{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AGnxWL3OE-xN"
   },
   "source": [
    "Name: Ali Gowani<br>\n",
    "Contact: https://www.linkedin.com/in/aliagowani/<br><br>\n",
    "\n",
    "Title: Regression Experiment for Intelligent Contact Center Employee performance<br>\n",
    "Pycaret Version: 2.1<br><br>\n",
    "Created: Monday, August 24, 2020 <br>\n",
    "Updated: Thursday, September 3, 2020 <br>\n",
    "<br>\n",
    "\n",
    "Use Jupyter Notebook Viewer to view this notebook properly: https://nbviewer.jupyter.org/github/aliagowani/Pycaret_2.1_Regression_EmployeePerformance/blob/master/Pycaret_2.1_Regression_EmployeePerformance.ipynb\n",
    "\n",
    "\n",
    "## 0. Overview: Real-Life Employee Performance Case in Machine Learning (Regression) using Pycaret 2.1\n",
    "\n",
    "We are going to utilize a low-code Machine Learning Python library, Pycaret (version 2.1), to predict the First Call Resolution (FCR) metric for Customer Service Agents (Employees) in Call Centers. FCR is an important metric in a call center as it indicates the percentage of issues that were resolved when the customer called the first time. We want to ensure that customers do not keep calling back to resolve an issue as it costs the company money when the issue is not resolved the first time.\n",
    "\n",
    "Below is the approach we will take to predict a Customer Service Agent or Contact Agents FCR metric:\n",
    "<ol>\n",
    "<li>Conduct Exploratory Data Analysis (EDA) on the real data from a global call center.</li>\n",
    "<li>Execute regression models to determine how accurately we can predict the FCR metric for each employee.</li>\n",
    "<li>Create a classification indicator to determine whether predicting an employee's increase or decrease in FCR metric performance is more meaningful than regression.</li>\n",
    "</ol>\n",
    "\n",
    "We will leverage a real-case data from a business process outsourcer (BPO) that supports many Fortune 500 companies. *Note: dataset has been sanitized of personal information as it is a real dataset.\n",
    "\n",
    "Let's get started!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KLSqH0j6FBGy"
   },
   "source": [
    "## 1. Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8V8nM8_vE2Gu",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Import libraries for data processing.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import time\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Import libraries for visualization and set default values.\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use(['seaborn'])\n",
    "\n",
    "from sklearn import set_config\n",
    "set_config(display='text')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3fTMUUPnJ_Wy",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confirm Pycaret version is 2.1.X?\n",
      "Pycaret Version:  2.1.2\n"
     ]
    }
   ],
   "source": [
    "# Install and import Pycaret library for transformation and classification.\n",
    "# !pip install pycaret\n",
    "from pycaret.regression import *\n",
    "\n",
    "# Confirm Pycaret version is 2.1\n",
    "from pycaret.utils import version\n",
    "print('Confirm Pycaret version is 2.1.X?')\n",
    "print('Pycaret Version: ', version())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i0z35t89F_Ud"
   },
   "source": [
    "## 2. Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "37X3rvNGGAr5"
   },
   "outputs": [],
   "source": [
    "# Load Dataset.\n",
    "url = 'https://raw.githubusercontent.com/aliagowani/Pycaret_2.1_Regression_EmployeePerformance/master/employee_performance.csv?token=AMLWIYQHZO4XANFWX3IP5B27LGQIY'\n",
    "dataset = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 400
    },
    "colab_type": "code",
    "id": "WkvhfNseGboM",
    "outputId": "fd2852c5-6fe6-47b7-9430-8886c2ad2342",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of dataset (observations, features): (102, 19)\n"
     ]
    },
    {
     "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>Agent_ID</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Site</th>\n",
       "      <th>Function_Field</th>\n",
       "      <th>tenure</th>\n",
       "      <th>Total number of calls</th>\n",
       "      <th>Assistance</th>\n",
       "      <th>Recommend</th>\n",
       "      <th>CSat</th>\n",
       "      <th>total coaching</th>\n",
       "      <th>total coaching improved</th>\n",
       "      <th>Actual Value</th>\n",
       "      <th>FCR Week before</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>384091</td>\n",
       "      <td>100.00</td>\n",
       "      <td>90.00</td>\n",
       "      <td>90.00</td>\n",
       "      <td>96.15</td>\n",
       "      <td>100.00</td>\n",
       "      <td>96.88</td>\n",
       "      <td>100.00</td>\n",
       "      <td>Kuala Lumpur</td>\n",
       "      <td>Agent</td>\n",
       "      <td>33</td>\n",
       "      <td>163</td>\n",
       "      <td>95.06</td>\n",
       "      <td>94.23</td>\n",
       "      <td>4.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85.71</td>\n",
       "      <td>97.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>369185</td>\n",
       "      <td>94.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>96.87</td>\n",
       "      <td>96.87</td>\n",
       "      <td>96.10</td>\n",
       "      <td>95.89</td>\n",
       "      <td>98.33</td>\n",
       "      <td>Kuala Lumpur</td>\n",
       "      <td>Agent</td>\n",
       "      <td>33</td>\n",
       "      <td>306</td>\n",
       "      <td>95.08</td>\n",
       "      <td>94.67</td>\n",
       "      <td>4.83</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>88.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>360854</td>\n",
       "      <td>94.44</td>\n",
       "      <td>80.00</td>\n",
       "      <td>92.94</td>\n",
       "      <td>92.94</td>\n",
       "      <td>100.00</td>\n",
       "      <td>93.94</td>\n",
       "      <td>96.30</td>\n",
       "      <td>Kuala Lumpur</td>\n",
       "      <td>Agent</td>\n",
       "      <td>32</td>\n",
       "      <td>138</td>\n",
       "      <td>94.16</td>\n",
       "      <td>94.74</td>\n",
       "      <td>4.82</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>80.00</td>\n",
       "      <td>92.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>374407</td>\n",
       "      <td>100.00</td>\n",
       "      <td>95.00</td>\n",
       "      <td>96.63</td>\n",
       "      <td>96.63</td>\n",
       "      <td>92.50</td>\n",
       "      <td>95.65</td>\n",
       "      <td>100.00</td>\n",
       "      <td>Kuala Lumpur</td>\n",
       "      <td>Agent</td>\n",
       "      <td>32</td>\n",
       "      <td>148</td>\n",
       "      <td>98.65</td>\n",
       "      <td>95.83</td>\n",
       "      <td>4.84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94.44</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>372496</td>\n",
       "      <td>96.88</td>\n",
       "      <td>95.83</td>\n",
       "      <td>94.28</td>\n",
       "      <td>94.28</td>\n",
       "      <td>83.33</td>\n",
       "      <td>95.35</td>\n",
       "      <td>100.00</td>\n",
       "      <td>Kuala Lumpur</td>\n",
       "      <td>Agent</td>\n",
       "      <td>29</td>\n",
       "      <td>142</td>\n",
       "      <td>97.18</td>\n",
       "      <td>98.55</td>\n",
       "      <td>4.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>90.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Agent_ID  Friday  Monday  Saturday  Sunday  Thursday  Tuesday  Wednesday  \\\n",
       "0    384091  100.00   90.00     90.00   96.15    100.00    96.88     100.00   \n",
       "1    369185   94.00  100.00     96.87   96.87     96.10    95.89      98.33   \n",
       "2    360854   94.44   80.00     92.94   92.94    100.00    93.94      96.30   \n",
       "3    374407  100.00   95.00     96.63   96.63     92.50    95.65     100.00   \n",
       "4    372496   96.88   95.83     94.28   94.28     83.33    95.35     100.00   \n",
       "\n",
       "           Site Function_Field  tenure  Total number of calls  Assistance  \\\n",
       "0  Kuala Lumpur          Agent      33                    163       95.06   \n",
       "1  Kuala Lumpur          Agent      33                    306       95.08   \n",
       "2  Kuala Lumpur          Agent      32                    138       94.16   \n",
       "3  Kuala Lumpur          Agent      32                    148       98.65   \n",
       "4  Kuala Lumpur          Agent      29                    142       97.18   \n",
       "\n",
       "   Recommend  CSat  total coaching  total coaching improved  Actual Value  \\\n",
       "0      94.23  4.87               0                        0         85.71   \n",
       "1      94.67  4.83               0                        0        100.00   \n",
       "2      94.74  4.82               2                        1         80.00   \n",
       "3      95.83  4.84               0                        0         94.44   \n",
       "4      98.55  4.86               0                        0        100.00   \n",
       "\n",
       "   FCR Week before  \n",
       "0            97.14  \n",
       "1            88.64  \n",
       "2            92.31  \n",
       "3           100.00  \n",
       "4            90.91  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check shape of dataset and view first few observations to ensure data loaded correctly.\n",
    "print(\"Shape of dataset (observations, features):\", dataset.shape)\n",
    "dataset.head(5).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HauKRNRaGoi9"
   },
   "source": [
    "## 3. Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 320
    },
    "colab_type": "code",
    "id": "Gj_syQpgGrDa",
    "outputId": "a127405c-90ac-4af0-e7c3-27b1906138d1"
   },
   "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>Agent_ID</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>tenure</th>\n",
       "      <th>Total number of calls</th>\n",
       "      <th>Assistance</th>\n",
       "      <th>Recommend</th>\n",
       "      <th>CSat</th>\n",
       "      <th>total coaching</th>\n",
       "      <th>total coaching improved</th>\n",
       "      <th>Actual Value</th>\n",
       "      <th>FCR Week before</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>102.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>102.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>371097.88</td>\n",
       "      <td>95.65</td>\n",
       "      <td>94.10</td>\n",
       "      <td>95.55</td>\n",
       "      <td>95.19</td>\n",
       "      <td>94.91</td>\n",
       "      <td>94.97</td>\n",
       "      <td>96.54</td>\n",
       "      <td>14.24</td>\n",
       "      <td>173.94</td>\n",
       "      <td>95.83</td>\n",
       "      <td>95.62</td>\n",
       "      <td>4.81</td>\n",
       "      <td>1.61</td>\n",
       "      <td>0.88</td>\n",
       "      <td>96.04</td>\n",
       "      <td>95.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10742.44</td>\n",
       "      <td>4.36</td>\n",
       "      <td>10.47</td>\n",
       "      <td>3.56</td>\n",
       "      <td>3.94</td>\n",
       "      <td>5.45</td>\n",
       "      <td>4.68</td>\n",
       "      <td>3.83</td>\n",
       "      <td>6.85</td>\n",
       "      <td>77.23</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.07</td>\n",
       "      <td>5.70</td>\n",
       "      <td>4.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>353039.00</td>\n",
       "      <td>77.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>80.56</td>\n",
       "      <td>80.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>80.00</td>\n",
       "      <td>83.33</td>\n",
       "      <td>3.00</td>\n",
       "      <td>52.00</td>\n",
       "      <td>89.66</td>\n",
       "      <td>87.16</td>\n",
       "      <td>4.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>80.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>362108.00</td>\n",
       "      <td>93.75</td>\n",
       "      <td>92.45</td>\n",
       "      <td>93.87</td>\n",
       "      <td>93.28</td>\n",
       "      <td>92.31</td>\n",
       "      <td>92.94</td>\n",
       "      <td>95.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>116.25</td>\n",
       "      <td>94.71</td>\n",
       "      <td>94.67</td>\n",
       "      <td>4.77</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>94.12</td>\n",
       "      <td>92.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>371781.00</td>\n",
       "      <td>96.15</td>\n",
       "      <td>94.94</td>\n",
       "      <td>95.83</td>\n",
       "      <td>96.01</td>\n",
       "      <td>96.00</td>\n",
       "      <td>95.86</td>\n",
       "      <td>97.33</td>\n",
       "      <td>15.00</td>\n",
       "      <td>158.00</td>\n",
       "      <td>95.81</td>\n",
       "      <td>95.59</td>\n",
       "      <td>4.82</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>96.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>380697.75</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>97.56</td>\n",
       "      <td>97.57</td>\n",
       "      <td>100.00</td>\n",
       "      <td>98.15</td>\n",
       "      <td>100.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>226.25</td>\n",
       "      <td>96.99</td>\n",
       "      <td>97.11</td>\n",
       "      <td>4.87</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>388627.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>33.00</td>\n",
       "      <td>388.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>4.98</td>\n",
       "      <td>9.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Agent_ID  Friday  Monday  Saturday  Sunday  Thursday  Tuesday  \\\n",
       "count     102.00  102.00  102.00    102.00  102.00    102.00   102.00   \n",
       "mean   371097.88   95.65   94.10     95.55   95.19     94.91    94.97   \n",
       "std     10742.44    4.36   10.47      3.56    3.94      5.45     4.68   \n",
       "min    353039.00   77.78    0.00     80.56   80.00     75.00    80.00   \n",
       "25%    362108.00   93.75   92.45     93.87   93.28     92.31    92.94   \n",
       "50%    371781.00   96.15   94.94     95.83   96.01     96.00    95.86   \n",
       "75%    380697.75  100.00  100.00     97.56   97.57    100.00    98.15   \n",
       "max    388627.00  100.00  100.00    100.00  100.00    100.00   100.00   \n",
       "\n",
       "       Wednesday  tenure  Total number of calls  Assistance  Recommend  \\\n",
       "count     102.00  102.00                 102.00      102.00     102.00   \n",
       "mean       96.54   14.24                 173.94       95.83      95.62   \n",
       "std         3.83    6.85                  77.23        1.78       2.00   \n",
       "min        83.33    3.00                  52.00       89.66      87.16   \n",
       "25%        95.00    8.00                 116.25       94.71      94.67   \n",
       "50%        97.33   15.00                 158.00       95.81      95.59   \n",
       "75%       100.00   17.00                 226.25       96.99      97.11   \n",
       "max       100.00   33.00                 388.00      100.00     100.00   \n",
       "\n",
       "         CSat  total coaching  total coaching improved  Actual Value  \\\n",
       "count  102.00          102.00                   102.00        102.00   \n",
       "mean     4.81            1.61                     0.88         96.04   \n",
       "std      0.09            1.78                     1.07          5.70   \n",
       "min      4.41            0.00                     0.00         75.00   \n",
       "25%      4.77            0.00                     0.00         94.12   \n",
       "50%      4.82            1.00                     1.00        100.00   \n",
       "75%      4.87            2.00                     1.00        100.00   \n",
       "max      4.98            9.00                     4.00        100.00   \n",
       "\n",
       "       FCR Week before  \n",
       "count           102.00  \n",
       "mean             95.58  \n",
       "std               4.67  \n",
       "min              80.00  \n",
       "25%              92.92  \n",
       "50%              96.49  \n",
       "75%             100.00  \n",
       "max             100.00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Below is a high-level description of each feature. The data set is comprised of 102 agents and we have 19 features to evaluate. The data is between the time period of June 1, 2020 and July 31, 2020, with the 'Actual Value' as the Actual FCR value of each agent for August 7, 2020. The goal is to create a model that predicts the FCR for an employee can have at the end of the week (Friday). \n",
    "\n",
    "# 'Agent_ID': unique identifier of the employee or agent.\n",
    "# 'Friday', 'Monday', 'Saturday', 'Sunday', 'Thursday', 'Tuesday', 'Wednesday': shows the FCR percent for each agent as an aggregate (mean). The higher the percentage the higher the rate at which the customer's issue was resolved in the first call. \n",
    "# 'Site': city location of the call center.\n",
    "# 'Function_Field': this is the role of the employee. In our case, it should all be Agents. \n",
    "# 'tenure': tenure of the agent at the company in months. \n",
    "# 'Total number of calls': number of all phone calls taken by the agent in the given timeframe.\n",
    "# 'Assistance': percentage of time the agent needed to provide additional assistance by escalating to supervisor. \n",
    "# 'Recommend': percentage of time the agent would be recommended by the customer to resolve an issue. \n",
    "# 'CSat': average survey response (on a scale from 1 (least satisfied) to 5 (most satisfied)) that the customer was satisfied with the experience. \n",
    "# 'total coaching': number of times the agent received coaching sessions from supervisor to improve FCR metric in the given timeframe. \n",
    "# 'total coaching improved': number of times the agent's FCR value increased after a week from the initial coaching. \n",
    "# 'Actual Value': the label we are trying to predict. It is the next Friday (August 7, 2020) FCR value for the agent. \n",
    "# 'FCR Week before': a strong indicator is how well the agent performed the previous Friday. This is the FCR value for the Friday before the predicted value ('Actual Value'). \n",
    "\n",
    "# Describe the features in the dataset, such as, count, mean, standard deviation, min, max, etc.\n",
    "dataset.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 736
    },
    "colab_type": "code",
    "id": "0zZ0wQbOGtfq",
    "outputId": "b9e5a819-fbb7-4171-e6c8-c5f6b9f295d5"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a correlation for the dataset.\n",
    "dataset_corr = dataset.corr().round(4)\n",
    "\n",
    "# Drop Agent_ID from correlation dataset.\n",
    "dataset_corr = dataset_corr.drop([\"Agent_ID\"], axis=1)\n",
    "dataset_corr = dataset_corr.drop([\"Agent_ID\"], axis=0)\n",
    "\n",
    "# Create a correlation matrix. Only bottom left corner valued. \n",
    "mask = np.zeros_like(dataset_corr.round(4))\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Generate the corrleation matrix (heatmap) using Seaborn. \n",
    "with sns.axes_style(\"whitegrid\"):\n",
    "    f, ax = plt.subplots(figsize=(12, 10))\n",
    "    ax = sns.heatmap(dataset_corr.round(2), mask=mask, vmax=1, center = 0, vmin=-1, square=True, cmap='PuOr', linewidths=.5, annot=True, annot_kws={\"size\": 12}, fmt='.1f')\n",
    "    plt.title('Heatmap (Correlations) of Features in the Dataset', fontsize=15)\n",
    "    plt.xlabel('Features', fontsize=15)\n",
    "    plt.ylabel('Features', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JiIiIiIiIHENi0Ri1hRV4k+Nw+Txdvs+XmoAdswhV1PZiddLfKWgSEREREREROYYEiiuxLYu4jKRu3edLiW+6f29Vb5QlA4SCJhEREREREZFjSG1hOQD+9G4GTakJANTt1ZvnpGMKmkRERERERESOIbWFTQ29/emJ3brPk+jHdDq0okkOSkGTiIiIiIiIyDHCilnUFVfiSfTjdLu6da9hGHhT4qmvrCPaGOmlCqW/U9AkIiIiIiIicowIllRhRWPEdXPbXLOWPk0lWtUk7VPQJCIiIiIiInKMqGnuz9TNRuDN/Pv6NGn7nHREQZOIiIiIiIjIMeJQ+zM186UoaJKDU9AkIiIiIiIicgywLZu6wgrc8T5cXvchjeH0uXG4nQQranq4OhkoFDSJiIiIiIiIHANClXXEIlH8aQmHPIZhGHiS4misCRGLRHuwOhkoFDSJiIiIiIiIHAOC+xp4N29/O1TeRD8A9ZV1h12TDDwKmkRERERERESOAYHSamD/m+MOlWdf0BQqrz3smmTgUdAkIiIiIiIicgwIlFRhmE1b3w5HS9BUoaBJ2lLQJCIiIiIiIjLAWTGLYFkNnkQ/puPwogBvYlNQFarQ1jlpy3mkCxARERERERGR3lVfUYsdsw67P9Omoiryq4IMcTmpKa3qoepkINGKJhEREREREZEBrrk/k/cQ+zPZts2bWwp56sNtvLmliKJwDLs+zIrPtvdkmTIAKGgSERERERERGeACLW+c637QZNs2r3yxhzc2FZLgcTJj/BCSU5tWRr29agvr8kp6tFbp3xQ0iYiIiIiIiAxwgZLqpkbg+xp5d8eGwio+2F5Cmt/NZacMZ2R6AukZiQCkGvDE62spLFe/JmnSa0GTZVncddddzJkzh3nz5rF79+4219TX1zN37lzy8vIAiEQi3HbbbVx++eVccsklvP32271VnoiIiIiIiMgxwYrGCJXX4E2KwzS7FwNEYxbLN+RjGnD++KHEe1wAmHFeAE7OTCQcjbH84209Xrf0T70WNL311luEw2GWLl3K/Pnzuf/++1ud37BhA1dccQX5+fktx1599VWSk5N57rnnePLJJ7n77rt7qzwRERERERGRY0KoohbbsvEeQiPwD7aXUBlsZMLgFFL87pbjpr8paEq0YmQm+1mzvZiymlCP1Sz9V68FTWvWrGHq1KkATJw4kY0bN7Y6Hw6Heeyxxxg5cmTLsfPOO4+bb7655XuHw9Fb5YmIiIiIiIgcEwIlTY3Au9ufKdAY4a0tRXicJqcPT291znCYGB4XsdoQpx6fjW3D22t39ljN0n85e2vgQCBAfPz+P2KHw0E0GsXpbJpy8uTJbe6Ji4trufemm27iZz/7WZfmysg4vNczihxr9MyIdJ+eG5Hu03Mj0j16ZqS3FNQEAMgclk5cctd7NL27ZicN0RjTThhMekrb+8IJPhrLazltzCBWbi5g5aYCrrlgEvE+dzujybGi14Km+Ph4gsFgy/eWZbWETAdTXFzMjTfeyOWXX86MGTO6NFdZmZqOiXRVRkaCnhmRbtJzI9J9em5EukfPjPSm8t2lGA6TMCaR6q5tb4tZFu9tKcLjMDk+NZ5AoLHNNZa7qV9TTXE1E0dm8f6GPfzz3c2cd+qoHq2/Iwpnj069tnVu0qRJvP/++wCsW7eO3NzcTu8pLy/n2muv5bbbbuOSSy7prdJEREREREREjgmxaIz6yjq8yXEYptHl+zYVVVPbEGHcoCRcjvajA9PnaZojUM+EEZk4TIOPthT2SN3Sf/XaiqZp06axatUq5s6di23b3HfffSxfvpxQKMScOXPavefPf/4ztbW1LF68mMWLFwPw5JNP4vV6e6tMERERERERkQErVFaDbdn4krvXn+nDHaUAnJid3OE1zUGTVVePL8fJ8MwkduytpqwmREZS17fodcaOxcA0MYyuB2Vy5PRa0GSaJgsXLmx1bNSotsvnlixZ0vL1nXfeyZ133tlbJYmIiIiIiIgcUwKlzY3Au77NrKyugW2ltQxJ8pEW5+nwOtPfvKKpaTve6MEp7Nhbzbq8EqZNGnEYVTeFS5GX/kFs0waorsbIGoTnxzdjpKQc1rjS+3pt65yIiIiIiIiIHFmBkiqge2+c+3hn02qmkwYfPNQxPC4wDGKBegBG7bt+bd7eQym1hW3bRJY+S2zl+9DQiJGegb23mMY//n/YNTWHNbb0PgVNIiIiIiIiIgNUoKQa02HiTvB16XrLtlmbX4HHaTIq/eDhlGEYmD4Psbp6bNsmzusmOzWebYWVBOrDh1xz9H9fI/bRKozMLFxXXYtz7hWYk0/DLiulcfEjTVvp5KiloElERERERERkAIpFotRX1uJNie9yf6Nd5QFq6iOMSk/AaXYeGZh+N0Rj2I0RoGn7nG3D+n2rorrLyt9D9H9fg8QknBdehOF2YxgGjjOnYI4dh11USOyLtYc0tvQNBU0iIiIiIiIiA1CwrAZsutUIfF1BBQC5GYlduv7AN88BjB6c2jROXkl3Sm0Ree0VAJznnIvhj2s5bhgGjtPOaJrr3bcPaWzpGwqaRERERERERAag5v5M3i72Z7Jsmy8KKvG5HOSkdO2tcfvfPNfUEDw1wUtKvJdNu8uIxqxu1RvL2461eSPGkKEYOcPanDdSUjCOG4G1cwfW7l3dGlv6joImERERERERkQEoWNK9N87lldURaIwyOj0Bs4tb7YyvrWgyDINhmYk0RmLsKe16427btom++k8AHGdO6XCrn2PiJACiWtV01FLQJCIiIiIiIjIABUqqMJ0O3PHeLl2/Ln/ftrnMrgVT0HbrHEDOvm13XxVWdnkca2ce1o7tGMeNwMwe3OF1Rs4wjNQ0Yp+v7vLY0rcUNImIiIiIiIgMMNHGCPVVAXxdbAQes2zWF1bhdzsYnNS1bXMAhssBTgexuv1B05D0fUFTQdeDptjK9wFwnDL54PMZBua4E8Dq3rY86TsKmkREREREREQGmGBZ07a1rvZn2l0RIBSOMiqt69vmYF/w43NjBeqxbRuABJ+b5DgP2worsSy70zHsYIDY2jWQnIIxNKfzOYcd1+X6pO8paBIREREREREZYJobgfu6GDRtKm66fmRa199Q18z0esC2sUKNLceGZiRSH45SUF7b6f3RTz6CaBTHSRO6tPrKSE+HA95IJ0cXBU0iIiIiIiIiA0xLI/DkrvVb2lRcjcs0GNrFt80dyPS5AbCC+7fPDe3i9jnbtpu2zTkcTVviusAwDMxhw7tdp/QNBU0iIiIiIiIiA0ygpAqHy4krztPptaV19ZTVNTAsNQ6n2f2YoP2G4E0BV2cNwa3t27DLSjGPz8Xw+ro8pzFcQdPRSkGTiIiIiIiIyAASbQjTUBPE28VG4JuKmlY/Hcq2OThgRVOgoeVYot9Dgs/NV4WVLb2b2hNb82nTGOPGd2/OHAVNRysFTSIiIiIiIiIDSKB037a5LvZn2lzcdP1xqYcWNBnefSuaDtg6ZxgGORmJBOrDFFcG2r3PjkabmoDHxWEMGdq9Of3d3+InfUNBk4iIiIiIiMgAEijpetAUbIyws7yO7EQffrfzkOYzXA5wmFgHbJ0DGLxvhdSOvdXt3md9uRlCIczjx2AcwpY9OTrpNykiIiIiIiIygARLm94g5+1C0LRlbw02h75tDvY15/a5iQUbWm2TG7RvhdTODoKm2Op92+bGjD3kueXoo6BJREREREREZAAJlFTj8Lhw+TpvBL6pqCmUOpygCcD0eiBmYTeEW45lJPlxmEa7QZPd2EhswxeQlIyRmXVYc8vRRUGTiIiIiIiIyAARqW+ksTaEL7nzRuDRmMWXJTUk+Vyk+N2HNW9zQ/AD3zznME0yk+MoKK8jEo21uj624QsIhzHHjOlSw3LpPxQ0iYiIiIiIiAwQ3enPtL2slnDUYmRq195OdzDmvtVTVrCh1fFBqfFYls2estpWx5vfNufI1ba5gUZBk4iIiIiIiMgAEdz3xrmu9GfatO9tcyPTD2/bHIDhbbuiCSA7JQ5o3afJDgawNm/CyMjESE077Lnl6KKgSURERERERGSACJQ09VzqbEWTbdtsLqrG4zTJTvQf9rwtK5q+FjS11xA8tu5zsCzM3DGHPa8cfRQ0iYiIiIiIiAwQgZJqnF53p43Ai2pCVNeHOS41Hod5+D2SDLcTTIPY17bOpcR78bgcrYOm1Z8BYGrb3ICkoElERERERERkAAgHGwgH6rvUn2lT0b5tc4f5trlmhmFgej3EAvXYtt3q+KCUeEqrQwQawthVVVh52zAGD8FISOiRueXooqBJREREREREZAAIdKs/UxWmAcNT43psftPnhmgMOxxpdXzQvjl2l9QQ/Xw12DbmGK1mGqgUNImIiIiIiIgMAMHm/kzJBw+aqkNhCqpCDE3243E6emx+w9fcELz19rnsA/o0xdZ8CqaJOTq3x+aVo4uCJhEREREREZEBIFDStKKps61zm5vfNpfWs1vXTG/7DcEzk5tWNNXs2oOdvwdj2HAMn69H55ajh4ImERERERERkQEgUFqNy+fB6XUf9LpNxU0rn0b0UH+mZmbziqZg66ApwefG63aStn1T03VqAj6gKWgSERERERER6efCgXoiwYZO+zM1RGJsK60lPc5DotfVozWYvvZXNBmGQWaij3EVu7CdTsyRo3p0Xjm6KGgSERERERER6ee6um3uq5IaYpbdY2+bO5DhcYFhtOnRBDDWrCczEiQ05DgM98FXXEn/pqBJREREREREpJ8LlO5rBN5J0NS8bW5kei8ETYaB6XVjfW3rHMCJlTsB2JM5vMfnlaOLgiYRERERERGRfq55RZP3IG+ci1k2m4triHM7yYz39kodhs+NHY5ihSMHTBxj6J6t1JluNngze2VeOXooaBIRERERERHpx2zbJlBShcvvwenpuO/S7ooAoXCUEWnxGIbRK7W09GkK7t8+59uxFVdDiM8ThrI7EO2VeeXooaBJREREREREpB8L19UTrQ93edvcqF7oz9TM3PfGu9gBDcHjN6wB4Kv04yiqbSRm2b02vxx5CppERERERERE+rHm/kzelISDXrepqBqXw2Boir/XajF9TUFT85vnzGAdvrytRFLSMNPTidqwNxDutfnlyFPQJCIiIiIiItKPdeWNc6V19ZQFGhiWEofT7L0ooHnrXGzf1rm4TeswbIv6Eblk+Z0A5Nc29tr8cuT12l+XZVncddddzJkzh3nz5rF79+4219TX1zN37lzy8vK6fI+IiIiIiIiI7NcSNB2kEfimoqZrRqYdfNXT4TI8LjD2rWiyLRK++BTbdFA/fFRL0LSnRkHTQNZrQdNbb71FOBxm6dKlzJ8/n/vvv7/V+Q0bNnDFFVeQn5/f5XtEREREREREZL/mRuDueC8Ot7PD6zYVVWEAx6XG9Wo9hmlieNzEgg14d27HVVVB/fCR2B4vmfuCpoJabZ0byHotaFqzZg1Tp04FYOLEiWzcuLHV+XA4zGOPPcbIkSO7fI+IiIiIiIiI7NdYGyLWGMF7kNVMdQ0RdlUEyE704T9IGNVTTJ8buyFM/OcfARA6fjwAHodJqsdBfm0jtq2G4ANVr/2FBQIB4uP3/6E7HA6i0ShOZ9OUkydP7vY9HcnI6N2lfyIDjZ4Zke7TcyPSfXpuRLpHz4wcisK9FQCkDk4lObn9Jt/rthRiA+OHphAf7+ny2DWRShqtBgwMEpxJeB1dayIeS/ATqgpgFBQRyxyEd9iQlnNDktxsKK0n5nKRHufuci3Sf/Ra0BQfH08wGGz53rKsTgOjQ7kHoKys7tALFTnGZGQk6JkR6SY9NyLdp+dGpHv0zMihKtpW3PSF10N1dajdaz7eVgLA0AQfgUDn/ZFKIwV8HniHgnBeyzEHDo73TeQk/zdJcqYf9P6Y0wFA2OUnPHocDQfMmeZu2li1qaCGkwcd3ja+QYd1t/SWXts6N2nSJN5//30A1q1bR25ubq/cIyIiIiIiInKsCpRUAeBNbj+0CTRGyCurZVCCl0Sv66Bj2bbNh7Wv82rlXygI55HuyGakezwjXCfgNfx8Wb+GlyoeY31w1UG3vjnNpnP1/mQaho5oda6lIbjePDdg9dqKpmnTprFq1SsP20QAACAASURBVCrmzp2Lbdvcd999LF++nFAoxJw5c7p8j4iIiIiIiIi0Zds2wdJq3Ak+HK72/3m/qagay4bRnWzNtG2bjwNvsLn+UxLNFCZ4zyTdmX3AeYui6C42NHzCp4E3qY6Wc1bi+TiMtvMm7fmSIMkE04cQb7Ze39IcNBXozXMDVq8FTaZpsnDhwlbHRo0a1ea6JUuWHPQeEREREREREWmroTpALBwlflBKh9esL6gEYHT6wYOm1cEVbAp9QoKZzFn+6XhMX6vzhmEyxDWSVEcWn4Te5KuGtdRbAaYlz8U0HC3XOeqDpG37nKJh36HRHc/XW5THu0zinAb5WtE0YPXa1jkRERERERER6T2BkmoAfB28cS4UjvJVaS2Z8V6SfB033i5o3M4XwQ+IM5PaDZkO5DPjmBJ3AZmOoeSHt7Gy9rVW2+iSN3yKMxrGYVpEw7E29xuGQZbfRUV9lFCk7Xnp/xQ0iYiIiIiIiPRDzf2ZfCntr1baWFSFZdsH3TYXscOsrH0NA4Nv+M7Ba3b+Zjmn4eQb/v9HspnOVw1rWRds6rXsqq0iacvnRL1+DK8HKxzDttr2csqKa9pcla/tcwOSgiYRERERERGRfihYWg1Gx43A1+6pACD3IEHTmsA7BKxqRrsnkORI6/LcTsPFGf7v4jcSWBN8h131m0n/+C1MK0bVuMk43E3b6aKN0Tb3Nvdpyq8Nd3k+6T8UNImIiIiIiIj0M7ZlEyitxpPgx3Q62pyva4iwrbTpbXMdbZsrjxSxKfQxcWYiYz2ndLsGr+nnDP80HDgp/+pV4gp3Up+eTWjQMExXU02xho6DpgL1aRqQFDSJiIiIiIiI9DP1VXVYkRi+lPb7M31RUIkN5GYmdjjG6sAKbGxO9p7V7tvjuiLRkcpk4zS+vxmiJpSfMAkM46ArmlK9Dlwmagg+QCloEhEREREREelnmhuBezsImtbmV2AAuRntB03lkSIKwttJcwwi0znk0Auxbb69toykBpO3R4f5MP4rgP0rmtoJmkzDINPnpKguTCTWtoeT9G8KmkRERERERET6mUBpx43AK4ON7KoIMCTZT5yn/ZVKa/c18B5zCFvmDjRo65ekFhVRm5LK6pFu1tnr2cve/Sua2tk6B5Dpd2LZsDegPk0DjYImERERERERkX4mWFINhoE3qe1b4tbmNzUBH9PBtrnKaAm7G78kxZFBhmPwIdeQVFzEsC/WEXG7KRh/MqdHT8XGZoX9LlEzhuEw213RBJAV5wK0fW4gUtAkIiIiIiIi0o/YlkWgtBpvoh/T0boRuG3brN5djsMwGJ3e/tvmvgiuBGCM+xQMwzikGuIqKshduRIMg90nnULU4yHTTmds7HhqjFo+4VMcbpNYYwzbars9LsunhuADVadB009/+lM+/PDDvqhFRERERERERDoRqqjDjlnt9mfKrwpSWtfAyPR4vK62b6MLxerY0bCJRDOFLGfOIc3vq65m7PvvYsai7DnxZELJKS3nJsROINFOYAMbaXA1hUixcNtVTZn+ptrya7V1bqDpNGiaNm0aixcv5nvf+x5//etfqa6u7ou6RERERERERKQd+/sztQ2aPttVDsAJWUnt3rutYR02Fse5xx3Saqa4ykpOWPEWrsZGCseOpzYjq9V5Jw7OiEzGsA3yXDuA9vs0uR0mqV4HBTWN2LYagg8knQZNF154IX//+99ZvHgxFRUVXHLJJdx2222sX7++L+oTERERERERkQME971x7uuNwCMxi7X5FcS5nQxLjWtzn21bfBn6HAdOclyjuz1vfHkZ41a8jTMcJn/ciVQOaX9FVLqdygmxXCrclQDEOmgInuV3EopaVHVwXvqnLvVosiyL3bt3s2vXLmKxGGlpaSxYsIBHH320t+sTERERERERkQMESqoxTANPYutG4JuKqqiPxBiblYjZzmqlwvBO6qwqhrhG4jLc3Zozce9exr2zAkcsSv74CVQNHnrQ60+MjcNu6vdNVaiq3Wuy/E19mvJrtH1uIGn/PYcHeOihh1i2bBk5OTlcfvnlPPLII7hcLkKhEN/5zne46aab+qJOERERERERkWOeFbMIltfgSYrDdLReO/LZ7qZtc+M62Da3tX4NAMe5xnZrzuTCAnJXrQQbdp80sc12ufY4MDnJGIONTXWomnQ7E6/hbXVNpn9/Q/CTB7VdgSX9U6dBU2VlJU8++SRjx7b+Q/T7/Tz44IO9VpiIiIiIiIiItBaqqMWOWfiSW/dnqq0Ps3VvDVkJXtLiPG3viwXY1fgliWYqKY6MLs+Xtmc3oz76ENsw2X3yKQTS0rt8b4qRTMgVJr4xjvfq3+d7/u8CTW/Gq7WixNwRLH8j6wKVnFTvIdvjw2V2aeOVHMU6DZpisVibkOmmm27i0UcfZcqUKb1WmIiIiIiIiIi0FixpvxH4mj0V2MAJg9pfzbSjcSM2FsNduV1uAp62exejP/oIy+lg58mTW71drqs8ThdEDPLqd/GuvY1gLIFtjQGqrUjTBRmQR4AF2yswMTgpIYnLsoeR7fF1ey45OnQYNP32t7+lpKSENWvWUFlZ2XI8Go2Sn5/fJ8WJiIiIiIiIyH6Blkbg+4Mm27b5bHc5DsMgNyOx3fvy6jcABkNcI7s0T2LJXkZ9/DGW08GOU06jPrH9AKsjtm1TjUWjCzLqHTgCU3k3GgWqcGGQ4/KRZDrJr7Soa7Q5IcdFaaSBL+qq2VBXw7npWczKGorHdHRrXjnyOgyaLrnkErZt28bWrVv53ve+13Lc4XAwceLEPilORERERERERPYLlFZjOMxWjcDzq4KU1NZzfEYCXlfbYKY2WklZtJBMxxC8pr/N+a/z1VSTu/IDAHZNOKVLIVNkX7BUQoy9doxiYtRhc7zLyTn4yIwaVDp3kO10M9V7RkuzcqOqkS/ropzsTmZwhovtoQArKkv4d/leChpC/Gz4GJzaTtevdBg0nXTSSZx00kmcddZZZGV13uhLRERERERERHqPFY0RKq/BmxyPcUD40lkT8LyGjQAMcY3qdA4zEiF35Qc4IxH2jJ9AMCUNy7YJYFODRRCbIBYh2yaETRCbun3HD+SyYSgOsp1NwddJlo8qVwXVVLHXGsRgxwgAUnxNn6MsGGVIspvj4xIY4Yvj1bJCNgdq+WvBDq7PGdXuW/Tk6NRh0HTzzTfzyCOP8B//8R/tnl++fHmvFSUiIiIiIiIirQXLa7Etu1Uj8GjMYu2eCvxuB8NT2765zbZt8ho2YOJgsOu4Tuc47vM1uAMBPh4zhi8y0yi0gpRjYXVwvWGDF4NMTBIwScYkDZNEDAyMltTBGTYYyyms5l2+iHxImjkIj+EjxdcURJUFoi1jOk2TGRlDeGHvHj6pqSDJ6WLu4OFd/jkdbZ544gk+/PBDTNPEMAxuueUWTjzxxEMaKy8vjwULFrBkyZIerrLndBg0XX/99QD85je/6bNiRERERERERKR9zY3AvQf0Z9pUXE19JMbkoantrvqpjJZQHSsj23kcLsN90PHjdu9iXbSRFWeeSrXbDYQxbEjGJB6DBAz8mHgx8GLgw8ADTYFSR0yIOWycjQZ+I54R9jjy2MT6yEec5j6HRK+BabQOmgBcpsnsrByeK97Fvyv2ckJCEhMSkrv6ozpqbN++nRUrVvD8889jGAZbtmzh9ttv59VXXz3SpfWaDoOm5nTtG9/4Bvn5+eTk5PDuu++yadMmrrzyyj4rUERERERERESa+jNB60bgn+0qA2BcR2+b27dtbuhBts3Zts32umrWOC0aRh+H07YZZTvJxkEGJs6DBUldEHOBu8HAiMFQcyRlFFNk7aQwtpMhjhEkeU3Kg1Esy8Y098/lczi4MHMIfyvcyT+KdnNCbiJO49D7NT39v+v4cMOew/osX3fmScO45vsd97FOTU2lqKiIl156ibPPPptx48bx0ksvMW/ePBYsWMCoUaN4/vnnKS8vZ9asWcyfP59BgwaRn5/PSSedxO9+9ztKS0v5+c9/jm3bZGRktIz9xhtv8Oyzz7Z8/8gjj/C3v/2NrKwsrrjiCmpqarjmmmtYtmxZj37mznT6G7rrrrt48sknycvL484776SgoIBf/epXfVGbiIiIiIiIiOwTKKlqagSe0NTQu7Y+zNaSGrISvKTFedpcb9s2Oxs348DJIGdOu2MGY1HerC5lVX0tpm0ztSrA+fiZhJtsHIcdMgHEnE39m5yNYBgGY5mIiYP1kQ9ptOtJ8ZnELKiqj7W5N8PtZWJCCnvDDayoKDnsWvpaamoqf/rTn/j888+ZM2cO5513Hu+8806H1+/atYt7772XF198kffff5+ysjKefvppLrjgApYsWcK5557b6tonnniCJUuWMGLECFauXMmll17K//zP/wDw2muvMWPGjF7/jF/X4YqmZhs3buSll17iiSeeaEnXZs+e3Re1iYiIiIiIiAgQi0QJVdThT03A2LfqZ82eCiy74ybgVbFSamOVDHaOwGG0/ed/RSTMm1UlNNgW4ysquXh3MV+ePBm7B8KlVrW7mv7XETaI+O19W+jGkscmvoh8SJJvKtC0fS4trm2dZ6WkszlYwyslhZyRnE6i03VIdVzz/YkHXX3UG3bv3k18fDyLFi0CYMOGDdxwww2kp6e3XGPb+xupDxs2jPj4phVrGRkZNDY2sm3bNmbOnAnApEmTeP755wFIS0vj9ttvJy4ujh07djBx4kRycnKIi4tj+/btLF++nMWLF/fVR23R6Yom27YxTZNVq1ZxxhlnANDQ0NDrhYmIiIiIiIhIk1B5Ldg23uSmht+2bbN6dzkOw2BMZmK79+xq2ALQbhPw0nADb1TtpcG2uKCwhJ+s38Le40Zhm4e+Na0jMVdTkOJq3B9gDWUkSaRSbO3C8jdtZ/t6n6ZmfoeTKckZ1Fsx/qekoMfr601bt25lwYIFNDY2AjBixAgSEhJITk6mrKxp2+PmzZtbrjfa6bM1cuRI1q5dCzQFVQB1dXU8+uijPPTQQ9xzzz14PJ6WwOqyyy7jT3/6E1lZWaSmpvbq52tPpyuahg0bxvXXX09BQQHf+MY3mD9/PmPGjOmL2kREREREREQECH6tP1NBVZC9tfWMTk/A63K0e8+uxi2YmGR9bdtccbiBt6tKiWHzrTBc8NV2ytIyqErpnVAi5gIbG2fD/hDFMAzG2BNZzXvsdnwMju9TFui4WfnExBQ+r61iZVUZs7KGknCIq5r62ne/+13y8vK49NJL8fv92LbNL37xC1wuFwsXLiQ7O5vMzMyDjnHzzTdzyy238PrrrzN06FAA4uPjmTRpErNmzcLv95OYmEhpaSkA5557LgsXLuSBBx7o9c/Xnk6DpkWLFvHmm28yefJkXC4Xp556KhdddFFf1CYiIiIiIiIiQLCsBgBvclPQtHpPBdBxE/DaaCWV0RKynDmt3jZXG43wTnUZFjan+ZI5b91HAOw4bmTvFW82hU3OBsCG5p15fiOekfY4trORuOzVlJVM7XAIh2FwSmIKKypLWFlVxvczBvdevT3sRz/6ET/60Y/aHP/Wt77V5tgLL7zQ7tdPPPFEm2sfeeSRdueLxWIMGTKEs84661DKPWydronz+/2ceuqp1NbWsmnTJiZMmMCOHTv6ojYRERERERERoemNc4Zp4EnwEbMs1uZX4HM5GJ4S1+71uxq/BCDbeVzLsYhlsaK6jLBtMcGbyIlVNaRUVVKWlkEgof3tdz0l5rIxbQNHuPXxIYwgiTSIL6DRXUCwsW1D8GYnxifhNAzeqSjFOqCvkez3+eefc9lll/HjH/8Ysxe2QXZFpyuaHnnkEZ566inS0tJajhmGwdtvv92rhYmIiIiIiIgIWDGLUHktnsQ4DNNka1EVwcYoE4ek4DDbb9y9q3ELYJDtHAY09XT6oLac6liEEW4/w9x+Rm/9FOjl1Uz7RN3gCYGrwSDm2R8SGYZBrn0Sn9nv4c76nL2BXEZ52g/PvA4HJ8QlsT5Qzca6GvrPmqa+M2nSJJYvX35Ea+g0aHrllVf497//TVZWVl/UIyIiIiIiIiIHqK+sw7YsfPsagTdvmxub1f4qpFCsjtJIPmmOQXhMHwBf1texp7GedIeb8Z4EkqoqSa2soDw1vddXMwHE3E3hkrPBgKTWq5HijERSw6OodG9nU/3njKLjLXQTE5NZH6jmncoSvturFcuh6nQdVXZ2tkImERERERERkSOkuRG4Nzme+nCUTUVVpPjdZMZ7270+P7wNgGzncADqYhHWBKpxGQaTfEmYhsHwHXlN1w4d1gefAKL7GoK7GtpfgTXCyMWK+NjrWE9ttKbDcQZ5fGR7vKyvq+6tUuUwdRo0ffOb3+T3v/89a9asYdOmTS3/iYiIiIiIiEjv298IPI4vCiuJWjbjspIwjPZDmz2NXwEwyDkM27b5sLaSqG1zoicRr+nA3djA4KICgj4/lb30prk2TIg5D2gI/jXxLidW+clgxFgd+PigQ52SkNLeEHKU6HTr3LJlywB44403Wo6pR5OIiIiIiIhI3wiWVYMB3qQ41mwoBGBMZvvb3WJ2lMLGPOLNJOIdSXwVqqM43ECW08NQV9MKqJxdOzEti4IhOdBBWNUbYm4bZ8jEEYaYp/U5w4D46FCCDSlsZysnRyaT5kpvd5zcuET+XbG3DyqWQ9Fp0LRixYq+qENEREREREREvsa2bQKl1Xji/VQ3RtlRXsfQJD+JXle71xeHdxElQpYzhwYrxmeBKpwYTPAmYhgGhmUxbNcOog4HxYP6tp12Rw3BmyV5barLTsKR8z6fBT7ivJQZ7Y7jNk2O87XfMPxo88knn3DllVfy0EMPMX369JbjM2bMYPz48dx///2HNO7zzz9PeXk5P/3pT3uq1B7T6da5YDDIwoULueqqq6iuruauu+4iGAz2RW0iIiIiIiIix7SG6iBWJIY3OY41e8qBjpuAQ+ttc18EaojYNmM88fhMBwDpZSX4GhrYm5VNzNnp2pMe1aoheDuSvBaxYDa+WDp7GneyN1zc4Vi5/oReqbE3jBw5ktdee63l+61bt1JfX38EK+pdnf5V3XPPPWRmZlJRUYHH4yEQCHDXXXfx4IMPHvQ+y7JYsGABW7duxe12c8899zB8+PCW8ytWrOCxxx7D6XRy8cUXc9lllxGJRLjjjjsoLCzENE3uvvtuRo0adfifUkRERERERKQfCpY1NwKPY82OChymweiM9kMW27bZ0/gVTly47DS+rN9LnOlghNvfcs2QPXsA+ng1k41JiJjLwCYed6j9q5K8FmDgqZ1AfcoKVgc+4oLU2e1eO/oQgqaG9/9AdNu/u33fwTiP/y7es39+0GvGjh3Lrl27qK2tJTExkVdffZUZM2ZQXFzMq6++yjPPPIPb7ea4445j4cKFLF++nPfee4+Ghgb27NnD9ddfz+zZs1m9ejX33XcfSUlJmKbJxIkTAXjwwQfZuHEjwWCQUaNGsWjRIubOncvdd9/N8ccfz3vvvce7777Lb3/72x797B3pdEXTli1buOWWW3A6nfh8Pv7whz+wZcuWTgd+6623CIfDLF26lPnz57daDhaJRFi0aBFPPfUUS5YsYenSpZSVlfHee+8RjUb5xz/+wY033sjDDz98eJ9OREREREREpB8LljY1Aq91OCgLNDAqLR6P09HutdWxMgJWNVnOoXwerMEGxnkSMPf1YXKGw2TtLSLoj6M2oeNVUYfPws1OEox3SDWWkmk8Rqb5BFnOxzE9+bgaDTKD/yQx8jFuay/YTSud/C4bp2lTV5NBpiOLonAB5ZHSdmfwOtr/GRytpk2bxptvvolt26xfv55TTjmF6upq/vjHP/LMM8/w/PPPk5CQwNKlSwEIBAI8/vjj/OlPf+KJJ54AYNGiRTz44IM8/fTTDB06tOW6xMREnn76af7xj3+wbt06SkpKuPTSS/nnP/8JwMsvv8wll1zSZ5+10xVNptk6i4rFYm2OtWfNmjVMnToVgIkTJ7Jx48aWc3l5eQwbNoykpCQAJk+ezOrVq8nNzSUWi2FZFoFAAGcfL+MTEREREREROZoES5tWNK2vatpqNTYrqcNrm7fNeezhbGqsJ9XhItu5v+t2dlEBDsuiOCu7V5qAOynHZ2zAy1YcRlO9tm0QJYmwta9ubzVm4zCc9T6SzI9Jin5MgzmEKtc5RM00krwWFSEHk525lMZKWB9cyznJ3+uR+rxn/xw6WX3UW2bMmMGCBQvIycnh1FNPBZp2go0ePZr4+HgATjvtNFauXMnJJ5/M2LFjAcjOziYcDgNQUlLCiBEjAJg0aRJ79uzB4/FQWVnJrbfeit/vJxQKEYlEmD59OrNmzeK6665j7969jB8/vs8+a6dJzmmnncYDDzxAQ0MDH3zwAc8++yynn356pwMHAoGWHxaAw+EgGo3idDoJBAIkJOxf5hYXF0cgEMDv91NYWMj3v/99qqqq+POf/9ylD5HRwbJBEWmfnhmR7tNzI9J9em5EukfPjHydbduEKmrwxPtYu7eGOLeT8TmpmGb7IVFhzXawDQojcUCYiQnJeF37/9mfU7AHG6jIGYrb3XMrghx2MT7rY9x2HgAWXuo5gUaOI2qkA07YV7LpcxBfA6HIt4l6MvFF8/DGChnU+Cwhz6mkx02hIuQgzsohyZFEXsNXfMfzbRKcvbkCq/fl5OQQCoVYsmQJt956K/n5+RiGQV5eHqFQCL/fz6efftoSJBntBIEZGRnk5eUxatQoNmzYQFJSEu+//z7FxcU8/PDDVFZWtqya8vl8nH766dx7773MnDmzTz9rp0HTz3/+cx5//HESEhJ46KGHmDp1Kj/+8Y87HTg+Pr5V03DLslpWKH39XDAYJCEhgb/97W9MmTKF+fPnU1xczFVXXcXy5cvxeDxtxj9QWVldp/WISJOMjAQ9MyLdpOdGpPv03Ih0j54ZaU9joJ5wsBEjLZFAXQMTBicTCoXbvbbBClHcsBsfIyhuDJPl9BBvOWhsjAHgDwRIrqigIiWVgOmCcOyw63NQSYLxPl5jJwBhO5OgdSKN9lBad+qx9n9pWliGhTPopdoaSsAxFK9RQEp0DXGNn3Kabzc7zcvZW+1hVFoun8c+45Oyzzgjccph13ukTZ8+nVdeeYURI0aQn59PSkoKF1xwAVdeeSWmaTJs2DB+/vOf869//avd+x944AFuv/124uLiiIuLIykpiQkTJrB48WIuu+wy3G43OTk5lJaWkpOTw2WXXcYPfvADFixY0Kefs9OgyeVy8ZOf/ISf/OQn3Rp40qRJvPPOO0yfPp1169aRm5vbcm7UqFHs3r2b6upq/H4/q1ev5rrrriMvLw+Xq+kVjUlJSUSjUWKxw//jFxEREREREelvmrfNlcSaehgdn9Hxqp78xm3Y2ISiwwDIdce1Op9dVNA0Vuagw67LoJF442P8rMMwLMJ2FgFrImF7EC1Llzq+magngrvBgyPiIOaO0WAOZa9rECmxz4hnJzOynmNDaC65WcPYZGxgS/1GJsWfhts8+CKUo9Hpp5/esits3rx5zJs3D4Czzz6bs88+G2jaVneg2bP3N0D3eDysWLECgNGjR/PSSy+1mePll19ud+5YLMZ5551HYmLfrgY7aND05ptv8pe//IWvvvoKn89Hbm4u1157bcsP42CmTZvGqlWrmDt3LrZtc99997F8+XJCoRBz5szhjjvu4LrrrsO2bS6++GKysrK4+uqr+dWvfsXll19OJBLhlltuwe/3dzqXiIiIiIiIyEDT3Ah8S12YOLeTwUm+Dq/Nb/yKaCyJQNRFhsNNitPd6vygokIsw6AsPfOwavKwjURjBQ4jRNSOpy52Go32MDoNmA4Q8YZxN3hwhzzU73sFnW04qXScgW2bZHjyOMX8/9m78+iq6nv//8+99xmTnMwhzMhQRKuMamoRROpwHbAVQe1A662tHa+9IBXkW6v1FoXqvbbqxaE/lQpOVFAvrSPSimJFQJAZY1DmzOM5yZn23r8/AlGEkARJIPB6rJVlTvben/3+HM9mrfNe78/78wyWex39vP3ZHN9IUbSQ01LO+FKxn0zmz5/PwoULuf/++zv83obr7mvv/gWvvPIK9913HzfddBODBg3CMAzWrVvHQw89xNSpU7n44os7OtZmqcRUpPVUli3SdnpuRNpOz41I2+iZkUPZsvg9Kov28ozrZWCPbMYMyD/keY5rM7/sD9REzyTmZDEyJZuczyWaUiJhxix5jfLsHD4cPPyIYjGpJ2QsJWgU4roWYWcwEfertGKh1MFjJU0yS7KJpUap7l554EHXJVaziQEpH5K0ulGVMZ5X618h39uVb+Zcc8CpYy4ee0RzkfbV7CfiySefZO7cuXTv3r3pb/3792fo0KHMmDHjuEo0iYiIiIiIiJxowqXVJEyTBtvgK4dpFl+c2EG97SPmZJFjeQ9IMkFjNRNAad6hE1Ut8fMxGcYbmEaUuNuFGnskNs3vftcSx+OQ9CbxRfyYSRPH87keToZBUXwkjpNkYNpGMhpW08XKpyRRTHWykkxP9hHfVzqG2dyBSCRyQJJpv759+xKLxdo1KBEREREREZGTWaIhRryugTKXxmVz6c0vm9sR+4hYsg8AX/GnHXS82+5dR7RsziBBurGELHMxBglq7XOotC/9Ukmm/WKpDRgYBGtSDzqWHkjwdsW/EXXT8Ta8x2k0zn1rw+YvfV9pf80mmiyr+a0Om1ltJyIiIiIiIiJHQaSssT9TmQMD8kKH3O5+v08aPiFh5xMyLfKsA6uZgpEIGTXVVGVmk9y3+VZreCgnx3iKFGM9CTebcnsc9e7ptKUX0+HEgjEcwyFYk3LApnQAGf44CdfPB5HLAIOe9StIdQ0KG7bguM4hx5PjR7OJJhERERERERE5NvYnmiowGZB7cJXSfjXJcsrjaYBJP1/qQQmp/OI9AJTmtb6aKcAmcoxn70huvQAAIABJREFU8BhVRJzTqbAvxyaz7ZM4HBNiqTEs2yIQPrBaK82XwDIctod7EQ0Mx3QjnG/XUu9E2BXfcXTjkKOu2R5NW7duZfjwg5uEua5LPB5v16BERERERERETmaR8sZEU8Sy6J7R/G7s26IfEU/2xGM49PQevLyuS/FeAMpz8lpx1yTpxlukGOtwXC/V9gXE3D5HFH9rxFIbCIQDpFSnEg01NBVLGQak++NUR/1EPGfitXaRkyyhq2FR2LCF3v5T2i2mo23WrFls3LiRsrIyotEovXr1Iisr66jtBheLxbj00ktZunTpURnvaGg20fTGG290ZBwiIiIiIiIisk/13iqSLuTlhjAPs2xuY7gEly708fqxvnCeJx4nu6KcmlA6cb//sPcziZBpLMZn7CXhZlFtX4BN+lGZS3Mcj0M8GMPfECC9JJPa/OqmZFNGIE5VNEB11Ic/WEBa+P84O1nNq9Ft2G4Sy2j7bnfHwvTp0wFYtGgR27ZtY+rUqcc4ovbX7P+ZHj16dGQcIiIiIiIiIgK4jkOiJkI1Bv1zm99tLmY3UBpLBRz6+w5OCnUpLcZ0XcpzcvG7EQJuhIBbj9+tJ+BGAKgzs4gYLn7rn1hGmAanLzXOSA6TLjiqIllhrKRFsC4Fx3KozwrjWA553jg2DlZZENPfi4S3gAzPCnrZVeyO7aR3oG+b7/XUqrms2L78qMZf0Gck3z3r+jZdM336dC677DJGjx7NsmXLePnll5k1axavvPIKc+fOxTRNRowYwdSpU1m9ejWzZ8/G4/GQnp7Ovffei2EYTJ06ldraWnr37t007vvvv8+DDz4IQDQaZfbs2bz//vt8+umnTJs2Ddu2+da3vsXChQvx+XzNhfeldY4UoIiIiIiIiMhJor4qjOG6VBsmZ2YdvCvbfh+Et+C4qWR5GgiYjRt6WU6cMyLLyEnsJrDdAoIMDb3KWdHoIccot+Ajv4FjGHSN+wjYcSLGGmqNDOqMTMJGOnH8jevZ2oMBdTm1pJdlklqdRmp1Go7hkO+anGkkodYLeIFRJLynMixnCRtTPzqiRNPxrLq6mgceeICFCxcSDAb59a9/zfLly3nnnXe46KKLuOGGG1i6dCm1tbUsXbqUgQMHMnnyZD788ENWrFgBQGFhIffccw/5+fk8/PDDvPrqq0yaNInx48czdepU3n77bQoKCto1yQRKNImIiIiIiIgcV/Z8WgqAkRLAYzW/h9fGcCUQoJ+vsVl4bnwH59a8QLpdiesYVFedDr4k1cFMYkaQGMF9/00hRhDb2o7jLcTAoGc0he52DB87gZ0H3MfBIEaAmBEkSpCYEWj8IUjUCBAngG1YOJi4mLgYTb87+18bJg2k4hoHz8e1XGrzqvE1+PFGfZi2ScKTYEM0SMJyGZ4XxqrzYkby8JRdQTDwd5yMi9r8vn73rOvbXH3U3lzXBWDHjh1UVlZy4403AhCJRNi5cyc//elPefjhh/nBD35Afn4+gwcPprCwkFGjRgEwZMgQPJ7G1E5+fj4zZ84kJSWFkpIShg8fTlpaGmeffTbvvPMOixYt4uc//3m7z6nFRNPSpUu54IILDruVooiIiIiIiIgcHTs/KSYAZGQfZre5RAPVCT8eo44eRh+G1r3BaZHlGLhsCQxnT/1Ahtmb2N0lnyLvyC9c7ZJirSDoKcR2A9TGR1FKFmtMFx8xQtQScutIo5ZUN4yfKH5ipLq1ZFIJ7pHNK46P3WYfdpunUGJ2xzWszyKyXGJpUWJpn1Ve7Sz1UxX3cmYgiZOSxFOZwFOdzsCyIRR3KQIuPLJAjjGfz0dZWRkAmzZtAqBnz55069aNxx9/HK/Xy6JFizjttNNYvHgxV111FdOmTeORRx5hwYIF9OvXj7Vr13LhhReyadMmkskkAL/5zW9YsmQJaWlpTJs2rSmJdc011/DnP/+ZqqoqBg0a1O7zazHRNG/ePH7/+98zYcIEJk6cSF5eazrVi4iIiIiIiMiRCJfVEgC6ds1q9pz3aj4GDLp66ris6v8jK1lCnZnB+6kXUuHtzsDtRQBUZmV+4UqbNM9b+K1PSDrp1MRH4bBveZ5hECdABQEqjC6HvK/pJvET2/cTxe82JqFMnKY6ps//12iqa7LJc0vp6xTS1ykkjo89Zm92mX0PSjrtl+GNUxn3URX3kBtIksxKYjRUY0VOJ7lrzRG8s8eHiRMnMmPGDBYvXswpp5wCQHZ2Ntdffz2TJk3Ctm169OjBpZdeSjweZ/r06aSkpOD1ernzzjvp1q0bt956K9/+9rfp168fXq8XgG9+85tcc801pKenk5ubS2lpY2XckCFD2L59O9/97nc7ZH6Guz/FdRg7duxgwYIF/P3vf2fw4MFcd911nHvuuR0RX6uUldUd6xBEOo28vJCeGZE20nMj0nZ6bkTaRs+M7FfXEOfdh/+OzzTIHXXmIc+xXYcHdywn4dpMSTxPQaKaj/1nsC5lJEmjsf/OuctWEmyI8m7BCFxz/3K1JCHvm/jMXSScXGri5+HSvv16DuC6ZFNBD3cH3d2dpNAANFY67TV77Us69cDZl3TaEQnyQVUWI3LrODVjX6VTPE5gVxaut5pv3TGl42LvxBzH4dvf/jaPPfYYaWnNV8kdLc0v9vyc3r17M3nyZKZPn86GDRuYMmUK48aNY926de0dn4iIiIiIiMhJY33hXkIGJP3NJ4C21xSRcE2C1h5OTSZYlnYlH6Re0JRkCjRESYvUU52R3pRkMoiT7n0Vn7mLmN2V6vjojk0yARgGlUYu683hvGZeyVvmhRQap5LEQx+niJHJJVyUeAG/25iAyvIlAKiMej8bw+cjmrIbI5HbsbF3Ujt37uSqq67im9/8ZockmaAVS+e2b9/OggULeOmllzj11FOZMWMGF1xwAR9++CH/+Z//ydKlSzsiThEREREREZET3sdb9zAACKSnHHzQdelS9QYvVdeA2Z0B7OIfGd8hYQYOOC2nrBKAqqwMAAxipHtfwWNWELV7UZc4Bzh4qVqHMgwqyaXSyGWDO5QsKunrfkwf9xNGJt7gLe9lpHnAYzhUxA5MXdSGkgTqj1HcnUyvXr146aWXOvSeLSaaJk6cyFVXXcX8+fOb1g4CDBs2jHPOOac9YxMRERERERE5aSRth8q9FQCkZKYedDy3+p/4S16kMPhdLLOKkHcACQIHn7cv0VSZmYlBdF+SqZKGZF/CyRG0cnFTxzEMqsihimwMXHq7n3J2chnveS4gw5ugIu4j4Rh4zcbOP/6UdByz4RgHLc1p8dN12223ceuttx6QZHrxxRcBmDVrVrsFJiIiIiIiInIyKdxdSZptA2ClHphA8sf20rt4Hks9ZzS+tnaTy8GbdRmOQ3ZFNfWBALEgn0sy9SOcPIvjLsn0eYbBGuNsysmjp/MpZ9qr9i2fM6j8XFWT3/BRlrbn2MUph9VsRdPSpUtJJpP86U9/IhAING2Ll0wmeeCBB/jWt77VYUGKiIiIiIiInOjWfVJKNi4uYKZ8lmgy3CT9d88BN8Gb3jOABLlmHO8heixlVtXgsW32ds34QpJpBGB02FyOlGNYvGeex/nOEk6113OeP4ePw+dRGfOQH0w0nVcVih/DKOVwmk00bd68mffee4+KigqefPLJzy7weLj++us7IjYRERERERGRk8aGT0oZa7iYQR+G9VnlUffShaRGP+GtlDGEsfBZu8k3Dq5mAsgpr8K2bGr6rsdjVnWqJNN+CcPPv8zRnO+8wVjzLVZ5u1MR7X7AOWn+4DGKTlrSbM3cL37xC+bNm8fUqVOZN29e088TTzzBpEmTOjJGERERERERkRNaZV0DtVVh/ICV+lkSJS2yhW4VfyNuZfGm90wAfJ7d5JF/yHGyKsvZMXgHhq+KqN2n0yWZ9osYId4zR+FicFP6QvyJ6gOOZ5NxjCJrm+9///usW7cOgHg8zogRI3jssceajn/ve99jy5Ythx2jqKioXfIwy5YtY/r06Ud93GYTTfu7ksdiMZ544omDfkRERERERETk6Ni0vZwsGlvWmPv6M1l2hH675wAGG7Ov5qNEEsuoJdMwCHLwrnTeWJSa3pupz4oQs3tSlzibzphk2q/SyOMDo4AUM8Yv0hZA4rMG4Obx3Gvqc8477zxWrVoFwOrVqznvvPP45z//CTTmW/bu3cugQYOOYYRHX7NL57Zv3w5AYWFhhwUjIiIiIiIicjLasL2M7M8nmlyXPnufwJ+spDR9DO+7WbiE8Xl2k0uXgwdwXXJibxDODWNGsqm1CjiuG3+30i6zD+EGmwt873NWw6t84LkCx/Ae0VhV85+i/r0VRzW+lK8VkPW97zZ7/Otf/zpz5szhhz/8IW+99RYTJ07k3nvvpa6ujo0bN3LOOefwyiuvMHfuXEzTZMSIEUydOpXS0lKmTp2K67rk5X22THLcuHGcc845bN26FcMwmDNnDqFQiP/+7/9m5cqVuK7L9ddfz6WXXspTTz3Fiy++iGmaDB8+nGnTplFUVMSMGTMIBoMEg0EyMhorw+bPn8/rr79OMpkkFArxwAMPcOuttzJu3DjGjBlDUVERs2fP5tFHH23xPWk20XTTTTcBcPfdd7f6DRYRERERERGRtnEcl807yjnXY4DduONcTs1ycmrfo97Xk5L0UXxYXYWBg9cqJo9zDhoj3X4X07+DYE2QqngBhKxjMJP2scn4KlY0yujAOoY2LOWD4MVgdI5KrdNPP51t27bhui4rV65kypQpnHvuubz77rts3bqVUaNG8cADD7Bw4UKCwSC//vWvWb58Oe+88w5XXHEF11xzDS+//DLPPPMMAJFIhMsvv5zbbruNm2++mWXLlpGWlsauXbt49tlnicViXHPNNYwcOZJFixZx2223MXToUJ5++ummDd9uuukmRo4cyaOPPsq2bdtwHIfq6uqmZNcNN9zA+vXrmThxIs888wxjxozh+eefZ8KECa2ac7OJpnHjxh32wsWLF7fhrRURERERERGRQ9leWkMkmiDXB2DiN6vpUzwX2/CxM/dqdtsOFY6N3yoniIcQ6Qdcn5pcS5q9Fm99gO4b+rJjeNoxmUd7yfLGeaz6cnp4KunPpwyKvceWwLltH+d73z1s9VF7ME2TQYMGsWzZMvLy8vD5fIwePZp//vOfbNmyhTFjxlBZWcmNN94INCaSdu7cSWFhId/85jcBGD58eFOiCRqTVwDdunUjFouxZ88eNm7c2NTHKZlMsmfPHu6++24ef/xx7r33XoYOHYrruhQWFjJ48OCmcbdt24Zpmni9XqZMmUJKSgrFxcUkk0kKCgqYOXMmFRUVLF++nClTprRqzs0mmm677bYjeAtFREREREREpC02bi/DxCWYTGKmBem/52EsJ8qu7KtIeLJZF64BwGPtIpcuGJ/ruxSwC8mwl+O4QU5Z04ua1IxOU+3TWkHLxme63F87gZk5j9Mvvo4SzynHOqxWGzlyJI888giXX345ACNGjGDOnDmYpknPnj3p1q0bjz/+OF6vl0WLFnHaaaexbds21qxZw6BBg1i/fv0B4xlf+P/br18/CgoK+K//+i8cx2HOnDn07NmTP/7xj/zud7/D7/dzww03sGbNGvr168eaNWsYPXo0GzZsAGDLli0sWbKEv/71rzQ0NDB+/Hhc18UwDMaNG8fMmTMZOXIkXm/rliw2m2jKycmhf//+bNy4sU1voIiIiIiIiIi03sbt5WQaLoYLIauEUEMhNSlfpTp1CEnXZUMsitdI4jEryKNf03Vep5is5Js4eLHKhuOL1VDVI/UYzqT9ZHqj7I2lsco8lzH2a/SNrzvWIbXa17/+dX7zm9/whz/8AQCfz0coFOL0008nOzub66+/nkmTJmHbNj169ODSSy/lV7/6FZMnT+bll1+mZ8+ehx1/7NixvP/++3znO9+hvr6eCy+8kLS0NE499VQmTJhAVlYW+fn5DBkyhNtvv53Jkyfz2GOPkZ2djd/vp0+fPgSDQcaPH4/P5yMvL4/S0lIAxo8fz5gxY5o2jGsNw3Vd91AHfvKTn/DII48wduzYgy8yDN58881W36S9lZXVHesQRDqNvLyQnhmRNtJzI9J2em5E2kbPzMmrIZbgVw+/wbCghyH1Eb6S+ib5aYV83O3nOGaQjbEGFtXVkObZhd9TyGi+gYmJ5daQG38ekxiVxgWcui5GflkNK4YNoCHoO9bTOuq2hLPYGM5hdG4x15iLyHArSZ+84ViHdcIrKSnhlltu4S9/+Uurr2m2oumRRx4BYOnSpV8+MhERERERERE5yNZdlTiOSy8/UA+pnlJ254zHMYMAfBhtAMCwdpBLHiYmhhsjO/53LKJUG+cQoxvZVRto8HtpCBzZjmzHuyxvFICKeIBP005jaGL5MY7oxPfaa6/x4IMPMnPmzDZd12yiab/6+noeeughli9fjtfrZfTo0fz4xz/G5zvxMqQiIiIiIiIiHWnj9jIAusdLiJJGLLM/kUBfAGptm22JOKlmDMuM0MUdCK5DVuI1vFQR5jTqjYGk10TwJm3KskMnXH+m/bK8MQAq4z6Kzd40GGsIHeOYTnSXXHIJl1xySZuvM1s64Xe/+x3FxcX8+te/5le/+hWFhYX8/ve/P6IgRUREREREROQzGz8tI8cbxWlw8ZlhKrPPazq2PtaAS2MTcMu1yCGPdPtdAu5OovSg1hgGQHZlGICqzBOzPxOAz3RIteJUxP04mGy3Tj3WIUkzWqxo2rRpE4sXL256XVBQ0LTFnoiIiIiIiIgcmbLqekpr6vlp3hri5SNITanDNRqXvrmuy7pYAybgWjvIpQtpdiFp9ockSKfKGAlGY+1ITmUdLlCdceImmqCxqmlXNETY9rDTGsDZxzogOaQWK5oyMjKorq5uel1fX08opAI1ERERERERkS9j044y0o0wgxJFALhpmU3H9iQTlNs2mVY9ppHkFMdPZvKfOPioNMbgGo3tbEzbJrM6Qjg1QMLbYi1Jp7Z/+VxF3E/SUDuf41Wzn8L9y+M8Hg/jx4/n4osvxjRNli5dyoABAzosQBEREREREZET0cbt5YxL+RfRZBYAnpTPvqJ/GGtsAo71CSkO9EusAByqjPOxjfSm87KqIpiuS9UJXs0EnzUEr4z7OSUlcoyjkeY0m2jKzGzMpJ511lmcddZZTX+/4oor2j8qERERERERkROY7Tjs2fkJvwyt5uP6SwHwBBoXHSVdl42xKAHDxTH2MCZRgYcItcZQYkb3A8bJrqwDTuz+TPtleWKAS0W881QzzZo1i40bN1JWVkY0GqVXr14UFhZy7rnnct999x31+y1btoyXX36ZWbNmHfWxW6vZRNMvf/nLZi+qr69vl2BERERERERETgafFFdzoedtfEaSGuMUAKxA445xW+NRoq5LV08tg+xKctwaGuhJmK8eNE5OZR2OYVATSunA6I8Nj+mS7olTGffjuMc6mtaZPn06AIsWLWLbtm1MnTqVFStW8Oyzzx7jyNpPiws4lyxZwv333099fT2u6+I4DtXV1axZs6Yj4hMRERERERE54Wwr+oh/C64mYmYSjQaxAgaG2ZhoWhdtXCKWaXzImclqkqRRbXwdDOOAMbzxBKFwlKqMVByrxRbMJ4QsT4zapJ/apLfN1255778p3vbGUY2na7+LGPS1m9t83fbt2/nRj35EZWUlF1xwAf/xH//BpEmTuOOOO+jfvz/PPPMM5eXlXHXVVfzsZz8jMzOT0aNHk5KSwosvvohpmgwfPpxp06ZRVFTEjBkzCAaDBINBMjIyAJg/fz6vv/46yWSSUCjEAw88wK233sq4ceMYM2YMRUVFzJ49m0cfffSovictfhL/8Ic/8NOf/pRu3bpx++23M2rUKK677rqjGoSIiIiIiIjIySR/x/P4DJvSrItwHfAEG7+e1zk2RYkYmUaS0cmt2JhUGuc3Nf/+vOzKMMBJ0Z9pv/0NwSvj/mMcyZcTi8WYM2cOTz31FPPnzz/suWVlZTz22GP8+Mc/ZtGiRfy///f/eO655+jVqxfJZJI//elP3HTTTcydO5dhw4YBNBUJzZ07l6effppkMsn69euZOHEiL7zwAgDPP/88EyZMOOpza7GiKRgMctlll7F582b8fj933HEHl19+OdOmTTvsdY7jcMcdd7B161Z8Ph+///3v6dOnT9PxpUuX8r//+794PB6uvvpqrrnmGgAeeeQRli5dSiKR4Nvf/jYTJ078klMUEREREREROX40VO5muPMeFWRR5zsNiDYlmtZHo7jAQNbgx2GPMRSMrEOO09Sf6aRKNDVWe1UcQaJp0NduPqLqo/bwla98BZ+vMXno8RycmnHdz9YG9uzZs+ncu+++m8cff5x7772XoUOH4rouhYWFDB48GIDhw4ezbds2TNPE6/UyZcoUUlJSKC4uJplMUlBQwMyZM6moqGD58uVMmTLlqM+txYomv99PPB6nd+/ebN68GdM0Mb5QrncoS5YsIR6P89xzz3HzzTcf0IgqkUg0vTnz5s3jueeeo6ysjBUrVrBmzRqeeeYZ5s2bR3Fx8ZebnYiIiIiIiMhxJrrqz3gNm43B80ns2zzNEzRxXZcPYw2YOAxxN7LDTMfl9GbHya4Kk7BM6tICHRT5sZfhjWF0sobgh3KovIrP56OsrAyATZs2Nf3dND9L3SxYsIDf/e53zJ8/n82bN7NmzRr69evX1N5ow4YNAGzZsoUlS5bwxz/+kdtuuw3HcXBdF8MwGDduHDNnzmTkyJF4vW1fgtiSFiuaxo4dy4033sjs2bO59tprWb16NVlZh86mft7q1asZNWoUAEOHDm2aLEBRURG9e/duWjc4YsQIVq1axaZNmxg4cCC/+MUvCIfD3HLLLa2aRF5eqFXniUgjPTMibafnRqTt9NyItI2emZODU7sXb+kSSuws/Kecg7OtAYC0rADFJCi3kwzgE+KmzR7PYPpz6ERAoD5GSkOc8pwQlufk6M8EYAGZ3hhVnXzp3KF8//vf584776Rbt2506dLlkOeceuqpTJgwgaysLPLz8xkyZAi33347kydP5rHHHiM7Oxu/30+fPn0IBoOMHz8en89HXl4epaWlAIwfP54xY8bw0ksvtcs8Wkw0/fSnP+XKK68kPz+fOXPmsHLlSq644ooWBw6Hw6SlpTW9tiyLZDKJx+MhHA4TCn32j2hqairhcJiqqir27NnDww8/zK5du/jZz37Gq6++2mIFVVlZXYvxiEijvLyQnhmRNtJzI9J2em5E2kbPzMnD897/4sHmb9FRjPK5FFcnMCyI2wlWhCsBOJWPWObNZ1AynwT2IcfpUlYDQGV6KrbdSbZgO0oyPTGqEp2rimv8+PFNvxcUFFBQUND0evny5QCcf/75nH/++Qddu2DBgqbfJ06ceFCLoS5duvDUU08ddN2TTz55yFhs22bEiBH079+/bZNopRYTTdBYgTRv3jw8Hg+jRo0iJyenxWvS0tKIRCJNrx3HaVp3+MVjkUiEUChEZmYm/fr1w+fz0a9fP/x+P5WVla26n4iIiIiIiMjxzAjvxSpazN5kNqWpQ8CBZL2DN80k6bpsiNWTQpwKK4JDT1IINjtWU3+mzJOnP9N+Wd4onzRkHOswOqXXXnuNBx98kJkzZ7bbPVqsr3v44Ye5++67CQQCmKbJbbfddshM2RcNHz6cZcuWAbB27VoGDhzYdKx///5s376d6upq4vE4q1atYtiwYYwYMYK3334b13UpKSmhoaGBzMzMLzE9ERERERERkeODtWEuhmvzQv0o+uV6SUQaq5U8QZPN9ZuI4eUUdrHFm05359BLpwBwXbIr64j5PDQEOnevoiORvW/nOWm7Sy65hMWLFzc1D28PLVY0/e1vf2PBggVNy+B++MMf8p3vfIfvfve7h73uoosuYvny5Vx33XW4rstdd93F4sWLqa+v59prr2X69OnccMMNuK7L1VdfTX5+Pvn5+axcuZIJEybgui6//e1vsSzr6MxURERERERE5Bgx6nZjFf2dCnL5V+yr/CrbJFGZAMD01/NhtB6AGm8VFh7yndxmx0oLN+BL2BTnZUArNus60YQ8cSycYx2GNKPFRJPf7yc19bNSvIyMDPz+lptumabJnXfeecDfPr/+b+zYsYwdO/ag61rbAFxERERERESks7A2PIHh2jwfHkV2ikVm0KQy3FjRVOe8zXbzLLKMeiKeOnrY+Vg0X3SRUxkGoCrj5Fs2B2AajQ3B5fjUbKLp9ddfB6Bv3778/Oc/Z+LEiViWxYsvvsgZZ5zRYQGKiIiIiIiIdGZG3U6sba8Q9efzTtlpnN2rMYmUCNuAy2bTxcXEZ1USA7o7+Ycd72Tuz7RflhJNx61mE03z5s074PUTTzzR9HtFRUX7RSQiIiIiIiJyArE2/AXDdVjt/wYuJv2zLVzXJV6XBE8Vm82BmLhErSJCTioht/kEkuE4ZFVFiAR9xH3eDpzF8SXLGz3WIUgzWp1oSiaTuK6L13vyfpBFRERERERE2qS+DOuT13BS8nm16itYhs0pWSbJaALXNoin1FBBLtlmGMeM0yPZA4Pm+y5l1NRjOQ5VGWkdOInjT2epaNq1axdXXnklX/3qV5v+VlBQwC9/+cum15MnT2b27Nn4fJ81dl+2bBkvv/wys2bN6tB4j4YWezRVVFQwbdo03nvvPWzb5uyzz+aee+4hP//wpXwiIiIiIiIiJzvP1ucxnCSR7hfw6b9s+mSZ+D0GFTvWAQMo8TVWLznWJ3hci66H220OLZvbL81KHOsQWm3AgAEHFfN83n333deB0bS/FhNNd955J0OHDuV//ud/sG2befPmcccdd/DQQw91RHwiIiIiIiIinVOiHqvwBVxvGh+6Q3CJ0D/bIhErJlpVDAyg0JOGjziuVUI3pyuewzQBh8ZG4C5QnZ7SIVM4Xh3JZnuP/Ws5b28rOqpxjOrXnxuLQ7ufAAAgAElEQVTOHdmma1asWMG9996L1+vlmmuu4f777+eVV15h165dzJgxg2AwSDAYJCMjA4D58+fz+uuvk0wmCYVCPPDAA9x6662MGzeOMWPGUFRUxOzZs3n00UeP6tyOlNnSCZ9++im//OUvSU9PJysri5tuuokdO3Z0RGwiIiIiIiIinZZV9HeMeB12r9GsL3UA6J9tUFv6Im48F4C9PoegVYqBS0+76+HHS9qk10aoSwtiew6fkJLjx8cff8ykSZOafkpKSojFYjz99NN861vfajrvT3/6EzfddBNz585l2LBhADiOQ3V1NXPnzuXpp58mmUyyfv16Jk6cyAsvvADA888/z4QJE47J3A6lxYqmZDJJLBbD7/cD0NDQgHEkqUMRERERERGRk4VjY215Ftf0YvcYxcZ1DaR4Id15n3BsF27yMhKmS9hyCXk+oYubSQrBww6ZVRXGdLVs7kjdcO7INlcfHQ1fXDq3YsUK+vbte9B5hYWFDB48GIDhw4ezbds2TNPE6/UyZcoUUlJSKC4uJplMUlBQwMyZM6moqGD58uVMmTKlw+bTkhYTTZdddhnXX38948ePxzAMFi5cyCWXXNIRsYmIiIiIiIh0SuauZZjhPdg9zmNPNEhVQ4QRXcNEKt8ANwU3HqLcbxM067HMCL0Sp7U4ZnZlGICqDCWaOjvTPHiBWb9+/VizZg2jR49mw4YNAGzZsoUlS5bw17/+lYaGBsaPH4/ruhiGwbhx45g5cyYjR448rjZuazHR9Itf/IKuXbvy9ttv4zgO48ePP65KskRERERERESON55NTwNg9x7Lxp1xwOXMlFdxnQQ+/1jiGFR4HQxrO6lukGw3s8UxsyvrsE2DmtDhK5+kc7r99tuZPHkyjz32GNnZ2fj9fvr06UMwGGT8+PH4fD7y8vIoLS0FYPz48YwZM4aXXnrpGEd+oBYTTT/4wQ/4y1/+wtVXX90R8YiIiIiIiIh0akbZOszyDTi5Z0JqPhuLK+mbspWA8xEeXzdINi6bqvIl8XqK6WOfgsHhW9T4YglCkSiVmam4h6iGkeNTz549WbBgwQF/KygooKCgoOn10qVLAejSpQtPPfXUQWM8+eSThxzbtm1GjBhB//79j2LEX16Ln866ujrq6+s7IhYRERERERGRTs+z6RkA7D7fIGG7FJXVMSpnCWCSkvl1qsONNR/V/nICmOQ7uS2OmV2lZXPymddee40f/ehH3Hzzzcc6lIO0WNEUDAa54IILOPXUU0lJ+Wz7xIcffrhdAxMRERERERHpbIy6XZg738JJ742bOYDCkjhnpC0nxaolkDYYy5NBJOIhHZdw8BNOtbtjtlwDQnZlHaBEkzS65JJLjtv+2S0mmtSPSURERERERKR1rM3PYuBi9/4GGAZb9+xiSMYKHCONQNpQojb4oya1HhufVUP35ICWB3VdsivrSHgswqmB9p+EyJdw2ETTRx99RGpqKkOGDCE/P7+jYhIRERERERHpfGI1WEV/xw1k43QZhuu6+MMvYXkdAukFGKaHLVUm3RyT4mANvdyueLBaHDalPkYwmqA0Jx2Mw/dyEjnWmq3PW7hwId/73vf485//zJVXXsk777zTkXGJiIiIiIiIdCpW4QsYdhS71xgwLXYXf0Cu9xPKEn0JpPQGYGd1Y71H2F9OL7t7q8bNragFoDIzrV3iFjmamq1omjdvHosXLyY/P581a9Zw3333cd5553VkbCIiIiIiIiKdgx3Hs+V5XE8Ap8fXcZwEu3e8hOOaRLwjMQyDsgS4DV4A/D7wttzNBoCc8sb+TJVZ6s8kx7/Ddhzbv1xu2LBhVFVVdUhAIiIiIiIiIp2N9enrGNEKnB4jwROkeO8/MJwq1teeTa/MIACrI5CTaPwanulpXXWSaTtkVYcJp/iJ+7ztFr+0n0cffZTrr7+eH/7wh9xwww1s2LCh2XOfe+45EonEEd1n5MiRRxriUdVsosn4wrpPy2p53aiIiIiIiIjIScd1sTY9g2tY2L0uIB6vYc+u12iwUyisP5ecoI3twtp6g+yEgW3YGJ7W9VrKqgpjOa6WzXVSH3/8MUuXLuWJJ57g8ccfZ+rUqcyYMaPZ8x955BEcx+nACI++1tXpcXDiSURERERERETA3LsCs2YbdtezIZDFro+fwnHivF/1DXplNvbv3lhvY9sBMhMWSX8cWvkVu6k/U5YSTV/WxldWsXvD9qM6Zo8z+vDVS89q9nh2djZ79uzh+eefZ/To0Zx22mk8//zzvP/++zz44IMARKNRZs+ezapVqygrK2Py5Mn84Ac/4Nlnn+W+++4DGquVli9fzvTp06murqa6upqHHnqIe+65h48//phevXoRj8eBxo3dZs2aheM41NbW8pvf/Ib6+noWLFjA/fffD8B1113H/fffT5cuXY7q+wGHqWjaunUrw4cPb/rZ/3rYsGEMHz78qAciIiIiIiIi0hlZm54GwOn9DSLhHZSXrSBGHlvCQ+ifGQNgWV2CnISJiYHtT7Z67JyKOpKWSU0opV1il/aVnZ3NQw89xAcffMC1117Lv/3bv/GPf/yDwsJC7rnnHp588knGjh3Lq6++ysSJE8nLy2tKLjXna1/7Gs8++yxr164lFouxYMECbr75ZhoaGoDGKqpp06Yxd+5c/v3f/51FixYxcuRIPvroI2pqavj444/JyspqlyQTHKai6Y033miXG4qIiIiIiIicKIyqQqzilThZA3FCPdmx6QHAZW3dNzAMgz6ZcXbEG6hMhhi8r/dO0te6HjzB+hip9THKs0O4plYZfVlfvfSsw1YftYft27eTlpbG3XffDcD69eu58cYbueWWW5g5cyYpKSmUlJS0WNDjum7T73379gWgsLCQwYMHA9C9e3e6desGQJcuXZgzZw6BQIBIJEJaWhqGYXDllVfyt7/9jV27djFhwoT2mC5wmIqmHj16HPZHRERERERE5GTn2fwMAHafb1BTvYm62kICKaewtqIvPUNx/JbLazURwKB3svEreLKVFU05+5bNVag/U6e1detW7rjjDmKxxsq2vn37EgqFuOuuu7jrrruYNWsWXbp0aUokGYaB4zj4/X7KysoA2L17NzU1NU1j7m9t1K9fP9auXQtASUkJJSUlAMycOZObbrqJ2bNnM3DgwKaxr776al599VVWrlzJ+eef325zbnWPJhERERERERH5nEgJ5iev46R2xckexM51fwAMKs3RuBj0z4qzI15NcTwLgyR5sVRcw8H2ti7RlFtRB0BVZmo7TkLa08UXX0xRURETJ04kJSUF13W55ZZbWLlyJddccw3p6enk5uZSWloKwFlnncWNN97I448/TigUYuLEifTv35+ePXseNPaFF17I6tWrmThxIt27dycrKwuAK6+8kp///Ofk5OTQtWtXqqqqAMjPzyc1NZWhQ4fi8bRfOshwP19/1UmVldUd6xBEOo28vJCeGZE20nMj0nZ6bkTaRs9M5+RZ9Uc8W54jefr3KPHAJ0VPk5F5OktKL2f1Hi//PriM/4t9RHF0EL2sMJd+0pWkP0FN98oWxzYchwv+uYGo38PKYQM6YDadz/dv+s2xDqHT+clPfsKMGTPo06dPu92j2aVzIiIiIiIiItKMWA1W4Uu4/kwSeUPYtfPvGIaH7NwCtpRbpPlsyj07KY/nAnC6a2FgkPS3rj9TVlUEy3Go1LI5OQqi0Sjjx49n0KBB7ZpkAi2dExEREREREWkza+vzGHaUZP/LKSl9h0S8hpzcsyiuzyASNzmjSw1vR/aSdArIsOJkRhu/fre2Efj+/kyVWUo0yZcXCARYtGhRh9xLFU0iIiIiIiIibZFswLP1r7jeVOJdhrF39xIsK0B2zgg2l1kAOFmbqE12AQz6+uL4GnyNl7ayoim3og7bNKhJT2mvWYi0CyWaRERERERERNrA+ngxRqwGp+f5FJe9i203kJ0zHMvys7nMg+mrYjs7SCR74MWhuzeOt8G3rxG43eL4gWictEiU6vRUHFNf26Vz0SdWREREREREpLWcJJ5NT+NaPqLdzqJ4zz+wrCBZ2UOIxGFHtUGox2riTh4Ofnr64nhcA0/MS9KXBKPlW+SWNS6bq9CyOemElGgSERERERERaSXz09cx6ktwuo+kuOw9HCdGTu7ZmKaXreUezPQdJP3lGPYpAPTxRfE2+NrUCDyvrAaAiuxQe01DpN0o0SQiIiIiIiLSGq6DZ+N8XMOiofvZlBS/hceTSmbWGQCsL3XwdVmL66QQsTPIthKELAdfvR+ARCDe4i2spE12VZi61AAxv7ddpyPSHpRoEhEREREREWkFc9dyzJpPcLqexd7yVThOgpzcczBND7YDW50tmJ4oIfcrAPTxxQDw7ks0taaiKbeiFtN1KVc1k3RSSjSJiIiIiIiItMR18Wx8EoD6bgWUlryN15tOZtbpAHxQUQcZhVjJVMoTOXgNh27eOLjgi/ixLRvH47R4mzz1Z5JOTokmERERERERkRYYpWsxyzfg5J7Jnuo1uK5NTt45GIaF67osqdmAYbhkOYOIuha9vTEsA6y4B8u2SLZi2ZzhuOSW1xL1eQinBjpgViJHnxJNIiIiIiIiIi3wbJwPQKT7OZSV/gufL5OMjEEArA3vJmxWkKzrQRUhDFxO8Tcum2vqz9SKZXOZ1WG8SbuxCbjRiu3pRI5D7ZZochyH3/72t1x77bVMmjSJ7du3H3B86dKlXH311Vx77bUsWLDggGMVFRWcf/75FBUVtVd4IiIiIiIiIq1iVH2MteddnMwB7K7dBK5Dbl4BhmHSYCf4e/kmXMciLT6QCttLV0+CFLNxmdz+RFNrKprySxt3mytTfybpxNot0bRkyRLi8TjPPfccN998M7NmzWo6lkgkuPvuu3n88ceZN28ezz33HGVlZU3Hfvvb3xIIqExQREREREREjj3PxnkAhLufQ3nZ+/j9OYTSBwLwZtVWGtw4iYrTsVMaq5D6+qNN1/rq/biGS9KXPPxNXJcupdXEPRY1GantMxGRDuBpr4FXr17NqFGjABg6dCgbNmxoOlZUVETv3r3JyMgAYMSIEaxatYpLL72U2bNnc9111/Hoo4+2+l55ecr2irSFnhmRttNzI9J2em5E2kbPzPHJqd5JZPsSjIyelMSKAJcevUaSFvKzu6Ga92o/xUymEq/5Cg1ptWR6knQNuBiGhWEbeKJe7GACr9c67H0yKuvwx5Ps7ZqF6VGXG+m82i3RFA6HSUv7rEu+ZVkkk0k8Hg/hcJhQ6LN/RFNTUwmHwyxatIjs7GxGjRrVpkRTWVndUY1d5ESWlxfSMyPSRnpuRNpOz41I2+iZOX553v8zHtehNv9sSnb/H/5AHl5vH8J1MRbsXYsLNOwdQSCrgTAGp3ijJJI2AP66AAYGcV+86W/NydlbCUBpdgjbdtt7WiLtpt3SpGlpaUQikabXjuPg8XgOeSwSiRAKhVi4cCHvvvsukyZNYvPmzUybNq1pSZ2IiIiIiIhIh2qoxCr6G24wh13RXYBLbu45GIbB+sgetkcrySGXZH03GoIJ/IZDd+9nvZh84caWMImW+jO5Ll1Ka0h4TKrTtWxOOrd2SzQNHz6cZcuWAbB27VoGDhzYdKx///5s376d6upq4vE4q1atYtiwYTz11FPMnz+fefPmcdpppzF79mzy8vLaK0QRERERERGRZnm2LsCw44S7F1BR8QF+fw5poX7EnSSvVGzGwsBTdSZOagzbMOjji2J9brM4fziAi9tioimjpp5ALEF5djquqd3mpHNrt6VzF110EcuXL+e6667DdV3uuusuFi9eTH19Pddeey3Tp0/nhhtuwHVdrr76avLz89srFBEREREREZG2iYexti7E9YXYnawAXHLyGquZ3qoqotaOMjSlG+8X5kLXWkxcTvHFmi43kibeqI9kINFiiUfX4ioAynLUp0s6v3ZLNJmmyZ133nnA3/r379/0+9ixYxk7dmyz18+bN6+9QhMRERERERE5LM/GeRiJMOFTLqK87B/4fFmEQgOoTNTzdk0RqaaXvER/Yl4bx+PQ0xvHb37WW8kf2defKRA7zF3AcFy6ljTuNleVmXbYc0U6A7WyFxEREREREfm8SCnWlmdx/ZnsJgI4TdVMr1ZswnYdvhbqxfbKEE56FIC+/ugBQ/j392cKHn7ZXHZlHb5EktLcdFxDy+ak81OiSURERERERORzPOv+jGHHqe9zAeXlK/H6MklP/wpFDeVsrC+mqzeNAf4cPqry4wYTZFkJMq0Dd5XzhwM4hkPSnzjsvbrtWzZXkpfRbvMR6UhKNImIiIiIiIjsY1QXYW17GSetO7uTFbiuTW7u2TjA38o3AHBeeh/KIn5qAkkA+n+hmsmMW3jiXpKBOBymSMlK2nQpraEh4KUuLdheUxLpUEo0iYiIiIiIiOzjWTMHw3VoOOVCykrfw+tNJz1jICtrd1CaCHNaMI88byobylJw02IEXJeungOrlvYvm4u3sGwur6wGy3Eoyc0ALZuTE4QSTSIiIiIiIiKAWfwB1u53cbIGsqdhF66bJCf3LOKuy9Kqj/AaJuek9cR1YUODBwwYEKg/KEcUqE0BIJ5y+Ebg3fdUAlo2JycWJZpEREREREREXAfPBw8CEO17EaUl7+DxhsjIPI1l1UVEnDjDUruRYnnZE/ZSH0hgOtDbf2AyyXAMAuEASW8Cx2sf6k4ABOtj5FSFqU5PoSHob9epiXQkJZpERERERETkpGduX4pZuRk7fwR76j5qrGbKGUGdnWB5TREpppfBKV0BWFaeAiZ0w8b6QjWTvy6A4ZotVjP12FMBwJ78rHaZj8ixokSTiIiIiIiInNzsBJ61D+EaFtE+F1Ja/DYeTyoZmafzZtVWEq7D2Wk98JoWcQd2OAY4BqeF6g4aav+yuVhq9KBj+xmOS/c9lSQsk/LsULtNS+RYUKJJRERERERETmpW4QuY4T04PUdTXP0hjhMnO2cEZckGVtftJMsTZFAwD4DlFQEc0yUtZhL0OAcO5EKgNoht2di+ZLP3yy2vxR9PUpKXgWPpa7mcWPSJFhERERERkZNXPIxn/eO4ngCxXqMp2fsWlieFzKwzeK1yMy7wtbSemIZBwoHVYT84Bn19By+N80UCmI5FPDUKh9lErufucgD2atmcnICUaBIREREREZGTlmfjPIxYDXafiymuWIXjxMjOGc6nsWq21pfS3Reijz8TgDV1PhKAWeene3r4oLGC1S3vNpcajpJbUUd1egqR1EC7zEnkWFKiSURERERERE5OkVKsLc/i+jOJd/8aJXv/gWUFycg8g1crNwNwbqgXxr5qpner/eBArg2+LyybMxyDYE0qtmWTCMSbvWXvnWUA7OqW3X7zEjmGlGgSERERERGRk5Jn3Z8x7Dh2/ysoLXsP246SnTOULQ3l7I7VMCCQTRdvGtBYzdTgmJh1QbodopopUJOC6ZjEQg3NLpvzxpN021tJg9+rJuBywlKiSURERERERE46RnUR1raXcdK6k+gyhL17l2JaftIzB7Ok6iMM4Jy0ngDEHPhXtR/DBbPWT3565KDxUqoaE1LRtIZm79ljdwWW47K7WzYYh2niJNKJKdEkIiIiIiIiJx3PmjkYroM94JuUlLyDnawnO3soGxrKKEuEGRTMI8PT2ENpZY2fesfEqAmSHYzh99oHjGXFPPgjAeKBGM4Xju1n2g69d5SRtEz2dsls9/mJHCtKNImIiIiIiMhJxdz7Ptbud3GyvkIysz9797yJafoJZQ3mzaqtmBj/f3t3HmVXVaD9/3uGO99bU1KZ54SEMSEJ4ssQX1F4oVFkEhHsNC0utZH+Ad3IMqA4BlFktbYusGloW0WRILJabYduIQgoCCYaICQh81BJDan5zmfavz8qqSSkklSgMvJ81rrr3HvPOfvsc1K7cu9Te+/D3OwYAEqhxYs9CVzA7k0xsnbvYXM7ezNVc/vuzTSuqZ2EH9A0uoHQdQ7JeYkcDRQ0iYiIiIiIyNuHVyD2wlcxlk14whW0tjzb15tp2GyWldroCsqckh5BzkkAfROAe8YiXYxjGRj1hmFzVmiR6cwS2RHVdGXAQ9phxKRNbQS2TdPoYYf8FEWOJAVNIiIiIiIi8rYRW/JNrFIr0aQLCdKNNG/rm5spV3caT3etxsVmTmY0AD2BxV9646TtiFJ7lvpMmWQ82KO8dFcWO3Qo1xT3+Q173NZ2El7A1tH1BDH1ZpLjm4ImEREREREReVuwN/++bwLw3ATCyX9DS8szhGGJhoY5/LnYTD6sclpmJGknDsDTnUlCLEYEDhYWY+ryexYYQXZ7DcaKqNSUBjymE4RM2tBGaFs0jVFvJjn+KWgSERERERGR41+lk9iLX8fYLsGp1xFEHi3bFuM4STJ1p/BM91rilsPpO3ozbak4rCzGaYiFFDoyWJbZa36mVE8GJ3Cp5MoYxwx42MkbWkn4AZvHDsePuYf8NEWONAVNIiIiIiIicnwzhtifvo5V7SacdilkRtHa/HvCsEzDsNn8qbCVUuQxKzOKpO0SGfhdRwqAk+IBPaUEw7NF4m60q8wIcm21GAzl2uKAh02VqkzcvJ1K3GWLejPJ24SCJhERERERETmuOet/jdP0LFH9dKLx7yYISrQ0P43jJEnWnsQfetaRtFxmpkcB8GohRqvnMCnpk+9JAzD6DcPmsu01uF6MSk2JaPcAajcnrNmGbQzrJ40kcvT1W94e9JMuIiIiIiIixy2r0Iz753/BuEmCU+aDZdPS35tpDn/MN1GJAmZnRxO3Hcoh/L4ziWMZZmU9NrdncOyIEbv1WrJ9h2xbLZEdUqorDHjcEa3djNzeQ08uRduwmsN1uiJHnIImEREREREROT6ZiNjzX8EKSoTTr4JkA0FQorX5aRwnRaz2RJ7v2UDajnFqeiQAv+9KUYpsTs14VCsxitUYI2oKuPauOZhqmuuxjU2xoTDg3EwxL+CkVU2EtsWqaWPAsg7bKYscaQqaRERERERE5LjkrFqE3fZXosZZRKPfCUDLtqcJwwoNw+bwXM8mfBMyNzsG17LZXHZYlo9T54aclPHZ3JEB9hw2Fy8kSPdk8BMe1Wx574Maw4mrmoj7ARvGj6CcShyWcxU5WihoEhERERERkeOO1b0B96/fxcRzBCddA5ZF4Bd3zM2Uxq45gZfym8g5CU5KNRIY+E17CjCcWVPFAjZ3ZIg5IcNzpb5CDdQ2NwBQbMjDAB2Vxm9pZ1RbNz25FE1jGg7b+YocLRQ0iYiIiIiIyPEl9Ik9/yWsyCc48RqI5wBobl5MFFVpGD6H3/dsIDARZ2TG4Fg2z3cn6Awcpqd9hscj2nqTVH2HUbUF7B2BUrozS6wSp5ItEST9vQ5b31Vg+pqteDGHFdPHacicvC0paBIREREREZHjirv8P7E7Xycc/X8wI2YB4Hk9tDY/jetmCDNTWJrfQr2bYnpqOFsrDs93J0jbEbOyHsBuw+Z6AbADm5rWOiI7otiw9wTg6WKFma9sBOC16eOoJmKH4UxFjj4KmkREREREROS4YbW/hrP8B5hkA+GMD/a/v63pt0SRz/DGd/JUzzoM8M7sOAJj8cvtKQxwVm2VmA1hBFs70yRjPvWZCgC51jrs0KFUV8A40R7HTJeqnLF0LXE/YM3k0fTUZg7jGYscXRQ0iYiIiIiIyHHByjcR//1nwBiCk+eDmwKgXG6jrfV54vE68skxLC82MyKWYVKijqc6k3QFDielfUYmQgC2daXxQ5vRdXksC2LlOOnOLEHMp1JT2uOY2UKZuUvXkvAC1k4aSfOo+sN92iJHFfdIV0BERERERETkLSu1E3vqZqxKB8GMqzAN0/tXNW3+JRAxfMTZPN69GoD/kx3P6lKMZfkEdW7IzJzXv/2G7VkAxtb39k0Avq0eC4visD0nAB/R2s0pKzbjhhFrJ46kacyww3KqIkczBU0iIiIiIiJybPPyxBf/E3ZhG+Hki4nGv7t/VSG/ka7OZSSTI2l1alhXXsX4eC1Ju45Ht6dxLMPZtVWcHQFSqerQ2pOkNl0mm/RJdWWIl5JU0xX8VF8Y5foBJ6zZxrhtnYS2xfIZ42gfVnMETlzk6KOgSURERERERI5dQYX407dhd68lHPcuwikX968yxrBl088BaBxxNt/veh2AOZnxPNGaxjMWZ9dWqIvtmnNpU3sGsBhX34sVWtS01GMsQ3FYL3YYMXZrB5M3tJLwAwrpBCtPGEsxkzyspyxyNFPQJCIiIiIiIsemKCD23Oewt79MOHIu4YyrwNo1tq2rcxn5/Foy2UlswGGb18PURAMv9Qyj3XeYnvaYlAr6tzcGNm7PYlsRo+oK5NpqcQIHP93J1A1bGLOtk7gfEtoW6yaMoGnMMIxtDVQzkbctBU0iIiIiIiJy7DERsRfuwtn6R6KGkwhP+Tuwdt3vKop8Nm/6L7Bsho88l8fbXsbGgmgGK4txGmMhs3eblwmgo5CgWHE5NdnCxM1FyI8lFpaZtXwJtonwXZuN44azdXQDfkxfp0UGcshaRhRFfPGLX+T1118nHo+zcOFCJk6c2L9+8eLF3Hfffbiuy5VXXsmHPvQhfN/njjvuYOvWrXiexw033MB73/veQ1VFERERERERORYZg7v02zgbfktUM4lg1sfB3vPrbcu2xXjVThoaZvNypZd2v8gwawZLe7NknYh59WUcC2Jln5r2EjXtRSZv9bm4sJSkCVk3ag69GZvRnatob8jSNryGjvosxtbN20X255AFTU8++SSe57Fo0SKWLVvG1772Nb773e8C4Ps+d999N48//jipVIprrrmG8847j2effZa6ujq+8Y1v0NXVxeWXX66gSURERERERPbgvPYD3FWLiDKjCE6/AZzEHus9r4dtW/8Xx0mRaZjNU9v+SBQ1sr46noQVcUW5jakvdlLTXiRV2NWrKQK2x+WR/KMAACAASURBVDO0DK+lNzUCY5d49ZQG0PA4kUE7ZEHT0qVLmTdvHgCnn346y5cv71+3bt06JkyYQG1tLQBz585lyZIlXHTRRVx44YX92zmOc6iqJyIiIiIiIscgZ81/EVv2ACbZQDD7HyGe3WubLZt/QRR5jBr9Hp7Jb6bHy1HxZuIYw//38nKmdecB8GM2nY1p8nUpXnMaWVyZwgnD2phTyuJ6UKzrVcgkcpAOWdBUKBTIZnc1eMdxCIIA13UpFArkcrn+dZlMhkKhQCaT6d/3pptu4pZbbhnUsRobcwfeSET6qc2IHDy1G5GDp3YjcnDUZg7Mf/23VF68BxI5kv/3VtK50Xtt09mxio7tL5FKj8AdNoWm51+hmjkdy8ANy1cyNqiydcYwukZlKdUmwLIwBv6wehK+Y3OiG+J6SfxUEZI+DgqaRA7GIQuastksxWKx/3UURbiuO+C6YrHYHzw1Nzdz4403cu2113LJJZcM6ljbt+eHsOYix7fGxpzajMhBUrsROXhqNyIHR23mAEyE8+r3cV95CJw4waxP4YV10F3eY7Mo8nntlR8CFiOsObz2xOusmDITsLhm2ybC6Vn+Wjd8153pfAMYWopp8l6cCZlOMt31GAzlTDcmNIf7TEWOeYdsFrM5c+bw7LPPArBs2TKmT5/ev27q1Kls2rSJ7u5uPM9jyZIlzJ49m/b2dq6//npuu+02PvjBDx6qqomIiIiIiMixwisQe2YBsVcehGQdwdybMTUTBtx029bfUam0UZ8fy8oXK/xw6omElsV7TCfWCRkK9aldIdNu1nQ3AHCaW8UJ4nipAsYJD+lpiRyvDlmPpgsuuIA//vGPfPjDH8YYw1e/+lV++ctfUiqVuPrqq1mwYAEf+9jHMMZw5ZVXMnLkSBYuXEhvby/3338/999/PwAPPvggyWTyUFVTREREREREjlJW93pizyzAzm8haphBcOpHIT7wEMNyuYXmpv/FrcZY0TKDx0+Zhm0Czol3Mmo/0//mvRhNhSwNsRLDizUYIqqZnkN0RiLHP8sYc8z3BVQXU5HBU7dskYOndiNy8NRuRA6O2sze7E1PEXvhLqygTDjxAsKpl4A9cGIUhQEr/3Q3vVY7f+n8f/ypYSIWVU6Jb2KOXbff4yxpHcma7gYuzG5nbLGWarqXSq77UJySDLG/u+lzR7oKMoBD1qNJRERERERE5KBFAe6yB3BX/AjjJPBP+xhm5Jx9bm7KVZqe+nfaG3r5X/8DbG4YiWPlGRZ7jVn2pP0eqhrarO+pI2t7jC5nMFZENdM7xCck8vaioElERERERESODpVuYn+4E6dlCVF6BOHMj2OyY/a5edSdp/u/fsqyE3r4rbmSfCxL2m4nFn+FM5iEc4Bpidd21xMam/+TKmKXM1QyPRg7GuqzEnlbUdAkIiIiIiIiR5zVsYr4MwuwSq1Ew08jOPU6cFP73D7ctp3iT3/FEzMtnrMuIcJmfKyNXudlpjCc4WT3e7wgsljdVU/KChhXTWKskGpavZlE3ioFTSIiIiIiInLkeAXcV/8T5/XHIAoJpryfaPKFYO27N1KweiMtv36Oh04bx7rEKNJUOTNZYJn1MlkT5yRGH/Cwa7vrqIQx3pPswa6mqGS6wT7mpzAWOeIUNImIiIiIiMjhF4U463+Fu+zfsCpdmOQwghOvxgw/Zb+7+a+u5tmXXuPRuadScWJMYBvz0gkWm1exgNlMwD3AkLkgsljROZyMFTDBixNZIdW0JmMXGQoKmkREREREROSwstpeJrbkm9idr2