LiL-HomePrediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ab430c70-f5df-476f-a593-167b6652c45c",
   "metadata": {},
   "source": [
    "# Predict the Cost of Homes in California Using Numpy + Pandas in Jupyter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b7b4b61-5503-4727-af4b-72153f2301c9",
   "metadata": {},
   "source": [
    "This notebook trains a machine learning model that predicts home costs in California.\n",
    "\n",
    "The overall objective is to predict the value of home prices using **9 feature variables and 1 target variable:** \n",
    "\n",
    "#### **Feature Variables** ####\n",
    "\n",
    "1.  **longitude:** A measure of how far west a house is (a higher value is farther west)\n",
    "2.  **latitude:** A measure of how far north a house is (a higher value is farther north)\n",
    "3.  **housingMedianAge:** Median age of a house within a block (a lower number is a newer building)\n",
    "4.  **totalRooms:** Total number of rooms within a block\n",
    "5.  **totalBedrooms:** Total number of bedrooms within a block\n",
    "6.  **population:** Total number of people residing within a block\n",
    "7.  **households:** Total number of households, a group of people residing within a home unit, for a block\n",
    "8.  **medianIncome:** Median income for households within a block of houses (measured in tens of thousands of US Dollars)\n",
    "9.  **oceanProximity:** Location of the house in proximity to the ocean\n",
    "\n",
    "#### **Target Variable** ####\n",
    "1.  **medianHouseValue:** Median house value for households within a block (measured in US Dollars)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f64a0504-dce2-43fd-bcff-424b66396066",
   "metadata": {},
   "source": [
    "## Background"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa8d65be-bbbc-4596-91ff-7a8f1031f143",
   "metadata": {},
   "source": [
    "## Scikit-learn\n",
    "\n",
    "Scikit-learn is a machine learning library for the Python programming language. It features various classification, regression and clustering learning algorithms. \n",
    "\n",
    "## Dependencies\n",
    "\n",
    "- pandas - To work with solid data-structures, n-dimensional matrices and perform exploratory data analysis.\n",
    "- matplotlib - To visualize data using 2D plots.\n",
    "- seaborn - To make 2D plots look pretty and readable.\n",
    "- scikit-learn - To create machine learning models easily and make predictions.\n",
    "- numpy - To work with arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad6f3574-f270-4be7-af64-35dce51c8d58",
   "metadata": {},
   "source": [
    "**A few things to note:**\n",
    "\n",
    "- You'll need to install the various libraries before importing them ```(i.e. %pip install pandas)```\n",
    "- You may need to restart the kernel to use updated packages."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63d53467-01c6-460b-89bb-465b90073944",
   "metadata": {},
   "source": [
    "# Obtain & Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "19d52d8a-e1f9-4bdc-b809-a4664e224528",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a3453354-a698-44a5-9059-e2e409bea7d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# Read in the data with read_csv() into a Pandas Dataframe\n",
    "housing_df = pd.read_csv('housing.csv')\n",
    "\n",
    "# Use .info() to show the features (i.e. columns) in your dataset along with a count and datatype\n",
    "housing_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "33a2e4b4-a6a3-493f-948e-516fe84e2462",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 10)"
      ]
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     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use .shape to understand how many observations (ie rows/records) of the dataset\n",
    "# The shape of a DataFrame is a tuple of array dimensions that tells the number of rows and columns of a given DataFrame.\n",
    "# (row count, column count)\n",
    "housing_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "27bd7e20-ddaf-4f24-bc72-fa3abcaf1a03",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using .head() function to view the first few observations (i.e. rows/records) of the dataset \n",
    "housing_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5940a3d5-7356-498b-95d4-6b7d4873216f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>-121.09</td>\n",
       "      <td>39.48</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>1.5603</td>\n",
       "      <td>78100.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>-121.21</td>\n",
       "      <td>39.49</td>\n",
       "      <td>18.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2.5568</td>\n",
       "      <td>77100.0</td>\n",
       "      <td>INLAND</td>\n",
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       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>-121.22</td>\n",
       "      <td>39.43</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1.7000</td>\n",
       "      <td>92300.0</td>\n",
       "      <td>INLAND</td>\n",
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       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>-121.32</td>\n",
       "      <td>39.43</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.8672</td>\n",
       "      <td>84700.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>-121.24</td>\n",
       "      <td>39.37</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>616.0</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>2.3886</td>\n",
       "      <td>89400.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "20635    -121.09     39.48                25.0       1665.0           374.0   \n",
       "20636    -121.21     39.49                18.0        697.0           150.0   \n",
       "20637    -121.22     39.43                17.0       2254.0           485.0   \n",
       "20638    -121.32     39.43                18.0       1860.0           409.0   \n",
       "20639    -121.24     39.37                16.0       2785.0           616.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "20635       845.0       330.0         1.5603             78100.0   \n",
       "20636       356.0       114.0         2.5568             77100.0   \n",
       "20637      1007.0       433.0         1.7000             92300.0   \n",
       "20638       741.0       349.0         1.8672             84700.0   \n",
       "20639      1387.0       530.0         2.3886             89400.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "20635          INLAND  \n",
       "20636          INLAND  \n",
       "20637          INLAND  \n",
       "20638          INLAND  \n",
       "20639          INLAND  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using .tail() function to view the last few observations (i.e. records) of the dataset \n",
    "housing_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c24ac416-9b05-4ae5-89b2-3b51d42c21f3",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
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       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use .describe() to see metrics about the dataset (count, mean, std, min, 25%, 50%, 75%, max)\n",
    "# Count is the count of non-null observations (i.e. rows)\n",
    "# Mean is the average of values for the given column\n",
    "# Std is standard deviation - how far off from the mean the values are\n",
    "# Min is the minimum amount of the value\n",
    "# 25% is the 25th percentile \n",
    "# 50% is the 50th percentile\n",
    "# 75% is the 75th percentile\n",
    "# max is the maximum amount of the value\n",
    "housing_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bda11c2-53cc-43df-bbe5-ce2f8a6317fb",
   "metadata": {},
   "source": [
    "# Prepare & Preprocess Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cad16fb8-f682-422f-b352-fe36073ad10d",
   "metadata": {},
   "source": [
    "### Understand missing data that could skew the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "70f8e8c1-e2dd-4935-a182-51699db1602a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude               0\n",
       "latitude                0\n",
       "housing_median_age      0\n",
       "total_rooms             0\n",
       "total_bedrooms        207\n",
       "population              0\n",
       "households              0\n",
       "median_income           0\n",
       "median_house_value      0\n",
       "ocean_proximity         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verify which features have missing values\n",
    "housing_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f9d4bac9-f30f-4e54-8ac5-97df8ba8355b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.002906976744186"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the % of missing data\n",
    "housing_df['total_bedrooms'].isnull().sum()/housing_df.shape[0] * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e1f4a76-514f-4e6b-a5c4-7a8a7f173738",
   "metadata": {},
   "source": [
    "### Use imputation to handle missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1272f885-3e7e-4e86-961a-c95152a5df7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import KNNImputer\n",
    "\n",
    "# create a temporary copy of the dataset\n",
    "housing_df_temp = housing_df.copy()\n",
    "\n",
    "# retrieve columns with numerical data; will exclude the ocean_proximity column since the datatype is object; other columns are float64\n",
    "columns_list = [col for col in housing_df_temp.columns if housing_df_temp[col].dtype != 'object']\n",
    "\n",
    "# extract columns that contain at least one missing value\n",
    "new_column_list = [col for col in housing_df_temp.loc[:, housing_df_temp.isnull().any()]]\n",
    "\n",
    "# update temp dataframe with numeric columns that have empty values\n",
    "housing_df_temp = housing_df_temp[new_column_list]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd13d34f-4e41-4503-ad52-434da406e465",
   "metadata": {},
   "source": [
    "### Impute missing data using machine learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "aa1639dd-86e4-4f99-9cb9-f3f0bb70f6cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize KNNImputer to impute missing data using machine learning\n",
    "knn = KNNImputer(n_neighbors = 3)\n",
    "\n",
    "# fit function trains the model \n",
    "knn.fit(housing_df_temp)\n",
    "\n",
    "# transform the data using the model\n",
    "# applies the transformation model (ie knn) to data\n",
    "array_Values = knn.transform(housing_df_temp)\n",
    "\n",
    "# convert the array values to a dataframe with the appropriate column names\n",
    "housing_df_temp = pd.DataFrame(array_Values, columns = new_column_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1085f8cf-e223-4c29-b2aa-668cfaa2ab4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_bedrooms    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confirm there are no columns with missing values\n",
    "housing_df_temp.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "564ace1f-133e-41db-8836-8b845b95952c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude             0\n",
       "latitude              0\n",
       "housing_median_age    0\n",
       "total_rooms           0\n",
       "total_bedrooms        0\n",
       "population            0\n",
       "households            0\n",
       "median_income         0\n",
       "median_house_value    0\n",
       "ocean_proximity       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# overlay the imputed column over the old column with missing values\n",
    "\n",
    "# loop through the list of columns and overlay each one\n",
    "for column_name in new_column_list:\n",
    "    housing_df[column_name] = housing_df_temp.replace(housing_df[column_name],housing_df[column_name])\n",
    "\n",
    "# confirm columns no longer contain null data\n",
    "housing_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "174efa66-67ff-49aa-ac71-952384605392",
   "metadata": {},
   "source": [
    "## Use visualizations to understand the relationship of the target variable with other features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2e9be11-9745-481a-b5bb-eab30d327edb",
   "metadata": {},
   "source": [
    "#### Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "74ce420d-9933-4faa-b3ae-3ff99f527b28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'House Values')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of the target variable (median_house_value) using a histogram\n",
    "\n",
    "# bins->amount of columns\n",
    "plt.hist(housing_df['median_house_value'], bins=80)\n",
    "plt.xlabel(\"House Values\")\n",
    "\n",
    "\n",
    "# We can see from the plot that the values of Median House Value are distributed normally with few outliers. \n",
    "# Most of the house are around 100,000-200,000 range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "af1bc3b7-9d15-46ac-a796-c0c1275a8b17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'longitude'}>,\n",
       "        <Axes: title={'center': 'latitude'}>,\n",
       "        <Axes: title={'center': 'housing_median_age'}>],\n",
       "       [<Axes: title={'center': 'total_rooms'}>,\n",
       "        <Axes: title={'center': 'total_bedrooms'}>,\n",
       "        <Axes: title={'center': 'population'}>],\n",
       "       [<Axes: title={'center': 'households'}>,\n",
       "        <Axes: title={'center': 'median_income'}>,\n",
       "        <Axes: title={'center': 'median_house_value'}>]], dtype=object)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EHKoAAAAAAAAAAAAAAACIgEMVAAAAAAAAAAAAAAAAEXCoAgAAAAAAAAAAAAAAIAIOVQAAAAAAAAAAAAAAAETAoQoAAAAAAAAAAAAAAIAIOFQBAAAAAAAAAAAAAAAQAYcqAAAAAAAAAAAAAAAAIuBQBQAAAAAAAAAAAAAAQAQcqgAAAAAAAAAAAAAAAIjwL366AtC0BuxYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2000x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's do histograms for the all the features to understand the data distributions\n",
    "# using housing_df as to not plot the encoded values for OCEAN_PROXIMITY \n",
    "housing_df.hist(bins=50, figsize=(20,15))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83ae2f7f-477a-4160-9bbb-66f2c20e0fb0",
   "metadata": {},
   "source": [
    "#### Use a heatmap to show correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "94a06cae-8e05-48db-a20f-78e9a74af526",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    longitude  latitude  housing_median_age  total_rooms  \\\n",
      "longitude            1.000000 -0.924664           -0.108197     0.044568   \n",
      "latitude            -0.924664  1.000000            0.011173    -0.036100   \n",
      "housing_median_age  -0.108197  0.011173            1.000000    -0.361262   \n",
      "total_rooms          0.044568 -0.036100           -0.361262     1.000000   \n",
      "total_bedrooms       0.069260 -0.066658           -0.318998     0.927253   \n",
      "population           0.099773 -0.108785           -0.296244     0.857126   \n",
      "households           0.055310 -0.071035           -0.302916     0.918484   \n",
      "median_income       -0.015176 -0.079809           -0.119034     0.198050   \n",
      "median_house_value  -0.045967 -0.144160            0.105623     0.134153   \n",
      "\n",
      "                    total_bedrooms  population  households  median_income  \\\n",
      "longitude                 0.069260    0.099773    0.055310      -0.015176   \n",
      "latitude                 -0.066658   -0.108785   -0.071035      -0.079809   \n",
      "housing_median_age       -0.318998   -0.296244   -0.302916      -0.119034   \n",
      "total_rooms               0.927253    0.857126    0.918484       0.198050   \n",
      "total_bedrooms            1.000000    0.873910    0.974725      -0.007682   \n",
      "population                0.873910    1.000000    0.907222       0.004834   \n",
      "households                0.974725    0.907222    1.000000       0.013033   \n",
      "median_income            -0.007682    0.004834    0.013033       1.000000   \n",
      "median_house_value        0.049454   -0.024650    0.065843       0.688075   \n",
      "\n",
      "                    median_house_value  \n",
      "longitude                    -0.045967  \n",
      "latitude                     -0.144160  \n",
      "housing_median_age            0.105623  \n",
      "total_rooms                   0.134153  \n",
      "total_bedrooms                0.049454  \n",
      "population                   -0.024650  \n",
      "households                    0.065843  \n",
      "median_income                 0.688075  \n",
      "median_house_value            1.000000  \n"
     ]
    }
   ],
   "source": [
    "# Plot a graphical correlation matrix for each pair of columns in the dataframe\n",
    "corr = housing_df.corr(numeric_only=True) # data frame correlation function\n",
    "print(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "3ac2ed86-90fd-43ab-b18f-ae99d2d1341d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make the heatmap larger in size\n",
    "plt.figure(figsize = (8,8))\n",
    "\n",
    "sns.heatmap(corr, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ef111a5-f2f7-4db5-9a74-16776813b6e0",
   "metadata": {},
   "source": [
    "##### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b1c4dde8-d8e6-4621-aeb6-7b7c039dc0f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 14 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   longitude                 20640 non-null  float64\n",
      " 1   latitude                  20640 non-null  float64\n",
      " 2   housing_median_age        20640 non-null  float64\n",
      " 3   total_rooms               20640 non-null  float64\n",
      " 4   total_bedrooms            20640 non-null  float64\n",
      " 5   population                20640 non-null  float64\n",
      " 6   households                20640 non-null  float64\n",
      " 7   median_income             20640 non-null  float64\n",
      " 8   median_house_value        20640 non-null  float64\n",
      " 9   ocean_proximity           20640 non-null  object \n",
      " 10  rooms_per_household       20640 non-null  float64\n",
      " 11  bedrooms_per_room         20640 non-null  float64\n",
      " 12  population_per_household  20640 non-null  float64\n",
      " 13  coords                    20640 non-null  float64\n",
      "dtypes: float64(13), object(1)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# Additionally we noted that several features (total_rooms,total_bedrooms,population,households) have very high correlation to one another, \n",
    "# so it's interesting to find out if a removal of a few of them would have any affect on the model performance\n",
    "\n",
    "#  a new feature that is a ratio of the total rooms to households\n",
    "housing_df['rooms_per_household'] = housing_df['total_rooms']/housing_df['households']\n",
    "\n",
    "# a new feature that is a ratio of the total bedrooms to the total rooms \n",
    "housing_df['bedrooms_per_room'] = housing_df['total_bedrooms']/housing_df['total_rooms']\n",
    "\n",
    "# a new feature that is a ratio of the population to the households \n",
    "housing_df['population_per_household']= housing_df['population']/housing_df['households']\n",
    "\n",
    "# let's combine the latitude and longitude into 1\n",
    "housing_df['coords'] = housing_df['longitude']/housing_df['latitude']\n",
    "\n",
    "housing_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "9f94582a-901f-4729-9e5b-d8fa027b8e74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 8 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   housing_median_age        20640 non-null  float64\n",
      " 1   median_income             20640 non-null  float64\n",
      " 2   median_house_value        20640 non-null  float64\n",
      " 3   ocean_proximity           20640 non-null  object \n",
      " 4   rooms_per_household       20640 non-null  float64\n",
      " 5   bedrooms_per_room         20640 non-null  float64\n",
      " 6   population_per_household  20640 non-null  float64\n",
      " 7   coords                    20640 non-null  float64\n",
      "dtypes: float64(7), object(1)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# remove total_rooms, households, total bedrooms, popluation, longitude, latitude\n",
    "housing_df = housing_df.drop('total_rooms', axis=1)\n",
    "housing_df = housing_df.drop('households', axis=1)\n",
