrYlaWkpNiSdr1791br1q315ptv2tZfvny57r77bgUEBMhisWju3LlatmyZBg4cqObNmysmJkYjRozQq6++ahf7rFmzNGDAALVo0UJ16tQptG/x8fEKDAy0fUJCQsrrsOEq0tLStHPnTuXm5tqV5+bmaufOnUpLS3NqPddajxZbKKnKutYBAAAAAM7l1i3/pPw+/2677TZt2rRJX3/9tT7//HMtWLBAr7/+umJiYvTFF18oPj5eBw4cUGZmpnJycmQ2m3Xp0iX5+/srPDxcDz/8sM6cOaONGzcqIiJC9evXV0pKisaMGaOtW7dq6tSptvpiY2P1z3/+U1OnTtUvv/yizz//XF9++aUk6fDhw7p06ZIGDBhgF+Ply5fVpUsXu7Lu3btfdb+mTZumSZMm2aYzMzNJAFaCkJAQdevWTbt371ZeXp6t3NvbW126dCm3c+BoPddar0WLFraypKQkmUymcom3MpnN5t9b2/k4qeVZgXrd4TgWPGYljbWyrnUAAAAAgHO5ffJPyv+xO2DAAA0YMEBPP/20YmNj9cwzzygiIkK33367HnzwQc2ZM0d16tTR5s2bNWbMGF2+fFn+/v4KCwtTnTp1tHHjRm3cuFFz5sxR/fr1NX/+fH3zzTfKzs5W7969bXWNHDlSTzzxhFJTU7V161Y1a9ZMffr0kSRbX4KffvqpgoOD7WL84yvJ1atXv+o+GY3GIl9jRsUyGAyaMGGCYmNjiywvr9cgHa2nNOuZTKYSvf7pypz12qnBYNCVF3894TgWpbKudQAAAACAc7n1a7/FadeunS5evKidO3cqLy9PCxcuVM+ePdWqVSv9/PPPdssaDAb16dNHycnJ+v7773XzzTerY8eOslgsevXVV9W9e3e7RF1QUJAGDx6s5cuXa8WKFRo1apRdvUajUcePH1doaKjdh1Y07iM4OFjR0dG25IfBYFB0dLQaNmzoEvVUVnzwfFxLAAAAAOD53Dr5l56erv79++utt97S3r17dezYMb3//vtasGCBoqKiFBoaquzsbCUkJOjo0aN68803tXTp0kLbiYiI0LvvvqvOnTsrICBAXl5e6tu3r95++227QTquiI2N1cqVK7V//37df//9tvIaNWpo8uTJevTRR7Vy5UodOXJEu3btUkJCglauXFmhxwLla9iwYQoKCpKUn/CtqEEfHK2nsuKD5+NaAgAAAADP5tbJv4CAAN1444168cUX1bdvX3Xo0EFPP/20xo4dq8WLF6tTp0564YUXNH/+fHXo0EFvv/224uPjC20nPDxcubm5ioiIsJVFREQUKrsiMjJSDRo00MCBAwu1kHn22Wf19NNPKz4+Xm3bttWgQYP06aefqlmzZuW9+6hAJpNJcXFxqlevnuLi4iqszzdH66ms+OD5uJYAAAAAwLMZrFar9dqLoaALFy4oODhYy5cv19ChQyulzszMTAUGBiojI0M1a9aslDrhfqxWq8xms6T8pI479tuWlZWlqKgoSZLXuGAZfIv/G4U1O095/zxRomVLo+B2k5OTXb7PP0847wAAAACAkitNnsgjBvyoLHl5efr111+1cOFC1apVS3feeaezQwLsGAwGl09Uofxx3gEAAAAAxSH5VwrHjx9Xs2bN1KhRI61YsUI+Phw+oEJlW2VVXrGzrdl5RX4vj3oBAAAAAPAEZK9KoWnTpuItaaDy5C3/+doL/T/r8pPi7gQAAAAAwJ5bD/gBAAAAAAAAoHi0/APgUkwmk5KTk0u0rNVqlcVikSQZjcYKGeiC0W8BAAAAAO6M5B8Al1LawSv8/f0rMBoAAAAAANwbyT8AACqR1WqV2Wx2dhhVQmW0Dq7qTCYTxxUAAMDFkfwDAKASmc1mRUVFOTsMoFwkJyeXqrU2AAAAKh8DfgAAAAAAAAAeipZ/AAA4iff9PSVfb2eH4bGs2bnKW/m1JMnr/p4ycKzLR3aucv//uAIAAMD1kfwDAMBZfL1JSFUSA8e63FidHQAAAABKheQfAJRQwYEa6OQeAACgaPybCQBcS5Xr8y8mJkaDBw++6jIRERF65JFHKiUeAO7jykANUVFRjNYKAABQDHf5N1NqaqpGjBih1NRUZ4dyVe4SJwDX5ZbJv5iYGBkMBs2bN8+ufM2aNdf8q9LLL7+sFStWlHtMTZs21UsvvVSofMaMGercubNtuiTJRwAAAABAxTGbzUpISNDp06eVkJDgsklKd4kTgGtzy+SflN98fP78+Tp37lyJls/NzVVeXp4CAwNVq1atig0OAAAAAOCyEhMTlZ6eLklKT09XUlKSkyMqmrvECcC1uW2ff5GRkTp8+LDi4+O1YMGCQvNXrFihRx55RKtWrdITTzyhgwcP6vDhw5oxY4bOnz+vNWvWSJIuXryoBx98UKtXr1aNGjU0efLkQts6efKkYmNj9eWXX6p+/fqaM2eO/v73v+uRRx4p1evBM2bM0MqVKyXJ1kJxw4YNioiIKPX+A3Au/uoKRxW8dqxWq+gFCe7Gav19yA+ehQCK4urPhhMnTigpKcn2PLNarUpKSlJkZKSCg4OdHN3v3CVOAK7PbZN/3t7emjt3ru677z5NnDhRjRo1KrTMpUuXNH/+fL3++usKCgpSvXr1Ci0zZcoUbdy4UcnJyapXr57+/ve/a9euXXav6o4cOVK//vqrUlJS5Ovrq0mTJun06dOljnny5Mnav3+/MjMztXz5cklSnTp1ilzWYrHIYrHYpjMzM0tdH4DyVfAfstHR0U6MBB4jJ0+q5uwggFLKybN95VkI4FrMZrP8/PycHYaN1WrV4sWLiy2fO3euSwxQ4i5xAnAPbvvaryQNGTJEnTt31jPPPFPk/OzsbC1ZskS9e/dW69at5e/vbzf/woULeuONN/T888/rlltuUVhYmFauXKmcnBzbMgcOHNAXX3yh1157TTfeeKO6du2q119/XVlZWYXqe/zxxxUQEGD3mTt3rm1+QECA/Pz8ZDQaVb9+fdWvX1/VqhX9qy8+Pl6BgYG2T0hIiCOHCAAAAADw/9LS0rRz507l5ubalefm5mrnzp1KS0tzUmT23CVOAO7BbVv+XTF//nz179+/yNd1q1Wrpo4dOxa77pEjR3T58mXdeOONtrI6deqodevWtukffvhBPj4+6tq1q60sNDRUtWvXLrS9KVOmKCYmxq5s0aJF+uqrr0qzS5KkadOmadKkSbbpzMxMEoCAk5lMJtv3pKQku2mgpMxm8++tpXzc+m9wqKoKXLc8CwEUpeD/61ztGRESEqJu3bpp9+7dysv7vSWzt7e3unTp4jK/udwlTgDuwe2Tf3379tXAgQM1bdq0Qok3Pz+/Sm0Kfd111yk0NNSurLjXeq/FaDTKaDSWR1gAKoDJZHKpV1jgnnhdB+6o4HXLsxCAuzEYDJowYYJiY2OLLHeV/ze7S5wA3INHNDmYN2+e/vWvfyk1NbVU67Vo0UK+vr7atm2brezcuXM6ePCgbbp169bKycnR7t27bWWHDx8u8SjDf1StWrVCTbcBAAAAAJUjODhY0dHRtgSawWBQdHS0GjZs6OTI7LlLnABcn0ck/8LCwjR8+HAtWrSoVOsFBARozJgxmjJlir788kv997//VUxMjLy8fj8sbdq0UWRkpMaNG6ft27dr9+7dGjdunMOtCps2baq9e/fqhx9+0K+//qrs7OxSbwMAAAAA4Lhhw4YpKChIkhQUFOSyAxi5S5wAXFuJX/st2P/ctbzwwgsOBVMWs2bNUlJSUqnXe+6553ThwgXdcccdqlGjhh577DFlZGTYLbNq1SqNGTNGffv2Vf369RUfH6/vv//eof4rxo4dq5SUFHXv3l0XLlzQhg0bFBERUertAAAAAAAcYzKZFBcXp1deeUUPPfSQy/VNeIW7xAnAtRmsVqu1JAv269fPbnrXrl3KycmxDY5x8OBBeXt7q1u3bvryyy/LP1IX8r///U8hISH64osvdMstt1RKnZmZmQoMDFRGRoZq1qxZKXUCsGe1WmU2myXl/0OMvlbgiKysLEVFRUmSvGNvksHX28kReS5rdq5yX98iiWNdngoe1+TkZPr8A1AI/2YCgIpXmjxRiVv+bdiwwfb9hRdeUI0aNbRy5UrbqLfnzp3TqFGj1KdPHwfDdl1ffvmlLly4oLCwMJ08eVJTp05V06ZN1bdvX2eHBqASGQwGfuQCAABcA/9mAgDX4tBovwsXLtS6detsiT9Jql27tmbPnq0//elPeuyxx8otQFeQnZ2tv//97zp69Khq1Kih3r176+2335avr6+zQwMAuLPsXJWo+T0cYs3OLfI7yohjCQAA4FYcSv5lZmbqzJkzhcrPnDmj3377rcxBuZqBAwdq4MCBzg4DAOBhcld+7ewQqow8jjUAAACqKIdG+x0yZIhGjRql1atX63//+5/+97//6cMPP9SYMWM0dOjQ8o4RAAAAAAAAgANKPOBHQZcuXdLkyZO1bNkyZWdnS5J8fHw0ZswYPffcc6pevXq5B1rVMeAHAHiGgp2go2JZrVZZLBZJktFopMP5CkBH/gAAAM5RmjyRQ8m/Ky5evKgjR45Iklq0aEHSrwKR/AMAAAAAAIBUQaP9FqV69eqqU6eO7TsAlFV5topyRqsfWsEAAAAAAFyJQ8m/vLw8zZ49WwsXLtSFCxckSTVq1NBjjz2mJ598Ul5eDnUlCAAym82KiopydhgOS05Olp+fn7PDAAAAAABAkoPJvyeffFJvvPGG5s2bp5tuukmStHnzZs2YMUNms1lz5swp1yABAAAAAAAAlJ5Dyb+VK1fq9ddf15133mkr69ixo4KDgzV+/HiSfwDKhc/9AyUfb4fXt2bnKHfVOkmS98g/yeBbpp4OipeTq5yV/66YbQMAAAAAUAYO/RI+e/as2rRpU6i8TZs2Onv2bJmDAgBJko93uSXsDL4+FZb8c3jUJAAAAAAAKphDnfN16tRJixcvLlS+ePFiderUqcxBASjMarUqKytLWVlZKsMg3UC549oEAAAAANflUDOYBQsW6LbbbtMXX3yhXr16SZJSU1OVlpamzz77rFwDBJCv4EAYDCoBV8K1CQAAAACuy6GWf+Hh4Tp48KCGDBmi8+fP6/z58xo6dKh++OEH9enTp7xjBAC4odTUVI0YMUKpqalKTU3V3XffrbvvvlupqamlWresdTu6TnHbWLFihQYNGqQVK1bYfXcFZT1ucD7OIQAAAMqbQ8k/SWrYsKHmzJmjDz/8UB9++KFmz56thg0blmdsJRYTEyODwaB58+bZla9Zs0YGg6HQ8m3atJHRaNSpU6cqK0QAqFLMZrMSEhJ0+vRpLVq0SC+//LIyMjKUkZGhRYsWyWw2l2jdhISEqy5bXuv/cZ2MjIwit5GRkaHExETl5eXpnXfe0bvvvqu8vDwlJiYqIyOjVHGWt7IeNzgf5xAAAAAVweHk3/nz57Vw4ULFxsYqNjZWL774olN/+JhMJs2fP1/nzp276nKbN29WVlaW7rrrLq1cubKSogOAqiUxMVHp6emSpPT0dLvBoNLT05WUlFTida+2bHmt/8d1Zs6cWeQ2ZsyYoby8PNt6V/o4zMvL08yZM0sVZ3kr63GD83EOAQAAUBEcSv7t2LFDLVq00IsvvqizZ8/q7NmzeuGFF9SiRQvt2rWrvGMskcjISNWvX1/x8fFXXe6NN97Qfffdp7/+9a9atmxZoflNmzbV7NmzNXLkSAUEBKhJkyb6+OOPdebMGUVFRSkgIEAdO3bUjh077NbbvHmz+vTpIz8/P4WEhGjixIm6ePGibf6SJUvUsmVLmUwmXX/99brrrrvKZ8dRJZnNZtsAC572KdjSxV0GjygYpyefm5KcM0k6ceKEkpKSrnr+EhMTdeLEiULlf1zXarUqKSmpyGWL4sj6Ra3z3//+t9A21q1bp++//77Y7fz3v/912v8Dy3rc4HycQwAAAFQUg9WBX9d9+vRRaGioXnvtNfn45I8ZkpOTo9jYWB09elRfffVVuQd6NTExMTp//rzuv/9+3XfffTp06JAaNWqkNWvWaMiQIbZ/SP/2229q0KCBtm3bpjZt2ig4OFjvv/++XT+FTZs21W+//aa5c+eqf//+evHFF/X222+rd+/eGj16tDp16qTHH39cP/zwg77//nsZDAYdOXJEnTp10uzZs3XbbbfpzJkzmjBhgjp16qTly5drx44d6tmzp95880317t1bZ8+e1aZNmzRx4sRi98lischisdimMzMzFRISooyMDNWsWbPiDiZc1rlz5xQdHe3sMCqV98g/ycvf5PD61uwc5byRPwiRz5g/y+Dr0BhH15R3yazcVesqZNvuJjExUc8995z27Nmj3Nzcqy7btWtXxcfH27pnsFqt+vvf/15oXW9vb3Xu3Flz584tsiuHKxxZv7h1/sjb21uSrrlPNWvW1HvvvScvL4cb1pdaWY8bnI9zCAAAgNLKzMxUYGBgifJEDrf8e/zxx22JP0ny8fHR1KlTC7WIq0xDhgxR586d9cwzzxQ5PzExUS1btlT79u3l7e2tYcOG6Y033ii03J///Gc98MADatmypaZPn67MzEzdcMMNuvvuu9WqVSs9/vjj2r9/v3755RdJUnx8vIYPH65HHnlELVu2VO/evbVo0SKtWrVKZrNZx48fV/Xq1XX77berSZMm6tKly1UTf1e2GRgYaPuEhISU/QABQAU7ceKEdu7cec0kmSTt2rVLaWlptum0tLQi183NzdXOnTvtli2KI+sXt84f5ebmlmifMjMztX379msuV57KetzgfJxDAAAAVCSHmsHUrFlTx48fV5s2bezK09LSVKNGjXIJzFHz589X//79NXny5ELzli1bphEjRtimR4wYofDwcCUkJNjF3bFjR9v366+/XpIUFhZWqOz06dOqX7++vv32W+3du1dvv/22bRmr1aq8vDwdO3ZMAwYMUJMmTdS8eXMNGjRIgwYN0pAhQ+Tv71/sfkybNk2TJk2yTV9p+Yeqy2T6vQVcUlKS3bQnMZvNv7dw9PF2bjAlVSBOTz43xSl4zlq0aKFu3bpp9+7ddn3jFaVbt252z7WQkJAi1/X29laXLl2u+Qx0ZP3i1vmj0rT869Gjx1WXKW9lPW5wPs4hAAAAKpJDyb/o6GiNGTNGzz//vHr37i1J2rJli6ZMmaJ77723XAMsrb59+2rgwIGaNm2aYmJibOX79u3T119/re3bt+vxxx+3lefm5ioxMVFjx461lfn6+tq+X3nNpqiyK/9Av3Dhgh544IEiW/M1btxY1apV065du5SSkqJ169Zp+vTpmjFjhr755hvVqlWryP0wGo0yGo2lPwCoEkwmk/z8/JwdRoVzl9fcCsZZVc5NcQwGgyZMmKDY2NirLuft7a24uDi7Y1fculfKr3U9OLJ+SeM1GAx69NFH9dxzz111uaeeeqpSX/mVyn7c4HycQwAAAFQkh36hPP/88xo6dKhGjhyppk2bqmnTpoqJidFdd92l+fPnl3eMpTZv3jz961//Umpqqq3sjTfeUN++ffXtt99qz549ts+kSZOKfPW3NLp27ap9+/YpNDS00KdatWqS8l+LjoyM1IIFC7R37179+OOP+vLLL8tULwC4ouDgYEVHR181YTFs2DA1bNjwmusaDAZFR0cXuWxJ6i7J+kWt06FDh0LbGDBggNq3b1/sdjp06KDOnTuXKM7yVtbjBufjHAIAAKCiOJT8q1atml5++WWdO3fOlkQ7e/asXnzxRZdorRYWFqbhw4dr0aJFkqTs7Gy9+eabuvfee9WhQwe7T2xsrLZt23bVERyv5fHHH9fWrVs1YcIE7dmzR4cOHVJycrImTJggSfrkk0+0aNEi7dmzRz/99JNWrVqlvLw8tW7dulz2FwBczbBhwxQUFCRJCgoKUp06dWzzgoKCrjp4zR/XLe1AN46s/8d1nnnmmSK3MWPGDFvLPoPBYEvUeHl5FdvfbGUp63GD83EOAQAAUBHK9G6Sv7+/wsLCFBYWdtX+65xh1qxZttdyP/74Y6Wnp2vIkCGFlmvbtq3atm1bptZ/HTt21MaNG3Xw4EH16dNHXbp00fTp021/ra9Vq5ZWr16t/v37q23btlq6dKnefffdq7YgAQB3ZjKZFBcXp3r16mnixIl6+OGHbQMYTZw48ar9IhZcNy4urtR9KDqy/h/XCQwMLHIbgYGBGjZsmLy8vHTvvffq3nvvlZeXl4YNG6bAwMBSxVneynrc4HycQwAAAFQEg9VqtZZ2pYsXL2revHlav369Tp8+XaiT9KNHj5ZbgMhXmiGc4ZmsVqvMZrOk/B+IntoHVFZWlqKioiRJPmP+LIOvQ12TSpKs2TnKeeOzctlWSetJTk6ucn3+VZVrEwAAAABcRWnyRA79Eo6NjdXGjRv117/+VQ0aNOCHHlAJDAZDlUsqwT1wbQIAAACA63Io+ff555/r008/1U033VTe8QDA73JyVeqmyQVYs3OK/F7ucnIrbtsAAAAAAJSBQ8m/2rVr23XeDgAVIWflv8ttW7mr1pXbtgAAAAAAcBcODfjx7LPPavr06bp06VJ5xwMAAAAAAACgnJS45V+XLl3s+vY7fPiwrr/+ejVt2lS+vr52y+7atav8IgRQpZhMJiUnJ5fLtqxWqywWiyTJaDRWSv+kjM4JAAAAAHAlJU7+DR48uALDAIB85T14hL+/f7ltCwAAAAAAd2OwWq2l6k8/JydHc+fO1ejRo9WoUaOKigt/UJohnAGUjNVqldlsdnYYZeaMFo6uyGQyVdl9BwAAAFC1lCZPVOrknyTVqFFD3333nZo2bepojCglkn9A+cvKylJUVJSzw0A5SU5OLtdWowAAAADgqkqTJ3JowI/+/ftr48aNDgUHAAAAAAAAoHKUuM+/gm699VY98cQT+u6779StWzdVr17dbv6dd95ZLsEBQGXxHTlU8nHokeh01uwc5by5WpLk89ehMvi65344JCdH2atWOzsKAAAAAHBZDv1CHD9+vCTphRdeKDTPYDAoNze3bFEBQGXz8fGIpJnB1zP2o6RK3W8FAAAAAFQxDv1CzMvLK+84AI9WcGAJBiUAAOfjuQwAAICqwqE+/wCUjtlsVlRUlKKiojxidFkAcHc8lwEAAFBVOJz827hxo+644w6FhoYqNDRUd955pzZt2uRwIGfOnNGDDz6oxo0by2g0qn79+ho4cKC2bNni8DYr24EDB2QwGPT111/blffs2VMmk8nux4XZbJbJZNIbb7xR2WECAAAAAACginAo+ffWW28pMjJS/v7+mjhxoiZOnCg/Pz/dcssteueddxwK5C9/+Yt2796tlStX6uDBg/r4448VERGh9PR0h7ZX2axWq0JDQ1W/fn2lpKTYyn/77Tft2rVLdevWtUsKpqamymKxqH///k6IFgAAAAAAAFWBQ8m/OXPmaMGCBUpKSrIl/5KSkjRv3jw9++yzpd7e+fPntWnTJs2fP1/9+vVTkyZN1KNHD02bNs02cvALL7ygsLAwVa9eXSEhIRo/frwuXLggKT/xVrduXX3wwQe2bXbu3FkNGjSwTW/evFlGo1GXLl3S6NGjdfvtt9vFkJ2drXr16tla4uXl5Sk+Pl7NmjWTn5+fOnXqZLf9lJQUGQwGff755+rWrZuMRqM2b96sfv362SX/Nm/erFatWumOO+6wK09JSVGTJk3UrFmzUh8vAAAAAAAAoCQcGvDj6NGjuuOOOwqV33nnnfr73/9e6u0FBAQoICBAa9asUc+ePWU0Ggst4+XlpUWLFqlZs2Y6evSoxo8fr6lTp2rJkiUyGAzq27evUlJSdNddd+ncuXPav3+//Pz8dODAAbVp00YbN27UDTfcIH9/f8XGxqpv3746efKkLUH4ySef6NKlS4qOjpYkxcfH66233tLSpUvVsmVLffXVVxoxYoTq1q2r8PBwW1xPPPGEnn/+eTVv3ly1a9dWv3799OijjyonJ0c+Pj7asGGDIiIi1KdPHy1ZskQzZsyQJG3YsEH9+vUr9bGC+6NvKddR8FxYrVbR3b/7sVp/H++XewulwfUCAACAqsKh5F9ISIjWr1+v0NBQu/IvvvhCISEhpQ/Cx0crVqzQ2LFjtXTpUnXt2lXh4eEaNmyYOnbsKEl65JFHbMs3bdpUs2fP1t/+9jctWbJEkhQREaFXX31VkvTVV1+pS5cutldw27Rpo5SUFFvSrnfv3mrdurXefPNNTZ06VZK0fPly3X333QoICJDFYtHcuXP1xRdfqFevXpKk5s2ba/PmzXr11Vftkn+zZs3SgAEDbNP9+vXTxYsX9c0336hXr15KSUnRlClTdPPNN+v++++X2WyW1WrV9u3bFRsbW+wxsVgsslgstunMzMxSH1e4joI/Mq8kmOFicnKlar7OjgKllZNr+8q9BUeZzWb5+fk5OwwAAACgQjj02u9jjz2miRMn6sEHH9Sbb76pN998U3/729/0yCOPaPLkyQ4F8pe//EU///yzPv74Yw0aNEgpKSnq2rWrVqxYISk/sXjLLbcoODhYNWrU0F//+lelp6fr0qVLkqTw8HDt27dPZ86c0caNGxUREaGIiAilpKQoOztbW7duVUREhK2+2NhYLV++XJL0yy+/6PPPP9fo0aMlSYcPH9alS5c0YMAAW6vEgIAArVq1SkeOHLGLu3v37nbToaGhatSokVJSUpSZmandu3crPDxcDRo0UOPGjZWammrr7+9qLf/i4+MVGBho+ziSVAUAAAAAAEDV5lDLvwcffFD169fXwoUL9d5770mS2rZtq6SkJEVFRTkcjMlk0oABAzRgwAA9/fTTio2N1TPPPKOIiAjdfvvtevDBBzVnzhzVqVNHmzdv1pgxY3T58mX5+/srLCxMderU0caNG7Vx40bNmTNH9evX1/z58/XNN98oOztbvXv3ttU1cuRIPfHEE0pNTdXWrVvVrFkz9enTR5JsfQl++umnCg4Otovxj68kV69evdB+REREaMOGDerYsaNatmypevXqScpPUG7YsME2OMjVEnrTpk3TpEmTbNOZmZkkAN2YyWSyfU9KSrKbhvOYzebfW4v5eDs3GDimwHnj3kJpFLz/uW4AAADgyRxK/knSkCFDNGTIkPKMpZB27dppzZo12rlzp/Ly8rRw4UJ5eeU3VrySdLzCYDCoT58+Sk5O1vfff6+bb75Z/v7+slgsevXVV9W9e3e7RF1QUJAGDx6s5cuXKzU1VaNGjbKr12g06vjx43av+JZUv379NHHiRLVr186utWHfvn312muvyWq1XrO/P6PRWGTfh3B/JpOJ18tckMFAj3/uqOB5494CAAAAgMIcTv5J0o4dO7R//35J+Qmzbt26ObSd9PR03X333Ro9erQ6duyoGjVqaMeOHVqwYIGioqIUGhqq7OxsJSQk6I477tCWLVu0dOnSQtuJiIjQY489pu7duysgIEBSfsLt7bff1pQpUwotHxsbq9tvv125ubm6//77beU1atTQ5MmT9eijjyovL08333yzMjIytGXLFtWsWdNu2aJc6fdv2bJleu2112zl4eHhtn7+xo8f79CxAgAAAAAAAErKoeTf//73P917773asmWLatWqJUk6f/68evfurcTERDVq1KhU2wsICNCNN96oF198UUeOHFF2drZCQkI0duxY/f3vf5efn59eeOEFzZ8/X9OmTVPfvn0VHx+vkSNH2m0nPDxcubm5dq3tIiIilJycbFd2RWRkpBo0aKD27durYcOGdvOeffZZ1a1bV/Hx8Tp69Khq1aqlrl27lmg042bNmqlJkyb66aef7FoONm7cWA0bNtSPP/5YZDwAAAAAAABAeTJYrVZraVcaNGiQzp8/r5UrV6p169aSpB9++EGjRo1SzZo1tXbt2nIPtCJcuHBBwcHBWr58uYYOHerscK4qMzNTgYGBysjIUM2aNZ0dDkopKyvL1h9mcnIyrya6iILnxXf0PTL4lqkxtNNYs3OUvSy/KwR33g9HFNx37i2UBs9lAAAAuLPS5Ikc+oW4ceNGbd261Zb4k6TWrVsrISHBNmiGK8vLy9Ovv/6qhQsXqlatWrrzzjudHRI8nMlkUnJysu07AMC5eC4DAACgqnAo+RcSEqLs7OxC5bm5uYVen3VFx48fV7NmzdSoUSOtWLFCPj5Vp5UMnMNgMNCqBABcCM9lAAAAVBUOZb2ee+45xcXF6ZVXXlH37t0l5Q/+8fDDD+v5558v1wArQtOmTeXA284APFlOjtz1qWDNzinye5WQU8X2FwAAAABKqcR9/tWuXVsGg8E2ffHiReXk5NhazV35Xr16dZ09e7Zioq3C6PMPKH8F+/yC+6PfNgAAAABVRYX0+ffSSy+VNS4AAAAAAAAAlcih0X5R+Wj5B5Q/q9Uqs9ns7DDKzGq1ymKxSJKMRqNdK+2qxGQyVdl9BwAAAFC1VPhov1L+iLmHDx/W6dOnlZeXZzevb9++jm4WACqNJ3X47+/v7+wQAAAAAAAuyKHk39dff6377rtPP/30U6GBMwwGg3Jzc8slOKAqcHbrM2e0GqOFFgAAAAAAlcOh5N/f/vY3de/eXZ9++qkaNGjAj3igDMxmc5UbdIKBGQAAAAAAqBwOJf8OHTqkDz74QKGhoeUdDwAAAAAAAIBy4lDy78Ybb9Thw4dJ/gHlzPevwyUfh7vidIg1O1s5b70jSfIZcZ8Mvr4VU1FOjrLffLtitg0AAAAAAIpU4izD3r17bd/j4uL02GOP6dSpUwoLC5PvH5IFHTt2LL8I4XEK9nFH329/4ONTccm3EjD4+lZY/QwrfnXcFwAAAACAilDi5F/nzp1lMBjsBvgYPXq07fuVeQz4gWs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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.boxplot(data=df_first_transform, y=\"Neighborhood\", x=\"SalePrice\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we could imagine, the neighborhood is a feature with a direct impact on the price sale. As we can see above, the boxplots are very different from one neighborhood to another. The most expensive neighborhood seems to be Northridge Heights (NridHt) and Northridge Heights (StoneBr) and the cheaper ones are Briardale (BrDale), Meadow Village (MeadowV) and Iowa DOT and Rail Road (IDOTRR). The top bought neighborhoods are: North Ames (NAmes) and College Creek (CollgCr)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Building Type (BldgType)\n",
    "\n",
    "Acronym and values:\n",
    "- 1Fam: Single-family Detached\n",
    "- 2FmCon: Two-family Conversion; originally built as one-family dwelling\n",
    "- Duplx: Duplex\n",
    "- TwnhsE:Townhouse End Unit\n",
    "- TwnhsI: Townhouse Inside Unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='BldgType', ylabel='Count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,3))\n",
    "sns.histplot(df_first_transform[\"BldgType\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='SalePrice', ylabel='BldgType'>"
      ]
     },
     "execution_count": 19,
     "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": [
    "sns.boxplot(data=df_first_transform, y=\"BldgType\", x=\"SalePrice\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The single-family detached home is the type of home most bought and also is the most expensive in general."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Foundation\n",
    "\n",
    "Acronym and values:\n",
    "\n",
    "- BrkTil: Brick & Tile\n",
    "- CBlock: Cinder Block\n",
    "- PConc: Poured Contrete\n",
    "- Slab: Slab\n",
    "- Stone: Stone\n",
    "- Wood: Wood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Foundation', ylabel='Count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3))\n",
    "sns.histplot(df_first_transform[\"Foundation\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='SalePrice', ylabel='Foundation'>"
      ]
     },
     "execution_count": 21,
     "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": [
    "sns.boxplot(data=df_first_transform, y=\"Foundation\", x=\"SalePrice\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main type of foundations are Brick & Tile and Cinder Block, the first one is way more expensive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XVbV4ltnROZp",
    "tags": []
   },
   "source": [
    "<a id='SEED'></a>\n",
    "## Seed questions to understand better the data and its peculiarities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CkXxRcoSRX6v"
   },
   "source": [
    "### Is there any old data?\n",
    "Let's check if we have recent data sales, otherwise, due the inflation the prices we'll try to predict may be not accurate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "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>YrSold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     YrSold\n",
       "min    2006\n",
       "max    2010"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_first_transform[[\"YrSold\"]].agg(['min','max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset only contains data about 5 years of difference, from 2006 to 2010. There isn't a huge difference in years, so we don't have to delete old data that could affect to our price prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uHR5753wS18G"
   },
   "source": [
    "### Is there a comparable selling price for the same main features?\n",
    "From the previous analysis of more important features correlated to the price, let's group by those features and let's check how different is the price in each group. We're going to create the groups using this features:\n",
    "- OverallQual\n",
    "- TotalSF (because is a continuous variable we'll have to create buckets of 200 sq length)\n",
    "- Total_Bathrooms\n",
    "- GarageCars\n",
    "- Neighborhood\n",
    "- MSZoning \n",
    "- BldgType\n",
    "- Foundation\n",
    "\n",
    "To check how different can be the price in each group, we'll see how dispersed the data is in relation to the mean, so we'll use the Standard Desvitation (std) metric. \n",
    "\n",
    "In the following chunk, you we'll see the data group by the previous features and ordered by the std:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">SalePrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <th>TotalSF</th>\n",
       "      <th>Total_Bathrooms</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>Foundation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>8</th>\n",
       "      <th>(3934, 4134]</th>\n",
       "      <th>3</th>\n",
       "      <th>3</th>\n",
       "      <th>NridgHt</th>\n",
       "      <th>RL</th>\n",
       "      <th>1Fam</th>\n",
       "      <th>PConc</th>\n",
       "      <td>2.0</td>\n",
       "      <td>346646.500000</td>\n",
       "      <td>132021.785795</td>\n",
       "      <td>253293.0</td>\n",
       "      <td>299969.75</td>\n",
       "      <td>346646.5</td>\n",
       "      <td>393323.25</td>\n",
       "      <td>440000.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>NridgHt</th>\n",
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       "      <th>1Fam</th>\n",
       "      <th>PConc</th>\n",
       "      <td>3.0</td>\n",
       "      <td>543914.666667</td>\n",
       "      <td>93566.270655</td>\n",
       "      <td>437154.0</td>\n",
       "      <td>510043.50</td>\n",
       "      <td>582933.0</td>\n",
       "      <td>597295.00</td>\n",
       "      <td>611657.0</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">8</th>\n",
       "      <th>(3134, 3334]</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>Crawfor</th>\n",
       "      <th>RM</th>\n",
       "      <th>TwnhsE</th>\n",
       "      <th>PConc</th>\n",
       "      <td>3.0</td>\n",
       "      <td>300833.333333</td>\n",
       "      <td>81866.252713</td>\n",
       "      <td>235000.0</td>\n",
       "      <td>255000.00</td>\n",
       "      <td>275000.0</td>\n",
       "      <td>333750.00</td>\n",
       "      <td>392500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(3934, 4134]</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>NoRidge</th>\n",
       "      <th>RL</th>\n",
       "      <th>1Fam</th>\n",
       "      <th>PConc</th>\n",
       "      <td>2.0</td>\n",
       "      <td>358500.000000</td>\n",
       "      <td>61518.289963</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>336750.00</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>380250.00</td>\n",
       "      <td>402000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <th>(2734, 2934]</th>\n",
       "      <th>2</th>\n",
       "      <th>2</th>\n",
       "      <th>NWAmes</th>\n",
       "      <th>RL</th>\n",
       "      <th>1Fam</th>\n",
       "      <th>CBlock</th>\n",
       "      <td>2.0</td>\n",
       "      <td>119750.000000</td>\n",
       "      <td>52679.455198</td>\n",
       "      <td>82500.0</td>\n",
       "      <td>101125.00</td>\n",
       "      <td>119750.0</td>\n",
       "      <td>138375.00</td>\n",
       "      <td>157000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                              SalePrice  \\\n",
       "                                                                                                  count   \n",
       "OverallQual TotalSF      Total_Bathrooms GarageCars Neighborhood MSZoning BldgType Foundation             \n",
       "8           (3934, 4134] 3               3          NridgHt      RL       1Fam     PConc            2.0   \n",
       "9           (4534, 4734] 4               3          NridgHt      RL       1Fam     PConc            3.0   \n",
       "8           (3134, 3334] 4               2          Crawfor      RM       TwnhsE   PConc            3.0   \n",
       "            (3934, 4134] 4               3          NoRidge      RL       1Fam     PConc            2.0   \n",
       "7           (2734, 2934] 2               2          NWAmes       RL       1Fam     CBlock           2.0   \n",
       "\n",
       "                                                                                                              \\\n",
       "                                                                                                        mean   \n",
       "OverallQual TotalSF      Total_Bathrooms GarageCars Neighborhood MSZoning BldgType Foundation                  \n",
       "8           (3934, 4134] 3               3          NridgHt      RL       1Fam     PConc       346646.500000   \n",
       "9           (4534, 4734] 4               3          NridgHt      RL       1Fam     PConc       543914.666667   \n",
       "8           (3134, 3334] 4               2          Crawfor      RM       TwnhsE   PConc       300833.333333   \n",
       "            (3934, 4134] 4               3          NoRidge      RL       1Fam     PConc       358500.000000   \n",
       "7           (2734, 2934] 2               2          NWAmes       RL       1Fam     CBlock      119750.000000   \n",
       "\n",
       "                                                                                                              \\\n",
       "                                                                                                         std   \n",
       "OverallQual TotalSF      Total_Bathrooms GarageCars Neighborhood MSZoning BldgType Foundation                  \n",
       "8           (3934, 4134] 3               3          NridgHt      RL       1Fam     PConc       132021.785795   \n",
       "9           (4534, 4734] 4               3          NridgHt      RL       1Fam     PConc        93566.270655   \n",
       "8           (3134, 3334] 4               2          Crawfor      RM       TwnhsE   PConc        81866.252713   \n",
       "            (3934, 4134] 4               3          NoRidge      RL       1Fam     PConc        61518.289963   \n",
       "7           (2734, 2934] 2               2          NWAmes       RL       1Fam     CBlock       52679.455198   \n",
       "\n",
       "                                                                                                         \\\n",
       "                                                                                                    min   \n",
       "OverallQual TotalSF      Total_Bathrooms GarageCars Neighborhood MSZoning BldgType Foundation             \n",
       "8           (3934, 4134] 3               3          NridgHt      RL       1Fam     PConc       253293.0   \n",
       "9           (4534, 4734] 4               3          NridgHt      RL       1Fam     PConc       437154.0   \n",
       "8           (3134, 3334] 4               2          Crawfor      RM       TwnhsE   PConc       235000.0   \n",
       "            (3934, 4134] 4               3          NoRidge      RL       1Fam     PConc       315000.0   \n",
       "7           (2734, 2934] 2               2          NWAmes       RL       1Fam     CBlock       82500.0   \n",
       "\n",
       "                                                                                                          \\\n",
       "                                                                                                     25%   \n",
       "OverallQual TotalSF      Total_Bathrooms GarageCars Neighborhood MSZoning BldgType Foundation              \n",
       "8           (3934, 4134] 3               3          NridgHt      RL       1Fam     PConc       299969.75   \n",
       "9           (4534, 4734] 4               3          NridgHt      RL       1Fam     PConc       510043.50   \n",
       "8           (3134, 3334] 4               2          Crawfor      RM       TwnhsE   PConc       255000.00   \n",