OcBMHUS4gmvBec2H73a1+2iu9vauXlk04ghs95PMfJqYk8RRNlfE42o2kgc8Djr+mupxq6/N9EL7aXpJztUm8mkSGioElEREREREQOj2ILsb/ch7PpSQDCUWcSTrsUknUH3PWFP7/GD4oexcZhjKOV9/IkNalZ/MUu0BL1MMrUMJXGA5bjRxYrO4eRswLG+nEiO8BTbyaRIaOgSURERERERA6toIL72o9wVvwIK6wS1UwknHEVpnbyAXct+AHfX7KKFw3EHId3s5JTeRYrfiJr7DSvRevIEGc2E7CwDljeax3DqYYu70n0Yu3szXTg3URkkBQ0iYiIiIiIyKERlHHW/Tfuaz/GKrViErUEM64mGv2O/U72vdOyzjwPrdpIDxaT8gXOq11N1vwZnJFsiU3ixWg1cRzeaaYQwzlgeb3VOKs6h9HoeIzyEoSOj58qDsWZisgOCppERERERERkaJU7cF9/HGf1z7C8PMaOEU76f4STLgQ3eeDdg5AfbWzm2bZunMjwgaatTJvchh/9GexamhOzeC5ag43NO80UsiQOWKYxsKRtJAaLc2MlrGqaaqZHvZlEhpiCJhERERERERkSVvcGnJU/wdnwW6zIx8SyBFMuJhr3LojnBlXGa90FHly7lQ7PZ3y+wN9u2IQ/x1AN/wxWlqbEHJ6J1gFwpplIPelBlbs5X0NrKcuURJ66aorQ9fCTpTd9riIyMAVNIiIiIiIi8uYZg936V5yVP8bZ+jwAUXoE4YT3EI1+JzjxQRVTCSMe29TC71o6sY3hfRu3cH57B1vPjlMN/ghWirWJ03jBbMLF5kwzmeFkB1V2wYvx59ZROFbEmXaARYJKVr2ZRA4FBU0iIiIiIiJy8IIK9ubFuKt+it25CoCodgrhxPMxjacNag6mnVb3lvj3tU20VjxGBAEfW/Yao0zIprM9/OAljJXkpfgUVtNKApd3msnUDbInU2Tg+eax+JHDvNoW0r0NBLEqQbz8pk5bRPZPQZOIiIiIiIgMmtW9DmfNz3HW/wbLL2CwiEacTjjhvZi6KQdVlhdF/GxzG7/Z1g7Au/N5rvzLq4Q1KTa+o4sgWItnZXgyPooOq8xwk2UOE0gSG1T5xsBf2kbSUUkxPt3F5HIGg6GS053mRA4VBU0iIiIiIiKyf0EFe9NTuGv+C7t9OQAmXkM46SLCsWdBavhBF7mhUOaBNU1sLVepj7n87cbNnLJ6Az1jY2w9cS0m7KTDzvBkbDiBBSeaUZzACKxBJkTGwMvtjazpbiAXq/DORBG3PIJqKk8Y8w66viIyOAqaREREREREZC+VoExP28v0bvgtva1/oTcK8C2LYNh0/OxYwmQDWBaJrtdJ9W4g6SRIOQnSToKsmybnpsm6KVzb6S+zvRiwqrXKk20drAt7wYJcOc7Hl7zC5Hw7a2YUqIzdjG0Mrzs1LHGHMZp6TjKjSTO4uZ6gL2R6tb2RlZ3DybhV5g3fTLZ1PJEVUs32HIrLJSI7KGgSERERERF5Gyv5RZrzTWzLb6G50ERzz0Za8pspRf6ujVK1u+0RQWFL32MQknYcxyTxvASFSpaqM47ISmLhM7K0hU+92kI6kWf5WS3YqQpVHJ53RtNcPImRwUjGpQzJVGnQQ91KvsufWsbQWsqQdqucM2o9w9pHYxuHUk0Hxo4Gf3FE5KApaBIREREREXkbqARlthdbaCls3RUq5ZvornTusZ1lDCMin8mhT9ZNk8iMIpYZQyaWxrVdHCxsy8beMdm3HwVUIx8v8qlGPtXQoxxWyftV2opVestVIruKRwNVdwpgE3e2cmrPaj643qN3+na2NxSxgWbTyOrKO+junUSpUsM6Y7MOcO2QUekiYzIFGtNlcjEP6w3BU68XZ213Het76vAjh1HpXk4fvoXaQi2xaho/UcJPFg/LtRZ5O1PQJCIiIiIicozzQ4+810tvtZt8tZe810O+2kN3pZO2YgttxWZ6q9177Zd100xzM0wstTO+2svYsEpDLENx+Ey6G08liOcOui7GwKtNFn9daVPyLNykjz2iQNWKSFiGmakezmx/nYSzieZZfXMlVWgkz4lg1zI9DdPTmwkji/ZKltZyjrZSjqZCDU2FGgBidkgm5hO3QyJjkffjVMO+r7dxO2DWsCYm5jqJVZOkuxqJ7IByTacmABc5DBQ0iYiIiIiIHKWMMZT8AttLrbQXW+kst9O7I0TKV3vIez30VnupBKV9FUCciEY3yeREjlonTo1tU1/tpr7USjK/jggwlk2QqqcjPZ62eC1YIXQvx7JjWE4Cy07sWMb7ljvfs/f8StlRgF+97LC1yyJme4yZ0MUWyyHCYqrdzLvMc2TLXZCBILTwvdH0xCfjWw17Vd2xDSPTeUam85gGKAZx2ko5uqppur0UBS9GYJJYGNKuR326xLhMF6MyvTiWwfZj5NrGAVCu6dSQOZHDREGTiIiIiIjIUaK91Mb6ztfZ2L2WLb0b2V5sobyvEAlIuRkysQyNqUbqbJv6qEIuLJEKCsS9Hmy/GyvyoLr3vl0AsZ1dfAwEndDbufeG+2M5WHYcLBsvsKgGNhdkfXpqcixmHpsYToYi5/Esk81mbN8l21WDXapn/ZgTqCaSgzuMBdmYR7a2A+jofz8yfUv7DT2VrMChpnU8duRSynUQJCoHd14i8qYpaBIRERERETlCIhOxvvN1frV+GX/ZsoT2Ulv/OsdyqEnUMTI7ltpEPXXJemoSdWRiGZJhlVilDVPcTJjfRNi1HBO8Yf4hO4Ydy5GMDJlqkXRQIWYAO0E5O4Z8bjx+PItlOYDBmAh2PIwJgQiiEBN5GOND5GN2exB5mMgnigKKVZsggtCyWWrP5lWmY7A5wW5irr2JsS0pxq+bRryUYNOkOtZPrsO8MR16EwYqwvHi5FrH44QxKpke/LTmZRI5nBQ0iYiIiIiIHGZtxRZe2PI0S7Y93z93UsyOM6V+OmNzExmVHcfw9Ahsy8ZU2gkLmwgLGwlbN/U9D4qEu5Vnxetwamdgp0biJIdRW+5hRPtaajo3YWGILIfumkl01k2hkBnJzpm0nbd4Hps6U/xqZSNF36FueA/FrE/Z2KStkNNSvZze4nPiCpdUGYrpFEvnDqenbnC9mA6agXixhkzHSGzjUMl0U830Hppjicg+KWgSERERERE5DCITsXL7K/x+429Z3fEaAAknycmNp3P6hFnUmEasahthYQth67OUi1uIClv26qm0R6iUHoWdGoFlx8n1bKauYyV1G1/ADfvGyhVTw+ism0pX7QQiJz5k5xJG8PzGel7cXAfxgPSEdtotG8dYzEiUeGdPOyf/ZTt13RVC22L9pFo2Tqolcuwhq0M/A7FKhlT3MGLVNIaIUm07fnLfQw5F5NBR0CQiIiIiInIIRSbiL81/4nfrfkFLvokkIVOdOoZHDaTL4G5eS3nLn7Boxd6jn9IbQqXUSOzUSCx3R48gY8jkm6jf9Bx1HauIBWUAPDdF67CT6KyfQjVRO+Tn0112+dXKEWwrxnAa81TTHj42o90q7+5pY+ayNuq7+uZEahmRZu20eiqp2MCFGbBCBzt0sSMXK9wRRFm7b2LA6nsYy2AZCytysIMYrpcgVs5gR31fbf1EiXKuC+OEAxxMRA4HBU0iIiIiIiL7YUxIFPqYyCOKPEzoEUU+Jqz2vY58otDrWx/2bRN6efxqF63dq2ntXgN+gXcSkTIRMUJg864DWBAYh3avkQ5vFO3eCDq8kZj4cCaNinPysJCGbH9lSOebqe9YSV3HKuJeAQDfSdBefwJdtRMpphv7h8YNtZWtGf5n7TCq2SpmbJHAghpCLmjfxrlrmklVAgDaGtOsn1xHIberF5UVOrjVJK6XxPGSuNUETvjWe1lFVkg1lcdPFQlj3lsuT0TeGgVNIiIiIiJyXDMmIvB68Cvt+JVO/EoHfrVvGXq9hGGZKCgR+iXCoEQUlIjCSn9ohHlrvWNG0Hd3tGqUoifMUTF1mFgNbrIGJ5HDTdQQz9QSFg2xqk2i4OD2uDR3x2hdY7FkDcwbvol31a5hYnUNiR1zOgV2nI66KXTVTtwx79IhGJa2gxdY/PfaYaz2bczoHrCgJvC5sKmJd2/chmMgcCy2jMuxZXwN5XhfoJTqTuJW+4IlJ9yzV1NkhQSxCpEdYuwIs2O5F9MXmlnGoq+rk8HYEZEdErk+kRPs0QNKRI4sBU0iIiIiInLMMybCK7VSKWymWmyiUmiiWmiiUtyCV2zGmODAhVgOtpPEdhLYbgonVoNluzseMSzLxd75fLf3LNvFtmNgOWyv9rKydyPbvQIVXIrFceQ7ZmKZBqaN8pk8xqMutXeYkkrYEPnkUhGj6gJmjeqmIb+ZXNcmxlY3kbI86IWKibExOY2gcSzF3CiM/Van894/P4Ln21Is64wTJHxSjsWIose8rW2c2tmNZdlsHTGOrtpaysksdhgn0REn9cZQyQ7w42XCmEfoeoQxD2OHCohEjkMKmkRERERE5JgShVXKvesp9ayh1LOGcvdaSj1riMLyXts6boZEdhxuvA4nnsON1+LGanY8r8Fx09hOEstJ9IVFb4IxhrW9m1i87QWaii19dSydRGX7qWTsLLMmVJg8Is9+58E2hmylgxHFjTQWNlJXbunPYMpumrXxSbxQPJHFHSfhE6O23efMCd2cMiqP+yY6MkW+RVRxCcsuUcUlqjoY3ybybULPJvBsotDGwuJUA6ftkQhlITWM9WN3eyuExI45y/tCpVJfqBTzCF1fcyaJvI0oaBIRERERkaOWiQLK+Y0UO1dQ7HqNYtdKyr0bgN17BdnE0yNJZE4mlhxBPNVILNVIPDUCx00furoZw7rezSze9gJbis197xXHUW47lURUwxkTKkwb1TtwwGQMGa+LhtI2GkpbaShvI6q69ARj6QxOYL15F2VTi2/SGOOAgbG24dpUlULo01ONUV5dz0vrammsqzKmoUQsvncPoci3MZ5DtPNRcYgqLibYdzrlWYaKbQhcg2VCkoFPOvBxHIPn2gQxG7Pb5NzGioicgMgNdgxjM0N4lUXkWKOgSUREREREjgrGGPxyG4WuFRS7VlDsXEGpexVRWOnfxrLjJGsmkcyMI5EZRyIzlnh6FLbz1ieVHqzIGFb3rOe5liVsLmzrq1dpLKXWU7GDOk4bW+GksT3Edvu2ZaKIdLWL+vI2akst1JZbcIMK+WAMHdXprPPPxwtr9jiOwWCcCGzTFyBFFrbvUGtsanfeiS0COhNUOxNUB1N3y1CNhZSSPj12xPaYoTMOBcdQdgw+EWMLReoLAW7RJZOyGdlYwbM1xk1EBkdBk4iIiIiIHBGB10uxayXF7lWUOldQ7F6JX+nYbQuLeHoUydxEkrlJpLITiWdGY1mHdl4iAD+K6K76dFd9Cn5IwQ/o9Tw25NvYWuymEkbAaEw0hchPYSwbd6TB2J28CrzSbDDGYIAIC4MF1IOpZ6R3GlOLMaaUXTJhX8+iqmXYlvJpToR0xCO6YhHlnQHTG1gG4hEkIovkzkdoEY+svTav2IbSjhCp5PT1VNq5kRtGNFYqNJYqjPUMeS9Nc9hAszOMeKqXU+o7STo+mkhJRA6GgiYRERERETnkwqBCqWc1xa4VlLpWUexaSbXYtMc2bryWTMNMUjuCpWR2ArabPCT1McZQ8EPaylXaylVayx7tZY+uqk9X1Sfv72vycAcYtltB4LiQsDySBMQin5jxcQlxTISNwcGQCGM0luoYVcqRCfq+hnl2xKZMlea0R2c6JDJ9Q84sa9cRrD2Wu9Zj7xn/VA10+DEKXowgtMiGHnVBmYawzESvTJ3nkfV8sr6PsRwKsRQtTh3L3JGsdDIAxK2Qiblezsm0ktacSiLyJh3zQVP5lzcTq/pgJzBuHJxE/8P0P4+Dm8TEazCJGkjUYhK1EK+BQ3yXBhERERGRtxNjDF6phXLvOkq96yj3rKPcu45KYTOYXfMq2W6KdN2JJLMT+nosZSfiJmqHvD5+FNFe9mgre7SWq7SWqjvCJY9SsHeY4liQjbmMSrtAiUrYQTXqwcLHxVBbzDC5AFPoYmqilbFOO5motEcZRTtFZ6yednsEXdWp+PnRxMp9c0UZy1DJlKlmy/gpj4wF04CY6+APUJ/BsMOI2p4i9V0F6ruK1PYWcaJd8yQ1x3KsTQ1jRXI0a7PDyO8W3mUcj4mxXsYkC4xKlNAIORF5q475oClY+xRvJSoysSwmWdcXPsVrIVmHSdRhkvU7ntf3rU/WYxJ14KZ2/AlBREREROTtK6j2UCk2US02USn0LauFJsr5jUTBnsGL7ST7eyglsxNJ5iYQSzZiDdHn6sgYOis+beUq28te37LSt+ys+LxxamobqIm7jMylqE/EqIu7pGMhYdBGd3kzzdVWuqO+O9jZFswMfc6pdjHX7+37ArUjpzFA0UqzJTGGzlgDHW493WYkVrGOZE+KeDGJZSxcDF6ySjVbxstUMfabnyzbDiMyxQo1+TI1PUVqe0tkihV2FmmAYjpBd02Gnpo0nbk03U6KQhgjHtnMoAfX6iJlB6SdgKR6LonIEDtkQVMURXzxi1/k9ddfJx6Ps3DhQiZOnNi/fvHixdx33324rsuVV17Jhz70oQPuM5DkJd+mt7cCkQ+hj7VjSbTjEXo73vPAL2L5xT2W+EWsai8UmrHNgX/JGicOyYYdYVQd7AilTLJ+x/M6TLIBEjWYWAZiGbDjCqdERERE5KAZYwj9Ak4sg2W9iXvYH+zxooDALxB6PQRensDrxqtsxy9vxyu341e245Xb8MvthEFhj33zJkvFSjM81UBN3YnEM2NIpMeQyIzBTTS86VApiCKKQUjRD+nx+uZM6qr6dFcDuqoe7ZW+4W7hANlN2rUZnYoxLAbD3ZBhVpkMvSSiHiphLx1+gTavyutWRNGywUAqSIFdZlZYYLZX5DSviBs5tEUNvB5rpJpKUYjl6HVrKFKLXUkRq8RxC3GShRh14a5JySO3SilbpZqrELnR3hXcBycISVZ8khWPZMUjVfbIFCtkixVSZW+PIXOhbZHPpujNpuiuzdCTSxPE9vxTfA6fnOsf7KUXEXlTDlnQ9OSTT+J5HosWLWLZsmV87Wtf47vf/S4Avu9z99138/jjj5NKpbjmmms477zz+Otf/7rPffbFSmRht1+kA/1tYFB/LzAGwgp4RSw/D14By8uDX8DyCuDlsfxC3/t+Aat7HXY0uF/WxnYhltkVPMUymFga3MwbhvvFMXZ8x1C/RN9wQCcOlg22DZbT9xx7wPeMveN9ywGsvuXO97B3hF37+Q9+n//572+f/Z35/la+mXoc8IDHsaG7RWwUz2AVikNW3pAxx/ttcHV+x7LIzWD1HoXtZkgd3/+G+h1z+IVOBqvneG83Q2gfP6MbVz1Ee/PvAQs3lsWN5XYt3TSWHcOyXCzbwbIc7B3PjTFgIowJ++5cZkIwEVHkEYUeYVglCqtEUd8yDMt9AdMbeiINxHaSxGJZUulRxBMNxBL1xBMNfHVjDd0BUIJM1SLba5N2LFJOO2mng5QDrrXzXA0WBowhMgYvMlQjgxfRvyyGUIgsytH+P/9ZeNhWibRdImWXSFMkSYEYBSzLJwosKiGsshzKA02bYcOwMGKcHzIyP5bRnediMPiWR4cd8WTMYMcMjg1WycbO29iBQzKwSZs9wz9jhdQUW6grbqem1E4srGIAP+bgx9y+petirL5PtZYxfXM8RRHxIMT1Atwg3GPY2+5816GnJk0xlaCQTZLPpiimEhiNdxORo8ghC5qWLl3KvHnzADj99NNZvnx5/7p169YxYcIEamv7xmDPnTuXJUuWsGzZsn3usy9WogYTH6p5lnKQbjyIYKraF0B5vX2hlJfv6x21M5QKKlhBGXY8rKAClS4IKthH4QdCeXsoAokjXQmRY4zajcjBK6F2MxRGOBC5FoFl8KNefK+XCgxdb3ljcADH9P17uQZipm/pGohhiEcQN5AwfUuHEn3/wm17FPUJZyIv2+NptWtpi3IUgiTtxAkHfYe4XdNex0xA1lRopEKNqZDb8ag1JYaZAsNMgeGmSLsT8qdkmm7bpcd2KVs2ZcumCLhY2MSxDGAsYoFDOnSxgwQECcIgjVcdTjUYSdFNYRIhQTwkkQvI+hYxP0Y8tCGEvou+45JZBuP2XZAg5mOSEVEqIkqGmJih6sUoduXId1ukCmVSxSquHxDzA1JlD3uAUNEAYcwhcF3KyThBzKWajFNNxvCScaqpOOVMkiAe22tftTMROdocsqCpUCiQzWb7XzuOQxAEuK5LoVAgl8v1r8tkMhQKhf3usy/OiBMZPuLQnIOIiIiIyJGUA/Y/kcTR4907HofTDOCcw3xMERHZv0M20DubzVIs7uouHUVRf2D0xnXFYpFcLrfffURERERERERE5Oh2yIKmOXPm8OyzzwKwbNkypk+f3r9u6tSpbNq0ie7ubjzPY8mSJcyePXu/+4iIiIiIiIiIyNHNMubQzI658w5yq1evxhjDV7/6VVasWEGpVOLqq6/uv+ucMYYrr7ySj3zkIwPuM3Xq1ENRPRERERERERERGWKHLGgSEREREREREZG3l0M2dE5ERERERERERN5eFDSJiIiIiIiIiMiQUNAkIiIiIiIiIiJDwj3SFXgzdk4a/vrrrxOPx1m4cCETJ0480tUSOer4vs8dd9zB1q1b8TyPG264gWnTprFgwQIsy+KEE07gC1/4AratzFlkdx0dHVxxxRV873vfw3VdtRmRA3jggQdYvHgxvu9zzTXXcOaZZ6rdiOyH7/ssWLCArVu3Yts2X/nKV/T/jcg+vPzyy9x77708/PDDbNq0acB28thjj/Hoo4/iui433HAD55133pGu9tvaMfmb68knn8TzPBYtWsStt97K1772tSNdJZGj0i9+8Qvq6up45JFHePDBB/nKV77C3XffzS233MIjjzyCMYannnrqSFdT5Kji+z6f//znSSaTAGozIgfw4osv8te//pWf/OQnPPzww7S0tKjdiBzAM888QxAEPProo9x4441861vfUrsRGcCDDz7I5z73OarVKjDw57Lt27fz8MMP8+ijj/If//Ef/Mu//Aue5x3hmr+9HZNB09KlS5k3bx4Ap59+OsuXLz/CNRI5Ol100UXcznuq9AAAC+9JREFUfPPN/a8dx+G1117jzDPPBOBd73oXzz///JGqnshR6etf/zof/vCHGTFiBIDajMgB/OEPf2D69OnceOON/MM//APvfve71W5EDmDy5MmEYUgURRQKBVzXVbsRGcCECRP4zne+0/96oHbyyiuvMHv2bOLxOLlcjgkTJrBq1aojVWXhGA2aCoUC2Wy2/7XjOARBcARrJHJ0ymQyZLNZCoUCN910E7fccgvGGCzL6l+fz+ePcC1Fjh5PPPEEDQ0N/X/MANRmRA6gq6uL5cuX86//+q986Utf4tOf/rTajcgBpNNptm7dyt/8zd9w5513Mn/+fLUbkQFceOGFuO6uGX8GaieFQoFcLte/TSaToVAoHPa6yi7H5BxN2WyWYrHY/zqKoj1++ERkl+bmZm688UauvfZaLrnkEr7xjW/0rysWi9TU1BzB2okcXX72s59hWRYvvPACK1eu5DOf+QydnZ3969VmRPZWV1fHlClTiMfjTJkyhUQiQUtLS/96tRuRvX3/+9/n3HPP5dZbb6W5uZnrrrsO3/f716vdiAxs93nLdraTN+YDxWJxj+BJDr9jskfTnDlzePbZZwFYtmwZ06dPP8I1Ejk6tbe3c/3113PbbbfxwQ9+EICTTz6ZF198EYBnn32WM84440hWUeSo8uMf/5gf/ehHPPzww5x00kl8/etf513vepfajMh+zJ07l+eeew5jDK2trZTLZc466yy1G5H9qKmp6f8iXFtbSxAE+owmMggDtZOZM2eydOlSqtUq+XyedevWKSM4wixjjDnSlThYO+86t3r1aowxfPWrX2Xq1KlHuloiR52FCxfym9/8hilTpvS/99nPfpaFCxfi+z5Tpkxh4cKFOI5zBGspcnSaP38+X/ziF7FtmzvvvFNtRmQ/7rnnHl588UWMMfzTP/0T48aNU7sR2Y9iscgdd9zB9u3b8X2fv/u7v+PUU09VuxEZQFNTE//8z//MY489xoYNGwZsJ4899hiLFi3CGMMnP/lJLrzwwiNd7be1YzJoEhERERERERGRo88xOXRORERERERERESOPgqaRERERERERERkSChoEhERERERERGRIaGgSUREREREREREhoSCJhERERERERERGRLuka6AiIiI7N+MGTOYPn06tr3r70Onnnoqd911F5deeikPP/wwNTU1gyorn89z44038sMf/nDA9a2trXzzm9/ktddew7IsEokEn/zkJzn//PP3W+4TTzzB//zP//DAAw8wf/58PvKRj3DRRRftsc13vvMdfvzjHzNy5EgAjDEUCgUuuOACFixYgGVZgzqHwVqwYAEnnHACH/vYx/a73fXXX8+9995LQ0MDH//4x/nMZz7DtGnThqQOhUKBm2++mfvuu49f//rX3HXXXYwbN26PbW666Sbe+973Uq1W+e53v8vvf/97jDFEUcQll1zCxz/+cSzLGtT1+/SnP80NN9zA1KlTh6T+IiIiIgdLQZOIiMgx4Ac/+AENDQ17vf/zn//8oMrp6enh1VdfHXBdZ2cnH/7wh7n55pu5++67sSyLVatW8dGPfpRUKsU555zzpuq+u4svvpjPf/7ze9TnAx/4AOeeey7z5s17y+W/GX/84x/7nz/44INDWva9997LVVddRTKZBOCMM87ggQce2Gs7Ywyf+tSnmDx5MosWLSKRSNDV1cUnP/lJSqUSt9xyC3Dg63fzzTdz6623smjRoiEP7kREREQGQ0PnREREjmEzZsygs7OTJ554gmuvvZbLL7+c+fPns337dq6//nouv/xyLr/8cr71rW8BcPvtt1OpVLj00ksJw3CPsh555BHmzJnDZZdd1h9SnHjiiXz7299m+PDhADz++ONcddVVXHbZZZx33nk88sgjb6n+7e3tVCoVamtrAVi3bh3XX389V1xxBZdeeimPP/44AMVikZtuuolLL72Uyy+/nM997nNEUQTAokWLeP/7388HPvABrr/+ejZs2LDP6/TG17fffjsA1113Hc3NzbznPe/pD+L2Ve6CBQtYuHAh8+fP54ILLuAf//EfKRaLex2zubmZp59++oC9wQD+/Oc/s379em6//XYSiQQA9fX13HPPPbzjHe8Y9PUbP348uVyOp5566oDHFBERETkU1KNJRETkGHDdddftMXTue9/7HsOGDdtjm7Vr17J48WKy2Sz33Xcf48aN43vf+x6lUonPfvaz5PN57r77bi655JIBe0ItX758wF5FO4OOYrHIT3/6U/793/+d+vp6li1bxkc/+lGuvfbaQZ/Hr3/9a5YuXUq5XKanp4eTTz6ZL33pS8ycOZMgCLjpppu45557OOWUU8jn81x99dVMmzaNjRs3UiwW+fnPf04YhnzhC19gy5YtbNu2jYceeohFixbR0NDAE088wY033sivfvWrQdXn7rvv5oknntirx9gLL7yw33KXL1/OD3/4QyzL4kMf+hC//e1vufLKK/co+6mnnuKss87CdXd93FqyZAmXXnpp/+tZs2bx5S9/meXLlzNz5kwcx9mjjEmTJjFp0qRBXb+dzj33XH73u98NKuASERERGWoKmkRERI4B+xo6t7sZM2aQzWYBmDdvHp/4xCdobm7m7LPP5tZbbyWXy9HT07PP/S3Lwhizz/WZTIZ/+7d/45lnnmHjxo2sWrWKUql0UOexc+iX53l85StfYe3atbznPe8BYOPGjWzevJk77rijf/tKpcKKFSuYN28e3/zmN5k/fz5nn3021113HRMnTmTRokVcfPHF/dfmiiuu4K677qKpqemg6vVGzz333H7LnTdvHvF4HIDp06cPeF3Xr1/PhAkT9nhvX0PnbNve77XfaX/Xb6dx48bxm9/8ZnAnKiIiIjLENHRORETkOJFOp/ufz5w5k6eeeoqrr76arVu3ctVVV7F8+fL97n/66aezbNmyvd5/9NFH+c///E9aWlq47LLL2Lp1K3Pnzu2fN+jNiMfj3HnnnRQKBe655x4AwjAkl8vx85//vP/x2GOPceWVVzJ+/Hh+97vf8YlPfIJCocBHP/pRFi9e3D98bnfGGIIg2OexPc87YP0OVO7OOZdg3wGdZVkDljOQWbNm8eqrr+41nPGVV17htttu22v7ga7fTq7r7tH7TURERORw0qcQERGR49C9997L/fffz/nnn89nP/tZpk2bxpo1a3BdlzAMBwxGrr76al566SV+8Ytf9K9fvnw53/72t5k+fTrLly+noaGBT33qU5x77rk8/fTTAHuFI4MVj8f5whe+wCOPPMKKFSuYPHkyyWSyf1hfc3Mz73//+1m+fDmPPPIIt99+O+eeey633XYb5557bn9Pp1//+tf98y/97Gc/o66ujokTJ+5xrIaGhv65l/77v/97j3WO4+wVTA223P2ZPHkyW7ZsGdS2s2fPZsqUKdx9991Uq1Wgb/6lhQsX7nWXup3eeP12ampqYsqUKYOup4iIiMhQ0tA5ERGR49B1113HggULeP/73088HmfGjBm8733vw3EcZs6cyfve9z5+/OMfU19f379PXV0dDz/8MN/4xjd44IEHsG2bVCrFXXfdxTnnnEO5XObxxx/noosuwrIszjzzTBoaGti0adObrucZZ5zBJZdcwpe//GV+8pOfcP/993PXXXfx0EMPEQQBN998M3PnzuWkk07ipZde4uKLLyaVSjF69Gjmz59PbW0tf//3f891111HFEU0NDT01313n/vc5/jyl79MTU0NZ599No2Njf3rLrroIubPn893vvOd/vfOOeecQZW7P+effz4PPfQQYRjuNffSQL797W/zzW9+kyuuuALHcYiiiMsuu4yPfexjg75+lmXx3HPP8bd/+7eDrqeIiIjIULLMYCYEEBEREZGDduedd3LWWWdx8cUXH5bjbd68mU9/+tMsWrSo/86BIiIiIoeThs6JiIiIHCK33XYbP/3pT6lUKofleN/61rdYuHChQiYRERE5YtSjSUREREREREREhoR6NImIiIiIiIiIyJBQ0CQiIiIiIiIiIkNCQZOIiIiIiIiIiAwJBU0iIiIiIiIiIjIkFDSJiIiIiIiIiMiQ+P8B/rAZ2fvAQgQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1169x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the FCR for specific day in the probability density chart. \n",
    "facet = sns.FacetGrid(dataset, aspect = 3, height=5)\n",
    "facet.map(sns.kdeplot, 'Sunday', shade = True, color='#4E79A7')\n",
    "facet.map(sns.kdeplot, 'Monday', shade = True, color='#F28E2B')\n",
    "facet.map(sns.kdeplot, 'Tuesday', shade = True, color='#59A14F')\n",
    "facet.map(sns.kdeplot, 'Wednesday', shade = True, color='#E15759')\n",
    "facet.map(sns.kdeplot, 'Thursday', shade = True, color='#B6992D')\n",
    "facet.map(sns.kdeplot, 'Friday', shade = True, color='#499894')\n",
    "facet.map(sns.kdeplot, 'Saturday', shade = True, color='#B07AA1')\n",
    "facet.set(xlim = (0, dataset['Thursday'].max()))\n",
    "facet.add_legend()\n",
    "plt.title('First Call Resolution for Days.', fontsize=12)\n",
    "plt.ylabel('Probability Density', fontsize=12)\n",
    "plt.xlabel('First Call Resolution (FCR)', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the dispersion of FCR values in a given day and identify the outliers. \n",
    "\n",
    "# Create a color set that matches the probability density chart. \n",
    "my_pal = {'Sunday': '#4E79A7','Monday': '#F28E2B', 'Tuesday': '#59A14F', 'Wednesday': '#E15759', 'Thursday': '#B6992D', 'Friday': '#499894', 'Saturday': '#B07AA1'}\n",
    "\n",
    "# Generate a boxplot using Seaborn. \n",
    "dataset_boxplot = pd.DataFrame(data = dataset, columns = ['Sunday','Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'])\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.boxplot(x=\"value\", y=\"variable\", data=pd.melt(dataset_boxplot), color='#cccccc')\n",
    "sns.swarmplot(x=\"value\", y=\"variable\", data=pd.melt(dataset_boxplot), palette=my_pal, alpha=0.5)\n",
    "plt.title('First Call Resolution by Days.', fontsize=12)\n",
    "plt.ylabel('Days', fontsize=12)\n",
    "plt.xlabel('FCR', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yEKKOHudHbcA"
   },
   "source": [
    "## 4. Conduct Regression Model in Pycaret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 973,
     "referenced_widgets": [
      "d9753680a0024e15975657a7832deb8d",
      "c5ac7182273c4c669aed0bf2893fb513",
      "633351d460b64632ad0c9d9fb56774f5",
      "66d6c0694aee4bedbadb31ac64334953",
      "ed0bdd23d4c74c32a8c87519c4d367d5",
      "1b62fff75ad14cd7a3649af48698ee92"
     ]
    },
    "colab_type": "code",
    "id": "8oJ2FeoUHqVW",
    "outputId": "7873c522-250e-4531-b9df-a17ae5600e5c",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Setup Succesfully Completed.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
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       "            background-color:  yellow;\n",
       "        }</style><table id=\"T_7666c324_eece_11ea_a903_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row0_col1\" class=\"data row0 col1\" >786</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row1_col0\" class=\"data row1 col0\" >Transform Target </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row1_col1\" class=\"data row1 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row2_col0\" class=\"data row2 col0\" >Transform Target Method</td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row2_col1\" class=\"data row2 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row3_col1\" class=\"data row3 col1\" >(102, 19)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row4_col1\" class=\"data row4 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row5_col1\" class=\"data row5 col1\" >16</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row6_col1\" class=\"data row6 col1\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row9_col1\" class=\"data row9 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row10_col1\" class=\"data row10 col1\" >(96, 19)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row11_col1\" class=\"data row11 col1\" >(72, 31)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row12_col1\" class=\"data row12 col1\" >(24, 31)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row13_col1\" class=\"data row13 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row14_col1\" class=\"data row14 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row15_col1\" class=\"data row15 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row16_col1\" class=\"data row16 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row17_col1\" class=\"data row17 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row18_col1\" class=\"data row18 col1\" >yeo-johnson</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row19_col0\" class=\"data row19 col0\" >PCA </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row19_col1\" class=\"data row19 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row20_col1\" class=\"data row20 col1\" >linear</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row21_col1\" class=\"data row21 col1\" >0.990000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row22_col1\" class=\"data row22 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row23_col1\" class=\"data row23 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row24_col1\" class=\"data row24 col1\" >0.100000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row25_col1\" class=\"data row25 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row26_col1\" class=\"data row26 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row27_col1\" class=\"data row27 col1\" >0.050000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row28_col1\" class=\"data row28 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row29_col1\" class=\"data row29 col1\" >0.900000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row30_col1\" class=\"data row30 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row31_col1\" class=\"data row31 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row32_col1\" class=\"data row32 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row33_col1\" class=\"data row33 col1\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row34_col1\" class=\"data row34 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row35_col1\" class=\"data row35 col1\" >0.100000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row37_col1\" class=\"data row37 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row38_col1\" class=\"data row38 col1\" >0.400000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row39_col1\" class=\"data row39 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7666c324_eece_11ea_a903_acde48001122level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
       "                        <td id=\"T_7666c324_eece_11ea_a903_acde48001122row41_col1\" class=\"data row41 col1\" >0.010000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9afee5ddf0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Transform dataset (transform, bin and create dummy variables) and split the dataset. In addition, we are logging experiments and plots for those experiment to be viewed later with MLflow. \n",
    "reg_fcr = setup(data=dataset, target='Actual Value', session_id=786, transformation=True, normalize=False, train_size=0.75, numeric_features=('Sunday', 'Monday', 'Saturday', 'Thursday', 'Tuesday', 'Wednesday', 'Friday', 'Total number of calls', 'CSat', 'total coaching', 'total coaching improved', 'FCR Week before'), remove_outliers=True, outliers_threshold=0.05, remove_multicollinearity=True, multicollinearity_threshold=0.9, feature_selection=True, feature_interaction=True, silent=False, ignore_features=['Agent_ID'], combine_rare_levels=True, polynomial_features=True, trigonometry_features=True, feature_selection_threshold=0.4, feature_selection_method='classic', folds_shuffle=True, pca=True, log_experiment=True, experiment_name='reg_fcr_experiments', log_plots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Turbo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lr</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>sklearn.linear_model.LinearRegression</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lasso</th>\n",
       "      <td>Lasso Regression</td>\n",
       "      <td>sklearn.linear_model.Lasso</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ridge</th>\n",
       "      <td>Ridge Regression</td>\n",
       "      <td>sklearn.linear_model.Ridge</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>en</th>\n",
       "      <td>Elastic Net</td>\n",
       "      <td>sklearn.linear_model.ElasticNet</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lar</th>\n",
       "      <td>Least Angle Regression</td>\n",
       "      <td>sklearn.linear_model.Lars</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>llar</th>\n",
       "      <td>Lasso Least Angle Regression</td>\n",
       "      <td>sklearn.linear_model.LassoLars</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>omp</th>\n",
       "      <td>Orthogonal Matching Pursuit</td>\n",
       "      <td>sklearn.linear_model.OMP</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>br</th>\n",
       "      <td>Bayesian Ridge</td>\n",
       "      <td>sklearn.linear_model.BayesianRidge</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ard</th>\n",
       "      <td>Automatic Relevance Determination</td>\n",
       "      <td>sklearn.linear_model.ARDRegression</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>par</th>\n",
       "      <td>Passive Aggressive Regressor</td>\n",
       "      <td>sklearn.linear_model.PAR</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ransac</th>\n",
       "      <td>Random Sample Consensus</td>\n",
       "      <td>sklearn.linear_model.RANSACRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tr</th>\n",
       "      <td>TheilSen Regressor</td>\n",
       "      <td>sklearn.linear_model.TheilSenRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>huber</th>\n",
       "      <td>Huber Regressor</td>\n",
       "      <td>sklearn.linear_model.HuberRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kr</th>\n",
       "      <td>Kernel Ridge</td>\n",
       "      <td>sklearn.kernel_ridge.KernelRidge</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>svm</th>\n",
       "      <td>Support Vector Machine</td>\n",
       "      <td>sklearn.svm.SVR</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knn</th>\n",
       "      <td>K Neighbors Regressor</td>\n",
       "      <td>sklearn.neighbors.KNeighborsRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>sklearn.tree.DecisionTreeRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>sklearn.ensemble.RandomForestRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>et</th>\n",
       "      <td>Extra Trees Regressor</td>\n",
       "      <td>sklearn.ensemble.ExtraTreesRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ada</th>\n",
       "      <td>AdaBoost Regressor</td>\n",
       "      <td>sklearn.ensemble.AdaBoostRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gbr</th>\n",
       "      <td>Gradient Boosting Regressor</td>\n",
       "      <td>sklearn.ensemble.GradientBoostingRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp</th>\n",
       "      <td>Multi Level Perceptron</td>\n",
       "      <td>sklearn.neural_network.MLPRegressor</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xgboost</th>\n",
       "      <td>Extreme Gradient Boosting</td>\n",
       "      <td>xgboost.readthedocs.io</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lightgbm</th>\n",
       "      <td>Light Gradient Boosting Machine</td>\n",
       "      <td>github.com/microsoft/LightGBM</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catboost</th>\n",
       "      <td>CatBoost Regressor</td>\n",
       "      <td>https://catboost.ai</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Name  \\\n",
       "ID                                            \n",
       "lr                        Linear Regression   \n",
       "lasso                      Lasso Regression   \n",
       "ridge                      Ridge Regression   \n",
       "en                              Elastic Net   \n",
       "lar                  Least Angle Regression   \n",
       "llar           Lasso Least Angle Regression   \n",
       "omp             Orthogonal Matching Pursuit   \n",
       "br                           Bayesian Ridge   \n",
       "ard       Automatic Relevance Determination   \n",
       "par            Passive Aggressive Regressor   \n",
       "ransac              Random Sample Consensus   \n",
       "tr                       TheilSen Regressor   \n",
       "huber                       Huber Regressor   \n",
       "kr                             Kernel Ridge   \n",
       "svm                  Support Vector Machine   \n",
       "knn                   K Neighbors Regressor   \n",
       "dt                            Decision Tree   \n",
       "rf                            Random Forest   \n",
       "et                    Extra Trees Regressor   \n",
       "ada                      AdaBoost Regressor   \n",
       "gbr             Gradient Boosting Regressor   \n",
       "mlp                  Multi Level Perceptron   \n",
       "xgboost           Extreme Gradient Boosting   \n",
       "lightgbm    Light Gradient Boosting Machine   \n",
       "catboost                 CatBoost Regressor   \n",
       "\n",
       "                                           Reference  Turbo  \n",
       "ID                                                           \n",
       "lr             sklearn.linear_model.LinearRegression   True  \n",
       "lasso                     sklearn.linear_model.Lasso   True  \n",
       "ridge                     sklearn.linear_model.Ridge   True  \n",
       "en                   sklearn.linear_model.ElasticNet   True  \n",
       "lar                        sklearn.linear_model.Lars   True  \n",
       "llar                  sklearn.linear_model.LassoLars   True  \n",
       "omp                         sklearn.linear_model.OMP   True  \n",
       "br                sklearn.linear_model.BayesianRidge   True  \n",
       "ard               sklearn.linear_model.ARDRegression  False  \n",
       "par                         sklearn.linear_model.PAR   True  \n",
       "ransac          sklearn.linear_model.RANSACRegressor   True  \n",
       "tr            sklearn.linear_model.TheilSenRegressor   True  \n",
       "huber            sklearn.linear_model.HuberRegressor   True  \n",
       "kr                  sklearn.kernel_ridge.KernelRidge  False  \n",
       "svm                                  sklearn.svm.SVR   True  \n",
       "knn            sklearn.neighbors.KNeighborsRegressor   True  \n",
       "dt                sklearn.tree.DecisionTreeRegressor   True  \n",
       "rf            sklearn.ensemble.RandomForestRegressor   True  \n",
       "et              sklearn.ensemble.ExtraTreesRegressor   True  \n",
       "ada               sklearn.ensemble.AdaBoostRegressor   True  \n",
       "gbr       sklearn.ensemble.GradientBoostingRegressor   True  \n",
       "mlp              sklearn.neural_network.MLPRegressor  False  \n",
       "xgboost                       xgboost.readthedocs.io   True  \n",
       "lightgbm               github.com/microsoft/LightGBM   True  \n",
       "catboost                         https://catboost.ai   True  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Below is a list of models that Pycaret can use for regression. The ID for each regression can be used to include or exclude models for various functions.\n",
