student.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# House Price Forecasting with Regression Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project Overview\n",
    "\n",
    "The project focuses on the creation of a machine learning project for house price forecasting for all stakeholders involved from investors to owners to buyers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background Information\n",
    "\n",
    "There are comparisons that have already been done on the difference in house prices in houses having waterfronts, and those without. Research shows that the closer a house is to a waterfront, the more expensive it is. However, it is notable that the different houses with the same waterfront have different prices. There are things shared by different houses on the same front such as geographical location, available amenities and a shared waterfront or ocean. There are also differences in the houses based on the overall size of the house, the proximity to neighbors and the different provisions in the houses among others. There are situations where some houses share certain attributes such as the same number of floors, number of bathrooms and an almost similar size and design in the house, yet the houses have a different pricing, and sometimes it is difficult to understand the difference in the pricing. There are no studies and experiments, though, showing how the different factors the house prices."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Challenges\n",
    "\n",
    "1. External Factors: The housing market is susceptible to external factors such as changing policies and economic fluctuations, impacting price trends.\n",
    "\n",
    "2. Insufficient Data: Limited data hampers the development of a recommendation system. We need more data on local amenities, security, and how they vary across geographical regions.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solutions\n",
    "\n",
    "* Explore feature engineering to create new features that can help improve our model.\n",
    "\n",
    "* To combine multiple models into an ensemble to capture patterns in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Statement\n",
    "Real estate developers encounter difficulties when assessing the precise influence of individual metrics and attributes on house pricing within the KC housing dataset. Their primary concern is the degree to which these factors interact to affect pricing outcomes. The current lack of clarity in pricing decisions can result in instances of both overpricing and underpricing of properties. And to address this issue, we aim to develop a more comprehensive understanding of the dataset's variables, enabling them to make more accurate pricing decisions based on a combination of factors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### General Objective\n",
    "\n",
    "* To develop and implement a ML based solution that leverages data analytics and predictive models to give accurate insights and enable real estate developers to make informed decisions\n",
    "\n",
    "\n",
    "\n",
    "### Specific Objectives\n",
    "\n",
    "* To develop a price prediction model  \n",
    "* To identify and select the most relevant features to build the model\n",
    "* To determine the peak season for house sales\n",
    "* To provide insightful recommendations based on our analysis\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project uses the King County House Sales dataset. The data contains the following columns.\n",
    "\n",
    "### Column Names and Descriptions for King County Data Set\n",
    "* `id` - Unique identifier for a house\n",
    "* `date` - Date house was sold\n",
    "* `price` - Sale price (prediction target)\n",
    "* `bedrooms` - Number of bedrooms\n",
    "* `bathrooms` - Number of bathrooms\n",
    "* `sqft_living` - Square footage of living space in the home\n",
    "* `sqft_lot` - Square footage of the lot\n",
    "* `floors` - Number of floors (levels) in house\n",
    "* `waterfront` - Whether the house is on a waterfront\n",
    "  * Includes Duwamish, Elliott Bay, Puget Sound, Lake Union, Ship Canal, Lake Washington, Lake Sammamish, other lake, and river/slough waterfronts\n",
    "* `view` - Quality of view from house\n",
    "  * Includes views of Mt. Rainier, Olympics, Cascades, Territorial, Seattle Skyline, Puget Sound, Lake Washington, Lake Sammamish, small lake / river / creek, and other\n",
    "* `condition` - How good the overall condition of the house is. Related to maintenance of house.\n",
    "  * See the [King County Assessor Website](https://info.kingcounty.gov/assessor/esales/Glossary.aspx?type=r) for further explanation of each condition code\n",
    "* `grade` - Overall grade of the house. Related to the construction and design of the house.\n",
    "  * See the [King County Assessor Website](https://info.kingcounty.gov/assessor/esales/Glossary.aspx?type=r) for further explanation of each building grade code\n",
    "* `sqft_above` - Square footage of house apart from basement\n",
    "* `sqft_basement` - Square footage of the basement\n",
    "* `yr_built` - Year when house was built\n",
    "* `yr_renovated` - Year when house was renovated\n",
    "* `zipcode` - ZIP Code used by the United States Postal Service\n",
    "* `lat` - Latitude coordinate\n",
    "* `long` - Longitude coordinate\n",
    "* `sqft_living15` - The square footage of interior housing living space for the nearest 15 neighbors\n",
    "* `sqft_lot15` - The square footage of the land lots of the nearest 15 neighbors\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries for data analysis and visualization\n",
    "\n",
    "# Pandas is used for data manipulation and analysis\n",
    "import pandas as pd\n",
    "\n",
    "# NumPy is used for numerical computations and data manipulation\n",
    "import numpy as np\n",
    "\n",
    "# Seaborn is a data visualization library based on Matplotlib, often used for statistical data visualization\n",
    "import seaborn as sns\n",
    "\n",
    "# Matplotlib is a comprehensive data visualization library\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Statsmodels is a library for statistical modeling and hypothesis testing\n",
    "import statsmodels\n",
    "\n",
    "# Import the Ordinary Least Squares (OLS) model from the statsmodels library\n",
    "from statsmodels.formula.api import ols\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>10/13/2014</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NONE</td>\n",
       "      <td>...</td>\n",
       "      <td>7 Average</td>\n",
       "      <td>1180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>12/9/2014</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>...</td>\n",
       "      <td>7 Average</td>\n",
       "      <td>2170</td>\n",
       "      <td>400.0</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2/25/2015</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>...</td>\n",
       "      <td>6 Low Average</td>\n",
       "      <td>770</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1933</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>12/9/2014</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>...</td>\n",
       "      <td>7 Average</td>\n",
       "      <td>1050</td>\n",
       "      <td>910.0</td>\n",
       "      <td>1965</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>2/18/2015</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>...</td>\n",
       "      <td>8 Good</td>\n",
       "      <td>1680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id        date     price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7129300520  10/13/2014  221900.0         3       1.00         1180   \n",
       "1  6414100192   12/9/2014  538000.0         3       2.25         2570   \n",
       "2  5631500400   2/25/2015  180000.0         2       1.00          770   \n",
       "3  2487200875   12/9/2014  604000.0         4       3.00         1960   \n",
       "4  1954400510   2/18/2015  510000.0         3       2.00         1680   \n",
       "\n",
       "   sqft_lot  floors waterfront  view  ...          grade sqft_above  \\\n",
       "0      5650     1.0        NaN  NONE  ...      7 Average       1180   \n",
       "1      7242     2.0         NO  NONE  ...      7 Average       2170   \n",
       "2     10000     1.0         NO  NONE  ...  6 Low Average        770   \n",
       "3      5000     1.0         NO  NONE  ...      7 Average       1050   \n",
       "4      8080     1.0         NO  NONE  ...         8 Good       1680   \n",
       "\n",
       "   sqft_basement yr_built  yr_renovated  zipcode      lat     long  \\\n",
       "0            0.0     1955           0.0    98178  47.5112 -122.257   \n",
       "1          400.0     1951        1991.0    98125  47.7210 -122.319   \n",
       "2            0.0     1933           NaN    98028  47.7379 -122.233   \n",
       "3          910.0     1965           0.0    98136  47.5208 -122.393   \n",
       "4            0.0     1987           0.0    98074  47.6168 -122.045   \n",
       "\n",
       "   sqft_living15  sqft_lot15  \n",
       "0           1340        5650  \n",
       "1           1690        7639  \n",
       "2           2720        8062  \n",
       "3           1360        5000  \n",
       "4           1800        7503  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = './data/kc_house_data.csv'  #the path to your CSV file\n",
    "\n",
    "def load_and_display(file_path, column=None, num_rows=5):\n",
    "    df = pd.read_csv(file_path)  # Load the file\n",
    "\n",
    "    if column:\n",
    "        if column in df.columns:\n",
    "            data = df[column].head(num_rows)\n",
    "        else:\n",
    "            print(f\"Column '{column}' not found in the DataFrame.\")\n",
    "    else:\n",
    "        data = df.head(num_rows)\n",
    "    return data\n",
    "\n",
    "df = pd.read_csv(path)\n",
    "\n",
    "  \n",
    "\n",
    "# Display the head of the entire DataFrame (default behavior)\n",
    "load_and_display(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                 int64\n",
       "date              object\n",
       "price            float64\n",
       "bedrooms           int64\n",
       "bathrooms        float64\n",
       "sqft_living        int64\n",
       "sqft_lot           int64\n",
       "floors           float64\n",
       "waterfront        object\n",
       "view              object\n",
       "condition         object\n",
       "grade             object\n",
       "sqft_above         int64\n",
       "sqft_basement     object\n",
       "yr_built           int64\n",
       "yr_renovated     float64\n",
       "zipcode            int64\n",
       "lat              float64\n",
       "long             float64\n",
       "sqft_living15      int64\n",
       "sqft_lot15         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the data types of each column in the DataFrame\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21597 entries, 0 to 21596\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   id             21597 non-null  int64  \n",
      " 1   date           21597 non-null  object \n",
      " 2   price          21597 non-null  float64\n",
      " 3   bedrooms       21597 non-null  int64  \n",
      " 4   bathrooms      21597 non-null  float64\n",
      " 5   sqft_living    21597 non-null  int64  \n",
      " 6   sqft_lot       21597 non-null  int64  \n",
      " 7   floors         21597 non-null  float64\n",
      " 8   waterfront     19221 non-null  object \n",
      " 9   view           21534 non-null  object \n",
      " 10  condition      21597 non-null  object \n",
      " 11  grade          21597 non-null  object \n",
      " 12  sqft_above     21597 non-null  int64  \n",
      " 13  sqft_basement  21597 non-null  object \n",
      " 14  yr_built       21597 non-null  int64  \n",
      " 15  yr_renovated   17755 non-null  float64\n",
      " 16  zipcode        21597 non-null  int64  \n",
      " 17  lat            21597 non-null  float64\n",
      " 18  long           21597 non-null  float64\n",
      " 19  sqft_living15  21597 non-null  int64  \n",
      " 20  sqft_lot15     21597 non-null  int64  \n",
      "dtypes: float64(6), int64(9), object(6)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# Display a summary of the DataFrame, including non-null counts and data types\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling\n",
    "\n",
    "Here we will work on data celaning, handling missing values, data transformation, handling duplicates, data reshaping and other processes to ensure that we have a clean, structured, and suitable format for analysis and modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_processing(df):\n",
    "    # Dropping specified columns\n",
    "    df.drop(['zipcode', 'sqft_living15', 'sqft_lot15'], axis=1, inplace=True)\n",
    "    \n",
    "    # Checking for null values and getting their counts\n",
    "    null_value_counts = df.isnull().sum()\n",
    "    \n",
    "    # Counting occurrences where 'yr_renovated' is equal to 0\n",
    "    yr_renovated_eq_zero = (df['yr_renovated'] == 0).sum()\n",
    "    \n",
    "    # Dropping the 'yr_renovated' column\n",
    "    df.drop(['yr_renovated'], axis=1, inplace=True)\n",
    "# Finding duplicates based on the 'id' column\n",
    "    duplicates = df[df['id'].duplicated()]\n",
    "    \n",
    "    return null_value_counts, yr_renovated_eq_zero, duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'yr_renovated' column has been dropped because even though the rows with data are many, more thean 85% of them contains zeros instead of actual years making it hard to use as it mostly lacked actual data of renovation years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NONE', nan, 'GOOD', 'EXCELLENT', 'AVERAGE', 'FAIR'], dtype=object)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display unique values in the 'view' column to inspect the different categories of views\n",
    "df['view'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['7 Average', '6 Low Average', '8 Good', '11 Excellent', '9 Better',\n",
       "       '5 Fair', '10 Very Good', '12 Luxury', '4 Low', '3 Poor',\n",
       "       '13 Mansion'], dtype=object)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display unique values in the 'grade' column to explore the various grade levels\n",
    "df['grade'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Average', 'Very Good', 'Good', 'Poor', 'Fair'], dtype=object)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display unique values in the 'condition' column to examine different property conditions\n",
    "df['condition'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'NO', 'YES'], dtype=object)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display unique values in the 'waterfront' column to check for potential outliers\n",
    "# Additionally, this column may be prepared for one-hot encoding\n",
    "df['waterfront'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove rows with missing values in the 'waterfront' and 'view' columns\n",
    "df.dropna(subset=['waterfront', 'view'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rows with missing values in waterfront and view were dropped to ensure data completeness for further analysis. We then replaced the string 'NONE' with 'NO' in the 'view' column to ensure consistency in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace the string 'NONE' with 'NO' in the 'view' column for consistency or further analysis\n",
    "df['view'].replace('NONE', 'NO', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>12/9/2014</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>7 Average</td>\n",
       "      <td>2170</td>\n",
       "      <td>400.0</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2/25/2015</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>6 Low Average</td>\n",
       "      <td>770</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1933</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>12/9/2014</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>7 Average</td>\n",
       "      <td>1050</td>\n",
       "      <td>910.0</td>\n",
       "      <td>1965</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>2/18/2015</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>8 Good</td>\n",
       "      <td>1680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7237550310</td>\n",
       "      <td>5/12/2014</td>\n",
       "      <td>1230000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4.50</td>\n",
       "      <td>5420</td>\n",
       "      <td>101930</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>11 Excellent</td>\n",
       "      <td>3890</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98053</td>\n",
       "      <td>47.6561</td>\n",
       "      <td>-122.005</td>\n",
       "      <td>4760</td>\n",
       "      <td>101930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id       date      price  bedrooms  bathrooms  sqft_living  \\\n",
       "1  6414100192  12/9/2014   538000.0         3       2.25         2570   \n",
       "2  5631500400  2/25/2015   180000.0         2       1.00          770   \n",
       "3  2487200875  12/9/2014   604000.0         4       3.00         1960   \n",
       "4  1954400510  2/18/2015   510000.0         3       2.00         1680   \n",
       "5  7237550310  5/12/2014  1230000.0         4       4.50         5420   \n",
       "\n",
       "   sqft_lot  floors waterfront view  ...          grade sqft_above  \\\n",
       "1      7242     2.0         NO   NO  ...      7 Average       2170   \n",
       "2     10000     1.0         NO   NO  ...  6 Low Average        770   \n",
       "3      5000     1.0         NO   NO  ...      7 Average       1050   \n",
       "4      8080     1.0         NO   NO  ...         8 Good       1680   \n",
       "5    101930     1.0         NO   NO  ...   11 Excellent       3890   \n",
       "\n",
       "   sqft_basement yr_built  yr_renovated  zipcode      lat     long  \\\n",
       "1          400.0     1951        1991.0    98125  47.7210 -122.319   \n",
       "2            0.0     1933           NaN    98028  47.7379 -122.233   \n",
       "3          910.0     1965           0.0    98136  47.5208 -122.393   \n",
       "4            0.0     1987           0.0    98074  47.6168 -122.045   \n",
       "5         1530.0     2001           0.0    98053  47.6561 -122.005   \n",
       "\n",
       "   sqft_living15  sqft_lot15  \n",
       "1           1690        7639  \n",
       "2           2720        8062  \n",
       "3           1360        5000  \n",
       "4           1800        7503  \n",
       "5           4760      101930  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                  0\n",
       "date                0\n",
       "price               0\n",
       "bedrooms            0\n",
       "bathrooms           0\n",
       "sqft_living         0\n",
       "sqft_lot            0\n",
       "floors              0\n",
       "waterfront          0\n",
       "view                0\n",
       "condition           0\n",
       "grade               0\n",
       "sqft_above          0\n",
       "sqft_basement       0\n",
       "yr_built            0\n",
       "yr_renovated     3402\n",
       "zipcode             0\n",
       "lat                 0\n",
       "long                0\n",
       "sqft_living15       0\n",
       "sqft_lot15          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm that there are no other missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the 'date' column to a datetime data type using Pandas to_datetime function\n",
    "df['date'] = pd.to_datetime(df['date']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               id       date      price  bedrooms  bathrooms  sqft_living  \\\n",
      "1      6414100192 2014-12-09   538000.0         3       2.25         2570   \n",
      "2      5631500400 2015-02-25   180000.0         2       1.00          770   \n",
      "3      2487200875 2014-12-09   604000.0         4       3.00         1960   \n",
      "4      1954400510 2015-02-18   510000.0         3       2.00         1680   \n",
      "5      7237550310 2014-05-12  1230000.0         4       4.50         5420   \n",
      "...           ...        ...        ...       ...        ...          ...   \n",
      "21591  2997800021 2015-02-19   475000.0         3       2.50         1310   \n",
      "21592   263000018 2014-05-21   360000.0         3       2.50         1530   \n",
      "21593  6600060120 2015-02-23   400000.0         4       2.50         2310   \n",
      "21594  1523300141 2014-06-23   402101.0         2       0.75         1020   \n",
      "21596  1523300157 2014-10-15   325000.0         2       0.75         1020   \n",
      "\n",
      "       sqft_lot  floors waterfront view  ...          grade sqft_above  \\\n",
      "1          7242     2.0         NO   NO  ...      7 Average       2170   \n",
      "2         10000     1.0         NO   NO  ...  6 Low Average        770   \n",
      "3          5000     1.0         NO   NO  ...      7 Average       1050   \n",
      "4          8080     1.0         NO   NO  ...         8 Good       1680   \n",
      "5        101930     1.0         NO   NO  ...   11 Excellent       3890   \n",
      "...         ...     ...        ...  ...  ...            ...        ...   \n",
      "21591      1294     2.0         NO   NO  ...         8 Good       1180   \n",
      "21592      1131     3.0         NO   NO  ...         8 Good       1530   \n",
      "21593      5813     2.0         NO   NO  ...         8 Good       2310   \n",
      "21594      1350     2.0         NO   NO  ...      7 Average       1020   \n",
      "21596      1076     2.0         NO   NO  ...      7 Average       1020   \n",
      "\n",
      "       sqft_basement yr_built  yr_renovated  zipcode      lat     long  \\\n",
      "1              400.0     1951        1991.0    98125  47.7210 -122.319   \n",
      "2                0.0     1933           NaN    98028  47.7379 -122.233   \n",
      "3              910.0     1965           0.0    98136  47.5208 -122.393   \n",
      "4                0.0     1987           0.0    98074  47.6168 -122.045   \n",
      "5             1530.0     2001           0.0    98053  47.6561 -122.005   \n",
      "...              ...      ...           ...      ...      ...      ...   \n",
      "21591          130.0     2008           0.0    98116  47.5773 -122.409   \n",
      "21592            0.0     2009           0.0    98103  47.6993 -122.346   \n",
      "21593            0.0     2014           0.0    98146  47.5107 -122.362   \n",
      "21594            0.0     2009           0.0    98144  47.5944 -122.299   \n",
      "21596            0.0     2008           0.0    98144  47.5941 -122.299   \n",
      "\n",
      "       sqft_living15  sqft_lot15  \n",
      "1               1690        7639  \n",
      "2               2720        8062  \n",
      "3               1360        5000  \n",
      "4               1800        7503  \n",
      "5               4760      101930  \n",
      "...              ...         ...  \n",
      "21591           1330        1265  \n",
      "21592           1530        1509  \n",
      "21593           1830        7200  \n",
      "21594           1020        2007  \n",
      "21596           1020        1357  \n",
      "\n",
      "[19030 rows x 21 columns]\n"
     ]
    }
   ],
   "source": [
    "# Sort the DataFrame by the 'date' column in descending order (most recent first)\n",
    "df.sort_values(by='date', ascending=False, inplace=True)\n",
    "\n",
    "# Remove duplicate rows based on the 'id' column, keeping the first occurrence\n",
    "df.drop_duplicates(subset='id', keep='first', inplace=True)\n",
    "\n",
    "# Sort the DataFrame by its index (restoring the default index order)\n",
    "df.sort_index(inplace=True)\n",
    "\n",
    "# Display the modified DataFrame after performing the data cleaning operations\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify duplicate rows in the DataFrame based on the 'id' column\n",
    "duplicates = df[df['id'].duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)\n",
    "\n",
    "Here we will explore the different features of the dataset to gain a better understanding of the data. We will use data vizualization to uncover trends and patterns. We will use Feature Engineering to create new features from existing ones and perform One-Hot Encoding on categorical variables that we will require for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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>id</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.903000e+04</td>\n",
       "      <td>1.903000e+04</td>\n",
       "      <td>19030.000000</td>\n",
       "      <td>19030.000000</td>\n",
       "      <td>19030.000000</td>\n",
       "      <td>1.903000e+04</td>\n",
       "      <td>19030.000000</td>\n",
       "      <td>19030.000000</td>\n",
       "      <td>19030.000000</td>\n",
       "      <td>15651.000000</td>\n",
       "      <td>19030.000000</td>\n",
       "      <td>19030.000000</td>\n",
       "      <td>19030.000000</td>\n",
       "      <td>19030.000000</td>\n",
       "      <td>19030.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.593750e+09</td>\n",
       "      <td>5.427404e+05</td>\n",
       "      <td>3.375355</td>\n",
       "      <td>2.119154</td>\n",
       "      <td>2084.463058</td>\n",
       "      <td>1.508839e+04</td>\n",
       "      <td>1.496847</td>\n",
       "      <td>1793.565791</td>\n",
       "      <td>1971.113190</td>\n",
       "      <td>82.772283</td>\n",
       "      <td>98077.657646</td>\n",
       "      <td>47.560114</td>\n",
       "      <td>-122.213797</td>\n",
       "      <td>1988.788650</td>\n",
       "      <td>12817.168208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.876278e+09</td>\n",
       "      <td>3.710512e+05</td>\n",
       "      <td>0.928520</td>\n",
       "      <td>0.769075</td>\n",
       "      <td>922.418436</td>\n",
       "      <td>4.087882e+04</td>\n",
       "      <td>0.540581</td>\n",
       "      <td>832.513113</td>\n",
       "      <td>29.405592</td>\n",
       "      <td>397.983369</td>\n",
       "      <td>53.407946</td>\n",
       "      <td>0.138700</td>\n",
       "      <td>0.140828</td>\n",
       "      <td>684.988741</td>\n",
       "      <td>27633.190102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000102e+06</td>\n",
       "      <td>7.800000e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>5.200000e+02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98001.000000</td>\n",
       "      <td>47.155900</td>\n",
       "      <td>-122.519000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>651.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.125049e+09</td>\n",
       "      <td>3.244625e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1430.000000</td>\n",
       "      <td>5.040000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98033.000000</td>\n",
       "      <td>47.471200</td>\n",
       "      <td>-122.328000</td>\n",
       "      <td>1490.000000</td>\n",
       "      <td>5100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.905081e+09</td>\n",
       "      <td>4.520000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1920.000000</td>\n",
       "      <td>7.615500e+03</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1570.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98065.000000</td>\n",
       "      <td>47.572300</td>\n",
       "      <td>-122.230000</td>\n",
       "      <td>1840.000000</td>\n",
       "      <td>7620.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.334501e+09</td>\n",
       "      <td>6.450000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2550.000000</td>\n",
       "      <td>1.072000e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2220.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98117.000000</td>\n",
       "      <td>47.678100</td>\n",
       "      <td>-122.124000</td>\n",
       "      <td>2360.000000</td>\n",
       "      <td>10095.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.900000e+09</td>\n",
       "      <td>7.700000e+06</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13540.000000</td>\n",
       "      <td>1.651359e+06</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>9410.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>98199.000000</td>\n",
       "      <td>47.777600</td>\n",
       "      <td>-121.315000</td>\n",
       "      <td>6210.000000</td>\n",
       "      <td>871200.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id         price      bedrooms     bathrooms   sqft_living  \\\n",
       "count  1.903000e+04  1.903000e+04  19030.000000  19030.000000  19030.000000   \n",
       "mean   4.593750e+09  5.427404e+05      3.375355      2.119154   2084.463058   \n",
       "std    2.876278e+09  3.710512e+05      0.928520      0.769075    922.418436   \n",
       "min    1.000102e+06  7.800000e+04      1.000000      0.500000    370.000000   \n",
       "25%    2.125049e+09  3.244625e+05      3.000000      1.750000   1430.000000   \n",
       "50%    3.905081e+09  4.520000e+05      3.000000      2.250000   1920.000000   \n",
       "75%    7.334501e+09  6.450000e+05      4.000000      2.500000   2550.000000   \n",
       "max    9.900000e+09  7.700000e+06     33.000000      8.000000  13540.000000   \n",
       "\n",
       "           sqft_lot        floors    sqft_above      yr_built  yr_renovated  \\\n",
       "count  1.903000e+04  19030.000000  19030.000000  19030.000000  15651.000000   \n",
       "mean   1.508839e+04      1.496847   1793.565791   1971.113190     82.772283   \n",
       "std    4.087882e+04      0.540581    832.513113     29.405592    397.983369   \n",
       "min    5.200000e+02      1.000000    370.000000   1900.000000      0.000000   \n",
       "25%    5.040000e+03      1.000000   1200.000000   1952.000000      0.000000   \n",
       "50%    7.615500e+03      1.500000   1570.000000   1975.000000      0.000000   \n",
       "75%    1.072000e+04      2.000000   2220.000000   1997.000000      0.000000   \n",
       "max    1.651359e+06      3.500000   9410.000000   2015.000000   2015.000000   \n",
       "\n",
       "            zipcode           lat          long  sqft_living15     sqft_lot15  \n",
       "count  19030.000000  19030.000000  19030.000000   19030.000000   19030.000000  \n",
       "mean   98077.657646     47.560114   -122.213797    1988.788650   12817.168208  \n",
       "std       53.407946      0.138700      0.140828     684.988741   27633.190102  \n",
       "min    98001.000000     47.155900   -122.519000     399.000000     651.000000  \n",
       "25%    98033.000000     47.471200   -122.328000    1490.000000    5100.000000  \n",
       "50%    98065.000000     47.572300   -122.230000    1840.000000    7620.000000  \n",
       "75%    98117.000000     47.678100   -122.124000    2360.000000   10095.750000  \n",
       "max    98199.000000     47.777600   -121.315000    6210.000000  871200.000000  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display summary statistics of the DataFrame using the describe() method\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import the Seaborn and Matplotlib libraries for data visualization\n",