    "housing_df = housing_df.drop('total_bedrooms', axis=1)\n",
    "housing_df = housing_df.drop('population', axis=1)\n",
    "housing_df = housing_df.drop('longitude', axis=1)\n",
    "housing_df = housing_df.drop('latitude', axis=1)\n",
    "\n",
    "housing_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6f5d86a-289d-46e3-b12b-1f87c4fe2314",
   "metadata": {},
   "source": [
    "##### Heatmap after removing correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a2638134-616c-4ad2-8884-76961aeae59e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = housing_df.corr(numeric_only=True) \n",
    "\n",
    "#make the heatmap larger in size\n",
    "plt.figure(figsize = (7,7))\n",
    "\n",
    "sns.heatmap(corr, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4137895-007b-4fb1-8728-8792928f53cf",
   "metadata": {},
   "source": [
    "## Encode categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "3a712bde-01c2-4f43-90fb-b1aabb67f3d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 8 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   housing_median_age        20640 non-null  float64\n",
      " 1   median_income             20640 non-null  float64\n",
      " 2   median_house_value        20640 non-null  float64\n",
      " 3   ocean_proximity           20640 non-null  object \n",
      " 4   rooms_per_household       20640 non-null  float64\n",
      " 5   bedrooms_per_room         20640 non-null  float64\n",
      " 6   population_per_household  20640 non-null  float64\n",
      " 7   coords                    20640 non-null  float64\n",
      "dtypes: float64(7), object(1)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# Most ML algorithms can only learn from numeric data (it's all Math) so categorical data must be encoded (i.e. converted) to numeric data\n",
    "\n",
    "# Let's review our data types again; showing that ocean_proximity is the only categorical data\n",
    "housing_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "df289acf-5df4-4ae2-a809-ac84983d0fe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NEAR BAY', '<1H OCEAN', 'INLAND', 'NEAR OCEAN', 'ISLAND'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's see the unique categories for OCEAN_PROXIMITY\n",
    "housing_df.ocean_proximity.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e9b33eb2-c363-4993-97fc-81b9273c0720",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ocean_proximity\n",
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's count\n",
    "housing_df[\"ocean_proximity\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aa7f116-8d10-4cc2-8279-322bbd99d4c5",
   "metadata": {},
   "source": [
    "### One-Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "85cda782-10c2-4c1f-bc1a-1bd88ffb9e80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       <1H OCEAN  INLAND  ISLAND  NEAR BAY  NEAR OCEAN\n",
      "0          False   False   False      True       False\n",
      "1          False   False   False      True       False\n",
      "2          False   False   False      True       False\n",
      "3          False   False   False      True       False\n",
      "4          False   False   False      True       False\n",
      "...          ...     ...     ...       ...         ...\n",
      "20635      False    True   False     False       False\n",
      "20636      False    True   False     False       False\n",
      "20637      False    True   False     False       False\n",
      "20638      False    True   False     False       False\n",
      "20639      False    True   False     False       False\n",
      "\n",
      "[20640 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "# Let's see how the Panda's get_dummies() function works\n",
    "print(pd.get_dummies(housing_df['ocean_proximity']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "3fdbad69",
   "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>housing_median_age</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>bedrooms_per_room</th>\n",
       "      <th>population_per_household</th>\n",
       "      <th>coords</th>\n",
       "      <th>ocean_proximity_&lt;1H OCEAN</th>\n",
       "      <th>ocean_proximity_INLAND</th>\n",
       "      <th>ocean_proximity_ISLAND</th>\n",
       "      <th>ocean_proximity_NEAR BAY</th>\n",
       "      <th>ocean_proximity_NEAR OCEAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>0.146591</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>-3.226769</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.155797</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>-3.228209</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>0.129516</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>-3.229590</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>0.184458</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>-3.229855</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>0.172096</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>-3.229855</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   housing_median_age  median_income  median_house_value  rooms_per_household  \\\n",
       "0                41.0         8.3252            452600.0             6.984127   \n",
       "1                21.0         8.3014            358500.0             6.238137   \n",
       "2                52.0         7.2574            352100.0             8.288136   \n",
       "3                52.0         5.6431            341300.0             5.817352   \n",
       "4                52.0         3.8462            342200.0             6.281853   \n",
       "\n",
       "   bedrooms_per_room  population_per_household    coords  \\\n",
       "0           0.146591                  2.555556 -3.226769   \n",
       "1           0.155797                  2.109842 -3.228209   \n",
       "2           0.129516                  2.802260 -3.229590   \n",
       "3           0.184458                  2.547945 -3.229855   \n",
       "4           0.172096                  2.181467 -3.229855   \n",
       "\n",
       "   ocean_proximity_<1H OCEAN  ocean_proximity_INLAND  ocean_proximity_ISLAND  \\\n",
       "0                      False                   False                   False   \n",
       "1                      False                   False                   False   \n",
       "2                      False                   False                   False   \n",
       "3                      False                   False                   False   \n",
       "4                      False                   False                   False   \n",
       "\n",
       "   ocean_proximity_NEAR BAY  ocean_proximity_NEAR OCEAN  \n",
       "0                      True                       False  \n",
       "1                      True                       False  \n",
       "2                      True                       False  \n",
       "3                      True                       False  \n",
       "4                      True                       False  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's replace the OCEAN_PROXIMITY column using get_dummies()\n",
    "housing_df_encoded = pd.get_dummies(data=housing_df, columns=['ocean_proximity'])\n",
    "\n",
    "# print the first few observations; notice the old OCEAN_PROXIMITY column is gone\n",
    "housing_df_encoded.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a37ea4cc-ca9c-4819-bdf5-83fcb2413385",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b988badf-4961-40e2-8d22-9fda6fcc44ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       housing_median_age  median_income  bedrooms_per_room  \\\n",
      "0                    41.0         8.3252           0.146591   \n",
      "1                    21.0         8.3014           0.155797   \n",
      "2                    52.0         7.2574           0.129516   \n",
      "3                    52.0         5.6431           0.184458   \n",
      "4                    52.0         3.8462           0.172096   \n",
      "...                   ...            ...                ...   \n",
      "20635                25.0         1.5603           0.224625   \n",
      "20636                18.0         2.5568           0.215208   \n",
      "20637                17.0         1.7000           0.215173   \n",
      "20638                18.0         1.8672           0.219892   \n",
      "20639                16.0         2.3886           0.221185   \n",
      "\n",
      "       population_per_household    coords  ocean_proximity__1h_ocean  \\\n",
      "0                      2.555556 -3.226769                      False   \n",
      "1                      2.109842 -3.228209                      False   \n",
      "2                      2.802260 -3.229590                      False   \n",
      "3                      2.547945 -3.229855                      False   \n",
      "4                      2.181467 -3.229855                      False   \n",
      "...                         ...       ...                        ...   \n",
      "20635                  2.560606 -3.067123                      False   \n",
      "20636                  3.122807 -3.069385                      False   \n",
      "20637                  2.325635 -3.074309                      False   \n",
      "20638                  2.123209 -3.076845                      False   \n",
      "20639                  2.616981 -3.079502                      False   \n",
      "\n",
      "       ocean_proximity_inland  ocean_proximity_island  \\\n",
      "0                       False                   False   \n",
      "1                       False                   False   \n",
      "2                       False                   False   \n",
      "3                       False                   False   \n",
      "4                       False                   False   \n",
      "...                       ...                     ...   \n",
      "20635                    True                   False   \n",
      "20636                    True                   False   \n",
      "20637                    True                   False   \n",
      "20638                    True                   False   \n",
      "20639                    True                   False   \n",
      "\n",
      "       ocean_proximity_near_bay  ocean_proximity_near_ocean  \n",
      "0                          True                       False  \n",
      "1                          True                       False  \n",
      "2                          True                       False  \n",
      "3                          True                       False  \n",
      "4                          True                       False  \n",
      "...                         ...                         ...  \n",
      "20635                     False                       False  \n",
      "20636                     False                       False  \n",
      "20637                     False                       False  \n",
      "20638                     False                       False  \n",
      "20639                     False                       False  \n",
      "\n",
      "[20640 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# remove spaces from column names and convert all to lowercase and remove special characters as it could cause issues in the future\n",
    "housing_df_encoded.columns = [c.lower().replace(' ', '_').replace('<', '_') for c in housing_df_encoded.columns]\n",
    "\n",
    "# Split target variable and feature variables\n",
    "X = housing_df_encoded[['housing_median_age', 'median_income','bedrooms_per_room','population_per_household','coords','ocean_proximity__1h_ocean',\n",
    "                        'ocean_proximity_inland','ocean_proximity_island','ocean_proximity_near_bay','ocean_proximity_near_ocean']]\n",
    "y = housing_df_encoded['median_house_value']\n",
    "\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03aed0f6-6014-49e5-a36d-7216c02bc28a",
   "metadata": {},
   "source": [
    "### Split training & test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "88db92d2-e9b3-4651-8602-cc18619ff409",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14448, 10)\n",
      "(6192, 10)\n",
      "(14448,)\n",
      "(6192,)\n"
     ]
    }
   ],
   "source": [
    "# Splitting the data into training and testing sets in numpy arrays\n",
    "# We train the model with 70% of the samples and test with the remaining 30%\n",
    "# X -> array with the inputs; y -> array of the outputs\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, shuffle=True, test_size=0.3)\n",
    "\n",
    "# Confirm how the data was split\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b607798f-076f-4696-8cc5-5281103e8247",
   "metadata": {},
   "source": [
    "## Linear Regression - Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "098f04c4-b616-4be8-8f97-db0715f25226",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use scikit-learn’s LinearRegression to train the model on both the training and evaluate it on the test sets\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Create a Linear regressor using all the feature variables\n",
    "reg_model = LinearRegression()\n",
    "\n",
    "# Train the model using the training sets\n",
    "reg_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93d08eaf-ee43-478e-8ec4-baab001eb262",
   "metadata": {},
   "source": [
    "## Run the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "4cf2303e-5928-43b2-985e-ec86a6095413",
   "metadata": {},
   "outputs": [],
   "source": [
    "#run the predictions on the training and testing data\n",
    "y_pred_test = reg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "138bcdf9-7535-4ba9-8e13-09a7c53b73bc",
   "metadata": {},
   "source": [
    "## Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d96200b1-6570-45ef-bccf-bb0d27b3de4c",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20046</th>\n",
       "      <td>47700.0</td>\n",
       "      <td>103743.050896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>45800.0</td>\n",
       "      <td>92451.250932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15663</th>\n",
       "      <td>500001.0</td>\n",
       "      <td>219490.963844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20484</th>\n",
       "      <td>218600.0</td>\n",
       "      <td>283292.425471</td>\n",
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       "    <tr>\n",
       "      <th>9814</th>\n",
       "      <td>278000.0</td>\n",
       "      <td>244228.861575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17505</th>\n",
       "      <td>237500.0</td>\n",
       "      <td>210121.340663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13512</th>\n",
       "      <td>67300.0</td>\n",
       "      <td>74907.098235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10842</th>\n",
       "      <td>218400.0</td>\n",
       "      <td>216609.962950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16559</th>\n",
       "      <td>119400.0</td>\n",
       "      <td>127975.072923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5786</th>\n",
       "      <td>209800.0</td>\n",
       "      <td>202803.254310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6192 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Actual      Predicted\n",
       "20046   47700.0  103743.050896\n",
       "3024    45800.0   92451.250932\n",
       "15663  500001.0  219490.963844\n",
       "20484  218600.0  283292.425471\n",
       "9814   278000.0  244228.861575\n",
       "...         ...            ...\n",
       "17505  237500.0  210121.340663\n",
       "13512   67300.0   74907.098235\n",
       "10842  218400.0  216609.962950\n",
       "16559  119400.0  127975.072923\n",
       "5786   209800.0  202803.254310\n",
       "\n",
       "[6192 rows x 2 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compare the actual values (ie, target) with the values predicted by the model\n",
    "pred_test_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred_test})\n",
    "\n",
    "pred_test_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "282b54bc-e7bd-4342-aab0-8e94f5f9d538",
   "metadata": {},
   "source": [
    "### Evaluate the model using the 𝑅^2 metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ec03d269-e46c-43db-b729-7555215a097f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 Test: 0.56\n"
     ]
    }
   ],
   "source": [
    "# Determine accuracy uisng 𝑅^2\n",
    "# 𝑅^2 : R squared is another way to evaluate the performance of a regression model.\n",
    "# 1, means that the model is perfect and 0 means the the model will perform poorly.\n",
    "r2_reg_model_test = round(reg_model.score(X_test, y_test),2)\n",
    "\n",
    "print(\"R^2 Test: {}\".format(r2_reg_model_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbcf91ce-3c4c-458d-881c-33fdad5feb11",
   "metadata": {},
   "source": [
    "##  RandomForestRegressor - Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b80e8e93-156e-442c-b465-d647a582469e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_estimators=10, random_state=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_estimators=10, random_state=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(n_estimators=10, random_state=10)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# try another machine learning algorithm : Randorm Forest\n",
    "# Use scikit-learn’s Randorm Forest to train the model on both the training and evaluate it on the test sets\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Create a  regressor using all the feature variables\n",
    "rf_model = RandomForestRegressor(n_estimators=10,random_state=10)\n",
    "\n",
    "# Train the model using the training sets\n",
    "rf_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35eab0b8-9fe9-403f-88a1-50d91c7bb1cb",
   "metadata": {},
   "source": [
    "## Run the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "35564066-85b0-46a5-961b-a9b39d95e971",
   "metadata": {},
   "outputs": [],
   "source": [
    "#run the predictions on the training and testing data\n",
    "y_rf_pred_test = rf_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bc16242-1fdd-4870-9e9b-27942452595b",
   "metadata": {},
   "source": [
    "## Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "7b80d01e-0538-41a7-8139-a24a6cc0d309",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20046</th>\n",
       "      <td>47700.0</td>\n",
       "      <td>47840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>45800.0</td>\n",
       "      <td>92680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15663</th>\n",
       "      <td>500001.0</td>\n",
       "      <td>446000.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20484</th>\n",
       "      <td>218600.0</td>\n",
       "      <td>265320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9814</th>\n",
       "      <td>278000.0</td>\n",
       "      <td>240800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17505</th>\n",
       "      <td>237500.0</td>\n",
       "      <td>231680.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13512</th>\n",
       "      <td>67300.0</td>\n",
       "      <td>69680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10842</th>\n",
       "      <td>218400.0</td>\n",
       "      <td>203930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16559</th>\n",
       "      <td>119400.0</td>\n",
       "      <td>126170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5786</th>\n",
       "      <td>209800.0</td>\n",
       "      <td>198160.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6192 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Actual  Predicted\n",
       "20046   47700.0    47840.0\n",
       "3024    45800.0    92680.0\n",
       "15663  500001.0   446000.5\n",
       "20484  218600.0   265320.0\n",
       "9814   278000.0   240800.0\n",
       "...         ...        ...\n",
       "17505  237500.0   231680.1\n",
       "13512   67300.0    69680.0\n",
       "10842  218400.0   203930.0\n",
       "16559  119400.0   126170.0\n",
       "5786   209800.0   198160.0\n",
       "\n",
       "[6192 rows x 2 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compare the actual values (ie, target) with the values predicted by the model\n",
    "rf_pred_test_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_rf_pred_test})\n",
    "\n",
