{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align='center'style='color:#940e04;font-size:70px'>Car Price <span style=\"color: black\"> - Prediction</span></h1>\n",
    "\n",
    "download dataset from here [Car price prediction dataset](https://www.kaggle.com/hellbuoy/car-price-prediction)\n",
    "\n",
    "<h1>Problem statement:</h1>\n",
    "\n",
    "A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts.\n",
    "<br>&emsp;&emsp; They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to understand the factors affecting the pricing of cars in the American market, since those may be very different from the Chinese market. The company wants to know:\n",
    "<br>&emsp;&emsp; Which variables are significant in predicting the price of a car How well those variables describe the price of a car Based on various market surveys, the consulting firm has gathered a large data set of different types of cars across the America market.\n",
    "\n",
    "# Business goal:\n",
    "We are required to model the price of cars with the available independent variables. It will be used by the management to understand how exactly the prices vary with the independent variables. They can accordingly manipulate the design of the cars, the business strategy etc. to meet certain price levels. Further, the model will be a good way for management to understand the pricing dynamics of a new market.\n",
    "\n",
    "# Attribute information:\n",
    "<br>**Car_ID:** Unique ID of each observation (*Integer*).\n",
    "<br>**Symboling:** Its assigned insurance risk rating, A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe (*Categorical*).\n",
    "<br>**Car_Company:** Name of Car company (*Categorical*). The following are the list of car companies available in the data set. 'alfa-romero', 'audi', 'bmw', 'chevrolet', 'dodge', 'honda', 'isuzu', 'jaguar', 'mazda', 'buick', 'mercury', 'mitsubishi', 'nissan', 'peugeot', 'plymouth', 'porsche', 'renault', 'saab', 'subaru', 'toyota', 'volkswagen', 'volvo'\n",
    "<br>**Fueltype:** Car fuel type i.e. gas or diesel (*Categorical*).\n",
    "<br>**Aspiration:** Aspiration used in a car i.e. Std or turbo (*Categorical*). Mode of air intake for the internal combustion engine i.e. natural (standard) or turbocharger.\n",
    "<br>**DoorNumber:** Number of doors in a car i.e. 2 or 4 (*Categorical*).\n",
    "<br>**CarBody:** Body of a car sedan, hatchback, wagon, hardtop, convertible (*Categorical*).\n",
    "<br>**DriverWheel:** Type of driver wheel 4wd, fwd, rwd (*Categorical*).\n",
    "<br>&emsp;&emsp; **4wd:** Four-wheel drive, also called 4×4 or 4wd, refers to a two-axled vehicle drivetrain capable of providing torque to all of its wheels simultaneously.\n",
    "<br>&emsp;&emsp; **fwd:** Front wheel drive is called as fwd. A fwd can only supply power to the front wheels.\n",
    "<br>&emsp;&emsp; **rwd:** Rear wheel drive is called as rwd. A rwd can only supply power to the rear wheels.\n",
    "<br>**EngineLocation:** Location of car engine Front or Rear (*Categorical*). Out of 205 only 3 cars engine location is rear. All the 3 cars belong to Porsche company.\n",
    "<br>**WheelBase:** WheelBase is the distance between the front and rear wheels. Continuous from 86.6 to 120.9 (*Float*).\n",
    "<br>**CarLength:** Length of car continuous from 141.1 to 208.1 (*Float*).\n",
    "<br>**CarWidth:** Width of car continuous from 60.3 to 72.3 (*Float*).\n",
    "<br>**CarHeight:** Height of car continuous from 47.8 to 59.8 (*Float*).\n",
    "<br>**CurbWeight:** The weight of car continuous from 1488 to 4066 (*Int*).\n",
    "<br>**EngineType:** Type of engine dohc, ohcv, ohc, l, rotor, ohcf, dohcv (*Categorical*).\n",
    "<br>**CylinderNumber:** Number of cylinders continuous from 2 to 8 (*Categorical*). A cylinder is the power unit of an engine; it's the chamber where the gasoline is burned and turned into power.\n",
    "<br>**EngineSize:** The Size of engine continuous from 61 to 326 (*Int*).\n",
    "<br>**FuelSystem:** Fuel system is to inject a precise amount of automized and pressurized fuel in to each cylinder at a proper time. Types of fuel system are mpfi, 2bbl, mfi, 1bbl, spfi, 4bbl, idi, spdi (*Categorical*).\n",
    "<br>**BoreRatio:** Bore-Stroke Ratio is the ratio between the dimensions of the engine cylinder bore diameter to its piston stroke-length. Bore ratio continuous from 2.54 to 3.94 (*Float*).\n",
    "<br>**Stroke:** stroke or volume inside the engine continuous from 2.07 to 4.17 (*Float*).\n",
    "<br>**CompressionRatio:** Compression ratio, in an internal-combustion engine, degree to which the fuel mixture is compressed before ignition. Compression ratio continuous from 7.0 to 23.0 (*Float*).\n",
    "<br>**HorsePower:** Horsepower is a unit of power used to measure the forcefulness of a vehicle's engine. Horse power continuous from 48 to 288 (*Int*).\n",
    "<br>**PeakRPM:** RPM stands for revolutions per minute, and it's used as a measure of how fast any machine is operating at a given time. how many times each piston goes up and down in its cylinder. PeakRPM continuous from 4150 to 6600 (*Int*).\n",
    "<br>**CityMPG:** CityMPG refers to driving with occasional stopping and braking. Mileage in city continuous from 13 to 49 (*Float*).\n",
    "<br>**HighwayMPG:** It is based on continuous acceleration. Mileage on highway continuous from 16 to 54 (*Float*).\n",
    "<br>**Price:** Price of car continuous from 5118 to 45400 (*Float*)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Importing libraries\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#### Ignore all warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "#### allow plots to appear directly in the notebook\n",
    "%matplotlib inline\n",
    "#### set the maximum number of dataFrame columns display to unlimited\n",
    "pd.set_option(\"display.max_columns\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>CarName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginetype</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero giulia</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero stelvio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>alfa-romero Quadrifoglio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>six</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100 ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_ID  symboling                   CarName fueltype aspiration doornumber  \\\n",
       "0       1          3        alfa-romero giulia      gas        std        two   \n",
       "1       2          3       alfa-romero stelvio      gas        std        two   \n",
       "2       3          1  alfa-romero Quadrifoglio      gas        std        two   \n",
       "3       4          2               audi 100 ls      gas        std       four   \n",
       "4       5          2                audi 100ls      gas        std       four   \n",
       "\n",
       "       carbody drivewheel enginelocation  wheelbase  carlength  carwidth  \\\n",
       "0  convertible        rwd          front       88.6      168.8      64.1   \n",
       "1  convertible        rwd          front       88.6      168.8      64.1   \n",
       "2    hatchback        rwd          front       94.5      171.2      65.5   \n",
       "3        sedan        fwd          front       99.8      176.6      66.2   \n",
       "4        sedan        4wd          front       99.4      176.6      66.4   \n",
       "\n",
       "   carheight  curbweight enginetype cylindernumber  enginesize fuelsystem  \\\n",
       "0       48.8        2548       dohc           four         130       mpfi   \n",
       "1       48.8        2548       dohc           four         130       mpfi   \n",
       "2       52.4        2823       ohcv            six         152       mpfi   \n",
       "3       54.3        2337        ohc           four         109       mpfi   \n",
       "4       54.3        2824        ohc           five         136       mpfi   \n",
       "\n",
       "   boreratio  stroke  compressionratio  horsepower  peakrpm  citympg  \\\n",
       "0       3.47    2.68               9.0         111     5000       21   \n",
       "1       3.47    2.68               9.0         111     5000       21   \n",
       "2       2.68    3.47               9.0         154     5000       19   \n",
       "3       3.19    3.40              10.0         102     5500       24   \n",
       "4       3.19    3.40               8.0         115     5500       18   \n",
       "\n",
       "   highwaympg    price  \n",
       "0          27  13495.0  \n",
       "1          27  16500.0  \n",
       "2          26  16500.0  \n",
       "3          30  13950.0  \n",
       "4          22  17450.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### loading dataSet as pandas dataFrame\n",
    "data = pd.read_csv('./Data/CarPrice.csv')\n",
    "\n",
    "#### Let’s take a look at the top five rows using the DataFrame’s head() method\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The no.of rows = 205\n",
      "The no.of columns = 26\n"
     ]
    }
   ],
   "source": [
    "#### checking dimensionality of the Data\n",
    "rows, cols = data.shape\n",
    "print(f'The no.of rows = {rows}')\n",
    "print(f'The no.of columns = {cols}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 205 entries, 0 to 204\n",
      "Data columns (total 26 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   car_ID            205 non-null    int64  \n",
      " 1   symboling         205 non-null    int64  \n",
      " 2   CarName           205 non-null    object \n",
      " 3   fueltype          205 non-null    object \n",
      " 4   aspiration        205 non-null    object \n",
      " 5   doornumber        205 non-null    object \n",
      " 6   carbody           205 non-null    object \n",
      " 7   drivewheel        205 non-null    object \n",
      " 8   enginelocation    205 non-null    object \n",
      " 9   wheelbase         205 non-null    float64\n",
      " 10  carlength         205 non-null    float64\n",
      " 11  carwidth          205 non-null    float64\n",
      " 12  carheight         205 non-null    float64\n",
      " 13  curbweight        205 non-null    int64  \n",
      " 14  enginetype        205 non-null    object \n",
      " 15  cylindernumber    205 non-null    object \n",
      " 16  enginesize        205 non-null    int64  \n",
      " 17  fuelsystem        205 non-null    object \n",
      " 18  boreratio         205 non-null    float64\n",
      " 19  stroke            205 non-null    float64\n",
      " 20  compressionratio  205 non-null    float64\n",
      " 21  horsepower        205 non-null    int64  \n",
      " 22  peakrpm           205 non-null    int64  \n",
      " 23  citympg           205 non-null    int64  \n",
      " 24  highwaympg        205 non-null    int64  \n",
      " 25  price             205 non-null    float64\n",
      "dtypes: float64(8), int64(8), object(10)\n",
      "memory usage: 41.8+ KB\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "The info() method is useful to get a quick description of the data, in particular the total number of rows, \n",
    "and each attribute’s type and number of non-null values\n",
    "'''\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 205 instances in the dataset.Notice that there are no null values present in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>car_ID</th>\n",
       "      <td>205.0</td>\n",
       "      <td>103.00</td>\n",
       "      <td>59.32</td>\n",
       "      <td>1.00</td>\n",
       "      <td>52.00</td>\n",
       "      <td>103.00</td>\n",
       "      <td>154.00</td>\n",
       "      <td>205.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symboling</th>\n",
       "      <td>205.0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.25</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wheelbase</th>\n",
       "      <td>205.0</td>\n",
       "      <td>98.76</td>\n",
       "      <td>6.02</td>\n",
       "      <td>86.60</td>\n",
       "      <td>94.50</td>\n",
       "      <td>97.00</td>\n",
       "      <td>102.40</td>\n",
       "      <td>120.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carlength</th>\n",
       "      <td>205.0</td>\n",
       "      <td>174.05</td>\n",
       "      <td>12.34</td>\n",
       "      <td>141.10</td>\n",
       "      <td>166.30</td>\n",
       "      <td>173.20</td>\n",
       "      <td>183.10</td>\n",
       "      <td>208.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carwidth</th>\n",
       "      <td>205.0</td>\n",
       "      <td>65.91</td>\n",
       "      <td>2.15</td>\n",
       "      <td>60.30</td>\n",
       "      <td>64.10</td>\n",
       "      <td>65.50</td>\n",
       "      <td>66.90</td>\n",
       "      <td>72.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carheight</th>\n",
       "      <td>205.0</td>\n",
       "      <td>53.72</td>\n",
       "      <td>2.44</td>\n",
       "      <td>47.80</td>\n",
       "      <td>52.00</td>\n",
       "      <td>54.10</td>\n",
       "      <td>55.50</td>\n",
       "      <td>59.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>curbweight</th>\n",
       "      <td>205.0</td>\n",
       "      <td>2555.57</td>\n",
       "      <td>520.68</td>\n",
       "      <td>1488.00</td>\n",
       "      <td>2145.00</td>\n",
       "      <td>2414.00</td>\n",
       "      <td>2935.00</td>\n",
       "      <td>4066.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>enginesize</th>\n",
       "      <td>205.0</td>\n",
       "      <td>126.91</td>\n",
       "      <td>41.64</td>\n",
       "      <td>61.00</td>\n",
       "      <td>97.00</td>\n",
       "      <td>120.00</td>\n",
       "      <td>141.00</td>\n",
       "      <td>326.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boreratio</th>\n",
       "      <td>205.0</td>\n",
       "      <td>3.33</td>\n",
       "      <td>0.27</td>\n",
       "      <td>2.54</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.58</td>\n",
       "      <td>3.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>205.0</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.31</td>\n",
       "      <td>2.07</td>\n",
       "      <td>3.11</td>\n",
       "      <td>3.29</td>\n",
       "      <td>3.41</td>\n",
       "      <td>4.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compressionratio</th>\n",
       "      <td>205.0</td>\n",
       "      <td>10.14</td>\n",
       "      <td>3.97</td>\n",
       "      <td>7.00</td>\n",
       "      <td>8.60</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.40</td>\n",
       "      <td>23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>205.0</td>\n",
       "      <td>104.12</td>\n",
       "      <td>39.54</td>\n",
       "      <td>48.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>95.00</td>\n",
       "      <td>116.00</td>\n",
       "      <td>288.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peakrpm</th>\n",
       "      <td>205.0</td>\n",
       "      <td>5125.12</td>\n",
       "      <td>476.99</td>\n",
       "      <td>4150.00</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>5200.00</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>6600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citympg</th>\n",
       "      <td>205.0</td>\n",
       "      <td>25.22</td>\n",
       "      <td>6.54</td>\n",
       "      <td>13.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>49.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highwaympg</th>\n",
       "      <td>205.0</td>\n",
       "      <td>30.75</td>\n",
       "      <td>6.89</td>\n",
       "      <td>16.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>34.00</td>\n",
       "      <td>54.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>205.0</td>\n",
       "      <td>13276.71</td>\n",
       "      <td>7988.85</td>\n",
       "      <td>5118.00</td>\n",
       "      <td>7788.00</td>\n",
       "      <td>10295.00</td>\n",
       "      <td>16503.00</td>\n",
       "      <td>45400.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count      mean      std      min      25%       50%  \\\n",
       "car_ID            205.0    103.00    59.32     1.00    52.00    103.00   \n",
       "symboling         205.0      0.83     1.25    -2.00     0.00      1.00   \n",
       "wheelbase         205.0     98.76     6.02    86.60    94.50     97.00   \n",
       "carlength         205.0    174.05    12.34   141.10   166.30    173.20   \n",
       "carwidth          205.0     65.91     2.15    60.30    64.10     65.50   \n",
       "carheight         205.0     53.72     2.44    47.80    52.00     54.10   \n",
       "curbweight        205.0   2555.57   520.68  1488.00  2145.00   2414.00   \n",
       "enginesize        205.0    126.91    41.64    61.00    97.00    120.00   \n",
       "boreratio         205.0      3.33     0.27     2.54     3.15      3.31   \n",
       "stroke            205.0      3.26     0.31     2.07     3.11      3.29   \n",
       "compressionratio  205.0     10.14     3.97     7.00     8.60      9.00   \n",
       "horsepower        205.0    104.12    39.54    48.00    70.00     95.00   \n",
       "peakrpm           205.0   5125.12   476.99  4150.00  4800.00   5200.00   \n",
       "citympg           205.0     25.22     6.54    13.00    19.00     24.00   \n",
       "highwaympg        205.0     30.75     6.89    16.00    25.00     30.00   \n",
       "price             205.0  13276.71  7988.85  5118.00  7788.00  10295.00   \n",
       "\n",
       "                       75%       max  \n",
       "car_ID              154.00    205.00  \n",
       "symboling             2.00      3.00  \n",
       "wheelbase           102.40    120.90  \n",
       "carlength           183.10    208.10  \n",
       "carwidth             66.90     72.30  \n",
       "carheight            55.50     59.80  \n",
       "curbweight         2935.00   4066.00  \n",
       "enginesize          141.00    326.00  \n",
       "boreratio             3.58      3.94  \n",
       "stroke                3.41      4.17  \n",
       "compressionratio      9.40     23.00  \n",
       "horsepower          116.00    288.00  \n",
       "peakrpm            5500.00   6600.00  \n",
       "citympg              30.00     49.00  \n",
       "highwaympg           34.00     54.00  \n",
       "price             16503.00  45400.00  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### The describe() method shows a summary of the nummerical attributes.\n",
    "data.describe().T.round(decimals = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above it is observed the **price** column ranges from **5118** to **45400** with a standard deviation of **7988.85**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CarName</th>\n",
       "      <td>205</td>\n",
       "      <td>147</td>\n",
       "      <td>peugeot 504</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fueltype</th>\n",
       "      <td>205</td>\n",
       "      <td>2</td>\n",
       "      <td>gas</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aspiration</th>\n",
       "      <td>205</td>\n",
       "      <td>2</td>\n",
       "      <td>std</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doornumber</th>\n",
       "      <td>205</td>\n",
       "      <td>2</td>\n",
       "      <td>four</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carbody</th>\n",
       "      <td>205</td>\n",
       "      <td>5</td>\n",
       "      <td>sedan</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drivewheel</th>\n",
       "      <td>205</td>\n",
       "      <td>3</td>\n",
       "      <td>fwd</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>enginelocation</th>\n",
       "      <td>205</td>\n",
       "      <td>2</td>\n",
       "      <td>front</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>enginetype</th>\n",
       "      <td>205</td>\n",
       "      <td>7</td>\n",
       "      <td>ohc</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylindernumber</th>\n",
       "      <td>205</td>\n",
       "      <td>7</td>\n",
       "      <td>four</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuelsystem</th>\n",
       "      <td>205</td>\n",
       "      <td>8</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count unique          top freq\n",
       "CarName          205    147  peugeot 504    6\n",
       "fueltype         205      2          gas  185\n",
       "aspiration       205      2          std  168\n",
       "doornumber       205      2         four  115\n",
       "carbody          205      5        sedan   96\n",
       "drivewheel       205      3          fwd  120\n",
       "enginelocation   205      2        front  202\n",
       "enginetype       205      7          ohc  148\n",
       "cylindernumber   205      7         four  159\n",
       "fuelsystem       205      8         mpfi   94"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### The describe(include=['object']) method shows a summary of the object attributes.\n",
    "data.describe(include = ['object']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from the above it is observed that \n",
    "* **98%** of the cars have **enginelocation** as **front**.\n",
    "* **90%** of the cars have **fueltype** as **gas**.\n",
    "* **75% carName** are **distinct**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CarName :\n",
      " ['alfa-romero giulia' 'alfa-romero stelvio' 'alfa-romero Quadrifoglio'\n",
      " 'audi 100 ls' 'audi 100ls' 'audi fox' 'audi 5000' 'audi 4000'\n",
      " 'audi 5000s (diesel)' 'bmw 320i' 'bmw x1' 'bmw x3' 'bmw z4' 'bmw x4'\n",
      " 'bmw x5' 'chevrolet impala' 'chevrolet monte carlo' 'chevrolet vega 2300'\n",
      " 'dodge rampage' 'dodge challenger se' 'dodge d200' 'dodge monaco (sw)'\n",
      " 'dodge colt hardtop' 'dodge colt (sw)' 'dodge coronet custom'\n",
      " 'dodge dart custom' 'dodge coronet custom (sw)' 'honda civic'\n",
      " 'honda civic cvcc' 'honda accord cvcc' 'honda accord lx'\n",
      " 'honda civic 1500 gl' 'honda accord' 'honda civic 1300' 'honda prelude'\n",
      " 'honda civic (auto)' 'isuzu MU-X' 'isuzu D-Max ' 'isuzu D-Max V-Cross'\n",
      " 'jaguar xj' 'jaguar xf' 'jaguar xk' 'maxda rx3' 'maxda glc deluxe'\n",
      " 'mazda rx2 coupe' 'mazda rx-4' 'mazda glc deluxe' 'mazda 626' 'mazda glc'\n",
      " 'mazda rx-7 gs' 'mazda glc 4' 'mazda glc custom l' 'mazda glc custom'\n",
      " 'buick electra 225 custom' 'buick century luxus (sw)' 'buick century'\n",
      " 'buick skyhawk' 'buick opel isuzu deluxe' 'buick skylark'\n",
      " 'buick century special' 'buick regal sport coupe (turbo)'\n",
      " 'mercury cougar' 'mitsubishi mirage' 'mitsubishi lancer'\n",
      " 'mitsubishi outlander' 'mitsubishi g4' 'mitsubishi mirage g4'\n",
      " 'mitsubishi montero' 'mitsubishi pajero' 'Nissan versa' 'nissan gt-r'\n",
      " 'nissan rogue' 'nissan latio' 'nissan titan' 'nissan leaf' 'nissan juke'\n",
      " 'nissan note' 'nissan clipper' 'nissan nv200' 'nissan dayz' 'nissan fuga'\n",
      " 'nissan otti' 'nissan teana' 'nissan kicks' 'peugeot 504' 'peugeot 304'\n",
      " 'peugeot 504 (sw)' 'peugeot 604sl' 'peugeot 505s turbo diesel'\n",
      " 'plymouth fury iii' 'plymouth cricket' 'plymouth satellite custom (sw)'\n",
      " 'plymouth fury gran sedan' 'plymouth valiant' 'plymouth duster'\n",
      " 'porsche macan' 'porcshce panamera' 'porsche cayenne' 'porsche boxter'\n",
      " 'renault 12tl' 'renault 5 gtl' 'saab 99e' 'saab 99le' 'saab 99gle'\n",
      " 'subaru' 'subaru dl' 'subaru brz' 'subaru baja' 'subaru r1' 'subaru r2'\n",
      " 'subaru trezia' 'subaru tribeca' 'toyota corona mark ii' 'toyota corona'\n",
      " 'toyota corolla 1200' 'toyota corona hardtop' 'toyota corolla 1600 (sw)'\n",
      " 'toyota carina' 'toyota mark ii' 'toyota corolla'\n",
      " 'toyota corolla liftback' 'toyota celica gt liftback'\n",
      " 'toyota corolla tercel' 'toyota corona liftback' 'toyota starlet'\n",
      " 'toyota tercel' 'toyota cressida' 'toyota celica gt' 'toyouta tercel'\n",
      " 'vokswagen rabbit' 'volkswagen 1131 deluxe sedan' 'volkswagen model 111'\n",
      " 'volkswagen type 3' 'volkswagen 411 (sw)' 'volkswagen super beetle'\n",
      " 'volkswagen dasher' 'vw dasher' 'vw rabbit' 'volkswagen rabbit'\n",
      " 'volkswagen rabbit custom' 'volvo 145e (sw)' 'volvo 144ea' 'volvo 244dl'\n",
      " 'volvo 245' 'volvo 264gl' 'volvo diesel' 'volvo 246']\n",
      "\n",
      "fueltype :\n",
      " ['gas' 'diesel']\n",
      "\n",
      "aspiration :\n",
      " ['std' 'turbo']\n",
      "\n",
      "doornumber :\n",
      " ['two' 'four']\n",
      "\n",
      "carbody :\n",
      " ['convertible' 'hatchback' 'sedan' 'wagon' 'hardtop']\n",
      "\n",
      "drivewheel :\n",
      " ['rwd' 'fwd' '4wd']\n",
      "\n",
      "enginelocation :\n",
      " ['front' 'rear']\n",
      "\n",
      "enginetype :\n",
      " ['dohc' 'ohcv' 'ohc' 'l' 'rotor' 'ohcf' 'dohcv']\n",
      "\n",
      "cylindernumber :\n",
      " ['four' 'six' 'five' 'three' 'twelve' 'two' 'eight']\n",
      "\n",
      "fuelsystem :\n",
      " ['mpfi' '2bbl' 'mfi' '1bbl' 'spfi' '4bbl' 'idi' 'spdi']\n"
     ]
    }
   ],
   "source": [
    "#Checking for the unique values present in the categorical features.\n",
    "for i in data.select_dtypes(include='object').columns:\n",
    "        print(i, ':\\n', data[i].unique(), end=print())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### spliting car brand from carName column\n",
    "data['car_brand']=data['CarName'].str.split(\" \", n = 1, expand = True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['alfa-romero', 'audi', 'bmw', 'chevrolet', 'dodge', 'honda',\n",
       "       'isuzu', 'jaguar', 'maxda', 'mazda', 'buick', 'mercury',\n",
       "       'mitsubishi', 'Nissan', 'nissan', 'peugeot', 'plymouth', 'porsche',\n",
       "       'porcshce', 'renault', 'saab', 'subaru', 'toyota', 'toyouta',\n",
       "       'vokswagen', 'volkswagen', 'vw', 'volvo'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Priting all kinds of brands availabel  in the dataset\n",
    "data['car_brand'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['alfa-romero', 'audi', 'bmw', 'chevrolet', 'dodge', 'honda',\n",
       "       'isuzu', 'jaguar', 'mazda', 'buick', 'mercury', 'mitsubishi',\n",
       "       'Nissan', 'peugeot', 'plymouth', 'porsche', 'renault', 'saab',\n",
       "       'subaru', 'toyota', 'volkswagen', 'volvo'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### There seems to be some duplicate values in in the car_brand column.\n",
    "data['car_brand'].replace({'maxda': 'mazda',\n",
    "                           'nissan': 'Nissan',\n",
    "                           'toyouta': 'toyota',\n",
    "                           'porcshce': 'porsche',\n",
    "                           'vw': 'volkswagen',\n",
    "                           'vokswagen': 'volkswagen'\n",
    "                          }, inplace=True)\n",
    "\n",
    "data['car_brand'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no of outliers in 'wheelbase' is 3\n",
      "no of outliers in 'carlength' is 1\n",
      "no of outliers in 'carwidth' is 8\n",
      "no of outliers in 'enginesize' is 10\n",
      "no of outliers in 'stroke' is 20\n",
      "no of outliers in 'compressionratio' is 28\n",
      "no of outliers in 'horsepower' is 6\n",
      "no of outliers in 'peakrpm' is 2\n",
      "no of outliers in 'citympg' is 2\n",
      "no of outliers in 'highwaympg' is 3\n",
      "no of outliers in 'price' is 15\n"
     ]
    }
   ],
   "source": [
    "#### Printing no of outliers in each column\n",
    "outliers_columns_list = []\n",
    "for i in data.select_dtypes(exclude='object').columns:\n",
    "    q1 = data[i].quantile(0.25)\n",
    "    q3 = data[i].quantile(0.75)\n",
    "    IQR = q3-q1\n",
    "    ub = q3+(1.5*IQR)\n",
    "    lb = q1-(1.5*IQR)\n",
    "    outliers_count=data[(data[i]>ub) | (data[i]<lb)][i].count()\n",
    "    if(outliers_count>0):\n",
    "        print(f\"no of outliers in '{i}' is {outliers_count}\")\n",
    "        outliers_columns_list.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "for i in range(len(outliers_columns_list)-1):\n",
    "    plt.subplot(2, 5, i+1)\n",
    "    sns.boxplot(data[outliers_columns_list[i]], orient=\"v\")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above **boxplots** it is observed that all the outliers are nearer to **Upper and lower boundaries**. So there is no need of outliers treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing our dependent variable for outliers and skewnwss\n",
    "plt.figure(figsize=(15,4))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.boxplot(data[\"price\"])\n",
    "plt.title(\"Boxplot for outliers detection\", fontweight=\"bold\")\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(data[\"price\"])\n",
    "plt.title(\"Distribution plot for skewness\", fontweight=\"bold\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above plot :\n",
    "* There are few outliers towards the higher price range, suggesting that there are few high price models.\n",
    "* Price distribution plot is **right skewed**\n",
    "* maximum number of cars are in range of 5000 to 20000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### Count plot for all categorical columns\n",
    "object_columns_list = data.select_dtypes(include='object').columns[1:-1]\n",
    "plt.figure(figsize = (15, 10))\n",
    "for i in range(len(object_columns_list)):\n",
    "    plt.subplot(3, 3, i+1)\n",
    "    sns.countplot(data[object_columns_list[i]], order = data[object_columns_list[i]].value_counts().index)\n",
    "    plt.xlabel(object_columns_list[i], fontweight=\"bold\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above visuals:\n",
    "* **fueltype:** Majority of the automobiles are gas fuel type.\n",
    "* **aspiration:** Majority of the automobiles use standard aspiration.\n",
    "* **doornumber:** Majority of the automobiles are 4 door models.\n",
    "* **carbody:** Sedan is the most common model, convertible is the least common model.\n",
    "* **drivewheel:** Forward wheel drive is the most common model, 4 wheel drive is the least common model.\n",
    "* **enginelocation:** Almost all the models are having engine location as front.\n",
    "* **enginetype:** Majority (almost all) of the models are having 'ohc' engine type.\n",
    "* **cylindernumber:** Majority (almost all) of the models are 4 cylinder models.\n",