    "sns.histplot(df['price'], kde=True)  # Create a histogram plot for the 'price' column\n",
    "plt.title('Distribution of Price')  # Set the title for the plot\n",
    "plt.show()  # Display the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1080 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#set up a function to Create histograms for all columns in the DataFrame\n",
    "def plot_histograms(data_frame, bins=50, figsize=(20, 15)):\n",
    "    data_frame.hist(bins=bins, figsize=figsize)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_histograms(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sqft_above', ylabel='price'>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a regression plot using Seaborn to explore the relationship between 'sqft_above' and 'price'\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.regplot(x='sqft_above', y='price', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the 'sqft_basement' column to numeric, replacing non-numeric values with NaN.\n",
    "df['sqft_basement'] = pd.to_numeric(df['sqft_basement'], errors='coerce')\n",
    "\n",
    "# Replace NaN values in the 'sqft_basement' column with 0.\n",
    "df['sqft_basement'] = df['sqft_basement'].fillna(0)\n",
    "\n",
    "# Convert the 'sqft_basement' values to integers, handling NaN values as None.\n",
    "df['sqft_basement'] = df['sqft_basement'].apply(lambda x: int(float(x)) if pd.notna(x) else None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace string values with numeric values in the DataFrame for the 'view' and 'waterfront' columns\n",
    "# Mapping 'view' categories to numeric values\n",
    "view_mapping = {'EXCELLENT': 4, 'GOOD': 3, 'AVERAGE': 2, 'FAIR': 1, 'NO': 0}\n",
    "df.replace(view_mapping, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping 'waterfront' categories to numeric values\n",
    "view_mapping = {'YES': 1, 'NO': 0}\n",
    "df.replace(view_mapping, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping 'condition' categories to numeric values\n",
    "view_mapping = {'Average': 3, 'Very Good': 5, 'Good': 4, 'Poor': 1, 'Fair': 2}\n",
    "df.replace(view_mapping, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the numeric grade from the 'grade' column and store it in a new 'grade_int' column\n",
    "df['grade_int'] = df['grade'].str.extract(r'(\\d+)').astype(int)\n",
    "\n",
    "# Drop the 'grade' column from the DataFrame\n",
    "column_drop = 'grade'\n",
    "df = df.drop(column_drop, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>grade_int</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2015-02-25</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7237550310</td>\n",
       "      <td>2014-05-12</td>\n",
       "      <td>1230000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4.50</td>\n",
       "      <td>5420</td>\n",
       "      <td>101930</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3890</td>\n",
       "      <td>1530</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98053</td>\n",
       "      <td>47.6561</td>\n",
       "      <td>-122.005</td>\n",
       "      <td>4760</td>\n",
       "      <td>101930</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id       date      price  bedrooms  bathrooms  sqft_living  \\\n",
       "1  6414100192 2014-12-09   538000.0         3       2.25         2570   \n",
       "2  5631500400 2015-02-25   180000.0         2       1.00          770   \n",
       "3  2487200875 2014-12-09   604000.0         4       3.00         1960   \n",
       "4  1954400510 2015-02-18   510000.0         3       2.00         1680   \n",
       "5  7237550310 2014-05-12  1230000.0         4       4.50         5420   \n",
       "\n",
       "   sqft_lot  floors  waterfront  view  ...  sqft_above  sqft_basement  \\\n",
       "1      7242     2.0           0     0  ...        2170            400   \n",
       "2     10000     1.0           0     0  ...         770              0   \n",
       "3      5000     1.0           0     0  ...        1050            910   \n",
       "4      8080     1.0           0     0  ...        1680              0   \n",
       "5    101930     1.0           0     0  ...        3890           1530   \n",
       "\n",
       "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "1      1951        1991.0    98125  47.7210 -122.319           1690   \n",
       "2      1933           NaN    98028  47.7379 -122.233           2720   \n",
       "3      1965           0.0    98136  47.5208 -122.393           1360   \n",
       "4      1987           0.0    98074  47.6168 -122.045           1800   \n",
       "5      2001           0.0    98053  47.6561 -122.005           4760   \n",
       "\n",
       "   sqft_lot15  grade_int  \n",
       "1        7639          7  \n",
       "2        8062          6  \n",
       "3        5000          7  \n",
       "4        7503          8  \n",
       "5      101930         11  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview the dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm the unique values in the waterfront column. They should be '0' and '1'\n",
    "df['waterfront'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>grade_int</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.006519</td>\n",
       "      <td>-0.012911</td>\n",
       "      <td>-0.133958</td>\n",
       "      <td>0.017828</td>\n",
       "      <td>-0.004880</td>\n",
       "      <td>0.014068</td>\n",
       "      <td>-0.022813</td>\n",
       "      <td>-0.012369</td>\n",
       "      <td>-0.002847</td>\n",
       "      <td>0.022633</td>\n",
       "      <td>-0.010416</td>\n",
       "      <td>-0.007255</td>\n",
       "      <td>-0.006524</td>\n",
       "      <td>0.018949</td>\n",
       "      <td>-0.002234</td>\n",
       "      <td>-0.139255</td>\n",
       "      <td>0.006593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.019219</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.309329</td>\n",
       "      <td>0.526036</td>\n",
       "      <td>0.703850</td>\n",
       "      <td>0.086395</td>\n",
       "      <td>0.256893</td>\n",
       "      <td>0.271527</td>\n",
       "      <td>0.395255</td>\n",
       "      <td>0.035922</td>\n",
       "      <td>0.608692</td>\n",
       "      <td>0.319018</td>\n",
       "      <td>0.050753</td>\n",
       "      <td>0.122900</td>\n",
       "      <td>-0.048031</td>\n",
       "      <td>0.305905</td>\n",
       "      <td>0.019495</td>\n",
       "      <td>0.580949</td>\n",
       "      <td>0.080894</td>\n",
       "      <td>0.666696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>0.002728</td>\n",
       "      <td>0.309329</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.516747</td>\n",
       "      <td>0.578524</td>\n",
       "      <td>0.029797</td>\n",
       "      <td>0.181755</td>\n",
       "      <td>-0.004183</td>\n",
       "      <td>0.083868</td>\n",
       "      <td>0.024352</td>\n",
       "      <td>0.480910</td>\n",
       "      <td>0.295752</td>\n",
       "      <td>0.156668</td>\n",
       "      <td>0.016556</td>\n",
       "      <td>-0.152507</td>\n",
       "      <td>-0.013258</td>\n",
       "      <td>0.131794</td>\n",
       "      <td>0.393864</td>\n",
       "      <td>0.028025</td>\n",
       "      <td>0.360649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>0.006519</td>\n",
       "      <td>0.526036</td>\n",
       "      <td>0.516747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.755730</td>\n",
       "      <td>0.085276</td>\n",
       "      <td>0.505359</td>\n",
       "      <td>0.064609</td>\n",
       "      <td>0.185797</td>\n",
       "      <td>-0.129021</td>\n",
       "      <td>0.687386</td>\n",
       "      <td>0.277298</td>\n",
       "      <td>0.506215</td>\n",
       "      <td>0.047145</td>\n",
       "      <td>-0.200882</td>\n",
       "      <td>0.024612</td>\n",
       "      <td>0.222399</td>\n",
       "      <td>0.568908</td>\n",
       "      <td>0.086194</td>\n",
       "      <td>0.666311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>-0.012911</td>\n",
       "      <td>0.703850</td>\n",
       "      <td>0.578524</td>\n",
       "      <td>0.755730</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.172888</td>\n",
       "      <td>0.355893</td>\n",
       "      <td>0.107508</td>\n",
       "      <td>0.285337</td>\n",
       "      <td>-0.059540</td>\n",
       "      <td>0.877730</td>\n",
       "      <td>0.426675</td>\n",
       "      <td>0.315741</td>\n",
       "      <td>0.049859</td>\n",
       "      <td>-0.195441</td>\n",
       "      <td>0.052498</td>\n",
       "      <td>0.238614</td>\n",
       "      <td>0.755316</td>\n",
       "      <td>0.182870</td>\n",
       "      <td>0.763484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot</th>\n",
       "      <td>-0.133958</td>\n",
       "      <td>0.086395</td>\n",
       "      <td>0.029797</td>\n",
       "      <td>0.085276</td>\n",
       "      <td>0.172888</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008036</td>\n",
       "      <td>0.023128</td>\n",
       "      <td>0.071214</td>\n",
       "      <td>-0.014176</td>\n",
       "      <td>0.183716</td>\n",
       "      <td>0.014117</td>\n",
       "      <td>0.055218</td>\n",
       "      <td>0.002112</td>\n",
       "      <td>-0.130061</td>\n",
       "      <td>-0.085895</td>\n",
       "      <td>0.229976</td>\n",
       "      <td>0.145875</td>\n",
       "      <td>0.721689</td>\n",
       "      <td>0.114249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floors</th>\n",
       "      <td>0.017828</td>\n",
       "      <td>0.256893</td>\n",
       "      <td>0.181755</td>\n",
       "      <td>0.505359</td>\n",
       "      <td>0.355893</td>\n",
       "      <td>-0.008036</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021192</td>\n",
       "      <td>0.027781</td>\n",
       "      <td>-0.266144</td>\n",
       "      <td>0.523979</td>\n",
       "      <td>-0.239496</td>\n",
       "      <td>0.489822</td>\n",
       "      <td>-0.001385</td>\n",
       "      <td>-0.057422</td>\n",
       "      <td>0.050587</td>\n",
       "      <td>0.126026</td>\n",
       "      <td>0.279070</td>\n",
       "      <td>-0.011448</td>\n",
       "      <td>0.457470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterfront</th>\n",
       "      <td>-0.004880</td>\n",
       "      <td>0.271527</td>\n",
       "      <td>-0.004183</td>\n",
       "      <td>0.064609</td>\n",
       "      <td>0.107508</td>\n",
       "      <td>0.023128</td>\n",
       "      <td>0.021192</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.407505</td>\n",
       "      <td>0.017118</td>\n",
       "      <td>0.073470</td>\n",
       "      <td>0.086184</td>\n",
       "      <td>-0.026910</td>\n",
       "      <td>0.087869</td>\n",
       "      <td>0.031905</td>\n",
       "      <td>-0.013013</td>\n",
       "      <td>-0.040344</td>\n",
       "      <td>0.087340</td>\n",
       "      <td>0.031916</td>\n",
       "      <td>0.085743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view</th>\n",
       "      <td>0.014068</td>\n",
       "      <td>0.395255</td>\n",
       "      <td>0.083868</td>\n",
       "      <td>0.185797</td>\n",
       "      <td>0.285337</td>\n",
       "      <td>0.071214</td>\n",
       "      <td>0.027781</td>\n",
       "      <td>0.407505</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.046807</td>\n",
       "      <td>0.168069</td>\n",
       "      <td>0.274744</td>\n",
       "      <td>-0.057609</td>\n",
       "      <td>0.098802</td>\n",
       "      <td>0.089685</td>\n",
       "      <td>0.009991</td>\n",
       "      <td>-0.078505</td>\n",
       "      <td>0.275601</td>\n",
       "      <td>0.071331</td>\n",
       "      <td>0.247601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <td>-0.022813</td>\n",
       "      <td>0.035922</td>\n",
       "      <td>0.024352</td>\n",
       "      <td>-0.129021</td>\n",
       "      <td>-0.059540</td>\n",
       "      <td>-0.014176</td>\n",
       "      <td>-0.266144</td>\n",
       "      <td>0.017118</td>\n",
       "      <td>0.046807</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.158258</td>\n",
       "      <td>0.167786</td>\n",
       "      <td>-0.364634</td>\n",
       "      <td>-0.061146</td>\n",
       "      <td>0.001751</td>\n",
       "      <td>-0.019051</td>\n",
       "      <td>-0.106342</td>\n",
       "      <td>-0.094257</td>\n",
       "      <td>-0.005553</td>\n",
       "      <td>-0.147841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_above</th>\n",
       "      <td>-0.012369</td>\n",
       "      <td>0.608692</td>\n",
       "      <td>0.480910</td>\n",
       "      <td>0.687386</td>\n",
       "      <td>0.877730</td>\n",
       "      <td>0.183716</td>\n",
       "      <td>0.523979</td>\n",
       "      <td>0.073470</td>\n",
       "      <td>0.168069</td>\n",
       "      <td>-0.158258</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.050412</td>\n",
       "      <td>0.420531</td>\n",
       "      <td>0.018100</td>\n",
       "      <td>-0.257596</td>\n",
       "      <td>0.002058</td>\n",
       "      <td>0.341822</td>\n",
       "      <td>0.732908</td>\n",
       "      <td>0.192329</td>\n",
       "      <td>0.757520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_basement</th>\n",
       "      <td>-0.002847</td>\n",
       "      <td>0.319018</td>\n",
       "      <td>0.295752</td>\n",
       "      <td>0.277298</td>\n",
       "      <td>0.426675</td>\n",
       "      <td>0.014117</td>\n",
       "      <td>-0.239496</td>\n",
       "      <td>0.086184</td>\n",
       "      <td>0.274744</td>\n",
       "      <td>0.167786</td>\n",
       "      <td>-0.050412</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.129477</td>\n",
       "      <td>0.066107</td>\n",
       "      <td>0.074885</td>\n",
       "      <td>0.105047</td>\n",
       "      <td>-0.143236</td>\n",
       "      <td>0.194739</td>\n",
       "      <td>0.017332</td>\n",
       "      <td>0.164060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_built</th>\n",
       "      <td>0.022633</td>\n",
       "      <td>0.050753</td>\n",
       "      <td>0.156668</td>\n",
       "      <td>0.506215</td>\n",
       "      <td>0.315741</td>\n",
       "      <td>0.055218</td>\n",
       "      <td>0.489822</td>\n",
       "      <td>-0.026910</td>\n",
       "      <td>-0.057609</td>\n",
       "      <td>-0.364634</td>\n",
       "      <td>0.420531</td>\n",
       "      <td>-0.129477</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.224358</td>\n",
       "      <td>-0.344447</td>\n",
       "      <td>-0.147469</td>\n",
       "      <td>0.408554</td>\n",
       "      <td>0.323921</td>\n",
       "      <td>0.070378</td>\n",
       "      <td>0.444908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_renovated</th>\n",
       "      <td>-0.010416</td>\n",
       "      <td>0.122900</td>\n",
       "      <td>0.016556</td>\n",
       "      <td>0.047145</td>\n",
       "      <td>0.049859</td>\n",
       "      <td>0.002112</td>\n",
       "      <td>-0.001385</td>\n",
       "      <td>0.087869</td>\n",
       "      <td>0.098802</td>\n",
       "      <td>-0.061146</td>\n",
       "      <td>0.018100</td>\n",
       "      <td>0.066107</td>\n",
       "      <td>-0.224358</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068515</td>\n",
       "      <td>0.031981</td>\n",
       "      <td>-0.070835</td>\n",
       "      <td>-0.005253</td>\n",
       "      <td>0.002523</td>\n",
       "      <td>0.011821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zipcode</th>\n",
       "      <td>-0.007255</td>\n",
       "      <td>-0.048031</td>\n",
       "      <td>-0.152507</td>\n",
       "      <td>-0.200882</td>\n",
       "      <td>-0.195441</td>\n",
       "      <td>-0.130061</td>\n",
       "      <td>-0.057422</td>\n",
       "      <td>0.031905</td>\n",
       "      <td>0.089685</td>\n",
       "      <td>0.001751</td>\n",
       "      <td>-0.257596</td>\n",
       "      <td>0.074885</td>\n",
       "      <td>-0.344447</td>\n",
       "      <td>0.068515</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.272302</td>\n",
       "      <td>-0.563184</td>\n",
       "      <td>-0.276985</td>\n",
       "      <td>-0.146419</td>\n",
       "      <td>-0.182082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>-0.006524</td>\n",
       "      <td>0.305905</td>\n",
       "      <td>-0.013258</td>\n",
       "      <td>0.024612</td>\n",
       "      <td>0.052498</td>\n",
       "      <td>-0.085895</td>\n",
       "      <td>0.050587</td>\n",
       "      <td>-0.013013</td>\n",
       "      <td>0.009991</td>\n",
       "      <td>-0.019051</td>\n",
       "      <td>0.002058</td>\n",
       "      <td>0.105047</td>\n",
       "      <td>-0.147469</td>\n",
       "      <td>0.031981</td>\n",
       "      <td>0.272302</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.137032</td>\n",
       "      <td>0.047255</td>\n",
       "      <td>-0.083806</td>\n",
       "      <td>0.114909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long</th>\n",
       "      <td>0.018949</td>\n",
       "      <td>0.019495</td>\n",
       "      <td>0.131794</td>\n",
       "      <td>0.222399</td>\n",
       "      <td>0.238614</td>\n",
       "      <td>0.229976</td>\n",
       "      <td>0.126026</td>\n",
       "      <td>-0.040344</td>\n",
       "      <td>-0.078505</td>\n",
       "      <td>-0.106342</td>\n",
       "      <td>0.341822</td>\n",
       "      <td>-0.143236</td>\n",
       "      <td>0.408554</td>\n",
       "      <td>-0.070835</td>\n",
       "      <td>-0.563184</td>\n",
       "      <td>-0.137032</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.335792</td>\n",
       "      <td>0.254020</td>\n",
       "      <td>0.197083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living15</th>\n",
       "      <td>-0.002234</td>\n",
       "      <td>0.580949</td>\n",
       "      <td>0.393864</td>\n",
       "      <td>0.568908</td>\n",
       "      <td>0.755316</td>\n",
       "      <td>0.145875</td>\n",
       "      <td>0.279070</td>\n",
       "      <td>0.087340</td>\n",
       "      <td>0.275601</td>\n",
       "      <td>-0.094257</td>\n",
       "      <td>0.732908</td>\n",
       "      <td>0.194739</td>\n",
       "      <td>0.323921</td>\n",
       "      <td>-0.005253</td>\n",
       "      <td>-0.276985</td>\n",
       "      <td>0.047255</td>\n",
       "      <td>0.335792</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.183676</td>\n",
       "      <td>0.713068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot15</th>\n",
       "      <td>-0.139255</td>\n",
       "      <td>0.080894</td>\n",
       "      <td>0.028025</td>\n",
       "      <td>0.086194</td>\n",
       "      <td>0.182870</td>\n",
       "      <td>0.721689</td>\n",
       "      <td>-0.011448</td>\n",
       "      <td>0.031916</td>\n",
       "      <td>0.071331</td>\n",
       "      <td>-0.005553</td>\n",
       "      <td>0.192329</td>\n",
       "      <td>0.017332</td>\n",
       "      <td>0.070378</td>\n",
       "      <td>0.002523</td>\n",
       "      <td>-0.146419</td>\n",
       "      <td>-0.083806</td>\n",
       "      <td>0.254020</td>\n",
       "      <td>0.183676</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.120269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade_int</th>\n",
       "      <td>0.006593</td>\n",
       "      <td>0.666696</td>\n",
       "      <td>0.360649</td>\n",
       "      <td>0.666311</td>\n",
       "      <td>0.763484</td>\n",
       "      <td>0.114249</td>\n",
       "      <td>0.457470</td>\n",
       "      <td>0.085743</td>\n",
       "      <td>0.247601</td>\n",
       "      <td>-0.147841</td>\n",
       "      <td>0.757520</td>\n",
       "      <td>0.164060</td>\n",
       "      <td>0.444908</td>\n",
       "      <td>0.011821</td>\n",
       "      <td>-0.182082</td>\n",
       "      <td>0.114909</td>\n",
       "      <td>0.197083</td>\n",
       "      <td>0.713068</td>\n",
       "      <td>0.120269</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     id     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
       "id             1.000000 -0.019219  0.002728   0.006519    -0.012911 -0.133958   \n",
       "price         -0.019219  1.000000  0.309329   0.526036     0.703850  0.086395   \n",
       "bedrooms       0.002728  0.309329  1.000000   0.516747     0.578524  0.029797   \n",
       "bathrooms      0.006519  0.526036  0.516747   1.000000     0.755730  0.085276   \n",
       "sqft_living   -0.012911  0.703850  0.578524   0.755730     1.000000  0.172888   \n",
       "sqft_lot      -0.133958  0.086395  0.029797   0.085276     0.172888  1.000000   \n",
       "floors         0.017828  0.256893  0.181755   0.505359     0.355893 -0.008036   \n",
       "waterfront    -0.004880  0.271527 -0.004183   0.064609     0.107508  0.023128   \n",
       "view           0.014068  0.395255  0.083868   0.185797     0.285337  0.071214   \n",
       "condition     -0.022813  0.035922  0.024352  -0.129021    -0.059540 -0.014176   \n",
       "sqft_above    -0.012369  0.608692  0.480910   0.687386     0.877730  0.183716   \n",
       "sqft_basement -0.002847  0.319018  0.295752   0.277298     0.426675  0.014117   \n",
       "yr_built       0.022633  0.050753  0.156668   0.506215     0.315741  0.055218   \n",
       "yr_renovated  -0.010416  0.122900  0.016556   0.047145     0.049859  0.002112   \n",
       "zipcode       -0.007255 -0.048031 -0.152507  -0.200882    -0.195441 -0.130061   \n",
       "lat           -0.006524  0.305905 -0.013258   0.024612     0.052498 -0.085895   \n",
       "long           0.018949  0.019495  0.131794   0.222399     0.238614  0.229976   \n",
       "sqft_living15 -0.002234  0.580949  0.393864   0.568908     0.755316  0.145875   \n",
       "sqft_lot15    -0.139255  0.080894  0.028025   0.086194     0.182870  0.721689   \n",
       "grade_int      0.006593  0.666696  0.360649   0.666311     0.763484  0.114249   \n",
       "\n",
       "                 floors  waterfront      view  condition  sqft_above  \\\n",
       "id             0.017828   -0.004880  0.014068  -0.022813   -0.012369   \n",
       "price          0.256893    0.271527  0.395255   0.035922    0.608692   \n",
       "bedrooms       0.181755   -0.004183  0.083868   0.024352    0.480910   \n",
       "bathrooms      0.505359    0.064609  0.185797  -0.129021    0.687386   \n",
       "sqft_living    0.355893    0.107508  0.285337  -0.059540    0.877730   \n",
       "sqft_lot      -0.008036    0.023128  0.071214  -0.014176    0.183716   \n",
       "floors         1.000000    0.021192  0.027781  -0.266144    0.523979   \n",
       "waterfront     0.021192    1.000000  0.407505   0.017118    0.073470   \n",
       "view           0.027781    0.407505  1.000000   0.046807    0.168069   \n",
       "condition     -0.266144    0.017118  0.046807   1.000000   -0.158258   \n",
       "sqft_above     0.523979    0.073470  0.168069  -0.158258    1.000000   \n",
       "sqft_basement -0.239496    0.086184  0.274744   0.167786   -0.050412   \n",
       "yr_built       0.489822   -0.026910 -0.057609  -0.364634    0.420531   \n",
       "yr_renovated  -0.001385    0.087869  0.098802  -0.061146    0.018100   \n",
       "zipcode       -0.057422    0.031905  0.089685   0.001751   -0.257596   \n",
       "lat            0.050587   -0.013013  0.009991  -0.019051    0.002058   \n",
       "long           0.126026   -0.040344 -0.078505  -0.106342    0.341822   \n",
       "sqft_living15  0.279070    0.087340  0.275601  -0.094257    0.732908   \n",
       "sqft_lot15    -0.011448    0.031916  0.071331  -0.005553    0.192329   \n",
       "grade_int      0.457470    0.085743  0.247601  -0.147841    0.757520   \n",
       "\n",
       "               sqft_basement  yr_built  yr_renovated   zipcode       lat  \\\n",
       "id                 -0.002847  0.022633     -0.010416 -0.007255 -0.006524   \n",
       "price               0.319018  0.050753      0.122900 -0.048031  0.305905   \n",
       "bedrooms            0.295752  0.156668      0.016556 -0.152507 -0.013258   \n",
       "bathrooms           0.277298  0.506215      0.047145 -0.200882  0.024612   \n",
       "sqft_living         0.426675  0.315741      0.049859 -0.195441  0.052498   \n",
       "sqft_lot            0.014117  0.055218      0.002112 -0.130061 -0.085895   \n",
       "floors             -0.239496  0.489822     -0.001385 -0.057422  0.050587   \n",
       "waterfront          0.086184 -0.026910      0.087869  0.031905 -0.013013   \n",
       "view                0.274744 -0.057609      0.098802  0.089685  0.009991   \n",
       "condition           0.167786 -0.364634     -0.061146  0.001751 -0.019051   \n",
       "sqft_above         -0.050412  0.420531      0.018100 -0.257596  0.002058   \n",
       "sqft_basement       1.000000 -0.129477      0.066107  0.074885  0.105047   \n",
       "yr_built           -0.129477  1.000000     -0.224358 -0.344447 -0.147469   \n",
       "yr_renovated        0.066107 -0.224358      1.000000  0.068515  0.031981   \n",
       "zipcode             0.074885 -0.344447      0.068515  1.000000  0.272302   \n",
       "lat                 0.105047 -0.147469      0.031981  0.272302  1.000000   \n",
       "long               -0.143236  0.408554     -0.070835 -0.563184 -0.137032   \n",
       "sqft_living15       0.194739  0.323921     -0.005253 -0.276985  0.047255   \n",
       "sqft_lot15          0.017332  0.070378      0.002523 -0.146419 -0.083806   \n",
       "grade_int           0.164060  0.444908      0.011821 -0.182082  0.114909   \n",
       "\n",
       "                   long  sqft_living15  sqft_lot15  grade_int  \n",
       "id             0.018949      -0.002234   -0.139255   0.006593  \n",
       "price          0.019495       0.580949    0.080894   0.666696  \n",
       "bedrooms       0.131794       0.393864    0.028025   0.360649  \n",
       "bathrooms      0.222399       0.568908    0.086194   0.666311  \n",
       "sqft_living    0.238614       0.755316    0.182870   0.763484  \n",
       "sqft_lot       0.229976       0.145875    0.721689   0.114249  \n",
       "floors         0.126026       0.279070   -0.011448   0.457470  \n",
       "waterfront    -0.040344       0.087340    0.031916   0.085743  \n",
       "view          -0.078505       0.275601    0.071331   0.247601  \n",
       "condition     -0.106342      -0.094257   -0.005553  -0.147841  \n",
       "sqft_above     0.341822       0.732908    0.192329   0.757520  \n",
       "sqft_basement -0.143236       0.194739    0.017332   0.164060  \n",
       "yr_built       0.408554       0.323921    0.070378   0.444908  \n",
       "yr_renovated  -0.070835      -0.005253    0.002523   0.011821  \n",
       "zipcode       -0.563184      -0.276985   -0.146419  -0.182082  \n",
       "lat           -0.137032       0.047255   -0.083806   0.114909  \n",
       "long           1.000000       0.335792    0.254020   0.197083  \n",
       "sqft_living15  0.335792       1.000000    0.183676   0.713068  \n",
       "sqft_lot15     0.254020       0.183676    1.000000   0.120269  \n",