    "rf_pred_test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "494ee151-d0bc-4599-bb56-4f3465793fb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 - 75.0%\n"
     ]
    }
   ],
   "source": [
    "# Determine accuracy uisng 𝑅^2\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "\n",
    "score = r2_score(y_test, y_rf_pred_test)\n",
    "\n",
    "print(\"R^2 - {}%\".format(round(score, 2) *100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "57599279-d2a2-4e08-b2c9-f68f8f63fee5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on test data:  57289.11495447338\n"
     ]
    }
   ],
   "source": [
    "# Determine RMSE - Root Mean Squared Error on the test data\n",
    "print('RMSE on test data: ',  mean_squared_error(y_test, y_rf_pred_test)**(0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d30e865e-e072-4212-8735-b8d503dea5cc",
   "metadata": {},
   "source": [
    "# Determine Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ad910ca1-383d-4502-9ef2-5e87366c22cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Determine feature importance - random forest algorithm is that it gives you the ‘feature importance’ for all the variables in the data\n",
    "# plot the 6 most important features \n",
    "plt.figure(figsize=(10,6))\n",
    "feat_importances = pd.Series(rf_model.feature_importances_, index = X_train.columns)\n",
    "feat_importances.nlargest(6).plot(kind='barh');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c2d7b27-b193-47eb-91e0-19ed7a6737bf",
   "metadata": {},
   "source": [
    "Now, we are going to train the same random forest model using these 6 features only and observe the change in RMSE values for the train and the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e2fb7168-d51b-47c0-9e00-552c2fbbf769",
   "metadata": {},
   "outputs": [],
   "source": [
    "# training data with 5 most important features\n",
    "train_x_if = X_train[['bedrooms_per_room', 'housing_median_age', 'coords', 'ocean_proximity_inland','population_per_household','median_income']]\n",
    "test_x_if = X_test[['bedrooms_per_room', 'housing_median_age', 'coords', 'ocean_proximity_inland','population_per_household','median_income']]\n",
    "\n",
    "# create an object of the RandfomForestRegressor Model\n",
    "rf_model_if = RandomForestRegressor(n_estimators=10,random_state=10)\n",
    "\n",
    "# fit the model with the training data\n",
    "rf_model_if.fit(train_x_if, y_train)\n",
    "\n",
    "# predict the target on the test data\n",
    "predict_test_with_if = rf_model_if.predict(test_x_if)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e79b953b-9a31-41a4-844d-9ec9b31a9d5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on test data:  57366.910692045196\n"
     ]
    }
   ],
   "source": [
    "# Root Mean Squared Error on the train and test data\n",
    "print('RMSE on test data: ',  mean_squared_error(y_test, predict_test_with_if)**(0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0260ed49-babd-4e83-8e31-2fb2262d087c",
   "metadata": {},
   "source": [
    "## XGBoost - Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97a7db38-81ac-470c-89ed-21897abb673a",
   "metadata": {},
   "source": [
    "**A few things to note:**\n",
    "\n",
    "- You'll need to install the various libraries before importing them ```(i.e. %pip install xgboost)```\n",
    "- You may need to restart the kernel to use updated packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c02f7f22-f10e-4240-8329-c788872b96fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extreme Gradient Boosting (XGBoost) is an open-source library that provides an efficient and effective implementation of the gradient boosting algorithm.\n",
    "# Use the scikit-learn wrapper classes: XGBRegressor and XGBClassifier. \n",
    "\n",
    "# try another machine learning algorithm : XGBoost\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "xgb_model = XGBRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "6117c6c3-9da1-449b-9c43-b99b96c706ba",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model using the training sets\n",
    "xgb_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf6aa487-bd9a-44b0-8fae-234bcaf868da",
   "metadata": {},
   "source": [
    "## Run the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "032ccd75-8fcf-442a-9d98-e7d0f44193e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#run the predictions on the training and testing data\n",
    "y_xgb_pred_test = xgb_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d215cd90-ba15-4019-a88d-c4d2c3af31f3",
   "metadata": {},
   "source": [
    "## Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "79ede4fb-b359-457c-906a-2364382b26b3",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20046</th>\n",
       "      <td>47700.0</td>\n",
       "      <td>66404.914062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>45800.0</td>\n",
       "      <td>86681.765625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15663</th>\n",
       "      <td>500001.0</td>\n",
       "      <td>449666.093750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20484</th>\n",
       "      <td>218600.0</td>\n",
       "      <td>262887.281250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9814</th>\n",
       "      <td>278000.0</td>\n",
       "      <td>218322.796875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17505</th>\n",
       "      <td>237500.0</td>\n",
       "      <td>227466.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13512</th>\n",
       "      <td>67300.0</td>\n",
       "      <td>64712.433594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10842</th>\n",
       "      <td>218400.0</td>\n",
       "      <td>218226.109375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16559</th>\n",
       "      <td>119400.0</td>\n",
       "      <td>123181.968750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5786</th>\n",
       "      <td>209800.0</td>\n",
       "      <td>227016.828125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6192 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Actual      Predicted\n",
       "20046   47700.0   66404.914062\n",
       "3024    45800.0   86681.765625\n",
       "15663  500001.0  449666.093750\n",
       "20484  218600.0  262887.281250\n",
       "9814   278000.0  218322.796875\n",
       "...         ...            ...\n",
       "17505  237500.0  227466.500000\n",
       "13512   67300.0   64712.433594\n",
       "10842  218400.0  218226.109375\n",
       "16559  119400.0  123181.968750\n",
       "5786   209800.0  227016.828125\n",
       "\n",
       "[6192 rows x 2 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compare the actual values (ie, target) with the values predicted by the model\n",
    "xgb_pred_test_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_xgb_pred_test})\n",
    "\n",
    "xgb_pred_test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "75dfbbd9-e49b-4979-86dd-ef9b22740317",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13023cb80>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig= plt.figure(figsize=(8,8))\n",
    "xgb_pred_test_df = xgb_pred_test_df.reset_index()\n",
    "xgb_pred_test_df = xgb_pred_test_df.drop(['index'],axis=1)\n",
    "plt.plot(xgb_pred_test_df[:50])\n",
    "plt.legend(['Actual value','Predicted value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "218027a7-d038-4b1a-9fea-7543e7d8c8d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 - 78.0%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "score = r2_score(y_test, y_xgb_pred_test)\n",
    "\n",
    "print(\"R^2 - {}%\".format(round(score, 2) *100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "e0c3845e-f9cc-46bf-96b5-b651d614ed24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2939759040.9080276\n",
      "54219.5448238735\n"
     ]
    }
   ],
   "source": [
    "# Determine mean square error and root mean square error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import math\n",
    "\n",
    "mse = mean_squared_error(y_test, y_xgb_pred_test)\n",
    "rmse = math.sqrt(mean_squared_error(y_test, y_xgb_pred_test))\n",
    "\n",
    "print(mse)\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "7f07f27e-e46e-4dfd-ab65-a30601af1d16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36285.050324826894\n"
     ]
    }
   ],
   "source": [
    "# Calculate mean absolute error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "print(mean_absolute_error(y_test, y_xgb_pred_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49706f5e-3140-400b-b31e-307c73398238",
   "metadata": {},
   "source": [
    "## Cross Validation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "de609af5-6f83-4a8b-9691-db81dc7e1117",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7850403811484551\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:    1.8s finished\n"
     ]
    }
   ],
   "source": [
    "# We can build and score a model on multiple folds using cross-validation\n",
    "from sklearn.model_selection import RepeatedKFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "\n",
    "# define model evaluation method\n",
    "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "\n",
    "scores = cross_val_score(xgb_model, X, y, scoring='r2', error_score='raise', cv=cv, n_jobs=-1, verbose=1)\n",
    "\n",
    "#average of all the r2 scores across runs\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f8bf03-cead-43b8-a5cf-a599ab161546",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning\n",
    "\n",
    "Good hyperparameter values can be found by trial and error for a given dataset, or systematic experimentation such as using a grid \n",
    "search across a range of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "99db52c9-dda9-4b62-a475-23817784e68a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'objective': 'reg:squarederror',\n",
       " 'base_score': None,\n",
       " 'booster': None,\n",
       " 'callbacks': None,\n",
       " 'colsample_bylevel': None,\n",
       " 'colsample_bynode': None,\n",
       " 'colsample_bytree': None,\n",
       " 'device': None,\n",
       " 'early_stopping_rounds': None,\n",
       " 'enable_categorical': False,\n",
       " 'eval_metric': None,\n",
       " 'feature_types': None,\n",
       " 'gamma': None,\n",
       " 'grow_policy': None,\n",
       " 'importance_type': None,\n",
       " 'interaction_constraints': None,\n",
       " 'learning_rate': None,\n",
       " 'max_bin': None,\n",
       " 'max_cat_threshold': None,\n",
       " 'max_cat_to_onehot': None,\n",
       " 'max_delta_step': None,\n",
       " 'max_depth': None,\n",
       " 'max_leaves': None,\n",
       " 'min_child_weight': None,\n",
       " 'missing': nan,\n",
       " 'monotone_constraints': None,\n",
       " 'multi_strategy': None,\n",
       " 'n_estimators': None,\n",
       " 'n_jobs': None,\n",
       " 'num_parallel_tree': None,\n",
       " 'random_state': None,\n",
       " 'reg_alpha': None,\n",
       " 'reg_lambda': None,\n",
       " 'sampling_method': None,\n",
       " 'scale_pos_weight': None,\n",
       " 'subsample': None,\n",
       " 'tree_method': None,\n",
       " 'validate_parameters': None,\n",
       " 'verbosity': None}"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# determine hyperparameter available for tuning\n",
    "xgb_model.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "e499df15-36fd-4b65-b628-f8f3bd27e2c8",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "xgb_model_2 = XGBRegressor(\n",
    "    gamma=0.05,\n",
    "    learning_rate=0.01,\n",
    "    max_depth=6,\n",
    "    n_estimators=1000,\n",
    "    n_jobs=16,\n",
    "    objective='reg:squarederror',\n",
    "    subsample=0.8,\n",
    "    scale_pos_weight=0,\n",
    "    reg_alpha=0,\n",
    "    reg_lambda=1,\n",
    "    verbosity=1)\n",
    "\n",
    "xgb_model_2.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "#run the predictions on the training and testing data\n",
    "y_xgb_2_pred_test = xgb_model_2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "b73c9f33-7a65-46c3-a8b0-47f50079b8cc",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20046</th>\n",
       "      <td>47700.0</td>\n",
       "      <td>57542.468750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>45800.0</td>\n",
       "      <td>90140.296875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15663</th>\n",
       "      <td>500001.0</td>\n",
       "      <td>441852.906250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20484</th>\n",
       "      <td>218600.0</td>\n",
       "      <td>254412.796875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9814</th>\n",
       "      <td>278000.0</td>\n",
       "      <td>240307.781250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17505</th>\n",
       "      <td>237500.0</td>\n",
       "      <td>234835.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13512</th>\n",
       "      <td>67300.0</td>\n",
       "      <td>64357.855469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10842</th>\n",
       "      <td>218400.0</td>\n",
       "      <td>220460.828125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16559</th>\n",
       "      <td>119400.0</td>\n",
       "      <td>125676.593750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5786</th>\n",
       "      <td>209800.0</td>\n",
       "      <td>208793.187500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6192 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Actual      Predicted\n",
       "20046   47700.0   57542.468750\n",
       "3024    45800.0   90140.296875\n",
       "15663  500001.0  441852.906250\n",
       "20484  218600.0  254412.796875\n",
       "9814   278000.0  240307.781250\n",
       "...         ...            ...\n",
       "17505  237500.0  234835.000000\n",
       "13512   67300.0   64357.855469\n",
       "10842  218400.0  220460.828125\n",
       "16559  119400.0  125676.593750\n",
       "5786   209800.0  208793.187500\n",
       "\n",
       "[6192 rows x 2 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compare the actual values (ie, target) with the values predicted by the model\n",
    "xgb_2_pred_test_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_xgb_2_pred_test})\n",
    "\n",
    "xgb_2_pred_test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "835ec23d-271d-4246-a688-e93b34c49581",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12f10a9a0>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3ve/Fnj17cPPNNwNga42POuooXHHFFV09jnvuuQcnnXQSdu/eXTH03y6ef/55rFq1Cn/4wx9w1FFHdeRrtItYwvcHP/hB3ffncjlcffXVuPrqq2s+ZuXKlbj99tvrfp4TTzwRf/jDH+o+5vzzz8f555/f0rEMEiYfiqFsIEH0JwZFHQiiZd773vfiW9/6FgBAVVWsWLEC73nPe/Bv//ZvyGRiSaLY/OQnP4GqqpEe2w2xOqh09qdMJAYv6kA1SATRl4QcX5uexwTRLG984xvxzW9+E7qu4/bbb8d5550HVVXxyU9+suKxhmFA07S2fN158+a15fMQrdHScBvRP1g2Ob4E0c/QcBtBtIdsNoslS5Zg5cqV+MhHPoJTTjkFP//5zwEwR/hv/uZv8PnPfx7Lli3DQQcdBAB48cUX8Y53vANz5szBvHnz8Na3vhXPP/+89zlt28ZFF12EOXPmYP78+fjEJz4B13VDX/fEE0/EBRdc4P2/ruv4l3/5FyxfvhzZbBb7778/rrvuOjz//PM46aSTAABz586FJEl473vfC4AN81966aVYtWoV8vk8jjzySPzoRz8KfZ3bb78dBx54IPL5PE466aTQcVbjXe96F84666zQ20zTxIIFC/Dtb38bAPDLX/4Sr3nNa7x/31ve8hZs2LCh5ue84YYbKpzqm2++GZIkhd72s5/9DMcccwxyuRz23XdffOYzn4FldXbfAAnfAcHwMr7kFBFEP2JQxpdIMq4LGNO9+a9MYMYln8/DMAzv/++66y6sX78ed955J2699VaYpom1a9dieHgY9913H377299iaGgIb3zjG72Pu+yyy3DDDTfg+uuvx/33349du3bhpz/9ad2v+573vAff//738Z//+Z94+umn8bWvfQ1DQ0NYvnw5fvzjHwNgjVRbtmzBlVdeCYBtmP32t7+Na6+9Fk8++SQuvPBCvPvd78a9994LgAn0M844A6effjrWrVuHD37wg/jXf/3Xusdx9tln45ZbbsHU1JT3tjvuuAMzMzN429veBgCYnp7GRRddhN///ve46667IMsy3va2t8Fxmn8tuu+++/Ce97wH//iP/4innnoKX/va13DDDTfg85//fNOfMwoUdRgQyPEliP7GtKjHl0gw5gzwhWW9+dr/thnQirE/zHVd3HXXXbjjjjvwsY99zHt7sVjEN77xDS/i8J3vfAeO4+Ab3/iG51h+85vfxJw5c3DPPffg1FNPxRVXXIFPfvKTOOOMMwAA1157Le64446aX/svf/kLbrrpJtx555045ZRTAAD77ruv934Ri1i0aJHnnOq6ji984Qv43//9X6+add9998X999+Pr33ta3j961+Pr371q9hvv/1w2WWXAQAOOuggPPHEE/jSl75U81jWrl2LYrGIn/70pzjnnHMAAN/73vfw13/91xgeHgYAnHnmmaGPuf7667Fw4UI89dRTOOyww+p9m2vymc98Bv/6r/+Kc8891/u3fO5zn8MnPvEJXHLJJU19ziiQ8B0QxAILcnwJoj8xaIEFQbSFW2+9FUNDQzBNE47j4F3vehc+/elPe+8//PDDQ7nexx9/HM8++6wnAgWlUgkbNmzA+Pg4tmzZghNOOMF7XyaTwbHHHlsRdxCsW7cOiqLg9a9/feTjfvbZZzEzM4M3vOENobcbhoGjjz4aAPD000+HjgNAw/0FmUwG73jHO/Dd734X55xzDqanp/Gzn/0sVGjwzDPP4OKLL8ZDDz2EsbExz+ndtGlT08L38ccfx29/+9uQw2vbNkqlEmZmZlAoFJr6vI0g4TsgGNzxtRwXlu0go1DKhSD6CaozIxKNWmDOa6++dgxOOukkfPWrX4WmaVi2bFlFm0OxGHaPp6amsHr1anz3u9+t+FyizjUu+Xw+9seIKMJtt92GvfbaK/Q+sQuhWc4++2y8/vWvx/bt23HnnXcin8/jjW98o/d+scPhv//7v7Fs2TI4joPDDjssFBEJIstyheg3TbPi3/OZz3zGc8mD5HK5lv499SDhOyBYZW4RCV+C6C9ogQWRaCSpqbhBLygWi9h///0jP/6YY47BjTfeiEWLFmFkZKTqY5YuXYqHHnoIr3vd6wAAlmXh0UcfxTHHHFP18Ycffjgcx8G9997rRR2CCMfZDjS4HHroochms9i0aVNNp/iQQw7xBvUEv/vd7xr+G1/1qldh+fLluPHGG/GLX/wCf/u3f+tVr+3cuRPr16/Hf//3f+O1r30tAOD++++v+/kWLlyIyclJTE9PexcS69atCz3mmGOOwfr162P9LNoBqZ8BQWR8AbpNShD9CLU6EERvOPvss7FgwQK89a1vxX333YeNGzfinnvuwT/8wz/gpZdeAgD84z/+I774xS/i5ptvxp///Gd89KMfxZ49e2p+zn322Qfnnnsu3v/+9+Pmm2/2PudNN90EgO08kCQJt956K3bs2IGpqSkMDw/j4x//OC688EJ861vfwoYNG/DYY4/hv/7rv7xu4g9/+MN45pln8M///M9Yv349vve97+GGG26I9O9817vehWuvvRZ33nknzj77bO/tc+fOxfz58/H1r38dzz77LH7961/joosuqvu5TjjhBBQKBfzbv/0bNmzYUPU4Lr74Ynz729/GZz7zGTz55JN4+umn8YMf/ACf+tSnIh1vs5DwHRDMwOQl5XwJov8wAxevJHwJonsUCgX85je/wYoVK3DGGWfgkEMOwQc+8AGUSiXPAf6nf/onnHPOOTj33HOxZs0aDA8Pe40ItfjqV7+Kt7/97fjoRz+Kgw8+GB/60IcwPT0NANhrr7284a/Fixd7C7s+97nP4f/+3/+LSy+9FIcccgje+MY34rbbbsOqVasAACtWrMCPf/xj3HzzzTjyyCNx7bXX4gtf+EKkf+fZZ5+Np556CnvttRde/epXe2+XZRk/+MEP8Oijj+Kwww7DhRdeiH//93+v+7nmzZuH73znO7j99ttx+OGH4/vf/34oRw2wobpbb70Vv/rVr3Dcccfhla98JS6//HKsXLky0vE2i+TWSl4TmJiYwOjoKMbHx2ve3ugXXvvlX+PFXbMAgLs/fiJWLeiPW1IEQTC+99Am/NtPnwAAnHXscnzp7Uf0+IiIQaZUKmHjxo1YtWpVR/OYBCGo9zsXR6+R4zsgBKMO5PgSRP8R3txGji9BEEQzkPAdEEzK+BJEX2NQxpcgCKJlSPgOCBZlfAmir6EeX4IgiNYh4Tsg0NYnguhvqM6MIAiidUj4DgimQxlfguhnjNACC3oOE8mA5uOJbtGu3zUSvgNC+QILgiD6C8r4EklCURQAqLm5iyDajfhdE797zUKb2wYA23ERMHzJ8SWIPoRaHYgkkclkUCgUsGPHDqiqClkmH43oHI7jYMeOHSgUChUrpuNCwncAMMtOkuT4EkT/QY4vkSQkScLSpUuxceNGvPDCC70+HGIAkGUZK1asgCRJLX0eEr4DgOWEczE6Ob4E0XdQqwORNDRNwwEHHEBxB6IraJrWljsLJHwHAIscX4Loe8jxJZKILMu0uY3oKyiUMwAEl1cAlPEliH6EhC9BEETrkPAdACjjSxD9T2i4jZ7DBEEQTUHCdwCwyPEliL4nlPGlVgeCIIimIOE7AJhOmeNr0kmTIPoN0/IvYA3LocUBBEEQTUDCdwCocHxp6xNB9B3lLm95dp8gCIJoDAnfAaAi40uOL0H0HeW5XlpiQRAEER8SvgNAufAlx5cg+o/y5zENuBEEQcSHhO8AULnAgk6YBNFvVDi+JHwJgiBiQ8J3ACDHlyD6H3J8CYIgWoeE7wBQPgRDji9B9B+VGV+6gCUIgogLCd8BoHxlMTm+BNF/lAtfWkRDEAQRHxK+A4BwfDOyBIAcX4LoR0SLA38aU9SBIAiiCUj4DgAWX2AxlMsAIKeIIPoN13U94TuUpecxQRBEs5DwHQDM8hMmrSwmiL7CclyIRW3DORUAOb4EQRDNQMJ3ABBRB3KKCKI/CTY6FLMKABK+/c6v/7wN3/ztxl4fBkEMHJleHwDReawy4WvYDmzHhSLCggRBJJqgyA0+j4n+5ZM/eQLbJnScfPAirJxf7PXhEMTAQI7vAOBFHXL+dQ65RQTRPwiRK0lAQePCl57Dfc3ErAUA2D1j9vhICGKwIOE7AJRnfAGgRDlfgugbhMhVFRlaRg69jehPxMXMtG71+EgIYrAg4TsAiJXF2YziV5rRSZMg+gYhcrOKDE1hL9s6RR36FovHzQASvgTRbUj4DgBigYWqSMipbDCGHF+C6B/EgKqWIcc3DQTz2dMGCV+C6CYkfAcAg580VUVGlp80yfEliP6Bog7pIvizm9bJhCCIbkLCdwAQjm+GHF+C6EuEQ0iObzoI/uxmyPEliK5CwncAEBlfcnwJoj/xHV/Jy/gaNl289ivB198pcnwJoquQ8B0ARKtDRpaQJceXIPoO3/FVvItXcnz7l6DwnaHhNoLoKiR8BwDT9vOB5PgSRP9hWhR1SBOhjK9BJgRBdBMSvgOA5Q23Scip7EdOji9B9A+e46tIvuNLdWZ9S6jVgRxfgugqJHwHAFGFlFFkZDMs6kCOL0H0D2aV4TZ6DvcvesB4oOE2guguJHwHAMvxM77k+BJE/6EH68wUEr79TtDxnSLHlyC6CgnfASDoFpHjSxD9h8iEaooMjT+HKePbv4TrzMiEIIhuQsJ3APCiDrJMji9B9CHVog4kfPsX3aKML0H0ChK+A0BwgQU5vgTRf4QdXxK+/Q5tbiOI3kHCdwDwF1j4E+E6Ob4E0TcYwTozhVod+p1wnRk5vgTRTUj4DgBGYDBGrCwmx7cJnr0LePLmXh8FMYCEc/rk+PY7uhVsdbDhum4Pj4YgBotMrw+A6DzC8c3I/kmTMr4xcV3gpvcA5gyw7+uB/NxeHxExQOiBJTQUdeh/gsaD7bjQLcczJQiC6Czk+A4AlnfSlMjxbRZzFjCmANcB9MleHw0xYJgWu3gNDbdR1KFvKf/Z0YAbQXQPEr4DQGiBBbU6NIc54//d0nt3HMRAYtjs+Rrs8SXHt38p/9lRpRlBdA8SvgOAGXR8qdWhOYwp/+8kfIkuI4RSlja3pYLynx0tsSCI7kHCdwDwWx3I8W0aw3d8XRK+RJcRd23CdWb0HO5XKh1fEr4E0S1I+A4AwvHNyNTj2yz67IT3910TU3UeSRDtx29mkajOLAWUC98p6vIliK5BwncAsGxyfFtlcsIXvpY+28MjIQYRw6szU6jOLAXoZW79DEUdCKJrkPAdAMxAFRJlfJtjanKP93fLLPXuQIiBJOj4irs2jus3thD9RflFyzQNtxFE1yDhOwCYwZXF5Pg2xfSk7/jaBglforuENrdl/JdtuoDtT6jOjCB6BwnfAcAbboNDjm+TzE4HhC85vkSXERev2TLhS3GH/qTS8SXhSxDdgoTvAGDZLk6U/4C9v3Yg5m/8GQByfOOiz/hLK8jxJbqNEYgrKbIERZZCbyf6C2E8jOZVAMAMDbcRRNcg4ZtyXNeFYTs4Xl4P2ZrF0JbfASDHNy7mrC98HaozI7pMMOoAgJZY9Dni9XdugQlf6vEliO5Bwjfl2F7Mgb2wZqxpAOyE6fD3EY2xS36FmUNRB6LLBB1fALTEos8RFyxzixoA6vEliG5CwjfliHyvBhMAoJjT3vvoNml0bN3/vrkkfIkuU+H4UqVZXyMuWOYVmPClVgeC6B4kfFOO1//JHV/Z9J1LyvlGRwqsLKbNbUS3EcNtIuJASyz6G7F1b44QvhR1IIiuQcI35YjlFZrEHF/ZnPYGY+g2aXQky19a4VpGD4+EGETKHV9aYtHfiAuWeUUabiOIbkPCN+WIgvssd3yhT3knTXJ8o+E4LlR7xn+DRVEHort4wlehqEMaKM/40nAbQXQPEr4px+QZ36zERa4xhZxKXb5x2DNrIodAvIGiDkSXMcWdm/KMr00Xr/2I3+pAw20E0W1I+KYck7/AZiX+wmpMk+Mbk7EpHUUEXF6bog5E9xCVhECg1YHqzPoao0z40nAbQXQPEr4px3