    "* **fuelsystem:** Majority of the models are having 'mpfi' and '2bbl' fuel systems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4gAAAJNCAYAAABz4lt9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXyU1dn4/8+ZmWSyhyQkLAkQlrBrVdDaWmvV4la7/PrY1+P3++3z2Gprq+ACLizaulXr1oq2RSuuKHXHBRAEQVoQBAFRAdkhECAEss6+nt8fMxMSshBgZu6ZyfV+vfLK5Mx9z1wh4eS+zn2dc5TWGiGEEEIIIYQQwmR0AEIIIYQQQgghEoMkiEIIIYQQQgghAEkQhRBCCCGEEEKESYIohBBCCCGEEAKQBFEIIYQQQgghRJgkiEIIIYQQQgghALAYHUC89ezZU5eXlxsdhhAiitatW3dEa11sdBynQvomIVKT9E9CiETUWd/U7RLE8vJy1q5da3QYQogoUkpVGh3DqZK+SYjUJP2TECIRddY3SYmpEEIIIYQQQghAEkQhhBBCCCGEEGGSIAohhBBCCCGEACRBFEIIIYQQQggRFtMEUSm1Ryn1tVJqg1JqbbitUCm1WCm1Pfy5oMXxU5VSO5RSW5VSl7ZoHxN+nR1KqaeUUircblVKvRFuX62UKo/l9yOEEEIIIYQQqSwedxAv1FqfobUeG/56CrBEa10BLAl/jVJqJHA1MAq4DJihlDKHz3kauB6oCH9cFm6/DqjXWg8BngAeicP3k3Tmz5/P9OlPGh2GEAlDBq8SQ01NDZMnT6aqqsroUIQQopWnnvobr7zyitFhCGEII0pMfwq8HH78MvCzFu2va609WuvdwA7gHKVUHyBPa71Ka62BWcecE3mtt4GLIxdo4qjHHnuM99571+gwhEg0MnhlsP/85z+sXr2auXPnGh2KEEK0MmfOOzz//PNGhyGEIWKdIGpgkVJqnVLq+nBbL631QYDw55Jweymwr8W5VeG20vDjY9tbnaO19gONQFEMvg8hROqTwas4C/2zgd/vNzgSIYQQQkTEOkE8T2t9FnA5MF4p9f1Ojm3v4kl30t7ZOa1fWKnrlVJrlVJrDx8+fLyYU1bkYkwIIYNXQojEJCXwxgsEAkaHIIShYpogaq0PhD/XAO8C5wCHwiPvhD/XhA+vAvq1OL0MOBBuL2unvdU5SikLkA/UtRPHs1rrsVrrscXFxdH55pKQ1+s1OgQhEoUMXiWAyKCV3FwVog0pgTeQ2+02OgQhDBWzBFEpla2Uyo08Bi4BNgIfANeED7sGeD/8+APg6vDI1kBCndma8Ei+TSl1bnj063+POSfyWlcBS7XcJuuQx+MxOgQhEoIMXiWGSGIo3bYQxyUl8HHUMkGU/kl0R7G8g9gLWKGU+hJYA8zXWi8EHgbGKaW2A+PCX6O13gS8CWwGFgLjtdaRe/w3AM8R6vh2AgvC7c8DRUqpHcAkwiNqon0yIiaEDF4lEinjEqJdUgJvMIfD0fxYqq9Ed2SJ1QtrrXcB32qnvRa4uINzHgQebKd9LTC6nXY38ItTDrabkARRCCA0ePVueMDcAvxLa71QKfU58KZS6jpgL+G+RWu9SSkVGbzy03bw6iUgk9DAVcvBq1fCg1d1hErAxDGkTxKiXedprQ8opUqAxUqpLZ0cG9MSeEIlqvTv37/ziFNMywTR4XBgtVoNjEaI+ItZgigSj9PpNDoEIQwng1eJI9InSfm7EEe1LIFXSrUqgddaH4xiCXzV8UrggWcBxo4d260qIFomiHa7ncLCQgOjESL+jNgHUcSRz+drfiwJohAikbhcLqD1xZgQ3ZmUwCeGpqam5sc2m83ASIQwhtxBTHHHlkkIIUSiiPRJMnglRDMpgU8ALZPClsmiEN2FJIgp7tgyCSGESBSR/skmfZMQgJTAJ4rGxsZ2HwvRXUiJaYprOQomCaIQIpFEEkMp4RJCJBJJEEV3JwliimuZFEqCKIRIJJHSLbtN+iYhROJoaGhAZeSBMkmCKLolKTFNcZIgCiESlS2cGNod0jcJIRJHQ0MDAYsVsw7Q0NBgdDhCxJ3cQUxxzaVbSkkZlxAiYWitcYQTQ5/XK1tdCCESRl19PUFLBkFLhiSI3UAgEMDv9xsdRkKRBDHFRZJCnZEnCaIQImF4PB78Ph/B9BxAKhyEEImjvr6BoCWTgNlKfX290eGIGJt46y1c/9vfGB1GQpEEMcXZbDYwmQlYsmiSBFEIkSAi8w+DGbmtvhZCCCNprbE1NaItGWhLBnX1cgcx1X319UZ27d5jdBgJRRLEFGez2VAWK9pilYnWQoiEEaloCFrzAEkQhRCJwW63EwgE0GkZ6LQMGqXEVHRDkiCmOJvNhrakoy3pNDXJHUQhRGI4egdREkQhROKIDKZH7iC63S68Xq/BUYl4kHmIR0mCmOKampoImKxosxWHw47W2uiQhBDiaIJozW/1tRBCGOlogmhFWzIA6Z9SWSAQaH7scrkMjCSxSIKY4hobm9DmdLBYCfj98ssvhEgIzRdhcgdRCJFAjr2DCMhKpinM4XA0P5bF0o6SBDHFNTY1hkfBrACykqkQIiEcvYOYAyazzJEWQiSE5gQxLaP52kkGsFJXy5+t/JyPkgQxxdmabK1GweQiTAiRCBobG1HmNDBZUGkZ8odZCJEQWpeYSoKY6lpeF8s18lGSIKYwt9uN1+tBpx3t5OSXXwiRCBoaGiAtNHAVtGTIXmNCiITQ1NQEygSmNBlc7wZalg9LKfFRkiCmsMgvurZkErRktmoTQggj1dfXEwhffAXMVuokQRRCJIDGxkZUegYoJYPr3UDLwcm6ujoDI0kskiCmsMgvfTC8l0/LNiGEMFJtXR3BcIKo0zKpq5O+SQhhvIaGhuY7h5jMKNlHOqVFkkKFJIgtxTxBVEqZlVJfKKXmhb8uVEotVkptD38uaHHsVKXUDqXUVqXUpS3axyilvg4/95RSSoXbrUqpN8Ltq5VS5bH+fpJJJBnUlkwwp4PJLAmiECIh1NbWEUwLVTbotEzq6+tkGx4hhOHq6+vxm63NX+u0DEkcUlhtbS056YriLEkQW4rHHcRbgG9afD0FWKK1rgCWhL9GKTUSuBoYBVwGzFBKmcPnPA1cD1SEPy4Lt18H1GuthwBPAI/E9ltJLkeOHAFCF18ohUrPam4ToruTwSvj+P1+bE2N6LQsAIJp2fh9PlllWQhhuCO1daGB9bCARRLEVFZbW0uP9CD5aX5qa+UaOSKmCaJSqgz4EfBci+afAi+HH78M/KxF++taa4/WejewAzhHKdUHyNNar9Kh4eVZx5wTea23gYsjF2gCDh8+HKqhTw9dhPktWdTU1BgclRAJQwavDHLkyBG01uj0bIDmPurw4cNGhiWE6Oa01tTX1TUPXgEELZkcPlJrYFQilg4frqFHuo8Ca4DDco3cLNZ3EKcDdwLBFm29tNYHAcKfS8LtpcC+FsdVhdtKw4+PbW91jtbaDzQCRdH9FpLXoUOHUNac0GpcQDA9m4PV1QZHJWJFa82sWbN48skn2bt3r9HhJDQZvDLWoUOHAAim54Q/Z7dqF6I7k+oG49hsNnw+L8H0owmiTs+mNjyoJVLPkcOHKbAGKbAGmwcvRQwTRKXUlUCN1npdV09pp0130t7ZOcfGcr1Saq1Sam13GqGu2r8ff1p289dBay5HDh/G7/cbGJWIlerqal544QXeffdd5s+fb3Q4iU4Grwx08OBBAILWUIKorbkAHDhwwLCYhEggUt1gkMggVaS6AUIDWB6PG7vdblRYIkb8fj/19Q3NCaLb45Wfc1gs7yCeB/xEKbUHeB24SCn1KnAoPPJO+HPkfm4V0K/F+WXAgXB7WTvtrc5RSlmAfKBNobjW+lmt9Vit9dji4uLofHdJYN++KgLWvOavgxl5BINBuQhLUdUt7g5LKXHHZPDKeFVVVeHy93CCaMlAmdPYv3+/wZEJYSypbjBW5O9oMDxoBTT3U9VSgZVy6urqCGpNkTVIoTU0Xtyd/hZ3JmYJotZ6qta6TGtdTmiEa6nW+pfAB8A14cOuAd4PP/4AuDpc/jCQ0IjXmvBIvk0pdW64E/vfY86JvNZV4feQe8OE9uyxNTUSzMxvbgtmhB5L+WFqityVKckMsL9q33GO7tZk8MpgVVVVkJEHpvDNDqUIZOSzb5/83opuT6obDHS0uuFoghjMCD2WAazUExlML8w4miDKAHuIEfsgPgyMU0ptB8aFv0ZrvQl4E9gMLATGa60D4XNuIDSatgPYCSwItz8PFCmldgCTCJddCNizZw8AwYwezW3BzB6tnhOpZe/evVhMcFqhj3379kkdfQdk8Mp4u3bvxt+iugEgkJHPrt17jAlIiASQSNUN4Xi6XYVDVVUVKi0DLEe3uQiG+6qqqqqOThNJKlJSXGQN0jMj2Kqtu7PE40201suAZeHHtcDFHRz3IPBgO+1rgdHttLuBX0Qx1JSxa9cuAIJZhUcbzemojFx27txpUFQilnbt2kXf7CD9cvy49nuorq6mT58+RoeVTB4G3lRKXQfsJdy3aK03KaUig1d+2g5evQRkEhq4ajl49Up48KqOUCIqAJ/PR1VVFYGSUa3ag5kF1FXtxGazkZub28HZQqS0SHXDFUAGkNeyukFrfTCK1Q1VnVU3QKjCAXgWYOzYsd1igKuycm+bwSvMaShrtlQ4pKBI2XDPjADpZjCbpJQ4wog7iCIOtm/fjkrPbLVUM4A3o4Bt27cbFJWIFa0127Z8w4AcL+W5oUWItm7danBUiU9rvUxrfWX4ca3W+mKtdUX4c12L4x7UWg/WWg/TWi9o0b5Waz06/NyEyF1CrbVba/0LrfUQrfU5Wutd8f/uEtPevXsJBgIEMwtbtQcyQwszRga3hOhupLrBeLv37G6ejtOSz5rPrt27DYhIxNLBgwfJsyoyLGBS0DNTN5cZd3eSIKaobdu248sogGPmngezijiwfz9Op9OgyEQs7N+/n4YmG4Pz/PTLCZBmgk2bNhkdlhBt7NixAzimuoFQ39TyeSFEM5maEwcNDQ00NTYSyOzR5rlgRg/2Vu4lGAy2c6ZIVvurqijO8DV/3cvqo0rWcADiVGIq4svn87F79y4CxSPbPBfILkJrzc6dOznttNMMiE7EwoYNGwAY1sNPmgkG5/nZ8MV6g6MSoq3t27ejzBaCGa3LuHRaJio9UxLEFOV0OlmzZg0jRoygV69eRoeT8GRqTvw1r92QWdDmuWBWAZ4aN4cOHZKpGylk375KhmUe3fqtV1aAFVVVaK3p7ov7yh3EFFRZWUkgEGgekW8p0rZdykxTypo1ayjIgL5ZocHj0YVetu/YSV1du1NLhDDM9u07QuWk6pg/P0rhyyhk2zbpm1LRe++9x7333ssTTzxhdChCtGt3uIS0vQQxUgK/W8pMU4bT6eRIbT19so7eFe6TFcDl9lBbW2tgZIlBEsQUdHSBmradnE7LQqVlSCeXQjweD2s/X8O3Ct3NFcXfKvICsGrVKgMjE6I1rTXbd+zAf8z8w4hgViF7Kvfg9/vbfV4kr8jKgIeqZX6PSEy7d+9GpVnbrN0AR1eBl2un1BHZ8q1PVqC5LfK4srLSkJgSiSSIKaiyshKUiaC17URrlMKfkS+dXAr5/PPPcbrcjC32Nrf1zwlQnKX55JOlBkYmRGu1tbU4HfZ2R+ghNEof8Ptlv7EUFFkZ8NChQ7IFj0hIe/bswW/Nb7N2AxBaBd6aI9uEpZBIgtg3+2iCWJotCWKEJIgpaN++feFNqNv/8Qas+ezbJ/v5pIrFixeTZ4WRBUcnWisF5xa7WL9uvZRKiIQR+YMcbGcRiJbtkeNE6qjaF/qZOl1uGhoaDI5GiLYqK/e2u4JphM+aR6X0TSljz549mE3QK/NogpifrslOUzIQgCSIKan60CH8adkdPq+tOTQ2NuDxeOIYlYiFhoYGPl2xgu+UuLAc87/5e308BLVm4cKFxgQnxDEidwaDx+4zFhZZuEY2pE4tHo+HgwerGZIXGsSSChaRaJxOJ42NDW0Wz2opaM2jqkqqG1LFnj176J0VbHXtpBT0zfKxZ4/0UZIgpqDa2lp0WmaHz0eeq6+vj1dIIkbmz5+PPxDgB33bJvt9soKMKPDzwfvvEQgE2jlbiPg6dOgQKIVObzvHBwiVcVms1NTUtP+8SEo7d+4kqDXn9wn1U9u2bTM4IiFai5RAB625HR4TtObidNix2+3xCkvEUOXuXfTN9LVp75vlZ6/cQZQEMRXZ7Xa0xdrh89psbT5OJC+fz8ecd95mZKG/uW7+WD8sdXGo5jArVqyIc3RCtNXQ0IBKy2y7gmkLOj1TShBTTGRP1jN6einOkj1aReI5fPgwALqz6qv00HMybSP5+Xw+Dh6qabVATUSf7AANTTaampoMiCxxSIKYgvx+P1qZO3xem8zNx4nktWTJEmrr6rm8n7PDY8YUeynJ0rz22r9kYQhhOJvNhrakd3pM0JQug1cp5osv1lOSpSmwaobludnwxXqpahAJJVJRFexC9ZVsH5X8Dh48SDAYpHc7CWLv8JzE7r5YmiSIKSi0uWdnyYAkCskuEAgw+9VX6J8b5PTCtiUSESYFV/RzsGXLVtatWxfHCIVoy+fzdTp4BRBUJpkfnUK8Xi/r161ndEHoZzq60IfN7mDr1q0GRybEUZFBqU6rr8KDWzKAlfwOHgxtt1OSGWzzXKTtwIEDcY0p0UiCmILS09JRwY7vDkaeS0/vfCRfJK5ly5axr2o/PxngaHdF7pbO7+OhIANefulFuYsoDKWO98t6gseJxLd+/XrcHg9nhPdmPa3Ih0nBp59+anBkQhzVPChl6qz6ytL6WJG0InNOe7aTIBZlBFod011JgpiCcvPyUP6OO7DIc/n5HS/nLBKX3+/nxReepywn2Grvw46kmeDH/e18vXGT3EUUhkpPT0cFOy8tNAUDMniVQpYtW0ZmGowKVzrkpmlGFPhY9slSGbASCaO55LmT+dEQGriS6TnJ78iRIygFPdLbJoiZFshMU91+rqkkiCmopLgnJl/H89KU14HJZJIEMUktXryYqv0H+PlAB6Yu3mi5oK+HokyYOfNZuSgThsnNzUUFOh99Nwc95OZ2vJKgSB5ut5v//HsZY3u6SWtxtXFuiYf9Bw6yefNm44ITogWzOXznULdNGI4K/e00dbDHtEgeDQ0N5KarDq+h8tN1t1/pX37LU1Dfvn2xeG0dPm/y2CguKcFiscQxKhENXq+Xl158gYF5Acb0PP7dw4g0E/xsgJ2tW7fJiqbCMEVFRWivCzoqgdcavE569uwZ38BETPznP//B6XJzfu/WgwLnlHixmmHBggUGRSZEa81VC51UOESm52RmdryQjUgOTU1N5KR1PBiQbfZjs3V8Hd0dSIKYgvr374/2OMDffgJhcTcysLw8vkGJqJg3bx6Hag5z1cDjzz081vd6e+idrXn+uZmygqAwRO/evQEwedpf5EH5XeiAv/k4kdw+eP99emdrhvVoPSCQadGcU+Lm48WLcTgcBkUnxFE5OTkAqEDHA68qfE2Vnd3xVhgiObhcLqymjhPEDHMQl7PjSrzuQBLEFDR48GAAzK52lmIO+lGuhuZjRPJwu928+soshvfwM7qDlUtf3ZbFq9va34TcbIKfl9vZU7mXTz75JJahCtGu/v37A2Byt7/PocnV2Oo4kbx27tzJxk2buLCPs93BrItKPbg9HhYtWhT/4IQ4Ro8ePQBQPleHxyi/q9WxInn5fD7SVMcJosUEXm/3XoxIEsQUNHToUABMjsNtnjM560AHm48RyeP999+nrr6B/xrU8d3DvXYLe+0dlw6fU+KlX26QF194Xibai7grLy9HKRXqh9phcoYWBRg0aFA8wxIxMGfOHNLNoVWU2zMo18/AvADvznlH5kULwxUXFwNg8nZ8R1t5na2OFckrGAx2uoaDSdHt+6WYJYhKqQyl1Bql1JdKqU1KqfvC7YVKqcVKqe3hzwUtzpmqlNqhlNqqlLq0RfsYpdTX4eeeUuE10JVSVqXUG+H21Uqp8lh9P8mksLCQ4pJemO1tE8RI24gRI+IdljgFbreb1/41m1GFvjblWifCpOBnAxzsP3CQpUuXRjFCIY4vMzOT0rJ+mB3trw5ndtRSUFhIYWFhnCMT0dTQ0MDiRYs4r5ebnLT2L7KUgktKnezdV8Xnn38e5wiFaK1Xr14AqA7K3yFUGm/NyGguRxXJy2w2E9AdZ4hBLdstxfIOoge4SGv9LeAM4DKl1LnAFGCJ1roCWBL+GqXUSOBqYBRwGTBDqeYdlZ8Grgcqwh+XhduvA+q11kOAJ4BHYvj9JJXTTxtNuvNwaNGHFsyOGop6FlNSUmJQZOJkfPjhhzQ0NvHTAadeEz+m2EtpTpDZr75CMNjZim2pSQavjDV61EjSnEfa9E0Aaa7DjBo50oCoRDTNmzcPr8/HuDJ3p8ed08tLDyu8/fZbcYpMiPbl5uaSk5uLydPU4TEmTyOlpaXdPnFIBWlpafg7SRB9QUhPt8YxosQTswRRh0SGYtLCHxr4KfByuP1l4Gfhxz8FXtdae7TWu4EdwDlKqT5AntZ6lQ7d7511zDmR13obuFjJ/1wARo0ahfY4UMeUS6Q7DnP6aaMNikqcDL/fz5tvvE5FfoDhBadeFmpS8OP+Dir37uOzzz6LQoRJRwavDDRq1Ci0z4U65kJM+VzgamL0aOmfkpnf7+fdOe8wqtBHWU7ni2GlmeCivk7WrPmcysrKOEWY2GQAyzj9+/fH7G7s8Pk0j43yAQPiGJGIlYyMDDzBjlMgT9BERjdfrTamcxCVUmal1AagBlistV4N9NJaHwQIf47cyioF9rU4vSrcVhp+fGx7q3O01n6gESiKzXeTXEaNGgWA2V7T3Ka8DrTH3vycSA6rVq2i+lANl/WL3opa55R4KciAOe+8HbXXTBYyeGWsSALYsm8KfX2o1fMiOf373/+mtq6eS8s6XuyjpYtK3VhM8O6778Y4sqQhA1gGKR8wAIungwQx4Ee7m2QBrRSRnZ2NO9Dxn2RPwNztV6uNaYKotQ5orc8AyghdUHX2l7+9n5TupL2zc1q/sFLXK6XWKqXWHj7cdl5eKho8eDAWSxpmx5HmtshjmX+YXObO/YDCDDjrBPY9PB6LCS7s42TtuvXs378/aq+bLGTwyjgDBgwgKzsHs+1Qq3azrQaLJU0W0Epyc955h15ZmtOL2l9p+Vh56Zpvl7j5aOEC2fICGcAyUnl5OdrrQvnalkZHVl4uly3CUkJWVhZOX8e/8s6AkgQxHm+itW4AlhEavToU7rgIf44MI1cB/VqcVgYcCLeXtdPe6hyllAXIB9osj6e1flZrPVZrPba7rD5lsVgYNHgQJtfRxSBMzlqUUrLFRRI5cuQIn3++lvN7OzFH+X/r9/t4UNAtl5mXwSvjmEwmRo8aRZqz9fdrcdQwdNjQoxtWi6SzY8cONm3ezMV9nZ2uEHiscWVuXG4PH330UeyCSyIygGWMSPJnctW3eS7SNnDgwHiGJGIkOzsbt18T7GChUqdPEsRYrmJarJTqEX6cCfwQ2AJ8AFwTPuwa4P3w4w+Aq8O18QMJlUOsCXeENqXUueERrv895pzIa10FLNXdfV3aFgYPGkRai3p6k6uB3n36kpGRYWBU4kR88sknaK35bq/o78dTmBFkRIGPJR8v7rbLOcvglTFGjhyBctZDIHyXSQcxO+tkgZokN3fuXNI62dqiI4PyAgzMCzDvgw+6bV/UkgxgGaM5QWxnn1aTqxGz2UxpaWmb50TyiaxE6/K3/a8Q1OD2a0kQY/jafYBPlFJfAZ8TGgWbBzwMjFNKbQfGhb9Ga70JeBPYDCwExmutIzPcbwCeI1Q6sRNYEG5/HihSSu0AJhGuyRchZWVlaK+z+SLM4rXRv1/Zcc4SieTTFSsoywnSJzs2q42eXeyhav8B9u7dG5PXT0QyeGW8SBmpObzvocnVgA76GTZsmJFhiVPgdrtZvGgRZ/f0kN3B1had+UEfF7v27GHLli0xiC45yQBWfBUXF2PNyMDkajsP0eRuoE/fUiyWjvcZFskjcqPEE2ybIHoDrY/prrqcICqlBiilfhh+nKmUyu3seK31V1rrM7XWp2utR2ut7w+312qtL9ZaV4Q/17U450Gt9WCt9TCt9YIW7WvDrzFYaz0hcqGltXZrrX+htR6itT5Ha73rRP8BUlmkQ1e+0OImJp9LtrdIIk6nk683fs23CqN/9zDiW+F5QmvWrInZe8RaePXDXOha34QMXhkuUuYeKduKfB40aJBhMYlTs3z5cpwuF9/v2/nWFh05t5eXdDMsWLDg+AcnkRPtn2QAyzhKKcpKyzC1s1BNmtfGgP792jlLJKNIoh9oZ+w9sv1FWlpaPENKOF1KEJVSvyU0kfmf4aYy4L1YBSWiIy8vDwDlDyUY2ucmN/d4184iUWzcuJFAIMiowq4t9nAyemYGKcnSbNiwIWbvEUszZ87kqquuAoisPX7cvkkGr4xXUlJCutWKyR3a6sLkbgxdnJVJhUOy+uijhfTM1Azv0f5WPK9uy+LVbVkdnp9p0Yzp6WHpko/xeqO3IJeRTqZ/QgawDFVa2heL14F172dY94a3gdIa5bFJeWkKOXZNpvb6p+6+blNX75WPB84BVgNorbcrpeRWVIKLjH4oHQxtSq2DsgBEEtm8eTMKGJwXuwQRoCLPw6aNX6O1TroO8R//+Adr1qzBarUGQfqmZKGUonfv3uxstAFg8tgpKCyS/ilJ1dXVsX7den7U39Xh4jR77ce/3Divt4dVXzpZvXo1559/fpSjjL+T6Z+01l8BZ7bTXgtc3ME5DwIPttO+Fmgzf1Fr7QZ+0aVvopvp1asXymvH5Khrnqmp/B50wC8VWCkkMghlMYVunLfsn9LCbakyUHWyulpi6tFaN/9LhWvWu305QqLz+0MjuVqZ2rSJxLdt2zb6ZGsyYzzlYWBugIbGJmpra49/cIKxWq2tkgrpm5JHz6IizP5QOaLyu+jZs9svopi0li1bRlBrvtv71C6oRhX4yLPCxx9/HNh9AN8AACAASURBVKXIjCX9U/IpLCxEB/y0/DEpv6v5OZEa7PbQTjJZlrb/HdNNYFZgs9niHVZC6WqC+G+l1DQgUyk1DngLmBu7sEQ0NO8pZU4DpVAWa/N/CpH4du/aSVl27Eew+ueEBg12794d8/eKtgsuuICHHnoIQEnflFxyc3MxBUN3x00BH3lS/p60Pl68iH65QUqzA8c/uBNmE5zT082qlStxOp1Ris440j8ln8g0HKVbTE7zh/4OR1a+FMmvtraWDIvCam77nFKQn0FSDppHU1cTxCnAYeBr4HfAh8DdsQpKREdkWepgWqiuWqdn0R2Wqk4FXq+X6kM19M06tQuurugTvqhLxpVMH3744chiTC6kb0oqaWlpqPBUKaUD3X5BgGR14MABNn+zhe+UuKLyeuf28uD1+Vi+fHlUXs9I0j8ln6N3fFvcQQz3U1ar1YCIRCxUVVVRktnx9VWx1UdV1b4On+8OupogZgIvhBdduAp4IdwmElhlZSUqPRMsoU7Nl57H7j17jA1KdMnBgwfRWtMrDgliXpom06LYv39/zN8r2lwuF9deey3ALumbkkswGESHJ/loFMFgbLZyEbG1aNEiAL7TKzrVDhX5foqzNIs++igqr2ck6Z9Siyz0mjp27thGWVbH6zuUZQfYvWtXt/671NUEcQmtO7VMIDUmCaSwzZu/wZdxtGY+mF3EwQMHun1ddTI4ePAgACWZse+clILizEDzeyaTiy++GJer1Z0L6ZuShMvlQptCE2yDJktKlBR2N8FgkIULPmRkgY+ijOj0VUrBeSUu1n+xPin7pJakf0o+R39eR1dbivRTHk/stpwS8VNdXc2R2noG5XW8JsegPD9Ol5s93fimSlcTxAytdfPktfDjjterFoZraGhgz57dBHJ7NbcFcnqhdfJuadCdRC6MijNifwcRoNjq58D+qri8VzS53e5W80Kkb0oe9fUNBMyh6gZtyaC2rt7giMSJWr16NdWHavjBSe592JHv9w1diM+dm9zT9aR/Sj6NjaE9EFsu7qfDVVj19dJHpYL169cDMLyg4zuII3qEnlu3bl1cYkpEXU0QHUqpsyJfKKXGEKqpFwlq5cqVAPjzj27sGsgpQVmszc+JxHXgwAHSzZCfHp+SltAdxOqkK6fIzs5u7uxB+qZkcuDgQYLpoYtnnZ7N4ZqapPv96+7efOMNCjJgbHF0F9PqmRFkTE8Pcz94P6nvLEv/lHwOHDiAsmaHbmWH6XA/lex3tEXIihUrKMyAfp0sqtUzM0hpTpAVK5J/LvTJ6mqCeCvwllJquVJqOfAGMCF2YYlTtWjxYsjII5hVeHTDV5MZb34/li37N253dEd8RXTt27ePXllB4rUtYe+sAF6fjyNHjsTnDaNk+vTp/OIXvwAYJn1T8mhsbKSpsYFgRj4AwYx8fD4v1dXVBkcmuurLL7/kiw0buLzMgaWrVxIn4EcD3NjsDt55553ov3icSP+UfLZu24bPmt+60WSGrB5s377dmKBE1DQ1NfH5mtWcXew67vXVOcVuvvrqK2pqauITXILpUreutf4cGA7cANwIjNBad9/7rglu3759bPjiCzxFQ0ApTM46TM46AHw9K3C5nHzyyScGRyk6U7lnN30yOy5/iLbIaqnJVm9/9tlns2XLFoBKpG9KGlu3bgXA7KjBuvczAllFrdpFYgsEAsz4x98pzIALS2Mz2Dg4z89ZPb289q/ZSbvcvPRPyaWpqYk9u3cTyClp85wvq5ivvv6aQCA+0z5EbCxevBifP8D5vY8/n/S7vTxoDQsWLIhDZImn0wRRKXVR+PPPgR8DQ4EK4MfhNpGAXn/9dZTJjK94WJvnArm90VmF/Ou116WcK0HZ7XYOVh+if078/hCVhd9r586dcXvPU7F06VIA5syZE5mnlIH0TUnjyy+/DO3N6nNjctYRzCpCmdNC7SLhzZkzh63btnP1YFu7+4hFy/8Z4sDncTN9+vSkWkFS+qfktHr1arTW+PPL2jznzyvFbrOxadMmAyIT0RAIBHjn7bcYkh+gf+7xr696ZQU5rdDH+++9i9cb+z2pE83x7iBeEP7843Y+roxhXOIk7d+/nwULFuLpWYFOa2c1baVw9z6NfXsrm/+IicQSHnGmPLfjFbaiLSdNU5IFmzdvjtt7nop///vfQGgRi/AFWA+kb0oaK1etCo3SR2p8TCZ8Ob1ZuXJVUiUC3dH27dt59tl/ckZPL98uie1FU6+sID8f6GD58uXMnz8/pu8VTdI/JaePFi0Caw7B7J5tnvP36IcypzVv6yKSz9KlSzlwsJrL+3V9XvPl/V3U1Tfw4YcfxjCyxNRpgqi1vkcpZQIWaK1/fczHtXGKUZyAGTNmoJXC2+eMDo/xFw5CZxfxzD//eewS3CIBfPnllygV2g8sniry3Hz15YakuLN83333EQwGufzyy3nxxRcB9kjflBz27dvH7l278PXo36rdX9CfmppDUmaawGpra7n7rmnkmv38Zrg9LnOkL+/vZnShjyenP8FXX30V+zeMAumfkk9lZSVr164NT81p59LYnIanoJxFixbT0NAQ/wDFKfF4PLzw3Ez65QYZcwKLao0q8DGsh59ZL7+U1AtmnYzjzkHUWgeRSdVJYcWKFXz66ae4+5yBTu9kJW2lcPX7NkcOH4788RIJZPVnqxiS5yfTEt87KacV+mhssrFt27a4vu/JMplM/P3vfzc6DHGCPvroI1AKf+HgVu2+gnKUyczChQsNikx0xmazMfnOO2ioPczNoxvIi9MKyyYFN4yyUWT1c9e0qezYsSMu73uqpH9KLq+88kpoak7JiA6P8fUejdfr4e23345jZCIaXn/9dQ4equH/DrZhOoGBLaXg6iEO6uobut31clfXHluslLpdKdVPKVUY+YhpZOKE1NXV8eijj6Gzi/D2GnXc4wO5vfEWD+PNt96SfRETyMGDB9m2fQdn9oz/hrynF/kwq6PlUclg3LhxPP744wBp0jclPo/Hw/sfzMWfX9Z2EMtixVtQzoKFC7Hb7e2/gDBEfX09Eyfeyp7du7hpdBOD8uK7UEdumuaO0xtI8zuYNPHW5jL8RCf9U3LYsmULH3/8Me7ike1PzQkLZhbgKxzEG2+8waFDh+IYoTgVO3bs4JVZszi3xMOowhOvzBqc5+fCvm7eefttNm7cGIMIE1NXE8RrCa3A9W9gbYsPkQD8fj/3338/NrsDZ/n3Q0syd4Gn3zmQkce9992ftKvEpZrI/IZYz+1pT06aZnShl8WLPkqaldpeeOEFZsyYAaFVlqVvSnDz58/H1tSIt9fodp/39hqNx+1mzpw5cY5MdGTv3r1MGH8je3fv5JbTmji9KH6rK7dUnBlk6hn1WP02Jt56C6tWrTIkjhMh/VPi8/v9PPrYY5Cehbfv6cc93lM2Fn9Q85e//lXmSycBp9PJfffeQ05agF8OdZz061w9xElRZpD777uXxsbGKEaYuLqaII4E/gF8CWwA/gYc/zaViIsZM2awYcMGnAO+QzCroOsnmtNwDLqQxqYm/vCHP+LxxP+ulTjK7/cz94P3GVXoozjTmHmAF/T1cKS2jpUrVxry/idq8+bNjB8/HsCJ9E0Jzel0MuuVVwjk9iaQ27vdY4LZRfh79Oe111+XeT4JYNWqVdx4w++x1R5k8hmNfMug5DCiV1aQu8+qp1e6i7umTWP27NkJfZEu/VPimzVrFrt27sTV/1wwpx/3eG3NwdV3DGtWr06qhZO6o0AgwJ8eeID9+/dzw4jGUyqLz7Roxo9spK72CPf88Y/4fMb2hfHQ1QTxZWAE8BShDm5EuK1D4XLUT5RS3yilNimlbgm3FyqlFiultoc/F7Q4Z6pSaodSaqtS6tIW7WOUUl+Hn3tKqdDUeKWUVSn1Rrh9tVKq/ES++VTw5ptvMmfOHLy9RuHvWXHC5wezCnGWn8/mzZv404MPJs2do1S0dOlSjtTWcUmZcQsHnVnkpWem5rXX/pXQF14R11xzDd988w1ADV3sm4QxZs2aRUN9Pe6ysXS2uomnbAwul4uZM2fGMTrRkt/v59lnn2Xq1KkUmezcc1Z93BfN6kiBVXPXmfWcXexm5syZTJs6NWEHE6R/Smxr165l1iuv4Csagr+gvMvn+XqNIJDXlyeffJJdu3bFLkBx0rTWTJ8+nZWrVvH/KhyMKDj1/mtQXoDrhtnY8OWXPPzww0mxoN+p6GqCOExr/Rut9Sfhj+uBtpvsteYHbtNajwDOBcYrpUYCU4AlWusKYEn4a8LPXU1odO0yYIZSKlIr+TRwPaF9hCrCzwNcB9RrrYcATwCPdPH7SQlz585lxowZ+ArKQ+WiJ8lfOBB3v2+z/D//4bHHHkv5X/pE5Pf7eWXWy5TlBA0dpTeb4Ip+DjZv/oZ16xJ/P+etW7fy3HPPAdhOoG8ScbZlyxbefPNNvD2HEmxnE+qWgpkFeEtGMn/+fL744os4RSgiqqqquGnCeP71r39xQR83fzir3rCKho5YzXDjKDu/rHDw+ZrPuO7aX7N2beJVbp5M/ySD6/Gxf/9+7rn3PnRmD9wDvnNiJysTrkHfx6fSmDJ1WsIOUHRXWmtmzJjB3LlzuXKAi3Fl7qi99nl9vPxikIMlS5bwl7/8JaWvl7uaIH6hlDo38oVS6tvAp52doLU+qLVeH35sA74BSoGfcnQE7WXgZ+HHPwVe11p7tNa7gR3AOUqpPkCe1nqVDt3SmHXMOZHXehu4ONIBprp58+bxl7/+FX9+Ge5BF3Q6It8Vvt6j8PQ9k4ULF/LYY4/JncQ4W7hwIfuq9vP/lTtOaIWtWLigr4eiTHj2n88kfOd35pln8tlnnzV/3ZW+SS7A4svpdPLAn/5EMC0TT7+zu3SOp3QMZObx4EMP0dTUFOMIBUAwGOS9997juuuupXLnVm4cZeO6EQ7SuzalPe6Ugkv6ufnjmAasnjpuv/12nnrqqYTauulk+idkcD3mGhsbmTxlCk6PD8fgi8GcdsKvodOycAy+iMOHD3PX3XfLFJ0EEQwGmT59Om+99Rbjylz8YlD0t6a4coCbnwxwMn/+fB5++GH8/sSoroi2riaI3wZWKqX2KKX2AKuAC8IXRsfdmCh8cXQmsBropbU+CKEkEogMJ5cC+1qcVhVuKw0/Pra91Tlaaz/QCBR18XtKWu+88w6PP/44/rxSXEMu6vKiNMfj7XsGnr5nsGDBAh566KGU/aVPNA6Hgxeef44h+QHGnsD+PLGSZoKrBtrYtn1HaEuCBLZ69Wq++93vApx2An2TXIDFidaav/zlL+zfvz+0gJbF2rUTzRYc5RdwpLaWhx56KOEHKpJddXU1d9x+O9OnT6ci28FDZ9dzbi/j+6KuKM8NcN/YUGn+nDlz+M111ybMfokn0z/J4Hpsud1upk6dxv79B3AMvgidkXfSrxXMKcE58Hw2bdzIn/70J7lmMpjX6+WBBx7g/fff54r+Ln5Z4YzJXq1KwX8NcvFfA50sWrSIu+++K6EGpqKlqwniZcBA4ILwx0DgCuBK4MednaiUygHeAW7VWnc2FNzej1F30t7ZOcfGcL1Saq1Sau3hw4c7Czehaa158cUX+dvf/oa/YACuIReDyRK9N1AKb+lZeMrGsGTJEu66627c7ujdmhfte/HFF6mvb+CXFbZT6sxe3ZZFpc1Mpc3MQ+vzeHVbJ3thHsd3enkZnB/gn888jc1mO/mgYmzhwoXs3r0bYCtd7JvkAix+XnvtNZYsWYKn71kE8vqc0LnBnGLcZefw2Wef8fzzz8cowu5Na828efO49te/YtNXX/CrYXbu+FYThRnJlZBbzfDLoU6mnNmIt/4At9xyM//4xz8Mv6tzMv1TSzK4Hl1er5dpd93F5m824xx4QYeLZZ0If+Eg3P2/zfLly2WKjoEaGxu5/bZJfPLJJ/z3YAdXD4lNchihFPx0oItfDbOzevVqbrn5ppTbDaBLCaLWurKzj47OU0qlEUoOZ2utI+uWHwpfWBH+XBNurwL6tTi9DDgQbi9rp73VOUopC5AP1LUT/7Na67Fa67HFxcVd+ZYTTiAQ4K9//Ssvv/wy3p4VuAZfGLU7h8fy9vkW7gHfZfXqz5g06TYp8YqhLVu2MOedd7iw1H3Ke4vttVtwBUy4Aia2NKSx137ygwcmBdcMtdHY1MQzzzxzSnHF0oABAxgwYACA90T6pgi5AIudZcuW8ezMmfgKB+Ltc/zl49vjKxmBt3gYs2fP5sMPP4xyhN1bbW0tUyZP5vHHH2dAhp0Hz67jolJPTC+qYm1kgZ8Hz67jwr5u3nrrLX5z3bWG7pl4Kv2TDK5Hl9fr5Z577mH9unW4yr+Hv7A8aq/t6zUKT+lZfPTRRyk/Ly0R7d27lxtv+D2bN23kxlE2fjQgfjc2Lir1cOtpTVTu2sHvf3c9O3bsiNt7x1pX7yCesPBo+fPAN1rrv7Z46gPgmvDja4D3W7RfHZ67M5BQudaa8IWaTSl1bvg1//eYcyKvdRWwVCfD0osnyOPxcO+99zJ37lw8vU/DU/49UDH70QHgKxmOa/CFbN6yhfETJlBTU3P8k8QJ8fl8PPrIw+RZdUzq5E9VeW6Ay/q5mD9/PuvXrzc6nKiTC7DYWb9+fWjeYU4J7oHnn/wcaaXw9P8OgfxSHn/8cT799HjTt0RXrFy5kl//6hq+WPc5/zPUweQzGhNuIZqTlWGBXw1zcOcZTdiP7Gf8+BuZPXt2Ul20y+B6dEWSw1WrVuEe8J2TWvH9uO/R51t4+pzO/PnzJUmMo3Xr1jVvxTP1zEZDSuPP7OnjrrMa8NtrmTB+fNJsE3Y8scwyzgP+B7hIKbUh/HEF8DAwTim1HRgX/hqt9SbgTWAzsBAYr7WO3FK5AXiOUGnXTmBBuP15oEgptQOYRHjOUCqx2+3cccedLF++HHe/b+Ptd/YpL0jTVf7CgTgrLqHqQDU33Dieysrj3pARJ+CVV15h1+49/HpoE9lpiTmu8fOBTnpnax595GGczsRLYk+WXIDFzqZNm5g6dRr+9FwcQ3546mXwJhPOwRfhzyrinnvuTYrVdROV3+/n6aefZtq0aRTQxP1j6xhX5o75wljRLH/vqtGFPh46u44xRaEtU+68846k2OBaBtejy+PxcNdddzcnh76SEbF5I6Xwlo5pThIfffRRWewvxubNm8cdd9xBvjJ+K57y3AD3jqmjj9XJXXdN480330yKrcI6E7MEUWu9QmuttNana63PCH98qLWu1VpfrLWuCH+ua3HOg1rrwVrrYVrrBS3a12qtR4efmxDpyLTWbq31L7TWQ7TW52itU2pDmvr6em655Va+2vg1rkE/wNc7/vvrBvL6YB92OXVNDsZPmGBouU4q2bJlC6+++irn9XZzZs/E3XDVaobfDm/i0KEaZsyYYXQ4USEXYLGzbds2br/jDjwqHUfFpV1flOZ4zGk4KsbhS89l6tRpCbMISTKx2+1MvvNO3njjDX5Y6uaPY+rpmx2fuxzRLH8/EdlpmvGj7Px6mJ0N69fx++t/mwwDnTK4HiUul4vJU6bw+edrcJefF7vkMCKSJPY9g4ULF8pifzGitWbmzJk8/vjjjC7w8MezGhKiAqLAqpl2ZgNje3qZMWMGTz75ZFIPEsS2TlGctNraWm66+WZ27dmDc8gP8RcNMiyWYFYRtmFXYPfBrRMnsnHjRsNiSQUej4c/P/gneqQH+WVF4t+Vq8j3c3l/F/PmzWu1ZHsSkwuwGNixYwcTJ92GK2jGPvQydHqU7xJZMnAMvQSvOZM77ryTTZs2Rff1U1hDQwM33zSBDV+s5zfD7fzvMAdp3eSvv1JwYamHu85sxNFQw4Qbb0zogU4ZXI8Ou93O7XfcwYYNG3AN/D6+4jhtj3vMYn/33nsvXm9yrAicDPx+Pw8//DCzZ8/mwr5uJp7WRKYlccZerWYYP9rG5f1dvPfee9x7772GL5Z1srrJn4jkUltby8233ML+A9U4Ki4hkF92/JNiTGfkYR92BW7Sue322/n666+NDilpzZw5k8p9VfxmeOKWlh7r5wOdlOUEeeyRh5OiTKszcgEWfbt27eLWiRNx+DT2ikvR1pyYvI9Oy8I+9DI8ysrtt9/BN998E5P3SSUOh4NJE29l3949TDq9ie/3Tc6LlVM1ON/PPWfVk6Ht3DZpYmR1UZGCGhsbmThpEps2bcY16Af4ew6JewzePt/C3e/brFixQlaEjxKv18t9997LRx99xM8HOvnVMAfmBMxiTAr+zxAn/7fCwfLly7lr2tSk/Pkn4D9t99bU1MSk227jwMFDOCrGRWUZ5mjR6dnYh16OR1m5887JbN++3eiQks4XX3zB22+/zQ9L3YwuTNzS0mOlm+F3I5poaGhg+vTpRocjEsiePXu45daJ2L3B0J3DU9hXrCt0eihJdGkLk267jW3btsX0/ZKZ1pqHHnow9DMa3cRpRcnT58RCcWaQqd9qIC3gYtqUydjtdqNDElEWGWDfsWMnziEX4y8caFgsvt6jcJefx+efr+HOOyen1Dz+ePP7/dx/330sX7GCX1Y4+NlAV9SW44jVHOnL+rn5zXA769atZ9q0qUl3J1ESxATi8XiYPGUKe/fuwzHk4oRKDiN0ehb2iktxBU1Muu12Dhw4cPyTBABOp5NHHv4zvbI0/z3EYXQ4J2xAboCflTv55JNPWLZsmdHhiASwb98+br11Ina3Ly7JYURosOoyXEEzEydOYufOnXF532SzaNEiPv10Jf892MHp3Tw5jOiZGeSm0Y1U19Tw9NNPGx2OiKLq6mom3HQTe/ftDw2w9+h3/JNizFc8DNegC/jq66+YOHGSbBt2ErTWPPLII6z49FP+Z6iDS/pF925cLOdIf7+vh9+MsLN+/Rfcf/99STUnURLEBKG15s9//jPfbA5v4JrX1+iQOqStOdgrLsXucnPn5MkJvZF6InnmmWc4dKiG3w5vwhqbLSxj7soBLgbmBXjir3+hoaHB6HCEgaqrq7l14iQanW7sQy9FZ+TH9f21NQf70Mtw+GHipNuSYfGRuPL7/Tw381kG5QW4NMoXVMmuIt/PJWUuFnz4Ifv27Tv+CSLhVVVVMX7CTVTX1GIfemlCXUP5iwbjHHwxW7dv5+abb0m5DdVj7YUXXmDx4sX810An48qSry87v4+HX1Y4+PTTlfz97383OpwukwQxQbz66qssW7YMd9nZUd3A1br3M8zOWszOWjK3fIh1b3QWGdGZ+TgGXUTV/v088MADSTUqYoQNGzbwwQcfcEk/F0N7JO+qZmYT/Ha4DbvdxlNPPWV0OMIgtbW13DpxInUNTdgrLiGYWWBIHNqai73iUmwuD5Mm3UZ1dbUhcSSiNWvWcPhILT8Z4Ij5NhbJ6MoBLhSaDz/80OhQxCnauXMnN46fQF2THfuwywjmlJzS68XiuilQ0B/nkB9SuW8fE266SfqqLlqyZAmvvPIKF/Rx85Nyl9HhnLRL+rm5rJ+Ld999l7lz5xodTpdIgpgAPvvsM55/4QV8RYPx9R4d1dc2OetQAR8q4MNiq8bkbLMV20kL5PXB3e9c1qxZw4svvhi11001Ho+Hxx97lOIszVWDkn8OQllOgJ8McLJ06dJUWdVUnACbzcZtt9/BoZoj2CvGEcwqMjQenZmPveJS6hptTJw0ibq66PVxyWzNmjVkWFRClJa6/IqMjAyuuuoqMjIycPmNz1jz0zXDevhYs1r6sGS2ceNGbrr5ZprcfuxDL49KfxSr66ZAfin2ikuprqll/PgJUvVwHDt37uTRRx5maA8/1wxzxGsL8Ji5eoiT04t8PDl9elKswi0JosEOHDjA/Q88gM4qxF1+Hsn2P8BXMhxvz6G8+uqrrFixwuhwEtLrr79O1f4D/KrClrSlpce6coCLvtma6U/8NekmXouT53K5mDx5CpWVlTgGX3RKI/XRHKUPZhVirxhH9aHD3H7HHVL2DuzevYt+2T4sCfBX3ulXXHnllUyYMIEf/ehHOBMgQQQYlOensnKvVMAkqdWrVzNx0iScQQv2YZcTzOxhdEjHFczthX3YZdTZnLK3dCdsNht//MPdZJp8TBjVlBD92KkyKfj9SBsFVj/3/PEP1NfXGx1Sp1Lgnzx5OZ1Opk27C5fXj2PQhWCKz+bB0eYZcC7B7GL+9OCDMiJ2jOrqama/+irnlHhSagXBNBNcM7SJ6kM1vPbaa0aHI+LA6/Vy9x/+wOZvwvOk80tP6fWiPUofzCnBMfhCdu3ezeQpU3C5krccKRoa6uvpkZ4YiU+WRTNv3jz+9re/MX/+fLISZN+yvPQg/kBAVpdMQh999BFTp07Dm5aLfdgVaGuu0SF1Wcu9pW+55VbWrFljdEgJJRAI8NCDD1J98CDjRzbSw5oY/UU05KRpbhrVSGNDHffecw9+f+JOOZIE0SCBQIAHHniAPZV7cAz8QdxW/4sJkwXn4AvxBGDylCmyeEkLzz77Twj6+D9DUu8CZESBn3NKPLz2r9kcPnzY6HBEDPn9fu6//37WrV2La8B5UZ0nHU2B/DJcAy9g8+bN3HX33d367nZ4S86EkGnRuN1u3nnnHdxud8JsbB25j5lI/1aic1prXn31Vf785z/jy+mFfdjl6LRMo8M6YaG9pX+E25LNlClTWbBgwfFP6iZmzpzJqs8+4/9V2BmWxGs2dKQ8N8C1w2x8+dVXPPHEEwnb/0iCaACtNdOnT2fVqlW4+337lEfiE4G25mAffDGHDh1m8uQpOBzJt41DtG3bto2lSz/hsjInRRlBo8OJif8e7MTv9/HSSy8ZHYqIEZ/Px33338+KFStw9z8Xf/FQo0PqlL9wIK7y77F+3bpunSTm5OQmTClnoorMhczKis6+ZyK2fD4fjz76KM899xy+wkE4K8aBOd3osE6aTs/CPuxyfDm9eOSRR3juuecIBlPzWqGr8yS8FgAAIABJREFU3nzzTV5//XUuLnVzcWnq9t3n9fby4wFO5s+fn7BreEiCGGfBYJAnn3ySuXPn4ulzOr5eI40OKWqCOSU4B13A1m1bueOOO7t9kvjSiy+SnQZXDIjPssxGLARRnBnkwr4uFi5cwMGDB2P+fiK+XC4X06bdxfL//Ad3v28nTX/l71mBq/x7rP38c26//Y5uuSF6z+Ji6rxpRoeR0GrdJgp65GGxJOf0ju6koaGBiZMmsWDBAjx9z8A96AIwpcCkfnM6zopLmtdyuOeee7ptyfMbb7zBjBkzOLvYy/8MTf5FaY7nqkEuzu/jZtasWbzwwgsJdydREsQ48ng8PPDAn3jvvffw9hqNt3SM0SFFnb9gAK5BP2DzN5uZcNNN3bb0cNeuXaxctYpLy5xxm29j1EIQVw5wo3SQN954Iy7vJ+KjtraWW2+dyOdrP8ddfh6+3qOMDumE+IuH4hp0AV9v3MiECTd1uwGMsrIyalwKf/e+IdGpapeF0jLjN1MXndu6dSu/+e1v2bTpG1yDLsBbelbSLejXKZMJT/l5uPudw/IVK7jxxvFUVVUZHVXcBAIB/vGPf/D0009zTomHG0bZusXWPErBdcMdfD+cJD7++OMJNSdREsQ4qamp4daJE/nkk6V4ysbi6Xd2anVwLfgLB+KsGMeeyn387ve/5+uvvzY6pLibM2cO6Wa4OI6buhq1EEShNch3StwsWPBht7xTk4o2b97Mb6+/nm07duAafCG+4mFGh3RS/EWDcVaMo7LqANf/7nd88cUXRocUN4MGDSIQhAOOFLjLEgNBDXsdFgYPHmJ0KKIDWmvmz5/P+PETONLkwj78CvxFg40OKzaUwtd7NM6KS6jcf5DfXn89K1euNDqqmGtoaGDKlMm89dZbXFLm4oaR9pRYsbSrTAquHe7gJ+Fy00kTb02YGyvd6MdgnJUrV3LtddexZet2XIMvxNvn9JRNDiMC+WXYh19BncPHLbfcwuzZs7vNUuIul4uPFy/m3BI3uWnxKxkwciGIi8vceDxelixZErf3FNEXDAaZPXs2EybcRJ3Di334j/AXlBsd1ikJ5JdiG34lNp+JiZMm8cILLyTUKG2sDB8+HICdTVI+2Z6DTjMu39F/J5FYXC4Xf/7zn3nsscfwZBVjH/ETgtk9jQ4r5gL5pdhG/BinymLatGk888wzKdtfrVmzhuuu/TUb1q3l18Ps/HKoE3M3zEpMCq4a7OL3I21s/WYTv7nuWpYvX250WJIgxlJTUxMPPvgg06ZNwxZMxzbyJ/gLBxodVtwEs4qwjfgJnvz+zJw5k/ETusfGsKtWrcLt8fC93qk7wfpYA3MD9M3WLFnysdGhiJNUWVnJTTffzMyZM/Hk98c24idR2XQ6EejMfGwjfoyvaAizZs3ihhtvZMeOHUaHFVOlpaUUFuTzTYPMQ2zPN/WhxPm0004zOBJxrF27dnH99b9j0aJFePqeiXPoJei0DKPDihttzcU+/Aq8xcN5/fXXuenmm6murjY6rKix2Ww8+uij3HnnnVg9ddwzpoELU3hBmq76bm8v942po4du5A9/+AP333+foXslSoIYA8FgkAULFvDL//lfFn+8BE/fM7APvxKdkW90aPFnScc9+EJcgy5g647dXHvddTz33HO43fErvYy31atXk5MOQ1NweeaOKAVjerrYuHGTbFKeZDweDy+99BLXXncdm7duxzXwfNyDfwAWq9GhRZc57f9n777DoyqzB45/TzKZJCSBQOi9SgdRLIB0EQuKDVYFRUTBta+ooK5i7yvorrsiFizYG+iPKl0pivReAkEglDTSJzPJ+/vj3ugY6WRa5nyeh2eS2+YMMznz9kthk+4UNOvN9l2/MXLkSCZOnFhhF4QQEc4593zWZ0ZTrPMQ/2JNupM6tWpSr17oryJeURhjmDp1KqNGjWLPgTTyW15MUb1OIGFYVI1w4GrclYKmvdi8dTu3jBjBwoULAx3VaTHGMGvWLG4ccgMzZkznsoYFPNU5g4YJ4TG67ETUjSth3NmZXN0kn4UL5nPj0CFMnTo1ICPwwvCvzrc2b97MnXfdxYsvvkhWsZO8NpdbE6orwmpbp0oET1IzctpeRWGVRnz00UcMvfFG5s+fH3SrNpWHdWtX06qKKywmWXtrU9VNSUkJmzZtCnQo6gQYY1iwYAFDhg5l8uTJFFZuQE7bq/FUb1Ghh8B7qjUhu+3VuKo145NPPuGGIUOZNWtWhVxe/oILLiC3CDZkai+it1y3sD7TSfeevZAK/FkPJTk5OTw+bhzjx4+nsFItctoMpLhy3UCHFXCepKbktL6CXKnEuHHjePXVV0Py1j2bN2/mzjvv4Pnnn6e6ZPF05yz+1jwfZxgXjY/GEQFXNingmXOyaODMZvz48dw+apTf1/PQyQnl5MCBA0yaNIkffvgBccZS0KQ7nqTmFbqgdbJMVCyFTXvirtGSQ7uX8eSTT/LFF19y55130LZtaK2QeDQul4t9qQfo0iT8WsRKWwF37drFueeeG+Bo1LGsXr2aNydOZPOmTZhK1ShoeQnFlesEOiz/cURT2KQ7RTVaUvLbcp5//nk+//wLRo0ayTnnnFNhKg3nnXcelRPimb+3iA5J7kCHEzQWp1q9qhdddFGgQ1FYlYfHHx/HwUOHKKx/Du7a7bTs5MXEVCav5aVE713JtGnTWLtuPc88/RT169cPdGjHlZGRwdtvv82MGdNJcMKIVrl0rxN+Deinol5cMWPPPMyyA04+Td7O3XffTd++fRk1ahQ1a9b0+fP7rAdRRN4VkYMist5rWzURmSMi2+zHql77HhaR7SKyRUT6e20/W0TW2fteF/ubW0SiReQze/tyEWnsq9dyLHl5eUyaNImhQ4cyd94CXHU6kN3umgrfCn86ihNqk9vmCgobd2PT9p3ceeedPPnkkxViGfr09HQAqsVUvN6I44l3GJyRkJaWFuhQ1FFs2WLdo/S+++5jc/JuCht3I7fNFeFVOfRSEl+TvFYDKGjakx17D/LQQ9b/zfr1649/cghwOp1cMfBKVqY52ZOrTfUARcUwc08cHTq0p3nz4FvBNFzKTqWmTp3KnXfdxcHDeeS1uhR3nfZadjqSiEhcDc75fVXmW2+7LSgWMjkaj8fDF198wdAhNzBrxnT61y/gpfMy6Fk3+CqHgbiH9IkSgS61i3jpvHSuaJTPovlzufHGoUyZMgW327eNfr4cYjoZuLjMtrHAXGNMC2Cu/Tsi0ga4Dmhrn/NfESn9NvsfMBJoYf8rveYIINMY0xwYD7zos1dyBMXFxUybNo3rrr+BKVOmkF+5ITntrqaofmeIdPozlNAkEbhrtCS73TW46p7JgkWLGXrjjbz55pvk5eUFOrpTVvoH65CKN3T2eEQgKkIq7IproSw5OZlHH32UUaNGsWL1Wgrrn0NOu2us21eE4/web95D4Buez9pNW7nrrrt48MGH2Lx5c6CjO23XXnstsbGxfLI9jgo4ov+kzdgdS2YhDB9+S6BDOZrJVOCyUymPx8Orr77K+PHjccXVJqfNQErifd8rEuqKExuQ0/oK8iMTeOyxx5g8eXLQDY/fvHkzI2+7lTfeeINmsTk8d24mN7Tw3z2hT1ag7iF9MqIjrZVOXzgvk3aVc5k0aRIjbhnO2rVrffacPisZGGMWARllNg8E3rd/fh+40mv7p8YYlzFmJ7AdOFdE6gCVjTFLjTVZ7YMy55Re60ugb2kLma+tXbuW20aO5NVXXyXLxJLX5goKm/bERMf74+krlsgoiuqdRU67ayhMbMKnn37KDUOGMHPmzJCcn1ipUiUACoqDL8H4WnEJFHjM7/8HwSxcWul3797Nk08+xS0jRrBk+S+46nYiu/21Vit9hM4w+JOISNy12pDd7lpc9TuzYvVabr/9dv75z3+SnJwc6OhOWWJiIiNuvZV1GVH8uL+CLTx0kvbkRjI1pRI9e/SgU6dOgQ7niCpy2amUy+XikUcfZdq0abhqt6egxYUVb1EsHzLR8eS1vBh3UnMmT57Myy+/HBS3EfN4PLz77rvccccdZKXu4u52OTzQMZs6ccFVgS0rUPeQPhU1Yku4t30OoztmU5D+G/feew///e9/KSoqKvfn8nfTcS1jTCqA/VjaXFQP+M3ruD32tnr2z2W3/+kcY4wHOAz4dE32goICXnvtNe655x6S9xygoFkv8lteEtz35iku+lPXOcXl/yEqD8YZR2GT7uS1vpxMj5MXXniB0aNHh9zSzklJSURHO8Py5tQHCiIoMYTKqoCTqcCt9Glpabz00ksMGzaMBYsW4ardnuz2g6wVAXWEw7FFRlFUpwPZ7a/FVbcTPy37mVtGjOCZZ54JuXxU6sorr+TMjh15f2s8KTn+z00N4z3ERpYQG1lCq0Q3DeP9P8ogzy28vqEKCZWrcO999/n9+U9TSJedvBUVFTFm7Fh+Xr6cwkZdKWpwTvCNYgiFclOEg8Im3XHVPZMZM2bw/PPPB7RRPS8vj4cfHssHH3xA15r5PHduBufULAqJ0cKBvIf0qeqY5ObZzhn0rlvI559/zgOj7+fw4cPl+hzB8ld5pI+QOcb2Y53z14uLjBSRFSKy4tChQ6cU4K5duxh+yy188803FNVqQ07bq/BUaxr0Y+XFU/SnrnPxBGGi81ISX4O8VpdR2Kgrq9as4+bhw4N6nH1ZERERtG3blg1Z0WE3nGujvVJiu3btAhzJ8VXUVvqCggLee+89brhhCDNmzsJVsw057QdZQ9+1hf7kRDopqteJ7PaDcNVqz9x5Cxg69EYmTpxIbm5uoKM7KZGRkTz2+ONUqZrEv9YlcrDAv1/9Q8/Ip1FCMY0SinnkrGyGnuHfW4u4imHCusqkFTp44smnqFatml+f34eCvuz0pycxhgkTJrB61SoKmnTHXbPVaV/TF0Km3CRCUb2zcNU7ix9++IEPP/wwIGF4PB7GPPQgv65YwS2tchnZJi+oe+EqihgH3Nwyjzva5rBxw3pG3/+Pcl3h1t8VxAN2oQr78aC9fQ/QwOu4+sA+e3v9I2z/0zki4gCq8NcCHwDGmLeMMZ2NMZ1r1Khx0kGvWbOGv99xBwfSD5Pf6lJcDc+HyNBYNtw4nH/qOjeOEOg9EMFdsxU5ba8kPzKexx5/nK+//jrQUZ2wHj16sjc3gp0BaKkPFGNg8f5YmjRuFBIrqx1FSLfSr1ixgmHDbub9998nL74uOW2vwtXwPExUrC+ftuJzRFPUoDM57a+hoEojPvnkE268aRhLliwJdGQnJSkpiRdfepliRxzPr65Kan6wtA/7VoFHeHVtFbYdjuLhRx6hY8eOgQ7pVIRk2ams5cuXM336dFx1OlgL+QWpUCs3FdXpiDupGe+99x47duzw+/N//PHHrN+wkZGtc+hVN/RuwRHqzq9VxN3tstm+I5l33nmn3K7r72+IacAw++dhwFSv7dfZ83aaYA3V+tkupOWIyPl26/tNZc4pvda1wDzjg/719PR0HnnkUfKNk5xWl1GcULu8n8K3Ip1/6joPpeFlJjqBvDMuwVOlAa+//jq//vproEM6IRdeeCFxlWKZuiv45+KVl3UZUezMjuTKq66uMLcI8OKzVvryaKH3eDxMmDCBBx54gIM5heS3upTCZr0xMZVP6Xp+EwrDuLwYZxyFTXuQ1/pyMgoNjzzyCM8//7xP5n74StOmTXl1/ASKoxJ4emVVtmRV7Hmo6YURPLcqka3ZTh5+5BH69u0b6JBOVciVnY7k3Xffg5jKFNU9yx9Pd+pCrdwkQmHD8yHSyeT33z/+8eVswby5tKnqpmvt0MmFFU2n6m7OruFi/ry55XZNX97m4hNgKdBSRPaIyAjgBaCfiGwD+tm/Y4zZAHwObARmAncaY0pn3P4deBtrWNcOYIa9/R0gSUS2A/djzxcqb5MmTSK/oJC8Zn0x0Qm+eAp1LJEOCpr2gtgqvPLKvwIdzQmJj4/nhiFDWZXmZHVaaPQ0n46iYvhoewJ1atfi0ksvDXQ4p8PvrfSn20Kfn5/Pww8/wrfffktRrbbWzaVDpBErZIZxlVESX4Pc1pfjqnsms2bN4v7Ro8nOzg50WCesRYsWvPG/N6lSvQ4vrKrCnD0xFXI4/MZMB+N+rcohTyWee+55+vXrF+iQTkhFKTuVVVBQwNatW3AlNYeI8Oi99itHNK7ERqxatdrvT13ocuGIqIBJJMRERVCuDZY+az40xlx/lF1HbMIzxjwLPHuE7SuAv0xqMsYUAoNOJ8YTsW3bdtwJtTGxVXz9VOpoIh24kpqTuudXCgoKiI0N/iFzgwYNYs7sWbyzZTdPJ2SQGF1xk+fH2+PYnye88uSDREWFdIW4tGX9Bf7aSv+xiLwK1OWPVvpiEckRkfOB5Vit9P8uc62l+LCVfsKECfyy4hcKG3ezblkRQkqHcRlj7GFcIdTjHhFJUb2zKIlJZMOGxbzw4os8+8wzIdN7Xq9ePd6c+BbPPvsMHy5bzsaMKIa3yqWyM/TzlKcEvt0Zy3cplWjQoD5PP/MsjRo1CnRYJ6yilJ3KysrKsp7fEePvpw4bJiqGvPQc3G63X7+Le/fpy5QpU/gp1Um3OqHR0FfRrDjk5OeD0Vx+RZ9yu6Y24xxHdEw0Dlc2lAR+CeFwFpGfSZQzyId5eHE6nTw+7gkKcfLa+iq4/PDxCcRKgfP2RjNvbwzXXXcdnTt39vnzlZeK0Eq/bt06Zs+ejat2h5CrHAKhN4zrCDxJTSmoexZLfvqJ5cuXBzqck5KQkMBzzz3PHXfcwdqsWB75pRorDoXee+Btd04kT/5alWkpleh/8cVMfGtSSFUOK7JatWoRF59AZE5qoEOpmIzBkbOfxk2a+L2h9qabbqJTpzN5a1MCH22tRKHeCtlviorhix2xvLE+gVatWjFq1Khyu7ZWEI9j2E03QWE2Mbt+0kpiIBhD1IGNRGUk87fBg0Oi97BU06ZN+ec/HyM5O5L/rE/A7eNbAfl7pcCfDzp5f2s85513LrfeeqtPn6u8GWOuN8bUMcZEGWPqG2PeMcakG2P6GmNa2I8ZXsc/a4xpZoxpaYyZ4bV9hTGmnb3vrtJeQmNMoTFmkDGmuTHmXGNMud9Ir3QxAnet1uV9aXUSSv//A7E4xOmKiIhg8ODBTHxrEjXrN+X1dQn8e108Ga7QKhq47ELSuBWJHI5I5Omnn2bs2LEh9X1R0UVERHDZpZcQlbGTyGytJJY3R/oOInMPclkApnlER0fz3HPPc8XAgczZG8vYn5OY/VuMXxrGw1VRMczfG83Dv1Tju5RK9Ot/MS+9/Eq53oc6tL4FAuDcc89l2LBhRKVvJ27rTCIKsgIdUtgQdyHRu34kZvcyzj//fIYOHRrokE5a9+7duf/+0axJd/L6ugSKKkjCXHbAyX83JNC2TVvGjXsCh6NiL3YRjJx2j3pEfmaAIwlvpf//oTy8umnTprw58S1uu+021mTFM3Z5VWbujsET3Pe3BmBVWhSP2IWkCy/qz/sffEj37t0DHZY6gltuuYU6desRt2MekTmheU/RYOTI2EVsyk+0aduWq666KiAxxMbG8o9//IN///s/1G3Wlo+2xfGPpUl8sSOWA0G+YnIw3Kf1RKUVRPDtzlhGL0vivS3xVKt/Bv/6178YO3Ys8fHx5fpcWqo7DhFh+PDhNGzYkFde+RcRG76hKKkFRfU6YZxxgQ6vYip24zywkZgD66DYzQ1DhnDLLbcQGRmat424/PLLMcYwfvyrvLSmCve1zyY+yjdzffyR2Gb/FsOUbXG079CeF154sVxbrNSJ69u3L++//wEHdi8lt8VFwb9qaQUkrlwqpfxIYtWqXHbZZYEO57Q4HA6GDBlC7969eW3CBD7++WcW7o/lxuY5tKl2+nmlvHPT/vwIpmyLY026k0YN6jPhuQc488wzy/U5VPmKiYlhwvhXuX/0aPZtnU1Bg3Ot4fEhMnc36JgSnKlrid63itatW/PC888HvJzUrl07/vPGG6xbt45PPvmE/1u6lO9SKnFGoofutQs5u0aRz8o/p2roGfnszrWqQ4+cFXwLjuV7hJWHovhxfywbM604zzmnM9dffwOdOnXy2dx38dPqxkGjc+fOZsWKFad0blZWFh9++CHffvstxSUGd9VGuGu2pji+VtAmuOjdy4hK2wZAcaUkSipVs+7jGISk8DDOg5uJTt+O8bjo2rUrI0eOpHHjxoEOrVzMnz+f5559hqRoD/9on0WdSiHQPO+luAQ+3l6JOXti6datK48/Po7o6OC4+bqI/GqMCZ1JkEdwKrlp/fr1jBn7MPmFReQ37oYnsVHQ5qKyYjdPx+HVi+BJqE1Bq9BZBTfy8B7idi0mOhKeefrpkJqDezzGGH766Sf+8+/X2X/gIOfUcHFd83xqxAY+ZxV4YNquSszcE0t0dAzDbh7ONddcE9SjGMI1Px1NVlYWTz31FCtXrqS4cl0KGnUNqgauUCg3ReSnE5uyhIjcQ/Tp04cHH3wwKIdUHzp0iNmzZzNzxnR+27OXSIE2Vd2cU9PF2dWLSAiShbGeW2l9/oKlgpjnFlamOfnloJP1mU48JVCndi0uufQyLrroImrXLp/Vyo+Vm7SCeApSU1P5+uuv+b/p08nPy8NUqoYrqTmeak0xzuDrTYndPB0gOAtfxW4cmSk407cTmb2PiMhIevXsyTXXXEPbtm0DHV25W7duHf989BHcBTnc3jqbM6u7Ax3SCckpEt7YUJmNmQ4GDx7MqFGjAt5S6S2cC2Cpqak88uij7ExOpjihNoX1O1MSX9MHEZavUK0gRuSlE7N3BZGH91Kvfn2eeza0Vsk8GS6Xi88++4wpUz6ixFPEpQ3yGdCogOgA/OmXGFiy38nnyQlkuaB///6MHDmSpKQk/wdzksI5Px2NMYZp06bx3//+D5e7iKIarXHV7QhBssppsJabpCiP6L0riUrbRnxCAg+MHk2vXr0CHdZxGWPYsmULCxcuZMH8eaTuP4AItKjioVOSi7OqF1EnLnANUB9ttcruvl674VgO5EewKs3JqvRotmQ5KDFQs0Z1evXuQ8+ePWndujUR5XyLGK0geinPJFdQUMDcuXOZOm0a27ZuBRGKE+pQVK0pnqqNwBEcvStBl+hKionM3ktUejLOw79hit3UqFmTAZddxoABA0LiC/90pKam8tg/H2XHjmQGNs7nyiYFRARxp8+ObAf/2VCFbI+D++8fzSWXXBLokP4i3AtgHo+H77//nnffm0z24SyKK9elqGZrPIkNQIJz/kcotNL/zhgis/fiPLAJx+HfiItPYNhNNzJw4MCg6UX3pYMHD/Lmm28yb948kmLhhmY5dK5R5LfO6l05kXywNYHthyNp3aol99x7H61bh87iTOGen44lLS2Nd999lxkzZlgrG9dohbtWG0xUYHvDgq3cJK5cnPvXE522lYgIuPaaaxg6dCgJCaF3f25jDNu3b+fHH3/kp59+ZPt2a4Gv2nGGjtUKOTOpiJaJHhzB+dVVbopLYNthB6vTnaxOj2FfnpVQmzRuRLcLutOtWzdatWrl09snaQXRi6+S3O7du5k7dy6zZs9mf2oqSASeynXwVG2MJ7FhQJNdUCS6Yg+Ow3twZO7Cmb0H4ykiLj6Bvn16c+GFF9KuXbtybxkJZoWFhYwfP55Zs2bRtpqbv7fJCbp7kBkDc/bE8MmOOGrUqMmTTz1Ny5bBeTsFLYBZ8vPz+eabb/j6629IT0+DmARcSWfgTmqGiS7fCezlIShy0zFIUT6OjB3EpG2BgmyqJCZy5cCBXHvttSFZMDtda9euZcL4V0neuYt21dwMOyOXWj4cKp/vEb7cUYm5e2OoUqUyo27/O/379w+57wrNT8eXnJzM5MmTWbR4MRIRiav6GRTVaouJDszfWbDkpoiCTJz71xOVvoOICOGifv0YNmwYderUCWhc5enAgQMsWbKEpUuXsGrVKtxuD7EOoV3VQs6s7qZjUlHQlY9OVY5bWJcexeo0J2szo8l3gyMyko4dO3J+ly5069aNunXr+i0erSB68XWSM8awefNmFi1axPwFC+zKolAcXwt3YiM8VRv6PeEFLNF5XDiyfsORlYIzex+m2E18QgI9unenR48enH322SG98t/pMsYwffp0XpswgUqRbm5vfZg2VYNj9aw8t/DO5nhWHHLStUsXxj78MJUrB88ckbK0APZnHo+HJUuW8NXXX7Nm9WoAiivXoahaMzxVG4MjOO53FyyFsD+xh71Hpe/AkbMPjKFNm7Zcc83V9OjRI6xzFlifrWnTpvH2pLdwuwoZ2DifSxsWlHtr/y8HnXy4LYHsIuHKq65i+PDhIVsp1/x04lJSUvj444+ZM+cHSkwJ7ioNcddu6/e1HqJ3LwMIzKgGY4g8vIfoAxuIzN5HlNPJFZdfzuDBg6lVq5b/4/GjgoICVq5cybJly1jy04+kZ2QiQNMqxXRKctGpehH144pDZao9APvyIliZ5mR1ejTbDjswBhKrVKZL12506dKFzp07B2yxP60gevFXkgOrArBjxw4WLVrEokWL2bVrp7U9LomiKg3wVG1ESWw1nyc9fyY6ceXiyNpNVFaKtYy1MVStVo0e3bvTs2dPOnToENSLCQTCjh07eGLc4+zZu5crG+czsHFgh5zuyHbw3w2VySiKZOTIUQwePNinQxzKgxbAjm7fvn3MmTOHmbNmk7pvLxIRSVHletbohqoNA3qD+qCpIBa7rcas0hEOxR5q1qxF//4X0a9fPxo2bBjY+IJQWloa//736yxcuIjGlUsY2Sqb+vGnfx+fHLfw/pY4fj4YTfPmzXjwwYeCduTCidL8dPIOHjzI1KlT+XbqNPJyczBxSbiqt8Sd1AwiK2gjjcdFVNo2e8TCYaolJXHN1VczYMAAqlSpEujo/K50KOrSpUtZ8tOPbN6yFYCalQxQ6KvDAAAgAElEQVSdkgo5u3oRZyR6gm6KTomxylErDzlZmR5Dqj10tEXzZnTtdgFdunThjDPOCIqREFpB9OLvJOdt7969/PTTTyxavJgN69djjIGYBIqqNMRTtRHF8TWDdr7QURlDRGGW1eKetZuIvDQAGjRsSM8ePbjggguC5g8hmOXn5zN+/HjmzJlDm6oebm+TTWK0f/82jYFZe2L4bEcc1avX4PFxT4TMQkFaADs+YwybNm1i7ty5zF+wgIz0dIiIxFO5Lm57KLy/500HtJW+uMirUrgXU+yhSmIivXv1ok+fPmE37P1ULVy4kPH/eoW8vByub5ZL33quU27z3JTp4M1NVchxR3Lz8OFcd911FaJBUfPTqSssLOSHH37gq6+/ZmdyMuKIxpXUjKIarTGxFaPSFJGXRtTBTURn7sQUe2jbth1XX30VPXv2rBCf//KSnp7O0qVLWbx4MSt/XYHbU0xiNHSuUcB5NYtoUSVwlUVjIDnbwbKDTn45FEtGIURGRtDpzE5c0L07Xbt2pWbN4Fs8TiuIXgJZQfSWmZnJkiVLWLR4MStWrKDY40GcsbiqNMJTrQnFCbWCt7JoDBEFWTgydxKduQsKsgBo1br175XCBg0aBDbGEGSMYebMmUwY/yqx4uaONodp5achp/ke4e1N1pDSbt26MnbswyE1nEsLYCenpKSEjRs3smDBAubPX2DNV5QIPAl18FRthKdqo4AvEuEL4i4kMms3UZkp1vDRkmISq1alV8+e9OrVi/bt2wfV6ryhIiMjgxdffIHly3+mSy0XI1rl4jyJ/0ZjYPruGD5PjqNB/fo89vg4WrRo4buA/Uzz0+kzxrB+/Xq++eYbFi5cSHFxsbUYV41W1kiIYC0vHU2JB0fGTqIPbSYi9xBOZzQXXdSPK6+8kubNmwc6uqCXn5/PsmXLmD9/PsuXLaPI7SYpFrrVzKd7HZdP50Z7SyuI4Mf90Sw+EMuhfCHKEck5555Lr1696dKlS9CXo7SC6CXQSe5I8vPzWb58OQsWLGDp0mUUFbkQZyVciQ1xJzWnJK5GUNzbTAqziUrfTnRWCuRnIiJ07NiRXr160a1bN2rUqBHoECuE5ORkHn/sn+zbt4+/Ncvj4gaFPn379+RG8vqGKhwsiOT2229n0KBBQT+ktCwtgJ0673nTCxYuJHXfPgCKE2pbPYtVG2GccX6Pq7yIu8Aa4ZC5i8icVDCGmjVr0atXT3r06EGbNm20p7AclJSU8PHHH/POO2/TJKGY+zscPqGFJYpL4L0tcSxKjaFnz56MGTMmYPNxfEXzU/nKyMhg+vTpfDt1KmmHDkF0PK7qZ+Cu0TLoG7bElUvUwc3EpG/FuAup36ABV191Ff369Qv6ykSwys/PZ8mSJcyePYsVv6ygxBhaJnroV7+As6sXEVnO6b3EwOq0KObsjWVjRhQG6NTpTPr3v5hu3bqF1PuoFUQvwZTkjqSgoIDly5czf/58lixdiruoCGKr4KrWDHdSc/+vROgpIipzJ8707UTkHEBE6NChA3369KF79+5Uq1bNv/GEiby8PF544QUWL158Si3yJ2rFIScTNyUQl5DIE08+RYcOHcr/SfxAC2DlwxjDzp07rXtVLVxIyq5dAJTE16SoahM81ZoE5b1eyxJ3AY6MnXal0LrXYt169ejTuzc9evSgRYsWIdcIEioWL17M0089RXVnEWPPzDzmUHlPCfxvQwK/HHJy0003MXz48Ar5vmh+8o3i4mKWLVvGV199zcqVv0JEJO6qTSiq3ZaSSkF0uyxjiMw9QNSBDURl7UaAbt0u4Oqrr6JTp04V8jMfKIcOHWL27Nl8/900UvcfICkWLqqXR596had9/1Z3CSzcF83MPXEczBdqVE/isgGX079//5BdVVYriF6CMckdTV5eHosWLWLGjJmsXbsGAE9iA4pqtqG4cl2f9ipG5GdYY+IzdmCKPdRv0IBLLr6Yfv36BeU46orIGMOUKVN45523aVq5mPvaH6ZKOS31bAz83+4YPt8RR6tWLXnmmWepXr16uVw7ELQA5hspKSksWrSIefPnszM5GbBWQ3VXa4q7auOgudcrYM0pzEwhKj3599VHGzRsSJ/evenZsydNmjTRgpifrFq1irFjx1DbWcgjnTKJPcI0KmPg7U1xLN4fw5133smgQYP8H6ifaH7yvZSUFL755htmzJiJy1VIceW6uGq3o7hyvcCNwDIlODJ3EX1gAxG5h4iLT+CKywcwcOBAateuHZiYwkRxcTFLly7li88/Z83atSRGwxWNculV13XSKy6XGFicGs23KfGkF0CbNq0ZPPhvXHDBBSE/R1QriF6CPckdTWpqKtOnT2fqtO/IPpwFsYkU1mqLO6kFlNfwKGOIzN5H9P61RGanEhUVRb9+/bj88st9frNOdXSLFy/mmaeforKjiIc6ZJ322PoSAx9sjWPe3hj69OnNmDFjQ/5m31oA872UlBTmzZvHnB9+YN/evVZrfWJD3NXP8HmD1VEZQ2TOfqLStuLMSrFWH61Vm4v6XUifPn1o2rSp/2NSACxfvpyHH36Ys5IKubtdzl8+HjN3x/Dx9jiGDRvG8OHDAxOkn2h+8p+cnBymTZvGl19+RWZmBiYuicI6Z1qLcPkrR5WU4MjYQez+tVBwmNp16nDd3/5G//79iY0N7iGwFdHatWt5e9JbrF23ngYJJYxomU3Tyie24vJvuZG8szmB5OxIWrVqyW23jeTss8/2ccT+oxVEL6GS5I6mqKiIBQsW8MWXX7Jt61aIqUxB7Q54qjc/rUnakdn7iNm3ioicAyRVr8G111zNpZdeGpZLKwejzZs3M+ahBzGuHMZ0zKLBKS4nX1wCEzfGs+xgNNdffz233XZbhZh/pQUw/zHGsHXrVmbNmsWsWbPJy8v1mgPUChMV4/sgPC6iDm21loMvzCY2thIXXtiXiy++mDZt2mhjVpD49NNPefPNNxnRKpeedV2/b9+TG8ljKxLp0qUbTz/zTIV/vzQ/+Z/b7eaHH37ggw8+JDV1H6ZSNQrqnU1xlfq+qygagyN9B7Gpq6EwmyZNmzLsppvo3r27Ln4VYMYYfvzxRyaMf5WMzEwGNc3jsoZHX9/BGJi7N5op2+OJj0/g7nvupW/fvhUuV2kF0UuoJbmjMcawfPly3nn3XbZt3YqJSyK/YVdK4k9uoRgpyiN693KiMneRlFSdm266kUsuuQSnMzhupK3+kJKSwv3/uI+i3EwePjPrpO85VmLgvxvi+flgNKNGjeL666/3UaT+pwWwwHC5XCxZsoTvvvuelSt/RSIcuJKa4q7VjpLYxHJ/PinMxnlgA9Hp2zHFbtp36MDlAwbQo0cPYmL8UDFVJ6WkpIT77r2H5C3refm8DOKiDMbAC6ursNeTyAcffkRiYvl/ToJNMOYnEbkYeA2IBN42xrxwrONDMT8BeDwe5s2bx3uT3yd1316KK9ehsP45lMSV75SKyOxUYvf8guSl0ax5c0bccgtdunSpcBWKUJebm8srr7zCggULuKB2Ibe2zvvLrTGMgY+2VWLOnljOP/88xo59uMLmKa0gegnVJHc0xhgWLlzIhNdeJyszg6La7XDV73xCvYmOtO1U+m0ZDjEMGzaMwYMHa8UwyO3Zs4d7776L4oIsHuuUSY3YExtuagy8uzmOhakx3H777Vx33XU+jtS/grEAdrJCPTft3LmTr776ilmzZuF2u3EnNcNVtxMmpvJpX1tcuTj3rcaZvo3IiAguvPBCrr322gp1K4SKauvWrYwcOZJrm+ZzReMCNmc6eG5VFe655x6uvvrqQIfnF8GWn0QkEtgK9AP2AL8A1xtjNh7tnFDPTx6Ph2nTpvHe5MnkZGdTVKsNrnpnQ2TUaV1X3IVE/7acqPQdVK9Rg1EjR9K3b98KMTKnojLGMHnyZN5//3361Ctk2Bl5f+pJ/Gx7Jf5vdyyDBg3i9ttvr9C9vxW6ghgurWDHk5uby8SJE/nuu+8orlyHgqa9jz7Uy5QQvftnnAc30qFDR8aMeYh69er5N2B1ypKTk7nn7rtIlFweO+vIC0CU9X8pMXy2I46hQ4dy6623+j5IPwu2AtipqCi5KSsri88++4wvv/oKt9tDUY1WuOqfYkGsxINz72qiD24gMkK4cuBAbrjhBpKSgmiFQnVcDz74AFvXruCCWgVsyIwig0S++PKrkJ/7fKKCLT+JSBfgCWNMf/v3hwGMMc8f7ZyKkp9yc3OZNGkSU6dNA2cc+U16UJxwagvGODJTqJSyhIiSIoYMGcKQIUPC5jNdEUycOJFPPvmEQU3zaJhgjcg6kB/JR9viuPzyy7n//vsrfA/wsXJTSDdx2K1gbwCXAG2A60WkTWCjCoz4+HhGjx7N2LFjic5PI27bLPC4/nqgMcTsXIzz4EYGDRrEq6/+SyuHIaZp06Y88eRT7Mt38O7meI7XxrMxw8HnO+Lo3bs3I0aM8E+QKmwlJiYyatQoPv3kE664fADOQ5tI2PANkVm/ndR1IrNTSdjwLdH713LRhX35eMoU7r77bq0chqBBgwZTJDHMTk1gb2EM1w4arAXpwKoHeP9B7rG3VXjx8fH84x//4D///jd1kipTacsMnKlrOO4XqbeSEqJ3Lyd2+1yaN67P22+/zS233KKf6RBz66230r59O75IjuNfayrzrzWV+WhbHM2bN+Puu++u8JXD4wnpHsRwbgU7lhUrVjBm7FjcsUnktbzkT8NNo3/7Bef+ddx6660MHTo0gFGq0zVlyhQmTZrEba1z6V7nCI0BQJ5beOSXaiTUqM+bE9+qcDegLhVsLfSlTmaEQ0XNTRs2bOCFF1/it90puOqeSVHdTsdeJMIYnPvXEb1nBbXr1OGhBx/krLPO8l/ASpWzYMtPIjII6G+MudX+/UbgXGPM3WWOGwmMBGjYsOHZKSkpfo/Vl/Ly8nj55ZdZsGAB7uotKGzc7fjTc4o9VNoxj8jDe7jqqqv4+9//rlNzQpjb7Wbbtm1/2tasWbOwqexX2B5EwrgV7Fg6d+7M2DFjiMg5gDN17e/bI7P34dy/jiuuuIIhQ4YEMEJVHq6//nrat2vLlO3xZBcducD9+Y5KHC6K4NF/PlZhK4fBSkc4WNq2bcs7b0/i4osvJnrfamKS54M5ytxZY4je9RPRe1bQs2dP3nv3Xa0cKlX+9gANvH6vD+wre5Ax5i1jTGdjTOcaNU5uAbxQEBcXx7hx4xg2bBhRaduI3bHg6LkJoMRD3LZZOLL38sADD3Dvvfdq5TDERUVF0aZNmz/9C5fK4fGEegXxSKXiv3SJishIEVkhIisOHTrkh7ACr1+/fvTs2ZPo1DWIOx+MIfa3n6ldpw533HFH2HedVwQRERGMfuBBCosj+GbnXyt/u3MjWbAvhquvvpqWLVsGIMKwdy6w3RiTbIwpAj4FBgY4poBwOp2MGTOGUaNGEZWxi5hdP/11SJcxRO9ejjNtK0OHDuWJJ57Qe4Yp5Ru/AC1EpImIOIHrgGkBjikgRIThw4dzxx13WDe13/3zkQ80htjkhUTkHuTxxx9nwIAB/g1UKT8L9QqitoIdw2233YaYEuJXf0rCiveQ/AxuGT5cl4OvQBo3bsyAAQNYkBpDWuGf/5y/2VmJSpViGTZsWICiC3s6wsGLiHD99df/3lofdWjzn/Y7MpJxHtzINddcw4gRI7QRSykfMcZ4gLuAWcAm4HNjzIbARhVYgwcPZtCgQTgPbsSRsfMv+6MObMCRmcKdd9xB7969AxChUv51AusfBrXfW8GAvVitYDcENqTgUb9+fcaNG0dycjIACQkJ9OnTJ8BRqfI2ZMgQvv/+e37YE8N1zfMBOJAfwcpDToYMvYaEhIQARxi2jjvCocwcH3/EFHA333wzGzZu5NeVK/BUaYCJjkfchVTavYzWbdrqCAel/MAYMx2YHug4gsmoUaNYvWYN23cuJbtyXXBYQw2lMJvYvb/SpWtXrr322gBHqZR/hHQF0RjjEZHSVrBI4N1wbwUrq1evXvTq1SvQYSgfqlWrFt26deXHn3+karQ1f2JzlgMRYeDAsBzRGCyOO8LBGPMW8BZYi9T4L7TAEREefOABbrjhBmJSluCpXI/I3AOY4iIeeujBCn3PKaVU8HI4HIwdM4YRt95KzK6fKI6vZW0/vBuHI5LRo0dr45UKGyFdQQRtBVMKYODAK1m8+EembIv7fVv3C7oRTkOqg5COcDiKWrVqMXDgQL7++msch/cAcNFFF9G4cePABqaUCmvNmjWj/0X9mTVrJlGZu37ffv1NN+ktdlRYCenbXJyKirqUvFL5+fkUFxf//ntcXBwREaE+zfjEBNsy8qVE5FJgAn+McHj2aMeGW24yxpCbm/v77/Hx8do6ryqkYM1PJyOc8lPZ3ASan1TFdKzcFPI9iEopi97GIvjoCIejExGdH6uUCjqam5QK/VVMlVJKKaWUUkqVE60gKqWUUkoppZQCtIKolFJKKaWUUsqmFUSllFJKKaWUUkAYrmIqIoeAlEDHEQDVgbRAB6H8Ihzf60bGmJC+p4fmJhUGwvW91vwUmsL18xquwvH9PmpuCrsKYrgSkRWhvsy2OjH6XqtQop/X8KHvtQol+nkNL/p+/5kOMVVKKaWUUkopBWgFUSmllFJKKaWUTSuI4eOtQAeg/EbfaxVK9PMaPvS9VqFEP6/hRd9vLzoHUSmllFJKKaUUoD2ISimllFJKKaVsWkFUKgSIyBMi8oCIPCUiF5bjdXuJyPfldT2lTpSI3CcilY6y72YR+Y+/Y1InTkQSReSOUzhvgYjoSoEq4E71M6xCj4jcIyKbRGRKoGMJFVpBVCqEGGMeN8b8EOg4lCoH9wFHrCCqkJAInFThWkQifRSLUqfipD/DKmTdAVxqjBlyqhcQS9jUm8LmhVZ0IvKYiGwWkTki8ond23SbiPwiImtE5KvS1noRGSQi6+3tiwIduzoyEXlURLaIyA9AS3vbZBG51v75bBFZKCK/isgsEaljb79HRDaKyFoR+dTeFici79qfh1UiMjBgL0yFHfvz9392zlkvIuOAusB8EZlvHzNcRLaKyEKgW0ADVifiBaCZiKy288rvIxFE5D8icrP98y4ReVxEfgQG2YcMFZEl9mfhXPu4aiLyrZ23lolIB3+/IBV2vD/D74nIFQAi8o2IvGv/PEJEnrF/vt/+zK4XkfsCGLc6CSLyJtAUmCYio4+UZ0pHaXmds15EGtv/NonIf4GVQIPAvAr/0wpiBWAP17kG6ARcDZQO3/naGHOOMaYjsAkYYW9/HOhvb7/C3/Gq4xORs4Hr+OM9PafM/ijg38C1xpizgXeBZ+3dY4FOxpgOwO32tkeBecaYc4DewMsiEufzF6KU5WJgnzGmozGmHTAB2Af0Nsb0ths3nsSqGPYD2gQuVHWCxgI7jDFnAg8e59hCY8wFxphP7d/jjDFdsVr137W3PQmssvPWI8AHvghaKS/en+FZQHd7ez3+yEEXAIvt7+ThwHnA+cBtItLJz/GqU2CMuR37+wZozMnnmZbAB8aYTsaYFJ8FGmS0glgxXABMNcYUGGNygO/s7e1EZLGIrAOGAG3t7T8Bk0XkNkCH/ASn7sA3xph8Y0w2MK3M/pZAO2COiKwG/gnUt/etBaaIyFDAY2+7CBhrH7sAiAEa+vYlKPW7dcCFIvKiiHQ3xhwus/88YIEx5pAxpgj4zP8hKh8q+35+AmCMWQRUFpFErO+xD+3t84AkEani1yhVOFsMdBeRNsBG4IDdcNUFWIL1+fzGGJNnjMkFvuaPCqUKHaeSZ1KMMct8HlmQcQQ6AFUu5CjbJwNXGmPW2MN9eoHVmiIi5wGXAatF5ExjTLo/AlUn5Vj3oBFggzGmyxH2XQb0wOodfkxE2trHX2OM2fKni4jUKq9glToaY8xWuwX+UuB5EZl9pMP8HJYqPx7+3OAcU2Z/Xpnfy77XhiN/j+lnQvmFMWaviFTFGu2wCKgGDAZyjTE5InK0cpYKLUfLM8fKYWXzV1jQHsSK4UfgchGJEZF4rAoCQAKQag9H/H1irog0M8YsN8Y8DqQRRmOqQ8gi4CoRiRWRBODyMvu3ADVEpAtYQ05FpK09gbqBMWY+8BDWJPx4rOEzd5d+yenQGOVPIlIXyDfGfAS8ApwF5GDlKIDlQC8RSbLz1aAjX0kFEe/3LwVoIyLRdmt83+Oc+zcAEbkAOGz3KC/C/p4SkV5Amj16Qilf8f4MAyzFWjxrEVaP4gP2I/a2K0Wkkj094yqvfSp0HC3P7ML6XkJEzgKaBCi+oKE9iBWAMeYXEZkGrMH6ol4BHAYewyp4pWAN8SpNhC+LSAuslpS59nkqiBhjVorIZ8BqrPdvcZn9RfZiNa/bBTIH1ryurcBH9jYBxhtjskTkaXv/WruSuAsY4LcXpMJde6y8UwK4gb9jDd2aISKp9jzEJ7AKaKlYiwHo8PcgZoxJF5GfRGQ9MAP4HGt4+zZg1XFOzxSRJUBl4BZ72xPAeyKyFsgHhvkkcKVsR/gMLwYuMsZsF5EUrF7ExfaxK0VkMvCzffrbxpjjfc5V8HmCI+eZr4Cb7Gk4v2CVpcKaGKMjOCoCEYk3xuSKtVLpImCkMWZloONSSimllFJKhQ7tQaw43rInV8cA72vlUCmllFJKKXWytAdRKaWUUkoppRSgi9QopZRSSimllLJpBVEppZRSSimlFKAVRKWUUkoppZRSNq0gqoATkftE5JCIGBF55hSvscA+v7qI1BWRJ0TkyvKOVSmljkVEetm56D+neP4jInJfeV1PKRWaROQB+2//5kDHcjJEZLIdd+dAx6JOna5iqoLBo1irrw7j+PfPOhF1gXHA+8C35XA9pZQ6URuB6znKfbRExGGM8Rzj/EeANKz7lh73ekopdSJOIPcElVCLt6LRHkQVUCKyAKgOxGNV6L7z6gmsbv+8wD62ioi8KyIHRSRNRN6y7/tY1i/24zD7/NEiUiAi39rXcYhIuogss383IrJFRN4WkSwRmS4iVe19rUVkjohki0iKiPzDt/8jSil/E5EaIrJKRHLtf4tFpK2IdBORtSJSaI9y+MQ+/gk7b7wmIltFJFVEhtiXawN8gn0DeK/RDa+JSBowQEQm2NdziUiyiIwqPRaIAxrZ50w+wvXaishcEcmxc9JjIiL2PiMi20TkPRE5LCKzj5IjlVJByO41TBORX4H2XtsvEJHldn7aLiIjvfbdZv/d54nIzyJygb39ZjsnfCYiG4DPvba9b+e8TBG5t8zxD9i/f2//3tj+Z0RkkYjMsPPPGLt8dVhEVotI4zIv5zoR2SUiO0Wkr31Np4i8IiJ77fLWFyJSw95X2vP4PxHZC9zlu/9pdTxaQVSB9hTgwmoxvx6QYxw7AbgRmAy8DYywzy/rUftxkX3Nr4EvgMtEpCbQC6gGfOB1zhnAAeBz4BLgMRFxAFOxCmgvAcuBV0Xk8pN8jUqp4FaClSfuBV4AOmLlm4eApvb2p7DylLe+wCv2+W+LSK1jPEcnYAywBdiElacewMo7b4hII/6aD//nfQERiQKmAefZ56+1zxnudVhz4BCwFOgHXHNi/wVKqUASkY7Ay8B+YCJwob0rCevvvhFWzjgITBSRPiLSB3gL62/+fqAhME1Ekrwu3d++3gdltr0NGOAFEXGeYJhdgDlAOvA8VnlpMlbOvK/MsefarycJ+EhEooGHgdHAd1g59hLK5DmgO9YosKUnGJPyAa0gqoAyxswDPECeMeZTYOcxDh+ANSz6QayCVgRw0RGOm20/7jTGfGqM2YmVgBzAUKwCUxHwqdc5e4wxj2K1WJVgVSJbAi2whqw+DQyyj+13cq9SKRXkooGLgUlYf+sJWK3324BYrDxTGXijzHnjjTFvAe9iDZM/7xjPcZcx5h1jzCasSud44HXgfCASaF02Hxpjlpe5Rkv73KnGmNexCoRgFbJKpRpjHsIqtAE0Pu6rV0oFg172o3deAavRqCrwjjHmTazKE1h/95faP48zxkwE3rGPPd/ruu8aY143xnxbZtsbwM9YuetYjVvelhtjXgV+wmrQfx4rjwE0KXPsk/ZzTAVqY+WvAfa+UfbriOOv5bhHjDFvHyH/KT/SOYgq2BTbjw6sYadl7cfqRSzlOsIx5i8bjFkqIquxhmklAd8bYzKOcK4c4edZWL0E3jEopSqOe4CuwH+wWrbfwaokPoQ1EqEr1oiFh0Wk/hHOP9bIh1L7AESklX3d1cCTwOVYeSnGPu4v+esIjnVMaV4rnbsTeQLXU0oFn7J55Vh/98fat+8I246UJ7zLXwCJRzgvy35024+Hvc47Wq4pW67yYFUUS88r21l1pHiVn2kFUQWbXfbjSKzWJm/fAzcDVwBrgLOxevsWlzku037sJCLXA3OMMWlYvYgT7X0flDmnvog8C9TASlbzgc1YPQgXAHOBfKwhH98A60/p1SmlglFpASYea3hTfayCzyNYjVAbgN+wWsgre533DxGJxBriWYA1DL31CT5XLFar/YVl9mcCNURkGH/Mpy61BdgBDBSRu73OnX6c51RKBb8F9qN3XgFrhEMmMEJEfuOPRvLpWPlkNPCkiDTDamzKBJZhNT6djF3242Uiks6feyFPxeN2g9gVQCpW/voOq+w2DPgBawpPE/4Y+aWChA4xVcHmFawkcg9/7am7D2vM/GDgNazk9VPZCxhjkoGPseYVfgy0sndNAbKx5veULVBtAWra154JPGOvnjXQfo5/8sfQs3Wn8wKVUkHndazK2JVYQ6FKG4BKsHLRO1j5ZJwxZrfXebOx5gRFArcaYw4c74nsIabjsYau3wrMKHPIS1hD4CcDV5c5142Vk34BnsOa1/g4fwwnVUqFKGPMGqwpNLWxprvMsXelY1WydgOv2vtHGWPm28PSR2KVX14F9gBXGGPSTyGEH7HKTGdi5Z4VpzfO2JwAACAASURBVP5qfr/eGKzeyhuNMS6sIakvYzXE/QdrmOzC03we5QNizImMZlEqtNmL0/QEPgTeMMaM9tpngA3GmHaBik8pFTpE5Ams+TODjDFfBjgcpZRSqlxpD6IKF5diLUqzCqvlXSmllFJKKVWG9iAqpZRSSimllALCcJGa6tWrm8aNGwc6DKVUOfr111/TjDE1Ah3H6dDcpFTFpPlJKRWMjpWbwq6C2LhxY1asON15t0qpYCIiKYGO4XRpblKqYtL8pJQKRsfKTToHUSmllFJKKaUUoBVEpZRSSimllFI2rSAqpZRSSimllAK0gqiUUkopFTREZJeIrBOR1SKywt5WTUTmiMg2+7Gq1/EPi8h2EdkiIv29tp9tX2e7iLwuImJvjxaRz+zty0Wksb9fo1IquGkFUSmllFIquPQ2xpxpjOls/z4WmGuMaQHMtX9HRNoA1wFtgYuB/4pIpH3O/4CRQAv738X29hFApjGmOTAeeNEPr0cpFUK0gqiU8qnXX3+dEbeMoLi4ONChqNM0YcIEXnr55UCHoVQ4Ggi8b//8PnCl1/ZPjTEuY8xOYDtwrojUASobY5Ya64bXH5Q5p/RaXwJ9S3sX1R+effY5Jk2aFOgwlAoIrSAqpXzqyy+/ZNv2bbhcrkCHok7T119/zffffRfoMJSq6AwwW0R+FZGR9rZaxphUAPuxpr29HvCb17l77G317J/Lbv/TOcYYD3AYSPLB6whpc+bMZsqUKYEOQ6mACLv7ICqlAkN7EJVS6oR0M8bsE5GawBwR2XyMY4/U82eOsf1Y5/z5wlbldCRAw4YNjx2xUqpC0R5EpZRfuN3uQIeglFJBzxizz348CHwDnAscsIeNYj8etA/fAzTwOr0+sM/eXv8I2/90jog4gCpAxhHieMsY09kY07lGjRrl8+KUUiFBK4hKKb/weDyBDkEppYKaiMSJSELpz8BFwHpgGjDMPmwYMNX+eRpwnb0yaROsxWh+toeh5ojI+fb8wpvKnFN6rWuBefY8RaWUAnxcQRSRRBH5UkQ2i8gmEemiSzUrFZ6CqQdRl5FXSgWpWsCPIrIG+Bn4P2PMTOAFoJ+IbAP62b9jjNkAfA5sBGYCdxpjSsfz/x14G2vhmh3ADHv7O0CSiGwH7sdeEVUppUr5ugfxNWCmMaYV0BHYhC7VrFRYCqYKok2XkVdKBRVjTLIxpqP9r60x5ll7e7oxpq8xpoX9mOF1zrPGmGbGmJbGmBle21cYY9rZ++4q7SU0xhQaYwYZY5obY841xiT7/5UqpYKZzyqIIlIZ6IHVUoUxpsgYk4Uu1axUWCoqKgp0CMejuUkppZRSYc+XPYhNgUPAeyKySkTetsfT61LNSoWhIKsg6jLySimllFJH4MsKogM4C/ifMaYTkMexx7n7dKlmEVkhIisOHTp07KiVUuXGe92DILsPYjdjzFnAJcCdItLjGMdqblJKqTCii6qpcOfLCuIeYI8xZrn9+5dYFUZdqlmpMOE97zCYehB1GfnTowseKqUqsiCcM6+UX/msgmiM2Q/8JiIt7U19sVbZ0qWalQoT3r2GhYWFAYzkD7qM/KnxDl9b15VSFVkwNWgqFQgOH1//bmCKiDiBZGA4VqX0cxEZAewGBoG1VLOIlC7V7OGvSzVPBmKxlmn2Xqr5Q3up5gyslQaVUkGioKDg95+DpYKItYz8N/aaMQ7gY2PMTBH5Bc1NR1W2NzgqKiqA0SillO9oD6IKdz6tIBpjVgOdj7Cr71GOfxZ49gjbVwDtjrC9ELsQp5QKPt49iMEyB9Fe0r3jEbano7npqLxb1F0uF3FxcQGMRimlfMc733k8HhwOX/enKBVcfH0fRKVUGPPuQfT+WYUe7x7gIOoNVkqpcuddQdThpiocaQVRKeUz3pXC/Pz8AEaiTlcwzidVSilf0AqiCndaQVRK+Yx3pVB7EENbkM4nVUqpcqcVRBXutIKolPIZ7wqi9iCGNh0urJQKF96VQl2wRoUjrSAqpXymtFLoiIwiLy8vwNGo06HDhZVS4cK7UhgsC6wp5U9aQVRK+Uxubi4AlZwJWkEMcTpcWCkVLrQHUYU7rSAqpXwmNzcXESEmKp7cnNxAh6NOgw4xVUqFC60gqnCnFUSllM/k5eUR5YjGGRlLTk5OoMNRp8G7B1h7g5VSFZkuUqPCnVYQlVI+k5OTg9MRg9MR/ftwUxWatIKolAoX3r2GWkFU4UgriOXs7nvu4ZJLL2XHjh2BDkWpgMvJycEZEU1UZDS5eVpBDGW5ublIZBQSFaMVRKVUheZdQdQhpiocaQWxnK35f/beOz6u6s77f587XV2yLTfZcsEdYxvTQ4BASKHuZiFhkwD7LFkSQpaUzW7g2Ww2eTYE9hcCCUlIQiqmg2OCwTYugG3cMdjgjqtc1PtIU+/c8/vj3hlJVhtJU6Xzfr3mpZkz9975jjQ6cz7n23bvpr2tjWPHjqXbFIUi7XhbvdhtLpx2D+FwWPXPy2La29vB7gS7U3mDFQrFsEYJRMVIRwnEBNI5DEHtsCsU0NLaitPmwWlzA9Da2ppmixSDxev1Im1ODM2p8kkVCsWwRhWpUYx0lEBMIJ131dUOu0IBrS0tuOxuXHZTICphkb14vV4iNieGzUlrq/o7KhSK4YvyICpGOkogJpDO3pGWlpY0WqJQpB/DMGhrb8Np9+C0ewD1f5HNNDW3IG0upN1Fs/o7KhRJQwhhE0LsEkK8bj0uEUKsFUIctn4Wdzr2ASHEESHEISHEpzuNLxZC7LGee1wIIaxxlxDiRWt8uxBiSqrfXzagitQoRjr2dBswnOi8+FULYcVIp729HcMwlEAcJrS0tCDtxaDZaG1tTLc5CsVw5pvAAaDAenw/8KaU8mEhxP3W4+8JIeYCtwHzgAnAOiHETCllBPgNcDewDVgJfAZYBdwFNEkpzxFC3Ab8L/CF1L217EB5EIc3uq7z1ltv9bomyc3N5ZOf/CROpzPFlmUOSiAmkKamJgAkgubm5jRbo1Ckl+j/gNvuwWUJRJWDmJ1IKfG2tiJHjwdho73Ni2EYaJoKQlEoEokQogy4HngQ+I41fDNwlXX/KWA98D1r/AUpZRA4LoQ4AlwkhDgBFEgpt1rXXAL8HaZAvBn4oXWtpcCvhBBCSimT+sayjFAohLA7kXoIXdfTbY4igRw+fJhHf/YIBw4e6vO4pS+/xL9999+ZN29eiizLLJRATCBRgWjklNDQoHbYFSOb6M5cZw+i2jjJTvx+P+FwCOxupGbDMAxaW1spKipKt2kKxXDj58B/APmdxsZKKasApJRVQohSa3wipocwymlrLGzdP3s8es4p61q6EKIFGAXUJ/h9ZDXhcNis2qyHVIjpMOHo0aM8++yzvP32W+Q74atzvSwa3bN3eF+jg2eOnODee+/l8ss/xpe/fDuzZ89OscXpRQnEBNLQ0ABCYOSUUN+g5tp0U1NTw+jRo7HZbOk2ZUQSFYMuuwebZsNpdyuBmKVE/27S4UFqttiYEogKReIQQtwA1Eop3xNCXBXPKT2MyT7G+zqnJ3vuxgxTZfLkyXGYM3wIBoNIYUdodoLBYLrNUQySYDDIpk2beG35cnZ/8AFuu+D6yX6un+wn19G70/zC0hDzSxpYdcrD6u2b2bRpM/PmzuWmm2/myiuvxO12p/BdpAclEBNIfX09wpmDdObSXHUEXdex29WvOB3U1tZy6623cuedd3LXXXel25wRSUwgOnKsn56MykEUQtiAncAZKeUNQogS4EVgCnAC+LyUssk69gHM3J0IcJ+UcrU1vhj4C+DBzPP5ppRSCiFcwBJgMdAAfEFKeSJlby7BNDaaERHS4UEKW2xsypQpabRKoRh2fAy4SQhxHeAGCoQQzwA1QojxlvdwPFBrHX8amNTp/DKg0hov62G88zmnhRB2oBDoMeRJSvkk8CTABRdcMKJCUIPBIFKzI2x21b83y9B1nd27d/P222+zYf3btLX7GOORfH66j6smBMnrQxh2xm2Hv5/q5zOTAmyocvFWxT4eemg/P3/sUT5+xZVcc801nH/++TgcjiS/o/Sg1EsCqaurI+IwBaKUksbGRkpLS/s/UZFwou0UNm7cqARimujwIJoC0WnzxMKwMwRVCCJOGhoaAJCOHKSVdxgdUygUiUFK+QDwAIDlQfyulPLLQoifAncCD1s/X7VOWQ48J4R4FHNumgHskFJGhBBeIcQlwHbgDuCXnc65E9gK3AK8pfIPu+P3+zGEDU0JxKwgEAjw3nvvsXnzZja9s5FWbxtuu2Dx6AAfmxFkbnEYrSffeRx47JLPTArw6bIAh5rtbKp2sentNaxZs4a83BwuvexjXH755Vx44YXk5OQk9o2lESUQE0hVdTWGIxfpygPMEEclENODqjqWfpqbm7HbHNht5u6ay55DY4bk5qpCEAOjvt4MmTecHhD2LmMKhSLpPAy8JIS4CzgJ3AogpdwnhHgJ2A/owL3WxhXAPXREN6yybgB/BJ625rFGzM0vxVm0+3xIzY7UHPh8vnSbo+iB2tpatm/fztatW3hv506CoTAeBywqCbK4PMSCUSGcCcwwEgJmF+vMLta502hnb6ODd2sDbN2wlrVr1+Jw2Fm0aBGXXnoZl156KePGjUvci6cBJRAThJSSmpoaZMlMDGeHQJw/f36aLRuZqJyB9NPc3IzbmRt77LLn0Nx8Ko0WdUEVghgA9fX1oGlgFRsSNocSiApFEpFSrsfcpEJK2QBc08txD2JudJ09vhM4t4fxAJbAVPROW1s70ubEMMJKIGYIkUiEgwcPsnXrVrZu2czRY8cBGOOBK0r9LBodZnZRGHsKims7NFg0Osyi0WEiRjsftdjZVe9k974d7NjxLr/4xS+YUj6ZSy/7GJdeeilz587NupSz7LI2g2lqaiIcCmG48mMexOrq6jRbNXJRAjH9NDc347J52HXyLQDcjhxaGlvS3h4hkwpBZEsRiLq6OoQz19xCBaQrVwlEhUIxbGlra0M6x2IYIVqtlBVF6vH7/ezcuZPNmzezbctmmlu9aAJmFOp8fnqQhaPCTMyNRL+a0oJNgznFOnOKdb6Ijyqfxu56Jx80HOWlF07y/PPPk5+Xy8WXXMrll1/ORRddlBWhqEogJoiqqioApCsfbA6E0xMbU6QeteOXfhobG3Hacmj2mfUUJhadg2EYeL1eCgsL02laxhSCyJYiEDU1NUQcHV9oEXsO1TU1abRIoVAokoOUEl97G9IzCWlz0tqqBGIqaW9vZ/PmzWzcuIEd23cQCofJccB5JUEWlYU4b1S4zwqk6WZ8jsH4yQE+OzmATxfsbXSwqz7Ato3rWLduHQ67jfMXX8BVV13F5ZdfTn5+fv8XTQNKICaIykpzXWi4zD90xJnP6TNn0mnSiMbv96fbhBFPU2MTBY4JtAXNHlLRaqZNTU1pFYiqEMTAqa6pjYXOAxjOXKqrlUBUKBTDj0AggK7rYHchjTBeb126TRr2hMNhtm3bxtq1a9m6dQvhsE6xG64Y6+eC0SFmFulJDR195iNzffLlmYl1LuTYJReVhrioNIQh2zncYmdnnZP3PtzO9u3b+dkjj3DRRRdx7ac+xWWXXYbL5Uro6w8FJRATRFQgSrcpEA1XPqdPn+7rFEUSaWtrA8CQRpotGZlIKWluaWHMmOm0Bc1qpm6rmmkG90JUhSB6wDAMGurrMMbMjY1JVx7NlaqVj0KhGH5Eq6BLuwsiYXzt7WlPjRiunDp1itdee43Vb6yipdVLoQs+MdbPxWNDTC/QB115dKCcbEv+95gmYFaRzqwinS+e4+O418b2Ghfbdm1ly9at5OXmcO2nPs2NN97ItGnTkm5Pf6hv9gRx+vRphCsPNPNXargLqK88RjgcHrY9UjKZ6ARvGEogpoO2tjYiET3mNYSuHsRMQRWC6J+mpiZ0XUd28iBKZx5SSurq6hg/fnwarVMoFIrE0traCoC0ucAeRkpJe3t7xoYCZhtSSnbs2MHSpS/z7rs7sQk4f3SQK84Lcm5JGNsI0OFCwLSCCNMKfHzhHB/7mxxsrAry+quv8Morr7BwwQJuufVWLrvssrRtTCiBmCDOnDmD7uyYPKSrACkl1dXVTJo0qY8zFckgKhDb29vTbMnI5OweiNAhEDPYg6jogRor11C6OirSGtb9mpoaJRAVCsWwoosH0QjHxpRAHBq6rvPWW2/x/HPPcvxEBUVu+NxUH1dNCFDkytoMjCGjCTi3JMy5JWG8oXY2VLl486MP+P73P6Bs4gRu+8cv8qlPfQqn05lSu5RATBCnTp/BcI+JPTasUNPTp08rgZgGohO8V1UfSwtREeju7EG0ebo8p8gOamvNWj1nexA7P6dQKBTDhWiKirQ7ERFzUa7WEoMnFAqxevVqnnvmaapqapmYZ3D3nHYuGRtKSUuK/njmoxwqvGbDxJ+8X8DkPD3huYjxku+U3FAe4LOTArxb52TlqdM88sgjPPWXP/OPX/wS119/fcryFJVATAB+v5+W5iZk2fTYmHQVAB25iYrUEg0RCYfCBIPBjEr8HQn05EHUNBsuh0cJxCwjKgINZy7Oiq0AhMou6PKcQqFQDBdiAtHmRNqcXcYU8RMMBlm5ciXPPfsMdfUNTCuI8K357SwcHU5ZbmE8nGyz44+YSvVgcwYoVszWGZeMDXFxaYi9jQ6WV+g8/vjjPL3kKW77xy9y00034fF4kmpD0gWiEMIG7ATOSClvEEKUAC8CU4ATwOellE3WsQ8AdwER4D4p5WprfDEdhSBWAt+UUkohhAtYAiwGGoAvSClPJPs9nU2sgqm7U4ipw4OwOZRATBPNLR0ipLW1lTFjxvRxtCLRdAjErhOYy64EYrZRX18Pmg3sLrT2BnPQ5kDYXaoXokKhGHZE22RJmyMmEFXrrPgJh8OsXLmSp5c8RX1DIzOKdP5pgY9zS8Jp7VeYjQgB80eFmT+qhYNNdv5WofOb3/yG5597li99+XZuuummpDlAUiGVvwkc6PT4fuBNKeUM4E3rMUKIuZiV/uYBnwGesMQlwG8wm0nPsG6fscbvApqklOcAjwH/m9y30jNdeiBGEQLpyle9ENNES2sLUpgx7VFvoiJ1tLS0AOB0dBWIDptH/T2yjPr6eoQrl7O/2aUzh4aGhjRZpVAoFMkhEAiYdzQ70mbvOqboFSkl69ev544vf4nHHnuMYr2O7y1s4fuLWpg/SonDoTK7WOf+hS385/ktTLA18utf/5ovf+mLrF69OikFGZMqEIUQZcD1wB86Dd8MPGXdfwr4u07jL0gpg1LK48AR4CKrYXWBlHKr1UdsyVnnRK+1FLhGiNR/BKurq4GOHohRdGcuZyqVQEwH3lYvWClTKncg9TQ3N2O3ObBrXSv4uuwemhozp4qpon+ampqI2NzdxiN2txKICoVi2BETg8IWq0yvBGLf1NXV8e///l1++MMfYm+v5t8WtPL985uZV6IrYZhgZhWZQvH+RS3kh2p56KGH+NY370u4QyrZHsSfA/8BdJa2Y6WUVQDWz1JrfCJwqtNxp62xidb9s8e7nCOl1IEWYFRi30L/1NTUIDQ72LsuoqQzj9oa1Uw61RiGQXtbe0wgKo9V6mltbcXl6B4f77S7Y95FRXbQ0NCI0cPfUtrdNCixr1AohhmhUMhc0wlhikTMsElFz5w8eZKv3v0v7Nn9PnfMbON/LmxiQRZ5DP26wO12c8stt+B2u/Hr2WH43GKdHyxu5iuz2zhycC93/8tXOHz4cMKunzSBKIS4AaiVUr4X7yk9jMk+xvs652xb7hZC7BRC7Kyrq4vTnPipra2FnkKwXHm0t7fh9/sT/pqK3mlvb0dKicxTIabporW1Faetu6hw2T1425RHN5toaW0xy72fhbS78XrV/5ZCoRheRCIRsHrPSaF1jCl65Kc//f/Qfc389/lNfLIsmFEFaOLBpwtuuOEGvvGNb3D99dfjyxKBCGaLjCsmBPnR4iYcejsPPfjjhF07mUVqPgbcJIS4DnADBUKIZ4AaIcR4KWWVFT4aLYN3GujcD6IMqLTGy3oY73zOaSGEHSgEGs82REr5JPAkwAUXXJDwZiv19fXo9h522K0S//X19arVRQqJCUIVYpo2WltbcWjdRYXD5iYUCqnKsllEe3s7FE/oNi7tTnzWZkwaIvsVCoUiKUQikY4Nf+unEoi9U3HiBDPyQ0zMzc7fUY5d8vrrryOlZMWKFYy1Z19PxrE5BvOKAmw5dQrDMNC0ofv/kuZBlFI+IKUsk1JOwSw+85aU8svAcuBO67A7gVet+8uB24QQLiHEVMxiNDusMFSvEOISK7/wjrPOiV7rFus1Uv6XratvQDpzuo1LKyxL5emklliT2xwJmvIgpoPWVi9Oe/e8NafliVKiPTuIRCKEgkGkzdH9SZsTwzAIhUKpN0yhUCiShLmMjG56mT+TUQRkuHDDjTfxXr2Tx/fmUdmeGW0iBoLHLgkEAvz1r38lEAjgyTKBWOvX+O2+PN6pdnP99TckRBxCevogPgy8JIS4CzgJ3AogpdwnhHgJ2A/owL1Syuh2xD10tLlYZd0A/gg8LYQ4guk5vC1Vb6IzLc3NyIIp3cajAlGV9U8tMfHhAM2pKTGSBtra2ii0FXQbd1rFTtrb2xk9enSqzVIMkJj407oLRKmZuTmBQEB5gxUKxTDFFAsqSqJ3vvKVr5CXl8dTT/2F97a7mFsc5rKxQRaNCZHvyC6xlS34dMHuegdbalzsaXBid9i5884vcfvttyfsNVIiEKWU64H11v0G4JpejnsQeLCH8Z3AuT2MB7AEZrrQdR2/34csMRe+sUbS5ZciLQ+K8mCllpggdJo31eA29bS3tzO6yNlt3GEzhYT6m2QH0cIMsqcdSVW8QaFQDENMD0xXYaMEYu9omsYXv/hFPvvZz/Laa6+xauUK/nCwBnEIZhTqnFscYk5xmOkFOvYMdDBOztOp8ZmGjc0xmJynp9mi7ugGnPDa2d/kYF+Tk4+a7UQkjBk9ii/ffh0333xzwjfd0+FBHFZEF7rRIg6xRtKYOTqgwulSTXt7u3nHAYbdUGIkxUQiEYLBAHZbTzmI5v9E7G+kyGg68m56EogqN0ehUAw/bDYbSCuk1Ppps9n6OEMBUFxczB133MHtt9/OoUOH2Lx5M9u3beWVI0dZdlzitMH0/DAzCsPMKNSZVqhnhIfxyzN9fHmmL91mdKE9LDjaaudIi52PWhwc9ToJ6ubvavq0qXz+M5dw+eWXM2fOnISFlJ6NEohDxOezPlQ95ehYZZJjxyhSQmeBKO2StnYlEFNJtGpvVAx2xm6Nqf+JLKPHzXO1o65QKIYfdrsdDCu01BKIdrtaLseLEILZs2cze/Zs7rrrLlpaWvjggw/48MMP+fCD3bx+9BhGhfl7Lc2RTMsPMS1fZ2qBzpR8HdcI0+KhCFS02TneaudYq51jbU6q283vV00Ipk6dwnVXLeS8885j4cKFFBcXp8Qu9YkfItHFcI9FHIRA2BxZ2ebC6/WSn5+fbjMGRez3bTdvSoyklujv3671IBCtsWz8nxiJxMKqeqz9ZY4la/dSoRiJCCHcwEbAhfkttlRK+d9CiBLgRWAKcAL4vJSyyTrnAeAuIALcJ6VcbY0vpqN+w0rgm1JKKYRwAUuAxUAD8AUp5YkUvcWMx263Iw3dnPcsgehw9LDGU8RFYWEhV1xxBVdccQVgfv9/9NFH7N+/n/3793PwwH62HTGj74SACbkGU/NCTC2IMDVfZ3KejnOYiMawAafabBxvtXPca+d4m5MzbVp0P4JRxUXMXjiPG+bMYe7cucyePZucnO5FMFOBEohDJBgMmne0Xn6Vmr3jmCxh1apVPPTQQzz55JPMnj073eYMmEAggLAJECBtZnUqReqICcQeNk2iY+pvkh3EFkWyhzBSQ+2sKxRJIAhcLaVsE0I4gE1CiFXA54A3pZQPCyHuB+4HvieEmItZoG8eMAFYJ4SYaRX5+w1wN7ANUyB+BrPI311Ak5TyHCHEbcD/Al9I7dvMXDrmPQOMSNcxxZDxeDwsWLCABQsWxMYaGho4dOgQhw4d4uDBg+w9eIBN1Wb9DpuAsjyDqfkhphXoTCvQKcuNZHy/RUNCZbvN9Ap67Rz3OjjZZiNiRS8X5Ocxc9ZsrrS8rbNmzWLMmDHpNboT6pt9iMSKOIhedtE1W9YVcXj33XcBOHnyZFYKxHA43JEyZYOQX5XhTyXRDRFbD5smNqsaZrZtmoxUnE7T4yt6KvEu9S7HKBSKoWO16ormRTismwRuBq6yxp/CLPz3PWv8BSllEDhuVXW/SAhxAiiQUm4FEEIsAf4OUyDeDPzQutZS4FdCCJGONmGZSGxOkwbC2hxTlZqTy6hRo7jsssu47LLLALPVSF1dXYdoPHCA9w4eYH2lGRHmsgum5YU4x8pnnFGok5vmfEa/LjjcYudwi50jrQ6OeZ34w6ZNOR43s2bP4RJLDM6cOZNx48ZldPEjJRCHiK5b1Y56E4hCZF0Rh+h3RLZ+V0QiEUR0a0moIhqppkMgdt9xjYrGdApEFcIVP3a73fwCM7pXdROGWjgpFMlACGED3gPOAX4tpdwuhBhr9YVGSlklhCi1Dp+I6SGMctoaC1v3zx6PnnPKupYuhGgBRgH1SXpLWUV0ThOGHvMgqo2w1CKEoLS0lNLSUj7+8Y8D5pr0zJkzHDhwwAxP3beXlUePEqkwEEBZvsGcwiBzi8PMKdaT3s8wGIGDTQ72NTk42OKiwqshpZk3OG3aVD718fmxMNFJkyZlXTqGEohDpENE9bILILSsbbA6LKp/Cs6uVp1V7N+/n/z8fCZNmpRuU+Im6jG3CRu7Tr5Fs68WgLcPvkBRTikCke7m6iqEK06EELjcbkJGD1EQkTB2u12FmCoUCcaaWxYKIYqAV4QQ3dp8daKnxYfsY7yvdFEpSgAAIABJREFUc7peWIi7Mec3Jk+e3KfNw4mYGDQipkhECcRMQAhBWVkZZWVlXHvttYCZrnLgwAH27NnDBx/sZsOePaw5HcYmzBYbi0YHWTwmRKknMevw+oDG+3VOdjU4OdTsQDfA4bAzb+48rly4kPnzTVGYrrzBRKK+2YdITPz14SXONk9c9D21tLSk2ZLBoWlax1edkb1FNMLhMF/72tfIy8tj5cqV6TYnbqICURM2mn21hCOmt7DOe8oc12wdnvc0oEK4BobH48Eb6S4QRSSM2+NJg0UKxchAStkshFiPufFUI4QYb3kPxwO11mGngc47iGVApTVe1sN453NOCyHsQCHQ2MPrPwk8CXDBBRdk3dw1WHryIKpIiczE7XazaNEiFi1aBNxBKBRi3759vPvuu2zbsoXnj5zg+SO5TC2I8LGxAT42LjjgUFS/DttqXGyq9nC4xayWM3lSGZ/75GVcdNFFzJ8/f1h+PpRATBiZG0c8UKJ9G5ubm9NsyeAwK5BZE4AEuyM7P+ZRgZ5tntyo+BO9hF2nWyCCCuEaCJ6cHISvBw+iEcbtVgJRoUgkQogxQNgShx7gk5gRCMuBO4GHrZ+vWqcsB54TQjyKGeEwA9ghpYwIIbxCiEuA7cAdwC87nXMnsBW4BXgrGzevkkVssS8jqkhNluF0OmOC8e6776aqqooNGzawbt1anjl8lJeP5fLx8X5uKA9Q4urbq9gaEqw46WF9pQe/DlPKJ/OVWz/FlVdemVVRXYMl7pWzEKIcmCGlXGdNWnYppeoAH2P4zK2traYwiQrFbMPpdCJ1idglIAJOR3aGhjQ0NMTuRyKRrGnUG8351HoTiEJLqECsqKgAyAeId25SIVzxk+vJAW/3VjEiEianQAlEhaIvBjE/jQeesjaxNOAlKeXrQoitwEtCiLuAk8CtAFLKfUKIl4D9gA7ca81vAPfQkSO9yroB/BF42oqGaMQMoVdYxMSgEYkVqVEhptnJ+PHjue2227jtttv46KOPWLZsGWvXrmFjlYePjQ2Q5+hZJPp1jU01bkIRwVWf+AT/8A//wNy5czO6qEyiiUsgCiH+BXMRUwJMxwxV+C1wTfJMyw7i+bBk2wfKbzWaz9ZedW63GwDRLMAOnvzsXMTW1tbG7jc2NmZU+eO+iArE3j73IoF5ub///e958sknAcqtoQHNTSqEq39ycnIQRmu3cRHRyckpSINFCkV2MJj5SUr5IbCoh/GG3s6TUj4IPNjD+E6g2+aXlDKAJTAV3YnmVTur96IXl3cZU2QvM2fO5P777+fOO+/kt7/9Le9s2oTsw7lz8UUX87V77snYzdtkE+8n/l7gIswwBaSUhzuFX41oOhpJg7NiK5rP9Pq497+OkTsKpMw6gRjNIUtzIZFBExWISCACOZ7sTBauqqrqcj9bBGJU/PUWYio6HTNUfv3rX7Njxw5cLjNWJJ65SYVwDQyPx43WUx9EqZOjchAVil4ZzPykSD/RaB0t0Gr2QkQJxOHE+PHj+dGPfpRuMzKeeD/xQSllKCp0rB3xrFzsJBOtvQFhFXOweavTbM3g0Wzmwj5bQhrPxhNdtEoQEdHxOMuorKyM3T9z5gznnXdeGq2Jn351UAI3TFwuV5fQnzjnJhXCNQCcTmeskl9nNBkZlon5CkWiGOT8pEgzHZv6socxhWJkEK9A3CCE+L+ARwhxLfB14LXkmZU9xBbDw2juiBaeyNbFX2cPomZoHY+zjNOnT1MqBHVSdhGL2YLo5Z9CkLjKvldeeSU/+clPAES8c5MK4RoYDocD0cOaVkhD7aorFH0wmPlJkX56al+Wre3KFIrBEm/9//uBOmAP8FXMfl/fT5ZR2UT/fRBF1k0seflmXlF+fn6aLRkcHRXIACN7k8urKysZLSWFmtYl3HQ4kKjd2IcffjgaeutHzU1JwWwb07Ogz9YWMgpFKlDzU3bSpYialSoRza1XKEYK8X67e4A/SSlvlVLeAvzJGhvxxCaNXha8UmhZN7EUFhYCUFCQnQUouoTGGtkbKltXX08BkG8Y1NXVpducAdNb8nci46v8fj///M//DHBMzU3JoS9vb7ZtfikUqUTNT9lJrP6CECDM9UMwGEyjRQpF6olXIL5J10nNA6xLvDnZR7SgS3QS6YbQOo7JEqIet6KiojRbMnLRdR1/IEAOkAu0ZFFPyphXqTdhkcBaLddcc83Z1XbV3JRgwuEwsoeCQzLB7UoUiuGGmp+yk46/mUDaHGeNKRQjg3gFoltKGevWbd3PztKQCSa60yS1ngWigZZ1O0/R8L9s9SB2iHZAIysXsdHPlQMzUTiURZ+h6OenNxkoSVxoYiAQIC8vr+Paam5KOH6/H0PrnmtoCLtaNCkUfaDmp+ykrc1a7goNaXN2HVMoRgjxrtLahRDnRx8IIRZjxtSPeGILJM3R4/NSc+DLskVUNKQs1iw2y/D5rKbeAqRNdjzOYrKpglpPFeC6krjWL7m5ubz//vudX1vNTQmmtbUVw9Y9j1fanLS09tXvW9EboVBoWMxLir5R81N20mxF7EghkHazpkFLS0s6TVIoUk68Jei+BbwshIiWUhwPfCE5JmUX0V2laBjC2UibA683u3aeskmM9ERrq9XUW4DhNGhuyZ7wzCjRQjsh6+bOolYdUe9gr7lrCUxC/PnPf86tt94KMEsI8Q5qbko4DY2NYO8eTSAdHhoba9JgUfbzH//xH5w6eZK/LluWblMUSUTNT9lJQ0ODWZxGCKQzp2NMoRhBxCUQpZTvCiFmA7MwA/cOSimzK7EuSXi9XjORuTeBaHfh9apFVCqJFXQRIN2S2tra9Bo0CGw2G3k5Ofh8PnwIxg2jfFCJTFjhoAsvvJCDBw/idDorMEvIq7kpgei6TkN9Pca48d2ek85cWuqaCQaDWdsSJ1109iophi9qfspOampqOvKuNTvC6aGmRq3jFCOLPgWiEOJqKeVbQojPnfXUDCEEUsoRv/3Z3NyMcHp6r2Lq8OBtaMUwDFUSPkWcOXMGbJhbGbnQcKqBUCiUde0uiouLafP5aNcExcXF6TYnbjpyEHupYpqAIjVvvfUWV199Ncs6PDBuYAZqbkoo1dXVGIaBdHX3IBpuc6yyspKpU6em2rRhQTgcztpQfkXvqPkpuzl1+kyXuhIRZ565rlAoRhD9eRCvBN4CbuzhOQmM+EmuoaEBw957+J90eDAMg+bmZkpKSlJo2eBJVBPzdHH8xHGkzXoPBeb7OXXqFNOnT0+vYQOkZNQoWs6coV3KrBSIQz2mLzZs2MDVV1/Na6/Fek4X0TFPqbkpQRw/fhwAI6f75096zLFjx44pgTgAOs+vTU1NlJaWptEaRTJQ81P2EgqFqKmugk7ruoirkOMnTqTPKIUiDfTp0pJS/rcQQgNWSSn/z1m3f06RjRlNdU0tEUfvRcmkMxcgK/vYZVt7DjAL1FRVVsW2PmShuRg7fPhwGq0aHAWFhbQLjZCUWVVRNhU5rD/60Y8wDIPPfvaz/PnPfwY4oeamxHPw4EEQAiOn++aW4SlCaDbzGEXctLe3x+43Njam0RJFslDzU/Zy/PhxM2qiU+HBSE4xTY2NseI1CsVIoN+YRymlAXwjBbZkJdXVVUhXfq/PS1eedVx1qkwaMtEd7qampjRbMnAOHDhg2h+d2wtAOAT79+9Pq12Dwel0EiCLK8om2ROtaRq/+tWvkvoaI50PP/wQmTsaemhzgWYjkjuaDz78MPWGZTGdF5nZOMcq4kPNT9lJbMPL1jHnGTmjAXN9oVCMFOJNilsrhPiuEGKSEKIkekuqZVlAa2srfp8Pow+BGH2usrKy12MyjRar6mc2JmV/8MEH5p3o3C7AKDHYtXtX2mwaLMFgEDemNy7aFzEbiIXQ9eFJTFQY87XXXssjjzwC4FBzU2Lx+/3s3bePcP64Xo/R88fz0aFDqkfYAOj8u1K/t+GNmp+yjz179iCcOUjRIRAjuaNBaOzZsyeNlikUqSXeNhf/jBk3//Wzxqcl1pzs4vTp0wBIdx/hf3YXwuGOHZsNnKqoMH+eOplmSwbOuzvfhRK6bH3IUknFngrq6+sZPXp02mwbKNVVVRRJA5+mZVUl1qj4E/QsEBMZgvqnP/0per3ZwM5OT43ouSkR7Ny5k4iuEymcBICzYiuazyz17t7/OkbuKPSSqRiVu9mxYwdXX311Os3NGnRd7/G+Yvih5qfswjAMdry7k1DeeES4IxQcm4NI3hjeffdd7r777vQZqFCkkHg9iHOBXwMfALuBXwLzkmVUtnDypCmgDHdhn8dF3IVUVGSH2PJ6vdTWm4vAo0eOZFXBmtbWVg7sP4Ax1ugyLsea72HHjh3pMGtQ+Hw+jh8/znhgnGGwN4vC+AzD/P33JgSFEEQikYS81v79+7n33nsBfKi5KaFs3LgR4XBh5I8FQGtvQETCiEgYm7carb0BI68U4fSwcePGNFubPXQOF7fb492jVWQjan7KLg4cOEBrSzN64cRuz+kFZRw+fDirNmsViqEQr0B8CpgDPI45wc2xxkY0FRUVILQeS8B3JuIu5ITllct09u3bB8BFpSFaWr1ZFRq7bds2M7l8/FmitghEjuCdTe+kx7BB8M477xAxDLMuOnC8ooITWVJFLeoVEaLn6UWQOIF45513RvNCalFzU8IIBoNs3PgOoaJy0ProWSk0QkXlbNq8GZ/PlzoDs5jc3NzY/by8vDRakt1s2rSJG2+6ketvuJ7f/OY36TanR9T8lF28/fbboGnoRZO6PacXlwNmhVqFYiQQr0CcJaX8ipTybet2NzCrrxOsfMW3hRAHhBD7hBDftMZLhBBrhRCHrZ/Fnc55QAhxRAhxSAjx6U7ji4UQe6znHheWa0II4RJCvGiNbxdCTBnoL2AonDhxAjyF0E9/Q8NTRGtLMy0tLakxbAi899572DW4cUoAMMPMsoX169cjcoQZYtoZAZEJEXZs35EVi1jDMHjxhRcYo2lMBhYAdiF48cUX021aXETFn+hlehFCS5hAPHToEH/4wx8AvPHOTYr+eeedd/D7feijzun3WH30OYSCQbVwipPCwsIe7ysGxqpVq2hpbqG1rZXlry3PyHBdNT9lD+FwmNVr1hIunAx2V7fnDU8RRu5oVqxYmVWRVQrFYIlXIO4SQlwSfSCEuBjY3M85OvBvUso5wCXAvUKIucD9wJtSyhnAm9ZjrOduwwy/+AzwhBAiunX9G+BuiDlUPmON3wU0SSnPAR4D/jfO95MQjh47hu4u6ve4aL+wTPcASSl5Z8N65hSHmZIfoTQHNm7MjkVfW1sb27ZvIzIhQk+pb7JMEg6H2by5v49t+lm1ahVHjh7lSsNAQ5CH4EIpWbVyJYcOHUq3ef0SXahpvXgQNc2WsMXcokWL2LZtW+xxnHOToh+WL38N3PkYBeP7PdbIGwueIl5dvjwFlmU/nb2GRUX9f38outPW1mZGjEw3MC4xaG9rz8jNTDU/ZQ/r16/H29pCeMzMXo8JjZ7JiRPHVbEaxYggXoF4MbBFCHFCCHEC2ApcaXn1ekyOklJWSSnft+57gQPAROBmOkIsngL+zrp/M/CClDIopTwOHAEuEkKMBwqklFuluW2z5KxzotdaClwT9S4mm2AwSE11NYan/y94I0sE4q5du6isruGysSGEgEtL/by3872sqGa6ceNG9LCOnNzLzt5oM8x07dq1qTVsgFRXV/Orxx+nXAjmdxr/BJArBA89+CDBYDBd5sVFtH+m1ktook3YEtZjc/v27Vx22WUA8+OdmxR9U1FRwe7duwiNntVnJdoYQhAcM4v9+/Zx5MiR5BuY5XT+ilIhpoPjjTfeIBwOI6dIGA/CLfjb3/6WbrO6MZj5aSREX2UaUkqee/558BQRKeiefxglPOochMPN888/n0LrFIr0EK9A/AwwFbjSuk0FrgNuAG7s72Rr8lkEbAfGSimrwBSRQKl12ETgVKfTTltjE637Z493OUdKqQMtwKg439OQqKioQEoZE399IZ25CLuD48ePp8CywfP888+R74SLx5otFT5RFgQkL7zwQnoNi4M3Vr+ByO8hvDSKgMikCDt27MjYZrfBYJAf/Nd/oQcCfE5KtE6uUA+CvzMMjp04wc9//vOMDnGJtuSw9dQ7D9CEPWEi94033oj+Xx0izrlJLcD65pVXXkFoNsKl8UfC6WNmIGx2li1blkTLhh82Wx/5nYoeCYVCPPf8czCaWMXqyLQIW7Zs4ejRo+k2rwuDmZ8YAdFXmcamTZs4fuwY/nHz+94Us9kJlM5l69atWRHNo1AMhbgEopSyoq9bX+cKIfKAvwLfklK29nVoTy/dx3hf55xtw91CiJ1CiJ11dXV9mRs3sQqmcXgQEQLDXcSJE5lbqGbXrl1s376D6yb7ePGIh6cPeRjtlnx8fJDlr/4to4vV1NTU8MHuD4hM6jm8NIoslxiGwbp161JnXJxIKXnkkUc4eOgQn5OSkh7eyEwEVwIrVqzI6IV4VPzZhI1wJIjH4+GWW27B4/EQjgTRhC1hfR3Ly8spLy8HCA1gblILsF7wer2sWLGSUMk0cHjiP9HuJlQyndWr12TsBkym0HlzJxtyojONV155hfq6eiJzO/KY5QyJcIqMK1YzmPlpuEdfZRq6rvO7J58ETyH6qOn9Hh8aOxfhcPPb3/4uozdqFYqhEq8HcVAIIRyY4vBZKWV0RVtjTVxYP6M1g08DnUtHlQGV1nhZD+NdzhFC2IFCoPFsO6SUT0opL5BSXjBmzJhEvDVTIAqB7KfFRZSIK3Mrmeq6zs8fe5TRHvj0pCAVXhsVXnP9+w/T/GgY/OIXv8jYyXDdunVIKZHl/dhXCKJY8MbqN1Jj2AB49tlnWb16NVcDcy1xuBLJyrP2O67GbKj1y8cfZ/v27Sm3Mx5iAlFzENaDXH/99dx3331cf/31hPUgNmEnEAikzT61AOudlStXEgwG0McNvBJ/eNy5hMMhXnvttSRYNnzoLKCrqqrSaEn2UV9fz5/+/CcYB4zt9IQTInPMCJFNmzaly7yEMxyjrzKN119/ndOnTuGfeAH0kjffBZsT//gF7Nr1Plu3bk2+gQpFmkiaQLQWQ38EDkgpH+301HLgTuv+ncCrncZvs0KzpmLuxu+wJkKvEOIS65p3nHVO9Fq3AG/JFKmY06dPI1x5fZeA74ThKaSxoT4j88deeOEFjp+o4PYZbTjPejslbsnnprazdevWjK1SuHrNavOrzUrnEbsFNAPNoK3XzMcWkUkRPjr0EadOnerxWulg/fr1PPnkk8wHruo0XmXdOqMhuAVzbfSD//qvjAxbDgQC2DQ7QggcdhcrVqzg8ccfZ8WKFTjsLmyaI60CsTNqAdZBJBLh5aVLMQrGY+SOHvD5MqeYSOFElv51WUZWlMwUDh48GLtvtUBQxMnjjz9OIBggsrB7FWR5jkQUCh597NFh4ZkdrtFXmURzczO///0fiBSMRy+aHPd54TFzIKeYx3/5y4xc0ykUiSCZHsSPAbcDVwshdlu364CHgWuFEIeBa63HSCn3AS8B+4E3gHullNFvgXuAP2Du3B8FVlnjfwRGCSGOAN/BCglLBWcqK9Gd+XEfb7jMYzNtx7iyspK//PnPXDgmxOLSnguHfGZSkCkFBr/4+WO0t7en2MK+OX78OCeOn8CYbMTGRLNAhK1bnUA0d3wXRovYvPnmmym3tSeOHj3Kgz/+MZOE4O8xewT2hwvBl6TEHgpx//e+h9frTb6hAyAQCOCwOwFw2Fz4/X6WLl2K3+/HYXNhtznw+9MvENUCrCtbtmyhtqaG0Ni5g75GeNy5NDU2ZOxmUiawdetWnDYY45FdKlwq+mbTpk2sX7+eyJwI9PTVq4G+WKe+rp7f//73KbcvkQzn6KtM4ne/+x3tPh+ByZfEV5AriqbhK7uI6qoqVbBGMWxJmkCUUm6SUgop5XlSyoXWbaWUskFKeY2Ucob1s7HTOQ9KKadLKWdJKVd1Gt8ppTzXeu4bUS+hlDIgpbxVSnmOlPIiKeWxZL2fs6mursFwxV+BTlrH1tbW9nNkavnVL3+JJnVun9X7jqtNg/8zq43GxiaWLFmSQuv65+233wbMNhZx4QHGwJtvpV8g+nw+vv+f/4lT1/lHKXHEIQ6jFCK4zTCoranhoZ/8JKPCf/1+P3bN0evzds2B3+9PoUXdUQuw7ix75RWEK4+I1RB6MEQKy8BTwLJXXkmgZcOHSCTChvVvs6AkxCVjA+zatUvlbMaBz+fjZ4/+DFEokLP6mOtGgXGOwbJly9i/f3/qDEwgwz36KlPYs2cPq1atIjR2XlzFBs8mUjiRcMlUnnnmWc6cOZMECxWK9JLUHMThSiQSoaW5CenIOeuJUJeCHEQ6CnFEj21oaEilqX2yf/9+Nm3ezI3lPkrcfX83TC+McPn4IEuXvkx9fX2KLOyf9RvWwxjAHf85xkSDihMVaQ8zfeKJJ6isrORWwyB/AOIwymQEn5KSTZs3s2LFiiRYODj8fj+2vgSizUkgjQJRLcC6U1lZyXs7dxIcPTO+PJzeEILQ6Fns+fDDWCEvRQd79uyhqbmFi8eFuGRsGMMw2LhxY7rNyniWLFlCQ30D+mK931WLPFeCBx597FEMw+j74MxkWEdfZQK6rvPoY4+BK4/ghIWDvk5w0kVEJPzi8cczapNWoUgESiAOAq/Xi2EYSEdXVSL0UJeCHELvLBDNioCNjd2cCGlj6dKl5DgEn5oUX7jfzVMD6GGd5RnSEPvMmTNmeOmEgS0C5ERzIn/nnXeSYVZc7Nu3j+XLl3MpMLUHcbgSGctB/GMPxWqiXAJMQfDEr36VMZ6IuDyIgUA6v1DVAuws1qxZA4DeR5PoeAmPmQFCsHr16iFfa7ixdetWbBosHBVmcl6EMTmoMNN+qK+v5+WXX8YoN+LL4nVAZJ6Za56Noc7DPfoqE3j99dfNthZlF4Kt9++q/pDOXPwTFrJj+3ZVsEYx7FACcRC0tbWZd2yuLuPS7uxSkENaeVgAaHYQWsbk8AUCAd7ZuIFLSwN4em5X141xOQZzS3TWrV2TXOPiJCrwooIvbnKAYtK6c//bJ54gX9O4upfnq4CgdTtB92I1UTQENyBp9/l45plnkmDpwPH5/NhE3x5EKY2EtboYKGoB1p11697EKBgfC4UfEo4cIgUTWbN2ndpVP4sDBw4wJT+C226mPM0qCHJg3950m5XRLFu2jLAeRs6N/7MkyyUiX/Dsc88m0TJFNtLW1sYf/vhHszBN8ZQhXy9cOhc8Rfzq10+o4lyKYYUSiIMgWoFR2s5SVjZnl4Ic2DoJRCEQtvTnXkX54IMPCIbCLC4d2CJ98ZgQp89UZkTM/foN6xFFAnIHfq4xweDAgQNpCZfdu3cvH+zZw8cNA9cgQkvPZiyC84BX//a3jChY4/f5sHf+7J9F1LuYKf8LI53Tp09z8mQF4SHkHp6NXjKFmuqqjKyym05qqqsodXdU4BzjMWhoalYLy16QUrLqjVXIcTJWpTouBESmm17EigxtL6VIDy+++CJtXi+BsgsHVpimNzQbvokXUHnmNKtWrer/eIUiS1ACcRDEvswHmqujaRmzEHj//fexaTCzaGD2zCsxK53u2rUrGWbFTW1tLfv37ScysXu583iQZRIpZVpCkF555RXcQnB+Aq95GRAMhTIirM8XR4gpKIGYKbz33nsARIom9XNk/EQKy7pcW2ESCoVw2jo8YS7rfjjccwXpkU5FRQUN9Q3ICQP3REfP2blzZ6LNUmQpXq+Xl5cuJVw8ZVCtfHojUjQJI6+UJU8/nTFrPIViqCiBOAiG0u86U3pl79i+nRmFOu742jjGmJBjUOSGHTt2JMewOFm3bh0ActIgQ9gKQBQJ3njjjQRa1T8+n4+N69czX8qEeA+jTEAwQQhWrVyZsGsOloDfj72PvI7oc0ogZgZ79+5FOHOQroKEXVO68hDufPbuVeGTnTl7+o/1RMmQ74VM48SJEwDI4kHM87mguTTlxVbEWL58OQG/n9CEBX0e5zq5DZuvAZuvAc/BlbhO9pMnLASB8edRV1vL+vXrE2ewQpFGlEAcBA6HtfgdaIU0Q+84N40cP36co8eOcf7ogeeACQGLRwXZumVz2sIZDcNg+WvLYTQ998OKk0h5hEOHDnHkyJGE2dYfmzZtIhgOc14Srr1ASg4fOZL26pGBYLDPKqbR56Kh2or08tHhI4Q9Jf2HW/VRpbknwp4SDqfwfysbCAaCOLUOseO0voHV/0LPtLS0mHcGUKW6C+5O11CMaCKRCMte+RuRgvEYOX1XO9J8jYhIGBEJY/dWo/n6Ly4YKZwEnkKW/vWviTJZoUgrSiAOAo/HrEgqjAGEBUmJ1MPk5OT0f2ySWbJkCU4bXD6++wLv6UMeKrw2Krw2frwzj6cPebodc3VZkGAozMsvv5wKc7uxfft2Ks9UYkwfWglzOUUi7CKl72PtmjUUahqTk3DtczG7s69duzYJV4+fUCjYZ4ipTbNbx6WnSI2iK9VVVUh3/97Dvqo094R0FVBdXa0K1VjU19fjbfcxxtMxb5XmmCHyx45ldA2jtBFrUzFIB6sUMltbXSgSzHvvvUdDfR2hMbOT8wJCEBg9k4MHDqi8V8WwQAnEQVBQYC6mRLjrrq+ROwppcyBtDiL54zByO+1S6cEu56aLLVu28Oabb3LdZD8Fzu4LtwqvDX9Ewx/RONjsoMLbPQa1PD/CJWNDPPvM0xw9ejQVZseQUrLk6SWIXIEsG+LC02l6EdesWUNNTU1iDOyD2tpa3n33XRYYBloCw0ujFCCYjmDlihVEIoPLzRwquq5jGAaa1nvssk0ogZgphEIh/H5frA1PX/RZpbkHDKcHPRzOmMrN6SYazr5gdMfG4uwiHaeNlIe6Zwtut+U6HGRal4iIjmsoRjRr1qxBOFzoRcm+m8eKAAAgAElEQVTYnjXRR50DQqR9k1ahSARKIA6CnJwcnC4XIuzrMh4qvxQjZxRGzigCc28gVH5p7LnosaNGxdPIKTmcOHGC//l/P6I83+CmKUMLabp9lo9ce4T/+8D9Ke3tuHPnTvbt3UdkZiQhn145W2JIg6effnroF+uHZcuWIaXkgiS+xgVI6urr09bjMVpsQxO9C8SoeFQCMf0Eg+bGldTi6HXTV5XmnrCuqcInoa6ujmefeZoFo8JMzO3waHnscPWEAGtWr+bAgQNptDAzyc21SlQPtu5HqNM1FCOWYDDIO5s2ESwshz42L4eKdHiI5I9XLX4UwwIlEAeBEILS0lJEsC3uczTr2NLS0mSZ1ScVFRV8+1vfxBHx8+3zvDiHOEcWOiXfnt9KY10t3/n2t2hqakqMoX0gpeR3T/7O9B5OTdDkmwORqRFeX/F6Ult3NDc388pf/8o8oDgO72EAuuR7xbvEngOM0jT+8uc/pyW0Kuq57FMgWtV/VbW39GOzRf9OSVjMWAskuz3ORqvDlEgkwo9//D/owQC3zzI3Cp8+5ImF7//9tABFLsmP/9+P8Pl8fV1qxOF0WpsQgwyIkBHZcQ3FiGXbtm0EAwH0UdOS/lqhkmnU1lRz6NChpL+WQpFMlEAcJJPKyrCHuhdpMXJHdQ0ttRCBVgDKysqSbtvZ7N+/n2/c+3XC7U3cv6iF0Z7ECIfphRG+vaCV0ycruPfr91BZWZmQ6/bG5s2b+ejQR0TmRCCBm4ByjkQKyV/+8pfEXfQsnnrqKQLBIJ+I8/gAdMn3ilcgagg+YRgcO348LWEuUVEq+hDBwhKI6QqDVXTgdrvRNK3ffMLBIKwiNpmQd51O/vjHP7Jr127umNXGuBzz/yOa5w2Q65B8fZ6XM5WVPPTQQ8rz0ImY93mQ872wi5iXXDFyWbduHcLpIZI/LumvpRebXso333wz6a+lUCQTJRAHSXl5OcLfDLKr2AqVX9oltDSK5m8iNy+fwsLCVJkIwPr16/nmff+KU/fyg8UtlOUl1qt0bonO/YtaaKqr4mtfvZs9e/Yk9PpRpJT85am/IPIFsryfBVS4q/eN/moJeSAyzcxFTIbIPXnyJH975RUWA6Vx5h66oUu+10CyaOYDE4Tgyd/+NuXhfTGB2EdFzL7EoyK1aJpGYVExIpT4PEERaicvv2BEe3C2b9/OM888w1UTglw5oXcRPrtY5wvn+NiwYQPLli1LoYWZzalTp8w7g9xjMHIMKk6qgiEjmebmZrZs3UqweOrAe1cPBruLcEEZq9esVf1NFVmNEoiDZNq0aUgjgvDHV0Lb5m/inOnTUtbvyjAM/vCHP/CDH/yAyTkBfri4ObZ7nWhmFkX478XNuPQWvnnffbz22msJf40DBw6Y3sMZceQehrt63/oViICcaXoRX3311YTY25nf//732KXkmgGc44Yu+V4DEYgags9KSV1DQ0YuNmUywhkVg6a8fDK2QOJbAWj+FsonT0r4dbOF9vZ2Hn7oJ5TlSe6Y1X/o6HWTgywYHeY3TzyR1HD3bOKdTe8gigQMco/BGG2wZ88e1epiBLNq1Soiuk54zKyUvWa4dBatLc1s3LgxZa+pUCQaJRAHycyZMwGwtdf3f7BhYPM1xs5JNl6vlwfuv58lS5ZwxfggDyxqpdCV3EX5hFyDH13QwpyiID/96U955JFHErp7tn79eoQmkJPjeB+Ort434mk96QE5VvLmW4kNC6msrGTjxo1cLCV5KfScTbEqmr70wgspzfWL5rRJ2cdmhBVC15H/pkgn50yfjs3f2C0aYkhIid3fwDnnnJO4a2YZzz33HA2NTfzLnPhyvoWAu2a3I6TO7373u+QbmOHs3LmTgwcOEpky+FB0OUUS0SM8//zzCbRMkS0Eg0FefOklIgUTMDzFKXvdSMFE8BTy9DPPqjYriqxFCcRBUl5ejsvlRmuv6/dYzd+INHRmz05S/51OnDp1iq999W52bN/GnbN8/Mtc34AK0vh10SU806/HL2ryHJJ/X+jlhvIAy5cv51vf+ibNzc2DeBfd2bt3L7JExif2HF29b3GdA8hSSW1NbUKrsm7ZsiXplUt74wIkjc3NKa2OGBV9Rh9iI/qcEoiZwbx585ARHa29IWHXFP4mpB5i7ty5CbtmNuH3+1n216VcWBpiemH8AqfELfl0mY8NG9YnPac7k2ltbeXh/33YTCmYNoTNzUIwyg1eePGFpKU/KDKXpUuX0tzURHD8gtS+sBD4xy3gxPFjKhdRkbUogThIbDYbs+fMxh6PQGyrBcyFWDI5dOgQ93ztqzTXnuGB871cOynIQCNafbroEp7pG4BABNAE3DbDzzfObePg/n3c87WvJqTHYGNTI4Y7uTtx0mMuRBJZkbW6uhqnEHFVLk000Xq51dXVKXvNaL5ZRPbutYw+N5Jz0zKJhQsXAmBrTZwgsbWY1zr//PMTds1sYsOGDbT7/Hx60sALpHyyLIjAjIIYiQQCAR74vw9QV1eHfqE+5IJkcqFEeiQP/OcDHTmNimFPVVUVTz21BL24nEjB+JS/vj5qGkbeGH796yfwersXNFQoMh0lEIfAgvPOQ7TXQ6TvUEqbt5riklGMH5+8Saqqqop/+863cept/PcFLcwuHlxYYY5ddgnPzLEPbvf2knFhHljUQlNdNd/59rdoa4u/JUhPFBQUIILJFVnR6+fn5yfsmmPGjCEkJU1pyLur7WRDqrDb7dhsdiKRPgSiYT7ncrlSZZaiD0aNGsW06dOxtyRu8WxvOUXZpEmMHTs2YdfMJlauXEFpDswqGvg8XOKWzCsJs/qNlSOuFYzX6+W7//5d9ny4h8iFEUhE22An6JfreANevvGv3+Do0aMJuKgik9F1nf/5nx8TNiSBSRenxwih4Z98Kc0tLfzsZz9T1YkVWYcSiEPgvPPOAymxefvwkEmJo62GRQsXJLVAzS8ff5ywv537F7UMqRiNxy67hGd6BikQwSxe853zWjlz5gxLliwZ9HUA5p87H61RgyRWLBdVgtFjRidUUF111VU47HZWAkYKRWIAyZtCY/y4cZx77rkpe10Aj9uNbpgVG4tySvE48vA48hiTP4minFJ0q/2Bx+NJqV2K3rni4x9H89ZA2N/rMUbuKKTNgbQ5iOSP67GdDwB6EFtrFR+//PIkWZvZ7N27l927P+DqCb4BR3BE+eTEILV1DWlpVZMuKisruefr9/Dhng8xLjbiyzePl3zQr9BpDjTz9Xu/zrZt2xJ3bUXG8eSTT7J//z58ky9FuvLSZoeRO5rgxEWsX7+epUuXps0OhWIwKIE4BM4991w0TUPzVvV6jAi2IoPtsTCuZGAYBlu3beWK8f6kVSodLLOLdeaXhNmyedOQrnP99dcjIxJxKEkiuwFEteCmG29KqJAfO3YsX7vnHg4CrwKRFIhEH5KnhaBRwP0PPJDyRuU5OTmEI6aSXzT5am5ceA83LryHT8y+jUWTr449l5eXvi9uRVeuvPJKs7BM4/FejwmVX4qRMwojZxSBuTf02M4HwN54AqTBJz4Rb9fP4UNLSws/efDHlLjNUNHBsmhMmOmFBr/65eMjIixy+/bt3PWVuzhVfYrIxyNxiUOxW6Ct12I3sbufebsQ9E/oBFwBvve97/H000+rAiLDkNdff52XXnqJUOlc9FHT020OoXHnoReX88QTT7B169Z0m6NQxI0SiEMgJyeHWbNmY/f2nuNlazXFYzIFoqZpFBUWcsJrx8iwKAa/DpV+O8XFQ6sgNm3aNK677jq0jzTop3CsLJJIh3UbI5FF/fdNtL9rp2RUCZ///OeHZGdP3HLLLfzTP/0T7wN/EoLmOETieKDAuk2xHsfDKSS/1TSqbDZ++MMfsmjRosGaPWjyCwpiIrAnQrr5XCJDeRVDY9q0aUwun4KjYejhd/aGo4yfMIFZs1JXVj4TqKys5L5//QY1VZV8fZ4X9xD2ZTQB98zzQqid+/71Gxw8eDBxhmYQkUiEP/3pT/8/e/cd51SVPn788ySZzsDMMPQ2SBUEUVgUBKQIWFBQcUVXRVHxa0Hs4lr3Z5cV1y6wKtjWrqCIirjWpQhiQ0EQ6X0KTC/J+f1xTyAMUyEzSWae9+uVV27OLTk3ubm5zznnnsMtt9xCniePkqEl+2+eroRkCbIr4JFVhYK9eCgZXIK3tZeZM2cyZcoU9u7de3g7ocLGV199xaOPPoq3UWsK2/YNdXYcIuS3H4QvvjF33X03P/30U6hzpFSVaIB4mHr3PhZXzq5y70N0791Go6Qk2rVrV6P5uPSyy1md5WHajw3YU8P36lXV9jwXD3zfiPQCNxdfMuGwtzdp0iRatGiBZ5EHKril0fQykAQkgW+wz3ldHh+4lriQXOGuO+8iPv4QR2SugIgwYcIE7r77bnZFR/O0CMswFTY5PRXhZvu4FOHUSjq5KcbwKYZ/A9GNG/PkU08xePDg4O5IFSUlNaKwpPymikXefESEhISEWszVgUTkBRHZKSK/BKSliMgCEVljn5MD5t0mImtFZLWIjAxI7y0iP9t5T4itfhaRGBF5w6YvEZG02ty/6hIRTh45Alf2DuQwxkSUwhzce7dy8siRtTbma6h5vV7ef/99JlxyCTu2bOTGo7MP+R7wQM3jffz92D1IfiZXXXklL7zwAoWFNdjGvpZlZWVx4003MmvWLLztvJQMKYHaaFTgAXOcwXeMjyVLl3DJhEvqbABenyxevJh//OMfeBOakNdhCEgYXd66o8jtNJxidxy33jpFjzcVEcLoFxSZevXqBcZX9n2IxhCVs53exx5b4xdLo0aN4vrrr2dlViy3LE5m7voYCkLUv8HeIuG1NXHctqQRO0sSuO++++jdu/dhbzchIYGpj0wlISoBz9ceqHzs6Yr5QJYKsk24/vrra7zHxWHDhvHi7Nl07dmTOTi1iTuD0OT0DwxPu1x8DYw85RRenD07pMMLJCcnU+QtP0AsKM4jsUFiqIe5mAWcXCptCrDQGNMJWGhfIyLdgHFAd7vOMyLiz/yzwESgk334t3kpkGmM6Qg8BjxcY3sSJCNtUOfZteaQt+HZvWbftuo6n8/HF198wSUXj2fatGmkxWVzf98sjmocvBNvmwY+7uubRd8mecyaNYvzxp3LnDlzKCoqCtp7hMLvv//OhEsnsOKHFfj6+DB/MVCbLeEFTEdDyZASdufu5qqrr+Ljjz+uxQyUTwuvqm/ZsmXcccedlMQmk9tpOLirOLZVLTJRceR0Gkm+8XDjjTexZs2hn2eVqg0aIB6mHj164HK7y7wP0X//YW018zvzzDN5cdZsevTuy5tr45n8vxTeWBtLekHVg9N2iV7i3D7i3D66JhXTLrHqY3htyXXx4m/xXPdtMh9vjGPoSSN5+ZVXGRDEziratm3LY9MeI9YXi+dLD+SWvZxJqqRpqQ0OXZtcXHHFFYwePTpoeaxIy5Yt+dfjj3PLLbeQHh/PMyJ8hqH4EALFXAxvYZgFxDZrxmOPPcZtt90W8nv7kpOTKSgu54sBCopzSWmcUos5Opgx5iug9ICXo4HZdno2MCYg/XVjTKEx5k9gLdBXRFoADY0xi4zTRd1Lpdbxb+ttYJiEeZVakyZN6Nv3OGLS10AF41iWyxiid/9Or17H0LJly+BnMEzk5eXx/vvvc+EFf+Ouu+6icPcGru2Rw23HZNMkrvLP7eXVcWzIdrMh2819yxrw8uqKO2tqEGW46qg8bu+dTaOSXTz66KOc+9dzePnll4M2zmxtWrp0KVdfczXpuemUDCnBtA/hfREpUDKshJLkEh544AFefPHFcOhtchZaeFVlP/zwA7f9/e8URyeS03kEeMK3d2wT04CcziPJLYHrb7iBdevWhTpLSpWrdnuvqIPi4uLo0rkLKzdvp3QjU/de597Eo4+uvUFa27ZtyyOPTGXlypW8/vp/+PCrr5m3IY7eqUUMa11I95SSCnvWu7BLfpnT5Snxwfe7ovhsSyy/ZniI8rgZcfLJjBs3rsaa1Xbp0oXH//U4199wPXlf5FEyqARK3c5WYbNSL7gWu5Ctwv/93/9x/vnn10g+y+NyuRg1ahQnnHACTz31FAsWLOA3cXGm8dG6iuMl/oLhQ5eLQhHGX3ABF1xwQdgMG9G4cWOKS4oo9hYR5T54rMPCklxaprYKQc4q1cwYsw3AGLNNRPx3Q7UCArs93GzTiu106XT/OpvstkpEZA9Op/2V3EEbWqefPoolSxbjztqEN7l6v1/X3i1QkM0ZZ5xeQ7kLrT/++IMPPviAj+fPJy8/nyMa+rjmqDz6NivGVY3Qf0O2m3yvUza7KqvqZbRHJpdwT5+9/JLhYd6GEmbOnMmsWS8yePAQzjjjDHr27Bn2zXqXLl3KrVNuxZfoo+SEEgiHjoxjwDvQiywXXnzxRbxeL5dddlnIsmOM+aqMWr3RwGA7PRv4AriVgMIr4E8R8RderccWXgGIiL/war5d5x67rbeBp0RETBhExtW1Zs0aptx2G0XueHI7jwRPbHA27C0iNjaWUaNG8eGHH5LjDV6NvYlJJKfzycjqj7jxppt59pmnad68edC2r1SwaIAYBL16Hc1vq98AXwm49n+kruztJDZsWOP3H5ale/fu3HvvfWzbto3333+feR9+wHcrcmiRYBjWKp9BLQuJL+fbr0pgmFUofL4lhv9ujSOzAJo1SeXyy8/k9NNPJykpKch7c7CuXbvy5BNPMvm6yeR8meMEiQ2rsKIXXItcyDZh8uTJnH322TWe1/IkJydz5513MnLkSB5+8EFmZmQw3BhOAKScQLEYwzxgOdClY0f+fvvttG/fvjazXanU1FQA8ouyiYo7eCiE/OKcWh2bMQjK+jJMBekVrXPghkUm4pTy07Zt20PNX9D079+fpORk0neuqnaAGLVjFQ0SGzJo0KAayl3ty87OZuHChXw070NWrf4djwv6Ni3kpO6FdGrkPeRhLA6VCPRoXEKPxtlsznGxcHMM3/z3MxYsWECbVq04ddQoRo4cue83GE62bNnCHXfega+BzzlfH1x2FDouMH0MPvHx0ksv0b59e4YNGxbqXAWq94VXpaWnp3PzLbdQ4HOT23UkJip4pQ1SUsSoM0ZxzTXXYIzhzQ8+Cdq2AUxsQ3I6jYDV87n55luYPv25Gun/QKnDoU1Mg6BHjx7g8zmd1QSIyt3J0SEu1W3RogVXXnkl77z7HrfffjvJbbvxyu/xXPtNCq/+HkdGNZqfAmzNdTFjZTyTv03i3XVxdOrZlwceeIDX33yLCy+8sFaCQ7+OHTvy9FNP0zCmIZ6vKu64BnA6pFnsBIc33XRTSIPDQH379mXWSy8xcOBAPgHeoezhMHIxvCjCcuDCCy/k2eeeC7vgENg3OHpeUfZB83w+L3mFOeE6gPoO22wU+7zTpm8G2gQs1xrYatNbl5F+wDoi4gEacXCTVowxM4wxfYwxfcIhaPZ4PJw+ahTuPZuRwsp+UPtJUR6erI2cduopREeH05V/9Xm9XpYuXco999zDmDGjmTZtGnlbV3FB5zyeHJDFVUfl0Tmp9oPD0lo38DG+az5PDszg8m65xOdtZPr06Yw9+2xuvvkmPv/887Dq1OaJJ5+g0Fvo1BwG4xApdlrwjB071hlTtex+4qpOwBxroDFMe2wa+fmVF5SGgRorvAKnAEtElonIsl27dpW1SEh4vV7+8Y//x569OeR2HI6JDm6HZ8YTzYcffsiTTz7JvHnzMJ7gn9N88SnkdhjCps2bePTRR4O+faUOl9YgBoF/IHJ3zk58De2ABMX5kL/HCR7DQExMDCNHjmTkyJGsWrWKN998k08+/5zPNsdyUusCxrQvICGq/BYm6QXCW3/E8e22GKKjoxg95nTOPvts2rRpU+46tSEtLY0nn3iSq66+ityvc52e8MpqZWJAlguyVbjhhhs444wzaj2vFUlMTOT/3XsvL730Es8//zwlwF8xuOx/eT6GWeIiw+Pm3rvucsatC1P7A8SDu4/PK84GDE2bVrEv+9o1FxgPPGSf5wSkvyYi04CWOPfzLDXGeEUkW0SOB5YAFwFPltrWImAs8HmkNOEaNWoUr7zyCp5dqyluXbXOpTy7fgfjC7vfVXVs27aNefPm8dG8D9mdnkGDaGFws3wGtSwiLTH0AWF5Yt1wYssiTmxZxPY8F19vjebrn5ayZMlSEhskMGLkyYwaNYoOHUI3JlxWVhaLFy3G29ULwaooKXbGx7322msBeGv+W4e/TRd4e3jJ/iKbRYsWMXTo0MPfZnDsEJEWtvYwWIVXmysqvAKnAAuYAdCnT5+wOX8tWLCAn376kfy0AfjiD28IrTK5oynIy+Cdd95xXifWTMG3t2FLClv0YuHChYwaNSokw1IpVR4NEIMgKSmJ5i1asDmgBtFtp4888shQZatcXbt25a677uKyyy7jpZde4uP58/l2RyyXds2hd5MDi2GNgc+3RPPa2gYY8TDuvLGcd955tVpTWJm0tDT+OfWfTJo0CbPI4D3Re1DduPwuuNa7uPjiixkzZkzZGwoxEWH8+PFER0fz7LPP8gUwFDAY3gF2CTz84IP07Rsm4zuVIzU1FZfLRW7hwcMl5BU6QWOo77kQkf/g3NOTKiKbgbtxAsM3ReRSYCNwDoAxZqWIvAn8CpQAVxtj/L03XYnTqUQczv09823688DL9p6gDJyOJCJCixYt+Mtf/sJ3P66kuNUxlXcXH9A5TagLjKrLGMOKFSt44403WLx4MYKhR0ox5/co5JgmxURFWBub5vE+zulYwNkdCliZ4eHLrUXMee8d3nnnHXr0OIpzzvkrAwcOrPUehHfs2IExBpMcxBgjCubNmwfY52Ddgm3jja1bt1a8XO3SwivLGMPs2S/hS0ilJLVTqLNz2Ipa9CAmfQ2zZs/WAFGFFQ0Qg6R7t27s+HYJ/gY9rtzdiAidO3cOab4q0rJlS6ZMmcJZZ53Fww89yGM//sHYI/IZc0QBAF4fPP9bPF9ti+EvfXpz4003hW3vhN26dWPKlCnce++9yG+C6R7wf5cJrl9cDBo0iEsuuSR0mayicePGsXbtWhYu+IweGLYBq4FJV18d9sEhOM0Um6Q2KTNA9KeF+jgyxpxXzqwybzwyxtwP3F9G+jLgqDLSC7ABZiQ644wzWLp0aZU6q3E6p9kbcZ3TrFy5kqeffopffllJwxg4Iy2fIa0KSY2t2Wvl/BIhLi6O0047jXnz5pFfEvzxiFz77lUsIbsoj6+3RbPgj1+4665faNe2DVdedTX9+/cP+vuWp2nTpogIkimYVkH6fKMgPyuft99+23kdrM6bM52nFi1aBGmD1aOFVxXbvn0727Ztpajt8YRttX51uDwUJrfnl59/prCwMGw6m1MqwspHDyYiJ9vxf9aKyJRQ5aNz586YghwodoIrV+5uWrVuHRE3Hnfu3Jlnn5vOyJEjeXtdHAs2OSeo19bE8dW2GC6++GKm/vOfIb+or8zw4cMZPnw47lVu8N/+ZsDzvYfkpGRuvfXWsO/lD5yaxEmTJhEdE82/gbeAI9LSOOuss0KdtSpr1boVuUUHd8GfU7gHl8sVrk1MldW/f38aJSU7TUcrEbVzNQkNEiOqc5pXXnmFq666ks1rV3Jx11z+1T+TczoU1HhwCJBXIvuaRp522mnkldTsOSkx2nBqu0Ie7ZfJNUflUJS+kSlTpjB16lS83qoPY3Q4kpOT6de/H+61AefmcOQF909uGjZqSL9+/UKSBWPMecaYFsaYKGNMa2PM88aYdGPMMGNMJ/ucEbD8/caYDsaYLsaY+QHpy4wxR9l51/hrCY0xBcaYc4wxHY0xfY0xETXWwu7dTl86vpjESpaMHCYmEa/Xy549BxeqKhUqER0g2vF+ngZOAboB59lxgWpdx44dAXDlOeftqIIsuoRx7WFp0dHRTJkyhX7HH8/s1fE89XMCn2yKZezYsUyYMAGXKzIOlauvvpro6Ghkpb3o2gImw3DVlVeRmBg5fyhJSUn87YILiE9JoWlqKpdefnmoB5avlpYtW5JbVFYNYhZNUpvg8WjjhXDm8Xg49ZST8WRtRIryyl+wuICorI2cPHJExHROs3z5cmbMmMFxTYuYenwmJ7UuIroWf1rxHsO8efN44oknmDdvHvGe2mnd5xI4vnkxDx6Xxah2BXzwwQfMnTu3Vt4b4LrJ15EQm4Dnmyp0KBYKJU4P12TArbfcGhGFu/WRv3DRVRB5Y4CWx5WfhcvlIiUltOMDKxUoMq76y9cXWGuMWWeMKQJexxnjp9b5e5N05WeCtxhTsJe0tLRQZOWQud1ubrjxRuJiY1m8I5qmTVKZOHFiqLNVLSkpKZw55kxcm12QD+61bpq3aM5JJ50U6qxV2/jx43n3/fd5+913GThwYKizUy2tWrWioCiP4lLjR+UWZdG6Tety1lLh5JRTTgFjcKevLXcZT/o6jM/LaaedVos5Ozzr168HYGirQuJCUE4R5zHk5ztNI/Pz84mrpQDRz+OCk9o4LV02btxYa+/bvHlzHpv2GAmSgOdzD2yrtbeuXA64v3Dj2u7ixhtvjLjzbX3StGlTjuzWjdidv4E3+M2za5sU5xOTvoYTTzxRC05VWIn0AHHfeD5W4Fg/tapx48bExSc4JUH5TslWKMY/PFzNmjVjzty5zJ07l9f+8zqxsUEaeLYWjRo1yum1dI3ALhh12qiIqn2rC1q1cn6GOYWZB6TnFGbtm6fCW1paGl26diU6/Y9yl4lOX0v79kfsa0ERCYYMGUKzpk145IdE/rMmjvRqDvUTyXKKhQ/Wx/D3JcnEx8c558pa1KVLF2ZMn0G7lu1wf+NGvpdDHp7CJBlMk4BH0iEE2gbkD8HzmYf4ongeeughRo8OSRmzqiIRYeLll0NRLnF/fgnGF+osHTpfCfFrF+JxwcUXXxzq3Ch1gEgPEE9/+rAAACAASURBVKs8GHVNj+UjIrRt2wZXwR6kwOmpMRwGvj4UsbGxJCUlRUyTsdLatm1Ly1Ytca12Du8TTjghxDmqf/z3q+YENAMqKimgsDhfA8QIMnLECCQ3HclzAn1fQmN8CY0BkIK9SM5ORo4cEcosVltKSgrPPjedIcOG89HGOK7/NolHVjTgy63RZBfVvWAxvwSW7ojiiZ8SmPRNMm+sjeeoXn2YPn1GSIa+aN26NTNnzGTs2LG417nxLPA4RbvVjO9ML4NvsG/fw/Sq5gb22FrD710c0+MYZs+aHbL7DlX1HHPMMVx91VV4MjcQ+8cXQa9J9MWnYNxRGHcUJYnN8cXXQNPPkgLi1yzAlbuLO+64IyIrFFTdFun12eWNAXSA2hrLp03r1vy+YSle25V/uHfqUpf17NGTrVu2EhsXG5aDydd1+2sQ9weI/mkNECPH4MGDefLJJ/Fk/ElxfDJF7fZfQHsy/gQIp7Hiqiw1NZU777yTSy+9lA8++ICFny1g5q87EaBDIy89Uoro0biYIxp68QS5GLVdopcN2e590+0Sg9tRjM/Ahmw3KzM8/JwRzaosD14fJDdqyGmnD2X06NEhHRMRnHF5r732WoYOHcojUx9h/aL10By8vbxQ07eKF4H8KrjWumjQoAHXTLmGU045JSI6MFP7jR07Fq/Xy3PTp+Ne/RF5RwzGxDYMyrYL2x6/rz+J/K6nBmWbgVy5u0lY9wXukjxuve22sB7XWNVfkR4gfgd0EpH2wBac7prPD1VmmjVrhinIQQqzaZSUFJHNM+sKf5O39u3bR0wHO3VJQkICjRo2IjcwQCzQADHSpKamctRRPfh53QaKWx97wLyozA107tIl5GNaHo6WLVtyxRVXMHHiRFavXs2iRYtYvOh/vL/6d9770xDjETo3KuLI5GK6JZeQlnj4AeOFXfL3BYh39Dn83lp8BjbluFmV6eG3TA+r9sSQU+SUg6a1a8tfR57A8ccfT48ePcLuHqejjjqKF55/gXfffZfnX3ie/E/z8XX0YboZiArymxmQ9YL7FzcUwumnn87ll19Oo0aNgvxGqjaICOPGjaNt27bcd//9uH+dQ36r3hQ3PTJ8h7/weYne9iMx234kOTmF++59nO7du4c6V0qVKbz+LarJGFMiItcAnwBu4AVjzMpQ5adZs2ZgfLhzdtGsTbNQZUPh/Pk3bdqUTp0ifyDdSNWqVSt2b9kfIPqDxVCNL6YOzcCBA/j552eQwmyM7VpeivKQnJ0MGnhGiHMXHCJC165d6dq1K5dccgnZ2dmsWLGC5cuXs+L75by51unMJcYjdG5YxJHJJXRLKQ5KwFhd/oDwNxsQrg4ICFs0a8qgk/pw7LHHcuyxx5Kamlq7mTsEHo+Hv/71r5x00knMmDGD+fPnwybw9vRi2piybySprkxwr3BDOnTv0Z3rJl8X1mMUq6rr378/s2fNYurUqSxdupjojHXktz0eX0J4HfvuvVuJ27gYyc9i+PDhXHvttRHVs7qqfyI6QAQwxnwEfBTqfIDTUQ04PZk2baqlQqEUFxfH4MGDQ52Neq1lq5Zs/HPZvtc5hXto1ChJu4+PMP369eOZZ57BnbWZkmZHAuDes3nfvLooMdEZ19E/tmNmZiY//vgjP/zwgxMw/rEB/ogj1iMcmVRI95QSejYupkW8r0qVF9VtVppeIPyUHsUvGVH8mhlDtg0IWzZvxqCTetOrVy969eoV0bW5KSkpTJkyhdGjR/PotEf5fcnvsB68x3qhwSFutATkF6c5aVJSEtfccQ3Dhw/X5qR1TJMmTXj44YdZsGABTz39DK5f51LUpCtFrY7BRMWFNG9SmEPM5u+IyviTZs2aM/nOB+jfv39I86RUVUR8gBhO/AEioOPZqHqvRYsW5BbuxWd8uMRFbmEWLVtr7WGkadu2LalNmrJ9z5aAAHELjZKSIqr30sORnJzM4MGD9xU6ZWZm8sMPP/D999+z7LulrPjdGbOhaTwc07iAvzQtpnNSCa5y4pALu+RX+H7G3kf43a4ovt8dy6ZsZ0ONU5LpP6QvvXv35phjjnFardQxRx55JNOfm87cuXN59rlnKVxQiPdoL6Z9NWsT08Gz1IPJMYwZM4aJEyfSoMGhRpoq3IkII0aMoF+/fsyaNYv33nuPmMw/yW9xtNPs1FXLPZl7i4ne9hOxO1bicQvnjx/P+eefT0xMTO3mQ6lDpAFiECUlJe2bTk5ODmFOlAq9Fi1aYIyPvKK9NIhJIr9kLy1bapPfSCMi9Ol9LJ8s/IJC49RcReVsp/cJx9Xbmpjk5GSGDBnCkCFDANi2bRtLlixh0aL/8fmyZXyyqYTkWDihWQGDWxXSPL5qXfFnFQpfbo3hm+2xbMsVXC4XPXv2YFS//hx//PGkpaXVi8/c7XZz5pln0r9/fx586EG+X/49vp0+TB9T+VWLAfldcP3soknTJtzxwB306tWrVvKtQi8xMZFJkyZx+umn8/Qzz/Dd0qXE7l5FXqu+eJPaVPn+xEPuudQYPOlrid+yHFOUx9Bhw5g4cWKdLMxRdZsGiEEUGCA2bBic3rSUilT+5m55hXtJiG5EbkF2RDeBq8969uzJxx9/7Azh43JhCnPp2bNnqLMVNlq0aMGYMWMYM2YMeXl5LFq0iAULFjB/yWLmbYjlmNRizu6QX27T0l35Lt5bF8u3O2Lw+qBXr6O5cMRIBgwYcMD/Sn3TrFkzpj06jddee42ZM2dCDpQMKIHy+n/zgSwXXOtdDDpxEFNunaK1hvVUWloaUx95hCVLlvDkU0+xee1neBu2pKDt8fjiKv9NFbY9vtrv6crZSdzGxbhyd9O5S1euvXaSdkKjIpYGiEEUHx/PZZddxo4dO/bdu6JUfeUvMc0r2ktBcS4+49VS1AjVrVs3AFy5u0CcplpHHnlkKLMUtuLj4xk2bBjDhg1j9+7dzJ07l7ffepM7lkQxsk0B53bKJ8p2bOMz8NGGGN5ZFw9uD2POHM1ZZ51FmzZtKn6TesTlcnHBBRfQoUMH7rzrTvgSSk4sI0g0IEsF1yYX48ePZ8KECfWitlVV7LjjjqN3797MmTOH5194AffK9yls1p2ilr3AHaSucksKiNm0jOjdv5OUnMJVk//OSSedpD2oq4imAWKQXXTRRaHOglJhoWnTpgDkFWWTV7T3gDQVWdq2bUtUVDTFubsxLjcutzvkY+lFgtTUVCZMmMA555zDzJkzef/991mfE8XNvfYS5YIZK+P5ZnsMAwYM4LrrrtPfRwX69evHP6f+kxtvuhHzP4N3sBcCrr/lFyc4nDhxIhdccEHoMqrCjsfj4eyzz2bo0KFMnz6djz/+mJisDeS1OwFvw8O7L96TsZ74TYuQkiLOOfdcxo8frx2xqTpBizeUUjUiJiaGxMSG5Bdlk1eUDTi9zanI4/F4aNeuHZKfiSsvk1atWhMdHR3qbEWMxMREbrjhBu644w5WZ7mZvSqeeRti+GZ7DBMmTOD+++/X4LAKevXqxR233wHpzmD3++wE1yoXp59+ugaHqlzJyclMmTKFxx9/nObJCcSvnk/MxiXgq16vwgB4i4n940vi/vicI9q2ZubMGVx55ZUaHKo6QwNEpVSNaZKaSn5xLvnFzoDgGiBGrrS0dkQV7sVTtJf2ae1CnZ2INGLECMaNO4+vtsXwxtp4Bgw4gfHjx2tTyGoYMmQII0eOxPWbC1kiyHLB/Y2b5i2aM2nSpFBnT0WAo48+mlkvvsiZZ55J9I6VJKyejxTlVnl9V34mib99QHTmOi655BKmP/estqhQdY42MVVK1ZjUJqms3bmRgqJc3G63dt4UwVq3bo0p+AyAVq1ahTg3keuCCy6gqKiI4uJizj//fA0OD8EVV1zBr7/9yt5sp+m6O8nN9dddT2xseb3XKHWg2NhYJk+ezNFHH81DDz2Ma9U8cjsNxxdXcQ/07uztJKxdSGJCHP94YBrHHHNMLeVYqdqlAaJSqsY0btyYld5VFJTkkpSUpDftR7DADoa0N9pDl5iYyOTJk0OdjYiWmprKq6+8GupsqDpg8ODBtG7dmptuuhlZPZ+cLqeUGyS6924jYc0CWrRoxrRHH9XzoKrT9GpNKVVjkpOTyS/KpaA4l8YpjUOdnVonIieLyGoRWSsiU0Kdn8MR2DxYmworpeqKjh078swzT9OoQRwJaxYgRXkHLePKzyLhj89p1aolzzz9tAaHqs7TAFEpVWOSkpLw+bzkFu4hKbl+jecmIm7gaeAUoBtwnoh0C22uDl1KSkqZ00opFelatmzJ1EceIdoUE7f+azBm/0yfl/h1X5CYEMfURx6u12OTqvpDA0SlVI1p1KgRANkFGfum65G+wFpjzDpjTBHwOjA6xHk6ZIEXRXqBpFRkq0utG4KlU6dOXH31Vbj3bMGTvnZfevT2n5G8DG6bcistWhzesBhKRQoNEJVSNSawU5p62EFNK2BTwOvNNi0iJSYmljmtlIosda11QzCdccYZdOrcmbhtP4DPByWFxO74hQEDBtKvX79QZ0+pWqMBolKqxgQGhfUwqCire0pzwAIiE0VkmYgs27VrVy1l69AEjnuoY30pFdHqVOuGYBIRLh4/Hgqy8ezZRFT6WkxJEePHXxTqrClVqzRAVErVmISEhH3T9TBA3Ay0CXjdGtgauIAxZoYxpo8xpk+4d/wSOByD2+0OYU6UUoepTrVuCLbjjjuORklJeNLXEZ3xJ0d06ECnTp1CnS2lapUGiEqpGhMYIDZo0CCEOQmJ74BOItJeRKKBccDcEOdJKaUqbd0AkdXCIZg8Hg/HH3ccUdlbcOXuYsAJJ4Q6S0rVOh0HUSlVY1JSUmjfvj179uyhQ4cOoc5OrTLGlIjINcAngBt4wRizMsTZOixDhw6lqKg41NlQSh2eSls3gNPCAZgB0KdPn4MCyLps6NChLF6yBLfbzcCBA0OdHaVqnQaISqkaExUVxezZs0OdjZAxxnwEfBTqfATLPffcE+osKKUO377WDcAWnNYN54c2S+HluOOOY87774c6G0qFjAaISimllFL1RF1s3aCUCi4NEJVSSiml6pG61rpBKRVc2kmNUkoppZRSSilAA0SllFJKKaWUUpYGiEoppZRSSimlABBj6lXPxYjILmBDDb9NKrC7ht+jpuk+hIe6sA9Q8/vRzhgT3iPNV6KWzk3BUFeOyVDTzzE4IuFz1PNTZIqEY0sFT338vss9N9W7ALE2iMgyY0yfUOfjcOg+hIe6sA9Qd/ZD6XcZLPo5Bod+jqqm6LFVv+j3fSBtYqqUUkoppZRSCtAAUSmllFJKKaWUpQFizZgR6gwEge5DeKgL+wB1Zz+UfpfBop9jcOjnqGqKHlv1i37fAfQeRKWUUkoppZRSgNYgKqWUUkoppZSyNECsJSKSJCJXBbxuKSJv2+mLReSpctbLKSMtTUR+qcZ7jxGRbpUsM1hEPixn3noRSa3q+1XwHgftS02q7uekDhTsz8///dvtnh+s7arwUNE5pD4SkWtF5DcRebWa6wXlfFtXiIhbRFYc6rFV2/87Kvwd6m+zjO3of5mqszRArAUi4gaSgH0BojFmqzFmbC1lYQxQYYCoVDCJiKeC2WmA/qmGOXHof8Shuwo41Rjzt1BnJMJNBn4LdSZUnXLQb7OS/6zypKH/ZRHlcP/X7PV8vVBv/vxF5CIR+UlEfhSRl0WknYgstGkLRaStXW6WiDwhIv8TkXUiMtamvyEipwZsb5aInG1LN6eKyHd2W1fY+YNF5L8i8hrwM/AQ0EFEfrDLl66daSMiH4vIahG5u5x9uFlEvgPmA81EZKaIrBSRT0UkTkQut/n4UUTeEZF4EekPnAFMte/dQUQ6ishndrnvRaSDfYsGIvK2iKwSkVdFRALe/mYRWWofHW1+TheRJbZ09zMRaWbTG4jIiyLys/1Mzi61H6kiskhETqvid5cgIvNsfn8RkXNFpLeIfCkiy0XkExFpYZftbZdbBFwdsI00Efna7u/39nPxf09fVLDfh0VEbhGRa+30YyLyuZ0eJiKviMizIrLMfo//CFjvVJufb+zx+KFNTxGR9+3nulhEetr0e0TkBbsv6/zvGQTuqhxnNg+zRGSaiPwXeFhE2tvv+TsRuTdgmw8BA+3xeL2IxAYcLytEZIjd3sUiMqey34UKHvs7+U1EngHWAc/b9Mkiss5OdxCRb+z0yf7jFDgrZBkPMyLyHHAEMFdEjDiSRMQnIoPsMl/bc3Fj+9taISLTgaCdfyKdiLQGTgP+bV83FZHldvpo+9n6/7v/EOc/r7zzjlKlf5t7RGSGiHwKvCTVvC6k1H9ZiHZJVaLU/9r3wJ2y/5o98LrrfXGuKVeKyMSA9BwR+X8isgToF4JdCA1jTJ1/AN2B1UCqfZ0CfACMt68nAO/b6VnAWzjBczdgrU0/E5htp6OBTUAcMBG4w6bHAMuA9sBgIBdob+elAb8E5Gnfa+BiYBvQ2G7zF6CPnZdjn0fg9LAkdvs+YIKd9yZwAdA4YPv3AZMC9mlswLwlwJl2OhaIt/ndA7S2+74IGGCXWQ/cbqcvAj6008ns7+joMuBRO/0w8K+A90v27wvQzL7/8Gp8f2cDMwNeNwL+BzSxr88FXrDTPwEn2umpAZ9xPBBrpzsBy+x0ufsdpGPveOAtO/01sBSIAu4GrgBS7Dw38AXQ034nmwKOnf8EfOZPAnfb6aHAD3b6HvuZxACpQDoQdZh5TwNKgF7VOM4+BNz29VzgIjt9NfuP5cH+/bGvbwRetNNdgY32M7iYcn4X+qiZh/3Offa4bQ58Z9PfBr4DWgHjgQcDjtNOOOelNwO/1/r+wDlvpgIf4/wHjbKf4e32d/qnXe4J4C47fRpgsP9V9f1hj7vegecMYCXQELjGfp5/A9oBi+z8Ms87+tCH/xHw27wHWA7E2fTqXhce8F+mj/B8lPpfC7yWdtlrlkF2Of/1mP96o7F9bYC/hno/avtRX2oQhwJvG2N2AxhjMnBKAV6z818GBgQs/74xxmeM+RUnoAGn1m6oiMQApwBfGWPycQ62i0TkB5zApzHOBRPAUmPMn1XM4wJjTLrd5rul8oN9nxHACmAezoW7v6R5Oc4P4ChbKv0zzp9m99JvIiKJQCtjzHv2sygwxuQF5HezMcYH/GC36fefgGd/CUpr4BP7fjcHvN9JwNP+FY0xmXYyClgI3GKMWVDpJ7Lfz8BJIvKwiAwE2gBHAQvs534H0FpEGgFJxpgv7XovB2wjCphp8/oWBza5rWi/D9dyoLf93AtxAtA+wECcgPGvIvI9zvfa3earK7Au4Nj5T8D2Bvj3yxjzOdDY7jfAPGNMoT3Od7L/2D0cfxpjfgjYlzQqPs7eMsZ47fQJAXkP/C5KC9ynVcAGoLOdV9nvQgXfBmPMYmPMdpxWBYk4v7nXgEHsP3a74hwfa4zzL/pKyHIc3r7G+dwG4QTWA4C/4AQ32PRXAIwx84DMMrZR74jIKGCnMWZ5qVn/wzm3DAIe4MBjEqp+3lEKYK79f4HqXxeqyLHBGLOYA6+lv8f5H/Nfs18rIj8Ci3H+8/zpXuCd2s1u6NWXAFFwSgAqEji/sNS6GGMKcGp4RuLUWL0eMH+SMaaXfbQ3xnxq5+VWI4+l81f6tQAPGmN6AacCvxtjnrfzvIAHp5TrGmNMD+AfOCX8pVXUfClwv/3bLCs//ukngafs+10R8H7lfd4lOEHGyArycBBjzO84pcg/41xgnQ2sDPjMexhjRlTwvgDXAzuAo3ECtOiAeRXt92ExxhTjlFZegnNh8zUwBOgA5AM3AcOMMT1xAv9YKv6Oyprn3+ea2I+ytjmL8o+z0sd8VcbRqWh/K/tdqOAL/A4X4Ry7q3GO3YE4F1Hf2vn6fVTO/7n1BT7CuR99MPBVwDL6OR7sBOAMEVmP8387VEReYf/n2Q6Yg3NOH4B+nurQVHSdVuF1oYoo/u9537W0fXQ0xjwvIoNxKjf6GWOOxgkg/dc2BQEF3/VGfQkQF+LU1DQG5z4unIv1cXb+34BvqrCd13EulgYCn9i0T4ArRSTKbruziCSUsW42kFjBtoeLc39ZHE6nMt+Wmv8JMEFEGtjXHhFpWmqZRGCbzUtgxwj73tsYsxfYLCJjbH5j/PeQVeLcgOdFdroRsMVOjw9Y9lOc5j/Y90i2kwan2UZXEZlShff0r98SyDPGvAL8EzgOaCIi/ez8KBHpbozJAvaIiL/UL/AzaARss7WEF+I06awtX+EEgl/hXNz8H05NZUOck9Yece7fPMUuvwo4QkTS7OtzS23rb+DcPwnstt9pbSrvOCvtWw78jfmV/i0E7lNnoC1OQAKV/y5UzQo8dlfgFG4UGmP24Byn7WX/PcznhSaLYW8J0B/w2YLGH3AK1Pw1XoHH/yk4TffrPWPMbcaY1saYNJzzyOfGmAtwPq8LgDX2fJ6BU2jqPzeUd95RqjLVvS6s7LpOhZ8DrqVFpJW9lm4EZBpj8kSkK05z1HqtXgSIxpiVwP3Al7b6eBpwLXCJiPyEEzBMrsKmPsVpzvKZMabIpv0b+BX4XpxOZ6ZTRs2NMSYd+FacTlamlrHtb3CaNPwAvGOMWVZq/U9xmj4swrmnpS0Hn5juxLkYWYBz8eb3Ok4nMyvsxdyFOFXpP+GcEJtXYd9j7A26k3Fq48Bpv/+WiHwN7A5Y9j4g2e7rjzgXlf798OKcgIdIwLAflegBLLXNSW8H7gLG4nSE8iPOZ9bfLnsJ8LQ4ndTkB2zjGWC8iCzGab5Yndrdw/U10ALnHpkdQAHwtTHmR5yL7pXAC9gLHNvc5SrgY3E6/9iBc58kOJ95H/vdPcSBgXltKe84K20ycLU4HSs1Ckj/CSgRp5Ob63G+G7dtsvoGcLExxl9aW+HvQtW4r3Ga2nxlf7ubsBdNNtiZCMyzx+mGkOUyjNljeRNOsyVwPtNEnBYR4NTCD7JNzUfg3IOrymGMWW8n/TWG3wBZAbcylHfeUaoy1b0uLP1fpsJc4LW0veZ4G+d8/DFOxctPwL3sP1/XW/4ORpRSYUREGhhjckREcO7nXGOMeSzU+apNInIxTqc011S2rFJKKaWUCo56UYOoVAS63NaYrsQpBZ8e4vwopZRSSql6QGsQlVJKKaWUUkoBWoOolFJKKaWUUsrSAFEppZRSSimlFKABolJKKaWUUkopSwNEVSeIyD0iYkRk7GFu5xcR0RtzlaqHROQmex65uJz560UkpxbyEdT3EZHBdr+eCtY2lVLhwf62fznMbcyy2+kTrHwFbPt8e42WFJB22HlWNeug8fqUijQiosexUqpGiYgbmAREhzovSikV4DwgK9SZqMD5wGnALPbnM9zzXO9pDaIKCyJyoYisFJF8EflDRHqIyK8ikiciWSLykYi0ssv6awtfEJF1wNSATQ0Rkd9EZJuI/M0uLyJyu4hsEJFsEfmviHS385JEZJ6IZIrILCAqIE/fichOEYmyr+eKSEFgKZhSKrLZWsPdIrIc6BGQvl5EckXkGRHZY+c9CcwWEbc9x/wYsPx3djtRItJPRBaJSI6I/C4i59llHrDnri52GSMit9p5u0Xkm1J5u1dE0u22m9u0I0VkgYjstee06wOWH2UH7c61zyfV5GenlKo+EZkgIqvt7/R/InKsiFxszwezRWSFvSaZbJcXEXlURDJE5AsRec8uO9hu8j/AP+2y/uujp+y5Z5eInGPnRYvIP0Vki72uektEmpSTx8tFZI3N41IRGRCwjQftuSdfRL6y6RfatEIR2S4iz9rz5D04wSHAnyKyvow8J4tTg7nLPl4SkWQ77wu7P/fb67FVInJkUL8QVSYNEFXIiciJwEs4NdrXAq8DRcBs+/opYCRwT6lVRwAPAx8GpJ0IPAb4gH+LSDPgEuA+4CfgduAvwBwb+N0NnAq8BWwBOgds61mgCTBKRBoAw4G5xhgt9VKqDhCRo3EKmLbjjDVaOqCKB1oCNwE7/YnGGC/OeaqniHQSkXZAH+ANIBHnnJQE3A+sB14WkV7AV3YT/ewDoJ+IdAEaA18HvHcC0Br4yG77cnFaS8wBugGPAEuAaSJyuoh0Bt4B8nHOd4XAeyLS4lA/H6VUcNmg7nmc88J9OL/7uUCsXWQk8G/AAA+JSDRwOnADzrjI7+Bci1TmJOBpnHGUH7JptwE3Ah8A/wJOwbnOKZ3HocAMYJd937bAXBFpDEyxj5XANcD3drXdOAHfZGAh8H/AOOBtYIVd5lqcVhilPQ6Mx6lhfBG40KYF6gm8CnTBOR+rGqZN81Q4GGWfbzDGzAMQkR44zRJ6BizXo9R6jxhjptvlB9q0x4wxz4tIG+AO4DicANC//TUicpzddmdgME4weY0xpkhELsK5KIP9JVyX4DQri8UJZJVSdcNg+1z6vBFovDFmD4CIBKa/ClwHnIMTjAG8ghP4pdjHAwHLD8W58PPaZZKBTzgwWAwMEH3AlUAv4AIgDefiqJOdf2/AssNxLuKicc55xwXM6wdklLn3Sqna5q9NG2EffvH2+QVjzNMicjpOsNgMGGLn/cMY85mIHI9zDVORacaYGSJyJfvPGf5rrSsClhvBwfzXTHcbYxaISFvg78DxOMGqAc41xmQHrNMIJwANLJDqYYx5VUS2AscAHxhj1pfzfluMMTeDc88iTvAa6EZgL845N63sXVbBpAGiCle34wSHdwOLcUrkY0sts7WC9aWMtPI6nwlM37eeMSZfRGbjlJIl4tQgfFxxtpVSEaz0eSPXHxyWZoxZJiKrgbE4AeIfxphFIuK/CHsJeDlglfXGmL0i8hP7A8T/A97DCQB9wLcBy+cbYwpEpMS+dgfk7xNs8yxrO/uD3UeABQHzfmP/BaJSKrT8v+EbcVo1gdOar7ud9hfmBP7u/arTgV7gdvytBcW+HoVTUAUVtySsyjWT379wgtyLcM5tj7P/mi0YhVv+IQAAIABJREFUHf8FFnK5y11KBY02MVXh4AP7PM22e7+P/fcCNgDODHhdmetFZCJOrV8+ThOseQHbnwScAfwB/A78F+dk85SI3A+0KrW953AKUgYDrxljSlBK1RVf2OfA80Z1vIpTMn6cnQb4H87FzMlAV+AonCZZ/nPLVzatBfAl8AMwDPipvGA0wCpgDTDAvm8X4GrgWOBTnKb5ZwHt7fwHqfq5UylV8/y3xJyHU+t/HPAEkFnBOv+1z3fZa5jRh/jeH+Bcz4y3730yB9Ym+n1kn/8hIlcAE2z+FtttuIA37L2U/wpYLxqnMH1Mqe359218wH2TgeYBrUTkYRF5GOdc+VEZy6lapAGiCjljzFc4pU5enE4gzsO533AVzgVbOlDZhZPfpzjt093AZcaYHTjt2u8Ejsa5YFoGjDbGFOM005oPnItzUlpTKm+rgc/tS21eqlQdYoz5EbgZaI7TUmBBxWscxB8UCk7zUowxGTgl9Gtx7v25HcjDuecInGakAvxijMkBFgWkV5bfEpyLw29xmsLei3NB9rMx5nec4DAHp/T+epyCsIouPJVStcgY8wXOdU0DnHsEJ+IUKlXkA2AaTquqsewPGKvbH8KDOPdcD8Tp2+EUnEKq0nn83OarqX3fzcAZxph0nHPaQziFXM/gFE6Bc77Zi3M+/abUJqcDG3Gu60o34Qen2ehLwKX28bJNUyEkxuiQb0qVRURigEE4pXt5xpjeIc6SUkoppeoZEbkBp0lqc5wCoHyggzGmsMIVlTpEGiAqVQ4RSQP+xCn5OtcYszikGVJKKaVUvSMiX+L0wF6E0wrqZmPMiorXUurQaYColFJKKaWUUgqoh72YpqammrS0tFBnQykVRMuXL99tjClzwN9IoecmpeomPT8ppcJRReemGg0QRSQJZ9yno3C6uZ0ArMYZTDgN56b9vxpjMu3yt+HcoOoFrjXGfGLTe+N0NBKH07PRZGOMsfeIvQT0xunI5NxyxljZJy0tjWXLlgVzN5VSISYiG0Kdh8Ol5yal6iY9PymlwlFF56aa7sX0ceBjY0xXnB4kf8Pp7nuhMaYTsNC+RkS6AeNwxoI5GXhGRPxjnTyL06NSJ/s42aZfCmQaYzoCjwEP1/D+KKWUUkoppVSdVWMBoog0xOkB8nkAY0yRMSYLp4vu2Xax2ewfL2U08LoxptAY8ydOF+F9RaQF0NAYs8g4N0y+VGod/7beBoaJSFkDpCullFJKKaWUqkRN1iAeAewCXhSRFSLybxFJAJoZY7YB2OemdvlWwKaA9TfbtFZ2unT6AevY8aH2AI1rZneUUkoppZRSqm6ryQDRgzOA5rPGmGOAXGxz0nKUVfNnKkivaJ0DNywyUUSWiciyXbt2VZxrpZRSSimllKqnajJA3AxsNsYssa/fxgkYd9hmo9jnnQHLtwlYvzWw1aa3LiP9gHVExAM0AjJKZ8QYM8MY08cY06dJk4juSKxMxcXFoc6CUkopVafk5eWFOgt1QklJSaizoJSqphoLEI0x24FNItLFJg0DfgXmAuNt2nhgjp2eC4wTkRgRaY/TGc1S2ww1W0SOt/cXXlRqHf+2xgKfm3o2sOM777zDsGHDyMrKCnVWlFJKqTrhq6++4uSTT2b27NmVL6wqNHbsWF5++eVQZ0MpVQ013YvpJOBVEfkJ6AU8ADwEDBeRNcBw+xpjzErgTZwg8mPgamOM127nSpzhMtYCfwDzbfrzQGMRWQvcQMVNWOukd999F4A9e/aEOCdKKaVU3bBx40YA1q1bF+KcRL6MjAxmzpwZ6mwopaqhRsdBNMb8APQpY9awcpa/H7i/jPRlOGMplk4vAM45zGxGNH+Fqc/nC3FOlFJKqbrB37xUm0cqpeqjmq5BVDXMP6qH1+utZEmllFJKVUVOTg4Aubm5tf7eIrJeRH4WkR9EZJlNSxGRBSKyxj4nByx/m4isFZHVIjIyIL233c5aEXnCPwyYvZXnDZu+RETSansflVLhTQPECOevQdQAUSmllAqO7OxsAPbu2RuqLAwxxvQyxvhbYU0BFhpjOgEL7WtEpBswDugOnAw8IyJuu86zwEScPh062fkAlwKZxpiOwGPAw7WwP0qpCKIBYh2hAaJSSikVHP6O3/aETwdwowF/jzmzgTEB6a8bYwqNMX/i9NXQ1/YS39AYs8h23vdSqXX823obGOavXVRKKdAAMeJpDaJS1adNuJRSFcnKdALDrL17CEHn6Ab4VESWi8hEm9bM9uqOfW5q01sBmwLW3WzTWtnp0ukHrGOMKQH2AI1rYD+UUhFKA8QI5y/00xvplao2bcKllCpTRoYzpHJxcfG++xFr0QnGmGOBU4CrRWRQBcuWVfNnKkivaJ0DNywyUUSWiciyXbt2VZZnpVQdogFiHaEBolKHTZtwBZHX6+Wee+7hu+++C3VWlKqWkpISsvZk0TQ+BYD09PRafX9jzFb7vBN4D+gL7LDnHOzzTrv4ZqBNwOqtga02vXUZ6QesIyIeoBGQUUY+Zhhj+hhj+jRp0uRQ9+WQ1lNKhZYGiHVEcXFxqLOgVCTRJlw1LCcnh88//5wHH3ww1FlRqlrS09MxxtC+kRNf1WbtmYgkiEiifxoYAfwCzAXG28XGA3Ps9FxgnG3W3h6nJcNSew7LFpHjbeHURaXW8W9rLPC5qaFITgNEpSJTjY6DqGqPBohKVcsJxpitItIUWCAiqypYtkabcOE0UaVt27YV5zjC+MdmzczMDHFOlKqenTudyrkOSW1Ysu2nfa9rSTPgPdvgwAO8Zoz5WES+A94UkUuBjdgxoI0xK0XkTeBXoAS42hjj75TgSmAWEAfMtw+A54GXRWQtTs3huJraGR2jWanIpAFiHaEBolJVF9iES0QOaMJljNkWxCZcmytrwgXMAOjTp0+dKmrXjrNUpNq+fTsARyS1QUT2va4Nxph1wNFlpKcDw8pZ537g/jLSlwFHlZFegA0wlVKqLNrENML5b2sqKioKcU6Uigx1rQlXuNKaAxWptm3bBkDT+BSS4xrte62qT88DSkUmrUGsIzRAVKrK6lQTrnDlr0GsZ33zqDpg69atNIpNJMYTTWpsElu3bK18JVWmelYuplSdoQFihDO2dE4DRKWqRptw1Q7tWVlFqq1btpAa5wyD2iQumV+3rA9thiKYBohKRSZtYhrxnJNvQUFBiPOhlFL76T2IKlJt2rSZpjZAbBqfQmZWJnl5eSHOVWTSJqZKRSYNECOc15bSFxYWhjgnSim1n7/jLK1BUJGkoKCA9Ix0msY7o9I0sWMhbt68uaLVVDn0969UZNIAMcIV2MBQaxCVUuHEHyDqPYgqkmzZsgXYHxj6A0UNEA+N1iAqFZk0QIxwhRogKqXCkA69oyLRpk2bAKdpaeCzP3BU1aMBolKRSQPECGaMoaBAA0SlVPjRAFFFIn8g6A8MYzzRNIpN1BrEQ6QBolKRSQPECFZUVITPtu/XG+hVVc2ZM4c5c+ZUvqBSh8Hfs7Leg6QiydatW2kQk0BcVOy+tCZxyVqDeIg0QFQqMukwFxEsPz+/zGmlKvLoo48CMHr06BDnRNVl/ubveg+iiiRbt26lie3B1K9JXDJrNEA8JFpApFRk0hrECBZYa5iflxvCnCil1IG0Z2UVibZu2UpqbBJv/DafN36bD0BqXDK703frMX0IdLgbpSKTBogRzB8gCoa8XA0QlVLhw39ftNYgqEjh9XrZuWsnjeOS2Jy9nc3Z2wFoHJcEwM6dO0OZvYikTUyVikwaIEYwf4CYFGP0HkSlVFjRZu8q0mRkZOD1ekmJa3RAuv/1jh07QpGtiKYBolKRSQPECOYPClNifOTpxZhSKoz4A0StQVSRwl9DmBJbKkC0r3ft2lXreYp0GiAqFZk0QIxgubZZaXKMj/z8Ar0QU0qFDX8Bls/n03u3VETwB4BJMYkHpDeyrzVArD69B1GpyKQBYgTbV4MY68NnjI6FqJQKG4HN3rUJvIoEGRkZwP6A0C/aHUV8dBzp6emhyFZE0xpEpSKTBogRLLAGMfB1qOTl5TFr1iy990gpdcD5KNTnJqWqIiMjAxGhQXT8QfMaRjcgMzMzBLmKbFqDqFRk0gAxgvlL5X/NiDrgdah8+OGHvPDCC8yfPz+k+VDlCyzN1ZJdVZNycnL2TWuAqCJBZmYmDaLjccnBl0aJUfEaIB4C/Z9RKjJpgHgI3nzzTW666eaQ3/OXl5eHAJmFztcY6ouwvXv3HvCswk9xcXGZ00oFW052NlEu5xwZGCwqFa6ysrJIjE4oc15idDxZGbUTIIqIW0RWiMiH9nWKiCwQkTX2OTlg2dtEZK2IrBaRkQHpvUXkZzvvCRERmx4jIm/Y9CUiklaT+6I1iEpFJg0QD8FTTz3F0qVLKCkpCWk+cnNzcQu4xLkIC3UNoj9gtv9DKgwFdhZSVFQUwpyoui577x6axDm1B9nZ2SHOjVKV27t3LwmeuDLnJUTFs2fPntrKymTgt4DXU4CFxphOwEL7GhHpBowDugMnA8+IiNuu8ywwEehkHyfb9EuBTGNMR+Ax4OGa3BENEJWKTBogHoZQ18Dk5eXhEnDJ/teh5A8MQ12zqsoX2JGR9iypalJ2TjZN45yLQw0QVSTYk7WHhKjyAsQ4snNzavz/TURaA6cB/w5IHg3MttOzgTEB6a8bYwqNMX8Ca4G+ItICaGiMWWScDL9Uah3/tt4GhkkNlur+f/bePE6uqsz/f59bW1fvnU5nD0kgO0lAkkHUGRcQB2XTr4jLDOA2+FX8AY6IMCqg32FEB1SQZWQZQFkUERUMQTGsQoCEBBKTkH3rdKf3pbq7urb7/P6491ZXdap6rS3V551Xv7rq3O106ta55znP83yefC+kazSasaENxHFQGAaixA3EfIeYagqfRKNQq95qsoVpmgQCvUzTHkTNMUR3d3daA7HcW0osFsvFc/ZnwNVAYvLeVBFpBLB/T7HbZwKHEvart9tm2q8HtycdIyJRoAuozeyfMID2IGqKna1bt3LJJZfQ2tqa765kFG0gjoN8e2D6ensxlOAqEA+ipvBJvGfzff9qipe+vj5MEfYFXLgNchmap9GMCREh0BOgzOPnN9vXcChwhEOBI9zyxv38ZvsaSt0lQHYXO5RS5wDNIvLmSA9J0SZDtA91TKr+XKqU2qCU2jDWGpDag6gpdh577DH27dvH5s2b892VjKINxHGQ7wl2X18PBjoHUTNyEkuQTHQPYjEJQRQaccGqsEG5V2kPoqbg6e/vJxKJUOYppT5whGA0RDAaYmfHAeoDR+KexSyLsL0POE8ptR/4NXC6UuohoMkOG8X+3WzvXw/MTjh+FtBgt89K0Z50jFLKDVQB7ak6IyJ3i8gqEVlVV1c3pj9IG4iaYsdR6i22ua82EMdB/g3EIIaylgMV+Z/w6xzEwifxntX1KotHCKLQcDyGLgUVHlN7EDUFj2P4pc1BtGsjZtNAFJFrRWSWiMzFGnOeE5F/BZ4ELrF3uwT4o/36SeAz9oLUPKwx6A07DDWglDrNXrS6eNAxzrkusK+RtYe2NhA1mmMTbSCOg3xPsPv7g/H8wxKPynt/NIVP4j0yke+XYhOCKDQGDEShzBXTBqKm4HHu0TJvepGaxP1yzE3AmUqpXcCZ9ntEZCvwGLANeAa4TEScpL+vYo1vu4E9gFOg+D6gVim1G/h37IWwbKFzEDXFTrE6R9zZvoC90r4BOCwi5yilJgG/AeYC+4ELRaTD3vdarJX3GHC5iPzZbl8JPAD4gaeBK0RElFI+rEnZSqAN+LSI7M/23+SQ7wl2f3+IEnvK6XPl34OoQ0wLH20gxnGEICoS2pKEIJRSiUIQryXs5wg+RBihEIRSyhGCKK4s9jQ4XhaXARVek+aOlBFsGk3B4Bh+6eoglntKk/bLNiLyAvCC/boNOCPNfjcCN6Zo3wAsS9HeD3wqg10dEu1B1BQ7xTr3zYUHsajCuBIHu3zm/IkIoVAYw84t97kk7waipvDROYiFJQSRCRGIQiQxxLTcI3QHspq3pdGMm87OTgAqbENwMOXeUhQqvp9mZORb7V2j0YyNrBqIxRjGlWgU5rOsRCQSwZSBEhdeQ/KeE1msbvZiQhuIQAEJQWRCBKIQiXsQlVDuMekOZL9+3HCEQiHtzdCkpaOjA4AKX3nK7YYyKPeV0t6uveGjQYeYajTHJtn2IBZVPR+Anp6e+Ot8GojhcBgAxxz2FICBqCl8Eo3CiRpiWoxCEIVGV1dXvPxOhUeIxcy8jpciwsc//nG+8Y1v5K0PmsKmvb0dt+GKl7NIRaW3PG5IakaGXpTRFDvF6hzJmoFYrGFciZOcRGMx18QNRPu9W5nxNo0mHcFgEJehMJQWNUrBMSsEUWh0d3fHoxvKPRJvyxdOgfO33347b33QFDZtbW1UlpQPmUdU6S2jrciKYWebRA9isU2gNZpiJpsiNU4Y18eAEqAyMYzLFoHIVBhX/XBhXMDdAKtWrRrXCJVoFOaztpcT15/oQYxoA1EzDJaBaK0LTeAQ0zjFIgRRaHR3d+Oy67OW2QZiPsdLfa9rhqO1tZUqb8WQ+1T7KtjdcjhHPSoOEg3EWCyG2511bUSNJi8U2wJI1jyIxRrGlWgg5tODGDcQ7fduBZFIYRiIxabkVEyEQiHcysClDD1p1mSNQHfXgIHozr8HUd/rmuFobWmlyps6/9ChyldBW0e7zqsbBYMNRI2m2HDMjmKL4stHHcRjOowrvgru8uZ1RdyJ63dMMZcSHeuvGZb+/n4MZalL6kmzJlv0BALxHMQyj5WCns8FtXwqTmuODVpbW6kuqRxyn+qSSkzT1EqmoyBxzV4biJpixLnHi02xNye+/mIK43ImOTFfeV4nPM5A6zjrXEoPvprhCYVC8dwwLWqkyRa9vb3x+8xvexDzKVKTeO1oNKrD3DRJBINBevt6qfENH2IKljFZW5tVPbyiwTQHNAqLLQRPo4GBqLliMxDz4UE8pnGMQvGWEQjkz0BMHHTBMhRNUxuImqEJh8MYWF98bSBqskVvX3DAQLSr2ebTQEyM9sjnwp6mMGlrawOsENKhcAxEZ3/N8GgDUTNR0CGmE5ze3l6Uy4O4ffTkccLjDLpOiKmhwIyZ6Q/QaLCMQoV13xTbYKYpDKLRKJFoFMPOQXQ8iPkM80zMf9ThgZrBOLUNq9LUQHRwtmsDcWxoA1FTzBTbnErH2YySvr4+cHvB8BAMFk5eSyHJwuiHQOESiUQw7HCIaJGFQ2gKA6d8iuNBNBR4XPnNeU00Cru6uvLWj2xy2223sXv37pxec/78+Vx++eU5vWY2cAzEimFEaiq8ZQC6FqJGo4njpHcVW1SWNhBHSTAYBMONuDwFJ/KRb8NMq5cWPpFI2Jq4S/HFy2sKA2dcTAxP8bnyW3czcUKvJ/eawTiLBuXe0iH387g8lLh9RbvIkA0S5wV6jqApRpxnnvYgTnD6+/sRww2Gm2gkQiwWw+Vy5a0/zUGDtn7F4V4Qb/5k5CH/BqpmeGLRGAoFWvVWkyWch2TiXNBj5Pfh2dbWZlmspiUwUowUgycvXzh5qWWekmH3LfWW6DzWUaANRE2x43gOi82DqHMQR0k4HMZULjBc8fd57Y+pCMYMAhGDSCS/E37HQNQPgcIlZpp2DqIipkWNNFlgcI1WAI+R3wWJ5uZmqAZlKFpaWvLWD01h0tfXh1IKj+EBIBgN4ff7ueCCC/D7/QSjAxO/EpdPl00ZBYZhpHyt0RQLxWogag/iKIlEIogyEOWKv/f7/TnvR8qBVttlmmEQ00QpEEBM7fHVZJ54jdaE8chlkFcD8UjTEcQvGBHDMhY1mgTC4TAelye+uBmM9HP2OWfHvbIv/Omv8X3dhjvvC8PHEtpA1BQ7oaAVYqoNxAlONBoFZcRnP/mqPZh6oM2vhag9h8cO+pPSZIt4jdaENlceQ5pFhKamJmSuEIvEaGhsyEs/NIWLaZpx8S4Av6eE1atXA7B69Wome6ri2wyUrjk8ChLnKvlMx9FosoVjGBaaLsl40QbiKDFFABU3EAfXI8wVqYyxQrHPdC5i4aKUQn86mmyS6vuv0rTngra2NiLhCJSBRITDhw/npR+awkUplXR/+t0+goEgjz/+uPW+Zkp8myB6MXQUuN0D00xtIGqKkQ5bJbvYPIja33+MUogeRG0YFj7KcOJL7dcaTZHjGIRSLlAO3V3d9Oaxhq2m8PD5fERi0RE9wyJmFK/Xm4NeFQeJRqE2rDXFSCRq5d0XmwdRG4ijRGHXCLCfI/ka8FKtxOV78M339TXD4zJciP3PZejVXE3uyNf4UF9fb70ot43ExDaNBvD7/ZhiEjGtid6simn43T78bh8La+Ywq2JafN/+WIiysrKs9UUpVaKUekMp9bZSaqtS6vt2+ySl1LNKqV3275qEY65VSu1WSu1QSv1zQvtKpdQWe9ttyv4SKqV8Sqnf2O2vK6XmZuvvSfQgajTFiBNJGOrXHsQJjdvtAjEB64bIV8hEISZ7axXTwsflciECIvrBrckOzve/OWhwIODiPzeU096v8jZmHTx40PKWlwEVVtuhQ4fy0hdNYVJdXQ1AIGypk356yUeZXTGN2RXT+OapX+DTSz4KWM+4nlAfVVVVac+VAULA6SJyEnAycJZS6jTgGmCtiCwA1trvUUotBT4DnAicBdyplHImJncBlwIL7J+z7PYvAR0iMh/4KfCjbP0x+jmjKWYSc+t1iOkEx+PxoBDbSMzf4JdqslUohplO4C9cPB4PzvKGx+PJd3c0RYizaOaU4Hmn00Mklr9FrUOHDlmGoQLKrbaDBw/mpS+awqS2thaAzv6hawn3RfsJxyLx/bOBWDiFFj32jwDnAw/a7Q8CH7dfnw/8WkRCIrIP2A2cqpSaDlSKyDqxVm9/OegY51yPA2eoLE0gtIGoKWYSFY2LTd1Yf3NHidvtRkkMZbuU8zXJLkQPolP/rNi+JMWEx+u1hZas15nitttuY/fu3Rk730iYP3++Lg5egKSaEEqa9lywd99ezHJbTMwFRrnBgQMH8tIXTWEybZoVQtoW7OSEmuPS7tcW7EjaP1vYHsA3gfnAHSLyulJqqog0AohIo1LKUc6ZCbyWcHi93RaxXw9ud445ZJ8rqpTqAmqB1kz/LVqYRlPMJM53nTlwsVB4VkaB4/V6UWKCWF6yQvLC5Nt/6Hw5iu1LUkx4vV5Ho6ag7l1N8ZBqQpgvAzESiXCk8QhSOSA+EquIsf/A/pz3RVO4zJgxA0MZHOlrG3K/pl5r++zZs7PaHxGJicjJwCwsb+CyIXZP9eiXIdqHOib5xEpdqpTaoJTa0NLSMly3U6I9iJpixjEQFaronCMj/uYqpeYAC0Tkr0opP+AWkUD2ulaY+Hw+lBkDM4bH4y2YsE4g73UutIFY+Hi9XsyE15kin5482xtUATCRx6ZCIdXCQ75yXuvr6zFNE9Wu4C2QkwWpEA7tO0QsFtPeDQ1gPddnTJ9OY0/zkPsd7mnGMIxRGYjjGZ9EpFMp9QJW7mCTUmq67T2cDjidrQcSOzQLaLDbZ6VoTzymXinlBqqA9hTXvxu4G2DVqlVjkinX3zFNMePMdw2lim7uOyIPolLq37Di1H9hN80C/pCtThUyXq8XJIYyY3i8BeaByXOZCSdZV+cgFi4+n8/KQRTr9bHOPffcwwUXXAAwx24admwqNpXAQiOlgZimPdvEcw1DoDrtBbRKiEaiNDU15bw/msJl/sIFHOoZuCdmVUxLUi8FONR9hONmHzfixbUxjk91Sqlq+7Uf+DDwDvAkcIm92yXAH+3XTwKfsceceVhiNG/Y4agBpdRp9rh08aBjnHNdADwnWapTpQ1ETTEzYCAa8XIXxcJIQ0wvA94HdAOIyC5gypBHFCkejwfMGEis4EL08l2F0DEME1WdNIWF1+vFFEukphgMxDvuuINXXnkFbFnhEY5NRaUSWGgUkgcxrlaaMEeVCmuk1HmImkQWLlxIS287PQlKpo56KVgKpgcCDSxesnjE5xzj+DQdeF4ptRlYDzwrIn8CbgLOVErtAs603yMiW4HHgG3AM8BlIuKs0n4VuBdLuGYPsMZuvw+oVUrtBv4de6zLBgUVZaXRZBhnvmsohYgU1fx3pE/skIiEnS+6HZKQb3skL7jdbjBNMGO43fkzEFN76fL7kTh90h7EwsXn82GKIEXiQfT5fEmr+SMZm+yV8nQqgR+02x8EXgC+TYJKILDPnlSdqpTaj60SaF/bUQlcYx9zg32ux4HblVIqW6v0hUQ6kZp8FBc/dOgQRqlBzEgYk2wlU10LUZPI0qVLAdjXVc/yuoVHbW8NdhAI9cb3GwljHJ82A+9K0d4GnJHmmBuBG1O0bwCOyl8UkX7gU8N0PyNoA1FTzDgGobL9bbFYrGjybkfqQXxRKfUfgF8pdSbwW+Cp7HWrcHG73SAmSkyrJmKeSGWE5Xvq6fTJKRqqKTxKSkqImSYx0ywKA/EDH/gA//Vf/wWgRjM2KaVcSqm3sPJ4nhWR14EklUAGVvrjin82jhrgTEaoEgg4KoFFT7rIinyEmtXX12OWDRqPfKA8isOHD+e8P5rCZcmSJRiGwZ7O1DUynfZly4bSi0lmrONTMaENRE0xEzcQ7fu8mBwkIzUQrwFagC3AV4Cnge9mq1OFjGEYcUtMqfyJwKY2EPNrmGkPYuHj8/mIFpGBeNNNN1FXVwcQZBRjUzGpBBYa6VZP8yJSc7ges3TQuGjXQ2xoaEh5jGZi4vf7WTB/Abs6UtfI3N1xkLLSMubNmzfic451fComJkDQhGYC48x3jQlsIPqB/xWRT4nIBcD/2m2aPJFKTlfM/A7EjuewmL4gxYbX60XEslSKwUAMBoN88YtfBNg7lrFJRDqxQknjKoEAGVQJdMLK0qoEisgqEVllTySPedJ5DHJtIIbDYTo7OqHfQGrIAAAgAElEQVTs6G2m36ShURuImmROOvkkDnQfJhI7WmxiV+dBlq9YPipP+HjHp2JARxRpihnn/lZMXANxLcmDmh/4a+a7U/jEYjEwFKKMvN4IoVDoqDYzzx5Ex9VeTEm6xUZiPkyhiSyNhTPOOINgMJjYNOzYVGwqgYVIKhMx1yGmra2tlvei9OhtUioUi8dWkzlOPvlkIrEo+7qSw4+7Qz0c6WnhpJNOGtX5xjI+FRvaQNQUMwMGYvL7YmCkS7olIuKIOiAiPUqpFI/d4icUCoHhBsNFOHy0kZbTfgzCzLMHUZe5KHwSDcR8iIZkmv7+fsrLy+PvRzg2TQcetJVIDeAxEfmTUmod8JhS6kvAQWwRBxHZqpRyVAKjHK0S+ADWxG8NySqBv7IFbdqxVFAnDoqjAmpznYvU1mYVNRe/xFd34/gh2BckGAzi908oh45mCFasWAHAro4DLJw0N96+q8NSvD355JNHdb4xjk9FRbHVhtNoEonPd+3n20Q0EHuVUqeIyEawan9hxdRPOPr6+sDlAcM9eGUwp6S6dr5vTOdBkCr8VVMYJHoNi8FALCsrY+PGjfH3Ixmbik0l8Fgh1w7Ujo4O60WqSGrfwD7aQNQ4VFZWcvy849k9KA9xd8dBfF4fCxcerW46FGMZn4oNbSBqipliDjEdqYF4JfBbpZSTtDEd+HR2ulTYBAIBTJcPcfsIh0KEw+G8TLRTGYhODZZ8SeyGbI+qNhALl0QDsRikmH/2s5/xqU99CmCRUuplJvDYVEiksgVzvYDV1dVlvUhhIIrP6mB3dzczZszIYa80hc6Kk1bwzOo1mGJi2EJ0u7sOsvTEpaMOy9fjkzYQNcWNxEUrrff5dtRkkhHlIIrIemAxVjjV14AlIvJmNjtWqLS1tRNz+RCPteocX6XOMb29vSnb+/r6ctyTAfr7+wEI9k+oBdJjisQ8sHyUHcg0//AP/8A777wDcIAJPjYVOrl+cPb02FkRqeb0dlsgEMhZfzTHBsuWLaM/EqKhx9KoCkXD1Hc3jaq8hYMen5IXsydQGrZmgjDYg1hM9/iQLgSl1Oki8pxS6v8M2rRAKYWIPJHFvhUkR5qaEN80xGtJ4zU3NzN16tSc9yM++UnRXllZmePeWDgPAsdQ1BQehmGkfH2s8dxzz3H66afzxBPxIagESzxmwo5NhUK6EJtcexKcxTL1dwWdVpvxgoFUCzJXkvbRaByWLl0KwN7OemZVTONAdwOmmJx44okjPocenwZYu3Zt/HU4HC4K9WyNxmFg4XPihZh+AHgOODfFNgEmzCAHltcu0N2FzF6I6asA4PDhwyxfvjznfQkEAql0IPK6Ih73IOYxN1MzNIlCIcdyAeMXX3yR008/naeeitecrmZgnJpwY1MhkUpAC3Ifet7f349yK1SXQkXse90WLhWXNXKm66tm4jJz5kwqyss50G1l1Di/Fy9ePOJz6PFpgES14L6+Pm0gaoqKuAfRfsRMGA+iiFyvrGrwa0TksRz1qWA5eNBKXDdLqhBfJSgj3pZruru7cSmIDroX82kgOoahXpUvXIpl8Pr+97+PaZp89KMf5cILL+SBBx7YLyJfyHe/NKmNLkPlPrIgEomgXGkWQezoap0vrRmMUooFCxdyaM8RAA51H2Fy7WQmTZo04nPo8WmARI9KT08PNTU1eeyNRpNZBoeYTqgcRBExga/noC8Fz549ewAwS2vAMKC0Ot6WawKBAIZx9GQ/LsyQY6LRKOGQNdkK9YeKys1eTCQOXsf6Z2QYBrfffnu+u6EZRKoFIkPlPrIgGo2mf8IZCftoNIOYP38+DT3NmGJyuLeJE+afMOpz6PHJIvE7pnN+NcWGM48qRhXTkSYhPauUukopNVspNcn5yWrPCpCdO3ei3F7LewhE/JPYtn17XrwyXV2duI4KMM3fAOyI5kipzu0pZBIf1sUwkJ155pncfPPNAJ6JPDYVEqnyo4007dkkFosxuPxhHJWwj0YziHnz5hGJRWnua+dITyvHH3/8mM6jx6fk71h3d3cee6LRZJ64gWjHmBbTouNIde6/iBU3/7VB7WMbNY9Rtvz970RLJw8UxCybQteB3Rw5coTp06fntC9dHR24DWDQ/CZfHsT45E8G3ldUVOSlLwDbtm3j2WefZd68eZx33nl560ehkSgUUgzhdf/7v//rDMyLgQ0JmybU2ORgmibr1q1j1apVecv1SWUIupTkfPFKRNIbiBrNEBx33HEAbGvdQ9SMMXv27DGdR49P1oRZlAslMTo7O/PdHY0mozgGoaEmrgdxKXAH8DbwFvBzYOSSXkVAb28ve/fsIVYxoFhq2q+3bNmS8/502TmIiRjkz0CMX9c2EPO9Uvg/d93F7373O26++Wb9UEogMQ+sGAQ6tm3bxmWXXQbQxwQdmxJ58803ufbaa3n88cfz1odU3zeXEro62/PQG41m9MycOROAbW1WCsmsWbPGdB49PtmLki7LF5GvsmAaTbZwDEQnxLSY6n6O1EB8EFgC3IY1wC2x29Jih6M+r5TarpTaqpS6wm6fpJR6Vim1y/5dk3DMtUqp3UqpHUqpf05oX6mU2mJvu03ZS3JKKZ9S6jd2++tKqbmj+eNHw+bNmxERYhXT4m1maQ3K4+Ott97K1mVTIiJ0B3pwqeQQU5eRP8Msfl1b/CFfhipAY2MjmzdvxtGc+8tf/pK3vhQaiaG/xRAGfMkll7B9+3aAZkY4NhUz7e2WEZav3GhIYyAa0NlReAs1xSLapMksNTU1+Lw+drbvBxhzhNBEH59ExPIgGm6U4dYGoqbocAxCQ1nmVDGFmI7UQFwkIl8Wkeftn0uBRcMcEwW+KSJLgNOAy5RSS4FrgLUisgBYa7/H3vYZrNW1s4A7lVJOJe+7gEux6wjZ2wG+BHSIyHzgp8CPRvj3jJqNGzeC4Yp7DQFQBpHyaaxfvyH9gVmgt7cX0zRxDfr0XEryZpg5E1MnaDmfD4L7778fBZwDzFGKRx5+uCiMoUzQ29uL2zBwG0bOc8KywY4dO7j33nsBAqMYm4qWQihdEh8LEnAroaOrO6cKb0Maf/n/b9IUMEop6urqCMXCGMqgtrZ2TOeZ6ONTfOFYGeArpa2tLb8d0mgyzGADcSJ6EDcppU5z3iil3g28MtQBItIoIhvt1wFgOzATOJ+BFbQHgY/br88Hfi0iIRHZB+wGTlVKTQcqRWSdWE/8Xw46xjnX48AZKkszpPXrNxArnwJGctpmrHI6TU1HaGhoyMZlU+IMukeFmCohEMiPBzE+8Nv/Pa2trXnpx7p163jmmWd4jwhVKP5ZhI6ODm677ba89KfQ6O7uxuty4XEVh4H4rne9i9deey3+fiRjUzFTCB6xtra2o8Ymt2HlR+Yy3Hsk/xfFJEmuySyT6yYDUF1dhds9UrmGZCb6+BSfBygXMbc/qSaiRlMMOFoOLttALAZtB4eRGojvBl5VSu1XSu0H1gEfsMM+Nw93sB36+S7gdWCqiDSCZUQCU+zdZgKHEg6rt9tm2q8HtycdIyJRoAsY21LfEHR2drJ37x5ilTOO2hartLry5ptvZvqyaXHEHoxBkzCXgkCePIgtLS3WqrwByqtobm7OeR/27NnDD264gelKcbrdNhvF+4Gnn36axx6b8KU86ezsxG2Ax1BFEe7z+uuv8973vhdg+VjGJk3maW1txTOoBI/bGNiWK0zTHFbFVBuImnQ4XsOamrGLjo5lfCqm9BzHIBTDhekppalZG4ia4sIxCB2RmmIyEEe6LHbW8LukRilVDvwOuFJEuodw8KXaIEO0D3XM4D5cihWiGlcnGw0bN24EIFY186ht4q9G+cp48803Offcc0d97rHgeH6ipuD3+zn77LNZvXo1Eumlpzc/XqHGxsZ4/iFlcOTIkZxef9++fXzjyitxhUJ8TgRPwq1xOtAC3H777fh8Ps4///yc9q2QaG9rw6sUArTlycubSZ555hkA5s6duwPIzRewgCmE8JbWlmbchkBs4Dvotg3GXIaYDVkHUZe50AxDZaVVzqq6pnrM5xjj+OSk52xUSlUAbyqlngU+j5Wec5NS6hqs9JxvD0rPmQH8VSm1UERiDKTnvAY8jTWXW0NCeo5S6jNY6TmfHvMfmoa4x1AZiLeM1tbDiEhBhMJrNJkgFAqhUPF7OlEI8FhnRAaiiBwYy8mVUh4s4/BhEXnCbm5SSk0XkUY7fNRxNdUDiVrSs4AGu31WivbEY+qVUm6gCjgqAUZE7gbuBli1atWoY7A2bdqEcnsxyyan+iOJlE9j46ZNORv4nHy6UExx9nlnc/nllwOw+vePEenLbTFqh8MNhxF7Emj6TQ43HM7Ztbdt28a3rroK+vr4gmlSPWjdwEDxKYRfA7fccgs9PT187nOfm5APqZaWFkrt5NWWIjAQ58yZ47wMj3WcKiYKIde2ubkZ96CvlkcNbMsVkUgkPiYdhWtgH40mFY6B6PweC2MZn+zIKifKKqCUSkzP+aC924PAC8C3SUjPAfYppZz0nP3Y6TkASiknPWeNfcwN9rkeB25XSinJcIx6/PtuuDC9ZUTCYbq7u6mqqsrkZTSavNHf34+hVDwHsRjU4R3GFlg/AuxQhvuA7SLyk4RNTwKXADfZv/+Y0P6IUuonWKtgC4A3RCSmlArYOZCvAxdjqYElnmsdcAHwXKYHOIBNb71NtGwKKAPvgXW42vYBICWVmGW1xCqm0bl/D/X19WOulzQagkHLCCx1C6tXrwZg9erVeF1CVzhCLBbD5XINdYqMYpqm5TG07yYpExoPNObEYH799df57ne+Q1k0yiWmyaQ0MWVuFJ9BeAL4xS9+QXt7O5dddhmGMdIo67HT1dXF/v37cblcLFmyJKefTSKRSITOri4mVZaCCA2dnUQiETweT176o8k8+V697Ovro6e3j7qSwSGmgktBU1NTzvrS39+f3kBUoAyV9/8vTeHykY98hJ6eHs4444y89WGo9BylVGJ6zmsJhzlpOBFGmJ6jlHLSczK6atjc3GwJ1ADiLQesRUptIGqKhbfeeouYmBwOWIsha9eu5ZOf/GSee5UZsmYgAu8DLgK2KKWcOhD/gWUYPqaU+hJwEPgUgIhsVUo9BmzDCrG4zA6RAPgq8ADgx1r9WmO33wf8yl4xa8cKs8goPT09HDywn9islQAYvW0YEXuV3v4dqbNEybZu3ZoTA9GZ1JS6oaE7GK95Vl5ibQ+FQpSWlma9Hw6tra1EI1Fw6nKXQyQcob29fczqbyPhhRde4Ps33MAUES4WoTzBOHzajjT+WEKbG8UFCGXAb3/7W3p6erj66quzarBFo1GuvPIb7NmzG4Avf/nLXHzxxVm73lAcOXIEEcFvexBFhKampjHX+NIUHs7qZb5y6xyPgSfFuktNCTkVqejr60PckiLpwEJ5VUF4XDWFyezZs7niiivydv1iSM9pbm5GbDF68ZUB1hgwf/78UZ9LoylEnMoB/THr2VtMQkxZMxBF5G+klwhIuSQnIjcCN6Zo3wAsS9Hej21gZoudO3cCYJbVpd1H/NUol4cdO3Zw1lljTtccMU4S7ODnhSNaEw6Hc2ogxvMNbTtLyqznTGNjY9YMxHXr1nHDDTcwS4SLRCgZdKs1pjnOQPExxFppWLMGt9vNVVddlTVP57333suePbtZPO1UWgKHeOCBBzjllFNYtuyo2znrNDZa/yul7gGDuKGhQRuIRYQzNuQrdNIxEN0pPHeTfNGcehC7A93gAdJpBritsi8aTaFRLOk5O3bsADOK0deG94Dl5MyHgJ1Gky0G57EXk/BZ9uPrjnH27t0LgFk6hJKZMoj5a3JWnNqZ/KlBC35q0PZcEZ/0OXdT6aD2DFNfX88N113HNNtzONg4fBrBSeK4D4l7Ex0UitNtddOnnnqKP/7xj2Qa0zS55557eOSRRzi+7iRWzP4A/7jwk/g9FVx11bfYtGlTxq85HIcOWSLBpR4XpR5XUpumOHC++/lSUhvKg1jrM2luSrd0k3m6A92IN/2c1vSaA3XaNJoCYQTpOXB0es5nbGXSeQyk5zQCAaXUafY5Lx50jHOurKXn9Pb2ohBULIKrxxobisnDotEMNgiLSfhMG4jDUF9fj3J7EY9/yP1iJVUczNFk2xnHj3J65Um6PS5d79xN9n9Vth4EP7/tNiQc5nMi+FI4qRuBkP2zn/TexDOA+SjuuuOOjNZn6+zs5Nprr+VXv/oVx9et4JQ5HwbA5/bzgQUX4qGEf//Gv/PrX/86p5/VwYMH8bhceA0Dr2Hgcbk4ePBgzq5fKBSTjPxgnPDzfOXWOd/5iAl+v58LLrgAv99PMKqoKTFpaW3LSa1GESHQHRgIe0+1j1fo6Dz2S71oig4nPed0pdRb9s/HsNJzzlRK7QLOtN8jIlsBJz3nGY5Oz7kXq670HpLTc2rt9Jx/x1JEzSh9fX1Hf9eVkbcayRpNNhg8h8v1/LulpYV33nknK+fWBuIwHDlyBPGVp7DGkhFfOR3t7Za0epZJN8FSw2zPFq2trSi3GribPKDcKiuS9k1NTax77TXeI0JV2gjmkWGgOAshGArx7LPPZqR/b7zxBp+/5PO88fobvOu4M1g55yNxdSuAUl8lH1r0L0yrOp4777yTb37zmzkLudm/fz9lbgOlLEnmMrfB/v37c3LtAsORkV8CnAZcZkvFX4MlI78AWGu/Z5CM/FnAnUopJ07XkZFfYP84MeZxGXngp1gy8lnHKYHT1ZUfz1hraytVPkUwqjj7bEth+eyzz6YvqpjkM4lEovGcjWwSCAQwY+bQBqJPaG3Tk1VNYSEifxMRJSIrRORk++dpEWkTkTNEZIH9uz3hmBtF5AQRWSQiaxLaN4jIMnvb1x0voYj0i8inRGS+iJwqInsz/Xekev6LcuW01I1Gk23MPIeYfu1rX+PSSy/NSgSjNhCHoaOzk5irZKAhFk5aGSdmhXKJx4+I5GTy4+TLDTYDZdD2XNHe3o7yJ1xTgSpRtLcfldIwbnbt2gXAwiH26SfZezGUL2UqiirDiOeajpVQKMRPf/pTrrrqKmIhg9MX/wsLpp6S8rPwun2894TzWTnnTN5+azOXXHIJzz333LiuPxL27t1DWUL+YZnbxb69GZ8XFDwi0igiG+3XASBRRv5Be7cHsSThIUFGXkT2Ya3Gn2rnAVWKyDp74vXLQcc453ocOEPl4IvZZC825KuESWtrK9W+WFxh+bbbbmP16tWUuoVqn/XgzMUEMX6NkiF2KoGujq6cL6hpNBOBjo4U3nll0KoNRE0REctjiGk0OpDXrw3EPNDb24u4vPH3KhpOWhlXUdtAdFmlAnKhihdX3ZTk+aYzz3G7sylOezStra2YvkFudp+ZlYlgPLx2iH36IekzGi7YzmB8qz6HDx/mK1/5Cr///e9ZMHUlH15yETVlU+PbNx18jk0Hkw1ApRQnTDmZM5deQomq5IYbbuCWW27JWv5oR0cHXV3dlHsG7o1yj5vOrq6MhtceawwlIw8kysgnxo87cvEzGaGMPODIyGcNEeHgAStkuLcnkJfPtb2tlSpPDL9bCAYtheVgMIjfLVTb+YDZWDgajDP2iH8I489vPcxzsain0Uw0UhmIogw6Oibu80ZTfOQzxPTAgYHSqjt27Mj4+XNrSRyDRKNRUANxSuL2JtUeFLedcGeHEeZi9cDrtQzWmWUxDvVY151TYV23KeiKb88VR5qOYPpNVP+A2Wb6TRqzIEjhFB4+zMBMfDAlkPQZDVVxqQehwxTmzp07pv5s27aNb33rasL9Yf5pwSeZXn38Uft09qUPIa0oqeFDiz7LlsMv88c//pEDBw7wwx/+kLKysjH1Jx2O2FKFN9lABGvlaeXKlRm93rFAMcjIJ3LgwAGCwT4ikxfgad3F1q1bed/73jeuc46Wjo4OFnpNWoNHrz1Wes34Ptkmnuc0hAdR7FqNbW1tVFdXZ71PGs1EIhAIHN2oDHp6UrRrNMcg0Wj0qAgUM5Y7A/HNN98ErIg553Um0R7EYTAMF0jCB+7yJq2M43gX7ZskF0XQS0qsWc+58/qZUxFjTkWM767qYXmtlf/o8w2ReJNhotGoJUwx2J4pg5amlowbzLNnz2bmjBlsHiJarwSSPqOhosy22L/f+973jrov27dv5xvf+AZEXJy++F9TGoebDj5HZ18znX3NPP/Or4/yJIJ1j500+4O8e97H2Pz2Zr75zW9a91YGccIPEj2IjrGYK/XdQmIoGXl7e6Zk5BlORl5EVonIqrq69KV0RsKGDRsAiExfgTLc8fe5pLu7mwpPaq+d054Lj13cSzmUtpi9TedEaTSZJ6VCsFKEQ6G8qSxrNJkk1TwtZuYuxPTFF1+kvLycmTNnsnfvXg4fPpzR82sDcRjKykrBHD7sT8Wsffz+odVOM4Fzjf5ospHUHwOXy8ipB7G+vt5aMakYtKHSMh4zfcMqpfj4Jz7BAREOpauAPUJiCOsMgxOXLuWEE04Y1bFNTU1c/a2rcYmPDy76DBUlNSn36+xrJhILEYmFaAkcGtKbOGfyiZx2/Lls376dH/zg/2U0VGHPnj2UuN34XANfeZ/LwOd2x72LE4VikpFP5OWX/wZuL56Wd4hUzuCll1/OaX5dOBymPxSmPI2BWGa3p/QsZJj29nZLOMsNRJJzknGGc3sdLRceTY1mopGyxqgdaZWLVByNJtukMhBzFWLa0NDAli1bqKurY8oUKxvmz3/+c0avoQ3EYaiprsYVDcXfm2W1iMuDuDzEKqZhlllpRSpqZbpVVlZmvU9O+GHfIAOxL6ooKy3N+vUT2bZtm/WiCei0fowXDOt94vYMcu6551JZXs7acaqYbgQ6TJOLL7lk2H0TiUQifPe73yXY188/zv8/+L3l4+pHIrMmLeSkWR/ilVf+xiOPPJKx8+7ZvZsy99Ff93K3we7duzN2nWOEopCRT6Szs5O33tqEaXgwetuITZpLS3Nz1uSvU+FM+vyu1AaioaDErXJSnL6rqwtVYo8PkeSc5MEG4kTOwS0kTNPkd7/7Hffffz9r167Nd3c048SZPCctztjDpjYQNZlmx44drFmzhldeeSVnC6PpDMRcXP/JJ59EKcX06dMpKSmhtraWJ598MqOVFHQO4jDU1dVhRN6Ovw/PeQ9GrxWS1L/0nHi7CvVQUVmVE+9dRYXlruuNJBtIvRGDyorBrrzs8sYbb2CUGEifoJz+tACTQfkU69ev56yzzhryHKOltLSUiz//eW6//XZ2AgvHYCj2IzxnGCxfupTTTjttVMfed9997Nixg/eecD6V/szrjiyYegptvQ3ce++9nHLKKSxdunRc54vFYuw/cIDpvqPDn8s9Lvbv349pmhjGxFgvEpG/kV7n6Iw0x9wI3JiifQOwLEV7P/CpcXRzVPztb39DRBC3FVAdrTkOnzJ46aWXWLJkSU764Dwsfe70D0efOzc1GgOBAOJ4Mj3JOcnx0hceQOXGo6kZnldeeYVbb701/n7RokXMmjVriCM0hYwVRjpQ7gbgsT/8KWGbRpMZmpubueLyy+mzn0HXX389Z5yR8lGeUdI9y8LhcFZTvUKhEE8++SSTJ0+Op5zNnDmTzZs389JLL3H66adn5DoTY0Y4DmbMmIGEgxAdGNDMstq459DBCAWYOXNGTvpUVWXJrvREVDwH0XlfVZ061DEbBINBXl33KtHp0aOn2wpi02P87ZW/EQqFUh4/Hj7xiU8wa+ZMVhsGkTGEmq4FekX4/y6/fFRlQXbu3Mmjjz7KvMkrmDVpqGIbFpFYKGkFNRIb/v9CKcXKOWfi95Rz0w9vGveKUGNjI+FwOCn/0KHc4yYcDtPYmHlBIU3uWLduHaqkHGw1ZdwlxCqm8sqrr+asD86kz2Ok/z56DcnKeDCY3t5eTJcd6uNJzknG/i9CgfIo7c0oAKLRKPfecw+lHjfvn1GLyzC455578t0tzThwDMTEcjcY7oRtGs34CQQCXHvNNYRDIU6pq6LC6+HHP/pRVqLXBpNOKyLbz7jnnnuOnp6epAW02tpaSktL+f3vf5+x62gDcRgcZUGjfyAMKTznPYTnvCdpP3eok7m2wma2cRT3uiKKixYFuWiRdZN2R91U5VCNb+3atfQH+5E5qSeEMkcI9gWzEi7k8Xj45lVX0W6ajLaC4EGE14Hzzz+fxYsXj+rYBx54AK+7hJNmf2BE+0eioaTwtkh0ZAOH113CSbM+xP4D+3nxxRdH1cfB7N+/HyCtgQjJcsmaYwsRYdNbbxGuSF6gilXOZP++fanFIrKAs5DhHmK9xaVyo/QcCoVgBHphyqVyYrBqhuaee+5h3/79LKoqw+92Ma/Cz/PPP89f/vKXfHdNM0ai0SgYapCon7U6k8tacZripb6+nq9+9avs2bOHFbUV1Pl9nDK5EhWLcsUVV/Dyyy9n9frpnh2ZFhkczJ/+9CfKysqorq5m586d7Ny5E6UU06ZN4+2336a+vn74k4wAbSAOgyNeYvQNUbsrEkRCfRx//NEqltnA7/dT4vPRHUr++LrCBpMmTcpJH6LRKA89/BCqRsHkNDvVgapWPPTwQ1l5IKxcuZJzzjmHV4ADI/QihhCeMAzqJtfxla98ZdTX3LDhTWbVLMLrHkobdQCP25e0gupxjzzsYGbNAkq8paxfv37U/Uzk0CGrhN/h3iAvHG7lhcOtvNHUwfaOAKVuV9I+mmOPlpYWegIBzLLkL6LzvpBUapXKTRJ/zIwNXSw13qHc1q3SHM1TTz3Fo48+yqzyEqaUWuPjvMpSJpV4+dFNN7Fp06Y891AzFobKw8qleJamOHnxxRf58pe+xJHDh1lZV0Wd3xo7StwuTp1SRQkm3/nOd7jrrrsympeXSLoQ02wuOra2trJlyxamTJmCUoqenh56enoAmDZtGgAvvPBCRq6lDcRhmD59Ov7SMoze1rT7uOycxHh/ZFMAACAASURBVIULhw85zBS1tTV0hgc+vpgJ3f1CbW1Wa3HHefrpp2k43EB0SYrwUgcF0SVR6g/Vs2bNmjQ7jY+vf/3rTJ82jccNg6BtJE4HKu2fufZ7h9VAuwjfve57Y6o16PeX0BfqHvEDzuPyJa2gelwjNxBD0T6isQil4xQeamhowOt20RuJEYqZhGImHaEIgXAUr8vA63ZlbMVJk3uamixFKPEl5x+bJRVJ27ONE6o91DdDJDelgFyGa+iOxDvEhMm9LUSefPJJbr75Zur8PpbUDNy/hlKcVFtJiaG4+lvfykqNL03+0AaiZqzEYjHuuusuvve97+Exo5w2tZpJJcnaHz6Xi3+YUs3scj+PPvooV155ZVbEyBxDMEmIiewaiBs3bgRg8uSjPTMlJSVUVFSM26ngoJ+Mw6CUYumSxbiHMBCN3haUUjk1ECfXTaEjwYPYFVYIqW+aTBMIBLj7nrstz+FwaZczgVr4xd2/iK9yZJLS0lKu//73CQC/BwThYyi+Zf98CcXHbAv2LYRNwEUXXcTJJ588putdeOGFNHbt5a2Dz2W13k1vqIuXd/0OZcB55503rnM1Njbid6X/qvtdhs5BPIZxwlnElfyQdN7nKsfO47HCx6JmetddVBRud/a10XwlPrC/nlItiMf+qROkemByKlHJad1YjYWI8NBDD3HzzTczucTLSZMrMQblgntdBivrKvEiXP2tb4071F6TW4bK7R9N3r9G4xAOh7nuuut49NFHmV3u59Qp1fjtKKjtHQG2dwwIjrmUYumkCpbXVrL173/n0ksvpaGhId2px9wfGKSSTXYNxJ07d+JyuSgvT62eX1FRwc6dOzOyCKMNxBFw4oknovraIJa6HqIRaGL27OPSfmDZoK5uCh2RgYmWYyzmwoN477330t3dTezkEYRxKYi9K0ZXZxf33XdfVvqzZMkSvvJ//y/bgXTrJu0If1KKFcuX8/nPf37M1/rsZz/LBRdcwK7mjfx1+y850rUvo6uhUTPCO42v85etD9BvdnPjjTcyd+7ccZ2zqakJn5H+g/IZKmdeJk3mGZhsJd+Hyr4vc+Uhcwyt8BARm6EYcdW1bFJRXoERtf5uOVmgGqgG84Om9R7ABIlITsdtjRXSe+utt3L33XczvdTHyZMrcaUxGHwuF6vqqihzGVx33XX84Q9/yHFvNWPF5Urvxc9FFIGmuIjFYvzgB9/n5ZdfZnF1OUsnVcQXlbZ3BGjo6aehpz+ePuMwo6yEVXVVtLe0cPnll9Pamt7ZM1ocQzBJiInsijA1Njbi9/tRSrFz504CgQCBQICNGzeyc+dOSktL6e3tzUg5KV3mYgSceOKJIILR24JZOchlJoKnr4WT3v+RnPaprq6OjqAVsqUUtNsGolMwM1vs3LmTP/zhD5gnmDBSwdQaME8weeKJJzjnnHNGXZR+JFx44YWsX7+eZzZsYL4IkxIsVxPhCaVw+/1877rrxuXBMAyDyy+/nJUrV3Lrz27lpZ2PU1cxiyXT38PUyjlHrYxWl04Z8r1DNBZhb8tmdjavpy8U4LTTTuPKK69kxozxK+O2tbVR43IRMVPH4ftcLjrah8ix1RQ0Tqi0ig56KMWs9+MNUR4pznWC0fSLEcGIxMNwskl1dTUqNMzqlf3fVVOTO+XniU40GuWH//VfPPvXvzKnws+i6vJhvUlel8GquirebuvmJz/5Cd3d3Vx88cU56rFmrFgRBYMsRHvRKhflwDLNbbfdlvWawfPnz4+XBNEkc/fdd/PSSy+zqLqcOZXJz7RAOErUvrc6Qkc7cqp9Hk6pq2RDcyvXXHMNd9xxR0YiRxxD0EkjcohEUjuTMkEgEIjPYXt6euL6Hk4IrbPgGQgExr34qT2II2DZMqvMmStwtJdFBTuRSCi+T66oq6sjYkLArj3Y1p99A1FEuP2O28EHcuLovGayTMALP7/951npm2EYfPvb38bt87F60LZNwAERLr/iCqZOnZqR673vfe/joYcf4oorrsD09vPSzt+y9p2HOdyxK8mj+K7jTudDiz8T/3nXccn1aSKxENsbXuPpv9/NW4eeY/6iedx66638+Mc/zohxaJomvb29eIbwIHoMRaCnR4t1HKM4ZW9UNDlh3nlfnSNl49LSUgyljqrP6hCOQdQcqOOaTerq6jCDZjzMNCV25G0uwvI1lgfgP//zP3n2r39lQVVZSuNwcJiYg8tQnDy5kumlPu69914efPDBXHVbM0YsI3DwPMF674Sj54LW1lad83iM8/zzz8fDSudWjm3Bs8rrYfkkK/zylltuyUi/0onfZNNAjEQiQy6qORFDmRDm0R7EEVBRUcFxc+ayt/NoA9HVY7Xl2kB0DMG2foNKb4z2fgOvxxOfLGaDLVu28NamtzBPNmHwAmDEStQ9++yzWb16NX2RQXlPXogtjrHxzY1s2bKF5cuXZ7x/U6ZM4fNf+AJ33XUXBxGOQxFDeN4wWLJwIWeddVZGr+f1evnkJz/JueeeyzPPPMPDDz3MK7v/QE3ZVJbP/CemVc1Le2zMjLKr6U12NL1BKNLPqaeeysUXX8yKFSsy2se+vj5EBI9hEDXNpM8oanuc