    "models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "        }</style><table id=\"T_87bc7a60_eece_11ea_a903_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>        <th class=\"col_heading level0 col7\" >TT (Sec)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row0_col0\" class=\"data row0 col0\" >Lasso Regression</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row0_col1\" class=\"data row0 col1\" >3.7130</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row0_col2\" class=\"data row0 col2\" >25.3413</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row0_col3\" class=\"data row0 col3\" >4.9250</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row0_col4\" class=\"data row0 col4\" >0.2483</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row0_col5\" class=\"data row0 col5\" >0.0538</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row0_col6\" class=\"data row0 col6\" >0.0407</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row0_col7\" class=\"data row0 col7\" >0.0035</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row1_col0\" class=\"data row1 col0\" >Elastic Net</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row1_col1\" class=\"data row1 col1\" >3.7677</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row1_col2\" class=\"data row1 col2\" >25.4430</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row1_col3\" class=\"data row1 col3\" >4.9550</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row1_col4\" class=\"data row1 col4\" >0.2274</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row1_col5\" class=\"data row1 col5\" >0.0541</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row1_col6\" class=\"data row1 col6\" >0.0413</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row1_col7\" class=\"data row1 col7\" >0.0037</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row2_col0\" class=\"data row2 col0\" >Orthogonal Matching Pursuit</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row2_col1\" class=\"data row2 col1\" >4.0510</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row2_col2\" class=\"data row2 col2\" >27.4890</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row2_col3\" class=\"data row2 col3\" >5.1716</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row2_col4\" class=\"data row2 col4\" >0.1303</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row2_col5\" class=\"data row2 col5\" >0.0556</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row2_col6\" class=\"data row2 col6\" >0.0438</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row2_col7\" class=\"data row2 col7\" >0.0094</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row3_col0\" class=\"data row3 col0\" >CatBoost Regressor</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row3_col1\" class=\"data row3 col1\" >4.0196</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row3_col2\" class=\"data row3 col2\" >28.5505</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row3_col3\" class=\"data row3 col3\" >5.2430</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row3_col4\" class=\"data row3 col4\" >0.1355</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row3_col5\" class=\"data row3 col5\" >0.0574</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row3_col6\" class=\"data row3 col6\" >0.0443</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row3_col7\" class=\"data row3 col7\" >1.3449</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row4_col0\" class=\"data row4 col0\" >Bayesian Ridge</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row4_col1\" class=\"data row4 col1\" >4.0471</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row4_col2\" class=\"data row4 col2\" >28.6559</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row4_col3\" class=\"data row4 col3\" >5.2558</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row4_col4\" class=\"data row4 col4\" >0.1271</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row4_col5\" class=\"data row4 col5\" >0.0571</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row4_col6\" class=\"data row4 col6\" >0.0442</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row4_col7\" class=\"data row4 col7\" >0.0048</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row5_col0\" class=\"data row5 col0\" >Random Forest</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row5_col1\" class=\"data row5 col1\" >4.1942</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row5_col2\" class=\"data row5 col2\" >29.8542</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row5_col3\" class=\"data row5 col3\" >5.4258</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row5_col4\" class=\"data row5 col4\" >-0.1348</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row5_col5\" class=\"data row5 col5\" >0.0589</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row5_col6\" class=\"data row5 col6\" >0.0455</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row5_col7\" class=\"data row5 col7\" >0.1358</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row6_col0\" class=\"data row6 col0\" >AdaBoost Regressor</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row6_col1\" class=\"data row6 col1\" >3.9844</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row6_col2\" class=\"data row6 col2\" >31.9570</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row6_col3\" class=\"data row6 col3\" >5.6004</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row6_col4\" class=\"data row6 col4\" >-0.0837</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row6_col5\" class=\"data row6 col5\" >0.0613</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row6_col6\" class=\"data row6 col6\" >0.0441</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row6_col7\" class=\"data row6 col7\" >0.0612</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row7_col0\" class=\"data row7 col0\" >Extra Trees Regressor</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row7_col1\" class=\"data row7 col1\" >4.4468</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row7_col2\" class=\"data row7 col2\" >31.7656</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row7_col3\" class=\"data row7 col3\" >5.6088</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row7_col4\" class=\"data row7 col4\" >-0.1164</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row7_col5\" class=\"data row7 col5\" >0.0609</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row7_col6\" class=\"data row7 col6\" >0.0483</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row7_col7\" class=\"data row7 col7\" >0.0992</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row8_col0\" class=\"data row8 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row8_col1\" class=\"data row8 col1\" >4.3154</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row8_col2\" class=\"data row8 col2\" >34.5705</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row8_col3\" class=\"data row8 col3\" >5.8095</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row8_col4\" class=\"data row8 col4\" >-0.1048</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row8_col5\" class=\"data row8 col5\" >0.0630</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row8_col6\" class=\"data row8 col6\" >0.0472</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row8_col7\" class=\"data row8 col7\" >0.0104</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row9_col0\" class=\"data row9 col0\" >Gradient Boosting Regressor</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row9_col1\" class=\"data row9 col1\" >4.3556</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row9_col2\" class=\"data row9 col2\" >35.3551</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row9_col3\" class=\"data row9 col3\" >5.8438</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row9_col4\" class=\"data row9 col4\" >-0.4162</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row9_col5\" class=\"data row9 col5\" >0.0636</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row9_col6\" class=\"data row9 col6\" >0.0474</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row9_col7\" class=\"data row9 col7\" >0.0508</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row10_col0\" class=\"data row10 col0\" >Lasso Least Angle Regression</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row10_col1\" class=\"data row10 col1\" >4.5154</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row10_col2\" class=\"data row10 col2\" >36.6081</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row10_col3\" class=\"data row10 col3\" >5.9376</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row10_col4\" class=\"data row10 col4\" >-0.1017</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row10_col5\" class=\"data row10 col5\" >0.0647</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row10_col6\" class=\"data row10 col6\" >0.0497</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row10_col7\" class=\"data row10 col7\" >0.0039</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row11_col0\" class=\"data row11 col0\" >Support Vector Machine</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row11_col1\" class=\"data row11 col1\" >3.9780</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row11_col2\" class=\"data row11 col2\" >40.7379</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row11_col3\" class=\"data row11 col3\" >6.1438</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row11_col4\" class=\"data row11 col4\" >-0.1249</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row11_col5\" class=\"data row11 col5\" >0.0671</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row11_col6\" class=\"data row11 col6\" >0.0453</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row11_col7\" class=\"data row11 col7\" >0.0032</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row12_col0\" class=\"data row12 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row12_col1\" class=\"data row12 col1\" >4.8463</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row12_col2\" class=\"data row12 col2\" >48.0118</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row12_col3\" class=\"data row12 col3\" >6.5678</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row12_col4\" class=\"data row12 col4\" >-1.5531</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row12_col5\" class=\"data row12 col5\" >0.0720</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row12_col6\" class=\"data row12 col6\" >0.0522</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row12_col7\" class=\"data row12 col7\" >0.0316</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row13_col0\" class=\"data row13 col0\" >Decision Tree</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row13_col1\" class=\"data row13 col1\" >4.6632</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row13_col2\" class=\"data row13 col2\" >56.7530</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row13_col3\" class=\"data row13 col3\" >6.9887</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row13_col4\" class=\"data row13 col4\" >-2.0352</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row13_col5\" class=\"data row13 col5\" >0.0770</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row13_col6\" class=\"data row13 col6\" >0.0507</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row13_col7\" class=\"data row13 col7\" >0.0042</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row14_col0\" class=\"data row14 col0\" >Ridge Regression</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row14_col1\" class=\"data row14 col1\" >5.3578</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row14_col2\" class=\"data row14 col2\" >51.6905</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row14_col3\" class=\"data row14 col3\" >7.0107</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row14_col4\" class=\"data row14 col4\" >-0.8219</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row14_col5\" class=\"data row14 col5\" >0.0750</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row14_col6\" class=\"data row14 col6\" >0.0572</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row14_col7\" class=\"data row14 col7\" >0.0049</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row15_col0\" class=\"data row15 col0\" >TheilSen Regressor</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row15_col1\" class=\"data row15 col1\" >5.8157</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row15_col2\" class=\"data row15 col2\" >55.3237</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row15_col3\" class=\"data row15 col3\" >7.2968</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row15_col4\" class=\"data row15 col4\" >-0.8380</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row15_col5\" class=\"data row15 col5\" >0.0782</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row15_col6\" class=\"data row15 col6\" >0.0621</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row15_col7\" class=\"data row15 col7\" >0.7838</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row16_col0\" class=\"data row16 col0\" >Linear Regression</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row16_col1\" class=\"data row16 col1\" >5.5428</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row16_col2\" class=\"data row16 col2\" >57.1311</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row16_col3\" class=\"data row16 col3\" >7.3383</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row16_col4\" class=\"data row16 col4\" >-1.0322</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row16_col5\" class=\"data row16 col5\" >0.0787</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row16_col6\" class=\"data row16 col6\" >0.0591</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row16_col7\" class=\"data row16 col7\" >0.0040</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row17_col0\" class=\"data row17 col0\" >Passive Aggressive Regressor</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row17_col1\" class=\"data row17 col1\" >5.6631</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row17_col2\" class=\"data row17 col2\" >56.8023</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row17_col3\" class=\"data row17 col3\" >7.4761</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row17_col4\" class=\"data row17 col4\" >-1.3372</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row17_col5\" class=\"data row17 col5\" >0.0786</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row17_col6\" class=\"data row17 col6\" >0.0602</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row17_col7\" class=\"data row17 col7\" >0.0118</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row18_col0\" class=\"data row18 col0\" >Least Angle Regression</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row18_col1\" class=\"data row18 col1\" >5.8003</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row18_col2\" class=\"data row18 col2\" >63.1026</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row18_col3\" class=\"data row18 col3\" >7.7016</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row18_col4\" class=\"data row18 col4\" >-1.2282</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row18_col5\" class=\"data row18 col5\" >0.0830</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row18_col6\" class=\"data row18 col6\" >0.0617</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row18_col7\" class=\"data row18 col7\" >0.0127</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_87bc7a60_eece_11ea_a903_acde48001122level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row19_col0\" class=\"data row19 col0\" >Huber Regressor</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row19_col1\" class=\"data row19 col1\" >6.1915</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row19_col2\" class=\"data row19 col2\" >61.8013</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row19_col3\" class=\"data row19 col3\" >7.7522</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row19_col4\" class=\"data row19 col4\" >-1.3569</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row19_col5\" class=\"data row19 col5\" >0.0824</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row19_col6\" class=\"data row19 col6\" >0.0656</td>\n",
       "                        <td id=\"T_87bc7a60_eece_11ea_a903_acde48001122row19_col7\" class=\"data row19 col7\" >0.0258</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9aff52f460>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can do a compare_models() function without assigning it to a variable. However, we have top 5 models selected using n_select and assigning it to top5 variable. We plan to use this for Stacking and Blending purposes. We have excluded 'RANSAC' (Random Sample Consensus) and 'KNN' (K-Nearest Neighbor) models and have adjusted the default fold value from 10 to 5. \n",
    "top5 = compare_models(n_select=5, exclude=(['ransac', 'knn']), sort='RMSE', fold=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "      <th>TT (Sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lasso Regression</td>\n",
       "      <td>3.7130</td>\n",
       "      <td>25.3413</td>\n",
       "      <td>4.9250</td>\n",
       "      <td>0.2483</td>\n",
       "      <td>0.0538</td>\n",
       "      <td>0.0407</td>\n",
       "      <td>0.0035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Elastic Net</td>\n",
       "      <td>3.7677</td>\n",
       "      <td>25.4430</td>\n",
       "      <td>4.9550</td>\n",
       "      <td>0.2274</td>\n",
       "      <td>0.0541</td>\n",
       "      <td>0.0413</td>\n",
       "      <td>0.0037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Orthogonal Matching Pursuit</td>\n",
       "      <td>4.0510</td>\n",
       "      <td>27.4890</td>\n",
       "      <td>5.1716</td>\n",
       "      <td>0.1303</td>\n",
       "      <td>0.0556</td>\n",
       "      <td>0.0438</td>\n",
       "      <td>0.0094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CatBoost Regressor</td>\n",
       "      <td>4.0196</td>\n",
       "      <td>28.5505</td>\n",
       "      <td>5.2430</td>\n",
       "      <td>0.1355</td>\n",
       "      <td>0.0574</td>\n",
       "      <td>0.0443</td>\n",
       "      <td>1.3449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bayesian Ridge</td>\n",
       "      <td>4.0471</td>\n",
       "      <td>28.6559</td>\n",
       "      <td>5.2558</td>\n",
       "      <td>0.1271</td>\n",
       "      <td>0.0571</td>\n",
       "      <td>0.0442</td>\n",
       "      <td>0.0048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>4.1942</td>\n",
       "      <td>29.8542</td>\n",
       "      <td>5.4258</td>\n",
       "      <td>-0.1348</td>\n",
       "      <td>0.0589</td>\n",
       "      <td>0.0455</td>\n",
       "      <td>0.1358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AdaBoost Regressor</td>\n",
       "      <td>3.9844</td>\n",
       "      <td>31.9570</td>\n",
       "      <td>5.6004</td>\n",
       "      <td>-0.0837</td>\n",
       "      <td>0.0613</td>\n",
       "      <td>0.0441</td>\n",
       "      <td>0.0612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Extra Trees Regressor</td>\n",
       "      <td>4.4468</td>\n",
       "      <td>31.7656</td>\n",
       "      <td>5.6088</td>\n",
       "      <td>-0.1164</td>\n",
       "      <td>0.0609</td>\n",
       "      <td>0.0483</td>\n",
       "      <td>0.0992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Light Gradient Boosting Machine</td>\n",
       "      <td>4.3154</td>\n",
       "      <td>34.5705</td>\n",
       "      <td>5.8095</td>\n",
       "      <td>-0.1048</td>\n",
       "      <td>0.0630</td>\n",
       "      <td>0.0472</td>\n",
       "      <td>0.0104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gradient Boosting Regressor</td>\n",
       "      <td>4.3556</td>\n",
       "      <td>35.3551</td>\n",
       "      <td>5.8438</td>\n",
       "      <td>-0.4162</td>\n",
       "      <td>0.0636</td>\n",
       "      <td>0.0474</td>\n",
       "      <td>0.0508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Lasso Least Angle Regression</td>\n",
       "      <td>4.5154</td>\n",
       "      <td>36.6081</td>\n",
       "      <td>5.9376</td>\n",
       "      <td>-0.1017</td>\n",
       "      <td>0.0647</td>\n",
       "      <td>0.0497</td>\n",
       "      <td>0.0039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Support Vector Machine</td>\n",
       "      <td>3.9780</td>\n",
       "      <td>40.7379</td>\n",
       "      <td>6.1438</td>\n",
       "      <td>-0.1249</td>\n",
       "      <td>0.0671</td>\n",
       "      <td>0.0453</td>\n",
       "      <td>0.0032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Extreme Gradient Boosting</td>\n",
       "      <td>4.8463</td>\n",
       "      <td>48.0118</td>\n",
       "      <td>6.5678</td>\n",
       "      <td>-1.5531</td>\n",
       "      <td>0.0720</td>\n",
       "      <td>0.0522</td>\n",
       "      <td>0.0316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>4.6632</td>\n",
       "      <td>56.7530</td>\n",
       "      <td>6.9887</td>\n",
       "      <td>-2.0352</td>\n",
       "      <td>0.0770</td>\n",
       "      <td>0.0507</td>\n",
       "      <td>0.0042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Ridge Regression</td>\n",
       "      <td>5.3578</td>\n",
       "      <td>51.6905</td>\n",
       "      <td>7.0107</td>\n",
       "      <td>-0.8219</td>\n",
       "      <td>0.0750</td>\n",
       "      <td>0.0572</td>\n",
       "      <td>0.0049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>TheilSen Regressor</td>\n",
       "      <td>5.8157</td>\n",
       "      <td>55.3237</td>\n",
       "      <td>7.2968</td>\n",
       "      <td>-0.8380</td>\n",
       "      <td>0.0782</td>\n",
       "      <td>0.0621</td>\n",
       "      <td>0.7838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>5.5428</td>\n",
       "      <td>57.1311</td>\n",
       "      <td>7.3383</td>\n",
       "      <td>-1.0322</td>\n",
       "      <td>0.0787</td>\n",
       "      <td>0.0591</td>\n",
       "      <td>0.0040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Passive Aggressive Regressor</td>\n",
       "      <td>5.6631</td>\n",
       "      <td>56.8023</td>\n",
       "      <td>7.4761</td>\n",
       "      <td>-1.3372</td>\n",
       "      <td>0.0786</td>\n",
       "      <td>0.0602</td>\n",
       "      <td>0.0118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Least Angle Regression</td>\n",
       "      <td>5.8003</td>\n",
       "      <td>63.1026</td>\n",
       "      <td>7.7016</td>\n",
       "      <td>-1.2282</td>\n",
       "      <td>0.0830</td>\n",
       "      <td>0.0617</td>\n",
       "      <td>0.0127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Huber Regressor</td>\n",
       "      <td>6.1915</td>\n",
       "      <td>61.8013</td>\n",
       "      <td>7.7522</td>\n",
       "      <td>-1.3569</td>\n",
       "      <td>0.0824</td>\n",
       "      <td>0.0656</td>\n",
       "      <td>0.0258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Model     MAE      MSE    RMSE      R2   RMSLE  \\\n",
       "0                  Lasso Regression  3.7130  25.3413  4.9250  0.2483  0.0538   \n",
       "1                       Elastic Net  3.7677  25.4430  4.9550  0.2274  0.0541   \n",
       "2       Orthogonal Matching Pursuit  4.0510  27.4890  5.1716  0.1303  0.0556   \n",
       "3                CatBoost Regressor  4.0196  28.5505  5.2430  0.1355  0.0574   \n",
       "4                    Bayesian Ridge  4.0471  28.6559  5.2558  0.1271  0.0571   \n",
       "5                     Random Forest  4.1942  29.8542  5.4258 -0.1348  0.0589   \n",
       "6                AdaBoost Regressor  3.9844  31.9570  5.6004 -0.0837  0.0613   \n",
       "7             Extra Trees Regressor  4.4468  31.7656  5.6088 -0.1164  0.0609   \n",
       "8   Light Gradient Boosting Machine  4.3154  34.5705  5.8095 -0.1048  0.0630   \n",
       "9       Gradient Boosting Regressor  4.3556  35.3551  5.8438 -0.4162  0.0636   \n",
       "10     Lasso Least Angle Regression  4.5154  36.6081  5.9376 -0.1017  0.0647   \n",
       "11           Support Vector Machine  3.9780  40.7379  6.1438 -0.1249  0.0671   \n",
       "12        Extreme Gradient Boosting  4.8463  48.0118  6.5678 -1.5531  0.0720   \n",
       "13                    Decision Tree  4.6632  56.7530  6.9887 -2.0352  0.0770   \n",
       "14                 Ridge Regression  5.3578  51.6905  7.0107 -0.8219  0.0750   \n",
       "15               TheilSen Regressor  5.8157  55.3237  7.2968 -0.8380  0.0782   \n",
       "16                Linear Regression  5.5428  57.1311  7.3383 -1.0322  0.0787   \n",
       "17     Passive Aggressive Regressor  5.6631  56.8023  7.4761 -1.3372  0.0786   \n",
       "18           Least Angle Regression  5.8003  63.1026  7.7016 -1.2282  0.0830   \n",
       "19                  Huber Regressor  6.1915  61.8013  7.7522 -1.3569  0.0824   \n",
       "\n",
       "      MAPE  TT (Sec)  \n",
       "0   0.0407    0.0035  \n",
       "1   0.0413    0.0037  \n",
       "2   0.0438    0.0094  \n",
       "3   0.0443    1.3449  \n",
       "4   0.0442    0.0048  \n",
       "5   0.0455    0.1358  \n",
       "6   0.0441    0.0612  \n",
       "7   0.0483    0.0992  \n",
       "8   0.0472    0.0104  \n",
       "9   0.0474    0.0508  \n",
       "10  0.0497    0.0039  \n",
       "11  0.0453    0.0032  \n",
       "12  0.0522    0.0316  \n",
       "13  0.0507    0.0042  \n",
       "14  0.0572    0.0049  \n",
       "15  0.0621    0.7838  \n",
       "16  0.0591    0.0040  \n",
       "17  0.0602    0.0118  \n",
       "18  0.0617    0.0127  \n",
       "19  0.0656    0.0258  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sometimes you want to include the output of the compare_models() as a screenshot into a report. However, with the yellow highlights it gets difficult to read. Pycaret has thought of that and you can use the pull() function to show the model results in the sort by or ascending order.\n",
    "pull().sort_values(by='RMSE', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        }</style><table id=\"T_f62bed50_eece_11ea_a903_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_f62bed50_eece_11ea_a903_acde48001122level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row0_col0\" class=\"data row0 col0\" >3.6922</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row0_col1\" class=\"data row0 col1\" >28.7731</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row0_col2\" class=\"data row0 col2\" >5.3641</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row0_col3\" class=\"data row0 col3\" >0.2916</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row0_col4\" class=\"data row0 col4\" >0.0599</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row0_col5\" class=\"data row0 col5\" >0.0419</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f62bed50_eece_11ea_a903_acde48001122level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row1_col0\" class=\"data row1 col0\" >2.9477</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row1_col1\" class=\"data row1 col1\" >11.8495</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row1_col2\" class=\"data row1 col2\" >3.4423</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row1_col3\" class=\"data row1 col3\" >0.0112</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row1_col4\" class=\"data row1 col4\" >0.0351</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row1_col5\" class=\"data row1 col5\" >0.0300</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_f62bed50_eece_11ea_a903_acde48001122level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row2_col0\" class=\"data row2 col0\" >4.3477</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row2_col1\" class=\"data row2 col1\" >30.2222</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row2_col2\" class=\"data row2 col2\" >5.4975</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row2_col3\" class=\"data row2 col3\" >0.3182</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row2_col4\" class=\"data row2 col4\" >0.0601</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row2_col5\" class=\"data row2 col5\" >0.0476</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f62bed50_eece_11ea_a903_acde48001122level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row3_col0\" class=\"data row3 col0\" >5.1968</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row3_col1\" class=\"data row3 col1\" >43.0441</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row3_col2\" class=\"data row3 col2\" >6.5608</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row3_col3\" class=\"data row3 col3\" >0.1431</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row3_col4\" class=\"data row3 col4\" >0.0724</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row3_col5\" class=\"data row3 col5\" >0.0579</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f62bed50_eece_11ea_a903_acde48001122level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row4_col0\" class=\"data row4 col0\" >4.0851</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row4_col1\" class=\"data row4 col1\" >29.4094</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row4_col2\" class=\"data row4 col2\" >5.4230</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row4_col3\" class=\"data row4 col3\" >-0.1452</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row4_col4\" class=\"data row4 col4\" >0.0578</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row4_col5\" class=\"data row4 col5\" >0.0440</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f62bed50_eece_11ea_a903_acde48001122level0_row5\" class=\"row_heading level0 row5\" >Mean</th>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row5_col0\" class=\"data row5 col0\" >4.0539</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row5_col1\" class=\"data row5 col1\" >28.6597</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row5_col2\" class=\"data row5 col2\" >5.2575</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row5_col3\" class=\"data row5 col3\" >0.1238</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row5_col4\" class=\"data row5 col4\" >0.0571</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row5_col5\" class=\"data row5 col5\" >0.0443</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f62bed50_eece_11ea_a903_acde48001122level0_row6\" class=\"row_heading level0 row6\" >SD</th>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row6_col0\" class=\"data row6 col0\" >0.7414</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row6_col1\" class=\"data row6 col1\" >9.9248</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row6_col2\" class=\"data row6 col2\" >1.0089</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row6_col3\" class=\"data row6 col3\" >0.1740</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row6_col4\" class=\"data row6 col4\" >0.0121</td>\n",
       "                        <td id=\"T_f62bed50_eece_11ea_a903_acde48001122row6_col5\" class=\"data row6 col5\" >0.0090</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9affb38e20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x396 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can tune our top 5 models dynamically with a higher iteration rate (n_iter) to find more optimal hyper parameters over a larger search space. \n",