       "            (3934, 4134] 4               3          NoRidge      RL       1Fam     PConc       336750.00   \n",
       "7           (2734, 2934] 2               2          NWAmes       RL       1Fam     CBlock      101125.00   \n",
       "\n",
       "                                                                                                         \\\n",
       "                                                                                                    50%   \n",
       "OverallQual TotalSF      Total_Bathrooms GarageCars Neighborhood MSZoning BldgType Foundation             \n",
       "8           (3934, 4134] 3               3          NridgHt      RL       1Fam     PConc       346646.5   \n",
       "9           (4534, 4734] 4               3          NridgHt      RL       1Fam     PConc       582933.0   \n",
       "8           (3134, 3334] 4               2          Crawfor      RM       TwnhsE   PConc       275000.0   \n",
       "            (3934, 4134] 4               3          NoRidge      RL       1Fam     PConc       358500.0   \n",
       "7           (2734, 2934] 2               2          NWAmes       RL       1Fam     CBlock      119750.0   \n",
       "\n",
       "                                                                                                          \\\n",
       "                                                                                                     75%   \n",
       "OverallQual TotalSF      Total_Bathrooms GarageCars Neighborhood MSZoning BldgType Foundation              \n",
       "8           (3934, 4134] 3               3          NridgHt      RL       1Fam     PConc       393323.25   \n",
       "9           (4534, 4734] 4               3          NridgHt      RL       1Fam     PConc       597295.00   \n",
       "8           (3134, 3334] 4               2          Crawfor      RM       TwnhsE   PConc       333750.00   \n",
       "            (3934, 4134] 4               3          NoRidge      RL       1Fam     PConc       380250.00   \n",
       "7           (2734, 2934] 2               2          NWAmes       RL       1Fam     CBlock      138375.00   \n",
       "\n",
       "                                                                                                         \n",
       "                                                                                                    max  \n",
       "OverallQual TotalSF      Total_Bathrooms GarageCars Neighborhood MSZoning BldgType Foundation            \n",
       "8           (3934, 4134] 3               3          NridgHt      RL       1Fam     PConc       440000.0  \n",
       "9           (4534, 4734] 4               3          NridgHt      RL       1Fam     PConc       611657.0  \n",
       "8           (3134, 3334] 4               2          Crawfor      RM       TwnhsE   PConc       392500.0  \n",
       "            (3934, 4134] 4               3          NoRidge      RL       1Fam     PConc       402000.0  \n",
       "7           (2734, 2934] 2               2          NWAmes       RL       1Fam     CBlock      157000.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "length_bins = 200\n",
    "bins_total_SF = pd.cut(df_first_transform.TotalSF, np.arange(df_first_transform.TotalSF.min(),df_first_transform.TotalSF.max(),length_bins))\n",
    "groupby = df_first_transform.groupby([\"OverallQual\", bins_total_SF, \"Total_Bathrooms\", \"GarageCars\", \"Neighborhood\", \"MSZoning\", \"BldgType\", \"Foundation\"])\n",
    "groupby_metrics = groupby[[\"SalePrice\"]].describe().sort_values(by=('SalePrice','std'), ascending = False)\n",
    "groupby_metrics.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the value of the std (for those non null values) and we'll see how is distributed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='std', ylabel='Count'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupby_metrics.reset_index(drop=True, inplace=True)\n",
    "plt.figure(figsize=(5,5))\n",
    "sns.histplot(groupby_metrics[\"SalePrice\"]['std'].dropna(), bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "As we can see above, for a large number of groups the Standard Desviation is near 0 and then you can see a few large values of the Std. This could suggest that for most of the houses with the same characteristics, the price is consistent and we could try to predict with good results. On the other hand, there are a few others houses that the price is not consistent for the same characteristics, this could due because some outliers. We'll check for outliers in a later section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RAdMtg0vNJH1",
    "tags": []
   },
   "source": [
    "<a id='SUMMARY'></a>\n",
    "## Summary of bussiness analytics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we've said many things about this dataset. Let's just summarize all above in the following take aways:\n",
    "\n",
    "- There are 4 features with more than 80% missing data. In case we were a real state business, it could be interesting to check why is missing this information and if there is any way to retrieve it.\n",
    "- All high correlation with the sale price feature are positive. This means that a high value of any of these features also has a high value for the sale price and low value of any of these features also has a low value for the sale price.\n",
    "- The most correlated features with the sale price feature, in order, are: rate of the material qualities of the house, total of square feet, number of bathrooms and the year of construction.\n",
    "- The rate of the condition of the house is not highly correlated, but it is the rate of the material qualities.\n",
    "- The big picture of the houses sold in the last 5 years is:\n",
    "    - Houses with medium-high qualities.\n",
    "    - Relatively new houses.\n",
    "    - Around 2000-3000 sq.\n",
    "    - With 2 bathrooms and a garage for 2 cars.\n",
    "    - Residencial in a low density zone.\n",
    "- The more expensive zone is residential with low density and the most expensive neighborhoods are Northridge Heights and Northridge Heights.\n",
    "- The most bought type of home (and the most expensive) is a single-family detached home. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vphHk1rcPhMb",
    "tags": []
   },
   "source": [
    "<a id='ML'></a>\n",
    "# Machine Learning\n",
    "\n",
    "In the previous part of this project, we've had examined a dataset with many features that describe the houses of the Iowa region. We explored and discovered interesting insights about the real state market and how the features of a house can influence about the selling price of the house. Now, we're moving on to the Machine Learning part of this project.\n",
    "\n",
    "Our main goal here is to find the best model that could accurately **predict the selling price of a house**, so its market price. Also, in this section we'll clean and transform the data to ease that the data could fit in several models.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='OUTLIERS'></a>\n",
    "## Outliers\n",
    "\n",
    "Let's check if there are outliers in our data that we should remove before finding a model that fits our data. \n",
    "\n",
    "First of all, let's plot the histogram of our target feature SalePrice:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='SalePrice', ylabel='Count'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "sns.histplot(df_first_transform['SalePrice'], bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, the distribution of the SalePrice feature seems to be a gaussian distribution positively skewed. Let's find out the skew value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8828757597682129"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_first_transform['SalePrice'].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the skew value > 0.5, we can numerically confirm the positively skewed distribution. On the other hand, also in the right side of the distribution, seems to have a long tail. That would confirm the presence of outliers in the right side of the distribution. The kurtosis is the statistical value that measure how long or short are the tails of the distribution. Let's find out its value: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.536281860064529"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_first_transform['SalePrice'].kurtosis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "For values >3 of kurtosis we can say that we have a heavy-tailed distribution (Leptokurtic) an this confirm the presence of outliers. [Here](https://www.analyticsvidhya.com/blog/2021/05/shape-of-data-skewness-and-kurtosis/) for more information. We are going to eliminate the outliers to prevent those affecting the results of the model we are going to build. The [method](https://towardsdatascience.com/5-ways-to-detect-outliers-that-every-data-scientist-should-know-python-code-70a54335a623) we're going to use is the z-score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The outliers removed are the following:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>TotalSF</th>\n",
       "      <th>Total_Bathrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>104.0</td>\n",
       "      <td>21535</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>755000</td>\n",
       "      <td>6760</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1182</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>15623</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>555</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>745000</td>\n",
       "      <td>6872</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1169</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>118.0</td>\n",
       "      <td>35760</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>CulDSac</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>625000</td>\n",
       "      <td>5557</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>100.0</td>\n",
       "      <td>12919</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>611657</td>\n",
       "      <td>4694</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>107.0</td>\n",
       "      <td>13891</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2009</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>582933</td>\n",
       "      <td>4556</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1046</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>16056</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>556581</td>\n",
       "      <td>4860</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>105.0</td>\n",
       "      <td>15431</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>555000</td>\n",
       "      <td>5496</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>47.0</td>\n",
       "      <td>53504</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR2</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>CulDSac</td>\n",
       "      <td>Mod</td>\n",
       "      <td>...</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>538000</td>\n",
       "      <td>4929</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>63.0</td>\n",
       "      <td>17423</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>CulDSac</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2009</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>501837</td>\n",
       "      <td>4450</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSSubClass MSZoning  LotFrontage  LotArea Street LotShape LandContour  \\\n",
       "691           60       RL        104.0    21535   Pave      IR1         Lvl   \n",
       "1182          60       RL        160.0    15623   Pave      IR1         Lvl   \n",
       "1169          60       RL        118.0    35760   Pave      IR1         Lvl   \n",
       "898           20       RL        100.0    12919   Pave      IR1         Lvl   \n",
       "803           60       RL        107.0    13891   Pave      Reg         Lvl   \n",
       "1046          60       RL         85.0    16056   Pave      IR1         Lvl   \n",
       "440           20       RL        105.0    15431   Pave      Reg         Lvl   \n",
       "769           60       RL         47.0    53504   Pave      IR2         HLS   \n",
       "178           20       RL         63.0    17423   Pave      IR1         Lvl   \n",
       "\n",
       "     Utilities LotConfig LandSlope  ... ScreenPorch PoolArea MiscVal MoSold  \\\n",
       "691     AllPub    Corner       Gtl  ...           0        0       0      1   \n",
       "1182    AllPub    Corner       Gtl  ...           0      555       0      7   \n",
       "1169    AllPub   CulDSac       Gtl  ...           0        0       0      7   \n",
       "898     AllPub    Inside       Gtl  ...           0        0       0      3   \n",
       "803     AllPub    Inside       Gtl  ...         192        0       0      1   \n",
       "1046    AllPub    Inside       Gtl  ...           0        0       0      7   \n",
       "440     AllPub    Inside       Gtl  ...         170        0       0      4   \n",
       "769     AllPub   CulDSac       Mod  ...         210        0       0      6   \n",
       "178     AllPub   CulDSac       Gtl  ...           0        0       0      7   \n",
       "\n",
       "     YrSold  SaleType  SaleCondition  SalePrice  TotalSF Total_Bathrooms  \n",
       "691    2007        WD         Normal     755000     6760               5  \n",
       "1182   2007        WD        Abnorml     745000     6872               5  \n",
       "1169   2006        WD         Normal     625000     5557               5  \n",
       "898    2010       New        Partial     611657     4694               4  \n",
       "803    2009       New        Partial     582933     4556               4  \n",
       "1046   2006       New        Partial     556581     4860               4  \n",
       "440    2009        WD         Normal     555000     5496               3  \n",
       "769    2010        WD         Normal     538000     4929               5  \n",
       "178    2009       New        Partial     501837     4450               3  \n",
       "\n",
       "[9 rows x 78 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_remove_outliers = df_first_transform.copy()\n",
    "# Define a Z-score threshold (the usual one is 3, we'll set it as 4 to delete only the highest outliers)\n",
    "z_threshold = 4\n",
    "# Calculate Z-scores for each column\n",
    "z_scores = df[['SalePrice']].apply(zscore)\n",
    "# Differenciate outliers from not outliers\n",
    "outliers_index = (z_scores.abs() > z_threshold)[\"SalePrice\"]\n",
    "# Delete the outliers from the dataset\n",
    "df_remove_outliers = df_remove_outliers[~outliers_index]\n",
    "\n",
    "print(\"The outliers removed are the following:\\n\")\n",
    "df_first_transform[outliers_index].sort_values(by=\"SalePrice\", ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, the deleted 9 outliers from the right tail, now the distribution of the SalePrice feature is the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='SalePrice', ylabel='Count'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "sns.histplot(df_remove_outliers['SalePrice'], bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0lzSnXmfW4IL",
    "tags": []
   },
   "source": [
    "<a id='TRANSFORMCAT'></a>\n",
    "## Transform categorical variables to numerical\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zRCCVzMWXDrB"
   },
   "source": [
    "There are ml algorithms that only work with numerical data, for that reason we need to transform our category features into numerical features. We'll use numerical imputation with ordinal features, where exist an order in the possible values of the feature, for example ratings, size classifications, etc. For nominal variables we'll use one hot encoding. For more info [here](https://medium.com/@brandon93.w/converting-categorical-data-into-numerical-form-a-practical-guide-for-data-science-99fdf42d0e10)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ordinal features\n",
    "The ordinal inputation has been done in [this](#FFE) section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Nominal features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the nominals features we have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSZoning', 'Street', 'LotShape', 'LandContour', 'Utilities',\n",
       "       'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2',\n",
       "       'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st',\n",
       "       'Exterior2nd', 'MasVnrType', 'Foundation', 'Heating', 'Electrical',\n",
       "       'Functional', 'GarageType', 'SaleType', 'SaleCondition'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_numerical = df_remove_outliers.copy()\n",
    "df_numerical.select_dtypes('object').columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8gc0COxmIf51"
   },
   "source": [
    "Let's use **dummy encoding** to encode the rest of categorical features. First, let's input an 'unknown' value for those nan values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NjHFqhKsL191",
    "outputId": "6d543375-6f04-4c84-fa1a-155b4e18ec7d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>196.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>162.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>350.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 231 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0          60         65.0     8450            7            5       2003   \n",
       "1          20         80.0     9600            6            8       1976   \n",
       "2          60         68.0    11250            7            5       2001   \n",
       "3          70         60.0     9550            7            5       1915   \n",
       "4          60         84.0    14260            8            5       2000   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  ExterQual  ExterCond  ...  SaleType_ConLw  \\\n",
       "0          2003       196.0          4          3  ...               0   \n",
       "1          1976         0.0          3          3  ...               0   \n",
       "2          2002       162.0          4          3  ...               0   \n",
       "3          1970         0.0          3          3  ...               0   \n",
       "4          2000       350.0          4          3  ...               0   \n",
       "\n",
       "   SaleType_New  SaleType_Oth  SaleType_WD  SaleCondition_Abnorml  \\\n",
       "0             0             0            1                      0   \n",
       "1             0             0            1                      0   \n",
       "2             0             0            1                      0   \n",
       "3             0             0            1                      1   \n",
       "4             0             0            1                      0   \n",
       "\n",
       "   SaleCondition_AdjLand  SaleCondition_Alloca  SaleCondition_Family  \\\n",
       "0                      0                     0                     0   \n",
       "1                      0                     0                     0   \n",
       "2                      0                     0                     0   \n",
       "3                      0                     0                     0   \n",
       "4                      0                     0                     0   \n",
       "\n",
       "   SaleCondition_Normal  SaleCondition_Partial  \n",
       "0                     1                      0  \n",
       "1                     1                      0  \n",
       "2                     1                      0  \n",
       "3                     0                      0  \n",
       "4                     1                      0  \n",
       "\n",
       "[5 rows x 231 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Input 'unknown' value to the features we want to apply one hot encoding\n",
    "df_numerical[df_numerical.select_dtypes('object').columns] = df_numerical[df_numerical.select_dtypes('object').columns].fillna('unknown')\n",
    "#Apply dummy encoding\n",
    "df_numerical = pd.get_dummies(df_numerical, columns=df_numerical.select_dtypes('object').columns )\n",
    "df_numerical.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vIMTqEgPPle5",
    "tags": []
   },
   "source": [
    "<a id='NULL'></a>\n",
    "## Input missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1h_VtpMLP5oz"
   },
   "source": [
    "Let's refresh how much null values we have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fm0qv7CuPkS6",
    "outputId": "d2e12275-7a74-4832-b4d0-63dc2fa17823"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_nulls</th>\n",
       "      <th>perc_null</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <td>690</td>\n",
       "      <td>47.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>259</td>\n",
       "      <td>17.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual</th>\n",
       "      <td>81</td>\n",
       "      <td>5.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish</th>\n",
       "      <td>81</td>\n",
       "      <td>5.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>81</td>\n",
       "      <td>5.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond</th>\n",
       "      <td>81</td>\n",
       "      <td>5.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <td>38</td>\n",
       "      <td>2.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure</th>\n",
       "      <td>38</td>\n",
       "      <td>2.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>37</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <td>37</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond</th>\n",
       "      <td>37</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>8</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_VinylSd</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_CBlock</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_BrkFace</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count_nulls  perc_null\n",
       "FireplaceQu                  690      47.55\n",
       "LotFrontage                  259      17.85\n",
       "GarageQual                    81       5.58\n",
       "GarageFinish                  81       5.58\n",
       "GarageYrBlt                   81       5.58\n",
       "GarageCond                    81       5.58\n",
       "BsmtFinType2                  38       2.62\n",
       "BsmtExposure                  38       2.62\n",
       "BsmtQual                      37       2.55\n",
       "BsmtFinType1                  37       2.55\n",
       "BsmtCond                      37       2.55\n",
       "MasVnrArea                     8       0.55\n",
       "Exterior1st_VinylSd            0       0.00\n",
       "Exterior1st_CBlock             0       0.00\n",
       "Exterior1st_BrkFace            0       0.00"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_null_values = df_numerical.isna().sum().sort_values(ascending = False)\n",
    "perc_null_values = round(df_numerical.isna().sum().sort_values(ascending = False)/len(df_numerical)*100,2)\n",
    "total_null_values = total_null_values.to_frame(name=\"count_nulls\")\n",
    "perc_null_values = perc_null_values.to_frame(name=\"perc_null\")\n",
    "df_null_values = pd.concat([total_null_values, perc_null_values], axis=1)\n",
    "df_null_values.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1X44yerJRQpQ"
   },
   "source": [
    "We have 12 features with null values. Because there are libraries like sckitlearn that don't allow null values, let's **input a value to this nulls**. This can be approached by simple methods. For categorical features, we can input the mode and for numerical features, the mean or the median. This time we'll use a ML approach using **K Nearest Neighbours**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "mMDJcIKbQpcT"
   },
   "outputs": [],
   "source": [
    "from sklearn.impute import KNNImputer\n",
    "df_inputed_nulls = df_numerical.copy()\n",
    "knn_imputer = KNNImputer()\n",
    "X = knn_imputer.fit_transform(df_inputed_nulls)\n",
    "df_inputed_nulls = pd.DataFrame(X, columns = df_inputed_nulls.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 273
    },
    "id": "ttcSSZlyVkR1",
    "outputId": "c60870c7-0a20-49ab-811e-96b105d276b1"
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>60.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>2003.0</td>\n",