3IaBiBAMBsck3KPJL5WVldaLQSVUlP0+vj3LGIZBZUU53ZF+5lTEOBCwwsicWq3dtuGYC4N1+nRbmqoPSGOPqh6rP5lYiNEMz89//nOee+45FlaXMa8y9TgTCKef1BhKsby2EkWA++67j9raWs4555xsdVczTnw+X9xjGMd+n4swc4DNmzfz9a9/nWuvvZaPfvSj4zrXseTZExFEpCgEuBoaGrjphz+k2udlcc34PGJTSn0cX1nKM888w8qVK/nnf/7ncZ0vnRGWLdVUyK3A07F/9+SIk1Ysx9PbctSAZwSaqKyqTipYmQscA7Hd9hy2hQzq6mqzmvz91FNPobwKmZfiBo0MStRNsYAixwvKq+Jx2tng4x//OOWlpbxuv98BdJkmF19ySdb+b7xeL+eddx4PP/Iw1157Lb5yxUs7H+eV3X+gP3J0HHhz90H+svUBNte/xMmnnMTdd9/NzTffnHHjEAZkmA0FEVOSazKa1ufoUsn7ao4tnJBNNTiEOBZN2p4LaiZNojts1Wd1DMPvrurhokVBOu0w+FyU4nHq1zJUCciAlb+Z67F7IrJmzRqeeOIJ5lT40xqHI0EpxYm1FUwu8XLLLbewdevWDPZSk0lSh7Zbz5xcjUnvvPMOQMZUHY8FRIR/+/KXOfecc7Ki3Jlr7r77biKRMCtqK44SsnJwFr8dJdHoENFQ86vKqPF5uP3nPx+3mEy68m3ZrPMpIiMSgMpEH7SBOEKWL1+OREOoYEdSu6e3mRXLl+VclSvuQbQnXe39LqZMnT7UIeNmw5sbiE2NpfY7ewYl6qaKIHFDbEqM9RuyN1j7/X7e/8EPsksZmAg7gPKyMt797ndn7ZoObrebj370ozz08EN85StfobnnAM9u+yUdvQOe5x1HNvDCjseomOTnJz/5Cf/93//N4sWLs9YnZ5AwlMJjqOSajHbYaSYHFE3uSbuiqIbZngXqpkylPZQ6MCWXQlqOsJPqGuJB2qWYPmO6VjHNMg0NDfzkJz9hUomXRdXpPQDbOwIEwlEC4ehRQhOJGEqxYnIlPkPx/e9/P+tFqTVjI6WBaE9uc2UgOuVR9u3dm5PrFQLbt29n565dBHp6eP755/PdnXGz/o03mFrijauVpiLd4ncqlFIcV+Gnq7t73DWC06XlZHMu1dvbO6TIk7MtE+OiDjEdISeeeCIArp5moqX2CnikH4Jd8W25pLq6Go/bFfcgtofdHJ/l/MO21jZYkmYHDwQ7ExJ1080DyqF1R+ZUpFKxbNkynn76abqARqVYunRpTqT1HbxeL//yL//CaaedxtVXf5sXdz7Gu2afTmewhR1H1vOB93+A//jOf+TUswNWKGkgIZm6xpdsxWvp8WOT7m7LTSaDa2za753tuWDq1Km883bqh5eTJ52pPOChKC0tZcbMGRzuPIykkVJ0dblY9A+Lst6XiYyIcPPNN2NGIyybWjPkGDOc0EQiHsPgxJpy1h85wv3338/Xvva1jPY7nyil/hc4B2gWkWV22yTgN1ilffcDF4pIh73tWuBLWBm3l4vIn+32lcADgB94GrhCREQp5QN+CawE2oBPi8j+TP8dKQUyxKSkxJ+T0McjR47w+muv4XcJe/buZevWrXmZq+US0zT5n7vuotQwKBfhlw88yIc//OEx533nQpgHhhbnmTFjBof27SUUM/GlKdflLH4DSYvfqRARmvpCGIYxbs2OfOS2trW1DZmm4QhAtbW1jfta2oM4QmbNmkVZWTlGT0u8zdXTDJCXQccwDCbX1tLWb2AKdPRLVgVqlFL4S/1x5b8xE8Y6Txapq6sDIAD0KkVdlpVd03HCCSdw660/w+118fq+p9lxZD2rVv0D199wfc6MQ0cMwBxiIHO25VI4QJM5Dh8+DID4kidkpv3e2Z4Lpk2bRndI6E+xgNocNPCX+LKaJ53I0iVLcXWkWWkNgdlj5rR27UTk5ZdfZsOGDZxQWTqkB2AsTCrxMrOshN8+9hgHDhzI6LnzzAPA4IT5a4C1IrIAWGu/Rym1FPgMcKJ9zJ1KKec/+i7gUmCB/eOc80tAh4jMB34K/Cgbf4ST+yyGG3F5iFVMA5eHihzlRD/++OOICCvrwpR54De/+U1OrptPHnnkEd56+23ONE0+IUJHRzs//vGPj2mRniuuvJKYMnitqZPmvlDKv8VtGHEl0WAwiDvNAkRvJMr6li6O9IX48pe/fMwJlHV2dtLb2zvk/NHZdvDgwXFfT3sQR4hSiiVLl7B+2964jWT0tqKUytsko27qVDoONtAVVpgyYBhli2UnLmPD9g1EJTp8/cNUmOBqcmVFoCYRx1sYs3/yafjMmjWLhx9+iPr6egzDYMmSJTn1ZjpiALEhHhDONh1qd2zi5NmYZcmhm1JSiXJ5eOedd8Yt0DBSHMGXluDRD+jmoMGMGTNy5qlevHgxf/3rXyFVpI1d1WXp0qU56ctEJBqNcuedd1Dh9TC7PDsLYgury2nub+euu+7ipptuyso1co2IvKSUmjuo+Xzgg/brB4EXgG/b7b8WkRCwTym1GzhVKbUfqBSRdQBKqV8CHwfW2MfcYJ/rceB2pZSSDFsRzkKQ+CoQt4/+pefge+cZaqqzL4QWCoVY8/Rqyj0mrf0GH5jRz59ffom2trachLjng9dee41777mHZUAT0AycCfz5xRd56KGHuOiii0Z9zkIQ5lm2bBl33HknP/jBD9h08CC1JV4WVJVRlRABVeFNnlMNfh+Kxdjb1Ud9bz8lJSUZES2C9DWGs/WM27FjBzC0ErjH48Hv97Nz585xX097EEfB4kWLUH0dYItBGL2tzJo1O2d1xgZTVzeFjog7ntuT7dWQ8847D+kR1P6x3fxqv0J6hfPOPS/DPUvGSTz22D/5Fl+pra3lpJNOYvny5Tk1DsFaTXK5jCFj8qOm4HIZOVOW02SWjRs3gr8S8Q6aeCmDaPkUNth5OLnAEXxp7HXFRWocmoJeZs0+Lmd9iStLt4FUC1I98B1QbQrDMLKa/zvRWbNmDQ0NjcyvKk0rLjFevC6DueV+Xn31VbZt25aVaxQIU0WkEcD+7YTFzAQOJexXb7fNtF8Pbk86RkSiQBeQcaspXl9UBvK0XLH+nNQdfeKJJwj09FLjs659+swQZszkoYceyvq188H+/fu54frrmQp8AjgCNALvA1YA9957Ly+//HI+uzguFi5cyP3338/Xv/51wh4frzV1sKm1i96INRdfUlPBqVNr4j9LaiwDKmKa7Ozs4W+NHdT3hTjn3HN55NFHM7Zgms5AzFYI9fbt24EBAzEajSaL89jqqeXl5RkR8NIG4ihYsGABiIkRtJShPP0dLFqUvxCl2tpaOvtVzgzE97///Zy47ERcm13xGmIOUi2Ix/6pS56MAdALri0ulq9Yzj/+4z9mtZ+BgCVu4AdKsIqJTlSUUlRWVhGOmVR43biVwq0UNT5PfJUtHDOprKzSOYjHIMFgkPUbNhCpnIX3wDqMvjaMvjZKtv0J74F1RKtmc+jgQerr64c/WQaYPXs2AEf6XFy0KMhFiyz3XdSE5r6B7blgwYIFeDweVKtCThbk5IExyWgzOP6E43OeBzxREBEeeeQRqnwe6kpGVhR9NEqEiRxX4cfrcvHoI4+Mp8vHKqkGbRmifahjjj65UpcqpTYopTa0tLSk2iUtcbXiBAPRiASzqmIcjUb51a9+xS/+539YWReh1G39WdNKTT48q5/f/e53/PznP8/7onEm6erq4pqrr8YIhficCN6Ej1eh+DjWisD/+8EPxi3Kkk88Hg8XXnghjz32GF/4whfoNhWvHulgR0cPsUEL4CJCQ28/rxzpYF93Hx/40If41a9+xVVXXZXR+y/dgn+2HAG7du2irKwsfv5oNJokzuMYiBUVFTQ1NcXnwmNFG4ij4IQTTgDA6GuHaBjpD3D88cfnrT+1tbWEYsKRvtzIxyul+M5/fAefy4drnSupCLWcLFANVIP5QTNpMkYU3OvclLhL+M5/fCfrhohjEPoAn2lOaAMRYHJtLaGYyZKaCiq8biq87qRVtlDMpO4Yi8XXWLz66qtEwmGiNXMxettQsQgqFsEVOILR20Zs0lwA1q5dm5P+lJaWMrm2hoa+5EdLc9AgJrk1ED0eD4uXLMZoG/SYM0G1K05acVLO+jLR2LlzJ4cPH2Z2WcmIx/vRKBEm4jYMppd6eeXVV4tZ0bRJKTUdwP7dbLfXA4lfqllAg90+K0V70jFKKTdQRTzoOhkRuVtEVonIqtGmsPj9fkr8fjDtiYKYSLgvKwvZIsIrr7zCFz5/Cffccw+nTgnxtWXJz/1/XRjkI7P7+e1vf8u//svn+POf/5xWhfJYIRqNct33vkdTUxOfNU2qU9j+HhSfFcEbifDtb11Ne3vKj/qYoaysjC984Qv85je/4aMf+xj7A3283txJMGrdZ6YIf28LsKWtm+MXLOSee+7h+uuvz8qzxxGEGWn7eDl8+HBSpJfb7U5SpncMRyeqsbGxcVzX0wbiKJgxYwZutxsV7MCwy13Mm5e+GHq2cQzCQ3ZB6lzUF5s1axbf++73oB3UGyrNumMCAsYbBnTC9dddn5Oi1OGwlSVaKCGm+WbqtGmEhkgvCYkwddq0HPZIkyme+tOfoKQCszL15ye+cmKV0/nT6tU5mwzNmTOPht7kFdSGXpe9bU5O+uCwYvkK6AQSS0R2gkRlIARVk3EcT8VgpeShSFeGZyTU+LxEo9FiE6tJ5EngEvv1JcAfE9o/o5TyKaXmYYnRvGGHoQaUUqcpy0K/eNAxzrkuAJ7LdP6hw+TJk1FiTdxVJAgiGTcQ9+/fz5VXXsG1115LuO0g3ziph68v7+Wx3X4OBFwcCLj4zw3lPLLLz8WLgnx3ZYDSUDM33ngjl/7bl4/Z0GTTNPnRj37Eprfe4uMiHGcbh08jNGKFmN6H8DRCJYrPmSYdba1cc/XV9PX1DXnuY4Gamhq+/e1vWyrJbg/rW7ro6A+zqaWLhr5+vvjFL3LnnXeyaFH2lKpzbSAGg8Ek76Tb7U4W57G3Ob/HO/fVBuIocLvdzJg5CyPYhdHfBeR+wpOIE8t/sMdFRXlZzvLb/umf/omvfvWrGPUG6u8DD/HBeT4AaotCHVZcdtllvPe9781J/xJJF2szkZgxYwbBSAwRiXsQHUSEvkiM6dP///bOPL6q4nrg3/PeCyEhYU3YgoIEEBBQREFcKqJ1wQWtoqJVUIsbqIitSm0ruFexVetSqiC1tYrLry0qam0Vta6IGyCCUDZF2QnZtze/P2ZechOyvzXJ+X4+7/PuOnPu3Hvnzpk5c050Y2gqkWfdunV8umwZJRkHgtRelZd2HcTWH37gv//9b0zk6nPAAWwpCOBtcsZLQRwyZAgEAU/4WtkplfuUqFDhKKwRakdDPRHWRHkL8sQsIs8AHwAHisi3InIZcA/wYxH5But75B4AY8xK4DngK+A1YKoxJmTbcxXwBLAWWId1UAMwD+jiHNrMwHlEjQbdunYF1zElxfkAEfO2bozhxRdf5LLLLmXNyi+YdGABd4/aw4hMGyJlY66fwnIfheU+vt6TxEbXkT6wUxm3HZ7D1UPy2LF5LVdfdRXz589vVnGAi4qKuO2223j99dc5HjjE08r5Hih2vw1uHSALYYIxrFmzhunXXsuOHdENNxYrRo4cyf2/+x0lQcPH2/awo6iEKVOmMHny5DrjBUaC2qYoRGvqQnp6OqWldYcAgspBkhpDzTQC9WLaSHrvvx+blq0gWJSDz++nexxHXkJewr7L99Mzq/a4KNHg/PPPZ/Pmzbz88ssE2wcxvavO8QGQjYJvtY/x48czYcKEmMkWGl63laTQsV30vaYlMllZWZQFgxVmpl6Ky4OUB4MVzkWU5sOTTz6JBNpQ2q1uRyvlnftASnvmP7mAo48+OuoxyHr37k1RmWFXsdClra0Tvsv3kdmlM+1i/C6GQhDJLsFkuvppJ3Tu0jkm8RhbK8OHD8fv87Ext4AhXaIb1iBoDJvz7Ny2eHbYRgpjzMRadh1fy/F3AnfWsP0TYJ9eEGNMERCTD3K3bt0qRxBL8iq2hUtZWRlz5sxh8eLFHJJRypRB+XRIbnhvhE/gyO6lHJKxm7+sTmXBggWsXv01t946K25OBxvK8uXL+e3dd7P522/5MXBMI84dhDARw/PffMMlkyYxfcYMxo4d2+z9DwwePJh58+axfv16OnTowIgRI2KSb+hZSfa3wSdCh+R0fsjfEbXvXP/+/Vm/fj3BYBCfz0daWlrFPMP09PQKhXDv3r0kJSWFbVarI4iNpGfPnkhRLr6iXLp27Rpzr5ReQsEyy43QsWP0PYN5ERFmzJjB0GFD8X/qh+qxuHPAv8zPIcMP4brrrotpBRQytc0F8nzS7GLdRJr997eeI/PL9u0hDW2L5dywREBE5ovINhFZ4dnWWUTeEJFv3H8nz76ZIrJWRFaLyEme7SNEZLnb95Az58KZfC102z+qwW19WHz22We88847FHcbAoF6vM+Kj6Keh/K/dWtZvHhxJMWokVAjPTRqCPBdQRJ9+mZHPe/qdOzYkW7du1WMGgL49/gZcpCOHkaTzMxMzp84ke/yi1i/Nz9q+QSNYeWuXPYUlzJ16tS4fo+VfenatSuYIMHUzhUKYrgjiMXFxfxy5kwWL17MmQcUMuPgvEYph15SA3DFQQVMOrCAjz78kOunXxe2Y49okZuby7333svUqVPJ2bKFScCPEKSRNlIDEa40hrS8PGbPns0NM2bENFZutOjbty/HH388hx12WMzamyFFsGtqZ/ZL787Y3qOqbI80Rx55JKWlpezcuROw3l3T09NJT0/n0EMPZcCAAQSDQbZv386oUaPCtqhQBbGRdO/eHRMsw5+/nZ5xNsvzxkKJVfBZL4FAgNmzZpOWmoZ/qb9yPmIQAp8ESE9P59bf3Brzj3ZonuM2ICcYbPXmk3369AEgr7Rsn315JXZbPOfSxokFNNNg1IWFhdzz299C2/aU9hjWoHPKu2QTbN+dhx95hMZ6I2wsIQXxO6cgBo0LexGn0Z3Bgwbjz3G3qwRMrtHwFjHgsssuY+xxx7FmTz4rdu6lrB6nM+ltAiT7fST7fVW8LNdGcXk5n27PYUu+nW/04x//OJLiKxEgZGFV2n0IvuI82qak1BnDrT6MMdx11518+NFHXDown3Oyi6htqmphmVTxiltYVrvS8OP9irluWB7frFnNzTfdVOENMlFYv349kydNYvHLL3MUcE0wSHYtimERVLnummahZSJcbgynAis+/ZRLJk/m/fffj+IVtExCz3K5GyUvKC2qsj3SjB49mszMTDZt2kRo2nBaWloVU9Lvv/+e4uJizjzzzLDzUwWxkYR6v6Q4N+qB6esjJSWFgLOxjtYDWR8ZGRlMv266dVrj4iPKBsHsMlx/3fVxCUrbq1cvfCKswuqsoRG01krnzp1JT0urUAa95JaW0T49PSaxqRIJY8w77Ou5bzw2CDXu/0zP9meNMcXGmPXYOT0jnTfB9saYD5yTh6eqnRNK6wXgeIlQt+YDDzzA91u+p/CAo8HfwM4XEYoOOIbCohJuu/32qM636dSpE+lpqRUjiLuLheJyEzcFccCAAQTzglCCdVjjtinRJRAI8Ovf/IaLLrqILQXFfLh1N7uKSmo9flCndMZkZTAmK6OKl+XqhFzYv//DHnKDMHPmTCZPnhylq1DCIWROKsV5SEl+2OalK1as4K23lnBudiFje9X+LAEUlEkVr7gFdSiIACMyS/nZoHyWr1jB22+/HZackeb22bMp2rWLKcDJCMl1jBoWQZXrrs1NiQ/hCIRpxtClpIRZt97a6j2+N5b2bmCm3M2zzS8tpE1SG5KTk6OSXyAQ4OKLLyYnJ6diDumAAQMqvmfl5eVs3LiRQYMGcfjhh4ednyqIjcSr8MTbdFFEaNfOToYNdzJqOJxwwgkMOHAA/tV+CIJ/tZ+BAwcyduzYuMiTnJxMj+7dWeXWW+HoWBVEhOx+/cirwcQ0r6ycvtnZzX4OQoRI+GDUr7zyCq+++iolPQ8m2L5xHoFN2w4U9R7NF59/zrx588IVpVZEhP327833BVZBDCmK8eqoCYUnIgckR6puU6KK3+9nypQpPPDAA6R3yWDptj2s2LmXkvKmedTNLy1j2fYclu/cS/aAAcybNy9iQa+VyBNSCH0lefhL8sO2ugopMD4x1Od3NTVgqnjFDcVErAu/mCr5JAq5ubmkGkNDWnltocp11zMBgWQg3RhKSkoqnJsoDSPkByS9TTt6pXcnryS/Ylu0OPXUU9l///1Zt27dPp7JN27cSFFREVOnTo1Im04VxEbidLQy+wAAIABJREFUDSURi7AS9ZGaam2dY+38wYuIcP5552NyDfKVYPIM5513XlyVjj4uPqVPRB2wYBvEec6TaQhjDPml5fTr1y+OkjULohaMujGBqFetWsWc+++nvEMWpb0OrU/mGinLHEBp5oH89a9/jWov+f7792ZrkZ3/8INTFOM1zzXUQSQ5AnuhXXq7hKi7WxPDhw/nqaee4sILL+SHwhLe27qbLfmFNDS6QtAY/peTzwdbd1MgfqZPn86jjz7aIpzStGRCVlZSko+vJC/s+YeHH344Rx45mmfXpnLbsvZ8uTNQq6KYEjBVvOKm1KEgfrPHz72fpfHIijQGDxrIiSeeGJackeYXN91ETps2PCTCaxj21hFfrC1Uue7aFMRiDO9heNDnY7UIV0+dqvViI0lNTSUpKYm+HXtx3qBTyC0poFOn6DqMDAQCTJs2jYKCgipzR4uLi9m8eTPHHXccw4Y1bOpJfTR7BVFETnaOI9aKSNTcNYcIOYYBot5T0BBCbnzjqSACHHXUUQSSAvhW+Uhqk8RRRx0VV3lCjdGumZlRG+5vTvTr14+yYJACzyhiQVk5ZcGgKoiVxDwYdUMDUefk5HDLLb+iPJBCUfZxdYa1qI+SPkdi0rpyx513snnz5vpPaAJZWVnsKjQUl8PWAh9tk9vExdwc7LSA5LbJkAuSKxzQ+wAdMY8Dbdu25YorrmDe/Pn0G3Agy3fm8tmOvRTXM5qYV1rGR9v28E1OPkcf8yOefvppfvKTn0Tdhb0SPsnJybTv0AEpysGUFoU9LScQCHDXXXfzi1/8gj2Brtz7WTq/XtqBpduS6h1RrImVuwLcsSyd2Z+0Z0NJJ6666ir+8PAjUQtT0FRGjhzJn596ijEnnMAHIvxOhBcwbKk3EPW+7MHwGoY5Ph+vAQMOPpjHHnuMc889N/KCt3BEhI4dOrK3xDriyi3Np1MMlOxRo0YxYsQINm7cWDFdZMOGDQBcccUVEcunWSuIzlHEI8ApwGBgonMoETW8ykb7ODiG2QfXUGzbtj5DguiSkpLCoEGDAOsUIt7yhBzTtImzHIlCSAnM9TiqCS2rglhBwgajvv/++9mxayeF2WMhqZZnurykinMCymsxF/L5Kew3lpJymDX7tqg4ZAiN2m8r9LG10EePHj3ippSJCFlZWUie4M/3q0VBnOnbty+PPvoo11xzDXtKy/lw6x72FNcc2+uH/CI+2roHSU7hjjvu4Pbbb4/71A6lcWRmZuLP216xHC4+n4/TTz+dZxc+x0033URZei8e/DKN25a1Z/3eyk6D3unlpPiDpPiDDOxYSu/0ys7RHwp83PdZGnd/ms4OX1emTZvG8y+8yMSJExM2lmaPHj349a9/zd+eeYazzj6bNcnJPIb1trbJoyj2wJqNJgN93DrATgwvYPg98IHPx5FjxjB37lweePBBBg+OarO5RdO5S2f2FluT5L0l+TEZhRURLr30UkpKSvj+++8r/k8++eQKJ42RoLn7hB4JrDXG/A9ARJ7FOof4KloZehs58Zz3V514K2QA2X2zWf7l8oRQOEKNiESPaRQr+vTpg4iQW1JGd1ckuSVl+Hy+Vmmm5YJRjwEyRORb4FZs8OnnXGDqTbhYYcaYlSISCkZdxr7BqBcAKdhA1N5g1H9xwah3Yb2gNolly5axZMkSSnqNIJhWewNLyko49QzrnADguUWv1XqsSU6jsPdovlnzFq+88grjx49vqng1Euqg2V7oZ3txEr0PjK9SltUzi/WfrSdYEIzoB1RpGj6fjwkTJnDooYcyc+ZMPtm2jeEZ7enStk3FMZtyC1i1O48hBx3EbaoYNlsyMzJYt3YtEFm/DUlJSZx66qmcdNJJvPbaazz+p7ncutTPCb2KOCe7kIsOLGRjrlUYf3WYbcCXlMPLG9uyaEMKbZJTuPrqSzjrrLOalZVRz549ufbaa7n00ktZtGgRzz7zDI/n5DAEwzhgHEJoNsM4hBIMr2N4H1tm55x1FhMmTNA4sBGiS5cubPp+HcYY9hbnxczh39ChQznwwAPZsmULwWCQYDAY8VHg5q4g1uQ8YlSsMk8k5SMRFMTQXJ9QWIV4MmTIEI4++mjOOOOMeIuSECQnJ9MrK4u8ndsqtuWVltErK6tZfRwjRXMKRv38888jye3qDWlhAm145ZVXAOukwATqNpMq79yXYNoKnnv++YgriKHGx44iHzsLfYxy7u7jRbdu3TB5pmJZSQyys7OZO3cu06+7js83b2JU146kJQXYVljMqt15HHnkkcyePbtV1lEtBa9peTRGVwKBAKeddhrHHnssjz/+OP/85z/4eHtbfto/r8qo4YqdAZ5cnc7WAhg7dizTpk1r1p0OaWlpXHDBBZx11lksXLiQvzz1FOuDQc4PBp2SCLsxPC0+tpog48aNY8qUKXEz9W+pdOnShRXFX5JfWkh5sDym5XviiSfyhz/8gZKSErKzsyPukLFZm5gSBUcQjSER7NRDSmqbNm3qOTL6jB8/nqeffprTTz893qLQqVMn7rrrLo444oh4i5IwZPfrR3555euRX27IToDRXqVuvvjiS0o67Ae+euZc+dtUcU6Av546QYTSzgewedMm9u7dGzmBse9fwO/nu3wfhWUmbOcU4eI1bYt3eCKlKp06dWLO/feTmtqOZdty2JxXyJc79pKdna3KYQvAqxRGc3QlPT2dGTNmMHfun+i2f38eXp5GQZkwsX8hz36Twj2fpdOmUxa///3vmTVrVrNWDr2kpKQwefJk5s2fT6cePXhKhE3Okc08n4/81BTmzJnDzTffrMphFOjcuTO5JfnsLrLf0FiW8ciRIwHroGbUqMiPjTV3BbE25xFVaKgjiIYy6ogj8Pv9VRzWxIurrrqKcePGMXTo0HiLgs/nY7/99sPna+6PVcukT58+5JeUUh40lAcN+SWlrT4ESHPAurKO1vw9m26k4yL6fD4yunRmXY41Uom3Uub9aLe2mJ/NgczMTKZdcw1F5eV8tSuXIHDTTTepctgC8Drzi4XfhoEDB/LYH+cyefJk3v0+mclvduLljW0544wzmL9gASNGjIi6DPGgT58+PPrYY2R268bzPh8vIhQFAjzw4IMVioQSebp06YIxhu/ytgKxjW7g9Qw+cODAiKff3E1MlwL9neOI77DzfC6Idqb33H03ZWVlCfHxGjFiRIut8JTIEpprmO9xShKv2HRKwxk6dAhLv1hJSfko8EewyjaGNrvW07NnVlQ6u7pkZvLVSmvSHO/eeu/1JULHnrIvJ510EsOGDaOsrIzU1NS4PzNKZEhPT69YDgRi0+QMBAJceuml7Nmzh3/84x8ceeSR3HDDDS3ee3HHjh35+Y03MmPGDPYAV1xySUUQdSU6hBTCzXt/AGI7gujz+bjiiitYs2YNhx7atNBXddGsFURjTJmITANeB/zAfGPMymjn6/f71cW20uwIKYMFZeUVhtit0UFNc+Oiiy7i44+voc2G9yjp+yOIUCMnacsXSN42Jl0zMyoNp86du2DcCGW8R+28jdRECE+k7IuIqAOhFoj33Ys1V155JcOHD2f48OEtXjkMMWLECPr368fWrVsZN25cvMVp8YS+baERxFh/6y688MKopd2sFUQAY8xiYHG85VCURCcrKwuAgtLyiom62iBLfA4++GAmT57MggULwJ9ESe/RNSqJwXZd8BXstMupXQi2q70nM/D9ctp8+wknnHACJ598clTkTqRRO6/H6ViNYiiKEt8YzampqRx33HFxyz8eiAjz5s+PtxithgoFMXcrSYFA3GOSRxL9UipKKyE1NZWOHTpQUFYMQKeOHRPC0ZJSP5dccgmFhYUsXLgQKcmnOHsM+KvG6yrpPRpfvlUQiwafVnNCwSBtNn1I0tavOPbYY5k5Mzqjh1A5UicicR1FgPg2UhWlNZNI3t4VJdKEFMS9JflkdMloUSPV6k1EUVoRPXv2pLCsnMKycnro6GGzQUSYOnUq11xzDUl7NtHuq0VIYU7jEiktIGX1qyRt/Ypzzz2XWbNmRTUodEgp9Pl8cXdclQhhgBSlNaLvntKSSU1NJeB8A8TbUibS6AiiorQiunXvzqZ1NmixxoNrfkyYMIG+ffvy69/cinz1Twr7Hkt5p/rnkfrytpG69j8EgqXceMstnHTSSVGXNZFG7dq2bUv/Af3pe0DfiKb70EMPsdYFAY8V/fr149prr41pnorSVBIhBJeiRAsRoX37dHbt3k2Hji1rfrsqiIrSisjMzKSorLxiWWl+jBgxgifnz+OXv7yFb9b8m+L9D6es+1AQqXHeoX/nOlL+9w6ZmRncfddd9O/fPyZyhhTERDC58fl8zHtiXrzFUJRWhyqISksnPc0qiLEI4xJLVEFUlFZEly5dKAsGK5aV5km3bt149NFHuPPOO1myZAlSWkTpfodbBzYeAltXkbzhPQ4aMpS77rozpiYwoblHxph6jmy+6Eie0lwRkZOBB7Ee4J8wxtwTjXxUQVRaOmnt7XQKrzO0loDOQVSUVoTXBXO8Qw8o4ZGcnMysWbMYP348bb7/kqQtX1TZ79+5juQN73HEEUfw+9//LubzIxIhTqyiKPsiIn7gEeAUYDAwUUQGRyOvkII4fPjwaCSvKHEnpBgm0rSKSKAjiIrSivAqCaogNn98Ph/XX389+fn5/Pvf/6E8LZNghyykcA8p699lyNBh3H777XFR1kLOKbp37x7zvBVFqZORwFpjzP8ARORZYDzwVaQzSk5O5uGHH6ZXr16RTlpREoLQ97WleexVBVFRWhFeG/l4hx5QIoPP5+Omm25i1aqv+W7j++QPOZvkje+TmtKW2bNnxW0kb8CAAUyZMoURI0bEJX9FUWolC9jsWf8WGBWtzIYNGxatpBUlYWhpYcPUxFRRWhFeG3lVEFsOycnJTJs2FQpzaLP5Y/w5W7hk8mQyMjLiJlMgEOCiiy5i8OCoWK4pitJ0avIctc9kYRG5XEQ+EZFPtm/fHgOxFKX50tJCuqiCqCitCK+NfEuzl2/tjB49mozMriT9sIJAUhLjxo2Lt0iKoiQm3wL7edZ7AVuqH2SM+ZMx5jBjzGHq9VpRaibkqbulzbtXBVFRWhFeG/mWZi/f2vH5fBwxaiQABx10UIvzqKYoSsRYCvQXkQNEpA1wPrAozjIpSrMmKSkp3iJEFJ2DqCitCG8PV0vr7VIgOzsbgK7a268oSi0YY8pEZBrwOjbMxXxjzMo4i6UozZJAwKpSfr8/zpJEFlUQFaUV4fP5OO2004DECGCuRJaRI0cy/NBDOf300+MtiqIoCYwxZjGwON5yKEpzZ8KECezdu7fFzbdXBVFRWhk33nhjvEVQosR+++3Hgw88EG8xFEVRFKVVMHjwYO6///54ixFxdA6ioihKlBCRk0VktYisFZGb4y2PoiiKoihKfaiCqCiKEgVExA88ApwCDAYmikjLskFRFEVRFKXFoQqioihKdBgJrDXG/M8YUwI8C4yPs0yKoiiKoih1ogqioihKdMgCNnvWv3XbFEVRFEVREhZVEBVFUaJDTW5iTZUDRC4XkU9E5JPt27fHSCxFURRFUZTaEWNM/Ue1IERkO7AxAkllADsikE4kUFlqRmWpmUSSBSIjT29jTEIF/xOR0cAsY8xJbn0mgDHm7lqO17opuiSSLJBY8qgsNRMpWRKufmosYdZP8bynmnfryFfzbhq11k2tTkGMFCLyiTHmsHjLASpLbagsNZNIskDiyRMpRCQArAGOB74DlgIXRDsgdSKVp8pSO4kkj8pSM4kkS3MmnuWoebeOfDXvyOetcRAVRVGigDGmTESmAa8DfmB+tJVDRVEURVGUcFEFUVEUJUoYYxYDi+Mth6IoiqIoSkNRJzVN50/xFsCDylIzKkvNJJIskHjyNHcSqTxVltpJJHlUlppJJFmaM/EsR827deSreUcYnYOoKIqiKIqiKIqiADqCqCiKoiiKoiiKojhUQawFEZklIj+vY/8SEYmZxyIR6SMiK2KVX2NIVNlEJC8BZOgjIhfEOf9G3RsRuU9EVorIfWHkm1DvT0tBRK4VkVUi8nS8ZakPEXlCRAbHML9Q2ewWkZtjla/Lu6OIXO2Wx4jIy/HIO8x0NohIRiRkqiOPiMgaaURksYh0rOeYGussETlERMZFT7qWi/dbIyJXisjFjTh3PxF5y73zK0XkOre9tvtU4zcpFu0XEUkWkX+LyOcicl44daOIzBeRbV6ZE/GaXT6ZIvKRiHwmIsc05D2rJz2/S+tltx6X6xaR92vZvkBEznHLMf3+1SBLnvvvKSIvNCUNdVKjKBFARARrsh2stqsPcAHwt0akFTDGlEVQvMZyBZBpjCmOowxKzVwNnGKMWd/UBOp4ViOKMeZn0Uy/BsIumzDo6PJ/tKEniIjfGFMej7zjSELKaowJR8E7BDgMdUbVFML51pQBNxhjPhWRdGCZiLwRWfEixnAgyRhziFtfGEZaC4CHgafCFSoGHA98bYyZ5NbfDTO964BVQPsw0wkLY8yRDTgm1t+/GjHGbAHOacq5OoLoQURuEZHVIvJv4EC37RAR+VBEvhSRv4tIJ88pE0TkYxFZIyLHuOP9IjJHRJa7c65poiwzRGSF+013m/0i8rjrLfuXiKS4Y/u53qkvRORTEckWkYXeXk3Xs3F2kwomwrJFQoZEwPVGrRKRR4FPgXmuTJaLyHnusHuAY1zP4fUi0lZEnnTHfCYix7m0JovI8yLyEvCvMGQK97lZBLQDPvJcQ0Pzjsr7IyKniMhznnzGuHJqVYjIH4G+wCIRuUFE/uHK6EMRGeaOqdJz6p6DPjU8q/tFWLZ2IvKKe5ZWiO0lXyIih4lIbxH5RkQyRMQnIu+KyIkRzt9bNteLyMMi0kHsqJjPHZMqIptFJMk966+JyDInz8AwRbgHyBaRz4H7gDQReUFEvhaRp0VEnAwbROQ3IvJf7PN/ooh84N6/50UkzR03QkTedvK9LiI9GpK3q1vOcGn8XUTmu+XLROQOt/xT9959LiJzRcRfrSx/K55RPvdM3eCWfyEiS91zNzuccmqgrDXVZ2FR0/WLZ/RURH7t7tsbIvKMVB2JqFJniUgb4DbgPJdeo+rM5o6rV74WO1qywj3rJ4jIe+6dH+men7+IyJtu2xR3bpVvTfW6qz6MMd8bYz51y7lYxSHL7f6piLzvZBrpOe3g6nLEogyAvwKHuGckW8KwoDHGvAPsqmFXTK5Zaq7rN7h642P36ycihwD3AuPcdadIGFYKItILOBV4otqumN1rjyyh0TkR+635SkReAbp6jgnbSqqhZe2OPUDst2SpiNzuSaPpo6bGGP1ZRz0jgOVAKrZ3Yi3wc+BL4Fh3zG3AA255CXC/Wx4H/NstXwW8CATceucwZGkHpAErsT1QZcAh7pjngJ+65Y+As9xyW3cNZwF/dtvaAJuBlAiWU5Nli+E9zYty+n2AIHAEcDbwBjbeXTdgE9ADGAO87DnnBuBJtzzQHdcWmAx825TnJdL3pinlFs33B2vpsAlo57Y9FrqG1vYDNgAZwB+AW922scDnbnkW8HPP8Svcc1rxrEZJrrOBxz3rHdw9Psyt/wx4AfgFMDfKZTMZeNht+ydwnFs+D3jCLf8H6O+WRwFvhpl3H2CFWx4D5AC9sJ2wHwBHe2S80S1nAO94nuubgN8AScD72JGVkNzzG5j3+cB9bvlj4EO3/CRwEjAIeAk7mgF2JO/iauU3HHjbk/5XwP7AiVhveeKu62XgR2GUU32y1lifhXmfarx+z7UfBnwOpADpwDe494na66yK5621/dz9LAOGumdiGTDfPSPjgX9g66QvXJlmYNsiPd35eZ60ZuGpu5ogxybst2cJri4CfuR53mqUw/tMRrEMxlC1HbAEVzeGkeeKaunF5Jqpua7fANzi1i8OXWv1dyP0njUx3xewdcIYT/oxvdceWfLc/0+obPf1BPYA50TiHjehrBdRWZdP9cjY5GvWEcRKjgH+bowpMMbsxRZ2O6CjMeZtd8yfsQ9hiP9z/8uwNwHgBOCPxpkIGmNq6umpj6OdLPnGmDyXzzHAemPM5948xZpWZBlj/u7yKzLGFACvAmNFJBk4BXjHGFPYBFmiIVtLYqMx5kNsuTxjjCk3xmwF3gYOr+H4o4G/ABhjvgY2AgPcvjea+Lx4047XvYna++OWXwNOF5EAthfxn2HI2hLwPkdvAl1EpEM954Se1WiwHDjB9WweY4zJ8e40xjyBbXBfie04iBULsQoWWIVkodhRuiOB58WO+M3FduZEko+NMd8aa8b7OZXPd0gmsB1Lg4H3nByTgN7Y0fchwBtu+6+wymZDeBdrsTAYq9htFTv6OBqrdB6PbWQtdWkfjx15rcAY8xnQVezclYOB3caYTVgF8UTgM+wo9ECgfwPlaoqstdVn4VDf9R8N/NMYU2jsqFR1S4Wa6qzWznpjzHL3rK8E/mNsy3Q5lWUUKtMdwFvAyJqTajzufX4RmO6+PQDPQMVoW3upnPcWLTkaUgbRJlbXXFtd/4znf3SYeVRBRE4DthljltWwO9b32suPqGz3bQHejHD6jSnrozzb/xKJzHUOYlUaG/MjZDdfTmVZShPSqY7Uk18oz5TajjXGFInIEmxP7HlUPjjhErZsLYx899/Qa63ruPw69oWTdqzuTTTfn4XYXrFdwFLXeGvN1HT/DLYn29vx19azHO7zVSvGmDUiMgI7snK3iFQxkxaRVCqVnDQgVvdvkZOnM1YxeBPbcbHHVM4HigbV3znvt9ZbZ7xhjJnoPVFEhgIrjTGNbmQZY74Ta8Z9MnZ0sjNwLrY3OVdEBGtZMrOepF7AzlvpDjzrkfduY8zcxsoVhqyRpsbrF5HJnv11UVOd1drxPutBz3qQyjKqXqdHJL6aiCRhlcOnjTH/59lVW35RkYOGlUG0ick111HXe9ONdPy8o4AzxE6baotVBP9aS17RvtfViVa6TSnriMqiI4iVvAOc5eyk04HTsR/y3eLmRwEXYUeG6uJfwJVupAPXMGmKLGeKnTPTDmsuWuPkXtdj9q2InOnyS3aNMbAf9kuwva6vN0GOaMrW0ngHOw/FLyKZ2J6lj7EN4fRqx10IICIDsKZbqyMoQ7zuTbTfnyXAocAUwpvg31LwPkdjgB3unm7AlhMicihwQCyEEZGeQIEx5q/AnJAMHn4LPI01oXw8FjIBuJGnj4EHsaY45a6c1ovIBCe7uJGycKj+njeED4GjPHNIUl2dsBrIFJHRbnuSiBzUiLw/AKZjn5F3sSO2oXrgP8A5ItLVpd1ZRHrXkOaz2BHXc7DKIthvyKVSOU8yK5ROI2iMrA2uzxpBfdf/X6ylQlt3nac2IM2m3PvWxnhXpl2wJoJLw03QdSDMA1YZY35Xbfd57pijgRzPyEvE5UggYnLNddT153n+Pwgnj+oYY2YaY3oZY/pg66U3jTE/9eYbp3v9DnC+a/f1AI6LZOKNLOv3sGUDrm0QLtoD5jDWE9ZCrDnQRio/RJOAP7rG8/+wClddPIE1GfxSREqxjaGHmyDLAmzDJpTm7jpOuQiYKyK3AaXABCfrv7CerhYZY0oaI0MMZGtp/B071P8FthfnRmPMDyKyEygTkS+w3scexT5Py7GjPZONMcWR6CyP572J9vtjjCkX69p6skuztTMLeFJEvgQKqCyTF4GLnfncUmBNjOQZCtwnIkHss3QV9oOGiByLNbc+yt3Hs0XkEmPMkzGSbSHwPLaREOJC4DER+RV2zt+z2He3SRhjdop1SrECKAS2NuCc7W7k6hmxUwEAfuV6jc8BHhJrNhwAHsCartWX96vYd+9EY8xaEdmIHZl71x37lbvmf4l13lOKHZnfWC3Nla6j5ztjzPdu279EZBDwgauv8oCfAtuaWE71ybpPfebMX5tMHdcf2r9UrPOUL7Bl8gl2PmldvAXc7N65u40x2oG1Lx8Dr2A7RG935njhchT2G7bclT3AL93/brGhCNoDl9Ylh4j0iYAsMUNEnsHWZRki8i1wq9sVq2uuqa5/AUgWkY+wA08T6zg/0sTzXv8d6wNgOfZbW18HeGNpTFlfB/xNbLiXFyORuVgzaUVRFEVRlNaNiKQZY/Jcp9Y7wOXGectUGo+IzMKaDc+JtyxKdBCRDViHLDviLUtLJ5ZlrSOIiqIoiqIolj+JdZzTFjtfUZVDRVFaHTqCqCiKoiiKoiiKogDqpEZRFEVRFEVRFEVxqIKoKIqiKIqiKIqiAKogKoqiKIqiKIqiKA5VEJVmh4gY5yY9nDTGicis5ubiWlGU8BGRJa4eyRCRMW65UeGIXDp5zqtcQiIiG0QkL95yKIrSeERkuohsd/XTHU1Mo6Kui7BsvxSR6ZFMU0ks1Iup0hyZCOwJM41x2PhXS7DBxRVFaZ18ha1TYhUzEhEJGGPKYpVfuIiI3xhTHm85FKWVcQvWm+4kIKwYoFHgl8AObIxWpQWiI4hK1BGRS0VktYjki8j7InKoiEx2vVp/FpHPRGS3C/CJWO4XkV2u9+vv7tgxLslnqAzCPSvU+y8ia1xv2wS3r42IzBGR70Rkj4g8LyKZLjh1KDjyW+78ae7/THfuT9z6lR5Z54nIpyKyQ0R+Xtf1xaZkFUUJISIXichKESkUkXWuLtgmIklu/yIRKRKRjtVOHYytUy51x4V63O9053/tAsQjIvu7d3yHiNxbLf8a6xu3b4FL8zER+Q6Y5tl2n4h8KyKbReSYascf5tYrRio99dECEfnKyXiKiDzt6qB/iEigmmy/dTK/LyL7u21ZIvKiq3u3iMg9YoPHh0Ye80XkURHJwQZsVhQlRojIEiADSAP+DLwklVYPGW55iTu2g4jMd3XBDhH5k9g4ntXTPFBEPnJ15G4ReUdEktz7/5nnuC9dPeYTkT+6tlihq2/GunzbAb1DdZE7b6aIrBeRXBF5XUQ02YBtAAAGRElEQVT6uu2hdtojIrLR/Y52x+SJyNzolqbSFFRBVKKKWKVuHnaU7g6gC7AI2ysGcBLwBGCAe0SkDXA6MANYCbwI/LgBWZ0APAJ0AO5x22YCNwAvYXu5TgEeA94G/uWOuR07evAUkAdc4rb/BCgGFnryOBmYC/wA3CciB9d2fSLSFkVRYoKIHIt9hwPAtcCzwK+ATOA0EUnD1iOLjDENtT4YBjwNHAiEOoQeBEZj64GO2EZSiNrqGy/HALcCH3i2HQX8EegFzGqgbABjXfoZwMtYq4r/AuOB0zzHtQM6OZlHU9nj/1dsmTyIrZNvAq72nJcK9MRe+7ZGyKUoSvjchm2D7MC2UaSOYx8ALgIWYNtTl7nzq3M1MBI7+jcT2GSMKXXnHCIiw0WkH7ZD6GngYOAK4C137j+xdWx12R4TkUnAXcBH2DbYMOC5avkf7fLaH9sO+xBYDVwuIoc0oEyUWGKM0Z/+ovYD7sMqf9V/M9z/Xe6419z6fsDv3fIJbt/Tbn2MWzfACrc8y61f7ta/Bsrd8tIa8t3r9j3sTdNtmwuUOhn2AC+47ZPdsbe79cvc+rV1XN+h8S57/emvtfw87+Gpnm0pwC6s8nOe23+a27fErWcAY9zyw9X2DQC6u+X/uH27gc1uuQ1QDmxw63XVNwvc+hke+ULbTnTrRcC6avsOc+t5nnxC9dEdbv07J0cbT910vdu3IbTPrW92ZZIGBGuQd5HnPAN0iPe91Z/+Wuuv2nvvrbMy3PISt297De/ylzWcN80tv4pV8g53x/QCyoCHsB1FBhgCdAPyseb3DwMXAEnVZXPrz9cggwE6U9lO+xmQ5Ja/defd7tbPind566/qT+cgKtEm1Ot1A/ClW/YBB7nlXe4/NB/H7znXNCIfbzqhkXFx66dhG0l49tWU9qPA5cB87EjkU7XkJTUsV7++9Y2QXVGUCGOMKRSRP2MbRenYUbDXGpHELs+yv4b91Xv066pvQmypI58yTz6h8wMikoxVdqsTGgktBQqNMSUiEjqvtnq0usxfUDk6CpDjWc43xuSgKEoiUFEnYDt4qvMDdhQxRHH1A4wxD4vIKuBYrKXBLSIy2BizWkRewiqAm4BPjTErAETkIOBMYBS2s34w1jqjehsqVLdcSKXFgQ8o8ByzxxhTKiJQWX/VVGcpCYCamCrR5mX3PxFrVjAK20u1u45z3nL/vxGRa7AVWVN4CVuZTnJ5n4w1l8CT/zkiciqAMeYLrOnXCdgeuVerpXepiFwOTMdWjm9Ty/UZY+q6PkVRIstL7v93IjJFRO4Qkf5Y080AdpTwbyZ8xzBvAb1E5E7gD1T9htZV3zSWDe7/Iqy5Vjjfaj/wsJM5C3jLGJOHrb+GYs1e+wIX0zBzfkVRYs8G9385MLvavpex1g5nAL2xU2TOq56AiFyJNTNf634+7CghWHP1LsBwXOe4iAwAbgRysaajYM3OwbahMkVkkogMprIOnoS1wjoW+LUxpqhJV6vEHVUQlahijFmCndeXhp0jeDnwfj2nvQT8DmvDfg6VCmNjPZfejTU9OwZrHnEKtlEEtifsa6xd/YOec0Jzhp4x1jbfy2LgSmxFfKMx5osmXp+iKBHEGPMOVsEpxypuE4EyY8xq4E13WG0WAY1hOrYT6WpsfeTtHa+rvmksj2NNVi/EKniFTUwHrInYNmzd9SFwvdv+U+D/sCOsc4Bs4OMw8lEUJXrMwc7XuxY7WuhlOnZu37nY9swRwHs1pFGCba88jlXgHvEc9wZWaSzDOu0Ca/Y+AjvH8S5s2+a3bt+9Lr0FwE+MMX8Gbgb6Y9tRE4F3mnitSgIgzgZYURIKEZmBNdnsjq3wCoFsY8w+ZhMRzHMYthF1FTDMGLPcbZ8MPAn8whgzJ1r5K4oSOZxp5o+wFgsFxpgRcRZJURQl4RCRDlhnWQuAd40xZ8dXIiUR0DmISqIyHusVtAT4BKucRU05dDyE9fB1R0g5VBSl2dID6614E5XeiRVFUZSqDAdeAVZhndQoio4gKoqiKIqiKIqiKBadg6goiqIoiqIoiqIAqiAqiqIoiqIoiqIoDlUQFUVRFEVRFEVRFEAVREVRFEVRFEVRFMWhCqKiKIqiKIqiKIoCqIKoKIqiKIqiKIqiOP4fVKfu/4PMoWgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### violin plot for all categorical columns v/s price\n",
    "object_columns_list = data.select_dtypes(include='object').columns[1:-1]\n",
    "plt.figure(figsize = (15, 10))\n",
    "for i in range(len(object_columns_list)):\n",
    "    plt.subplot(3, 3, i+1)\n",
    "    sns.violinplot(x=data[object_columns_list[i]], y=data['price'])\n",
    "    plt.xlabel(object_columns_list[i], fontweight=\"bold\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above visuals:\n",
    "* The **price** doesn't show more effect on **fueltype, aspiration, doornumber**.\n",
    "* There is high chances that the **enginelocation** at **rear** as **high price** compared to **front**.\n",
    "* The **higher** the number of **cylinders** makes **higher** the **price**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x180 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x180 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAusAAAC+CAYAAACMJUFQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOyde5xcZX3/3885c937JZt7AlmSEAj3SwiKEIFqFIvaogatoIKhXortr1rQttZibaG2KlhUEFCwykVaC1XAAiEGNBASELkk5LIJ2Vz3vjs713N5fn+cM7MzszOzs5uZndnd5/16zWt2n5lz5syc7/Ocz/k+3+f7FVJKFAqFQqFQKBQKRfWhVfoAFAqFQqFQKBQKRW6UWFcoFAqFQqFQKKoUJdYVCoVCoVAoFIoqRYl1hUKhUCgUCoWiSlFiXaFQKBQKhUKhqFKUWFcoFAqFQqFQKKqUiop1IcQ+IcSrQojfCyG2um0tQognhRC73OfmtPd/WQixWwjxphDi3WntZ7v72S2EuE0IIdx2vxDiQbf9BSHE8WMd09q1ayWgHupRrkdZUHarHmV8lA1lt+pRxkdZUDarHmV85KUaPOvvlFKeIaU8x/3/RuBpKeUy4Gn3f4QQJwPrgJXAWuB7Qgjd3eb7wHpgmftY67ZfA/RLKZcC3wZuGetgenp6SvKlFIrJRNmtYiqi7FYx1VA2q6gE1SDWs3k/cK/7973AB9LaH5BSxqWUe4HdwCohxDygQUq5WToVnu7L2ia5r4eBS5Jed4VCoVAoFAqFotqptFiXwP8JIbYJIda7bXOklIcB3OfZbvsCoDNt2wNu2wL37+z2jG2klCYwCLSW4XsoFAqFQqFQKBQlx1Phz3+7lPKQEGI28KQQYkeB9+byiMsC7YW2ydyxc6OwHmDx4sWFj1ihqBKU3SqmIspuFVMNZbOKSlNRz7qU8pD73AX8AlgFHHVDW3Cfu9y3HwAWpW2+EDjkti/M0Z6xjRDCAzQCfTmO404p5TlSynPa2tpK8+UUijKj7FYxFVF2q5hqKJtVVJqKiXUhRK0Qoj75N/Au4DXgUeBq921XA4+4fz8KrHMzvCzBWUi6xQ2VCQkhVrvx6FdlbZPc1xXABjeuXaGYVKSUxAyr0oehUCgUCoViilHJMJg5wC/c9Z4e4GdSyieEEC8CDwkhrgH2Ax8CkFK+LoR4CHgDMIHPSSmT6uczwI+BIPC4+wC4G/iJEGI3jkd93WR8MYUinZhh0R2K4/dqBLz62BsoFAqFQqFQuFRMrEspO4DTc7T3Apfk2eYbwDdytG8FTsnRHsMV+wrFZCOlpD9iMBBJAOD3Vno9t0KhUCgUiqlGpReYKhTTkqQ33bDsSh+KQqFQKBSKKYwS6wpFCUl60wejBmp5hEKhUCgUimNFiXVFWdm4o4s7NnXQ2R9hUXMN113YzpoVs8fecAoSNx1vesKc+t70mXTeFJVH2ZtiqrDjSIgr73xe2ahiUlFBtIqysXFHF1999HW6QjGagl66QjG++ujrbNzRNfbGUwgpJX3hBIcGYtNGqM+E86aoDpS9KaYSHk0oG1VMOkqsK8rGHZs68OqCGp8HIZxnry64Y1NHpQ+tZMRNi4MDUQYiiWkT9jITzpuielD2pphqKBtVTDZKrCvKRmd/hGBWqsKgV+dAf6RCR1Q6pJT0TyNvejrT+bwpqg9lb4qpiLJRxWSixLqibCxqriGaVQgoalgsbK6p0BGVhqQ3vX8aedPTma7nTVGdKHtTTEWUjSomEyXWFWXjugvbMSxJJGEipfNsWJLrLmyv9KFNiGP1plv21BD20+28KaobZW+KqYayUcVko8S6omysWTGbmy5fyez6AINRg9n1AW66fOWUXEF/rN7033cOcNXdW8pwZKVnOp03RfWj7E0xlbBsqWxUMemo1I2KsrJmxewpPaBJKRmIGAxMMG/64cEod/ymg027espwdOVjqp83xdRC2ZtiqnDi3HruX7+60oehmGEosa5Q5OFY8qZHEiY/fWE/D287gGE5Iv/UBY2lPkSFQqFQKBTTHCXWFWVjqhY6ORZvui0lv379KHc/t5e+cAKAtjo/6y9cwuVnzC/H4SrGyVS1y5mMOmeK6Y6ycUUhlFhXlIVkoZOEaRGKmRwZjPHS/n4+t+YErr90eaUPLy/H4k1/9cAg//HMbnZ1DQPg92h85NxFrDt3EQGvjhCi1IdbNqbrhSNpl15dZBTguQmmxfebqhSyN3XOFNVEOSqYKhtXjEXFF5gKIXQhxMtCiF+6/7cIIZ4UQuxyn5vT3vtlIcRuIcSbQoh3p7WfLYR41X3tNuGqIiGEXwjxoNv+ghDi+Mn+fjOVOzZ1kDAtesMJTEvi0QS2lNy+cU9VVn1zvOkTy/RyZCjG13/5Bl948PcpoX7Jitnc+8lz+cTbjieQlUO62pnOFSVVAZ7qYyx7U+dMUU2Uo4KpsnHFWFRcrANfALan/X8j8LSUchnwtPs/QoiTgXXASmAt8D0hRFIFfR9YDyxzH2vd9muAfinlUuDbwC3l/SqKJJ39EUIxEw2BpgmEEOiawLTtqhuA4qbFocEYfeHxZXqJGhb3/HYvn/jRizzzZjfgLD767pVn8LeXncTshkC5DrmsTOcLhyrAU32MZW/qnCmqjVKPicrGFWNRUbEuhFgIXAbcldb8fuBe9+97gQ+ktT8gpYxLKfcCu4FVQoh5QIOUcrN0lNZ9Wdsk9/UwcEnS664oL4uaa4ibNum/tpTg17WqGoCS3vR4VlGWQthS8uQbR7n6ni385/P7SZg2rbU+blx7Ird/9ExWzp/aC0mn84VDFeCpPsayN3XOFNVIKcdEZeOKsai0Z/07wN8A6XEHc6SUhwHc52TA1gKgM+19B9y2Be7f2e0Z20gpTWAQaC3tV1Dk4roL29E1gSUlEoktJVJCY423KgaghGlzcCA6bm/6G4eG+Iv7X+ZfHt9Bz3ACry742HmLue9Tq3jXyrlo0+BecDpfOFQBnupjLHtT50xRjZRyTFQ2rhiLiol1IcT7gC4p5bZiN8nRJgu0F9om+1jWCyG2CiG2dnd3F3k4ikKsWTGbz605AU0IDMtGF9Ba58Wr6xUfgAYiCQ4ORMflTe8Oxfnnx7bz+ftfZvvhEABrlrdx76dWcc0FSwj6Jj8uvVx2O50vHKoAT+XJttux7E2dM0WlSbfZxPBAycdEZeOKsRATKfRSkg8W4l+AjwMmEAAagP8GzgXWSCkPuyEuG6WUJwohvgwgpfwXd/tfA18D9gHPSClXuO1Xuttfl3yPlHKzEMIDHAHaZIEvfc4558itW7eW5TvPRJJZHg70R1hY4awiCdOmezg+LpEeMywe2trJA1s6ibkLT5fOruNz7zyB0xc2jevz6wIeZtcHyuJ6L7XdVtN5U1Scsk0XJe1W2ZuiDJTFbusXnijf99V7lY0qykFem61Y6kYp5ZeBLwMIIdYAX5RS/pkQ4pvA1cDN7vMj7iaPAj8TQnwLmI+zkHSLlNISQoSEEKuBF4CrgO+mbXM1sBm4AthQSKgrSk+1VCYciCTojxSfN11KyTNvdnPnpg66QnEAmmu8XPuOdt69cs64w12EEPg9UycrTLWcN8XMQNmbYqqgKpgqKkE15lm/GXhICHENsB/4EICU8nUhxEPAGzje+M9JKZMu0s8APwaCwOPuA+Bu4CdCiN1AH042GcUUoRS5vuOmRc9wYlze9B1Hhrj9mT28fmgIAK8uuOLshXzsvMXU+MbXZYQQ1Ac8NAW9ePRKLxFRVJrpmr9+slG/o2Kmo/rAzKJiYTDVigqDKR3HMpikF4kIenWihoVhyaLj+CZShbRnOM7dz+3l168fTbVdsHQW113UzoKmYFH7SCKEoCHgoXG0SJ8SYTDTmUpd5I7VpitI2cNgsim2SNIU+x0Vk0tVjbWlHHdUH5i25LVZ5epTlIVjLaxzLLm+46bFwYEo/ZHiMr0kTJufvvAWV92zJSXU29tq+fcPncZN7185LqGuCUFj0Mvilhpa6/zKm15lVLLg03TOX19KVJEkxXSj1OOO6gMzj2oMg1FMA9IHE3CKSEQSJnds6ijqzr+zP0JT0JvRNlZe2/F606WUbNrVwx2/6eDIUAyAxqCXT739eN576jx0zbnJ3dLRxwMvdnJ4KMq8hiDrzl3EqvaWjH1pQtAQ9NIY9Ka2U4yfcnu9j9Uuj4WJ2PR0J1fp9rHOkfodFdXAeMaqUo87qg/MPJRYV5SM9MGrOxRnboM/4/XxDCaLmmvoCsUyYsQL5bWNmxbdoTgJ0875eja7job43sY9vHJgEHBKSH/wzAV8fPVx1AVGPnNLRx+3btiFR3PCWnrDcW7dsIsvsIxV7S1KpOdgooI7fWo33ft0E5RMSFfyIjdem54JpJduT57nzv4IuoCO7mESlo1P15hV58sokqR+R0UlKWasKuX1MBvVB2YeSqwrcjJewZU9ePWE4hwciAGOmIXxDSbXXdjOVx99nUjCzIjJy85rO15vel84wT2/3cvjrx5JJdw/v72VP7+onUUto4/tgRc78WgiVWExeSwPbu3k3afMpTHoRVMiPcXGHV188eFXGI6bWLakZzjOFx9+hX+74vQxBfdkeL0reZEr1qZnGtnnuc6ns7s7jC4EuhCYlqSzL0rAq3PBLRuo8+kMRQ0A9TsqKkKusao7FOP6B16mIeil3u+hezhOY9BbkuthNmosmXmogFrFKCYSX5cdQze3MQDA0VBsQoV1iikSMZ7Y9IRp88CLnVx1zxYec4X68a013PKnp/KND56SU6gDHB6KEvCmdRMBtX5nYG6u9SmhnsXNj29nIGIgbdCFQNowEDG4+fHtY247Vtn5UlDJgk+q8El+0s+zSKZFFc7DkjY2YNo2TUEvhi2RgE/X1O+oqAjZY9VQ1KA3nCCcMGkKetnbE2YgYmBasiTXw2zUWDLzUJ51xSju2NSBYVn0DpupaeiGoKeghzM7vKA+4GVBk+TIUJzBqHFMhU5yyfBi86ZLKfndnl6+/5s9HBpw4tIbAh6uftvxXH76/DFDV+Y1BOkNxwn6dHTN8fRFDYtFLbXj/h4zgb29EUA6gkqCEKCJZHthJsPrvWbFbG6CihfgUTm4Mkk/z6G4yYKmAD3DCRKWjS1BF2BYkjePhvDpGj6P4OBANOWlVCgmk+yxqmc4ju3eRL55NIRpSXTNaW8Iekt6PcxGjSUzAyXWFaPY1RViMGKgaQJdE5i2pCeUwLBCebfJJbQ8usZZi5snVEAiX0zgVy2bkxY0FpU3vaN7mO9t3MNL+wcARzR+4IwFXHX+cUVf5D+6ahG3bdiNadn4dI+abhwD25aY9kj+KSnBlCCKuKRM1tRupQrwTEZM/lQk28uYHEva2+oAeOPwEJYtEYCuCeKmRTjhCPjFLTXqd1RMOrnGKls6NTl0zQndMm2QaWuojuV6mI0aS2YeKgxGMYqEq7Y0IRAIp1qnoODizVKHF+RKTaUJ+I+Ne8YU6gORBN9+aifrf7ItJdRXHd/M3Vefw+cvXlqUUPdoGq21fj507iL+6QOnMKchqKYbi8DryQwZytmeh+k+tavSrY3GsuWo85w9liRnz7y6hkBgu/d9EtTvqKgI2WOVEAJdc64bAoHXTdmbtN9Sh9upsWTmoTzrilF4dUHUcLykQjjeUQCfnj9kpNThBekZIeKmhVfXaK7xcmQwmncbw7L5n98f4r7N+wjHHUG/qDnIZ9acwOr21qI+16NpNNZ4aQh4UrGzqhR68QS9GnHTwrYdu3G8oVDjLc4vMJ1/67GynMxEcpVuzx5LNOGEUwkc8ZMS69KpNqx+R0UlSB+rzv76/zEUM1PXzOS4J6DkYS+gxpKZiBLrilEsn9PA3p5hQrGRmPX6gJcls+qK2r4UMXT1fg87j4bQNYHmhuIcHYpzXOvoWHEpJS/s7eP7G/fQ2e+I+Tq/h6vOP44PnDG/qMJEXt0R6fX+EZGuGD/L5zSw48ggg1EzFbPeEPCwbE5DpQ+t4tT7PezqGnbWPrg2fXAgxrLZxfWrmYjEybSha5AwJQnLdkS7+7r6HRXVQK5rZo3Hg2WXJ6ZcjSUzDyXWpwmlLCaTjMeb2+gpOna41DF0lu2E3CS9s6kRL2tB6b7eMN/fuIcX9/UDTlz6H582n0+87Xgaa8YOd5lpIr3cRYfOb29hy74+dE3gFWBLGIpZnJ9VRGomkloMnbLlrHYFMHosMS2b7uEEs+t9tNb6efPIEIbtCHUkJfsdy903FNOX7GtmbzhOVyhBW52vLDHlyfCahOksahU41z41lkxflFifBpRaKE8kpKVUObKTedMHYyZzGvz0RwwMy3bDYHxE3Hj1oajBvZvf4pHfH0xNi5+9uInPvnMpS2aNnanFq2s01XipmySRnhQCmzt6O/bdfFlFVqdOxqKkzR19tNX5smZlPGzu6OP6knxC9TKW2BtOWBlZTny6xtw6P+HE2IulZxLZY0lbvZP2Lhy38GgGmqbR4IXhhE3MlGgCWmu9x/Q7qgV7imMh+5oZjlu01flStlvqmhE94YTjt0pOM7nhqj3hRN5t1M3o1EaJ9WlAOYrJjDd2OFdlSNOyeWl/PxfcsqGowSG9CmkyZeKitLR9UcOircbHL14+yL2/28dQzARgQVOQP7+onbed0Dqm8E6K9PrA5KV8SxcCQN+kfXAWk1F0qLM/wqw6f+oiBc4NWDXFUpbjolWM2MvOcgJOJpTZab+VIvdY4vdoTqpWHA9iOGHj1bTUmpr+iMnSNn/uHRbBZPQNxfQm/Zp5wS0bxl0peTzjUsK00XWBRxsJ8TRtO28SCHUzOvWpWDYYIURACLFFCPGKEOJ1IcQ/uu0tQognhRC73OfmtG2+LITYLYR4Uwjx7rT2s4UQr7qv3SZcxSaE8AshHnTbXxBCHD/Z33MymIxiMmOxqLmGaFqWllDM4OBADCEYs7CS401PcGgglhps1p27CNOWRA0LifMcjpscHorx3Q27GYqZ1Pp0rruwnbuvPoe3L51VUKh7dY3ZDQEWtdRMqlCH0UKgUkyGnWTbAVRXGeyJFPwqhmKyM1SyINNUYqyxxLIllgQbmSqcBBzTDFk1jKGK6cN4x8Hxjkuu48fJ7S4ltju9nC8JhMoeM/WpZOrGOHCxlPJ04AxgrRBiNXAj8LSUchnwtPs/QoiTgXXASmAt8D0hRHJ0/T6wHljmPta67dcA/VLKpcC3gVsm44tNNtUgkLKFyJFBpwDRnPpAwcEhYdocGozRF86sQrqqvYUvXLyM1lo//eEEg1GDvohBVyiOAC47dR73XbOKj5y7CF+BtIA+z4hIr/NXRiznEgKVYDLspNoFabkuWsWIvememrJUjDWWSJwc65YtsWyJRxMsaAowHDcn/JnVMIYqpg/jHQfHOy4tn9NAa60Pjy6wpMSjC1prfXkX8qub0alPxcS6dBh2//W6Dwm8H7jXbb8X+ID79/uBB6SUcSnlXmA3sEoIMQ9okFJulo7auy9rm+S+HgYuEcfifqlSqkEgZQsRCSxoCmTkNM8eHAYiCQ4ORPPmTT95fgNL59TSHzVTIS+nL2zkjo+fzV+/aznNNb68x+PzaMxpCLCwuXIiPUkuIVAJJsNOql2QluuiVazYW7NiNvevX82zN1zM/etXV83vUk2MNZb4dC2VBWPF3Aba2+rw6NoxCetqGEMV04fxjoPjHZeuu7Adn0dnbmOAE+fUM7cxgM+j57VXdTM69amoinE949uApcDtUsoXhBBzpJSHAaSUh4UQSeteADyftvkBt81w/85uT27T6e7LFEIMAq1AT5m+UkWolhLq6TF7V975PF2hWMbrycEhYdp0D8fzinTLlvzq1cP86Lf7GIwaAMxrDHDdRe28Y4xwF79Xp7nGW/GQk3TSq91Vksmyk2rOlZ6r0m4pLlqTVX11plBoLJlV5+fgQBSPJpBSluS3rpYxVDF9GM84ON5xabz2qsanqU9FFY2U0gLOEEI0Ab8QQpxS4O25FJos0F5om8wdC7EeJ4yGxYsXFzzmaqXaBFK+weFj5y3m4EA0b4qpl/b3871n9tDREwYc78LHzlvMFWcvLBjuUo0iPUn6wLqnO1yyHIYTsdtqs5PJplwXLSX2ime8dpt9zjy6oKnGS1udv6QFZ2Z631Dkp9waYSLj0njsVY1PUx9RLXk5hRD/AISBTwNrXK/6PGCjlPJEIcSXAaSU/+K+/9fA14B9wDNSyhVu+5Xu9tcl3yOl3CyE8ABHgDZZ4Eufc845cuvWrWX7nuXitqd2ctdzewknLGp9OtdesITrL11e0WNKrm4/0B9hflOQD5+9kDOPa8753oP9UX6waQ+/3d0LOHdZ7145l2suOJ7WuvxZHqpZpOehLGFYxdptNdrJZJNul+qiVRRlCx0s1m4n45ypvjHtqOhYO17Ga+PKXqcleW22YgpHCNEGGFLKASFEELgUZwHoo8DVwM3u8yPuJo8CPxNCfAuYj7OQdIuU0hJChNzFqS8AVwHfTdvmamAzcAWwoZBQn6rc9tRObt2wG02AR3Omz27dsBugop03eec/GDHoiyRyetPDcZOfvrCfh7cdwHRXtJ8yv4HPX7yU5XPq8+474NVprvER9FV+4eZUoVrtZLJRHtSpR7nPmeobikozHhtX9jrzqGQ2mHnAM0KIPwAvAk9KKX+JI9L/SAixC/gj93+klK8DDwFvAE8An3PDaAA+A9yFs+h0D/C423430CqE2A38P9zMMtONu57b63ZaDU1o7rPTXkkSps3BgSi94fgooW7Zkl/94TBX3bOFB17sxLQls+v9/P1lJ3HrujPyCvWAV2deY5D5TUEl1MdJtdqJQlFpVN9QTCWUvc48KuZZl1L+ATgzR3svcEmebb4BfCNH+1ZgVLy7lDIGfOiYD7bKCScsssO5ncIhlctAUsib/sqBAW7fsIfd3U4yoIBH48pVi/nwOQvx50lxqDzpx0412olCUQ2ovqEoFrsKJueVvc48pkygryI/tT5nQYqWFu1kS6d9simU6eXIYIw7NnXwm53dqbZLT5rNp9/RTlt97rj0oM8R6YEqyFM+1akmO1EoqgnVNxTFYlqSzr4ILbU+aiuUFljZ68yjkmEwihJx7QVLsKVTbtiWtvvstE8WySqkufKmRxMWdz+3l6t/tCUl1E+eV8/tHz2Tr7z3pJxCPejTmd8UZF5jUAn1ElENdqJQVCOqbyjGg2HZHB2KcXAgSqwCNTSUvc48lGd9GnD9pcvZ2zPMo384gmFJdE1w+WlzJ22hSdy06BlOjBLptpQ8+cZR7np2L73hBACz6nysv7CdS1bMzpkvvcbnoanGqwR6Gai0nSgU1YrqG4qJEDcsDg1EqfF5aK714vdMznVL2evMQ4n1acDGHV1s2z/I8a01qRyt2/YPsnFHV1kzKEgpGYwa9EeMUbHprx0c5PaNe3jzSAhwKoquO2cRH1m1aFSlNlAifTKolJ0oFNWO6huKYyGSMIkkTOr8HpprfXj18gYtKHudeSixPsXZuKOL6x94mXDCJODRaav3Ux/wEkmY3LGpo2wdN25adIfiJEw7o/3oUIw7N3XwzJsjcenvPLGN9Re2M6chMGo/SqSXh2TO3s7+CIvcnL13bOrAq4tUTvoan6fsdqJQFMKwJHHTmhSPZK4+kbR71TcUxXJkKMZbvWGOa60d9dpw3CScsGgIeGiq8aFr5SlRcMemDgzLonfYJGHZ+HSNhqBH2es0Ron1KczGHV1u1TMLjyYwbcmhgRjzm6DO7+FAfyTnNvkuWMXgxKYbDEQzvelRw+LBFzt58MVO4q6AXz6njs+/cymnLGgctZ9av4fGoBLp5SBpF15d0BT00hWKparjzc26YQp69Zx2MhM51r6hGD+2lBzsj47bIznec5WvT9yEk9+6sz9CU9CbsY3qG4pcDEYNPvnjraxub+HD5yzi9IWNGSGdyRnnUMykMeilMehF00RJx5ddXSEGIwaaJtDda39PKIFhhUr1NRVVhhLrU5ikN8jv0TAtiaYJbCTdoTi6JljYXJPx/rEuWGORy5tuS8mGHV3cuamDnmEnLr2l1se1FyzhXSvnoGXFpdf6HU/6ZMX2zUTyeQkTpk3UsDKqvUYNa5SdzESOtW8ojo2kR7I+4KEp6MVTQLRP5FyN5Tlf1FxDVyim+oZiTJI3lM939PF8Rx/LZtfx4XMWctHytgy7taWkP5JgKGbw2oFBbn5iBz6PVpLxJWHaIEhdX4UAW8hRM92K6YPKBjOF6eyPEPTqzKrzYyOxbQlIYqaFYUmuu7A94/3pFywhnGevLrhjU0fBz5FS0hdOcGggljEYbD88xPX3v8w/P7aDnuEEXl3wsfMW85NPrWLtKXMzhHqNz8P8piBzGgJKqJeZpF2kE/Tq+HSBYUkiCRMpnedcdjITmWjfUJQOKSVDUYPO/ih94YQ7no1mIucqX59Ies6vu7Bd9Q1FUSyZVcs/Xr6SU+Y3ALCra5hvPLaDP7t7Cw++2Mlw3Mx4v2VLfvjsXoQAv0cvyfji1Z1rq21LpJSpvuLTyxN2o6g8yrM+xUifShuKGpiWTVu9E9rQMxwnbkpqfR5uunzlqDv2iUz1xgzHm25YIyK9OxTnruf28uQbR1NtFy6bxXUXtTOvMZi5f5UnfdLJ5yVcNqchFbt+oD/Cwhka6pFrOlqFQVSGrqEY+/siLG4Z8WAn08CGYkYqjCA9zKDYc1VorIRMz/maFbO5CWZ831CMjQDesWwW71g2izcODfHQtk6e29VDVyjOHZs6+Mnzb3HZqfP4k7MWpNZpHR6K0hDwYFo2li3waOKYxpflcxrYfniQoZiJLZ2CSA0BD8vmNJTwmyqqiaLFuhDiOGCZlPIpIUQQ8EgpVYBUGckWFee3t/DwSwdT07+WbdMVSqZE9ONxPae5hDrkF3G5pnqT3vTBqJFqixsWD207wP0v7CfmethPaKvl8+9cyumLmjK2D/p0moKq4mgluO7C9lSMejJTQNJLuGbF7EkVINUWB54vhKLOLTKiwiAml4GowSd+9CKr21u44qyFnLm4KSXMLXtkDGoK+mgIOp70Ysax7POcPVam94kkpegb1WbvivJy8vwGvuC/X+gAACAASURBVDZ/JYcGovzXSwd5/NXDRBIWP992gP966QBrTpzNh89ZyLyGIL3hOEGvjpQytbB6fpZzq1jOb29hy74+dE3gFU5BpFDc4vz2ltR7lC1OL4oS60KITwPrgRbgBGAh8APgkvId2swml6i4feMeWmq9NAadu/VZdc5zOG4xGDXG9AalizjTsjkaimNYEp+uZaR8yvamSyn5zc5u7tjUwdGhOADNNV4+9fYlrD1lbsaKd5XdpfJUi5ewGuPA88UuCyEYjDpFvSzbyVtc5/fw95edXJHjnClkx/+2z6rlT89eyCUrZuNz66lbtqQ3HGcwatBY42X9O5bwD//7Rs6b0SS5smU0BjxEEsWNlROhGu1dMTnMbwryFxcv5erzj+OXfzjML14+SG84wYYdXWzY0cWSWbUMx50Qq6BPJ2bYmLbkg2cu4OhQjOYaX8rei2FzRx+z630MRTOzwWzu6ON6HFv80sOvEIqZmLZNTyjOlx5+hW9ecbqyxSlKsZ71zwGrgBcApJS7hBDqjJeRXKLCsiWDESMl0gFaa/14NINnb7g4777S77Dr/R5C0QSHQ3G8msbCpgAJy+arj77OP0rJqYuaGErzpu88GuL2Z3bz6sEhADya4E/PWsCfrT4uo9SyWjhaXUy2Bz0X1ZgOL18IxZGhGAnDImE6lQAtW5LQJr8y4UxjSWstX7x8JQ9vO8CrBwfp6AnzzV+/yV3PdnD56fO5/Iz5NNf4AKdaY+9wnKVz6vny2hXc9/xbGTejAFfe+Tyd/RGODsawpcSja6lsGYmYSWONt+BYeSxUo70rSo+mCTQhsOXoNRUNQS8fPW8xV5y9kGfe7OLnWw/Q0RNmb08YgEjCwu/ROL61lo+uWsyq9hbCcZNw3KQ+4KW5ZmRxdSHPeGd/hNZaf4YWkFKmwmpueWIH/REDXRN4dA0poT9icMsTO5QtTlGKFetxKWVCpFYeCw+Qe/WPoiTkEhV+j0bMzBQQhwaiDMZMTvjKY9T6dK69YElGFbNsb0/UsOgOG7TV+TPiN4fjBrdt2M2/f/h0APrCCe56di+/fv1I6kS/fWkrf37hCSxoHpm6UyK9OrntqZ3c9dxewgkrp11MBtUYB54vhCISt0hYNl5NQwiQEiKGzc2Pb1cXt3IiRuJ/dx4N8fOtB9i4s5v+iMG9m9/iZ1v2c+lJc/jTsxbQ3lYHOKXeT5xXzzc/dBottT5qfJ5R49yhgSi2BF06GTNyZcsodR+pRntXlB6PJljcUkM4YTIcN4kmRt/U+zwa7145l3edPIetb/Xz860H2PpWP3HTJm7a7O+LsP3IEMvn1tHk3oyGYgbDcZOGgIdX9g/wtV++kXeWZqxQsI6eMFpWthhD2mw/EsqrFRTVTbHzLr8RQnwFCAoh/gj4OfC/5TssxaLmGqJG5iBQH/Dg0bRUxoKD/RH63OqhHs3prLdu2M1tT+1MbZMrc4Jp24Rizop1KSWmZePRBIcHoyRMm/u37Ofjd2/hCVeoL5lVy79dcRpff/8pKaFe6/ewoFlld6lGbntqJ7du2E3UsPLaxWSQy4YrHQeeL+tHwrKci5smEEK43jPY26uE1mSxfE49f3vZSfzs2vO4ctUi6gMeDEvy+GtHuPa+bXzp56/wfEdvyqOZMG2ODMY4NBDlexv3ZIxzSQzLzpktoxx9pBrtXVEeNE1QH/AyrzHI4pYaWmpzh7EIITj3+Bb+9YrTuOuqs3n3yjl4NMFA1LkZXffDF/jWkzvZ3+eMM8kc7bc9sxtNODd7ubLHjDd7kWE5M4ZARa8JiolTrFi/EegGXgWuAx4D/u5YPlgIsUgI8YwQYrsQ4nUhxBfc9hYhxJNCiF3uc3PaNl8WQuwWQrwphHh3WvvZQohX3dduE+5oLYTwCyEedNtfEEIcfyzHPJnk6ow+j87n1pzA7PoAg1GDwZiJ5qaD0oSGR9PQBNz13N7UfnKlLPPrGnHTxradhS6WLYkmLAJenU/++EV++OxeooZThe0vL13GnR8/m7OOc06DEunVz13P7UUTuPaQ2y4mg2pMh7dmxWxuunxlqg/Nrg9w0+Ur0TWVxbZaaKv38+l3tPPA+tX85aXLWOQ6CLbtH+Arv3iNT/7oRR595RAxVxjHDIu3+sJ4tJHQhKBXJ5nFzpISjy5orfWlsmWUo49Uo70ryo9H12iq8bGwuYYFzUEag148OcaT9rY6bli7gp99+jw+6t6MJkybX/7hMJ/40Yv87S9e45XOAaSUHB6M4tUFCcvGctMzps/S5BvHkrOAS1prsOVIakczLQ1qJa8JiolTbBhMELhHSvlDACGE7rYdi9vJBP5aSvmSEKIe2CaEeBL4BPC0lPJmIcSNODcKNwghTgbWASuB+cBTQojlUkoL+D7OAtjncW4k1gKPA9cA/VLKpUKIdcAtwEeO4ZgnjUKLBK9333PCVx5L5VtNogkIp03L5Zouawh66AsbDMUMAl6NoahJf8Qg4S4o1TXBB8+cz1Wrj6cu4GxX7nAXtXK9dIQTjrcwnWy7mAwma6HreG0nVzx/+6xadnUNI6RMhcHYEpa1jS4prigdPo9GS62PUMzMSA8LjuC+/PT5vO+0eWzZ28fD2w7w0v4BOvujfOepXXx3w278Ho0lrbXU+pzFo0Gv4/WcVefj4EAMv0ewtK1u1CLUcvSRalnYragcfo+Ov06ntQ4ibphMOG5lVPueVefn2ne087HzjuOJ14/w8LYDHB6Msbmjl80dvZw4p56AR6c/HCecsDEsG6+7gHRJa11qP4XWJd34npP44sOvMBw3sdKEujctGUQlrgmKiVOsWH8auBQYdv8PAv8HvG2iHyylPAwcdv8OCSG2AwuA9wNr3LfdC2wEbnDbH5BSxoG9QojdwCohxD6gQUq5GUAIcR/wARyx/n7ga+6+Hgb+QwghpMyxMqQKSe+MSUHyd4+8lhIktW66ubT+hy2hNi1dYnYav3DCRBMaH1u1mC37+unoGSZqjFwkV7e38OcXnZDKe1zr9/CHzgG+9eROOtxFMktaa7jxPSeV7CKksiiUlmLsYrIo5ULXXKIcyGs7QNEi/oa1K/jCgy8zFDWROLmUG4Iebli7oiTHrsiNAJpqfDTV+IibFsMxR9yY9siYpAnB6vZWVre3sqd7mB9s7GDb/n4sWxJJWLx+eAhdQI274D3g1bClUzgmati8dmgIXRNcftrc1PkvVx+phoXdiuqgxuehxufBrpU549uDPp0PnrmAy0+fz29393DPb/exvy/Cm0dHMmI7cedOGEt3KMH7Tm1gOG5S5/cUXHOxZsVs/u2K01M3jocGY064bEaF1cpcExQTo1ixHpBSJoU6UsphIUTJAvHc8JQzcbLNzHGFPFLKw2lZZxbgeM6THHDbDPfv7PbkNp3uvkwhxCDQCvRkff56HM88ixcvLtXXKhn5xOwlK9p49A9HMG0bzc21aku49oIlqW2T3p4f/GYP+/sizK4PcMXbF3BgIMq+3nBKqB/XUsNn1pzAqiVOntZkCsbn9/TyxYdfYTA6UpVtx9Fhrr//JW678qySXJhUFoWJkc9ur71gCbdu2F3QLqYa+VKRtdb6ctrOzY9vJ2LY47oBtO0Rr7oQ5K2gqTg28tntKK9kzCSSsDKybpzQVkd/OI4gM8OBJSEUM7FtiVcX+D1ahhPCsiWPvHKYJbN2cv2ly6dlH1GUj2PRCMn49vqAF8OyGY45wt1Im8kOeHRCMQOPBmlroFN26dGgMeBh21sDdA3F+M7zb3HP7/Y52V7cGPTvPL2Lh7Z2gluL4LoL27l//WpgZI2GsvepS7FiPSyEOEtK+RI4MeJAtBQHIISoA/4L+Esp5VD64qDst+ZokwXaC22T2SDlncCdAOecc86ErtDlDOPIJ2aPDCW4/LS5PPqHIxiWTHmPsld4n9feSntbHYZlsbmjlx/8poMD/c7pEzg5Yte/o51VS1pG5Um/5YkdGUI9yVDcKlkaKJVFYWJk2226Dc5vDNAzHCdhySm18j9fP8qXiqw/YrCgKUBH93Aq3/CsOh8H+uMsbA4WfQN48+PbiSQskrpQSifN2s2PbweK99ArxqaY8TbplZRSEk44Hveo4YQTvNUXyZuKrNC0vi3h+xv3cP2ly1N9odIZk4pBhQhWnlJoBHDqCjTX+miu9RFNWITiBpG4xZ3PdjAUNdA0gc8DCTPzI0wb+qMmAwcHuOIHv2MgYiAlmJZMiSAJHBiI4dGgJxTnCw++zLyGAMMJi0XNNVx+2lye3tFd9faeRNl9JsWK9b8Efi6EOOT+P48SxH4LIbw4Qv2nUsr/dpuPCiHmuV71eUCX234AWJS2+ULgkNu+MEd7+jYH3HSTjUDfsR53NscSxlGMQXb2R9AFKUGiAQjB3p4wXl2jrc6Xqsq3bf9gqsCRbUt6w07Z7r09Yb73zG627R9I7bfOrzO73o9hSW7/zW7a6v2865S5GZ+9s2uYfCTDYo6V8VRWVeRm++EhrvvPbTTXeFO20Ja16KgSjGfALdSPcqUik8JZIH1wIIYuBLoQmO7/AkYtrC50A7i7O4yVdn2UON7aXV3DKkSrggjhFKfaurcvNTtoFZBKAY+Wqq6ci6hpMxBJ0Bj0Zoj2YimFgChVn1D2N7UJ+nSCPh27diQ/elJ8p9Na66M/ksCWjhOhL2yM2lf6Nh5dc2qyRE2iiTDL59TTFYpxcCDKbevOLGhr47HtcoppZfejKSoFgpTyRWAF8Bngs8BJUsptx/LBbsaWu4HtUspvpb30KHC1+/fVwCNp7evcDC9LgGXAFjdkJiSEWO3u86qsbZL7ugLYUI549VzpEdPTLOUjObX/8v5+jgxGeXl/P196+BU27ujKeF+938PBgZizoltK4pZ0srlIiJs2PcMJQjEz43OjCYuDA1EO9EW49aldfPq+rSmhXu/3MLfBz/zGID6PTkPQS9Cr86Pf7Rt1jNYkhAKoLArHjmk7NtGdwxYqRXLA7QrFMgbcbPtOMtF+JKUkYdnETJuEm6rP69HGlUbPzGPnlhv7PN5jUpSOpB11D8dpqfUVfK8QoOednHXoCyfY3xdh0E17O97jSLfnLz38Cmu//RsuuGUDV975fF7bLrSPcvQJxdRB00TKOZDLGltqveia4z33aGMYN4AcuW4blizKbsZrl+N9/3hRdj+agp51IcTFUsoNQog/yXppmRCCNG/4RHg78HHgVSHE7922rwA3Aw8JIa4B9gMfApBSvi6EeAh4AyeTzOfcTDDg3ET8GGfh6+PuA5ybgZ+4i1H7cLLJlJyJhnHc8sQOeocTJP1Apm0TG05w3U+20tYQSN2tpi4oEowcTiPTlhwditEQ9BLwaLzVG6azL8wjrxzi3t+9xXDcCWNZ2Bzks2tO4DtP7XTSS+laylM5kbCTJa2l8XyrLAqlw7IlR1xbqHQo0XjXInT2R4gbJnt7wtjSWVjVWuslYdosaa1hd3cYYWdma0lFzQkQaUFxuiB1A5hdkj7fQtV8jMdDn0RN4ZaObDvy64J4Hve6aUt0XWCZ+UX4Z3/6Eh86eyEXLm9jIKrRFPTREMzMz54kfREfQENAZ2GzkyHIsiX9EYNQ3GRpW11R3r87NnVgWBa9w5ll4gv1CRUiOP0pJMGdmiiCWp/GcBHZWySQy/dgWjYv7e/ngls2sKi5hvPbW9jc0Udnf4ShqEGtX6cx6BRKHGusHq8djxdl96MZKwzmImAD8Mc5XpPAhMW6lPI58tvoJXm2+QbwjRztW4FTcrTHcMV+OZloGMfOrmFyTdjGLcmh/mhqEZ0tJc01HrqHR09/pbYxbWwpCcVNAh6da+/bliq0UOvXuer84/nAGfNpDPp45OVD9ITj+DwjP3/68aYLjULc+J6TUn8fqzhRWRRKR9wNA6h0KNG4B1zbzrBxW0L3sMHCRo0b33NKRioyXRM0+b2E4yZx007FbKaeheCmy1eOugEEci5ULUTUsMbVt/Mthv3mFacrG58A2XY0tzFIZ38klfFF1wQxw7EBPSW484v1HUdCfP1X26l9cicNQS+mbbOwqYZrL1jCe0+blxLttz21k+88vStD+PRHTIZjgyAEli0RgGWT8v6NtTB+V1eIwYgTm6xrAtOW9IQSGFYo5/tViODMwM4zw+PR4Lc3XsKFt2zg4GCsqH3F08LAJLD98CC6EBi2REo4MhilayjG8x29eDTnPaYN4biJ36NTH3D6WqGxerx2PF6U3Y+moFiXUv6DEEIDHpdSPjRJxzTlyE6PmJ3TNx8FQ0yE04mSi+qSF4Z8W0igdzhOf8RIdVZNwGWnzeOTbzue+U01qYWjn1lzQt7jTU5vJUwrVeV0LFR8WfURihmYluQTbzsOw61QW2DxdlkY74B7OJTI256diiwpvj9//7YMMTWyQNTOeQP4nu9soi/sxn8Clm1jhHN/bpJ8Hvp85FsMW6oF2TONbDtqCHqZbfqJJCwag14WNtfw6sF+4qZMZfIpRK1fJxy3CCechxAQTVh87X9fJxw3efcp82gIevjBpg5n9gac8di1LWd2c8To0i+iY3n/EqYNWWsvbCGd9hxM9NqimFromoaUNpKRbFTCbdc1wZGh4oR6LgRkzESZafZr2uD3aljSxpJwZDCWEuuFxurx2vF4UXY/mjEXmEopbSHE5wEl1vNQjjAOKZ2OIN0OUEiog9MhjwzFU/+fubiJz645gdMWNvGHzgG++eudGV7vXF7HNStmc+Wdz5MwLbqHE2Omrrv58e2sWTFbpV6sQpqCPtadu4hlc+rpdGdYvLqGx/VEejQNXQg0zUkdpgmBV3cuDKVivANuvpvXZHsu8R3PFRcGhOI2V975/Kh+uLtrGMsVYMLtVIUWLC5s9OftK/nItxi20IJsFTaTn+subOeLD7/CwYFoalalzu/JWCx35Z3Ps7dnmFDMTBV3y0c8ay2DlDActxiOW/zTY9vRdcE5x7UQSYYc5OgS6eNx+qeN5f1zcr9npgkF8OUJtFchgjODZJifR4iUXRiWjS4EF9yyoeAYNRZ5hkggORsp8AiBISUx01nzM9ZYPV47Hi/K7kdTbDaYJ4UQXwQeBFJXHCllyTOrTFUmEsbh0wWJPL0w3TtUTD9Nvmd+U4A/v/AE3rVyDs21Pjbv7uXrv9o+2ut9+UruX786s9jSphp2Hh1iOG6lPPn5bhI8GuztdUSgii+rHgTOwP+tj5w+6jXDsjHGCHnUNUe0e3UNX4a4d57H452fjAG30IUouWA7PfzETF5V0mLdccW73yOIpcU6BzyCf/rgaWUP0VIzU2MjAKSzmBgpRunn5I3h3EYPQa/Oa4eG8u5LFwIzz6g6FDP565//gaVtI5Ui861BTY6NhiWLEjgAy+c0ZNxU+HSN+oCXJbPq8m6jQgSnP6MqjkpnUGqs8dAU9KZSLY9Fc42XWbU+9vSEkXkWrObCo2vY0sIGBqPGmGP1ROx4vCi7z6RYsf4pnPP+2az2mTsnUQJm1/k4MBjP+ZruFmVJOhvH6nQ1Pp0/W30cHztvMXMaAqk86YW83kDGANEzHCdh2ClPUa4k9poAj6ZlHJGKL6s8AvB5BLU+D+svPGHC+7FsiWVbxPKoel1L88xrAq/uhHp4tNye+UoOuMkwsvTwE12AKUcLMCFwpn/dOHOPplEfKHZ4zCTfYtils3L3BzUzVZg7NnXQEPQytzGYausOxbj+gZdpCHpzzhYWQtM0sHLbd1KA7+7On7IWSI2vhmVhyeIEDoy+qVDT+wpwxsmrVh/nLGa2LEDQGNSZVRcY137q/R48uobfo5Ew7ZTzpWD9AdsGHEfMiW21PPFXF435OcqOJ5+iUjcCJwO3A68Avwe+C6ws10HNGDSNtjpvzpcMGwzbpsY79imq9+s8dN35/NWlyzmutTZ1IQHH650vm8XNj293iivYrrfJtEcteM2+SfB5NCc+TUL7LCcrgkq9WHk0IVjUVMNHVy1mbmOA7lCcSMLMu3Bpoli2E5YVSZiEYgZ94QRdQzEODUR5qzfM3p4wnX0RDruLmLpDcQYiCYZiBpGESdy08oa75PPZT3RiVRMCTWTWA1jaVodGpmNdA/yekVkEIdwbkgmmCrvxPSfRVONFaGBJidCgqcabsSA7nUJ9VDH69xmKGvSGE4QTZsZMBMD961fz7A0XF9xfdibr9MkiXcDtHz2Td57YRqGIsKjh3NBKCSfOruPZGy7m/vWrx7y5WrNiNjddvpLZ9QEGowazq6AWgqLybNzRxcMvHaSt3s9Jc+sByXDcYiiaP6lEklMXNHJ8aw0eTdA1HEfXBI1BDxJoDHqZ31RY8Ju2BCEJugkniklDqux48inWdXQvMATc5v5/pdv24XIc1FThWONMFzXXsK93mFqfTsKNT8su6DGW1KoPePi3K07jlAWNeT8jn9f7pf39Tmyte1UqZmmIaTlex+YaLzesXQGo+LJqwJKSPT1hbt+4B9iT8VrQq1PjFuCoST08qbbk68m2Gp+Hzr4Iz+7qoS8SZ059gCvOWsjbl83C5yl88yilU6horHAbzRXEmuYUM9LdXMM5KSCafJogMY5aANnTzcn457hh0RNKlCS7Qb7FsPn6Q3IcGIpmpkE7vrXwlLKU0i2UIlML0yTOIsuMv3GyTcjke6X7f9o2yGQJcpn2Xuf/492b8kqRPYb1DMex3FnH1w4NoQloCnqLnonIvllM3s/q7lj4juVtnLG4mc7eMN/buIfndvfkTIWXzI09HDdT6fCKGffKMduk1jxMbbJTIUrpjOmd/RH0wcLuilcPDqIJqPNpxC1nlmfJrHrOb/fx9I5u9nQXLl5oSfAJgRSCqGHRGPAUFYpXajtWNlyYYsX6iVLK9CDYZ4QQhfOdTXNKEWd6fnsLL+ztTV0IDFeuaDgebCkhHC+sej59wRLefcq8vK8XWuR33X9m1rUqpHka/Drzm4KEE1ZO8aHiy6qXqGE5BYImWHC2L2zw9ce2A05Rjmyxn3r2egreFNS470nfRktza+ZdRC1hf28kYzFsUuwLkd9oLcvGkpKlbTWYlo0mBBed2JYx3RzUtdT/lpRYVmZGkULZDaSUqRj+pAhOHs07lrdxwbJZbtYZ57WYYbliOE1g23DGwkZe2NvrLkqFuGlxdMjifafO59BA1BHbdrbILu2MSTWTawxLjlUCt6pjxOD1Q4NF7e/EOfVsPzJE9jpUS8K8ej8NAS8NAS+z6nxccdZCBiIGe3vDDMdyR7p3DcVY5laJLOYaUGpRotY8TH2yUyHacuSm0lvE1KItYShus7DRz7M3XJyyibZ6P4tbanjzyFDO9T1eXWBYkqj7YsKM01IraarxIqVV8Aa4lHasbHhsihXrLwshVkspnwcQQpwH/LZ8h1X9lCLO9PHXjjiL29JWU4Pj4Y6bdlGLQ37y/FuctrCp4N1vPq93+6xath8J5Y3fTOe2K89SnaaKcarbgW07xa9u+sApRBMWkYRJJGGlHtGEScRI/p1sNzP+73Y9l7kwbclQzGSoyLSeYxHwailBnw9dE9z/4n5qvDq1/swbg4I1QjRo8Hn55NvbUzUHtnT08aPf7XNi8qUkZlj8+Hf7iBpmzhSQlmWxNy2MpliRvKWjjwde7OTwUJR5DUHWnbuIVe0tOd+7uaOPlhov4YSFYdl4dY1an86L+/q58rzFRX3edCZ7DEsJ9XQRI8mIy/VoyRR1oxmMGvh1nYhtZeTnF0Cdf+SS6CzOf4OhqIFpjwTPZN9UJmw4PBSjtcaHR2NMgVNqUaLWPEx9slMhSjlivGNNHCazWkmgL+qMy9k20VTjo3t4dIraq1cvpn12PV979HUSllMJ+shQjO7hOE1BL5H4cIZTIkmp7VjZ8NgUK9bPA64SQux3/18MbBdCvApIKeVpZTm6KmaiGVDS70YPDkRTCzZN284UC0UeRzH5m/N5vZuC+U9/emkRTai726mArmkIbA4PxVjcMvHFvVf+8HkaAp6MUAhLSoZjJn+zdkVO0R9OmGni3xq5UTCcv6MJK6dNxwybmJGgr4DX37Ql39+4J/8b8m1nSWq8Oo+9fpiNO7up8els2NHFYNRw8hgLZ6p5IGLkDwET2ri92Fs6+rh1wy48mqAh4KE3HOfWDbv4AstyCvbDQ1Gaa3201I5cECWSI0PFZYCYCaSPYUtu/FUqhCed9FSz+bI3CuDZGy7mgls2UGuY9IaN1ExKa603Q/Cn58v3egS24ebBZvTNQF/YoC9sEPRqTjEsy8ajjw4ZK4coUdm4pj7ZqRCTpqwJWDG3gdcPDeYX7a79esRIQaRsmxjIE/v+6ze6ePo9J/PfLx3kQH+EcNwi5IYI9rr1J1Z+9Qna2+r4q0uXc8nJc4DS27Gy4bEpVqyvLetRTEEmkgEl+270QH8UC2e6fqJkL6AbD1veGij4uhBOSE6tf2JZMRRTk3kNQXrDcWdRnwAdgWlYLG6pZdWS3N7hfCQ9zIcGI8ypD/LHp8/jxLn1mR59V/R/56ldeffTPqs2tU0ylGssbAkHB2M5K//JpOoag0jC4qp7thBMevW9nlSIz0g4j4eatHUB9zy3D9uW6B4Ny3YWr0rD4oEXO3OK9XkNQQ4OhBmOj3jW6/w6C5oqGyterdQHPISyQlKE256kgK4BoM6nc3ggmlZEyxHcy2b7U+91xlXppmbM3KdHdxws2UQNm6iR4EM/2MyfrV7Me0+dRzDtGlEOUaKycU19slMhaiK5hsSJSS9EMqGEadvUun9n20S+8fJAfxSfR+Pz71zK3z/yGrPqdNrqfRwejKVCYyKGzWuHhvjsz17iMxedwGffubTkdqxseGyKUmFSyrfKfSBTjfEUfEl601/a34/AWaF9sMi8qWORsCQ5HDhFkQx1SM5wpXuqfB6RGiyuvWDJMR6lYjIwTRsL8AjJ/3vwlYKhF4VYd+4ibt2wi6hhEfBqxAwb05asO3fRuPazUfG0RAAAIABJREFUpaOPv/+fVzFcu+oKJdhxeJCvf+BUzjuhdZTHupBYv+vqczL+NyybSMLip5vf4r9ePogmMosbJYseSemIuFVLnCI3m/f0pt5TjL9cQtE5jsfi4ECMdXc+Pyqefzhu0D1spI45btrEDJtTF3h56a1+gj6dWl/+WP+ZxiUr2vjF7w9ntEm3vVjCCWukOJbbZmWF0thS4kYnjIp9iWUFAPt0x5uftMGXOwd4uXOAf/zfN2gIejFtmyWtddT5nGtFKUWJqvY49clOhbivZ5jhRHFOPFvao67V2TaRDwmc8JXHqPXpzG/0s6s7ktIFQa+gzu+lP+KEgcVNm+88vYvvPL0LAYSicRa31qf2dSx2rGx4bJTLdAIkxXc4bmBYEp9HY9ns+pwLLNK96baU2LbkaCh3bvVcjFW5FBzRfdtTO7n+0uXj+h66JjJik9Nj500ban06116wZNz7VUw+EkjKDFvCG4cH+ddfh/m7y07m7ctmObMkQqTSGQq3Up4jEDOF3/vOmE9jjZcf/XYfBwciLGiq4ZoLjueiEzNt+9md3dz13F4OuAuM/vyiE7joxBHB9P7/eDYl1JMYEr7x2Ou8+o/vGdf3W+SG9SQzlST//toHTmFOo5/7Nr9FyF2MrQunWquzMNMmalh85b1O2sRrf/wib/VF0NIqBVq2zBsG49cFn3nn0pHQn/RYf8MiEh8J9Ul6/vN5sSTQVaDvZ4d2bHizmw1vdud8b3qsf3q2n9pRC38zvf6ZWX+cv736+IpdVZojQwlqfRrhNDFT69M4MjQ6JjcfXaG4s8ZDjpR310Xm+fFozuI7CWMOwhKn5oDtVpsO+HQiCStjfUfXUJwan4bf44inUomSiWTjUpk3qovscxg1ZVHXfsh9rc7eXyGstHVIyeJwcVMSNST1flg2u44D/ZHU+Ip7XIMxm11HhziupRbTzQI2UTsuRUa56W7TSqyPk3TxPa8xmBpsz29vGakEmmYoyZRMXUNGUVP32RSzhSbgruf2jltUX37aXH7x+8OjYj8/eMY8vr3urHHtS1FZklLL6+YKl9KpxvjDZzv4k7MXjnt/Qa+ekXPc79Ez8vdv3NGVqozbXOOjezieqoybHCBD8dwSOF97IbwFpo++tPYkvrT2JE78u8eRUqJrznsFIIXzHZbMqsWW8LeXnczf/Fdm6sb6oJdI3CRuylGhFR5d4/LT54/rWDfv7uVf/28HkbiJZYOmOVPVl5++gAVNgVELfJ947XDG2JB9DBON9S8W3c3wM5LC0xX57t93fPycsXcyiezqCpEwJX635oOUkDAlu7pG0mzm+93Sb0k0ITLsysoKa6nx6c5Cf1ncOJyw7LQsQqNXPtsShuM2pgVtdRoDkQSLWmpHiYqJiI7xZONSmTeqk/Rz2P7lX40UJcxKQJGOAPb883uL2l++mPd0J52zRk1DE07Gpa7hBP3R/NolZkre7HIKiF26YlaGs2a8HEtGuZlg0xUV60KIe4D3AV1SylPcthbgQeB4YB/wYSllv/val4FrcJyI10spf+22nw38GAgCjwFfkFJKIYQfuA84G+gFPiKl3Hcsx5xrYUV3KMbtG/ewsDk4ylB2dYXoHU4UvWA0m2TsWiEEFKxQlg9HkL/Eo384khIul582Vwn1KYpHc7wkpiVdT/rE1jMUM/CVY6Fc0Kul4iSz25PHlUvE3PbUTu56bm9qcZVmWanUp8nKoUIIdAGXnDyHf9fOGOXB+Yv7XyKWJbCci6Wkrd6PYUlMyyZh2W6V1/ydMpleUmgCDYnmVhE8bUFjzrCkp7cfxZZW6iYDHOHo9+g88vm3EzfsUfH9ObP8ZHn9owmTcGJsr79lS0Ixk1CJMvyUm+zMGUKALWRGms0T59Sx4+joKqQnznFy1xdTZXb5nAZePdBfVDhC9u9aaMyOmTY7joZYOb+By06bx+mLGlMZNyZDdKjMG9WPSBrlGBNeSXsei+Wz63jz6HBOHZJ9IxDNKpJRrJPxqR09/NG3fsM//8mpnHNcS6p+y2QwE2y60p71HwP/gSOok9wIPC2lvFkIcaP7/w1CiJOBdTiVU+cDTwkhlkspLeD7wHrgeRyxvhZ4HEfY90splwoh1gG3AB85lgNOX1gxFDXoGY4TcTNdmJZE+ESGoUTiubNgFIstxw6FsaQzXT8Rvr3uLL69bkKbKqqM9PhaKcGUFMxDno87NnWQMEcKdPh0jfqAJ2PgK8dCuWxPZjKGu9an5xUxZ//+AI+8cjhDHNk4gijg1Wjyj64cmsuDE81zsxszbOoDo6sMSykx3Rsjw7YxTCeu37QlD27tpM7voa1uZLFitMACU68uiJsjmSCSF0+f7oj+oBvC0lrUr1gY0431HwndGRH54TTRH0lY7OsJ8+bREMff+KuOfTdfVjXBo9mZM9J/ryT5il8lbSHX69m2MrfBx+Yi44YnwuuHhvi7/3mN727YxZ+e9f/bO/P4OIor8X/fnBppJFmy5UuysQ0Gc9rYDpiEKwQChgRCYgjkItkQs/w25252Q7I/NuTaTTb7S0KyuySEZMkJCw4GAiGE4JgrXD7BBgPGxrbkQ7J1SyPNVb8/umc0Gs2MZqS5ZL3v59NST013V1XPq+rXVa/ea+Lq5XO47Yk3C650pGq74UiUTXs7cgrupBQOn1voHTQpR9Rjs6eJ8jwayfI+lln+dCSa0+5s6+PqnzxHtdfFR86cy+cvOh5vBpv5fLGvox+nwK623vgza5rfc1R5kympsm6MeVJE5iUlXwGcb+//AlgPfMlOv9sYMwjsFpGdwBki8hZQY4x5FkBEfgm8D0tZvwK4xb7WGuA/RUTMOCKKxFYthyOG/V2BYVOke9v7afB7mFHriysuA6OFcsyCE2b42XOkj0A4fbHDGYK3KEc/8UXC8T8WrjGMbrx+qJvugTAOrOii4Yjlxisc6Y4fU4jV+8keEWIvCfOn+UdE+ItF+UxW1GMIcPqcuqyVjnRNK126iGWj7HaCj+EPo7beQZwCzR398bLWV3lo7RnA43IQjlhRQmPMm+qnuaMvyc+6i6a6/HuDcTkd1Pgc1PhGvoAk8sKudh7dfpC+wTBAY94LMg5Sy4mb+dOGRhlHiyKbTZTZx3ekXi+Qipj9ebYsnF7F3vYAg+Eoh7oH+e/1b3Lb+jcRgQa/hwqXMz4ymW8XdslttzsQoqVzAJfj6DUhmGic2ljHqwe66B6w4j84BCpcDlxOB7U+N011lWlNb1ORLO/78rRoHkZGBAboGQxz25O7WLtlP9efM58PLG2irsqTtzyT8Xuc7Gzrs6Jh28+sls4Bjms4ejxqlXpkPRUzjDEHAIwxB0QkJn2NWCPnMZrttJC9n5weO2effa2wiHQBU4HDiRmKyGqskXnmzk0fhGT9jlY6+4O8daTfmrZkuAcKg2XjVel14XQIHqek99+cAzetPJGbH9iWsYGN/5VAmWgkyq27NnUnPRZlPTbqElMWRKxRzGCCsBdi9f4N5y7gxl9viL+UhiIRotEoN5y7gC+u2Toswl84ajjcE0xrbpDpbTxmNtMXjMQXZuWTaq+L1w/1WG3fQMBEOdA1wPEzquMvM9bolmVS86lz5vOvj+ygyuvC63IyELbuZa7ed/LJ7U/tojsQislAXu1jsu1v05HsOSOd7I1mAzva97kE/sp1UCZq4NhpVRzoHqCj3/KBHVtgfKgnSNdAmAa/lyqvk2A4mlcXdslt91CP5dp0Zm0FIkMzwzc/sI2uu0PD2slkdTYwXpnNldhvNNXvHSbjsTVBYzGXSpT3Y7/yh4ymfPniYPcA33z4VW5b/yYfWNrEh8+cS2OdL2UMgvEQXyCf4N4pGjG83tob93Yz0eW3HJX1dKTSOkyG9EznDE8w5nbgdoDly5enlODExtE0pYI97ekV573t/fi9rrzYgMZmdlt7BvE4ZZjCpExuEuW2cvbxJqaXx7xbwEhPL9ngcTkIBCNETYKZgbHSY+Rj9X4yD2xpHjF7FAgbHtjSnNZOOROb93bwj2u28t1Vi+Pl+uGfX+fWdTvtYGTWbMCt63aOucyp6AkEh73EY6yX6Z7AkLcSp0NwOqwR+csWz6bK6xp2Lz91znzOXthAOBolFLFs5MPRaHxNQmgcsRmyYV9HP474/c5vn5NNf5uJQsjeeMm1Ege7BuLrH1IxEIqyryNgz944EGN4+7cf55gUC1JzJfn+GQONUyqGmXv1BEK09gZxO2VEO5nICs9YGa/M5spoMj5eG+3o2I0LcsYhcKQvyO1P7eKXz73FylNm8dEVx9DWPcCdz+7Ji/eWnsEwjVMqONwbJGj3jbHBEq/z6JDfclTWD4nILHtUfRbQaqc3A4lDTU3Afju9KUV64jnNIuICaoH20QoQG3lLDLzhdAgVLuu1LTFoQSqiJrdRmUzEwlcrSiZE7IWNDLkkjGKG2fEmk2qE+bMXHs/C6dW83NJJb4KrLr/XycLp1cPOH8/q/VQ8sPVA2vS6SndKO+VMDIajhPqCwyL83vH07njUYLAeJOFoNK8zU219IRwwbFbNYaenI9299JB6BMrYrtLC0SihsG03H7H2UwXrOdrIt+wVm9g6p9EIRQyhSIQ3j1iDQ/s7B9jw1hF+/JHl8WiSqRjNo0zi/bv29ud47WA3+zoCcZOL2LMt2bZ5LF7HlLGRScbHu2bIKelN/PLN3atXcO+GZh7cup+BUJS1m1tYa8fGiMlZc0eAjXvacTkcDEaiOY+Ex0y7FjRYpnDb91uBpKwBB0e8n08nv8nPwpNnVbP9QE9ZzSqVo7L+IHAd8G37/wMJ6b8Vke9hLTBdCLxgjImISI+IrACeBz4G/CjpWs8Cq4B1o9mrt3YPcOu6nRgz3I1bJGroCxocgMdt2Z0Wg8EIbN7bzvypVexsS+/ZY+J4SVYKgdflZGqVJ6MdbyKZRphn1nh4dtdw9bV3MMLMmsLZHELml990dsqHugdHXXy9s3XIK0hfMIIrSf/Nt9OCqO23PTGYTtROT8cX7s7NK5OI4HGJpcwn/SzRqKW8hyNDC2Bjo/HZjsjPmeJjT3v/6K6ojmJymVPIdf4hpVvJFMHpUhGKwvW/3MCM2grm1lVy43kLeOeJQ4p7riYSVnsfepHM9JPnaxBKGR/jXTM0s6aC5s6RkZ0LwfJj6mntHuTJN9oYCEXoG4wQjpoRchaMGIIR67nTPRDmB49bQfKyUZKTTbti13YmzCw7JLXXvORnYe9gmGd3dwCWz/lyGZXPr+FQjojIXViK9Aki0iwin8RS0i8SkTeAi+zPGGO2A/cArwB/BP7O9gQDcCNwB7ATeBNrcSnAz4Cp9mLUv8fyLJORw73BzKPmwGAomueJ4cwMhA03rTyRKZXpF4UlhtpWJh8N1V48Liczays4YUY1M2sr8LicaW3I73h6N2CZVwTDMVeEhjue3s3jO9rsEQlLCYnt57LgLt/ccO6ClPXL2A7tfjqcoP1UeZwj2na+9dFhPuFNmvQEvnD3JtZuORA3iYhEDWu3HOALd28aU/4O2y9+lddFbaWbaX4vM2srmFNfyfxpVTTVVTKztoKpfi81PrcdGMkxzGRq9bnHUuNzI1aRJ2XnMtVf2JfTZIzJbsYILLHqDoTYfbiXL699mQc2t8Tt5hNNJGI26G6npJ2hTdXeS8X6Ha1ce/tznP2ddVx7+3Os39E6+kmTkBvOXUAoYugPhjHGxN2yZrtmyO91ZT3AN15xcDiE3zy/146S6mP+tOxeKKIGfvxEdlYF5y+aztcvP5np1RV0BUK2+1yG2cZHjdX/J5M42+oQx7DngUMcdnrsmZmaYshtSZV1Y8y1xphZxhi3MabJGPMzY8wRY8y7jDEL7f/tCcd/yxhzrDHmBGPMIwnpG4wxp9jffTo2em6MGTDGXGWMOc4Yc4YxZtRfPmLMqJ1VvhV1ARaN4i81tpq7pmJ4I3MI1Fe6OXl2bZ5LpUwkqitcwzqr6dUVwwIUJdNrB+wx9qoPK5Knld4XjOB2DgVC8rqcuJ0yJl/++SK5M47VLxMmProylHb92fMJRwyBUCS+5XuWzOdxEHtGDJnRQaUndXebyfwn31gj8lb001rfcEV+3tRK5tRbivzK02bxtctP5hSrX2nJe0EmABVuJ7VFHATJVSnqD0Zo6w1ypC/If/zpNd463Me+9n72HOmjImn6KJOJRKr2nq8y5kJsRqC1Z2DYjECuik9McZp308NHrf1ouv4wW9Oww33BuGvc0chH77ivo59KjwuX05FRvpLpD0XoHghlnJWMcf6i6dy1egVPfekCPnfBcRjDiH4+lTOBvmBkVJ0v3ag85Edus5HZSTlikgmnSNFnfk+Y4eePXzgv7Qptpy1J5y+azg+vOT0+xZkvLxzK0UEudrwjgm7Y8/giQpXHkqvEDizdqEQxydVOOeYY4LiG4S/CyS0s3809kwvKVGQy/ykmQ+4oLUXv8iWNXL6kEWBSdi5z6ippdQ0wd6rl/u3llq60x+bjp/LYYd6zJRZELBQx7OsIcM3tz3H5ktnUVXroHghR5XXhEGuEMZOJRKr2nr6MhRvfy0dgm0QTILJYnzaRGc+6jWA4itMp8bU7mdyOjuaWNBsTsESznVydHry4u51jG/xUep3UVLiHRdLORLb9fDbyn+n5N165zVZmSzqyXo5M83uK9pB0O+CYel/8je3y02amPC4xfbxv1IoCVtANGJp6j41CV7qF68+eT9RYC3KiJmr/J+8uDguN1+2g3u8ZFjjkjqd3x190Y5t7jAHF0pHOZEdfqCcWyaYGmUiWoeSR7WSGvDVZ/60FcLmVb05dJXPrfFTaSkRnIMQvn93DjkM9HO4N0tEfJBSxRiY7+4Mc6R1IOU2farYpHdECLl7e19GPL0kRy9XHfLLipKQmJq/RqMEYk1YRdMCosu9xjd5/5tKWkvnkLzbw3h89zU/W76Klo5997f10BUJ8/q6NHPuVPzDvpoc59it/4Nqf/DVuihKzQU/u51OZsiQ/74YPUo3+/Buv3GYrsyrRSbT3h1hS7WF/92DBlHaBePCk/mCY6dUVAPaCstEXmk10TwhK6UkVdKOmwsWJs2rji2hSeYrJF6m8VeT7nFRBkYqxwLQcXQsquZP8O9ZUuBgMhRlM0mWrPE4WNPjZ1dYbV3QjxlDlcRKOROLHOx3CGcdMYWdbHz0DYcLRKC6HNesScy8676aHsy6f0zYyr/W5+fwFC9nT0c/vt+6neyBMIGop3R19QeqrPESihkAoSqXbyYGuADc/sI1vcArnL5rO7sOpw9CnopCx97JZNDmal5tUXlKUkSTP/vmSZBWswcSIsaJBJxMbTRcyy0TMiuz8RdNZ1dwZf6bkSs9gmJ89s5uHXz7Ax99+DC/sPsK614bC5USihmd3d1DlcTB/mp9mOyZNOBKN262nM2VJft75va6cvMGMZbFvohy39Qwys8ab9tgYqqwn4XIIXo+LusooJmroGgiTD5NWh1g2kE6B6TUV+NzOlItCvn/NUr5/zfjzU5RMpAu6EZPFz154fEFXvqfyVpHs7jCG25Hew0W62AM+t4O7Vq8YkZ7OxCff5PJCfeLMal492JMyXSktib9jogz63E4O9w7S1hukxufCGEN1hYuBcJTp1R6mVnlHBLKJEXtQp3qRa6r10tw1mFXZegfDNE7x8ZEVx3BqUy2RqOHDZ87lT68c4r5NLext7ycYMRzsHsTlEDxOJ16XA4/TQSAa4UfrdvK2+fXxtRGJ1gmxwc/kNEcBV5+OFmgtGy83qRQnZSTpAovFZDXmIcXpALdAOGKIGEs/irkJ9ntd/If9knnt7c+xdV8H/aGhHrzS7WDxnDrA+u3WbGqhodrLXLeTbfu70xVtBLNrK+joDxIIRTnYPcC3//ha2mP7glHENv2KGuulOSYJmUxZxvO8yzVAYLIcH+4dpKVzYFTzIJXoFLgdlrVr9+D4FXW3U/jMO4+LC0KmjlpRikUxRn8rXMJAGhvcVDZ+ksYNk8/jSmsXGLUXhBszNNIjkr5Tvv7s+dy6bifh6FCshFJ7J/zSJYv4xzVbR4y2fumSRaUtmDKM5DYzf5qfD51Rz7O72lN+TtemMr3IVfs8OLoGMVnEEnjqSxfE96NRQ1cgRFcgxOWLZ/Oe02bxwu52frexmY17OwlHDa29gxzuG6TW52aKz0VLZz+Hugfi8p8YTC1OUhkKuW4lH4GAEhUnJT2j3etnd7XT4B9yBVxhm5FEjTWTk3x87L6nW0uX/Ns5HZJ1BFW/14Xf6yIQCtPWYwU9ynRqKBJlapWHtt6g3b9H4/18IUw5c32WJt+LGdUVtHQGONiV2ZWmKutJRKKGPe2B+HSm02EtuMvFv6wATXW+lD+amrAo5UKhZfH0ufW81NxBX3BotKXK4yAcJaWNnzEGtzN1YKd0QUCcDgfTKt1ZL+ZMZ+LzvT+/kefaZ8/5i6bz3VWL9SV+ApCqzXw26Zjkz7nQMxhmTr0vHokxOShRDGfSCLfDIdRVeajxueNK+4oFU1mxYCo3/noTzR399AcjRA109Ifo6A9RV+nmtYM9w1wVJ78giP2dQ6DO5+aEmTXjqN3ojDcQUKLi9GZbX30hyzrRGe1eT/N7abBNdMGyXe8KhIa9JCZeK5PCmvzbVbgcDIQseXTZ9vOpZH1GtRe300E4ag3FLJpZwz+tPIEP/fT5tPWKRg11lR6C4Sg9g5b1gt/rKmhgo1yepcn3osbnBqwZMCCtzKqynkQwEo0r6tUVLv7rQ6fTNxDh5ge30dkfik+fp5p6j7FoZjWPfP7cYhRXUcqW+FRr0mhLpdtBIBQZYePn97qo9DjTBnZKZRc4f2ol/aHoiOncTPbsqaY8n33zcDwQRiJnza8b723ICn2JV2BkJMbXD/UwmGQU7ACOn576ZdTpEOqrPNRUuOgKhOgeCPOJt8/j1nVvUOtzMxCO0NkfiivtN/5mE1N8LjoDIwejvE6YNaWSKo+TgXC05F7HsrUNTmhLuqJ7jIzFDjtTH5Z8vYZqL80dAbwu4bgGP4FQhMO9gwTDUYIRa4DG43Lgr3AhdvRRA3z4zLnMrKngwkUN/DlN3I+9Hf1UeVz4PE6+vPJEzlhQj9vpoKbCPcyGvVSkurcup4Olc+u4a/WKtDKr3mBS4HIIdZVuvnH5ybzjuAZWnjaL/1i1mOMaqhARRIQ5db6UN6/Wp9PXigLpPRfdtPLElAE9rj97flovKumCgNy08sS8eEe664a3j1DMz5pfx103vD2ft0RRMpIs53WVbhwCHqfgclgjklP9nlGfMS6ng6l+L3PrK7no5Bl87l0LmV5dgcfp4JTZtaxa2siCaZZLymRFXYALFzXwtctPZYrPw5G+ILU+N1+6+ATOOb6hUFUflfEGAlKyJ9/3Ovl6TlvHmldfGe+3b/3g6bz6jZW8+a+X8uo3VnLrB08f1q9/84pTWLW8ialVXm5+78lcuKghPngqWOuUAAZCUY70BQlHDAe6BxgIRQhFohzpG2Rvez/7OwNZ+24vBGO9t5KrG52jncrZx5t3f+Xn3HjesVx86qyMx67f0cq3H3mV3UesabgF06r40iWLdIRMyURBVmgtX77cbNiwoRCXLgjp1m5kWtOh6z1KRsFWFU40uS0GyXJ+1oLR7eBHIxI1dPYH6R4Ycp1njGHz3k7WbGrmuV1D7p2rPE4uPXUWV57eyMzaimHXEbFmyaq8TisKZo4+s8dLjn2A9rXjIN/9bT6vF0lYozFMnvd1cs+GZl7YPSTPNRUuLl8ym/ctaaS+aigqcSymSJU9o1tMWc5wL9IWQpX1JJYtW25efPHFgq56VyY1+gBRJhqqrB8lhCNROvpD9A4O93e9t72f+za18Oj2g3GzG4fA2QunsWppEyfPrhmhzDhEqLSV9jL1vqJ97VFOOnnefbiP321s5rFXD8Vt4d1O4aITZ7BqeRPz7EBnMWKyXO114ytt8D9V1rNFG6JSYPQBokw0VFk/ygiGo3T0B+kbHG4C0x0I8dBLB7h/SwuHe4Px9EUzq1m1rIlzF05LafPrcjio8lqjlNlGmCwC2tdOEtLJc3tfkPu3tPDglv3DnIScMb+eq5c3cfqcKSNeQl0Oy1a+usIVj+ZcRFRZzxZtiEqB0QeIMtFQZf0oZSAU4UhfkMGkqKXhSJQnXm9jzcYWXjs0FAOgwe/lytNnc9lps6iuSB18yG17ZKrylkTZSUT72knGQChCe1+QgSR5HghFeHT7IdZsbKalMxBPP67Bz1XLm3jnCQ0pX0J9tpmM3+MqlrWFKuvZog1RKTD6AFEmGqqsH+X0Dobp6AsSigz3PGOMYVtLN2s2NfPMzsNxF48VbgcXnzyTDyxtzOghpMLtxF/hosrjGuFusghoXztJ6RsM055CniNRw7NvHuHejft4uWUoMFOD38uVSxt5z2mz8HtHmnTF7Nv9FQU3+ZrcyrqIXALcCjiBO4wx3053rDZEpcDoA0SZaKiyPgkwxtAzGKazL0Q4OjKW8P7OAGs3t/DItoP022HbBVixYCqrljWyJIVJQQwRodLjjLtnLdJiPu1rJzndA6G08vzqgW7u2dDMU2+0xV9CfW4nl546kw8sbRqxuDqGy+GIr9UogMnX5FXWRcQJvA5cBDQDLwLXGmNeSXW8NkSlwOgDRJloqLI+iTDG0B0I0xkIpowy2TsY5pFtB1m7qYWD3UNRF49r8POBZY2884TpeFzpzV8cIlR5LZvgAtu3a1+rxIM5WfEFRsrzga4Av9vUwh9ePsBAaGhx9XnHN3DV8iYWZQgE5nY6rAir+bNvn9TK+lnALcaYi+3PXwYwxvxbquO1ISoFRh8gykRDlfVJSDRqrJHJNEpOJGp4Zudh7t3YzPb9QyYF9VUerlg8m/cunsWUSs+I8xKJLebze10ZFfwxon2tEieV+9JEegfCPPTSfn63uYUjCYurT2uq5eqIb+VDAAAYMUlEQVTlTaxYMBVHhhmhmMnXOO3bJ7Wyvgq4xBhzvf35o8CZxphPJxyzGlgNMHfu3GV79uwpSVmVSUHeHiAqt0qRyKvSo3I7sRhNyQHLpGDNxmaeeH3IpMDjcnDhidNZtWykq7xUeFwOqr1uqrzOfEWZ1L5WGUE4EqW9P0jvwMiovQChSJS/7Gjlno3N7Grri6fPqfOxalkT7z5pBt4MM0LjtG+f1Mr6VcDFScr6GcaYz6Q6Xt+alQKjoz3KRENH1hXCkSidgRA9GZT21u4B7t+yn4deOkBvghu95cfUsWpZE2+bV5eVvXqevHBoX6ukZTBseY4JBCMpvzfGsGlvJ/du2McLb3XE02t9bq5YPJsrTp9NXRYzR1VeS3H3urIy+Uors2UZySDPNANzEj43AftLVBZFURRFmXC4nA6m+b1M8blTBqIBmF5TwepzF/DRFcfw6PaD3Le5heaOABv2dLBhTwfH1FfygWVNXHTi9Iyjk4FghEAwwhEJxiOmVhXPfZ4yCfC6nMyq9Vly1jdIMDx8EaqIsOyYOpYdU8eutl7WbGzh8R2H6AqE+OVze7jrxb28+6SZXLWsiblTU3tECkejdAWidAVCcZemfq9rTDNHk2Fk3YW1wPRdQAvWAtMPGWO2pzpe35qVAqOjPcpEQ0fWlREEw1E6+4PDRtCTiRrD87vauXdjM1v2dcbTaypcvHfxbN63ZDZT/d6s8htDeHjta5WsSee+NJEjvYPcv2U/D27dT0+CGc2KBfVcvXwOi5tqs5458ntdqV5AJ68ZDICIXAr8AMt148+NMd9Kd6w2RKXA6ANEmWiosq6kZTRzghg7W3tZs7GZdTtaCduG7S6HcMEiy679uOn+rPPM0qOM9rVKThhj6B4I09mf2hNSjEAowqPbDrJmUzP7O4c8Ii2c7ufq5XM47/jUkX6TcYhQ6XVS7XXj8zhhsivruaANUSkw+gBRJhqqrCuj0h8Mc6Q388gkWCHgH9jSwoNbD9AVCMXTFzfVsmpZE2cdm9nrRjIZPMpoX6uMiWjU0BkI0RUIpV2fAbZHpDcPc++G4R6Rpld7ef/SRi47dRZVKYIspcLtdDCnvlKV9WzRhqgUGH2AKBMNVdaVrMkUiCaRwVCEP7/ayppNzew50h9Pb5zi48rTG1l5yszYaGPWJHmU0b5WGRfhSJSO/hA9A6FRj31lfzf3bNjH0wmRfis9Ti47dRbvX9rIjJrUQZYSWdDgV2U9W7QhKgVGHyDKREOVdSUnRguslHzshj0d/G5j8zCvG36vi8tOncmVpzcyPQtFJ5FKj4uZtRXa1yp5IRiO0tEfpC/D+owY+zutIEuPvHyAgfBQkKXzT5jO1cubOH5GddpzMynrk8EbjKIoiqIoRUJEqK10U13hyhhYKXbs2+bV87Z59bx1pI/7NrXwp1cO0TsY5n83NHPvxmbOO76BVcuaOHFW+miSilIoPC4HM2oqGAhZ6zMGQunXZ8ye4uMzFxzHdWcdw0MvHWDt5haO9AVZt6OVdTtaWdxUy9XL53DmgvrczL3yURFFURRFUZREHA5hSqWH6gr3qIGVAOZNreLvLzqeT75jPr9/aT/3b9lPe1+Qv7zWxl9ea+OkWTWsWtbEOQunUSArF0VJS4XbyewpPvoGw7SP4jmmxufmQ2fOZdWyJv7yWiv3bmhm1+E+tjZ3sbW5i7n1laxa1shFJ2YOshRDzWCS0CkupcDo1Kwy0VAzGCUvhCKWOUG66JHJBMNR1r/WypqNLexs642nz6jxcuXpjVx66iz8KRbwqRmMUgyyXZ8BQ+Ze92xoZuOeIXOvKT43VyyZzRVLZrP0mHq1Wc8WbYhKgdEHiDLRUGVdySuD4QgdfSH6g9kp7cYYXmruYs3GZv765hFiWovP7WTlKTO5cmkjjVN88eNVWVeKhTGGrkBmU69k3mzr5d4Nw92YelwOXv/mSrVZVxRFURSl9HhdTmbWOgkEI7T3BxnMYAMMll374jlTWDxnCi0dAe7b3MIj2w4QCEW4b3MLaze38PbjprJqWROnNdYWqRaKYslmLqZeAMc2+Llp5SKuP2c+aze38PutBzIGFwNV1hVFURRFKQE+j5NGjy+r6JExGuusBXyfePs8Hnr5APdvbqG1Z5Bndh7hmZ1HWDjdz4fOnMsn3jG/CDVQFAunQ5jq91Ljc9PRlzmyb4xpfi+fOmcBHznzGB7ZdjDjsaOHWFIURVEURSkQfq+LpjofU/3erBeO+itcXPO2Ofz6k2dw82UncuIsyyXeG629fO33rxSyuIqSFrfTwfSaChrrfFnHCfB5nLx/aWPGY3RkXVEURVGUkiIi1PrcVHtddNnRI7OxAXY5Hbxz0XTeuWg62/d3sWZjC0+90VaEEitKerwuJ7NqffQHLc8xwfDos0aZUGVdURRFUZSywOEQ6qo8ljlBf5CeLGyAY5w8u5aTZ9fSFRg94qSiFINKj4tKj4uegRAdWXqOSYWawSiKoiiKUlY4HcI0v5emOl9K94yZmFXrG/0gRSki1RVu5tT7qK/y5BQMKYYq64qiKIqilCWJNsCVHjUGUCYuMc8xc+orqfG5kRyU9pIo6yJylYhsF5GoiCxP+u7LIrJTRF4TkYsT0peJyMv2dz8Uu5Yi4hWR/7XTnxeReQnnXCcib9jbdcWqn6IoiqIo+cNy91jBrFpfVhEfFaVcGcusUalG1rcB7weeTEwUkZOAa4CTgUuA/xaRWKu8DVgNLLS3S+z0TwIdxpjjgO8D37GvVQ98FTgTOAP4qojUFbBOiqIoiqIUEJ/HSeMUH9NrKnA71ThAmbjEZo1mT/FRMcoLaEkk3RjzqjHmtRRfXQHcbYwZNMbsBnYCZ4jILKDGGPOssVaa/BJ4X8I5v7D31wDvskfdLwYeM8a0G2M6gMcYUvAVRVEURZmgJLp7dDlUaVcmLhVuJ7OnZF5nUW4S3gjsS/jcbKc12vvJ6cPOMcaEgS5gaoZrjUBEVovIBhHZ0NamLp+UiYHKrTIRUblV8kXM3eN4Fu5lmY/KrFJSCqasi8ifRWRbiu2KTKelSDMZ0sd6zvBEY243xiw3xixvaGjIUDxFKR9UbpWJiMqtkm8SF+7V+tzkW2dXmVVKTcGWVhtjLhzDac3AnITPTcB+O70pRXriOc0i4gJqgXY7/fykc9aPoUyKoiiKopQ5sZDv2fplV5SJQrmZwTwIXGN7eJmPtZD0BWPMAaBHRFbY9ugfAx5IOCfm6WUVsM62a38UeLeI1NkLS99tpymKoiiKcpSSi0s8RZkIlMRpqYhcCfwIaAAeFpEtxpiLjTHbReQe4BUgDPydMSZin3YjcCfgAx6xN4CfAb8SkZ1YI+rXABhj2kXkG8CL9nFfN8a0F752iqIoiqIoipIfSqKsG2PWAmvTfPct4Fsp0jcAp6RIHwCuSnOtnwM/H1dhFUVRFEVRFKVElJsZjKIoiqIoiqIoNqqsK4qiKIqiKEqZosq6oiiKoiiKopQpoi6OhiMibcAeYBpwuETFKGXepc7/aK/7YWNM3iPpJshtvinV7zFZZbAc8y6IzEJB5TYTpe5jskXLOT7Kta8t1/sVQ8s3dsZbtrQyq8p6GkRkgzFm+WTLu9T5T+a6lyOluh+TVQYna97FZKLUU8t5dFLu90vLN3YKWTY1g1EURVEURVGUMkWVdUVRFEVRFEUpU1RZT8/tkzTvUuc/metejpTqfkxWGZyseReTiVJPLefRSbnfLy3f2ClY2dRmXVEURVEURVHKFB1ZVxRFURRFUZQyRZV1QETeEpGXRWSLiGyw0+pF5DERecP+X5fH/H4uIq0isi0hLW1+IvJlEdkpIq+JyMUFyPsWEWmx679FRC4tUN5zROQvIvKqiGwXkc/Z6cWqe7r8i1L/ckFEnCKyWUQesj/nXH8RWWa3mZ0i8kMRkSzzzqmt5Sv/NPkWpd4iMkVE1ojIDlv2zipGnTPkXbTfu5SMpb8pUTkrROQFEdlql/Nr5VjOGCn6j7IsZ6kpd/mbKHJXzvKW5rlSmPIZYyb9BrwFTEtK+3fgJnv/JuA7eczvXGApsG20/ICTgK2AF5gPvAk485z3LcAXUxyb77xnAUvt/WrgdTuPYtU9Xf5FqX+5bMDfA78FHhrr7w+8AJwFCPAIsDLLvLNua/nMP02+Rak38AvgenvfA0wpRp0z5F2037vEcp5Tf1PCcgrgt/fdwPPAinIrZ0J5k/uPsixnqbdyl7+JInflLG8UUXfUkfX0XIH1oMP+/758XdgY8yTQnmV+VwB3G2MGjTG7gZ3AGXnOOx35zvuAMWaTvd8DvAo0Ury6p8s/HXnNvxwQkSbgMuCOLA5PWX8RmQXUGGOeNVaP9EvG1z5y+v0LkH+q8uQlXxGpwXpB/hmAMSZojOksRp0z5F3wepcDY+hvSoKx6LU/uu3NUGblhLT9R9mVsxwod/mbCHI3QeWtIOVTZd3CAH8SkY0istpOm2GMOQBWowOmF7gM6fJrBPYlHNdMZgVzrHxaRF4Sy0wmNm1TsLxFZB5wOtbbfNHrnpQ/FLn+JeQHwD8B0aT0XOrfaO8np2dDLm0tn/mnyhcKX+8FQBvwP/ZU7h0iUkVx6pwu72LUu6zIsr8pGfZU/xagFXjMGFOW5SR1/1GO5SwrylX+JoDclbu8FU13VGXd4h3GmKXASuDvROTcUhcogVS2ofl24XMbcCywBDgA/L9C5i0ifuB3wOeNMd2ZDi1S/kWtf6kQkfcArcaYjUlf5Vr/8dyXXNpaPvNPlW8x6u3CMju7zRhzOtCHNTWajmLkXczfu+Tk0N+UDGNMxBizBGjCms04pdRlSiZD/6FkoJzlr5zlboLIW9F0R1XWAWPMfvt/K7AWy9ThkD39i/2/tcDFSJdfMzAn4bgmYH8+MzbGHLIbbRT4KUOmHnnPW0TcWB3Xb4wx99nJRat7qvyLWf8S8w7gchF5C7gbuEBEfj2G+jfb+8npo5JjW8tb/qnyLVK9m4Fme8QKYA2WAl3wOqfLu5i/d6nJsb8pObaZ0nrgEsqvnCn7D8qvnGXDRJG/MpW7spe3YuqOk15ZF5EqEamO7QPvBrYBDwLX2YddBzxQ4KKky+9B4BoR8YrIfGAh1mKvvBETLJsrseqf97xFRLDsZ181xnwv4aui1D1d/sWqf6kxxnzZGNNkjJkHXAOsM8Z8JNf621N7PSKywr6nHyOL9jGGtpaX/NPlW4x6G2MOAvtE5AQ76V3AK4Wuc6a8i/V7l5ox9DclQUQaRGSKve8DLgR2UGblTNd/UGblLBfKXf7KXe7KXd6KrjuaEq7yLYcNy65zq71tB/7ZTp8KPA68Yf+vz2Oed2FNP4ewRq0+mSk/4J+xPDO8xji9MKTJ+1fAy8BLtqDNKlDeZ2NNn78EbLG3S4tY93T5F6X+5bQB5zO0uj7n+gPLsTqmN4H/xA6wNkqeObe1fOSfId9i1XsJsMHO536grtB1HiXvotS71FuG9l6wvn2M5TwN2GyXcxvwL6O1i1JvDO8/yracJb5HZS1/E0nuylHeKLLuqBFMFUVRFEVRFKVMmfRmMIqiKIqiKIpSrqiyriiKoiiKoihliirriqIoiqIoilKmqLKuKIqiKIqiKGWKKuuKoiiKoiiKUqaosn6UISLzRGTb6EcqyuRBRO4UkVWlLocyuRGRvxWRj9n7HxeR2aUuk3J0k04nEJGvi8iFo5x7i4h8sXClU7LFVeoCKOWDiLiMMeFSl2M0Jko5lYmFiDiNMZFSl0M5ejHG/Djh48ex/FtPiGiwytGFMeZfSl0GJXt0ZP3oxCkiPxWR7SLyJxHxicgSEXlORF4SkbUiUgcgIutF5F9F5AngcyJylYhsE5GtIvKkfYxTRL4rIi/a599gp58vIk/a13tFRH4sIg77u2tF5GX7Wt+x064Wke/Z+58TkV32/rEi8rS9v0xEnhCRjSLyaELY3mHlLO7tVEqFPSq0Q0R+YcveGhGpzCAnn7LldKuI/E5EKlNc8xv2SLtDRN4SkX+x5e8qW85+ICJ/tWX3DPucW+wy/Mk+5/0i8u+2jP9RrLDiijIMEfmYLbdbReRXsZFKe5ZnOfAbEdkiIpeJyNqE8y4Skfvs/V4R+Y4t638WkTNsOd0lIpfbx3xcRB6wZfE1EflqwrVuttvQYyJyl46UTkpS6QTx2UYRudSWkadF5Ici8lDCuSclyNtn7eP/KWH/+yKyzt5/l4j82t6/TUQ22Hl+LeF7lfOxUOrIVLrldwPmAWFgif35HuAjWFHKzrPTvg78wN5fD/x3wvkvA432/hT7/2rg/9r7XqyIiPOxoooNYEXycgKPAauA2cBeoAFr9mYd8D5gJvCifZ01wItAI1ZI3n8D3MBfgQb7mA8CP09VTt0mx2bLswHeYX/+OfCPGeRkasK53wQ+Y+/facvmvwM/gXhAuLeAf0o4Zz3wU3v/XGCbvX8L8LQto4uBfuwIn8Ba4H2lvle6ldcGnIwVCXaa/bnelqMv2p/XA8vtfcEK9R6T6d8C77X3TZKs/SlBDrfY6R/Hikw9FfBhjdgvt7ctdlo1VlTFL5b63uhWVDmcR2qdINYnVgD7gPn293cxFC30Fruv9QLTgCO27K0A7rWPeQp4wU7/KnCDnV5v/3fasn6ayvnYNzWDOTrZbYzZYu9vBI7FUryfsNN+AdybcPz/Juw/A9wpIvcA99lp7wZOkyGb31pgIRAEXjDGxEbI78IKsRwC1htj2uz03wDnGmPuFxG/iFQDc7Aa6rnAOXZeJwCnAI+JCFiN/ECaciqTh33GmGfs/V8DXyG9nJwiIt8EpgB+4NGE69wMPG+MWZ10/WS5ugvAGPOkiNSIyBQ7/RFjTEhEXrbz/KOd/jLWA1FRErkAWGOMOQxgjGm35XUExhgjIr8CPiIi/wOcBXzM/jrIcFkbTJDDeQmXecwYcwTAHq08205/wBgTsNN/n6/KKROKZJ1gXsJ3i4Bdxpjd9ue7sAboYjxsjBkEBkWkFZhhX2OZ/SwfBDZhKcznAJ+1z7taRFZjDdjNAk4yxrykcj42VFk/OhlM2I9gKS6Z6IvtGGP+VkTOBC4DtojIEqy34c8YYxIVH0TkfKy34USMfXw6ngU+gTXi9BTwN1gN9h+AucB2Y8xZo5VTmVQky1gP6eXkTqxR7q0i8nGs2Z8YL2I9YOqNMe0J6clylUqmwW5XxpioiISMPdQDRNG+VBmJMFKWMvE/wO+xZivvNUPrcpJlLVEOE+UuldyqqasCI3UCX8LnTM/rVOe6bCX6Laxn+V+xZu7fiTUw+KqIzAe+CLzNGNMhIndijeCDyvmYOOorqADQBXSIyDn2548CT6Q6UESONcY8b6zFJ4exRsAfBW6M2eWKyPEiUmWfcoaIzBfLVv2DWKYCzwPnicg0EXEC1ybk9yRWI34S2IzVwAeNMV1YCnyDiJxl5+MWkZPzdxuUCcrcmExgydJzpJeTauCALasfTrrOH4FvAw/bI0Lp+KB93bOBLls2FSVXHscaXZwKICL1Sd/3YMkrAMaY/ViLTf8v1ktnrlwkIvUi4sMyO3wGqz9+r4hUiIgfaxBGURLZASwQkXn25w9meV7is/wp4G+xzFUMUIM1CNIlIjOAlbGTVM7Hho4GTR6uA34s1oK7XVhvxKn4rogsxHrbfhzYivXWPA/YJNY8bhtWIwFrpPzbwKlYjXat/Sb8ZeAv9nX+YIx5wD7+KawXgCeNMRER2YfVWWCMCdqmNj8UkVos+fwBsD1P90CZmLwKXCciP8GyRfwR1gtkKjm5GetlcQ/WVOowpdwYc6+tqD8oIpemya9DRP6K9cD5mwLUR5kEGGO2i8i3gCdEJII1OPFWwiF3YvXJAeAsewr/N1j2vK+MIcungV8BxwG/NcZsABCRB7H68T1Y64305VOJY4wJiMj/Af4oIoex7M+z4Sngn4FnjTF9IjJgp2HPbG7G6pN3YSnUiaic54gMzTooSm7YZjBfNMa8p9RlUY5O7NGeh4wxpxQpv/VYMr2hGPkpSiIi8p/AZmPMz3I87+NYi1U/neI7vzGm1x6oeRJYbYzZlJcCK0cFCTIiwH8Bbxhjvl/A/FTOc0RH1hVFURSlxIjIRizTgX/I86VvF5GTsGyGf3E0KTBK3viUiFwHeLBmgH5SqIxUzseGjqwriqIoiqIoSpmiC0wVRVEURVEUpUxRZV1RFEVRFEVRyhRV1hVFURRFURSlTFFlXVEURVEURVHKFFXWFUVRFEVRFKVMUWVdURRFURRFUcqU/w9tpEc0J2EAFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x180 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### fitting the regression line between all numeric columns v/s price \n",
    "num_columns_list = data.select_dtypes(exclude='object').columns[1:-1]\n",
    "plt.figure(figsize = (15, 10))\n",
    "for i in range(0,len(num_columns_list),5):\n",
    "    sns.pairplot(data, x_vars=num_columns_list[i:i+5], y_vars='price', kind='reg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above visuals:\n",
    "* The higher price cars will have high wheelbase, carlength, carwidth, carheight, curbweight, enginesize, boreratio and horsepower\n",
    "* The high price cars have low mpg in both city and highway\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19696959588>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### heatmap to visualize the pearson's correlation matrix between the numeric variables\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.heatmap(data.corr(), annot=True, cmap=\"YlGnBu\", mask=np.triu(data.corr(), k=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above heatmap:\n",
    "* wheelbase has high positive correlation with carlength,carwidth and curbweight\n",
    "* carlength has high postive correlation with curbweight\n",
    "* carlength has negative correlation with highwaympg\n",
    "* carwidth has high postive correlation with curbweight and engine size\n",
    "* enginesize has high positive correlation with horsepower\n",
    "* curbweight has high positive correlation with engine size and horse power, negative correlation with highwaympg\n",
    "* horsepower has negative correlation with citympg and highwaympg\n",
    "* citympg and highwaympg are highly correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row0_col1{\n",
       "            background-color:  #08306b;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row1_col1{\n",
       "            background-color:  #083877;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row2_col1{\n",
       "            background-color:  #084d96;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row3_col1{\n",
       "            background-color:  #2d7dbb;\n",
       "            color:  #000000;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row4_col1{\n",
       "            background-color:  #8cc0dd;\n",
       "            color:  #000000;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row5_col1{\n",
       "            background-color:  #8dc1dd;\n",
       "            color:  #000000;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row6_col1{\n",
       "            background-color:  #a1cbe2;\n",
       "            color:  #000000;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row7_col1,#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row8_col1{\n",
       "            background-color:  #add0e6;\n",
       "            color:  #000000;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row9_col1{\n",
       "            background-color:  #afd1e7;\n",
       "            color:  #000000;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row10_col1{\n",
       "            background-color:  #d7e6f5;\n",
       "            color:  #000000;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row11_col1{\n",
       "            background-color:  #d9e7f5;\n",
       "            color:  #000000;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row12_col1{\n",
       "            background-color:  #dbe9f6;\n",
       "            color:  #000000;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row13_col1{\n",
       "            background-color:  #dceaf6;\n",
       "            color:  #000000;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row14_col1{\n",
       "            background-color:  #deebf7;\n",
       "            color:  #000000;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row15_col1{\n",
       "            background-color:  #e1edf8;\n",
       "            color:  #000000;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row16_col1{\n",
       "            background-color:  #e3eef8;\n",
       "            color:  #000000;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row17_col1{\n",
       "            background-color:  #e6f0f9;\n",
       "            color:  #000000;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row18_col1{\n",
       "            background-color:  #e8f1fa;\n",
       "            color:  #000000;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row19_col1{\n",
       "            background-color:  #eaf2fb;\n",
       "            color:  #000000;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row20_col1{\n",
       "            background-color:  #eaf3fb;\n",
       "            color:  #000000;\n",
       "        }#T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row21_col1{\n",
       "            background-color:  #f7fbff;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >car_brand</th>        <th class=\"col_heading level0 col1\" >price</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row0_col0\" class=\"data row0 col0\" >jaguar</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row0_col1\" class=\"data row0 col1\" >34600.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row1_col0\" class=\"data row1 col0\" >buick</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row1_col1\" class=\"data row1 col1\" >33647.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row2_col0\" class=\"data row2 col0\" >porsche</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row2_col1\" class=\"data row2 col1\" >31400.500000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row3_col0\" class=\"data row3 col0\" >bmw</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row3_col1\" class=\"data row3 col1\" >26118.750000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row4_col0\" class=\"data row4 col0\" >volvo</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row4_col1\" class=\"data row4 col1\" >18063.180000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row5_col0\" class=\"data row5 col0\" >audi</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row5_col1\" class=\"data row5 col1\" >17859.170000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row6_col0\" class=\"data row6 col0\" >mercury</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row6_col1\" class=\"data row6 col1\" >16503.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row7_col0\" class=\"data row7 col0\" >alfa-romero</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row7_col1\" class=\"data row7 col1\" >15498.330000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row8_col0\" class=\"data row8 col0\" >peugeot</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row8_col1\" class=\"data row8 col1\" >15489.090000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row9_col0\" class=\"data row9 col0\" >saab</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row9_col1\" class=\"data row9 col1\" >15223.330000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row10_col0\" class=\"data row10 col0\" >mazda</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row10_col1\" class=\"data row10 col1\" >10652.880000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row11_col0\" class=\"data row11 col0\" >Nissan</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row11_col1\" class=\"data row11 col1\" >10415.670000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row12_col0\" class=\"data row12 col0\" >volkswagen</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row12_col1\" class=\"data row12 col1\" >10077.500000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row13_col0\" class=\"data row13 col0\" >toyota</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row13_col1\" class=\"data row13 col1\" >9885.810000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row14_col0\" class=\"data row14 col0\" >renault</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row14_col1\" class=\"data row14 col1\" >9595.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row15_col0\" class=\"data row15 col0\" >mitsubishi</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row15_col1\" class=\"data row15 col1\" >9239.770000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row16_col0\" class=\"data row16 col0\" >isuzu</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row16_col1\" class=\"data row16 col1\" >8916.500000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row17_col0\" class=\"data row17 col0\" >subaru</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row17_col1\" class=\"data row17 col1\" >8541.250000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row18_col0\" class=\"data row18 col0\" >honda</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row18_col1\" class=\"data row18 col1\" >8184.690000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row19_col0\" class=\"data row19 col0\" >plymouth</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row19_col1\" class=\"data row19 col1\" >7963.430000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row20_col0\" class=\"data row20 col0\" >dodge</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row20_col1\" class=\"data row20 col1\" >7875.440000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row21_col0\" class=\"data row21 col0\" >chevrolet</td>\n",