KE8GUZXxhTyGco4xuHsSkd+YDjK9nk+BLdQ7i9QKXjS8/h/sQo6/Gl4TaC6B4kfFOOOGl6GV8AoxnmWJLjG42dUwaKku/4SuT4El0k2NxAGd/+x3Zcr2Zyntfja1OvOkF0CRK+Kae81QEA5ihMuOkmnTSjMDalo0COL9EjguK2YnMb1Zn1HcGfpxhuA4BZMiIIoiuQ8E05fo+v/6I6ojDhVrLohTYKOyd1FAIZX9khx5foHqLDV5Elr4owKwZU6eK17wgK35GcCv4jpbgDQXQJEr4pR9SZBR3fUS586aQZjd2TU8hI/vdKts06jyaI9iKEkqpIgG0Bxgw5vn2Mzg0HSWI/06KWAUADbgTRLUj4phxvZbHri7VhnlclxzcaU5Pjof9XHIo6EN3DCG5tu+E04MojUJTZ7yBlfPsPPdDJLEkSilkufMnxJYiukOn1ARCdRdwmVQPDbSMyOb5xmJmeCP2/QlEHoouEtrZtfgywDSxydoTeR/QP3oUMz2sXsiy2QsKXILoDOb4pR7Q6qPAdX9FQQFVI0SiVC1+Xog5E9xDiNivD65DO8+gSRR36D+/nmWGCV0QdZijqQBBdgYRvyrGqOL5DmAVAdWZR0WcmQ/+fIceX6CLirk0x4z9f82C/g+T49h+6J3zZ6bfIHV9aW0wQ3YGEb8oRK4vVgEsptpCR49uYGcOCYs2E3pZxSfgS3UOI2yHFF0Z5iVcS0nO47whFVxB0fEn4EkQ3IOGbcizbgQQHmYDjWyDHNzI7pwxva5ut5AEAGYo6EF1EF45vQPjmJHJ8+xUjMNwGAAVvuI1ejwmiG5DwTTmm7UBD2EnIu+T4RmVsSvcccis7BwAJ30HDcVw8tXkCdo82a5lWFeHr8lYHyvj2HaLOLKuy0+8QDbcRRFch4ZtyTNutInzZrXudHN+GjE0ZKEjc8c3NBQBoIOE7SPzyya1483/eh4t/9qeefH0hbguBLu6sl/Gl53C/UeH4Uo8vQXQVEr4px7Kd0GAbAGQdFnUgx7cxO6f8rW1Obg4APijo0PduUNiwfQoAcOMjL+Kl3TMNHt1+zGpRB1CPb79SXmcmenwp40sQ3YGEb8qxHLfCocw67ORNGd/GjE3pKHCR4eTn+++wacBtUBAXiJbj4mv3Ptf1ry/EbV7yhZFGUYe+paLVQaNWB4LoJiR8U45hO9Ck8AuqRo5vZFjUgTm+yM/z32GVenNARNcJXiDe+PsXsX2iuz/7asI365Lj26/oZa0OYrhthobbCKIrkPBNOZZd6fhq9jQAcnyjsHPa8BxfKT/qv4Mc34EheIFoWA6+cf/Grn59gy+hKcj+75xKwrdv8evMmNPrDbdR1IEgugIJ35RjVWl1UHkvLTm+jRmb9KMOcnYIJVcFALjk+A4MYgr/+H2Y4/+d372A3dPdu/ARQikXcHxVh68dp+dw31FzuI2iDgTRFUj4phzTqWx1yFjk+EZl57TurXhWckMwwISvaZDwHRRKJhMqp75iMV6xbAQzho1v/rZ7rq8YbssFWh2E8CXHt/8orzOjlcUE0V1I+KYc03L8qIPEbqkp5PhGZiywwELJFqGDnaSM0mwvD4voIkKo5FQF5520PwDghgeex2SpO7V2XsYXvsuccaiLu18pd3xpZTFBdBcSvinHclx/uK3AbtXK5jQAlxzfBli2g90zhuf4ZnLDnuNrkeM7MASn8N/4iiXYb2EREyUL//O7F7ry9UVzQzbg+GZsanXoV4zyVocsOb4E0U1I+KYctrmNnzALrI5LgosCdHKLGrB7xoTrwnN8Ja1IwncAEReIWVWBLEv46InM9b3uvo2Y7YJY8YRSwPFVuONLUYf+w7uQEa0OGm1uI4huQsI35Vi26y+wyI0CEncZMEuObwPGppjgHZHZn9CKsCjjO3CIC8QcFyp/fdQy7D03j53TBn7wyKaOf31v4UGgnUWxSfj2K7oZrjMb4o6vbjmwyMEniI5DwjflmLaDrBC+mSygDQEAilIJuuXAdd0eHl2y2TnFHLaiJIRvAabEhK+tU8Z3UBBCJasyZ05VZHzkxP0AAF//zXMdF59mNcfXYr9/FHXoP8o3t4lWB4DWFhNENyDhm3JMx4UmsoGKBmhFAEARNBzTCOH4ipXF0IZgShoAwDLJ8R0USmIKP+O/XL599d5YPJLFlvESfvLYSx39+p5Qcn3hK/OMr+24sB26eO0nyofbtIwMVZEA0NpigugGJHxTTqjHV/Ed3yESvg0RwjfLM75QC7BlJnxtQ+/VYRFdRji+Oe74AkA2o+BDr90XAHDNPRs6eovaE0qBqINkzVa8n+gP/Doz//dJDLhRzpcgOg8J35QTGm7LaECWC1+Zry2mnG9Ndk4byMCC6vLvn1aExaMOjklRh0FBL5vCF7zrhBWYV9SwadcMbv3jlo59fdHjK7p7ARK+/Yxe5vgCfpfvNK0tJoiOQ8I35ZjB4baA4ztHYbdNyfGtTXBrGwBAK8LmUQfHJMd3UNCrRB0Als18/6v3AQBcffezcDoUORDPUTUQdYA5C0ni77dJLPUT/srigPDNUrMDQXQLEr4px3ICUYeM5gnfUYUJN2p2qM3OacPP98oZQNG8qAMJ38GhWtRBcM6afTCczeCZ7VP41VPbOvL1hVDKBISvZM56jiE5vv1FtTsI3tpiGm4jiI5DwjflmHbZcBuPOozyii5yfGszNqWjIBod1CIgSbAVLnwtGm4bBBzHrehdDTKaV/GeV60EwFzfTrSkiKhDUPjCMZHPsK9Fwre/qOf40nAbQXQeEr4pJ1RnFog6DMvk+DZi51TA8eVtGA53fF1yfAeCYF1YtorjCwDvf/Uq5FUFT7w8jt88M9axYxDb2gQjilVxjETyKa8zA/yML60tJojOQ8I35Vi2W3W4bViiVod6uK6LsSkdRfjLKwDA5Y4vbBK+g0DwwjBXxfEFgPlDWbzrhBUAgKt//Wzbj8G0mLMbcnwBDCvseU2Ob3/hryyubHWYoeE2gug4JHxTjmk7VYfbhmQmfMnxrc6UbkG3HOQl4fgWAACOnAUAuBYJ30FAXBgqsoSMUvvl8u9fty80RcbDz+/CQ8/tbOsxCIdQLrvYGs5YoWMk+oNqw5Le2mKKOhBExyHhm3Isx6063EY9vvURW9vmZUSVGfu+eY4vCd+BwNvaVsPtFSweyeHtx+4NALjq7va6vsIhVJyw4zskW6H3Dywzu4BdG3t9FJGplvEdoh5fgugaJHxTjmk7VYfbRHaVHN/qiOUVC3P8+6MyxxcZJnwl26j2YUTKqLa1rRYfef1+UGQJ9z0zhsdf3NO2Y/Ad3/BA5RCvJBx44XvDW4CrT2ACuA8o39wGUKsDQXQTEr4pJ5TxDUQdCuALLAb9pFmDMe74LtD85RUA4Co59v+U8R0I6lWZlbN8XgFvPXIZANbw0C6EUCqPOhRl9rs58M/hXc+x5+PE5l4fSSS8OjO1SqsDOb4E0XFI+KYctrmNuwiBqEPBZcKXHN/q7JxmImOeyk9EXPiS4ztYiDzmImUKiFBV9tGT9gMA/Oqpbd5dg1YxyzO+/O5Dkd/JGehWB9cFxBa7PqgYdBwXFl90EtrclhWtDvR6TBCdhoRvymEZ34Djy6MOeZcc33qMTTJhO1flApcLX0lhw23l7huRTkqmg7+SH8XPZt4D/OY/Gj5+/0XDGM2ztdZ7ZtpzcSQcX0kIu/xcAEBBplaHkNjtgzXiwYsUrcpwG/X4EkTnIeGbckwrsLlNUT3HN0uOb12E4zvCc5TCZZNUIXzJ8R0EdMvGwdKL7H/+9ONIH5Pjt7BLZuuCVDiEMhxIDr+Azc0BABRlyviGxG4fOL5BoyFYZ0bDbQTRPUj4phzTCQy3ZbJAdhgAkLVnAJDjWwtxm3pEFj2+7IJByrCMr+yQ8B0EdCvw/NnxNDDZeC2xyAO346LSW3Yg7toAnuObl4TwHeCL1z5zfPXAz0pVJO/vNNxGEN2jJeH7xS9+EZIk4YILLvDeViqVcN5552H+/PkYGhrCmWeeiW3bwieLTZs24bTTTkOhUMCiRYvwz//8z7Cs8JXuPffcg2OOOQbZbBb7778/brjhhoqvf/XVV2OfffZBLpfDCSecgIcffjj0/ijHknYqh9vYLXvNmQXgkuNbAzHcVpBE1KHM8SXhOxDolo1sUHRuvLfhx+QyQvi2flHprUsOCd85AIACjNBjBpI+c3yDVWaS5AtfGm4jiO7RtPB95JFH8LWvfQ1HHHFE6O0XXnghbrnlFvzwhz/Evffei82bN+OMM87w3m/bNk477TQYhoEHHngA3/rWt3DDDTfg4osv9h6zceNGnHbaaTjppJOwbt06XHDBBfjgBz+IO+64w3vMjTfeiIsuugiXXHIJHnvsMRx55JFYu3Yttm/fHvlY0o7rstukapXhNhkO8tDJ8a2BcHzLVxYrGSZ8MyR8B4KS6YRF53MRhK8XdWj9otLkz88cF7mQFCA7wt4msd/RgY469Jnj621tK1uG4g+3kfAliE7TlPCdmprC2Wefjf/+7//G3LlzvbePj4/juuuuw1e+8hWcfPLJWL16Nb75zW/igQcewO9+9zsAwK9+9Ss89dRT+M53voOjjjoKb3rTm/C5z30OV199NQyDvbhfe+21WLVqFS677DIccsghOP/88/H2t78dl19+ufe1vvKVr+BDH/oQ3ve+9+HQQw/Ftddei0KhgOuvvz7ysaQd0+bTwxWOL3MaitDJ8a2BWGCRc/mJVWXCV+aOb/kyASKd6GaZ4/vcPQ3bHbI86tCOi0rh5hZlsYQmB6h5AEAONNzWb45vtSozACjyqMOMYcON0B5CEETzNCV8zzvvPJx22mk45ZRTQm9/9NFHYZpm6O0HH3wwVqxYgQcffBAA8OCDD+Lwww/H4sWLvcesXbsWExMTePLJJ73HlH/utWvXep/DMAw8+uijocfIsoxTTjnFe0yUYylH13VMTEyE/utnLIffVpPEcJsGSJLn+halWXJ8q2BYDsZnmahgkRB4jq+sMdGhuGbVjyXShW45yEqBn/XES6w3tg5tzfjy5+cQX08MNSh8meOrU9Sh8u8JpdryCgAo8KiD5biDHV0hiC4QW/j+4Ac/wGOPPYZLL7204n1bt26FpmmYM2dO6O2LFy/G1q1bvccERa94v3hfvcdMTExgdnYWY2NjsG276mOCn6PRsZRz6aWXYnR01Ptv+fLldb4TyUc4vp5jxTtohYgbQgk6Ob4V7Jpmbq4iS1BERyjP+CoqG25TXXJ8B4GS6YQHywDgubvrfkyO11SV2jB0Jjp8h5RKxzcLanUIubx94Ph6w4qZ6o4vAExTly9BdJRYwvfFF1/EP/7jP+K73/0ucrlcp46pZ3zyk5/E+Pi499+LL77Y60NqCat8Ipx30Iou3yLI8a2GyPfOK2qQjGn2Ru6SKxr7vc+Q4zsQhIbbRvZmfzbI+fqOb+vPLfH8LHhRhywTvwCyLmV8+9XxDVaZAewiW2TDqdKMIDpLLOH76KOPYvv27TjmmGOQyWSQyWRw77334j//8z+RyWSwePFiGIaBPXv2hD5u27ZtWLJkCQBgyZIlFc0K4v8bPWZkZAT5fB4LFiyAoihVHxP8HI2OpZxsNouRkZHQf/2McHxDw22Av71NKlHGtwpC+M4vaoDJhS/v8c1owvEl4TsI6MEe7ANPZX9u/A3g1H7etHO4TQilcMaX/S5qJHz7zvEVdWblji8QzvkSBNE5Ygnfv/qrv8ITTzyBdevWef8de+yxOPvss72/q6qKu+66y/uY9evXY9OmTVizZg0AYM2aNXjiiSdC7Qt33nknRkZGcOihh3qPCX4O8RjxOTRNw+rVq0OPcRwHd911l/eY1atXNzyWtGPWcny58B1CiRzfKojBtoXDWcBzfFk8RAjfTPntbyKVlILDbSvWsEaF0h5g6x9rfoxwfNsRIxIXr8VQ1IH9DgrhO9DPYXMm8Pf+cXyrCl9qdiCIrpBp/BCf4eFhHHbYYaG3FYtFzJ8/33v7Bz7wAVx00UWYN28eRkZG8LGPfQxr1qzBK1/5SgDAqaeeikMPPRTnnHMOvvzlL2Pr1q341Kc+hfPOOw/ZLBNmH/7wh3HVVVfhE5/4BN7//vfj17/+NW666Sbcdttt3te96KKLcO655+LYY4/F8ccfjyuuuALT09N43/veBwAYHR1teCxpR2x8ykj8xKhwx1dEHcjxrYrY2ragoPguEr9YEMI3C5NN9we6OIn0wYbbAtv79nkNsP521u6w7OiqH+NFHdrR6uBFHURO33d8VXJ8AbPfHF8RdagUvrS2mCC6QyzhG4XLL78csizjzDPPhK7rWLt2La655hrv/Yqi4NZbb8VHPvIRrFmzBsViEeeeey4++9nPeo9ZtWoVbrvtNlx44YW48sorsffee+Mb3/gG1q5d6z3mrLPOwo4dO3DxxRdj69atOOqoo/DLX/4yNPDW6FjSjmWXDeaURR2GKONbFbG8YmkhUCvEh9uy2UC23TZY5pJILaGoQyYHrHo9F773Aq+5sOrHeMNtbXF8a2d8NYcL30FuAbD6K+OrR3B8KeNLEJ2lZeF7zz33hP4/l8vh6quvxtVXX13zY1auXInbb7+97uc98cQT8Yc//KHuY84//3ycf/75Nd8f5VjSjFEufMuG2wogx7caIuO7OMe/N5LsiY1MtuA/0NJJ+KacUNQhowH7nsj+vulB5jaqlUO+2TbWmQmhlBOVamrez5s75Pj2m+Nbq84MCApfek0miE7S0spiItmwdcWBF1FFZX9qftSBHN9KhOO7MCu6U4tepCHk+Fp6tw+N6DLM8Q3EDBYeBAwtYSLrpYerfkw7Wx2Em5sXwjeT9cR2xmZCb6CFb585vnUzvhR1IIiuQMI3xViOEx5sE3nUYNShDSfntLGTO74Ls/x7xwfbAEDLKNBd5szYZvIdJqI1dNP2F1iIBTD7vp79/3P3VP2YbDujDlwo5atkfBWH/f7RAgtOPzi+dvU6MyA43EaOL0F0EhK+Kca0XWjBk7YgONxm0YrMckSrw1yVOy+aH2/QMjJ0MOfc1JN/oiVaQ7ecQNSBu/2rhPCt3ufb1uE2LpRyCDi+GXJ8PUI9vsl/PgqjQcvIbDjW9t1dcnwJojuQ8E0xph0czAkIXxF1QAmu61cmEYDrul6rw9wMn+YPOL7ZjAxDCF8j+SdaojVKph2IOvA8t3B8Nz8GzO6p+Jh29viK4TYv45vJe5vbZE/4DrBDGOrx7YOog81+VtmMDNz4buDKI73KxAJlfAmiK5DwTTEs41vW4QuEhC/QntWqaWFi1vIuBIYVUWPlC9+MIsPgM6GmPlPx8US6MEKOL38Oje4NzN8fcB3ghd9WfEwu074eX+Hm5vh6YpbxZcJX4aJvoFsd+szxNYJ1Zs/dA0y8BIw9AwAYolYHgugKJHxTTE3Hl0cdhiR20qCcr88Onu8dzmWg2fykGnB8AcAkx3dgsEzT78HOBAYbRbtDlZxvJ1YWh+IWXPhKroUMrMGOOvSZ4+vVmSnwl+PoEwD8Ht9pijoQREch4ZtiWMaXv4gqVaIOEhN5VGnmIwbbFgwFt7YVQo8xJfa9tEj4pp7QAGPwOVQn5+tFHdpwJ6Vi+2Imy+IO4mvBGGzh26eOb0EyAfCIWWkcAK0sJohuQcI3xVS0Ogg8x5dPhQ/yibOMndPslvL8ohYQvkOhxwjHl4TvAGAHKuuCju+q1wKQgLH1wMTm0Ifk2tjj69VfiaiDmueRC9bQkifh6//dmmUDYwlG/KyKUuC1o8QcX1pZTBDdgYRvijFtt8FwGztpkOPrMxZ0fE2e4VXDjq8lM+HrkPBNP/xWuispgBLY95OfCyw7iv19429CH+IPt7VvZXHWDWR8Jcn7ncxKxmBnfINRB9cBbLP2YxOAMBnEfAV7I486ZKnVgSC6AQnfFGPZDlQhfKsMtxVQAuCS4xtALK+YPxR0fMMZX0six3cQsB0XshMQnOXUyPmKjtZ2tjqo3nAbd535Eos8DJi2C8dJttPZMcqXViQ85ytea/MI3EkQjq9GrQ4E0Q1I+KYYM7iyuMpwmwI2sd6O6fO0MFY141sufNn30qHNbalGt+zAHZMqwjeY8w3cYhdRB91yWu7IFm6u6pYLX+b4iraHgXV9y5dWJDzn623iQ0Cgi4wvd3yp1YEgOgsJ3xRTc7gtUM81hFlyfAP4w221HV9b5sK3D1akEs2jm46/OEKpInxXvJK9fXKzV0kF+FEHoPX8vGEx4aw6ZcI3IxxfvS1fp28xyyoFE+74is7lvBuMOtBwG0F0ExK+KcZyAh2kQeEry574LUolyvgG2OlFHWpnfB1P+JLjm2ZKlr+8Qqrm+Kp5YMUJ7O8b/XYH4fgCrVcFCocw45ZFLnilWU7iju/ACt/+cnzFBUrODTq+4YzvtGHRNk2C6CAkfFNMeLit7MQtmh3I8Q0RjjpMsTeWtToIx9elqEOq0U0HWalsXXE5VXK+qiJDkVnrQquVZsIhzDh6+Di48B1W2PENZNTBcfzWDeHIJ97x5cOKTmXUQSywcF1glswIgugYJHxTjFlruA0IDbiR4+uzMzTcxh3fsqiDcHxdcnxTjR7a2qZVf9CqE9mfG+8DHP95lMu0Z22x2CLoOb5qWPgOyez5PZCObzDfm5/L/ky441tV+PJWh7yqQGLXSzTgRhAdhIRvigmtLC4/cQe6fMnxZZRMG5N8sKTeAgtHEY5vsk+yRGvogahDTcd32VFAdpTlNDev897cru1tQihl7DLHNyOE7wBHHYIZeyF8E+74epvbnMqogyRJgWYHGnAjiE5BwjfFmI5TfbgNCHT5kuMrEMsrVEXCSC4DmNUXWLjie2kZ3Tw8osuUzGBGvkrGFwBkhS+zALDxHu/N7VpiISIMco2oQ0FEHQZR+AqRq2j+xWmfOL7eOnTAc3wBWltMEN2AhG+KCTm+tYSvRBlfgWh0mF/MQpIk3/EtG25zuQhybYo6pJmw41tD+AJVc75ZtT1RByGUFLu8zowJ36IkMr4DePEqRG4m769xTrjj6w0r2oE2Cp7xBfztbRR1IIjOQcI3xbAe3/rDbeT4+niDbcP8IqFGnZkQvhINt6WaSMNtgN/nu+kh7/Z7TiyxaLnOzIEMB7Jbdhye8GWCeCAvXoXIVXN+9rlPHF81KHxtwzvuYpYcX4LoNCR8U0yo1aHGcFsRlPEVeFvbilk2MW5WH24T30vJpqhDmilZduPhNgBYcAAwvIw1DGz6HYDg2uJWh9scZBH4PRMXsKLHd6AzvsLxzfkXBAl3fHXR0mFNl72DV5qJLl9yfAmiY5DwTTFWrc1tgCd8h6jH1yNUZRYsxi8XvhkSvoOAbjqNh9sAQJKAfbnry/t825nx9cR38Dh4/KYwyD2+wZ5t7oAn2fF1HNdr6VCsssUbYnubRtvbCKLTkPBNMZZTY3MbEIg6UMZXIKrMFgxpYeEr8oMcscxAcijqkGZCdWblz59yynK+3triFlsdzOAxyBlAYY6guLU/0CuLRauK2h+Ob/BnJJtlji9vdvAyvhR1IIiOQcI3xRhBx7fGcFuBHF+PndWWV6hFtukuCHfPZRpuSzUl0/YvHOs5voCf8928Dpjdjazo8W11gYXteNvZQsfAHd/8QDu+XORm8n3h+AYNBrnc8aW1xQTRNUj4phir3nAbv30/RBlfj7GqyysKFY8Tjq/imBXvI9JDpAUWgpGlwIKDALjAxvvaFnUIH0PgOSxWFmOAhW9Vxze5wjf4M5LE4KzET8Fla4unKOpAEB2DhG+KsWy39ua27DAAFnUgx5chMr7zQ8srihWPkzUmOmSHMr5pRrdsf7CskeMLhHK+/nBbi1GHYMY3eAwZIXx1fqwDKHyrOr7JjzpoiuwL36HF7M+ytcUzJHwJomOQ8E0xpuMG6phqD7cN5EmzCmOhjK/o8K0ifLnzliHhm2pKZp07JtUI5HyzmTYNt1lOdfHNhZ7mDrDjK0Sumu8rxzebkf0L65Fl7M+yVodpijoQRMcg4ZtiTCtw4q4x3FZACTo5vnAcF7umgxnfOo6vKqIOJHzTjG7Z/oVjrc1tQVa+mt263vksFjpjAFpzfC3bgeMCuWpdwlz4Zl32OzuYw23BHt/kO76iykxTJH+GYHgp+5NHHYay1OpAEJ2GhG+KsZzgcFv1jG+RHF8AwJ5ZEw5rGsK8Yv2Mb4ZP1CsuZXzTjG5FrDMT5OcAy44BABww/XsArQ23ieor3/GtzPgKx3cgn8OhzW394/gOZWzA5b8XwvHlUQdyfAmi85DwTTGhBRYVUQeW8R2ijC8AP987p6BCVWTfkeGRkCCyxk6yGZcc3zSjmzGG2wQ857vPBBe+LTy3vFvj4hjUQK1eRgjfUuixA0WfOb7iZzQnE3jdGF7C/tRFnRk5vgTRaUj4phjLceoMtwU2t5Hw9QfbilzgBMvxy8jwk6xKjm+qCW9ui+D4Al7Od+/dDwNwW+rxFfGFXB3HVxVRh0EUvn3m+ApXfkThNYhqAcjPZX8v7/El4UsQHYOEb4oxLReal1FUw+/kTmZGcuAkuPuyW/iDbVxc1Mn4ZjT2GBK+6UY3ncBwaISMLwDsfTwgqygYY1iGna05vlz4FuUqXcI8bqPaIuM7gBevweG2PnJ8R2QufLUikB1hfy9fWUxRB4LoGCR8U4zpNO7xBYCMXVamPoCEllcAdYWvkuVuG0j4phk96PhGGW4DmCDNMTEzJM22lPEVQqmgVHkO8zsRGUcH4A6m42v1V6uDcHyHZe7ga0UgN8r+XtoDgKIOBNENSPimGMt2aw+3yQpcnhPMmFNwXbfLR5csQuuKgbrCV+UZXxUmMODftzSjx60zE/AKvDz0llodTO74FjzHN5jxZb+DMmyosAdT+HpRh/7I+IpWh2HP8R0KCF8eddBoZTFBdBoSvikmvLmtcjjH5UssCijBcgZbwIWWVwB+xrea8M0K0eECNrm+aSW8wCKO8GUirCDpnthpBiFm83K1zW1+9jwPfcDrzPrD8fVaHaTaUQeR8S2ZDuwBf00miE5BwjfFGKEe3yonbs3v8h30ZofQumLAb3WoMtwmhC8AgGcsifShB58/UYfbAK8CLwejJcdXiNlCtR5fRfXW3WZhkOPrOb4JFr62EL78GLWiF4tBaQJwXRQ0xXs8ub4E0RlI+KYZx4QscdegfLgNgJRlbiZtb/MdXz/jKxzfyjozLevfcnYtEr5ppWTa8YfbAO9iqQC9LXVm3gILNSB8Jcn7OnnJGMznb7B5xXN8kxt1ED/PYkj48qiDawPGNLIZGRlZAgDM6INtRhBEpyDhm2IkJ3AbvsqJW+JdvkXq8sVOb2tbeca30vHNZlQYLnNmLCO5J1qiNfTguuCow21AQJC2lvH1hW8N15n/f25QHV8Rawj2+NoG4CTztUxcnBQhhO8Q+12RuMurT0CSJM/1JceXIDoDCd8UIwVvw1c7cYsuX3J8MTYZvc4sq8owwBx0U0/urVWiNcJRh/gZ3zz0ljqyxXBbXqqRMxYCe1AzvmKQLdjjCyQ25+u1dCDg+EpSOO4A6vIliE5DwjfFSDY7YbqSDCiZygdo/hKLQXZ8ZwwLs/zf7w+3ceGrVgpfTZFhgH0/DRK+qaVkWoEFFjGErxZodWjDcFuu1hINNee9nxzfQONFQnO+3rBiUPgC/oAbX1vsC9/BfU0miE5SRQ0RacBx3Npb2wT8hbeIwXZ8RZVZNiOjKIZL6ji+sizBAItEWEYyT7JE69iWCVnjGfkmWx1My4XtuFB4bjMOwsXNVhtuC3ydvKRj5yA+f7nA/cKdL+A3O3fjF7LK4l0JzfmKho+8G4g6AH7OVzQ7aNTlSxCdhBzflMKWV9TY2ibgdWZD0mBnfHcEBtskiQsUo3adGYBA1IGWf6QRy3aQcQz/DXFaHVS/1QFA088t4RD6lWrlwrfgvX8gHV8+3PbL9Xvw562TcMT3J+GOb9blwrxc+JY7vpTxJYiOQMI3pZi2W7/KDAjVmZHjGxhsc12/zqyG8LUkdnKyyfFNJWywLTAc2sRwWwHsgqpp4csdX61W3IILvfwgCl/HBvjw7oTFnouOkuxmB/HzzDlC+FaPOtDaYoLoLCR8UwpbXiFOmJXLKwB4w21DUqmlIZx+p6LKzCoB4Le4q/T4AoApiagD1ZmlkZJpe88fV1YBOcZLJW8CKfLVtKUmRakZ0fHNScbgDbcFNrSVeOzIERcnCd3eJsyFbLnwLY860NpigugoJHxTimkHMr618omU8QUA7PS2tpVVmQE1HV9f+CbzJEu0hm45XrZWipPvBTxBOiS3x/FV3So9voH/z0MfPMc30Nyg89iRLfeH8NWc8qhDrVaHwTUjCKKTkPBNKZbjQOP9n1LNqAP1+AL+1raKKrNMDpCVqh9jS+xk65jk+KaRpqvMAD/qwIVzqxlfzeW/YzWG2way1YHnex0lC5efxiwv6pDM+JH4Gam1og5lw20zlPEliI5AwjelmJbrZxRrDrdRjy/gRx3mR+jwFVhc+NoJPckSrVEy7eaWVwCeIC1KwvFt7rll2Cxu4zm+FRlfLnwl1uPrum5TX6cv4QNstuJfDFgJd3w94WuXDc7mqmd8pyjqQBAdgYRvSgm1OtSMOlCPL1BluM1bhVpb+Irbqg5lfFNJaLgtruPr9fiy36tm8/OeUHJFxjcffoDn+LL3D1TOlw+wefEGAKYUzOgnD1Fnplpl69C9Vgfm+A5labiNIDoJCd+UYsVodShKJegtrFbtdyqG2xo0OgCALTPH103oSZZoDd20vahQrCozINTjC6DpJRZic1vGEVGH8s1tYkMcF76DdNeGO75mSPiKC9dkO76Zcse3LOpQoOE2gugoJHxTimk7geG2Bq0OmB3oqMPOaSYc/OG2+h2+AODIfJKcMr6ppGQ5gTaFGs+fWvA7BTm0GHUQQqlBxjfPv85ACV/u+HouLwBDTrbja/DXZJnXsNWKOhQ16vEliE5CwjelmLY/3Aalxok70OM7qFEHy3awe6bGcJtWvcoM8G+xuhYJ3zSimzayaM3xzfENXa0Mtymwobh29ePg0Ye8zITUQEUduKtrBISvjuQ7vgUERHl5nRm1OhBEVyDhm1Isxw1sbqslfNkLrybZA7t6d9eMAdcFJAmYWxAnTiF8h2p+nMu/pyR800ko41vr+VMLfsGU9YRvkz2+wS5uoEqdmRiiG8SoAxO3osMXgLdGPKmOr245KArhq2T9oeMsrSwmiG5CwjelhE6aDYbbAMDVJ7twVMlDDLbNK2hQZLGumAvfGssrAF/4JvUkS7RGybSRlWosjmgE/71hNWRu046vbjve4BqAyqy+iDoMovDlz7ug8O0Lx1firxfBGJWIOuiTgOOgQMNtBNFRSPimFMt2/Vu1tYbblAwsmZ3UpeDShgGiYrANiJTx9YSvbdR8DNG/tKPHVwarFGx6uC3oOssZQMmUfZ2w8B2onD4Xt7OuX9VYSrjjawQd3+DdJDHcBhfQJzAkhtso40sQHYGEb0qJNNwGwMywk7RkTnXjsBKHcHy9wTYgUquDKy4mKOqQSlqqMwvcKchDb7oxxbD97XEVVWaBt+X5cQ6U8OXidsapInwT6vjqluM1fYReW9Scb07oE16PL0UdCKIzkPBNKabtQpMaZxTtDHsBlk1yfD3Mxo6vEEOyTcI3jeiWHYgKxYw6KBnvOVeA3rTjazQS36q/wEI8fmDgz9Epx39t80RwXzi+Za8tgbXFYrjNtN3B+pkSRJcg4ZtSLCdwq7ae8OXVS8qAOr5jVR3fxhlfIUQkijqkkpLZwnAb4P3u5KXmHV8zmPGtJr75sFt2EBdY8B7facePf8y6yXV8XdeFYQdaHcqFb9avNCto/pp0WltMEO2HhG9KYQssGt+qdYTwFduEBoydVTO+IupQu9VBUkj4phndsgMxg5iOL+ALX+gt1ZnVd3zZ18i5A+j4cld3wvKF74ybCb0vSYgYSlEMt2WHww/I+c0OqiJDy7BTM60tJoj2Q8I3pRjBjG+t4TYALhd3mYGPOgQdXxF1qOP4qjzq4JDwTSO6GaEVpR6aEL5G88LXdn3xrVbJ+PK3ZQdycxtzdScDwndaxB7M5Alf4cbXdHwDUQeA1hYTRCch4ZtSLNtFVmo83OZyx1ezB9TxFVvbikHHV/T41s74yiLj61DGN43olt38cBsQWlvc/OY2OxB1qHIMfLhN1KYZ9gCJJEtEHfzhNi/2YCUv6iAuSoqoMtwGVK4tpi5fgugYJHxTCsv4Ns4oSnxtsWoPpuMrWh0WDAeH20TGt7bwlbjjq5Djm0qY49tknRkQWlvc9HCbHYw6VMv4MuErw4UGa8AcX3ahPgv/ZzNlcxGcRMeX/2yG5RrC19vetgdAYG0xbW8jiLZDwjelmLYbabhNZM2yTvJckk7jui528KjD/GKV4bZ6ji8XHYpj1nwM0b/oluMvsKgTFaqJcHxbyPialuvFGOq1OgBMYA+W8K1cYDFlJ9fx1SuEb9n8QMXaYuryJYhOQcI3pUTa3AZAzjHhqzmDF3WY0n2XrPoCi9oZX5k7vhlyfFNJybRbc3z5RVNeMpqPOjTq8VVUttgCQA7mgPX48pXFgQUW/eD4DskNWh3E2uIsdfkSRKcg4ZtSrIjDbQqPOuQG0PEVVWZFTUE+UCHkO761Wx0UfutZcUn4ppHwAotmWh3EcolSS60OdTO+gL/EQtIHss6sBA1i0/hkgh1fT/hWW2ABBIbbxgEEMr403EYQbYeEb0phCywaD7cp3PEtYBbWIJ044VeZzQ+6va4byPjWdnyVLHd8XYo6pBE23NZAdNZD9VsdmnViG2Z8gYDANgYr6sDFrQ7Ne/561WYJdHx1nvOuurIYqBJ14K0O5PgSRNsh4ZtSog63ZfLMaSiiNFi3SuE7vqEqM9sAHH6yqZPxzXDBQcI3nZRMJ3Dh2LzwZa0O8V0713XDPb5qLeHL3p4bNOHL68xm3awXU5qw+F0ba5ZdwCYI8bOpurIYqIw6dHFt8WTJxOV3/gXPbp/s+NciiCRAwjelmLaLbIThtkyeOb5Fqflbsv3KWDXH1wi0W9QRvorGPkajqEMqCdWZNTPcprW2wMJymHDLSXU2twGhDXGDJXxF1EHFQt7I4mV8AcBKVs2gbos6Mx7DaNDj62V8uxB1uOXxLbjyrmfwX79+tuNfiyCSAAnflBJ5uI1nfAfR8d3ldfhWaXRQNDY8VANVY46vCnJ804hutbjAwosgNNfjK0Rswy7hjL+2eKAyvmK4DRoW8gvXYMND0nK+4ueZdxtFHVjGt8gzvt1YWbx5D/teiddDgkg7JHxTimW7kYbbRJ3ZkDQ7cI6vWAc6kg8IXFM0OtR2ewEgkxXClzJ4aUQ3G6wLboQaaHWwbLgxb71XCt8Gju/ARR384bYFw0zwGsjAhRR6f1IQpkK+1ua28gUW3PGd6kKPr7jz1Xdb4ia3ATedC2z8Ta+PhOgzMo0fQvQjph3IKNZxLoXzUEAJk4N04oQvfEWeDgBgTLE/6yyvAABN460OcADbAhR6KqWJkmUHqsRa6/F1XT6ollEafJCPyd1b/xgaZ3wH6o6NV2emYU5eg6pIMG3AzeQgWbOJdXxzbq2oA3d8zRnANjHEe3y7Mdy2Y5IJ376rTvvzrcBTN7OZjFWv6/XREH0EOb4phS2wiHDiDkQdBs7xLXHhmw1WmUVzfNVsQIhYyXKXiNZhm9taqDPT/M1tAGLHHXRvGCpqxneAHF/b9AZQZ6FhOJdBjl9UuOL7lDDH17AcKLD9mYDyqAO/8wYAKE2goAnHtwvClzu+fbcsg7vjobkMgogACd+UwlodImxu4yforGTB0JN1sug0wuEYygYdX9HhW7vKDADUrL9QwE3YIA3RGq7rtj7cJhxfPsWvx7yoFI5vXm7QLJEZQMfX9N1cnQvfrMqEr6Nw4Zswx1e3bBQQeJ0ov7BWVP8ukz7uXYx3I34w5jm+fWZ8iNdqM1k/ayL5kPBNKVZUx1fznQZzdqLDR5UsvKhDUPiajZdXAEBWU2G67OSk6/TCmyZM24Xjwm9FaanOjDl8cR1fMaiW8+rMqmxuC7w9hwFaYMHvsDiQoEPFSE5FNsNOZbaSXMe3IPK9cqa6GRFodvDqzDrswrqu69U69l3UQQjfhF3kEMmHhG9KMSwbmsSv4Os5VkoGOp+GtktTXTiy5CBOKlUd3zrLKwBAy8gweETeNOiFN02wZQNuixlf9vtT5I5vyYrnpnmZ0EbHIISvZMCI+TX6Fu7wGVABSCzqoArhy79PCRNDhuWgKAUG2ySp8kGBAbdurSyemLW8CybdcvpriZHOe4fJ8SViQsI3rdiB22r1htsAlGR28rRLg1VgLm7thRzfiBlfTZH5iRewBiwiknZC64qB5oSv5rctAIidnxdRh1zD4bYB3Nxm+lVmAGtlyfGogy3zn1XSHF874PgG7rKFCFSaiZXFMx2OH+yYCn+f+iruQFEHoklI+KYVO9DJ2ODErXPh6+qDJXz9qENwuI273g0yvpIkecLXJOGbKkqm7efjgeaG21R/gQWA2Plb3asza7A2mX+d3CD1+FpiaxsTvsO5jBd1MIXwTdjAqW45KKLG1jZBIOowlPWjDnGr8OKwYzLc3dtXA26e8J3p7XEQfQcJ37QSHLhqMJxjyOzk6Q6c41tvuK1+xhcATOH4Gsk6yRKtUeH41hsOrQUXpFmv1SGu4+vyjxeOb42MLxflA9XqwN1cX/j6jq/lOb7JcgENy0FBqtHhKwhEHUSPr+PGz4fHQTQ6CPoq5ytMioS5+0TyIeGbVmx2wnSkDCDX/zEbCnc3B6gWxnFcb2I6PNzG3YMGGV8AMCUhfJN1kiVaI1RlpmSr5zEbofoLTjKw4g+3NeP4Dorw5Y6vDg2yxLaceY6vlGTHt4Hw9RzfcRRU/y5UJ11Y0ejgf61+ijoI4TsDdNAVJ9IHCd+UIvGMrxPBrTIVUaMzOMNtwZNJ2PEVUYf6GV8gKHyTdZIlWqPl5RVA6PcnDyO24ytErOZGXWChD47wDWxtG8pmIEmS5/gaEn+9S5jjq1t2wPGtcTfJy/hOQJalruR8+9rx9c5XbvgOJ0E0gIRvSpG44+vK9QfbAMDKMNdIMgdI+PKTiSJLnlsEIPJwGwBY/CRr0622VNHyumKAxSMk9nuVh970cJsa0fHND1LGl9+VKbkahnPs9c0Tvkim42tEcXy9qMM4AHRliUWF49tPwjd4hzJhLR5EsiHhm1Ikh72guXJjx9fKsBdi2Rgc4TsVyPdKwVvZXsY3ivBlJ12bHN9UoVt2a1vbABaP4AsJ8pKOUkw3Vri3qtj0VavHVyywkAZogQUXtbPQMJJnz0Fx8VqSEpzxbTjc5rc6APDXFncw6lDh+PbjcBuQuJ83kWxI+KYUmbc6uBGiDjY/QcvW4EzHVh1sA2JlfG1+UeGYdJstTYSG25oZbBOI7W3QY29u02224jYD/nFUZ+YTqDMbzrHnr3B8RSd50hxf3XIwJHFxFiHqAHTJ8eXCV1w49E2dmev6sTSAhC8RCxK+aUVEHSKsW3W48FXMwRluq1plBgQyvo1bHWzh+FLUIVWUTBtao/7cKGh+pVnsqEPULmEufLM86tDJ6qvEYPkZ3xEufLN8gYXo9k2aEAptbovQ6gCgK2uLd/Cow8r57He1b6IO5gwAt+z/CSIaJHxTiuKIbGBjx0oIX9UeROFb5vjGyPiKsnxyfNNFyPGN8PypicjfSnpTK4u9RgegseMr6XBdvwYt1YjhNlf1Mr7ZDBOJsy6faUiY42vYjrfFL0qPL4COb29zHBc7+brilfPZMfVNq0P5IDaZD0QMSPimFNkRUYfGw20u3ySUsQZH+NaMOngZ3whRB34b3E3YSZZoDd1y/AUWrTi+gcGzZhzfnBDfsgrISvUH8n7fHBfJAzHgJobbkA1EHdipTHT79rXjyzO+Ra2zwnfPrAnLYRdKK+f1meNbPo9Cji8RAxK+KUUI30bLKwBAyrIXYs1O1smik4gXeHFy8TCjL7BweGOGS45vqtBNu3F/bhQCGd+SFbPOzHaQlcQx1BHfgYwvgMHI+YaiDrzVIeGOr27ZgVaHBhlfXWR82b+pUy6siDnMLagY5UOCnRykayvlnfMJu9Ahkg0J35SiOMwtkiKcuCX+QqzZg3PVPKVXWV4B+C+oEYbbHIULEuqQTBW65fg9vhEuHGvCnb1cM1EHK2Klmhd1MAC4gyF8xXCb6w+3iYzvjKOGHpMUIm1uE1EH2wDMkvfa1CkxKgbbFgxlva811S/DbeWOL9WZETEg4ZtShPCNMpUu5VjUIesMjvD1ow6BW8i2yU46QKSMr2jMcG2jwSOJfkI37UDUoU2Ob9wFFnZA+NaqMit7XxbmYAhfz/H1M77C8Z12kun4sh7fBhlfbRgAr1YsjXvDbZ1qWhCO78LhbOBrkeNLpB8SvinEdV0ovP9TijCcI3Phm3MH58Wj6nBb8MU0hvCVEnaSJVqjZDltijrwHl8YTTm+uSjHkPGFbw4GDLtPHLtWqFNnNp1Qx1cPZXxrRB1kOdTsUOhwxrea49s3wlefDP8/ZXyJGJDwTSG240LljlWUqENmAIXvdDXhK1485Uy0/laxHIQc31TBMr7tc3zzUgl6zIyvaQfiFvUyvkqGDb+B1aYNxBILLmpnA8NtXg+tzZ/PCbsYNSwHxUZRByDU7DDkRR264PgKkU0ZX2IAIOGbQizH9TZPRRG+Che++UESvkaVVgcv31tkm7ca4KpMkEg2ZXzTRPsyvs23OoQzvg2aJbjAzkkDssSC5zn1KiuLpzzHN1nCV7cjtDoAobXFYritUwssxNY2FnXgIrtvMr7lwjdZP28i2ZDwTSGm7dcxRRG+aoFnfGF6iy/STtXhthjrigFAElEHEr6pomTa0UVnPfiAJMv4NtPjG0/4Dsz2NtPP+I7mw8Ntk57jm5yLeNd1YVqW3+ObHa794Jxfadbp4Tbh+LKoQ2dFdtsxKOpANA8J3xRi2S40ib2AyWrjE7eaH/X/pzw7lVKq9vjG6PAF4N0GlynqkCpYj2+ERoVGcOHLWh3iOr5utIwv4Du+fHtb2nGtYMY3PNw2afHnc4IcQMN2vLo5AA2iDv7a4k43LYzx5RXBqEMnt8S1FYo6EC1AwjeFMMeXnbjlCMNtuVwWuui/LH9BSSlTpSrC14y+tQ0AZCF8HRK+aUKPWiXWCC3g+DbV4xvR8c0MVtTBNSrrzMQCiwkreY4va3RgQtyV5Po/z8BwW1ETK4s77fhq/nCbYfXH2mtxnpK4hCHHl4gBCd8UYjquX8cUYUgrm1EwDXaCt0uD4fj6rQ6BOjPRDalGE77gbrrsDEY8ZFDQLbutjm9zrQ6BJRqN7tp4UQd9MIQvd/cMOYs8z/aKlcWe8HUswE7Gbftwh+9Q/fmBKlGHTtSZ2Y6LXdOVdWauC8zGvDvRE8TK4sIC9mfChhmJZEPCN4VYdrxbtTlVxrTLTp7m7HgnDy0x1B1ui+n4KpTxTRUls03DbUL4NhF1MG3XX1kcdbhtUKIOXPgqWgESF5HC8d1jBS5kE+L66gHHV2r02hKMOnSwzmzXtAHHZRp8XkFDXlU8Pd4XOV9hUhQXsj/J8SViQMI3hZh2fMd3Cuzkas4MhuNbtc7MEFGHaBlfWeXC16WoQ5rQLbs9UQfP8Y1fM2ZYwZXF0TK+eRgDUWcmerOVbGB5B3d+SyKyBSQm5xuMOjS8qA72+HIXdta0YTvtjR+IDt/5RQ0ZRYYkSX7Otx+aHTzhyx1fyvgSMSDhm0JM24EqRe8hVWQJM+CO7wBEHXTLhmmzE0lY+PIX01oF82WIwUGFog6pQjeDd0xaaHUIZHwNy4ETQ7yYcVod+Ptz0gAIX9eFbDMRqeV8ESl6fF3IcIVLnxDH17AjdvgCVXt8gfbHD4KNDoK+anYQd+c8xzcZFzlEf0DCN4VYtt/jG2kRA4CSxISvPZt+4RvMzIkBEgCxh9sUjX3PMuT4popS2xxff+gMQCxRGq/Hl7dHDELG1zYguezfqOX8OzPZjOzdqnfF9yshYsiIsrVNkPUzvtmMDJn/m9oddxgLdPgK+qrZoUL4UtSBiA4J3xRiOo6/eSqq8JW58C1NdOqwEoM4ieRUGRkl8BTwFlhEizooPOqQccnxTRO66Xh1gO1YWSxET5ycrx5aWdxI+HLHF2b6hW/glraW90WkJEm+6yu+XwlxfHXLjuH4zuEfNB6KH7Rb+FZ3fPtobbGo3aSoA9EEJHxTiNlED2lJZmLPKaW/zmyqWocvEBhuixZ1yGjsBEvCN12wOjMuOlsabvOztwBiVZqFVxY3yvj6Q3SpF74832u7Egq58AWBaHZwlGQ5vrrloAA+ABsj6gAgsMSivS5sNce305vi2go5vkQLkPBNIZbjQo3p+Orc8XUHYIFF1cE2IPYCC4ULX5WiDqkivLmtlR5fJnLykgEJTqxKs9DmNjVf/8Ei4wsDht0Ht6lbwfSXV4zkw69totnBTlrGt5nhthJr1yl0KHcb7PAVDHV4U1xbKW91oDozIgYkfFOIace/VWsoXOyJfsQU43X4amXCN2bGN8NvMXvuOtH3uK7LN7eJ508rK4t9wZqDESvqYAZd56iO7yCsLLbEumINI7nw81c4vnYSHV8pYsZX1Jnpk4DrBnK3bRa+1RzfDm+Kaxu25QvdoUXsT4o6EDEg4ZtCmhluM4XwLd+BnkLEcNtQrtzxjbfAQuO3WlUSvqlB9OD6MYNoz5+qZHzhW0C8Ll8jTquD6rc6pF748ovT4LpigXB8LbmPHV8RdXBtwJgONC20OeowyS6qghnfIf61ZpIedTADcTwv40tRByI6JHxTCFtZHC/qYGXYC7I0AC8g0zUzvvEcX5U7ehk4idkSRbSGiCO0pc5Mlj3xm5eib29zHBem7cbO+Oagp3+BBXdxg+uKBTne5esJ34Q4vkacjK9aACTeNKNPBLp1u+D48q81lfSog7grKau+Q+5YgE0GBBENEr4phK0sjpdR9ISvkf6ow2SbMr5qLpC9pO1tqUDnA2jZmHdMaqL5SyyiDreZDnedvahD1Iyvmf4eX8vP+JY7vqLVwZQS5viGenwbRB0kqfra4jYOt5m2g90z7HdrYZVWh8QvsAhu2Aw28FDcgYgICd8UYgUzvhGn0u0MewFRzPS3OviOrxJ+hxmv1UELbI6yTRK+aUBvp+MLhLe3RYw6iLhCLurFq9ceMQCtDqaf8a3l+JqyFnpsr9FNO9DjG+FuUnBtMX+NamfF2K5pA67LFhfNLQSH29r/tTqCiONpQ/zClJcdk/AlIkLCN4VYoZXFav0Hcxwu9hQr/Y7vdK3htpg9vpqmwXbZi66h04tuGtAtBzIcaBIXqa20OgDe71JB0iNHHcRWwWzkHl9/UUbqha9VO+oghtv62vEFwmuLRY9vG+MHotFhflGDLDZkAB35Wh1BvE5nh5hDLl6vByCmR7QHEr4pxLDj9/g6fKArY6X/xWOqZtQhXsY3m5Ghgzkmpp4Md4lojZJph1s6Wha+vhsbdbjNc3xFxleNJnzzMAYg4+sPt43ky6IOfLhNPCeT4vjG2twGBBzf8Y7ED0S+NzjYBvgzD9P9FHUA/PYUqjQjIkLCN4VYTQy3QWUvyOoACN+qw22O7TtEUevMFBkG2Ocw9fR/3wYBPbj8BWg96iC6fJsQvrSyuBLHYM/R2WpRB+746kiW4xtrgQUQFr5a++MHY5OVg22Av8Ai8Y6vGG7zhC85vkQ8SPimEKuJ4TZkufC10//iIaqBQo5v8EUzovAFAAPMdSLHNx3oVmB5BSRAztR9fEO4G1WQdJQiilLh2uakiD2+YrhNSv/KYkNnbp8ODSM16sx0ib89QY5v5JXFQDjqkO1A1KGh45tw4SsGsLVh9qe4I0IZXyIiJHxTiGVZyEj8BBh15Sq/Bae6euqrufzNbYHhNnH7DFIsl8/kJ1nTSMZJlmgN3XSQ9Za/5FiGsBU8Nzb6AgshXiMP2AUG6NIedTBm2QWqAc1rcRCIjG/JTZ7jG7nHFwitLfa6ddvY6iA6fCsc376JOpQ7vjzqQMKXiAgJ3xQSahiIONwm54b9/0l5pZlwT0JRByPQ6BBD7Jjc8bVJ+KYC5vgKp7XFKjPAH25DKfJwm2E7UGCzfmgg+gILGF4rRVoRkSInk4NU9jwVju+smyzHVzftgPCNMdxWGve7ddvowvqOb/j322t1SHrUITjcBgSiDiR8iWjEEr5f/epXccQRR2BkZAQjIyNYs2YNfvGLX3jvL5VKOO+88zB//nwMDQ3hzDPPxLZt20KfY9OmTTjttNNQKBSwaNEi/PM//zMsK/xEu+eee3DMMccgm81i//33xw033FBxLFdffTX22Wcf5HI5nHDCCXj44YdD749yLKnFCgjfiFEHVcvBcLkDmnLhW3W4LWaHr8CS2MnDSshJlmgN3YqxMS0Kmr9OOKrja9oOckJ8RzkO4SpLJkwr4aKlRawSe566Vb4nos5sVgy3JWTYyTVnIUusqSNWxje0wKKdjm+tjG+/RR349zJDUQciHrGE7957740vfvGLePTRR/H73/8eJ598Mt761rfiySefBABceOGFuOWWW/DDH/4Q9957LzZv3owzzjjD+3jbtnHaaafBMAw88MAD+Na3voUbbrgBF198sfeYjRs34rTTTsNJJ52EdevW4YILLsAHP/hB3HHHHd5jbrzxRlx00UW45JJL8Nhjj+HII4/E2rVrsX37du8xjY4lzThB4RtxuC2nypgGv2Wkp1v4Vh1uK58UjoglCceXXnTTAGt1EFGHFhsdAD+GIOnecoxGGEHxHeU4giLQSneftMUdXzdTeYEqog+zjnB8k/GclILd6FGqEgNRB39lcfsd34VlGV9hBJi2m+ysuDfcVu74pn8+hWgPsYTv6aefjje/+c044IADcOCBB+Lzn/88hoaG8Lvf/Q7j4+O47rrr8JWvfAUnn3wyVq9ejW9+85t44IEH8Lvf/Q4A8Ktf/QpPPfUUvvOd7+Coo47Cm970Jnzuc5/D1VdfDcNgDse1116LVatW4bLLLsMhhxyC888/H29/+9tx+eWXe8fxla98BR/60Ifwvve9D4ceeiiuvfZaFAoFXH/99QAQ6VhSDT/5WZIa+bZ9NqNgGvwEmnLHV2TYQsLXbE74mtzxpQUW6SDk+EbNx9cjkL+NE3XwjkFWAVmp/wGqv0hFSkiutVOIVgepSsWbcHxnRNQhIY6vbLLXU1PJszXWjchWbm6baWP8YKzKumIAXoMEkHDX1yhbNER1ZkRMms742raNH/zgB5iensaaNWvw6KOPwjRNnHLKKd5jDj74YKxYsQIPPvggAODBBx/E4YcfjsWLF3uPWbt2LSYmJjzX+MEHHwx9DvEY8TkMw8Cjjz4aeowsyzjllFO8x0Q5lmrouo6JiYnQf/2Ia7GTpi1Hy/cCrANzyuUvICkWvo7j1o86qPGEr/geO5TxTQW66UCT2hh1EK0OiOn4ikaHgKitiazA5dvKZDvlwpe7uLJW+X0Rju9M4hxf5kRaVVzqqgSiDn7FWHuiDoblYM8M+/0ub3XIKLL3PUx0zrdWjy85vkREYgvfJ554AkNDQ8hms/jwhz+Mn/70pzj00EOxdetWaJqGOXPmhB6/ePFibN26FQCwdevWkOgV7xfvq/eYiYkJzM7OYmxsDLZtV31M8HM0OpZqXHrppRgdHfX+W758ebRvSsJwbSbCHCmG8M0omBH9lymOOswEcpbhqEO85RUCmwsOJ+W3mAeFUJ1ZO4bbRI+vZER3fC0n+rpijstP/hk75RdgXMwqVbL4wvGdcvjzOiEOoFgDb0cWvsFWB/ZvMSwHZhsaO3ZOs9cpVZEwmq88PxT7odlBrCzOijozanUg4hFb+B500EFYt24dHnroIXzkIx/Bueeei6eeeqoTx9Z1PvnJT2J8fNz778UXX+z1ITWHxdwiIcqikBsQx1fcwpMlfwocQGBgIt5wmyd8KeqQCkpmm4fbmtjcZtpO9HXFHDHsJadd+PIoh5KtdHzF83naTpbjK7ZhOpmIF9XZoOPrX5y3Y8DNX1ecDa0rFhT7odmhpuObjJ83kXxit7Nrmob9998fALB69Wo88sgjuPLKK3HWWWfBMAzs2bMn5LRu27YNS5YsAQAsWbKkon1BNC0EH1PevrBt2zaMjIwgn89DURQoilL1McHP0ehYqpHNZpHNtiHX12ts9uLmxBC+2YyCPSLjq0924qgSQTDmEKpDErfJotQNBRDfYzch7hLRGrplx1/+Uo9QxreJ4bao4puf/LOuAct2kFHS2VQp8eeZmq0UkaLHN3GOrxC+UQbbAN/x1SegyS40RYZhO5g2LIwWot/Fq8aOGo0OgmI/NDvU3NxGwpeIRsuvjo7jQNd1rF69Gqqq4q677vLet379emzatAlr1qwBAKxZswZPPPFEqH3hzjvvxMjICA499FDvMcHPIR4jPoemaVi9enXoMY7j4K677vIeE+VYUo3N3KI4wjfU6mBM139wH1O10QEIZHzjOb6e8KU6s1SgWw6yUvuH2wpS9OE203YCW9uiCV+JC9+cZKR6iYXCL+q1XBXhyx3fSStZjq/Kc9du1PkBMdwGAPokCtn2rS0eq9HhK+iPqEPZcBvVmRExieX4fvKTn8Sb3vQmrFixApOTk/je976He+65B3fccQdGR0fxgQ98ABdddBHmzZuHkZERfOxjH8OaNWvwyle+EgBw6qmn4tBDD8U555yDL3/5y9i6dSs+9alP4bzzzvOc1g9/+MO46qqr8IlPfALvf//78etf/xo33XQTbrvtNu84LrroIpx77rk49thjcfzxx+OKK67A9PQ03ve+9wFApGNJM5IQvhGrzADe6iA2HqU46lB1sA1ous7MVYTjazR4JNEPlEw7dr62Llpgc1vE4bZwl3C0Y5CC29ssB4U2xJOTiMKjHNl85fNUZHwn7WQ5vqrDK9iivraoOXbRZetel++eGbMtA24NHd9+WFtsUJ0Z0RqxhO/27dvxnve8B1u2bMHo6CiOOOII3HHHHXjDG94AALj88sshyzLOPPNM6LqOtWvX4pprrvE+XlEU3HrrrfjIRz6CNWvWoFgs4txzz8VnP/tZ7zGrVq3CbbfdhgsvvBBXXnkl9t57b3zjG9/A2rVrvcecddZZ2LFjBy6++GJs3boVRx11FH75y1+GBt4aHUuaEcLXjdHqMCg9vsLJaJfw9S4uaLgtFeiWg9EORB0KKEXeqhaqM4vr+MJMdgdri2Qc9jzL5mv3+E5YAeHruq2vnW4R4fjGilHlRoDpHbzSrJ2Ob/V1xQJRadbO+rS24rq+8M1SnRnRHLGE73XXXVf3/blcDldffTWuvvrqmo9ZuXIlbr/99rqf58QTT8Qf/vCHuo85//zzcf7557d0LGmlKcdXVTDlih7f9GZ8/ahDWTeq2Vyrgytuh9skfNOAbsZ3W+viLbCIsbnNcv1jqNJXW/3r8CE6SYeeYuGruex5litUikjh+I5bgee2VYpWCddBNLuJ15bcKBe+E23dqCYc3/IqM4EwBKaSGnWwDcDh3weqMyOaJJ0TEAOO5PDb7jGEby7jO76unt6Mrxd10NqT8fVyoOT4poKSZXekxzfWcJttx874wnN8053xFcI3X0f4eo4vkIjcZ9blSzdE/VakD/IH3ITjO9OOqEON5RUC4fgmNuoQvBupUqsD0RwkfFOIZLMTtytHd6yyqt/j66S41aHhcFvMVgeXu4LCZSf6m5DjG+PCsSaixxd65IyvabvxXeeg8E2r4+u6yPGat+JQpYj0FlhYMlyJu74JuP2ddfjSjSpNFDUJri3WhAvbhqhDRMc3sXVmIuaQyQMKfw0n4UvEhIRvCpF5Ds5VYmR8M36PrzsAwrd2xjem48uFieyQ8E0DrM6Mn/Tb6PgqkouMY0RaQhCuM4t4mz7gLKc16mAHmlOGirUdXwCJEUOu6yLPHV85F+OiukNrixs6vkkfbqs2i0F1Zk1zy+Ob8YXbn4btuL0+lK5CwjeFKE78zVMZRcasJIRveofbRHZtKFcmfJvM+Eqe40tRhzTAFliImEH7Mr4Ab3aIEHfQgyuLox5DJlBnllLhOz3lX5APDVWKSOH4Av5Cj147vqbtogB2DEqcqIO3tnjcX1vcYu62ZNqYLDFBW9PxbfOK5LZjlHX4AlRn1gJf/MWf8fXfPIffPjvW60PpKiR8U4h32z1mD6mp8JN0int8p3R2UVAZdeAvqFG7NjkSvx1Ojm860C0bWUk4vm0QvorqtasUEK3L17SDK4tjDrelOOowNc2Er+kqyGYrvy+qIkPh28hcRYih3gpfw3ZQBLsozuTjtDpw4RtYW9yqCys6fLWMjJHyC39OIfGOr2h0CFxEpLzO7DO3PIk3X3kfZjtwMbJrmp23Htiws+2fO8mQ8E0hituc8LX4LnkpxT2+Xp2ZVtbqYDTp+KpiVSwJ3zQQ6tBtxwILAJImmh2iDbixqENMxzc03JZQt65FZqbZ65Iu1b6TleOur+MNnfbWBTQsBwWJie9MM8NtpXG/1aFF4eN1+A5lw1srAwiR3Y71yB2hfGsbkPo6s5v/8DKe2jKBp7aMt/XzGpaDWf569OAGcnyJPqeZqAMAWHyXvJxi4dt4gUW8jK/Mha9Cjm8qaGZ5REMCyyX0CANuoYxv1CouNf1RByF8jTrCN8tzvnZCHF/dslHkUYdYGV8v6hBsdWjV8WWvUQtq5HsBeLGKdgzSdYRqQ8jC8bVKgJOu333HcTE+y14LJmbb+zOZLJne3594edz7OoMACd8UIoSvFFf4quzFRLZLgJPQK/4Wqdrq4DiBjG+8VgeZi6OMS8I3DZRMG1rcmEEjAsI3atQhfsaXHWsORmqH20ozTPSYUu3viXB87QQ6vvF6fAOtDm2KH/iOb+3zwlAbB+k6QrWMb7Drusc/73YzZVgQc2cTpfYK04mS/zN2XOCh5wYn7kDCN4WIqIMU07FyMoEXk5S6vlUdX2sWAH91iRl1UFT2PSbHNx2wwbLm7pjUhN9FKESNOjSxuQ1lK4vTiF5ir0lWnZpG0exgyclwfA3L8RzfWBfVgR7fdg23jTVodADgxSoSu8CifF0xEG4+SdmA2/iML3Yn2uzIln++Qcr5kvBNIRlXOL7xhK+sZmG6PPua0mYH0U8ZEr7BYb6o9VEcWWOPF99zon9xXReG5XTQ8TUiOb5GM3ELVTi+ZmoXWOiz7K6MF2OogsYdX08c99gB1E0bBT7c1pzjO+4Pt7Xowjba2gb0g+PLX6uzAeEry6ltdgjGD4IObTsod5AfJOFL9DPNCt+cqmAaYm1xSoWvqDOrJnzVInsRjUFGY98vijr0PyIi0O7htnDUIa7jGzXj6w/QpdXxNUuNha9wfE1ZEx/U8eOqh2GUoEr8Zx53ZTHQ1pXFXtShjuMb3BLnJLHbtdpwG5CY3uZ2E3Rl2+/4st+nAxaxi4j12ya935G0Q8I3hYiMr6zGO3FnVQVTfG1xWoWvH3UItDo0u7wCgMKdNpUc375HN8uEb7uH26Ro29sMywmsLI6X8c2muM7M5KvU3brCl53SvBxwjx1fu1RlxW4UOrCyWEQd6jm+wTthMxFXbHeValEHwL9ATFmlWdjxbXfGl32+lfMLOHQp+3373YDkfEn4phCVn7iVuI5vRsaMyz8mhVEH03Y8URByfJtcXgEAiiaG20j49juicUGT2hx1EBnfOMNtTWd8Uyx8S1zEqrW/J9kME4mGEL49dnytEuse1qH5K3ajIBxfcwbFDHNeW21aaLS1DWBLQEQXciK7fGutlk+p47sn5Pi2OerAP/dITsWr9psPYHByviR8U4brun7UoQnHdzrFjm/whTyc8W1ueQUAqFn2/RIXG0T/IkRpzltZ3KbhtkDHbvw6s6jCl2d8JSO1GV/HiyTVjn8Ix9erPOux4+twx7ckxbyIEo4vgCGwC/MZw4brNh8/GIuQ8ZUkKdmVZtVaHYBApVm6hG83HN+RvIpX7S+E72D0+ZLwTRmW40LjJ24l6kmTk8vImHL5x6TQ8RUv5FpGhqoEfvWbXF4B+BlfjYRv39Mxx5dfUBWkUkTH10XOW2BBrQ4Ch7t5klo7kiQyvjqSkfF1uVDT5XhDs1Ay/u+NywS/7bhNV9XNGJa3AKOe4wskfIlFteE2wL9ATJnjGxS+7e7ZFQ7ySC6D4/aZB0WW8MLOGby0O11xkWqQ8E0ZrAO02YyvnHLHl72QD7dpeQUAaHx1qgo7deXpg0blcFt7HV/W6tBYTIQr1ajH18NgokbRaovILG918IRvjx1Al+eSYwtfwGt2KDh+60yz8YOxSXYhlVeVyq2VZfSH4zsYUYfxTg63BRzf4ZyKI/Zm8ZpBaHcg4ZsyTNuF2rTjq2Aa/ESbQuE7pbMnesXWNrNGbiwCIuoAAG5KV2YOCkKUtr3OTAtsbou0stgOrCyO5/hmJQumldK7D5ZY/dvY8S0lzvGNf1Et4g6KPuFFOJodcNsxxb4PC4a1muuKBYmuNNNrCV/+/U2z8G13nVkg4wsAr95vAQASvkQfYtmOF3XIqPEcq6wqY9rlQi6VUQd20qi5rrjOLdRaaAGX2DJI+PYzuuVAgQ0F3DHtSKtDtKhD/OE2/wLMMdJ18hdIEYSvcHxLCXF8xWuLqTTj+AbWFotKsybF6A7u+C6sk+8V+EssEih8vbtzg1ln1krGu+JzcyE9kmc/7+CAWzu/ThIh4ZsyWMa3yR7fTPM9vonsfCzDX1dcdquvhYxvNqvBcZmDYujpetEdNHTL9gUn0HbhW4jc42vHz/gGH9djl7NTyDZ7fqm5xo7vrMtcrF5/L2T+OmoqTTi+bVxbvCNClZmg6Dm+Scz41hhuS2md2Z7A5jbLcTHbxoq5csf3mJVzoWVkbJ0o4bmx6Xof2veQ8E0ZbPOUmEqPn/GdcuNnfO9evx2HffoO/Gzdy7G+Xrepuq4YqP1iGgEto8AA+3ymnk7BMSjophMeUmzzAotchIyv7biQHAuKxC8koz6HZdnfVpaykz/A7mRlHCbetHztSJInfJ1kOL6S5/g2H3VAabzltcVjEZZXCERvcOLqzBwnMNw2HH5fSh3f8oG2dlaaBTO+AHvurF4xF0D6a81I+KYMy3GhSvzJEXM4J6cqmBEZ3xhRh189uQ0zho37nkl2Fcp0LeHbQo+vLEveII1JUYe+phR0fCUlXu9qPUSPr9S4xzfU4QvUre4qxxZCvde39zvAlG4hx78vuXzjqMNMUhxfPj9gNeX4+lGHoR44vs2K7I5hzQLgF4S1og4p+92vEL5trDTzWx1U720i7vBgymvNSPimDJbxbW7zVDYjN7W5bcMO9tjd08le2+tFHbT2ZXwBwPQc3/Q5bYOEbgbbFNo02AYEWh0aRx10q0z4xnCdHZ4jlVN28geAyZLlxT+UOu0rWe74Tjv8Od7j74Vs8TXLmdaiDgUhRpuMH8RyfIW7nLThNs+MkSpfq1Po+DqO6wld8TNpV7ODYTlebEJkfAF4fb4PbtjZF/HFZiHhmzLYYEzzju90Ez2+z3Hhu2sm2cK34XBbE44vAJgSu2Km4bb+Rreav2isC+9jzaPxcJtpO57AcxUNkKO/RDt8la+UwnaRiZIZWONcZ2Uxd3ynnWQ4vgoXvk4zF9Xe2uJxT/g027TQnOObMOEbrDIrb6ZQ05fxndQtiBmz5fPY70+7unwnA85xcIvpEXvPQVFTsHvGxJ+3TrblayUREr4pwww6vjGFbzYTv8d3z4yBsSl2Quobx7diuK1F4QsSvmmgZAaiDm0VvtzxlRpnfI1Ah68U03V2+OPlNArfWQs5MPFWL/4hHN8pmwvfHn8vhPC1m9gK6UUdSuOeGG22aWEswrpiQauxio5RbxbDqzNLz+++cHdzquxdsLQr6iAaHYayGWQCy5xURcbxq+YBSPcWNxK+KcOy7RaG2xRMu/F6fDfs8Kc/d/WJ8G1nxhcATL4e1TL0po+tb5neCUxs6fVRtIVQzKCdwpf/XhVQatjjG1pXHFP4ukL42uk5+QsmS6aX8a27spg7vlM2f473+Na3arPXFrcl4TvhO75N5G5d18UOEXWIUWfWbKyiY9Ta2gakMuogGh1G86oXR2jXcJvf6FA5x/Aq3ueb5gE3Er4pw7QsyGIivBXHN2LUYcN2/3ETJQumndytUZ1odQAAi0cd7JT2p9bELAFfPxG45gQmgPsc3bL9jG+7Gh0A76TMWh2iRx1iC1/ueil2+n4Pgxlfr7qqCqLVYTIhjq8nfJt5bfGiDsGMb3zhM23Y3u/dguHG54TEtjrUuzOXwjozEWsYzaveAFq7Mr6TXoevWvG+NXzA7aHndib6fN4KJHxThmMGXMcmMr5TMXt8xWCbINg7mDTESWO4/CpX9Pg248oAsGT2fbZTdJstEk/+BBjfBJTGgefu7vXRtEzJ7JDjG9yqZta/K9KS68yFsmKn787DZDDjW2cjpWh1mLKS4vjyr9+M8M35dWatxA+E21vUFM/N9bBN4IH/Aq44HFj3Pf64hEYddJ45rbZhM4WOb0j4coHavqhDuMM3yKFLRzCaVzFt2Hji5fG2fL2kQcI3ZdjBnGkTrQ7e5jZzBnAa3+oqF767EzzgNsWvcou1Wh2adHxt7viGLjrSjusCD33N//8N/S98dcvu0HBbYLCpgSNl2g6yEYa4qn8d9tzNpDLqEM/xHbd4jt+1mbjrEZrDhJikxl+HHow6COG7cyr+6+uOWo0Oz/8WuPa1wK8+BezZBPzpJwACw21JjTpUFb78OZaiRhNf+GpeJKHtUYd8ZdRBliWs2ddvd0gjJHxThnAdHUiAHK+HNKcGNrcB/gtNHYIZX6C5F+ZuUTvq0JrwFY6vM0iO78uPAlvW+f//3N1An6+51M3m87V1yWThSuylVmrgSIUyvnWczWpIQvi66bsAm9Qt5BHB8VXZ93nSCjzHe+gCZoXwzbUWdThiL/b3hzfuin37eay80WFyG/CTvwdueDOw42n/PDHDhpmSG3WoN9zGfyfI8Y1EPccX8GvN0jrgRsI3ZbgWe5GzoFZWvjQgm5GhQ4Xl8l+LBnEH3bKxaRdzsFbwupUkO76ikD1Y3wLXBczWhK/jCd/0CY6aPPx19uehf8PysBMvA2N/6ekhtUqpha2HdZEkuNyllKz6jq/RQsZX4v22ahod3xndz1/XqQbLZcocX6CnOV8hfOVqLmUjRNTBNnDEkhzmFTVM6hYefWF3rE8jHN/FQxl2l+aqY4E/3ghAAo59P/DOH7AH8px+4uvMBmS4LSh8R9stfGdrZ3wBf8Dt98/vjrRmvd8g4ZsybIudNEW3bByyGQWA5Lu+DQbcXtg5A9txMZzN4MDFbIVkkpsd/FaHspOiyx2UJhdY2Fz4iouO1DO1HXjyp+zvr/4HYOUa9vc+jzvoZoeG2wDvdytjl+DWccZZ1KG5uIXMT/5aCh3f2dnABUO9Hl8edShZrv+4HoqhnMsd32pirRHaMABmXijGJF53ABMj96zfEevTjE3pOEb6Cz61+aPALz4B6BPAsqOBD90FvOVyYP7+7IHT7PMWA60O9X5Xu06UqEOqhtvYuTQ43NauHl/f8a1+V3i/hUUsGs5Ctxw8tinehVY/QMI3ZQjX0W5C+Ob4bcKo29tEo8O+i4Ywv8jEX1K7fF3X9YbbQo6vEXihbNbx5SLJTWF/alUe+xZgG8BexwJ7rQb2PYm9vc8H3Dq2wALwTsx56NDrLLFopc5MzrKvobk67JRtXdJnA5Gqej2+fLjNdlyv3q1njq9ter9Pmdxw/I+X5VDc4aSDFwEA7lm/PfrnmB7D657+DH6S/TSWzj4D5OYAp30F+OBd7LkLAEUmqGHNAsa0ZwzYjlv3d7Xr6PWiDsLxTc9rsO/4ZjpXZ1bD8ZUkKbC+OH05XxK+KcPlwtdq2vEFZtxozQ5isG3/hUOYy4VvUre3zZo2hBYIZXzFvzGTA2Sl8gMj4HLHF4Pg+Nom8Mj17O/H/z37cz8ufJ+/H7CS+fOPgm51aIEF/BhCXqq/ttiw3aajDrLm16YZSRIsbcAsMeHrSGrd56lwfAG/17hnjm9gRkLJNeH4AqG1xa87YCEkCfjz1kls3tPg3+TYwO+vB/5rNY7bfRsAYMPebwM+9ihw3AfC30NtyL/DMT0Wan5IVNyhnuMbrDNLkkvdAp7wLQTqzNq8wKJWxhdId58vCd+U4drNC19VkSBLiBx1EINt+y0qYl6Rfb2kOr5isE2SgIIWeNFvcXkFwFfLAoMhfP98GzC5GSgsAF7xN+xtiw9n/29MAS890tPDa4WO1ZkhIHw76Pgq/Hc4J6VQ+Opc+Db4uQjHF0DvHV9+Ua27GajZJoclheNb2oO5RQ1HL58DIELc4ccfBG69ECjtwXPKKpyhfxrPvepLvrsbRJKA4kL29+kxKLKEvCpWJCco31l3uE3cBXBT8zoshO+cvOYPt82abYmf1Gt1EIg+38df3NP0xsCkQsI3bfAnvS3HF76SJCGbUTAV0fF9lkcd9ls4hLkF4fgms8dXDLYVtQyk4NCfcBGa7PAFAsLXTqbobysP/zf7c/V7fXEoy8C+J7K/93HcQbccaFJzorMhPOpQQAPH12o+46tkhbg2oNsJEixtwNKZw+nWqTIDWBWTxlewOkoyHN8Z5Lxjio2oNNMnAAAnHhQh7jCxhXVsQwLe+CW8N/PveMw9sP664iITOeXNDokSPJ7wrTPcBqSm0mw8EEcQzqzjtqdmrlGrAwAsn1fA8nl5WI6LR57fxYywu78AbP1Ty1+/15DwTRmOEL5SvOUVgpwa2N5WR/i6rutFHfZbOIR5Cc/4Vh1sA1quMgMAlwsUKSVOQ022PQm8cD8gKWwaPIiIO/TxgFso6hBz+UtDuPDNSfW3t5l2sM6svsgrR1LTG3WwdHZnRorwPRGVZraSDMd3GjnvmGITiDoAwElc+P722THoVg0B9Oyd7M+9joF7wv+HrVPstW/BUJ3f6QJ3gqeF8GVO4EwTm+I6Rr2VxYrq17KlpNlhPLCyOKfKUBVm2LRje1ujVgfBq/ZlvxcPbtgJPPUz4N4vsd7nPoeEb9rgGctmHF+A5XyjRB22TpQwY9jIyBJWzi/4Gd+ECt/GHb7NNToA8PNxKdyYFUK4vYe8BRjdK/w+MeC2+TFgtj+ngHUzONzWZsdXi+H4ehnfmHELLgrzkp4q4WvaDmRe0RZF+Iqcry2el712fN1s845v1t/eBgCvWDaCBUNZTBs2fv98jefZM79ifx5wKiZKFgze++v1+FbDizqwCIXI+U7pCbpzUG+4DQg0O/S/8LUd18vhjuZVSJLU1pxvFMcX8Pt8f/vsGLB7I3vj5j/0fY6ahG/a4MJXdMvGJafKmI4Qddiwnb2or5xfgKrImMejDknt8RWO71C58G1DxlcIFCnNUYfZPbz7E/5QW5DRvYAFB7FquI2/6eqhtYvwcFtnHN98I+FrB4VvTPHNYwBZGJ7YSQPBrW1igK8eIudryVzo9cjxdblQm0EulD2ORc5vdQBYlOP1BzKRWjXuYBnAhnvY3w841evwHc5lQoN/FYjsL486DPE7YzOJjDrUaMhQAwNufc5kQNyKDl+vy7fFZgfTdrzsdr2ML+DnfJ/aMgF97AX2xtIeYPyllo6h15DwTRt28xlfQDi+IupQe3NbMOYAwHN8Zww7kYXXU7WEr3gxbSHjK4Sv7KRY+K77LjuhLDoUWPnq6o/p87iDbgbztZ3J+OYlHaVGw23NHoNwfFMWdZgsmZ7wjeP4esK3Rw6gzYXvtJvzGnNiE1hbLDjpYCZ876424LbpQcCYBIqLgKVHeVvb6uZ7AV/4lkUdkpXxbRBLS1Glmcj35lUFGr9oGs63p8t3suT/TCvOh2UsGs7hgEVDcF1gevtG/x1b/9jSMfQaEr5pgwvf1hxf/iKpT9Z8nDfYtogJ35FcBorMMkhJdH1rRx1ad3xlIXzTGnVwHD/mcPyHam8E7PM+35Jldy7qEBCljR3f1oRv2jK+Qcc3Su5Z9JH32vG1S77jqzXr+GbDji8AvHb/hVBkCc9un8KLu8rcTS/m8AZAlj3Ht27MAajM+IolFokSvg2iDpn0OL5eo0PBN7DEsolWM77i44eyGWQiRHBEny8mXvbfuIWEL5EkbPZL3azwDTu+daIOZY6vJEl+s0MCc741ow5tyPhKqnB8k9lo0TIb7mL5ruwocPg7aj9un1ezAZPdzwO7nuva4bUD23Fh2m7nhtv4ybqAUl3ha1rBlcXNZnzTJXwnZk3kJPE9iRJ1YO6qKfXW8bVKYrgt27zwzYUzvgDrdV29Yi6AKnGHoPAF4ju+Za0O7WgQaAu25V/AZBtFHfo/4xtcVywYadPa4kZb28pZs98CyHAwbAR+18jxJZKEcB1dpcmogypjKsJwmy98/atvv8s3eQJQDGlUtDqYdUrRIyJxgaKkNerw8NfZn0efXX2iWpAdBpafwP7eZ3EHIRQ1cIerU46vpEOv0+rQkuOb8R1fPUUZ34mQ49v4eyIcX0PqrePrcOE7i7x3Nyw2VaIOAPD6g0TONxB32LURGPsLa13hd1+E47uwkeMb6PEF/OG2xDi+QROm0XBbCurMxqtsVvOG21rM+EZtdBC8ct95WCiNQ0Xg65LjSyQKPmDlyjHdIk42o2DarZ/xnSyZ2DbBXlBF1AGAV2mWxO1t0w1bHZqPOihc1KRS+O7cADzD65GO+2Djx/dp3EG4sH6+tlPDbQZKtWqoUJbxjSDywl9DCN90tTpMlkzkEd/xNUSlY48cQDHcpsstXERlRY/veOjNXq3ZhjH/DoJ4nq5YA+TnAAgI30aOb4Hfzp4Ww22iziwhjq94nZbV2ndC1B73NreRPTPVHF8edWib4xtN+M4paHjtQnbxaKj893HiJWBmV0vH0UtI+KYMWQjfJm/Vsh5f/sJiVM/4Psc3ti0azoaePEnu8vWiDlqNjK/afNRB5lGHjJu8f3fL/P56AC6w/xuA+fs1frwYcNv4G3Z7sk8Q29RyHcv4Rqwzs52mVxYL4atJNkwjPb+LkyULWSm+46ujt8LXEcJXitfHHKJK1AEADlk6jMUjWZRMBw9t5AKkLOYA+FGHuh2+gB91sGYBYxqFpC2wqGJQlEwb//Pg83hpt3gNT1/Gd7Sq49uejG+jRocgr17InkMvqSuBufuwN255vKXj6CUkfFOGxHOmzQrfkONbI+oQ3NgWJMkZ39rDbXW2AUVE0djJWHGTF/FoCWMa+MP/sL9XqzCrxrKj2e3Z0jjre+wTxDKAXJNb0xqiBVod6kUdrBbWJgcGv2yj/10vQezhNu74esK3V3VmYmWx3ILwzYYXWAgkScKJBzLX9+4/b2cX8M/fx9554FrvcTuiZny1If9Ca3pHwPFNivDlJkzgdfrWP27B//3Zk7jsV39hb0hRj+9EVzK+0eOQRwyz379n9DnAkiPYG/s450vCN2WISq1mM77M8a3f4+vlexeF4wGe45vgqEPtHt/mHd9MWh3fP97EBOzcfYD9T4n2MbICrHo9+3sfxR2E45uV+IleabPwjdjja7aU8fUfb+u1qwj7jWCdWaSoA3d8Sz12fIVLaSotLMfxVhZPViwNELVm9/5lBxO9VgkYXQ4sPNh7zNgk+741bHWQpECzw87kLbCosrVNNFpsn+QXNimsM5sTEL7t6vGNm/EFgBUKu6vwrD4He+Ycwt7YxzlfEr4pQ0Qdmj1xZzMKphr0+JY3OgiS7PhOe8NtHcj48lJ9LU2Or+v6FWbHfQiQY7xU9GGfr5fx7XDUgdWZdajHV5K8gS7H6P/bvYKJkulHUCJEHUTGd9blJ/YeOb4Sf20xlDZEHVy74vX41fsvQEaWsHFsGhN/vI298YBTvbpBx3GjtzoAgS7fHclbYFFla5v4t3m9tL2sMzNngakqC0WaxIs6VKkza7XHN26rAwCoU5sBAJvdBfijtYK9cesTLR1HLyHhmzK8JQpNDufkVBkzwc1tTuVJegPP+O6/KCx8k+z4+lGHslaHNiywyPCoQyZNwveFB4DtTzLBdvTZ8T5WDLi99HDdLugk4Tu+HRpuC0Yd6g23tZLxBWDyoVYnBbd7BZMlC3mJd2RHcHzFAouS21vHV+KNMVYrjq9aYC0NQEXOdzin4rh95gFwIT3rrykWjM+asBzmEs8vxhC+M2MBxzchwtczKPxzjhjcmxLCt5d1Zt85E7ji8LaJ347WmVVpjGh8QC8CAF525+Ou3UvZ23Y+48/I9BkkfFOGlzNtyfENnHBLe0LvN20HL+xkL0IVjq9odUhgndm0UavHt/UFFkL4qmj+3/3irhn8ZVuCRKKoMDviHUB+bryPnbcKmLsKcCzg+fvbf2wdQFSMea59xxzfBlEHy20+4wvAlNhxuwOc8RXrgWd67PjKQvhmWhC+khSIO0xUvPvEgxZif+llDJe2sNf8Va/13ifyvXMKarQe4cASi2LiWh0qZzE8x1eI817WmW39E/s92/50Wz6daHWoXmfWquPLvl/DMRxfsaJ4s7sA62cKbDOg6wDbnmzpWHoFCd+U4S1RaHI4J6fKKEHDmLoXe8O9Xwq9f9OuGZi2i4KmYMlIWBzMKyS/1aF21KH5k5OabS3q4LouzvzqA/jrq+7HzqkEbH+b2Aw8fQv7+3Efau5z9FncQYhRtVMLLAKtDvV6fHXLRjaGyCvHEhe8KZhsF4QyvjFWFk87vXV8FU/4trAOHQg0O1QK35MOXoSTZTZEaq98TTgKELXDVxCIOngLLBLj+FaLOrDfCd/x7VGdmeP4FyUddXzZuWtSt+A4btWPi4Ln+EYdbtOngNndAIDN7nwWLVkqBtz6s9mBhG/KEF2yUtOtDjIACT9Y+A/sDQ9dy257czbwRod9FxYhl5Wyz+ULLHbNGHDd5p+YnUDkwCqH21pfYKFyx1eTrKrRkEbMGDa2T7Jp/4c3JqAb8ffXs0zhylcDSw5r7nP0WZ8vizq4vujslOMrGdCN2hdIrmVAkfhzp4mLV4t3xropGPARsDqz6E68qDObcflzvVeOr8UuPpxWHF8g0OwwXvGuAxYN4Y1ZlrXcMGdN6H07vCqzmMJ3Zqe/sjgxrQ6Vw20i6jBr2jBtp3d1ZsYkAP6cnW6P8K3a6sCFqusCUy38XLyMb9SoA19VbGkjmEKBnUuXHM7e16cDbiR8U4aIOkhNO77sSn9ddjVw9DnsjT87z4sEiHxvecwB8DO+huUk5xYZAMt2vAxnzZXFLfT4CscXAFw7vmO7J3Dr6uHnIwjf0gRw96Xe7ae2YunAozewvx/fpNsLAKteB0gy2yTVieNsM7plQ0Xgd7ZDdWZAg/xt8PenCfFtK/xjrPQ4vhOxow7crbR76/hm+M/AbtnxrR11kPQJHOmw2+u3zh4Rel/k5RWCQtDxZa+TJdOBlYQtgGJWgDu+07qF2UBkaFq3eldnFrwgmdrW8qezHdeLbwRbHXKq4kVWWok7eK0OUR3fPSzfaw+zu8CTJbPvK81I+KYMUaklqU06vqIKyHSAtZ8HhpcBu54D7v48AL/RYf8qwjevKl6+LknNDtOBSp5Q1MEyWA4VaCnjq2V9gaKX4rtLewLDgJEc3wevAu79InDPF2N/rYY8fQswvYP93A9+S/OfJz8H2Gs1+3sfxB1KpuO7vUD7hW9gKMut50gF3ckmcvo2/xgpRY7vRMlEHmK4LbrjO+300PF1bGQc9nXdbJuEbxXHFxvuhgIbG5yl+MnzauhOW3zH119bHBwCnk6CiVE23CZEvWCyZPWuziwkfFt3fIOittyVbcfaYt/xjZjx5YNt7sjeANj32hXCd9tTgJ28mZ5GkPBNGRlHOL7N3ar1yt8tm73gnn4le8eDVwObHgp0+FYKX0mSEtnsIG4LaYocHvKIsv89AkHhazYx5To+479wPL1lovHU7ku/Z392YrDgxYfZn694G9BkF7RHH8UddMuGFtxF3+4eX1n23dg6vyMyd3wdJRuvQo7jcIEt9WLApwPolg3DCjRdRLgzIxzfKZv//vbiIiBQPea2K+pQxfEV29rudY/BS7tnvTtygN/hG9nxDUQdshkFqsKibIlYYlEmfMfKZiGmdKt3dWbB7HUbhK/I9xY1BaoSfg0YbXFtsWn7d2MjO778jp08dzkAwHJczA6vALRhdodq7C9NHUsvIeGbMlqNOoQcXwA48FTgyHcBcOH+7Dy8uJ05ktWiDkAyu3yna1WZiRdIRWtJ5GkZBTqfIDeacHx3B4Sv4wKPvrC79oNdF9iyjv197JmKUvuW2fFn9ueiQ1r/XPudzP587p6mss/dRA9uTJPVpkRnI6KIUpm7k26Twtvh4lq20+H4imy+t1EvxsriSVs4vj24COBCzXJlyBGOuS411hbDcYBn7gQAbFvyOgDAPet94bUj6rpiQWC4Da7rVZolYsCtbLitXPiGHd9eRh1aF757quR7BcIBbrbL1+s8RoxWBy581bkrIMZ6JnXHn//owz5fEr4pQ3TJKk1GHUKOr+CNXwCGlkDa+Qw+aN0IWQJWzq/uYiTS8W3U6NBCvhdgTrcB9rlNo4mow2z4e/VIvbjD+IvAzE72d2OSNTC0E3H1Htj+1DR7H8tcgZmdic+ClUwbWalDg20c4fzJdfK3End8mxW+LhfXco8GutqNJ3xjbG4TcwqTFr/QtQ3A6fLtev7aMoMcsmqM2qhqeFGHMsd36+NsmEobwuLD2N2Ve9bv8N491mzG1yoBxrQ3DzGdhO1tQvhmhwFURh2mdDOQ8e2y4xt04tuQ8R2v07PbaqXZRMBNzigR5R8XvtKc5RjmXz+U8+3DATcSvilD1DHJ7XJ8Adbj+pbLAQAfUm7FqaMveSeXcpLY5VtzXXGVUvRmMfh6VKsZ4csdX+FU1c35bl4X/v+x9bG/Xk1K48DkFvb3hQe2/vkUFdjnNezvCY876JbjRx3ane/luGpjUSqiDlGczapfg4t2JTWOL39uiIuSSJvb+ACQHRAO3b4Q4EJtGrloHbr1qBV1+AtfWrHviXj9oWzw6KGNO73Xu9gZX63oX/TNjKGgJajSrGzD5o6psFnAHF8x2NnDjO/MWMsXWdWqzAT+EovmfiaxGx0AL+OL0b09l3giVGlGwpfoMSp3fJu9vZat5vgCwMFvxsalp0GRXPxf+2o2/V+FeXzFYpK6fDvZ4SswJfbvtvQmMr78he51B7Dhksdf2lN7ycHmP4T/f0cb81Xicw0t8V2mVumTPl/ddFpaHBEJ7khlaji+rut6dYTNus5CXKdF+E7MWpARvCiJ7vhOmIGL827nfIXj62ZbF761og7P+Nva9l1QxIp5BZi2i98+Owbbcb242aKojq8klQ24iUqzBDi+ZSuLKx3fhLQ6uI5/R65JxPlgTqGa48uFZ9OOb8xGB8f27yqO7h1wfK1ws0PC6ksbQcI3ZQjHV1GbX2ABlDm+nJsWno8d7ij2Ml+oWGwh8BzfREUd2At3hfAVt8RaGGzzPpUQvk04vuIi4Yi9R7FoOAvTdrHuxT3VHyzyveIE1U7HV3yuhQe173OKnO+mBxO93pINt3VY+PILrFqi1HaCPcJNHkMmXcI3tLwCiLjAgvf42hIg9yjny4XvNHLQot5SroXX4xtwfKfHgJcfZX8/4FRIkoSTDmKvCXev34HdMwZsx4Uk+fGzSBTme58/UUssGgy3hTO+3R5uK7sgaTHuUK3DV9Dq2uLYjQ5T2wDHZGuzh5d6ju9kyWRxOFll//49LzR1PL2ChG/KyLjsRUppt+ML4E+7FXzKfB/7n/uvqHQfEcj4JtDxHSofbquyBrNZLHDhazbf4zunoOH4VfMA1Ig7uK7/PT/s7ezPtjq+HRC+8/cHRvZmOctNDzR+fI8omY6/JKHdjQ4cmQtfzSnBrrJ5ybD9SrVmW1nEyV9UafU7oXXFQCQnXLyGlUw7MOnfm6gDy/i26vhW6fF99n8BuGyRwMhSAMCJBy0CANy7frvniM4raNGznEB4e1uSllgYosc3LHyXjrLfh6mg8HWs7lZsVQjf1gbc6kYdWqwzi721TXSwj+wFyErAcbaAjAYs4rMgfZbzJeGbMsQtwWaH24IZ3/Lta8/tmMYdzvHYtc9b2Gavm89jXbgBktjq4A23aeVRB+4MtDjcBgCmzP7djhHfWRJ1ZnMDwveRaoss9mxiqyNlldWNAe11fIXwXdCGfK9AkoD9TmR/T3Dcga0K7qzjK4RvXtKrRlmMQLOEpMVfVxz8uEwTi1SSyETQ8Y1Y8RZ6DfNynz1yfN02OL7VVhb/5Q725wFrvTe9ct/5yGZkbB4v4bfPjgGIke8ViDtJM4GoQ68dX9et2NwmhP0+89ndulCdGdDduEN59rpF4St63as7vq3VmcXO+AbyvYAvmEX2HkuOZH/2Wc6XhG/K8KIOWpM9voGhNSOwsWfGsPDyHvZiIp/272wCePuTwH3/Efr4JLY6NM74th51sHjUwW7C8RXfqzkF1RO+j76wu3JjknB7F7+C/Qew6qGZNq059qIObWh0CBKsNUsobLgt+lrcZpD5IoM8aghf2/GGuJp1fCV+Eae66XF881K8gb/Q4K34PvYq44usN2zXNNmyBRa2BWy4i/39gFO9h+U1BWv2Y1GFH/6eOXWRGx0EVaMOPc742uFFQ67reo7vqoXsOTVZsvgFK+/b6qbwFT8XmYvJFqMO9Rxf8bbWM77xqsyE8PWjDvznsbQ/mx1I+KYIx3aQlYTj22SrQ+BFWqz5BZjbCwDzixrmLFwGnMYF732XhX7pfcc3Oa0OwvGt6C002zfcZkvs3203cYIN9jYeuGgYo3kVM4aNJzeXOQki37vsKOZ88E06bSkQN2eB3Tyn1c6oAwCsOhGABGz7EzDZet1PJyiZQce3ubsljRCiNA8DJasyQx9yfJt0nWXu+GpOOhxfFnUQP5doLnjwNcxReuX48lYHN+dFL5pGRB2MSTZs9NIjTGzl57LKwAAnHsgc2/XbWDQgcoevIDjclpQeXzHYBgBqEVO65c2grJovhK/J7i71otJMCN95q9if0ztqPzYCUerMmu3xje/4lgvfcsdXDLj1V5cvCd8UYQZEVybb3K1STZEh8YvmoCvlbWwTiyte8TbgkL9mV+I/+6iXqQo6vuVRiV5Rs8d3mt0ORH5ey1/D5lf7TkzH13VdL+owp6BCliUct89cAFVyvsLxXXY0+1NUju1oQ9xh7BkALpCb45/82kVxvu8MJNT11a1AxrdDjq+4s1CoEXUwbTcgvps7BkUTjm86hO9EyfQH/iIMtgHh1zCnZ46vn/FtW6sDAOiTwDM85rD/KYAcFtUi5yuI7fh629uCrQ49Fr5iFiOTB5QMxniVWVFTsGiE/fvEa7z3O9LNSjMRQZl/APuzZceX/VvmFCovWoRgnWy2zqzZjG8tx3fJYQAkYHKzfz7tA0j4pgjL8E92apOOryRJnmOiB5odNmwXq4oDsYDTLmOicesTwP2s51dUsNiO23TXYLupGXUQNS0jy1r+GrbMvt9uzBPsrGl7kRLhlh+3Dx9wC+Z8Xdfv8F16FPtzAXdm2+H4BhdXCNXQThK+vji0uU3pjOMrTsq5WlGH4DE0OZyqZJnwzaZE+E6WzECHbzThK0mSt4ind45voNWhVeGbyfoDl6Vxb1tbMOYg2GdBEfsu8F+j40cdxHBbsMe3x1GHGlvbFgxnPSFWIXx74fguaI/wrdvq0GqdGT8nR2518DK+bF2xcHy9jHF2GJi3L/v7lsebOqZeQMI3RdgB4ZtpMuML+Bm5YLOD2AEfWlU8tAh487+zv9/7ZWDjb5BTFRT5C2ZSmh3EC3dFq0Mbha+jNOf4inXFqiJ5J5rggJsjpv93Pw+U9jBRtuhQ9rZ2Or5eo0MbB9uCiJzvhrsT2fmoh6IOHXJ8+W3YAvSqdYGG5bS8PU44vpqbjOdeq4RaHWJ8T8SAm7gg7V2PbxuG2wDf9d3xZxYZgsQc3yoEXd+mh9umxwKb23rt+FYfbFswlMVQlr3uTpXKhW+XLnRc1x9u84RvB1sdhOOrW1WbYRoR2/Hdw4XvnOX86wcWWAj6cJEFCd8UIdblWq6MTKb5NZnC8Q2enCuiDoLDzgQOOo11/X3rdOC7f4tX5jcBSE6Xb81WB7GlbLgNwlc4vjUWe9TCn+DVIHGn9bC9RpFXFeyZMfEMd9q9fO/iV/gZVM/xbYfw/XP4c7abFa9ktyqntgLbn+7M12iB8Oa2zgrfvKRDrzHc1nLUQTi+SIvjayEfM+oA+KvXLSF8e5TxnWpHnRng53yf+BH7c+/jgEL1iNaJB/lRpfhRBz7cNjPmGRiJiTqUVZktHMp64nyiQvh26ULHKrHhOyAQdWhe+Jq2452vqgnf4JzKVBN3VGNlfPVJZrYArM4MCC+wEPTh6mISvilCDFYZUD0R1Qzljq/tuHhujF1177+oTPhKEvA31wBHv5uVXD/zK1yn/zO+rl4G86Vk3PqourLYcXzhy3swW8HhdWauFU/s+1Vm/guRqsg4ZuUcAIG4Q3m+F/CH0Pa82PpyiGDUoRNkssDKV7G/JzDuoJt2wG3tUNRB1JlBR6lKT3Yo6tCk8FVz7GvkYPh3C/qY0AKLJhxfS0mJ4yuWWPz5NvbngZUxB8Hxq+Z5d4+WzYk56yEcX6uEEYV9z3oedSjb2jYmHN9hLRB1KBuA7FbUQcQcJNm/5T+7q6LmMyrBCEO15oVsRvEWtDRTaRZrc9v4y+zP3Kh3xyG0wEJAji/RS0SVloHm3V4AFRnfl3bPsJNyRq7+QpqfA7z1auD8R4Aj/g4OZJyqPIoTfvVW4Kb39Nzhq5rxnRnjFTkSMLS45a/h8il8KebGrD011lMevw9zXrwBt/J8L8AGUfLzALjAzmfiHrKPbQE7N7C/dyrqAAD7vJr9Kf4tCaLUTccXRtWog2k7TYm8IJkcEwc5GKE6wn5lsmTFzvgCvuNrSr1yfNuY8QX8qINooqmS7/Ueqir46rtX4/NvO6zyDl0jtKInHkcddgu/945veGvbDs/xzXlCrGQ6MG2n+1EHMdiWHWZVcGJTYJPNDiLmMJTN1Fw80kqzQ6zNbd5g2/LA1y4bbgN8x3fnhnADR4Ih4ZsiLO5qWFJrwlc4vsKVEjGHVQuKUOQ6TvL8/YAzvoYv7XsDfm6vgQsJeOpnwDVrgB99gDcHdJ+qrQ4T/Gp2aDGgRMw71cHlji9iRh12B6IOQY5bxZodHtm4C67jBKrMjg49znN9W9ngtnsji6qoBb8irROMrmB/Cqc9QYQyvh0bbuMZ33oLLLxmieaGU9Ws7/j2u/B1XTe8wCKO8OWumNmzjK9odci2XmcG+FEHABha4ouNGrz+wIU4+4SVzX0t3uww7DA3s/cZ37Dju2OS/T4sGNZCr+nTutX9OjPh+OZG2XIVLyPdXNyhXr5XMNrk2mLLdjBjsNedaI5veHkFEK4z81qbhhax30m4PH+efEj4pgiHD7eZaE3IlTu+G7bzwbbymEMNzLkH4B/Mj+G6w7/LKs/gAn/6EXD18cBPPwzseq6l44uD67qYNsRwW1D4ti/mAMC/PW7Hu8W1Z6a643v08rlQFQlbJ0rY8vyf2QuskgUWHRL+BGLLWis5Xy/fe2CkzVhNM7yE/Tm5tXNfo0lYq0NrbmtDQq0OVYbb7NaPQcux56gq2TD0/s756pbDK96aiDpwsWn02vF12+T4ZgOVZge8oTPNKwIufIvWbgDATK+jDmUZX+H4LhjKQlVk7yJnMri2uFt1ZnpA+AJMBAJN53yjCN+RJpdYBF3aik77qgcTrjILfpzjwjuvAgjEHfqjz5eEb4oQGd9WhW8txzfqbbN5Rfb1n3GXA2f9D/D//QY46M2A6wCPfx/4r2OBG9/NmiCe+jlzgu3OuAq65XjTr8Vgq8Mkb3Row2AbAK9uSI65Kla80M0tE755TcHhe7EX001/+i1745LDKt1pz/FtRfiKRocODbYJhvlFRsKEr2U7sBw3EHXoVMaX9/jWqDMz7dbrzKSAK2qWuljp1AGEo1VoIuogMr4G+M+yZ5vbcq1vbgPCjm+dmENb4JVmBZMJ32nD6m0ne1mrg8j4isE90ezAhK/obe6y4yu264nYXJOVZpGEr1dpFu+cKZ5PRU2pGaMIH0yl8M2rCjL8rm8o5+sNuCVjrqcRrd0TJxKFwwP1ptRmx5cL34rBthrM5UssvFaHpUcC7/w+8PKjwN2XAs/eCTx9C/tPoGhsKnbRwWzAauHBzN2cuwpQmv81nQrcpgu1OrSxygyAd2taiu34inXFlWLr+FXz8dimPShtepS9IZjvFXiObwtRB/GxCzqY7wWAYX5SMCbZxHB2uLNfLyJiQ2HHF1hw4ZaXjKrDbbrlYH6rrnMmC8eVIEsurNJ0s0eaCIRDNZyJn70WF++GJCJIvcv4tkX4CsdXVoF9T2z989WDO745YzeAxXBclqHNa22IbDRDYLgtuK54Ia9qG8llMDals9d6L+rQrYxvmeNb7KLjGzPq4A22Rd7aFu7wBVhH9nAug90zJiZLFpaK67E+G3Aj4ZsiHJ4vtVoVvsLx5a7Us2J5xcJizY8JMs9bW1wmAvdaDbz7R0wAb7yPOY07nmZ/mjPA9ifZf0EUjYng0y4Dlh8f+98iKl8KmgI5mE9uc9RBrJiVnHjCV/T4VnuhO37VXFx7LzCym+emyvO9gC9Wd25grnkzFwki6tCpRgdBdhjQhpnwndyWOOGrobV8bUO84bZ6Pb4tHoMkQZc05KHDMlIifBULsOGLmgh4lYy9cHwdB64xDQm81aEdwneIZ0dXviq8ya0TcOGrlnZ6b5rSrd4J30DUYVK3vOer6CgeCjY79Gq4TfxMWo061DkfCEQ+N27UwRtsi7y1rVL4Aizny4RvFcd3+9Nsi2sb5mY6CQnfFOF4w21tcnwtB7umDU+c7bsgnuNbc4HFXqvZfwLHYU+yHX9mT5xyQbz1j8Dvv9mc8K1WZQa0P+rAb7HJMR3f8RoZXwBYvXIeZMnB/tYGQAKw7KjKTzC6nAkCc4YNqYkS9ag4jj902OmoA8Byvjsn2YDbgv07//UiIGr78hJ3FpXOC1+9yqR8KOqQiX5bv5wSsshD7/+og5hwV0wufOM7vp7w7abja81CAosGTCPbHuF7xFns1vNRZ7f+uRrBow7SzE4UNQXTho0ZwwLQoedFIwKtDiLmMJTNeEJcvLZPlqxAnVmPHN92RR2qnA8EVZdIRMBbXhGl0cGx/buigagD4Od8Q19/7j4s7qGPs/P4ksNjHVu3IeGbIlyTia7Wha9wfB0v5rDXnHzkK/555VGHRsgyMHcl++/Atf7bHQd49Hrgtn/yrz5jUrXDF2h71EHmDp3ixFxgMcu+R3OrRB1G8ypOXjiNkYkZ2HIWSjVHVpaB+fuzi4Md6+ML34mXmGiWVRYr6TTDS1j1WoJyvsJ9zUnxb6nHgvf4ZiQHVpUNf+Ee3+ZFhiFpgAvYejoc36Isfi7xWx1Kbg8cXy7UHFdCCVp7eny1IvBXF7f+eaLgNRPsQCGbwbRhhyJjXSfQ6uBvbfNfL0PCt+uOb7nw9b93zRAt49sFx3dqG6v7lBR/KJkzXG1tsiQxsfvC/WyRRcKFLw23pQiXd8jaLQpfcdLQLRsbtsfL9wK+iBufNWG1Uqkky8CiV7C/Nyt8jSpVZkAg6tAm4asK4du+qAMAvHEeE4ibc/vVvn0knNpmmh3EYNv8/VrKUkfGG3BLTqWZcHxzXsygs3VmAOBUWTjSjh5fANB5k4FtdDnX2mbErdSiN9wWv9Vh1uXPmW5N+QOhKjNZjjhIlCR41AEzwbXFPWx2CAy3jU2x34XgRjpRsTXVizozsa5YZLC7MdzWjYyvGGwb2QuQw4bXSLXtbUBf5Xz77BlJ1MPxHN/WTtzVHN84Rejitr3rNleyHULcZhl/mTnAMZniL9ihRgd9kuVMAV+ItYhwfGUn+r/Xdd26UQcAOEZ9HgDwB6uOGyvWDDfT5dutRgdBAivNdM/x7fBwm6LC5h3bTpWi97ZkfOEvbXD0/o46iBNr3tuoF9/x9YRvtxxAINTo0Ba3t9vwqAOmd3ob4Hq6xELnr9XakDfYJvK9gO9ATvWizqxm1KELPb5NtjpU2whXeSCVHb6CqmuLAd/l7YPVxX34rCRqwvOlttw+x9cbbFsUbbANYCt3xZNrd9S4Qy2Gl7LbLY7Z1FV01aiDcHuzI15FTqvIGnvBVd3o/95Z0/aWDFSLOgDA3iUmZu+b3ssTyRWIbWvNOL7iYxZ0S/gmz/EVQ5yaF3XoXJbRVtjviVvFjdVDdWbNZ3xFd63TTbHXAYTj612QxKkz4xfvMz1xfEWHb5vyvd1GOL7TO1AUwrenUQc/4+tHHfznqB91CA63davOrGy4TcRE9ImmLrbiRB3imkp+xjeG41tV+FZZWwz4A25bn2jKpOomffisJGrCWx2cFoVv2PHlyytirr70cr7TLTq+SsaPIzQRd6i6rlhsbWtXlRkAhd+GjRN1EMsrVEXynJUQjgNtG7t6/qOzL37/wq7qn0iI1rFnmM0eB3J8/TozETPo1HAbfOELq0rUwXIDUYcWHF++rcytEqfoJ8TwTBY8D93E5rZpR2R8u3gRoIuoQ5uqzLqNEL62jvkq+33s6RILT/gW/SqzQNRBtDpM6gnI+OZG/dePJlzfaFEHMVwWN+PLow5RMr57+Ll2zvKKd1VdWwywc4iSZXdT9zwf69i6TR8+K4lauFz42i1GHcRJY6Jk4sXd7OQZV/h6Xb61mh3iIOpUmhC+VdcVC7exTTEHAFB4xjfjRn8xCq4rlqptYtr1HGBMwpCyeNbdCw8/X0P4ztuXueLGlC/qo+C6PRC+yXN8/TqzLji+IkZRRZQalhUYbms+bmHJ7GPdPnd8xYldc+PnnkWrw7TNn/c9yPhOo01VZt1GK3qxksUKixkkb7itQdShV8JXklqKO3R0uC1Oq0Ndx9dfWxxCUf3NogmPO/Ths5KoiRd1aE/G989bJ+C67EkYnKKNgujybTnqAPhPvj3NO77hqEObl1cAyGTZSVlF9BejRvlebP4DAGByziGwoeDhjTWEb0Zj4heIt8FtegdQ2gNAYs0Q3SDo+PZyG1QAL+rQBre1ES4fvpGr1Gs5pglZcls+Bks4vn0ufIWjpLnxHV/htE47Pcz4tqvDtxfwW/YLFe5e9yrj6ziB4bbh6o5vVvT49qDOrHy4DQg0O8QTvqbtYIavAa55ToAfVZg27FjD47FaHTzhW8XxrVen1icDbn36rCSqIfF1uU6L5dHC8X1xF3vx2G9hsbojWYe2Or7idot4MsbAG27r5NY2AIoaP+O7p8a6Yo8t6wAA2vJjAABPvDSOWaPGLUev2SHGgJtYXDF3ZUuZ0lgI4WvN+m5Jj/EcX7d1t7URLj8xS1UyiE7QlWyhx9dWxNrWfhe+PAokKgKbcHwne+L4iq1t2f4cbgOA4nwAwHwwYTfVq6iDOQPwTmQWdWCvr0EjZjh4672bjq9t+W50bo7/9iabHYKZ3eE64nQ4MJxWETeoQ7xWhyjDbVVMHm91MQlfolvY7BfRbZPjK4gbcwD8jG/NJRZx8JodWsn4Bv5NHYg6aFnmQMRxfPd4VWY1fl7c8R1adSyWjuZgOS7+sGl39ceKDW5xHF8v5tDhjW1B1Lx/kkhIzlfUmXkxlS44vhm7yok5eLJu4RiE8JW6vaa3zYiTusJrGpsZbpuyA45vt+4wiKiDm/e2YPYd3PGdB3Zx2jPH19s+KMHN5GsMt/Wozky4vUB4m16T29uE8B3OZaDItY0mVZG9mZA4Od/Ijq8+ye8EgtWZlTFcK+MLAEuPZH+S40t0C8/xbVOrg2C/GB2+AtFSEHmJRT1GV7A/m3B8uxZ14K0Omhv9BCFiIFVvazkOsOVxAIC01zE4ftU8AKid823G8RWPFaK5WyQs56ubDiQ4/kVLB4fbJBF1sCsdSC+jL2ssK9gkvuPbRZezA/jCt4moA38Nm7TE8971omAdx3N8c8j2q+PLK81GXOH49kr4+uuKJ3S/BSfc41vF8e2Gwy/uWKmFcMd6sTnhuyfCumJBM5VmkTO+43xOJDdadT12zTozAFj8CgASc7snm+sy7gZ9+qwkqsJf2B25tRN3exxf9uRoq+PbRMZ3UgjfXGeFr5plL7hZyYzsLI3XizrsfJa96KsFYMGBOG4fLnxr5Xxbcny7NNgmSFizQ8my/cE2oKOOr6TVcXz5ydoTrk3i8JhEtRxxPzFZMpGBBUlcTMaJOvDXsHEr8FrWreiH1+Pbp3VmgBd1GHG449urqEOVwbbhbMaLsgDBjK8ZFr6drtQqH2zzDkgI33jCbyLCYJvAG3CL6PhatoNpHpNr6PjWyfcCwZXFVb62VvS3hybY9e3TZyVRDZkLXzfTXsc3ztY2ge/4tlhnBvjCVx+PnQutqDOzTX+d5HD7hS/7EtHWFu/xHN8qUQcec8CSIwBZwQnc8X1s024YVpUXdCF8Z8aAmRriuJxeRB2ARDq+2WBEpZPCN8v6sDNVHF+ZC1+nxaiSw4VzNVe5X3BdFxMly693A5qqM5uyFADcPe9WzteLOvRpnRngRR2GLBat6tkCi9DWtsrBNsAXYiXT8ar8ALA5gk5SbbANaLrVIUqjg0C4tlG7fIPu7HCjBRbjm9ifDYTvlG7BcaqYPN4ii8cjHVsv6NNnJVENyREZ3/Y5vqoiYfnc+IM284famPHNDgH5uezvMeMOFVGHya0AXEBWgcL81o+No2V9N8rQo73g1r21xQfbsOwoAOziY25BRcl08KfNVcR/dggY4RcIUVzf0jgwxR1XcYXeLRLm+OpWmfBVOrSyGIDMHV/NmYVbfmdACN8WoxYuF4j9LHxnTRu24yIXuiCJv7K4ZLn+x3Xd8e3jVgcedSiYXPj2Kuqg+45vta1tQLiq0st0A53/edd0fFsbbqvX6CCIW2km3NmiFmGFdp0qs+DXdl1gqtoFUXCRRULp02clUQ3h+KJNrQ4AsM/8YlO75oXj2xbhCwS6fOMJ34pWBy/msBSQ2/frn21B+FZ9oROO77KjAQCSJDWOO8TZ4CbWGw8vrXzh7jQJc3xLpg0t2J/bQr62EUqO3T3JwfDyigKR0XdbFb5c6Cl9HHUQDlVBFj+XfKyfi7d90rQBvlyme46vn/HtW+HLl1jkDCF8ex11CGxtGw5fmKqKjDyPPkwZjp/R77jwLdvaJvDqzHbEGqiM5/jGizrEa3SoL3xzquK1lVQfcEt+pVmfPiuJakh8a1irJ86g49tMvhfwWx0mdav6rfm4COG7Z1OsD6t0fLnwbWPMAQAymQx0l30NM6rwnWU/r4p1xY7t18EsPcp7sxhwe6RmzpdndXeEB9xKpo0Xd81gomT6LqOoMut2vhdIpuMrdX6wDQAU7vjmUULJrCF8W61T4wN0Sh87vqIqaZ7GBZca73siMqC65XS/2zXQ49u/UQcmfDWDvdb0brjNX1fsRR2GKp+jQ72oNKvl+IrhNnPGF+4RGI+xUlhsT4s63NZch2914QvUWVsMAEt4s8Ou5/yLg4QRYYUH0S9463JbzCgGHd/9FhWb+hwjORWyBDguy7IuGmnxZN5El6/tuJjlywm8OrMJ7jKOtK/KTGBCRRYWTD2a4KgZdRh7BjCnAbUYiiF4wvf5XXAcF3J55U2Z42vZDr7/8CZcdudfvK+VkSXMKWj4hHwX3gHgrrG5+NWP/oi5RQ1zCyrmFjQsHMni1fst6Jxb5Tm+SRG+dle2tgGAwjO+eclgbmTgZy+EasvCl398xulf4SvK8ednbUBH7F5jITgN24Gr5ljKt9sZX2Qxt1+FL486ZEq7ALg9rDOrv7VNMJzNYMekzoSYWmB1XJ2uNAsIX7F8YjSvstiZNsSOfWo7kB2O9OnitDrEd3zjbG0THb7VM74AE747p43qjm9xPqtBm3gZ2PYnYOWrIh1jNyHhmyJkLnylFjOKQce3mcE2AJBlCXMLGnZOG9jVDuHbRJdvcCCj2GHHFwAMiTu+RuMTrOu6taMOIt+7lA22CQ5dOoKipmCiZGH9tkkcsrTsFlvA8X3g2TF85pansH4bWzmakSVYjgvLcTE2pWO++jygAHfvnIMbt1d+Tz928v74p1M75AYLx3eKb2/rYLQgCmy4rfNb2wC/1aEAvcLx9aJKLR6DqEzLONGGLJOIOFHP1bjwbdLxBQBX4cK3B46vpvRrjy8TvrKtYwizmNZ79O8Qwjc7hLFdfHnFcG3Hl3X5dqnSLDDc9r5vPoI/bNqNez9xEhPmxYW+8J2/X6RPFyvq0GTGt6Hj69h+HLCu41tniQXAcr4TL7M7lyR8iU6iOEzouS2eOLNBx7fJqAPAtrftnDbas72tiYyviDlkZMm/5diBKjOBCfZiYEUQvrOm30lZ0epQlu8VZBQZx6yci/ueGcPDG3dVCl8RWxjfhA98417MIoc5BRX/dOpBeOdxy2E5LnbPGNg9bWLf7/0LMAW89tWvxaLsgdg9Y2DPjIn1Wyfx1JYJPLW5g7eoxPCHbQCzu4HCvM59rQjolu0Pt3VY+ELlji90lKxwblLxNpS1tkVP4p3Sah8LX+EkzVH596hJxxdg9W4yQBnfOGhF5pyaM5gnTWKTXYBhOd3/9+h+xrde1GG4mvDtouP7+Et7MG3YeOLlcZx00CL2Grd7Y6wBtzh1Zl6Pb8TNbZEzvlPbAMcC5IxvUFTBW1tcK2qx9AjgL79IbM6XhG+KUPi6XLllx1fG0tEcZgy7accXAOZ5A27tqDQTGd8Yjm+gysxbudzJqIOkAS5gRcj4CrdXVSQUtTI3ZfM69mcg3ys4YdU8Jnyf34VzX7WP9/Zp3cI194/hg+4w5kqTOEDZimNOOBEXnHKAJ6wzCrB0NI+lBQBT7AJi7eteh7ViGAPA3X/ejvfd8Ai2jHdQJGQ0dit1ZowNuPVc+DrQJP4C3uGMrzgp5yUdJbOW8G3V8WVfo58dXyF8RzMi4xtP+GYU2bvLYStZdqLrQatD32Z8AfYcHd+E+ZjAJizGjGFBy3Su8aQqXsY3EHWo5vhme5fxdbKjXgb6xV1cbDexvc1rdai1yTOALzzjOr4NJJ84v44sC91tLGc4G8HxBRK7upiEb4pQeJ2Z1OqJU5Jwy8deA8t2UdCa/xWZy5dYtGV7m8j4Tm5hXbwRmitEo0Noa1sHow6WpLIFURE2ZgXXFUvBW/2O7V8llzm+AELNDq7rwnWBm9e9jC/+4s/YPqnj9doyHC+tx9feOISlr31F9S8+9gwAl1XE8VuagqVz2C3lLeMdPmkML/WF7+Iax9klSqaNkS5FHeANtxmYKYs6ZBwDkAAp5m39crzKNLd/M77ihDqq8guSmMIXYHGHKd3yF4J0w/F1XcBg8aJpt48dX4C9NoxvwuLMJGAC04aNOYUuHwMXvq5Wu8cX8NcW90L4lpSiV97wws4y4TsdX/jGiTpE7fGdiDo4FyHfCwSXWNRxfAFgx9OApXf+dTUmffysJMoRji/U1q/KFwxlsWS0tROwaHZoS6VZYQF341yWHYqA7/jyK1fXDTi+HRK+iCp8a6wrHvsLu0WnDQHz96/4uCOXz4GmyNgxqePnj2/GGV99ABfd9Di2T+pYOb+AZfuzidqlRp32i+DiirJ87dJRdtLYPWNWOJJtJUHNDrrlBIbbWsyiN0IVwleHXhZ1EA5ty8I3K4Rvl1b0dgDh+A4rgZq5mAi31Ra95t1wfK0S4LILmhlk+9vx5RfFSzN8WK8XzQ78ImJWysG0mbqcX6w8v/lRB7N7LR5c+E5L/gC4L3zjd/l2tM6MP5+ib22rne8FGqwtBphwzs1hsQnRIJQg+vhZSZSTcdmTQG5x5Wm78La3tUP4ynJgwC1azlc8Kb3BtpldAK+M8poF2oglsROsHSHju6fWumKR7116ZNWe4Zyq4MjlrD7nH3+wDute3IOipuBf3ngwfnXh67D3AUexB9br8hXvE9veAozkMijw6EVH4w6e8O19l29oc1unb+UK4SuFh9tc1/WjSk24m0EUnvHVXD1Wj2iSEI7vsCIc3/ivaWLAzdvm1Q3HV9yaRwqiDnx722Kll8KXfT8nHfYzHM6F1xULPOHbTceXD7dNwrfBN+36/9t78zhH7vLc96lFJamllnrvnp59PON934d9MRjjEEwcDgRO4rAkgYy5GJ+EG3KI4ZDcYy4EAiQOzkkOkNxA2O6FgCGAYxubxevYgxc84xl7PPv0TK/qVmutqvvHb6mSWktJKu3v9/OZT2ta1VJ1l6rqraee93n59q/R6pDJmzKBqLbmNq8eX4+pDp4L3wpxZgATVM6+HjjvLYDSeQ2eZHXoITRe+Co+KL5+IBVfP6wOANsZ55/37PMtm+E7MNaUAievsoOR5WFksdvqUEAFf69g57ZRPPoiC5b/7cs24MPXnuWkZpTJ8i1AKr5rUxsURcG6eAjPn07ixFIKW8fqi7OrSgdFmmXyppPj2yLFl6U6OIpvzrTleF61QcVXD7FtpsFitqBW+zJ9QChUETHAIlD7PXbRpCsuSOHhTkzD8BSCjBKEBbW7rQ58suW4yq0b7RhiwQvfxTzbhqVsDoDL45vJO5+VFjW3JewIAPbZOjy/Ctu2oURqK3yF2qsoJUYKP/wPwJFHgLfcKS1+ooBN5UxPTYeeUx1qLHwrNtfd8PeV36uNUOHbQwSE4tshfhpfFV+g5ixfEWcmC98mJjoAgKWw39eT1SFVxupQJtHBzftedQbCho6dZ4zi4o1DhU+KLN/558t7oSsUvgCzOzx/OokTi61QfNtf+KZzljO5rYnjigG4PL6FhW/WdFTnRhXfQND187nVrix8hZIUacjqwJSmrFR8W2B14IVaWmHr29WFL7c6jCpM2Uy2I8uXpzrM59lxrFSGL1A0wGKgBYqvbcvhDIu2s7+lcxZOL2cwIa0O3gpfocgOBvXCfHbbBu79K6YuX/FeYPNOAIV9K8vpHEbL/F2c1+fWIc+Fb2WPr7BalFV8O5wu3iuJYqTVIdAZha//iq8ofL1Nb1vJFFkdmlz4mlzxtfPeFd8h920tM+/MN5++uOzPDhg63v+qM9YWvQAQ28AUDysPzB8ssZI5VhQDjjpcxLp4CxrcOsnqUBBn1hrFN6jkkck6+0Uu7xS+mtHYOgQCYZg2P3m2KsLLZ4SSFBZKfF3Nbez0luMXpK1RfFnhm1K4z7pbc3wBaXUYEYVvW6wOrPCdzbJtWE3xLbA6NPNCJ5sEbHbhOp8v3F8Pza8WNrd5sBvJRIfiaMuVGScvePGQ/LauqfJ39hJpJhXfqlYH0dxWWfGNuS80uhAqfHsFy4IOtiOqzT55e2RYNrf5dFVYY5bvWqsDL7Ka4O8FAJNHYdk1NLcNuxs1Zvexg7UxCIx4Cz1fg6o6095K+XznD7KiOBApe3BzCt/+UHxZc1urcnydW/ZmxvGDZk1nbLJqNKb4GgENaYhir8m3e5uEOKGGISLe6vD4CsVXaaXiywq1FNj6drXHl09vG7J4E1e2HVYH9vc8nWHH8FIZvoBzC78wx7eJ21tk+Ko6FnKFKuqhuVV50QAzy6bIVaFsY9usy7K2WCj4yCxfD8kO0uNbSfHNLDvr6rm5jRRfop2YjsqoGp1xa3PEb6uD2Bk9e3yLxxU3V/G1Vfb72vnqv2/J8ZTS31u6sc0z0udbovAVxfD4mWUnpq0bYieO5ha+Lo+vZVVetonYto10zu3xbXLhqwdhsTlihYVv3pIe30ZVZ0NXkUILfa1NQJxQ5US9OhRf4fHNoPWK7yp6werAPL6DovBtY3PbyRQ7ho9FS5/bSk5ua+ZFn2tq21KR6nl4LsmaMUOsCdmL3cFb4Xuo4CnHZ1u5+MyblrxoqRhnJgSl0FDVMcuDpPgSHYHpFFt6g80xfiFyfFM5Eyk/1AK3x9fD7aNWWx0sTRS+NVgd3B5f6e+9uLEVET7f2RINbiJapozNAWiR4huZAKCw24Wrs817nyrkLRuWDQRbFWemKMip7MRsZpwTs9vj22jxbeiqS/Ft0dAGnxEn1EYKX6H4ZvhExVZ6fFf5hUd3F75MtRw0FwHYrS98zby06hxPccW3WnNb2h1n1sTjl2tqm1BTxbY+LIdYeI80KymEADxznbNQWPiKIrZalq+7OF3TOOfGo7+XvU6VOLMOp4v3SqIAl8qodYjHNxrUEdCYuuWLzze2nn3Np4DVuaqLt9rqYImpX14KX9Hc5k51OLGHfa3Q2OaJSoqvSHsQxXEJRJZvUz2+mu744Nro883kmdosC6xmN7cByPNmK6tI8ZXr0ODIYkNTkbb5RVguWWXpzsO2ban4yiziOv4mQvFNt1L95rfmk3bvWB10O4coUq1PdeB/SwA4usr+jmWb21o9uY03tiEUl4qrGCF/SBS+NSQ7LJUbMFHB6uA10kys34ChIaBV+Dx69Pey93YUdtPqvsjELt4riQK41SFra9D1zmioUBTF32QHPQhEuTd0qbrdQSq+RmsUX1k0mXUovgWNbQ0WviKtYXb/WhuBtDqcXfbHxfS2xdWcP0p9OTrA5yuSFVrW3AYgr7ETs7sozZkWQj7ZLZjVgX0W85nu8/gmsybEuTQgxi7Xk+PLj4Mpu/WKryh8u1rxNQZYLwBYskPLFV+RiawGcHKFfSDKKb6iCMzkLeRFjn0zrQ5S8Y3JwvOC9azwPVw8va0hq8MB53HiGDtPcOTY4ipWB7F+fkWZAYXpECtdqPp28V5JFMBVxiwCMCpd1bWYpmT5Ap58vkm31SG76hj3m+Xx5YqvYlb+XW3blgMsZOF7ei+7rReMAcNbG1uRkW2AqgO5ZOGUO8tyFN8KVofBoI6IHGLRzGQH4fNtv+IbUoXVofl3S0xe+CLr/G0LFd/Giu+gy+qQT3df4SvUXl1VoJr8b1SH4itSHVJ26z2+yzb7HHW14gtIn+8oEq2PM+OKr21EMJdk57dyiq/s44Bb4W+m4rvIvroU3/Onmad3LplloksNVoel4vMBwM5ZIsFIUVlTsutY6Si+VQpfz4kO3gtfQ1flZ9vr9LhOosv3SkLCi60sdOha6aaldtDOLF9xay4a1J0DRiDCistmwBVfpYriK0LHAVd8TZWJbbWtR4AVv0BhssPSEaZ6aQYwvKXsjyuK0qIGt/Yrvhmu+IZbWfjqa5tvsnkfPb6aihQvvPLZbix8ReaoDkUUq3UovsFA+xTfFUsUvp1x961uuN1hRFlug+LLC99AxBlXXKa5TddUhPn2XrVasL1lc5vj8Z0eCkuh5/Dcak2Kb6KU4jvH1d7wiHO8djW4eR1b7CnRAaip8AW62+dLhW+vwAvfHHTojRZOPiIVX7+THWqxOgQ1l81hXdk0g4bhJ2e1SuErbA4BTZHKquPvvdifdRHjiN0T3IRfbHQ789hWoDWRZu1XfMXY4JDSusLX4oWv4lKkCprbGhxgoaoKsmKYShdaHeS44lDAKV7qam5jx0FZCLXQ4ytG7Ha11QGQDW6jSqL1cWb8IiKnM7tFPByoeCEhkh2SUuFvQZxZKC4Lv1g4gE0jLK7w8HyyMMu3CiWtDuJ4PXYmMLSJPXb5fIXP1qvHt2KiA+DcRRXvVYVYtbHFHUyX75WEhDe3Ze2AbCjrBESyw/yqX1m+fKesofCNBvXm+3sBKB6tDu5xxYoowj1MbKsJ6fN1Kb4y0aF8Y5tAFr6LrRhi0UbFN88VX+Gv1Zpf+No8y1fNFyq+IUVYHRpfhwzPrrWyLVA5fcaZMqU7xWpdzW1cAWyH4ssV906yndWFmN6GNnh8+dS2LE9BKRdlJhCJBUl5odOawtdRVHVsHmX79qG51cZTHUSiw9gOYGgzX9ApfOOeFV/h8a0gdlimY4vzrPh6H6DRaXT5XklIRHMb9Mqdmy1GZPn6rvhW8fjathO/Ew3pwDIvfAebWPjyNA3VqlL4Fo8rtixg5hn2eN3F/qyMTHZwKb5VRhW7mRLJDoneVnylx7dVOb4AbF7Eqfkyiq8PDXY5Wfh2n+IrFapQwJk8V4/VgautK2YLFV9erCV7IccXAAa4x1dJYLVNim9aFr6V981BMb3NbEXhy6wOVnAQy/w8E3cpvgXT2+ptbiul+C6UsDp49vhWUHyXT7JoSVV3CvYqdPPY4i7fKwmJq7mtozy+3Oow71dzm0ePbyZvIc9bwyNBHUjw4irWnCgzAFB1j4Vv8bji5Gl2gldUz7eZqiKzfF2KrziQeih8p/tG8eVxZtLq0IIMbIOdHDXT+dvmfC58s2r3Kr5uj6/0QdfV3MYU3xWTK12tGN/MrQ6rvRBnBhSMLV5pucd3GYAzDKRcooNAWB0SLSl8meKb1pxBD4Mhl9VhbtWJM0vOMkW1AqULX6H4nllS8RWe3Wo5vp48vuJ8GpsGVG++9G4eYtHleyUhMLnVIQMdgX7w+K7Osq7XMrhvy0UM3bmN01TFlx2gNa+Fr2hsE+sWnWSNaX4g7Ayrc0Byjg388DC8QtDS6W0rpwpielrJ2jizFmRgc6uD5irEsjnT18JXZAXbXTiyuKAIaKC5TRS+Lbn1LRBxZghCVVjTVVfjsjqstinObNli276a4iuyfJdk4bvqadBRXfDmtiTYvhwOaDB0FZtHmR/58Pwq/9uJIT3zZV8qnTPlBXjcfRdQNLeN7QCGSxS+YW9Wg4T0IFcaXiEyfKsPrxAMBvtE8b399ttxxRVXYHBwEBMTE7jhhhuwb19hSH46ncauXbswOjqKaDSKG2+8ETMzhR6Xw4cP4/rrr8fAwAAmJibwp3/6p8jnCzfeT3/6U1x66aUIBoPYvn07vvKVr6xZnzvuuANbtmxBKBTCVVddhUceeaTmdekVTH6CyPV6qkNoCDD4VbY7qqsIkegQDmjQVMW5nd5Ej68YHKLXanUQv4cY0OEHRsQ5iM3uY8VleompyqPbq/54S5rbBsYARQNge2oAaQbihGO0sPBVDHZy1C2nEMvns1AV27d1yKts+9ldOLltdoXdvRqNGK7mtoGaX0eorctC8bVyVZW3hnGNLO56mwOwprnNauWwAv63TPBGwWqKr0gZWMqJAs/2NEyoLrjiu6KwfVkUlcLje2wxhRw0aRWp5PMViqyqAFGZOX+UffbVAFN7xZ3AxFHALFRwq1kdltM1KL4e/b1AHym+999/P3bt2oWHHnoId999N3K5HF7/+tcjmXSC2D/0oQ/h+9//Pr71rW/h/vvvx/Hjx/Fbv/Vb8nnTNHH99dcjm83il7/8Jf75n/8ZX/nKV3DbbbfJZQ4ePIjrr78er371q7Fnzx7ccssteO9734sf//jHcplvfOMbuPXWW/Gxj30Mjz/+OC666CJce+21OHXqlOd16SXMLCtQWHNb5xxwfc/xVRSXz/dw2cXWjitugdWBF76aXflAtFRsdVgSTQU+Fr6AK9lhn2N5GN7iST0The9SKofVZuV3qqrL7tAen6+IM5OFbwua21SDqekBy7moMN2WBD8UXxni332F79wKO1ZMRFTA5gNY6vibCMVX3voGmv/3cA2w6PooM0AWbiMKtx3kWujzzbD3XMyzc8i4V8U35/q7N6uhkRe+SzYrdEVROTEYRFBXYVo2ji+mPCU7uKe2qSoXrWQCzxksgScywY5NtiWFEuGxzeQteeeqFLK5rZLHt67CVzTX9Xjh+6Mf/Qi///u/j/POOw8XXXQRvvKVr+Dw4cPYvXs3AGBpaQn/+3//b3z2s5/Fa17zGlx22WX48pe/jF/+8pd46KGHAAA/+clP8Otf/xr/+q//iosvvhjXXXcd/vIv/xJ33HEHsll2wLvzzjuxdetWfOYzn8E555yDm2++Gb/927+Nv/mbv5Hr8tnPfhZ/8Ad/gHe9610499xzceedd2JgYABf+tKXPK9LL2HlOrO5bVhaHXKw/brt5MHnK8LWo0GN3UYXV9xNtDpovKCppviKiwBH8RX+Ku8HHU/IZIfnnMY2DzYHgB3UxImkl7N800LxlaNxm1/4asEoACDgUnytrOtv7MM6mGr3Fr7i7tBE2HUyryfOjA+wWC4ohJrs8xUji3tG8eU5vkgAsFtrd+AXEfO88B0b9JbqsJQFa9ICmvf5581tSzb7XIqiUlEUp8HNY5bvYkV/7w72VVXXRJoNBnWZzFlJdU14Unxrtzp4nRzXiTS0Zy4tsauekZERAMDu3buRy+VwzTXXyGXOPvtsbNq0CQ8++CAA4MEHH8QFF1yAyUmnc/Daa69FIpHAM888I5dxv4ZYRrxGNpvF7t27C5ZRVRXXXHONXMbLuhSTyWSQSCQK/nULpqu5TVM7x+ogUh2ypuVfDqSHLN8CxTd5ivmsFM05EDUBjSupehXFd63Ht0lRa27FVyY6VI8yEziRZr2b7CAUX72FVgctxE6MhuXchhWWhJxi+JIzbXHFV2lFhJfPCKvDWFBcKCvOOPAaEIpr2oTz8y1SfFftUPdHmQFygEVQyWMQqdY2uPG/5WyWFVjj0cqqv7hQX0nnHWtMM7Z3PiuV5HmTF76uqDBhdzg87y3SbKlklJkr0UFQlOygqor8nSsVn7K5raLHVyi+NXh8+3GAhWVZuOWWW/DSl74U559/PgDg5MmTMAwDQ0NDBctOTk7i5MmTchl30SueF89VWiaRSCCVSmF2dhamaZZcxv0a1dalmNtvvx3xeFz+27jR+4eg3Vg5ppLkFZ+ao3wibGhSefGvwc2D4usufIXNYXDKc8dqPWgGOzAHqhW+xeMpm2V1cCu+s7UpvgAwJX2+vZvsIDy+cpu1oPDVueIbstPSM2lzj75oSmsUUxeFb5MuWtJLwAOfBuZf8P2l5/hxYiTIL5QD4bouBsRxJ50znVSIZiq++SzzEYMpvl2f6ACwBJIA87GOtDrSjKvnpzPsOFld8eWFWCbv3CFoRnNnxhHE5vNsf3XbCDaNuBvcmEe6kuJbNdFBUGKIRdxDpJlsbvOk+Nbj8e0jxXfXrl14+umn8fWvf93P9WkrH/nIR7C0tCT/HTlSfUhCp2DzA3peqTKPuw0I1XeujsJ3biWDT3z/1zgy7zqAicK3QpZv0j28Qmb4Ns/fCwABXvhK9bAMi8LqEC5KdfDb6iCK3KUjwIkn2ePxsz3/+LTI8m2J1aHNiq+wp7QgzkwPsRNjWMnKwlsUvqZau7JZCktbmxXsK09+E7j3r4D7P+Xry1qWLa0OIwb399Y5yU4ovpm85fjam6n48kINAFYR7A2rAwBEeJYvWhxpJqfgsW03GvEWZ7aSzjv7cTOym8XwCmMQS2n2GXUXlc4Qi6RL8a218BWK7w7ne6WSHar4bPOmJbdZWY9vOuH8TjWIL33T3Ca4+eabcdddd+G+++7Dhg3OyXpqagrZbBaLi4sFy8/MzGBqakouU5ysIP5fbZlYLIZwOIyxsTFomlZyGfdrVFuXYoLBIGKxWMG/bkF4fPOKPydOPxluINLsiz99Hl/6xUF85ieu9BDp8a1kdWAFTaRFU9sAQA/y0Hp4jTMLsC7zZiVOREadruL0IvvqPpBWYd1QKxRfYXVon+KrwYQGrmS1oLktEGKKbxgZ2ZQiL1x9UnwtfuJvWuErbt3OH/T1ZZdSOZhcBY/rfJvUkeELFCu+vBBqpuLLb82bqoE89N5QfIGCZIemNbqWQg4DCWJoIFD1QkIMsFjO5FxWhyYovuJYGoqXjAor9Ph6sDoUF77pJWf5UdfxWiq+7iEWesFrFOO+UBksN7lNCC+hISA4WHqZEsSk1aHHFV/btnHzzTfjO9/5Du69915s3bq14PnLLrsMgUAA99xzj/zevn37cPjwYezcuRMAsHPnTjz11FMF6Qt33303YrEYzj33XLmM+zXEMuI1DMPAZZddVrCMZVm455575DJe1qWXsLnH1+wwqwPgJDvUE2n2yIss//CxQwvON8XtmMSxsvFEK2lXc1urCl+uKhkVrA62bRdaHVZOAVae+Y8HS1+QNYTb2jA4DYS8X8y1JNKsA6wOhluhb0lzG8/+RAZpPjJZFGSmT4WvnA5nNjfOqVKkYD3MJdn6xkK6k3pRR4Yv4KQ6pHMmbHnru5mKLy98de7h7pXCl/t8R5RlKSi0BFc0XLUMX6BI8W3m9uaNbQjFpMXArdZu4orvkflV2MLqkDxd9uXWFL6zPL83OlV4vK4wxKKc1UEkOgwYWvmm9zr8vUB3K7413RfftWsXvva1r+Hf//3fMTg4KL2y8Xgc4XAY8Xgc73nPe3DrrbdiZGQEsVgMH/jAB7Bz505cffXVAIDXv/71OPfcc/G7v/u7+NSnPoWTJ0/iox/9KHbt2oVgkH243/e+9+Hv/u7v8OEPfxjvfve7ce+99+Kb3/wmfvCDH8h1ufXWW3HTTTfh8ssvx5VXXonPfe5zSCaTeNe73iXXqdq69BK2VHw7r/AVWb61RpolM3k8c5wdZI4upDCTSGMyFmIqoaKxgnFlpmRBK1IdIobuKKpNtjoYIXawNZCHbVlQSgwSSecsZPnt7aEBAzgtBmusa47/ePxM4PAv+WPv/l4AWCesDj3c3JbOmTDgOnC3ZIAFszoMKBmkc/x2Pr8la9XRxFX6PfgwFbNJhZ4sfI+zi0+fPruzPMpsLBp0oqjqVHyF4mrZgK2HoAAtUXzz2gB//x6IMwMcxRdLbUl1WLHDVaPMAKcQW3F7fJtxx0N89kPxkokJG4bDUBQgmTWxqA1jGKhN8S1lcwBcWb7HmZ9cN5yxxWVU19oSHWqz2onXXM2ayJlWR6VJVaOmwveLX/wiAOBVr3pVwfe//OUv4/d///cBAH/zN38DVVVx4403IpPJ4Nprr8Xf//3fy2U1TcNdd92F97///di5cycikQhuuukmfOITn5DLbN26FT/4wQ/woQ99CJ///OexYcMG/NM//ROuvfZauczb3vY2nD59GrfddhtOnjyJiy++GD/60Y8KGt6qrUsvYZvshGGpnVf41qv4PnF4Ud72BIDHXlzA9RfyAjG2Hlg6zHy+JQpfcYsnGtKB2dYovgE+ilZVbGRzORjBtQdrUfzrqoKIobmutn1ubBO4Fd+aC98WWh1W51jYfCsKTxeZvGtUsKI6MUjNhJ+UQ8himVsdFK7MijSGRhEKp9YsxTe1yN/ILHvxWQ/iGDEaNYAc98w2qPgC7O+qAi3x+Oa0HlN8hcdXWW6xx1eMLA5iS5XhFYCT6rDcbMVXNLcFY0gsr/XPBnUN0/Ewji2mcDg7yArf1Tk2eKLEZM7yhW9RAk9knF0E5lMsAnNkm0vxLb1dPCU6iF6ZodoU36jLOrGSzktLYzdQ01HeSw5rKBTCHXfcgTvuuKPsMps3b8YPf/jDiq/zqle9Ck888UTFZW6++WbcfPPNDa1Lr2DzkcVmJ3p861R8H32xcMzj7kO88AXYTrp0mF+tXrXmZwua21pkdQiGnBN0NrNasvB1R5kpitL8dXPHlxUfSKsgxhYn0nkkM3lnGIifhIdZ1JSZZQWUUDVaRCZvwlBco4J9iBKrCr9AGnB5fEX6guWTx1jRRaZ0E1MdBEvHfPv8zsmpbUFHna27uc0pPOXftQWKb5Y3FvZEnBngsjokcKRVqQ62XbviG3QGOlh6mF/oNMPj6yi+S6dKK6obR1jh+0LSwEWKxi4Qk7MlBygtFaf8lCt8FYUdH2f3sUizkW1Vs3S9Kb61D68AgICmIhzQkMqZWO6ywrdH9kwCXNkxO1LxZetUq+IrCt8rt/Kc6EOuQrhKlq+MMzO01lkdgs4JOpcpfYJtybhiNwWKr/dEB4BdNAw2e4iForTV55vJuRRfv2wG1eCNNwNKBumsUHzZ39f2q/Dl7xGwMqyI8Bt34SsGsPiAsDowxbcxq4OiKFJ1NVsxyU4UviovfHtG8RVWh4Q8rjadfIZZ2cA9