    "tuned_top5 = [tune_model(i, n_iter=120, optimize='RMSE', fold=5) for i in top5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "        }</style><table id=\"T_f9d595d2_eece_11ea_a903_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_f9d595d2_eece_11ea_a903_acde48001122level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row0_col0\" class=\"data row0 col0\" >3.4107</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row0_col1\" class=\"data row0 col1\" >24.5344</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row0_col2\" class=\"data row0 col2\" >4.9532</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row0_col3\" class=\"data row0 col3\" >0.3960</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row0_col4\" class=\"data row0 col4\" >0.0554</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row0_col5\" class=\"data row0 col5\" >0.0387</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f9d595d2_eece_11ea_a903_acde48001122level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row1_col0\" class=\"data row1 col0\" >2.7098</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row1_col1\" class=\"data row1 col1\" >10.7143</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row1_col2\" class=\"data row1 col2\" >3.2733</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row1_col3\" class=\"data row1 col3\" >0.1059</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row1_col4\" class=\"data row1 col4\" >0.0334</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row1_col5\" class=\"data row1 col5\" >0.0276</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f9d595d2_eece_11ea_a903_acde48001122level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row2_col0\" class=\"data row2 col0\" >4.2827</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row2_col1\" class=\"data row2 col1\" >27.5811</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row2_col2\" class=\"data row2 col2\" >5.2518</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row2_col3\" class=\"data row2 col3\" >0.3778</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row2_col4\" class=\"data row2 col4\" >0.0574</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row2_col5\" class=\"data row2 col5\" >0.0468</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f9d595d2_eece_11ea_a903_acde48001122level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row3_col0\" class=\"data row3 col0\" >4.6942</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row3_col1\" class=\"data row3 col1\" >38.4357</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row3_col2\" class=\"data row3 col2\" >6.1997</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row3_col3\" class=\"data row3 col3\" >0.2349</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row3_col4\" class=\"data row3 col4\" >0.0692</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row3_col5\" class=\"data row3 col5\" >0.0527</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f9d595d2_eece_11ea_a903_acde48001122level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row4_col0\" class=\"data row4 col0\" >3.4236</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row4_col1\" class=\"data row4 col1\" >23.0011</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row4_col2\" class=\"data row4 col2\" >4.7959</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row4_col3\" class=\"data row4 col3\" >0.1043</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row4_col4\" class=\"data row4 col4\" >0.0514</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row4_col5\" class=\"data row4 col5\" >0.0369</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f9d595d2_eece_11ea_a903_acde48001122level0_row5\" class=\"row_heading level0 row5\" >Mean</th>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row5_col0\" class=\"data row5 col0\" >3.7042</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row5_col1\" class=\"data row5 col1\" >24.8533</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row5_col2\" class=\"data row5 col2\" >4.8948</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row5_col3\" class=\"data row5 col3\" >0.2438</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row5_col4\" class=\"data row5 col4\" >0.0534</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row5_col5\" class=\"data row5 col5\" >0.0406</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f9d595d2_eece_11ea_a903_acde48001122level0_row6\" class=\"row_heading level0 row6\" >SD</th>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row6_col0\" class=\"data row6 col0\" >0.7026</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row6_col1\" class=\"data row6 col1\" >8.8923</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row6_col2\" class=\"data row6 col2\" >0.9458</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row6_col3\" class=\"data row6 col3\" >0.1262</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row6_col4\" class=\"data row6 col4\" >0.0116</td>\n",
       "                        <td id=\"T_f9d595d2_eece_11ea_a903_acde48001122row6_col5\" class=\"data row6 col5\" >0.0086</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9aff1ec160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x396 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Blending models is an ensemble method of combining different machine learning algorithms and use a majority vote to build consensus of final prediction values. Let's try building a blending model from our top 5 models and evaluate the results. \n",
    "blender_specific = blend_models(estimator_list=tuned_top5[0:], fold=5, optimize='RMSE', choose_better=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingRegressor(estimators=[('Lasso_0',\n",
       "                             Lasso(alpha=0.777, copy_X=True, fit_intercept=True,\n",
       "                                   max_iter=1000, normalize=False,\n",
       "                                   positive=False, precompute=False,\n",
       "                                   random_state=786, selection='cyclic',\n",
       "                                   tol=0.0001, warm_start=False)),\n",
       "                            ('Elastic Net_1',\n",
       "                             ElasticNet(alpha=0.8300000000000001, copy_X=True,\n",
       "                                        fit_intercept=True, l1_ratio=0.26,\n",
       "                                        max_iter=1000, normalize=False,\n",
       "                                        positiv...\n",
       "                                                       tol=None)),\n",
       "                            ('CatBoost Regressor_3',\n",
       "                             <catboost.core.CatBoostRegressor object at 0x7f9aff702d60>),\n",
       "                            ('Bayesian Ridge_4',\n",
       "                             BayesianRidge(alpha_1=0.001, alpha_2=0.0001,\n",
       "                                           alpha_init=None, compute_score=False,\n",
       "                                           copy_X=True, fit_intercept=True,\n",
       "                                           lambda_1=0.3, lambda_2=0.3,\n",
       "                                           lambda_init=None, n_iter=300,\n",
       "                                           normalize=False, tol=0.001,\n",
       "                                           verbose=False))],\n",
       "                n_jobs=-1, verbose=False, weights=None)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Below is a view of the model parameters. \n",
    "blender_specific"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_0672b22a_eecf_11ea_a903_acde48001122row5_col0 {\n",
       "            background:  yellow;\n",
       "        }    #T_0672b22a_eecf_11ea_a903_acde48001122row5_col1 {\n",
       "            background:  yellow;\n",
       "        }    #T_0672b22a_eecf_11ea_a903_acde48001122row5_col2 {\n",
       "            background:  yellow;\n",
       "        }    #T_0672b22a_eecf_11ea_a903_acde48001122row5_col3 {\n",
       "            background:  yellow;\n",
       "        }    #T_0672b22a_eecf_11ea_a903_acde48001122row5_col4 {\n",
       "            background:  yellow;\n",
       "        }    #T_0672b22a_eecf_11ea_a903_acde48001122row5_col5 {\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_0672b22a_eecf_11ea_a903_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_0672b22a_eecf_11ea_a903_acde48001122level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row0_col0\" class=\"data row0 col0\" >3.6171</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row0_col1\" class=\"data row0 col1\" >27.1878</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row0_col2\" class=\"data row0 col2\" >5.2142</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row0_col3\" class=\"data row0 col3\" >0.3307</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row0_col4\" class=\"data row0 col4\" >0.0584</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row0_col5\" class=\"data row0 col5\" >0.0411</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0672b22a_eecf_11ea_a903_acde48001122level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row1_col0\" class=\"data row1 col0\" >3.0085</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row1_col1\" class=\"data row1 col1\" >14.1218</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row1_col2\" class=\"data row1 col2\" >3.7579</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row1_col3\" class=\"data row1 col3\" >-0.1784</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row1_col4\" class=\"data row1 col4\" >0.0386</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row1_col5\" class=\"data row1 col5\" >0.0306</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0672b22a_eecf_11ea_a903_acde48001122level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row2_col0\" class=\"data row2 col0\" >4.0868</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row2_col1\" class=\"data row2 col1\" >27.8063</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row2_col2\" class=\"data row2 col2\" >5.2732</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row2_col3\" class=\"data row2 col3\" >0.3727</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row2_col4\" class=\"data row2 col4\" >0.0582</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row2_col5\" class=\"data row2 col5\" >0.0451</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0672b22a_eecf_11ea_a903_acde48001122level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row3_col0\" class=\"data row3 col0\" >5.0266</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row3_col1\" class=\"data row3 col1\" >39.0431</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row3_col2\" class=\"data row3 col2\" >6.2484</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row3_col3\" class=\"data row3 col3\" >0.2228</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row3_col4\" class=\"data row3 col4\" >0.0686</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row3_col5\" class=\"data row3 col5\" >0.0557</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0672b22a_eecf_11ea_a903_acde48001122level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row4_col0\" class=\"data row4 col0\" >3.3589</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row4_col1\" class=\"data row4 col1\" >22.7910</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row4_col2\" class=\"data row4 col2\" >4.7740</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row4_col3\" class=\"data row4 col3\" >0.1125</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row4_col4\" class=\"data row4 col4\" >0.0510</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row4_col5\" class=\"data row4 col5\" >0.0360</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0672b22a_eecf_11ea_a903_acde48001122level0_row5\" class=\"row_heading level0 row5\" >Mean</th>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row5_col0\" class=\"data row5 col0\" >3.8196</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row5_col1\" class=\"data row5 col1\" >26.1900</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row5_col2\" class=\"data row5 col2\" >5.0535</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row5_col3\" class=\"data row5 col3\" >0.1720</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row5_col4\" class=\"data row5 col4\" >0.0550</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row5_col5\" class=\"data row5 col5\" >0.0417</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0672b22a_eecf_11ea_a903_acde48001122level0_row6\" class=\"row_heading level0 row6\" >SD</th>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row6_col0\" class=\"data row6 col0\" >0.6985</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row6_col1\" class=\"data row6 col1\" >8.0747</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row6_col2\" class=\"data row6 col2\" >0.8073</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row6_col3\" class=\"data row6 col3\" >0.1972</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row6_col4\" class=\"data row6 col4\" >0.0099</td>\n",
       "                        <td id=\"T_0672b22a_eecf_11ea_a903_acde48001122row6_col5\" class=\"data row6 col5\" >0.0085</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9b02a4f310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x396 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Stacking models is an ensemble method of using meta learning, where a meta model is created using multiple base estimators to generate the final prediction. Let's try building a stacking model from our top 5 models and evaluate the results. \n",
    "stacker_specific = stack_models(estimator_list=tuned_top5[1:], meta_model=tuned_top5[0], fold=5, optimize='RMSE', choose_better=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StackingRegressor(cv=5,\n",
       "                  estimators=[('ElasticNet_0',\n",
       "                               ElasticNet(alpha=0.8300000000000001, copy_X=True,\n",
       "                                          fit_intercept=True, l1_ratio=0.26,\n",
       "                                          max_iter=1000, normalize=False,\n",
       "                                          positive=False, precompute=False,\n",
       "                                          random_state=786, selection='cyclic',\n",
       "                                          tol=0.0001, warm_start=False)),\n",
       "                              ('OrthogonalMatchingPursuit_1',\n",
       "                               OrthogonalMatchingPursuit(fit_intercept=True,\n",
       "                                                         n_nonzero_coef...\n",
       "                                             compute_score=False, copy_X=True,\n",
       "                                             fit_intercept=True, lambda_1=0.3,\n",
       "                                             lambda_2=0.3, lambda_init=None,\n",
       "                                             n_iter=300, normalize=False,\n",
       "                                             tol=0.001, verbose=False))],\n",
       "                  final_estimator=Lasso(alpha=0.777, copy_X=True,\n",
       "                                        fit_intercept=True, max_iter=1000,\n",
       "                                        normalize=False, positive=False,\n",
       "                                        precompute=False, random_state=786,\n",
       "                                        selection='cyclic', tol=0.0001,\n",
       "                                        warm_start=False),\n",
       "                  n_jobs=-1, passthrough=True, verbose=0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Below is a view of the model parameters. \n",
    "stacker_specific"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Evaluate Results and Finalize Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1742.4x2613.6 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can use Pycaret's built in plot_model() function to generate side-by-side plots: the Cook's Distance Outliers and t-SNE Manifold charts. \n",
    "fig = plt.figure(figsize=(20,30))\n",
    "ax = fig.add_subplot(5,2,1)\n",
    "plot_model(blender_specific, plot='cooks', save=True, verbose=False, scale=1.1)\n",
    "ax = fig.add_subplot(5,2,2)\n",
    "plot_model(blender_specific, plot='manifold', save=True, verbose=False, scale=1.1)\n",
    "plt.savefig('plots_cooks_and_manifold.png', dpi=300, pad_inches=0.25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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84ciRIw2LxWLMmTPHrj0hIcE8r+vWrTPbP/roI/OevDOnuW/fPuOll14y6tevbxjG//KTLVq0MAzDMLZs2WJ4eXkZgYGBdjm3O8+D7ToZxv+ud/Pmze3yshcuXDB8fHwMq9VqXLx40TCMpOtau3Zto0yZMkZERITZNzEx0RgyZIh5jv78809zm+363dlmGIZx+/Zt4++//zbWrVtn1KpVy7BYLMaECRPs+qTlXZHWvO693hWHDh0yPD09jXr16hkXLlyw2/bll18aFovFGDlypNnWsmVLo3z58sY///xj19d2zWfOnGnGkto867Vr14yAgADD09PTzNPabN682ShTpozh5+dnjmm7DywWi7F27VoDAICnCUtLAwCQAe3fv1/79+9X9erV1ahRI7ttL730kvk158aNGyVJefLk0dixYzVixAi5urra9Q8ICJAkXb58Odk4fn5+yp8/f4oxFCtWzFwmw6Zu3bqSkpZISQ3brHg27u7u5leTtn0cOXJEe/fula+vr1q2bGn3+27duqlo0aKpGsu2jK/ty8j00rhx42Rtr732miQpNDTUrn358uVydXVVkyZNJCV9ER4VFaXXXntNfn5+dn1r1qypWrVq6fDhw6laPiQlCxculCQNHjzY7jq4urpqyJAhypEjh1asWKHY2Fi739WuXdv8Gjg1KlasqNKlS2vPnj3Jls+wLSt950yNCxYskCR9+umndl/GZsuWTR999JGcnZ01f/78VI+fkvfee0+ZMmUy/1yjRg25urra3btLlizR7du31aNHD7sviLNnz66BAwfedd+2pWLu/Mu2NMyoUaOUP39+TZkyRdWqVTN/s2zZMsXFxalr164qWbKk3f7atWsni8WijRs36uLFi5KkRYsWSUqahSBLlixm31KlSumNN964a2yvvPKKXFzsJ12fN2+epKQv193c3Mz2zJkzm8uN2M63YRhKTEzU+fPn7c6Vq6urpk6dan5tL8lcZufAgQN295CHh4dWrlypLVu2mNfghx9+kGEY6t27t0qVKmX2dXJyUq9evVSsWDHt2rUr2TIx93pHAQAAAACAjI0cZxJynE9GjvPll1/WqlWr1LlzZzNXeObMGS1dulT9+/dX7dq1NXfu3FTtq1GjRvroo4+SrV7j4uKiKlWqSEpaelqSuRpN9uzZNWjQILucZrly5TRgwAC1b98+2RLH4eHh6tmzp/Lmzavvv//eLud2P126dLHLy77wwgvy9vaWYRhmfjc8PFyRkZFq1KiR3fLlTk5OGjBgwD1Xsqlbt65d7tTT01P+/v7q1auXrl69qn79+qlPnz5m/7S+Kx40r5vSu2LhwoW6ffu2+vbtazcDpyT16tVLuXLl0qJFi3Tr1i1JSfnTGzduJLuf+/Tpo61bt6pLly5mv9TmWTdu3KhLly6pSZMmyWacrVmzppo1a6aYmBitWrXKbluWLFnM9xkAAE8LlpYGACAD2rdvn6SkJSWCg4OTbY+MjJSU9B/4jRo1MpfQNQxDx48f1+nTp3X27FkdO3ZMe/bskSTdvn072X7ulUD7d7GVJDM5ER8fn6rjSCl5Ylviw7aP3377TZJSXNrDxcVFvr6+OnPmzH3Hsi3XYEv2pZeUloVp3LixRo8erRUrVqh3796SpMOHD+vo0aN65ZVXlCtXLkn/u86RkZEpXuerV69Kkn7//XeVL18+zbH9/vvvcnFxSZZAlJKuQ5kyZfTzzz/r6NGjduc/tYnVO7Vq1Uqff/65QkND1aNHD0lJidzdu3fLz8/PvBeuXbumU6dOqVChQimOU7RoUeXPn1/nzp1TVFSUXXIytdzd3ZMlpDJlyqRs2bIpOjrabLvXvVexYkW7pOGdbEvFSEnFfMePH9ePP/6oTJkyaejQoWrdurW5pJCN7VofPnw4xWstJSW3Dhw4oICAAB08eFC5c+dW8eLFk/WrVKnSXY48+bWLi4vTH3/8ocyZM5sFjXeyvRd+//1389hatmypxYsXq0GDBvLy8pK/v7/8/f3l5+dn938iBAYGqnTp0tq2bZuqVasmPz8/Va1aVdWqVUu2LJNt/3cu/2Jje85Pnz6tgwcP2r03HuReBAAAAAAAGQM5ziTkOJ+cHGehQoU0ePBgDR48WCdPntTu3bu1Y8cObdu2Tf/8848+/vhjJSYmqmPHjvfcj23Z77i4OB06dEh//vmnTp8+rSNHjmjHjh2S/veR8JkzZxQTE6PKlSvbfdBsY8tD3ikyMlI9evTQzZs39eKLL6b5WB/2ns6ZM6esVqt2796d4v6DgoKUM2dOGYahS5cuafXq1eby2R9++KHdx9ZS2t8VD5rXTek82cbeuXNnso+sJSlr1qyKjo7WyZMn9eKLLyooKEgDBw7UW2+9peLFi6tq1ary9/dXQECAXS47LXnWAwcOSEo5dyol5YOXLl2qgwcP2rUXKlQo2UftAAA86fgnGwAAGZAtUfXbb7+Z/1Gekn/++cf8+3Xr1mncuHE6ffq0pKQEWalSpeTl5aVjx47JMIxkv8+aNetd9/3vZIKkZMVZ93OvfdjiuXLliiQpX758Ke4jtbOx2ZIQp06dum/fW7du6cyZMykmMtMqpeRS9uzZVb9+fS1fvly//fabfHx8tHz5ckmy+yLbdp23bdumbdu23XWMO4vv0iI2NlZZsmRJ9gW7jS2xcv36dbv2e90Xd9OsWTONGzdOy5cvNwsZlyxZIsMw1KZNG7uYpKRzdDceHh46d+5csrhSK6X7LiW2e+/fX9pKSV/G2hLH/5YzZ0716tXLru2XX35R165d9fHHHytHjhxq2LCh3Xbbtf73V7P/Fh0drStXrsgwjBTjku79TPz72tkSxTdv3tSkSZPu+rs73yWffvqpfHx8tGTJEu3du1f79u3TtGnTlDNnTnXq1Ek9e/aUs7Oz3NzcNH/+fM2cOVOrV69WeHi4wsPD9eWXX6pIkSLq16+feR5s1/1uX2o/ynsRAAAAAABkDOQ4/4cc55OR47xTiRIlVKJECbVu3VpxcXEaP368QkJCFBwcrNdff/2e91F8fLzGjh2rRYsWmXHlyJFD5cqV04svvqjdu3eb947t/rcVEqZGdHS0ihYtqmzZsmnLli1aunSpWrRokerfP+57unPnzipcuLD55169eqlz586aO3eu+TH4ndL6rnjQvG5K94Rt7PutEGQbu3nz5sqXL59CQkK0fft2zZ8/X/Pnz5erq6saNGigoUOH6vnnn5eU+jyrLYd7v9xpXFycXXtKzy0AAE86ChkBAMiAbEte9OrVSz179rxv/61bt5pLH4wZM0blypVT0aJF5erqquPHj2vZsmWPO+QHZito+/fSHzbXrl1L1X4qVKig5557TmfOnNHJkydVokSJu/bdunWr3n33XXl5eWnx4sV222xfwt7p3wmC1GjZsqWWL1+uFStWyNvbWytWrFC+fPkUGBho9rFd588//zxNiabUyp49u6KjoxUbG5ti4aAt+WJLrDyM3Llzq169elqzZo327t0rb29vLV++XLly5dIrr7xi9rMdc1RU1F339SjjuhdbLLGxsXruueeSbY+Li0t1UWSFChU0atQo9e3bV/3791eRIkXk5eWVbKyQkBBVrlz5nvuy3fMP+0zcOW6hQoXMpVfuJ1OmTGrTpo3atGmjmJgY7d69W+Hh4QoNDdXkyZOVLVs2de3aVVJScq1Pnz7q06ePzp07p127dmnTpk0KCwtT3759VahQIXl7e5v33/nz51M81+l1zQEAAAAAQPohx/k/5DgfXHrkOLt06aI//vhDa9euTbGYzN3dXYMHD1ZYWJiioqJ0+fJl5cmT5677+/DDD7Vy5UrVrVtXr7/+ukqXLm0Wo3355Zd2MxnalhG3FbP92/Xr15U5c2Y5OzubbXnz5tWcOXN05coVtWrVSp999lmyGQEf1qO6p6WkgsMpU6botddeU0hIiIoVK6ZOnTqZ29P6rniUeV3bvjZu3KhChQql6jcBAQEKCAhQfHy89u3bp4iICIWGhmrlypWKjY3VtGnTJKU+z3pn7jQltmJLcqcAgGeB8/27AACA9GZblnXv3r0pbl+7dq0mTJhgLnuwcOFC3b59W5988omaN2+uUqVKmV+oHjt2LH2CfkDe3t6Skma0S8nd2v/N1dXVTJR99dVXd+13+/Ztffvtt5KkGjVqmO22xEZKCZiTJ0+mKoY7ValSRUWKFFFYWJi2b9+uixcvqnnz5nbLWnh6ekq6+3VesGCBgoODdeLEiTSPL0lly5aVJO3atSvZths3bmjv3r3KnDlzikvHPAjbzIurV6/Wnj17FBkZqaZNmypz5sxmn+zZs6t48eKKjo7WkSNHku0jKipKp06dUv78+c0kVFq/kk8t21I2Kd1jhw8fTlMyTpIaNWqkFi1aKCEhQf369dONGzfMbfe71tOnT9fkyZN14cIFZcuWTaVLlzaX1/631D4TUtL5LlKkiP766y9dvHgx2faoqCiNGjVKP/zwgyTpyJEj+uKLL8yix5w5c6pOnToaPny4vvzyS0lJy6xI0qZNm/Tpp5+aMwQULFhQzZs311dffaUuXbrIMAzz3rvXvXhnu8ViSfWxAQAAAACAjI0c5/+Q48zYOU43NzddunRJa9euvWsfJycnGYahLFmymMtqp5S3jImJ0apVq/TCCy9o8uTJqlatml2Boe1ets18WLJkSWXOnFmHDh1KcbnzQYMGydvbW4cPHzbbPDw85OHhoTJlyqhr166KiYlJNsvhw7rXPX3r1i3zuU2t4sWLa8iQIZKksWPH6o8//jC3pfVd8Sjzuve6fw3D0Lhx4zRt2jTFxcXpn3/+UXBwsGbOnCkp6b7x8/NT7969tWTJErm6upq507TkWe+XO7UtR261WlN9XAAAPKkoZAQAIAOqUKGCSpUqpa1bt5rLddicPHlSI0aM0NSpU80CMdv/Xrp0ya5vZGSk+R/FCQkJ6RB52nl7e6ts2bKKiIjQ6tWr7bbNnTtXBw8eTPW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      "text/plain": [
       "<Figure size 3188.81x2551.05 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can use Pycaret's built in plot_model() function to generate side-by-side plots: the Residuals chart, Prediction Error and Cross Validation (learning) charts. Let's compare the Blend and Stack model plots in a side-by-side comparison. \n",
    "fig = plt.figure(figsize=(25,20))\n",
    "ax = fig.add_subplot(3,2,1)\n",
    "plot_model(blender_specific, plot='residuals', save=True, verbose=False, scale=1.1)\n",
    "ax = fig.add_subplot(3,2,2)\n",
    "plot_model(stacker_specific, plot='residuals', save=True, verbose=False, scale=1.1)\n",
    "ax = fig.add_subplot(3,2,3)\n",
    "plot_model(blender_specific, plot='error', save=True, verbose=False, scale=1.1)\n",
    "ax = fig.add_subplot(3,2,4)\n",
    "plot_model(stacker_specific, plot='error', save=True, verbose=False, scale=1.1)\n",
    "ax = fig.add_subplot(3,2,5)\n",
    "plot_model(blender_specific, plot='learning', save=True, verbose=False, scale=1.1)\n",
    "ax = fig.add_subplot(3,2,6)\n",
    "plot_model(stacker_specific, plot='learning', save=True, verbose=False, scale=1.1)\n",
    "plt.savefig('plots_blender_vs_stacker.png', dpi=300, pad_inches=0.25)\n",
    "plt.show()"
   ]
  },
  {
   "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>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Voting Regressor</td>\n",
       "      <td>4.5013</td>\n",
       "      <td>35.0045</td>\n",
       "      <td>5.9165</td>\n",
       "      <td>-0.7728</td>\n",
       "      <td>0.0619</td>\n",
       "      <td>0.0479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Model     MAE      MSE    RMSE      R2   RMSLE    MAPE\n",
       "0  Voting Regressor  4.5013  35.0045  5.9165 -0.7728  0.0619  0.0479"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can execute the predict_model() function to use the model to generate the predicted values. \n",
    "pred_tunded_blender = predict_model(blender_specific)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stacking Regressor</td>\n",
       "      <td>4.5898</td>\n",
       "      <td>39.7708</td>\n",
       "      <td>6.3064</td>\n",
       "      <td>-1.0142</td>\n",
       "      <td>0.0657</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Model     MAE      MSE    RMSE      R2   RMSLE   MAPE\n",
       "0  Stacking Regressor  4.5898  39.7708  6.3064 -1.0142  0.0657  0.049"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can execute the predict_model() function to use the model to generate the predicted values. \n",
    "pred_tunded_stacker = predict_model(stacker_specific)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingRegressor(estimators=[('Lasso_0',\n",
       "                             Lasso(alpha=0.777, copy_X=True, fit_intercept=True,\n",
       "                                   max_iter=1000, normalize=False,\n",
       "                                   positive=False, precompute=False,\n",
       "                                   random_state=786, selection='cyclic',\n",
       "                                   tol=0.0001, warm_start=False)),\n",
       "                            ('Elastic Net_1',\n",
       "                             ElasticNet(alpha=0.8300000000000001, copy_X=True,\n",
       "                                        fit_intercept=True, l1_ratio=0.26,\n",
       "                                        max_iter=1000, normalize=False,\n",
       "                                        positiv...\n",
       "                                                       tol=None)),\n",
       "                            ('CatBoost Regressor_3',\n",
       "                             <catboost.core.CatBoostRegressor object at 0x7f9b02cd9820>),\n",
       "                            ('Bayesian Ridge_4',\n",
       "                             BayesianRidge(alpha_1=0.001, alpha_2=0.0001,\n",
       "                                           alpha_init=None, compute_score=False,\n",
       "                                           copy_X=True, fit_intercept=True,\n",
       "                                           lambda_1=0.3, lambda_2=0.3,\n",
       "                                           lambda_init=None, n_iter=300,\n",
       "                                           normalize=False, tol=0.001,\n",
       "                                           verbose=False))],\n",
       "                n_jobs=-1, verbose=False, weights=None)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x396 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The Blend model seems to perform better in both our train and test so let us finalize it. The finalize_model() function trains the model on the entire dataset. \n",
    "finalize_blender = finalize_model(blender_specific)\n",
    "finalize_blender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Analyze the Performance of Final Model on Entire Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Agent_ID</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>tenure</th>\n",
       "      <th>Total number of calls</th>\n",
       "      <th>Assistance</th>\n",
       "      <th>Recommend</th>\n",
       "      <th>CSat</th>\n",
       "      <th>total coaching</th>\n",
       "      <th>total coaching improved</th>\n",
       "      <th>Actual Value</th>\n",
       "      <th>FCR Week before</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>371097.882353</td>\n",
       "      <td>95.650593</td>\n",
       "      <td>94.096218</td>\n",
       "      <td>95.552565</td>\n",
       "      <td>95.186928</td>\n",
       "      <td>94.907817</td>\n",
       "      <td>94.968364</td>\n",
       "      <td>96.544911</td>\n",
       "      <td>14.235294</td>\n",
       "      <td>173.941176</td>\n",
       "      <td>95.832315</td>\n",
       "      <td>95.622119</td>\n",
       "      <td>4.807622</td>\n",
       "      <td>1.607843</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>96.036176</td>\n",
       "      <td>95.578725</td>\n",
       "      <td>96.181909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10742.442598</td>\n",
       "      <td>4.355132</td>\n",
       "      <td>10.465289</td>\n",
       "      <td>3.558242</td>\n",
       "      <td>3.941495</td>\n",
       "      <td>5.453729</td>\n",
       "      <td>4.675461</td>\n",
       "      <td>3.829237</td>\n",
       "      <td>6.846516</td>\n",
       "      <td>77.234608</td>\n",
       "      <td>1.784522</td>\n",
       "      <td>1.997818</td>\n",
       "      <td>0.089011</td>\n",
       "      <td>1.775281</td>\n",
       "      <td>1.074402</td>\n",
       "      <td>5.700526</td>\n",
       "      <td>4.674600</td>\n",
       "      <td>2.478407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>353039.000000</td>\n",
       "      <td>77.777778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>80.555556</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>83.333333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>89.655172</td>\n",
       "      <td>87.162162</td>\n",
       "      <td>4.413793</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>86.818100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>362108.000000</td>\n",
       "      <td>93.750000</td>\n",
       "      <td>92.445055</td>\n",
       "      <td>93.873767</td>\n",
       "      <td>93.284134</td>\n",
       "      <td>92.307692</td>\n",
       "      <td>92.938312</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>116.250000</td>\n",
       "      <td>94.711729</td>\n",
       "      <td>94.666667</td>\n",
       "      <td>4.767000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.120000</td>\n",
       "      <td>92.920000</td>\n",
       "      <td>95.324600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>371781.000000</td>\n",
       "      <td>96.153846</td>\n",
       "      <td>94.935897</td>\n",
       "      <td>95.833414</td>\n",
       "      <td>96.013439</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>95.861872</td>\n",
       "      <td>97.329060</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>95.806136</td>\n",
       "      <td>95.589688</td>\n",
       "      <td>4.819492</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>96.490000</td>\n",