       "                        <td id=\"T_b34d1c1c_ead5_11ea_87be_24ee9ae80742row21_col1\" class=\"data row21 col1\" >6007.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x196964c0c88>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### average price for each brand car\n",
    "(data.groupby('car_brand')['price'].mean()\n",
    "    .sort_values(ascending=False).reset_index().round(2) \n",
    "    .style.background_gradient(cmap='Blues'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above dataFrame:\n",
    "* **jaguar** make is having **highest average** price.\n",
    "* **chevrolet** make is having **least average** price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Based on above visuals Dropping Few columns\n",
    "data.drop(['car_ID', 'CarName', 'highwaympg', 'fuelsystem', 'aspiration', 'doornumber'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### converting 'cylindernumber' column to numeric\n",
    "data['cylindernumber'].replace({\"two\": 2,\n",
    "                                \"three\":3,\n",
    "                                \"four\": 4,\n",
    "                                \"five\": 5,\n",
    "                                \"six\": 6,  \n",
    "                                \"eight\": 8,\n",
    "                                \"twelve\": 12\n",
    "                               }, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>symboling</th>\n",
       "      <th>fueltype_1</th>\n",
       "      <th>fueltype_2</th>\n",
       "      <th>carbody_1</th>\n",
       "      <th>carbody_2</th>\n",
       "      <th>carbody_3</th>\n",
       "      <th>carbody_4</th>\n",
       "      <th>carbody_5</th>\n",
       "      <th>drivewheel_1</th>\n",
       "      <th>drivewheel_2</th>\n",
       "      <th>drivewheel_3</th>\n",
       "      <th>enginelocation_1</th>\n",
       "      <th>enginelocation_2</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginetype_1</th>\n",
       "      <th>enginetype_2</th>\n",
       "      <th>enginetype_3</th>\n",
       "      <th>enginetype_4</th>\n",
       "      <th>enginetype_5</th>\n",
       "      <th>enginetype_6</th>\n",
       "      <th>enginetype_7</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>price</th>\n",
       "      <th>car_brand_1</th>\n",
       "      <th>car_brand_2</th>\n",
       "      <th>car_brand_3</th>\n",
       "      <th>car_brand_4</th>\n",
       "      <th>car_brand_5</th>\n",
       "      <th>car_brand_6</th>\n",
       "      <th>car_brand_7</th>\n",
       "      <th>car_brand_8</th>\n",
       "      <th>car_brand_9</th>\n",
       "      <th>car_brand_10</th>\n",
       "      <th>car_brand_11</th>\n",
       "      <th>car_brand_12</th>\n",
       "      <th>car_brand_13</th>\n",
       "      <th>car_brand_14</th>\n",
       "      <th>car_brand_15</th>\n",
       "      <th>car_brand_16</th>\n",
       "      <th>car_brand_17</th>\n",
       "      <th>car_brand_18</th>\n",
       "      <th>car_brand_19</th>\n",
       "      <th>car_brand_20</th>\n",
       "      <th>car_brand_21</th>\n",
       "      <th>car_brand_22</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>152</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>109</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>136</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  fueltype_1  fueltype_2  carbody_1  carbody_2  carbody_3  \\\n",
       "0          3           1           0          1          0          0   \n",
       "1          3           1           0          1          0          0   \n",
       "2          1           1           0          0          1          0   \n",
       "3          2           1           0          0          0          1   \n",
       "4          2           1           0          0          0          1   \n",
       "\n",
       "   carbody_4  carbody_5  drivewheel_1  drivewheel_2  drivewheel_3  \\\n",
       "0          0          0             1             0             0   \n",
       "1          0          0             1             0             0   \n",
       "2          0          0             1             0             0   \n",
       "3          0          0             0             1             0   \n",
       "4          0          0             0             0             1   \n",
       "\n",
       "   enginelocation_1  enginelocation_2  wheelbase  carlength  carwidth  \\\n",
       "0                 1                 0       88.6      168.8      64.1   \n",
       "1                 1                 0       88.6      168.8      64.1   \n",
       "2                 1                 0       94.5      171.2      65.5   \n",
       "3                 1                 0       99.8      176.6      66.2   \n",
       "4                 1                 0       99.4      176.6      66.4   \n",
       "\n",
       "   carheight  curbweight  enginetype_1  enginetype_2  enginetype_3  \\\n",
       "0       48.8        2548             1             0             0   \n",
       "1       48.8        2548             1             0             0   \n",
       "2       52.4        2823             0             1             0   \n",
       "3       54.3        2337             0             0             1   \n",
       "4       54.3        2824             0             0             1   \n",
       "\n",
       "   enginetype_4  enginetype_5  enginetype_6  enginetype_7  cylindernumber  \\\n",
       "0             0             0             0             0               4   \n",
       "1             0             0             0             0               4   \n",
       "2             0             0             0             0               6   \n",
       "3             0             0             0             0               4   \n",
       "4             0             0             0             0               5   \n",
       "\n",
       "   enginesize  boreratio  stroke  compressionratio  horsepower  peakrpm  \\\n",
       "0         130       3.47    2.68               9.0         111     5000   \n",
       "1         130       3.47    2.68               9.0         111     5000   \n",
       "2         152       2.68    3.47               9.0         154     5000   \n",
       "3         109       3.19    3.40              10.0         102     5500   \n",
       "4         136       3.19    3.40               8.0         115     5500   \n",
       "\n",
       "   citympg    price  car_brand_1  car_brand_2  car_brand_3  car_brand_4  \\\n",
       "0       21  13495.0            1            0            0            0   \n",
       "1       21  16500.0            1            0            0            0   \n",
       "2       19  16500.0            1            0            0            0   \n",
       "3       24  13950.0            0            1            0            0   \n",
       "4       18  17450.0            0            1            0            0   \n",
       "\n",
       "   car_brand_5  car_brand_6  car_brand_7  car_brand_8  car_brand_9  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   car_brand_10  car_brand_11  car_brand_12  car_brand_13  car_brand_14  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   car_brand_15  car_brand_16  car_brand_17  car_brand_18  car_brand_19  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   car_brand_20  car_brand_21  car_brand_22  \n",
       "0             0             0             0  \n",
       "1             0             0             0  \n",
       "2             0             0             0  \n",
       "3             0             0             0  \n",
       "4             0             0             0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the columns to perform one hot encoding\n",
    "cols_to_ohe=data.select_dtypes(include='object').columns\n",
    "\n",
    "# perform one hot encoding on the following columns\n",
    "import category_encoders as ce\n",
    "\n",
    "ce_ohe=ce.OneHotEncoder(cols=cols_to_ohe)\n",
    "data=ce_ohe.fit_transform(data)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Creating dependent and independent variables\n",
    "# independent variables\n",
    "x = data.drop(columns=\"price\")\n",
    "\n",
    "# dependent variable\n",
    "y = data[\"price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>symboling</th>\n",
       "      <th>fueltype_1</th>\n",
       "      <th>fueltype_2</th>\n",
       "      <th>carbody_1</th>\n",
       "      <th>carbody_2</th>\n",
       "      <th>carbody_3</th>\n",
       "      <th>carbody_4</th>\n",
       "      <th>carbody_5</th>\n",
       "      <th>drivewheel_1</th>\n",
       "      <th>drivewheel_2</th>\n",
       "      <th>drivewheel_3</th>\n",
       "      <th>enginelocation_1</th>\n",
       "      <th>enginelocation_2</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginetype_1</th>\n",
       "      <th>enginetype_2</th>\n",
       "      <th>enginetype_3</th>\n",
       "      <th>enginetype_4</th>\n",
       "      <th>enginetype_5</th>\n",
       "      <th>enginetype_6</th>\n",
       "      <th>enginetype_7</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>car_brand_1</th>\n",
       "      <th>car_brand_2</th>\n",
       "      <th>car_brand_3</th>\n",
       "      <th>car_brand_4</th>\n",
       "      <th>car_brand_5</th>\n",
       "      <th>car_brand_6</th>\n",
       "      <th>car_brand_7</th>\n",
       "      <th>car_brand_8</th>\n",
       "      <th>car_brand_9</th>\n",
       "      <th>car_brand_10</th>\n",
       "      <th>car_brand_11</th>\n",
       "      <th>car_brand_12</th>\n",
       "      <th>car_brand_13</th>\n",
       "      <th>car_brand_14</th>\n",
       "      <th>car_brand_15</th>\n",
       "      <th>car_brand_16</th>\n",
       "      <th>car_brand_17</th>\n",
       "      <th>car_brand_18</th>\n",
       "      <th>car_brand_19</th>\n",
       "      <th>car_brand_20</th>\n",
       "      <th>car_brand_21</th>\n",
       "      <th>car_brand_22</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.690772</td>\n",
       "      <td>-0.426521</td>\n",
       "      <td>-0.844782</td>\n",
       "      <td>-2.020417</td>\n",
       "      <td>-0.014566</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.074449</td>\n",
       "      <td>0.519071</td>\n",
       "      <td>-1.839377</td>\n",
       "      <td>-0.288349</td>\n",
       "      <td>0.174483</td>\n",
       "      <td>-0.262960</td>\n",
       "      <td>-0.646553</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.690772</td>\n",
       "      <td>-0.426521</td>\n",
       "      <td>-0.844782</td>\n",
       "      <td>-2.020417</td>\n",
       "      <td>-0.014566</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.074449</td>\n",
       "      <td>0.519071</td>\n",
       "      <td>-1.839377</td>\n",
       "      <td>-0.288349</td>\n",
       "      <td>0.174483</td>\n",
       "      <td>-0.262960</td>\n",
       "      <td>-0.646553</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.708596</td>\n",
       "      <td>-0.231513</td>\n",
       "      <td>-0.190566</td>\n",
       "      <td>-0.543527</td>\n",
       "      <td>0.514882</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.604046</td>\n",
       "      <td>-2.404880</td>\n",
       "      <td>0.685946</td>\n",
       "      <td>-0.288349</td>\n",
       "      <td>1.264536</td>\n",
       "      <td>-0.262960</td>\n",
       "      <td>-0.953012</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.173698</td>\n",
       "      <td>0.207256</td>\n",
       "      <td>0.136542</td>\n",
       "      <td>0.235942</td>\n",
       "      <td>-0.420797</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.431076</td>\n",
       "      <td>-0.517266</td>\n",
       "      <td>0.462183</td>\n",
       "      <td>-0.035973</td>\n",
       "      <td>-0.053668</td>\n",
       "      <td>0.787855</td>\n",
       "      <td>-0.186865</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.107110</td>\n",
       "      <td>0.207256</td>\n",
       "      <td>0.230001</td>\n",
       "      <td>0.235942</td>\n",
       "      <td>0.516807</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.218885</td>\n",
       "      <td>-0.517266</td>\n",
       "      <td>0.462183</td>\n",
       "      <td>-0.540725</td>\n",
       "      <td>0.275883</td>\n",
       "      <td>0.787855</td>\n",
       "      <td>-1.106241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  fueltype_1  fueltype_2  carbody_1  carbody_2  carbody_3  \\\n",
       "0          3           1           0          1          0          0   \n",
       "1          3           1           0          1          0          0   \n",
       "2          1           1           0          0          1          0   \n",
       "3          2           1           0          0          0          1   \n",
       "4          2           1           0          0          0          1   \n",
       "\n",
       "   carbody_4  carbody_5  drivewheel_1  drivewheel_2  drivewheel_3  \\\n",
       "0          0          0             1             0             0   \n",
       "1          0          0             1             0             0   \n",
       "2          0          0             1             0             0   \n",
       "3          0          0             0             1             0   \n",
       "4          0          0             0             0             1   \n",
       "\n",
       "   enginelocation_1  enginelocation_2  wheelbase  carlength  carwidth  \\\n",
       "0                 1                 0  -1.690772  -0.426521 -0.844782   \n",
       "1                 1                 0  -1.690772  -0.426521 -0.844782   \n",
       "2                 1                 0  -0.708596  -0.231513 -0.190566   \n",
       "3                 1                 0   0.173698   0.207256  0.136542   \n",
       "4                 1                 0   0.107110   0.207256  0.230001   \n",
       "\n",
       "   carheight  curbweight  enginetype_1  enginetype_2  enginetype_3  \\\n",
       "0  -2.020417   -0.014566             1             0             0   \n",
       "1  -2.020417   -0.014566             1             0             0   \n",
       "2  -0.543527    0.514882             0             1             0   \n",
       "3   0.235942   -0.420797             0             0             1   \n",
       "4   0.235942    0.516807             0             0             1   \n",
       "\n",
       "   enginetype_4  enginetype_5  enginetype_6  enginetype_7  cylindernumber  \\\n",
       "0             0             0             0             0               4   \n",
       "1             0             0             0             0               4   \n",
       "2             0             0             0             0               6   \n",
       "3             0             0             0             0               4   \n",
       "4             0             0             0             0               5   \n",
       "\n",
       "   enginesize  boreratio    stroke  compressionratio  horsepower   peakrpm  \\\n",
       "0    0.074449   0.519071 -1.839377         -0.288349    0.174483 -0.262960   \n",
       "1    0.074449   0.519071 -1.839377         -0.288349    0.174483 -0.262960   \n",
       "2    0.604046  -2.404880  0.685946         -0.288349    1.264536 -0.262960   \n",
       "3   -0.431076  -0.517266  0.462183         -0.035973   -0.053668  0.787855   \n",
       "4    0.218885  -0.517266  0.462183         -0.540725    0.275883  0.787855   \n",
       "\n",
       "    citympg  car_brand_1  car_brand_2  car_brand_3  car_brand_4  car_brand_5  \\\n",
       "0 -0.646553            1            0            0            0            0   \n",
       "1 -0.646553            1            0            0            0            0   \n",
       "2 -0.953012            1            0            0            0            0   \n",
       "3 -0.186865            0            1            0            0            0   \n",
       "4 -1.106241            0            1            0            0            0   \n",
       "\n",
       "   car_brand_6  car_brand_7  car_brand_8  car_brand_9  car_brand_10  \\\n",
       "0            0            0            0            0             0   \n",
       "1            0            0            0            0             0   \n",
       "2            0            0            0            0             0   \n",
       "3            0            0            0            0             0   \n",
       "4            0            0            0            0             0   \n",
       "\n",
       "   car_brand_11  car_brand_12  car_brand_13  car_brand_14  car_brand_15  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   car_brand_16  car_brand_17  car_brand_18  car_brand_19  car_brand_20  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   car_brand_21  car_brand_22  \n",
       "0             0             0  \n",
       "1             0             0  \n",
       "2             0             0  \n",
       "3             0             0  \n",
       "4             0             0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save numeric columns in a variable\n",
    "cnames = ['wheelbase', 'carlength', 'carwidth', 'carheight',\n",
    "          'curbweight', 'enginesize', 'boreratio', 'stroke', \n",
    "          'compressionratio', 'horsepower', 'peakrpm', 'citympg',]\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "for col in cnames:\n",
    "    x[col]=sc.fit_transform(x[col].values.reshape(-1,1))\n",
    "\n",
    "x.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Splitting train and test dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "#### instantiate and fit\n",
    "model_LR = LinearRegression()\n",
    "model_LR.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.9706000959758017\n",
      "Test Accuracy  : 0.8920483520083704\n"
     ]
    }
   ],
   "source": [
    "LR_score_train = model_LR.score(x_train, y_train)\n",
    "print(\"Train Accuracy :\",LR_score_train)\n",
    "\n",
    "LR_score_test = model_LR.score(x_test, y_test)\n",
    "print(\"Test Accuracy  :\",LR_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1602.070849236117\n",
      "MSE: 7128842.973392517\n",
      "RMSE: 2669.989320838665\n"
     ]
    }
   ],
   "source": [
    "#### predicting 'price' using lin_reg\n",
    "predictions_LR = model_LR.predict(x_test)\n",
    "\n",
    "#### calculating RMSE, MSE, and MAE\n",
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions_LR))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions_LR))\n",
    "LR_RMSE=np.sqrt(metrics.mean_squared_error(y_test, predictions_LR))\n",
    "print('RMSE:', LR_RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_results = []\n",
    "dict_LR = {'MODEL':'Linear Regression',\n",
    "           'Train_ACCURACY':LR_score_train,\n",
    "           'Test_ACCURACY':LR_score_test,\n",
    "           'RMSE':LR_RMSE\n",
    "          }\n",
    "final_results.append(dict_LR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "model_LR_Ridge = linear_model.RidgeCV(alphas=np.logspace(-6, 6, 13))\n",
    "model_LR_Ridge.fit(x_train,y_train)\n",
    "model_LR_Ridge.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.9703273024088487\n",
      "Test Accuracy  : 0.8885484567286488\n"
     ]
    }
   ],
   "source": [
    "LR_Ridge_score_train = model_LR_Ridge.score(x_train,y_train)\n",
    "print(\"Train Accuracy :\",LR_Ridge_score_train)\n",
    "\n",
    "LR_Ridge_score_test = model_LR_Ridge.score(x_test,y_test)\n",
    "print(\"Test Accuracy  :\",LR_Ridge_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1632.8053066911716\n",
      "MSE: 7359966.854654508\n",
      "RMSE: 2712.9258844750084\n"
     ]
    }
   ],
   "source": [
    "#### predicting 'price' using Ridge Regression\n",
    "predictions_LR_Ridge = model_LR_Ridge.predict(x_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions_LR_Ridge))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions_LR_Ridge))\n",
    "LR_Ridge_RMSE=np.sqrt(metrics.mean_squared_error(y_test, predictions_LR_Ridge))\n",
    "print('RMSE:', LR_Ridge_RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_LR_Ridge = {'MODEL':'Ridge Regression',\n",
    "                 'Train_ACCURACY':LR_Ridge_score_train,\n",
    "                 'Test_ACCURACY':LR_Ridge_score_test,\n",
    "                 'RMSE':LR_Ridge_RMSE\n",
    "                }\n",
    "final_results.append(dict_LR_Ridge)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LassoCV(alphas=array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,\n",
       "       1.e+02, 1.e+03, 1.e+04, 1.e+05, 1.e+06]))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "model_LR_Lasso = linear_model.LassoCV(alphas=np.logspace(-6, 6, 13))\n",
    "model_LR_Lasso.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.9670849805859022\n",
      "Test Accuracy  : 0.8901462183776644\n"
     ]
    }
   ],
   "source": [
    "LR_Lasso_score_train = model_LR_Lasso.score(x_train,y_train)\n",
    "print(\"Train Accuracy :\",LR_Lasso_score_train)\n",
    "\n",
    "LR_Lasso_score_test = model_LR_Lasso.score(x_test,y_test)\n",
    "print(\"Test Accuracy  :\",LR_Lasso_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1704.319461370362\n",
      "MSE: 7254454.876684296\n",
      "RMSE: 2693.4095263595354\n"
     ]
    }
   ],
   "source": [
    "#### predicting 'price' using Lasso Regression\n",
    "predictions_LR_Lasso = model_LR_Lasso.predict(x_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions_LR_Lasso))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions_LR_Lasso))\n",
    "LR_Lasso_RMSE=np.sqrt(metrics.mean_squared_error(y_test, predictions_LR_Lasso))\n",
    "print('RMSE:', LR_Lasso_RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_LR_Lasso = {'MODEL':'Lasso Regression',\n",
    "                 'Train_ACCURACY':LR_Lasso_score_train,\n",
    "                 'Test_ACCURACY':LR_Lasso_score_test,\n",
    "                 'RMSE':LR_Lasso_RMSE\n",
    "                }\n",
    "final_results.append(dict_LR_Lasso)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(n_jobs=-1, n_neighbors=4)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import neighbors as nb\n",
    "model_KNN=nb.KNeighborsRegressor(n_neighbors=4,n_jobs=-1)\n",
    "model_KNN.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.9124677696162664\n",
      "Test Accuracy  : 0.8259113078399183\n"
     ]
    }
   ],
   "source": [
    "KNN_score_train = model_KNN.score(x_train,y_train)\n",
    "print(\"Train Accuracy :\",KNN_score_train)\n",
    "\n",
    "KNN_score_test = model_KNN.score(x_test,y_test)\n",
    "print(\"Test Accuracy  :\",KNN_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=KNeighborsRegressor(n_jobs=-1, n_neighbors=4),\n",
       "             param_grid={'n_neighbors': [3, 5, 7, 9, 11],\n",
       "                         'weights': ['uniform', 'distance']})"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Parameter tuning/Grid search\n",
    "import sklearn.model_selection as model_selection\n",
    "model_KNN=model_selection.GridSearchCV(model_KNN,param_grid={'n_neighbors':[3,5,7,9,11],'weights':['uniform','distance']})\n",
    "model_KNN.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 3, 'weights': 'distance'}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_KNN.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.9985246605173365\n",
      "Test Accuracy  : 0.8783507393636467\n"
     ]
    }
   ],
   "source": [
    "KNN_score_train = model_KNN.score(x_train,y_train)\n",
    "print(\"Train Accuracy :\",KNN_score_train)\n",
    "\n",
    "KNN_score_test = model_KNN.score(x_test,y_test)\n",
    "print(\"Test Accuracy  :\",KNN_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1832.775503909773\n",
      "MSE: 8033397.294435981\n",
      "RMSE: 2834.3248392581927\n"
     ]
    }
   ],
   "source": [
    "#### predicting 'price' using lin_reg\n",
    "predictions_KNN = model_KNN.predict(x_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions_KNN))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions_KNN))\n",
    "KNN_RMSE=np.sqrt(metrics.mean_squared_error(y_test, predictions_KNN))\n",
    "print('RMSE:', KNN_RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_KNN = {'MODEL':'KNN Regressor',\n",
    "            'Train_ACCURACY':KNN_score_train,\n",
    "            'Test_ACCURACY':KNN_score_test,\n",
    "            'RMSE':KNN_RMSE}\n",
    "final_results.append(dict_KNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DT_Regression_Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=3)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn.tree as tree\n",
    "model_DT=tree.DecisionTreeRegressor(max_depth=3)\n",
    "model_DT.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.9216374578674762\n",
      "Test Accuracy  : 0.8679424987382512\n"
     ]
    }
   ],
   "source": [
    "DT_score_train = model_DT.score(x_train,y_train)\n",
    "print(\"Train Accuracy :\",DT_score_train)\n",
    "\n",
    "DT_score_test = model_DT.score(x_test,y_test)\n",
    "print(\"Test Accuracy  :\",DT_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(418.5, 380.52, 'enginesize <= 1.326\\nmse = 62413097.511\\nsamples = 143\\nvalue = 13299.679'),\n",
       " Text(209.25, 271.8, 'curbweight <= -0.022\\nmse = 19869258.311\\nsamples = 129\\nvalue = 11141.381'),\n",
       " Text(104.625, 163.07999999999998, 'curbweight <= -0.508\\nmse = 5353442.13\\nsamples = 84\\nvalue = 8564.036'),\n",
       " Text(52.3125, 54.360000000000014, 'mse = 1072432.475\\nsamples = 49\\nvalue = 7284.122'),\n",
       " Text(156.9375, 54.360000000000014, 'mse = 5842577.678\\nsamples = 35\\nvalue = 10355.914'),\n",
       " Text(313.875, 163.07999999999998, 'carwidth <= 1.258\\nmse = 11419572.218\\nsamples = 45\\nvalue = 15952.426'),\n",
       " Text(261.5625, 54.360000000000014, 'mse = 9422357.639\\nsamples = 39\\nvalue = 15325.107'),\n",
       " Text(366.1875, 54.360000000000014, 'mse = 5216916.667\\nsamples = 6\\nvalue = 20030.0'),\n",
       " Text(627.75, 271.8, 'compressionratio <= 1.604\\nmse = 16001889.551\\nsamples = 14\\nvalue = 33186.857'),\n",
       " Text(523.125, 163.07999999999998, 'carwidth <= 2.52\\nmse = 7696192.8\\nsamples = 10\\nvalue = 35104.0'),\n",
       " Text(470.8125, 54.360000000000014, 'mse = 4317654.222\\nsamples = 9\\nvalue = 34453.333'),\n",
       " Text(575.4375, 54.360000000000014, 'mse = 0.0\\nsamples = 1\\nvalue = 40960.0'),\n",
       " Text(732.375, 163.07999999999998, 'curbweight <= 2.319\\nmse = 4606060.0\\nsamples = 4\\nvalue = 28394.0'),\n",
       " Text(680.0625, 54.360000000000014, 'mse = 1573219.556\\nsamples = 3\\nvalue = 27325.333'),\n",
       " Text(784.6875, 54.360000000000014, 'mse = 0.0\\nsamples = 1\\nvalue = 31600.0')]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import plot_tree, export_text\n",
    "\n",
    "features  = x.columns\n",
    "plt.figure(figsize=(15,8))\n",
    "plot_tree(model_DT, feature_names=features, filled = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 2076.496298188754\n",
      "MSE: 8720730.136760743\n",
      "RMSE: 2953.0882372121464\n"
     ]
    }
   ],
   "source": [
    "#### predicting 'price' using lin_reg\n",
    "predictions_DT = model_DT.predict(x_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions_DT))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions_DT))\n",
    "DT_RMSE=np.sqrt(metrics.mean_squared_error(y_test, predictions_DT))\n",
    "print('RMSE:', DT_RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_DT = {'MODEL':'DT Regressor',\n",
    "           'Train_ACCURACY':DT_score_train,\n",
    "           'Test_ACCURACY':DT_score_test,\n",
    "           'RMSE':DT_RMSE}\n",
    "final_results.append(dict_DT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_bd842fb4_ead5_11ea_8920_24ee9ae80742row0_col1{\n",
       "            background-color:  #08306b;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_bd842fb4_ead5_11ea_8920_24ee9ae80742row1_col1{\n",
       "            background-color:  #084e98;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_bd842fb4_ead5_11ea_8920_24ee9ae80742row2_col1,#T_bd842fb4_ead5_11ea_8920_24ee9ae80742row3_col1{\n",
       "            background-color:  #115ca5;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_bd842fb4_ead5_11ea_8920_24ee9ae80742row4_col1{\n",
       "            background-color:  #2676b8;\n",
       "            color:  #000000;\n",
       "        }#T_bd842fb4_ead5_11ea_8920_24ee9ae80742row5_col1{\n",
       "            background-color:  #3b8bc2;\n",
       "            color:  #000000;\n",
       "        }#T_bd842fb4_ead5_11ea_8920_24ee9ae80742row6_col1{\n",
       "            background-color:  #4a98c9;\n",
       "            color:  #000000;\n",
       "        }#T_bd842fb4_ead5_11ea_8920_24ee9ae80742row7_col1{\n",
       "            background-color:  #4e9acb;\n",
       "            color:  #000000;\n",
       "        }#T_bd842fb4_ead5_11ea_8920_24ee9ae80742row8_col1{\n",
       "            background-color:  #5aa2cf;\n",
       "            color:  #000000;\n",
       "        }#T_bd842fb4_ead5_11ea_8920_24ee9ae80742row9_col1{\n",
       "            background-color:  #5ca4d0;\n",
       "            color:  #000000;\n",
       "        }#T_bd842fb4_ead5_11ea_8920_24ee9ae80742row10_col1{\n",
       "            background-color:  #abd0e6;\n",
       "            color:  #000000;\n",
       "        }#T_bd842fb4_ead5_11ea_8920_24ee9ae80742row11_col1{\n",
       "            background-color:  #bed8ec;\n",
       "            color:  #000000;\n",
       "        }#T_bd842fb4_ead5_11ea_8920_24ee9ae80742row12_col1{\n",
       "            background-color:  #c6dbef;\n",
       "            color:  #000000;\n",
       "        }#T_bd842fb4_ead5_11ea_8920_24ee9ae80742row13_col1{\n",
       "            background-color:  #d6e5f4;\n",
       "            color:  #000000;\n",
       "        }#T_bd842fb4_ead5_11ea_8920_24ee9ae80742row14_col1{\n",
       "            background-color:  #d6e6f4;\n",
       "            color:  #000000;\n",
       "        }#T_bd842fb4_ead5_11ea_8920_24ee9ae80742row15_col1{\n",
       "            background-color:  #e0ecf8;\n",
       "            color:  #000000;\n",
       "        }#T_bd842fb4_ead5_11ea_8920_24ee9ae80742row16_col1{\n",
       "            background-color:  #e6f0f9;\n",
       "            color:  #000000;\n",
       "        }#T_bd842fb4_ead5_11ea_8920_24ee9ae80742row17_col1{\n",
       "            background-color:  #eff6fc;\n",
       "            color:  #000000;\n",
       "        }#T_bd842fb4_ead5_11ea_8920_24ee9ae80742row18_col1{\n",
       "            background-color:  #f7fbff;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Number of trees</th>        <th class=\"col_heading level0 col1\" >ACCURACY</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row0_col0\" class=\"data row0 col0\" >50</td>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row0_col1\" class=\"data row0 col1\" >0.912008</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row1_col0\" class=\"data row1 col0\" >20</td>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row1_col1\" class=\"data row1 col1\" >0.911499</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row2_col0\" class=\"data row2 col0\" >60</td>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row2_col1\" class=\"data row2 col1\" >0.911267</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row3_col0\" class=\"data row3 col0\" >40</td>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row3_col1\" class=\"data row3 col1\" >0.911264</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row4_col0\" class=\"data row4 col0\" >70</td>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row4_col1\" class=\"data row4 col1\" >0.910807</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row5_col0\" class=\"data row5 col0\" >90</td>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row5_col1\" class=\"data row5 col1\" >0.910469</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row6_col0\" class=\"data row6 col0\" >100</td>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row6_col1\" class=\"data row6 col1\" >0.910244</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row7_col0\" class=\"data row7 col0\" >10</td>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row7_col1\" class=\"data row7 col1\" >0.910193</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row8_col0\" class=\"data row8 col0\" >110</td>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row8_col1\" class=\"data row8 col1\" >0.910033</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row9_col0\" class=\"data row9 col0\" >80</td>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row9_col1\" class=\"data row9 col1\" >0.910015</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row10_col0\" class=\"data row10 col0\" >130</td>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row10_col1\" class=\"data row10 col1\" >0.909085</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row11_col0\" class=\"data row11 col0\" >120</td>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row11_col1\" class=\"data row11 col1\" >0.908817</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row12_col0\" class=\"data row12 col0\" >140</td>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row12_col1\" class=\"data row12 col1\" >0.908712</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row13_col0\" class=\"data row13 col0\" >150</td>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row13_col1\" class=\"data row13 col1\" >0.908355</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row14_col0\" class=\"data row14 col0\" >160</td>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row14_col1\" class=\"data row14 col1\" >0.908344</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row15_col0\" class=\"data row15 col0\" >190</td>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row15_col1\" class=\"data row15 col1\" >0.908114</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row16_col0\" class=\"data row16 col0\" >170</td>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row16_col1\" class=\"data row16 col1\" >0.907999</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row17_col0\" class=\"data row17 col0\" >180</td>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row17_col1\" class=\"data row17 col1\" >0.907784</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row18_col0\" class=\"data row18 col0\" >30</td>\n",