vlWizAAgEnQU/lwzB7iUsjoUKaqbeaTZofm0K9KstN3BPbkNwNrhFW6KIs28enz9ntomEPaSbhti0SN7JiFmkvsVh+Qn7ultXsmZFp44vAgAeP8rzwAAPHM8gZRQHKpk+YpbcnE15Vz1N1nxVTUNOZsdfLPZ0gfcteOK6z/oeCK+gRXVgQFg8ryaf7y1yQ6t9/mm86ZT+LYgygyA03EOIJthxZLC91/fCl8+RVCBLY8NvlKs+PqEaG4bjbgK3zqtDgAQ4sWnTMtopuLLu/0zvPDtnVQHpviOKgnZO9F0+EUEwKwOXprbdE3FgMGOvzlx57MpcWZrm9uKFdVNBUMsKk9vK7A6mHknG7vUHbqiSDOZ41umwcxRfP0dXiHo1ga3HtkzCQiPb6tUqxoQOb7zNVgdnjmeQCpnIh4O4JVnjmMyFkTesvGro4tsAVEolsnyTfLu41GTd9OGh+u+ZVoLWbADUT5TulBcWC26rSUV3yYV5YoCvPc/gff9HAgP1fzjrWlw6xDFV2+t4gsA+TQ7wat8ipvt02dUdb1Hcxp8Fp3HPiY7zEnFt/HmNsDx+crCt1mKbz4DPHwnAGA2vA1ADym+A8Ljm5DH1abD/dJpBGFCw7gHjy/g+HwzitjezbM6mEZMTiMtVlTl9LaCscVrFV/btgsL38VDbAiKHi59F7Ao0ixWZYBFolhNLsYynf23DvGlWnNdp9Ijeyah8MLX7sRUhzpyfB/jNocrtgxDVRVcvlnYHXis2VBlxVfckovnZtk3Bpur9gqyiih8q1kdiscVN0nxBVhRPXpGXT/amkiz9im+mbyFoNJixVdVkQXb/vk0O8GLJjS/FF/dMJC3+eHd72Ivny0sKCpMUKyVuQKrQ2NxZgAQ5Fm+uWYrvr/4AjB3AIhO4hdT/xVADxW+/Fb9CBJYaVWBwwvfVbDt5sXjCzgNV2k08UKHN7eltaj8VnFGrszydY8tTq5VfN0pP/FwwOXv3Q6USAVaa3XgVoNyhW+1qW3LJ5n/WNWd9ayBbh1b3CN7JiEUX7MTFV9R+K5m5YjWajxykBW+l29hBe9lm4cBOAWxY3UorfjKVIccV3xLNBU0gxxXfHPZ0ifYAquDmXeKvWalOjSIVHx7dGwxizNr3bhiQYZ7EM0MKyA1rvgqDRR5boK6ilSzTv6uka0AfFV8Z6XVwaX4utXrGgnxSLG80sRCaP4g8LO/Zo+v/Z9YQa/FmTGrg6GYsIu3fbPgVodlm+0PXqwOgDPEIm3z82AT48xWVR5LWCIjV3h8Ty9nkAuzv18pq4NQezVVYWp1ucY2QVGWb9U4s2qpDuKiNTZdVyRht44tpsK3R5CKbwcWvuK2vmV7uyVi27YcWHHFFlbwisL38cOLrHgWt2UyiUK/IUc2t6X57aUm+3sFIkc5X6bwFckWbHjFScC22NV2ZKIl61crfaH4yua21hW+ovnGlIov+1woPqnOhq4hzS/CfC/2RJSZwEePr2iAHXMrvg38TYTimxWeT78VX9sGfvin7HW3vhI4/0ap4PWM4hsII6+zQk5PzbXmPTM8yowXvqNRb+c1oUCmIDy+zSt8V8AU31JqanwgIP238wo7d5WyOrhtDoqieC98l08AubTL6pAvmbpVNdVBZvjW7u9lr0seX6KNyMLXp8lPfhLUNem98pLs8MJsEvPJLIK6ivPXxwEA507HEA5oWErl8PzpFcCIAGGmBhf7fC3Llt6rkCh8W2R1EE0V+TLNbe44M2lzGJwufVurA2hNc1sbPb551wCLVlkd4BS+FrcMaDbfL3xSfA1dRdpu0u19caEplNiVGWZ/aJCcacn9YzQadOwUDfieheKbbZbi++z3gAN3s0SQ6z8DKAoyfBpfzzS3AciH2LE2mJmvsqRPcMU3iRCGBwKehyOI80xSKL5N8fgy1TsB9rmMl/HPCp/vjMmnr62snd62NsO3QqIDAAyMyAE4WDoqC8+saSHDL7jcVE11aLDwHezSscW9s2f2ObI5pkRAdicwzCPNvGT5PsptDhdtHJK3CwOaios2siL4sSo+39Wc04BhrArFtzVWB6H4mrnSnfQF44qbPbzCB9qp+O4+NI+f7ivdCe0X6ZyFIPhnslXNbQDyPF7L5qkOAb7/qn4WvmjSyV80tg1v5RFwti9qvegBUBVuBco3rviK5raMUL/9vAjILAP/8Wfs8Us/KIsVofj2UuFrhlmDWyi7UGVJnxCFrx3y3NgGuMYWW0262wHIC78lixW+5WwEwud7JDfIvlFB8Y1VG14hUBRXssMhRAwdKo8eL+Xzra74NpYqNBgkxZdoI4rZ4izSGpHJDh4izR59kR1cr+T+XoGwO8gGtzI+3xW+E6oKoC67VNUWIApfq0yMjuPxNZqf4esDwuO7nM43b2qTUHzTS0CWFWn/3+NH8dY7H8S7v/JoU9XmTN5sfXMbnLG2Nj8x6xa3OvhV+Gqq63av34rvIvsaHnYsRD74fEWG70jEgKoqrjiz+hVfUXxmmuF3/ukngeXjwPAW4OX/TX47a/aY1QGQQywG8q0qfJkFKImQZ38v4Ci+K6YofH3+7FuW9LgvmLzwLVNUisL3wCpXaCt4fOPhAJCcA1ILABRgpEIzsivZQVWVij5fzx7fOgtfSnUg2opqda7HF3A1uHmwOojBFZdzf69gTbJDucLXNbVNEUpUqzy+PEfZ9OLxFd7IFq1bPUSCuryddmKxSQVoMOa6bX4S33z0CP7bt34Fy2a+8F8dWevh9gPbtpHJW47VoYX7jhyowAt93eaKr+FPnFmwqYov3x7hISeNRNh2GmDO3dgGNDy5DXAU37Tfiu/Jp4GHvsgev/GvC9ZRenx7ZXIbACXKCt9oftHTBNeGEYWvXVvhKz2nsvD1+bOfXQbAfv+5vFB8K1sd9q7wfT2ztKYQX+Tng4JEh/hGOd2xJGWGWCyVGFtcNdWhgQxfgHJ8iTajWq3vTK8FEWlWLct3JpHG4flVqIqj8Aou3cT+f3A2idmVTNksX9HYNmJYbEY60LLi0uKFr13C6mDbdqHVoYH8xFbiJDs0cXobj9L58UN78OH/90nYtnMSe+Z4cwrfrGnBtuGyOrRO8bVENm2enZgN7vHVfMrxZR7fJjX4uCZXSZuOD5FmBVFmgGtyW+PNbc7fwofPsGUBP7iVxUCd85vAjtcVPJ3pteY2ACrPoh3GklS0m0rGUXzrsTok8k2yOojPvhbEfJZt33LDITbxZIe9C6pzUV0UaZaQiq870aGMv1dQXPiWGVucNy0pApUszs0ci+ADgJGtld+zDMLjW26ARqfSO3tmn6NxxbfTrQ7VFF+h9p49FZM7lSA+EMCOCdZJ+/ihhbIeX1H4bjR49I4WZLdlW4CYnGeVmJblzmxkzW2db3UAWju97Xs/fwIA8K6XbsGfXMvGLT99rDmFryxQZHNb6y4abV74qvzEHOCFr2r45/GVcWZ+RzqJVIdQ3Pns+mB1mEu6hlcAPlkdmOK76me81Z5/BY48DBhR4A2fXPN0Rnp8eyTODEBgkGf5tmqIBff4rtZsdWDnjMUc/9v7/dl3T21LF/lzixCK79HFFOwy09uE1WEobFT39wpEssMCH2JRZmyx25pWnDMMADi9l90BCcaZX78OKM6MaCtS8W1hJFMtSMW3SuH7mPD3bh0p+bywP+w+tOAopWWsDhu1RfaN2DRTFVuAVHxL3FIVwyt0VUHE0ByrQwc3twGtaXB7Ps0uaCaVBfzRK7bhtt84VyZ6PH28OdmhmRwrUEJK6++WWNzaoeRTsCxbZgnrPnp8082KdHIrvuJOig+RZnMrrnHFgE/NbewUl7J98nwm54C7b2OPX/WRkvtuz8WZAdCiYojFshQWmgq3OqzU2dy2mOeFXhM/++XGFQsmYyEYmoqcaSMbKp3lW+DxrZboIChjdSgufEWiQ6mcYQDAscfZ1+mL6k4VIqsD0VZkHFILO9NrwT3EohLO4IrSCu1l3Of72KEFIM4PAMsnC+KUxDz5aY17gVvooXUK37WK70LSsTkoZs7p8u10xbfJY4vvuO8A7jvOFJo3bLbxZ9edDUVRcM5UDKrCQuBPJfx/bxE7NaC2wSbEC189v4qs6SRLaMH6hzW4MXQVqaZbHYaci8+Ef1aHsWKrgw8eX98U3/+8jTUgTZ4PXPW+kov0otVBTG8bUxLy+NpU5OS2kPN58IAoxKTi26zhLcGY458t0zimqQo2jLDPblJnqRjFyQ6Fha9XxZef95KngFzKZXUo3C5VEx2Os7trmL6k8vtVoFzR3en00J7Z32hc8VU71eM7UF3xTaRz2HuSHViu2FJa8RW+36eOLiETHOZqkF1wq3WF34qbVHjmpIjLagGyubCEx7dgXPHKSQA2s6bwjulOZUoovj4Xn7Zt42/ufg6f/vE+zNhsu145mmVB7gDChoYdEywK6Okm+HzTXPENq/zWbSvvlvDCVzXTyJoWQkLx9am5rTDOrJmKr/D4+mF14IqvuLXtQ3ObSHVIWkIBbOAzfPgh4Il/ZY+v/wyglS54sj2Y44sIK9xabXWoNdVBxGvNZ8T2blJjpwfFFwA282SHeXWIfaOc4mvYwCKzLlQtfMPDrCEYABYPV1B8qyQ6yML30srvVwHx3pm8Y+PrBnpoz+xjzDxU8A9dhxa+juJb/srw8UMLsGwWAzMZK317c8voAEYjBrKmxW6BS7uDoziJW3ETNi98W6j42qJ4MtcW+AXjit2JDh06vEIwLRVf/woo27bx1z/Zh8/fw27vXXnhueyJojzY89azA/zTx/y3OwjFN6y0foCFwru2dTOFXN5RfH0rfDUVaRQVkH4h4sxCcWf/W51t2EbgjjMD4Gpua1zxTYpc13oVXzMH3PUh9viS3wU2XV120V6OMxtBAskW+Dlt0dxmhzBRh9VhLsP/9laebTu/cBe+VTy+ALB5lDW4ySEWRc1totl5Mn+MTfEMxgDhBy6HohTYHcpFilVUfPMZYOYZ9rgBxTfq8g53k8+3h/bMPsZ01EWlYwtfPr6xguIr/L3lbA4AoCiKVH0fe7G0z1cUvqNWaxMdAJfia5by+JZIdOhwmwPgbm7zp4CybRv/84fP4o77ngcAfPT6c/C6Ky9mTxZNbzt/mvl8n2pCg5u4JR1SW9/cpgb5CFiu+IosYX/jzNg+Z2ebGGcWHnYK0wYb3ITiu9bq0IDHVyq+DXp8H74TOPVrNi3ydZ+ouKjwjvdSnBkirPA1FBOZleZn+ebTfGSxEnIuhDwgcnxPZ12NhX7e8XA3t3EPbbnJbQCwscoQC6HKjqaE2rvDWz+KK8s37hpbXPjaFRIdZp4GrBz7PIvXqgNN9Kugu3y+PbRn9jEudVELdGbhK6wOS6kc8mXicB7hiQ7FgyuKKRhkEV+b7CCa24bys+wbrbQ6cNVQKTHC1cnw7Z5EB8BpblvJ5Bu+qrdtG//j+7/GP/7sIADgE28+D+99+TbX9Laiwpc3uD3ThMI3nROKb+s9vipXfA0rhWzeQsjnSDXm8WW/j93E271QFKfBq9HCV8SZRYKAbTvqbAOKb5Arviui2SmfYq9dC0tHgftuZ49f9wk2NrYCQvHtKatDIIyUwraDuTLb9LezuOKrBgeh13ABIZKAVvIabPAC0tfCd5F9dSu+HqwOz6fWDrGwbduxOqyy42FVm4NAJDssHi4bZ+asXwmrg7A5rL+04cZvZ2wxFb5EK3EVWWqHjiyOhwNy/1osYYTP5E386sgiAODyKoWvO9nBllm+h+XzQvGN5/hBppXFpVR8PVodOjzRAQAGDF2qCo2ovpZl46PffRpf+eWLUBTgf77lAvzezi3syUGW44vsMhsFyzl3mt0iPL6Ull3/fiEV3zYUvhpXfAN2BjnTQhD+roOhq1gGO+laIn7MD2y7MM4M8MXnm8qaWM2yC5HRqMFuxQoaam5jpzg50AAofG0v/OjPgFwS2Hg1cPE7qy6e7cE4MwBY0YcAAFaJCWS+wwvf4ECsph8Tii+gOJ8bPy/8+EWfacTk57WshxZOpNm+Zb4urr9dKmciZ7KLsIEldveraqKDQKi0C4c8eHxL1ATHGm9sE4jfn6wORGvhVoeMHYDeoQdbXVNl8VQqy/fpYwlk8hZGIgbOGI9UfK3z18dhaCrmklnMarxgKvD4mlBgIZLlykSsdYqvKFwUq0Rz22p3Wh0AfyLNvv34UXz14cNQFOBTN16Id1zlusUWHAQMfjtw2bkdGA3q2DbGPg/P+BxrJm5JB4XHt4XNbTovfINWGpmc6Sq+/YszW7BZRJy96uOt6VyK3SIFWKoD4Euyg7A5GLrKihe3F9eHHN8V03VcrMXn+9xPgGe/Dyga8Buf9eTH78U4MwBY1ZngoKw2X/FVc6y5LRwdrOnnNFXBAL/1bonpiH563HmqQ1qLym85xfZahNXhUJYv7yp8hdqrqwq0BT5IwrPiu9bju1Rc+Faa2uZDY5vAGWJBhS/RSrjim4GOgNaavNp6GKmQ7CDHFG8ell395QjqGi7YwNSmX69yRcDl8V3O5DGKZah2HoAzFawVCI+1Zq4tfIXVIT5gOIV6txW+DTS4PfDcaQDA+155Bt56eYkRmYNT7OuaBjeR5+uv3UE0t/mttnpBD/HC104j704A8SnHV9dULCvsZKukfCx8hc1B0QCDX6D6oPjKKLOIwfZ/cXta0YAG7mIJxXclpwIKP9159fnaNvCT/84e7/xjYPK8qj9iWjbyFlPxeq3wTQdY4aum5pr7RpYF3WTbfyA6VPOPi0LU1Jun+K6qUflelawYoYCGyVgQp+0hvi5JqWZLm0NIhyIzfD0WvsMuq0OZ6WllUx2yq8DpZ9ljHxRfESHXTdPbemvP7Fd4kZVFoHRQdYcwXCHL9zHh7y0zuKKYy7nP9+F5fvJdOiq9e8lMHlMKPzhHJxo6cdaKKHzVEp3EwuIxPBAAEsfZN7vA6gAAUz6MLX7i8CIA4GXby8S3ycK3uMGNXdw843Oyg2xCgr9qqxcCYXbiDCGDfMZ1MeHjOqyo7O+mpOZ9e82CRAdxgeqDx3dNlJkPGb4AEOKKbzpvOV5hr4rvwkGWraoGgFd82NOPuCOdesrjCyATZMdmvdmFb24VCtixfHBwqOYfF4WYKRTfJjS3rXAbUblxxW42j0SwihDymrA7sDta4g7gttAyyy1WNO8T1ERvy+os4hrbdxKpHGyXf72sB/nkkyxBIjrly91Q8vgS7YH7SXPQoaudq/g6Wb6FRaFl2XhUJjp4K3xFg9t9x3UACrudlWS34Fjh2/rhFYCr8LXKe3yHDduJtYltaNm6NcK0tDrUdxI5lUjj2GIKigJctHGo9EKywa1Q8T2/yYqvU/i2bvhLIOQUvpkUu61rQfF15HhSY383NbNYe0NXOdyJDgLxGW5A8V0TZebD1DYACHLFN5O3HDXdq+L7wv3s68YrgZA3r6m78O01xTcXYsdmI+PjhVQpeIavZSuIx+M1/3iUF2I5RVxE+Wh14J//hCh8KyQ6CDZxn28ywM9tSXbnSyi+Zwf4hf7wFu/HoPCQ9NjHMuzn85aNVM7JWC6b6uBubPOBbhxb3Ft7Zr/CrQ5ZW+9oxVdEmhUrvgdOr2AplUM4oOG8aW8nmEt54fvs6QwsYWXgdoeVTB5TcnhFawtf1WAnV61E4SsGWIzZXDHRQ1U7xDuFqQY9vo9ztfesycHynrgyiq/4TByaW13jY2uEdBsVXyPM7lSEkUFyld2KzcHwdbR2SueKr5UvaBhsCHeig0Aqvg14fEWig4wyE8MrGptkJzy+6ZxZu+J7kBe+W1/p+f3ExZSioKNFiHqwQmyIRSjb7MKXZ/gihPHB2vdJMcQip4rC13+rw5IlFN/qha9IdlhQeEwnV3zFsWyHxi/0vdocBDzZIZw8Co1/1tyRZmUVXx8mtrnpxrHFnVslEd5xWR30Dvb4CqtDscdXjCm+ZNOQ58J9LBrEVt70tBzkBRMvfJOZPCal4tvCxjYAKi+e5Ahpjm3bcnjHsEybmPa10Gkm00ONWR2eOMy2xyWbymc0l1N8hwYMbBhm7/9rHxvcRJESsHnh28LmNoNbHcLIIp1iJ/qs4q8lx9bDSNv8Nf2yOxQnOgCOxze9JP2LtSISO+SULlGcNuh5FgMsalZ8LQs4+AB7vK2WwteJMqvWq9BtWDzLN5xfbO4byXHFQYzVMLxCIC6sM0qRbaZRbFs2ty1Y7HhUKdFBIBTfGZvvM7zBTXhwN9v8TonXRAcBb3BTFg87Wb4u1bWsx/fY4+yrT4VvN44tpsK3FxCKLzpc8eVWh+JUB+HvLTemuBzC7nAc3DPKG8aSGRPrhOLbYquDZrCDrV6k+KZzzkjHWO40X7fu8PcChc1tdh23zR+Xhe9Q+YXKKL6AM8jiGR/tDqJICYiLlBY2tylcyQwoJrJJ9jvlFH+tFkFdxQJ4V/yqT4VvKcU3FHNGqNbp8xUXw6NrprY1aHXgdoOaFd+Zp4HVOcCIAusv8/x+cmpbBx+H60WJjAMAok0vfJnVYcUOY7yGccUCoUBm4LPim09LW+Fcnn0uvSi+m7jie7Qo2UEovuvzvDG7TsUXi4ek19hdfJZMdUgngDneSOdb4UuKL9EOCprbOldlkIpvkdVB+HvrLXz3p4fYNxaPIJu3kDUtTKI9VgctIBTfwqtfYXPQVQXBVa5oxrvD3wsA63hzWzJrYjlT2wEuZ1p48igrmC6tQ/EFgPPl6GL/C1/dbr3VQSYiALCS7LOa97nwNXQVizzSzDfFVxa+Q4XfjzXW4DYrCl+/m9tciq9di+IrbA6bX1pTc6wTZdaZsZKNoA2ywnfQXGzq+5gppqoyxbf2fUKM0U2hyC/eKGJqGxTM5dhnwovHV4wtPpQpnN4mCt+JDM+gr7nwLT+2OG9acpDToLsB78Sv2Nf4JjmNr1Fkc1uGFF+ilZiO4qt7yJlsF6UU32OLKRxbTEFTlcpqYAlEssOeZX5AWToih1dMtcnqoHGPb6DI6uDO8FVkhm9ri/JGCBsayx8GcGKxthPJsydYRnMs5GTylsSt+Bapyk6kmX9WBzG5Tbd5nFgLm9ugBZAHL454USo9iT5RWPgu+vOi7lQHN8LnW2eDm7A6SI+vT81tIs4MqDHXVTS21WBzAAqtDr2GzgvfmJ3wr1myBCsr7OIqibA8Z9SC8PiKyYW+Kb7yoi+GRJrfvfNQ+A4PBDAY1HEahVaHxdUcwkgjluW55bVaHYbdim9hlu+KS5wYdCu+x4XN4eLa3qsC5PEl2oOrua0rPL4uxVfYHM6bjiFSIQi8FGeMRxEPB3Aoz5XipSNyh3esDq21E+hcoQoUKb6F44qPt2XdGmUqxgqH4zUmO4gYs0s2DUOt1PAjCt98yjnJcITV4fnTK1jN+nOAZXFmNjSrDYovgDS/Faum2UWaqfqs+GoqFsALX9+sDovsa3Hh26Di64wrLrI6+NTcBgCW5tHzmc8Ch37JHtfQ2Aa4p7b13qk1GJsAAASQX7N/+slKYhEAkNPCNY0rFgjFd1X42/3y+IrfORivPA64CEVRsGl0ALPC45t0rA7bFG7rGhirvdG5QPEVVod8wddwQCtMF/E50QGgODOiXbisDp3sLRNRRQuuODNncEXt6QaqquDSTUM4ZjMlAktHkczmEUEKUYUf7AZbq/jqXPHVUVj4Fo4r5t3vXWR1AJwGt5M1NrgJf29FmwPAbmuLW+hFPt/xwSCmYiHYNlOQ/SCTN6HDhAoeQeVjlJin91f4ZyXD/j75piq+Plsd3HFmgGuIRe3JDrZtr83xFapsg81tAU2BuNYyvSq+x3azQQMDY8DEuTW9X69ObQOASCSKZZtbT5LNm96WWmH7t6lXnuBZDlGIJS2fC9+M42+vOA64BJtGBpwhFi6P7xkKF0FqVXsBp/BNLWA8wC4cxXrJwrzJjW3u96A4M6K15EXhW3mKTLsRt61WMnnZUf8Y9/deubVKUVSGy7eM4LjNYnawOofUSsKJMgvGgGC0/A83gUCQnbiNNR7fUuOKu8fqALgizWqc3uYovkPVF/bk8/Wn8E3nLGdqG9ByxVcUvkaWn+j9Vnz1Zii+ZTy+DQyxSKTzyJns1rmj+PLb03pjHl9FUaTP11Q9Kr4v/JR93foKTyOK3chs6B4sfAcMDfM2s5WZK6eb9j7pJNsfbKO+Y7dIdVg2m6T4huKVxwGXYNPoAE7LVIcZwLaRSOVwhtpA4RscBMJMMNqgsu0hCl5ZmLvXb3UeWDzEHq+7uPb3K4McWZwixZdoJdLjG+jo7MjBkC7zBhdXc1hazWHfDMsXvawOxZf93DASiMhJOrmFI64M39aqvQCgG+xEbaC01WE0aLNucaDrrA7TdWT5zq5kcHh+FYoCXOyp8C2f7HAetzs85VODWyZvFm6nFqY6AECWF2Ih3iVvqv4W3obmVnx9GltcKs4MaGhssUh0iAZ1WaQ6Ob6N/02E7UB6qKspvgfr8/cCvW11iAR1zINdfGaWZpr2Pjne3KbWKVoIq4P/hS+/4A7FpJfWS5wZwKa3zQqPr5kF0ktFim+NjW0Crvqus0VEGrc6pEso0sLmMHLG2js2DSA8vlnTkn0TnU7v7Z39iGtyWyfHmamqwsb1gp3sdh+eh20D28YiGK8jrxEALtowBF1VcNRiqq+9eARTaM/UNgDQg07ha1lOA4iwOmzS+boFBoBwfSp3u1hXx9jixw+x33f7eNSbOlJR8eUNbr4Vvi7FV9UBtbWd+DmV/T0H8uyEavlstTB0FYtoUaqDsO0kjtXc+LSmsQ1wIscaVHwBJ9kh70XxzawARx9lj7e9qub3knFmPVj4BnUVc7zwzSVONe198ikmhmjBOq0OUvHlRanvzW3x0opqBTaPDiADAytgv5O9MuNr4TtpsgsRqfhKRdpVmB/33+YAAFFDl3H03eLz7b29sx8pmNzWuYov4IwtXkhm8chBMaa4/gIwbLBpb8dsFs2iJY5hsk0ZvgBgBHmcmWIjm3Oa+ESqw7TqarrrsoD7dXWMLX7iyCIAD/5eQaUsX2512H9qxRdlIZ0zYSj8QN1imwMA5LnnNGqJwtd/j++C3YIcX8BRfLMrNTc+zRY3tgEuxbfxwlcqvooHxffwg4CVZxmpw1tqfi+R6tDJvRb1oigKEirb7vnl5lkdLD4EJTDgbYpnMeLW+xKPHPMvzkw0t8VkgRmvweMLAKe43SG9cAKmZWKrIqa21WF1AGSyw3CuqPAt5UE+vod99bGxDWCCVtToLp9v7+2d/Qhvbssg0NGKL1CY7FDv4IpiLtvs+Hz15aNOokMbrA6i8AWAbMZRGkSO74TNm0K6zN8LAOtc09u8DrEQiu+lm4e8vUkFxXcqFsJoxIBp2dh3svERvAWKb4sb2wAgr7G/Zxzsd5FxWz4R1DUs+Gl1sKzyha/huoNRo893TWMb4NvkNsBRfLNKUWJEKYS/tw6bA+AqfHtQ8QWAFY1tYzvZvMJXybHCN1hn4SusDgs5nxVfPrUtbwzKcedeFd918RB0VcFpm/1OqYUTWK/MIaTkYGuGM4yiVvjPxdNMOXasDiU8yD6PKnbTbZFmvbl39hsFzW2drSKKBreTS2k51KDxwndYKr6h1eOuDN92KL6OQpVNO0qDGFc8avITRpclOgBOnNlq1vTUyJB3Da6oOKrYTQXFV1EUV55v43YHVviKqW2tV3xFysAQ2Ine9lnxDfptdcguA+AXPMWFLwDE+Ge6Rp/vmigzwDW5zQfFVwyx8KL4ivzeGmPMBI7Ht/cGWABAUh8CANhNTHVQeaEaipb4jHlANLet+J3qwC/60vqg814e4swAQNdUbBgOy2SH7OJJaXNQRrfXb7PiVodIiu1zwnu8Zlzx8gy7IFVUYOrC+t6rAsUDNDodKnx7ALugua2zN6lQfH+67zSypoXxwSA2jzaW1Xn5lmEc54VvMHm8rVYHRQsgb/Nbq1nnBCs8vvEuHFcsCBua9GifSFQ/mew9uYxUzsRgUMf2cY+NKlLxXVv4AsD50/4lO6RzJgwIq0PrFV+TF3VRhX1ObJ+b6wrizNJLgNmgGiPUXj1UWomVyQ61RZqV9Pjm/FN8hdWhquKbnAVmnmKPGyx8e1XxTQfYBayammvaewTybGRxdLCxwjcNDwp/LfDmtpTCfLruZm0vbBqNyGQHM+EUvhjdXv86ccU3uMIKX8fjW+RBFmrv2FlNSToixZdoORZXfHO23vHespEI2xEfeoEdOK/YMgylQa/rZCyEbJSddAsU3zZYHQB2AQIAuYxzwBVWh0iaF3Tx7it8AWBKNLh5mN4m/L0XbxqqPLjCjVR8T5RskhINbs/4pfgq7RleAQBWkZpp+7wOhuZSfAFn+ES9lEt0ENSZ7CDHFUfcVgcxuc2/5ra0XWWE7cEH2NfJ84HoeF3v1ctxZgCQCbK7c3qTCt+8aSFoseNmdHCortfQVAURQ0PK9rvwZcecFV74erU5CDaNhGXhaydPNd7YBgBDGwEAWnYJMSSRSOVg27a8Iyc9vk20OQDuIRak+BItws65RhZ3uNVBNLfleeJBPYMrSjGx8QwAwLg1izHwoqhNqmpWYQeBfNZV+HLFN5TihW8XKr5AbZFmT3B/r2ebAwBEJ9lXK1eyIUtMcNt7Yhk53kFfL5mc6Xh8WxxlBgC2XnSnw+/CV1dhQkNa5d3xjfp8y/l7BXVm+c4Lq0O0Oc1tIV6EZqopgAcbszkAvR1nBgBZXvgGMj41SxYxn8xiAGzbD8brb3qOhnSk4DG32Sv887/Mkxm8Dq8QbB6J4DSGAABa8rQrw7eBwteIsEErADYop2HZQDJrllB8m5PoICDFl2g5Flcwsgh0fOE7Eim8pXzlVn8K3zPP2I6crUFXLKiKDVPRgYFRX167VnIQhS/bLqmsKZteAiu8aatLC991Q96THZyJbUPe30A35IG8VIPbxpEwBkM6sqaF/TMr3l+3BIXNbW0ofIuKOqUJhS8AJDXeJNRoskO5KDOB9PjWaHXgzW1jJZvb/PP4plBF8RX+3job24DetzpYYXZMDWUXao6t88Kp5Qwi3PqjNXBLPhrUXVYHf5vblvj0Oi/jit24h1gEUqcbm9rmhvt8N2vMd51I5Qo9vrbdlFHFbkThm6DCl2gVtqu5LdAlHl8AiBgazp4arLC0dy7dMo6TtlNEZ0ITNU9d8ou8Ulj4CptDVM1CSXPVrUutDl6zfOeTWbw4x044l2ysUbmp4PNVFEWqvgUNbrYNzDwjo/2qYdt2YeHbBsUXgaKc0oC/6yBPRgovfBttcGuS4jtXUvEVzW0+pDrwIjRVqdlp4RCwcJDlOW9+Sd3vJS5wgx1uOasXi4sJqp1v3DpTgtmVDCJc8W3EixoNBapbW2qFf/4XTHanpmbFd3QAs7zwHUwdxrjC96dGC18eabbDYPaTRDonlddYiE8KTZ5mn+3J8xp7rzLEyOpAtBx+ss8rAe9eyjYhUh0A4NLNw76NWD5rahAnFceXlx2Y8uV160EUvmaWXZAIm8NZYX6gM6JsnHIX4jXL9wmu9p4xHkF8oLYTRIHPtwQiz/cZ9yCL+/9v4IsvAR74tKe3kLFTShsLX6PQ6qD44Gd1Izyzvo0tFoVONY9v4rhnNdC0bMyvlvD45vxTfENeFF9hc1h/GRsFWye9rviGwhEsc8UTTRhbPLuUREjsk3WOLAaYGiutDvk0i+JrBDPPMqoBzJsh/h61Hdc2Dg/IVIegyUSBZWOioc8bAJfiywvfVL4wx/cYtzlMnOPL/lSKbhtb3Jt7Z59h88LXVGosMNqA2+rQaIyZG01VkIk4KQ75aHsa2wAgx7vHTe7xFYXvNmORLdCFwysEU6LwrdLc9sThRQA1+nsFFSLNANcEt+M82eHYbuD+T7HHYupWFaQyJxXf1je3qYGiwteHBAM3QkGd5wqVbx7fcuNORYpKPu25yF5Yzcoaedh9gSSb2/xLdUhZIte1xGe3wRgzgZjc1qtxZhFDx1GbCwzzL/j++ktLrotZo77JbQCzOsgLHcCxztRLxkmROZ1jBbXXccWCSFAHIoVNk4no1sbWC5CF7waFWR0WVrNYzrgmt8nGtubYHAC3x5cUX6JV8AEWptr6SKZaGW5S4QsAgdFNzn/alOgAAHlR+PIT7CJXtDYH+EG9S20OADDtsjpUGmLh+HvrKXzLD7EAnML318cTMDOrwHfeB9h8ktvCi57eIsMnvwXbqPiqRSNZVb8LX76vzeRFc1uTrQ56EIhMsMceI82EzWF4IFB490cqvo1FHQKO4psUVofiIsi2nUSHOsYUu+l1xTcS1LDf5sev08/6/vrLy4sAwHo0GtgnCzy+QOMNbuKzHxjAIjvd1qz4AsCGsRjmbEfhTcX8KHy3AADW2Wx627EF53cdDAWanujA3oea24hWkxeFb+crvhE+Ynj9UBiX1NL05IGR6W3ysRJrX+ErtoOZ41YHfttpg3tccZciFN9UzpRh6cWYlo1f8SizurZxFcV362iExRXlTCR++DFg9jkgyIuxpSOe8mqF4htRecHchuY2NVhY1KmG34ov+51OC8W3UatDtTgzwLmo8xhp5mT4Fv39heLrY45vspzH99SzQPIUCoyxNQAASthJREFUK7I3XNHQe/V6nFkkqOM5izcxnt7n++snE4sAgJzW2AVPNKTDhipFiIYLX6H4BmPOVLQaPb4AsHnE8fkCQH64gQxfAVd8x80ZADaO8sI3HNBgaErTG9sAl8c3Q4ov0Sr4AAurCxRfRVHw3V0vxX1/8iqpxPjFhi1ny8fB0Y2+vnYtCOXdkoovOxhMgmdfdnHhGwpo0q5SrsHtuZllJLMmokEdZ07W4V+r4vFVVQXnTsdwlfIshn71j+ybv/UPbOywlffUXCUKlAFNDLBofeGrhwo9jJoP6qabWEhHQFOwIBQm3+LMhiq8aW0NbnMyw9d17LKspuT4rpjC6lBUBIkxxZt2NjzIJNPjcWYDhkvxPeW/4ptKss+Y1eC+IDynWdWnSDPX3Q7pn60x1QEoTHYA0FiUmYBn+YatVcSRxNEF5h+OhXXWsJleZBf24+c0/l5lIMWXaD288LW7QPEFgICmNkURCY85886jY5sqLNlcxAWISNsQVodxS4wr7t7CF6je4CZsDhdtjNc02UhSRfEFgEsmdXw6cCcU2MAlvwucdZ1UPrzYHdI5VqCElTYWvkWKrxb0V/FVFAUjEQOLdousDoAzittjpJlQfAujzFwXVD4ovqEAO9asmPz4aJuA6VKmDjYeYybodatDNKhjv8238exzjTeNFZHhha9tNNbwNcint2XFmOpGI8341DaEYjIjN16H4rtpZEBm+QJAYPLs8gt7JRCW+ecblVNS8Y25bQ5T5zd1OqUcWVzmLmCn0Zt7Z5+hSMW3DZ3pnYQ46QJAW60OvPDNFaY6DOe7d1yxm3VVhljIxrZaY8wEwuO7MgNYZslF3rb4j9iknsYpbQK49n+ybw5vYV8XD1V9C6n4qu0rfLUixVdtQsf1SCSIRfAiYrVRxXeRfa1U+IoGt1oVX3eUmbvw9UHxFY1mUvEFHAXQzAMv/oI9brCxDXClhfRonNmAoeOQPYksdFZMLh327bVXs3msLLPCt5EMX4BZHQAg4052aIRSim89VgdXpFnSDiI67tOdSVeDm6P4uhIdmtjYBhQqvpV6PzqF3tw7+w1e+Jpadyi+TcMYAF72IeDS35MzzNuBXaz48hzfwewptkDXF76VxxbLxrbNQ/W9QWQCgMKUueTs2uf3/yfOOPRNAMCf5d8HS6hDYpt7UHwzXPENycK39akORlHhqwf9L3zHogYWbP4+fim+5VIdgJrHFs+uVIgyUwOAVvvt5GKCXPFdzrteSxRCxx8HsstAeBiYurDh95KT23y2cXUK0aAOExoOK6LBzT+f7317TyNgsm0fjDQW9xjliq9vQyzcha87I7dGNo1EpNXhBXsd4gM+XXDLwve0a/104Pge9nwTG9sAx1qSt2x5N62TocK3B1B54duOBp2O45qPA7/5t22NC7OEeph3FN8IUjDyy+z7XW51EA1ux0tYHRZXs3jhdBIAcHG9iq+mA1GeDlDs800tAN+7GQDwz9Z1uDdzNg7P85OaUHy9WB244hsWqQ5a6/3xRrgw1UE3mqH4GlgUOb7NHlkMOHddPKc6sH1kpNTwCp8UcOHxTZu2c4wU7yFjzF7hy8AbEWfWs4pvkP0tm+Hz/eFTJxDlU9uUBjJ8AUeBXLWb0NzmnopWI2NRA3s15ut9GOf71+fCL/o3KE62cjykASf2sP80ufCNGBqEq60bIs16c+/sMxQeZ2b3u+LbIQjF1134TilcbQvGGw8sbzPTfGzxyRJWhyd4msPWscia8dQ1Uc7n+8MPs2J4dDvumvgDAK4JbjUUvkLxDSptVHzDhSf3QBMU39FIEItC8c2tls6w9YKZkwH+3prbTnjyfwqrw5j7syLixnzaJqLRLJ2zHM+wUHxFY5sPNgegPzy+APBsnltafFJ8V7N53LN3BgNialsDGb6AU/im/Cp8+UVf3ohJO0s9VgdFUXBq5HJclv4i/sH4/cbWyQ1XfDe6Ct9tygm2zwYGgPGz/HuvEiiKIj8b3TC2uDf3zj5DsdgVlt0FqQ79gC0UX9OxOkwrPNGhy9VeAJiKlR9b/MQhpio2HFVXKsv31/8OPPVNQFGBG+7EmeuZKvz0Ma7G1FL48pNXqI05vsHQACzbuTNR3OzmB6NRAwkMwARXluq1O4jmHqDy1MHBdWz7WDkWEVaFeenxdVsd/IsyA1yKb950PMO5FJBdBY4+wv7fYH6vQHjHeznVAYAr0swfxfe+vaeRzlmYDvOiqVGPb5AVpWUj7GqFF75pja2XogBRoz4bzqaRAcwhjviAj+drl9VBcEb+OfZg3UWA2nzrjbgQIMWXaD62Dc0iq0NHwW+bCyV+cTWHdaLwFc0/XYxQfE8spdY0Mjwh83vrtDkIihXfldPAXR9ij1/2IWDjFXKQxTNS8eUe39U5ILNc8eVlgYL2Fb4ho3C6VCDUhMI3YgBQkFRFg1u9he8i+2oMApqOg7NJvOMfH8IDzxWNrdV0IMq3nQef76zM8S2l+PprdcgUK76HH2T9EbENwMi2Cq/gnWzPx5mxYs8ZYuFPssMPnjoOADhvjBdoDSq+orlt2fJX8U1pbL0GgzrUehJrwBrcgPpSIcpSYHVgx+RNaV74NtnmIJBji0nxJZqOO5aniXElRA3wCxDFzCKdM5HJW47i2+WNbQAwGWPFQzpnycQKALAsG3t4osOlfim+KyfZZK27bmEF7eT5wCv/TwDA+dN8dPGxJVaAh+JAmE8DXKic7CAaMAzwg3QbLhoDmooU7zq3bAVGwP91EEpqQmnQ51vk7/3hUyfwy+fn8OVfHFy7rLirUcXnm8mbMvdzLFJK8fWn8BVFaKZY8XXHmPnUE9DrVgdNVRAOaDhkT7L4zFySDY1pgNVsHvfuZXcHdgzx7dBonNkaj68/zW0rYIVvPTYHwbZxti+OFw9taQSe5RtRMhgBu+ifWvk1e67JiQ6Cbhpb3Jt7Zz/BVUUAsEnx7Qx0p/Bd4Bm+0wovONyRa11KKKDJgQNuu8OB0ytYzuQxYGg4q57BFW7ciu+T3wD23sW6/N9yp/z7njkVha4qWFjN4bhYj2FvyQ6O4svvlrRB8QWANC98Mwgg0IRiSfis5xtNdiiKMju9zI47+0+trF3WY7KDsDnoqlLYKCSKFL+b24oVX9HY5pPNAXDFmfVo4QsAE7Eg8tCxHN3CvtGgz/fevaeQzlnYNDKA4QDfHxtUfCNcmU7bYkx1g3FmvLltGUytrSfRQfCbF03jA6/Zjltet6OxdXKjB2FHmViwQTkNDSZGEnvZcy1SfGNdNMSid/fOfsGt+LahM51Yi8KLKNXKSkV0oy7GFXe/1QEA1rnsDoLHub/3wg1x6I12tQvFd+bXrKENAF71Z8DUBXKRoK7JyXBPH6utwU00twXQvuY2AEgr7H0zCEBpQhLJGLcQzJq8kKjb6lAYZSYK36MLKaxmi050MtmhcuE7x6PMRiJG4e8up7b55fEtofgmjgMnfsUeb32FL+9jWTbyFrvNLLKDe5E3X8wubJ7O8n20QZ/vD55kPv7rL1wHJcsSYRotfDVVYWPNRY6vT4rvki0U3/pj9iJBHf/t9Wfh7KnGItuKUYYdn+8O5Rg0K8P8+D7ZeKohrA6k+BLNhycHmLYCXadUh46Aq0qqmZGFby9ZHQCnwe24S/EV+b0N+3sBR/FdOgxkloD1lwMvvWXNYhcIn2+Nha+IMzNsofi256JRTJbKoDnvL6wOc6Lw9cnqIApfAHj+VLJwWan4VrY6OP7eIrXd5zgzUYTmTBu2KKYP/CcAGxg/2/msNYiIMgN6W/F9x5WboKkKHlrmkYMNKL7JTB737WM2h+svWOckh/iQfBMNuTz0Pnl8F032+fHVn+sX3Oe7UTmNC9Xn2femL/Ylps8L3TS2uHf3zn6BWx2yCEDX2pddSzioXPHVrCwfV2xjwhapDt1vdQDckWbOCeUJ6e/1o/B1Td7TQ8ziUGKYwfnrmWry9PHakh0cxVcUvu1RfDMqe9+s0pzCN2JoMHTVleVbp+KbWmRfReG74hS++08VNRJKj683xXcsWvS7i+I84E+zn1B8AcAUdjCfY8wAx+YA9G6OL8ByvK89b9KXLF9hc9g8OoDzpmNAhhe+DSq+AFMg0354fG1bpprMW+xirBGrQ9NwJTtcqLzAvtcimwPg/E26YWxx7+6d/UKenTyy0BHo4YNtN6EGhNUhh8VUDoNIIWzzArFHrA5iiIWY3raUykm/Z8NRZgAwMOY0nF3zcWCstB/uvPVOgxsAz2OLRZGi22KARXs8vjludcgpzTmRKoqCsYhrelu9Y4ul4jsEAJh1Kb7PzRT5fGP84s6jx3e0OO/5hfvY1/WX1bWqxbhtB6bGL3BEIbTNz8LXGa8d6HER4nev3oLnbLad7dP7WHFYBz98itscLljH7C7S6tBYnBnAMofT0urQgMc3m2RTJAHM5njh25GKr1P4XqDyptMWNbYB3aX4Nj4PkmgvbsW3zngVwl9UbnXQ7GxhlFloyBcloxOYFmOLudXhVzzGbNPIAMb86FZWVeA3v8A6xq/8o7KLnTMVg6oAp5YzOJVIY0IqvodYzFKZ23zpHDuR6VZ7m9tyWgjIO5aHZjASNbC4wm8d193c5lgdUlkTyxnn5HagnOK7chIw82XHDs8mS1gd0kvAoV+yx2e9ob51LUJTFQQ0BTnThqm63ktRgS0v8+U9gMIos2b4tTuJq7eNwBg/A9klDYZIduCFl1eSGSfN4Y0X8Ds8WT8VX5+sDmJqm6JhLssuojpS8eWNvduUE845p4WKL8WZEa3DdCm+Pewr6yY0XvjqVrZoeEVv2BwAl+LLrQ7C39twjJmbi94OvOJPK3rUwoaG7RNMHXr6+BJTGxWNXRCunCz7c0Lx1ez2Wh3yGruAyDXJ6gCw6W1S8a3b47vIvobi0psrWJPsEJlgCRy2VXEbCKtDQYbvgXsAKw+MnelrU05I+Hzdhe/0pZXHL9dIr0eZuVEUBe94yXYctFnBas3srfk17tl7Cpm8hS3C5pBaZE2HABDf2PA6RoM6UrYPzW2uiz5R1DXS3NY0+IXHZvUUDMVk0Y41Xow0AsWZEa1DWB1sHQFSfDsCofjqdg6LSde44h5pbAMKFV/btqW/15fGthpx8nwTTF0UFxgVfL7itrSj+Lanuc3khW++qYWv4Xh8fUh1OMVtDmJE6eH5VamgA2AXKjGu4FWwO8zxArogw/e5H7OvZ15b33qWIch9vnl34eujzQFwLqZ6dXhFMW+5ZD0OKmxfe3Hv7pp//ofuNAdFAY49BsAGhrcC0fGG128wpCPth+IrC9+YU/h2ouIb2wALrhpg/aW+5VN7oZusDv2xh/YyBc1ttDk7Ad1gJ1fdZopvL01tE0zGeRpB3sJ8MosnpOLb+sK3rM+3YuFrAXBNPWyT4is8p00tfKMGFhvO8XVUL5HosH0iiuGBAGwbOFCs+gqfb4UhFnNJJ84MAGCZwP6fsMdnXlffepZBJju4LSU+NrYBbqtD70aZuYkGdQTXnQcAOL5/T00/605zkDaHI3x89MarfFq/gFP45hspfLnVIRSXjVsd6fHVDawYE87/W2hzAFwjizOk+BLNxtXcRqkOnYFmMBXPsHNYXM1hGsLq0DuKb1DXZDf+zw/MIpHOIxRQcfa6xmOIauX8aZbs8EwNyQ7pnOlMbQPaloFt6Sy5oECJ9JnRaJHVoZ5GJFeqg0h0mBgMYscE295rCt949SEWa6wORx9lhXko7lvxIxDJDlmVv5ce8v09RJxZP1gdBGdfeAUAIJrYj2OL3otLYXPYOhbBuet4nq0sfK/wZd2iIbfVwQ/FN44Ev40vhjV0Githl7jS6sKX/00SKVJ8iWbjUnx7OUKnmwgEudUBOSylXM1tsd7x+ALAOm53EJ3ZF64fakuyyLm88D22mGJJAe4GtzJk8haCcI/7bo/imwuwwjGj+RPdVYoRt9XBygOZ5co/UApXqoNQfMcHg9g+yV53TaSZuLtRJtLMtm3pFZbNkM/9iH3d/rqyDXH1IlTYjMqbpjZe5Uxx