       "      <td>96.715000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>380697.750000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>97.556895</td>\n",
       "      <td>97.568007</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>98.146168</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>226.250000</td>\n",
       "      <td>96.988326</td>\n",
       "      <td>97.113066</td>\n",
       "      <td>4.865194</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>97.755875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>388627.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>388.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4.984127</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.294000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Agent_ID      Friday      Monday    Saturday      Sunday  \\\n",
       "count     102.000000  102.000000  102.000000  102.000000  102.000000   \n",
       "mean   371097.882353   95.650593   94.096218   95.552565   95.186928   \n",
       "std     10742.442598    4.355132   10.465289    3.558242    3.941495   \n",
       "min    353039.000000   77.777778    0.000000   80.555556   80.000000   \n",
       "25%    362108.000000   93.750000   92.445055   93.873767   93.284134   \n",
       "50%    371781.000000   96.153846   94.935897   95.833414   96.013439   \n",
       "75%    380697.750000  100.000000  100.000000   97.556895   97.568007   \n",
       "max    388627.000000  100.000000  100.000000  100.000000  100.000000   \n",
       "\n",
       "         Thursday     Tuesday   Wednesday      tenure  Total number of calls  \\\n",
       "count  102.000000  102.000000  102.000000  102.000000             102.000000   \n",
       "mean    94.907817   94.968364   96.544911   14.235294             173.941176   \n",
       "std      5.453729    4.675461    3.829237    6.846516              77.234608   \n",
       "min     75.000000   80.000000   83.333333    3.000000              52.000000   \n",
       "25%     92.307692   92.938312   95.000000    8.000000             116.250000   \n",
       "50%     96.000000   95.861872   97.329060   15.000000             158.000000   \n",
       "75%    100.000000   98.146168  100.000000   17.000000             226.250000   \n",
       "max    100.000000  100.000000  100.000000   33.000000             388.000000   \n",
       "\n",
       "       Assistance   Recommend        CSat  total coaching  \\\n",
       "count  102.000000  102.000000  102.000000      102.000000   \n",
       "mean    95.832315   95.622119    4.807622        1.607843   \n",
       "std      1.784522    1.997818    0.089011        1.775281   \n",
       "min     89.655172   87.162162    4.413793        0.000000   \n",
       "25%     94.711729   94.666667    4.767000        0.000000   \n",
       "50%     95.806136   95.589688    4.819492        1.000000   \n",
       "75%     96.988326   97.113066    4.865194        2.000000   \n",
       "max    100.000000  100.000000    4.984127        9.000000   \n",
       "\n",
       "       total coaching improved  Actual Value  FCR Week before       Label  \n",
       "count               102.000000    102.000000       102.000000  102.000000  \n",
       "mean                  0.882353     96.036176        95.578725   96.181909  \n",
       "std                   1.074402      5.700526         4.674600    2.478407  \n",
       "min                   0.000000     75.000000        80.000000   86.818100  \n",
       "25%                   0.000000     94.120000        92.920000   95.324600  \n",
       "50%                   1.000000    100.000000        96.490000   96.715000  \n",
       "75%                   1.000000    100.000000       100.000000   97.755875  \n",
       "max                   4.000000    100.000000       100.000000  100.294000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The predict_model() can be executed with the final blend model over the entire dataset and saved to a csv file. \n",
    "pred_final_blender = predict_model(finalize_blender, data=dataset)\n",
    "pred_final_blender.to_csv('pred_final_blender.csv')\n",
    "pred_final_blender.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5ORkHDx6Ep6enVi/ZoEGDIJVK8dFHH6n327p1q7rdyckJzZo1w9q1a3Hz5k38/vvv2LZtm7q9YcOGePjwIX755RekpaXhp59+Uj/89nRxW5G4dBk9ejTu3buH+fPnIzk5Wf0g04ABA+Di4lKhnDZu3Bj/93//h/nz58PT01PjwaxJkybh5MmTiIyMREpKCg4dOoTIyEiMHj0aUqkUNjY2EIvFSEpK0jnjRVlatWqFPn36YM6cObh48SJiY2OxcOFCALp730uaN28esrOz1fmdOHEiTpw4gYiICNy4cQPHjh3DokWL1Lftx40bhz/++AMbN25ESkoKvvzyS8TGxpZ5jfLOmZaWhvnz5+PMmTNITU3FDz/8gDp16qBt27ZQKBRYvnw5Dh48iLS0NBw7dgxpaWnw9vaGq6srnn/+ecyePRt///03kpKSMGPGDBQVFamHVlRU586dYWtriw0bNmjNFgE86VUfO3Ysli1bhl9//RXXr1/HJ598gqtXr2L06NEQiUQYP3481q9fj8OHDyM5ORmzZs1Sz0ICAK+99hp27dqFLVu24NatW/jxxx+xatUqvYdwEBHpg4UwUTmGDx8Of39/LFmyBAUFBQgLC8OUKVOwatUqhISEYN++fVi5ciV69uwJkUiE1atXQy6XIzQ0FMOHD4dKpcKXX36pdV57e3ts3LgRDx48wEsvvYQ1a9Zg4sSJ6naRSIQlS5bg/v37GDRoEFavXo33339f3T5gwACEhobio48+wgsvvIDvvvsO8+fPh1QqxeXLlzWuVZG4dHFxccHXX3+NxMREDB06FLNmzcLAgQOxbNmySuW0eE7hp+cObteuHSIjI3H06FEMHjwYK1aswKRJk/Dee+8BeNJLXlxg6TOk42mLFy/GM888g7Fjx2L69OnqQrDkLXldWrdujQkTJmDbtm1ISEhA+/btERkZiePHj+OFF17AwoULERYWhrfffhvAk3G0S5YswXfffYchQ4bg8uXLeO6558r80FHeOWfMmIGuXbvinXfeQUhICH799Vd89dVXcHFxwaBBg/D2229j5cqV6N+/P5YvX473339fPaxg8eLF8PPzU89K8fjxY2zdurXCH2KKicVi9O3bFxKJRD2++2nh4eF44YUX8PHHH6t/3hs3blSP0Z44cSImTpyIhQsXYsSIEWjRooXG+O3nnnsO8+bNw5YtWxASEoK1a9dixowZGDlyZKViJiLSRSToO/COiKgGKyoqQnR0NHr16qUuSM+dO4dXXnkFv/76K5o2bWqwa124cAF2dnZo3bq1etvEiRPh7OyMJUuWGOw6RERUNewRJiKLIJVKMXfuXHz++edITU3F5cuXsWLFCvj5+Rm0CAaejAeeOHGiem7c77//Hn/99RcGDRpk0OsQEVHVsEeYiCzG+fPnsWzZMsTHx8Pa2ho9e/bERx99ZPBlr4tXTfvpp5/w6NEjtGrVCpMnT8aQIUMMeh0iIqoaFsJEREREZJE4NIKIiIiILJJFLKhx+fJlKJXKCk0YT0RERDVbUVERJBKJxiqGJbE+MK7y8m8OTNIj/PXXX2POnDnq7yMjI9GzZ08EBATgvffeK3V+0LfffhudOnWCn58f/Pz81NMKlUepVKpXp7JEgiAgNzdX75W5LAFzoo050cacaGNOtDEn2swlJ+X9/bf0+sDYKprf6q4PgWruEZbL5VizZg3WrFmDESNGAAAOHTqEqKgo7Ny5E/Xr18eMGTPw2WefqVfyKikxMRG7d+9GmzZtKnTd4k96np6eVX8RNZBMJsPFixfh7u6u1+IJloA50cacaGNOtDEn2pgTbeaSk8TExDLbLb0+MLby8l/MVPUhUM2F8JIlS3Djxg2MHDlS/SkxOzsbU6ZMQZMmTQAAw4YNQ2RkpNaxBQUFuHfvHlxdXSt1bUEQ9FpKtjaSy+Ua/yXmRBfmRBtzoo050cacaDOXnKhUKojFZd/8tuT6wNj0yT9g2vqwWgvhqVOnwtnZGREREbh//z4AYNSoURr7REdH6/xklpSUBBsbG4wfPx7Jycnw9/fHvHnz4OTkpNe18/LycPHixaq/iBosISHB1CGYHeZEG3OijTnRxpxoY060mUNO6tWrV2Y76wPjKi//gGnrw2othJ2dncts37dvH6KiorBr1y6ttvz8fPj6+mLmzJlo0qQJlixZghkzZuCbb77R69p2dnZwd3evVNw1nVwuR0JCAry8vMpdStZSMCfamBNtzIk25kQbc6LNXHKSnJxc7j6WXB8Ymz75B0xbH5rNrBFbtmxBREQE1q9fjxYtWmi1d+/eHd27d1d/Hx4ejh49eqCwsBA2Njblnl8kEln82C1ra2uLz8HTmBNtzIk25kRbdeVEoVBAoVAY/TpVoVKp1P8t/trSVVdOJBIJrKysIBKJdLbrc1ue9YHx6JP/8hi7PjSLQjgyMhI//PADNm/eDA8PD537/PHHH8jNzUVISAiAJ582xWIxrKzM4iUQEZGB5ebmQqVSmX2RIpFI4O7uDolEYupQzEZ15UQmk+Hx48dwcHBg/muh6qgPTV5F/vzzz9iyZQt27dqF5s2bl7qfQqHA4sWL0aFDBzg7O2PFihUYMGAAC2EiolpIpVJBoVDAwcHB1KGUS6VSQRAE2NjYGKQHrDaozpzUrVsXDx8+hKOjY6k9w1TzVFd9aPIqctOmTcjJycHgwYPV29zc3LB7927s378fX331FaKiohAcHIzk5GSEhYUhJycHPXv2xMKFC00YORERGYtCoTD7nmAyDyKRCDY2NlAoFByjXYtUV30oEkw923U1KJ7HzlLnCSyez9Hb25t/WP7BnGhjTrQxJ9qqKyeFhYUAoNcYP1NTqVQoKCiAra0te4T/Ud05Ke39Ut7ff0uvD4ytJuTX5D3CNZVCqcL22BTEpGQgq0AGB1spgtycEervBisJfxESERERmTsWwpWgUKrw/k9nEHf7Iaz++aT7IK8ICRnZiE17iBWDO7MYJiIiIjJzrNYqYXtsikYRXMxKLEbs7Uxsj00xUWRERETA6dOnMWLECIwZMwbh4eEmX+GNyFyxEK6EmJQMrSK4mJVYjJiUjGqOiIiI6F8tWrTA5s2bsXXrVjRr1gzHjx83dUhEZolDIyohq6DsNcmzCvnJm4iITKdJkybqr62trTnHbhmir6ej/bq/9dpXuTLMyNFQdWMhXAkOtlI8yCsqvd2G07cQERmaXKlCfHq2Ua/RzqUBrPV4xmPSpEk4e/YsAKCgoAB16tRRz44QFRWFpk2b6nW9M2fOYPHixdi9e7feMebm5qJLly6wtbWFSqVCgwYNMHnyZIwdO1Zr39TUVMTExOD111/X+/ylycrKwuzZs/Hnn3/CwcEB06dPx5AhQ0rd/7XXXsPFixfV87k6Ozvj8OHDAIDbt29j/vz5OHfuHKRSKQYOHIiZM2eq963otYgqi4VwJQS5OSMhI1vn8AiFSoUgt7LXzCYiooqLT8+G38oDRr1G3HsvoFPThuXut2HDBvXXffr0weLFixEQEFDhqcK6dOlSoSIYABISEmBvb4/Tp08DAI4dO4a33noLwcHBGgV4dnY2PvjgAyxdutQg8+suWLAA1tbWiImJQXx8PKZMmYJ27dqhbdu2pR7z8ccf45VXXtF5LkdHR8TExODx48eYMGECtm/fjrCwsEpfi6gyOEa4EkL93eDX3BGKp9ZPV6hU8G/eCKH+biaKjIiITO3UqVN4+eWXMXLkSPTs2RO5ublYvXo1+vfvD19fX4SEhCA2Nla974ABA3Dq1CmMGTMGH3zwAfz8/DBs2DBcvXpV5/mvXLmC9u3bq78PCAiAIAjIyspSb5PJZHj33XcRHh4Od3f3Kr+m/Px8HDlyBO+88w7s7OzQpUsXBAcHY9++fZU6X2pqKkJCQlCnTh04OTkhKCgISUlJRrkWUVlYCFeClUSMTwd3waSAtmjT2B6N69mgTWN7TApoy6nTiIgIly5dwvTp03Ho0CFcvHgRe/fuxbZt23D27Fk899xz+Pzzz7WOOXPmDLp164ZTp06hU6dO+Oyzz3SeOz4+Hh06dADwZJjEihUr4O7uDg8PD/U+u3btQnx8PCIiIhAWFoaDBw9qnWfKlCno0qWLzn8lV/MCgBs3bkAsFsPN7d+OHi8vL1y7dq3MPHz++ecICAjAqFGjcOrUKfX2cePGISoqCgUFBUhPT0d0dDR69epVpWsRVQaHRlSSlUSMsK6tEda1talDISIiM1O/fn306NEDAODr64stW7bA0dERd+/eRd26dfHgwQOtYxwcHDBixAgAwPPPP49PPvlE57mvXLmCmzdv4vvvv0deXh66d++OTZs2qcfXAsDo0aMxevToMmP86quv9H49+fn5sLe319hmb2+PvLy8Uo95++230aFDB9SpUwdRUVGYOnUq9u3bh5YtW6Jr167YuXMnOnfuDKVSiaFDhyI4OLjS16qKnu4uUK7sZZRzk/lj1yUREZGBOTo6qr8WBAHLly9H9+7d8dZbb+HixYsQBEHrmIYN/x2bLJFIoFQqtfaRyWRITk7Gnj17EBsbi6+++goXLlzQKIKNoW7dusjNzdXYlpubCzs7u1KP8fb2hp2dHaRSKV566SX4+/vj999/h1KpxMSJE/Hcc8/h3LlzOHXqFPLy8rBkyZJKX4uoslgIExERGZhIJFJ/vXHjRuTm5iI6Ohq7d+/GsGHDKn3epKQkWFlZoVWrVgCAZ599Fp6enjh06FCFzzVp0iT4+fnp/Ddo0CCNfV1dXaFUKnHjxg31toSEBLRp00bv64lEIgiCgOzsbNy7dw9hYWGQSqVwcHDAyy+/jOjoaINdi0hfLISJiIiMKDc3F1KpFGKxGPfu3cMXX3wBhUJRqXNduXIFbdu21ZidolevXpVaMGPDhg2Ii4vT+S8qKkpj37p166Jfv3744osvkJ+fj9jYWBw/fhwvvviiznM/fvwYf/75J4qKiqBQKLB//36cOXMGQUFBcHR0RIsWLbBt2zbI5XLk5ORgz5498PLyqtS1iKqChTAREZERvfrqq7h//z66du2KMWPGYPDgwXjw4AHy8/MrfK74+Hh4enpqbOvZsyf+/vtvreEEhjZ37lzIZDIEBgYiPDwcc+fO1ZjObNKkSVi7di0AQKFQYPXq1QgMDET37t2xZcsWfPnll+oZLCIjI/Hnn38iMDAQffv2hSAImD17tt7XIjIUkaBroFItk5iYCABavzwshUwmw8WLF+Ht7Q2pVGrqcMwCc6KNOdHGnGirrpwUFhYCAGxsbNTbLtx5ZDbzCJekUqlQUFAAW1vbCs8jXFtVd050vV+A8v/+W3p9YGw1Ib+cNYKIiGqEdi4NEPfeC0a/BhFZDhbCRERUI1hLxBXurSUiKgvv4RARERGRRWIhTEREREQWiUMjiIgAKJQqbI9NQUxKBrIKZHCwlaJ7S0e0l9T654mJLFr09XS0X/e3XvsqV4YZORqqbiyEicjiKZQqvP/TGcTdfgirf55wf5BXhCvpWWgpVWF9RxU4ZwQRUe3DoRFEZPG2x6ZoFMHFrMRiJDwswI7zN00UGRERGRMLYSKyeDEpGVpFcDGJWIQTNx5Uc0RERFQdWAgTkcXLKpCV2Z5dIK+mSIiIqDpxjDARWTwHWyke5BWV2t7A1roaoyGi6tTT3QXKlb1MHQaZCHuEicjiBbk5Q6FS6WxTqgT8n2vjao6IqGJOnz6NESNGYMyYMQgPD4dczrsYRPpgIUxEFi/U3w1+zR21imGFSgUvR1u84tPKRJER6adFixbYvHkztm7dimbNmuH48eOmDomoRuDQCCKyeFYSMT4d3OXfeYQL5XCwsf5nHuEcWEnYZ0DG9/DhQ9jZ2aFOnToVPrZJkybqr62trSGRSAwZGlGtZZLf7l9//TXmzJmj/n7btm0ICgpC165dsXLlylKPO3LkCIKDg+Hv749Zs2bx1g8RGYyVRIywrq3x1cge2DGuF74a2QNj/N1gJRaZOjT6h0qlxMO8u0b9p1Ip9Ypl0qRJ8PPzg5+fH9q3b4/AwEB07twZfn5+uHPnTqVe38CBA/H48WOt7bm5ufDy8oKfnx98fHzQq1cvbNmyRec5UlNTERMTg2effbZSMZSUlZWFN998E50YCykAACAASURBVH5+fujTpw/279+v13GPHj1Cjx49MGHCBJ1tAQEBCA0N1WqLiorCwIED4evri759++LMmTNVfg1Us5iiPqzWHmG5XI41a9ZgzZo1GDFiBADgwoULWL16NbZu3QobGxuMHz8enTt31vqfOD09HbNnz8bXX38NV1dXvP766/jhhx8wZsyY6nwJRERkIlkFGdgf94VRrzHE7x042j1T7n4bNmxQf92nTx8sXrwYAQEBEJcyDZ8+srKydG5PSEiAvb09Tp8+DQA4duwY3nrrLQQHB6Np06bq/bKzs/HBBx9g6dKlsLau+gOeCxYsgLW1NWJiYhAfH48pU6agXbt2aNu2bZnHLVu2DB4eHpDJtGdjKW5TKBQa20+cOIFPP/0Un3/+OTp16oT79+9XOX59cWU50zNlfVitPcJLlizBuXPnMHLkSPW2Q4cOYciQIWjVqhVcXFwwbtw47N27V+vYo0ePIjAwEJ06dUL9+vUxZcoUnfsRFVMoVdh8OhlTfjiJVzb9jik/nMTm08lQKHU/FEVEZChxcXEYNmwYunbtismTJ6sLO6VSiY8//hgBAQHo06cPVqxYAQAYN24cAKBfv35ISUnRONeVK1fQvn179fcBAQEQBEGjcJbJZHj33XcRHh4Od3f3Ksefn5+PI0eO4J133oGdnR26dOmC4OBg7Nu3r8zjTp06hVu3buHFF18stW3o0KFabREREXjjjTfg6+sLsVgMFxcXuLi4VPl1UM1gyvqwWnuEp06dCmdnZ0RERKh/Kdy4cQPBwcHqfVxdXbFt2zatY2/cuAE3NzeN/ZKTk/W+tiAIOj+dWoLiWwSWNJREoVRhxsFzOHfnkXqhhIzcQlxJz8LpWw+wsN+TPyqWlJPyWOL7pDzMibbqyolcLodEIoGqxAOMQikzexiSoFJpXLNCxwoCVCoVHj9+jNdffx0LFy5Er169sGHDBnz44Yf4+uuvcfjwYdy4cQO//fYbcnNz8corr2DQoEHYuHEj2rVrh8OHD8PJyUkjhuJCWKVSITc3F59++inc3d3Rpk0b9X47d+5EfHw8/ve//wEARo0ahZCQEI34pk6ditjYWJ2xP/PMMxpF7vXr1yEWi9GqVSv1NTw9PXH69OlS8yOTybBgwQKsXLkSly5d0shJybbLly8DgPo8SqUSly5dQu/evdG3b18UFRUhODgYH374IWxtbfXKvUqlglKp1OqRV6lUVeql1/UaSX/65t+U9WG1FsLOzs5a2/Lz8zXe6HXq1EFhYaHO/ZycnMrdrzR5eXm4ePFiBSOuXRISEkwdQrWJuv4If17LgkQswtOzw564mocvVDkY5N7QonKiL+ZEW03MiUIl4PCNLJy7X4AcmRL2Ugl8nWzR39XBIOOeqyMn7u7uEARB/X1hUelzPRtKYVERCsQFFTqmOMbiv0lHjx6Fh4cHAgMDoVAoEBYWhvXr1yM1NRVSqRQpKSnYu3cvevbsif3790MkEqGgoEB9juKvi12+fBm3bt3Cjh07kJeXh65du2LNmjWQy+XqDyRDhw7V6ml9+jyff/55ma+j5P6PHj1CvXr1NLbVqVMHOTk5WucttnbtWnTv3h0tWrRAXFycRk6eblOpVOrz3L9/H3K5HIcPH8aGDRtgZWWF8PBwREREYNq0aWXGXEwmk+H69es62+rVq6fXOfRh6XVEZeiTf1PWhyafNcLW1hZFJX65FRUV6fwEqO9+pbGzszPI7aKaSC6XIyEhAV5eXgYZN1YTrEk6jfr1S3/o5YZcCgAWlZPyWOL7pDw1NSfqOyL35LASSwFr4JEAHL0nxwOxEktDfCo9E0Z15aSoqAgSiQQ2NjbqbYWqis+mUFE2depU6G8LAIhETz5Y2NjYQCQS4eHDh4iNjUXv3r3V+6hUKjx69AjBwcFIT0/H999/j4ULF+LZZ5/FokWL4ODgoD5HyevLZDKkpKRg3759cHNzw2+//Yb33nsP9vb2FY6zIho2bIi8vDytWEq77o0bN3Do0CHs2bMHtra26veGjY0Nbt68qdEmlUohFovV52nQoAGAJ8NDWrZsCQCYMGEC1q5diw8//FCveEUiETw8PLRm3KhIz6A+vL29DXq+2q4q+a+u+tDkhbCbmxtu3bql/v7GjRtwdXXVuV/xJ8yy9iuNSCSCVCqtSqg1nrW1tcXkIKdIAbGo9F6vnKInt+QsKSf6Yk601bSc7DidjAt3syB9agotqUSC83cf4cdLaQjr2rpK1zB2Topvm5e8rSoy4C3u0ojE4krfSheJRBCLxXB2dkafPn0QERGhbktOTkarVq2QlpaGwMBAjBkzBnfu3MGMGTOwadMmTJ8+HcCT11vy+snJybCysoKbmxvEYjGCg4Ph6emJn3/+ucIPi0+aNAlnz57V2da0aVNERUWpv3d3d4dSqcStW7fUf2sTExPRtm1bnfk5d+4c0tPT0bdvXwBPimaZTIbAwEBMnz5dZ1uPHj0QHR2Nhg0bokmTJhqvvfjDhb4/i+Jjn35PGnJYBIAa9XvAHFQl/9VVH5p8cswBAwZg9+7dSElJQUZGBjZv3oyBAwdq7ffcc88hJiYG586dQ05ODtavX69zPyLgyZK5ZeGSuVSbxaRkqMfGP81KLEZMSkY1R2RZevXqhdOnT+PkyZMQBAF79+5FaGgo5HI5/vrrL3z44YfIzs6Go6MjpFIp6tevD+DJh4v8/HyNc125ckWr+OzVq1elFszYsGED4uLidP4rWQQDQN26ddGvXz988cUXyM/PR2xsLI4fP67zITjgydRvR48exb59+7Bv3z5MmzYNnp6e2LNnD1588UWNtnfeeQft2rXDvn371IXlsGHDsHnzZmRmZiI7OxubNm0yyBRwVHNVV31o8kLYx8cHb7zxBv7zn/9g8ODBeP7559UvYP/+/Rg0aBCAJwP5P/nkE3zwwQfo06cPXF1dMX78eBNGTuasrCVzFSoVl8ylWi2roOwHerIK+fCfMTVq1AirVq3CsmXL0LlzZ2zcuBGrV6+Gra0thg0bhvbt2+P5559HUFAQGjdujLFjxwIABg8ejBdffBEXLlxQnys+Ph6enp4a5+/Zsyf+/vtv5ObmGvV1zJ07V92rGx4ejrlz52pMnTZp0iSsXbsWwJPb002aNFH/a9CgAaytrdGkSROttvr166vbir3xxhvw9vZG//79ERISgnbt2uH111836usj81Zd9aFIKPkkQi2VmJgIAFq/TGorhVL17wpZBTLY17FCS0kB3n8hCHVtbco/QS2gUKrw/k9nEHf7oUbPmEKlgn/zRvikvzfir1yGt7c3b3X9QyaT4eLFi8xJCTU1J1N+OIlrD3JKbW/T2B5fjexRqXNXV06KH3YpOUZYpVIiq8C4vdkOts4Qiyu2Klvxg1+2trYGvxVfU1V3TnS9X4Dy//4nJiYi+no6phy7qdd1OI9wxdSE+svkY4TJsHQVgBm5hYh7nIMHB8/js6FdLWK52NKWzA1yc0aovxtUSkX5JyGqoYLcnJGQka1zeIRCpUKQm/YT2jWBWCzRa7ELIiJ9sRCuZbbHpmj1ggKARCxC3J2H2B6bUuWHZGqK4iVzdb1emX6rqBLVSKH+bjibllnqHZFQf7cyjiYishy1v2vQwvAhGSIqviMyKaAt2jS2R+N6NmjT2B6TAtpixeDOFnFXiIhIH+wRrmX4kAwRAWXfESEioifYLVDLlDdtmIMNpw0jIiIiAlgI1zrlTRtWUx+SISLLIpFIoFDwoVbSj0wmg5UVb3JTxfFdU8uU9pCMUiXAr6kjH5IhohrBysoKeXl5yM3NNfsCR6VSQSaTqVeWo+rLSfF1rKyszP59QuaJ75paRte0YfZSCVo6S/B+iA8fkiGiGkEkEqFBgwZQKBRQKs17mhelUonr16/Dw8ODhfA/qisnVlZWsLGxqdI1erq7QLmylwGjopqEhXAt9PRDMsUT4LMIJqKaRCQSwdraGtbW5v1sQ3ERVqdOnRq18IoxMSdUU7AyIiIiIiKLxB5hIoJCqcLW2BT8FHsHkgs5cLSro16Fj3cSiIiotmIhTGThipflPpuaiaJ8GeyEIjwskCEhIxuxaQ+5AAMREdVaLISJLFzJZbmLSmy3EosRezvTopblJiLLE309He3X/a3XvsqVYUaOhqobu3mILByX5SYiIkvFQpjIwnFZbiIislQshIksHJflJiIiS8VCmMjCcVluIiKyVHxYzoIplKp/V6ArkMHBVsopsyxQ8bLcZ1MzNbYrVCr4N2/EZbmJiKjWYiFsoYqnzCqeLQAAHuQVccosC1S8LPfm00n4KfYqJLZ1OI8wERFZBBbCFqrklFklccosy2QlEWOMvxs6WefC29ubS6ISEZFFYFePheKUWURERGTpWAhbKE6ZRURERJaOQyMslIOtFA/yikpv55RZRERkAXq6u0C5spepwyATYY+wheKUWURERGTpWAhbqFB/N/g1d9QqhjllFhEREVkKDo2wUMVTZqnnES6Uw8HGmlNmERERkcVgIWzBrCRihHVtzWnSiIiIyCKx24+IiIiILJJZ9Ajv378fc+fOVX8vCAIKCgqwfft2+Pv7q7dv2bIFS5cuhbX1kxkN6tatixMnTlR7vERERERkGKasA82iEB4yZAiGDBmi/n7VqlVISkrSePEAkJiYiFmzZmHMmDHVHSIRERERGYEp60CzKIRLunbtGrZv346oqCittsTERLz44osmiIqIiIiIjK2660CzK4Q///xzTJgwAY0bN9bYLggCkpKSsG7dOkybNg3NmzfHxx9/DB8fH73OKwgCZLKyV1OrreRyucZ/iTnRhTnRxpxoY060MSfazCUnKpUKYnHZj0NZcn1gbPrk/2nGqgNLIxIEQajSGQzo1q1bePnll/HLL7/A3t5eo+3Ro0d4++23MWXKFHTr1g0HDhzAihUrcOTIEa19n5aYmIjc3Fxjhk5ERERmqF69evD09NTZxvrA+MrK/9OMVQeWxax6hA8cOIC+ffvqfEENGzbE5s2b1d8PGzYM3377Lc6fP4+goKByz21nZwd3d3eDxltTyOVyJCQkwMvLSz3A3NIxJ9qYE23MiTbmRBtzos1ccpKcnFzuPpZcHxibPvkvyZh1YGnMqhD+448/MHnyZJ1tKSkpOHr0qEa7XC6HVCrV69wikUjvfWsra2tri8/B05gTbdWVE5WgxPWM80h/nAKZogBSK1u41HeDu7MPxCKJ0a9fEXyfaGNOtDEn2kydE31uy7M+MJ6KDoswZh1YaoxVOtqAVCoV4uPj0alTJ53t9vb2WLNmDX755RcolUps27YNMpkMvr6+1RwpEVWVSlDi9PWDuJr+N3IKM1GkyEdOYSaupv+N0ykHoRKUpg6RiIiqkanqQLMphB89eoSioiI0atRIve3OnTvw8/PDnTt30LhxY6xatQorV65E586dsXfvXqxevZqf4ohqoOsZ55GZlwaxSPNXkFgkRmZuGq5nnDdRZEREZAqmqgPNZmhEo0aNkJCQoLGtadOmiIuLU3/fu3dv9O7du7pDIyIDS3+colUEFxOLxEh/nII2Lv4624mIqPYxVR1oNoUwEVkOmaKgnPbCaoqEiCxdenYKTsZ8q9e+44OWGjkaqm5mMzSCiCyH1Mq2nHabaoqEiIgsGQthIqp2LvXdoBJUOttUggou9d2qOSIiIrJELISJqNq5O/ugkV0zrWJYJajQqF4zuDtXbaUgIiIifXCMMBFVO7FIgq7uISXmES6E1MrGbOcRJqLay6WBG3p5DjB1GGQiLISJyCTEIgnauPhzdggiIjIZDo0gIiIiIovEHmEiUhMEFa4/OIfMvFSzX/aYjEehVGF7bApiUjKQVSCDg60UQW7OCPXnQ4xEVLuwECYiAE+WPb4rPw9RhgxWkie/GooU+cguuI/MvDR0dQthMWwBFEoV3v/pDOJuP4SV+MlNwwd5RUjIyEZs2kN80t/bxBESGRbnEbZsHBpBRACAG5kXUaB6xGWPLdz22BSNIriYlViM2NuZ2HH+pokiIyIyPBbCRAQAuJ9zE6Jylj2m2i8mJUOrCC5mJRbjxI0H1RwREZHxsBAmIgBc9pieyCqQldmeXSCvpkiIiIyPY4SJCACXPaYnHGyleJBXVGp7A1vraoyGyPiuZtbFlGMd9Np3fJCRg6Fqxx5hIgIAONm3gsBljy1ekJszFCrd7wOFSoX/c21czRERERkPC2EiAgC4NvKGrbghlz22cKH+bvBr7qhVDCtUKvg3b4RXfFqZKDIiIsPj0AgiAvBkpbdnrH1Qz1n4Zx5hLntsiawkYnw6uMu/8wgXyuFgY62eR1ilVJg6RCKD6unuAuXKXqYOg0yEhTARqYlEYrg39oZX026mDoVMyEoiRljX1gjr2lqrTaY0QUBEREbCoRFEREREZJHYI0xEZMFUghLXM84j/XEKl9UmIovDQpiIyEKpBCVOXz+IzLw09YqCXFabiCwJC2EiIgt1PeO8RhFcrOSy2m1c/E0UHVH1SM9OwcmYb/Xad3zQUiNHQ9WNY4SJiCxU+uMUrSK4GJfVJiJLwEKYiMhCcVltIrJ0LISJiCwUl9UmIkvHMcJERBbKpb4bsgvu6xwewWW1yVK4NHBDL88Bpg6DTIQ9wkREFsrd2QeN7JpxWW0isljsESYislBikQRd3UNKzCPMZbWJyLKwECYismBikQRtXPw5TRoRWSQOjSAiIiIii2Q2hfCyZcvg7e0NPz8/+Pn5Yfjw4Vr7XLt2DcOHD4evry/Gjh2Le/fumSBSIiIiIjIkU9WBZlMIJyYmIjIyEnFxcYiLi8OuXbs02gVBwPTp0zFs2DD8/fff6NixIxYvXmyiaImIiIjIUExVB5rNGOHExER4eHiU2p6UlITMzEyMHj0aADBt2jT06NEDubm5qFevXrnnFwQBMpnMYPHWJHK5XOO/xJzowpxoY060MSfamBNt5pITlUoFsbjsPj9Lrg+MTZ/8FzN2HVgasyiEHz58iEePHmHevHk4f/48PD09MW/ePLi5/TuH5c2bN+Hq6qr+3s7ODg0bNsTNmzfRoUOHcq+Rl5eHixcvGiP8GiMhIcHUIZgd5kQbc6KNOdHGnGhjTrSZQ07KK5JYHxiXPkVqddSBpTGbQrhbt25466234OnpifXr1+ONN97AgQMHIJE8mb4nLy8PtraaqyDZ2NigoKDsJUKL2dnZwd3d3eCx1wRyuRwJCQnw8vKCtbW1qcMxC8yJNuZEG3OijTnRxpxoM5ecJCcnl7uPJdcHxqZP/oHqqQNLYxaFcJs2bbBx40b192+88Qa++eYb3Lp1S/1pwNbWFkVFRRrHFRYWom7dunpdQyQSQSqVGizmmsja2tric/A05kQbc6KNOdHGnGhjTrSZOif63JZnfWA8+g6LqI46sNQYq3S0gZw/fx7bt29Xf69SqaBUKjXemG5ubrh586b6+/z8fGRmZqJly5bVGisRERERGY4p60CzKITr1KmDFStW4MKFC5DL5fjiiy/g5eWFZs2aqffx8PCAvb09tm7dCplMhsjISAQGBlZpgDQRERERmZYp68AqFcKGehrUy8sLc+bMwfTp09GtWzdcunQJn332Ge7cuQM/Pz/cuXMHABAREYH9+/cjICAAly5dwsKFCw1yfSIiIiIyDVPWgRUaI3zz5k1ERETg008/xfHjxzF9+nTY29tj9erV8PHxqVIgQ4cOxdChQ7W2x8XFqb9u3bo1duzYUaXrEBEREZF5MVUdWKFCeMGCBXBxcYEgCFi+fDmmTZsGe3t7LFy4UGviYyIiS6BQqrA9NgUxKRnIKpDBwVaKIDdnhPq7wUpiFqPPiIioFBUqhBMSErBu3TrcunULaWlpGDNmDGxtbbF8+XJjxUdEZLYUShXe/+kM4m4/hNU/T0c/yCtCQkY2YtMeYsXgziyGicxc9PV0tF/3t177KleGGTkaqm4V+g0tEolQWFiIX3/9FT4+Pqhbty7u3r1b5akriIhqou2xKRpFcDErsRixtzOxPTbFRJEREZE+KlQIDxgwAKGhofjyyy8xevRoXL16Fa+++qrOMR1ERLVdTEqGVhFczEosRkxKRjVHREREFVGhoRGzZ8/G0aNHUb9+fXTv3h2pqamYPHkyhg8fbqz4iIjMVlaBrOz2QsPMrENERMahVyGclZWl/rpbt27qbfb29ujXrx+ys7Ph4OBgnAiJiMyUg60UD/KKSm+34XK7RETmTK9CuHv37hCJRDrbBEGASCRCfHy8QQMjIjJ3QW7OSMjI1jk8QqFSIcjN2QRRERGRvvQqhI8fP27sOIiIapxQfzecTcvUemBOoVLBv3kjhPq7VUscnMKNiKhy9CqESy5xV1BQgOzsbKhUKgBPVpdLSkrS2IeIyBJYScT4dHCXf4vQQjkcbKyrtQjlFG5ERJVXoYfldu7ciUWLFkEm03xApGnTpujbt69BAyMiqgmsJGKEdW2NsK6tTXJ9faZwM1VsRETmrkKF8Jo1azBnzhzUqVMHMTExmDp1KpYvX4727dsbKz4iIiqDPlO4sRAmItKtQvfLHj16hJdffhmdO3fG1atX4erqioULF2L37t3Gio+IiMrAKdyIiCqvQj3Czs7OyM3NRZMmTXD79m2oVCo4OjpqTK9GRPQ0PsxlPJzCjYio8ipUCAcFBWHy5Mn48ssv4e/vj4ULF0IqlaJ58+bGio+IKsGcCk8+zGVcnMKNiKjyKvTXZ8aMGejVqxckEgnmzJmD1NRUnDt3DosWLTJWfERUQcWF54ZTSbj2IAcP8opw7UEONpxKwgc/nYVCqarWePR5mIsqL9TfDX7NHaFQaf5cq3sKNyKimqhCPcJSqRRTp04FANSvXx8bNmwwSlBEVHnmNosAH+YyLnOYwo2IqKaqUCE8a9asUtuWLFlS5WCIqOrMrfDkw1zGZ+op3IiIaqoKdRXUrVtX459MJsOxY8fQoEEDY8VHRBVkboWng6207HY+zEVERCZSoR7h//73v1rbzp07h4iICIMFRERVY26zCPBhLiIiMlcVKoR18fHxwblz5wwRCxEZgLkVnqH+bjiblqk1bpkPc5mWOc0sQkRkKhUqhC9fvqzxvVwux5EjRzh9GpEZMbfCkw9zmR9OaUdE9ESFCuGXX35Z43uxWAxXV1fMmTPHoEERUeWZY+HJh7nMS2kzi0hEYhyKT8P5tIdwqmfDXmIiqvUqVAgnJCQYKw4iMiAWnlQWXTOLCAJwLfMxcosUyCmSQyQSsZeY6CmS9zbrtZ9yZZiRIyFD0asQvnPnTrn7NG3atMrBEBGR8emaWSQ9twC5RQqIAChVgnq7qeafJiKqDnoVwsHBwRCJRBrbBEHQ2BYfH2/YyIiIyCh0zSySXSBH8W90iVjz9z0XPiGi2kqvQvjkyZMAgK1btyIxMREffPABmjVrhvT0dKxcuRJt27Y1apBERGQ4umYWKV6iWRAEONhoz/3MhU+IqDbSa8BXw4YN0bBhQ2zfvh3Lli1Dy5YtIZFI0LRpUyxatAjffvutseMkIiIDCfV3g19zR3XxCzzp9RUEAfXqSOFsb6N1DBc+IaLaqEIPywmCgMzMTNStW1e97fbt27C25i9IIqKaQtfMIoIg4M7jfDxTvy7ETw2F48InVJv1dHeBcmUvU4dBJlKhQnjUqFF49dVXMXr0aDRp0gR37tzBli1bMH78+CoHcuzYMaxcuRLp6enw8vLC/PnztYZcbNmyBUuXLlUX3nXr1sWJEyeqfG0iIkvz9MwiJecWLlkIc+ETIjI2U9aAFSqEp02bBmdnZ0RFRSEjIwMuLi4IDw/H0KFDqxTEvXv3MGvWLKxduxa+vr749ttv8e677yIqKkpjv8TERMyaNQtjxoyp0vWIiEiTOc4/TUS1n6lrwAoVwiKRCKNGjcKoUaMMGsSdO3cwfPhwdO7cGQAQGhqKFStWID8/X2MYRmJiIl588UWDXpuIiJ7g/NNEVN1MXQPqVQi/++67WLVqFaZOnVrqPmvXrq10EP7+/vD391d/Hx0djRYtWmgkQBAEJCUlYd26dZg2bRqaN2+Ojz/+GD4+PnpdQxAEyGTac2daArlcrvFfYk50YU601eacqAQlbmRexP2cm5ApCiC1soWTfSu4NvKGWCQp9bjanJPKYk60mUtOVCoVxOKy72ZYcn1gbPrkvzpqwLLoVQi3a9cOANCxY8cqX7A8V69exdy5c7Fo0SKN7VlZWejYsSPGjRuHbt264cCBA5g6dSqOHDkCe3v7cs+bl5eHixcvGivsGoErA2pjTrQxJ9pqW04EQYW78vMoUD2CSPTvH6m7D24g6eYFPGPto7FdF0PnRKEScPhGFs7dL0COTAl7qQS+Trbo7+oAq6fmNTZXte19YgjmkJN69eqV2R6TkoEO60/rda6/R7c3REgWpbz8l2SsGrAsIkEQhPJ3000ulxt0xoizZ8/ijTfewPTp0/UafjF48GDMmDEDQUFBZe6XmJgIQRDg7u5uqFBrFLlcjoSEBHh5eXGGj38wJ9qYE221NSfXH5zDtYwzEOsodlWCCm2cu8C9sa/OY42RE4VShRkHz+HcnUdacxv7NXXE0hAfsx6jXFvfJ1VhLjlJTk6GWCyGp6enzvbExESkZ6fguuw3vc43utsCA0ZX+5WX/5KMVQOWp0JjhG/evImIiAh8+umnOH78OKZPnw57e3usXr26yt3T0dHRmD59OhYsWICQkBCt9pSUFBw9ehSTJ09Wb5PL5ZBKtSd+10UkEum9b21lbW1t8Tl4GnOijTnRVttykpmXCiuJ7l//YoiRmZcKr6bdyjyHIXOy43QyLtzNglSiOSRDKpHg/N1H+PFSWo0Yt1zb3ieGYOqclHdbvqL4860YffNv7BqwLBUqhBcsWAAXFxcIgoDly5dj2rRpsLe3x8KFC7Fr165KB5Gamop33nkHn376KYKDg3XuY29vYO/l5QAAIABJREFUjzVr1qBNmzbo3bs3duzYAZlMBl9f3b0WRESkm0xRUE57YTVF8kRMSoZGT3BJlV3eWaFU/TsDRoEMDrZSzoBBZIZMXQNWqBBOSEjAunXrcOvWLaSlpWHMmDGwtbXF8uXLqxTErl27kJ+fj/fee09je1RUFAYNGoSoqCg0bdoUq1atwvLlyxEeHg4PDw+sXr2an86IiCpIamWLIkV+Ge3aK8s9TaFUYcfpZIMUmlkFZT+oVNHlnUvOiVxcYD/IK0JCRjZi0x5ixeDOLIaJzISpa8AKT59WWFiIX3/9FT4+Pqhbty7u3Lmj8WRfZUyfPh3Tp0/X2RYXF6f+unfv3ujdu3eVrkVEZOlc6rshu+B+qWOEXeqXvXiGQiVgxsFzuHA3yyCFpoOtFA/yikpvr+DyzttjUzSK4GJWYjFib2die2xKjRhqQdXjamZdTDnWQa99J+7ZrNd+ypVhVQnJopi6BqxQITxgwACEhobi7t27mDdvHq5evYo333yzygtqEBFR9XF39kFmbhoy89I0imGVoEKjes3g7uxTYpsS1zPOI/1xCmSKAliJ6+DUvUKcv9MI1k+NM65soRnk5oyEjGydwyMqs7yzMYZaEFHtVKFCePbs2Th69Cjq16+P7t27IzU1FZMnT8bw4cONFR8RERmYWCRBV/eQEgVuIaRWNnCp7wZ3Zx/1PMIqQYnT1w9qFMwFqlyIrDMR0DwbcXfbQIB2r2tFC81QfzecTcvU6sWt7PLOhh5qQUS1V4WHRvTu3Ru//fYbvv32W4waNQodOnSASFQz5ngkIqInxCIJ2rj4o42Lf6n7XM84r9VrDABylQiNbHPg6pCOlKxntI6raKFp6OWdDT3UojTFveV3s5Jxvygd+SnX8YxDa40PE0Rk3ipUCCcnJ2PChAmwtbVFeno6goODMXLkSKxcuRL9+/c3VoxERGQC6Y9TdI4jthIDcojgYpelsxCuTKFpyOWdDT3UQpeSveUQACWKkFOYiZz0TGTmpaGrWwiL4RrCo1E+vn7psqnDIBOp0MfsBQsW4LXXXsPPP/8MKysrtGrVCqtWrUJkZKSx4iMiIhMpbZq1etYSCIIAa4lCq81QhWZVhPq7wa+5IxQqlcb2yg610KW03nKxSIzM3DRczzhf5WsQkfFVqEc4Pj4e33zzDQCoh0P07dsXM2bMMHxkREQ1zNMPlkmtbLXG3dYkpU2z5mhjBblIQNpjzddkyEKzKgw91EKX0nrLgSfFcPrjlDKHnRCReahQIdyoUSMkJSXBy8tLve3atWtwdjbtp38iIlPT9WBZkSIf2QX3a+yt8tKmWROJANeGdVHf1g2PZPZGKTSrypBDLXQxt0VJiKhyKlQIT5gwAZMmTcK4ceMgl8uxbds2bNq0CRMmTDBWfERENYI+t8prWg9hadOsCYIKjeo1xSDf5/Fqj5pV3BuKIRYloZpnfNBSU4dABlahQnjEiBFo0KABdu7ciaZNm+LYsWN4++23MWjQIGPFR0RUI9TGW+W6plmzEkshLXSCf8v+Na6H25CquigJEZmHChXCAPD888/j+eef19h29+5dPPOM9pPDRESWorbeKn96mjWZTIaLFy9adBEMaPaWl6RrURIiMl96F8I7d+5EYmIiAgMDERwcrN7+ww8/YPny5Thz5oxRAiQiqgks9VZ5bXtAUF8le8vvZiWjME8Be5tGnEeYqIbRqxBetmwZ9uzZg27dumHmzJmYP38+nn32WYSHhyM6OhpTp041dpxERGbNFLfKFUrVvzMjFMjgYCut1gfWauMDghVR3FvesmFHXMy9CG83b0ilUlOHRUQVoFchfPDgQWzYsAEdO3ZEdHQ01q9fj82bNyM7Oxs7d+5Eu3btjB0nEZFZK+3BMmPdKlcoVXj/pzMayxI/yCtCQkY2YtMeYsXgzkYvhmvjA4JEZFn0+i35+PFjdOzYEQAQFBSEM2fOoH79+ti1axeLYCIi/Hur3MOlG+xtGqGOlR3sbRrBw6WbUXpGt8emaBTBxazEYsTezsT22BSDXk8XfR4QJCIyZ3r1CBcvnlH8tbW1NRYvXgxbW1ujBUZEVNM8/WCZMcWkZOhcQhh4UgzHpGQYbQ7dYuU9IFgkL8Dm08kmG7pBZGiS9zbrtZ9yZZiRIyFDqfCsEcD/s3ff8VHU+R/HX7M9lUACCUUg9JOaEHoVGwpBQOBEimLD40APUQF/ihULCqJYsRwK6qFgoamIpyegh7QLFjpJKIEA6WWzbeb3R2DNZpMQIdndJJ/nQx+6M7Mzn3x3M3nv7He+XzCbzTRo0KCqaxFCCFFJ2VZ7xeuLHNVeQ0U3CGqaxq4Teazdf9BvXTeEqIwDGcFM3dTR32UIP6lUENY0jd9//x1N0wBwuVwejwE6dpQ3kRBC+EpEkImzBbby11uM1V5DRTcIpucW8nt6/Qq7blT3FWshhLiQSgVhq9XK6NGjPZaVfKwoCnv37q3ayoQQQpSrf2wj9p3OKbN7hFNV6R/bqNprqOgGwbS8EI7mxpT5PF913RBCiAupVBDet29fddchhBDiTxgfH8vOExleN8w5VZX4ZpGMj6/+mc3KmnnOZLAQHR7L+7tz0Ci/e4Yvum4IIcSFXFQfYSGEEP5l0Ot4ITHhj3GEixxEWIw+vxmtvBsE6wX9xJmC8sOuL7puCFEZA1pF41o40N9lCD+RICyEEDWUQa9jUo/WAdnFIBC6bgghxIVIEBZC1Dj+nlFNXFggdN0QQogLkSAshKhRAmFGNXFhFXXdGNu1hXyQEUIEBAnCQogapTIzqgViV4G6qKyuG/JBRggRSCQICyFqlECYUU1cPPkgIwBUzVVitBErJkMQ0eGxtGrUtcqnI7+QzUfSuXzpzz49ZkkyC51/SRAWQtQogTCjWm1XUR/sSyUfZISqudh+ZIPH+NM2ZyE51jNkFJygR+z1Pg/Dou6SICyEAIrDz/ojWbx+cDt5NmfA9tsMhBnVarMLdV2Yf23nS9q/rz/IyI2VgefI6SSvSVgAdIqOjPwTHDmd5DUcnxDVRYKwEAKnS2X2hv/x46FswsNd6BQlYPttyrBc1etCXRdWJqXS5RI+a/jyg4z0Rw5M6bnJZU7LDcVhOD032adBuF1kIe+M+q1S297+Wcdqrkb4WsCcAXbs2MGwYcPo1q0b06ZNIz8/32ubQ4cOMWbMGLp168bEiRM5deqUHyoVovb5aFcy/0vLQq9TPJaX7LcZKMbHxxLXrAFOVfVYLsNyVY0fjqRzNr84LP52Kpt9p3M4lWtF1TQMOh1bU85e0v77xzbyeu3Oq+oPMpXpjyx8z+60XmB9kY8qEYHEXzkwIK4IFxUVcc899zB//nz69OnDnDlzePXVV5k9e7Z7G03TmDlzJuPHj2fMmDEsWrSIp59+mpdfftmPlQtRO5zvt1nWdbpA67cZKDOqVaeL/Tr/UrsBOF0qPxxO50x+EYpS/KHI4YKT9kLybE5aR4WSY3UAFpwulZXbD5c4loG+l+XSOrIAh6uo3JuffDm+sPRHDkwmQxA2Z2EF6y0+rMb/9LOW+7uEKnOxN/75MwcGRBD+6aefaNKkCVdccQUAM2bMYNKkSR4NcPDgQTIyMrj55pvd2/Tp04f8/HxCQ0MveAyn00lubm71/AABzm63U1BQQG5uLiaTyd/lBARpE09nMrNwFdlw2QpxGXRoiueV4TNZroD7/bmhfUNuaN/QY1lhgfcVhEvhj/eJ06Xy8Je72ZOW5Q5x6cBvR0/y34PHePK6bmWG2ot9Xkn/2pVMbm4eOF1opdbl26ycwk7z5pHk5OUzc9VWfksv7qKioNIk+ggpafmcOWskNjIURVE4eeYYR9MPEt/iGo8wPG9QO1YlpfLf1DPuDzK9W0QzstNlvLv5l+Ll54J87xYNGdO1RaWDvHu/Vjv/O5GJ2aCnYagFXan3NFTd+1rOJ94qapMQpSEn846V2T1C1VRigjpU2fnG6XRiMFQcdVyqhrWg4r7rbvaKr2bXdaVft8q0P/gmB5YnIIJwamoqsbF/XAVo0aIFGRkZZGdnExER4d6mZcuW7m1CQkKoX78+qampdOx44T47P//8MwMGDKjy2oWoK1rO9XcF4nfgIx8+r7R0YO25f0tLusR9fw7M+RPL/4z0CtbJ+7p2W7hwIX379q1wm5S9p/i/Wcsqtb96VVBTbdbyA8/f1sq0P/gmB5YnIL5DLCgoICgoyP3YYDBgNBopKioqdxsAi8WC1SqfzoQQQgghaip/5sCAuCIcFBSEzfZH70Sn04nD4fD4gUtvA8V9SoKDgyt1jB49enDgwIGqKbiGcTgcHDx4kLZt22I0ytBSIG1SmtOl8sjXSew4cpLQkBCUc1+tO1WVLjH1eeyaTrWi7+2f5Y/3yZ0fbyOjgiHGIoNMvDWuV5U9r6RZa3eRnFmApmmcKSgit8iJU9Uw6BTCLQZ6NItkwfVdmPDedzj0Zvf7pN9lv2M2ON37MeoU2jcMdz82GYPp22p0pY5dntgGISxMLH8kgbKer2kayZn5FNhdBBn0tGkYBlT9+1rOJ94CpU0qczNVy7/EMP+jWyu1v+nrOlxiRbVb+qOev+eVvZnNFzmwPAERhGNjY9m4caP7cWpqKvXr16devXoe26SmprofFxYWkpGRQfPmzSt1DKPRSFRUVNUVXYPY7XbS0tKIjIyU/mvnSJt4e2nsAF5Yt4WjriDy7K5adwPan+V0qSzffpC1v+agP7yfBiFmn7RHo6gG5J7Nq2B9WJnnsot9XklXdWrN29sOYtTpaBocStMS65yqyjVd2hAZGUlEeBhZmsnd71ZnDsVi/uOqTJDRQGj4H3+cwiyRFzx2kc6MMaiC9XpLhfso7/ntgkJJz7dic7po3DCyWt7Xcj7xFihtkpGRccFt9DqFoJBK1miq4E0qvH5HK9P+4JscWJ6ACMK9e/fmoYce4ttvv6Vfv3689tprXHfddR7btGvXjrCwMD744APGjh3LK6+8Qt++fS+pg7QQ4g8GvY5hrerTuXPnOv/H/Pz4szuPZWArtBOi2ci02n0y/uzFjpNcFeMrV2ZEB9XlpFvDIL455cCkL74BLr0ggnBzIRoKmgb1SowFrGoq0eEXHgniUscXLu/5igIxYUG0iQrjzXF9LlhHXed0qazYcYT3dxwmNasAFGgREcLkhNZMTGhVKz8UH8gIZuom/40PLFMs+zcHBsQ7OigoiFdffZXFixfTt29fbDYbs2bNIi0tjbi4ONLS0gBYsmQJa9asoVevXvz66688+eSTfq5cCFEb+XP82YsdJ7kqxlc+PzTdHb3a0iYqjKhQC22iwrijV1uP8H9tywi6NanvPlZKdjSZ1jDQNMLMRqLDioe/UjWVyNCmtGrU9YLHvtTxhX05PnFt5XSp3LdmOw9/uZv/ncgk22onu9DO/9KyePir/zFrzQ6crrLbWIhL4c8cGBBXhAHi4+NZu9bzXuTQ0FB2797tfty6dWtWrlzp69KEEHXM+fFnVa30IGLVP/7sxY6TXFXjKxv0Oib1aF3hz2fQKTx3fTdW/3rCfaxsRxyXx5wfR9iGyWApcxzh8lTmanRF4yT7cnzi2uqjXcls3JdGod3pHkcaQAEK7U6+3pdGQrNkGXtZVAt/5cCACcJCCBEosiu46Qwgu8hRrcevTBityuddjKo+1oWCPHDB6ZJr+0QrVcXpUvlw52H2n9qFWX+GYJNKVEgYB84GUWDXeYTg8xQg3+aUSUhErSNBWAghSrnU/qri4lQUrpdvP3zB7irnnytBrXzF/d9/xqhuJyo4Hw0FpwtO5hRi0Tm5pnUQa/Y1QdW8w7BTVav9Q6A/tIss5J1Rv1Vq29s/819fYlE9JAgLIUQp5288K2s2Mulv6h8yXXLV+GhXMrn5+2gfVRyCz1MUcKrQOKyAuMZZ7Exr4PVcg04nHwIrSW6AqzkkCAshRCnn+5vuPOY59I/0N/Uff3dXqalK96v+5WQ217bJ9AjB5xn0OmxOjdj6+V5BWANCzQb5EChqHQnCQghRyvn+qsu3H2TtrgPog8w+G0dYlE26q/w554dBW/jDb6TnFmHU64iwmMgosKFpNqwOlSCj502MJr0OTdMINalomubuK6wBwSYD13ZoIh8CRa0jQVgIIcpg0OuYEB9LF2O+jK0cAKpinOS64vw42F/tTeN0fhEK4FJdnLQX4nBpWB16Qk0u7C4VU6kPdfUsJlrUj6Jb0wakZheioNFcxhEWtZgEYSGEEAFPhkcrW1lDygHsTc8m3+b06AChKAqqpnIgI5hGITacLg1TiYvCmgYRFgP923Tl1n7lT2ctRG0iQVgIIUTAq6pxkmsTu9PB0xs/p8h+gsbBLqLMBtILIvjqQBA6RY/D5fJ6jtmgY2dafVpGWLmsXhFQnIQ1DcLMejrEtK7UBCiiYvpZyyu9rdxY518ShIUQQtQIvhwnOdCpmosPf/4XFuU4FkvxdV+LwVE81XVbE1/83hgVndctcYqiEKQ3sP1EW/LtZ+jc2E6wwUVUaBh9YjvTNiauUhOgCFFbSBAWQgghApzd6eDDnzdx4PQBHGoRIQYHISY7dpcZo0HvDrwaCs3CrMQ3yWZHWn00FK8wbNDriAy1MKLLEPlQgYwjXNfVve+ShBBCiBrE7nSw+N/LSD67A52Si1nvoH5wESEmJ2FmK0UOJyUnA9frdcTWz8ek0xNqNnis0zSNMLOhTverFqIkuSIshBBCBJiSN8EVWvfSIuIkqqa4J3nRKcXx1qxXsRgcOJw6TIbia1smvY5Qs4ZOB20iw0nPt5JjdWB3uYgJD+a+gZfX2hEghPizJAgLIYQQAeT88GfnR8hoH5mBSy0OwC409IqCqinoz4XhIIMLq0PFVOJLXqPeTGLHywBoGGap8zcWClEeCcJCCCGEH5UeAu1MfhFpuVaahAcDYNE7/9hYAxWNIoceo0UFDXQ6zaP7A5pGkKkpTyX2ktArxAVIEBZCCCH8oLzZ37KsNoqcKoUOF20iwyhyGTCXCMMakGc3YDa4MBlUNFXBqNNh1Osw6KBeUBNu7jlSQrAQlSBBWAghhPARVXNx5HQSp3KOsP1oGqlZTmKCLZzKqU+RQ+OkvRCbS8Ws15Fvc5CebyXSEkH9qJOoKoBSnIQVhbOFZsJMDoqcJlpERtK2YRTR4bG0atRVhkATopIkCAshhBA+oGouth/ZQEbBCU7n2ShyWAk3u+jdrIBm4YWs298UFQWXqmFHxWzQk2N1kFHUjDMFuUQFF6BpGpy7YU4BDmVFEBzUl//r1VOuAAtxESQICyGEED5w5HQSGQUn0Ck6coocKApoaMVj/4ZbiWucxc60Bhj1OhwuFTPFU0grip5DWZeTVXiMBsFZBJs0XKqRAkckA1r3ZWKPNhKChbhIEoSFEEKIKnS+7+/7Ow6TmlUACrSICGFytzSiw4qv5jqL+zmgoLjDcGz9fHcQVjUNTdMw6IoDrqLoOV3UnGZRcTyf2F2CrxBVRIKwEEIIUUWcLpX71mzn06SjFNqdKOe6MWQV2omLSSfPZqBNVBgGXfFVX4Newe48Nwya0QUUd3mIsJgIsxhoEh5MVKgMfyZEdZEgLIQQZXC6VD7YlczaXWno9+TRIMQsQUSUSdVcnLUn88H2JFKzsiiyOmldP5jdJ+tj0OlRlOJpjvNtOjIL7aTnFVHPYqTQ7sSk1+FSNVyqhtVRfIObpmmEWQxc95dmcvVXiGomQVgIIUo5P6HBzmMZ2ArthGg2Mq129p3OYdeJTAknwk3VXOxM+Yrfcg9QpCqoqkpksEq/FjYuq2fls71NMOuLw3ByVigxoZnkFDlo1zCMPJuDfJuTIKMeh8vFidxw9DpFZn8TwockCAshRCkf7Up2z+plK7HcoNOx63gGH+1KZlKP1n6rT/hP6ckv2tQ/RaPgVAodKnqDAQ2N4n8UmkcU0b1xJrtORmI26Nl9sj7NI6yEBxV3mWgTFUZ6XhE5RTaszgiaNujMwu4x8q1DHaOftbxS27kWTqrmSuomCcJCCFHKluTTGHTFNyyVZtDp2JJ8WoJwHXP+BrhF//mdU3lWTHo99YKMtK53irOFdtA0gvXFN7+hnAvDmkKbKCs/n9AwA6qmsPFQc6LDVMIsBuzOIto2krF/hfAnCcJCCFFKttVe8foih48qEYEg32qn78tfsf9sLk5VQwfodQr5dgd2VxFBOlA13De/OYtnPgYg+NwNcFC8LNhkpEPjzvRrKx+khAgEEoSFEKKUiCATZwts5a+3GH1YjfCnIruTTs+v4USOtXhCN4oDrUPVcNmc5NoULOf+kjpVlWCToXhCDFdxGi48fwMcEGwycG2HJoyPj/XTTyPKciAjmKmbOvq7DOEnEoSFEKKU/rGN2Hc6B925oa9Kcqoq/WMb+aEq4Q/3fr6dk3lWdLriGd/OUwAV2H8miOgQGxp/XAUOMurRKwoOl4sjmSHUCzLSMTqCyQmt5QY4IQKMBGEhhChlfHwsO09ksPNYhsdyp6oS3yxSrujVMnang4+2b+JUbgqqWoROZyEmvCXje1zF5iOnz0164U0B/nusHm0j7TQJK0Djjw9OZoNCnj2CuOY92PT3HhJ+xSWTm+qqhwRhIYQoxaDX8UJiAsu3H2TtrgPog8wyjnAtVWiz8cSXb2PWZaKioKBg0Fs5mZ3NS/8+QYG9HoqioGkailLcF7jk9wQuTeHrw5fRv2kmXZu5MOhcFDr12JxRdG4Wz83dW8v7RYgAFhBBeNOmTSxcuJD09HQ6dOjA448/Ttu2bb22W7FiBc8++yxGY3H/vODgYLZu3errcoUQdYBBr2NCfCxdjPl07twZk8nk75JEFVA1F0dOJ5Gem4zNYeXno8epby4gz178+mpo2J0aLp1CEGfo1MjBD4X1sDm1czFZc/cVPi/EaETnuox7Bl1JcJDFLz+XuHjtIgt5Z9Rvldr29s+kL7GvVXdG9HsQPnXqFHPnzuWNN96gW7du/POf/+Qf//gH69ev99p2//79zJ07lwkTJvihUiGEEDWZqrnYfmQDGQUn