       "grade_int      0.197083       0.713068    0.120269   1.000000  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate and display the correlation matrix of the DataFrame 'df'\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yNi2z2n0yac1ze9eyYPBfsFMGVpzYR1xWCn9pcw0AX8TvYUxIB6a0jqRIm8gzFfHgjq+Ly8/tex19Alrh4+TKlvEP8M7Bzaw4ua/aeoUQQgghRNNjbmJTv6uyzwDVewKW6dvXYOkcDQCOATdjmaTiXcALS6fwba31x2UmyPgRy3NdXawTZPTSWt9n3a4vlue7wig/QUZFy58FjFrrN6zljVprd+v7ZwEjsAT4FsskHAp4Q2v9SVX71/nRuQ3bwEBBr5zqg+rBsWlPNnQKQgghhBBXs0Y/+8TncX3r5LvxzLa/Ncp9b/DO1tVOOlslpLMlhBBCCFGnGmWHo7Sm1tlqLLcRCiGEEEIIIa5yMvW7EEIIIYQQQogrJiNbQgghhBBCiHphbmJjPU1rb4UQQgghhBCinsjIlhBCCCGEEKJemJrY1O/S2aprjeAZwIGhxxs6BYrMdtz82+0NnQYAn/Wd39ApCCGEEEI0SebG8OW4HjWtrqUQQgghhBBC1BMZ2RJCCCGEEELUi6Z2G2HT2lshhBBCCCGEqCcysiWEEEIIIYSoF6YmNtYjnS0hhBBCCCFEvTDrpjVBhnS26sHA9q2ZM3EodsrAVztjmL9pZ7mYxyYOZXBEGLmFhTyxfB2x55OqLTtzQHdm9u+GyazZfPgEb/6wheY+nnz3j79xMjkNgP2nE1hLdJX5pe9P58RnJ8AMgUMDCZkUYrM+81Amh+cexinACQC/3n60vK4l5gIzMS/GYC4yo00avz5+tLq+1WW1UUZ0GqcXx6PNmoAhwTSf2NJmfVZsBsfePoRTgDMAPr38aDGlNfmp+cTPO0JhRgHKoAgYGkzw6BaXlYMQQgghhBB/pHrvbCmlQoHVWusuNYy/BVintT5v/XwS6KW1TqmrHP9IBqV4YnIUd/xvJYmZ2Sy7bya/xB7neFJaccygiFBa+3sz9o2FRLYM5ukpUfzlg6VVlu3TJoSojuFc9/ZiCk0mfN1cird3JjWD699dUvy5z/WV56fNmvhP4uk8pzOOvo5EPx2Nb09fXFu42sR5RnjS8Z8dbZYpB0Xnxztj52yHuchMzAsx+HTzwaOtR63aSJs1pz49TsS/uuDo68TBZ/bhc40vLi3cbOLc23sR8XBn2xzsFK3+0ga3UHdMuUXEPL0Pry7e5coKIYQQQoiG19RuI/wz7O0tQPPaFFBKNZoRu64tgzmTmsHZtEwKTWbW7D/CsE7hNjFRncJZtScWgOgzCXi4OOHv4VZl2en9ujF/004KTSYA0nJyLys/43EjLkEuOAc6Y7A34N/Pn7TdadUXBJRS2DnbAaBNGl2kLzOHbJwCnXEOdMFgb8CvXwDpe2qWg6O3I26h7gDYudjj0tyFgvSCy8pDCCGEEEKIP1JDdUrslVKfAD2Ao8BfgX8CEwEXYDtwF3A90AtYopTKBfpby9+vlJoIOADTtNaHlVLPYumUhQIpSqnHgAVAAJAMzNZan1ZKta5k+SIgF+gAtAZmA3+z1vmb1voWpZQd8D9rThpYoLWeW9WOBnm6cyEzu/hzYqaRyJbBNjGBnu4kZNjGBHm6V1k21N+bnqEt+L9RA8gvMvHGms3EnE0EoIWvFysemIUxr4B3122HKm4jzE/Px9HXsfizo68jxuPGcnHZcdnse3wfjt6OhM4MxTXEMvKlzZr9T+4nLzGP4JHBtR7VAihMz8fJz6lMDtnl4oxxWRx4Yg+OPo60nBGGa4jt6FV+ch4XT+XgHl77HIQQQgghRN0zy9Tv9SICmKe1jgSygHuA97XWva23F7oAE7TWK4BdwCytdXet9aXhmxSt9TXAh1g6aZf0BCZrrWcC7wOfWutYArxrjalsOYAPEAU8BHwHzAU6A12VUt2B7kALrXUXrXVXYGG1e1rBM4Aa2xEgVVlMFWXtDAY8XZz4ywdLeXPNZt6cOR6A5KwcRrwynxveXcJr32/itRljcTY4V55fDQaj3ELd6Pl2T7q/1J1mo5pxeO7hktwNiu4vdafXu70wHjeScyan+g1eBrdQd7rP7UPXf19D0MjmHHvnkM16U56JY+/F0mpWG+xcGs3AphBCCCGEKMWEqpNXY9VQna0zWutt1veLgYHAMKXUb0qpA1g6PJ0rLQ0rrf/uxjKSdcmqUh2y/sDn1vefWeuoajnAd1prDRwAErXWB7TWZuCgtZ54oI1S6j2l1BgsHcVylFJ3KqV2KaV2xe3aQTOvkpGWIC93krJsOySJmUaCvcvHJGYaKy2bmGlk/cE4AA6cTcSsNT5uLhSaTGRezAPg0LkkzqRlEOxsO5JWmpOvEwVpJbfdFaQV4OjjaBNj72pffLugT3cftElTmF1oG+Nmj1dHLzKiMyqtqzIOPk7kp+aXycHJJsbOpSQH726+NjmYi8wce/cQfv0D8O3tX+v6hRBCCCGEqAsN1dkqO56igQ+AG6wjRh8DVQzHcOmbuQnbWyGrGlapbAyn9PJL2zWXen/ps73WOh3oBmwE7gXmV7hBredprXtprXudcgmglZ8PLXw8cbAzMK5bBL8cireJ/+VQPJOusUw+EdkyGGNeASnZOcScTai07M+HjtM33DJjX2t/bxzs7EjPycXHzQWDdagsxNeL1n4+JOcnV9oo7m3cyU3IJS8pD3ORmZRfU/C9xtcmpiCjAEsfFLKPZ6O1xt7dnsKsQopyigAwFZjIiMnApblLuTqq497Gg/zEPPKTLTmk/pqMd4/KczAezwYz2Lvbo7XmxP+O4dLclWZjQyravBBCCCGEaCTM2lAnr8aqoe63aqWU6q+13gH8BdgKDMDyrJU7cAOwwhqbDVzOQzjbgRlYRq9mWeuoanm1lFL+QIHW+iul1HFgUXVlTGbNv1dtYN6tUzEYFF/vOsjxpFRu7BsJwJe/RbP5yAkGdwjlh0dmk1dYxJPL11VZFuDrXTG8cMMovnnwZgpNJp5Y/iMAvcJacN/IAZjMZkxmM89/8zMXO1XeB1V2ijZ/a8Oh1w6hzZqgIUG4hriS8HMCAMHDg0n9PZWEnxNQdgqDg4H297ZHKUVBRgFx/41DmzVaa/z7+uNbppNUo3a1U7T+aziHX4sBrQkYHIRriBtJGy4AEBjVjPSdKZbPBoXB0UD4vR1QSpF9JJPUbUm4tHQl5sk9AIRMC8W7W+3zEEIIIYQQ4o+kLo0W1FuFlqnf1wCbsXSwjgE3A49j6QSdBM4Ap7TWzyqlrgdewjJ5RX8gFuvU70qpXsAbWuuh1gkyjFrrN0rVswDwx3YijMqWL8IyJf2KstPTX1pnzXUhJSOCj2mtf6hqfzvPmVu/DVyBPtdX/Ttb9aHIbNfQKRT7rG+FA5JCCCGEEH92jffhJauXD42rk+/Gj3Va0yj3vd47W02NdLYspLMlhBBCCFHnGmWHo7SG7GxZ51x4B7AD5mutX6kgZijwNpZZz1O01kOuJC+Ztk0IIYQQQghRLxrq+SrrTzj9BxgJnAV2KqVWaa0PlYrxxjKPxBjrnW+BV1qvdLaEEEIIIYQQ9cLUcJNZ9AHitNbxAEqppcBkoPTvCc0EVmqtTwNorZOutNLGO3WHEEIIIYQQQvwxWmCZF+KSs9ZlpbUHfJRSG5VSu5VSf73SSmVkSwghhBBCCFEvzHX0WJlS6k7gzlKL5mmt55UOqaBY2efH7IGewHDABdihlPpVa330cvOSzpYQQgghhBDiT83asZpXRchZoGWpzyHA+QpiUrTWOUCOUmozlt/YvezOltxGKIQQQgghhKgXJm2ok1cN7ATaKaXClFKOWH5yalWZmG+BQUope6WUK9AXy89OXTYZ2apjhqKGzgCm+O1t6BTYlt2uoVMA4HrvXfx2Kqyh06Bv6xMNnYIQQgghRL0z64aZnV5rXaSUug/4EcvU7wu01geVUndb13+ktY5VSq0FogEzlunhY66kXulsCSGEEEIIIa56Wus1wJoyyz4q8/l14PU/qk7pbAkhhBBCCCHqhamJPcXUtPZWCCGEEEIIIeqJjGwJIYQQQggh6kVDPbPVUKSzJYQQQgghhKgX5iZ2Y510thrAnMlDGdQxjLyCQp5cto7Yc0nlYlr4evLaTePwcnEm9lwSj32xliKTmbAAH16YPoqOIYG8+8N2Ptm0GwBHezsW3XMjjvZ22BkM/BR9jA/W7ah1bkd2mfj2o0K0GfqMsWPYjQ426zeuKGTvLyYAzCZIOqN5Zqkzrh5X9leKxH3ZHFh4DszQargv7acE2qxPOWjkt9dO4hroCEDzvl5E3BAEwPHVyZzakAYKPFs60+Oeltg5Xt6FHL1TsfhDe8xmGDLGxMQZZpv1339pYMcGy7ZNJjh/RvGfLwtx94QcIyx4y56zJxUouP3hItp1KvtbeUIIIYQQoqmQzlY9G9QhlNYB3ox/ZSGRrYJ58vooZr27tFzcQ+MH8dnmPazdd5Snrh/O1D5d+HJHNJm5ebz87UaiOofbxBcUmbjtoxXkFhRibzDwyX03svXwCaJPJ9Q4N7NJ8/V/CrnjJUe8/BXv/V8+nfraEdS6pOMy9AYHht5g6YAd+tXElm+Krrijpc2a6P+dY8CTYbj4ObDpsTiCe3niGeJsE+fX0Y1+c2ynbc9NKyT+hxSi5kZg52hg51unOLc9g1ZDfWudh9kEn75vz79eKcTXH565355r+ptp0bokZvyNZsbfaOmA7d2hWLvSDndPy7rFH9jRtbeZ+582U1QI+fm1TkEIIYQQ4qpmamK3Ef4pxvGUUk5KqfVKqX1KqelKqcdrUMZo/be5UmpFNbGTlFJz/qh8qzKsczirdll+Gy36dAIezk74e7iVi+vTtiU/RR8DYNWuQ0R1sXSu0oy5HDyTSJHZXK5MbkEhAPZ2BuwNBmo7pnLmqBn/5gq/ZgbsHRTdhthx8FdTpfH7NpnoPsSulrWUlx53EbdgR9yCnDDYG2gxwJuEnVk1Lm82g6nAjNmkMRWYcfZxqL5QBY4fUQQ21wQ2A3sH6DfEzJ7tlV8iOzYa6DfMchxyc+DIAQNDxlg+2zuAm/tlpSGEEEIIIa4Sf5aRrR6Ag9a6OxR3pF6qSUGt9XnghmpiVlH+F6TrRKCXOwkZ2cWfEzONBHq5k5KdU7zM29WZ7Nx8TGZLdykhI5tAr+q/uRuUYtmDM2nl783S7fs5UItRLYDMFPAKKPlrg5e/4syR8p06gII8zZFdJqbcc3kdm9Ly0gpx8SvZjoufA+nHLpaLSzt6kV8eOYqzjwOdb26GZ0tnXHwdaDsxgHV/P4ydoyKwmweB3TwuK4/0FPALKOmi+gbA8cMV//UlPw8O7DLw13stHdykBIWnt+bjN+w4Ha8Ia6e56e8mnFwuKxUhhBBCiKtSU5sgo8FGtpRSbkqp75VS+5VSMdYRqzFKqcNKqa1KqXeVUquVUoHAYqC7dWRrOeBifb+kBvWEKqVirO9/U0p1LrVuo1Kqp1LqFqXU+9Zli6x1b1dKxSulbrAuNyilPlBKHbTmtebSutrtdwULtS4TU0FQDYapzFozbe4SRrwwny4tg2kb7Ffb9Grs0G8mQjsZrvgWQii3+xZlNusV5sKoDzow7PX2tBnjx++vnwSgwFhEws5MRv6nA6P/24miPDNnNqdfcU6V5XHJ3l8NtOuki28hNJng5DHF8AlmXvywCCdn+G7ZlY/6CSGEEEJcTczaUCevxqohMxsDnNdad9NadwHWAh8DE4FBQDCA1joJuB3YorXurrWeBuRa38+qZZ1LgRsBlFLNgOZa690VxDUDBgITgFesy6YCoUBXaz79K6tEKXWnUmqXUmpXWvQOZgzoxvKHZrH8oVkkZeYQ7F0y8hLk5U5SVo5N+fScXDxcnLAzWL7pB3t7kJRlrPFOZufls/P4Wa6NCK1xGQAvf8hMLun5ZKZoPP0q7m3s32Si+9A/pjPh4udAbmph8efc1MJytwI6uNph72ypL+gaT8wmTX5WEckHjLgGOuLkaY/BXtGsrxdpR8uPitWEjz+kJpfsb1oy+PhW3Mv9rdQthAC+/hrfAAjvaInvPcjMqbim9ZcbIYQQQghhqyE7WweAEUqpV5VSg4Aw4ITW+pjWWmMZzfqjfQlMs76/EVheSdw3Wmuz1voQEGRdNhBYbl2eAPxSWSVa63la615a616+kf1Zun0/0+YuYdrcJWw4eJxJvToCENkqGGNegc0thJfsjDvDyMh2AEzq1YlfDh6vcsd83FzwcHYCwMnejn7tWnEiKa3KMmWFtDeQcl6TlmCmqFCzf5OJTv3Kd6hyczTxB8x07v/HdLa8w13JuVBATlIB5iIz57ZnENzL0yYmL6MQbR0CS4+7CGZw9LDDxd+R9GMXKco3o7Um5YARjxZOl5VHmwhN4jlF8gUoKoRfNxno0b98Z+tiDhw+oOjZv6Sz5e0LvgGaC2csnw/uNdC8lcxEKIQQQghRmglVJ6/GqsGe2dJaH1VK9QTGAS8D66jRzXJXVOc5pVSqUioSmA7cVUlo6XnkVJl/r8iW2BMM7hDKmjmzySss4sll64rXfXDbFJ5Z/hPJWTnM/X4rr900jvvHXMvhc0ms/O0gAH4eriz7v5m4OTti1pqbB/Vg8uufEuDpxoszRmOnFMqgWLf/KJtjT9QqNzs7xeS/OzD/yQLMJug9yo7g1gZ2fF8EQP/xltPl4HYT7a+xw9H5jzmxDXaKyFubs+Pf8WgztBrmg2dLZ06sSwUgbJQf53/N5OS6VJSdws7RQK8HW6GUwredK837ebHp0WMoO/AKdaH1iNrPRGjZf/jrfUW89rgD2gyDR5sICdVsWG35m0TUBEvnavc2A12uMZd7Huvme018+Io9piIICIY7/ll0+Y0ihBBCCCH+9JSu8IGZeqhYqeZAmtY6Tyk1Bbgb6AQM01ofV0p9AXhorScopYYC/9RaT7CWTQcCtdaFFW/dMomG1tpdKRUKrLbeqohS6l4stwD20Fp3ti67Beiltb5PKbXIGr+izHamAX8DJgEBQCxw56W4ynT959wGH9548Z5PGjoFtmW3a+gUALjee1dDpwBA39a16wgLIYQQQtRA4x3isbp798118t34o56fNcp9b8jZCLsCryulzEAh8HfAH/heKZUCbAW6VFJ2HhCtlNpzGc9trQDeAV6oZbmvgOFADHAU+A3IrOU2hBBCCCGEEE1EQ95G+CPwYwWrOgBYR7O6WGM3AhtLlX0UeLSa7btb/z1JqU6b1jqRMvuttV4ELLK+v6WS7ZiVUv/UWhuVUn7A71ieOxNCCCGEEELUQGOeObAu/Fl+Z6uxWK2U8gYcgResE2UIIYQQQgghasDc+O90/EM12s5W2dGsilhHmH6uYNVwrXVqHeQ09I/ephBCCCGEEOLq1Gg7WzVh7VB1b+g8hBBCCCGEENUz6aY1stW0bpoUQgghhBBCiHrypx7Z+jNwSWnwmd955Iu/NXQK2Oc2dAYW37gMaOgUKPQ0A282dBoAnLj/4YZOQQghhBBNiEyQIYQQQgghhBB1wCy3EQohhBBCCCGEuFIysiWEEEIIIYSoF01t6ncZ2RJCCCGEEEKIOiAjW0IIIYQQQoh60dSe2ZLOlhBCCCGEEKJeyGyEol49PGsYAyLDyCso5Pn5P3LkVFK5mGnDuzNj1DW0DPJm5H0fkGnMA2Bwj3DumjoArTUmk5m3Pt/I/mPnq61zUJvWPDFqKHbKwPJ9MczbsbNczJOjhjIkPIzcwkLmrF7HoQRLXrf06cG07l3RWnM0OYU5362jwGQC4OZe3ZnVqxsms2Zj3Ale37Clxu0wsF1rHhs3FDuDgRW7Y5i/uXxOj48fyuD2lpwe/2odsReScLS349Pbb8TRzg57g4F1B4/x/oYdNa63Ltri/kH9uLFHV9IuXgTgrV+2sen4yRrnNLhVKM8MHoZBKZYdiuGj3b/brG/j48vrw0fTOTCQN3ds4+O9uyzLvX14b8yE4riWXl7M/XU7C/fvqXHdQgghhBDijyOdrQY0IDKMlkHeXP/oArqEN+PRvw7n1he+KBe3/9g5tu6P58M502yW7zx0ms17jwPQNsSfl+6dwI2PLaqyToNSPDMmitmfryQhK5uvbp3Jz8eOczwlrThmSHgoob7ejPxwId2aB/PcmCimLVpKkIcbN/fuwbj/fkJ+kYm3rxvP+M4RfB19iL6tQxjePpyJHy+m0GTC19Wlxu1gUIonJ0Zx+8KVJGZls+zumfwSe5zjySU5DW4fSms/b8bMXUhkSDDPTIpixn+XUlBk4tYFK7hYUIi9wcDiO25k89ETRJ9NqFG9ddEWAAt/28OC33bXuA1K5/T80OHc/M0KEozZfDt9Fuvj44hLL8kpMy+X5zZvYFSbtjZl4zPSGb/0s+Lt/Dr7LtbFH6t1DkIIIYQQdaWp3UZ41Y/jKaWclFLrlVL7lFLTlVKP16CMsZr1oUqpmVea2+Ae4azZZvlyHnP8Ah6uTvh5uZWLO3o6mQspWeWW5+YXFr93cXJA6+p/QDmyeTCn0jI4k5FJodnM94eOMKJ9uE3M8PbhfB0dC8D+8wl4ODsR4G7Jy95gwNneHjulcHGwJynb0lR/uaYb87bvpNA6ypV2sea/Ytw1JJjTqRmcTc+k0GTmhwNHiOpom1NUx3C+3WfJKfqsJSd/a04XCyztYG9nwN6u5qd0XbXFlegWFMypjAzOZFly+u7oEUaW6VSl5uYSnZRIodlc6XauDWnFqcwMzmVnX3FOQgghhBDi8jSFka0egIPWujsUd6ReusJthgIzgc+vZCOBPu4kppV8GU5KNxLo405qZk6NtzH0mrbcM20gPh6u/GPu19XGB3m4k1DqC3hClpFuLYLLx2SVxCRmGQnycCfmQiL/+3U3G++/nfzCIraeOMW2E6cBCPPzplerFjw0dAD5RSZe/XkzBy4k1mgfgjzdSci0zSkyxDanQA/bmMQsI0Ge7qQYczAoxYp7ZtLK15vPf9tfo1GtumwLgJt6dWNK147EJCTyyvrNZOXl1yinYDd3LhhL5WTMpntwsxqVLW1C+w58d+xwrcsJIYQQQtQlmfr9T0Ap5aaU+l4ptV8pFWMdsRqjlDqslNqqlHpXKbVaKRUILAa6W0e2lgMu1vdLalCPUkq9bq3jgFJqunXVK8Ag63YeuvwdKb+oJqNTpW3cE8eNjy3iX+9+y11TB1xOleXqVJXk5ensxPD2bYj6zwIGvvsxrg4OTOrSAQA7ZcDT2Ylpi5by2obNvD11fI33ocJLriY5YYkxa83U/yxh2Ovz6RoSTNtAv8uu949oi8/3RDPig4VMnr+YZGMOc0YMrlE+lvrKV1jLUwIHg4ERYeGsOXa0dgWFEEIIIcQf6k/Z2QLGAOe11t201l2AtcDHwERgEBAMoLVOAm4Htmitu2utpwG51vezalDPVKA70A0YAbyulGoGzCm1zbllCyml7lRK7VJK7Uo6ajtZww3Du7H4+ZtY/PxNpGTkEOTrUbwu0Med5Iyaj2qVtvfoOUICvfFyd64yLiHbSLBHSZ3Bnu4kGW3rTMgyEuxZEhNkjRkQ2oqzGVmkX8ylyGxm3ZE4eoQ0L97uusNxAESfT0RrjU8Nn9tKyDIS7FUmp2zbnBLLxAR5upOUZRuTnZfPzhNnGdQutGb11lFbpOZcxKwtXcEv98YQ2cx2tKwqF4zZNHMvlZO7B4k5tbs9cWjrMA4mJ5KSe7FW5YQQQggh6ppZqzp5NVZ/1s7WAWCEUupVpdQgIAw4obU+pi1DE4v/oHoGAl9orU1a60RgE9C7ukJa63la615a616B7fvbrFvx835uenoxNz29mE174hh3bScAuoQ3w5hbUKtbCEMCvYvfR7QOxN7ernimwsocOJ9AqK8PIV6eOBgMjO8Uwc9H421iNhyL57rIjgB0ax6MMb+AZGMO57Oy6d6iGc72lrtP+4e2It46mcT6o8fpF9oSgFBfbxzs7Eiv4XNbMecSaO3nQwsfTxzsDIztGsEvh8vkFBvP5O6WnCJDgsnOLyDFmIOPqwsezk4AONnb0T+8JKfq1FVbXHqmC2BkRDjHklNrlA9AdGICod7ehHhacprYPoL1J47XuDzAxPYdWHVUbiEUQgghROPT1Dpbf8pntrTWR5VSPYFxwMvAOqCWN1vVSJ0euW37TzAgMoyVr91KXn4RL/zvx+J1cx+6jn8vXEdKRg43jujBzeN64eflxucv/JXt0Sf498KfiOrVjnHXdqTIZCa/oIgnPlhdbZ0mrXn+xw387y9TsTMoVuw/SFxKKjOuiQRg6Z5oNsadYEh4KOvvmU1uYRGPrV4HQPT5BH48fIxvbptFkdlMbGIyS/ceAOCrfTG8NGEUq++4mUKziUdX/VhpDuVyMmv+vXoDH/9tKgaD4uvdB4lLSmV6b0tOy3ZGs/noCQa3D2XtP2aTV1DEEystOQV4uPHy9aMxGBQGpVgbc5RNR07UrN46aot/RQ2iQ1AAWmvOZWbx9A8/17wttOaZTRv4dNL1GAwGlh+K4VhaKjO7WHL6PCYaf1dXVk2/CXdHR7TWzO5+DaMWL8JYWICzvT0DW7bmiV9+qnGdQgghhBCibqjaPiPUGCilmgNpWus8pdQU4G6gEzBMa31cKfUF4KG1nqCUGgr8U2s9wVo2HQjUWhdWvHXLJBpaa3el1FTgLiydOl9gF9AXaAG8pbUeUl2ufW55q8EbOKNdw/f27Ws+OWGdKqr5jPR1ptCz8lkE69uJ+x9u6BSEEEII8cdp+C991Zi45f46+W783aD3GuW+/ylHtoCuWJ6fMgOFwN8Bf+B7pVQKsBXoUknZeUC0UmpPDZ7b+hroD+zHMnL2L611glIqFShSSu0HFlX03JYQQgghhBCiaftTdra01j8CFd2n1gHAOprVxRq7EdhYquyjwKPVbN/d+q8GHrG+Sq8vBIZfXvZCCCGEEEI0TY35+aq68KfsbAkhhBBCCCH+fJra72xdlZ2tsqNZFVFK+QEVzVwwXGtd8+njhBBCCCGEEKICV2VnqyasHaruDZ2HEEIIIYQQTUVTu43wz/o7W0IIIYQQQgjRqDXZka36Ymze8L33gpYFDZ0CBdmN41Tr3/NIQ6dATFKzhk4BAL3Fmy6PNPxEmjGvP9TQKQghhBCinjS1ka3G8Q1YCCGEEEIIcdVrap0tuY1QCCGEEEIIIeqAjGwJIYQQQggh6oWMbAkhhBBCCCGEuGIysiWEEEIIIYSoF1pGtoQQQgghhBBCXCkZ2apnA9u15rEJQ7EzGFixM4b5m3eWi3l8wlAGR4SRW1DI41+tI/Z8EgAvTh3JkA5tSMu5yOR3PiuO79AsgGcmD8fJ3o4is+aFVT9z4GxijXMa0jyMp3sPx04ZWBa3nw9jfrNZPzmsE3d36QvAxcJCnvztR2LTkwHwdHDilQFjifD2R2v41/Y17Ek5X+t2GdIqlKcHDsPOoFh2KIYP9/xusz7c25fXh4+mc0Agb/y6jY/37Sped1u3a5jeqStaw5HUFB7ZsJZ8k6nWOQBkRqdxdkkcmDV+Q5oRPKGVzfrs2AyOvxODU4AzAN49/Wk2JRSAU/OPkLkvFXtPBzq91Puy6ge4NqAtj3Ydi51SrDy1h//FbbVZPyw4gvs6RGHWGpM282rMWvamnQbAw96ZZ7tPop1HIBp4et837E8/W+O6H5s8lEEdwsgrLOSJZeuIPZdULqaFjyev3zQOLxdnYs8lMWfpWopM5irL3zyoB9f36YpGc+xCCk9+uY6Coss7RkIIIYT48zIjI1tXBaXUA0qpWKXUOaXU+w2dD4BBKZ6cFMVdi75h4tufMK5bBOGBvjYxg9uH0trPmzFvLuSZb9bzzOSo4nVf7znEnYu+Lrfdh8cM4oMNvzL1/SW8v347D48ZVKucnu87klt+Xs7IVfOZFNqJtl5+NjFnjJlM//Fzxn63kPeit/NyvzHF657pM5xN5+IZ/u18xq5eQFxmao3rtslh8HBuWb2SkZ8vYlK7CNr62LZLRn4uz27ZwMd7d9ksD3Jz55bIa5j45RJGL/0Eg0ExsV2HWucAoM2aM58eo+3DXen4cm/Sf00i91xOuTj39l50fKEXHV/oVdzRAvAdGETbf3a9rLovMaB4InI89/y6mMkb/sPYFl1p4x5gE/Nr8gmu3/gh0zZ9xNP7vuW5bpOK1z3adSzbkuKY9Mv7XL/xQ+KzU2pc96AOobTy92bcqwt5dsV6npoaVWHcQ+MH8dnmPYx/bRFZuflc36dLleUDPd2YNbAH099ZwnVvfobBYGBs94jaNo0QQgghrgJmrerk1VhdtZ0t4B5gHPDEH7ExpdQVjwJ2DQnmdGoGZ9MzKTSZ+SH6CFEdw21iojqF8+3eWACizyTg4eyEv4cbALtPniPzYl657WqtcXNyBMDd2Ymk7PIdhMp092vGqewMzhgzKTSb+e5kLKNatrOJ2ZN8jqyCfMv7lHMEu3lY6nJwpE9gS5bFRQNQaDaTVZhf47qLcwgM5lRmBmeyrDkcO8KosLY2Mam5uUQnJVJkNpcrb6cMONvbY6cULvb2JOYYa50DQE58Fk5BLjgFumCwN+DTN5DMPTXvPHp08MbOzeGy6r6kq08LTuekcfZiOkXaxA/nYhgWbNt5zDWV/Ei1i50D2vrezd6Jnr6tWXl6DwBF2kR2UfnzpTLDOoezarf13Dtte+6V1rdtS9YdOAbAt7sPEdU5vNry9gYDTg722BkULg72JGdd3jESQgghhPgzuSpvI1RKfQS0AVYBC0otb239HAAkA7O11qerWL4ISAN6AHuUUquAd6yb08BgrXV2TfMK8nInIbMkPCHTSGTLYJuYQE/bmMQsI0Ge7qRU0YF65ftNfDz7Oh4ZOxiDUsz679KapkSQqwfnc7KKP1+4mE13/2aVxk9v242N5+IBaOXuTWr+Rd4YMI6OvoEcSE3guZ0/k1tUWOP6AYLc3TlvLNnnC8ZsugdVnkNpiTlGPt63k+1/u4O8oiK2nDnFljOnalX/JYXpBTj6OhV/dvB14uLxrHJxOXFZxD65CwdvR1rMCMclpHyH5HIFOnuSkJtZ/DkxL5NIn5BycVHBHXiw4wh8ndy497clAIS4+pBekMOL3afQ3iuYQxnneTXmB3JNNTseQZ7uJGSUOvcyjQR52Z573q7OZOfmYzJbuniJGdkEerlXWf7g2UQWbdrN+iduJ6+wiO1HT7H96OlatIoQQgghrhYNOUGGUmoMlu/ydsB8rfUrlcT1Bn4FpmutV1xJnVflyJbW+m7gPDAMSC+16n3gU611JLAEeLea5QDtgRFa64eBfwL3aq27A4OA3NrkVfGppauN0VpXsLTEjL6RvPL9Joa/Np9X12zihamjap5TBRVWVlv/oFZMbxvJK3s2AmBnMNDFN5jFR/cyfvUicosK+XuXfjWuuziHCva66j0u4enkxMiwtgz6dD59F/0XV3sHprTvWOscKq20TGquoe50easfHV/sRcDIFsS/e/Dy6qpExce//LINCYeZ9Mv7/N/vS7mvg+V2PTtloKNXM5ad3MmNmz4i11TIbW1rfktphedCmcpVBUGXQior7+nixLDObRj98gKiXvgYF0cHJlxzebd6CiGEEEJcDqWUHfAfYCzQCfiLUqpTJXGvAj/+EfVelZ2tKvQHPre+/wwYWM1ygOVa60tP8m8D3lJKPQB4a62LKqpEKXWnUmqXUmpX+t4dxcsTMo0Ee3kUfw72cicpy3bEKjHLNibI073a2wInX9OJnw7GAbD2wFG6hgRVGV9aQk42zd08iz83c/Ug6WL5W7w6eAfwyoAx3PHLV2Tk5xWXTbiYzb6UCwCsOXWELr41r7s4B2M2zd1L9rmZuwdJNbwVcGBIa85kZZKWl0uR2cza+GP0DG5e6xwAHHwdKUgruQ2yMC0fB28nmxg7F3vsnO0A8OrmhzaZKcqu3UheVRLzsgh28Sr+HOTsRVJe5YOnu9NOEeLqi7ejK4l5WSTmZXEg4xwAP50/SEfvqkcIZwzoxoqHZrHioVkkZeUQ7F3q3Kvg/EzPycXDxQk7g6VnFeTtUXxLYEKmscLy/dq14lxaFuk5lmP0c0wc3Vtf3jESQgghxJ9bAz6z1QeI01rHa60LgKXA5Ari7ge+AsrPEnYZmlpnq6zKBlBKLy/+tmkdarwdcAF+VUpV+Od5rfU8rXUvrXUvnx79i5fHnEugtb8PLXw8cbAzMDYygl9i423KboiNZ3IPy8hMZMtgsvMKqryFECApy0jvMMutZv3CW3IqNaPK+NL2p14g1MOHEHcvHAwGJoZ25KczcTYxzd08+GjodTy09XtOZJcMFCbn5XA+J4s2npbJLK5t1ppjmTWfkKE4h6QEQr28CfHwtOTQLoKfTh6vUdnzxix6BDfD2d5yR+y1Ia2IS0+rdQ4AbmGe5Cfmkp+ci7nITPpvSXj1sJ0spDCjoHi0J+d4FtoMdu5/3N24MRnnae3mSwtXb+yVHWNbdGFj4mGbmJZuJZOHdPRqhoPBjoyCi6TmG0nIzSLUzZJz34A2HM9OrrK+pdv3c8PcJdwwdwkbYo4zqaf13GsVjLGSc+/3uDOM6mp5rm9yz05sOGg5VhsPxldY/kJ6NpGtmuHsYGmnvm1bEZ90ecdICCGEEH9uWqs6edVAC+BMqc9nrcuKKaVaANcBH/1R+3tVPrNVhe3ADCyjV7OArdUst6GUCtdaHwAOKKX6Ax2AwxXFVsRk1vx71QY+nj0Vg1J8vfsgcUmpTO8TCcCy36PZfOQEgyNCWfvwbPIKi3jiq3XF5V+fPpY+YS3xdnNmw6O38/76HazcfZBnvl5fPJ18QVERz3y9vsYNYtKap3//iU9H3IidUnwZd4BjmSnMat8dgCVH9/FA5LX4OLnwYt+RABSZzUxa8ykAz/6+nrcHTsDBzo4z2Rn8c/uaGtdtk8OWDXw66XrslIEvY2M4lpbKrM6WdllyMJoAV1dWTbsJd0dHtNbc2u0aRn6+iH2JCfxw/Bjf33gzRWYzB1OS+OJgdK1zAFB2ipY3tyXu9QNos8ZvcDAuIW4kb7BMZR8Q1Zz0ncmkbDiPslMoRwNh93QsvrXuxAeHyD6cSZGxkAMP7qDZdaH4D6nZs2clbWHmpQNr+KjfzdgpA1+f3svx7GSmte4FwPJTuxjZrBMTQ7pRpE3km4p4ZPfy4vIvH1jDKz2vx8Fgx9mcdJ7a902N6958+ASDOobyw5zZ5BYU8dSXJefeB7dO4ZkVP5GclcPcNVt5fdY47h9zLbHnklj5+8Eqyx84k8BPB47x5YOzMJnNHD6XzPJfD9SqXYQQQgghqqKUuhO4s9SieVrreaVDKihWduDlbeBRrbWpokcnLiuv6p4H+rNSSp0EegETgF5a6/uUUqFYJsLwx3YijMqWLwJWX3owTin1HpbnwEzAIeAWrXWV0+91enxugzfwxQ4F1QfVtezG0a/v3/NIQ6dATFLtOmB1RW/xbugUAIh5/aGGTkEIIYS4WjTeOdCt+qx9vE6+G/8+5qUq9906UPKs1nq09fNjAFrrl0vFnKCkDf2Bi8CdWutvLjevxvENuA5orUOtbxdZX2itTwLlfjyoiuW3lPl8/x+ZoxBCCCGEEKJe7ATaKaXCgHNY7mqbWTpAax126X2pQZdvrqTSq7azJYQQQgghhGhcGuqmOq11kVLqPiyzDNoBC7TWB5VSd1vX/2HPaZUmnS0hhBBCCCFEvTA34J2OWus1wJoyyyrsZJW9w+1yNfXZCIUQQgghhBCiTsjIlhBCCCGEEKJe1HCa9quGjGwJIYQQQgghRB2Qka06Zihq6AxAGcwNnQJupxpHv35PaseGTgGTU0NnYOHo2NAZABo6PT63obMA4NBLMgW9EEIIUdfMMrIlhBBCCCGEEOJKyciWEEIIIYQQol401NTvDUU6W0IIIYQQQoh6IRNkCCGEEEIIIYS4YjKyJYQQQgghhKgXTW1kSzpb9eSxSUMZ1CGMvMJCnvhyHbHnksrFtPDx5PVZ4/BydSb2XBJzlq6lyGSutrxBKZY9MJOkLCP3LvwWgIhm/jw1dTiujo6cMaXz4LZVGAsLKsxtcLM2PNN7BAZlYFncPj46+KvN+smhnbm7cz8AcooKeOq3H4nNSKKNpy/vDZxSHNfS3Zu50VtYeHhno2qLh8cPYkjHNhSZTJzKzOSxb9eRnZ8PwKDw1jwxeigGg4Hle2P4eFv53J8YPZQh7Sz1zfl2HYcSLPV5ODnx4sSRtA/0Q2vN49/9xL6zF7hvSD9u7NGVtIsXAXhrwzY2x52ssk1KGxzWmieHD8XOYODL/TH89zfbnNr4+vDquFF0DgrkzS3b+d/vu4vXvTx2JFHhbUi9eJFxCz6rcZ0AA9u15rHxlnpX7Iph/ubybfH4+KEMjggjt7CQx79aR+z5JIK93Hn5hjH4u7uiNXy58wCLd+wF4P4R/YnqGI7WmlRjLo9/9SPJ2TnV5zHBmsfOSvKYYM2joEwe08rksd2Sx+gu7bh3eH/aBPgy/cMvOHgusVZtI4QQQghxOf5UtxEqpR5USrleRrkOSql9Sqm9Sqnw+s5jUIdQWvl7M+61hTz71Xqeui6qwriHxg3isy17GP/aIrJy87m+d5calb9pYA/ik9Jslj13w0je/mErU+d+xo9njnJnp34V1mlQiuf7jOKWDV8y6rt5TArtRFsvP5uYM8YMpv+0hLHf/4/3DmzjpX5jAYjPSmP8mgWMX7OAiT8sJM9UyLozRxpdW+w4eorr3vqUqXMXczI1nbsG9i7e96fHRnH7598w/oNPmNA5gnB/X5uyg9uGEurnzaj3F/LU6vU8O76kvifGDGXL8ZOM/eATJv93MceTS+pd9NsepsxbwpR5S2rV0TIoxbMjo7ht+TeMmf8JEzpF0NbPNqeMvDyeX7+R+aU6WZesPHCIW5d/XeP6Stf75MQo7vrkGya+8wnjIiMIDyjTFu1Dae3vzZi3FvLMN+t5ZpKlLYrMmtd+2MzEdz5lxkdfMLNft+KyC7bs5rr3FjP1/SVsOhLPPVEVn4c2eUyK4q5F3zDx7U8Y1y2C8MAK8vDzZsyb1jwml8pjzWYmvv0pMz605mEteywxlQeWfMeuk2dr3TZCCCGE+OOYtaqTV2P1p+psAQ8CtepsKaXsgCnAt1rrHlrr46XWKaXU5bRBrfIY1imcVXtiAYg+nYCHixP+Hm7l4vq2bcm6A8cA+HbXIaI6h1dbPsjLncEdwvjq9xibbYUG+LAr/hwAWy+cYEzLiApz6+bXnFPZ6ZwxZlBoNvPdyVhGhrS3idmTco6sgjwA9qacJ9jVo9x2rg0O5VR2BudyshpdW2w/dhqT2TL1zb6zFwj2dAcgskUwp9IzOJuRSaHZzPcHjzA8wrYvPjwinG/2W+rbfy4BTycnAtzdcHN0pHerFqzYa6mr0GwuHi27Et2aBXMqI4MzmdacYo8wop1tTmkXczmQkEiRufzvp+08e46M3Lxa19s1JJjTaRmcTc+k0GTmh+gjRHW0rTeqYzjf7rW2/ZkEPJwtbZ+SnUPsecto38WCQuKT0wi0tnFOfsloqouDA7qaKYi6hgRzOrWaPDrVMI+kkjzik9M4mZJe63YRQgghxB9L67p5NVYN0tlSSv1LKfWA9f1cpdQG6/vhSqnFSqkPlVK7lFIHlVLPWdc9ADQHflFK/WJdNkoptUMptUcptVwp5W5dflIp9bRSaiswHUvn6Hal1C9KqVClVKxS6gNgD9BSKfW6UipGKXVAKTXduo2hSqmNSqkVSqnDSqkl1s5ZuTyqE+TlTkJGdvHnxAwjQV7uNjHers5k5+YXdwoSM7MJtMZUVf7RiUN5a82Wcl9i4xJSGdapDQDjWnegmVv5DhJAsKs7Fy6WdJASLmZX2Jm6ZHp4JJvOHy+3fELrjnx38lCl5S5piLYo7foeXYpHmoI83EnILLWtLCNBHra5BHm4k5BVEpOQbYlp6eNF2sVcXp40iq/vmMWLE0bg4lByV+6s3t1YdddNvDRxJJ7ONf8V4yAPdy6Urc/dvYoSf4wgT9u2SMgyFrf5JYGeFbSXp21Mc29POjYLIPpsQvGy/xs5gJ8fuZ0J3Tvw3vodVefhVSaPTGNxh6nWeTQPIPpMAkIIIYQQDaWhRrY2A4Os73sB7kopB2AgsAV4QmvdC4gEhiilIrXW7wLngWFa62FKKX/gSWCE1voaYBfwj1J15GmtB2qtPwc+AuZqrYdZ10UAn2qte1jr7w50A0YAryulmlnjemDpqHUC2gDXls2jJjtb0cBm2Q6BUuWjLoVUVn5IxzDSjBc5VMEzT08tX8dfBnRn2QMzcbN3orCCURDLtiuqt+LOSr+gVtzYthuv7Nlos9zBYGBESDvWnI6tsJxtfRXvi03MH9wWl9wZ1QeT2cyqA4cr3xZlc6k4xt5goFOzQL7YHc11Hy8ht7CIO6+13J74xa5oRr63kMn/XUySMYc5IwdXmlNZNcmpLlS0n2X/TFRhW5SKcXV04J2ZE3j5+002I1rv/LSd4a/PZ/W+w8zq373qPCpcqquNKZfHrPJ5CCGEEKLhaa3q5NVYNdQEGbuBnkopDyAfywhTLywdsAeAG5VSd1rza4alsxNdZhv9rMu3Wb+cOwKl/2y+rIr6T2mtL80CMRD4QmttAhKVUpuA3kAW8LvW+iyAUmofEApsrW7nlFJ3zpkz54m//e1vAcvuuo7YlGyCvUtGi4K83UnKsp0kID0nFw8XJ+wMCpNZE+TlQXKWEbD8db+i8qO6tmNopzYM6hCKk4M9bk6OvDJjDHOWruVEcjp3zl8JQOC1rkS1qPhRtQsXs2nm6ln8OdjVg8RcY7m4Dt4BvNJvHLM3fElGQa7NuqHNwzmYlkhK3sUK67i5/TXMjOoBQMyZxHpvC4BJPTsxuGMYf1vyVXHZhGwjwV6ltuXpTlKZyRsSsowEe5bEBHtYYrTWJGRlE33OMnKyNvYYd17bC4DUnJJ2WL4nho/+MrnCdqlIQraRZmXrM1Y9ocQfISHTti2CPcsfl8TMytvL3mDg7ZkTWL3/MOsPxVVYx/fRh/nwr1N4/+fKR7fK5eFVQR5ZNchj32HWH6w4DyGEEEKI+tIgI1ta60LgJDAb2I5lNGsYEA7kAv8EhmutI4HvAecKNqOAn7TW3a2vTlrr20qtr+obaul1VXWFSz+EY6KGnVOt9byXX365dYcOHVyn//drNhw8zqRrOgIQ2SoYY24BKRXMyPb78TOM6toOgMm9OrHhkOV2vY2H4iss//babYx4aT6jX1nAI0vW8PvxM8WdC183F8vOKbiv6wCWHNtbYa7RqecJ9fAhxM0LB4OBiaEdWX/2mE1Mc1dPPhxyPf/Y9h0nstPKbWNiaCdWnTxYaXt8dnQPN7y9hBveXtIgbXFt+9bcNrQX9y9aRV5RUXEdB84lEOrrQ4i3Jw4GA+M7R7DhaLxNHhuOxjOlm6W+bi2Cyc4vINmYQ0rORRKyjIT5+QDQP6xl8QQZAe4lz6CN6BDOsaTUStumrOgLCbT28SHEy5pTxwh+jouvvuAVijmXQGs/H1r4eOJgZ2BsZAS/HC7TFofjmdzD2vYtLW1x6di9MHUk8UlpfLJtj02Z1n7exe+HdQgnPrnq56ZiziXQ2r9MHrFl8ogtk0demTySy+chhBBCiMZBRrbqz2YsnapbgQPAW1hGvDyxdIYylVJBwFhgo7VMNuABpAC/Av9RSrXVWsdZZwcM0VofvYw87lJKfQL4AoOBR4AOVZQpnUf1FRw+waAOofzw6GxyC4p4avm64nUf3DqFZ1b8RHJWDnPXbOX1meO4f/S1xJ5PYuXvB6stX5lx3TswY0A3AH5IPMzy42UHBi1MWvPMzp/4dPgMDEqx/Hg0xzJTmNnOMhL1+bG9PBB5LT6OzrzQZzQARdrM5B8WAeBsZ8/AZmE88dvamjRFg7TFE1OicLS34+M7pqINsP9sAs+s+RmT1jz/wwbmz5qKnVJ8te8gccmpzOgZCcDS3dFsOnaCIW1D+em+2eQWFvH4qpL6XvjhF964biwOdgbOpGfymHXdIyMG0SEoANCcy8ji6e9/rlHbgOV4PPfTBhbeaMlp+YGDHEtJ5S/dLTl9sS8afzdXvvnbTNwdHTFrzexePRgz/1OMBQXMnTiWvq1a4uPizNZ7buedrTtYHl15R7i4XrPm399t4ONbpmJQiq/3HCQuKZXpfSz1Lvs9ms1HTjC4fShr/zGbvMIinlhp2d9rWjdnco9OHElIZuV9swB4e902Nh89yUOjBhIW4INZa85nZPPct+urz2PVBj6ebc1jdyV5RISy9mFrHl+VyuOaThy5UD6P4Z3CeWLiMHzdXPjwb5M5fD6ZOxfVftZGIYQQQlyZRjyXRZ1Q1c0OVmcVKzUcWAt4a61zlFJHgY+01m8ppRYBfYF4LKNLq7TWi5RS9wP3Ahesz21FAa8Cl2YgeFJrvUopdRLopbVOsdb1LGDUWr+hlAoFVmutu1jXKeA1LJ06DbyotV6mlBoK/FNrPcEa9z6wq6I8qtrPLv+a2+DnVE5k7Wen+6O5RVc0OFn/Cut+rolqmWo+X0adcqx64sj60eBXR4lDLz3U0CkIIYQQV6rxDvFYRax8vk7+639k6tONct8brLPVVEhny0I6WyWks1VKg18dJaSzJYQQ4irQKDscpbX/6oU6+a//0eufapT7/mf7nS0hhBBCCCGE+FNoyGe2hBBCCCGEEE1JI7qrpT7IyJYQQgghhBBC1AEZ2RJCCCGEEELUi8Y8TXtdkM6WEEIIIYQQol40tbn55DZCIYQQQgghhKgDMrJVx7RdQ2cAAf7ZDZ0CGZ6NY+r3Al9TQ6eAwbugoVMAwHTcpaFTwK7hf5UAAK8hifRfN6eh02DHqFcaOgUhhBCiTjW12whlZEsIIYQQQggh6oCMbAkhhBBCCCHqRxMb2ZLOlhBCCCGEEKJeyAQZQgghhBBCCCGumIxsCSGEEEIIIepHExvZks5WPRvYvjVzJgzFzmDgq50xzN+0s1zMYxOHMjgijNyCQp5YsY7Y80kAvHD9SIZ0aEOa8SJT3vmsXLlbBvXkkXGDufaFD8m4eHnTvPXzb8fDHSdgwMC3Z3fy6YnNFcZ19GzBgv5/54l9S9mQGHNZdQ1q05onRg/FThlYvi+GedvLt8WTo4YypG0YuYWFzPluHYcSLG1xS58eTOvRFa01R5NTmLNqHQWmkpkGb+3XkzkjBtP3zQ9Jz615WwxpGcrTA6OwMyiWHTrAh3t/t1kf7u3L61Fj6BwQyBu/beXjfbuK182OvIYZHSNRCpYeimZB9J7aNgkAg5u14emeIzEoxZfH9/PRoR026yeHduaujv0ByCkq4KmdazmcYW2XiN5MD++OApYd38fCI+XbtDKDwi3Hw6AMLN8bw8cVHI8nRluOR15hIXNWWY5HmJ8Pc6eOK45p6ePFuxt38MnvexnTsR33DelPuL8v0/73BTEXEqvNY2C71jw23nKNrNgVw/zN5fN4fLz1Giks5PGvLNdIsJc7L98wBn93V7SGL3ceYPGOvQB0aBbAM5OG4+RgR5FZ88KqnzlwtvpcAPr5tefBDhOxU4pVZ3fy2clNFcZ19Azh47738FT05/xivSae6HwDAwI6kF5g5Kbtb9eoPiGEEEJcPZr0bYRKqeZKqRX1VZ9BKZ6YFMXdC79h0txPGNctgvBAX5uYQRGhtPbzZuwbC3n26/U8PSWqeN03uw9x18KvK9x2sJc7A9q24nx61uXnh+JfnSbxf7sWMX3r24xu1o0wt8AK4+6PGMOvKccuvy6leGZsFHd88Q3jPvqECZ0jCPe3bYsh4aGE+noz8oOFPLVmPc+NtbRFkIcbN/fpwdT/LWHCvM8wKAPjO0cUlwv2dOfasFacy6xdWxiU4vnBI7jl+68Y+cVCJrXrQFsfP5uYjPw8nt26waaTBdDe158ZHSOZ/NVixi77hKjW4YR6edeq/ks5PNdrNLN/Wcbo7+cxsXUn2nr628ScMWYwY/1ixv0wn/djtvJSn7GWHLwCmB7enet+XMj4H+YT1aItoR4+Na736TFR3P75N4z/8BMmdCl/PAa3tRyPUf9ZyFPfr+fZcZbjcSI1nSkfL2HKx0uYOv9zcguL+OlIHABHk1O5f/l37Dx1tsZ5PDkxirs++YaJ73zCuMgIwgPK5NE+lNb+3ox5ayHPfLOeZyZZ8igya177YTMT3/mUGR99wcx+3YrLPjx6EB/88itT31/C++u38/DoQTXLB8XDHSfzjz0L+cu2uYxs1p3QSq6Je9qP5beUozbLvz+/m4d2L6hRXUIIIURToLWqk1dj1aQ7W1rr81rrG+qrvq4tgzmTmsHZ9EwKTWbW7D/CsI7hNjFRHcNZtTcWgOgzCXg4O+Hv4QbA7pPnyKxkxOrR8UN584ct6CsYm+3sHcLZi6mcz02nSJtYlxDN4KCO5eJubN2fDYkHSS8wXnZdkc2DOZWWwZmMTArNZr4/eIQR7W3bYnhEOF8fsLTF/nOWtghwt7SFvcGAs709dkrh4mBPkrEkl8dHDuX1n7ega/kEZvfAYE5lpnMmy5LTd3GHGRVmm1Nq7kWikxIoMpttlrf18WVv4nnyioowac1v588wOqxdreoH6ObXnFPGdM7kZFBoNrP61CFGhthuZ0/KObIKLefB3pTzBLt6AhDu6ce+lHPkmaw5JJ1mVEhEuToqEtk8mFPpGZwtdTyGR5Q5Hu3D+Sa65Hh4ljoel/QPa8mZ9EzOZ1p+2y0+JY0Tqek13v+uIcGcTiu5Rn6IPkJUBdfItxVcIynZOcWjwBcLColPTiPQ0x0AjcbNyREAd2cnkrJzapRPJ6+W1msijSJtYn3CfgYHdioXN63VADYmHiC9wHa7+9JPkFWYW+P9F0IIIa56uo5ejVST6WwppV5VSt1T6vOzSqmHlVIx1s92SqnXlVI7lVLRSqm7rMs/UEpNsr7/Wim1wPr+NqXUi7XJIcjTnQuZJT8wnJhlJMjL3SYm0MudhIxSMZlGgjxtY8oa1rENiVlGjiSk1CadcgKcvEjMzSz+nJSXSYCTZ5kYT4YGdWbl6d+uqK4gD3cSskr2MyHbSJCHe5UxiVmWmMTsHP63YzcbH7idbQ/eSXZ+PtviTwMQ1a4NidlGDifVvi2C3Dw4byyp74LRSJCbR43KHklLoU/zELydnHG2t2dY6zY0c69Z2dKCXTy4kFMyInfhYjZBrpVv58bwbmw6fxyAo5nJ9AlsibejC8529gxtHk4zV89Ky5YW5FlxW9vElD1mFcSM7xzB6pjDNaqz0jwybesILHuNlIlJzCp/jTT39qRjswCizyYA8Mr3m3hkzCB+fuR2Hhk7mLfXba1RPgHOniTlVX9NDAnszNdnruyaEEIIIcTVp8l0toClwPRSn28ESj8MchuQqbXuDfQG7lBKhQGbgUv3HLUALv1ZeyCw5UqTKjv6UtEgaFUjNM4O9tw5rA/v/7T9SlOpsO6y/tFxPO8fWYv5Cv+EoCqorOyoXGVt4ensxPCINkS9v4CB73yMq4MDk7p0wNnenr8P7MM7my6vLSrMqYajY8fT0/ho7+8snjSNTyZcT2xqEiZtrr5gDVSWQr/A1twY3o1X9/1iySErlf8e+pVPo/7ComEzOJxe8xxqct5V1z4OBgNR7cNZG3v5t5dWVEfZBqguD1dHB96ZOYGXv99ETn4BADP6RPLKmk0Mf30+r36/iReuG1WzfCpombKH48GICfzn2A9XfE0IIYQQTUFTu42wyUyQobXeq5QKVEo1BwKAdOB0qZBRQKRS6tJthV5AOywdqgeVUp2AQ4CPUqoZ0B94oKK6lFJ3AncCNBszDZ/ulskMErOMNPMqGaUI8nQnKcv2tqPETCPB3h5wyhrj5V7lLU8tfb1o4ePFyv+7ybpND1bcP4sZ//mCFOPFatultKT8TIJcvIo/Bzp7kZxv+9xTR88WvNh9BgDeDq4M8I/ApE1sSoqtVV0JWUaCPUvaItij/H4mZNvGBHm6k2TMYUBYK85mZJF+0XJ71rrDcfQIac7hxGRCvL1YdYelLYI9Pfj69lncsOALUnKqb4sEYzbNS41GNXN3J+lizW+V/DI2hi9jLRMjPNJ3IBeMtb/NMiE3m2ZuJSMnzVw9SMrNLhfXwTuAl/uO49aNy8goKLlN7cv4/XwZvx+Af3YbQsLF8mUrrDer4rauKia4TMzgtqEcvJBEag3autI8Mo0Ee5Wpo6JrpOx1ZD137A0G3p45gdX7D7P+UFxxzORrOvHS9xsBWBtzlOevG1GjfJLyMgl0tr0mUspcEx28QnghciYAXg6u9A+IwGQ2szn5UI3qEEIIIcTVqymNbAGsAG7AMsK1tMw6Bdyvte5ufYVprddprc8BPsAYLKNcW7CMihm11hV+k9Vaz9Na99Ja97rU0QKIOZtAK38fWvh44mBnYFy3CH6Jjbcp+0tsPJN6WJ6TimwZjDGvgJQqOlvHElMZ/O//Muq1BYx6bQGJWdnc8N6SWne0AA5lnqOlqz/NXXywV3aMCo5kS5lO1JTNbzBl0+tM2fQ6GxJjeO3Qqlp3tAAOnE8g1NeHEG9PHAyWCS5+PmrbFhuOxnNdV0tbdGthaYtkYw7nM7Pp3qIZzvaWvxX0D2tFfEoaR5NT6T/3v0S9v4Co9xeQkJXNdfOX1KijBbA/KYFQLx9CPLxwMBiY2LYDP504XuN98nNxBaC5uwdj2rRjVVzt2yU69TyhHj6EuFlymNC6E+vP2Y4UNXf15INB1/PwjlWcyE6zzcHJtThmdEgHVp2s2Rf+io7HhgqOx5TIkuORbT0el4zv0oHvD17+LYQAMecSaO1Xco2MjYzgl8Nl8jgcz+RS10h2fsk18sLUkcQnpfHJNtuZIJOyjPQOCwGgX5uWnErNqFE+sVlnaenqRzPrNTEiuBtbkmzb9PotrzF1y6tM3fIqvyTG8EbsN9LREkIIISrTxJ7ZajIjW1ZLgY8Bf2AI4FRq3Y/A35VSG7TWhUqp9sA5rXUOsAN4EIgC/LB02mo9i6HJrPn3qg3Mu3UqBqX4etdBjielcmOfSAC+/D2azUdOMDgilB/+OZu8wiKeXLGuuPzrM8bSO6wl3m7O/Dzndv6zfgcrdx28nHaoOD9t5vVDq3i312wMSvHd2d3EG5OY2rIPACvP/F7NFmpTl+b5tRv431+mYmdQrNh3kLiUVGZcY2mLpXui2Rh3giFtQ1l/72xyC4t47DtLW0SfT+DH2GN8c/ssisxmYhOTWbr3wB+S09NbfubTiddjpwx8efgAx9JTmdW5GwBLDu4nwMWVVdNuxt3REa01t0b2ZOQXCzEWFvDh6En4OLtQZDbx1OafycrPv6wcnt21jk+GzbBMwR6/n2OZKcxs2wOAz+P2cn+Xgfg4ufB87zGWMmYzk39cCMAHg67H28mSwzO7fiyeSKMm9T6/dgPzZ07FTim+2n+QuGTb47HJejx+unc2uUVFPL6q5Nx0trdnQFgrnv5+vc12R0SE89SYYfi6uvDfGZOJTUzm9s8rnlHTsi+af3+3gY9vsV4jew4Sl5TKdOs1suzSNdI+lLX/sFwjT6y05HFN6+ZM7tGJIwnJrLxvFgBvr9vG5qMneeab9cXTyRcUFfHMN+srzcG2Xcy8eXgVb19zKwZlYPW5XZzISeK6kL4AfH226ue0nus6g2t82+Dt4Ma3gx9j/vGf+O7crirLCCGEEOLqoWo7Y9ufnVLqAJCitR6mlAoFVmutuyilDMCLwEQso1zJwBStdaZS6jbgBa11c6WUA5AB3Ky1XlldfZ0fm9vgDew2JLmhUyBjV0BDpwBAga+p+qA6ZvAuaOgUAHA47tLQKWB3eT8H94fzGlKz39yqaztGvdLQKQghhPhza7wPL1mFfvpqnXw3PvnXRxvlvje1kS201l1LvT8JdLG+NwOPW19ly/wP+J/1fSHgVjZGCCGEEEIIUY0GH4aoX03tmS0hhBBCCCGEqBdNbmRLCCGEEEII0UBkZEsIIYQQQgghxJWSkS0hhBBCCCFE/WjEP0BcF6SzJYQQQgghhKgXTWwidOls1TVDI5jlO3tbw0+7bm9u6AwsvHY2/J2zhe4NP+U6gMmp+pi65h/dCC4QIFEHNXQKmB2gw7a5DZ0GAIefe6ihUxBCCCGuCtLZEkIIIYQQQtSPJjay1fB/5hdCCCGEEEKIq5CMbAkhhBBCCCHqh0yQIYQQQgghhBB/PCW3EQohhBBCCCGEuFLS2RJCCCGEEELUD11HrxpQSo1RSh1RSsUppeZUsH6WUira+tqulOp2BXsKyG2EDWLOlKEM6hhGXkEhTy5dR+y5pHIxLXw9ee2mcXi5OhN7LonHPl9LkcnM+Gs6cOuwXgBcLCjkhRU/c/RCCo72diy690Yc7e2wMxj4KfoYH/y4o8L6B7ZtzePjh2JQBlbsjmH+lp3lYh4fN5TB7cPIKyzk8ZXrOHQhCUd7Oz67zVKHvcHAjweP8f6Gkjpm9e3OrH7dMJk1m46c4I11W6psh4HtWvPY+KHYGQys2BXD/M0V5DF+KIMjwsgtLOTxr9YRez6JYC93Xr5hDP7urmgNX+48wOIde23KzR7Yk0fGDmbAvz8k42JelXmU9fDMYVwbaTk+z/3vR46cKn98pg3vzl9GXkPLIG9G3P8BmUZLHYN7hHP3dQPQWlNkMvPWFxvZf+x8reoH+Nf1QxnY2ZLD04vXcfhs+Rya+3ny6i3Wc+RsEk98ajlHAHq1DeGR64dgb2dHujGX299dXqN650wudW4uq+bcdLGem19Y6g0L8OGF6aPoGBLIuz9s55NNu23KGZRi6YMzSco0ct+Cb2vcFvffM5y+vcPJyy/k1TfWcCwusVzME3Mm0L5dMCaTmcOHL/DmOz9iMpmZPq0PI6I6AWBnZ6BVSz+uu/E9srOrPicGtmvNYxOs5+bOSs7NCdZzs6Dk3AR4cepIhnRoQ1rORSa/81lx/JszxhHm7wOAh4sT2bn5TH1/SdV5tG3NE2Ot1+qeGD7eWj6PJ8YOZXA7y7X62DeWa/USg1KsuGsmSVlG7v7c0uZvTRtHmJ8lD09nJ7Ly8rnuo6rzEEIIIf7slFJ2wH+AkcBZYKdSapXW+lCpsBPAEK11ulJqLDAP6Hsl9V71nS2l1CJgtdZ6hVJqPvCW1vqQUupxrfVLpeK2a60H1HU+gzqE0trfm/EvLySyVTBPXh/FrHeXlot7aPwgPtu8h7X7jvLU9cOZ2qcLX+6I5mxaJrM/WE5Wbj4DO4TyzLQRzHp3KQVFJm77cAW5BYXYGwx8ct+NbI09QfTpBJvtGpTiqYlR3LZoJYlZ2Xx590x+OXyc48lpxTGD24XS2s+bMW8vpFtIME9PjGLGPEsdsxeu4KK1jsW338iWoyfYfzaBPmEhDO8YzuT3F1NoMuHrVvVvSRmU4smJUdy+0JLHsr/P5JfYMnm0t7TVmLcWEtkymGcmRTHjo6UUmTWv/bCZ2PNJuDo6sOLeWeyIO1VcNtjLnf5tW3E+PavWx2dAZBitgryZOmcBXdo0Y87Nw5n94hfl4vYfO8fWffF8NGeazfKdh06zee9xANqG+PPyPROY9viiWuUwsFMorQK9mfT8QrqGBvPE9ChufrP8OfLgpEEs/mUPP+45yhPTh3Nd/y4s3xqNh4sTj90Yxb0ffk1CejY+Nfxdr0EdQmkd4M34Vy7v3MzMzePlbzcS1Tm8wu3fNKgHJxLTcHN2rHFb9O3dhhYtfLlp9jw6dmjOQw+M4p4HPisXt/7nQ/z7ldUAPPnYRMaPjWTV6n0sW/47y5b/DkD/fuHcMLV3tR0tg1I8OSmK2xdYz817rNdIUplz08+bMW9az83JUcz40NJWX+85xJJf9/PKtNE223146Zri9/8aO5js/Pxq83h6fBS3fmrJY/mdM9lwpOJrdfS7lmv1mQlRTP+45Jj9tV8P4pPTcHcqafN/LC/J49HRg8nOqzoPIYQQ4g/VcBNk9AHitNbxAEqppcBkoLizpbXeXir+VyDkSittUrcRaq1vL9V7fbzMujrvaAEM6xLOqt2xAESfTsDDxQl/D7dycX3ateSn6GMArNp1iKiuli+w+09eICvX8uUo+tQFgrw9isvkFhQCYG9nwN7OUOGIamRIMKdTMzibnkmhycyaA0eI6mj75TiqYzjf7rPkuP9sAp4uTgS4W3K8WKoOh1J1zOjTjY8376TQZAIgLSe3ynboGhLM6bSSPH6IriSPvda2OpOAh7OlrVKyc4pHES4WFBKfnEagp3txuUfHDeXNtVvQl/FDDkN6hPP9dsspEhN/AQ9XJ/y8yh+fo6eTuZBavjOXm19Y/N7FyQF9GT+TPrRrOKt/t+z3gZPWc8SzfA6927dk/T7LOfLdb4cYFmlpv7G9ItiwP46E9GwA0o1VH4tLhnUOZ9WuUuemcyXnZtsy52YXS71pxlwOnkmkyFz+F6yDvNwZ1DGMr36PqVEul1w7oB3rfrKUiT18Hjc3J3x9y+f028744veHj1wgwN+jXMzwoZ3Y8EtstXV2LXONVHhudqr43ATYffIcmdWMpo7u2p41+49UGRPZwvYaWRNzhOEdbPMY3qHMtepccq0GebozpH0Yy/dU3uZjOrfn+wNV5yGEEEJcJVoAZ0p9PmtdVpnbgB+utNJG19lSSv3Vep/kfqXUZ0qp1kqpn63LflZKtbLGLVJKvWu9nzJeKXWDdblSSr2vlDqklPoeCCy17Y1KqV5KqVcAF6XUPqXUEus6Y6nyryulYpRSB5RS063Lh1rLr1BKHVZKLVFK1bprHujlTkJGdvHnxEwjgV7uNjHebs5k5+ZjMlu+qCdkZtt0Ji65rm8Xth4+UfzZoBTL/zGLTc/dxa9HT3OgzKgWQKCnOwmZtvUHedhuO6hMTEKmsbh+g1KsvGcWWx+9i+3HTxN91lJHqJ83PUNbsPTOGXx66zS6tAiqsh3K1ZFVvh3K5ZplJKhMOzT39qRjs4DiPIZ1aENSlpEjCSlV1l+ZAG93EtNK6kxKNxLoU77tqzL0mrYsf+kW5j54HS8sWFfrHAK93Ys7SgCJGdWfI4kZ2cUxrQN88HR1Yv4DN/D5IzOZ0KdjzeqtybnpWubcLFVvVf41eShzV2/BXMvOp7+fO0nJJZ3alJRs/P3Kd6QusbMzMHJ4Z37fdcJmuZOTPb17hbF5a/UdiyCvys//S2pyblamZ2gLUo0XOZWaUXUenu5cKJNHuWvVw50LWbbX0aU8Hh8zlDfWbam0w9+rtTWPtKrzEEIIIf5QdfTMllLqTqXUrlKvO8vUXNH39gr/I6mUGoals/XoFewp0Mg6W0qpzsATQJTWuhvwf8D7wKda60hgCfBuqSLNgIHABOAV67LrgAigK3AHUG7ESms9B8jVWnfXWs8qs3oq0B3oBowAXldKNbOu6wE8CHQC2gDX1nofK1yqy8RU34frHR7C1D6dmbt6a/Eys9ZMe2sJI56fT5dWwbQN9qtR/WVHgKqKMWvN1A+WMOyN+XRtEUy7QEsd9gYDns5OzJi3lNd/3Mzc6eOrzL/CbmqZL4UVxZT+4ujq6MA7Myfw8vebyMkvwNnBnruG9uG99dvLF6yh6uqsiY174pj2+CIeee9b7r6u9gOmFeZQ9hhVEHQpws7OQMeWQdz30Tfc88FK7hzdl1YB3pdVb/ljUmFyVRrcMYw040UOVfD8V/U51fj/FwF48P5RRB84y4GYszbLB/RrS8yhc9XeQgg1vUYriKjheTK+WwRrog/XKLZ8FmXqqOR8Hdo+jNScixy8UHmbj+8awfcxl5eHEEIIcdnqqLOltZ6nte5V6jWvTM1ngZalPocA5R6sV0pFAvOByVrr1Cvd3cb2zFYUsEJrnQKgtU5TSvXH0gEC+Ax4rVT8N1prM3BIKXVpKGUw8IXW2gScV0ptqGUOA0uVT1RKbQJ6A1nA71rrswBKqX1AKLC17AasPek7AZqPmMY9f7+b6/t2ASDmTCLBpW79C/JyJykzx6Z8ek4uHi5O2BkUJrMm2MuDpCxj8fr2zfx57saR/P3jryu8XSk7L5+dx89ybYdQ4hJsz5HELCPBXmXqz7atP6FMTLCXO8lZtjHZefn8fvIsA9uFciwplYQsIz8digPgwLlEzFrj4+pC+sWKb2FLyCxTh6c7SWXqSCwTE+RZkqu9wcDbMyewev9h1lvrbenrRQsfL76+/yZrvAdf3TuL6R9+QYrxYoV5AEyL6saUIV0BOHQikSDfkjoDfdxJzsiprGiV9h49R4tAb7zcnYsn0KjM9EHdmDrAco4cPJ1IsE+p/fZ2J7nsOWK0PUeCvD1IzrScI4kZRjKMueQVFJFXUMTu4+eIaBHA6eSMcvXOGNCt6nMzq5pz09v23KxIj9DmDOvUhkEdQnGyt8fN2ZGX/zKGx75YW2H8lIk9GD/OMvnP4SMJBAZ4AucA8Pf3ICW14vr+etO1eHu78PRz5bc7bGhHNvxyqIJS5ZU7Nytoh3LXkWf566gidgbFiM5tmfb+59XGJmYZaVY2j+zyeTTzLHMdZecwunM7oiLaMKRdKI729rg7OfLa1DH8a+Xa4jxGdmzL9f+tPg8hhBDiKrETaKeUCsPyxWIGMLN0gPUOupXAzVrro39EpY1qZAvL32mr+/Nw6fWln+xWlcRcTg6VKV2fiUo6q6V71r6R/Vm6bT/T3lrCtLeWsCHmOJN6Wm7rimwVjDGvgJQKvqTtjDvDyMh2AEzq1YlfYiyTLgR7ezD3lok89sVaTqVkFMf7uLng4ewEgJO9Hf3ateJEYlq57R44l0BrPx9aeHviYGdgXNcIfjkcbxPzy+F4Jne35NgtJJjsvAKSjTn4uNrW0b9NK05YH9b/OfY4/dpY/lgQ6ueNg51dpR0tgJhLefhY8hgbWT6PDYfjmdzD2lYtg8nOL2mrF6aOJD4pjU+27SmOP5aYyqCX/8vINxYw8o0FJGZlc/1/llTZ0QJYvmE/s55ZzKxnFrNxTxzjB1hmr+vSphnG3AJSM2ve2QoJ9C5+H9E6EAd7u2o7WgDLtuxn+qtLmP7qEn6JPl5861/XUOs5klU+h13HzjCiu+Ucmdi3ExsPWM6RjdHH6RHeAjuDwtnBnq6tg4mv4FwAWLp9P9PmLmHa3CVsOHicSb0u49w8eLzKfXvnh22MeHE+Y15awCNL1vB73JlKO1oA33y3lzv+vog7/r6IbduPMmqkpTPYsUNzcnLySUsrn9O4MZH07hnGCy99V3YwDjdXR7p1bcm2HXFV5nlJzLkEWvuXOTdjy5ybsWXOzUraqqz+4a04kZxOYjUdVIAD5xNo7VvqWu0SwYaKrpEKrtW31m9j6FvzGf72Ah5esYbfTpwp7mgBlms3pWZ5CCGEEH+oBpr6XWtdBNwH/AjEAl9qrQ8qpe5WSt1tDXsa8AM+sD5utOtKd7exjWz9DHytlJqrtU5VSvkC27H0PD8DZlHBSFIZm4G7lFKfYnleaxhQ0Z9vC5VSDlrrwjLLL5X/BPDFMlL2CNDhcneqtC2xJxjcMZQ1j80mr7CIJ5eWPNPzwe1TeObLn0jOymHu6q28dvM47h97LYfPJbHyt4MA3D2qL96uzjw5NQoAk1kz4+3PCfB048W/jMZOKZRSrNt/lM2xJ8rVbzJrXly9gfl/m4rBoFi55yBxSalM7x0JwLKd0Ww6eoLB7UP58SFLjo+vtOQY4OHGy9db6jAoxdqYo2w8aqlj5Z4YXrxuFKvuu5lCk4nHvvqxynYwmTX//m4DH98yFYNSfH0pjz7WPH6PZvMRSx5r/2HJ4wlrHte0bs7kHp04kpDMyvssd4G+vW4bm4+evNzDUmxb9AmujQzj61dvJa+giOf/V7Ifbz90HS8uXEdKRg7TR/Tg5rG98PNy44vn/8q2Ayf498KfiOrVjvEDOlJkMpNXUMTjH66udQ5bDp5gYKdQvnvast/PLC45R96/ewrPfW45R97+diuvzh7HvROu5cjZJL7eYTlHTiSmsT32JF/OuRmtNV/viOH4hepHwbfEnmBwh1DWzLGem8tKnZu3TeGZ5dZz8/utvHbTOO4fY3tu+nm4suz/ZuLm7IhZa24e1IPJr39KTn5Brdvgkl9/j6dvn3AWL7qT/PwiXn2jZCa9l1+8gTfeWktqmpF//N9oEhIz+c87llHNLVuP8ukSy+2kA69tz649J8nLK3upV8xk1vx71QY+nm09N3dXcm5GhLL2Yeu5+VVJW70+fSx9wlri7ebMhkdv5/31O1i529JGYyMjqp0Yo3QeL6zZwP9utlyrX+09SFxyKtN7WfPYFc2mY5ZrZN3/Wa/Vb2r2jOD4LhGslokxhBBCNDFa6zXAmjLLPir1/nbg9j+yTnU5s6XVJaXU37B0bkzAXuBZYAHgDyQDs7XWp0tP6W4tZ9Rau1snrXgPyy2Jl4b/Flunft8I/FNrvUsp9SowCdijtZ5VpvxrwFgs/eQXtdbLlFJDrWUnWOt7H9iltV5U1f50fXhugzdwkWtDZwCq/AR1DcLtfIMfDgrdG2zKUxsmp4bOAPyjL78j9kdK7FXz6ejritmhoTMocfi5hxo6BSGEEJencXzJqELof96sky9jJ+99uFHue2Mb2UJr/QnwSZnFURXE3VLms7v1X41liLCibQ8t9f5RSs0wUqb8I9ZX6bIbgY2lPldYhxBCCCGEEKJiquH/7l2vGtszW0IIIYQQQghxVWh0I1tCCCGEEEKIq5SMbAkhhBBCCCGEuFLS2RJCCCGEEEKIOiC3EQohhBBCCCHqRVObIEM6W3WtEUxCedOMnxs6BXZltG7oFACYEfR7Q6fANPfMhk4BgDZf3dXQKdBr+uGGTgGA82sjGzoFCrwbx+8jBP0K/Wa92dBp8OuShxs6BSGEEOKKSWdLCCGEEEIIUT90IxiJqEfyzJYQQgghhBBC1AEZ2RJCCCGEEELUjyb2zJaMbAkhhBBCCCFEHZCRLSGEEEIIIUT9aGIjW9LZEkIIIYQQQtQLmfpd1Lk5k4cyqGMYeQWFPLlsHbHnksrFtPD15LWbxuHl4kzsuSQe+2ItRSYzYQE+vDB9FB1DAnn3h+18smk3AEFe7rz0lzH4e7hi1rDi1wMs2bq3Rvmc25PDzgXJaDO0HeFJ16m+5WISYi6yc0EyZhM4e9gx+sUQALa9n8i5XTk4e9kx6Z3Ln97deCCFhM+PorXGZ1AL/MeHVhiXeyKTEy/uJOTvXfHsFQRA6rpTZGw+DwqcWrjT/LZOGBzsLiuPY7uKWDMvD23WXDPKkcE3OpWLORFdxA/z8jCZwNVTcdurbgDs+Daf3T8WojX0HO3AgCnly9bElt/gpffAbIYbxsMds2zXZxvhXy/ChSQoMsGt02HqOMu6RV/Ciu9BKWgfBi/NAafLS4MhrUJ5euAw7AyKZYdi+HCP7bT54d6+vD58NJ0DAnnj1218vG9X8brbul3D9E5d0RqOpKbwyIa15JtMtc4hfX86Jz47AWYIHBpIyKQQm/WZhzI5PPcwTgGWnfTr7UfL61qSn5rPsY+OUZhZCAqChgXRfEzzGtc7KLw1T4weikEZWL43ho+37ywX88TooQxpG0ZeYSFzVq3jUEISYX4+zL10MICWPl68u3EHn/y+l/8b2p/h7cMxa01qTi6PrfqRJGNOjXMa3CqUZwYPw6Asx+Oj3bbHo42P9XgEBvLmjm18vNdyPNp4+/DemAklOXl5MffX7Szcv6fGdZf2j78Oo3+3MPILinjhv2s5crL8/3/dMLI708dcQ8tgH0bf9QGZxtziddd0DOHBm4dhb2cgIzuXe1788rLyEEIIIf4MGm1nSynlBHwP+AMvA+Fa65cuc1tGrbX7H5nf5RrUIZTWAd6Mf2Uhka2CefL6KGa9u7Rc3EPjB/HZ5j2s3XeUp64fztQ+XfhyRzSZuXm8/O1GojqH28SbzJo3vttM7LkkXJ0cWPbgLHYcO0V8YlqV+ZhNmt8+TmbkMy1w9bNnzb9O07K3G94tS76hF+SY+G1eMsOfao57gAO5GUXF69oO86TDWC+2vZt42W2izZoLi4/Q+uEeOPg6E//873h098ephXu5uMTlcbh38SteVpieR9r6M4S/2B+Dox1nP4gm67dEvAfW/Iv1JWaTZvWHufztRTc8/RX/fSiHDv3sCWxV0nHLNWpWf5DHzc+74h1owJhh+W2kxJMmdv9YyJ1vuWHnAJ89dZGI3vb4tahdp89kghfehv+9CUEBcONdMOxaaBtaEvP51xAeCh++AmkZMO4mmDAS0jNg8Vew+lNwdoKHnoE1G+C6sbVuCgxK8fzg4dy0agUJxmxWTZvFTyfiiEsvOZ8y8nN5dssGRoW1tSkb5ObOLZHXMOLzReSbinh/9AQmtuvAisMHa5WDNmviP4mn85zOOPo6Ev10NL49fXFt4WoT5xnhScd/drRZpgyK0JmhuIe5Y8o1sf+p/Xh39S5XtrJ9f3pMFLOXrCQxK5sVt89kw9HjHE8p2ffBbUMJ9fVm1H8W0q1FMM+Oi+LGBUs5kZrOlI+XFG9n84N38NOROADmb9/NOxt3AHBz7+7cO7gfz6yp2W/gGZTi+aHDufkby/H4dvos1sfbHo/MvFye27yBUW1sj0d8Rjrjl35WvJ1fZ9/FuvhjNaq3rP7dwmgZ7MO0hxfQuW0z/jV7BLc983m5uOij59m2N54PnrzRZrm7qxOPzB7Bg69+RWJqNj6eLpeVhxBCiD+xJjay1ZgnyOgBOGitu2utlwGPN3RCf4RhncNZtSsWgOjTCXg4O+Hv4VYurk/blvwUbflCtGrXIaK6WDpXacZcDp5JpMhs+wOoKdk5xSNkF/MLOZGYRpBn9f3L1Lg8PJo54BHsgJ2DInSgB2d+t/1re/zmbFr1c8M9wAEAF++SPnpQZxecPC5vFOmS3PhMHANdcAx0Rdkb8OobRPa+5HJxaevP4NkzEDtPR5vl2qTRBWa0yYy5wIy99+UN5Zw9asK3uQHfZgbsHRRdBztw+Ncim5gDGwvpOMAe70DLpePubfk3+YyZkAg7HJ0VdnaK0K72HNpRVK6O6kTHQqsW0LI5ODrAuCjYsNU2RinIuQhaw8Vc8PIEe+shMJkgLx+KiiA3HwL9a98OAN0DgzmVmcGZrEwKzWa+O3akXKcqNTeX6KTy5yKAnTLgbG+PnVK42NuTmGOsdQ7G40ZcglxwDnTGYG/Av58/abur/uPBJY4+jriHWc5/Oxc7XJq7UJBWUKOykc2DOZWewdkMy75/f/AIwyNs/7gxvH0430RbruP95xLwdHYiwN32Ou4f1pIz6Zmcz8wGIKegpH4XRwe0rvl/bboFBXMqo9TxOHqEkW0qPh6FFRyPS64NacWpzAzOZWfXuO7SBvcMZ82WQwAcjLuAu6sTft7l///r6KkkLqRklVs+ekAHNu48RmKqpf70rNxyMUIIIcTVpF47W0opN6XU90qp/UqpGKXUdKXUGKXUYaXUVqXUu0qp1UqpQGAx0F0ptU8ptRxwsb5fUsX2v1FK7VZKHVRK3Vlm3ZtKqT1KqZ+VUgHWZd2VUr8qpaKVUl8rpXyUUh2VUr+XKheqlIq2vu+plNpkreNHpVSz2rZBoJc7CRklX3QSM40Eetl2irxdncnOzcdktnwZS8jILhdTleY+nnRoEUD06YRqYy+mFuHmV9J5cvWz52KabSch+3whBUYzPz51ltX/PM3xX8p/iboSRRn5OPg6F3+293GmMD3fJqYwPY/sPUn4DLO9jczBxxm/Ma05+shWjj60BYOrvc3IV21kp2q8/EsuCU9/RVZqmU7teTN5Rs2COTl8+ICRfT9bvkAHtTZwKsbExSwzBXmao7uKyEqu/EtvZZJSIDiw5HNQACSm2MbMmgrxp2DwVJg8Gx67HwwGS+zsGTD8Rss6Dze4tnetU7DU6+7OeWPJeXrBmE2QW83OwcQcIx/v28n2v93B77PvJruggC1nTtU6h/z0fBx9SzrWjr6OFKSX7zBlx2Wz7/F9HHrtEBfPXiy3Pi85j5xTObiH1yz/IE93ErJKXaNZRoI8bMsGedjGJFQQM75zBKtjDtsse3DYADY+cDsTu3TgnU07apQPQLCbOxdKHY8EYzbB7rUfrJ/QvgPfHTtcfWAlAnzdSUotySMpLZsAn5rn0TLYBw83Zz544kYWvXgTYwd2uuxchBBC/EnpOno1UvU9sjUGOK+17qa17gKsBT4GJgKDgGAArXUScDuwxTqyNQ3Itb6fVcm2AW7VWvcEegEPKKUufet2A/Zora8BNgHPWJd/CjyqtY4EDgDPaK1jAUelVBtrzHTgS6WUA/AecIO1jgXAvytKQil1p1Jql1JqV1r0jjLrKihQ5i/cqqKgGp5ELo4OzP3bBF79dhM5+dX/Jb8mmzWbNanH84l6ojkjnm5B9Io0ss7XbJSgRipKokwTJHxxlMBp7VAG2xWmnEKy9ybT7tVraf/WIHS+iYwdFy4vjQryKHskzCbN+TgzNz3ryl9fcGXj0gJSzpkIaGXHwBsc+eTJi3z29EWCwwwY7Gr/C+k1yWHr79ChHWxeCSvnw4tvgzEHMrMto2A/LYVNKyE3D1atq3UK1jrL517T/x/zdHJiZFhbBn06n76L/ourvQNT2nesvuBlVOgW6kbPt3vS/aXuNBvVjMNzbTsSpjwTR945QthNYdi71uyu6Yov0bLXaNUxDgYDUe3DWRtre7ve279sZ+i78/ku5jA39e5eo3ws9VVwPGr5HxYHg4ERYeGsOXa0dgVL51HReVGLROzsDHQIC+Qfb6zk/175iluv60fLYJ/LzkcIIcSfj9J182qs6vuZrQPAG0qpV4HVQDZwQmt9DEAptRi4s4ry1XlAKXWd9X1LoB2QCpiBZdbli4GVSikvwFtrvcm6/BNgufX9l8CNwCtYOlvTgQigC/CT9YuPHVDht3qt9TxgHkDXf87VMwZ04/q+XQCIOZNIsLdHcWyQlztJWba37aXn5OLh4oSdQWEya4K9PUjKqv42LHuDgbl/m8D3ew7zc0xctfEAbn725KSWjGRdTC3C1df2tHD1s8fJww4HZwMOzhDUyYW0k/l4Nncsu7nLYu/jRGFaXvHnovQ8HMrcCph3MotzHx2wrDcWYoxOAYMCk8bR3wV7662FHtcEkBuXiXf/Wg864umvyEwpGY3KStF4+Nn+PcLTz4Crp8LR2fIK7WxHQrwZ/xZ29BztSM/Rljx++iQPL7/a/y0jKAASSs03kJhc/lbAlT/AHTMtX/hbh0BIM4g/DecToEUz8PW2xI0YBHtjYNKoWqdBgjGb5u4l52kzdw+Sangr4MCQ1pzJyiQtz3KL2Nr4Y/QMbs43R2NrlYOTr5PNrX8FaQU4+tiec6U7UD7dfYhfFE9hdiEOHg6Yi8wceecIAQMC8Otd89HOhCwjwZ6lrlFP93ITWZSNCS4TM7htKAcvJJGaU36kDWB1zGH+O2MK79VwdOuCMZtmpY5HsLtHrW/NHNo6jIPJiaTk/j975x0fRfH+8fdcElJIhxRKIBAgIUDoTVoIvTcpgqgolq+9i4Jib6jYC4qAgtKkixB6V3pCGiQkdNJJJf3m98ceSS53abTgz3m/XvfKZfeZnc8+88zczs7srHlN5TFuQDtG9W0DQGRsPO51SnS4uzqQnFb1RT4SU7NIz8whN6+Q3LxCjkVdoHkjN87HX6mWJoVCoVAo/i3c1pEtKeUpoCNap+sDYCQ3aeBPCBEI9Ae6SynbAscAm3LMK8tzGTBBCNECkIbOoADCDaNr7aSUbaSUVbqMXbo/hPFzlzB+7hK2h59mZCftLn9AI0+ycvNJzjS9WDkUc54BAc0BGNnJnx3hpyvN560JA4hNSOWX3VVfZaxOMxsyL+eTmVBAUYHkzN5MvDobP4Ph1cWexMgc9EWSwjw9yadycWpwczpaALZNHMlPyCE/KQdZqCf9nwTs27kZ2TT/uCfN52gfx07u1Jvqh2MHdyxdbciJTUefV4SUkuzIK1jXq3wRBHM0aGFB6kU9V+L1FBZITuwuwK+rccezZTdLzoYXUVQkyc+VXDhVhJuXVo2uLZaRlqgncn8hbfpYVVtDGz84ewEuXIb8Am2Bi749jG3qucPfhiJOToW48+BVD+p5QEiENqIlpWbjc50LRIYkxuPt5ExDB0esdDpGNPdly5nKYxDgUlYG7T3rYWOp+a5Hw0ZGCzlUFfum9uTE55CbmIu+UE/y38m4djBeKTM/Lb94ZCXzdCZSSiztLZFScvqn09jWt6X+0OotlnLiUjzeri40dNbOfVgrX7afijWy2X4qltEBWj1u28CTzNx8kkp1toa19uPPcONRtsbXesFAUAsfYlOq3sEITYjH29mZho6G8mjhy9a4qpXHNUa08GPdqepPIfxjy3Hue+1X7nvtV3YdjmFoL23qX6tm9cjKySOlGp2tPUdiaOvbAAudwLqWJa186nHmUkq1NSkUCoXiX4wUt+Zzh3JbR7aEEPWBVCnlYiFEFvAY0EQI4SOlPA3cU0HyAiGElZSyoJz9TsAVKeVVIYQf0K3UPh1wN7AUmAzslVKmCyGuCCF6SSn3AFPRphgipTwthCgCXqdkROwk4CaE6C6lPGCYVthCSlmtJdb2RMbR28+bjTOmkVtQyKxlJfO8vn1oNLNXbCEpI5u5f+7l43uH8tTgHkRdTGTVP1o2dRzsWPbMZGrb1EIvJVN7tWfUnF9oUa8uIzv5c+pSEiue02ZafvnXPvZEnalQj85C0GW6O1vfvqgt/d7PEedG1pzcnAaA7yBnnBvWon772qx/7hxCaMvDuzTWRp52f3aZhLAccjOLWDk9jraTXGne36k6LkFY6PC815dznx1D6iXOPetj08Ce1B0XAHAt85xWaex8nHDo5E7sW/8gLAQ2jRxw7lO+fUVYWAiG/c+GX16/il4v6TCgFu6NLTi0URtd6Ty0Fm6NLGje0ZJvn8hG6KDDQCs8vLXVKZa+n0NOhkRnCcP+Z4OtQ/UrvqUlzHoWpr+oLf0+dig0bwJL12r7J42Cx++HVz+AkQ9odw1eeBRcnLXPoD4w7mGwsICWzWDCiOtyBUVS8sae7fwychwWQsfyyDCiU1OY0ioAgCXhobjZ2bFu/L3Y16qFlJIH23ZgwG8LOZ4Qz1+no/lzwlQK9XrCkxP5PTy02hqEhaDp/U2J+DgCqZd49PHArqEd8du0ZxE9+3mScjCF+G3xCAuBzkpHiydaIIQg42QGSXuTsPOy4/hrxwFoPKExLu0qn7JWJCVvb9rOT5PHYiEEf4SEE5OUwqQO2rkvPRrKrpg4+jTzZssT08gpLOS1UvM1bSwtuatJI974c6vRcV8I6kmTOi5IKbmYnsnsjcb7K9M0e5dWHjqdjhURWnlMbq1p+i0slLp2dqybWFIe09p1YODihWQV5GNjaUlPr8bM3LGlynmaY//xOO5q15SVnz1Ebn4B7/6wuXjfZy+N4f0fg0lOy2bCoPbcO7wzrk61WfzhfRw4Hsf7PwVz5lIqf4eeYfGH96PXS9btPEHsBdXZUigUCsX/X0R15tvfcGZCDALmoE3rKwD+h7a0++dAMrAXaC2lHG4YqXpRSjnckPYjtJGwo+ae2zIsFb8GaIChYwS8KaXcaejYzQWGAunARCllkhCiHfA9YAfEAtOklFcMx3vRoLWJlPKMYVs74Eu0jp0l8LmU8seKzrnNi3NrfBbppGlVW176VnI47frfwXUzmeRxsHKjW8x4+/SalgBA0z8erWkJ9O0SVtMSANi/KaCmJZDvXP0FVW4FHn/XtAKNv5e8UNMSFAqF4t/InTvEY6DZR7fm2jjmlefuyHO/rSNbUsrNwGYzu/ygeCpga4PtTmBnqbSvAK9UcOw8wOwbhUq9Y+v1MtuPYzwCVnrfJ8AnZux7l6dBoVAoFAqFQqFQlM+dvJjFreBOfs+WQqFQKBQKhUKhUPxrud2rEVZI2dEscxiWczc3L66flFJN/lcoFAqFQqFQKO5U/mMjW3dUZ6sqGDpU7Wpah0KhUCgUCoVCoVBUxL+us6VQKBQKhUKhUCj+nfzXntlSnS2FQqFQKBQKhUJxe1CdLcXNRN4BS5Asiuxa0xIoKrSoaQkAHD8ztqYl8I1n9V/weyuofaHmgzN2Y8ualgCAvKumFQAu+TWtAICEbjfvheXXi2OMjrbPzK1pGQCEfPFcTUtQKBQKxb8Y1dlSKBQKhUKhUCgUt4f/2MhWzd/aVigUCoVCoVAoFIr/h6iRLYVCoVAoFAqFQnFb+K8tkKFGthQKhUKhUCgUCoXiFqA6WwqFQqFQKBQKhUJxC1DTCG8Tr44MpJdfE3ILCpi5PJjIi4kmNg1cHJkzZShOdjZEXkxkxtJNFBbpy03v6WTP+5MGU9feDr2Elf+cYPG+YwD41nPjjbH9sLayoMCmkLeO/UXolUtmtfXy8GFmu0FYCMGKuGPMO7nfaH+/ei14plUgEkmhXs/7IcEcSTmPp60jH3cehZuNPXopWRZ3lF9iDt6wr3p7NuX1DgOwEIJlsSH8EHnAaH//Bs15rk0f9FJSJPW8c3QLR5Iv3HC+AL3rNWV2p/7ohI5lMcf5PuJvo/2jvFvxmH83ALIL83n94GYi07SyfNCvMxN92iKBk2lJvHRgA/n6ompr6OLqy9MtRqITOv68dJAlZ3eYtfNzaMh3nZ/izbDF7Eo8QS2dJV91+B9WOksshI6diSdYEBdc5Xx7tmjMjOGBWOh0/HEojJ92HTKxeXVEIL19m5CTX8DMlcFEXtLO/Z1xA+jj15TUrKuM/uJXk3QP9OrIS0N70+Od70i7mltlTQBPP9qPrp2akpdXwAdz/yL6dIKJzawXh+Pb3JPCwiKiTl3mk6+DKTLUHQC/5p58++m9vPXROnbtO1Vpnr2aNmbmwEAshI4Vx8OYd8DUF7MGBtLHpwk5BQXM2BBMRLzmiwe6tGd8uzZIKTmVlMyM9cHkF2lxMLVTO6Z0akuRXrIzJo452/dU2Q+96zVldudSsRluJjZblYrNf7TYbOroylc9Rxfbedk7Mzd0DwuiTM+pUg2NvJnduy86IVgWEcb3R4zre1MXV+b0G0Qrd3c+PbCPH48d1rY7u/DV4OElGpycmPv3fhaEHK22hmu8MjaQnv5am/j6kmCiLphpU10d+ej+oTjWtiHqfCKvLdba1E7NGvL59JFcTEkHYHtoDD9s/ue6tSgUCoWiGvzHphGqztZtoJefN43qOjP04wUENPLk9TFBTP56qYndc0N78eueo/wVcoo3xvZjXOfWLPs7tNz0hXrJnA27ibyYiJ21FcufnsL+6LPEJqbywrBefLf1b/aePEP3kZ68FNCPqbtML4J1CGa3H8y0PUuIv5rBH/2ms+3SKU5nJhfbHEiMY9tl7QLV18mdL7qOY3DwdxRJPR+GbiEiLZ7alrVY1W86+xJijdJWF50QvNlpEPfv+J34nAxWD5jGtovRxGSUHHN/whm2Xow26HHjqx5jGbjxh+vOs3Teb3ceyNTtS4m/msHawQ+w9UI0MRkpxTbns9KYuHUJGfm59KnflPe7DmHM5kV42NrzgG8nBmz4kbyiQr7uOZoR3v78EXuiehoQPOc7huePzSMpL515nZ9mb3I4Z7MTTeweazaMQykni7fl6wt59tgP5BTlYyF0fNPxCf5JiSIi41yVzn3myCAenr+KhIxMlj0xmR2RpzmdWLJMfS9fbxrXcWbIJwsI8PLkjdFB3POtFsdrjkTw24EQPhg/yOTYnk723NWsEZeuZFTLFwBdOzWlYX0Xpjz8I/6+9Xj+iQH87/nFJnZbdkbw7icbAHjj5REMHxTA2o3HtXPTCR6d1odDR+OqlKdOCGYPDmLab6uIz8jkjwcnsy36NKeTS3zRx8cbb1dnBny3gLb1PXlrcBDjFy7Fw6E2Uzu3Z+gPi8grLOLzMcMY1sqX1aERdG3ckH4tfBjx42IKiopwtbOtsh90QvB2l4FM3WaIzSGG2EwvE5tbSsVmtyGM2bSI2IxUhm38ufg4f499kuDzJ8vLqmINgf2YumYl8VmZrJ04ha2xMcRcKfFLem4Ob+3ezsCmzYzSxqZdYdjSX0s0THuU4Njoamu4Rk9/bxq5OTPi3QW0aezJrPFB3DvXtE19ZmQvFu88yqZjp5g1oR9jurVmxb5QAI7FXuSpeWuvW4NCoVAoFFXhpk4jFEJYCyG2CiGOCyEmCiFeq8TeWwgRdjM13A4MuidX1b6vvw/rjkYCEHouHgdba+o61Dax69rMi+AT2gXI2sMRBLXyqTB9cmZ28QjZ1bwCYhNT8XCyB0BKib2N9r4ceytrEnOyzGoLcK3P2awrnM9Oo0Dq+fN8OP3r+xrZXC0qKP5ua2FVfEMiKTeLiLR4QLuTfjozGQ9bh6q6xSxtXetzNtOgR69nw7kI+jdobqynsESPnWUtpLw5t0ja1jHknaXlvf5sJAO8WhjZHE2+SEa+NjJzLPkSnnYl52shdNhYWGIhBDaWViReNe/zimjp2IiLOclczk2lUBaxLeE4Peu2MrEb59WDXUknuJKfbbQ9p0h7V5OlsMBS6JBVvH3UxsuT8ylpXLiSTkGRno0hJ+nb0sfIJqilD+uOGeLwfDwONiVxfOTMRdLLGbF6ZVggn/61p8paStOzWzM2bw8HIOLkZexr2+DqYlp3/jkcW/w98tRl3OqWlMvYER3Yte8UV9KvVinPgPqenE1N43xaOgV6PX9GnKR/C2Nf9Gvhw+pQzRchlzRfuNlruix1OmwstTiwtbIkMVOLg3s6tGXe/kMUGEa5Uq/mVNUNprF5JpIBDasem9fo4enN2cw0LmZXv+Pb1sOTs2lpnM/Q/LL+1EkGlOlUpeTkEJqYQIFeX85RoEfDRpxNT+NiZma1NVyjb2sf1h/S/H/irKFNdDSNiy7NvdgSorWp6w5GENTGx8RGoVAoFLcXIW/N507lZj+z1R6wklK2k1IuAyrsbP2L8Qaq3NnycLInPq3kwiIhLau4U3QNZzsbMnPyKNJr0ZKQnom7waYq6eu7ONKyvhuh57TOz0frd/HC0F5sfW06MwL682nYdvPabB2Jzym58IrPyTDbYRpQ35dNA//HvJ738OrhdSb7G9g54e/sSUjqxQp9URketg5cvlpaT6ZZPQMbtCB46KP81HsCMw7+eUN5XsPT1t4476uZeFbQeZzoE8CuS6cBSMjJ4sfIf9g3+gn+Gfs0mfl57Imv2khKaeraOJKYm1b8f1JeOm7WTsY21o70cmvN2gsHKIsOwfwuz7G212wOp0YTmXG+Svl6ONpzOb1UjGWYxph72ThMz8LD0dimLH1bNiUhI4uT8dc32lm3jgOJSSVlkpSciVud8svEwkLHwL6tOHgkzpDenl7dW7Dur+NVztPDwZ74Uh2B+IwsPBzsTW0yyvjLwZ6EzGzm/32EnU9NZ98zj5CZl8e+OG1ksUkdZzo1asCKByax+N7xtKnnUWVNnnZmYtNMZ+oapWOzNMMbt2T9mYgq52ukobY9l7NK+SUrE0/7isvfHMNb+LE+Ouq6NFzD3dmehDKx6F62Ta1dpk1Ny8TducQmwLsey1++l28eHY2PZ50b0qNQKBQKRXlU2tkSQtQWQvwphAgRQoQZRqwGCyGihBB7hRBfCiE2CCHcgcVAO8PI1grA1vB9SQVZWAohFgkhQoUQK4UQdoZ83xBCHDLkOU8IIQzbnxZCRBjsl5bS+LPB/pgQYpRh+wNCiDVCiPVCiDghxJNCiOcNNn8LIVwNdj5CiE1CiCNCiD1CCD/D9oWG89svhIgVQtxt0Pwh0Mtwbs9V6kMz28qOxhhOr4xN1dLb1rJi7tThfLR+F9l52sjGxG4BfLR+F/3f/4n3Q7bwfsfhZo5SzrHNjEBsuXSSwcHf8fj+5TzbKtBon52FFV91H8/7x4PJLsw3m09VMeMGswRfPMXAjT/w2N6VPNem9w3lWZK3mTIoZzSmm0cjJvi05cNjOwFwrGXDgIbN6b32W7qt+go7SytGe5uOSFWqwUyJlNXwVPORfB+zEb0ZbXokDx2cy9373sXPyYsmtat+QW+Sb9kYrYJNaWysLHmkbxe+3rK/XJvKMBcPFY2QPf/4AELCzhMarj3D99QjQfywYCd6fdVveVWtvpq3cbSxpl+LpgR98zM9v/wROysrRrb2A7SRT0cba8YvXMrH23fz+dhh1dBkrn2oIDabteXDozuNtlvpdPRv2JyN5yKrnK+RhgraqKpipdPRv4kPG6Mrf26uupjGa/l6I88nMvjN+Uz4eDG/7znO3OkjbroehUKhUJSDvEWfO5SqPLM1GLgkpRwGIIRwAsKAICAGWAYgpUwUQkwHXpRSDjfYZkkp21VyfF/gISnlPiHEz8DjwCfA11LKtw3H+RUYDqwHZgBNpJR5QghnwzFmAtullA8ath0UQmw17GuNNuJmY9D7ipSyvRBiLnAf8DkwD3hMShkthOgKfGs4P4B6QE/AD1gHrDRoKD7PsgghHpkxY8bM+++/323Zo2OITM7E07nkLrSHsz2JGcbTv65k5+Bga42FTlCkl3g4OZCUoU0/ik/PKje9pU7H51OH8+exKLaGxRTbjOzozwfrdgLw14UI3iunsxWfk4GnrWPx/562juVOOQQ4nHwOL3sXXGrZciU/B0uh46vu41l/7gTBl27sbjVod+zr2ZXW40BCTvnTjQ4lnadRKT03wuWyeds5kGDGF37ObnzYdSjTdiwnzZBnT09vzmelk5qn/b/5/Ek6uDVkzZnwamlIyk3H3ca5+H83ayeS84ynfPk5ejG79RQAnKxq062uH0V6PXuTS/LKKszl+JVYutbxIy7bdEGJsiRkZFHPqVSMOZrGaMK1ODxrsHGyJzHT2KY0Xq5ONHBxYtUz9xqO6cDKp6Yw6ZvfSc4qf0rf6GHtGT44AICTp+Jxd3MEtBFTt7oOJKeYj8/777kLJyc7Pvl6dfE232aevPHKSACcHG3p1qkpRUV69v4dY/YYAPGZWXg6lPjC09GexCzj84zPyMLTsYy/srK5y7sRF9IyuGKYIhh8Mob2DeuzLiyK+MwsgqO0fEMvJSClxMXOtti2IqoVm92GMm17SWxeI7C+D+GpCSTnVm06pYmGrEzq2Zfyi70DCdnVmyob2LgJ4UkJJOdUX8PEnm0Z2701AOHnEvAo3SY62ZNUWZvq7EBSuqb32k0pgL0RZ3jtbh3OtW1Iy67e4i0KhUKhuA7u4I7RraAq0whPAP2FEB8JIXoBTYA4KWW01G4lmj6tXj3OSyn3Gb4vRuvYAPQVQvwjhDiB1vG5NkwQCiwRQtwLFBq2DQRmCCGOAzvROlaNDPt2SCkzpZRJQDpah+3aeXkLIeyBu4AVhvQ/oHWwrrFGSqmXUkYAVRomkFLO++CDDxr7+fnZTfxhNdvDTzOyQ0sAAhp5kpWTT7KZi9SDp88zsI32fNKoTv5sj9CmAe2MiC03/dvjBxCbmMove4xX9UrKyKJz04YAdHf35kxWKuY4ceUS3vauNLRzxkroGObVqngxjGs0qu1S/N3f2ZNaOovijs37nUZwOjOZBdE3ZyWv0NRLeDu40LC2E1Y6HcMb+bPtovGD9I3tS/S0cvHAqpSeG8o7xTjvEY1bsvWCcd717Rz5rvc4nt+/nrjMEp9eys6gfd362Fho9y/u8vTmdHr1p85FZZ6noV1d6tm4YCks6OfRjn3JxtO+Ju7/oPizK/EEn51cxd7kcJysamNvaQNALZ0lHV2bmSysUR5hF+JpVNeFBi6OWFnoGNrWlx2RsUY2OyJjGdneEIdenmTlmo/ja0QnpND7vR8Y+PHPDPz4ZxIyMrn7qyUVdrQA1vx5jOlPLWL6U4vY83c0g4K0qu/vW4/s7DxSr5jmOWxgAF06NuHtj9cbjbZMemgekx78gUkP/sCufSeZ++2WCjtaACcuxePt6kJDJ0esdDqG+fuy7ZSxL7ZHxzImQPNF2/qeZOXlk5SVzaWMTNo1qIeNpRYH3b0bEWtYWGPrqdN08/YCwNvVGSsLiyp1tMBMbHqXE5t9xvH8PuPYvMYIb3/WVbPzb6QhIR5vZ2caOmp+GdHCl61xplMVK2JECz/Wnbq+mzLL9oYwcc4SJs5Zwo4TpxnRWfN/m8aGWMwwjYtD0ecZ0FZrU0d28WdHmKa3joNdsU3rRh7odEJ1tBQKhUJxS6h0ZEtKeUoI0REYCnwABHNz+6RljyWFEDZoo0udpJTnhRBvonWgAIYBvYGRwOtCiFZoM3/GSSmNltgyjFLlldqkL/W/Hu38dUBaBSNwpdNXcZKbMbuj4ujl581fr0wjJ7+Q11eULMf97YOjmb1yC0kZ2czduJc5k4fy1KAeRF5KZNXB8ArTt/euz8iO/py6nMTKZ7WRji827WNP1Blm/7GVGSMDsdTpyLXJ5/UjG8xqK5KSt49vYn6vyVgIwcozIcRkJDGpaQcAlsYeZVDDloxuFEChLCK3qJBn/14FQMc6XoxuHEBUWgJr+z8MwGdhO9gVX/HFbEUUSclbR4JZ2GcSOp2OlbEhRGckc49PewB+P32MQQ19GdOkDYV6PblFBTy9f3UlR6163rMPb+GXoEnohGDF6VCi05OZ3FzL+7foYzzdpgcutWx4p7O26l6h1DNq00KOp1zir3Mn2TDkQQqlnogrCfwec/w6NOj5/OQaPmn/MDp0bLx8kDPZCYxsoC3pve7i3+WmrWPtyGv+E7FAhxCCHYkhHEip2pSxIr3kvXXbmffgWHRCsPpwOKcTU5jQRRthWn4wlN0n4+jt681fL04jt6CQWStL4njOpCF0buKFc20bts2YzjdbD7Dq8PVf2F/j70OxdOvUlN9+epi8vEI+nPtX8b6P3hzHx19uJiU1i+efHEhCYjrffqrVgz37o1n0+/VNXyySkrc3b2f+PWOx0AlWhoQTk5zCpA6aL5YeDWVnTBx9fLzZ+vg0cgoKeXWD5ovQS/FsjopmzUNTKNTriUxIYukxbUXKP46H8f7wgWx4eCoF+iJeWbe5WppmH9rCL/0qiM0AQ2x2KRWbfy0EwMbCkp71mjDzn03X5ZNiDbu288vIceh0OlZEhBGdmsLk1ppffgsLpa6dHesm3ot9LW3hmmntOjBw8UKyCvKxsbSkp1djZu7Yct0arrEnIo6e/t5seH0aufmFvPFbSSx+/eho3vpda1M/X7+Xj+8fyhPDehB1IZHVB7SYHNCuORN6tKVQryevoJBXFm68YU0KhUKhqBp38mIWtwJR2UpuQoj6QKqUMlcIMRp4DPAH+kopTwshfgccpJTDhRCBGE8jvAK4SykLyjm2NxAH3CWlPCCE+BGIAuYDJ9EWorAA/kabvvc20EhKeUYIYQVcQJuG+DLgCDwlpZRCiPZSymNCiAfQOmxPGvI7Y/g/ufQ+IcR+YK6UcoXh2bAAKWWIEGIhsEFKudKQPktKaW/ofH4mpexTmYNbvzy3xkMqv0v1V8W72RQVWtS0BAD0RdfVX76peHmaH2W83aRsrV/TEqgbarZpuO1cvsuqpiVQ0PgOGVm5UqumFeAYc7PXbrp+Qr6o9LFchUKhuJOo+QudSmj5xq25No58+7k78tyr8ovWBu0ZqONoz0bNAh4B/hRC7KX4CQ6zzANCK1kgIxK4XwgRCrgC30kp04Af0ab6rQGuvX3TAlhsmFp4DK2DlAa8A1gZ8goz/F8dpgAPCSFCgHBgVCX2oUChYdEQ9UusUCgUCoVCoVBUBbVAhjFSys2Aufku11bsC0RbhAIp5U60Z6aupX0FeKWCY59BGyUzt28WWseuLD3N2OYAj5rZvhBYWOp/b3P7pJRxaAuBlE3/QJn/7Q1/C4B+5nQrFAqFQqFQKBQK8/zXphHeOXM1FAqFQqFQKBQKheL/EVVZ+r1Cyo5mmUMIUQfYZmZXPyllyo1qUCgUCoVCoVAoFP8C/mMjWzfc2aoKhg5Vu9uRl0KhUCgUCoVCoVDcCdyWzpZCoVAoFAqFQqFQ/NdGttQzWwqFQqFQKBQKhUJxC1AjW7cYfc2/vgddmH1NS6DQ5c64jdGta1RNS+DIBa+algCAzrqmFYD7zLialgBA6soWNS0Bu3ibyo1uAx5702paAtlzcmpaAgB5SzzpPO2zmpbBoQXP17QEhUKhuGnU5GqEQojBwBdor5P6SUr5YZn9wrB/KHAVeEBKefRG8lQjWwqFQqFQKBQKheL2UEPv2RJCWADfAEPQXj11jxCi7CuohgDNDZ9HgO+u9zSvoTpbCoVCoVAoFAqF4v87XYAYKWWslDIfWAqMKmMzCvhFavwNOAsh6t1IpqqzpVAoFAqFQqFQKG4PNTSyBTQAzpf6/4JhW3VtqoXqbCkUCoVCoVAoFIp/NUKIR4QQh0t9HilrYiZZ2W5aVWyqhVogQ6FQKBQKhUKhUNwWbtUCGVLKecC8CkwuAKVXKWsIXLoOm2qhOlu3mZ7NG/PqsEAsdDpWHg7jp92HTGxeGxZIb98m5BQU8NofwUReSsTTyZ4P7h5MXXs7pITlh06w+MAxAJ7q352glj5IKUnJyuG1PzaTlJldvoZmjZk5OBCdTsfKo2H8uNdUw8whgfRu3oTcggJeXRNMxOVEALY9+yDZeQUUST1Fesnd834DYJB/c54M7I6PmysTfvydsEsJ1fJL78bevNEnEJ3QsTz8BN8fNtbU1MWFjwcMopWbO58e2MdPR48U73OoZc2H/QfQok5dJJJXtgRzLP5ytfK/RnpoKheWxIBeUqdPPTyHNzLanxmZxukvwrB201aOc+5Yl3qjvQE4+9NJ0o+nYOlohf/7na8rf4BeHj7MbDcICyFYEXeMeSf3G+3vV68Fz7QKRCIp1Ot5PySYIynn8bR15OPOo3CzsUcvJcvijvJLzMEq53sr4uLJwG6M79CG1KtXAZi7bR+7o89UWVPmiRQu/hYNeolr73q4D/M2a3c1NoOYdw/T6H+tce7sDkBy8HlSdl8CCa596uM2sHqrQM4YE0ivlk3IzS9g1u/BRF5MNLFp4OrIx1OH4mRnQ+SFRF79bROFRXqGdfDjwaBOmra8At75YxunLiXj7ebCnPuGFqdvWMeJbzYdYPHuY+XqeHlcID1baTreWBxM1AVTHfXrOPLRAyU6Zv6i6ejUrCFzHxnJpZR0ALaFxDBv0z8A3Nu3PWO6t0FKSfTlZGYvDia/sKhSv/zv5SF06dGc3NwCPp29hpgo07r23OxRtPCvD8DFcyl88sYacnPyAQjo6M1jLw3G0tKC9LSrvDR9QaV5lqVrnRY802IUOiHYcPEgi8/uNGvn59iQHzo/yewTS9iZeAJ3aydmtZqEq7U9UkrWXfyHFef3VTv/a7wwuS89ArSyeWv+Zk6eNS2b8f3acc+ADnh5ONP/qW9Jz8oFoHd7Hx4bcxdSSgqL9Hz2+05Com/o91yhUCj+HdTcaoSHgOZCiCbARWASMLmMzTrgSSHEUqArkC6lvL6LSgOqs3Ub0QnBrBFBTF+wioSMTJb9bzI7Ik9zOim12KZ3C28a13Vm8GcLCPDyZPbIICZ9v5RCveTjv3YTeSkRu1pWrHxiCgdiznI6KZWf9xzhq60HALi3ezseD+rGW2u3lavhjaFBPPirpmHFw5PZfrKMhubeNHZ1ZtCXC2jb0JPZw4KY+NPS4v33LVpB2tVco+NGJ6bw9LL1vDWi33X55a3AIO5b/QfxWZmsmTSFrbGniUkt0ZSem8vbu3YwoGkzk/Rv9Alk19kzPLFxA1Y6HTaW17fevtRLzv8STfOXA7Bytebkm0dxal8H2wa1jezsWzjR7Pk2Julde3rg1r8+Z+Zd//LyOgSz2w9m2p4lxF/N4I9+09l26RSnM5OLbQ4kxrHt8ikAfJ3c+aLrOAYHf0eR1PNh6BYi0uKpbVmLVf2msy8h1ihtufneorgAWPT3UX7ef8Rke2VIveTirydp8mJ7rFytiXn7MI7t3LApUx5SL7m8IgaH1nWKt+VeyCJl9yWav94JYSmI+ywEx4A6WHvaVSnvXi21ejjs/QUENPZk1t1BTPliqYndc8N78euuo2w6forX7+7H2K6tWb4/lAup6Uz7ZgUZOXn09PNm9vj+TPliKWeSrjD+0yWA5vNtsx9m24mYcnX09PemkbszI99eQBtvT2ZODGLqp6Y6nh3Zi8U7jrL56ClmTuzHmO6tWbE3FIBjpy/y9A9rjezdnWpzT5/2jH1vEXkFRXw8bRiDO/qy7p+ICv3SuWdzGjSqw7RRX+LXpiFPvTacZ+770cTuh082cTU7D4BHXhjEyEldWL5gL7XtbXjytWHMfGIxSfHpOLnUNklbGToEz/uO4bljP5KYm85PXZ5ib3IEZ7ITTez+12woB1NOFW8rknq+jt7AqcyL2FpY83OXpzmUGm2StircFdCERh7OjJ3xM62b1mPG1H5Me/d3E7uQ6IvsPR7L9zPGG20/FHGO3cdOA9CsYV0+eHw4419bWG0dCoVCoagaUspCIcSTwGa0pd9/llKGCyEeM+z/HtiItux7DNrS79NuNN//zDNbQghvIURYNdOMFELMMHx/UwjxouH7A0KI+tXV0KahJ+dS07hwJZ2CIj1/hZ4kqKWPkU1QSx/WHosEIPR8PA421tR1qE1yZjaRl7QLgqv5BcQmpeLuqL0/Kzsvvzi9rZUVUpZ/yyCggbGGjWEn6edrrKGfrw9rQzQNIRficbSxxs2+4oui2ORU4lKuVNETxrT18ORsehrnM9Ip0OvZcCqKAU2NNaXk5BCakEChXm+03b5WLbo0aMjycK1oC/R6MvPzrktHdmwG1h62WLvborPU4dLVnfSjKVVO7+DnjEXtG3uxWoBrfc5mXeF8dhoFUs+f58PpX9/XyOZqUUHxd1sLq+IbREm5WUSkxWvnUpjP6cxkPGwdqpbvLYqLG+FqbAa13O2Ky8O5izsZx5JM7JK3XsCpkzuWjiW+z718FbumjuisLRAWOmr7OpN+1DRtefRt7cO6w4Z6eDYeB1utHpalSzMvtoRGA7DuUARBrTWfhZy5TEZOniH9ZTycTcuha3Mvzqekc/lKZrk6Atv4sOGgpuPEGYMOR1MdnVt4sfW4pmP9PxH0DfAxsSmLhU6HtZUlFjqBTS1LktKzKk3TvY8fWzccByDqxAVqO9jgWtf0PX7XOloA1tZWxXcx+w5pw75tkSTFayNt6VfKH4Evj5ZOXlzISeZSTiqFsoitCSH0dGtlYjfOqwe7Ek9wJb/kvFLyMzmVeRGAnKI8zlxNpK61U7U1APRp78Of+7XOaVjsZRzsrKnjZFo2p84lcTklw2R7Tl6pemxdcbutUCgU/58Q8tZ8qoKUcqOUsoWU0kdK+Z5h2/eGjhaGVQifMOxvI6U8fKPn+/9uZEsIYSGlrHwuTBWQUq5DG04sywNAGNWcw+nhaE98esmFVXxGFgFenkY27mVsEjKy8HC0J7nUtMD6zo60rOdG6IX44m3PDLiLke38ycrL44GfVlao4XKGsYa2DT0rtfFwtCcpKxspYf7UsSBh2ZETLD9yohoeMI+nvT2XM0vyu5yVRTvPqq2y6eXoRGpODh8PGETLum6EJSbw9q4d5BQWVltHwZV8armWvOnXytWaq6dNL5KyYzKInHUYK+daNJjkg23Dm9fh8LB1JD6nJM/4nAzaupougjOgvi8vtA7C1aY2j+w1vZvewM4Jf2dPQlIvVi3fWxgXU7q0ZVTbloRdSuCjzbvJyK1aZ7jgSh5WlZRHwZU8Mo4m0fTl9lyIK9ln06A28X+cpjCrAJ2VjszQFGy9HauULxjqYVqpepiWhbuTcT10rm1DZm4eRXqthY9Pz8TdybTjMaZra/ZGmr68eUh7X/46VvEoqLuzPfFXzOjIKKMjp0RHQpqxjoAm9Vg2416S0rOYu3oPp+NTSEzP5pdtR9j09nRy8wv5O+osB6LOVeYW6ro7kBRf4ufkhAzquDuSmmzaUXvhzdF07tmcc7FJzPtsMwANG9fBwtKCj398ADs7a9b8/jdbN4RUmm9p3KydSMxNL/4/KTcdfyfjKaJ1rR3p7d6aZ478wAx/89NHPW1caOFQn4j0ys/brA5nexJSS8om8UoW7i72pKRXvQMZ2KEZT9zdExcHO577fPV16VAoFArFnc2/bmRLCPGOEOKZUv+/J4R4WgixQwjxG1DR1b+lEGKRECJUCLFSCGFnOMYZIURdw/dOQoidhu8PCCG+LpP/3UAnYIkQ4rgQwrbq2s1sLHM305xN6TuedrWs+GLycD74c5fRiNYXW/bTb85PbDgexZTu7aoqyeT4ldlM/nkZ4374jYeXrGZy57Z0anxDq2HekCYAS52OVu7uLAkNYcTvi7laUMBjnbpcZ6ZmtpUpDztve1p/1o2W73bCbUADYr8Mv768ysH8EjimwrZcOsng4O94fP9ynm0VaKzRwoqvuo/n/ePBZBfmm6StKjcjLn4/FMqALxYw+vvFJGVm88qg3tetBzCpIJd+O4XneB+Ezni7Tf3auA9tTOycY8R9dhwbL3uEhTnvVikbA2XqqtnSMqZzs4aM7dqKuRv2Gm23tNAR2MqHYMNoVHV0lI0HYcbomkXkhUSGvDGfiR8uZumu48x9eAQADrbWBAY0ZdibPzNw1o/YWlsxtJNfpedjTlB5cfLpm2uYPPATzsUl0WegNvJkYaGject6vP7UEl574lcmP9yHBo3qmE1frgQz28oqeKbFSL6P3oi+nAcDbC1q8V7AVL44uZ6rRdc3El5ZW10Vdh6NYfxrC3npq7U8Nuau69KhUCgU/zpqbun3GuHfOLI1H1gFfCGE0KE93PYy2ovKWkspTW8hl+ALPCSl3CeE+Bl4HPikOplLKVca5nu+WN7QomGpyUcAPIeMx6V9dwDi07PwdCqZTuTpaE9ihvFd0IQyNh6O9iQa7qZb6nR8Pnk4G0Ki2Bph/jmPP0Oj+O6+0Xy97YDZ/QkZWdRzLKOhzGIaFdlc+5uancPWqBgCGnhy+GzVRk/KIz4ri3oOJfnVs7cnMbvyKU0Al7Myic/KJCRBG+XbFBPNY52ub3EKK9da5KeWXHgVpOZh5WxtZGNhW1JlnNrW4fwv0RRmFmDpcGPTB68Rn5OBp23JCIynrSOJOeX74nDyObzsXXCpZcuV/BwshY6vuo9n/bkTBF+q+rNjtyouUrKvFtuvOBrGd5PLvjuwfKxcrCkwKY9aRjZXz2Ry7jutw1uUVUBGaArCQuDUwQ3X3vVx7a3N9r288rTRKJk5JvVoy7hurQEIO5+AZ6mpfx7O9iSWGbG4kp2Dg401FjpBkV7i6eRAYqmpeC3q1eWtCQP434+rSS/zLFsvP28iLyaSknWVskzs1Zaxd2k6ws8l4OlirCOprI6sHBxsS3R4ODsUTwnMzi3pbO+NOMNrE3Q417ahc3MvLqZkcCUrB9AWzmjXtD4bD5vGzIgJXRgytgMAp8Iv4eZZEp91PRxJTSp/GqReL9kVHMb4+3oQvO44SYkZpKddJS+3gLzcAk4cPUvTFh5cPFf16bqJeem425RM/XOzcSI5z3jE09exIW+20Z55drKqTfe6fhRJPXuSwrEQOt4NmEpw/DF2J1VrZjnjg9oyuo/2vGZEXAIeriVl4+5iT1Ja9adFAhw7dZEG7s442dsUL6ChUCgU/2+5gztGt4J/3ciWlPIMkCKEaA8MBI4BKcDBSjpaAOellNeWnloM9LxFGudJKTtJKTtd62gBhF2Mp3EdFxq4OGJloWNIgC87omKN0m6PimVU+5YABHh5kpmXXzx16Z2xA4hNTGXRvqNGaRrXcS7+3tfPh9ik8p+dOnHJoMFZ0zC0tS/bT5bRcDKWUW01DW0bahqSsrKxtbKkdi2tU2FrZUkPn8acSqx88YXKCE2Ix9vZmYaOjljpdAxv4cfW2NjKEwLJV69yOTOTJs4uANzl1YjoUgtrVIfaTRzJS8ghLykHfaGeK/8k4tTe+K57QVp+8d3r7NMZSD1Y2N+8exYnrlzC296VhnbOWAkdw7xaFS+GcY1GtV2Kv/s7e1JLZ8GVfO2i+f1OIzidmcyC6H+ql+8tiovSz3T19/MhOrHqF9V2TRzIT7xKvqE80g4m4ti+rpFNyzl30fIT7ePUyY0GU31x6uAGQGGG1tHIT8kl40gSzl09Ksxv6b4Qxn+6hPGfLmH7idOM7GSoh409ycrNN5pCeI1DMecZENAcgJGd/dkRpi144OnswNxpI3j1t02cTUozSTekgx9/HTXfGV62J4SJHy1h4kdL2BF6muFdNB1tvA06Mkx1HI4+T/92mo4RXf3ZeULTUcehZEGQ1o09EEKQlp3L5SuZBHjXw8ZKi92uLRoRG2++3qxffpDHJ33P45O+Z/+OSPoPbweAX5uGXM3KNTuFsL6Xa/H3br19OX9Gi4cDO6No3b4xOgsd1jZW+LVuwLm46rUhURkX8LKtSz0bFyyFBf092rIvyXhhjwn7PmS84bMz8QSfRq1mT5LWKX/VfzxnsxNZdm5PtfIFWLE9hCmzFzNl9mJ2Ho1h2F3+ALRuWo+snPxqTSFs6O5c/N23sTtWlhaqo6VQKBT/D/k3jmwB/IT23JQn8LNhW1V+5cr2pa/9X0hJx9PmRsWVR5Fe8t767fz4wFh0QrD6aDgxiSlM7BIAwLKDoew+GUfvFt5sen4auQWFzFwVDECHxvUZ1d6fk/FJrHpyCgCfB+9j96kzPDewJ03cXNBLyaW0TN5au7VCDe9s3M78qZqGP46FE5OUwsROBg2HQ9kVHUfv5t4EP61peG2tpqGOfW2+nqhNQ7LQ6dhwIoq9MWcB7UJ61tC+uNrZ8v3kUUTFJzF9cdWeQSiSkjd37mDR6HHohGBFRBjRqSlMbqNp+u1EKHXt7Fg7aQr2tWohkUxr14FBixeRlZ/Pmzt38PngIVhZWHAuPZ2Xt2yubtEAICwEXlObETPnBFIvqdPbE9uGtUnarj2a5xZUnyuHkkjefglhIRC1dDR5vGXxNK64byPIjEqnMKuAE88eoN4Yb+r2qdqzZ6V98fbxTczvNRkLIVh5JoSYjCQmNdVGFpbGHmVQw5aMbhRAoSwit6iQZ/9eBUDHOl6MbhxAVFoCa/s/DMBnYTvYFV/+anfF+d6iuHhxQC9aerohkVxMy2D2evOrZJpDWOioP6UFsZ8eB73EpVd9bBrYk7JDG0mt07fiKaxnvj5BUXaBdpypLbCsxuIleyLj6N3Sm42vaec66/fg4n3fPjya2cu2kJSRzdwNe/n4vqE8NbQHURcSWfWPdkH/2MCuONvZMGtcEKD5d9JcbTl8GytLurdoxNsryq+nxTrC4+jp7836NzQdsxeX6Pj6sdG89Zum4/O1e/lo2lCeGN6DkxcSWX1A09G/fXMm9GxLoV5PXn4hMxZuBCDsbDxbj0fz+ytTKCrSE3UhiT/2V/785cG90XTu2YIF654hL7eAT99cU7zvna+mMPftdVxJzuLFt8dgV9saISD2VAJfvb8BgPNxyRzeH8P3y/+H1Es2rT7K2dPVWwmwSOr57ORaPms/HZ3Q8eelQ8RlJzCqQTcA1l78u9y0AU7eDK7XkZjMyyzo+iwAP8Rs4u+U6q8gui80jh4BTVj90YPk5hfy9vySdufz58bw7oJgktOymdi/PVOHdKKOU21+f/s+9p2I470FWwjq1Jxhd7WksEhPbn4hr323odoaFAqF4t9I1Sf1//9A/BtXQBJC1EJ7NssKaA70QpvWN7yCNN5AHHCXlPKAEOJHIEpK+akQYivwqZTyLyHEXKC9lDJQCPEA0ElK+aQQ4k0gS0r5iRBiPfCZlHJHZVr9Z86tcQfrb84Mtxsi36XG3QBA167Xvyz7zeLIheq97+lWoQszXczhdtNm4KnKjW4DUStb1LQELAoqt7kdeOxNq2kJZM/JqWkJAOQt8azc6DZwaMHzNS1BoVD8e7jj+zIBz9+aa+PQz567I8/9XzeNEEBKmQ/sAJZXc+XBSOB+IUQo4Ap8Z9j+FtozYHuAqhxvIfB9dRfIUCgUCoVCoVAo/tOoBTLufAwLY3QDxgNIKXcCOytKY3jWy7+cfXsAk1vbUsqFaB0rpJRvltr+B/BHtYUrFAqFQqFQKBSK/wz/upEtIYQ/2ludt0kpK143WaFQKBQKhUKhUNwx1ORLjWuCf93IlpQyAmha3n4hRB3A3FP4/aSUVV8KTaFQKBQKhUKhUNxc7uCO0a3gX9fZqgxDh6pdTetQKBQKhUKhUCgU/23+33W2FAqFQqFQKBQKxR2KGtlS3Ex0+TWtAPKdaz6qbePvjNU4j/3VsqYl3DEPSlrcAe9PjVxT80uuA+TXqdzmViPvkMBI6Olc0xLI317zGgCKmtS0AvA4UkjvUXNqWgYAu9e+VNMSFAqF4l+H6mwpFAqFQqFQKBSK28KdvJjFrUB1thQKhUKhUCgUCsXt4T/W2bpDJq4oFAqFQqFQKBQKxf8v1MiWQqFQKBQKhUKhuC3816YRqpEthUKhUCgUCoVCobgFqJEthUKhUCgUCoVCcXv4j41sqc5WDfPqqEB6+TUht6CAmcuCibyYaGLTwMWROfcOxcnWhsiLicxYuonCIj1N3Fx4Z+JA/Bu48+Wm/SzcdeS6NPT29mZWv0AshI7loSf44eAho/1NXV34aMggWrm78+nefcw/pOVTz8GeOUOHULe2HVJKloacYNHRY1XOt2eLxswYHoiFTscfh8L4adchE5tXRwTS27cJOfkFzFwZTOQlzT/vjBtAH7+mpGZdZfQXvxbbf3LPUJrUdQHAwdaazJw8xn21pHwNzRozc0ggOqFj5dEwftxrqmHmkEB6N9fK6NU1wURcLikjnRCsfHQyiRlZPPbbWgA+Gz+UJnU0DY421mTk5jHm+/I13ExN2559kOz8Aor0eor0krvn/VZxXi0aM2OkVvZ/HArjp51mymCkoQwKCpi5vKQMykv7wtBeBLZsSkFREedT0pm1IpjM3Dyc7Gz4/N7htG7owZojEby3dodJPsV1YXkFdWHKUJzsjOtCRenfGT+A3i21WBnz2a8mx3ygd0deHN6brp99x5UcbT38Xk0bM3Ogdm4rjocx74CpX2YNDKSPj+aXGRuCiYjX8nugS3vGt2uDlJJTScnMWB9MflERLT3ceGtIP6wtLSjUS97atI3QSwkVlk+vpo2Z1V+rI8uPhzHvb1Mdrw8o0fHKhmAiEjQd93dqz4R2rREIloecYOEhrW76udfl7cH9sLOqxcX0DF5Y9xdZ+RW/o+LlcYH0bNWE3PwC3lgcTNQF07KpX8eRjx4wlM2FRGb+UlI2nZo15KVxfbC0sOBKVg7Tv1wBwJuTB9C7dVNSM69y9wemZVOans0b86qhvVh5KIyfdpv64rXhJe3Fa3+UxOq7Yw3tRfZVRpVqL/zquTF7VEmZvLNuGyculF8mvXwaM3NQIDqdjhXHwvhxn5m6OSiQPoa6OWNtSVw4WFvz7ogBtHCvg5SS19Zv4fiFywDc27kd93ZuS6Fesismjjlb91Toi7I8/XAQ3To2JS+vkA++2MipWNPyef35Yfg286SwsIjI6Hg++TaYoiI9A/q0ZPLYrgDk5Obz6XdbOH0mqVr5KxQKRXVQ0wgVt41eft40quvM0I8W8ObKrbw+Nsis3XPDevHr7qMM+3ghGTl5jOvSGoD0q7l8uGbndXeyQOssvDkgiIdWrmbwzwsZ3tKPZnVcjWzScnN5e9sOfjpknE+hXvLBjl0M/nkRdy/+nXvbtzNJW1G+M0cG8diCNYycu4ihbX3xcTdO28vXm8Z1nBnyyQLeXL2VN0aX+GfNkQgeXbDa5Lgv/r6RcV8tYdxXS9gSFsPW8JgKNbwxLIiHF69h+DeLGNbGFx83Yw29m2saBn25gDfWb2X2cOMyuq9be2KTUo22Pb9iI2O+X8KY75cQHBnDlsjyNdwSTQtXMOb7JZV2tHRCMHN0EI/9vIaRn1VQBnWdGTJnAW+u2sobY4IqTXsg+iyj5/7C2M8Xczb5Cg/37QxAfkEhXwXvZ86fpheSxXXh4wW8+cdWXh9TTl0Y2otf95SqC51bV5p+zeEIHptvGisAnk72dG/eiIvpGUZ+mT04iIeXrmHoD4sY3soXn7rGfunj4423qzMDvlvA6xu38tZgLT8Ph9pM7dyesT8vYfiPv6ITOoa18gXgpaBefL3nb0b9tIQvd+3npaBe5ZRMiY43BwYxffkahsxbxHB/X5P61cfHm8YuzvT/fgGv/7WVtw06mtetw4R2rRm38HdGzP+VQJ+mNHZxBuC9oQP4ZMdehs//lS2nYpjerWOFOnr6e9PI3ZmRby/gnaVbmTnRfNk8O7IXi3ccZeQ7C8m4mseY7lrZONha8+qEIJ6Zt45x7//CSz9vKE6z7p8IHv/WfNmU9cWskUE8unANIz43H6u9W2j1YvCnC5i9ZiuzR5XoXH00gkcWmubzwuBefLv9b8Z+vYSvt+7nhcHll4lOCN4YEsT039Yw7FvzcdG7mTfedZwZ+PUCXt+wlTeHlWiYOTiQPafPMOTbRYz6YTGnDe1GV++G9PP1YcQPixn+/S/M33+4Un+UplvHJjSs58Lkx35izjebef5/A8zabdkVwb2Pz+eBpxdiXcuS4QMCALickM5Tr/3OtGcWsmjZAV56YmC18lcoFApFxfznO1tCiAeEEPWrmcZbCBF2o3n3beXDuiORAISei8fBxpq6DrVN7Lo28yL4RDQAa49EENTKB4DU7BzCLiQU3z2+HtrW8+TslTTOp6dToNfzZ1QU/Zv5GNmkXs3hRHwChXrjfJKyswlP1O6gZhcUcDolBQ97+yrl28bLk/MpaVy4kk5BkZ6NISfp29I436CWPqw7ZvDPeWP/HDlzkfSrFb+Vd1CbFvwZcrLc/QENPDmXWkpD2En6+Rlr6Ofnw9rjmoaQC/E42ljjZq9p8HC0p0+LJqw4Wn4oDG7Vgj9PlK/hZmuqDsVlkFqqDPzLlEHZGLXVyqCitPujz1Gk125bhZy7jIeTFhM5BYUcPXOJ/MJCEy19/X1Yd9Q0n7IY1YXDJXWhovRH4sqPlZdHBPLZxj1IWXKbLaC+J2dT0zifZqgTESfp36JMGbTwYXWooQwuabF5rQwsdTpsLC2xEAJbK0sSM7MAkFJiX6sWAPbW1iRmZpvVZKTjSikdkSfpV0ZH/+Y+rAnTdBy/FI+DtTVutWvjU9eV4xcvk1tYSJGUHDp/gYEtmgHaSPXB8xcB2Bt3lkG+zSvUEdjGhw0HtTxOnDH41tG0bDq38GLrca1s1v8TQd8ATeuQTr5sD4kh/komAFeycorTHD19kYxK6jFAm4aenCvVXvwVepKgsu2Fvw9rq9leSCmpbW0oE5uKyySggVYeF66VR/hJ+vmWiQtfH9aEGOLiYjyO1lpc1K5Vi86NGrDymNZWFOj1ZOblAXBPx7bM23eIgqIiQGtvq0PPLs3ZvCMcgIhTl7GvbUMdF9Py+ftIXPH3yOjLuNXV6mVY1CWysjUt4Scv4VbHoVr5KxQKRbWRt+hzh3JLO1tCCIsq2AghRE12+h4AqtXZull4ONoTn5ZZ/H9Celbxhek1nO1syMzJK754TUjLxN2pah2aKmmwt+dyZomG+MwsPOyr/2PbwNERfw93Qi7HVy1fR3sup5c69wzTc3d3MuMfx6qde0fvBqRkXeVcSlqVNcSnZ+HhYHx8Dwd7LmeUssko0fDa4EA+CTa+UC9Np8aahrOp5Wu42ZokMH/qWP54dDITOrapOC8ney5XEn/ujvbEp5uWQVXSAozt1Jo9J89UqOOaFqOyTqtCXUgvqQtVSV+WQP+mJGZkcfJysrEWB3viM8v410wZxGeUiV8HexIys5n/9xF2PjWdfc88QmZeHvvizgHw/pZdvNyvF7uems6M/r35dMfeCvV52pcp58wqxILBJjophc6NGuJsa4ONpSV9fLzxNMTIqaQU+jVvCsAQvxZ4OlRc392d7Ys7SqD5tmwb5Fy7/HaqsZsLjnbW/PT03fz20mSGd2lZYX7m8HAyjsP49CzcHSuJ1YzK24sP/9zFS0N6se3l6bw0pDefby6/TDwczBy/kri4Vh5eLk6kXs3hg5EDWf3wFN4d3h9bK20Wv3cdZzo1asDyhybx6/3jaVPfo0LNZalbx57E5JI8k5IzqVun/PO2sNAxKLAVB4/GmewbPiCAf8xsVygUCsX1c0OdHCHEO0KIZ0r9/54Q4mkhxA4hxG/AiXLSeQshIoUQ3wJHAS8hxEtCiENCiFAhxFtl7H4UQoQLIYKFELaGfe2EEH8b7FcLIVyEEC2FEAfL5BNq+P6G4fhhQoh5hk7e3UAnYIkQ4rgQwlYI0VEIsUsIcUQIsVkIUc+QvqMQIkQIcQB44kb8VqLPdFvZC3dhxqica/vr02Bmm6zm7QE7Kyu+GTWCd7fvrPTZj4owOfcq2JTH0La+bAyJqr6GsudeThkFtmhCSvZVwi+bPhtxjWFtfPkzrPoarlcTwOT5yxj3w288vHg1k7u0pVPjBtXLqyplUE58lE37SN8uFOr1bDhWuQ+qUtYV1YXqxoqNlSWPBHXh6+D916nFvI2jjTX9WjQl6Juf6fnlj9hZWTGytR8A93QM4P0tu+jz1U+8v2UX7w+vZLpWVdoHc1qRnE5JZd6BQyycNJafJ44hKiG5uCP06p/B3NuxHasfmExt61oU6IsqlmFOB1UoG8NfCwsdLb08ePL7NTz+7SoeGdSVRm7OFeZposHs1htvLyZ1DeDDP3fR7+Of+GjjLt4ZW36ZVKUulOcrS50O/3ru/H4klDE/LiGnoJBHemjTay10OhxtrJkwfykfb9nN5+OGVajZRJfZOCnf/vnH+hMSfp7QiItG29u38WJY/zZ8v2hXtfJXKBSKaqNGtqrFfOB+AMPo1CTgItAFmCml9K8grS/wi5SyveF7c0O6dkBHIURvg11z4BspZSsgDRhn2P4L8IqUMgCtUzdbShkJ1BJCNDXYTASWG75/LaXsLKVsDdgCw6WUK4HDwBQpZTugEPgKuFtK2RH4GXjPkH4B8LSUsntlThFCPCKEOCyEOJwacsBo36S72rLyuSmsfG4KiRnZeDqX3FX2cLInMcN4GsuV7BwcbK2x0Gm/qB7ODiRlZFUmocrEZ2VRr9SdbU8HexKzqn58S52Ob0aNYF1kJMHRVX82KSEji3pOpc7d0fTcE9KzTP1TydQrAAudoH+rZmwKPVUtDZ5mjp+QkUU9x1I2jppNh0b1CfJtyrZnH+TTu4fStYkXH48dbKRhQMtmbAyrWMPN1AQU/03NzmFrZAwBDTzLzys9i3qVxF9CRhaeTqY2laUd1cGfPi2b8MrSv8rN/57ubVn57BRWPmumLjhXoS44ldSF+LKxYiZ9abzqONHA1Yk/nr2XzTMexNPRgdUPTaFubTviM7OMRns8He1JzDI+VnxGFp6OZeI3K5u7vBtxIS2DK1dzKNTrCT4ZQ/uG2sD5mDb+BJ/U6shfkacIqGQEIz6zTDk7mNFhzsYQAytDwxm94DcmL1lBWm4uZ65cASA29QrTlq5izMLf2BARxbkr6SZ5T+zVlmWvTGHZK1NISs/G08XYt0npZcomy0w7la6VTUJaFvsjz5CbX0hadi5HTl/Et4Fbhedu4ot04zj0rEqsOlbeXozq4M8Ww3Odm06cok3D8sskPrPy45eNi2vlEZ+RSXxGJqEXtZH/TZHR+NdzL9a9JUrTcOJSAnopcbGzrVD3mKHtmT/3fubPvZ/k1Czc65bk6VbXgZRU8234AxPvwtnRjq9/Nl6cpmljN15+YjCvvr+ajMzKp3UqFAqFourcUGdLSnkGSBFCtAcGAseAFOCglLKyuQhnpZR/G74PLJX+KOCH1skCiJNSHjd8PwJ4CyGcAGcp5bVbcIuAa52z5cAEw/eJwDLD975CiH+EECeAIKCVGU2+QGtgixDiODALaGgmvwqXzZJSzpNSdpJSdnJta9w3W7o/hLvnLuHuuUvYHnaakR21KTUBjTzJys0n2czFwcGY8wxso7ljVEd/toefrij7ahF6OZ7GLs40dHLESqdjmJ8f22Jiq5z+g8EDiUlJ5efDR6uVb9iFeBrVdaGBiyNWFjqGtvVlR6RxvjsiYxnZ3uAfr/L9U5buzRoRl3SFhEo6pScuxdPY1YUGzgYNrX3ZHmWsYXtULKPaaRraNvQkMzefpKxsPtu6j8DPfqLf5z/zwsqN/BN3npdXbSrR0LQRccmVa7iZmmytLKldywoAWytLevg05lRiskke1wi7EE+jOpWUQUSs2RitKG3PFo15KLATTy5aR26B6fNZ1/j9QAh3f76Euz9fwvbw04zsUCqfnHLqwulSdaGTP9sjtLqwMyK2SumvER2fQp+3f2DQhz8z6MOfic/IZMz8JSRnX+XEpXi8XV1K6oS/L9tOlSmD6FjGBBjKoL4nWXlaGVzKyKRdg3rYWGpTxLp7NyI2WVsIITEriy6NGhq2e3GmkumlJy7F4+1SSkdLX7ZFG+vYFh3L6Naajnb1PcnMyycpWztvV8MFez1HBwb6NmNDxEmj7QJ4/K6uLD0WapL3sj0hTPxoCRM/WsKO0NPFU//aeBtiwExH9nD0efq308pmRFd/dp4wlE3oadr7NMBCJ7CxsqRNY09iE1JN0ldE2MV4GpdqL4YEmMbq9shYRpVqLzKr0F4kZmTRuYlWJt18vDhbwbTjExcNceFsKI9WvmwvGxenYhnd1hAXDQzlkZVNcvZV4jOyilcp7d7Eq3iBjK0nT9OtiRcA3q7OWFlYcKWS57ZWbzzGQ88t4qHnFrHn7xgG9dV+zvxb1CM7O4+UK6bnPWxAG7p08OatTzcYjXy513Xg3VdH8d7nf3Lh0pUK81UoFIqbgZC35nOncjOWfv8J7bknT7SRIIDKr4iNbQTwgZTyh9IGQghvIK/UpiK0UamKWAasEEKsAqSUMloIYQN8C3SSUp4XQrwJ2JhJK4DwsqNXQghnbsEA5e6oOHq19OavGdPIyS/k9eXBxfu+fXA0s1duISkjm7kb9zJnylCeGtyDyIuJrDqoPQxdx8GOZU9Pxt6mFnopubdne0Z98gvZeVWfylckJW9t3cGCu8dhoROsOBFGdEoK97TVVqr6PSSUurXtWDN1Cva1tHymdezA4J8X4etWlzGt/IlKSmLd/fcC8OnufeyKq3zOf5Fe8t667cx7cCw6IVh9OJzTiSlM6KLlu/xgKLtPxtHb15u/XpxGbkEhs1aW+GfOpCF0buKFc20bts2YzjdbD7DqsOaXIQG+bKxgYYzSGt7ZuJ35U8ei0wn+OBZOTFIKEztpGpYdDmVXdBy9W3gT/Iym4bU1wZUcVWNYa182VGNhjJuhqY59bb6eNALQpiZtOBHF3pizFeb13trtzHtIy2v1oXBOJ6QwoauhDP4JZXeUoQxenkZufiGzVgRXmBZg5qggrCwt+Gn6WABCzsXz9uptAAS/8iD2NtZYWegIauXDIz+uIjYxVasLft789YqhLqyooC5MHspTg3oQeamkLlSU/uPJQ+jcVIuVra9N59stB1h1KLx8v0jJ25u3M/+esVjoBCtDwolJTmFSB80vS4+GsjMmjj4+3mx9fBo5BYW8ukHLL/RSPJujolnz0BQK9XoiE5JYekybTT3rz63MHBiIpU5HXmEhr2/cWnEsSMlbW7bz86SxWAjBylBNxz3tDXXzWCg7T2s6tj2m6ZjxZ8l5fz12BC62NhQU6Xlr83YycrWmdIS/H1M6ttXK42QMK0PL9wXAnvA4evp7s/4NLd5mLy6Vx2Ojees3rWw+X7uXj6YN5YnhPTh5IZHVB7TjxiWksj/yDMtnTEVKyeoDYZy+rMXKBw8MoVMzL5ztbdj89nS+23iANX+b6rnWXvw4zdBeHAknJjGFiYb2Ylmp9mLTC5rOmX+Uai8mDqGLob3Y/sp0vt56gFVHwpm9emvxcvL5hYXMXl1+mRRJydt/beenKVp5/HFcq5uTOhri4ohWN/s082bLk1p5vLauRMM7f+3gkzFDsLLQcf5KOq8a9v1xLIz3Rw5k/WNTKSgqYsbazRWWR1n+PhJL905N+f37h8nLK+CDr0pGkz9+fRwffbOJlNRsXvjfQBISM/juoykA7P77FIuWHeCBSXfh5GDLc48OMPhazyMvVLwMv0KhUNwQd3DH6FYgqvoMTLkHEKIW2jQ+K7TRqF7Ai1LK4RWk8QY2GKb0IYQYCLwD9JNSZgkhGgAFgF0ZuxcBeynlm0KIEOBJKeUeQ+fJSUr5nMHuEBAFnJBSfmzoLJ0EvAEL4G9gpeE464HPpJQ7DOcSAUyVUh4QQlgBLaSU4YZnvx6XUu4VQnwEDLumqyJavzS3xkMq173GJWCdav6pi9tNUa2aVnDnYHEHzBa6U+5E5TvVtAKQd8jasLUv1bQCyL9DFsQrMndL7jbjcaT80eHbze61L9W0BIVCUTl3xgVXBXR8+NZcGx/58bk78txveGRLSpkvhNgBpEkpi8w9KF2FYwQLIVoCBwzps4B70UayyuN+4HshhB0QC0wrtW8ZMAdoYjh+mhDiR7RO4Rmg9JsoFxqOkwN0B+4GvjRMHbQEPgfCDcf/WQhxFajerUeFQqFQKBQKhUKBuJkrvf0LuBkjWzq056zGSymjb4qq/0eokS0NNbJ156FGtkpQI1slqJGtEtTIljFqZEuh+FdwZ1xwVUCn6Z/dkl//wz89f0ee+40u/e4PxADbVEdLoVAoFAqFQqFQVMh/bOn3G5pGKKWMAJqWt18IUQfYZmZXPyllyo3krVAoFAqFQqFQKP5d3CmzWm4XN2M1wnIxdKja3co8FAqFQqFQKBQKheJO5JZ2thQKhUKhUCgUCoWiGDWypfj/Ru0LNf+8oG3KnVGzLHP0NS0BWfPFAUCBfc2vyJDvcGc4Q38HLJxSWNkbBG8TBXY1rQDcj1b9XYG3kss9aj4wbC9drWkJAJwZ6YjvO3NrWgYnX3+upiUoFApFtVCdLYVCoVAoFAqFQnFbUM9sKRQKhUKhUCgUCsWt4D/W2ar5eUQKhUKhUCgUCoVC8f8QNbKlUCgUCoVCoVAobgv/tWmEamRLoVAoFAqFQqFQKG4BamTrNvHqqEB6+TUht6CAmcuCibyYaGLTwMWROfcOxcnWhsiLicxYuonCIn256b3dXPjk3qHF6Ru6OvH15gMs3nuMJwd1J6iVD3opuZKRw+tLNpOUkV1s+8rYQHr6a8d7fUkwURfM6HF15KP7h+JY24ao84m8trhED0CrRh78+twkXl64ka0h0QDcG9iesd3aIJFEX0rmjd+CyS8sqpKPnp/al+7tmpCXV8A78zZz8oypprsHtGPi4A54eTgz6LFvSc/KBaBDy4Z8/NwoLiWlA7DzUAw/r/m7SvlWxDMPBtG9QxNy8wt5/6u/OBVnqumNZ4bi5+NJYZGeyOjLfPzDFoqKbt6qh88+GET39pqG9742r2H2M0Pxa6ppiIi5eRpemNKXuwKakJtfwNs/bebkWdO8x/drx6SBWpkMeLKkTHq39+HRsXchpaSoSM9nv+0kJPpSlfJ9eVwgPVtp+b6x2Hx81q/jyEcPDMXJzobIC4nM/EWLz07NGjL3kZFcStFiYVtIDPM2/UNjdxc+nlZSXxrUceK7jQdYsvOYybF7Nm/Mq0MDsdDpWHkkjJ92HzKxeW1YIL1bNCGnoIDX/ggm8nIitSwt+GX6BGpZWGCp0xEcHs3X2w8A8OKgXgT6NaWgqIjzqenMXBVMZm5elfwB0KtpY2YODMRC6FhxPIx5B0w1zRoYSB8fTdOMDcFExGt+u69zeya0a40QguXHTrDokOk5G51/i8bMGKnl9cehMH7aaZrXqyMD6e2r5TVzeTCRlxIrTDuwTXOeGNCdpm6uTPr6d8IvJgBgqdPx9t0DaFnfHQsLwbojkawN2VepP576Xz+6dfYhN6+ADz/dSHRMgonNzJeH49vCk6JCPZEnL/Ppl5uL60W7AC+efLQfFpYWpKdf5dmXf680z14+jZk5KBCd0LHiWBg/7jf1y8xBgfRpprWtM9ZpZdCkjgtzx5bEnpeLE1/uPMCig8d4snc3JrRvQ+pVbdXBz3bsY3fMmUq1lOZ/LwymS4/m5OYW8Olba4g5GW9i89yskbRoWQ+E4OK5FD55aw25OQXF+1v41+fznx/i/ddWsnd75C3zBYCDtTXvjhhAC7c6SCSvrdvC8YuX8fWoy1tD+2FXqxYX0zJ4cfVfZOffGStTKhSKm4ga2fp3IYTYKIRwvoXH3ymE6HQjx+jl502jus4M/WgBb67cyutjg8zaPTesF7/uPsqwjxeSkZPHuC6tK0x/JukKd89dwt1zlzDh89/ILShkW1gMAAt2HmHsZ4u5e+4SdofH8ujgbsX59PT3ppGbMyPeXcDbS7cya7x5Pc+M7MXinUcZ+a6mZ0y31sX7dELw7Iie7I86W7zN3ak2k3u3555PlzDuw1/R6XQM7uBbJR91b9sEL09nxr/wMx/M38rLD/Qzaxd66iJPf7CSy4ZOVWmOn7zIfTMXc9/MxTelo9WtQxO86rkw6cn5zPkumBcfGWDWLnhPJJOf/pn7nluItbUlI/q3ueG8r9G9fRMa1nNh4lPz+fj7CjTsjuSeZ35m6vMLsa5lyYh+N67hroAmeHk4M+6Vn/lg4VZeuc98mYREX+TJOSu5lGxcJocizjHl9V+5943FvDM/mJkPDqxSvj39vWnk7szItxfwztKtzJxoPj6fHdmLxTuOMvKdhWRczWNM95L4PHb6IhM/WsLEj5Ywb9M/AJxNvFK87Z6PtfqyPSTG5Lg6IZg1IohHf1nDiC8XMbSNLz5urkY2vVt407iOM4PnLmD2mq3MHqlpzC8s4sGfVzL2m8WM/WYxPZs3JqChJwD7T59l1Fe/MObrxZxJvsLDvTtXyR/XNM0eHMTDS9cw9IdFDG/li09dY019fLzxdnVmwHcLeH3jVt4arGlq7laHCe1ac/eC3xn546/0bd6Uxi7OFeY1c3QQj/28hpGfLWJoW1983I3z6uXrTeO6zgyZs4A3V23ljTFBlaaNSUjhmV/WczjugtGxBgU0x8rSgjGf/8qEL39jQtc2eHo4VuiPrp2b0rC+K1MenMenX2zmuSfNx9bWHRHcN/0npj32M9bWlgwbHACAfW1rnn1iIK+9+QfTHp3Pm++trTC/a+f2xuAgpv+2hmHfLWJ4a9My6N1MK4OB3yzg9T+38uZQzS9xKVcY/eMSRv+4hLE//UZOQSFbTpbE3sJ/jhbvr25Hq/NdzWjQyJVpY7/ii/fX89SMYWbtfpi7if9N+YH/Tf6exPh0Rk7oUnJuOsFDT/bnyN+nq5TnjfgCtE7YnpgzDPluEaN+WMzp5FQA3hs+gE+37WXkD7+yNSqG6Xd1rJYvFAqF4k7kX9/ZklIOlVKm1bSOiujbyod1R7Q7haHn4nGwsaauQ20Tu67NvAg+oY0QrT0SQVArnyqn79bci/Mp6VxOywQgO6/kbqBNLSukLLmN0Le1D+sPacc7cTYeB1tr6jqa6unS3IsthhGrdQcjCGrjU7zvnt7t2BoSQ2qm8TtgLHQ6rK0ssdAJbGtZkpSeVRUX0bujDxv3RgAQfvoy9rWtqeNsqunU2SQuJ2dU6Zg3Sq/Ozdi0K1zTFF2+pr+PxhV/j4iOx72Ow03T0LNzMzbtLNHgYGdew4FjJRoiY26Oht7tfdi4TyuTsNOGvJ3MlMk582WSk1dy19zW2jgGKyKwjQ8bDhri80z58dm5hRdbj2vxuf6fCPoG+JjYlEdXXy8uJKdz+Uqmyb42DT05l5LGhSvpFBTp+evESYJaGh87qKUPa48b6uQFQ5201zRezdfO29JCh6VFSRO7P+YcRXrNByHnL+PpZF9lvQH1PTmbmsb5tHQK9Hr+jDhJ/xbGmvq18GF1qKYp5JKmyc2+Nj51XAm5dJncwkKKpOTguQsM8G1Wbl5tvDw5n5LGhVTt/DeGnKSvf5nzL9sm2WptUkVpYxNTOZN8xSQ/KcHOygoLncDaypKCIj3Z2RWPZvTo3pzN28IAiIi6hL29Na6upjHyz6HY4u+RJy/jVlerF/36+rNn/ykSk7TyT0uv/F1WAfU9OXsljQvXyiD8JP18TctgzbUyuBiPo6EMStO9iRfnr6RzKd009q6H7n382PpnKABRYRep7WCDax3T2LpayqfW1lZGd5ZHTezC3h2RpF3JNklnjhvxRe1atejcqAErj2vlV6DXk5mnjfA2qePCoXMXAdgXd5aBfs2r6AWFQvFvQshb87lT+Vd1toQQjwkhjhs+cUKIHUKIM0KIukIIbyFElBBikRAiVAixUghhZ0jXWQixXwgRIoQ4KIRwEELYCCEWCCFOCCGOCSH6GmxthRBLDcdYBtiWyn+gEOKAEOKoEGKFEKJKV0sejvbEp5X8sCakZ+FR5kLL2c6GzJy84ouxhLRM3A02VUk/pK0vG49FGW17evBdbJ05nWGd/Ph244Hi7e7O9iSUOZ57WT21zehx1mzcnWoTFNCMFftCjdIkpmezaMcRNr85na3vPEJmTh4HTp6rgofAzcWexJQSTYmpWbi5VP1iFKBNs3r8+t5U5r40hiYN6lQrrTnqutqTmFxKU0omdc1cxFzDwkLHoD7+/F2q43OjuNUp65dM3CrT0Nuff47fuAZ3F3sSUkvlfSUL92qWSWCHZiz/4AE+e24M784Prlq+zvbEl+oEJaRVMT5L2QQ0qceyGffy9f9G4+NpGguDOvjy15Eok+1gqG+lLoTjM7JwdzTO393B2CYhIwsPg41OCFY9MYW9Mx5lf8w5Qi+YTuka27E1e06dKc8Fppoc7InPNNbk4WBvapNRRpODPdFJKXTyaoizrQ02lpb08fGmnmP55ejhZF980wbMtzfuZXyUkK6df1XSliX4RDRXCwrYOfMRtr46nYW7j5BpmIpaHm517ElKKungJyVl4lbBDQYLCx0D+7Xi4GGtXng1cMXe3obPP76HH766n4H9WlWYHxjiwox/jWzKlIG5chrWypcNYcaxN6VzW9Y9ci/vjxiAo411pVpKU9fNgaSEklHl5MQM6rib98ULb4xk6aYX8PKuw9pl2ohvHTcH7gr0488/Dlc5zxvxhZeLE6lXc/hg5EBWPzyFd4f3x9ZKe6LhVGIK/Vo0BWBwyxbUc7x5N64UCsUdhJS35nOH8q/qbEkpv5dStgM6AxeAz8qY+ALzpJQBQAbwuBCiFrAMeEZK2RboD+QATxiO2Qa4B1gkhLAB/gdcNRzjPaAjgBCiLjAL6C+l7AAcBp6vim4hzJ5LGRtTo2smlaW3tNAR2MqH4NBoI5svN+2n/3s/8efhKCb1blehRhM9lK/npTGBfL5uD/oyaRxsrenbuilD3/qZAa//iG0tK4Z18qsw3+L8quCjiog6k8joZ39i6sxfWR58nI+fG1nltOVrMieqfPsXHu5PSMQFQiMv3nDexRoqKAdzvGjQEHIzNNxgmQDsPBrDhFcX8vKXa3l07F1Vy9as26tQXwx/Iy8kMuSN+Uz8cDFLdx1n7sMjjOwsLXT0aePDlmPRJscAs6dt4vSKNOqlZOw3S+g75yfaNPSkmbtxZ+/RPl0o0utZH2K+s1dVTaZtiHmb0ymp/HjgEAsmj2X+PWOISkymUF+9cjRtH8zYlFM5KouZNl6e6PV6+r73I4M+nM/9vTtQz9OpYkFm28vy83nuyYGEnrjAiXBtCqOFhcC3mSczXl/JyzOXc9/ku2jYwKXiLM1sq2oZXMNKpyOohQ+bIkti7/cjoQz4egGj5i0mMSubGQN6V6ijKsLKc8Wnb69j8tDPOHcmmT4DtWm3jz0/iPlfbUVfjZi4EV9Y6nT413Pn98OhjPlxCTn5hTzSQ5tSO3N9MJM7teOP6ZOpbV2L/KKqPe+rUCgUdzL/1gUyvgC2SynXCyG+KrX9vJTy2pPVi4Gngc3AZSnlIQApZQaAEKIn8JVhW5QQ4izQAugNfGnYHiqEuDZ80w3wB/YZLvRqASXDRaUQQjwyY8aMmffff7/bssfGEJmciadzyR06Dyd7EjOMp2tcyc7BwdYaC52gSC/xcHYgKUObghefnlVh+l5+3kReTCQly/xUmL+ORPHrc5MIbK3dMQw/l4BHmeMlVUWPYUpgq0YefHS/9rC3i70tvfybUKTXY2mh42JqBleycwDYFhpD2yb1+fOw+YvKcf3bMqqv9mxRZGyC0dQ3d1d7ktOqNqUF4GpOyRSZAyFxWD4QhJO9TfFiDVVl7OB2jOivPdcRGROPe91Smuo4kJxqflrktPHdcXayZebHVRu9qUzDyH4GDafjy/ilEg2Otrz2w/VruLtfW0b30cokIi4BD9dSebvYk1SNMinNsVMXaejuXG6ZTOzVlrF3aRd/4ecS8HQpFZ/O9iSll4nPrPLjMzu3JBb2RpzhtQk6nGvbkJat5dvT35uo84kmU2CvEZ+RhadTSf6ejvYkZhrnn1DGxsPRtE5n5uZxKO4CvZp7E5OYAsCo9v708W3Cgwv+KMdT5onPzMLToYymLOP84jOy8HQso8lgszIknJUh2nTU5wN7GI2SlSUhPYt6lbRXJudvsLGysKg0bVmGtfNl78mzFOr1pGbncOzMJXyb1+NyvPEzgKNHtGf44LYARJ2Kx83NEdBuKri5lV8v7p/SA2cnW17/clPxtqTkTNIzcsjNKyA3r4CQsAv4NHXnwkXTaY7XqMi/5dmULafezbwJv5xISnZJ7JX+vuJoGN9PGlWuhmuMGN+ZIaM7AHAq4hJuHk7AeQDqujuSmlR++er1kl1bwhl/710Erz9Oi5b1efW9uwFwcrajy13NKSrSc2DXyXKPcSO+kFISn5FJ6CVtxHdTZDSP9NAei45NucJDv60CwNvVmcBmTSr1hUKh+PdxJ0/5uxX8q0a2AIQQDwCNgbfM7C5bfBLtJpy5YjV7A7uc41yz3yKlbGf4+EspHzKbWMp5H3zwQWM/Pz+7id+vZnvYaUZ2bAlAQCNPsnLzSc40vQA5GHOegW20OeqjOvqzPVx7WHlneGyF6Ye28zOZQtiornPx98DWPhyPu8zEOUuYOGcJO06cZkRn7XhtGhuOZ+aC6FD0eQa01fSM7OLPjjBNz9C3fy7+bDkezXsrtrPjxGnir2QS0LgeNoYpIV1bNCIuPtWciwD4Y2tI8YIWu47EMLSnPwCtfOqRdTWflGpc2Ls62RV/92/qiRCi2h0tgFWbjjPtxV+Y9uIv7DkYw+A+2vSiVs3rkXU1z6ym4f3a0KWdN2/O/fOmjGKv2nScB176hQde+oXdB2MYHFi5hhH92tC1nTezP78xDSu3hXDvG4u5943F7Doaw9AeWpm09qlHVk4+KelVL5OG7s7F330bu2NpaVFumSzbE1K8eMWO0NMM72KIT+/y4/Nw9Hn6t9Pic0RXf3ae0OKzjkNJLLRu7IEQorijBTC4ox+byplCCBB2MZ7GdVxo4OKIlYWOIW182REVa2SzPTKWUe0MdbKhJ5l5+SRnZeNiZ4uDYRqYtaUF3X0aEWt4+L9n88ZM79WJJxavI7egsNz8zXHiUjzeri40dHLESqdjmL8v206V0RQdy5gATVPb+p5k5eWTZLgAdrXTZkPXc3RgoG8zNoSXfyEddiGeRqXOf2hbX3ZEGue1I8J8m1SVtGW5nJZJ12ZeANhaWdK2UT3OXUgxsVuz/hjTn1jI9CcWsvfAKQb10zrn/n71yc7OIzXVNEaGDQ6gc8cmvP3heqN6sfdADG1aNdSeE7O2xN+3HufOmeZZmuIycDaUQStftpctg1OxjL5WBg08ycwtKQOAYa39+DPcOPZKP9PV38+H6KSKdQCsX3GIx6f8wONTfmD/zij6D9Nuzvi1bsDVrDxSU0w7nvUblozcdevVgvNnkwG4f/SX3D/qC+4f9QV7tkfw1Ud/VtjRulFfJGdfJT4jiyZ1ND3dm3hxOkmrI9fiVAD/69WVpUeMp6orFArFv5F/1ciWEKIj8CLQS0ppbl3rRkKI7lLKA2hTA/cCUUB9IURnKeUhIYQD2jTC3cAUYLsQogXQCDhZavsOIURrIMBw7L+Bb4QQzaSUMYbnwRpKKU9Vpnt3VBy9Wnrz14xp5OQX8vrykpGHbx8czeyVW0jKyGbuxr3MmTKUpwb3IPJiIqsOhlea3sbKku7NG/HWH1uN8nxuaE+83VyQUnI5JZN3l5fs3xMRR09/bza8Po3c/ELe+K3keF8/Opq3ftf0fL5+Lx/fP5QnhvUg6kIiqw+EV3ieJ87GsyUkmqUvTaFIryfqQhIr95+ozD0A7D8ex11tm7Dy0wfJzS/k3Xmbi/d99uIY3v8pmOS0bCYMbM+9wzvh6lSbxR/cx4GQON7/aQtBXVowtl8ARUWSvIJCXv/mzyrlWxEHjsbSvUMTln0zndy8At7/puTO+JyZY/nw282kXMnmxUcHkJCUwQ/vTwZg1z/RLFxhdtDzujUs/9qg4dsSDZ+8NpYPv9tM8pVsXnxE0zDvvRINC1bemIZ9IXHcFdCEVR8/SG5eIe/MLymTuc+N4b0FhjLp356pQztRx6k2v71zH/tD43hvwRaCOjVnaI+WFBbpycsvZOa3G6qU755wLT7XvzGN3IJCZi8uFZ+Pjeat3wzxuXYvH00byhPDe3CyVHz2b9+cCT3bUqjX8p2xcGNxehsrS7r5NeLdpVtN8r1GkV7y3obt/Hj/WHQ6weoj4cQkpjCxs9YULDsUyu5TcfRu4c2m57U6NHOVptHNoTYfjBuETifQCcGmsFPsOqk9JzRreBBWlhbMnzYWgJDz8by1bluVfFIkJW9v3s78e8ZioROsDAknJjmFSR00TUuPhrIzJo4+Pt5sfXwaOQWFvLqhlN/GjcDZ1oZCvZ63Nm8no4Il54v0kvfWbmfeQ4bzPxTO6YQUJnTV8lr+Tyi7o+Lo7evNXy9r5z9rRXCFaQH6tfLhtVF9ca1ty7fTRnHychKPzF/N7wdCeHf8QNY+fx8CWH04nNi4pAr98ffBWLp29mHJz4+Ql1fIR5+VlPGHb9/NnM83kZKaxfNPDSI+IZ1v594LwO59p/jlt/2cO5/CwSNxzP/uQaSU/LkplDhD56PCMti0nZ8mj8VCCP4ICScmybgMdsXE0aeZN1uemEZOYSGvrSvVVltacleTRrzxp3HsvdSvF36ebiAlF9MzeOPPqsXENQ7ui6Zzj+YsWP0UebkFfPp2ycqK73w+mbnvruNKShYvvjkau9rWCCGIjY7nqw+vv428UV+8s2kHn4wegpWFjvNp6bxq2De8tR+TO2mjl1uiYvgjpOLfHIVC8S/lPzayJar7DEZNIoRYAAwCrr105zDaM1idAHtgI1pn6S4gGpgqpbwqhOiMNmXQFq2j1R8oBL5HeyarEHheSrlDCGELLECbMngcaAY8LaU8LIQIAj4Crj3BPEtKua4iza1fmlvjDra4A15TYptS424AwDLn5r3/6nqRFY2p3kYK7Gt+YDvf4c5wRn4ljwjdDgptK7e5HVjdnsU+K8Qt5A5otIDLPWrVtAS8190BBQKcGVnxcvy3i5OvP1fTEhSKO50744e1Au6a+OktuSjcv+yFO/Lc/1UjW1LKaeXtM6wMqJdSPmYm3SG0Z67K8oAZ2xxgUjn5b0dbnEOhUCgUCoVCoVAoKuRf1dlSKBQKhUKhUCgU/2LujMlOt42an0d0k5BSnpFStq5pHQqFQqFQKBQKheLfgxDCVQixRQgRbfhr8j4QIYSX4R2/kUKIcCHEM1U59v+bzpZCoVAoFAqFQqG4sxHy1nxukBnANillc2Cb4f+yFAIvSClboj2e9IQQwr+yA6vOlkKhUCgUCoVCobg9SHlrPjfGKGCR4fsiYLSpbHlZSnnU8D0TiAQaVHZg1dlSKBQKhUKhUCgU/2U8pJSXQetUAe4VGQshvIH2wD+VHVgtkKFQKBQKhUKhUChuCzdhyp/54wrxCPBIqU3zpJTzSu3fCniaSTqzmvnYA38Az0opK30/h+ps/QfI7pFd0xLQH6xd0xIAKLK2qGkJ6K1qWoGGXXxNK4DcOjWtQMMqq6YVgCisaQUaujtAR1Lbmn+/FdwZdTXNz6GmJQBgUf57sG8bOX65NFn8QU3LACDu3ldrWoJCoSiDoWM1r4L9/cvbJ4RIEELUk1JeFkLUo+SdvmXtrNA6WkuklKuqoktNI1QoFAqFQqFQKBS3B3mLPjfGOuB+w/f7gbVlDYQQApgPREopP6vqgVVnS6FQKBQKhUKhUPyX+RAYIISIBgYY/kcIUV8IsdFg0wOYCgQJIY4bPkMrO7CaRqhQKBQKhUKhUChuC7fqma0bQUqZAvQzs/0SMNTwfS8gqnts1dlSKBQKhUKhUCgUt4cbX6b9X4WaRqhQKBQKhUKhUCgUtwA1snWbeHVUIL38mpBbUMDMZcFEXjRd5KSBiyNz7h2Kk60NkRcTmbF0E4VF+grTO9hY89b4ATTzrANS8vqKLYScvczjA7oxrmsbrmRfRW+r59MTO9gVf9qstt6eTZnVbhAWQrA87jg/RO032t+/fguebd0HvZQUST3vHt/CkeTzAHzQeThB9ZqTkpfN0M3lLgBTLjNGBdKrZRNy8wuYVZ5fXB35uJRfXv1d80sTNxfemTiQlg3d+fKv/SzadQSAWpYWLHx8ArUsLbDQ6dgSGs23wQfM5t+zeWNeHRaIhU7HysNh/LT7kInNa8MC6e3bhJyCAl77I5jIS4l4Otnzwd2DqWtvh5Sw/NAJFh84BsATQd24u7Pme4DPg/ex+9SZCv3Qs1ljZg4ORKfTsfJoGD/uNdUxc0ggvZtrMfDqmmAiLmu+2vbsg2TnFVAk9RTpJXfP+w2Alwb0oq9vUwqKijiXms5ra4PJzK14SbGXJgTSs7VWHrMXBRN13rQ86tdx5IPpQ3GqbUPUuURmLdDKw96mFu8+OARPVwcsdDp+3XKYdQciALC3teaNqQPwqa/F6Vu/bCE07vJt88Ug/+Y8GdgdHzdXJvz4O2GXEir0A9R8bJr4pUVjZowIxELo+ONQGD/tMvXLqyNKYnXmCi1WAd65ewB9/JqSmnWV0Z//WqX8jHwxupQvllbiCzuDL34z+MK9jC92HilOM6VXe8Z1bY0Qgj/+PsHiPcdMz3lkqXPeaeacR5Y65+Ul51xe2heG9iKwpVYvzqekM2uFVi+c7Gz4/N7htG7owZojEby3dkeFPunVpDGz+mttx/KQMOb9baytqasLHw4bSCsPdz7bvZ/5B0vO+4OhA+jr05SUq1cZNr/65VGW56f25a62TcjNK+CdeZs5eda0fO7u345Jgzvg5eHMwP99S3pWLgAd/Boy57lRXEpKB2Dn4Rjmr/m70jx7Nm/Mq0MNbeeRCtrOFqXazsuJ1LK04JfpE6hlYYGlTkdweDRfb9fqwIuDehHoZyib1HRmrqq8zSpN73pNmd25PzqhY1nMcb4PNz6PUd6teKxVNwCyC/N5/Z/NRKYl0tTRla96ji6287J3Zm7oHhZEmZ6TQqG4ce7EaYS3EtXZMoMQIktKaV/BfmdgspTy26ocr5efN43qOjP0owUENPLk9bFBTP5qqYndc8N68evuo/wVcoo3xvZjXJfWLDsQWmH6GaMC2XfyDM//ugFLCx22ViVrFf+65ygLdx0ht3P5S7/rhODNDkO4f9cS4nMyWNX/IbZdOkVMRnKxzf7EOLYGnwLA18mdL7uPZdCm7wFYFRfK4ujDzOk6siquMPFLYzdnhn2ondescUFM+bJ8v2w6forXx/VjbJfWLD8QSnpOLh+s3UlQKx8j+/zCIh76fiU5+QVY6nQsenICe6PiCD1nvNa5TghmjQhi+oJVJGRksux/k9kReZrTSanFNr1beNO4rjODP1tAgJcns0cGMen7pRTqJR//tZvIS4nY1bJi5RNTOBBztjjtL/uOsmDvEaqCTgjeGBrEg79qOlY8PJntJ8voaO5NY1dnBn25gLYNPZk9LIiJP5X46r5FK0i7mmt03P2xZ/ls216K9JIX+vfkkZ6d+XTr3nJ19GjtTSN3Z0a9sYA2TTx5dXIQ939kWh5Pj+3Fkm1HCT58itcm92N0j9as3B3KhMC2xF5O4dlv1+Jsb8vqtx5g48EoCov0vDQhkP3hZ3h5nhanNrXMr6l9q3wRnZjC08vW89YIk+nYZqnp2DTnl5mjgnh4/ioS0jNZ9qQhVhNL/NLLV4vVIZ9osfrG6CDu+VbTvOZIBL/tD+GDCYOqdP4mvqjrzLAPrtMXV3P5YM1Oglob+6KZZx3GdW3N5C9+p6CoiO8fHsvuyDjOJaeVnPPoIB7+qdQ5R5RzznM0bW+MCeKeb5ZWmPZA9Fk+36TVi+eH9OThvp357K+95BcU8lXwfpp51KW5Z8XvJNAJwZsDg3hg6SriMzP544HJbI8+TUxKiba03Fze2bKT/i18TNKvOhHBr0dCmDO8+uVRlrvaNsHLw5m7X/yZ1j71eHlaPx5683cTu9Doi+w7Hsu3r4032Xf85EVe+GxNlfM0aTsfK6ftrOPM4LkLCGhoaDt/WEp+YREP/rySq4Y6sPjhCew+FUfohXj2nz7L3C2GshnYk4d7d+az4PLbrLKa3u4ykKnblhJ/NYO1Qx5g64VoYtJTim3OZ6UxccsSMvJz6VO/Ke93G8KYTYuIzUhl2Mafi4/z99gnCT5/ssr+UCgUiopQ0wivD2fg8aoa923lw7ojkQCEnovHwcaaug6m753q2syL4BPRAKw9ElF8oVZe+trWtejYtAF/HAwDoLBIX627gABtXetzNiuV89lpFOj1/HkunP71WxjZXC0sKP5uZ2lltLrmoeRzpOXnVCvPa/Rt5cO6w5X7pUszL7aEan5Zdzii+KItNSuH8PMJFOr1Jmly8jXNlhY6LHU6syuCtmnoybnUNC5cSaegSM9foScJaml8YRTU0oe1xwwaz5doTM7MLr6DfjW/gNikVNwdy+2fV0hAA2MdG8NO0s/XWEc/Xx/Whmg6Qi7E42hjjZt9xe8u23f6HEV6aUhzGc9K9AUG+LDhby2PE3HxONhaU9fRNI/Ovl5sO6qVx4YDEfRtq2mVEuxstPcj2VlbkZGdS5FeT22bWnRo3oA1+0riNCvHfJzeKl/EJqcSl3KlQpvS1HRslqWNlyfnU9K4kGrwS8hJ+vqXiVV/H9YdLRWrtiWaj8RdJD0n1+S4VaFv6zLtj205vmhexhdtyviiyNgXTd1dCT13mdyCQor0ksOnL9CvTbPqnXPZttGgraK0+6NL1Ytzl/Fw0upFTkEhR89cIr+w8heNBdTz5OyVNM6np2vtZsRJ+jU31pZ6NYcT8eZj4ND5i6TnXl95lKV3Bx/+2quNIIedvoyDnTV1nEzL59TZJC4nV/ruzSrRpqEn51JKtZ0nymk7jxvK5oKhDhnq6dXSdcCi5DJkf0ypsjl/GU+nqrepbevU52zmFc5nab9l689EMqCh8W/Z0eSLZORrfj+WfAlPO9N3mPXw9OZsZhoXs2+OrxQKhRnuzKXfbxlqZKsCDG+IXgu4AFbALCnlWrTlIH2EEMeBLVLKlyo6joejPfFpmcX/J6Rn4eFkT3JmyYiTs50NmTl5xT80CWmZuBt+aMpLX6jXcyUrh3cnDsS3nhsRFxL4cO1Ocgq0i4V77mrLyI4tCcm9yAfHt5JRYPrj7mHrwOWrJT8q8TmZtHWtb2I3oIEvL7bpSx3r2jy81/Su9vXg7mR6Xu6V+CW+lF8qQicEy56dTKO6zizdH8IJMyMHHo72xKeX5B+fkUWAl/GLxd3L2CRkZOHhaKyxvrMjLeu5EXqhJI/J3doysn1Lwi8m8PHG3WRU0An2cLTncoaxjrYNPSu18XC0JykrGylh/tSxIGHZkRMsP3LCJI9x7VuzMbziO7XuzvYkXCnJIzEtCzdne5IzSpVHbRuyrhrHqZuzVh7Ldh5n7uOj2PzRI9S2tmLGTxuREhrUdeJKVg5v3j+QFg3ciDyXwJzlO8nNN72ovR2+qAo1HZtl8XC053K6sR6zsVq2nSgTq9dDlXxRu4wv0jMrvfkQHZ/CU0N74GRnQ15BIb1aehN+oWR6p4eTPZfL5BvQqJL6aTjnqqQFGNupNX+FVn8Ew9PBnsuZpWIwM4u29U2Pfztwc7EnIbVUvU3Nws3VnpT0qpd7m2b1WPzeVJKuZPHl77uJu5hSob3ZtrNMPXV3KKftzMpGJwQrH59MI1dnfvsnxKjtvMbYjq3ZdKLqZeNpZ2/8W3Y1k3Z1TX/LrjHRJ4Bdl0yn1g9v3JL1ZyKqnK9Coag+ahqhojS5wBgpZYYQoi7wtxBiHTADaC2lbFeVgwgzi0TKMiuxCDNG10zKS2+p09GygTvvr9nBifPxzBgZyENBnfl68wGWHQjl+63/IJH874HOvNquP68e2mCqzcwKlubqwJaLJ9ly8SSd6zbi2daB3L9ridlzrQ7mzqvsCjXm/FKVuxd6KRk/dwkONtZ8/sAImnnWISbe+AKiavmbMymxsatlxReTh/PBn7vIzssHYOk/oXy3Q/P90/3v4uWhvZm1akvlosvJozKbyT8vIzEzG9fatvw8dRyxyakcPnux2O7RXl0o1OtZHxpV8QHNLmZaRoe5ODX87d7Km1MXknh07kq83Jz49plxHHv3IhY6HX5e7ny8dAdhZ+J5cUIg0wZ15rv1VXtW6Wb6oqrUdGyaCjKXVRVi9Sbc6jO/xm2ZvKu/Ei5xian8vP0Q8x4dS05eAScvJVNUVLFek3bTrDLzxyib9pG+Wr3YcKySelFFboavr4eqxGpFnDyTyKjnfiInr4C72jZhzrMjufulBRXnaW5jVdpOg4/0UjL2G60OfDl5BM3c6xCTWFIHHu3ThSK9nvUhVS8bs79l5fihm0cjJjRry/jNi422W+l09G/YnDnHd1Y5X4VCoagM1dmqGAG8L4ToDeiBBoBHpYmEeGTGjBkz77//frdlj40hMjkTT+eS6QoeTvYkZhjfdbySnYODrTUWOkGRXuLh7EBSRhYA8elZZtNLJAnpmZw4r90VDD4RzfS+nQBIybpabL8s9hg/9ppoVmt8Tgb17ByL//e0dSAxJ9OsLWjTBhvVdsGlli1XrmP64KS72jKua2sAws4nVNsvns4OJBr8UhUyc/M4dPoCPXy9TS5o49Oz8HQqyd/T0TT/hDI2Ho72JBru6FvqdHw+eTgbQqLYGhFTbJOSXeL7FYfC+O6+URVqTMjIop5jGR1lRiMqsrn2NzU7h61RMQQ08CzuYIxu60/fFk144Jc/zOY9oU9bxvTUyiP8bAIeLiV5uDvbk5RmrCMtKwd7O+M4TU7TymNkd38Wbj4MwPmkdC4lp+Pt6UJ8aiaJaZmEndHidNvRaB4Y1Om2+6Iy7qTYLEtCehb1nCrWk3CtnThbvk1VmdSjEl+kV+ILp6r5YvXBcFYfDAfg6SE9SCgzSlWvkjJIyChTPw02VhYWFaYd1cGfPi2b8NCP5utFZcRnZlHPoVQMOpjG6a3k7v5tGRXYBoCI2AQ8XEvVW1d7kq5UXUt2bn7x9/0hcbx0fxBO9jbFC2iYIz7DTNtppp6atJ1lyi8zN49DcRfo1dy7uLM1qr0/fXyb8OCC6pXN5auZxr9ldg4k5JjGoJ+zGx92G8q07ctNpsAH1vchPDWB5NyrJukUCsVNRP/fGtpSz2xVzBTADehoGMVKAGwqSySlnPfBBx809vPzs5v4/Wq2h51mZMeWAAQ08iQrN9/s1J6DMecZ2KY5AKM6+rM9XJvisDM81mz6lMyrxKdl4e3mAkC3Zl6cTtAeUC79TMXAhr6cSk8yqzU09RKN7V1pWNsZK52OYY1ase3SKSObxvYuxd9bOXtipdNdV0cLYOn+EMbPXcL4uUvYHn6akZ0q98uhmPMMCND8MrKTPzvCza+qeA2X2rY42FgDYG1pQbfmjYgr9VD9NcIuxtO4jgsNXByxstAxJMCXHVGxRjbbo2IZ1d6g0cuTzLwSje+MHUBsYiqL9h01SlPa9/39fYhOqPhC+sQlgw5nTcfQ1r5sP1lGx8lYRrXVdLRtqOlIysrG1sqS2obFJmytLOnh05hTidriJj2bNWZ6z0787/d15BaYfw5l+a4Q7nlvCfe8t4Sdx08zvJuWR5smhvIwc7F++OR5+nXQymN4d392hmrlEZ+aSRc/LwBcHexo7OnKxaR0UjKukpCaRWMPLY66+HkRd9m0PG6lL6rCnRSbZQm7EE+jUrE6tK0vOyKM/bIjIpaRHUpitTzNVWHpvhDGf7aE8Z8tqXL7ZeKLsIp9AeBqbwuAp7MD/QOa8dexkmljZs850sw5m9FWUdqeLRrzUGAnnlxUfr2ojBOX4/F2daGhk6PWbvr7si0mtvKEN4mVW0OYOmsxU2ctZveRGIb09AegtU89sq7mV2sKoauTXfF3/6ae6ISosKMFZtrONmbazshYRrUzlI2hniZnZeNiZ1wHuvs0IjZZqwM9mzdmeq9OPLG4+mUTmnIJbwcXGtZ2wkqnY4R3S7ZeiDayqW/nyHd9xvH8vvXEZZrWuxHe/qw7E16tfBUKhaIyRFWm6PzXuLYaoRDiGaCZlPIpIURfYDvQBMgEjkopG1d2rNYvzZUAM8f0paevNzn5hby+PLj42YRvHxzN7JVbSMrIpqGrE3OmlCydPOP3TRQUFVFRet/6brx99wCsLHWcT0nn9eXBZOTk8cGkwfjWdwMk5/VXmHVkI0m55u809/H0YVb7gVgIHSvijvNd5D7u8ekAwO+nj/KIX3fGNA6gQF9EXlEhH4ZuK176fW63MXR1a4SLtR0pudl8Eb6bFXHHTfKwPWh+AYOZY/rSw9eb3IJCZi0LJuKaXx4azewVJX65tqR01MVEZvym+aWOgx3LnplMbZta6KUkJ6+AUXN+oYGrI+9O0payFzpBcMgpvt/yDwBF1sb5927hzYxhgeiEYPXRcH7YeZCJXQIAWHYwFIBZI/rSs7mmceaqYMIvJtChcX0WPzKRk/FJxVNVri3x/uHdg/Gr54ZEcvFKBm+u3WZ0cao3sxBf7+bevDZY0/HHsXB+2HOQiZ0MOg5rOl4f2pdezTQdr60NJuxSAg1dnPh64ggALHQ6NpyI4oc9BwHY/PQ0allYkJajdYxDLsTz5oZtxXnamXlUaMakvnRv5U1ufiFvLgom8pxWHl8+OZq3f91Ccno2Deo6aUu/29kQdV5b+r2gsIi6TrV56/5B1HWqjQAWbj7ExoPaNKAWDd14Y+oArCx0XEhO581fgsm8msdVM4+53Apf9PfzYdbQvrja2ZKRm0dUfBLTF68uztPKTNW43bGpr2SeQS9fb2YMD0SnE6w+HM68HQeZ0FXzy/J/DLE6qi89Whg0r9BiFWDOpCF0buqFc20bUrKu8s2WA6w6bP6iUldgum3m2FK+WFrKF9NHM3t5KV9MLeWLJaV88WwZX3z8C9l5+Sx8YgLOdjYU6vXMWbeLf6LPG/mil683M0YYzvlQBefsq8Vs6XM2lxbgr5emYWVpQfpVQ704F8/bq7V6EfzKg9jbWGNloSMjN49py1cZrTBYmj5NvZnZPxALIVgZGs53Bw5yTztN2+/HQ6lb247V90/G3lo776v5BQz56Rey8vOZO3IIXRp54WJrQ0r2Vb7Ye4CVoebLwzW88t/ol+4PolsbzQfv/LiZqDjNB3NfHMN7PwWTnJbNhIHtmTqsE65OtbmScZX9IXG8P38Ld/dvx7h+ARTpJXn5hXz+205ORJu+liGzsfE0vd4tvJkx1ODfI+H8sOsgEzsb6ukhQ9kM70vPFpqumauCCb+UQAuPunwwbhA6nUAnBJvCTvHdDq0ObHquTNmcj+etdSVtVo5fxZ3AwPo+vNGpPzohWHE6lG/C9jO5eXsAfos+xofdhjDYy7d48YtCqWfUXwsBsLGwZP/YJ+mz5jsyCypfaCru3lcrtVEoaojqz+u+zfQZ+vEt6Xzs2vjyHXnuqrNlhlKdrbrAerTFMY4DPYAhUsozQojfgADgr4oWyLjW2apJKlr6/XZRXmfrdlO2s1UTmOts1QTmOlu3G3OdrZrAXGfrdlNZZ+t2Ya6zdbu5U3yR71K5za2mKp2t20HZzlZNUFln63aiOluKO5iar6yVEDjk1nS2dv51Z3a27pCftDuLa+/YklImA93LsZl8W0UpFAqFQqFQKBSKfxWqs6VQKBQKhUKhUChuD/+xWXVqgQyFQqFQKBQKhUKhuAWokS2FQqFQKBQKhUJxW/ivvdRYjWwpFAqFQqFQKBQKxS1AjWwpFAqF4v/YO+uwuI62D9+zQHANCRCFEDfirsRd2nhTSfWt+1tJ3Zu6t2mbtEnaeNNI42lcGoUYUaIEd4fd+f44G2DZBRZalv3ezn1dXOyeM7PzO3OeZ+aMHoVCoVAobMO/bGRLNbYUCoVCoVAoFAqFTRD/sg0yVGOrmrGHdyrVOlbz77jKDrIPxyr01te0BISDfeRFTsOaVgCOKfZRBDmn1vyrORyza1qBhj28Zyt7ZHpNSwDA7zfPmpaAbmZ8TUsAwPn3gJqWQP3bj9S0BADSZvak2+aPaloGf/30ZE1LUCgUVmAfTzoKhUKhUCgUCoXifx9DTQuwLWqDDIVCoVAoFAqFQqGoBtTIlkKhUCgUCoVCobAJas2WQqFQKBQKhUKhUFQH/662lppGqFAoFAqFYFdIHgAAnJ1JREFUQqFQKBTVgRrZsjF9mjXm+dEDcNDpWH7wBN/vPGgW5oXRA+jXIoSc/AJeWLGJ0zHablRvThxC/5ZNSM7KZtynC4rCtwj055Xxg3CrVYvrKek8u3Q9WXn5dq2hNP2Cg3lp4AAchI4lJ47z7V+mmpr4+fLesGG0qVuXj/bs4ftDhwGo5eDA4ilTqOXggINOsOHcOT7du8/qdEvTv0Ewr/QYhIMQLD4TydeRf5mcHx/aigfCugOQXZDPi3s2czo5gSB3Tz7uP5I6bu4YpOSXqAjmnazazln964fwcndNw5KzkXx9/IDJ+XFNWvNAu26ahsICZu/dxOmUhKLzOiFYM+Z2YrMzuXvLiippuKnjlR6DcNDdzAtTHeNDW/NAe6OOggJe3LuJ08kJODs4sHTUdGrpHHDU6fgj+gwfH91TJQ39goN5KdxoF8fLsIvhRrvYXcouppawi7N/zy4A/jtxAH1ahZBbUMBLv2wi6pr5LnH1/bx4746ReLm5EHUtnhcWbqBQX7wSuE3DABY8MZVnf/qDLRHnrEr32UkD6N1GS/eVnzcRddU83Xq1vXh31ki83V04fTWe2fO1dG8f3JmRXVsC4OCgIyTQj/BnvyE9O49pAzsysXdbBIKVe47zy59HrdLzzNQB9G4XQm5+Aa/O20TUFXM9kweGMX1wJxrW9WHQE1+TmpkLQHCgL6/cOZSWjery1aq9LNh02Ko0S9OnbigvtB+OTuhYfvkI3581ta/woBY82mogBinRSwPvHN/AkaSrRed1CJYNvJf43Az+s+/XKmkAePK2gfQKCyE3r4A35m7kzGXzvLh1cAemDutEwwAfhj74FWnGvLhJq5AAfnhlGrO/XMe2g9bZREl61G7O4y3H4CAEq68dZMGlHRbDtfJqwNzuD/JS5C/8GXcCgBfb3EqvOi1Jyc/ktr2fVDrtItvML+CVBRXYppvRNn+yYJs6o23+V7PNV24bQr+2TUjOyGbSWwvMfrM8Hvz0LrqN6ERedh5z7vqS80ejzcI88+NDtOvfmuw0bQvQOXd9yYWISwC079+aBz++CwcnB9ITM3hq4CuVzBV4aobRLvILeL0Mu5g0uANTh2p2MeQhy3bx48vTePHLdWw7VHm7UCjsmn/ZNMJ/3ciWECKzptLWCcHsseHcP38VYz75iZFhLQit62cSpl/zYBrX9mH4h/N4ZdUWXhkXXnTutyOnuG/+b2a/+/rEIXy0cTfjP1vA1lPnmdW3s11rsKTp1UHhzFr5G8Pmz2dMi5Y09TPVlJaTy+vb/uSHQ6YPZ/l6PbctW8boBQsYs2Ah/YKD6RAUZHXapXW80WsId2xczuAVPzI2tBXNfGqbhLmakcbktb8yfOV8Pju6j3f6DAVAbzDw5oE/GbT8R8avXsjtrTuaxbVWw+s9BnPnpmUM+e0HxjZpRVPvUhoyU5my/ldG/D6fz4/t5Z3ew0zO39W6M+dTkyqddmkdb/QazB2bljF4habDPC9SmbzuV4b/Np/PSujI0+uZ9sdiRqyaz4jf5tO/QQgd61T+nuiE4NXB4cxa8RvD5s1nTMuWNK1dyi5yy7GLpcsY/fMCxvy8kH4hVbcLgD6tgmlUx4cxb83j9SVbmD0p3GK4x8b0ZeH2I4x9az7p2XlM6NHW5HoeH9OHvVGXrU+3TTCN6vow7tV5vLloCy9MLSPd8X1ZtO0I416dT0Z2HhN6aen+vOUwU99ZxNR3FvH573s4fO4a6dl5hAbVZmLvtsx871emvL2Afu2a0KiOT4V6ercNpmFdH8a/OI83F2zh+RmW9UScj+E/H60gJjHN5HhaVi5zFm+vciMLtIbSS2EjuW/vIsZs+ZJRDdoS6ulvEmZ//EXGb/uGiX9+y4tHfueNjmNNzs9s2p2LGYlV1gDQq30IDQN8uPWZH3l33haevXOQxXCR567zyHvLiUlIMzunE4KHp/TlwHHrbcIkPoKnWo3jySPzmLbnY4YEdSDYva7FcA82H8GBxLMmx9fFHOaJwz9WKe0+bTSfGPfqPN78xQrbfM062wRYs/8UD31pXs9URLcRHanfNIg7mz/CJ/d/y6Nf3Vtm2LnPLuCBTs/wQKdnihpa7t5uPPrlvbw07j3ubfckb0z+sNIaerUPoWGgD7c8+yPvzNvCf++wbBcRZ6/z8Ptl28Ujk/uyv4p2oVAo7It/XWOrJmnXIJArSalcS0mjQG9gfeQZwluFmoQJbx3K70dPAxB5NRZPF2f8PbX3ZB2+dJ207Fyz3w3x9+VQ9HUA9p6/zNC2zexaQ2nCAgO5nJrK1bQ0CgwG1p6JYnBTU01JOTkcj4ujwGC+X2h2gfZiIEedDkedDlnFHpMOdYK4lJ7C1QxNx5qLUQxp3NQkzOH4GNLztQeCI/ExBLlr7+GJz8niRJLWe5lVUMD51CQC3D0qr8E/iMsZqVzNvKnhNEMbmWo4UlJDQgyBbsXvAgp08yC8QSiLz0VWOm0THXWCuJSeWiIvTjOkkXV5AdqIG2j3xEnnUKXp2WGBgVxOKWEXUVEMDi1lF9k5HI+tXrsAGNgulDUHNZ84fjkWT1dn/L3M31/XrVlDNhtHrFYfPEV4u2K90/p1YEvkeZIzrX+hVv/2oaw9YEz3UiyebpbT7dqiIVuOaumu2X+KAWGhZmGGd2nBhkNnAAgJ9ON49A1yCwrRGySHz11jYIemZnHM9HQIZd1+Tc+Ji7F4uDnj722u58zVBG4kmb8rKyUjh1OX4kxG+ypLe7/6XMlK5lp2KgXSwB/XThIe1NIkTLa++GVhbo61kCUsMMDFk/4BzVh+6e+9s6lfp1DW7zkFwIkLN/B0c6a2hbw4ezmBG4mW3xs2eWgH/jx4juT0qr1krbV3Q65lJxGTk0yh1LMlNoJ+dVubhZvUqBfb446Tkp9lcvxYSjTpBTlVStvMNsvwia7NS9jmgVMMaF++bQIcOX+dtCzzeqYieo7rypYF2sje6QPn8PBxxy/Qx+r44dP7sPu3AyRc1RriqQmVf99bv06h/GGNXVwpxy6GdGDboXOkVNEuFAp7R8jq+bNX/rWNLaExRwhxQghxXAgxxXh8gBBiuxBiuRAiSgixSAghjOdGGo/tFkJ8JoRYW5k0A7w9iE3LKPoem5ZJXS/TB/K6XqZh4tIzCfAq/6H9XFwS4a2aADCsbXMCvct+Eac9aDDT5OHBjYwSmjIyCfCwPr5OCNbMvI2//vMAey5fISI21uq4JQl08+BGVrGOG1kZBLqVfd1TW7Rn+zXzKSoNPLxoUzuAY/E3Kq0hwM2DmJIasjMIcC87L6Y0b8/268UaXu4+iHcObf9bDQuwkBfZGQSWo2Nqc9O80AnBH+Pv4MiMh9kVc4ljCVXIC89SdpGZSYBnJe3i9tv468G/ZxcAdb09iEsp4ROpmdT1NrUNH3cXMnLy0BukMUxGUZi63u6Et2vKsj2VawTX9fEgtmS6KZnU9bGQbnapdEuFcXFypFfrYLYaH3ov3EiiU9MGeLu74OLkSJ82wQT6Vtw5UNfXg7jkYj3xKZnU8al8p8Lfoa6LJ7E5xQ+pcTnpBLiY28XgoJasG/wQX/eczuwjq4uOP99+OB+c3ILhb67QruNXKi+SM6njZ31e1PH1oH/nZqzcVvWOkTouXsTnFo+MxOemUcfZyzSMsxf967bht6sHSkf/W9T19iA2tZRPWLLNkj6RUo5tHvv7U+X86/kRf7V4VD/xWhL+9f0shr3rzWl8e+wDHvjoDpxqaSsqGjSvh6evOx9se5UvD77H4Jn9Kq2hrq8HcUmmdlHXCt+6SR1fDwb8TbtQKOweKavnz0751za2gIlAByAMGAzMEULcnGfUEXgcaA00AXoLIVyAb4ERUso+QJ3KJigsHpUVhqnowXn2yk1M69GBZQ9Nx925FgV6vV1rMNNkUZT1TmOQkjELFtL7u7mEBQbSvHblp+9pQqxX0TOoIVNatOOdv0zXR7g5OvHN4HG8vn8bmQXWr1krkmAhM8rK+56BjZjSrD3vHtoOQHiDUJJysjmRFFfpdC0osV5HUCOmtGjPOwe3Fx0zSMnIVT/RY/HXdPAPormvv8W4lVNApQpTg5SM+Xkhvb812oV/Fe2iDErnh7CYZ9r/ZyYM4JM1uzBUsjKw5Btm6Vq0GdPv/do34djFmKJpWtGxyczffJCvH5nIlw9P4Oz1RAr1FWurStnwT2Mxny2E23IjilFbvuSR/Yt5tNVAAAYENiM5L4tTqZVv/JvrsEAl8uKJGQP4cknlbcJUQ8V58XiL0Xx5bv3fblyapf1P2WY7U9v8e5oqTg/ghxcWMavVYzzc7Tk8fT2Y8t/xADg4OtCsUxNmj36H54e/yW2zb6V+s6pPPy7SUIm8f3L6AL5Y+vfsQqFQ2Bf/5g0y+gC/Sin1QJwQYgfQFUgH/pJSXgMQQhwDgoFM4KKU8mb3/a/AfZZ+WAhx381zgcMn4duxJ6CNIpUc8Qn09iA+3XRaR1y6aZgALw/iM0zDlCY6IYV7560EoHFtH/q1CCkzrD1oMNOUkUlQiRGLQE8P4jIrv7QuIy+P/deu0i8kmLNJlV+zFJuVaTIVLsjdk7hscx0t/erwXt/h3LFhOal5xVNdHIWObwaPY9X502y4VLVe2tisDOqV1ODmSbwlDb51eLf3MO7cXKyhS0B9BjdqysAGTXB2cMCjljMf9xvFEzvXVV5HdoZpXriVkRe+dXivzzDu2GiaFzdJz89jX+wVBtQP4WxK5dbImNmFx9+wi6tX6RcczNlE6+1iSp8wJvbU1pecvBJHgK8nGL0/wMeDhFJ+k5KVg6erMw46gd4gCfDxJCFd09umYQDv3TESAF93V/q2CkFvMPDn8Qtm6U7uF8bE3sZ0L8cR6FvCF309SEgrlW5mDp5updJNM82nYZ1bsOFglMmxVXtPsmrvSQAeHtubuBKjFCWZNCCMCf00Paei4wjwK9ZT19eDxLTyy4Z/mrjcdAJdi0dvAly9iM+1rB3gUNIVGrr74lPLlY5+jRgY1IJ+Ac2o5eCIh6Mz73WewH8PW7c+6NZBYYwb0A6wkBd+HiSkWJ8XrUICeONBzSZ8PF3pFRZCod7AziPmNlEW8blp1HXxLtbg4k1inunUtJbeDXij/XQAvJ3c6FmnBXqDgZ0Jp6xO5yZmtulTwjZ9yrDNkj7hW4ZtHjK1zcow9sFhjLxnMABnDp2nbsPanDSe829Qm6SYZLM4ybGpABTkF7Jx/p9Mekpb05dwLYm0xHRys/PIzc4jctdpQsMac/1c+Y3zWweFMb5/Cbuo7QnGKqAqdvHmf0ztQm8wsKMSdqFQ2Dui6jPJ/1/yb25sWR7k0SjZxaZHy6fywpsgpfwO+A6g9QsfF3VPnbgeS2N/X+r7ehGfnsmI9i14dsl6k7jbTl9kRo8w/og8Q/uGgWTk5pNYQUPHz92V5KwchIAHBnZn6V9lTz+wBw2liYyNJdjHhwZeXsRlZjK6RUue+OMPq+L6ubpSYDCQkZeHs6MjvRs14tuD5rsrWkNEwg1CvHxp6OFNbHYGY5q05NE/TWeK1nP35NtB43hi+zqi01NMzr3fbzjnU5P4/sShKqUPEJF4g2AvXxp4eBOXncGYJq14dMcaMw3fhI/niV2mGt4/vJP3D+8EoEdgQ+5t261KDS2wlBeteHS7uY5vB4/niR2mOvxcXCk0GEjPz8PZwZE+9Rqb7epoDZGxsQT7+tDA24u4jExGt2zJE+uqaBeNG5ntZFgRS3ZHsGR3BAB9W4cwtW8YG46coV3jQDJz8klMN/eJg+evMiSsGRuOnmVs19ZFjamRbxRvQvD69KHsPBltsaEFsHRnBEt3aun2aRvC1P5hbDh0hnbBZad76OxVBndsxsbDZxnTozXbI4t/28OlFp2bNeDF+aZ+7uvhSkpmDoG+noR3aModcxZb1LNsewTLthv1tAth8sAwNv51hrZNjHps3Ng6nnKdxh61qe/mQ3xOOiMbtOGZgytNwjRy9+VKlmaTrb0DcdI5kJqfw8entvLxqa0AdPVvzKxmvaxuaAEs3xrB8q1aXvQOC+HWwR3YtP8MbUODyMzOJ6kSeTHhqR+KPr907zD2HLtYqYYWwOn0azR0q02Qqy8JuekMDgzjlUjT3RVv2fV+0efZbSaxJ/F0lRpaUMo22xht83AlbLN7Gbb503qzeNay+quNrP5qIwDdRnZi3EPD+XPxHlp1b0ZWWnZRw6okfoE+Rcd7j+/GpZPaTpX7fj/Iw5/fjc5Bh1MtR1p2a8rKjyteLVDaLiaVtIucytnF+KeL7eLle4ax+9hF1dBSKP6f829ubO0E7hdC/AT4Af2AZ4CWZYSPApoIIYKllJeAKZVNUG+QvLV6G3PvmohOCH47fJLz8UlM6dYegCV/RbLzTDT9WgSz4am7yC0o5MUVm4riz5kygm4hDfFxd2Hbf+/hiy37WHn4JCPDWjK9RxgAm0+eZ+XhkxbTtxcNZpqk5LVtfzL/llvQ6QTLT5zgXFIS09prmn6NjMTfzY1Vt83Ao1YtpJTc2akTw+f/RB13d+aMGI6DEOiEYN2Zs/x50XwdlbU6Xt67hZ9H3IqD0LH07HHOpSYxo6V2XYuiInisUy98XVx5o/cQY34aGPP7AroE1OeWZm04nZzAHxPu0PLq4E7+tLCmq0IN+7fw89BJOAjB0nNGDS06aBrOHOPRDr3xdXblzR6ahkIpGbvm5ypdc7k69m3h5+FGHUV5YdQRdYzHOmo63uh1My8kY1b/TF1XDz7qPxKd8Z6svXiGbVcr/7Cgl5LXtpawi+NGuwgz2kWE0S5mlrCLzp0YPq+EXej+vl0A7DoVTZ9WwaydfRe5+YW8/GuxT3xx33heW7yZhPQsPlmzm/dvH8lDI3sTdT2e3/Zb7weW2H0imj5tgln9mpbuqwuK0/38wfG8vmgzCWlZfPrbbt69eyQPjunNmWvxRSNWAAM7NGX/6cvk5hea/PYH943Bx92FQr2Bd5dsIyOn4mlcu49H07tdML+/ZdQzv1jPp4+O542fNpOYlsXU8A7cPrwLtb3cWfzKTPYcj+aNn7dQ28uNBbOn4+6i3a9pgzsy6eWfycq1fsqtXkrejPiD73vfhg7BysvHOJ+RwJRgbQfUJZcOM7Rea8Y1ak+BwUCeoYAnDy63+vetZU9ENL3CQlgxZxa5+YW88f3GonMfPzWBt37YRGJqFpOHdGTmqC74ebuz6K3b2RsRzds/bv5HNOilgQ+jVvNJp1nohI611w8RnRXPhAba6yl+u1b+Oq3X2k2lk18TfJzc+b3f83x/YTNrrlvXWbT7pNE2XzXawsIybHPVbt6dZbTNq/Gs2lexbb5z1wg6N2uIj4cLG968h2/W7TOJVxZ//XGE7iM78tO5z8nLzueDWV8WnXtr7fN8dO83JN1I4bmFj+FTxwsEXDh2iU//MxeAK1HXObjxGN9FfIjBYGD9D1uLGmLWsiciml7tQ1g5Zxa5eaXs4skJvPVjCbsY2YXa3u788ubt7I2M5q1/yC4UCrvnXzZNVth6vn1NI4TIlFJ6GDe9eB8YgTbN/U0p5RIhxADgaSnlaGP4L4BDUsr5QogxwBwgEfgLCJBSzigvvZIjW/9m8mrbRzYUelu/lqy6EA72kRfSDobxHVPso7/H45rVA9fVhq7yS/yqBV1BxWGqm+yRld8Frjrw+s36DVmqC91t5u9oqglyfw+oaQn4f723piUAkDazZ01LAOCvn56saQkK+6TmK7QKGNL7zWp5ENq8Z7ZdXrt9POnYECmlh/G/RBvJeqbU+e3A9hLfHy5x+k8pZUtjQ+1LoOrzxRQKhUKhUCgUin8b9tHnbDP+dY2tv8m9Qog7gFrAUbTdCRUKhUKhUCgUCoUViH/ZrDrV2KoEUsqPgY9rWodCoVAoFAqFQqGwf1RjS6FQKBQKhUKhUNiGf9nI1r/5pcYKhUKhUCgUCoVCUW2okS2FQqFQKBQKhUJhG+xgN2Rbohpb1YyusOIw1c3o2/bUtAS2xTSvaQkA9Auq+ZdDTvKp/At+q4NpKx+paQkYgip+t5MtyMlzqWkJFATn1rQEAHx31nxefNnxl5qWAMCs2HtrWgK1MtxrWgIAtexAhn5g55qWAEB6sB3sLh2WQcuVr9e0CgCiJr5c0xIU/8/4t22QoaYRKhQKhUKhUCgUCkU1oEa2FAqFQqFQKBQKhW1QI1sKhUKhUCgUCoVCofi7qJEthUKhUCgUCoVCYRv+ZSNbqrGlUCgUCoVCoVAobMO/bDdCNY1QoVAoFAqFQqFQKKoBNbJlI54fO4C+LUPILSjgxaWbOH093ixMfV8v5swYibebC6evx/Pc4g0U6g1lxg/09uDtqcPx93DDIGH5geMs3HMUgKdG9aV/qyYU6vUUuPVi2dUvyTVkW9QWdzSDyHkxSAM0HuRLiwl1Tc4nnMxk/3uXca9bC4B63b1oOSkAgPNrE7m0NRkEeDdyodODDXCoVfk2fA//ZjzVajQ6dPx+7SA/R+80Od+vbivubzYYKSV6aeCj0+uISL0MwJTGvRjfoCsCWHXtIIsv7610+jeJP5bOiXnXkQZJo0G1aTY+wOR84skMDr4fjZsxL4K6+9D81kAyY3I5/PGlonDZ8fm0mBxIk1GmeWkNkQcFC792xGCA/sP1jJlq2gW0bqmOfdu0PNbrIeaq4MulBXh4QVYm/PiRI9cuCRBwz1OFNGtdteH6fo2DeXnAAHQ6HUtPHOebgwdNzjfx9eX9ocNoU7cuH+7dw/eHDxed2znrbrIKCtAbDOilgXG/VG0r735BTXily2B0QseS88f45tR+k/PjgtvwQOseAGQV5vPSXxs5nar51qyWXZkSGoYEzqQm8My+teQb9Fal27dJY14cOgAHoWPZsRN8t++gWZjZQwfQPzSEnIICnlu7iVOxWrp3duvIpA7tkFJyNiGR59ZsIl+v55MJIwmp7QuAp7MzGXl5jPt+kd3nBcAzUwbQp20IufkFvDJ/E1FXzcuverW9eOderfyKuhrP7B+18svTzZlXbh9Kwzre5BXoee3nTVyISSqKpxOChS9MJyE1k8e+/N0qPScPwtJvQOqh9wgYNsX0/KZlcHCb9lmvh9irMGcJ1HKBD5+CwgIw6KFjXxhzu9XZYEL/hsG83CccByFYcvo4Xx81faVDqI8fcwYOp02dunxwYDdzIw4VnburXSemtm6PABafjuTHyCNVEwH0DQjlxbBhOAjBsuijfHfWtPwbFNScx9oMQEpJoTTwdsQmDiddJdDVi/e7jKOOiwcGJEuij/Dz+YpfS1EddRnAxudmkZVXgEEa0BskUz7TyoyHh/YkvE0oBinJjMnivffXkZSUWaa+hx8aTPfuoeTmFfD+++s4dy7OLMwLz4+hRYtACgsNREXd4KOPN6DXG3B3d+aF58dQt64XDg6CpUv/YsPG4xXmSZ+mjXlxxAB0QsfyIyeYu9u8vHhxxAD6NdOu+/lVmzh1ozjfdEKw/P7pxKdn8sAvmg98NKm4vPBycSY9N48J31hfXvQJCOXF9sM0TZeOMves6StgwoOa81jrgRiMderbkRs5knS1WBOC5eH3EJ+TwQP7FludrkJhDf+2rd9VY8sG9G0ZTCN/H0a+P4/2jQJ5aUI4078wL7yeGNmXBbuOsD7iLC9PHMQtXduyZH9kmfELDZI5a3dy+no8bs5OLH10BnvPXeZifDL7zl7mk/W70RskXzwdwoC6E9kQu9AsTamXRPwQQ++XQnD1c+TP5y8Q1MULr4am79qp3cqdXs8HmxzLSSrgwh+JDP64OQ7OOv766ArX9qTReKBvpfJHh+DZ1mN5+OCPxOem81PPB9kVH0V0VnFldDDpAjvjTwPQ1COQtztMY/Luj2niEcD4Bl25c99XFEo9n3a+kz0JZ7ianVRWcmUiDZLjP1yjx+xQXGs7sev5swR28cazgWle+LXyoPtzTUyOedRzof+clkW/s/n+kwR286m0BoMefv7CkWffLcDPH155xJFOPQ3Ub1wcZtRkA6Mmaw8uR/cJNqx0wMNLO7fwKwfadTXwyMsGCgsgr4qvsdIJwWvh4dy+cgWxGRmsmj6DLRcucD45uShMWm4ur2//kyGhTS3+xvRlS0nJrfq7o3RC8HrXoczctpjY7HR+H34nW66d43x68b29mpnKlC2LSM/PpX+9JrzdfQQTNv5EgKsHd7bowpC1c8nTF/JFn/GMCW7NiosVPzjphOCV4eHc9ctKYtMzWDFrOlvPXeBCYvG19w8NJtjPhyFfzyOsXiCvDQ9n0vzFBHi6M7NrR0Z++xN5hXo+mTCKUW1a8FvkKR7/7Y+i+M8N6kdGJW5OTeUFQO+2wTSq68O4l+bRLiSQ52eEc8e75uXXoxP7smjLETYdOssL0wcxvndblu+M5O4R3Th7LYGnv1lDcIAvz00P54GPVxTFmzaoI9GxyXi41LJKj0EPi7+ER98BX3949xFo3wOCSvjI0EnaH0Dkfti6Ety9tGUCj78PLq6gL4QPnoQ2XaFJK6uSLkInBK/3Hcxta5YRm5XB6ltuY/OlC5xPKb4fqXm5vLp7G0NDTP2juZ8/U1u3Z9yKhRTo9fw0+la2Xb7IpbTUyolAKztf6TCcu3YvIjY7nRXh97D1xlkuZCQWhdkXH83WG2cBaOFVl0973MLwTV+jlwbePb6ZU6mxuDvWYmX4PeyJu2gStzTVVZfdZNa3y0jNNi0z5u04zBeb9gFwf6O2zJzZm08+2WhRX/duTajfwJeZt39Lq1b1ePyxYTz08M9m4bZuPcnb76wBYPaLYxk1MozVa44yblwnLl1O5MXZy/H2duWn+fexZetJCgvLnvOkE4KXR4Uz6+eVxKVnsOy+6Ww7c4ELCcXlRb9mwTSu7cOwz+YR1iCQV0aHM2Vu8XXf3qMjFxOS8XAu9oEnlxWXF/8d1o+M3EqUFwheDhvBrN0LictJZ9nAe9h244zJvd0fH802o10096rLJ91vZeTmr4o1Ne3OxYxEPBydrU5XoVBYpkamEQohnIUQW4QQx4QQU4QQL1gRJ9P4v54QYnkFYccKIZ77G/oeFkKcF0JIIYR/ieMDhBBpRt3HhBBWvclvYOtQVh/RGgqRV2LxdHXG39P8DZHdmzZk0/FzAPx+6BThbULLjZ+YkVXUK5idV8DF+GQCvD0A2HvuCnqD1nNwNfss3k61LWpLPp+Ne2At3ANqoXPS0aC3NzcOpVtzWQBIA+jzDRj0ksI8Ay5+lW+/t/FpwLXsJGJyUiiUejbFRtIvwPTpJ0efX/TZ1dEJiXZtIe51OJF6hTxDAXpp4EhKNAMCWldaA0DK+WzcA51xD3BG56ijXi9fYg+mVfp3Eo5n4BbojFsd6x4eS3LhjKBuPUndIHB0gh79DRzZW7ab7tuuo8dA7UEgJwvOHNfRf7j23dEJ3D0qLQGAsMBALqemcjUtjQKDgbVnohgSGmoSJiknh8i4OAoN1TP5Oqx2PS5npHA1M5UCg4E1l08zpKHpy7GPJF4nPV97ODuaGEOgm2fROQehw8XBEQchcHF0Ij677N7wkrSvF8jl5FSupmrXvu7UGQY3N732Qc1D+S1S88mImFg8XZyp46H5tKNOh4ujlq6rkyPxGebpjmjdnLUnz9h9XgAMCAtl7X7tWo9HG8sfL/Pyq2vLhmw9opVfa/efYmAHLc9Cgvz46/QVAC7FpRBU2ws/TzcA6vp40LddCKt2n7Baz6UzUKce1DH6SJcBELGv7PAH/4SuA7TPQmgNLdAaW3q9dqyydKgbyOW0FK5maDay5nwUQ4NL+0c2kQmxZv7R1MePo3Ex5BYWopeSAzFXGRbSrPIigPZ+9biclcLVrFQKpIF1104yuF4LkzDZ+oKiz66OTkXr0hNyMzmVGgtoI6EXMhIJcPWkPKqrLiuPrLzist/FxanchfW9ejdj8ybNlk6fjsHDwxk/P/PfP/DXxaLPUVE38K+jXbeUEjdXrdx2da1FRkYuen355Vv7+oFcSU7lWkoaBXoDf5w4w6CWpcqLlqH8fsxYXlyLxatEeRHg5UH/5iEsO1K2Dwxv05x1x60vL9r71edKVgrXsjW7+OPaSQYFlW0Xbo61kCXyNcDVk/6BzVh26ajVaSoUlULK6vmzU2pqZKsj4CSl7ABFDam3rYkopYwBbq0gzGpg9d/QtwdYC2y3cG6XlHJ0ZX4swNuD2NSMou9xqZkEeHuQmJFVdMzHzYWMnLyiBlJcWgZ1jQ0na+LX8/WiVb06RF6JNUu/i184Eal7zI4D5CYX4lrbqei7q58TKefMpxsmn81m69PncPV1pO3tQXg1dMG1thNNx/iz4T9ncKglqBvmQUBY+ZW1Jeo4exOXU9yoic9No413Q7NwA+q25sHmw/Ct5c6TR34C4EJmHP9pPhRvJ1dy9YX0rtOC02nXK60BIDe5wCQvXGo7kWohL1LOZrHjmSicfZ1oM7Meng1dTc7H7Emlfm+fKmlISYTadYoLDL86cCHK8tNgXi4cP6Tj9oe0SjM+VuDlI5n7gQNXLgpCmklu+48eZ1eL0csl0MODGxnFNncjM5MOgUFWx5fATxNvQQK/Ho9k8XHrRlFMNLh6cCO7uOEfm51Bh9r1ygw/JbQ9O2IuABCXk8nc0wfYM/4hcvWF7LoRza7YaKvSDfD0ILbEtcemZxJWP9A8THoJn0zPJMDTgxM34vhh/2G2P3IPeQWF7I6+zJ7oKyZxuzSsT2JWNpdTUq3SAzWXF6A1iOKSi681PjWTOr4eJKaXKL/cXcjMLlF+pWRQx0crv85dSyS8U1OOXYihTXAAQX5eBPh6kJyRzdOTB/Dpil24WTmqBZCaBL51ir/7+kN0lOWw+blw6hBMfaj4mEEP7zwMCTHQfwyEtLQ66SIC3D2JySrhH1mZdKhrnX+cSU7k6e598HF2IVdfyMBGTYhMMC+3rdLh6kVsSbvISSfMr75ZuCH1WvBU23D8nN25b8+vZufru3nT2ieQiOTyy87qrMsk8N29E5ESlh04zvIDxWXGo8N6MbZza3JScnnyqbKnJPv7exKfUPz7CQkZ+Pt7kpycZTG8g4OOIUPa8MWXWwBYteoIb755C8uWPoybWy1ef+P3Cp/fArw8uJFWorxIyySsgXl5cSPdtEwJ8PIgITOLF4YP4INNu3B3tuwDXRrXJykzm8vJqeULKZmeiyc3StSpZdnF4HoteLLNIPyc3Xlgb7FdvNB+GB+c2IK7Y+U7DBUKq7DjhlF18I+NbAkh3IUQ64QQEUKIE8YRq+FCiCghxG4hxGdCiLVCiLrAQqCDcXRoGeBq/FzhhGQhRLAQ4oTx8wEhRJsS57YLIToLIe4UQnxhPDbfmPZeIcRFIcStxuM6IcRXQoiTRl1/3DwnpTwqpbz0j+WNhWOylKEJC92rN4NUFN+1lhMfzxzNe2t2mPQCAtwX3g2D1HMsdVclBJum6BPiyvCvWjDog2Y0GVGb/e9ra6XyM/XcOJjOsC9bMOK7VujzJFd2plifzs3krAy3Pf4Uk3d/zLNHF3J/0yEAXMpK4OeLO/i8yyw+63In59JvoJfWr0UxwZLvlxLnHeLG4K9a039OS0KG1+HgHNOHVkOhgdjDadTr4VM1DZYoI4OO7tfRrLUsmkKo18Olc4JBow28+XUhzi6wZonDPyajtM2Wx6Qlixn7yyJm/baSmWEd6FrfvKKvCIs+YfEmQY+ARkwODePdo9sB8KrlwpAGzej3+1f0WPk5bo5OjA9uYzGuWboWjpn7q+UwXi7ODGrehPAvf6TPZ3Nxc3JibFvTp/nRbVqw7mQZrYOyNNVQXmiJWzhW2hYs6tOYt+EgXm4u/Dp7BlMHduTM1XgKDQb6tgshOSOb01fM1/yUhyUzLGt0KnI/hLbRphDeROcAL34Nby/SRsmuX6pU8lp6lnSVcT9KcyE1mW+O/sXCMZP4adQtnE6KR1/F0WFrbBVgc8wZhm/6mgf3LeXxNgNMzrk5OPF5j0m8HbGJrMJ8s7iVTa+qddnMr5Yw+dNf+M8PvzGtZxidQ4rLjM827mXw29+zZetJxo/v/Lf0leTxx4YSGXmV48evAdC1awgXzsczafIX3Hvfjzz6yBDc3Crf4DCzhTLKiwHNQ0jKyubkjbJ9YFS7Fqw7UbnywmJ6FoJtiTnDyM1f8fC+JTzaegAAAwKbkZSXxcnUG5VLU6FQlMk/OY1wOBAjpQyTUrYFNgBzgTFAXyAQQEoZD9yDNkLUQUo5Ccgxfp5RyTQXA5MBhBBBQD0p5WEL4YKAPsBo4F3jsYlAMNDOqKenlWn2NDYo15ds6JVECHHf888/fzkqKip7yf0TiE/PItCneMQnwMeD+HTTnraUrBw8XZ1x0GmlZIC3Jwnp2lSf2LTMMuM76nR8MnM0645GseXEeZPfHNu5Nf1ahbD4yqdlXoyLnyM5ScXTCXKSC8ymAjq5OeDoqj20B3byQuoleemFJBzPxL1uLZy9HdE5Cup19yL5jOVNOMojPi+NAFfvou91XbxJyCt7KuPRlEs0cPPD20mbirT6+mFu3/cl9/81l7SCHK5kVX69FmgjWSXzIjepABdfJ5MwTm4OOLpoeRHQyQuDMS+KruVoBt4hbjj7mMazFl9/SEoorimTE8DXz/LDwoESUwgB/PwlfnUgtJUWvmtfA5fPV2GOFBCbmUmQZ7HNBXl4EJ9l/dSz+CzNPpNycth0/jxhgYEVxDDnRnYGQW7FT8mBbp7E5ZhraOlTh3e7j+S+HStIzc8BoE9gMFcz00jOy6FQGth49Qyd6jSwKt3YjEwCS1x7oJcH8Zmm/hqbnkmgVwmfNIbpFdyIa6nppGTnUGgwsOnMeTo2KB6BchCCoS2asu7UWesywYit82LygDB+nT2DX2fPICE1iwC/4mut6+NBQqppfqRm5uDhVqL88vUkMVXTl5Wbz6s/bWLam4t4ad4GfD1ciUlMJyy0Hv3DmrD2rVm8c89IurRsyJuzhleYF77+kJJQ/D0lEbwtz5Lm0A5tmqEl3DygWRicMt/LoEJiszKo517CP9wr5x9Lo04wevkCpvy+hNS8XKKrsF4LtBGLwJJ24epFfG7ZOg4lXqGhuy++tbThbkeh4/Oek1hz9TibYiw/0M9o0oXfB93L8sdnVGtdlmD8n5yVw9aT52nX0LzM2Lb1FP36mk6HGzeuE999exfffXsXSUmZ1K1T/Pt16niWuZnG7TN74+3jxldfby06NnxYO3bt1qbrxcSkEhubRqOGZRiXkbj0TIK8S5QX3h7EZ2SZh/EqVaZkZNGpUT3CWzRh6+Oz+PDWkXQPacj7E4t9wEEnGNKqKX+cqFx5EZeTQVCJOjXQ1Yv4nIwywx9KukIjd198arnSqXZDwoNasHXYo3zY7Ra61wnh/S7jK5W+QlEh/7JphP9kY+s4MFgI8Z4Qoi8QAkRLKc9JrWvJfHeGv89SwLgMmsnAsjLCrZJSGqSUp4Cb28v1AZYZj8cCf1qR3hGgsZQyDPgcWGUpkJTyu3feeadxy5Yt3aZ8+xvbTl5gbCdtDVL7RoFk5uSbTLu4yV8XrjK0nTZ3f1yX1mw7pU0D2n7qYpnxX580hIvxyfy8y3Q3q97NG3P3gC48Mn81BbLs3krfpm5k3sgjKy4fQ4GBa3vSCOriZRImN6WgqHcw+Vw20gC1PB1w9Xci+Vw2hXkGpJTEH8/Es0HlF9OeSrtOQzd/6rn64igcGBrYnl3GzTBu0sDNr+hzC696OOocSCvQGna+tYxz3128GRjQhk03IiqtAcAn1I2sG3lkx+dhKDQQszeFwNJ5kVqcFynns4ry4ibX96RUeQohQJMWkrjrgoQb2o5p+3fo6NjTvADJzoKo44LOPYsbWz5+4FdHcsO4odTJozrqNapa4RMZG0uwrw8NvLxw0ukY3aIlWy5erDgi4OroiLuTU9