8wkRkddPx+F121lxtV05hq8++KLnn/vBk8zL+8YLppjNwTKBo4+xJ326IBkM6kiDK7MNNbfxz34w1tmKL4Bs1HWOaWGiA+A0t61k8myEfAfTP3tor8Kb23LQobcoqJqojM5PqIadw8JqFuuEx7eHFF/AaXD76b7TAIBLNg+1ZT0GQwFsHWMFzTPHl7xZHXIWjILCtz2pDnvHr8W/5F+H/4zd2LT3GIsaSCPoDMmox+5QwuowPhjEDt5YuL9spFlpq8Nq1pSeWKn4Cn/vWf7aHABH8T2y7vXApb8HXPMx398ja/Ix2IE+Og6PbIOl6Igqadz/6BMFkW7lWMnk5THj+gv4BdLpvUB2mcWYTZzry6qxVAf/FF87FJdqZqcWvlObzwIAZMMTjs++RQiPr2nZWM1W/xy0kz7aQ3sUV3MbWR06g4DBCsIAclh0F7491NwGANO88BUFzCUbW+/vFZzHVd+vP3oEezMj7JuVrA5501F8NaOlTSBuzIFx3JZ/F2YGtjftPUZ489hSvQ1uubS8s1Rc+IqR0WuSHaoMsRA2h3BAw4ChM3X+1K9ZIscZr6lt/TwQCjDFd1kfAX7zb33LCHYjUx36SPGFbkAZZZ/dsfSL8u5PJe55dgaZvIVtYxGcI6xRRx5mX9dfBqj+eKR9m9zGC1/TGJR2lo6c3AYgNM0818bWl7T8vcMBDRpvsO/0Brc+2kN7FH5CyiDQ86Hp3UIgxG5bB5FDKL+MAYUXDT3U3AYAU/HCkbKXtknxBZymuh88eQJv/xaP0Fo5iTvvfgqPvji/RonK5CwEFV74tsnmADgxW830hYoBEfP1NriJKDNFBYyo9PiORx3F99BcsjDZIVbZ4+tk+BapvZuuBgZGals/D4ikBWFHaAaZPoozc6NMMJVxh3IU//zLyoNjALaPAqypTeYdH3mUffXRdx0N6ki748zqvf3Om9vSGivSVYVNROxIzvlN4L/8C/DGT7f8rRVFkT7fTo8060yHNuEdmtzWcQjFV1csbFDYLT17YBRKIFzpx7oOMbYYADYMhzEx2L4C8h1XbYKmKvjFgVk8clBHwgojpqTw7Xt/iU/eswFBXcWlm4Zx1bYRXLV1FKmciahb8W0T4vZgM0+korhcsKKABqdRzSsumwNUVU5tGx8MYnwwiFhIRyKdx8HZJM4RjUoiUi55Cshn1lhJnMa2In/vmf4MrShGKL5pD7fi3Xz8e8/gP54+gdFIEBOxIMaj7OvEYAgT/PefGAxhIhbs28IX4+cA+HecpR3DPx1ZxFNHl3DBhtL5yCuZPH76HLc5XOi6FS8U341X+rZaBXFmsNnnsB5fN//8p7g/PBYOdO6AEk0Hzn1z295+MBTAwmpONgF2KlT4djsUZ9ZxGCHn4LpZmQEAKD2m9gJOcxvQnvxeN6GAhptesgU3vWQLLMtG9o5twNwz+K2tefzvGQNzySwefGEOD74wB2A/AGBDByi+v3nxNI4tpvD2KxoP7C/HgKEjHNCwUK/VwVX42rbtKL6DQSiKgjMnB/HYoQU8N7PsFL4Do+zvmk+zoQQjWwteUmT4jkYM1mz34s/YE00qfOtRfNM5E//PQ4dgWjZmEhn8uspdfHHHrV9yfCXjTPG9YmAGyAL/8uCL+PRbLyq56D3PziDLbQ5nT3GbQ3IWmH+ePd5wuW+rNRgKOFYHgEWa1VP48sltSYUXvp2Y4dshiAv5Tp/eRoVvtyOa22iARceguw6uW3jh22uJDgCwLu78nr5ObGsQVVUQmmCF7x9fpOP9V16DA6dW8NDBeTz8whwePjiP08sZDAf5rc82NbYBLNHgL37Dn2aeSoxGDSwu12l1cEWZJdJ56WUVaQw7JqN47NBCYaSZojBrz/wLzOdbXPjyDN/RiAG88FN2HBveCoztqPVX84RUfHPeFd/nT6/AtGzEQjo+//ZLcHo5g1PLaZxazvDH/P+JDDJ5CzmTfZ62jkWb8jt0LBPnAAA2mkcA2Pjer47jz994DoYja++k/KB4aAXAYuwAYOwsIOzfBXQ0qCMPHTlbQ0Axmc+3ntfnF37LovDtxKltHUK3DLGgLdjtuKwOIVJ8OwJFC8C0FWiKLRXfXkt0AIDJWAiKwqxz7VZ81+BKdlAUBTsmB7FjchC/e/Vm2LaNF+dWMXwyAHwbbS18W8VoxMDCsivLtxZKRJkNhnRZTG7nWb5rkx3Ws8K3hM9XRJmNRoOFNocm3UKux+qw7ySLaDt7XQyvPnui7HK2bWM5k8epRAbJTF42WvYNI2cAqg4tt4JXTGbxwEwQ39p9BH/4ijMKFltO51pmcwBYcxsApGEggFT9DW7885+w2B0uUnzLIyLNOt3jS5VSt+OyOlBzW4egKMgq7ACwReWNVj2W6AAwL+N7X7YVv3HhOlywvrSnr21UiDRTFAVbxyIYMtqv+LaK0WjQmd5Ws9VhkX0tSnQQyEiz4izfePnpbfNc8R2L6MBzfFrbWc2xOQD1WR1E4XvWZOWhLKypJ4DtE1FctHGo/yxnusGKXwDvPpN9Pv71ocOwrMJmsnuePcVsDuORwr9pExrbACBiOIUvgAYKX2Z1WLBZ0zIVvuUhxZdoDVLxDVBzWweRQwBhZB3FtwcLXwD479c3/zZ9XQxtYV8rRJohn2Zftd4vfAuGWNSb6uCa2jYedf5mItLsxblVZPKmM7K3QrKDaG7bnn+ONcAZg8Cm5kUwBetQfPeKwneq9dMIu47xs4DZfXhp7DRioREcnl/F/c+dLlDKf8Cjzn7DneZg5oBju9ljnxVfTVUQDepI2QagoGHFdz4fBrBCVocKxEjxJVqCjDPToZPHt2PIcpVhSuG3lXvQ6tDRuBXfcjFGeR4z1xeKr4EFmxdw9Ta3hYdKKr6TsSAGgzpMy8aLs6vOz1XI8hVWh63zv2Df2P4aphw2CTHAoh7F92wqfKvDfb6B+efw1stZo+a/uCa5LadzuJ/bHN7otjnMPM2azkJDwKj//u5o0D3EYrXywqXIZ9n6AZjLs9chxbc83TK2mArfbsfd3EaKb8eQV4oOjj2q+HYsQxsBKOxkl5wtvUwfFb5jkSAWbT6ut26Pb2mrg6IopUcXi4bOEoWvaG6bOHEf+8aZ/k9rcyNU6HTeW+G7tJrDyQS7I3AmFb7V4ckOOL0X//XqzQCAnz53GofnWLEpbA5nrLE5PMK+brgCaMLk0ag70qwexZcnOgDAbI69TqdObesEyONLtAbK8e1I1ha+vRdn1tHoQedio5zdQVgd+qDwHYkYWAAvOOpOdSit+ALAmaUa3OKlrQ6WZWM+mcUU5hCeewaAAux4XW3rVCNC8fWa6rBvhhXw64fCpPB5YZwpvji9D1tHB/CKM8dh28C/PswGWtwl0xymCzNwZWObv/5eQTToKnzzdRS+4qLPiGKRHy6EqkmspVs8vlQpdTsFOb5kdegU3IVvJjjaF8VVx1GhwQ2AvFvSzhzfVjEaNZzmtvQSYNZwYnKnOpTw+AIs0gwoVnx54ZuaB7LObealVA6mZeM12h72jQ1XAJEx7+tTB2JCXsaj4rvvJFP6yN/rkdEz2LjpTAJIHMfvcdX3G48ewanlNB4QaQ4XrCv8OdnYdkVTVmswpCMlp7c1UPiG4nIoQ3yALoTK4Si+VPgSzaSguY0K304hrzp+xWxkXYUliaZRrfDto+a20UjQaW4DnIY1L1SxOgDAdpHs4FZ8Q3HA4O+ZOC6/LWwO1waeYN9oYpqDIFij4isa286skuhAcPQgK34B4PRevPrsCWwYDmMplcOt3/gVsqaF7RNRnDnp+gwmTgBLh9ko7PWXNWW1Cqa31ePxdRe+KVb40h2A8ojGv06f3EaFb7cjPL7QoDfBI0XUh6k4ha81SP7etjDMVKfyhW//eHxHowZMaFi2+bS9Wny+rpP/7ErpwncHLxAPziaRM7mqqiiO6uuKNJtbySCEDK7G0+wbTZrW5kYqvl6tDtTYVjsun6+mKtLr+/MDzGN/vTvNAQCOcn/vxHlAsDl/Z9bc5oPHNxiT08jI41seUnyJ1iBSHWyKM+skTNU5OKqU6NAehOK7eKj0831U+I7wKVoL9WT5cnXYDMbkqOHiwnc6HkLE0JC3bLw4m3SeKOHznUtm8RL1GQSRBeIbgYnmR+KJARZerA62bUuPL1kdakD6fPcCAP7L5RthuMY3FwytAJzGNp9jzNxEgwEWZwYAuXTtL0CKb004I4tJ8SWaCVkdOhLTZXUwRnpvXHFXUNXq0D+FbyigIRrUa29ws2158l+0IrBsJuSODBRGj7FkB97g5h5dHFsbaTa3ksFrVW5zaOK0Nje1WB1OLKWxnM5DVxWcMd5n44cbQSi+p1jhOxIx8KYLWVPvjonoWtuILHyb09gGCKtDA3Fm/LNvh2KymKMc3/KIwnclk18zwKSToMK325HNbXr/TQzqYCzVKaaMkU1tXJM+RhS+S0flBWIBpih8e7+5DShqcPNqdciuADZTSU/l2Gd6NGKUPNbsKOXzlUMsHKvD7HIGr9FchW8LEFaHtIccX2Fz2DYeKVAsiSpMOMkOIjv7lmt24CVnjOLDbzi7cNlcGjixhz1uUmMbUOzxrae5jVkdzEAMOZP9TqT4lkf8bWwbSGY71+5Ae3W341J8aYBF52BpjiKmkNWhPUTGgcAAABtYOrL2eaH4as0bnNBJFExv82p1EFFmmoFTKXZ8GYuWVsjPLJXsUGKIhTH7NNYp88iqYWDLyzyvfyMIxTfjYXKbM7Et1tR16jlGt/NkhyVgmcWXbRwZwNf+4Gq87tzJwmVP/Ir1p0TGgeGtTVslObkNaCjOLK2zz7amKhgwNL9Wr+cI6qq889zJPl8qfLsd1wALUic6B9tdTNHwivagKC67w8G1z8sc3z5RfCNBx+Pr1epQEGXGjjXF/l7BjlJZviXGFm+c/RkA4OTY1UCgNX/72hRfHmU2STaHmtCDwMg29pj7fMsiGts2XNlUq0s05J7c1kDhq7LhL7GQXtigRxSgKIprbDEVvkSTsN1WB1J8OwaLR2RZUGh4RTsZqpDsID2+/aH4jtaj+HqIMhOISLMXZleQF8kO8bXT285dZmOKFza8poa1bww5wCJvwi43wpqzjxfupPjWQZHPtyxycEXzGtsAljLQUJwZT3VYUXjhS4kOVemGBjcqfLsdt9WBPL6dA1d8E9oIoNHBsm1IxbdEskOePL5VEXm/Hgrf9UNhhAMacqaNQ/O8yBCKbybB/JLLM9ieew4AkNva3GltbsTIYtuG9GqWImdaeJ4351GUWR1MFCY7lMS2W5LoABRNbmtA8V1RBgCQv9cL3TC2mCqlboefvDPQYVDh2zlwxTcRGG/zivQ5lZId+q65rQGrQ7j81DaBqirOBDdhdwhG2SALgKm++38CANhjbUN8onVpJ8LjCzDVtxwvziaRNS1EDA3rh8KtWLXeYpw3sVUqfBcPAyszgKoD05c0dXUKJ7fVE2fGFN+EzRTfOCm+VemGscVUKXU7plvxJatDp7BpchgAMDC+uc1r0udUKnz7rLmNWR24irnqVfF1Wx1Y4VBO8QXcE9zco4t5gbt0DNa+/wAA3GteitEyBXQzCLr6HzIVfL5yYtvUIFSyjtWOu/AtZyk5yscUT10IBJp7ceEeYGE3EGe2aLH1pCiz6jhWByp8iWbham4jj2/nMLGDRfSMnffqNq9Jn+MufItPxP3W3BY1ald8RapDKI7ZKs1tgKvBzZ3lK5Id5l8AXrgPAHCffQmGWqieKYoii99KWb4iyuwsGlVcH6Pb2Qji9BKwfLL0MtLf27z8XoE7zqyRwnfe5IUvWR2qEiOrA9FULAuKxT5cphqgbtNO4pw3Af/ni8DV72/3mvQ3QzxDOZNY62sV2b590txWEGfm2ePrSnXgHt+JCoWvE2lWItnhya9Dza3ipD2ME+EzW66oOtPbKhS+NLGtMQKh6skOLWpsA4CIoSPN48zsbI0eX8uSzW1zeaH4UuFbDeHxTaRI8SWagemE8tta70+f6jrCw+1eA8IYAKJT7HHx6OI+U3zHokGnuS236s3zyAvfvBHDEh/ZWi7HF3AU3+dPr8AUk5uE4ntsNwDgXvMSjEZb/zeXyQ4VrA5S8aXCt34q+XyzSeDk0+xxCwpfVVWgGKwxza61uS27DIB9hk/z4S2xEFkdquF4fEnxJZqBaM4BYFFyAEGUZrhMpJm4cOyDkcUAMDxgIIEB5G1+2Pdid+CF7zKPcwpoSsUGn/XDYYQCKrJ5C4dlskNhE9s91iUYjbZeZRfJDuUU32QmL9f5bIoyq59Khe+xxwHbZHcB4q1pblR54avUWvjyxjZoBuYz7O4EKb7VoeY2orm4x7Cq/XG7liBqplyDm1B8++RuiaGriIUCtWX58jizJYsVD+PRYEVLlaYqOGO8qMHNNbkwrwbxC+v8lja2Caopvs/x9R0fDGIkQsfTuhGFb6ksXzm4onljiovRg7zwrXVym6uxM5FmF0vk8a0OeXyJ5iIb2zRoGo1RJIiSlC18+0vxBZhNYYlHM3ny+fKT/5zJC98K/l7BmZNFDW6uyYWHYpcjjSBG21BYVlN8hc2B8nsbZKJCsoPM721+Y5tAD7HPu2rnAbOGYkwUvsGYHMZAqQ7VIcWXaC6uqW0ByvAliNJUU3z7qPAdiRhYEJFmNVgdTueYJ9dL4bsm0sxV+P5q4GoAwFgbrA7VFF8ZZUaJDo0xuoMnOyyyvF5BCwdXuAnwwhdAbUMseGMbQnEkuL+dFN/qCDsIFb5Ec3BNbQtQhi9BlKZU4WtZAE9E6ZfmNqAo0syL1YHHmc1kWcHrpfDdMVGU7BAIAesuAoIx/EJjt7hHIu2wOjDFt1yc2XOU6OAPgRAwvJU9dvt8555nF1takGX4tohQKAzL5ufHWgpfaXVwGjvJ41sdGllMNBeX4kvjigmiDKLwXToKmFyFcDWG9ssAC4AVnJ7HFpt53tkOHMuwv1G5qW1uhGJ64JQr2eH3vgfsegTPp1nTWHua29gxMpMvrfiS1cFHSvl8RYzZ9CUtjRAcDBmuscU1ZPny5jY7FJfDGGhyW3WckcWk+BLNgCu+OdDwCoIoS3SKqUxWno3NBRybA9BXiu9Y1J3lW0XxFbd6ARxZZSczL4rvxpEBGLqKTN7CsQWusIWHgNg6zCezcj1aTbCC4nt6OYO5ZBaK4kSyEQ0wUSLZ4WjrbQ4AEA0509sK9vtqiCi/QExewJHVoTpC8V3J5J0L3w6DCt9uhje3ZewADJ02JUGURFWdQRbC7iATURSgj6IARyJuq0MVxZcnOiAQwakkU0m9FL7uZIfn3KOLAcytMKV9tB1WB10UvmsVX6H2bhmNIGxQo3DDlIo0a0NjGyDGFvPPW02K7yIAIKM7UX7CJ06UZ9CVdbyS6UzVl7ZiN+O2OpDiSxDlKfb5uhvb+mji4Wg0iEWvzW3C4xh2prZ5KXyBEj5fAKmsiWTW5OvRDsVXWB3WKr57TzJ1m0YV+4S0OjzLmtrSS+wx0HLFdzCkI2Pzi9s6mttSKvtMxEI0HdULQV2TtqJOjTSjwrebcTW3kceXICqwpvDlHt8+SnQAgLGIgUWvcWa88LVDcVn4Vpra5sYZXewovnNJ9hqGriIabH0sVCXFVyjTZ5K/1x/G3MkOp4CjjwGw2X4YnWjpqgyG3Ipv7VaHVZVF+VFjm3c6fWwxVUvdTEGcGV2JEkRZigtf0dzWJ8MrBCNRV5xZtVQHnuhgGTGkuC/Wa+G7fcJpcBPMrbAL9dGI0RblLFRB8aXGNp8JhJ197vRe4Oij7PGG1qq9ABANBhyPbx3NbStgF4o0rtg7sQ4fW0yFbzfDw7hzNuX4EkRFxEl48RD7KhXf/mlsA5i3VqQ62B6tDmmdFYMRQ0PEo1K7Qyi+MyuweIOLUHzbYXMAnAEWxYqvZdl4boYV6BRl5iNun69IdGixzQFgzW1pWxS+tceZJUCKb610+hALqpa6mbxQfAPQVdqUBFGW4c3sa59bHYYHAk5zW2ph7WQtN/zEn1LZ8l79vQCweWQAhqYilTNxbJEVG7NS8W3P31wqvkWpDofnV5HKmQjqKraMRkr9KFEP0uf7a251QHsK36COdF3Nbezzv8SnFlKig3dkpFmGFF/Cb8jqQBDeGOKF7+ocu4XZh1PbAEDXVCjhYQCAYuWBzHL5hfmJf1lhxWAtha+uqdg2zn5O2B1ElFm7FF8xwKI4x1dMbNsxGYVGTcL+IQrfvT9gjWKBCDBxXstXI1ZvnBlvbpu3wux1aFyxZ0jxJZoHNbcRhDdCMWBglD1ePNS3ii8ARAZjSIsu90p2Bx7ntGQzxauWwhdwjS7mDW4iysyrT9hvRKd5cY6vbGyjRAd/EVm+ydPs6/pLAa31xWM0pCNls8+cna1d8Z3LsZ8lxdc7sQ4fYkHVUjfDFd8MKb4EUR13g1ufNrcBPMvXS4MbP/HP81u9Xqa2uRGDIIR/1t3c1g7kyOKi5jZqbGsSozsAuM5LLc7vFTCrAyvEzEzS2w/l0jIn/3Se9QGQx9c7cmxxiqwOhN/wHTNn6wiQx5cgKiML3/5WfMeihrexxbzwneUn/loVXyfSjBW+s9Lq0F7FN5MrtjrwDN+pWMvXqacxBpx9Dmhb4RsxHI9vNu1R8RUZ1lBwmo/rpsLXOzLOjBRfwncKrA6k+BJERdyKb5+mOgBM8fVU+PI4s5ksKxpqLXxFssOBmWXYtu2a2tY5im86Z+LFOVYMkeLbBITPFwA2XN6WVVBVBabG9vN8psbCNxjDUoZ9XijOzDuDFGdGNI2C5jbalARRkSFXsoMsfNtThLWT0UgQCxBji6tbHY5n6lN8N49GENAUJLMmji+lHatDu+LMAsLj6yi+B06twLRsDA0EMFHj70d4QPh8x84EBkbathq2Lgpfj1YH3tiGUBwJXryR4usdam4jmodL8SWPL0FUoUDxFakO/af4jhZYHaoXvkdXWaE6Hq3tbxXQVGwdY8kOz80su3J822V1EKkOjuIrGtvOmhykcbTN4Mw3AFCA83+7rathB5hP3fKs+C6yr6G4nD5GzW3eERcJnar4knbfzbgUX0p1IIgquIdYiMJX60/F9xC8eHwXAQCHV9lJbGyw9r/VjolBPDezgicOLyJn2vz922V1WKv4isY2GlzRJDZdDfz5MUAPt3c9AmEgDVheB1ikheIbQ+I0K97iFGfmmZ5TfB944AG86U1vwvT0NBRFwXe/+92C523bxm233YZ169YhHA7jmmuuwf79+wuWmZ+fxzvf+U7EYjEMDQ3hPe95D1ZWVgqWefLJJ/Hyl78coVAIGzduxKc+9ak16/Ktb30LZ599NkKhEC644AL88Ic/rHlduhre3Ja1dQQof5IgKhNbD6g6228W+AS3PlV85RCLclaHXFpeHMxbTC2rZ/CEiDR76IU5AKzDXnhtW43M8XXFme2lwrf5GBGgzc3XqsE+w54GWGRXgce+BACwI+MymYAUX+/EZHNbZyq+NX8ak8kkLrroItxxxx0ln//Upz6FL3zhC7jzzjvx8MMPIxKJ4Nprr0U67QRHv/Od78QzzzyDu+++G3fddRceeOAB/OEf/qF8PpFI4PWvfz02b96M3bt349Of/jQ+/vGP43/9r/8ll/nlL3+J3/md38F73vMePPHEE7jhhhtwww034Omnn65pXboayvElCO9oOhDfyB7P7mNf+zDVYTRiYBFVrA7c42hDwQpCGB4IwNBrP8aIBrc9hxfZe7fJ3wu4cnzzaxVfamzrbTRZ+FZRfHNp4OvvAF78GWAMInXlB8AnbpPHtwY6XfGtWbu/7rrrcN1115V8zrZtfO5zn8NHP/pRvPnNbwYA/Mu//AsmJyfx3e9+F29/+9vx7LPP4kc/+hEeffRRXH456/L827/9W7zxjW/EX//1X2N6ehpf/epXkc1m8aUvfQmGYeC8887Dnj178NnPflYWyJ///Ofxhje8AX/6p38KAPjLv/xL3H333fi7v/s73HnnnZ7WpZhMJoNMJiP/n0gkav3ztBZqbiOI2hjeAiwcBE73ceEbDWLBZoWetTpfWv3giQ6mMQg7rdbc2CYQQyGyJis222VzABzFN5u3YFk2ltN5nEwwEYSGV/Q2ovBVKk1uy2eAb7wTeOE+NmXuv34bi7HzAMzA0FR54URUR8SZrWZN5E2r44Q5X9fm4MGDOHnyJK655hr5vXg8jquuugoPPvggAODBBx/E0NCQLHoB4JprroGqqnj44YflMq94xStgGM5B8tprr8W+ffuwsLAgl3G/j1hGvI+XdSnm9ttvRzwel/82btzYyJ+j+VBzG0HUhvD5iq7tPrQ6DIUDSIA1nVnJMoovb2zL6Czbtt7Cd8topGAM8Egddgm/cFsssqYl83vXD4XliZroTfQQ+7xrZhnFN58FvnkTcOA/mR/5nd8ENl3tSnTQqfmxBgZd0W8rmc5TfX0tfE+ePAkAmJycLPj+5OSkfO7kyZOYmJgoeF7XdYyMjBQsU+o13O9Rbhn389XWpZiPfOQjWFpakv+OHDni4bduI1zxzdk6dPL4EkR1hjcX/r8Pm9tUVYEVZtFSSrnmNl74plRmVah1apvA0FVsGR2Q/x/rAKsDwPJ7RaID2Rx6HyPMPoOaWULxNXPAt98FPPcf7EL4HV8HtrwMALC0SlFm9RDQVIT5hWYn2h2oTdFFMBhEMNhFtz5NtlNSqgNBeMQ9SQroS8UXANSBEWAZ0LJLgJln/mc3PNFhRWFKWb2KL8BsBM+fZvmp7fT4BjQVmqrAtGykc5ZsbDuTCt+ex+CKr15c+Jp54P99L7D3LnYR/PavAtteJZ8Wk8eosa12BkM6UjkTS6kcOu3eua/V0tTUFABgZmam4PszMzPyuampKZw6darg+Xw+j/n5+YJlSr2G+z3KLeN+vtq6dD154fENwKDClyCqs6bw7T/FFwAC0THnP3I8qwte+C7ZjRe+O3iyA1BfMoSfyLHFeZMa2/qIYJh9BgO208MDywS++z7g198F1ADwtq8C2wvtkzLRgRTfmtkxGe3YfcvXamnr1q2YmprCPffcI7+XSCTw8MMPY+fOnQCAnTt3YnFxEbt375bL3HvvvbAsC1dddZVc5oEHHkAu50Rh3H333TjrrLMwPDwsl3G/j1hGvI+Xdel6uNUhA51GFhOEF0jxBQAMD4aRsHm2aqlkB14ML1hsmUYK3+2uxrF2Kr6A4/NN5Uzsm6Eos34hNMC2sWFnActiRe+/7wKe+haLOPwv/wKc+fo1Pyc9vjSuuGa++t6r8aNbXoHz18fbvSprqLnwXVlZwZ49e7Bnzx4ArIlsz549OHz4MBRFwS233IK/+qu/wve+9z089dRT+L3f+z1MT0/jhhtuAACcc845eMMb3oA/+IM/wCOPPIJf/OIXuPnmm/H2t78d09PTAIB3vOMdMAwD73nPe/DMM8/gG9/4Bj7/+c/j1ltvlevxwQ9+ED/60Y/wmc98Bnv37sXHP/5xPPbYY7j55psBwNO6dD0UZ0YQtREeBkKuA3EfpjoAPNKsUpYvL3zn8rzwrXFqmxu34jvWpqltghBXfF+cTWI5nYeuKtg2Fq3yU0S3Ex6IOP/JJYHv/x/Ar/4NUDTgt78EnP3Gkj8np7aR4ttT1HwZ89hjj+HVr361/L8oRm+66SZ85StfwYc//GEkk0n84R/+IRYXF/Gyl70MP/rRjxAKOQfOr371q7j55pvx2te+Fqqq4sYbb8QXvvAF+Xw8HsdPfvIT7Nq1C5dddhnGxsZw2223FWT9vuQlL8HXvvY1fPSjH8Wf//mfY8eOHfjud7+L888/Xy7jZV26Gj7AIkcDLAjCO8NbgBO/Yo+1Pi18o0EsYBCbcLq04svjzGZy7O9Tz9Q2wbbxCFQFsGxgpI1xZgAQ5Irvr46ywv6M8Whd+cREdzEQcV3cfO8DwDPfARQVuPEfgXPfXPbnHMWXCt9eoubC91WvehVs2y77vKIo+MQnPoFPfOITZZcZGRnB1772tYrvc+GFF+JnP/tZxWXe+ta34q1vfWtD69LViMltlONLEN4Z2uwUvn1qdRiJGNK/W3JsMVd8T2bZ36feVAcACOoa3vXSrXhuZrlA/W0HwuP75NFFANTY1i8MhoPI2AEElRwreqEAb/kH4PwbK/6c4/Elq0MvQVuzm3E1t5HHlyA84vb59mlz21jUwAJ40VfB6pCwWQ7v8EBjf6e/+I1zG/p5vxAe3yePsN+vU5tvCH+JhnSkEUAQOdhQoNzw98CF/6Xqz5Hi25uQTNjN0OQ2gqidgsK3PxVfNr2twthinuqQwADGogbUHrFSCcV3mYfqn0UT2/qCwVAAh2yW6Z+//nPAxe/w9HPk8e1NSPHtZnhzWwYBKnwJwisFhW9/enxHIgYWUb25bcmONJTo0Gm4p7cBlOjQLwwENLw792EMYxlfPfvtmKj+IwAo1aFXoWqpm3E1t5HVgSA84i58+7S5bSwSlKkOZiWrAyIN+Xs7jVDAOeVFgzo2DIfbuDZEq1BVBRljFPvtDTVNEhOFb5wU356CCt9uJbMM2CYAIA0DAZU2JUF4Ir6RdXQDQKA/rQ6xsI6EwtTO/Mpc4ZO2LVMdEvZATym+Qd1RfM+cjEJRSDDoFzbx0dk/eWamypIONLK4N6FqqVt54X4AwBFMYQkRUnwJwiu6Abz8vwEXvJUlPPQhiqIgH2TDgKxkkeKbTcqL6iX0mtXBOeWRzaG/ePdLtwIA/ulnLyCVNasub1m29IJTc1tvQYVvt3LgbgDAz3ExAIU8vgRRC6/5KHDjPwH9rPiFWeGrpIsKX25zyENHGkaPWR0cxZca2/qL37x4GhtHwphLZvFvjxyuuvxKNg+R3DpIHt+egqqlbsS2gf3/CQC437oYABAgxZcgiBrQo6Psa2ap8Ame6JBUIwAUjA/2jh0kqLsV31gb14RoNQFNxftfuR0A8A8PPI90rrLqKzJ8g7q6pimS6G6o8O1GTj0LJI4CWhC/NM8BABpZTBBETRiDvPA1U0Au7TzhSnQAWOZvr+AuYCjDt/+48bL1WBcPYSaRwbd3H624LEWZ9S5ULXUj3OaArS/HssV2ShpZTBBELYQHR5C3+SnAneXLC99FizUD9ZLHVyi+E4NBDLd5fDLReoK6hj96xTYAwBd/+jxyplV2WYoy612o8O1G9rPC1zrjGulBIo8vQRC1MDYYcrJ83WOLZeHLor56qfAVii81tvUvb79yE8aiQRxbTOE7Txwru5wzrpgU316DqqVuI50ADj8EAMhte638NqU6EARRCyMRQ9oZCoZYiCgzRBAKqIgGe0fxeu05k7hiyzBu2rml3atCtIlQQMMfvoIlPPz9fQdgWnbJ5RJpSnToVajw7TYO3g9YOWBkG7LxrfLbpPgSBFELoxEDC+DKZwmrg5ja1ktZt1vHIvjW+16Ca86dbPeqEG3knVdtxvBAAC/OreKuJ4+XXIYU396FqqVug9scsP11yJvOlapOHl+CIGpgNBrEgl1ibLGc2jbQU1FmBCGIBHW852VMOPq7ew/AKqH6OlPbeueOB8GgwrebsG3gAIsxw47XIWcxY76iABoVvgRB1MBoxMBSSY/vIgAgYffW8AqCcPN7L9mCwZCO/adW8ONnTq55XqY6kNWh56DCt5s49SyQOAboIWDLy6TiG1DVnrodSRBE8xmNGlLxzbnHFgurQ49NbSMIN7FQAO96yRYAwN/eewC2Xaj6LpHVoWehwreb2P8T9nXLy4FAWEaxUGMbQRC1Eg3qWFaYxze7POs8IawO9gDGo70zvIIginnXS7ciYmj49YkE7tt3quA5J86MCt9egwrfbsJlcwCAHFd8yd9LEEStKIqCXHAIAJB3K74y1WGAFF+ipxmOGPivOzcDAL5wT6Hq6zS3kce316DCt1tIJ4DDD7LH268BAOS5x9fQaTMSBFE7VmgYAGCvrs3xXSKPL9EHvPdl2xDUVew5sohfHHAuACnOrHehiqlbOHg/YOWBkTOA0TMAQHp8dZU2I0EQdTAwAgBQ06VSHSI9Na6YIEoxPhjE71y5CQDwhXv3y+9TnFnvQhVTtyD8vdzmAABZ8vgSBNEAgcgoAEDPLLJvWCaQcXl8SfEl+oA/euU2GJqKRw7O4+EXmOpLI4t7Fyp8uwHbBvZzf+92p/CVqQ40vIIgiDoIDI4BAIL5JXacySTkc0zxpcKX6H3WxcP47cs3AAD+7j6W67uS4VYHUnx7DqqYuoFTvwaWj/MYs5fKb+e54hsgxZcgiDoYiLPCV7NNILMsbQ6rdhDhUAihgNbO1SOIlvH+V54BTVXws/2z+NmBWYg+N/L49h5U+HYDYlrb1lcAgbD8ds4ijy9BEPUTj8WRtvmJPTVfOLWNbA5EH7FxZABvuWQ9AOD2Hz4LAAgHNGoe70Foi3YDrjHFbnJ5UnwJgqif0aiBBbAsX6zOO1Fm5O8l+pA/ftUZUBVg78llABRl1qtQ4dvppBPAkYfY4x3XFDwl4sx08vgSBFEHIxEDi3aE/Se1UDS1jYZXEP3FtvEofuPCafl/sjn0JlQxdTov/JTFmI1uB0a2FTyVk81tpPgSBFE7Y9EgFm2m+NqrLquDHcE4NbYRfciuV2+Xj6mxrTehwrfTOVDa5gA4ii+lOhAEUQ/M6hAFwMcWpxcBkMeX6F/OmhrEG86bAkBRZr0KbdVOxh1jVmRzAIBcnkYWEwRRPwOGjmWFKb6ppVkEQ+xYQlPbiH7mz647GwurWbydD7YgegsqfDuZmWd4jFkY2PyyNU/nyONLEESDZANxwASyK7MA2KS2BAawhaa2EX3KlrEIvvFHO9u9GkSToMK3kzngjjFb22giBlgYVPgSBFEnueAQsAqYK/OAxo4zCVJ8CYLoUahi6mSkzWGtvxcAcjSymCCIBrHDw+xBah52QaoDFb4EQfQeVPh2Kukl4PCD7PH2tf5ewEl1oAEWBEHUizIwCgBQ0wvIJRcAACsYwGiECl+CIHoPsjp0Ki/8FLBNHmO2teQiNLKYIIhGCURH2NfsIqxUFgBgB4egUdMsQRA9CBW+nYqY1rbj9WUXESOLKc6MIIh6CcbGAACh3BKUdB4AoEeG27lKBEEQTYMK307EtoED3N9bxuYAkMeXIIjGCccnAAAD1grMDFN8jUEqfAmC6E1IKuxEZp4Glk8AgQFg80vLLuZYHWgzEgRRH4PD4/KxZrHCd2BwtF2rQxAE0VSoYupE9leOMRPQyGKCIBplZHAACTss/2/ZCqLxkTauEUEQRPOgwrcT8WBzAJyRxZTqQBBEvYxGg1i0o/L/KwhjPBau8BMEQRDdC1VMnUZ6CTj8EHtcJr9XIEYWk+JLEES9jEYMLGBQ/j+BAYzR1DaCIHoUKnw7DRljtgMY3lJxURpZTBBEo4QCGlYUR/GlqW0EQfQyVDF1Gvt/wr5WiDET5E2KMyMIonFW9bh8vGRHMEGFL0EQPQpVTJ2EbQMH7mGPd1T29wKOx5esDgRBNEI2MCQfJzCA8Wj5plqCIIhuhgrfTsJjjJkgm6eRxQRBNI4ZGpKPV5QIYmGKeCcIojehiqmTkDFmrwT06rcaZaoDKb4EQTSAHXYGVuQCMSgKHVMIguhNqPDtJPQQEN/oyeYAOB5fgzy+BEE0gBpxBlZYwXiFJQmCILobup/VSez8Y+Dq9wOW6WlxGllMEIQfBKJO4auEqPAlCKJ3Iamw01AUQPN2PSILX/L4EgTRAMHYmHysRYYrLEkQBNHdUMXUxeQtGmBBEETjROLj8rERpcKXIIjehQrfLiZHOb4EQfhAdHhCPg4PjrRxTQiCIJoLVUxdTJ48vgRB+MDIyDjyNjsdROKjVZYmCILoXqjw7WKEx5cUX4IgGmEkGsTPrAtwxBrHwNSOdq8OQRBE06BUhy5GWB10lRRfgiDqx9BVfHTgYzi9vIqfT5DVgSCI3oUK3y7GGVlMii9BEI3xlXdfiflkFhODNK6YIIjehQrfLiZPzW0EQfjEjsnBdq8CQRBE06GKqYOYXcng0FwStm17Wj5LzW0EQRAEQRCeIcW3g/jO48fwf/3wWcTDAVy4IY4L1sfZ1w1DmI6HoCiFBa5UfGmABUEQBEEQRFWo8O0gllI5GJqKpVQOP9s/i5/tn5XPjUYMXLAhjgvXx3HhhiFcuCHueHx1UnwJgiAIgiCqodhe76v3IYlEAvF4HEtLS4jFYi15z2zewnMzy3jy6BKeOraIJ48uYd/JZTmlrRSP/vdrMD4YbMn6EQRBEARBdBK11Guk+HYYhq7i/PVxnL8+DmATACCdM7H35DKeOsoK4SePLmH/qWVYNjA0EMBgiDYjQRAEQRBENahi6gJCAQ0XbxzCxRuH5PdWs3k8eyKByVgIoYDWvpUjCIIgCILoEqjw7VIGDB2XbaageYIgCIIgCK9QHABBEARBEATRF1DhSxAEQRAEQfQFVPgSBEEQBEEQfQEVvgRBEARBEERfQIUvQRAEQRAE0RdQ4UsQBEEQBEH0BVT4EgRBEARBEH0BFb4EQRAEQRBEX0CFL0EQBEEQBNEXUOFLEARBEARB9AVU+BIEQRAEQRB9ARW+BEEQBEEQRF9AhS9BEARBEATRF1DhSxAEQRAEQfQFVPgSBEEQBEEQfQEVvgRBEARBEERfQIUvQRAEQRAE0RdQ4UsQBEEQBEH0BVT4EgRBEARBEH0BFb4EQRAEQRBEX0CFL0EQBEEQBNEXUOFLEARBEARB9AVU+BIEQRAEQRB9ARW+BEEQBEEQRF+gt3sFOhnbtgEAiUSizWtCEARBEARBlELUaaJuqwQVvhVYXl4GAGzcuLHNa0IQBEEQBEFUYnl5GfF4vOIyiu2lPO5TLMvC8ePHMTg4CEVRWvKeiUQCGzduxJEjRxCLxVrynkRzoG3ZO9C27B1oW/YOtC17h0a3pW3bWF5exvT0NFS1souXFN8KqKqKDRs2tOW9Y7EY7cg9Am3L3oG2Ze9A27J3oG3ZOzSyLaspvQJqbiMIgiAIgiD6Aip8CYIgCIIgiL6ACt8OIxgM4mMf+xiCwWC7V4VoENqWvQNty96BtmXvQNuyd2jltqTmNoIgCIIgCKIvIMWXIAiCIAiC6Auo8CUIgiAIgiD6Aip8CYIgCIIgiL6ACl+CIAiCIAiiL6DClyAIgiAIgugLqPDtIO644w5s2bIFoVAIV111FR555JF2rxJRhQceeABvetObMD09DUVR8N3vfrfgedu2cdttt2HdunUIh8O45pprsH///vasLFGR22+/HVdccQUGBwcxMTGBG264Afv27StYJp1OY9euXRgdHUU0GsWNN96ImZmZNq0xUY4vfvGLuPDCC+UUqJ07d+I//uM/5PO0HbuXT37yk1AUBbfccov8Hm3P7uDjH/84FEUp+Hf22WfL51u1Hanw7RC+8Y1v4NZbb8XHPvYxPP7447joootw7bXX4tSpU+1eNaICyWQSF110Ee64446Sz3/qU5/CF77wBdx55514+OGHEYlEcO211yKdTrd4TYlq3H///di1axceeugh3H333cjlcnj961+PZDIpl/nQhz6E73//+/jWt76F+++/H8ePH8dv/dZvtXGtiVJs2LABn/zkJ7F792489thjeM1rXoM3v/nNeOaZZwDQduxWHn30UfzDP/wDLrzwwoLv0/bsHs477zycOHFC/vv5z38un2vZdrSJjuDKK6+0d+3aJf9vmqY9PT1t33777W1cK6IWANjf+c535P8ty7KnpqbsT3/60/J7i4uLdjAYtP/t3/6tDWtI1MKpU6dsAPb9999v2zbbdoFAwP7Wt74ll3n22WdtAPaDDz7YrtUkPDI8PGz/0z/9E23HLmV5ednesWOHfffdd9uvfOUr7Q9+8IO2bdN+2U187GMfsy+66KKSz7VyO5Li2wFks1ns3r0b11xzjfyeqqq45ppr8OCDD7ZxzYhGOHjwIE6ePFmwXePxOK666irarl3A0tISAGBkZAQAsHv3buRyuYLtefbZZ2PTpk20PTsY0zTx9a9/HclkEjt37qTt2KXs2rUL119/fcF2A2i/7Db279+P6elpbNu2De985ztx+PBhAK3djrqvr0bUxezsLEzTxOTkZMH3JycnsXfv3jatFdEoJ0+eBICS21U8R3QmlmXhlltuwUtf+lKcf/75ANj2NAwDQ0NDBcvS9uxMnnrqKezcuRPpdBrRaBTf+c53cO6552LPnj20HbuMr3/963j88cfx6KOPrnmO9svu4aqrrsJXvvIVnHXWWThx4gT+x//4H3j5y1+Op59+uqXbkQpfgiCIInbt2oWnn366wH9GdBdnnXUW9uzZg6WlJXz729/GTTfdhPvvv7/dq0XUyJEjR/DBD34Qd999N0KhULtXh2iA6667Tj6+8MILcdVVV2Hz5s345je/iXA43LL1IKtDBzA2NgZN09Z0L87MzGBqaqpNa0U0ith2tF27i5tvvhl33XUX7rvvPmzYsEF+f2pqCtlsFouLiwXL0/bsTAzDwPbt23HZZZfh9ttvx0UXXYTPf/7ztB27jN27d+PUqVO49NJLoes6dF3H/fffjy984QvQdR2Tk5O0PbuUoaEhnHnmmThw4EBL90sqfDsAwzBw2WWX4Z577pHfsywL99xzD3bu3NnGNSMaYevWrZiamirYrolEAg8//DBt1w7Etm3cfPPN+M53voN7770XW7duLXj+sssuQyAQKNie+/btw+HDh2l7dgGWZSGTydB27DJe+9rX4qmnnsKePXvkv8svvxzvfOc75WPant3JysoKnn/+eaxbt66l+yVZHTqEW2+9FTfddBMuv/xyXHnllfjc5z6HZDKJd73rXe1eNaICKysrOHDggPz/wYMHsWfPHoyMjGDTpk245ZZb8Fd/9VfYsWMHtm7dir/4i7/A9PQ0brjhhvatNFGSXbt24Wtf+xr+/d//HYODg9JXFo/HEQ6HEY/H8Z73vAe33norRkZGEIvF8IEPfAA7d+7E1Vdf3ea1J9x85CMfwXXXXYdNmzZheXkZX/va1/DTn/4UP/7xj2k7dhmDg4PSZy+IRCIYHR2V36ft2R38yZ/8Cd70pjdh8+bNOH78OD72sY9B0zT8zu/8Tmv3S18zIoiG+Nu//Vt706ZNtmEY9pVXXmk/9NBD7V4logr33XefDWDNv5tuusm2bRZp9hd/8Rf25OSkHQwG7de+9rX2vn372rvSRElKbUcA9pe//GW5TCqVsv/4j//YHh4etgcGBuy3vOUt9okTJ9q30kRJ3v3ud9ubN2+2DcOwx8fH7de+9rX2T37yE/k8bcfuxh1nZtu0PbuFt73tbfa6detswzDs9evX229729vsAwcOyOdbtR0V27Ztf0tpgiAIgiAIgug8yONLEARBEARB9AVU+BIEQRAEQRB9ARW+BEEQBEEQRF9AhS9BEARBEATRF1DhSxAEQRAEQfQFVPgSBEEQBEEQfQEVvgRBEARBEERfQIUvQRAEQRAE0RdQ4UsQBEEQBEH0BVT4EgRBEARBEH0BFb4EQRAEQRBEX/D/A5ilBFiE7DhYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig= plt.figure(figsize=(8,8))\n",
    "xgb_2_pred_test_df = xgb_2_pred_test_df.reset_index()\n",
    "xgb_2_pred_test_df = xgb_2_pred_test_df.drop(['index'],axis=1)\n",
    "plt.plot(xgb_2_pred_test_df[:50])\n",
    "plt.legend(['Actual value','Predicted value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "2daa6e3b-a504-4b10-9d05-0bb752369953",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 230.63\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "mse = np.sqrt(mean_squared_error(y_test, y_xgb_2_pred_test))\n",
    "print(\"RMSE: %.2f\" % (mse**(1/2.0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "9a0e6c8c-8253-4a94-990b-47e8f60c226f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 Test: 0.78\n"
     ]
    }
   ],
   "source": [
    "# Determine accuracy uisng 𝑅^2\n",
    "r2_xgb_model_2_test = round(xgb_model_2.score(X_test, y_test),2)\n",
    "\n",
    "print(\"R^2 Test: {}\".format(r2_xgb_model_2_test))"
   ]
  }
 ],
 "metadata": {
  "instance_type": "ml.t3.medium",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}