0Ck6TuVaCTbkY9CrmAw2MgrNnI+4LlXDrmq0i7Sy9WgE+nN9hHVKcRTWikdII8Ji5P7Bf6GzMV+u/ApRxXyREf3+W5uWlsaYMWPo3r07er2e8ePHc+jQIQoLC7223b9/P+3bt/dDlUIIIWoyp0tl+X838t+UvexNz2X/6VxO5VnRKcXXd80GlTCTs9RzNKJCFVo1CMVs0GE26NDrFPSKgl4HzSOCSX14NJMTWmHQed9YKYS4NL7IiH6/IhwfH098fLz78ebNm7nssssIDg722E7TNA4ePMjSpUuZMWMGzZo14+GHH6Zr166VOo6madjtFY8NWls5HA6P/wppk7JIm3iTNvFWk9rE6VJZmZTK5iNn2Jx8hqtbHyIm1IlRr8OuqBTYnQQZFHTnOjtYjC7y7H8MjaehoVNMfH/3lcxat5styacpsLsIMenpH9uIhcPjMOlrVpv4SqC0iaqq6HR+v+ZXIeszf63S/QVS1rnU9vdFRvRZEF6/fj333Xef1/JnnnmG0aNHA3DgwAEeffRRnnrqKa/tsrOz6dSpE5MnT6Znz56sW7eOu+++m40bNxIWFnbB4xcUFPDLL79c+g9Sg+3bt8/fJQQcaRNv0ibepE28BWKbaJpKtusoea4zpBUUsD9TZe/pIHakRWBzgVHnwOZ04XC5MOt1KJpGoUNHPV3xlWCF4j+o5yloGB3hHNy/l7vbWri7bXOP4x3cv9fjcSC2ib8FQpuEhob6u4QK1fZsUpn292dGVDStjDlE/WDnzp1MmzaNmTNnctNNN1XqOYmJicyePZv+/ftXuN3+/fvRNI1WrVpVRak1jsPhYN++fXTo0MHdibyukzbxJm3iTdrEW6C2iaq52HX0azLy00jJKiSz0IbdpaIDUrItfPJbDBO6pNE4zIGGhllfPNGF3eUiMtiG2aDicOk4U3D+ZjcVp9qAh6+dgslQ8c8ZqG3iT4HSJocPH0an05X7lfn+/ftJz0nmiP37Su2vOm6Wq+orwoHkQu1fWdWZEf3eNQKKL3XPnDmTJ554guuvv77MbZKTk/nmm2+466673MscDkel7+RWFKXO3/VtNBrrfBuUJm3iTdrEm7SJN3+3idOlsmLHEd7fcZjU7AI6NzpLn8sysBgNFNqduFTNPf7vZfWs9GyazcGMYGLCslDQ/TELnKaRaTUTYXGSXWSgwGHA5tTjIoYXR40j2GyudE3+bpNA5O82CfRuEUCtfs9URftXd0b0exA+duwY9957Ly+88AJDhgwpd7uwsDBef/112rRpw6BBg1i5ciV2u51u3br5sFohhChbyWG57E4rJkMQ0eGxtGrUFZ2i93d5tUqR3cmwt//N5iPpuM59pzmoeSbZVidKkRODTucxAYaGQpvIQj7a04SWEUW0iChyT34RZNSjoMPmiuZgdjvqWcz0by3jRQsRCHyREf0ehFetWkVhYSGzZs3yWL5p0yZsNhvDhg1j/fr1NGnShMWLF7NgwQLuu+8+2rVrx2uvvVarP0kJIWqG0sNyAdicheRYz5BRcIIesddLGK4CTpfKe9sP88DaHeQUeY7wEGR0uYc1c7pc6HQK54dCU4AggwtVU1j1Wwz9W+TRJqoAvU5HkVPPX6LbM6n3NfIaiQuqjvGGKztjnD/5a7Y6X2REvwfhmTNnMnPmzHLX79692/3/gwYNYtCgQb4oSwghKu3I6SSPEHyeTtGRkX+CI6eTaBMdX86zRWU4XSqzvtjBip1HvEIwQKFDT5jZBYBKcfhVzo0GoVMUipz64rF/FR07TtTnRF5TokLNxDeLZELP7l6vnRDC/3yREf0ehIUQoqZLz00uN0jpFB3puckShC+C3engo+2bOJWbQp6tgKIilcsbWvjv8QhUzXPc3gMZwTQOs7mX65TiAOxSVRQzXs4rAAAgAElEQVTgaE4YJoMOo06hYVgQfWMbMrBVtHSBEKKOkyAshBCXyO60XmB9kY8qqfmcLpWPdiXzw5GTGLXtNAzOR6/Xo0OlQbDGgJZWmtcr4uPfYjzC8M/HI871/7WiagoKYDHqcTrhaE4Qv6Q3oFVkMPcNvJyJCa0k/AohAAnCQtRp50PHluTTZBbYcFnzSHSEMqlHWwkKf4LJEITN6T3T0R/rLeWuE8VUzcXBU7tZ++t28ooKaRZchMVgI89uxOVUUc/Naayi0KK+lZ7Nsvnvsfolnq/w8a8x9GyWTfuoQi5vFIxLM5Jpq4fO2ILnE2Pk6q+4ZNUxfJrwLwnCQtRRTpfK/Wt3sPt4JgadDlXTKMi38+72I+w5lcvzid0lNFRSdHgsOdYzZXaPUDWV6PBYP1RVc5y/2XDvyUM4nDaCjBBssGLUq5gNGmcLTR6jQKiaQrvIQo8gfH75f4/VJ9fWgqXjE+X9K4S4IAnCQtRRH+1Kdofgkgw6HbuOZ/DRrmQm9Wjtp+pqllaNupKRf8LrhjlVU4kMbUqrRpWbCr6uKLI7uffz7Ww+cpp8u4NeTbPo3zILTVNQzvV2UBQNDQWzwUWY2UlOkeHcrW8KGhrBRleZ+25WL4if7rlOQrCos/w1wkNNJUFYiDpqS/JprxB8nkGnY0vyaQnClaRT9PRodX2JcYSLMBksMo5wGfKtdjo9v4aTeVYUFBRFIcKSyel8OwoKIWbDuamOFaA4DFsMLvIUIypacRjWFKxOPToF1HOXii16HePjW/LK6F5YTPKnTQhROXK2EKKOyrbaK15f5PBRJbWDTtHTJjpeRoeogNOl0vflrziRY0WnKx7cTNM0zAZXcaDVNKx2F8EmPXaXAYPeBujQKcVp16BTMOh02F1O0vLCaV4/lBYRwUxOaC03wAkhLooEYSHqqIggE2cLbOWvtxh9WI2oCz7alczhzLxzk138odChJ8xU3NXBqaqAHodmxuFyYdQ7UbXiK+oGvQ6TTiHI1IQ1d96CySDvUSHEpZEgLEQd1T+2EftO55TZPcKpqvSPbeSHqkRtUdaU0/tOOsHjtrdihzKCiQm1FfcRBjQNFEWh0BmMyWUj22bA4TIRYg6lcb1Yxve4SkKwEKJKSBAWoo4aHx/LzhMZXjfMOVWVhMuiGB8vIx2Ii1PelNMNgrIYc7mej3+L9hgDePuJCFpEWLmsXhEmg4HG4UHkFDlwqi5szia0jbmCm7u3lq4Pwu/kRrTaR4KwEHWUQa/jhcQEj3GE6yt2EuNbyTjC4pKUN+W0QaenZX0rCU2y+PlEA/dyVVNY9VtjejTNYlgHIy0ahMvNhkIIn5AgLEQdZtDrmNSjNZN6tMZut/PLL7/QubNMOiAuTNVcHErf5dH14XxwLW/K6XoWIwV2Bx0aWvnvcQ2d8sdVYacLjuU25d4rbpBRH4QQPiNnGyGEEH+KpqnsOvo12dZTHl0fcqxnyCg4gc1R9ix70WEW8mwOGocpRIdayLY6cGkaOgVaNwznxxlDJQQLIXxKzjhCCCH+lGzXUYoK0jHoPf+E6BQdGfknAMXjau95iqLQJiqMMIuJYZc3I7vIQYTFSP/YRjL9sRDCLyQICyGE8OJ0qe7+49lWOxFBJvrHNuLGTk0pUM9iLKPrAxSHYU3TUDW1zO4RGhr9W3fl1r4y3rIQwv8kCAshhPDgdKncv3aHx4giZwts7Dudw/ajZxnU2I6R8ocvsxhDCDbVkymnhRABT4KwEEIIDx/tSvYaVg+Kp97enZZJ2zCN1iHlP99kCJIpp4UQNYIEYSGEEB62JJ8uc6IVKA7DB8+GENvIhg7vbVRNJTo8VqacFkLUCBKEhRBCeMi22itcv/90A27qoXiMGgHS9UHUTAcygpm6qWOltr21fzUXI3xOgrAQQggPEUEmzhbYyl0fYjIQ3/wqjmfvla4PosYb0Coa18KB/i5D+IkEYSGEEB76xzZi3+mcMrtHOFWVbjFB0vVBCFEryKCNQgghPIyPjyWuWQOcquqx3KmqxDVpwLUtI/xUmRBCVC25IiyEEMKDQa/jhcSEP8YRLjHxxY2dmrL399/8XaIQQlQJCcJCCFFLlDcJxsXM2mbQ65jUozWTerT2WG63V3wjnRBC1CQShIUQohaoaBKMXScyeT6xu0xhLEQZNh9J5/KlP1fpPl0LJ1Xp/kT1kSAshBA1UOmrv2fyi0jLtdIkPNhjO4NOx67jGXy0K9nr6q4QQtR1EoSFEKKGKevq7/4zuVgdLgodLtpEhqEof2xv0OnYknxagrAQQpQi35MJIUQNU9YUyC5VQwHybQ7S861ez8kucviwQiGEqBkCIgg/99xzdO7cmbi4OOLi4hgzZkyZ2x06dIgxY8bQrVs3Jk6cyKlTp3xcqRBC+F9ZUyDrdcWXgBUgx+odeiMsRl+UJoQQVaq6M2JAdI3Yv38/r7zyCoMGDSp3G03TmDlzJuPHj2fMmDEsWrSIp59+mpdfftmHlQohhP+VNQVyhMXESXshiqKUOf5v/9hGvipPiBpFZpYLbNWdEQPiivD+/ftp165dhdscPHiQjIwMbr75ZkwmEzNmzOD7778nPz/fR1UKIURgiAgyeS1rFGYh1GxE0zSPq8VOVSW+WSTj42N9WaIQQlSJ6s6Ifr8inJmZSVZWFo899hhJSUm0b9+exx57jNhYz5N2amoqLVu2dD8OCQmhfv36pKam0rFjxwseR9O0Ojv+pcPh8PivkDYpi7SJt0Btk97NG/B7erZX94hWkaGczLPSOCyIBkEm6gUZ6dcyir92bYHqcmJ3XfqxA7VN/EnaxFugtImqqujKmCq8pLqcD6pbZdq/Ir7IiD4LwuvXr+e+++7zWj5v3jx69uzJ9OnTad++PW+99RbTpk1j3bp16PV693YFBQUEBQV5PNdisWC1et8UUpaCggJ++eWXS/sharh9+/b5u4SAI23iTdrEW6C1yeV6jctMLvZnFrj7BkPxDXMJDYK4Jz4Kg3t5frXMBBdobRIIpE28BUKbhIaGVrhe8kH1ulD7g38zos+C8LBhwxg2bFiZ6yZMmOD+/2nTpvHuu+9y9OhRj8QfFBSEzWbzeF5RURHBwZ5jZpYnJCSEVq1aXUTlNZ/D4WDfvn106NABo1FumAFpk7JIm3gL5DZ5u1MnVialsjXlLDlWh8fV3+qcOCOQ28RfpE28BUqbHD58+ILb1OV8UN0q0/7g34zo964RSUlJ/P7774wfPx4ovozucrkwmTz7wMXGxpKamup+XFhYSEZGBs2bN6/UcRRF8dpnXWM0Gut8G5QmbeJN2sRbILaJCZjSuz1Terf3y/EDsU38TdrEm7/bpDJfy0s+qD6X0i0CfJMR/X6znNls5vnnn2fPnj04HA5eeuklOnToQNOmTT22a9euHWFhYXzwwQfY7XZeeeUV+vbtW6lL7kIIIYQQombxRUb0exDu0KED8+bNY+bMmfTs2ZNff/2VRYsWAZCWlkZcXBxpaWkALFmyhDVr1tCrVy9+/fVXnnzySX+WLoQQQgghqokvMqLfu0YAjBw5kpEjR3otb9KkCbt373Y/bt26NStXrvRlaUIIIYQQwk+qOyP6/YqwEEIIIYQQ/iBBWAghhBBC1EkShIUQQgghRJ0kQVgIIYQQQtRJAXGznBBCBDJVc3EofRfpucnYnVZMhiCiw2Np1agrOkV/4R0IIQJWek4yP235Z6W2vbX/s9VcjfA1CcJCCFEBTVPZdfRrsq2n0CnFX6LZnIXkWM+QUXCCHrHXSxgWQogaSrpGCCFEBbJdR8ksSHOH4PN0io6M/BMcOZ3kp8qEEEJcKgnCQghRgQL1rFcIPk+n6EjPTfZxRUIIIaqKdI0QQogKuDQHxgpOlXZnkQ+rEUJUteh6sQxsP9TfZQg/kSvCQghRAb1irHC9yWDxUSVCCCGqmgRhIYSoQIguClVTy1ynairR4bE+rkgIIURVkSAshBAViNA3p0FIE68wrGoqkaFNadWoq58qE0IIcamkj7AQQlRAUXTEN7+W49l7z40jXITJYJFxhIUQohaQICyEEBegU/S0iY6nTXS8v0sRQghRhaRrhBBCCCGEqJMkCAshhBBCiDpJgrAQQgghhKiTJAgLIYQQQog6SYKwEEIIIYSokyQICyGEEEKIOqlODJ9ms9lwuVzs37/f36X4haoWTwRw+PBhdDr57APSJmWRNvEmbeJN2sSbtIm3QGmTwsJC9Pryx/qu6/mgul2o/QNBnQjCgf4iVDedTkdoaKi/ywgo0ibepE28SZt4kzbxJm3iLVDaRK/XV5gB6no+qG4Xav9AoGiapvm7CCGEEEIIIXxNvsMRQgghhBB1kgRhIYQQQghRJ0kQFkIIIYQQdZIEYSGEEEIIUSdJEBZCCCGEEHWSBGEhhBBCCFEnSRAWQgghhBB1kgThWuinn37i+uuvp3v37kyaNImUlBQA3nvvPQYMGECvXr144IEHKCoq8m+hPvTVV19x9dVXk5CQwNy5c7FarQB8+OGH9O/fnx49erBw4UI/V+lb5bXJK6+84n6fzJo1i7y8PD9X6jvltcl5S5Ys4fbbb/dTdf5RXpv8+OOPJCYmkpCQwF133UVWVpafK/Wd8tqkrp5j33nnHebNm+d+XN551el08sgjj5CQkMDgwYPZsGGDP8r1sGPHDoYNG0a3bt2YNm0a+fn5/i7poj333HN07tyZuLg44uLiGDNmjL9Lqhk0Uas4nU6tZ8+e2o4dOzSXy6UtWrRIu+2227TffvtNu+KKK7T09HQtPz9fu+WWW7Q33njD3+X6RHJysta1a1ft+++/12w2m/b4449r8+bN05KSkrR+/fppKSkp2qlTp7ShQ4dq3333nb/L9Yny2mTDhg3a0KFDtZMnT2oFBQXa9OnTtccee8zf5fpEeW1y3r59+7TOnTtrt912mx+r9K3y2iQlJUVLSEjQtm/frtlsNm327Nnaww8/7O9yfaK8NqmL51i73a699NJLWocOHbRHHnlE0zStwvPq22+/rU2aNEnLy8vTtm/frvXo0UPLzMz0W/1Wq1Xr06eP9u9//1uzWq3avffeqz377LN+q+dSTZkyRfv+++/9XUaNI1eEa5mcnByys7NxOp1o5yYNNJlMHD16FKfT6Z7/XVEUzGazP0v1ma1bt9K7d28GDRqEyWRi+vTprF+/nvXr1zNixAhatGhBdHQ0kydP5vPPP/d3uT5RXpvk5OQwdepUYmJiCA4OZvTo0ezZs8ff5fpEeW2iqioul4tHHnmkzl1hKa9N1q5dy4gRI0hISMBkMvHQQw9xxx13+LtcnyivTVJSUurcOfaZZ57hf//7H+PGjXMv+/LLL8s9r65fv57bb7+d0NBQEhIS6NevH19++aW/yuenn36iSZMmXHHFFVgsFmbMmMEXX3zht3ou1f79+2nXrp2/y6hxJAjXMg0aNGD06NFMnjyZzp07s2rVKh566CH69etHgwYNGDRoEAkJCRQVFTFhwgR/l+sTqqoSFBTkfqzT6cjLyyM1NZXY2Fj38pYtW3L48GF/lOhz5bXJtddey8iRI93LN2/eTPv27f1Ros+V1yY5OTm88847dO/enU6dOvmxQt8rr022bdtGcHAwEydOpHfv3jz++OM0aNDAj5X6Tnlt0rVr1zp3jr377rt59913iYqKci9LSUkp97xa+pzbokULjhw54ruCSymrnoyMDLKzs/1W08XKzMwkKyuLxx57jN69e3PLLbeQnJzs77JqBAnCtYzT6SQsLIz33nuP3bt3M2rUKO6//35sNhudOnXim2++4ccff8RsNvPyyy/7u1yf6Nu3Lz/88APbt2/HbrfzyiuvoNfrKSgo8PiDZjab60yfvvLaxG63u7f54osvWL9+PX/729/8WKnvlNcmBw8e5IsvvuCee+7xd4k+V16bKIrCqlWrmDt3Lt9++y1FRUUsWLDA3+X6RHltAtS5c2yjRo28lhUWFpZ7Xi0sLMRisbjXWSwWr374vlT6b4DBYMBoNNbIvwOZmZn07NmT6dOn88MPP9CzZ0+mTZuGy+Xyd2kBT4JwLbNx40ZOnjxJ7969MZvNzJw5kwMHDjBnzhzat29P8+bNqV+/Pv/4xz/47LPP/F2uT7Ru3Zonn3yShx9+mKuuuoo2bdoQHBwMgM1mc29ns9k8Toq1WXltEhoaCsCKFSt4+umneeutt7jsssv8XK1vlNUmFouFp59+mocffrjOvDdKKu99oigKw4YNo2PHjoSEhDBt2jS+//57f5frE+W1ySuvvFJnz7ElBQUFlXtetVgsHuuKior8+ntVulan04nD4aiRv+tt2rRh2bJldO7cGZPJxLRp0zh9+jRHjx71d2kBz+DvAkTVSk9Px+FwuB/rdDoURcFgMHgs1+v1GI1Gf5Toc/n5+bRv356vv/4aKP46LDw8nE6dOnmcJFJSUmjZsqWfqvSt8tokJCSEV155hY8//pjly5fXqf5mZbVJREQER44cYcaMGQA4HA6cTieJiYmsXbvWn+X6RHnvk7Zt23rcXe9yudz3JNR25bVJVlZWnT3HlhQbG1vuefX8uvMfrlNTU0lISPBHme56Nm7c6H6cmppK/fr1qVevnt9qulhJSUn8/vvvjB8/HsB9b4PJZPJzZYFPrgjXMr179+ann35iy5YtOJ1O3njjDVq0aEGfPn1YsWIFJ06cID8/n1dffZWrr77a3+X6xJkzZ5gwYQInTpwgLy+PBQsWMHr0aIYOHcqnn35KcnIyp0+fZvny5Vx33XX+LtcnymuTr776ihUrVvDhhx/WqRAM5bfJnj172LFjBzt27ODRRx+ld+/edSIEQ/ltMmzYML7++mv+97//UVRUxJtvvsk111zj73J9orw26du3b509x5ZU0Xl16NChLF26lPz8fHbv3s3WrVu58sor/VZr7969SU1NdXfvee2112rs3wCz2czzzz/Pnj17cDgcvPTSS3To0IGmTZv6u7SAJ1eEa5m//OUvzJ8/nyeffJKMjAy6dOnCkiVLaNy4MRkZGfz1r3/F5XJx9dVXM3PmTH+X6xOxsbHMmDGDv/71rzgcDoYPH87dd9+NwWBg2rRpTJkyBavVyvjx42vsSfDPKq9NJk2aRF5eHomJiR7bfvrpp36s1jfKa5O6rKLfnfnz5zNnzhxOnz7NoEGDuO+++/xdrk+U1yaKonD27Nk6eY4tqWvXruWeV2+99VbS0tK48sorCQkJ4amnniImJsZvtQYFBfHqq6/y6KOP8sADD9C3b19mzZrlt3ouRYcOHZg3bx4zZ84kMzOTuLg4Fi1a5O+yagRFqyvfZwkhhBBCCFGCdI0QQgghhBB1kgRhIYQQQghRJ0kQFkIIIYQQdZIEYSGEEEIIUSdJEBZCCCGEEHWSBGEhhBBCCFEnSRAWQtR4ubm55OTk+LsMIYT4U44dO1arjlMTSRAWQlRa+/bt6dq1K3FxccTFxREfH8+ECRPYsWNHlR0jLi6Obdu2ATBs2DA2bdp0wedcffXVHtO6/hlTp05lyZIlXstfe+01rr322jKfs3TpUkaNGlXhfufMmcMTTzxxUTUJIQJTyXNgt27d6NWrF9OnTyclJcW9zRtvvME999xzwX0999xz/POf/yx3/bx583j22WeBSzufrFixgvnz57sfx8XF8fvvv1/UvmojmVlOCPGnrFixgs6dOwPF89m///773HXXXXz33XfUq1evSo+1fv36Sm2XnZ1dpccFGDt2LK+99ho7duwgISHBvVzTNFavXs0dd9xR5ccUQgS+kufA7Oxs3njjDW6++Wa++OILGjZsWOkZKbOysggODi53fVV9kM7KyqLk3Gm7d++ukv3WFnJFWAhx0XQ6HePGjaOgoICjR4+ybds2rr32Wv72t7/Ro0cPvvnmG2w2G8899xyDBw+mb9++zJ4926MbwwcffMCgQYNISEhg8eLFHvsfMmQIX331FQDp6elMnz6d7t27069fPxYtWoSmaYwYMQKAiRMnsmbNGgA++eQThg4dSkJCApMmTeLQoUPufW7bto0RI0bQrVs3pk+fTn5+fpk/W8OGDbnyyiu9ppfetm0bmZmZDB8+nG3btnHzzTfTp08f4uLiuOOOOzh79qzXvkpfzfnll19o3769+/GhQ4eYMmUKPXr04Nprr2XlypWVan8hhH9FREQwZ84cYmNjWbZsGQBLlixh6tSpACQnJzNx4kQSEhK48soreeaZZ3C5XLz11lusXbuWjz/+mDvuuIPjx48TFxfHI488Qo8ePXj//fe9zhvp6enu80TJ89q2bduIi4vzqGv48OF8+umnbNiwgTfffJPNmzczbNgwoPiq9i+//AJAamoqd999N7169WLw4MG88MIL2O12988xa9Yspk+fTlxcHNdcc437HFubSBAWQly0/Px83nrrLRo2bEibNm0ASElJYcCAAWzZsoWBAwfy/PPPk5SUxCeffMLGjRtxOp089NBDAGzevJlFixaxZMkSfvzxR/Ly8igsLCzzWDNmzMBisfDDDz/w6aef8uWXX/LJJ5+4T8wrVqxgxIgRbNy4kcWLF7Nw4UJ++uknrr76am677TasViuZmZn87W9/Y9KkSezYsYOhQ4dW2K1j/PjxfPnllx41ffLJJ4wcORJN0/j73//OxIkT+emnn9i0aRMZGRmsWLHiT7VhQUEBU6ZMoW/fvvz444+89NJLvPrqq5XqEiKECAyDBg3i559/9lr+3HPP0atXL7Zv387y5cv58ssv2bp1K3feeSeJiYmMGzeOt99+G4DCwkIiIiLYunUrN954o9e+Nm/ezF133cXWrVuJj49n6tSpOByOCuu6/vrrmTp1KgMGDPD6hs1ut3PbbbfRuHFjvv/+ez788EN+/PFHXnzxRfc2GzZsYPTo0Wzfvp2xY8fy+OOPY7PZLqaJApYEYSHEnzJ58mQSEhJISEjgqquuIikpiddff52goCD3NiNGjMBsNmMymfjkk0+4//77adiwIaGhocyePZtNmzaRmZnJunXrGD58OF26dMFkMnH//fdjNBq9jnns2DGSkpKYPXs2ISEhREdH89ZbbzFo0CCvbT/++GMmTZpEx44dMRqNTJ48maCgIL7//nu+++47oqOjGTt2LAaDgeHDhxMfH1/uz9q7d29iYmL4+uuvgeKvQb/55hvGjx+P2Wxm9erVXH/99eTn53Pq1Cnq169Penr6n2rP//znPwQHB3PnnXdiNBrp0KEDEyZM4OOPP/5T+xFC+E9ERAR5eXley8PDw9m6dSvffPMNYWFhfP/99wwcOLDc/QwfPhyTyURISIjXuuuuu44+ffpgMpmYPn06mZmZJCUlXXTNO3fuJCMjg7lz5xIUFESTJk34xz/+werVq93bdOzYkSFDhmAwGLjhhhvIz88nIyPjoo8ZiKSPsBDiT3n//ffd/ePKEhQURGhoKACZmZkUFRVx5513oiiKexuz2czx48c5c+YMPXr08HhuZGSk1z7Pnj2LwWCgYcOG7mUtW7Ys8/hpaWm88cYb7qssAE6nk7S0NBwOBzExMR7bN2vWrMKf96abbuLTTz9l1KhRfP7553Tv3p1WrVoBxVdopkyZgtPppH379mRnZ9OoUaMK91faiRMnOH78uEc/ZFVVL1iXECJwZGRk0KRJE6/l8+bNY8mSJSxYsIC0tDQGDhzI448/TnR0dJn7KXmOK63k/o1GI1FRUZw+fbrMc2Zla27YsCEmk8m9rGnTpuTk5FBQUADgsW+DoTgyqqp6UccLVBKEhRBVqmTgrV+/PiaTiX/961+0bdsWKA6lqamptGjRgujoaNLS0tzb2+12srKyvPbZuHFjnE4nZ8+eJSoqCii+kpqbm0tiYqLHttHR0UyYMIEJEya4l6WkpNCwYUM2btzocTwo7nfXvHnzcn+eUaNGsXjxYo4fP87q1avdd4MnJSWxcOFCVq1aRevWrQGYNWtWmfvQ6XQeX2GWvLkvOjqaDh06eFyFycjIwOl0lluTECKw/Oc//6FXr15ey/fu3cvf//535s6dS2pqKg8//DAvvviiezSIP6Pk/Qd2u50zZ87QrFkz7Ha7VxeJytxA3LhxY86cOYPdbneH4ePHjxMcHFzmFenaSrpGCCGqjU6nY9SoUSxcuJDMzEzsdjuLFy9m8uTJOJ1ORo4cybp169ixY4d7XVn9z2JiYujZsycvvPACVquV9PR0nn32WYqKigAwmUzum95uvPFGli1bxsGDB9E0jU2bNjF8+HCSk5O54ooryM7O5r333sPpdLJp06Yy+/WVFB4eznXXXceLL75Ibm4uQ4YMASAnJwedTofJZEJVVb799ls2bdpUZp+9li1b8vPPP5OZmUlOTg7vvfeee93gwYM5deoUn3zyCU6nk1OnTjFlyhTeeuuti253IYRvZGZmMn/+fNLS0pg8ebLX+oULF/Liiy9it9uJiopCr9cTEREBFJ+3yupOUZ4NGza4z5UvvPACLVq0oFOnTjRv3hyXy8W3336Lqqr861//8gjNZrO5zJuCu3TpQtOmTXnmmWewWq2cPHmSl156iRtuuOEiWqLmkiAshKhWc+fO5bLLLmPUqFH06dOHpKQk3n77bSwWC7169eKRRx7hwQcfpHfv3lit1jK/XgRYtGgRVquVK664ghtvvJHhw4czduxYAMaMGcPdd9/NihUrGD58OLfddhszZswgPj6eF198kQULFtCpUyciIiJYunQpX3zxBd27d2fFihVl9jMubfz48axbt45x48ah1+sBGDBgACNHjuTGG2+kT58+LFu2jPHjx3PgwAGv59900020bduWa6+9ltGjR3PVVVe514WHh/POO++wbt06+vbty+jRo+nevTsPPvjgxTS3EKKaTZw40T2W+siRI8nJyeHDDz8ss4vCggULSElJoW/fvgwePJiGDRsyffp0oLjP7w8//J+GUbgAACAASURBVMBf//rXSh13yJAhPPPMM/Tu3ZsjR47w2muvodPpaNSoEbNnz2b+/Pn06tWL33//nX79+rmfN3jwYI4dO0b//v09hlEzGo288cYbnDx5ksGDB3PjjTfSo0cP5s6de4ktVLMoWslWEUIIIYQQoo6QK8JCCCGEEKJOkiAshBBCCCHqJAnCQgghhBCiTpIgLIQQQggh6iQJwkIIIYQQok6SICyEEEIIIeokCcJC1EJDhgyhffv27n8vv/xyBg4cyLPPPuueOrOqTJo0ifnz5wPw6aefekwVXJH09HQ2bNjgUfOyZcuqtLbztm3b5tEepf/du3dvtRy3Inv27OGaa66hc+fOfPTRR5e8vwcffJDrr7++zHWapjFo0CCWLFlywf1U5+tS1utw+eWX069fP+bOnVvmoP9CCFGdZIplIWqpe+65h3HjxgHFc8MfOXKE+++/n7y8PHdwrWrXX399pSaoAHjiiScICwtzh7dVq1YRFBRULXWd99VXXxEaGuq1vH79+tV63LK8+eabREZG8s4771TJ8UeOHMkXX3zB/v37ad++vce67du3c+rUKUaOHHnB/fjidSn5OjgcDpKSknjssccwGAw8+eSTVXosIYSoiARhIWqpkJAQGjZs6H4cHR3N5MmTeeutt6otCFssFiwWS6W2LT2XT4MGDaqjJA+RkZGEh4dX+3EqIzc3l44dO3LZZZdVyf569+5NTEwMX375pVcQXrduHd27d6/UsXzxupR+HZo0aUJycjLLli2TICyE8CnpGiFEHWIwGDCZTEBxN4bExERmz55NfHw8ixcvBuDzzz9n6NChdOnShREjRvDVV1957GPZsmUMGjSIuLg45s+fj8vlcq8r3TXi1KlTTJ8+nfj4ePr06cNjjz2G3W5nzpw5fPvtt3z22WcMGTIE8PwKXtM0PvjgA4YOHUrnzp1JTEzk66+/du93yZIl3H333Tz//PP06tWLfv368cgjj2C32y+pfSZNmsSjjz7KsGHD6N27N/v27WPIkCE899xzXHHFFQwYMICMjAzOnj3LnDlz6NevH3Fxcfz973/nxIkT7v2U9ZyShgwZws8//8zy5cvdoTU/P5/58+czaNAgunbtyq233sq+ffsqrK0knU5HYmIiX375pcdyh8PB119/zahRowAqrP1Cr8ucOXOYN28eDz30EPHx8QwePJiFCxd6hOcVK1YwePBgunXrxgMPPMCsWbOYM2fOBdveZDKh0/3xJ+ns2bPMnDmT+Ph4+vbty4MPPkhmZqZ7fVpaGrfffjvdunXjmmuuYeXKlbRv357jx4+X+xpcaJ8ff/wx11xzDZ06dXLv87wdO3YwduxYunbtSr9+/Xjqqac83m8bNmzghhtuoEuXLlx99dV8+OGH7nXl/a4JIfxPgrAQdYCqqvzyyy+sWLGCq666yr38wIEDWCwWPvvsM8aMGcPmzZt56qmnmD59OuvWreOWW25hzpw5bN68GYDPPvuMxYsXc//997Nq1Sry8/PZuXNnmcd0OBxMmTKF/Px8VqxYweuvv87WrVt54YUX+L//+z8GDBjAddddx6pVq7yeu3TpUhYtWsS0adNYs2YNw4cP5x//+Ac7duxwb7NlyxYyMjL48MMPeeCBB1i9ejVffPHFJbfV6tWrmT17NkuXLqVdu3ZAcfeAl156iVdffZV69epx6623kpqayuuvv86KFSvIy8vjzjvvxOFwuPdT8jmRkZEex1i1ahVxcXGMGzeOLVu2AHDvvfeydetWFixYwCeffEKDBg2YMmUKubm5FdZW0siRI0lJSfHo87x161asVitDhw7F6XRWWPuFXhcoDnWRkZGsXr2aSZMmsXTpUn766ScA1q9fz4IFC7j33ntZvXo1BoOB9evXX7DNd+7cyT//+U+uu+4697Lp06fjdDr56KOPePvtt8nMzGTatGlomobT6eSuu+4CYOXKlcyZM4eXXnrJa7+lX4OK9rlv3z6eeOIJHnzwQb7++mvuvPNOHn30UX777TdcLhczZsxg4MCBrF+/nkWLFrFu3To++OADoPiK+4MPPsjYsWNZs2YNd9xxB8888wxr1qxx11L6d00IERika4QQtdQLL7zgDgd2ux1FURgyZAj333+/x3bTp093d6GYM2cOt956K8OHDwegefPmHDp0iHfffZcBAwbw4YcfMm7cOBITEwF4/PHH3SG5tB9//JFjx46xfPlyoqKigOL+p/v37ycsLAyTyYTFYvH66l3TNJYtW8bUqVMZMWIEAFOnTuW3335j6dKl7ivORqORxx9/HLPZTOvWrVm9ejV79uxh7Nix5bZJWf2XL7/8cnegAejVqxcDBw702Oa6666jS5cuAHz33XccOXKEf//738TExACwePFirrjiCjZt2uQOcyWfU1qDBg0wGo1YLBYaNmzIgQMH2LJlCytXrqRbt24APPvss1x11VV8/PHH3HHHHeXWVlKbNm3o2LEjGzZs4C9/+QsAa9eu5aqrriIsLKxStZf3upzXpEkTZs2aBcDtt9/OBx98wJ49e+jbty/Lly9n3Lhx7qvPTzzxBD/++KPXPkq+Dna7nfDwcBITE937/e9//8vevXt5//333d9gLFq0iN69e7Nr1y6sVispKSksW7aMqKgo2rdvz4wZM3jsscfKfd0utM/MzEwURaFZs2Y0bdqUsWPH0qxZM5o0aUJeXh5ZWVk0atSIpk2b0qxZM95++2139453332X0aNHM3HiRABatmxJSkoKb775pvs9DJ6/a0KIwCBBWIha6vbbb2f06NFAcWiMiopyB4DzzGazxx/mgwcPkpSUxDvvvONe5nQ63aHo4MGDTJgwwb3OZDK5A1dpBw8eJCYmxh2CAfr06UOfPn0qrDszM5PMzEx3IDyve/fuvPfee+7HMTExmM1m9+OwsDCcTmeF+/7ggw8ICQnxWFZyHwBNmzb1el6zZs3c/3/o0CFiYmLcQRKKg21sbCyHDh0q8zkXcujQIQwGA507d3YvM5lMdO7c2WOfZdVW2g033MDy5cuZNWsWVquVf//73+6v4itbe0WaN2/u8bhku+/fv5+bbrrJvc5oNNKpUyevfZx/HU6dOsVTTz1FkyZNuPfee92vxaFDh7DZbPTq1cvjeedv+szNzfV6b8XFxXkdp/TrVtE+ExMT6dSpEzfccAOtW7dm4MCBjBw50n0j4y233MK8efN4+eWXGTBgAEOHDnX/bIcPH2bSpEke++3evTvvv/++u23+n707D4u63P8//pwFGAZEQMUlN8Dd3FjSXKIEEZfhdCzzoFl2Wixzqeykfn9Wtpmn056nTottph6PtoGK6THz5JKpuJCKGuKChgjIwMDAMMvvD2JyZBFlYBh4P66rK+dzz3w+74EZeM3NvVz5XhNCNA4ShIVoogICAujSpUuN97kyGFssFubMmcOoUaMcjleM3VQoFJXO4eHhUeW5PTw8Kk28qo0rg2kFq9WK1Wq1376ydqg80etKHTt2vOpkuaquf/m1qqvPYrFctb5ruWZV56zufpfT6XS88sorpKamcvbsWXx8fBg+fPg1XacmNX3d1Wp1rb7nFd+HLl268OGHHxIfH8+8efNYunQpUP7hq23btnz++eeVHhsYGMhXX31Vq3ovr/Vq59RoNKxatYoDBw6wbds2tm7dyhdffMGbb75JTEwMCxYsYPLkyWzdupUff/yRGTNmcO+99zJv3rwqv65Wq9Vh/Py1vB6EEA1HxggLIey6detGZmYmXbp0sf+3efNm+3jRHj16sH//fvv9LRZLpUlbFYKDg7lw4YLDZKQNGzbYh1VUFaoBfH19CQoKcrgOlI8jDQkJqdPzc4bQ0FCysrLIysqyH8vNzeX06dPXXV9oaChms5lDhw7Zj5lMJg4fPnzN5wwMDGTEiBFs3ryZ5ORkdDodKpWq1rVX932pje7du3P48GH7bYvFctU1mtu1a8f8+fPZvHmzff3ibt26kZOTg0ajsb8OfXx8WLx4MefPn6dHjx5cuHCBnJwc+3lSU1NrvM7VzvnTTz/x7rvvMnDgQObMmcM333zD4MGDSU5O5ty5cyxatIh27doxbdo0li1bxpw5c0hKSgIgJCSkytdrly5dUKulv0mIxkyCsBDC7sEHH2Tt2rV88cUXnDlzhi+//JI333zT/if5adOm8eWXX7JmzRpOnjzJCy+8wG+//VbluYYPH05wcDDz5s3j2LFj7Nu3jzfffNPeO6nVajl37hwXLlyo9NiHHnqIDz74gKSkJPtYyy1btnDvvffW6fnl5uZy8eLFSv8ZjcZan2Po0KH07t2bxx9/nEOHDnH48GEef/xx2rRpQ3R09HXV1bVrV2JjY/l//+//8fPPP3PixAnmzZtHaWmpfbzttfjTn/7Ed999x/bt2x3WDq5N7TV9X65m2rRprFmzhm+++cb++jh37txVw/WECROIjIy0b/gydOhQevbsyWOPPcahQ4c4duwYc+fOJT09na5duzJkyBC6devG/PnzOX78OD/++KN9PHx117raOT09PVm6dCkrVqwgMzOTXbt2ceTIEfr164e/vz/Jyck8//zzZGRkcPToUbZu3WofyvLQQw/x1VdfsWLFCk6dOsXq1atZuXJlnV+vQoj6J0FYCGEXHR3NokWL+OKLLxg7diz/+te/mDdvnn1jjjFjxvDss8/y3nvv8ec//5ni4uJqN9BQKpW8++67KJVKJk2axOzZs7n11lt5/PHHAbjzzjtJT08nPj6+0p+57777bqZPn87rr7/O+PHj2bhxI++8806tN+uoTlxcHMOHD6/03+XLZF2NQqHg3XffpU2bNtx7773cc889BAQEsGLFCrRa7XXXtnjxYgYNGsTMmTO56667KCgoYMWKFbRt2/aazxUdHU1eXh5dunRxWFO4NrXX9H25mtjYWObMmcOrr77KhAkTKC0tZdCgQdUOn7ncs88+S15eHkuXLkWpVPLee+8RFBTEfffdx+TJk/H29ubjjz/Gy8sLhULB0qVLKSsr44477uDFF1+0T5Ks7lpXO2dYWBiLFy/miy++YMyYMcybN49JkyZxzz334OPjwwcffEBGRgYTJkzgnnvu4YYbbrCvxz1y5EgWLVrE559/zvjx4/n000955plnmDx58jV9/YQQDU9hu55BfEIIIcQV9uzZQ/v27R0mqcXFxTF+/HhmzpzptOvk5eWRmprq8MFo/fr1zJs3jwMHDshwBCFErUmPsBBCCKfYunUrjz76KIcOHeLs2bP885//JDMzk9GjRzv1OgqFglmzZvHxxx+TmZnJvn37ePfddxk9erSEYCHENZEeYSGEEE5RXFzM4sWL+f777zEYDHTv3p3HHnuMESNGOP1a27Zt46233iI9PR0fHx9Gjx7Nk08+WWl5PCGEqIkEYSGEEEII0SzJ0AghhBBCCNEsSRAWQgghhBDNkswqEMJJDh8+jMViqdXuX0II4SqlpaWoVCr69u3r6lKEcDnpERbCSSwWi8OWqlC+9azBYLiurYZdyR3rlpobhtTcMOqz5qp+VgnRXEmPsBBOUtETfPkGBiaTidTUVEJCQvD09HRVadfMHeuWmhuG1NwwnF2zXq/H09MTb29vjh075oQKhWgapEdYCCGEaML0ej0rV65k9erV17SduBDNgfQICyGEEE1URQjW6/Xo9XoyMjJQqVSuLkuIRkN6hIUQQogm6PIQDBATE0OfPn1cXJUQjYv0CAvRQKxWK2azGavV6upSrqqsrAwon13uDvWC82pWqVSo1WoUCoWzShOiwVUVgiMiIlxclRCNj/QIC9EAioqKKCgowGw2u7qUWlGpVISEhLjVn1CdVXNpaSl6vd6tVhgQ4nISgoWoPekRFqIBKJVKWrZs6eoyas1qtWKz2dBoNCiV7vF52Zk1GwwGzGYzHh4eTqpOiIZRUFAgIViIa+Aev+GEcHMSqNyLWq2WdVaFW9JoNPj5+QESgoWoDekRFkIIIZoIT09PJk6cSHp6Or1793Z1OUI0etIjLIQQQrixK+cfeHp6SggWopYkCAshnO78+fPXdP+ysjIuXrxYT9UI0XTp9XpWrFjBN9984zaTcYVoTCQIC9HM7d69m7i4OKed74svvuBf//pXlW1fffUVkZGRxMTE8Mwzz/DJJ58AMHfuXH7++Wen1SBEc3D56hC//vorJ0+edHVJQrgdGSMshHCqS5cuVdv2zTffMHv2bKZOnVrrxwghKqtqibQePXq4uCoh3I8EYSFcZN++fTW2K5VKBg0aZL9dXFzM0aNHa3yMVqu9rrGBZrOZBQsWsGXLFnr27MnixYsJDAwEYM2aNbz//vsUFxczduxY5s2bh4eHB3v27OG5557jwoULhIaG8uyzz3Lp0iXef/99bDYbJpOJJUuW2K/xxBNPsGfPHvbv309WVha5ubmEhIRQUlLC3r17OXDgAMXFxUycOPGa6xeiqTBbrKxKyWBbehansy7S5UQJUaHtSAgLRq0q/yOurBMshPNIEBbCRUaNGlVju4+PD2fPnrXfPnXq1FUfEx4ezubNm6+5lrNnz3Lvvffy3HPP8e677zJv3jw+/PBD9uzZw9tvv82nn35K27ZtmTt3LsuWLePhhx9m0aJFPPXUU0RFRfHBBx/w3nvv8fbbbzN9+nQuXrzI888/73CN119/nYsXL/KXv/yFcePGMX/+fABmz57Nnj177MeFaK7MFitPJu1lf2YeSoWColIL6bkGTuScIOVcHv/QhVNkKJQQLIQTSRAWQtC+fXv7cIUZM2bw0UcfkZOTQ2JiIpMnTyY0NBSARx55hPnz5/Pwww/TokUL1q9fT8uWLbn//vvdahc6IRqjVSkZ7M/MQ61UYr1sZ0O1UklKZi7L/ncIa9ouCcFCOJEEYSFc5Go9t1fujta1a9erPkar1V5XLe3atbP/29PTk5YtW5KXl8eFCxdYv349y5YtA8Bms6FQKAB49dVXee2113jggQfQaDTMmzcPnU53XdcXQsD2jGzU1eyKqFYq2X02lyG/f+CUECyEc0gQFsJFwsPDr+n+Wq32mh9TWzk5OfZ/l5SUoNfradu2La1bt2bu3Ln23mKj0UhOTg4Wi4WzZ8/yxhtvYDab2bhxI/PnzycmJqZe6hOiOcg3mmpsL8KDhIQETp06Rb9+/RqoKiGaNlk+TQjB2bNnWbNmDSaTibfeeoshQ4bQsmVL4uLi+OKLLzh79iwmk4mXX36ZRYsWoVQqmT9/PklJSajValq1aoW3tzdqtRpPT0+Ki4uv6frX8xghmhp/b89KxyymEmy/D5Pw13jQokULCcFCOJEEYdFsLFu2jGeeecZ+e+XKlQwfPpzIyEhee+01+3Gz2czTTz9NREQEt956Kxs2bHBFuQ2qb9++/PDDDwwePJgTJ07w4osvAnDLLbdw9913M23aNG6++WbOnz/P4sWLUSgUvPbaa3z44YcMGjSI5557jtdffx0PDw9GjBjBjh07mDlzZq2vP3r0aBYvXsynn35aT89QiMZveHAQZqvVfttSUkze3u8pOL6fMouF4cFBLqxOiKZJhkaIJq+srIz33nuP9957z74016FDh3j33XdZsWIFGo2GadOmER4ezq233spnn33G6dOn+eGHH0hLS2PGjBncfPPNBAQEuPiZ1I/Bgwfz1VdfORyzWq0YjUYApk6dWmndX4CIiAgSExMrHe/Tpw+7du2q8lrLly+3//vypdXuuusu7rrrruuqX4imIiEsmH3nctmfmYetpJjC1J2obGYMZ39l4I19SQgLdnWJQjQ5EoRFk/fyyy9z6tQp7rrrLvufGJOTk4mPj6dLly4A3HPPPXzzzTfceuutrF+/njlz5uDr60tERATDhg0jOTmZyZMnX/VaFevnVigrK7P/23pZT09jV/F1stlsblO3M2u2Wq1YLJZKExadreL1cfnrpLGTmuvX4tH9+XTHL/x79b/xMBvx8/Fm9KhRPDVpFFaLGZOl7tewWq31/toWwl1IEBZN3sMPP0xQUBDvvPMOFy9eBMrX5B05cqT9Pl27dmXlypUAnD59muDgP3peunTpUuutS4uKikhNTa10vLS01B7U3ElJSYmrS7hmzqjZZDI16Ha1aWlpDXYtZ5Ga64fBYODc1k0M0pZBl5ZERkbSu3cXjh457NTr+Pr6OvV8QrgrCcKiyQsKqjyurri4GG9vb/ttLy8ve4AqLi5Go9HY2zQajT1AX42Pjw8hISH222VlZaSlpeHl5eVwzsbOZrNRUlKCRqOxL5fW2DmzZoVCQY8ePfDy8nJSdVWreH306tULDw+Per2Ws0jN9Uev17N69Wq0Wi0ajYbg4GD+/Oc/O73m9PR0p55PCHcmQVg0S97e3pSWltpvl5aW2oOxRqNxaCspKXEIzTVRKBR4elae+Q2V1wVuzCqGFigUCrep25k1K5VKlEpltd9LZ/Pw8GiwazmL1Oxcer2etWvXYjAYUKlUREdH4+HhUS81u8t7WoiGIO8G0SwFBwdz5swZ++1Tp07RtWvXKtuuHCohhBDOZjKZ7GOYY2JiCAsLc3FFQjQPEoRFsxQXF8dXX31FRkYG2dnZLF++nDFjxtjbPvjgAwwGA/v372fHjh1ER0e7uGIhRFPWpk0bEhISGD16tOwYJ0QDkqERolkaMGAAM2bM4L777sNoNJKQkGAPwtOmTeP8+fNER0fj4+PDiy++6LAFsRBCOENpaanDOPQ2bdrQpk0bF1YkRPMjQVg0G7NmzXK4nZCQQEJCQqX7eXp6smjRIhYtWtRAlTVt58+fp0OHDrU+LkRzoNfrWblyJWFhYQwePNjV5QjRbMnQCCEEe/fuZcKECZWOL1iwgA8++OC6z3vkyBGmT58OlAffil/4lx8XormpCMF6vZ6tW7eSlZXl6pKEaLakR1gIQURERKXd5ZyhsLDQPgGoQ4cO7N69u9JxIZqTy0MwQHR0tMPQK7PFyqqUDLalZ3E66yJdTpQQFdqOhLBg1CrpuxLC2eRdJYSLnD9/vsb/fvvtN4f7l5WVXfUxtV3v+Eq7d+8mLi4Og8HA7NmzCQ8P569//Su5ubn2+xgMBp566ikGDx5MXFwc33//PQCZmZmMGjWKJUuWEBERQVxcHLt378ZkMvHggw9y6tQpYmJiyMzMpF+/fpWOv/XWWyxYsMB+nfT0dG655Ra33IBEiJpUFYIjIyPt7WaLlSeT9vLR7hOk5xrQl1pIzzXw0e4T/C1pH2aLe+zyKIQ7kR5hIVzk888/r7Hd09OTJ554wn770qVLV31Mhw4duOeee667pjfffBOLxcL27dvZt28fM2bM4KabbgLgpZdewmq1sm3bNn799VemT59Ojx49ADhz5gxarZZdu3bx8ccf8+KLL5KUlMSHH37Is88+y8aNG8nMzLQ/r8uPHzt2jHvuuQeLxYJKpeK7775j9OjRbrORhxC1cbUQDLAqJYP9mXmolUqsl30QVCuVpGTmsiolg6mRoQ1atxBNnfQICyHsNm/ezEMPPYS3tzfh4eHceuutQPlmFevWreNvf/sbGo2GG2+8kejoaDZs2GB/7F//+lc8PDyIi4vj7Nmztb5mz549CQgIYO/evQBs2rSJuLg4pz4vIVypNiEYYHtGNupqNrtQK5Vsz8iu1zqFaI6kR1gIF7laz+2VPaIBAQFXfUxdt2LNzc11WL6pffv2AOTl5WEymRg3bpy9zWKx2CfYeXp64uvrC4BKpcJisVzTdePi4vjvf/9Lhw4dyMvLk80ERJOi1+spLi4Gqg/BAPlGU43nyS+RcfVCOJsEYSFc5FqXDvPw8Kj35cZat25NVlaWffJOTk4Obdq0wd/fHw8PD3744Qd74L1w4QIajYbCwsI6XzcuLo5Zs2bRsWNHYmNjZViEaFI6d+7MHXfcQW5uLuHh4dXez9/bk5yi0urbNXX7oCuEqEyGRggh7MaOHcu7775LUVERv/zyi31CnFqtJiYmhjfeeAOTyURmZiYJCQls3769xvN5enpSUlKC1Wqt8XivXr1QKpWsWbNGhkWIJuHKv4p07dq1xhAMMDw4CLO16glxZquV4cFBTqtPCFFOgrAQwm727Nm0atWKqKgoFi9eTFRUlL3t2Wef5dKlS9xyyy1MmjSJCRMmOAyVqEr37t3x9/dn6NChDqtAXH68IgzHxcWRn58vwyKE29Pr9Xz00UekpaVd0+MSwoIZ1DGwUhg2W62EdWxFQliwM8sUQgAKm6xRJIRTHDt2DCif/FXBZDKRmppK79690Wq1rirtmlmtVoxGI97e3iirmbzjbCtWrODkyZM8/fTT1/V4Z9ZcUlICgEajqdN5rqbi9dGvXz88PT3r9VrOIjXX7PKJcUqlkgcffJCAgIBaP77EZGbON3v4X3oWl4pKCPDRcEtoO966PRKNp3NGM1b1s0qI5kp6hIUQLlVUVMTRo0dZtWoV8fHxri5HiOt25eoQI0eOvKYQbLZYmb8hhbRsPe38tHRu4Uk7Py1p2XoWbNgv6wgLUQ8kCAshXOrChQtMnjyZiIgIBgwY4OpyhLguV4bgmJgYIiIirukcq1IySDmbR05RKccvFpCuL/9/TlEp+86WryMshHAuWTVCCOFSISEh7N+/39VlCHHdnBGCAbalX+DUJQOGUjMAFqsNY5kFY5mRwlIz29IvyIYaQjiZ9AgLIYQQ18lZIRggNesShlIzVy4eqAAMpWX8knWpbsUKISqRICyEEEJcp/Pnz1NQUADULQQDFJZUDsEVFEBBifm6zy2EqJoMjRBCCCGuU+/evbFYLJSUlNQpBAO08FKTVWCrckMZm81GC9lQQwinkyAshBBCXAObzTGs3njjjU45b/8OAfxWYMRQWgaXnd9ms+Hr5Un/9v5OuY4Q4g8yNEIIIYSoJb1ezyeffEJmZqbTz31LSFu6BvrQ3k+Lt1qFWgneahXt/bR0DdRyS0hbiSmFdgAAIABJREFUp19TiOZOgrAQolb27NnDxIkTmTJlCk888QRlZWWuLkmIBlUxMS47O5s1a9ZgMBicev6EsGDCOrWita8XPYL8CG2poUeQH619vQjv1Fp2lhOiHkgQFkLUSqdOnVi+fDkrVqzghhtuYMuWLa4uSYgGc+XqECNGjMDX19ep11CrlCwZG0avoJZkFRRzprCUrIJiegW15OWxg1Cr5Fe2EM4m7yohRK20a9fOvuWwh4cHKpXKxRUJ0TCcuURaTSrvLOclO8sJUc8kCAshADAYDPTq1YtBgwYxaNAg4uLiWLFiRaX7nT17lu3bt3PrrbfW+Zr5+fk8+uijDBo0iNtuu43ExMRaPe7SpUsMHjyYhISEa2pfv349Y8aMYeDAgcTExLB37946PwfRtDVUCIbyneX2Z+ahVjr+alYrlaRkys5yQtQHWTVCCAFAWloaLVq0YM+ePVitVpKTk5k7dy7R0dF06NABKA8Ff/vb31iyZAkeHnVfyun555/Hw8OD7du3c/ToUaZPn07v3r3p3r17jY/7+9//To8ePTCbq15Xtar2HTt28Oqrr/LGG2/Qv39/Ll68WOf6RdPWkCEYYHtGdqUQXEGtVLI9I1t2lhPCyaRHWAgBwJEjR+jTp4/9dnh4ODabjfz8fABMJhOPPfYYTzzxBCEhIXW+XnFxMZs2bWLOnDn4+PgQERHByJEj+fbbb2t83O7duzlz5gy33357le0///xzle3vvPMOM2bMYODAgSiVStq2bUvbtjILX1Tv+PHjDRaCAfKNpprbS2SCqhDOJj3CQjRiZouVVSkZbM/IJt9owt/bk+HBQSSEBTt94szRo0fp27cvUD5M4u233yYkJIQePXoAsHbtWo4ePco777wDQEJCAmPHjnU4x/Tp09m3b1+V52/fvj1JSUn226dOnUKpVBIc/MdM+F69erFnz55qazSZTLzwwgu89tprHD58uNr2119/3aHdYrHwyy+/cOuttxITE0NpaSnR0dHMmzcPb2/vq31pRDMVGRlJaWkpGo2m3kMwgL+3J9mGErILS8g3migpM6EptuLv7UlQCw3+sqGGEE4nQViIRspssfJk0l6HMYM5RaWkZetJOZfHP3ThTg3DR44c4fTp0/z73/+mqKiIyMhIPvnkE9Tq8h8TkydPZvLkyTWe4/3336/19YqLi2nRooXDsRYtWlBUVFTj+YcPH07Pnj2rDMIff/xxle05OTmUlZWxadMmVq5ciVqtZsaMGbz77rvMnTu31jWLpu/KzTKGDx/eYNe+uUsb1h/JpNhkBoUCixWMZgvGgmIKSk3cGyHDIoRwNhkaIZq1jRs3MmrUKCIiIliwYAFGoxGAzz77jBEjRjB48GD+9re/UVJS0uC1NeTEGZPJRHp6Ol9//TUpKSm89957HD582CnjgKuj1WorrcNqMBjw8fGp8v6nTp3i22+/ZdasWdW2b9iwgZkzZ1Zqq1jtYurUqQQFBREYGMh9993Hjz/+WMdnIZzBbLGyfE860/+zi0mfbWP6f3axfE96g6+SoNfrWbFiBZcuXWrQ6zpQ4LCrXPkxBTYqb7sshKg76REWzdapU6eYP38+b731FjfffDNLlixhyZIlTJo0ic8++4wvv/wSHx8fHn30UT777DOmT5/eoPU15MSZEydOoFar6dKlCwC33nor3bp1Izk5mbvvvrvW53nggQeqHRrRoUMH1q9fb7/dtWtXLBYLp06domvXrkD5hL1u3bpV+fiUlBQuXLjAyJEjgfLwbjKZGDx4MD/++CP79+8nOzubmJiYKtvbtWvn0NMnGoeG/stHdS6fGLdy5UoeeOABvLy86v26l9t1+iI9WrfkgsFYPjTCZkXjocLf25O2vt7sOn2RaYOrfn8IIa6PBGHRbO3YsYMhQ4YQFRUFwMyZM4mNjWXw4MGYzWas1vLeKIVCUetfiDabDZPpjwkvl+++VnG+2rpUXIqthvZ8o+maz1mdw4cP21dqsFqt2Gw2hg8fzpYtW646HOJyH3zwQY3tl9er0WiIiYnhzTff5MUXX+TYsWNs2bKFlStXVvm8Ro8ezZAhQ+y3N27cyIYNG3j77bdRq9WMHj2aQYMG4eXlhUKhqNT+5z//meXLlzNs2DDUajWfffYZUVFRVV7LarVisVhQVvNBxFkqXh/utEufs2tekZLBvrO5qJVKrLY/XvFKhYK9Z3NYvucEU+q4o9rVatbr9axevdo+MS48PByFQuHwXm4IeUWl2LAR5KuhjdaT4uJitFotCqUSGzbyikqdUpPVaq3317YQ7kKCsGi2rFarw0QppVJJYWEhAwYMIDAwkKioKJRKJQMHDmTKlCm1OmdRURGpqamVjpeWlmKz1RRrK/P1VJJdaKm23cdDYR/KUVepqamEhoY6nG/o0KF8+OGHXLx40ek7aFV46qmneP755xk2bBj+/v7Mnz+fjh072uuYOXMmgwYN4v777wegZcuW9sd6e3ujUqlo2bIlRqMRhULhsArEle3Tpk0jJyeHuLg4vLy8iImJ4d57763ya2gymTh58mS9POeqpKWlNdi1nMVZNSelnKe02ERpte3H6e/hnK2Mq6rZYDCwadMm+zCdyMhIPDw8qnwf1zeLsZAig2PQLS4utv87QGFyWl319Z4Wwt0obNf621mIJiI9PZ277rqLf/3rXwwYMIBXXnmFlStXsnnzZt577z0eeughWrRoweOPP06/fv2uOqnq2LFj2Gw2h6XFysrKSEtLo3fv3vZxqrW1fO9Jlv38a5XDI8xWK/ff1I2pEXVfxqwqNpuNkpISNBqN2wwncGbNJSUlWCyWev/TeMXro1evXvU6HtuZnF3zlBU7ySmuLgZDa60XK6YMrdM1qqv5yp7g6OhowsLC6nStuliRksHHe06iViqxWa0OPcJmq5W/RobUuXccyn/2KZVKevbs6YSqhXBv0iMsmq3Q0FBeeOEFFi5ciNFoZMaMGWi1WpYuXUqfPn3o3LkzAI899hgzZ86s1eoCCoUCT0/PKtuu9U+RU8JD2H8+r9KEObPVSnjHVkwJD6m3P29ePizEXf6E6syalUolSqWy2u+ls3l4eDTYtZzFWTUH+niRV8P6uYE+Xk772lxes16vZ+3atRgMBlQqVYOsE3w1UyO7czBLz/7MPPtrWPH7kJGITq2ZGtndKeOl3eU9LURDkCAsmi2DwUDPnj357rvvADh9+jR+fn5cunTJYSyhSqVySW+dWqXkVV3EH+sIl5Thr/Got3WEhXCF4cFBpGXrq/3Lx/DgoHq5bkpKSoNullEbl7/nt6VncabMSOdWvkSFtqv2PZ+fn09ycjJJSUn07t2bp59+2gWVC+G+JAiLZuvixYtMmTKFL7/8Ej8/P1555RUmTJiAn58fn376KaNHj6Zly5b885//ZNSoUS6pUa1SMjUyVLZVFU1WQlgw+87lVvmXj7COrUhwwlCAqkRFRVFcXEzbtm0bRQiuSm3GLf73v//l0UcfBconvS5cuNBthjMJ0RhIEBbNVnBwMLNmzWLSpEmUlZUxfvx4Hn74YRQKBTk5OUyaNAmLxcKoUaN4/PHHXV2uEE2Sq/7yoVQqGTt2bKMKjZcvJadUKCgqtZCea+BEzgm2Hz5BeFkmu3buYNmyZfbhDbGxsXh6etKpUyd0Oh0lJSWyW6IQ10AmywnhJMeOHQNwmIBiMpXP8u7duzdardZVpV0zq9WK0WjE29vbbcYTOrPmig1UrnWC47WqeH3069fPbcYIu2vNu3btIisrC51O12jfi8v3pPPhT8fJLTKRbzRhvHQBj9O/QMZ+ys79Cr//uk5OTmbw4MH2x509e5aOHTvWOtRX9bNKiOZKeoSFaADOWu9XNAyr1WrfWlq4P71ez6ZNm9BqtaxevZqpU6c2yu/v/05e4OSp0xQf/RlOHoDsU1y58nG/fv0qLfnXqVOnhitSiCam8f0kEKIJKi4uxsfHp1H9GVZUz2Qy1XtvsGgYFUukGQwGtFotN954Y6MMwQCHzl+iOOV7OPhfx4agLhA8iC6RI9j2/H2uKU6IJqpx/jQQoolp0aIFeXl5aDQaVCqVq8u5KqvVislkcrvl05xRs8lkQq1Wu83zFtW7fNtkgJEjRxIZGeniqsrXvD569CiJiYlcunSJv//97wAUlpohdBAc3ALtQrAGD0AZGoaiRSAApVoZ+yuEs0kQFqIBqFQqAgMDMZvNWCzV7xbXWFgsFk6ePEmPHj3cJhA6q2atVttoewxF7V0ZgiMiIggPD3dZPTabjYMHD5KUlERiYiLp6elA+drGCxYswN/fnxYaNVlBXeGel8DHH5vFAr9/cLYBfhp5XQrhbPKuEqKBKBQKPDw83GIHsYog6eXlvM0M6ps71izqR1U9wa56Tezbt49vv/2WxMREzpw549CmVqsZPnw4ubm5+Pv7069dAL/pjeRZ/bCUWbBarSitoFIqCNR6cmO7AJc8ByGaMgnCQgghmpTt27c7bJs8YMAAUlNTXVLLSy+9xA8//GC/7enpyW233UZ8fDxxcXEEBPwRbocHB7Hx2Lkqz2O12eptcxEhmjMJwkIIIZqU2NhYCgsLCQ0NJTIyEpOp+i2cncFsNrNjxw4SExPp0qULs2fPtrfFx8eze/duoqOjiY+PJzY2Fj8/v+pPZgNPpQJUSspHRqjAZkOm2QpRPyQICyGEaFI8PDy466676nV8u8lkYtu2bSQlJbFhwwby8vIACAkJYdasWfYVYiZOnMjEiRPx8fG56jl3nb5I9zZ+ZBeWkG80UWKzoFGr8Pf2JKiFhl2nLzJtcLd6e05CNEcShIUQQrg1vV7Pjh07GDVqlH0Mfn2EYLPZzKZNm0hKSiI5OZmCggKHdj8/PyIjI+3LJQK1CsAV8o0mlAoF7fy8CWqhochgwMfXF+XvoTq/5MpVhYUQdSVBWAghhNu6fGJcYWEhEydOrLeeYIVCwRNPPEF2drb9WGBgIGPGjCE+Pp6oqKg6Tcrz9/Ykp6i0+nZN459oK4S7kSAshBDCLV25OkRoaKhTQnBBQQGbN28mMTGRiRMnMn78eKB8vO748eNZt24d48aNQ6fTMXz4cKcttzc8OIijF/LtWyyXlJnQFFvx9/aklY+nTJYToh5IEBZCCOF2rgzBMTExREREXPf58vPzSU5OJikpie+//94+wU6hUNiDMMAzzzzD3//+93rZGGfigC68se0I5wuKUSgUWKxgNFsoLihG46Fi4oAuTr+mEM2dBGEhhBBuxVkhuKCggK+//pqkpCT+97//YTabHdo7depEjx49HI7VuOJDHa05eBpfLw86tNT+PlnOisajfLKcj6eaNQdPMzUytN6uL0RzJEFYCCGE23BmT7DBYODxxx93OBYSEkJ8fDw6nY6BAwfaV39oCNszslFXM7RDrVSyPSNbgrAQTiZBWAghhNvYvHnzNYfgc+fO8dVXX7Fw4ULeeustey9vhw4diIyMpKCggPj4eOLj4+nTp0+Dht/L5RWX8mtuAYbS8p5pi9WGscyCscxIYamZ1r5eLqlLiKZMgrAQQgi3MWbMGP79738zYMCAGkPwqVOnSExMJDExkZSUFPvxpKQk5s6da7+9Zs2aeh3ucC1yi0oxlJpRALbLjisAQ2kZOYYSF1UmRNMlQVgIIYTb8PHx4d57761ypYbMzExWr15NUlIShw4dqtQ+cOBAOnXq5HCssYRgO5sNquqRttkqHxNC1JkEYSGEEI2WXq/nwIED3HLLLfYhC9UtV3b06FFeeukl+22FQsFNN93EuHHjCA0NJTo6uk7r/Na3Vj5e+Hp5YCgtcwjDNpsNXy9PWvlqXFidEE2TBGEhhBCN0uUT44qLi4mLiwPg4MGDJCYm8sMPP7BhwwY0mvKAGBUVRUBAAH379iU+Pp5x48bRvn17TCYTqamprnwqtRKo9SK0dYtqt1gO9G68IV4IdyVBWAghRKNzeQi22Wzk5OTwzDPPkJSUxJkzZ+z327ZtG6NHjwbA09OT1NRUtFqtq8quk+HBQaRl66vcYtlstcqGGkLUAwnCQgghGpWKEHz8+HFSU1M5c+YMOTk5Dvfx9PTktttuo2XLlg7H3TUEAySEBbMnM4dNaecpLDHbd5ZroVEzutcNJIQFu7pEIZocCcJCCCFcymyxsiolg+0Z2VzMu8Sl/dto7wVFp086rPjg7e1NdHQ08fHxxMbGNr6Jbk6gsCkARflSEZf/XwhRLyQICyGEuKqKsLotPYvTWRfpcqKEqNB2JIQFo1ZVvQlEbRQbS7j3H8tI+d8WzPpc/HoOwmIs4jebjV433oJPSgqxsbHEx8cTExODj4+PE59V47IqJYMD5/O4oaUWq83mMDTiwLk8VqVkyIYaQjiZBGEhhBA1MlusPJm0l/2ZeSgVCopKLaTnGjiRc4KUc3n8Qxd+TWHYaDSydetWkpKSSFy3HmORwd6mCeqISqPFv+cgLrUP5fl/b+K+Yb3r42k1OtszslEqFGQVGMsny/0+NKJispzsLCeE80kQFkIIUaNVKRnsz8xDrVRivWw9W7VSSUpmbq17Kjdu3MiaNWvYvHkzBoPBoU2lbUGLbv1Qenjh12MgPp26A/DTuXzuc+7TabTyiktJzym0L59msYLRbMFYUPz7znKyfJoQziZBWAghRI22Z2SjVlbd46tWKmvdU7lmzRq+/vpr++2goCA8uw3Cq0cELYL7oFCpsJaZUHr8sUxYfklZ3Z+AmyjfWa4MhULhuLOcQoGh1ESu7CwnhNNd/8AuIZqAjRs3MmrUKCIiIliwYAFGoxGAnTt3otPpiIiI4KGHHuLSpUsurlQI18k3mmpuvyys5ufns2rVKhISEpg/f77D/XQ6He3bt+ehhx5i/fr17Ny5k54jRuPXrR8KlQrAIQQD+Gs8nPQs3ERVu8rVdFwIUScShEWzderUKebPn8/ChQvZuXMn3t7eLFmyhNOnTzNnzhyeffZZdu7cSWBgIK+//rqryxWiTswWK8v3pDP9P7uY9Nk2pv9nF8v3pGO2WK/6WP+rbOTgXVbMZ599xp133kmPHj149NFH+e677/j666+xWCz2+40fP57U1FSWLFlCnz59WL16NR7njqI/eaTqmpvZ2rnlO8upuXIzZRvg6+UhQyOEqAcyNEI0Wzt27GDIkCFERUUBMHPmTGJjY2nTpg3x8fFEREQA8H//93+17hG22WyYTH/0npWVlTn83124Y91Sc/XMFivzNhzgwPlL9iEO2YYSjlzIZ8+ZHJaMHVDjZLchnQM5ciEftVKJzVoenK1mMzn7vif/8G4OnE4j0eoYqENCQhg/fjwFBQUOKz1YrVb0ej2rV69Gr9fTO6gFNi9/Mi0Wh+EXZquVQR0CuePGGxzeU9fDXV4bLTUehAT6km0oQW80UWKzolEraentSZCvBj8vdZ2/FlD+PVBWM9RFiOZGgrBotqxWK97e3vbbSqWSwsJCdu/ezcCBA7n77rv59ddfGTZsGIsWLarVOYuKiqrcyjUtLc1ZZTcod6xbaq5s/clL7Pw1H5VSQekVbTuOF/GqtZBxIQHVPr6PykYnTwtpuQZ7YC42GsneuYGyvAv2+3Xt2pURI0YwYsQIunbtikKh4OTJkw7nMhgMbNq0yT5ZLjIyktievfjuVD4HLhoxmCz4eqoY2M6b0Z1VHD1y2DlfBBr/a6Ozysj+QgO+SgW+WhWg+r3FQmGhgc5t1U7bKtrX19cp5xHC3UkQFs3W0KFDefPNN9mzZw8DBgxg6dKlqFQqFAoFa9eu5aOPPqJr16489dRTvPLKK7zwwgtXPaePjw8hISH222VlZaSlpdGrVy88PNxnrKM71i01V++9E3vw87NU237G4k2/fv2qbDt9+jTr1q0ja906wofGYOk9jMzsPDoGBdJ1VBxZv+xFp9Mxbtw4unfvXmMdFT3BWq0WrVZLdHQ0YWFhAAwacP3P72rc5bXRu4+Vi7/33KuA4uJitFotFmBQh0CevErPfW2lp6fX+RxCNBUShEWzFRoaygsvvMDChQsxGo3MmDEDrVaLQqFg3Lhx9O3bF4AZM2bw8MMP1+qcCoUCT8/K4yk9PDyqPN7YuUPd9bXRQ0Oq769zYakZZQ2TrQpNFofrHz9+vHyN38REhx5IvxYt+M/Tc0lNTaVfv36oJg1DpVJVdcpK9Ho9a9euxWAwoFKpiImJsQ8/aiiN/fXsCbxx+0321/OZrFI6t/Fz+utZhkUI8QcJwqLZMhgM9OzZk++++w4o7/ny8/Oje/fuDmucWiwWbLYrp6+IxsDZGz24g8u3I843mvD39mR4cFCNQcnf25OcoisHRVzWrvHg0qVLvP/++yQmJlYaQqBQKLjpppsYN26cw/HahmCr1cratWvR6/UALgnB7kKtUjI1MpRJAzrZP3A05vAuhLtrWr8hhLgGFy9eZMqUKZw7d47CwkJeeeUVJkyYwLhx4/juu+84cOAAJSUlvP/++8TGxrq6XFGFyzd6uNzlGz00JRXB/6PdJ/g1p5CcolJ+zSnko90n+FvSvmpXgBgeHIT5islsNpsNm81mX5lBqVTyxhtv2EOwUqlkxIgR/OMf/+Dw4cMkJyfz4IMPXlfdSqWS2NhYPD09JQQLIRoV6REWzVZwcDCzZs1i0qRJlJWVMX78eB5++GHUajUvvfQS8+fPJzs7m6ioKJ544glXlyuq4KyNHtxFbYJ/Vc83ISyYfedySTmTg+m3DPJ/+Yn8I3voeMcMRgy92d6bPGrUKEpKSuxjflu3bu202jt16sSDDz5IixYtnHZOIYSoKwnColmbMmUKU6ZMqXR87NixjB071gUViWtxLRs9NAXXE/wtFgs/796N585vOfvNt+hzsu1tnfOO8w/dTPuQis8//9xp40f1ej1ZWVn07NnTfkxCsBCisZEgLIRwW7UZ+9qUXEvwP3HiBO+//z7r1q0jOzvb4X7e3t5ER0czZaLOYVyxM0PwypUrKSgoQKfT0adPH6ecVwghnE2CsBDCbQ0PDiItW19lL2lT3JWspuBvs1odgn9+fj4ff/yx/bavry+xsbHodDpiYmIcNrlwpooQXDExrqioqF6uI4QQziBBWAjhtirGvlasGlHBbLUS1rEVCWHBLqzO+a4M/tYyEwW/HiL/8G4KThxk0uff2O8bHh5O79696d+/PzqdjpEjR6LR1O8WvVeG4OjoaCIjI+v1mkIIURcShIUQbkutUvKqLuKPdVfLjHRu5et26wjXVkJYMD+dPMeOH7ZScORnCo7tx2oqsbe3yEoDBgLlwxy2b9+Ooob1g51JQrAQwh1JEBZCuLXmsu7qjz/+yIcffsiWLVswGo0ObS0CApnwp3hu7Os4FldCsBBC1EyCsBBCNEJmsxm1+o8f0enp6axbt85+u0OHDowfP574+HgGDx5c680tnM1sNvPvf/9bQrAQwi1JEBZCiEYiJyeHDRs2kJiYyLlz59i5c6e9V3fs2LG88847jBs3Dp1OR3h4eKPYKletVjNixAjWrVvHbbfdJiFYCOFWJAgLIYQL5ebm8sknn7BhwwZ27NiB9bId4I4cOULfvn0BCAoKYu/evQ023OFa9OnTh3bt2hEYGOjqUoQQ4ppIEBZCCBdYsWIFy5cvZ8+ePdhsNoe2Xr16odPpCAgIcDjeWEKwwWAgMzOTkJAQ+zEJwUIIdyRBWAghGoDJZHKYxJeSksLPP/9sv92/f3/i4+PR6XR0797dFSXWil6vZ9OmTbRs2ZK//OUvdO7c2dUlCSHEdZMgLIQQ9eT48eMkJSWRmJhISEgIn3zyib0tPj6eAwcOEBERwQMPPNCow28FvV7P6tWrMRgMaLVaLl68KEFYCOHWJAgLIYST2Gw2jh49yrfffktSUhJpaWn2tvT0dIqLi9FqtQBERUWRnJxMamoqXbp0cVXJtVbVEmnh4eEurkoIIepGgrAQQtSRzWZj8eLFfPvtt/z6668ObUqlkmHDhhEfH19pLLC7uDIER0ZGEhYW5uKqhBCi7iQICyHENbJarZhMJvuWxQqFgv/973/2EKxWq7nlllvQ6XSMGzeO1q1bu7LcOqmqJ9jDw8PFVQkhhHNIEBZCiFqwWCzs3r2bxMREkpKSuP/++3niiSfs7XfeeSetWrVCp9MxZswY/P39XVitc5SWljqE4JiYGPr3709qaqqLKxNCCOeQICyEENUwm81s376dpKQk1q9fT3Z2tr0tMTHRIQg/+OCDPPjgg64os954eXkxaNAgfvjhB2JiYoiIiMBkMrm6LCGEcBoJwkIIcYXc3FwWLVpEcnIyeXl5Dm2+vr6MGjXKPua3saztW1+GDBlCly5daN++vatLEUIIp5MgLIRo9kpKSlCpVPaxr76+viQlJVFQUACAn58fY8aMQafTMXLkSPvY4KZIr9djNptp1aqV/ZiEYCFEUyVBWAjRLBUVFbF582aSkpLYvHkzH330EbGxsUD5kICEhASKiorQ6XRERUU5bIZRX8wWK6tSMtiekU2+0YS/tyfDg4NICAtGrVLW+/UrJsaZzWYmT57sEIaFEKIpkiAshGg2CgoK2LRpE4mJiWzZsgWj0WhvS0xMtAdhgJdffrlBazNbrDyZtJf9mXmoleWhN6eolLRsPSnn8viHLrxew/CVq0OcPXvWLYNwVR8mhnQOpI/KPZeuE0LULwnCQogm7/Dhw7z44ots3bq10mSv9u3bo9PpuOOOO1xUXblVKRkOIbiCWqkkJTOXVSkZTI0MrZdrXxmCY2JiGDhwYL1cqz5V92HiyIV8Onta+fBGK/Xfry+EcCcShIUQTY5er6dly5b2215eXnz33Xf22507dyY+Ph6dTkd4eDhKZf0PO7ia7RnZlUJwBbVSyfaM7HoJwlWF4IiICKdfpyHU9GEiLa+I1QdPc9+Qni6qTgjRGEkQFkI0CVlZWXz77bcsWrSIn376if3799OxY0eEJ0h0AAAgAElEQVQAunXrxrhx4+jRowfx8fH079+/0a32kG+seVmy/JIyp1+zKYVgqPnDhEqpYMepHAnCQggHEoSFEG4rMzOTpKQkEhMT+fnnnx22MF63bh0PP/yw/fby5ctdUWKt+Xt7klNUWn27xrm7uRUVFTWpEAxX/zChNzr/w4QQwr1JEBZCuJ2NGzfy6quvkpKSUqntxhtv5E9/+hNxcXEuqOz6DQ8OIi1bX2WPptlqZXhwkFOvp9VqCQ4O5sCBA00iBMPVP0y09G78W0NXTPbblp7F6ayLdDlRQlRouwZbOUSI5kaCsBCi0cvNzXVYwcBoNDqE4LCwMMaNG0f37t2JjY1tkKXOnC0hLJh953IrjXE1W62EdWxFQliwU6+nUCgYPXo0vXr1omvXrk49t6vU9GHCYrUxrGtrF1RVe5dP9lMqFBSVWkjPNXAi50SDrBwiRHMk7yjRrG3cuJFRo0YRERHBggULHJbTAnjnnXe4//77XVRd82Wz2Thy5Agvv/wyQ4cOZejQoVgsFnv7qFGjuOWWW1i8eDGHDh3iv//9L48++qhbb/ygVil5VRfBA4O70611C1r7aujWugUPDO7utACk1+sxGAz22wqFosmEYCj/MDGoYyBmq9XhuNlqpVegN5MGdHFRZbVTm5VDhBDOJT3Cotk6deoU8+fP56233uLmm29myZIlLFmyhOeeew6AY8eO8eGHHxIZGeniSpsHm83GwYMH7WN+09PTHdp/+uknhg0bBpTv/PbNN9+4osx6pVYpmRoZWq+rQ3h4ePCXv/wFX19fp1/D1So+TNjXES4pw1/j8fs6woWNvjfVVSuHCNGcSRAWzdaOHTsYMmQIUVFRAMycOZPY2FieffZZbDYbTz/9NHfeeSenT5+u9TltNpvDOrVlZWUO/3cXDV33v/71L5YtW8bZs2cdjqvVakaMGMH48ePp1q1bpTWAL+eOX+uGqlmv17N69Wr7xLgTJ07Qt2/f6zqXO3ydJw3oxKQBney3y8rKSEtLa9Q1A+QVlWL9fcKn7fdebZvVivX3cJxXVFrje6C2rFZro1gyUIjGQIKwaLasVive3t7220qlksLCQvR6PWvWrCE8PJzu3btfUxAuKioiNTW10vG0tDSn1NzQ6qNui8VCQUEBAQEBDtepCMEeHh6Eh4czYsQIbr75Zlq0aAGUrxCRmZnpkprrW33WbDAY2LRpk31IRGRkJFartcrX6bWQr7PzWYyFFBkcg25xcbH93wEKU52/bxWa4l8EhLgeEoRFszV06FDefPNN9uzZw4ABA1i6dCkqlYoTJ07w7bffsnbtWpKTk6/pnD4+PoSEhNhvV/RE9erVCw+Pxj9jvYKz6zabzezcuZN169aRnJxM7969+c9//mNvf+CBBygsLGT8+PHExMTYw68ra24I9V1zRU+wVqtFq9USHR1NWFhYnc4pX+f6oyvz5eM9J1ErldisVoqLi9FqtSiUSsxWK7qwEPr1q/ukySuHHQnRnEkQFs1WaGgoL7zwAgsXLsRoNDJjxgw0Gg2LFy9m4cKFDr3FtaVQKKpcscDDw8MtVzKoS90mk4lt27aRmJhIcnIyeXl59ra8vDwMBgOBgYFA+aoPzlrn1x2/1vVRs16vZ+3atRgMBlQqldOXSJOvs/NNjezOwSx9+aoRvw9dUCiVWG02Ijq1Zmpkd6eMc5ZhEUL8QYKwaLYMBgM9e/a0b717+vRp/P39OXnyJLNmzQLKe5LMZjM6nY6kpCRXlus2bDYbc+bMITExkYKCAoc2Pz8/xo4di06nw8fHx0UVNn0FBQVNbrOM5uDyyX7b0rM4U2akcytfWUdYiHokQVg0WxcvXmTKlCl8+eWX+Pn58corrzBhwgRmzpxpv89XX33F+vXrWbZsmQsrbdyKioooKSmxr/OrUCjIysqyh+DAwEB7+I2KimrUPXJNhVarpVWrVuj1egnBbqZi5ZBJAzqRmppKv3795D0jRD2SICyareDgYGbNmsWkSZMoKytj/PjxDlvyiuoVFBSwadMmEhMT2bJlC3/961954YUX7O2TJ0+mU6dOxMfHM2zYMNRq+VHTkNRqNRMmTCA9PZ2ePXu6uhwhhGi05LeTaNamTJnClClTqm2fMGECEyZMaMCKGq9Lly6RnJxMYmIiP/zwg8MyTomJiTz//PMoFAoAbr/9dm6//XZXldosFRQUoNFo7L2HarVaQrAQQlyFBGEhRI3y8vJ48MEH+fHHHzGbzQ5tnTt3Jj4+Hp1O56LqBPyxWYa/vz933HGH/CldCCFqSYKwEMLBb7/9hkajsd/29/cnLS3NHoJDQ0P505/+hE6no3///vZeYOEaFSFYr9ej1+s5efIkvXr1cnVZQgjhFiQICyHIzMwkMTGRpKQkfv75Z9555x37n9WVSiXTp0+nuLiY+Ph4evfuLeG3kbg8BANER0dLCBZCiGsgQViIZiojI4OkpCQSExNJSUlxaFu3bp3D+NLZs2c3dHniKqoKwZGRkS6uSggh3IsEYSGamYMHDzJ79uwqt2oNCwsjPj6eMWPGVFoDWDQeEoKFEMI5JAgL0YTZbDaOHTtGz5497cMZ2rdvzy+//AKUr/k7ZMgQdDod48ePp2PHjkD5rnBVBWXhehKChRDCeSQIC9HE2Gw2Dh48aB/2kJ6ezvbt2+nTpw8AQUFBPPLIIwQHBzNu3DjatWvn4orFtfDw8ECj0aDX6yUECyFEHUkQFqIJsFqt7N27l6SkJJKSkjhz5oxDe2Jioj0IA7z44osNXaJwEq1Wy6RJk8jIyKBv376uLkcIIdyaBGEh3NzXX3/NwoUL+e233xyOe3l5cdttt6HT6RgzZoyLqhPOYDAY0Gq1KJVKoDwMSwgWQoi6kyAsGpVp06bx1FNPOfReij+YzWaOHz/u8PUJCAiwh2Bvb2+io6OJj48nNjYWPz8/V5UqnKRiTHDHjh0ZN26cPQwLIYSoOwnCwqW++OIL7r77bvvtgQMHcvfdd3Pbbbfx+OOP2ydvNQdmi5VVKRlsz8gm32jC39uT4cFB3HHjDezY/iOJiYls2LABk8nEsWPH0Gq1AAwbNozJkyczatQoYmJi8PHxcfEzEc5y5WYZffv2JSQkxNVlCSFEkyFBWLjEyZMnWbhwIYMHD3Y4/thjjzF58mTefvttdDodEydO5JFHHiEgIMBFlTYMs8XKk0l72Z+Zh1qpxFpm4te9h9j83k88eTyFMmOxw/2///57xo8fD5RPnlq6dKkryhb16MrVIWJiYiQECyGEk0kQFi6xZcsWvL29eeSRRyq1BQUF8eKLLzJt2jReffVVRo0axQMPPMC0adMctv5tSlalZNhD8G9b1pC9Yx1WU6nDfQIDAxk7diw6nY6oqCgXVSoaQlUhOCIiwsVVCSFE0yNBWLjEAw88gKenJ1OnTmX16tVV3qdbt2688MILfPLJJ7z11lusWLGC2bNnc+eddzaZLX4LCgo4f/482zMuof597KfSS2MPwWrflvj3uYm+Q29jzf89gFotb9mmTkKwEEI0HPmtKlxCoVBw7733Ehsb63B8y5YtHD58mCNHjvDLL7+Qm5tLmzZtGDlyJD169ODTTz9l5cqVvPnmm3Tp0sVF1dfNpUuXSE5OJikpia1bt9K7d2+Cpi2yt/v3HUyZPg//vjfh07knCqUST1+NhOBmQEKwEEI0LPnNKlyqffv2Drfnz5/PjTfeSP/+/bnzzjvp168fbdu2tbfPmjWL1157jccff5yvvvqqocu9bjk5OWzevJnExER+/PFHzGazve3gwYOMLcoDyie5eQUE0XHcvQ6P99d4NGS5wkWsVitWqxWQECyEEA1BgrBoVPbs2VNju1Kp5L777uOTTz5poIrqbtGiRezatcsecCp069YNnU5HfHw8B0t9WPbzr/bhEZczW60MDw5qqHKFCwUEBDB58mTOnDnDgAEDXF2OEEI0eRKEhdtp1aoVy5cvd3UZtabRaOwhuFevXsTHxxMfH0/v3r3tY537WqyknM+zT5irYLZaCevYioSwYJfULuqfyWTCZrPZbwcEBDT5VVKEEKKxkCAs3I5CoSA8PNzVZdTamDFjuOmmm7j99tvp3r17lfdRq5S8qov4Yx3hkjL8NR4MDw4iISwYtUo2UWiK9Ho969atw2g0cttttzWZSaBCCOEuJAgLUc8GDBhAv3798PT0rPF+apWSqZGhTI0MbaDKhCvp9XpWr16NwWBg165d9OzZkxtuuMHVZQkhRLMi3UxCCNHArlwdIjo6WkKwEEK4gARhIYRoQFeG4MjISMLCwlxclRBCNE8yNEIIIRpIVT3BHh6yNJ4QQriK9AgLIUQDqGqzDOkJFkII15IeYSEaCbPF+seqEUYT/t6esmpEE2IwGCgpKQH+2CzDZDK5uCohhGjeJAgL0QiYLVaeTNrrsI5wTlEpadl6Us7l8Q9duIRhN3fDDTdw1113ceHCBekJFkKIRkJ+s4pmbePGjYwaNYqIiAgWLFiA0WgEYOnSpYwYMYLBgwczd+5cCgsL67WOVSkZlTbTAFArlaRk5rIqJaNery/qR1lZmcPtG264QUKwEEI0IhKERbN16tQp5s+fz8KFC9m5cyfe3t4sWbKE5ORk1q9fz5o1a9i6dSv/v727D46iPsA4/lyOJOQIUaID2gF5UwMKgZALhiCNEtAYEWtmlNIYptYpUApWsFZxQG0zVkurRcDW+o4gihOsFBMgvo6CtI1VxJcJVUyio0giL8sdebvLbf+wOcmrAXK3l9vvZ8ZhdnO7PPmxmIff7f22qalJDzzwQEiz7Kis6fDxytK3ZXhHZU1If3/0PMMw9Pjjj+uDDz6wOgoAoBMUYdjWzp07lZmZqezsbMXFxWnhwoUqKSmRYRiaN2+ezjrrLLlcLuXn52vPnj0hzXKkvut7RY80+Lr8OiJLywfjjhw5otLSUh08eNDqSACADnCPMGwrEAgoISEhuB0TEyOPx6PLL79cAwYMCO5/6623lJKS0q1zmqbZ6gNQLW+Nt32LvK3+8X1U423o/OtxzrB+sKq7uSNJpGRueWLc8Uuk9e/fv8M/v0jJfCLIHB6hzBwIBBTTyTtQgN1QhGFbWVlZWrlypcrLyzVu3DitWbNGTmfrwrl582aVlJSouLi4W+c8duxYh2+FV1RUdHncOc56vXfUI2eMo93XmgOmzhnotOQt9u/LHYmszOz1elVWViav1yvp24dlxMbGfu+fHeMcHmT+TmJiYkjOC/Q2FGHY1siRI1VUVKRly5apvr5eCxYskMvlCv6AWL9+vVavXq1HH31UQ4YM6dY5+/XrpxEjRgS3fT6fKioqNGrUqC4fnDD6goBqS3dr91eHW90r7A8ElPaDZP06b1xYV43obu5IYnXmlplgl8sll8ulnJyc7/1gnNWZTwaZwyOUmfft29ej5wN6M4owbMvr9SolJUXbt2+XJFVXVyspKUn9+vXTmjVr9Pzzz2vdunU6//zzu31Oh8OhuLi4dvtjY2M73N8iTtKffzTxu3WEG3w6vW+s5esIf1/uSGRFZsMwVFxcLK/XK6fTGVwnuLsY5/Ag87e4LQL4DkUYtlVbW6uCggJt2rRJSUlJWrFihfLz87Vt2zatX79excXFGjx4cNjy9HHGqDBjpAozRobt90TPqKmpCS6xd6IlGABgHYowbGv48OFatGiRZs2aJZ/PpxkzZmj+/PkqLCyUx+PRVVdd1eq1L7zwgoVpEcnOO+88XX311fJ4PJRgAOhFKMKwtYKCAhUUFLTa9+yzz1qUBr1J20/ed3dlEQBA5OBGIQA4QYZh6IknnlBVVZXVUQAAp4AiDAAnoOVhGd988402bdqko0ePWh0JAHCSKMIA0E0tJbjlYRk//OEPlZSUZHEqAMDJoggDQDe0LcE5OTnKyMiwOBUA4FRQhAHge1CCASA6UYQBoAuUYACIXhRhAOhCVVUVJRgAohTrCANAF8aNG6fGxkY5HA5KMABEGYowALRhmqYcDkdwe+LEiRamAQCECrdGAMBxDMPQ008/rdraWqujAABCjCIMAP/X8sG4/fv369lnn1V9fb3VkQAAIUQRBgC1Xx0iKytLCQkJFqcCAIQSRRiA7bUtwdOmTZPb7bY4FQAg1CjCAGyNEgwA9kURBmBblGAAsDeKMADb+vDDDynBAGBjrCMMwLaysrJUV1enAQMGUIIBwIYowgBsy+FwaPr06VbHAABYhFsjANiGYRjauHGjPB6P1VEAABGAIgzAFlo+GFdZWalnn31WPp/P6kgAAItRhAFEvbarQ0yYMEGxsbEWpwIAWI0iDCCqsUQaAKAzFGEAUYsSDADoCkUYQFSiBAMAvg9FGEBU+te//kUJBgB0iXWEAUSlnJwceTweDR06lBIMAOgQM8KwtW3btmn69Olyu91aunSp6uvrJUkbNmzQxRdfrIyMDN1///0Wp8TJcDqdys/PpwQDADpFEYZtVVVV6fbbb9eyZcv09ttvKyEhQffdd5/27Nmjv/zlL3rmmWf00ksv6ZVXXtEbb7xhdVx8D8Mw9NZbb6mxsTG4z+FwWJgIABDpKMKwrZ07dyozM1PZ2dmKi4vTwoULVVJSopKSEs2cOVNDhw7VoEGDNGfOHL344otWx0UXWp4YV1lZqU2bNikQCFgdCQDQC3CPMGwrEAgoISEhuB0TEyOPx6Pq6mrl5OQE9w8bNkwbNmzo1jlN01RTU1Nwu+XpZb3tKWa9KXdLCT58+LAk6dxzz5Xf77c4Vff0pnFuQebwCGXmQCCgmBjmwQCJIgwby8rK0sqVK1VeXq5x48ZpzZo1cjqdOnbsWKuCHB8fr4aGhm6d89ixY/rggw/a7a+oqOix3OEU6bm9Xq/Kysrk9XolSRkZGUpISOjwzyCSRfo4d4TM4RGqzImJiSE5L9DbUIRhWyNHjlRRUZGWLVum+vp6LViwQC6XS5Ja3Wfa2NjYqhh3pV+/fhoxYkRw2+fzqaKiQqNGjepVj/TtDblbZoJdLpdcLpeys7OVkJAQ0Znb6g3j3BaZwyOUmfft29ej5wN6M4owbMvr9SolJUXbt2+XJFVXVyspKUljxozR559/HnxdVVWVhg0b1q1zOhwOxcXFtdsfGxvb4f5IF6m5DcNQcXGxvF6vnE6npk2bptTUVH3wwQcRm7krZA4PMn+L2yKA7/C3AbZVW1urgoICffnll/J4PFqxYoXy8/OVm5urF154QZWVlaqpqdG6det0xRVXWB0Xx3nttdd4WAYA4JQxIwzbGj58uBYtWqRZs2bJ5/NpxowZmj9/vvr06aMFCxbohhtuUH19vWbPnk0RjjC5ubkyDENjxoyhBAMAThpFGLZWUFCggoKCdvtnz56t2bNnW5AI3ZGQkKDCwkI5nU6rowAAejFujQAQ8QzD0CuvvNJqfWBKMADgVDEjDCCiGYahDRs2yDAMHTt2TDNnzuSJcQCAHsGMMICIdXwJlqQf/OAHlGAAQI+hCAOISG1LcE5OjjIyMixOBQCIJhRhABGHEgwACAeKMICIQgkGAIQLRRhARCktLaUEAwDCgiIMIKLk5eXptNNOowQDAEKO5dMARJTTTjtNN954o+Li4qyOAgCIcswIA7CUYRj65z//KdM0g/sowQCAcGBGGIBljv9gXENDgy655BKrIwEAbIQZYQCWaLs6RGJiosWJAAB2QxEGEHZtS/C0adPkdrstTgUAsBuKMICwogQDACIFRRhA2FCCAQCRhCIMICxM09Tf//53SjAAIGJQhAGEhcPh0OWXX66EhARKMAAgIrB8GoCwOfvss/Xzn/9cLpfL6igAADAjDCB0DMPQhx9+2GofJRgAECmYEQYQEsd/MM7n8yktLc3qSAAAtMKMMIAe13Z1iObmZosTAQDQHkUYQI9iiTQAQG9BEQbQYyjBAIDehCIMoEdQggEAvQ1FGMApa25u1saNGynBAIBehSIM4JQ5nU5NnTpVTqeTEgwA6DUowrC1Xbt2KS8vT+np6SosLFRVVZUkae3atZoyZYouuugi3XrrrWpoaLA2aC9w7rnnau7cuZRgAECvQRGGbTU3N+vmm29WUVGRysvLNWHCBBUVFenjjz/W2rVrtWnTJr322muqra3V2rVrrY4bcQzDUGVlZat9p512mkVpAAA4cRRh2JZhGDpy5Ij8fr9M05QkxcXF6fPPP5ff71cgEJAkORwOxcfHWxk14rR8MK64uFj79u2zOg4AACeFJ8vBtpKTk5Wfn685c+bI6XRqwIABeu6553T66acrOTlZ2dnZiomJ0fjx41VQUNCtc5qmqaampuC2z+dr9Wtv0VVuwzBafTCutrZWQ4YMCWu+jvTGsSZzeJC5tUAgoJgY5sEASXKYLVNhgM34/X6tWLFCU6dOVVpamlavXq3y8nI99NBDWrlypebOnav+/ftr8eLFGjt2rG655ZYuz7d37155vd4wpbeG1+tVWVlZ8PvMyMjQ6NGjLU4F4EQlJiYqJSXF6hiA5SjCsK3S0lJt3bpVq1evlvTtPcNut1vp6enKzs5WYWGhJGn37t1auHChduzY0eX59u7dK9M0NWLEiOA+n8+niooKjRo1SrGxsaH7ZnpYR7nbzgTn5ORowoQJVsZspTeONZnDg8yt7du3TzExMRRhQNwaARs7cOBAq7cdY2Ji5HA41KdPn1b7nU5nt38QORwOxcXFtdsfGxvb4f5I15LbMAwVFxfL6/VG/BJpvXGsyRweZP4Wt0UA3+FvA2wrMzNTu3bt0o4dO+T3+/Xwww9r6NChmjRpktavX68vv/xSXq9XDz30kKZPn251XMs0NTXxxDgAQFRiRhi2NXr0aN1zzz0qKirSwYMHlZqaqtWrV+vss8/WwYMHNWvWLDU3N2v69OlavHix1XEtExcXp4kTJ+rll1+mBAMAogpFGLY2Y8YMzZgxo93+JUuWaMmSJRYkikzp6ekaMmSIBg4caHUUAAB6DLdGAGjHMAwdOnSo1T5KMAAg2lCEAbTSsjpEWVmZDhw4YHUcAABChiIMIKjliXGGYaipqUlfffWV1ZEAAAgZijAASa1LsCS53W6lpaVZnAoAgNDhw3IA2pXgqVOn9rr1VgEAOFHMCAM217YE5+TkKD093eJUAACEHkUYsLG6urp2JTgjI8PiVAAAhAdFGLCxhIQEpaSkSKIEAwDsh3uEARtzOBy69NJLdd5552nIkCFWxwEAIKyYEQZsxjAMHT16NLjtcDgowQAAW6IIAzbS8sG4DRs2tCrDAADYEUUYsInjV4c4cuSIqqqqrI4EAIClKMKADbRdIm3atGlKTU21OBUAANaiCANRrqMS7Ha7LU4FAID1KMJAFKMEAwDQOYowEKU8Hg8lGACALlCEgSjlcrl01llnSaIEAwDQER6oAUQpp9OpmTNnqrKyUueee67VcQAAiDjMCANR5OjRo2poaAhuO51OSjAAAJ2gCANRwjAMPfPMM9q4cWOrMgwAADpGEQaiwPGrQ+zfv1+fffaZ1ZEAAIh4FGGgl+toibQLLrjA4lQAAEQ+ijDQi7FOMAAAJ48iDPRSlGAAAE4NRRjohY4ePUoJBgDgFFGEgV4oPj5eiYmJkijBAACcLIowbG3Xrl3Ky8tTenq6CgsLVVVVJUl6++23ddVVV8ntdmvu3Lk6fPiwtUHbiI+P13XXXaeZM2dSggEAOEkUYdhWc3Ozbr75ZhUVFam8vFwTJkxQUVGRqqur9atf/Up33XWX3n77bSUnJ+uBBx6wOq48Ho+am5uD2/Hx8awOAQDAKeARy7AtwzB05MgR+f1+maYpSYqLi9OWLVtazbTecccdls8It3wwbtCgQbr66qvldDotzQMAQDSgCMO2kpOTlZ+frzlz5sjpdGrAgAF67rnntGLFCg0bNkzXX3+9Pv30U02ePFl33313t85pmqaampqC2z6fr9WvJ8MwDG3cuFGGYejQoUMaPXq0Ro4cedLn646eyB1uZA4PModHKDMHAgHFxPCGMCBJDrNlKgywGb/frxUrVmjq1KlKS0vT6tWrVV5eroSEBO3du1ePPfaYhg0bpt/85jdKTk5WUVFRl+fbu3evvF5vj2b0er0qKysLnjcjI0OjR4/u0d8DgP0kJiYqJSXF6hiA5ZgRhm2VlZVp//79yszMlCQtXrxYbrdb6enpuvLKK3XhhRdKkhYsWKD58+d365z9+vXTiBEjgts+n08VFRUaNWqUYmNjTyhfy0ywy+WSy+VSTk6OJkyYcELnOFmnktsqZA4PModHKDPv27evR88H9GYUYdjWgQMHWr3tGBMTI4fDodjY2FYzu83NzeruGycOh0NxcXHt9sfGxna4vzOGYai4uFher1dOp9OyJdJONHckIHN4kDk8QpGZ2yKA7/C3AbaVmZmpXbt2aceOHfL7/Xr44Yc1dOhQzZ07V9u3b9fu3bvV0NCgv/3tb7rsssvClosnxgEAEB7MCMO2Ro8erXvuuUdFRUU6ePCgUlNTtXr1ag0ePFj33HOPbr/9dtXU1Cg7O1tLliwJWy6HwxGcsaEEAwAQOhRh2NqMGTM0Y8aMdvvz8vKUl5dnQSIpKSlJs2fPVlVVlVJTUy3JAACAHXBrBBAB6urqWt2HnJSURAkGACDEKMKAxQzD0Nq1a/Xqq692+0N5AADg1FGEAQsd/8G4d955R1988YXVkQAAsA2KMGCRtqtD5OTk6JxzzrE4FQAA9kERBizQUQnOyMiwOBUAAPZCEQbCjBIMAEBkoAgDYUQJBgAgclCEgTBqbGwMPtaZEgwAgLV4oAYQRgMHDtSsWbP01VdfKS0tzeo4AADYGkUYCLGWGeAWgwYN0qBBgyxKAwAAWnBrBBBChmFoy5Ytevfdd62OAgAA2qAIAyFiGIY2btwor9erV199VQcOHLA6EgAAOA5FGAiBjlaH4HYIAAAiC0UY6GFtS3BGRoYmTJhgcSoAAPTWdKoAAAq2SURBVNAWH5YDepDf7283ExwbG2txKgAA0BFmhIEeVFNTEyzB06ZNYyYYAIAI5jBN07Q6BBAN9uzZo/r6enk8Hp1++unq37+/AoGA6urq5HK5FBPTe/7d2Rtzkzk8yBweocxcV1cnp9Op1NTUHj0v0BtRhIEe8tFHH6m5uVnx8fFWRwGATjU2NsrpdOrCCy+0OgpgOYowAAAAbKl3vEcEAAAA9DCKMAAAAGyJIgwAAABboggDAADAlijCAAAAsCWKMAAAAGyJIgwAAABboggDJ2nXrl3Ky8tTenq6CgsLVVVVJUnKz8/X+PHjlZaWprS0NN13330dHl9WVqapU6dqwoQJWrp0qXw+nyWZ77zzzmDWtLQ0jR07VmPGjOnw+JtuukmpqanB1950000hz7xt2zZNnz5dbrdbS5cuVX19vSRpw4YNuvjii5WRkaH777+/0+OtGOfOMq9Zs0ZTpkzRRRddpFtuuUUej6fD4yNpnCP5eu4sd6Re048//rjuvPPO4HZn17Df79fy5cvldrt1ySWXqLS0tNNzPvjgg7rooos0adIkPf300yHLDkQtE8AJ8/v95sSJE8133nnHbG5uNh944AHzZz/7men3+81x48aZXq+3y+O//vpr0+12m++//75pGIb5k5/8xFy/fr0lmY/X3NxsXn/99eZTTz3V4Tkuu+wy85NPPglpzuNVVlaa48aNM9944w2zsbHR/O1vf2veeeed5vvvv29OnjzZrKqqMr/++mszNzfXfP3119sdb8U4d5a5tLTUzM3NNffv328eO3bMXLhwoXn33Xd3eI5IGedIvp67yn28SLimm5qazAcffNAcNWqUuXz5ctM0zS6v4ccee8wsLCw0PR6PWV5ebmZkZJiHDh1qd96tW7eaV155pfnNN9+Yn376qZmVlWXu3bs3pN8LEG2YEQZOgmEYOnLkiPx+v8z/P5wxLi5OVVVVOvPMM9WvX78uj3/55ZeVlZWl1NRUJSUlad68eXrxxRctyXy8559/Xj6fT3PmzGl3fH19vb7++msNGzYspDmPt3PnTmVmZio7O1txcXFauHChSkpKVFJSopkzZ2ro0KEaNGiQ5syZ0+H4WTHOnWU2DEPz5s3TWWedJZfLpfz8fO3Zs6fd8ZE0zpWVlRF7PXeVOxAIBF8TCdf0vffeq927d+u6664L7tu6dWun13BJSYluvPFGJSYmyu12a/Lkydq6dWu785aWlmr27Nk644wzNHLkSF1zzTXavHlzSL8XINpQhIGTkJycrPz8fM2ZM0djx45VcXGx7rjjDu3du1emaSo/P1+TJ0/W0qVL5fV62x1fVVWl4cOHB7eHDRumffv2WZK5RVNTk9asWaPbbrtNDoej3fGffPKJ+vbtq5/+9KeaNGmSfvnLX6q2tjakmQOBgBISEoLbMTEx8ng8qq6u7tb4WTHOnWW+/PLL9aMf/Si4/6233lJKSkq74yNpnP/73/9G7PXcVW7DMCRFzjU9f/58PfHEEzrzzDOD+7oas7bX99ChQ/XZZ5+1O29VVZVGjBjR6nXhGHcgmlCEgZPg9/vVv39/rV27Vu+9956uueYa/frXv1YgEFBqaqr++te/qrS0VIcPH9Yf/vCHdsfX1dW1+gEeHx+vhoYGSzK3eOmllzR8+HClpaV1eHxdXZ3Gjx+voqIivfbaazrjjDN02223hTRzVlaW3nzzTZWXlwdLjdPp1LFjx7o1flaMc2eZm5qagq/ZvHmzSkpK9Itf/KLDzJEyzn6/P2Kv565yt4x1pFzTAwcO7PD37mzM6urq1Ldv3+DX+vbtG7xnu+052r4uHOMORJM+VgcAeqOysjLt379fmZmZkqTFixfL7Xbrwgsv1IwZM4KvW7RokebNm9fu+ISEBDU2Nga3GxsbW/1QDGfmyspKDR8+XP/4xz+Un5/f6fGZmZnBYyVpyZIlmjRpkhoaGlr9MO5JI0eOVFFRkZYtW6b6+notWLBALpdLkro1flaMc2eZExMTJUnr16/X6tWr9eijj2rIkCHtjo+kcc7JydHMmTODr4uk67mr3C1jHYnXdIuuxqxv376tvtbQ0NCt67uz1wHoHEUYOAkHDhxo9an4mJgYORwOlZWVacyYMZo8ebIkyefztbsPV5KGDx+u9957L7hdVVUV8vsUO8vcp08f1dfX6z//+Y9WrVrV6fFvvvmmvF6v8vLyJH37vcXExKhPn9D9b8Tr9SolJUXbt2+X9O1bxklJSRozZow+//zz4Os6Gz8rxrmzzP369dOaNWv0/PPPa926dTr//PM7PD6Sxvn111/XgAEDIvJ67ip3v379IvaabjF8+PBOr+GWr7X8Q6m6ulput7vTc7SU+erq6rDeWw5EA26NAE5CZmamdu3apR07dsjv9+vhhx/W0KFD5XK5dN9996m2tlaGYWjVqlWtZohb5OTkaMeOHdq9e7c8Ho8effRRXXHFFZZkHjx4sCoqKjR48GAlJSV1erzf79fvf/97VVdXq76+Xn/84x+Vm5sb0tJQW1urgoICffnll/J4PFqxYoXy8/OVm5urF154QZWVlaqpqdG6des6HD8rxrmzzNu2bdP69eu1YcOGTkuwFFnjfPjw4Yi9nrvKLSlir+kWXV3Dubm5euSRR+T1evXee+9p586dysnJ6fAc69atU01NjT777DO9+OKLYRl3IKpYu2gF0Htt2bLFvOyyy8z09HTzhhtuML/44guzubnZvPfee81JkyaZbrfbXL58udnY2Giapmlu3rzZzMvLCx7/8ssvm9OmTTPT09PNZcuWmT6fz5LMpmmaJSUl5vXXX9/u9W0zP/LII+aUKVPM8ePHm4sWLTKPHDkS8szr1683J0+ebE6cONH83e9+FxynDRs2mNnZ2ebEiRPNP//5z51mtmKcO8r84x//2LzgggvM8ePHB/+75pprOswcKeMc6ddzZ7lNMzKv6VWrVgWXTzPNzq/hxsZG86677jInTpxoXnrppeb27duDX1u+fHnwHIFAwFy1apU5efJkMysrKyxL1gHRxmGa/19HCQAAALARbo0AAACALVGEAQAAYEsUYQAAANgSRRgAAAC2RBEGAACALVGEAQAAYEsUYQAAANgSRRgAAAC2RBEGAACALVGEASDCmKYpt9utV155pdX+lStXas6cORalAoDoQxEGgAjjcDg0ZswYffjhh8F9NTU1Wrt2rW699VYLkwFAdKEIA0AESk1N1ccffxzcXrVqlS655BKNHTvWwlQAEF0owgAQgcaOHauPPvpIkvTpp59qy5YtWrx4scWpACC6UIQBIAKlpqbqm2++0YEDB/SnP/1J1157rc455xyrYwFAVKEIA0AEGjRokAYOHKinnnpK//73v7VgwQKrIwFA1KEIA0CEGjt2rJ588kndeOONSk5OtjoOAEQdijAARKjzzz9fZ555pm644QarowBAVKIIA0AEam5uVllZmRYuXCiXy2V1HACISn2sDgAA+E4gENChQ4f05JNPKjY2Vtdee63VkQAgajEjDAAR5N1339WUKVO0c+dOrVy5Uk6n0+pIABC1HKZpmlaHAAAAAMKNGWEAAADYEkUYAAAAtkQRBgAAgC1RhAEAAGBLFGEAAADYEkUYAAAAtkQRBgAAgC1RhAEAAGBLFGEAAADY0v8AgqMMgNAzWbUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 784.08x871.2 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can use the Pycaret's built-in plot_model() function to generate Residuals and Error plots for the finalized blend model. \n",
    "fig = plt.figure(figsize=(9,10))\n",
    "ax = fig.add_subplot(2,1,1)\n",
    "plot_model(finalize_blender, plot='residuals', save=True, verbose=False, scale=1.1)\n",
    "ax = fig.add_subplot(2,1,2)\n",
    "plot_model(finalize_blender, plot='error', save=True, verbose=False, scale=1.1)\n",
    "plt.savefig('plots_pred_final_blender.png', dpi=300, pad_inches=0.25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9b1cfd4af0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# An interesting view is looking at the Actual Values and Predicted Values (Label) in a histogram over the entire dataset. This shows the distribution between the values. We can see how the Predicted Values seem to peak in a more distributed manner and skew to the left. \n",
    "plt.figure(figsize=(15,5))\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.distplot(pred_final_blender[\"Actual Value\"],\n",
    "                bins=20,\n",
    "                kde=False,\n",
    "                color=\"#c6690c\")\n",
    "sns.distplot(pred_final_blender[\"Label\"],\n",
    "                bins=20,\n",
    "                kde=False,\n",
    "                color=\"#664697\")\n",
    "plt.title(\"Distribution between Actual Value and Predicted Value (Label)\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xlabel(\"FCR Value\")\n",
    "plt.xlim((74,101))\n",
    "plt.legend(('Actual Value', 'Predicted Value (Label)'), ncol=2, loc='upper left', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9aff06ddf0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can plot the Predicted Value (Label) and Actual Value over the entire dataset. \n",
    "sns.regplot(x=\"Actual Value\", y=\"Label\", data=pred_final_blender, lowess=False, scatter_kws ={'s':50}, line_kws={\"color\": \"#664697\"}, color=\"#c6690c\")\n",
    "plt.title(\"Linear Relationship between Actual Value and Predicted Value (Label)\")\n",
    "plt.ylabel(\"Predicted Value (Label)\")\n",
    "plt.xlabel(\"Actual Value\")\n",
    "plt.xlim((74,101))\n",
    "plt.legend(('Best Fit', 'Actual Value vs Predicted Value (Label)'), ncol=2, loc='upper left', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9aff71ae50>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can compare the Predicted Values (Label) and Residuals in an error plot over the entire dataset. \n",
    "sns.residplot(x=\"Actual Value\", y=\"Label\", data=pred_final_blender, lowess=False, scatter_kws ={'s':50}, line_kws={\"color\": \"#664697\"}, color=\"#c6690c\")\n",
    "plt.title(\"Residuals for the Predicted values in Final Blend Model\")\n",
    "plt.ylabel(\"Residuals\")\n",
    "plt.xlabel(\"Predicted Value (Label)\")\n",
    "plt.xlim((74,101))\n",
    "plt.legend(('Best Fit', 'Predicted Value (Label)'), ncol=2, loc='upper left', fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Client Presentation and Next Steps\n",
    "\n",
    "We presented the results of our experiment to the client, thinking they would be impressed. Initially, they shared their excitement of leveraging the models to predict performance. However, having a RMSE of ~4.5 is not going to be viewed in a positive manner with employees and their supervisors. \n",
    "\n",
    "However, we decided to approach it differently. Instead of looking at a finite value, we determined that if we can predict whether the employee was going to increase or decrease performance then this would be quite beneficial. In particular, if we can determine that the employee's performance was going to decrease then the supervisor can preempt it and try to mitigate the decrease in employee's performance before it occurs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generating the classification label based on the regression analysis. \n",
    "pred_final_blender.loc[(pred_final_blender['Actual Value'] >= pred_final_blender['FCR Week before']) & (pred_final_blender['Label'] > pred_final_blender['FCR Week before']), 'Pred_Class'] = 'True Positive'\n",
    "\n",
    "pred_final_blender.loc[(pred_final_blender['Actual Value'] < pred_final_blender['FCR Week before']) & (pred_final_blender['Label'] < pred_final_blender['FCR Week before']), 'Pred_Class'] = 'True Negative'\n",
    "\n",
    "pred_final_blender.loc[(pred_final_blender['Actual Value'] >= pred_final_blender['FCR Week before']) & (pred_final_blender['Label'] < pred_final_blender['FCR Week before']), 'Pred_Class'] = 'False Negative'\n",
    "\n",
    "pred_final_blender.loc[(pred_final_blender['Actual Value'] < pred_final_blender['FCR Week before']) & (pred_final_blender['Label'] > pred_final_blender['FCR Week before']), 'Pred_Class'] = 'False Positive'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True Positive     47\n",
       "True Negative     29\n",
       "False Negative    23\n",
       "False Positive     3\n",
       "Name: Pred_Class, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Saving the classification results to a CSV file and create a data type to store the classification metrics. \n",
    "pred_final_blender\n",
    "pred_final_blender.to_csv('pred_final_blender.csv')\n",
    "\n",
    "cf_values = pred_final_blender['Pred_Class'].value_counts()\n",
    "cf_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Performance Increase'), Text(0, 1.5, 'Performance Decrease')]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create confusion matrix table, along with labels, counts and percentage\n",
    "cf_matrix = np.array([[cf_values.loc['True Positive'], cf_values.loc['False Negative']],\n",
    "            [cf_values.loc['False Positive'], cf_values.loc['True Negative']]])\n",
    "\n",
    "group_names = ['True Positive', 'False Negative\\n(Type II Error)', 'False Positive\\n(Type I Error)', 'True Negative']\n",
    "\n",
    "group_counts = ['{0:0.0f}'.format(value) for value in\n",
    "                cf_matrix.flatten()]\n",
    "                \n",
    "group_percentages = ['{0:.2%}'.format(value) for value in\n",
    "                     cf_matrix.flatten()/np.sum(cf_matrix)]\n",
    "                     \n",
    "labels = [f'{v1}\\n{v2}\\n{v3}' for v1, v2, v3 in\n",
    "          zip(group_names,group_counts,group_percentages)]\n",
    "          \n",
    "labels = np.asarray(labels).reshape(2,2)\n",
    "\n",
    "# Create confusion matrix plot using Seaborn\n",
    "ax = plt.subplot()\n",
    "plt.rcParams.update({'font.size': 14})\n",
    "sns.heatmap(cf_matrix, annot=labels, fmt='', cmap='PuOr')\n",
    "\n",
    "# Set labels, title and ticks\n",
    "ax.set_xlabel('Predicted Class', fontsize=14)\n",
    "ax.set_ylabel('Actual Class', fontsize=14)\n",
    "ax.set_title('Confusion Matrix', fontsize=14, fontweight='bold')\n",
    "ax.xaxis.set_ticklabels(['Performance Increase', 'Performance Decrease'], fontsize=12)\n",
    "ax.yaxis.set_ticklabels(['Performance Increase', 'Performance Decrease'], va='center', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  74.51%\n",
      "Sensitivity (Recall):  67.14%\n",
      "Specificity (True Negative Rate):  90.62%\n",
      "Precision (Positive Predictive Value):  94.00%\n",
      "F1 Score (harmonic mean of Precision and Sensitivity):  78.33%\n"
     ]
    }
   ],
   "source": [
    "# Below are the classification metrics. \n",
    "accuracy = '{0:.2%}'.format((cf_values.loc['True Positive'] + cf_values.loc['True Negative']) / (cf_values.loc['True Positive'] + cf_values.loc['False Positive'] + cf_values.loc['True Negative'] + cf_values.loc['False Negative']))\n",
    "print('Accuracy: ', accuracy)\n",
    "\n",
    "sensitivity = '{0:.2%}'.format((cf_values.loc['True Positive']) / (cf_values.loc['True Positive'] + cf_values.loc['False Negative']))\n",
    "print('Sensitivity (Recall): ', sensitivity)\n",
    "\n",
    "specificity = '{0:.2%}'.format((cf_values.loc['True Negative']) / (cf_values.loc['True Negative'] + cf_values.loc['False Positive']))\n",
    "print('Specificity (True Negative Rate): ', specificity)\n",
    "\n",
    "precision = '{0:.2%}'.format((cf_values.loc['True Positive']) / (cf_values.loc['True Positive'] + cf_values.loc['False Positive']))\n",
    "print('Precision (Positive Predictive Value): ', precision)\n",
    "\n",
    "f1 = '{0:.2%}'.format((2 * cf_values.loc['True Positive']) / (2 * cf_values.loc['True Positive'] + cf_values.loc['False Positive'] + cf_values.loc['False Negative']))\n",
    "print('F1 Score (harmonic mean of Precision and Sensitivity): ', f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Conclusion\n",
    "\n",
    "Getting accuracy over 70% is more likely to move this model in to production. We can confidently predict whether the employee will increase or decrease in performance. Even more so, for employees we think will decrease in performance, we can get ahead of that and ask the supervisor to coach them and support them to mitigate the decrease in performance.\n",
    "\n",
    "We can see how versatile Pycaret is in experimenting real case scenarios. Most companies do not have terabytes of data so as a citizen-data-scientists, we can use Pycaret in many daily business situations to make more meaningful and data-driven decisions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2020-09-04 12:00:01 -0500] [16848] [INFO] Starting gunicorn 20.0.4\n",
      "[2020-09-04 12:00:01 -0500] [16848] [INFO] Listening at: http://127.0.0.1:5000 (16848)\n",
      "[2020-09-04 12:00:01 -0500] [16848] [INFO] Using worker: sync\n",
      "[2020-09-04 12:00:01 -0500] [16850] [INFO] Booting worker with pid: 16850\n",
      "^C\n",
      "[2020-09-04 12:14:16 -0500] [16848] [INFO] Handling signal: int\n",
      "[2020-09-04 12:14:16 -0500] [16850] [INFO] Worker exiting (pid: 16850)\n"
     ]
    }
   ],
   "source": [
    "# Note: since we had enabled log_experiement and log_plots in the setup() function. Pycaret does a wonderful job in leveraging MLflow so all experiments can be logged and analyzed. This can be accomplished with a simple one-line command and viewed in your browser. \n",
    "!mlflow ui"
   ]
  },
  {
   "attachments": {
    "Screen%20Shot%202020-09-04%20at%2010.10.33%20AM.png": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Screen%20Shot%202020-09-04%20at%2010.10.33%20AM.png](attachment:Screen%20Shot%202020-09-04%20at%2010.10.33%20AM.png)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## 9. Sources and References\n",
    "\n",
    "Matplotlib. Retrieved August 24, 2020, from https://matplotlib.org\n",
    "\n",
    "Numpy. Retrieved August 24, 2020, from https://numpy.org/\n",
    "\n",
    "Pandas. Retrieved August 24, 2020, from https://pandas.pydata.org/\n",
    "\n",
    "Pycaret. (2020, May 11). Retrieved August 24, 2020, from https://pycaret.org/\n",
    "\n",
    "Python. Retrieved August 24, 2020, from https://www.python.org/\n",
    "\n",
    "Scikit Learn: Learn. Retrieved August 24, 2020, from https://scikit-learn.org/\n",
    "\n",
    "Seaborn. Retrieved August 24, 2020, from https://seaborn.pydata.org/"
   ]
  }
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