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       "      <td>196.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1915.0</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 231 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0        60.0         65.0   8450.0          7.0          5.0     2003.0   \n",
       "1        20.0         80.0   9600.0          6.0          8.0     1976.0   \n",
       "2        60.0         68.0  11250.0          7.0          5.0     2001.0   \n",
       "3        70.0         60.0   9550.0          7.0          5.0     1915.0   \n",
       "4        60.0         84.0  14260.0          8.0          5.0     2000.0   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  ExterQual  ExterCond  ...  SaleType_ConLw  \\\n",
       "0        2003.0       196.0        4.0        3.0  ...             0.0   \n",
       "1        1976.0         0.0        3.0        3.0  ...             0.0   \n",
       "2        2002.0       162.0        4.0        3.0  ...             0.0   \n",
       "3        1970.0         0.0        3.0        3.0  ...             0.0   \n",
       "4        2000.0       350.0        4.0        3.0  ...             0.0   \n",
       "\n",
       "   SaleType_New  SaleType_Oth  SaleType_WD  SaleCondition_Abnorml  \\\n",
       "0           0.0           0.0          1.0                    0.0   \n",
       "1           0.0           0.0          1.0                    0.0   \n",
       "2           0.0           0.0          1.0                    0.0   \n",
       "3           0.0           0.0          1.0                    1.0   \n",
       "4           0.0           0.0          1.0                    0.0   \n",
       "\n",
       "   SaleCondition_AdjLand  SaleCondition_Alloca  SaleCondition_Family  \\\n",
       "0                    0.0                   0.0                   0.0   \n",
       "1                    0.0                   0.0                   0.0   \n",
       "2                    0.0                   0.0                   0.0   \n",
       "3                    0.0                   0.0                   0.0   \n",
       "4                    0.0                   0.0                   0.0   \n",
       "\n",
       "   SaleCondition_Normal  SaleCondition_Partial  \n",
       "0                   1.0                    0.0  \n",
       "1                   1.0                    0.0  \n",
       "2                   1.0                    0.0  \n",
       "3                   0.0                    0.0  \n",
       "4                   1.0                    0.0  \n",
       "\n",
       "[5 rows x 231 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_inputed_nulls.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SAog5zfdPMKG",
    "outputId": "3b4b1c16-7f53-4627-db81-8a270f951e4b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check we don't have any null in the dataframe\n",
    "df_inputed_nulls.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j3iTLkWeSa0c"
   },
   "source": [
    "There's no nulls left and all features are numerical, so we could use this dataset in the case that the model/library we'll try on demands a numerical and non null data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='SPLIT'></a>\n",
    "## Split training and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "ymSbkXEKT1Ga"
   },
   "outputs": [],
   "source": [
    "df_numerical_no_nulls = df_inputed_nulls.copy()\n",
    "X_numerical_no_nulls = df_numerical_no_nulls.dropna().drop(columns=['SalePrice']).values\n",
    "y_numerical_no_nulls = df_numerical_no_nulls.dropna()['SalePrice'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_numerical_no_nulls, y_numerical_no_nulls, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_results = pd.DataFrame()  # Here we'll store the performance metrics of each model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, you can check how many instances and features we have on our training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1015, 230)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare data in training and test dataset\n",
    "\n",
    "It's important to check if train and test data are similar in order to guarantee that we can rely on our metrics performance in the test and training set. The goal in every ML problem is being able to generalize with good results the model we've trained with unseen data (the test set). That's why we have to check that training and test data are similar, because otherwise if they are different we can't expect them to have good and similar metrics performance, even the training data has good performance metrics.\n",
    "\n",
    "First of all, let's compare that in both datasets we have the same proportion of the positive target class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "712.9703597415064"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(y_train.mean()-y_test.mean())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, the difference between the two mean prices are very low (below 1000$), so we can be sure that for the target feature, the training and the test dataset are similar.\n",
    "\n",
    "Secondly, compare all the principal statistic metrics for the other features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": []
   },
   "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>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>94.921173</td>\n",
       "      <td>613.270709</td>\n",
       "      <td>191.0</td>\n",
       "      <td>59.50</td>\n",
       "      <td>383.0</td>\n",
       "      <td>427.50</td>\n",
       "      <td>56245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GrLivArea</th>\n",
       "      <td>15.746138</td>\n",
       "      <td>15.273485</td>\n",
       "      <td>146.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>33.5</td>\n",
       "      <td>44.50</td>\n",
       "      <td>966.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <td>14.476667</td>\n",
       "      <td>12.376460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.25</td>\n",
       "      <td>3384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>13.198321</td>\n",
       "      <td>12.592640</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.25</td>\n",
       "      <td>471.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <td>10.667422</td>\n",
       "      <td>4.286352</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>8.5</td>\n",
       "      <td>63.75</td>\n",
       "      <td>2910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <td>10.517804</td>\n",
       "      <td>7.238467</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.50</td>\n",
       "      <td>207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageArea</th>\n",
       "      <td>9.861420</td>\n",
       "      <td>7.257330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.50</td>\n",
       "      <td>8.5</td>\n",
       "      <td>11.75</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscVal</th>\n",
       "      <td>5.820423</td>\n",
       "      <td>62.829722</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <td>5.602278</td>\n",
       "      <td>5.416604</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalSF</th>\n",
       "      <td>5.078716</td>\n",
       "      <td>35.829846</td>\n",
       "      <td>386.0</td>\n",
       "      <td>42.25</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.50</td>\n",
       "      <td>3938.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScreenPorch</th>\n",
       "      <td>3.913470</td>\n",
       "      <td>7.763547</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolArea</th>\n",
       "      <td>3.421675</td>\n",
       "      <td>44.923181</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>738.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <td>3.357545</td>\n",
       "      <td>17.080358</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass</th>\n",
       "      <td>2.606228</td>\n",
       "      <td>1.062093</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <td>2.571338</td>\n",
       "      <td>9.430302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <td>2.290001</td>\n",
       "      <td>1.712194</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>347.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3SsnPorch</th>\n",
       "      <td>2.213841</td>\n",
       "      <td>10.665330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>2.063559</td>\n",
       "      <td>3.711030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.10</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1stFlrSF</th>\n",
       "      <td>1.870789</td>\n",
       "      <td>10.598426</td>\n",
       "      <td>146.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8.25</td>\n",
       "      <td>1464.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <td>1.519243</td>\n",
       "      <td>4.222609</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.25</td>\n",
       "      <td>9.5</td>\n",
       "      <td>21.00</td>\n",
       "      <td>183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <td>1.378345</td>\n",
       "      <td>11.221274</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.25</td>\n",
       "      <td>129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <td>0.868636</td>\n",
       "      <td>0.670508</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>0.620513</td>\n",
       "      <td>1.071262</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold</th>\n",
       "      <td>0.162196</td>\n",
       "      <td>0.058724</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <td>0.084740</td>\n",
       "      <td>0.010956</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    mean         std    min    25%    50%     75%      max\n",
       "LotArea        94.921173  613.270709  191.0  59.50  383.0  427.50  56245.0\n",
       "GrLivArea      15.746138   15.273485  146.0  14.00   33.5   44.50    966.0\n",
       "BsmtFinSF1     14.476667   12.376460    0.0   0.00   26.0   26.25   3384.0\n",
       "MasVnrArea     13.198321   12.592640    0.0   0.00    0.0    9.25    471.0\n",
       "TotalBsmtSF    10.667422    4.286352    0.0   9.00    8.5   63.75   2910.0\n",
       "2ndFlrSF       10.517804    7.238467    0.0   0.00    0.0    8.50    207.0\n",
       "GarageArea      9.861420    7.257330    0.0   9.50    8.5   11.75     28.0\n",
       "MiscVal         5.820423   62.829722    0.0   0.00    0.0    0.00   7200.0\n",
       "EnclosedPorch   5.602278    5.416604    0.0   0.00    0.0    0.00    259.0\n",
       "TotalSF         5.078716   35.829846  386.0  42.25   20.0   13.50   3938.0\n",
       "ScreenPorch     3.913470    7.763547    0.0   0.00    0.0    0.00     40.0\n",
       "PoolArea        3.421675   44.923181    0.0   0.00    0.0    0.00    738.0\n",
       "LowQualFinSF    3.357545   17.080358    0.0   0.00    0.0    0.00     59.0\n",
       "MSSubClass      2.606228    1.062093    0.0   0.00    0.0   10.00      0.0\n",
       "WoodDeckSF      2.571338    9.430302    0.0   0.00    0.0    0.00    121.0\n",
       "BsmtFinSF2      2.290001    1.712194    0.0   0.00    0.0    0.00    347.0\n",
       "3SsnPorch       2.213841   10.665330    0.0   0.00    0.0    0.00    101.0\n",
       "LotFrontage     2.063559    3.711030    0.0   0.10    0.3    1.10    160.0\n",
       "1stFlrSF        1.870789   10.598426  146.0  11.00   22.0    8.25   1464.0\n",
       "BsmtUnfSF       1.519243    4.222609    0.0  18.25    9.5   21.00    183.0\n",
       "OpenPorchSF     1.378345   11.221274    0.0   0.00    2.5    1.25    129.0\n",
       "YearRemodAdd    0.868636    0.670508    0.0   2.00    1.0    1.00      1.0\n",
       "YearBuilt       0.620513    1.071262    8.0   2.00    0.0    0.00      1.0\n",
       "MoSold          0.162196    0.058724    0.0   0.00    0.0    0.00      0.0\n",
       "TotRmsAbvGrd    0.084740    0.010956    1.0   0.00    0.0    0.00      2.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_X_train  = pd.DataFrame(data=X_train, columns=df_numerical_no_nulls.columns.drop('SalePrice'))\n",
    "df_X_test  = pd.DataFrame(data=X_test, columns=df_numerical_no_nulls.columns.drop('SalePrice'))\n",
    "describe_X_train = df_X_train.describe().T\n",
    "describe_X_test = df_X_test.describe().T\n",
    "abs(describe_X_train.sub(describe_X_test).iloc[:,1:]).sort_values(by='mean', ascending = False).head(25)  # Substract the metrics for each feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The difference between the principals statistic metrics of training and test dataset are low or near or equally 0, so we can be sure that the training and the test dataset are similar enough to generalize the results of the model in unseen data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='METRIC'></a>\n",
    "## Performance Metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this regression problem we'll be using these following two performance metrics:\n",
    "\n",
    " - **RMSLE** (Root mean squared error). This metric is the root of MSE, which is the mean of all the squared differences between the real value and the predicted one. This metric is interesting because is in the same unit that the target feature, so is easily interpretable. It is a differentiable function, which is pretty important for the optimization algorithms during training. On the other hand, this metric is more sensitive to outliers that others metrics as MAE. In this project, we've already removed the most important outliers so this shouldn't be a huge problem.\n",
    " - **R2** (Coefficient of determination). This metric tries to measure how good or how bad is the model trained respect to a model that always returns the mean of the target. This metric is upper bounded by 1, and for R2 values near 1 it means that our model is far much better that the mean estimator. For values around 0, means that our model performs likewise the mean estimator. This metric is not lower bounded.\n",
    " \n",
    "We'll be using both metrics to check how the trained models are performing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define error metrics\n",
    "def RMSE(y, y_pred):\n",
    "    return np.sqrt(mean_squared_error(y, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='MODELS'></a>\n",
    "## Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we'll try to find a model able to predict the house price sale based on all the features. To do so, we'll use a hyperparameter tuning and a cross validation. \n",
    "\n",
    "We'll use 10 folds for cross validation and we'll use the GridSearchCV for the hyperparameter tunning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Setup cross validation folds\n",
    "kf = KFold(n_splits=10, random_state=42, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WzlPVuPVXRsk",
    "tags": []
   },
   "source": [
    "<a id='SVM'></a>\n",
    "### **SVM**\n",
    "\n",
    "Support vector machine model is commonly used for classification problems, but also can be used for regression and its called SVR. The main idea of SVR is to fit our data with a hyperplane. It could be very possible that our data is not linear, so SVM can also **transform the data into a greater dimension** (with a kernel function) in order to **find a hyperplane** that is able to fit data in this new dimension.\n",
    "\n",
    "Similar to SVM for classification, to find this hyperplane this model uses support vectors, which are those instances that fall in an epsilon \"tube\". This **epsilon-tube** (which is a parameter) measures how much we can tolerate the errors in the prediction, because for the instances that fall in this tube there is no error, for that reason, it's also called epsilon-insensitive tube (this tube can be more or less width depending on the problem error tolerance). The model is trained to minimize the differences between the actual and predicted value within the specified epsilon-insensitive tube\n",
    "\n",
    "Another important parameter for this model is the **'C' parameter**, which is a regularization parameter that controls the trade-off between training error and model complexity: for a smaller 'C' we can get more training error, but we generalise better, for greater 'C' we get the opposite.\n",
    "\n",
    "This model is effective when we have a **high number of features**, that is because the curse of dimensionality works in our favor, because when the dimensionality grows also grows the sparsity and the distance between our data, so it could be easier to find a hyperplane that fit our data.\n",
    "\n",
    "Support vector machine works better with **standardized data**, that's why we'll create a pipeline that includes the standardization of the data as a first step of a grid search hiperparameter tunning.\n",
    "\n",
    "For more information [here](https://towardsdatascience.com/an-introduction-to-support-vector-regression-svr-a3ebc1672c2) and [here](https://medium.com/it-paragon/support-vector-machine-regression-cf65348b6345)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FlZS6pZBmoVV",
    "outputId": "3e348510-3748-45ce-866d-d8a1252506cf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 48 candidates, totalling 480 fits\n",
      "Best params:\n",
      "\n",
      "{'svc__C': 1, 'svc__gamma': 0.01, 'svc__kernel': 'poly'}\n",
      "CPU times: total: 4.33 s\n",
      "Wall time: 2min 53s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pipeline_svm = Pipeline([ ( \"scaler\" , StandardScaler()),\n",
    "                       (\"svc\",SVC())])\n",
    "param_grid_svc = {'svc__C': [1,10,100], #Regularization parameter. \n",
    "                  'svc__gamma': [0.01,0.001,0.0001,0.00005], #Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’\n",
    "                  'svc__kernel': ['rbf', 'poly', 'sigmoid','linear']}\n",
    "grid_pipeline_svc = GridSearchCV(pipeline_svm,param_grid_svc, n_jobs=-1, cv = kf, verbose = 10)\n",
    "grid_pipeline_svc.fit(X_train,y_train)\n",
    "print(\"Best params:\\n\")\n",
    "print(grid_pipeline_svc.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5TkoAfZWoqAv",
    "outputId": "0b7f9a7f-d9af-47eb-bb31-9be013a62d52"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         SVC Score\n",
      "RMSE  55511.432899\n",
      "R2        0.452671\n"
     ]
    }
   ],
   "source": [
    "pred_grid_pipeline_svc = grid_pipeline_svc.predict(X_test)\n",
    "svc_df = pd.DataFrame(data=[RMSE(y_test, pred_grid_pipeline_svc),r2_score(y_test, pred_grid_pipeline_svc)],\n",
    "             columns=['SVC Score'],\n",
    "             index=[\"RMSE\", \"R2\"])\n",
    "print(svc_df)\n",
    "df_results = pd.concat([df_results,svc_df.T])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o8YT9QB6bKCA",
    "tags": []
   },
   "source": [
    "<a id='DF'></a>\n",
    "### **Decision forests**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision forests are a type of model made of multiple decision trees. There are different ways to **ensemble multiple decision trees** and there are two types of decision forest:\n",
    "- **Random Forest**: is an ensemble of decision trees in which each decision tree is trained with a specific **random noise**.\n",
    "- **Gradient boosting machines** (GBM): is an ensemble of two types of model: a weak one and strong one. This model is made in an iterative way: in each step the weak model is trained to predict the error of the strong one and then this weak model is added to the strong one to reduce its the error in each iteration.\n",
    "\n",
    "In tree algorithms, the value of the feature does not matter, but the order of the values, that's why in these models the **standardization of the data is not necessary** and in this section we won't standardize the data.\n",
    "\n",
    "Finally, these models can be used for classification and regression and they are **robust to noisy data**, have interpretable properties and  are well suited for training on **small datasets**, or on datasets where the ratio of number of features / number of examples is high. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Here](https://developers.google.com/machine-learning/decision-forests?hl=en) for more information about trees and decision forests."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### **GBM**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### **XGBoost**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "params_grid_xgb = {\n",
    "    'n_estimators' : [550,600,650,700],\n",
    "    'max_depth': [2,3,4,5,6,7]}\n",
    "xgb = XGBRegressor(n_jobs= -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 24 candidates, totalling 240 fits\n",
      "Best params:\n",
      "\n",
      "{'max_depth': 5, 'n_estimators': 600}\n",
      "CPU times: total: 12.6 s\n",
      "Wall time: 2min 37s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "grid_xgb = GridSearchCV(xgb, params_grid_xgb, n_jobs=-1, cv=kf, verbose=1)\n",
    "grid_xgb.fit(X_train, y_train)\n",
    "print(\"Best params:\\n\")\n",
    "print(grid_xgb.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance metrics:\n",
      "\n",
      "         XGB Score\n",
      "RMSE  22903.288331\n",
      "R2        0.906829\n"
     ]
    }
   ],
   "source": [
    "pred_grid_pipeline_xgb = grid_xgb.predict(X_test)\n",
    "xgb_df = pd.DataFrame(data=[RMSE(y_test, pred_grid_pipeline_xgb),r2_score(y_test, pred_grid_pipeline_xgb)],\n",
    "             columns=['XGB Score'],\n",
    "             index=[\"RMSE\", \"R2\"])\n",
    "print(\"Performance metrics:\\n\")\n",
    "print(xgb_df)\n",
    "df_results = pd.concat([df_results,xgb_df.T])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### **LightGBM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "params_grid_lightgmb = {\n",
    "    'boosting_type':['gbdt','dart','rf'],\n",
    "    'max_depth':[4,5,6,7,8,9],\n",
    "    'num_leaves':[8,10,11,12,13,14]}\n",
    "lightgbm = LGBMRegressor(n_jobs= -1,verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "Best params:\n",
      "\n",
      "{'boosting_type': 'gbdt', 'max_depth': 8, 'num_leaves': 11}\n",
      "CPU times: total: 6.62 s\n",
      "Wall time: 1min 21s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "grid_lightgmb = GridSearchCV(lightgbm, params_grid_lightgmb, n_jobs=-1, cv=kf, verbose=0)\n",
    "grid_lightgmb.fit(X_train, y_train)\n",
    "print(\"Best params:\\n\")\n",
    "print(grid_lightgmb.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance metrics:\n",
      "\n",
      "      Lightgmb Score\n",
      "RMSE    21965.389512\n",
      "R2          0.914304\n"
     ]
    }
   ],
   "source": [
    "pred_grid_pipeline_lightgmb = grid_lightgmb.predict(X_test)\n",
    "lightgmb_df = pd.DataFrame(data=[RMSE(y_test, pred_grid_pipeline_lightgmb),r2_score(y_test, pred_grid_pipeline_lightgmb)],\n",
    "             columns=['Lightgmb Score'],\n",
    "             index=[\"RMSE\", \"R2\"])\n",
    "print(\"Performance metrics:\\n\")\n",
    "print(lightgmb_df)\n",
    "df_results = pd.concat([df_results,lightgmb_df.T])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### **CatBoost**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model allows us to train with categorical and missing data, so for this model we'll use the version of our dataset prior to transforming out categorical features and inputting them a numerical value to the nulls:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_modeling_categorical = df_remove_outliers.copy()\n",
    "\n",
    "categorical_columns = df_modeling_categorical.select_dtypes(include=['object']).columns.tolist()\n",
    "df_modeling_categorical[categorical_columns] = df_modeling_categorical[categorical_columns].fillna('NaN')\n",
    "cat_indexes = df_modeling_categorical.columns.get_indexer(categorical_columns)\n",
    "\n",
    "X_cat = df_modeling_categorical.drop(columns=['SalePrice']).values\n",
    "y_cat = df_modeling_categorical['SalePrice'].values\n",
    "\n",
    "X_train_cat, X_test_cat, y_train_cat, y_test_cat = train_test_split(X_cat, y_cat, test_size=0.3, random_state=81)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "params_catboost = {\n",
    "                  'iterations': [250,300,350,400]\n",
    "                 }\n",
    "catBoost = CatBoostRegressor(random_state=42, verbose = 0, cat_features=cat_indexes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
      "Best params:\n",
      "\n",
      "{'iterations': 350}\n",
      "CPU times: total: 45.2 s\n",
      "Wall time: 4min 21s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "grid_catBoost = GridSearchCV(catBoost, params_catboost, n_jobs=-1, cv=kf, verbose=1)\n",
    "grid_catBoost.fit(X_train_cat, y_train_cat)\n",
    "print(\"Best params:\\n\")\n",