HnPo0bczax8g3gyKQYgj19aeDujZNOx5jGrdhy7ZxJmHpuXnzd7xae3LuG6IziBekxWel09K+Hi4PWcdArMJgLaWUv/C/J8ZhYgv18aeCtXfuo1i3Yetb02redu8iE9ppPhtULJDMvn4TMLGLSM+hQPwgXRy3dnsGNuFhiY41eIY24mJRCnIV1XPaUF0u3RzDtzUVMe3MR249dYHQP7VrbhRjLn3Tz8uvQmasM6qSVX6N7tGZ7hFZ+ebg64+igVTUT+rTlyLnrZOXm88WqPYx47ntGv/gjz3//B4eirjL7xw0V5kXjFhB/HRJjNR85tF3bIKM0OVlwLhLCehUfy0iFm8vV8vMg6ggEms9YrpCI+FiCfXxp4Gm8H01bsvnSBavj13bVOorqeXgyPKQZq8+driCGZY6nxBDs4UcDNx+chI5RDdqwNcb0wbyRe/GGRa19AqmlcyDF2BB/u/MYLqQnMu/cgTLTWHTxEOO2zuXWTxZVW13m6uSIm7OxzHBypFezxpyL1Wy0kb9P0e/26tWMK1dNy5Lffz/CfffP477757F7zzmGDG0LQKtW9cjKyrM4hXDkyPZ07RrCm2+uNnk+i49Pp1PHYAB8fd1o2NCPmBupZeYNaOVFYz9f6vt44eSgY2TbFmyLKlVeRF1kXAdjedEgkIxcrbz4aMseBnz0PYM++ZGnlv/BgeirPLuy2Ad6NmlEdGIKcemVKy+Op1ynsYcf9Y12MbJBm6LNMG5S2i6cdA6k5ufw0cltDFj/CYM2fsZTf63gQEI0zx5aVan0FYr/jwgh/IQQm4UQ54z/y9ztTQjhIIQ4KoRYa81v/2NrtqSUZ4UQnYGRwDvAJiyPXP9jSCmvCyGShBDtgSnA/WUELbmNjyj1vzLppZf4/IdxGqK/lLLcJ5edUdH0bRnM+v/eRU5+IS8t21R07qtZ43ll+WYS0rP4+I/dzJk+kkeG9eZ0TDwr/zpZbvyOwfUY27k1Z28ksPxxbVDw0w172BV1iRfHh1PL0YG5907Ey3cYV7LPser6d2badA6CsLvrseetaDBA44G+eDV0IXqTVqGFDK3N9f1pRG9KRjgIHGoJuj7RECEEfs3cqN/Dmz+fPY9wAJ9gV4IH+5mlURF6aWDOqdV81uUudEKw5tphLmbGM7FhNwBWXv2L8IC2jKzXkUKpJ89QyIvHindyeq/DDLxquaE36JlzajUZhVXbAU/nIGg7qwH737qINEgaDvTDs6ErlzZptzd4qD839qdyaVMSOgfQ1dLR+fHgomkzhXkGEiIzaH9fFZ7ejDg4wO0PF/L+C05IA/QbpqdBsGTbWu1hNXy01rg6vEdH204Gs/VYMx/S8/W7jugLoU4g3Pt0YekkrEIvJa9u+5OfJt6CTgiWnTzBuaQkprdvD8AvkZH4u7nx+/QZeNTSFlff1bETw37+CV9XV74ZM1a7Hp1gdVQUOy9fqpKGVw5t5ufwqZqGC5GcS0tkerOOmoZzR3m0XW98a7nwRtdhABRKA+M2zOdYUgzrr5xh7YhZFEoDp1Li+PX8MavTfX3jNn6YNhEHnWB5xEnOJyYxtZN27YuPRLL9fDT9Q4PZ8uBd5BQU8vxazScjY2LZGHWOVXfPoNBg4HRcAouPFq89GdW6BWtPWb/QvabzAmD3iWj6tAvm9zfvIje/kFd/Ki6/Pnt4PK8v2ExiWhafrdzNO/eM5KFxvYm6Gs+qPVr51STIj9fvHIZeSqJvJPHaz5srff0lcXDQ1mB9/gIYDNBrKNQLhp3GKq+fcUXtsT3QqjM4l9hMNC0ZfvpA29jHYIDO/aCdhYZaReil5OVdW/l59C04CB1Lo45zLiWJGa3DAFh0KoI6rm6svnVmkX/Mat+ZIYvnkVmQz9fDxuLr7EqhQc9Lu7aSnl+1bUP1UvL6sQ380Gc6DkKw/FIE5zMSmBrSCYDF0UcYVr8V4xu3p9CgJ1dfyOMHVgLQuXZDxjduT1RaHL8PuheAj07+yY7Y82WmV111WW1Pdz69fQwADjodfxyLYs9Zbbr6EyP6EFzHFyklSZfS+PiTshvkBw5coHv3JixccD+5uQW8P6d4R7933p7EBx+uJykpkyceH05cXBpffD4TgF27z7JgwR4WLNzLf58dxfdzZyGE4Lu520lPzyn/Hhgkb/yxjR9mTkSnE6w4epLzCUlM6aKVF0sORbLjXDT9mgez6bG7yC0o5IVVm8r9zZuMatuCtZXYGKNIk5S8cWw9P/SegU4IVlw+xvmMBKaEaFMwl0QfZmj9Voxr1J5Cg4E8fSFP/LWigl9VKP5B7POlxs8BW6WU7xo32XsO+G8ZYR8DTgNeZZw3QVRmHUa5PyREPSBZSpkrhBgPPAC0BgZKKS8IIX4FPKWUo4UQA4Cnb240IYRIAepKKQss/7q2iYaU0kMIEQysNU5VRAjxENoUwI5SyjbGY3cCXaSUDwsh5hvDLy/1O5OAO4CxQB20TLvvZjhj2EvG30k0fg8E4qSUUgjRDViONtJVZia2ffbjGh/XHH2b5c0xbMm2mOYVB7IB/YKs732uLib5VPwuG1swbeUjNS0BWbeKe9P/wzhdcqk4UDVTEFz1bfL/SXx31nxefPD8NzUtAYBZ6++taQnUqlv+w76tqHWgilub/oPUPWwf5UVMHzvYDj2s7GmBtiZqolUbMytsR9XWD9iQ4W1erJZn4w0n36rytQshzgADpJQ3jEuTtkspW1gI1wD4CXgLeNKaTfP+yd0I2wFzhBAGoAD4D+APrBNCJAK7gbZlxP0OiBRCHKnCuq3lwKfAG5WMtwIYBJwAzgIHgDQAIcSjwLNo68wihRB/SCnvQdsF8T9CiEIgB5haXkNLoVAoFAqFQqFQFGOnLzUOkFLeADA2uOqWEe4TtDaC1dtv/5PTCDcClt402BK0d1RhbGxJKbdTYlt1KeV/KXuo7mYYD+P/S5RotEkp4yh1HVLK+cB84+c7y/gdgxDiaSllphCiNvAX2rozpJSfAZ9Z0PAF8EV5OhUKhUKhUCgUCkUZVFNjSwhxH3BfiUPfSSm/K3F+C8YN+0rxopW/PxqIl1IeNrZrrKKm3rNlL6wVQvgAtYA3jBtlKBQKhUKhUCgUiv9HGBtW5hsUFJ8fXNY5IUScECKoxDRCS+9k6A2MFUKMBFwALyHEQinlbeXpslljq/RoliWMI0xbLZwaJKWs2n7e5Wsa8E//pkKhUCgUCoVCoSgDg11OI1yNtpfDu8b/v5cOIKV8HngeimbsPV1RQwvsbGTL2KDqUNM6FAqFQqFQKBQKxb+Gd4GlQoi7gSsYXy1l3ADweynlyKr+sF01thQKhUKhUCgUCsX/MHa4QYZxwGeQheMxaK+1Kn18OxXM2LvJP7b1u8Iy3e74qMYzOLVZze8C6mgfu1pT6FpxmOom38c+XjDx7cgfaloCT31V81trAxjsYCdnvR1oAHCygx2lM0P0NS0BALerDhUHqmb0dlBmAThW7r261YLHjRqvTgEocK/5OjW5Z35NS9DIsI8++0sPPVXTEuyJmjfQChjR7Nlqceb15963y2vX1bQAhUKhUCgUCoVCofhfxD66JBQKhUKhUCgUCsX/Pv+yWXVqZEuhUCgUCoVCoVAoqgE1sqVQKBQKhUKhUChsg31u/V5tqMaWQqFQKBQKhUKhsA3SPjYKsxWqsVXDPDVjIL3CQsjNL+D1uRs5c9n8hdWTBndg6tBONAzwYchDX5GWabq1X6uQAH58eRovfrmObYfOVZhm3yaNeXHYAByEjmXHTvDd3oNmYWYPHUD/piHkFBTw3JpNnIrVdN3ZrSOTOrZDSsnZhESeW72JfL2e4a2a8Ui/noT6+3Hrj79y4kZchTr6NGvM8yMH4KDTsfzwCb7faa7jhVED6Ndc0/HCik2cvhFPoLcH79wyHH8PN6SEpYeOs3DfUQBaBtbhlXGDcHZ0oNAgeWP1Vo5fL1tL3yaNeXFoibzYV0ZehBrzYm2pvOhQIi/WaHkBMLNLB2Z0CUNvkGw/H82cbbsqzI+b9G8UzMt9BuKgEyw5dYKvj/xlcj7Ux485g4bRpk5dPti/h7nHDhWduzusE1Nat0NKOJOUyDPbNpCnr9rObqcOSVZ+bcBggJ7DBUOmmM463rrMwKE/td4pgx5ir8LbS3Tk58KCOQYyUkAI6DVSMGB85WYsPz9uAH1bhpBbUMCLSzZx+rq5X9T39WLObSPxdnXh9PV4nlu8gUK9gZA6vrwxZSit69flsw17mb/jcFGcNyYNoV/rJiRnZjPhwwVlpl9V26zl6MDP90ymloMDjjodm06e44tt+wB4elhfBrRsQoFez9XkNF5cuYmM3Lxy86FvqOarOqFj2dETzLXgqy8O03w1t6CA51Zr9hlS25ePJxbvVNvQ15vPtu/jp7+OMrxVMx7ur/nqpB/K9tXnx5a4B0vLuQczRuLtZnoPyou/8blZZOUVYJAG9AbJlM9+Kfq96b06MK13GAUOBrZdvsi7+3da1Na/YTAv9wnHQQiWnD7O10ct+MjA4ZqPHNjN3IhiH7mrXSemtm6PABafjuTHyCNl5L45fZo15vnRRrs4WIZdjB5AvxYh5OQb7SJGu+43Jw6hf8smJGdlM+7TYtv7cOpIQvx9AfB0dSYjJ4+JXyyyWlN12EiZ19+8Mc+N1crLFQdP8P1287SeH2u8/pv33Xj9ZcV9amRfBrQy+kVSGrOXaX5Rz9eLNU/dwaWEZAAirsTy0VdbKsyPJ28z1ql5BbxRRp166+AOTB2m1alDHyyuUzu1bMCcx8cRk5AGwPZD5/nh9/0VpgnwzOQB9Gmj1eWv/LyJqKvm6dar7cU7d4/E292FqCvxzJ6v+YuHSy3evGsEgX6eOOh0LNhyiNX7ThXF0wnBwuenk5CayWNfmb1n1SL964XwctdBOAgdS85H8PWJAybnx4W05oG23QHILihg9oGNnE5JAGD3xAfILMjHIA0UGgyM/eNnq9I002AndZlCUdOoxlYN0qt9CA0Dfbjl2R9pGxrEf+8YxKzXfzULF3H2OruPXeTr5yaZndMJwSOT+7L/+GWr0tQJwSsjwrlr0Upi0zNYcfd0tp69wIXE5KIw/UODCfbzYchX8wirH8hrI8KZNG8xAZ7uzOzWkZHf/EReoZ5PJo5iVJsW/BZ5inPxSTy8bA2vjzJ7RUGZOmaPCeeeeSuJS89gyQPT+fP0BS4kFOvo1zyYxrV9GP7xPNo3COSVseFM/XYxhXrJ++t3cvpGPG61nFj+4Az2nb/MhYRknhrel6+27WfXuUv0ax7MU8P7cucPy8vOi+Hh3PWLMS9mTWfruTLy4ut5hNUL5LXh4Uyab8yLrh0Z+a0xLyYU50X3xg0Y1DyUMXMXUqDX4+dm/d7NOiF4vd8gblu9nNjMDFZPmsHm6POcTynWlJqXw6u7tjE0pKlJ3AB3D+5s34nBv8wnT1/IF8NGM6ZZS5ZHnbQ6/ZsY9JJlXxp46G0dPv7wwaMG2vaQBDUu3lV10CQdg4wmeXy/ZPtvBtw9BYX5kgn36mjYTJCbLZnziIEWHU3jlkfflsE08vdh5HvzaN8okJcmhjP988Vm4Z4Y1ZcFO4+wPuIsL08cxC3d2rJkXyRp2bm8u2o74W1DzeKsOnSKX/ZG8PbUYWWm/3dsM79Qz6wfl5OdX4CjTsfCeyez82w0kddi2XvhMh9v3o3eIHlyaB/u7deVjzbtLlfHy8M1X41Lz2D5PdPZVspX+zXV7HPol5qvvjoynMk/LiY6KYXxcxcV/c7Ox+9l85nzAJxNSOKRZWt4bWTZvlp0D9433oMJ4Uz/wsI9GNmXBbtK3IOubVmyP7LC+LO+XUZqtmmnUdfQBgxsE8rEjxaS0iif2q5uZebL630Hc9uaZcRmZbD6ltvYfOkC51OSisKk5uXy6m5zH2nu58/U1u0Zt0LzzZ9G38q2yxe5lJZaZl6UTHf22HDu+dFoFw9O58+oC1yIt2AXH86jfcNAXhkXztSvtev+7cgpFu2P4N1Jprb31OI/ij4/O6IfGXnlN8BLa6oOGykrrRfHh3Pv9yuJS8tgycPT+fOU6fX3bRFMY38fRszR7vvLE8KZ9uXicuPuO3eZTzYY/WJEH+4d2JWP1mt+cTUplVs+LW54elSQH73ah9AwwIdbn9Hq1GfvHMTdr5nXqZHnrrPn2EW+et68Tj129jpPfbSqgpRM6d0mmEZ1fRj3yjzahQTy/LRw7njf3F8endCXRduOsOnQWV6YNojxvduyfGckkweEcfFGEo9//Ts+Hq789uqd/PFXVFHHxbTwjkTHJuPhUssqPToheL37EG7bvITY7AxWj7yDzVfPcz6t2EeuZqYxZeMvpOfnMaBeE97pMZzx64s7AaZt+pWUvJxK5YOZBjuoyxR2itogw34RQjgLIbYIIY4JIaYIIV6wIk65bwcRQgQLIaaX+F5bCPGnECJTCPFFqbDbhRBnjOkfE0LUrfrVQL9OofyxR+u9OnHhBp5uztT2djcLd/ZKAjcS0y3+xuQhHdh26Bwp6dlWpdm+XiCXk1O5mppGgcHAupNnGNzc9KF0UItQfjt+GoCI67F4ujhTx0PT5ajT4eLoiIMQuDo5Ep+pZe+FpGSik1Osu3CgXYNAriSlci0ljQK9gfXHzxDeylRHeKtQfj+m6Yi8punw93AnMTOL0ze0XsPs/AIuJiRT10urhqWUuDtrFZKHizPx6VnW58UpC3nRPJTfIo15EVNBXmRoeTGtUxjf7T1IgbEXLjnb+gqrQ91ALqelcjVd07Tm3BmziigpJ4fI+DgKDebD8A6ihCZHR+KyqvZynMtnoE4Q+AcJHJ0EnfoLju8ru3A8sl3SeYDWmPKuLWhofLebi5sgoCGUqOMrZGCbUFYfNt73K8b77mnuF92bNmTTcW0k9/fDpwhvo9275KwcTlyLK3pQKcnh6OukZZf/0re/Y5ug2SSAo4MOR4fiInbv+SvojfPUI67eINC7/EfH9vUCuZySyrUSvjqohbl9roos9lWvEvZ5k54hDbmakkZMmvYCrYuJyUQnle+rA1uHsvpIiXvgasU9OFR8D6yNX5IpPcL44c9iv0nKsVymaT6SwtUMo4+cj2JosGm+JOVkE5kQa+YjTX38OBoXQ25hIXopORBzlWEhzcrVdRMzu4i0YBetQ/n9qPG6r5ra7uFLFdvesHbN+SPijFV6oPpsxBLtGgZyNSmVa8na9f8RcYaBrUtdf2nfNd738uLuPVfCL67cIKACvyiPfp1CWW9NnXq57Dq1KgwIC2Xtfu26j0fH4unmjL+XebpdWzRk6xHNX9buP8XAMC0PpAQ3Y0PKzdmJ9Kxc9EbbrevjQd+2Iazac8JqPR1qB3E5I5WrmUYfuXSaoQ1N7fxIwnXS87WG/ZHE6wS6e1byqivQYCd1mUJhD/y/amwBHQEnKWUHKeUSoMLGlhUEA9NLfM8FXgKeLiP8DGP6HaSU5vMEKkFdXw/ikoort/jkTOr6Wl/R1PH1YEDnZqzcFml1nABPD2LTi9OMzcgkwNOj3DBx6VqYuIwsfth3mO2P3sOex+8jIy+PPRevWJ22SRpeHsSWqNhj0zOLGkw3qetpGiYuPZOAUmHq+XjRKqgOkddiAXj3jx08M7wvW5+5h2eG9+OTzWWPHAR4ehCbYaqhUnmx/zDbH7mHPY8Z8yJay4uQ2j50aVSfZXdOZeFtk2gXFGBtthDg4UFMZnF6NzIzCHC3zibisjKZe+wge++4l7/ueoCM/Hx2XbVuxLM0qUngU6d4JMrHv+wGU36u5PQhSVgf85GrpFjJ9QvQuIX1aQd4eRCbWiLP0zLNHsB83FzIyMkrekiLS82g7t94SDNL/2/Ypk4IVj40g93P3c/e81eKbLMkEzu3ZdfZSxXrsGB7JmFK+7OFMKPatGDtiahy0zJL27vUPUi14h6kFd+D8uJL4Lt7J7Lk0enc2r1dUZjgOj50DqnPLw9PZcm4KbSvE2hZm7snMVklfCQrkwArHxTPJCfSLagBPs4uuDg6MrBRE4I8rIsb4F3KLtIs2IVXxWVWWXQOrk9SZjaXk1KtCg+2tZEAbw9uVOCXZtefpl2/NXEBJnZpy64zl4q+1/fzZvmjM5h//yQ6BdcvVx9AHT8P4pJN69Q6fpUrF9o1DWLhmzP5+KkJhNSvbVWcuj4exKWUSDclkzo+pfzF3YXMbNMy62aYJduPERLox8Z372Pp7JnMWba9qOP/6UkD+PS3XRgqsaFAgJsnMVnFjckb2RkEuJWdD1OahrH9+sWi71JKFgyezJpRdzCtWZjV6ZposJO6TGGnGGT1/NkpNd7YEkK4CyHWCSEihBAnjCNWw4UQUUKI3UKIz4QQa42jSAuBDsZRpWWAq/FzhRPchcYcYxrHhRBTjKfeBfoaf+cJKWWWlHI3WqPL5kisN5Ynpw/gi6W7MFRiOFZYmMlVOk1Lk72klHi5ODOoRRPCv/iRPp/Oxc3JibFtW1qddkVplB5WrkirWy0nPp02mnf+2EFWXj4AU7u1590/djBozve898cO3pgwtFIapDUabuZF8yaEf/kjfT4zzQsHocPLxZlJ8xfz/radfDJxVJkazDWZJ2jt3fVydmZISFP6/vw93ed/i5ujE+Obt7I67YoStZQXACcOSELagLunaYC8HMkPbxqYeL8OV3frX+peVp6bhrGQT/9QOft3bdMgJRO/XMTAOd/TrkEgTeuaPrDd378beoOBNRHlP9z+Hfu8iZNOR3jzUDacrngtZ+XTLvselBd/5ldLmPzpL/znh9+Y1jOMziHaQ7SDToeXqzPTv1jM2/t28OXQMdZrs9JLLqQm883Rv1g4ZhI/jbqF00nxRSMIFWHZgq0rO61hVFgL/oisXKO4Jm3EYlqWwpRxb0rHvW9gNwoNBtYe1fIgIT2Lwe98z62fLeL9tTt4f9oI3CuYRmeN75bHmUvxjHvie26bvYBlm48x57GxVse1kHApcWX7S8/WwZy9lsCw575j2tsL+e+Ugbi71KJv2xCSM7I5faVy/bqWyyfL9AxoxJSm7Xn3yPaiY7dsWMTodT9x59Zl3N6iE93qNqhU+mBHdZlCYQfYw5qt4UCMlHIUgBDCGzgBhAPngSUAUsp4IcQ9wNNSytHGsJlSyg5WpjMR6ACEAf7AQSHETuC5kr9pBfOEEHpgBfCmtFCTCiHuA+4DaNzjVuo271l07tZBYYzvr/XmnoqOI6C2JxjruLp+HiSklD3trTStQgJ48z/aAmcfT1d6hYWgNxjYceRCmXFi0zMJ9CruyQ309CA+wzTN2AzTMAFeHsRnZtErpBHXUtNJMU6L2xR1no4N6rG6kr3mRTq8S+jwMtcRVypMgJdH0bRAR52OT6aNZm1EFFtOFa8zGNexNW+v2w7AhhNneX384LI1ZGQS6FlKQ2apvEgvIy+CS+XFmeK8iM3IZFOUpikyJg4pJb5urkVhy82XzAzqlehpD/LwJN7K6RN9GjTmanoayblaOhsunqNzYD1WnT1tVfyS+PhDakKxaacmgpef5bBHdhRPIbyJvlDywxsGugwUFke8SjO1Vxi3dm8LwImrcQT6lMhzbw+z6aApWTl4ujrjoBPoDZIAH08S0v+ZaSZ/1zZvkpGbx8Hoa/RtFsz5eG1YcFzH1vRvEcKseSus02HB9soLU9qG+zUN5uSNeJKyKp5mPL1LGJM7tkUYLNwDHyvugXfxPYhNyywzfoLxf3JWDltPnqddw0AOR18nLi2TLSc0v4mIj8UgJX4urkX2XHTNWRnUKzGSFeTuYbWPACyNOsHSKG1K1jPd+3Aj07q4sWml7MKCXVq0i4yKy3QHnWBwm6ZM+uKXCsOaaLKhjcSlZRJUgV+aXb8xjJODQ7lxx3VqTf9WIdw9t9gvCvR60rK1KaWnrsdzNSmVhkG+REWbbuhy66Awxg0oUaf6FadT2To1Kze/6PPeyGiecQjH28PFbFMqgMn9w5jQWyuzTl6OI8C3RLq+HiSkmqabmpmDh5tpmZWYptne2J6tmb9J2xziakIaMUlpBAf4EhZaj/7tm9CnbTC1HB1xd63Fm3cOZ/b8DeVeh+YjXkXfg9w8ic82t/OWPnV4t9dw7tyyjNS84muMz9HCJuVms/HqWcL86/FX/LVy0zTTYCd1mcJOUWu2bM5xYLAQ4j0hRF8gBIiWUp4zNmQW/kPp9AF+lVLqpZRxwA6gayV/Y4aUsh3Q1/g301IgKeV3UsouUsouJRtaAMu3RnDbywu57eWF7DhynpG9WwPQNjSIzJx8ktKsrxjGP/1D0d+2g+d4/6et5Ta0AI7HxBLs50sDHy+cdDpGtWnB1rMXTcJsO3uRCe20XqSw+oFk5uaTkJlFTFoGHeoH4eKotdF7hjTiYomF2JXhxPVYGtf2pb6vF04OOka0a8GfUaV0nL7IuA6ajvYNAsnIyyfR+JDwxoQhXExI5qe9pjuJxadn0jVE64Xr0aRhuVNyivLC25gXrS3kxbmLTGhvzIt6gWTmGfMivVReBBfnxZazF+gR3BCAYD8fnBwcrGpogfaQGeztQwNPTdOYZi3YfKn8e3qTmMx0OgYWa+rdoJHJYuTK0KgFJMRo0wALCyRHdkja9TBvNOVkSc5HQruexeeklPzysSSgkSD8FuuKmMV7I7j140Xc+vEitp24wNjOxvveSLO/RAsPrH+dv8rQdto6hHGdW7PtpHX5VBF/xzZ93VzxdHEGwNnRgZ6hxXbRp1lj7unbhYcWria3oLBCHZZ8dZsFXx3fvthXM4y+epNRbVuy7qR1nSG/HIpg/NxF3PrJIradvMDYTiXuQU4Z9+BCiXvQpTXbTmn3YPupixbjuzo54ubsBICrkyO9mjXmXGyidi0nL9CtqeY3Id6+ODnozBpaYPQRH18aeHprPtK0pdU+AhRtvFHPw5PhIc1Yfc66B7gT12Np7F/CLtq34M/TFuyio/G6G2r3w1K+laZnaCOiE1KIq2SHgS1t5MS1WBqV8IuRYebX/+epixZ9t7y4fZo35u4BXXj4J1O/8HV3RWccomng501jf19i4tPMdC3fGsHMlxYy86WF7Dx8nhEl69TsytWpft7Fm7K0bhKITicsNrQAlu6IYNrbi5j29iK2R1xgdA/tutuFGO3dwnrhQ2euMqiT5i+je7Rme4Rmt7EpGXRrodm+n6cbjQP8uJ6Yxhe/72HEC98zevaPPP/DHxw6c7XChhZARNINgj19aeBh9JHgVmy+arr5ST13T74ZMIEndq8jOqN4DaeroxPujrWKPvcNCuFsakKFaZppsJO6TGGnSFk9f3ZKjY9sSSnPCiE6AyOBd4BNWD/aXBmsn8dUBlLK68b/GUKIX4BuQNX2RAX2RETTq30IK+fMIjevkDe+31h07uMnJ/DWj5tITM1i8pCOzBzZhdre7vzy5u3sjYzmrR83VylNvZS8vmEbP0ybiINOsPzYSc4nJjG1U3sAFh+JZPv5aPo3DWbLQ3eRU1DI82s2ARAZE8vG0+dYdc8MCg0GTsclsPjocQCGtAjlpWED8XNz5bsp4zgdl8Ddv/5Wtg6D5K2125h7x0R0OsFvh09yPj6JKV01HUsORrLzbDT9mgez4cm7yM0v5MWVmo5OjesxrmNrzsQmsPKhGQB8snkPO89e4pXftxRt2Z1fWMgrv5e9VbBeSl7fWCIvIsrIi9BgtjxozIu1JfIi6hyr7jbPixXHTvD26KGsvXcmBQY9/129sUwNljS9vGsbP4+9BQehY+npE5xLTmJGG03TopOR1HFzY/Wk2/CoVQspJbPCOjHkl/kci4tl/YVzrJs8k0KDgZOJ8fx60vr1fCVxcBDc+qCOr17Utn7vMVQQFCzYvU6bctVnlNaIitwjadlZ4OxS7F4XT8LBrZJ6wfDeg1rP9Og7dbTpZp0L7oyKpm+rYNY/dxc5+YW8tHRT0bmvZo3nleWbSUjP4uM/djNnxkgeGd6b09fjWfmXtlNVbU83ljw6HQ+XWhik5LY+HRn3wc9k5eXz/vQRdA1tiI+7C1tevIevNu1j5UHTHa7+jm3W8XTnnVuGodMJdEKw4cRZdpyJBmD26HCcHB344a6JAERcjeW11VvLzIebvvr99Ik4CMGKiJOcTzC1zx1GX9380F3kFBbywurivHJxdKRXSCNeXmfqA4NbhPLScM1Xv52q+eo9v5j66s6oaPq2DGb9f433YFk592D6SB4Z1pvTMcX3oKz4tT3d+fR2bXqgg07HH8ei2HNWW4ux8uAJ3pw0lN+enEmeg56ntq0vM19e3rWVn0cbfSTqOOdSkpjRWltbsuhUBHVc3Vh968xiH2nfmSGL55FZkM/Xw8bi6+xKoUHPS7u2Fm0SUBF6g+St1duYe9dEdKKEXXQz2sVfkew8E02/FsFseOoucgsKeXFFcb7NmTKCbiGa7W377z18sWUfKw9r+TWifYtKbYxRMi+qw0bKvP7ft/Hd3Ua/OHiSC3FJTO6upbX0QCQ7o7TrX/+s5hezjfe9rLgAL47T/OL7e4x+cSWW13/bSpeQ+jw8tBd6vQG9NPD6b1tJzyp/dv+eiGh6hYWwYs4scvNL1alPTeCtH0rUqaO64OftzqK3bmdvRDRv/7iZ8K7NuSW8PXqDJC+/kNlfrrPqPuw+EU2ftsH8/rp23a/+XJzHnz00ntcXbiYxLYvPVu3mnbtH8tCY3kRdjWfVXu3+z/3jAK/dPowls2ciBHz22y5SK7jW8tBLyct/bebnwZNxEIKl549zLi2RGc07ALDo7DEebd8bX2dX3uw+BKBoi3d/Fze+G6DdCwedjt+jT7EjJrpqGuygLlMo7AFh7XzyahMgRD0gWUqZK4QYDzwAtAYGSikvCCF+BTyllKOFEAMwnUaYAtSVUhaU8/uZUkoPIcRE4H60Rp0fcAjoDtQHPpJS9i8V706gi5TyYeN3R8BHSpkohHACfgW2SCm/Ke/6ut3xUY03tVOb/e125t/GsUZWwJlTaP0u7NVGvo99vMzv25E/1LQEnvrq3pqWAIDBuaYVgN4ONAA4lb0hnc3IDLGP9+m4XXWoaQno7aDMAnC0g83gPG7UeHUKQEEl1qBWF8k98ysOZAsyarzPHoBLDz1V0xLsiZo30AoYUf+RanHm9dc/t8trtwcvaQfMEUIYgALgP2hrqtYJIRKB3UDbMuJ+B0QKIY5IKWdUkM5vQE8gAm3k7FkpZawQIgkoFEJEAPOllB8LIS4BXkAtYwNwKHAZ2GhsaDkAW4C5Vb1ohUKhUCgUCoVC8b9NjTe2pJQbAUvzrFoCGEez2hrDbge2l4j7X+C/Ffy+h/G/BJ4x/pU8XwAMKnUsuIyf61xeWgqFQqFQKBQKhaIcrNwF9n+FGm9sKRQKhUKhUCgUin8JdryZRXVg942t0qNZlhBC1AYsrTQfJKUs41WsCoVCoVAoFAqFQlF92H1jyxqMDaoONa1DoVAoFAqFQqFQlMO/bGTLHt6zpVAoFAqFQqFQKBT/c/xPjGwpFAqFQqFQKBSK/wcY/l0jW6qxVc1kBdX8lv/PTV5R0xL45Ex4TUsA4OkW22paAu1crtW0BACmLX6spiXQbMylmpYAwIVdwTUtgfwG9vHeHJ8tTjUtgR8e+rKmJQAwdfXDNS2Bpu3so7yI2diopiXgkGsfD2hJbWu+Xie75t8BB/D5iJ9qWgJPrLiTpnM+qmkZAJx/5smalvD/Ain/XbsRqmmECoVCoVAoFAqFQlENqJEthUKhUCgUCoVCYRv+ZdMI1ciWQqFQKBQKhUKhUFQDamRLoVAoFAqFQqFQ2Aa19btCoVAoFAqFQqFQKP4uamTLBvRp3pjnxgzAQehYcfAE3+84aBbm+TED6NcihJyCAl5ctonTMfEVxp3eqwPTe4ahN0h2RkXz4fpdADQP9OeVCYPwcHHGICX7Mtagl2XvdHbxcD5b52ZiMEjChrjSY5KbWZgrx7Uw+kJw89Ix/V0fkq4Vsvr99KIwqbEG+sxwo+s48/gV0btOU/7bdiQ6IVh55Qg/nt9lcn5AQEsebhmOQUr00sD7J9dzNPkKAJ6OLrwaNo6mXnWREl6OWEVkytVKawD7yIuIg4IFXzliMAgGjNAzdqre5PzapQ7s3ar1kxgMcP2K4Otl+Xh4QVYmfP+RI9cuCQRw79OFNGtdtR6kfiGNmT1Is72lkSf49oCp3Tbx8+W9EUNpE1CXD3ft5YeDh4vOvTN8COGhTUjKzmbkvAVVSh+gs29LHgidiE7o2BC7n2VXt1gM19yjER91fIJ3T89nd2IEAO4OrjzefCqN3YOQSD4+8ytRGZesSrdvk8a8OFS79mXHTvDdPnOfnT10AP1DNZ99bu0mTsVqPntnt45M6tAOKSVnExJ5bs0m8vXaPZzZpQMzumg+u/18NHO27TL73bLoXy+El7sNwkHoWHIugq9PHDA5Py6kNQ+07Q5AdmEBs/dv5HRKAgC7b3mAzIJ8DNJAocHA2HU/W51uaZ6cOZBeYSHk5hXwxncbOXM53izMrYM7MHV4JxoG+DD0P1+RlpkLQKeWDZjzxDhiEtIA2H7oPD+s2l9pDZEHBQu/dsRggP7D9YyZarqz1bqlOvZt03xEr4eYq4IvlxYU+ciPRh9BwD1PVc1H+jUK5pV+A9EJwZJTJ/jm8F8m55v4+jFn0DDa1K3Lh/v2MPfooaJznrWceW/QUJrX9kdKybNbN3I09kalNQB08WvJg00noBOC9TcOsOTKVovhmns25LNOj/PWqZ/ZlRBRdFyH4MvOT5KYn8ZLx7+3Ot0+zRvz3OgBOOisqNfyC3hxeXG99sYtQ+jfsgnJmdmM/7S4fHhqRF8GtGxCgV7P1eQ0Zi/fREZuntWaAJ64ayA9O4aQm1fIm19t4Gy0uX3eMqwDU0Z1okGgLyPu/oq0jBwA3F1r8cqjIwmo7YmDg45f1xxi3faTlUrfHsrN0vRvGMzLvcNxEIIlp4/z9TFTWx3XrBUPdOgGQHZBPrN3beF0UsLfTjfqkIHfvynEYJB0H+5A+GTTR84/lxdy9E/Nd/V6iL8qeW1xLdw8BUs+KuDUXwY8fATPfFPrb+noFxxc4p4c59u/LN0To6/u3lN0T4I8PZgzcgT+7m5IKVkccZyfjhz9W1oURgz/rt0IVWOrmtEJwYvjwrn3h5XEpWWw5OHp/Hn6Ahfik4vC9G0RTGN/H0Z8MI/2DQN5eXw4075aXG7cbk0aEN4qlAmfLKRAr8fP3RUAB53g3SnDeX7pBs7cSMTbzYV7pxaWqc+gl2z+JoMpb/jgWVvHT0+m0LR7LfwbFZtGbqaBTV9nMvlVb7zqOpCVqjlJ7QaO3PWZX9HvfHVnEs17Olc+jxC80G409+3/ibicdH7tez/bY6O4mFlc2B9IvMj2HVEANPMM4IMukxn35+cA/LftCPYknOOpw0twFA64OlRt62p7yAuDHn763Inn3svHzx9eftiJzj0N1G9c/DA4erKe0ZO1h/cj+3RsWOmAh5d2bsFXjrTvYuCxlw0UFkBe5Z5TitAJwauDw7lj6UpiMzJYeft0tp6/wPmkYrtNzc3l9a3bGdIs1Cz+yhOnWHg0gjkjh1VNAJpdPNR0Ei8c/4rEvFQ+7fgUB5KOcyU7zizcXU3GcCQlyuT4A00ncijlNG+dnoejcMBZZ12FrROCV4aHc9cvK4lNz2DFrOlsPXeBC4nF194/NJhgPx+GfD2PsHqBvDY8nEnzFxPg6c7Mrh0Z+e1P5BXq+WTCKEa1acFvkafo3rgBg5qHMmau0WfdXK3PCyF4vccQbtu0hNjsDFaPuoPNV89zPi2pKMzVzDSmbPyF9Pw8BtRvwjs9hzP+j+IHtmkbfyUlL8fqNC3RKyyEhgE+3Pr0j7QNDeLZuwZx96u/moWLPHedPccu8tULk8zOHTtznac+WlVlDQY9/PyFI8++W4CfP7zyiCOdehqo37g4zKjJBkZN1nzz6D5h4iMLv3KgXVcDj/wNH9EJwesDBjFz1XJiMzP4fcoMtlw8z/mUYhtJy83htZ3bGNqkqVn8V/oNZMflSzy4fg1OOh0ujlUrs3QIHml2C/+N+IbEvFS+6PwE+xJPWPSRe5qM4XBylNlvTGjQjyvZcbg5ulifrhC8ONZYN6VnsOShMuq12ub1GsCqw6f4ZV8E70wyLR/2nb/MJxt3ozdInhzeh3sHdOWjDbut1tWzYwgNAn2Z/OiPtGkWxDP3DObeF38xC3f8TAx7jlzky1cmmxy/ZXgHLl1L4tn3VuHj6criT+9i467TFOqtezC0h3LTkqbX+wzmtrXLiM3KYPXE29h8+QLnU0qUHelpTPl9sVZ2NAzhnX5DGf/bor+VrkEv+e3LAu57uxbe/vDpYwW07m4gsHHxhKqBtzoy8Fbt88n9enau0uPmqW2r32WIA73HOvDrB2U/v1iDTgheHRLOHUtXaPdk5gy2XrB0T/5kSFNTXy00SN75cwcn4+Nxd3Ji1e23sefyZZO4iiqiphH+byCEuCSE8K9CvD+EED4VhLlTCFHPmt9r1zCQq0mpXEtOo0Bv4I+IMwxsbVrIhrcOZfWR0wBEXo3F09UZf0/3cuNO6RHG9zsOUmDsMU/O0h6iejVrzNnYRM7cSAQgLTsXSdkVxY1zhfgEOeAT6ICDk6BVPxfOHTAdBTu1I4/mPZ3xqqu918Pdx9xsLkcU4BPkgHfdyr/7o61vA65kJXM9O4VCqWdDzHEGBrY0CZOjL9bk6liryE/dHZ3pXDuYlVeOAFAo9WQU5lZaA9hHXlw4IwioJ6kbBI5O0GOAgcN7y3bTfX/q6DlQs4HsLDhzXMeAEdr9dnQCd49KSwAgLCiQy6mpXE1Lo8BgYN3pMwxuamq3ydk5HI+No9BCD9XBa9dJzanafbhJc8/GxOQkEJubRKHUsyPhCD1qtzMLN7Z+P/YkRJCan1F0zM3BmbbeoWyM1UZNCqWeLL11DY329QK5nJzK1VTjtZ86w+Dmptc+qHkov0VqPhsRE4unizN1PNwBcNTpcHF0xEEIXJ0cic/IBGBapzC+21vCZ7Otb/h08A/icnoqVzM1TWuiTzO0YTOTMEcSrpOen1f0OdDd0+rft5Z+nUJZv/sUACcu3MDTzZna3u5m4c5eTuBGYrrZ8X+CC2cEdUv6SH8DR8rzke06egzUbDTH6CP9h/89HwkLMPpHuvF+nD3DkFKNqqScHCLj4ygo5R8eTrXoVq8BS04dB6DAYCAjv2q9Ii28GhGTk1jkI9vjj9LLv61ZuHEN+rI7IYLUgkyT4/7O3nSv3Zr1Nyo3ulhUN6WUqJtalarXWoWy+miJes1Fq9cADl+6Tlq2efmw99wV9MZdyiKu3CDAu3I3p2+XUDbs1Ozz5LkbeLg7U9vHgn1eiic2wdw+pQQ3F61TxtXFifTMXPSV6IG3h3KzNB3qBnI5PYWrGUZbvRDF0GBTTUfiYorLjrgYAj2qWHGU4MpZSe16gtpBAkcnQYf+Ok7uLzsvj+0w0LF/cZ0Z2k5X1PD6O4QFBXI5pcQ9iYqy+p4kZGVxMl4bGc0qKOBCUhIB/0DeKP59/L9qbAkhqn0kTko5UkqZWkGwOwGrGlsBXh7cSCt+CIxLyyTAy9RZ63p5EJtqHqa8uMH+PnQOrs+vD05l/n2TaNsgwHjcFynhu1kTWPbIdGb161KuvowkA17+xQWcZ20dmUmm09aSY/TkZhr45flU5j+ewolt5pXB6V15tOpnfc9oSQJcPInLSSu+ztx06rp4mYULD2zF7wMf4ctuM3g5YhUADdx8Sc7L4o0OE1jS7z+82n5clUe27CEvUhIFfnWKe3z8/CUpiZYrnLxciDyko2sfrYJIuCHw9JZ8N8eRFx9wYu6HjuRWcSAjwMODGxnFthebkUmAp20rGX9nbxLyUou+J+alUruWt0mY2rW86VW7PX/c2GNyPNDFn7T8TJ5sPp0vOj3DY82mWj2yFeDpQWzJa083v/YATw9i00v4pjFMXEYWP+w/zPZH7mHPY/eRkZfHnmhtumtIbR+6NKrPsjunsvC2SbQLCrBKD0CAmycxWcUPhzeyMwgop5UwpVkY269dLPoupWTBkMmsGX0H05qFWZ1uaer4ehCXXHzd8cmZ1PGrnF20axrEwrdm8vHTEwipX7vSGlISoXZJH6kDKUll+8jxEj4SHyvw8pHM/cCB2f9x5IePHKjKYF+guwc3MkvYSGaG1Q+oDb29Sc7NZs7gYaydOpN3w4fi6li1qs3f2aeUj6Th72zuI33827E2Zq9Z/P80ncDcC2swULleZrO6KT3TrGFU19tyvWYtE7u0ZdeZS5XSVcfPg7jE4jQTkjIqZZ8rNhylcX0/Vn97Pws+vINP5v1ZqQ54eyg3SxPg7klMCVu9kZlJQDkdMVNatWP7lei/nW5aosSnTrFf+vgL0pIsZ2Z+riTqkIH2ff75R1KL98Sj8h1R9b28aB1Ql4gbsf+kvH8t0mColj97xa4aW0KIl4QQUUKIzUKIX4UQTwshtgsh3hZC7AAeE0KMEUIcEEIcFUJsEUIEGOPWFkJsMh7/FhAlfvc2IcRfQohjQohvhRBlDjncHBETQgQLIU4LIeYKIU4af9tVCHEr0AVYZPy98ucCWXgGkKUqNlFWmHLiOuh0eLk6M+2rxXz4x04+nD6q6Hin4Ho8u3g9M79ZyqA2oQS5di5bn6Wyr1S6Ui+JvVDIra94M/k1b/Yuzib5evHQvr5Acv5AHi17V37anMUEMc8jgG2xpxn35+c8fvBXHm4RDoCD0NHKO4illw4yZefX5OjzmdW0b9Vk2EFeWKzYy+jcO7pfR/M2hqLpUXo9XDonGDRGz1vfFODsIlmzpPKja1CGTdp82L/iXs37QyfwY/Rqs4dFB6GjqWcD1t3Yw8NH5pBryGdyw8FVTrX0tZeVP14uzgxq3oTwL3+kz2dzcXNyYmzblkWavFycmTR/Me9v28knE0dZpadsTZbD9gxsxJSm7Xn3yPaiY7esX8TotT9x55Zl3N6yE90CGlidtomOygixwJlL8Yx74ntue3EByzYfY87jY6ukw1yY5cNH9+to1lqa+8hoA29+XYizC1XyEWEhI6zNBkedjjZ1Alh0PILRixeQXVDAfzp3q7QGsM4uHmw6nu8vrjXzke61W5Oan8G5zGtVSts83VI+YkWYsrhvQDcKDQbWHjOf9lgelu+L9fbZPSyYc5cTGHv/t9zxzAKevHsQbq7Wrxeyj3LTlMrch571GjKlZTve3b/TZloATh0wENz6nxnJsiZNS88X5eHm5MSX48bw5rbtZOaXvf5doSgLu1mzJYToAtwCdETTdQS4uXLUR0rZ3xjOF+ghpZRCiHuAZ4GngFeA3VLK14UQo4D7jOFbAVOA3lLKAiHEV8AMwJoV4s2AaVLKe4UQS4FbpJQLhRAPA09LKQ9ZiiSEuO9m+qMff4FbuxZP6wjw9iA+PcskfFxaJoE+nnDZNIyTgwNB3p4W48alZbLl5HkAjl+LwyAlvu6uxKVlcCj6GqnGKRq7zlyie8fm3Mg5jCU8/XWkJxaP3mQkGfDwM3348KztgKuXjlouglouggZtnYiP1uNXXzOfi4fzCQh1xN23am33uNx0AlyLe2MDXLxIyM0oM/zh5Ms0dPfDp5YbcbnpxOWmczxVe2DYfONUlRtb9pAXfnUkyQnF1UNyosC3tuWKYd92HT0HFvfk+NWR+NWBpq208N36GVizuGqNrdiMTII8i20v0NOD+MyscmL88yTmpVLH2afou7+zD0n5aSZhmnk24rlWdwDg5eRBV7/W6KWBqPRLJOalciZDc6rdCcesbmzFZmQSWPLavcyvPTY9k0CvEr5pDNMruBHXUtNJMU4R3HTmPB0b1GP1iShiMzLZFKX5bGRMHFJKfN1ci8KWqyk7g3ruxaO9QW6exGdnmoVr6VuHd3sN584ty0jNKx51jc/RwiblZrPxylnC/OvxV5x1D9m3Dg5j3ABt+uapi3EE+BVfd10/DxJSrLeLrNziB5W9EdE8c0c43h4uRRtoWIOvPySV9JEE8PWz7CMHSkwhBG2k2K8OhBp9pGtfA2ur0Ni6kZlBUIne8UAPT+KyzO9HWXFjMzM4Fqf1kK+/cJYHqtjYSjDzEW8LPtKQF1rfDoC3kztd/Vqhl3paejamp39butVuTS2dI24OLvy31QzeO13xWp249EzTusnLynoto2JbGdepNf1bhXD39ysqDAswcVgHxg7S7DPqQiwB/p5wRjtXp7YniZWwz1ED27JglbZ5xPW4VG7Ep9G4nh+nL1g3mmEP5WZpYrMyqFfCVoM8PCyXHX7+vNt/GHf+scKk7Kgq3v6C1IRiv0xNlHjVttyYOrbDQMcB1dP3H5tp6Z5Y56ugdY58OW4Mq0+fZtO589Uh8d+JWrNVY/QBfpdS5kgpM4A1Jc4tKfG5AbBRCHEceAZoYzzeD1gIIKVcB6QYjw8COgMHhRDHjN+bWKkpWkp5zPj5MBBsTSQp5XdSyi5Syi6XXevQqLYv9X29cHLQMTKsBX+eumgS/s9TFxnbqRUA7RsGkpmbT2JGFieuxZYZd+upC3QPbQhAY38fnBwcSMnKYc+5yzQP9MfFyREHnaBLSANS8y+VqTWomSMpMXpSY/XoCySnd+bStJtpT17THrW4drIAg15SkCu5caaA2g2LH1BO7cyjVf+qTZsDOJl6ncbuftR39cFRODC8Xju2x5r2aDZ08yv63Mo7CEfhQGp+Nkl5mcTlpBPsrk1H6u7fhIsZ5rtPWYM95EWTFpLY64L4G1BYAPu36+jU03xoPDsLoiJNz/n4aQ2umKtahXbyqM5kY43KEHkjlsa+vjTw9sJJp2NUqxZsPX+x4oj/IGczrlDPtQ4BLn44Cgf61+nE/qQTJmHu+ut17jT+7U44xpfnl7Ev6TgpBRkk5KVS37UuAB18m3Ml27oHpuMxsQT7lbj21i3Yetb02redu8iE9prPhtULJDMvn4TMLGLSM+hQPwgX47SwnsGNuGjcWGPL2Qv0CNZ8NtjP6LNWrtuKSLxBsJcvDTy8cdLpGBPSis3XTCv+eu6efDNgAk/sWkd0ekrRcVdHJ9wdaxV97lsvhLMp1u80tnxLBDNnL2Tm7IXsPHyeEX1aA9A2NIjM7HyS0qx/mPTzLt6ds3WTQHRCVKqhBZqPxF0XJNz0kR06OvY0t/PsLIg6LuhswUduGDcrPXlUR71GlfeRyLhYgn18aOCl2ciY5i3YEn3BqriJ2dncyMygiY8vAL0aNOJ8clIFsSxzJuMq9V3rEGj0kQF1O7Iv0XT3vNsPvMnM/W8wc/8b7EqI4PNzK9ibeIIfo9cxfd9rzNz/Bm+d+pljqeesamgBWt3kX6puOl2qXjt9kbEdzeu18ujTvDF39+vCwz+vJrfAuo0RVm48xp3PLuDOZxew86/zDO+n2WebZkFkZeeRlGq9fcYmptOlXSMAfL3daFTPl5j4tApiFWMP5WZpIuJjCfb2pYGnsewIbcnmS6a2Ws/Dk2+GjeOJbX8QnZZSxi9VjobNBYkxkqRYSWGB5NgOA216mD9y5mRJLhw30KZn9TyOavfEp/ietGxZqXvyzvChnE9K5sdDR6pF378Wg6yePzvFbka2KH/OUMnS8nPgIynlaiHEAODVEufKmoT1k5Ty+SpoKrlqWQ9Yv33YzUgGyVurt/HdrInodILfDp3kQnwSk7u3B2DpgUh2nommX8tg1j9zF7kFhcxetqncuAC/HTrBG7cOZdXjMynQ63lx2UYA0nPy+GnXEZY8PB0ppTbnPWhfmfp0DoIhD3iw9JU0pEHSbrALdRo7cnS99hDYcYQr/g0dCelcix8fSUEIaD9UCwNQkCu5dCyf4Q9VfV66Xhp4+8Q6vu5xOw5Cx6qrR7iQmcCkxtp6s2WXDzE4qDVjGnag0KAnT1/Is0eWFsV/58Q63ul0K046B65lp/DSsd+qpMMe8sLBAe54uJD3n3fCYBD0H6anQbBk6xqtIho0RntwPLRbR7vOBlxKWeQdDxXy9TuOFBZC3SDJfU9XbScnvZS8tmUb8yZNxEEIlh0/ybmkJKZ10Oz212OR+Lu7ser26XjUqoVBSu7q0pHhP/xMZn4+H48ZQfeGDfF1dWH3f+7h0937WHa8clsoGzDw9fkVvNn2PzgIHZti93MlO5aRQb0BzNZplebr8yt4tuVMnIQjN3IT+fis+a5kZV376xu38cO0iTjoBMsjTnI+MYmpnbRrX3wkku3no+kfGsyWB+8ip6CQ59dqPhsZE8vGqHOsunsGhQYDp+MSWHxU2whhxbETvD16KGvvnUmBQc9/V2+0Oi/0UvLygc38PHgyDjrB0nPHOZeayIzmHQBYdPYYj7bvja+zK2/2GAJQtMW7v4sb3w2cCGjTjH+/eIodMVVbk7EnIppeHUJY8cEscvMLeWNu8TV8/PQE3vp+E4mpWUwe2pGZo7rg5+3OordvZ29ENG//sJnwrs25ZVB79AZJXn4hs79aV2kNDg5w+8OFvP+CE9IA/Yw+sm2t5iPhozUfObxHR9tOBpxL+cjMh/R8/a4j+kKoE6i9HqGy6KXklR3b+HnsLeh0OpadOsG55CSmt9Vs5JcTkfi7ubF6ym141KqFlJK7OnRi6ML5ZBbk88qObXw8dCS1HBy4kp7GM1s2VFoDgEEa+OLcCt5pfz86oWPjjQNczo5ldL1eABbXaf0TmNRNokS91s1Yr/1lrNdaBLP+aWO9tnxTUfw5U0fQNaQhPu4ubH3uHr7cso+Vh07y4thwnBwc+H6WZq8RV2N5fZXlrewtsfdoND07NWHZZ3eTm1/AW18V2+cHz03g3W83kZiSxaQRHZkxtit+Pu78POd29h2N5t1vNzF/xX5mPzicBR/cjkDw1aJdRdvCW5UvdlBuWtL08u6t/DzqFm3r8zPHOZeSxIzW2trNRacieLRzT3xdXHmzrzb6X2gwMHblwr+VroODYMJ/HJk7uwCpl3Qd6kBgYx1712mzR3qN0jopT+w10KKTDmcX00fAhe8WcCHSQFY6vHFbHkNnOtJ9WOVHobV78ifzbr0FB51g2fET2j0JM96TCOM9mTmj+J507sTwH3+iRR1/JrRpTVRCAqvvuA2AD3fuYUf031/Tpvh3IWp6PvFNhBBdgW+BXmiNwMPAXGA0JabsCSGOAvdIKQ8LIeYBIVLKAUKIz4B4KeWbQogRwB9AHaAu8DvaNMJ4IYQf4CmlvFyGjktoa7I8gLVSyrbG408DHlLKV4UQa9AafH9WdF1tnvu4xjP4qVnWTceoTj45E17TEgB4vMW2mpZAO5d/Zo3E32Xa4sdqWgLNelyqaQkAXNgVXNMSyG9gH2sBArZUbYOZf5LP3vy8piUAMHX1wzUtgabt7KO8iNnYqKYl4H3BPhbAx3ep+UlBhV76igPZgM+HVv2dff8UT6y4s6YlFHH+mSdrWgJYs+C5hhnmMqNano035i6yy2uv+RLDiJTyILAaiABWAocAS+P3rwLLhBC7gMQSx18D+gkhjgBDgSvG3z0FzAY2CSEigc1A0N+UOx/4xqoNMhQKhUKhUCgUCsW/EnuaRgjwgXHkyA3YCXwopZxbMoCU8ne0kSpKHU9Ca2Td5IkS55Zguu6rTKSUwcaPiUDbEsc/KPF5BVDzw0UKhUKhUCgUCsX/I6Qdr6+qDuytsfWdEKI14IK2zkqtSFQoFAqFQqFQKP5XkPYxJdhW2FVjS0o53VZpCSEOAKVfhjRTSnncVhoUCoVCoVAoFArF/y521diyJVLK7jWtQaFQKBQKhUKh+Dfxb5tGaDcbZCgUCoVCoVAoFArF/xL/2pEthUKhUCgUCoVCYWP+ZWu27OY9W4qyEULcJ6X87t+uwV502IMGe9FhDxrsRYc9aLAXHUqDfemwBw32osMeNNiLDnvQYC867EGDPelQ/LOoaYT/P7ivpgVgHxrAPnTYgwawDx32oAHsQ4c9aAD70KE0FGMPOuxBA9iHDnvQAPahwx40gH3osAcNYD86FP8gqrGlUCgUCoVCoVAoFNWAamwpFAqFQqFQKBQKRTWgGlv/P7CH+bv2oAHsQ4c9aAD70GEPGsA+dNiDBrAPHUpDMfagwx40gH3osAcNYB867EED2IcOe9AA9qND8Q+iNshQKBQKhUKhUCgUimpAjWwpFAqFQqFQKBQKRTWgGlsKhUKhUCgUCoVCUQ2oxpZCoVAoFP9jCCGcrTmmUChqFuWr//uoxpadIYSYWN5fDehpLIQYbPzsKoTwtLUGe0AI8ZgQwkto/CCEOCKEGFoDOizZxSAhRF0banjMmmPVrMFdCKEzfm4uhBgrhHCysYat1hyzgY7XhRBDhBDutk7bgpZeQojpQojbb/7VgIYAo4+uN35vLYS428YaatxHgH1WHvufRwjhJoR4SQgx1/i9mRBidA3oeM+aY/82hBB+Na2hhlG++j+O2iDDzhBCzDN+rAv0ArYZvw8EtkspbdbgEkLci/aCPT8pZagQohnwjZRykK00GHW8D7wJ5AAbgDDgcSnlQhtqiJBShgkhhgEPAS8B86SUnWylwahjHdAT+NN4aACwH2gOvC6lXGADDUdKX7cQ4qiUsmN1p10ivcNAX8AX7foPAdlSyhk2SNsFcEO7BwMAYTzlBayXUraqbg2l9MwC+qDZRQawC9gppfzdxjoWAKHAMUBvPCyllI/aWMd6YB7wotFnHYGjUsp2NtRQYz4ihAgE6gMLgemY2uc3UsqW1a3BqOM4UOYDhpSyvS10GLUsAQ4Dt0sp2wohXIF9UsoOttJg1GHJLiJtnBdrML8vaWhl6LdSytxqTn+2lPJN4+fWwCrACc1Op0gpD1Rn+sZ0/aWUiSW+3wZ0A04Ac6WNHoztxVcV1Y9jTQtQmCKlvAtACLEWaC2lvGH8HgR8aWM5D6EVQAeM2s7ZcgSlBEOllM8KISYA14BJaA+6NmtsUVwIjkRrZEUIIUR5EaoJA9BKShkHWi8+8DXQHdgJVFtjSwgxDa1CCBFCrC5xyhNIqq50y5Ijpcw2jlh8LqV8Xwhx1EZp3w88DtRDe4C7aQfp2N5HkVL+CPxorLgnA0+jdZLYehS6C1qZVdM9eP5SyqVCiOcBpJSFQgh9RZH+CezER4YBdwINgI9KHM8AXrCRBoCbI0cPGf/fLJtmANk21AEQKqWcYrw/SClzbFl+CyH+AzwINBFCRJY45QnssZUOIxeBOsCvxu9TgDi0Dru5wMxqTn8iWucpwBzgMSnleiFEN+ATtE7m6mYT0Am0xh9ax90vaDbbCnjCBhrAfnxVUc2oxpb9EnyzoWXkZmFoS/KklPk36yRjD3FNPEjdnB42EvhVSplcA+2cw0KITUAI8LxxOqXB1iLQ7CKuxPd4oLkxTwqqOe29wA3AH/iwxPEMINJijOpDCCF6oj243ZwiZpPyTEr5KfCpEOIRKeXntkizPIQQ3wOt0cqIXcCtwJEakHICCESzkZokSwhRG2NZJYTogdZzbwtq3EeklD8BPwkhbpFSrrBFmmXouAwghOgtpexd4tRzQog9wOs2lJNvHM26aROhQJ4N0/8FWA+8AzxX4niGlDLZhjoAOkop+5X4vkYIsVNK2U8IcdLGWupJKdcDSCn/Mt4jW1DyAWIi0FdKmSWE+AUblp324quK6kc1tuyX7UKIjWi9TxKYSvHUMVuxQwjxAuAqhBiC1jO3xsYaQKsMotCmET4ohKgDVOtUBwvcDXQALhpHVGoDd9lYA8Au46jnMuP3W4CdxvU6qdWZsPHh6TLQ0zii1tV46rSUsrA607bAY8DzwG9SypNCiCbY2D+klJ8LIXoBwZQoS6WUP9tSB1AbcEC7/8lAYg3cD9AaGKeEEH9R4kFWSjnWxjqeAlYDocaH+jpoDdBqp6SP2CI9SwghnrT0+SZSyo9KH6tm3IUQfaSUu42aegG2Xl/4CtoU9IZCiEVAb7QRBZsgpUxDa/BPAzDOEHEBPIQQHlLKK7bSAtQRQjS6maYQohGa7wLk2yD9JsZRXwE0EEK4SSlvjnTaat2tqxCiI9q+BQ5SyiwAKWWBrUbBS7FWCDEd87rElh0SimpErdmyY4S2IUZf49edUsrfbJy+Dq2RMRStYNwIfF8T04SEEL5AupRSL4RwA7yklLE21tAe88JwpY01CLQGVm+0e7IbWGHLeyKEmAR8AGw3augLPCOlXG6j9B2Ad6WUz9givXJ02MUapRJ6WqFNS3kC7QGigY3T72/puJRyhy11GLU4Ai3Q7POMlLK6R31Lpz8ReA9t7a0w/kkppZcN0n6lvPNSyteqW0NJhBCdgR8Bb7SOwzRglpTSpqOvxg6yHmj3Yn/JNTs21DAGbbpYPbRZCY3ROqva2FDDSOAb4AJaXoSgdaRuB+6VUn5SzemXLicOSykzjR14t0opq30qthCidMfcdCnlDaONbJRSdqluDaX0bEDzi8MU1yVIKT8sM5Li/xWqsaUoE+NoSa6UUm/87gA4l+iFspUOB2AU5g0dm/XQCiF+BNoDJymePiillLNspcFeEEJEAEOklPHG73WALVLKMBtq2CalDLdVemVoOI0drFES2q5qfYF+aBuG7AN2Gddy2VpLyRHPv27aiI01RABLgCVSygu2Tt+o4TwwRkp5uibSt0eEEF5ozxy2mtKJEKLcDYxqoMEXAYSjlZcdhRADgWlSyvtsrMMZaInW2Iqq7k0x/r9Qg884J6SUbW2ZpsK2qGmEdoYQYreUso8QIgPT9VE26xktwVZgMJBp/O6KtrDUFgtYS7IGbdrgcWpmnRRADyll6xpKu4ia7DEvga7UQ3QStn+NxFHjVJRlQNbNgzYeabSXNUoj0DZH+VRKGVNTIoQQk9EWvG9Hs8vPhRA2G/EswVi0Rf9LhRAGtIbXUhtP1YqrqYaWEOJZ44Yxn2Nhja2tR16NDfC30dbnjBDaDnQ9pZQ/2CD5myMDLmgbuESg2WZ7tI2f+thAQ0kKpJRJQgidEEInpfxT1MzW750p7rxsL4SoienPZgghvrN1w7MkxpkzjYAoGye9VwjRTkp53MbpKmyEamzZGVLKPsb/9vA+Kxcp5c2GFsahfrca0NFA2nBr3DLYJ4RoLaU8VcM63qfme8w3lFhPCNqD7R821uCH1sgrObolAVs2tuxijZKU8iEhRGO0TTJijIvMHaWUGbbUAbwIdC094gnYtLFlXDf1PvC+0F5X8RJaB4WDDWUcEtp246swtQ1b2OfNsuGQDdKyhvkYt+I3fj+L1gCu9saWlHIggBBiMXDfzYdZIURbtF07bU2qEMIDrXNkkRAiHrDp+sqypj8DNmlsibLfqSXQNsGqaTYBjWycZh/gTiFENFp5cbMTtaafexT/EKqxpSiPLCFEp5tTLYxz73NqQMd6IcRQKeWmGkj7Jj+hNbhiqdnCsMZ6zG8ipXxGCFFy3dh3tl5PKI2vSKhhXq1pAWD6Pjy0h6gGaGsybPo+POxjxBMAIUQw2jb4U9AeKJ+1sQQvtO3NS7743CadAVLKNcb/P1V3WlZSY1vxl6BlyVEDKeUJIUQHG2sAGIc2S+MJtJ1UvbHtroxQ869oSEDbRKbkjoDS+N0mr5YRQnxW1inAxxYaSjGiBtJU2BDV2FKUx+PAMiHEzalJQWgPL7ZmP/CbccOOAmpm6tyPaO8fqcmpjFCzPeZFSG2b2hrbqlYI0QD4HK3BJ9E2CnlMSnnNVhqklDvsYY0S9vM+PHsY8UQIcQBtV7NlwCQp5UVba7CHzgDjJgCWphHaeq1jTW7Ff5PTQntFwkKjjtsoHgG0GTd3vTNSU43hmp7+fBEYZGlarxDiqo003IW2a6ml7f+n2UgDQggvKWU62qshFP/DqMaWokyklAeFEC0p3tUryta7ehn5EG0r5eM12Bt3RUq5uuJg1U6N9ZhbWEdYdArbN37nob27ZpLx+23GY0NsJcCO1ijZxfvw7GHE08gdUkpbr7kwQQjRHO1l4wFSyrbGnUzHSinfrCDqP0nJaXIuaLuY1sQrAZ7EfCv+SeVH+ce5C/gP2isjQJvG97WtErezsrOmpz9/graRj6U1lO/bSMNB4ISUcm/pE0KIV22kAYpfpHyY4tG9m0igiQ21KKoRtRuhwgwhRLiUcptxMwYzbD2KYuwtHyGlrLERJSHEV2jTC9ZQgyNK/x8QQvhKKVOqOY1jUsoOFR2rZg01viujMd330d6xdTvwCNo2zqeklC+WF+9/FSGEN9p7lW6+uHUH8LqNd8HbATwDfCul7Gg8VuM7jgkhdkgpLW7RX41pOqNN5Szaih9tyqktXyqMEKKWUYOkBl4HYC/Y0ysaagrjurFcW+86WFWEEG2klLZ+4bTiH0SNbCks0R/YBoyxcM7WmxCANt1huxBiPaYNHVu+nNPVmLbNR5TA/nYYq4CtQLlbLv8DJAohbqN4yto0tDVCtsRe1ig9h/Y+vOPA/WhT9763VeJ2toMqaFN+T6Ct2QJt+u88wGLnUTXhJqX86+ZooxFbb4RQciMCHdoOdIG21GBkn5SyE9prMwAQQhyh+suIIoQQA9Cm7V1Cs8uGQog7pJQ7baXBXrCX6c9ldOamoc1gqVY9Uspko4bHpJSfltJldswOWIAN/UXxz6MaWwozpJSvGNdHrZdSLq1pPUC08a+W8c/m2MEaDHvbYaw8RMVB/jazgC+Aj43f9xiP2RK7WKNkHPGda/yzOXa2gypAqJTylhLfXxNCHLOxhkQhRCjF65RuxfZrZEpOTSpEK0PvtlXiQohAoD7gKoToSHG54AXYelfbD4GhUsozRm3N0fy2s4111Dh2NP35brTlATdfMDwAbX12cyHE61LKBTbQcAdQumF1p4VjNY0t6lRFNaIaWwqLSCkNQoiHgRpvbEkpXwMQQnhqX4u3o7cVNb0hw80dxoBIKeVRW6T5N6j2ucnGxdU23WK9JEIbsvgMrXe4DzWwRkkIsVRKOVkIcRzLo5023SlTCLFASjmzomM2IEcI0UdKuduooTe230X1IeA7oKUQ4jpaQ+c2WwqQUobYMj0LDEN7cG0AlJyFkAG8YGMtTjcbWgBSyrNCCCcba7AX7OIVDWgbTbWSUsYZdQSgraPrjramrtoaW0KIacB0IOT/2rvzIDurMo/j31+QNSYI2gqMAySMBCWskQEDUqJIaQUplUUZUEQBwQVxwwJEQB0tF6xS0URkjyImiCC4hAwGwmKGJUBAdAYNS4E4GkFQwYnAb/4456VvN71ktO85b/d9PlVU9327u84D3O73Pec853mU+jU2plA+Q2JNxHmfcS4mW2EkiyV9hNQTpbNx7MMlg8g9UeaTSlsjaRXw9sI5zNULMmRfkrQpqdLaRb2ax92Cya8lXWp7FuXTahvNYf9zgRuBUpW8hrNt54tcqKPGzsExwPn57JaAh0kr2MXkCoh7S5pMSjctXm1M0nqk83t70P87Mtf2X0uMn0vPny9p/1y9tKabJZ1N/wP8IaSdv17UlvTnLZuJVvY7YGvbD0vq9nm6G0g7zS+gv/E1pIWAFV0eO/SgKJARhpUb7A21Yl60Qo6kG4CTbC/Jr18FfMb27IIxVC/I0DHuJvT3EJoKfLdwlbMRSbq1KQrQxTEWkya/zcPTocAhtktWI/wacJ7tm0qNOUwcp5DeDw8DFwEXD3qI6fb4J5B2KtYnVcqENMlZTdrtO6FULIPimgqQSyuXHvt5pIIlW9KxqFnybKWkBaSHx2/lSwcDG9kuXQkQSXNIk/H1mmu2i/WXykU63kv/LvRS4Ouli3S0gaQvANszMP15he2PFY7j66TmwQvzpf2BB0iFZa5wbkhdII7q59dGI2mZ7d1qxxH+fjHZCsOStD4DV0avBebZLpqSI+n2wRXehrrW5Rj+AziPgQUZDrddunFsZ0zbkZq1vsV2sbNso6WLSdq427ufbZj8SroL2JrUoPMv1Gt03cSzPenBaX/gAdt7Fx7/s7UmVoPieD6pGmHnjs4nbRdLD8oLRMsY1JfPBRsNt+HvZh5zHumM1l6kwi0HkB5qS54fm0yqPvdUfr0WsO54qUY31ga1aFhaMv25IwaR/lY1cVwHfM8FH0olHQh8kf7za68Eip9fy/8tDgGm2/6kpM2BTWzfWDKO0D0x2QrDyiujjwHfzpcOBp5n+6Dhf6orcXwfWM7AXYyX235jwRg2JxVkeEW+dD0pbe2+UjHkOF5KeqA+gJT+cRHpBlVsNU7S8lxdrHm9FqmC1MsKxlBt8itpmu17JG0x1NdLvycaecfzQOCtwJRSkz5J29j+paQhq2XZXl4ijo54FpN2LpodnUOAV5WcfA7+HalB0nmkxbFl+fWupB5k7ykcxwrb23d8fC5wie19Rv3hsYthGbB3c943x3BlyeyI0D5qT/uOuaRFmVfbfqmkjUjvz11G+dEwTsSZrTCSGYP+6CzJf5xKeydwGulsTJMCUrQ6YO2CDB3OJU0w9rH9m5IDd6aLSWpSs55JFysZCwOrEZqUg1+qGuHFpLNI59Tc2WxIOoY0Ae8jxXak7bsKhvBh4EgGnn1oGHh1wVgANrb9qY7Xn5b0xsIxzJd0JHAFA9tVdP28a0fBlLWBt0u6P7/eAij5vmg0mRCPS9qMtEhUunjHep2FlWz/WVLpiohVqV2NlZvS758DXphjqBFHW86v7Wp7Z0m3Ath+RKkvXJggYrIVRnKrpN0GrYxeXzoIpwa5x+YzGE+7B6sRNirnbf/K9pSmCl7FOGpPfiflc1JbS/rQ4C+6bP83SA/Rx9m+rfC4ANg+Mn8scsZiDSyR9Fb6K6keAPywcAyrSeW1T6L/AddAifOu+67JN6lA8/HsinyG7QukDAVTvk3BXyTt3OyySppF+QqVVbk9rRkanwfeYPsXo35n97SifQfwt5wh0rSK6KMj/TiMf5FGGIYl6RfADOD+fGlzUr+npyl4NiWfTbqAXI0QWEVKh7mzxPg5hqoFGTR8me9i54Sa1KiaKVKSvjLS10sUIJA0A3gjcBwwb4gYTut2DG2ioZuTPsN2qcbfzcq9gMn0P6xMAv5ccsVc0q9Jq9WrSo35/1Xj9zgXqljP9qOFx92FlHLdZANsSjrr2qsVCauTdL3t3VsQRxvOrx1CmujtTGq+fQDwcdsLR/zBMG7EZCsMa7gzKY1SZ1OiGiFI2tT2QzXPCeUJ53OAHUnFUgbH0PWdJkkPkHYLNgKetSpfqgCBUtPvt9j+zqjfPMFJOjd/+kJgNvDT/Hov4GrbI07GJiKl3j1vbXMBhhJVQ/M4t5Pah3zX9q+7Pd4IcaxNWjwU8Evb3S4vHkYg6cvAJsClDEy1rdVKoypJ2wCvIb0/r6q84xfGWKQRhmHVOug/hMnNRAvA9tW5ulRJqyQdysCCDMWqm9l+KH+s+f9kDmnlbT5Dn88p4TFS5agfkB7mq3Bq+n0M/e+HnmX7cABJVwAva96rSv3gvlYjpnzA/CUMLDW+tGAITwG3SVrCwAfJYqXf10Cpldb9SKv2CyQ9TZp4LcipwEXo2T3HrpU0z4V6joUhTSW1iugslGIK9C1sy/k1SRt3vPwdHfeTElV9QzmxsxVar4XVCJuCDMWqEbbl5pBj6bP9+1LjDRr7WFLT2unAg51fIv13KNYDTtLJpHMfVZt+t4WkO23P7Hg9idS/Z+YIP9aNOI4gNXx+MXAbsBvwM9vFCnVIGrKJcqmd1zVRKY3wJcDJpBTstQqO25qeY2H86Pa5RvX3MhXpmMYj+fPnAffbLl1IJnRJTLZC6+VV6tNIq5KQqhGeVuhwd1Pa/Hzbh5YY7x9R4tC7pMsZYVW8UDrhXNvHdHucUWK4Z4jLRSd8bSLpDNJu0ndI74+3koqqvL9wHHeQmpQus71jTs85zfZbCsexPrC57f8qOe6aKpVGmMfakv5G7E+RUgqL7Y6rJT3HAkg63vbnJX2VIe4jbdr9LbUgodSL7ge2f5Rfv57UquDD3R47lBFphKHV8kRnoQs3aO1k+ylJfZLWsb26Vhxr6CpSql83rSTl2neuEt8LLOryuM+oPdHKMcSqYwfb78vFMl6ZL51Z47A5qXntXyUhaV2nHmAzSgYg6Q2kZqnrANMk7UhqrFykguYa7ioWaVsg6T9JZegXAgfaXlli3EFaUVk3AKnIFsDNVaNYMyo0zi62j25e2P6xpE+N9ANhfInJVmi1PNF5XNKGpStYDXIvcH0++N6ZMla6zPdoStwcdrK9Z8fryyUttX1igbFbI/fp+RBp9+KonCI1w/YVlUOrJh9ur33A/YFcavxSYLGkR+ivQlfKqcC/ks4XYvs2ScUm5/lM4e2SNh/ubFTBdNfDbP+y0FjD2ZX+nmOQK+s21V1LVdYNYPvy/OkK27dWDWZ0pVK/Vkn6OGkB06SjEsXOhIfui8lWGA/+CtyRq+F1TnRKphv8Jv8zCWhbv5JOJW4OfZKmNyvUkqaTGur2mnOBW0gV+AAeIK3e9+RkS+1oUortN+VPT80FKjYEftIRZ4n+Uk/aflQasPZROmd/U+Dnkm5k4N/N0v3pHpL0JaBZoLmGtMtXcvHsdQXHCmvmS7mIzkLgIts/rx1QRQcDpwBNJsDSfC1MEDHZCuPBDynflHQA91jvpFEcB1wtaSXpAXIacFTViOrYyvZbJB0MYPsJDXq67jFtaFI6gO1rhrhcItX2Tkn/BqyVdzyPJRXVKen7pEbstQu2nAPcSTqzBfA20kJF11sCSJpq+zFScYxn6dViNm1gey9Jm5DeF2dKmko6y/fpyqF1KvL3PL8PP1BirFBHTLZC67WhgpekrYGPAFvS8XtTssLZGipxc5gKzCRNsvYj7ey0tnlrF63ORRAMIGkrOsp896D/adNEawQlfkfeT+oH97+kgiGLgNJnMF5EeoBbTprwLHKdilhb2d6/4/Vpkm4rNPaFwL6kHeim6lvDpKqmoRLbvwW+knegjwc+ARSbbEmab/ttI1wrda6xj/Tvvy0D21W07fki/J1ishVaq8mnH+7rhfPsFwLzgLNI1bSqaMnN4WTbCyVNAV5L6rk1l3QuopecSkpP+2dJ3wZ2Bw6vGlFdN0v6Lu1vUtr1CYdTM+OTgJNykZ/JpXs62f54bk+wD+l9eUYugX62yzYXfkLSHravA5C0O6llQtfZ3jd/eg9wuu1nMiQkfbNEDGFokl5Kqk55AOl80kVA6ep72w6KaS1gVvO64M7nt0ktRPYFjgYOA6q0VwndEZOt0GbNjfK9+WPTZ+sQUjPEkp60PbfwmENpw82hmWzOAebZvkzSqQXGbRXbV0q6hdTHSaS+a724w9eo1qS0bSRdSHpoeoq0q7KhpC/Z/kLJOGxb0m+B3wJPAhsBF0tabPv4QmEcDVwgaUPS78nDwDsKjd3YEjhe0izbn8zXZo3w/aH7ziXt+u5ju2gBG0knACcC60t6rLkMrAbOLBlL9nzbZ0v6QE59vkbSUCnQYZyKPluh9SRdb3v30a51aeymw/uxpA7v32fgqn2Rla/OmwP9E81nbg62TygRR47lClJD4b1JDyxPADf2Ws8aSVfZfs1o10K7lOgvJem23OPrENLvyMeAW0ruxis1AD+MlOJ7FnCp7b/lsvB3296qVCw5nqkA+QxVUZKWk6pDfgX4Z1K1tyUleiiF9pF0YM7OWGD7oNF/ouvxLLO9m6RFpPfob4CLS/+Ohu6Jna0wHkwelIYyG5hcaOzBuf4f7fhayZz/X9me0pKbw0Gk6l5ftP3HXFHqo6P8zIQhaT1gA+AFSg23m/fGVGCzaoFVJunFpIIMu5N+N64j7fY9UDiONqTari1pbeCNwBl5klN6ZfMFwJtt39d5MZeF33eYnxlzktYF9iefd21qyHTsMBUJw/aTwHskvYP03tyo4Pgha+5hQxwTaKqXlliQOIF0NOBfCoy1Jj6dd34/TPobOhX4YN2QwliKyVYYD94FnJP/GBl4FHhniYGbxrWS1ht85iI/dJfSmptDPo9yScfrh4CH6kVU3LtJFRk3I03Gm8nWY8DXKsXUBueSChIcmF8fmq+9tnAcbUi1/QapN9/twFJJW5DeH8XY/sQIXytZyOQy0t/sW6hXQGZe84nt8/KD/ntH+P7QPU3VvWIT/iH8IRflmJZ7Zw5Qsj1C/vv0ktyf8VFgr1Jjh3IijTCMGzkNRTWaG0taPjjlZKhrXRx/MWlxZEfg2sFfr9A7p+dJer/tr9aOoy2a1LnRrnVx/Nak2g5F0nPy7kpPkXSn7Zm14wihIWkdUvuH+cARg78+TMuIbsazxHZMsiaw2NkKrSfpRcBngM1sv17Sy4BX2D67wNibAP9EOki7EwNTxjbo9vgd5tB/czi94LhhGLa/Kmkm8DIGluu9oF5UVa2SdCjp0Dukppx/KDh+m1JtkTSHQaWcgZKpc21xg6TtbN9RO5BQn6Q/MXRF0GJN0G2vBpZJmm27DVX/bpB0BqkiYWcD8uX1QgpjKXa2QutJ+jEpHekk2ztIeg5wq+3tCox9GKly1suBmzu+9CfgvNJlrSX1teTm0PMknQK8ijTZ+hHweuA62wfUjKsWSZsDZwCvID1M3QAca/v+QuMvt71zyR3nEWKZR1qM2YtUnOIAUhGZd9WMqwZJd5HSn+8hpRGWPJsTxilJG9l+pMtjXM7I7WWKZIzklEY6Yml+R6LP1gQRk63QepJusr1LZxWxkulJebz9bX+v1HgjxNGKm0N4pg/cDqSJ/w55B/Ys22+oHFoVks4HjmsekHIlzy/aLnK+sk2ptpJW2N6+4+NzgUts7zPqD08w+bzaswwu3BFCpxKLJpK+DGwCfCtfOph01nIRlEsnlPRhBhbiMumM5822bysRQ+iuSCMM48FfJD2fPMmQtBvpIGkxtr83VFpQ4YpaACsZ4eYQivprruz2ZD5P+DvKVadso+07V6JtP5xTb0tpU6pt07T3cUmbkdIpp1WMpxrb90nag1QE4FxJfcBza8cVWk+jf8s/bCfbe3a8vlzSUtsnFhi70yxS9swPSP/ec4CbgHdLWmj784XjCWMsJlthPPgQ6Y/QdEnXA32ktJxihksLKhlD1pabQ4CbJD0P+Cap0tqfqfOeaItJnak/eWer2D2mZecwrsjvjS8Ay0kLRd+sGlElOd325cAMUjr42qTFoq73SQzjWom0qz5J022vBJA0nfR8UdrzgZ1t/znHcQpwMbAn6d4Sk61xLiZbYTy4i9RM+HHSWalLgf8uHMPsjrSg0ySdTkf584LacnMIMIVU5vxq4CfAVNsrqkZU1+mkg94Xkx6UDgL+vUIc54zU06pEOqHtT+VPv5ebgK9Xo4pqS7wJ2Ik06cT2byRNqRtSCEBq4XG1pJWkv1nTgKMqxLE5qWpq42/AFrafkFSrXUIYQzHZCuPBBaT85c/k1weTUoUOHPYnxl5b0oKOox03h5BW6fcgNaGcDtyWdxm/XDesOmxfIOlm4NWkVJg3276rQijVU21zD773kN4fBq6TNHdwr74esdq2mwmwpFIN6cP4ViKNcCowk3Qf3Q+YDawqMO5gF5J25S/Lr98AfCf/rtT4GxrGWBTICK0n6XbbO4x2rcsxnEx6qH41/Y1rz7J9cqkYchwHkh4aO28OJ0WJ2DpyQ8pdSOmlRwNP2N6mblS9LU949xztWpdjWEDahe+c8G1ku+QCUStI+gjwElJz68+SGtJfGD3qepuk+bbfNtw1SRt3uwF5RwGbPUiLuacDJ9retZvjDhPLLNLijEhVbW8e5UfCOBI7W2E8uFXSbraXAUjaFbi+cAxfBI4BXgn8jFTtbG7hGABOtr0wp+G8lnRzmAsUvzn0OklXAZPpfz/sYvt3daMKtCPVdsagxaAlkm4vHEN1kkTqHbQNKTthBvAJ24urBhbaYNvOF3nhalbzutsTreyp/HEOMM/2ZZJOLTDus9i+hXQ+K0xAMdkKrZVLa5t0oPrtku7Pr7eg/Nb6+aSV6q/k1weT0htLN09tzc0hsIL0cDCTVB3zj5J+ZvuJkX8sdNlx1E+1bcMCUXU5ffBS27OAmGAFJJ0AnAisL+mx5jLpzNKZhcN5UNI3gL2Bz0laF5hUOIbQA2KyFdps39oBdGjLSnXcHFrC9gcBcg+lw0lnuDYB1q0ZV6h3DqNlC0RtsUzSLrZvqh1IaIVf2Z4iaYHt0ouVgx0EvI7UD/CPkjYFPlo5pjABxZmtENaApPNIO0mdK9WH2X5P4Tg2IN0c7rB9d745bGf7ypJxBJD0PlJa6SzgPmApcK3tn1YNrMfVPIcxXAPfRtPIt7NE/kQn6S5ga9LvyF9Iuxi2vX3VwEIVTbPiEk2LQ2iLmGyFMIJBK9UzgAEr1bZnVgwvVCTpo6QJ1i22n6wdT0gk3Wp7J0mfJS1KXNhcqx1bo5ceNIebgPbixDOApMWkrKodSWddByjRmiGE0mKyFcII1nSlOoTQDrmv1YOkVNtZpLYNN5asXjqatk3+auqliWcASesAO5Patxwx+Ou2rykeVAhdFpOtEEIIE8Z4SLWNCUa/mHj2Jkl9tn9fO44QSogCGSGEECYM248Dl3S8fgh4qF5EYRSx4tubzmkaXQ8l0gnDRBKTrRBCCKEs1Q4ghMpWkqq3djb+vhdYVCugELol0ghDCCGEMSRpvu23DXdN0saFmra2XqQR9iZJS23vOdq1ECaC6M8TQgghjK1tO19IWotUrAOAXploSZok6c5Rvu01RYIJbdMnaXrzIn/eVzGeELom0ghDCCGEMSDpBOBEYH1JjzWXgdXAmdUCq8T205Jul7S57fuH+Z6emHiGZzkOuFrSStK5vWnAUVUjCqFLYrIVQgghjI1f2Z4iaYHtg2oH0xKbAj+XdCOpqTEQBRACU4GZpEnWfsBsYFXViELokjizFUIIIYyBpqR7lHbvJ+n9wAPAgB2s6KfU2yStsL29pD2AzwCnAyfa3rVyaCGMudjZCiGEEMbGHyQtAaZJ+sHgL/bobs6LgA8Ay4FzgEWOVd4AT+WPc4B5ti+TdGrFeELomtjZCiGEEMaApHWAnYH5wBGDv96ruzmSBOwDHA68HFgAnG3711UDC9VIugJ4ENibVDzmCeBG2ztUDSyELojJVgghhDCGJPXZ/n3tONpE0g6kydbrgCXAbsBi28dXDSxUIWkD0nvhDtt3S9oU2M72lZVDC2HMxWQrhBBCGEOSLidVWBtSL6UTSjoWOIxU/OAs4FLbf5M0Cbjb9lZVAwwhhC6LM1shhBDC2FoJbAJ8K78+GLgXWFQroIpeALzZ9n2dF3NZ+H0rxRRCCMXEzlYIIYQwhiQttb3naNdCCCFMfJNqBxBCCCFMMH2Spjcv8ud9FeMJIYRQSaQRhhBCCGPrOOBqSStJZ7emAUdVjSiEEEIVMdkKIYQQxtZUYCZpkrUfMJtUICKEEEKPiTTCEEIIYWydbPsxYArwWmAeMLduSCGEEGqIyVYIIYQwtp7KH+cA82xfBqxTMZ4QQgiVxGQrhBBCGFsPSvoGcBDwI0nrEvfbEELoSVH6PYQQQhhDkjYAXgfcYftuSZsC29m+snJoIYQQCovJVgghhBBCCCF0QaQ1hBBCCCGEEEIXxGQrhBBCCCGEELogJlshhBBCCCGE0AUx2QohhBBCCCGELojJVgghhBBCCCF0wf8BClHlKPNxRaYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a heatmap to visualize correlations\n",
    "plt.figure(figsize=(15, 8))\n",
    "mask = np.triu(df.corr())  # Create a mask to display only the upper half of the matrix\n",
    "sns.heatmap(df.corr(), annot=True, cmap='viridis', mask=mask)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above, the `sqft_living`, `grade_int` and `sqft_above` have the highest correlation with price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a boxplot to compare 'waterfront' and 'price' with relevant labels and title\n",
    "sns.boxplot(x=df['waterfront'], y=df['price'])\n",
    "plt.title(\"Waterfront vs. Price\")\n",
    "plt.ylabel(\"Price in USD\")\n",
    "plt.xlabel(None)\n",
    "plt.xticks(np.arange(2), ('NO', 'YES'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Houses that have a `waterfront` are more expensive than houses without."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean price for houses with waterfront: 1692505.86\n",
      "Mean price for houses with no waterfront: 533912.48\n"
     ]
    }
   ],
   "source": [
    "# Calculate the mean price for houses with and without waterfront, and print the results\n",
    "waterfront_mean = df[df['waterfront'] == 1]['price'].mean()\n",
    "nonwaterfront_mean = df[df['waterfront'] == 0]['price'].mean()\n",
    "print(f\"Mean price for houses with waterfront: {round(waterfront_mean, 2)}\")\n",
    "print(f\"Mean price for houses with no waterfront: {round(nonwaterfront_mean, 2)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating Mean House Prices for Properties with and without Waterfront\n",
    "\n",
    "The provided code calculates and prints the mean prices for houses in a dataset, distinguishing between properties with a waterfront view ('waterfront' == 1) and those without a waterfront view ('waterfront' == 0).\n",
    "\n",
    "**Output:**\n",
    "- The mean price for houses with a waterfront view is approximately $1,692,505.86.\n",
    "- The mean price for houses without a waterfront view is approximately $533,912.48.\n",
    "\n",
    "These calculated mean prices provide valuable insights into the pricing differences between properties with and without waterfront views in the dataset. It indicates that, on average, houses with waterfront views tend to have a significantly higher price compared to those without such views."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets take a lok at some of the categorical features' relationship with `price`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "catfeats = ['bedrooms', 'floors', 'condition']\n",
    "\n",
    "# Create subplots for bar graphs\n",
    "fig, axes = plt.subplots(len(catfeats), figsize=(10, 12))\n",
    "\n",
    "# Plot bar graphs for each categorical variable\n",
    "for i, feature in enumerate(catfeats):\n",
    "    sns.barplot(x=df[feature], y=df['price'], ax=axes[i])\n",
    "    axes[i].set_title(f\"Bar Graph of {feature} vs. Price\")\n",
    "    axes[i].set_ylabel(\"Price in USD\")\n",
    "    axes[i].set_xlabel(f\"{feature}\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the visualisations above:\n",
    "- It can be noticed that as the number of `bedrooms` increase, so does the house's selling price.\n",
    "- More `floors`, preferably up to 2.5 have a higher price  \n",
    "- Houses prices also depend on the condition of the house"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How Location affects price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot scatter plot\n",
    "plt.figure(figsize = (9,7))\n",
    "ax = sns.scatterplot(x = df['long'], y = df['lat'], hue = df['price'],\n",
    "                palette = 'nipy_spectral',s=10, legend = None, data = df)\n",
    "\n",
    "norm = plt.Normalize(df['price'].min(), df['price'].max())\n",
    "smap = plt.cm.ScalarMappable(cmap='nipy_spectral', norm=norm)\n",
    "smap.set_array([])\n",
    "\n",
    "ax.figure.colorbar(smap, label = 'price in USD')\n",
    "\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "plt.title('House sales according to location')\n",
    "plt.show()\n",
    "#run code to visualize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the visualisation above, houses around latitude 47.6 to 47.7 and longitude -122.0 to -122.5 have some of the highest prices."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the necessary libraries for statistical modeling\n",
    "import statsmodels\n",
    "from statsmodels.formula.api import ols\n",
    "\n",
    "# Define the dependent variable 'y' and the independent variable 'x'\n",
    "y = df['price']\n",
    "x = df['sqft_living']\n",
    "\n",
    "# Create a linear regression model using Ordinary Least Squares (OLS)\n",
    "model = ols(formula='price ~ sqft_living', data=df).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.495\n",
      "Model:                            OLS   Adj. R-squared:                  0.495\n",
      "Method:                 Least Squares   F-statistic:                 1.868e+04\n",
      "Date:                Mon, 30 Oct 2023   Prob (F-statistic):               0.00\n",
      "Time:                        10:31:20   Log-Likelihood:            -2.6454e+05\n",
      "No. Observations:               19030   AIC:                         5.291e+05\n",
      "Df Residuals:                   19028   BIC:                         5.291e+05\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "Intercept   -4.743e+04   4721.791    -10.046      0.000   -5.67e+04   -3.82e+04\n",
      "sqft_living   283.1303      2.071    136.680      0.000     279.070     287.191\n",
      "==============================================================================\n",
      "Omnibus:                    13059.763   Durbin-Watson:                   1.993\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           480934.401\n",
      "Skew:                           2.822   Prob(JB):                         0.00\n",
      "Kurtosis:                      26.972   Cond. No.                     5.63e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 5.63e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "# Display a summary of the regression model\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OLS Regression Results\n",
    "\n",
    "Here we have the outcome of an Ordinary Least Squares (OLS) linear regression analysis that we used as our baseline model. It was performed on a dataset with 'price' as the dependent variable and 'sqft_living' as the independent variable. These results provide important information about the model's fit, coefficients, significance, and other statistical metrics.\n",
    "\n",
    "Key Statistics:\n",
    "\n",
    "\n",
    "- **R-squared (R²):**  R² is approximately 0.495, indicating that about 49.5% of the variation in 'price' can be explained by 'sqft_living.'\n",
    "\n",
    "- **F-statistic:** A measure of the overall significance of the model. The high value (1.868e+04) suggests that the model is statistically significant.\n",
    "\n",
    "- **Intercept (Intercept):** The estimated value of 'price' when 'sqft_living' is 0. The coefficient is approximately -47,430.\n",
    "\n",
    "- **sqft_living (Coefficient for sqft_living):** The estimated change in 'price' for a one-unit increase in 'sqft_living.' In this case, it's approximately 283.1303.\n",
    "\n",
    "These regression results provide valuable insights into the relationship between 'sqft_living' and 'price,' including the strength and significance of the relationship, potential issues, and diagnostic statistics for assessing the model's quality.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to extract the month from the 'date' column and add it as a new 'month_of_sale' column\n",
    "def month_of_sale_feature(data):\n",
    "    data['month_of_sale'] = pd.DatetimeIndex(data['date']).month\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>grade_int</th>\n",
       "      <th>month_of_sale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2015-02-25</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7237550310</td>\n",
       "      <td>2014-05-12</td>\n",
       "      <td>1230000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4.50</td>\n",
       "      <td>5420</td>\n",
       "      <td>101930</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1530</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98053</td>\n",
       "      <td>47.6561</td>\n",
       "      <td>-122.005</td>\n",
       "      <td>4760</td>\n",
       "      <td>101930</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id       date      price  bedrooms  bathrooms  sqft_living  \\\n",
       "1  6414100192 2014-12-09   538000.0         3       2.25         2570   \n",
       "2  5631500400 2015-02-25   180000.0         2       1.00          770   \n",
       "3  2487200875 2014-12-09   604000.0         4       3.00         1960   \n",
       "4  1954400510 2015-02-18   510000.0         3       2.00         1680   \n",
       "5  7237550310 2014-05-12  1230000.0         4       4.50         5420   \n",
       "\n",
       "   sqft_lot  floors  waterfront  view  ...  sqft_basement  yr_built  \\\n",
       "1      7242     2.0           0     0  ...            400      1951   \n",
       "2     10000     1.0           0     0  ...              0      1933   \n",
       "3      5000     1.0           0     0  ...            910      1965   \n",
       "4      8080     1.0           0     0  ...              0      1987   \n",
       "5    101930     1.0           0     0  ...           1530      2001   \n",
       "\n",
       "   yr_renovated  zipcode      lat     long  sqft_living15  sqft_lot15  \\\n",
       "1        1991.0    98125  47.7210 -122.319           1690        7639   \n",
       "2           NaN    98028  47.7379 -122.233           2720        8062   \n",
       "3           0.0    98136  47.5208 -122.393           1360        5000   \n",
       "4           0.0    98074  47.6168 -122.045           1800        7503   \n",
       "5           0.0    98053  47.6561 -122.005           4760      101930   \n",
       "\n",
       "   grade_int  month_of_sale  \n",
       "1          7             12  \n",
       "2          6              2  \n",
       "3          7             12  \n",
       "4          8              2  \n",
       "5         11              5  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply the 'month_of_sale_feature' function to the DataFrame 'df' to create the 'month_of_sale' column\n",
    "df = month_of_sale_feature(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to categorize months into quarters\n",
    "def quarter(month):\n",
    "    if 1 <= month <= 3:\n",
    "        quarter = 'Q1'\n",
    "    elif 4 <= month <= 6:\n",
    "        quarter = 'Q2'\n",
    "    elif 7 <= month <= 9:\n",
    "        quarter = 'Q3'\n",
    "    else:\n",
    "        quarter = 'Q4'\n",
    "    return quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>grade_int</th>\n",
       "      <th>month_of_sale</th>\n",
       "      <th>quarter_of_sale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2015-02-25</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1933</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1965</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7237550310</td>\n",
       "      <td>2014-05-12</td>\n",
       "      <td>1230000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4.50</td>\n",
       "      <td>5420</td>\n",
       "      <td>101930</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98053</td>\n",
       "      <td>47.6561</td>\n",
       "      <td>-122.005</td>\n",
       "      <td>4760</td>\n",
       "      <td>101930</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>Q2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id       date      price  bedrooms  bathrooms  sqft_living  \\\n",
       "1  6414100192 2014-12-09   538000.0         3       2.25         2570   \n",
       "2  5631500400 2015-02-25   180000.0         2       1.00          770   \n",
       "3  2487200875 2014-12-09   604000.0         4       3.00         1960   \n",
       "4  1954400510 2015-02-18   510000.0         3       2.00         1680   \n",
       "5  7237550310 2014-05-12  1230000.0         4       4.50         5420   \n",
       "\n",
       "   sqft_lot  floors  waterfront  view  ...  yr_built  yr_renovated  zipcode  \\\n",
       "1      7242     2.0           0     0  ...      1951        1991.0    98125   \n",
       "2     10000     1.0           0     0  ...      1933           NaN    98028   \n",
       "3      5000     1.0           0     0  ...      1965           0.0    98136   \n",
       "4      8080     1.0           0     0  ...      1987           0.0    98074   \n",
       "5    101930     1.0           0     0  ...      2001           0.0    98053   \n",
       "\n",
       "       lat     long  sqft_living15  sqft_lot15  grade_int  month_of_sale  \\\n",
       "1  47.7210 -122.319           1690        7639          7             12   \n",
       "2  47.7379 -122.233           2720        8062          6              2   \n",
       "3  47.5208 -122.393           1360        5000          7             12   \n",
       "4  47.6168 -122.045           1800        7503          8              2   \n",
       "5  47.6561 -122.005           4760      101930         11              5   \n",
       "\n",
       "   quarter_of_sale  \n",
       "1               Q4  \n",
       "2               Q1  \n",
       "3               Q4  \n",
       "4               Q1  \n",
       "5               Q2  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new 'quarter_of_sale' column by applying the 'quarter' function to the 'month_of_sale' column\n",
    "df['quarter_of_sale'] = df['month_of_sale'].apply(quarter)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract the categories (quarters) and values (prices) from the DataFrame\n",
    "categories = df['quarter_of_sale']\n",
    "values = df['price']\n",
    "\n",
    "# Sort the data in descending order based on price values\n",
    "sorted_data = sorted(zip(categories, values), key=lambda x: x[1], reverse=True)\n",
    "quarters, prices = zip(*sorted_data)\n",
    "\n",
    "# Create the bar graph with sorted data\n",
    "plt.bar(quarters, prices)\n",
    "\n",
    "# Add labels and title to the plot\n",
    "plt.xlabel('Quarter_of_sale')  # Label for the x-axis\n",
    "plt.ylabel('Price')  # Label for the y-axis\n",
    "plt.title('Bar Plot Quarters vs Price')  # Title of the plot\n",
    "\n",
    "# Display the final bar plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph above shows that the most expensive houses are sold during the fourth quarter of the year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>id</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>...</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>grade_int</th>\n",
       "      <th>month_of_sale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.019219</td>\n",
       "      <td>0.002728</td>\n",
       "      <td>0.006519</td>\n",
       "      <td>-0.012911</td>\n",
       "      <td>-0.133958</td>\n",
       "      <td>0.017828</td>\n",
       "      <td>-0.004880</td>\n",
       "      <td>0.014068</td>\n",
       "      <td>-0.022813</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002847</td>\n",
       "      <td>0.022633</td>\n",
       "      <td>-0.010416</td>\n",
       "      <td>-0.007255</td>\n",
       "      <td>-0.006524</td>\n",
       "      <td>0.018949</td>\n",
       "      <td>-0.002234</td>\n",
       "      <td>-0.139255</td>\n",
       "      <td>0.006593</td>\n",
       "      <td>-0.017817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.019219</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.309329</td>\n",
       "      <td>0.526036</td>\n",
       "      <td>0.703850</td>\n",
       "      <td>0.086395</td>\n",
       "      <td>0.256893</td>\n",
       "      <td>0.271527</td>\n",
       "      <td>0.395255</td>\n",
       "      <td>0.035922</td>\n",
       "      <td>...</td>\n",
       "      <td>0.319018</td>\n",
       "      <td>0.050753</td>\n",
       "      <td>0.122900</td>\n",
       "      <td>-0.048031</td>\n",
       "      <td>0.305905</td>\n",
       "      <td>0.019495</td>\n",
       "      <td>0.580949</td>\n",
       "      <td>0.080894</td>\n",
       "      <td>0.666696</td>\n",
       "      <td>-0.008294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>0.002728</td>\n",
       "      <td>0.309329</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.516747</td>\n",
       "      <td>0.578524</td>\n",
       "      <td>0.029797</td>\n",
       "      <td>0.181755</td>\n",
       "      <td>-0.004183</td>\n",
       "      <td>0.083868</td>\n",
       "      <td>0.024352</td>\n",
       "      <td>...</td>\n",
       "      <td>0.295752</td>\n",
       "      <td>0.156668</td>\n",
       "      <td>0.016556</td>\n",
       "      <td>-0.152507</td>\n",
       "      <td>-0.013258</td>\n",
       "      <td>0.131794</td>\n",
       "      <td>0.393864</td>\n",
       "      <td>0.028025</td>\n",
       "      <td>0.360649</td>\n",
       "      <td>-0.001241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>0.006519</td>\n",
       "      <td>0.526036</td>\n",
       "      <td>0.516747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.755730</td>\n",
       "      <td>0.085276</td>\n",
       "      <td>0.505359</td>\n",
       "      <td>0.064609</td>\n",
       "      <td>0.185797</td>\n",
       "      <td>-0.129021</td>\n",
       "      <td>...</td>\n",
       "      <td>0.277298</td>\n",
       "      <td>0.506215</td>\n",
       "      <td>0.047145</td>\n",
       "      <td>-0.200882</td>\n",
       "      <td>0.024612</td>\n",
       "      <td>0.222399</td>\n",
       "      <td>0.568908</td>\n",
       "      <td>0.086194</td>\n",
       "      <td>0.666311</td>\n",
       "      <td>0.009107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>-0.012911</td>\n",
       "      <td>0.703850</td>\n",
       "      <td>0.578524</td>\n",
       "      <td>0.755730</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.172888</td>\n",
       "      <td>0.355893</td>\n",
       "      <td>0.107508</td>\n",
       "      <td>0.285337</td>\n",
       "      <td>-0.059540</td>\n",
       "      <td>...</td>\n",
       "      <td>0.426675</td>\n",
       "      <td>0.315741</td>\n",
       "      <td>0.049859</td>\n",
       "      <td>-0.195441</td>\n",
       "      <td>0.052498</td>\n",
       "      <td>0.238614</td>\n",
       "      <td>0.755316</td>\n",
       "      <td>0.182870</td>\n",
       "      <td>0.763484</td>\n",
       "      <td>0.013389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot</th>\n",
       "      <td>-0.133958</td>\n",
       "      <td>0.086395</td>\n",
       "      <td>0.029797</td>\n",
       "      <td>0.085276</td>\n",
       "      <td>0.172888</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008036</td>\n",
       "      <td>0.023128</td>\n",
       "      <td>0.071214</td>\n",
       "      <td>-0.014176</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014117</td>\n",
       "      <td>0.055218</td>\n",
       "      <td>0.002112</td>\n",
       "      <td>-0.130061</td>\n",
       "      <td>-0.085895</td>\n",
       "      <td>0.229976</td>\n",
       "      <td>0.145875</td>\n",
       "      <td>0.721689</td>\n",
       "      <td>0.114249</td>\n",
       "      <td>-0.005449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floors</th>\n",
       "      <td>0.017828</td>\n",
       "      <td>0.256893</td>\n",
       "      <td>0.181755</td>\n",
       "      <td>0.505359</td>\n",
       "      <td>0.355893</td>\n",
       "      <td>-0.008036</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021192</td>\n",
       "      <td>0.027781</td>\n",
       "      <td>-0.266144</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.239496</td>\n",
       "      <td>0.489822</td>\n",
       "      <td>-0.001385</td>\n",
       "      <td>-0.057422</td>\n",
       "      <td>0.050587</td>\n",
       "      <td>0.126026</td>\n",
       "      <td>0.279070</td>\n",
       "      <td>-0.011448</td>\n",
       "      <td>0.457470</td>\n",
       "      <td>0.012816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterfront</th>\n",
       "      <td>-0.004880</td>\n",
       "      <td>0.271527</td>\n",
       "      <td>-0.004183</td>\n",
       "      <td>0.064609</td>\n",
       "      <td>0.107508</td>\n",
       "      <td>0.023128</td>\n",
       "      <td>0.021192</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.407505</td>\n",
       "      <td>0.017118</td>\n",
       "      <td>...</td>\n",
       "      <td>0.086184</td>\n",
       "      <td>-0.026910</td>\n",
       "      <td>0.087869</td>\n",
       "      <td>0.031905</td>\n",
       "      <td>-0.013013</td>\n",
       "      <td>-0.040344</td>\n",
       "      <td>0.087340</td>\n",
       "      <td>0.031916</td>\n",
       "      <td>0.085743</td>\n",
       "      <td>0.009211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view</th>\n",
       "      <td>0.014068</td>\n",
       "      <td>0.395255</td>\n",
       "      <td>0.083868</td>\n",
       "      <td>0.185797</td>\n",
       "      <td>0.285337</td>\n",
       "      <td>0.071214</td>\n",
       "      <td>0.027781</td>\n",
       "      <td>0.407505</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.046807</td>\n",
       "      <td>...</td>\n",
       "      <td>0.274744</td>\n",
       "      <td>-0.057609</td>\n",
       "      <td>0.098802</td>\n",
       "      <td>0.089685</td>\n",
       "      <td>0.009991</td>\n",
       "      <td>-0.078505</td>\n",
       "      <td>0.275601</td>\n",
       "      <td>0.071331</td>\n",
       "      <td>0.247601</td>\n",
       "      <td>-0.005572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <td>-0.022813</td>\n",
       "      <td>0.035922</td>\n",
       "      <td>0.024352</td>\n",
       "      <td>-0.129021</td>\n",
       "      <td>-0.059540</td>\n",
       "      <td>-0.014176</td>\n",
       "      <td>-0.266144</td>\n",
       "      <td>0.017118</td>\n",
       "      <td>0.046807</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.167786</td>\n",
       "      <td>-0.364634</td>\n",
       "      <td>-0.061146</td>\n",
       "      <td>0.001751</td>\n",
       "      <td>-0.019051</td>\n",
       "      <td>-0.106342</td>\n",
       "      <td>-0.094257</td>\n",
       "      <td>-0.005553</td>\n",
       "      <td>-0.147841</td>\n",
       "      <td>0.020945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_above</th>\n",
       "      <td>-0.012369</td>\n",
       "      <td>0.608692</td>\n",
       "      <td>0.480910</td>\n",
       "      <td>0.687386</td>\n",
       "      <td>0.877730</td>\n",
       "      <td>0.183716</td>\n",
       "      <td>0.523979</td>\n",
       "      <td>0.073470</td>\n",
       "      <td>0.168069</td>\n",
       "      <td>-0.158258</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.050412</td>\n",
       "      <td>0.420531</td>\n",
       "      <td>0.018100</td>\n",
       "      <td>-0.257596</td>\n",
       "      <td>0.002058</td>\n",
       "      <td>0.341822</td>\n",
       "      <td>0.732908</td>\n",
       "      <td>0.192329</td>\n",
       "      <td>0.757520</td>\n",
       "      <td>0.011901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_basement</th>\n",
       "      <td>-0.002847</td>\n",
       "      <td>0.319018</td>\n",
       "      <td>0.295752</td>\n",
       "      <td>0.277298</td>\n",
       "      <td>0.426675</td>\n",
       "      <td>0.014117</td>\n",
       "      <td>-0.239496</td>\n",
       "      <td>0.086184</td>\n",
       "      <td>0.274744</td>\n",
       "      <td>0.167786</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.129477</td>\n",
       "      <td>0.066107</td>\n",
       "      <td>0.074885</td>\n",
       "      <td>0.105047</td>\n",
       "      <td>-0.143236</td>\n",
       "      <td>0.194739</td>\n",
       "      <td>0.017332</td>\n",
       "      <td>0.164060</td>\n",
       "      <td>0.005838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_built</th>\n",
       "      <td>0.022633</td>\n",
       "      <td>0.050753</td>\n",
       "      <td>0.156668</td>\n",
       "      <td>0.506215</td>\n",
       "      <td>0.315741</td>\n",
       "      <td>0.055218</td>\n",
       "      <td>0.489822</td>\n",
       "      <td>-0.026910</td>\n",
       "      <td>-0.057609</td>\n",
       "      <td>-0.364634</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.129477</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.224358</td>\n",
       "      <td>-0.344447</td>\n",
       "      <td>-0.147469</td>\n",
       "      <td>0.408554</td>\n",
       "      <td>0.323921</td>\n",
       "      <td>0.070378</td>\n",
       "      <td>0.444908</td>\n",
       "      <td>-0.004039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_renovated</th>\n",
       "      <td>-0.010416</td>\n",
       "      <td>0.122900</td>\n",
       "      <td>0.016556</td>\n",
       "      <td>0.047145</td>\n",
       "      <td>0.049859</td>\n",
       "      <td>0.002112</td>\n",
       "      <td>-0.001385</td>\n",
       "      <td>0.087869</td>\n",
       "      <td>0.098802</td>\n",