       "                        <td id=\"T_bd842fb4_ead5_11ea_8920_24ee9ae80742row18_col1\" class=\"data row18 col1\" >0.907609</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x196972dbd88>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "list_RFR=[]\n",
    "#Tune number of trees\n",
    "for i in range(10,200,10):\n",
    "    model_RFR=RandomForestRegressor(n_estimators=i,random_state=10)\n",
    "    model_RFR.fit(x_train,y_train)\n",
    "    dict_RFR={}\n",
    "    dict_RFR[\"Number of trees\"] = str(i)\n",
    "    dict_RFR[\"ACCURACY\"]=model_RFR.score(x_test,y_test)\n",
    "    list_RFR.append(dict_RFR)\n",
    "    \n",
    "(pd.DataFrame(list_RFR)\n",
    "     .sort_values(by=['ACCURACY'],ascending=False)\n",
    "     .reset_index(drop=True)\n",
    "     .style.background_gradient(cmap='Blues'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_estimators=50, random_state=10)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_RFR = RandomForestRegressor(n_estimators=50,random_state=10)\n",
    "model_RFR.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.9874907945466967\n",
      "Test Accuracy  : 0.9120081880470541\n"
     ]
    }
   ],
   "source": [
    "RFR_score_train = model_RFR.score(x_train, y_train)\n",
    "print(\"Train Accuracy :\",RFR_score_train)\n",
    "\n",
    "RFR_score_test = model_RFR.score(x_test,y_test)\n",
    "print(\"Test Accuracy  :\",RFR_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1603.7387358064516\n",
      "MSE: 5810747.8859931175\n",
      "RMSE: 2410.5492913427674\n"
     ]
    }
   ],
   "source": [
    "#### predicting 'price' using lin_reg\n",
    "predictions_RFR = model_RFR.predict(x_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions_RFR))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions_RFR))\n",
    "RFR_RMSE=np.sqrt(metrics.mean_squared_error(y_test, predictions_RFR))\n",
    "print('RMSE:', RFR_RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_RFR = {'MODEL':'Random Forest Regressor',\n",
    "            'Train_ACCURACY':RFR_score_train,\n",
    "            'Test_ACCURACY':RFR_score_test,\n",
    "            'RMSE':RFR_RMSE\n",
    "           }\n",
    "final_results.append(dict_RFR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BaggingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_c71a0624_ead5_11ea_8c04_24ee9ae80742row0_col1{\n",
       "            background-color:  #08306b;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_c71a0624_ead5_11ea_8c04_24ee9ae80742row1_col1{\n",
       "            background-color:  #3080bd;\n",
       "            color:  #000000;\n",
       "        }#T_c71a0624_ead5_11ea_8c04_24ee9ae80742row2_col1{\n",
       "            background-color:  #3888c1;\n",
       "            color:  #000000;\n",
       "        }#T_c71a0624_ead5_11ea_8c04_24ee9ae80742row3_col1{\n",
       "            background-color:  #5aa2cf;\n",
       "            color:  #000000;\n",
       "        }#T_c71a0624_ead5_11ea_8c04_24ee9ae80742row4_col1{\n",
       "            background-color:  #6caed6;\n",
       "            color:  #000000;\n",
       "        }#T_c71a0624_ead5_11ea_8c04_24ee9ae80742row5_col1{\n",
       "            background-color:  #72b2d8;\n",
       "            color:  #000000;\n",
       "        }#T_c71a0624_ead5_11ea_8c04_24ee9ae80742row6_col1{\n",
       "            background-color:  #75b4d8;\n",
       "            color:  #000000;\n",
       "        }#T_c71a0624_ead5_11ea_8c04_24ee9ae80742row7_col1{\n",
       "            background-color:  #81badb;\n",
       "            color:  #000000;\n",
       "        }#T_c71a0624_ead5_11ea_8c04_24ee9ae80742row8_col1{\n",
       "            background-color:  #a6cee4;\n",
       "            color:  #000000;\n",
       "        }#T_c71a0624_ead5_11ea_8c04_24ee9ae80742row9_col1{\n",
       "            background-color:  #abd0e6;\n",
       "            color:  #000000;\n",
       "        }#T_c71a0624_ead5_11ea_8c04_24ee9ae80742row10_col1{\n",
       "            background-color:  #add0e6;\n",
       "            color:  #000000;\n",
       "        }#T_c71a0624_ead5_11ea_8c04_24ee9ae80742row11_col1{\n",
       "            background-color:  #b5d4e9;\n",
       "            color:  #000000;\n",
       "        }#T_c71a0624_ead5_11ea_8c04_24ee9ae80742row12_col1{\n",
       "            background-color:  #b7d4ea;\n",
       "            color:  #000000;\n",
       "        }#T_c71a0624_ead5_11ea_8c04_24ee9ae80742row13_col1{\n",
       "            background-color:  #bed8ec;\n",
       "            color:  #000000;\n",
       "        }#T_c71a0624_ead5_11ea_8c04_24ee9ae80742row14_col1{\n",
       "            background-color:  #cee0f2;\n",
       "            color:  #000000;\n",
       "        }#T_c71a0624_ead5_11ea_8c04_24ee9ae80742row15_col1{\n",
       "            background-color:  #cfe1f2;\n",
       "            color:  #000000;\n",
       "        }#T_c71a0624_ead5_11ea_8c04_24ee9ae80742row16_col1{\n",
       "            background-color:  #d4e4f4;\n",
       "            color:  #000000;\n",
       "        }#T_c71a0624_ead5_11ea_8c04_24ee9ae80742row17_col1{\n",
       "            background-color:  #d9e7f5;\n",
       "            color:  #000000;\n",
       "        }#T_c71a0624_ead5_11ea_8c04_24ee9ae80742row18_col1{\n",
       "            background-color:  #f7fbff;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Number of trees</th>        <th class=\"col_heading level0 col1\" >ACCURACY</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row0_col0\" class=\"data row0 col0\" >40</td>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row0_col1\" class=\"data row0 col1\" >0.917846</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row1_col0\" class=\"data row1 col0\" >30</td>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row1_col1\" class=\"data row1 col1\" >0.914527</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row2_col0\" class=\"data row2 col0\" >50</td>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row2_col1\" class=\"data row2 col1\" >0.914173</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row3_col0\" class=\"data row3 col0\" >20</td>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row3_col1\" class=\"data row3 col1\" >0.913007</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row4_col0\" class=\"data row4 col0\" >60</td>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row4_col1\" class=\"data row4 col1\" >0.912395</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row5_col0\" class=\"data row5 col0\" >80</td>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row5_col1\" class=\"data row5 col1\" >0.912235</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row6_col0\" class=\"data row6 col0\" >70</td>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row6_col1\" class=\"data row6 col1\" >0.912139</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row7_col0\" class=\"data row7 col0\" >90</td>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row7_col1\" class=\"data row7 col1\" >0.911879</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row8_col0\" class=\"data row8 col0\" >100</td>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row8_col1\" class=\"data row8 col1\" >0.910822</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row9_col0\" class=\"data row9 col0\" >110</td>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row9_col1\" class=\"data row9 col1\" >0.910629</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row10_col0\" class=\"data row10 col0\" >150</td>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row10_col1\" class=\"data row10 col1\" >0.910575</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row11_col0\" class=\"data row11 col0\" >140</td>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row11_col1\" class=\"data row11 col1\" >0.910301</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row12_col0\" class=\"data row12 col0\" >130</td>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row12_col1\" class=\"data row12 col1\" >0.910254</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row13_col0\" class=\"data row13 col0\" >120</td>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row13_col1\" class=\"data row13 col1\" >0.909993</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row14_col0\" class=\"data row14 col0\" >180</td>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row14_col1\" class=\"data row14 col1\" >0.909279</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row15_col0\" class=\"data row15 col0\" >160</td>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row15_col1\" class=\"data row15 col1\" >0.909222</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row16_col0\" class=\"data row16 col0\" >190</td>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row16_col1\" class=\"data row16 col1\" >0.908950</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row17_col0\" class=\"data row17 col0\" >170</td>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row17_col1\" class=\"data row17 col1\" >0.908668</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row18_col0\" class=\"data row18 col0\" >10</td>\n",
       "                        <td id=\"T_c71a0624_ead5_11ea_8c04_24ee9ae80742row18_col1\" class=\"data row18 col1\" >0.907015</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x196972667c8>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingRegressor\n",
    "\n",
    "list_BR=[]\n",
    "#Tune number of trees\n",
    "for i in range(10,200,10):\n",
    "    model_BR=BaggingRegressor(n_estimators=i,oob_score=True,random_state=200)\n",
    "    model_BR.fit(x_train,y_train)\n",
    "    dict_BR={}\n",
    "    dict_BR[\"Number of trees\"] = str(i)\n",
    "    dict_BR[\"ACCURACY\"]=model_BR.score(x_test,y_test)\n",
    "    list_BR.append(dict_BR)\n",
    "    \n",
    "(pd.DataFrame(list_BR)\n",
    "     .sort_values(by=['ACCURACY'],ascending=False)\n",
    "     .reset_index(drop=True)\n",
    "     .style.background_gradient(cmap='Blues'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingRegressor(n_estimators=40, oob_score=True, random_state=200)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_BR=BaggingRegressor(n_estimators=40,oob_score=True,random_state=200)\n",
    "model_BR.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.9886259453086359\n",
      "Test Accuracy : 0.9178461822917738\n"
     ]
    }
   ],
   "source": [
    "BR_score_train = model_BR.score(x_train,y_train)\n",
    "print(\"Train Accuracy :\",BR_score_train)\n",
    "\n",
    "BR_score_test = model_BR.score(x_test,y_test)\n",
    "print(\"Test Accuracy :\",BR_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1561.133266532258\n",
      "MSE: 5425222.097138057\n",
      "RMSE: 2329.2106167408\n"
     ]
    }
   ],
   "source": [
    "#### predicting 'price' using lin_reg\n",
    "predictions_BR = model_BR.predict(x_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions_BR))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions_BR))\n",
    "BR_RMSE=np.sqrt(metrics.mean_squared_error(y_test, predictions_BR))\n",
    "print('RMSE:', BR_RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_BR = {'MODEL':'Bagging Regressor',\n",
    "           'Train_ACCURACY':BR_score_train,\n",
    "           'Test_ACCURACY':BR_score_test,\n",
    "           'RMSE':BR_RMSE\n",
    "          }\n",
    "final_results.append(dict_BR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ElasticNet Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElasticNet(random_state=0)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import ElasticNet\n",
    "model_ENR = ElasticNet(random_state=0)\n",
    "model_ENR.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.8563475978333301\n",
      "Test Accuracy  : 0.8235110288331771\n"
     ]
    }
   ],
   "source": [
    "ENR_score_train = model_ENR.score(x_train,y_train)\n",
    "print(\"Train Accuracy :\",ENR_score_train)\n",
    "\n",
    "ENR_score_test = model_ENR.score(x_test,y_test)\n",
    "print(\"Test Accuracy  :\",ENR_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 2177.5442315710934\n",
      "MSE: 11654867.57628227\n",
      "RMSE: 3413.922608420154\n"
     ]
    }
   ],
   "source": [
    "#### predicting 'price' using ElasticNet Regressor\n",
    "predictions_ENR = model_ENR.predict(x_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions_ENR))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions_ENR))\n",
    "ENR_RMSE=np.sqrt(metrics.mean_squared_error(y_test, predictions_ENR))\n",
    "print('RMSE:', ENR_RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_ENR = {'MODEL':'ElasticNet Regressor',\n",
    "            'Train_ACCURACY':ENR_score_train,\n",
    "            'Test_ACCURACY':ENR_score_test,\n",
    "            'RMSE':ENR_RMSE\n",
    "           }\n",
    "final_results.append(dict_ENR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor()"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "model_GBR = GradientBoostingRegressor()\n",
    "model_GBR.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.9942508585108231\n",
      "Test Accuracy  : 0.907653503437977\n"
     ]
    }
   ],
   "source": [
    "GBR_score_train = model_GBR.score(x_train,y_train)\n",
    "print(\"Train Accuracy :\",GBR_score_train)\n",
    "\n",
    "GBR_score_test = model_GBR.score(x_test,y_test)\n",
    "print(\"Test Accuracy  :\",GBR_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1589.7051600620339\n",
      "MSE: 6098319.806888355\n",
      "RMSE: 2469.4776384669603\n"
     ]
    }
   ],
   "source": [
    "#### predicting 'price' using ElasticNet Regressor\n",
    "predictions_GBR = model_GBR.predict(x_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions_GBR))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions_GBR))\n",
    "GBR_RMSE=np.sqrt(metrics.mean_squared_error(y_test, predictions_GBR))\n",
    "print('RMSE:', GBR_RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_GBR = {'MODEL':'Gradient Boosting Regressor',\n",
    "            'Train_ACCURACY':GBR_score_train,\n",
    "            'Test_ACCURACY':GBR_score_test,\n",
    "            'RMSE':GBR_RMSE\n",
    "           }\n",
    "final_results.append(dict_GBR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram-Based Gradient Boosting Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HistGradientBoostingRegressor()"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.experimental import enable_hist_gradient_boosting\n",
    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
    "model_HGBR = HistGradientBoostingRegressor()\n",
    "model_HGBR.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.9636892964789147\n",
      "Test Accuracy  : 0.889279745320581\n"
     ]
    }
   ],
   "source": [
    "HGBR_score_train = model_HGBR.score(x_train,y_train)\n",
    "print(\"Train Accuracy :\",HGBR_score_train)\n",
    "\n",
    "HGBR_score_test = model_HGBR.score(x_test,y_test)\n",
    "print(\"Test Accuracy  :\",HGBR_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1833.2560112069134\n",
      "MSE: 7311674.4789742185\n",
      "RMSE: 2704.010813398167\n"
     ]
    }
   ],
   "source": [
    "#### predicting 'price' using Histogram-Based Gradient Boosting Regressor\n",
    "predictions_HGBR = model_HGBR.predict(x_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions_HGBR))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions_HGBR))\n",
    "HGBR_RMSE=np.sqrt(metrics.mean_squared_error(y_test, predictions_HGBR))\n",
    "print('RMSE:', HGBR_RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_HGBR = {'MODEL':'Histogram Based GBR',\n",
    "             'Train_ACCURACY':HGBR_score_train,\n",
    "             'Test_ACCURACY':HGBR_score_test,\n",
    "             'RMSE':HGBR_RMSE\n",
    "            }\n",
    "final_results.append(dict_HGBR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting With XGBoost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=100, n_jobs=0, num_parallel_tree=1, random_state=0,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "model_XGBR = XGBRegressor()\n",
    "model_XGBR.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.998523693850276\n",
      "Train Accuracy : 0.9055217582551045\n"
     ]
    }
   ],
   "source": [
    "XGBR_score_train = model_XGBR.score(x_train,y_train)\n",
    "print(\"Train Accuracy :\",XGBR_score_train)\n",
    "\n",
    "XGBR_score_test = model_XGBR.score(x_test,y_test)\n",
    "print(\"Train Accuracy :\",XGBR_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1696.7096537928428\n",
      "MSE: 6239094.653319247\n",
      "RMSE: 2497.8179784202143\n"
     ]
    }
   ],
   "source": [
    "#### predicting 'price' using XGBRegressor\n",
    "predictions_XGBR = model_XGBR.predict(x_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions_XGBR))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions_XGBR))\n",
    "XGBR_RMSE=np.sqrt(metrics.mean_squared_error(y_test, predictions_XGBR))\n",
    "print('RMSE:', XGBR_RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_XGBR = {'MODEL':'XGBoost Regressor',\n",
    "             'Train_ACCURACY':XGBR_score_train,\n",
    "             'Test_ACCURACY':XGBR_score_test,\n",
    "             'RMSE':XGBR_RMSE\n",
    "            }\n",
    "final_results.append(dict_XGBR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting With LightGBM Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor()"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lightgbm import LGBMRegressor\n",
    "model_LGBR = LGBMRegressor()\n",
    "model_LGBR.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.9609605280364496\n",
      "Test Accuracy  : 0.8780216588918559\n"
     ]
    }
   ],
   "source": [
    "LGBR_score_train = model_LGBR.score(x_train,y_train)\n",
    "print(\"Train Accuracy :\",LGBR_score_train)\n",
    "\n",
    "LGBR_score_test = model_LGBR.score(x_test,y_test)\n",
    "print(\"Test Accuracy  :\",LGBR_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1876.6765463516463\n",
      "MSE: 8055128.903472544\n",
      "RMSE: 2838.155898373545\n"
     ]
    }
   ],
   "source": [
    "#### predicting 'price' using XGBRegressor\n",
    "predictions_LGBR = model_LGBR.predict(x_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions_LGBR))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions_LGBR))\n",
    "LGBR_RMSE=np.sqrt(metrics.mean_squared_error(y_test, predictions_LGBR))\n",
    "print('RMSE:', LGBR_RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_LGBR = {'MODEL':'LightGBM Regressor',\n",
    "             'Train_ACCURACY':LGBR_score_train,\n",
    "             'Test_ACCURACY':LGBR_score_test,\n",
    "             'RMSE':LGBR_RMSE\n",
    "            }\n",
    "final_results.append(dict_LGBR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting with CatBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x1969cd39d48>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from catboost import CatBoostRegressor\n",
    "model_CGBR = CatBoostRegressor(verbose=0, n_estimators=100)\n",
    "model_CGBR.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.992900343375794\n",
      "Test Accuracy  : 0.9192606254631139\n"
     ]
    }
   ],
   "source": [
    "CGBR_score_train = model_CGBR.score(x_train,y_train)\n",
    "print(\"Train Accuracy :\",CGBR_score_train)\n",
    "\n",
    "CGBR_score_test = model_CGBR.score(x_test,y_test)\n",
    "print(\"Test Accuracy  :\",CGBR_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1509.6383901542376\n",
      "MSE: 5331815.989395706\n",
      "RMSE: 2309.0725387903485\n"
     ]
    }
   ],
   "source": [
    "#### predicting 'price' using CatBoost\n",
    "predictions_CGBR = model_CGBR.predict(x_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions_CGBR))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions_CGBR))\n",
    "CGBR_RMSE=np.sqrt(metrics.mean_squared_error(y_test, predictions_CGBR))\n",
    "print('RMSE:', CGBR_RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_CGBR = {'MODEL':'CatBoost Regressor',\n",
    "             'Train_ACCURACY':CGBR_score_train,\n",
    "             'Test_ACCURACY':LGBR_score_test,\n",
    "             'RMSE':CGBR_RMSE\n",
    "            }\n",
    "final_results.append(dict_CGBR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing ACCURACY and RMSE for all models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row0_col1{\n",
       "            background-color:  #005a24;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row0_col2,#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row0_col4,#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row1_col1,#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row4_col1,#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row12_col3{\n",
       "            background-color:  #00441b;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row0_col3{\n",
       "            background-color:  #f5fbf2;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row1_col2,#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row4_col4{\n",
       "            background-color:  #016e2d;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row1_col3{\n",
       "            background-color:  #dbf1d5;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row1_col4{\n",
       "            background-color:  #00471c;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row2_col1,#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row3_col4{\n",
       "            background-color:  #004d1f;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row2_col2{\n",
       "            background-color:  #006729;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row2_col3{\n",
       "            background-color:  #e0f3db;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row2_col4{\n",
       "            background-color:  #004a1e;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row3_col1{\n",
       "            background-color:  #005c25;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row3_col2{\n",
       "            background-color:  #005723;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row3_col3{\n",
       "            background-color:  #eaf7e6;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row4_col2{\n",
       "            background-color:  #53b466;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row4_col3{\n",
       "            background-color:  #7dc87e;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row5_col1{\n",
       "            background-color:  #005120;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row5_col2,#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row10_col2{\n",
       "            background-color:  #55b567;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row5_col3,#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row12_col1,#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row12_col2,#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row12_col4{\n",
       "            background-color:  #f7fcf5;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row5_col4{\n",
       "            background-color:  #097532;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row6_col1,#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row7_col1{\n",
       "            background-color:  #147e3a;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row6_col2{\n",
       "            background-color:  #29914a;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row6_col3{\n",
       "            background-color:  #b0dfaa;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row6_col4{\n",
       "            background-color:  #137d39;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row7_col2{\n",
       "            background-color:  #319a50;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row7_col3{\n",
       "            background-color:  #a4da9e;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row7_col4{\n",
       "            background-color:  #17813d;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row8_col1{\n",
       "            background-color:  #1a843f;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row8_col2{\n",
       "            background-color:  #2e964d;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row8_col3{\n",
       "            background-color:  #a9dca3;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row8_col4{\n",
       "            background-color:  #19833e;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row9_col1{\n",
       "            background-color:  #218944;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row9_col2{\n",
       "            background-color:  #2f984f;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row9_col3{\n",
       "            background-color:  #a7dba0;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row9_col4{\n",
       "            background-color:  #1f8742;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row10_col1{\n",
       "            background-color:  #268e47;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row10_col3{\n",
       "            background-color:  #7cc87c;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row10_col4{\n",
       "            background-color:  #2f974e;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row11_col1{\n",
       "            background-color:  #83cb82;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row11_col2{\n",
       "            background-color:  #7fc97f;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row11_col3{\n",
       "            background-color:  #52b365;\n",
       "            color:  #000000;\n",
       "        }#T_cf3195a6_ead5_11ea_823f_24ee9ae80742row11_col4{\n",
       "            background-color:  #7ac77b;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MODEL</th>        <th class=\"col_heading level0 col1\" >Train_ACCURACY</th>        <th class=\"col_heading level0 col2\" >Test_ACCURACY</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >ACCURACY</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row0_col0\" class=\"data row0 col0\" >Bagging Regressor</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row0_col1\" class=\"data row0 col1\" >0.988626</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row0_col2\" class=\"data row0 col2\" >0.917846</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row0_col3\" class=\"data row0 col3\" >2329.210617</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row0_col4\" class=\"data row0 col4\" >95.323606</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row1_col0\" class=\"data row1 col0\" >XGBoost Regressor</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row1_col1\" class=\"data row1 col1\" >0.998524</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row1_col2\" class=\"data row1 col2\" >0.905522</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row1_col3\" class=\"data row1 col3\" >2497.817978</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row1_col4\" class=\"data row1 col4\" >95.202273</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row2_col0\" class=\"data row2 col0\" >Gradient Boosting Regressor</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row2_col1\" class=\"data row2 col1\" >0.994251</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row2_col2\" class=\"data row2 col2\" >0.907654</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row2_col3\" class=\"data row2 col3\" >2469.477638</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row2_col4\" class=\"data row2 col4\" >95.095218</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row3_col0\" class=\"data row3 col0\" >Random Forest Regressor</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row3_col1\" class=\"data row3 col1\" >0.987491</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row3_col2\" class=\"data row3 col2\" >0.912008</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row3_col3\" class=\"data row3 col3\" >2410.549291</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row3_col4\" class=\"data row3 col4\" >94.974949</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row4_col0\" class=\"data row4 col0\" >KNN Regressor</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row4_col1\" class=\"data row4 col1\" >0.998525</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row4_col2\" class=\"data row4 col2\" >0.878351</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row4_col3\" class=\"data row4 col3\" >2834.324839</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row4_col4\" class=\"data row4 col4\" >93.843770</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row5_col0\" class=\"data row5 col0\" >CatBoost Regressor</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row5_col1\" class=\"data row5 col1\" >0.992900</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row5_col2\" class=\"data row5 col2\" >0.878022</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row5_col3\" class=\"data row5 col3\" >2309.072539</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row5_col4\" class=\"data row5 col4\" >93.546100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row6_col0\" class=\"data row6 col0\" >Linear Regression</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row6_col1\" class=\"data row6 col1\" >0.970600</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row6_col2\" class=\"data row6 col2\" >0.892048</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row6_col3\" class=\"data row6 col3\" >2669.989321</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row6_col4\" class=\"data row6 col4\" >93.132422</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row7_col0\" class=\"data row7 col0\" >Ridge Regression</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row7_col1\" class=\"data row7 col1\" >0.970327</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row7_col2\" class=\"data row7 col2\" >0.888548</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row7_col3\" class=\"data row7 col3\" >2712.925884</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row7_col4\" class=\"data row7 col4\" >92.943788</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row8_col0\" class=\"data row8 col0\" >Lasso Regression</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row8_col1\" class=\"data row8 col1\" >0.967085</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row8_col2\" class=\"data row8 col2\" >0.890146</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row8_col3\" class=\"data row8 col3\" >2693.409526</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row8_col4\" class=\"data row8 col4\" >92.861560</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row9_col0\" class=\"data row9 col0\" >Histogram Based GBR</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row9_col1\" class=\"data row9 col1\" >0.963689</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row9_col2\" class=\"data row9 col2\" >0.889280</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row9_col3\" class=\"data row9 col3\" >2704.010813</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row9_col4\" class=\"data row9 col4\" >92.648452</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row10_col0\" class=\"data row10 col0\" >LightGBM Regressor</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row10_col1\" class=\"data row10 col1\" >0.960961</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row10_col2\" class=\"data row10 col2\" >0.878022</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row10_col3\" class=\"data row10 col3\" >2838.155898</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row10_col4\" class=\"data row10 col4\" >91.949109</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row11_col0\" class=\"data row11 col0\" >DT Regressor</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row11_col1\" class=\"data row11 col1\" >0.921637</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row11_col2\" class=\"data row11 col2\" >0.867942</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row11_col3\" class=\"data row11 col3\" >2953.088237</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row11_col4\" class=\"data row11 col4\" >89.478998</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row12_col0\" class=\"data row12 col0\" >ElasticNet Regressor</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row12_col1\" class=\"data row12 col1\" >0.856348</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row12_col2\" class=\"data row12 col2\" >0.823511</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row12_col3\" class=\"data row12 col3\" >3413.922608</td>\n",
       "                        <td id=\"T_cf3195a6_ead5_11ea_823f_24ee9ae80742row12_col4\" class=\"data row12 col4\" >83.992931</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1969256be08>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results = pd.DataFrame(final_results)\n",
    "df_results['ACCURACY'] = ((df_results['Train_ACCURACY']+df_results['Test_ACCURACY'])/2)*100\n",
    "\n",
    "(df_results.sort_values(by=['ACCURACY','RMSE'],ascending=False)\n",
    "    .reset_index(drop=True)\n",
    "    .style.background_gradient(cmap='Greens'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Predicting car price ***Bagging Regressor*** provides high **Accuracy** of **95%** with **Root Mean square error** of **2329.21**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Validation for Bagging Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores        :  [-3388.330717460857, -3867.33524921731, -5878.525518966552, -2762.0386476277286, -2428.067541938594]\n",
      "mean          :  -3664.8595350422083\n",
      "std deviation :  1213.4678964953684\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score,KFold\n",
    "\n",
    "kfold=KFold(n_splits=5)\n",
    "scores=cross_val_score(model_BR,x,y,cv=kfold,scoring=\"neg_root_mean_squared_error\")\n",
    "print(\"scores        : \", list(scores))\n",
    "print(\"mean          : \", scores.mean())\n",
    "print(\"std deviation : \", scores.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Predicting car price Bagging Regressor provides high Accuracy of 95% with Root Mean square error of 2329.21.\n",
    "\n",
    "The varaiables which are significant in predicting car price are symboling, fueltype, carbody, drivewheel, enginelocation, wheelbase, carlength, carwidth, carheight, curbweight, enginetype, boreratio, stroke, compressionratio, horsepower, peakrpm, citympg, car_brand.\n",
    "\n",
    "A model like this is very helpful in predicting car prices based on above varaiables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Saving model to disk\n",
    "pickle.dump(model_BR, open('CarPricePrediction_model.pkl','wb'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}