    "print(grid_catBoost.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance metrics:\n",
      "\n",
      "      catBoost Score\n",
      "RMSE    19111.318220\n",
      "R2          0.919822\n"
     ]
    }
   ],
   "source": [
    "pred_grid_pipeline_catBoost = grid_catBoost.predict(X_test_cat)\n",
    "catBoost_df = pd.DataFrame(data=[RMSE(y_test_cat, pred_grid_pipeline_catBoost),r2_score(y_test_cat, pred_grid_pipeline_catBoost)],\n",
    "             columns=['catBoost Score'],\n",
    "             index=[\"RMSE\", \"R2\"])\n",
    "print(\"Performance metrics:\\n\")\n",
    "print(catBoost_df)\n",
    "df_results = pd.concat([df_results,catBoost_df.T])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### **Random Forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "params_random_forest = {\n",
    "    'n_estimators': [100,120,200],\n",
    "    'max_depth': [15,17,19],\n",
    "    'min_samples_leaf': [2,3,4,5,6]\n",
    "        }\n",
    "rfc = RandomForestRegressor(n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 45 candidates, totalling 450 fits\n",
      "Best params:\n",
      "\n",
      "{'max_depth': 17, 'min_samples_leaf': 4, 'n_estimators': 100}\n",
      "CPU times: total: 10 s\n",
      "Wall time: 10min 7s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "grid_random_forest = GridSearchCV(rfc, params_random_forest, n_jobs=-1, cv=kf, verbose=1)\n",
    "grid_random_forest.fit(X_train, y_train)\n",
    "print(\"Best params:\\n\")\n",
    "print(grid_random_forest.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance metrics:\n",
      "\n",
      "      Random Forest Score\n",
      "RMSE         25188.925280\n",
      "R2               0.887305\n"
     ]
    }
   ],
   "source": [
    "pred_grid_pipeline_random_forest = grid_random_forest.predict(X_test)\n",
    "random_forest_df = pd.DataFrame(data=[RMSE(y_test, pred_grid_pipeline_random_forest),r2_score(y_test, pred_grid_pipeline_random_forest)],\n",
    "             columns=['Random Forest Score'],\n",
    "             index=[\"RMSE\", \"R2\"])\n",
    "print(\"Performance metrics:\\n\")\n",
    "print(random_forest_df)\n",
    "df_results = pd.concat([df_results,random_forest_df.T])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='LR'></a>\n",
    "### **Linear Regressions**\n",
    "\n",
    "The simple idea behind any linear regression, is trying to fit the data with a hyperplane. In linear regression, we can expect overfitting, especially when the number of observations is low respect the number of features. In that case, exists linear regressions that add regularization terms to the fit in order to make the model simpler and deal with the overfitting. Make the model simpler, is done by penalizing the values of the weight of each feature. Let's see which types of penalizations and linear regressions we'll deal in this project:\n",
    "- **Simple linear regression**: This model only fits the data by a hyperplane, without any regularization.\n",
    "- **Ridge linear regression**: This model adds a regularization term that encourage non-informative weights of the model to be towards 0 with a normal distribution. Also, two informative features, nearly identical, would have half of the weight they'd have without ridge normalization. This regularization is also known as L2 regularization.\n",
    "- **Lasso linear regression**: This model adds a regularization term that encourage non-informative weights to become exactly 0.0. Also, strongly informative features on different scales, informative features strongly correlated with other similarly informative features may have a 0 weight. This regularization is also known as L1 regularization.\n",
    "- **ElasticNet linear regression**: This model adds two regularization terms, the lasso and the ridge regularization.\n",
    "\n",
    "[Here](https://developers.google.com/machine-learning/crash-course/regularization-for-simplicity/l2-regularization) for more information about L2 regularization. [Here](https://developers.google.com/machine-learning/crash-course/regularization-for-sparsity/l1-regularization) for more information about L1 regularization. \n",
    "\n",
    "Let's try to fit our data to each of these linear regressions:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Simple Linear Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 219 ms\n",
      "Wall time: 132 ms\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "linear_model = LinearRegression()\n",
    "linear_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance metrics:\n",
      "\n",
      "      linear model Score\n",
      "RMSE        30611.436295\n",
      "R2              0.833563\n"
     ]
    }
   ],
   "source": [
    "pred_linear_model = linear_model.predict(X_test)\n",
    "linear_model_df = pd.DataFrame(data=[RMSE(y_test, pred_linear_model),r2_score(y_test, pred_linear_model)],\n",
    "             columns=['linear model Score'],\n",
    "             index=[\"RMSE\", \"R2\"])\n",
    "print(\"Performance metrics:\\n\")\n",
    "print(linear_model_df)\n",
    "df_results = pd.concat([df_results,linear_model_df.T])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### **Ridge Regression** (L2 Regularization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params:\n",
      "\n",
      "{'ridge__alpha': 250}\n",
      "CPU times: total: 6.97 s\n",
      "Wall time: 2.97 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pipeline_ridge = Pipeline([( \"scaler\" , StandardScaler()),\n",
    "                       (\"ridge\",Ridge())])\n",
    "ridge_params = {'ridge__alpha': [0.1, 1, 10,50,100,150,200,250,350,400,450]}\n",
    "ridge_grid = GridSearchCV(pipeline_ridge, ridge_params, cv=kf)\n",
    "ridge_grid.fit(X_train, y_train)\n",
    "print(\"Best params:\\n\")\n",
    "print(ridge_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance metrics:\n",
      "\n",
      "       Ridge Score\n",
      "RMSE  26606.641233\n",
      "R2        0.874263\n"
     ]
    }
   ],
   "source": [
    "pred_ridge = ridge_grid.predict(X_test)\n",
    "ridge_df = pd.DataFrame(data=[RMSE(y_test, pred_ridge),r2_score(y_test, pred_ridge)],\n",
    "             columns=['Ridge Score'],\n",
    "             index=[\"RMSE\", \"R2\"])\n",
    "print(\"Performance metrics:\\n\")\n",
    "print(ridge_df)\n",
    "df_results = pd.concat([df_results,ridge_df.T])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### **Lasso Regression** (L1 Regularization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params:\n",
      "\n",
      "{'lasso__alpha': 100}\n",
      "CPU times: total: 53.8 s\n",
      "Wall time: 13.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pipeline_lasso = Pipeline([\n",
    "                       (\"lasso\",Lasso())])\n",
    "lasso_params = {'lasso__alpha': [0.1, 1, 10,100,150,200,250,300]}\n",
    "lasso_grid = GridSearchCV(pipeline_lasso, lasso_params, cv=kf)\n",
    "lasso_grid.fit(X_train, y_train)\n",
    "print(\"Best params:\\n\")\n",
    "print(lasso_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance metrics:\n",
      "\n",
      "       Lasso Score\n",
      "RMSE  26290.517656\n",
      "R2        0.877233\n"
     ]
    }
   ],
   "source": [
    "pred_lasso = lasso_grid.predict(X_test)\n",
    "lasso_df = pd.DataFrame(data=[RMSE(y_test, pred_lasso),r2_score(y_test, pred_lasso)],\n",
    "             columns=['Lasso Score'],\n",
    "             index=[\"RMSE\", \"R2\"])\n",
    "print(\"Performance metrics:\\n\")\n",
    "print(lasso_df)\n",
    "df_results = pd.concat([df_results,lasso_df.T])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### **ElasticNet Regression** (L1 and L2 Regularization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params:\n",
      "\n",
      "{'elastic__alpha': 1, 'elastic__l1_ratio': 0.5}\n",
      "CPU times: total: 22.7 s\n",
      "Wall time: 5.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pipeline_elastic = Pipeline([( \"scaler\" , StandardScaler()),\n",
    "                       (\"elastic\",ElasticNet())])\n",
    "elasticnet_params = {'elastic__alpha': [0.1, 1, 10], 'elastic__l1_ratio': [0.01, 0.1, 0.5, 0.9]}\n",
    "elasticnet_grid = GridSearchCV(pipeline_elastic, elasticnet_params, cv=kf)\n",
    "elasticnet_grid.fit(X_train, y_train)\n",
    "print(\"Best params:\\n\")\n",
    "print(elasticnet_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance metrics:\n",
      "\n",
      "      elasticnet Score\n",
      "RMSE      26519.716080\n",
      "R2            0.875083\n"
     ]
    }
   ],
   "source": [
    "pred_elasticnet = elasticnet_grid.predict(X_test)\n",
    "elasticnet_df = pd.DataFrame(data=[RMSE(y_test, pred_elasticnet),r2_score(y_test, pred_elasticnet)],\n",
    "             columns=['elasticnet Score'],\n",
    "             index=[\"RMSE\", \"R2\"])\n",
    "print(\"Performance metrics:\\n\")\n",
    "print(elasticnet_df)\n",
    "df_results = pd.concat([df_results,elasticnet_df.T])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='KNN'></a>\n",
    "### **K-nearest neighbour**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model can be used for both classification and regression problems. The main idea of this model is to **find the closest 'k' neighbours of an instance** (here you can considerate different metrics distance and different values of 'k'). For classification problems, the output of the instance would be the class with more frequency in their k-neighbours. For regression problems, the output would be the mean of their k-neighbours.\n",
    "\n",
    "This model is easy and intuitive and it can capture **non linearity** in a simple way. Despite of that, this model is **sensitive to outliers** and is not very efficient when dealing with large datasets. Finally, because of the **curse of dimensionality**, when greater the number of features, the distance between instances is also greater and less meaningful.\n",
    "\n",
    "Finally, in Knn is also important to **scale the features**. Because the algorithm calculates the distances between instances, if the features we're dealing have very different scale, this could imply that the features with larger scales contribute in larger distances between instances. On the other hand, features with shorter scales contribute in shorter distances. That's why we should our data in the same (standard) scale, in order that all features contribute with the same weight to the distance between instances.\n",
    "\n",
    "[Here](https://www.analyticsvidhya.com/blog/2018/03/introduction-k-neighbours-algorithm-clustering/) for more information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "grid_param_knn = {\n",
    "    'knn__n_neighbors':[5,8,9,10,11,12,15,20,25,30,35,40,45,50],\n",
    "    'knn__weights': ['uniform', 'distance']}      \n",
    "knn = KNeighborsRegressor(n_jobs=-1)\n",
    "pipeline_knn = Pipeline([( \"scaler\" , StandardScaler()),\n",
    "                       (\"knn\",knn)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 28 candidates, totalling 280 fits\n",
      "Best params:\n",
      "\n",
      "{'knn__n_neighbors': 10, 'knn__weights': 'distance'}\n",
      "CPU times: total: 2.23 s\n",
      "Wall time: 9.66 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "grid_knn = GridSearchCV(pipeline_knn, grid_param_knn, n_jobs=-1, cv=kf, verbose=1)\n",
    "grid_knn.fit(X_train, y_train)\n",
    "print(\"Best params:\\n\")\n",
    "print(grid_knn.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance metrics:\n",
      "\n",
      "         Knn Score\n",
      "RMSE  36966.075330\n",
      "R2        0.757289\n"
     ]
    }
   ],
   "source": [
    "pred_grid_pipeline_knn = grid_knn.predict(X_test)\n",
    "knn_df = pd.DataFrame(data=[RMSE(y_test, pred_grid_pipeline_knn),r2_score(y_test, pred_grid_pipeline_knn)],\n",
    "             columns=['Knn Score'],\n",
    "             index=[\"RMSE\", \"R2\"])\n",
    "print(\"Performance metrics:\\n\")\n",
    "print(knn_df)\n",
    "df_results = pd.concat([df_results,knn_df.T])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='NN'></a>\n",
    "### **Neural Network**\n",
    "\n",
    " A neural network is a model that is able to learn non linear data itself, without the necessity of having to specify manually the non-linearity. This model is represented by different **layers of neurons** connected to each other, which try to imitate the neurons of our brain. Neural networks comprise an input layer, one or more hidden layers, and an output layer. Each layer contains neurons with connections having associated weights. During the training, the model adjusts the **weights between the neurons**. The non-linearity comes from an **activation function** in each one of the neurons, this activation function is a non-linear function and some examples of that are the Relu, Sigmoid, Tanh, etc.\n",
    " \n",
    "In this case, we won't use the package sklearn form training the neural network, instead we'll use a more extended library as **keras**. For the hyperparameter tunning will use a method of the keras library, which the hyperparameters to test are defined meanwhile you're creating the NN model. As in the sklearn library, the search of hyperparameters can be performed in several ways: can be performed all the combinations (Gridsearch), can be performed by a random pick of combinations (RandomSearch). Also, there are other methods that improve the efficiency, like HyperBand or Bayesian Optimization. Hyperband tries to improve the RandomSearch by picking randomly hyperparameters using just a few epochs of training, and then it does a full training with the best combinations. On the other hand, in Bayesian Optimization, only the first combinations of hyperparameters are picked randomly, then the algorithm, based on the best results, chooses the next best hyperparameters combinations. In this notebook we'll be using the Hyperband approach with keras.\n",
    "\n",
    "[Here](https://medium.com/swlh/hyperparameter-tuning-in-keras-tensorflow-2-with-keras-tuner-randomsearch-hyperband-3e212647778f) for more information.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reloading Tuner from test19\\untitled_project\\tuner0.json\n",
      "CPU times: total: 1.56 s\n",
      "Wall time: 2.42 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "num_features = X_train.shape[1]\n",
    "\n",
    "# Define the TensorFlow model for regression with optimizer, activation function,\n",
    "# number or neurons per layer and learning rate as a hyperparameters\n",
    "def build_model(hp):\n",
    "    model = tf.keras.Sequential()\n",
    "    #Input Layer\n",
    "    model.add(tf.keras.layers.InputLayer(input_shape=(num_features,)))\n",
    "\n",
    "    # Hidden Layers\n",
    "    # First hidden layer\n",
    "    activation_choice_layer1 = hp.Choice('activation_layer1', values=['relu', 'sigmoid', 'selu','tanh', 'softsign','softplus','softmax'])\n",
    "    model.add(tf.keras.layers.Dense(units=hp.Int('units_hidden_layer1', min_value=8, max_value=256, step=16),\n",
    "                                    activation=activation_choice_layer1))\n",
    "    # Second hidden layer\n",
    "    activation_choice_layer2 = hp.Choice('activation_layer2', values=['relu', 'sigmoid', 'selu','tanh','softsign','softplus','softmax'])\n",
    "    model.add(tf.keras.layers.Dense(units=hp.Int('units_hidden_layer2', min_value=8, max_value=256, step=16),\n",
    "                                    activation=activation_choice_layer2))\n",
    "    # Add output layer\n",
    "    model.add(tf.keras.layers.Dense(1))\n",
    "\n",
    "    # Choice optimizer\n",
    "    optimizer_choice = hp.Choice('optimizer', values=['adam', 'sgd', 'rmsprop'])\n",
    "    if optimizer_choice == 'adam':\n",
    "        optimizer = tf.keras.optimizers.Adam(learning_rate=hp.Float('learning_rate', min_value=1e-4, max_value=1e-2, sampling='log'))\n",
    "    elif optimizer_choice == 'sgd':\n",
    "        optimizer = tf.keras.optimizers.SGD(learning_rate=hp.Float('learning_rate', min_value=1e-4, max_value=1e-2, sampling='log'))\n",
    "    else:\n",
    "        optimizer = tf.keras.optimizers.RMSprop(learning_rate=hp.Float('learning_rate', min_value=1e-4, max_value=1e-2, sampling='log'))\n",
    "    \n",
    "    # Create the NN\n",
    "    model.compile(optimizer=optimizer, loss='mean_squared_error')\n",
    "    return model\n",
    "\n",
    "# Hyperparameter tunning\n",
    "tuner = Hyperband( \n",
    "    build_model,\n",
    "    objective='val_loss',\n",
    "    executions_per_trial=1,\n",
    "    directory='test19',\n",
    "    seed = 123\n",
    ")\n",
    "\n",
    "# Perform hyperparameter tuning\n",
    "tuner.search(X_train, y_train, epochs=50,validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters are:\n",
      "\n",
      "{'activation_layer1': 'selu', 'units_hidden_layer1': 184, 'activation_layer2': 'selu', 'units_hidden_layer2': 72, 'optimizer': 'rmsprop', 'learning_rate': 0.0008204040606605068, 'tuner/epochs': 100, 'tuner/initial_epoch': 34, 'tuner/bracket': 1, 'tuner/round': 1, 'tuner/trial_id': '0197'}\n",
      "\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\rojol\\anaconda3\\Lib\\site-packages\\keras\\src\\backend.py:277: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\rojol\\anaconda3\\Lib\\site-packages\\keras\\src\\saving\\legacy\\save.py:538: The name tf.train.NewCheckpointReader is deprecated. Please use tf.compat.v1.train.NewCheckpointReader instead.\n",
      "\n",
      "Summary best model:\n",
      "\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 184)               42504     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 72)                13320     \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 73        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 55897 (218.35 KB)\n",
      "Trainable params: 55897 (218.35 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n",
      "None\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "best_hyperparameters = tuner.get_best_hyperparameters(1)[0]\n",
    "print(\"Best hyperparameters are:\\n\")\n",
    "print(best_hyperparameters.values)\n",
    "print(\"\\n\")\n",
    "best_model = tuner.get_best_models(num_models=1)\n",
    "print(\"Summary best model:\\n\")\n",
    "print(best_model[0].summary())\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model summary:\n",
      "\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_3 (Dense)             (None, 184)               42504     \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 72)                13320     \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 73        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 55897 (218.35 KB)\n",
      "Trainable params: 55897 (218.35 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n",
      "None\n",
      "WARNING:tensorflow:From C:\\Users\\rojol\\anaconda3\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n",
      "Model evaluation:\n",
      "\n",
      "996563136.0\n",
      "CPU times: total: 1min 27s\n",
      "Wall time: 1min 5s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "# Tune the model with the best hyperparameters\n",
    "tf.random.set_seed(123) ## to get initial same weights\n",
    "model = tuner.hypermodel.build(best_hyperparameters)\n",
    "print(\"Model summary:\\n\")\n",
    "print(model.summary())\n",
    "history = model.fit(X_train, y_train, epochs=500, validation_data=(X_test, y_test), verbose=0)\n",
    "print(\"Model evaluation:\\n\")\n",
    "print(model.evaluate(X_test, y_test, verbose = 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 2ms/step\n",
      "Performance metrics:\n",
      "\n",
      "          nn Score\n",
      "RMSE  31568.388096\n",
      "R2        0.822994\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "nn_df = pd.DataFrame(data=[RMSE(y_test, y_pred),r2_score(y_test, y_pred)],\n",
    "             columns=['nn Score'],\n",
    "             index=[\"RMSE\", \"R2\"])\n",
    "print(\"Performance metrics:\\n\")\n",
    "print(nn_df)\n",
    "df_results = pd.concat([df_results,nn_df.T])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1MC'></a>\n",
    "## Model comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see in the next cell, there are all the performance results for each model we've tried to fit to our data. Our main metrics have been the RMSE and R2, so we'll take the Top 3 performing models taking account their values. For this models, we'll try to increase the model performance using the PCA technique, because we're dealing with a high number of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "tags": []
   },
   "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>RMSE</th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>catBoost Score</th>\n",
       "      <td>19111.318220</td>\n",
       "      <td>0.919822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lightgmb Score</th>\n",
       "      <td>21965.389512</td>\n",
       "      <td>0.914304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGB Score</th>\n",
       "      <td>22903.288331</td>\n",
       "      <td>0.906829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest Score</th>\n",
       "      <td>25188.925280</td>\n",
       "      <td>0.887305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso Score</th>\n",
       "      <td>26290.517656</td>\n",
       "      <td>0.877233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elasticnet Score</th>\n",
       "      <td>26519.716080</td>\n",
       "      <td>0.875083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge Score</th>\n",
       "      <td>26606.641233</td>\n",
       "      <td>0.874263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linear model Score</th>\n",
       "      <td>30611.436295</td>\n",
       "      <td>0.833563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nn Score</th>\n",
       "      <td>31568.388096</td>\n",
       "      <td>0.822994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Knn Score</th>\n",
       "      <td>36966.075330</td>\n",
       "      <td>0.757289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVC Score</th>\n",
       "      <td>55511.432899</td>\n",
       "      <td>0.452671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             RMSE        R2\n",
       "catBoost Score       19111.318220  0.919822\n",
       "Lightgmb Score       21965.389512  0.914304\n",
       "XGB Score            22903.288331  0.906829\n",
       "Random Forest Score  25188.925280  0.887305\n",
       "Lasso Score          26290.517656  0.877233\n",
       "elasticnet Score     26519.716080  0.875083\n",
       "Ridge Score          26606.641233  0.874263\n",
       "linear model Score   30611.436295  0.833563\n",
       "nn Score             31568.388096  0.822994\n",
       "Knn Score            36966.075330  0.757289\n",
       "SVC Score            55511.432899  0.452671"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results.sort_values(by='R2', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='PCA'></a>\n",
    "## Apply Principal Components Analysis to Top performing models\n",
    "\n",
    "Principal Components Analysis (PCA) is a method of **reduction dimensionality**. Shouldn't be understood as a feature selection method because the result of PCA is a **linear combination** (principal components) of the original features. The principal idea of this is getting the linear combinations with the **maximum variability of the original features**, with the first principal component explaining the most variance, the second explaining the second most, and so on. This is done by transforming the original problem in finding the eigen vector and eigen values of the covariance matrix, because the eigen vectors are the components and the eigen values are the weight of how these vectors explain the variability of the original features. Is useful to use PCA especially when we're dealing with highly correlated features.\n",
    "\n",
    "An important parameter for PCA is how many principal components we pick. This can be done by using cross-validation, but also there are several rules of thumb to know in advance which could be a good number.  \n",
    "\n",
    "In this problem, we have a large number of features, so let's try to improve the performance of the top models by **reducing the overfitting** using PCA.\n",
    "\n",
    "For more information [here](https://builtin.com/data-science/step-step-explanation-principal-component-analysis), [here](https://towardsdatascience.com/principal-component-regression-clearly-explained-and-implemented-608471530a2f) and [here](https://dorukkilitcioglu.com/2018/08/11/pca-decision-tree.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Find the optimal number of components\n",