       "      <td>-0.061146</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066107</td>\n",
       "      <td>-0.224358</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068515</td>\n",
       "      <td>0.031981</td>\n",
       "      <td>-0.070835</td>\n",
       "      <td>-0.005253</td>\n",
       "      <td>0.002523</td>\n",
       "      <td>0.011821</td>\n",
       "      <td>0.011124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zipcode</th>\n",
       "      <td>-0.007255</td>\n",
       "      <td>-0.048031</td>\n",
       "      <td>-0.152507</td>\n",
       "      <td>-0.200882</td>\n",
       "      <td>-0.195441</td>\n",
       "      <td>-0.130061</td>\n",
       "      <td>-0.057422</td>\n",
       "      <td>0.031905</td>\n",
       "      <td>0.089685</td>\n",
       "      <td>0.001751</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074885</td>\n",
       "      <td>-0.344447</td>\n",
       "      <td>0.068515</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.272302</td>\n",
       "      <td>-0.563184</td>\n",
       "      <td>-0.276985</td>\n",
       "      <td>-0.146419</td>\n",
       "      <td>-0.182082</td>\n",
       "      <td>0.000441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>-0.006524</td>\n",
       "      <td>0.305905</td>\n",
       "      <td>-0.013258</td>\n",
       "      <td>0.024612</td>\n",
       "      <td>0.052498</td>\n",
       "      <td>-0.085895</td>\n",
       "      <td>0.050587</td>\n",
       "      <td>-0.013013</td>\n",
       "      <td>0.009991</td>\n",
       "      <td>-0.019051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.105047</td>\n",
       "      <td>-0.147469</td>\n",
       "      <td>0.031981</td>\n",
       "      <td>0.272302</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.137032</td>\n",
       "      <td>0.047255</td>\n",
       "      <td>-0.083806</td>\n",
       "      <td>0.114909</td>\n",
       "      <td>0.015048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long</th>\n",
       "      <td>0.018949</td>\n",
       "      <td>0.019495</td>\n",
       "      <td>0.131794</td>\n",
       "      <td>0.222399</td>\n",
       "      <td>0.238614</td>\n",
       "      <td>0.229976</td>\n",
       "      <td>0.126026</td>\n",
       "      <td>-0.040344</td>\n",
       "      <td>-0.078505</td>\n",
       "      <td>-0.106342</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.143236</td>\n",
       "      <td>0.408554</td>\n",
       "      <td>-0.070835</td>\n",
       "      <td>-0.563184</td>\n",
       "      <td>-0.137032</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.335792</td>\n",
       "      <td>0.254020</td>\n",
       "      <td>0.197083</td>\n",
       "      <td>-0.007683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living15</th>\n",
       "      <td>-0.002234</td>\n",
       "      <td>0.580949</td>\n",
       "      <td>0.393864</td>\n",
       "      <td>0.568908</td>\n",
       "      <td>0.755316</td>\n",
       "      <td>0.145875</td>\n",
       "      <td>0.279070</td>\n",
       "      <td>0.087340</td>\n",
       "      <td>0.275601</td>\n",
       "      <td>-0.094257</td>\n",
       "      <td>...</td>\n",
       "      <td>0.194739</td>\n",
       "      <td>0.323921</td>\n",
       "      <td>-0.005253</td>\n",
       "      <td>-0.276985</td>\n",
       "      <td>0.047255</td>\n",
       "      <td>0.335792</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.183676</td>\n",
       "      <td>0.713068</td>\n",
       "      <td>0.003975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot15</th>\n",
       "      <td>-0.139255</td>\n",
       "      <td>0.080894</td>\n",
       "      <td>0.028025</td>\n",
       "      <td>0.086194</td>\n",
       "      <td>0.182870</td>\n",
       "      <td>0.721689</td>\n",
       "      <td>-0.011448</td>\n",
       "      <td>0.031916</td>\n",
       "      <td>0.071331</td>\n",
       "      <td>-0.005553</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017332</td>\n",
       "      <td>0.070378</td>\n",
       "      <td>0.002523</td>\n",
       "      <td>-0.146419</td>\n",
       "      <td>-0.083806</td>\n",
       "      <td>0.254020</td>\n",
       "      <td>0.183676</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.120269</td>\n",
       "      <td>-0.000709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade_int</th>\n",
       "      <td>0.006593</td>\n",
       "      <td>0.666696</td>\n",
       "      <td>0.360649</td>\n",
       "      <td>0.666311</td>\n",
       "      <td>0.763484</td>\n",
       "      <td>0.114249</td>\n",
       "      <td>0.457470</td>\n",
       "      <td>0.085743</td>\n",
       "      <td>0.247601</td>\n",
       "      <td>-0.147841</td>\n",
       "      <td>...</td>\n",
       "      <td>0.164060</td>\n",
       "      <td>0.444908</td>\n",
       "      <td>0.011821</td>\n",
       "      <td>-0.182082</td>\n",
       "      <td>0.114909</td>\n",
       "      <td>0.197083</td>\n",
       "      <td>0.713068</td>\n",
       "      <td>0.120269</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.008452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_of_sale</th>\n",
       "      <td>-0.017817</td>\n",
       "      <td>-0.008294</td>\n",
       "      <td>-0.001241</td>\n",
       "      <td>0.009107</td>\n",
       "      <td>0.013389</td>\n",
       "      <td>-0.005449</td>\n",
       "      <td>0.012816</td>\n",
       "      <td>0.009211</td>\n",
       "      <td>-0.005572</td>\n",
       "      <td>0.020945</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005838</td>\n",
       "      <td>-0.004039</td>\n",
       "      <td>0.011124</td>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.015048</td>\n",
       "      <td>-0.007683</td>\n",
       "      <td>0.003975</td>\n",
       "      <td>-0.000709</td>\n",
       "      <td>0.008452</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     id     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
       "id             1.000000 -0.019219  0.002728   0.006519    -0.012911 -0.133958   \n",
       "price         -0.019219  1.000000  0.309329   0.526036     0.703850  0.086395   \n",
       "bedrooms       0.002728  0.309329  1.000000   0.516747     0.578524  0.029797   \n",
       "bathrooms      0.006519  0.526036  0.516747   1.000000     0.755730  0.085276   \n",
       "sqft_living   -0.012911  0.703850  0.578524   0.755730     1.000000  0.172888   \n",
       "sqft_lot      -0.133958  0.086395  0.029797   0.085276     0.172888  1.000000   \n",
       "floors         0.017828  0.256893  0.181755   0.505359     0.355893 -0.008036   \n",
       "waterfront    -0.004880  0.271527 -0.004183   0.064609     0.107508  0.023128   \n",
       "view           0.014068  0.395255  0.083868   0.185797     0.285337  0.071214   \n",
       "condition     -0.022813  0.035922  0.024352  -0.129021    -0.059540 -0.014176   \n",
       "sqft_above    -0.012369  0.608692  0.480910   0.687386     0.877730  0.183716   \n",
       "sqft_basement -0.002847  0.319018  0.295752   0.277298     0.426675  0.014117   \n",
       "yr_built       0.022633  0.050753  0.156668   0.506215     0.315741  0.055218   \n",
       "yr_renovated  -0.010416  0.122900  0.016556   0.047145     0.049859  0.002112   \n",
       "zipcode       -0.007255 -0.048031 -0.152507  -0.200882    -0.195441 -0.130061   \n",
       "lat           -0.006524  0.305905 -0.013258   0.024612     0.052498 -0.085895   \n",
       "long           0.018949  0.019495  0.131794   0.222399     0.238614  0.229976   \n",
       "sqft_living15 -0.002234  0.580949  0.393864   0.568908     0.755316  0.145875   \n",
       "sqft_lot15    -0.139255  0.080894  0.028025   0.086194     0.182870  0.721689   \n",
       "grade_int      0.006593  0.666696  0.360649   0.666311     0.763484  0.114249   \n",
       "month_of_sale -0.017817 -0.008294 -0.001241   0.009107     0.013389 -0.005449   \n",
       "\n",
       "                 floors  waterfront      view  condition  ...  sqft_basement  \\\n",
       "id             0.017828   -0.004880  0.014068  -0.022813  ...      -0.002847   \n",
       "price          0.256893    0.271527  0.395255   0.035922  ...       0.319018   \n",
       "bedrooms       0.181755   -0.004183  0.083868   0.024352  ...       0.295752   \n",
       "bathrooms      0.505359    0.064609  0.185797  -0.129021  ...       0.277298   \n",
       "sqft_living    0.355893    0.107508  0.285337  -0.059540  ...       0.426675   \n",
       "sqft_lot      -0.008036    0.023128  0.071214  -0.014176  ...       0.014117   \n",
       "floors         1.000000    0.021192  0.027781  -0.266144  ...      -0.239496   \n",
       "waterfront     0.021192    1.000000  0.407505   0.017118  ...       0.086184   \n",
       "view           0.027781    0.407505  1.000000   0.046807  ...       0.274744   \n",
       "condition     -0.266144    0.017118  0.046807   1.000000  ...       0.167786   \n",
       "sqft_above     0.523979    0.073470  0.168069  -0.158258  ...      -0.050412   \n",
       "sqft_basement -0.239496    0.086184  0.274744   0.167786  ...       1.000000   \n",
       "yr_built       0.489822   -0.026910 -0.057609  -0.364634  ...      -0.129477   \n",
       "yr_renovated  -0.001385    0.087869  0.098802  -0.061146  ...       0.066107   \n",
       "zipcode       -0.057422    0.031905  0.089685   0.001751  ...       0.074885   \n",
       "lat            0.050587   -0.013013  0.009991  -0.019051  ...       0.105047   \n",
       "long           0.126026   -0.040344 -0.078505  -0.106342  ...      -0.143236   \n",
       "sqft_living15  0.279070    0.087340  0.275601  -0.094257  ...       0.194739   \n",
       "sqft_lot15    -0.011448    0.031916  0.071331  -0.005553  ...       0.017332   \n",
       "grade_int      0.457470    0.085743  0.247601  -0.147841  ...       0.164060   \n",
       "month_of_sale  0.012816    0.009211 -0.005572   0.020945  ...       0.005838   \n",
       "\n",
       "               yr_built  yr_renovated   zipcode       lat      long  \\\n",
       "id             0.022633     -0.010416 -0.007255 -0.006524  0.018949   \n",
       "price          0.050753      0.122900 -0.048031  0.305905  0.019495   \n",
       "bedrooms       0.156668      0.016556 -0.152507 -0.013258  0.131794   \n",
       "bathrooms      0.506215      0.047145 -0.200882  0.024612  0.222399   \n",
       "sqft_living    0.315741      0.049859 -0.195441  0.052498  0.238614   \n",
       "sqft_lot       0.055218      0.002112 -0.130061 -0.085895  0.229976   \n",
       "floors         0.489822     -0.001385 -0.057422  0.050587  0.126026   \n",
       "waterfront    -0.026910      0.087869  0.031905 -0.013013 -0.040344   \n",
       "view          -0.057609      0.098802  0.089685  0.009991 -0.078505   \n",
       "condition     -0.364634     -0.061146  0.001751 -0.019051 -0.106342   \n",
       "sqft_above     0.420531      0.018100 -0.257596  0.002058  0.341822   \n",
       "sqft_basement -0.129477      0.066107  0.074885  0.105047 -0.143236   \n",
       "yr_built       1.000000     -0.224358 -0.344447 -0.147469  0.408554   \n",
       "yr_renovated  -0.224358      1.000000  0.068515  0.031981 -0.070835   \n",
       "zipcode       -0.344447      0.068515  1.000000  0.272302 -0.563184   \n",
       "lat           -0.147469      0.031981  0.272302  1.000000 -0.137032   \n",
       "long           0.408554     -0.070835 -0.563184 -0.137032  1.000000   \n",
       "sqft_living15  0.323921     -0.005253 -0.276985  0.047255  0.335792   \n",
       "sqft_lot15     0.070378      0.002523 -0.146419 -0.083806  0.254020   \n",
       "grade_int      0.444908      0.011821 -0.182082  0.114909  0.197083   \n",
       "month_of_sale -0.004039      0.011124  0.000441  0.015048 -0.007683   \n",
       "\n",
       "               sqft_living15  sqft_lot15  grade_int  month_of_sale  \n",
       "id                 -0.002234   -0.139255   0.006593      -0.017817  \n",
       "price               0.580949    0.080894   0.666696      -0.008294  \n",
       "bedrooms            0.393864    0.028025   0.360649      -0.001241  \n",
       "bathrooms           0.568908    0.086194   0.666311       0.009107  \n",
       "sqft_living         0.755316    0.182870   0.763484       0.013389  \n",
       "sqft_lot            0.145875    0.721689   0.114249      -0.005449  \n",
       "floors              0.279070   -0.011448   0.457470       0.012816  \n",
       "waterfront          0.087340    0.031916   0.085743       0.009211  \n",
       "view                0.275601    0.071331   0.247601      -0.005572  \n",
       "condition          -0.094257   -0.005553  -0.147841       0.020945  \n",
       "sqft_above          0.732908    0.192329   0.757520       0.011901  \n",
       "sqft_basement       0.194739    0.017332   0.164060       0.005838  \n",
       "yr_built            0.323921    0.070378   0.444908      -0.004039  \n",
       "yr_renovated       -0.005253    0.002523   0.011821       0.011124  \n",
       "zipcode            -0.276985   -0.146419  -0.182082       0.000441  \n",
       "lat                 0.047255   -0.083806   0.114909       0.015048  \n",
       "long                0.335792    0.254020   0.197083      -0.007683  \n",
       "sqft_living15       1.000000    0.183676   0.713068       0.003975  \n",
       "sqft_lot15          0.183676    1.000000   0.120269      -0.000709  \n",
       "grade_int           0.713068    0.120269   1.000000       0.008452  \n",
       "month_of_sale       0.003975   -0.000709   0.008452       1.000000  \n",
       "\n",
       "[21 rows x 21 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th>  <td>   0.691</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.691</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   3861.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 30 Oct 2023</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:31:54</td>     <th>  Log-Likelihood:    </th> <td>-2.5988e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 19030</td>      <th>  AIC:               </th>  <td>5.198e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 19018</td>      <th>  BIC:               </th>  <td>5.199e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    11</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>     <td>-3.629e+07</td> <td> 1.61e+06</td> <td>  -22.508</td> <td> 0.000</td> <td>-3.95e+07</td> <td>-3.31e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_living</th>   <td>  132.9392</td> <td>    4.220</td> <td>   31.502</td> <td> 0.000</td> <td>  124.668</td> <td>  141.211</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_int</th>     <td> 1.077e+05</td> <td> 2227.994</td> <td>   48.328</td> <td> 0.000</td> <td> 1.03e+05</td> <td> 1.12e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th>     <td> 3.421e+04</td> <td> 3330.035</td> <td>   10.272</td> <td> 0.000</td> <td> 2.77e+04</td> <td> 4.07e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>waterfront</th>    <td> 6.234e+05</td> <td> 1.89e+04</td> <td>   33.048</td> <td> 0.000</td> <td> 5.86e+05</td> <td>  6.6e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view</th>          <td> 5.312e+04</td> <td> 2301.202</td> <td>   23.082</td> <td> 0.000</td> <td> 4.86e+04</td> <td> 5.76e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condition</th>     <td> 2.736e+04</td> <td> 2512.419</td> <td>   10.890</td> <td> 0.000</td> <td> 2.24e+04</td> <td> 3.23e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yr_built</th>      <td>-2556.9171</td> <td>   72.768</td> <td>  -35.138</td> <td> 0.000</td> <td>-2699.549</td> <td>-2414.285</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_above</th>    <td>   39.8137</td> <td>    4.238</td> <td>    9.395</td> <td> 0.000</td> <td>   31.508</td> <td>   48.120</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_of_sale</th> <td>-2785.3987</td> <td>  480.243</td> <td>   -5.800</td> <td> 0.000</td> <td>-3726.718</td> <td>-1844.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lat</th>           <td> 5.725e+05</td> <td> 1.13e+04</td> <td>   50.623</td> <td> 0.000</td> <td>  5.5e+05</td> <td> 5.95e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>long</th>          <td>-1.089e+05</td> <td> 1.22e+04</td> <td>   -8.909</td> <td> 0.000</td> <td>-1.33e+05</td> <td>-8.49e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>16684.180</td> <th>  Durbin-Watson:     </th>  <td>   1.993</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>1801406.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 3.732</td>   <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>50.076</td>   <th>  Cond. No.          </th>  <td>3.81e+06</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.81e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.691\n",
       "Model:                            OLS   Adj. R-squared:                  0.691\n",
       "Method:                 Least Squares   F-statistic:                     3861.\n",
       "Date:                Mon, 30 Oct 2023   Prob (F-statistic):               0.00\n",
       "Time:                        10:31:54   Log-Likelihood:            -2.5988e+05\n",
       "No. Observations:               19030   AIC:                         5.198e+05\n",
       "Df Residuals:                   19018   BIC:                         5.199e+05\n",
       "Df Model:                          11                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=================================================================================\n",
       "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "Intercept     -3.629e+07   1.61e+06    -22.508      0.000   -3.95e+07   -3.31e+07\n",
       "sqft_living     132.9392      4.220     31.502      0.000     124.668     141.211\n",
       "grade_int      1.077e+05   2227.994     48.328      0.000    1.03e+05    1.12e+05\n",
       "bathrooms      3.421e+04   3330.035     10.272      0.000    2.77e+04    4.07e+04\n",
       "waterfront     6.234e+05   1.89e+04     33.048      0.000    5.86e+05     6.6e+05\n",
       "view           5.312e+04   2301.202     23.082      0.000    4.86e+04    5.76e+04\n",
       "condition      2.736e+04   2512.419     10.890      0.000    2.24e+04    3.23e+04\n",
       "yr_built      -2556.9171     72.768    -35.138      0.000   -2699.549   -2414.285\n",
       "sqft_above       39.8137      4.238      9.395      0.000      31.508      48.120\n",
       "month_of_sale -2785.3987    480.243     -5.800      0.000   -3726.718   -1844.079\n",
       "lat            5.725e+05   1.13e+04     50.623      0.000     5.5e+05    5.95e+05\n",
       "long          -1.089e+05   1.22e+04     -8.909      0.000   -1.33e+05   -8.49e+04\n",
       "==============================================================================\n",
       "Omnibus:                    16684.180   Durbin-Watson:                   1.993\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          1801406.023\n",
       "Skew:                           3.732   Prob(JB):                         0.00\n",
       "Kurtosis:                      50.076   Cond. No.                     3.81e+06\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.81e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a multiple linear regression model using Ordinary Least Squares (OLS)\n",
    "model = ols(formula='price ~  sqft_living + grade_int + bathrooms + waterfront + view + condition + yr_built + sqft_above + month_of_sale + lat + long', data=df).fit()\n",
    "# + sqft_lot\n",
    "# Display a summary of the regression model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OLS Regression Results\n",
    "\n",
    "The presented output displays the results of an Ordinary Least Squares (OLS) linear regression analysis. In this regression model, 'price' is considered the dependent variable, and several independent variables are included to predict the housing prices. Here's a breakdown of the key statistics and coefficients:\n",
    "\n",
    "Key Statistics:\n",
    "\n",
    "- **R-squared (R²):** R-squared value is approximately 0.691, indicating that approximately 69.1% of the variation in housing prices can be explained by the included independent variables.\n",
    "\n",
    "- **F-statistic:** The F-statistic measures the overall significance of the model. The high F-statistic value (3861) suggests that the model is statistically significant.\n",
    "\n",
    "  - 'sqft_living' has a coefficient of approximately 132.93, indicating that a one-unit increase in square footage of living space corresponds to an increase in price.\n",
    "  - 'grade_int' and 'bathrooms' also have coefficients, suggesting their impact on the price.\n",
    "  - Similar interpretations apply to other coefficients such as 'waterfront', 'view' and 'condition'.\n",
    "\n",
    "These regression results provide insights into the relationships between various independent variables and housing prices, as well as the overall model fit and diagnostic statistics.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the models predicting performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of test samples: 3806\n",
      "Number of training samples: 15224\n",
      "Root Mean Squared Error (RMSE): 200639.32145128777\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries and modules for data preprocessing, model creation, and evaluation\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# X = df.drop(['price', 'date', 'quarter_of_sale', 'sqft_basement'], axis=1)\n",
    "X = df[['bathrooms', 'sqft_living', 'waterfront', 'view', 'sqft_above', 'yr_built', 'grade_int', 'month_of_sale', 'lat', 'long', 'condition']]\n",
    "y = df['price']\n",
    "\n",
    "# Split the data into training and test sets using train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Display the number of test and training samples\n",
    "print(\"Number of test samples:\", X_test.shape[0])\n",
    "print(\"Number of training samples:\", X_train.shape[0])\n",
    "\n",
    "# Create a data preprocessing pipeline that includes data scaling and a linear regression model\n",
    "pipeline = Pipeline([\n",
    "    ('scaler', StandardScaler()),  # Scaling the data\n",
    "    ('regressor', LinearRegression())  # Linear Regression model\n",
    "])\n",
    "\n",
    "# Fit the pipeline on the training data, which includes scaling the features\n",
    "pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test data\n",
    "y_pred = pipeline.predict(X_test)\n",
    "\n",
    "# Calculate the Root Mean Squared Error (RMSE) as a measure of the model's prediction accuracy\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared Score: 0.6920211643612745\n"
     ]
    }
   ],
   "source": [
    "# Create a separate Linear Regression model, fit it on the test data, and calculate the R-squared score\n",
    "reg = LinearRegression()\n",
    "reg.fit(X_test, y_test)\n",
    "r2_score = reg.score(X_test, y_test)\n",
    "\n",
    "# Print the R-squared score to evaluate the model's performance on the test data\n",
    "print(f\"R-squared Score: {r2_score}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating the Model's Performance\n",
    "\n",
    "In this code, a linear regression model has been trained, and its performance on the test data is assessed using two key metrics: Root Mean Squared Error (RMSE) and R-squared (R²) score.\n",
    "\n",
    "#### Root Mean Squared Error (RMSE)\n",
    "\n",
    "RMSE is a measure of the model's prediction accuracy. In this analysis, the RMSE is approximately 200,639.32. This value represents the square root of the average squared differences between the predicted housing prices and the actual prices in the test data. A lower RMSE indicates that the model's predictions are closer to the actual values, signifying better prediction accuracy.\n",
    "Although our RMSE is large, in this context it is acceptable because the houses price ranges upto 7 million.\n",
    "\n",
    "#### R-squared Score (R²)\n",
    "\n",
    "R-squared (R²) measures the proportion of variance in the dependent variable ('price') that is explained by the independent variables used in the model. The R² score is approximately 0.692, which means that roughly 69.2% of the variation in housing prices can be explained by the features included in the model. A higher R² score indicates a better fit, suggesting that the model is successful in explaining a significant portion of the variance in housing prices.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a New Model with Random Forest Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 128296.20\n",
      "R-squared (R²): 0.87\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Initialize the Random Forest regression model\n",
    "rf_model = RandomForestRegressor(n_estimators=100, random_state=42)  #\n",
    "\n",
    "# Fit the model to the training data\n",
    "rf_model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test data\n",
    "y_pred = rf_model.predict(X_test)\n",
    "\n",
    "# Calculate RMSE\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "print(f'RMSE: {rmse:.2f}')\n",
    "\n",
    "# Calculate R-squared\n",
    "r_squared = r2_score(y_test, y_pred)\n",
    "print(f'R-squared (R²): {r_squared:.2f}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Random Forest Regression model yields the following results:\n",
    "\n",
    "- Random Forest RMSE: 128,296.20\n",
    "- Random Forest R-squared (R²): 0.87"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimizing Random Forest Regressor using Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'max_depth': None, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "Optimized RMSE: 127472.58\n",
      "Optimized R-squared (R²): 0.87\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Initialize the Random Forest regression model\n",
    "rf_model = RandomForestRegressor(random_state=42)\n",
    "\n",
    "# Define the parameter grid for hyperparameter tuning\n",
    "param_grid = {\n",
    "    'n_estimators': [100, 200],  # You can adjust the number of estimators\n",
    "    'max_depth': [None, 10],     # You can adjust the maximum depth\n",
    "    'min_samples_split': [2, 5] # You can adjust the min samples split\n",
    "}\n",
    "\n",
    "# Create a GridSearchCV object with the model and parameter grid\n",
    "grid_search = GridSearchCV(rf_model, param_grid, cv=5, scoring='neg_mean_squared_error')\n",
    "\n",
    "# Fit the model to the training data with hyperparameter tuning\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Get the best estimator and best hyperparameters from the grid search\n",
    "best_rf_model = grid_search.best_estimator_\n",
    "best_params = grid_search.best_params_\n",
    "print(\"Best Hyperparameters:\", best_params)\n",
    "\n",
    "# Fit the best model to the training data\n",
    "best_rf_model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test data\n",
    "y_pred = best_rf_model.predict(X_test)\n",
    "\n",
    "# Calculate RMSE for the optimized model\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "print(f'Optimized RMSE: {rmse:.2f}')\n",
    "\n",
    "# Calculate R-squared for the optimized model\n",
    "r_squared = r2_score(y_test, y_pred)\n",
    "print(f'Optimized R-squared (R²): {r_squared:.2f}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The optimized Random Forest Regression model yields the following results with a slightly lower RMSE:\n",
    "\n",
    "- Optimized Random Forest RMSE: 127,472.58\n",
    "- Optimized Random Forest R-squared (R²): 0.87"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqft_living: 0.3124\n",
      "grade_int: 0.3087\n",
      "lat: 0.1712\n",
      "long: 0.0829\n",
      "yr_built: 0.0291\n",
      "waterfront: 0.0289\n",
      "sqft_above: 0.0254\n",
      "view: 0.0166\n",
      "bathrooms: 0.0106\n",
      "month_of_sale: 0.0097\n",
      "condition: 0.0045\n"
     ]
    }
   ],
   "source": [
    "feature_importances = best_rf_model.feature_importances_\n",
    "feature_names = X_train.columns  # Assuming you have feature names or column names\n",
    "\n",
    "# Print and sort feature importances\n",
    "sorted_feature_importances = sorted(zip(feature_importances, feature_names), reverse=True)\n",
    "for importance, feature in sorted_feature_importances:\n",
    "    print(f'{feature}: {importance:.4f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract feature importances and feature names\n",
    "feature_importances = [importance for importance, _ in sorted_feature_importances]\n",
    "feature_names = [feature for _, feature in sorted_feature_importances]\n",
    "\n",
    "# Create a bar plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(range(len(feature_names)), feature_importances, align='center')\n",
    "plt.yticks(range(len(feature_names)), feature_names)\n",
    "plt.xlabel('Feature Importance')\n",
    "plt.title('Feature Importances')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importance Analysis\n",
    "\n",
    "After analyzing the feature importance of the dataset, the following key features stand out:\n",
    "\n",
    "- **sqft_living**: This feature has a high importance score of 0.3124, indicating that the living area square footage of a property plays a significant role in predicting house prices. A larger living area tends to positively influence the property's value.\n",
    "\n",
    "- **grade_int**: Grade is another crucial factor, with an importance score of 0.3087. A higher grade is typically associated with a better quality of construction and design, leading to increased property values.\n",
    "\n",
    "These results provide valuable insights into which features have the most influence on predicting house prices, with `sqft_living` and `grade_int` being the most significant factors.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "- **Baseline Model (OLS Regression Results):** The baseline model used 'sqft_living' to predict house prices. It explained approximately 49.5% of the price variation. The high F-statistic indicates the model's statistical significance.\n",
    "\n",
    "- **Model with Multiple Variables (OLS Regression Results):** A more advanced model using multiple variables, including 'sqft_living,' 'grade_int,' and 'bathrooms,' explained about 69.1% of price variation. The model's high F-statistic suggests its statistical significance.\n",
    "\n",
    "- **Random Forest Model:** A Random Forest model was developed, resulting in an RMSE of $128,296.20 and an R-squared of 0.87.\n",
    "\n",
    "- **Optimized Random Forest Model:** After optimization, the Random Forest model achieved an RMSE of $127,472.58 and maintained an R-squared of 0.87.\n",
    "\n",
    "- **Feature Importance:** The two most influential features for predicting house prices are 'sqft_living' with an importance score of 0.3124 and 'grade_int' with a score of 0.3087.\n",
    "\n",
    "In conclusion, the analysis of various regression models and a Random Forest model for predicting house prices has provided valuable insights into the factors influencing housing prices. The findings can be valuable for real estate professionals, prospective buyers, and other stakeholders in the housing market to make informed decisions.\n"
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