    "\n",
    "In this section, we'll take a look to two methods in order to know with how many principal components we should pick in our cross validation process in order to reduce as much as possible the dimensionality of the model and still having high variability explained with these principal components. \n",
    "\n",
    "The methods will use are: observing the ratio of explained variance and the Elbow method. [Here](https://towardsdatascience.com/how-to-select-the-best-number-of-principal-components-for-the-dataset-287e64b14c6d) for more information "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Observing the ratio of explained variance**\n",
    "With this method, we can see how much variance is explained by adding the principal components one by one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TaVi+V3sLkUUvhCPEy0niREREZG4VLpAuXLiAXbt24e7duwC0lz4TVTeXbubinR+PAQBGdwxC7+a+EiciIqLKYHSBdOvWLXTt2hUNGzZEr169cP36dQDAmDFj8M4775g8IJFU8lVqvLn+CLLzi9A6sBam9wqVOhIREVUSowukyZMnw8rKCikpKXBwuH8zzsGDByM2Ntak4YikIoTAjC0ncea6Ep5ONvj8pZawVvCMNBFRTWH0SNNff/0Vu3btQp06dfSWN2jQAJcvXzZZMCIpbfz3Cv53+CrkMuCzoS3g42ondSQiIqpERv+TODc3V6/nqFhmZiZsbTmjMFm+k6l3MGvbKQDA/0U2QodgT4kTERFRZTO6QHrqqafwzTff6F7LZDJoNBosWrQInTt3Nmk4osqWlVeIN749jMIiDbo29sYbT/MmtERENZHRp9gWLVqELl264NChQygsLMTUqVNx6tQpZGZm4u+//zZHRqJKodEIRP1wDFdv30VddwcsHhQOOW9CS0RUIxndg9S0aVOcO3cOTz75JPr164fc3FwMGDAACQkJCA7mv7bJcn2x9wJ+T8yArZUcy19uCVd7a6kjERGRRCo0HbCrqyvef/99U2chksy+8zcRvfscAGB+/6YI83OVOBEREUnJ6B6k1atX48cffyy1/Mcff8TatWtNEoqoMl2/cxcTvk+ARgCDWwdgUOsAqSMREZHEjC6QFi5cCE/P0lf1eHl54cMPPzRJKKLKUlikwVvrjyAztxBhfi6Y2y9M6khERFQFGF0gpaSkICgoqNTywMBApKSkmCQUUWX5cOcZJKRkwcXOCsuHtYKdtULqSEREVAUYXSB5eXnh+PHjpZYfO3YMHh4eJglFVBm2HbuGNfsvAQA+HRyBuh6l5/ciIqKayegCaejQoZgwYQL27NkDtVoNtVqN33//HRMnTsSQIUPMkZHI5M6nZ2PaJm2hP65zMLo09pY4ERERVSVGX8U2f/58XLp0CV26dIGVlfbtGo0Gw4cP5xgksgg5BUV449vDyCtUo2OIB6K6NZI6EhERVTFGF0g2NjbYuHEj5s+fj2PHjsHe3h7NmjVDYGCgOfIRmZQQAtM2HcfFG7nwcbHDkiEtoOBkkEREVEKF5kECgIYNG6Jhw4amzEJkdmv2X8L249dhJZdh2bCW8HTi/QOJiKg0owsktVqNNWvWIC4uDhkZGdBoNHrrf//9d5OFIzKlw5czsWDHGQDA+70bo1VgLYkTERFRVWV0gTRx4kSsWbMGvXv3RtOmTSGT8fQEVX03cwowbn0CijQCzzX3xcgO9aSOREREVZjRBdL333+PH374Ab169TJHHiKTU2sEJmxIQJoyHyFeTvh4YHMW9kRE9FBGX+ZvY2ODkJAQc2QhMovo3Wex/+ItONgosOLllnC0rfDQOyIiqiGMLpDeeecdLFmyBEIIc+QhMqm4M+lYtuciAOCjgc0R4uUscSIiIrIERv9Tet++fdizZw9++eUXhIWFwdraWm/95s2bTRaO6HGk3MrD5I1HAQAjO9RD33A/aQMREZHFMLpAcnNzw/PPP2+OLEQmk69S4831h6HML0LLum54r1djqSMREZEFMbpAWr16tTlyEJnU7K2ncOqaEu6ONlg2rCVsrIw+m0xERDUY/2pQtfPDv1ew8dAVyGXA0qEt4OtqL3UkIiKyMBW6nOd///sffvjhB6SkpKCwsFBv3ZEjR0wSjKgiTqbewcytJwEA73RvhI4hnhInIiIiS2R0D9Jnn32GUaNGwdvbGwkJCWjTpg08PDyQlJSEnj17miMjkUHu3FXhrfVHUFCkQZdQL7zZKVjqSEREZKGMLpC++OILrFy5EkuXLoWNjQ2mTp2K3bt3Y8KECbhz5445MhI9kkYj8M4PR5GSmYc6tewRPSgCct6EloiIKsjoAiklJQUdOnQAANjb2yM7OxsA8Morr2DDhg2mTUdkoBV/XsRvZzJgYyXHipdbwdXB+tFvIiIiKofRBZKPjw8yMzMBAHXr1sU///wDAEhOTubkkSSJ/Rdv4j+7zgIA5vUNQ1N/V4kTERGRpTO6QHr22Wexbds2AMCoUaMwefJkdOvWDYMHD+b8SFTpMrLzMWFDAjQCeLFVHQx+IkDqSEREVA0YfRXbypUrodFoAADjxo2Dh4cH9u/fj759++L11183eUCi8mjHHR3DzZxChPo4Y37/prwJLRERmYTRBZJcLodcfr/jaciQIRgyZIhJQxEZYuVfSfjr/E3YWyvw+UstYGetkDoSERFVEwYVSMePH0fTpk0hl8tx/Pjxh27bvHlzkwQjepiElNu6cUdz+jbhTWiJiMikDCqQIiIikJaWBi8vL0REREAmk5U5IFsmk0GtVps8JNGDlPkqvL0hAUUageea+2JQa447IiIi0zJokHZycjJq166te56UlITk5ORSj6SkJKMDLFu2DPXq1YOdnR3atm2LgwcPlrutSqXCvHnzEBwcDDs7O4SHhyM2NlZvm+zsbEyaNAmBgYGwt7dHhw4d8O+//+ptM3LkSMhkMr1Hjx49jM5OlU8Igfc2n8DV23cR4G6PDwc047gjIiIyOYMKpMDAQMhkMqhUKsydOxcajQaBgYFlPoyxceNGREVFYfbs2Thy5AjCw8MRGRmJjIyMMrefMWMG/vvf/2Lp0qU4ffo03njjDTz//PNISEjQbfPqq69i9+7dWLduHU6cOIHu3buja9euSE1N1dtXjx49cP36dd2DczhZhh8OXcH249dhJZfhsyEt4GLH+Y6IiMj0jBqkbW1tjU2bNmHmzJkmOXh0dDTGjh2LUaNGAQBWrFiBHTt2YNWqVZg2bVqp7detW4f3338fvXr1AgC8+eab+O2337B48WJ8++23uHv3LjZt2oStW7fi6aefBgDMmTMHP//8M5YvX44PPvhAty9bW1v4+PgYnLWgoAAFBQW610qlEoC2V0ulUhn/4auJ4s9eGW1wPiMHs7edAgBM7hqCpr5OVabtK7MdqjK2gxbb4T62hRbbQasqtIOhxzb6Krb+/ftjy5YtmDx5stGhHlRYWIjDhw9j+vTpumVyuRxdu3ZFfHx8me8pKCiAnZ2d3jJ7e3vs27cPAFBUVAS1Wv3QbYrt3bsXXl5eqFWrFp599ll88MEH8PDwKDfvwoULMXfu3FLL9+zZAwcHh4d/2Bpg9+7dZt1/oRqIPqlAvkqGUFcN/JRnsHPnGbMesyLM3Q6Wgu2gxXa4j22hxXbQkrId8vLyDNrO6AKpQYMGmDdvHv7++2+0atUKjo6OeusnTJhg0H5u3rwJtVoNb29vveXe3t5ITEws8z2RkZGIjo7G008/jeDgYMTFxWHz5s26geHOzs5o37495s+fj8aNG8Pb2xsbNmxAfHw8QkJCdPvp0aMHBgwYgKCgIFy8eBHvvfceevbsifj4eCgUZV8qPn36dERFReleK5VKBAQEoHPnzg8trKo7lUqF3bt3o1u3brC2Nt/prjk/n8H1vCvwcLTBqtfbo7azrdmOVRGV1Q5VHdtBi+1wH9tCi+2gVRXaofgM0KMYXSB9/fXXcHNzw+HDh3H48GG9dTKZzOACqSKWLFmCsWPHIjQ0FDKZDMHBwRg1ahRWrVql22bdunUYPXo0/P39oVAo0LJlSwwdOlQv64PzNjVr1gzNmzdHcHAw9u7diy5dupR5bFtbW9jalv6jbG1tXaO/7MXM2Q6xJ69j/cErAIBPB0fAz93JLMcxBX4ftNgOWmyH+9gWWmwHLSnbwdDjGl0gJScnGx2mLJ6enlAoFEhPT9dbnp6eXu7YoNq1a2PLli3Iz8/HrVu34Ofnh2nTpqF+/fq6bYKDg/HHH38gNzcXSqUSvr6+GDx4sN42JdWvXx+enp64cOFCuQUSSSM16y6m/k8799brnerj6Ya1JU5EREQ1gdH3YjMVGxsbtGrVCnFxcbplGo0GcXFxaN++/UPfa2dnB39/fxQVFWHTpk3o169fqW0cHR3h6+uL27dvY9euXWVuU+zq1au4desWfH19K/6ByOSK1BpM3JAAZX4RwgPc8H/dG0kdiYiIagije5AAbUGxbds2pKSkoLCwUG9ddHS0wfuJiorCiBEj0Lp1a7Rp0wYxMTHIzc3VXdU2fPhw+Pv7Y+HChQCAAwcOIDU1FREREUhNTcWcOXOg0WgwdepU3T537doFIQQaNWqECxcuYMqUKQgNDdXtMycnB3PnzsXAgQPh4+ODixcvYurUqQgJCUFkZGRFmoPM5LO48zh0+Tacba2wdEgLWCskq+eJiKiGMbpAiouLQ9++fVG/fn0kJiaiadOmuHTpEoQQaNmypVH7Gjx4MG7cuIFZs2YhLS0NERERiI2N1Q3cTklJ0bvvW35+PmbMmIGkpCQ4OTmhV69eWLduHdzc3HTb3LlzB9OnT8fVq1fh7u6OgQMHYsGCBbpzjgqFAsePH8fatWuRlZUFPz8/dO/eHfPnzy9zjBFJY//Fm1i65wIA4MMBzVDXg1cKEhFR5TG6QJo+fTr+7//+D3PnzoWzszM2bdoELy8vDBs2rEKzUY8fPx7jx48vc93evXv1Xnfq1AmnT59+6P4GDRqEQYMGlbve3t4eu3btMjonVZ7M3EJM3ngUQgCDWwegT7if1JGIiKiGMfqcxZkzZzB8+HAAgJWVFe7evQsnJyfMmzcPH3/8sckDUs0ihMCUH48hXVmA4NqOmN23idSRiIioBjK6QHJ0dNSNO/L19cXFixd1627evGm6ZFQjrf77EuISM2BjJcfnL7WEg02FhskRERE9FqP/+rRr1w779u1D48aN0atXL7zzzjs4ceIENm/ejHbt2pkjI9UQJ1PvYOEv2tmxZ/ZujMa+LhInIiKimsrgAikzMxPu7u6Ijo5GTk4OAGDu3LnIycnBxo0b0aBBA6OuYCN6UE5BEd7ekACVWiAyzBsvtzPuxsdERESmZHCB5Ofnh/79+2PMmDHo1q0bAO3pthUrVpgtHNUcs7aeRPLNXPi52uHjgc0hk8mkjkRERDWYwWOQvvzyS9y4cQM9evRAvXr1MGfOHFy6dMmM0aim2HzkKjYfSYVcBsQMaQE3BxupIxERUQ1ncIH0yiuvIC4uDhcuXMCIESOwdu1ahISEoFu3bti4cWOpCSOJDJF8MxcztpwEAEzq2hBtgtwlTkRERFSBq9iCgoIwd+5cJCcnIzY2Fl5eXhg9ejR8fX3NeqNaqn4KitR4e8MR5BWq0a6+O8Z1DpE6EhEREYDHvBdb165dsX79enzzzTcAgGXLlpkkFNUMi2LP4mSqErUcrBEzuAUUco47IiKiqqHCk8xcvnwZq1evxtq1a3HlyhV07twZY8aMMWU2qsZ+T0zH1/uSAQCfvBAOH1c7iRMRERHdZ1SBVFBQgE2bNmHVqlXYu3cv/P39MXLkSIwaNQr16tUzU0SqbtKV+fi/H48DAEZ1rIeuTbwlTkRERKTP4ALprbfewvfff4+8vDz069cPO3fuRLdu3Xg5NhlFrRGY9P1RZOYWIszPBdN6hkodiYiIqBSDC6R9+/Zh9uzZePnll+Hh4WHOTFSNLd97AfFJt+Bgo8DSoS1ga6WQOhIREVEpBhdIx48fN2cOqgEOXcrEp7+dBwDM69cU9Ws7SZyIiIiobI91FRuRoe7kqTDx+6NQawSeb+GPgS39pY5ERERULhZIZHZCCLy76ThSs+6inocD5vdvyrFrRERUpbFAIrNbfyAFsafSYK2QYenQlnCyrfDsEkRERJWCBRKZVWKaEvO2nwYAvNsjFM3quEqciIiI6NEM+qe8MQO0mzdvXuEwVL3cLVRj/HcJKCzSoHOj2hjdMUjqSERERAYxqECKiIiATCaDEOKRY0fUarVJgpHlm7f9FC5k5MDL2Rb/eTEcct5KhIiILIRBp9iSk5ORlJSE5ORkbNq0CUFBQfjiiy+QkJCAhIQEfPHFFwgODsamTZvMnZcsxPbj17Dh4BXIZEDM4Ah4ONlKHYmIiMhgBvUgBQYG6p6/+OKL+Oyzz9CrVy/dsubNmyMgIAAzZ85E//79TR6SLMuVzDxM33QCADDumRB0CPGUOBEREZFxjB6kfeLECQQFlR5LEhQUhNOnT5skFFkulVqDtzckILugCK0Ca2FS1wZSRyIiIjKa0QVS48aNsXDhQhQWFuqWFRYWYuHChWjcuLFJw5Hlid59DkevZMHFzgpLhkTASsELJYmIyPIYPSHNihUr0KdPH9SpU0d3xdrx48chk8nw888/mzwgWY6/zt/Aij8uAgA+HtgcdWo5SJyIiIioYowukNq0aYOkpCSsX78eiYmJAIDBgwfjpZdegqOjo8kDkmW4mVOAyRuPQQhgWNu66NnMV+pIREREFVahKY0dHR3x2muvmToLWSiNAKZuOombOQVo5O2Mmc81kToSERHRY6nQAJF169bhySefhJ+fHy5fvgwA+PTTT7F161aThiPLsPe6DH9duAU7azmWvtQCdtYKqSMRERE9FqMLpOXLlyMqKgo9e/bE7du3dRND1qpVCzExMabOR1Xc8at38HOK9ms067kwNPR2ljgRERHR4zO6QFq6dCm+/PJLvP/++7Cyun+GrnXr1jhx4oRJw1HVlq9SY/KPx6ERMvQM88bQNgFSRyIiIjIJowuk5ORktGjRotRyW1tb5ObmmiQUWYYVf1xESuZduNkIfNCvySNvQ0NERGQpjC6QgoKCcPTo0VLLY2NjOQ9SDXIlMw/L92ov6e8fqIGLvbXEiYiIiEzH6KvYoqKiMG7cOOTn50MIgYMHD2LDhg1YuHAhvvrqK3NkpCrogx2nUVCkQbugWojwuCF1HCIiIpMyukB69dVXYW9vjxkzZiAvLw8vvfQS/Pz8sGTJEgwZMsQcGamK+ePcDew6lQ6FXIZZvRvj/GEWSEREVL1UaB6kYcOGYdiwYcjLy0NOTg68vLxMnYuqqMIiDeZuOwUAGNmhHhp4O+G8xJmIiIhMrUIFUjEHBwc4OPB2EjXJqr+TkXQzF55OtpjIG9ESEVE1ZfQg7fT0dLzyyivw8/ODlZUVFAqF3oOqr7Q7+fgsTttfNL1nKFzsODCbiIiqJ6N7kEaOHImUlBTMnDkTvr6+vLS7Bvlw5xnkFarRKrAWnm/hL3UcIiIiszG6QNq3bx/++usvREREmCEOVVX/JN3CtmPXIJMBc/uGQS5nYUxERNWX0afYAgICIIQwRxaqoorUGszeqh2YPaxtXTT1d5U4ERERkXkZXSDFxMRg2rRpuHTpkhniUFW07p/LOJuejVoO1vi/7o2kjkNERGR2Rp9iGzx4MPLy8hAcHAwHBwdYW+sP1M3MzDRZOJLejewCRP96DgAwJTIUbg42EiciIiIyP6MLpJiYGDPEoKpqUWwisguK0MzfFYOf4M1oiYioZjC6QBoxYoQ5clAVdCTlNn48fBUAMLdfGBQcmE1ERDWEQQWSUqmEi4uL7vnDFG9Hlk2tEbqB2S+0qoOWdWtJnIiIiKjyGFQg1apVC9evX4eXlxfc3NzKnPtICAGZTAa1Wm3ykFT5Nv57BSdS78DZ1grv9giVOg4REVGlMqhA+v333+Hu7g4A2LNnj1kDkfSy8grxya5EAMDkbg1R29lW4kRERESVy6ACqVOnTmU+p+pp8a/ncDtPhUbezhjePlDqOERERJWuwjerzcvLQ0pKCgoLC/WWN2/e/LFDkXROpt7B+gOXAQBz+obBSmH0VFlEREQWz+gC6caNGxg1ahR++eWXMtdzDJLlEkJg9rZT0AigT7gf2gd7SB2JiIhIEkZ3D0yaNAlZWVk4cOAA7O3tERsbi7Vr16JBgwbYtm2bOTJSJfkpIRWHL9+Gg40C7/XiwGwiIqq5jO5B+v3337F161a0bt0acrkcgYGB6NatG1xcXLBw4UL07t3bHDnJzLLzVfhwp3Zg9tvPNoCvq73EiYiIiKRjdA9Sbm4uvLy8AGgv/79x4wYAoFmzZjhy5Ihp01GlWfLbedzMKUB9T0eMfrKe1HGIiIgkZXSB1KhRI5w9exYAEB4ejv/+979ITU3FihUr4Ovra/KAZH7n0rOxev8lAMDsvmGwtVJIG4iIiEhiRp9imzhxIq5fvw4AmD17Nnr06IH169fDxsYGa9asMXU+MjMhBOZsOwW1RqB7E290alhb6khERESSM7pAevnll3XPW7VqhcuXLyMxMRF169aFp6enScOR+e08kYb9F2/B1kqOmc81kToOERFRlVDheZCKOTg4oGXLlqbIQpUsr7AIH+w4DQB4o1MwAtwdJE5ERERUNRhUIEVFRRm8w+jo6AqHocq1bM8FXL+Tjzq17PHmM8FSxyEiIqoyDCqQEhISDNpZWTexpaop+WYuvvwzGQAw87kmsLPmwGwiIqJiBhVIvEFt9SKEwNyfT6FQrcHTDWujexNvqSMRERFVKY91o60rV67gypUrpspClSTuTAb2nr0Ba4UMs/s0Yc8fERFRCUYXSEVFRZg5cyZcXV1Rr1491KtXD66urpgxYwZUKpU5MpIJ5avUmLddOzB7zJP1EVzbSeJEREREVY/RV7G9/fbb2Lx5MxYtWoT27dsDAOLj4zFnzhzcunULy5cvN3lIMp0v/0xCSmYevF1s8fazIVLHISIiqpKMLpC+++47fP/99+jZs6duWfPmzREQEIChQ4eyQKrCrt7Ow7K9FwAA7/duAkfbx57lgYiIqFoy+hSbra0t6tWrV2p5UFAQbGxsTJGJzGTBjjPIV2nQNsgdfZrztjBERETlMbpAGj9+PObPn4+CggLdsoKCAixYsADjx483aTgynb/O38AvJ9OgkMswt18YB2YTERE9hNHnWBISEhAXF4c6deogPDwcAHDs2DEUFhaiS5cuGDBggG7bzZs3my4pVVhhkQZztp0CAAxvH4hQHxeJExEREVVtRvcgubm5YeDAgXjuuecQEBCAgIAAPPfccxgwYABcXV31HoZYtmwZ6tWrBzs7O7Rt2xYHDx4sd1uVSoV58+YhODgYdnZ2CA8PR2xsrN422dnZmDRpEgIDA2Fvb48OHTrg33//1dtGCIFZs2bB19cX9vb26Nq1K86fP29sU1iMNfuTcfFGLjwcbTCpa0Op4xAREVV5RvcgrV692mQH37hxI6KiorBixQq0bdsWMTExiIyMxNmzZ+Hl5VVq+xkzZuDbb7/Fl19+idDQUOzatQvPP/889u/fjxYtWgAAXn31VZw8eRLr1q2Dn58fvv32W3Tt2hWnT5+Gv78/AGDRokX47LPPsHbtWgQFBWHmzJmIjIzE6dOnYWdnZ7LPVxWkK/Ox5Ddt8fduz1C42ltLnIiIiKjqM7oHKTExsdx1u3btMmpf0dHRGDt2LEaNGoUmTZpgxYoVcHBwwKpVq8rcft26dXjvvffQq1cv1K9fH2+++SZ69eqFxYsXAwDu3r2LTZs2YdGiRXj66acREhKCOXPmICQkRHd1nRACMTExmDFjBvr164fmzZvjm2++wbVr17Blyxaj8luChTvPILdQjYgAN7zQso7UcYiIiCyC0T1ILVu2xCeffIJx48bplhUUFOCdd97BV199hfz8fIP2U1hYiMOHD2P69Om6ZXK5HF27dkV8fHyZ7ykoKCjVw2Nvb499+/YB0E5iqVarH7pNcnIy0tLS0LVrV916V1dXtG3bFvHx8RgyZEi5x35wYLpSqQSgPe1XVSfIPHgpE1uOXoNMBszq3QhqdRHUatMeo/izV9U2qCxsBy22gxbb4T62hRbbQasqtIOhxza6QFqzZg3efPNN7NixA6tXr8b169fx0ksvQaPR4K+//jJ4Pzdv3oRarYa3t/59wLy9vcvtpYqMjER0dDSefvppBAcHIy4uDps3b4b63l99Z2dntG/fHvPnz0fjxo3h7e2NDRs2ID4+HiEh2kkR09LSdMcpedzidWVZuHAh5s6dW2r5nj174ODgYPDnrixqAfznuAKADO1ra3Dl2N+4csx8x9u9e7f5dm5B2A5abActtsN9bAsttoOWlO2Ql5dn0HZGF0iDBg1Chw4dMGrUKISFhSE3NxcjR47E4sWLzV4oLFmyBGPHjkVoaChkMhmCg4MxatQovVNy69atw+jRo+Hv7w+FQoGWLVti6NChOHz48GMde/r06YiKitK9ViqVCAgIQOfOneHh4fFY+zaHdf+k4FpeIlztrfDp6Cfh7mieOapUKhV2796Nbt26wdq65o5vYjtosR202A73sS202A5aVaEdis8APUqFp1IuLCyEWq2GWq2Gr6+v0YObPT09oVAokJ6errc8PT0dPj4+Zb6ndu3a2LJlC/Lz83Hr1i34+flh2rRpqF+/vm6b4OBg/PHHH8jNzYVSqYSvry8GDx6s26Z43+np6fD1vT9ZYnp6OiIiIsrNa2trC1tb21LLra2tq9yX/WZOAWLitDNm/19kKLzdHM1+zKrYDlJgO2ixHbTYDvexLbTYDlpStoOhxzV6kPb333+PZs2awdXVFefOncOOHTuwcuVKPPXUU0hKSjJ4PzY2NmjVqhXi4uJ0yzQaDeLi4nT3eCuPnZ0d/P39UVRUhE2bNqFfv36ltnF0dISvry9u376NXbt26bYJCgqCj4+P3nGVSiUOHDjwyONaik9iz0KZX4Qmvi54qU1dqeMQERFZHKMLpDFjxuDDDz/Etm3bULt2bXTr1g0nTpyAv7//Q3tgyhIVFYUvv/wSa9euxZkzZ/Dmm28iNzcXo0aNAgAMHz5cbxD3gQMHsHnzZiQlJeGvv/5Cjx49oNFoMHXqVN02u3btQmxsLJKTk7F792507twZoaGhun3KZDJMmjQJH3zwAbZt24YTJ05g+PDh8PPzQ//+/Y1tjirn6JUsbDx0BQAwr18YFHLOmE1ERGQso0+xHTlyBI0aNdJbVqtWLfzwww9Yt26dUfsaPHgwbty4gVmzZiEtLQ0RERGIjY3VDaBOSUmBXH6/hsvPz8eMGTOQlJQEJycn9OrVC+vWrYObm5tumzt37mD69Om4evUq3N3dMXDgQCxYsECvS23q1KnIzc3Fa6+9hqysLDz55JOIjY21+DmQNBqB2VtPAgAGtPRH63ruEiciIiKyTEYXSCWLowe98sorRgcYP358ufdw27t3r97rTp064fTp0w/d36BBgzBo0KCHbiOTyTBv3jzMmzfPqKxV3Y+Hr+DY1TtwsrXCtJ6hUschIiKyWAafYmvSpAkyMzN1r9966y3cvHlT9zojI6NKXu5eU9zJU+Hj2LMAgEldG8DL2bJ7w4iIiKRkcIGUmJiIoqIi3etvv/1W71I5IYTBk0SS6UXvPovM3EI08HLCiA71pI5DRERk0YwepF1MCFFqmUzGAcFSOH1NiXX/XAYAzOkbBmtFhf+zEhERER6jQKKqQQiB2dtOQiOA3s180THEU+pIREREFs/gAkkmk5XqIWKPkfS2Hr2Gfy/dhr21Au/1bix1HCIiomrB4KvYhBDo0qULrKy0b7l79y769OkDGxvtLSweHJ9ElSM7X4UFO88AAMY/GwJ/N3uJExEREVUPBhdIs2fP1ntd1uzVAwcOfPxEZLClv1/AjewC1PNwwKtPBUkdh4iIqNqocIFE0lLmq7D672QAwOw+YbC1UkiciIiIqPrgIG0LdfLqHajUAnVq2aNzqJfUcYiIiKoVFkgW6njqHQBA8zquEichIiKqflggWagTV7UFUjN/N2mDEBERVUMskCzUidTiAok9SERERKb2WAUSby0ijay8QqRk5gFggURERGQORhdIGo0G8+fPh7+/P5ycnJCUlAQAmDlzJr7++muTB6TSTqZq74EX6OEAVwdridMQERFVP0YXSB988AHWrFmDRYsW6SaJBICmTZviq6++Mmk4Ktvx1CwAQFP2HhEREZmF0QXSN998g5UrV2LYsGFQKO7PvRMeHo7ExESThqOynSy+go0FEhERkVkYXSClpqYiJCSk1HKNRgOVSmWSUPRwx69ygDYREZE5GV0gNWnSBH/99Vep5f/73//QokULk4Si8t3OLcTV23cBAGEskIiIiMzC4FuNFJs1axZGjBiB1NRUaDQabN68GWfPnsU333yD7du3myMjPaD48v56Hg5wtecAbSIiInMwugepX79++Pnnn/Hbb7/B0dERs2bNwpkzZ/Dzzz+jW7du5shID9DNf1THTdogRERE1ZjRPUgA8NRTT2H37t2mzkIGuD+DtovESYiIiKovo3uQXn31Vezdu9cMUcgQ92fQdpM2CBERUTVmdIF048YN9OjRAwEBAZgyZQqOHj1qhlhUlls5BUjNKh6gzR4kIiIiczG6QNq6dSuuX7+OmTNn4t9//0WrVq0QFhaGDz/8EJcuXTJDRCpW3HtU39MRLnYcoE1ERGQuFboXW61atfDaa69h7969uHz5MkaOHIl169aVOT8Smc5J3QBtXt5PRERkTo91s1qVSoVDhw7hwIEDuHTpEry9vU2Vi8rACSKJiIgqR4UKpD179mDs2LHw9vbGyJEj4eLigu3bt+Pq1aumzkcP0PUgsUAiIiIyK6Mv8/f390dmZiZ69OiBlStXok+fPrC1tTVHNnrAzZwCXLuTD5mMM2gTERGZm9EF0pw5c/Diiy/Czc3NDHGoPA8O0HayrdD0VURERGQgo//Sjh071hw56BFOcPwRERFRpTGoQBowYADWrFkDFxcXDBgw4KHbbt682STBSB9vMUJERFR5DCqQXF1dIZPJAAAuLi6651R52INERERUeQwqkFavXq17vmbNGnNloXJkZOcjTXlvgLYfZ9AmIiIyN6Mv83/22WeRlZVVarlSqcSzzz5rikxUQvHl/cG1neDIAdpERERmZ3SBtHfvXhQWFpZanp+fj7/++sskoUhf8QSRzXl6jYiIqFIY3B1x/Phx3fPTp08jLS1N91qtViM2Nhb+/v6mTUcA7vcgNWWBREREVCkMLpAiIiIgk8kgk8nKPJVmb2+PpUuXmjQcael6kHgPNiIiokphcIGUnJwMIQTq16+PgwcPonbt2rp1NjY28PLygkKhMEvImixdmY+M7ALIZUATDtAmIiKqFAYXSIGBgQAAjUZjtjBUWvHl/SFeTnCw4QBtIiKiylDhv7inT59GSkpKqQHbffv2fexQdJ9ugkh/N2mDEBER1SBGF0hJSUl4/vnnceLECchkMgghAEA3eaRarTZtwhrufoHE02tERESVxejL/CdOnIigoCBkZGTAwcEBp06dwp9//onWrVtj7969ZohYcwkheIsRIiIiCRjdgxQfH4/ff/8dnp6ekMvlkMvlePLJJ7Fw4UJMmDABCQkJ5shZI6UrC3CjeIC2L3uQiIiIKovRPUhqtRrOzs4AAE9PT1y7dg2AdhD32bNnTZuuhivuPWro7Qx7G14hSEREVFmM7kFq2rQpjh07hqCgILRt2xaLFi2CjY0NVq5cifr165sjY4114moWAE4QSUREVNmMLpBmzJiB3NxcAMC8efPw3HPP4amnnoKHhwc2btxo8oA12fFUThBJREQkBaMLpMjISN3zkJAQJCYmIjMzE7Vq1dJdyUaPTwjBW4wQERFJxCQzD7q7u5tiN/SA63fycTOnEAq5jAO0iYiIKplBBdKAAQMM3uHmzZsrHIbue3CAtp01B2gTERFVJoMKJFdXnuKpbMW3GOEEkURERJXPoAJp9erV5s5BJXCCSCIiIukYPQ8SmZ/eDNocoE1ERFTpjB6kHRQU9NCr1ZKSkh4rEAHX7uQjM7cQVnIZQn2cpY5DRERU4xhdIE2aNEnvtUqlQkJCAmJjYzFlyhRT5arRiieI5ABtIiIiaRhdIE2cOLHM5cuWLcOhQ4ceOxDdH3/ECSKJiIikYbIxSD179sSmTZtMtbsa7fhVThBJREQkJZMVSP/73/84YaQJPDhAmz1IRERE0jD6FFuLFi30BmkLIZCWloYbN27giy++MGm4mujq7bvIylPBWiFDIw7QJiIikoTRBVL//v31XsvlctSuXRvPPPMMQkNDTZWrxiruPWrk4wxbKw7QJiIikoLRBdLs2bPNkYPuuT//kZu0QYiIiGqwCt+sNiMjAxkZGdBoNHrLmzdv/tiharL7txjh+CMiIiKpGF0gHT58GCNGjMCZM2cghNBbJ5PJoFarTRaupuEAbSIioqrB6AJp9OjRaNiwIb7++mt4e3s/dFZtMs6VzLu4c1cFG4UcDb05QJuIiEgqRhdISUlJ2LRpE0JCQsyRp0Yr7j0K9XWGjRVvk0dERCQVo/8Kd+nSBceOHTNHlhrveGoWAE4QSUREJDWje5C++uorjBgxAidPnkTTpk1hbW2tt75v374mC1fTnCwef8QCiYiISFJGF0jx8fH4+++/8csvv5Rax0HaFSeE4C1GiIiIqgijT7G9/fbbePnll3H9+nVoNBq9R0WKo2XLlqFevXqws7ND27ZtcfDgwXK3ValUmDdvHoKDg2FnZ4fw8HDExsbqbaNWqzFz5kwEBQXB3t4ewcHBmD9/vt4VdyNHjoRMJtN79OjRw+jspnT5Vh6y84tgY8UB2kRERFIzugfp1q1bmDx5Mry9vR/74Bs3bkRUVBRWrFiBtm3bIiYmBpGRkTh79iy8vLxKbT9jxgx8++23+PLLLxEaGopdu3bh+eefx/79+9GiRQsAwMcff4zly5dj7dq1CAsLw6FDhzBq1Ci4urpiwoQJun316NEDq1ev1r22tbV97M/zOIoHaDf24QBtIiIiqRn9l3jAgAHYs2ePSQ4eHR2NsWPHYtSoUWjSpAlWrFgBBwcHrFq1qszt161bh/feew+9evVC/fr18eabb6JXr15YvHixbpv9+/ejX79+6N27N+rVq4cXXngB3bt3L9UzZWtrCx8fH92jVq1aJvlMFaWbQZvzHxEREUnO6B6khg0bYvr06di3bx+aNWtWapD2g700D1NYWIjDhw9j+vTpumVyuRxdu3ZFfHx8me8pKCiAnZ2d3jJ7e3vs27dP97pDhw5YuXIlzp07h4YNG+LYsWPYt28foqOj9d63d+9eeHl5oVatWnj22WfxwQcfwMPDo9y8BQUFKCgo0L1WKpUAtKf9VCqVQZ/5YY5fuQ0AaOLjZJL9VZbirJaU2RzYDlpsBy22w31sCy22g1ZVaAdDjy0TJafDfoSgoKDydyaTISkpyaD9XLt2Df7+/ti/fz/at2+vWz516lT88ccfOHDgQKn3vPTSSzh27Bi2bNmC4OBgxMXFoV+/flCr1briRaPR4L333sOiRYugUCigVquxYMECvULs+++/h4ODA4KCgnDx4kW89957cHJyQnx8PBSKsm8QO2fOHMydO7fU8u+++w4ODg4GfebyaAQw/V8F8tUyTG1eBH/Hx9odERERlSMvLw8vvfQS7ty5AxcXl3K3M7oHKTk5+bGCPY4lS5Zg7NixCA0NhUwmQ3BwMEaNGqV3Su6HH37A+vXr8d133yEsLAxHjx7FpEmT4OfnhxEjRgAAhgwZotu+WbNmaN68OYKDg7F371506dKlzGNPnz4dUVFRutdKpRIBAQHo3LnzQ3ueDHHpVi7y//kbtlZyjBzQA9YKyxmDpFKpsHv3bnTr1q1Ub2JNwnbQYjtosR3uY1tosR20qkI7FJ8BepQK36z2cXl6ekKhUCA9PV1veXp6Onx8fMp8T+3atbFlyxbk5+fj1q1b8PPzw7Rp01C/fn3dNlOmTMG0adN0RVCzZs1w+fJlLFy4UFcglVS/fn14enriwoUL5RZItra2ZQ7ktra2fuz/yKfTcgEAjX1d4GAn7WDxijJFO1QHbActtoMW2+E+toUW20FLynYw9LgVuhfbw5Q3wLokGxsbtGrVCnFxcejfvz8A7emxuLg4jB8//qHvtbOzg7+/P1QqFTZt2oRBgwbp1uXl5UEu1++BUSgU0Gg05e7v6tWruHXrFnx9fQ3KbmoneYNaIiKiKsXoAun27dt6r1UqFU6ePImsrCw8++yzRu0rKioKI0aMQOvWrdGmTRvExMQgNzcXo0aNAgAMHz4c/v7+WLhwIQDgwIEDSE1NRUREBFJTUzFnzhxoNBpMnTpVt88+ffpgwYIFqFu3LsLCwpCQkIDo6GhdYZeTk4O5c+di4MCB8PHxwcWLFzF16lSEhIQgMjLS2OYwCU4QSUREVLUYXSD99NNPpZZpNBq8+eabCA4ONmpfgwcPxo0bNzBr1iykpaUhIiICsbGxujmWUlJS9HqD8vPzMWPGDCQlJcHJyQm9evXCunXr4Obmpttm6dKlmDlzJt566y1kZGTAz88Pr7/+OmbNmgVA25t0/PhxrF27FllZWfDz80P37t0xf/58SeZC0mgETl3Tng9lDxIREVHVYJIxSHK5HFFRUXjmmWf0enMMMX78+HJPqe3du1fvdadOnXD69OmH7s/Z2RkxMTGIiYkpc729vT127dplVEZzSr6Vi5yCIthZyxFS20nqOERERIQKTBRZnosXL6KoqMhUu6sxiscfNfF1gZUFXb1GRERUnRndg/Tgpe6A9iar169fx44dO8q9SozKVzz+qBnHHxEREVUZRhdICQkJeq/lcjlq166NxYsXP/IKNyrtRHGBVMdN2iBERESkY3SBZKr7sBGg1gicusYeJCIioqrG4EEvd+/exbZt25CdnV1qnVKpxLZt2/TuVUaPlnwzB7mFathbKxBcm/cXISIiqioMLpBWrlyJJUuWwNnZudQ6FxcXfPbZZ/jqq69MGq66O3FvgHaYHwdoExERVSUG/1Vev349Jk2aVO76SZMmYe3atabIVGNwgkgiIqKqyeAC6fz58wgPDy93ffPmzXH+/HmThKopeIsRIiKiqsngAqmoqAg3btwod/2NGzc4D5IR1BqBk6naGbQ5QJuIiKhqMbhACgsLw2+//Vbu+l9//RVhYWEmCVUTJN3IwV2VGg42CtTnDNpERERVisEF0ujRozF//nxs37691Lqff/4ZCxYs4DxIRigefxTm5wKFXCZxGiIiInqQwfMgvfbaa/jzzz/Rt29fhIaGolGjRgCAxMREnDt3DoMGDcJrr71mtqDVTfEVbM383aQNQkRERKUYdW35t99+i++//x4NGzbEuXPncPbsWTRq1AgbNmzAhg0bzJWxWtIVSHVcJE5CREREJRk9k/agQYMwaNAgc2SpMYrUmgdm0HaTNgwRERGVwtkJJXDxRi7yVRo42ihQ35MzaBMREVU1LJAkcPxqFgAgzN8Vcg7QJiIiqnJYIElAN0Ek5z8iIiKqklggSeC4boA2CyQiIqKqiAVSJStSa3D6GmfQJiIiqsqMvootPz8fS5cuxZ49e5CRkQGNRqO3/siRIyYLVx2dz8hBQZEGTrZWqOfBAdpERERVkdEF0pgxY/Drr7/ihRdeQJs2bSCTcZCxMYrnP2rq78IB2kRERFWU0QXS9u3bsXPnTnTs2NEceaq9E1eL5z/i6TUiIqKqyugxSP7+/nB2djZHlhrh/gzabtIGISIionIZXSAtXrwY7777Li5fvmyOPNWaSq3B6escoE1ERFTVGX2KrXXr1sjPz0f9+vXh4OAAa2trvfWZmZkmC1fdnEvPRmGRBs52Vgh0d5A6DhEREZXD6AJp6NChSE1NxYcffghvb28O0jZC8QSRTf04gzYREVFVZnSBtH//fsTHxyM8PNwceaq14/cGaDfnBJFERERVmtFjkEJDQ3H37l1zZKn2dD1IHH9ERERUpRldIH300Ud45513sHfvXty6dQtKpVLvQWUrLNLgzPVsAOxBIiIiquqMPsXWo0cPAECXLl30lgshIJPJoFarTZOsmjmXno1CtQYudlaoywHaREREVZrRBdKePXvMkaPaO/HADWo5sJ2IiKhqM7pA6tSpkzlyVHu6AsnfTdogRERE9EhGF0gAkJWVha+//hpnzpwBAISFhWH06NFwdeXYmvLwFiNERESWw+hB2ocOHUJwcDA+/fRTZGZmIjMzE9HR0QgODsaRI0fMkdHiFRSpkZimHcDOAdpERERVn9E9SJMnT0bfvn3x5ZdfwspK+/aioiK8+uqrmDRpEv7880+Th7R059JyoFILuNpbo04te6njEBER0SMYXSAdOnRIrzgCACsrK0ydOhWtW7c2abjqonj8UXMO0CYiIrIIRp9ic3FxQUpKSqnlV65cgbOzs0lCVTcnUrMAcIJIIiIiS2F0gTR48GCMGTMGGzduxJUrV3DlyhV8//33ePXVVzF06FBzZLR4uluMsEAiIiKyCEafYvvPf/4DmUyG4cOHo6ioCABgbW2NN998Ex999JHJA1q6fJUa59K1M2izB4mIiMgyGF0g2djYYMmSJVi4cCEuXrwIAAgODoaDA2eHLsvZtGyo1AK1HDhAm4iIyFJUaB4kAHBwcECtWrV0z6ls92fQduMAbSIiIgth9BgkjUaDefPmwdXVFYGBgQgMDISbmxvmz58PjUZjjowW7f4EkS4SJyEiIiJDGd2D9P777+Prr7/GRx99hI4dOwIA9u3bhzlz5iA/Px8LFiwweUhLxluMEBERWR6jC6S1a9fiq6++Qt++fXXLmjdvDn9/f7z11lsskB7w4ADtZpxBm4iIyGIYfYotMzMToaGhpZaHhoYiMzPTJKGqi8S0bBRpBDwcbeDnaid1HCIiIjKQ0QVSeHg4Pv/881LLP//8c4SHh5skVHVx4moWAO3l/RygTUREZDmMPsW2aNEi9O7dG7/99hvat28PAIiPj8eVK1ewc+dOkwe0ZA/eYoSIiIgsh9E9SJ06dcK5c+fw/PPPIysrC1lZWRgwYADOnj2Lp556yhwZLVbxDNqcIJKIiMiyVGgeJD8/Pw7GfoR8lRrnM3IAsAeJiIjI0hjcg9SlSxds3ry53PU3b95E/fr1TRKqOjh9XQm1RsDTyQY+LhygTUREZEkMLpD27NmDQYMGYfbs2WWuV6vVuHz5ssmCWbr7E0RygDYREZGlMWoM0vLlyxETE4Pnn38eubm55spULTx4ixEiIiKyLEYVSP369cM///yDU6dOoV27dkhKSjJXLov3YA8SERERWRajr2Jr3Lgx/v33XwQEBOCJJ57Ab7/9Zo5cFu1uoRrnM7QzaHOANhERkeUxukACAFdXV+zYsQNjx45Fr1698Omnn5o6l0U7ff0ONAKo7WwLbw7QJiIisjgGX+ZfcqCxTCbDRx99hIiICLz66qv4/fffTR7OUhWfXmvO02tEREQWyeAeJCFEmcuHDBmCffv24cSJEyYLZemOp3KCSCIiIktmcA/Snj174O7uXua6iIgIHD58GDt27DBZMEt2krcYISIismgGF0idOnV66HoPDw8MHz78sQNZutyCIly4N4M2r2AjIiKyTBUapE3lO31dCY0AvF1s4cUB2kRERBaJBZKJcf4jIiIiy8cCycR0M2j7u0kbhIiIiCqMBZKJ3b/FiIvESYiIiKiiWCCZUE5BES7e0A7Q5iX+RERElosFkgmdvqaEEICvqx28nDlAm4iIyFKxQDKh41ezALD3iIiIyNKxQDIh3QSRLJCIiIgsGgskE9LdYoQzaBMREVk0yQukZcuWoV69erCzs0Pbtm1x8ODBcrdVqVSYN28egoODYWdnh/DwcMTGxupto1arMXPmTAQFBcHe3h7BwcGYP3++3r3khBCYNWsWfH19YW9vj65du+L8+fOP9Tmy81VIvpkLgHMgERERWTpJC6SNGzciKioKs2fPxpEjRxAeHo7IyEhkZGSUuf2MGTPw3//+F0uXLsXp06fxxhtv4Pnnn0dCQoJum48//hjLly/H559/jjNnzuDjjz/GokWLsHTpUt02ixYtwmeffYYVK1bgwIEDcHR0RGRkJPLz8yv8WU7dG6Dt52oHTyfbCu+HiIiIpGfwvdjMITo6GmPHjsWoUaMAACtWrMCOHTuwatUqTJs2rdT269atw/vvv49evXoBAN5880389ttvWLx4Mb799lsAwP79+9GvXz/07t0bAFCvXj1s2LBB1zMlhEBMTAxmzJiBfv36AQC++eYbeHt7Y8uWLRgyZEiZWQsKClBQUKB7rVQqAWh7tVQqFY6lZAIAwvxcoFKpHrttLEXxZ61Jn7ksbActtoMW2+E+toUW20GrKrSDoceWrEAqLCzE4cOHMX36dN0yuVyOrl27Ij4+vsz3FBQUwM5O//J5e3t77Nu3T/e6Q4cOWLlyJc6dO4eGDRvi2LFj2LdvH6KjowEAycnJSEtLQ9euXXXvcXV1Rdu2bREfH19ugbRw4ULMnTu31PI9e/bAwcEBu87JAchhk3MdO3deM7gdqovdu3dLHaFKYDtosR202A73sS202A5aUrZDXl6eQdtJViDdvHkTarUa3t7eesu9vb2RmJhY5nsiIyMRHR2Np59+GsHBwYiLi8PmzZuhVqt120ybNg1KpRKhoaFQKBRQq9VYsGABhg0bBgBIS0vTHafkcYvXlWX69OmIiorSvVYqlQgICEDnzp3h4eGBT8/tA5CHgZ2fwFMNPI1qC0umUqmwe/dudOvWDdbW1lLHkQzbQYvtoMV2uI9tocV20KoK7VB8BuhRJD3FZqwlS5Zg7NixCA0NhUwmQ3BwMEaNGoVVq1bptvnhhx+wfv16fPfddwgLC8PRo0cxadIk+Pn5YcSIERU+tq2tLWxtS48tsra2xl01cOmWtiKNCPSokV9+a2vrGvm5S2I7aLEdtNgO97EttNgOWlK2g6HHlWyQtqenJxQKBdLT0/WWp6enw8fHp8z31K5dG1u2bEFubi4uX76MxMREODk5oX79+rptpkyZgmnTpmHIkCFo1qwZXnnlFUyePBkLFy4EAN2+jTnuoxTPf+TvZg93R5sK7YOIiIiqDskKJBsbG7Rq1QpxcXG6ZRqNBnFxcWjfvv1D32tnZwd/f38UFRVh06ZNusHWgPbcolyu/7EUCgU0Gg0AICgoCD4+PnrHVSqVOHDgwCOPWx7dBJGc/4iIiKhakPQUW1RUFEaMGIHWrVujTZs2iImJQW5uru6qtuHDh8Pf31/X+3PgwAGkpqYiIiICqampmDNnDjQaDaZOnarbZ58+fbBgwQLUrVsXYWFhSEhIQHR0NEaPHg0AkMlkmDRpEj744AM0aNAAQUFBmDlzJvz8/NC/f/8KfY7jV+9NEMn5j4iIiKoFSQukwYMH48aNG5g1axbS0tIQERGB2NhY3QDqlJQUvd6g/Px8zJgxA0lJSXByckKvXr2wbt06uLm56bZZunQpZs6cibfeegsZGRnw8/PD66+/jlmzZum2mTp1KnJzc/Haa68hKysLTz75JGJjY0tdIWco9iARERFVL5IP0h4/fjzGjx9f5rq9e/fqve7UqRNOnz790P05OzsjJiYGMTEx5W4jk8kwb948zJs3z9i4pWTfVekGaDf1Y4FERERUHUh+qxFLdyY9GwAQ4G6PWhygTUREVC2wQHpMp65pCyTef42IiKj6YIH0mBLTtBNONfN3kzYIERERmQwLpMfEHiQiIqLqhwXSY0rNygfAAomIiKg6YYFkAnXdHeDqwKnjiYiIqgsWSCbQjPMfERERVSsskEygOU+vERERVSsskEyA44+IiIiqFxZIJhDGAomIiKhaYYH0mAJq2cPVngO0iYiIqhMWSI+pia+z1BGIiIjIxFggPabGvi5SRyAiIiITY4H0mML82INERERU3bBAekyNvJ2kjkBEREQmxgLpMTnbcYA2ERFRdcMCiYiIiKgEFkhEREREJbBAIiIiIiqBBRIRERFRCSyQiIiIiEpggURERERUAgskIiIiohJYIBERERGVwAKJiIiIqAQWSEREREQlsEAiIiIiKoEFEhEREVEJLJCIiIiISmCBRERERFQCCyQiIiKiEqykDmCphBAAgOzsbFhbW0ucRjoqlQp5eXlQKpVsB7YD2+EetsN9bAsttoNWVWgHpVIJ4P7f8fKwQKqgW7duAQCCgoIkTkJERETGys7Ohqura7nrWSBVkLu7OwAgJSXloQ1c3SmVSgQEBODKlStwcXGROo5k2A5abActtsN9bAsttoNWVWgHIQSys7Ph5+f30O1YIFWQXK4dvuXq6lqjv+zFXFxc2A5gOxRjO2ixHe5jW2ixHbSkbgdDOjY4SJuIiIioBBZIRERERCWwQKogW1tbzJ49G7a2tlJHkRTbQYvtoMV20GI73Me20GI7aFlSO8jEo65zIyIiIqph2INEREREVAILJCIiIqISWCARERERlcACiYiIiKgEFkgVsGzZMtSrVw92dnZo27YtDh48KHWkSrdw4UI88cQTcHZ2hpeXF/r374+zZ89KHUtSH330EWQyGSZNmiR1FEmkpqbi5ZdfhoeHB+zt7dGsWTMcOnRI6liVSq1WY+bMmQgKCoK9vT2Cg4Mxf/78R97zydL9+eef6NOnD/z8/CCTybBlyxa99UIIzJo1C76+vrC3t0fXrl1x/vx5acKa0cPaQaVS4d1330WzZs3g6OgIPz8/DB8+HNeuXZMusBk96jvxoDfeeAMymQwxMTGVls8QLJCMtHHjRkRFRWH27Nk4cuQIwsPDERkZiYyMDKmjVao//vgD48aNwz///IPdu3dDpVKhe/fuyM3NlTqaJP7991/897//RfPmzaWOIonbt2+jY8eOsLa2xi+//ILTp09j8eLFqFWrltTRKtXHH3+M5cuX4/PPP8eZM2fw8ccfY9GiRVi6dKnU0cwqNzcX4eHhWLZsWZnrFy1ahM8++wwrVqzAgQMH4OjoiMjISOTn51dyUvN6WDvk5eXhyJEjmDlzJo4cOYLNmzfj7Nmz6Nu3rwRJze9R34liP/30E/75559H3vZDEoKM0qZNGzFu3Djda7VaLfz8/MTChQslTCW9jIwMAUD88ccfUkepdNnZ2aJBgwZi9+7dolOnTmLixIlSR6p07777rnjyySeljiG53r17i9GjR+stGzBggBg2bJhEiSofAPHTTz/pXms0GuHj4yM++eQT3bKsrCxha2srNmzYIEHCylGyHcpy8OBBAUBcvny5ckJJpLy2uHr1qvD39xcnT54UgYGB4tNPP630bA/DHiQjFBYW4vDhw+jatatumVwuR9euXREfHy9hMunduXMHwP2b+NYk48aNQ+/evfW+FzXNtm3b0Lp1a7z44ovw8vJCixYt8OWXX0odq9J16NABcXFxOHfuHADg2LFj2LdvH3r27ClxMukkJycjLS1N7/8PV1dXtG3blr8379yBTCaDm5ub1FEqnUajwSuvvIIpU6YgLCxM6jhl4s1qjXDz5k2o1Wp4e3vrLff29kZiYqJEqaSn0WgwadIkdOzYEU2bNpU6TqX6/vvvceTIEfz7779SR5FUUlISli9fjqioKLz33nv4999/MWHCBNjY2GDEiBFSx6s006ZNg1KpRGhoKBQKBdRqNRYsWIBhw4ZJHU0yaWlpAFDm783idTVRfn4+3n33XQwdOrRG3rz2448/hpWVFSZMmCB1lHKxQKLHNm7cOJw8eRL79u2TOkqlunLlCiZOnIjdu3fDzs5O6jiS0mg0aN26NT788EMAQIsWLXDy5EmsWLGiRhVIP/zwA9avX4/vvvsOYWFhOHr0KCZNmgQ/P78a1Q70cCqVCoMGDYIQAsuXL5c6TqU7fPgwlixZgiNHjkAmk0kdp1w8xWYET09PKBQKpKen6y1PT0+Hj4+PRKmkNX78eGzfvh179uxBnTp1pI5TqQ4fPoyMjAy0bNkSVlZWsLKywh9//IHPPvsMVlZWUKvVUkesNL6+vmjSpInessaNGyMlJUWiRNKYMmUKpk2bhiFDhqBZs2Z45ZVXMHnyZCxcuFDqaJIp/t3I35taxcXR5cuXsXv37hrZe/TXX38hIyMDdevW1f3uvHz5Mt555x3Uq1dP6ng6LJCMYGNjg1atWiEuLk63TKPRIC4uDu3bt5cwWeUTQmD8+PH46aef8PvvvyMoKEjqSJWuS5cuOHHiBI4ePap7tG7dGsOGDcPRo0ehUCikjlhpOnbsWGqah3PnziEwMFCiRNLIy8uDXK7/a1WhUECj0UiUSHpBQUHw8fHR+72pVCpx4MCBGvd7s7g4On/+PH777Td4eHhIHUkSr7zyCo4fP673u9PPzw9TpkzBrl27pI6nw1NsRoqKisKIESPQunVrtGnTBjExMcjNzcWoUaOkjlapxo0bh++++w5bt26Fs7OzbiyBq6sr7O3tJU5XOZydnUuNuXJ0dISHh0eNG4s1efJkdOjQAR9++CEGDRqEgwcPYuXKlVi5cqXU0SpVnz59sGDBAtStWxdhYWFISEhAdHQ0Ro8eLXU0s8rJycGFCxd0r5OTk3H06FG4u7ujbt26mDRpEj744AM0aNAAQUFBmDlzJvz8/NC/f3/pQpvBw9rB19cXL7zwAo4cOYLt27dDrVbrfm+6u7vDxsZGqthm8ajvRMni0NraGj4+PmjUqFFlRy2f1JfRWaKlS5eKunXrChsbG9GmTRvxzz//SB2p0gEo87F69Wqpo0mqpl7mL4QQP//8s2jatKmwtbUVoaGhYuXKlVJHqnRKpVJMnDhR1K1bV9jZ2Yn69euL999/XxQUFEgdzaz27NlT5u+DESNGCCG0l/rPnDlTeHt7C1tbW9GlSxdx9uxZaUObwcPaITk5udzfm3v27JE6usk96jtRUlW8zF8mRDWf4pWIiIjISByDRERERFQCCyQiIiKiElggEREREZXAAomIiIioBBZIRERERCWwQCIiIiIqgQUSERERUQkskIiIiIhKYIFERBbjmWeewaRJk6SOoSOEwGuvvQZ3d3fIZDIcPXpU6khEZCIskIgksHfvXshksnIfnTt3ljpiKcWZw8LCoFar9da5ublhzZo10gSTUGxsLNasWYPt27fj+vXr5d6DTwiBlStXom3btnBycoKbmxtat26NmJgY5OXlVXLqqqv4O5aVlSV1FCIWSERS6NChA65fv17q8d///hcymQxvvfWW1BHLlZSUhG+++UbqGCajVquh0Wgq9N6LFy/C19cXHTp0gI+PD6ysyr7/9yuvvIJJkyahX79+2LNnD44ePYqZM2di69at+PXXXx8nPhGZi7S3giOiYqdPnxbOzs7i/fff11u+d+9e8cQTTwgbGxvh4+Mj3n33XaFSqXTr8/Pzxdtvvy1q164tbG1tRceOHcXBgwd164tvGhkbGysiIiKEnZ2d6Ny5s0hPTxc7d+4UoaGhwtnZWQwdOlTk5uaWm694P1OmTBEBAQEiPz9ft87V1VV3o+Lim3ImJCTo1t++fVvvppwVzdSpUycxbtw4MW7cOOHi4iI8PDzEjBkzhEaj0WuPd955R/j5+QkHBwfRpk0bvZuBrl69Wri6uoqtW7eKxo0bC4VCIZKTk8v8zA9r+xEjRujdhDMwMLDMfWzcuFEAEFu2bCm1TqPRiKysLCGEEGq1WsydO1f4+/sLGxsbER4eLn755RfdtsXtunHjRvHkk08KOzs70bp1a3H27Flx8OBB0apVK+Ho6Ch69OghMjIydO8bMWKE6Nevn5gzZ47w9PQUzs7O4vXXX9e7ga6h36HffvtNtGrVStjb24v27duLxMREvc+zZcsW0aJFC2FrayuCgoLEnDlz9L6rAMSXX34p+vfvL+zt7UVISIjYunWr3udDGTc2/fHHH0XTpk2FnZ2dcHd3F126dBE5OTlltjeRqbBAIqoCbt++LRo0aCD69Omj98f+6tWrwsHBQbz11lvizJkz4qeffhKenp5i9uzZum0mTJgg/Pz8xM6dO8WpU6fEiBEjRK1atcStW7eEEPf/uLVr107s27dPHDlyRISEhIhOnTqJ7t27iyNHjog///xTeHh4iI8++qjcjMX7SU1NFb6+vuKTTz7RratogWRspk6dOgknJycxceJEkZiYKL799lvh4OAgVq5cqdvm1VdfFR06dBB//vmnuHDhgvjkk0+Era2tOHfunBBCWyBZW1uLDh06iL///lskJiaWWRg+qu2zsrLEvHnzRJ06dcT169f1ipIH9e3bVzRq1Kjcdi0WHR0tXFxcxIYNG0RiYqKYOnWqsLa21uUubtfQ0FARGxsrTp8+Ldq1aydatWolnnnmGb12fOONN3T7HTFihHBychKDBw8WJ0+eFNu3bxe1a9cW7733nm4bQ79Dbdu2FXv37hWnTp0STz31lOjQoYNuH3/++adwcXERa9asERcvXhS//vqrqFevnpgzZ45uGwCiTp064rvvvhPnz58XEyZMEE5OTuLWrVuiqKhIbNq0SQAQZ8+eFdevXxdZWVni2rVrwsrKSkRHR4vk5GRx/PhxsWzZMpGdnf3INiV6HCyQiCSmVqtFz549RePGjYVSqdRb995774lGjRrpFU3Lli0TTk5OQq1Wi5ycHGFtbS3Wr1+vW19YWCj8/PzEokWLhBD6//ovtnDhQgFAXLx4Ubfs9ddfF5GRkeXmLN7P7du3xYoVK4S7u7uu96OiBZKxmTp16iQaN26s1x7vvvuuaNy4sRBCiMuXLwuFQiFSU1P1snfp0kVMnz5dCKEtkACIo0ePlvtZhXh02wshxKefflpuz1Gxxo0bi759+z50GyGE8PPzEwsWLNBb9sQTT4i33npLCHG/Xb/66ivd+g0bNggAIi4uTrds4cKFegXZiBEjhLu7u14RuHz58sf+Du3YsUMAEHfv3hVCaNv4ww8/1Mu/bt064evrq3sNQMyYMUP3OicnRwDQ9ZQ9+B0rdvjwYQFAXLp06VFNSGRSHINEJLH33nsP8fHx2Lp1K5ydnfXWnTlzBu3bt4dMJtMt69ixI3JycnD16lVcvHgRKpUKHTt21K23trZGmzZtcObMGb19NW/eXPfc29sbDg4OqF+/vt6yjIwMgzKPGTMGHh4e+Pjjj436rCVVJFO7du302qN9+/Y4f/481Go1Tpw4AbVajYYNG8LJyUn3+OOPP3Dx4kXde2xsbPSOXZZHtb2hhBCP3EapVOLatWt6/x2Lj/eo/44A0KxZM71lJdssPDwcDg4Outft27dHTk4Orly5UuHvkK+vLwDojnXs2DHMmzdPr93Hjh2L69ev6w1Ef3Afjo6OcHFxeej3Ljw8HF26dEGzZs3w4osv4ssvv8Tt27fL3Z7IVMoeUUhEleL777/Hf/7zH+zYsQMNGjQw67Gsra11z2Uymd7r4mWGDla2srLCggULMHLkSIwfP15vnVyu/XfXg4WBSqUyeyYAyMnJgUKhwOHDh6FQKPTWOTk56Z7b29vrFT7m1LBhQyQmJppsfyXbrKxlFR10XpFjFx8rJycHc+fOxYABA0q9z87Orsx9FO/nYXkVCgV2796N/fv349dff8XSpUvx/vvv48CBAwgKCnqsz0P0MOxBIpLI0aNHMWbMGHz00UeIjIwsc5vGjRsjPj5er9j4+++/4ezsjDp16iA4OBg2Njb4+++/detVKhX+/fdfNGnSxKz5X3zxRYSFhWHu3Ll6y2vXrg0AuH79um6ZKecHOnDggN7rf/75Bw0aNIBCoUCLFi2gVquRkZGBkJAQvYePj49Rx3lU2xvqpZdewrlz57B169ZS64QQuHPnDlxcXODn56f337H4eKb473js2DHcvXtX9/qff/6Bk5MTAgICTPYdatmyJc6ePVuq3UNCQnRF86PY2NgAQKlpJGQyGTp27Ii5c+ciISEBNjY2+OmnnwzORlQR7EEiksDNmzfRv39/PPPMM3j55ZeRlpamt16hUKB27dp46623EBMTg7fffhvjx4/H2bNnMXv2bERFRUEul8PR0RFvvvkmpkyZAnd3d9StWxeLFi1CXl4exowZY/bPUVZxZ29vj3bt2uGjjz5CUFAQMjIyMGPGDJMdMyUlBVFRUXj99ddx5MgRLF26FIsXLwag7a0ZNmwYhg8fjsWLF6NFixa4ceMG4uLi0Lx5c/Tu3dvg4zyq7Q01aNAg/PTTTxg6dChmzJiB7t27o3bt2jhx4gQ+/fRTvP322+jfvz+mTJmC2bNnIzg4GBEREVi9ejWOHj2K9evXG91GJRUWFmLMmDGYMWMGLl26hNmzZ2P8+PEm/Q7NmjULzz33HOrWrYsXXngBcrkcx44dw8mTJ/HBBx8YtI/AwEDIZDJs374dvXr1gr29PU6dOoW4uDh0794dXl5eOHDgAG7cuIHGjRtXtDmIDMICiUgCO3bswOXLl3H58mXdWI4HBQYG4tKlS/D398fOnTsxZcoUhIeHw93dXfeHrthHH30EjUaDV155BdnZ2WjdujV27dqFWrVqmf1zPPvss3j22WdLzeWzatUqjBkzBq1atUKjRo2waNEidO/e3STHHD58OO7evYs2bdpAoVBg4sSJeO2113TrV69ejQ8++ADvvPMOUlNT4enpiXbt2uG5554z6jiGtL0hZDIZvvvuO6xcuRKrVq3CggULYGVlhQYNGmD48OG6AnPChAm4c+cO3nnnHWRkZKBJkybYtm2bSU69dunSBQ0aNMDTTz+NgoICDB06FHPmzNGtN8V3KDIyEtu3b8e8efPw8ccfw9raGqGhoXj11VcN3oe/vz/mzp2LadOmYdSoURg+fDjeffdd/Pnnn4iJiYFSqURgYCAWL16Mnj17GtMEREaTCUNGEBIRkUUaOXIksrKysGXLFqmjEFkUjkEiIiIiKoEFEhEREVEJPMVGREREVAJ7kIiIiIhKYIFEREREVAILJCIiIqISWCARERERlcACiYiIiKgEFkhEREREJbBAIiIiIiqBBRIRERFRCf8PAoYiOMYWPpQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Apply PCA method to the training data\n",
    "pca = PCA()\n",
    "pca.fit(X_train)\n",
    "\n",
    "# EXPLAINED VARIANCE\n",
    "# Plot cumulative explained variance against number of components\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlabel('Zoom Number of Components')\n",
    "plt.ylabel('Zoom Cumulative Explained Variance')\n",
    "plt.xlim(0,15)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see in the previous plot, with 10 components we can explain almost the 100% of the data, and we get to reduce the dimensionality from 230 features to 10 components."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Elbow method**\n",
    "Another method is to plot (sorted decreasing) the eigen values of each component, i.e., how much variability explains each component. To know the ideal number of components we should look for an \"elbow\" in the plot of the eigenvalues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ELBOW METHOD Plot explained variance against number of components\n",
    "plt.plot(range(1, len(pca.explained_variance_) + 1), pca.explained_variance_)\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Eigen values')\n",
    "plt.grid(True)\n",
    "plt.xlim(0,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, the \"elbow\" is when the number of components is 2.\n",
    "\n",
    "For the two previous methods, we can guess that the best number of components in between 2 and 10, so we'll be using this range of values for the grid search CV: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "param_grid_pca = {'pca__n_components': [2,3,4,5,6,7,8,9,10,11]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how decreasing the number of inputs in the model (keeping those components who explain more variance in the data) will affect in the performance of the top previous models.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### PCA for CatBoost\n",
    "\n",
    "CatBoost has been the top performing algorithm, so let's try this one first. For this testing, because PCA needs numerical features, we won't able to do the test with the same dataset (with categorical features) that we've used before for CatBoost, so we will use the full numerical dataset that we've used in all others models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params are:\n",
      "\n",
      "{'catBoost__iterations': 500, 'pca__n_components': 10}\n",
      "CPU times: total: 10.8 s\n",
      "Wall time: 3min 15s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pipeline_catBoost_pca = Pipeline([\n",
    "                       ('scaler', StandardScaler()),\n",
    "                       ('pca', PCA()),\n",
    "                       ('catBoost',CatBoostRegressor(verbose = 0))])\n",
    "params_catBoost_pca = {'catBoost__iterations': [400,450,500]} | param_grid_pca # union the params for each step in a unique set.\n",
    "catBoost_pca_grid = GridSearchCV(pipeline_catBoost_pca, params_catBoost_pca, cv=kf, n_jobs = -1)\n",
    "catBoost_pca_grid.fit(X_train, y_train)\n",
    "\n",
    "# Get the best parameters value\n",
    "print(\"Best params are:\\n\")\n",
    "print(catBoost_pca_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance metrics:\n",
      "\n",
      "      catBoost Pca Score\n",
      "RMSE        25335.329688\n",
      "R2              0.885992\n"
     ]
    }
   ],
   "source": [
    "pred_grid_pipeline_catBoost_pca = catBoost_pca_grid.predict(X_test)\n",
    "catBoost_pca_df = pd.DataFrame(data=[RMSE(y_test, pred_grid_pipeline_catBoost_pca),r2_score(y_test, pred_grid_pipeline_catBoost_pca)],\n",
    "             columns=['catBoost Pca Score'],\n",
    "             index=[\"RMSE\", \"R2\"])\n",
    "print(\"Performance metrics:\\n\")\n",
    "print(catBoost_pca_df)\n",
    "df_results = pd.concat([df_results,catBoost_pca_df.T])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA for LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params are:\n",
      "\n",
      "{'lightgbm__boosting_type': 'gbdt', 'lightgbm__max_depth': 6, 'lightgbm__num_leaves': 8, 'pca__n_components': 7}\n",
      "CPU times: total: 19.7 s\n",
      "Wall time: 2min 23s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pipeline_lightgbm_pca = Pipeline([\n",
    "                       ('scaler', StandardScaler()),\n",
    "                       ('pca', PCA()),\n",
    "                       ('lightgbm',LGBMRegressor(n_jobs= -1,verbose=0))])\n",
    "params_lightgbm_pca = {\n",
    "    'lightgbm__boosting_type':['gbdt','dart','rf'],\n",
    "    'lightgbm__max_depth':[4,5,6],\n",
    "    'lightgbm__num_leaves':[8,9,10]} | param_grid_pca # union the params for each step in a unique set.\n",
    "lightgbm_pca_grid = GridSearchCV(pipeline_lightgbm_pca, params_lightgbm_pca, cv=kf, n_jobs = -1)\n",
    "lightgbm_pca_grid.fit(X_train, y_train)\n",
    "\n",
    "# Get the best parameters value\n",
    "print(\"Best params are:\\n\")\n",
    "print(lightgbm_pca_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance metrics:\n",
      "\n",
      "      Lightgbm Pca Score\n",
      "RMSE        26499.870835\n",
      "R2              0.875270\n"
     ]
    }
   ],
   "source": [
    "pred_grid_pipeline_lightgbm_pca = lightgbm_pca_grid.predict(X_test)\n",
    "lightgbm_pca_df = pd.DataFrame(data=[RMSE(y_test, pred_grid_pipeline_lightgbm_pca),r2_score(y_test, pred_grid_pipeline_lightgbm_pca)],\n",
    "             columns=['Lightgbm Pca Score'],\n",
    "             index=[\"RMSE\", \"R2\"])\n",
    "print(\"Performance metrics:\\n\")\n",
    "print(lightgbm_pca_df)\n",
    "df_results = pd.concat([df_results,lightgbm_pca_df.T])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA for XGMBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params are:\n",
      "\n",
      "{'pca__n_components': 10, 'xgb__max_depth': 5, 'xgb__n_estimators': 600}\n",
      "CPU times: total: 21.1 s\n",
      "Wall time: 4min 3s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pipeline_xgb_pca = Pipeline([\n",
    "                       ('scaler', StandardScaler()),\n",
    "                       ('pca', PCA()),\n",
    "                       ('xgb',XGBRegressor(n_jobs= -1,verbose=0))])\n",
    "params_xgb_pca = {\n",
    "     'xgb__n_estimators' : [600,650,700],\n",
    "     'xgb__max_depth': [-1,2,3,4,5]} | param_grid_pca # union the params for each step in a unique set.\n",
    "xgb_pca_grid = GridSearchCV(pipeline_xgb_pca, params_xgb_pca, cv=kf, n_jobs = -1)\n",
    "xgb_pca_grid.fit(X_train, y_train)\n",
    "\n",
    "# Get the best parameters value\n",
    "print(\"Best params are:\\n\")\n",
    "print(xgb_pca_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance metrics:\n",
      "\n",
      "      Xgb Pca Score\n",
      "RMSE   29708.378194\n",
      "R2         0.843238\n"
     ]
    }
   ],
   "source": [
    "pred_grid_pipeline_xgb_pca = xgb_pca_grid.predict(X_test)\n",
    "Xgb_pca_df = pd.DataFrame(data=[RMSE(y_test, pred_grid_pipeline_xgb_pca),r2_score(y_test, pred_grid_pipeline_xgb_pca)],\n",
    "             columns=['Xgb Pca Score'],\n",
    "             index=[\"RMSE\", \"R2\"])\n",
    "print(\"Performance metrics:\\n\")\n",
    "print(Xgb_pca_df)\n",
    "df_results = pd.concat([df_results,Xgb_pca_df.T])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### PCA for Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params are:\n",
      "\n",
      "{'lasso__alpha': 150, 'pca__n_components': 10}\n",
      "CPU times: total: 1min 28s\n",
      "Wall time: 24.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pipeline_lasso_pca = Pipeline([\n",
    "                       ('scaler', StandardScaler()),\n",
    "                       ('pca', PCA()),\n",
    "                       ('lasso',Lasso())])\n",
    "params_lasso_pca = lasso_params | param_grid_pca # union the params for each step in a unique set.\n",
    "lasso_pca_grid = GridSearchCV(pipeline_lasso_pca, params_lasso_pca, cv=kf)\n",
    "lasso_pca_grid.fit(X_train, y_train)\n",
    "\n",
    "# Get the best parameters value\n",
    "print(\"Best params are:\\n\")\n",
    "print(lasso_pca_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance metrics:\n",
      "\n",
      "      Lasso Pca model Score\n",
      "RMSE           30708.029664\n",
      "R2                 0.832511\n"
     ]
    }
   ],
   "source": [
    "pred_lasso_pca = lasso_pca_grid.predict(X_test)\n",
    "lasso_pca_df = pd.DataFrame(data=[RMSE(y_test, pred_lasso_pca),r2_score(y_test, pred_lasso_pca)],\n",
    "             columns=['Lasso Pca model Score'],\n",
    "             index=[\"RMSE\", \"R2\"])\n",
    "print(\"Performance metrics:\\n\")\n",
    "print(lasso_pca_df)\n",
    "df_results = pd.concat([df_results,lasso_pca_df.T])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='BEST'></a>\n",
    "## Best Model\n",
    "\n",
    "As you can see in the next cell, using the PCA method has had little effect on the performance and the best model is still the CatBoost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "tags": []
   },
   "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>RMSE</th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>catBoost Score</th>\n",
       "      <td>19111.318220</td>\n",
       "      <td>0.919822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lightgmb Score</th>\n",
       "      <td>21965.389512</td>\n",
       "      <td>0.914304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGB Score</th>\n",
       "      <td>22903.288331</td>\n",
       "      <td>0.906829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest Score</th>\n",
       "      <td>25188.925280</td>\n",
       "      <td>0.887305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catBoost Pca Score</th>\n",
       "      <td>25335.329688</td>\n",
       "      <td>0.885992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso Score</th>\n",
       "      <td>26290.517656</td>\n",
       "      <td>0.877233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lightgbm Pca Score</th>\n",
       "      <td>26499.870835</td>\n",
       "      <td>0.875270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elasticnet Score</th>\n",
       "      <td>26519.716080</td>\n",
       "      <td>0.875083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge Score</th>\n",
       "      <td>26606.641233</td>\n",
       "      <td>0.874263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xgb Pca Score</th>\n",
       "      <td>29708.378194</td>\n",
       "      <td>0.843238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linear model Score</th>\n",
       "      <td>30611.436295</td>\n",
       "      <td>0.833563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso Pca model Score</th>\n",
       "      <td>30708.029664</td>\n",
       "      <td>0.832511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nn Score</th>\n",
       "      <td>31568.388096</td>\n",
       "      <td>0.822994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Knn Score</th>\n",
       "      <td>36966.075330</td>\n",
       "      <td>0.757289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVC Score</th>\n",
       "      <td>55511.432899</td>\n",
       "      <td>0.452671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               RMSE        R2\n",
       "catBoost Score         19111.318220  0.919822\n",
       "Lightgmb Score         21965.389512  0.914304\n",
       "XGB Score              22903.288331  0.906829\n",
       "Random Forest Score    25188.925280  0.887305\n",
       "catBoost Pca Score     25335.329688  0.885992\n",
       "Lasso Score            26290.517656  0.877233\n",
       "Lightgbm Pca Score     26499.870835  0.875270\n",
       "elasticnet Score       26519.716080  0.875083\n",
       "Ridge Score            26606.641233  0.874263\n",
       "Xgb Pca Score          29708.378194  0.843238\n",
       "linear model Score     30611.436295  0.833563\n",
       "Lasso Pca model Score  30708.029664  0.832511\n",
       "nn Score               31568.388096  0.822994\n",
       "Knn Score              36966.075330  0.757289\n",
       "SVC Score              55511.432899  0.452671"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results.sort_values(by='R2', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the R2 of our best model is above 0.92, so we could say that **our model performs well**. Also, this value of R2 means that our model explains the 92% of the variability of the data. Its value of RMSE tells us that the average difference between the predicted values from the model and the actual values in the dataset is around 19K$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have find a good model, we'll save it in order to use it in the future:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "filename = \"regressor_sale_house_price_catBoost.pickle\"\n",
    "pickle.dump(grid_catBoost, open(filename, \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='INTERPRET'></a>\n",
    "### Interpret de model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's find out which features the model considers more important in order to predict the price of a house:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Access feature importances\n",
    "importances = grid_catBoost.best_estimator_.feature_importances_\n",
    "\n",
    "feature_importance_df = pd.DataFrame({'Feature': df_modeling_categorical.columns.drop(\"SalePrice\"), 'Importance': importances})\n",
    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=True)\n",
    "                                                          \n",
    "plt.figure(figsize=(20, 15))\n",
    "plt.barh(feature_importance_df['Feature'], feature_importance_df['Importance'])\n",
    "plt.xlabel('Importance')\n",
    "plt.title('Feature Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, the most important feature is **TotalSF**, i.e., the total surface of the house, including the basement. Secondly, the most important feature is **OverallQual**, i.e., the rate of the overall material and finish of the house. With a large difference importance, the following more important features are: total of bathrooms, the total surface of the living zone, the neighborhood and the rate of the exterior qualities and the capacity of the cars in the garage among others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='PYCARET'></a>\n",
    "## Bonus: Pycaret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PyCaret is a Python library for simplified machine learning workflows, offering automated model training, hyperparameter tuning, and comprehensive visualizations. We'll use it to compare its results with ours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "bcaWpL58cf_h",
    "outputId": "2a1f138e-e9f3-4f55-99b9-3f7286803c54"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.2.0'"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check installed version\n",
    "import pycaret\n",
    "pycaret.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 677
    },
    "id": "gu94qfCXcnZ7",
    "outputId": "c6206e9d-23c0-4c08-840d-1cb99fc724a8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_69485_row8_col1 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_69485\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_69485_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_69485_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_69485_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_69485_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
       "      <td id=\"T_69485_row0_col1\" class=\"data row0 col1\" >123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69485_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_69485_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_69485_row1_col1\" class=\"data row1 col1\" >SalePrice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69485_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_69485_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
       "      <td id=\"T_69485_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69485_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_69485_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
       "      <td id=\"T_69485_row3_col1\" class=\"data row3 col1\" >(1451, 231)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69485_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_69485_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
       "      <td id=\"T_69485_row4_col1\" class=\"data row4 col1\" >(1451, 231)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69485_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_69485_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
       "      <td id=\"T_69485_row5_col1\" class=\"data row5 col1\" >(1015, 231)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69485_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_69485_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
       "      <td id=\"T_69485_row6_col1\" class=\"data row6 col1\" >(436, 231)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69485_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_69485_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
       "      <td id=\"T_69485_row7_col1\" class=\"data row7 col1\" >230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69485_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_69485_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
       "      <td id=\"T_69485_row8_col1\" class=\"data row8 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69485_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_69485_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
       "      <td id=\"T_69485_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69485_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_69485_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
       "      <td id=\"T_69485_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69485_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_69485_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
       "      <td id=\"T_69485_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69485_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_69485_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_69485_row12_col1\" class=\"data row12 col1\" >KFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69485_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_69485_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
       "      <td id=\"T_69485_row13_col1\" class=\"data row13 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69485_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_69485_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_69485_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69485_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_69485_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
       "      <td id=\"T_69485_row15_col1\" class=\"data row15 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69485_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_69485_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_69485_row16_col1\" class=\"data row16 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69485_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_69485_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_69485_row17_col1\" class=\"data row17 col1\" >reg-default-name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69485_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_69485_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
       "      <td id=\"T_69485_row18_col1\" class=\"data row18 col1\" >9c57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2152e0c5210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import pycaret regression and init setup\n",
    "from pycaret.regression import *\n",
    "s = setup(df_inputed_nulls, target = 'SalePrice', session_id = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 520,
     "referenced_widgets": [
      "54da000a17904b769b7701104aadd5c6",
      "9ef30e5583d94ab0868b0197347a8a51",
      "220ea549248e4f0c9f0ddb450e1ad0af",
      "563f2b6820114d61a937f9b8dbc425f0",
      "1575c177ab4146728b3a9c4adfbad822",
      "58d1a59c08b443ee9b02673f6c1cd8e6",
      "69e0a24d4fa84e58b5e67ad7f560b760",
      "4dc7f0388372476eb9abe2122854cff9",
      "4ab2b87ecd4f4b0ea422e2550f1937ba",
      "ebe67d35e206480fbdb316ffb55984b3",
      "4c5ed71c8e41472498eb22471bb0adbf"
     ]
    },
    "id": "dxgYTboVcy2P",
    "outputId": "1d3d46de-91ca-4162-e5ae-9befae2fe731"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
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       "#T_ce682_row0_col7, #T_ce682_row1_col7, #T_ce682_row2_col7, #T_ce682_row3_col7, #T_ce682_row4_col7, #T_ce682_row5_col7, #T_ce682_row6_col7, #T_ce682_row7_col7, #T_ce682_row8_col7, #T_ce682_row9_col7, #T_ce682_row10_col7, #T_ce682_row11_col7, #T_ce682_row12_col7, #T_ce682_row13_col7, #T_ce682_row14_col7, #T_ce682_row15_col7, #T_ce682_row16_col7, #T_ce682_row17_col7, #T_ce682_row19_col7 {\n",
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       "</style>\n",
       "<table id=\"T_ce682\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_ce682_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_ce682_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_ce682_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_ce682_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_ce682_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_ce682_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_ce682_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "      <th id=\"T_ce682_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row0\" class=\"row_heading level0 row0\" >catboost</th>\n",
       "      <td id=\"T_ce682_row0_col0\" class=\"data row0 col0\" >CatBoost Regressor</td>\n",
       "      <td id=\"T_ce682_row0_col1\" class=\"data row0 col1\" >13936.2905</td>\n",
       "      <td id=\"T_ce682_row0_col2\" class=\"data row0 col2\" >508768359.6845</td>\n",
       "      <td id=\"T_ce682_row0_col3\" class=\"data row0 col3\" >21938.0727</td>\n",
       "      <td id=\"T_ce682_row0_col4\" class=\"data row0 col4\" >0.8993</td>\n",
       "      <td id=\"T_ce682_row0_col5\" class=\"data row0 col5\" >0.1170</td>\n",
       "      <td id=\"T_ce682_row0_col6\" class=\"data row0 col6\" >0.0823</td>\n",
       "      <td id=\"T_ce682_row0_col7\" class=\"data row0 col7\" >4.5270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row1\" class=\"row_heading level0 row1\" >lightgbm</th>\n",
       "      <td id=\"T_ce682_row1_col0\" class=\"data row1 col0\" >Light Gradient Boosting Machine</td>\n",
       "      <td id=\"T_ce682_row1_col1\" class=\"data row1 col1\" >15547.6534</td>\n",
       "      <td id=\"T_ce682_row1_col2\" class=\"data row1 col2\" >584812851.2968</td>\n",
       "      <td id=\"T_ce682_row1_col3\" class=\"data row1 col3\" >23695.2510</td>\n",
       "      <td id=\"T_ce682_row1_col4\" class=\"data row1 col4\" >0.8816</td>\n",
       "      <td id=\"T_ce682_row1_col5\" class=\"data row1 col5\" >0.1281</td>\n",
       "      <td id=\"T_ce682_row1_col6\" class=\"data row1 col6\" >0.0918</td>\n",
       "      <td id=\"T_ce682_row1_col7\" class=\"data row1 col7\" >0.4170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row2\" class=\"row_heading level0 row2\" >et</th>\n",
       "      <td id=\"T_ce682_row2_col0\" class=\"data row2 col0\" >Extra Trees Regressor</td>\n",
       "      <td id=\"T_ce682_row2_col1\" class=\"data row2 col1\" >15895.0145</td>\n",
       "      <td id=\"T_ce682_row2_col2\" class=\"data row2 col2\" >611611904.8429</td>\n",
       "      <td id=\"T_ce682_row2_col3\" class=\"data row2 col3\" >24135.7852</td>\n",
       "      <td id=\"T_ce682_row2_col4\" class=\"data row2 col4\" >0.8787</td>\n",
       "      <td id=\"T_ce682_row2_col5\" class=\"data row2 col5\" >0.1285</td>\n",
       "      <td id=\"T_ce682_row2_col6\" class=\"data row2 col6\" >0.0938</td>\n",
       "      <td id=\"T_ce682_row2_col7\" class=\"data row2 col7\" >1.8250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row3\" class=\"row_heading level0 row3\" >gbr</th>\n",
       "      <td id=\"T_ce682_row3_col0\" class=\"data row3 col0\" >Gradient Boosting Regressor</td>\n",
       "      <td id=\"T_ce682_row3_col1\" class=\"data row3 col1\" >15596.6724</td>\n",
       "      <td id=\"T_ce682_row3_col2\" class=\"data row3 col2\" >607080630.3706</td>\n",
       "      <td id=\"T_ce682_row3_col3\" class=\"data row3 col3\" >24061.3562</td>\n",
       "      <td id=\"T_ce682_row3_col4\" class=\"data row3 col4\" >0.8781</td>\n",
       "      <td id=\"T_ce682_row3_col5\" class=\"data row3 col5\" >0.1297</td>\n",
       "      <td id=\"T_ce682_row3_col6\" class=\"data row3 col6\" >0.0931</td>\n",
       "      <td id=\"T_ce682_row3_col7\" class=\"data row3 col7\" >0.7680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row4\" class=\"row_heading level0 row4\" >rf</th>\n",
       "      <td id=\"T_ce682_row4_col0\" class=\"data row4 col0\" >Random Forest Regressor</td>\n",
       "      <td id=\"T_ce682_row4_col1\" class=\"data row4 col1\" >16542.8376</td>\n",
       "      <td id=\"T_ce682_row4_col2\" class=\"data row4 col2\" >652120559.3498</td>\n",
       "      <td id=\"T_ce682_row4_col3\" class=\"data row4 col3\" >25026.3396</td>\n",
       "      <td id=\"T_ce682_row4_col4\" class=\"data row4 col4\" >0.8697</td>\n",
       "      <td id=\"T_ce682_row4_col5\" class=\"data row4 col5\" >0.1347</td>\n",
       "      <td id=\"T_ce682_row4_col6\" class=\"data row4 col6\" >0.0982</td>\n",
       "      <td id=\"T_ce682_row4_col7\" class=\"data row4 col7\" >1.9910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row5\" class=\"row_heading level0 row5\" >br</th>\n",
       "      <td id=\"T_ce682_row5_col0\" class=\"data row5 col0\" >Bayesian Ridge</td>\n",
       "      <td id=\"T_ce682_row5_col1\" class=\"data row5 col1\" >16261.0548</td>\n",
       "      <td id=\"T_ce682_row5_col2\" class=\"data row5 col2\" >659567059.2000</td>\n",
       "      <td id=\"T_ce682_row5_col3\" class=\"data row5 col3\" >24413.7656</td>\n",
       "      <td id=\"T_ce682_row5_col4\" class=\"data row5 col4\" >0.8688</td>\n",
       "      <td id=\"T_ce682_row5_col5\" class=\"data row5 col5\" >0.1455</td>\n",
       "      <td id=\"T_ce682_row5_col6\" class=\"data row5 col6\" >0.0988</td>\n",
       "      <td id=\"T_ce682_row5_col7\" class=\"data row5 col7\" >0.0620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row6\" class=\"row_heading level0 row6\" >xgboost</th>\n",
       "      <td id=\"T_ce682_row6_col0\" class=\"data row6 col0\" >Extreme Gradient Boosting</td>\n",
       "      <td id=\"T_ce682_row6_col1\" class=\"data row6 col1\" >17092.7906</td>\n",
       "      <td id=\"T_ce682_row6_col2\" class=\"data row6 col2\" >691612886.4000</td>\n",
       "      <td id=\"T_ce682_row6_col3\" class=\"data row6 col3\" >25918.4770</td>\n",
       "      <td id=\"T_ce682_row6_col4\" class=\"data row6 col4\" >0.8598</td>\n",
       "      <td id=\"T_ce682_row6_col5\" class=\"data row6 col5\" >0.1363</td>\n",
       "      <td id=\"T_ce682_row6_col6\" class=\"data row6 col6\" >0.0995</td>\n",
       "      <td id=\"T_ce682_row6_col7\" class=\"data row6 col7\" >0.4210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row7\" class=\"row_heading level0 row7\" >ridge</th>\n",
       "      <td id=\"T_ce682_row7_col0\" class=\"data row7 col0\" >Ridge Regression</td>\n",
       "      <td id=\"T_ce682_row7_col1\" class=\"data row7 col1\" >17064.6634</td>\n",
       "      <td id=\"T_ce682_row7_col2\" class=\"data row7 col2\" >738229705.6000</td>\n",
       "      <td id=\"T_ce682_row7_col3\" class=\"data row7 col3\" >26015.5662</td>\n",
       "      <td id=\"T_ce682_row7_col4\" class=\"data row7 col4\" >0.8552</td>\n",
       "      <td id=\"T_ce682_row7_col5\" class=\"data row7 col5\" >0.1784</td>\n",
       "      <td id=\"T_ce682_row7_col6\" class=\"data row7 col6\" >0.1043</td>\n",
       "      <td id=\"T_ce682_row7_col7\" class=\"data row7 col7\" >0.0250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row8\" class=\"row_heading level0 row8\" >llar</th>\n",
       "      <td id=\"T_ce682_row8_col0\" class=\"data row8 col0\" >Lasso Least Angle Regression</td>\n",
       "      <td id=\"T_ce682_row8_col1\" class=\"data row8 col1\" >16539.2279</td>\n",
       "      <td id=\"T_ce682_row8_col2\" class=\"data row8 col2\" >765245331.2000</td>\n",
       "      <td id=\"T_ce682_row8_col3\" class=\"data row8 col3\" >26123.5359</td>\n",
       "      <td id=\"T_ce682_row8_col4\" class=\"data row8 col4\" >0.8518</td>\n",
       "      <td id=\"T_ce682_row8_col5\" class=\"data row8 col5\" >0.1607</td>\n",
       "      <td id=\"T_ce682_row8_col6\" class=\"data row8 col6\" >0.1001</td>\n",
       "      <td id=\"T_ce682_row8_col7\" class=\"data row8 col7\" >0.0290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row9\" class=\"row_heading level0 row9\" >en</th>\n",
       "      <td id=\"T_ce682_row9_col0\" class=\"data row9 col0\" >Elastic Net</td>\n",
       "      <td id=\"T_ce682_row9_col1\" class=\"data row9 col1\" >17726.1774</td>\n",
       "      <td id=\"T_ce682_row9_col2\" class=\"data row9 col2\" >783889942.4000</td>\n",
       "      <td id=\"T_ce682_row9_col3\" class=\"data row9 col3\" >26842.7387</td>\n",
       "      <td id=\"T_ce682_row9_col4\" class=\"data row9 col4\" >0.8451</td>\n",
       "      <td id=\"T_ce682_row9_col5\" class=\"data row9 col5\" >0.1517</td>\n",
       "      <td id=\"T_ce682_row9_col6\" class=\"data row9 col6\" >0.1059</td>\n",
       "      <td id=\"T_ce682_row9_col7\" class=\"data row9 col7\" >0.0690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row10\" class=\"row_heading level0 row10\" >lasso</th>\n",
       "      <td id=\"T_ce682_row10_col0\" class=\"data row10 col0\" >Lasso Regression</td>\n",
       "      <td id=\"T_ce682_row10_col1\" class=\"data row10 col1\" >17687.4437</td>\n",
       "      <td id=\"T_ce682_row10_col2\" class=\"data row10 col2\" >817440176.0000</td>\n",
       "      <td id=\"T_ce682_row10_col3\" class=\"data row10 col3\" >27266.5982</td>\n",
       "      <td id=\"T_ce682_row10_col4\" class=\"data row10 col4\" >0.8395</td>\n",
       "      <td id=\"T_ce682_row10_col5\" class=\"data row10 col5\" >0.1844</td>\n",
       "      <td id=\"T_ce682_row10_col6\" class=\"data row10 col6\" >0.1085</td>\n",
       "      <td id=\"T_ce682_row10_col7\" class=\"data row10 col7\" >0.0590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row11\" class=\"row_heading level0 row11\" >lr</th>\n",
       "      <td id=\"T_ce682_row11_col0\" class=\"data row11 col0\" >Linear Regression</td>\n",
       "      <td id=\"T_ce682_row11_col1\" class=\"data row11 col1\" >18503.2964</td>\n",
       "      <td id=\"T_ce682_row11_col2\" class=\"data row11 col2\" >843757708.8000</td>\n",
       "      <td id=\"T_ce682_row11_col3\" class=\"data row11 col3\" >27747.9467</td>\n",
       "      <td id=\"T_ce682_row11_col4\" class=\"data row11 col4\" >0.8349</td>\n",
       "      <td id=\"T_ce682_row11_col5\" class=\"data row11 col5\" >0.2393</td>\n",
       "      <td id=\"T_ce682_row11_col6\" class=\"data row11 col6\" >0.1149</td>\n",
       "      <td id=\"T_ce682_row11_col7\" class=\"data row11 col7\" >0.3550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row12\" class=\"row_heading level0 row12\" >omp</th>\n",
       "      <td id=\"T_ce682_row12_col0\" class=\"data row12 col0\" >Orthogonal Matching Pursuit</td>\n",
       "      <td id=\"T_ce682_row12_col1\" class=\"data row12 col1\" >19587.2015</td>\n",
       "      <td id=\"T_ce682_row12_col2\" class=\"data row12 col2\" >873386400.0000</td>\n",
       "      <td id=\"T_ce682_row12_col3\" class=\"data row12 col3\" >28615.6967</td>\n",
       "      <td id=\"T_ce682_row12_col4\" class=\"data row12 col4\" >0.8254</td>\n",
       "      <td id=\"T_ce682_row12_col5\" class=\"data row12 col5\" >0.1784</td>\n",
       "      <td id=\"T_ce682_row12_col6\" class=\"data row12 col6\" >0.1210</td>\n",
       "      <td id=\"T_ce682_row12_col7\" class=\"data row12 col7\" >0.0300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row13\" class=\"row_heading level0 row13\" >ada</th>\n",
       "      <td id=\"T_ce682_row13_col0\" class=\"data row13 col0\" >AdaBoost Regressor</td>\n",
       "      <td id=\"T_ce682_row13_col1\" class=\"data row13 col1\" >21144.3587</td>\n",
       "      <td id=\"T_ce682_row13_col2\" class=\"data row13 col2\" >866664378.0229</td>\n",
       "      <td id=\"T_ce682_row13_col3\" class=\"data row13 col3\" >29188.4413</td>\n",
       "      <td id=\"T_ce682_row13_col4\" class=\"data row13 col4\" >0.8240</td>\n",
       "      <td id=\"T_ce682_row13_col5\" class=\"data row13 col5\" >0.1746</td>\n",
       "      <td id=\"T_ce682_row13_col6\" class=\"data row13 col6\" >0.1372</td>\n",
       "      <td id=\"T_ce682_row13_col7\" class=\"data row13 col7\" >0.2480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row14\" class=\"row_heading level0 row14\" >knn</th>\n",
       "      <td id=\"T_ce682_row14_col0\" class=\"data row14 col0\" >K Neighbors Regressor</td>\n",
       "      <td id=\"T_ce682_row14_col1\" class=\"data row14 col1\" >26505.5283</td>\n",
       "      <td id=\"T_ce682_row14_col2\" class=\"data row14 col2\" >1396843801.6000</td>\n",
       "      <td id=\"T_ce682_row14_col3\" class=\"data row14 col3\" >37053.9914</td>\n",
       "      <td id=\"T_ce682_row14_col4\" class=\"data row14 col4\" >0.7170</td>\n",
       "      <td id=\"T_ce682_row14_col5\" class=\"data row14 col5\" >0.2087</td>\n",
       "      <td id=\"T_ce682_row14_col6\" class=\"data row14 col6\" >0.1637</td>\n",
       "      <td id=\"T_ce682_row14_col7\" class=\"data row14 col7\" >0.0590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row15\" class=\"row_heading level0 row15\" >huber</th>\n",
       "      <td id=\"T_ce682_row15_col0\" class=\"data row15 col0\" >Huber Regressor</td>\n",
       "      <td id=\"T_ce682_row15_col1\" class=\"data row15 col1\" >25524.1098</td>\n",
       "      <td id=\"T_ce682_row15_col2\" class=\"data row15 col2\" >1435446893.7169</td>\n",
       "      <td id=\"T_ce682_row15_col3\" class=\"data row15 col3\" >37397.4639</td>\n",
       "      <td id=\"T_ce682_row15_col4\" class=\"data row15 col4\" >0.7084</td>\n",
       "      <td id=\"T_ce682_row15_col5\" class=\"data row15 col5\" >0.2052</td>\n",
       "      <td id=\"T_ce682_row15_col6\" class=\"data row15 col6\" >0.1538</td>\n",
       "      <td id=\"T_ce682_row15_col7\" class=\"data row15 col7\" >0.3240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row16\" class=\"row_heading level0 row16\" >dt</th>\n",
       "      <td id=\"T_ce682_row16_col0\" class=\"data row16 col0\" >Decision Tree Regressor</td>\n",
       "      <td id=\"T_ce682_row16_col1\" class=\"data row16 col1\" >25313.1354</td>\n",
       "      <td id=\"T_ce682_row16_col2\" class=\"data row16 col2\" >1408980684.2620</td>\n",
       "      <td id=\"T_ce682_row16_col3\" class=\"data row16 col3\" >37201.8405</td>\n",
       "      <td id=\"T_ce682_row16_col4\" class=\"data row16 col4\" >0.7060</td>\n",
       "      <td id=\"T_ce682_row16_col5\" class=\"data row16 col5\" >0.2021</td>\n",
       "      <td id=\"T_ce682_row16_col6\" class=\"data row16 col6\" >0.1479</td>\n",
       "      <td id=\"T_ce682_row16_col7\" class=\"data row16 col7\" >0.0530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row17\" class=\"row_heading level0 row17\" >par</th>\n",
       "      <td id=\"T_ce682_row17_col0\" class=\"data row17 col0\" >Passive Aggressive Regressor</td>\n",
       "      <td id=\"T_ce682_row17_col1\" class=\"data row17 col1\" >35359.6257</td>\n",
       "      <td id=\"T_ce682_row17_col2\" class=\"data row17 col2\" >4331790874.5136</td>\n",
       "      <td id=\"T_ce682_row17_col3\" class=\"data row17 col3\" >54541.2186</td>\n",
       "      <td id=\"T_ce682_row17_col4\" class=\"data row17 col4\" >0.2677</td>\n",
       "      <td id=\"T_ce682_row17_col5\" class=\"data row17 col5\" >0.2595</td>\n",
       "      <td id=\"T_ce682_row17_col6\" class=\"data row17 col6\" >0.2166</td>\n",
       "      <td id=\"T_ce682_row17_col7\" class=\"data row17 col7\" >0.0260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row18\" class=\"row_heading level0 row18\" >dummy</th>\n",
       "      <td id=\"T_ce682_row18_col0\" class=\"data row18 col0\" >Dummy Regressor</td>\n",
       "      <td id=\"T_ce682_row18_col1\" class=\"data row18 col1\" >53769.6633</td>\n",
       "      <td id=\"T_ce682_row18_col2\" class=\"data row18 col2\" >4992875545.6000</td>\n",
       "      <td id=\"T_ce682_row18_col3\" class=\"data row18 col3\" >70136.6227</td>\n",
       "      <td id=\"T_ce682_row18_col4\" class=\"data row18 col4\" >-0.0076</td>\n",
       "      <td id=\"T_ce682_row18_col5\" class=\"data row18 col5\" >0.3854</td>\n",
       "      <td id=\"T_ce682_row18_col6\" class=\"data row18 col6\" >0.3451</td>\n",
       "      <td id=\"T_ce682_row18_col7\" class=\"data row18 col7\" >0.0220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce682_level0_row19\" class=\"row_heading level0 row19\" >lar</th>\n",
       "      <td id=\"T_ce682_row19_col0\" class=\"data row19 col0\" >Least Angle Regression</td>\n",
       "      <td id=\"T_ce682_row19_col1\" class=\"data row19 col1\" >1428130440357468342059008.0000</td>\n",
       "      <td id=\"T_ce682_row19_col2\" class=\"data row19 col2\" >inf</td>\n",
       "      <td id=\"T_ce682_row19_col3\" class=\"data row19 col3\" >inf</td>\n",
       "      <td id=\"T_ce682_row19_col4\" class=\"data row19 col4\" >-inf</td>\n",
       "      <td id=\"T_ce682_row19_col5\" class=\"data row19 col5\" >29.6543</td>\n",
       "      <td id=\"T_ce682_row19_col6\" class=\"data row19 col6\" >10848298145755928576.0000</td>\n",
       "      <td id=\"T_ce682_row19_col7\" class=\"data row19 col7\" >0.0520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x215311e77d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare baseline models\n",
    "best = compare_models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 601,
     "referenced_widgets": [
      "d9497efc8c854d51869004d77678d12e",
      "f2dfca736a954f32b7262998dbbe9cde",
      "003e80d6af2c407ca5afcd5066d9d03b",
      "66db0c16e1d94b47a772c1eca97388d3",
      "b9075358edbe407fa202fe1c02dd71f6",
      "014401894a47414f9af8fe888bb26171",
      "d271c1892ccf4ead9fa99aae9f356670"
     ]
    },
    "id": "yUzN0QE4dtcT",
    "outputId": "ce3c05fe-5fd1-433b-cabd-9e1011206ac2"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4b1bcd729b57409eb8419da2f6007130",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 513,
     "referenced_widgets": [
      "0c31745dc3c846728ec542c4ee75dea0",
      "475f8410903c4228aa8eab407f210512",
      "611c51cf22ff4bff82325ce0645289f0",
      "a3b73398533049acaa51bb385a760b23",
      "6c291f6089e14577b6101a718265a54f",
      "c8e6da5e26974ff695b3f6fbb1b49086",
      "290a1161518344028f56997155bdf3b3",
      "12d0941455f141ad84fec57dfc890387",
      "02152169fb754483ad6dc08ce538b016",
      "024d4fd943884726b8619d9b8a2698a0",
      "9f12500c08504ed499492f073bec6164"
     ]
    },
    "id": "7isH3-mSgTbT",
    "outputId": "f838683e-7b46-471b-f9ff-87becb2dd3cc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_8597f_row10_col0, #T_8597f_row10_col1, #T_8597f_row10_col2, #T_8597f_row10_col3, #T_8597f_row10_col4, #T_8597f_row10_col5 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_8597f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_8597f_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
       "      <th id=\"T_8597f_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
       "      <th id=\"T_8597f_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
       "      <th id=\"T_8597f_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
       "      <th id=\"T_8597f_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
       "      <th id=\"T_8597f_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_8597f_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_8597f_row0_col0\" class=\"data row0 col0\" >15389.4346</td>\n",
       "      <td id=\"T_8597f_row0_col1\" class=\"data row0 col1\" >628195629.4495</td>\n",
       "      <td id=\"T_8597f_row0_col2\" class=\"data row0 col2\" >25063.8311</td>\n",
       "      <td id=\"T_8597f_row0_col3\" class=\"data row0 col3\" >0.8785</td>\n",
       "      <td id=\"T_8597f_row0_col4\" class=\"data row0 col4\" >0.1222</td>\n",
       "      <td id=\"T_8597f_row0_col5\" class=\"data row0 col5\" >0.0868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8597f_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_8597f_row1_col0\" class=\"data row1 col0\" >15301.7831</td>\n",
       "      <td id=\"T_8597f_row1_col1\" class=\"data row1 col1\" >505125240.0008</td>\n",
       "      <td id=\"T_8597f_row1_col2\" class=\"data row1 col2\" >22474.9914</td>\n",
       "      <td id=\"T_8597f_row1_col3\" class=\"data row1 col3\" >0.8560</td>\n",
       "      <td id=\"T_8597f_row1_col4\" class=\"data row1 col4\" >0.1236</td>\n",
       "      <td id=\"T_8597f_row1_col5\" class=\"data row1 col5\" >0.0884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8597f_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_8597f_row2_col0\" class=\"data row2 col0\" >13205.9407</td>\n",
       "      <td id=\"T_8597f_row2_col1\" class=\"data row2 col1\" >333561528.5047</td>\n",
       "      <td id=\"T_8597f_row2_col2\" class=\"data row2 col2\" >18263.6669</td>\n",
       "      <td id=\"T_8597f_row2_col3\" class=\"data row2 col3\" >0.9129</td>\n",
       "      <td id=\"T_8597f_row2_col4\" class=\"data row2 col4\" >0.1248</td>\n",
       "      <td id=\"T_8597f_row2_col5\" class=\"data row2 col5\" >0.0878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8597f_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_8597f_row3_col0\" class=\"data row3 col0\" >15740.0056</td>\n",
       "      <td id=\"T_8597f_row3_col1\" class=\"data row3 col1\" >460878132.6264</td>\n",
       "      <td id=\"T_8597f_row3_col2\" class=\"data row3 col2\" >21468.0724</td>\n",
       "      <td id=\"T_8597f_row3_col3\" class=\"data row3 col3\" >0.9256</td>\n",
       "      <td id=\"T_8597f_row3_col4\" class=\"data row3 col4\" >0.1205</td>\n",
       "      <td id=\"T_8597f_row3_col5\" class=\"data row3 col5\" >0.0918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8597f_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_8597f_row4_col0\" class=\"data row4 col0\" >16817.0621</td>\n",
       "      <td id=\"T_8597f_row4_col1\" class=\"data row4 col1\" >1073548190.6676</td>\n",
       "      <td id=\"T_8597f_row4_col2\" class=\"data row4 col2\" >32765.0453</td>\n",
       "      <td id=\"T_8597f_row4_col3\" class=\"data row4 col3\" >0.8037</td>\n",
       "      <td id=\"T_8597f_row4_col4\" class=\"data row4 col4\" >0.1536</td>\n",
       "      <td id=\"T_8597f_row4_col5\" class=\"data row4 col5\" >0.1059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8597f_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_8597f_row5_col0\" class=\"data row5 col0\" >14746.5690</td>\n",
       "      <td id=\"T_8597f_row5_col1\" class=\"data row5 col1\" >407079416.5315</td>\n",
       "      <td id=\"T_8597f_row5_col2\" class=\"data row5 col2\" >20176.2092</td>\n",
       "      <td id=\"T_8597f_row5_col3\" class=\"data row5 col3\" >0.8926</td>\n",
       "      <td id=\"T_8597f_row5_col4\" class=\"data row5 col4\" >0.1322</td>\n",
       "      <td id=\"T_8597f_row5_col5\" class=\"data row5 col5\" >0.0914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8597f_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_8597f_row6_col0\" class=\"data row6 col0\" >18433.4601</td>\n",
       "      <td id=\"T_8597f_row6_col1\" class=\"data row6 col1\" >748159086.7817</td>\n",
       "      <td id=\"T_8597f_row6_col2\" class=\"data row6 col2\" >27352.4969</td>\n",
       "      <td id=\"T_8597f_row6_col3\" class=\"data row6 col3\" >0.8894</td>\n",
       "      <td id=\"T_8597f_row6_col4\" class=\"data row6 col4\" >0.1305</td>\n",
       "      <td id=\"T_8597f_row6_col5\" class=\"data row6 col5\" >0.1007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8597f_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_8597f_row7_col0\" class=\"data row7 col0\" >14347.6615</td>\n",
       "      <td id=\"T_8597f_row7_col1\" class=\"data row7 col1\" >405071080.6799</td>\n",
       "      <td id=\"T_8597f_row7_col2\" class=\"data row7 col2\" >20126.3777</td>\n",
       "      <td id=\"T_8597f_row7_col3\" class=\"data row7 col3\" >0.9174</td>\n",
       "      <td id=\"T_8597f_row7_col4\" class=\"data row7 col4\" >0.1004</td>\n",
       "      <td id=\"T_8597f_row7_col5\" class=\"data row7 col5\" >0.0787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8597f_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_8597f_row8_col0\" class=\"data row8 col0\" >17306.2942</td>\n",
       "      <td id=\"T_8597f_row8_col1\" class=\"data row8 col1\" >914167154.5253</td>\n",
       "      <td id=\"T_8597f_row8_col2\" class=\"data row8 col2\" >30235.1973</td>\n",
       "      <td id=\"T_8597f_row8_col3\" class=\"data row8 col3\" >0.8589</td>\n",
       "      <td id=\"T_8597f_row8_col4\" class=\"data row8 col4\" >0.1711</td>\n",
       "      <td id=\"T_8597f_row8_col5\" class=\"data row8 col5\" >0.1057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8597f_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_8597f_row9_col0\" class=\"data row9 col0\" >12517.2711</td>\n",
       "      <td id=\"T_8597f_row9_col1\" class=\"data row9 col1\" >286480835.2491</td>\n",
       "      <td id=\"T_8597f_row9_col2\" class=\"data row9 col2\" >16925.7447</td>\n",
       "      <td id=\"T_8597f_row9_col3\" class=\"data row9 col3\" >0.9169</td>\n",
       "      <td id=\"T_8597f_row9_col4\" class=\"data row9 col4\" >0.0970</td>\n",
       "      <td id=\"T_8597f_row9_col5\" class=\"data row9 col5\" >0.0746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8597f_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_8597f_row10_col0\" class=\"data row10 col0\" >15380.5482</td>\n",
       "      <td id=\"T_8597f_row10_col1\" class=\"data row10 col1\" >576226629.5016</td>\n",
       "      <td id=\"T_8597f_row10_col2\" class=\"data row10 col2\" >23485.1633</td>\n",
       "      <td id=\"T_8597f_row10_col3\" class=\"data row10 col3\" >0.8852</td>\n",
       "      <td id=\"T_8597f_row10_col4\" class=\"data row10 col4\" >0.1276</td>\n",
       "      <td id=\"T_8597f_row10_col5\" class=\"data row10 col5\" >0.0912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8597f_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_8597f_row11_col0\" class=\"data row11 col0\" >1721.1592</td>\n",
       "      <td id=\"T_8597f_row11_col1\" class=\"data row11 col1\" >247567918.3321</td>\n",
       "      <td id=\"T_8597f_row11_col2\" class=\"data row11 col2\" >4967.2663</td>\n",
       "      <td id=\"T_8597f_row11_col3\" class=\"data row11 col3\" >0.0357</td>\n",
       "      <td id=\"T_8597f_row11_col4\" class=\"data row11 col4\" >0.0209</td>\n",
       "      <td id=\"T_8597f_row11_col5\" class=\"data row11 col5\" >0.0099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2152e350b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n",
      "Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one).\n"
     ]
    }
   ],
   "source": [
    "tuned_best_model = tune_model(best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 179
    },
    "id": "1WRoDJKCzx6D",
    "outputId": "b36aa626-5d44-46a2-b7af-09e8c3ba3ec0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x2152e1f7b50>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_best_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, we've get better results than pycaret library."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='PRODUCTION'></a>\n",
    "# To production"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To recap, what we have done so far is analyze a dataset on houses in the United States and we have created a model capable of predicting the selling price based on the features of the dataset. Now, we are going to leverage this model and truly see the value that this work will bring us.\n",
    "\n",
    "First of all, let's load the new data, the model we've just created and apply the feature engineering necessary to being able to apply the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "tags": []
   },
   "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",
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       "    }\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>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "<p>3 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0  1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
       "1  1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
       "2  1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... ScreenPorch PoolArea PoolQC  Fence MiscFeature  \\\n",
       "0         Lvl    AllPub  ...         120        0    NaN  MnPrv         NaN   \n",
       "1         Lvl    AllPub  ...           0        0    NaN    NaN        Gar2   \n",
       "2         Lvl    AllPub  ...           0        0    NaN  MnPrv         NaN   \n",
       "\n",
       "  MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0       0      6    2010        WD         Normal  \n",
       "1   12500      6    2010        WD         Normal  \n",
       "2       0      3    2010        WD         Normal  \n",
       "\n",
       "[3 rows x 80 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('test.csv')\n",
    "df_test.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's apply the feature engineering:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "tags": []
   },
   "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>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>TotalSF</th>\n",
       "      <th>Total_Bathrooms</th>\n",
       "      <th>SalePricePredicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1778.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>117847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2658.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>165324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2557.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>183397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0  1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
       "1  1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
       "2  1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ...  Fence MiscFeature MiscVal MoSold YrSold  \\\n",
       "0         Lvl    AllPub  ...  MnPrv         NaN       0      6   2010   \n",
       "1         Lvl    AllPub  ...    NaN        Gar2   12500      6   2010   \n",
       "2         Lvl    AllPub  ...  MnPrv         NaN       0      3   2010   \n",
       "\n",
       "  SaleType SaleCondition  TotalSF  Total_Bathrooms  SalePricePredicted  \n",
       "0       WD        Normal   1778.0              1.0              117847  \n",
       "1       WD        Normal   2658.0              2.0              165324  \n",
       "2       WD        Normal   2557.0              3.0              183397  \n",
       "\n",
       "[3 rows x 83 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the model from disk\n",
    "loaded_model = pickle.load(open(filename, 'rb'))\n",
    "\n",
    "## Apply feature engineering\n",
    "df_test_copy = df_test.copy()\n",
    "ids = df_test_copy.pop('Id')\n",
    "df_test_transformed =  feature_engineering_sale_price_dataset(df_test_copy)\n",
    "categorical_columns = df_test_transformed.select_dtypes(include=['object']).columns.tolist()\n",
    "df_test_transformed[categorical_columns] = df_test_transformed[categorical_columns].fillna('NaN')\n",
    "\n",
    "## Apply the model to get the price prediction\n",
    "prediction_house_price = loaded_model.predict(df_test_transformed)      \n",
    "\n",
    "# Join all features in the same dataframe\n",
    "df_final_results = df_test.copy()\n",
    "#Add useful features\n",
    "df_final_results['TotalSF'] = df_final_results['TotalBsmtSF'] + df_final_results['GrLivArea']\n",
    "df_final_results['Total_Bathrooms'] = df_final_results['FullBath'] + df_final_results['HalfBath'] + df_final_results['BsmtFullBath'] + df_final_results['BsmtHalfBath']   \n",
    "df_final_results[\"SalePricePredicted\"] = prediction_house_price.round().astype(int)\n",
    "df_final_results.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the results in a csv file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_final_results.to_csv('house_dataset_w_predicted_price.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='NEWINSIGHTS'></a>\n",
    "## Which insights we can get from new data?\n",
    "\n",
    "Let's imagine, that we work in a real statement business. With the historical data of the houses sold by the last 5 years, we've built a model that is able to predict the price of the house that will be sold, so, we can assume that actually we're predicting the market price.\n",
    "\n",
    "Now, we have a portfolio of houses to sell and we want to know the market price for each house to help the seller owner to pick the right price and also to guide new buyers what they can afford. \n",
    "\n",
    "In the next sections, we'll be answering those questions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which are the cheapiest and the most expensive neighborhoods?\n",
    "\n",
    "Knowing the market price of the houses, we can share with new buyers which neighborhood they could afford, telling them the mean of the house prices and the mean of the price per sq."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "tags": []
   },
   "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>AVG Price</th>\n",
       "      <th>AVG Price/SQ</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MeadowV</th>\n",
       "      <td>93594.15</td>\n",
       "      <td>56.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BrDale</th>\n",
       "      <td>102318.14</td>\n",
       "      <td>63.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDOTRR</th>\n",
       "      <td>107452.14</td>\n",
       "      <td>57.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BrkSide</th>\n",
       "      <td>121976.88</td>\n",
       "      <td>61.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OldTown</th>\n",
       "      <td>122156.74</td>\n",
       "      <td>58.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SWISU</th>\n",
       "      <td>125466.61</td>\n",
       "      <td>55.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sawyer</th>\n",
       "      <td>134047.95</td>\n",
       "      <td>66.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edwards</th>\n",
       "      <td>134369.89</td>\n",
       "      <td>64.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blueste</th>\n",
       "      <td>140079.50</td>\n",
       "      <td>76.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAmes</th>\n",
       "      <td>143540.99</td>\n",
       "      <td>63.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NPkVill</th>\n",
       "      <td>151577.64</td>\n",
       "      <td>70.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mitchel</th>\n",
       "      <td>164527.77</td>\n",
       "      <td>69.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SawyerW</th>\n",
       "      <td>180853.71</td>\n",
       "      <td>70.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gilbert</th>\n",
       "      <td>189034.81</td>\n",
       "      <td>77.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NWAmes</th>\n",
       "      <td>192181.24</td>\n",
       "      <td>68.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ClearCr</th>\n",
       "      <td>194509.50</td>\n",
       "      <td>68.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crawfor</th>\n",
       "      <td>196065.35</td>\n",
       "      <td>73.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blmngtn</th>\n",
       "      <td>196103.82</td>\n",
       "      <td>73.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CollgCr</th>\n",
       "      <td>204511.39</td>\n",
       "      <td>76.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Somerst</th>\n",
       "      <td>225556.00</td>\n",
       "      <td>79.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timber</th>\n",
       "      <td>247913.82</td>\n",
       "      <td>80.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Veenker</th>\n",
       "      <td>276132.31</td>\n",
       "      <td>77.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NoRidge</th>\n",
       "      <td>305682.87</td>\n",
       "      <td>80.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NridgHt</th>\n",
       "      <td>311091.75</td>\n",
       "      <td>85.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StoneBr</th>\n",
       "      <td>312845.54</td>\n",
       "      <td>85.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              AVG Price  AVG Price/SQ\n",
       "Neighborhood                         \n",
       "MeadowV        93594.15         56.88\n",
       "BrDale        102318.14         63.48\n",
       "IDOTRR        107452.14         57.74\n",
       "BrkSide       121976.88         61.12\n",
       "OldTown       122156.74         58.47\n",
       "SWISU         125466.61         55.45\n",
       "Sawyer        134047.95         66.31\n",
       "Edwards       134369.89         64.80\n",
       "Blueste       140079.50         76.20\n",
       "NAmes         143540.99         63.90\n",
       "NPkVill       151577.64         70.88\n",
       "Mitchel       164527.77         69.55\n",
       "SawyerW       180853.71         70.83\n",
       "Gilbert       189034.81         77.07\n",
       "NWAmes        192181.24         68.01\n",
       "ClearCr       194509.50         68.51\n",
       "Crawfor       196065.35         73.34\n",
       "Blmngtn       196103.82         73.90\n",
       "CollgCr       204511.39         76.43\n",
       "Somerst       225556.00         79.74\n",
       "Timber        247913.82         80.04\n",
       "Veenker       276132.31         77.89\n",
       "NoRidge       305682.87         80.72\n",
       "NridgHt       311091.75         85.90\n",
       "StoneBr       312845.54         85.88"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final_results[\"PricePerSq\"] = df_final_results[\"SalePricePredicted\"]/df_final_results[\"TotalSF\"]\n",
    "price_per_neighborhood = df_final_results.groupby([\"Neighborhood\"])[\"SalePricePredicted\"].mean()\n",
    "price_sq_per_neighborhood = df_final_results.groupby([\"Neighborhood\"])[\"PricePerSq\"].mean()\n",
    "df_price_per_neighborhood = price_per_neighborhood.to_frame(name=\"AVG Price\")\n",
    "df_price_sq_per_neighborhood = price_sq_per_neighborhood.to_frame(name=\"AVG Price/SQ\")\n",
    "pd.merge(df_price_per_neighborhood, df_price_sq_per_neighborhood, on=\"Neighborhood\").round(2).sort_values(by=\"AVG Price\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, for low budget clients we can suggest neighborhoods as MeadowV or BrDale and so on. For high budget clients we, can suggest NridgHT and StoneBr neighborhoods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which type of building can I suggest by price?\n",
    "\n",
    "As we've done before, let's show the average price per type of building and its average price/sq."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "tags": []
   },
   "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>AVG Price</th>\n",
       "      <th>AVG Price/SQ</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BldgType</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2fmCon</th>\n",
       "      <td>125862.29</td>\n",
       "      <td>53.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Twnhs</th>\n",
       "      <td>132762.96</td>\n",
       "      <td>67.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duplex</th>\n",
       "      <td>147466.70</td>\n",
       "      <td>55.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1Fam</th>\n",
       "      <td>180446.33</td>\n",
       "      <td>70.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TwnhsE</th>\n",
       "      <td>199061.81</td>\n",
       "      <td>76.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          AVG Price  AVG Price/SQ\n",
       "BldgType                         \n",
       "2fmCon    125862.29         53.47\n",
       "Twnhs     132762.96         67.56\n",
       "Duplex    147466.70         55.53\n",
       "1Fam      180446.33         70.34\n",
       "TwnhsE    199061.81         76.63"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_per_BldgType = df_final_results.groupby([\"BldgType\"])[\"SalePricePredicted\"].mean()\n",
    "price_sq_per_BldgType = df_final_results.groupby([\"BldgType\"])[\"PricePerSq\"].mean()\n",
    "df_price_per_BldgType = price_per_BldgType.to_frame(name=\"AVG Price\")\n",
    "df_price_sq_per_BldgType = price_sq_per_BldgType.to_frame(name=\"AVG Price/SQ\")\n",
    "pd.merge(df_price_per_BldgType, df_price_sq_per_BldgType, on=\"BldgType\").round(2).sort_values(by=\"AVG Price\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cheapest building types are: the Two-family Conversion (2fmCon) and Townhouse Inside Unit (TwnhsI) and the most expensive building types are: Single-family Detached (1Fam) and Townhouse End Unit (TwnhsE)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Optimizing real estate profitability\n",
    "\n",
    "The fact that we could know in advance an estimation of the market price of the house, this doesn't mean that this finally will be the sale price. Maybe some sellers prefer to set a lower selling price to speed up the selling or some sellers don't want to sell below a certain price. So let's imagine we have all the selling prices for the portfolio we're working on (for that purpose will add some random noise to our predicted price and we'll imagine that this is the selling price)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set the seed for reproducibility\n",
    "np.random.seed(42)\n",
    "random_vector = np.random.normal(0, np.sqrt(100000000), df_final_results.shape[0]).round().astype(int)\n",
    "df_final_results['SellingPrice'] = df_final_results['SalePricePredicted'] + random_vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For those sellers, that are willing to sell the house below the market price, we can set a larger profit for the company until reaching (or almost reaching) the market price for which we believe the property will be sold. These properties are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>Id</th>\n",
       "      <th>Price Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>1670</td>\n",
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       "      <th>478</th>\n",
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       "      <td>1640</td>\n",
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       "      <th>755</th>\n",
       "      <td>2216</td>\n",
       "      <td>-26324</td>\n",
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       "    <tr>\n",
       "      <th>1453</th>\n",
       "      <td>2914</td>\n",
       "      <td>-26017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>2694</td>\n",
       "      <td>-25797</td>\n",
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       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>2115</td>\n",
       "      <td>-25734</td>\n",
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       "      <th>762</th>\n",
       "      <td>2223</td>\n",
       "      <td>-25601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>2341</td>\n",
       "      <td>-25269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>2752</td>\n",
       "      <td>-24930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  Price Difference\n",
       "209   1670            -38527\n",
       "478   1939            -30789\n",
       "179   1640            -27202\n",
       "755   2216            -26324\n",
       "1453  2914            -26017\n",
       "1233  2694            -25797\n",
       "654   2115            -25734\n",
       "762   2223            -25601\n",
       "880   2341            -25269\n",
       "1291  2752            -24930"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final_results['Price Difference'] = df_final_results['SalePricePredicted'] - df_final_results['SellingPrice']\n",
    "df_final_results[['Id','Price Difference']].sort_values(by='Price Difference').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, we can see the identification of those properties with lower selling prices respect to the market price.\n",
    "\n",
    "Also, let's check which properties are being sold with higher selling prices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Id</th>\n",
       "      <th>Price Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>1723</td>\n",
       "      <td>32413</td>\n",
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       "      <th>1101</th>\n",
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       "      <td>2522</td>\n",
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       "      <th>646</th>\n",
       "      <td>2107</td>\n",
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       "      <th>668</th>\n",
       "      <td>2129</td>\n",
       "      <td>26510</td>\n",
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       "      <th>74</th>\n",
       "      <td>1535</td>\n",
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       "      <td>25910</td>\n",
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       "      <th>1346</th>\n",
       "      <td>2807</td>\n",
       "      <td>25539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1160</th>\n",
       "      <td>2621</td>\n",
       "      <td>24994</td>\n",
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       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>2005</td>\n",
       "      <td>24716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  Price Difference\n",
       "262   1723             32413\n",
       "1101  2562             28963\n",
       "1061  2522             28485\n",
       "646   2107             26969\n",
       "668   2129             26510\n",
       "74    1535             26197\n",
       "1355  2816             25910\n",
       "1346  2807             25539\n",
       "1160  2621             24994\n",
       "544   2005             24716"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final_results[['Id','Price Difference']].sort_values(by='Price Difference', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For properties above, we should set a lower profit and also suggest a lower selling price to the seller."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='SHARE'></a>\n",
    "\n",
    "### Share results with the company\n",
    "An important part of an ML project is being able to make the trained model available for other teams or colleagues of the company to take advantage of the insights they can get of it. One technical way could be creating an API that our colleagues could make their own predictions. Another approach it could be to present the results in an interactive report and enable anyone with the link to get their own insights.\n",
    "\n",
    "That's why I've created an example of a [report](https://lookerstudio.google.com/s/p-JgLucqaOc) that we could create with the results of the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"500\"\n",
       "            src=\"https://lookerstudio.google.com/embed/reporting/69313d85-34c5-4552-b44d-cefe48c3a18f/page/8ZboD\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x2152e203d10>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame, HTML\n",
    "ulr= 'https://lookerstudio.google.com/embed/reporting/69313d85-34c5-4552-b44d-cefe48c3a18f/page/8ZboD'\n",
    "IFrame(ulr, width='100%', height=500) "
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "L746_pI63Pfa",
    "YBUPi-IQCSE5",
    "XVbV4ltnROZp",
    "FjQprZL9Uzi7",
    "CFrH5r8EXrYQ",
    "Ub5LSZMw0NPX",
    "uBEjIZNyHhDP",
    "0lzSnXmfW4IL",
    "vIMTqEgPPle5",
    "3RBFAUPkPuV2",
    "o8YT9QB6bKCA",
    "owAzcWMbcbHa"
   ],
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "toc-autonumbering": false,
  "toc-showcode": false,
  "toc-showmarkdowntxt": false,
  "toc-showtags": false,
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "003e80d6af2c407ca5afcd5066d9d03b": {
     "model_module": "@jupyter-widgets/output",
     "model_module_version": "1.0.0",
     "model_name": "OutputModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/output",
      "_model_module_version": "1.0.0",
      "_model_name": "OutputModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/output",
      "_view_module_version": "1.0.0",
      "_view_name": "OutputView",
      "layout": "IPY_MODEL_d271c1892ccf4ead9fa99aae9f356670",
      "msg_id": "",
      "outputs": [
       {
        "data": {
         "image/png": 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